Navigator / docs / intro / intro_triangulation.ipynb
intro_triangulation.ipynb
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Triangulation Using Navigator Module\n",
    "This intro with demonstrate how to use the navigator module to triangulate IGS data or any RinexV3 data, do some basic data analysis of the results, see VDOPS, TDOPS, and GDOPS, and plot the results on a map."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Triangulation Interface\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The triangulation API is designed using and interface design. The main class user needs to know about is the `Triangulate` class in satlib submodule.\n",
    "\n",
    "The `Triangulate` class always works with an interface class. These are hard coded algorathims to perform GNSS triangulation!\n",
    "Currently, two interface are provided: `IterativeTriangulationInterface` which used WLS to perform triangulation and `UnscentedKalmanTriangulationInterface` which uses UKF to perform triangulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by importing the necessary modules and defining the interface object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# These can be imported from top level of satlib\n",
    "from navigator.core import Triangulate\n",
    "from navigator.core import IterativeTriangulationInterface\n",
    "\n",
    "\n",
    "# Create a triangulation object\n",
    "# IterativeTriangulationInterface is a class that implements the WLS algorithm\n",
    "traingulator = Triangulate(interface=IterativeTriangulationInterface())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the data into epoch objects which are the basic unit of data in the navigator module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 2022-11-19 23:59:30: 100%|██████████| 2880/2880 [00:05<00:00, 519.35it/s]\n"
     ]
    }
   ],
   "source": [
    "from navigator.epoch import from_rinex_files\n",
    "from pathlib import Path\n",
    "\n",
    "obsPath = Path(\"./data/JPLM00USA_R_20223230000_01D_30S_MO.crx.gz\")\n",
    "navPath = Path(\"./data/JPLM00USA_R_20223230000_01D_GN.rnx.gz\")\n",
    "\n",
    "\n",
    "epoches = list(from_rinex_files(observation_file=obsPath, navigation_file=navPath))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'from_rinex_files' method is used to load the data into epoch objects. It return a iterator[Epoch] object which can be used to iterate over the data. For small data, it is better to directly convert it to a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Epoch(timestamp=2022-11-19 00:00:00, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:00:30, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:01:00, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:01:30, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:02:00, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:02:30, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:03:00, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:03:30, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:04:00, sv=8),\n",
       " Epoch(timestamp=2022-11-19 00:04:30, sv=9)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epoches[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, the epoch object is sorted by time and contains the range and ephermris data the satellites at that time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the profile of the epoch before triangulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each epoch has a profile which can be customized to apply necessary parameter for triangulation. These parameter can be wheather to apply tropospheric correction, ionospheric correction, and the type of ionospheric correction to apply."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set the dual frequency profile for the epoch object so the triangulation can be performed with dual frequency utilizing the L1 and L2 frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from navigator.epoch import Epoch\n",
    "for epoch in epoches:\n",
    "    epoch.profile = Epoch.DUAL # Set the profile to dual frequency \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Triangulating the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The triangulation is performed quite easily using the 'Triangulate' class. The 'triangulate_time_series' method is used to perform triangulation on continous epoch of data which are sorted by time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Triangulating:   0%|          | 0/2880 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Triangulating:  56%|█████▌    | 1603/2880 [02:54<02:19,  9.14it/s]"
     ]
    }
   ],
   "source": [
    "df = traingulator.triangulate_time_series(epoches=epoches, override=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the true coordinates of the receiver since this data is from IGS station and we know the true coordinates of the receiver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "realCoord = pd.Series(Epoch.IGS_NETWORK.get_xyz(station=\"JPLM00USA\"), index=[\"x\", \"y\", \"z\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this we can caluclate the ENU error of the WLS triangulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enuError = Triangulate.enu_error(\n",
    "    predicted=df,\n",
    "    actual=pd.DataFrame(realCoord.values.reshape(-1,1).repeat(len(df), axis=1).T, columns=realCoord.index),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the distribution of the ENU error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "sns.boxenplot(data=enuError.abs().clip(upper=50) , ax=ax)\n",
    "\n",
    "ax.set_title(\"Error in ENU coordinates\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Save the plot\n",
    "\n",
    "# fig.savefig(\"ENU_error.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "positionError = enuError.apply(np.linalg.norm, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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77+H//b//h7/97W/Yv38/rr766gHcaiIiIiIS5IxQR09YLbcz6kcCgDy3PX47BklkXQM6AvzRRx/V/Hz77bdj4cKFWLNmDY444gh0dnbiH//4B+644w4sXLgQgBI0nX766Vi5ciVqa2sHYKuJiIiISPAH5XK7kFpuZzTZDkhMt2OQRFZmqXWSOjs7AQAlJSUAgNWrVyMUCuHII49UrzNx4kSMGDEi6yApEhn4D6LYBitsCyn4mlgPXxPr4WtiLXw9rGeovyZy2Vy7L4hAKAwAcNnths+JOx48+YLhPnvOhvprYkVWeU0yfXzLBEnRaBS/+93vcNhhh2HKlCkAgKamJrhcLhQXF2uuW1FRgcbGxqzuv66uLmfbeqCstC2k4GtiPXxNrIevibXw9bCeofiahKMxzcjvbfX7sMbZDgCIRUNYuXJl0m327u4BADS3dhj+PpeG4mtidYPlNbFMkHTLLbdg06ZNePrpp/vk/mfNmgWHw9En952pSCSCuro6S2wLKfiaWA9fE+vha2ItfD2sZyi/Jp3+MPCPBvVnd2EJxk0cA7zTjII8r2HVzy77XmDplygoLOyz1omh/JpYlVVeE7Ed6VgiSPr1r3+N999/H3//+98xbNgw9fLKykqEQiF0dHRosknNzc2oqqrK6jEcDodlPiRW2hZS8DWxHr4m1sPXxFr4eljPUHxNgtGQ5mdfMIpoTCmncznshs+Hw54Y6NDXz9dQfE2sbrC8JgM63S4Wi+HXv/413nrrLTzxxBMYPXq05vczZ86Ey+XC4sWL1cu2bt2KPXv2cGgDERER0QAL6NY66gkl1kkyGwFui89ziBn+lsgaBjSTdMstt+DVV1/Ffffdh4KCArXPqKioCF6vF0VFRTj33HNx++23o6SkBIWFhbj11ltx6KGHMkgiIiIiGmD6tY58wYgaJLlNptvZwCiJrG9Ag6RnnnkGAHDRRRdpLr/ttttwzjnnAABuuOEG2O12/PjHP0YwGMTRRx+Nm266qd+3lYiIiIi0eoK6ICkQQSiiRD/OtJkkRklkXQMaJG3YsCHtdTweD2666SYGRkREREQWk5RJ0pTbmWWSFDHGSGRhA9qTRERERESDl1gQ1mlXQp8eqdyOPUk0mDFIIiIiIqJeEUFSWYEbgNKTFI6X25kFSSKXFGMqiSyMQRIRERER9Yoot6uIB0k9oQgCYeUy03I7ZpJoEGCQRERERES94o+PAC+PB0mxGFDf1qO5TI89STQYMEgiIiIiol4R0+3K8hMB0fambgBAdZF3QLaJKBcYJBERERFRrwTCSiYpz+2A16UcVu5o9gEAqos9hrexxevtmEgiK2OQRERERES9IvqP3E478t3KyjLbm1NnktROJdbbkYUxSCIiIiKiXgnGM0kepx15LgeARJ9SjWkmSfk/QySyMgZJRERERNQrIkhSMkkOze9MM0kiSGKURBbGIImIiIiIeiUYXzjW49AGSTYbUFloNt1O9CQxSiLrYpBERERERL0SCMmZJKd6eUWBG06zxWSZSaJBgEESEREREfWKmklyOjSZpFTjv7lOEg0GDJKIiIiIhpjN+ztx/Yt12B1f+LW35J6kPDlIMhnaAHAEOA0OzvRXISIiIqKDyVn3foquQBhbGrvw/PcX9vp+AiaDG8ZXFpjeJpFJYphE1sVMEhEREdEQ0xUIAwDW7ek4oPtR10lyaHuS5owqNb2NzWb6KyLLYJBERERENETl6cZ2Z0tdJ8llh9uZOKycParE9DbqdDsmksjCGCQRERERDVEFngPrvBCDG9wOO3a3JvqbxlWkKLdTF5NllETWxSCJiIiIaIjKcx1YJkk7AjxxX3a7eU0dp9vRYMDBDURERERDiDwwQQQ2oUgUdbvbMXtkifn6RgbUTJLTjmtPnoL2nhAuOXJc6hupmSQi62ImiYiIiGgI6Q5G1H/nx8vtHv14G86571P8bcmOrO5L7UlyOjCiNA8PXTwXR06qzN3GEg0QBklEREREQ0hrd1D9t6iK29jQCQDY0tiV1X0lgqTMDykTgxuYSyLrYpBERERENIS0+ULqv0WQ09ylBE6t0u8yoY4AzyZIYrkdDQIMkoiIiIiGkLaeRCZJDZK6A8rvfEHD25gRt3dn0cekjnRglEQWxiCJiIiIaAiRs0UBXSappTu7TJIY3OBxZZNJipfbZfVIRP2LQRIRERHRECJniwLhCGKxmBokZZNJikZjCEWUUCerTJIot2NPElkYgyQiIiKiIeLVVXtw4ytr1J+D4Sg6A2E1I9SaRZAkbgNk2ZMU/z9DJLIyBklEREREQ8S/V+3V/BwIR9UsEgD4Q1H4QxH9zQyJUj2gl4MbGCWRhTFIIiIiIhoi9MFMMBxFc1dAc1m6bFJDhx+xWEwd2gBkV24nckkx5pLIwhgkEREREQ0R4XgP0SVHjgOgZIOaurRBUWuK4Q0vLN+F+b97B79/fYNabud22tVhDJlgJokGAwZJRERERENEKB7YFHmdAOKZpG5tJinV8IZb/rUWAPDAB1sQiJflebLKIkk9SQySyMIYJBERERENESJIKvDEg6RIFI2d+nI780ySXK4nZ5KykU3WiWigMEgiIiIiGiLCUSV9I4IkANjb5tdcpyVFJknuPRI9SZ5sg6T4/zkCnKyMQRIRERHRECECm0KPQ71sT3uP5jpt3eZBksuZyAKJ+8o+k6T8nyESWRmDJCIiIqIhQpTb5bmcarCyp00JkoYVewEAHf4U5XZSJinQ2yBJTLdjlEQWxiCJiIiIaIgQ5XZup00NePa2K+V2o8vzAChrJZlxOxMZqN5mkogGA76riYiIiIYIEdg47Xa1l8gXVKbUjSxVgqSeFIvJuh2Jcjux6KxHCpwykSi3YyqJrItBEhEREdEQIcrtXA47PC5tcDOqLB9AIvgx4pLK7cQUvOwWkk1guR1ZGYMkIiIioiHCqNwOULI7w0qUnqRUQZI8vbu5KxC/Lw5uoIMPgyQiIiKiISIkl9u5EoeB5fluFMbHgqcqtxPlegDQHJ+Cx8ENdDBikEREREQ0RITimSSXw67JJFUUuuGNB02pBjcEpCCpMZ5JynqdJDUbxSiJrItBEhEREdEQIXqS3E6bpieposADb/znnqB5JkkuxWvsUIIkr6uXgxsYI5GFMUgiIiIiGiJCBtPtACWTlBcPdvxh8yBJziRtaewCAFQVebLaBrXcLqtbEfUvBklEREREQ4RabufUBkmVhYlMkj9FJilg0JM0PD7wIVOJTBLDJLIuBklEREREQ4Q6Atxu0wRJw0u8yHOLTFKKniSDoQ7DirMMkuL/Z4hEVsYgiYiIiGgIiERjah+Qy2HXrHk0d1w5vM70PUkBgwBqWK8zSVndjKhfMUgiIiIiGgJEFglQyu12tfrUn2eNLIHXHZ9uF44YlsKFI1F1nSVZtkES1BHgjJLIuhgkEREREQ0BQSlIctptWL27Q/3Z7bSrPUmxmHHGyOgyp92GyoIsBzdwMVkaBBgkEREREQ0B4UgiLHE57Lhw/hgAwDcOHwUA6nQ7QDvqWzAKkgo8TtjttqTLU+EySTQYOAd6A4iIiIio74lyO4fdBofdhhtOn45jJlfh+KlVAJTAyWm3IRyNGS4oG4iPBhfXAYD2nlDW22GzcQQ4WR8zSURERERDgAiSnPHMT4HHidNmDtMsBqsuKGuUSYoHTl6XAyPifUh5WS4kC0iZJCILY5BERERENASE4uV2bof54Z+6VpJBkCQWmfU47fj7f83Hggnl+OslR/R6ezi4gayM5XZEREREFtbaHcT6fZ1YMKFcLVXrDXWNJGeqIEn5XbpM0oSqQjx7xcJebQcHN9BgwEwSERERkYV95c8f4YKHl+DfdXsP6H705XZG8lJkksTgBk+KICsTNnUE+AHdDVGfYpBEREREZGH7OvwAgDfWNBzQ/YhyO1cvy+3E4Ab3gQZJaiaJURJZF8vtiIiIiA5igXAEwXA0UW7nSJ9J6gkaTLeLl9t5ejGswQgzSWRlDJKIiIiIBoHedCO99EU9/uf/ViESjeEHx08EkCaT5M5scMOBYE8SDQYMkoiIiIgGgd7MbHhjdYNaZrd8eyuANEGSM7PBDQfCxiiJBgH2JBERERENAiJGauwM4M01+xCJpo4yotEYWnxB9efuYBhAmnI7KZO0s9mnGdOdu8ENCvYkkZUxSCIiIiIaRM69/1Nc8bfP8cLyXabX+fuSHZh9y5tYtq1FvazLL4KkVJkkJUi6662NOPaP7+GedzervwvkutyOMRJZGIMkIiIiokFAlKntbPEBQMqR4L98eTW6AmHNZV0BJchJFSSJTFJ3ULnun97aqP7OLwY3OA+w3E6MAD+geyHqWwySiIiIiCxKLnfr/TKyiu540ORMUW5X4DEPgLoCIQBAvvtAe5KU/8eYSiILY5BEREREZFFhXd/RgQQWYhiDO0UmqaLAY/q7fe0BAMCwEm+vtwGQe5KIrItBEhEREZFFBcPSekU2wBdMnjqXrVSZpMoibZAk1k3a3+HH3vYeAMDwAwySwJ4kGgQ4ApyIiIjIogJh7aKuLd2JaXViJLcRp92WlIUSUvUkVRa4NT/nux14ZeVuXPPsSvWyA88kHWjhIFHfG9BM0meffYYrr7wSRx99NKZOnYq3335b8/vrrrsOU6dO1fx32WWXDdDWEhEREfUvOZMUicbQLAVJzd0B09uV6YIdWapyO30myety4Jcvr9ZcNqIkz/T2mejNek9E/W1AM0k+nw9Tp07Fueeei6uvvtrwOscccwxuu+029We32/xDT0RERHQwEWO3ASVgapECIzmrpFfkdaKx0ziISlluV6gNkvLdDhR4HOj0d6mXHWgmSRaLxRKLyxJZyIAGSccddxyOO+64lNdxu92oqqrqpy0iIiIisg45kxQMR9HclQiM2npCiERjcNiTg4xwxLzhJ1W5XWmeS/NzDErmaGNDIkjyug50BLh0/zFmlsiaLN+TtGzZMixcuBDFxcVYsGABfvKTn6CsrCzr+4lEDrzR8UCJbbDCtpCCr4n18DWxHr4m1sLXw3r68jXxxcduA0pWqbkrkR2KxYCmzp6k7A8ABMPm2+K02zLeVn8ogkhU2/t0oH9nVLq/cCQCRyz3URI/J9Zjldck08e3dJB0zDHH4OSTT8aoUaOwa9cu/OlPf8Lll1+O5557Dg5Hdmcx6urq+mgrs2elbSEFXxPr4WtiPXxNrIWvh/X0xWuyvimROWpp68D6bX7N7z/9fBXGlLj0N4PPb16K19rciJUrzfuZZF2+ABps2kVpV65cmdFtzXQGEkHSypUrDTNhucLPifUMltfE0kHSV7/6VfXfYnDDokWL1OxSNmbNmpV1YJVrkUgEdXV1ltgWUvA1sR6+JtbD18Ra+HpYT1++Jj1bm4H3WgAA7rx8OAsKAXSrv68ZMxG148uTbhf951sAIshzOfDLr07DL15eo/5u5PBhqK2dYv6gL7yu/jMMG8J2FwAlo3Xy9GrU1tYeyJ+ENl8Q+Oe7AIDZc+akLP/rLX5OrMcqr4nYjnQsHSTpjR49GmVlZdixY0fWQZLD4bDMh8RK20IKvibWw9fEeviaWAtfD+vpi9dEnvIdDMfQ6gtpft/YFTR8zGBY6Ul6+2fHoaVLm1VyOTPfTn8oik6/Up502dHj8aMTJx3w3+h0JA4/7XYHHH0QJAn8nFjPYHlNBtVisvv27UNbWxsHORAREdGQoBncEImiM6CUvok+pGeW7Uy6TSwWQzCi3M7tsMPj0h7uudKUtx0xLtH7HY7GlMwPgEuOHIfS/BxMGZYePgauKEvWNKBBUnd3N9atW4d169YBAOrr67Fu3Trs2bMH3d3d+P3vf4+VK1eivr4eixcvxlVXXYWxY8fimGOOGcjNJiIiIuoXAd10u0BIyepcc9IkuB12LNnaguXbWzS3EQESALiddnic2sO9fE/qQqK/fPswXHHsBPVnsShtsTe596k35Gl2McZIZFEDWm63evVqXHzxxerPYj2ks88+GzfffDM2btyIl19+GZ2dnaiursZRRx2Fa665hmslERER0ZAgZ5JCkagaNI2vLMSxUyrx9rr9WLe3A3PHlcMfisDrcmhu43Ha4dYFSUVpgqSaYi+u/8o0PPThVs3lhd7cHDZy4jcNBgMaJM2fPx8bNmww/f2jjz7aj1tDREREZC36xWT98UySx2VHRYFSctfeE8K9723GXW9txPNXLsTY8nz1Nm6HHR6ntv+jKINgx2azweO0q0FZoceZsyl08uKxzCSRVQ2qniQiIiKioUS/mKw/PsnB63SgJF8pf2vzhfDHNzYgHI3hplfWqOV2TrsNdrstKZOUaUZIXjS2OEdZJEC3mCx7ksiiGCQRERERWZTckxSIROGPZ5a8LjtK8pQgqb0nMfHOZksEViI40vckFWXYW+SVBj5keptMsCeJBoNBNQKciIiIaCgJhLSZJFHx5nU5TIIkW1KQ5LTbYLcB8fkLKEzTkyTkSZmkTEr0MmWTckmMkciqmEkiIiIiGkAPfrAFZ9/3CTr8oaTfBSMRzc+i3M7jNMkkIZF9csfXH7LZbJoFWzMtndOU2+X1VSaJYRJZE4MkIiIiogF023/W44udbXj+s11Jv5MzSTKPaSYpMQJc7kWKSsFIpj1Jnj7KJMkYIpFVMUgiIiIiGiDyYAZ56pv6+4hxkOR12VEaH9zQGl/sFVAySfpyOwAIRRLhiFxGl4rXKWefcpdJIhoMGCQRERERDZA9bT3qv+VBCYJRJslmU0rpRCapoSMg/U7qSXIYH+YZBWNG5HK7sRX5Ka6ZHQ5uoMGAQRIRERHRANnZ4lP/3R0IAwD+tmQHznvgU7T5goaZJI/TDpvNhtI8d9LvguGoGiTpp9plS844TawuPKD7ksmDG1hvR1bF6XZEREREA0QOkrr8SpD0q5dXAwD++vE2zWKygsjwFHmdsNm02ZiuQNiwJ6k3nI5EMDOpKodBkiZGYpRE1sQgiYiIiGiA7JKCpPve34IlW1vUn7uDEU3PkuB1KkGS3W5DkceJjnhwBcSDJIOepN7Y35ko4xtZmndA9yXTLCbLGIksKqMg6eqrr876jm+55RZUVFRkfTsiIiKioULOJIWjMSzbngiSvC67ZjFZ+XKhNN+tDZL84bQ9SZmql7bNbs+sjykTck8UYySyqoyCpLfffhtf+cpX4PV6M7rTf/3rX/D5fAySiIiIiFLY1eoz/V1PMGo4uMHjTPQKlejWL+oJReCPl+gdaCbpQG9vRptJYphE1pRxud0vf/nLjIOe119/vdcbRERERDRUdElZIL2W7gC6Asm/12aSkkdzt3QrI8HdzuRR39kkhO48vxa/eKkOvzpjRuY3yoCNcxtoEMgoSHryySdRUlKS8Z0+/PDDqKmp6fVGEREREQ0FfpPFYgGgxRdCZyCUdLm8yOuY8uTR3K0iSDIot8smO3T42DK8/pNjM75+pjTldoySyKIy+qTMmzcPTmfmMx7mzp0Ltzt5LCURERERJfSEkqfXCa3dQXTGM01yWZ28ftGhY8qSbtesZpKSD/MmVxf1elv7AqfbkVX1erpdc3MzmpubEY1qz4BMmzbtgDeKiIiIaCjwpwiSWqQgqabYg/YeJaskr39UO7o06XatvmDS9f7vyoV44IOt+NUZ03Ox2QdMHV3OGIksKusgafXq1bjuuuuwZcsWtdnOZrMhFovBZrNh3bp1Od9IIiIiooPFu+sbsHx7K3568hTD6XXC7rYe9d8TqwqxsaELgDaTNKGyIOl2zV3JmaS548rxyLjyA972XLFBiY8YI5FVZR0k3XDDDRg3bhx++9vfoqKiQlNXSkRERESpXfr4cgDAyLLM1h5y2G0YU5HoPfJKwY/dboPTbkM4mgg3RCbpQEeA9yVbPJXEniSyqqyDpF27duGee+7B2LFj+2J7iIiIiIaEFTvaki77ysxh+PXXZ2Lhbe+ogU+hx4maosQyLHImCQDe/ulxeGf9fry1dh+WbG2RpttZOEiK/589SWRVWX96Fi5ciPXr1/fFthAREREd1HzBxEjv/Z1+AIDLkajKyXM5UFXkQVlBYgBWkdeJ6mKP+rNHF/yMqyzAZUePR0Whcp1QRAk8LB0kxf9kZpLIqrLOJN1666247rrrsGnTJkyePDlp6t1JJ52Us40jIiIiOpg0dQbVf+9oVhaS9bocCEWU4EkENuX5bjR2BgAARV4XqlNkkoQij/aYzMrldkRWl3WQtHLlSqxYsQIffvhh0u84uIGIiIjIXFN3QP33zpZEkCSm2IkgqbrYgw0NnQDimaQi80ySUKALkgo8xsGUFdjioxuYSCKr6lUm6Wtf+xquuuoqVFZW9sU2ERERER2UmjoDSZd5XYmg54j4BLpR0lCHYq9LU24nD2mQFeqCpCKvy/B6lqCW2zFMImvKOkhqbW3FJZdcwgCJiIiIKEtNXcGky7xOB9689lis3NmGM2YPBwCMKktMsyv2OpHvThyydfhDhvetD5L0P1uJOriBMRJZVNafnlNOOQVLly7FmDFj+mJ7iIiIiA5azV3JmaQ8twNTaoowpaZIvWx0eSJIKvJqD9c6esIwoi+309/OSriCDFld1p+ecePG4c4778Tnn3+OKVOmJA1uuPjii3O2cUREREQHkyaDIMnrTO4dGi2V2+nL5maMKDa870Lv4Cm3s8VzScwkkVVlHSS98MILyM/Px7Jly7Bs2TLN72w2G4MkIiIiIhNG5XYeV/IgBrncTvQsvXXtsfhwUxMuWmC8VmWhblBD8SDIJHF0A1lV1p+ed999ty+2g4iIiOigJzJJHqcdgXAUgPFI78rCxDpJYvLd5JoiTJZK8vQKPdrMkbUzSQpmksiqOECfiIiIqA88vXQnrn+xDhFpGp0IkiZUFaqX5RkESTa5aSfD/h155LfDbtNMzbMa8fcxRiKryujTc9ttt8Hn82V8p3feeSfa2tp6u01EREREg1ooEsUNL9XhmWU7sXRbs3p5RzwrNLY8uZxO76YzZ2D68GJcdvT4jB6zSMokFXmd2kDLYhKZJIZJZE0ZBUlPPvkk/H5/xnf61FNPobOzs9cbRURERDSYrd+bOA7yBSLqv7viQdJIaTCDUbkdAHzvqPH4zzXHoLrIm9FjypmkArd1+5EAJNZJGtitIDKV0ScoFovh1FNPzfiMRE9PzwFtFBEREZFVRaMxRGMxOB3m55o/39Gi/rulWxnWEIpE0RNSAqaRpYkgyajcrjfk6XZ261baAWBPEllfRkHSbbfdlvUdV1RUZH0bIiIiIqv71sNLsL/DjzeuPRYeg/Hdj3y0Fbf+e536c1O30ofUHUisbyRnkjw5CpLkbbFl2sg0QBIn3hklkTVlFCSdffbZfb0dRERERJYRi8Xwk+dWIhCK4v7vHKYe1PcEI1i2TckSrd3TgUPHlCXd9v73t2h+bo6P/RZT6rwuOyoKEtPr+mLAgt3aMVJiBDhjJLIoixesEhEREfW/+tYevLJyDwCg1RdCeTyo2d+Z6NEWpXN6zd3atZCa4xPtRJBU6HGhJC8xZMFoMdkDZeWhDYBUbjegW0FkzuIVq0RERET9b8O+xOCFQDgRDO3vDKj/NloYFgDcTuXw6uenTAGQCJq64uV2xV4niuUgKUfldjKLx0iWD+KIGCQRERER6azd26H+2x+Kqv/e35EIkhqlgEmIxWIIRZTrDytR+o6a1HK7EABlwIKcSYr0Qc3ZYAlBWG5HVsUgiYiIiEhnnSZIkjNJiXI7oyApEo2pB/4jSpTR3aLcTmSSirxOTfYoYFK2dyCsnqlJlNsxSiJryipICoVCmDFjBjZu3NhX20NEREQ04NaaBkmpM0mhSOKgf1g8SGrpDiIajUk9SdqW8GAkilw5apIyXfg788fk7D77Agc3kNVlNbjB5XJh+PDhiEZz92EmIiIi6gud/hA8TofaI5SpQDiCHc0+9WfTcruu5CBJDnhEkBSOxtDhD2kGN8gmVRVmtX2pPHTRXKze3Y6548pzdp99Q4mSGCSRVWVdbnfllVfiT3/6E9ra2vpgc4iIiIgOXJsviFk3v4mT/vR+1rf1BbTlb/5w5uV2ISlIynM5UBxf4LWxM4CugNKTVBS/7NUfHY3fnj0TJ8+oyXobzRR4nJg/oQIOi88AVzNJLLcji8p6BPhTTz2FHTt24JhjjsGIESOQn5+v+f1LL72Us40jIiIi6o2l8bWMdrX0ZH1bn65HSO4ZakxbbqcESW6HHTabDeMqC7Cqvh2b9nehy5/oSQKAmSNLMHNkSdbbdzBQe5IYI5FFZR0kLVq0qC+2g4iIiMgSeoK6TJJcbicFRi3dAUSi2qP8UFj52eVQwoBZI0uwqr4dv3x5NVrio8BFkDSUWXyuBFH2QdLVV1/dF9tBREREZAnJQZLycyQaQ6svsTZSNKYMZSjPTxxOiZ4kV7wPavaoEjy1FGqABCT3JA1FNvYkkcX1+lTG6tWrsWXLFgDA5MmTMWPGjJxtFBEREVGuxGKxrEZi94SMg6SOnpB6UF+a70KbL4TGzoAmSBLldi6HEiTNGlmadP+FzCSxJ4ksL+tPaXNzM6699losW7YMxcXFAICOjg7Mnz8fd911F8rLrT5NhYiIiIaSUCQGt9M8SGro8MNht6Gy0APAIEgKK4FPW098MViPE8OKvUqQ1BXA1JoC6bESPUkAMLkmeXJdkYdBEnuSyOqynm73m9/8Bt3d3fj3v/+NZcuWYdmyZXj11VfR1dWFW2+9tS+2kYiIiCgr8sF3OMXSJT3BCOb/7h3MvfVtROP9RT3BsOY6IpPUFi+1K8lzoapICaj0wxsSmSRb/P92XH3CJBw2plS9jtUnz/UHkdljjERWlfWpjI8++giPPfYYJk6cqF42adIk3HTTTbj00ktzunFEREREB0pe4FVvX0dipLcvFEGhx2lQbqfNJJXmmwdJQXVwQ+I89M9PnQpgKv7xeT2+2NWKoyZV9v6POcjEmEoii8o6SIpGo3C5khsOnU4nF5klIiIii0gcfIcj5scn8nS6Ln8YhR4nfCaDG9p96YMkfU+S7NzDR+Hcw0dl80cctBI9SUTWlHW53YIFC/Db3/4WDQ0N6mUNDQ247bbbsHDhwpxuHBEREVFvyHFROGp+KC5PsusKhJMuA4BAWFtuV5rnRlW8f6mpyyRIcmZ9iDWkqEESoySyqKwzSTfeeCN+8IMf4KSTTsKwYcMAAPv27cPkyZPxxz/+MecbSERERJQtuQ8plCKT1C31H5kFSfpyu5IMMkluB/uOUrGBzw9ZW9ZB0vDhw/HSSy/h008/xdatWwEAEydOxJFHHpnzjSMiIiLqjWA4ERiFU/Qk+aQgqVsESfHyOqfdhnA0lii3E0FSnkvNJDXqMknBSHJPEqXCVBJZU1ZBUigUwpw5c/Dyyy/jqKOOwlFHHdVX20VERETUa3KJndl0u1gshu5AImvU6VeCJNGTVFbgRmNnILknKdV0u7B5TxIlsNyOrC6rIMnlcmH48OEc0EBERESWJg9rMJpuF4nGcPZ9n2BVfbt6mcgkiaCoLN8VD5LMp9u194QQCCeX9jFISk1dJ2lAt4LIXNaf4CuvvBJ/+tOf0NbW1gebQ0RERHTg5MDIqNxuS2OXJkACgD1tPejwhxKZpHw3AMAf1q+T5EZJngvu+HCGjzY1YUtrKP648Z6kFIvXkrROEqMksqise5Keeuop7NixA8cccwxGjBiB/Px8ze9feumlnG0cERERUW/IwxpCugqYWCxmODbgzrc24p73NuOY+DpGapBkkEmy2Ww4dnIl3l63H9//+wo4bcAJ84LsScqQmklilEQWlXWQtGjRor7YDiIiIqKc0fQkSZmkSDSGc+7/FE26XiIhGI5if/x3ZQXKupABg3WSAOAbh4/C2+v2K48RU/qTWG6XIa6TRBaXVZAUDiu1ut/4xjfU8d9EREREViNnkuT+pPX7OvDlrraUtxVrHyUySRF8urkJLfFyO3H5idNqUFPsQUOHcv3uYJiDGzKUyCQN6GYQmcrqE+x0OvHoo4+qwRIRERGRFcnZo5CUVXJnELyIiXUiGGr1hfCDp1YgFgNOO2QYaoq9yn057XjpqqPgiq+J5AtGuE5ShtSeJOaSyKKyPs2xYMECfPbZZ32xLUREREQ5IfchRaR/i4PzVESpXlmBEiT1hCJo7wlhVFke/nxBrea6I0rzMGtkCQCgOxBhT1KG1FeBMRJZVNY9ScceeyzuvPNObNy4EYcccgjy8vI0vz/ppJNytnFEREREvREKS5mkSPo1k4yUxXuPhLNqR8LjdCRdr8CtXOYLhhM9SU4GSanY2JNEFpd1kHTLLbcAAB577LGk39lsNqxbt+7At4qIiIjoAMjBUDjNOHAzIpMkfL12hOH18j3K4ZRcbsdMUmo2cAQ4WVvWQdL69ev7YjuIiIiI0orFYnjsk+2YNaoER4wrN72eWfZIHuiQTnl+Ikg6+9CRmFxTZHi9/HgmqZs9SRlLZJIYJZE1ZR0kEREREQ2UN9Y04NevrgUAbL/9q6bXkyfaaQOmzA/KS/Jc+OnJU9DqC+KG06ebXq/AHc8kBcIIhtmTlA1mksiqMv4EX3755ejs7FR/fuihh9DR0aH+3NraitNPPz2rB//ss89w5ZVX4uijj8bUqVPx9ttva34fi8Xw5z//GUcffTRmz56NSy65BNu3b8/qMYiIiOjg8WV9m/rvaIqAx2wEeDaZpAKPEz8+aTJuOvOQlEFPvtqTxHK7TCWm2xFZU8af4I8//hjBYFD9+YEHHkB7e7v6cyQSwbZt27J6cJ/Ph6lTp+Kmm24y/P3DDz+Mv/3tb7j55pvx/PPPIy8vD5dddhkCAeMF4IiIiOjg1ukPqf9u6jY/HpDHfodMFpZNpTTfBXeGwxcS5XZhtbSPgxtSYzEiWV3G5XYxXT5U/3NvHHfccTjuuONMH+/JJ5/ED37wAyxatAgA8Ic//AFHHnkk3n77bXz1q+YpdiIiIjo47Wj2qf/e0+ZHdZGyZpE/FIHHaVczFOEsM0mTqwuxaX+X+nNVoSfjbSqQBjeIcjv2JGUmF8eTRH3Bsj1J9fX1aGxsxJFHHqleVlRUhDlz5uCLL77IOkiKRCK53sSsiW2wwraQgq+J9fA1sR6+JtYy1F+PLY2JQGZ3SzdmjShCmy+Ik/70EcoKXLjzG7MxZ3QpQuFEMBQMR9TnKxAyft5e+P58PL+8Hr/7zwYAQGWhO+Pn2OtUAqLuQFjtf3LYhu5rlAkRQkYi0T55nob658SKrPKaZPr4GQdJNpstowXYcqWxsREAUFFRobm8oqICTU1NWd9fXV1dTrYrF6y0LaTga2I9fE2sh6+JtQzF18MfjmJPm1/9+bO1WzAsvA/rm4Jo6wmhrSeEH/79M/zlK1Vobku0BOys342VK5WfN+/qMbzvjWtXo2V/4nfOsA8rV67MaLuaG5Tb7W9JPOae+p1YaWvM+G8banp6lIzglq1bUdqzu88eZyh+TqxusLwmWZXbXXfddXC7lXGYwWAQN998s7qYrNyvZEWzZs2Cw5G8AFx/ikQiqKurs8S2kIKvifXwNbEevibWMpRfjzV7OgDsV3+2F5ajtnY6Ojc1AWgBAASidtTW1iLv82XqZVXVw1BbOwkAsAN7gCWr1PuYNbIYX501HIcfNh57nXuBz78EAEwZPQy1tdMy2q5G1z5g6UrYXF64nQ4AQUyaMAG1h9Qc8N98sMr/5FOgrQMTxo9H7bTqnN//UP6cWJVVXhOxHelkHCSdffbZmp+/9rWvJV3nrLPOyvTu0qqqqgIANDc3o7o68eFpbm7GtGmZ7bRkDofDMh8SK20LKfiaWA9fE+vha2ItQ/H16Apoy2S2NflwyePL0epLnKgNRWNwOByISMMaojGoz5V+bsOvzjgE88Yr6y0V5yXWRaou9mb8/BbmuQAAPaEobHZlYIPXPfRen2zY7Up1ks1u79PnaSh+TqxusLwmGQdJt912W19uR5JRo0ahqqoKixcvxvTpytoEXV1d+PLLL3HBBRf067YQERHRwAvohi68tyG5nE1Mr5PXRtJMt9ONDReT6QCg0Js4LKoqymJwQ3ydpO5gWB397bRzul0qooGDcxvIqgZ0cEN3dzd27typ/lxfX49169ahpKQEI0aMwMUXX4z7778fY8eOxahRo/DnP/8Z1dXV6rQ7IiIiGjrEMIZ8twO+oHHztRjBLf4PmE+6E/cliGAHyC5IUtdJCkRQ4OY6SRnhOklkcQMaJK1evRoXX3yx+rPIVp199tm4/fbbcfnll6Onpwc33ngjOjo6cPjhh+ORRx6Bx5P5jouIiIgGTiwWy9ngp2A8wJkxvBir97TDH0oe5x2KxBCLxRAKx5Iu+8lzK/H22gbN9cX4bgAo6mUmKbGYbBihiFJ653ZyBHgqiUwSwySypgENkubPn48NGzaY/t5ms+Gaa67BNddc049bRURERLmwbm8HvvPIUlyzaDIuXjgu6ffBcDTjBVvF9QEgz+3AISNK8PmOVsPrRaIxhORMUjSKVfXteGXlnqTrypkkryvx7/ICd9J1zYhAKxiJoTue4WImKTURNzNEIqviJ5iIiIj6xK//tRbN3UHc+Moa9bI9bT2IRGPY1NCJObe8id+/vl5zm00Nnej0hwzvTywE63bYMWtkienjhqMxtTcJUPqUgiaLyOZLJXYVBW4cMa4M88eXZ7WYbJ4UXLX3KNvOICk19iSR1Vl2MVkiIiI6uHy8qQnfeXQpvjJzGLwuB3pCEdz//hb872nK1NrX6vbiqqdW4PRZw3DfhYcDUIKO7zyyFKfPGo5CjxKMuJ12VBebBzGhSFTTexSKxGBU/PaVmcPgsCd+Y7fb8Pz3FwJAViWCbqcdTjsQjkrZLpf1p3cNpMTzyyiJrImnOYiIiKhPeF3aw4yHPtoKAPjP6n0o9GjP00aiMVz11AoAwGt1+9TLn/h0O+p2t+P3r69HIJwYinDe4aNNS+KUzJE80S4KfcxzyZHjcP93Dk+6rc1m61UPVYHub60p9mZ9H0MJM0lkdQySiIiIqE/kubXZFI/Uf1Sa71L/7QuGsWRrs+a6IhMkT7ETY73dTjuqijxY/otFOHRMadLjhqJR3XS7GHSTv+G053awQmV+4m8ryXMl/e2kxZ4ksjoGSURERNQn5EEI/lBEEyTJZW572vxo7g5qbtvQGQAATQZIlLKJYQ92uw1FXhf0whFtT1IoElXHhwvOHPcMVeYn/tZhzCKlZYvnkphJIqtikERERER9wiUtqNrmC8HjTAQSnf6w+u+97T3wh7TrHu1p60m6v0BYuY5bCnDksd1COBJThzwAyiAH/eAGlyPXmaTE31ZTwiApLTWTxCiJrIlBEhEREeVMe08If3xjPTY1dMIfTgQ+Ld1BzdpBe9sTQdCeth4EdEHS7tbkIEkEVvLY8GKDTFIoGtUESaFIVM1CCU57bg+BqjSZJK7nSDTYMUgiIiKinPntv9fi3ve24PS7P0KP1E/U5gtqFn+Vg6A9bf6khWF3xzNJcjlWq08pyZMzScUGmaRgOKrpQVIyS9qMhbMPM0kst0uPgxvI6hgkERERUc58tl1Z4DUUicEvZW9afEF0BRIldrvbtJkkfbmd+H1PMHEbESS50pTb6e8rHI0iGNFe5s5xT5Imk1SSl9P7PhhxcANZHYMkIiIiypmYlBrwS5mk1u4gfFLA09SVGNSwt92vluaJ9YVET1J3UC7ZUxZqlcvt5OEQRfGx4nIGC1B6kkLhvs4kJbapotB4NDklJAY3MEwia2KQRERERDkjH/L2SBmdVl8I3YFI8g0ANHUF1HK7sRX56mUANIFVmyi3M5mSV5znSnpcQCm3C0T6drpdiSd1dou0erEUFVG/4qeYiIiIckZODMhlby26TJKsuTuoXlcsECvWR+oKyIGW6ElKHGHbpaNtsTaRPkgyGtzgyvE6STabDf972lRs3t+NBeMrcnrfByO13I6JJLIoBklERESUM/JIZzlYafMFTTNJLd1B9bplIkgKRLC3vUfNHgFQs01yJkmOdcQCsYbldn2cSQKAK44ZD4eDi8hmQi23Y1cSWRSDJCIiIuoTciaprSeEbpNMUiQaw/4OpbyuPF8JkvZ1+LHwtncNry8HScdPrQawBjXFHnWgQ9LgBqNMUo57kig7zCSR1bEniYiIiHJGPuiVF4ztDoThM8kkAYlBDSKTlIo83W50eT4+ue5EvPuz49VhDL6gvtzOIJOU43WSqHcYJJFVMZNEREREOSMf9Aak7E2rL4SgLlABlBK5cDSG+niQVJ6fvDisnn5898hSZeS2Kx746IOkcNRgMVlmkgaUzSbK7YisiadRiIiIKGf0pW7C/g6/4eWjypQARwQxmWSS5HI7mQh85PWYAGW6nT5Ay/U6SZSdxGKyDJPImriHIKKc8gXD+PuSHWgwOSAiooNbZ8C476jDb3z5qLJ8zc/FXpc6gMGMWYAjhjF067bBaLodM0kDi4vJktUxSCKinLrttfX45curcd4Diwd6U4ionwXCkaRgRK9UV04nSuUEj8uujvI2Y5ZJEmO9RaAm1ivyh6PsSbIYNURllEQWxT0EEeXU2+saAAA7W3wDvCVE1N/MRnzLCtzadujKIm15ndflQH4vgySRHRKZpIp46V4wHE3qU+J0u4GV6ElilETWxCCJiHLKzmXUiYYsozLbMl3mqMDjQIEUBFUUeDS/9zodyHennitlHiQpl3fFS/vKpf6mNl/I8Lo0MBI9SQO6GUSmuIcgopxy5HgVeyIaHD7e1ISv/PmjpMsLPE7kuRJBUb7biUJvIgiqKNRnkuya6xtxmQQ4otyuSy23S/Q3tfUENddN1/dEREMbgyQiyikeeBANTQ9+uMXw8jyXAwWeRFBU4HGgUPq5qlCXSXI5UOBJU26XZnCDCJK8Lrv62K3d2kySWaBF/YODG8jquIcgopyyM0giGpImVxcZXu51OVAoBT2leW4UeRMleFVFyUFSnq7cbs7oUs3PHrPBDboR4B5norSvvUdfbsd91cCK9yQxSiKLYpBERDnlYE8S0UGt0x/C3xZvR2NnQHN5cZ5xH1Gey6EprxtW4sX4ygL15+QgyY58qdzu0e/OxWOXHKG5jlkWSEys65YySfnxTJJ+7SSukzSwEpkkRklkTak7I4mIssRMEtHB7YaXVuNfX+7B08t24T/XHKNeHo4YH+x63Q7I506GFXtx5XETsbe9B9+ePxYledrBDsrghkSQVFXkQanuOumm24Xi2+J1aYdEGF2XBgYHN5DV8TQKEeWUfHLWbCV1fyiCRz/ehq2NXf20VUSUK6+v3gsAWLe3Q3O5vA5RTXEiO1TgdqjrFQFATYkXVUUePHvFQnxtzgh1FLRgt9s06yQVeJzKZVJ2yXSdJF12yOO0m07K4zpJA4s9SWR13EMQUU7J5XbBiPGikve9txm/eXUtTrzzg/7aLCLKERuMMzAie3P5MeNx4xmHAFAGuXz3yHGawQ3DS7xJt5WDKACagEgMeZDL+cwGxOgvTzUEguskDSz1fcRUElkUy+2IKKfkcrueYAQeZ/IBytJtLf25SUSUSyaxhcgk5bmd+MrMYXj68vmYMbwYpflu/PPLPer1hhUnB0nFXhc6/YmeIXk/IoKkkjwXGjqUPih99knQr32UMpPEnqQBxUwSWR2DJCLKKbkvoTsYQWl+8nV44pBo8DLLv4SjSpDksttgt9tw5MTKxO+krHJ1sSfptvpMklyqK/qTir3aviQjriwySVyuYGDZmEgii+NpFCLKqUA4ov67Jxg2vA6nGREdfES5ncugX0ieLGeUXdYHQFFpFyGyRsV56YOkpEySy2GaSeI6SQPLpo4A5/cBWRP3EESUU4Fw4oyxLxgxvE6U34lEg5bZlH9RbmeUoZFL6YzoM0kRg52EfgqeEX2fkcdpN51u52AmaWCx3I4sjkESEeVUIJQ+SOKZQ6LBy2xwgyi1NcrQHDelCgBQWeg2vO3XakcAAKrjayYZ7SOKvek7BIwGN+R72FlgRRwBTlbHPQcR5ZS23M4kSOqvjSGinDPLJIlplkZB0sULx6Es340jJ1UY3vZrc0ag2OvCISOLAQAnTKvGE4t3aDJMh4wsSbttRoMbCnRB0v0XHobxVQWggSXKKPl9QFbFIImI0mrvCeH11Xtx2iHDUZKfuuSF5XZEQ5MYzmC0SKvbace5h48yva3NZsMJ06rVn4+bUoVnr1iASdWF6mXnHjYK9S0+zBtvHGgByeV2RovJHjmxMu1+jPpeIpPELwSyJgZJRGRoW1M3HvpwC35w3CTc8q81eGf9frxWtw9PXDov5e20QZJJHwK/FIkGLfPpdsrn2p2DgQg2mw0LJmiDIYfdhp+eMjXl7fQLxHoNRoCbLURL/cssI0lkFQySiMjQj55ZgdW7O/DhxibsbusBAHywsTHlbcKRqKbhuifEcjuig43ZGkXBsHkmqb/oH9vjcqDAo13UmkGSNTBGIqtjkEREhjbu6wIANUDKhJxFAoDugNnght5vFxFZk8gk6bM5/UnfD+V12RGJJg517DZOtbMafh+QVfF0ChEZGl2el/Vt9EGS2TpJUX4rEg1aZiGGGAHudg5gJsmuHwGuXUzWaI0mGhiJwQ38PiBrYpBERIbGlOdnfRt5sh2QagR4rzaJiDLwf5/X4/g/vodNDZ198wCm6yRZM5NUU+RVs0eXHzN+IDaLDHAEOFkdy+2IyFCRN/vpT/6QNpPkY08SUb/7+QtfAgBu+ucaPH35gpzfvxwjxWIxNSMQSjECvL/oe5K8TgfKCtx45YdHwetyaKbl0QDjYrJkcQySiMiQOODJhj6TZLpOEk8dEvW5cD/M2g9HY+rY7bAaJA1kuZ1unSSX8vPMDNZYov4lFiXm1wFZFcvtiMiQUZCUbmRrQJdJ0gdNAr8USa+1O4goF9DKqXx33/TfyNPt5P2EWm43gJkkfYDGHiTrsqmZJH7uyZoYJBGRIXHAI9M3RevpBzfogyaBX4okW7mrDYf+5i387z9WDfSmHFQK3LkpFtFnhOWTJcGwHCRZIJMkBWguh42T7CyMPUlkdQySiMiQUSYp3QGHPnMUNCnZ45eiuU5/CBv7quHeov7y7iYAwAuf1w/wlgx+ctCSl4NM0r++3IPpN76OZ5btVC+TM37y44nyvgHtSZL2UcwiWRsXkyWrY5BERIaMgiRXmqlVyeV2ZpmkBJZYaZ1xz8c45a4PsXJX20BvSr/RD/yg3mvzBdV/e3KwaOqPnvkCAHD9i3XqZXKWWT4REgoP/OCG0eX58Mb7kMZWZD+hk/pPoieJ3wFkTRzcQESGjMrt7FmW2wVNgiR5naRwNAY3S2LQFQhjf4cfO5p9AID/1O1F7ejSgd2ofmLWu0bZa/WF1H/3ZviKntNuSxoAId+vptwuGlVvM1BK8lxYesMi1NW3Y3INJ9lZmdqTxBiJLIpBEhEZMjrASt+TFNH9bHKQJn0phqNRuJnUxhl3f4Tt8QBpqJHfJ/JI6YHW6VcCjt6Mwx8oLd2JTJLp5y8LBR4n2nsSgVc0GtMETXImKRwZ+HI7QAmUjp5cOaDbQOnZOAKcLI5HJkRkqHc9ScptirzK+Zeg2XQ76d/9MaZ4MNAHSEbPytNLd+Ksez9BU1eg14+zclcbfvzMF9jd1tPr+8g1uUzTKqV34UgUs25+E7NufjMnGZn+Ipfb+U3WKctGoUd7LlVkiwSRSYrFYlJPkjWCXLI6jgAna2OQRESGwr2Zbhc/KCuOn3k3H9yQuO+IweOQcZ3+DS/VYeWuNtz73uZe3+9Z936Cf365Bz97fuUBbF1u+UJh9d8d/lCKa/YfuWytzWeNbcpEiy/XmSTt8AN9Ga4IIOXLB3IEOA0eHAFuLXvbe/CnNzegocM/0JtiGdyTEZEhowDHkeYMsTgoE2efzUaAy9kjZpISi3DKfCYL8QK5yRBsa+o+4PvIhVgshqbOxIF9R481ApKwlDEZTJmkVrncLgdZuQIpkxSNxtThDOpjhEWQlLjczSCJMsAR4NZy2ePLcfe7m3H5k8sHelMsg3syIjJk3JOUZrqdvtzO5OBSzlKFo4PnALSvdAXCSZfp+0Dkg99cjDZ2WKTvpzsYQY8U9KXKJL2/YX+/lQnKawOlClitRs6A5WIghrwgbYc/lLRfEOV2YU0myRrvLbI2i+yCKG7t3g4AwKr69gHeEutgkEREhozK7dINrQom9SQZB0Ahg2bvoayjJ3WQ9LMXvsShv3lL/dnjOvBdt1lWsL7Vh9+/vr7fSi4aO7X9VUbPBQB8sLERlzz2GY66/d3+2CxN4KZfTNXK5GA6F/1d8jmMlu5g0okP8RmXe5UGcrodDT78BiCrYpBERIaMskDpvszEbQrjPUlmPRFykBRhuZ1h9kQOkl76Yrfmd7lYW8osk3Txo8tw//tb1PVxcq3DH9L0WyUFSSaZpA83NvbJ9piRA6OeHJQ39sa+dj/eXLMvq3Vk5OcvF5kkeT/Q6gsa9CTF4v8XayTZLDOdkKxNrJNk5Xq7SDQ2qMptKbcYJBGRIaMvhnQBjT6TFInGDG8TirAnSWbUh9OeojenK3DgB79iUmFDhx919e3q67Q13qu0bFtLxve1eX8nvvngYny6pSnl9ZZubcbsm9/E7a+vVy/bp8tYmfUkGfVtpbJmTzv2tfc+GyYHRr6gcXarLwXCESy47R1c8bfPsWJnW8a365beG9kObgiGo/hyV5smCJezwS3dBuV2EeXxREY4XUkukWD1EeCxWAwn3/UBjvvDe1nvf+jgwL0ZERkyKoNLVxqn70kCjEvuNOV2g6QnqdMf0pQy5VKqTJLRkIZugx6mbDnsNjy/fBfm/+4dnPmXj/HMsp34eFMiyMmm+f4rf/4IS7e14MfPrMTLX+w2DfB+/epaAMCDH2xVL9u8v0tznQ6/8d8WyiKY3t7Uja/e/TEW3PZOxrfR02SSBqDc7vFPtqv/3tWS+fpZckCXbbndY59sw9fv/QQ/eW6lepn8+W3tDiZ9nsXPQSmTRJQJqw9u8Iei2NrYjT3tfuxp48S3oYhBEvW5nmAEj3y0NasvehpY+gUjhXQBjZpJ8pgHSfJ6KsDg6EmKRmM4456PceTt76bM8PSWUR9OR08I0WjMcE0ko0EP2XLY7Vi6NZEt+uXLq/GdR5eqP7udmX09tPeE1MxgU1cAP3luJRb96QPD6xoFdxv3dQJInFU2K7eTz+SmKz/7sr4t3WanpelJGoByu1dX7VX/nU1JandQziRlt93vbdgPAPjnl3vw9tqGpPto8QUNMknKtlllIVkaPERZplVHgMvfd1bdRupb3JtRn/vda+tw67/X4dLHPxvoTaEM6ReMFNKW28UPoPLcTnXIg/5ATd/TMBh6krqCYexo9qEnFME/v9zT6/v5ZHMTHvtkW9JBvlFgEI0pj9vUlZy96m2QJD+u025LOUrck2GQ9MaafUmXNXYG8PmO5HI9o+3e2KAESUeMLQdgPrhBDqbTlZHZpZ6Y3o5LH+jpdq3yorBZBDs+6TnOttxOLpV7Kx4kacvtknuSgroR4JxsR9myaiZJ3udYdRupbzFIoj737Gc7AQCbdGU1ZF1m2Z10/UPB+MGc22lXMxH6AzV9NsoKPUk9wYjpJD7xe+Hfq3ofJF34yFLc8q+1WL6jVXO5WR9Ouy+UNNgA6H25ndyEb7fbUmZIvK7E6OdUB9tr93QYXv700l1Jl+mDJH8ogu3NSg/U3HFlAMwzSXK5XbqgJSod0fR2IdiBnm4nvyeyeXyftN3BcDSroQ9y1nJzo7K/lt8zTZ0B0xHgicENPKygzFi9J2kwloVTbnFvRn1Of+aRrM9smk8kzWspDpg8Drva06KfkhcKa+9joBti/aEIDv3Nmzjuj++ZXkcOSpZsbUnZm7R5fxeWb0/OorRJmYHkiW7GQU97T8iw3K63QZJ8sO2wpT74FkHuv1ftxZxfv4WPdhqvT7Sj2XhRWqPL9T0ym/d3IRoDSvNdmFhVCMA8YJT/5nSDFOTns62nd31k2sEN/RskRaMxdPYyI+TTDfXI5rby+3JTQydisZjm9g2dftMR4OJkB4MkypSYbmfVLE1IM8DEohtJfYp7M+pTB3oAvL/Dj+P/+B7ue39zjraIMiEOhPSTfM3K8PS3UzJJSiYioDsw1t/HQJfbbW3shj8Uxd52v2lwqD9IXr3HfLG9RX/6AN94YDG2NWmDBHlAgb4E0SwwWLOn3TCT1NvpdvLfEY1psw56Isj94dMrEIrE8P+WGv/NO5qNew0zWfRVTNKbXF2IyiIPgOQAUsgmsyJft7eZJP8AjgDvCoY1B46ZZpKC4WhSEKP//JkJR6JokQL5Dn8YjV0BTYZ1X7sfIV3QJT4z4nIObqBMJTJJ1gxA5Pe62cLodHBjkER9art0ACVPPMvUAx9sxfZmH/7w+oZcbhalYdaEnekIcLfTrva0JGWSdD8PdLmdfFCnPwsv6A+SV+82LjGTTwos29as+Z0cJLV0aw/c9SVm588dBQD43Wvr1Z4dWVegdwf+Pk1Tf1QTCOilC4gB5f2wq9U4SGro0Aad+uxXOBJVh2CUF7gxqiwPAFDf2mNYIiYPzEiX2ZGfz9yU2/XvCHB90JxpX5VRMJXp8IYWXxCxmLJgtHgtNjd0aTJJ+zsCyT1JIkiKcgQ4ZUfd81ozRtKU2GWzVtKuFh9W7mrrgy2i/pb9UStRFtbvSxxMdgXCiEZjsGexGrtVzzAd7NT+ArsNcrFS+p6keJDkkIIkfU+SxQY3yI/eHQyjJN+VdJ1MM0nygbx+ZOyWRjlI0pXbxYcV/OXbh+K4KVXwuhz4bHsrtjV14991ypSz/zltKg4fU4ZvPrQE/lAU4UgUzixLm+SD6GA4krIUNlUAJexp6zG9j2hMyTxUFnpw/wdbMGtkieb3vlAEnfFgpsjrwshS5cC8KxBGe08IpfluzfXlwCddkNQpldu197Lczpfl4AaxJlimUwFT0Q+vyDST5Qspt3M5bHA57PAFIxmX24kMXnmBB1NrilDf2oN1+7QBemcgnFS+qPYkMZNEWbJ6T5JcYpeqZ1XvmD8opdvv/fx4jK8syPl2Uf/hKR/qU/LZ81gMmjr7THicjvRXopzyBcPqxDKX7oAvFoNmoUm9QFgutxODG7QHeOkyS/1N/vIz63Xx6d63a3YbB0ltUpCkX/8nk0xSSZ4LRV4XXA47pg0rApCo1x9Tno/aMaXqbbp70Scj/32BcDTlwbe+FM9jcPArSu0mVhXAaXDyo761B798eTXufmcTLn9yufb+AxE1mCnyOuF1OVAVL7nb1ZJcqicHDj2hND1JOSi3y3YE+LcfXoJj//BeTtaw0mcWM13vSCwkm+92qicpMs1CiSmKlYVujK1QDuy2SoG9WHx4d6v2tQmoPUkc3EDZUUeAW7QpSc4kZRMkCWtSlGXT4GDpvdk999yDqVOnav477bTTBnqzKAs7df0KZr0XZrwuS79FD0r//X+r8LvX1gMwPuBJlU2Se5IGSyYpqCkJSz6gfK1uL/6xYjcAYPYoJRuyvdlnOM5aPiBfJ2VRY7EYNjYkDjhX727Hhxsb1edmb7uSdaou8qrXqSlO/BsAhpfkweN0qGfqH/xgC27/z3r1PqLRGPZ3JrJXO5q78dCHWzSBkX7yWapskb50y2NwvkJMphtfWaAppxXlWrvbevCPFfWG998dDCcySfF1tRIld9r9RlAX0KUvt5MHNxjvcxo6/Gg2GIoh+LOYbheLxbB0Wwv2dfjx0abGlNfNRFK5XYYlc+K1LnA71BNMmWSSlm5txnf/ugwAUFXkQb5bua2ckRtTng8A2KULksRjiowiR4BTprJdTDYWi+HBD7Zg2bbkwTh9QT6Bx56kocny5XaTJ0/GY489pv7scDCzMJjs0C0g294Twugsbi9nkvyhiGYsMQHbm7qxeGszvnH4qJydwf23tIilyyA7EI5G4TY5vxI0yCTpgySr9STJ29etyyT5gmFc9dQK9efhJV5sbOiEPxRFa3cQhdKiuY99sg1/X7JD/Xl7U7f6nt20v0szyKBudzsu/usyDCv24oUrF6pleiJIEI8lG12u/K7A40SbL4T73t8CAJg2rAhnHToS1724Cs8vr8fz31+IeePLcc59n6K5O4iGjgB+dcYMANqDfTmT5HLYksrmAuGoJlBwO5PfC6IfaVRZPtbt7URrPEicPrwY9a092LQ/uZ9K8AUi6FIzSUqJ4+iyfHyxsw31ugNxfWblz29vwrBiL+aOKze8707p+kaL/3YFwjj5Tx8gHI3hy5tOwWt1e5HvduLkGTXqdbJZJ0nO6hmta5Ut/bTDTEofgcR25rkd6smHTHqS7n53k/rvIm8iCyWeR6fdhmHFXmxr6k4KYEUgxRHglLUsy+3+XbcXt/1HOYG3/fav9s02SXpTbidnxSyaIKMsWH5v5nA4UFVVpf5XXm78pUjWtNMgSMqGXN+ebgHNWCyG+97fnJMzuYPF8Xe8j+tfrMNT0sF5LunL7YA0mSSpJ8md4eCGAc8kyeV2ukyS/j2X73aiJE85oJffy8FwFLf8ay22NCYm2kVjSgMvALwZL1/M0wX5+zr8amljeYEbBVLQNUwKkrwuO6oKlVI0fSZOTNF7frmSsRGTIJvjY8rfXb8/8fdpDvzD6mv5/PcX4pIjx0FvjxTYGZXT7YtnwEaUagO66fFSwb9+vC3pNoKSSUqU2wGJIFE/DEKfWdm0vwvfeGCx6X3L12/sDOBbDy3Gzf9co162s9mHDn8YvmAE//i8Htc8uxKXP7lc817UPFdpStbkx9vbnn6qXyqxWCxp7HvWmSSPUz3B5A9F4QuG8dn2FtPPmjyjw+t0qJ9d8fq4nXbUFCvvPxHAikBKBLpmw16IzNiQXdZRX8Lc13ozuEH+jFkpRorFYimXriBjls8k7dixA0cffTQ8Hg9qa2vxs5/9DCNGjMj6fiKR/l8M0GwbrLAt/cEXDKvNwJOrC7FpfxdauwNZ/f3ytLB2XwBleeZv2f+s3qdOwdvy28zKMg+W1+Sz7S24aMGYnN+vy27Dfd8+FNc+/6VathMMhRExKYMUAYfTnhgh3RMIa57fgK6fJBDW/r6/XxO/lD3q9Ac1j9ulOzjPc9lR7HWhoSOgeS9vbzL+8m7u8mNCZT7eWtsAADhv7kg8uXin5jordykLy44qzdM8dlVhYnDBqNI8RONf2PrAbUdzN0LSc+px2vHJpkRgtK2pG0fc+jYevvgwdPkTX5Jy5mhqTSF+9dVp+HhTIzZLgd6O5sTfFQzH1O1r8wXx/Oe78cXONgBATZEHEem06VGTKnD3u5tTDobo6gmqGaICtwORSAQj4oHhzmaf5rlo6zYuiwsEQ4bDK+T+JfHcL9nagutOmwKXw46mrkRZory8QGu3H2XxgRFyJqknGE75fpS3b1tj9wG9d294aTWeiwe8FQVuNHcH4QtGMvqMdMbfr3kuh3pGuycQwh9eX4/HP92Bu86fja/NSf7+DEr3c9nR47A0Xs4kXh+3w45q3Yj28gI39rb70eEPIRKJqJ9rh23w709742D5LulPsZgoFY5m9LzJxwOZXP9AX5OA9N3gT7MPEORJmJn+Xcp1Y+gORno1BRhQTmh9vLkZX68doZ7AkN3/wRbc8eYmPHzRYThxWnXa++ur97FVPieZPr6lg6TZs2fjtttuw/jx49HY2Ih7770XF154If71r3+hsLAwq/uqq6vro63MnpW2pS/taFe+YAtdNhQ7lH+v3rgVw8P7Mr6PnfWJg7QVq9airSx58pjw9qpEac/KlSuz2tbB/pp0d7Rl/TdnIhQMoCa0F387qwrf/L8GxAB8uaoOpV7jskcRcGzZuB6+LuX12LJjJ1Y6m9TrrN+vPeDdvn0nVtqboNdfr8nG+sQB8/ot2zEmlggwtrdpg6SutmY4Isplq9Ztwrufr0dbIIrJ5cbvyxVrNgItHqyOD3qYnpccTC3bomQ+C20BzWvY0pX4si12hE1f3/W7GvHekkRJ4BtrGvDGmgbNdRq7Avjp05+hMj/5dbMDWFv3JWw2G352RD7e2gq8uF4JlJasSpRh+cMx9TW597N2vLs9kTHp2r8LwWAiAHO27sCxY7z4cKcf1fkO5Lts2N6u/D3V+Q7s90WwdtMW7G9VHmf/7h1YGdkHX7PyWuxqbNX8vSv3GQdJby9egWGFyV9j7T3G13/r0xUYUeTEF9LCuDulIRGLP1+FEUXK/bV2JoLFtq6elJ+vtY2Jv33tribc/+oS7OsK4+xpyvdUky+CNn8Uk0zeJ7Lnlif2j8WuKJoBtHV0GT6+/jOyfpuSgQv7uxGOD3tYv3krPt2g/C2frt6ieX8Le1uUz+otx5XBv28L9u9V7qelQ/m/PRaBr02boc+3K69nc0c3Vq5cie07lcfo7uzok33RYDHYv0v60/54Oe7+xkasXGneHyjs2du77/jeviYbdye+G7bu2ImVruYU11Z0BROB3Pbt27Ey2pDi2gl/XtqGxfV+/L9TKw33aelc/q/9aPFH8fn6bbhgZpHmd9FYDHe8qWzHr19ZhXJ/Zdr76+vP8GD5nFg6SDruuOPUf0+bNg1z5szBCSecgP/85z8477zzsrqvWbNmDXg/UyQSQV1dnSW2pT/sX9sAoBnjq4swuqoQn+/dg+LKYaitnZDxfXzQvAmAcmA5fOwE1E6oML3uM1vqAChf1LW1tRnd/6B/TV54HQAwoqYKtbWH5PQ+AaC4MF99Lp0vvYFQJIZp0w/RlILJwv94AwAwZ9ZMVO/eAOzei5phI1BbO069TvvGRuCDz9WfR4wchdraRBasv1+TnbY9wOI2AEBF9XDU1o5PbMuOVgCJL8axI4ej09aB9c2NyK8YjscWrwMAHDXR+H1ZWj0SJSNLEYk1oNDjwFnHzcX1776puc7+buWM1qwJw1FbO1W9fFooAvznLQDA6Jpy1NbOAQA8UdiE11c34Kuzh+E7j36GlqAdeTXjAKQuM93aFsbWtuSS1Ty3A4ceeqj68ylHAe/99h20+kKwFVYAUAZQ+CMxzJw5E06nE+vefF9zH8fOnYW7ly8BoBzo1NbW4s9TQ3jk4+04c85w3PDSamxvbwMAzBhVjv0bG1E1fDQiG7YACKP2kKmoHV2KSFkr8PFShGwuzWe4YU0DgNakbc+vGYvayVWaywLhKIIvGJ+Iyasei9qpVfiyZweA5MlTw8ZORO2YMuWHtz8AoDxfEThS7lOa1+0HoGRf9vfEcEf8/XTmwkMwd2wZJv5C+Uy99ZOjMaEqzQk+6fM3proU29qaYHN6NI9v9hlZ6dsOoAPDKsuUctCmZgwfNQb7vlgLAPAUVaC2dkbSQ/r//Q6ACI6YMwNTa4qwLbYb+LwO/qgdQBQFeR5MHT8GqFur3mbi8ApsaW1AIGpHbW0tPuvcBmADqivLUVs7O/XfeBAa9N8lA+Dt/RuB9VtRWVmF2trpaa//xr4NwHqlhDeT7/gDfU12O/YCn7YBAGqGj9R8jwl1u9vR6gvi2Ph+qLEzALyinIgYPWYsamuTM7eb93dh8/4unDZzGB77ZDve39iITa0xhKJAtGQUamcNy3pbW+L7jTWt9qTn5vMdrQCUICk/z2v+3En7njlz5qjTB3PJKp8TsR3pWDpI0isuLsa4ceOwc+fO9FfWcTgcltlxWWlb+tLeduWAaUx5gbrmSWcgktXfLvdK+oLRlLdtkSaLZfv8ZvqaxGKxPtlx9IbcVJ/ndvbJe8rtTDwvDrvS3B+FzfCxotGY2uOS53bCE++/CUZjmutHY9rnz+z++utzIk9X7glpt9Uf1paLFXhdak/SWums5idbjM8wtvsj2NqkZComVRchz5PIJDjtNk1/1+jyAs1jF0j/ll+H46bW4LipNWjzKdmL/Z0BbGjofa2+0Xsnz+VAK0Kol9Z6isaASMwGj8OB0eX56kQ+ABhemg+5Yt/hcKCs0IH/Pm1afPsT5R9F8eevJxRVe15K8t1wOBwoj/ddtflCmm0KmPQD7Gr1J227r8e8d3FHSw8cDofpWPAOf2L/1CO9Mfyh1PutLpMhD/Wtfswbn/jbV+/txORh2vWiZPpRyMOK81I+vv4zIt6vhR6XWh67ryOgDoJo7Qkl3U8kGlMnAFYWeeFwOJDnVl4jUdrpdtpRrFu3alRZvnodu92uLu9QkucaEt9vZobK93suiDUTbTbj7wA9+dORzXPc29dE/q4K6b7HAOXzetZ9Sm/kx/97AkaV5UP+yggb3AYATv3zxwCAxy45ArfGJ8kKLb7kz2h225z83Ly1TqqOaPYhBlvaNfaisKsl831hsHxOBlWHZXd3N3bt2oWqqqr0V6YBJxrbS/Ndhs3umZCbJdMNbmju46bEWCyGbz60BF+/95MBX9sH0D6XRk31vaE/SJPv12lXdhdmzd/ygIZUI8D1z91AP5ep1knSTzUrcDvU9/KqevM1MMriC9K2+YLY2KAEU1NqlAzCcVOqkOdy4KrjJ2puMzo+YtnIpOrk7IOyppJynuvDTcnlipnKcyd/DeTFR0Cv29uhufxr936Kz7a3oLJQe8DsdNhTTnK64fTpyHc7cN1XpqEgft/dgbD6mRbT7UryxMmUsGY9LrPpctubfEmXiSEKhR5n0sKu2+NDLsz2FSJ4enFFvWZ4gi8USbmWS6ffeN/UHQxrPqfLtrXi0Y+3md6Xflx3/CMHv8FkrXe3+fC1ez/VDNcQazTlexzIdyvvDdE3BkANrGUdPSGIp7o0/vzrexrcTgeK87SlgiPjQzaiMWW6n5hsqF8EmMiMGNyQ6TpJ/T0JVf5OM5puJ0+hFOuHyX2Y6SbiPfTh1qTL9ENbshUxeC7lhc0D4Si2NnUnXUdvoL+XrcLSQdLvf/97LFu2DPX19VixYgWuvvpq2O12nHHGGQO9aZQBcQBU6DWeCJYJeYeTNkg6wJ1LOl2BMJZta8GXu9qwdGv/rNOQSqt0wJPJWiiZ0C9QKh9kivVPzL6o5G1IOQJcd/uBnm4nfxkYjQCX5bmd6sHihgbz8dZilfVWX1Adgz25WqkTf/S7c7HsFydh+vBizW0mGKzM/tdL5uKCeWPwXYPJczabTV275kAmOuon7gGJIEme1gcAmxu7cd4Diw2DlmOnKHXuYgqabPaoUtTdfCquPG6ievDe1BVQX3sR7In9RCymDTzM1ikS6zTJxLYVeZ0YV5FveP3m+Jju+eO101JbfUF0+kP46fNfai6PxVJ/xszWf+v0J4bXAMAzy3biN6+uxWKTzKP+fibF3zNGf/+9yzuwZk8HbnxltXqZ+Nvz3Q71dVi6LfFY+kWMgcR+pEgKKvXBpdtpR7Guoby6yKOeROnyh9UATJwgIErHluUI8FQLmfcFeZKoUdCwT8qmiwmY8vdduu/lxVuT9wMHGiQZPUf6YyejRW71gSqDJIWlg6R9+/bhpz/9KU477TT85Cc/QWlpKZ5//nmOAR8kxBobxV4XCuIrUaZblFFPPpNjdrZWaJbWJ+mLA295R7PEYOfW31qlA550z2tPMILl21vSfsl06tajiUo7TnFAZJpJkoMkaQS4/osipF9c1sIjwPVBY76USRLOO3wUakeXqhkSABhbIYKkkLqI7OR4JsnpsKPI60J5QeKMu9dlx8jSPOidOK0Gt50zy3R9MLGWUjbrcXx11nDNz4ZBknRZdZEnaeJSl8Fn8aYzD8F/nzoV/3flkYaP64i/f8S+oKEjoF4uHs/ttKv//tUrq/HZduVkhDj412dMd7UkZ5JEGarX5cC4Cm3guTUe9LXEM0mnHKKt/W/zhTSfK1lLiky1fh0n9fKekCZIEvbED662NXXj3fWJxm75fm47ZxbOmK28Vv6wkskKhCPY3+HX3Nfq3Ylsnwjq891OdTFieb9pNAJYBEmlBYn3tT6T5HHa1WyfUJLnQmH8fdHpTzxvZQXMJFFmsl1MVs6S9MfJtVCaTJI87l98tuQy+N6cvDzQddaMMkni2EVMDzWqgtB/D3PxXIWlg6S77roLH3/8MVavXo0PP/wQd911F8aMyf2YY+ob8hoo4iDPn2a9ET35gDpVkBSJxtSFMYHcnQXZ1eJTFwGVDww/THHmvicYUc+qvriiHne8sSHjcoJstPckdqY9aZ7XHz69At94YDEe/3R7yut16Po5/FJfhjjIldeOkImdqtthh81mU9dp0X+56Nd8GehMUqrFZHuSMkkOFOsOFueNL8dLVx2JlTedol5WFR+X3NodVA/kx+syRRWFiYzLxKpCtT4/G+VZHpC6HDb85duHquOcARgGYPJlX5szIimLIB/M337OLADKQfMPT5iUsmwQgJpJ2hc/2C/yOjV9fiII/eeXe3BefC0k8f7WHwAYve/Fe9bjtCc957vbetDhD6E5PrJ7+vAi/PvHR6vBSKsvqPnbpg0rUjMy+w2CHUH/uREauwJoNDgz7HbaEY5EccId7+PSx5fjd6+tw/Uv1qknekaV5eGCeWPUjF4spny+fvzMF5j3u3ewfl8ii7lPCppEUF/gdmB4SXLQ3eILJu2LRHBTLpXJeXTvCSVI0r4HiqVyzw5/OBFssdyOMhX/3McyzCXJ3xX9kemQH8NoOQO5L7OlO4jLn1yOr9/7iXpZMBzF3xZvx0+fX6kZX57KAWeSDI41RBnuUZOUbP8XO9uwtbFLc9JUv/5epovnHuwsHSTR4JaTIEnTk2ReqqffseTiLEggHMExf3gPR93+LvyhiNqYDCgTbbY1dePqp1fggoeWaHY2R//+XdT++i20+5Synb+8txmfbU+ezHWgWqXmc6ODxVgspp79FguKPvJRcg20TJ9JkjNUoidJvzMV1IVk42ehzXqS9AeUmX559BX5vaIvI+vWZZYKpHI7YVxlAWw2G1wOO75/3AScekgNjpui9E3uaPGpZxPFmX2hQgpw9BmPTGV71j7P5VACWGmdqzx3cpCUL1121ORKNbARRHbshSsX4lvzsjtxlcgkJYIkmT5TByTeh4W67TA6UxuIB+EelwOjpIBNZKjW7O5QPxcVBR4cMqIEh49VJtrta/er/UpjK/Lx2o+PwbD466bP4MjMMkmNnQHDTFIgFFHXbwKU3oRnlu3EI/HFd0UgLmf09ncE1NHuYgFiQex/GuIHbaX5bgwrSS57DIajSe/xFlEmJ72XknqSHPakkwPFXieK4oNIOv0htZ+L5XaUqawzSf0eJCUez2hfIwdJe9r8ms80oHy3/OqVNXhxxW78a9UeAOm/73oTJMnfsUbnMEUm6ejJSpC0clcbTrzzA7xat1ezrbJUa9wNJQySqM+IA+4ij0sNkuSJUZnQ9CSlyCTJtcFAcklXb8gTsDY2dGoePxYD/vjGery6ai8Wb23GtuZubN7fhZW72tSDrA+kbFNDigOsXGyfUfB5++vrcdhv3sL7GxKTbYwawGX6gz0565PIJGUWJInJOPqdr/4xrFRu162r3dYHn/luB4p1CxrLAc71X5mOBy+aq2Z4xAFySZ4rKWMjBwNlBb07sCxPc9Z+wYRybPrtV9SfxWsoTy0yKreTA9nDx5YZXgfo3QGxCIrEAUahJ7mMS0+UkV20cCwWTCjHd+ILJwfSZJLkDJg4QFhV36ZOcxOvk1hA9p31+/Hdvy4DAFQWemC321BVFA+SUmWSTIKkpq6gYZDU6Q/j6WXJU1rr4mUw4j3mctjV1+w/qxMHNIUeJ+Q4ZmeLD+FIFGv2KKV3M0cWJwXlgggQY7EY9nf41TKhsvwUQZLTjny3A/JgzyJvIpO0fHsr9nf6k+6HKJVse5LkrxKzk3W5lK7cbp9Ubvf5zuQTofJttsVLfdN9Bzd1Zl9uJ/fOGlV6iCBphq4P9p8r9yRuZ7GBSlYxqEaA0+DSKQ1usMf3hkYHNalk2pOkPzuai7MgXbrMkfjyz3M50BOK4LW6xNlcl92OY//4nub2O6WmcnF2qNMfQoc/bNh/ki15UpVRkPTgB0rW6OZ/rlEvS9e7pH+OA1JQKwY3pOtJEgfg3ni2Qr9t+ub0AS+3kwc3xDNHD36wBV6XIylo0mddxlXkq6V1Mn0ZnNEwA7m8rrrI+IA2nXSZpFAkBpcUEDnsIsuX+DuMAqDVUmNvsddlWOcOAAWe7L9CxAALISmTZBB4ic93ZaEHz16xEPWtPvx9yU7DjLHck3TS9BqMKPFixohizB5ZgrfWNuDjzU3qmWsR5JUaPKYI1qpFuV38RMcdb2xAY2cAvzlrpnpCQHxuxL5BMMskdfrDWClNnRNE6ZyctclzOdAVCOMV6YCm1RfSLI+wfl8n/OEIekIRFHqcmFBZiHA0BptNOaFT5HUi3+1AQ0cArb4gRpfn4w9vbMD9729R90VycGM0uEG/9EGR16n2Kf3lvc3q5UbPJZGRxHS7zK4f1JS/5e4gfvGWZriddjWjLITTPJ6cSfpyV1vS7+XKjMau5J4lIz2hCLoDYRR4nPhoUyPeXtuA60+fbtqXCmiPVfTHQrFYTP0eK/Q6MaLEq/ZEytnmpJ4kltsBYCaJ+pC23E55q6XrndELZRgk6ftccrEDlQ+QV+9uVzNJCydWoFB3cBgyOHuzvTnRVF4fHw960p0f4Kjb30VjZwCv1e3Fra+u7fXEnrY05XaC3HAuX29rYxdu/896LN7SjOtfrMPe9p6kAEa+vjNtT5JyXXGAZVZiKcamiveEpTJJwTBauoO47T/rcdM/16jDBQT94IaFZovI6g4UzYKgb88fg+ElXnxnwdhebbucSTLK6ugHHYh4ST4I9hqU2y2IL9o8a6Sypo/ZZEn95yATU2qK4HIktks/gU6fSYpEY2pwL8oARZAXCEcRi8UQicbw23+vxT+/3KOWxXiddhR6nPjwf07AwxfPxcxRyt/yUXxc+sjSPHWtEKPsh8hC1UiZpJ5gBH95bzOeW75LM1VOfG70iyy3dAc0PUPCjpZuTflu0mNLz4H4nIgsEaA9OAOUUe1/W7wDgJJFstttcDvtqChQDoImVBWiPP7vlm5lLP39728BALXncri07XIQrfysbIN8MOt1OZJ61Zx2W6/eEzQ0JeLuzL4D5JOs+impvdXuC+GCh5fg3Ps/RSgSxXX/WKWeWAymGeet/xzqyUMYxEmWTIY5iJOqFz26DE8s3oH74p9VAHhzzT781xOfab7X5cCoS7d8QiAcVU8aF3qcuP3cxELPcjm5/pgp25aFxs4A/vuFL/Hp5t4vR2FF3JtRn5Gn24kD5QPpSVq1uw3tPSHDcpxAyPgDvnhLM2qKPelXuTcgHxg+s2wXCuL9EIUeJ8oL3JrfG5UC7tQEST7s7/SrJTtr93bgqqdWAAAOG1uG03UTx4w0dwWwsaELCyaUw2azaUaAyxmit9c24MklO9SfO0yCy/MfXIymriAe+EDZAW/Z34XjpmrXIJNfL0eadZICunK7RImlcSapPN+NPe3+ge9J0qyTFNFk6PTrBHmcDuS7Ewf4h43RnnmUr1fgdqiN9NUGmSQA+N3Zsw5ogeJyab2isgK3pk+tLN+FG8+cobm+067tFwOMM0m3njUTs0aW4IJ4v5FZqWuBO/uvELfTjqnDitSpbMdM1r7n9J/v7mBYPQgQmTzRUxWLKdmyf365Bw9/pPTz3HjGjPh1lOuKQGj2SO0irnNGJ342CpKSMkmdAU3Z7LOf7cJ3jxyH6cOL1c9YdZEH26Q1SKIxYIM0ZEFYvbtdvb5RGZ+cSdIHLAA0ayMBwJ/f2aT+e/aoUvXfw0o8aOoKYGJVgbrtbb4Q3l6n7Z0AgNNmJib9yT1rQHJmSXA6tO/b4jyXZRbbJuvLtidJLlXL1fdGmzQA6cUV9Xj2s10AgP8+dWraTFJDmiBJziKL5Qf0x0B2G6D/Sm3qCmgqFL6QSvmu+NvnAIA/vL5eDXjkY5FYDOgKhtV9iHyyt8DtxLFTqnD7ObNw3Yt1mkyXvnwx25aFRz7eihc+r8cLn9fjyUvn4dgpVYhGY/hiVyumDSvuVdWBFTCTRH0iFImqvQHy4IasM0nS8tX+UBQvrqg3vF7AIJP0+Y5WXPDwEpxxz8dZPaagb9oXTdWFXmdSmZNRT8LWpi713/WtPfhcGt7gkA4k0p2NEk6+60Nc8PASdQiDvOaUPIXuv55cjg83pl83Rz9qdNn2luSeJKNMUrrBDfGD0jyTPjTxGOI5HPBMkqbcLqz2qwCJs+yVhW5876hxKC9wo0ja2R8ZnxZkZHJNoqzMrD8EwAEdVGozSYl/zxpZghW/OhmHjFACgV+cPh0uhw13nDcHgPagN98gk1RT7MWPT5qsflEbZT0K3I5eTeQDgCppst/RuudQH7R1+cPqfkMMkJB7qgLhiBp0AIneIa/uwL6i0INDRiRq8rXBhBejy7UlsCKbIyYB7u/0J/UWPvrxNkSiMfWzOLwk+XU2WrhWDL6YVF2IBROUJS3kt4Hc92Y0WGNPin3GyTNqEn9XsfI3TawqVN8fTV0B7G3T3r7Q49RMJUwe3GBc6iNntwDjxWqJzKg9SZkGSdL30QcbGw2XAMiW/P0jZ2z8oYi2J0kXJEWjsZTZYEA7hEH0DepP6I4zWB+vqSuoOUG3zWDx1037E8cX+qUr5KobcRyTL+2vE6P7E9dLXuQ98+/lWCyGN1Yn2g+eXqr0Wz63fBfOvX8xvvfYZxnfl9UwSKI+IX/4Cj3ydLtoVuOwxY5JTAuT6/Jl+h1PKBzDs/HGaKOFLzOh70cRijxOzWQywLgUUA5C6lt7NBPu5FHTEZPyNT2RXn8vPohB/ruyydClev71f7NcOpPt4AZxcOfXPf/tuqb5ge5Jkr8cAuEoNhksEnvrWbNw05mHAFB6iV686kg8e8WClL1lc6X69hqDvqVckIN1Odix222a4OvyYyeg7uZT1fJA+SA4kzN8RqUmhd7enxmcJQUo+hMO+s9rVyBsUG6X2P5gOIq10gHFhn3Kv41q+I+dkshazZG2we20492fHY9F06vVy0QmSQS4DR0BNMQDMPEef2Xlbuxo7kYkGoPdBnVx30yNrcjHQxfPxUf/cwIukkou5TWJvK7kr+ndukyS8MZPjsUR4xLrCF561Dgsml6Nsw4dqf4d+zsDaIrvS75eOwKVhR48fPFczf3IQShg/lpfpCsTHeCPMg0ytixHgMulaje+sgbH/OG9FNfOjFyFsUOq/vCHo5qSPn2ZnL7E34gcJIUiMexq7Um6ndFk06augDrEBVCOH/SfeXk/qV+6Qs4QdcanAsvf5eLfcgZKHxRl07KweX+Xpr1AHKv8PV7Rsiy+3t1gxCCJ+oT4kOa7HXA67Jov+mwWWBMf1OPjZWAbGzoNe3j0maRgJIql2w7sgyl2ICfPqNGcZS70OJPKc/Sjs/Xae0L46yfbEvftl4Ok7LZLlN/IWblsMnSpgkYRbB4zuRLThhXhse/NU3/nUgc3pFknKW25nfK3l1slk6R7P769bn/SdcTYauGwMWVq346ZueOkIClFJulAyD0hNptNPUj/6qxhSdeVgwa5LO/EadVJ19W78YzpAIArjhnf622VXXX8RFx53ET86+qjk36n/3Lu9IfhC4k+NuVvsNls6vusKxDWNE2L8jZ9NgSA5jWbNUpbfudyaBf0FeUqIpPU2BnAO/EytVMPGYbyAjdCkRiW71BOflQWerIOHMdWFKDY68Lo8nx1DRPlsRP3Y9TPJs5zyFm30nwXptRoy4qPnFSJR757BEaW5qlZrj1tPWiOH7xdvHAclv9yUVJvnfz86rdHdt7c0XjhyoWZ/KlEByzbwU+ZMDvB6A9FNCVn+v2SvtLEiD7w2NXiS3o8OasuNHUGsVqXpa2rb9cc+8gT7fQnN+XppGI75SBJnISRT+7qe43F93l9qw+/enk1tjZ2wczH8T4kcXJFLCvQHxMI+xqDJOoT4sMnPpjyAVo2WQ+xY5pUXQiXwwZfMGJ4FlUfeHX0hLBTSsX3ZjFXdSKMx6mulQIoZ1XLdSObUw2VMCI3XRot/paKOPiTz4D1hCKIxWIZ/Z0tBuU/gggMj59ajdd/cqxm2k+mmSSxfXlmQZJfm0ka6J4k8d4R79V3DPo1jErS0jl8bOKMfpG3byZ+ydmiWCyG//vBQtxzwaG49KjUwcx2qXxjilQWaObiBWNw/+lV+J9Tp6iXZfuel3ldDlz3lWlJgQoA/Ncx4zVf6EaZJCDxPltV3675/ItSNKNM0lETK/DNuaPx05OnGA4YqJQOWES5XUWhR/38i0z2sOLEZSJAG1biRZ5Jj1aByftHHlohByryPnLasMTro5+aOKk6cRa6PN+dsnRzRDwA3NvuVxetrSw0n44oB5lGfaCAsk84Ylw5HvjOYQCUPg6ibPWm3C5XzE4a9gQjmu86/ck0EaTkux2YNbIEBW6HpnrASH1rT9Kxin5JCUDJJMklxICyeLy2jE76d1Bfbpc4CSbWl5RP4IiJok1dAXyyuQlBabiD0NDhx4MfbMFljy/H35bswDcfWmL6d+1qUY7J5sdLh8WyAgfDGHEGSdQn5Ml2gHKWVvS0+LNYK0l8cPNcDkyoVM6Sbt6ffEZDv/PcoCuZyiZ7JYidUIHHgWo5SPI41UlRgtlwBAB45OK5+P25s/CPHyxUz9o3dSfS8JmcbZH/PnGGVz6TFIspZXhmU8hkrSn6BkTK3OgsfMaLyTrMy+38oYh6PdFPY5VMksgyGG2OfjHVTFQVeTB/fDnK8l2YPTo5GOgL1UVenDlnhDqswMyNZ85AeYEbz1y+IKP7tdlsqC5waA7Ce1vGms7YigJ8cePJmDde+cLt8icGN2iDJOXfO036Egzfww47fv+N2fjxSZMNb1NZJAdJymvusNvw8g+P0oxxryn2qpmZL+vbACjPfb7ZelIFbtxx3hwcOqZUc7ncj1DsdWHmSKVnauHERFZpmrS2iT5TVKqZgpc6kBeT97Y0dqknLioMzmIL8sAIETCK/il9/9ZpM4djxa9OxlXHT0y5DUSyTNZJauwMqAfbvfkeT8esCiMQjmj6kJKDpESf5PPfX4iP/vdETc+jkV2tvqRsmMthV/d1Yv23+laf2nO0MP691N4TUjM0gHKyU2yTL6Avt5OGSsUzSfKQHXGCyBeM4MJHluLP72xMOll54ytrcNt/1qvHUkZLGQh74+tFzYwPx2nrCSEajWU9Ic+KGCSRoeauAJ5cvF0zHCAb6kKy3uQv8WxKw8ROwOWwY1L8AGHT/uSeEf3OU1/+pu9ZykSXmqZ2aTJJRYaZJPPn6dgpVfjmEWNw+Nhy9cCqRepXEqVEqcgDFcS6N/qD1EsfX44nF+9AOkaN5MKWeErd6AAzbSZJV25nlEkSf4fdllgPZ6B7ksR2m43zBno3xQ0Anr58AT697iTNtLK+ks3aW1+vHYkVvzo55d88kFwOuzogo9MfUt9DeQaZJLP+nHRBgxE5kyRnT4aVeNWDFQCoLvaiJv5ZFlP6aoo9phnH8gI3vnH4KNx1fq16mdNuU0/8CM9/fyE++p8TMKk6cfmM4YlMkn6NKbmfTB+A6Y0oUd4fYukAj9NumuESvxfEc3H3tw7F94+dgKcuSw6uywtSZ7KI9NKtk7SxoRNH/PZtXPjIUgDGmaRHP96GT7f0fuy0ebldNGW5nThJWeBxIM/tQHmBO+0+5+NNTXhHV87tsNvw1H/Nx7JfnKSeqPt4cxMi0RgqC92YGs8kt/eEdBUowIqdrXh22U6s103QlHs0Eyd7kzNJwr3vbUmZKUpHTNsUi9VGojF0+sPMJNHB6/t/+xw3vrIG1/1jVa9ur88kAebr5qQiPmQuhx1T4gcImxrSZ5KSFkXNoMlStqmhE2viC2oWehzacjuPy6AnyTjQKct3aWr7xY5Kbug0G68sPPHpdiy68wP150A4ikg0ZnhW7a8fb0u6TK81RZCkH74gE5lA054k/eCG+OsdjsbU11HUShd5XWqwN9CZJLFtEyoLDMdhA8YTxjLhsNt6fdtMPfrduThlRs1BV+okPivN3UH1IErO6ImD+L3xL+jputXkjQYepCOXn+kD28OlgQg1RR4M1/WZ1RR7Na+1vG6V2F/I+8MxFflJn7N8t3bKHKBt7HbYbZo1pgo8Tjx9+Xyce9go/M+p01L+bVVFHs26WZWFnpRBjTwGXO3PKvbi+tOnY0xFdgMqiIwkMknG3wH/97kyzXZZvL/YqArlN6+uxbcfXtrrbTDLiPtD6crt4idupO8MsyBJlMzW7W7HC59rJ/Qqn2k7qou86kkaUUEzc2SJmsVt7wklfXdf+ffPcd2LdXjpi90AgAlVyr7ipS92q5mhLoNjsd6e9GuPn2DRL0ovSpzHVxaoJ15afUH2JNHBSzQj/0ca66j5/fYWnHjH+3hvfXKTOyBnkuQgSXm79SZIcjtt6tnVLQYNhPqAQR94ZFPi5wuGcfJdH6qDHwo8Ts06N4VeJyoKMxvcoG+6Fmea5WyO2RQ94aZ/rtGU8wVCEU12Rj7OyaTcrrkr/Zheo7VZxJooZkGNeF29Tu1aNvLvRCapOM+ZyEz14dmmV1buxuc7WlNeR+6lGisd/F0wb7T6bysvkHnS9Bo8dPHclKVTuXT1CZMAJE82yzVRQ79fGrstH5C4dZmkiVXaKVFG7+F05Mx3sa4PR+43qCryqJkkQckkJd4nI6TMngiY5L6ATDN/cunklJoiTYarwOPAkRMrcef5c9TMrBmH3aYZIKLfh+nJE+7S3TdRb6RbS1b+bovFYhlNlBPXNfPKyt14f0PiuEV/wC/4Q1HdCHDtfYqBCHKGxuyE2AzdCRyZ3BNaqduHzxxRon7e23vCmnI7QLugPACcMXsEKgrcaOwM4MNNjegJRtQ10eThQ/ZeLvq8pakL76xrwCE3vY4nPt0OQDkBLUrxRpTmqdNKW3xBltvR0HXdi3XY2tSN7z1uPP9erI2T55J2IL0otxNnVFwOO8oKkieyCPpyOv36BdlkkvQ7ngL94AbD6XbGwYl+EVFxBkcOVDIJbGT+UERN9dts2lKFTFfzTpcG1y8mCSR6kszK49TyxPiBoMdpV7/kxGsuFpIt9rrUTEBf9bZ8sbMV1zy7Eufe/2nK68kZsPFSj8hJ02rwqzNm4DdfP6TPs0GDyU8WTcY/fnAkfnXGjPRXPgCi3E6se+Rx2tXAGkgsFrsnvuaPfsFoo/dwOmMr8jG2Ih/ThhUlTXSbUlOECVUFGFHixaiy/KQ1kWqKvZpyO02QFD9wkAM3ozWVzPzr6qPx36dOxXlzR2mCt2zPCMuPqV/GIBWz6XZEByJdT5ItEUYhFImlHfDQHQjjhDvex4Lb3lHHT8u2NHbhmmdX4pLHPlO/d82OR5LWSdIdQ8iDGwT92mzCDINepdHlefjF6dPxFWkRZ/2ku1mj5CApOZOk1xMM48w5IwAA/6nbh5+9sFJzslfWm0z71sZu/OKl1YjGlJO3ALAvnkXyOO0oy09U2bR2BzXPX2+GZ1kB93yUUm9LzNWz89IHUaSis+kPCkrldmq5nkHAo79Mn9lJlUl6cV0XNkfq8c15yplxfaBR6HFqzsAWepxJZVlGi8kC0KyaDQD58bM5mnK7LIOkQDiqnv3KczmyDjL2dwbS3iZVT5LZInNduvUYbDabun3+oPKcih63kjyXmmVr7DJvCD0Qcl12KnLfm9xIX5rvwqIZuRl5fTBxOuyaqYd9pVAXJOkDVY8jMQIcAMaW52tOGvSmJ8nlsOOta4+DzZa8yK/DbsPr1xyLSDQGt9NuGCS5pOyLnCkqz08OSIaXZN5DNmtUiToJUB7WkO3Z4JFleWqVQLrMo5zx7215DlEqiZ4k4+8Uea1qs+9Y2Zo9HerCq3e+uQEXHDFK83t5QffFW5px8owa80xSOKL5rtNnRYyGyZidTBtVllyeevSkSlx+7ATNZfpJd0dNqsTiLc0A4kFS/ASuy2FTt80p9QrPHFmCqiIPHv90e1JZn/4pzmSEud6Wxi7NiapYLKaepBpZmgebzaaeELrsieWa2wYj0V5l9wcaM0mUksMkSpLr5I3KxYIR5QMol2xkm0mKxWKaniRRxmUU8OgDL33gYZZJ2tbUjadWd+F/X1ytZkh8uoXZCnRBUnGeM6nx0ayvyKzcTg7EUgVJRqWJSiYpeQedqcbOQNLfqGdYbpemJ8mo9ln/msuj4cW0sIZ2P/qC2ZefniijcDvtGF8hB0mZn2mn3FPL7TqV94d+cpw+U1Rd7FGzT4BxoJ8Jt9OuCXb0vxMHQvI+weuyY1xFgebzKAdRpVLWZtH0auS5HPj2/DG92j45QNWv35XO2YeOVP+d7sSuvI+y23t5towohbSZJOlt15TmZFosFlOnrAGJCWsyufT6w42NAFJlkqKaUnA5YLrrrY345curAWhPIJidmCnJcyWVJxt9x8onZkaW5qHQ41QzSR1SJkneB4wqy8OSG07Cn86fgzNmj8C8ceWGJ08W6tb1y6aiR9iwr1Ozz9nb7leHNgwvVfZ3ZSalueJE6WDDIIlScph8OcofFP1kFSARtMgHMp4se5Ii0UR63S0tSKu//Ztr9mH9Pm3WINOeJPlAWiywqL//wvj0mheuXIhnr1iAfLcTdrsNH//vCZgXb+Y2LbfTZ5IMzsiandEJhqOGz60/FE00jWYRJIn9b+MBZpLMepK6pHWlBP1EQ3XtKa9THaveHYxknU3LRKYZNlFG4XbaNU3zpezDGFBqJqnDJJOke49WFHg0vTO9ySRlo8irLN5a5HHi6csXIM/t0ARJcqmtXK724EVzsfyXi3q9wPDlxyTOPmc78+T4qdU4apJysHT05NSTDftiTRoiI2YBe490YN3UmbrULByNYa90wi0WSy67/3xnIkj6wCRIEif5/KGIpg8pEo0hEo2hvSeEP7+zSb1c3i+Z7XOK85z4zVkz8cMTEiPyzcqBxXj97x+nfM7lcjvRkyQHSSPL8lBZ6ME5h42Cw26D02HHeXOVDNqREyvwxa9OxvJfLsIJGSwans6aPe2aUeBr9nRgX7xndFixst36VgShN0GZFTCHPoQt3tKM+97fjF9/faamF0NmFiTJB6Dr9nYkld+I1LTnADJJ8pkbl9NmWK736ZYmXPG3zxPXi6ehM51uJ2/Lvg4/qou9mh0zkKjlPUKabgUoKfQpwwqxbHuLaZCkP9A2Klsxu+1lT3yGjzYljzb1hyOJxTVdmX+Ex5bnY3uzD/s7/WmzLGZrzABAxKTcTs0SyZmk+BeIeDx1HKnbiUKP8l9XIIyGDj8KdT0lB0p+j8ZiMdNJXurococd4yoTQZLZAprUP8TBishojNANOtCfia0sdMensPXEf9/35wD/efXRCEai6vQ3+YAp1QKs+v6AbFQXe/Gbr8/A4x9uxFdnDUt/A53HvzcPq+rbUDs6dclkX6xJQyQT+2SzWF+uUkmXSQqGo2p/jCAvYdLeE9KssbizxadZqFoYVuxFp78LgVAkaahQKBLFGt0ir5rBDSZBkii3K81LBBBek9KzJ743D2v2dOCM2cMBaIOkrfGhVYdKn125b0u44fTpuHD+GEysKsx6LL/baU+a5AcoJ1kbOrSvwZo97WoPt2gtaOgwrgwZrEESM0lD2AUPL8FHm5rws+dXai6Xdxpm5XbyWUajxV0TmaTksyyZTpqTa4BdDrt60BOMRNXSOP2q1OLss/4MknjMQDiCr979EX763EoA2lI3sYPVf5hT1eO7HY6kbZXpy+HyDcpjzKbbGQVIgPLcinK5PLcDvzlrpjr6U08O0sSOutUXUgcoKH+DHV+vHaG5Xapyu3SZJKMvDfF+6dJNBBJn2812rAeiRyopNOujArSDG4aX5OHO8+bgngsONS25ov6xcGKlWpLpddnxi69O1/xePz67vMCtGdvd15kk8RjyY8rlxfLBy6iyzPuPMvHteWPwh0WVSdOwMuFy2HH42HLTE2ACM0nU18Q70KwnqVvah6dazBRQgnq53A7QDmESvysvcKslYTuau5ODpHiZrD8cTRpwtK/dj/c2aCf6phsBXuB2qIGOnOk2yyRNqCrEmXNGqMGN6FOKRGPY0qj0W8knpZ2O5M+xy2HHpOqilAGSvP8Ui9kCSlmeWDRaJpeiCxsbOtXgVSyfcOyUKsPHG6z7Ex4FkGYxxpe/2I3pN76e9jbyjsWooVI+Oy/oD5jTkXdQTrtNswMSB7b6nZIY4as/EyIySUu2tmDNng68GF9XQC51a4jvhPVBUqrmaJcz9YHGJP3ijwYBV4/BGatU/OHECPB8twMXLRiLd392vOF15al8+W6HGujsaPEBAA4ZUYy6W07BKTO0Z6SNduCJcjvjbRXBXpFBkJRUbhcPFmviPVv7O3I/vKFbeo+aTfOLRGNqyZJ4r557+Ch1QhANnJI8F/5zzbG45qTJeOJ78zBtmHZClJwpKst3wemwa7I3/REk6ckHJaPL8/DY947AjWfMwOFjkw86rG6Aly+jISBdT5J8AjHdgB+jTJJ8bCIWcC8vcGNs/IB/Z7Mv6ftefGcq0+20W3b8He/j4Y+0axHKrQdGmaThmqUA0meS9PJcDs3aaFNrilBW4MaPTpwEt9OOn5/Su/Xxnrl8Po6ZXIm3rj0Wz39/oXp5dyCMJy+dj4cvnqu5vtGEvm1NPjVIEpmk8+eOxt0XHJp0XWaSaNASo50B4CfxDIvQHQwbnuWR3/A+g56agNTnIZj1FJmNlA5JgZbNpg2SxH3oS2rMAhqRSZKDkUA4otkJiwECPQaDG8x4TLINx0yuxIMXHa6u7SSYDVp4etlOXP9infq86RtOZf5QYrqdfH8/P2UKCj1O3HfhYepl8vMRCEfVHdn1L9apt/c4HUnjQA3L7XSZpK2NXbjiyeX4clcbAKknSV4by63LJAW12aaaPs0kpQ+S5GDaaAFdGljlBW5ce/IUzNc1HQPa96jIqMjTofqj3M7IP36wEPddeBgmVRfhhKnVuPRoTkgkMpJunST5JOa/V+1NeV/BcFRd1FQsaCqX24m1CZUgSamq2NHiS8ok1WiCpHipb4px/XKfcZ47eZ+jGeCSQSZJz2azaU7+HDFeySL97JSpWHXTKZg5siSj+9E7fGw5/nbZfEyu0Z7I7QqE4XbacfKMGs3l86Vsk5jcub2pW83wifHlDrsNX5szIumYwt9HS330NR4VkGG6VojGjMvj5CCp22BSmrxAp+A1yCRt2NeJObe8ib+8uwl6obBYI0nZPnm1eTHyW59O1k+dE9TgQ9oZd/rD2nK7DhEkJbbvvgsPS3nwbFaStWBCBU49JLlfwCxIuvGVNXhm2U48/sl2ZdtSDDIISNPt8qQd9NUnTsaXN52CIycmDijl19YfiiQ1v4vb68+AGZXbOcRisvGza9c+txJvrm3A1+/9RNlmv8Hghvhzp88kqUFS/AtEX+ucC/Jra1YOySBp8JJLecXCqIWegc0kAcrBx+mzhg/IY+eSWL/lpBw0fBMZSfQkpS7hBhIVL+Um63t1BcJqVmNafPHWTzY3Y3+38t3TGh96UJ7vxtj4gJ4dUiZpeIkXlx09Xh1hLS8mO3eceSZY/k43+t4cIY36l8f3Z5pJAoAWaX2khRMqE/fRB/s4syFKF8wbg3MOU6ZjfvOI0XDabegJRdQSwErdkCr9sgfMJNGgla42vTOQXE4nnxUw6qkJSH0egn7SGQD85tW16AqEccebG9XLwpEonly8XV3nxiXfh24MeMBkMk3S9savL4++1gdJIpshFsL9xuGj0h7suEwOrM3OYqdr2F6yNb4mgm5B2+8sGIP74xmiQDiaKLfT7SQdurJEOQnoD0WS7lfc3qsLnowChsJ4QCUmB+5qTZRpBsNR9TUvkg5UzQY3iEBKlNs1dGaeSVq7pyPp7zAin0U060kKRESwnciU0eAgf8bEmj/addn49XYgfv+N2bjzvDm461u1A70pdJBKNVMgEI4kHXuMKPHiqybfybtafYjFlMoTMYjqueX1+MFryhQ7sYB7eaEbY0S5XUu3+l165/lz8KszZmgqXsQJwbnjEj1APz5pMn6yaLL6szaTlBy0DJMySXJPktmxgxExtKbI48Sph9SkufaBkY+J5KmcTocdd543Bx/+9wm4+oRJGFOuXftJ3x/5rXnaJQ4Ga5DE6XZDTF19Ox79eKu6kwC0B4fyYoxCdyACaDOy2nI7gzRqQM0kpR7cIActkWgMLyzfhW3N3Xjwg63q5XK2xuNyoDMQVrNR+iyXWbmdyCR1SJPkukyDpMRCrem4TTJJZkGSx2mH3WZe7/9lfTui8TGjsqpCLybEp78p6yQlBjekemz5YYrzXPj5vDFqqR2QOAsmn9Vy2m2GgbM4EG3uVs7WFXud6hkuOVA2qtE2G9wgvkD2tmkbbs2s2NmKc+77FMNLvFh8/UmG1/EFw/j6Xz7BJmmgiFHP19bGLvXsoijppMFD/uyJUg/5vT8YFy60kmKvC+cePir9FYkOkP6YY3tTN06/+6OkY4vDxpaZnvzY2az02Q4r8Sat1eMPRdR9fYVUbre9yadeRwQ76onYcFStQDhsTCJI+uqs4djVIt8udU/SFKmcTZ5uF8pieuTNZx6CV77cg199dbo6ZbavyN/lT1w6D796ZTV+cfoMAErmb0z8uRtXWYCt8YV7HXabJksGAD84fiLGlOfjoQ+3Yu3ejowHdlkNgyQLeWPNPizd2oJffHV62uxObz300Vb868s9msvEAXs0GoPdZkNEt8cyWihV3nkZpWeDBpmkvPjOTS5nk8ugnvh0O3796tqk+5IPhvR9Tfr+piKv9oOa73bAF4yoH9BOqZGz0x/S1DyLpk9xn5msQWSeSTK+rc1mQ4HbaVpO19IdxNq9HWjr0a4JYbclDgD9mnK71AvSIQY88J3DcP8HW9VR78dMrsTRv38PQOL1ydOUDBj/TaKkSdR2F0s7RfEeyHM5NDtxERh3ByN4ffVe9ctFDG4QU7/krFQqb6zZBwCa9TD0lm5r0QRIQHJPUlNXACfe+YH6c28W5aWBJWeNKuIlMvLnrq/2oUSUG4npdtrL735nk+HJ16k1RfCZZCS2NysH7MNKvEnj9/e2+9XvrbJ8NybEM02723rU4wsR4MhtAaL/tqrIg9+cNRNd/jCm1BRqjjvkk4JyFcei6TWoLvaoZauA9ngom8zKohk1WDSjbzNIgnz8cOiYMrz6o2MMrycvG1NR4E5acNrlsOOsQ0fi9dX7sHZvB3732jr868s9uGj+aKRefMBaGCRZyPfj6/3MGFGMb/TRGbyW7uTeDxE4dPhDhkMU9EFQNBrTrKGROpOU2CmIhTpX7GxVAjK7Te07AoBXdMGb4NacHRaBgnL/ft36R4W6cruSPBd8wYiaSZLXJOrwhzX9VB1+JUMlgrhMMklmgxtSNWUWehNB0vjKAmyLn40R6lt7kibIdQcjiXWiwtLghjTbGEMMp80cjtNmJkoURkrTdkTTpdelzdYZEQeiomxBHn0sMkr6ckIRfN3//hbN5eJ6o+NjyRs7A/CHlL8xGo3h7W0+lIzqxqQa7USdVAMtBKND42BYe7t1e7WLD2fyWpO1yAGRyHKyr4xoEDHpSRJZH70pw4qS1ikSdsZPwA03CJJO/fPH6rFNRaEbFYUeLJpeg7fXNagnahNBUuJkrriNy2HHRQvGqvdXJfXf5ElrFconZr55xOik4Qcys9aAgfLXS+bi+hfrcOd5tRldv3Z0qfpvswVkgcQxQEt3EB9sbMSoUi/OGWt6dcvhN4oF7WjuTn+lXmoz6OXo6FEO2Ju6jHdM+iBJH5h065orr3n2C/UgVD5oOWpSJYo8Tuxt92NFfOVr+Qy/nMKWyeMv1bM84czK7cTO0iyTpM+StXQHU2ZpkrbNZAR4qlKfQ0YkptHMHZt8TqWx05/0Oi2cWKHuvMPRxGK56bbRKKaQzxSJZlg5SDA7A6+W23WJwCpxG3EWT7/jNws+xCj00nyX+prVx7NJTy/bhfuXd+Cs+z5Nup2cEDJbW8NocV59Jkn/vtH3ZJH1aafbuZMuIyJr02eSGjr8OPYP7+G9DY2G159aU2Q6LEmTSdIdtMsnf8UB/VUnTNRcR3yXiu81eXy4/uSLqKoAgKjJ95DZd9+fv1WLC+aNMe2tGignTqvB0hsW4ejJlemvDOAUqTdqQ0On6fXk44TxlQX4xenTer+RA4DfKBakH0mZS/peF0BJ+4YiUfXgF0iM0ASSBzPoty8Qjqo9Hz9//ku8sjKREdJPtxNnVl6Nj/Nskc4YyRNcZC6D8q2ASbldse4gXWQ7jDJJXYFw0mS+5q5gotwug+yC2Q47VSbp6EmJ6XNHSGM1hcbOgPo6HTO5Eo9/7wgcO7lSs7P5d53y/OWnWOgWMA8kptQo/U0nxidXaYc9GN9GfDF0ByPwhxLZOQDYEi9v0wepZs+huJ7NZpNK7pQgWfxtXQaj5eUvJLNyhUyCJP1tmUkafNwGgxvOnDMCFQVuTYkLEVmTfp2kN9bsUzNCRkaX55v25OxqUU6yDS9OziTJxHS8w8aUacZaJ4Ik5f7F2n0Ou01zPAQoJ0EXTa/GtGFFmDpM27D9qzNm4LzDR2mmzMq+XjsSt50zq897i/qax+lQp91doBvSIJO/Wy89erxppYpVWSvfRwCSMzW5ZBQkAUrfkcgkHTGuDM9dsRA/eOpzvLGmIal/RhxgygMIuoMRlOTZsblR2wuiPwNz3NQqvPjFbtTtboc/FDHMbOk5DXqSRDlfUkZA9wEUfTMvrtgNj9OhOYDu9IfR5dc+103dgdwMbkixAzxqUuJMjbzY6yEjirFmTwcauwJqb9XUmiIcP7Xa9LHS9dKYFac99V8L8N6G/ThztrJoqhzMmlW0FXmccDvsCEaiaO4OagLULfGSQX2QNLo8D0bypTruUWX5WL+vE/XxL8d9KcaByz1snf6wYZDYmWJxY0H/OWCQNPjI2VqRSSrJc2Hx9Sdpss9EZE22eC5JnPuqT9GbWl7g1iwDYmZYSZ7piT5xP8JVJ0zC0m3LlMXq49+BYr8ijgOKvU7DoT6PfPcIxGKxpN9dNoTWRfvDubNx/NRqHD3JPPskl/JPrCowvZ5VMUiyCPlD3VdTQCJSmZZehz+kTi2rKPDAbrepfSP6TJI4OC7Oc6E7EEYoEoMvGEZJngsO3Q5DX/4iFlfdJi1Cls5MaaXnxAjweCZJF1DqgzL5jNIzy3biCGmUZ6c/pC5umudyoCcUQXNXUM2UZVKCZTa4IVXTuLzAbE2xF3+9ZC5W7GjDsBIvfvnyauzvCEC0JMmLz9ntNridds3aPvqzWHpm3xVVRR6cP3e0+rO8ozcrH7DZbCgvcGNfhx/NXQHN+1TNJOkyeaceMgyv/uhojCrLQ+2v31Ivlw9wRSAlviD3pVhYVg5uOv1h6FqW1Mv19CPA23RZy0xKK8lajBaTBdiXRDRYJL52lP3zFt3AHeHlHx6F0fGKA7PqDWFEqRdjywvUE3p6cpB07ORK/PepU1GS51JPxupPtKbKSg31iahOhx1fmzMi5XXkY6GJVYUprmlN/DaxCPkgTl9ClitGZ9gTv0tkkkRZVVE8SNLfrieYaHTUB1L64EDfmyMmorR0B3HMH97LaLuvkdYkEDuwpVtbcP6Di5NW4dbvQPU7uKRyu/h2j4kfqDd3BUzXIDJiljFKNV7AZrPh9Z8cgwe+czimDivCidNq8PNTp6pZpcaugDrdTr/9coD02S8WYfpwgyhB3o4UZ9TMb2P+O3nCnfw+3WqSSbLZbJg5sgSlKRo7R8WHN+xq9SEUiWr+xqNufxf3vrdZ/bmjR9tTZkRc/qMTJ2FOvLlUP261VZfBHKiFR6n3REmr12XndEKiQUjfk7QlXony9H/Nx3nS8Kra0aVqSa0zbSbJi5J8F5becBIeuPDQpN/L+3qbzYYfnjAJ35GGMuhHjKcKkii9/VJlSLVuwdnBgEGSRcgZEfkMfU8wgu1NuRnkIErbCtwOHDulSvO7s+79BF/EhymInZGY4NIgvcn3tPXg+pdWAYgHSfFyp+1NPsRisaQdmP6sbr7bieHS4mqpXDBvDJ64dB6qixLXFwdGL36xG8u2tSTdJl2QJE/N6ZDWSRobXzequTuRSTqQEeDpTBtWjNN0fRPi+ZZ7kvQNqEbXNyKCp7MPHZn1tpllkgB5eENQ854VgU1vvlBGxN8PDR2BpPf67rYe/PGNDerP+kySEbEWVpHXCXf8/ajvSdJPT2K53eAjAqOqIs+QP6NLNBjJPUmBcERdCmJidSF+cvIUFHmduOTIcZrb2FN81p12GyoLlO+osgK3pmrjyUvn4V9XH512m8xK9ql35O/swbifZrmdRQTkwCiUOPj71sNL8OWuNrzyw6PUs+K9pR5457lw77cPxYqdbbjzzQ1YVd+OcDSGjzY1qb8HgOElSnZln7Qmzdf+8gma4gMe8twONXP0X08uxy9On5623A5Qsklm69zIKfLbzpmV9Pt0Z/z19coledq3uDzBT85CiUxSk5RJyiS7YNaTlGokphkR9DR1BdQpcfoF2oQik0VzhWevWIBV9W04cmJmk2pkKTNJ6hjwgJpRlJXmm3+hFLgd6DYYSiKyTK2+ILY0pj4hIPewmQVJIpNU5HWpQXNI12ilD5LMFigk65o9qhTfnDsaR04ybpAmImtL9CTFsLPZh0g0hkKPE9XxEx9f/OrkpAEHqU7iVRd5NOv1jK8swE8XlGD+rGmYn+F3of6EGTNJB+bqEyfho02NuOqESQO9Kb3CIwOLkEuXPtncjCc+3Y5YLIYvd7UBAP7v83rN9T/b3oLbXluXVWmeCJKK81wo8rpw3JQqTfmSIA7QRcZnb7tydqeuvl0NkABtuR0A/Pa1dUnldkb9AfKY6GU3nITLj0k0Os6fkDztTWY24nfhhAo8c/mCpKBFfxbIaB0oABgbX8OpuSuY1eAGOXPltNtw1zfn4PqvTEvbK2RE9FWEIjFsbFDKDkaWGQ8+GF6aOhtXkufCMZOrerWgZspMUkGi3C5g8N4zC+qA5H4lQdSIt/lCaOwy7lMTZYPtGZTbyZkk8QWbrtyOmaTBx+Ww4/ffmI2v12afLSUiC5AySWK9wPGVBWrGwWgCXKq18ozKuo8anYe5Ui9yOvluh6Z8l0HSgZk5sgRf3nQKfjhIgyRmkiwioDuIu+mfazSLfupHFp/3wGIASvbhv46ZoF4ei8XQE4oYTv2SM0mC0bhNMTZ7mBok+RGLxfD0sp2a6+W5HUkH1ElBksFOTmSoAKC62KvJDvz66zNx9n2fJKXYBbPszo9PmoyFEyvw2XZtCV6RN7MdnAgIm7sD8GdRbueW1kmqKfbi7EN7vwiw22lHWb5LPYC32xKLrSZvr3HwlAupMknD4+/JXS0+wymMZQXmGbRCjxMNSA6CyuLZpzZf0HSYx+b9XXjko20Zldt1qkGSy7Tcrk2fSWJPCxFRv5J7kkSVR01x6r4VsxOdQOpKhoy3yWbD8BKvWtXAIOnADeZx54N3yw8yRhkheZy22Zowe9q0ZWt/fGMDZtz4BlbsbMXm/Z34f29vVO/bKEj63dnJJW3FaiZJOSD2BSP4x4rd+PeqPZrr+YIRtSdJ0PcEGX04frJoMs6cMwJPXz4fAPC9o8bD47TjB8dPxPjKAqz45cn4yaIphn+v12SRVlEupc80pRtcMH1YEX4yv0TTa6MObsgkSHIkrpNpr1UqcvAzsiwvKRMn1pm66njtQni5lCqTNL5SCdo27+9KmhgHGJ/JE37x1ekAgIsXapfbFreJxhJnE/VOvutDPLd8l+Yy/Wh69XK13M6ZKLfTBUn6NbmYSSIi6l8iYxRDYpHyioI0QVKKr/RcBEkAMEI6Qc0gaWhjJqkfrapvx5J6P2prk3+nzyQBQJN0Vt0v9XLIi3jKKz8DwH3vbwEA3PrqWtTtbkcoEkM0GsNPT5mqBknyjuRb88bA47Lj2ue+VC8TJWp5bgdK811o84Xw8xeU38s9Q/WtvqTem0yqu0rz3bjngsTUmRkjirHq5lPUSXj2FHdi1jsiMkwTdCMmj51ShcPGlGLFzjbD2z1z+XxsWb9aLSPb1+FXMymZ9CS5pEzS8NIDz+7MGV2KtXs7AADjKpLXFLj324ehocOP0eXGGaZcSBVXim3aZDKqNVW53YnTarDshpM045oBJYMm+pU2m9yvEfPpdkrwVCz1JAUjMYQiUVz06FJMrCpMykIxSCIi6l+JTFIMzd3a6bpmUpXbleRl3wtsRD7hySBpaGMmqR/97IVV+OPiNuwyKHEz6u9Yt69D/XdTd1DNisiDFPQrQQt72/3qmf4l8SlwRpkkIPnsv9wzJC92CgDHT01MxWvoCGB/pzaTJQ9GyIZ+VLgZs8BFXF7ocWr+Pq/LgRevOgrTpB6hMeX5uOO8OXj8e0eof6syIUsbIGS7mGwuMklzxyZqp42CJLfT3qcBEgDEUgwwH1WWn7LPKd3Aiupir2EQLMr0xDoZmawFalRuF4vF1OCpWJdJ+mRzE5ZsbcFTS3cm3Y7rJBER9S95zpMIkspTlGwDqSsdcpVJGlbCTBIpGCT1o3B8hdCGzgCeX74Lv3y5Tq2vNervWL+3U/33l7vaMPvmN7G7rQe72xKrUvsNMlAANNPj9rT14NMtTWj3GQdJxV7zn5ulsqQjJ1bgxydNxmFjSgEA88eXawI2AJpt6wtyJqks32V4+fThyUMT5OCqyOvENw4fheOnVquXuZ12TUColGqlP1KXR4BXFR74GgBHjEsMrhiWg6CrN1KcqIPbadf0ynmcdk3QVFrQuy8UEVyJLOXwouQkd5nuC9Aok9QdjKjbX+R1qT1ja/d0mJbyAVwniYhoIIlyO32lgd60FGsDpqpkyMYIZpIojkFSPyryKB+2zp4Q/uf/VuHvS3Zi8ZZmANoR4EKzrm+iMxDGi5/Xa/qQxO027+/Efe9vhpH61h58++Glak+Hfn0d/ZhseQqZyI54nHY8ffkCzBxZggcvmotrTpqMu75Zi0XxHpn+IreWyGsgyL1KM4aXJN1O7i8qMpmyNkqaJCdP2ElFziSlKxPIxOjyxDakWgepL5wQzxJeOH9MyuuNq0xkuPLcDk2JZbrR5Gb0ZwAr85J3TTeeOQO/PXsmrjhWGVRilEkSgZPTboPXZVczSf/8cg9u+dda08dnuR0RUf9S10mKKf3AQPrv0WMnV+LO8+bgm3NHJ/0uV5kkuXSe6yQNbexJ6kci+Fi9J1FGJ4YqyJkkh91mOsHlky1Nmv4afziCWCyGRX/6MOPtOHysdhymnDmSDywB4A/fmI073tiA674yTb2sqsiDa09WBitc/5VpmD68GH9bvF0dW92X5OlmclpezgR8e/4Y/PWTbThkROKMU54mk2S80xtZmofPoCyoO74yudTNiEsTJB14UGOz2fDAdw7Dp1uae7UQ7IG459uH4dPNTUkLDeuNr8iHeLd5nQ74AhEgXqLX28Xi9CUWBe7kIGlMeT4OH1uOd9c34KEPt6LNF0IwHMV1/1iFBRMrcFbtSHz3r8sAKIGwzWZLGiRihkESEVH/UtdJQizjcjubzYZzDx9lWEmQq6wPe5JIYJDUj0QGQ/QIAUBnQPmgi4zQounV+PO3DsUhN71heB9LtmpHXPtDEXwaz0YZmVBZgK1SmVFZvgsTdcMN5DMl+sVgpw0rxiPfPSLF3+TCRQvGYvn2ln4JkuSyOrmPSZ5qN6m6EB/+9wkok0q/8jLKJCV6fYz6gYzIpWZGZX69cdrM4Tht5vCc3Fc2Cj1OnHLIsLTXk3uicrUIq9zLZLcBec7kYEs87oTKQthswNq9HfjNq2vx4he78eIXuzGxqlB9D9bGF17OOEhiTxIRUb8ShxuRaAwt3ZmV2wlGp5FzNbhBLnUvyVF2igYnBkn9qDBeiiQHOq3dSpAkMkqe+AKtxV6nuihmKoFwNGn9ItnD352LJVub8YuXVgMA8t3OpLP9coCRemC2uep+Kg375hGjsWRrM06ZMQxLtiaCQ/0wgDEV2uEGcrmdvgdL0JfbZeqdnx2HnmAE1UUD00PU3+Rg0utyJEYUHQC5TKK8wI1Cg0yS6PkaV1mAbxw2Ci98Xo+/Ldmh/n5jQ2d8m+x4+OK5AKCuk5QOe5KIiAZGmy+k9pKmG/4jGM1vyFW5XbHXhT+cOxuRWMz0eIGGBvYk9SOjDIZY1FKMABcBi3y2/huHj8ID3znc8D79wYja16RX4HZgYlUhLpw/FkdNqgAAXHb0+KTr9bZEStZf/TP5bicevGguzj18FDxZZDHyXOkzSSN7GSRNrCrEzJHJfVAHKzmY9LocuYiRNF+MFQVunDWtAAsmlGteB/l9eqXBOlHic3DEuHJ1fa5UmSQ5C8hyOyKi/iX26ev3KSe4ir3OpLUBzSyantwPnasgCQDOP2I0LpiXuj+XDn4MkvqR0cF5qy8EXzCM7c1KSZw4o31ofIIcoBywnywNSDhyYgVuPnMGAKXkSL8wplAgNdHf++3D8MB3DsclR45LuY1p1l41lenZn1zKdGw4AORJi96aBUnydLtxWQRJQ83oMm2Wbs6oUgDJC/lmQ168b1xlAYrcdjx12Tz88+qjsGh6Ne765hzN9SdUFiS9jovjmUV5pL0rxTbVSIE9y+2IiPqXftBPqjUS9cZU5GPZL07C3dKai6U5KrcjElhu14+MBga0+oI4575P1TMp4kBz3vgK/H2JUkZXnOfSnPWeO65cDabE7YwUSjug0nw3TpuZvt+ktwYmSOptJsn4bNPEqkIcP7UK5fluNmumUCxNQ2zuDuDZKxbizjc3GGYpM3XitGrcfOYMOBx2nH5INbZvVCbRFXldhj1xNpsNs0eV4JPNiSxqY3zxZXkMrDPFl25VsRd74iPsc9VbRUREmTl6ciV+dcYM3PveZrR0BzEry4qM6iIvItHEyFvuxynXGCT1I6PxyO09IU2gI4KfedJ6OWKYwuPfOwLvrt+PH54wEf+p26e5nwK3A91B7VpL+Z7sz46nWkg0lbI0E2n6wskzanDf+1sySrFnMgLcbrfh8e/Ny9n2HazksrfmriBGlubhT+fXHtB9Ouw2XHKUEmRFIslrhhkZX1mgCZIE+f2QqnSjQnrPstyOiKh/uRx2XHb0eFw4fwz+uXIP5o0vT38jHblnKBetA0QyBkn9qNDg4HyvbjFWkR2Rp6uIdQOOn1qtLoCqz6LMG1+O9zY0ai77aXxMdzZ6W25nNLazt2vmZOrQMWV49UdHa0q1zHjd6TNJlD1fMLOApi+MKc83vFzOAqbqSSqWPo8c3EBENDC8LgfOPyJ53aNMnDC1Gt876v+3d+/BUZTpHsd/mVwgIUMISUQDCghOYm6QGAyEBAgCR1fwKCDeWBHRlQNeqOAKrK7HIOBtsTSKu2yxu4UI7rLcagVBQRYLBES3uEREgQVFhJUwghBAApn3/JGTsYcETQSmO8P3U5WqpPudnqfnSWfmydv9dDt/R1PgfKJICqK6ioadBwLbZls/rM3/n27asPtQnRconvmhztp0oE18tObc17VWh7cf0y+tld799Bvd+zNPmYq3/Pc+3BWm+wuv1K25bX7Wthqivg0TYurRuAGNy9CubbVh9yHltovXs0s/8y+vb5FkHXcu11MBAOzhcoXpfwek2x0GQhSfFoPI+uG8fWIz7bbcv6iG9cPaNW1b6pq2dU8/n9nZ7bK4H4okd9PIBhVIkvTS7Z318ReH1PXKhAY9roZ1yrtJhCvg5rNOEB3QApxf+3M1a8S1eujNjZp8c6ZtMcRERWjGsOpW39Pf/7cOHa9upx/QuOFHWoB375ioD3d/qzbx0ZymAQAAAvBpMYisRdJVl8TWWSTV97SfMzu7WTuzxfyMTl0xURHq4Ulq8ONqWLvS2HkK1tlEc7rdeVV4VZI2/ravY4qLS+OiLUWS5Zoky0xS37RWinCFaekn1dfzxTeL0tJHCoMbKAAAaBQ4xySIrB/OO1wSq7oab9W3O8uZ4y6zXMPERei11ec+SWgYpxRIkpRs+f1vcZbT7e7t3l7/lf5Dh0d30+obKztpPwAAgDNQJAWRtSV3XHRknW2m63vvnzNnnFpZiyTu+VJLzexauCuMIjIEWRudxFlmkqz3SXI3jQgokJlRBAAAZ0ORFETNLMVLdGS4OiTF1hpT33upWa9dahLhCrhPUX3vWH0xqXl9kmKbMHMQgqydIwMbN/yQa/cZd3NnRhEAAJwNnxKCyHrdTkxUuK67upU+/vJQwJjvTpyq17aannH6WDPLPZHMz+3jHcIubxmj5wdnqU38T7cLR+NjnR20zsaGh1mLpMiAFvfNovjzBwAA6saUQ5D1uKKprmgZrRsyL1PftEtqrbd2qfsxgTNJ4QEfDC03oA6qH+sk5gRDci9XfodEu8PABdAmvu5ujidP/3AwxDaJULuEZv6fw+s7bQsAAC46/Cs1yB7Ja6FOnTopIiJCHZJi1SImUoePn1LpHdk6fLxShVfV70O8dSbpzOLEZ9NM0qP9UvTM0s80JAj3RwKs/rtzstb++6Dyzrhj+/enfui0GBXh0hUJMXr93mvrvPkxAABADYokG9RcExMWFqb3invq0PFKdbzE3aBtWLt2nXkNks+ms+3uL7xS3TsmKuXShu0LcK4iw116cUjnWstTL21ea9m5tLoHAAAXB4okmyXENlFCbJNz2oa1YJLsm0lyucKU0TrOlucG6nJFQozeerBALWOZOQIAAPVHkRQCas8k0bgBqJHZhsIdAAA0DI0bQkDU/88k1VyHntc+wcZoAAAAgMaNmaQQUDOTtHJsL63eeVC35V5uc0QAAABA40WRFAJqrklql9hM7RKb/cRoAAAAAD+G0+1CQFQ4aQQAAADOl0bx6Xr27Nnq3bu3MjMzdeutt2rLli12h+QI17arvifMnXlX2BwJAAAAEDocXyS9/fbbeuaZZzR69GgtXLhQqampGjFihLxer92h2W7Wfddq5die3PcFAAAAOI8cXyT95S9/0ZAhQzRo0CB17NhRJSUlatq0qebPn293aLZrEhGuK5Ni7Q4DAAAACCmObtxQWVmprVu36oEHHvAvc7lcys/P18aNGxu0raqqqvMdXoPVxOCEWFCNnDgPOXEecuIs5MN5yInzkBPncUpO6vv8ji6SDh06pKqqKiUkBN73JyEhQbt27WrQtsrKys5naOfESbGgGjlxHnLiPOTEWciH85AT5yEnztNYcuLoIul8yszMVHh4uK0xVFVVqayszBGxoBo5cR5y4jzkxFnIh/OQE+chJ87jlJzUxPFTHF0kxcfHKzw8vFaTBq/Xq8TExAZtKzw83DEHiZNiQTVy4jzkxHnIibOQD+chJ85DTpynseTE0Y0boqKilJ6ernXr1vmX+Xw+rVu3TtnZ2TZGBgAAACBUOXomSZKGDx+ucePGKSMjQ1lZWZo5c6ZOnDihgQMH2h0aAAAAgBDk+CLpF7/4hb799luVlpaqvLxcV199tWbMmNHg0+0AAAAAoD4cXyRJ0tChQzV06FC7wwAAAABwEXD0NUkAAAAAEGwUSQAAAABgQZEEAAAAABYUSQAAAABgQZEEAAAAABYUSQAAAABg0ShagJ8LY4wkqaqqyuZIfojBCbGgGjlxHnLiPOTEWciH85AT5yEnzuOUnNQ8f02NcDZh5qdGNHKVlZUqKyuzOwwAAAAADpGZmamoqKizrg/5Isnn8+n06dNyuVwKCwuzOxwAAAAANjHGyOfzKSIiQi7X2a88CvkiCQAAAAAagsYNAAAAAGBBkQQAAAAAFhRJAAAAAGBBkQQAAAAAFhRJAAAAAGBBkQQAAAAAFhRJAAAAAGBBkQQAAAAAFhRJQTR79mz17t1bmZmZuvXWW7Vlyxa7QwpJH330kUaOHKmCggKlpKRoxYoVAeuNMXr55ZdVUFCgrKws3XPPPfriiy8Cxhw+fFhjx45VTk6OcnNz9Zvf/EbHjh0L4l6ElunTp2vQoEHKzs5Wt27dNGrUKO3atStgzMmTJ1VSUqK8vDxlZ2froYce0sGDBwPG7Nu3T7/61a/UqVMndevWTc8995xOnz4dzF0JCXPmzNGAAQOUk5OjnJwc3XbbbXr//ff968mF/f74xz8qJSVFkydP9i8jL8H1yiuvKCUlJeDr+uuv968nH/b45ptv9OijjyovL09ZWVkaMGCAysrK/Ot5jw+u3r171zpOUlJSVFJSIqmRHycGQbFkyRKTnp5u5s2bZ3bs2GGeeOIJk5ubaw4ePGh3aCFn1apV5sUXXzTvvvuu8Xg8Zvny5QHrp0+fbq655hqzfPlys23bNjNy5EjTu3dv8/333/vHjBgxwtx0001m06ZN5qOPPjJ9+/Y1xcXFwd6VkHHvvfea+fPnm+3bt5tt27aZ+++/3/Tq1cscO3bMP+bJJ580PXv2NGvXrjVlZWVmyJAh5rbbbvOvP336tOnfv7+55557zKeffmpWrVpl8vLyzNSpU+3YpUbtvffeM6tWrTK7d+82u3btMi+++KJJT08327dvN8aQC7tt3rzZFBUVmQEDBphJkyb5l5OX4CotLTU33nijOXDggP/L6/X615OP4Dt8+LApKioy48ePN5s3bzZ79uwxq1evNl9++aV/DO/xweX1egOOkQ8++MB4PB6zfv16Y0zjPk4okoJk8ODBpqSkxP9zVVWVKSgoMNOnT7cxqtB3ZpHk8/lM9+7dzYwZM/zLjhw5YjIyMszixYuNMcbs3LnTeDwes2XLFv+Y999/36SkpJj//Oc/wQs+hHm9XuPxeMyGDRuMMdU5SE9PN0uXLvWPqcnDxo0bjTHVxW9qaqopLy/3j5kzZ47JyckxJ0+eDGr8oahLly5m7ty55MJmFRUVpl+/fuaDDz4wQ4cO9RdJ5CX4SktLzU033VTnOvJhjxdeeMHccccdZ13Pe7z9Jk2aZPr06WN8Pl+jP0443S4IKisrtXXrVuXn5/uXuVwu5efna+PGjTZGdvHZu3evysvLA3LhdrvVqVMnfy42btyo5s2bKzMz0z8mPz9fLpeLUyTPk6NHj0qS4uLiJEmffPKJTp06FZCXDh06KDk5WZs2bZIkbdq0SR6PR4mJif4xBQUFqqio0M6dO4MXfIipqqrSkiVLdPz4cWVnZ5MLm02cOFE9e/YMeP0ljhG7fPnllyooKNB1112nsWPHat++fZLIh11WrlypjIwMPfzww+rWrZtuvvlmzZ0717+e93h7VVZW6h//+IcGDRqksLCwRn+cRNj67BeJQ4cOqaqqSgkJCQHLExISal2XgQurvLxckurMRc05sgcPHlTLli0D1kdERCguLs7/ePx8Pp9PU6ZMUU5Ojjwej6Tq1zwyMlLNmzcPGJuQkOB/zQ8ePBjwR1SS/2fy0nCff/65br/9dp08eVIxMTGaNm2aOnbsqG3btpELmyxZskSffvqp5s2bV2sdx0jwZWVl6ZlnnlH79u1VXl6uadOm6a677tJbb71FPmzy1Vdf6c0339Tw4cM1cuRIlZWVadKkSYqMjNQtt9zCe7zNVqxYoaNHj+qWW26R1Pj/blEkAQiqkpIS7dixQ3PmzLE7lIta+/bttWjRIh09elTvvPOOxo0bpzfeeMPusC5a+/fv1+TJk/XnP/9ZTZo0sTscSOrZs6f/+9TUVHXq1ElFRUVaunSpmjZtamNkFy9jjDIyMlRcXCxJSktL044dO/TXv/7V/8Ec9pk/f7569OihVq1a2R3KecHpdkEQHx+v8PBweb3egOVer7dW9YwLKykpSZJ+NBeJiYn69ttvA9afPn1a3333nf/x+HkmTpyoVatWaebMmbr00kv9yxMTE3Xq1CkdOXIkYLzX6/W/5omJibU64tT8TF4aLioqSm3btlVGRobGjh2r1NRUvf766+TCJlu3bpXX69XAgQOVlpamtLQ0bdiwQbNmzVJaWhp5cYDmzZurXbt22rNnD/mwSVJSkjp06BCw7Morr/SfBsl7vH2+/vprrV27VoMHD/Yva+zHCUVSEERFRSk9PV3r1q3zL/P5fFq3bp2ys7NtjOzi06ZNGyUlJQXkoqKiQps3b/bnIjs7W0eOHNEnn3ziH7N+/Xr5fD5lZWUFPeZQYIzRxIkTtXz5cs2cOVOXX355wPqMjAxFRkYG5GXXrl3at2+fOnfuLEnq3Lmztm/fHvDmt3btWsXGxqpjx45B2Y9Q5vP5VFlZSS5s0rVrV7311ltatGiR/ysjI0MDBgzwf09e7HXs2DF99dVXSkpKIh82ycnJ0e7duwOWffHFF2rdurUk3uPttGDBAiUkJKhXr17+ZY39OOF0uyAZPny4xo0bp4yMDGVlZWnmzJk6ceKEBg4caHdoIefYsWPas2eP/+e9e/dq27ZtiouLU3Jysu6++279/ve/V9u2bdWmTRu9/PLLuuSSS9SnTx9J1RcVFhYW6re//a1KSkp06tQpPf3007rxxhtDZgo52EpKSrR48WK99tpratasmf88Y7fbraZNm8rtdmvQoEF69tlnFRcXp9jYWE2aNEnZ2dn+P6QFBQXq2LGjHnvsMf36179WeXm5XnrpJd11112Kioqyce8an6lTp6pHjx667LLLdOzYMS1evFgbNmzQn/70J3Jhk9jYWP81ejViYmLUokUL/3LyElzPPfecioqKlJycrAMHDuiVV16Ry+VS//79OU5sMmzYMN1xxx36wx/+oBtuuEFbtmzR3LlzNXHiRElSWFgY7/E28Pl8WrBggW6++WZFRPxQWjT648TW3noXmVmzZplevXqZ9PR0M3jwYLNp0ya7QwpJ69evNx6Pp9bXuHHjjDHVLUJfeuklk5+fbzIyMsywYcPMrl27ArZx6NAhU1xcbDp37mxycnLM+PHjTUVFhR27ExLqyofH4zHz58/3j/n+++/NU089Zbp06WI6depkRo8ebQ4cOBCwnb1795r77rvPZGVlmby8PPPss8+aU6dOBXt3Gr0JEyaYoqIik56ebrp27WqGDRtm1qxZ419PLpzB2gLcGPISbGPGjDHdu3c36enpprCw0IwZMybgfjzkwx4rV640/fv3NxkZGeb66683f/vb3wLW8x4ffKtXrzYej6fW62xM4z5Owowxxt4yDQAAAACcg2uSAAAAAMCCIgkAAAAALCiSAAAAAMCCIgkAAAAALCiSAAAAAMCCIgkAAAAALCiSAAAAAMCCIgkAAAAALCiSAACONX78eI0aNSroz7tgwQKlpKQoJSVFkydPrtdjxo8f73/MihUrLnCEAIALKcLuAAAAF6eUlJQfXf/ggw/q8ccflzEmSBEFio2N1bJlyxQdHV2v8Y8//rjGjh2rgoKCCxwZAOBCo0gCANhizZo1/u/ffvttlZaWatmyZf5lMTExatasmR2hSZLCwsKUlJRU7/Fut1tut/sCRgQACBaKJACALawFiNvtrrMoGT9+vI4cOaLXXntNkvTLX/5SHo9HLpdLixYtUmRkpMaMGaP+/fvr6aef1rJly5SYmKgnnnhCPXv29G9n+/btev755/Wvf/1L0dHR6t69uyZMmKCWLVs2KObZs2dr5syZ2r9/v9xut3Jzc1VaWnoOrwIAwIm4JgkA0KgsXLhQ8fHx+vvf/66hQ4fqqaee0iOPPKLs7GwtXLhQ3bt312OPPaYTJ05Iko4cOaJhw4YpLS1N8+bN04wZM+T1ejVmzJgGPW9ZWZkmT56shx9+WMuWLdOMGTOUm5t7AfYQAGA3iiQAQKOSmpqqUaNGqV27dnrggQfUpEkTxcfHa8iQIWrXrp1Gjx6tw4cP6/PPP5ckvfHGG0pLS1NxcbE6dOigtLQ0TZkyRR9++KF2795d7+fdv3+/oqOj1atXL7Vu3VppaWm6++67L9RuAgBsxOl2AIBGxdrwITw8XC1atJDH4/EvS0xMlCR5vV5J0meffaYPP/xQ2dnZtba1Z88etW/fvl7Pm5+fr+TkZPXp00eFhYUqLCxU3759693YAQDQeFAkAQAalYiIwLeusLCwgGVhYWGS5O+Kd/z4cRUVFenRRx+tta2GNGaIjY3VwoULtWHDBq1Zs0alpaV69dVXNW/ePDVv3vzn7AoAwKEokgAAIS09PV3vvPOOWrduXavAaqiIiAjl5+crPz9fDz74oLp06aL169erX79+5ylaAIATcE0SACCk3Xnnnfruu+9UXFysLVu2aM+ePVq9erUmTJigqqqqem/nn//8p15//XVt27ZNX3/9tRYtWiSfz1fv0/UAAI0HM0kAgJDWqlUrvfnmm/rd736nESNGqLKyUsnJySosLJTLVf//Fbrdbi1fvlyvvvqqTp48qbZt22rq1Km66qqrLmD0AAA7hBm7bmUOAIBDLViwQFOmTNHHH3/c4MempKRo2rRp6tOnzwWIDAAQDJxuBwBAHY4ePars7Gy98MIL9Rr/5JNP1tlBDwDQ+DCTBADAGSoqKvwtxN1ut1q2bPmTj/F6vaqoqJBU3TUvJibmgsYIALhwKJIAAAAAwILT7QAAAADAgiIJAAAAACwokgAAAADAgiIJAAAAACwokgAAAADAgiIJAAAAACwokgAAAADAgiIJAAAAACz+D4Q8za/+rNPsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_error , ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "err = positionError.clip(upper=100)\n",
    "\n",
    "sns.set_style(\"whitegrid\") \n",
    "sns.lineplot(x=err.index, y=err)\n",
    "\n",
    "ax.set_title(\"Position Error\")\n",
    "ax.set_xlabel(\"Time [30s]\")\n",
    "ax.set_ylabel(\"Error [m]\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Save the figure\n",
    "pos_error.savefig(\"Position_error.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "navigator-96XIUzCO-py3.10",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}