{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03191cf2",
   "metadata": {},
   "source": [
    "# OFDM-based Communications with Superimposed Pilots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a59c999",
   "metadata": {},
   "source": [
    "This notebook provides an implementation of superimposed pilots for OFDM communication systems, based on the approach presented in \"[End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication](https://ieeexplore.ieee.org/abstract/document/9508784)\" [1].\n",
    "\n",
    "We focus specifically on neural network-based receivers that leverage superimposed pilot signals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d6fba6",
   "metadata": {},
   "source": [
    "## Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d84bd69e-59f2-4a42-8dd3-730c8c1d821e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T17:58:58.053730Z",
     "iopub.status.busy": "2026-02-13T17:58:58.053620Z",
     "iopub.status.idle": "2026-02-13T17:59:00.881233Z",
     "shell.execute_reply": "2026-02-13T17:59:00.880109Z"
    }
   },
   "outputs": [],
   "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",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "# For plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# For the implementation of the neural receiver\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sionna.phy import Block\n",
    "from sionna.phy.channel.tr38901 import Antenna, AntennaArray, CDL\n",
    "from sionna.phy.channel import OFDMChannel\n",
    "from sionna.phy.mimo import StreamManagement\n",
    "from sionna.phy.ofdm import (\n",
    "    ResourceGrid,\n",
    "    ResourceGridMapper,\n",
    "    ResourceGridDemapper,\n",
    "    LSChannelEstimator,\n",
    "    LMMSEEqualizer,\n",
    "    RemoveNulledSubcarriers,\n",
    ")\n",
    "from sionna.phy.utils import ebnodb2no, insert_dims, expand_to_rank, sim_ber, PlotBER\n",
    "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n",
    "from sionna.phy.mapping import Mapper, Demapper, BinarySource, QAMSource\n",
    "\n",
    "# Set seed for reproducible results\n",
    "sionna.phy.config.seed = 42\n",
    "device = sionna.phy.config.device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d17183",
   "metadata": {},
   "source": [
    "## Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4dea9622",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T17:59:00.884574Z",
     "iopub.status.busy": "2026-02-13T17:59:00.884304Z",
     "iopub.status.idle": "2026-02-13T17:59:01.151138Z",
     "shell.execute_reply": "2026-02-13T17:59:01.150037Z"
    }
   },
   "outputs": [],
   "source": [
    "# Bit per channel use\n",
    "NUM_BITS_PER_SYMBOL = 2 # QPSK\n",
    "\n",
    "# Minimum value of Eb/N0 [dB] for simulations\n",
    "EBN0_DB_MIN = 0.0\n",
    "\n",
    "# Maximum value of Eb/N0 [dB] for simulations\n",
    "EBN0_DB_MAX = 10.0\n",
    "\n",
    "# How many examples are processed by Sionna in parallel\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "# Coding rate\n",
    "CODERATE = 0.5\n",
    "\n",
    "# Number of convolutional channels\n",
    "NUM_CONV_CHANNELS = 128\n",
    "\n",
    "# Create an RX-TX association matrix.\n",
    "# RX_TX_ASSOCIATION[i,j]=1 means that receiver i gets at least one stream\n",
    "# from transmitter j. Depending on the transmission direction (uplink or downlink),\n",
    "# the role of UT and BS can change.\n",
    "# For example, considering a system with 2 RX and 4 TX, the RX-TX\n",
    "# association matrix could be\n",
    "# [ [1 , 1, 0, 0],\n",
    "#   [0 , 0, 1, 1] ]\n",
    "# which indicates that the RX 0 receives from TX 0 and 1, and RX 1 receives from\n",
    "# TX 2 and 3.\n",
    "#\n",
    "# In this notebook, as we have only a single transmitter and receiver,\n",
    "# the RX-TX association matrix is simply:\n",
    "RX_TX_ASSOCIATION = np.array([[1]])\n",
    "\n",
    "# Instantiate a StreamManagement object\n",
    "# This determines which data streams are determined for which receiver.\n",
    "# In this simple setup, this is fairly easy. However, it can get more involved\n",
    "# for simulations with many transmitters and receivers.\n",
    "STREAM_MANAGEMENT = StreamManagement(RX_TX_ASSOCIATION, 1)\n",
    "\n",
    "# Resource grid configuration without DMRS pilots for superimposed pilot transmission\n",
    "RESOURCE_GRID_0P = ResourceGrid(num_ofdm_symbols=14,\n",
    "                                fft_size=76,\n",
    "                                subcarrier_spacing=30e3,\n",
    "                                num_tx=1,\n",
    "                                num_streams_per_tx=1,\n",
    "                                cyclic_prefix_length=6)\n",
    "\n",
    "# Resource grid configuration with two DMRS pilots for baseline comparison\n",
    "RESOURCE_GRID_2P = ResourceGrid(num_ofdm_symbols=14,\n",
    "                                fft_size=76,\n",
    "                                subcarrier_spacing=30e3,\n",
    "                                num_tx=1,\n",
    "                                num_streams_per_tx=1,\n",
    "                                pilot_pattern=\"kronecker\",\n",
    "                                pilot_ofdm_symbol_indices=[2,11],\n",
    "                                cyclic_prefix_length=6)\n",
    "\n",
    "# Grid dimensions for neural network\n",
    "NUM_OFDM_SYMBOLS = RESOURCE_GRID_0P.num_ofdm_symbols\n",
    "FFT_SIZE = RESOURCE_GRID_0P.fft_size\n",
    "\n",
    "# Carrier frequency in Hz.\n",
    "CARRIER_FREQUENCY = 2.6e9\n",
    "\n",
    "# Antenna setting\n",
    "UT_ARRAY = Antenna(polarization=\"single\",\n",
    "                   polarization_type=\"V\",\n",
    "                   antenna_pattern=\"omni\",\n",
    "                   carrier_frequency=CARRIER_FREQUENCY)\n",
    "BS_ARRAY = AntennaArray(num_rows=1,\n",
    "                        num_cols=1,\n",
    "                        polarization=\"single\",\n",
    "                        polarization_type=\"V\",\n",
    "                        antenna_pattern=\"omni\",\n",
    "                        carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# Nominal delay spread in [s]. Please see the CDL documentation\n",
    "# about how to choose this value.\n",
    "DELAY_SPREAD = 100e-9\n",
    "\n",
    "# The `direction` determines if the UT or BS is transmitting.\n",
    "# In the `uplink`, the UT is transmitting.\n",
    "DIRECTION = \"uplink\"\n",
    "\n",
    "# Suitable values are [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "CDL_MODEL = \"C\"\n",
    "\n",
    "# UT speed [m/s]. BSs are always assumed to be fixed.\n",
    "# The direction of travel will chosen randomly within the x-y plane.\n",
    "SPEED = 3.0\n",
    "\n",
    "# Configure a channel impulse response (CIR) generator for the CDL model.\n",
    "CDL_CHANNEL = CDL(CDL_MODEL,\n",
    "                  DELAY_SPREAD,\n",
    "                  CARRIER_FREQUENCY,\n",
    "                  UT_ARRAY,\n",
    "                  BS_ARRAY,\n",
    "                  DIRECTION,\n",
    "                  min_speed=SPEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb3265",
   "metadata": {},
   "source": [
    "Let's inspect the two OFDM resource grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b3b1bbd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T17:59:01.153336Z",
     "iopub.status.busy": "2026-02-13T17:59:01.153202Z",
     "iopub.status.idle": "2026-02-13T17:59:01.468171Z",
     "shell.execute_reply": "2026-02-13T17:59:01.467296Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resource grid without DMRS pilots\n",
    "plot = RESOURCE_GRID_0P.show();\n",
    "plt.title(\"OFDM Resource grid without DMRS pilots\");\n",
    "\n",
    "# Resource grid with DMRS pilots\n",
    "plot = RESOURCE_GRID_2P.show();\n",
    "plt.title(\"OFDM Resource grid with DMRS pilots\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eb9259",
   "metadata": {},
   "source": [
    "## Neural Receiver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c85b8f",
   "metadata": {},
   "source": [
    "The next cell defines the PyTorch modules that implement the neural receiver.\n",
    "Convolutional layers are leveraged to efficiently process the 2D resource grid that is fed as input to the neural receiver and residual (skip) connections are used to prevent gradient vanishing during training [2].\n",
    "\n",
    "For convenience, a PyTorch module that implements a *residual block* is first defined. The neural receiver is then built by stacking multiple such blocks. The following figure shows the architecture of the neural receiver."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f2c755",
   "metadata": {},
   "source": [
    "![Neural 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3kyJF+jKkEn37ub7n4t4NEibKjC54tKcBFy2KcPu644/BXwerMT9Q5Rmvs7mhi9CuyG9d2VC+jbV7dww47DE8VDPw4kUfZkTXO4mw4Exxm2IKd4yDiicsaUOLibEMU1pWSOBHZHIYcGTNgAicXfPT5wDLe8yhv8mW/GgrAzyiL6AAhzlV2pHnvvfdIjcITngIg4vH84RKjDjKlJGyOycYy7CVPYTbccEMs9DjYsG71/fffZ9jALbBwNtr3nQWpzANSGL44y3CiVrb/qGweSEACEpCABIqPAOawLbbYAuFefLfmHeUhgeK3uCNw2UGlVhZ3BHr0qPBRQbDiMMM2L1idWZZ6+eWX4yrD8Br9ikTGuQ2HcraaQeziM0NeCG7cVIJLOknxShMYozXmasL07t0bVY0W5xLOM+w/gzWdDVvwpXnnnXcQzWSNhiYYnuVcwsaPnkbcjx8/nm9CYfi/5557MO0Tlz1tonKGA1aUPvDAAwwGKDA3fuCBB+Kw3rFjRzZ2ZPksjuz7779///79n3vuOSYNcMGnoeFfnIhwo2d8cv311yPQCcC4guWzLFcNBnjWqvIXfPRZZXv11VeTbCyluGL4UwISkIAEJFAiBOjl6bIRCSVyv95m3RKoz/6DdVuCbOeOuR35u++++6JNk8wLb2+EMvKXfV2wVaPd0ansIYNlnY2fWMEZZDQObRiqN9lkE5QxKz5ZfoohHJM24p6T2MhR4WhrdpjhX9xpEOucQe6TMokwQMcxDos4XivERejzEzmO/Rt1jpkcAzkbRJIOX28lCmZ7LOisWMXujtmewQD5RneE1MYWThi81bHfkyz7ybAel8Ij/UmNdDhgnEC+BCZHbPwUDDlOCWl3QmGw9HOJQjIaYXqBkBSD6T/yClvcYKqneAwzmG2IcvdAAhKQgAQkUJoEsHAxp83KNNenlmYFyPFdl6MCc5xljrNDZLMJDHI2g/miaxkP4BqOy3iULAs3eXs5U52iRaBjBcejhsFAFIsDHgELRokVmxrnEc38EZg/ciRxDoKdm7w4js2IxBlI4FdDSGKx5BQtHpsLxwxI0O7hPMdVBiOd4FkUm3hcOv6UgAQkIAEJSCAQoN9EtTNf/cYbb8hEAtkmoHDPNmHTl4AEJCABCUigmAlgOGM+nK0pivkmvbf8IKC3Q348B0shAQlIQAISkIAEJCCBhAQU7gnxeFECEpCABCQgAQlIQAL5QUDhnh/PwVJIQAISkIAEJCABCUggIQGFe0I8XpSABCQgAQlIQAISkEB+EFC458dzsBQSkIAEJCABCUhAAhJISEDhnhCPFyUgAQlIQAISkIAEJJAfBBTu+fEcLIUEJCABCUhAAhKQgAQSElC4J8TjRQlIQAISkIAEJCABCeQHAYV7fjwHSyEBCUhAAhKQgAQkIIGEBBTuCfF4UQISkIAEJCABCUhAAvlBQOGeH8/BUkhAAhKQgAQkIAEJSCAhAYV7QjxelIAEJCABCUhAAhKQQH4QULjnx3OwFBKQgAQkIAEJSEACEkhIQOGeEI8XJSABCUhAAhKQgAQkkB8EFO758RwshQQkIAEJSEACEpCABBISULgnxONFCUhAAhKQgAQkIAEJ5AcBhXt+PAdLIQEJSEACEpCABCQggYQEGiS86sWqCSxatOiLL74YM2YMB1OmTKk6kGerIbDmmmt269atXr1622yzTf369asJlZXT33777YgRI0iag6xkkLlEGzRo0LVr1zXWWKNJkyZbbLFF5hKuIaWiqdurrroq1axx48YbbbTRKqusUsNtZ/Tyd9999+9//3v58uUcVFRUZDTtIk/Map/MA67Duv3DDz+MHDly8eLFHCxbtiyZ0uZnmLXWWoueiPpGN5SfJbRUEqiOQPmKFSuqu1Yc5ydMmLDddttlSqgFWfOf//xn8uTJxcGnru7i+++/32qrrbp37/7b3/42B/I9SHb+LawKj/ibNm3a1ltvvc4662Rbvhdl3WZc3atXr/XWW+83v/lNDuR7kOzffPMNer2walpdtQNV5mu1rxJL3Mkc1+0g2cePH49eL466zV1MmjRp2223pRtKX77Pnj0bU8usWbPiHpM/JZBxAgr3WiDlzXz99ddHjx6NERQpgCWPA4wftUjCoGVliBsENH9YJZE455xzzk477ZRV7c5Tw9BOM40Jtn///jwEDvL8UdA7Bkrjxo178803586de95552VPu9PfgKiY6jayJgBkmD127NhDDz10n332yap2J6OXX34Z0dmlSxeGoxjzMOkxs5TnNS2vime1T+Zx1EndphXF0N65c2c0Lh0fk4HU8GRKm59hvv76a9oH/n3jjTd+/PHHCy+8ME3trnDPzwddlKVSuCf7WHkthw8f/vbbb6P80E/q9WTBVR+OKVc6A7pqbB5QzZJ2J4v77ruPWReyyH+9XpkWhnBuAVFIZxnqXuUwaZ5Btb/wwgvFWrdxaQMg98hIG4BZ0u48oAcffHDjjTempmHAU6+nWSet9skAzGXd3mCDDajbTP0VtF6Po0rvQ+NAT8RN0Tiko90V7nFs/Zk9Agr3pNgGW/vnn39O37/rrrsmFcdAyRGg0bz//vsPPPDAHXbYIePanUaZP/T6oEGDkitOnoZCx7zyyivPP//84Ycfnlm7O4oWRNiki7tuI3H++c9/IqyxuzMEyuxjRrXDsH379kceeWSjRo0ym3gpp2a1T+bpR3V7l112yfi4NKrbhxxySIsWLZIpT8GFQb6/9tprjz76KO9vytpd4V5wz71wC+w0bs3PjhfyX//615NPPolkV7XXzKuWIWgoMXU8/PDD+IRkdiUfWuqyyy4j8UJX7RBFa2LuwuiF+sTZtJaMqw2Oan/qqaeo3kVft4O5nVkFlpXTT1dLpPYXUDZXXXUVnvSMqVTtteeXKIbVPhGd/12L6vaHH37IUOd/pzPw/9i6XayqHUyY22ldWQbDpBmenBkAZxISyCYBhXvNdBE3mDSwZNA+1hzaELUnMHDgQGZg//GPfyxdurT2sauNgXDHrx3hXm2IgrpAx3nwwQe3adPmsccey1TBqdv4tZdI3WZwwivMCHz+/PmZAkg6iBvUEr1+xg35GSxk4SZltU/m2YW6jdkYd+1kwicZpnTqNkPuww47bLXVVhs2bFiScAwmgboioHCvgTxdMktY2FSuCKy2NdxqnV5Gu7/77rssFcqU0Z2nxoZCl156aZ3eVoYzR8TsueeeOBdlxOhO3QYRcrN06jb65pNPPmFuJ1NGdx4ENe3UU0/NuItChqtOISdntU/m6VG3WbSKzxvvdTLhawxTanUb7X7QQQexZYJG9xrrhgHqloDCvQb+mttrAJShy2Fzd/xAlixZkpEkMbeTTtGY2wMTpnTx10cjZsToXoJ1O2yAjWvQvHnzMlLNgkmSVQea2zPCs8pErPZVYok7Geo27R7yPe5Saj9LrW6zoJzWFYwa3VOrMIljYSthKMhKLYZGGDsyO7ueOOviu6pwr+GZ4uBOJdNJpgZMmbiMsztdBbvpZSKxMjow3BYzklReJcLkDyssAZV+qRDubMdZanUbkY13EBvbpQ+QFHgQAOShZCQ1E6mOgNW+OjKx56mKvNH0WbEnUz4uwbrN7ghbbrklU5opQzNilQSwx7311ltDhgy56667br755iuvvJJ9zNjjuMrAnOTrJa+++ir/VhcgwXm8xdjFYcGCBQnCFPolhXsNT7A0xU0NULJzGeE+ceLETLnKsCt5kZnbA3Usu5tuumlGXGVKc1CKcKc/yJSrDOKGcZTm9uw0Cb+karX/hUX1Rwh3/GQy5SpTgnWbuR0+C6irTPVVLMUrX3755QMPPIAfF1s/09FD+NZbb2WbvursdJ9++ikSn680xuVH+Bo//vX+++8zNpgzZ05c3GL6mfUPKKDD8KNlYpoNMQoRHI0g2t1d23Pw7PCWQY9mSrgzT8K8Zw6KneMs6FqojRkR7qVZt/lwDL4EmRLuPAgeBw8lx9Wg1LKz2ifzxKmK9FaZEu4lWLfxlqF9ULgnU9mSD0OfjrmdLY9Y/vv73/8eyOuuu+7ZZ5/Np6+aN2/O+JDP1a299tqcx1e2U6dOdNxsIcC8BzsxbLLJJnvttRdVkQ1CysvLsaY3bNiQwRXmkpkzZ2JZj41LRJYXs8vFqFGjbrjhBnJh0QJZJF/UQgmZdYs7HeSzzz575513ZoQIEy7vvfceM90ZSc1EJCABCUhAAhKQgASyRGDhwoXjx49n7S975jICR6CjtlHY7BOA0f2OO+5gi95gsGM7TpzgUeREQToyJ8wfVnZUHxZ6JP5XX331xBNPXHPNNZ999hn2+Li4DA/YMQwnGeKSCK44mbIDZolMyslm3VAEdJ4NxGOLGGjy/GJPMgPC+cSf4OFJ3HvvvT169GC8FRvXYwlIQAISkIAEJCCBvCKAmEa5tWvXjk/UhYI1a9YMQziinNkhlhtF8hrLLJq7Y8eOODRiNT/iiCNYctCyZUtkJIZ29k3q06cPq9cQ7m+++eZ6661XOe7qq6+ON85HH3100kknYYBnQ6q8QpGpwmRduEcFRZfjM/PMM8/wPfAPPvigVatWzHdsttlmPKq//e1vPEhGSAyVEOU8Hh4SAy+eFp855EHi8MRYinmWF198kckRPKVIlg9t9u3bN0rfAwlIQAISkIAEJCCB/CGAnOMPdYcdPZQKhy580JHycdbbcBXrLYKQf9k/jb9wEqHftWvXnj174j/Dp9Yx0uPUVPkesegTl2RJvHXr1pUDFMeZX9m8s3pLDKrYpfuKK654+umnJ0+ezL/XXnstWwIxZrrxxhvZgGnSpEnsE8Tx448/zjNmyzbmRCgSYfCNYT0ygzPOI/T5d8aMGRxktcAmLgEJSEACEpCABCSQMgHM3hjRp0+fHi0eQHZPnToVIc4lPNfReOjDuFWn2G35q5wpwfgjVvirMi7W3siKXzmFIjiTO+EOLHCzShVD+7nnnnvAAQdgPscGz0ko4/w0ePDgM844o2nTphjgUfM8D/4CYp4BZ9hYgH1CGGYxFXL66acX6GrXIqg03oIEJCABCUhAAhKokQDmdtQaGp0dHnGXwLWdRaiou80335z1o5z/+OOP0fSsVUXNB8GNWR3Vjmc8y1Kx2KIS+ZfdwNhzApMuwTC987X1uLgEozA45CAXyQgDcbGad3PnKhOeLhMZrEtg/5Add9yRJ4cNnqEY8xp4I3Xu3Bn/Gbzh//73v2NQr1wbGGAxCcISByZNmAepHMAzEpCABCQgAQlIQAL5Q+A3v/nNvvvuyz4l+FxgqMViixc0/uvsg7T11lvjCM1KU3Qgm8aEMvfu3Rtp/vDDD3PpqKOOQn/jSo0yRPuxZhIfaay3BKgyLgsg2aPmtttuY/3rKaecgit84pWT+UMp+ZLkWriHkiHB0egMjxgYxZaVM/yFMzzFaLQUN+sRnY+N67EEJCABCUhAAhKQQF4RYJNHdmbEOIvbMwZ41jHutNNOLCRF5rECFZ2NdXyttdbCAM+3CFiNikA/8cQTWYGKoR1XCxQjvhiY4du2bUsYPC+Q43yULS4u3hwYdrEC/+EPf3j33XcRk/zMKw6ZKkzdCPe40qPLGWnhAsWiVaZLWL2KGZ6Hyre1mCtBpr/99tshCiMnFhxwkvAY77W7x5H0pwQkIAEJSEACEsgrAjg5H3zwwWzKvnKlasOGUdnwoll//fURgag7BHp0nt1HMKij7AmMIwarVI899lgCo9c5GYJVF5dcdt55ZyQif1GCxXSQi7uKfRiwCz9j/8Vn5pFHHnnttddYaIwXDZMpKPL99tuPtaqM0rDNRxZ3hmI8+z//+c9Mu/DM+LZWMT2M6u6FoctPP/1ExWUYGgezuiiel4AESoEAbSPzzjSYtJOlcL/eowQkULgE8HauXHiEeKTFY6+i0cNPTPU4wCCBsLvHBuC4urjF/TXrrAt30LPq9Oijj2Y4xUewnnvuObyawM2Ux/Dhw/Fk4pgR1R577LHLLrswr8ETYt6EwAzC2HKfbxzypOmT2Ekm7PKz5557MqWCE3yVmwHFPdTi+MlKgIsuuui4445jhiiqysVxa96FBCSQDgF2WMOWcfLJJ+MJmk46xpWABCSQnwTwaMeFBn+b/Cxe7kuVdeHOeIjFv+HGGDBts8024RixzsPgGCs7YdDiCHfMyUj2EIABE0sQkO/RmSgRJlAw0sfOtoRLxfovgxaWXeNKFC0AKNY79b4kIIFaEWDvBb5Uwl66tYplYAlIoJgITJ475umxg8MdrdZyo73Xu6GY7g47L3/FdEdp3kvWhXuN5cMJicXFmM+r9EaKU+1RaqWj2qNbrnzwzTffvP/++/TZDI340hg1m+8AY55nBXeHDh0Iz0oAOnUWc4Q5ChzF2FyJSwyZGLwyyc5XrojF1xDYV5X5kH79+hXrYo7K9DwjgSImwNZpOB+yVwNNKzYRlgYxe/nyyy/j+hkMV7z+bMrGPrzMQbMCjPlPdmTj0nbbbcekKDs/4KkIHxaEvfPOO5zBO7GIcXlrEihoAguXzf565oiCvgULnzyBuhfuWNYvueQSjPHJF9qQEGCKnA//jhgxAq8v5h9YZH3ZZZfhCn/TTTf9/ve/32233TjPTvkPPPAAeAlA4DFjxrBIAJmOMxKBkftsmY/ux2jHAIkUBCsBCRQHAT5v99hjjzFK532//fbb77nnHhwOr7zySnZVO/XUU7lHvgqOA94dd9zBtzVoMd577z3cFNnbga+iEJeh/tChQxnPMx3K2J4tIIoDi3chAQlIoNAJ1L1wp2NARxY6x9yXH9M4eyoF+1kQ6OhytkPCkMZuPJtuuindMN87IBhiHYHOVw9OOukkvI/wurnhhhv4ci1bKdFPY7a//vrr6acxzDuPkfvnaI4SyAYBpDZ+hozGx40bd9hhhz311FPo9SDHaQcYqPNd6tmzZ2NuZ4n///t//+/mm29mxRHfN2FJEouLGOczpGfL5KuvvprZPPfvysYzMk0JSEACKRCoe+GeQqGNAgG63s022wyzGe4u+MMwM860OB8S4/tWzzzzDC4xeMbjOcNnDgiMOS38xCQ/d+5cOmwusbqAS3jIbL/99uy/WUBUK1ZU1CvP6Ud/CwhObFEFFUujVseFjo6vEuLiwtCdgTprYxi302IMGjTo8MMPp7mg6Xj00Ud33313vjKIYZ7FRV9++SUNCK0ExyNHjoQVB+zxdeihh+JmUyt0dR640J9dDgCKKAeQE2fhI0jMx6sJCJS6cC/clwdXGbxiUOR4vGAqo3tm10h6aMzwfBOYrw/gHoP1HWsZB4h1ND31gC9eYYPHXRXTe9g/jq8VVN5iKUGNyYdLoyc9gEtf31UPbNV4tXwoT36WYe7iyZ9Ofbpp/dY92+/WtGGBaa86R/rRlIfnL5nWu+PeqzTtUl5WeKNEfGCQ5sy8bbjhhi+99BLNBUgHDBiAt/pDDz2EKOdrGMy8cZIDpDln8HTn39/97nf4xnCeuVD84wtOtVvtk3l3Cr16J3OP+Rxm+YqlP8we9cW0FzZe9dAOLUrLSXXa/HEspV28fN66HXZt2WjlHoP+1ZZASQv3JcvnfzvrzclzPtyg0/5tm3WvLbu6Dc+Xqthbk152//33ZyUZdnfKg3Bne3t0/Ouvv47RHe+XLl26sIMkPjDsAb/33ntHXkn8DI4x/EtvXbf3Utvcpy746sPJwz756bE+HQ/YeLWBzRu1r20KNYYPXwRjnAM9nI5qlC9MX+A9zFfDYt2N4k7ydEiWf/kUc40FSD/AkooFE2a++d2stz/66fG+HffPuHxnWDh16lSqGaZcHKNZxZh+mUmByoz7FqNN0qSWhjSxBDNZxL/gZQEl3l/ZrrTTF3w9evL9H/74cK/2e2y02sDCku8TJ04cMmQIa1LPPPNMqu59991HrYMkxzQXfEgcyLQMYY8veBIex7loq9mwQh3C4SAjjzVniWS72lMzqfbsRwxD6if0ggWkuhuEPC0DsVjHFRGufJK6TZq8RFGdry7BjJzPQfXG1QrPKzZxphvKlG0IbqQJf7q5KE0aIrZkCN9TT6atzgjANBNZsaJizuLJ73x/+1czXurRdueSku9Lls8bNelvP80fO3Pht22adlO+p1CXSlq4V6xYNn3B+JEThoyd/vx67XbLc/mOCzsLeUPTj8spDT2dAT8xjLFEFVcZvsPCSea+cW9leRm+rWz9Tr9CLGbGcXxHzdMT00kTEed4zqdQY/IhyoqyijlLJs1cNGHGgq8/+fGJPp32y7h8Zzbj+eefp58AL35EhxxyCI7CVW58BBC6cAJj43zwwQfpOQKiyiexaP7pT39iKTCLBXODcWnFomkLx81e/MPkOWM++vHxvp0yJt/plf/xj3/glIUoARGbkxx//PH4XKV5X6hzllMHZYltmK+w8YcBmG8qYx6meyYvOmz8u8gOQVDdE0mzGESnjs1dMmXagnEzFoz/fOqz63XYgzqWt/J97Nixzz77bHTXrHVhySkvOLKJzWEYXHFMZUaLs9077uwY4zkIGnHgwIEXXngh4p6BPYte8JnBzT3IoCjBwjrIXrWHA+3nnXfeSXOKXqT6MeHJPvo0uQwmq6REY8vuPZ9++un//d//MfsRwlQ+GWp4eIlyMF7KavWmTeAjiXzvHMsFo3r6oPvvv59/q+ST/El6tzPOOANQ2FNok9n+6I9//CPpM5uEQxePgxpO48AT4Yv3KPvkU66TkDyCeUunzp81fcb8caUm3+cunjJz4YR3Jt7ZoF5j5XsK1a+khTu80O5zlkyeN3PqtHlj81y+IwrxgQmGRjZ8POWUU9BJfHH2ySefxBUV+9mHH37IXu8c06KxvAwFiT8MHjJoHXaNQDXizMqKNFJgAh0XmuA8k0KlyV6Upq3K5i+Z3mDJ0sRZLFu+cAWzC2UVsxdPnLN40oyF436W77/DMtqi0cp9MNP/QyMyEPrLX/7Cv3ga/PWvf+3Tpw96scqUg+2ZrTlj5U7lk5xhmw767CoTSf4kEySNW1bgyJE4yoIlM5dVLCYMOmb6wnFzFn/PBCXyfYNO+6270nkm9X2cqGZsOTJs2DCECOOZ+fPnk0tG1i9iS8PczjwS1ZjO/sYbb6QDprazKgMvbSzE/CQArts4ePB0WH8Z3ojEKGKvLlgyA3oLl86Yv+S/5vzYq9Hx0ooF6ICgb+Yt+XHGwvFjV8r33aljbZqumT/OM+H2mXxjs9dQeM6wgoVXnmHV6NGjGfZgemf5KYqcoQ7TcUDDs44HF8LzdTwqOeMlwnPH3bt3xxUexR+hyJODOq/2gQMykRpI44ntgx17WA/A0n8g0+pWCQqkLB7gxcdfMQpQ+SRJ8VAwutNKRMFSOGjcvGxxxewaG4fsVW8ME2effTbDSL56jsGITUhpLjLS12CceuuttxhnUoEZZ95yyy077LADH3UhR1xG8f9kzQY1mUHpMcccgz9Yz549UwBIlKatVtQIsHLK85fMST4iLfPipSvrQ2X5vtGqh3YsDecZ5DsElO+V61KNZ/JUuFOb6V9rLH0yAeihq1M5ixDty1Zqjjj5vu7P1vd2eeM8QwOEiAlequGWmXXt0aMH7SPyhWYL2yQ9Ae0j3gUEwJCGRwEdCdod1c4ZeusLLriA/iM4u2P8wKkDuw62N+Z5oznHkHgd/rv3+WWPfXlogwb//QhXdSWZtei7ihX/lb8o+Bj5/mSfThmT73Q2GHKwXPLNtiOPPJJdsYNwhyFSHismGIEM/OrKmaXzzVYp2/bkn+4bvXfi9JdVLJq56LsozNKKxZF8//jHJ5DvC5emKM7QiAwXGRaec845OF8xt0AlDOZbxocYF5kdQj7yWQAqHtUMyYjtDfMk8oV/GTeiy5EpzHqTSLdu3SjkVz//4USEuReHAYQjY6fbbrsN6x2Jh7vggw98OJnseCOYEmFnJOAzxRTdYzIHj396HPSen3hc4+n//Z52lbGYpkDchEvUMazv/5Pvz+WVfAcUM2lxt8Bbj20SHYORkloKT9akhooKf6KwmQwD0RCLAFguqeqMmhh98UebQAPCxjJxydbtzzqv9rG3T7U/8MADcT3ij6knRkpBuNMssPYXjzjaXpoLmtnYWDk4/u3RZR8s/eO40a0S55VM9U6cQnVX+VYALyYymheZKkeDwF8YBzImYXIYVrQVWI54i6moTBahyLGgv/nmmzS5O+64I6MgWhgaDcaWGNTJiN2NWGnNu8+upgwsqZ+YSDBCMauMcA8lwYhAHcaIwPYMjPzZDYkBVXWFTHC+YZOyXc+ZX2PrWjkFWsHkI+Iqs2D5zCiRX8v3F9dpOwDnmTj5Pm/JlIc+PCSKEnfAoq/oDAaaO0f1j37GHbRrtk7/bue1b75O3Hl+xn7FqfLVTJ3BVWb6gnFRasr3CEXyB3kq3Oct+emB0fslfxsJQi5etLg6lYNen7f0F7NljHz//Ivpz63bbnecZxKknLNLNGfsEVE5OxQPhnYUUlDnCBqaOUwdtGg0hWzlHu0Vw3naO8LTdHIcrHQkiCStnGwdnll13bKJ899NoQCV5XsKicRFgRLT34xqgMZx8OHGl+P0009ncgOJGW16zSAqLm5Wf9ZvVNau++IJs99OIZdY+T5/VoseWzdfvGxe4wYrFy4n/4fxG1cZ3IcwfSEEo4gMchAxGOOpipzEj4upfwxvLJXGoMtJSDL+pCqyIyGK/O9//zviEocuZiEwCWM24zyakrgMREENf/5C9eYkzwKdhNcHzgnMNREe8VRb4T5x7gfQm7L4g7KVsxG1+Kss3zdZ/Sh63FokkYWgvM5V2nqRjMgmgIcHxJCekBgscdsImzzG+mPQjPCHGEKvR41DvjkbZKra/ziuRcWKVKp97NMDFDWTukfl5C/YPhCXeMHxdsAcmAh35j2iAVJs9Owdt+9a9vXMT2f+ouKSzapy9S5rMres9qufcBpkfgzhzuCcKhdrFcJ/hllfrE68+0h5fvbv3x9XLsznvNR0c9ih7r77bl5tjFB8foR2lbE6N8AonTE/yzCil522l/PRki2OaW1IgT8motHutCdUfh5Nsvf/v3D16pd17Lk8tda1Y8+y1CKGzGPl+7gZL/ZAvq92RIf/KWya7iQ/sZT4Y0yLls1COv/vdn/1/8QRfxU00z+U77Ui+kunW6to2Q7MRFI6L0Bc8dp1r8Xr9LN8nzJv5jScZ5Dvc37s2Cpd37y44mTyJy1jlFzooYPDxnnnnUd3TmsYXeXg5y6m1g1ZbAp5fhzke1l5vfrlDdMvKpPXl156KZ+TRHeie3BjxbcSKw5GI4yR7JAdbXoddudIP8ecpUAfMHvx9wsWty5bsW79eo1qlS8QMCgyJkR/x6p2EsHWiC5EAjJVjZkN8xhjSNzfMbczY45Gx4sa7chJTPL0r3TqaB22OkHxY2OjV456YgKgyzGnYbmMpGRUTqzCFIDBQ+w0VHQ1qwdB31DHysvK69dvzL9ZzS6dxGPf96hx4EHw3SWWqFZOOVbKV75aBGdCtZ+zhC10al3t426fZvaqq66iicAZiVkjJpd4KR5//HGaCwarWH9pJfjJ1FxUpeNSyM+fsdV72ZKVnhy1/cOgjjQHSGzfRCK84DSeGNThg138tNNOC58ICD5CrKVmxI7R/fzzz2cujui82jjjIdwZwOP9cvLJJ0cJMueJvmfiLgzy40rImAqDAt82IWWM93FX8/8n8n3+0ukr5tdbu82yxg0SufPl/73UtoRBvrN6bd2OuzZtkLozZ23zLbjweSrc65wj8n3ukp8aLGy2YlmbpYvqvDi1KABmCeyXGNswCFVWPLVIqC6CLpyDf2H7enizJvxbunx+5MYQG7B1ky79VjuiV4c9f3ZzOjn2UgrHWIixrKPawYgeZWs8+hJmgfHcwPyDMyV+CFwKm16nkH7KUVZUlC2eW69N23aJU6hYsXxpxfzlP7u5x4ZkPVCnFhswFTtleZO/f/RIg1oKd+xYEKBvrtwpggV3C9Q5opxg0GNVBh4v5E5gpneYH4cYTkd4bVFF6Xqxz/EtApQ9vSzKPswRwRnRj90OfVndDjw8HYRmCha1Jg3bLZ76fatV2tRPaI2jji2rWLmUIhYdxy0br9630wG9Ou7TqVnP5g2RBTXU1bjodfuTrzrgmR2cZ+q2JCnknqlqP2lxk6/Kal3t4wqMMzqSnaE7BnWUKF4fDDWpydiS8fnCYMwlwuANz5m4uFn9uWh+WcOyVRCviXNJpnrXr0hlDT0jdt7i4DgXWwZEORKcJdFh9pj1KqxB560nDG0C7z5vOn+sk2b8z7IWJtMY+bCan2+HMX3BuosoNQzwONIg6yvnEsJgXCDNWGN/FLfGA9a1LJpT3rZd7RzwSJa1sbNmTE8yIg3L8hVLliybE1ceDAE0UH077den4/4dW/TK1JKtuFzy9mfP9rtuuvqg1Vtt1KZJ1wb1fmV2zNsy10nB8lS41yur16xhzasMK8oqFi2dTs9Zv16TRvWrXtS/vKJizqyZVaqclS9PxaLK00asPGvVpAtbcCABP1v444vz7qiTZ5NapnwcMbWI+RDrmb+UPfb4sJYta/Dc+M/3d46ZMmx5xS+rPGMle4tGmZkiwSuD6VqMPayCwk6JvQcxSk/MeVjRP9E3RJteJ6AX/EZCgNjjBFESX1owq2zkbR2xWycONnvRxLe/v338jFejYJFk795uh7ZN1nx3wseLqp4yjWJUccCUNGNCbOQI67jLWMJQDOjCoBuCsickwTiDzoYYcZklhx6am7VriHicYhlqEgtZH4Q4DvSYKg844ACUPdnF5cJPFAB/eIPEzSlVDln5zIHr373PhXvceuttq62WaHHCexPvHT15GDvGRinESvaWTVbPn/WpUQlrPMB7ocYweRsgg9U+/XukZmIiwR8D0zsynVqKTA8eINRzqjeTQohRRGpw764yR9oT/sIlWgbcw6oMVquTb9xbdvigP/frt9LfLMFfMtU7tRqO+Mb1HAK86bEFoAnl7WaEEwzn9FPIaxoH7ppmIVBC8TM1xDCekwyHMKszsL/33nsZcIblWySIrKdBxhWHmbrY9GOPGUHRnqTQOJAIdroXrmn+4otPxSaYzPHcufP233+/JCMuq1jy7cw3Xxh3QZTy/yT7/n067odk5xMl9cp/Jc/Y8vyEzV6PwscdTJo75pmxg8PJ1VtutNd6N8QFiH42qt+8Sgd3AhAxQRZRCmkeTJ8/7rVvr4p1cydBJXutqP6qZtQqZlYDt2jc8eh+Nb85cxZPeWDMfvXLG63VdusBa19RZZEmT55y8pUnValy8O79YtrwNyZcH0WMJHvvjnu1b9YTCfhD45EV/10aF4XyIFsEJn9RtkbLzYMyTpDHpz89E3Uq2ZDsIWu0JlPemJYRkfjGbLvttvQudC30K6xVjfwKooO4AtMb4RZPf4zNPjjB06/jXoxbZ1zI2v5cvrRs2vjGa7Ze6f2Z4G96o/HNGv7XKh8n2ZuuNBWn+EfvSyfKZDeuLLhjRcMYksMARmccTIwYyVhogW0+yPQoM6Lzx0/6ZvDiSI3hDcggDWNOHGDwecVDBst9hw5VjN5JE1McNntGTcijKOUkDzq36ge9VZv2W7P1ypXc1f19Oe3leuX/9UMrAsle3W0W0Pm6rfZxoHi18dNAGjKSR75jd8e7mvpPTWZxUaQyOVml3Ze2hfM4alONQ8os6ebd4W2Kcz+Ly7fGn9MmlLWpt36NjUP2qjemdNZZMfZmt6IwgGeEz3JSXlVGKdwvkpoWgHkJ7pT7pZ2Mbipo+jCAwcWOpvLKK69kyo7djUOjwVwcJnnC4+4F2yhi7AHNNaMpVmPHphwbIPExEztTvqhfI8DKicwum518RJyBWcgXEokkO2vqOjRfr7JkD8Fow7u32a5yvpXPNGnQOsmQcXFTjhiXTuKfTeq3blz/FxcgJXtiXFVezVPhXr+8Bl3CNNO3M0a+/s3V3BWLM5s1aF/dm7Zi1oTqVM6iZbP5CkDgUlmyR912leDy8yR73NJK0qPQf1BCjmkraTQj78DUio0xiXYzrpFNLakMxmrVeA1WB/bqsAeOMZmysscVj6YfD5lzzz332GOPvf766y+//HKcNxDx7DVG90y/y4wtmyeEngaNjv0YF08SISKWJ7pzZD0Tu6hY+i3mfLEwsaYzLpes/vyfZB/Yvd32WNnTkexROXE954NfCG5qF9odpc7nwKh1rH6mV8ZIhuxmEhw3U/zg2VimOm8iahRG9+HDhwdzO6wwvROLIQGiHEmE4w0KKVolyRAIccM2FGxcyM6G9OukEJUqGwc/S/YDe3XcG8eYArWyR1iwg7KiAA+EMNSEJCMrnlQUILUDbKu0D7nfPiVxabNR7eNypElkv3zsvrQG1HmswlRU3gv+qJm4vMMZFRsUJ6hZ3RFNUmF4ZvaD14dNfpH7WKPDahDWh6Qp3OMKmfhnNqo3LeRdd92F/zptHRMOcGC1OpsX491Oy8kGryzYRYsHSnFW+djSIvrZ+Z6l7UCO9uFl7S+LB1huRAo4IxGeL4WHWHjGMzvKeIDHQWPLFgKxqeXncZKSPT8Ln36plOwpM8xT4V7j/axYsfLjBd/P/k+NIWsMUBySPdwmogfLDTs/BuGOoQLpw9co+CZFjRyiAEgunJXxSI68FFgwRDOKbK3s1hzFyuVBq8ar/7br2T3a79yx+bpZkuyhu+Wm6EfZLZjeCJdK9iPDjMSOb8ze0tGi1xGmOF8G+YgSwiMzGJmIhcEY4xAb7bGnIejoxenJsCuTWm5YNShvjHW5a+stMyjZQ8mpTvTEfHAK7R44INlxnsYzlV2W6acZ6iBKOMn9MtuAe2tk/Yo94BiXA8QN9YpBDomzIweinBqImme7CQIwHoBeyJfs6LYZiDJq4omwBC16TBlH2rLxqlt3OX2tdv2LQLIHOIzqsVPuscceQbizpQ9b/cC5VoMf1nWQDk8t+CEwIqUaYGFlt5BapZPx5xUlmL1qH2URDqicGH332msv1nJQaamNVH6m46ilKEggo+lZt0p1JSSCEgePYH3nJ68PERmaEphdfWk60Jq8LLwycblk6Wf2qjcGC3YZpiXkWyI0BdQQzjBRyWp+qh+anmWmzNexZpe2MdQigFR5mzQpNDUnnHBC6IkQ61hPYIW3DDxDFMKE6HwSC6MJ43xUPoqfoVGVaebJScrctEHbzdf4v8RW9jwpbcaLgWTvv9b5+rKnDLZQhXvKNxwbEVN968Zdtul6Ru+OewbHmEK0ssfeEaYvzMA0beEkxk7EYnVTirERY48Rmnh0sMw/Eu6IJ/oh/mKD1eExjV298notGnXEtJ2lYqC5I/MYoyC6HAQ6s7f0rxwzD4tjK8f80TEj1vnAZ2QYpki0y/RVeIlgqmdvMixqPBQ6MDpmwmepzHHJ4m/GjETDeo0zYmWPTZyagKUcIysrdFEqyBFuDQsit4alnB0huF8IMFnBSa5ijMRIH4yynLn44oupWsHYhsUXEcM4J/xca621wMuOE1F2yEHCsOFM+JQBWROXha140YQxUhQyswe9O+xdXr6ieaOOkVNWZtPPfWqsQEApMqAKWdM4MNCqbnlflcVjTgk/BLwXUOpBcjFwQrBiVa1VOlUmnqmT2av2oYS4wbD0hTnMsDUhApS9pxhqcp76T93GEszwhlaXekudp5birs03sGJvkHaAtwNJynk+xco7QpVmiFvbtjo2zVodZ6968+IzFGeakVpBO0AzyB+3RoVhZMIuMZynYeR+mTQDIzOQNA7RjTMtCZnQ19AmYGKnPQm3xrwc1vS426TpIJHwKQOOIYkVgEYme0P6uAKk9pN9z7q03rRD857VOcaklmxBxMLDfqs1T2nesL3LT1N+XiUt3BvVb7Z22+26rrJ580adMivZsQfQlNNkY7qmNUHl0BjRlGCjxWzAjD+dKLYErNq0MngaMM3HVZo8FDOmAswG6DwC4xWARy/2LewW2HWCRwFXCUkfjCcfDsGkw6q+yrZwZmYRVegb2jUs8Zjeg0dHqCt4ONAsYrrA2YMEMQOTOMlitMDfA6My+6hgTmZ7EMyllIF0uBHikmbYFYQzxKL3IhZfsqStZMxAdJrjxEuyUq6sUUQau+g4Swdo9ChlbpCOlr9whh6aP54Iz5dL4SQ9NH9RlOggiHs8QGAYtE50KdsHrP3IHijUM9UDJtwXVZc6Fm6HesgfJ/kZnYyFQ5+NZIzunUoVaxtjjMRLEV2NDtCFYTOK6Ey2D1o2zswS58rlrPKLMyEYzQW75mGPxDzJGwpAtDJzGkxH4BwcFkigaWgxGM8gVlCKvIM4/oaJCKyPvMskhWNGeElpdpCGlW3hbIHCi8/jgz8vL81IVE4ypb1CdOL+RBgqLVnwrGk9aEPYVYnGCjsxLz6LLwlAg8a7HxoHXgryDU0KHg7EYpxAsUOBafeQa9xXpNKiTDN4kNVqTzmpwLGNA40ALXBUfkaeNA7cNRijxoGT/EVhogPeC0a8NA68QQFgdCnbB9mr3pSce+EdhxK9GA1FxIGWgbEi90uA6CSVh7/ofhH90TEHrCyKyNAIUHlir0bH1Z2PAuTfQTkO5fyXfwXLeoka1W/Bf1nPpqgzKGnhXl5Wv2lD+rmVXV1m//AlDV+cobfD+kIHduKJJ9IG0asxYUofTLOFuGG2FIssUhhPR3R56HSx5mKzwb4YbGNcpW+g+aNxZzBAImeddRaiGX9BemsMDLSDdPOXXXYZ/8beBcKd+WvmalHSzOTS3XKVwJiK8aTkPJ9JD1OZeKky380sJJqJT2DQixMGH0SEOL0vB3hw4rVJ1ow3+Fgmch+VjzUF7xEM8/RJrCYkMIlzX1j9cSlh/reyXIgtXqEfB5tQknfBE+QvycAFFIw6yV/lAkeSvfKlymeqTKFysKI5U+UXZxixsLsOTkFMkSENme3hI1O8RLxTfKcGlYyapwohUNDZeGox6uYPicwx4p7ovLl4v7BQkmD4yPHu00rQYmCzZG/NOOFIa8Om2iTOCg2i4P0CXhor3n1aIV5n3nredJLi6ZAFHsNs8sOrzX4dZEdREWGM6rEdkCmtE+0bzQj2ZozxlBalzr9/+ctfmKzDhwErAK0BrwzbnGNAJeuifB1CFV35SlT1UlRZgWkwa9WSVJlIfp7k1qo0VdTq0fC1rp4AAEAASURBVEeqPT/v0VJJoE4IlLRwzx5xBC5CFndSnASwWGMzo6fEEsMBxjBmTjFI8H04OjnmELFMMHuI2QzJHjwKCMYZZD3REc2IePwCucREIdKf9TqYaliUg4ENAY01nUECJjTSj70jnIAJTA9KL8IHKSkSV0mZb1sSF1Mchnz6e2xgGNIIgLjH9onxnrJhzsddAX2PbQyvbubH6bMZaVASyoNvIsEYCeDVgO3t1FNPRcSzPR8zyERh+RHTnXTkxS3cY1F7LIHkCSCmeXfivjiDwEXmotdxREHy4h985513MgDGKB78kfA9wDrLIBydTXS0Ne0GDQLp8D7iqcXLjtBnUM1gm9V7eLAwkmdBKt+N531EoMeWkKYDgz3nOYlDMNYBDjA38OE2XBdoSRg80Caw7Q92hzPPPBPtTrODGR4rOxZ0dDyjAloPMmIcwuJjGgcOaIjQ9+zVzQDgjDPOCMu4sQIwwECs0+ZgLCApflK82PJ4LAEJSEACSRKowlqWZEyDJSaARQoDNjODzP3Rw2FlZ9kos+R0t/THdK4Yz7Cp04cxv0xS9JF0ipijUNL8pPPmDNFJhA4PPwGOMXERGImMbYzpbDpXDGN01QwGSB+LV2yRsLGRL2YPzJ9kSgqY6ND3LJmiAFyiX0cWsByQ3pTumWl6LHChPw7TwUwBY/BAzTNHSTokTnb4fhCd3OnICcywhCEHl+jsuSkGAxSSkQDjluAvEVskjyUgAQjwNoWXHZnL68krzFCZD/qgg3l9eNPZ+4WhMmfCS8R8F+d5uXgxQ3T2saZl4CQBiMXxoYceyjEGAgLwstOMYJvHNEBbwTseh52Rf2hnOE87QHiG6wwJcEHGRkAutDm0RWh0RuYMJ2i7CEk7gHanwERBeWMqJh1sB1zCDI98Z7qAKcTQCPAvjQNnuEpIrBg0GjQdNDXcFyf9k4AEJCCBFAhocU8BWlJRmBCku6KHRi7T4aGqsTwx381KHfQuVnCUPZc4E4Q7/V/oAkPqITrHHDATHZLCpZWIdM/EwgLHrnloa3pcLHAkElxrqiscFnfMePSadPBoetLBXRULGcWgD6YDJgX6+Oqih/PMA+CPiBk+3Bc3EizxXMVpJzhIcBeUhIxqTC1xXl6VQLES4PUJEpzWgLExBm9G47yACOLgSMCSR94mXrfwUnOeFiCiQXReN36GRMIxI2rOhFE0nm9sbIJ0ZvDPdFk4GUWv8uCvf/0rK3OYUkPQ08KgwtmNimKQNY1MZekflwjlZNqNliqsWOAuaGTYsiYI96jA2CM4DrN/cSn4UwISSJlAu2br7LXufz+61Lpxunu8plwMI+aGgMI965yRyPyhYtG7dLRYx1Hw9I70yhyj3UPvS/8dhG+CAtHnhau4jfIxeVxuMHsj4sOa+iCUq1Pw+NZjgcMJFaMaIoBelp250A3Mg9PXkgKFDImTCza2KocBhMc0iGGekPS+eKzi6hNXbG6Eq6r2ANN/JZCAQHh3eFkY7vICIp0RzahehDIH+MaEtxJBnCCRcCl6f1khc8UVVxxzzDG4rhGRwXnsy1iliMchniUubCdF40BqWBnYKQUjOm5vlJAlhpFwp3EgNYbrkcE+yp3GgcYHV3u2V6LwRMHFOQwnosJH9xud8UACEkifAPsQbNHlhJBO0WyElT6WYk1BV5ncPVn8Vdgdgr0aWBLKai1WdmIqo5OLetzki4KjC7GQ7Eh/5rLZUAxnVoxbmMPxUcH1hfOxqWH6Ikcc0Om2WdLKTyzi6AN6aPQ3pnd2g+GPHppYFBIXVSbZcVqlh47t9dmbnFHHddddh0MtnrUEY8qe+YTYvDyWgARqSwD7NP4qfG+SZoH3Ec913nG86cIwuFapoZVD44Ahn8YBJY2G5i3GnYZ0SBkn+9gEEfqsd8cHhgk9zPOsmWFAzkQcQ3dedpzrKA+KHLcZZDeJMG6nBeM8KUfp0JiwJocobOCNQx2Kn+1xaC44H4XxQAISyBIBxDq7K4b/6tf7ZXYuS9mZbN0S0OKeFf7YpfiLkg4/cSxhI1t6Tb5PQedHT4nJnK3W2E+NkCFMiBJ3XFnZ08fjI8u8NlZzlrUxnU0/ilhnzRl+pezzwGfqmJUmNeLSf7OzBO712PPYBSKkRkS2rbj77rtZEYs/K8VgcSodPGYzVsTSf2OuoxsmANvJReXhCyMslWN5K/ICKcA38AjM8CO2hAQO+YZ78V8JSCCOQHhHYk8i0BkPI6D52BnvHS8vrQRNRNzClRClcvToPG8iM3t8AAtvGcbnjAdwjaOh4NVmIykWqoa16bjFRy8132tjhQwFYFtx0sFMzkcMaJpY6sp6GJaY06ow7Kd9wPRAU0OCbFPDkIAwuNOEdMiX9PleJi43YQKQJih8nZ40qytwKLb/SkACEpBA8gRWznsmHzp/Qi6rWPTpT08N+2jl1+Mb1mvSu+M+A/v+s8riITQxXGFVqvJqjSexTyNhQ1dUY+AoAGZsTNrBDQaNjucJHRsmNLpkjlHYnMS+hW0MczXWLNQ87iv0kaGHi42OjRzlzR5tBOCYXWJIB+8aouDuQviweQumcZaH0ulyHgM8HTa5s0iUAGRErDgFwCiC0lIS8iIFEsR8jsGeWFQJLG0kQmoY6SkhGWE5owzcBeUnC1LGJkf5SZwscPvhUjD1UUiSZQCQzPx+RCwccGs8r3BHcZdq+5NSFWjdrvFO06zSUfqp1e0oeuEedOvWjXuP24UptdtJISneIN5oRstUUTLFvM1LRM3HvYQXhxcZHxVeLrQ7bxB1GFXNq8eLGUpIdP7C1te81Lx6eNQED3gqBiFJjZea87yJvJKMq4NLG+84iROXvAjGW0wwXnDc6wkZ3T6lIgDvPudpB0iBkQCxeMEJTHloTGgrCM+4gpBcYq6P2+EMjR63QCyikAVxSS3KiADcI3F5wSlklGOSB1b7ZECxjpmtgenykgmcOEwKdTtxggVxlfpMy0ClrW1pU45Y24wMLwEt7lmpA/Sy/IWkkbzB+M3PoG7plTkO3TYHdLrI3BA4/BsbnX6Xv3A+9pguk86PBMMlUgjHdJZcCr6kQZ0TIHbZawgf/g09cYhIrHBAwYhIOlHisf6sdLr80U+HLEI6selTSJKNzcVjCUggIhDeoOhnkOD85IXiPQpNQdQ4cBAFCFFioyOvY4cf0TENCNIZkR1eYaKEA1oe/sKAlvedP9KMa3yigvGOs2Y9RCRkiEV5SAFRHiX+czIr0+GPsQGlJWLUdHAyNgD3aOPwMyr/kYAEJJAiAYV7iuDSiRb1yukkQtzY3jH2OFZSJ86CkkSFiU0hLvHKiSSfReW4npGABKojEL2P1QVI8vzPb/Z/vfXiXu3ks4iNGBsrNvHK5YmNVfmqZyQgAQlIIB0CLk5Nh55xJSABCUhAAhKQgAQkkCMCCvccgTYbCUhAAhKQgAQkIAEJpENA4Z4OPeNKQAISkIAEJCABCUggRwQU7jkCbTYSkIAEJCABCUhAAhJIh4DCPR16xpWABCQgAQlIQAISkECOCCjccwTabCQgAQlIQAISkECWCPw0/7OXx1+epcRNNn8IKNxreBbslc62xJ999lkN4bycZwS22GILPrqeZ4XKr+JYt9N/Hny3mMZh8eLF6SdlCrkhYLVPkrN1O0lQeRJs9uIf3v7uzi+nPffjPOVKnjyTbBVD4V4DWfYk5mPgfNy0hnBeTpvA7bffzlfTM7VDPN+miftYbNoFzIsE+OjmQw89RJ+afmlKs27fc889a6+9dgqf9a0SOOnQOISPE1UZwJMZIWC1TwbjY489xrCEv2QC1ximBOs2I/C77rprs802qxFOHgaYufDb9yfdM23BuDFThuVh8SxSBgko3GuAibkd2y0NYg3hvJw2AT4Zvc022zBMSjullQkMHjz4hhtuyEhSeZVIRUUFSpE6mX6pSrNuIwE333xzxnXpAySFY445hsaBNDOSmolUR8BqXx2Z2PPz5s3r06dP+CBu7PnUjkuwbjMCnz9/Pt1QasTqMBbm9jGTH168fN6CpTN/mP1uHZbErHNAIDMiKQcFrassMAA3bdqUBrGuClA6+WJxHzlyZKYUFZ95ZyRQfPTQiCjFt956K/1bK826jcV92LBhmbJKks7cuXO1uKdfGxOnYLVPzCdcpWU45ZRTunTpkkzgGsOUYN3G4k778PHHH9cIJ98CYG7/YNJQSrWirGL6wq8/mvJY31UPyLdCWp5MEdDiXgNJGq/evXv/8MMPGt1rIJXeZVT7/vvv36FDh/SS+SV2sEkXmdEd+fLoo48edNBBGdGdJVi3eYsHDBjQuXNn3IR+qStpHOGzhEcBnb1G9zQo1hDVal8DoJ8vU7cxt/fo0SNTk5alVreDaj/ssMMw+iQDPH/CROb2UKRIxOdPCS1JZglkpvfKbJnyKjU6+O7du+P0dtNNN7lENUuPZtSoUX/+85/POOOMTJnbKSdJ4S1z6aWXFs0SVfqV11577d577+W+MvIgQt3edNNNS6Ru8/5yp4MGDcpgx4xqP/bYYxHuPBoeUEaei4nEErDax9Ko7jjU7f79+3ft2rW6MLU9X1J1e/ny5W+88caQIUPOO++82oKq8/BxSr1ixfKfFozF6F7nBbMAWSKgcK8ZbJs2bfbee2/s7syw1xzaELUngF18v/32y6C5PRRhl112Ofroo6+66qralygfY+D58+CDD2bK3B7ukLq9zz77lEjd5v3lTjNobg8Mt9tuu8MPP5xHU5SuWXX+Jljtk3kEoW5n0NxeanWbnQyY9S0Cc3t4cHFSPpkqZJgCIqBwr/lhYZjs2bPnySefPGnSpN133127e83Ikg6Brf2oo45ileQFF1yQQXN7yJ8EL7nkkl69enXr1q2g7e4YHV988cWzzjqrX79+mTK3B0TUbTr7oq/bvLN/+MMfWKnCnWbQ3B4YYphksohHs8MOO7zyyiuY7pKu/gZMRMBqn4jO/67F1u0MmttD8rF1+5lnninKOSVe2BEjRhx//PEbbLBBEZjbw4PT6P6/96M4/69wT+q5sowPW93pp5/Ogn2m2vV3T4paTYGwcBxwwAHt27c/88wzO3XqVFPwVK7zvM4999zf/e53++67b4H6u+Pgy6QBipMayL8Z8W6PRYlHLGObIq7bvK28s9OmTcOnhTtlrBJ7+xk55qHwaKjMzPBce+21RalvMgIq+USs9smwiqvbmfJuj806qtuMTmmIimwtB68qL+wRRxyBbY4bzPioPpZkNo7jvNtjs9DoHkujyI7LC3Q/hGUViz796alhHx3C82hYr0nvjvsM7PvPKp/NhAkTmMv+9ttvq7xaq5MMzWfNmjVu3Dj8jD/55BP66bC8r1aJGBgCSPY333yTxQO0mLvtthvaPatYeGpMuPPUhg4dGizWGdlOMatlJnH6SEBR0zbaaCOcMbbffvuMq/boFpYtWwair776CkRFU7eRNcxUMHjDEQuPIHbbyIayiRjyvGbOnInTAjWNQQLWStYPZDXHKOtiOrDaJ/M0c1+3aUVZGU/dDt56YelqMkXNzzBI9ptvvnn06NHrr78+s7477bRTmqqd9pMZDyjl7H5R7SO+ufa9iX9nF8gqM+3ccuODN3iwU4veVV71ZOESULjX+tlF8n348OHsBIeOr3USJR9h4MCBaJrmzZujRNltMzc8IvlOdmPGjMlNpunk0rJlS0DRr+DzQ6eCEEwntWTiRvKduo1jSaHX7V133XXbbbdt27ZtqGbZsLVXpkr/TU1DvqMM2FdOz5nKiBKfsdon5hOu1kndZkwV5DsdHy46Bf1dQppTzEa4x9AB0T6k76iZe+G+cOmMj3589L2fd4FctGzOT5U+mNq8YbvfdPn9Luv8KZkaZZgCIqBwT/Fh0R+jbMJnQVJMooSj5VKvx2EOFpGCcGYoLy/PjV6PQ4R8p24zF1fQHTM31aJFC3rl3Oj1OIb04gAsiGoWV/I6/2m1T+YR1GHdRr7T8RV63aaaZUSvRw8r98KdLduXLl+45Gdz+4/zPvnX2NOCdu/UvPfu616Lob28vF7jes2bNmwbFdKD4iDgB5hSfI54vac5s5ZixkZLj0CmPiuYXinyOjbeHVJK8wnZOKQJMPfRrfZJMkfvJhnSYFklUF5Wr1H95vxHLnMbTmlQr1HIrn69hq0ardamScY2Bs3qXZh4CgQyv04rhUIYRQISkIAEJCABCUhAAhJITEDhnpiPVyUgAQlIQAISkIAEJJAXBBTuefEYLIQEJCABCUhAAhKQgAQSE1C4J+bjVQlIQAISkIAEJCABCeQFAYV7XjwGCyEBCUhAAhKQgAQkIIHEBBTuifl4VQISkIAEJCABCUhAAnlBoPiFO99Z6Nu376hRo/KCt4WQgAQkIAEJSKCICPBdl3fffXfLLbcsonvyVvKXQPELd76zwBdY/H5h/tZBSyYBCUhAAhIoWAJ8bY2PUvF1l4K9AwteSASKX7gX0tOwrBKQgAQkIAEJFBQBLe4F9bgKvrDFL9x1lSn4SuoNSEACEpCABPKYgBb3PH44xVa04hfuTZo02Wijjd55551ie3TejwQkIAEJSEACEpBAKRFoUPQ3i3DfcMMNzznnnKK/U29QAhKQgAQkIIEcE1i0aNHtt98+YcKEHOdrdqVJoPgt7jzX5s2b9+7d++OPPy7NZ+xdS0ACEpCABCSQDQKLFy++7bbbTjzxRPxys5G+aUogjkBJCPdVVlnl8MMPf+CBB+Ju3p8SkIAEJCABCUggHQJLlixRtacD0Li1IlASwh1vmU022eTVV1/V6F6rymFgCUhAAhKQgAQSEAh+MmeccUaCMF6SQAYJlIRwh1e7du222GILje4ZrDomJQEJSEACEihlAvrJlPLTr6t7LxXhjrfMUUcdNXXqVI3udVXVzFcCEpCABCRQNARQ7X/5y19uvPHG8847r2huyhvJfwKlItx5Euuss87WW2+t0T3/K6UllIAEJCABCRQEAZelFsRjKqZClpBwx+jer1+/cePGDRs2rJgeofciAQlIQAISkEAuCWBuv+aaa8aMGaO5PZfYzQsCxb+Pe/SYy8vL+RLTueeee+2113Jy4MCB0SUPJCABCUhAAhKQQDIEgpPMHXfcMX78ePeTSYaYYTJIoIQs7lCrV69ez549WaWKU5rO7hmsRiYlAQlIQAISKAUCy5cvf+2112655Zb77ruPr8SUwi17j3lFoLSEO+hxmNlnn32Q7yeccILaPa/qooWRgAQkIAEJ5DMBVPtLL7102GGHnXrqqTvuuGM+F9WyFSuBkhPuOMysvfbaF154If8ecsgh+rsXa832viQgAQlIQAIZJICHzBVXXMEOdSgHXNsbNCghZ+MMYjSpNAmUYrVDu2NxZ1nJ/ffff/nll0NQf/c0q5HRJSABCUhAAkVMIKxGfe+991AO2NpV7UX8rPP81krO4h6eB9q9U6dOgwcPfvzxx99//32mvTS953lNtXgSkIAEJCCB3BPAPebll1/ef//9Ue0PP/ywqj33j8AcYwmUosU93D/avVGjRuuvv/5pp5126aWXXnDBBV999VXXrl0HDRoUC8hjCUhAAhKQgARKkMCyZcteeeWVq666iq1j8LDdZpttShCCt5xvBEpXuEdPArF+7733TpgwAfl+ww03PPXUU6xeVb5HfDyQgAQkIAEJlA4B9PrIkSPxjQmSHb92JXvpPP38v1OF+3+fUZDvaPehQ4eOGDHi6KOP7tatW5DvrVu3ZgP4/H+WllACEpCABCQggdQI4BLz+uuvI9lDdKzsSvbUSBorqwQU7r/Ci3y/5JJLODVkyBDkOx9F4xhjPGb4X4XzhwQkIAEJSEACRUSA9abbbbcdGoADTexF9GCL7VYU7lU/0bDdOz4zVV/2rAQkIAEJSEACEpCABHJLoER3lcktZHOTgAQkIAEJSEACEpBAugQU7ukSNL4EJCABCUhAAhKQgARyQEDhngPIZiEBCUhAAhKQgAQkIIF0CSjc0yVofAlIQAISkIAEJCABCeSAgMI9B5DNQgISkIAEJCABCUhAAukSULinS9D4EpCABCQgAQlIQAISyAEBhXsOIJuFBCQgAQlIQAISkIAE0iWgcE+XoPElIAEJSEACEpCABCSQAwIK9xxANgsJSEACEpCABCQgAQmkS0Dhni5B40tAAhKQgAQkIAEJSCAHBBTuOYBsFhKQgAQkIAEJSEACEkiXgMI9XYLGl4AEJCABCUhAAhKQQA4IKNxzANksJCABCUhAAhKQgAQkkC4BhXu6BI0vAQlIQAISkIAEJCCBHBBQuOcAsllIQAISkIAEJCABCUggXQIK93QJGl8CEpCABCQgAQlIQAI5IKBwzwFks5CABCQgAQlIQAISkEC6BBTu6RI0vgQkIAEJSEACEpCABHJAQOGeA8hmIQEJSEACEpCABCQggXQJKNzTJWh8CUhAAhKQgAQkIAEJ5ICAwj0HkM1CAhKQgAQkIAEJSEAC6RJQuKdL0PgSkIAEJCABCUhAAhLIAQGFew4gm4UEJCABCUhAAhKQgATSJaBwT5eg8SUgAQlIQAISkIAEJJADAgr3HEA2CwlIQAISkIAEJCABCaRLQOGeLkHjS0ACEpCABCQgAQlIIAcEFO45gGwWEpCABCQgAQlIQAISSJeAwj1dgsaXgAQkIAEJSEACEpBADggo3HMA2SwkIAEJSEACEpCABCSQLgGFe7oEjS8BCUhAAhKQgAQkIIEcEFC45wCyWUhAAhKQgAQkIAEJSCBdAgr3dAkaXwISkIAEJCABCUhAAjkgoHDPAWSzkIAEJCABCUhAAhKQQLoEFO7pEjS+BCQgAQlIQAISkIAEckBA4Z4DyGYhAQlIQAISkIAEJCCBdAko3NMlaHwJSEACEpCABCQgAQnkgIDCPQeQzUICEpCABCQgAQlIQALpElC4p0vQ+BKQgAQkIAEJSEACEsgBAYV7DiCbhQQkIAEJSEACEpCABNIloHBPl6DxJSABCUhAAhKQgAQkkAMCCvccQDYLCUhAAhKQgAQkIAEJpEtA4Z4uQeNLQAISkIAEJCABCUggBwQU7jmAbBYSkIAEJCABCUhAAhJIl4DCPV2CxpeABCQgAQlIQAISkEAOCCjccwDZLCQgAQlIQAISkIAEJJAuAYV7ugSNLwEJSEACEpCABCQggRwQULjnALJZSEACEpCABCQgAQlIIF0CCvd0CRpfAhKQgAQkIAEJSEACOSCgcM8BZLOQgAQkIAEJSEACWSdQXlavfr1GWc/GDOqOgMK97tibswQkIAEJSEACEsgcgcYNWnVq0Ttz6ZlS3hFQuOfdI7FAEpCABCQgAQlIQAISqExA4V6ZiWckIAEJSEACEpCABCSQdwQU7nn3SCyQBCQgAQlIQAISkIAEKhNQuFdm4hkJSEACEpCABCQgAQnkHQGFe949EgskAQlIQAISkIAEJCCBygQU7pWZeEYCEpCABCQgAQkUBoEVZRXLKpYURlktZdoEFO5pIzQBCUhAAhKQgAQkUEcEFi2b89O8z+ooc7PNNQGFe66Jm58EJCABCUhAAhKQgARSIKBwTwGaUSQgAQlIQAISkIAEJJBrAgr3XBM3PwlIQAISkIAEJCABCaRAQOGeAjSjSEACEpCABCQgAQlIINcEFO65Jm5+EpCABCQgAQlIQAISSIGAwj0FaEaRgAQkIAEJSEACEpBArgko3HNN3PwkIAEJSEACEpCABCSQAgGFewrQjCIBCUhAAhKQgAQkIIFcE1C455q4+UlAAhKQgAQkIAEJSCAFAgr3FKAZRQISkIAEJCABCUhAArkm0CDXGWYnvxVlFYuWzcpO2qYqAQlIoAoC9es1Li8rr+KCpyQgAQnkkMDyiiU5zM2s6phAMQj3FWUr5i356cXxl9UxS7OXgARKicDabbZbvmLJihUrSummvVcJSCDvCPw4/7O8K5MFyhqBYhDuyyoWfz1jBP9ljZIJS0ACEqiCwAcT7124bHYVFzwlAQlIQAISyAIBfdyzANUkJSABCUhAAhKQgAQkkGkCBWxxr1dev03TrpkGYnoSkIAEkiLQrEGbpg1XKdPNPSlaBpKABHJBoHH9FrnIxjzqjkChCvfy8nqrNF5zp+6X1h06c5aABEqaQKfmvfFxX7J8XklT8OYlIIF8ItCu6Tr5VBzLknkC5a6syjxUU5SABCQgAQlIQAISkECmCejjnmmipicBCUhAAhKQgAQkIIEsEFC4ZwGqSUpAAhKQgAQkIAEJSCDTBBTumSZqehKQgAQkIAEJSEACEsgCAYV7FqCapAQkIAEJSEACEpCABDJNQOGeaaKmJwEJSEACEpCABCQggSwQULhnAapJSkACEpCABCQgAQlIINMEFO6ZJmp6EpCABCQgAQlIQAISyAIBhXsWoJqkBCQgAQlIQAISkIAEMk1A4Z5poqYnAQlIQAISkIAEJCCBLBBQuGcBqklKQAISkIAEJCABCUgg0wQU7pkmanoSkIAEJCABCUhAAhLIAgGFexagmqQEJCABCUhAAhKQgAQyTUDhnmmipicBCUhAAhKQgAQkIIEsEFC4ZwGqSUpAAhKQgAQkIAEJSCDTBBTumSZqehKQgAQkIAEJSEACEsgCAYV7FqCapAQkIAEJSEACEpCABDJNQOGeaaKmJwEJSEACEpCABCQggSwQULhnAapJSkACEpCABCQgAQlIINMEFO6ZJmp6EpCABCQgAQlIQAISyAIBhXsWoJqkBCQgAQlIQAISkIAEMk1A4Z5poqYnAQlIQAISkIAEJCCBLBBQuGcBqklKQAISkIAEJCABCUgg0wQU7pkmanoSkIAEJCABCUhAAhLIAgGFexagmqQEJCABCUhAAhKQgAQyTUDhnmmipicBCUhAAhKQgAQkIIEsEFC4ZwGqSUpAAhKQgAQkIAEJSCDTBBTumSZqehKQgAQkIAEJSEACEsgCAYV7FqCapAQkIAEJSEACEpCABDJNoEGmE6w6vYqKis8//3zq1KkcNGnSZN11123Xrl3VQZM+u2LFirlz544fP75v377169dPOt4vAadNmzZq1KjevXt37dr1l7MeSUACEpCABCQgAQlIIP8I5Ei4L1my5L777vvoo49Q7YsWLVp//fUPPPDALbbYIh0gy5YtGzNmzF133XX99dd37NgxhaS+++67u++++9hjj1W4p0DPKBKQgAQkIAEJSEACuSSQI+G+fPny0aNHz5o164gjjpg8efKjjz76008/RcIdMzz3XK9evN8O0rxBg1+VMPZMeXl5y5Yt+/TpU6O5PTZWgIu1nkwZQvzwww/z58/PJXHzkoAEJCABCUhAAhKQQAoEfiWLU4iffBR0eY8ePU477bTZs2d/9dVXb7zxBmb4BQsWPPfcc/zkas+ePXfffXe0OHr6P//5z3vvvTdnzhxM6b/97W/xZiHMSy+9hLNN27ZtN9tssy233HLcuHHDhw9v2rQpcTGcYzUfMGAAIwRCvvDCC4cccsgqq6zy73//m3RIsEuXLrvttlvnzp2XLl36xRdfvPrqqxQDTxvKkPwtGFICEpCABCQgAQlIQAJ1RSB3wp07DC7pEyZMwLk8aOihQ4c++eSTa6yxBoIbXd6sWbNddtllxIgReL8gx1HbGOYR9Kj5a6+9Fs8WYmG5HzlyJJfmzZv3yiuvcHLfffd97bXXSHy77bZDiD/xxBNPP/307373O9K57rrrWrRoQbKMExgGDB48GJ/4IUOG4HCP0J85cyaavq7Qm68EJCABCUhAAhKQgASSJxDvnZJ8zNqGRJp/9tlnl19++VVXXYVcPvnkk5Hvt956KzZ1/N3R2Xi8PPTQQ7jT3HvvvT/++OPZZ599ySWXEAw7PQL92Wef3XHHHQ8++ODtt98e13ZM5muttRYqf/Hixc2bN99nn30+/fTTjz/+GC2OuX2nnXbiJKMC3HJI+aCDDurVq9czzzyDXifiW2+9ddhhh51//vko/saNG9f2RgwvAQlIQAISkIAEJCCB3BPIncUdl3TENCZzpDOLU/fcc8933nkH6/vGG2/8/vvv43HeunVrVDuGc85zdYcddgAH4dHib7/9Ni4uOLeguRcuXMjJ77//Hjs6bjOEQXz/5je/wQb/8MMPb7vtthjp0fcEw8reqVMnHGOCRztONQh3RP+qq66KIw3b2mCzV7jnvs6ZowQkIAEJSEACEpBACgRyJ9xxfWEh6cUXX4yCHzZsGGIdLc5x+/bt2R2SomMUx60FuzsivmHDhtHNILux1nO+W7duSH/Ob7LJJpjbowAhEbQ+O8xgYkfE4xM/adIkYiHWQ+IExoEeHU/KlES9HtHzQAISkIAEJCABCUigIAjkzlUm4EAxH3300auvvjqO5iwexU8Gr3QWm/bv3x+T+c4774wdHX3/5ptvsj4V8c3S0okTJ/br1w+13apVK/xkOO7evXu0I01IFoGOewxuM+zLjhsMKp99J9H3+OSst956XOLfDTfckLHBBhtsQLKsiMXYj8p3cWpBVFMLKQEJSEACEpCABCRQ/9JLL80BBYzrjzzyCPL6gAMOQH9jWceRHaWOzwxLSD/55BNkOm4wm266KVIezc1PJPiHH37IUlRcaDCis4cMK1BxZCc8ezgi9DHME5GfJ510ElH4Y6Eq+v7MM89s1KgR2p0xAF43SP8PPvgAt5kOHTqEvSM5w24zrFLFi4YoLGmlGDmAYBYSkIAEJCABCUhAAhJImUCOhDveLChpPnGKEwvaOji94GuOMzo6HmcY/sUWjrDGbZ39XlZbbbVgNUdSEwVLOUtU8XIhHRzcsZ3jAEMA/giJ9Z3t3vnjEufJBRz8JB18Y4hCRKITixwZM7CJDUMIdqohMPZ7RgVo+pQJGlECEpCABCQgAQlIQAI5IFCOaM5BNomzwKEFNY+ZPDYYHurY6bGjRycpKktO0dxo8ehk7AEBiBKXDl9fwh8G63tsSBLnLy5kbACPJSABCUhAAhKQgAQkkFcE8kK45xURCyMBCUhAAhKQgAQkIIE8JJDrxal5iMAiSUACEpCABCQgAQlIIP8JKNzz/xlZQglIQAISkIAEJCABCZQp3K0EEpCABCQgAQlIQAISKAACOfoAE1s3spPj9OnTWRLK6tKePXvy3aUCwGMRJSABCUhAAhKQgAQkkB8EciTc2dflgQce+Oijj9glhj1k2PaRDd3jPqKUH0AshQQkIAEJSEACEpCABPKRQI5cZTC0jxkzBgB//OMfTzzxxJdffvmCCy6IdqJkx0YCxOGp/E1TPowaF6a6n6QcG5ifcalVPlNdUp6XgAQkIAEJSEACEpBAPhDIkcWdW2XzdT6utPnmm8+fP//xxx9/9dVXMb3PmjXrpptu4iOp7OPOh5AGDx7cvHlzZP2wYcOmTZu28cYbn3HGGaussspLL700dOjQGTNm8E2lo48+esstt+RLqJjwb7nlFj6oNHXq1LPOOuu4447jE05/+tOf+JbT119/TcoMElZfffU77riDT6Xiq7Ptttsef/zxfHfp9ddfv//++4lFavvvv/9ee+2VD0/CMkhAAhKQgAQkIAEJSCABgRxZ3CkBRm6UNxr9sccew/qOKEdMo7Nfe+21HXbYAdX+/PPPP/roowjua665Bm/4PfbYI3yV6cMPP7zkkkvQ/Shs1DzH6PJJkyaNGDGCzy2RMl9lQotPmTIFKzsnEfTo9T333BPFf+mll77wwgt8HhWB3rp1az6h+s4771x99dX42e+2226Ev+uuu8aPH58AkJckIAEJSEACEpCABCSQDwRyZ3FHpr///vvYvOfNm7f22mtfdNFF33777SOPPLLvvvuuv/76aGgEOpp+9913nzlzJpIdD/jVVlutfv36zz33HNbxe+65p0uXLuutt96gQYOw1mNoj/Wu4Zj0GRug77t163bKKacgzceOHfviiy8ec8wxOOdgyEffkws6nlHBUUcd1bFjR3T/E088QWqUJx8ehmWQgAQkIAEJSEACEpBAdQRyZ3FHi2Nl//Of/4zZGyeZXr16BYeW77///umnn8bcjuzGKI6jCy4urGE988wzn3zyyblz5xJszTXXxA2mXbt2SHzCJLCRN27cmFw6d+7crFkzBgZI81122WXVVVfFQwaljpfOl19+iVF/5MiR//rXv9johpDkWx0dz0tAAhKQgAQkIAEJSCBPCOTO4o4tHOk8YMCAs88++/zzz0c349CCQX3rrbfeZ599Ao5GjRqhy/FywWr+zDPP4MeCWEeLz5kzB5s60n/BggVIeVQ4xwhujOht2rSJVd7kgjGe1DjA6E4srPURa7JjSICOP/bYYxkhcJ4zZBEF8EACEpCABCQgAQlIQAL5SSB3Fvdw/5jb0eWI9SFDhuC+gin9rbfeQos3aNDggw8+QFKjxYcPH84mMCxjxaN94sSJW2211eTJk++77z4M5DjMoMj79++/7rrrshcNpvrPP/+ck3GbxpAXGeFXg6xnVSuO7x9//DH2ewYGJIsrDk47SH80PemTaX4+G0slAQlIQAISkIAEJCCBiEBOLe4hVz699Ic//OGggw568MEHr7jiCpaK/v73v8cQjq19k002wa8dhf3FF18g5VlUisrHvs7S0uuvvx6hj+sL/uubbbYZ+p7VpewY89BDD3Xo0CG6H+R4OOaAUcF555136623nnzyySxU3XTTTfG3Ida4cePuvPNONpbBlr/jjjsyDEDQRyl4IAEJSEACEpCABCQggTwkUB7rZ5K98rFy9JNPPkGL9+7dm1wwkI8ePRo39LXWWguncwzqXELQ9+jRA/8WjOjYwrGsYwvnG6scYBfH0x3DPGFYSIrLDT4w3333Hf7xJIXWx22dkEj/zz77DI2Opw25cGtEYQxAaiSLZw7LW7HEkx2psUaWkGTRvXv3SO5nj4ApS0ACEpCABCQgAQlIIB0CORLuiYuI0wvqHHfzKBiam784PY25HdkdheGgymCxAcIxKp/0Y1MjIicZLVQO7BkJSEACEpCABCQgAQnkIYG8EO55yMUiSUACEpCABCQgAQlIIK8I5Hpxal7dvIWRgAQkIAEJSEACEpBAoRBQuBfKk7KcEpCABCQgAQlIQAIlTUDhXtKP35uXgAQkIAEJSEACEigUAoUq3FmoyjY177zzTixoFrmyXztbTPLBptjzHktAAhKQgAQkIAEJSKDQCRSqcJ8/fz4bsfNp1dgHwEYx33zzzcMPP8wHVmPPeywBCUhAAhKQgAQkIIFCJ5DX+yGyRzt7OLJZO9u9R6DZyRFzOx9jOvLII+MEOuEXL148e/Zsto2PwnsgAQlIQAISkIAEJCCBIiCQj8Id/T1q1Cg+ifrVV1/xgSQ+rnTVVVexEfvFF1+8xRZbvPHGG+z4vtVWW40cOXKbbbbZfPPN+YrqE0888cgjjyxatCh2s/YieDzeggQkIAEJSEACEpCABAKBfHSVmTFjxpVXXsm/e+yxB64vP/zwAx89nTlz5nPPPffUU0/1799/r7326tWr17Rp0z799FNM7C+//DLh+ZrS9ttvj3b30UpAAhKQgAQkIAEJSKD4COSdxZ0Fpl9++eWHH344ZMiQbbfddu7cuUOHDg1ynM+mDhgwYNCgQWh0/GFQ87jEEOCBBx5o3LgxVvkWLVrgQnP11VcX33PyjiQgAQlIQAISkIAESpxA3lnc8XVp3bo1dvRx48bxLw7u7dq14wzPqUmTJkj5Nm3atGzZkmN0PCcJ89lnn+E506NHj7Zt2+JaU+JP1NuXgAQkIAEJSEACEihKAnlncUe4d+3a9aijjrrttttwjMEAf8IJJ2BQhz6XMKjHPQas7x06dMCvJu68PyUgAQlIQAISkIAEJFBMBPJOuAOXxak4r7OTDI7sa6+99q677hoRZ4lqdBwOmjZtyhJVVrKyOLVv375EjAvgTwlIQAISkIAEJCABCRQBgXwU7mDF0P71118HBc9Xlq6//vrKkj2cadas2T777PP6669fdNFFnTt3xn+mcsgieE7eggQkIAEJSEACEpBAiRMoZ1v0fEPAUtS33nqLUiHKJ06cOHjwYDaNQZ1jTe/Tpw8O7lxiK/fx48dTeKzyuLmPHTt2ypQpeNQE//iNN944eNfk261ZHglIQAISkIAEJCABCaRGIB+F+7fffrvvvvuivPv168dW7qxSxQ2G/doT3yEiXlt7YkRelYAEJCABCUjg/7d3HnBSFPnbZ9kAS85pySCggILkoIAoyimmQxAUxHgqBjCcmM6I4JnFdJhRzAE9AyiIBAUUFQURSZIl75KXZWHfL9T/+m1ndmdnd7p7emaekc/a01NdXf2t9NSvflUtAiIQuwT8KNx5odK0adO+/PJLDlq2bNmlS5c2bdqkpaXFLmWlXAREQAREQAREQAREQAQiJOBH4c4j4f2CamebdvQ6Oz+ydUyEz6nLRUAEREAEREAEREAERCCmCfhUuMc0UyVeBERABERABERABERABBwn4LsXMDn+hIpQBERABERABERABERABOKAgJMuKIfycv/YPstAKZmU0qjKCXEASI8gAiIgAiIgAiIgAiIgAn4g4KRwP3Bw7xsLzzdPVbJkStPKJ/vhCQtNQ6mUCs2qnnJMjTMLDakAIiACIiACIiACIiACIhAtAk4K97wSebtzNltPsnDTO9axnw/KpdWoWKquhLuf80hpEwEREAEREAEREAERcFK4B9A8cCg74Iw/v5JOnHz8mTalSgREQAREQAREQAREQAQMAS1OVUkQAREQAREQAREQAREQgRgg4KTFPTW5zHmtXo2Bh2af+Nydy7ZN/W3LRzGRWiVSBERABERABERABERABJwU7slJqW1qDYgJpvji79y/TsI9JjJLiRQBERABERABERABEYCAk8L9cHQlS8cEVtLJhpUxkVQlUgREQAREQAREQAREQAQgIB93FQMREAEREAEREAEREAERiAECEu4xkElKogiIgAiIgAiIgAiIgAhIuKsMiIAIiIAIiIAIiIAIiEAMEJBwj4FMUhJFQAREQAREQAREQAREQMJdZUAEREAEREAEREAEREAEYoCAhHsMZJKSKAIiIAIiIAIiIAIiIAIS7ioDIiACIiACIiACIiACIhADBCTcYyCTlEQREAEREAEREAEREAERkHBXGRABERABERABERABERCBGCAg4R4DmaQkioAIiIAIiIAIiIAIiICEu8qACIiACIiACIiACIiACMQAAQn3GMgkJVEEREAEREAEREAEREAEJNxVBkRABERABERABERABEQgBghIuMdAJimJIiACIiACIiACIiACIpDifwT79+///vvvnU3nvtzMRRvXrfvzcKzpqQcqbF5V6s/Zzt5CsYmACIiACIiACIiACDhLoHXr1hUrVnQ2zhiKLSkvL89vyUWpT5069dlnn83Ozp4/f36pUqXatm3rbCIP5uVkZa/ZvncF0SYnpVUq3aBKmcbO3kKxiYAIiIAIiIAIiIAIOEtg8eLFO3fuRL53794dBT948OD69es7ews/x+Yj4X7w4MHJkyc/9thjCxcu7Nmz5yWXXGJJ9pQUh2cGdu/fPHfds1//8RB5Uza1epf6V/dsdLOf80lpEwEREAEREAEREAERQC5idEa+z507d/bs2d99912/fv3Q7ldddVUiWOIdFsTFK0/kwfTp0x9//PGyZcvedNNNqampXbp0SUtLIzbHJbtJYV5q2dJl0tLSD38rlZZUOj2VW5uf9FcEREAEREAEREAERMDPBDp27Hj88cdfccUVaMh33333tttuy8rKOvnkk0844QTMvn5OeYRpi7JwN5L94Ycf/umnn9544w239XqEsHS5CIiACIiACIiACIhA1AkkH/kYjT5kyJD+/fu/9NJLQ4cOveiii0aNGhXHpvdo7iqDav/yyy8HDhzI8Gjp0qU9evQoU6YMJnaXrOxRL2RKgAiIgAiIgAiIgAiIgLME8NFAqV999dW//fZbjRo1cH+/9dZbd+zY4exdfBJb1CzurEAdM2YMK1AxtCPZS5cu7RMiSoYIiIAIiIAIiIAIiEBsEcD6zgf5jsJkwSR+8Mj3+DO9R8fiDtOHHnqITR4nTJjQu3dvqfbYqhtKrQiIgAiIgAiIgAj4kADafeTIkfhxrFmzBvkef3b3KAh3S7W/8847qHY5xviw3CtJIiACIiACIiACIhCLBNDuGNpfeOEF5Hv8aXevhbvxkBk3bhweMmzkItXuwyqxa9cu1gpv27bNh3v8+xCXkiQCIiACIiACIuA3AiybjEvt7qlwN3vIoNrxkNH2i4UW8RUrVrDfzgcffGCFnDdv3hlnnMGYZ/fu3dZJxw+4y9///vcZM2bk5OQ4HnmICNkJtE+fPq4+Woi76ycREAEREAEREIF4IoB2/8c//vHyyy+zFQqG4/h4NE+FO5rsggsuuO666/CQiQ98rj4FuhnLN9sb8XIBc6N9+/atWrUqMzPz0KFD7t06NzeXnOKve7fIN2b275eNP18yOikCIiACIiACIlAMAt26dUO433zzzZs2bSrG5T68xDvhzljnmWeeQbXfcsst8pAJsyignqdOnfr6668bOzS6NkDaMomxceNGfrXOc2CX9favnDfBtm/fvnXrViPNcYxhAQfxhJkkEzl/169fv2fPHuu+mzdvZkRhfbViO3DgwNq1a/lrneGAW3PSPvwlwjvuuIPphXLlymVnZ9uN/fzEGSvm4GuJ0EoVz8Lwxn4vHYuACIiACIiACCQmAQTniSee2L59+xdffDE+Fqp6J9zRXrwbFdWuPWSKVHmQ1MjZH3/8MUBb8xWPI94c1qJFC970e84558yfPx9R++abb7Zp02by5MkmPKKfr6h/pC1hzjrrrL59+7LF6Zlnnjlp0iReVdCsWbNWrVoRD1tzIsRDp+2rr75q3rz5jTfeeNxxx3HVk08+ySUzZ85kQ8+jjz66YcOGxDNlyhQju5Hy11xzDWkjZEZGxvDhw5HUpJANhZo0acLJ2rVr86KEP//8E1FOqgh52mmnMQjhmNehWRWMuxAzDjxEG3AtQwUS/Omnn1apUuXee+810YIl9FPoVxEQAREQAREQgQQhgHZHjbzyyiu8WjUOHtmjfdyNuZ3NNaXai1poTj31VN4swLLoWrVq2a99++23H3jggXr16vHCMKzX77333r/+9a9HH30Udbtz505L5TNeQgGbrxjXZ82axetpb7jhhpYtWyJz33rrrZ49e6KSzeuCOdm1a1f7XQKOMZwT2/jx4xHcKH6GAVi4cSDDy4UXlZHL+N/z9ZNPPjnqqKM4wFGe9w+fe+65y5YtS09PL1myJCscRo8efcopp6DRcQEiAcTJ8hGmYm6//fa9e/dyR8IzwOPav/3tb7wZjbFH48aNjznmmKeffjrgWgK/+uqrPDKpYj0ANZMRAg76AcnWVxEQAREQAREQgYQlgMMMEpRtIp977jne0BTTHDwS7sbcvnr16piGFZXEV6tW7fTTT7/rrrvQxFi1TRqQyK+99hpeJffddx+yG2s6mnjixIkbNmwwTiMFJbVChQpE1b17d/OuYIzTaHqcbdg76e677/7ll1/atWtX0LWcN/4qLJC97bbbSBgjiuuvvx7LOnMCHTp0IE4UNmdmz57N28vmzJmDjf/f//43uzKZkQNpYwSCvR8JXrly5cGDB+O0g8rHbM/45KmnnjIuaJwfO3Ysmh7Rz1QDoh9Rzt2Dr2W88fzzz5tU8QpeHi0pKYlUhXgE/SQCIiACIiACIpBQBDC6jxgxAgsgPgL9+vVD88Tu43sh3GVuj6R8oMgR02effTa6nE1XjAM3Jnac1BcsWICGNgsGsKbzE4ZnfFFC3I6osElbRfbDDz/EfwllTB7xYQvI0LrfxMzaYmS30ccLFy7EwN+/f39UO78SCWlAfzOEYLRGgitVqsRPJpEEZqIK3xi0Owqb8HzlEv4yDDBnOFm1alWM7vj88IwMV3CDwbTPqC/fa423DFfFelXkEfQRAREQAREQARFwgwDKB0MnasRaMufGXTyI0wvhDiPEmZxkip2dCF/mdxYtWoSB3DiT4ByCDRu3lssuuwwhbsXctm3bjz76yPoafICR3mhofsJhHZfxmjVr4otSvnx5LNbhqHYuJDDDCRM5OYtBHQeY6tWrW7fDT4bNcMj3gE0/kfKcxEmGGSu7XbxBgwaWajeR8Fy4o/G8uABdeuml3ALhnu+11n0JYyVAByIgAiIgAiIgAiJgJ4A/Lbuk4DAc04rUC+GOtmPho/xk7KWnqMcYpHEiZy9IjNZcyzpOJPjKlSubNm3KelBL9SLxUdV4nGPwxgBPGIzoqPx8b/f+++9jw8aV5aSTTsKEbwl6I8qxoxek463bES2O8uxZif86owhLixMDa1hJxrRp03B3McZ4AuMWz11+/fXXzp0747RjpcoKYJ3BJI8/Dw4zpGHYsGHEXNC11hDCniorHh2IgAiIgAiIgAiIAATwdMcUiA0xpmm4vqsMqp3xjZalRlhKUKWdOnW65JJLjH0dRY4zCXGyoBMnb7zDBwwYgOcWGp2hJK4m7FqKa82YMWP4taC9S9HWRPvDDz988803vPwIiT937lwKNGZ7VDVjLc4XJPqtx2EFKs4wuLw/8cQTuLWwaJVbMy2AXz52d0zmrDrFas5y2Kuuugr9TZ1hUMGDvPPOOyylxfpuTOlWhOYAOU4YTOw4ydStW5d0YlAPvnbLli0BF+qrCIiACIiACIiACAQTwHSIwmE5H9I0+NdYOeO6xR3tBaCYnpWIbl6iWfmQBszSOJBg3jb+JxzjLYO8RnPzE6tC8fbGwb1OnTqXX375f/7zHzZmYc8ZLOKY1c0jWFGZr6wcpfgiuDG6s38LnuvsOcNqUQQ3Ay12PGW/SHziLTu6xcEycnMG2zl+Mvfccw/LZMlrFmtzCQdIf8YMt956K+MHPswMsJEq5vM777yTq3Ck+eKLL6hCLLdlwMBuj6SNaO0x477PxAIDA6vw5HuttdmO/VorqToQAREQAREQAREQAUMA0cL6QASJtdgv5sgkobFcTTQWXLYEwe2BbQddvVGRIt+ds/mbNU9+tXI0V5VLq9G13nW9m9xepBg8CIwuZ8yDokW5Gu3OTbExU9qwuKNTMYezHwt6HU8VzhAM7xTCcJ4LOW/cZpDLaH3EPVukcxXBjMblPMFYAMoZAlASsIXjbo5S5zyR4GyDi45dELN1Iyd5h7DlV8PtOEkuY9cnHu7IX1MfiJ/AuNwQFVZ5TvIT4QmMSZ4HYWaAk9za3MK8ZIoHIYz5/PHHH4w97PfK91oSQIRcGOxy87+Y9H8REAEREAEREIFEJ8Cud+xQhycIC/xilIXrFnfUJ9sC+kq1x0pWIUORyAGpte++gsLmgya2ZL0JbM4jZAPO2zUxIZHLKH7jiGIuxC5uLkFP80GFB9ydgYEZG9jPm5PBtyN+RDkfhgT2lCDf+bAvjf0kEQYkjzPGEm+/V77XkgCtTLVT0rEIiIAIiIAIiEAwAUyB6NLg8zF0xnUfd5w3cJXG4h5DUPyc1ACxS1KDz5j0F3Q+4OnswezHAcEK/Rri2nx/yvdkwF0KClPQ+YDL9VUEREAEREAEREAE4omA68I9nmDpWURABERABERABERABGKUgPFxx+02RtNPsiXcYzfvlHIREAEREAEREAEREIEEIiDhnkCZrUcVAREQAREQAREQARGIXQIS7rGbd0q5CIiACIiACIiACIhAAhGQcE+gzNajioAIiIAIiIAIiIAIxC4BCffYzTulXAREQAREQAREQAREIIEISLgnUGbrUUVABERABERABERABGKXgIR77OadUi4CIiACIiACIiACIpBABCTcEyiz9agiIAIiIAIiIAIiIAKxS0DCPXbzTikXAREQARFa1211AABAAElEQVQQAREQARFIIAIS7gmU2XpUERABERABERABERCB2CUg4R67eaeUi4AIiIAIiIAIiIAIJBABCfcEymw9qgiIgAiIgAiIgAiIQOwSkHCP3bxTykVABERABERABERABBKIQEriPGvuoewV22fOXPXvvBIlDubl7Ni3zjz7vgNZ8ze8tCJzOl8rlKrTPuPiplV6JQ4WPakIiIAIiIAIiIAIiEBMEEgg4Z5SsnSFUjX35mZt2PmDPW8Q8dv3reRfyaTkxpV7ZZQ/3v6rjkVABERABERABERABETADwQSy1WmcnrD9nWGFcS9QqmM42oNSE+tWFAAnRcBERABERABERABERCBaBFILOFeOqVi48on1KnQLhg35vZqZZq1rjkg+CedEQEREAEREAEREAEREIGoE0gs4Q7ugozuMrdHvSwqASIgAiIgAiIgAiIgAiEIJJxwz9fofsTc3lzm9hAFRT+JgAiIgAiIgAiIgAhEl0DCCXdwBxvdZW6PbinU3UVABERABERABERABAolkIjCPcDo/j9z+3mFwlIAERABERABERABERABEYgWgUQU7rC2G91lbo9W4dN9RUAEREAEREAEREAEwieQoMLdMrrL3B5+WVFIERABERABERABERCBKBJIUOEO8SrpjTpmXFo5vdHxtS/Q3u1RLIK6tQiIgAiIgAiIgAiIQDgE/P7m1Nzc3HAeoxhhkkuUaVChe1b1Dc2rnuneXUoe+RQjeY5ccvDgwby8PEeiilYkKSlRK6LulYpowfTgvirwHkB2/BZRrGU8iypaMTI0OTk5KSmpGBc6comyrHgYo1jRUALogeIl2ydXRbfM+wSCSUbUVFFoCgcOHMjJydm/f//3338fOmQkv+blHSqR1PmbVd9FEknoa+se+VDgSpcu7VmlpX6Cjoq6cOHCHTt2hE6hz39t37493EqVKgVAb5JKnwQ97jV79mxv7hhPd1GBj8Xc7Ny5M8n2spZxO28a+VjMjnDSfMwxx1SoUCEtLY1cY7QcziWRh6FtpF+mZ6FfNo1k5HEmTgzkVNu2bXle+jKOvXlwMsvkGkoAPeDNTV26S1TKvEvPEmG0vhPulDNK2Oeff/7iiy+mpqYieSN8wuhevmbNmlWrVtEv3njjjd26dfNAvtOw0qq+8MILGzZsQPJG0SrjCPm5c+dmZ2dfe+21I0aMqFixotvynXtNmzbtscceAx0fRx4hoSJRgY/F7B44cCC9+x133HHVVVd5IN/jrJGPSo4jwrZv3z5gwICRI0c2adLEA/mOUseW8fTTT+/du1e2z2JkOp3L/Pnzy5Qpc/PNN19yySUeyPdDhw5t3rx50qRJ77//Pt1ZrKsp78t8MXLZo0toQ139ZGVlobfCvAXlbP369dddd139+vURT999912YF/o52OrVqydOnHj66aefeuqpX3zxhbGFu5RgmtTx48e3bNmSDviTTz4Bvks38jLab7/99qGHHqpZs+aoUaO2bt3q3q13796NdqG4jhkzZtasWe7dKL5jVoGPxfylwN911101atSg8FMR3HuEuGzk3cMVIuZffvnlmWeead269aWXXrp48WLjGBkifCQ/7dmzh0a4Xr16NMJffvklGjSS2BL2WrhBD4aQfPTRR6HqHgrKA6Vi2LBhlBDKCaXFvXt5FrNTZb5BgwZYVD1LtuM3KuF4jAERFkm4L1269OSTT7744osDIomPr3SNl19+ObZwl7T7rl27rr/++r59+8ZHFQ3OdFo6Bj+bNm0K/inyMxTU3r17M76KPCrFYAhYBd5MrzuOJe4LvOPECo0QYXHfffedeeaZLml3VHt8N/KFEnYjAIah/v37z5kzxyXtzhz4oEGD/va3v7nU9rrBxOdx0t0wToaqS9qdCTSmR0455RQGCT5HUbzkRVjmY124e+QbF870AY3CCSeccO6557700kvhhI+5MN27dx86dOi8efNee+01/DudTT/a6Oyzz6bffe655xhhOxu5T2JjUhjhDkOm/5xNEtyw6DNoZJrC2ZgTOTarwL/66qsq8DFREvC4YB6/Q4cOgwcPRlI4m2Z66GXLlsV3I+8ssTBjI7POOeecRx55hDlqhkZhXhVmMMxMLDRiDvyNN95gQibMqxQsNAHmdenOmjVrdtlllzFPHjpwUX+lDEyfPp15GMztTKAV9fKYCO92mQ+oRwyJkVgBZAhD7UA88JeRUsCvrn5NojF19QYM1hncML4MfRcCPPHEE7QL+HiEDhnrvzIORsd07dr1oosucmpREUXq9ddf37hx44UXXkgLG+uIQqcfH6rPPvvsvffeo+0LHTLMX6l4Y8eOxeMQ40SYlyhY+AQo8K+88goLPFTgw4cW3ZD0Q/hFsLwE71inVnrQ0TDexqyQCI18VLIPYU2riLMfKyCdWtpEScDLghby6quvdqq9jQocf94UdUR3tmTJErpvpyoaanL58uVvvfVW06ZNUbf+fHCnUlXsMt+wYcMZM2YgTYNTwmQjCg37AmthrUzBi2Hq1KnDhw+3ryrmJC0k4Vk80KpVK6akKleuHByhG2d8YXGnXXjwwQchEveqnSzEDNmjRw/8gP/44w+nchRhRITU0rhX7RDDUMF2CtQ6p8a499xzD9FKtTtVGgPiocD37NlTBT4Ai5+/0hRTy+iNqGVOpRON8vjjjyP+EqGRdwpakeKh/Uer/fjjj4WaycKPliwj48gyqfbwoYUf0lQHxrTffPNN+FeFDklLix5ACcS9aoeDG2We6oMhj2XE9llirBj3338/StUOf8GCBYT8+uuvGT/gBd2nT5/MzEx7APeOfSHcaRqeffZZ+1DGvQf2Q8wnnngitvaZM2cGTMcUL22Y29955x0cjZyy3xcvGV5eddNNN1FgHJnKpypi83B7sxov4fjwXirwPsyU0EmiRrCVE1XDqeEx5vaXX345cRr50Hhd+nXIkCFIQExCjkykY25nbzdaSKfs9y49dUxHW6lSJXKN7syRioaiWLFixQcffMA+oTGNJfzEO1vmw7+vFfLTTz9lnSuOaosWLWKdCfmIDX7Lli3I2mDvGuuqCA+iL9xpFyi12Dux8UT4MLFyOaPhxo0bO2WDZLSHUROAiWBuN1ncpUsXNtVyxBzIiBl0fGKl8MRiOlXgYy7XMLdTy5wyutPI00xRy2Rud7UksLqpevXqThnd6ZexaJJrMre7l2sMZVlS4pTRnd14cU0kyxLB3G4yxdkyX4yMRqljm8evhtaSHVrJAlaKt2jRomPHjkx92M32xYi8oEuiL9xJGcNEnrmgJMbleaQ2z0Udi/zpWDYBvUQzijDMw/nSEXqWH1vksSmGggiowBdExrfnkRR4NmN0dySFCdjIO8KtqJGg2OhWUA9FvTA4fGL2LMEc3D7DuIhcY5jkyI0SsKI5WOaLkQVfffUVE1PsEVS+fPmTTjoJ9wf8atj05p///OfRRx/tkh9E9IU74xWe0/TrxaAWo5eY7KSORZ5+HK1YacGMW+RRxVAMjFUcoYeDGh7YMfTgMZpUFfiYyzhsAeQa6i3ylNPI00yZ97NGHptiCEHANIyOuMrQL7dp00ZuhCFoO/KTgxUN/2zcNsg1RxIWK5E4WOaL8ci8d4jXa/72229vv/02e9NVrVqVDOWNPaxVNZOWxYiz0EuiL9zpGNjEiicsNK3xFMC4tThiF+F1YsSWaLOZFBiKTeRFAutUog0aI4dWjBhU4IsBLbqX0B0y24vXZuTJoJFnBwZcAiKPSjGEJoDnAN0K/rWhg4XzK2/gPvbYY7UsIRxWkYSBMJyhHUkk5lryndyP1/2gC+LjYJkv6BYhziPZ2YOLPT3NojteQ3TLLbewvxMGQbx5HTF8BN89+sI9OE06IwIiIAIiIAIiIAIiIALeE0Bw457OVCEfa27ffDV/rSRhX7/iiiuwSrC0gDWpnMePF4NglSpV8J/Zt2+fFdLBgxQH41JUIiACIiACIiACIiACIhC7BFjhnZ6ebqabWGmKiEfK835GdsUwD8Vbaa2nS01NZZcLfGOeeuopNoDn2l69eiHc//zzTyuMswcS7s7yVGwiIAIiIAIiIAIiIAKxR4CFPbwo5s0jH5N69sKqVasWkv3666+3nufLL7/EmdBaW4hXIa+qfe211wjG26/+/e9/N2nS5O6773ZpiYiEu5UROhABERABERABERABEUhQAnXq1Pn111+DH96u2s2vuMdccMEFVkjeIsqHr7fddhuLDVD/eNFYvzp7IOHuLE/FJgIiIAIiIAIiIAIikKAE3N4sRItTE7Rg6bFFQAREQAREQAREQARii4CEe2zll1IrAiIgAiIgAiIgAiKQoAQk3BM04/XY3hNgWTof+/ZSRU2DtS+V/UJetrJ///58f7IHCz4mMTk5OcHndcYQOJJdh/OLg2IzyTdfiJAsK0acxcvoYtwodi+BrcmyYucaFYpPAAHOmO3hAs4X+tVsHhccYaEXJk4AcopPJBXtcIYFZRkAad+IthgkVdGKAU2XeEZAPu6eodaNEpcAnQo7vLLkhdc0sGClbt26LDlntynzPtEwubAj7JIlS3iLsn2hOrpwxYoVkyZNGjBgQIMGDcKMimC7d+/+5ptv1q1bd+GFF+odK/lyY1evzMxMunDeZQ32ypUrp6Wl5RuyoJO8yHD16tUtW7ZMSflLSztr1ixeH8ZqpyKRR4U88MADZ555Zrt27Qq6Y4KfZ00Yr2illsGhRo0aTZs2LVeuHJs/hI+FWrZ+/XoWlnG5/ao//viDvZkbNWrEdm/286GP0aO8m3nz5s1sD1e7du3QgRP2V+rCzp07Kd5gp7LgH8z+ekWisWzZMprTevXqBVz13HPPNW7c+Iwzzgg4H/oriXnyySe5KtFeQRoai371DwFZ3P2TF0pJ3BLA6sM7kE866STkNZ+uXbsil1euXBn+A9OroQDoSz7++GP7VSgDhPsjjzzCm67t5ws9RrhPmzbt5ZdfLp7pt9D44yDA+eefD/BBgwaxQW+nTp1efPFF82688B+NbX3JdEZHAeZAJODDDz+cnZ0dflSEJPy99947f/78Il2VUIFR7SeffPI555xz3nnn9e7d++9//zu1pkgEFi1axLWPPvpowFXUr9dff52MCzgf+isVn1rGznIbNmwIHTKRf6WW9evXb/DgwX379mWnDjgzuVEkIGedddaVV14ZfMnTTz+NUSP4fOgzDP8YIf/000+hg+lXEYgWgb/YgaKVCN1XBBKBADu8Pv/889WqVZs+ffpjjz2GKQjBbR7cvN8BYzzWQcs6i1jHoI5VHvsTJ3mx87/+9a8ePXqYS9CCaG5zoUWPSwhvYuBadAPXEq0VjJ/Mr9YlOghBAPM2gmDjxo3o7DvvvJOXbmDEJTxsQUoWwNaymvOVk/xkIDOmQkGS3ewvRjCuIjv4cEwYc1MuIcsw5FsB+MnY9a0sIweLZDMO8TiJ8BOW8osuuoicQrK/9NJLVDRs5AYpOQJVIMDTsumaLIM/J6k7GRkZvO/QmrwyeWr95VoyiHgIaTKFY86YazkgNgKbOmvyNBGYR/6M/fv3Z7dsbBljxoy56aabkPLVq1c3tE0GQdia7zL5CF4qGuepQQxoqWhWMjhDGH4lL8xJ6p1VszhjtZME4JifyDKrGlrx6EAE/ElAwt2f+aJUxSEB+gbewXb88cd37tz5v//97/fff89D0mdgjfvss88w8JQtW7ZLly5oDg6Y/J0yZQrW9KOOOooz9E9Yx5kONq9Q3rVrF/PLkydPNv2TgYWh6D//+Q/xY3TEOvvDDz9gncVgTA/HK99w/CABiJgTTzwxwA0gDlk79EioAfxScJLB0+mee+75/fff69evT2cPzM8//5yTiHLUefPmzVEJ5CA5gspnuv/cc8/FdvvVV1/BnCwmOcy/884OzhAnLk/mJMb48ePHDxkyhFwmm8hxrhoxYgS5jJn2559/5vLTTjutW7duVatWdeiZ4jwa5Bd5hGsKbi34KcETYznDra1bt2Ivx0kJ8sceeyyzKDVr1ty0aRP5iA8bNYJ6wYUTJ05EyZHjYKJyrVmz5u233+Za3G+2bdvGSarkjBkzyC8zhMbfbOnSpebauXPnTp06lcBUc+ogYeKctXOPh0zH1k7rRGNIRfvtt984QyMG0k8//ZS607BhQ2bA+IuOp/EkK2kMqRenn376s88+i3cTc2ImOWvXrn3jjTeWL1+OowvziuYkNYs6y9jbfOX9OO3bt+/Tpw9TNB9++CGXE/PZZ5+NR5w1onPu4RSTCDhMQMLdYaCKTgQKIoDgw2caYYeSQLExlU9Ivt54441IAfoMuhl0Ay9gu/baazE7ffvttz179pwzZw42XTohLIhYpHC94C1udF14SON7jTr55ZdfkPVEhZp/4YUXeCUEogFjPMJ9woQJhEdJjB49Gs94xgPvvPPOqaeeilgsKJE6byeAgDaiDZjQgzb9Ot5KN9xwAwKRzh5Vwdvy3nvvPTyYyTic2pkYIQBjM953zQEZTf4i6x8/8kEsohFR5MYWiEzhNPqDNQ9kIln8wQcfYHok0x966CHkI8L9lVdeufrqq++//357wnRcEAHUNvUIRY7CRr1RWTCik4nPPPMMHs+4QTPxRX6hsDHuItow1lK5uIpLDHkqFKIQLY5fO7/i60IM5CxVlZtSB5HyjKaMcGf8jChE8fMTlXfmzJk4ajMO/+ijj5CJ5GxB6dR5OwEqGsNgxq6YM3gbJbOR/EpdoOSTZVQ0PF5effVVxl0LFy7EPE+zRhM3b948hDvDYOogZgs8o8hofiXrW7VqRfvJ6gJzF7KbXLOE+7hx4y6//HJy8IknnqDScTtG1Pi1k5VaQGLPFx37k4CEuz/zRamKQwL0NGPHjuUvyoC5+GHDhmFSokfB/Z3Zebze6XXoWuiEsP1gcUfK46OJQET2oSRuv/12pCFc6OF4Qxv2PIQCuoQXLN96660F8UIOYltCoGCyRUrSUdHzoUjcfkNEQemJrfMY9kaOHIkyQAEAH/s6WYaGxhyICkTPodsuvvji999/n1kRLLLY8PgVky1q47jjjiMTMQfyyMQAedQeztNce9111zGCCoGCeBh0YcintOD7i4LBSypEeP1kEWBsjMEVBUY5Z+RDrlF3yCZ44glNRUMjUnH4inzHHo/mxqGZuoYpl6wZNWrUNddcQ2yY2JGJ6Dn82aiPGOYx01p3CT6gPvLGREZ0VapUIXLmx6jmEu7BoPI9g/cgAyeI0QYOHz6cWSn4Y7xAfyO4sVxg2kCRk3HkLzH885//hC2jKY6pYsxlmWhpMJlmhD8zjWYUne/tzElsGffddx/2DjOWxj+H4YGEewhi+sknBCTcfZIR+ScjL+9QiST+0xri/PkUevZQXm7JJL8UcuxJ7HHBTD3mPQR627Zt6VqQFCgGOht0Bo6YCES6E/oSRAaLtPCaQOQR0npSrsVMi/LGjosBmGvD2a2CThHXCyaLsUGiY5D+/hTufivw5AWgMOaxnhitgC1w8eLFTKxjOEfPMabCcI4NHsmOMwZh6Pi5BLd48oVfrVxDdiBE8L1GGjKUIssoDNavwQdcThnAPxsDJP4elApuFPqS4Eg8O+OrWgZebOrAJ19wWBo6dCjkMZOjCBlHGZsr5Z8A8MTmStZwElu7mQGzoCEQqZVIOrLVFAMmuKxfgw+omAwJEPp4d1CXMc9bfhrBgaN+xldZBg0aJSoazMkRxj8Ax4kFQwMDYDNkpfxT+6BKnjIHRavIGInjAJL4qjG6RrUzVcUIivY2IEDAV6JlcM4EFw0j5cGy0AcE889Xv2Wcf8h4kJK8EoedHv2gx/yiaRyETgNKt0cltBaNFTty4sHARjdAZ1DsSCK5cHfOlv0Hd6anVC6TVsWl4oKO5DH5S0/m4GOaXMBUzMciYFYI0VZyI/t5K4DjB5t2L05PrVImtXJaclnHIy9qhBTIO+64A2MSrixMy2JVBTvumygMDEtGSeNigUsGxlo8PpnAZTErBlfMQvh3mtsBFgkCPbq04ARQ7AnAeSDz4QCTLdYsBgCof5ZaomDo2+wXmmD2M1E8NgW+bGr10qkVXCrwRXo6nG5xqMDUSkbQuzMvT36Rawh31hIgL4iNnMKpHZWADR7jHwsiWVHAMMlyuiUMyh7OGHfRE/YEmK8myzhPzOZXPHAwFeOHzbQM1kS7ed4KY48nisd0Zpt2/1YmtUp6aiU/1DKs7Exe4Upx88034+VCxaGmoKGpfQx0mX0yrFDh5CA5wkwUm/8wDEbtUdEskrSKDNjIYvSiddI6OFy7/jqUYlKFy9F/qMlmzZoxSUIAE/5I2P87tmKI7sHWPctSU8rSs5ROQdqGGkN6k07WJDCLSN4hyqlotJDGms6klpVlVDSyrGPHjgyPqZK33HIL1YTJLtpPK5EMychTmkHrjDngDBUnIMsYG5PvjI0vueQS6hoDBqsmcpXfKhpJOph3YMvu38uWql46uUJqcnrAMzrylWIPBNolqAZjLN4tIEnXz7X2OC0xQF/moPAoXgrDvGpX9pZ9B7ZWSK9TOoXuKTqa0CT1/4uqMJPu52CUD0bMLCfC4IFlCw2EvYQEU0T4mF42/PRTgpm5w2cRzznagvAvdDDkhl0Lvl07rnRK5R4Nb6xcuqHjaob+DPMhtg32uWOymA/yMfLqirhBIJppaNQnKse0reQLLr+AReXgK0IznW+n6CDA+Rte+W3LJ93qX9eyxtl+kO+wxSBED4EDBi6brErEHQIlDSV6DppLBopYiWjL6FTQHAhHhDudmSXc+YkY6IFwzaRkEiEGRUOMy5EjZChn0BxY2TlPT0b8AMfNnYxg3plryQIuZKiGrN++fTtXGQXpIPniRWUKfM2yrdvUHuhGgS9eqrDtMfhBwTOgotwayysb2KEqIAlP3JqJmUaGxQloDmy36Db7PAmSkc6JjKBFIn/JZZMSoiJDWfNAlhEPQzL+8hMO2eQyozsCowXJHdxvsEpSjwjDMXlXvGdx/KqDh3LmrR+/cvvXXepdc0z1M3wi3/GK/sc//sF4FVGOyEO7Q4xOAU1PRpBrYIcnGUEVQ2fjy47TmnFFM4j4lSxA1dGO4QRPSPKXn7DjkB3kghnCYSQ25zG0s3YZvynmWygtjBkQQHRJ3Ihrqc589Y9A+WXTews3v39czQGta/Uvn1bbJ/KdhhFumNipaLgekQUAZEDFAbMZYKeiAZPuGE8kvJ7IWWbACGyVaq4CPpNUpuuHufmJ4ZlpEul0GEUTCTFT75D++BlyX5QDOUt7SHhuRK4RzFcVjYTlHNz97dqntuz9vWPGP5pW7eWsfKcY88hoHnoQagq2JCYGI29nQM0UMauNyQtyh4qJEgM+q4booTigV+JG2LOwWEWuPayS4MZBZvaK134+75jqZ3XKuKxK2UZRlO/xI9zp8CgfNJo0tRhXqHgDBw6kr6WNpuVFR6JyipSXXEijgP0My1m0hDsJ3ntg25Itn67MnN621hBn1QwdD5uQsE4R+U6FoZHC5oFMpJUsEqjgwDga8uE8bQENJWZIekc6Ldx/WbbFGdOeYk2kZ8VL29Xqum3vso+XXPvNmie61b/eD/Id7cVW7hiKkBQoPBwrcYlhJSLNJeTxZkFwoOPxu6WBo5kDo314Q6fC+5vgic7A6515fLo6kwV0PN27d8cZFxdPZpmJlvaX1hAvDrx1WarFrRHudIHUCIa1xxxzDD6jlHBywT+bllDgZ6wa+9PG1xwv8IZSMf6CEcscq4GRC/jd4vGMQZ3KwvpFNiSBMBP3sOUntishFzhGkdtLNR7waHeEOOMoLIjMzpsqwLCN0TI+2YRHbbCY1UzuoxQpD+QarRD2e/QE/Rwu8lxLDnJfszKyGM/ixiV5eRjdF3205Oo5a8d1qXetH+Q7ggPrLG0+1lnGS+QLvsssGKVrQMSzD4zZWIYz1DiyAFVnBlGGD3nHWBfIzHVQN1nPzXpHTEKcpzYh9MlBGk+GW8Rg5soQH0gQglEBmSRBxCNTyG6qMI0eJ6nUjPrc4F+8OLP2/TFl+e3z1j/fKeNyn8h3ACKj8XHnnQkcYK0g++6++27c2dm9By2OwsYZiZaNyUPcZiBPXbOyjAMGZjR0DJ+Q++S15fpC3WF9EVMi5ClrGxhH0ZCahcsMjHGXwg6CfuAkdY1omUMjGK0rLWrx8Lp01cG8XAbJa7Pm1avUsWPGlU7Jd3pqVszT6WCpBCmtE7OLOAeyP1KED0IjhgyjnUQAkCNsGcQ2XHCmtaTq0dwZwwfKjW0V3BYDET4Ll+/P3Tlv3bMLNk48rtYFnTMurVK2cVTke/wId+obVZpZNtpZajUNKG03ZYLqzXojJBF9JyWSAwZ5GJgpqZQkvlo1n/OMDtGvZA8lLCCPOc+vaKnIx6DEjJmqRHJOXokkxtABN7J/zT2071DeYZvBzuz1zqoZaiZ2XAx7tE1Yf4GAOZz5SvPg5mEJw8MiROiuDDSO6as4DzcTEvVvbFdGpgAW5vSCqE+8S7Gv804NFCrzm1zLg9B7cV+0I8qVno/5UHyFjVixP3ihx3BLLX3YAhE65GHORz7b9i6PonwHDo9Pt22gQY8xDHxQbJiX0HP0KDg0Qxs7h7H6MJqis6fIgY4MMjGgOYgBSUELSx+GuQg7HxuScJ44wYgJH8FHFeBeyDsygr4QWz6e9NyCY5QfwwCEC+HZehJFQpOK5clt4R71Ah+6nOT7KwMk7ED0MfyK/wP6jw/KAPKUeUQ2q4pRe5RwyjzGKtoTvGXIKYBfccUV5BSozcQ9HRK1AF9qchxhgdYny4iZwMTGHheYD5GDeE8Z9cCWFyxjwNJPnlJOCEkOIj5QMyxvRSx6I9zDqWUHaKMOHd4c/Yh8/zW68p32mVwzEyAwR/nhp049opyj/xhrMaaFOWIasU47RvMFWAIgUHBz5zwtEoZ2YqAy0jYyNgY4FQTVSIRIOgQ6IzeyiVEcrRkfqjb3ZYKFksDKcgoJF3JM5UXro/zojIgBXxoPhPuBg/uS0w7m5u0L3TYeysuh0JJrWftWRV2+U0dof8gX0kOWsWUWvQPyna6BSkRFY/6KqoTgIwAdEE0cmpLsoNZgfeAkmW5aMC7HSEcuIBaR++QOVZgAjOIYXVNxqLPYSsh9IsSEhDag9l122WVsBoV2ZBE/Op7hNAN16jIjLo+Ee1KJ1FJ5obOMp9ifu/tQ3uG6RqVbuX3G2qzvnJLvFFGIMfdOZwQKttSEjymuVBNKMhYEWiF6DaQCJcd0UqSEn4ysMsf8pS4Q0oThWiQBnQ4NF/OTdEzMM+PyZCyhNGgwJ4zJL9pPRlzcl0iK+klJK1Fomf9rnNDeUyjwv14CdmZKD9ca5Pt36579OXryPcnU3oD0OfgV6UD2G3+1fKMtNEC+VwWfpAlmSE2xYMiIcYuiQxhKFXUVx2JaYcZzWFkoK9RM6if9IjqG5pWT9I7oeNpWZtnM0JCyhYqlkjMZh75ETmFiYbqHnpViHXz3op5ZlfXtC+/ck5dUos8pfUJcu2XP78u2f5G1b7U9TIXSGW1rXdim9vnGl6BXz5MYxRa1I2c8QxuH4QHbBuY9e/y0jIySsUCQNTRwrJKkWSQH4cBdsHwgx3Eh5ZhujHqOiEFboGAQ92xHyF/EjdHi1HZ6RBQJ1gvECgYwIGPeML+iTrBcomPAa/LLnozQx7PXPHX77Tffe9/9oYMt3vLxqsyZAWGqlml6xHnmHJxnSqUcHvEHBPD+K9AowGaYZN2dMskIB7DWGfsByTaeu1xlP09UnIewHSmNI9lKk0ojy6iMaMk7riIS7sLfgEjsETpyHPUC78hT2COh6wIpGO2cIU91gLM9pP0Yzwp6JqNR7Oepa4jFgPNWYPKU/s8qCWQfPaj9cjeODx7a/+XSh8eMvj90LcPvFm+0NVnf2NOQlFSyRtmjjzjP9MN5Zt/uXPwZqPv2MN4fg5EegQJvlL1JAF/JR9gGwLeSB22OA4BzFY0beU2FskJyYAWmTlFCKBumZhGY+AlvD+zG8YKNbz/42O2nnNqnSeMmIeJfvm0qs7i5hw57hlifSukN8QFoXes84zzTsGEj2moPBhtWAvI9IMvIIKwV9l9x8EN2F5Rl0CYj7LlsriUqyoAxIVmxkU18CEyW0QjTT1nZ5E1FIyV/bll98e1tb73tditV+R7kHNrL9Pu6HfPsv6aWTD8i33GeOWne7J/uv3csfbc9QKHHPD6jIzZTYpqC3Wnt4SHJgJNJXZQ9QyD6fbPrDmdgBSis5sgDdBEZhOWUk0gsrBK0hOzshGzgK5KdOOmDEO4MkjFhMKDiPOoCbYaIp/XD5MRogelfu4ehPSWhj6+8q2fHHseELvP2GM7vPvrxt6+ulXH4dQ3hf3bsWzV3/XjLGmguLJVSwXvru+vtSPhQIgzJ0ByfFjIeyyLDaMqKkZKUCaof7SzFgvLEV6ae0aBMf1O2EI4EZm0KohzTF9YX5jcZu+Mkg6w3SaJY035hLSN+DGkRptNcvmzbFwcafsHxZ0sP/y3S54j1/cGfNr5u5PvBEn9pfMOMCmcJphHxuAgwtdJ4wQQzMLWUnxDcTPvi60KTR32DG3OI6AkcbDCH4OtCJFjQMe5yHisUYydUO42jSQZ3YbIYWW81hfbk8Wpx7oVTHRXYrn7sYQo6/nL5HZ2HZH+29KaCAoQ4f8T6ft03a55Evperygj6YHQXmpBUHj/Y0gA0S6sFPw6dVr6/ElVwj4UxwwqMkjBigjiJJEB5BN/IkTNRL/COPIU9EhocPvYzHJsRacBJ+1fUhv2rdWzGUdZXc2AFJk+t7OOnABEZcJVTXzEvTV9DLaONKnIt+5/1ffictU8h3zPSTkz6y6Jcp9JYtHjAGCDauJ5aFlxf7PHmS5ur+NiDmWMrMDXLPn7zppaRhnlrn63fe8Xvuc/+vjQ4dYWcOWJ9v2Pe+heMfM9L+j8H8UIuc/lnMNpJmrsF6PiAJEA7X+DB8XChVZHJsoBqaOVmQPyOfy1dvsRx/TOLUdFIid36fijruPSKRe7REEX0wrRdzO4GPBoTVszu8ivaicES+p4138amiZslVjkQMYuIWRP5xMgc1yYYEhgDKMIANWXmSYgW703UP5cE93RcwiQVMgOhjwIhIwKSUejXtmeU+D2zCGV+b26Jr1eOrrCn0IgLDxBgfa9evkVaSdd3wvBBa1o4mbBCUP0oT7yvAaHJXBg+1kxe00wzrckYjmEixYJRHWIUmY4exc0A5wQC4CtMcWF+Ex8vplMZNTJPhyMpMp0bo/hxmmekyLgQOWvvPsNKlmuBdmWv/3bNuA8XX7Uz7/eS+fQghdwYiztaHFt7QC3CBg8WbORUUciwcp8ZDGqv8SDCAx6pzRnsZ5jeGRFhtqeJhB5GEeYxkOw4FRj5QvzsXQ0xpLylFO3JYmhEFWWFCnXVft6b4+17l09ZdnvfG0vs3n/4hYj6+JxAhAXe508Xl8lDvm/e/euUZbd9tOTa0oXsyxeXAGLyoTArTv9j7PuLrjhUaqsfhlsxCdHzRCPfV2fOXpH0QvvzDuzL2Vmk+9NTowdQAgFz75zH6QDfBHQRzghY7ujZsbUTmPhxgEEXIbdY/EMnzhIddD/zFfjbYHfnNdJIAlZSmSEWlnsuZKEObsz4OAUnzwTjQhRX8K/+P4N8/3HDSxN/Gbhy20y27HQ7wUVXfG6nKIL4MZxgPudlk/hv4JiIrxtuagHxoSyxoDP7g4sIZYsCRzFixElpw3mRBRlGYhIVxY5rcVLEzMxVuHnkW+AC4g/za0pSWtLBwx4O+SpaKxIc3HPz9tP/WWc4YDSaguUotWqzaqd2rHv5W3kjDhW9nKC2sT9RzbDF2iMHGgNrXnVBRSIA/jAPP/ww8w94fxIMd2ouxNSEZyEnUfM4DjHCYRTEJQh3ttRghMOFaHEmxZgsY58NptLyHUNjrScYVPP91Z6q4ONSyeV27dpRqIGTuWCm8gMuB2BacvkyqdVxN3ps4APlbz289ZA+rhKIeoF39eniNXJqmXG7Cv2ABdWyUikVy6XWaltncLNy5z2ys2/oSPSrIwRwnDiUk0y3EtCwB0SeeyjnsJv7X8/SMKYmlyuTUrVlzbM71btyzL5T/trz/DW0vjlIIK9EbnYS3XFhUeYdOJxxgT1aclJyWnLFamWa5Wxv+uW7a8tcmP+0XkGR01/TrVNgAhKA6ZPlBJjP6e4JgPmcldmodiPcmZPnw7WsCSFmpBQT+BhJmWZHoyOoUAKIe36il8ejgaUL9PWY/IjK2AHt6UF18BXTe74TWfaQ+R7n7i+RUiI9dJm3X5hUYk9acplSyRT5InzQYwyQgi9IKVmqdErFhhVP6Nrw2saVuicl/UVTBYeP/ExcCXdw0GDhtoHPNItLUI2UPMMI/ypzwDZeCHFcsdnZAxcOZnb4iUEexmPm0YIn4BgpUhoou/jK4+1t9Gvk3DMqtE/eeLhMd+raNURsmdmrVmd9u3P/BhOGUmaX7Bnl2ySXTONfiBgK+oknYraBAQwmdmv6mArGIJu/zNFTIbmWmsyBOWmPivMEM2cwqONBxPYLbKTAmiEzKUGFZ/0ld2GVarBHAReCnfk1JDsLEsKvb1Ya2tW5+LF3H7r22iutM/kerMz8et3O+dZPdsnepd7VFUtn7N72gPWrDtwjEPUC796jxWvM9EZtql88/sPxoWsZ5qWVmTM27PrJ4kAtsyR7h4zLKpaug2XE+lUHrhI4uvqZX3/6K44H9erWC3GjNTvnrsv6Ljcvx4Qhy+ySvUaZoxAfHuiPEClMqJ9y9pZYPrP8FZdfEfqpcw9mr9wxc+OuX6xglmRvV2cIixN+2L/44x13Wb+GeYC7Cz01nvEB66cRP3yQB8ZlCHFFV45Y4qQ9Zs7zwbiJbMDojucCSw3p31mjT+dOSKr/u+++i3ZnfbDxd7dfzjHqCzsgygFLXDGseMSwbE6JFm3PDF3m7TctlTK+Ta3za2Qc3sY3/A9KbOHmdw8eWYtvrvJesv/ffcNPtP9DYkpnqgVNidA0HuoMDRnkMcJDr7O6iwM83fFxx6kGzy0md1gZiWc2xY7RIaUWJxB0P0WHomkKEEqd1dbMIjESwNmG7X7zlaFFhdOsWp/U1d9yi9MvDlXNft86Zcf+dRQXGlZLsneqe0WdCsclJxVHr1vpZAzNqiP8YXheFs6TEoa81ElwARDfGPNSOrQ1g2MM7SHGwSxMgRLbhLNynwE3kMkIJtFwaMNhCVGO0Y5BkYmB6m2mw3iLITs8MP7GOd4MEqy0hXNw6lH3DXx93OSnHgod+L+/32CEOwAtK3vX+sMrlDpsJPDJhyEQhRM+AS6YnGdYRaMJ0iIllUaT+SIa3GKMiIp0o/ADR73Ah5/UcEJSwqkXwd0M7Qbkma8zrUc4UZkwRMgntLN1+LE5EjKlZOneDe675PUJoWtZ7qHs//5+oxHu1DJLsjMZWKFUbUdS4kgk1Cb6Bdof2jd7hJwnK8k4zhcp16hlREi1daRHsCcpkuMu9a+69au3L+1xVY/mPULEM3XFfei/3NwcssyS7J3rXVW9bNOoL/ixJ7ugekGDSWZZJif7JaGP6YxoFY0SDR3Sy1+zd5f4+b3Kpz9cSHe2Lzfzk99vNsL9/0v2jCHH1hxQNq1asRNMs4OTDO4JeKtjIwcOlIzHAa7F6CK6cjQAB7jNsDgQQ3tB98Inmel3PI1xXmAqnjxCVOBvg7kdTc8EPhmH3KLSEQOVjg4O4YGNdd68eez7h/woKObQ5xd8WuKcjoWUeXsM6SnvntTktqIuvMaKynYXB0scHreklixNW9ewYnfPrOz29MePxZ1CgDM6HjKIcvQ6kpTd7pCnlD8KEwqSUaDZHIrpHnQ8szmsn0C4M0zkK2ucKXBodE5SsNg4hV1oIEWjjAsNPt+4gjBqJHJ2FitS+27HXYzjw5I9qVTZtBo4xjgi2U0aGIpgKWf0wj5NeK7zyEwp4NOCgzv1xyxIpRax5RlDZKbA6KUKemoQsWU+9nWWEBjlgT/cW2+9xTGDbEhyR8Q9Gp0YcI8hL6iuONLQgLKfV6HuLsWAZr/Ez5LdpBMarOOhZaSxs6ecyUcWAeMUGOB6aA8TfIw0wd+J0SnOXQxHgwP4/MxhJfE/TzAHC7yzT83IlhUgOHcGLBFhFRcOYwzyrXWlYd6XbT3NxvwBg7cwL496MHLNt5LdwMHsB2FUQsAGA2gU7DW4RLL2qUgSnEuIEJsi/oFR51+MBBxpGP/PMcaHkt08EZYjINMvBzwgS63oTewvzAoIUNBXWlSsy+yLUFAA/593ULKbh8UwhJMCDgWs7mP/U47xIqanZts31DaL2diukT6Fk9g6WUAYwu+A6sAl7EqHFQ+THPFjoWM3TyoLnjZoDM4gzFiiygG2eXwfqJhUQJwdECTG68akyrd/LcnereG1jTxxjAlGET/CHb1Co0whYLcTCgGlkD1PkIbYUVAwdLRs4kuJxGSOIscejO81JQnFyYIJfDx4DyLb0eAcj6xklIlAx5rCgB7ZSmvOEJNI2PmIgSM7IiFzg1G6cSY5KaVMao2jqp52cpPba0dsZQ9IIc5CPBoNGYtO+Ikqx0pTZDQrUWgTqcMQ4CR7TSLomZfgr2ViBBHjGcugy6iGgRCK31gyqOeodoZSvCnD3JQKCXwG6wTAqYamEynPxBlZUAxze8CDhPiKx2el0o3b1h7Upf7VvrKy29OMdGBUiTXCLtyxSTCepEAi6Isk3BlimX21MHjEnHA3Bb5N7Qs6173c8QJvZx7hMY0G82+0MAHCnQlfbAdsC835gga6+d4agxP7SfNSklgU7mnJZaumNz++zgUd6l7mKyu7HTVdA2voERABwh0TIJuGYcGhXyiScKeW0ZRRbWNRuCM+KpRq0LRq7y71rqzmMyu7PdfIF2xJwcKd7SXoX4oh3DFFMS0cs8I9iWUnGeU7HJ8x9Nia50ViZbdD5pgemY0o6LWxTrJ7vRE8gKIdo4KwIJDWCVnF4Ja+Hv1jnGnNVDASCwcH4yJLo4dwxz8Wy72pTZhTeW0IMXPAx9wX6Y9ZkK/Y2umk2IaSHdwRG0VqMwMewYOvOI+VT6tZu1ybKEp285jxI9wpJdgmGQ7SQCO4rSYYXcjwDhmKhwbnKXMY4Nm+3cxoQ4H6z4efkK3UZ85zrSXN0euGFFsWMnD0oHDYb1G1zFF9j3qgUnr9Usn5b+ZtD1zUYyoJW+7wwbLLI7MuxFQbKiEvZoIDk1zURnOSSSWMgtYtEPfWMQdoejS6qbp8nThxov1X6xitYx17c9C8Wl+/OcYU9OAIboou40/ygtKYbzCGUnz4iTDWbK/xAWBOmQPOB/jV4AbAJchBq0jnG7NPTpoCX63sURF6gnnzONQRtkalhaHrKsiXjG6PLKASEcaeNYzWqF+kk6zhY08w+UVuMki2Bsb2X/12XDIptVWNs09oMNK3kt1OjPrFBBfkqQ5gz5cwYcg1wpCn1DKTs1YtIzYuDKhNBCbLKAlUQFctEfZnieS4cZWex9YaWCk9w1eOMQU9EdkBXjKCSlRQlpkKRRWzrEvERqNqZWXwtZQEwljdVkF398957FBsqNC78R0OSnbr6bBRMm2OfAc1TCjJ5ickNbZwGiXKvHUSA6h1IWqKj/UVMxNGOiubWEnIx/rVOmCEYB3HykGFtDrnH/tGvQrtor78I36Eu8l7Gs18J6k5b7eN0QQwBDSXUNX5mGNKm6+qceV0L1wdzHqAgMpTkHYMCGZ9Rfdbx/45aFS5u38SEyIldPxM+zARxHJq7BzMF/GmRnt4dAMTSrgwMXziGHsGY1SKOiLjzz//ZLqDeUx6Kd4mxkjMupDBKnPNOCwxv+HIi8OsmF068KbAO5J4yNP3MOilxSAvyDWUQUDM/IrJAO87Ojwm8djwisBciP8Yy+LJcSQgBiprq2Mup9fE2QanUvpL5qnMsDkgWl99LZmU3KBSqBX2vkoti+axSjDjj/cjaoPprIDkMcoiX5hZJSSTir1796aukQuY6qlK1DK6ErIMQ7t1IfWOSX88Cpg/ZMlQcDGwQvrnoF7FDv5JTOiUUF8wk0OYJhE7Gu2Y1V+bC2k8qWJURrw7yDIMcLz6lGxiySP5hU8m1ZCqRC3DXdO6F83m888/j7mXeTPrpM8PWApZv2JnVxNJA8Un4BZHJFK466wQV3wCYoiPr5V41VV6qDXfnj1mfPL1DJ9uJAKOEEAu4M1F+8jqH2aBWc1DX2WPGQmO2wz74iM4UIGs/sG5Aq8whP4NN9yAGygrN+iEOMnciLF2IAEdf3GYPUkJfsw0HfKOsRNrtphKRgTYzU7AQTfwQm/cPZniY9UNAXgNM140KEJWfuN7ZnZGI++s16ojAfmKwwwufHij+V+1x1YZYMQLdjwB4MyACo9bHADsK+3QiAhEXPgIRoViHp9g1EQqFK+W4xUfnMSgSyXlr9E3xIknAPGQrVgrY0K1x1auUdFYMQVY8+ZEVq+xZ479Edh/kIVnzI2YlZG8hJFKhzMGM+S8DRSDHU7VeEnxKkD8YM2FmOF5Kwt1Da9Oe1Q6FoGYICDhHhPZpETGOQF6HbQa/Qq9C4sxWKuKqrBc242NllX/rMRA/6EIWdSLvscrl6U/9Em8vgD5ju5H39PD0W9hhcKDkK4ONe+rF4fFTUZiVcIljHEUVj2EOK87QB/Yn458YQCGUxlrWBmYsTkaYcwCG6QeQy/UBpHgbGNNBi5cuBCpgUGXqwL8Z+wx67h4BBgIoe3IF8a0bKBBvUDksYOtFRvDYLYoYG0JL37H8XLOnDnmjX5oR8bVDKjwj2cqktzEoMsgmQsZkrFMiLUNVECWP1lR6cApAtQRZhSxVgCcOSteYc5Xe+RssYCfDJnF5hOsQ+vQoQMNKTWId3xyhjYQMzxZRqabqzhmpM36BFw3rTGzPUIdi4DPCcTPm1N9DlrJE4EQBJAUxn0QOytTvSh15KAVHhW+fPly/qIM0HNIB2bq6X4Ig+cMlj+zztjuD8bMPu8rYP0cc/3F3mPLSoAOgglghTVZhpMMJkD8KyxlYAKjIbDsov8YUGGg7datGx4yyHR8KvDQwKfCTChjszeWdTKUFfPYerHKF9VRLTh5OpMvAZjjqkuV4Z2OGGWZLbEHw+mZM6yhJ3fIXyoOTheIcobHeACjCLmQ8wyzTdaTv/g1MQbDqZcIqYD22HTsCAGrorHankpkLXC0IkfQUwfR6JxhMxNyjbf/MDbGGQb3GFQ7502mm0sYg6H+yX08Eq1IdCACMURADU0MZZaSGv8E6Ptx5UTMoQl4WibizYe1QfzEeU5ygFmd8yyOxNREn4TUCECDjZAOD7cZLPfI94Bf9dVZAnhQIL4ZbployRoOyDJkImtpjC43a+aYLWFWBG0RLPK4Cl2InzQeUOh7Z1Oo2AIIUIPYSYNaZs81vlJxGBvzK+HJPjQ6bhVUNPKR3AmIhK+MytD0+FhjBuba4AA64yABtojAfkGWUadMLSNy6pTVAPIT81dUvaysLH4ia4LvzviZyS7+8iIRK/eDg+mMCPiWgIS7b7NGCUsgAnRC2GtNf4N5DzmODwbaDvHNmlR6F/btAQfWJn5iQh+rLTY/tktC53EGlwyMhXRgBMZwS0jsSeyhhmGJM8wIBxiDE4ism4+KpDPMWf7LXmbkCIMoJAXuE0hAsoaMYwafrGG/IDa2Q/wh2Xm5BDY/1keacRe5aSQIeY3nBqodhxmccJRlbmQd+UKu8cElnbWM5BrYyTVoU1OQ7Kh5cgSTLWHQduQs+Yi/2apVq8gX6hofctPkDnmNhxtTWwhKfGzwj7fUpBuJT9g4YU5FI1/IAiY3aBgZL5EL5BFMgE+FIvtoP9lunDkTzjCWZtDFegbGwGQWOp4BswGIewxO8xjjcUokfMJS1YPHLgH5uMdu3inl8UMAWx1bg7MxAi4xOGWiJ+hXkAXM/7I8DqdndDxz8czLI/4QH3Q8eMCzUQmCHrsRXp7IfdxsiIQ9dOGCiGSOmKui9eKw+Mmbgp+ELVBRdawo4IMDNBZZZuox+LGVO5qAXflZTMwBggMxwaCLN5QhDdl8Gh8nfNlxcEJ5cGzWzKFFkPX4ZrApLd7w7A6OxA82zBecHP1SOAE8kXjnCwMqNhth4ISzBBUKzoyUGDXh0Y5rGRKc/Z1wSyNbCUMtY+jFMctLuJxsQgJisuVmiH4uJ9PZyoksY6cg9KK1YqHw1ChEeAQwPbD1GSqcARXbunMRbktMcZAjI0aMYHNx1hOzVoEqQ+bCn/0HGYCxBQ25zIph8hR3GjaWoZJyLQdUq9tuu+3rr79mW0MiMY5P4aVFoUQg+gRkcY9+HigFCU6A7h+RjexmtRy7HNCvsDaOM+ywidMzUo9dIHn5BRZ0VB19GAoDrYA6ZEIfzcH7s3DDYOkqW1tAEqlBP8QcMX/NezTwqGb/E83jO1jMAM4yR3Yd4fVkmNLZPQb1wEl0PMeY8XCMNu9DIPtGjx6NpENGoC34ishDLmAvZLjFEkmUH0IQ0Y9Z18h0Xl5IGUBKmskTB5OdyFGZWoZPBUKNARWjXxYvsk0qdllWFSPEcXdBl/OWaLKJHGQLIHaPYZTFGd6rzdpTKpR5LTyrV8kpvpLFRqaj8hmnMTyzL01JZNpOPTv1ggUkVBMqBa8lZgrRbArOIm/yzox4OUadM7IiQ9n5kYbRLDmlFaXSsXUM58lcs6QES4fZRwhlz5p+LB1E61RqFY8IeESAqT1XP0xRoT9C3KLQACGujemf2BiEle+RPwLrFLEcRB5PzMUQulyF+ThUszBDuhcMDYEU4C/Twdj2Am6EdZApYHOSY+NfERyGC5kRDjjvq6/xVOChTUbgbos1nRFRAGfOW1nGTzRx9q8mMFnJtfwNuNZXX51qnImH5RbRfTQyhVwjI/iQHqpbQHrIR6sGccxoioGTPQwxkOlWGPtPvjp2qkcgy3AQiu6jkQswJ7PYbis4yzhpJY/c4WtwhSK/ghtV6yqfHMDZkQqCEiD3ffJQXiajqGXeD2U7Ej5ylfFogKTbiEBBBLDeYW7nV8yxwWFwmMG2Z85zjIEw3zCYe4PP64xLBCzazIQE3wLjLh/rfL7vJiMr873WukoHzhIgR6xcsyqU/RbGImvOcBy8FJUY8q199kh07CwBKxfs2+1bt7CfJHfsX60wLGDgY33VgQjEAQG5ysRBJuoRREAEREAEREAEREAE4p+AhHv857GeUAREQAREQAREQAREIA4ISLjHQSbqEURABERABERABERABOKfgIR7/OexnlAEREAEREAEREAERCAOCEi4x0Em6hFEQAREQAREQAREQATin4CEe/znsZ5QBERABERABERABEQgDghIuMdBJuoRREAEREAEREAEREAE4p9A9IU7+xnz7kDeIxj/sG1PyEtA+Jbvvt22UGEdsjkxsfH6ibBCx0sgCgzFJvKn4cWHvI0l8ngUQ2gCKvCh+fjwV94Pwutv8t2EvqippZGnmSK2ol6o8EUlsHv3broV+570RY3BCk8DS2y80sg6owM3CEAYzo50Z+Q7uU9sbqTTt3E6WOZ9+4wBCYu+cOe9Cby2feHChQEpi++vvCmNB+T1XZE/ZtOmTTdu3JhodXX27NmdO3eOnF6bNm0WLFgQeTyKITQBFfjQfHz4K7aAOXPmmLfHR5g8GnmaqcWLF0cYjy4vlACtWcOGDR0ZbtEvL1++nNfHFnpTBYiEAEaNn376yZHujOFxrVq1yLVI0hNz1zpY5mPl2aMv3DHG1KtXb82aNbGCzJF0IrWJhzoWeWxEkpmZyYudI48qhmJgpEe/EnmCGTutXr068ngUQ2gCKvCh+fjwV94wj9Ru3bp15Gmjka9du/a6CtpRwAAAG99JREFUdesij0oxhCaAaKNHsN4RGzpw6F/r1q37559/UgxCB9OvERJgaESuOdKd8UrgypUrm8Y2wlTF0OUOlvlYeeroC3eK2iWXXPLMM88klLcM7hl0ZvhpRF5Q+vfvP3fu3CVLliSUtwwjPcZ7kdMbMWLE448/Lm+ZyEmGjkEFPjQfH/6KqwxSu379+pGnjUaeZuqFF16Qt0zkMEPHgGhDujnihEm//N5771EG5C0TmnmEvzI0ItcYJkUYD5cTCZb7t956K6Fm4B0s85FngTcxRF+4M4vasmXLbdu2ffrpp948c9TvgohhcofZTPw0Ik8Mc9BEMn36dBhGHltMxIBqnzlz5nnnnRd5askC5is++uijyKNSDAURUIEviIxvz6PVVqxY8d133w0ePDjyRNLIN2/efNOmTV988UXksSmGggigYDDidOrUyZG5XGzAeHF8/vnn+/btK+iOOh8hAcxty5YtW7RoESPbCKPicmZazBwyrqSRxxYTMThb5mPikUlk9IU7icA8cMMNN7z++uuJMJeKiEEmYm7v2bOnU6Vk2LBhWNwZDJglgE5F6894UO0PPPDAyJEjHbEq8Yx33333K6+8Ypyw/fnIMZ0qCvykSZNU4GMoE1HtiAlmokaNGuVUspldvOyyy7AFJkIj7xS0IsWDgnnuued69eqFj3uRLgwRmAnJqVOnJtp0bgggzv6Eav/111/pfeDsVMzk/mmnnUaTS3lwKk7fxuNGmfftw9oT5gvhnpaWdsEFF9SsWfPRRx+N77lUhPWUKVPefPNNHtYRc7vJSyoqsREtrUB8O8zgTzVmzJj58+cPGDCAYmMvysU+ZtiDleKee+5BYhY7El2YLwFT4N9++20V+Hz5+PAkHjKbN2++//77f//9d0fM7eYZsQUOHDiwfPnycd/IRyVPcY3AE2natGndunVzxNxungIzcIsWLcaNG7d27Vo5zDibs/CE6oMPPogpyhFzu0keuX/WWWex5o3yEN8OMy6VeWdz2aXYfCHceTacILHH0O7EsUkGXThjxgxmHvFCO/vss53NUWr+1q1b0e4///xzvNrdaeCYlvnxxx+ffPLJMmXKOAgQgwe+Rq+++qrs7g5StQo8c/cq8A6CdS8q4yEzYcIE/GRYd+TsjdjwbtCgQfHdyDtLLMzYsDtiYf3mm2+GDx+Ozg7zqjCD4enOGuXXXnuNgVx8W4XCBOJIMEjCc+LEiQySH374YUfitCLB0713796YCOPY7u5qmbdI+vYgGT8BVxPHyC+cKdeSJUsyUqTbQJmxVqNOnToMpxzZ2dTVpwszcpT0+vXrqUg0fzSsV155pYNGEZOGKkc+n332GdKzRo0atAt4kjiym2+Yz+hqMAztS5cuffHFFz/88EOKE0IQr1kH70h2MJXPRDM5xVQjitORdcMOpjC2olKBj638MqllsQdTdv/5z38YxPLXkf1k7Byos0y84DQVl428/Uk9O6aXXLlyJTNa77777qWXXnrGGWc4sp+MPf3Vq1dntSsmIQxDGRkZe/fu5Rb01/YwOg6fACJn+/bt2NfGjx9vjFCO7CdjTwBz0WgAnBcQ7uRUtWrVaJAdLxj2O3p57EiZR0VcfPHFsdvLJzEx6ip0JBd+CGE6IbAvEo7av/zyC1NISM++ffs6stS6wAdMKlEij7dLuNgGMW7h2RkdslErf7G149biuGq3HpB9kQDIXxrZLl26tGrViqkM69dYPKD88GHHRlajYmXHpapdu3bOqnYLC+j4MPLhjliIKbfWTzoIk4AKfJigfBWMAk8zhV87VluE2pAhQxxX7dbzet3IWzeOrwPkC7nGggFWox44cADJzo777okzjO60jQwSEJ2nnHJKs2bNXGqE4yuX/vI0aC1qGQCpaCz7xjqJK5rjqt26JSWELGPZK0ZDrHjY4N0THtZNXT1wsMxjnsP9IXa7eH8Jd5PrZM8bb7xhmglH3kpQYGFKyT5Yelvynlol8pw039pvR0VlZQ/VBiHoqmS333Ty5MksKgcgG87E7pjSPBHuMRgnGIQMHTrUPclup8dSIdDxcXD1sD3++D5WgY/F/GWwSoHv06ePq5LdTsa7Rt5+1zg6xgyEJqOFP//8812V7HZm7A6J4qSoHH/88U7tDWCPP76PsUUy8uFz8sknuyrZ7RgpJ+QaWYYtz8FldfZbeHbsYJmXcC8k17AKhG9xD4iLxp0yF3DSqa+H8nKzkhZmlp1WZceZlVOaORVtQDyMca1PwE8efEXBU9Y9uJF7t8Dyh2nH/HXvLvnGjILP97xOhiBglXYOQgRz6ac4KPAukQkdrenRo9Kvu9rIh37qmP4VyY593fz1/kHol8k47+8b03fEgwX7uvnr/YOgBGgevb+vg3d0sMxLuBeSL5EI90KijuznHfvXf71y7IKNb3ase0WfJncnl3Rmi5LIEqWrRUAEREAEREAEREAE3CIQ68LdRfdug5ylSKwxRb67lQPFihdz++bdixdtmbT3wPZl277YvOe3YkWji0RABERABERABERABGKDAMt2Y32BtevCHT8HpocWLlzoqyzdlbNp8eaPd2avY3Vq5r5VCza+ffBQjq9SqMSIgAiIgAiIgAiIgAg4SICV1njdOPUeGAcTFn5Urgv38JPiWchDeQeMud3cUUZ3z8jrRiIgAiIgAiIgAiIQLQLG4o4zSLQSEPl9XRfuWNzZEoRNTiJPq1MxZGavmbtu/BFzu4kyb+vepd+seSrnoFbbOMVY8YiACIiACIiACIiAvwjI4l54fvAOILZ09I9wP3Bw79qs71du/8qe9P25u9ZkzVmVOcd+UsciIAIiIAIiIAIiIAJxQ4BXH7Rt2zam9zN13eLOi7vq16/P28LYkNsPGb8z58+Fm9/HPSYgMVn71/yy6R0Z3QOw6KsIiIAIiIAIiIAIxAEB3lnGayvat28v4R4qN41w59VuvFMpVDhPfsvX3G7u/D+j+7eeJEQ3EQEREAEREAEREAER8I4A5nZeWBHrb5R33eJOhuDm7hNvmYLM7abUZGVjdH9XRnfv6pDuJAIiIAIiIAIiIAKeEODdYbzPMabN7XDyQriz7Q4v+I26twx7t+/evyXn4K66FTvUKd+mfKk6ppyUTEqpUDqDk9XLHZ17KDtrH3tE6iMCIiACIiACIiACIhAnBDC3Z2dnd+rUKdaFe1JeXp4HeQKsBx98kOmJUaNGeXC7fG+BcN+RvWHj7sM7ymfnZi3e8t9fNr7NcXpKpRbV+x1XayDHqcnp1dObVUyvm28MOikCIiACIiACIiACIhBzBM4///wTTjjhoosu4gVMMZd4e4JT7F/cO8boPmDAgJEjR3Y/8nHvRiFixrJeOb0+/wizO2ez9bbU5JJp1cs0P7r66SGu1U8iIAIiIAIiIAIiIAKxSCBuzO3A98JV5vBtSpZs1KhRhw4dHnjgAZ9sLxOLJU9pFgEREAEREAEREAERCJ8Am8k8/PDDF198catWrdijPPwL/RnSI+HOw2N0HzRokE+2l/FnZihVIiACIiACIiACIiACDhJgTeqePXviwLvdMPFOuGN0b9as2c0337xixQo/bA3pYJlQVCIgAiIgAiIgAiIgAn4jgJPM8uXLhw8fXqlSJb+lrXjp8XTKgBmKk046ieWwjz32GG9lwt29eInWVSIgAiIgAiIgAiIgAiIQggCq/euvvz7jjDN69uwZ65vJWI/pqXDnrka7r127dsKECXyVdrdyQgciIAIiIAIiIAIiIAKOEEC1v/7666eddlo8qXbIeC3cuWVqairb8eA589VXX7FQlS3eHckhRSICIiACIiACIiACIiAC+LUvWbKE96T26tUrbmztJlujINy5Mdp96NCh06dPf/PNN1euXMlgSKZ3VTMREAEREAEREAEREIFICLCHzOzZs/GQ6devX+/eveNMtUMmOsL98I1TUhgG8TrVMWPGgPiOO+6Qdo+kpOpaERABERABERABEUhYAkaysxQ1Nzf3nHPO6dGjR/ypdjI3asL98L1TUhgM8XfWrFlTp05leHTyySd37tw5YcucHlwEREAEREAEREAERKBIBDZu3IiG5JOdnd2lSxf8OJo0aYK8LFIksRI4yk9ltDujohkzZiDfJ0+ejIJv+L9P3bp1Y4Wj0ikCIiACIiACIiACIuAZAfQ6K1CzsrI4wNBeu3ZtfDfi1dBuUU1ic0brS3QPmNqYNm0aeWAlCVmPdjd/nU3bvtzMXzd9+OOG14g2PbVyy+rnHJ8xxNlbKDYREAEREAEREAEREAEHCaDRMasToTlYsGBBo0aNWrRo0bRp027dusWlb0wAPR8Jd5My5PuqVatWr17NX9yVOGnp+ICkR/I15+CetTu/+2P7TCJJSy5br0LHRlVOjCRCXSsCIiACIiACIiACIuA2gaSkJHOLWrVqodTZOqZ58+aJINnNU0fZVSY4d7GvM2ziw0+IeLS7+RscMpIzWNwXbdyUu/FwHOmpaS2rN2pXp2ckEepaERABERABERABERABVwkYsc4trANXb+fDyH0n3O2MEPG4u3PG6Hj7TxEe787ZXHLND1tXHo6mXFpqm3qNezbpGWGculwEREAEREAEREAEREAE3CNQ0r2oFbMIiIAIiIAIiIAIiIAIiIBTBCTcnSKpeERABERABERABERABETARQIS7i7CVdQiIAIiIAIiIAIiIAIi4BQBCXenSCoeERABERABERABERABEXCRgIS7i3AVtQiIgAiIgAiIgAiIgAg4RUDC3SmSikcEREAEREAEREAEREAEXCQg4e4iXEUtAiIgAiIgAiIgAiIgAk4RkHB3iqTiEQEREAEREAEREAEREAEXCUi4uwhXUYuACIiACIiACIiACIiAUwQk3J0iqXhEQAREQAREQAREQAREwEUCEu4uwlXUIiACIiACIiACIiACIuAUAQl3p0gqHhEQAREQAREQAREQARFwkYCEu4twFbUIiIAIiIAIiIAIiIAIOEVAwt0pkopHBERABERABERABERABFwkIOHuIlxFLQIiIAIiIAIiIAIiIAJOEZBwd4qk4hEBERABERABERABERABFwlIuLsIV1GLgAiIgAiIgAiIgAiIgFMEJNydIql4REAEREAEREAEREAERMBFAhLuLsJV1CIgAiIgAiIgAiIgAiLgFIEUpyKy4sk5uOeVH/tZX/15cDAvJyt7vT/TplSJgAiIgAiIgAiIgAiIQDAB54X7obzcFZnTg++kMyIgAiIgAiIgAiIgAiIgAsUmIFeZYqPThSIgAiIgAiIgAiIgAiLgHQEJd+9Y604iIAIiIAIiIAIiIAIiUGwCSXl5ecW+ON8LcZVZmTkr35/8eTIlKa1C6Ywq6Q39mTylSgREQAREQAREQAREQAQg4LxwF1YREAEREAEREAEREAEREAHHCchVxnGkilAEREAEREAEREAEREAEnCcg4e48U8UoAiIgAiIgAiIgAiIgAo4TkHB3HKkiFAEREAEREAEREAEREAHnCUi4O89UMYqACIiACIiACIiACIiA4wQk3B1HqghFQAREQAREQAREQAREwHkCHgn33NzcyZMn5+TkmCc4ePDgrFmzpkyZUtADEXL+/PkzZswoKIDOi4AIiIAIiIAIiIAIiEBCEfBIuO/fv/+JJ554/PHHDdwDBw68++67r7zySkGsd+/e/eKLLz711FMFBdB5ERABERABERABERABEUgoAinePC2vedq8eTPavWvXrt27d+frnj17du3aZb97dnZ26dKlzZlKlSr961//Qu5bATguVaqU9ZUYsMrbz1g/6UAEREAEREAEREAEREAE4o+ARxZ3Aw6xfsMNN2zdutXOES+a999/v3PnzkcffXSfPn0+++yzHTt23HXXXf369Rs/fvy+ffvefPPNE0888bjjjhs2bNi6detQ8Hjd9OjRo0WLFgwDnn32WXtsOhYBERABERABERABERCBuCTgnXBPS0s788wzsZHfcsst2MsNzUOHDv38888jRoxo0KDBgw8+WLZs2VGjRq1fv/6UU06pUKHChg0bMjMzx44dW7Fixbvvvrthw4YcLFy4cOTIkY0aNXrggQfat2+PFX/BggVxmTd6KBEQAREQAREQAREQARGwCHgn3LllzZo1x4wZM3Xq1JdeesmkAGf3SZMm4dGOakfW33TTTYh11qQ2b968Ro0a6PukpKSSJUuuXLmycePG6HXOfPzxx6j53r17lytXrnXr1gQgBut5dCACIiACIiACIiACIiACcUnAU+GenJzcrl2766+/fvTo0YsWLQIoFvc1a9ZgSsfijoN7/fr1q1WrxhlC8iFA1apV0fSVK1ceNGjQo48+mpWVtXTpUrzhke+vvfYa+9Ig6MuXLx+XeaOHEgEREAEREAEREAEREAGLgEeLU637paenDxkyZPr06djdsZpjL8clZtOmTWalKd7t27dvr1KlihUeB5tevXodddRRH3zwwbhx4zIyMhDxKPV7770XTW+CEacVXgciIAIiIAIiIAIiIAIiEJcEPLW4G4IIbtzT8YTha0pKCgtScX155JFHsMHjsM4ZlLrFGi+aCRMmLFu2DOcZDO2ExP2dRa4vv/wyTjU//vgjC1WR/lZ4HYiACIiACIiACIiACIhAXBLwzuKOq7ohyAEq/KGHHsLXBZnes2fPG2+88ekjH0zpd95557HHHrtz506M8YRHrH/yySc//PADpnd2nhk8eDALWAnPhjMTJ07E1n7CCScMHDjQ2kcyLjNJDyUCIiACIiACIiACIiACSdYGL66y4C4bN25EXuPoYm7EPo/sHtO0aVO+ItO3bduG/zoO7vjJIM3xfcdnhqswz2/ZsoUABGNzd74i6LG4s6ck1ndiIzBXGZXv6iMochEQAREQAREQAREQARGIIgGPhHu+T4g6t8zwBAj4GnAJIj5AnYcOH3C5voqACIiACIiACIiACIhATBOIpnCPaXBKvAiIgAiIgAiIgAiIgAh4SSAKi1O9fDzdSwREQAREQAREQAREQATig4CEe3zko55CBERABERABERABEQgzglIuMd5BuvxREAEREAEREAEREAE4oOAhHt85KOeQgREQAREQAREQAREIM4JSLjHeQbr8URABERABERABERABOKDgHfCff/+/cHIOFnUjeQPHDiQm5ubb1RsEBl8XmdEQAREQAREQAREQAREIA4IuCvcUdJz5sxp27bt5Zdfzt927drxwlRkN2J9yZIlf//734855pg2bdrcddddvGKJn84++2x+Rc1PmTJl3Lhx8OX8mDFjLrzwQqJaunTp0KFDW7ZsySUjRozYvHnz7t27CXbqqadee+21HTt2vPfee+MgS/QIIiACIiACIiACIiACIhBMwF3hjkDfu3cvGr1evXqoamT6o48++v777yO4r7nmmtWrV995551nnXXWyy+//OKLLyLH58+f/9NPP+3atYuvEydO5O2qhPzoo4+aNWvGi1RvvfXWlStX3nTTTRdddNHs2bMnTJiAmt+0adPcuXN37NhB/P379w9+Qp0RAREQAREQAREQAREQgTggkOLBM6Smpp577rlNmzbt0KEDgnvSpEkNGzb8/vvvx44dO3DgQDS3OYkcr1q16meffXb66ad//vnnFStW/PjjjytUqLBhw4a+ffv+9ttvM2bMwOJes2bNsmXL1q1b94MPPhg2bBhjg/Lly6PpGzdunJyc7MHj6BYiIAIiIAIiIAIiIAIi4D0BL4Q7T4XULl26dPXq1Vu3bv3HH3+sXbsWY3mnTp3S09OTkpLwfvnvf/9bpkyZHj16YI/np+bNm3ft2vX5558/9thjjzrqqBYtWnz44YdY3xcsWLBu3TrEek5OToMGDTgoWbIkUh6TvFS796VHdxQBERABERABERABEfCMgEfC3TzPwYMHMZwjyqtVq4Y6R74ff/zxnFy+fDm29rS0NHzcn3766czMzNGjRyPWe/XqRfibb765VKlSXIKyHzRo0ODBg01sKH4c4vmLZJdq96zE6EYiIAIiIAIiIAIiIAJRIeCFcEeaz5s3DzmO2zp+L6NGjcLEXqdOneeee46/+LXzueqqq7Cdo+kxyQPinHPOyc7O7tOnDz/hBI+mP/roo3GPwa+9fv36+M/g186KVQR9VKjppiIgAiIgAiIgAiIgAiLgMQEvhDtrTNn1hb1i8JYZPnw4JnOE+DPPPHPdddeddNJJHPfr149dYnhyvNXPP/98jPEcIMqvuOIKTuISw9+MjIzHHnvsn//8J+7y/NSqVasTTzwRLxp+wujOX31EQAREQAREQAREQAREII4JJOEm7t7jYWv/+uuvcYCZNWsWWrxKlSo4tSPfuSM/4bOOGR4HmHLlyvGXkySGLWX4y8pUvuLIzmYytWrVMinkkj179mRlZbHaldiICg8ZDPPs7M5XE0Z/RUAEREAEREAEREAERCAuCXhhcccijhBnJxm7aRzNzUmcXuwnOeaMBRpjPAtPra9cwq9odPslDAPMSMAKpgMREAEREAEREAEREAERiD8C7u7jjsJGeeMJg0HdrrYtjvmetH7lIDhA8Bl7eB2LgAiIgAiIgAiIgAiIQFwScNdVBmQ4rOPNYrZ9jEuCeigREAEREAEREAEREAER8ICA68Ldg2fQLURABERABERABERABEQg7gm46yoT9/j0gCIgAiIgAiIgAiIgAiLgDQEJd2846y4iIAIiIAIiIAIiIAIiEBEBCfeI8OliERABERABERABERABEfCGgIS7N5x1FxEQAREQAREQAREQARGIiICEe0T4dLEIiIAIiIAIiIAIiIAIeENAwt0bzrqLCIiACIiACIiACIiACEREQMI9Iny6WAREQAREQAREQAREQAS8ISDh7g1n3UUEREAEREAEREAEREAEIiIg4R4RPl0sAiIgAiIgAiIgAiIgAt4QkHD3hrPuIgIiIAIiIAIiIAIiIAIREZBwjwifLhYBERABERABERABERABbwhIuHvDWXcRAREQAREQAREQAREQgYgISLhHhE8Xi4AIiIAIiIAIiIAIiIA3BCTcveGsu4iACIiACIiACIiACIhARAQk3CPCp4tFQAREQAREQAREQAREwBsCEu7ecNZdREAEREAEREAEREAERCAiAhLuEeHTxSIgAiIgAiIgAiIgAiLgDQEJd2846y4iIAIiIAIiIAIiIAIiEBEBCfeI8OliERABERABERABERABEfCGgIS7N5x1FxEQAREQAREQAREQARGIiICEe0T4dLEIiIAIiIAIiIAIiIAIeENAwt0bzrqLCIiACIiACIiACIiACEREQMI9Iny6WAREQAREQAREQAREQAS8ISDh7g1n3UUEREAEREAEREAEREAEIiIg4R4RPl0sAiIgAiIgAiIgAiIgAt4QkHD3hrPuIgIiIAIiIAIiIAIiIAIREZBwjwifLhYBERABERABERABERABbwhIuHvDWXcRAREQAREQAREQAREQgYgISLhHhE8Xi4AIiIAIiIAIiIAIiIA3BP4feeycwessdS0AAAAASUVORK5CYII=)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf95e03d-882b-4af7-aafa-4b86a3ab334a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T17:59:01.470580Z",
     "iopub.status.busy": "2026-02-13T17:59:01.470442Z",
     "iopub.status.idle": "2026-02-13T17:59:01.478372Z",
     "shell.execute_reply": "2026-02-13T17:59:01.477504Z"
    }
   },
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    \"\"\"\n",
    "    Convolutional residual block with two convolutional layers, ReLU activation,\n",
    "    layer normalization, and a skip connection.\n",
    "\n",
    "    The number of convolutional channels of the input must match num_conv_channels\n",
    "    for the skip connection to work.\n",
    "\n",
    "    Input shape: [batch_size, num_conv_channels, num_ofdm_symbols, num_subcarriers]\n",
    "    Output shape: [batch_size, num_conv_channels, num_ofdm_symbols, num_subcarriers]\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, num_conv_channels: int, num_ofdm_symbols: int, fft_size: int):\n",
    "        super().__init__()\n",
    "        # Layer normalization over the last three dimensions (C, H, W)\n",
    "        self._layer_norm_1 = nn.LayerNorm([num_conv_channels, num_ofdm_symbols, fft_size])\n",
    "        self._conv_1 = nn.Conv2d(\n",
    "            in_channels=num_conv_channels,\n",
    "            out_channels=num_conv_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,  # 'same' padding\n",
    "        )\n",
    "        self._layer_norm_2 = nn.LayerNorm([num_conv_channels, num_ofdm_symbols, fft_size])\n",
    "        self._conv_2 = nn.Conv2d(\n",
    "            in_channels=num_conv_channels,\n",
    "            out_channels=num_conv_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "        )\n",
    "\n",
    "    def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n",
    "        z = self._layer_norm_1(inputs)\n",
    "        z = F.relu(z)\n",
    "        z = self._conv_1(z)\n",
    "        z = self._layer_norm_2(z)\n",
    "        z = F.relu(z)\n",
    "        z = self._conv_2(z)\n",
    "        # Skip connection\n",
    "        z = z + inputs\n",
    "        return z\n",
    "\n",
    "\n",
    "class NeuralReceiver(nn.Module):\n",
    "    \"\"\"\n",
    "    Residual convolutional neural receiver for superimposed pilots.\n",
    "\n",
    "    This neural receiver is fed with the post-DFT received samples, forming a\n",
    "    resource grid of size num_ofdm_symbols x fft_size, and computes LLRs on\n",
    "    the transmitted coded bits.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    y : [batch_size, num_rx, num_rx_antenna, num_ofdm_symbols, num_subcarriers], complex\n",
    "        Received post-DFT samples.\n",
    "    no : [batch_size], float\n",
    "        Noise variance.\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    llr : [batch_size, 1, 1, num_ofdm_symbols, num_subcarriers, num_bits_per_symbol], float\n",
    "        LLRs on the transmitted bits.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        num_conv_channels: int,\n",
    "        num_bits_per_symbol: int,\n",
    "        num_ofdm_symbols: int,\n",
    "        fft_size: int,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self._num_bits_per_symbol = num_bits_per_symbol\n",
    "        self._num_ofdm_symbols = num_ofdm_symbols\n",
    "        self._fft_size = fft_size\n",
    "\n",
    "        # Input convolution: 2*num_rx_antenna + 1 input channels (real, imag, noise)\n",
    "        # For single antenna: 2*1 + 1 = 3 input channels\n",
    "        num_input_channels = 2 * 1 + 1\n",
    "        self._input_conv = nn.Conv2d(\n",
    "            in_channels=num_input_channels,\n",
    "            out_channels=num_conv_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "        )\n",
    "        # Residual blocks\n",
    "        self._res_block_1 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        self._res_block_2 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        self._res_block_3 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        self._res_block_4 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        # Output convolution\n",
    "        self._output_conv = nn.Conv2d(\n",
    "            in_channels=num_conv_channels,\n",
    "            out_channels=num_bits_per_symbol,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "        )\n",
    "\n",
    "    def forward(self, y: torch.Tensor, no: torch.Tensor) -> torch.Tensor:\n",
    "        # y: [batch, num_rx, num_rx_ant, num_ofdm_symbols, num_subcarriers]\n",
    "        # no: [batch]\n",
    "\n",
    "        # Assuming a single receiver, remove the num_rx dimension\n",
    "        y = y.squeeze(1)  # [batch, num_rx_ant, num_ofdm_symbols, num_subcarriers]\n",
    "\n",
    "        # Feeding the noise power in log10 scale helps with the performance\n",
    "        no_log = torch.log10(no)\n",
    "\n",
    "        # Stack real and imaginary components\n",
    "        y_real = y.real  # [batch, num_rx_ant, time, freq]\n",
    "        y_imag = y.imag  # [batch, num_rx_ant, time, freq]\n",
    "\n",
    "        # Reshape noise to [batch, 1, 1, 1] and broadcast\n",
    "        batch_size = y.shape[0]\n",
    "        no_expanded = no_log.view(-1, 1, 1, 1)\n",
    "        no_expanded = no_expanded.expand(batch_size, 1, y.shape[2], y.shape[3])\n",
    "\n",
    "        # Concatenate: [batch, 2*num_rx_ant + 1, time, freq]\n",
    "        z = torch.cat([y_real, y_imag, no_expanded], dim=1)\n",
    "\n",
    "        # Input conv\n",
    "        z = self._input_conv(z)\n",
    "        # Residual blocks\n",
    "        z = self._res_block_1(z)\n",
    "        z = self._res_block_2(z)\n",
    "        z = self._res_block_3(z)\n",
    "        z = self._res_block_4(z)\n",
    "        # Output conv: [batch, num_bits_per_symbol, time, freq]\n",
    "        z = self._output_conv(z)\n",
    "\n",
    "        # Transpose to [batch, time, freq, num_bits_per_symbol]\n",
    "        z = z.permute(0, 2, 3, 1)\n",
    "\n",
    "        # Reshape the input to fit what the resource grid demapper expects\n",
    "        # Add dimensions for num_rx and num_rx_ant: [batch, 1, 1, time, freq, num_bits_per_symbol]\n",
    "        z = insert_dims(z, 2, 1)\n",
    "\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e896684",
   "metadata": {},
   "source": [
    "The following cell defines the end-to-end OFDM system with superimposed pilots.\n",
    " \n",
    "Let $N$ denote the number of subcarriers and $M$ the number of OFDM symbols.\n",
    "The transmitter implementation follows the architecture shown in the figure below. Coded bits are first mapped to a QAM constellation and then arranged into a resource grid $\\mathbf{X}_{\\text{d}}$ that *contains only data symbols (no pilots)*.\n",
    "A separate resource grid that contains fixed QPSK pilot symbols is then *superimposed* onto the data resource grid $\\mathbf{X}_{\\text{d}}$ according to:\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathbf{X} = \\sqrt{1-\\alpha} \\mathbf{X}_{\\text{d}} + \\sqrt{\\alpha}\\mathbf{X}_{\\text{p}}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\boldsymbol{\\alpha} \\in \\mathbb{R}^{N \\times M}$ is a *trainable* power allocation matrix such that $0 \\leq \\alpha_{n,m} \\leq 1$ for all $n = 1,\\ldots,N$ and $m = 1,\\ldots,M$. Each element $\\alpha_{n,m}$ represents the fraction of energy allocated to pilots at resource element $(n,m)$. The square roots and products in the equation are applied element-wise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31153988",
   "metadata": {},
   "source": [
    "![sip notebook.png](attachment:08818eeb-b1c9-4d7c-a82a-0576fcefcbcd.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0a13ad",
   "metadata": {},
   "source": [
    "Training of the end-to-end system is done on the bit-metric decoding (BMD) rate [3] which is computed from the transmitted bits and LLRs:\n",
    "\n",
    "\\begin{equation}\n",
    "R = 1 - \\frac{1}{SNMK} \\sum_{s = 0}^{S-1} \\sum_{n = 0}^{N-1} \\sum_{m = 0}^{M-1} \\sum_{k = 0}^{K-1} \\texttt{BCE} \\left( B_{s,n,m,k}, \\texttt{LLR}_{s,n,m,k} \\right)\n",
    "\\end{equation}\n",
    "\n",
    "where\n",
    "\n",
    "* $S$ is the batch size\n",
    "* $K$ the number of bits per symbol\n",
    "* $B_{s,n,m,k}$ the $k^{th}$ coded bit transmitted on the resource element $(n,m)$ and for the $s^{th}$ batch example\n",
    "* $\\texttt{LLR}_{s,n,m,k}$ the LLR (logit) computed by the neural receiver corresponding to the $k^{th}$ coded bit transmitted on the resource element $(n,m)$ and for the $s^{th}$ batch example\n",
    "* $\\texttt{BCE} \\left( \\cdot, \\cdot \\right)$ the binary cross-entropy in log base 2\n",
    "\n",
    "Because no outer code is required at training, the outer encoder and decoder are not used at training to reduce computational complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5d2605f-3b5f-481e-b5d1-48e638b9afd1",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-02-13T17:59:01.486214Z"
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   "outputs": [],
   "source": [
    "class OFDMSystemNeuralReceiver(Block):\n",
    "    \"\"\"\n",
    "    End-to-end OFDM system with neural receiver and superimposed pilots.\n",
    "\n",
    "    When training=True, returns the BMD rate loss for training.\n",
    "    When training=False, returns (bits, bits_hat) for BER evaluation.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, training: bool = False):\n",
    "        super().__init__()\n",
    "        self._training = training\n",
    "\n",
    "        n = int(RESOURCE_GRID_0P.num_data_symbols * NUM_BITS_PER_SYMBOL)\n",
    "        k = int(n * CODERATE)\n",
    "        self._k = k\n",
    "        self._n = n\n",
    "\n",
    "        # Transmitter components\n",
    "        self._binary_source = BinarySource()\n",
    "        if not training:\n",
    "            self._encoder = LDPC5GEncoder(k, n)\n",
    "        self._mapper = Mapper(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self._rg_mapper = ResourceGridMapper(RESOURCE_GRID_0P)\n",
    "\n",
    "        # Channel\n",
    "        self._channel = OFDMChannel(\n",
    "            CDL_CHANNEL,\n",
    "            RESOURCE_GRID_0P,\n",
    "            add_awgn=True,\n",
    "            normalize_channel=True,\n",
    "            return_channel=False,\n",
    "        )\n",
    "\n",
    "        # Neural receiver\n",
    "        self._neural_receiver = NeuralReceiver(\n",
    "            NUM_CONV_CHANNELS,\n",
    "            NUM_BITS_PER_SYMBOL,\n",
    "            NUM_OFDM_SYMBOLS,\n",
    "            FFT_SIZE,\n",
    "        ).to(device)\n",
    "\n",
    "        # Resource grid demapper\n",
    "        self._rg_demapper = ResourceGridDemapper(RESOURCE_GRID_0P, STREAM_MANAGEMENT)\n",
    "\n",
    "        # Decoder (only for evaluation)\n",
    "        if not training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "        # Generate superimposed pilots\n",
    "        # A fixed resource grid of superimposed pilots is randomly generated\n",
    "        # by sampling a QPSK constellation\n",
    "        pilot_source = QAMSource(num_bits_per_symbol=2, seed=1)\n",
    "        pilots = pilot_source([1, 1, 1, RESOURCE_GRID_0P.num_ofdm_symbols, RESOURCE_GRID_0P.fft_size])\n",
    "        # Center the pilots (remove DC component)\n",
    "        mean = torch.mean(pilots)\n",
    "        pilots = pilots - mean\n",
    "        # Normalize the pilots\n",
    "        norm_factor = torch.sqrt(torch.mean(torch.abs(pilots) ** 2))\n",
    "        pilots = pilots / norm_factor\n",
    "        # Move to device and register as buffer (not trainable, but saved with state_dict)\n",
    "        self.register_buffer(\"_pilots\", pilots.to(device))\n",
    "\n",
    "        # Trainable variable that controls the amount of energy allocated to the pilots\n",
    "        # We train its logits, i.e., the actual scaling is obtained by applying a sigmoid\n",
    "        self._scaling_logit = nn.Parameter(torch.randn(pilots.shape, device=device))\n",
    "\n",
    "    def forward(self, batch_size: int, ebno_db: torch.Tensor):\n",
    "        # Compute noise power\n",
    "        no = ebnodb2no(\n",
    "            ebno_db,\n",
    "            num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "            coderate=CODERATE,\n",
    "            resource_grid=RESOURCE_GRID_0P,\n",
    "        )\n",
    "\n",
    "        # Ensure no has shape [batch_size]\n",
    "        if no.dim() == 0:\n",
    "            no = no.expand(batch_size)\n",
    "\n",
    "        # Transmitter\n",
    "        if self._training:\n",
    "            # Skip outer coding during training\n",
    "            codewords = self._binary_source([batch_size, 1, 1, self._n])\n",
    "        else:\n",
    "            bits = self._binary_source([batch_size, 1, 1, self._k])\n",
    "            codewords = self._encoder(bits)\n",
    "\n",
    "        # Map data to constellation symbols\n",
    "        x = self._mapper(codewords)\n",
    "        # Map symbols to resource grid\n",
    "        x_data = self._rg_mapper(x)\n",
    "\n",
    "        # Add superimposed pilots\n",
    "        scaling = torch.sigmoid(self._scaling_logit)\n",
    "        scaling = torch.complex(scaling, torch.zeros_like(scaling))\n",
    "        x_rg = torch.sqrt(1.0 - scaling) * x_data + torch.sqrt(scaling) * self._pilots\n",
    "\n",
    "        # Channel\n",
    "        no_expanded = expand_to_rank(no, x_rg.ndim)\n",
    "        y = self._channel(x_rg, no_expanded)\n",
    "\n",
    "        # Receiver\n",
    "        llr = self._neural_receiver(y, no)\n",
    "        llr = self._rg_demapper(llr)  # Extract data-carrying resource elements\n",
    "        llr = llr.reshape(batch_size, 1, 1, self._n)\n",
    "\n",
    "        if self._training:\n",
    "            # Compute BCE loss for BMD rate\n",
    "            bce = F.binary_cross_entropy_with_logits(\n",
    "                llr, codewords.float(), reduction=\"none\"\n",
    "            )\n",
    "            bce = bce.mean()\n",
    "            # BMD rate\n",
    "            rate = 1.0 - bce / math.log(2.0)\n",
    "            return rate\n",
    "        else:\n",
    "            # Decode\n",
    "            bits_hat = self._decoder(llr)\n",
    "            return bits, bits_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4982feda-3b93-46dc-a936-bd225d7260dd",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597a8a07",
   "metadata": {},
   "source": [
    "The following cell implements a training loop that performs stochastic gradient descent (SGD) optimization using PyTorch.\n",
    "\n",
    "Each training iteration consists of:\n",
    "- Sampling a batch of signal-to-noise ratios $E_b/N_0$, where $E_b$ is the energy per bit and $N_0$ is the noise variance\n",
    "- Executing a forward pass through the end-to-end system\n",
    "- Computing gradients using PyTorch's autograd and applying them via the Adam optimizer\n",
    "- Periodically displaying the current BMD rate to monitor training progress\n",
    " \n",
    "Upon completion, the trained model weights are saved to a file for later use.\n",
    " \n",
    "**Note:** Training may require significant computational time. For convenience, [pre-trained weights are available for download](https://drive.google.com/file/d/1GAgLXQxpqcVb1skHKLYPsWM4P-N3XqEV/view?usp=sharing). Skip executing the next cell if you prefer to use the pre-trained model instead of training from scratch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c53960db-28ee-4f33-8840-7b1373348162",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-02-13T18:03:23.000125Z"
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   },
   "outputs": [],
   "source": [
    "# Instantiating the end-to-end model for training\n",
    "model = torch.compile(OFDMSystemNeuralReceiver(training=True), mode=\"reduce-overhead\")\n",
    "\n",
    "# Number of iterations used for training\n",
    "NUM_TRAINING_ITERATIONS = 5000\n",
    "\n",
    "# Adam optimizer (SGD variant)\n",
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "\n",
    "# Training loop\n",
    "for i in range(NUM_TRAINING_ITERATIONS):\n",
    "    # Sample a batch of SNRs\n",
    "    ebno_db = torch.empty(BATCH_SIZE, device=device).uniform_(EBN0_DB_MIN, EBN0_DB_MAX)\n",
    "    \n",
    "    # Forward pass\n",
    "    optimizer.zero_grad()\n",
    "    rate = model(BATCH_SIZE, ebno_db)\n",
    "    \n",
    "    # Loss is negative rate (we maximize rate by minimizing negative rate)\n",
    "    loss = -rate\n",
    "    \n",
    "    # Backward pass\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Print progress\n",
    "    if i % 100 == 0:\n",
    "        print(f\"{i}/{NUM_TRAINING_ITERATIONS}  Rate: {rate.item():.2E}\", end=\"\\r\")\n",
    "\n",
    "# Save the weights in a file (use _orig_mod to get unwrapped model from torch.compile)\n",
    "torch.save(model._orig_mod.state_dict(), 'weights-ofdm-sip.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c374949",
   "metadata": {},
   "source": [
    "## Evaluation of the End-to-end System with Superimposed Pilots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc192a74",
   "metadata": {},
   "source": [
    "The next cell implements two baseline systems for comparison: one that assumes perfect knowledge of the channel coefficients, and another that uses least-squares (LS) channel estimation. Both systems employ linear minimum mean square error (LMMSE) equalization with \"app\" (a posteriori probability) demapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22f04eb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T18:03:23.003750Z",
     "iopub.status.busy": "2026-02-13T18:03:23.003449Z",
     "iopub.status.idle": "2026-02-13T18:03:23.009122Z",
     "shell.execute_reply": "2026-02-13T18:03:23.008234Z"
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   },
   "outputs": [],
   "source": [
    "class OFDMSystem(Block):\n",
    "    \"\"\"\n",
    "    Baseline OFDM system with conventional channel estimation.\n",
    "\n",
    "    When perfect_csi=True, uses perfect channel state information.\n",
    "    When perfect_csi=False, uses LS channel estimation.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, perfect_csi: bool = False):\n",
    "        super().__init__()\n",
    "        self._perfect_csi = perfect_csi\n",
    "\n",
    "        n = int(RESOURCE_GRID_2P.num_data_symbols * NUM_BITS_PER_SYMBOL)\n",
    "        k = int(n * CODERATE)\n",
    "        self._k = k\n",
    "\n",
    "        # Transmitter components\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(k, n)\n",
    "        self._mapper = Mapper(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self._rg_mapper = ResourceGridMapper(RESOURCE_GRID_2P)\n",
    "\n",
    "        # Channel\n",
    "        self._channel = OFDMChannel(\n",
    "            CDL_CHANNEL,\n",
    "            RESOURCE_GRID_2P,\n",
    "            add_awgn=True,\n",
    "            normalize_channel=True,\n",
    "            return_channel=True,\n",
    "        )\n",
    "\n",
    "        # Receiver components\n",
    "        if perfect_csi:\n",
    "            self._removed_null_subc = RemoveNulledSubcarriers(RESOURCE_GRID_2P)\n",
    "        else:\n",
    "            self._ls_est = LSChannelEstimator(RESOURCE_GRID_2P, interpolation_type=\"nn\")\n",
    "\n",
    "        self._lmmse_equ = LMMSEEqualizer(RESOURCE_GRID_2P, STREAM_MANAGEMENT)\n",
    "        self._demapper = Demapper(\"app\", \"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size: int, ebno_db: torch.Tensor):\n",
    "        # Compute noise power\n",
    "        no = ebnodb2no(\n",
    "            ebno_db,\n",
    "            num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "            coderate=CODERATE,\n",
    "            resource_grid=RESOURCE_GRID_2P,\n",
    "        )\n",
    "\n",
    "        # Transmitter\n",
    "        bits = self._binary_source([batch_size, 1, RESOURCE_GRID_2P.num_streams_per_tx, self._k])\n",
    "        codewords = self._encoder(bits)\n",
    "        x = self._mapper(codewords)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        # Channel\n",
    "        no_expanded = expand_to_rank(no, x_rg.ndim)\n",
    "        y, h_freq = self._channel(x_rg, no_expanded)\n",
    "\n",
    "        # Receiver\n",
    "        if self._perfect_csi:\n",
    "            h_hat = self._removed_null_subc(h_freq)\n",
    "            err_var = 0.0\n",
    "        else:\n",
    "            h_hat, err_var = self._ls_est(y, no)\n",
    "\n",
    "        x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n",
    "        no_eff_expanded = expand_to_rank(no_eff, x_hat.ndim)\n",
    "        llr = self._demapper(x_hat, no_eff_expanded)\n",
    "        bits_hat = self._decoder(llr)\n",
    "\n",
    "        return bits, bits_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2226b31a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-02-13T18:04:01.006015Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 3.4980e-01 | 1.0000e+00 |       40834 |      116736 |          128 |         128 |         0.3 |reached target block errors\n",
      "    0.526 | 3.3498e-01 | 1.0000e+00 |       39104 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.053 | 3.1812e-01 | 1.0000e+00 |       37136 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.579 | 3.0530e-01 | 1.0000e+00 |       35640 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    2.105 | 2.8666e-01 | 1.0000e+00 |       33463 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    2.632 | 2.7215e-01 | 1.0000e+00 |       31770 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    3.158 | 2.4588e-01 | 1.0000e+00 |       28703 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    3.684 | 2.2557e-01 | 1.0000e+00 |       26332 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    4.211 | 1.9661e-01 | 1.0000e+00 |       22952 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    4.737 | 1.5916e-01 | 9.9219e-01 |       18580 |      116736 |          127 |         128 |         0.0 |reached target block errors\n",
      "    5.263 | 8.3518e-02 | 6.9531e-01 |       19499 |      233472 |          178 |         256 |         0.1 |reached target block errors\n",
      "    5.789 | 4.2863e-02 | 3.3594e-01 |       15011 |      350208 |          129 |         384 |         0.1 |reached target block errors\n",
      "    6.316 | 2.1327e-02 | 1.6562e-01 |       12448 |      583680 |          106 |         640 |         0.1 |reached target block errors\n",
      "    6.842 | 1.4006e-02 | 8.6806e-02 |       14715 |     1050624 |          100 |        1152 |         0.3 |reached target block errors\n",
      "    7.368 | 7.5223e-03 | 5.1758e-02 |       14050 |     1867776 |          106 |        2048 |         0.4 |reached target block errors\n",
      "    7.895 | 6.2090e-03 | 3.5511e-02 |       15946 |     2568192 |          100 |        2816 |         0.6 |reached target block errors\n",
      "    8.421 | 3.2974e-03 | 1.8601e-02 |       16167 |     4902912 |          100 |        5376 |         1.2 |reached target block errors\n",
      "    8.947 | 1.8058e-03 | 1.1660e-02 |       14124 |     7821312 |          100 |        8576 |         1.9 |reached target block errors\n",
      "    9.474 | 1.6657e-03 | 9.6451e-03 |       15750 |     9455616 |          100 |       10368 |         2.3 |reached target block errors\n",
      "     10.0 | 1.3584e-03 | 7.3437e-03 |       15858 |    11673600 |           94 |       12800 |         2.8 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 2.3542e-01 | 1.0000e+00 |       27482 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    0.526 | 2.0970e-01 | 1.0000e+00 |       24480 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.053 | 1.8082e-01 | 1.0000e+00 |       21108 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.579 | 1.4050e-01 | 9.7656e-01 |       16401 |      116736 |          125 |         128 |         0.0 |reached target block errors\n",
      "    2.105 | 6.3134e-02 | 6.9141e-01 |       14740 |      233472 |          177 |         256 |         0.1 |reached target block errors\n",
      "    2.632 | 2.8012e-02 | 2.8385e-01 |        9810 |      350208 |          109 |         384 |         0.1 |reached target block errors\n",
      "    3.158 | 1.7532e-02 | 1.4583e-01 |       12280 |      700416 |          112 |         768 |         0.2 |reached target block errors\n",
      "    3.684 | 8.3485e-03 | 5.7478e-02 |       13644 |     1634304 |          103 |        1792 |         0.4 |reached target block errors\n",
      "    4.211 | 4.9114e-03 | 3.7202e-02 |       12040 |     2451456 |          100 |        2688 |         0.6 |reached target block errors\n",
      "    4.737 | 3.3270e-03 | 2.3208e-02 |       13205 |     3969024 |          101 |        4352 |         0.9 |reached target block errors\n",
      "    5.263 | 1.6077e-03 | 1.0702e-02 |       13700 |     8521728 |          100 |        9344 |         2.0 |reached target block errors\n",
      "    5.789 | 1.0911e-03 | 7.6562e-03 |       12737 |    11673600 |           98 |       12800 |         2.8 |reached max iterations\n",
      "    6.316 | 6.9396e-04 | 4.8437e-03 |        8101 |    11673600 |           62 |       12800 |         2.8 |reached max iterations\n",
      "    6.842 | 5.4559e-04 | 3.5156e-03 |        6369 |    11673600 |           45 |       12800 |         2.8 |reached max iterations\n",
      "    7.368 | 2.4979e-04 | 1.7188e-03 |        2916 |    11673600 |           22 |       12800 |         2.8 |reached max iterations\n",
      "    7.895 | 2.5810e-04 | 1.6406e-03 |        3013 |    11673600 |           21 |       12800 |         2.8 |reached max iterations\n",
      "    8.421 | 1.1282e-04 | 7.0312e-04 |        1317 |    11673600 |            9 |       12800 |         2.8 |reached max iterations\n",
      "    8.947 | 9.9113e-05 | 8.5938e-04 |        1157 |    11673600 |           11 |       12800 |         2.8 |reached max iterations\n",
      "    9.474 | 8.4635e-05 | 4.6875e-04 |         988 |    11673600 |            6 |       12800 |         2.8 |reached max iterations\n",
      "     10.0 | 7.3585e-05 | 5.4688e-04 |         859 |    11673600 |            7 |       12800 |         2.8 |reached max iterations\n"
     ]
    }
   ],
   "source": [
    "ber_plots = PlotBER(\"Advanced neural receiver\")\n",
    "\n",
    "baseline_ls = OFDMSystem(False)\n",
    "ber_plots.simulate(baseline_ls,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occurred\n",
    "                  legend=\"Baseline: LS Estimation\",\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=False);\n",
    "\n",
    "baseline_pcsi = OFDMSystem(True)\n",
    "ber_plots.simulate(baseline_pcsi,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occurred\n",
    "                  legend=\"Baseline: Perfect CSI\",\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de79d49",
   "metadata": {},
   "source": [
    "Next, we instantiate the end-to-end system with the neural receiver and evaluate its performance. We also visualize the learned energy allocation matrix $\\mathbf{\\alpha}$ by comparing it before and after training. The initial matrix represents random initialization, while the trained matrix shows the optimized energy distribution between pilots and data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a0e6e6b-3650-4524-9c83-46c715493e3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T18:04:01.009317Z",
     "iopub.status.busy": "2026-02-13T18:04:01.009187Z",
     "iopub.status.idle": "2026-02-13T18:04:01.410522Z",
     "shell.execute_reply": "2026-02-13T18:04:01.409539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiating the end-to-end model for evaluation\n",
    "model = OFDMSystemNeuralReceiver(training=False)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.imshow(torch.sigmoid(model._scaling_logit[0,0,0]).detach().cpu().numpy())\n",
    "plt.colorbar(label=\"Ratio of energy allocated to pilots\", orientation=\"horizontal\")\n",
    "plt.title(\"Initialization\")\n",
    "\n",
    "# Run one inference to initialize the model\n",
    "model(1, torch.tensor(10.0, device=device))\n",
    "\n",
    "# Load the trained weights (strict=False because training model doesn't have encoder/decoder)\n",
    "model.load_state_dict(torch.load('weights-ofdm-sip.pt', weights_only=True), strict=False)\n",
    "    \n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.imshow(torch.sigmoid(model._scaling_logit[0,0,0]).detach().cpu().numpy())\n",
    "plt.colorbar(label=\"Ratio of energy allocated to pilots\", orientation=\"horizontal\")\n",
    "plt.title(\"Trained\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442fe049",
   "metadata": {},
   "source": [
    "Finally, we evaluate the performance of our end-to-end system with superimposed pilots and the neural receiver and visualize the bit error rate (BER) curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc0e47a5-7d61-4e8b-b077-8fc63a33057f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T18:04:01.412566Z",
     "iopub.status.busy": "2026-02-13T18:04:01.412433Z",
     "iopub.status.idle": "2026-02-13T18:04:42.507957Z",
     "shell.execute_reply": "2026-02-13T18:04:42.507019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 2.4155e-01 | 1.0000e+00 |       32897 |      136192 |          128 |         128 |         0.1 |reached target block errors\n",
      "    0.526 | 2.1634e-01 | 1.0000e+00 |       29464 |      136192 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.053 | 1.9181e-01 | 1.0000e+00 |       26123 |      136192 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.579 | 1.4497e-01 | 9.8438e-01 |       19744 |      136192 |          126 |         128 |         0.0 |reached target block errors\n",
      "    2.105 | 9.6144e-02 | 8.0469e-01 |       13094 |      136192 |          103 |         128 |         0.0 |reached target block errors\n",
      "    2.632 | 4.4697e-02 | 3.8802e-01 |       18262 |      408576 |          149 |         384 |         0.1 |reached target block errors\n",
      "    3.158 | 2.7191e-02 | 2.2461e-01 |       14813 |      544768 |          115 |         512 |         0.2 |reached target block errors\n",
      "    3.684 | 1.6002e-02 | 1.2054e-01 |       15255 |      953344 |          108 |         896 |         0.3 |reached target block errors\n",
      "    4.211 | 1.0647e-02 | 7.6705e-02 |       15950 |     1498112 |          108 |        1408 |         0.5 |reached target block errors\n",
      "    4.737 | 6.2511e-03 | 4.5956e-02 |       14473 |     2315264 |          100 |        2176 |         0.8 |reached target block errors\n",
      "    5.263 | 3.0440e-03 | 2.2991e-02 |       14510 |     4766720 |          103 |        4480 |         1.6 |reached target block errors\n",
      "    5.789 | 2.1036e-03 | 1.6103e-02 |       14038 |     6673408 |          101 |        6272 |         2.2 |reached target block errors\n",
      "    6.316 | 1.1000e-03 | 8.6806e-03 |       13483 |    12257280 |          100 |       11520 |         4.1 |reached target block errors\n",
      "    6.842 | 9.9433e-04 | 7.1094e-03 |       13542 |    13619200 |           91 |       12800 |         4.6 |reached max iterations\n",
      "    7.368 | 6.1964e-04 | 4.6875e-03 |        8439 |    13619200 |           60 |       12800 |         4.6 |reached max iterations\n",
      "    7.895 | 5.1097e-04 | 3.6719e-03 |        6959 |    13619200 |           47 |       12800 |         4.6 |reached max iterations\n",
      "    8.421 | 2.8636e-04 | 2.0313e-03 |        3900 |    13619200 |           26 |       12800 |         4.6 |reached max iterations\n",
      "    8.947 | 2.3731e-04 | 1.7188e-03 |        3232 |    13619200 |           22 |       12800 |         4.6 |reached max iterations\n",
      "    9.474 | 9.4499e-05 | 7.0312e-04 |        1287 |    13619200 |            9 |       12800 |         4.6 |reached max iterations\n",
      "     10.0 | 1.7101e-04 | 1.1719e-03 |        2329 |    13619200 |           15 |       12800 |         4.6 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing and plotting BER\n",
    "ber_plots.simulate(model,\n",
    "                   ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                   batch_size=BATCH_SIZE,\n",
    "                   num_target_block_errors=100,\n",
    "                   legend=\"SIP + Neural receiver\",\n",
    "                   max_mc_iter=100,\n",
    "                   show_fig=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48182e7d",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c51d8f6",
   "metadata": {},
   "source": [
    "[1] F. Ait Aoudia and J. Hoydis, \"End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication,\" in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2021.3101364.\n",
    "\n",
    "[2] M. Honkala, D. Korpi and J. M. J. Huttunen, \"DeepRx: Fully Convolutional Deep Learning Receiver,\" in IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3925-3940, June 2021, doi: 10.1109/TWC.2021.3054520.\n",
    "\n",
    "[3] G. Böcherer, \"Achievable Rates for Probabilistic Shaping\", arXiv:1707.01134, 2017."
   ]
  }
 ],
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