{
"cells": [
{
"cell_type": "markdown",
"id": "c0222cc3-b667-4784-9b43-ca3129793c96",
"metadata": {},
"source": [
"# OFDM MIMO Channel Estimation and Detection"
]
},
{
"cell_type": "markdown",
"id": "220ef1ff-de24-451a-83ce-28cf0608de9d",
"metadata": {},
"source": [
"In this notebook, we will evaluate some of the OFDM channel estimation and MIMO\n",
"detection algorithms available in Sionna PHY.\n",
"\n",
"We will start by evaluating the mean square error (MSE) preformance of various channel estimation and interpolation methods.\n",
"\n",
"Then, we will compare some of the MIMO detection algorithms under both perfect and imperfect channel state information (CSI) in terms of uncoded symbol error rate (SER) and coded bit error rate (BER).\n",
"\n",
"The developed end-to-end models in this notebook are a great tool for benchmarking of MIMO receivers under realistic conditions. They can be easily extended to new channel estimation methods or MIMO detection algorithms."
]
},
{
"cell_type": "markdown",
"id": "d4a374f2-c8f3-45b7-9fe5-89123decb076",
"metadata": {},
"source": [
"For MSE evaluations, the block diagram of the system looks as follows:"
]
},
{
"cell_type": "markdown",
"id": "1151e856-875a-4c6a-8291-e1c00313eb25",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "7158bf5c-afb3-42f6-a5db-c928cb3b0422",
"metadata": {},
"source": [
"where the channel estimation module is highlighted as it is the focus of this evaluation. The channel covariance matrices are required for linear minimum mean square error (LMMSE) channel estimation."
]
},
{
"cell_type": "markdown",
"id": "627b6480-7c04-42ad-98eb-2c4fc14d369e",
"metadata": {},
"source": [
"For uncoded SER evaluations, the block diagram of the system looks as follows:"
]
},
{
"cell_type": "markdown",
"id": "c9d2cbb9-499d-4615-b34c-13cad61228b1",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "53fc4e24-26e0-4812-a45f-b55f3b68042f",
"metadata": {},
"source": [
"where the channel estimation and detection modules are highlighted as they are the focus of this evaluation."
]
},
{
"cell_type": "markdown",
"id": "f622bbb9-9078-4881-8462-2eb69c1122d6",
"metadata": {},
"source": [
"Finally, for coded BER evaluations, the block diagram of the system looks as follows:"
]
},
{
"cell_type": "markdown",
"id": "4ff3e463-d95d-4816-9ae9-c0c185bf6b18",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "8123dc2f-f759-4615-a137-f10a1c5ef062",
"metadata": {},
"source": [
"## Table of Contents\n",
"* [Configuration and Imports](#Configuration-and-Imports)\n",
"* [Simulations parameters](#Simulation-parameters)\n",
"* [Estimation of the channel time, frequency, and spatial covariance matrices](#Estimation-of-the-channel-time,-frequency,-and-spatial-covariance-matrices)\n",
"* [Loading the channel covariance matrices](#Loading-the-channel-covariance-matrices)\n",
"* [Comparison of OFDM estimators](#Comparison-of-OFDM-estimators)\n",
"* [Comparison of MIMO detectors](#Comparison-of-MIMO-detectors)"
]
},
{
"cell_type": "markdown",
"id": "6fd7faab-7720-4039-9a16-65d53233dbb1",
"metadata": {},
"source": [
"## Configuration and Imports "
]
},
{
"cell_type": "code",
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"source": [
"# Import Sionna\n",
"try:\n",
" import sionna.phy\n",
"except ImportError as e:\n",
" import os\n",
" import sys\n",
" if 'google.colab' in sys.modules:\n",
" # Install Sionna in Google Colab\n",
" print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
" os.system(\"pip install sionna\")\n",
" os.kill(os.getpid(), 5)\n",
" else:\n",
" raise e\n",
"\n",
"sionna.phy.config.seed = 42 # Set seed for reproducible random number generation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9d3ff139",
"metadata": {
"execution": {
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"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\n",
" \"ignore\",\n",
" message=\"Torchinductor does not support code generation for complex operators.*\",\n",
")\n",
"warnings.filterwarnings(\n",
" \"ignore\",\n",
" message=\"`torch._prims_common.check` is deprecated.*\",\n",
" category=FutureWarning,\n",
")\n",
"\n",
"from sionna.phy import Block\n",
"from sionna.phy.mimo import StreamManagement\n",
"from sionna.phy.utils import sim_ber, ebnodb2no\n",
"from sionna.phy.mapping import Mapper, QAMSource, BinarySource\n",
"from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n",
" LMMSEChannelEstimator, LinearDetector, KBestDetector, \\\n",
" EPDetector, MMSEPICDetector\n",
"from sionna.phy.channel import GenerateOFDMChannel, OFDMChannel, gen_single_sector_topology\n",
"from sionna.phy.channel.tr38901 import UMi, Antenna, PanelArray\n",
"from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder"
]
},
{
"cell_type": "markdown",
"id": "81ead318-3f42-48b0-b140-62dcbbb7baff",
"metadata": {},
"source": [
"## Simulation parameters"
]
},
{
"cell_type": "markdown",
"id": "575c8802-bc4c-4db7-bb96-67c897735d00",
"metadata": {},
"source": [
"The next cell defines the simulation parameters used throughout this notebook.\n",
"\n",
"This includes the OFDM waveform parameters, [antennas geometries and patterns](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.tr38901.PanelArray.html), and the [3GPP UMi channel model](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.tr38901.UMi.html)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c770ffeb-5396-4c2f-af31-4b4d82a805ae",
"metadata": {
"execution": {
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"source": [
"NUM_OFDM_SYMBOLS = 14\n",
"FFT_SIZE = 12*4 # 4 PRBs\n",
"SUBCARRIER_SPACING = 30e3 # Hz\n",
"CARRIER_FREQUENCY = 3.5e9 # Hz\n",
"SPEED = 3. # m/s\n",
"\n",
"# The user terminals (UTs) are equipped with a single antenna\n",
"# with vertial polarization.\n",
"UT_ANTENNA = Antenna(polarization='single',\n",
" polarization_type='V',\n",
" antenna_pattern='omni', # Omnidirectional antenna pattern\n",
" carrier_frequency=CARRIER_FREQUENCY)\n",
"\n",
"# The base station is equipped with an antenna\n",
"# array of 8 cross-polarized antennas,\n",
"# resulting in a total of 16 antenna elements.\n",
"NUM_RX_ANT = 16\n",
"BS_ARRAY = PanelArray(num_rows_per_panel=4,\n",
" num_cols_per_panel=2,\n",
" polarization='dual',\n",
" polarization_type='cross',\n",
" antenna_pattern='38.901', # 3GPP 38.901 antenna pattern\n",
" carrier_frequency=CARRIER_FREQUENCY)\n",
"\n",
"# 3GPP UMi channel model is considered\n",
"CHANNEL_MODEL = UMi(carrier_frequency=CARRIER_FREQUENCY,\n",
" o2i_model='low',\n",
" ut_array=UT_ANTENNA,\n",
" bs_array=BS_ARRAY,\n",
" direction='uplink',\n",
" enable_shadow_fading=False,\n",
" enable_pathloss=False)"
]
},
{
"cell_type": "markdown",
"id": "a07a86d6-73e8-457d-b44c-98753023ca52",
"metadata": {},
"source": [
"## Estimation of the channel time, frequency, and spatial covariance matrices"
]
},
{
"cell_type": "markdown",
"id": "4682a1c0-4c2f-41b0-b244-d178d06c1264",
"metadata": {},
"source": [
"The linear minimum mean square (LMMSE) channel estimation method requires knowledge of the time (i.e., across OFDM symbols), frequency (i.e., across sub-carriers), and spatial (i.e., across receive antennas) covariance matrices of the channel frequency response.\n",
"\n",
"These are estimated in this section using Monte Carlo sampling.\n",
"\n",
"We explain below how this is achieved for the frequency covariance matrix. The same approach is used for the time and spatial covariance matrices.\n",
"\n",
"Let $N$ be the number of sub-carriers.\n",
"The first step for estimating the frequency covariance matrix is to sample the channel model in order to build a set of frequency-domain channel realizations $\\left\\{ \\mathbf{h}_k \\right\\}, 1 \\leq k \\leq K$, where $K$ is the number of samples and $\\mathbf{h}_k \\in \\mathbb{C}^{N}$ are complex-valued samples of the channel frequency response.\n",
"\n",
"The frequency covariance matrix $\\mathbf{R}^{(f)} \\in \\mathbb{C}^{N \\times N}$ is then estimated by\n",
"\n",
"\\begin{equation}\n",
"\\mathbf{R}^{(f)} \\approx \\frac{1}{K} \\sum_{k = 1}^K \\mathbf{h}_k \\mathbf{h}_k^{\\mathrm{H}}\n",
"\\end{equation}\n",
"\n",
"where we assume that the frequency-domain channel response has zero mean.\n",
"\n",
"The following cells implement this process for all three dimensions (frequency, time, and space).\n"
]
},
{
"cell_type": "markdown",
"id": "0c6fdb83-e5b9-4521-a15a-59bfe337f041",
"metadata": {},
"source": [
"The next cell defines a [resource grid](https://nvlabs.github.io/sionna/phy/api/ofdm/resource_grid.html) and an [OFDM channel generator](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.GenerateOFDMChannel.html) for sampling the channel in the frequency domain."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0ae51854-9597-409d-8ecf-a1d85e02b3cd",
"metadata": {
"execution": {
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"source": [
"rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n",
" fft_size=FFT_SIZE,\n",
" subcarrier_spacing=SUBCARRIER_SPACING)\n",
"channel_sampler = GenerateOFDMChannel(CHANNEL_MODEL, rg)"
]
},
{
"cell_type": "markdown",
"id": "65802f06-150a-4e91-a68b-05fa44aa3eaf",
"metadata": {},
"source": [
"Then, a function that samples the channel is defined.\n",
"It randomly samples a network topology for every batch and for every batch example using the [appropriate utility function](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.gen_single_sector_topology.html)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5a71816c-bd1e-4789-9ea1-db6f5cf1525c",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"def sample_channel(batch_size):\n",
" # Sample random topologies\n",
" topology = gen_single_sector_topology(batch_size, 1, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n",
" CHANNEL_MODEL.set_topology(*topology)\n",
"\n",
" # Sample channel frequency responses\n",
" # [batch size, 1, num_rx_ant, 1, 1, num_ofdm_symbols, fft_size]\n",
" h_freq = channel_sampler(batch_size)\n",
" # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n",
" h_freq = h_freq[:,0,:,0,0]\n",
"\n",
" return h_freq"
]
},
{
"cell_type": "markdown",
"id": "2bd07c16-595e-4880-b2aa-e162e75f9d55",
"metadata": {},
"source": [
"We now define a function that estimates the frequency, time, and spatial covariance matrcies using Monte Carlo sampling."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1cc6d3e1-9abb-45f4-ad66-27fa3ff5654a",
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"source": [
"def estimate_covariance_matrices(num_it, batch_size):\n",
" freq_cov_mat = torch.zeros([FFT_SIZE, FFT_SIZE], dtype=torch.complex64, device=sionna.phy.config.device)\n",
" time_cov_mat = torch.zeros([NUM_OFDM_SYMBOLS, NUM_OFDM_SYMBOLS], dtype=torch.complex64, device=sionna.phy.config.device)\n",
" space_cov_mat = torch.zeros([NUM_RX_ANT, NUM_RX_ANT], dtype=torch.complex64, device=sionna.phy.config.device)\n",
" for _ in range(num_it):\n",
" # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n",
" h_samples = sample_channel(batch_size)\n",
"\n",
" #################################\n",
" # Estimate frequency covariance\n",
" #################################\n",
" # [batch size, num_rx_ant, fft_size, num_ofdm_symbols]\n",
" h_samples_ = h_samples.permute(0, 1, 3, 2)\n",
" # [batch size, num_rx_ant, fft_size, fft_size]\n",
" freq_cov_mat_ = h_samples_ @ h_samples_.mH\n",
" # [fft_size, fft_size]\n",
" freq_cov_mat_ = freq_cov_mat_.mean(dim=(0, 1))\n",
" # [fft_size, fft_size]\n",
" freq_cov_mat += freq_cov_mat_\n",
"\n",
" ################################\n",
" # Estimate time covariance\n",
" ################################\n",
" # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n",
" time_cov_mat_ = h_samples @ h_samples.mH\n",
" # [num_ofdm_symbols, num_ofdm_symbols]\n",
" time_cov_mat_ = time_cov_mat_.mean(dim=(0, 1))\n",
" # [num_ofdm_symbols, num_ofdm_symbols]\n",
" time_cov_mat += time_cov_mat_\n",
"\n",
" ###############################\n",
" #Â Estimate spatial covariance\n",
" ###############################\n",
" # [batch size, num_ofdm_symbols, num_rx_ant, fft_size]\n",
" h_samples_ = h_samples.permute(0, 2, 1, 3)\n",
" # [batch size, num_ofdm_symbols, num_rx_ant, num_rx_ant]\n",
" space_cov_mat_ = h_samples_ @ h_samples_.mH\n",
" # [num_rx_ant, num_rx_ant]\n",
" space_cov_mat_ = space_cov_mat_.mean(dim=(0,1))\n",
" # [num_rx_ant, num_rx_ant]\n",
" space_cov_mat += space_cov_mat_\n",
"\n",
" freq_cov_mat /= torch.complex(torch.tensor(float(NUM_OFDM_SYMBOLS*num_it)), torch.tensor(0.0))\n",
" time_cov_mat /= torch.complex(torch.tensor(float(FFT_SIZE*num_it)), torch.tensor(0.0))\n",
" space_cov_mat /= torch.complex(torch.tensor(float(FFT_SIZE*num_it)), torch.tensor(0.0))\n",
"\n",
" return freq_cov_mat, time_cov_mat, space_cov_mat"
]
},
{
"cell_type": "markdown",
"id": "d5e444cb-58d7-42c7-9e49-cdb6f9eb4aaa",
"metadata": {},
"source": [
"We then compute the estimates by executing the function defined in the previous cell.\n",
"\n",
"The batch size and number of iterations determine the total number of samples, i.e.,\n",
"\n",
"```\n",
"number of samples = batch_size x num_iterations\n",
"```\n",
"\n",
"and hence control the tradeoff between the accuracy of the estimates and the time needed for their computation."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8ea94466-bb28-4ed5-a7cb-6e1cf82f86c0",
"metadata": {
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"source": [
"batch_size = 100\n",
"num_iterations = 1000\n",
"FREQ_COV_MAT, TIME_COV_MAT, SPACE_COV_MAT = estimate_covariance_matrices(num_iterations, batch_size)\n",
"FREQ_COV_MAT = FREQ_COV_MAT.cpu().numpy()\n",
"TIME_COV_MAT = TIME_COV_MAT.cpu().numpy()\n",
"SPACE_COV_MAT = SPACE_COV_MAT.cpu().numpy()"
]
},
{
"cell_type": "markdown",
"id": "0ddaf455-cae8-4248-8979-a9428c4298d6",
"metadata": {},
"source": [
"Finally, the estimated matrices are saved (as numpy arrays) for future use."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6048932c-486a-419f-81c2-62b1bc6c9ff5",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# FREQ_COV_MAT : [fft_size, fft_size]\n",
"# TIME_COV_MAT : [num_ofdm_symbols, num_ofdm_symbols]\n",
"# SPACE_COV_MAT : [num_rx_ant, num_rx_ant]\n",
"\n",
"np.save('freq_cov_mat', FREQ_COV_MAT)\n",
"np.save('time_cov_mat', TIME_COV_MAT)\n",
"np.save('space_cov_mat', SPACE_COV_MAT)"
]
},
{
"cell_type": "markdown",
"id": "2770ca3d-19aa-4337-ab2b-8b184f2a0ddd",
"metadata": {},
"source": [
"## Loading the channel covariance matrices"
]
},
{
"cell_type": "markdown",
"id": "d4bab810-fc78-4a0c-bac4-f1da9f2bc07d",
"metadata": {},
"source": [
"The next cell loads saved estimates of the time, frequency, and space covariance matrices."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b20bce1a-9a6c-4842-aaad-3b659931cb6e",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"FREQ_COV_MAT = np.load('freq_cov_mat.npy')\n",
"TIME_COV_MAT = np.load('time_cov_mat.npy')\n",
"SPACE_COV_MAT = np.load('space_cov_mat.npy')"
]
},
{
"cell_type": "markdown",
"id": "c1ee48c1-6817-4c40-9b7f-447c5a34c9b8",
"metadata": {},
"source": [
"We then visualize the loaded matrices.\n",
"\n",
"As one can see, the frequency correlation slowly decays with increasing spectral distance.\n",
"\n",
"The time-correlation is much stronger as the mobility low. The covariance matrix is hence very badly conditioned with rank almost equal to one.\n",
"\n",
"The spatial covariance matrix has a regular structure which is determined by the array geometry and polarization of its elements."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7e316b0f-bbfd-4c75-9204-9a0cd49aa63b",
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{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3,2, figsize=(10,12))\n",
"fig.suptitle(\"Time and frequency channel covariance matrices\")\n",
"\n",
"ax[0,0].set_title(\"Freq. cov. Real\")\n",
"im = ax[0,0].imshow(FREQ_COV_MAT.real, vmin=-0.3, vmax=1.8)\n",
"ax[0,1].set_title(\"Freq. cov. Imag\")\n",
"im = ax[0,1].imshow(FREQ_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n",
"\n",
"ax[1,0].set_title(\"Time cov. Real\")\n",
"im = ax[1,0].imshow(TIME_COV_MAT.real, vmin=-0.3, vmax=1.8)\n",
"ax[1,1].set_title(\"Time cov. Imag\")\n",
"im = ax[1,1].imshow(TIME_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n",
"\n",
"ax[2,0].set_title(\"Space cov. Real\")\n",
"im = ax[2,0].imshow(SPACE_COV_MAT.real, vmin=-0.3, vmax=1.8)\n",
"ax[2,1].set_title(\"Space cov. Imag\")\n",
"im = ax[2,1].imshow(SPACE_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n",
"\n",
"fig.subplots_adjust(right=0.8)\n",
"cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
"fig.colorbar(im, cax=cbar_ax);"
]
},
{
"cell_type": "markdown",
"id": "b5867869-195c-4daf-9b25-f226ea34aca2",
"metadata": {},
"source": [
"## Comparison of OFDM estimators"
]
},
{
"cell_type": "markdown",
"id": "0dabc5a8-37a4-4609-9966-ca3374602cf7",
"metadata": {},
"source": [
"This section focuses on comparing the available OFDM channel estimators in Sionna for the considered setup."
]
},
{
"cell_type": "markdown",
"id": "1bf539fc-04fd-48be-99a4-7afc25ae0b2f",
"metadata": {},
"source": [
"OFDM channel estimation can be performed in two steps:\n",
"\n",
"1. Channel estimation at pilot-carrying resource elements using [least-squares (LS)](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LSChannelEstimator.html).\n",
"\n",
"2. Interpolation for data-carrying resource elements, for which two methods are already available in Sionna:\n",
"\n",
"- [Nearest-neighbor](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.NearestNeighborInterpolator.html), which uses the channel estimate of the nearest pilot\n",
"- [Linear](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LinearInterpolator.html), with optional averaging over the OFDM symbols (time dimension) for low mobility scenarios\n",
"\n",
"Alternatively, one can use an [LMMSE channel estimator](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LMMSEChannelEstimator.html). This estimator requires knowledge of the time and frequency covariance matrices to simultaneously denoise the channel estimate and the pilot-carrying resource elements and interpolate between them.\n",
"The LMMSE channel estimator also supports optional spatial smoothing, which requires the spatial coviarance matrix. The [API documentation](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LMMSEChannelEstimator.html) explains in more detail how this channel estimator operates."
]
},
{
"cell_type": "markdown",
"id": "93b9fb03-97cd-4d9b-adb6-e0b00be506fb",
"metadata": {},
"source": [
"### End-to-end model"
]
},
{
"cell_type": "markdown",
"id": "b1568b07-702f-40a8-afb8-66115a5c4ea0",
"metadata": {},
"source": [
"In the next cell, we will create a Sionna Block which uses the interpolation method specified at initialization.\n",
"\n",
"It computes the mean square error (MSE) for a specified batch size and signal-to-noise ratio (SNR) (in dB).\n",
"\n",
"The following channel estimation methods are available (set through the `chest_method` parameter):\n",
"\n",
"- `\"ls-nn\"` : Least squares channel estimation with nearest-neighbor interpolation\n",
"- `\"ls-lin\"` : Least squares channel estimation with linear interpolation\n",
"- `\"lmmse\"` : LMMSE channel estimation\n",
"\n",
"When LMMSE channel estimation is used, it is required to specify the order in which interpolation and optional spatial smoothing is performed.\n",
"This is achieved using the `lmmse_order` parameter. For example, setting this parameter to `\"f-t\"` leads to frequency smoothing and interpolation being performed first, followed by time smoothing and interpolation, and no spatial smoothing.\n",
"Setting it to `\"t-f-s\"` leads to time smoothing and interpolation being performed first, followed by frequency smoothing and interpolation, and finally spatial smoothing. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e83d1d4a-7153-4856-b1ab-79177ad846a1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:15:34.941067Z",
"iopub.status.busy": "2026-08-06T20:15:34.940922Z",
"iopub.status.idle": "2026-08-06T20:15:34.947389Z",
"shell.execute_reply": "2026-08-06T20:15:34.946623Z"
}
},
"outputs": [],
"source": [
"class MIMOOFDMLink(Block):\n",
"\n",
" def __init__(self, chest_method, lmmse_order=None, **kwargs):\n",
" super().__init__(kwargs)\n",
"\n",
" assert chest_method in ('ls-nn', 'ls-lin', 'lmmse')\n",
"\n",
"\n",
" # Configure the resource grid\n",
" rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n",
" fft_size=FFT_SIZE,\n",
" subcarrier_spacing=SUBCARRIER_SPACING,\n",
" num_tx=1,\n",
" pilot_pattern=\"kronecker\",\n",
" pilot_ofdm_symbol_indices=[2,11])\n",
" self.rg = rg\n",
"\n",
" # Stream management\n",
" # Only a sinlge UT is considered for channel estimation\n",
" sm = StreamManagement([[1]], 1)\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" self.qam_source = QAMSource(num_bits_per_symbol=2) # Modulation order does not impact the channel estimation. Set to QPSK\n",
" self.rg_mapper = ResourceGridMapper(rg)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
" self.channel_model = UMi(carrier_frequency=CARRIER_FREQUENCY,\n",
" o2i_model='low',\n",
" ut_array=UT_ANTENNA,\n",
" bs_array=BS_ARRAY,\n",
" direction='uplink',\n",
" enable_shadow_fading=False,\n",
" enable_pathloss=False)\n",
"\n",
" self.channel = OFDMChannel(self.channel_model, rg, return_channel=True)\n",
"\n",
" ###################################\n",
" # Receiver\n",
" ###################################\n",
"\n",
" # Channel estimation\n",
" freq_cov_mat = torch.tensor(FREQ_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" time_cov_mat = torch.tensor(TIME_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" space_cov_mat = torch.tensor(SPACE_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" if chest_method == 'ls-nn':\n",
" self.channel_estimator = LSChannelEstimator(rg, interpolation_type='nn')\n",
" elif chest_method == 'ls-lin':\n",
" self.channel_estimator = LSChannelEstimator(rg, interpolation_type='lin')\n",
" elif chest_method == 'lmmse':\n",
" self.channel_estimator = LMMSEChannelEstimator(rg, time_cov_mat, freq_cov_mat, space_cov_mat, order=lmmse_order)\n",
"\n",
" def call(self, batch_size, snr_db):\n",
"\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" x = self.qam_source([batch_size, 1, 1, self.rg.num_data_symbols])\n",
" x_rg = self.rg_mapper(x)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
"\n",
" no = torch.pow(torch.tensor(10.0, device=sionna.phy.config.device), -snr_db / 10.0)\n",
" topology = gen_single_sector_topology(batch_size, 1, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n",
" self.channel_model.set_topology(*topology)\n",
" y_rg, h_freq = self.channel(x_rg, no)\n",
"\n",
" ###################################\n",
" # Channel estimation\n",
" ###################################\n",
"\n",
" h_hat,_ = self.channel_estimator(y_rg,no)\n",
"\n",
" ###################################\n",
" # MSE\n",
" ###################################\n",
"\n",
" mse = torch.mean((h_freq - h_hat).abs().square())\n",
"\n",
" return mse"
]
},
{
"cell_type": "markdown",
"id": "e20e3285",
"metadata": {},
"source": [
"The next cell defines a function for evaluating the mean square error (MSE) of a `model` over a range of SNRs (`snr_dbs`).\n",
"\n",
"The `batch_size` and `num_it` parameters control the number of samples used to compute the MSE for each SNR value."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2e89a42b-ca66-4448-b6da-ccf98d0ca3e1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:15:34.949399Z",
"iopub.status.busy": "2026-08-06T20:15:34.949274Z",
"iopub.status.idle": "2026-08-06T20:15:34.952340Z",
"shell.execute_reply": "2026-08-06T20:15:34.951536Z"
}
},
"outputs": [],
"source": [
"def evaluate_mse(model, snr_dbs, batch_size, num_it):\n",
"\n",
" mses = []\n",
" for snr_db in snr_dbs:\n",
"\n",
" mse_ = 0.0\n",
" for _ in range(num_it):\n",
" mse_ += model(batch_size, snr_db).cpu().numpy()\n",
" # Averaging over the number of iterations\n",
" mse_ /= float(num_it)\n",
" mses.append(mse_)\n",
"\n",
" return mses"
]
},
{
"cell_type": "markdown",
"id": "dc14c206",
"metadata": {},
"source": [
"The next cell defines the evaluation parameters."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d8de113a-07ba-40e3-83ce-03c2fe0fd640",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:15:34.954356Z",
"iopub.status.busy": "2026-08-06T20:15:34.954231Z",
"iopub.status.idle": "2026-08-06T20:15:34.957344Z",
"shell.execute_reply": "2026-08-06T20:15:34.956525Z"
}
},
"outputs": [],
"source": [
"# Range of SNR (in dB)\n",
"SNR_DBs = np.linspace(-10.0, 20.0, 20)\n",
"\n",
"# Number of iterations and batch size.\n",
"# These parameters control the number of samples used to compute each SNR value.\n",
"# The higher the number of samples is, the more accurate the MSE estimation is, at\n",
"# the cost of longer compute time.\n",
"BATCH_SIZE = 256\n",
"NUM_IT = 20\n",
"\n",
"# Interpolation/filtering order for the LMMSE channel estimator.\n",
"# All valid configurations are listed.\n",
"# Some are commented to speed-up simulations.\n",
"# Uncomment configurations to evaluate them!\n",
"ORDERS = ['s-t-f', # Space - time - frequency\n",
" #'s-f-t', # Space - frequency - time\n",
" #'t-s-f', # Time - space - frequency\n",
" 't-f-s', # Time - frequency - space\n",
" #'f-t-s', # Frequency - time - space\n",
" #'f-s-t', # Frequency - space- time\n",
" #'f-t', # Frequency - time (no spatial smoothing)\n",
" 't-f' # Time - frequency (no spatial smoothing)\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "cb63359c",
"metadata": {},
"source": [
"The next cell evaluates least squares with nearest-neighbor and linear interpolation, as well as LMMSE channel estimation.\n",
"For the LMMSE estimator, we loop through the configuration listed in\n",
"`ORDERS`. This can take a while."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "28b4a2dd-eb4e-449f-958c-9d3985296d03",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:15:34.959308Z",
"iopub.status.busy": "2026-08-06T20:15:34.959183Z",
"iopub.status.idle": "2026-08-06T20:19:32.350920Z",
"shell.execute_reply": "2026-08-06T20:19:32.349941Z"
}
},
"outputs": [],
"source": [
"MSES = {}\n",
"\n",
"# Nearest-neighbor interpolation\n",
"e2e = MIMOOFDMLink(\"ls-nn\")\n",
"e2e.channel_model.allocate_topology_tensors(batch_size=BATCH_SIZE, num_bs=1, num_ut=1)\n",
"MSES['ls-nn'] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n",
"\n",
"# Linear interpolation\n",
"e2e = MIMOOFDMLink(\"ls-lin\")\n",
"e2e.channel_model.allocate_topology_tensors(batch_size=BATCH_SIZE, num_bs=1, num_ut=1)\n",
"MSES['ls-lin'] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n",
"\n",
"# LMMSE\n",
"for order in ORDERS:\n",
" e2e = MIMOOFDMLink(\"lmmse\", order)\n",
" e2e.channel_model.allocate_topology_tensors(batch_size=BATCH_SIZE, num_bs=1, num_ut=1)\n",
" MSES[f\"lmmse: {order}\"] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n"
]
},
{
"cell_type": "markdown",
"id": "cf1d55b0",
"metadata": {},
"source": [
"Finally, we plot the MSE."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8a83a603-88ed-41d6-9eb6-927f73efe384",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:19:32.353886Z",
"iopub.status.busy": "2026-08-06T20:19:32.353740Z",
"iopub.status.idle": "2026-08-06T20:19:32.779010Z",
"shell.execute_reply": "2026-08-06T20:19:32.778017Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"\n",
"for est_label in MSES:\n",
" plt.semilogy(SNR_DBs, MSES[est_label], label=est_label)\n",
"\n",
"plt.xlabel(r\"SNR (dB)\")\n",
"plt.ylabel(\"MSE\")\n",
"plt.legend()\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"id": "e4e29531-1b22-4c01-a46a-91bc8834ea96",
"metadata": {},
"source": [
"Unsurprisingly, the LMMSE channel estimator leads to more accurate estimates compared to the two other methods, as it leverages knowledge of the channel statistics.\n",
"Moreover, the order in which the LMMSE smoothing and interpolation steps are performed strongly impacts the accuracy of the estimator. This is because the LMMSE channel estimator operates in one dimension at a time which is not equivalent to full-blown LMMSE estimation across all dimensions at once.\n",
"\n",
"Also note that the order that leads to the best accuracy depends on the channel statistics. As a rule of thumb, it might be good to start with the dimension that is most strongly correlated (i.e., time in our example)."
]
},
{
"cell_type": "markdown",
"id": "b4556af5-ecaa-4770-86ae-165238ef0e21",
"metadata": {},
"source": [
"## Comparison of MIMO detectors"
]
},
{
"cell_type": "markdown",
"id": "07870ad0-b59f-4227-ab15-fba5331a89a8",
"metadata": {},
"source": [
"An OFDM MIMO receiver consists of two stages: **OFDM channel estimation** and **MIMO detection**.\n",
"\n",
"While the previous section focused on OFDM channel estimation, this section focuses now on MIMO detection.\n",
"\n",
"The following MIMO detection algorithms, all available out-of-the-box in Sionna, are considered:\n",
"\n",
"- [LMMSE equalization followed by APP demapping](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.LinearDetector.html)\n",
"- [K-Best detection](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.KBestDetector.html)\n",
"- [EP detection](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.EPDetector.html)\n",
"- [MMSE-PIC detection](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.MMSEPICDetector.html)\n",
"\n",
"Both perfect and imperfect channel state information is considered in the simulations.\n",
"LMMSE channel estimation with time-frequency-space smoothing (in this order, i.e., `order='t-f-s'`) is used."
]
},
{
"cell_type": "markdown",
"id": "5cda3dc1-1317-4b46-bcb0-8b3ef8a02e97",
"metadata": {},
"source": [
"### End-to-end model"
]
},
{
"cell_type": "markdown",
"id": "1bad6adf-eff6-451a-bef4-fe24731b1769",
"metadata": {},
"source": [
"An end-to-end model is created in the next cell as a Sionna Block, which uses the detection method specified at initialization.\n",
"\n",
"It computes either the coded bit error rate (BER) or the uncoded symbol error rate (SER), for a specified batch size, $E_b/N_0$ (in dB), and QAM modulation with a specified modulation order.\n",
"When computing the BER, a 5G LDPC code is used with the specified coderate.\n",
"\n",
"The following MIMO detection methods are considered (set through the `det_param` parameter):\n",
"\n",
"- `\"lmmse\"` : No parameter needed\n",
"- `\"k-best\"` : List size `k`, defaults to 64\n",
"- `\"ep\"` : Number of iterations `l`, defaults to 10\n",
"- `\"mmse-pic\"` : Number of self-iterations `num_it`, defaults to 4\n",
"\n",
"The `det_param` parameter corresponds to either `k`, `l`, or `num_it`, for K-Best, EP, or MMSE-PIC, respectively. If set to `None`, a default value is used according to the selected detector.\n",
"\n",
"The `perf_csi` parameter controls whether perfect CSI is assumed or not. If set to `False`, then LMMSE is used to estimate the channel.\n",
"\n",
"You can easily add your own MIMO detector and channel estimator to this model for a fair and realistic benchmark."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "de827343-04d6-4a52-8ffe-f26c76bd8a6d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:19:32.781583Z",
"iopub.status.busy": "2026-08-06T20:19:32.781441Z",
"iopub.status.idle": "2026-08-06T20:19:32.791105Z",
"shell.execute_reply": "2026-08-06T20:19:32.790249Z"
}
},
"outputs": [],
"source": [
"class MIMOOFDMLink(Block):\n",
"\n",
" def __init__(self, output, det_method, perf_csi, num_tx, num_bits_per_symbol, det_param=None, coderate=0.5, **kwargs):\n",
" super().__init__(kwargs)\n",
"\n",
" assert det_method in ('lmmse', 'k-best', 'ep', 'mmse-pic'), \"Unknown detection method\"\n",
"\n",
" self._output = output\n",
" self.num_tx = num_tx\n",
" self.num_bits_per_symbol = num_bits_per_symbol\n",
" self.coderate = coderate\n",
" self.det_method = det_method\n",
" self.perf_csi = perf_csi\n",
"\n",
" # Configure the resource grid\n",
" rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n",
" fft_size=FFT_SIZE,\n",
" subcarrier_spacing=SUBCARRIER_SPACING,\n",
" num_tx=num_tx,\n",
" pilot_pattern=\"kronecker\",\n",
" pilot_ofdm_symbol_indices=[2,11])\n",
" self.rg = rg\n",
"\n",
" # Stream management\n",
" sm = StreamManagement(np.ones([1,num_tx], int), 1)\n",
"\n",
" # Codeword length and number of information bits per codeword\n",
" n = int(rg.num_data_symbols*num_bits_per_symbol)\n",
" k = int(coderate*n)\n",
" self.n = n\n",
" self.k = k\n",
"\n",
" # If output is symbol, then no FEC is used and hard decision are output\n",
" hard_out = (output == \"symbol\")\n",
" coded = (output == \"bit\")\n",
" self.hard_out = hard_out\n",
" self.coded = coded\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" self.binary_source = BinarySource()\n",
" self.mapper = Mapper(constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, return_indices=True)\n",
" self.rg_mapper = ResourceGridMapper(rg)\n",
" if coded:\n",
" self.encoder = LDPC5GEncoder(k, n, num_bits_per_symbol=num_bits_per_symbol)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
" self.channel_model = UMi(carrier_frequency=CARRIER_FREQUENCY,\n",
" o2i_model='low',\n",
" ut_array=UT_ANTENNA,\n",
" bs_array=BS_ARRAY,\n",
" direction='uplink',\n",
" enable_shadow_fading=False,\n",
" enable_pathloss=False)\n",
"\n",
" self.channel = OFDMChannel(self.channel_model, rg, return_channel=True)\n",
"\n",
" ###################################\n",
" # Receiver\n",
" ###################################\n",
"\n",
" # Channel estimation\n",
" if not self.perf_csi:\n",
" freq_cov_mat = torch.tensor(FREQ_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" time_cov_mat = torch.tensor(TIME_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" space_cov_mat = torch.tensor(SPACE_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" self.channel_estimator = LMMSEChannelEstimator(rg, time_cov_mat, freq_cov_mat, space_cov_mat, order='t-f-s')\n",
"\n",
" # Detection\n",
" if det_method == \"lmmse\":\n",
" self.detector = LinearDetector(\"lmmse\", output, \"app\", rg, sm, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n",
" elif det_method == 'k-best':\n",
" if det_param is None:\n",
" k = 64\n",
" else:\n",
" k = det_param\n",
" self.detector = KBestDetector(output, num_tx, k, rg, sm, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n",
" elif det_method == \"ep\":\n",
" if det_param is None:\n",
" l = 10\n",
" else:\n",
" l = det_param\n",
" self.detector = EPDetector(output, rg, sm, num_bits_per_symbol, l=l, hard_out=hard_out)\n",
" elif det_method == 'mmse-pic':\n",
" if det_param is None:\n",
" l = 4\n",
" else:\n",
" l = det_param\n",
" self.detector = MMSEPICDetector(output, 'app', rg, sm, num_iter=l, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n",
"\n",
" if coded:\n",
" self.decoder = LDPC5GDecoder(self.encoder, hard_out=False)\n",
"\n",
" def call(self, batch_size, ebno_db):\n",
"\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" if self.coded:\n",
" b = self.binary_source([batch_size, self.num_tx, 1, self.k])\n",
" c = self.encoder(b)\n",
" else:\n",
" c = self.binary_source([batch_size, self.num_tx, 1, self.n])\n",
" bits_shape = c.shape\n",
" x,x_ind = self.mapper(c)\n",
" x_rg = self.rg_mapper(x)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
"\n",
" no = ebnodb2no(ebno_db, self.num_bits_per_symbol, self.coderate, resource_grid=self.rg)\n",
" topology = gen_single_sector_topology(batch_size, self.num_tx, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n",
" self.channel_model.set_topology(*topology)\n",
" y_rg, h_freq = self.channel(x_rg, no)\n",
"\n",
" ###################################\n",
" # Receiver\n",
" ###################################\n",
"\n",
" # Channel estimation\n",
" if self.perf_csi:\n",
" h_hat = h_freq\n",
" err_var = 0.0\n",
" else:\n",
" h_hat,err_var = self.channel_estimator(y_rg,no)\n",
"\n",
" # Detection\n",
" if self.det_method == \"mmse-pic\":\n",
" if self._output == \"bit\":\n",
" prior_shape = bits_shape\n",
" elif self._output == \"symbol\":\n",
" # For symbol output, prior needs num_points = 2**num_bits_per_symbol\n",
" prior_shape = list(x.shape) + [2**self.num_bits_per_symbol]\n",
" prior = torch.zeros(prior_shape, device=x.device, dtype=x.dtype)\n",
" det_out = self.detector(y_rg, h_hat, prior, err_var, no)\n",
" else:\n",
" det_out = self.detector(y_rg, h_hat, err_var, no)\n",
"\n",
" # (Decoding) and output\n",
" if self._output == \"bit\":\n",
" llr = det_out.reshape(bits_shape)\n",
" b_hat = self.decoder(llr)\n",
" return b, b_hat\n",
" elif self._output == \"symbol\":\n",
" x_hat = det_out.reshape(x_ind.shape)\n",
" return x_ind, x_hat"
]
},
{
"cell_type": "markdown",
"id": "cce1c898-f431-4613-b6fc-ee702e932ecf",
"metadata": {},
"source": [
"The following function is used to evaluate all of the considered detectors for a given setup: It instantiates the end-to-end systems, runs the simulations, and returns the BER or SER."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "990cefa6-abed-42c6-969c-75bdfdab7946",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:19:32.793229Z",
"iopub.status.busy": "2026-08-06T20:19:32.793101Z",
"iopub.status.idle": "2026-08-06T20:19:32.797548Z",
"shell.execute_reply": "2026-08-06T20:19:32.796785Z"
},
"tags": []
},
"outputs": [],
"source": [
"def run_sim(num_tx, num_bits_per_symbol, output, ebno_dbs, perf_csi, det_param=None):\n",
"\n",
" batch_size = 64\n",
"\n",
" lmmse = MIMOOFDMLink(output, \"lmmse\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" lmmse.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" k_best = MIMOOFDMLink(output, \"k-best\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" k_best.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" ep = MIMOOFDMLink(output, \"ep\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" ep.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" mmse_pic = MIMOOFDMLink(output, \"mmse-pic\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" mmse_pic.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" if output == \"symbol\":\n",
" soft_estimates = False\n",
" ylabel = \"Uncoded SER\"\n",
" else:\n",
" soft_estimates = True\n",
" ylabel = \"Coded BER\"\n",
"\n",
" er_lmmse,_ = sim_ber(lmmse,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"default\");\n",
"\n",
" er_ep,_ = sim_ber(ep,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"default\");\n",
"\n",
" er_kbest,_ = sim_ber(k_best,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"default\");\n",
"\n",
" er_mmse_pic,_ = sim_ber(mmse_pic,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"default\");\n",
"\n",
" return er_lmmse, er_ep, er_kbest, er_mmse_pic"
]
},
{
"cell_type": "markdown",
"id": "f5b2eb9f-685c-4481-a5c1-a2ce7d132db1",
"metadata": {},
"source": [
"The next cell defines the simulation parameters."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "386ef947-c7db-48be-be7e-ddaade014537",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:19:32.799774Z",
"iopub.status.busy": "2026-08-06T20:19:32.799649Z",
"iopub.status.idle": "2026-08-06T20:19:32.802264Z",
"shell.execute_reply": "2026-08-06T20:19:32.801541Z"
}
},
"outputs": [],
"source": [
"# Range of SNR (dB)\n",
"EBN0_DBs = np.linspace(-10., 20.0, 10)\n",
"\n",
"# Number of transmitters\n",
"NUM_TX = 4\n",
"\n",
"# Modulation order (number of bits per symbol)\n",
"NUM_BITS_PER_SYMBOL = 4 # 16-QAM"
]
},
{
"cell_type": "markdown",
"id": "c0f7deac-a797-4ae9-a1e1-de32cb4b4ab2",
"metadata": {},
"source": [
"We start by evaluating the uncoded SER. The next cell runs the simulations with perfect CSI and channel estimation. Results are stored in the `SER` dictionary."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d8fc9f89-dff2-4a46-b712-3b032aff67bd",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:19:32.804405Z",
"iopub.status.busy": "2026-08-06T20:19:32.804280Z",
"iopub.status.idle": "2026-08-06T20:33:56.049238Z",
"shell.execute_reply": "2026-08-06T20:33:56.048269Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.0649e-01 | 1.0000e+00 | 89430 | 147456 | 256 | 256 | 48.8 |reached target block errors\n",
" -6.667 | 4.8136e-01 | 1.0000e+00 | 70979 | 147456 | 256 | 256 | 31.9 |reached target block errors\n",
" -3.333 | 3.3661e-01 | 9.9609e-01 | 49635 | 147456 | 255 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 2.0353e-01 | 9.4922e-01 | 30011 | 147456 | 243 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 9.9881e-02 | 8.5938e-01 | 14728 | 147456 | 220 | 256 | 0.0 |reached target block errors\n",
" 6.667 | 3.8571e-02 | 6.2695e-01 | 11375 | 294912 | 321 | 512 | 0.0 |reached target block errors\n",
" 10.0 | 1.2092e-02 | 3.6589e-01 | 5349 | 442368 | 281 | 768 | 0.0 |reached target block errors\n",
" 13.333 | 4.8014e-03 | 1.7031e-01 | 3540 | 737280 | 218 | 1280 | 0.1 |reached target block errors\n",
" 16.667 | 9.7030e-04 | 6.2800e-02 | 1860 | 1916928 | 209 | 3328 | 0.2 |reached target block errors\n",
" 20.0 | 1.7632e-04 | 1.7069e-02 | 1196 | 6782976 | 201 | 11776 | 0.6 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.0251e-01 | 1.0000e+00 | 88843 | 147456 | 256 | 256 | 41.1 |reached target block errors\n",
" -6.667 | 4.5643e-01 | 1.0000e+00 | 67304 | 147456 | 256 | 256 | 40.5 |reached target block errors\n",
" -3.333 | 2.8220e-01 | 9.9609e-01 | 41612 | 147456 | 255 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 1.1741e-01 | 9.4141e-01 | 17313 | 147456 | 241 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 3.7320e-02 | 7.2656e-01 | 11006 | 294912 | 372 | 512 | 0.0 |reached target block errors\n",
" 6.667 | 6.8427e-03 | 3.3333e-01 | 3027 | 442368 | 256 | 768 | 0.1 |reached target block errors\n",
" 10.0 | 1.1875e-03 | 9.3750e-02 | 1576 | 1327104 | 216 | 2304 | 0.2 |reached target block errors\n",
" 13.333 | 2.3004e-04 | 2.0868e-02 | 1289 | 5603328 | 203 | 9728 | 0.7 |reached target block errors\n",
" 16.667 | 5.3972e-05 | 3.2417e-03 | 1918 | 35536896 | 200 | 61696 | 4.4 |reached target block errors\n",
" 20.0 | 6.4426e-06 | 5.7812e-04 | 475 | 73728000 | 74 | 128000 | 9.2 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.0544e-01 | 1.0000e+00 | 89276 | 147456 | 256 | 256 | 183.1 |reached target block errors\n",
" -6.667 | 4.4425e-01 | 1.0000e+00 | 65507 | 147456 | 256 | 256 | 182.4 |reached target block errors\n",
" -3.333 | 2.5538e-01 | 9.9609e-01 | 37658 | 147456 | 255 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 9.7826e-02 | 9.1016e-01 | 14425 | 147456 | 233 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 2.7306e-02 | 6.3281e-01 | 8053 | 294912 | 324 | 512 | 0.1 |reached target block errors\n",
" 6.667 | 6.8071e-03 | 2.3535e-01 | 4015 | 589824 | 241 | 1024 | 0.1 |reached target block errors\n",
" 10.0 | 8.0411e-04 | 5.7199e-02 | 1660 | 2064384 | 205 | 3584 | 0.4 |reached target block errors\n",
" 13.333 | 1.9638e-04 | 1.1114e-02 | 2056 | 10469376 | 202 | 18176 | 1.9 |reached target block errors\n",
" 16.667 | 3.6621e-05 | 2.3674e-03 | 1782 | 48660480 | 200 | 84480 | 8.8 |reached target block errors\n",
" 20.0 | 9.6164e-06 | 4.9219e-04 | 709 | 73728000 | 63 | 128000 | 13.4 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.0287e-01 | 1.0000e+00 | 88897 | 147456 | 256 | 256 | 196.8 |reached target block errors\n",
" -6.667 | 4.6070e-01 | 1.0000e+00 | 67933 | 147456 | 256 | 256 | 38.5 |reached target block errors\n",
" -3.333 | 2.9604e-01 | 9.9219e-01 | 43653 | 147456 | 254 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 1.3808e-01 | 9.7266e-01 | 20360 | 147456 | 249 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 4.0087e-02 | 7.8906e-01 | 5911 | 147456 | 202 | 256 | 0.0 |reached target block errors\n",
" 6.667 | 8.7382e-03 | 4.1797e-01 | 2577 | 294912 | 214 | 512 | 0.0 |reached target block errors\n",
" 10.0 | 2.2719e-03 | 1.6875e-01 | 1675 | 737280 | 216 | 1280 | 0.1 |reached target block errors\n",
" 13.333 | 2.8545e-04 | 3.5689e-02 | 926 | 3244032 | 201 | 5632 | 0.4 |reached target block errors\n",
" 16.667 | 4.5790e-05 | 6.2500e-03 | 844 | 18432000 | 200 | 32000 | 2.5 |reached target block errors\n",
" 20.0 | 5.8594e-06 | 8.2031e-04 | 432 | 73728000 | 105 | 128000 | 9.9 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.5622e-01 | 1.0000e+00 | 96764 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" -6.667 | 5.4234e-01 | 1.0000e+00 | 79972 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" -3.333 | 4.0232e-01 | 1.0000e+00 | 59325 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" 0.0 | 2.5247e-01 | 9.8828e-01 | 37228 | 147456 | 253 | 256 | 0.2 |reached target block errors\n",
" 3.333 | 1.4337e-01 | 8.7891e-01 | 21141 | 147456 | 225 | 256 | 0.2 |reached target block errors\n",
" 6.667 | 7.2978e-02 | 7.6953e-01 | 21522 | 294912 | 394 | 512 | 0.5 |reached target block errors\n",
" 10.0 | 2.7605e-02 | 4.6484e-01 | 8141 | 294912 | 238 | 512 | 0.5 |reached target block errors\n",
" 13.333 | 1.0308e-02 | 2.8385e-01 | 4560 | 442368 | 218 | 768 | 0.7 |reached target block errors\n",
" 16.667 | 2.8890e-03 | 1.7031e-01 | 2130 | 737280 | 218 | 1280 | 1.2 |reached target block errors\n",
" 20.0 | 3.0501e-03 | 1.0107e-01 | 3598 | 1179648 | 207 | 2048 | 1.9 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.4738e-01 | 1.0000e+00 | 95460 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" -6.667 | 5.0831e-01 | 1.0000e+00 | 74953 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" -3.333 | 3.7462e-01 | 1.0000e+00 | 55240 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" 0.0 | 1.8438e-01 | 9.7656e-01 | 27188 | 147456 | 250 | 256 | 0.2 |reached target block errors\n",
" 3.333 | 6.2920e-02 | 7.9688e-01 | 9278 | 147456 | 204 | 256 | 0.2 |reached target block errors\n",
" 6.667 | 2.4967e-02 | 5.5859e-01 | 7363 | 294912 | 286 | 512 | 0.5 |reached target block errors\n",
" 10.0 | 5.2846e-03 | 2.6465e-01 | 3117 | 589824 | 271 | 1024 | 1.0 |reached target block errors\n",
" 13.333 | 1.4113e-03 | 9.9826e-02 | 1873 | 1327104 | 230 | 2304 | 2.2 |reached target block errors\n",
" 16.667 | 1.0434e-03 | 5.7478e-02 | 2154 | 2064384 | 206 | 3584 | 3.5 |reached target block errors\n",
" 20.0 | 1.1074e-03 | 3.8318e-02 | 3429 | 3096576 | 206 | 5376 | 5.2 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.6142e-01 | 1.0000e+00 | 97530 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -6.667 | 5.0846e-01 | 1.0000e+00 | 74975 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -3.333 | 3.3659e-01 | 9.9609e-01 | 49632 | 147456 | 255 | 256 | 0.3 |reached target block errors\n",
" 0.0 | 1.5254e-01 | 9.4141e-01 | 22493 | 147456 | 241 | 256 | 0.3 |reached target block errors\n",
" 3.333 | 5.4654e-02 | 7.8906e-01 | 16118 | 294912 | 404 | 512 | 0.5 |reached target block errors\n",
" 6.667 | 2.1657e-02 | 4.7656e-01 | 6387 | 294912 | 244 | 512 | 0.5 |reached target block errors\n",
" 10.0 | 3.2810e-03 | 1.8672e-01 | 2419 | 737280 | 239 | 1280 | 1.3 |reached target block errors\n",
" 13.333 | 3.5468e-03 | 8.7674e-02 | 4707 | 1327104 | 202 | 2304 | 2.3 |reached target block errors\n",
" 16.667 | 1.1045e-03 | 5.3385e-02 | 2443 | 2211840 | 205 | 3840 | 3.9 |reached target block errors\n",
" 20.0 | 4.0453e-04 | 4.1016e-02 | 1193 | 2949120 | 210 | 5120 | 5.1 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.5055e-01 | 1.0000e+00 | 95928 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -6.667 | 5.2709e-01 | 1.0000e+00 | 77722 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -3.333 | 3.5575e-01 | 1.0000e+00 | 52458 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" 0.0 | 2.0386e-01 | 9.8438e-01 | 30060 | 147456 | 252 | 256 | 0.3 |reached target block errors\n",
" 3.333 | 7.6158e-02 | 8.8281e-01 | 11230 | 147456 | 226 | 256 | 0.3 |reached target block errors\n",
" 6.667 | 1.9667e-02 | 6.1914e-01 | 5800 | 294912 | 317 | 512 | 0.5 |reached target block errors\n",
" 10.0 | 7.1863e-03 | 3.6589e-01 | 3179 | 442368 | 281 | 768 | 0.8 |reached target block errors\n",
" 13.333 | 2.1306e-03 | 1.4974e-01 | 1885 | 884736 | 230 | 1536 | 1.5 |reached target block errors\n",
" 16.667 | 2.2408e-03 | 6.6406e-02 | 3965 | 1769472 | 204 | 3072 | 3.0 |reached target block errors\n",
" 20.0 | 1.3035e-03 | 4.3380e-02 | 3652 | 2801664 | 211 | 4864 | 4.8 |reached target block errors\n"
]
}
],
"source": [
"SER = {} # Store the results\n",
"\n",
"# Perfect CSI\n",
"ser_lmmse, ser_ep, ser_kbest, ser_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"symbol\", EBN0_DBs, True)\n",
"SER['Perf. CSI / LMMSE'] = ser_lmmse\n",
"SER['Perf. CSI / EP'] = ser_ep\n",
"SER['Perf. CSI / K-Best'] = ser_kbest\n",
"SER['Perf. CSI / MMSE-PIC'] = ser_mmse_pic\n",
"\n",
"# Imperfect CSI\n",
"ser_lmmse, ser_ep, ser_kbest, ser_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"symbol\", EBN0_DBs, False)\n",
"SER['Ch. Est. / LMMSE'] = ser_lmmse\n",
"SER['Ch. Est. / EP'] = ser_ep\n",
"SER['Ch. Est. / K-Best'] = ser_kbest\n",
"SER['Ch. Est. / MMSE-PIC'] = ser_mmse_pic"
]
},
{
"cell_type": "markdown",
"id": "a7b436e3-1fc4-4c6a-b2b3-338cf7158851",
"metadata": {},
"source": [
"Next, we evaluate the coded BER. The cell below runs the simulations with perfect CSI and channel estimation. Results are stored in the `BER` dictionary."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "56dda021-51c5-4c23-8675-e9e08e556c4f",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T20:33:56.051828Z",
"iopub.status.busy": "2026-08-06T20:33:56.051539Z",
"iopub.status.idle": "2026-08-06T21:22:13.608642Z",
"shell.execute_reply": "2026-08-06T21:22:13.607615Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.7119e-01 | 8.3203e-01 | 50485 | 294912 | 213 | 256 | 0.1 |reached target block errors\n",
" -6.667 | 1.0098e-01 | 5.3711e-01 | 59562 | 589824 | 275 | 512 | 0.2 |reached target block errors\n",
" -3.333 | 3.9557e-02 | 2.5293e-01 | 46663 | 1179648 | 259 | 1024 | 0.3 |reached target block errors\n",
" 0.0 | 1.5465e-02 | 1.0205e-01 | 36487 | 2359296 | 209 | 2048 | 0.6 |reached target block errors\n",
" 3.333 | 4.3876e-03 | 3.0093e-02 | 34937 | 7962624 | 208 | 6912 | 2.1 |reached target block errors\n",
" 6.667 | 9.2260e-04 | 6.6774e-03 | 31834 | 34504704 | 200 | 29952 | 9.1 |reached target block errors\n",
" 10.0 | 1.6646e-04 | 1.2656e-03 | 24545 | 147456000 | 162 | 128000 | 38.9 |reached max iterations\n",
" 13.333 | 2.5187e-05 | 2.4219e-04 | 3714 | 147456000 | 31 | 128000 | 38.8 |reached max iterations\n",
" 16.667 | 4.1097e-06 | 3.1250e-05 | 606 | 147456000 | 4 | 128000 | 38.8 |reached max iterations\n",
" 20.0 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 38.8 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 20.0 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.7207e-01 | 8.2031e-01 | 50745 | 294912 | 210 | 256 | 0.1 |reached target block errors\n",
" -6.667 | 8.1184e-02 | 4.8633e-01 | 47884 | 589824 | 249 | 512 | 0.2 |reached target block errors\n",
" -3.333 | 2.9045e-02 | 1.9062e-01 | 42829 | 1474560 | 244 | 1280 | 0.4 |reached target block errors\n",
" 0.0 | 5.6734e-03 | 4.0820e-02 | 33463 | 5898240 | 209 | 5120 | 1.8 |reached target block errors\n",
" 3.333 | 5.4229e-04 | 4.3140e-03 | 29107 | 53673984 | 201 | 46592 | 15.9 |reached target block errors\n",
" 6.667 | 8.2852e-05 | 6.7187e-04 | 12217 | 147456000 | 86 | 128000 | 43.9 |reached max iterations\n",
" 10.0 | 1.1447e-05 | 1.1719e-04 | 1688 | 147456000 | 15 | 128000 | 43.9 |reached max iterations\n",
" 13.333 | 1.5327e-06 | 1.5625e-05 | 226 | 147456000 | 2 | 128000 | 43.9 |reached max iterations\n",
" 16.667 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 43.9 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 16.7 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.0061e-01 | 9.2969e-01 | 59163 | 294912 | 238 | 256 | 0.1 |reached target block errors\n",
" -6.667 | 9.2989e-02 | 5.3906e-01 | 54847 | 589824 | 276 | 512 | 0.2 |reached target block errors\n",
" -3.333 | 2.6276e-02 | 1.8516e-01 | 38746 | 1474560 | 237 | 1280 | 0.5 |reached target block errors\n",
" 0.0 | 4.7367e-03 | 3.3040e-02 | 33526 | 7077888 | 203 | 6144 | 2.4 |reached target block errors\n",
" 3.333 | 5.3953e-04 | 4.2652e-03 | 29436 | 54558720 | 202 | 47360 | 18.3 |reached target block errors\n",
" 6.667 | 7.2428e-05 | 4.9219e-04 | 10680 | 147456000 | 63 | 128000 | 49.5 |reached max iterations\n",
" 10.0 | 1.4859e-05 | 1.0156e-04 | 2191 | 147456000 | 13 | 128000 | 49.4 |reached max iterations\n",
" 13.333 | 1.0647e-06 | 1.5625e-05 | 157 | 147456000 | 2 | 128000 | 49.5 |reached max iterations\n",
" 16.667 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 49.5 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 16.7 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.7660e-01 | 8.1250e-01 | 52080 | 294912 | 208 | 256 | 0.1 |reached target block errors\n",
" -6.667 | 9.2489e-02 | 5.2539e-01 | 54552 | 589824 | 269 | 512 | 0.2 |reached target block errors\n",
" -3.333 | 3.9936e-02 | 2.6302e-01 | 35333 | 884736 | 202 | 768 | 0.3 |reached target block errors\n",
" 0.0 | 8.5123e-03 | 6.4002e-02 | 32635 | 3833856 | 213 | 3328 | 1.2 |reached target block errors\n",
" 3.333 | 1.1311e-03 | 9.6227e-03 | 27354 | 24182784 | 202 | 20992 | 7.5 |reached target block errors\n",
" 6.667 | 1.1610e-04 | 1.1016e-03 | 17120 | 147456000 | 141 | 128000 | 45.6 |reached max iterations\n",
" 10.0 | 1.2227e-05 | 1.2500e-04 | 1803 | 147456000 | 16 | 128000 | 45.6 |reached max iterations\n",
" 13.333 | 6.7817e-09 | 7.8125e-06 | 1 | 147456000 | 1 | 128000 | 45.6 |reached max iterations\n",
" 16.667 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 45.6 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 16.7 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.9858e-01 | 8.8672e-01 | 58564 | 294912 | 227 | 256 | 0.3 |reached target block errors\n",
" -6.667 | 1.2695e-01 | 6.2891e-01 | 74881 | 589824 | 322 | 512 | 0.6 |reached target block errors\n",
" -3.333 | 6.4579e-02 | 3.7891e-01 | 57135 | 884736 | 291 | 768 | 0.8 |reached target block errors\n",
" 0.0 | 2.5956e-02 | 1.5625e-01 | 38274 | 1474560 | 200 | 1280 | 1.4 |reached target block errors\n",
" 3.333 | 8.0132e-03 | 5.5990e-02 | 35448 | 4423680 | 215 | 3840 | 4.1 |reached target block errors\n",
" 6.667 | 3.2711e-03 | 2.0662e-02 | 36658 | 11206656 | 201 | 9728 | 10.5 |reached target block errors\n",
" 10.0 | 1.1048e-03 | 7.5120e-03 | 33886 | 30670848 | 200 | 26624 | 31.1 |reached target block errors\n",
" 13.333 | 3.5476e-04 | 2.3508e-03 | 34944 | 98500608 | 201 | 85504 | 92.5 |reached target block errors\n",
" 16.667 | 1.4900e-04 | 1.1328e-03 | 21971 | 147456000 | 145 | 128000 | 138.3 |reached max iterations\n",
" 20.0 | 1.4360e-04 | 1.1953e-03 | 21174 | 147456000 | 153 | 128000 | 138.2 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.9708e-01 | 8.7109e-01 | 58121 | 294912 | 223 | 256 | 0.3 |reached target block errors\n",
" -6.667 | 1.1754e-01 | 6.1328e-01 | 69325 | 589824 | 314 | 512 | 0.6 |reached target block errors\n",
" -3.333 | 5.0105e-02 | 2.9818e-01 | 44330 | 884736 | 229 | 768 | 0.9 |reached target block errors\n",
" 0.0 | 1.5282e-02 | 9.8524e-02 | 40562 | 2654208 | 227 | 2304 | 2.6 |reached target block errors\n",
" 3.333 | 2.7306e-03 | 1.9341e-02 | 33017 | 12091392 | 203 | 10496 | 11.7 |reached target block errors\n",
" 6.667 | 8.2513e-04 | 5.5408e-03 | 34311 | 41582592 | 200 | 36096 | 40.4 |reached target block errors\n",
" 10.0 | 3.2446e-04 | 2.9151e-03 | 25644 | 79036416 | 200 | 68608 | 76.8 |reached target block errors\n",
" 13.333 | 2.5470e-04 | 1.9387e-03 | 30571 | 120029184 | 202 | 104192 | 116.6 |reached target block errors\n",
" 16.667 | 1.7617e-04 | 1.4141e-03 | 25977 | 147456000 | 181 | 128000 | 143.3 |reached max iterations\n",
" 20.0 | 1.4984e-04 | 1.2891e-03 | 22095 | 147456000 | 165 | 128000 | 143.4 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.2746e-01 | 9.3750e-01 | 67082 | 294912 | 240 | 256 | 0.3 |reached target block errors\n",
" -6.667 | 1.3562e-01 | 7.3633e-01 | 79990 | 589824 | 377 | 512 | 0.6 |reached target block errors\n",
" -3.333 | 5.1472e-02 | 3.0990e-01 | 45539 | 884736 | 238 | 768 | 0.9 |reached target block errors\n",
" 0.0 | 1.1238e-02 | 7.7060e-02 | 36456 | 3244032 | 217 | 2816 | 3.3 |reached target block errors\n",
" 3.333 | 2.7477e-03 | 1.6872e-02 | 38085 | 13860864 | 203 | 12032 | 14.0 |reached target block errors\n",
" 6.667 | 6.5204e-04 | 4.2213e-03 | 35767 | 54853632 | 201 | 47616 | 55.4 |reached target block errors\n",
" 10.0 | 3.2493e-04 | 2.2103e-03 | 34210 | 105283584 | 202 | 91392 | 106.4 |reached target block errors\n",
" 13.333 | 1.6335e-04 | 1.3984e-03 | 24087 | 147456000 | 179 | 128000 | 149.0 |reached max iterations\n",
" 16.667 | 1.6349e-04 | 1.2109e-03 | 24108 | 147456000 | 155 | 128000 | 149.0 |reached max iterations\n",
" 20.0 | 8.4303e-05 | 8.2031e-04 | 12431 | 147456000 | 105 | 128000 | 149.0 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.1092e-01 | 8.7109e-01 | 62203 | 294912 | 223 | 256 | 0.3 |reached target block errors\n",
" -6.667 | 1.3839e-01 | 6.9336e-01 | 81623 | 589824 | 355 | 512 | 0.6 |reached target block errors\n",
" -3.333 | 6.4082e-02 | 3.5547e-01 | 56696 | 884736 | 273 | 768 | 0.9 |reached target block errors\n",
" 0.0 | 1.8703e-02 | 1.2221e-01 | 38611 | 2064384 | 219 | 1792 | 2.0 |reached target block errors\n",
" 3.333 | 3.7313e-03 | 3.3040e-02 | 26410 | 7077888 | 203 | 6144 | 7.0 |reached target block errors\n",
" 6.667 | 9.9988e-04 | 8.0541e-03 | 28603 | 28606464 | 200 | 24832 | 28.1 |reached target block errors\n",
" 10.0 | 3.5812e-04 | 3.0912e-03 | 26826 | 74907648 | 201 | 65024 | 73.6 |reached target block errors\n",
" 13.333 | 1.7352e-04 | 1.6209e-03 | 24665 | 142147584 | 200 | 123392 | 139.7 |reached target block errors\n",
" 16.667 | 1.4918e-04 | 1.2266e-03 | 21998 | 147456000 | 157 | 128000 | 144.9 |reached max iterations\n",
" 20.0 | 1.5997e-04 | 1.4219e-03 | 23588 | 147456000 | 182 | 128000 | 144.8 |reached max iterations\n"
]
}
],
"source": [
"BER = {} # Store the results\n",
"\n",
"# Perfect CSI\n",
"ber_lmmse, ber_ep, ber_kbest, ber_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"bit\", EBN0_DBs, True)\n",
"BER['Perf. CSI / LMMSE'] = ber_lmmse\n",
"BER['Perf. CSI / EP'] = ber_ep\n",
"BER['Perf. CSI / K-Best'] = ber_kbest\n",
"BER['Perf. CSI / MMSE-PIC'] = ber_mmse_pic\n",
"\n",
"# Imperfect CSI\n",
"ber_lmmse, ber_ep, ber_kbest, ber_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"bit\", EBN0_DBs, False)\n",
"BER['Ch. Est. / LMMSE'] = ber_lmmse\n",
"BER['Ch. Est. / EP'] = ber_ep\n",
"BER['Ch. Est. / K-Best'] = ber_kbest\n",
"BER['Ch. Est. / MMSE-PIC'] = ber_mmse_pic"
]
},
{
"cell_type": "markdown",
"id": "2755232f-5704-4246-9fc2-c08304f8cf5a",
"metadata": {},
"source": [
"Finally, we plot the results."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "818ccd65-02ab-4636-9cf5-887862ec978d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-06T21:22:13.611546Z",
"iopub.status.busy": "2026-08-06T21:22:13.611404Z",
"iopub.status.idle": "2026-08-06T21:22:14.155244Z",
"shell.execute_reply": "2026-08-06T21:22:14.154308Z"
}
},
"outputs": [
{
"data": {
"image/png": 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hhBBCCCGEECLfSaJSCPFeGj9+PI0bN87VOr/77jtq1qyZq3UKIYQQQoi326VLl3B3d+fQoUP53RQhhHjjSaJSCJHnfH198fLyokmTJlSrVo2uXbsyf/58oqKi8q1NISEh+Pv752qdoaGh+Pn55Wqd75ITJ07g7u7OiRMn8rspQgghhBAvLSAgAHd3d51P9erV+fDDD9mwYQMqlUqnfHx8PDdv3iQmJkZ77PTp07i7u+Pj4/O6m//WadCgARMnTszvZgghXhNJVAoh8tRff/1FuXLluHv3LuPHj2fp0qW0atWKX375hYoVK+Z388RrFBsby82bN4mNjc3vpgghhBBCvLTExERu3rxJkyZN2Lp1K1u3bmXevHlUqFCBHj168Mknn+iUr1y5Mjdu3KBJkybaY3FxcemSlyJjt27d4sGDB/ndDCHEa2KY3w0QQrzbPvvsM9zd3fn3338xMDAAoEqVKnTv3j1dECeEEEIIIcTbws7ODnd3d+33WrVqceHCBZYuXcqMGTOwsLAAwNTUVKecEEKIzMmISiFEnklMTOTBgwd4enpqk5RprKysWLVqlfb7H3/8gaenZ4ZvS9etW4e7uzvXr18Hnq0vmZiYyKRJk6hZsybNmzdnz549gGZ6zTfffEOtWrVo0KABf/75Z6ZtTEhI4Ouvv6ZmzZrUr1+fmTNnkpKSkq7cjRs3GDJkCNWrV6datWoMHjyYa9euvVS/gGaa+Jdffkm9evWoVKkSXbp0Sbdu0fPPOW3aNOrUqUPXrl0BqFmzJlOmTCEwMJBBgwZRpUoV5s6dC0BMTAzff/89jRo1omLFirRr146///5bp+5Dhw7h7u7O+fPn2bZtG61bt6ZcuXLcu3cvw/ZmVf7evXs6U58qV65Mly5d+Pfff7XXb9y4kYEDBwIwcOBAbdl169Zpy6SmprJ06VLatGlDxYoVadKkCb///jvJyckv3c9CCCGEEK9TwYIFSU1N1Zn+/eIalVu2bKFv374ADBkyRBsXrV69Osu6U1NTWb58OW3btqVixYo0btyYWbNm6cRKPj4+uLu7c/r0aXbs2KGN2QICAvjuu++oVq0aqamp/PLLL9SrV4+WLVtqr/3333/p1KkTlSpVon79+kyePJmnT5/qtCGrGDQjWZXv2bOn9tnLly9PkyZN+P7777Wzb5KSknB3dycsLIwtW7Zoy/bs2VPnHqdPn6Zfv35Uq1aN6tWr4+XllWlMK4R480miUgiRZ0xMTChbtiyHDx8mMDAw3fnnk5dt27bF19eXpUuXpis3Y8YMlEolnp6ewLP1JUeMGEGxYsWYO3cunp6etGvXjjNnztCvXz+cnJyYM2cO1atXp3fv3hw4cCDDNg4ePBhHR0fmzp1L9+7d+frrr+nXr59OmaNHj1KtWjXu3r3LTz/9xC+//EJwcDDVq1d/qUXRb9y4QaVKlTh48CATJkxgyZIleHp68sEHH7BmzRptubTnHDx4MDY2NsycOZNKlSoB4Ofnx7Vr1xgwYABt27bl119/xcLCgtjYWOrXr8+cOXMYMmQIS5YsoUaNGvTu3ZuxY8dq646JieHmzZusXLmSTZs2MXHiRIYMGZJpUjCr8o6OjtppT1u3bmXBggWULVuWDz/8UPs8LVq0YOrUqQBMnTpVW7Zt27YApKSk0L59e8aPH0+bNm1Yvnw5Q4cO5ZdffqFr166o1eoc97MQQgghxOt08eJF/vvvP1q2bImlpaX2+ItrVDZt2pTvvvsOgMmTJ2vjovbt22dad2pqKp07d+azzz6jZcuWLFu2jOHDhzNz5kw6deqkTYymxWxr1qzh77//ZuLEiQwbNoykpCRCQ0O5efMmn376KQC//fYbtWrVAmDKlCl07tyZMmXKsGjRIkaNGsWKFSuoVasW4eHh2nZkFoNmJqvyP//8s/bZ165dS//+/Vm8eDFdunQBwMjIiK1bt2Jra0vTpk21ZX/++Wdt/StWrKBu3boYGxszY8YMfvvtN+7du0e1atW4ffu2/r95Qog3h1oIIfLQ4cOH1ba2tmoTExN169at1VOmTFHv3LlTHRcXl65s69at1S4uLuqUlBTtsRMnTqgB9W+//aY91rt3b7VCoVCvXbtWe0ylUqldXV3Vzs7O6pUrV+ocL1GihLpjx44690qrY+nSpTrH586dqwbUBw4c0B7z9PRUlyhRQp2cnKw9lpKSoi5btqy6dOnSapVKpVar1WovLy+1tbV1tn1St25dtaurqzomJkbn+MiRI9W2trbq2NhYnTYuXrxY53nUarXa2tpabWxsrL57967OualTp6oB9bFjx3Tq/vrrr9WA+syZM2q1Wq3etm2bGlA3b95cp1xa/S/KaXm1Wq3u37+/2t3dXft97969akC9d+/edGX/+OMPNaDet2+fzvFjx46pAfWWLVsyvY8QQgghxOt048YNNaC2s7NTly1bVl22bFm1o6OjWqFQqD/88MN0MV5aPLtt2zbtsYMHD6oB9c6dO/W65/z589WAeteuXTrHT506pQbUGzZsUKvVavXOnTvVgLpx48Y65VQqldrLy0utUCjUv/zyi87xW7duqZVKpXrEiBE611y7dk2tVCrVo0eP1h7LLAbNTE7L79u3Tw2oz549qz3m4OCg7tevX7qyISEhalNTU3X//v11jicnJ6vLlCmj7tKlS6b3EUK8uWREpRAiTzVs2JDAwEAWL16Mi4sLO3bsoG3btjg4ODBv3jydsp988glBQUFs27ZNe2zBggUYGRnx8ccf65RVKBR069ZN53vlypUJCQmhR48eOserVKmCr69vhu376KOPdL6nTcPx9vYGNLs6Xr9+nd69e2No+GxZXwMDAz7++GNu3brFzZs39e6P4OBgjh8/Ts+ePdO9fe7cuTMRERGcOnVKe0ytVuu0UaFQaH9dr149ihUrpnNu+/btlC5dmrp16+rUPWDAAJ3nStOrVy+d78/Xn5HMyp88eZKBAwdSs2ZNPDw8cHd3Z9u2bfj7+5OampplnQDr16/H1dWVZs2a6RyvW7cuhQsXZufOndnWIYQQQgjxOnXr1k07ys/b25v58+dz+PBh+vXrl27n71e1fv16nJ2ddaZqg2ZqtZOTU7pYKbOYTa1W07t3b53jO3fuRKVS0b9/f51rPD09qVmzZrr4MaMYNCuZlQ8LC+Pbb7+lSZMmlC9fHnd3d4YOHQqQaez+vP/++4+EhAQGDRqkc9zQ0JB27dqxa9eubOsQQrx5ZDMdIUSes7Kyom/fvtok4IMHD+jTpw+ffvopbm5utGnTBoA2bdrg5ubG/Pnz6dSpE0+ePGH9+vW0b9+ewoUL69RZuHBhjI2NdY7Z2NhgZ2eHqalpuuNPnjxJ1y47OzvMzMx0jllaWmJtba1dKzMkJARAJ7hK4+rqqi2j7wLpd+/eBWDNmjV4e3trpzSr1WoSEhIAzfqVz7cxs+k0GbUpJCSEsmXLZlo27XmyqiMrGZXfvn07HTp04KOPPuK7777DyckJQ0NDZs+ezaJFi0hOTk63RumL7t69y5MnTyhfvjyg6Y+0T2RkpE6fCCGEEEK8CV7cTKdq1aoYGxszaNAg/vrrL52E4Ku6e/cuERERGcZKT548SRcrZRbjGRoa4ujoqHMsu3j30qVLetWdmYzKR0VFUbNmTUxMTPj2228pW7Ys5ubm3L59m3bt2hEfH59tvWlx9eDBgzE0NNT2B2iSoLGxsURHR+tMwxdCvPkkUSmEeO0cHR2ZNWsWlSpVYvv27dpEpVKpZNiwYUycOBF/f3+2bt2a4VtSINPEV2bH1RmscRgZGYlKpUKpfDa4PDk5WSegSftvREREuuvTkp85CX7Syvbv3z/dKNE0Tk5O2l+/mHR9XkbnLC0tM2xr2tpCL7Y1q/r1vefvv/9OqVKlWL16tc4bdX0CzDSWlpbY2dnx119/ZXi+QIECOWqnEEIIIUR+qF69OgAnTpzI1USlpaUlFhYWbNiwIcPzL8ZKmcV4JiYm6UZAPh/vFilSROfckydP8iR+3Lp1KwEBAZw6dYqaNWtqj9+/f1/vetPaNWfOHIoWLZphmazWzxRCvJkkUSmEyDNpOxMOGTIk3bnExEQAnSQhwKBBg5g8eTLz589n27ZtGU5xyS3JyckcO3aMBg0aaI8dOXIElUqlnTrt4eGBjY0N+/fv54svvtC5ft++fVhaWlKhQgW97+np6YmDgwMXL17k+++/z50HeU7dunVZu3YtoaGhOoHmvn37AM3Um9wWHR1NkSJF0iUpd+/erVMuLUjNaFf1pk2bsmzZMgoWLJhu9KwQQgghxNsibVZOdi+ys4qLMtK0aVMWLFiAjY1NumTiq0qLe/fv34+Hh4f2eFRUFGfOnKFp06a5ej/QxI9AutGdW7ZsSVfW1NQ00/gRwN/fnxYtWuR6G4UQ+UPWqBRC5Bm1Ws3QoUPp3bs3t27d0h6/ffs2o0ePxtDQUDsdPI29vT3du3dnzpw5+Pv7079//2ynDb+sQoUKMXv2bIKCggC4d+8eo0ePplSpUnTv3h3Q7DY4ceJEdu/ezdy5c1GpVKjVahYuXIi3tzdffvlljt4qGxgYMGvWLHbs2MHXX39NXFwcoOmrq1evMmTIEL3WdMzM559/jlKpZODAgURGRgJw+fJlJk6cSJUqVejQocNL152ZRo0acfLkSY4dOwZAbGwsgwYNomTJkjrlSpQogUKh4OLFi+nqmDhxIjY2NnTt2lXnz0p4eDgzZ85k7969ud5uIYQQQojcFBISwuTJkzE2NqZPnz5ZlnVzc0OpVGYYF2VkwoQJ2Nvb06VLF/z8/LTHw8PDmTVr1iutx9i4cWMaNmzItGnTtGulx8bGMnjwYOLi4vjqq69euu7MNGjQAKVSyaxZs7Tx9bp16zJcm7JkyZJcu3YtXbKyVq1a9O7dm4kTJ7JlyxbtDKrk5GR27typ3VldCPF2kUSlECLPGBoa8ueffxIeHk6lSpVwdnamSJEiuLu7o1Ao2LNnDzVq1Eh3nZeXFykpKSgUCu0mMHnB2NiYqVOn0rJlS5ydnSlevDg2Njbs3r1bZ+3K8ePH89tvvzFt2jRsbW2xtbXlm2++4ZdffmHixIk5vm+vXr3Ytm0bO3fuxNrammLFimFlZUWfPn2oXbv2KyVm3d3d2bdvHw8fPsTe3h4nJyeqV69O7dq12b17t86GQLnlm2++oVOnTjRs2JCiRYtSokQJGjVqlO7NtpOTE19++SXffvstrq6uuLu7s27dOu25kydPUqRIESpWrEjhwoVxcHDAw8OD0NBQKleunOvtFkIIIYR4FQsXLsTd3R13d3eKFStG8eLFMTU15eDBg5QrVy7Lax0cHPjqq6+YNm2aNi5avXp1puWLFCnCyZMncXFxoVKlShQqVAgHBwfc3d0JDg6mSpUqr/Qs//77L61ataJRo0YUKVIEW1tbbty4wc6dO7XT2XNTxYoVWbBgAcuWLcPe3h57e3u8vb2ZOXNmurJTp04lJCSEwoUL4+7uTs+ePbXnVq5cyfjx4/Hy8sLCwgJXV1dsbGxYsGBBnowEFULkPYU6o4XbhBAil6nVah48eEBiYiKOjo5ZjkKMiIigUKFCNGzYkAMHDqQ7/+DBA+Li4tKN2Hvw4AGxsbGUKlVK53hoaCgxMTE6x5+vQ61WExISgpGRUZbTjlUqFcHBwajValxcXNJNW3/48CFRUVGULl06y754XmRkJOHh4Rn2SWbPCXDr1i2srKxwcHDItO4nT54QGRlJkSJFMDc31zkXGxvL/fv3cXV1TbehUEb0KR8XF0dYWBiOjo4YGRkRFhZGWFgYZcuW1ZkWHhcXR0hICCkpKRQpUgQbGxudepKSkggJCdGuWymEEEII8SZJSkrizp07OsdMTU1xdnbGyMgoXfmEhAQCAwMpWrRoujUT4+PjCQkJITk5GQcHB2xtbfW6f0hICAUKFMDe3l7nXFrMVqxYsXTx38OHD4mMjKRMmTKZ1p2QkKCNwwoVKpTuvD4xaE7Kq1QqgoKCsLW1xdLSkuTkZG7fvo2joyPW1tY65YKDg4mLi8PU1FS7qeXzQkNDSU5OxtnZOV2cLoR4e0iiUgjxxlm+fDmDBg1i7dq1uboIuRBCCCGEEEIIId5c7/xrhri4OL744gs++OADfvjhB1QqVX43SQiRhYSEBObNm4erq6t2nUghhBDibffnn3/Stm1b+vfvr10bWQghhBBC6HrnE5UjR44kKCiIzz77jH379jFjxoz8bpIQIhNt2rTB2dmZoKAgVq9eneHUGSGEEOJtc/DgQb766iuGDx9OiRIlaNu2bX43SQghhBDijfRWTP1OTExEpVJluY5acnJyhkkNKysr7t69i62tLefPn2fQoEFcuHAhL5srhHhJAQEBqNVqihYtKklKIYQQb5TU1FTi4+MxNzfPdO0ztVqNSqVKtyna8OHDKV++PJ9++ikAnp6ebNiwIdvNNoQQQggh3jdv9IjK/fv307VrVywtLWnQoEGGZU6ePEmVKlUwMzPD2tqaL774Qju9++nTpxgaGmoXJC5ZsqRMtRHiDebm5kaJEiUkSSmEEOKNce/ePSZNmoSLiwuWlpZcv349XZmoqCj69OmDmZkZpqamNG/enICAAO35oKAgSpQoof0uMakQQgghRMbe2ERlYmIiP/zwA927d2fIkCEZlnn06BGtWrWiWbNmREZGsm/fPlasWMF3330HgIWFBQkJCaQNGo2JicHS0vK1PYMQQgghhHi7zZs3D1NTU5YvX55pmUGDBnHx4kVu3rzJw4cPMTMzo23btqSkpABgaWlJTEyMtrzEpEIIIYQQGTPM7wZkxsTEhP379wNw/PjxDMusWLECpVLJTz/9hKGhITVq1GDkyJHMmTOHr7/+GiMjI9zd3fH29qZ9+/Zs2LCBmjVrZnrPxMREEhMTtd9VKhXh4eHY2dmhUChy9wGFEEIIIV4DtVpNdHQ0Tk5OmU5ZFpn7+eefAbh48WKG54OCgti0aRNbtmzB1dUVgN9//52SJUuye/du2rZtS40aNdi8eTPdu3fn/v37XLlyJctp3xKTCiGEEOJdkpN49I1NVOrjxIkT1KlTB0PDZ4/RpEkTpkyZgr+/P2XLluW3336je/fuFC5cmOjoaPbu3ZtpfT/++CNTp059HU0XQgghhHit7t+/j4uLS343451z8uRJ1Go1DRs21B4rUaIERYsW5eTJk7Rt25bBgwfz999/U7JkScLDw5k+fTrW1taZ1ikxqRBCCCHeRfrEo291ovLRo0eUKVNG51ihQoW058qWLUvTpk25d+8eAQEBlC5dGmNj40zrmzhxIuPGjdN+j4yMpFixYgQEBOTp9Jzk5GQOHjxIkyZNZG2+XCJ9mjekX3Of9Gnukz7NG9Kvue919Wl0dDRubm4y1TiPPHr0CENDQ2xsbHSOFypUiEePHgGazR1PnjyJv78/dnZ22NnZZVlnfsSk8nc8b0i/5j7p09wnfZo3pF9zn/Rp3ngd/ZqTePStTlQC2o1zXvz+/LQYc3NzvXZVNDExwcTEJN3xggULYmVl9YotzVxycjLm5ubY2dnJX7ZcIn2aN6Rfc5/0ae6TPs0b0q+573X1aVrdMmU476Sth/48lUql0+dKpTLdC/bM5EdMKn/H84b0a+6TPs190qd5Q/o190mf5o3X0a85iUff6oWKnJyctG+q06R9d3R0zI8mCSGEEEKI94iTkxOpqak8efJE5/ijR48kHhVCCCGEyKG3OlFZv359jh07prPY+N69e3F0dKREiRL52DIhhBBCCPE+qFOnDkqlUrsJJMCNGzcICQmhfv36+dgyIYQQQoi3zxudqIyPjycmJobk5GRUKhUxMTHExMRoz/fv358CBQowdOhQAgMD2bZtG3/88QcTJkyQ6U1CCCGEEOKVJScnExMTQ3x8PKAbnwI4ODgwYMAAvvzyS06dOoWvry9Dhw6lZs2aNG3aND+bLoQQQgjx1nmj16hs1KgR169f134vUqQIAA8fPsTCwgIbGxv279/PuHHjqF69OgULFmTKlCmMHj06v5oshBDiDZKamqpNJohXk5ycjKGhIQkJCaSmpuZ3c94JudWnRkZGGBgY5GLLxPMWLFjAV199BYCFhQVNmjQBYObMmQwdOhSAuXPn8vXXX9OtWzcSExNp3rw5M2fOlBfnQgghAIlJc4vEo3kjN/o1N+PRNzpRefr06WzLeHp6smvXrly977x585g3b578wRdCiLfYw4cPiY6Ozu9mvDPUajVFihTh/v37knzJJbnZpzY2NhQpUkR+b/LAqFGjGDVqVJZlTE1NmTFjBjNmzMjVe0tMKoQQbz+JSXOPxKN5I7f6Nbfi0Tc6UZlfvLy88PLyIioqCmtr6/xujhBCiByytLQkKioKBwcHzM3NJZDJBWlLsBQoUACl8o1eOeatkRt9qlariYuLk80E31ESkwohxNtNYtLcJfFo3njVfs3teFQSlUIIId4pqampWFpaUqhQIezs7PK7Oe8MlUpFUlISpqamEhjmktzqUzMzM0Czy3ThwoVlGrgQQgjxBpCYNPdJPJo3cqNfczMeld9ZIYQQ75SUlBSUSiXm5ub53RQhXpu0P++y/pUQQgjxZpCYVLxvciselUSlEEKId4parQaQqTXivSJ/3oUQQog3i8Sk4n2TW3/WJVEphBBCCCGEEEIIIYTId5KoFEIIId4Dq1evxtXVFaVSyaRJk/K7OUIIIYQQ4j1z584dGjRogJmZGS4uLvndHPGGkkRlBubNm4enpyc1atTI76YIIYR4j1SuXBmFQoFCocDMzAxPT09+//33V643ISGBYcOG8fPPP5Oamsp3332X7TUqlYp58+ZRo0YNChQogJ2dHS1atGDJkiUkJSVpyy1evJhKlSpRoEABXF1dGTBgADdv3tSe37hxIwqFgpiYmCzvt337dkqWLJnhuezqSDtfpEgRUlJSdM4dOXJE26dp53JaPifPmdEnMDAwy2cXIjMSkwohhHjdZs+erY1hlEolLi4u9OvXjwcPHrxy3dOmTaNgwYJEREQQFBSk1zWnT5/mww8/pHDhwpibm1OzZk1GjRrF/fv3tWUuXrxI+/btKVy4MDY2NjRu3JhNmzZpp98DFChQgLlz52Z5L7VaTZEiRTh69GiG57Oro0CBAigUCry9vdPVW6pUKRQKBUuXLn3p8vo+Z0bx6JQpU7J89jeJJCoz4OXlxfXr1zlz5kx+N0UIIUQ+mLXXjz/238rw3B/7bzFrr1+e3Xv06NGo1WrCw8OZMGECY8eOZdmyZa9UZ1BQEAkJCdSrV0+vtWNUKhVdunThxx9/ZPz48QQFBREQEMDPP//MhQsX2L9/P6BJ3o0dO5ZvvvmGBw8ecOLECZo1a8b333+f4zZ6e3vTrl27HF/3PFNTU7Zv365zbOnSpbi6ur5S+Zw8Z3R0NGq1WudTvHjxV3ou8f6SmFQIId5f+RmPWltbo1arSUlJYceOHZw+fZru3bu/cr3+/v5Ur14dU1NTvcpv2LCBBg0aUKZMGU6dOkVERAR//fUXnp6e/PbbbwCEhobSrFkznJycOH/+PEFBQXz//fesX7+eGzdu5Kh9p0+fJjk5mbp16+b42dK4urqmi90PHjxIXFwcxsbGL10+J885Z86cdPGoJCqFEEKIt5iBUsHMDILDP/bfYuZePwyUeb8oupmZGf369aNBgwb8999/ABw+fJh69eppp8t4eXkRHR2tvWbu3LmUKVOGH3/8EWdnZ5RKJYsWLaJ06dIAFCtWDIVCwdWrV7O898qVK9m6dSubNm2iW7du2NjYYGVlRdWqVZk/fz6tW7cGYMuWLbRt25auXbtiaWmJk5MTffr0YfXq1Tl+3h07drxyorJ///46gV5kZCSbNm2if//+r1Q+N59TCCGEEEIfb0I8qlQqqVixIuPHj8fHx4fw8HDCw8MZOHAg9vb2WFlZ0bhxY86dO6e9JiYmBoVCwezZs6lWrRomJibMnj0bQ0NDjh07xrfffotCoeDzzz/P8t5Pnz5l8ODBDB48mJ9++gk3NzdMTEwoWbIkw4cP1846Onz4MOHh4cycORMXFxcKFChAvXr1+Oeff/D09MzR83p7e9OqVSsMDAxy3ln/17dvX3bu3MnDhw+1x5YuXUq/fv0yHDCgb/ncfM43nSQqhRBCvPPUajVxSSl6fwY3cGNk01LM3OvHjD03iUtKYcaem8zc68fIpqUY3MBN77qen4rxKs6ePUv79u355JNPePToEUePHsXX15chQ4bolLt16xaXLl3izJkzqFQqhg0bpn3Lev/+fdRqNeXLl8/yXuvWraN+/frUqlUry3IFCxbk4sWLPHr06JWe7dKlS0RERNCoUaNXqqd///7s2bOH0NBQQPMcVapUwd3d/ZXK59ZzCiGEEOL99S7EoyqVipYtW5KSksKFCxcIDg6mQ4cONG/eXBtPpZkxYwZ//PEHsbGxjBkzhpSUFGrVqsX06dNRq9XaEZGZ2bZtG1FRUYwbNy7LcgULFgRgz549r/Zw5M4Mn2LFitGoUSPWrFkDQEREBFu3bmXgwIGvVD43n/NNZ5jfDRBCCCHyWnxyKp7f7n6pa+cc8GfOAf9Mv2fn+rSWmBvn/MdtfHw8mzZtwsfHh4ULF/Ljjz/St29fevfuDYClpSWzZs2icuXKLF26lAIFCgBgZGTEokWLsLa2zvE909y6dYsWLVpkW27y5Mm0adOGokWLUrt2bWrVqkWrVq1o2rRpju7n7e1NixYtMpwOkxMuLi40bdqUVatWMWHCBJYtW8ann376yuVz8pyWlpY63x0cHNIF7kIIIYR4/7yN8aharebatWv89ttv1K1blxMnTuDv78+JEycwNNTUN27cONauXcumTZvw8vLSXvvVV19Rr169HN8zza1btzAyMsLNzS3Lcs2bN6dv3758+OGHlC1bljp16tC4cWM6d+6MlZWV3vcLDg7m6tWrtGrV6qXbnGbQoEFMnjyZzz//nLVr11KjRg3tDKeXLZ+T5xw5ciQjR47UObZz585cebbXQUZUCiGEEG+Q33//HYVCga2tLd999x2//vorQ4YM4ezZsyxYsABDQ0MMDAxQKpVUqlQJtVpNQECA9noXF5dXSlKCJijVZy3LMmXKcP36dby9vWnQoAGnTp2iefPmdO7cOUdv7nPj7XWaQYMGsXz5ci5cuICfn1+26ynpUz4nz/niGpWSpBRCCCHE2yYyMhKFQoGBgQEffPABVapUYf369Zw9e5anT59iamqKgYGBNia9cOECd+7c0amjXLlyr9QGfeNRhULBqlWruH79OsOHDychIYExY8ZQtmzZHK1R6e3tTd26dbG1tX2VZgPQqVMnHj9+zIkTJ1i2bBmDBg165fI5ec6M1qh8W5KUICMq88/T+xD3BACF3z5aXZ6FwmYclG6mOW9uBzZF87GBQgjx7jAzMuD6tJY5vm7BodvMOeCPkYGC5FQ1I5uWYkTjjHemzureOTF69Ghmz56d7rhKpWL69Ol89dVXWV7/qqMSQZOYe35H6+zu16JFC+0IzH/++YeePXuyb98+vUZlhoWFcfbsWbZu3foqTdbq0KEDI0aMYNiwYfTs2RMLC4tcKf+qzymEEEKI99vbFI9aW1vz9OnTdMdVKhWlS5fGzy/7jXxeNSYtU6YMSUlJBAQEULJk9s/r4eGBh4cHoIkvq1atynfffce6dev0ul9uvjg3NjamT58+jB49moCAALp27Zpr5V/1Od8GMqIyA/PmzcPT05MaNWrkzQ2e3oc/KsHiRrC4EYaHpmOSGoPhwWnaY/xRSVNOCCHEK1MoFJgbG+bos/RoAHMO+DOuRRlufd+GcS3KMOeAP0uPBuSoHn3eBOujatWq7Nq1K1fqys5HH32Ej48Pp0+fzvG1aetMRkRE6FV++/btVK1aFQcHhxzfKyNGRkZ8/PHHnDlzJtO1gF6lfJqcPqcQLyPPY1IhhBCvzbsSj96+fZtbtzLejTw3tWvXDktLS2bOnJnja+3t7fH09NQ7TktISODAgQO5lqgEzaydM2fO0LNnT8zNzXO9POT8Od8WkqjMgJeXF9evX+fMmTN5c4O4J6BKzbqMKlU74lIIIcTrlbab4rgWZRjVTLM+zKhmpRnXokyGuy++Dl9//TUnT55k3LhxBAcHExERwa5du7J9Q5uRs2fPolAo8PHxyfD8gAEDaN++PR9++CEbN24kMjKS6OhoLly4gJeXFzt37gRg8ODB/P777/j6+pKQkMCdO3cYN24ctra2NGzYUK+25Obb6zQzZsxArVZTp06dXCmfG88pxMvI85hUCCHEG+tNjEfbt29PlSpV6NGjB2fOnCE2NpYrV64wcuRIDh8+nOP6ypcvz+DBgzM8Z2try+LFi1myZAkTJ04kMDCQpKQk7ty5w8KFCxk9ejQAGzduZOjQoRw/fpzIyEiePn3KihUrOHjwIB07dtSrHfv376dIkSLakYq5oUKFCqjVahYtWpQr5XPjOd8WkqgUQgghXpCqUusEhWnSgsNUVe7snJgTNWvW5ODBg1y6dImyZctSunRp5s6dy/jx43P9Xkqlki1btjBhwgR++uknnJyccHV1Zfz48VSqVIlmzTTLlHzzzTfcv3+fzp07Y2trS/369YmPj9cGe9lJTk5mz549eicqLS0tUSgUOp+UlJRXelZ95OQ5M2rjoUOH8ryNQgghhHi3vInxqKGhIfv27aN27dp07NgRe3t7Pv74Yzw8PF5p45zM9OzZk8OHD3Pjxg1q1KiBjY0NPXr04Nq1a3z++eeAJnlau3ZtJkyYgKurK8WLF2fu3LnMmTOHYcOG6XWfnLw4HzlyZLpYb+nSpS/9jPrKyXNm1Mb+/fvneRtzi0KdW/vUv4OioqKwtrYmMjIyR7tFZSv4PCxpkn25IQfBuWru3fc9kpyczI4dO2jTpg1GRkb53Zx3hvRr7pM+zX3R0dH4+fnh4eGh97QJkT2VSkVUVBRWVlYolbnznnP//v3069ePoKCgXKnvbZObfZqQkEBAQABubm6YmprqnMuzeEa8Nq/j91B+HuUN6dfcJ32a+6RP84bEpLkvL+JRAFdXV5YuXfrerjueW/2aW/GobKaTH0Iu6lduSXOwLwXF60G5zuBSE4xMs79OCCGEeAts376dtm3b5nczhBBCCCHEe+ry5cuEh4dr1x8X+U8SlfkgOugalnqVTIWwm5rP2eWaQ+b24FQFSjbTjLYsUh6Ms97RVAghhHgTvczi6EIIIYQQQuSWihUrEh0dnd/NEM+RRGU+iCpQQs9E5f8pDUH1/zW44sLAf6/mA4AC7MuAY6XnPhXB1DqXWy2EEEIIIYQQQgghRN6RRGU+iC1YXq9yjzwHUjjOHzovAlUyXNsCR36DpP9n+w2MITXp2ajLK+ufXVywxAvJy8pgXjD3H0YIIYQQQgghhBBCiFwgicp8cC3KhJJqBQaKzPcxSlUr8CnUgw+b1H52sP4YSIyGkwshOUaTpHyRhQPEPoTwO5rPtS3PzlkX04y2dKz8LIFp6ZBrzyWEEEIIIYQQQgghxMuSRGUG5s2bx7x580hNTc2T+i9GFeDXxN9pY3CCLww3YKJI0Z5LVBvya0p3dqTWJmT3E1Zc86GlpwM1ihekUlEbTJt9A02+hsc3INBH8wk4CgkRoDCAcdcgIQoeXIC1XYHnkqGR9zQfX+9nxwoU0SQsnSo/S15aOYNCkSfPLoQQQggh9JPXMakQQgghxJtGEpUZ8PLywsvLS7t9em4r42DJKuxZmtqef1Prc8hkHOYkEospTRJn8BhbbdkrQZFcCYoEwEipoGJRG2oUL0hNN3uqVRyIda1hoFJppn4/uQ0GRmBhp9kh3MAEUhMyaYUCUENMKNwKhVu7n50yt9OdMu5YCWyLS/JSCCGEEOI1yuuYVAghhBDiTSOJynxQ3vlZoPkYW8YnD2Oy0SqmJPfTSVK+KFml5tzdCM7djWDhYc0x9yKW1ChekBpuBalZvDlF0gqbWEKr7zWjLQMOQ3zEC7U9N9LSsojmfEoioIC4J3D7gOajrc/6/9PG/5+8dKoMBUuCUpn5gx78EZQG0Gg8XN0MOydAm1+gXGc4/AuoUqHJxOw7TAghhBBCCCGEEEK88yRRmQ8MlLojE7erarM9sXa6cts+rUd0QjK3w+LYfTWUE3eekKrSXdfSNzQa39Bo1py8C0DRgmaaxGXxgtQo3pOS1QehAAjz00wTv3NYk7i0dtEkER9cgujQZxVW6g0uVTS7jF/ZBEGnQaGExEgIPKr5pDEuAEUq6G7aY18WDP7/x0ppAAe/h6RYOLcCEiJh22gIuQjHZmumsAshhBBCCCGEEEIIgSQq84WthTEGSkW6pOPzDJQKChYwoYKLDXVLwce1XYmMS+bHHdfZde0hSiWExyanu+5+eDz3w4PZfD4YADsLY6oXt/1/4rIL5aoOwFCpgOQ4MLaA2CeaJOKB6ZoKLq3VfAyM0UwPB4rVhVY/apKaIRfhwmpNIjMpBu6d0HzSGJqCQ/lnictqAzRJybS6EqKeJSkbjX/VrhRCCKGn69ev8/vvv3P//n169uxJ375987tJQgghhBDiPRIfH8/MmTM5e/YsBQoUYM2aNfndJPEGkkRlPnC2MePI+CZExCbxJCaRYWvOkZCSiqmRAYv6VMOugAm2FsY425jpXGdtbsRPXSvxU1fN94CwWA7dfMT8g7d5HJOIkYGCpf1qcCYgnNOB4ZwOCOdJbBK7rz1k97WHAJgbG1C12P8Tl262VClqi1mDz8CzE9z1ebZBT/SDZze2df3/tO+KEBem2W3cwBj6eUNEgCaBeXMnRN6HlAQIPqv56FDr/ldpCE/vg03RXO9fIYTILakqNacDwnkUnUBhS1NquhVMNyo+N3l5eXH79m0AzMzMcHNzY9CgQZQrV+6V6k1NTaVly5Z07NiRkSNHUqpUKb2u8/PzY82aNdy4cQNDQ0OKFy/OkCFDKFmypLaMv78/S5Ys4fbt29jZ2dG0aVO6deuG8v9Lg/j4+PDdd9+xdetWTE1NM73XlStX+OGHH/jrr7/SnUurIyOrVq3CwcFBp4xCocDBwYF69erRv39/jIyM9HpeIYQQQog3zeuOR7du3crChQsBUCqVODk50axZM3r06KGN717W9OnT2bZtG9OnT8fGxkava+Li4li7di0+Pj5ERkbi5OREmzZtaN++vbZMfHw8y5cv59SpUyQlJVG5cmUGDRpEoUKFtGU6d+7MwIEDda7LSP/+/fn000+pXr16unOdO3cmPj4+3fE+ffrQp0+fdGUKFChAmTJlGDFiBEWLSu5DX5KozCfONmbaROTPH5Zn0pZLfP9heRqVLax3HW72FrjZu9G7litHbj0mKUVFozKFaFSmEAd8H3E6IFxb1lCp2TwnLikVH/8wfPzDtMfLO1tT060gNYq3pnqrj7A1N4LwO5pp4r7eUG3gs5v6btf8NzUJln8AVk5QvAGYWkFECjT6EuxLa5Kd51Zk3vj9U+HA9zDiOBQuq/czCyHE67Lr6gOmbrvOg8hnm5I5Wpsyub0nrco75sk9jx07hpubG8OGDSM+Pp6NGzdSuXJljhw5Qp06dV663uDgYIKCgvj8888pXry4XtfMmzePcePG0adPH7p21bwhu3LlCu3atWPhwoU0atSIY8eO0bRpUz7++GN69OhBdHQ027ZtY+3atWzbtg2A0NBQdu/eTUpKSpb327hxY6bn0urYsmVLumSnlZVVhmX8/f2ZNGkS3t7e/Pvvv3o9sxBCCCHEmyQ/4tHAwEB8fHzYuHEjKpWKq1evMmzYMHx8fJg3b94r1X3y5Em6dOlCp06d9Crv6+tL69atsbe3Z+DAgRQpUoRbt26xdOlStm3bxuLFi0lISKBBgwakpKTw6aefYmNjw6VLl6hduzb79u3Dzc0NgL1799KsWbNsn/2vv/5izpw5GZ7fu3cv/fr1S5fsfH4QwPNloqKiWLJkCfPmzePSpUt6x+HvO0lUvgHaVCgC98/TunyR7AtnwNhQSXMPB51jSampWJoaEp2g+YdhygvTzK3NDDEzMiQ0KoGL959y8f5TFh+5A0AZhwJUL16QmsVbUqN9L92RnUVrw8PrkByr+R4VApf/+f9JBfjvBQt7KNks60QlgDoFFtaDkk2gbBso21qzsY8QQuSzXVcfMGLteV5coCM0MoERa8+zoE/VPAsOXV1dadWqFQCdOnXiwoULzJ07lzp16hAeHs68efM4f/48dnZ2dOzYUSdQ2rZtG//99x9Dhgxh0aJFPHjwAC8vL3766ScABgwYgImJCYsWLcLV1TXTNvj4+DBy5EgWLVrEkCFDAFCpVLRq1YopU6YQG6v5GfD777/TqFEjli5dqr124MCBhIWF5fi5vb29GTduXJZlmjdvToECBfQuY2trS58+fQgICNAGqUIIIYQQb4P8jEcNDQ218WibNm1Qq9V8/fXXzJgxA1NTU9avX89///1HUlISVatWZdSoUZibmwOQkJBAp06dmDZtGv/++y9Xr15lwIABLFmyhPPnzxMUFMTJkyfp2rUrgwcPzrQNqampfPjhh7i6urJv3z4MDQ1RqVRERUXx+eefEx6uGZh14MABzp07R3BwME5OTgB07dqVr776CrU68+X2MrJt2zYaNmyIpaVlpmU8PDy0faNPmXbt2uHo6MiKFSuYOnVqjtrzvpJE5TuqVTlHWnoW4caDaLZfCeHfiyEERTwbohwZn0IJ+wJ0quLEk5gkNp0PwthQSUKyCr+HMfg9jOHPU/cAzejPtHUua1aeQKnm01BG3YPr/8KNbfDwKiTHA2oIPqf56EuVDLf2aD7eY8C52v+Tlm2gsAco8m5IuxDi/ROXlPmoPqVCgamRAakqNVO3XU8XFIJm8QoFMGXbdVp4FtFOu8msXnPjV/sxq1AoKFasGI8fP+bRo0fUqlWL+vXr89FHHxEWFsbw4cO5ceMG48dr1vy9e/cua9eu5fjx43z22Wc4OTlRrlw5+vXrh4+PD4MGDcLe3h47O7ss7ztnzhzKlCmTYfBoYGCgnaoTFxdHUlJSujL29vY5es4HDx5w+fJlWrdunaPrspOWjH38+LEkKoUQQgjxRnjb4lHQxFSpqamEh4fz/fffc/DgQcaOHYuNjQ2rVq3i77//5syZMxgZGZGSksLu3bs5d+4cw4cPZ8iQIVSoUIHRo0dz584datasSa9evbKNzfbs2cONGzdYunQphobpnyEt3oyLiwMgMTFR57yZmVm6a7Lj7e1Nu3btcnxdVszNzSlUqBCPHz/O1XrfZZKofIcpFAo8nazwdLLii5buBIbFsvtaKP9dCuFaSBQX7j/lwv2n2vIJySoMlApKFrLA2syIu0/ieBydSPDTeIIvxvPvxRAAbMyNqO5akBrF21Hjg76Ud7LGODIQrm7SJBbvHILAY4Aq+0Y2mqBZ7/Lmzv+vbfn/ROeB6WBb/FnSslidZ7uJCyHES/L8dnem55qULcSKATU5HRCuM73mRWo0b7JPB4RTp6Qm4Vf/54OEx6ZP2AX+1PaV2hsYGMiJEyfw8vJi+vTplC9fXmfR8ZIlS9KpUyfGjh2rXYcxMTGRrVu3Urp0aW25+vXrA9C4cWNcXFyyve+5c+eoWbMmimxeFg0dOpQPP/yQOnXq0KFDB2rVqkW9evUwMTHJ0XN6e3tTu3ZtChYsmGW5zp07Y2BgoP1esGBB/vzzz0zL//3331hYWODu7p6j9gghhBBC5JW3LR5NTU1l/fr1ODo68uDBA5YsWcK9e/coUkQzG7JTp06UKlWK9evX07t3b+11w4YNY/r06drvrq6uWFtbU7p06WxHJIImHlUoFFStWjXLch988AElS5akdu3a9OrVizp16tCoUSNt+/QVExPD4cOHmT9/fpbl5s+fj7e3t86x77//nmrVqmVY/uTJkwQEBGS45qXImGR+3iPF7S0Y1qgkwxqV5GFUAnuuP2TPtVCO+YehUms22olLSsXvYYzOdaOaaf6xeyYgnPP3wnkal8y+Gw/Zd0OzQY+pkZIqRW2p4daRGsVtqVprDBbHf4XDP2XfKAMTaPgZPPGHgm5QyB3un9YkOyMC4eR8zcfUBsq01CQtSzUDk8yHYgshxKt4FJ15UPgy5XJq69at+Pr6Eh8fz7lz56hbty5fffWVNrhp164darUatVpNfHw88fHxBAQEUKZMGQAcHR11kpQvIy4uDltb22zLdejQgQsXLrBy5Uo2bdrEt99+i5mZGT///DMjRozQ+376vr328vLSWaMyo4RoWjLzzp07PHnyhBUrVmjXsRRCCCGEeBvkdzwaGxtLq1atUKvV3Lx5k+TkZNauXcvevXsxNjZm8ODB2mnVarWa2NhYrl27plNHo0aNXqkNcXFxmJmZZbkZI2jWK7948SJLly5l165drFmzhvDwcDp37szKlSv1jgP37t2Lm5ubzqaRGWnSpEm6NSpfXFIpLZkZHR3NuXPn6N27N/3799erHUISlRmaN28e8+bNIzU1Nb+bkmccrEz5uLYrH9d2JTIumf2+D/FwtMLc2IBDNx+z/uw9roVEAzC0QXEKmBoD0GfpKc7fi6C6qw1GBgacuxfB07hkTtx5wok7TwAwUCoYaxvDp/o0xLwgRIdq1rlUq2D4MWj4OSTGwJ2D4LsD/HZBfLimzOV/NCMw3Rpq1rQs20azoY8QQujh+rSWmZ5T/n/0YGHLrIOhNM+X85nQ5NUa9pwqVaowbNgwTE1NcXNz0wY+kZGRtGvXji5duuiUnzBhAo6Oz9YnympNHX05OTkRGBioV9kKFSowY8YMAKKiopg4cSJeXl7Uq1ePihUrZnt9YmIi+/fv54cffsi2rD5rVHp5eWFmZkahQoVwd3fXrpckxNvofYhJhRDiffM2xKMmJiaMGTMGpVJJkSJFKFu2LCYmJuzduxd7e3s+/TT9v/Zf3CjmVWNSJycn4uLiePz4sc7u3RkpUKAAY8aMYcyYMahUKnbs2EGPHj2YNm0av/32m1730/fFuT5rVKYlMy0sLChVqpROrC6yJ4nKDHh5eeHl5UVUVBTW1tb53Zw8Z21uxIdVn00F7FfXggImhkzZdo2axQtqk5RqtZoTd56QqlJz5NYTiliZ0tKzCM62ZpwJ1Cxke+dxLMFP49kd7sCn+sz8c6wEF9ZpkpQAq9prko8e7SD0Khibw8BdEPcEbu7QJC7Db4P/Ps1n+2fgVOXZFHGHcrKupRAiU/qs0VPTrSCO1qaERiZkuC6QAihibUpNt2fTlHNj7Z80z2+m8zw3Nzfi4uL0mirzqtq0acPMmTN58OBBjgIrKysrpkyZwvz587ly5YpeicoDBw5QqFAhypUr9ypN1tInmSnE2+J9i0mFEOJ98DbEo89vpvM8Nzc3QkNDqVevXq68HM9Kq1atUCqV/PXXX4waNUrv65RKJe3ataNx48ZcunRJr2vUajU7duzg77//ftnm6tAnmSkyp8zvBog3U5dqLpyb1IJfu1XSHrt0/ympz+0eHhqVwD9n7zNzrx9Hb4VxKegpx75syrEvmzLuAw/9bqRQPrdrOJqRkxfXwl89NVPHzyzVbNrjWhc++A5GngOv09B8CrjUBBQQcgEOfq/ZQfz3irBzAorAIyjUmS9SLIQQmTFQKpjc3hPQBIHPS/s+ub2nduHy12Xo0KH8888/7N79bF2jmJgY5s2bl+O6bt26RatWrdJN0UkzZswY7O3t6d+/f7odvP/77z/OndNsmrZo0SJ8fX11zm/cuBGFQkGlSpXQR14sWi6EEEII8TZ7U+PRrl27YmFhwejRo0lOTtYe3759e6ZxZVaGDx/O7NmzMzxXqlQpvLy8mDJlCocOHdI5d/v2bVatWgXA8ePH2bx5MyrVsz0y7t69y9mzZ6lcubJe7Th79iwJCQnUq1cvx88gcp+MqBSZMjZUUtDQWPu9oIUJQxuWYNfVUO6Fx6Urb2FsyMOoBJxtzFAVL0bCISNMFcnpyqVJUBvx+da7dKqxiMZR2zC8uBpiM9gJy8r52a/Pr9asYVl/DNQfCzGPNFPDfXdopoo/vQenFmJ4aiGtDMwxSN4OHm2hVAswlTXKhBD6aVXekQV9qjJ123WdhcyLWJsyub0nrcq//ukb/fv35/79+3Tu3JmSJUtibm7OvXv3GDt2bI7rioyMZPfu3UyaNCnD8wULFuTo0aMMHz6cYsWKUalSJZRKJX5+ftSvX585c+YAmt0Wu3fvTlRUFMWLFyckJISwsDDmzZtH+fLl9WrL9u3bWbRokV5lX9xMB+CXX37Ra+SmEEIIIcTb5E2MRwsWLMj27dvp06cPRYsWpXTp0ty5c4dq1aqxePHiHNfn4+NDSkrmA4xmz55NoUKF6NixI46OjtrliUxMTPjpJ82eGI6OjsyePZthw4ZRqlQp1Go1ly5donXr1kyZMkWvdnh7e9OqVasMdxd/UUab6bRo0YLPPvtMr3uJ7EmiUuitmJ05X7XxYGJrd248iGb3tVB2XwvFN1SzluWDyARq/bCfKsVsUKlUPE6cga0iGiUqyikDKUg04VhyTVUcFUoi1JaE3DPE+14Ilqa16Fi+DQNtL+F2508UwWef3fj0IlAaQLnOcPhniAqG2wegxkBwbwdVPoaqfSEpTpOsvLkD9c1dGMeFwbVNmo/SCIrXB/e2mrUtrbPf9VYI8X5rVd6RFp5FOB0QzqPoBApbaqbX5OWb6/nz52e58/U333zDmDFjuHTpEqamppQrVw4zMzPt+Q4dOmT45rho0aLs3LlTu75PmTJl2LlzZ5bJxKJFi7J9+3YeP36Mr68vBgYGODo64urqilKpmZDRpUsXunTpwt27dwkMDMTW1pYyZcroLHreoEEDdu7cqdPONFeuXOHJkyc0btw4y35JqyMjabuYZ3UfIYQQQoi3UX7Eo507d6ZChQqZnq9duzZ+fn5cvXqViIgIypYtq7PDtpmZGTt37sTDI/0sy9mzZ1O4cGHt90WLFmFjY5PpvZRKJd988w0TJkzgypUrREZGYmdnR/ny5bUvr93c3Fi/fj2RkZHcuHEDgBIlSujcBzQbVqZtPvkib29vxo0bl2k7nq8jo8Sqs7OzTpnM7iP0o1CnbdUk0klbDygyMjJPdwxNTk5mx44dtGnTBiMjozy7T14JDIvVJi3P33uao2srOFvxJCaJkOfeEBUraM6I0k9pn7idArf+hdQkzQkzOzC1hog7upVYOUMBB6gzEsp1AqWS5MQETmycSz37pxj47YInt3SvKVLxWdKySEVZ11JPb/uf1TeR9Gnui46Oxs/PDw8PD9lIJRepVCqioqKwsrLSJipf1Y8//sjp06fZsmVLrtT3tsnNPk1ISCAgIAA3N7d0u2O+rnhG5J3X8XsoP4/yhvRr7pM+zX3Sp3lDYtLclxfx6IMHDyhWrBgPHz7McsDAuyy3+jW34lEZUSleWXF7C4Y1KsmwRiV5GJXAnusPWekTwO2w2Gyv7VXTlQ+rOHP+fgSbzgWz8+oD7oXHMfGUMRPpTLOiHzLK9gQVHmxEGR0C8U/A3B6afA1+O+HOIc0Iy6hg2DwYyrbSbMCjNCDCohSqpm0xaPkdhN3SbMZzcyfcOwmhlzWfQz+ClYsmYeneBlzrw3PT3YUQQuSdNm3a0L179/xuhhBCCCGEeE8ZGRmxf//+9zZJ+SaSRKXIVQ5Wpnxc25WKztZ0nHcs2/LlnKwY/c8FnsQk8VVbD6Z3Ksfua6FsPh+Mj38Y+++r2X+/NmaGtRhX9BZdU3ZgXboOyhoDNVO/EyJh81DNhjqOlTVJSoCoED64NgaDmLVQqSeUbgH1Rms+sWHgt1uTuLx9AKKC4MwSzcfECko114y2LNUczGzytL+EEOJ9pu+GO0IIIYQQQuQFe3t7GjZsmN/NEM+RRKXIE/qumREVn8zuaw8B+O9iMJ6tPehcxYXOVVwIjUxg68VgNp0L4tajGL4PKMP3lKFwuAHtk67zYVVnPGPPoPDbBdbFoNdf2nqVJ+ZglhwB/ns1H6URlGj0/+nebaBKb80nOR7uHIab2+HmLoh9BNc2az5KQ3Ct92yKuE2xPOkrIYQQQgghhBBCCCGJSpHPzt0Lx8rUkKiEFFYev8v2K6EMb1QSE0MlEbFJ9KntyrCGJbgWEsXGc0H8dymER7FJLPMJYJlPAP3trjHO1AXDEh9grvz/TrBqNVg5EWFeAmvnMijDb2vWqPTfp/l4j4XGX0HjCWBkppkuXrYVqFQQfO7/Scud8NgXAg5rPjvHg0OFZ1PEHStr1rU8+KNmo59G49M/3OFfQJUKTSa+1j4VQgghhBBCCCGEeBtJolLkq22XHhCVkIKzrRmJyak8jk5kuvd1lApQqcHKzJB+dd0o72xNeWdrvm7rweGbj9l8IYh91x+x8kk5VvMT5ieTqPrkNF2qOtOqQAAmB6ZiaOKI2q0RdFkC0aGa9SivbdbcuLDns0Y8ugFXNmhGTrpUh6I1oPkUeHJbk7C8uQPunYCHVzSfI7+ApZMmaZkUC5f/1tTzfLLy8C9w8HvNWppCCCGEEEIIIYQQIluSqBR5wtbCGBNDJYkpqkzLmBgqaeHpQOiJuwRHxANQ0cWah5HxPIzW7PRdzfXZgraXg56iUkNzTweaezoQGZeM95UQNp8P5tzdCI74PeaI32M+MvHhWwNzLBMfwJ6JcOh7qNRLs+Zk7GNwrKRZszLNnm/Bfw8cnaFJQLq31XyK14e6n2o+ceH/X9dyO/gfgOgQOLtMc72BsSYpGXoFOi2Ak/OfJSkzGmkphBBCCCGEEEIIIdKRRKXIE842Zhz4vDERsUmZlrG1MMbZxoyB9d2YucePf87e53JQJMaGSpq6F6ZYQTPKO1try4/feBnf0GimtPekfz03rM2N6F3Lld61XAkIi2XLhWA2nw/iz4j6/Es1Ohv4MMh4L25J/98sB6BEYyhWBwyMNN+Dz2uSlAoDMDDRJCDTNtYxtYYyraDlj2BhB5V7aT7JCRBw5Nku4jGhmrpu/Kf5gGbTHklSCiGEEEIIIYQQQuhNEpUZmDdvHvPmzSM1NTW/m/JWc7Yxw9nGLNtyhS1N+alLRT6u48p07+ucvBPOAd9HFLI0wdPJmq5VXQgIi+VmaDQAwU/jiUpIxsrUiKdxSZgaGeBmb8G4FmUY06w0ZwLD2XjuPhsumrI2vjl1ldfob7CbZgYXMLhzCO4c0myMU2OwZgp40Vqa7x3mataj9PWGq1s0O4r77YaO85419v4ZKOgGZT7QfNrOhOO/w5HfICnmWbljf8ATf2g3GwoUztV+FUIIIcT7QWJSIYQQQrxvJFGZAS8vL7y8vIiKisLa2jr7C0SuKOdkzV9DarPn+kN+2HGDu0/iGL/xMqtPBNK9elHcCllw53EsS44G8M+Z+wys70ZwRDwHbz5iSodytKvohFKpoFYJO6oWtaKmwV0MXavw7+XCDPcrj1PKY/oY7KOHwUFsn96Dvd+iNjRFUaEb1B0NRqZQpiU4VYWLf2l2Cm888dnoS7UaNg6AqGAoWhs82oGhCeybCqhfeBo1+G4Hvz1Qrb9mhKVN0dfco0II8UxSUhLbt2/n/v371KhRgzp16uR3k4QQ2ZCYVAghxLvm7NmznDt3DkNDQwYNGpTfzRFvIElUijeKQqGgZbkiNC5biNXH7/LH/ltcDY7iavA1WpUrQt86xVl38i63HsUwe98t7aY75kYG6eoyNoA2FR3pXK0Yj6IS+O9SCBvPlWBWaBc6GBynv8FuyqXcRX1hLTdLD6GsvRqFQgExD8GxoiYxWXv4swpv7QVTK4i8D/eOaz7ZUSVrppGfWwEVe0L9MWBfOvc6TAjxTlm3bh2PHz8GwMzMDDc3N5o2bYqh4av/uG7ZsiXR0dHUq1ePUqVK6XWNSqXixIkT3LhxAwMDA1xcXGjatGm6MocPH+b27dvY2dlRt25dHBwctOf9/f3x9vbGy8sLIyOjTO/18OFDNm3axCeffJLuXGZ17Nu3j2vXrtGnTx/s7Oy0x+/evcuWLVu039P6slmzZhgYpP958bJ8fX05evQoQ4YMybU6hRBCCCHy04ULFzh8+DAASqUSJycn6tati5OT0yvXvXLlSsaOHUuPHj0oUqSI3tcFBgZy/Phxnj59SuHChWnatCkFCxbUKePn58fZs2dJSkqicuXKVK5cWef8/PnzqVevHpUqVcryXmvWrKFOnToZxssZ1RESEsL69eupXr069evX1ym/ZMkSYmNjATAwMMDBwYFGjRrpxMq54Y8//qBjx464urrmar35RZnfDRAiIyaGBgxpWIJDXzSmT+1iKBWw78ZDGpa2Z/eYhsz7qCplHSxRqcHMUEnVYrbaa1cdD2Tk35cIin1WX2ErUwY3KMGuMQ3ZMqoZ1nUG0M9oBl0SJ/NTck9arQ7ig1lHWHDoNrHHF0HpltB1+bMKUlNg2ygI84cea6HVz+BQQb+HKVIRVClwcS3MrQEb+ms23hFCvLGCn8ZzNTgy00/w0/g8ue+vv/7KqlWrCAwM5Pz58wwdOhRPT09CQ0Nfqd6HDx9y6NAh1q5dy++//06bNm2yvebcuXN4enrSu3dvfHx8OHLkCJMmTaJq1arcuHFDW2/FihUZPHgwJ06c4K+//qJOnTpMmTJFW8/FixcZO3YsiYmJWd5vw4YNbN68OcNzGdWxaNEi2rRpg5mZmU6SEuDGjRuMHTuW69evExgYyLlz5xgxYgQVKlQgLCws22fX19mzZ/nmm29yrT4hhBBCiDT5FY8ePnyYCRMmEBgYiL+/P/Pnz6dkyZKsW7fulev+559/GDZsGAsXLtSJFzMTGxtLv379KFeuHP/88w+XL19m8eLFVKtWjfnz52vLffbZZ1StWpXNmzfj4+PDkCFDaNCggXYAAMD48eM5evRolvd7+vQpgwYNwsTEJMPzL9Zx69Yt6tWrx+7du6latWq68t988w0bN24kMDAQPz8/lixZQvHixTONeV/WuHHjtPH5u0BGVIo3ml0BE77rVIGPaxfnwr0IShQqAEDbio4UNDciIi6JyIQUbCyMAUhNVTFrrx9P45OxKqHIsE5PJys8nTz5srU7R/0rsfl8MCbXQrn1KIa1u44y1GQNKNTsVdSmbu2iWJgYajbZMbeH1GRNEtPQGCzsYZMeQ9XrjQYbV82u4n474doWzad0S2j4ORStmWv9JYR4dcFP42n62yESU1SZljExVHLg88Z6rcObU40aNWL27NkAREZGUrZsWb777jvmzp0LwJkzZzh//jx2dnY0bNiQwoWfrYN76dIlLl26xIcffsjOnTt58OABjRs35u+//wY0waG1tTUff/xxuuTe8+7fv0+LFi3o3LkzixYtwtDQEJVKRVRUFMHBwdpRiZMnTwbg+vXr2oAuKSmJ/fv35/i5vb29adeunV5lf/jhB6ZNm8Zff/1Fly5dMi03bdo07dv6yMhInJycWLduHaNHj9Ypd/78ec6cOUPBggVp2LBhurfcAQEB+Pj4oFQqadCgAcWKFePu3bvs3r2buLg47e9X7dq1qV27dg6eWgghhBAivfyOR83MzLTxDcCIESP45JNP6NWrF0qlkocPH7J3716SkpKoWrWqzujF5ORk5s2bR8+ePbl16xZXr16ldu3aHD16lBs3bqBUKpk9ezbVqlWjQYMGWbajX79+nDlzhqtXr+Lm5qaNR5VKJadPnwbg5MmTzJw5Ex8fH+rVq6e99sSJE6jVLy7RlrVdu3bh6elJ0aLZL9t24cIFWrVqRdOmTVm9enWmM4fatWvHl19+qf3ep08fpk+fzocffqhT7tGjR+zbt4/4+HgqV65MtWrVdM4nJCSwe/duHj16RLly5ahbty6gGbWpVqvZunUrvr6+WFlZMXDgwBw995tGRlSKt0LZIpb0rFlM+/1qcCQfLTvFnIO36Vj52RD043ee8DQ+GQMlPIxTEBajGX1zJjCcv07fIyH52WL0hgZKmpQtzJxeVTgzqTk/d6lAseIlGJvsxdKU1gzZGUON7/cxbv1F7h1YhqrWcBi8X5OkBCig53DtAg5QtAZ89DcMPwblu4BCCbd2w7IWsLId3D6omWouhMh3EbFJWQaFAIkpKiJik/K8LdbW1lStWhVfX19SU1Pp0aMHXbt25fTp06xZswYPDw+dpODRo0f57LPPqFmzJhs2bODOnTtER0cTHBwMaBKQgYGBJCcnZ3nf3377DQMDA2bPnp1u2rmHhwdlypQB4Pbt23h4eOi8dTY2NqZ169Y5es7Y2FgOHTqUbaJSrVYzbtw4fvzxR7Zv355lkvJFZmZmGBkZ6QSsKpWKvn370qFDB06dOsWff/6Jh4cHO3fu1Jb5888/qVChAt7e3uzdu5eWLVuyfv164uPjefz4MampqQQGBhIYGMjTp09z9NxCCCGEEBl5k+JRgCZNmhAVFaWd5ly2bFm2bNnCsWPHaNWqFSNHjnzWrsRExo4dS6dOnfjss8+4du0aUVFR3L17l8TERCIiIggMDCQiIiLLe164cIFNmzbx008/4ebmpnOuQIECNG/eHNDEo0C6Kd116tTReaGvD31fnB85coTGjRvTrVs3/vzzzyyXN3qRlZVVugTq1q1bKVOmDBs2bOD48eN06NBBZ2mhR48e4eHhwU8//cS5c+eYOHGiNg6+f/8+arWahw8fEhgYSFBQkN5teVPJiErxVgqKiMfSxJDShQtgbvzsj7GpkQEVXay5HBTJ4VAlTWYe5aOarlwNjuR0YDj3wuOY0Mo9XX1Wpkb0qFGMHjWKcT+8OlsuBFP8fBCBT+I4ev4aP5nMQXk5lThDGxIqfkzBRsPBrCBqIONxmxrpzhcpr5lS3uRr8JkFl/6GwKOaj3M1aPAZlGkNSnmHIEReiEtKybbM8y80XrXe5///9DKSkpK4fv06zZo14/fff+f69ev4+vpiZqZ5cz5//nwGDhzI3bt3tdeEhYWxfPly2rdvrz1mZ2fH6tWrmTJlCi4uLtne98CBA9SrVw9LS8ssyzVt2pTJkyczdepUOnToQPny5XMUqKXZu3cvxYsXz3btzIEDB3Lo0CEOHjxI9erVs613yZIlWFpaEh8fz86dO6lSpQp9+/bVnl+4cCGnT5/G19eXAgU0I/aXL1/OwIEDCQoKwsDAgLlz5/L1118zceJEQBN8X758GXd3d/r06cPFixd1RhwIIYQQQmTlbYtHQbMMj4mJCampqfTv35/du3drR0M+ePAAd3d3OnfurLOWuYuLCxs2bNDsA4Fm1tCxY8do06YNkyZNyvaeBw4cAMj2BXjdunUxNTWle/fujBo1itq1a2NjY5PjZ0xNTWXXrl14e3tnWW737t188cUXTJgwQa/p68eOHWP27NmoVCr8/PzYuXMnCxcu1J5//Pgxffr0YevWrdrk6+PHj7V92qZNG7Zu3YqpqSnHjx/X9qePjw+gmUH0ww8/MGzYMFq1apXj534TSaJSvJValS9CLbeCpKievYkIiohjz7VQ1gysyemAML7bcp67MSqWHwvAQKnA0sSQD6s6a8s/jEogMVlFMTtznbqLFjRnVLPSjGxaivP3Ith+xpcFV7vTVb0H55QnmJ+fQ+r5eTyx8iS79zMKgHXdoONcqND12Qm7kppjjb+EY3/A+VUQfA7+/ggKe0L9cVCuMxjIX1EhcpPnt7vzpN76Px8kPIM32oE/tc1xXRcuXGD27NnEx8ezbds2IiMjmTBhAr1798bZ2Zlly5ahVqtRq9WEhYVx79497t+/r52iYmtrq5OkfBlhYWE0atQo23Ljx4/HxMSEZcuWMWXKFCwsLGjatCnfffcdFStW1Pt++r69TiuX0RpAGbl//z7m5uYkJiYSExND4cKFdda5/Ouvv3BxcWHlypXaPo2MjCQ0NJQ7d+5QunRp7O3tOXbsGA8ePMDR0RETExNq1Kih97MJIYQQQjzvbYhHExMTmT17Nmq1mmvXrrF69WqmT5+Ot7c3JiYmXLp0iYsXLwKaGS+FChXi2LFjOonKvn37apNqLyMsLAwLC4tsk45ubm4cOHCAqVOn0qlTJ5KSkqhUqRIjRoxgyJAherfhxIkTKJVKatbMemm2ffv2YWFhQf/+/fWqN20EqVqtJjQ0FGNjY5KSnv0+/ffff4Bmk8a0dSaf79M2bdpgb29PaGgoPj4+1K9fH4VCkW7jnneJZEHEW8v2/+tSpvl51022XQph47kgRjctyahyqdiWrcncQ3c4f+8phSyNKWFfQFt+3kF/1p68yxct3RnRuGS6+hUKBdVcC1LNtS4JHWtx8HoI/x7bQJXQDdRRXqdw1FX9GpoSr1nL8vZBaPMLGFs8O2ftojnW8As4OQ9OL4VH12HzYDj4PdQfC5V6gmHGi/kKId49UVFRBAYGYmpqSt++fenevTsFCxYkKCgICwsL/P39dcqPHj0a5XOjsHM6xSUjNjY2em3gY2BgwLhx4xg3bhyPHz/Gx8eH77//noYNG3Ljxg0cHR2zrUOtVrNjxw7+/PPPbMtu3ryZ7t27M2DAAFasWIFSqeTs2bPaN8oAPXv21P76+TUq1Wo19evXx8vLS7uAeVBQEC4uLhn2qbGx5mfMnDlzGDt2LKVKlaJEiRK0b9+eL774AltbW4QQQggh3kUqlYrAwECUSiVlypTh+PHjVK9enYkTJ2JkZJQudmrXrh3ly5fXOfaqMamNjQ2xsbHExMRoZ75kpk6dOuzatYvExEQuXrzImjVrGDZsGKmpqYwYMUKv+3l7e9OmTRuduDoj3377LTt27KBRo0YcPHiQEiVKEBcXx+LFi7VlKlasqE3avrhG5aJFi+jVqxcBAQE4ODgQFBSEiYlJuj5t1aqVdu3Pzp07c+XKFXr16kVSUhLNmjVj9OjR7+za6JKoFO+MLlWdufEgCv9HMUzx9qWImQE/uMOmEXU55v8ENWoMlJq3KfFJKRzxe4xKDZVcrLV1JKeqMFAoUCp137qYGhnQulJRqDSOsBgvNvscofC5mdRPPp59w6r0hQurNbt+h9+GATvhxbc6BQpB8ylQbwycXgIn50NEgGan8UM/Qb1RULUfGJtndAchhJ6uT2uZfZmQKLouPJGjen0mNHnZJqXz/GY6z7Ozs6Ny5cqvZZpx3bp12bNnD4mJiZnueviiQoUK0blzZ6pWrUrx4sU5fPiwTtIwM+fOnSM+Pl6vt8INGjRg9+7dtG7dmtTUVFatWkVkZCSBgYHaMpntLq5QKKhXrx6rV6/WHrOzs6NcuXJZ9qmrqyubN28mMTGR48eP8+WXX3L27Fn27NmTbXuFEEIIIV70NsSjL26mk8bOzo6UlBRmzZr1SqMl9ZG2WcyRI0do06aNXteYmJhQq1YtatWqxbVr1/j3339zlKjUZyq3paUlu3btom3bttpkpYODg0486uzsnOn1DRo0ID4+XrsZj52dHQkJCcyYMUO7YeWLFAoFkydPZvLkydrdwxs1aoSvr2+69TvfBbIQnnhnNC5bmF2jGzCtYzlszY0IjVcwcNV5Bq06SxFrUxqULqQtu/6sZv1JQyX8dymY++FxAPx1+h7NZx1m55UHmd7HvoAJH7ZqQf0BP+rXsMofaXYMNzCBqv3TJymfZ2YDjb6AMVeg5Q9g6ajZcXzXlzC7PBz5DRIi9buvECIdc2PDbD+mRhkHCC9Tb27q0KEDa9as4eHDhzrHr17Vc3T3cx49esTs2bMJCQnJ8HzaCMnp06enO/fkyRPtIt3nz59HpdJd6D1t454Xd87OjLe3Ny1btky3aU9m6taty969e9m+fTt9+vTRJnbTPq6urplee/nyZYoVe7YxW4cOHfj777+1bU7zfJ+m/drExIQmTZowYMAArl+/DmgWQ4+Li9Or3UIIIYQQ8HbHo+3atSMyMpJly5bpHH/y5Iles3FetHbtWvbt25fhuQYNGlC/fn3Gjx+fbsNClUrF5cuXAQgMDCQsLEznfEpKCo8ePdI7Hr179y63bt3igw8+0Ku8hYUFO3bsoGzZsjRq1IgHDx7oxKPdunXL9Nq0dqfFpG3atCExMZEFCxbolIuIiNDGqH5+ftrNMMuUKcO0adNITk7WbiT0rsWkMqJSvFMMDZT0rVOcNuUK8/mK/fg8NOCA7yOO+D2mT21XxjQvjY25MeWdralb0o7jt5/w95kgNp4L5sOqzly8/5Q7j2O1u4XnhqQHVzFOjNaMmizX8dmJuyfAvgxY2KW/yKQA1PGCGoPh4p+ajXee3oUD0+HY71BzCNT+BCzsc62dQog326RJk/Dx8aFy5cr06dMHc3NzTp06hYGBAdu3b89RXffu3WPs2LFUr14dJyendOfLlSvHhg0b6NevH4cOHaJFixYoFAquXr3K2bNn2bBhAy4uLqxfv55evXrRpEkTihcvTkhICOvWraNTp056rXEJmkTlmDFjctT+mjVrsm/fPlq0aEGvXr3466+/Mkx0pm2mkzYa8vDhw2zbtk17fvz48Rw5ckS7yU6BAgU4ffo0iYmJ2t3Ux48fT2pqKnXq1CEpKYmlS5dq38xXr16dpKQkhg0bhoeHB7Vr135np+AIIYQQQri7uzNz5kxGjBjBwYMH8fT05M6dOxw5coStW7dql9zR108//UTt2rW1m8i8aOPGjXTp0gUPDw969OiBo6Mj/v7+nDx5ki5dulCxYkWCg4Pp3bs3NWvWpFy5cqjVarZt28ajR4/46quv9GrHtm3baNCgAVZWVnq33dzcHG9vbzp16kTjxo05cOAA7u7pN+5N20xHrVZz9+5dVqxYwaBBg/D09ASgRIkSzJ07l5EjR+Lj40OFChUICAjg8OHDbNiwAWdnZ06fPk3Hjh354IMPcHR0ZPv27Xh4eFCnTh0A6tevz48//sidO3coWLAgAwcO1Ps53kQyolK8k6zNjOhcXMWOkXVp7uFAikrNyuOBNPr1ECuPBVDRxZo/h9Rmw/A6NChtT4pKzfqzQfg/iqFSUWs6VX42VPv47TAm/3uVe09e7g3FoH1qNtfdTFKnxWCk2amXp/dh7YewoC4EHMn8YkMTqD4ARp6HzouhkDskRsHRGTC7AuyaCJHBmV8vhMgxWwtjTAyz/vFoYqhMt05ubujdu3emCT5zc3MOHTrE4sWLMTExwcjIiM8//1wnSVm5cmX69euX7tqCBQsyevRo7S7eDg4OjB49OstpKe3btycwMJDhw4eTlJREamoqLVu25NKlS1SrVg3QBJe7d++mSpUqREZG4uLiwubNm9myZYt2fZ/SpUvrrPn4vNDQUC5dupTtbo4Z1VGtWjUOHDiAs7Mzhw4d0ilfvHhxRo8ezZMnTwgMDOTJkye0aNGC27dv6wTCpqam7N27l5UrV2JmZoaBgQGjR4/WJikBduzYweeff45CocDc3JxNmzYxdepUAJycnDh16hRFixbl7t276d72CyGEEEK8jPyMR6tUqZLldOnRo0dz+fJlKlWqRExMDA0aNODcuXOUK1cOAGNjY0aPHp3hWuU9e/akVq1a2u8ff/wxLVq0yPReDg4O+Pj48Ndff1GoUCHCw8Nxd3dnw4YN2mna9erVw9fXlx49eqBWazbbHTNmDIGBgZQtW1Zbl5eXl3bNxxfpu7Hji3WYmpry77//MnjwYLZu3ZpuptHQoUMpWbIkgYGB3Lt3DwcHB/777z+WLl2qU2748OFcu3aNatWqERMTQ926dTlz5oz2Xn369GHHjh2UKFGC6OhohgwZwtmzZ7Gw0Ox/sXbtWvr378/Dhw+1M5/eZgp12u+kSCcqKgpra2siIyNzlFnPqeTkZHbs2EGbNm0wMjLKs/u8T17s02P+YUz3vo5vaDQAJQtZ8H3nCtQuoRnNeO5uBH/sv8Vhv8c09yjM0n7PdnTtu/w0R/we06+OK1M7PrdAcMhFWJz9iKGfknuwMLUjzjZmjG5Wmg+rOmN4YRV4j3lWqMHn0Hhi9rt8q1Rwc7tmCviDi5pjSiPN9PL6Y6Bgiew75xXIn9XcJ32a+6Kjo/Hz88PDwwNz85db1zX4aTwRGeyamMbWwhhnG7OXbeJbSaVSERUVhZWVVbaLjOtr6dKlrFy5UmcznPdJbvZpQkICAQEBuLm5YWpqqnPudcUzIu+8jt9D+XmUN6Rfc5/0ae6TPs0brxqTSjyaXl7Eo7GxsdjZ2XH16lVKlSqVK3W+bXKrX3MrHpWp3+K9UK+UPdtHNeCfM/eZsecmtx/HkpTy7G1HNVdbVg2sycX7T7EwfrYeSMjTeFJVKmoUt2VQ/WdJwOCn8dwJVlNHYYyhOvMfHioUfGn0DwVMjPjtaRvGb7rMgsO3+dlTQQ1zexQFCmt2+T76m2ZkZZelYJv5+mooleDRHtzbwe0DmpGVd4/B+VVwYQ2U7wL1x4GD56t1mBDvOWcbs/cu8MsPFhYWTJgwIb+bIYQQQgjxxpF49PUIDQ3liy++eG+TlG8iSVSK94aBUsFHtYrRrpIjO688oGGZZ5vrHL31GE9HKyoXtdG5ZuHh2xzzf4JCAT/v9mVU09KULWLJkiN3WHk8hGGV/qSeE/y862a6+01oVYaGsfvh4jqG9BuOyW0LFhy+TUBYLN2PFKJK4Xl41S1FM4NLKLzHQNBpmFsd2s6Aqn2zfhiFAko103zuntAkLP33wpUNmk/ZttDwM3Culgs9J4QQeaNXr1753QQhhBBCCPEeK1myZIYbSIr8I2tUiveOlakRPWo82/X1cXQiI9aep/Fvh/B/FK1TtkeNorQqVwS1GrZffkDL2UcYsfYcKSoVlqaGVChXjk8PqriuduPac5/rajc+PagmrOE0GHkOE+cKDGlYgiPjmzCxqTOWpoZceJTK4L9v0vGwAyc/2Ira3A5Sk+DwLzl7INc60GcjDD0Mnh0BhWZ6+JKmsLoTBBwFWeFBCCGEEEIIIYQQbzhJVGZg3rx5eHp6UqNGjewLi7deRFwSxQqa42ZvQQn7AjrnyjlZs/Djauwc3YC2FRxRKGDn1VDWnrxHw9L2bLsUQkxiCi+mAdVATGIKk7ZeBUsH7fECoacZdqETJzsn8mmTUpgbG3A5KJKeG0KZbjKORDMH6LLsWUWqVP2TjE6Voftq8DoFlXqBwgDuHIRV7WB5S/DbIwlLIYQQ4i0iMakQQggh3jeSqMyAl5cX169f58yZM/ndFPEalHGwZNvI+iztVx2lUgFAbGIKI/+6wI0HUQB4OFoxr3dVdo9pSPtKTigUYG5syO5rD1FlkvtTqWHX1VD8Hj43SvP0YoiPwGLLx3xu+h9Hv2jM4PpuGBsqWf7AjbIRM/l4L1y4F6FJUs6vDfNqwSNf/R+oUFnovBBGXYDqg8DABO6fgj+7waIGcG2Lpm4hhBBCvNEkJhVCCCHE+0YSlUKgWb+ysOWzXakWHbnDtkshtP3jKBM3XyEsJhHQJDXn9KrC3rEN+aJlWUwMs/4rZGKopGSh50ZpfrgEag7V/Prgd9jtHMqkD1w58kUT+tQuhpGBkqO3wug8/zjL5v8EYX4QdhM2DYa48Jw9lK0rtJsJYy5D3ZFgZAGhV2BDf03y88I6SE3OWZ1CCCGEEEIIIYQQeUQSlUJkoFs1F9pWdESlhr9O36Pxr4dYePg2iSmakYilClty+3Esic/tHJ6RxBQVu66GPjtgYARtfoX2f4DSCK7/C8s+oIjqId91qsCBzxrTrZoLSgVMD6rEH8kdSUUJD6/AwvoQeAyiHuQswWhZBD74DsZehUZfgqkNPLkF/34Cf1SB00sgOf4lekkIIYQQQgghhBAi90iiUogMFC1ozryPqrJheB0qulgTk5jCTzt9aTHzCDuvPECtVvMoOkGvur7ZekWb4NSq1g/6e4NFYXh4FRY3hoCjFC1ozq/dKrF3XCPaV3JmZmoPOiROJ0BVBKKCUa9sq9kkZ34deHApZw9lXhCaTNQkLJtP1dw78j7s+BxmVwSf2ZAYnW01QgghhBBCCCGEEHlBEpVCZKFG8YJs/aQeM7pVwsHKhHvhcYxYd54ei08SHKHfKMRW5R0xMTRIf6JYbRh6EBwrQ3w4rO6oGd2oVlOyUAHm9KrCztENcPKoTdukH9iY2hAFaogOQR0RoEk0vgwTS6g/RjMlvM1vYF0UYh/Bvskwqzwc/DHn08yFEG+FmJgY/Pz8CAsLy++mCCGEEEKI91Bqaip3797l9u3b+d0U8YaSRKUQ2VAqFXSp5sLBzxszqllpTAyVnA4I55fdN/W6vnt1F+2vH0UlEJuY8uyktQsM3AUVuoE6VTO6cdtoSEkCNJv4LOlbnT+9mvFv8UmMTvqEMLUlk5IHMP1IhHbtTG5sy3ly0cgMag7RbLrTcT7YlYKEp3D4J03Ccs8kiA7VJC4P/5JxHYd/0ZwX4h3mfTmEGt/tZfvlB3l+r8DAQHx9ffH19eXu3bukpKRkf5GepkyZQpEiRWjfvj3r1q3T+7rU1FSCgoJ4+PBhtmViY2PTnYuOjsbX1xe1OpOdx/4vKSmJmzcz/v9qWh337t3L8N5pffay5XPyHBl9kpMzX47j+etu3rzJ06dPMzyfUf/o0/dCCCGEePe9zng0IiJCG7v4+fkRHZ17s+7OnDmDs7MzjRo1YtCgQTm6Njw8nMDAwCzj4/DwcEJDQzM85+fnly4Oy8jdu3eJiorKtI7MYr+goCB8fX2JjIx86fL6PkdG8Wh2AxGevy4oKAiVSncZO39//0z7R5++z02SqBRCT+bGhoxrUYYDnzemY2Unva9L+6dnQnIqQ1afpfP8YwSGPfePYCMzzSY7LaYBCji/Cla1h5hH2iKVi9qwZlAtPhr8OWOKrGFdchOW+QTQ8JeD7Fz1M+oN/WFOVc36lTllYARVeoPXaei2EhwqQHIsHJ+jmRJ+aw8c/D59svLwL5rjygxGiwrxjgiLSeSrzVd4HJPExM2Xn70cyCOdOnWibt26dOrUiYYNG1KgQAFGjBjxykFBTEwM06dPZ+fOndy8eZPRo0dne010dDQjR47Ezs6O6tWrU61aNYoXL87nn39OeLjmxUhKSgrjxo3DysqKWrVqUbRoUerUqcPu3bu19ezevRsPD48Mk3/P27p1K926dcvwXFodFSpUIC4uTufc9u3b8fDwwMPDQ9tPOS2fk+fo2LEjnTp10vk8evSIzDx/XceOHXFxcaFcuXL4+PgAcODAAcqVK6fTPy/2fZUqVbC1teWzzz7T9r0QQggh3g+vOx5dtWoV5cqVo1OnTrRr1w4HBwdq1arF1atXX7nuX3/9lbZt2xIYGMihQ4f0uubPP//E09OTYsWK0axZM1xdXfnggw84ceKEtsy2bdsoXbo0bm5uVK9eHUdHR77//nsSEp4t11a1alXWrl2b5b2Sk5OpVKkS/v7+GZ6vWrUqHh4e/PPPP+muq169Oh4eHmzYsOGly+v7HI0bN04Xj/79999ZPtvz19WsWRNra2umTZumPd+4ceN0gxme7/smTZpgbW1NixYtdPo+L0iiUogccrYx4/eeVfi1W0W9yhsZaP6aBUXE8yAygUfRiRgoFbqFFAqoNxp6bwQTa7h/Eq5uTldXrRJ2rBnemNUDa1LJxZqyyb60uPMjSSoFQUZuxJgUevkHUxpAuc4w/Ch8tB5cakJqIoScBxRw8HuUuydqih79TZOkbPI1NBr/8vcU4g2mVqv5essVYpM0a8zGJqUyaeurB2jZ6du3r3ZE5aFDh1ixYgUzZszQnlepVNy9ezfDt9tPnz4lICAAgMjISO2bYx8fH1QqFSqVCl9fX5KSkrJsQ3x8PE2aNOHYsWMcPXqU0NBQgoKCuHLlCsWKFePatWsA/Pzzz6xZs4aTJ08SHBxMeHg4c+bM0SbhcsLb25t27dplWaZYsWI6wRzA0qVLqVChwiuVz8lznDt3Lt0bbGdn52yfL+26sLAwPDw86NKlC/Hx6ZcQyajvQ0JCuHfvnk7fCyGEEOLdl1/xqKWlpXZEZWhoKCYmJuleKIeFhRESEpJhm319fUlISCA5OZmAgAAiIiK4efMmt27d0tb9+PHjbNvx66+/MmjQIMaPH8/Tp0+5ffs29+7d46uvvuLo0aMA3Llzh27dujFkyBDCw8O1MWtSUhLBwcE5eu6jR49iYWFB1apVMy1Tvnx5li1bpnNs27ZtWFlZYWJi8tLlc/IckyZNShePfvrpp9k+X9p1ISEhLF++nMmTJ7Np06YMy77Y9wEBAURFRfH1119r+z6vSKJSiJfkUcRK77JqtZoTt8NYN7gWS/tWp2hB84wLlm4OQw5AvTFQa1iGRRQKBQ3LFGKrVz2++sCNKKU1JiRjF3mFWT9NZPFhf+Jjo2GrF4TdyvmDKRRQpiUM2gP9vKFEE9LGhRqcXUKHC/0wOPKTJCnFO8/78gN2X3tIqkrz5z9VpWbX1VC8L6cPyPJK7dq1adCggfaN8/Lly3F0dKRu3bq4uLjQrFkz7t+/ry2/du1amjRpQr9+/ShevDgdOnRgz549fPLJJwAMGjSITp06cffu3SzvO2/ePC5fvsw///yjk9SztLRk1KhRNGjQAIBDhw7RrFkznTLVq1dn+vTpOXpOlUrFzp07s01UDho0iOXLl2u/P3jwgD179tCvX79XKp9bz6EPU1NTRo4cyaNHj7h+/Xq681n1/ejRo7V9L4QQQoh335sQj1pZWTFs2DB8fX15+PAhfn5+1K1bl5IlS1KjRg0cHR3ZvPnZIJvY2Fg8PDyYOHEiTk5OtGjRgm3bttGlSxd8fX1Zt24dnTp1YsWKFVneNyQkhEmTJvHll1/Sv39/DA0NATAwMKBx48aMH6/5t+iJEydITExk1KhRGBhoZvvZ29szdepUSpYsmaNn9fb2pm3btlmW6dOnDydPntRZY3PZsmWZTmXXt3xuPoc+unXrRvHixTMc2apv3+cVSVQK8RosPxbIN/9e45N153GyMdMevxocyeBVZwmPfW50k30paDFVkzAESIyB43NBpbtzuEKhoEaTjtiMPc2jwvUxUyTzjXoxLvtG8PeMUXBxLep13eCFtSf0plCAWwPouxUGH4Cymv9hK1Br0pYGxpCSt9MOhMhtcUkpxCWl6KwHmJSiIi4phcSUZ3/HwmIS+WrLFV4Y+4wC+GrzFYIjdKcTp9WrUj2rNzn1Jf/uvSAiIoICBQqwc+dOvvjiC7Zv305wcDCPHz/GxcWFvn376pS/e/cujo6OhIeH4+vrS/fu3dmxYwegScj5+vpSunTpLO+5ZcsWGjVqlG25kiVLcvjwYU6dOvVKz3jy5ElAk5jNSu/evTl9+rR2Os7KlStp1qxZpiMa9S2fk+d4cV2gzKYGZSUiIgKAAgUKpDunb98LIYQQ4u30tsajCoUCpVJJy5YtadWqFeHh4QQHB7Ns2TI+/vhj/Pz8dK7Zv38/V69exd/fn759+3L16lUqVarE6NGj8fX1zTbZtX37dpKSkhg8eHCW5dKSeL///rvOFOmXoc8MHzs7O9q3b69NtAYHB7N///50MXlOy+fkOR4+fJhuRGV2yyy9KDU1lejo6AzjUX37Pq9IolKI16BeKTscrEy49SiGLguOc+thNCqVms/WX2LfjYf8siv9pg4AqNXw7yew52v4N+Oh3EorBwoP30Zq8+moFIa0MThN69SDnE0tw+SYD1l/LpiUtB9QLyQ79eZSDZwqa5qE5ocj+ybD/Npwc6emnUK8BTy/3Y3nt7t1Xg4sPnIbz293M/lfzZTatCk20QkpvPgnWw1EJaTQdaHuuiz1fz6I57e78X8coz228VzQS7UxbQHzCxcuMGnSJM6dO8fHH3/M7Nmz6dSpEzY2Nty6dYvAwEB69uzJoUOHtIkvADMzM6ZNm4ZC8WJYq7/AwEC93txOmzaNChUqULt2bRwcHOjQoQPz588nMTFnLzG8vb1p3bo1SmXWYYmtrS2dOnVi+fLlqNVqli9fnuVC7PqWz8lz9OrVS2c9oAEDBuj1jGkJzh07djBhwgRq166dYTJS374XIlc8vQ8hFzWfB5ewjguEB5eeHXt6P8vLhRBC5NzbEI+mLRd048YNNm7cyA8//EDnzp3Zt28fT58+pVevXty5c4dbt25RqlQpSpcuzfbt23Xq+OKLL3BwcHip+4MmJjI1NcXJKev9IWrXrs0PP/zA1KlTsbGxoU6dOkycOJFbt3I2u/DWrVvcv3+f5s2bZ1t20KBBrFq1CpVKxfLly/nggw9wdHR8pfI5eY4lS5akW6PywoUL2bY7LcF55swZBg8eTHR0ND169EhXTt++zyuG+XJXId4z7kWs2DSiLn2Xn+bO41i6LjzB8v7V+b1XZX7Y4cvE1h4ZX6hQgGdHuHMIqvXP/AZKJQb1R4FbfdQbB1IkIoB1xp1ZHV0VNl1mweHbfF/hIXX8Z6Jo9SOUbJqzB/j/xjmpDb/EO8qddvEbMLi5HcLvwF89oWQzaPUTFCqTs3qFeAP5PYxh97Wsd1l+EJmA38NoyjhY5vr9t23bxqlTpzA1NaV48eLaKSiffPIJly5d4tixYzrly5Yty6NHj7C1tQXA2dkZY2PjV2qDsbFxuk1oMlK4cGH27NnD/fv3OXz4MEePHmX8+PEsWbKEkydPZrhOT0a8vb2ZNGmSXmUHDRpEv379aNq0KZGRkbRv3z7TtXX0LZ+T5zh37lyGb54BnZ3EraysdIK7Xr16oVQqKVSoEJ07d+aLL77IMDGrb98L8cqe3oe51bSzI4yAxgA3nytjaAKfngOboq+/fUII8R7L73g0NjaWTp06oVQqKVKkCJ988gljx47lxx9/JCEhgfbt26e75sUXvCVKlHilNhgbG5OcnExKSgpGRkZZlp04cSKjR4/myJEjnDp1in/++YdZs2axZ88eGjZsqNf9tm3bRpMmTTA3z2SZtue0aNECpVLJrl27WLFiBbNmzcqV8vo+x6RJkzJdk/LBgwc6O4mXLVtWO4BhyZIlbNiwAQsLC8qUKcPhw4epXLlyuh3Ac9L3eUESlUK8JFsLY0wMlSSmZD6c3sRQqZ3B7WJrzqbhdRm46gwX7j3loyWnmPtRVVYPrKlzzQHfh9QrZY+J4f930y7fBUo1B1PrZ4Viw8DCPv0NnauiGH4Uzq/Bq9pQrE/dY/6h2wSExWBwbCYKpS+BJ//FtUQT/Udbpe3u3eRrVHXHwo4dqLqu0qxTefQ3UCjh9n5YUAdqDoPGE3TbKsQb5Pq0lgCYGT3brX5ow5IMrO+m3eSqjEMBWpZzYN/1R6RmMFrYQKGgqUdhnaDQZ0ITAEwNn9XbtZrLS7Wxb9++zJ49O91xIyMjBg4cqLM7X0bS1rR5FRUqVODKlSt6ly9atCh9+vShT58+fPzxxzRo0IDdu3fToUOHbK+9d+8evr6+tGzZUq97NWvWDGNjY4YOHUrfvn2zDZ5yUv5VniM1NZVOnTppv7ds2ZLff/9d+z2jBOeLQSHkvO+FeGlxT7JfwiUlUVNOEpVCCJFr3oZ4NG3DmxcZGRlRqFChDM+la+MrxqQVKlQgNTWV69evU6lSpWzLm5ub06pVK1q1asVXX31FpUqVmD17tt6JSm9vb7p27apXWaVSSf/+/fn000+Ji4vLdl3LnJR/1eeYNWsW//33n/b7+fPntcnXrBKcz8tp3+c2mfotxEv6H3vnGRbV0QXgd3fpHQQLoIKoKIqCXUHF3nuNRmM39mhMomlqPk0xiRq7xl5jb9i7YhcromJBxU6RJtJ29/txZWFlgQWxz/s8+8jeOXdm7oD3nnvmFCcbUw6M9sN/mG+Wn8kdK9B57gm2XpSSHduaG7GyX3XqlylIUqqKwSsDuZ8ht8jB60/pu/Qsneee4EVyhjDtjIa/x0HwjxccnaI75NrYEmoOxsTIgH61S3BkRGVOOvzGenlj5qS2ovXlWrSeeYxD15+ifh4BL55l7iMjKqXuwjkNfpKOV+0HpZuCKhVOzoLplSBwad7DzAWCN4iZkQFmRgZahnojAzlmRgaazQGZTMakdp6YGyt05gQyN1bwW3vtqtFp/crl6WcYKvL3Eevr68umTZtQKrX/b6WkpOS6r8TERK5du6az6jRAv379uHDhgpaSk5G0quG6qocXK1YM0G2E04W/vz++vr5YW+u3wSGTyRg+fDhGRkbZhn3nRj4/rkOhUGjlCcpopMwN+q69QCAQCASCD5MPXR8NCwvTmdc7LzppaGgojx8/1tnWvHlzHB0dmTBhgs727PRRQ0NDChcurLceFxMTQ0BAQI75KTPSu3dvjI2NGT58uKbYzOvI58d1AEyePFlLJ9XHQ/RV9F37N4XwqBQIXgMnG1OcMhTHeZU1Z8J4nqzkq//Oo1KpaevthJmRAfN6VOb7jZcp72SNs236jcNALsPa1JDyTtaYGmWxA3V1GyTHwf4J8CQIWs8Eo6xvPhZn52ARF8SfBBHo3IV5D025/CCGXovPsMRmIT7qcxi2mwVlstjVqTc26wXIaLy8sQ92jYHIG7BtOJxdCM0mQ7Hsi2MIBO8j9hbGTGrnybDV2rle1MCv7T2xt9AvpDk/GTduHNWrV6ddu3aMGDECMzMzTp06xfr16wkICMhVX0FBQVStWpWjR4/i6+ubqb1169aMGjWKbt26MWbMGBo1agTAhQsXWLNmDd988w3NmjWje/fu2Nvb06RJE1xcXHj48CG//vorrq6u1KtXT6+56JO0/FVGjhzJyJEj800+N9cREhKSSeErVqxYnpRAXehae7lczvXr11m0aJFm7QUCgUAgEHzcvI/6aIMGDWjTpg0dOnTgt99+w8PDg9u3bzN//nx++uknvb3+0mjVqhU1atRgwYIFmdpMTEz477//aNWqFe3atWPgwIEULlyYa9euceLECWJiYliyZAlbtmxhxowZ9OrVi3LlyqFWq9m0aROHDx/WqkaeHbt376ZMmTKajWp9cHFx4erVq/kmn5vrSMs1mRFra+ts82TmBl1r7+joyL179zR5SpcsWZIvY+lCGCoFgjfIhNblSFGq+O9MGCPXXkCpUtOhsjOGCjmTO1bQ2kWLep5MLTd7tg+vjb1Fen655FQVhgpZumy9sWBREHZ+C0EbIOIGdF2VdVhWndGQ/BxOzqLy4zWcLRTEgkI/MudCMkUSrmMof8bPByJpZ/YM72K2eb/YUg2hxAk4PR8O/S4l41/UBMp3hEa/gLXuqrwCwftKywpF8L/0kH1Xn6JUqVHIZTTyKETLCm8uqbSrq2uWScdLlizJuXPnmDx5Mt999x0mJibUqFGD9evXa2RsbW115gMyNjbG3d1dE/ZsamqKu7t7tsa1v//+m6ZNm7J06VI2bdqEoaEhJUqU4Oeff8bPzw+A5cuXs2LFCpYuXcqdO3ewtbWldu3aDBs2TOMhaWVlhbu7u858jAkJCRw8eFBnqHtG0vrIKmXFq+25lc/NdXTr1i1Tf8uWLaNatWqZjud0/SCFVr3armvty5Qpw/jx43P9AiAQCAQCgeDD5V3oo3Z2dpQunXXtgfXr1zN79mwWL17Ms2fPcHd35/vvv9foKHK5HHd3d0xNMzv0uLi4YG+fnsKsRIkS2RrXateuTVBQEDNnzmTSpEnExcXh6OhIs2bN6N+/PwCdOnXCycmJxYsXM3/+fECqoL1//36NzgpSrsa0nO6vou/Gubu7OzY2Nnq350Y+N9exbt061q1bp9VXu3bt+O2337IdK6vrB+ldI+Ncda198eLFadSo0RuvBi5Tq0W53qyIjY3F2tqamJgYrKys3tg4KSkp7Nixg+bNm7+TRKUfI+/TmqpUan7aEsTKU/eQyeCPDhXoXEXbqBidkEynuScoWdCCqV28MMmQr+S79ZeIfpHMX50qYmmS4VruHIO1PSEhAszsoctyKF4r64mE7IHNgyR5QzNi/CYx5Yk3d8/t5lCqFDbQsGxBfnJ/QPFSnlAgc9XZ1IvrSN32NQatp2BQIZv8HfHhcOAXOLccUIOhGfiOglrDwNBEr3X7VHif/lY/FuLi4ggJCaFs2bKv7eUWEZ9E/b8OEZuYipWJAQdG+72T3ev3AZVKRWxsLFZWVjlW59aXrVu3Mnr0aEJCQvKlvw+N/FzTxMREQkNDcXV1xcRE+z77tvQZwZvjjfwOH16A+XVzlmv/L1TonD9jfoKI53z+I9Y0/xFr+mbIL51U6KPpvAl9VKVSUahQIbZs2UKtWtm8T3/E5Ne65pc+KnJUCgRvGLlcxsS25elZszhqNXy7/hL/nb6nJXP5QQx3IxPYGfSYLxadJuaFlN/jVng8G8/fZ2/wE648jNXu2MUHBhyEwp6S8XFpKzi7KOuJlG4Mg46Ba11IScB670gmFDrK/0YNp3MVZ+QyOH31NpY7h5A6oxr3Lx7UPj8+HMWOURinxqDYMUoyRmaFhQO0niHNr2gNSEmAgxNhVlUI3qo7t6ZA8B5ib2HMr+09cbAw4rf2FT5ZpfBNcenSJXr27PmupyEQCLJjY39Y3wcib73rmQgEAsEnidBH3yxXr17FxcWFGjVEyrL3BRH6LRC8BWQyGRNal0Muk7Hk+B3GbLyMUq2me/XiANQu5cCSPlUZuCyQU6FRdJl3gqV9quHmYMHagTUJehhLjRIFMndsUwz67IEtQ+DKRvAfKRXbafo7GBhllrcsDD02w7FpcGYhVPyMohZmTO5YkS/rurFk5zEu3nDDURZJy//iaHX9AlYmhtiZGTI84hdIfo4MUCfFw/ZRTLf/GaVKzchGWYQGOHpDn11weT3s/Rmi78HaHuBaB5r+AYU88muJBYI3RssKjm80vOZT5scff3zXUxAIBPoQtAGCt0ClnlDnW7DKnxxYAoFAINAPoY++OcqVK8eZM2fe9TQEGRAelQLBW0ImkzGulQd9fV0B+GFTEMtO3NG013KzZ83AmjhYGnPtcRztZx/n5tN4vIvZ0qNGcY3cs+fJzNh/g1Tly8pfRmbQcRE0+BmQSUVslrfN2uNRLofao2DoGcnz8SUlogL45fNGFBnizxzXmaSoFWw894BlJ0KpfPgLuLYNmVqqNixTK+HqVq7vX4ZCrjsHXIYLhwqdYNhZqPMNKIwh9AjM9YUd30BCVG6XUiAQCAQCwWvyOMUMVQ4BDio1RLRZCSUbgSpVityY7g37xsOL6LcxTYFAIBAIBJ8YwlApELxFZDIZP7Yoy8A6UrGLn7dcYfGxUE27h6MVGwfVooS9OQ+iX9Bp7nEu34/RtKvVakasucDfe0MYu/Fyxo6h9tfw2X9gZAl3j8G/9eDRpawnk7FS+OX1sLoLLG9LGfMEpvWux5YhPtQp7UBn2QF8FMGZorVVavjbbDHDq9vod/FG5lD/Rxh6Gsq2ArVSKrwzozKcWQAqpX79CAQCgUAgeG3iYqPJaa9RLoMYtQV0Xwe9toNzNUh9AQFT4Z8K2esZAoFAIBAIBHlAGCoFgreMTCZjTLMyDPKTitVM2BbMgqO3Ne1F7cxY92VNKha1wczIgIJWxlrnfla1KPYWxvR56ZmphXtT6L8f7NwgJkwK1dIHlVIqeBN6GObUgpA9VCxqw7LeVfnO9TYqZLxaPFcuAxNlAmwflbsFsHWBLiug5xZwKAsvomD71zCvDoQezV1fAoFAIBAI8kSSbWnOqdyyTButVkOgqiT2l+dKURAyBfTdA11XS89vUzsoWPbtTlogEAgEAsFHj8hRKRC8A2QyGd82ccdALmPGgZtcexyHWq1G9tIaWMDCmNX9qxMRl0whK+1qWc08i1CvTEGtyuAPol/gaG0ine/gDv0PwMk5Uqi1PlTsIuWTXN8HnlyGVZ2gxhCo0BnbBwezPu9lCDhPr+b+ZaWEH3wZIIWRHZwET4JgaUvwaAONJ0r5NwUCgUAgELwZ1CocZZHZijjLwrG4/xhS4sHEWorgKNMcSjeBmPugeFkdODUZVncF7+7g0U5KMyMQCAQCgUCQB4QWoYNZs2bh4eFB1apV3/VUBB8xMpmMUY1KM/fzyvzRoYLGSJmGmZEBxQqkh2dvufCAWQdvolartYyUdyOf02zaEUavu0RiysvwaVMbqDcWFC/3IlKTYf8vkPhK5fCMOJSGfvug+pfS95OzYOswKFEPZLpvFSpkvCjZIu8eFQoDqD4Ahp+HKn2lcYK3wMyqcPBXSE7IW78CgUAgEHwEvEmd1PzxaQrLojNFTKQhk0EhWQz3/f6BDgu1C+Adngz7J0gF/AAurIBb+2HnGCk0XCAQCAQCgSCPCEOlDoYMGUJwcLCo/CR448hkMpqWL6wpSJOqVLEr6HEmuTsRzxm97iJ/7r7O+K1XUGbIfn/2zjPik1K5HRGPPKu3jT0/wNG/YUV7sozxAjA0gWZ/SLkuTe3g8SVwKINarcp0mloNMrWa324WY2/wk1xfuxZmdtByCgw8Ci61ITURDv8hGSyDNmQ/Z4FAIBAIPlLepE5qkPBULzm7K4ukqIs0UpOkHNNBGyDqZeoaz07g971U2M/IXDqmUsGji/k8a4FAIBAIBB87wlApELwnqNVqRq+7yJcrApm+/4ZWm4u9OWObSV6LS0/cZfjq8ySlSt6THSo7s6JfdWZ3r4SRQRb/pSt+BtZFoc63ZOk6kRH3ZjDoGLSYQtz5DZJR8pXTZDJQA1+q1zJw2WnGb72S7tGZVwqXhy+2Qael0nxj70vh6EtaiIT9AoFAIBDkI6lmBfWSs3x4DPWMSrC2J9wPBANj6LEJagwG9+aSkLGlFF0R9xieR0jHrmyU8k//1x3Cr7+hqxAIBAKBQPCxIQyVAsF7gkwmw72wFQZyGe6FLTO19/F1Zfpn3hgqZGy//Ihei84Qm5gCQC03e4pYm2pkV526xz/7bqBK87x0qgRDz0LpxukdRt/L3lPRyhHsS2OZ/DTLqqByGTjKImkrD8D/+EXazT7OrfD4XF+7FjIZlGsLQ89I3hkGplIV8/l1YdtX8Dz7fFoCwadApUqV2LZt27uehkAg+IB5XrgacWrTbGXS1AQZSKlZFtSHxS0g/ik0+TU9xYxaDUf+hIMT4dwy6Vj4dSmlyzV/mF0DNg+B6LA3dj0CgUAgeLv8+uuvfPHFF+96GoKPEGGoFAjeIwb5ubFvVF2alCuss711RUcW96qGuZGCE7cj6TLvJE9jE7VkwqISGLc1iKn7QtiZMYzcMENRnqhQycth00BIySaXVLx+Id2TzVawxmQSTx+F0XJ6AGvPhqF+3XBtQ1Pw+04yWJZrD2oVBC6GGd5wci4oU16vf4HgPSUiIoJx48bh6+uLu7s7DRo04OeffyYiIkIjc+/ePZ4/f/5a4zRv3hwXF5dMn5EjR+p1fs2aNVm/fn2exu7SpQurV6/Ocl6//PJLjvNevnx5pra+ffvi4uLCH3/8kWf5Z8+e8cMPP+Dj40P58uXp3Lkze/fu1TmHvK6dQPA+YGtpylF1RdRqUL3yyFapJdvjPlUlVqibcVFVgm3URSU3hLsBUtG92TXhwiopD7ZaDbWGQbFaULmX1En9H6DTMnCqLD3DL6yAGZVg1/di01EgEAjec9RqNatXr6ZNmzaaXMlffPEFp06d0shERUXx5MnrpQBbtGiRTp3KxcUFpTLnaL2///6brl275mnsdevW0aFDhyznVaFChRzn3aZNm0xtO3fuxMXFRSu/dG7lAdauXUuzZs0oV64cDRs25O+//9bS/1937d5nRNVvgeA9w8XeXPNzWFQC/pce8WXdEppiO76l7FkzsCa9Fp/m6qNYNpx7wCA/N805Re3MmNTOk+M3I2juqdvgyYNAqbDOpTUQEQJdVoK1U2Y5i0J6zdnAyJhidrZUUdizOzSFb9df4tjNCCa2LY+liaH+F68Lm6LQaTFU7Qc7v5Oqku/6TjJaNv0d3Oq9Xv8CgS4O/gZyBdT9NnPb4cmgUkoFq/KZa9eu0aBBA8qUKcMPP/yAm5sbDx8+5MSJE3Tr1o09e/bk21gPHz6kfv36/Pzzz1rHLSws9Do/LCyM+Pjce1DHxsayadMmJk+enOW8oqKisjz/4cOHvHjxgtmzZ9OjRw/N8adPn7Jy5UosLCx49uxZnuSVSiVNmjRBJpMxceJEnJ2duXbtGlOmTCE5OZkWLVpo+nydtRMI3gecbEz5rugvbAndwTjDZTiS/v/uMQWYkNKD567N+bW9J4OWn+bK4+f8Lu/EPLcAyoWtQRZ+FTYPgv3/g5qDodIXUKGz9iBXNko6R/mO0ubnnaNSsb5zy8BnuBQ+biz+3wgEAoFO3pE+qlKp6NKlC0eOHGHcuHFMmjSJlJQUgoODGTVqFLNmzcLLyytfxoqNjSUqKopLlzKn+VIoFDrO0ObZs2c8fpy5xoM+rF+/PktjZGxsLPfu3cvy3LR57927l+vXr+Pu7q5pmzNnDi9evCAsLCzP8tOnT2fs2LFMmzYNHx8fwsPD2blzJ0OHDmXx4sVafeZ17d5nhKFSIHhPeZGspNuCk4RFvSDqeRLfNy+rMVaWd7Jmw6BarD0bxpd1S2Q6t3OVonSuUlTzXalSc+xmBHVKO0gHPDuCRUFY+wU8PA/z/aDLCihWXbuj4rWkEPDYR0gZKV9FJrX33IKhWQFmm9gy9/AtpuwNYcuFh1wIi2bGZ95UcLZ5/QVx8YGBh+HcUumlKPwaLG8L7i2gyUSwy7wOAkGekSvg4CTp54zK4eHJ0vF6P7yRYT///HOcnJzYs2ePRsEoXbo0fn5+fPPNN1qyERERfPnll5w6dQoLCwtGjRpFu3btcjWelZUVLi4uWbZv376df/75h/v371OyZEmGDRtGgwYNaN68OY8fP2b06NGMHz8eY2Njrl/XLwfd7t27cXd3p3jx4rmaa0Y6dOjA0qVLCQ4OxsNDqkS8dOlS6tWrp6Xk5Vb+ypUrnDlzhrNnz1K5cmUAypYtS7t27UhNTdXqM6e1Ewjed1KVKk7cjkCprsbepCpUk1+jINE8xYbTqjKokKO4HYGTrSnrB9dmzMZLbLkAC27ZMc0og04Q9xD2/AiH/4QqvaHGILB8uVFqX0oqzuf7FRQqD7cOSLJPg6V76al50j22ci8p96VAIBAI0nlH+uisWbPYuHEjgYGBWgZJb29vunfvnslbb86cOaxdu5aoqCgaNWrE//73P0xNs08tkhG5XJ6tTnX//n3GjRvHmTNnsLKyonnz5nzzzTcsWbKEmTNnkpiYqDl/xowZtGrVKscxU1NT2b17N99+q8MIrCdmZmY0bNiQRYsWaaJzHj16xJ49exg0aFCm6KHcyC9ZsoQ+ffrQv39/zbG6detm0kdzWrsPFRH6LRC8p5gaKRhYR/KU/PdoKL/4B2uFUxcvYM43TcpojJeJKUrO3tHthfT3nuv0XHSa33deSz/oWgcGHJReHJ4/lQrWpOWVSkOugKZpIZG6ElWqofH/pBcRMzsUchlD6pVkR2s1ZaxTuRuZQIc5x/n3yO30fJmvg1wBVfrA8HNQfRDIFHB9O8yqDvsmQNJr5scUfLyo1ZD8XP9PzSFQ5xtJCTwwUTp2YKL0vc43Uru+femZBuHChQsEBgby/fff69wFNTDQ3lv8/vvvqVSpEitWrKB169Z06tSJa9euZTovr1y9epX27dvTsWNHNm7cSP/+/Rk3bhxKpZIFCxbg4ODAmDFjOHToUK48Pf39/WnZsuVrzc3KyoqOHTuyaNEizbGFCxfSt2/f15I3NJQ8wHWt46vrLxB86Gw89wDly9uTCjknVR5sVdXipMoD1ctXBKUaNgTex9RIwbQuXvzc0oNt1OZb6z9RFa2Z3plMDkkxcGwaTPOELUMhPATqfQ9fX4PCnlIO6pINoMzL//8m1pAQATu/hZlV4HLeUkkIBALBB8MHoI+CpCO1atUqS6/JjHrq3r17OX78OH///TdTp05lzZo1TJo06XVXSosuXboQExPD0qVL+euvv3j8+DGrVq2iY8eO9OjRAy8vLw4dOsShQ4eoX7++Xn0ePXoUMzMzKlWq9Fpz69u3L0uXLtUYEJcsWULDhg1xctIRrZgLeUNDQ27evJnJKPyp6KOfxlUKBB8on9cojkIuY+zGyyw+dgeVSs341uU0xsk0lCo1I/47z/6rT5ncsQLtKzlr2tRqNQYvq+F4OFppD2DrAn12S6FbV7fC1mHw+PLLBPkvQ7Y9WkPnZVK4dezDDCfLADU8OAflM+T2CNmD+95ebLd351unSWwIjmfSjqsE3Izg784VsbfIB48JU1to9rvkgbFrDNw+CAFT4OJqaDhBCj3Tp7q54NMhJQF+dczbuUf+lD5Zfc+J7x+CkXmOYkFBQQCUK1dOr24HDx7MgAEDNOfMmzePAwcOUKZMGb2ntmjRIjZv3qx17JdffqFnz55cuXIFOzs7zRilS5fGx8cHhUKBo6MjCoUCe3v7XO3iqlQqdu7cyaZNm/Q+Jyv69u1Lp06d+O233zh58iSRkZG0bt2a8ePH51m+bNmyfPHFF/To0YO5c+fi6+tLnTp1qFevHiYmJlr9Zbd2AsGHgJGBfs9JY0PJaCmTyejj60o5Ryuc7eoht+4PN/dJG4VPLkvCCkNQJsP55dLHvTn4jIBiNdI7jH8CyKD535AcB4f+kAr83T0mRXwIBALBx8oHoI+CFGGSlu4mJ4oUKcLixYs1BrSBAweydetWJk6cqPfUYmNjM+mTFSpUYOvWrQBcvHiRn376CW9vb1QqFWXKlMHc3BxDQ0Osra0xMTHJtVehv78/LVq0yPRenVv8/PwwNzdn+/bttG7dmkWLFjF58mRu3br1WvI///wznTt3xs3NjUaNGlGjRg2aNGmCs7OzllxOa/ehIgyVAsF7zmfViqGQyfhu4yWWnriLUq3ml9blkWcoxa1SqzE1VJCqUjNq7UXC45IYUEfKaymTyRjV2J2WFR0pXSi9mniqUoWBQi7lhuq0FI7+Je3OnZ4PT69Kx8wLSMIeraFMC1JvH+HC0d141W6CgToVDv8hvYBkxKYoGFuhKODGX+1rUjnwMRO2XeFwSDjN/jnKtC5e+JS0z5/FKVgGemyC6ztg9/fw7A5sGgBnFkCzP6Rq5wLBB0LazuqrBrGs8PT01PpeuHBhwsPDczVmx44dM+VZLFBA+n9fu3ZtTc7Grl274ufnh62tba76f5VTp06hUqmoWbNmzsI5UKdOHaysrPD392fz5s18/vnnGBkZvbb8kiVL6N+/P/7+/pw8eZKpU6dSqFAhtmzZouVZkN3aCQQfAoWs9AvL23juAZWL2+JsawZA9RIZ/s5LNWJKaFE8bPbS5OkCZMV9pFyVx6fDte3S8/n6DnCuJukL7s2h9XTwHQnWzpJhs0JX2DoULq2VjtX+WtJDXjyTUtAIBAKB4K2hUqlQKpV666Nly5bV8vLLiz5qYWHBoUOHtI4ZG6c7t7Rv354BAwYwcOBA/Pz8KFOmzGvnYPT3988yX3pukMlk9O7dm0WLFmFlZUVMTAwtW7bkn3/+eS35Fi1aEBISwsaNGzl69Cg//vgjX375JZMmTdIKV89p7T5UhKFSIPgA6Fy1KHK5jG/WX2TFyXsoVTCpbbqx0lAhZ0pnLxwsjfn3aCi/7bzG07gkfmheViOT0UiZkJxK53knaO/tTG8fF2RyuZT3pFA52DhASnb/rx90XQ2Fy0snyRWoi/vy4EosFYv7gqEhlGyY2XOxYFnovx+snJEpDOhWvRiVi9sybPU5Qp7E8/nCUwyq68bIRqUxVORD9gmZDMq0ALcGUoL+I3/D/dPwb33w7g4Nxkn5OAWfNoZm0k5ybgmYKu1WK4wkL6E630gv2LkdWw/SdkNv3bpF0aJFsxdGd5JsdS7CeiD7PIuFChXiypUr/Pfff2zZsoXhw4fj4+PDpk2bcpV3KCP+/v40a9YMuTx/Ms/06dOHadOmcfbsWa0qlK8r7+Pjg4+PDwCPHz+mfv36jBw5koMHD2pkRI5KwYdONVc7ilib8CgmMVu5wyHh9F58hj0j62TyPLnyMIbpB24BJVjVeye1ipmBqQ0UWwk39ko6RVKs9Fxe0x0KlJKqg1fokh65YWT2MhwyHlQq6djuH+DWfmj4C/i+siEqEAgEHyofgD4ql8spVqxYlh6Br5If+mhOeRaXLl3Ktm3b2LFjBz179iQpKYm1a9dSq1beNrNu3rzJ3bt3adiwYZ7Of5VevXoxceJEEhIS6NmzpyaV0OvKOzk5MWzYMIYNG4ZKpeLbb79lzJgxdOrUCVdXV0DkqBQIBO+YjpWdmdK5InIZrD59j7EbL2vlfZTLZfzQwoMfmpcFYGFAKF+tuUByqipTXxvOPSDoQSyzD90iOiElvaFMC+i3D2xdpTCshY3gzEJ4eEH6PLqIdcIdeHRR853ol4Uozq+E8yukn21dQJG+D+L+ZAdb+lWgW/ViqNUw+9AtOs87QVhUQv4tkKGJ5IUx7Kz0AoRams/0SnBsOqQm599Ygg8PmUwKd8nN58QsSSms9wP8FC79e+RP6Xhu+tEzpKRWrVoULlyYuXPnvuHF0B8HBweGDRvG5s2bCQkJISAgQBO2rVAocq2I5kd+yox88cUXHDt2jPLly1O+fPl8lwfJM6Bhw4bZVn4UCD5EFHIZ41p56CX7OPYFK07eJUWprVOUc7Tmjw6eDKhTglrujpKRMo2gDfAiClSpYOUk3Q8jb8C24VIey6N/S16TAB0WQrd1UjEeZQrYFgeFMVxYARdeFhfI5f1GIBAI3js+AH0UpCKEGzduzLVn5JtCJpPRunVr5s6dS0hICFWrVmXChAlA3vTRbdu2Ua9ePczN9QuFzwlnZ2fq16/Pvn376NOnT77Lg2SQ7NatG2q1mvv377/ulN97hKFSIPiAaOftzNQuXshlsOZsGN9uuITylSI1/euUYFoXLwzkMrZefMjItRcy9fN59WL83NKDWd28sTV/JfSxYFnofwBK+El5VHaMhvl1YX5dDBc1wO/6zxguaqA5xszKELQRtgyGrcPh+i7t/k7OgU0DMF3bhV9blWZ290pYmhhw/l40zacfZfulR/m7SFaO0H4+9N0Ljt5S/qu9P8GcmhCif8EPwSdOxmqKaVUW634rfT84SWrPZ4yMjJg9ezYbNmxgwoQJxMXFAVJI+IkTJxg9enSu+hswYABdu3bN83w2btzI0qVLSUyUvK0iIyNJSUnBzs4OAEdHR27cuKH3mGFhYVy9epWmTZvmeU6v4ujoyN27d9m5c2e+yF+6dIlvvvlGy4vg8uXLbNq0iXr16uXLnAWC94mQJ1IROjMjbY+YtO9pWWbiEpX8tOUKjace4e8911lx8o7mxbBL1WJ8/3KTFCA8LondVx5L+a59RoCBCcQ+kIo5OLiDeUGpiN/+X2Bqedj1vdReujGY20ueli2nSmlnIkKkPNQAmwfD9tEQ9+QNr4pAIBC8J7wDfRTghx9+oFChQnTo0IGrV69qjj948IAff/yR4OBgvfvas2cPLi4ueTauJSQkMGzYMM35iYmJxMTEaOmjd+/eJSUl3fkmpzHze+McYPXq1dy9excPD/02AHOSHzZsGPv27SM5WXK2iY2NZdq0adja2lKxYsV8m/f7ijBUCgQfGG28nJj+mTcKuYz1gff5Zt3FTMbKtt5OLOpVFXsLI/r4uGbqIy0hfsY8U4F3n7Ev+KXyb2YH3TdIleTUmT0ytUhNAjtXqNgN1EpY1wvuZQipLFYTTGyk0GwDY5p7FmHH8NpUKmZDXGIqQ1adY+zGy7xIVmY1Qt4oWg36HYA2s6SXosibsKoTrOwEETfzdyzBx4dKqa0UppGmHKry+e/1Je3atWPv3r3s27cPBwcHHB0dsba25rvvvqNJkya56uvp06c8fvw4W5lFixbh4uKi9enUqRMANWvW5NChQzg4OFCkSBF8fX356quvaNy4MQDffvsts2fPxtHREXd39xzH9Pf3x8fHB2tr6xznrmtef/zxh05ZJycnjbKqD9nJlypViiJFitC8eXOsrKywt7enVq1aNGvWLFPuoOzWTiD4UFCq1IxqVJrL45sw1K8EJgo1w+qV4PL4JoxqVJrOVYpS0dma0Y1LU8DciNCI58w4cJMfN1/hl22ZX1RTlCqGrjrHwOWB/H74KcoGE2D4ean4nUwB4dfhebi0GVqwnBTufXIWTPeCjQPhyZX0zpr/CY3+BzWHQuQtuLgKzvwLU8rCpkHwIvotrZJAIBC8I96RPmpnZ8eJEydwd3enZs2aODg4YGNjQ82aNbGwsNCEHetDQkICd+/e1eRi10VaQZhXP3fv3sXU1BR3d3dq1aqFg4MDBQsWRK1W8+efUiGhTp06YWNjoynwuG3btmzHjI2N5ejRo3oZKnXNq2rVqjplra2tKVasmJ6rkrN827Zt+euvv7Czs6NIkSI4ODhw+/ZtduzYgZVVeoHc7NbuQ0amzq2f7CdEbGws1tbWxMTEaP0x5DcpKSns2LGD5s2b55jPQKAfn8Ka7rz8iGGrz5OqUvNVw1J81bB0JpmE5FTMjNJDsDUFdF4hIj6JFtOP8iQ2iZndvGlZ4WU1uocXJK/JnBhwWMpv+V93uLFbMkz22S0VuwHJ+8GykNYpKUoVU/eGMOfwLdRqKF3IgpndKmnl0sw3EmPhyGQ4ORdUKSA3hBpfQp1vweTN/d/Wh0/hb/VtExcXR0hICGXLlsXMTL98PO8jL168IDo6moIFC2bK/xMWFoadnZ1WyMrjx48xMTHBxsYGkIyGKpWKwoUL6+z/4cOHml3ajJiYmGido1QqiYyMxM7Ojvj4eKysrDQ5JlUqFU+ePCE5OZnixYtnO2aLFi2oX78+X3/9dbbXndW8rK2tsbW15eHDh5iammZZ2OfV9tzKpxEfH09SUpLOAjn6rl1OqFQqYmNjtdY0ryQmJhIaGoqrq2umBPhvS58RvDnexu8wq+eRWq1GJpMRn5TKvMO3mH3olmaDtEGZgnzbtAzuhaVnd6pSxR+7rvHv0VAAfEvaM/0zb+zMjaRNwoMT4comKX+070i4uR+OTZNyY6dRsqHkielSWztUMfSIZMyMe5nfzcRGSvlSrT8Y5i1v7ttAPOfzH7Gm+Y9Y0zfDx6CTqlQqwsPDMTc3x8LCQqvt2bNnJCcnU6hQ+ntefHw80dHRmurUCQkJPH36FGdnZ62iO2nExsYSFRWlc+xXz4mIiMDMzIzk5ORMutOzZ8+IjY3F3t4emUyW5Zjr1q3jl19+4fLly9led1bzUigUFC1alNjYWGJiYrLMK/9qe27l01AqlYSHh2Nvb5/pWnKzdjmRXzppfumjopiOQPCB0syzCDNlMuYduUXvWrp3tTIaKYMexDB01Tmmf+ZNBWcbLTkrE0OalS9CwM0I/NzzWHhGYQidlsCy1nD/DKxoD333SBU8MxopVUo4PgPDagP4tmkZfEra89WaC4Q8iafVjAB+buVBt2rFMiXsfy1MrKDxRKjUC3aPhRt74PgMuPif9LIUHSbl1Hx1txKkkAqVEuqNzb/5CAR6YGpqmmXRGl1KzqsGsoIFs/+/7OjoqNc8FAoFBQsWRKXK7F0tl8spUqRIjmO+ePGCgwcPMmXKlBzHy2leuW3Pa38WFhaZFHJ9+xQIPibSnscWxgaUc7RCqVJjY2pAbGIq+6895eD1p5R3smZcKw8qF7fjhxYeeDrb8N36SwTcjKDVjADm9ahMeaeSkp7gMwLs3SUjZKmGIANsikNitFQh/OY+6ePoDbWGQ9nW0jPatQ50XysV2okIgbhHUmqXk3OkKIqmv4NVkewuRUpVs/M7aD4ZyrV700snEAgEHzxyuVzLEJkRXZvAr+pPZmZm2RZ7sbKy0nsDzt7eHpVKpXOz2NbWVms+WY2pb9h3TvPKbXte+1MoFFlugudm7T40ROi3QPAB07R8YTZ8WQtrM23PB138tec6dyIT6Dr/JEdCtBMjGxnIGd+6HJuH+GBhnG7cjHqeywI0RmbQbS3Yl5byTa3oAEnx2jLbv4Z942BtD1Cr8Slpz84RtfFzdyApVcUPm4IYsuocMS9SdI/xOtiXhO7rpIT9BUpK4Wdbh8L5ZbrzvKTlhZFnrmYnEAj0x8DAgKtXr2pCxAUCwYeJlYkhBcyNiH6RiqmRgkrFbFCp4dL9GDrMOcGEbVeIeZFC64qObBpSi+IFzHgQ/YIOc46zPvBlrjBHb0lfAKnK995xUtGcu8ckI2blXmBgCg/Pw/reUi7s0/9CcgIU9oQvtsLIK9BmNlg5Sx6WwZthWnnJWzOrYLH4cPD/SsqPuW2E9F0gEAgEnxS///47P/7447uehiAHhKFSIPjAkcvTPQ8XHwtl4PJAnZW+Z3zmjU/JAiQkK+mz5AxbLjzIJJPRSLnz8iP6Lj2b+wmZ2cHnG8HSETzaSlXmMlLxMzC1fZmvSpq7vYUxi76oyg/Ny2KokLHj8mOa/3OUwLvPcj++PpRuDINOSLmvjCwlrwyQjJJ7fpJ+1pW8WiAQ5AlDQ0OKFy/+rqchEAhek1ol7fEf7kulYjY8T1Jy7l40jcsVwvKl/rD42B3q/nmQBUdv42pvztahvtQvU5CkVBWj113k5y1B2jqKTAZ+YyUPyxfPIGAq3NgLDX6W0rOY2sGzO1Jhv2nl4dDv8DxS2kD07g7DAqFybyn/pSpVypP9bz24fUgKFVcppaiJBy+NnklSkTKS4mBDHynFTXTYW15FgUAgELwrihQpkm/VvgVvDmGoFAg+Eh5Gv+D3ndfYE/yE7ZcfZmq3NDFkUa+qtKroSKpKzYj/LrDg6O0s+9sZ9JhkZQ6FdLLCpigMPiGFS78awl2sOoy4CGVbaR2Wy2X0r1OCDYPSPTA6zzvBrIM3MxULyhcMjMBnuPSS4/V5+vHj02GCnTBSCgQCgUCggyLWpvw3oCa9arkAsOfKE8o7WTG5gyelCloQnZDCxO1XqTZpP/uuPmb+55UZ0aAUAMtO3OWzf0/yJDZR6kwmg7ItYdBxqfidlbMUkbF7rOQd2WYmNPtTCg1PiIRDv8HUcrDjG8mAaWgCrabBd3egzjdgaC55Yi5rA0tbSUV6ZlSCf/2kPJhpBQLVKsmQOb+u5LEpjJUCgUAgELw3CEOlQPCR4Ghjyr89qzC8fknaejnplDE2UPBPFy9NJfCJ26/y646rqHQYAqd18WJEw1L6DZ6alPmYqU36z8kJcGp+ejiWSYaqv4kxsPdnTR8VnG3wH+ZLGy9HlCo1f+6+Ts9Fp3ia9lKT31gWgrazoP8BcK4mHVMrARl4f57tqQKBQCAQfIqkpYyZ8Zk3ZkYKTtyOwtLEkJ0javNHB0+MDeTEvEjh67WXaD3rGJWL27LwiypYmhgQePcZLWcEcOZOhgIACgPpmTssEJr8KnlSRt6QDIrVB8Cwc9BxERSpCKkv4PR8mO4N63pLhkkTK6j/o7QRWv1LSc7UDuxKgjKHNDapSZIRVCAQCAQCwXuBMFQKBB8RdUo7MKqxuybx/YtkJYkpSi0ZuVzGTy3LMqaZVJH76qNYlDryOcnlMhpVLkuKzCjngQ/8D1KzeBFQKaXCOju/keQyolZLlcKP/QNbh2sOW5oYMq2LF392rICpoYJjNyNp9s9RDl5/mvNc8opTZanSaPrkpJegoA1vbkyBQCAQCD5gWlV0ZOtQH75vXoZmnkUwUMjpXKUo3zYtg7GBHDNDBcGPYum56DSLj93hr04VKV3IgvC4JD6bf5ID155od2hoAjWHSAbHFn9DmZcFDxQGoDCCltOg51ZwayAZMa9shPl+kvfkzX1gbg/N/oD+h2BUsNSXQCAQCASCDwpR9Vsg+EhJTFHSf9lZZDL4t2cVTAzTC8LIZDK+rOtGCXtzapW0x1Che88iJNGW3i/+wlYWp7O9nOwOvxguweTOUdjYX/J2eLXwjFwh5aW8dwKO/g0WhaD6wLSJQJ3REBUKtYZqnSaTyehUpSjexWwZtvo8Vx/F0nvxGfr5uvJt0zIYGeTzPsvhyXDoVync26OtVL087hGs7wPXdkCLv6TcmgKBQCAQCDSULGhJyYKWmu/hcUncCo/nyLf1MFTImXngJstP3iHgZgQBNyNoXr4wzjZmhEY+p4qLne5OTaygar/074mx0obmiyjwaCMZI1MT4dh0aUMx9Ij0KVReKshTrh0YmkJSrH4XERUKjl55XwSBQCAQCAT5hvCoFAg+Um4+jefcvWccvRFB36VneJGszCTTuFxhTQEdtVrNgqO3iYhPD+MuXcgCz3LluCYrwRW1a6bPBnV95jtOBLmhVHFz63CpguerVP4C6r2srrbzO20vxRJ+MPycVMlTByULWrBpcC1NLqwFAaF0nHucOxHP87Isunm1cI5DafjqMrj4Su1B62F2Lbh1MP/GFAgEAoHgI0OtVjNy7QVWnbrHZ/+eJCI+iZ9bebB2QE0MFVK0x46gxxy58ZQaJexIVao152XUPzKRmgSlmwAyCN4Cs6pL4d8Nx8OIC1BjsJSf8kmQtHE63RtOzAaLwvpN3M71ta5bIBAIBAJB/iEMlQLBR0p5J2uW9qmGuZEUOt1nyRkSklOzlF9wNJSJ26/Scc5x7kUmAJJX46R2npgbKXilJA4ywNxYQbfuvSVPSpkCLqyQEuDrCCWnzmio2h9Qw8aB2kY/A+P0nyNvwfavQZmiOWRiqGB863LM71EZGzNDLt2PocX0ozorl+cJlTJz4RyFIfTaLlUnN7GFuIewvK1kaE15kT/jCgQCgUDwESGTyRjVqDSFrUy4Hf6cNjOPseXCAwpZm1DVxY5SBS3wLVmAVBWsPh1G3ckHmXXwJguOhtJwymGO3gjX3bGFA7SbC4OOgXtzKZf0uWWSQfL0fKmQzsggKU+luQPEhEn6yKpO+k08pzyWAoFAIBAI3hrCUCkQfMRUdbFjWd9qWBgbcOJ2JL0Wn+F5km5jZf2yBXG2NeVOZALt5xwn6EEMAPYWxkxq58mrpkc10MfXFXsLY/BoDW1nSw2n5sKBiZkHkMmkUC2PNqBKgTWfw8ML2jKpybC8HZxZkDmfJZIH6M4RtanmasfzZCUj/rvA6HUXs7wmvak3Nuvq3q3+gVFXoErf9OubV1dK3i8QvCNGjx7N2bNn3/U0BAKBIBOVi9vhP9yXWm4FeJEiPavnHb7F4l5VWTuwJiv61WB532p4FLEkLimVP3df549d14hOSOHqoxxCtQuVg89WQ589UKwWKJPg+EyIewxmdpLB8qsgKZelnRskx+s36WvbX/u6BQKB4FNjw4YNzJgx411PQ/ARInJUCgQfOZWL27G8bzV6LjzN6dAovlh0miV9qmlCvtNwc7Bg46BafLH4DFcfxdJ1/knm96hMrZL2tKxQBP9LD9kX/BSlWo1CBubGBsw+dIsGZQrh6WwNFbtCUhzsGA1H/wJjC/AdqT0ZuQLa/wsJUfD0qpQIPyMGRtD8Tzj4K9QcpvN6ilibsrp/DWYcuMH0/TdYH3ifc/eeMeMzb8o5Wus857UxMoeWU8C9GWwZAhHXYUFD8BsDPiOlJP8CQT5y4sQJ9u/fT3h4OM7Ozvj4+FCrVi1N+5IlS6hSpQpVqlTJ8xiTJk0iLCws0/Hq1avTu3fvHM8fO3YsrVu3pmbNmrkee+bMmZQtW5YGDRrkaV4ZZUxNTXF1deWzzz7DwcEh13MRCAT5j72FMcv7Vmfq3hBmHrzJ0hN3uXg/hvk9KgNQu5QDYTUS+H5TEMYGcpJSJX1gzZkwXO0taFi2oKYwoE6KVYfeO6QCOg/PQyGP9Lb7Z8CrO1TqCSdmwd6fcp6we4vXuVyBQCD4KHn06BGbN2/mxo0bmJub4+HhQbt27TAxMQEkfTUoKIhhw3S/t+nDgQMHWLt2rc622bNnI5dn71u3detWbt68yahRo3I9dmBgIDt37uTHH3/M07wyysjlchwdHWnQoEGedGOBNsKjUiD4BPAuZsuKftWxMjHg7N1n9Fx4irjElExyBa1MWDOwBjVK2BGflMoXi0+z7eJDZDIZv7Qpj7GhHFBjbKiggrMN5kYK4pIy9FOtPzScIP28bzyc/jfzZAyMoesq6LsHnCplbi/dBPoflMK8skAhl/FVw9Ks7l9DE17WbtZxlhwLRa0r7Dy/KNUIBp+Esq1BlSp5ji5uKoWrCz5OVEoIPQqX10v/qjLnes1P4uPjadOmDS1atCA8PBw3NzciIyMZO3YsXbt2zdex1q1bx40bN/Dy8tL6FC9eXK/zly9fzvXr13M9bmpqKj/99BPW1ro3FvSZV0aZYsWKsWHDBkqWLMm1a9dyPR+BQPBmUMhljG7izsIvqmBlYkB8UirmGTZJjQwUmBspGNWoND+2KIuNmSG3wp/Tf9lZOsw5Tud5J7gVno1HpEwmPZczRkRE3oJlbWBmFSkftkcb7fQyWREwFZSpUp7tazt0p7ARCASCd8Vb1kcB5s+fj5ubGzt37sTR0REzMzN27NhB+fLlCQ0NzbdxLl26xLJlyzLpfV5eXtlvWL3k9OnTbN26NU9jL168mMePH+d5Xhllypcvz8OHD6lbty6TJk3K03wE6Qg3IIHgE6FiURtW9qvB5wtPce5eND0WnmZZ32pYmRhqyVmZGLK0TzVGrbnI9suPGLnmAjEvUph18CYJyUpARkKykhtP4xjduDS13Oy1B/L9SvKsPPqX5F1pUVB6UciIiZX0SePRRbB1TT+Wcefs1kGpUE+LKZkqilcvUYCdI2rzzfpL7Lv6hPHbggm4GcmfHStga270WuuVJWZ20HkZXFoDO76RPDfm+kKTSVC5t/TiJPg4CN4Ku76D2Ifpx6wcoekfUrqDN8CgQYM4ffo0QUFBODo6arVdvXo1k/zmzZs5deoUFhYW9OrVCycnp1yN5+npyZdffpll+9OnT1mxYgX379/Hzc2N1q1bY2VlxaRJk4iOjmbp0qWcPHkSQ0NDvUN/AgICMDExoXLlynme16syQ4cOxd3dnT/++IPFixfrNQ+BQPB2aFC2EP7DapOiUmkMlWq1mvbeTviULEBBSxMUchmdqhTlz13XWHM2jHP3ogFoMvUIP7fyoGdNF/0Ge3YHzApA9F2pqE6h8tBiKiREwKHfISVBW97WFaLvQcgO2DQAHMpIxfW8P4c2s/JtDQQCgSDPvAN9dN++fXz55ZcsXLgwU5TNkydPMDTUfn+8evUqW7duJSoqioYNG9KoUaNcjWdkZJSt3qdUKtm4cSNnzpzB0tKSevXqUatWLQ4cOMDOnTt59OiR5vx+/frpHXG0fft25syZk+d56ZKxs7Pjl19+4dtvv820TgL9ER6VAsEnhKezNSv7VcfGzJALYdH0WHCKmITMnpXGBgqmf+ZNr1oudKlalJ82B/EoJlFL5mlsEj9uvsKuoEcAJKZk2Nmr/yNU/1Kq5F2sFtly6wAsaiblrEx9peJnQpR0PHCJbu9MwNbciH97VmZC63IYKeTsu/qEZv8c5eTtyBzXI8/IZFKo+6Dj4FJbevHxHwmrOkPckzc3ruDtEbwV1vbUVgoBYh9Jx4PztnObHWFhYaxcuZIff/wxk5ESoGzZslrfv/32W5YtW4a9vT0BAQF4e3sTHR2db/MJDw+nQoUKHDt2jGLFinH16lWaNm2KSqXC3d0dQ0NDihUrhpeXFxUrVtS7X39/f1q0aKHXLrm+GBoaUqZMGZ0h4wKB4N1TrIAZbg4Wmu/zj9ym79IzmBoqUMile4GlsQEhT+ORy2RULW4LQKpKzc9brtBi+lEeRetRyK5kA6kKeP2fwNhKqgK+ZTDs/TmzkRLgWahULVymkDwwr/lLPztXy4/LFggEgtfjHeijAH/88QdVqlTRmQqoUKFC2NnZab6fP3+eLl26oFQqUavVtGrVipUrV+brfPr378/333+Pra0tKpWKESNGsGvXLhwcHChUqBAWFhYab0dbW1u9+gwKCiI8PJx69erl61wrVKhAcnIyT56Id8LXQXhUCgSfGOWdrFn10rPy4v0Yui88yYq+1bEx0/ZAVMhl/NTSA98/DmQqpANSMR0ZMGFbMEWsTRmy6hw/tfSgSbnCkiGvyW+Q8hyMLbOfkKmtJB96GDZ9CR0WpntUmtlBm5nSy0OVrHPmyWQyvqjlQhUXW4atPs/t8Od0+/ckw+qXYlj9khgo3tCejE1R6LkVTs2BfRPgxh6YXUMqwPOGdjgFr0ny86zbZAowNJHCaXZ9B9n95e/6Dsq0SPfyzapfI3O9p3bq1CnUajW+vr56yfv4+LB69WoAhg8fjpOTE5s3b6ZXr156j3ngwIFMO8Vdu3bFz8+Po0ePIpPJ2LBhAwAqlYrg4GBkMhkdO3bkq6++ol69erkaDyRD5e+//57neekiOjqas2fP5nt4vEAgyH+iniczff8NnicraTkjgDndK+PpbE18cirGBnIUchlTungRl5jCwBWBhEW94MrDWHz+OEBfX1eGNyiFpUk2XipG5lBnNFTpA0f/hhMzs59Q8ssCPjK5FOFRspGU4zINZarIRS0QCPKX91gfBSn3ZP/+/fWSffHiBfv379fkCU9OTubff/+le/fuOZyp3cerel/x4sUZO3YsAOvXr2fdunU0adIElUrF0KFDSUhIoGjRolSqVImEhIQcPR9fxd/fn4YNG2JsnHV6kJzmpYt9+/ZhbW2t0+lAoD/iqSsQfIJ4OFqxun8Nuv17kofRiYTHJWUyVAKcDo3K5EmZETXwKCaRf4/e5v6zF8w4cINGZQshl8skY2NGI+W55WBXAlx8tDtx9IYuy2FlZ7iyEcwdpOrgad5W5dqBR1vtkGq1WmeIdTlHa7YN9WXc1iusD7zPP/tvcOJWJNO6euFoY5qLFcoFcjnUHAIl6sHGAfDkMqztARW7QbPfweQNFfgR5I1fs1EaSjWG7uvg7vHMO9daqKX2u8fBtbZ0aJonJOjw4h0fo/fUYmIk2UKFCukl37hxY83PhoaGuLm55dqj0MHBAS8vr0zHAEqVKkVERARTpkzhs88+o1ChQjg7O7+WJ+StW7e4c+dOjiFB2c0rjTRjZpqCXKBAAZ3J0AUCwfuFnbkRa7+syaAV57gXlUCHOceZ0KYcXasWZVmfatwKj6eonRkAR7+tzzfrL7Lu7H1Uavj3aChrz4QxslFpulUvjpFBNhuRZnZQumnOhso01CpABjf3wsZ+0H4BqJWwpKWUP9t3lHZqGoFAIMgr77E+qlQqef78ud76qLe3t5aOVrZsWbZt26b3eAAKhSKT3lewYEHNz+7u7sycORN7e3sqVqyIQqHIdbqjV/H3989xsz2neUG6MVOtVnPlyhXOnz/PwoULcywCJMgeYagUCD5R3Atb8t+AGqQo1ZQqpNvr8Wlc1kbKjNQvUxBXe3P61S4hGSlf5ao/bB0KRpbw5VGwc9Vud6sP7ebChr5weh5YFoLaX6e3ZzSMnJoH4dehxd86jZXmxgb81akitUvZ88OmIE7fiaL59KNM7lCBxuUK63U9eaKQB/Q/AId+g2PT4OIquBMA7eaAi34ecoL3hHg9QzX0ldOTNMUnLCyMwoVz/ls1NdU2visUCpTK3CVXzy4XpKenJ1u3bmXmzJmMGzeOokWL0rt3b77++mud8vqwbds2/Pz8MDfPfmdfnxyVacZMExMTevfuTe3atVEoFNmeIxAI3g/KOVqzbZgvX6+9yL6rTxi78TJn7zxjYtvylCyYrpPcfBrHlgsPcS9kSXh8IlHPU4hJTGX8tmAWH7/D6MbutPAsolv3AP3v0zUGS7mn017wr2ySNkVLN4H7pyEiRPKytCrymlcuEAgEevKO9FGFQoGdnZ3em9/5oY/mlAty8+bNTJo0iQ4dOhAbG0uLFi3466+/9DamvkpUVBSnTp1i3bp1rzUvSDdmyuVyWrRoQY0aNTIZMwW5RxgqBYJPmFcNlIF3oyhewBx7C8kFvqCliV79FLE2pX0l56wFSjaQcjk6eoGti24Zz47wPBx2jYH9v4BFISmZfUYibkjtapXUZ5kWWQ7ZxssJr6I2DFt9nkv3YxiwPJAvahZnbPOymBi+IWOGgRE0HCfthG4aKCXzX9ISag2V8mXpU3lU8Gb5PpudadnLvwsLPZWejHJfXc77nF7i4+ODsbEx/v7+VK1a9bX7yw+aNWtGs2bNSElJYcuWLXTt2hUvLy8aNWqUJ89Kf39/2rZtmy9z08eYKRAI3l+sTQ2Z36My847c5s/d19hw7j43nsaxabCPJm/l3cgEjBVyitiY8N+A6oz47wJHbkRo2oatPs+/R28zplmZzMX9QP/7uXtz8PkKtg6DG7ulY8GboWwraDUdrJyEkVIgEOQf77E+CtCgQQN2795NSkrKe1EQxsnJidmzZwNS4Z5u3brx9ddfs2LFijzpozt27KBixYoUKfL693V9jJmC3PPR+6M+ffqUoKAggoKCSEjQkURbIBAAcPZOFD0Wnuaz+SeJiJeK2lRztaOItQnZ3f6LWJtQzdVO69jBa09ZduJO+gFDU/h8AzT6X/ZVsWsMAt+R0s/Xd0reDBmxLyVV4aw9WnqpyIHiBcxZ/2Ut+teWPDiXnrhLu9nHuRUen+O5r0XxmjDoGHj3ANRwfAbMrwePg97suIKcMTLP+mP40jBfvJZUTTHLv3yZ9NJaPEOhqKz6zAV2dnZ8/fXX/P3335w4cUKrLSkpiU2bNuWqv7lz5zJlypRcnZORwMBA7t27B0ih5c2bN8fMzIyICMlIYGtry7Nnz/QeMy4ujqNHj9KyZcs8z0kg+FBJTk7W6KMPH2YXyvdpIZfLGOTnxop+1bG3MKJDJWeNkRKkiuF7RtXhjw4VsDU3ZnHvagyq65Z+vgwu3Y+h27+n6LX4NFcfxWoPkOP9HGkTsXAFKZqj2xpoOQ0ULzcW/UeCgYm0OZrG48twNXdhjQKBQKDFe6yPAvz00088ePCA77//HpVKpdUWEBDA/fv39e7r4sWLfPnll0RG5q3QaXJyMtu3b9d8d3d3p2LFitnqozmN6e/vL/TR95yP3qNy/fr1zJ49mzt37rBr1y69ixQIBJ8aBSyMsTIxpLC1CRbG0q1BIZcxrpUHg1acQ4buVM6D/UpqvVRcuh9Nn6VnkCEV7qlU7GXltYzehCmJkmdk7a+lgjQZaTAOCpSCCl10GzW9uml/V6kkuSwMoEYGcn5o4UGtkvaMXnuRq49iaTk9gAltytGp8uvl28sWY0upEJB7c8lD4+kVmO8nVUSvNSw96bXg/UOugKZ/SNUUM/3lv/x7afr7G/kd/u9//wOkneyqVavi5ubGo0ePuHr1Kl988QXt2rXTu69du3YRHR3NqFGjspTRVbSmZMmSjB49mpSUFBo3boyTkxPFihXjxIkTuLu706KF5MncunVrfv/9dy5fvoy5uTkzZszIdszdu3dTqlQpXFxccpx7dvMSCD5Enjx5QteuXYmOjqZly5bMnTv3XU/pvaKWmz17R9bFxizdc+dh9AsKWhpTxDo9rFAhl2FqpMBALkMul5GcqqJ4ATMePHvBoevhHA4Jp723M6Mal8bJxjSH+/lLUpNgWSvothYsC0vF+1zrSJER98/ApgFSKHirf6TIif+6SxET7eZBRVHASyAQvCHeoT7q6enJjh076NOnDxs2bMDHx4fk5GSuXr2KnZ0da9as0buv0NBQ5s2bx5gxYyhQoIBOGV1Fa0DSi21sbJgzZw7fffcd3t7ehIeHc/z4cc0GftOmTfn+++9p164dhQoVol+/fty/fz/LMVNTU9m9e7deOmV283o1d7ogf/noDZWDBw9m8ODBNGzY8F1PRSB4r3G1N2f9oJrYWxhrhUY3LV+EOZ9XYsK2YK3COoYKGSlKNXuCH9OjZnHNcU8na9p7O2NmpKC8YxaFZHZ9B4FLpErfvXdJXgxpyGTgnaFKnFothYRb6Mj1oVLBtuFgapOjt2Y994LsHFGbkWsvcOxmJN+uv8SxmxFMbFs+++qhr0uZ5uBcVZrn9R2wbxyE7JZyV2YVBi9493i0hs7LpL/VjInMrRwlpfANVXWXy+VMmjSJr7/+moCAACIjI3FycqJy5cpaitbff/+dKTz8m2++0UosPmjQIJKSkrIc68cff9TsRmckLQymRo0aXLx4kWPHjnH//n169+6Np6cnFhYWAEycOJGmTZty48YNTS6i7MbUd/c6p3mlyYhqioIPiaJFixIUFMSSJUs4efLku57Oe4mteXpRv9jEFLr9e5KCVibM7OatSUWjVqsJehBDqkrND03LcCjkKVO7ePE8Sclfu6+z/fIjNpy7z7ZLD+lVy4UhfiWxzvJ+7gRV+0vFdh5dhAWNpAIWBctAATdJPzk2FQ7+BiE7YdoB6LgYyrSE69ulNC8CgUDwJnlH+ihAvXr1uHHjBqdOneLmzZuYm5tTrlw5ypYtq5Hp2LEj9evX1zrP19eXSZMmab57eXkxZ86cLI2UDRo04J9//tHZZmxsjKGhIf7+/gQHB3Pu3DnMzc2ZN28eRYtKzi4eHh5cv36d48ePExsbi62tLfb29lmOGRAQgImJCZUrV872+nOaV5qMtbUonPomkKnVr8ZWvl0iIiLYsmULlpaWdO7cWafMhQsXCAwMxM7OjsaNG2sl4b937x6xsbGZzrG3t9cqRtCwYUPGjx+fK4/K2NhYrK2tiYmJwcrKKhdXlTtSUlLYsWMHzZs3fy9yQHwMiDV9fdRqNfOP3Ka1lyNFrE1RqtScuPmUPUdP0bh2dUoUtOLnLVeY1K48hay0c1kqVWotL8tMxDyARU0h5h4U9IBe26XqnK+iTIXto+DmPui7B6xfyYMZegSWtgKZHAYcgiIVc7wupUrN3MO3mLI3BKVKTfECZtQsUQBHG1OGNyiVSX76/hsoVWpGNiqdY9/ZolbD+RWSJ2lyPBhZQLM/SCnXmR07d4q/1XwkLi6OkJAQypYti5mZ2et1plJK1RTjn0g5gIrX+mS9YVUqFbGxsVhZWeW6kqFKpaJIkSJs2LBBRDZk4HXW9FUSExMJDQ3F1dUVExPte/Lb0mc+ZJRKJbt27SI0NJRu3bphZ5f5mRQREcHevXtJSkqibt26uLqmF4aLjY3VpErIiIGBAWXKlNF8TzNU5taj8m38Dt8n3enMnSh6LTrN82QlDpbGzOpWSZNmRq1Wc+xmJD4lC2iiIh7FvMDBwpi5h29z+PpTztyVwgCtTAyo4CxFd4xqWDLT/Xz6wdtYPA+jz53REHULKn4mFffTmsxC2P41Gm+mqv2gzrfam6zPI8Fc90v4+7SuHwtiTfMfsaZvhnzTSYU+quF1dafRo0cTHR3NggUL3sDsPlzySyfNL330nXlUKpVKevXqxYEDBzA3N8fKykqnofK7775jzpw5NGvWjOvXrzNy5EgOHTqkCR37+++/2b9/f6bzevTowXffffemL0Mg+GiZf+Q2v+28xtLjd5jYzpOClsZYGBtQ0ESNhbEBUc+T+aphKVJVmfc6Mhop1Wo1p0KjqFEigwJv7QRfbIFFzeBpMKzoAD23gMkrN6zkeLh3EmIfwPL20GeXtkHTtQ40/wtMbfUyUqbNbUi9ktQoYcfw1Re4G5lAWFQCKrU01xEN0w2S0/ffYMreEEa9rpESJG/PSj2kCuCbB8G9E7BlCIqr2zEyyjnfpuAdIVeAa+13PYsPnufPn/PLL79Qs2bNdz0VgSATy5cv5+eff8bBwYEzZ87g5+eXyVAZEBBAy5Yt8fb2xtramsGDBzNr1ix69+4NwKlTpxg5cmSmvm1tbTl69OhbuY6Piaoudmwd5sugFYGEPInns39PMqZpGfrVdkUmk+FbKr1wTlKqkp4LT5OYqiQs6gUVnK2Z36Myf+8J4fqTOAJuRhJwM5LrT+KZ3d1Xo6NoPeP77oVDv0rRGZkm01fyvgzeDBdXw5kFcPsQtJsPzpXhxj4pNLPVNKig2+lCIBAIXguhj+YbderUoVSpzM4pgveLd2aoVKvVNG7cmH///ZcxY8YQEBCQSeb48eNMnjyZQ4cOUbduXVJSUvDz82PEiBFs2bIFIEt3XIFA8Hq0rOjI0uN3eBiTSJ8lZzK0GPDX5fSwNWMDOQdG+3H2ThRRz5Pp7ZPuYaJWqxm78TL/nQnj9/aedK1WLL0buxLQczMsbg4Pz8HqrtB9PRhl2G00tYEeG6VwrIjrsKqLZNDMKFOtv/bEU5OlHFI5ULm4HTtG1GbsxkvsuPwYgKn7bvAiRcmYZmW1XmB0eVrmGTtXyYP0+HQ4MAl5yA7qGQQg87AFD5HUWfBxYmlpycCBA9/1NAQCnRQoUIDDhw8TFRWFt7d3pna1Wk2vXr3o1KkT//77LwDTp09nyJAhtGjRgoIFC9KoUSOCgkTBtPzEzcGCzUN8GLvxMlsuPGTSjqsE3n3G5E4VsMqQsiXkcTxPYqXUNNamBviWtKdxucI0KFuIjefuM2VvCI9iEtl95THVf93HlM5eXAiLzvyMb/F3+uBqNdzYI4V3y2Tg3lT6VOgCmwdD5E1Y0BD8voOIEEh5DncChKFSIBAI3nNat35z4fKC/OOdGSoNDAzo0aNHtjJr1qzBw8ODunXrAlLV0QEDBtCvXz/i4uKwtLTMcZyYmBjCwsJ4/vw5oaGhFCpUKEsLelJSklZurbSQ8pSUFFJSUvS9tFyT1vebHONTQ6zp61PQ3IAJrcvSf/n5bOWSUlUcDXnCmI1XkMmgiKURDcqm55MsYmWMXAYxCUmZfx+2JeGztRisbIfs7jFUa3qg7LQMFBkMjWaFJJllLZDdP41q7ReSjFzH7SspDsWaz1C71EFV59scr9HMAKZ18qSmqx0Td1wjKVXF3MO3WXA0lFSVmhH13RhUx+XN/B1VHwrF66LY8iUmEddhbXdUXj1QNvqfFBYuyDOpqamAZFx4tVKhIO+kZYoR65p/5OeaqlQq1Go1KSkpKBTa4WDiWZg9zZtLXu1RUVE628+ePcutW7dYt26d5ljfvn357rvv2Lp1K/369dNrnKCgIB48eMCzZ88ICgqiZMmSmcKi0ngXOun7qDsZyuDP9uXwdrZi0s7r7LryGCsTBZPaltPIlClkxo5htbj/7AVOtqY4WBiTkpJCZHwSDdwL0KRsLZafCmP6gVtExCfTc9FpgGyf8fIjf6A4+ifKSr1RNfktXeco5ouswQQUWwYiU6vg0G+oinijrv0NqlpfgY6+3sd1/dARa5r/iDV9MwidNP8R+uibIb/WNb/00XeeoxLgq6++IiAggLNnz2odb9SoEdbW1qxfv15z7PTp01SvXp3AwEAqVaqUY9/+/v6MGTNG871EiRJs3bpVp+z48eOZMGFCpuOrVq16/TxnAsEHSFg8/HU55/2Mrz1TOfFEzvGncozkakaUV+L8MpWsSg0PnkPRbGxvdvHXqXnzTwzUyTy0qcpZl8GoZYpXZEKodfMPFOoU7trV5kKxfpmK5zhFnaDK3TmkKMw4UPY3Eg1t9b7WhwmwNETB4xdSn3KZmqk1lHqfn1fkqmTKPlyPW/huZKh5blSQwOIDeWYhQhLyioGBAYULF6Zo0aIYGeXsXSsQfAwkJycTFhbG48ePNS9GaSQkJNCtWzeRozIHLly4gLe3N5cvX6Z8+fKa40uXLqVXr168ePFCy7BYunRpWrZsyZQpU/TqP2OfIG3IlytXTqes0EkzcycONt1R0L+MEoscUujdioXZwQrMDcHJVE1DZxVxybD4hoK0arm1HJTUKqzG3BDsjLXPL/F0D+UfrESGmsdWXpx1GYJSIQk5xF6m+u1pKNQpqFAgR0mqzIgrTl25Y98AZDLKPlzHY2tvnpmXfAMrIRAIPhSETir41MgvffS9rvodFxdH8eLFtY7Z2kqGB10FdHTRsmVLvaqMAowdO5ZRo0ZpvsfGxlK0aFEaN278xovp7N27l0aNGonkxfmEWNP84crDWK0w76yo7etL30IW9Ft+juO3olh2x5wNA6tnKrIDUjEb4JViO83htjfqtd1wjD5DS9VulC2nS0VyMsioQ9xRr/+CYs+O49h2HBTxeqX35ihPOyNzrkZ9x8zhezkRv+8msw7fBkCllnE4sRh/tC+fw1mvh/S3aoRz/X4Y7xyBeewDat+chKrmCFR1vtH2LhXoRXx8PLdv38bc3BxTU9N3PZ2PBrVarYlmkL2ySSDIG/m5pomJiZiamlKnTh2dycsFeScuLg5jY+NM62pra5urtc1NaPi70Ek/BN1pkFqt9X9l26VHNPYohLGBduL//22/Rqr6HrHJEJMsIzgmc2GA4+EKjodLKWz2jPDB0Sbj86I5ymsNUWz5ksKxF2j+dCbKLqukIhY0R327Oup1nyNPTURtaofBiygq3l+Gp9EDVKUaYXB+G6Ui95I6+CwpJgXe+3X90PgQ/lY/NMSavhmETpr/CH30zZBf65pf+uh7bag0NTUlLi5O61jaxb2J3WRjY2NNqfmMGBoavpUb9tsa51NCrOnrYWCg3y3CwMAAMxNj5nxehfazj3Er/DmDVl1kzcAamBml9xH1PJnhq89T3smaMc3KaHfi3gg6Loa1PZFf+g+5sSU0/1Pba7Jca0ieCeYOGBarqnsyPkO1vyfFgXHOaSKm77/BrMO3GV6/JJcexHDoejgbzz8EmYwpnb30WofXQVHSD9ngE7DzO2QXV6M4PhXF7f3Qfj4ULPvGx/+YSPu7lclkr11JWZBOWhiIWNf8Iz/XVC6XI5PJdD73xHPw9TA1NSUpKYnk5GQtj5jY2Ng35t34LnXSD0V32hB4n6/XXaai8z1mda+Es23672JCm/JUcS3As/gkxm0LzrafpFQVsUkqir96zZ7twMYJVnVB/vgi8qXNoPsGcCgt6Syf/QeruyJ7EQUOZSAqFPnt/cgfBoJTFWRlmmNoV1QTDv6hrOuHhFjT/Eesaf4idNL8R+ijb4b8Wtf80kff699sqVKlCA0N1Tp2+/ZtZDIZbm5u72hWAoEgK6xNDVncqxp25kZcfhDDV/9dQJWhKviZO1EE3Ixg2Yk7msT3WpRtCW3nADI48y8cnpxZxrs7lG6c/l2ZmlkmjWd3YHYtOD4j23lnLJwzqrE783pUpuHLPJsbzz3gq/+yz9OZb5hYQ7u50GmpVMn88SWYVxdOzAaRg0UgEAjeCWm5ze/cuaM5lpKSwv3790Xl0HdIAQsjbMwMuXg/hpYzAjh0/ammTSaT0bqiI5Vd7LLpIZ2FAaHozIZVtBr02we2rhB9D5Y0h6R4qc2tnmSsNDCB8GvgXAUKe0JiNDw4C0+vQ2IMAIap8RD74HUvWSAQCASCT4L32lDZunVrzp49y7Vr1zTHVqxYgY+PDwUKFHiHMxMIBFlRrIAZ83tUxkghp4SDdmLKJuUK80Pzsmwa7KMzLByAil2g5RQws4fSTbIfLPIWzKkJtw7qbr+2A2LuQeASSH6eZTdKlVqr8qexgYLZ3SvTtFxhALZefMjuK4+zn0t+Uq4tDD4JJRuCMgl2j4XlbSDm/tubg0AgEAgAqFGjBg4ODqxYsUJzbNOmTbx48YIWLVq8w5l92vi5F2TbUF8qOFsTnZBC7yVnmLYvRGuDVN9U/JsvPOSnLUFa52oo4CYZK52rQaNfwDiDbuNWD7qtAQNTuHtMCg33+UpKXXN5DcyuhSz0MJXvzMFgYQO4e/w1r1ogEAgEgo+fdxr6vXr1aiIjI7l48SLh4eHMnDkTgIEDB2JoaEjLli1p06YNTZo0oXfv3gQFBXHgwAEOHszCKCEQCN4LqrjYsf/ruhS1yxwS179OCT066APl2klehdlxYiZEhMCaz6HXdnD00m6vORjkCijbGozMs+xmZKPSmY4ZGciZ0c2bkWsu4H/pEUNWnmP6Z9409yyS8/zzA8vC0H09nF0Ee36E0COSd2iLv8CzU6ZCQoJPi507d1KhQgWcnJze9VQEgg+eCxcuEBAQwIMHksfb6tWrKVKkCHXr1sXT0xMjIyNmzJhBjx49CA8Px9ramjlz5jB27FhKlNDjmSZ4YxS1M2PdlzX5ZVswK0/dY9q+G5y7F820Ll7YmRtxNypB775WnLxHYoqKPzpUeCWPNmBuD312STpFGglRkp5Swk8yVq7qAjf3ATLouQ22DoVnoShWdcDGwBJI0SsVjUAgEHwoXLlyhdjYWGrWrPmupyL4yHinhsrQ0FAePnxIuXLlKFeunMZzMuPu54YNG1i7di1nz56lUqVK/PXXX7i4uLzRec2aNYtZs2ahVL75ir8CwfuMrbkRxgZyklKzDjs2VMiwNc9c8CWjkTIxRUloxHPKFtEuAHAn4jmrz9xjTNMymZP2ZjRShp2GqFDJ2zIjTX+HyJuSEW9lR+izW/J8yEj1gdrf45+CRcEsr0f72uRM6+KFgVzG5gsPGbb6PClKFW283pJxSCaDqn3BtS5sGgAPAmFjf7i+A1pMATP9QtoEHx4pKSmaTTxnZ2dKly6tla+uR48ezJw5k65du+Z5jIMHDxITE5PpeNGiRalcuXKO5+/evZuyZctSrFixXI995swZrK2tKV068ybBwYMHsbe3x9PTU3MsNTWVnTt3Ymtri6+vr5b80aNHiYyMBKRwTwcHBzw9PbG0zF+DwP79+ylRogSurq752q/g3RMZGanRQYcMGUJMTAwxMTFUqFBBI9OlSxfc3d3ZuHEjSUlJrF+/nkaNGr3xuQmdNGeMDRRMaudJpWK2/LD5MkdCwrl0Pxo/94IU17FhqovRjUszdd8N1gfeJylVxZTOFTFUvBJ4ltFIGf8UFjSUIj+a/g4l6mYwVu4F1JIX5oH/IQtcgnFqHGqbYqCnh6dAIBC8L4SGhnLjxg3Mzc1xd3fH3t5e07Z48WKCgoLYtWtXnvu/desWly9f1tnWpk2bHAu7XL16lcjIyEz6oT48efKEy5cv07BhQ53zunbtWqbIibNnz/LgwQMaNWqklaf67t27nD+fni7M3NycUqVK5bvt6saNGzx8+JC6devma7/vG+/UUPn999/nKCOXy+natetrvYzlliFDhjBkyBBiY2OxtrZ+a+MKBO8bTjamHBjtx7PnyYBkLAgICMDX15fQyETUqKleogBONllXsYtOSKbv0rPcfBrPxsG1cHsZDp6QnErHuceJiE/GwcKYfrWz8EqJuAHL2kLqC7BwALf66W0GxtBlpZQz6vFlWNEe+uwBy0K6+7pzDFZ1lkK3qvbVaw0MFHL+7uyFgULO+sD7jFxzgVSlmg6VnfU6P1+wLyldV8AUOPQ7XNkEd09A21lSeLgg/4kOg4TIrNvNCoBN0Tcy9Jw5c/jpp58oUKAAbm5uPHz4kKioKL799luGDh2acwd6MnLkSOLi4rQMggB+fn56GSr79u3LxIkT6dWrV67H7tq1KzNmzNBpqBw5ciR+fn5MmzYNgBcvXtC5c2cuXrzInj17MsmPHTuW+/fv4+XlhVqt5u7du4SGhjJ//ny6dOmSST6vDBkyhKFDh+br70DwftCgQQMaNGiQo5yXlxdeXl5vfkIZEDqp/nSo7Ew5JytO3orEz13akNS3cqmfe0HcHCwY/t95tl18SHKqkumfeWNsoNB9wu3DUs7K0/Mh5gF0WCAZK7uvhZWdJc/KjQPgs9WkujUideMgTKLvwb/1of4PUqTH1uHQZgbYCa9cgUCQBe9QH718+TL9+vXj+vXrVKlShZSUFEJCQvDz82PWrFnY2eWPw8S2bdv45ptvdKZSad26dY738ZUrVxIQEMChQ4dyPfa8efMICgrSaajctm0b48ePJzo6WnNs8eLFDBw4kClTpmQqprd3714GDBhA69atAYiLi+PEiRO0bduW5cuXo1Bk8TzJJRs2bGD9+vWcPXs2X/p7X3mvq34LBIJ3j5ONqcYQmZKSwl0LKOdohVdx/fLEmhgqUKvVxLxIoc+SM2we7IOtuRFmRgaMauTO+sAwWlV0zLqDAiXBs4OUn7FodR0DWElVOBc2kornrOwohYGbWGWWvXUAkuPh2nao3Bv0rGimkMuY3KEChgoZq0+HMXr9RVJVKrpUzb0nWZ5RGEDdbyXD5MYBEHkDVnSAqv0lw6vRm6k8+0kSHQYzK0NqUtYyBsYwNDDflcOpU6cyZswY1qxZQ9u2bTXHo6Ki2LBhQyb5Z8+ecenSJSwsLPD29s51lb5WrVppDIJZERwczP3793Fzc9Pkhz548CCJiYmcO3cOGxsbFAoFrVq10mvM4OBgHj9+TP369XOUjY6OplWrVkRGRnL8+HGcnXVvEDRs2JAFCxZovn/11VcMGTIkk6FSpVJx7tw5oqKiKFOmjE5v0GvXrnHv3j3c3Nw0hfuOHj1KfHw8ly5dYvPmzYB+u/wCgeDtUqawFWUKpz//H8foKNyXBc08izDPUM6XK86x+8oTBi4PZO7nlTEx1PFyWaETKAyl5/H17bC0JXy2BlzrQPd10qaobXGQG6Iu1YSDZX6lSfIO5CE7YN94CJgqFdrZ8Q18nvneLhAIBO9SH719+zZ16tShbdu2HDx4UGOUU6vVrF+/npiYGC1DZUpKCsHBwURFReHt7Y2NjU2uxjM3N9foV1kRHh7O5cuXsbCw0Hgp3rp1i5CQECIiIjTnV61aVe/USP7+/npvQP/111/88MMPLFu2LEsnOrlcrnUdFy5cwNvbm27dutGyZUst2bCwMK5cuUKhQoUoX758porYkZGRXL58GTMzMypWrIixsTF3794lODiY6OhozTje3t4UL15cr2v4kBCGSoFA8NoEPYjhf/7BzO5eiQIWxlptJoYK5vesQttZx7gbmcDAFYEs71sNYwMFn1UrSucqzhi8Gl6VEZkMWk4DVar0MNaFZSHosQkWNpYqZe/5AVrrqPRd/0ewKQYVOuttpExDLpcxqa0nhgo5y07c5bsNl0lRqvm8xlt+MDhVgoFHpBed0/Ok6ui3D0K7+eCcsxecQA8SIrNXCkFqT4jMV8UwLi6On3/+maFDh2oZKQHs7Ozo37+/1rGVK1cyduxYSpYsyYULF/Dy8mLXrl35tmObkpJCmzZtOHfuHF5eXty+fZsKFSqwZs0atm7dyvPnzwkICODevXsYGRnpbaj09/enQYMGmJhkUVDrJY8fP6ZJkyaYmJhw9OjRXBXRK126NPHx8ahUKo3x9saNG7Rt25bU1FSKFy9OYGAgHTp0YN68echkMpRKJZ07d+bIkSNUrlyZu3fvUrZsWdasWcPOnTuJiYnh1KlTPH0qVRbWZ5dfIBC8O9RqNVP3hegla/rSGOlb0oFFX1Sl37IzHLoeTp8lZ1jwRRXMjHS8MpVrK+WTXt1VSs2ysKGUW9q1Ngw4LG20yuWgVJJsaIWy9VLkV9bBzu8kI6VMIaV2UatF3mmBQJCZd6SPAkyYMAFzc3Nmz56NqWl65JxMJqNTp05asmFhYVSrVg1LS0uio6N5+PAhe/bsoVKlSvk2nwULFjBy5EgqVapESkoKz549Y+3atdy5c4eLFy8SGRnJkiVLALC3t9fLUPnkyRPOnz9P8+bNc5QdM2YMM2bMYMuWLTRt2lTveZcqVQqZTKaVakmpVDJkyBDWrVtHpUqVePDgATKZjM2bN1OqlFTYddWqVXz55ZdUrFgRtVpNZGQkK1euJCoqisDAQK3rtbCwEIZKgUAgeBW1Ws236y8R/CiW33de489OFTPJ2FsYs6hXVTrMPs7p0Ci+3xjEX50qIJPJMFCkK+eX78dQqpBFZu8FuSI9N5RaDUf/ghL1tQ1zBdzg8/WSAa/BeN2Tlcmg8hfax6Jua4ddBW2UXiKaT5YK+mSchlzGhNblMJDLWXQslB83B5GqVNHL5y3nrDMyk+ZXuglsGSLl6VzYSPK4rP215OUh0E021d81pL7Iv36zKeL0KgEBAcTHx2dSALPi4cOHmp3lhw8fUrJkSfz9/WnTpo3eY966dSvTDnbFihVxdXVl3759nDhxgnv37mFpaYlKpWLJkiXSy//Uqaxbt47hw4fnOvTb39+fzz//PFuZ0NBQfH19KVGiBBs3bsTCwiJb+bt377J582bUajVhYWFMnjyZsWPHaoyUarWaDh060L59e/73v/8BEBERQaVKlVi6dCm9evXi+PHj7NixgwcPHmi8BLZs2UJKSgq//vorGzdupH///iL0WyD4QJDJZPzYvCzdFpwiu8yQ9uZGuNibExaVQLcFJ/mphQfL+lSn9+LTHL8VSc+Fp1ncuyqWJjqercVqQN99sLKDFNWxsJG0ceronS6jTKHU462QWg+8u4OLD2waBPeOw96f4N5JaPUPPL4IzlXBRIT4CwQfPe+xPgqwa9cu2rVrp2WkzIpr165x6NAhateuDUC7du343//+x6ZNm/QeLyUlJZM+amtrq8nD+MMPPzB79mx69OiBSqXi0qVLxMfH06pVK06dOkVAQECOHpmvsn37dqpWraqVc/NV1Go1/fv3Z+PGjezbty/HokFqtVozj/j4eFasWEHFihU14eAAU6ZM4dixY9y6dQsbGxvUajVDhw6lf//+mvD1n376iT/++INBgwYBkjH47t27NGzYkB49erB+/fpcX++HhjBUCgSC10ImkzG1ixfTD9zghxZls5QrXciSWd0r0XvJGTacu08JB3OG1CupaV97NowfNl2mrZcTkztWyNpTKXAxHJgIx2dKId6Fy6e3OXpDzy36Tz5wCfiPgjazwOsziA8H/68kT4dtI6C4r5QX85Xr/allWQwVMuYduc34bcGkqtRZ59h8k5RsAIOOw/av4cpGOPQb3NgjeVfal8z5/E+RX7NJM/A6TPPUnUNofOZiNVnx6NEjAL13Rfv06aMx4Dk6OuLh4cG1a9dyZagMDg7W7MimMWDAAFxdXTE0NCQlJYV79+5Rrlw5ANq3b5/r8PKMPHv2jBMnTrB69eps5bZt24ZCocDf3z9HIyVI3pJp1/H48WPMzc218myeOnWKy5cvM3bsWPz9/VGr1ajVajw8PNi3bx+9evXC0NAQlUrFnTt3NIbK3KylQCB4D5HJsjVSAkQ8T+Z0aBQ7gx4RFvWCGQdusnmIDyv6VeeLRac5e/cZny84xdI+1bAxy1w8EPuSkrFydRdIjAUb7Xu4YvtXeDxaj2pjrJTD0tYFevnD8RmSPnN9O9w9JqWmsXWB3rsy6R4CgeAj4z3WR5VKJU+fPtVbH61UqZLGSAlS3udZs2bpPR5AcnJyJn20ZMmSGkOloaEhoaGhqFRSgVcXFxesrHSk+coF/v7+mcKxXyUuLo4FCxbwww8/6FXZXK1Wa64jISGBy5cv07NnT60oooULF+Lj40NAQIBGHy1evDjz5s0jKSkJY2NjDA0NuXPnDkqlEoVCQdGiRSla9M3kIn1fEYZKgUDw2rgXtmRWt5zd++uUdmB863L8tDmIBUdv0716MY3S72htilKl5nlyKilKNUYGWRgqPTvDxf8g7BQsbysp9FkZ5c4uhhfPoPYo3e2PL4Na+fLfruA/EpLipbakeNg+Crosz3SaTCZjTLMyGCrkzDx4k4nbr5KsVDHY7x0YB83soNNiKNNCmu+DQJjrC43/B1X7iXCyDwhzc2m3OyoqiiJFiuQo/2ootImJCS9e5G73PbsclQ0aNKB///5Ur14dNzc36tevT5cuXahWrVquxsjIzp078fT0zDEkp0+fPty4cYNGjRpx8OBBSpaU/m/5+/uTmpoKQMGCBalVqxaQOUdlWo7PoKAgypQpw+3bt5HL5axZs0ZrHBMTE00eyho1avD111/j5+dHsWLFqF+/Pn379qVixcxe4gKB4MPgaZx+OSqfxiVStrAV3sVsmNO9Mgq5DO9itqzqX4MeC09x8X4MXeefZEW/6thb6EhDY+EAX/hLOoeZdoEJldfnpARvR1GlL/K0Z7JcAb5fSRuOGwfA02DpuDIZDLJPiyEQCARvEoVCgYmJCVFRUXrJ54c+mlOOyoULFzJo0CBmzZpF3bp1ad68OT169MjVGBlJTk5m7969jB8/Pls5KysrfvnlF0aOHImTk5PGw/HSpUvcvn1bI5cWDv5qjsqoqCg8PT0xNDRk4sSJgJT/09LSkshIbYNyy5YtiY+Px9jYmLlz5zJgwAAWLVpE3bp1adeuHZ999tlrOQt8aAhDpQ5mzZrFrFmzUCqV73oqAsEHh1qtZl3gfeqUcqCwdWZlu0eN4sQnptKkXCEtzwTfUvZsHOxDRWfr7PO+GVtAt7VS4vrHl2FZG+izU8o9mZH7gZJ3JIC5PVTqmbmvZn+CS23waCN5JF7bluFClHB1qxQKXr59plNlMhmjm7hjqJAzdV8Ik3ddJyVVzYiGpbJbnjeHZ0coVhM2D4LQw7BjNFzfKXmLWuVs9Ppk+P5hzjKPL8Ei/fPPAPDV5bzNJwNVqlQB4PTp0xoPxneJTCZj6tSp/Pbbb5w8eZIVK1ZQt25dzp07l+f56bN7DVK+nZ07d9KqVSvq1q3LwYMHKV26NCtXrtQovxUrVtQYKl+lbdu2KJVK9u/fT5kyZTSh6//++y8ODll7Kv3666+MGzeO06dPs3btWqpUqcKpU6fyNc+SQJAbhE76ehS01M/o9zxJyfebpPv4ncjnGv0lPimVJX2q0XfJWa49jqPr/JOs7FedQlY6+jUy0y5sd2YBRNxAXX8C+8r9TWM3HZXlC3tC/4NwcKLkYRl9D+b5vsw7LT0TNKlvBALBx8N7rI+CpJOePn06X/rKD5o0acLt27e5du0au3btYvTo0dy6dUuTzie3HDp0CDs7OypUqJCj7PDhwzE0NGTo0KEolUqGDh3KqVOn2L59u0Ymo0dpRuzs7KhTpw67d+/WGCotLS3p3Lkz33zzTZZj+vn5ERISwo0bN9izZw+jR48mMDCQKVOm5PJKP1w+HZNsLhgyZAjBwcGcOXPmXU9FIPjgmL7/Jt+uv8Sw1edIUap0ygzyc6OEQ3o4p1otBWZ5FbXRMlJmdT6mNtBjM9iXhtj7krEy7rG2jHNl8H3pSblthGS0exW5XEqG/zxC8qbkVQOpTDJ2xodndbmMaFiKb5q4AzB1Xwh/77muuZ63jrWTtC5N/5A8Mm7thzk14Yr+OWI+eozMc/4Y5JyPR+9+c4GbmxstW7bkf//7n1bS7TQeP36s46ysScvZk1fCw8NRq9WYmJjg5+fH/PnzMTMz0zwbzc3NSUrSTvKe3ZhKpZJdu3bpZagEMDMzw9/fn3LlyuHn58e1a9dYvXo1mzdvZvPmzUyYMCHLc0NDQwHJ6xLAx8cHMzMzLa9LkO494eHS/++IiAhUKhXGxsbUrl2bGTNmUKRIEU6ePJnl9QoEbxqhk74e1VztKGJtkunpnoYMKGJtQpeqRRnk58boxqWp5iJ5RJ69E0XPRaf5YdNlFvSsTBFrE24+jafLvBM8iM7BW+jZXSnf9am5KDb2QSXLYGyMvAUb+kNygvTd0AQaT5Q8Mq2LSrkuFzeFxc1geXtI0M+rSSAQfEC8x/oowFdffcWRI0fYunVrprbnz58TFxend1+PHj1i8+bNJCQk5HoeIOmPad6HZcqUYfjw4XTp0oVjx44BuvWznMb09/enRYsWes9h0KBBzJ07lxEjRjBt2jT69++v0Uc3b96cbcHHO3fuaPRRgEaNGrF06VJNhFAaacUaM/5cqlQphgwZQv/+/TX69aeijwqPSoFAkK+08XJkwdHbnLnzjL92X2ds86zzVgIcCQln5oGbLOxVRZOoPlWp4s/d17l0P4blfavprgpubi/lo1zUVCqIs6wt9N6hHXLV4Gd4/hTOr4B1vaDnVihWXbsftTpDyPerBkZ1tiHgaQypVxJDhYxfd1xjxoGbpCjVfNfU/d1UBJbLocaXUMIPNg2ARxelaz/8p7Q+pjba8ocng0oJ9ca+/bkKMrFo0SJatmyJl5cXAwYMwM3NjUePHnH8+HFiY2PZuVOHwT0LfvvtN6KjozWJuXWhq5hOgQIFqF27NgcPHuTPP/+kc+fOFCtWjAMHDqBWqzX5gtIK0dja2mJqakqrVq2yHfPYsWMYGRlRtWpVva/B1NSUrVu30r59e/z8/Dhw4AAeHh6Z5NKK6YBUxXH69OmUL19eo4Ta2dnxzz//MHjwYO7du4ePjw+PHj1i/fr1jBgxgm7dunHq1Cl+/PFHunbtiouLC8ePH+fZs2c0aNBAc72rVq3C2dkZY2Nj2rRpI6p+CwTvOQq5jHGtPBi04hwytJ/yaf97B9V1QyGX8V3TMlrnyuUyLIwNKGJtiqezDWsH1qTbgpPciUyg89wTrOpfneIFsjAA2BaH9v/CpoHIr2/Hx+waPPcFq4JSlfCIEIh7BN3WpBsRXGvDoGOSgfPiainFDUg/1xySn8siEAgE2dKhQwfGjRtH586d+eKLL6hTpw7JyckEBwezYcMGdu3ahaWlpV59nTp1inbt2hEaGoqLi4tOGV3FdEAy6ikUCqpXr07btm3x9vYmPDycNWvW8NNPPwGSfvbLL78wb948ChUqRNWqVTlz5ky2Y27fvp0ZM2bouxwA9O/fHwMDA/r164dSqeTrr7/OJJOxmM6LFy/YtWsXZ86cYe/evRqZyZMn4+vri4+PD71798bAwICAgAAiIiLw9/cHpA32Fi1aUKVKFaKjo1mwYIGmmGOlSpW4du0as2bNwsnJCW9v74+y6rfwqBQIBPmKi705f3aS3OjnHbnNnitZe4ElpigZs+ESp+9EMXz1eVJfelA+jE5kxcm7nLgdyeGQrL0ZsXKUjJWWRSD8KqxoLyWyT0Mmg5b/QOmmkJoIqzrD06vafTy9KoV8q7MIq0sLAX/1vFcYUMeNca0kA8rcw7eYuP3qu/OsBChYRkruX3s0IIOnV2Baebh9OF3m8GQ4OEmElb2KWQEw0JGDLCMGxpJcPuPg4MDx48f566+/CAsLY8OGDdy9e5cuXbpohZg0b94cZ2dnrXPr1KlDmTLpL9rVq1fH19c3y7Hq1auHQqFgyZIlWp80Y2jnzp2ZP38+4eHhbNmyBXt7ew4fPqxRhqZPn079+vXZsGEDy5cvz3FMf39/mjdvnqNxr169enh6emq+m5iYsHnzZjp27Mg///yTKe9RnTp1MDc3Z8mSJSxdupTTp08zaNAgTp06hZlZehhmv379OHnyJKampmzbto2IiAjmzJlDt27dAGjRogUrVqzg2bNnbN68GVNTUwIDA3F3lzymJ0+eTKtWrdi0aZOm+rlAIHj/aVq+CHM+r5QpHU3hl56U47ddYeO5+1ptL5KVhDyOY/PgWvzT1QuFXEZROzPWDqxJCXtzHkS/oPO8E9x8Gp/1wOXbQ88tqE1ssEu4hcHSZpK3ZOuZYGQJd47Cqi7aVXpNrKHdXOi0FIxfForYNwFOzgFVFlEmAoHg4+Qd6qMA48eP5+zZsxQsWBB/f3+OHTtGkSJFOHPmDKVLlwagfPnymdLwuLi40LhxY833IkWK0KZNGy2dLCNubm40atQokz66ZMkS4uLiMDExITAwEEdHR3bu3Kkx0g0bNgyQwsLnzJnD8ePHWbp0KaGhodmOGRwczOPHj6lfv3621+/m5pbJ67J3796sWLGCgICATKHxLi4utGrVSjP3rVu34uTkxJUrV6hXr55GrlixYly8eJHOnTtz5MgRzpw5Q4MGDdiyJb0g7Pnz53F1dWX37t1cvnyZWbNm8f333wOSEXPp0qWcPXuWpUuXcuPGjWyv40NFphaadpbExsZibW1NTEzMa1eVyo6UlBR27NhB8+bNMTQ0fGPjfEqINX0z5GZd/+cfzMKAUCxNDNg+rDbFCuh+OF0Mi6bL/BMkpqjoVcuF8a2l3He7gh6jVKlpUUGP/Irh16UQqYRIKFYLPt+gnScqOUEKD79/Gqyc4MuAdM9LtRrW9IDrO3QbK2UKqVBNNh6VGVl+8i4/bQ4C4IuaxRnfuly2hpm38rcadhpWdoLEaOl7jcFgbAmH/4B6P0Ddb9/MuO+IuLg4QkJCKFu2bJZKUY5Eh+mumpiGWQGw+bSq76lUKmJjY7GysspTMm8PDw8mTpxI+/aZc75+qrzummYkMTGR0NBQXF1dtapLwtvTZwRvjrfxO/zYdSelSs3p0CiexiVS0NKEaq52/M8/mCXH7yCXwezulWhavggpShXtZh8j6EEsk9qVp3v1dE+VmQduYGQgZ33gfUKexGNvYcSKftUpUzjr30nKo2CSF7fCPDlCenZ89p/UsLw9JMdJubIzelamEfcYtgyBm/uk78V9wbkq1PseDHRUH/+E+Nj/Vt8FYk3fDK+tkwp9NBOvqztNnjyZgIAAnWHtnzL5pZPmlz4qPCoFAsEbYUyzMlQqZkNcYiqDVwWSmKLbY7FiURumdvYCYMnxOyw7cQeApuUL62ekBHBwhx6bwNga7h2HNZ9DaobcHUZm0kuAQxmo0gdMbdPbZDJoOVUq0qMrR6WxJbTQP3FxjxrF+b29JzIZLD1xlx82B6FSveP9oKLVYOQVcPSWvp+cLRkp63z30Rkp8w2bouDolfXnE1MKX5eYmBjc3d21dtgFAoHgbaKQy6jpVoA2Xk7UdCuAQi7j55YedKrsjEoNw1af53BIOIYKOa0qOAIwYWswl+5HA3A6NIq/9oTw645r/NjCg3KOVkTEJ9N1/kku38+cV1iDfSmOlh6HqoiXZHC4d1J6LvfYmO5ZubKztmclgGVh6L4eWvwt5aq7GwDHpkr5KwUCwaeB0EfznaSkJPr16/eupyHIAWGoFAgEbwRDhZyZ3Spha2ZI0INY/ucfnKVsM88ifNtUCq8cv/UKh64/1Wp/npTKRP9gYhNTsh6wSEXovg4MzaQiMhv7S96SaZjZwYBDUGe0ZJzMiIWDZKzUlaOy1nCpPRd0rVaMPztWRCaDVafu8d2GSyjftbHS2EK6fnmG1MQPAyEl8Z1NSfDpYG1tzaZNm7CwsMhZWCAQCN4ScrmM3ztUoIVnEVKUagYuP8uZO1EMqFOCxh6FSFaqGLTiHNEJyVR1sWVEg1IMrFuCOqUdWNW/Bl5FbYhOSKHbvycJvJt10ZskQ2uUn2+BltOglhSuKBkrN0kh3ncDpMiHpFdCyWUyqNpPigSxc5OOPQiE9X1EkR2BQCDIAz/99BOtW7d+19MQ5IAwVAoEgjeGo40p07p6I5PBylP32Hz+QZayg+q6abwahq46z+3wdGV96KpzLAgIZcyGS9kPWKw6dF0FhuZQtnVmg6Rhhup5SfFSjkblS+NnufZQppUU6g1ovCuDNuQpL1THys5M6+KFXAbrAu8zet1FTQ7Od8bhyaBKTTdW3twHa7oLY6VAIBAIPlkUchlTu3jh5+5AYoqKPovPEPQglj87VaR4ATMeRL9g5JoLqNUwslFpxrwsumNtasiiXlXxLmZDXFIqPRae5sStbEI0jcyhSu903SQpDsJOQvcNL42Vx3QbKwHsS8KQ0+D7taSnBG2AObUgaKP2pixIx/4sBVc25dMKCQQCgUDwdhGGSh3MmjULDw+PXFUmFQgEuqlb2oFh9UsBMHbjZW48idMpJ5PJmNTOkxol7GjhWQRn2/Q8LiMblcbZ1pQ+Pq45D+hWD766BJ4ds5ZRq6XCOgcnwbavpO9aIeBIId8udaHDAqmSdh5o4+XEjM8qoZDL2HT+ASPXvkNjZVrhnHo/wM+R4NVdOi6MlQKBQPDeInTSt4ORgZy5n1emuqsdcUmpHLr+FGtTQ+Z0r4yxgZyD18OZdfAmgCbvtEqlZvzWK0TGJ1O5uC0JyUp6LT6dfRHANNRqWNcL9vwIJ2ZIeSuNraT0NVkZKxUG0PBn6LcXCpSUqoav7w2zqqd7V8aHg/9X8PwpbBshfRcIBAKB4ANDGCp1MGTIEIKDgzlz5sy7nopA8FEwokEpfEva8yJFyaCV53ielKpTzshAzuJe1fi9gydGBum3pwrONhwc7UcVFzv9BjS3T/859iGcmKXtcSCTSaFXMgVcWAH7f5GOm9lB9S+ll4Uag6HnJijkkdvL1aJFhSLM6lYJQ4WMbRcfMmz1eVLetrEyo5EyLSdl29nCWCkQCATvOUInfXuYGCpY2KsqkztWYFgDaYPVw9GK/7UtD8CUfSEE3IjQyEc+T+bcvWc8jH7BsPolaVCmIEmpKvovPcve4CfZDyaTQcXPQGEEV7fC/gkvK32/zLW9sqPkcakLp8ow8CiUeFmxNuI6TK8EW0eA/8h0I2dSPGwf9VIH+O211kYgEAgEgreJMFQKBII3jkIuY1pXLwpZGXPzaTw/vqyKrQtTI4XGW0GpUrM+8D4qlRpDRfrt6mlcIvefJeQ8cPJzqRr47u/h1FztNvdm0Gqa9HPAFNg8BKaVl4rMJMXC4d+l78EvK8I9uyNV3ssDTcsXZk73yhgp5OwMeszgledIStVdXOiNoFLqru6dZqyUGwhjpUAgEAg+eSyMDehcJb04RWKKEr/SDnSpUhS1Gob/d55HMS8AcLA0ZtNgH+Z8Xhk/94LM+bwyzcoXfpnXMhD/Sw+zH8yzo5Sj0sQawk7BjtHQdtZLY+UJODAx63ONzKTN1AY/g5k9JD6Dc0vg2jZQv9Qv1ErJCHpwEsgVWfclEAgEAsF7hjBUCgSCt4K9hTEzu1XC3sKYNl6OOcqr1WoGrwxk9LqLTN0Xojl++X4MLacHMGBZ1pXENRiZQ7UBYOsK7s0zt1fqCfV/kn6+sELyvsxI7CNY21N6WZhbGzb0A6Vub9CcaOhRiPk9K2NkIGdv8BO+XK7H/POLemOzru7ddjb02CwVIbq5D/7rJoyVAoFAIPjkiU9Kpdfi03T99yTDGpTEo4gVUc+TGbLyHMmpUmSEg6UxjTwKAVJUyPhWHng6WZGqUjN89Xk2BN7PfhAXX6W+Q6kAAQAASURBVOi7F6yLQdQtKR1N09+gVGNpgzEnan8NQ89A6WZZyxiYQOXeel61QCAQCATvHmGoFAgEb42qLnYEfFcPP/eCOcrKZDIalJWU/xkHbrLxnKTsF7AwQqlSk6JUEfU8OedBaw6RqmXaFtfd7vOVZNDUyctw8XPLQK2SvifG5DxmFvi5F2TRF1UxMZTyXfVfdvbtGSuzw7W2dsV0YawUCAQCwSdO7IsU7kUmcDv8Of2XBfJnxwpYmhhw7l40v+28mkk+Raniy5XnuPwgFo8iVqjUMHr9Rf47k4Ox0sEd+u2DIhUhIQKO/gVdV4OJVbpMajb6jqmt5DEpy+K1TpkihYALBAKBQPCBIAyVAoHgrWJimB5+FBaVQExCSpaynasUZZCfGwBjNlzmzJ0oHG1MWda3GpuH+OBoY5rluVqkFcgBuOovVcRM494JKUQ8S9QQ/wSa/Aq9doB5Af3GzALfUvYs6V0NMyMFR29EMGDFeZLeA1slLr7CWPkBkZSUhCoP1egFAoFAoB+ONqas7F8Dewtjrj6K5actQfzazhOAxcfusP3SIy15Q4Wc9pWcKWBuxPSuXvSq5YJaDT9tDebQI1n2g1kWknSMcu2lXJUKg/S2gKmwpAUkxuo+9+lVuOb/ckNVB2kh4E8zG1cFAoHgdUhNTSUlJet3OYEgrwhDpUAgeCccDgmnxfSjjF5/EXXGQjev8E1jd03OpwHLznI38jnlHK0xN05X4vWupB12Rgrl3tgfru+SjsXnkPA+DSNz7ReH16BGiQIs7VMNcyMFJ25HMe+qgvgsCgy9VYSxUjdBG+HPUnBl01sfOitjpJOTE2vXrn2tvpOTk0lMTMz00VfhTEpKQqnMm5U9JSWF1FTdf/Np89J17ampqSQmJmqdm1v5jGR178nr2mQ8T9faJCcnZzkXyPr3LRAI3g2u9uYs71sNa1NDzt2LZvXpe/Sv7UpFZ2sqFrXOJN+jRnEOfeNHyUKWjGvlwcC6JQDYdEfBvCOh2Q9mbAGdFkORCunHQo9AwDS4fxqubtN9XsGyUKaVVCBQFzI5lG0tyQkEgg+Xd6iPZqUvjRkzhlatWr1W30qlUqfOlZio3ztIamoqycl6RNnpQKVSkZSUlO28dOl+arWaxMRErXNzK/9qe3ZzyO3aZDxP19oolcos55LdfN4mwlApEAjeCXZmRiSmqIiITyI2MesXd7lcxpTOXlRwtuZZQgp9lpzR8sLccfkRjacd4WmcHg8zp0pQvj2oUiWDZegRsCik34TT5NRqODVfKr7zGjfxqi52LO9XHQtjA27FyeizNJDYxPdgR1IYK7WJDwf/r+D5U9g2Qvr+hrly5QpdunTB3t4eIyMjnJyc6Ny5M+fPn8/XcapVq4aFhQU2NjZan4EDB+p1vpubG8uXL8/T2L6+vqxcuTLLeZmamjJlypRMbU2aNMHU1JQff/wxz/I3b96kQ4cOFChQAFNTUypUqMCMGTO0lL68rk3G88zMzChRogR//PGHRuGrVq0ao0eP1jrn6tWrdO3a9Y3/vgUCQd4oW8RKs7l4/FYkt8LjWdm/Os62ZjrlLU0MASmFTXtvJ2xMpe9/7b3BlL0h+r8A3jsFKzuBozc0GAfe3XXLyWTQcmrWm6lqFRStrt+YAoHg/eQd6KMJCQmMGzcOd3d3jIyMsLa2pnr16ixdujTbTdfcMmPGDExNTTPpXDY2NnptiI8fP57GjRvnaex58+bRtGnTbOfl7e2dqW3FihWYmppSvHjxPMunpqbyyy+/4OrqirGxMUWLFqV///7cunUrU5+5XZuM51laWmJvb0+XLl0ICwvTtBcqpP0OnJCQwG+//UbZsmXf6O9bX4ShUiAQvBM8na1Z1b86awbUxPqlEp8VpkYKFvSsgqO1CQ+jE7n6WAp/SlGqmLYvhNvhz1lwNAdPBZByOLWdA+4tQJkEq7qCwhCsHIFswrKsnKB4Lenn8Ouwa4xUfOfWAT2vVjeVitmyrHdlTBVqzofF0GPhaWJeCGPle4NaDf4jISle+p4U/8bzfB05coSqVatia2vLmTNnSElJITAwkM8++4xp06ZlMc28G8yHDh2aaYd20aJFOY6RtjubkpKS7Q6xLp4+fcq5c+do3lxHgauXuLq6ZprH7du3OX78OI6OmYtx6SuflJREo0aNALhw4QLPnz9nzZo13L59m8OHD2udr+/avEraefHx8fz22298//33zJ8/X6fskSNHaNCgQa5+3wKB4O3jVdSGBV9UxdhATuDdaB7HpN/zgh7EZHkf/nN3CNEvUnAwkdqn77/B7zuv6Xffjr0PKiXcPgg39kBClHQ8KT5zvuzAxZCazX14zw9wfEbOYwoEgvePd6CPJiQk4Ofnx8aNG5kzZw4JCQk8evSI2bNnc+TIEa5cuZLFVPOmk1pbW+v0GlQotD3FX+1fqVSiVCpRqVSac3ITneLv70/Lli2zbLexseHBgwecPn1a6/iCBQtwdXV9Lfmff/6Z2bNns2zZMhISEggMDMTHx4c5c+Zoyem7Nq+Sdl5SUhLHjx8nODiYzp0765RNSEigfv36bNu2jVmzZun9+36TCEOlDmbNmoWHhwdVq1Z911MRCD5qqrjYYWSQfhvKLoS7oJUJC//P3l2HR3F1ARz+zUo2npCEEDw4BJfg7m7FpVCg0EJbCqVuX1taKDWgUIq7FndKcXcPEtwJCRDX3f3+GEgIsQ1sBDjv8+Rhd+buzN0JsHfP3HtOX1+WDKpB9cJqnki9VsOkXpV5r0FRPmlWwrKTavXQaQYUrg+x4bCgC1Qb/HhnCsHKUm3VICeAZ0lo/C20+AWKNLTsnKkom9eF90obyWGv58SNR/Sctp9HEc+3fMGqJFgJZ5bDuTVqfi9IyPP1dI5TKzIajfTt25cGDRrw999/U6hQIRRFwcvLiw4dOjB79uzE3Ttzhvr16+Pk5ETOnDkZM2aM1fs0btw4ChQogMFgoFSpUixatAiAOnXqcOvWLQYPHoyrq2uSu7KpWbduHVWqVCFnzpwptmnXrh337t1j37598dumT59Ou3btyJEjx3O3P3XqFFevXuXbb78lf/78aLVaSpUqxR9//EGzZs0sfg+W0Ov1dO3albp167J27dok+41GI/369aN27dpMmjQpzd+3eD3JmDT7qFHEncm9K7NoYHWKeqq5r3//9zyt/9zNvAPXk33N2G4V6FE1H8PKGPm6VUkAJu+8zLerz2AypfGFvswb0Hs5GFzUfNrTm8LdM+osy7kdIPJRQluTEep/kXgJuKJVK3675Fef//sVbPnhhVaDCCGyQCaPRwFGjx7NqVOnWL9+PQ0bNkSv12Nvb0/lypWZPn065cuXj28bGRnJ0KFDyZMnD/b29rRu3ZrAwECr9ufYsWPUqlULOzs7vLy8+PDDDwkODmbSpEn88ssv7N69O36m4bJlyyw6ZmRkJNu2bUs1UGkwGOjRowfTp0+P3+bv78+hQ4fo3r37C7VfvXo1PXr0oE6dOuh0Ojw9Penbty+//vqrRf1Pj+LFizNixAj2799PUFBQkv1Pft9LlixJ8/edWSRQmYwhQ4bg5+fHoUOHsrorQrwW4owmRm04y1uzDmFMZeBeKrczZfMl5ISKjDFSJKcjI5qVQKdNx39nelvotkBdChUVDLt+gxrvJy2Uo7NV/zwwCQ7PTNheayhUG6gut7KCfA4w960quDvYcPpWCN2nHiAozPIZahnGuzb0XPpqBStjwtWfp7+oxcWo256ejRJ2H9Z8SNLgtaIuvXl0I/njPn0X15i+2bEHDhzgypUrfPjhhxa1nzx5Mt9//z2PHj1ixowZfPbZZ0nu4KYludw3T5aSHDp0iE8++YR58+YRFRXF2rVr2bVrF0ajkQMHDpA3b16mTp1KVFQUjx49svicad29BnWg17Nnz/gZjEajkVmzZtG/f/8Xau/h4YGiKKxZsybNu/6pXZv0iIuLS/au95Pf97vvvpvuY4rXh4xJs5f6JTwplTuhGrfx8X8j14OSL8rnaNDxXRsfHPTwZvUCjOqoFuOZs+8any8/leqYB4BCdaHfRnDOB0H+MLsV3DsNt44kDlY2+Bzqf6ouAX9SQNDgBIP3w7DT6tJxUCuKr/848WeVECLzZePxKMCiRYvo2LEj+fPnT7Ptzp07cXNzw9/fn0uXLnHt2jW++eabdJ/z2THX07kVe/XqRdWqVXnw4AHnz5+nSpUq/Pfff7z33nt89tln1K1bN/51nTt3tuh8//33H3nz5qVEidQnu/Tv359FixYREREBqLMjO3TokOyN8/S0z5kzJ7t37+bBgwdp9jW1a2OpJ8u3kxuTLlq0iA4dOpAvX750HzejSKBSCJHlbj6MZM7ea+zyD2TcFn+LXnP8xiPq/7qNTWfuxm8zm81M23WZs3dSqIz5NBsH6LEEcpaEqIewbzyEP3P3L+6poNzaD2HvhKTHMcbBpW0W9Tk1JbycWDQwobpo96n7uR+aHYKVtV6tYOVPedSfiKfuJu4dp25b/zh3YPwSmxDg2S+RZjW4PeOZ2Xdjy6rHCDyfsO148jkYU+Lvr/7dL1asmEXthw4dSt26ddHpdLRp0wYfHx/27t2brnNOmjQpSc6bqVOnAnDjxg1cXFyoXr06Go2GIkWKMHHixDSXmqQmJiaGzZs3pxmoBHWgt3jxYsLDw1m/fj16vZ5GjRq9UHtvb29GjhzJ//73v/iZi3/88Ud8zp6npXZtUvMkwPnw4UOmTZvGnj17aN++fZJ2T37fRYoUSfOYQojs58DlIGbtuUKnynn5spWPRa/xcrGNv8e5+PANhi85nnZBwFw+MOA/8CoLkQ/BGAO2LnD7aNKZlY45ofVYcPCENmPB7fFSwzrDoeXjWTqHpsLKd9TxixAia2Tj8ajZbObSpUsWj0eLFi3Kt99+i4ODA3ny5KFPnz7pHo8GBwcnGXM1aNAgfv+NGzeoW7cu9vb2uLi40KtXL9544410neNZltw4B6hUqRKFCxdm6dKlxMXFMWfOnBRvnKen/a+//srdu3fJnTs3derU4eOPP06ShgjSvjapiYqKIjIyksOHDzNmzBjq1auHq6trojbp/X1nFglUCiGynLeHQ/wsgz+3+rPzQtoJolcdv8W9kGg+XHSc07fUXE2Td15m5LqzvDPvCOGWVNG2c4UWP1ve0X+/hLNPLeGMjYRZLdUvCld2Wn6cFBTL5cTiQdXJ5Wzgwr0wuk3ZR0BINggKvmrByrQEnFWX2KQm5JbazoqUx99eLc2tU7hw4UTPXV1d0zWzEZLPw/jOO+8A0KRJE7y8vPDx8eHTTz/l33//tbgieEp27NiBq6urRUtIypcvT/HixVmyZAnTp0/nrbfeQqNJedhiafsvvviCGzduMGbMGLy8vPjjjz8oWbIkmzZtStQutWvzpJr4k5+nZ2c+CXAWKlSICRMmMGHCBPr27ZukH+n9fQshspfz90IJjzGy9Mgtpu26DKgrRFJb0h0da8JGq6F6YTe0Cqw6fpv3Fx4jJi6N/wecc8NbG6BIIyjWFN5cA3Zuj4OV7dUA5hNlOsLH/lC6Q8K2qGA1z6WNI6CBk4vVooKv8me5EC+7LBqPgjpGyczxaHJ5GPfs2RO/f/jw4fTu3ZvevXszc+ZM7t9/8WJC69evtyhQCerN8OnTp7N27Vrs7OzSDBRa0r5y5cr4+/uzdu1aGjRowIEDB6hfv36SoGZq1+ZJNfEnP08XvXkS4HR3d6dLly7UqVOHxYsXJ9vf9Py+M4sEKoUQ2UL7innpUa0AZjN8uPg4d4IjU23/ZctS1CnmQWSskf6zD3E3OIquVfJTyMOB/rULYW9j4awvW1fL2vkOgFJtoPhTleH0dpCzhDrwfzax/XMqktORxQNrkMfFlkv3w+k6ZX+a1yJTvCrByi9uqz/2Ty3zrzlU3fZktolnqcR5vp6laNWCTJ6lErZ9eEo9hsdTy0cqpFChNQVPlp6cO3fOovaKlVIPpMTJyYkjR44wadIkFEXhgw8+oE6dOhYtUUnJ2rVradWqlcXt+/fvz++//86GDRuSDfY9b3svLy/69OnDpEmT8Pf3p0KFCokqg6dl9OjRie5snzp1Kn7fkwDno0ePOH78OIMHD072GE9+3xcuXLD4vEKI7OPNGt6MaFocgJHrzjJ5xyV6TDvAn1svpvia5mW8WDmkFnP6VWNy7yrYaDVsOH2Xd+YdISo2jdQSBifosRg6ToU85aHPGvWz7PYxmNkSruyC28eT/wm7D2EBYIqDuh+B1gDn18GCzhAdap0LIoSwXDYejyqKQrFixbLNeBTUyt5HjhyhcuXK/PPPP1SoUIEVK1Y89/GOHTtGSEgIdevWtah9z549OXToEN999x39+vVL8z1b2t5gMNCkSRO+//57du7cyZ9//smMGTMsLl5z/fr1ROPRzz//PH7fkwBnREQEly9fZtq0acnmlE/v7zuzSKBSCJFtfNPahzJ5nXkQHsOQ+UeJTWU5lE6rYWLPShTzdOReSDT9Zx/CRqdh44d1eLOGt/U/NCv2hs5zQKtTn5uMav6X5qPh3d1qENNKvD0cWDyoBvly2HElMJyuk/dz82GE1Y7//B17NljZXZ1V+jKxcVB/nv77obNRt+kM6nNFeSrPVzI5gQxO0GZc8sd9egafNvVq9s/y9fWlRIkSVkuiHRsb+1w5bJ6m1+tp0qQJo0eP5vjx49y5c4d169bF73s2Z2Na51y3bp3Fd68BevTowc2bN2ncuDEFCxa0entQB4kVK1YkJMSClBGPffXVV4nuYJcrV87i1z7x5Pc9YUIyKSWEEC+FIQ2K8k49NX3DqA3nOHjlAWO3XEh1ZUip3M7Y6DQ09snF9L5V0Gpg67kABsw+TERMGqtBtHo1zzZArtKQtzJo9BDgB7Nbw5R6yf/8XVMtAtj/X2j4FfRapt5kvbIT5rRPqCguhMgc2Xg8CmpOyJUrV3LxYso3Xiz1pCL381YEf6JEiRJ8+OGHrF+/ns6dO/PXX38ByY9H0zrn2rVradq0KXq9ZdcmR44cdOrUifPnz1t04zy97Z+oXr06gMVj0oIFCyYaj/7yyy8Wn+tpvXr1YtWqVVy+fPm5Xp8RJFAphMg2bPVa/upRGSdbHUevP2L0htTv7Djb6pnR1xd3BxvO3A5h6KLj6J76YI6JM1mWr9JST45tMsGaD9QcT1oD5PBOaGOlapr53exZPKgGBdzsuf4ggq6T93M9KDsFKx3g0tbHMytfsmClJRxzqoPD5HICtf5D3W9lGo2G2bNnc+TIEbp37x5/t/f8+fPMnDkz3bl4OnfuTNOmTVNtk1zBmCeBxhkzZvD5559z6tQpgoOD2bhxIxERERQvrs4gKlSoEHv37iUkJITo6Og0z3n27Flu375Nw4YNLX4PLi4uPHz4MD44+qLtDx48SJs2bVi3bh03b94kMDCQf/75h7lz5ya5vqldG2vQaDTMnDmT48eP06NHjxf+fQshMp+iKHzavAS9qhdQn6MOA4YuOsbtR2l/NoZGxfHknuzui4H0nXGIMEtS1wDcOaF+DpssSMkRF60GQXI/TrtRqA68uQrscsCtwzCrFYTeTf0YQojMlwXjUYARI0ZQo0YNmjdvzooVKwgKCuLOnTts2bKFTp06cfToUYuPtXr1auzs7Lh27Vqq7Z4dcz0JNIaHh9OkSRP+++8/goKCOH/+PMePH080Hr1w4QLXrl0jKioKk8mU5jktzU/5tDlz5hAREWFxwZm02rdu3ZoJEyZw5swZgoODOXr0KB9//DGFChWiYsWKidqmdG2s5cnvu1OnTi/8+7YWCVQKIbKVAu72/NpZHUhP332FjafvpNo+v5s9U96sgo1Ow39n7zFzzxUAHoTH0H3qfrpO3se1FKpxPre7J+DEIjXH0z99Eqrz3TsD0xrB/fOpv95CeV3tWDKoBoU9HLj1KJKuU/ZxJdDK7+V5eNeCnv+8+sHK0h0TL7lRtFCqrZr/K4NUq1aNo0eP4ujoyBtvvEH+/Pnp0KED+/bt4+efE/Kp2traJilqYzAY0Ol08c9tbGywsbFJ8VwGg4GpU6cmSdDdokULALp3746rqys9e/akUKFC/PTTT/z1119Uq1YNgB9//JETJ06QP3/++KUkqZ1z7dq1NGrUCDs7u1SvgcFgSPUO97P709Pe19eXd999lylTplCjRg1KlSrFzz//zLfffssPP/xg8bWxVt+rVavGjh070vx9CyGyL0VR+L5tGTpWzIsZcLLV8TAilsHzj6aZe7JxqVx0rJiXXtUL4GSr4+DVB/SadoDgCAuCj3kqqDMj9Q7p7/TDa7DhU2j+Mzh6qTMyZzRXtwshspcsGI8aDAY2b97Mu+++y08//UTRokXx9fXl559/plevXvGBNL1en2Tcp9PpMBgM8c+1Wi0GgyHF1W46nY6oqKgkYy5XV1euXLmCg4MDn332GWPGjKFEiRK0bNmSqlWrMmbMGAC6dOlCgwYNqFq1Kjly5GDZsmWpnjMgIICjR4/SsmXLVK+BTqfD1tbW4v3pbT9+/HguXrxIt27dKFiwIF27dqVIkSJs3bo1vl1a18ZafTcYDGzatIl+/foxevToFH/fmUkxWzMU+4oJCQnBxcWF4OBgnJ2dM+w8sbGxrF+/npYtW1o8/VikTq5pxsjM6/rjOj+m7rqCk0HHmvdr4+2R+kB81fFbrDt5hz+6VsDBoCM6zki3Kfu5GBDG5F6VqVnUI/kX3j6uLotKy8Ad6peCJ86tV4OUxhg1uX3XebC0H1x4nOy+93KL3qcl1zQgJIoe0w5wMSAMTycDC96uTlFPR4uOn6Gu7oH5nSE2HIo0hG4L1LydWSw0NJQLFy5QqlQp7O3tX+xgYfdhQmU1B6mtK7x3OMPuXmd3JpOJkJAQnJ2dUy1qk5J69erRo0cPBg0alAG9ezm96DV9WlRUFFeuXKFQoUJJBqeZNZ4RGSczfocydnoxcUYTs/ZepX4JTzr+tYeQqDj61vTmyxbFU72uT76KnbkdQu/pB3gYEUtJLyfmD6iGu6MhSfskTi+HpW+l3e7pcczq9+HoHMhVFrrMgXkd4OFVcMoNvVeCZ0mL33dWkL+r1ifXNGNYbUwq49F4Lzp2mjlzJlOmTGHfvn0Z0LuXl7XGpNYaj8qMSiFEtvRJ85JUKZiD0Og4Bs8/mmaS+XYV8jK5d2UcDOqMMoNOy6SelVn9Xu2Ug5TpYXxmdkPJltBjSUK+xvmdoOkPULYLdJj84ud7iqezLYsGVqdELicCQqPpNmU/F+5lg+T3r8PMSsec0HosOHhCm7Gv7aDwRT1Z0pKeQjpCCPEy0Wk1DKhTmKKejvzepQIAs/ZeZe3J1FeGKIqCoiiUyevC/AHV0WsVzt0NpdPf+wgItaBo3dPpZ1Jjfmp2Z7NRUK4rdF8I7oXhrY2QsxSE3oGZLeBW5i/zE0KkQsajVrN582batWuX1d0QaZBApRAiW9JrNUzoUQl3Bxv87oTw3Zq0q589md5vNpuZvOMSYdFxFHpqJqbRlMwEcnv3hKTVKdHqwckr6fYiDdSZBwYXuLYHlg+EFj9nyODBw9HAwoHV8cntTGCYGqz0u23F/JvP69lg5cKXsMBOWsp0hI/9oXSHrO7JS8vFxYXQ0FCL8/oIIcTLrGFJT8rmdQHg85VnuGthimkbnQZbvbq880kxvTRzXd45YdnBn25ncISOU8A1v/rcOTe8tR7yVILIBzC7LVzdbdlxhRCZQ8ajVrFgwQI+++yzrO6GSIMEKpMxceJEfHx88PX1zequCPFa83KxZVy3iigKrDh2ixsPLBvp/73jMqM2nKPfrEM8CFeLX5y/G0rzsTs5dPWZypau+eG9I+qSqGR/tsP7xxIG888G4QpUgz6rwc4N7p5Uf564fkBdqmElbg42LHi7GmXzuvAgPIYe0/Zz+law1Y7/3J4OVl7e9moGK4UQIgvImPTlFBlrxPR4BmNUrImp57WEW1Akp6inIysG1+LnN8qRL4cdVwLD6TJ5X+rjH0srdqfW7tZRuLpLHc9414GYUJj3BlzYZNmxhRBCCCuSQGUyhgwZgp+fH4cOHcrqrgjx2qtdzIMf2pVh5ZBa5HezLLdLlyr5yO9mx/UHEQyae5joOCPTdl3GPyCMH9edTVolzTW/mrcp2Z+KCUHKgLMwviKce6aicJ4K8NYG6DwbCtdXtx2ZBTObw6rBVqsEDuBqb8O8AdWokN+VRxGx9Ji6nxM3Hlnt+M/Nuxb0WirBSiGEsCIZk76cHAw65vavTiEPddwSGKUwYtkpi6q0FvV0pKtvfpYMqoG3uz03H0bS+s/dXL4flvwL7N0s61RK7W4fUwvpLB+o5qnsuRSKt4C4KDWly6mllh1fCCGEsBIJVAohsr1e1QtS0svy4gHujgZm9PHFyVbHoasP+WzZKf7X1ode1Qswo69vilXn0nRwipq/aXHvpAN3z5JQqnXCc+c8oNGpMy2fVAW3Ehc7PXP7V6VKwRyERMXRa9oBjlx7aNVzPJeCNSVYKYQQQqCuglg0sAaejmpF3P/O3ufvHZctfn0eVztm9vXFRqshODKWthP2cP5uMvmpizS07IAptfMqB4XqqvtdC4DeFrrOVXNum+Jg2QA4PMPifgshhBAvSgKVQoiXypFrD/l+jV+asxKK5XJiUs/KaDUKK47dYvruq4xsXxY3B5vnP3mLX6BcNzAb1YH7kdnJtwu+CWs/AoMT1BqqDvqtzMlWz+x+ValayI3Q6DjenH6Ag1csXP6VkbJBsPLpXKVCvC7k77sQ2U8uZ1sWD6yGrVb99zn2vwsEhVt+87KguwPdq+ZHp1EIi46j25R9SVO+5CgIrf5I/UDFmqntkqPRQpfZ0HU+2Kp5NdHq1cKAvgMAM6wdBrvHWtxvIYRKxqTidWOtv+sSqBRCvDQCw6LpOW0/M/ZcYdnRW2m2r13Mg+/blQbg980XWHPidvy+vRcDWXvydkovTZ5WB+0nQZV+gBnWfAD7/kqmnQFsnSEiCGa1TKieaeVBioNBx6y3fKlV1J3wGCN9Zhxk76VAq57juWRxsFKn02EymYiIsLB6gRCvgCd/3/V6fRb3RAjxtHw57BhWxoidjYaYOBPHrz+y+LUajcJ37cqwZXg9yud35WFELN2n7ufwtWduTPr2gy5z1dUcTzM8Djz6b4Kza1I+kY0DaJ76Whjorz5v+SvU+Ujd9t+38N93Vh/LCPEqkzGpeN1Yazyqs0ZnhBAiM3g4GhjRtATHbzyieZlkqnAno2e1gly5H86svVeJjDUCcOjqA3pNP4CNTkMxTydKeDlZ3gmNBlr9rg7q9/4Jmz6H2HCoMwKeLCl3zAl91sD8TnDrCMxqA0UbgqMntPotvW87VfY2Oqb38WXg3CPsvHCffrMOMfXNKtQpZv3K4+nyJFg5r1NCsLL7QtDbZfiptVotoaGh3L9/H41Gg729/fMv9xfxTCYTMTExREVFodHIfU5rsMY1NZvNREREEBAQgKurK1qt1sq9FEK8KC97WDSgKreDY2hUKle6X1/Qw4F5/avSb9YhDl19SJe/9zGuawXaVMib0MinLZRsBdf2Qtg9cMylfhZv/BwOToZlA6HvasiXSmEmsxm2j4IdY9Tl36XaQKNvwOCsBip3/w5RwWoAUz4HhEiTjEmtT8ajGeNFr6u1x6MSqBRCvFT61y4EkK4P+c9blqJ9xbyUyavOLKhUIAd1i+fEw9FAQXfLCvQkoijQ5AewcYLtP8HWkRAdBo3/lxCstHeDN1epAbqru+DsanW77wDwLJX+c6bCVq9lSu/KDJ5/lK3nAug/+zCTe1emQQlPq54n3ZIEK7tB90WZEqwMDQ2lePHiBAQEZPi5Xhdms5nIyEjs7OxkkG0l1rymrq6ueHlZdgNHCJH5fHI7U76AHrPZzKy9VwkMjWZYk+LotJZ9IXSy1TPrLV+q/rSF8GgjQxcfx9XBJvGNSY0WCtVJ/MJmP8KtQ+rqjjntYOgpcHBP/iSKoo5nMKs3Wku1UbfX/lBdFr52GByeDtEh6goTrczgFiItMia1LhmPZgxrXVdrjUclUCmEeKk8/R+n2Wxm05m7NPXxQqNJ+T9UrUaJD1ICBEfG8kuncng4GpL8R7z25G2+W32G/7UtQ6tyuVPrCNT/VJ1Z+e+XsGcsxIRDizEJswwMTtDzH7X4zsXNanGd0LtWD1SCGqz8u1dl3ltwlH/97jFozhEm9qxEE5/0z9ywqoI1odcymPcGXN6eqcHKXLlykTt3bmJjYzP8XK+D2NhYdu7cSd26dWV5sZVY65rq9XqZSSnES2L/5Qd8t8YPAL87IUzv45vqGOZpDgY96z+oQ9fJ+7gbEk3/2YeZ1LNSolmaScYxWj3U+xwWdFbHKSvfhZ5LUj5Jk++gcH0o3jTx9ipvqeOaFYPg1D9qQLPzrAzJwy3Eq0bGpNYj49GMYY3ras3xqAQqhRAvrQ8XH2fV8dt82rwk79YvYtFrLgaE0W/WIbw9HJjRpwo6rfrl4OydEHI6Gfhi+SlCouL4fPlJqhV2w8PRkPoBa76nBivXDoNDUyE2AtqMV/NZghqQ67YAlvWHoIuQu/yLvOVU2eg0TOxZiQ8XHWfdqTu8O+8IE3pUpHmZVAKumaFgjSwLVmq1WgngWIlWqyUuLg5bW1sZGFqJXFMhXj81irjTqJQn284FsO38fb5ZfZof2pWxeAZLQXcHdn7SkA8WHmPjmbsMmnuEYU2KM7h+EYLCY5IfxxRvCmXegNPL4P45NW90Sp/BWn3iIOWTnJSKAmU7qcHKJW/ChQ1qipvuC9VtQohUyZjUOmTslDGy23WVRf1CiJdW9cLq0qVf/z3PgctBFr0mMsZIQGgUOy/c5/u1fphMZr5ZdZoW43bx9pzDhMeoeSzDY4x8tfK0ZR2p8hZ0nAKKFo7PV4OScTEJ+3U20Gkm9F2nLgmPCobzG9P1Xi2l12oY160C7SrkIc5kZsiCY4mKCGWZJ8FKvUNCsDKTq4ELIYQQ2cH0Pr6M7VYRRYF5+68zeuO5dFVKtdFpmNCjYvxn/S+bztNz6n6+XH4q5XFM67HglAceXYPtoy07UWwkrHgHdj9VVbx4M+i1XE1/c3UXzG4LEQ9SPoYQQgiRThKoFEK8tLr55qdjxbwYTWbeX3iM+6HRab6mbD4XxnZVvxzM2XeNOfuu8mQOw7HrjzCa1C8KRpOZjafvWl4ZvFwX6DIbtDZqINJsSrxfq1ODlGEBMKk2LOquJrjPADqtht+7VKBjJfXaDF10jBXHbmbIudJFgpVCCCEEAG3L52FUh7IATN5xmb+2X0rX65981lfIr6a22Xv5AZv87iUZxwyZf5Q/Nl8AW+eEgn57/1THIFf3pH6S8xvg5CK1wE7wU+MI71rQdw3YucHtozCzBYTcSVf/hRBCiJRIoFII8dJSFIWRHcpQPJcjAaHRDF10LH6AnprmZbz4tHlJAL5f60fZvM7Y22h5dtGVAnyx/BSBYWkHQAE16XyfNdBtfso5mxxyQg5vNZC5/y80u35JWFZlRVqNwi+dytO1Sn5MZhi2+AT/HL6RpN34Lf7qF5jMklywMiYi884vhBBCZBPtK+bF3cEGgF82nWfmnivper1Wo7D83Vp4p1IYcN2pO0THqbMsKdkSfNqD2Qj7/4LFveDR9ZRPUKYj1Bqqfm675Eu8L09FeGsDOOVWl5PPaAYP0td/IYQQIjkSqBRCvNTsbXT81bMS9jZa9l4KYux/lgXdBtUtTJcq+TCZ4dNlp4iKNfJsuNAMhEenYwk4QIHqas5KUAOQB6dC5MOE/YoCXeZA3Y8B0O78GZ/bizIsWDmqY1nK5VNnW3y89CQLDyZ8IRm/xZ/fN19Aa2ESf6t5Eqy0cVSDlYu6S7BSCCHEa8dWr6V71QLxz6fvukJUrDFdx1AUKOGVco5IBbga9NRnbIsxagVvgMgHsLD740rfKWjyPRSqm/w+z5LQbyPkKKQuKZ/RHALOpqv/QgghxLMkUJmMiRMn4uPjg6+vb1Z3RQhhgaKeTozqqC6f+nPrRbadD0jzNYqiMLJ9Wcrnc8FohpQmYhrN6tKpC/dC09+x3X/A+hFq/qanc1ba54CGX0GzUQAUC9iAZuPHYDKlcKDnp9EorBpSi4r5XQH4fPkp5u67Gh+kHN6kOB80Kmb186apYA3ouVSClUIIkQoZk776hjUpTq0ias5tnVYhzoKVIU+7cC+MTWfupbjfDInHMU65oOmPCQ3unVYrgVsyBgkPhD3jE99czeGtBis9fSDsrroM/NaRdL0HIYQQ4mkSqEzGkCFD8PPz49ChQ1ndFSGEhdpVyEvv6gUBGLb4OLcepZ370EanYfZbVclhn3pls4r5XSme6zkqWhZrCg6eULGXWlDnWTUGY6zyNmZAe3SW+kXBGJf+86RBURSWD65J5YKuAHy96kzWBimfkGClEEKkSsakrz6tRmFc94p4OdtyNSiCz5adxGw2E2Rh2pniuRxpVjpXiqsjtIpC8zJeiccxFXuBd53HTxQ4uxp2jkn9RLGRMLUhbP4aDk1LvM/JSy0YmLeKuopkdlu4stOi/gshhBDPkkClEOKV8VXrUpTL58KjiFiGzD9KTFzaswOc7PTotan/V3jrUaRFuS+T8CoD7x2EaoOS328yodw5hgKYUdSE9efWpv88FlAUhaXv1OTp7zFVvHNkyLnSRYKVQgghXnMejgYm9qyITqOw9uQdhi0+Tp0x29h7KTDN1yqKwo8dyuJgo012v1aj8EO70s++CNqMA50tPEl8s30U+K1K+UR6O6g6ENyLPhXkfIq9G7y5Sl0mHhMG8zqpxXiEEEKIdJJApRDilWHQaZnYoxLOtjqO33jEqA1p50k6eOUBAWlUCw8IjebglQfP1ym7p4KBkQ9hfhe4/ziPpkaDsd0k/D1bYuw4Dep+Aj7tnu88Fvhz60VMZuKDlW/NPMSVwPAMO5/Fng1WSoEdIYQQr5nKBd34vGUpAFYdv01EjJG3Zx/m2PWHabxSDXTWKZYz2X0xRhN//OeP6dkbru5FoP5n6mOdARRt4pzayakxBAbtVHNTJsfgCD3+gRKtwBgNi3rCyX/S7L8QQgjxNAlUCiFeKfnd7Pm9SwUAZu65yvpTd1JtHxAaZdFxLW2Xqo2fg/8mNX/T3VPqthyF8MvbDXOpdtDwS3WWA0B0KESFvPg5H3s6J6Xf983xcrYlOs5Ex7/2EBwRa7XzPLeng5VXdkiwUgghxGunXy1vWpXNjRk1PU14jJG+Mw9x9k7q44HxW/xZd+oORXM6xi8B12oU3BzU1DYLDlzn46Unk64OqfEe5K4AvgPhrXVQuW/qHVSUhIKBoFb5fjoHN4DeVi0aWK6bWl18+dtJl4oLIYQQqZBApRDildPYJxeD6hUmp5MBN4dkckM+xdPJ1qJjWtouVU1/BK9yEBEIs1rBzcOJ95vNcHoZhN5Tq3DOaQcRzzmT8ynPFs6x1WtZ/X4tnAw6HkbE0mbCLmKN1i/kk25PVwOXYKUQQojXjKIojH6jLIU9HIiJM+FkqyM4Mpbe0w9y+X7KlbmNJjPDmxRn0aDq8UvAHWy0lPJyjm+z7OhNhi46lvjzXquHAf9Bs5FQoEbC9shHSQOQzzq/ESbXhU1fJN2n1UH7SepSccyw7iPY9ZsFV0AIIYSQQKUQ4hX1cdMSbBhah+qF3VNtV7WQG7ldbEk+Bb3KRqvga418jg7u0GcN5K8GUcEwpx3Ktd0J+//7Fpb2g9Xvw70zcPsozGoNYWlXMU/Nky8wTxfO8XSyZfGgGui1CtcfRPK/1Wcwm58jD6e1FaguwUohhBCvLSdbPZN6VcZOryU0Kg4PRxsCw6LpNe1AioUChz3+jPdwNPBTx7LkdLRhVMdy/NihLE19cjGyfRn0WjX/5ZD5R4mOMya8WPtUQUGTEe6chKkNYMPHiat7P0tRIDpErRoem8yqE40GWoyBuh+rz7d8D5u/Tf2YQgghBBKoFEK8onRaDR6OhvjnASHJL93WahS+beMDkGKw8o+uFdClUXDHYnau0Gs5FKoHMWFoF3XDM/iEuq9ka9AaIJ+vWj3T0QsCzsCM5hB887lPOSyF6t4+eZz5q2dlFAXmH7jO7L1Xn/scViXBSiGEEK+xEl5OjOpYFoCgsBhyuxi4HRzFiqNpjwVal8vDoa+a0Kpcbrw9HJjyZhV6VS/IlN5VsNFp+NfvHoPmHiEq1pj4hQFnYUYz2PaTuqT7yKzUl2wXbwY9l6k3YPUprDpRFGj4FTT5QX2+ZyysHaYGRIUQQogUSKBSCPHKW33iNvV/3c6SwzeS3d+8TG4m9aqEl0vSgXb/2oVoVS6PdTtkcIQeS6B4C5S4KKpdGYtybg3krwofnoJ6H0MuH+i3AVwKwINLMKMFBF2ybj+AJj65+Ky5mhT/+7V+bD//YrM3rUaClUIIIV5j7SvmpVf1AvgWcmPKm1X4vEVJhjQo+tzHa1DSkz+6VMBWr2H7+fv0m3WIiJi4hAYht+HmIbi+F+p9om7b+Blc2ZnyQYs1TjwjM6XZkrU+gDbjAQWOzFTzVhqzQX5sIYQQ2ZIEKoUQr7xrgeFExBjZePpuisubm5fJze5PG7Lw7eqM61aBTpXyAbDx9F1ijSbMZjPLj97kYXgaOZsspbeFrnMx+bRHYzaiXd4fji8Ep1wJbVy9oe8acC8KwdfVIjz3/Kxz/qcMrFuYzpXzYTLD+wuO4X8v1OrneC5JgpVdJVgphBDitfFN69IsGFCNsnldGVSvCMrjgnsxcSbCouPSeHUCs9nMhK3+DF9ynE+alcTBRsveS0H0mXGQ0KjHAcOijaDFLzB4P9T/HMp2AVMcLOmjzrBM/QRwYArM75zybMnKfaDTDNDo1Xzci3pCbPJL2YUQQrzeJFAphHjlDWlQlF86lWPqm1XiB/nJ0WoUahRxp12FvPzQvgxNfHIxtlsF9FoN36w6w/AlJ/hx/VnrdUyrx9huMtfc6qKYTbDynYRlVmEBsKAzHJ0Lb22AXGUgLhrM1i96oygKP3YoS1VvN0Kj4+g/+zAPrBWQfVGJgpU7JVgphBDitWGj0yRKPXPqZjBRsUYGzT1Mv1mHiIyxbAm1oijcDYkiOs7E5cAw5g2ohrOtjkNXH9Jr+kGCIx4HK6sNBOc86pLttuMhT0WIfACLekB0KjcxQ26pebYvbobTy1NuV6YjdF8IOjvw3wTz3oCo1CuaCyGEeP1IoFII8crTaBQ6V8mPVpNayZzE7Gy0TH2zCr7ebgB0qJQXexstxTwdrVt0RqPleIF+GH0Hqs/XfQR7xsH1fXDxP9g/CVDUHFB91oBXGeud+yk2Og1/965MATd7rj+I4J25RxIn289KBaqreT0lWCmEEOI1ZDab+W7NGdpM2M3MPVc4fPUhB6884N35R4iJs+wG5ifNSzKxRyV+aFeGigVysODt6uSw13PixiO6T91PUFh04hdc2wNNfwTHXBDgpxbCSYlLPmg3UW1ftlPqHSnWBHovB4Ozeo7ZbSA8yKL3IIQQ4vUggUohxGslOs7I1ytPM2tPGsuYnlHEw5G9nzVMtPTKahQNpiY/Qp2P1OeHZ0CRhmoC+re3gGNOsHeD3OUSXnNtH1zaatVuuDnYML1PFZwMOg5efcBXK05nj0rgAAWqSbBSCCHEa0lRFJwMOgBijWZmvOUbn2vyw8XHiDOmHax0ttXTqlzu+DFMmbwuLBpYAw9HA353Qug2ZT8BoY8LD+6bqM523DoSus5TxyQNvkj9BGU6Qs331NmYaSlYU735au8Od46rqW1Cbqf9OiGEEK8FCVQKIV4r60/dYe7+a/y4/izHrj9Ms73ZbGbcf/5UH7WFK4HhGdcxRYFG30CLMfDmajA4Qd2PwbNU0rYB59Q8UAu6wrl1Vu1GsVxO/NmjIhoF/jlykyk7L1v1+C9EgpVCCCFeU0MbF+efd2rwQaNi+Hq7qVW8tRrWn7rLZ8tPYTJZfmMxJs7EkkM3KObpyOJB1fFytsU/IIxuk/dzJzgSSrUBvYNaWOfeaei9Ahw8LO+sMQ52/Q5RwSm3yVMB3toIznkh8LxacTwDigYKIYR4+UigUgjxWmlfIS8tyngRazTz3oJjaRbHURSFGw8jiIw18v1aP8xmM1cDw+k9/QCnb6UyAH9e1QZBjoIJz28eVhPTP7iSEJR0KwRFGoAxBhb3hlNLrdqF+iU8+aa1DwCjN55js989qx7/hUiwUgghxGtIq1Hi09EAVC/szrhuFdBqFJYeucn3a/2IM5rYdymIVcdvse9SEMZkgpcmk5nOk/fxybKTLD58gyI5HVkyqAb5cthxOTCcLpP3ccPkAY2+Vl+w+VsIuZNwgGPz4c6J1Du7aghs+Q5WDk65EjhAzuLQbyO4FYZHT4oGnknPZRFCCPEKkkClEOK1oigKP3cqh7e7PbceRTJ8yfE0ZyF83KwE9jZajl1/xKrjtxm3xZ9d/oH8b3UGD6bPrYPpTWFhN/i7NiwbAPcvgM4AnWZC+e5gNqrbj8yy6qn71PSmV/UCmM0wdNExztzOgKDs85JgpRBCiNfYzYcRdP57L3eCo/i1s5oWZuHB69QYtZXuU/czdNFxuk/dT+2ft7Lx9J1Er9VoFNqWz4OHow2udnoACrjbs2RQDbzd7bnxIJIuk/dxuVAPyFsZokNg/Qj1xUfnwKrBsLCHWvQvJdUGqsu6y3VJeym4awF1ZmWuMhB2D2a2VG/SCiGEeG1JoFII8dpxttXzV8/KGHQatp2/z6QdqS81yuVsy5AGRQEYveEcw5oUo0UZL37vUiFjOxoXrQ7w7dzULwu5y4PeTt2n1UG7v6BKf8AMa4bCvr+sdmpFUfi2TWlqFXUnIsbI27MPJ+Suyg7ig5VOarByQRcJVgohhHgtbDsXwImbwfy0/iwF3OzpXb0A0XEm7j9TEOducBTvzjuaJFjZt6Y3W4bXp0XZ3PHb8rjasWRQDYp6OnInOIqu0w5xrdYo0Ojg3FrwWw2l2oJ7UQi5qa7oiEthVUreyvDhKfBpZ9kbcsoFfddCPl+IegSz28Ll7em4IkIIIV4lEqhMxsSJE/Hx8cHX1zeruyKEyCA+eZz5vl1pAH7ddJ75B65x+lZwsj+3HkXSv3Yh8rracTckiqVHbjGpV2UKuNtnbCfLdIR+m9RKml3mQN914Jo/Yb9GA61+g1pD1eebPoczK612er1Ww189KlPYw4HbwVEMnHOEqNhsUgkcHgcrl6nByqu7JFgphHjlyJhUJKdX9YK0KpebOJOZIfOPselM8ilanqwX+W6NX6Jl4FqNgou9Pkl7T2dbFg2sTkkvJ+6HRtNhWQj3y7+r7lz/sfpn90VgcIEb+2Hd8JSXdts4JDyODoXgW6m/Kbsc0HslFK4PseFqLm4r5+EWQgjxcpBAZTKGDBmCn58fhw4dyuquCCEyUJcq+WlRxgsz8OWK07T+c3eyPw1/3U5QeAxftFQL20zecYlbjyLjj3PrUWTGVcfOV0WdPWnnqj7f+QtEhaizLUGdcdn4O2jwFRRtDCVaWPX0LvZ6pvf1xcVOz/Ebj/hk6cnsUwkcEoKVGpuEYGXsM8HKHWNg26is6Z8QQrwAGZOK5CiKws9vlKNITgfuhkQREBqdYlszcCc4ioNXHiS7/8i1B/SbdYiImDgAPBwNLBpYnXL5XHgQHkOLY9WIcikMYXfhv2/Boxh0mg6KBo7NhYNTUu9s0CWY1lgtAJjWzUSDI/RYAiVbJ+ThPrEo9dcIIYR45UigUgjx2lIUhf61C6XZLjrOxMPwGFqW9aKqtxtm4Pj1RwBM23WZhr9uZ9nRNGYKWMOmL2DrSPirBoyvCGH31e2KAvU+hu6L1fyVoM5wMJmsctpCHg5M6lUJnUZh9Ynb/Ln1olWOazUFqkGFburjq7vQLu6B1vT4S9uOMbDtR9Bos65/QgghhJU5GnRM6lUZG61lX+eSS98SazQxdNFxtp4LYNL2hDQ4rvY2zBtQjcoFcxAYpWHQozfVHUdmwdU9UKwJNPle3bbxc7i0LeUT62whIggiAiH4Rtod1Rmg82wo30PNw71iEBycatF7FEII8WqQQKUQ4rVmq7c8gKUoCqPeKMuW4fVoVU7N6xRrNBMdZ2L7+VSSyltL+e5gm0PNDRVyCw5MSrxfq1P/NJth42ew8l0wxlnl1DWLePBD+zIA/L75AmtP3rbKca2m7Z9QSf0ipbm2m2qXfkez/Sc1SNngS6j3SRZ3UAghhLCu4rmc6F41f9oNAU0yRW30Wg3ftytN58r56Fcr8Y1bZ1s9c/pVpXphN3ZEF2exqZG6Y80HEBsFNd6Dct3UYOK90ymf2CUv9FgMA3dAzhKWvTGtTk17U3WQ+nz9CHVFSXZa0SGEECLDSKBSCCHSoUhOR/K7JeSmHFCnEH/3qsSf3Stm/MnzVIB+G8DOXX1+egUE30za7t4ZdfbByUWwtG/CMvEX1L1qgfgZqB8tOcGJG4+sclyreSpYmTPsLNo9v0PdjyVIKYQQ4pXVsVI+i9oVcEs+r3bDkrn4pXN5cjjYJNnnYNAx662q1C2ekx9juhFgzgFBF9WgoaJAm3HQewXUfD/1k+etrBbMecKSgKNGAy1+hnqfqs+3joQZzZJ/raR4EUKIV4oEKoUQ4jkdvf6Q3RcDaV4mN0oyMxUyhGcpGLAZXPLDw8swo4Wa/+lpXmWg6zzQ2sDZNbCoh5oXymSEK7vg1FL1T1P6C+N80bIUDUrkJDrOxNtzDnMnODLtF2Wmtn9i1ugSnt88BLHZrI9CCCGElWg1lo0/LG33IDxxJW9bvZapb1amaqnCfB3bB4DY/VMgKhj0tlCkYULj2Mi0xxbX9sLfdSDEgpUZigINvoBmP6nPbxyAKfUTn0NSvAghxCtHApVCCPEc/vO7R8e/9vLp0pOER6vLq2ONJmbsvhL/PMO4F4F+G8GtCARfh8n14PbxxG1KtlQT0uvt4eJ/MLUB/FEaZreGZf3VP8eWAb/V6Tq1VqMwvntFSuRyIiA0mgGzD8cn4M8WdoxBMcVhUh5/Ybm8XQ3UxibNzSWEEEIIVVSska9WnqLemG3cfpT4Bp9Bp1VzVZdux5jYrjQOH8ma8+GJDxB8U53xuHVkyicxmdSclvdOpd7uWTWGQNsJgAJ3jqOdWg/FFIdm16+S4kUIIV5BEqgUQojnUKe4BwXd7QkIjY5PQD94/lG+X+vHr/+ez/gOuOSDtzaAwQliQmF606TByiIN1CVZOju4fw5C7yTeH3IHlryZ7mClk62eaX2q4O5gw5nbIQxffAKTKRvkjXo8q8JY9zPWVJiJsXwPdfulrbC4l9WWwAshhBCvGhuthnN3QgmNjuO/s/eS7NdrNYzrVoG75QZzzeTB0EXHWHrkqfQzNw7AnROw+3d15UZyNBroPBMq94WWv6Svg5V6Q+dZoGjQBJ6jzYn+aHeOliClEEK8giRQKYQQz8Gg0/JFy1IATNl1mRsPIuhdvSA57PWUy+eSOZ1wyqXmZVS0YIyG2W3g+oHEbfL5gsExhQM8Di5u/Czdy8Dzu9kzubdabXTjmbv8tjkTgrOpebL0q8GXmOqMAMDUejxU6Knuv7hZDcpKsFIIIYRIQqNRGP1GWRYMqMabNbyTbaPTavi1c3m6+ebHZIZlyxawcvt+dWeZN6DWUPXxqiFw+1jyJ3IrrOa2tHFIfydLt4ee/2AGFMyYFY0EKYUQ4hUkgUohxGsth4MNBl3q/xUadJpkk8w39clFjcLuxMSZGL3hHHWL52TXpw3pUNGyxPZWUboDDD0JBWpCdAjMbQ+XtiXsv7YXwu+ncgCzWkH82t50n7qKtxuj3ygLwMRtl1h+NJnCPpnFZEx+VkX7v6BCDzWYe2Ej/NMX4mKSPYQQQgjxOivq6UTNoh6pttFoFH7qUJbJRfax0OZHXLZ8wszdl9Wdjb6FYk0hLgoW9oDQpDMzkzi9DG4dtbyTt47yJNumYjbBgm6Wv1YIIcRLQQKVQojXWl5XO7aOqM/a92sn+pnZ1xd7Gy1NSnmy/oM65HW1S/JaRVH4po0PGgXWnbrDgctBOBoSCrmYLalqaQ2u+aDXMjWhfWwE/PMWRIeq+8Is+JKQnnbP6FgpH4PrFwHgs2WnOHz1wXMd54U1+DzlWRXtJ6nXR2cL59fD0rfAGJu5/RNCCCGy0KOIGDaevpN2w8dCo2JZdzL59hqNQtP2vYlVDNw05+SntafUNDgaLbwxDTyKQ+jttNOuHF8IS/vB4t4QYcH44akUL2dzv6Fuu7BBncEphBDilSGBSiHEay+vqx1l8rok+mlQ0pNtI+oztY8vRTxTWjoNpXI7061qAQC+X+uH8XGuxmPXH9J+4h4u3Q/LlPeAjT00/FrNWVm6vfongGMuy15vabtkjGhagualvYgxmhg09wg3HkQ897EyTJEG0G0BaA1wbq36xUiClUIIIV5ylqwMsdFqeGfeEd5feMyicUlwRCxNft/JewuPcvT6w2TbKDlLoBt2nMB6o4hFx88bz/HH5guYDc7QfRHYusDNg7D525RPVLIluBeFcl3U9ql5JsXLhVxtMfl0UPcdm6cW6RFCCPFKkEClEEKkIJezbfxjs9lMdFzyeRw/alKcIjkd6FPTO3450vgt/py4GczPG85lQk8fu3NCnUl58b+EKte5SoNzHojv2bMUcM4LBWs+92k1GoXfu5andB5ngsJjGDD7MKFR2TAIWLQRdJsPWhs4uxqWDQBjNqpYLoQQQqRTSitDFgyoRtGcjtjoFBYNrE7VQu7EGs38b/WZNFd8uNjrqVnUnYJu9qm2VZzzMKxJcT5pXgIwM26LP6M3nsPsVhg6zYTcFaDG4JRPZOsCg3ZC42/V2ZipeTbFi6JgbD1OPQfAySUQFZL6MYQQQrwUJFAphBBpuBcSRZ+Zh/hujV+y+90dDWweVo8uVfKj0agBwe/blaFLlXyM6lg28zpauS80+R4Gbge9LQScgwm+UKTR4wbJBSvN0Hx02l8Q0mBvo2Nanyp4Ohk4fy+UoYuOx88uzVaKNYEuc0GjB7+VsGKgBCuFEEK81JJbGVKjiDtujjYYtFoeRcbwTWsfbLQadvkHsunM3TSP+b+2pdn4YV0qF3RLs+3gchr25BlPHc1JJu+4zHdr/DAXaQhvbwPXAqm/+OmiOmYz3E+hOF9yKV709tB9ITh6QUQgLH873cUBhRBCZD8SqBRCiDRcCghj54X7LDhwnS1nk8/l+CRACWAymcnvZs+YTuVxdzRkVjdBUdSKmw6PE+GfXaMO3G8dhYbfgIN7Cq+zzkdBbhc7pr5ZBYNOw9ZzAfy0/qxVjmt1JZpDlzlqsPL0Mlj5rnyxEUII8UpRFIVfOpVj28f1aVgyF94eDgyqVxiAH9aeJTIm9c89Z1s9tnoLb2IenEreBweY5DIHO6KYtfcqX6w4jenpG6TnN0DQpZSPERMBS96EKQ3gXvI3hpPvaJ6E1C4XNsKW7y1/rRBCiGxJApVCCJGGmkU9GFC7EACfLjtJYFjyieHNZjOrjt+i4W/buRYUnmhfpuWqfFrBmlC2CwRdhK3fQXhg8u0W94IHV6xyyvL5XfmtS3kApu++wsKD161yXKsr2RI6zwKNDk4tURPxS7BSCCHEK6SguwMeT90wHVy/KHld7bj1KJK/tl+0+DibztxlxD8nUl4G3uALcM6HY+RtVvnsRKPAwoPXGbH0BHFGE5xYBAu7w8JuEBWc/DG0Nuo+YwwEpCNQCZCvMrSbqD7eM1Y9nxBCiJeWBCqFEMICI5qVoKSXE4FhMXy27GSKg/WlR25yNSgi0WzCH9f50fj3HWw8nfZSK6u5tA1mt1arXBtTqbgJgBn2/WW1U7cul4dhjYsD8PXK0+y9lEKANKuVag2dZoCihRMLYfX7YDJlda+EEEIIq9tx4T77LwfxdetSAEzecZmrgeFpvEpNf/P+wmMsPXKT9adSGMcYnKD17wAUvzKHWc30aDUKy4/eYuji48R611NnPgZeUPNDJ3djUKtT81r22whlO6X/DZbrDHU+Uh+vfh9uHEr/MYQQQmQLEqgUQggL2Oq1/NG1AjZaDf+dDWDRoRtJ2iiKwtetfdAosOnMvfgAnV6rwWwmxcqZGcK7tppg3ru2Ze3vHFNzQ1nJB42K0rZ8HuJMZt6dd5QrFnwZyhI+7aDTdDVYeXw+rPlAgpVCCCFeKauO36LPjIN8ueIUdYvlpH6JnHSvmp8c9jZpvjaXsy3DGhdnSIMiNCrlmXLD4s2gTCcwm6h77nv+6lYWvVZh3ck7DF51i5jOc0FnC/7/wpbvkj+Ggzvkq5LwPL2fxw2+ghKt1FmZi3pA8M30vV4IIUS2IIFKIYSwUKncznzcrAQA36/xSzb4VjyXEz2rFQTUHFBGk5kPGhVjwYBqfNGyVOZ1VquHvmuh/ueWtW/xi5rj0koURWFMp3JUyO9KcGQs/WcdIjgiG1YCByjdATpOUXN1HpsL64ZJsFIIIcQro1lpL/K42HI7OIrpu68wvY8v37Urg4u93qLXv1u/CB83K5l2zsrmo8EuB9w9RbOQpUzpXQUbnYbNfvd4e7ORmNZ/qu32jIMTi1M/VshtmNkcLvxrUR8B0GjUz/NcZSA8QF1uHpNNb5QKIYRIkQQqhRAiHfrXLkSNwu5ExhoZtvi4mnvpGcOaFMfZVsfZOyEsPnQDW72WmkU9Mr+zT1fSTIsVg5RP2Oq1THmzMnlcbLkcGM6QBUeJTeZ6ZQtlO0GHyWqw8sgsWD/CqjNMhRBCiKxiq9fyaYuSAEzacSlRrm2z2ZzsWCY1Kd54dMwJzX5SH28fTYOcoczs64udXsuOC/fpc7AAMTWHqftXvw83j6R8kgOT4cYB9fPYmI4bnQZHtRK4vQfcPQkr3pGbj0II8ZKRQKUQQqSDRqPwW5fyONnqOH7jERO2JU1G7+Zgw4ePczT+9u95QqISBthh0XH8ucWf6LhsWLglOhT++w4irbdE3dPJlml9fLG30bL7YiDfrTmTcjL+rFauC7SfBChweDps+ESClUIIIV4JbcvnoWIBVyJijPy66TwA/vdC6TntAH9tT6Ua91MCw6IZNPcwHSbtSXkcU747FK4PcVGwZii1irgzu19VHA069l0OoufFxsQWbabmz764OeWTNfgSKvaGN1eqq0TSw7UAdJ0HGj2cXQ07fk7f64UQQmQpCVQKIUQ65XG1Y2T7MgD8ufUix5LJPdm7RkGK5HQgKDyGf8/cA9RZC72mHeC3zReYuM2yLwWZasmbsPt32PytVQ/rk8eZcd0qoigwb/91Zu+9atXjW1X5bo8rhypwcAps/FyClUIIIV56T/JoAyw9epPTt4I5ezeUvZeCmLjtIjceRKR5DL1Ww9Hrj7geFMGhKync1FQUaD0WdHZwdRccm0fVQm7MG1ANZ1sdh64H0/vBAMLbTIH6n6V8Mp0NtJsAboWTnsJvJc1OvY9ydlXKry9YA1r/oT7eMRpOL0/z/QkhhMgeJFAphBDPoV2FvLQtnwejycywxccJj45LtF+v1TCqYzkWDaxOp8r5APVLwsC6hcnvZoevd46s6Hbq6n6s/nl0NlzbZ9VDN/HJxWfN1WVn36/1Y/v5AKse36oq9oS2j/NoHZgE/34lwUohhBAvvUoFctCuQh7MZvWzuHVZL6oXdiM6zsTIdX5pvt7FTs8fXSqw9oPa1C6WSkobt0LQ4Av18b9fQug9KuR3ZcHb1clhr2f/7Vg67c5D0JMl6Ma4tD9n756GY/Mh7D7a9cMxxAWjXT8cwu6n8oZ7Q4331McrB8PtY2m+RyGEEFlPApXJmDhxIj4+Pvj6+mZ1V4QQ2dgP7cqQ28WWq0ERjN/in2R/1UJuVC/snmhbizJebB5WjzrFcmZOJ+3cgLTyTypg7w4Fa0KlN9VNaz+EuBirdmVg3cJ0rpwPkxneX3AM/3uhVj2+VVXqrc4IAdg3ATZ/I8FKIUSmkzGpsLZPmpfEoNNw8MoD/vW7x3dty6DVKGw6c48dF1IJ+j1Wu5gHJb2c0z5R9cGQuzxEBcPGTwEok9eFxYNq4OFo4OydELpO2c/9e7dgbnv1szYlgRdhehNY9R788ybEhKsjm+gwWDc89X40+R6KNoa4SFjYA0Lvpt13IYQQWUoClckYMmQIfn5+HDp0KKu7IoTIxlzs9fzWpTxtyufh3fpFUm17JziSM7eDURQlUdXMDM/XGBMGpHUO8+N2QOPvwCEn3D8He8dZtSuKovBjh7JU9XYjNDqO/rMP8yDcusFQq6ryFrT6TX28dzxs+U6ClUKITCVjUmFteV3tGFhXXU790/pzeHvY07emNwDfrT5DTJzlhWfuhUSx52Jg8ju1OnV1gkanFvd7XBCneC4nlgyqjpezLRcDwpgx/S91ifjmb8D/v+SP5V4ESrWBXKXg2l4Us5ofUzEb1RyUqS3r1mih0wzwKA6ht2FRD4iNsvg9CiGEyHwSqBRCiBdQs4gHf3aviKu9TYptdl64T8NfdySpEr713D1a/rmXh9EpvvTFeZaCkm1A0abSSEmoEG7vBs1GqY93/AJB1s2laaPT8HfvyuR3s+P6gwjemXskXV+KMp3vAGj5q/p49x+wdaQEK4UQQrzU3qlXBE8nA9cfRDBrz1WGNi6Gh6OBy4HhTN99xaJj+N0OofFvOxg8/yj3Q1MYyOQuD+8fUXM/P1UQp3BOR5YMqkG+HHZMCqnBam1jMJtgaT8ITLpCBUWBhl/Do5skXSWiqKtAUlsCbusC3ReBrSvcOgJrPpDPciGEyMYkUCmEEFZiNpvZeeF+klmS5fO5YtBruHAvjIUHr8e3/WvbJS7eD+ffmxn4X7GiqMnkDY4kO7jX6AAz7PwlYXPZTlCkoVqRc/M3Vu+Sm4MNM/r44mTQcfDqA75ccSr7VgIHqPo2NH9cMXTXr7B9dNb2RwghhHgBDgYdHzcrAcCErReJiTPxRUs1j/TG03cwmdL+TC6ey5GCHvYU8nBIkqc7kRzeyW4u4G7PkkE1KOThyEfhb3JcKQXRwbCwG0Q+StzYbFaL2yW7SsRs2RJw9yLQZbZ64/bkYtgzNvX2QgghsowEKoUQwgrMZjMfLj7OmzMOsvDgjUT7XOz1DG9SHIDfN18gOCIWRVEY/UZZ3q7tTQfvDJ5R6JjzceXLZAb3ddW8URybnzB7UlHUJc/luiYsfbayYrmc+LNHRTQK/HPkJlN2Xs6Q81hN9Xeg2U/q4x2jYceYrO2PEEII8QLeqJSPMnmdCY2OY9x//nSomJc/upZn6bs10WjSym0NOq2GGX19WfZuTbw9HNI+4aPrML8LXD8QvymPqx2LB1bH29OV/pEfcBcPCLqozqw0GRNeG3AWzq0BszGZA6NuP7tabZeawvWhxeMbj/99B+fWp91vIYQQmU4ClUIIYQWKolA2rwt6rUJkbNKBdI+qBSiey5GHEbGMe1x4p6inE580K45NaquyraV0x8RLwBUtlGoL9T+BgrXVYGZsREJ7t8LQcQo4eWVYl+qX8OSb1j4AjN54js1+9zLsXFZRYwg0+UF9vO1H2Plr1vZHCCGEeE4ajcLXrXxoUz4P79QvgqIodKiYD73W8q+Hnk62aC0IagKw63fw3wTrRyRadu3pbMuigdXxzJ2fftHDicQGLm1RU63EN0ojjc2TMY1nqbT7UfVtqNIfMMPyt+HeGcv6L4QQItNIoFIIIaykX61CbBhal/61CyXZp9Nq+PpxUG7OvqtcDAhL0sbvdkjGdS7REnDA4AStfgeTCSKC1CqY+/9O+fV3T2dIt/rU9KZntQKYzTB00bGMvQbWUOsDaPw/9fHWH9S8lUIIIcRLqFphd/7sXpG8rnaJtsfEmZh/4BqxRstWfJhMZubuv8afW5LJL/lEo2+gZGt4Y7o6JnmKu6OBhW9XQ5+3PB/FvMN1cnE2Z/OEBmmlsXkyprFUi5/Bu466lHxhNwhPoSCQEEKILCGBSiGEsBKNRqGop2P882dzPNUplpNGJT2JM5n5cZ1fQjszfLj4JC3H70q5eqY1OOaE1mPBwRPajFWfazTQcbI6YG/yXdLXmIzwz1vwdy24utvqXVIUhf+1LU2tou5ExBgZMPsQAaHZvBpn7WHQ8Cv18X//gz3js7Q7QgghhDWER8dhNpvpNe0AX644zey9Vy163YErD/h65WnGbvHH/15o8o3s3aDbfMhZPNndrvY2zB1QjXv5W9Ak6mc6L3/EwSsPEhqklsam9R/qfktp9dBlDuQopC5JX9wb4mIsf70QQogMJYFKIYTIAKdvBdNy/C6OXn+YaPuXrUrhYKOlhJczxseBTI0CrvZ6tBqFs3cyeEZhmY7wsT+U7pCwLXd56LMGYsJh1RC4cyJhn0arVssEWPMhxFm/RLleq+GvHpUp7OHA7eAoBs45QlQyy+ezlbofQ/0v1Mebv4Z9E7O2P0IIIcRzCgyL5r0FR2kzYTdxJjNvVM4LwNj//AkISfvmYY0i7nSunI+vW5WicE7HNNsD6koNU+IZm862eub0q0qlwrkJi46jz4yDnNq7AUJuqw0ep7ExP14Cbla0UKKVWjE8vezdoMdiMDjD9b1qMZ7sXNhPCCFeIxKoFEKIDDBjzxXO3Q1l2OLjiaphFs7pyL4vGvFZi5KJ8jp91KQYq4bUYkCdwlnRXXVZ1bYf4dg8WDtcnUX5ZHZB42/VWZhB/rB7bIac3sVez/S+vrjY6Tl+4xGfLD2ZvSuBA9T/FOo9Lka06YvUl84LIYQQ2ZRBp2H/5SCuBIaz/3IQnSvnp3x+V8Ki4xi14ZxFx/ilc3n61ipkWc7KrSPh79pweHqSXQ4GHTPf8qVe8Zw0MO6h5KaehMzqArGRCUvAbRzUeZU2jnDvNCzrDxe3pO9NA+QsAZ1mgKKBY3PhgHyOCyFEdiCBSiGEyADftilNHhdbrgVFMPKpZd6gzhh4lpOtjjJ5XTKre8mr+4m6BPz+WTizHA5MUrfb5YAWo9XHu36FwFRyUL2AQh4OTOpVCZ1GYfWJ2/y59WKGnMeq6n8OdUaojzd+CgenZm1/hBBCiHRystUzplM51r5fmzrFcqLRKPzQrjSKAiuO3Uq8BNsCJpOZ0KjYlBs4eAJmtfJ28M0ku231Wqa8WRmXIr6EYYfzg1Pcnvu2OuPRMSfGlr8TrXPB2Op3KNESnPI8/2zIYk0SCuVt+gIu/vd8xxFCCGE1EqgUQogM4GKn59cu5VEUWHjwRrIVrY/feMSAuUcJf2YsHxAaxZiN54izMIm91XgUhb5rofMcKPMGVHs3YV/pjlC0MRhjYO2wDFseVbOIBz+0LwPA75svsPbk7Qw5j9UoipqvsvYw9fn6EXAo6QwRIYQQIjtrWDIXpfMk3DAtl8+Vbr4FAPhm1WmLxySX7ofRefI+PlpyIuVGvv0hX1WICYV1I5IdUxh0Wr7v25rZ+b8nzqwhz/U1rJjwiZo728GD03l7gL071P+MSWUX8cfVAul7w0+rMQQq9FKXkP/TD+5feP5jCSGEeGESqBRCiAxSs4gHbz9eyv3ZspPcD03I72g2m/li+Sl2XAhkw82E/4rjjCbemLSXv7ZfYuaeq5ndZVWxxupSKO1TMz8VBVr9Bjo7uLoLji/IsNN3r1ogvnL6R0tOcOLGoww7l1UoCjT6Fmp+oD5fNxwOz8zaPgkhhBDP6fL9MK4HRfBxsxK42us5dzeUefuvWfTaOKOZEzcesftiIDceRCTfSKOFtuNBo4cLG8BvZbLN9FoN7731Fiu91M/XdoFTCB3pjW5ee6pcm4RuXntC//Dl+Pblli05T4miQOvfIX81iA6GhV0hIn2zSIUQQliPBCqFECIDfdS0OCW9nAgKj+HTZQl5FxVF4avWpQDYc1fB/14YADqthiH1i1ImrzM1irhnTacjHsCmL2FeR3WWw/6/Yf3H4FoQ6n8GznnBIR3VNZ/DFy1L0aBETqLjTLw95zB3giMz9HwvTFGgyfdQ4z31+doP4eicLO2SEEIIkV5LDt+g6R87+d+aM7g52DCiaQlqF/WgdjHLPvdLeDnxa+fybB5ej/xu9ik39CwFdYarj9d/nGJgUKfV0HHgt1xw9EWjgKMxcdFBh+gA/rYZywe5z8Ll7bDxC4v6mfREBug6D1zyw4PLsPQtMMal/TohhBBWJ4FKIYTIQAadlrHdKmCj1bD1XAALDl6P31eziAdNSnliQuGnjefjg5hdffOzcnCtrMtZGRel5lq8tBWOzIaNn8HBKeD/r7o8asgBKN40Q7ug1SiM716RErmcCAiNZsDsw0TEZPMvDIoCTUcmLJlf/QEcm5+1fRJCCCHSoUrBHABsPRfALv/79KhagLn9q1LU08Jq3kD7innJ62qXdsM6H4FHcQi/D5u/TrGZRjFTTLmFGfWjNvE+UFDUYOe8N2D/RPDfbHFfE3H0hO4LQW+vBj03PWfQUwghxAuRQKUQQmSwkl7OfNK8BAAj157l8v2w+H2fNi+OVjGz+2IQ284HAOpsS5024b9noymTq1875wHfAerjo7Oh9Vi1unWxpupycINTQtsMrMztZKtnWp8quDvYcOZ2CMMXn8CU2dcivRQFmo+CqgMBM6waAicWZXWvhBBCCIsUzulI7xoFAXXMYjKbUZ6KDsbEpS9/tv+9UPxuhyS/U2eANuPVx8fmweUdybe7thcl9DYpL+42Q9hd8GkHVQdBPt909TERr7LQcYr6+OBkODzj+Y8lhBDiuUigUgghMkG/WoWoVdSdyFgjwxYfJ/ZxUvqCbvbUz60G30auPZvoC4DZbGbRwes0+m07QWHRyR43w9Qeps4ouH1UnWHQ4IuEaQxmM5hM6mzLaY0hNirDupHfzZ7JvStjo9Ww8cxdftt8PsPOZTWKAi3GQJX+gBlWvgsn/8nqXgkhhBAWGdqoGK72es7fC2XRoRsABEfG8tXKU3T+e6/FN1A3nr5Dy/G7GL4kYdyTRMEajz8vgTVDITaZVC9hSQsSJqt4C2g5BuxcLWufklJt1EJ5oM7UvLLrxY4nhBAiXSRQKYQQmUCjUfi1c3mcbXWcuBnMn1svxu9rmteEh6MNlwPDWXn8Vvz2OJOZ2fuucTUogtl7r2Zuhx1zQrV31MfbflQDk6BW21z9Huz+Hbb9BLcOq48zUBVvN0a/URaAidsusfzozQw9n1UoCrT8FSr3VauIrhgIp5Zmda+EEEKINLna2/Bho2IA/L75AiFRscQaTaw6fpsTN4NZdOh6GkdQ+Xq74WDQkdfVjvDoVNK3NP4WnPLAwyuw4+ek+x1zWdZxJ6/Ez42xlr0uOXVGQJk3wBQHS3rDgyvPfywhhBDpIoFKIYTIJLld7Pixgxpwm7DVn1M3gwGw1cGXLUowumNZ3qiUL769XqthdMeyfNWqFB88/sKQqWq+DwZnuHc6oSLn+fXq8qxtP6mzLgF2/Q73M3amY8dK+RhcvwgAny07xZFrL0E1To0GWv0Bld5Ug5XLB8KZFVndKyGEECJNPasXpEhOBx6ExzBh60U8HA181KQ4AL9sOs/D8Jg0j+HuaGDdB3WY1qcKrvY2KTe0dYFWv6qP94yHOycT7y9Yk1AbT1KayGkyQ6ghFxSsqW4Iuw8r3oXFvZ4/RY2iQLuJkKciRD6Ehd0gKoUl7EIIIaxKApVCCJGJ2pTPwxuV8jGkQVFK5k7I9di6XG66VS2AVpM4A1P5/K4MqFM4Uc7KTGPvllDFettPavXLkq2h7sfQaTpUGwTFm4MpFtZ8mDDrMoOMaFqCZqVzEWM0MXDOEW48iMjQ81mFRgOtx0GFnmA2wtL+4Lcqq3slhBBCpEqv1fBVKx8AZu65wrWgcHpVL0hJLyceRcQyZpNlNyjzutolynGZopKt1ByTZiOsG544wKjRsr3wR48z0CQ+1pNmc13fBY1WfRL5EE79Axc2wt1TFvUzWXo76LYQnHLD/XOwbIC6skQIIUSGkkClEEJksl87l+OjpiXQpxB8jIo1cjc4ad5Hk8nMkWsPM7p7iVV/F+xyQJC/OuhXFDVvU+kOj5c3/wI6O7i+F45nbIVrjUbhj64VKJ3HmaDwGAbMPkxo1Ass68osGg20/RPKdXscrOwHZ9dkda+EEEKIVNUvkZM6xTyINZoZtf4cOq2G79uVAWDRoeucvPnI4mNFxRr5Y/MF1p68nXKjFr9AkYbQZlyS8t5tur2D0mUuOOdOtF1RYKepHONulUwo2pOzOLT4Gfr/B7nLWdzHZDnnhm7zQWcL/pvgv/+92PGEEEKkSQKVQgiRyZ6tnnn7qYmBh64+oOGv2xm+5Djmp2YTRMUa6TZ1P53/3suJG48yr7O2zlDrQ/Xx9lFJ8z3ZuibkhPr3K3W5VQayt9ExrU8VPJ0MnL8XytBFxzO/Kvrz0Gih/V9Qtoua7+qfvnBufVb3SgghhEiRoih83doHjQIbz9xl36UgqhZyo32FPJjN8M2qM5gs/AxeePA647b487/VZ1K+yeiUC3qvgFylk9/v0xY+PE1cr5UcLvguxsYjAairPU0B43UGzz9CyJNj+/aH/C9Q/ftpeSury8AB9o6H4wusc1whhBDJkkClEEJkkbvBUXSZeoAJZ7TcD1Wrens52xIYHsPeS0Fs9kuocmmr15LbxRaDTsvVoPDM7WjVt8HBEx5dU/NTPu3QVDX5vUYLUY/g0pYM705uFzumvlkFg07D1nMBjFp/NsPPaRUaLbSf9FRy/jfh/Mas7pUQQgiRouK5nOhRrQAAI9f5YTSZ+aJlKRxstFwJDOdyoGVjkp7VClKrqDvftS2Do0Fn2ckDLybNManRYi5Ym1tuNTBVewdKtkaDie/tFnI1KIIRS04kutELQMQDCL7FCynbSU19A2p18usHXux4QgghUiSBSiGEyCI5HPQYTWAGrgap0yrzu9nzdp1CAPy4/izRcQm5kL5p7cO/w+rSrkLezO2ojQPU+Uh9vPMXiH1qWXqtD8F3ALSfDAO2QPlumdKl8vld+a1LeQCm7b7CooOWVSDNcloddJiiLp03xaqVRP03Z3WvhBBCiBQNa1wcJ1sdZ26HsOzITTydbfm7d2W2jahPUU9Hi45ho9Mwf0B1WpXLbVnOyu2jYWJVOLk49XZNvgeNnhqmYzTSneRfv3tM2Xk5Yf/FLTChCqx+//kL6zxR/ws1V7cxBhb3hEc3Xux4QgghkiWBSiGEyCIGnZbxXcvxWXkjvt454re/W78oOZ0MXAuKYNaeq/Hb3R0N5Hezz4KeApX7gnNeCLml5qp8QqOFVr9Buc6Qr4q6LZMSzbcul4dhjdUKpF+tPM3eS4GZct4XptVBx6lQqq36ZWdRT7j4X1b3SgghhEiWu6OBDxoWo1w+F4o8DkzWKZYTN4dUKnmnITrOSExcKkX4tDZqXufr+9PoXBGoMRgqvUmLJk0B+HnjOfZfDlL35/CG6FAIua0W2XkRGg10mAy5ykD4fVjYHWIyeZWLEEK8BiRQKYQQWaiQhwMuz4zzHQ06PmlWAoA/t16MXxb+tEv3wxi51s/i3FAvTG8LzX5UA2wVeqTc7sFlmOALW37IlG590KgobcrnIc5k5t15R7li4RK0LKfVQ6cZj2dmRKvBykvbsrpXQgghRLLequXNysG1qFwwR6LtZrOZTWfucu5uiMXHOnz1AS3H7WLS9kspN6r5PvRaDm3Gpn3Axt9B2z95o24lOlbMi8kM7y04RkBIlBrI7LMG3tkF9m4W9zFFBkfovhDsPeDeKVjxDphSCbgKIYRINwlUCiFENvHvmbu8NfMgsUYTb1TKR7l8LoRFx/H75vOJ2kXExPHGpL1M232FJYczcdlR6Q5Qros6izIlm76CB5dg169wzy/Du6QoCr90KkcuZwPBkbH0n32I4MjESfrHb/Hnj80XMrwv6abVQ6eZUKIlxEXBwm5weUdW90oIIYRIQqfVoNEkXbI9acclBs09wtcrTyfNDZmC28FRXLofzpLDNxKluElEq4eijSzr3OOl5Iqi8GOHsvjksicwLJohC44SazRBgerq8azFtYBaCVyjh7OrYcdo6x1bCCGE9QOVd+7csfYhhRDilRcSGcvHS0+y7fx9/tzij0aj8E1rHwCCI2MTVba2t9ExpH5R6hXPSa2iHlnT4dhIiIlIuv2NqeCSX3289sNMmWVgq9fS/nHezsv3w/lg8QmMj087fos/v2++gDaZL1fZgs4GOs+CYs3UYOWCrnBlV1b3SoiX2p07d/jss8+yuhtCvJLCo+P4/d/zfLL0BADtK+TFTq/l0NWHrDxuWcGaNuVy8782Pqz/oA4GXSo3P58IC4B1IyA6LPV2gRexW9qDBYU24mjQcejqQ8ZsPJew32yGU0vhvhVuXhaoDm3GqY93/Aynl734MYUQQgBWDFTu37+funXr8ttvv1nrkEII8dpwttMzsn0ZACZsu8iRaw+p4u3Gv8Pq8lfPykkCbf1rF2LWW75Zk7PyzAoYXxH2/pl0n40D9NsINo5w4wAcnQ3GuAzv0uctS9GrekEA9l56wPKrGiZsu8Tvmy8wvElxPmhULMP78Nx0Bug6F4o2gbhIWNAFru3N6l4Jka2dP3+eadOmMXXqVAICAgCIiIjgm2++oWjRomzatCmLeyjEq+lqUDh/brvIksM38bsdQh5XO95rWBSAn9afIzQqNo0jqDMf+9YqhIu9BbMczWaY3wkOTYWtI1Nv++AyXNiI66kZTGzhCsDUXVfYePrxRJptP8Gy/rB+xIsX1gGo2BNqvKc+XjkYbh198WMKIYRIX6By165d1KxZE3d3dypVqsTWrVsJCQmhV69e1KhRAzs7O/r165dRfRVCiFdam/J56PA4t9KwxccJi46jeC6nZNtqNEqiqpmpJqS3NrMJQu+A36rkZ0y65IOGX6mPN30JEypDUCp5qKxkZPsytKuQB4Dd9zSM23qJYY2LZe8g5RM6A3SdB0UaQWwEzOuUdgEBIV5Ty5Yto2zZsrz99tsMHDgQHx8f9u3bR+XKlfn7778ZPXo0Bw8ezOpuCvFKKp3HhfcbFmNSz0qUyq2OUQbUKUQhDwfuh0Yz7j//dB/z0NUHBIYlzccNqMu6G32jPj4wCY4vgDsncIm4CndOwO3j6s+jG1CsCRRuAMYY6l2bwMC6hQEY8c9JLt8PUwOLtq7gXcd6hf+afP/4RmMULOoBIbK6UAghXpTFgcoHDx7Qpk0bbG1teeedd8idOzcdO3akffv2HD16lM2bN7Np0yZ8fHwysr9CCPFK+65dafK62nH9QQQ/rEnI8XgvJIqRa/2Iik08sI6JM/HnFn+a/LHDolkMVuHTAdr/DQO3qRUwk1N1IHiVh9hweHgVdv2eKV0b160i2qcCuGHRcRbnzMpyels151XhBup1m/cG3JBgixDP+vLLLxk+fDihoaEEBATQoEEDGjVqhI+PDxcuXOD9999Hr7diPjohRCLDmxSnRdnc8TdMDTot37ZRvwPO3HuVC/dCLT7WpO2X6Pz3PkauTSWvtUcJUB6PN1a+i35GI+qf/wb9jEYwpZ76M6EyBN+EZj+pbc+u5pNSQVT1diMsOo535x0lwiEfDDsD9T4Gre65338iGi10mq72MfSOGqyMjbTOsYUQ4jVlcaBy69atlCpViq1bt/Ljjz+ybt066taty61btzh06BCNGzfOyH4KIcRrwdlWz29dyqMosPjwDTaduYvJZKb71P1M232F6buvJGpvMptZevQm14IiWHHMstxQL0yjgQrd1VmAKbbRQtvxwOOgYZkOmdK18Vv8MZrNaFCDk1N3XeHLlaczrzr6i9LbQbcFUKguxITB3I5w83BW90qIbCMmJoarV6/yww8/4OjoSM6cOfnhhx+IiYlh1qxZuLq6ZnUXhXitBEfGEhljpH4JT5r65MJoMvPtqjMW3ySsWcQdrUbBzkabKB93IhFB6mqO1MRFq+1y+UClPgDo/v2SCd3L4+Fo4Py9UL5ccRqzjUN63p5lbF3USuB2OeD2UVj9vnWWlgshxGvK4kDlrVu3qFatWqJtNWrUoGnTpjg4ZMB/+EII8ZqqXtidgXXU5UqfLz9FYHg07z/O/zRx20UCQqLi29rqtfz8RjnGdatA78c5GjOVMQ5uHUl+X54KUGc4NPoWCtVL2B4XkyFdeVI4Z2jDIvxRw0jjkjkBWHDgOsOXHCfOmInL41+EjT10X6QuTYsJhbkdUr7GQrxmQkJCcHR0TDRj0sPDA1dXV5yckk+VIYTIGKuO36LBr9uZsvMyAF+39qGklxMD6xVOlJ4mNeXzu7J9RH1GdSxnvcJ3Db4AGye4cxzPq6uZ2KMiWo3CimO3mH/gutom6BIs6Ab3UpnJmR7uRaDLHNDo4NQ/sDtzVpIIIcSryOJAZWxsLDpd4inyer0egyGVGTVCCCGey/CmxSmV25kH4TF8svQkbcvloUJ+VyJijPyy6XyittULu9OuQl6LvxRYTdh9+KsazGwJIbeTb9PoGzVYqX0cVDg0DabUh/Agq3blSZByeJPivNegCACTelakVdncAKw8fpvB848SHWelnFQZzcYBeiyGAjUhOkQNVt4+ltW9EiJbiI2NZfv27fE/e/bsSXabECJjaRSFB+Ex/L3jEneDo8jvZs+GoXVoUMIzXcexemFAR0+o+5H6+MhsqhV259PmJQD4fo0fJ248gq0/wIUNsOlz6523UF1oMUZ9vOV7OLfOescWQojXSLqK6SxevJj69evH/0yaNCnZbdlNQEAAu3fv5uHDh1ndFSGEsIhBp2VctwrY6DRsP3+fBYdu8M3j/E9Lj97k1M3gZF8XE2fiwGXrBgFT5OABDjnVBPK7fku7ffgD2DEGAs7AiYVW7YrRZE62uvfEnpVoVyEPGgX+9bvHgNmHiYx5iYKVPf+B/NUhKhjmtFcLBwjxmgsJCaFBgwbxP+3bt0+yrV27dlndzSRiYmLYv38//v7pLzYiRHbUulxuKhfMQWSskTGbzgEkumn6bF7ttARHxvLlilOcvRPy4p2r9i60+AV6rwDg7TqFaVY6FzFGE4PnHyW41tdQoiW0tGD8kh6+/cH3bfXxsrfh7mnrHl8IIV4DFgcqy5QpQ6NGjfD29o7/qVOnTpJtbm5uGdnfdPvtt9+oVKkSn332Gd7e3mzYsCGruySEEBYpnsuJT5uXBODHdX642OlpXyEPZjN8tyZp/qdHETG0/nMXvaYfwD8dieyfm6IkVPc+MhseXku57b0zMKulGths8CXUGGLVrgxLJkj5xLhuFZnbvxr2Nlp2+Qfy5owDhGRW4aEXZXCEXkshX1WIegTTm8LdU0nb7RgD20ZleveEyGxubm5cuXIlzZ9jx7LXDOSjR49Srlw5hg0bRq1atRg4cGBWd0mIF6YoCt+0Vm+iLj96i5M3HwFgMpmZsfsKNUdv5dL9MIuP99O6s8w/cJ0vVpx68UJ4eluoNlD983Fff+lcHm93e249iuT9jUEYuy4Aj6Ivdp7kNB+lzq6MDYeF3dUVKEIIISxmcbmz5s2b07x584zsS4bw8vLi6tWr6HQ6Jk+ezLRp02jRokVWd0sIISzyVk1vtp67x56LQQxbfJyJPSuy6cw9Dl97yPpTd2lVLnd8Wxc7Pflz2BMUFsPdkCiK5cqEfG3etaFwfbi8HXaOgXYTk29nl0OtxhkTClXeUoOcoCabj4uO/yKRUWoV9WBu/2r0nXmQQ1cf0nPqAeb0q0oOB5sMPa9VGJyg1zKYWA1Cb8O0JvD2FshVWt2/Ywxs+1ENAAvxitNoNHh7e2d1N9ItODiYTZs2UbBgQYKCgsiXLx9jx47F3t7KS16FyGTl87vSoWJeVhy7xQ9r/VgyqAaKArv87/MgPIb/rT7DnH5VLUpPM6xJcc7dC+Wz5iWtm87GGAfX9uBcuB6TelWmw1972HnhPn9u9efDxsXVNtGh6uetNWj10Hk2TG0ID6/Akt7w5mrQvQRjDiGEyAbStfQ7I5w6dYqvv/6acePGJbvfbDazatUqvv76a/744w9u306cB+3YsWNs3Lgxyc+5c+ryg549e8bn1rx582aSgkBCCJGdaTQKv3YuT74cdvSp4U0+V3veb1SUQXULU6e4R6K2iqIw6o2y/De8HnWK5cy8TjZ4PKvy+EIIvJh8G+c80Ohr9fF/30HoXTCZYN1wWNAZYiMzvJuVC+Zg4dvVcXOw4dStYLpO2ZeoMFG2ZusMQ/aDU26Ii4RpjSHgbOIgZb1PsrqXQmS4mJgY1qxZE//cZDJx586dRG0iIyP566+/0nXc4OBgJk6cyIgRI7h7926ybfz8/Bg1ahT/+9//2LVrV6J99+7dS3Y8unXrVgAaNGhAwYJqwbP79+9TvHhxCVKKV8YnzUtgq9dw6Kp6E1VRFL5tUxobrYZd/oFsOnPPouN4udiycnBNqhV2t17nYiJgch2Y0xZuH6NUbmd+bF8WgHFb/Nl+7h7s+h1+90l+xcLzsndTc00bnOH6Plg3TCqBCyGEhSwOVM6aNYvffkvI4fHvv//yzz//JGozfvx4vvzSshkdcXFx1KlTh+7du7Nhwwbmzp2bbLvu3bszePBgYmJi2LJlCz4+Ppw4kZCja82aNYwdOzbJz44dO5L0/8KFC3z00UeWvmUhhMgWcrvYsW1Efd6onA9FURhcvyiftyyFs60+SVtPJ9vMnyWY3xeKNwezEXaMTrmd7wDIU0ktDrPhU3WWwcklcGUXXM2cwhdl8rqwZFB1cjkbuHAvjC6T93HzYUSmnPuF2brA4P3g6AWxEfBXdQlSitdOSEgIb731VvzzBw8eULZs2URtwsPD+eabbyw+5u+//06pUqXYvHkzv/32G4GBgUnarFixgooVK3LhwgUePXpEixYtGDlyZPx+f3//ZMejkydPTnScy5cvM2DAgCRjaCFeZrld7BhUVy1kN2rDWaJijXh7ODCwbmEAfljrZ3F+6KdnUkbExKkP7N1Bl0YBV51BbfcsG3vwevx/xKYvwWzmjcr56FGtAGYzfLjkBBHXj6ljk2PzLOqjxXKWgE4zQNGox96f/Wo5CCFEdmTx0u/AwMBEd5hPnjzJ3bt36dy5c/y2mJgYoqOjLTqeoij89NNP1KlThw8//JDdu3cnabNx40YWL17MqVOnKFOmDAAtW7bkww8/ZNu2bQAWDURHjRqFv78/CxYsQKvVWtQ/IYTITvTahPtKD8Nj0Os0OBp0mM1momJN2Nkk/b/tyLUHbDh1ly9blcr4iuANvoALG+HUUqg9HHL5JG2j0UKbcWrVb7+VUKGHWiwm+BYUa5yx/XtKUU8n/hlUk57T93M1KIIuf+9j3oBqFM7pmGl9eG52rjB4H4wpDJgBBWq+n8WdEuLlVqdOHQYNGoS/vz+rVq1Ksj8uLo7BgwczfPhwRo1Sc8H6+vrSt29fevfuTcGCBalduzYbN25M9TyHDx9m6NChzJs3j0KFCmXIexEiqwyqV5hFh65z82EkM/dc5d36RRjSoCgrjt3i1qNIJm2/yPCmJSw+3oZTd/hm9Rl+bF+GpqXzw3tHIEItFhgbF8eePXuoVasW+vt+kLss2HuAa/7kD9boG/BbDdf2wNk14NOWb1r7cPpWMCdvBvNe4BtMbtsCfcXu1rgUiRVrAk1HwqYv4N8vwaN4po55hBDiZWRxoNLatFotderUSbXNypUrqVSpUnyQEqBPnz50796dhw8fkiNHjjTPM3ToUPbt28d3333H5s2byZEjR4rLv6OjoxMFWkNC1IpzsbGxxMZmXOGFJ8fOyHO8buSaZgy5rtaX3mt64MoDPvrnFHWLe9CnegG+XXOW/Dns+KVT4hlFgWHRdJ96gJg4E+XzOdO8dC6r9z0RDx+0JduiObca09YfMXaalUK7UmiqDkJ74C/M6z4ibuBuyOMLT95/bKSa20nz/B9PllzT3M56FvT3pc/MI1wODKfL5H3M6lOZEl6ZkNfzBWn2T0aL+XGY0oxpelOM/bdm+Hnl37/1ZdY1ld9Z6nx9fVPdv3//fu7evcubb74Zv61Tp04MGjSItWvXMmRI2sXBtm/fTteuXRk9ejTnz5/n/Pnz1KtXDzs7u2TbZ8WYVP6NZ4zX5brqFRjRpBgfLzvNhG3+tC+fCw9HA583L857i07w987LtC3vRUE3y1IeHLv+gPuh0czac4UGxd3BwUv9Qb2Wwfa3iPXwgdzlE16U0jW2z4Wm+mC0u3/D/O/XxBVqiFZnYFyXcnSYtJ+td+Dbq6X5rmzci16G5FV+G+3dM2hOzMe8tC9xff8Fj+QLAGaV1+XvaWaT62p9ck0zRmZc1/QcO8sClZY4f/48RYoUSbStSJEimM1mLl68mObAEiA0NBQ3N7f4HJhlypRJMVA5atQovvvuuyTb//3330zJI7R58+YMP8frRq5pxpDran2WXtOLIRAQqmXHmZvkirzGkes6jlx/RCHjDbyfibE1yKXhUQyEXjzC+lQKcluLEzVowBo059eyc+lEgu2TnzGkNVakod6dcKMjRzesJEqf4/H2KKpd/oMovRtHC76tLpV6AZZc037eMClCy62wGLr8vZd3ShkpmI1jlcXvrqTUneWczd2Rh/ZFqXFpDJq7J7k7oRmHCg/NlD7Iv3/ry+hrGhHxkqQ3yKbOnz8PkGhMajAYyJcvHxcuXLDoGDdv3qRixYosXrw4flvFihVTDFRm5ZhU/o1njNfhuurMkN9By41wI8NnbqNbERNmM5Rw0eAfbGb66h1U97QsT2MxI7QrqFAnZwDr169Pts3T11QfF0a5G7O54VabAJfySdpqjSVorHPF9tFVzs8dwaVcanHVrgUVppzTsODgTTQPrlHVIw73sPMEOiWzMuQFaGhITYdDuIdfIHpWe3YW/5ZYXfZbyfE6/D3NCnJdrU+uacbIyOuanvFotg5URkRE4OzsnGibi4sLoOYfssSMGTMsPt/nn3/O8OHD45+HhISQP39+mjZtmqQf1hQbG8vmzZtp0qQJen3SnHMi/eSaZgy5rtb3PNfU52wAtYq4Y2ej5fby0yw/dpttwe4s6ZK4qmYLsznjl3w/w7zqKMrpf6gbtwtjy1RmGdWrhqtTbho+1T/lyk60py6A3g6v6iXA/flmG6T3mrZoGkv/uUc5fiOYyRcMTO5VkWqF3J7r3BlJs+tXtMeWY6z7GUXrjADAtEuDdudo8gQfobVmO6bmYzLs/PLv3/oy65o+mY33qoiNjWX79u2AWgTn6edPtllTREQEBoMBG5vE+X9dXFwsHo/26tWLXr16WXzOrBiTyr/xjPG6XVevMg/pPu0QB+5r+LxTLUrldqJM9QiiYo3pXrXQPoXtyV1Tzfaf0D46QF7TTeK6fATapPm6lQJRsPYDSgeto0SXb8HBg5aAfstFJmy/zLprRr6MGIPtg3PE9dsMuSukq79pCq+JeWZTHINv0Dx0EcZui9VVJNnA6/b3NLPIdbU+uaYZIzOua3rGo+kKVC5evJjDhw8DcOPGDWJiYuKfP9nWoUOH9BwyVY6Ojjx69CjRtifPnZysP+XFYDBgMCRN1KzX6zPlH0Fmned1Itc0Y8h1tb70XNMW5fLGP/60RSk2nrnH8RvBbPC7T7sKeVN8XWSMkS3n7vHd6jP8r20ZWpXL/cL9TqLB53BmOZpL/6G5cxQKJD+DHfeCSbcVbwRvTAPXgui9Xnwmg6XX1F2vZ/6A6rw95zB7LwXRf85RJveuTP0Sni/cB6tSgAZfoq33CfEZSet/CrcOwaUtaM+sQNvkO7VCeAaSf//Wl9HX9FX7fYWEhNCgQYNE25597u5uvarBjo6OREdHExUVha2tbfz2R48eZch4FLJ2TCr/xjPG63JdaxT1pFW53By88oAHkXHo9XqKerm80DHNZjPbz9+nTjEPdE/l7E50Tet/AoHnUep+hN7WIfkDVeoFh6eiGJzRmyLh8WuHNS3JiVsh7PIPZE9IThrZ3kUfERC/32pc80D3RTC9KZqrO9Fs+QZa/Wrdc7yg1+XvaWaT62p9ck0zRkZe1/Qc1+JAZZkyZWjUqFH8c29v7yRtvL29LVqObSkfH59Ed8gBzp07h1arpVix7JXXQwghMpvJZGbV8VvULurBv373GL3hHE18cmFvk/i/9rDoOH7ecI6d/vd5EB5DaFQcny8/SbXCbng4plFFM73ci0DFnnDyHwi8kHKg8omoYNjyg1oZs+rbUKZj0v0GZ8jgmaEOBh0z+voyeP5Rtp4L4O05hxnfrSItymZAMPd5Nfg86TaNBjpOhcl1IeQmrH4POs/O8OslRFZxc3PjypUrabazZvFEHx/1xsn58+cpX15dUhoREcGNGzcoVaqU1c4jxKviu7alsdNrcTAk/ap5/m4oZ++E0L5iyjdWnzXin5MsO3qTL1qWZGDdIqw/dZevDmtRCtylbcXHBXT0dtB9QeoH0mih9yqwd0v0OanVKIzrVpHW43fxSXAPGpbKxS8lGpAhn6ReZeCNqbCoJxyaCp4lwXdARpxJCCFeWhYHKps3b07z5s0zsi9JdO7cmQkTJrB3715q1qyJ0Whk2rRpNGvWLEOXYgshxMtg/ek7/LT+HLZ6DZ5OBu4ERzF5x2WGNSmeqJ3JbGbTmTsEhMageTzqDo8x8tXK0/zdq7L1O9bwa2jwFThZUMDnzEp1oG7jBCVbgXOehH2PrsPsNlCuW/JBOiuz1Wv5u1dlhi05zrqTdxiy4Ci/dCrPG5XzZfi5X4iDO3SeBTNbgN8qODAZqr+T1b0SIkNoNJpkb5ZnJF9fX7y9vZk8eTJ//fUXALNnz8ZkMtGmTZtM7YsQL4OUboKevhVMu4l70GkUKhfMQX4LC+tULZSD1SduEWs0ExgWzVer/QiNha9W+VGzmGfy57t3BmxdweWZgKhD8rOt3RxsmNizEl0m72Pp2UhK7r7CgDqFLepfupVsBY2+hi3fw/pP1FQ3hetlzLmEEOIl9GKVCh4LDQ3FZDKl+3W//fYbI0aMYNeuXdy8eZMRI0YwYsQIYmJiAKhbty7vv/8+rVq1om/fvtSsWZOLFy/GF8YRQojXWcsyuald1IOoWBO2enX20E7/+5hMiRPVO9vq6VRZnXHwZJfRZGbj6busPXnb+h1z9LQsSAlQsTfkrQIxobDhk8T7Lm2Dh1fh5CKIypwcezY6DeO7VaRLlXyYzPDRPyeYuz8TKhG9qPy+0HSk+vjfL+HGwaztjxBZIDIykqioqHS/bseOHYwYMYI//vgDSBif7tmzB1CDozNmzGDOnDm0aNGCbt26MXz4cMaOHUvu3Nlo1rUQ2YzJZGbFsZv8c/gGAKXzOOPrnYPoOBMj1/lZfJwuVfLz3/B6DK5fhC9XnCIi2ggohEerN12TOLUUptSHVYMhpe+okQ9h05dwaWv8pooFcvBNa3UG9agN5zhzaDvsGW9xP9Ol9nAo2xnMRljyJgRdypjzCCHESyhdgcoNGzZw6NCh+OdBQUHUr18fZ2dnXF1dmT59erpO7u7ujpeXF927d2fEiBF4eXnh5eWVqPjD+PHj2bBhA1WqVGHo0KGcPXuWokWLpus86TVx4kR8fHysuoxdCCGsTaNR+LVzeVzs9Fx/EEHLMl4sfacmGk3ixUqBYdHM238tyRImBfhi+SkCw6IzrpPX9qUeNNNooM040Ojg7Bo491Rlz8p9oPVYeGtDhuddfJpWozC6Yzn61vQG4OuVp5m84yX4AlFtEPi0B1Mc/NMXwoOyukdCZIjo6Gh++OEHevXqxdKlS4mNjaV37944ODjg6OhI586dCQsLs/h4Dg4OeHl5UbZsWX755RdKly6Nl5cXDg4Jee4aNGiAv78/nTt3pl69ehw7dox33sn4mcsyJhUvs01n7jJs8Ql+WOvHg/AYFEXhu7Zl0GoUNp25x44L9y06jqIoFHR3YO3JO2w6cw+jWb3rajSncNM1d3lQtHB5u7pqIzl7xsG+CbDxCzDGxW/uVb0g7SrkIY/5LiXXdcC8+Ru4eeR53n5abwra/gl5KkHUI1jYXU13I4QQwvKl35GRkbz33nvs378/ftvXX3+Nn58fEydO5Pr16wwePJimTZuSP39+i47Zt29fi9pVr16d6tWrW9rVFzZkyBCGDBlCSEhIfJVxIYTIjrxcbPmpQ1mGLDjKxjN3OX7jIZULJlSsNpvNfLniFOExRszPvNYM8bMRMmQJ+OGZsPZD9QvDwB0p5030KgM13oM9Y2H9x1CoLhgc1X1V3krcNuKBmlsqg2k0Ct+28cHBoGXitkuM2nCO8Og4hjUpnumV1C325EvPvdMQdBGWvw09l6rBYCFeIe+//z7//PMP5cuXp0+fPnTt2pVTp07x119/ERkZyW+//caYMWP4/vvvLTpelSpVqFKlSprtcufOTb9+/V60++kiY1LxMmta2ouq3m7UK5EText15UcJLyf61vRm+u4rfLf6DBs+rINBl3ZO2cCwaL5ccQoFEo1nntx0rV7YPWEJuEcxaPI9bPgYNn8DhRtAzsRpcag1FI7Mgvtn4dgcqKL+21YUhVEdy9L+TggrHtTG01FHTac86atAaym9HXRbAFMbQOB5WDZALbajsV6OXSGEeBlZ/O1lx44d+Pj4kDNnTkD98rts2TJGjhzJ4MGDGT16NK1bt2b9+vVpHEkIIYQ1tSqXm44V82Iyw7DFJwgMi2bhweuYTGYu3AtTZx+Yng1Tqp7MRrhwL9T6HSvVRs0PlacSxEam3rbep+BaUC0Is+2n5Ntc2wfjKsDxNJLlW4miKHzcrCSfNC8BwPitF/lh7VnM5uSvZbZg6wxd5oDODi5tgV3Zq5qoENawcOFCduzYwfbt25k5cyYzZ85kzZo1vPPOOwwbNozp06ezbt26rO6mEK89rUZh8aDqDGlQND5FDcDQxsXwcDRwOTCcGbuvpnkcS2+6JuI7QA1QxkXBikFgjE283y4H1H+c/3rrj4lmM9rb6JjUqzLfKe/y5sMB/LL3kcXvOd2cc6vBSp0t+P8L/32bcecSQoiXhMWBysuXL1OwYMH45xcvXiQgIIBmzZrFb6tQoQK3bt2ybg+FEEKk6X/tSpPX1Y7rDyJo9NsOPl9+ihXHblE8lyPNSudCq0l+FqBWUWhexoviuZys3ykHDxh2GtqMBZs0Eubb2EOr39XHJxclv/zpwgaIDoYTi1LOOZUBBtcvyvftSgMwY88VPl9+KsXAb7aQqzS0VnPtse0nNdenEK+I4OBgFEWhXLlyANSqVQsXF5dEq3nKly8v41EhsomnVyE8+ex0ttXzRcuSAPy51Z87wanfzHyum64aDbT/C2xd4PZR2PVb0hdW6acWsokITLK/SE5Hfu5cEYDJOy6z6cxdyKgblXkrqX0F2PsnHJuftM2OMbBtVMacXwghshmLA5Wenp6cOXMm/vm2bdvIkydPouBlYGAgHh4e1u2hEEKINDnb6vm9S3kUBYIj1VkDP288R0SMkR87lMXBRptsjkoHg5aR7ctkXMcM6QiAFmsMLX+FwQfULxbPavydur/H4kxfzvxmDW9+6VQOjQKLDt3gw8XHiTVmXrA03Sp0h0pvAmZY1h+CJWgjXg2xsbHY2NjEPzcYDOh0iRdl6nQ64uLinn2pECIL7bkYSKvxu9h+PgCADhXz0qBETj5qWiLFKuFPPPdNV+c8CTdBd4yBW8/kmtTqEwrR7Z+kFvB7SsuyuelfuxCORHBvyXBC/xli0Xt9LmXegIK11cer34frCenW1CDlj7IkXAjx2rD4m17Dhg05cuQIH330EbNmzWLkyJF07NgxUZuDBw9So0YNq3dSCCFE2qoVdmdQ3SIAaBQICI1m0vZLeDga+LFD2WSXS/3UsSyudvqM79zd07B2eKKE9cmq+jY45kx+n6Ko+/V2CdtC71mvj2noXCU/E3pUQq9VWHPiNu/OO0pUrDHTzp9uLcaAV1mICIKlbyVd9ibESyo2Npbt27ezfft29uzZk+j5k21CiOxl27kAzt0N5cd1Z4kzmlAUhRl9felfuxB6bepfSRVFef6brmU7QemOanXt5YOSpqIp3gwK1QNjTLKpZz5rUZI2eUJ5U1mHk998om5bXq083fqsAY8Sal/ndoBH1xOClA2+hHqfZNy5hRAiG7E4UOnm5sb8+fNZuHAhb731Ft7e3nz7bUIOjYMHD2I2m6UqoRBCZKHhTYrjk9uZJ6ujJu+8xM2HEbQulzvRbAStRp19YNBpafT7Dm48iMi4TsXFwNz2cHi6uqzbUufWQfDNlPcfnQvjK2Tq0uaWZXMzpXcVDDoN/529R//ZhwiPzqYzt/R2ar5KgwvcOAD//S+reySEVYSEhNCgQQMaNGhA+/btEz1/sk0Ikb2836gYOez1+AeEsfDgdSDxsvDoOGOqKxXSuuma6qzMVr+BU24I8k/6Wago0OwnqNhLLcDzDL1Ww4d9ezBZ04U+MZ/y5e7YjMtVrdHAwG3g4AmxETC2nAQphRCvpXStnWvTpg23b98mMjKSnTt3JlrmXblyZXbv3m31DmaFiRMn4uPjI0FXIcRLx0anYWy3Ctjo1P/eY41mRm04l2g2AoCDjZbv25Zm8o5LXAuK4K/tFzOuUzobtbomwPaf1cBlWrb9BIt6wPoUBuZmM1zYqA7k/Tcn2qX4raTZqfdRzq56wY4nr0FJT2a9VRUHGy17Lgbx5oyD8cvtsx23wgl5r/ZNAL/VWdsfIV6Qm5sbV65cSfPn2LFjWd1Vq5AxqXhVuNjpGd5Erbz9++YLiT43d/sH0nzsLmbvvZrqMeJvuj4V4FQAX2+31E9u7wbtJqiPD/yd9AanVxloNxGcvJJ9eS5nW8r1HM0uc3mWHb3JokM3Uj/fi7BxgLe3Pn7yOCBa5o2MO58QQmRDz5Xky9bWNsk2rVabJEfQy2rIkCH4+flx6NChrO6KEEKkW/FcTnzeoiSeTgYUYN3JOxy88gAPRwM/dSxLTkcbRnUsh6ezLX90rcDg+kX4X9vSGdupKv3B0QuCr8OxOWm3L90BNDo4vw7Orkm6X1Gg0wxoMw6a/ZiwPew+2vXDMcQFo10/HMLuW+89PKVGEXfmDaiGs62OI9ce0mPqfoLCojPkXC+sVGuo8Z76eNUQCLqUtf0R4gVoNBq8vb3T/Hm6uM7LTMak4lXSvWoBink68jAilj+3+Mdvv/UogiuB4Yz9z5+AkKgUX//kpqu9QQuY0WsVvm7lQw57mxRfE69oY3UsAupnYeSjlNuGByXZVKOIOx83UwsAjV51hDMXr6Z9zud1YmHi53/XhjsnMu58QgiRzWRuNQIhhBCZok8Nb7aOqE/3agVo4pOLXM7qkqjW5fJw6KsmtCqXG4D8bvZ80rwkBl0GJ2i3sYe6I9THO39NmiPqWZ6lEmZhrv8EokKSttEZoHJfNWgJaiXwFe9ATLiawyo6DNYNt9IbSKpigRwsGlgDdwcbztwOoeuU/dxL5QtWlmr8P8hfHaJDYEmftK+/EEIIYWU6rYavWvsAMHvfVa4EhgPQuXJ+yud3JSw6jtEbzqV6DA9HAyPb+uCkh986laVfnULxq0jS1PQHcCsCIbfh0tak+8MCYGF3mFwHYpKmxHmn3v/Zu+vwKK4ugMO/WYkbISRACBCc4O7uTqHQUmgptNSoKy3Vr+5KjVLBC5TiVtzdCR40QEICcd/d748LJCG2STbZEM77PDzMzo6c3crOnLn3nGo8HXiJpYaXuTDzWaISrJghkl8Za1K+dCJ9GvhvPeCs1N8VQtwdJFEphBClkE6n4eZo4H8D6zH5oeZ4WTHawGKxsGBfaNFceIPqQu0ZALGXYffveW/f8RUoEwixl2DtB7lva7HAzGFwejWaRTW40SwmOLoIDs+3QfDZC6rowZwn2lDB04lT4XEM+3lb0db7LCi9EYb9AS4+EHYIlr1i74iEEELchTrVKkfn2uVUaZplRwF1zfK/gfXQNJi/L5RdZ6/leoy+DcrzQXMTfepnP1U7Rw6uMHQyjFkG9Ydkfd/RHa4cgphQVS7lNpqm8USPhlTUIqlvOsIbs7ZgNtuwXuXtjXPc/eDpXerayZQMUwfCsWW2O58QQpRQkqgUQohSTK/TmL79HO0+WcuOM5FsOx3Bwv2hbDsdiem2i+tPVhzj+b/3M+GfQ0VTKN7gmF4MftNXasRjbozO0P8rtbzzVwjdk/O2URdyaKqjwZLni2wKOED1cm7MebwNlb1dOH8tgWE/b+NUeB6fzR48KsK9UwAN9k2DfTPsHZEQQoi70Jv96qLXaawKDmPr6QgAGgV4cX8LVbLhrQWHSculsU5GFouFdcfDGW1tvWj/ZlClbfbvGZ3VDASAzV9DzOUsm7hVa8XFnpPpZ/qSZScTmLTOhjW+zaasjXOcvVSysmxNMKfB36Ng/0zbnVMIIUogSVQKIUQppmkaW05FEJecxohftjNi8g6em72fEZO30+qj1fy64TShUWoacP8GFXE26mkY4ElRNbSk0QjV4CUhAnb+kvf21btCg+GABRY/py7ib2exwMo3cjiApcingIOaQj/3iTbU9HXjSkwS9/2yjeBL2UxXt7dqndVNEMDSl+DKYbuGI4QQ4u5Tw9edUa0qA/D+kqO3Hpy+0qsOns5Gjl2JZcaO81Ydy2KBj5cdZcOJq0zbdjZ/gUScgv/eJtNFT/2hUKmFmm6dw2yOym2H8ebgZgB8tfoEm07a6GFol9ez7+5tdIantqlrKIsJFjwJW7OO+BRCiNJCEpVCCFGKhUYl8l9wGAC3j02IiEvho+XH6PTZOkKjEmlQyZPNr3Xhqc410Om0rAezBb0ROr+ulrd8m3sx+5t6fQQVGkG3d0CXTS3N8KNwbLG6eM/OzSng4UcLHLY1/Dyc+PvxNtT39yAyPoX7f93G3vPXi/ScBdLhJdVUIC0R5jyUff1PIYQQogg9370WHk4Gjl6OYd4e1UXb29WBl3vVBmDPOet+P3U6jee71+LR9oEMa56PJlrJcTClu7oW2fNn+npNg14fq+X9M+DS/mx3H9Y8gBEtA+igHeCDWWu4FFXEtZ/1Rhj0I7Qer16vmgir36PoniwLIYT9SKJSCCFKsYjYZNLyqJ+UZrYQEas6Vpd1c7y13my2kJJm3dSrfKk/FMrVgaRo2P5j3tu7lYPHNkDNHtm/71sX6gwALYeGQJoe6g5U2xUxb1cHZo5rTbMqZYhJSmPUbztuTWsrMXQ6uOdX8KgE107DoqflRkcIIUSxKuPqwHPdawHw+coTxCWnAfBAy8pMe6Ql397f2Opj9W1QgTf7B+Hn4WR9AI5uqhZ2YKes1xcBLaD+vYAFVr2Z42/k+x6LmOrwKeNT/+SpGXuL5popI50Oen0I3d5Wrzd/lfNsEyGEuINJojIbkyZNIigoiBYtWtg7FCGEKJQjVk4/vn278JgkRv+xk4+XF8EoRJ0eutyYqr3tR4iPzHsfLcMIz4RrmW8aNA36f61uOshmJKiDC/T7qlAh54eHk5Fpj7SkXY2yJKSYGPPHLtYeCyu281vFtSwM+xN0RgheCDt+tndEQohsyDWpKM0ebF2FQB9XYpJSb42g1Os0OtQsh6YV0cyOjFo9CQ8uAM9KWd/r/i4YnOD6OYjL/jfcUG8AFp0D1/VlOXDhGh8uDS7ScAF1zdPhJej/DaDB3r9g7sOQllz05xZCiGIiicpsjB8/nuDgYHbt2mXvUIQQolCuxVt34Xr7dkcux7DpZAR/77pAWEyS7QOrMwDKN4SUWNj6rfX77fkLvm2kkmsZuZVTyUqyGfUw4Dv1fjFycTAwZXQLutf1IznNzGNT97D0YNai/HYV0EKNzAA1YuTCTvvGI4TIQq5JRWnmYNDx9X2NWfNiJzrVyvo7fS0+he/XnMzS/C8n5yMTeH3+Qf7eZV19S3Q69eem62fTl70CYNR81cjGPYfu4hUaob14hID7v8KCjr+2nWPh/lDrzl1YzceoB456B1XeZsYwSI4tnnMLIUQRk0SlEEKUYt6ujnlvlM12XWr78lb/IBY93T5/U6mspdNB1zchsCPU6W/9ftEXITkGlr+mpo5nVG8I1BmA5cYUcMvNKd/1h9gwcOs5GfX8NKopAxtVJM1s4ZlZe5m7+4JdYslRy8eg3j2qk+jch60b3SqEEELYSOMALwK8XbKsTzOZGTRpM1/+d4LZViYe1x4LY9bOC3y35pTVXcMBMKWqKdTfN4fLB9PXV20Hxjyugdx86VrHj6e71ABgwj+HOBFWTAnDeoPhgTlgdIUzG+CvAfI7LoQoFSRRKYQQpVi9ih4F3u6R9oHU8HWzdUjpavaE0YshoKX1+3R4CbyrQ9wVWPN+5vduTgF3cFXjKh3d0qd8Xz8L/4yDlHgbBW8do16NFrm/RQBmC7wy7yB/bT1brDHkStNg4PdQtgbEhML8R6XWlRBCCLvYfyGKw6HqIaRBr2Nsu0AAPl95nOvxKXnuf3/LygxoVJFv7m+MQZ+P21ydARIiwZwK8x+D1NtmkpjSVMOdXJKAL7T24E/vP/FKDeeJ6Xtu1dwsctW7qGspZ2+4tA9+7wVRJeyhqBBC5JMkKoUQohTTW9m9O6/tzkTE2346U0HqTxmdbkzxBnb9Bhdumw7pVg5T369INnhi6vuVmvJtNsPM++DQHFj1VuHjzie9TuPjIQ14pL264Xpn0REmrTtV7HHkyNEdhk8FgzOcXgsbv7B3REIIIe4yc3ZdYPCkLbzx7yHMN6Z6P9i6CnXKuxOVkMrnq47neQwno57vRzShRVXv/J1c01TNR1dfuHoU1t72IPTfx9SIyw2f5HgI/ZJn6Zywig9dZhJyNZ7X5h3EUlyN6io1g7ErwMMfIk+qZOXVvL8vIYQoqSRRKYQQIldnIuLp990mXp57gEMXo/PeIb/iI1WNxOUTrNu+WidoNAKwwJLn1ZStDCxBg1nZ4HssdQepFTqdSm4GtFYjMu1A0zTe7FeXZ7vVBNTokM9WHCu+m5i8+NVLTwCv/1glLIUQQohi0qWOL+5OBmr5uZOYqkb2G/Q63htYD4BZO89z8GJU0QXg6qNmGABsmwRnN6e/1/Qh9feuKXD1RPb7d3sHKrWkQr/XMeg0lh66zB9bzhZdvLcrVxvGroSyNdUMid97Q+ie4ju/EELYkCQqhRCiFCvj6oCjIff/1Rv1GmVcHXJ8v2pZFzrWLEfTymUo65bzdgUWcQK2fg+7Jls/Xannh2qaU9hh2P5j3ttXaatGG3j6Fy7WQtA0jRd71OL1PnUA+HH9ad5bHHxr5IjdNR4BTUcDFvjnUYgupoYAQggh7nrl3B3Z+EoXvhjWCFdHw631raqVZXDjilgs8PbCI1b9Zialmvh98xke/mNn/h4I1u59IylpgX+fhKQYtb5aZ6jVBywm9WA1O+XrwyOrqNusE2/2qwvAR8uOsvvsNevPX1heAepap2ITSLwGfw6A0+uK7/xCCGEjkqgUQohSzN/LmbUvd2bJM+0z/Zn3RBvcbtwIpJksJKbkXEtJ0zS+HN6ImeNaU9HL2fZBVmkDrcfDiNngWcm6fVzLQs8PQNNlbaqTk4xTzS/uydzdsxg93qk67w+uD8CfW8/y6j8Hre5oWuT6fAblG6haXfPGZBmtKoQQQhSVnB6avt63Lq4OevZfiGLungvsOHONPREaO85cy/b3MynVxJerjrP++FXWHQ/PXxC9PgKvKhB9Hla8nr6+5/uqluXJlTnPOrhxnTG6bVUGNfQlzWxh/My9RMQl5y+GwnD1UTUrAztCajzMHA7BC4vv/EIIYQOSqBRCiFLO38uZ+v6emf40r+rNmHZVAbAAHy87musxXB0NmepYxtu6SHzvj6Bmj/zVrWz8ADy1Hbq9nb9zHVsGf/SG2SOLvbnOTQ+2rsJXwxuh02Denos8O2sfKWn56FBaVIxOql6loydc2AGr37V3REIIIe4yZyPieXrmXi5HJwLg5+HEiz1r07GWD1/9d4JRv+9m6kk9o37fTftP17Li8OVM+3u5OPBiz9p8PKQB7Wr45O/kju5wz8+ABvunw7Glar1PTWjxqFpe+WbOjefMZrQ9f/D15Qdp5xNPWEwyz8zcl78u5IXl6A4j50HdAWBKgbkPq2ZAQghxh5BEZTYmTZpEUFAQLVq0sHcoQghRZB5qUxWjXiUG1xy7ytbTEXnuYzZbmLwxhI6frePCtYSiCSw10brtNE3VZMqvCo3AyRPKVAWL/ZKDQ5pW4seRTTHqVS2rJ6bvISm1BHTc9q4Gg29Mp9/2AwQvsm88QtzF5JpU3I0mLjjEkoOX+XxFekMYfy8nNp2IICwm8+jEK9FJPDl9b5Zk5SPtAxnRsjKOBn3+A6jSFto9q5YXPQtxV9Vyp9fAyQvCj8C+adnvq2lweD662MtMqrYDFwc920Ii+eq/HGpbFhWDIwz7S5V0sZhVM6BNX0FJqY0thBC5kERlNsaPH09wcDC7du3Ke2MhhLhDlXN35N5m6VOtP1x6NM/aT2aLhRVHrhAZn8LcPRdtG5DFojpOf1kHrhzO377hx+DvUZAYlfe2nv7w6BoYPk2NOrCj3vUrMPmh5jgadKw9Fs6YP3YRZ+vRqgVRtz+0fUYtLxwPkaftG48Qdym5JhV3owm966JpMH9fKPsvRGEyW3hvcTDZXaHcXPfe4mDbllHpMhF860FCBCx+Vl2juHhD5wlQuQ1UaJz9fpoGfb+A3p/gNehTPh3aEFB1qf8LDrNdfNbQ6WHAt9D+RfV6zXuqxqa5BMzgEEKIXEiiUggh7mKPdqjGzcnWRy7FsGB/7g1UDHod39zXmI+HNOCF7jVtG4ymQdgRSIqCdR9Zv5/FAv88AkcXw+p30M5txv/aNrRzm3OemlWmiuoGflNCMRa7v03n2r5MHdsSN0cD20IieXDKDqITSkBtyG7vqJux5BiYM9r6ka5CCCFEITSo5MmQJupB6vtLgtkREsnl6KQct7cAl6OT2Hkm62/5llMRDP9lW/6b2hgcYcgvoDPC8WWwf4Za3/IxGLMcKjbOeV/fOtD6SdAbGNCo4q1SOy/O2c/5yCKajZITTYPu76i63qBmSiwcD6YS8FBUCCFyIIlKIYS4i1Uv50b3ID/cnVRjnc9XHicxJffpxwHeLoxoWRktP/UkrdX5ddUg5/hSCN1j3T6apprAAOz5E8P0wTQ/9xOG6YPhm/q5T102m2DN+/B9M7s11wHV1XTGo63wdDay73wU90/eXrzF97OjN8K9v4OLD4QdQr9ygn3jEUIIcdd4tXdtnI169py7zsrgK1btEx6bNZm5+MAldp65xs8bCjAzoHwD6DpRLa95H9KS1SjFjNc/eU2ltlh4o6mJppW9iE1Ks1+Zl7bPwOCfQNPDgZkw50F5ACmEKLEkUSmEEHe5j4c0YOuErvh7OePuZOBKTM6jFm6XajIzad0pohJSbBNMuVrQ8H61vPZD6/dLiMx+fcxlmPNQzslKcxqErIPEa+kF8+2kUYAXfz/eGh83R45ejmH4L9tuNRKwG4+KcO8UQEN3YAaVIzfaNx4hhBB3BT8PJ57sXB2ApQcv57G14uvulGXdk52rM6ZdVd4fXL9ggbR9Flo9CWNXqFGWNyVFq2nUC57Med+kaPijD8bfu/NzP2/KujoQfDmGtxfms7yNrTR+AO6bDnpHNUp0+lAVoxBClDCSqBRCiLucj5sj7k5GZjzaimXPdiDQx9XqfV+ee4DPVx5nwj+HsNiqQHunV0FngNNr4NzWvLc3m2DFazm8eSOmFROynwZucFS1KodOgTbjCxyyrdQp78Gcx1tT0dOJkKvxDPt5W/FPE7tdtc6qVhfQ8MJfanq+EEIIUcTGdahGBU8nIuJScHc0kNM8Dg2o4OlEy0DvLO9VKevKOwPqUcHTuWBB6PTQ5xPwDsy8Puo8bP0BDsyC8zuy39fRAwxOoNPjG3+C70Y0QafBnN0X+XvX+YLFU1h1+sKD81Vs57bAn/0gLtw+sQghRA4kUSmEEAIA/zLOLD10mYQU6+sWPdq+Gj5ujgxqXNF2U8G9A6HJg2p57Qd5T6s6txViLuWygQViQnNOenr6Q4N7CxRqUahWzo05T7ShalkXLl5P5N6ft3IyLNa+QXV4CXP17ugtqRjmj4GkGPvGI4QQotRzdtDzWu86AKSYzFggx2TlOwOC0Ovyvg4p9EPVkA0QFqymhTcZpdatfD37BjWapprZjN8JQYNoV8OHl3rWBuCthUc4HGqn0YxV28PDS8C1HFw5BL/3gqhz9olFCCGyIYlKIYQQAIz+fSfPzd7PzB3n+XnDaU6F550ca1DJk82vdaFPgwq2DabjK2pq0rktELI+923jrOyiac12KQkw/3E4vda6YxaRSmVcmPN4G2r5uREem8x9v2633w0NgE6HaeCPJBi90a6FwKKn804gCyGEEIU0sFFFGgd4kZxmpl31spT3zDy9W6/BpAea0rt+7tchl6MTmfjvISb8c6jgweybDlMHwvzHIC0Fur4FRldVU/vwP9nvU6YKeAXcevlkp+p0q+NLSpqZJ2fssV/zvAqNYOxK8KoM10Iw/NUX98SL9olFCCFuI4lKIYQQAPRpUAEfNwdWHrnCJ8uP8cnyY1bt52TU31pOSEmzTZF4T39oPlYt5zWq0s3PumNas93W7+HgbJWsTLHvlGtfDyf+fqwNDSt5ci0+hRGTt7PnnP26k+Pize7AZ7DojBC8EHb8bL9YhBBC3BV0Oo23+gcBsDUkkl8ebMb0sc15oLoJF6MOkwWcHfV5HAUiYlOYseM8c/dc4FJUAes/1+ihGsxVaq5qXLv7QYcX1Hur3827OU3ESXQHZ/PV8MYEeDtz4VoiL87Zj9lspwd/ZavD2FXgG4QWF0b7kx+gXdxpn1iEECIDSVQKIYQAYHjzSmx+rSufDG2IQaex+mg4W09HWL3/kUvRDPh+M5+usC7Bmaf2L4DRBUJ3w4mVOW9Xpa1q+pJb9SoPf7VdXto9B7V6w7A/wMGlIFHbVBlXB2Y82ooWVcsQm5TGfb9sZ/PJrP9Mvltzkq//O1Hk8Vx3rY65+//Ui1VvwgW5oRFCCFG0mlUpQ7e6vlgs8Mb8Q7g66KnoYmFkq8o8160GHk4GQvNIPjao5MmzXWswc1xrKnhmbbpjFXc/NY17wDfp1whtngaPShBzEbb9kPO+V4/Dj21g0TN4Jpzlp5HNcDDoWHMsnJ8K0pHcVjwqwMNLMfu3wMGUgH7GUDj5n/3iEUIIJFEphBDiBkeDHiejnurl3BjZqjIAHy07avWT/vCYZE5fjWf5oStEJ9pgKpO7H7R8TC2v+yD7+k+gCt33/vTGi9uTlRpgudGgJ+8RFxid4IG/Vf2mEsLdychfY1tSxduFNLOF0b/vZHVw+jT279ac5Kv/TlhVm8sWzM0fhXr3qNEkcx+G+Bw6rgshhBA2EBqVyKYT6iHd4Usx3PPzDr44ZGDy5rN8u+YUQ3/aRtcv1ueZrHyxZ21aVytbuJrarmXTl81m0HTQ/V31esevkJac/X4+tVRzumqdQe9AfX9P3h9UD4AvVx1nyynrHwzbnIs3pgfmEebeEC0tEWbdD4fm2S8eIcRdTxKV2Zg0aRJBQUG0aNHC3qEIIUSxM5ktNKjkiYtRz+HQGBbsD7Vqvy51fPn83oYsf64Dns5G2wTT7jlwcFfF3o8uynm7oIEwfKoaGZCR8UaXz0Pzsu/6nZe48JzrThUjFwcDq17sSI1yrpgsFh6btptFBy7dSlK+2KMWz3arWTzBaBoM/B7K1lRNiuY/WrDvVgiRJ7kmFQKux6eQYsrhYeUNyWlmrsenWH3MQjfVib4I0wbD8ldVQ74uE+HxjWBwzH57TYPhf8HIuapuJXBfi8oMb14JswWenbWPK9FJhYupMBxc2VHtecz1hqgHkf88qhKvQghhB5KozMb48eMJDg5m165d9g5FCCGK3TerT/Dy3INU8FJToz5fedzqupPDmgdQxtXBdsG4eEOb8Wp5z5+5bxs0EJ4/TNqoBeyu8iRpoxbAuPWq0P3ZTbDxi/ydO+4q/NJJXazbubkOqBGvK57vSJ3y7rduaoo9SXkrGHeVGDY4q+8mv9+tEMIqck0qhPVSTHlfq5jMFv7ccoZuX27gamwOox+tcS0EzmxQ1yYn/1MzN25/WHo7B1eVsMzgf4PqE1TBg8j4FJ6asYeUtNwTskXJojNgGvTzjdksFlj+Cqz7WJrnCSGKnSQqhRBCZDKkaSU0DU5fjcfX3ZHL0UlM2Xwm38fZejqCWTvPFz6gNk9B3y9gxOy8t9XpsVRpT6h3GyxV2oNvbej/tXpvwydwdrP153X1gRpdwbu6qj9VAhj0OpY924GMs7wf61jNPsH4BaV/t+s/LhHJXCGEEHevLSfzLkWi0+Df/ZcIiYhnxo5zBT9ZYEdo/ZRaXvR05jIol/blntxLTYINn8G6j3Ey6vl5VDPcnQzsPR/Fx8uPFjwmW9B00Ocz6Py6er3hE1j2Ss7ld4QQoghIolIIIUQmgT6u9Aoqf2u5R5AfveuXz9cxDl6MYuRvO3h74WEOh0YXLiAnT2g5TtWPLIhG90HjkWAxq9GR1tZU1DTo+yWMWwPlahXs3EXgh3WnyFg2dOyfdhxp1XgENB0NWNR3G21dmQAhhBDC1pYdupznNpqm8Wqv2nwwuD5PdKpeuBN2ext8akNcGCx9QSXz5o2FXzvD8eU573duC6z7EDZ9AdfOULmsC18NbwzAH1vOsvjApSy7FFfTPEBd/3SeoB4So8GuyTB/HKRZP7VeCCEKQxKVQgghshh3Y5TevvNRfDi4PtXLueVr/wb+nvSuV54hTSoR6ONqu8BMaRBZgO6YfT9XNRVjL8OCJ62fxmR0UonSm6Iv2nUKVMaalN+NaALA1tOR/G/xEbvFRJ/PoHxDSIiEeWPAZINGSkIIIUQ+Hb0Sy5FLeT8cbVfDh1Gtq+BktKLJXm6MzjDkV9AZIHghHJoLXqoZIavezDmxV6MbNHsY7vkFylQFoEeQHy2rlgHgxTn7ORUee2vz4m6ad0vLcTD0N/X5Ds+D2SMgJb54YxBC3JUkUSmEECKLZlXK0LxKGVJMZv7cevbWepOVHcA1TeO7EU349N6GuDoabBNUxEn4sTX8NTDnrpo5cXCFYX+C3hHOby9YsvPYUpjUGrZ+l/99beD2xjkDGlagXQ3VffT3LWf5dnUxjbS4ndFJNQhw9IQLO2D1u/aJQwghxF1v+vb8l5wxW3ltk62KjaHTBLW87BVoeD+4loNrp2H3lJz3G/CtasKToWblzHGtCSjjTKrJwvBfthOfnGafpnkZNbgXRvytalKfWg1TB0PCteKPQwhxV5FEpRBCiGzdHFU5ffs5zkTE8dq8gzw5fY/V+xv1mX9iwmIK2c3SM0A9yU+Nh/AC1HAqXx/unQJPbAKfGvnfP+YSpMSqC3U7dLk2mS2ZblQ0TeN/g+pj1KubnJPhccUe0y3e1WDwj2p52w8QnEuHdiGEEKKILNgXSkySdSP795y7xohft/PrppDCnbT9C+DfHJKjVQOaLm+o9es/sS6pZ0qFlAQMeh3zn2qHq6Oea/EpNHh3pX2TlDfV7A6jF4GTF1zcCX/0VddEQghRRCRRKYQQIls96voR6ONKTFIac/dcZN7ei6wKDmPr6Yh8HScp1cTr8w/S/csNXLiWUPCAjE5w/wx47qAawVAQdQdAmSoF27fFozB0CoyaD7pCThcrgBeyuVGpXs6NxzuqGlt7z10nISWt2OO6pW5/aPuMWl44vmCjVoUQQojblHF1wNGQ+22ro0FHoI8Liakm5u+5aNVxQ67Gsy0kkr+2nrV6xki29AY1jdvgDGc2qmY5vvUgKUo1zcnN+R3wc3tY+wEA5dwdmTq2JQBmCxh0mn2TlDcFtIQxy8GtPFw9Cr/3kt95IUSRkUSlEEKIbOl0Go92CARg4b5LjGgZAMCHS4/ma5qUQadx/EoscSlpbDqZvyRnFv5NwcmjcMe46eRqWPqy9TUnNU1NgdIb09fZsV7lTeO71MDfy5lL0Ul8t+aUfYPp9g5UbgPJMTDnIUhNtG88Qggh7nj+Xs6sfbkzS55pz5Jn2rPgyda83CCN13rWxNVBT5tqZVn7cmce61idPvXL0zDAy6rjDm7iz/gu1Zn3ZNvC13/0qQE931fLa96D1k+o5V2TVemanCTHwtVjcPgfSFYzI7acSm/6l2a28OWq44WLzVb8guCRlWoWRdR5lay8fMDeUQkhSiFJVAohhMjR0KaVKOvqQGhUInXLe+DuaODIpRgW7Le+u7NBr+Pb+5swbWwrHmhV2TaBWSyqo2ZybN7bZic6FGbdr24g9k0r2Pm3/Qj/Pm73ZKWzg573BtYD4LdNIZwMK+B3Ygt6I9z7h6rPFXZY1esSQgghCsnfy5n6/p7U9/ekXkUPAtygU+1yxKeY2H4mkjSTmREtK/PTqGY0rVzGqmMa9Tpe6VUHfy9n2wTZ4lGo3g3SkmDXFKjZE9wrqK7gOanZHfp/A+O3g6PbrZqUz3StcSuu79ee4rs1uSQ7i1OZqjB2JZRvAPFX4c/+cHaLvaMSQpQykqgUQgiRIyejnofaVAVg1q7zPNlZTTP+fOVxklKtr9MY4O1C+5o+tgts/jiVaNz+c8H29/SHrhPV8rJX81/zMuIk/PcWHPwbTqwoWAw21D3Ij+51/UgzW3hr4WEs9kyeelRQXUI1nUoC75tuv1iEEEKUWjV93ehcuxwWC/yx5Wyhj1eo6d+gZl4M+kHVcry8Hyo0gqd3QdX2ue/XfAw4l8nUOOelnrV5f3C9W4f96r8TJSdZ6eYLDy+FKu3UDIrpQ+DYMntHJYQoRSRRKYQQIlcPtqmCk1HH4dAYgip64O/lzOXoJKZsPlOg40UlpDDx30NEJaQUPKhavdXfW7+HxOsFO0bb56B6V0hLhLljICUf9TPL1YI+n0Gvj9NjsbN3BgThZNSxPeQaC/fbuch9tc7Q+UYzgaUvwZVDdg1HCCFE6fRoe9X4b87uC0QnqCY6ZyLi+WBJMFeirWviF5WQwtsLD9Pvu02kmcyFC8ijIvT7EhreB22eBqP1ozVNZguftkrh2bbl4PB8ui5uz8Sqx7BYoLyHU+FjsyUnTxj1D9Tuq0aQ/j0K9s+0d1RCiFJCEpVCCCFy5e3qwLBmATjodZyNiOeVXrUBmLXzfIEumsfP3MuMHed5c8HhggdVbwj4BqkOm1t/KNgxdDpV/N7NTxWGX/l6/vZv8Qi0eUoNdSgBArxdeKarKrj/wdKjRCda1/W0yHR4CWr0UDcwcx6CpGj7xiOEEKLUaVejLHXKu5OQYmLmzvMAvPbPQX7bfIZZN17nxcGgY/GBSxy7EsvGk1cLH1SDe2HIr+DspV6bTbDnTzg0L9fdXnBYwH0HHob/3oYlz0N8OI9c/5bKjvFciUminIdT4WOzJaMzDJ8GjR4AiwkWPFnwazIhhMhAEpXZmDRpEkFBQbRo0cLeoQghRInwTLcabH6tCw+3C2Rgo4q82a8uS5/pgEGf/5+RCb3rUssvvVt1geh00OXG1O3tP0F8AZv0uPmqZCWauok4PL9gxzGlwuZvICW+YPvbyKMdAqlWzpWIuGS+snfxfZ1O3ah5VIJrIbDwabvX8xTiTiPXpELkTtM0Hu2gRlX+ufUMKWlmHmxdBVAPVFOteKDq4mDg3YH1mDmuFV1q+9o2QItFdfRe/ByseD332toBqts3p9feaqyjS4ljmt9sAD5bfozwGOtGiRYbvQEGTVKjRwFWTYTV78nvvRCiUCRRmY3x48cTHBzMrl277B2KEEKUCL7uTvjeeJKvuoFXw9PFmMde2WtQyZMVz3WkQSXPwgVVpx9UaAyp8bD564Ifp3oX6PCiWr6wo2DHmP8YrH4HFj1T8DhswNGg5/1B9QGYtv0ch0PtPIrRxRuG/wU6IxxdBDsKWFNUiLuUXJMKkbeBjSri6+5IWEwySw5eole98vi4ORIem8yqI7k0sslgUGN/2lb3QbPlLAlTGsx+ADZ/Be7lIT489+uVap2h10cQfUGNUASwmKgSvoanyh0kNjmN/y0Jtl18tqLTQc8PoPu76vXmr1Ri1mx9LXMhhMhIEpVCCCHy5XBoNFdjkwGwWCzsOXct38fQ6dJvBK5EJ+WrMc8tmgZd31LLu36DmMv5P8ZNnd+AB+ZAn08Ltn/Lx8C5jJqSXlSiLsCl/Tn/iboAQLsaPgxsVBGzBSYuOIy5sM0BCqtSc3XjBbDqTbiw077xCCGEKFUcDDpGt60KwORNZzDqNUa0DABg2vaz+T5eqslsm6Z0egN4+IPeAWr3U+u2/gBROUxJj7sKGz4Fbk+WaryU/CO+uhiWHLzMuuPhhY/N1jQN2r8AA75VzfT2/gVzH4a0ZHtHJoS4A0miUgghhNU+WnaU/t9v5rfNIZjMFkb+toOhP21j6+mCTb1eeyyM3t9u5NMVxwoWUI1uENBa1UHc9GXBjgHqZqJWr/TX+b1BqdIGnj8EdfsXPIbcRF2AH5rBr51y/vNDs1vJyjf71cXN0cCBC1HM3nWhaGLKj5bjoN49YE5TNy4FnaovhBBCZGNkq8o4G/UcvRzDttORjGhZGZ0G20OucTIsl+nWt5mz6wKdP1/PxpM2+p3q8T94bINqsFO1A5iS1dTo21kssOSFG1O+b78GsaBPjecvXzUF/K0Fh0lMKaGjFZs9DMP+VMnZo4tgxrDcp7sLIUQ2JFEphBDCai2reqPXacQmpaHXadTwdQNUArMgI/c0NKISUtlz7nohRlW+qZb3/JnzKIX8iI+AmcPhxKr87efonr6cFHMraWgTCZF5j0pIS1bbAb4eTrzYoxYAn644RmScnUc0aBoM/B7K1oSYUJg/TqaECSGEsBkvFweGNa8EwORNIVT0cqZ7XT8Apm8/Z/VxjofFEhqVyIx87JMrBxfwC1K/g70+BDQ4PA8u3FbOIfwoHFucPuX7dhYTdaPW0849nIvXE/l2zUnbxFcUggbByHng4AZnNsBfAyA+0t5RCSHuIJKoFEIIYbWudXzZ8EpnPrqnAQDPdauJm6OBw6ExLNgfmu/jdanjy+SHmjPvibY4GfUFCyqwAwR2AnMqbPisYMfIaPtPcHIVLHgCYi7lf//I0/BbN5h5n+2a61jy3139oTZVqFvBg+jE1IKPWLUlR3cYPhUMzqpRwMbP7R2REEKIUmRsu0A0DdYdv8qp8FgealMVT2cjni4OVh/jsY7VeG9gPb4b0cT2AZrTwNVHLa+amHn2hm9dqDMAtByuhTQ91B3ImHv6AvDbphCOXYmxfYy2Uq0TjF4MLmXh0j74vZdtH+AKIUo1SVQKIYSwmk6nUamMy63XZd0ceaqL6t79+crjBRoV2SPIDwdDIX+OWj6m/t43A44vxTPhLFw+kKV+o1U6vQrlG6rRifMfy//IP6MzJEZB4nWIvpj1fYtFTe1KyFDbMy1F1a1a/Z7qIH7TyonwaVWY3CV/MQAGvY4PBqvGOnN2X2T32fzXErU5vyAY8I1aXv+JSlgKIYQQNlDVx5Uet0ZRnqdt9bJsf73brRkG1vDzcGJ026oFf3iaE4sFlr8G8VfB2Rt6fqRGWd6kadD/a3B0I7salTi6Q7+v6B7kR+965UkzW3hj/iH716HOjX9TGLMCPCpB5EmVrLx63N5RCSHuAJKoFEIIUSCnr8ax9/x1xrYLxN/LmcvRSUzZfKbAx7NYLPy96zx/bT2bvx2jLsA/Y2+8MGOcN5rOx9/G+Hu3bOs35sngqOorObjB2U3WjfxLvA7hx+DsZri4C5qMhIbDYffv8M+jMHUw/NwBvgqCD8vDx/6w9MX0/XV6+O8t1SkzYwLTnKaOXUDNqpThvuaqocCbCw6TZsr/yEyba3Q/NB0NWNR3E53/kbhCCCFEdp7uWoOPhzRgQp866HQazg6FSzgWqCxNdjQNBk0CgxMkXoMr+7Nu41ZOJSuzqVF5q6M28M7AIFwd9Ow9H8WsXTYoeVOUytWCR1aCTy1V+uWXjrD4+ey33fAZrPu4WMMTQpRMkqgUQgiRb0sOXqLblxuY+O9hHA06XulVG4Cf1p8mooD1EDedjOC1fw7xwdJgToXno/B6Pus35iklHvRGaPuser3+E5WABEhJgNkjYUovNQrypuWvwY+t4M9+MOch2Pw1bPkGdvwMh+ZCyDq4clBdpKclqX2SotP31+mhyYPQ6gm1fFObp+GpHfDgAutiz8Zrferg5WLk2JVY/sxvErio9PksfdTq3IczjyIVQgghCqhhJS9GtKycaUSkxWJh66kIjl+x/triRFgsD07ZwdMz99kuuHK10xOOq95SpWJuLxFTb0jmKeCaHqq0g3Ufwr+PgdlMBU9nXr5x3fXp8mOExybZLsai4FlJjays2FRdA+35A/59IvM2Gz5Tn1Fn45GsQog7ksHeAQghhLjztK/hg4uD6q655VQkAxtVZMrmMySlmgiPScbHzTHfx+xQ04cBjSpSr6IH1XzciiBq4Px2CA9WRd0TIiEhQv0dH6FGMiZEQlribTvdGPn3xBZwLgPHl6makQmR4FFBbeLiA05eqvaUS1n12sVbvU6KgWNLoctEKF8v/X0H18ynGfhd1ni91GjIW8nNAvB2dWBC7zpMmH+Ir/87Qf+GFSnv6VTg49mE0QmG/wW/dIaLO+G/d6D3R/aNSQghRKliNlswWyx8+d8Jflp/mkGNK/Lt/dbVntRpGptPRWDQaYTFJOHnYaPfzZaPq+uIMxvhjz7qQd1T28FdTVm/NQX87Eb1QNPRHbq8CdOHQGyYuvZwK8dDbaoyf28oh0Kj+WDJ0aKpqWlLrmVh9CL4exSErIcDs9RD5GF/pCcpu0xU5XeEEHc9SVQKIYTINy8XB4Y3D+DPrWf5ZeNp2tf0YfJDzfFxc8CgL9hgfU3T+O7+xmja7bWZbGj3H3Bwdt7b6R1VktHZS01NNjipulKuZVX3age3zF2+e32YfaLNYlE3InFX1DTyZg/Z7KNkK/X2JKsyvHkAf+++wL7zUXywNJgfHmhatHFYw7sa3PMTzH4Atk+Cyq1Up1AhhBCikBYfuMTX/53gsY7V6Fu/Aj+tP82yQ5d5q3+QVQ9Ta/i68fE9DWhXw8d2SUoAnQ4G/Qg/tYG4MLVu3YeZH1a6lYP+36jZGn0/g6pt4cF/oWJjVQcb0Os0Ph7SgIE/bGbRgUsMbVaJTrXK2S7OouDoDg/MgfnjIHghHJkPRxepMjeSpBRCZCBTv4UQQhTII+0D0WlqyvbRyzGU93QqcJLypoxJSrPZwsmwfEwBt4Z/U6jdV02zbvc89PwABv8MD8yFcWvhuQPweii8GQYvBsOTW9X6xzeCbx11jCajoN7gGwXvbwWe0weCe39XzX4GfFvwuF3KqtqZudH04FEx27d0Oo33B9VHp8GSg5fZdPJqwWOxpTr90qfYL3xaTYMr7dZ9nHN3eqnPJYQQNhEWk0RIRDxzdl+gQSVPGgV4kWqy8Pcu65vr3d+yMgHeLnlvmF9eAdD3i/TXe6fClcOZt6k/BF45CfXuUa+rtLmVpLy1ib8nD7cNBOCtBYdtV0+zKBkc4d4/oNkY9dqcBjqDJCmFEJlIolIIIUSBBHi70LeBmvo8eWPIrfVJqSZ+2XCaHSFW1oTMRlRCCqOm7GDIj1u5cC2h0LHe0upxGDELBv0APd6Dts9A4xFQqyf4N4MyVVUCMmPisWx1cPJIf53feooeFaHv5+BQiJsdrwB4eg88tiH7P6OXqCRrmSo5HqK+vycPtakKwNsLj5CcVkJuaLq9DZXbQHKMqu+Zw6jQUkOnV6Nnbk9WSn0uIYSwmftaBPDOgCCmPtIKgAdbq9/HmTvOYypAp+yElDSbxkfD+6DuwBsvbnQEt1gZ18G5MP9xsFh4sWctKng6cf5aAt+vPWnbGIuKTg/uFdJfm9PUw0ohhLhBEpVCCCEK7LGO1QBYdOASl6JUgun7tSf5ePkx3l8ajLkANwMAbo4GklJNpJkt+Sp+X6QsFtjxC/zcPnMjnPw6OBdOrcn/fl4BatpXdn8CO6TXs0xNhJAN2R7ixZ61KOfuyJmIeH7dEJLtNsVOb1SjK1zLQdhhWPayvSMqWp1eVVPc1n2IbpMaUaPb9IXU5xJCCBtydzIypl0gbo6q0ln/hhXwcjESGpXIumPhVh8nzWTmvcVHaPXhGi5et+GDU01T07tdyqrX5zbDiRV57xd1HhY+pcrYHPkXN0cD7w2sB8AvG0I4YeuZKEVhw2ew/iPo/DrU6a/W7ZsGS0v5778QwmqSqBRCCFFgDSt50bqaN2lmy62O0mPbBeLuaOBwaAwLD4QW6LgGvY7vRjRhybPt6R7kZ8OICyElDrb+AFePweLnrR/5kNGRf2H+ozBvLERftHmIJEWrzuPTh8LZLVne9nAy8ma/ugD8sO6UbUerFoZHBRj6G2g62Ddd/bnTmVJV06ZrZ+DyQfXP4/hyODhHNVqq1hn9xk8YuG80+o2fSJJSCCGKiMWimurc11w90Ju6/ZzV+xr0Ok6ExRKbnMaiA5dsG5hrWRj8U/rrpS/mPWvDqzL0+gg6TbhV17lnvfL0CPIjzWzhjfmHCvyQuFhkbJzTeYL67Q9ord7bNRlWTrRvfEKIEkGa6QghhCiUxzpWY3vINWbuOM/TXWtQ1s2Rp7rU4NMVx/h8xXH61K+AkzH/01krlbFyqvTN+o1pyTlvY3BMH7VQUI7ucO8U+L23KgBfrTM0G52/Y9TqA/7N1b4Zpz3ZioM7eAbAtRAg+xuVgY0qMnvnBbaFRPLOoiNMGd28aBsYWataZ6jSTjUdWvoSVGgE5Rukv7/hMzCboMvrRRuHKRWSY9VU9ORY9ScpJsO6DOtvvReTdR8rO7VrWLDoDGiSpBRCCJvbERLJe4uDqVPenee61+TXTSGEXk8gMcWEs4N11yav9a5DdGIq7Wv42D7AWr2g8UjYPwNiLsO5Ler3MDctx2VZ9d7Aemw5FcHuc9eZs/sC97esbPtYbcFsyvxgzuisSvL83hsijqtu4B1fBucy9o1TCGFXkqjMxqRJk5g0aRImUwmp3yWEECVY51q+1PR142R4HLN3nuexjtUZ064q07efIzQqkSmbzzC+S41CneP01Tg+XnaML4Y1xMvFIfObN+s3JqiamKlpaWzZsoV27dphNNz4mXMpmz41ujACWkK3t2D1u6qeVEBL8K1r/f5GJxizHAwOeW9bEDqdGp0RFwbegdluomka7w+uR59vN7H2WDj/BYfRs175ooknv6p2UInKtCRVr/Kx9eDkmXkERk5MaVmTiBkTiEnZvZdNMjLNxjUyjS4qye3ocePvG3+izsOVg5jRoTOnqc8oyUpxG7kmFaJwnIx6gi/HcCIslld712Hx0+2pV9EjXw/oGlbyKroAAfp8Bmc2QNBgqNI+f/uazXByFRVr9eKlnrV5f0kwHy07Sre6fpRzz7u7ebHL7mGjizeM+gem9ITYSzBrhOpyflvzICHE3UMSldkYP34848ePJyYmBk9PT3uHI4QQJZpOpzGuYzVenXeQ3zef5eG2gTgZ9bzSqzbP/72fn9af5r4WAfi4FeyC2WKx8OysfRy5FMOHS4/y+bBGWTfyCshQozGVaJdQNSLPaCzEJ8tB2+fgzEY4vRbmjlFdwfPTKCdjktJshtDdKuFpKw4umZOU186oqWIZmrTU8HVnXIdq/Lj+NO8tDqZ9TR9cHErAJUHn11SicPPXalTo3DGARX3XlVrA9bMwe2T2ychUG09jNzirJkoZk4uOHpkTjrfe98iaiHTyUCNc9dl8rxs+g2NLMHWcwJLYIPq7B6Nf96F6T5KVIgO5JhWicBoFeNGyqjc7z17jr21nea13nUIdL81kJtVksXo0plUc3eCZvRAXrmo1W8xw+QAkXFNJvAqNVGmU2x+6Wizw9yg4vhQG/sDoNiOZv/fijeulYL65v4ntYixqXgEwah783gfOb4N/HoXhU6XBnBB3qRJwVyKEEOJON6hxRb5YeZwrMUksOXiJIU0rMbBRRaZsPsOh0Gi+XX2S9wfXL9CxNU3jkyEN+WLVcV7qWdvGkReATgf33Giqc/UorHwdBnyb/+OkpcC8MXBsqbo4r9Hd9rGeXK3O0fQh6PVhpree7lqDhfsvERqVyA9rT/FqIW/ebKb7u5B4Hfb8CaczNB26uEv9yYvBOWvCMFMS8bZlpxze0xdBkhsyjQ41t30Bli3D3OFl9Pob3cBBkpVCCGFDj3QIZOfZa8zYfo6nu9TA9UbDvivRSVT1cbX6OCuPXOGDpcEMbuxv++uRuHD4oVneZWye3pOerNQ0qNQMTq0GixmDXsfHQxoweNIWFuy/xNBmlehQs5xt4yxKfvVgxEyYdg8cWwLLXoF+X6rPKYS4q0iiUgghRKE5GvQ83K4qn604ztbTkQxpWgmdTmNiv7r8timE0W2rFur4DSp58tdYG446LCw3XxjyK8wYBl5V1KiG/F5I642qBpPeCPERRRPnzenN236AcrVVwvIGFwcD7wwI4rFpe5i8KYQhTf2p4eteNHHk14BvVUMdc5oaRdLi0dtGNnpmn4x0cCu6afW2krE+V2qGpgk3k5NmmeIrhBC21L2uH1XLunA2MoF5ey5S08+NJ6fvpWpZFxY+bf1Ua7PZwoVriSw5eJkXutdCp7NhAi0hMvckJaj3EyIzj6ps9wLUHQQ+qsROw0pePNSmKn9uPctbCw6z4vmOBaoTbjdV28OQyTD3Ydg9RTXb6/iKvaMSQhQzSVQKIYSwiZEtq9CiqjfNq6QXQG9drSytqxWyiU02jlyKpno5N/tefFfrDM8dAI+KBdtf09RIgZaPQYWGNg3tlvpDIOIErP8YlrwI3tWhartbb/cI8qNbHV/WHAvnrQVHmDmuVclorLPhM5Wk1DuAKQVcy5WeUYa5NQMqLZ9RCCFKEL1O45H2gby18AhTNp9h3hNtSEwxceBiNAcuRNEowMuq4/SqV57PhjZkQKOKtk1SgpruXZDtdLpbSUoAzCZe6lmLFYevcDYygUnrTpWM2Sj5UW8wxH0Ky1+FtR+o5oNNRtk7KiFEMdLZOwAhhBClg6eLkRZVvXNNdKWkWXkhnotp288x6IctfLriWKGPVWgZk5QpCWo6d34YHDMnKdOS1ehMW+r0GtQbAuZUVcvq2plbb2maxrsD6+Fo0LEtJJJFBy7Z9twFkbFxzltX1d/rPlTrhRBCiAIY2qwSns5Gzl9LYO/56/RrWAGA6dvPWX0MnU5jeIsA29anvOnygcJvd/0cTOmJ+6nFvDswCICfN5zmZFisDQIsZq0eh/YvqOVFz8KJlfaNRwhRrCRRKYQQwuaiE1K5HJ3ePTkyLpkJ/xxk2M9bMZsLl4jz93IizWwhLCYJUyGPZTPhR2FyV1jzXsGPER0Kf/SBLd/YLCxAjdwc/CNUbAKJ12DW/arD9Q0B3i48faMr+wdLjxKTlJrTkYpexiTlzdGFnV6VZKUQQohCcXEwMKp1ZQB+23SGUa2rALDowCWiEvL5kPGGgu6XrYRrhd/uwCzVoO+/d+hVpyzd6/qSarIw8d/Dhb72sotu70CjEWAxqangF3fbOyIhRDGRRKUQQgibmr/3Im0+WcMnyzOPeFxy8DIHLkazYH9ooY7ftY4f/zzZlkkPNEVv66lXBXUtRDXW2fZDwZ/6n14DoXtg6/eQFG3b+IzOcP8sNX3q6jH455FMtRAf61SNQB9XrsYm89WqE7Y9d35krN+Y0c1kpdRvFEIIUUCj21TFqNfYfe46GhBUwYPkNDPz9lzM13HCYpJ46Ped9PpmI8lpNvpdcvEu/HYdXlblZB5egmZw4L1B9XFx0LPz7LV8f8YSQdNg4Peq2WBqgqoLHnHK3lEJIYqBJCqFEELYVC0/dxJSTIRcjSfVpKZ6l3Vz5Kku1QH4fOVxklILd2HfrEqZklFL8aY6/aDl42r53ycgpgBTqJs8CF3fgkfXgJOnbeMDVZB+xCzVFfvkKvjv7VtvORr0/G9QPQCmbjvL4VAbJ0qt1eX1nOs0dno19/qOQgghRC58PZwY2MgfgCmbz/BgGzWqcvr2c/kacejlYuTElVgi4lLYc/a6bYKr0Kjw2+kN0PdzKKM+l7+XMy90rwXAh8uOEhGXR7OekkhvhGF/pc8KmX4PxIbZOyohRBGTRKUQQgibqu/vyYLx7Vj0dDuM+vSfmbHtAvH3cuZydBJTNp/J5QjWS0kz8/Hyo/y2KcQmxyuUnu9D+YbqQvqfcfkf/adp0PFl8A4smvhAXejf85Na3vYD7J16660ONcvRv2EFzBZ4c8EdOk1MCCGEyMWjHdRv7PLDl2lepQzujgbORiZwMB8P6BwNer4a3oh1L3WmbQ0f2wSmWXlbbu12AFcOMbbiOepW8CA6MZWPlh4tWGz25ugGD8wF72oQdR5mDM1UwkYIUfpIolIIIYTNNQ7wyjLi0cmo55VeqvPkT+tP2+TJ/n/BYfyyIYRPVxzjUlRi3jsUJYMj3PsHGF3h3GbY+HnhjnflEKx4w/bNderdA53fUMtLXoSzm2+99Wa/IFwd9Oy/EMWc3Rdse14hhBDCzupW8KBDTR/MFpi18wKfD2vE2pc60djKzt83ta3hQ+WyLrYLzKWsuo7Ijc6gtrPGuW0wuRv6f8byRS8fNA3m7wtl66mIwsdqD27lYNQ/4FpOXR/9PSr/DQyFEHcMSVQKIYQoMvHJaRy8GHXr9cBGFWng70lcchrfrj5Z6OP3bVCe+1sE8MMDTano5QzAskNXeHO3nuWHrxT6+PnmUwP6f62WN3yaKQmYL4lR8Ec/2D4Jdv1ms/Bu6fQq1B96oxP4gxClkpLlPZ14oYeaJvbJimNci5ebACGEEKXLE52qM7ZdIGPaVaV3/fJUK+dWqONdi08pfHM/rwB4eg88tgHGrYP+30DXt9XfHV6+sZEGaz+wbsaGf1PwrQOVWlAvwJcHbzQPmrjgcKHL79iNdzUYORcc3ODMBljwJJjN9o5KCFEEJFEphBCiSBwOjabNx2t49K/dt4rN63QaE/vVBdS0q/jktEKdQ9M0PhnakF71ygMQEZfMm4uCiU2FNxcG26ceU6P7oPFIqNwGyhRwGrezF3R/G6p3gwb32jQ8QE0zHzQJKjaFeoPBvfyttx5uW5U65d2JSkjl09saIhWrw/Ph85pw5F/7xSCEEKLUaVfDh7cHBBHgnXlEZEESeN+uPkm7T9ay8ogNHo56BUDFxirJ2HwMdHxJ/d31TdVQxpwKB2dnqjGdI4MjPLgAHvgbXMvycq/a+Lo7ciYinh/Xny58rPZSsQkMn6pGlx6eB/+9Ze+IhBBFQBKVQgghikQtP3ecHfSExyazcH96c5nW1cry2dCGrHmxM66OBpudz2KxMOGfgzeSnxrxySbeXHDYZsfPl35fwkOLwNO/4Mdo/giMnAfOZWwXV0ZGZ3h4CfT7ShWrv8Gg1/HB4PoA/L37AnvOXSua8+cm7ioseR7iw2Hxc+q1EEIIUQQi45J5asYeOn++npS0/I3QM1ksJKaaWH20CBu8aBoM/EGVlgFVY/rA33nv5+Kt9gU8nIy837cqAD+tP8Wp8LgiCrYY1OgGg35Uy9t+gK0/2DceIYTNSaJSCCFEkXAw6BjTTo0onLwxBEuGWovDWwTg6WLMadcCmbb9HKuPhnNz9pXJYmHF4SssOViADtyFZXRW3Tdvun42/8fQNNBl+JkO2QDJN24sbDXa0MH11k0MpjQIXgRA86reDGtWCYA3FxwhzVSMU6ssFljyQvpnTY6DpS8W3/mFEELcFfaev85jU3ez6eRV9py7zpWYJJYfvpyvY4xpW5U/x7Tgy2FWdu0uKI8KMOCb9NcLx0PoXuv2NaXBuo/ouXYAg2oaSTVZmPjvoUzXZXecRvdB9/fU8qqJcGiefeMRQtiUJCqFEEIUmQdaVcbN0cDJ8DjWH886Ks5isbDp5NVCd5iOiEvm85XHs6zXgDfmH7LPFHBQNwfLXoXvm1t/Q5GdnZNh6iB1YxIXbvvRhmYTzBwOcx6EPX8BMKFPHTydjRy9HMPUbecKfw5rHZkPxxaD5cYUPIsJji5SyVkhhBDCRjafjGBVcBi/bznLAy1VDcfp2/P3e1fG1YHOtX2zNBAsEg2GQZ3+atmcCrMegFgrRnKaUiB4IVrsJd6tdhwno44dZ64xb8/Foo23qLV7Dlo9qZb/fQJC1ts1HCGE7UiiUgghRJHxcDJyf4sAAH7dGJLpPYvFwripe3hwyk4WHggt8DksFjUyICEla20pC9h3CrhOD7GX1Q3FvDGQFFOw45RvoOoxOXnC4udtP9pQp4fKrcHooqaKAWXdHHmtdx0AvvrvBGExSYU/T0YpCaqJT2yGul5xV1UyNgtNJWdlCrgQQggbGdW6CiNbVear4Y24v2UABp3GrrPXOXq5YL/VaSYzV2OL8MGopqnmOs7qd5q4y+oBY1oe53RwgWF/wpDfKNPlGV7orprmfbTs6J3dNE/ToNdHUO8edZ01exRcPmjvqIQQNiCJSiGEEEVqbPtADDqNbSGRHLoYfWu9pmk0reIFwOcrjhe4C+WJsDhWHgnLsePmzSngJ8JiC3T8QtE0GPg9eFZW07+XPK+mNudX5dbw5Bao1gmOLy2a0YYdX4Ent0LdAbdW3d8igEYBXsQlp/FJxsY6Fguc2wYn/8t8g3R2M6x6Ew7OzXzs33rAd00gJsM0/D1/wDf1YeXE9GMueQFSE7MJziJTwIUQQtiUt6sDH97TgBq+7vh5ON1qzJffUZUAe85do9tXG3hu9j5bh5mZWzno/3X664RrkBSd8/Y3+daFhsMAdV1Wp7w71xNS+WjZ0SIKtJjodHDPL1C1A6TEwox7C1ZuRwhRokiiUgghRJGq6OXMgEYVAfh1U+ZRlWPbBVLR04lL0Un8vuVMgY5fy8+NXvX80Ouyn3al1zR61y9PLT/3Ah2/0Jy94N7fQdPD4X9g79QCHsdbJfK4/XPmMtrQYlFTxSNPgzlDnclL+2DXFDi7JX2d2QSr34XpQ1VSMPoiupQYfqm6geOOD9Hq2Cccj75xbk2DvwaoG4L4DOe9tB+2fg8nV2WO49ppuBaS+WbKwQ10GeqUhh9VU75zcjMpG36H31QJIYQokUa2qgzAv/tCiU1Kzde+5T2dCb2eyNHLMYTH2ngGwu3qDYb696o6010mgptvvnY3mpP5s/JyPLU45u25yLbTkUUTZ3ExOML9M8CvPsSFwbQhEH+HfyYh7nKSqBRCCFHkxnWoBsCyQ5e5cC3h1nono55XetcG4Md1pwtUS1LTND68pwGuDvosKTwAV0f9rS7WdhPQArq9pZaXv5b/ZFumBjO3j8i8Mdpw6iCY1Ao2f5P+VloSfFETvm8KKRk6fB5bpkYnHskwElOnh2NL4NRqOLsJfu0C88ZS3tMFRy0NVy2JeSE6km92RC1fH8o3VAnOm/ybQdtnoHbvzCEO+wvGrACvyunrmj4Eb0fAvVPUa9+6UGeASujmpEpbtZ0QQghhI6fC43hu9j5WHrlCDV83ElJMzN+bv5I0/l7OTHm4BZtf64qvu1MRRZpB38/h6d1Q/570dYnXrdv3n0cpf/BHZvjOBGDiv4fSf9vvVE6eMHIeeAaoh6Mzh0NKvL2jEkIUkCQqhRBCFLmgih50qOmDyWzJMnJyUCN/Gvh7EpecxrerTxbo+D5ujnx4T4MsKTyAdwbWw8fNsUDHtam2z0H1bpCWCHPHgCkfozVujja05DA93mKC8CNw9RjEZLi5MjipUYsO7pmnVPvWVQX5/eqlr9M0deMz6Edw9IDkWJW0jDpHzJMH+NrhccKTNH7fclZt/9h6eGITlKmSfowqbaDnB1B/aOb4Ajuo9xxcM58vI01T09kc3ch21KjRGYZlGI16dgtEF7y2qRBCCAFwNTaZhfsv8ffuC4xtH8jb/YMY3MQ/38fpVKscro6GIogwGy7e4KFmq2A2w7qPVeO+61ZMW+/wEnhUIrDX05RzdyQkIp5fNxZsVkuJ4lEBRs0H5zIQuvvGtVaavaMSQhSAJCqFEEIUi8c6qlGVf++6QHRCepJOp9N4o68aJTdz53lOhcdlu39e+jesoKaA30iA6TXoXb88Q5tWKmTkNnKzjpJPbejyOuiNee9zU16jDTU9VO8KoxdD66cyrNfgravwxkVw90tfX3+ImibVfGzm4zQfC01GQtV2cM/Pat3u3/G4uJ7n+jQG4McNIZlGxdrUrdpb2YwaHfSjeh/UKIl5Y+H7ZnB+e9HEIoQQ4q7Qupo39f09SEo1ExmbzNj2gXg65+M3OhtF9juZnWNLYNv3kBABsx/IeyShf1N4dh+uQT14u38QAD9tDCE8uxLRd5pyteCBOWBwhpMrYclzBasNLoSwK0lUCiGEKBbta/hQt4IHCSkmpu/I/MS/TfWydK/rRzUf13zXhbrp5hRwF0c9YMHV0WD/Kd+3cysHT22DoEH52y+v0YaO7nDPrxDYEbwDs+5bEPUGq9pXAEtfYpDnaWp4mElKNfPe4uCCHdOq8w7JnJTV9FB3oEqu3pQQqT6nmy9UbFJ0sQghhCj1NE3j0fbqYepf284VuLkfQKrJzCN/7qLj5+s4FV4MTfxSk2DF6yo5aXSFsMOw4Mm8k3MGB0A95O1TwwkHUwJzQnRYSkNSL6DljdrgOtg3HdZ9ZO+IhBD5JIlKIYQQxULTNB7rqJJoiw9cynIx/Pm9DVn+XAeaVC5T4HP4uDnywcAg3I3wwaAgfNwcuXg9gWnbzhYmdNvSZRgVGXMZos5bt19uow37f50+2tCWOr6ipnGb0zDMH8Mj/pcx6DRWHw3jv+Aw258PbkvKopKw/b7KvI1XZRizHB5ZpYrog7opW/qS6kReGm60hBBCFJt+DStQwdOJiLhkFu0PZd6eiwz9aSvhMflrjGPU69DrNDRge8i1ogk20wmdYOB30OZpGDFLlXsJXgibvrBqdy10D99HP8NHDn9wMkZjwf7LRRxwManTN707+sbPVANBIcQdQxKV2Zg0aRJBQUG0aNHC3qEIIUSp0r9hRT6/tyELxrdDu22kXxlXBwz6wv8s9W1Qng+am+hTvzzX41Po+uUG3lp4hOBLMYU+tk2d3w4/t4c5oyEtxbp9rBltaEuaBoMmQcWmaInX6X/pK55sVRaAdxcdITGl4KNOcuVWDvp/A66+MOCb7JOwmgbu5dNfn1oNu35T095irxRNXEIUM7kmFaJ4GPU6Hm5bFYApm88ya8c59py7zuxdF/J9rNf71mXtS50Z1bpK3hvbQo1u0OtDqNYJ+t1IUK79QDXOy4spFUPcFTq5nMWLOD5ecZzr8VZek5R0zR6Gzq+r5WUvw9HFdg1HCGE9SVRmY/z48QQHB7Nr1y57hyKEEKWKUa9jWPMAnIw5d3ZOSjXx68bTLD1Y+Kf6ZVwd6FWvPO1r+BT6WDbnWQnMaXBpL6x5z7p9rBltaGtGZxgxC4t7BdyTL/Ps9Y8J8DASGpXID+sK1vzIKvWHwCsnod49eW8LUKm56jje9llVUP+m1NJQdEvcreSaVIjic3/Lyrg66DkeFkuLQG8AZu44T5opfx2xA31cqerjmveGRaHJg1Crt1qe/xjEhee+fZU2cN80HJ7cgLOzG9cTUvl4+dGij7O4dHoNmo4GixnmPQLnttk7IiGEFSRRKYQQwi7MZgsRcclZ1s/eeZ6Plh3jw6XBhaoTddOXwxox/dFWBFX0KPSxbMqzEgz+SS1v+wFOrLRuP2tGG9qae3nShk0nTXPAeHYd0yqrUQm/bgzh9NWCNT+yOecyquN4t7fS10VdgK+CVDdU6fwphBAiF57ORoa3CADgcGg0ZV0duBKTxOqjeST7cnE9PqX4RiimpcAffeHECqjQGPp8qmo556VOP4wuntxXXV1zzdl9ke0hkUUba3HRNPVAt3ZfMCXDrPsgvBQlYoUopSRRKYQQotjtO3+d7l9t4KkZe7O8d3/Lyvh7OXMpOokpm88U+lwOhhL8U1enL7R6Qi0veBJiLlm3X35HG9pChUbsrfo4AFVPTeXdijtJNVl4e+Hhklt8f/9MSLwG57Zkrg0qhBBCZGNsu0B0Gmw+FUn3uirJN2372QIda/bO87T/dC3frz1lwwhzYXCAio3Vclw41OmXr90D3eG92md5xTCbif8eIjmtiMq7FDe9AYZOgUotISkapg+F6FB7RyWEyEUJvnsTQghRWlXwdObC9QSOXY7JUqjeyajnlV61Afhp/elsR10WRFKqiWnbzhIem7/C+EWux/+gfEPVyXr+Y2AuuTcGl71aYOqk6j2NjvqB1oaTbDkVyWIbTNMvEp1ehWF/Qa+P0rufp6VAyAb7xiWEEKJECvB2oXd9Vf84NikNTYMtpyI5FZ7/2QMVvZyJTzFx8GIUZnMxPdDr9g54V4fYS7BigloXHwG7/8hzV9ekyzx0biLjDYuoFLmVXzeEFHGwxcjBBR74G3xqQUwozLgXEq/bOyohRA4kUSmEEKLYlfd04rfRLdj6ejd8PZyyvD+wUUUa+HsSl5zGt6ttUwdx/Iy9vLXwCH9uOWuT49mMwRGG/QkObnB2E+ycbO+IcmVu9yLUvxetTn86duwGwAdLgolNSrVzZNnQNKg3GCo0TF+36zeYOhD+fcJuYQkhhCi5Hu1QDYD/jobRvrpqIDdjx7l8H6dDTR9mjWvN3CfaoNNpee9gCw4uqqyMpoMDs+DQPPi1Cyx5Hg7MznXXeKcKmNs+y6nqo9lqrsf3605xJiK+eOIuDi7eMOofcK8A4cEweySklrCH10IIQBKVQggh7KRTrXK4ORqyfU+n03ijb10AZu48X6CRDLe7OaXcbgXuc1O2umqS03gkNH3Q3tHkTtPUTdC9fzC2SxBVy7oQHpvM1/8VYWMdW0qOAZ0RKre2dyRCCCFKoKaVy9CsShlSTRa8XB1pX8OHjjXzXw9a0zTaVC+LphVTkvKmyq1UczlQoyqDBqjlRc9C6J5cdzV3fpPqo76ldc3ypKSZeXPBoZJb3qUgvCrDyHng6KHKwswfV6Jnsghxt5JEpRBCCLuyWCyEZNOQpU31snSv64fJbOGzFccKfZ5udXxZ/0pnhjcPKPSxikTD4TD4R3AogYnU2xkcQKfDyajnfwPrcY9uE9O2nuLIpWh7R5a3zhPg6V2qM+pN53fA+k8hpRSNHBFCCFFg4zoE0qGmDyNaBDD90VZ0qWNFU5pcmMwWToXH2ig6K3R+A8rVgfirEH0JavVRzWRmj4TYKznvp2lomsYHg+vjaNCIO72DhfutrJ99pyhfH+6fCXoHOLoIlr8GpSkZK0QpIIlKIYQQdhMZl0y/7zbT77vN2XbFnNCnDj2C/Hi1d+1Cn0un0zDq75CfPbMZDs+/Iy6cOx57j68dfuIN/XTeWnC4+OpwFYZ3YHpzHbMZVr4O6z+CdR/ZNy4hhBAlQu/6FZj2SCva1vAp9LEuXEugx1cbuPfnbcQnp9kgOisYneCen0HTQ/C/UHcA+NSG2Mvw94OQlnv97yplnFhc8U8WOr7NmsXTiUoops7lxSWwA9zzC6DBrsmw+St7RySEyOAOuWMTQghRGnm7OgCQmGpi+vas9Z9q+Lox+aHm1PB1t9k5LRYLW09HsO5YuM2OaVMWC8x5EOaNgW2T7B1N3mr2wqJ3IERXlb3no5i754K9I8ofTYM248G3HrR9Nn29qZhuJoUQQpR44TFJfLv6JAcvRuV734pezlhQP+/HrsTYPLacT9wEOryklle9CQO/BydPuLgTlryY+8NQnZ7qlStjQodX8iU+WV74mS0lTv0h0PsTtbzmf7Bvhn3jEULcIolKIYQQdqNpGo93UkXr/9p2lqTU3OsEJaYUvo7Qwv2XeGDyDt5ZdIQ0k7nQx7M5TYPqXdTy6nfzrCdld0ED0Z7dT+XuqjnNJ8uPZTs6tsTSNKg/FJ7cAu5+6etXvAYzhkPEHVJ7UwghhM2FxyTx+cpjvDTnAF+vPsEfBWjIp9dp/DSqKVsmdKVZFW/bB5mbjq9A+QaQeA22fANDp6hGO+e35dn1Wt/rA471/5dppp7M3nWBnWeuFU/Mxan1E9DuObW86Bk4+Z994xFCAJKoFEIIYWd9G1SgoqcTEXEpzN8bmu020YmpvD7/ID2+3pBnMjMvPev5UcHTiU61ypFYyGMVmeaPQN2BYE6FeWMhqYTXfvT05+F2Vant5w4JkUxetNbeEeVfxmYHCdfUyIqTK3Ov5SWEEKJU+2PrWSatO821G1Oflx68TGRc7tOms1OnvEeODQSLlMFBTXHWGeH4MoiPgGF/wbg1qgt2boxO1GvemftbqNreE+cfJCWtBD7gLaxu70LD+8FigjkPwcUS/oBYiLuAJCqFEELYlVGvY2z7QAB+2xSSbY1DR4OODcevcvF6IlM2nynU+VwcDGx4pQvvD66Pu5OxUMcqMpqmpmh5VobrZ2Hx8yW+XqVRr+OLrs4scHiboUef58DJs/YOqeBcvOGJzdD9XVXH6qYrhyA10W5hCSGEKF6j21SlVaA3z3StSQN/D1JMZubsvlioYx67ElO8CT+/eqqRHMCOn6BOf3Auk/5+HqVOJvSpQ22XeN6KepOli+cUYaB2otPBoB+geldITYCZwyDytL2jEuKuJolKIYQQdnd/y8q4OxkIiYhn9dGwLO87GfW8cqOhzk/rTxNRgNEMGTkY7oCfP2cvuPd30BngyHzYO9XeEeWpQbXKeDpYqK67TNrfD5OWegdNAb+dTw1o/0L665QEmDEMvm8G4UftF5cQQohiU97Tib8fb0Pv+uV5sE1VAGbsOIepgI3jXp9/kN7fbGLB/uxnkBSZds+rh28PL1OJOVAPQHdOht+6Qkpcjrt6uTjwY9WNdNQfovH+dzh3tRjrbBYXvRGGT4UKjSEhEqbdA7FZr0eFEMXjDrhTE0IIUdq5ORoY2aoKAL9uDMl2m0GN/Gng70lcchrfrrZN3cDQqET+2FK4EZpFKqAFdH1LLa96q+RPAXf3wzJiNok40ixtHyemPpv3PneK62dU0linhzKB9o5GCCFEMRvQsCKezkYuXk9kw4mCNeQL9HFF0+D01ZwTg0VCb1AP3xzd0tclRcGGz+DyAfSLnwFLzqM8q933CTucO/Joyku8uegolhI+y6NAHN1h5Fz1Gx91To2sTI61d1RC3JUkUSmEEKJEGNOuKka9xu5z19lzLmuBd51O442+dQGYufM8p8ILd5Efk5RK9y838N7iYPaez72gvF21fRaajYHRC1W3zhKuTPXm7Gz8MQBBF2YRs+kXO0dkI3714Old8MBcMDqpdRYLrP8Erp6wb2xCCCGK1PX4FKZsDqGWr0r0Tdt2rkDHGdmqCqtf7MTrferaMrz8MZvVSMqkGLhvOuiM6I4tplbYohx30Rzd8H1kNhf0AWw6GcGiA5eKMeBi5OYLo/4BFx+4fAD+fhDS7uDZIULcoSRRKYQQokTw83BicGN/ACbnMKqyTfWydK/rh8ls4ZPlhZt+6+FkpH/DCrStXhajrgT/HOp0MOAbqNhEvTab4MwmODRP/W0ueQ2B2g8cyzSXhwBwXTMBQjbYOSIbMTqDb53016fWwPqP4ddOkBhlt7CEEEIUrX0XrvPFqhMEX47ByaCjvKdTtjW18+LqaKB6Obe8NyxK/70Fy16GheOhUgvo/xUAdS/PRzu+LMfdAn1ceaZLDQD+XLyG2JDdxRJusStbXY2sNLpCyDr1PZlLYRMhIUqwEnxnJoQQ4m4zrmM1AFYGX+FsRHy220zoUwe9TmPX2euExyQV6nwf3tOAmeNa06BSyR+pCEDwIviyNvzVH/55RP39TX21vgTR6zQaj3ifBaZ26DGTOvvB0lmYvkxVqNUHmo9VNUVvKo1T4oQQ4i7WuZYv1cq5Ep9i4rnuNfl4SEN0Oq1Qx4xOSOVUuB2mFrd4RI0YDBqkXjd9CFPzcQDoFz2Zax3mxzpVY3CZs0xLe4XUWaNK70M6/6aqZqXOAIfmwOp37B2REHcVSVQKIYQoMWr5udOldjksFvhtc/ajKmv4ujHpgSZseKUzvh5OhTrfHdFU56bgRTDnQYi/mnl9zGWY81CJS1Y2CPDiQJP32WeugTElGsvM+0rfDY1PDXhgNvT4X/q66IswqSUcnCsJSyGEKCV0Oo1H26uHqdO3nyfNVLgRdhtOXKXdp2t5ac6B4q/36F0Nnj8ELcfdaqxj7v4/rrrVRUuJh1kj1LTwbDga9Iwa1JdIiwcnkr04cLYUN5yp2R0G/qCWt34H2360bzxC3EXuoDs0IYQQd4Oboyr/3RtKQkpattv0rl8BLxcHm50zKdXEjB3nuBSVaLNj2pTZBCtey+HNGzc4KyaUuGngz/duyATja4RayqJFnoS5D4Mp+3+mdzSdPn15y3cQcQL2/JnnblrwAnodegbt6MKii00IIYRNDGnqT1lXB0KjEllx5AqHLkaz59y1Ah2rfkUP0sxmktPMXIu3Qw1EB5f05eQ40HTsDnwaS5lANUvA0T3HXZvXCWRGnUk8kDKRV1eEkVrIpG2J1niE6pYOsPJ1OPyPXcMR4m4hiUohhBAlSptqZZnQpw4rnu+Ii4Mh120tFgtrj4WRlFq4BN2Lc/Yz8d/D/LaphHYAP7cVYnIrXG+BmFC1XQni6WLk8X5tGZfyEgkWR1XraeXr9g6raHV/V3Vq7/UBaDemBaalZJ36HncV/bIXcUyLRr/sRYi7muVQQgghSg4no55RrasA8OnyYwz4YTPvLylYveyybo4sero9y57tQFk3R1uGmT9nt8BPbdHt/IkUgztp4zZCu2fTf79y8OSgzni5OnE8LJbJm0LAlFpMAdtBu+eh5eNq+d8nSk/dbSFKMElUCiGEKFE0TeOJTtUJ8HbJc9uX5hxg7J+7+X1L4RKM97eoTEVPJ2r42rnAfU7irJxadXM7i6XEdKm8p4k/blWb8kLqU2rFzl9h/yz7BlWUHFyg48vpzY8Adv+upoOv/1S9tlhgyQuQEo8GajTL0hftEa0QQoh8eLBNFRwMOi5cT0Svg/0Xojh0MbpAx6rl517oOpeFFnkKos6hW/8x7omhqmncTcmxOSblyrg6MLFvXcBC9JpvSPmxg/otK400DXp/rGp6mlJg9ki4csjeUQlRqkmiUgghRImWmJLzaMkOtXwA+HHdaSLjkgt8jg41fdjwahceaFW5wMcoUm5++dsu4gR8Vk1NtT40z661ITVN44PB9VlDSz5LvY9rfm2gVi+7xWMXl/aBOQ3cyqnXR+bDscVoFvXvtmYxwdFFcHi+HYMUQgiRFx83R4Y08QegnLuqkz19+7lCHdNstrA9JLLQsRVI04egRg80UzJNzk9Wv1UA8RHwWw+YMQwu7sl21yFN/elW1YmHdUtxiDyKZf/MYgy8mOn0cM+vUKU9pMTC9HvheuH+uQshciaJSiGEECXS5ehExk3dTb/vN2EyZ19oflAjfxr4exKXnMa3a04W+FyapmHUl+CfxCptwaMikNPICw08/NV2ACf/UxfSR/5V3cE/rw5TB8POyRAdWkxBp6vl584j7QP50TSQe6JfItFwh3RZt5Uhv8DDy6DJQ2qK95IXyPrPUoMlz8sUcCGEKOEe7RAIQFh0EgALD4QSnVCwqc8paWb6freJ+3/dzsGLUbYK0XqaBgO/w+LkSZmEEHRbv1Prnb3BOxBMyTD7AdW4L8uuGhOHtuYl0zO8mTqGJY79ijn4YmZ0gvtngG89iLsC04dCQsFqlAohcleC78qEEELczTycjOw8c42Qq/Hsv3A92210Oo03+tYFYMaO85wKL9y0I4vFwo6QSFYduVKo49icTg+9b0wbzi7BBdD7k/SmLq2fgkfXQPsXwKe2GiERsg6WvQxfB8HZzcUV+S3PdqtJBU9nzkWl8OP6U2rl/lmQmP0/21Knajv1z2fJCzemx92efLfIFHAhhLgD1PB1p0vtclgALxcjSalm5u29WKBjORh0BFXwwMPJwLnIBNsGai2Piph6fgyAbtPnalqzTgf3/ALl6qik3N+jIDUpy67VyrnRsnN/ppt68N6So0QnluJalQDOXjBqHnhUgsiTMHM4pNjpn5sQpZgkKoUQQpRIro4GPru3Iatf7ESzKt45btemelm61/XDZLbwyfJjhTrnyiNXuO/X7byz6AgpaSWsi2XQQBg+FTwqZF7vUVGtDxqYvk6ng0rNVWOXp3fC03ugx/8goBU4uIN/8/Rtt/0IKyeqgvpF2DXc1dHAOwOCAPhlQwjXlv4PFjxRejuBZyf8KBxbDJYcvuebU8DDC9acQQghRPF4tEM1AOKT1e/X9O3nMOcw+yMvE/rWYcuErgxoVNFm8eWXpf4wLns2QzOnqoYxaSng5AEjZoGTF4TuVg/SLFk/45Odq1PNx5WIuGS+XHEYtv6QbVKz1PCoCA/OV9/LxV0wb8zdcx0jRDGRRKUQQogSq1e98lY1uJnQpw56ncbqo2H8sfUseyI0dpy5luOU8Zx0qeNLgLczXev4kljITuJFImggPH8YRi+BoVPU388fypykzI5PDWj3HDyyCl46qqYv3bTnD9j2A/zZF76oCQuegqNLimSEQK965elUqxwpJjNfnKuBxcEdAjuljwQt7XzrQp0BoOXweTU91B2othNCCFFita1elroVPEg1WXDQ60hMMXEpOrFAx/J1d8LdyWjjCPNJ0zgQ8DAWZ28IOwwbP1PrvavBsD9A08H+GbDjlyy7Ohr0fHhPAwA67n0RVk2EVW8WZ/TFr1xteOBvMDjBiRWw9IVsk7hCiIKRRKUQQog7wvX4nLtY1/B1o30NH/Q6jY+Wn2DqST2jft9N+0/XsuJw1rpKOXE06FnzYmc+vKcBns52vmnIiU4PgR2gwb3q7/wm+Rzd05ctFuj6FjQaAc5lICFS3Yj8PVI141n4tE1D1zSN9wbWw8GgY+Z5T/7rvhw6vKhqZN0NNA36fw2ObmQ7hd/RHfp9ZY/IhBBC5IOmaYzrEIi/lzNPdanO5te6UKmMS6GPeyIsltCogiU8CyvZ6Impz+fqxaavIPRGE53qXaHnB2p52w/ZPshsU70s9zarxDRTD2I1N9KqdCimqO2ocmu493eVxN07FdZ/Yu+IhCg1JFEphBCiRDOZLbzw935afbSGkKvZ16BccfgyG09czTKC8kp0Ek9O35uvZKWD4S76adQ0NRrznp/h5VNqhGbrp8CrMqQlkqmOosVM4NX/4FpIoU5Z1ceVpzpXB+DN/8KITbpRzyo5TnXHLu3cyqlkZXY1Kvt/nd4ZXAghRIk2sFFFNrzSmee718Jgg4Z8k9adoufXG/lu9UmWHLxEiw/+Y+lB669fbMFSdxDUG6JKkfz7ZPoU7tZPQbe34ZH/wCH7hOwbfety0Kk5bRO/4bfI+sUYtR3V6Qf9vlTLGz6B3b/bNx4hSom76G5MCCHEnUiv04hNSiXFZOa3zWeyvG8yW3hvcXCWtA+kp4LeWxyc72ngl6MT+W1TCJa7ZSqP3qBGaPb+GJ47CE9sgbbP3Xpbu7yfhhenYfypJUxqDWveV6MtzPmv5flEp+pUKetCeGwy36w+CbFh8HsvmDoIIk7Z8lOVTPWGQJ0BWG5MAbfcnPJdf4idAxNCCGEtg16XKUGZZjIXqqlf62pl0TSITkzhjfmHuBqXwuvzDxIRl2yLcK3X70tw9YWI47DuQ7VO06DDS1nrZGfg7erAG33rEosL36w+wYVrCeohpKmUN9hpPhY6vaaWl74Ex5baNx4hSgFJVAohhCjxxt0oWj9vz8UsF+w7z1zjcnTORdstwOXoJHaeuWb1+RJTTPT8aiMfLD3K9hDr9ys1NA3K14dytdLXmU1cdQtSSbWrR2HTFzC5K3xdD5a8CFePW314J6Oe9wbWA+DPrWc5FqWB0QWSomHaIDizCS7tz/on6oINP6QdaRr0+wIMTiqZbnSCvp/bOyohhBAFkGoy88uGU7T8cA0jJm8vcDO+ZlXKsOHlzliA+BRVJzs+xcSbCw7bMForuHjDgG/V8tbv1e/v7Q7Ng3/GZXlYeW+zSrQK9CYp1czkuQux/NoZ1n5Q5CHbXefXoelDYDHDvLFwfru9IxLijiaJSiGEECVey0BvGgV4kZJmZuq2c5neC4+1rrOktdsBODvoGdzEn1aB3jgZ5acSwFKpBVtrTiDtheMwZDIEDQYHN4i9BLunQHxE+saxYSrpmIvOtX3pU788JrOFiUtOYe59o3B/9EX4qz/82inrnx+alY5kZfAimNwFLTUeDdBS4mFyF7VeCCHEHeWr/07w8fLjxCWncTU2mVXBVwp8rAMXo1l5JOzWLBCT2cKKw1dYcvCSrcK1Tp2+0GwM9PgflG+Q+b3oUNV479Cc9KY7N2iaxof3NMCo1wg7dwwt8qRKaibFFGPwdqBp0O9rqNUH0pLUDJHsHuBu+AzWfVz88Qlxh5G7LyGEECWepmk83lGNqpy27SyJKekduX3dnXLaLRNrt7vp7QFB/P14G5pULpOv/Uo9Zy9oOByG/wWvnIaR86DVExDQKn2bjZ/DZ9Vh2j2wc7K6qcnGW/2DcHHQs+fcdVYfD8/73GnJquHPnSx4Ecx5CGJuu+mMuazWS7JSCCHuKPc1D8DPw5FmVdT1wrTbHqhaKyIumYn/Hsqu1RpvzD9U/FPAB3wD7Z7N2rTP0x/632j8tv5jOLo409s1fN14snMNVppb8qH+CWIfXgNOHsUTsz3pDaq5joe/Slb+1l39tt+04TM1lT6/TRCFuAtJolIIIcQdoVe98lT2duF6Qipz96SPqmsZ6E0FT6csF/YZVfB0omWgd77OZ7RBYfxSz+gENXtAn0/VBfpNkSfBnAqn18Kyl+HrIPi1s0pghgWrbuNARS9nnu9eE4A/sqk/WuqY0mD5q2RtpEP6uhUTwGzK5n0hhBAlUVUfV7ZO6MZX9zVCr9PYceYaJ8Ji83UMi8XCxH8PEZ9iyq7VGvHJdpgCnlFqIlw9kf66ySj1kBJg/uMQdiTT5k91rk6gjyuT4zvy+aYI7hoOLvD4JnD2huQYDL93xZAWj27TFypJ2WUidHrV3lEKUeLdFXdh69at44MPPmDhwoV3T1MEIYQoZfQ6jUc7BALw26Yzt6ZF6XUa7wwIAsgxWTmxX130utxSmTlLTjMxe+d5zkXGF2j/u9JDC+HpPWrKWEArQFMdvdd+ADPvy7TpmLZVqeXnRkxSmn1izQ+zGRKj4Po5uHIIzm5WRfP3z4TtP0Pk6fRtz2yEvwbCL53g28bwWTX4oBzE5tbB1QIxoXBuaxF/EGEP58+f54svvuDHH3/k+vXr9g5HCGFDep1GBU9netT1A2D69vyNqjwRFpdpyvftTBY1BTy/CVCbiDgFP3eAGUMhOcP5e34AgR0hNR5mjYCE9JreTkY9Hw5Wnb+nbT/HvvPX1e/i9p+LO/ri51oWHlsPDq5o8Vfpe+gp9Bs/kSSlEPlQ6hOVX3zxBZ9++impqam8+eabvP322/YOSQghRAENaxZAGRcj568lsPJIeg2o3vUr8NOoppT3zDy9+2Zu8kouzXbyMuGfQ0yYf4hfNoYU+Bh3JZ8a0O45eGQVvHwCBnwHtXqrztbajX8wplSMP7Vkus9UWuuCiz6mtBSIuwqpGf59iDwNe/6CLd+pROqyV2D+YzDzfvi9T+aC+AdmwqdV4NuG8HN7+LMfzH4AFjwJK15TydibkqLhzAa4vB+un1FT1i1WNliIC7PJxxUlx/Hjxxk8eDARERFs2rSJli1bkpZ2ByTnhRBWM5kt1K3gDsD8vaHEJVv/33gtPzd61fPL8aGqXtPoXb88tfzcbRJrvrj7gSlZ/YZeyzD7QW+EYX+BVxWIOgdzH1YzB25oW8OHIU38sVjg17mLsfw1EFa+Dud3qA0Oz4fPa8KRf4v38xSHMlVg7EosgIYFi6aDjq/YOyoh7hiGvDe5sw0dOpSXX34ZgK5du/Luu+/aNyAhhBAF5uyg58HWVfhu7Sl+2RhCn/rl0W4kvXrXr0CPoPJsOxXOqk076NmhFRejkpkw/xA/rDvFyFZVcHbIf12gES0rs+10JHXK2+HmoLRw84Vmo9WfjM5thchT+Eae4i2jlceKC4PwoyoRmBSj/k6OVgXsPf3VNidWwY6fIfnG+ze3TUtU74+cp6asA5zfBoufzfl80RfTl5081d8GJ7V884+jh/rbvUL6thWbwpDfbmxz4/2rx2Hubd9Bdtz8rPwyxJ3Cy8uLzZs34+LiAkBAQABhYWH4+/vbOTIhhK2sPhrG16tPotMgLjmNdcfCGdCoolX73mxCs+30emKT0jJN/9YAV0c9H9wYoVjsHN3h/lngUVF1BM/IxRtGzIIpPaFik/QHkTdM7FeXtcfDWX61LCer9qdWBS8oX189NFzyvPp9XvwcVGkPbuWK7SMVi+PL0VBT9zWLWTXYGS11qIWwhl0TldHR0UybNo2///6bwMBApk6dmmWbiIgIPvzwQ3bv3o23tzfjxo2jf//+t96fNm0aR44cybJf+/bt6d+/P4GBgSxatIgNGzawc+dOJk6cWKSfSQghRNF6qG1Vft4YwoELUew6ez1T7Um9TqNVoDeRRy20CvSmrd7AibA4HmgVUKAkJUCLqmXY9FoXqVlZFKq0g9GL4dgyTAdmoU+KynufmcOzX+9VNT1RGR8Op9fkfIyUuPTlMoEqyXkzmZgx8ejkAZVapG9bqze8GQ4Gx7zj9PSHhsMyr/OppW70Yi6TfZ1KTb1fpW3exxc2Y7FYWLNmDb/88gtnzpzh77//pnr16lm2mTJlCnPnziU5OZnu3bvz8ssv4+SkRnEfPnyY6dOnZzm2q6srb731Fn5+fly4cIH33nuP48ePM2TIEElSClHKdKvji7+XM6FRiTzVubrVScqbfNwc+fCeBjwza1+m9RbgoyEN8HGz4renqJTPJUnqVw+e3qV+v25T1s2RN/rU5dV/DjIkdAQr7utCJaOzqmuZfOO3ODkOlr4I900rouDt4EbjHFPHCRwOuUyji3+pWRZzRqtmhEKIXNktUZmSkkLdunUZPHgwZcuWJTg465Sv5ORkOnfujI+PD2+88QaHDx9m8ODBzJ49m3vvvRdQF4BeXl5Z9nV2ds607OnpiU6nY9u2bfTu3bvIPpcQQoii5ePmyNCmlZi18zy/bjyda5McnU7j7Rv1KwtK0zSM+oLVtxR50BtUfavAjuhdfGDd+9bt51I2czLRyVN1I7+pchsY/FM2iUdPNTIkY8fNqu3UH6vitXbYZw50euj9qerufWucxU03/h3r/Yl0BC1mTzzxBKdPn6Zz587MmzePxMTELNu89957fPPNN3z77bd4enryyiuvsGfPHv79V01ZNBqN2V6P3hxBCaDX6/Hy8sLHx4cdO3YQGxuLu7uM1BaitDDodYxpV5UPlh5l5ZErvNyzNrp81sfu37ACSw5eYvXR8Ez1KutV9LR1uAVjscCBWXD5gGqkd1PGJGVaMkSdBx/VLG9Y80rM23ORnWev8c7CI/zW7DzasQydwi0mOLpITQWvP6SYPkgRutndu8tEzG1f4GzsMupXdEa/82cIXgCLnoGB39s7SiFKNLslKo1GIydPnsTV1ZXnn3+eixcvZtlmxowZnDx5kk2bNlGmTBn69OnDmTNnmDhx4q1E5ZAhuf/PLDg4mB49etCjRw8ef/xxGjRowHvvvVckn0kIIUTxGNchkNm7zrP6aDhnIuIJ9HG1ar/jV2Kp4etW4MY6u89e42psMn0aVMh7Y5E/3oHWbTd0CjS4N/dtylZXf0qioIEwfKqqaRlzKX29R0WVpAwaaL/Y7lJffPEF7u7u7N+/n7feeivL+zExMXz66ad8++23jB6tpu77+vrSrl07du3aRYsWLahduzYTJkzI8RzHjx+nZs2avP766wD06NGDbdu20bNnz6L5UEIIu7ivRQDfrj7J6avxrD8RTtvqPjgZrX/4lHEKeExSGgadhqNBx/ErsVZf6xSpq8dg4XhVczmwE9Tpm/n9uKswewREXVANZTwq3PhM9en73Sb2HztJWuirZH3sp6mp4FU73PlTwM2m9MY5qalqVbf30EdfgONL4eBcaP+i9dc9QtyF7Jao1DQNV9fc/2e7Zs0a2rZtS5kyZW6tGzBgAD/99BMXL16kUqVKeZ7n888/JzY2lkqVKrF27VqGDh2a47bJyckkJyffeh0TEwNAamoqqTf+J1MUbh67KM9xt5HvtGjI92p78p0WTICXI890qU7jSp74exgzfX85fadfrDrJ5M1neLt/XUa2DMj3Odcdv8pj0/dR1tWB9tXL5OvGozQo8n9Xa/Uj2VgWl5RIsssjmy0Q7+CDU63+6sI/JR4cSsBNW0HU7APVe2I6s5nD21ZTv0139IHt1UjKIvh+5f8vuctrVOOWLVtISkqiX79+t9a1adMGHx8f1qxZQ4sWLXLZWzlx4gSjR4+mVatWXLhwgVOnTuW6nz2uSeX3qGjI92p7Jfk7ddLD8Ob+TNlyjtf+OUhSqpmVz7ajnLv107Y9HXW8PzCI95cd4+nO1RjcuCKujoaScT9apga6Vk+h3/4DlsXPkVah2W11K/UYkmPR4q5gnv0ApgcXgcGJqt5OjGtXlYbbnkVLjc/mwBYsyXFYFj+P6d4/bfWx7KO96o9Bhv9fp5rMMPBH9NMGortyAMuMYaSNXp55NoiwSkn+7/9OVhzfa36OrVksluyKJBWr559/ns2bN7N79+5M6zt06EDlypWZMWPGrXUHDhygcePGbNmyhbZt867hZLFYWLp0KWfOnKFhw4Z06tQpx23ffffdbEdbzpw5M9PUHSGEEHeejZc1/jmrx0VvYWITE275nMVrMsMnB/RU97AwoLIZ10LOAhZZVYjaRYsz32OxkClZabao+vy7Ap/hslcLKl3bQtClOWyt8RpxTvmrAXY3SkhI4IEHHiA6OhoPDw97h1Ni7d+/nyZNmnDo0CHq10+vx/bLL7/w5JNPkpqail6f/oCiQYMGdOjQgR9//NGq4x89epQ1a9bg4+ND//79cXNzy3FbuSYV4s51LRne36vHfKOkR78AEz0r2f2W22Z05hQ6HX8Hj6RQLnq1Yk/g+EzvuySH0en4uziY4jnv3Z59lceBpuEUf5FeJ97I8/hr63xErHPeA5LuRE6p1+l4/D2cU69x1S2IbdVfxqIr9f2NhQDydz1aov+rSE1NxdEx89Onm7Unrc3GapqWqflObl5//XVefPHFW69jYmIICAigZ8+eRXphn5qayn///UePHj0wGuXO1xbkOy0a8r3annyntmE2W27VgcrpO+1pMnPkp+0cC4vjkFaV9/vmv3Zl7z7mu7apTvH8u9oX07FmJCx+FY+U8Ftr4xz9cBnwKU3q9KeJOQ39H1+iS71OZ69QzF0fLaJYil5x/fd/czSeKJjU1FQMBkOmJCWoa9L8jA6oW7cudevWtWpbe1yTyu9R0ZDv1fbuhO90T+pBlhy6AsDeGFe+6NUeQyGvH85fS6CD8CKvAACFJklEQVSSl3O+615aI7/fqda0CpY/e1MpagflAx/DUndQ5vfPVMMyaxiVr23Gv1lvzC2fAIuFA98tp17sFgyaOcsx0yw6zpTtRIehj9nsc9lbtt9rWGMsU/tSLi6Y/pbVmPp8m6VbusjZnfDf/52oOL7X/FyPluhEZdmyZYmMjMy0LiIi4tZ7tubo6JglMQqqnmZx/EdQXOe5m8h3WjTke7U9+U4LJjHFxKR1p1hy8BJLn+2Aq2P6z9rt36nRCO8Nqs99v27n790XGdmqKg0q5a84vfwjKoZ/Vxvcg0e9gTzw1tf4WK4Tjhc/vvQ0BvebTfKM8OC/sPdP9O1eQK+78xPHRf2dyv9bCqds2bKkpqZmaX4TERFRJNejYN9rUvk9KhryvdpeSf5OH+tU/Vai8nJ0EptOX6dnvfIFPt6zs/ax+OAl/hrTko61iq6Go9XfaZWW0OFF2Pg5huWvQLWO4Oab/n6t7tDzQ1j5OvrVb6MvXx+qd6HRE38Q90UjXCzxmWZO3JxJUXPkV6XyYivT91qpMQz7C2YOR3dwJrpyNaDDS3aN705Ukv/7v5MV5fean+OW6Kv7pk2bsmfPHjLOTt+xYwdubm7UqlXLjpEJIYQoCRwMOpYcvMTZyAQWH7iU5/atqpVlUOOKWCzw9qLDmM0Fm4oVFpPELxtOUwKqp5RK360LYaupLovNbdluDmLMX3syb+BaVl3U30xSmtJUAX8hikDTpk0B2LVr1611YWFhnDt37tZ7QgiRUcNKXrQMTK/dOG37uUIdz9vVAYsF9p2PKmRkNtTxVfBrAInXYMkLKtuYUesnofFI1XhnyfPqt9qtHOZ+X2epRa1poNMb1bHuBjV7QJ/P1PKa/6mO50KIW0p0onL06NGEh4fzyy+/AHD16lV++OEHHnzwQRwcHOwcnRBCCHvT6zTe6FuXn0c1Y1hz6xrkvNG3Lq4Oevadj2L+vtB8nzM5zUTvbzby8fJjrD8hyTFb+27NSb767wQv9qjFzHGtAThwMZq3Fx7Ofoe0FJg3Bv7oDbFhxRipuFvUrl2b9u3b8+GHH5KSkgLAe++9h6+vr9XlhYQQd59xHardWt50MoIzEdk1kbHOE52qs+7lzjzXvaYtQrMNgwPc8xPojHBsCRyck/l9TYN+X0GjETBqPujVrBeP5sO54NeNNItKRaRZdKqD+MNLoFLz4v4U9tNyHLR+Si3/+wRc2GnfeIQoQeyaqLz//vtp3rw5s2bN4ujRozRv3pzmzZuTmJgIQI0aNZg2bRoTJkygatWqVK5cmbp16/L555/bM2whhBAlSM965eldvzx6K2s2+Xk48Uy3mng4GShIRSBHg557m1WiRdUyeDiV6Aoqd5yMScpnu9WkTfWy9L4xVW7qtnN8u/pE1p0SIuHSfog8BVMHQnxE8QYt7njTp0+nefPmjBw5Eki/Pp0zJ/2me8aMGURFReHr60uFChVYsmQJ8+fPl8Y2QogcdavjS6CP663X0wsxqrK8p1OmY5UY5RtA59fU8vJXIOa22S1GJ7jnZyhbPX2dprG8yqvE44TFAvE4M9l3IgS0TN8mPlKNwCzten4AtfuCKRlmjYBrZ+wdkRAlgl3vsN56661bScmMMtbkue+++xg0aBCnTp3C29ubihWLvrvnpEmTmDRpEiaTqcjPJYQQwnaS00xoVkzHHtsukGHNKlHWLWsNOGu82rvOXdtUpyiZzJZbScqbXu9bh7XHwkkxmTl9NS7rTh4VYPRC+KMfXD0G0wbDQ4vAxTvrtkJko0ePHtSpUyfL+ipVqtxarly5Mnv27CEkJITk5GRq1aqVpblOUZBrUiHuXDqdxtj2gby36AjNqpThsY7V8t7JCvHJaeh1Gk7Gov9/kFXavQDHlsGlvbDoGRg5L+fmMKfWsHnLej452pLAgOG0Cp/D72m9+H5DOIkOJ9Xvf9QFmDoIKrWAwT+ll3kpjXR6GDIZ/uwLlw/AzOHwyH/g7GXvyISwK7smKuvVq2fVdk5OTtSvX7+Io0k3fvx4xo8fT0xMDJ6e+Wu0IIQQwj5+2xTCzxtC+GZ4gzy3dTDoCpykBCRJWURe6JG1/nSVsq6MaV+VXzaEcDg0hpQ0Mw6G275/72owehH80ReuHILpQ+GhBeAkv+Eib35+fvj5+Vm1bbVqtkk0WEuuSYW4sw1rVone9cpTzr3g1xwZ/brxNN+vPcVrveswqnWVvHcoDnqDGjX5cwc4tRr2ToVmo7Nud/U45un30h4zR9w8cb4aDRq8YJzPSOMG3lrzIN8ximcrn4XrZ8GUCvFXwd26/z/fsRzdYMTf8Fs3iDgBcx6CUf+AXhrFiLuX3GkJIYS444VGJbLn3HUi4pL5evUpLsTBkUsxHA6N5nBoNKFRWUfvA1gsFv4LDuPluQcK1BgnJc3MnF0XOBUeW9iPIHLxdJca+Lg5EBIRn3NDAp+a8NBCcPZWozpmDIPkbEZgCiGEEMXEyai3WZIS1IPS2KQ01h8Pt9kxbaJcbej6Jrj4gKtPjtuc9WoLgHNadKa3fCyR/OzwDYFX16hGM/dNg7HLS3+S8iaPCvDA3+DgBmc2ZN+cSIi7iCQqhRBC3NFCoxLp+sV6lh++AsCe81F8ccjA4J+20//7zfT/fjNdv1ifbbLyalwyT8/cy7w9F1l66HK+z/32wsO8+s9Bflx3utCfQ+TM3cnISz1rA/Dt6hNci0/JfkO/oPSRlBd2wKz7ISWh+AIVQgghcvD75jMM+XEL+y9EFfgY9zarxB9jWvDrgyWw6Uyb8TB+J9Tpl/37ZhPVTCHZvqXTwAL0v/QdmE3qGJ6V0jeIuwuaF5ZvAPf+AZoO9k2DLd/YOyIh7EYSlUIIIe5o1+NTSE4z57pNcpqZ69kkt3zdnXiysyrw/uHSoySk5K9w+4iWlfF1d6Sev0zJLGrDmwdQp7w7MUlpfJNdU52bKjSCUf+Cgzuc3QR/j4TUpOILVAghhLjNzxtO878lwew9H8W0bQVvquPuZKRLbV90VjYQLFY6PbiWTX9tSs38/rmtEHtbs52MuwNaTKjaLqMLO+H7ZrDtR9vFWlLV6gm9P1XLq9+FI//aNRwh7EUSlUIIIe5qT3SqTqUyzlyOTmLSulP52rdRgBdbJnTlkfaBRRSduEmv03h7QBAAM3ac50RYLtPtKzWDUfPA6Aqn18Lc0ZCWwyhMIYQQooh1rl0O/Y3k4qIDodk+PM0vi8VCUmoJbbR1eD581wQiM8w4iQuzate4yNDMK85uguRoOLr47ugE3uoxaPWkWv73Cbiwy77xCGEHkqgUQghxV3My6nm7v0qATd54hjMR8fnaXxrrFJ+21X3oGeSHyWzh/SXBudcVrdwaHpgNBic4sQL+GXt33OAIIYQoceqU92D7612p7+9BqsnC3D0XCnW8dcfC6fn1Rr5cddxGEdqQxQK7f4foC7Dl2/T1btbVm5xz7LYkbvsXYeD36gGk3q69gItPrw+hVh9IS4LZI+B6wUfhCnEnkrurbEyaNImgoCBatGhh71CEEEIUgx5BfnSqVY4Uk5n3Fh8pUGOdPeeus+hAzlOahG280bcuRr3GppMRrD+eR82qwI5w/wzQO8DpdaqbphB3ELkmFaL0KOfuxIM3OnVP334es7ngzVIsWDgZHsfSg5cxFeI4RULTYNAk6PwG9P0ifX2VtuBREch+2roFjUuWsnwc7M3JjLMmNA2aPgQOrunr4iOKJvaSQqeHob9B+Yaq8/nM4ZAYZe+ohCg2kqjMxvjx4wkODmbXLhlmLYQQdwNN03hnQBBGvcb641fZl89C91tORTD0p628teAwcckyaq8oVfVxZUw7NdX+/aXBpJpyr09Kje4wfBo8uEA12xHiDiLXpEKULgMb+ePioOf8tQQ2nix4g5jOtXz5eEgDVrzQ8daU8hKlTBXo/BoYHNLX6fTp9RezJCs1NCDSpRrlzJF8sPRozsfePwu+aQghG2wcdAnj6KY6gbtXhKvHYM5DWet+ClFKSaJSCCGEAKqVc+ONvnX54+EWNK1cJl/7tq5Wltp+7vSq51dy60WVIk93rUFZVwdCrsYzfbsV06Fq94aADCPSoi+qqWlCCCFEMdp19hoJKeo6Yeq2swU+jk6nMaJlZTycjDaKrAilpcCu31T5laCBMHwqeFTIvI1HRajZkwaJu5jr+D/OnjzEuuPhWY9lscDxpZAaD8eWFk/89uRRUSUrja5wZgMsfVGuX8RdQRKVQgghxA1j2gXSpY5vvvfT6zSWPNuez+5thI+bYxFEJjLycDLyYs9aAHyz+mT+mhJc2gc/t4cVr8vFvhBCiGLVqpo33q5qlGEFT2ebHbfETf++yWKB6UNg6Uuw5Ru1LmggPH8YRi+BoVPU388fgv5fQ9ka+GsRzHH4H9MWrco6a0LT1D79voLenxT7x7GLCg1h2B+g6WDv1Mx1P4UopSRRKYQQ4o5WxtUBR0PeP2f5nZJ9NTaZsJgkq7eXpjrF677mAdQp7050Yirfrjlp/Y7hxyDxOlzcBamJRRegEEIIcRtHg56x7aoCcOBiVIFqYmd0MiyWx6bu5umZe20QXRHQNGgySi2v/wSuHFLLOj0EdoAG96q/dXrw9IcxyzH51MVPi+KzuNdZsfq/rMc0OEKLR0B347rLYoH4yOL5PPZSq1d6Ynb1O3BkgV3DEaKoyV2VEEKIO5q/lzNrX+7Mkmfas+SZ9ix4sjUvN0hjwZOtWTS+HQ0reeLv5YSbo/WdIlccvkLXL9bzzsIj+Y4nPDaJH9efKrmjG0oJg153q1v7tO3nOBUem8ceNzQeoWpWPrQAHFyKLkAhhBAiGyNbVcHJqONwaAzbQ64V+nirgsNYFRzG1dhkG0RXBBreB7X7gTkV/n1STQXPiZsv+rHLiPQIwkeLodO2McSd3pHz9hYL/PeWmilx/azNQy9RWj0OLR9Xy/8+Dhd32zceIYqQJCqFEELc8fy9nKnv70l9f0/qVfQgwA3qVfSgYYAXU0a3YNULnajv72n18ar6uJCQamLFkStsykex+zSTmf7fbeazFcf5L/hKQT6KyIe2NXzoEeSHyWzJvfD+7YIGgqN7+uvQEjoSRQghRKlTxtWBe5tVAuCdRYdZdyybWoxWqunnztv9g1j5fAfKuZfQ0jOaBgO+AWdvCDsEGz/PfXsXbzwfX8YRfR08iMc8+wFIzWGGS3IsnPwPYi/B2c02D73E6f0x1OoNaUkw6364bkWdbiHuQJKoFEIIUaqVc/9/e/cdV1X9BnD8cwd776GoqKjg3nvvUWZqaY6y1HJUajY0q197a1a2TNuOUnPvPUslB4gDcKEoogiyx73n98dREAEZApfxvF8vXnHP+Z5zn3tCOPe53+/zWGBz12xKYwFmOtbztGdUm+oA/G/1CdIy8uksfZtep+Xxlj40q+aIk7V5/geIBzazn39mt/ZcC+/nZ89smN8VDnxT/MEJIYQQuXi6vS8AZ6IS+Hzz6Qc7Vwdfarvb5T/QlGzdof/n6ve7P4NjSyDyaM6v2AgA9DZOxDy6lK2GpjyXNIGzsXmU77G0h1Er1bqVd5aYV2RanfpaPRtCYjQsegxS4kwdlRDFThKVuZg3bx4BAQG0bNky/8FCCCHKBaNR4ed95xj0zb4Cdeae2rMOLjbmhEcn8vP+cwV+nhe6+7F8Qjta13R5kHBFAfm62vBUuxoAvL/uZM7C+/nJuL1UbtMMtSupEGWI3JMKUTHVdLOlk58rAMGRtwiJvFUs533QmpclqmpLtSEMRnXp8g+dc3593TwzWdmxfg3+qPkJ+w3+fLD+lHqOtMSc57X3Umtd3pGRBinFcz3LJAtbGL4U7Lwg+hT8+SQY0k0dlRDFShKVuZg0aRIhISEcOnTI1KEIIYQoJrdS0vl6RzjHLsWx/L9L+Y53sDLj1b71AJi7NZRrBWysY6bTotFoHihWUTiTu/nhbGNO2LUEFv17sXAHd3kN2k9Rv1/3Evz3W7HHJ0RRyT2pEBXXpK61M7//ce/ZBzrXzcQ0Plx/kuHz/ym7ycqkG6Dk82FiRqo67rbX+/uj02rYejKKI4f3wZdNIXhF3senJ8PSEfDH0NyTmhWFQxV4YimY2cDZHer9S1n9/y5EEUiiUgghRKXgaG3OJ0Ma8s7A+jzRqlqBjhnSrCpNfBxJTDPw4YZThXq+dIOR5YGXim2WhMibg5UZ03rWAWDO1jPEJt2nUP+9NBro8T9oPUF9vPp5OP5X8QcphBBC3KWVrzM1XW0AWHMsklspRZ8Vp9Vo+PXABf45G8Oh8zeLK0STq+1ul1mK58LmbyEhCpY/A0cX535A7EW4+C9cOQZRIaUYqQl4NYYhC9RZqv/9Avu/NHVEQhQbSVQKIYSoNLrV82B02xoFnvGo1Wp4++H6mOk0OFiZFai+5R3vrzvJS38d4+sdoUUNVxTCsJY+1PWwIzYpnbnbCnnNNRq1QH2LpwFFXZJ2YmVJhCmEEEIAoNFoeLG7HwDpBoU/D0UU+VwO1ma81rce80e3oHl1p+IKsUx4sbuf+oHkrccJ8xmszspc+RwcXphzsFtdGLkMRq0An0pQMqNuX+j9ofr9lrcgZLVp4xGimEiiUgghRKWUkJrBssD8l4A39nFk76vd+N/D9dFqC76ke1grH9ztLGji41h2l2FVIHqdllkD/AH47cAFwq4lFO4EGg30+xyajADFoM7YOL2hBCIVQgghVP0aeWFvqTb8+2H32Qe6X3iyXQ16BnigK8S9SnngZGPOC939MKJleOTjpDUfp+5YOxUOzMt5gE8rqN4u63FyLBgLWb+6PGnzHLQaDyiwYjxcDjR1REI8MElUCiGEqHSS0wwM+HIP0/86xuYTV/Md72FvWejnqOdpz/7XujG+Uy2pWVlKOvq50cPfnQyjwvvrirDkS6uFh7+CBkPAmAF/joawrcUfqBBCCIFa13pcx5q3v9cQn5pHd+tKblSb6tR0tSE6MYPZumegw1R1x6aZsPvTvA+8dQUW9IL1FbyGY+8Pwa8XZCTDomHqEnghyjFJVAohhKh0rMx19G7gCcCMFUFcT0gt0HGnr8Yz/tfDxCQWrAaiXid/ZkvbzH7+6LUadpyOZufpa4U/gVYHg74H/4fBkAZLRsC53cUfqBBCCAE82b4Gvz/Tin2vdcfe0uyBzpVuMLL00EWG/XCAlHRDMUVYysK355gBaa7XMrOfumpi4b7zXGzyMnSdpe4M2652+s7N5UC4fgbObILE6JKM2rR0ehiyEDwaQuI1+OMxSIkzdVRCFJm8gxJCCFEpTetZh3qedtxITGPGiqB8l1spisLLy46xOSSKTzedLtRzHY2IZUUBOo2LB1fTzZYn29UA4L11J8kwFGG5l04PgxdAnT6QkQKLHocLB4o3UCGEEAKwtzSjg59bsZxLq9Hw5bYw/jkbw6qjl4vlnKVu29vw15M5Nnf3d6dDbVfSDEY+2nQKOr8Mg35Qu1/rzXM/l/8AGPwjjNkAtu4lHLiJWdip18LOC6JPwl9PgaHoDZqEMCVJVAohhKiULPQ6Zj/WBDOdhi0hUfyVT71KjUbDGwMCAFhy6CLHL8UW6HkCL8TwyLx9vLEymLgkuWEsDS9088PJ2oywawksOljE5U96cxj6C9TqBulJEF24ru9CCCFEYR29eJOVD5Bg1Gk1vNjDj5n96tG7vmcxRlYMrF1Ab3H/MVo96CzUZcz30Gg0zBrgj1YD64Ou8u/ZG9D4cbC0zxp0aj0Y75lJ2nAIOFXPepxccbqi5+BQBYYvATNrdWbq+pcr9pJ3UWFJojIX8+bNIyAggJYtK0GnMCGEqMQCvO2Z1rMuAO+sCSEiJum+41vWcOaRJt4oCry56kSBuoA3q+ZEgyr29GngRWpGOV2GVc44WJsxrWcdAGZvOVP0BLGZJTz+h3rT32JMMUYoRMHIPakQlceH60/yyDf7eW35cdKLshrgtsda+DC+Uy0crfOYZWgqjj4wORDG78r764Wj8HwgNB2ZdVz0aUhPBtT638NaVQPg3XUh2e/D9s6BJcNh5QQw5FHr83IgfNkM/vu1hF5kGeDdRF0VggYCf4IDX5s6IiEKTRKVuZg0aRIhISEcOnTI1KEIIYQoYeM71aRFdScSUjN46a9j+SYfZ/Tzx8Zcx9GIWJYXYDm3RqNh5cT2fP5YY9yL0JRHFM3wVtXwc7clNimdudtCi34ic2uo2zfrcVKM+qZJiFIg96RCVB6NfRwBSEk38sbKIL7eHsqify9yLCKW4MtxXI5NNm2AxcHRR02k5fXl6KN+3WlCmBQDvw2C+d0g6gSglu6xs9ATfPlW9vswBx/Q6OD4Ulg2Jve6lac3QHIMHPk958zLiqReP+j9gfr95jfg5BrTxiNEIUmiUgghRKWm02r4/LHGWJvrOHguhgV7z913vIe9JS/28APg442niEvOf7aeNNUpfXqdNnOp/q8HzhMenfDgJ028Dr88BD/3h+gzD34+IYQQArgcm8zUpUczHy85dInPNp9h5t9BDJy3jwFf7aXbZzsLlaw8eC6GSYv+40pcOU5wxl5Q6yxeC4EfusI/3+FqY87kbrUB+HTTaRLvdEpvOAQe/w105nByNSwdCekp2c/X9XXo8zGMXK42z6vI2kyAluMABZaPU2eTClFOyDsnIYQQlV51F5vMpNanm05z+mr8fcc/1c6Xmm42XE9I47cD5wv8PNcTUpm3I+yBlnSJgutUx41u9dzJMCp8sO7kg59Qq1NneWi0oMj/QyGEEMXjZmIaqRn3/7uSmmHkZmIe3a1zMXvLadYdv8Lv/1x40PBMx7spTNgPfr3BkAobX4VFj/FUExuqOVtzLT6V73eFZ42v1x+GLwa9JYRugkWPQVpi1n6NBto8pzaeuSM5ttReTqnSaKDPR1C7J2Qkw+LhEBth6qiEKBBJVAohhBDAsJY+dKvnTprByNSlR0m7zxsGc72Wdwc24I0BATzbuVaBzm80Kgz8eh+fblLfOIjSMbOfP3qthm2nrrH7TPSDnczKCUatUruHutcrngCFEEJUeoYC1LwuzDiAcR1rMryVDw83rlLUsMoGWze1m3W/z24nIDdj8UMHPm96DYDvd5/NPtO0dg91xqS5LZzbBb8PzrtmZdAymNsIIg6WwgsxAZ0ehv4EHg0gIUpN3KbcMnVUQuRLEpVCCCEEai3JjwY3xMnajJArt5i77f5Le9vXduWZDr6YFXBZt1arYXgrHxr7OOJul0/XS1FsarvbMqqt2u3zvXUhZDzobFYbF3C5Kzl9fh/cinywcwohhKjUTkQWLHlU0HEA3f09+PDRRtT1tMt/cFmn0UCrcTBuB7gHQGI0LfeN5xvnpZCRwicbT2UfX6MDjFoJlg5Qf5CasLuXosCxxZASp9a1rKgs7NREr62nuoT+r6fyTtwKUUZIolIIIYS4zd3Okg8GNQQg8MLNAie1UjMMhEbdf7k4wHOda7FyYjva1XZ9oDhF4bzY3Q9HazPORCWw+FAxLns6uxN+fxR+eRgSrhXfeYUQQlQqMYmpxTquwvIIUJOVrZ8DoF/SKlaZv0HIsYP8d/Fm9rE+LeH5/6D1s7mfS6OBx35Vm870/bSEAzcxh6rwxBIws4bwbbDhZTVRK0QZJYlKIYQQ4i59G3qx8KkW/DG2TYGa4JyNTqDvF3sYteBgVkH3POh1WjR3OlmKUuNobc7UHnUAmL35dIEaIBWIky9Yu8KNUPh1ICTeKJ7zCiGEqFScbQq20qKg4+52JS6Zzzad5si9ibzyyswS+n4MT/wFNm7U00awxvx1Dv35MYrxng+Ybe76YDgpRp1NePcqCHMbaDsJtHfd76XElWj4JuPdFAb/CGjg8EI4MM/UEQmRJ0lUCiGEEPfoVs8DnbZgCUVvRyvSjUau3kph3o6wAh2TbjDy95FLHL8U+wBRisJ4onU1arvbcjMpna+2hRbPSZ2qw5Ors5ZT/fYIJFeQN4JCCCFKTX1v+2Idd7cvtoTy9Y4wFuw9V+hjy7Q6vWDCflJrdMdSk86zCd9yaONveY9f8wKc+Bt+6gs3c2kwpCiw9W34vhPcqqC1xOv1h97vq99vngUn15o2HiHyIIlKIYQQIg8p6QbeXnOCHafzXtZraabjzQH1AZi/5yxnoxPyPe9nm04zdekx5m4tpoSZyJeZTsus/v4A/Lz/fIH+PxWISy14cg3YuMHV42rRfilUL4QQohAK+uFoQcfdbXS76rTydeahxt6FPrbMs3XH4snl7K41nU2GFkw96k1ymiH3sb0/AKcacPM8/NQPboRn358SBydWqPvDtpZw4CbUZiK0eAZQYMU4uPyfqSMSIgdJVOZi3rx5BAQE0LJlS1OHIoQQwoR+3HOWn/ad59Vlx/O+8QV6+LvTuY4b6QaFt9eEoORT92dYq2q421nQvIZTvmNF8elS152udd3IMCp8sP5U/gcUlFsdGL1K7Qp+ORD+GAqpxZQIFZWa3JMKUTk42Zhjob//W3MLvRYnG/NCn7u+twN/PtuW3vU9ixpe2abR0GrYTN6xnsnlW2n8uOes+jd496eQcVdNT8dqMGYDuNaBW5fUmZXXTmbtt3KE0ath4DfQbFSpv4xSo9FA30/U7ujpSbB4GMQWY/1uIYqBJCpzMWnSJEJCQjh06JCpQxFCCGFCYzvWpF0tFz4e0ggrc12e4zQaDW89FICZTsOuM9FsPXn/xiq+rjbsf60bE7vUlpqVpez1/gHotBq2noxib+j14juxR321w6iFA0T8o974pycX3/lFpST3pEJUDlUcrdg+vQtrn+/Aqknt+WBQQ6b3qkPjqg6ZY356qgVVHK1MGGXZZWmm49V+6qqJb3aGk7x6Omx/D/4cnX2gvTc8tR48GkJClDqzMvJo1n6n6tB0RNZjQzqkJZX8CyhtOj0M+Qnc66vXYdHjshpElCmSqBRCCCHyYGmmY9G4NnSt657v2JputoztWBOAd9aeICU97xmYQIEa9YjiV9vdllFtqgPw7tqQAnd2LxDvJjBqBZjbwvk9sGRE9tkcQgghRB6qOFrRoIoDjX0ceaJ1NSZ382P+ky24s9r76x3h9z9BPlIzDCwPvHTfcjbl2UONvGhWzZHkdAOL4xuBjTu0ez7nQFs3tb60dzNIjoGVE+HeJjwA6Snw55Ow5ImK+bfc0h6eWAq2HnDtBCwbA4b7N4UUorTIuyQhhBCigC7HJhN1KyXP/ZO71sbT3pKarrbcSilYZ+njl2L585AsuSlNU3r44WBlxumoeJYeLuZrX7UFjPgLzKwhfJvaYdRQTF3GhRBCVCrudpb0a+gFwP7wG5y6WvRZb7/sP89Lfx1j9uYzFbLsjEaj4Y0BAQC8c6Y6wUP3QI0OWQPCd0DiDfV7a2e1ZEvAQHj8t+xdv++4EQZnd8KF/XA1qORfgCk4+sDwJaC3UutybnxVbSokhIlJolIIIYQogB2nrtFnzm6m/3UMozH3mzgbCz2rJ7fn5zEtcbezzPecQZfiePjrfbyxKpjrCRXw0/oyytHanKk9/AD4fPOZAieVC6x6u9s3/pZwej0sf0ZmKQghhCiSGf38uVMk5s2VwUU+z+BmVanhYk2fBp4Y8riPKe+aVnPikSZq06B3Np7LSsheD1VnRn7bTk1Ygjqj8LFf1aZ4dyTFZH3v2QCGL1Y/fKzaopRegQlUaQaDfwQ0cOhH+OdbU0ckhCQqhRBCiILwcbYmzWBkT+h1fv/3Qp7j3O0tC1x3skEVe5pXd6J/Qy/Si3MJssjXiDbVqeVmQ0xiGl9vDyv+J6jZGR7/A3Tm6lJwqUUqhBCiCKo4WtGngdoI5+D5mxyLiC3SeVxsLdgxvQuTutau0OVnXulTD0szLQfPx7Ah+Kq60ZAODlUh4Sr89ghsfgMy0rIfGLoVvmgIp9ZlbavZWf26IzW+Ys449B8Avd5Tv980M/s1EMIEKu5vKCGEEKIY1Xa3ZUbfegB8sP4k4dH37+ock5jGjBVB7AmNznOMRqNh6fg2zH68CV4OUiC/NJnptMy6vUTsp33nOH89sfifxK8HjN0KD38N2rybMQkhhBD382of9f5Dr9VwMyktn9F5qwwN/LwdrRjfSZ0l+eGGk2rNcI8AGL8Lmo9RB+3/Ehb0UGda3hG8DNISYOkoCFqW88TxUbCgF2x5o2ImK9tOghZPAwosHwuRR0wdkajEJFEphBBCFNDotjXoUNuVlHQj0/48dt9GLN/vDmfxwYv8b/UJ0jLyHleRZzWUdV3rutO5jhvpBoX3158smSfxapxV+8qQAUcXV8w3OEIIIUpMDVcbfhjdnKC3etGlAA3+7kdRFP67eJONwVeKKbqy57nONfGwtyAiJpmf959XN5pbw0NfqKsdrJzhyjH4vhME/qL+XX74a2g0DBSDmqg78nv2k57fA9dC4PifkHi9tF9SydNooO+nUKs7pCfBomEQd8nUUYlKSt4dCSGEEAWk1Wr4dGgj7Cz1HIuI5ZudeXfgnNilNi425oRHJ/Lz/nP5nvtGQipfbw8lNeP+3cJF8ZrV3x+dVsOWkCj2hZXgGw9Fgb+fhZXPqf+NPApXjuGQdF59sxR5VP2KlcZKQgghcuoV4ImVhf6Bz7PzTDSPfrOfWStPVNh7DmtzPa/0Vmehfr09jOj4u+qA+w+ACfvBt7OakFvzAvw5ClJvwSPf3p51qcCqSXBwftZxDYfAw1/BmA1q5/CKSKeHoT+De4C6TH7R4+pydyFKmSQqhRBCiELwcrDi3YENAPhyWyhBl+JyHedgZcart5eKz90ayrX7dAtXFIWh3x3gs81n+Pu/y8UftMiTn4cdI1tXA+DdtSEl12BAowHPhur3x5fCD50xW9idLqffxGxhd/ihs/r1dXNJVgohhMjTreR0Zv4dxP4ifrjWobYr1V2s6VLXjcTUipmoBBjUtAqNqjqQkJrB7C1nsu+094JRK6HnO6A1g5Nr4Nv2cGEvDJgDbSaq49ZPh31zs45rNjp7853U+5cBKpcs7eGJP8HWA6KC4a8x0hBQlDpJVAohhBCFNLCJN/0bepFhVJiy9Iha/ygXQ5pVpYmPI4lpBj7ccCrP82k0Gp5oXY1GVR3wdpRalaVtSo862FvqOXU1nqWHSjBJWLNL/mMyUiHpRsnFIIQQotxKTM2g06c7WPTvRd5ZcyKrq3UhmOm0bJ3Wmc+GNsbZxrwEoiwbtFoNb9yuRb300EVOXrl17wBo/6JaS9qlNsRHwi8Pw7a3ocf/oON0ddy1k7mXbLlyDL5sCsHLS/aFmIKjDwxfAnorCNsCG1+TsjWiVEmiUgghhCgkjUbDe480wM3OgvDoRD7emHsSUqvV8M7A+mg08PeRyxw6H5PnOZ9qV4NVk9rTqU4FXU5UhjnZmDOlRx0APt98mlsp6SaOSAghhMjJxkJPwyoOAJyKSmBLSFSRzmNWSepjt6zhTP+GXhgVeG9dSO6JXe8m8OxuaPYkoMDeORDxL3R/Ax77Ta1dmVsTouN/QuI1+OdbMOZdi7zcqtIMBs8HNHBoPvz7nakjEpVI5fgNJYQQQhQzJxtzPhnSCICf9p3PcwlWo6qODGupLi2ec+/So7voddpK0Y2zrBrVtjo13Wy4kZjGvO1hpg5HCCGEyNXHgxsxrqMvALO3nMH4ACVLrsWnVOimOgCv9a2HuU7LvrAbbD15LfdB5jbw8JdqYrLjdPDtpG4PeFit2whgNMCRP7KSkj3fhe5vwsjlWU3zKhr/h9Tl8QAbZ8Cp9aaNR1QaFfRf1IOZN28eAQEBtGzZ0tShCCGEKMO61nXnidv1Daf/dYy45Nxn4r3cuy5j2tdg3hPN8j1nhsHIqqOXCbxws1hjFfdnptMyq78/AAv3neP89UQTRySE3JMKIXLydrRiclc/7G6XLFkbVLRE46WbSbT/aDvPLz6SvdlMBePjbM0ztxO7H6w/SVrGfWY/BjyszqS8I+4SrJwEyTfVpjurJsKa59WkpVYLHV8CS4es8WkV8N6h3fPQ/ClAgeXPqI3/hChhkqjMxaRJkwgJCeHQoUOmDkUIIUQZ93o/f6q7WBMZl8KGPN4sONuY89ZD9XEqQC2oL7eF8uKSo3y++XRxhyry0bWuOx39XEk3KHy44aSpwxFC7kmFELlysDZjXAc1+fbZxlNkGAq/9LiqkzUNqzjQqKojMYlpxR1imTKxSy1cbc05dz2RXw+cL/iBqybB0d9h1WSo0RE0WjjyO6wYD4Z7PpwOWQVzG8OV45mbNCEr6R30PJqTq4rnhZiCRgP9PoNa3dQu6YuHQZw0fhQlSxKVQgghxAOwsdAz5/EmfDeyOcNaVct3vKIoBF/OvVM4wGMtfXC3s6BtTZcHWs4lCk+jUQvvazWw6UQU+8OL1lFVCCGEKGkRN5MBuHgzmRVHipY4+vWZ1iyf0I66nnbFGVqZY2dpxvRedQH1A+GbBU3Mdn8TvBqry58bD4MhC0Grh+Bl8NdTagM8UBvN/PsDJEZD4M/qtoRodOunYZERh279NEiILvbXVWp0ZjD0Z3Dzh/grsOhxSI03dVSiApNEpRBCCPGAmlVzok8Dz3zHpWUYefKnQzz09V6ORcTmOqaqkzX7X+vG89390GqlZmVpq+Nhx4jW1QF4d+1JDJIsFkIIUQYNaOwNgFYDtdxsinQOWwt9cYZUpg1t4YO/lz23UjL4YmveNcOzqdIcxu8Cl1rq4/qD1GXQWjM4tRaWPAFpSeqsw+GLoNss6PepmrhcOxXSEtEApCbAumkl9MpKiaUDjPgTbNwhKgi+7wKGjJzjdn0COz4s9fBExSKJSiGEEKIYXYtP4evtobl2ljTXa3GxMUdR4M3VJ/KcMamvJN04y6qpPetgZ6nn5JVb/HU4ovhObO0Ceov7j9FbqOOEEEKI++jk50qjKg4YFdiWV5OYAkrLMBJys2J/OKrTanhjgFqL+vd/LxIaVcAZgXc3Ojy7Cw4tAK1OTVaGbYU/R6uJSUsH6PSyuu/ECji1Bo1iUE+hGODkagheUdwvq3Q5VoPhS9RZpTFhsLB39v27PoEd76vXQIgHIO+EhBBCiGKSkm7g4a/28dnmMyw5lHuCa0bfetiY6zgWEcuy/y7d93zBl+NY9O/FkghV3IezjTkvdvcD4LPNp4lPyb1JUqE5+sDkQHV2xvhdpD+9jZ113yH96W2Z25gcqI4TQggh7kOj0TC5W20Afj1wgbikov2tSssw0nvuXr4/pSPoPqVpKoJ2tVzpGeCBwajw/voi1KJ2rgnV20FGChjTQWcOTUdmT2YmRKuzKXPQwNop5XsJOEDV5uoSeIDLh+H3Ier3d5KUXV+Hzq+YLj5RIUiiUgghhCgmlmY6nungS31ve5pXd8p1jLu9JVN61AHg4w2n8uwUHhoVz4Cv9vLW6mCuxCWXWMwid6Pb1sDX1YbrCWnM2xFefCd29AHvJuqXV2PirGuo9a/ubJMkpRBCiALq4e9BPU87ElIzGDb/AD/uOVvoc5jrtTSv7oS9mULUrYrb/fuOmf38MdNp2Hk6mp2nCzkT1dEHnlwD3d4AjQ4MabB5Flw4oO6/s+Q7NSGXg5WKsQQcIGCgWrcTIGwLvOMiSUpRrCRRKYQQQhSjZzr48vfE9tTxyLsw/VPta1Db3ZYbiWnM2ZJ7nSQ/Dzva1XKhX0MvqZNoAuZ6La/3U5eILdx7jos3kkwckRBCCJGdVqthUld1VuXJK/F8vT2UhNRc6gbmY2bfurzVzEAPf/fiDrHM8XW14cm2NQB4b93JwndM1+qg03R4ZjM41YC4CPi5H6ybDj/2gFNr4PaS7xzuLAG/VoTZnGVNuxeg2ZPq98YMdTm4JClFMZFEpRBCCFGMtFoN5vqsP683EnLOTjDTafnfQ/UB+O2fC5y6eivXc/36dCvmDmtKVSfrkglW3Fd3f3c6+rmSZjDy4YYK8KZCCCFEhdOvoRe+rmozndjkDBbuPVfoczjbmKOvRJmB57v74WRtRti1BBYdLGKJnaot4Lm90PgJUIxwaL66FFpvCZq8LqYGfLuAu38RIy9DNBqw88p6bMyAXx5SZ5UK8YAq0a8jIYQQovQYjAqzN5+mw8c7OHklZyKyg58rfRt4Ut3ZmsTU3D95l6Y6pqXRaJjVPwCtBjYEX+WfszdMHZIQQgiRje6uWZUAP+wKJzYprcjnC74cR3phZxmWMw5WZkzrqZbhmbPlTJHre2JhB4O+VWs2mtuq2zJS1MRlrhQ4txNuVoD647s+gV0fQZeZ0PElddu53TC/a+7dwIUoBHkHJIQQQpQArQZCrtwiOd3A1KVHSc3ImYz88NGGbJzSKc96lnfcTExj3o4wktLkxq+01fW044nW1QB4d22ILMMXQghR5gxs4k1VJysAEtIM/LC78LUqASYuOsqAr/ayMfhqcYZXJg1vVQ0/d1tuJqXz1fbQBztZg8Ew8QC41ivY+OSYB3s+U7u7cU6XV6H7m9DvM3Vf5BH4pg2kSckcUXSSqBRCCCFKgEaj4cNHG+FiY86pq/HM2ZLzJtjR2jzbMvHcKIrC8Pn/8Omm0/yZRydxUbKm9qiDnaWeE5G3WB54/07tQgghRGkz02mZ0KVW5uOFe88RHV/4xjj1PG0x02m4cCOxOMMrk/Q6LbMGBADwy4HznLv+gK/ZsRoM/LpgY+/MuLxyHJJvPtjzmoLRkLNxTqtx8Pjvaq3KG6Hw60BIKucJWWEykqgUQgghSoibnQUfPNoQgO93h3PofO43bOkGIz/uOcvXuXyir9FoGNmmOvW97anuYlOi8Yrcudha8GJ3PwA+2XS6SI0KhBBCiJI0pHlVPOwtAEjJMBapA/joNtXZ91o3JnfzK+7wyqTOddzoUteNdIPCB+uLoRZ1VHDBxkUchMTr8MdQ+K4TROfeWLHM6joj98Y5/g/BU+vA0hEuHYQFveDm+dKOTlQAkqgUQgghSlDv+p4MaV4VRYFpfx7NNcl1IPwG7607ydxtoZyNTsixf3iraqx9vgNd61X8bpxl1ei2NajhYs31hFS+2RFm6nCEEEKIbCz0Op7tVAutBlrWcGJSt9r5H3QPR2sz3O0sSyC6smtWf390Wg1bQqLYH3b9wU5W0BmEG19VZxxmJKuzKy0dHux5y5JqbdSO6A4+6szKBb3gyjFTRyXKGUlUCiGEECXszYcCqOJoRURMMu+vC8mxv6Ofa+Yn+v9bE4JyT8dEnVaDRqPJfLz2eCQt39vCuuNXSjx2oTLXa3m9v7pE7Me954iIkdpLQgghypbhraqx6+Wu/PVcO+wtzR7oXDcSUrmVUsQmM+VIbXc7Rt6uRf3Og9aitnYu+NioYEiJg1uXYLY/zO8OlwMrRm1Ht7rwzBbwaAAJUfBTPwjfbuqoRDkiiUohhBCihNlbmvHZ0MZoNLD4YATbTkZl26/RaHjrofqY67TsPhPNlpCoXM9jMCos+vcCryw7TnRCGjNWHOd6QuFrUImi6eHvTvvaLqRlGPlowylThyOEEEJkY2Wuw8fZOvOxoihFasQ3b0cYbT/azm8HLhRneGXWlB51sLfUc+pqPH8efoB64F6NCzbu8UXwyLfQZIRa21IxwOXDEHEIvm4BF/+BEyth8xtwZjOk3Cp6TKZi7wVj1kONjpCWoC5zP7bU1FGJckISlUIIIUQpaFvLhWfa+wLw6vIgbtyTYPR1tWFsR3X/u+tCSEnP2SX8251hzPw7mKQ0dV9imoFZKwtYD0k8MI1Gw6z+AWg1sC7oCv+evWHqkIQQQohcrT0WSa85u4t0n+DlYElahpGjEbHFH1gZ5GRjzos96gDw+ebTxBd1JqmmgOkVB2+4dAgaDoEpQerXoz9C0J9w6zKc2QTBy2H/l7BoKHxcHX7oejtxuUmdiVkeWDrAyOXQYAgYM+Dv8bD3C1AeYNaqqBQkUZmLefPmERAQQMuWLU0dihBCiApkeu+6+Lnbcj0hldf/Ds6xxHtyt9p4OVgSEZPM97tyFsF3sjbP9thgVNgYfJW1xyNLNG6Rxd/LnmGtimmJmBD5kHtSIURRrDkWyeTFRwi9lsDf/10m7Fp8oY7v38iLFRPb8cOo5iUUYdkzqk11fF1tuJ6Qxrwd4UU7ibUL6C3uP0ZvoTbTObwQfhsEG2eAjTs0GgqjV0PXWWpH7YZDoekocPJV61hG/nc7cfkYfFYH0lOyzmkow03+9Bbw6HxoO1l9vPUt2PCq2jlciDxIojIXkyZNIiQkhEOHDpk6FCGEEBWIpZmOOY83Qa/VsPHEVf4+cjnbfmtzPTP7+QMwf8/ZbJ/oX09I5eONp9CQnQaYuSJIloCXomk962BnoedE5C2W/3fJ1OGICkzuSYUQRdHRzxU7Cz3udhYowJwtoYU63kKvo1k1p2z1sSs6c72W12/fgy0sai1qRx+YHAjjd8G4HWT0/ZwQzyFk9P0cxu1Qt08OVBOQLZ5Rj/nnG5jfDaJOgIUtdH4ZdHoIeBge/gpqdYMhP6vJvmajwbkmeDYCs7uaHi3sDd93gk2vw+kNkBz7wNejWGm10Pt96P2B+vjg97BsTPZkqxB3kUSlEEIIUYoaVHFgSg8/AN5bd5LktOyfKA9o5MWznWuyYmI77G4XwlcUhdf/DiIxzcC98/cUIDFVloCXJldbC57vrnZT/XTT6Vw7uQshhBCm4mhtzp5Xu/LrM63Q3C5XEny5aMuFMwxG4pIqflMdgO53alEbHqAWtaMPeDeBKs1Qmj1JqNfDKM2ehCrN1O2OPmBuDQNmw/ClYO0K106oS7sPfANGY9a5ji6Cwwtg5XNQs4uauHzhCIxelTUmNV6dbXnlGBz4GhYPg49rwHcdYeNMCN9R9AtS3NpOgiELQWcOIavUGaXJN00dlSiDJFEphBBClLLnOtdicLOq/DKmFVbmumz7NBoNM/r6U8fDLnPbmagENp2IynOZsUFRl4CfiSrc0i5RdE+2q0F1F2ui41P5dmeYqcMRQgghsnG0Nqeepz0PNfIGYM6WM4U+x5aQKDp9soP31oUUd3hl0r21qA+eiynZJ6zbByYeAL/eYEiFTTPg90fh1hV1f/1H1IY7Pf4Htu5Zx5lnNUzCwg6mhsDgBdD8KXDxAxS4ehz+mQdHfssaqyhwaj0klfDrup8Gg2HkCrBwgIv7YWEfiJPVKSI7SVQKIYQQpUyv0/L5Y41pWNUh37Gnr8ZTw8Wa3vU90GlzX4Kl02jo08AzW3JTlCwLve6uZfpFXCImhBBClLDHW1ZFo4Ftp67x38XCzV5ztjEjMi6F/eE3SDcY8z+gAvD3sufxlmot6nfXhmAs6VrUtu7wxFLo/znoreDsDvi2LYSsBnMbeOQbaP1c1vi4yzlnSdp7qY15HpoLzx+Gl07fTlyOAf+HssZdPwNLhsMnNeHb9mqtyJNrSj9x6dsRnt4Adt4QfQp+7KkufRfiNklUCiGEECYWEnmLsGsJObbP2XKGvnN38/P+87w/qCE25rpca1TaWOh475EGpRKryNIrwIO2NV1IyzDy0cYiLhETQgghSkh8Sjrjfg3MbLL81+HCzVxrVs2J70c1Z9tLnTHTVZ7UwUu96mBroSfochwr7qknXiI0Gmg5Fp7dpdafTL4Jf46CVZMgNUHdD2rTnOXPqEumD/+U9/nsPG8nLr+A+oOytideB9e6gAJRwfDvd7B0JHziC9+0g1PrChe30QDn9kDQMvW/hWmQ41Efxm4Bt3oQH6nOrDy3p3DPLyqsyvPbRgghhCiD1gddYeC8vUxdejTHbIWqTlYYFfhyWygGo8L7gxrmWqPyg0cb4mqbT5dJUew0Gg1vDAhQ638dv8Kh8yZcSiWEEELcw87SjEFNqwBQ18OO9wv5oaZGo6F3fU8szXT5D65AXG0tmNztTi3qUySWVi1qt7owdhu0nwJo4Mjv8F0HiLq99N6YoY6xsIOanQt//hrtYfJBmB4KQ39Wk6Nu9dR9106otSPvuPgvrH9ZrSWZeD3nuUJWwxcN4JcBavL0lwHq45DVBY/HoSo8vRGqtYPUW+qy9+DlhX9dosKRRKUQQghhQs2rO2FtrsfTwZLk9OyfRA9uVpWm1RxJTDPw4fqTDGjklW0JuE6rLvkecLv+lCh9Ad72DGvpA8A7a0phiZgQQghRCM91roVeq+F0VDzHLsU+0LnubQBYkY1pX4NqztZE3Url+13hpffEenPo+TY8uQbsq0BaAti4qfvMLNXl3RP/Ubt/3xEfVbjnsHVXZ1r2/xwm/QvTw2DoL1CtTdaYMxvg4A/w52j4tBbMawPrXoITf8N/v6vbb0VmP++tK+r2wiQrrZxg1N/g/zAY0mDZ02pTIVGpSaJSCCGEMCEPe0vWPt+BH0Y1x/52l+87tFoN7zzcAI0GVh6N5ND5m5lLwAFszHW82qcun28+zdrjkbmdXpSCaT3rlu4SMSGEEKKAfJytM2dVztsRRnKagfPXEwt1jtNX43n8+wM89dPBkgixTLLQ65jRV51t+MOes0TGJpduAL4dYcI+eOJPsHXL2p54HRyqZD2+dhK+bApb3lSXhheFrZvauMfirlrntbpDq2fBvb76OPokHPoR/noKVk+CHGt8yNq28bXCLQM3s1RneLYarz7eNAM2vZ69A7qoVCRRKYQQQpiYj7M1Gk1W9cm7u3s3rOrAsNtF3d9cFYyjlRkfPNoQN1tzPny0EVtCovhqexgfbThFWobc0JmCm13WErFPNpbiEjEhhBCiACZ0qYVWA1tPXqPdR9uYtOi/Qq0AsLfSc/jCTQ5fuMnl0k7YmVCfBp608nUmJd3IJ6aoRW3lBFWaZT0OWgZzm8CxpVnbzmyC9ES4GgSaYkzv+HaEfp/AxP3w8ll47Dc1celUI58DFbh1GS7sL9zzaXXQ9xPo8bb6+MDXsGIcZKQWJXpRzkmiUgghhCgj4pLSeWHxEd5dG5Jt+8u96+JgZcapq/EsOniRAY28OTSrJ/0beTGqTQ3a1XJhVv8AzHS5dwUXJe/OErFr8al8V5pLxIQQQoh81HSzzSwTE5+SwYnIW2w8cbXAx3s5WDH7scbsfbUrVRytSirMMkej0fBG/4DMlS1HCtk1vdgFLYO0eIi56z6jwxR4/A8Y9ANoSyi9Y+MCAQ+rictubxTsmKjgwj+PRqO+nkHfg1YPwcvgjyGQElf4c4lyTRKVQgghRBlx/HIsq49F8vP+8+wJjc7c7mxjzvRedbCz1GOm03Ag/Aarjl7mQPgNzPVaFo1rQ58GntlmZYrSZaHXMbPf7SViu89y6WaSiSMSQgghskzqqs78z7g9k3L2ljPZVnDkZ2CTKng5VJ4k5R0NqzowuFlVAN5ZG4KimLAW9bA/4OGvoNPLWdsyUsF/QPbl4Xs+h40zISOt+GOw9SjYuI2vwc8D4OTawj9H42Ew4i8wt4Vzu+Gnfmr9S1FpSKJSCCGEKCM6+rkxqk11AF7+6zhxSemZ+55oXZ03BwTw5bYwhs//hxeXHGX4/H/o8PF2NgbLzVtZ0Lu+J619nUnNMPLxxtOmDkcIIYTIVNfTjt711SSTmU5D2LUEVh0tWl3lwiQ4K4KXe9fF2lzHkYvqB8omo9VBs9Ggu13TPCMVFvSEzbOylkjfvADb34d/5kHopuKPoXo7sPcG7vPhuM5C/e/5PRD5X9Z2oxEKmuit1Q3GrAcbd3V25oKeEC33VpWFJCqFEEKIMmRGv3r4utpw9VYKb67OWjazJeQqryw7zpW4lGzjr8alMOH3/9gQFMmGoCsMnLePmMQS+ARd5Euj0fDmQ+oSsTXHIgm8EGPqkIQQQohMk7v6AVmzKr/YGkq6oeD1rWMS05ix4ji9v9hdqZKVHvaWTOhcC4CPN5wiJb2MdD8/sxGuHIP9X8GP3eHaKXCqDo//rtaTrDeg+J9Tq4M+H99+cG+yUqN+Df4Rpp6AbrOg6cis3aGb4avmsPsziCtAktyrMYzdAi61IS4CFvRCE/FvMb0QUZZJolIIIYQoQ6zN9Xz+WGO0Glh1NJK1xyMxGBXeXhNyv/6KvL0mhK+2h3EsIpYf95wtzZDFXep7O/B4Cx8A3lkTUqhmBUIIIURJaljVgS513VAUsNBruRiTxLLASwU+3tpcx8bgq4RdS2Bv2PUSjLTsGdepJt4OlkTGpTB/dxm5zwoYqNantHJWm+n80Bn+/QHq9lXrSd4pCZSRCnvnQHrK/c9X4Od9GB77Fey9sm+391a3BzwMDlXVJerONbP2B/2p1tfc/i580QB+exSCl98/Lqca8PRmqNoSUmLRLRqMV+zh4nkdosySRKUQQghRxjSr5pRZS2rWymA2nbiaYybl3RTg6q1UBjbx5oXufkzoUquUIhW5ealXXWwt9By7FMfKIi6rE0IIIUrC893U+4s7MylPXblV4GMtzXS8PbABfz7blk5+riUSX1llaabj1b5qLepvd4UTdauYkn4Pyn8ATDwAtbpDRgpseBn+GArxUVljNr8BW/8Hi4cV3/MGPAxTguHJtTB4gfrfKUHq9rw89CUMnAfV2oFihPBtsOxp+LwurHsp7w7fNi4wejXU7YcmI4WW575Ce3hh8b0WUeZIolIIIYQog17o7keDKvbEJqXz1fbQAh3j6WDJtJ51sLM0K+HoxP242VlkJpo/3niKpLQME0ckhBBCqJpXd6ZtTRfqedkzd1gT3h7YoFDHP9zYm1a+zpWygd/Djb1pWs2RpDQDn24qQ/US7TxhxDJ1SbbOAsK2wLft4PQGdb9fT7B2hTYTi/d5tTrw7QgNh6j/1eruP97CVl0K/vQGeP4/6Dgd7KtASixc/Bf0Fllj0+5pSmhuDY/9hqHpaDQo6Da9AlvfLnjNS1GuSKJSCCGEKIPMdFrmPNYEc72Wk1fiC3SMu51ltscZhag7JYrXmPY18HG2IupWKt/tKiNLxIQQQgjgu5HNWfd8BwY2qfJA5zFpB2wT0Gg0vDkgAIDl/10i+HKciSO6i1YLbZ6D8TvBowEkXVdnUK6ZojbAefEY1OmVNf5GOKQnmypacKkF3d9QZ2GOXAE9/pe1LzUeZteDJSPg1How3G4uqdNj7Ps5J70eVR/vnQ0rJ2TtFxWGJCqFEEKIMsrPw45XetcF7ttbEQAvB0ta+ToDEBGTxKQ//uOFJUdKOEKRF0szHTP7+gPw/a5wLsea8M2AEEIIcRcHa7NsMyIjY5MJuVnwGZLpBiM/7A6n9xe7iUuqXEmiptWcGNjEG0WBd9aGlL1krUcAjNsObSerjwN/gu87w427VuekxMFvg2B+d7h53iRhZtLqoHZ38OuRtS18hxrjqbWwZDjM9odNr8O1k6DRcMbzETL6zwWNDo4thkWPq8lNUWFIolIIIYQow55u70vbmi65NtK528QutdBp1TcZyekGNgRfYWPwVSJikvI5UpSUPg08aeXrTGqGkU82njJ1OEIIIUQ28SnpvLUqmO5z9vBbqJZbyQVLOuq1Glb8d5kzUQn8FRhRwlGWPa/2qYelmZaD52LYGHzV1OHkpLeA3u/D6FVg560mKX/sAWd3qvtjzkF6EqTFg6WjKSPNXcDDMPEfNdlq4waJ0XDga/imDbqFPXFIOofSZAQMXwJm1mqty58HQMI1U0cuiokkKoUQQogyTKvV8NljjbExV+v+WJtnr/+jv/2X/NLNrBl7dTzseOuh+qx/sSM+ztalFqvI7s4SMc3tDu6BF26aOiQhhBAi07O/BfLLgQvYWZqRZNDw0/4LBTpOo9EwrWcdPh7ckJFtqpdwlGWPt6MV4zuq3aw/2HCS1AyDiSPKQ80uMGGf2h3ctS74tFG3ezeB5/bCsMVg5Zg13lCGamq7+6vJ1mkn1TjrDQCtHs2Vo6Tq7dUxdXrBY7+pXc+vHFWTsTfCTRq2KB6SqBRCCCHKgdQMtd5kUlr2m+Hbm/l5//lsy4ufbFeDep72pRafyF2DKg4MbV4VUJeIGY1lbImYEEKISmt02+rUdLNhYGMvAH7af4GYxLQCHdurviePt6yGpVk+DVQqqGc718LD3oKImGR+2nfe1OHkzdoZhv4CY9aB2e1a5oYMuHIcPO9qpBS6Fb7roC6vLkt0ZlCvHwz7A6adwvDoAlLMXbL2//utOsbSAWIvwIKecCnQdPGKYiGJSiGEEKKMu5mYRkY+Ca7UDCM383hzEZeULgkyE5reqy425jqORcSy6thlU4cjhBBCANArwJMtUzszo09dqtooJKYZ+H6XzEgrCBsLPS/3rgfA19vDiI5PNXFE96HRgJVT1uO9s2HRUFg7VX1sNMLW/0H0SQj8xSQhFoitG4r/w1mP0xIh8ggkRKk1LQGSbsBPfSB4pUlCFMVDEpVCCCFEBXEzMY1PN53CcFdScuHec3T4ZDsbymINpUrC3d6SiV1rA/DxhtMkpZWhpVVCCCEqLa1Wg06rQavV0M9HXaLxy4HzXLuVUuBzbD8Vxcgf/yU0qvI1M3m0aRUaVnEgITWD2VvOmDqcgtNo1EY0d5aCa7Uw6m9o/Vz27ttlnbkNTA2BIT9B7R6guZ3eMqTBsidhyUjTxieKTBKVuZg3bx4BAQG0bNnS1KEIIYQQBTZ92THm7Qhn4d5zmdtupaQTn5LB2uORJoxMPNPBlyqOVly9lcL3u86aOhxRTsg9qRCiNKSmG4hJgWpOVqSkG5m3I6zAxy49FMHesOv8vP98yQVYRmm1Gt4YEADA0kMXOXX1lokjKqBOL8Okf6HRY1nbUmKh57tZy8MBdnwAV4NKPbxCMbOEBo/CyOUw9QR0nQUWduq+U2tg50egKJByC2IrX+On8koSlbmYNGkSISEhHDp0yNShCCGEEAX2WHMfAD7dfJqwawkAjO1Yk7nDmjDviWamDK3SszTTMbOfPwDf7w4n8q56okLkRe5JhRClYX1wFMvO64hLScfKTIuTjXmBjx3bsSbPdqrJc51rlWCEZVcrX2f6N/TCqMC7a0NQlHJSasfVT51ZCZB4Q+2avaAnXA9VtwUtg10fw4LekHjddHEWhr03dH4ZXr0IjZ9Qt+38ENa8AEf/gC8awq8D4fhfkC73YWWZJCqFEEKICqJXfQ861XEjLcPI9L+OYTAq2FroGdikClqtxtThVXr9GnrSsoYTKelGPtl4ytThCCGEEAD0b+iJk7lCXHIGU3rUYUqPOgU+tmUNZ2b088fH2boEIyzbXutbD3Odln1hN9h28pqpwym8G6GQkaJ2zv6+ExxeqHYM9+sFHaaCjWvexwavgE/94MTfpRVt/rRaGPQt9J+tLgf/71c48A2gwNmdsGIsfFYX1kxRG+/cL7lsNMC5PWri9twe9bEocZKoFEIIISoIjUbDR482xM5Cz9GIWObvyb7E2GhUMmdaitKn0Wio7WYLwMqjkRyJiM22/8ttocwpTzWuhBBCVAjmei3dq9yuUbn/PGkZRhNHVL74OFvzdAdfAD5Yf7L8Xb9qbWDiAfDtDOlJapOdVZNh4Dzo+FLWuMTrEHk063FCNKydAonXYM2L6uOypOUz8PjvoLeEuIvg0RDaPg8OPpAaB4E/wY/d4LuOYEjPeXzIaviiAfwyAJY/o/73iwbqdlGiJFEphBBCVCDejla88ZBaL2n25jOZxe2vxCXT/6u9DP52P3HJudyMiVLh5WiV+f37609nfoj/5bZQZm85g05mvgohhDCBNu4KbrbmRMal8PeRS/xz9kahalWGXUvgjZXB7DxdDmcUFoNJXWvhamvO2euJ/PbPBVOHU3j23jBqJfR6H3TmcGYDfNsewrep+41G+PtZdXn48b/UWYhrp0Lq7Q/AUxNg3TSThZ+nev1h9Gq163lUEJxeD6NXqV8NH1OTmI7VQGeWdcy5PepM0T9Hw617arzfuqJul2RliZJEpRBCCFHGOdmYY6G//59sc11WTamhzavSta4baQYjc7aqM/Tc7SwxGI0YjQonIuNKPGaRuxe6+zG+U00Ajl2KI/C6hq93hDN7yxmm9azDC939TByhEEKIyshMC2M71ABg7rZQhv3wD59tPs3pqwXr5v3n4Qh+++cCC+5q6FeZ2Fma8VKvugDM3XqGm4lpJo6oCLRaaDcZxm0HN391puQfQ2D9y5Acoyb1NFrwCIATK9RmNcrtpdCKAU6uVhN8ZU211vD0ZnCoBjHhsLAPWDrC4Pkw/Qz0+TBrbMy5rBmU5LYk/Pa2ja/JMvASJIlKIYQQooyr4mjF9uldWPt8hxxfLWs4AdC8uiNVbs/W02g0fPhoI55u78unQxoDoNNq+HJ4U3a/0pV2te5Ta0iUuJn9/GlfywWA38K0zN0eLklKIYQQJjesZVWcrM2IjE2hSVVHFAVmbzldoGNHtalOrwAPJlTSpjoAj7XwoZ6nHbdSMvhiazku5eLZEMbvhNYT1McHf4Cf+0OnV9TtNu7qbEruXQWiUZeCl7Ul4ABudeCZzery78Rr6usJ2waWDuBUPWtczFmwdALlfsv3Fbh1GS7sL/GwKytJVAohhBDlQBVHKxpUccjx9fHgRui1Gg6cjWHHqazlVp4Olrz5UAA2FvrMbfU87QvVyVOUnAVPtbz9nQadViNJSiGEECZnba5nbEd11n9MUhoaYNOJKI5fis33WB9na34Y3YJ2tSvvh6E6rYYAL3sAfv/3Yo664OWqFrWZJfT9CEYuB1sPiD4FP3aHM5vVJeCp8eSccaio21eMN0XE+bP3gjHr1VqcaQmw6DE4ujj7mNrdoe/HBTtf2DbYOFNt1hNxEFLK74olTchKegc9j+bkKlOHAkiiUgghhCjXarrZMqZ9DQDeXReSawF3o1Fh1dHLpBuy9p2+Gs+lm0mlFaa4xw+71UZHWo2Cwajw5bZQE0ckhBBCwKi21bGz1HMxJolWvs4AfLa5nCTXyoAarjYAGIwKH23Kum7lthZ17R4w4QDUGwDGdNj6plq3Mq8Zh4oRzm6Hj6rBpcNZ25Ni4OZ50y+XtrSHEcvU+pTGDFj5HOz5PHvnb60+7+Pvdu0E/DMPVj+v1u78qBp87g+/DYKNM9R6luVBQjS69dOwyIhDt35amZgRK4lKIYQQopx7vrufWsA9OpFfD5zPuX/xEV5ccpTvdoYDsGDvOfrM3c0nGwu2nEsUrztvVl7sVos5bQy82K0Ws7eckWSlEEIIk7O3NGNMuxoAxCSmodPA7jPRHDwXU6Djb6Wks3DvOVYeuVyCUZZdL3T3y/wAedeZ65yMrQC1qG1c1O7ZD30JNbsW7JiUOLBxy3ocsgrmNoYlI7KPO70RIo9ARinW9NSbw6Dvof2L6uNt78D66VlJVGffgp2nZjdoM1G9JnZe6rb4SAjfDv98k33s/q+yEpiBv8DFfyE5tlhezgO50xQpLVFdyF9GmiIVMFUshBBCiLLK3tKMl3vX5dXlQczdGsojTavgamuRub9XfQ/WBV3hy+2hdPf3oE1NdYaEQVHIMBjR6+Rzy9JyJ0k5rWcdJnSqwfr1p5nctRY6nY7Zt5eDlcs3MUIIISqMMe19+XHvOUKvJdC5jhu7zkTz2ebTLB3fBo3m/jMC1xyL5J21IVR3sebhxt5oy9sMwmLw1kP1OR4RS+DFWL47qYWTFaAWtUYDzZ8Er8bww478x7d7EW6Eqs1rFAUuH1ZnKmr1ELZVHaMY4c9RYEiDZ3er5wY4sRLO7YYaHaDBo+o2QwaEb0djyMA97hiaMDPQ6bI/p5JL85sqzcH2dsI0NgKuBoGtO1RtAT3fATtv2PgqHPoRIo9C28kQd6lg16R6W/CekPU4ORaiT6vL5G+EgZ1n1r4LB9QEZvj27Oew8wK3ejD0Z7ByzHqtulJK1d1uinTnX6nm7qZId669CUiiUgghhKgAhjT34bd/LhB8+Rafbz7Nh482ytz3cGNv1h2/wuaQKKb/dYyVk9qza3pXqrlYmzDiyslgVDLfrKSnp2duv/PmxWDMrcOkEEIIUXqcbMwZ1aY63+8+y/WEVGq72TCspU+Bjh3UtArLAy/xaLOqGBQFbY6GK5XDwqda0fidzVTaWtT756pf9zq1Rv26l+GuGZUHv1cb1Zzfm5UsS0+CRUPRA20BzhYwjhHLwa+H+v3ZHeoy7Tp94Iml6rY2z8Hm19Vl4JcPw7KnCnjiXFg5qh3Gq7XOua/Lq1Cnt5rEjD4F106psy/jr6izTy3ss8auGKsmNt3rqd3X3eqC++3/WjkVPb57JUTf1RTp7vvP202RanTMSvKWMklUCiGEEBWATqvhfw/VZ8h3B1hyKIIRravToIoDoHYBf39QQw6djyHkyi3m7Qhjas86Jo64crrfda90b2KEEEKUWc909GXl0cv0DPBgYpdamOt1+R+E2pBnxcT2JRxd2ffLXaV4DEaFL7acYUpluvdyrg1mVlmPc+Sr79lgbpf92KvBamLujkS1YaQCxFrVwMHREa1GoybbUm+BtYuaxNNosmZWajRqTco7bNygSgtwqZ39uX1aQ9INuB4KigF05tkTp8XBq3HWjNE7kmPh+hmIvwrau1Y3RZ+GhKvq19md2Y9xrAYvHMsafyMcrJ2zEpiKor5ugPgoiPgH9FZQp1fWOTa8BleOgUarLvXOtSnS7SXgj//2YK+7iCRRKYQQQlQQLWo483Bjb1Yfi+TtNSf489m2mUu03OwseGdgA55ffIR5O8LoGeBBgyoOxKeks+poJCNaV8t3OZcQQgghKgd3O0v2vdpNysMUwZ0yLxM6+/LrvrMkZmj4Ylso2so0s3LIAvBuUrRjB36lft3N0gl6/A9j4g12p7SkX79+aM3MYOkodalylxnQdqI6NiEavm4OrnXVpd93+LSGMRvUGpV3G7Ne/W9UCPwxBG6VUn1VK0fwaZVz+9MbIfoMRPyrLlWPv6ImJG9dUpfOb3tbXb7edhIsexquHFWTq8YMqNtPbYDkVk+dsbnsafBqkj1RefU4XNx//9juLAG/dlKdzVnK5LeOEEIIUYG81rcelmZaDp2/ydrj2bsNDmjkRb+GnmQYFV5dfpy0DAN9vtjDrJXBbAmJMlHEQgghhCiL7k5SphuM/PHvBcb8dBBjAcqUGI0KO05fY9G/F0syxDLn7lrU03r40d1b7Y5tb6WXxnkPwsYFOkzF2O2t7Ns7ToOH5kLt7lnbrp9Rl1MnRIH2rpnA66fD+55weGHWtrQkuPiP2pXcIwCe2QyONYov7nu7nIdthf9+VWc73nF+H/w8ANZMUR9bOoBPSzj6BxxfAh2mwrQT8FoEtJ8C+76A40vV2ZPpSeoxhjS15uepteqy7Z/6wOY31eTsnZmcJ9eoS8pbjoPBC8Ezq0xUrqq0MEmSEmRGpRBCCFGheDtaMbFLbWZvOcOH60/Su74n5nr1jYZGo+HdgQ2ISUxjZj9/zPU6BjbxZuOJq1iZF2xJlxBCCCEqD0VR2Hk6ml1noln+3yXiUzJYG3SFhxt73/e4f8/FMOanQ9ha6HmosRd2lmalFLFp3VuLuqOnwv4Yc64npNHd311qURc376bq192qtoQJByAlNvv22Ah1puCdDt0AUcGwsDfYV4FpIeBQFQZ+Db8MKNjzXw2GI79BRoraRdy5pro9ZDX8/away5Ors8ZvfhOunYBRf4Odh7otLRHO71GXsN/NxhWsXcFwu6a5pb3aYKjNRHDyVZd4Tz4EKbfUJeI3wtXl4jfC1OXjXo1h2B/qsYoCKydBatztc7tD4vX7v7bok2qiVVv67xEkUSmEEEJUMOM71eT4pTjGd6qZmaS8w8XWgiXj22Y+fqG7H9N61pGlXUIIIYTIITIuhbG/HsZgVBjRqhp/HLzIF1vO0K+B533vHdrUdKZpNUea+jiRbqg8ybl7a1Gb62BC55q8u+4UJy7fYt4TzUwUWTGxdgG9BWSk5j1Gb6GOMxW9uTo78l7PbFaXUVs6ZG1LvQUOPuBSK2ubY/WCPc/2d+HSIXX2JqgJR3d/MLOGmHPqbMeb5yF0q1qv08xSbZBj7QwZ6WqC0cxaTSgOXgD29yT/R6/Oqjd5h0st6PNh9m2W9hDwcM74DBlZ36clqsvMo09BXERmzc/7SktUmxr5dsx/bDGTRKUQQghRwVia6fjxyRYFGnvpZhJVnawpYI18IYQQQlQiVRyteKJVNazMdTzRuhobTlzl7PVEVhy5zGMt8u4ErtFoWDGhndS/Bh5vUZUFe88TGZfCon8v8nQHX1OHVHSOPjA5UG0+kxdrF3VcWaPR5EwG1u4BU4OzL9FOSyjY+cK2Zn8c9FfOMbEX4I/BObef35P1vdbsdiLz9pf+zvfWWQnOzO+tQW9ZwH13f1nDiL/Ua5AaD/98Czvez/81JpimNJQkKoUQQogKLjYpDUdr8xzbfztwnnfXnuSZjr680rsum0OiSEk3MLBJFRNEKYQQQoiy6N1HGmR+P6FzLd5ff5K5W0MZ2MQbi/t80ilJSpWFXsvz3f2YsSKIb3aGMayVD9bm5TgV4+hTNhORD+Lu5c3u/lDvITi9Xl0qfi+NFjwbQpOR6qzDxGjQ6SE9WX0cc1Zt/pORrG5LT1KXht/5Pj3ldm3J2zONjemQmp5z6Xex02QlMTUFXEll61GyIeWhHP/rEEIIIUR+vt4eyrwd4fw8piWta2ZfhuNmZ0mawcj3u8Kxs9DzyabTONuY093fA1sLuUUQQgghRHaj2lZn/p6zXI5N5s9DEYxqWyPfY85fT2Rf+HVGtC7gktoKaEjzqny7M5yLMUn8sv8CE7rUyv8gYRoaDQyYA2GbISOXRKXOHEYsB1u3nPuClqlNcKq3h0e+AacauT+HoqjL53MkMZPv+rr9OCP5nu157bsrCZqerJ43LfGuZKsC6YnqV/4XQZ19Wr1dAS9a8ZJ3IUIIIUQFFhmXQnK6gVXHInMkKvs08GRgE29WHY1kWWAEAV72dKvnjsx/EEIIIcS9Dp2P4evtYQxr6cOX28P4ansYj7eslqMe9t2u3Uqh++xdGIwK7Wq54utqU4oRlx1mOi1Tevgx7c9jfLcrnBFtqmFfSRoMlUuBP+VdhzMjRd3f+ZWc+wzpYGYDF/bBN+2g9/vQ/KmctSY1mtvLti2LPfRcY7o3iZmeDOFbYccHeR/X5yOTNNIBSVQKIYQQFdpLPevQrpYL/Rt65br/fw/VZ3/4Dc5eT2J8R1+m965byhEKIYQQojz4ftdZdp2JxtnGnEebVWF02xr3TVICuNtb0qWOGwqQbjCWTqBl1MAmVZi3I4zw6EQW7j3HlB518j9ImIbRAF1mwtWgrCXgGh3U668u+zbmMtMSoMlwqNYGVk6Ei/th7RQ4tRYe/ipnfczSojMDnUP2JkIAVZur3cGDl+c8psGjuTfoKSXS4lMIIYSowFxsLRjQyDvPOlFONuZ8MKghAPP3niPwws3SDE8IIYQQ5cTkbrUBWH0skqk96tDEx7FAx303qjkLn2pJHQ+7Eoyu7NNpNZldwRfsOUdsUpqJIxJ56joDuryqLgG3sFW3WdhB/9nqTMquM/I+1tkXnloHvT8AnYXadOebNnD8T3XJd1mx6xM1SdllBhkjV3K4+gQyRq6ELjPU7bs+MVlokqgUQgghKolbKelsCcnZva9ngAePNq2CosDLfx3j1NVbvLjkCJduJpkgSiGEEEKURU18HOno54rBqPDtrvDM7Snpecwuu81MJ2mHO/o18KKepx3xqRn8sPusqcMR+bF1gwFfgI07PPRF7nUpc6PVQttJ8Nwe8G4KKXGwYhz8OQoSr5dkxAVnNEDX16HLayjVO3DZuS1K9Q7Q5TV1e16zRkuB/MYQQgghKoFr8Sl0+2wnE34PJOxaQo79bz1UHx9nKx5v6cP7606y6mgkc7aEmiBSIYQQQpRVz3fzA2DZ4Uuci07knTUhdPh4B3FJ6fkem5iawbLASxiNZWhWWSnTajW81Ests/PTvvNcT8ijDqIoOxo8Ci+HQv1BhT/WrS48sxW6zgKtHk6ugXmt4eTa4o+zsLrOyL3OJuQ/a7SESaJSCCGEqATc7SxpXNWRDKPCe+tCcux3sDZj67TOPNu5Fi/1qkvXum6M7ehrgkiFEEIIUVa18nWmla8zaQYjvxw4z/7w61xPSOWHPeH3Pc5gVOg5exfT/zrG3rAyMqPMRHr4u9O4qgPJ6Qa+3Xn/6yYqAJ0eOr8M47aDewAkXYelI2DFs5Aca+royiRJVAohhBCVxKwBAZjpNOw8Hc2OU9dy7LfQq539mvg48u3I5pW2M6cQQggh8vb87VqVSw5dZGwH9UPN/GYH6rQaetX3xNfVhnSDkbXHI2n53hbWHb9SKjGXJRpN1qzK3/65wNW4FBNHJEqFV2MYvxM6TAWNFkI3qR3ERQ6SqBRCCCEqCV9XG55ur76heHdtCGkZuXffPH4plv5f7uGjDadKMzwhhBBClAMdarvS2MeRlHQjYdEJNK7qQFJa/rMDX+lTl23TOtPYx5GZK4KITkhjxorjlXL5c0c/V1rVcCYtw8jXO6TUTqWht4Ae/4OnN8Gg78HOM2tfRuX7d5AXSVQKIYQQlcjkbrVxtbXg7PVEftl/PtcxN5PSCY9O5Of955mx4jjjfj2MUpa6FAohhBDCZDQaDc93VWdV/nbgAs92rqV+/88FrsQl53mctbkejQZe/zuIxDS1UUdimoFZK4NLPugyRp1VqXYAX3oogogYaWBYqfi0gjq9sx6fXAPzWsH5faaLqQyRRKUQQghRidhZmvFKH3W50ZfbQomOz/npbec6bgxv5QPA4oMRbAmJ4p9zMaUapxBCCCHKru7+7vh72ZOYZuD01Vtq3coMI19vD7vvcWuPX2HTiSgMtxvqGIwKG4OvsvZ4ZGmEXaa0rulCRz9X0g0KX26TWZWVlqLAntlw8zyEbjZ1NGWCJCqFEEKISmZIs6o0rOJAfGoGn28+neuYmf38qeJoBUD72i60quFcmiEKIYQQogzTaDRMvj2r8qd955nYRZ1VufpYJPEpuXcAv56Qyut/B+U8FzBzRVClXAI+rac6q3L5f5c4G51g4miESWg0MHoVdHoFus7M2m7IMF1MJiaJSiGEEKKS0Wo1/O/hAACWHo4g6FJcjjF2lmZ8PLgRAPvCbnDo/M1SjVEIIYQQZVufBp7UcrPhVkoGIVdu8e7A+mx7qTN2lmY5xiqKkm3Jd7Z9QGJq5VwC3rSaE93ruWNUYK7Mqqy8LO2h2+tqDUtQk5Q/94MdH4Ah98R/RSaJSiGEEKISal7dmUeaeKMo8PaaE7nWoOzg58qI1tUAmPF3MEkZZC7VEkIIIUTlptNqmNytNn0beNK5jhuj2tbA3c4y17FnohKyLfm+l0FRl4CfiYovyZDLpKm3Z1WuPhbJ6auV7/WLXJxeBxH/wq6PYX43iAoxdUSlShKVQgghRCX1at96WJnpOHzhJmuOX8l1zIzbS8AdrM34MljH0sOXSjlKIYQQQpRVg5pW5duRzanv7ZBte/g9y5jreNjSu74HOq0m1/PoNBr6NPCkjoddicVaVjWo4kC/hp4oCszZcsbU4YiyIGAgDFkIVk5w9Tj80Bn2fgHGnDOSK6JKk6hcuHAhXl5enDp1ytShCCGEEGWCl4NVZk2pD9efJCktZy0cWws9yye049Gm3lxJ1vDLgQsYZValEEVy+fJl6tSpw7vvvmvqUIQQokRkGIyM+ekg3T/fRfDlrNIyGo2G9wc1xMZcx72pSg1gY6HjvUcalGqsZcnUHnXQaGDjiau5luQRlVCDwTDxX6jTBwxpsPUt+Kkv3Ag3dWQlrlIkKk+ePMn+/fvx8PAgI6PyFiQVQggh7jWuU02a+DgypYcfFnpdrmM8HSwZ1sKHPlUNLB7bqpQjFKJiMBqNvPTSSzz00EMkJiaaOhwhhChWETFJzPw7iOX/XcqsUXnv7EBXWwveH9SQez/uVIAPHm2Iq61F6QRbBvl52PFIkyoAzN6Se6NDUQnZecDwJTBwHpjbqcvBv+sAB+eD0Wjq6EpMmUhUhoeHEx6ed1Y4IyOD8PBwbty4Uehzp6Sk8NZbb/HFF188QIRCCCFExWRppuPvie14vGW1PJdjAZjrtXT1Upi9NZSZuXTsFKK8i4mJITg4mNTUvLvOXrlyhfPnz+da0zU/H3zwAWPHjsXLy+tBwhRCiDJp28koFv17kS+3hfF8t9rotBq2nbrGfxezN+Mb0MgrxxJwD3sL+jeU340vdvdDp9Ww43Q0gRdiTB2OKCs0Gmg6EibuB99OkJ4E66fDb49AbISpoysRJktUKorCggULaNGiBQ0bNuTxxx/PddzKlSvx8vKiffv2eHt7M3ToUJKTkzP3jx49GldX1xxfs2bNAmDWrFm88cYb2NralsrrEkIIIcobjSbrzUJaRt6fzl5OgqWHL7PkUARrj0WWRmhClLjAwEBGjhxJrVq1aNiwIaGhObuuRkZG0qFDB2rXrk2zZs2oU6cOgYGBmfvXrFmT6/1o3bp1AdizZw8ajYYePXqU2usSQojSNKxVNfo38mL2Y43x87BjcDN1duDnm7PPDrx7CTioJWbWvdAx271IZVXD1YahzasC8PlmqVUp7uFYDUatgr6fgt4Kzu2Cb9vBkd+hCB+glmUmS1Smp6ezf/9+vvnmG8aPH5/rmHPnzjFs2DBmzZrF1atXOXfuHIcOHeK1117LHDNv3jxOnTqV42vGjBlERUXx5Zdf0rVrV1xdXQkODqZjx44cO3astF6mEEIIUW6sPR5Jl0938M/Z3Fcw1LKHEa3UG+jnFx+plJ05RcWzY8cO+vTpw8aNG/McM3LkSLRaLdevX+f69et07tyZgQMHZn543rt371zvRw8cOADAO++8w+zZs3F1deWtt97iyy+/zPxQXQghKgJLMx3znmhG65ouADzfzQ8znYZ9YTfYH34921hXWws+eLQhbrbmfDy4UaVe8n2v57v7Ya7Tsj/8BvvDrud/gKhctFpoPR4m7IOqrSD1FqyaBIE/mzqyYqU31RObm5uzYMECABYtWpTrmJ9//hknJydeeOEFALy9vZk8eTLvvPMOn332GWZmZtjZ2WFnl3tnMCsrKyIjs2Z8dO3alW+//Zb69evnOj41NTXbcp+4OLWIbUxMDOnp6YV/kQWUnp5OUlISN27cwMzMrMSepzKRa1oy5LoWP7mmxU+uadHtOH6OS9di+HrjMfweb5xt353rOra9C0sPhJGWYWTqb/v5dUwLE0Vb/pXWz2p8vJpQLspy5cpg+vTpABw9ejTX/WFhYezYsYMtW7ZgZWUFwLvvvsvChQtZt24dQ4YMwdzcHFdX1zyfY9myZZn3kt988w03btxgxowZeY43xT2p/O4sGXJdi59c0+JXEtfUSlF4JMCRpYcv88Hfgfz8VPNssybbVrFg44RmANy4cYPkNAM3k9PwdrAqlucvC4pyXS2BRwIcWHL4Mh+u+o+fnmwms03vIv/+73CEgX+gPTwfbchKMqr2gCKUSryjNK5rYe5HTZaoLIjAwEBatWqV7R9mu3btiI+P58yZM3kmHO/QarXZbhp1Oh2Ojo7o9bm/7A8//JC33347x3ZfX98ivgIhhBCi/PkD+GNS/uMiANeXSzoaUVzi4+NxcHAwdRjlzuHDhwFo3bp15jYvLy9q1KhBYGAgQ4YMyfccd193a2trkpKSsLGxyXO83JMKISqSCMBN7hcKLQJwm27qKES5ML2aqSMosILcj5bpROWNGzfw9/fPts3FRZ1Kfv164adB79y5M8/ZlwAzZsxg2rRpmY+NRiMxMTG4uLiU6KcYt27dwsfHh4iICOzt7UvseSoTuaYlQ65r8ZNrWvzkmpYMua7Fr7SuqaIoxMfH4+3tXWLPUZHdmV1w7z2ki4tLke5HJ02alO9sAlPck8q/8ZIh17X4yTUtfnJNS4Zc1+In17RklMZ1Lcz9aJlOVOr1etLS0rJtu7MMpijTUR0dHe+738LCAguL7PUx8jumONnb28s/tmIm17RkyHUtfnJNi59c05Ih17X4lcY1lZmURafX6zEYDBiNRrTarPLuqampRbofvbN8/H5MeU8q/8ZLhlzX4ifXtPjJNS0Zcl2Ln1zTklHS17Wg96Mma6ZTED4+Ply5ciXbtjuPfXx8TBGSEEIIIYSoRHx8fDAajURFRWXbfvXqVbkfFUIIIYQoZmU6Udm1a1f27duXWUAcYN26ddSqVUtuDIUQQgghRIlr164d5ubmrF+/PnPbwYMHiY6OpmvXriaMTAghhBCi4jHp0u+wsDBSUlK4ceMGKSkpBAcHAxAQEIBWq2XkyJF89tlnDB06lJkzZxIUFMS3337Lr7/+asqwi52FhQVvvfVWjiU+oujkmpYMua7FT65p8ZNrWjLkuhY/uaZlw40bN7hy5Qrh4eGAen8K4O3tjbOzM46Ojrz00ku88sor2Nra4uDgwLRp0+jXrx9t2rQxZejFSn4eS4Zc1+In17T4yTUtGXJdi59c05JR1q6rRilIb/ASMmjQIEJDQ3NsP3ToUGb9nitXrvD2229z+PBhnJ2dGT9+fIG6KwohhBBCCJGfn3/+mc8++yzH9tdff53hw4cDajObefPm8ddff5GamkqPHj2YOXPmfTt3CyGEEEKIwjNpolIIIYQQQgghhBBCCCGgjNeoFEIIIYQQQgghhBBCVA6SqBRCCCGEEEIIIYQQQpicSZvpiCznz5/n0qVLtGjRAktLy1zHhIeHExsbi7+/P9bW1qUcYfl27Ngx4uPjs23z8PDAz8/PRBGVP9evX+fcuXNUq1YNDw8PU4dT7u3duzfHtlq1auHl5WWCaMovo9FIYGAglpaWNGzYMNcxqampnDhxAhsbG+rWrVvKEZZP9/ubFBISQkxMTLZtTk5O1K9fvzRDLHcuX77MjRs3qFmzJra2trmOiYuLIzQ0FE9PT6pWrVrKEQoBSUlJHDlyBB8fH6pVq5brmJs3bxIeHo6XlxdVqlQp5QjLt8jISM6ePZttm0ajoX379iaKqPxJT0/nxIkTWFhYUK9ePTQajalDKtfOnDnDtWvXsm2zt7enUaNGJoqo/Dp79iyRkZG0atUKc3PzXMeEhYURFxdHQEBAZk8OkbfExESOHj2a69+k69evc+rUqRzHtGnTBr1e0lx5SUhIyLzXzOt9p9Fo5MSJEwDUr18frdYE8xsVYVLbtm1Tevfurbi4uCiAEhoammNMTEyM0rlzZ8XOzk7x8/NT7O3tlSVLlpgg2vKrefPmSvXq1ZX27dtnfr3//vumDqvcePXVVxULCwslICBAsbCwUCZOnKgYjUZTh1WuAUqDBg2y/UwuX77c1GGVG2lpacqHH36o1KxZU3FwcFA6d+6c67j169crLi4uSs2aNRUnJyelefPmSmRkZOkGW44U5G9S//79FW9v72w/uy+99JIJoi0f1q5dqzRq1EipUqWK0rBhQ8Xa2lp5/fXXc4ybM2eOYmlpqfj7+ytWVlbKkCFDlJSUFBNELCqjq1evKi+++KLi5eWlWFpa5vozqiiK8tFHH2X+nFpaWipPPPGEkpaWVsrRll9z5sxRrK2ts/3+zOvvl8hp165diqenp1K9enXF1dVVadCggXL27FlTh1WujRgxQnF3d8/2Mzl+/HhTh1WubNq0SenRo4fi7OysAEpERESOMdHR0Uq7du0UBwcHpXbt2oqDg4OyYsUKE0RbPkRGRirPP/+84uXlpVhYWChvvfVWjjGLFy9W9Hp9tp/d9u3bK7GxsaUfcDlw8eJFZfjw4YqDg4PSpEkTxcHBQenataty+fLlbOOOHj2q+Pr6Kl5eXoq3t7fi6+urHD16tNTjlUSlic2dO1dZv369sm/fvjzfFI4cOVJp0KCBEhcXpyiKonz11VeKubm5cu7cuVKOtvxq3ry58uGHH5o6jHJpyZIlioWFhXLw4EFFURQlKChIsbGxUb7//nsTR1a+AcqWLVtMHUa5FRsbq7z66qtKeHi48swzz+T6Ri86Olqxs7NT3nnnHUVRFCU5OVlp06aN0rdv31KOtvwoyN+k/v37Ky+++GLpB1dOffXVV0pQUFDm47179yrm5ubKH3/8kW2bRqNR1q1bpyiKokRERCienp7KG2+8UerxisrpwIEDypw5c5SYmBilbt26uSYqt27dqmi1WmXr1q2KoijKuXPnFFdXV/ngtxDmzJmj1K9f39RhlEvx8fGKm5ubMm3aNEVRFCU9PV3p0aOH0rZtWxNHVr6NGDFCefLJJ00dRrn2+eefK5s2bVK2b9+eZ6JyyJAhSrNmzZSEhARFURTl008/VaysrJRLly6Vdrjlwr59+5S5c+cqN2/eVGrVqpVnotLFxaX0gyundu3apSxevFgxGAyKoqjvpVq2bJntfVF6erri5+enjBgxQjEajYrRaFSGDRum+Pn5KRkZGaUar9SoNLEXXniBvn375jmdNiEhgT///JMpU6Zgb28PwIQJE7C3t+f3338vzVDLvbi4OA4dOkRkZKSpQylXFi5cSN++fWnZsiUADRo0YNCgQSxcuNDEkZV/V65cITAwMMcyWpE/BwcHPvroI2rWrJnnmL/++guDwcBLL70EgKWlJdOnT2fjxo1cuXKltEItV/L7m3RHQkIChw8f5uLFiyiKUkrRlU+TJ0+mQYMGmY/bt29Po0aNspV/WLhwIc2bN6dfv34AVK1alaeeekp+z4pS06ZNG6ZMmYKTk1OeYxYuXEi7du3o3r07ADVq1GDkyJHyc1pIBoOB48ePc/LkSdLT000dTrmxZs0aYmJimDlzJgB6vZ7XXnuNAwcO5Lr8UxRccnIygYGBnDt3DqPRaOpwyp1p06bRq1evPMsQ3Lx5k7///ptp06ZhY2MDqPdb5ubmLF68uDRDLTfatWvHCy+8gKOj433HKYpCSEgIwcHBpKamlk5w5VSnTp0YNmxY5j2+g4MDI0aMyHY/umfPHkJDQ3njjTfQaDRoNBpmzZpFaGgoe/bsKdV4JVFZxoWEhJCWlkbz5s0zt+l0Opo1a8aRI0dMGFn58+WXXzJu3Djq1atH8+bNCQ4ONnVI5cKRI0ey/fwBtGrViqNHj0qC4gFNnTqVp59+Gm9vbx555BGio6NNHVKFcuTIkRw1fVu1aoWiKBw9etR0gVUAv//+O2PHjqVJkyb4+/tz4MABU4dUbtypQ1m7du3MbXn9nr18+bL8XhBlRl4/p+Hh4TnqgIu8nT59mmHDhtG7d2/c3NyYP3++qUMqF44cOUKNGjVwcXHJ3NaqVavMfaLo/v77b55++mlatmxJ7dq12b59u6lDqlCCgoIwGAzZfn+am5vTuHFj+dl9QDExMQwcOJCBAwfi7OzMxx9/bOqQypVDhw7luB+9t6Z//fr1sba2LvWfVakyWsyCg4OJjY2975j27dsXuPDznZlWd/9RvvO4Ms8IOnLkCImJiXnu1+v1tGnTJvPxCy+8wNChQ7GysiI+Pp7hw4fzyCOPEBQUJIWM8xETE5Prz19qaipJSUmZnwyKwvnhhx8YO3YsGo2Gixcv0rt3b8aOHcuqVatMHVqFkdfP7p19omhGjhzJokWLsLe3JzU1lfHjx/PII48QEhKS43qLnJ577jlsbW0ZM2ZM5rb8flbd3NxKNUZR/kVHR3P69On7jqlduzaenp4FPmd+P6d2dnaFD7Scu3TpEufPn7/vmPr162fOVG3UqBFnzpzJfGM4f/58nn32WWrVqkW3bt1KOtxyLbefPzs7O8zMzORv+gN45JFH+PLLL3F2diY9PZ2pU6cyaNAgTpw4IU3disn93s/Lz27R+fr6cvToURo3bgzAqlWrePTRR6lWrRrDhw83cXRl3+rVq1m0aBHLly/P3Jbb71kwzc+qJCqL2YIFCzh06NB9x2zfvj3PTmD3MjMzAyAlJSXb9uTk5AKfoyKaM2dOjq6Jd7Ozs2PDhg2Zj0ePHp1t32effYa/vz+HDx+mY8eOJRpreWdmZpbrzx9QqX8GH9S4ceMyv69WrRqvv/46o0ePJjExUZK/xUR+dkvGsGHDMr+3sLBg7ty5ODk5sX37doYOHWrCyMq+qVOnsnHjRrZv355tia38rIriduTIEd555537jnn55ZcZOHBggc8pP6c57dq1i2+//fa+Yz755BPatWsHkCMZOW7cOL7//nuWLl0qicp85Pbzl5GRQUZGRqX9+SsOQ4YMyfzezMyMzz//nPnz57Nhw4Zs96qi6OT9fMlo3bp1tscDBw6kb9++LFmyRBKV+di9ezfDhw/nnXfeYdCgQZnbc/s9C6b5WZVEZTGbM2dOsZ6vevXqAFy+fBk/P7/M7ZcvX8789KAy+vXXXx/oeA8PD0C9juL+qlevnuM6Xb58GU9Pz8w/vOLBeXh4oCgKV65cyTYFXxRd9erVc3xwdOdnuVq1aqYIqUJycHDA0tJSfp/m4+WXX+bnn39my5YtNG3aNNu+vH7P6nQ6vL29SzNMUUH06tWLXr16Fes58/o5tbCwwN3dvVifq7wYMWIEI0aMeKBzeHh4yO/PAqhevTrLli1DUZTMlWmRkZEoiiJ/04uRhYUFjo6O8jNZjO5+P+/j45O5/fLly3Tu3NlUYVVIHh4eHDt2zNRhlGl79+6lf//+vPzyy8yaNSvbvurVq3Pjxg1SUlKwtLQE1CTlzZs3S/33rNSoLONq166Nr68vq1evztx26dIlAgMD6dmzpwkjKz+Sk5Nz1FLcvHkzQLYGByJ3PXv2ZN26ddmKa69evVp+/h5AbmULNm/ejJ2dndxsF6OePXsSHh5OSEhI5rZVq1bh7OxMs2bNTBhZ+ZWWlkZGRka2bbt37yYlJUV+n97HK6+8wvz589m8eTMtWrTIsb9nz55s2bIl26fYq1atolOnTlhYWJRmqELkqWfPnmzatIm0tLTMbatWraJbt27odDoTRlZ+3Pv3Py4ujoMHD8rvzwLo2bMn169fz1YTedWqVVhbW9O+fXsTRlZ+ZWRk5GhAcuzYMa5duyY/k8UoICAAb2/vbO/nz549S1BQkLyfegD3/j5NTU1l9+7d8rN7H/v27aNv375MmzaN//3vfzn2d+/eHUVRWL9+fea2tWvXoihKqc/6lxmVJhYREcGFCxcy30gHBgZy9epV/Pz8Mmf9ffjhh4wcORJ3d3fq1q3LBx98QPPmzRk8eLApQy83Tp06xcSJExkzZgw1atTgyJEjfPDBB4wZM0Z+kRXAK6+8wuLFixk5ciTDhw9n5cqVnDlzhj/++MPUoZVbS5YsYe3atTz66KO4urqyZcsWvvrqK7744gtZAlIIhw8fJiUlhaioKOLi4jK71nXo0AFQ/9j26tWLoUOH8vbbbxMZGcn777/PnDlzZDZwHvL7m3Tt2jUeeughxo4di5+fH6dPn+a9996jX79+mZ2ARXbvvPMOn332GZ9//jmpqamZP6cuLi74+/sDat3K7777jkGDBjFhwgT27NnDhg0b2LFjhylDF5VIWloaBw8eBNQPeCMiIti7dy8ODg40bNgQgOeff54ff/yRIUOGMG7cOLZt28auXbtKvRNoeXbnd2WLFi2IjY3l888/x9ramilTppg6tDKvefPmDB06lFGjRvH+++8TFxfHzJkzmTVrFra2tqYOr1xKTEykQ4cOjB07lnr16nHu3Dnee+89OnbsyCOPPGLq8MqNixcvcvHiRYKCggC1Qcn58+epW7cubm5uaLVaPvzwQ8aOHYuLiwu+vr68++67dOjQgQEDBpg4+rIpNTU1c1VUSkoKFy9ezPE3aeTIkdSrV4927dqRkpLCvHnzuHnzJq+//ropQy+zjh49St++fenUqRM9e/bM1u27Xbt2aLVafHx8mDRpEhMmTCAhIQGA6dOnM3ny5GyzgUuDRpG2vSb166+/8sMPP+TY/tprr2X7xbVhwwYWLFhAbGwsrVu35pVXXsHBwaE0Qy3XgoKC+P777wkNDcXb25uBAwfKH+BCCAsL45NPPiE8PJzq1avz0ksvUb9+fVOHVa5t2rSJxYsXc/XqVXx9fTO7LYqCGzp0aK5Nxe7+w5uUlMTs2bPZvXs31tbWjBgxQuoo3kdB/iaFh4czb948Tpw4gYeHB7169WLEiBEFbhJX2Tz77LOcOHEix/ZOnTrxwQcfZD6+cuUKH330EcHBwXh6ejJ58mTatm1bmqGKSiw6Ojpbnao7GjVqxDfffJP5+NKlS3z00UecPHkSb29vXnjhBfnbVQi3bt1i3rx57N+/HwsLC5o3b87kyZMrZSOiokhLS2Pu3Lls3boVCwuLzMSlKLqIiAi++uorjh8/jouLC926deOpp56SWdKFMH/+fH755Zcc2998881sJThWr17NL7/8wq1bt2jbti0vv/yy/NvPw9WrV7PVT72jadOmfPXVV4CawPz+++/ZuXMnoP69euGFF6SxYx5WrFjB7Nmzc923ZcuWzAbDRqOR7777jrVr1wIwYMAAnnvuObTa0l2MLYlKIYQQQgghhBBCCCGEyUmNSiGEEEIIIYQQQgghhMlJolIIIYQQQgghhBBCCGFykqgUQgghhBBCCCGEEEKYnCQqhRBCCCGEEEIIIYQQJieJSiGEEEIIIYQQQgghhMlJolIIIYQQQgghhBBCCGFykqgUQgghhBBCCCGEEEKYnN7UAQghRHmVnp6OwWDIsV2r1WJubp7v8YqikJqaiqWlZbbzWVhYoNFoMscZjUbS0tIyx929XauVz5uEEEIIISqrO/eJubn3njIvaWlp6HQ6dDpd5vn0ej16ffZ0QWpqKnq9Hp1OlyMGuScVQhQX+W0ihBBF1K1bN+zs7HB0dMz2NWrUqAIdv3HjRvz9/TMf9+rVCysrKxYsWJBt3OLFi3FyciIjIwOA06dP07VrVywsLLC3t+e5554jJSWl+F6YEEIIIYQoF+bPn4+1tXWO+1EXFxcSExPzPd5oNFK1alX++ecfAH766SesrKzo0aNHtnHp6enY29vz119/ZT5+8cUXcXR0xMLCgvbt2xMUFFT8L1AIUelIolIIIYrAaDRy9OhRvv32W1JSUrJ9LV26tEDnWLt2LQMGDADU2ZVHjhyhatWqmTeAdwQGBtKoUSP0ej3Jycn06dMHT09PoqKi+Oeff9i4cSNTpkwp7pcohBBCCCHKuMDAQDp16pTjfjQxMRFbW9t8j//3338xGo20bds283xVq1Zlz549REVFZY4LCgoiLS2N5s2bAzBjxgyWLVvGzp07iY6Opk6dOvTu3Zv4+PiSeaFCiEpDEpVCCFEEp0+fJiEhgWbNmhX5HOvWrctMVIaFhREXF8ebb77J9u3biYmJyRwXGBiY+Tx///03ERERfPXVVzg7OxMQEMCsWbP46aefiIuLe7AXJYQQQgghypW77xOLYu3atfTt2zdz6XZgYCCDBw/G39+fFStWZHsee3t7ateuTXJyMt999x0zZsygSZMmODo6MnfuXG7cuMGSJUse+DUJISo3SVQKIUQR/Pfff5iZmVG7du1sn17nVSPoXkFBQVy/fp0uXbpkO9/o0aOpVq0aq1atArJmWt759PrAgQPUr18fV1fXzHN17dqVtLQ0AgMDi/dFCiGEEEKIMistLY3g4GAaNmyYY0aloigFOsfdK3wyMjI4fvw4zZs3Z/DgwSxbtixzXGBgIE2bNkWj0XD8+HESExPp3Llz5n57e3uaNWvGgQMHivdFCiEqHUlUCiFEEQQGBmIwGHB3d89WD2jMmDGZY2bPns13332X6/Fr166lZ8+eWFhYZJ6vQYMGWFhYMHjw4Mzl36GhocTHx2cmKqOionBzc8t2Lnd398x9QgghhBCicggODiYtLY0JEyZkux91dXUlOTkZgEuXLtGtW7dcj4+IiODkyZP07t0bgJCQEFJSUjITlbt27SI6OhpQP1S/+34UyPWeVO5HhRAPShKVQghRBIGBgYwfPz7Hp9d//PFH5ph///2XqlWr5nr83Z9e3znfnZu/wYMHs3XrVmJjYwkMDMTc3JwGDRpkjjUajdnOdedxQbo6CiGEEEKIiiEwMBA7OzuSk5Oz3Y8mJCRgbW0NwJEjRzI/1L7X2rVrad++PY6Ojpnns7GxoV69ejRq1AhfX1/+/vtv0tPTM2da3i23e1K5HxVCPChJVAohRCHdWY7dqFGj+447duwYtWrVyrH9xo0bHDx4kP79+2duu/tT6latWuHh4cGqVasyG+mYmZkB4OXlxbVr17Kd785jT0/PB3pdQgghhBCi/AgMDKRhw4b3TQ7mdT8KuX9w3qRJk8x6lXdW+Zw4cYLU1NTMe1UvLy+AXO9J5X5UCPGgJFEphBCFdObMGeLj4++bqExKSiI8PJx9+/ZRo0YN2rVrx7lz5wBYv349TZo0ybyRCw8PJzY2NvPmT6PR8Oijj7Js2bIcBdI7dOjAyZMnuXr1aua2bdu2YWlpmeNTbiGEEEIIUXHd+UD7fo4ePYpOp6Ndu3bUqFGD33//HYDk5GR27NiR5wofUBOVO3fuZPPmzdja2uLn5wdAo0aNsLOzY8eOHZljb968yZEjR2jfvn1xvkQhRCUkiUohhCik//77DwA/P788C5cHBQWh1+uxsrJi//79tGjRgi+++ALI+en1nUY6d99oDh48mC1btuS4YRw4cCB+fn6MGzeO8+fPs3//ft5++20mTJiAnZ1dKbx6IYQQQghhaunp6QQFBeHv75/jfjQjIyNz3LFjx7h8+TJ//PEHCxcuZOrUqQBs3bqVKlWqULduXQAMBkOO5d0tW7bE29ubTz/9lKZNm2bOtLSwsGDKlCl88MEH7Nq1i4sXLzJu3Di8vb15/PHHS/EqCCEqIklUCiFEIR0/fhwLCwuqVauWrXC5i4sL8fHxgPrp9ahRoxgxYgTe3t40bdoUvV5PRkYGmzdvzpaoPH78OE2bNs1srAPqzEl3d3fS0tJo0aJF5nZzc3M2bdqETqejWbNmPPbYY4wePZqPP/649C6AEEIIIYQwqZMnTwLwyiuvZLsfdXR05KeffgIgISGBmJgYvv32W3x9fWnTpg2WlpZAzg/OT58+jcFgyLFC57HHHiM+Pp7WrVtn2/6///2PCRMmMHr0aBo3bkxSUhJbt27NrI0phBBFpVHuTP8RQghRbCZOnMitW7f49ddfCQ0NZeDAgSxevJjY2FhGjBjB5cuXpdi4EEIIIYQoMfv37+ehhx7i1KlT2NjY8MILL+Ds7Mwnn3yCj48PP//8M927dzd1mEIIkY3MqBRCiBJw9OhRatasib29PZ07d+btt9+madOmbN68mYEDB0qSUgghhBBClKijR48yZMgQWrRogZubGxqNhvfee4/jx4+TkpJCp06dTB2iEELkIDMqhRCiBMTHx2NnZ0daWhrm5uamDkcIIYQQQlQyKSkpaLVa9Ho9RqMRvV5v6pCEECJfkqgUQgghhBBCCCGEEEKYnCz9FkIIIYQQQgghhBBCmJwkKoUQQgghhBBCCCGEECYniUohhBBCCCGEEEIIIYTJSaJSCCGEEEIIIYQQQghhcpKoFEIIIYQQQgghhBBCmJwkKoUQQgghhBBCCCGEECYniUohhBBCCCGEEEIIIYTJSaJSCCGEEEIIIYQQQghhcpKoFEIIIYQQQgghhBBCmJwkKoUQQgghhBBCCCGEECb3f0lISxY26qYkAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in SER.keys():\n",
" SER[k] = SER[k].cpu().numpy()\n",
"for k in BER.keys():\n",
" BER[k] = BER[k].cpu().numpy()\n",
"\n",
"fig, ax = plt.subplots(1,2, figsize=(16,7))\n",
"fig.suptitle(f\"{NUM_TX}x{NUM_RX_ANT} UMi | {2**NUM_BITS_PER_SYMBOL}-QAM\")\n",
"\n",
"## SER\n",
"\n",
"ax[0].set_title(\"Symbol error rate\")\n",
"# Perfect CSI\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / LMMSE'], 'x-', label='Perf. CSI / LMMSE', c='C0')\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / EP'], 'o--', label='Perf. CSI / EP', c='C0')\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / K-Best'], 's-.', label='Perf. CSI / K-Best', c='C0')\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / MMSE-PIC'], 'd:', label='Perf. CSI / MMSE-PIC', c='C0')\n",
"\n",
"# Imperfect CSI\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / LMMSE'], 'x-', label='Ch. Est. / LMMSE', c='C1')\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / EP'], 'o--', label='Ch. Est. / EP', c='C1')\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / K-Best'], 's-.', label='Ch. Est. / K-Best', c='C1')\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / MMSE-PIC'], 'd:', label='Ch. Est. / MMSE-PIC', c='C1')\n",
"\n",
"ax[0].set_xlabel(r\"$E_b/N0$\")\n",
"ax[0].set_ylabel(\"SER\")\n",
"ax[0].set_ylim((1e-4, 1.0))\n",
"ax[0].legend()\n",
"ax[0].grid(True)\n",
"\n",
"## SER\n",
"\n",
"ax[1].set_title(\"Bit error rate\")\n",
"# Perfect CSI\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / LMMSE'], 'x-', label='Perf. CSI / LMMSE', c='C0')\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / EP'], 'o--', label='Perf. CSI / EP', c='C0')\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / K-Best'], 's-.', label='Perf. CSI / K-Best', c='C0')\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / MMSE-PIC'], 'd:', label='Perf. CSI / MMSE-PIC', c='C0')\n",
"\n",
"# Imperfect CSI\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / LMMSE'], 'x-', label='Ch. Est. / LMMSE', c='C1')\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / EP'], 'o--', label='Ch. Est. / EP', c='C1')\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / K-Best'], 's-.', label='Ch. Est. / K-Best', c='C1')\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / MMSE-PIC'], 'd:', label='Ch. Est. / MMSE-PIC', c='C1')\n",
"\n",
"ax[1].set_xlabel(r\"$E_b/N0$\")\n",
"ax[1].set_ylabel(\"BER\")\n",
"ax[1].set_ylim((1e-4, 1.0))\n",
"ax[1].legend()\n",
"ax[1].grid(True)"
]
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"For this setup, the non-linear detection algorithms K-Best, EP, and MMSE-PIC, outperform the linear MMSE detection method.\n",
"It is remarkable that K-Best and EP with imperfect CSI achieve lower BER than LMMSE detection with perfect CSI.\n",
"\n",
"However, one should keep in mind that:\n",
"\n",
"- EP is prone to numerical imprecision and could therefore achieve better BER/SER with double precision (`dtype=torch.complex128`). The number of iterations `l` as well as the update smoothing parameter `beta` impact performance.\n",
"\n",
"- For K-Best, there is not a unique way to compute soft information and better performance could be achieved with improved methods for computing soft information from a list of candidates (see [list2llr](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.List2LLR.html)). Increasing the list size `k` results in improved accuracy at the cost of higher complexity.\n",
"\n",
"- MMSE-PIC can be easily combined with a decoder to implement iterative detection and decoding, as it takes as input soft prior information on the bits/symbols."
]
}
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