{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56986c3d",
   "metadata": {},
   "source": [
    "# Section 06 - Effect of Atmospheric Uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8eabf8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "018517b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8449b7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = Planet('URANUS')\n",
    "planet.loadAtmosphereModel('../../../atmdata/Uranus/uranus-gram-avg.dat', 0 , 1 ,2, 3, heightInKmFlag=True)\n",
    "planet.h_skip = 1000.0E3\n",
    "planet.h_low  = 120e3\n",
    "planet.h_trap = 100e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ad6da76",
   "metadata": {},
   "outputs": [],
   "source": [
    "ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert = planet.loadMonteCarloDensityFile2('../../../atmdata/Uranus/uranus-gram-mean-density-variations.txt', 0, 1, 2, 3, 4, heightInKmFlag=True)\n",
    "density_int_low = planet.loadAtmosphereModel5(ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert, -3.0, 2201, 1)\n",
    "density_int_avg = planet.loadAtmosphereModel5(ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert,  0.0, 2201, 1)\n",
    "density_int_hig = planet.loadAtmosphereModel5(ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert, +3.0, 2201, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b5b999e",
   "metadata": {},
   "outputs": [],
   "source": [
    "planet1 = Planet('URANUS')\n",
    "planet2 = Planet('URANUS')\n",
    "planet3 = Planet('URANUS')\n",
    "\n",
    "planet1.density_int = density_int_low\n",
    "planet2.density_int = density_int_avg\n",
    "planet3.density_int = density_int_hig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed9fb84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_array = np.linspace(0, 1000e3, 1001)\n",
    "\n",
    "d_min_arr = planet1.densityvectorized(h_array)\n",
    "d_avg_arr = planet2.densityvectorized(h_array)\n",
    "d_max_arr = planet3.densityvectorized(h_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4310d55c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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WbXS996goePhQeW7ConOGIjf/K7E0ZHWT1QvkdUtKSjJ1CM/F0dGRbt26sWrVqmdizUjUl12ha3N3yysbNmygePHiVK1aNVfH2djYULt2bd1rOzs7bG1t6dSpU65j+Prrr/nuu+9yfZypUDtA+UytWs9uy22/pEiBzP/VnAk8Q0r6s5kyM8qB8OT/XA4fzt0bmSE6LwmR1U1WL5DXLTcjCKZi5MiRREREsHXrVr3ty5cvZ9iwYXrboqOjmTNnDo6OjgQGBgLg4eHBwIED6dq1Kx4eHjRr1oySJUuyZs2aZ47x8/N75hgvLy86dOiAjY0NkyZNIjU1lXHjxmFra0vTpk0JDAxECMGqVasoUaIEAwYMAJQ0Le+8846uSkFuz5kdCxcupKMBSh9t3ryZjRs36pVSefz4MePHj6datWoUKlQIjUaje7z//vu6dp06deL48eO5Hj0yFeb/Vy4ZbdtCTIz+ttxO3C9SoAgpk5VOz7XQa1kmRCxZsqTypFAhuH9feb5gQS6jNT90XhIiq5usXiCvW37XZMoLnTt3pnbt2ixalDkNIDU1lb/++uuZW5Opqamkp6fj5+encytXrhx3797l9u3bXLhwgT///JMPP/yQcePGkZ6erndMBra2tnh5eXHv3j0uX76Mq6sro0aN4rfffuPnn3/m008/5dq1a0RGRjJz5kw0Gg2DBw/WW5XcuHFj+vXrl+dzZkVwcDCXL1+mfv36ef59+vr68u233zJixAi2bdumV4Liyy+/ZPXq1QwdOpSDBw/ywQcfsHnzZk6fPs2MGTN07ezt7SlTpgyHcpPczoSYdTFUWUlKgtKlITpaed24MTg6wp07UCAXV6RphaZ4hnhSZnYZzg86z2uVX9Ptu3HjBq+++qryolgxsLeHI0dg9mz4/nsD2uQvel6SIaubrF5gWW4JCXDrVs7aRkenUrq08WKpV0/5WHoZNBoNw4cPZ9y4cZw/f542bdqwa9cuPvjgA73i2KB0dlq1agVAcnIyoMzvcXR0RKPRMGLECAA+/vhjXFxcCA0NpVKlSrpjMqhduzb16tXjwYMHfPnllwB8/vnnzJgxgy5duujat2/fnjt37uiOe3pE7cnXeT3nk1y7dg3gmcnao0aN4sqVK8/7NXLmzBkAKlSoQL9+/fDz82PTpk04Ojoybdo0bt68iaurK9u3b+eTTz4BoG7dunTt2pVTp05hZ2end77KlStbzDwgtQNkAsqXh8hICAyELl2UDyVfX8jNf7oKWhfknyH/0GVjF074nuDI3SN6HaB3331X/w3v31eqwy9ebNEdID0vyZDVTVYvsCy3W7egRYuctjZi7wfw8IAnBkXyzIABA5g8eTILFy6kTZs2uLi46IrUPk3GyM+TE5qtrKz0OiPFixcH0I1+ZDUSVuCp/6U+XWgboHDhwqSmZp2jLSte9pyRkZGA0tF7koULF+Y4hqJFi9KiRQt27txJhw4dOHDgANOmTcPLywuALl266NpWrVqVunXr4uTkxLx58/TOU7x4cUJDQ3P8vqZEvQVmIjQaqFoV/vgjc9ucOfDvf05yhLWVNYu7LQZgyt9TSEjNrIeWcR9bh40NvP++shzegiduPuMlEbK6yeoFluVWr57S8cjJ4/Dh8By3zcujXj3DOJUqVYrPPvuMnTt3cvToUapUqfLCJevRGUPvEpHRYTJUKYyPPvqIQoUKAZkdxsTERL02devW5eTJk88cq9FoLOIWKqgjQCbn3/9wAPDDDzBxorIs3tk5Z/9bq2NXh9JFShOdFE3538vj3NmZoS2GMnny5Gcbf/457NkD9evD/v1KWmoLI0svSZDVTVYvsCy3YsVyM+pS1pihGJSRI0eyYsUK+vTpg5ub2wvbly9f3vhBPUWhQoX0OhBarVb30xATzh0cHABllMgQJCUl0aGDstK4Q4cO2Nra4ubmRp8+fXRtQkNDs0wPEBcXxyuvvGKQOIyNOgJkYtq1g7g48PEBNzclKeLff0PLlvDo0YuP12g0BP8vmMtDL1OycEmGHRjGjbAbWafo//BD8PODihXhvfeUyUgWhqylB0BeN1m9QF43cy6pkJiYqNeZaNSoER06dKBBgwY0adIk23YZCRCfdEtNTdV1Rp4ko+3TPzOeP/n6yc5MBkIIvTY1a9bk1KlTeHh4sHv3bnbv3g0oCRsfP36cp3M+SbNmzShRokSe5t6EhITg7OxMeHg4oEyo3rdvHxMmTACUlY5z587l119/Jf7fkkqRkZHs3buXcePGPXM+f39/i5kXp5bCyIL8KIXxPDLKYzRtmrvj9tzcoyuUmpKSkn3D5cuVN/i3gKwl8VwvC0dWN1m9hDA/N0OVwniy2LQ5ce7cOfHpp58KOzs7sWbNGhETEyOEUIpkb968WQihlCdZvXq1KFOmjADEtGnThJubm+jTp48AxKhRo0RERIQ4fvy4qFSpkrCxsRFbtmwR9+/fF/379xeAGD16tAgICNAdM3r0aN0xDg4OwtbWVmzZskUEBQWJESNGCED069dP3LlzR+zdu1dUqVJF2NjYiO3btwshhLh//76oX7++KFWqlPj999/F2rVrRatWrcS6devE4cOH83TOp/n000/FDz/8kOvf6bVr14Sjo6MoWbKk+OCDD8SYMWOyLP2xefNm0atXLzFhwgTRr18/ceDAgWfa+Pr6isKFC4uwsBcU6n4CU5bCUDtAWWDqDtAPP2R2gtq0EWLoUCGWLBHiRZ9J6dp0XQeo5tSaYu+tvVk3TE3NfIMlS3JXjt7EzJw509QhGA1Z3WT1EsL83AzVAQoKCjJQROaHrG5eXl6iUaNGJo1h8eLFYsSIEbk6Rq0FpqLHzJng7q7MA3J0hBUrYPhwZZL087DSWJE4KZF176/jnvYe7219D69Qr2cbFiiglKKvXFk58fbtxhExAhnLMGVEVjdZvUBetzJlypg6BKMhq1vDhg3p0aMHrq6uJnn/tLQ0du/ezfTp003y/nlB7QCZKS1bwvjxsGkTHD2qbPvxR5g06fnHFSlQhC+afMH0KsofYauVrQiNz2JJYs2a4O2t5AcaMQJCQgxsYBzOnz9v6hCMhqxusnqBvG4Zcz1kRGa3cePG4eHhwb179/L1fYUQzJgxgz/++MOikoOqHSALoG3bzOc5TR7WvpZSYTU5PRkrTTaX2cZGOXlkJOQiZ4UpqVKliqlDMBqyusnqBfK6ZSyBlhGZ3YoUKcJvv/3G7du3c1X1/mU5c+YMQ4cOfalM1KZAXQZvAVy8mPn8f//L2TFCCN1z+2L22TfMqIycmxTUKioqKipmS7du3fL1/TKWzFsa6giQBdCsmVLSCyCLBKFZ8uDBA0a2GgnAq6uesyQxo2jga69l38aMeF4xQEtHVjdZvUBetyfrQMmG6qaSgdoBsgDc3eHJv+snBneypU2bNrStotw7i0yMzL5hRt6MgICXiDD/aNOmjalDMBqyusnqBfK6ZZXgThZUN5UM1A6QBfDkfLatW5UyGi9ix44dnPA9AUD9ss+5L/tkz8oC/vewY8cOU4dgNGR1k9UL5HWLiooydQhGQ3VTyUAjRE7GE/5bVK5cmYcPH+Lg4MCDBw9MHQ5aLQwaBC4uUL26Ujj1RaSmpqLVaCkyvQgA4pfnXObfflOWl23fDma+rDc1NZWCBQuaOgyjIKubrF5gfm5JSUn4+vri6OhIkSJF8nweQ5VoMEdUN/PiRX+zxvw+tqzf1H8Ue3ul8wOwcWPOjpk1axZrPdcCMKLViOc3zqjc++RyMzNl1qxZpg7BaMjqJqsXyOsWYiFpMfKC6qaSgdoBsgAyihd37w45LbEyefJkohKV4dDY5Fi04tl6Nzrs/10l5uHxElHmD5ZUfDK3yOomqxfI61apUiVTh2A0VDeVDNQOkAVw4YLy8+BBKFgQpk598TFOTk780O4HADZc20DjpY1JSE3IunHG2vr33oOdOw0QsfGQtfgkyOsmqxfI62bOxVBfFtVNJQO1A2QBVK0KS5dmvs5JzsKvvvoKaytrQr9Vbm/dCL9BQats5ioUKwYZ91bNfBToq6++MnUIRkNWN1m9QF43e/vn5A6zcFQ3lQzUDpCZER2tzEWeMQOGDFFueVWqBN98o0yAHj8+ZyNAa/avYfLJydRdVBdrjTUz3ppBQetsOkB37sD774O1NXTubEgdg3P48GFTh2A0ZHWT1QvkdYuNjTV1CEZDdVPJQE3/a0YkJ8OTdfpatIAaNWDkSHj77cykzS9CCMFPIT9BCHzZ5EumvzkdB1uH7A9o3x7Cw+HkSXjjjZeTMDINGjQwdQhGQ1Y3Wb1AXreXWUFm7qhuKhmoHSAz4tixzOcHDkCbNvodorwwr8s8Shct/fxGGct4GzV6uTfLBx49emTqEIyGrG6yeoG8bvlZR+pl2LZtGxqNht69e+f4GEtxywsyuxkD9RaYGWFrm/m8Rw+ws1NWpuf2b1qj0fCarVLaoszsMuy5tef5ByxerPysUEEZCTJj4uLiTB2C0ZDVTVYvkNdNq33OqlEzYvny5axatSpXx1iKW16Q2c0YqB0gM6JjR6XMRXg4bNmibDt/HmbOzP25VndZzakBpwD4YNsHpGuf04t6/3346y+lpzV4cO7fLB955ZVXTB2C0ZDVTVYvkNfNEm6l3LlzhytXrnDixAn8/f1zfJwluOUVmd2MgdoBMkPs7aFPn8zXeUk1cvToUTpUy6zQu+X6lucf0KmT8nPfPkhKyv0b5hNHjx41dQhGQ1Y3Wb1AXjdLmEy7cuVKtmzZgkajYfXq1Tk+zhLc8orMbsZA7QCZKZcvZz4fOlQph5EbBg0aBGRmga5QosKLDxrxb8boiIjcvVk+kuElI7K6yeoF8rqZ+3LqlJQU7t+/z7vvvkuPHj1Yu3YtWq2WxMRExo4di0ajoWPHjvj5+QHg7e1N/fr1mTdvHvb29qSnpzN79mzGjBlD69at+eCDDwgKCuLx48csXbqUhg0bcuDAAd5++22qVq1KVFQUly9fpk+fPkyePJlXX32ViRMn6sXk7u7O119/zaJFixg6dCgRT32ORkZGMmHCBAYNGkSjRo0YP348qTnJaZILzP26mRvqJGgzJSP7M8CKFfDmm/qjQi/ijz/+YPLkybxe7XUWuy/mnQ3vEDchjhKFnlMt+JNPlPlAHTrkrOCYCcjwkhFZ3WT1AstyS0hN4FbErRy1DQ8Pp2zZskaLpZ59PYoVLJbn411dXXUTn7/++mu6du3K4cOH6datG/Pnz+fMmTOUL1+e6tWrA8qtylatWjFu3DiCgoJYtWoVvXr1omnTpiQlJdGqVSu++OILtm3bRpEiRbhx4wZbtmxh/PjxbN26lWLFivHRRx8xYcIEhg4dSseOHenSpQv9+/enfv36RERE8O677/LXX3/RuHFjzp8/T926dalfvz6ffvop33zzDePHj2fRokXY2Njg5+dH7dq1KVOmjEH/fkJDQ9Vs0LlA7QCZKW+/DZs3w6efKq9zu9o24x/VJw0+gX+TO1tpXjDg9/rrStbFf//XZI5YypdNXpDVTVYvsCy3WxG3aLGihanDAMBjqAfNKzbP8/H79u1j/fr1AHTp0gVHR0dWrlxJt27dAKVTNHLkSKKioihTpgwnTpyga9euANjZ2bFkyRIKFSqky+PUpEkTIiIiKF26NK+//joAvXv3plu3brpzvvfee3T6d6pAhQrKiHrGKM/p06eJioqibt26ALRp04bk5GRGjRpF7969OX36NJ6enizOWHACdO7c2eCrCNXOT+6wiA5QamoqU6dORQhBkSJF8PPz46effqJatWq6NtHR0YwaNQp7e3tCQkKYOnUqderU0TvPwoULcXd3x8rKivbt2zPYzCf8Tp+e+fzxY0hIUJI25wQnJ6dnPpy339jOl02+RKPRZH9guXIQEKBkY8zF0tL8IisvWZDVTVYvsCy3evb18Bias0zv+TEClFdu377NzZs3GTJkiG5bqVKlOHDgAKGhoZQvX55+/frxv//9j3Xr1jFu3Dj27NmDs7MzAOfPn+fRo0f88MMPWX4WZlRTL1mypN72+fPnc+PGDX766SfdaquMnxm3nsLCwqhSpQoAxYsX15Wm8PLyomLFivz444+68z353FAEBQWpnaDcICyAn3/+WcyePVv3+ty5c6Jx48a611qtVrRr104sX75cCCGEh4eHqFq1qoiNjdW1Wb58uWjbtq0QQoj09HTRrFkzsWfPnizfz8HBQQDCwcHBGDo5xt1dCGVdWOZj/PicHRsREaF77ubrJpiCYAri9bWvC61Wm/2BPj5CFC2qvNmWLS9pYHie9JINWd1k9RLC/NwSExOFt7e3SExMfKnzpKamGigiw/O///1P3L17V2+bv7+/0Gg0YubMmbptI0aMEPXq1RPh4eHixx9/1G2/dOmSAMT169f1zhERESG0Wq3w9fUVgPjrr7/09s+ZM0f0799fJCYmPtNGq9WK/v37iylTpgghhLh+/booXbq08PX1FUIIMXfuXFGxYkWRkpKid87w8PCX+VU8gzlft+x40d+sMb+PLWIS9J49e/RGe5o1a8a1a9eIjIwEYOfOnVy6dInPP/8cgObNm1O0aFHmz58PQGJiIpMmTWLgwIGA0sPv3bs3//vf/xBC5K9MLmjZUun2JCbCP/9AkyYwd64ySPPee8qoUHZs375d9/z16q+T9lMaczvP5W//v2m4tCF3o+5mfWCtWsoEpM6dYeBAOH7cwFYvx5NesiGrm6xeIK9bVFSUqUPIkqSkJO7evUvNmjX1tletWpWOHTuyevVq3Wf6119/za1btxg6dChffPGFrq29vT0FCxbkl19+0TvHypUrsx0dv3v3Lt999x0//vhjlkvNNRoNzZs3x9fXlz/++IPdu3fzzz//6OYgNWjQgODgYJYtW6Y7JiUlhQ0bNuTp95Ad5nrdzBWL6ADZ29vj7OxMSkoKAGfPnqVhw4bY2dkBSgeoXr16FC1aVHdMixYt2LZtGwBubm5ERETQvHlzvf337t3Dw8yLfwIUKaKUxfj7b1iyRMkTtG8ffPklZLfqsU2bNnqvra2sGfvaWCa0n4B3uDe1F9YmLjmbJG6FC4OLi7Ic/p13ICTEsEIvwdNeMiGrm6xeIK9biRLPWSxhQtatW0f79u2z3NejRw98fHz4888/AWjYsCHt2rUjPDxcL19T+fLlGTlyJK6urnTr1o1ly5YxaNAg3S2/jNtaycnJumOS/k0NsnnzZm7fvs2iRYsApWN04cIF7t69y/Tp0+nQoQPly5enbt263L59m5iYGADefvttWrRowbhx4xg+fDhLly6lZ8+edOnSxaC/H3O9bmaLwceUjMDJkyeFtbW16Nixo3B3dxedOnUSPj4+uv3Vq1cXnTp10jtm7NixwsrKSiQlJYkpU6YIQPj5+en2e3p6CkCsWLHimfczl1tg2XHu3LO3xo4f12+zb9++LI/VarXi55M/626JDdgzIPs3unxZiNKllTf4/nsDGuSd7LxkQFY3Wb2EMD83Q90Ci46ONkxABmTLli2iTJkyol27duLMmTN6+65fvy7efPNNAYgqVaqIrVu3CiGE2LBhg1i3bp1e2+joaJGUlCRGjx4tSpUqJSpVqiRmzZolhBAiODhYDB8+XADizTffFB4eHrrjBg8eLEqUKCG6du0q7t27J6pWrSq6dOkiHj16JOLj40WLFi1EuXLlROHChYVGoxGAsLe3192uCwwMFD169BBFixYVr7zyijh8+LDBf0fmeN1ehClvgVlEB0gIIbZu3SoKFiwoChQoIK5cuaK3r1ixYuLDDz/U2/bTTz8JQAQFBen+oJ+cE3Tv3j0BiN9+++2Z98r4hVtZWQkHBwe9h7Ozs1H8cktMjBBnzggxZEhmJ+jJf+f79+9/7vE3w2+KVitaCaYgxvw5Jvt5QeHhQrz7rvIGS5YY0CBvvMjLkpHVTVYvIczPTeYOkKEwhpuXl5du/k8Gjx8/FitXrhS///67wd8vOyzxuqlzgHJAUFAQX3/9NcWLF6dz585cv35dt0+j0ejd/oLMonAFCxbU3dd9ss2T+7NDq9Xy8OFDvceBAwcICgpi9erVuLu7c+TIEXbt2oW3tzcLFiwgNjYWJycnQFkhEhUVxeLFi7l27Rr79u3j4MGDXL58meXLlxMeHq7XNiEhAWdnZ27fvs327ds5ceIE586dw8XFhcDAQL22JUqkc+rUDCZO9Gf48NOAckusTJkEtm4N5MSJEyQlJekdExISwsqVK3F3d8fvHz++t/uedxze4Y+Lf2A3247vpn2naxsTE8OiRYvwCg5m7+DBBLdrB8OHc7l7d0KDgvTOm5iYiLOzMz4+Pmzbto2TJ09y9uxZ1q9fT0BAANOnT0er1eLk5IRWq2X69OkEBASwfv16zp49y8mTJ9m2bRs+Pj44OzuTmJiod/7Q0FBWrFiBh4cHvr6+7NmzBy8vLxYtWkRMTIxe20ePHrFw4UKuX7/O7t27OXz4MO7u7qxcuZKQkBC9tklJScyZMwcfHx+2bt2Km5sbZ86cYcOGDfj7+zNjxgzS09P1jgkMDMTFxYVz585x4sQJtm/fzu3bt3F2diYhIUGvbXh4OMuXL+fy5cscPHiQffv2ce3aNRYvXkxUVJRe29jYWE6dOoW3tze7du3iyJEjuLu7s3r1aoKe+n0nJycze/Zs7t27x5YtW3Bzc+P06dNs3LgRPz8/Zs6cSVpamt4xDx48YO3atVy4cIFjx46xY8cObt26xbx584iPj9drGxERwbJly/D09OTAgQPs378fT09Pli5dSmRkpF7buLg45s+fj7e3N66urhw9epSLFy+yZs0aXdyVK1fGycmJ1NRUZs2axf3799m0aROnTp3i1KlTbNq0ifv37zNr1ixSU1P1zh8UFMSaNWu4ePEiR48exdXVFW9vb+bPn09cXJxe28jISJYuXYqnpyf79+/nwIEDeHp6smzZMiIiIvTaxsfHM2/ePG7dusWOHTs4duwYFy5cYO3atTx48ECvbVpaGjNnzsTPz4+NGzdy+vRp3Nzc2LJlC0IIZs+eTXJy8jNxm+IzYunSpYCyGkkIQXBwMMnJyURGRhIXF0dcXBxRUVEkJSUREhKCVqvVrVQKCgoiNTWV8PBw0tPTefToEdHR0SQmJhIaGkpaWppe27S0NMLCwkhISCA6OppHjx7x+PFjwsPDSU1N1Wur1WoJCQkhKSmJqKgoYmNjiY+PJyIiguTkZIKDgxFCEBQUpBd3REQE8fHxxMbG5ijux48f6+JOSEggLCzsmbitra0JDQ0lMTEx13Fn/A4jIyP14h44cCBvv/02QUFBpKSkEBERgVarpUyZMjg4OOjiTk9PzzbumJgYYmJiso07PT0927hTUlIICgqiUKFCz8T95LV/Ou4nz58R95O/74xrn1XcGdc+L3FnXPuUlBTCwsIAWLp0aZafERlTX4yCwbtURsDFxUV89NFHQgghrl69KsqWLStq1Kihm1Ffq1Yt0aNHD71jRo8eLaytrUV6erpwcnISgN6KjcuXLwtAuLi4PPN+ljAC9DR+fkIUL545GmRr+1icOvXi47RarRh+YLhgCkIzRSMWXlyYdcOUlMzhpo4dhTDR/zSWLl1qkvfND2R1k9VLCPNzM9QIUGhoqIEiMj+M4ebo6Cjatm0rTp48KSIjI0VERITYu3evGDVqlMHf63lY4nVTb4G9gOrVq4sdO3boXp8/f15YWVnp7qF+9NFHonXr1nrH9O7dW7dt//79AhDe3t66/YcOHXpmWwbmPgfoecTG6s8NiozM2XHBccG6eUEeQR7ZN/z5Z+XEvXsL8bzl9EbC0MtGzQlZ3WT1EsL83AzVAXp6ubZMGMPt/v374osvvhCVKlUShQoVEvXq1RMzZ87M92Xplnjd1FtgL+DRo0cULlxY9/q1116jadOmutn6/fv35+rVq3qz9q9du8bHH38MKJlCy5Yty4ULF/T2169fX7pqziVKwPz5ULPmfaytwc4OnlrtmSUVSlRg/fvraViuIe3XtGfe+XlZN/zuOxg7VkmU+N57Sj8rH3lyGalsyOomqxfI6xYeHm7qEIyGMdwcHR1Zt24dDx8+JDk5mZs3b/LDDz9QoED+5hqW+boZBYN3qYzAiBEjxFdffaV7HRcXJ1q2bCni4uKEEEKkpaWJFi1aCFdXVyGEEBcvXhSOjo4iPj5ed8y8efNEz549hRBKsqimTZtmO4HRkkeAnuTatcyRoM8/F+LOnRcf8yjpkei4tqNgCuJXt1+znxy9dKly4mHDlNtjKioqJsdQI0AqKvmFOgL0An7//XeKFCnC0KFDmTdvHtOnT2fTpk26nAfW1tbs27eP7du38/3337Nw4UJOnDhB8eLFdecYO3YsLVq0YNiwYQwePJgffviBHj16mErJ6Dg5OdGoEQQHw7RpcOwY1KkDTZsqAzihoVkfZ1vYlv399jPutXH84vYLtRbWYvk/y59NGPn117BwIaxcCc2bw8WLxlYC0E0IlRFZ3WT1AnndMiaxyojqppKBRjzzzaZSuXJlHj58iIODAw8ePDB1OHkiPj5eLylWdDQcOAD798OOHco2Z2cYPz77c5wJOMO0U9M4eu8ojqUcuT/m/rONLlyALl2UjIyenkq6aiPytJdMyOomqxeYn1tSUhK+vr44OjpmmbE4p6Snp2NtbW3AyMwH1c28eNHfrDG/jy1iBEgl96xcuVLvdenS8PnnytSdL79Utv3vf/BvNoAsaV+1PYc/U6ol+8b4cj86iw7Qa6/Bv5lX+e03Q4T+XJ72kglZ3WT1AnndMqqcy4jqppKB2gGSlK5du2a778ns/S8a/9NoNFQvVR0AK002fy4tWig/t283+qTo53lZOrK6yeoF8rrZ2tqaOgSjobqpZKB2gCTFy8sr232DBmU+f6qmYJYs7rYYAMc/HElMTXy2QeHCypwggHnZrB4zEM/zsnRkdZPVC+R1S0zM4t+5JKhuKhmoHSBJKVWqVLb7ChSAUaOU5wEBLz5Xt9rdKFJAuTcb+jib2dP/Fgdk0qRcRJl7nudl6cjqJqsXyOuW38u38xPVTSUD9bclKTY2Ntnu27RJWcAFmT+fR2p6KsUKFiMpLYnktOSsG1lZQbly8FRJEkPzPC9LR1Y3Wb1AXjcrK3n/b6y6qWSg/rYk5ebNm9nuO3Ik8/mZM3D37vPPpRVaohKjAHDxdEErtFk3LFoU/P1h377chptjnudl6cjqJqsXyOuWlJRk6hCMhuqmkoHaAZKUd955J9t9q1bB998rz7dtg3Xrnn+uwgUK4zvGF4CZZ2fiFZrFvAeNBk6dUp737p2XkHPE87wsHVndZPUCed1knkyruqlkoHaAJMXFxeW5+2fPVn7a2sJPP734fNGJ0brnTSpkk+unfHnlZ3I2t8kMwIu8LBlZ3WT1AnndzHU59dmzZ+nWrRsajYYPP/zwuW0bNmyIRqNh6tSp3Lt3T7c9r26XLl2iTJkyZp0b7mm3Bw8eMHfuXNKfl+/kKR49ekS/fv0oWbIkNWvWZP369bmK4ejRoxw6dChXx5gMg+eWlgBZSmFkhaenECNHZpbI8PXN2XFL3ZcKpiAaL22cfaOoKOWkVaoYJFYVFZXc8V8oheHl5SUAYW1tLfz9/bNs89dffwlA2NnZGex9AwICRL9+/XQlmMwdf39/MXToUJGenv7MvqCgoGyPGzt2rDhw4IC4ePGi6NSpk7CyssqyaPjzWLFihVi7dm2O2qqlMFQMTnYp+ps3z1ywtXIlVK/+4nOladP45uA3AEx5fUr2DTMmFxlxZYyspQdAXjdZvUBeN3MuqVCiRAk6duyIEIKlS5dm2Wbp0qW8/vrrWWYWzqtblSpV2Lx5s1ll/n6aDLf09HQ+++wzJk2alOXE6AkTJmR5fFxcHMOHD6d79+60bt2atWvXotVquXHjRq7iGDJkCBs3bsTT0zPXDvmJ2gGSlB9//DHL7U9+Xv/+Ozx+/OJzFbAqwNs13gbgh+M/kJSWzUS7Xr2Un15eysMIZOclA7K6yeoF8rpVrFjR1CE8F0dHR7p168aqVauemfgbFBREamoq1bP53525u70MGW4bNmzA1taWqlWr5up4GxsbateurXttZ2eHra0tnTp1ynUsX3/9Nd99912uj8tP1A6QpPz+++9Zbp8wATJuE9++De7uOTvfsc+P0bdhX3yifFjpkU36/2LFwFeZLM2wYbmMOGdk5yUDsrrJ6gXyuoWEhJg6hBcycuRIIiIi2Lp1q9725cuXMyyLz5/jx4/Tr18/Ro8eTbNmzVj4RA6Qffv2UaVKFTQaDWvWrCEmJoZmzZrRs2dPAgICiI6OZs6cOTg6OuLn5weAh4cHAwcOpGvXrnh5edGhQwdsbGyYNGkSqampjBs3DltbW5o2bUpgYCBCCFatWkWJEiUYMGAAANeuXeOdd95Bo9Hk6ZxPk3HdFi5cyJtvvvnSv+PNmzezceNG7O3tddseP37M+PHjqVatGoUKFUKj0ege77//vq5dp06dOH78eK5Hj/ITNQ+QpPTt2zfbfXZ2mc8HDIBXX4XWraFtW+V5dqkk5neZz9brWxl9eDQewR70btCbLjW7YG31RPG96tWVcvPz5ysjQqNHw1tvKavEDMDzvCwdWd1k9QILc0tIgFu3ctTULiUFgoONF0u9esp/mF6Czp07U7t2bRYtWqTrUKSmpvLXX38xZcoUtmzZomubmJjIe++9x+7du3n99ddZt24d33zzDQMGDMDGxoZevXpRoUIF2rZtS3h4OKDc8nJ1daVgwYKEhYWRnp6u6/yAsuLKy8uL2NhYLl++jKurK/Pnz+e3335Dq9Xy6aefMmbMGDp06MDMmTNZvHgxgwcP1ptU3LhxY/r168fx48fzfM4nKVOmDMHBwVy+fPmlbs/6+vqyePFi5s+fT9++fenSpQuFChUC4Msvv+TYsWN8//33tG7dmqVLl/LJJ59QpUoV7J74crG3t6dMmTIcOnSIBg0a5DkWY6J2gCTlzJkz2Q4BA4SGKjVMr1wBDw/49ltl+4ABsHZt1seUL1Gee6PvsfHaRtZ6rmXd1XUMaT6EFT1X6DecMwcaN1busb3zDixeDMOH54uXJSOrm6xeYGFut25l1u17AYWMHAoeHsqExJdAo9EwfPhwxo0bx/nz52nTpg27du3igw8+0I2oZFCwYEF69+5NixYtiI+Pp0KFCmi1WqKjo3XJLFu3bs24ceNwcnLi2rVrzJs3j4IFCwJQrlw5WrVqpXfO2rVrU69ePR48eMCX/1aY/vzzz5kxYwZdunTRtW/fvj137tzRHff0nJwnX+f1nBnEx8dz7do1ACpUqKDbPnPmTM6cOaN7ffXqVXr06KF37LZt2yhevLju2H79+uHn58emTZtwdHRk2rRp3Lx5E1dXV7Zv384nn3wCQN26denatSunTp3S6wCBUsndnOcBqR0gSalWrdpz95crp1SFz6gM36YNXLgALi5QqZIyVyirQZsapWvw8+s/079xf2ouqMnKyytpWK4ho18dndmoQAEYOBA++QRsbGDECGVkqFs3o3tZMrK6yeoFFuZWr57S8cgBjx8/1n0ZGi0WAzBgwAAmT57MwoULadOmDS4uLnojPxkUKFCAtWvXcv78eVxdXXU1s7Ra/aSuU6dOZdeuXURFRVHzqUKJ1tbWPM3TpSeKZpEJv3DhwqSmpubY6WXOWahQISIjIwH0rt/Tc9UGDBjw3BQORYsWpUWLFuzcuZMOHTpw4MABpk2bpqt916VLF13bqlWrUrduXZycnJj3VC3I4sWLExqaTfkkM0DtAElKbvI+APzxh3L7C2DmTJgyBf79z0+W1Chdg4OfHqT75u784vaLfgcogxIl4OZNeOUV5YQG6ADl1suSkNVNVi+wMLdixXI86qKNi1P+82LmlCpVis8++4y1a9cyYMAAqlSpkm19tjFjxlCoUCEmT56Mp6cnS5YseaZNfHw8tWrV4vDhw/z555907drVyAaGJ6PDlJKSYpDzffTRR7pOZcYKuMTERL2ki3Xr1s0y949Go8my42guqJOgJSU4l/fvM5bGg5LI+XmdnwxuhitlAGKSYohLjsu6Udmyyk93d4iJyVVMWZFbL0tCVjdZvUBeN0N9eeYHI0eOJDU1lT59+jBixIgs2xw/fpwFCxYwZcqU53ZaJ02axPr16/nyyy/55ptveJyTZbK5pFChQnpV2zNGoZ4ejcoLKSkpODg4AEpCQ0OQlJREhw4dAOjQoQO2tra4ubnptQkNDc0yPUBcXBxVqlQxSBzGQO0ASUrr1q1z1X7ixMznW7fCiRMvPmZgs4G65wP2DiA+Jf7ZRnZ28NlnyvOePSE2NldxPU1uvSwJWd1k9QJ53Yx6++slSUxM1OtANGrUiA4dOtCgQQOaNGmSZbuMpfLr16/n/v37rP13oqOXlxdXr14FYOXKlbzzzjuUL18eZ2dnYmNj+T6jZhCZo31PdqDS09P1XmfVmRFC6LWpWbMmp06dwsPDg927d7N7924A3NzcePz4cZ7OmUHx4sVp1qwZJUqUwN/fP9vfYXapAEJCQnB2dtZNBA8ODmbfvn26vEE2NjbMnTuXX3/9lfh45fM+MjKSvXv3Mm7cuGfO5+/vz6sZtxbMEYOnVpQAGTJBz5o1K9fHJCbqZ4lu106I1NTnHxOTGCM+df1UMAXBFISbr9uzjbRaIaZOVU5avLgQhw7lOrYM8uJlKcjqJquXEObnZqhM0MHBwQaKyLCcO3dOfPrpp8LOzk6sWbNGxMTECCGE2LZtm9i8ebMQQojY2FixevVqUaZMGQGIadOmiVu3bokePXoIGxsb8cEHHwhvb29RpkwZ0b9/f5Gamiq2b98uihQpInbt2iWEEMLHx0fUrl1bAOKHH34QAQEBok+fPgIQo0ePFhEREeL48ePCwcFB2Nraii1btoigoCAxYsQIAYh+/fqJO3fuiL1794oqVaoIGxsbsX37diGEEPfv3xf169cXpUqVEr///rtYu3ataNWqlVi3bp04fPhwns6ZQcZ1+/TTT8WPP/6Y69/vtWvXhKOjoyhZsqT44IMPxJgxY0RgYOAz7TZv3ix69eolJkyYIPr16ycOHDjwTBtfX19RuHBhERYW9tz3NGUmaI0QQhi6UxUVFUWZMmUMfdp8o3Llyjx8+BAHBwezrvvyPJKTkylcuHCejr16FZo2VZ536wZffw3vvvv822Jbr2+ln2s/AJZ2X8pXzb6ikPVTa0lu3oT69ZXnq1crE6VzuTz+ZbzMHVndZPUC83NLSkrC19cXR0fHLLMg5xStVptlBmEZ+C+4Xb9+nS+//BKPHE56NwZLlizB29ubRU/Or8iCF/3NGvP7+KX+CrRaLcHBwQQEBOgevr6+/Prrr4aKTyWPzJkzJ8/HNmmiJEmcNg38/ZV0PoUKwblz2R/Tt2Ffrn9znU8bfco3B7+h8tzKegVUAWUydHAwfPEFDBoEgwfnOraX8TJ3ZHWT1QvkdbOERIh55b/g1rBhQ7p3746rq6tJ4khLS2P37t1Mnz7dJO+fY/I6dLRx40ZRqlQpYWVlpffQaDTCysrKcGNUJkCGW2CGZMkSIapVU+5gZVN7UI+NVzeKAlMLiE+2f5J9Iycn5YRHjxosThWV/zr/hWKoKjkjPT1dTJgwQdy9ezdf31er1YqpU6eKGzdu5Ki9RRZDHTVqFL179+bPP//k5MmTuseJEyf48MMPDddDU8kThizS+M03SiofgKcm/2fJZ40/o1vtbuzw3kGaNi3rRhMmKBOk163LVSyyFp8Eed1k9QJ53cy5GOrL8l9xs7Ky4rfffuP27dv5mq7hzJkzDB06lPoZ0x3MmDznAbKzs2PGjBlZzvUx52Vv/xW++uorg56vXDnl57VryhTpF03dGdp8KPtu72PPrT18XP/jZxtYWSlZGOfOVTJFlyyZozgM7WVOyOomqxfI6/Zk7SfZ+K+5dTNA/rXckLFk3hLI8wjQ/Pnz+fPPP7Pc5+Pjk+eAVAxDdtcmr2QUA3Z2Bnt7eFFi01YOrShZuCSf7PiELV7PZmYFlLlABQtCq1bw77LLF2FoL3NCVjdZvUBeN0PlkDFHVDeVDPI8AvTqq6/y1VdfceKphDFarZZTp05x//79lw5OJe80btzYoOerVg0ePVIGaqKilCoX27crk6OzolzxcniP8MZhrgOf7voU28K2dK/TXb9Rkyawfj3066fUKPL2VrJHPwdDe5kTsrrJ6gXyuhV7yUKl5ozqppJBnkeAevbsybFjx/D29sbX11fvkVGLRMV0REVFGfyctrZw+rTyfO9e+OEH5XZYdlSyqcT5QecB6LGlByd9Tz7bqG9fpWhqYKAyzPSCRInG8DIXZHWT1QvkdUtLy2bungSobioZ5LkDdPv2bc6dO8eFCxf466+/dI+///6bZcuWGTJGlTxgjBTuAO3bw717yvP585WpPMnJ2bd/rfJrnB6o9JreWv8Wv53+7dlG//ufUoLewwMqV4bn3EI1lpc5IKubrF4gr5shyjKYK6qbSgZ57gB99tln2aZLf/PNN/MckIphqGegastZUaMGPJmPKiHh+e3bV21P6LdKReBJJyfhE5lFB2fAADh6FOLi4L33lAREWWBML1Mjq5usXmC+buIl89u+TBJFc0d1My9e9m/1ZXipZfBbtmzRS4KYkQhx1qxZhoxRJQ8cP37cqOd3cIBvv1WeV6ny4knR5YqX090Oq7OoDmcCzjzb6J134O+/lQnRrVvD4cPPNDG2lymR1U1WLzA/t4IFC6LRaF56ZCr2JWv2mTOqm3nx+PFjNBoNBXNSgdvA5LkUhqOjIwEBAc9sF0Kg0WjyNe+AoZGhFEZsbCy2trZGf59PPoGdO5XnwcFQocLz2/tE+tB2TVsiEiL4qulXLOuxjILWT/3hP3igrBD76y9lgvSyZcoEJPLPyxTI6iarF5inW3BwMDExMdja2mJra0uBAgXQ5LLkTHp6OtbW1kaK0LSobqZHCEFaWhqxsbHExsZSqlSpbAu0GvP7OM+rwL755hvS09Np3bo1BQpkniYtLY2VK1caJDiVvLNgwQImT55s9PdZv165a3XkCFSsCFu2wEcfZV83rLZdbe6NvscPx35gmccyAmIDmN9lPg3KNchsVLmyUo5+3jz48UfldtjcufDqq/nmZQpkdZPVC8zTrUKFChQtWpSwsLA8jwg8evSIkjnMzWVpqG7mg7W1NRUrVjRZzHkeAQoLCwOgXEaGvCc4deoUHTt2fLnInsPRo0c5fPgwtWvX5vXXX6d+/fpER0czatQo7O3tCQkJYerUqdSpU0fvuIULF+Lu7o6VlRXt27dncDa1qGQYAcpPtFoYOVJJ6pyQoCzs2rz5xckSfzr5E/MvzkeDhpNfnqRlpZbPNjpwAMaMUVaJ7doFPXoYR0JFRTKEEKSnp6srg1TMkgIFCmBtbf3C0Umjfh/ntYbG2rVrs903ZsyYvJ72uWi1WjF+/HgxZMgQkZqaqre9Xbt2Yvny5UIIITw8PETVqlVFbGysrs3y5ctF27ZthRBKjZRmzZqJPXv2ZPk+MtQCmzZtWr6/Z1KSUt4LhFi2LGfHBMcFC6YgmIIIfxyedaPkZCEcHIQAsaV/f8MFbGaY4prlB7J6CSGvm6xeQqhuloYxv4/z3AFq3769SEtL09uWkpIihg8fbrRiqBMnThRvvvnmM++7fft2UbBgQZGQkKDbVrduXTF16lQhhBAJCQnC3t5erFy5Urd/xowZombNmkKr1T7zPjJ0gCIjI/P9PdPSMjtAuelav+HyhmAKoteWXtk3un4988RZXDMZMMU1yw9k9RJCXjdZvYRQ3SwNsyyGmpqaytChQ3V5B+7du0ebNm1Yt24dTZo0Mcjo1JNcvHiROXPmsHbt2mcmee3cuZN69epRtGhR3bYWLVqwbds2ANzc3IiIiKB58+Z6++/du4eHh4fBYzUHtmzJpvyEEZk2LW/H1bFTblXuu70v+0YNnpgjJGmuC1Ncs/xAVi+Q101WL1DdVDLJcwfo+PHjDBw4kCFDhrBhwwaaNWuGEIIrV67w999/GzJGAKZNm0br1q1xcXHh7bffpl27dpw8qWQWvnTpEnZ2dnrty5Urx82bN0lOTubSpUsAem0y5i5duXLF4LGaA/ldkE4I+PXXzNe5uVW7/cZ2ADpU7fD8nBAZS8z++ScPEZo/llREMDfI6gXyusnqBaqbSiZ57gCVKFGC9u3bM3DgQEaOHMmQIUO4ePEitWvXNniBwMePH3Ps2DEqVarEmDFj+PPPP3FwcKBbt274+voSFhb2TFV6GxsbtFotUVFRugnbT7axsbEBICIiItv3DQ4OpnLlynqPuXPnGtTNWPj5+eXr+z09zzIoKOfH/vHuHwCcDjiN4DkdoB9/VH527ZrL6CyD/L5m+YWsXiCvm6xeoLqpZJLjDlC3bt148803n3n8/PPPVKtWjStXrtC5c2def/11hg0bZtAg79+/T0pKCn369KFkyZIULFiQ6dOnk5yczMKFC9FoNHq3vwBdHqKMxGCAXpsn92eHVqvl4cOHeo8DBw4QFBTE6tWrcXd358iRI+zatQtvb28WLFhAbGwsTk5OADg5OREVFcXixYu5du0a+/bt4+DBg1y+fJnly5cTHh6u1zYhIQFnZ2du377N9u3bOXHiBOfOncPFxYXAwEC9tunp6cyYMQN/f382bNjAmTNncHNzY+vWrfj4+LBnzx6SkpL0jgkJCWHlypW4u7tz+PBhdu/ezfXr11m4cCGPHj3SaxsTE8OiRYvw8vJiz549HDp0CA8PD1asWEFoaKhe28TERObOna/3u3vvvSQaNAjG1TWE6dOno9VqcXJyQqvVMn36dAICAli/fj1nz56laHDmdbGfZs+Yg2P4ddqvuvOHhoayYsUKrrRoQbi9PURHk1inDrtHjyYmJkYvlkePHrFw4UKuX7/O7t27OXz4MO7u7qxcuZKQkBC9tklJScyZMwcfHx+2bt2Km5sbZ86cYcOGDfj7+zNjxgzS09P1jgkMDMTFxYVz585x4sQJtm/fzu3bt3F2diYhIUGvbXh4OMuXL+fy5cscPHiQffv2ce3aNRYvXkxUVJRe29jYWA4ePIi3tze7du3iyJEjuLu7s3r1aoKCgvTaJicnM3v2bO7du8eWLVtwc3Pj9OnTbNy4ET8/P2bOnElaWpreMQ8ePGDt2rVcuHCBY8eOsWPHDm7dusW8efOIj4/XaxsREcGyZcvw9PTkwIED7N+/H09PT5YuXUpkZKRe27i4OObPn4+3tzeurq4cPXqUixcvsmbNGl3c1tbWODk5kZqayqxZs7h//z6bNm3i1KlTnDp1ik2bNnH//n1mzZpFamqq3vmDgoJYs2YNFy9e5OjRo7i6uuLt7c38+fOJi4vTaxsZGcnSpUvx9PRk//79HDhwAE9PT5YtW0ZERIRe2/j4eObNm8etW7fYsWMHx44d48KFC6xdu5YHDx7otU1LS2PmzJn4+fmxceNGTp8+jZubG1u2bCEsLIzZs2eTnJz8TNzm/hkxZ86cbD8j7t27Z/DPCGdnZ3x8fNi2bRsnT57k7NmzrF+/noCAgBd+Rpw8eZJt27bh4+ODs7MziYmJeufP+Izw8PDg0KFD7NmzBy8vLxYtWvTMZ0RSUpLFfkYsWLDguZ8RGf/WLO0zIqNtVp8RKSkpGIscL4P/7LPPePDgAdWrV8fKKvt+U3p6Om5ublkmScwr58+fp23bthw+fJguXbrotletWpXGjRtz+/Zt6tWrx/79+3X7xowZw+LFi0lJSWHGjBlMnjyZiIgI3W2wK1eu0Lx5c1xcXPjyyy/13i9j2Z2VldUzyZnGjx/P+PHjDeZmLC5fvqw35ym/SEpSVqtfvQpz5ijbmjWDx4/hzBmwt8/+WDc/N9ZfXc/6q+spV7wcfRv2Zc47c7C2ypzzddnDg+YhITB7Nly4oCRMnDpVSUJk4ZjqmhkbWb1AXjdZvUB1szTMIhHi8OHDqV+/PqVLl35h2337njOZNQ9UrlwZeLbycvny5SlTpgxNmjQhMDBQb19ISAgtWrTAyspKNyk7LCxM1wEKCQkBoHXr1tm+b8WKFS02D5C7u7tJ/iEUKQKffqo8Jk6EGTMgozJKxYrw229KQdXGjeHpUnKdqneiU/VODGw6kI4uHZl3YR6nA07zXdvveKfGO5QuWhr3f/6h+bBhStmMceNgyRLYtg0WLoSPP372pBaEqa6ZsZHVC+R1k9ULVDeVJzD4ujIj0bhxYzFq1Ci9bTVq1BAuLi5i9+7donDhwiIpKUm3r169emL27NlCCGV5ftmyZcWaNWt0+2fOnCnq16+f5XvJsAw+LCzM1CHoSE8X4uhRIfr0EaJwYf2l8tmFqdVqxZG7R0SbVW10eYKaLmsq/IP89Rvevi1Er16ZJxw5UnlDC8ScrpkhkdVLCHndZPUSQnWzNMxyGXx+M2vWLLZs2aKbtPzPP/9QtGhRPv30U3r27EnDhg05ePAgoKwKS05OZvjw4YAyz2fixIns3r0bUMp1bN26VeqircuXLzd1CDqsrJQBm61bITYW1q7N3FeuHNy+/ewxGo2GzjU7c27QOfzG+AHgGeJJrRW1CHj0xO3VOnVg7164e1d5vWiRkoo6MdF4QkbCnK6ZIZHVC+R1k9ULVDeVTPJcCsMUbN++nW3bttG0aVMCAgL45ZdfdLfHgoKCGD9+PFWrViU4OJipU6fi6Oiod/yvv/5KUFAQycnJvPvuu/Tt2zfL91FLYRgfIWDmTOU2WQaffQYbN2Z/zGavzXx37DsiEyJpXrE56z9YT60ytTIbaLWwYQN89RWULAmjRsEvvyg9MBUVFRUVi8MsS2HIjAy3wCwlJbq/vxBz5mTewWrfXoh584Tw9Mw64fOkqZPEsP3DdLfFfjz2o7gQeEG/kY+PEJ07KycsVEiI9euVOh1mjqVcs9wiq5cQ8rrJ6iWE6mZpGPP72KJGgPILGUaAEhISKFasmKnDyBFCwIoVSmX59HTw9ITkZOXu1qZN0PKJGqkZXpuubWKj10Y8gjwITwinfdX2rOy5knr29TJPunixUp7+3DmoUkW5Pdarl0kcc4IlXbPcIKsXyOsmqxeobpaGMb+PDX5vwNnZmRo1ajBmzBjdnBuV/Gfp0qWmDiHHaDQwbBicPausbI+NhZ074c4daNUKnsw6kOH1WePP+POzPwn5NgTX3q54h3vzyuJXmHd+XuZJR45UTnrzptKbeu895T6bmVbHtqRrlhtk9QJ53WT1AtVNJRODd4CWLl3K48eP+eOPP7Czs+MfScsWmDs9evQwdQh5pmBBeLJCyZN/Qk97WWms+PCVD5n51kwATvqdfPaE9erB0aNQpgxs3gzR0cYI+6Wx5Gv2PGT1AnndZPUC1U0lE4N3gPbv369bjdWxY8cc5Q1SMTxXr141dQh5JiICpk/PfL1gQebz7Lz23t4LgLXGmuS05GcbWFlBw4bK8507DRWqQbHka/Y8ZPUCed1k9QLVTSWTl+oAxcXFcePGDQCio6N58OABr7zyCi2fmLRRs2bNl4tQJU88XRzWkihbFiIj9TNJZ+TAzM5rZ++d/NjuR/be3ks/135Zn/jgQXj9dRg+HK5fN0LkL4clX7PnIasXyOsmqxeobiqZ5LkDdOHCBWrWrMm4ceMAKFWqFGfOnGH48OEkWmAOFtl4ujaapVGmjDIvKINDh5Sf2XkVKVCEiR2UNfW7b+3Ouqp8iRIwf77yfOVKA0ZrGCz9mmWHrF4gr5usXqC6qWSS5w7Q2LFjee+993jllVcAJXFd3759sbe3Z+TIkQYLUCVv3Llzx9QhvDQ2Npklvj7/HN58E2bOLM6/uTCfbV/YhgolKgDQfm175pydQ2p6qn6jmjWVCdILFsC33yozrs0EGa5ZVsjqBfK6yeoFqptKJnnuAJUqVYqVK1dSqVIlve329vbq6i8z4K233jJ1CAbh7l0ll+HYscqo0MmTjWjVCn79FVxdwcdHWfGewdWvrzL9zek42Dgw4cQEum7qyjrPdZnZo21s4Px5GDECli6FN95QUlTHxZnE70lkuWZPI6sXyOsmqxeobiqZ5LkD1KBBg2e2PX78mBUrVlCyZMmXCkrl5Vm3bp2pQzAIxYrBlCkwb54yd/mLL9ZQv76S0ufjj5XV7dWrQ8+eMHQoBNwsx8QOE9n+yXa2fLSF8IRwBuwdQLX51Wi5oiWj/xzN5aoFlRMcP65Mju7XT8kT9Pnn8NdfJnOV5Zo9jaxeIK+brF6guqlkkudEiK6urgQHB+Pv788nn3yCh4cHv//+O76+vixfvpwhQ4YYOtZ8Q4ZEiP8FwsLg0iX4+2+4cQP+/FPZbmcHHh5QrZryOioxikM+hzh67ygbrm0AYHTr0cx/dz4ajQb8/WHVKnByUg6YOFF/GZqKioqKikkw21IYf//9t3jrrbdEuXLlRMmSJUXbtm3Fvn37DJCg2rSopTDMl+d5PXwoxIcfZpbVOHz42XIawXHBujIaow6NElEJUZk7Y2OFsLVVDp44UYjUVCNZZM1/8ZpZOrK6yeolhOpmaVhUKYyYmBiKFClCkSJFDHnafEWGEaD09HSsra1NHYbBeZGXVqvMF1q4UHn9yy/KQ6PJbBOVGMU3B79h+43ttKnchr8H/E1B64LKzrg4GDIEtm2DSZMyR4Xygf/qNbNkZHWT1QtUN0vDLEphBAQEvPDh5+fHn3/+qd6HNANmz55t6hCMwou8rKyUBV5JScrrX3+F99/Xb1OmaBm2fbyNvg37cv7BeUrOLJm5bN7GRpkUbW+v3AZbtszwEtnwX71mloysbrJ6geqmkkmOR4CqVauW495XvXr1dAkSLREZRoD8/f2pljEJRiJy4/X995nJFPv2VfoyT87P9432pcaCGgA0rdCUUwNOYVPYRtl56RK8+qry/MgR6NzZUArZol4zy0NWN1m9QHWzNMxiBOjrr79mzpw5HD9+nBMnTtCjRw+GDBnCoUOHOHnypO7x/fff88033xg0SJXcc+rUKVOHYBRy4zV7Nvj6wkcfKSvIRo1SbpFl4FjakeTJyUzuMJnrYdcZdmAYWvFvg9atlTX27dopFeS9vAxs8izqNbM8ZHWT1QtUN5VMCuS0YcaqLnt7ewA2bdrEsixuD7Rt25Y33nhDTYZoYhwdHU0dglHIrVf16krnZ8YMZXHX3r1KhfnWrZUBnldfLcS0N6dRtWRVhh0Yhne4N69Xe53WDq1p5dCKWgcPYNW2HXTsCF27Kge99ho0bQqFC5vUzVKQ1QvkdZPVC1Q3lUxy3AHK6PhkkN1Eq8uXL+Ph4fFyUam8NGlpaaYOwSjk1evHH6FTJyXNz6VLsHat0ikCJQXQq68OYXDL6vixjkM+f7LgklKBtVSRUrw5ognj3Gyo732Z0q6uaFJSoFAhpUhZRofo1VfB0VF/tnU+uZk7snqBvG6yeoHqppJJjjtAT5NR8mLEiBFUq1aN6Ohojh8/zsSJE3nttdcMGaNKHggJCTF1CEYhr14aDbRpozxAWSgfGKh0hi5eVH4emvoOCQnvYG0NDZpFUbPDPzRqc4lrkZf4uPUtQhuEUrAndI4vx/sxFekVY0u5gwczy9WXLat0hAYPhvfeyzc3c0dWL5DXTVYvUN1UMslzB2jq1Kl8++23NGrUSLeCRghB3bp1WbVqlcECVMkbLVq0MHUIRsFQXhoNVK2qPD7+WNmWlqYkVFQ6RWXYsbozXns6c/w4ODoKAmMDcX/ozqWHl1h47zDfhP/FJqdN9C73RmZP6sQJ+OADmDZNWUZvAjdzQ1YvkNdNVi9Q3VQyyXMpDCsrK+bOnYuPjw+LFi1i1qxZ7N27Fy8vL2rWrGnIGFXywJ49e0wdglEwpleBAtCkiZIGaNUqpQ/j66sM6gihoWrJqnxU/yNmvTOLC4MukKZNo8/OPpxKuAndu8PUqXD6NHTrBpMnw+LFuXp/9ZpZHrK6yeoFqptKJgZPhHjr1i1CQkLo1KmTIU+br8iwDD4pKcmik1FmR356paQodchmzFDKaly5AqVLZ+73jfal/+7+nAs8x+peq/mq2VfKjsREGD1a6UXlIpmies0sD1ndZPUC1c3SMItl8E/z5ptvPvPo1KkT7777Lgsz0vCqmIzff//d1CEYhfz0KlQIfvtNWfzl769Uo4+MzNzvWNqR/f32AzBo3yC2Xt+q7ChaFFasUFaKTZ+e47pi6jWzPGR1k9ULVDeVTPI8AlS9enVef/11rKwy+1BarRYvLy9atmzJihUrDBZkfiPDCJCKYXlycdeyZcqtsoYNoUQJpbSG3Ww7AEa2GkmLSi1oWqEpr9jVo3ChospBPXrA669Dy5bQqJFSsVVFRUVF5bmY5QjQ8uXLWbduHWvXrtU91q1bx9ChQ+nbt68hY1TJA075WMMqPzGVl4cHtGgB9erBiBHKajIbG2UJfZ/3StM1dTWtSnbn4O2jfLX3K5otb0bxGSXoOaE6J96sjn/QTdJ//gneeEMps1GpkpJdeuJE2LcP/PxwmjrVJG7GRta/RZDXTVYvUN1UMjH4HKCbN2/Ss2dP7t69a8jT5isyjACFhIRQoUIFU4dhcMzBKykJvL2VFWM3byrPL16EjBWoVkXiKdvoGsL+Ot2+vIFv4lXcg9xJTkqgdhS8GlWU9o9K8mZcWWr4hOsOFEWKoKldG/r1gx9+UAqbSYA5XDNjIaubrF6gulkaxvw+zvMy+PXr1z+zLTExkZ07dxIfH/9SQam8PPv379dl75YJc/AqUgSaN1ceGQgBfn5w5w74+ZXg77/bsmVLW1z+VCrTH/8xDe9wb3wifbgbdZfvz84iOsmLV3rXw7P7aQrd8uH8unW0TUxURoVmzVKW1tepYzJPQ2EO18xYyOomqxeobiqZ5LkDNGDAgGe2FSxYkJo1a7JmzZqXiUnFADRt2tTUIRgFc/XSaJRE0BmZ6IcNUwZyevVSapClpBRg0KDGNK7fGIDxbcbz3tb3+PPun7Q91pdJHSbhMH68UqdjzRr4+msl0/TPPysnK1XKdHIvibleM0Mgq5usXqC6qWSS5zH2yZMnk56ejlar1T2Sk5Px9vamW7duhoxRJQ9EPrlcSSIsyatnT+Xu1ttvw//+p1Skz6CgdUEOfXaI84POky7S+XD7hyy7vkzpSQ0aBBER0L+/UsPj3Xfh8WPTibwklnTNcousbrJ6geqmkkmeO0B169ZFk0Xdo+PHjxMeHv5SQam8PImJiaYOwShYmlf58rBhg/L88GGYM0e5XZbBa5Vf4+LgizQq14i1gWv57uh3PE55DCVLwvLlsGWLkoCoTh04eNA0Ei+JpV2z3CCrm6xeoLqpZJLnDtDZs2ez3N6yZUvey0MdJBXDUrt2bVOHYBQs0atCBWVuUJUq8P33SlHW1NTM/YWsC/HP0H/oU6MP8y7Mo83qNtwMv6ns7NtXmW1do4aylP6LL8DC/oNhidcsp8jqJqsXqG4qmeSqA3T9+nX69u3Lm2++yb59+7JMhtiiRQsCAwONFa9KDvnrr79MHYJRsFSv2rXh7l3o3RtOnVKSLH73nVKQFZROULuYdpz88iRRiVHUX1KfT3Z8wrF7x6BWLeWgP/6APXuUk40erdTpsAAs9ZrlBFndZPUC1U0lk1wvg4+NjaVfv37cu3ePNhmltTNOptFQvHhxBg0aZNGTsWRYBv/o0SNKlixp6jAMjgxeJ0+CqyusXKmMBP30E4wdC9bWiltcchxL/1nK+qvruRF+g+61uzP7ndnUL1tf6TEtXAguLhAdrawYGzlSqURvpshwzbJDVjdZvUB1szSM+n0s8kBSUpJYsWJFXg61CBwcHAQgHBwcTB1Knpk2bZqpQzAKMnmFhQnRoIEQIESpUkKMHLlMb3+6Nl0svrRYFJ9eXDAFMe3vaSI2KVbZGRMjxNixQlhbC1GypBCbNgmRkpL/EjlApmv2NLK6yeolhOpmaRjz+9jgiRABLl++TPMnk6QYkKioKOrVq8f27dt1BVejo6MZNWoU9vb2hISEMHXqVOo8lT9l4cKFuLu7Y2VlRfv27Rk8eHC27yHDCJCK5eDurqwYCw1Vpvp8+KGyaiwjn1lyWjJT3KYw8+xMClsXpnud7sx6exa1ytRSVosNGwa7dimjQMOGwZgxSrZpFRUVFQvHLEaA5s+fL44dO6Z7PW3aNPHrr78+8/j5559F8+bNDd5Ty2DQoEECEH/99ZcQQgitVivatWsnli9fLoQQwsPDQ1StWlXExsbqjlm+fLlo27atEEKI9PR00axZM7Fnz55s30MdATJfZPVKThbi00+3igEDlBGhQoWE+Ptv/TZ+0X7C+ZyzqPB7BWH9q7WYfGJy5s4rV4QYNUo5sHx5Ic6fz9f4n4es10wIed1k9RJCdbM0jPl9nOMOULNmzcSPP/6oe92lSxdRtGhRUaVKFVG9enXdo0qVKqJAgQIGD1QIIU6dOiUGDhyo1wHavn27KFiwoEhISNC1q1u3rpg6daoQQoiEhARhb28vVq5cqds/Y8YMUbNmTaHVarN8Hxk6QNHR0aYOwSjI6iVEptu1a0onaPTorNs9Tnksxvw5RjAFceD2Af2dQUFCtGun3Bo7eNC4AeeQ/8I1kw1ZvYRQ3SwNY34f53gV2OXLl5kxY4bu9ahRozh16hQBAQH4+vrqHgEBASxatMiww1RASkoKc+fO5YcfftDbvnPnTurVq0fRokV121q0aMG2bdsAcHNzIyIiQu+WXIsWLbh37x4eHh4Gj9Nc2Lhxo6lDMAqyekGm25w5yusLF/RzBmVQrGAxJnecDECPLT0IeBSQubNiRThxQqnU+tFHcPSoscN+If+FayYbsnqB6qaSSZ7zALVv356WLVs+s/3BgwcMGzbspYLKilmzZjF27FgKFy6st/3SpUvY2dnpbStXrhw3b94kOTmZS5cuAei1KVeuHABXrlwxeJzmwuuvv27qEIyCrF6Q6TZ2rDL/59Il6NoVVqyA4GD9tvbF7HF6wwn7YvY0XdaUX/76hbtR/xYgLlwYzpyBRo2gSxfo00dZeqbV5q/Qv/wXrplsyOoFqptKJnnuAE2YMCHL7ZGRkUyePDnPAWWFj48PISEhWV7csLAwypQpo7fNxsYGrVZLVFQUYWFhAHptbGxsAIiIiHju+wYHB1O5cmW9x9y5c19WJ1+4d++eqUMwCrJ6QaZb8+ZKh2fnTuXn8OFQvbqy2v3JjtCkjpO4M/IOvRv0Zv7F+dReWJu2q9uy1H0pUTUqKiXqV64ET0946y2oWRN+/VUpYW8CLxmR1U1WL1DdVDLJVQfI29ub9evXs379em7fvs2GDRt0rzMe27dvZ/HixQYN8ueff2batGlZ7tNoNHq3vwDS09MBpThrRrmOJ9s8uf95aLVaHj58qPc4cOAAQUFBrF69Gnd3d44cOcKuXbvw9vZmwYIFxMbG4uTkBICTkxNRUVEsXryYa9eusW/fPg4ePMjly5dZvnw54eHhem0TEhJwdnbm9u3bbN++nRMnTnDu3DlcXFwIDAzUa5uens6MGTPw9/dnw4YNnDlzBjc3N7Zu3YqPjw/79+8nKSlJ75iQkBBWrlyJu7s7hw8fZvfu3Vy/fp2FCxfy6NEjvbYxMTEsWrQILy8v9uzZw6FDh/Dw8GDFihWEhobqtU1MTMTZ2RkfHx+2bdvGyZMnOXv2LOvXrycgIIDp06ej1WpxcnJCq9Uyffp0AgICWL9+PWfPnuXkyZNs27YNHx8fnJ2dSUxM1Dt/aGgoK1aswMPDg6tXr7Jnzx68vLxYtGgRMTExem0fPXrEwoULuX79Ort37+bw4cO4u7uzcuVKQkJC9NomJSUxZ84cfHx82Lp1K25ubpw5c4YNGzbg7+/PjBkzSE9P1zsmMDAQFxcXzp07x4kTJ9i+fTu3b9/G2dmZhIQEvbbh4eEsX76cy5cvc/DgQfbt28e1a9dYvHgxUVFRem1jY2M5fPgw3t7e7Nq1iyNHjlC1qjujR6/m+vVgOnY8yYYNUKkSNG2qpU2bK0ydGs78mf/Qg89wfc2VEeVHUJSijDw0kgq/V6DO1LpMqfWA/p+15NaW1dypUoX02bOhfn0elyvHo/fe43jv3jz+5x+c/v335eTkREREBMuWLcPT05MDBw6wf/9+PD09Wbp0KZGRkXpxx8XFMX/+fLy9vXF1deXo0aNcvHiRNWvWEBQUhJOTE4UKFcLJyYnU1FRmzZrF/fv32bRpE6dOneLUqVNs2rSJ+/fvM2vWLFJTU/XOHxQUxJo1a7h48SJHjx7F1dUVb29v5s+fT1xcnF7byMhIli5diqenJ/v37+fAgQN4enqybNkyIiIi9NrGx8czb948bt26xY4dOzh27BgXLlxg7dq1PHjwQK9tWloaM2fOxM/Pj40bN3L69Gnc3NzYsmULkZGRzJ49m+Tk5GfiNvfPiDlz5mT7GeHn52exnxGHDh167mdEamqqxX5GLFiwQO8zwt3dndWrVz/zby05OZnZs2dz7949tmzZgpubG6dPn2bjxo34+fkxc+ZM0tLS9M7/4MED1q5dy4ULFzh27Bg7duzg1q1bzJs3j/j4eL22hv6MePLaPP0ZkZKSku139EuTmwlDWq1WbN26VZQqVUpoNJosHyVKlBC//vqrwSYpubi4iA0bNuhe+/r66k2CrlWrlujRo4feMaNHjxbW1tYiPT1dODk5CUBERETo9l++fFkAwsXFJcv3zJh0ZWVlJRwcHPQezs7OBnMzJv/884+pQzAKsnoJ8WK38HAhNm8W4tNPlfxBBQook6VBiN69hbh7V2kXEhci5p+fL7ps6CLsZ9sLpiCYgnhz3Ztiw/kVInX3LiWHUIsWQlhZKSdo2FCISZOEuHMn370sGVndZPUSQnWzNMxiEjQooy19+vTh1KlTjBkzRq8SfMYjLi6On3/+2WAdNBcXFz7//HM0Gg0ajQZHR0cA3njjDTp16kSTJk10t7kyCAkJoUWLFlhZWdGkSRMAvTYhISEAtG7d+rnvXbFiRR48eKD3GD9+vMHcjImsE7xl9YIXu9nbQ79+sGkTXL8O8fFw9iwMHAh//61UzOjeHW5cKs+wpmM43P8wYd+GETA2gMXdFqNBw+dHhmJ/ayCfd4zg753OiOho2LdPmS+0eLFSdLVtW5g/HwyUc+O/fM0sFVm9QHVTySTPiRDj4uJ0c2meJiQkhAoZWdxekrt37xIfH697HRQURPfu3Vm5ciWdOnXS1Sd79OiRboL0K6+8wldffcV3331HamoqDg4OzJo1i4EDBwLKhOr169dz48aNLN9ThkSIoaGhlC9f3tRhGBxZveDl3CIiYPNmWLAA7t1Tao21bAnt2imPtm2VPInXw67j6u3K5uubuRN5h/LFy9OuajvaVWlHe/sWNL8UQIFtO+DIEUhJgQYN4PXXoWNH5VGxYr56mTuyusnqBaqbpWHM7+McjwAFBAToPaKjo5/ZlrEkfubMmQYLsFatWjRt2lT3qF+/vm57rVq16NmzJw0bNuTgwYOAsiosOTmZ4cOHA8o8n4kTJ7J7924A0tLS2Lp1K7NmzTJYjObIypUrTR2CUZDVC17Ozd5eqY965w5cvgy//65Un9+8Gd5/H8qVU0aIlk1tyODav3B92C3cvnTjq2ZfEZEQwaSTk3h1cyds/IbywWfWHD+/idjVSxCvvQbHjytV6StVgldegalTlSGoHN6bV6+Z5SGrF6huKpnkeASoevXqOaryLoRAo9HoJhobGj8/PxwdHfnrr790pTCCgoIYP348VatWJTg4mKlTp+pulWXw66+/EhQURHJyMu+++y59+/bN9j1kGAFSUQFlhlBAgHKr7OefldEhAI1GWRTWsqWySr5bzxQ8Qzw57X+a9dfWcy30GgA2hWxo5dCKrsWbMjyxEcWO/aWU3YiPB2traNwY2reHAQOgWTPlxCoqKioGwpjfxznuAM2ePRsrKyuaN2+OtbV1tu3S0tJYtWoVW7ZsMViQ+Y0MHSAnJyeDpyMwB2T1gvxxu3ED/PwgKEh5vnYtxMbCO+8o84fefx+qVhVcD7uOT5QPdyLvcOHBBY7fPw5Az7o96VqxI588rk7R+wFKtsZjx+DhQ6hbVylq1rcvtGiRr16mQlY3Wb1AdbM0zKIDFB0dTXp6OvY5KLKYEaylIkMHKDEx8Zn0ADIgqxeYxi0pCVxcYP16OH8eSpcGZ2dlQOfJwZyARwFsuLqBHd478ArzwrGUI86dnXmv3nuQmqrcJtuxA/78E0JCYPBg+OUXqFxZvWYWiKxeoLpZGmYxB6h06dI56vwAnDt3Ls8BqRiGJUuWmDoEoyCrF5jGrUgR+PprOHdOyZcYHQ1ffaW8fpKqJasyqeMkPL/25NrX17gXfY/3t73Pjhs7oGBBJWX1mjXKyrF582DVKmUS0rVr6jWzQGT1AtVNJZM8Z4LOjj///JPvvvvO0KdVySW9evUydQhGQVYvML1b7dqZz3v0UCZVu7rCP/9AeHhmXbIG5RpwZuAZihQoQu+dvem4tiPL/lnG+cDzxKTGKbU89u5V0lc3acI3rq7KMJO3NxhpbqCpMPU1MxayeoHqppKJQTpA4eHhzJkzh3r16tG9e/ccTZZWMS6XL182dQhGQVYvML1bsWJKJ+fCBSW30LZt8PHH0KqVsoqsTBllug9Au6rtiP0xlq0fbaVwgcIMPzictmvaUnpWaZoua4pvh0ZKyY3163kcFaWcsEED5STz55vU05CY+poZC1m9QHVTyeSlOkCHDx/mo48+onLlyvz4449UrFiROXPm8MYbbxgqPpU8UrZsWVOHYBRk9QLzcXv1VZg7V5nKEx6ujADt3AkxMdC5szLAk5ICBa0L0qdhH459fozHEx9zZdgVNn24ibDHYTRe1phhx8YQ80lPvJYsUe6tnTwJ/fvDuHFK4sVFi0xWoNVQmMs1MzSyeoHqppJJgdweEBgYyOrVq3U1c6pXr06jRo3YvXs3VapUAaBr164GD1Qld2QkhZQNWb3A/Nw0GiW/kL29sqhrzx4lBdAffyi5hnr3VuYPFSgARQsWpWmFpjSt0JSWlVqy6vIqVl1exZnAM7S1aUuD1g0o/8Yb8MYb0K0bbNgAo0YpE6f79lUmHpmZf04wt2tmKGT1AtVNJZMcjwDt2LGDd999lxo1ajB9+nSaN2/OoUOHuHfvHo0aNdJ1fgBdskIV0yFrVWBZvcD83d57Dzw8YNIkOH1a6b907Phsuzp2dZj9zmz+HvA3lW0rs+r+Kio4V+BG2L+Z17t3h61b4fBhZQRo+HBo2lRZm29hmPs1yyuyeoHqppJJjjtAQghSU1OxsbHh4MGD7N69my5dugDoKq6rmA8ZSSJlQ1YvsBw3Jyfw91eenz+v3NnKikblG3Gk/xHWvbMOgJYrW7L1+tbMBl26KD2pI0eUE77yirKCLDXVyAaGw1KuWW6R1QtUN5VMctwB6t27NydOnODixYscP36cTz75hB07dhgt47PKy7FhwwZTh2AUZPUCy3KrWhXu3wdHR3jrLfjuOyU5dFYE/hWI3xg/3q/3Pv1c+/HOhndwf+ie2aBzZyU742efwbffKoXL1q2ziBVjlnTNcoOsXqC6qWSS52KoqampuLq6sm/fPm7duoWrq6uu/MT58+dp06aNQQPNT2RIhKjVarGyMniWA5MjqxdYptuhQ8odrQy6dFE6RK+9pgzo2NtnegkhmHxyMkv/WUp0UjRtKrfhTcc36VitI22rtKVEoRLKiNDEiXDmDFSurKSm7tYNOnUCM0zwZonXLCfI6gWqm6VhFokQn6ZgwYL07duXzZs3s3XrVpYsWcKHH37I3Llz+eGHHwwZo0oe+O2330wdglGQ1Qss061bN2Xp/OXLMH06WFkpNcc6dlSqz2s00KPHcYRQbpVPf2s6D8c/ZP3766loU5HlHsvpsrELpWeVZsi+IQQ3raV0gtzdlUlH+/crb2Jnp2SWfvTI1Mp6WOI1ywmyeoHqppJJnkeAsiI5ORkXFxcmT55MeHi4oU6b76gjQOaLrF4gj1tyMty9q/RdJkxQtp07B1kNCgshuBlxkzVX1uB83plWlVpxcfDFzHmFQsCtW7B4sfLo1EkZdjKT0SBZrtnTyOoFqpulYZYjQFlRuHBhhg0bxqJFiwx5WpU8MGPGDFOHYBRk9QJ53AoXVnIeDhiQuW3KFCWr9NNoNBrql63P751/5+P6H+Me5E69xfVYfGkxqempyhDSK68oOYPmzIFTp5QSG99/DxER+aWULbJcs6eR1QtUN5VMDDoCJAsyjAAFBARQtWpVU4dhcGT1Ajnd4uJg5cpIVq+2w9tbmTR96BDUq/dsWyEEpwNOM/f8XPbd3kdtu9rMeGsGH9T7IHNE6N49WLIEVq9WEhBNmQLDhin1yEyAjNcM5PUC1c3SsJgRIBXzwc3NzdQhGAVZvUBONxsbsLc/iJeXMiHa11cZ0Jk0CcLC9Bd6aTQaOlbryJ6+e9jTdw93Iu/w0faPqLe4HrcjbpOuTYeaNZVy9bdvQ/v2SjKiV15RelXJyfnuJ+M1A3m9QHVTyUTtAElKzZo1TR2CUZDVC+R1q1mzJlZWSlmNQ4eUbb/9BuXLQ6FCyl2unj315zf3qtuLtJ/S6NuwL3ci71BvcT2KTC9Co6WNcPF0Ib2svZKa+tQp5Z5b9+5KMbMOHZRJ1PlUYkPmayYrqptKBmoHSFKSTfC/4fxAVi+Q1+1Jr65dlb7JrVuwe7dyN6tcOThwAEqVgiJF4Ma/CaOtrazZ8tEW4ifEc/izwyx4dwGp6akM3DuQAtMKMPXvqSS3aQ3XryurxpYsUe65deyoDDctX55Zwj4f3GRCVi9Q3VQyUTtAkmLJq/Ceh6xeIK/b014aDdStq6T4GTZMKbq6bJmyLzkZGjbU77cUL1ScLrW68E2rb7g+/DrT3pgGwC9uv9Bray8S05KgZUvlZB4esGqV8iZffw3vvAPBwfnmJguyeoHqppKJ2gGSlObNm5s6BKMgqxfI6/YiL41G6bvcvp25zcpKqY965YpShT6DAlYFmNxxMo9+fMQ7Nd7h2L1jFPutGJ03dOZa6DVSSIdBg5T7bb/+ChcvQqVK8PrrSufIwGU2/qvXzJJR3VQyUDtAkrJv3z5Th2AUZPUCed1y6lWnjnJ7LCOz9Nq10Lw5lC6tzH2Ojc1sa1vYlqOfH8Xza08Ajt0/RpNlTSjsVJgFFxeQKtKVjIwBAUoColOnlFGi0qVh5UpIS8tXN0tDVi9Q3VQyUZfBZ4EMy+ATExMpaibJ4gyJrF4gr1tevITInDQ9cGDm9gMH9EtvZBCTFIOLpwvjjowDlA7ShUEXeKXsK0qDpCRl0tGIERAdDdWrK6+bNs2b1L+o18zyUN0sC3UZvEqucXZ2NnUIRkFWL5DXLS9eGo0yOXrAAEhMVNL+APToofRhLlzQb1+qSCnGvjaWpElJ7PhkB7HJsdRfUp9Rh0bhGeKpzK7u109Ze3/+vDLU1Lo19Omj3CbLRzdLQFYvUN1UMlFHgLJAhhEgFRXZOH4cVqyAHTuU1zNnQnZlB/1j/JlxZgbLPZYD8GunX/mp40+ZCRVjY5WZ12vXKkvS+vdXXhcvng8mKioqOUUdAVLJNU5OTqYOwSjI6gXyuhnK6+23Yft2ePhQef3jj8pIUbFiyh2uJ6lWqhrLeiwj/LtwOlXvxC9uv2A11YpeW3qRlJYEtrZKOY3r15Xl81u3KtVb331XGSXKZzdzQ1YvUN1UMlFHgLJAhhGg0NBQypcvb+owDI6sXiCvmzG8wsLgxAn49FPlddGi8OGH8MYb8Nlnyh2vDLRCy4n7J/hizxeExIdQolAJetfvzaeNPuWtGm8pje7cgblzlQnSQiiZGQcPVn7ms5s5IKsXqG6WhjoCpJJr9u7da+oQjIKsXiCvmzG8ypVTpvSkpytTen78Uak4P3iwUm/s/PnMtlYaK96p+Q7B/wvm5oibfNvmW47cO8LbG97mw20fEvgoUFmCtmyZkpTojz8gKAh69VImHd28ma9u5oCsXqC6qWSidoAkpUWLFqYOwSjI6gXyuhnTy8oKXntNWfF+/77SdylTRhkJ0miUkhtPUs++Hr90+oWAcQHMeWcO/wT9Q8OlDak8tzJXQ64qt8FGjYJLl5SEinfuQLNmyq2xK1fy1c2UyOoFqptKJmoHSFJCQ0NNHYJRkNUL5HXLT6/Ro+HkSaUSBihFV+vXf3YQx0pjxbdtv+XKsCvUtavLw7iHNF3elK/2fkVccpzSexo0CK5eVWZaHzmiJCUaMgSe8FGvmeWhuqlkoHaAJCUlJcXUIRgFWb1AXrf89ipfHgIDlfJg7dsrnZ/69ZX+zOrV+smg7YrZcWnIJa5+fZX+jfuz1nMttjNtGXt4LB5BHsrkol9/hQcPYPp0ZVSoQgUYPhwuX1avmQWiuqlkoHaAJEXWqsCyeoG8bqbw0miUxM8nTii3wb78UrmDNXiwUoF++nRl9XsGjcs3ZsMHG9j28Ta+aPIF229sp+XKlry/9X12eu9EVKoEEyfC5ctK5+fgQWjZkjcWLoSjR/Pdz9jI+rcIqptKJmoHSFL+/vtvU4dgFGT1AnndTOlVqBBMmAAuLkrfZe9eZbXY9OnwyivQoQPMnq2shhcCejfozbr31+E31o95XeYRHB/MJzs+oc6iOvzy1y/crFwEFi+Gu3dhwQJS7tyBLl2U22OLFkFEhMlcDYmsf4uguqlkoi6DzwIZlsHHxMRQqlQpU4dhcGT1AnndzNHr8WPYsgX274djx5Rs06CMCtWtq9/2tP9p1nquxfWmK7HJsbxd422W91hOjdI1iImOptSFC8oKskOHlFnZ334Lv/yi9L4sFHO8ZoZCdbMs/vPL4ENCQvjoo48oWbIktWrVYunSpXr7/f396dOnD+PGjaN///6EhITo7RdCMGnSJIYMGcJnn33G/v378zN8k7Bo0SJTh2AUZPUCed3M0at4ceV22N69EBWlTPMBqFcPDh/Wb9uhWgfWvLeG0G9DWdFjBcfvH6fmgprMOz+PhYsWQdeuyomCgmDoUOWeW4MGysRpC8Ucr5mhUN1UdAgLoGvXruKXX34RGzduFG+99ZYAxPr164UQQjx+/FjUrFlTHDlyRAghhKurq2jZsqVIS0vTHT9hwgTRr18/XfsqVaoId3f3bN/PwcFBAMLBwcGIVioqKubElClCKDfChGjdWojQ0KzbnfI7JcrOLiuYgmi5oqXYeWOnSEvP/LwRx48rJwAhXntNiL//FkKrzR8JFRXJMOb3sdl3gK5fvy527Nihe52amirq1q0rOnToIIQQYvbs2Xq/mLS0NFG8eHFdB+nBgweiUKFC4tixY7o2w4YNE2+++Wa27ylDB2jatGmmDsEoyOolhLxuluTl6ytE48ZK38XKSoi33hLi1Ckh0tP122m1WnHY57BwnOIomIKovaC2WP7PcvEo6ZHSIDVViJ07hWjWTDnZK68IsWqVEPHx+e6UFyzpmuUW1c2y+E93gIKCgoT2qf89jR07VjRo0EAIIUTr1q1Fz5499fa3bdtWdO/eXQghxJIlSwQgIiMjdftXrFghNBqNCM3mv3gydIBiYmJMHYJRkNVLCHndLNErLEzprzRsqPRfKlQQ4tChZwdyYmJixMUHF8WH2z4UmikaUcSpiPjh2A8iJvFf5/R0IY4cEaJXLyE0GiFsbZWhpsDA/JfKBZZ4zXKK6mZZGPP72OznAFWsWDGzgvO/pKWl8dprr5Geno6Hhwd2dnZ6+8uVK8eVf7O2Xrp0CSsrK0qXLq23XwjB1atXjS9gItavX2/qEIyCrF4gr5slepUtq+QN8vRUVrynp0O3blCrlpJcMWOa4fr162nt0BrX3q74j/VnSPMhzLswj7JzyvLBtg846nucxDc6KHOE7t+HL75Qlp1Vqwbduysnf/zYpK5ZYYnXLKeobioZmH0HKCv++usvxo8fT1RUFOnp6ZQpU0Zvv42NDRH/LkcNCwujVKlSep0oGxsbAF2b7AgODqZy5cp6j7lz5xrYxji88cYbpg7BKMjqBfK6WbKXtbXS8fHzg337lCLyv/0GFSsqyRUfPPhYt/K9SskqLOi6gDsj7zDr7VncDL9Jl41dqDS3EoP3DWZnwj+k/zEfgoOVVWPBwUqtsQoV4PPPlWVpGcvRTIwlX7MXobqpZGBxHaDdu3fz9ttvU79+fV2npmjRonpt0tPTKViwIAAajSbL/YCuTXZotVoePnyo9zhw4ABBQUGsXr0ad3d3jhw5wq5du/D29mbBggXExsbi5OQEgJOTE1FRUSxevJhr166xb98+Dh48yOXLl1m+fDnh4eF6bRMSEnB2dub27dts376dEydOcO7cOVxcXAgMDNRrm56ezowZM/D392fDhg2cOXMGNzc3tm7dio+PD3PmzCEpKUnvmJCQEFauXIm7uzuHDx9m9+7dXL9+nYULF/Lo0SO9tjExMSxatAgvLy/27NnDoUOH8PDwYMWKFYSGhuq1TUxMxNnZGR8fH7Zt28bJkyc5e/Ys69evJyAggOnTp6PVanFyckKr1TJ9+nQCAgJYv349Z8+e5eTJk2zbtg0fHx+cnZ1JTEzUO39oaCgrVqzAw8ODrVu3smfPHry8vFi0aBExMTF6bR89esTChQu5fv06u3fv5vDhw7i7u7Ny5UpCQkL02iYlJTFnzhx8fHzYunUrbm5unDlzhg0bNuDv78+MGTNIT0/XOyYwMBAXFxfOnTvHiRMn2L59O7dv38bZ2ZmEhAS9tuHh4SxfvpzLly9z8OBB9u3bx7Vr11i8eDFRUVF6bWNjY5k/fz7e3t7s2rWLI0eO4O7uzurVqwkKCtJrm5yczOzZs7l37x5btmzBzc2N06dPs3HjRvz8/Jg5cyZpaWl6xzx48IC1a9dy4cIFjh07xo4dO7h16xbz5s0jPj5er21ERATLli3D09OTAwcOsH//fjw9PVm6dCmRkZF6bePi4nRxu7q6cvToUS5evMiaNWt0cfv4+ODk5ERqaiqzZs3i/v37bNq0iVOnTnHq1Ck2bdrE/fv3mTVrFqmpqXrnDwoKYs2aNVy8eJGjR4/i6uqKt7c38+fPJy4uTq9tZGQkS5cuxdPTk/3793PgwAE8PT1ZtmwZERERem3j4+OZN28et27dYseOHRw7dowLFy6wdu1aHjx4oNc2LS2NBQtm0qiRH+PHb2Tjxn/o1eshWm0Ys2dXpGxZ6N07nc6dT+DlBSt/30afan0YX2Q8Lm1deKPUGxz1PsonOz6h/PTy9D8ynI+DjxBwcjfLRgwn8euvCXdzg08/JbliRYI6dsT/p5/YPH064U/9W8uvz4hDhw5Z7GfEoUOHnvsZ4enpabGfEQsWLHjuZ0TGvzVL+4zIaJvVZ4RRs1sb/KaaEYmKihIffvihSE5OFkIoExELFSokRo4cqdeuV69eonr16kIIIQYPHixKlCiht3/Xrl0CEG5ublm+T8Y9RysrK+Hg4KD3cHZ2NoKZ4fnzzz9NHYJRkNVLCHndZPUSQohZsy6IoUOF6NBBmeKTsYqsbVshvv9eiC1bhIiNVdr+5fuXGLZ/mGi2rJlgCoIpiAq/VxAD9wwUSy4tEQEXjgrx7bdCtGmjzMAGISpWFGLAACFWrBAiICDfvGS+ZqqbZWHMOUAFjNe1Mizp6el8//33LFiwgEL/JhjTaDQ0atSIsLAwvbYhISG0bt0agCZNmrBq1SoSEhIoVqyYbn+BAgVo3rz5c9+zYsWKFpsI8el5UbIgqxfI6yarF8Abb1jx/ffK87AwuH1bKblx7Bhs3apM97GyUpIrtmrViQ8+6MQvvSCpiB83I7z50+dPzgaeZcO1DQzXpuFQ0YHWY1rzQelZvB1ThvLnrmJ16jSsXw9aLVSvDm3bwttvwzvvQOXKRvGS+ZqpbioZWMwtsAkTJjB8+HAcHBx028LCwujfvz8XLlzQbUtLS+PmzZt8/PHHAPTp04eCBQty8eJFXZtr167RuXNn3VwgGfH09DR1CEZBVi+Q101WL9B3K1dOKa0xerSSYdrfH3x9YflyeOMNpWP0wQdQqRLUKFOdW/u78XrCQo5+dJnoH6LZ1XsXnzf+nIiECAZc+IFKNwdhX3EDE2Z3YYvbIoJWz1dOcPu2MkO7ShUl4eLYsUoWagNOpv6vXDPZkNnNGFhEKYxJkyaRnJzM22+/DSidnFOnTtGpUyc6dOhAgwYN2LNnD82bN2f79u3MnTuXc+fOYWWl9O/Gjh2LVqtlwYIFxMXF0ahRI3bt2pXtCJAMpTBCQkKoUKGCqcMwOLJ6gbxusnpB7t3u3oV168DJSamUkTG94fffYfx4pYgrQGRCJP8E/cNGr40cuXuE8IRwNGjoVrsbXWp2oU+Ftyl30UspxHr0KAQGQsGC0K4ddO6sjA41b64MP+WDlyWhulkWRv0+NvhNNQMzZ84cATzzKF26tEhJSRFCCOHt7S0+/vhj8e2334qBAweKiIgIvXOkpKSIkSNHilGjRon+/fuL48ePP/c9ZcgDJGNCLCHk9RJCXjdZvYR4OTetVoiJEzPnDYEQU6cK4eoqxPXrQsTFZbaNS44T0/6eJt5a95YoOLWgsPrVSvTY3EP8fvZ3sf/WPuF34YhIWzBfiJ49hShRQjlZ+fJCfPONEEuXCnH6tBDR0fniZe6obpaFMb+PLWIEKL+RYQRIRUXFMkhLU+5k3bmj5B8KD3+2zZUr0LSp8jwyIZI/Lv7BQZ+D3Iq4RUJqAgCFrAtRx64Oc17/jXcjS8GmTXDmjHLLLC1NObhyZejVCyZPVtbyq6iYOf/5YqgquSdjaaFsyOoF8rrJ6gWGcStQQOmjCKFMpA4OVvotmzZltjl5MvO5XTE7pr4xFY+hHsRNiCNgbABH+x/l93d+x0pjRdcdvWjvMwHt0iVw/TrEx8O1a8oJe/dWflarpiQ4emoBiSG9zBXVTSUDdQQoC2QYAUpKSqJIkSKmDsPgyOoF8rrJ6gXGdRNCfwqPtze88srzj0lNT6Xp8qZ4h3sDsLrXar5q9pV+o5gYmDgRli5VXr/7LuzaBU/kS1OvmWUio5s6AqSSaxYuXGjqEIyCrF4gr5usXmBctzt39F/Xr69U1MjqFlkGBa0Lcv2b6/yvzf8AGLRvEDX+qMHyf5bzMPah0qhUKViyRBn9+egjOHwYSpaEnj3B1RXi4tRrZqHI7GYM1BGgLJBhBMjHx4fatWubOgyDI6sXyOsmqxcY383XV1lO//bbSj2yDGbOVNIBOToqlTQKPJXRTQjBjfAb7L+9n7Wea/GJ8gHg2zbf8objGzSt0JSKJf6ts+jlpSQuWrkSbt2CUqV49PbblHzvPejUyWi5hkyF+vdoWagjQCq5xsPDw9QhGAVZvUBeN1m9wPhujo5KHyQtTekAvf++0tlxcoKOHZVUQMWKwauvwpgxSjmxBw+UJLENyzVkQocJ3Bl1B69vvPik/idsvbGV7pu74zDXAfs59vTY3IMZMQc49VFLkryuKHOF+vdHXLyo1CerUkWpADt4MGzcqCy3t3DUv0eVDCwmE7RK7pAtF0QGsnqBvG6yekH+ullZwe7dyvO0NGVO0IMHcO8eXLqk5EJcsEDZX7u2stirVi0lJVDDmg3Z/sl2hBD4RPlwM/wmniGenA08y4wzM4hLiaNEoRJ0rdWVFn1aUPStbxnZtg9Wp8+Am5vyWL1aOXmNGkqv7K23oGtXKF06334HhkD9e1TJQO0ASUqBp8fEJUFWL5DXTVYvMJ1bgQLQuLHyeJLwcDh9Wkm2uGMHBAQo26dOhS5doGFDDXXs6lDHrg7v1XsPgHRtOl5hXhy8c5BDdw9x+O5h4lLiGHN1DAu7LqT1j5/TZN7vFI6Jg1On4K+/4O+/Yc0aJflinz7KPbqOHZUhKzNH/XtUyUC9BSYpvr6+pg7BKMjqBfK6yeoF5udWtix8+KEyWfrePWjUSNn+88/KbbLixeGLLyA6OvMYaytrmlZoyqSOkzj71Vmif4jm/VLvU7VkVcYfGc+rq17FbrYdE6/OxbNtDZLn/a7cKnvwAH74QUlSNGCAMjLk6Ki82ZkzkJpqkt/BizC3a2ZIZHYzBuok6CyQYRK0v78/1apVM3UYBkdWL5DXTVYvsBy3hASlOOugQfrbAwKUaT5Pk+GVnJbM1dCrrL+6nmX/LCNdKDOxyxcvj+8YX4oW/HfpfFSUMvS0d69yny4mRrk11qMH9O2rLLXPY1kOQ2Mp1ywvyOimToJWyTWbN282dQhGQVYvkNdNVi+wHLdixeCrr57dXrUqrFr17PYMr8IFCtPaoTWLui0i+odozn11DoDQx6EU+60YdRbWYcCeAVxJ9kf06qXcFouIUCYljRgBGzZA9+5ga6vUKVuyBHx8lCRHJsJSrllekNnNGKgjQFkgwwhQeno61tbWpg7D4MjqBfK6yeoFlud2/z788w8kJcGXX2Zuv3ZNqYxRujRYWz/fKzY5lnOB5/CN9sXlqgueIZ6kpKfQrEIzPq7/MW86vkkduzqULlIaTWwsXLigZKQ+ehROnFCWs1Wpokyi7tgRGjZUMjyWKJEvvwNLu2a5QUY3dQRIJdfMmDHD1CEYBVm9QF43Wb3A8txq1FCqYXzxhVIhI+P2V+PGyvyhggXB3h7Kl4+hXTtlfvOUKbBtm9JJSkoC28K2vFvrXb5p9Q0XB1/k0Y+P2PzhZqqUrMLMMzNps7oNdrPtsJ1pS4NNbekRuZCJTSPZMXcwd++6o92/Dz7+GC5fVoalWrcGG5vM8hyTJyuZqf39jfI7sLRrlhtkdjMG6ghQFsgwAqSioqKSE2Ji4OpViIxUHhERSt9j+fKs2wcGZp8bMTU9lauhV/GN9iXgUQD+j/y5G3WXq6FXCYoLAsCmkA0req6gecXm1ClcSUm+6O2tPLy8lI5RSIhyQisrpWRHw4bKLG7JRjdUXoxRv48NXl9eAhwcHAQgHBwcTB1Knpk2bZqpQzAKsnoJIa+brF5CyOs2YcLvQpmok/UjL/Td2VcwBb1Hg8UNRFBs0LONHz4Uondv5c2srJSf5csLMW2asu8lkPWaCSGnmzG/j9URoCyQYQQoMDCQKlkt77BwZPUCed1k9QJ53Z70mjgRnr6z8uGHYGenPMqUUZbX16+v5EfMCes81zFg7wDd6yq2Vahftj6v2L9C/bL16Vq7K5Vt/x1mSk2FRYtg5044p0zCpmpVaNZMebRqBZ07P1sPJAdusiGjmzG/j9WsSZJy4sQJBgwYYOowDI6sXiCvm6xeIK/bk15OTsoCroQE5fbY2bPKz4CAzNtmsbHKcS1aKBOpS5RQpvVUr65ko65SRbltVrmyUnT+y6Zf0r9xf84/OM/96PvciriFd7g3B30OsuDSAooVLEb9svWxK2pH2eJlqdW8FnU7j6Fu7HfUuB1GiRs+aDw9YeFCJYCKFZX01xUrQs2aUK+eMrG6Th1lBVo2brIhs5sxUDtAklKnTh1Th2AUZPUCed1k9QJ53Z70srJSVrJn8M03z7Zftw7271eKysfHKw9fXzh+HIKD9dtWrgxNm0KpUtbY2ranZMn2lCwJXWyhtx1YV43m6KOFxGgDiU+J4Gb8HQ7cOUBUYpTuHKXsS9HksyZU/aYrLYI1tDkXgH10MiX9vLE59ReFgsMy37BCBaUjVLUqODjQMTlZmWTt4KA8sqoma6HI+vdoLOS46irPkJiYaOoQjIKsXiCvm6xeIK9bbr2+/FJ/Wf2TJCTAw4fK5OkHD5TVZLdvKxOtY2Ph0SPlERubUfG+NPDzM+cpbhdNMQdfCpW/T9EqtwmNv0aIjR9/WwcT0yic2ORYXdsSyVA3Aho9KkTTmETqx3jj6HGHcidTqRYWA/PnZ55Yo4Hy5ZVVaE2aKLfV2rWDBg3MJnljTpH179FYqB0gSYmMjDR1CEZBVi+Q101WL5DXzZBexYopd6dq135+OyGUzlJGZ+jJjpHyszSPHpUmJqY59+7B1WOZxelbt4ZX2yVTsGQ4RezCKVM5nBLlw0kgjNCEcNwfBXA19Co3w2+Srk2nsXUlepZoTo2EwlSJs6L644JUikim2IULaNasUarN2tkpHaHq1ZVhq/r1lQ6Sg4PSaTJDZP17NBZqB0hSmjRpYuoQjIKsXiCvm6xeIK+bKbw0GmUydfHiUKlSzo7x81MqbWi1cPxwYWJiKhMWVvnfkSSFKVPgraowqB5U7ZbE6Xt7OPv4JG7h3mwrEEqQJoiEoglgD0UbFmVM4xG0C7KmwY0IKtzwo/Cxo1gFPlDu64HSk3vjDWWE6JVXlIeZ3EaT9e/RWJj+iqkYhQMHDlC3bl1Th2FwZPUCed1k9QJ53SzFq3p1JdH0k6SkKFN8+vVT7mwtW5aZVgiKAH3/fSiULCU4fdqfU9GbORd4ju2++5kd44fWTgsd/20koFoMfFv0TUYGV4Hz58HFRXkzUHpvJUooWSTPnMl5D87AWMp1MxfUZfBZIMMy+ISEBIoVK2bqMAyOrF4gr5usXiCvm2xeycnKpOy7d2HatHQuXdJPqNinj3J3K2O1WvlKKaQW9yXw8V0iEiL0luy/Uf0NapepTZ2SNWicYEOdCEHFxxoKrV0PFy8qjVq1UpI3NmqU+bN8eaPfOpPtuoFaCkMlD8ydO9fUIRgFWb1AXjdZvUBeN9m8ChdWVsb36AFdu85m/nz44QcYNkypvnHzJsyZA59/rtzdql+3EE0q1+XjJt0Z0OxLfi4UzR9vL2NC+wnYFbPjUtAlfjkzjc4XR1D93kgKh4yg8scBfDO6Jjd+HERKvTrKbO9Jk5QcRRUrKrVGuneHrVshLOyFMecF2a6bsVFHgLJAhhEgFRUVFZXc8fix/oq1efOUMiEZ2Nkpt92qV4dq1QVlqoRSoNxdUm3u4pd6idXXluraVrKpRP0ydWmbUoHWUUWpG5xKtSPnKXj7rtKgXDmlCFujRpk/69dXEiWp6FBHgFRyjZOTk6lDMAqyeoG8brJ6gbxusnrB892KF1dSBr31lrKs39MTQkOV5I+bNsH48dC8ubIybf8+DVO/r8CPn7bnp54DWP3hEkotiaXu3+70SNnAq4UGIlLs2JnqxYdpG6ldZgNF+tyl8djCfDekOsc71+IhsaTsdkV89RW0bKnMI6pXD0aMUEaKbt1SZngbwE3lWdQRoCyQYQQoPDycsmXLmjoMgyOrF8jrJqsXyOsmqxcY1k2rVTpIfn6Zj2vX4NgxJUG1Dk063//mT8cPb3En8jZXQq5w+O5hwhPCAahRsBzNowozvWw/avvHofnzT+VkADVqKLmJRo+G9u2fm5tIxuumjgCp5Jpdu3aZOgSjIKsXyOsmqxfI6yarFxjWzcpKmd7Tpo2y6mzCBNiyRSkVEhkJf/2lrJZHWDN7Qg2+aNON1UPG8WDhejpfDWNUUijDSx6kWOF67LQJpG7SbCrUcKXlBDu+WNmNVU4fcbdWGXB1hddfVyZSt2mjTFaaMQP27lWSJxnB7b+AugxeUlq1amXqEIyCrF4gr5usXiCvm6xekH9uZcooxWGvX1dumV26pKyeDw9X5kAHBMCZM+Xw9+8GdINC8bw/6iyJhS9QQDwktLA/M208GdL2HkVawSexlekfU5Xq0YJqd25SeP9+JUOkoyO88w40aED7qlXzxU0W1A6QpAQ/XYBHEmT1AnndZPUCed1k9QLTuNnawttvK4+niY1VRo/Cw0tw62AX7t3rQmqqUky2USN4s3Eqfo6TuVXek6FpPgTG+VPAqgBNBzehV6Q9fd0iqXj6L4q6uNAgJUW5Zda+vZLFul07JVGjhZX0yC/UDpCkpD+ZClUiZPUCed1k9QJ53WT1AvNzs7Xl/+3deVhU5f//8efI4gKKImKKXyQ/fjJXvNzKPmqbGZmWmhEupa1m4m67aal8hVIxl8L8JRquhEvuG+YXN5QUBEUNU1wClUVlx2G4f3/wYWpSS3TgyJn347q4cs6cc3y/EMd3933Ofdi06Y/XSpU8RPbIkZLriaL3OXDi/wVRVFTyfqN/ZeHy+GIKbsSxsEYsn3U+iuqscDIaePWsKy9ctaPdwV24L12KobgY6tQpmTb7z39KGqOOHeVOs/+SBkinvLy8tC6hXOg1F+g3m15zgX6z6TUX3P/ZDIaSGa1nnvlj240bJQ+QjY+H+PhaJCSMIn5byS37OORi73EMV+94tngcYtvDJ/m9cwwO+cV0TXWgb2YNul5MoOn/7sIhtwAcHEpuZfvPf/74jezsbluPnkkDpFN79uyhTZs2WpdhdXrNBfrNptdcoN9ses0FlTObo2PJdFjr1jBo0B/bMzMhIcGJ+PhHiI9/hJ2be3P5ygPcMObRsPMR0tv8Qsi/fmHqY7+QklfAY9ecGZjlRZfzufxrZRg1Zs0qeY5Zr15/TJk1bXrfPuzV2uQ2+FvQw23wmZmZuLq6al2G1ek1F+g3m15zgX6z6TUX6D+bk5Mr69bBrl0lI0bHjv33Oa6up3HssBSnh/dTWPcwecWZtE+BYaec6HmuKg3PX8WgVMmK1f36Qe/eJVNmLi6aZpLb4K1k165d+Pr64u/vz9ixYykqnVTVoW+++UbrEsqFXnOBfrPpNRfoN5tec4H+s1WtWvJsswULSu46y8oqWVJoU1hTpjz1Oa3jtmOang5fn+Xw3tVMbPwOzYdWp84HCp9B8ENbAzk/LisZFXJ3Bz8/2LoV7rNrp6zBZkaAjh49yrPPPsuxY8dwc3Nj1KhR2NnZERwcfNO+ehgBEkIIIW7FZILffoPo6JLeZts2yDSm4NR8D3W7ruFyzQ00yshn2AV3hsZBveQrJQseDRsG/v4lzwSpIDICZAWffPIJ3bt3x83NDYCBAwcyd+5ckktX29QZvS6JrtdcoN9ses0F+s2m11wg2aDkmueHHoLXXoPly0vWJTqwvSHjnn0F18hVFE7L4MyOnwhy60HD17Lo+DaENzVinB5AsUdD1EsvQUQE5OeXc6LyZRMjQFlZWdStW5egoCDGjRsHQEFBAU5OTgQFBTFhwgSL/fUwApSVlUWtWrW0LsPq9JoL9JtNr7lAv9n0mgsk2504f75kVOj//g8i92ZzqeYmaL6W+h6bGHw8lyGJjrT+/QbFNZ2p4jcAPvsM/ud/rJDgZjICdI9iY2MpKiqi7p+G7apVq0atWrWIjY3VsLLys3jxYq1LKBd6zQX6zabXXKDfbHrNBZLtTnh6wttvw9KlkHK2Jklr/VjYYxXdLmUyP2UbbToO4OHhjkxtl8u1lT9gavov1Pjxf3kA2v3PJhqgK1euANx05X/NmjVJT0+/7XGpqak0atTI4mvWrFnlWqu1dL/VkqM6oNdcoN9ses0F+s2m11wg2crKYCi5M/6ttyB8hSMZh3qw0m8xXqmXmHJjNp5+Xkx5zMiNb+dBp06V6mJpm2iADP9d06D6X1a/NJlMODg43Pa44uJifv/9d4uvjRs3kpKSwvfff09MTAzbtm1jzZo1JCYmMmfOHLKysszzsNOmTSMzM5P58+cTHx/P+vXr2bRpE0eOHGHBggWkpaVZ7JuXl8fMmTM5deoU4eHhREZGsn//fhYvXsyFCxcs9jWZTEyfPp1z584RFhbG3r172b17NytXriQpKYnAwEAKCgosjrl06RILFy4kJiaGrVu3snbtWo4dO8bcuXO5fv26xb7Xrl1j3rx5JCQksG7dOjZv3szhw4f57rvvuHz5ssW++fn5zJw5k6SkJFatWsWuXbvYt28fP/zwA+fPnycgIIDi4mKmTZtGcXExAQEBnD9/nh9++IF9+/axa9cuVq1aRVJSEjNnziQ/P9/i/JcvX+a7777j8OHDLFu2jHXr1pGQkMC8efO4du2axb7Xr19n7ty5HDt2jLVr17J161ZiYmJYuHAhly5dsti3oKCAr776iqSkJFauXMnu3bvZu3cvYWFhnDt3junTp2MymSyOuXDhAosXL2b//v1ERkYSHh7OqVOnmDlzJnl5eRb7pqWlsWDBAo4cOcKmTZtYv3498fHxzJ8/n8zMTIt9s7KymDlzJomJiaxZs4Zt27YRExPD999/T0pKisW+hYWFfPnll/z222+sWLGC3bt3s2fPHpYuXUpycjKBgYEUFRVZHHPx4kVCQ0OJjo5mx44d/Pjjj5w8eZLg4GBycnIs9k1PTyckJIS4uDg2btzIhg0biIuL49tvvyUjI8Ni3+zsbGbPnk1iYiKrV69m+/btHDx4kEWLFpnrPnnyJNOmTcNoNBIUFMSZM2dYtmwZUVFRREVFsWzZMs6cOUNQUBBGo9Hi/CkpKSxatIiDBw+yfft2Vq9eTWJiIrNnzyY7O9ti34yMDL799lvi4uLYsGEDGzduJC4ujpCQENLT0y32zcnJITg4mJMnT/Ljjz+yY8cOoqOjCQ0N5eLFixb7FhUVERgYSHJyMkuXLmXPnj3s3r3b/L3/8ssvKSwsvKnu+/0z4quvvrrtZ8TGjRsr7WfE5s2b//Yz4vDhw5X2M2LOnDl/+xlR+netPD8jZs2axtNPp9Pn2VVE/u+TjG24hBrvBLL3x8Ws792bjL98v+/kM6J031t9Rty4ceMW/zpbibIBe/fuVYCKiIiw2F6rVi01dOjQm/b38PBQgKpSpYry8PCw+Jo5c2ZFlX1Ptm7dqnUJ5UKvuZTSbza95lJKv9n0mkspyVbZlP577OHhYfVz28RK0M2bN8fBwcE8FQaQl5dHVlYWnTp1uu1xDRo0qLQXQet1oS+95gL9ZtNrLtBvNr3mAskm/mATU2Curq707NmT6Oho87aEhAQcHR3p3bu3hpWVn/j4eK1LKBd6zQX6zabXXKDfbHrNBZJN/MEmGiCAiRMnEhkZSV5eHgBLlixh9OjRNGrUSOPKysdzzz2ndQnlQq+5QL/Z9JoL9JtNr7lAsok/2EwD1KFDB+bNm8ebb77JyJEjcXV1JTAwUOuyysWsWbMYPHhwpblj7U7pNRfoN5tec4F+s+k1F0i2yignJ8fiv9ZkEwshllVlXwixstd/O3rNBfrNptdcoN9ses0Fkq0ysrOzo7i4mCpVqmCy8i32NjMCJIQQQghRShogIYQQQtgcaYCEEEIIYXPkGqBbcHR0xGg0UqVKFRo0aKB1OWWWmppqnjOtjPXfjl5zgX6z6TUX6DebXnOBZKuMfv/9d/Ovrd2uSAN0C6UXXQkhhBBCewaDwer/LtvEStBlVa1aNQoKCrCzs8Pd3V3rcoQQQgibdOXKFUwmE9WqVbP6uWUESAghhBA2Ry6CFkIIIYTNkQbIhs2ePZs5c+Ywd+5c1q9fr3U5VpORkYGjoyMGg8H8lZiYqHVZVpOSksLUqVMJDw8nKytL63KsJjc3lzp16mAwGP72IcWV1XvvvcfixYu1LsOqJk+ejJeXF926dSMjI0Prcqzqgw8+oE6dOjz22GOcO3dO63KsKjMzkxEjRrB06VKtS7kn27ZtY9KkSYwbN87iYuk7JQ2QjTpz5gxRUVGMGjWKkSNHMn/+fK1Lspr9+/dz8OBBsrOzyc7Opk+fPrRo0ULrsqwiNTWVoUOHMnr0aHx9falVq5bWJVnNqlWrOHDgAKmpqURGRmpdjlXFx8ezbt06rcuwqqNHjzJo0CCSkpIwGAyEhYVpXZLVREVF0aVLF1JSUvD09GTcuHFal2RVBQUF5OXlUVRUpHUpdy07O5uAgACmTJnC+PHjef/998t8DrkI2kbl5uZy6NAhrl+/jrOzc6X+i/BXPj4+ODg4AJCWloaHh4fGFVnPW2+9xSeffKKrxqdUaGgoaWlpvP3229SsWVPrcqwqIiKCHj16aF2GVXl7e5t/3b59e5o2baphNdbl6elJt27dAPjoo48YMGCAxhVZV8OGDfH09NS6jHuyfv16WrZsCYCHhweHDh3i6tWr1KlT547PISNANqp169a0a9eOHj16MHXqVGbMmKF1SVZT2vwAbNmyRTdPSE5MTCQ6OpqTJ0/i5+fH1q1btS7JajIzM+nWrRsrVqygY8eOXLhwQeuSrGbNmjX069dP6zLKTXZ2NnZ2dvTq1UvrUqzGy8vL/Ovc3Fw6dOigXTHlxGAwaF3CPYmNjcXNzc38um7duvz2229lOoc0QDYsJCSE5ORkIiIidDVK8me7d+/mqaee0roMq4iKiuLRRx9l2LBhfPzxx7z00ktcunRJ67KswtXVlYCAAA4fPkzXrl2ZNGmS1iVZRX5+PqdOnaJt27Zal1Iurl27xujRowkODtbVFNifbdy48a6mV0T5un79Oi4uLubXDg4OXLlypUznkCkwHZsyZcotL/4dPnw43bp1Y/jw4Rw+fJixY8fy6quvsm3bNg2qvDt/l+3xxx8HwGQyYTQaqV69ekWXd9f+LldGRgZNmjTBYDDg7e3Nv//9bw4cOEDfvn01qLTs7uTPzM7OjuDgYJ588smKLu+u/V2u+Ph43n77bQ2qso5/+jOrXbs2ixYtonPnzgQHB/Pqq69qUOXduZOfx4sXL+Li4kKbNm0qurx7cifZKrs6depQWFhofp2fn2/REN0RJWxSbGys6t27t1JKqfz8fOXl5aXS09M1rsq69uzZo+bPn691GVYTGhqqfH19za+fe+45tXnzZg0rKj8vv/yy1iVYRYcOHVTjxo1V48aNlZOTk6pbt65aunSp1mVZ3dWrV1XHjh21LsOqjEaj+uKLL1RRUZHWpZSLyZMnq9DQUK3LuGthYWHK39/f/NrDw0Ndv369TOeQKTAb5erqSn5+PlCy8nW3bt3K3j3f57Zs2cLzzz+vdRlW06tXL44fP47JZAJKhoD18n9zqampXLx4ESiZ6nvjjTc0rsg6YmJiSE5OJjk5mf79+zNjxgwGDRqkdVlWUVBQQEFBAQBJSUm88sorGldkXcHBwfj7+2NnZ0dycjJJSUlal2RVqpKvgdy7d2+OHj0KlIzUderUqcw3h0gDVAkUFBTw9ddf8+ijj97y/V27duHr64u/vz9jx469ozu6PD096dOnD7Nnz2bZsmUMGDAAe/uKnxEtj2ylzp07R+PGja1VapmURy43NzcCAwOZOHEiQUFBTJo0iRo1ali79H9UHtliYmLo1KkTI0aM4OrVq/j4+Fi77H9Unj+LWiuPbBEREbRo0YJPP/2U/fv3M3bsWGuXfUfKI1tAQAATJ07E09MTZ2dn2rZtq8lnSXn9TJ4/f56YmBiioqJIS0uzZslldrcZXVxcGDNmDJMmTWLu3Ll3t5SLlUelhJUZjUa1aNEi1aJFC9W4ceOb3o+Li1P169dXaWlpSimlRo4cqcaMGVPBVd4dvWbTay6l9JtNr7mUkmyS7f6ldUZpgCqJDz/88JY/ID179lSDBg0yvz5w4ICys7NTZ8+erbji7pFes+k1l1L6zabXXEpJNqUk2/1Kq4wyBVZJ3OpJuFlZWWzfvp127dqZt7Vt2xalFBERERVZ3j3Raza95gL9ZtNrLpBsINnuV1pllAaoEouNjaWoqIi6deuat1WrVo1atWoRGxurYWX3Tq/Z9JoL9JtNr7lAslVWes5WqiIySgNUiZUu+uTq6mqxvWbNmqSnp2tRktXoNZtec4F+s+k1F0i2ykrP2UpVREZpgCqx0qXM/7rQn8lksngcRGWk12x6zQX6zabXXCDZKis9ZytVERmlAarEGjRoAJSsB/NnOTk51KtXT4uSrEav2fSaC/SbTa+5QLJVVnrOVqoiMkoDVIk1b978puef5OXlkZWVRadOnTSs7N7pNZtec4F+s+k1F0i2ykrP2UpVREZpgCoxV1dXevbsSXR0tHlbQkICjo6O9O7dW8PK7p1es+k1F+g3m15zgWSrrPScrVRFZJQGqJIwGo23XOVz4sSJREZGkpeXB8CSJUsYPXo0jRo1qugS75pes+k1F+g3m15zgWQDyXa/0iyjVVYTEuUqIiJCtWrVSlWpUkUFBwery5cvW7y/du1a5efnp/z9/dWnn36qTCaTRpWWnV6z6TWXUvrNptdcSkk2yXb/0jKjQalK/kQ0IYQQQogykikwIYQQQtgcaYCEEEIIYXOkARJCCCGEzZEGSAghhBA2RxogIYQQQtgcaYCEEEIIYXOkARJCCCGEzZEGSAghhBA2RxogIYQQQtgcaYCEEBb279/PwIEDMRgMNGzYkBdffBE/Pz8eeeQRfHx8WLZsWYXVUlBQgIeHB6tXry6X88fHxzNs2DBat25tlfNt376dsLCwuz5+7dq1NGvWDBcXF/r06UNaWppV6hJC3EwaICGEhccee4yJEycCMGXKFH766SdWrlzJgQMH8PHx4bXXXsPX15eKeIpO1apV8fHxoUmTJhbbExISrHJ+T09PcnNzyc7Otsr51q9fzwsvvHBXxx46dIjo6GjCw8OZMWMGmzdvZtSoUVapSwhxM3utCxBC3H9q1Khx07YqVaowZswYCgoK+Pjjj+nUqRMTJkwo1zoMBgPff/+9xbbk5GTmz59PSEjIPZ+/du3aNGnShL17997zuYqKisjNzcXFxeWujre3tycoKAgAb29v9u3bxy+//HLPdQkhbk1GgIQQZTJ27Fjq1avHrFmzMJlMFfp7X716lZdeeomCggKrnbNKFet8DP788888+eSTd318u3btLF7XrVv3rkeThBD/TBogIUSZVK1alaeeeorU1FSOHDkCQH5+Pp9//jnvvfcebdu25fXXXycrK4uMjAyCgoLw8vIiMTGRN954A2dnZ1544QWMRiMAJpOJ8ePHM2nSJHr37o3BYCAnJwej0cjy5cvp3LkzS5YsASAkJITU1FQOHDjAu+++y8GDB3niiScwGAwMHjyYq1evAhAbG4u7uzvBwcFlymYymejVqxctW7YkJCSErKwsAGJiYnj33XeZN28e77zzDunp6Tcd+9NPP/Hiiy+Sm5vLN998Q8uWLfn555/54osvqFevHi1btuTXX38lOjoab29vnJ2dzSM+f5WTk0N6ejqTJk0qU/1CiDJQQgjxF2fPnlWAWrhw4S3f/+CDDxSgIiIilFJK+fv7q4sXLyqllMrMzFT16tVTb775prp8+bKaN2+eAtTo0aNVYmKi2rp1qwJUeHi4Ukqp5cuXq7Fjx5rP3adPH5Wdna3y8vLUjh07FKBCQ0PN7z/++ONqyJAh5teXL19Wzs7OasaMGeZtN27cUD169LijrJMnT1aNGzdWSil14sQJ1b9/f5WVlWV+Py0tTbm6uqqjR48qpZTav3+/cnV1VV26dFHffPONUkqpoqIiNXToUHP+0NBQBagRI0aomJgYlZSUpOrXr28+5vLly2rKlCnKzs5OpaSkWNQTHh6uWrVqpR544AG1c+fOO8oghCg7GQESQpSZwWAAQCnFuXPn2LBhA2FhYQQGBrJgwQK6du1KYWEh7u7uNG/eHIBRo0bRvHlznn32Wdzc3EhKSgIgNTWV8PBwYmNjAfjwww9xcHCgevXqdzSl5O7uzuuvv87ChQvNF2Zv2rSJfv36lSnTiRMn+OyzzwgNDaVmzZrm7Xv27CEzM5NmzZoB0LlzZwoLCxk5ciTDhw8HSqa/nnjiCQDq1KlDly5dAOjfvz8dOnSgadOmPP7449jZ2TF8+HDc3d3x9fXFZDJx5swZizq6dOnCmDFjKCoqom/fvuZRLSGEdUkDJIQos/PnzwPQpEkTjh8/TrVq1fjoo4/MX6tXrzbfDl56jc2fr7VxcnLixo0bAAwaNAhnZ2c6dOjAgAEDcHd3p2rVqgDY2dndUT2jRo3i119/ZcuWLQCEh4fj5+d3x3mKiorw8fGhVatWODs7W7zn5uYGwJUrVyzqT0lJMb8unf4qZW9/8/0l1atXt3hdmrF0KrBUgwYNePPNN1mxYgXZ2dlERUXdcQ4hxJ2TBkgIUSZGo5Gff/4ZDw8PvL29KSwsJDk5mczMTIv9bnWdzK3Ur1+f2NhYvvjiCzZu3Ii3tzdxcXFlqqlp06Y8//zzBAcHk56ejoODQ5nuxrK3t2fcuHFMnTr1pjvCunTpwuDBg1m0aBEAx48fx2g00qdPH6DkuqHs7Gxq165dppr/ydNPP03t2rVxdHS06nmFECWkARJClMn8+fO5dOkSn332GXZ2drRo0YLCwkICAgIs9vvr7eu3s27dOqpVq8bEiRNJSEigZs2a5mbjVkqn3/5q9OjR7Ny5k08//ZSBAwfeeaA/He/j48PAgQMtmjmDwUC7du04e/YsX3/9NWvXruWXX37By8sLgN27d5unv6ypuLgYe3t7OnToYPVzCyGkARJC3EJeXt4tty9atIj333+fkSNHMmzYMACaNWtG3759mTVrFoMGDSIkJIR+/frRvn174I8pnuLiYotzld5Cf/LkSfPq0l5eXnTq1ImHHnrIYp8/327v5OTE6dOnycjIsJge6t69O61atWLTpk0888wzQMm0VYsWLRgzZsxts5pMJvP5Fy9ejNFo5LXXXjNfT3T69GkCAgLo2rUr9evXp1mzZpw6dYpr164BN09/3a7u4uJii+9B6flL91m1ahWbN282vx8YGMiECROoV6/ebWsXQtwDba/BFkLcb/bt26deeeUVBagHH3xQDRgwQL311luqe/fuqn///mrHjh03HXP16lU1ePBg5eTkpB588EG1ZMkSpZRS586dUy+//LIC1KhRo9SFCxfUggULlL29vWrbtq2Kjo5W06dPV/b29mrw4MHqww8/VOPGjVNFRUUqPz9fTZkyRQGqe/fu6sSJE0oppTZt2qTc3NzUc889p3JycizqCAkJUR999JH59YULF5S7u7uqV6+eysjIuKnuXbt2qTZt2ig7OzsVEhKicnNzVY8ePRSg/Pz8VFxcnMrJyVHt27dX7u7uqmrVqspgMChAubm5qdOnT1vckaZUyV1j/v7+ClADBgxQSUlJavPmzcrDw0O5uLiosLAwlZKSot59910FKF9fX5WcnKxGjBihatSooby9vdWQIUPM30MhRPkwKFUB69kLIUQF+PzzzxkwYID5jq1SEyZMICAgwHzhcVkcO3aM1atXM3nyZPO2vLw8li9fzvXr1xk/fvw91y2EqHgyBSaE0IWioiJOnTp1U/OTkJDAQw89dFfND8CwYcPo3r27xbYaNWrQqFEjHn744buuVwihLRkBEkJUakFBQSQnJ3Pt2jWGDBmCj4+P+b1Lly5x/Phxnn766bs+f5MmTWjQoAHTpk3D29sbpRT79u1j586dzJkzxxoRhBAakBEgIUSlduzYMcLDw+nWrZtF8wPwwAMP3FPzAxAZGUnTpk0ZPHgwDRo0oEuXLpw4cYJZs2bd03mFENqSESAhhBBC2BwZARJCCCGEzZEGSAghhBA2RxogIYQQQtgcaYCEEEIIYXOkARJCCCGEzZEGSAghhBA2RxogIYQQQtgcaYCEEEIIYXOkARJCCCGEzfn/BPbWcMtgXRoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 625x325 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25, 3.25])\n",
    "\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.plot(d_min_arr, h_array*1E-3, 'b-', linewidth=1.0, label=\"Minimum \"+r\"$(-3\\sigma)$\")\n",
    "plt.plot(d_avg_arr, h_array*1E-3, 'g-', linewidth=1.0, label=\"Average\")\n",
    "plt.plot(d_max_arr, h_array*1E-3, 'r-', linewidth=1.0, label=\"Maximum \"+r\"$(+3\\sigma)$\")\n",
    "plt.xlabel(\"Density, kg/m3\",fontsize=12)\n",
    "plt.ylabel(\"Altitude, km\",fontsize=12)\n",
    "plt.xscale('log')\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(np.logspace(-8, 0, 9), fontsize=12)\n",
    "plt.grid('on',linestyle='-', linewidth=0.2)\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.xaxis.set_tick_params(direction='in', which='both')\n",
    "ax.yaxis.set_tick_params(direction='in', which='both')\n",
    "ax.xaxis.set_tick_params(width=2, length=4)\n",
    "ax.yaxis.set_tick_params(width=2, length=4)\n",
    "ax.xaxis.set_tick_params(width=1, length=4, which='minor')\n",
    "ax.yaxis.set_tick_params(width=1, length=4, which='minor')\n",
    "ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)\n",
    "ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)\n",
    "ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)\n",
    "ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "\n",
    "plt.legend(loc='upper right', fontsize=12, framealpha=0.8)\n",
    "\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/density-variations.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/density-variations.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/density-variations.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7cec76ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot  limit : -11.3102 deg\n",
      "Undershoot limit : -12.3240 deg\n",
      "TCW: 1.0138 deg\n"
     ]
    }
   ],
   "source": [
    "planet.density_int = density_int_low\n",
    "\n",
    "vehicle=Vehicle('Titania', 3200.0, 146 , 0.24, np.pi*4.5**2.0, 0.0, 1.125, planet)\n",
    "vehicle.setInitialState(1000.0,-15.22,75.55,29.2877,88.687,-11.0 ,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "\n",
    "# Compute the corridor bounds and TCW for low density atnosphere\n",
    "overShootLimit, exitflag_os  = vehicle.findOverShootLimit2(2400.0,0.1,-25,-4.0,1E-10,500e3)\n",
    "underShootLimit, exitflag_us  = vehicle.findUnderShootLimit2(2400.0,0.1,-25 ,-4.0,1E-10,500e3)\n",
    "\n",
    "# print the overshoot and undershoot limits we just computed.\n",
    "print(\"Overshoot  limit : \"+str('{:.4f}'.format(overShootLimit))+ \" deg\")\n",
    "print(\"Undershoot limit : \"+str('{:.4f}'.format(underShootLimit))+ \" deg\")\n",
    "print(\"TCW: \"+ str('{:.4f}'.format(overShootLimit-underShootLimit))+ \" deg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d183aa0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot  limit : -11.1238 deg\n",
      "Undershoot limit : -12.1418 deg\n",
      "TCW: 1.0180 deg\n"
     ]
    }
   ],
   "source": [
    "planet.density_int = density_int_avg\n",
    "\n",
    "vehicle=Vehicle('Titania', 3200.0, 146 , 0.24, np.pi*4.5**2.0, 0.0, 1.125, planet)\n",
    "vehicle.setInitialState(1000.0,-15.22,75.55,29.2877,88.687,-11.0 ,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "\n",
    "# Compute the corridor bounds and TCW for low density atnosphere\n",
    "overShootLimit, exitflag_os  = vehicle.findOverShootLimit2(2400.0,0.1,-25,-4.0,1E-10,500e3)\n",
    "underShootLimit, exitflag_us  = vehicle.findUnderShootLimit2(2400.0,0.1,-25 ,-4.0,1E-10,500e3)\n",
    "\n",
    "# print the overshoot and undershoot limits we just computed.\n",
    "print(\"Overshoot  limit : \"+str('{:.4f}'.format(overShootLimit))+ \" deg\")\n",
    "print(\"Undershoot limit : \"+str('{:.4f}'.format(underShootLimit))+ \" deg\")\n",
    "print(\"TCW: \"+ str('{:.4f}'.format(overShootLimit-underShootLimit))+ \" deg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae7c50e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot  limit : -10.9809 deg\n",
      "Undershoot limit : -12.0031 deg\n",
      "TCW: 1.0221 deg\n"
     ]
    }
   ],
   "source": [
    "planet.density_int = density_int_hig\n",
    "\n",
    "vehicle=Vehicle('Titania', 3200.0, 146 , 0.24, np.pi*4.5**2.0, 0.0, 1.125, planet)\n",
    "vehicle.setInitialState(1000.0,-15.22,75.55,29.2877,88.687,-11.0 ,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "\n",
    "# Compute the corridor bounds and TCW for low density atnosphere\n",
    "overShootLimit, exitflag_os  = vehicle.findOverShootLimit2(2400.0,0.1,-25,-4.0,1E-10,500e3)\n",
    "underShootLimit, exitflag_us  = vehicle.findUnderShootLimit2(2400.0,0.1,-25 ,-4.0,1E-10,500e3)\n",
    "\n",
    "# print the overshoot and undershoot limits we just computed.\n",
    "print(\"Overshoot  limit : \"+str('{:.4f}'.format(overShootLimit))+ \" deg\")\n",
    "print(\"Undershoot limit : \"+str('{:.4f}'.format(underShootLimit))+ \" deg\")\n",
    "print(\"TCW: \"+ str('{:.4f}'.format(overShootLimit-underShootLimit))+ \" deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46aa1bf",
   "metadata": {},
   "source": [
    "## Target EFPA selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9a77af7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 625x325 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Polygon\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,3.25])\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "\n",
    "x1 = [1.0, 1.0, 2.0, 2.0]\n",
    "y1 = [-12.3240, -11.3102, -11.3102, -12.3240]\n",
    "\n",
    "x2 = [2.0, 2.0, 3.0, 3.0]\n",
    "y2 = [-12.1418,-11.1238, -11.1238,  -12.1418]\n",
    "\n",
    "x3 = [3.0, 3.0, 4.0, 4.0]\n",
    "y3 = [-12.0031, -10.9809 , -10.9809 , -12.0031]\n",
    "\n",
    "\n",
    "\n",
    "poly1 = Polygon( list(zip(x1,y1)), facecolor='xkcd:blue', edgecolor='k')\n",
    "ax.add_patch(poly1)\n",
    "\n",
    "poly2 = Polygon( list(zip(x2,y2)), facecolor='xkcd:green', edgecolor='k')\n",
    "ax.add_patch(poly2)\n",
    "\n",
    "poly3 = Polygon( list(zip(x3,y3)), facecolor='xkcd:red', edgecolor='k')\n",
    "ax.add_patch(poly3)\n",
    "\n",
    "\n",
    "plt.ylabel(\"Entry flight-path angle, deg\",fontsize=12)\n",
    "plt.xlabel(\"Atmospheric density\",fontsize=12)\n",
    "\n",
    "plt.tick_params(axis='x',          # changes apply to the x-axis\n",
    "                which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False)\n",
    "\n",
    "plt.axhline(y=-11.3102, linewidth=1.0, linestyle='solid' ,color='xkcd:blue')\n",
    "plt.axhline(y=-12.0031, linewidth=1.0, linestyle='solid' ,color='xkcd:red')\n",
    "plt.axhline(y=-11.70, linewidth=2.0, linestyle='dashdot' ,color='xkcd:black')\n",
    "plt.axhline(y=-11.40, linewidth=2.0, linestyle='dotted' ,color='xkcd:black')\n",
    "plt.axhline(y=-12.00, linewidth=2.0, linestyle='dotted' ,color='xkcd:black')\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.xaxis.set_tick_params(direction='in', which='both')\n",
    "ax.yaxis.set_tick_params(direction='in', which='both')\n",
    "ax.xaxis.set_tick_params(width=1, length=4)\n",
    "ax.yaxis.set_tick_params(width=1, length=4)\n",
    "ax.xaxis.set_tick_params(width=1, length=4, which='minor')\n",
    "ax.yaxis.set_tick_params(width=1, length=4, which='minor')\n",
    "ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)\n",
    "ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)\n",
    "ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)\n",
    "ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "\n",
    "\n",
    "ax.set_xlim([-1.5, 6.5])\n",
    "ax.set_ylim([-13.05, -10.5])\n",
    "\n",
    "plt.text(1.3, -12.45, 'Min', color='xkcd:blue', fontsize=10)\n",
    "plt.text(2.3, -12.27, 'Avg', color='xkcd:green', fontsize=10)\n",
    "plt.text(3.3, -12.15, 'Max', color='xkcd:red', fontsize=10)\n",
    "\n",
    "plt.annotate(\"Overshoot limit for min. density atmosphere\"  , xy=(0.20, -11.33), \n",
    "                                                                   xytext=(1.3, -10.650), \n",
    "                                                                   va=\"center\", ha=\"center\", \n",
    "                    arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=10,color='k',\n",
    "                     bbox=dict(boxstyle='round,pad=0.2', fc='w', ec='k', alpha=1))\n",
    "\n",
    "\n",
    "plt.annotate(\"Undershoot limit for max. density atmosphere\"  , xy=(0.20, -12.00), \n",
    "                                                                   xytext=(1.3, -12.750), \n",
    "                                                                   va=\"center\", ha=\"center\", \n",
    "                    arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=10,color='k',\n",
    "                     bbox=dict(boxstyle='round,pad=0.2', fc='w', ec='k', alpha=1))\n",
    "\n",
    "plt.annotate(\"Target EFPA\"  , xy=(4.5, -11.73), \n",
    "                                                                   xytext=(4.5, -10.75), \n",
    "                                                                   va=\"center\", ha=\"center\", \n",
    "                    arrowprops=dict(arrowstyle='->, head_width=0.2', facecolor='blue'), fontsize=10,color='k',\n",
    "                     bbox=dict(boxstyle='round,pad=0.2', fc='w', ec='k', alpha=1))\n",
    "\n",
    "plt.annotate(\"Assumed delivery error\\n\" +r\"$\\pm 0.30$\"+ \" deg. \"+ r\"$3\\sigma$\"  , xy=(5.3, -11.38), \n",
    "                                                                   xytext=(5.3, -12.225),\n",
    "                                                                   va=\"center\", ha=\"center\", \n",
    "                    arrowprops=dict(arrowstyle='<->, head_width=0.4', facecolor='blue'), fontsize=10,color='k',\n",
    "                     bbox=dict(boxstyle='round,pad=0.2', fc='w', ec='k', alpha=1))\n",
    "\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/target-efpa-selection.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/target-efpa-selection.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../../../data/acta-astronautica/uranus-orbiter-probe/target-efpa-selection.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
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