Genus 2 curve data - 5547.b.16641.1

Formats: - HTML - YAML - JSON - 2025-12-22T11:43:21.877899

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '547192357228946261', 'abs_disc': 16641, 'analytic_rank': 2, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[3,[1,3,5,3]],[43,[1,2,1]]]', 'bad_primes': [3, 43], 'class': '5547.b', 'cond': 5547, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[0,-1,-1,3,1,-3,1],[1]]', 'g2_inv': "['1188137600000/16641','31074368000/16641','126006400/1849']", 'geom_aut_grp_id': '[4,2]', 'geom_aut_grp_label': '4.2', 'geom_aut_grp_tex': 'C_2^2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['520','6292','896816','66564']", 'igusa_inv': "['260','1768','16776','308984','16641']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '5547.b.16641.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.307177595435768704473852207451928922525909372188', 'prec': 167}, 'locally_solvable': True, 'modell_images': ['2.30.2', '3.90.1'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 14, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '24.460379506288139697494350989', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0062790848228', 'prec': 50}, 'root_number': 1, 'st_group': 'SU(2)xSU(2)', 'st_label': '1.4.B.1.1a', 'st_label_components': [1, 4, 1, 1, 1, 0], 'tamagawa_product': 2, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 2, 'two_torsion_field': ['4.2.688.1', [-1, -2, 0, 0, 1], [4, 5], False]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '5547.b.16641.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'G_{3,3}']], 'ring_base': [2, -1], 'ring_geom': [2, -1], 'spl_facs_coeffs': [[[928], [-28376]], [[16], [-280]]], 'spl_facs_condnorms': [129, 43], 'spl_facs_labels': ['129.a1', '43.a1'], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'G_{3,3}', 'st_group_geom': 'G_{3,3}'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '5547.b.16641.1', 'mw_gens': [[[[0, 1], [-2, 1], [1, 1]], [[0, 1], [-1, 1], [0, 1], [0, 1]]], [[[-1, 1], [0, 1], [1, 1]], [[-1, 1], [1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.081387829883890546771594428612802', 'prec': 117}, {'__RealLiteral__': 0, 'data': '0.081387829883890546771594428612802', 'prec': 117}], 'mw_invs': [0, 0], 'num_rat_pts': 14, 'rat_pts': [[-5, -363, 7], [-5, 20, 7], [-1, -2, 1], [-1, 1, 1], [0, -1, 1], [0, 0, 1], [1, -1, 0], [1, -1, 1], [1, 0, 1], [1, 1, 0], [2, -2, 1], [2, 1, 1], [12, -363, 7], [12, 20, 7]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 2119
    {'conductor': 5547, 'lmfdb_label': '5547.b.16641.1', 'modell_image': '2.30.2', 'prime': 2}
  • id: 2120
    {'conductor': 5547, 'lmfdb_label': '5547.b.16641.1', 'modell_image': '3.90.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 114897
    {'cluster_label': 'c4c2_1_0', 'label': '5547.b.16641.1', 'local_root_number': 1, 'p': 3, 'tamagawa_number': 2}
  • id: 114898
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '5547.b.16641.1', 'local_root_number': 1, 'p': 43, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '5547.b.16641.1', 'plot': 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ragAAfvPAA9YHtW1rfRKiDwEwDiUklLzzys62E3ABAIiEFSuk0aNte8QI65MQffi3xKlu3aSuXe0S/DvucF0NAMAv7rjD%2Bp6uXa0vQnQKeJ7nuS4C4TF3rtS%2BveR50tdfS1lZritCrAgGg0pLS1NOTo5SU1NdlwMgRsyeLXXoIAUC0pw5dggY0YkRwDh27LFS7962PWiQBUFgf7Kzs5WZmaks3i0AKCfPK1npo3dvwl%2B0YwQwzv30k3TkkdLOndLrr0sXXui6IsQCRgABlNcbb0gXXSRVriz98IPUoIHrirA/jADGuQYNpFtuse1bb5Vyc93WAwCIP7m5JcuR3nIL4S8WEAB94NZbpbp1bYk4JocGAITayJHWx9StWxIEEd0IgD5QrZo0fLhtP/CAzcwOAEAorF9vfYtkfU21am7rQdkQAH2id2%2B7CnjrVqaFAQCEzh13WN%2BSlVVy4SGiHwHQJxISpFGjbHv8eJsWBgCAQ/H119anSNbHMOlz7OBf5SMdO0pXXmnbAwZIRUVu6wEAxK6iIutLJOtbOnZ0Ww/KhwDoMyNGSCkpe75rAwCgvIqPJqWklCw/ithBAPSZ9HRp6FDbHjJE2rzZaTkAgBi0ebP1IZL1KenpTsvBQSAA%2BtCAAVKrVtKmTVwQAgAovzvusD6kVauSw8CILQRAH6pYURo92rbHjJG%2B%2BsptPQCA2PHVV9Z3SNaXVKzoth4cHAKgT518sp2063lS375SQYHrigAA0a6gwPoMz7M%2B5OSTXVeEg0UA9LFHH5Vq1JDmzSuZIgYAgNKMGmV9Ro0a1ocgdhEAfax2benhh2377rulVavc1gMAiF4rV1pfIVnfUbu203JwiAiAPveXv0gnnSTV2bFc8065SV7TplKjRtLVV0sLF7ouDwDgysKF1hc0aiSvaVPN73KT6uxYrpNOsr4DsS3geZ7nugi4tfL1b1T94q6qrpw9v5GcLL31lnTmmW4KgzPBYFBpaWnKyclRamqq63IARNr770vdu0u7du3x5d%2BUpt9e%2B1CNLzrOUWEIFQIgpDZtpAUL9v299HRp9Wou8/IZAiDgY/n5UsOG0vr1%2B/5%2B69bS/PmRrQkhxyFgv5s9u/TwJ1kD8M9/Rq4eAIBb775beviTrM/45pvI1YOwIAD63erVobkP4kJ2drYyMzOVlZXluhQArtAv%2BAIB0O%2BaNDnwfRo3DnsZiA79%2BvXT4sWLNXv2bNelAHCFfsEXCIB%2B166d3Urh1a8v/elPESwIAODUWWdZ21%2BaA/QbiA0EQEjjx0u1au315W2qqo/6vCQlJjooCgDgRIUK%2BqjPS9qmqnt/77DDrM9AzOMqYJiffpKeekp65x2psFBfp3ZVr69v0sbqzbVggZSR4bpARBJXAQP%2BtWaNXeh72G9LNanDKHUIfmgDAeecI/XvLzVo4LpEhAABEPuUny%2BdcIJdJHzqqdKHH0oJjBf7BgEQ8KeiIqlrV2nGDKlDB%2Bmzz5gFLF7RpWOfKlaUJk6UqlSxhuCxx1xXBAAIt0cftTa/ShXppZcIf/GMAIhSHXmk9MQTtn3nnUz7BADx7JtvrK2XpJEjrQ9A/CIAYr%2BuuUa68EI7JHzZZVIw6LoiAECoBYPWxhcUWJt/9dWuK0K4EQCxX4GANHasrQq0bJnUt6/EWaMAED88z9r2ZcusrR871tp%2BxDcCIA6oRg1pyhS7CGzKFGnMGNcVAQBCZcyYPdv4GjVcV4RIIACiTDp3loYPt%2B2bb5bmzHFbDwDg0M2ZY226ZG18585u60HkEABRZn/7m3TuuVJurp0jsnmz64oAAAdr82Zry3NzrW2/5RbXFSGSCIAos4QE6YUXpKZNpZUrpV69pMJC11UBAMqrsNDa8JUrrU1/4QXO%2B/MbAiDKpUYN6Y03pMqVpfffl%2B6%2B23VFAIDyuvtua8MrV5amTuW8Pz8iAKLc2raVxo2z7eHDpVdecVsPAKDsXn655JzuceOkNm3c1gM3CIA4KJdfLg0ebNt//rMtGQcAiG6zZ0tXXWXbgwdbWw5/Yi1gHLTCQum886R//lNKT5e%2B/lrKyHBdFUKBtYCB%2BLNmja3vu3699Kc/SdOn29Qv8CdGAHHQiueMOvpoa1DOPpuVQgAgGgWD1kavX29tdvG8f/AvAiAOSWqq9O67NgL43Xc2pUBenuuqAADF8vKkHj2sjU5PtzabgX0QAHHIGja0BqVqVenDD%2B2cwKIi11XhYGRnZyszM1NZWVmuSwEQAkVF1iZ/9JG10e%2B%2Ba202wDmACJl//Us65xxbTLx/f2nUKOaVilWcAwjEPs%2BTbrpJeuopqUIF6Z13pDPPdF0VogUjgAiZM88smUz0qaeku%2B5yXREA%2BNddd1lbHAhIL75I%2BMOeCIAIqcsvl7KzbXvYMOm%2B%2B9zWAwB%2BdN991gZL1iZfdpnbehB9CIAIuRtukB591LbvvZcQCACRdN991vZK1hbfcIPbehCdCIAIi7/9TXroIdu%2B917pzjvtfBQAQHh4nrW1xeFvxAhri4F9IQAibIYMKRkJHDZMGjCAq4MBIBwKC62NLT7s%2B%2Bij0q23uq0J0Y0AiLD629%2Bk0aPtJOTi81B27XJdFQDEj127Ss6/DgSszWXkDwdCAETY3XCDNHmyVLGi9Oqr0hlnSL/%2B6roqAIh9v/5qbeqrr1obO3ky5/yhbAiAiIiePaX335fS0qTPPpOOP15asMB1VQAQuxYssLb0s8%2Bsbf3Xv6ytBcqCAIiIOe006YsvpGbNpBUrpE6dpIkTXVcFALFn4kRrQ1essDb1iy%2BkU091XRViCQEQEXXUUdJXX9khix07pN69pauvlrZtc10ZAES/bduszezd29rQM86wNvWoo1xXhlhDAETE1aolvfeedM89dsLy889Lxx5rhzEAAPv22WfWVj7/vLWd99xjbWmtWq4rQyxiLWA4NWOGdOWV0k8/WYPWt6/04INSjRqS1q%2BXRo6UXn9d2r7djncMHCiddJLrssutqEj67Tdp0yb7mJNj7%2BR37LAr%2BPLz7Vb8akxMtBO6k5KkKlVsEffUVKl6dalmTemww%2Bz74cJawIiU/Hxp40Zp82Z7bQSD9nLfsUPKzbXvFxbafQMBe95XrCglJ9tro1o1O/%2BtenULQtWrSwmxOLTx6afSE0/YsdyqVaWLLpJuvllKT9eWLTa/3zPPWBuRkWHLbnLIF4eCAAjnfvvNpix4/nn7/LDDpMf6r1DvsScpsHbtnncOBKwVvO66yBdaCs%2BTfvlF%2BvFHOx9n5UppzRoLtevWWY799deSTixUataU6taV6te3DqFRIzsX6IgjpCOPtI7wYBEAESq//Sb98IO0bJm9RlatstfH2rXSzz9b8AulxESpdm0pPV2qV09q0MBeH40bS02a2GukTh1rSqLGmDH27vcP3bHXoIFeuvZTDRrVWJs22df%2B8hfpsccO7fUNSARARJGZM6V%2B/aTFi6W3dL7O1/R937FSJetBDj88ovXl5kpLl1p9ixdL//2vtGSJdWxlPYexWjUb3UxLs%2B0qVWwko1IlqUIFG7nwPAuL%2Bfn2O3futJ8fDEpbtliHWpYJtdPTpcxMqXVrqU0bO3SUmXmAkcPCQumddxScNk1p48cr56WXlHrZZdarwj8KC6Vp0%2Byy0kBA%2BtOfpHPO2e/QWn6%2BvS7mzpXmz7crVBcvtjdAB5KQYIGmRg0b6a5WTapc2UbAK1a0p18gYM/7ggIpL89GznfssNdGTo69NsrzOjziCKlFC6llS3tdZGZKzZvb74yoDRssoebl7fPbb%2Bl8dddbysy0ef66dIlseYhfBEBElfx8adLjv6j3bfWUqP2knMcekwYNCksNRUXS8uXSd99JCxeWfPzhh9JH8QIBa8ObNrWRhkaNbOShXj0LYnXq2KhEpUqhqW/zZht1XLfORhrXrLGRxx9/tJD688/7fmxystSundSxo9S5s3TiiVabJBumPPNMae5cBSWlScqRlHr00dK//23DjYh/P/8sdetmT/rfO/ZYC4S1a0uy599nn0mffy59%2BaU0Z07pk7zXrWvhqlkze31kZJS8PurUsdHsUBy2zcuzp/Evv1jwLH59rFplr4/ly%2B21Ulqvl5hoo%2BdHHy0dc0zJx6ZNw3hY%2BbHHpFtuKfXbBUrUxIfWqtegOmE97QP%2BQwBE9Jk3zzqb/Vh78UDVevEfSk4%2B%2BF%2BTn2%2BHbJcskb7/Xlq0qGR0b8eOfT8mLc2utiu%2BtWxpHVuTJg5GDvYjGLQRyuIAO3eu3YLBve/bsqWdS3TPF2cpfd779nj9LgBK0imn2BAt4l%2BXLtKsWfv81vq2/6f7Or2nGTPs%2BfVHqan20j322JIA1bKlfT1a5Oba637pUvsbvv%2B%2B5JaTs%2B/HVKlSMkrYqpW99lu0sNf9oYSyXbukTVf%2BVfVfe2L/d5w7V2rb9uB/EbAPBEBEn40bbcigoKDUu9ykkXqm4k1q1coa5SZNbDShZk07f7pCBRspy82Vtm61w0MbNtiIwJo11gGsXl36iF5ysjXyxZ1Y8UhA/fpRdu5QORQVWaf39dd2nvlnn1lA9DzpSC3RErXcfd%2B9AqBkwbxNm8gXjsgpw5uvFvqvflALBQL2ujjxRLs%2Bq0MHezMUkxdgyF4Ha9eWjPgXv3n6/vvSRzYTE6WGDa39yciwNujww%2B1QdkqKvSlMSLCmbPt2G7lft87an8WL7U1n3/xRGqWbSy%2BsYkV70GGHhecPh28RAA%2BB53naunWr6zLiU58%2B0ltv7fNbuQnJ6lT9e/24ueYh/5rKle2wVIsWdise2WvSxEJkvNu82cLgljEv64qZ1%2B/%2BelBShqQ1KgmARSOfVMKfr3RQJSKlaMKLSrh5wH7vM/m0sUq75hJ16mRvuOJdQYEFtuJRwiVLSs79DcW65s1qbtYXvx2lpKJSftgFF9glvwiLlJQUBWL1Xf0hIgAeguIrJQEAQOzx80wHBMBDUDwCmJWVpdmzZ5frsdH6mGAwqIyMDK1Zs6ZcL4qw1LZlizRhgo0E7tghZWWp12efaVI5FxGO6X0Qocfk5uSoYps2StiyRdLeI4A5SlELLdFOVZVkh92vuEK69NI9j0xFy98TisfE%2B/Ng40bplVdsSbHFi%2B37VbRN/1VLpamUIxs1atjw1wFOeI2VfVAWEXseLFig6WefrfPS0%2B2kwwsukP785/9NihrC33MQj4nn14KfRwB9cJArfAKBgFJTU5WYmFjudxDR/BhJSk1NLdfjwlJbaqp07712%2B58fMzP9tQ8i9ZjUVGnEiL3mV0z9363yow9oUtO6mjRJevttCwx33CENHSr16CHdeKOdCxY1f0%2BIHiPF1/MgISFR8%2BenavRoaerUkplHKlWSzj1X6tUrVenLH1DFW0o5J23EiN1XAYe6tmh%2BjBSB58GJJ2pY/fq6ojiNh%2Bv3HORjpPh6LRzsPogniUOHDh3quoh40KFDh7h4TG5urh566CHdfvvtSirnZa3R%2BPcczGN8uw/at7ehvSVLlLthgx6SdGvz5qo8apQSr79WRx0lXXKJ1L%2B/TXOzfr1NsbFwoTR%2BvPTmm1Lbti119tlNy33%2BZNTsg9%2BJp%2BdBbq700kvS%2B%2B9fqlGjqmjhQrsAqn176e67baC9Tx87/zWx8/E2Sd6SJTaniqTCVq2U8NRTtmxPFPw9kXxMPD0PDvYx7IP4xCFg7IEVINgHkrR29mw16NBBa9asUYMGDUq935w50tNPS5Mnl0ydk55uK/bdcEN0Tf9RXvHwPAgG7f/zxBMlEzJXqSJdfrn9f9q12//jty5apKOOPlqLY3gfHKp4eB4cKvZBfGIEEHtJTExUly5dVMEPl8GWwu/7ILdSJT3yyCO6/fbbVbVq1VLvV7eudN55dgi4Zk2bV23dOunDD23Fvvx8m1UkmuZILI9YfR4Eg9LDD9s5mu%2B8YytkNGhg68lOmmQjuWWa1zstTTtidB%2BEUqw%2BD0KJfRB/GAEEsJeDfcefn2%2BjgQ89VDJRcK1aFjxuvDF2g2CsyM2VRo%2BWHnxQu9eObdlSuu02G/VjJQkAxWJ0yk4A0ahiRTuXbOFCacoUm1tx0yZbta9VK2l6Kcs749BNn277eNAg2%2BctWtj/YOFC%2B58Q/gD8HgEQQMglJko9e1r4GDvWzgv88Ufp/PPtkPGaNa4rjB9r1tg%2BPf9828fp6bbPFy60/0FiousKAUQjAiCAsKlQQbrmGluCbsgQG4V6%2B21bQmzCBFt%2BCwfH82wftmpl%2B7RiRdvHS5faPudULQD7QwDEHqZOnaozzzxThx12mAKBgObNm%2Be6pLDwPE9Dhw44Kdw7AAAgAElEQVRVvXr1VLlyZXXp0kWLFi3a72OGDh2qQCCwxy09PT1CFce2atXsvMC5c6WOHe0ihauussmkt21zV9fo0aPVpEkTJScnq3379vr0009Lve%2BECRP2%2Bv8HAgHtCsV6YOW0bZvtu6uusrWuO3a0ffvQQ7avD9Unn3yic889V/Xq1VMgENBbpSzLGOvK%2B3fOnDlzn8%2BB/xaf8Bpnhg8frqysLKWkpOjwww/XBRdcoCVLlrguCyFCAMQetm/frhNOOEEPPfSQ61LC6uGHH9bjjz%2Bup556SrNnz1Z6errOOOOMA67t3KpVK/3888%2B7b999912EKo4PrVpJn31mFykkJtoFIyec4OaQ8CuvvKKBAwfqzjvv1Ny5c3XSSSfprLPO0urVq0t9TGpq6h7//59//lnJyckRrNr21Qkn2L5LTLR9%2Bdlntm9DZfv27WrTpo2eeuqp0P3QKHSwf%2BeSJUv2eA40b948TBW6NWvWLPXr109ffvmlPvjgAxUUFKhbt27avn2769IQCh6wDytWrPAkeXPnznVdSsgVFRV56enp3kMPPbT7a7t27fLS0tK8Z555ptTH3XvvvV6bNm0iUaJzOTk5niQvJycnbL/j0089r04dz5M8LyPD85YtC9uv2qcOHTp4ffv23eNrLVu29G677bZ93n/8%2BPFeWlpaJEor1dKltq8k23effhr%2B3ynJe/PNN8P/ixwry985Y8YMT5K3ZcuWCFUVXTZs2OBJ8mbNmuW6FIQAI4DwnRUrVmj9%2BvXq1q3b7q8lJSXplFNO0eeff77fxy5dulT16tVTkyZN1LNnTy1fvjzc5catE0%2BUvvrKrlZds0Y6/fSSyYrDLS8vT99%2B%2B%2B0ezwFJ6tat236fA9u2bVOjRo3UoEEDnXPOOZo7d264S91t/Xqpa1fbVy1a2L478cSI/Xr8zrHHHqu6devq9NNP14wZM1yXEzE5OTmSpJo1azquBKFAAITvrP9fyqhTp84eX69Tp87u7%2B3L8ccfrxdffFH/%2Bte/NHbsWK1fv16dO3fWpuIJ11BujRpJs2ZJzZtLq1ZJF10kFRSE//du3LhRhYWF5XoOtGzZUhMmTND06dM1ZcoUJScn64QTTtDSpUvDXm9Bge2bVatsX82aZfsOkVW3bl2NGTNGb7zxhqZOnaoWLVro9NNP1yeffOK6tLDzPE%2BDBg3SiSeeqKOPPtp1OQgBAqCPTZo0SdWqVdt9298J8LHsj39nfn6%2BJCkQCOxxP8/z9vra75111lm68MILdcwxx6hr16569913JUkvvPBC%2BIr3gTp1pHfflVJSpP/8xyYyjpTyPAc6duyoK664Qm3atNFJJ52kV199VUceeaSefPLJsNeZnW37JiVF%2Buc/bZ8h8lq0aKFrr71W7dq1U6dOnTR69GidffbZevTRR12XFnb9%2B/fXggULNGXKFNelIESYKMDHzjvvPB1//PG7P69fv77DasLnj39nbm6uJBsJrPu79bA2bNiw14jQ/lStWlXHHHNMREaA4l3z5rZ02Q032EUN118f3lVDDjvsMCUmJu412lee50BCQoKysrLC/v/PzZWGDbPtRx6RjjgirL8O5dSxY0dNnDjRdRlhNWDAAE2fPl2ffPLJftcGR2xhBNDHUlJSdMQRR%2By%2BVa5c2XVJYfHHvzMzM1Pp6en64IMPdt8nLy9Ps2bNUufOncv8c3Nzc/X999/vESJx8K6%2BWqpXT9qwwdYSDqdKlSqpffv2ezwHJOmDDz4o83PA8zzNmzcv7P//Dz%2B0fVKvnvSXv4T1V%2BEgzJ07N27bAM/z1L9/f02dOlUff/yxmjRp4rokhBAjgNjD5s2btXr1aq1bt06Sds/5lJ6eHjdz3gUCAQ0cOFDDhg1T8%2BbN1bx5cw0bNkxVqlTR5Zdfvvt%2Bp59%2Burp3767%2B/ftLkm655Rade%2B65atiwoTZs2KAHHnhAwWBQffr0cfWnxJWKFaUzz5TGj5dmz5bOPju8v2/QoEHq3bu3jjvuOHXq1EljxozR6tWr1bdvX0nSlVdeqfr162v48OGSpL///e/q2LGjmjdvrmAwqFGjRmnevHnKzs4Oa52zZ9vHM8%2BM3HJu27Zt07Jly3Z/vmLFCs2bN081a9ZUw4YNI1NEBBzo77z99tu1du1avfjii5KkJ554Qo0bN1arVq2Ul5eniRMn6o033tAbb7zh6k8Iq379%2Bmny5MmaNm2aUlJSdo%2BYp6Wlxe2Aga84vQYZUWf8%2BPGepL1u9957r%2BvSQqqoqMi79957vfT0dC8pKck7%2BeSTve%2B%2B%2B26P%2BzRq1GiPv/vSSy/16tat61WsWNGrV6%2Be16NHD2/RokURrjy8nnrqKe%2Boo47yjjzyyLBPA7MvQ4bYFCcDB0bm92VnZ3uNGjXyKlWq5LVr126P6S1OOeUUr0%2BfPrs/HzhwoNewYUOvUqVKXu3atb1u3bp5n3/%2BedhrHDjQ9smQIWH/VbsVT3fyx9vv90c8ONDf2adPH%2B%2BUU07Zff8RI0Z4zZo185KTk70aNWp4J554ovfuu%2B%2B6KT4C9rVvJHnjx493XRpCIOB5LMYEYE/BYFBpaWnKyclRampqxH7vpZdKr74qDR8u3XZbxH5tVBs%2BXLrjDumSS6RXXnFdDYB4wTmAAKJCMCi9955tn3SS21qiSfG%2BeP9920cAEAoEQABRYfhwW9e2RQupUyfX1USPzp1tnwSDttYvAIQCARCAcx9/bNPASBZyEmiZdktIKAl%2BI0bYvgKAQ0UzC8CpOXOkHj2koiKpTx/pggtcVxR9LrjA9k1Rke2rOXNcVwQg1hEAATgzc6Z02mlSTo6ta/vMM64ril7PPGP7KCfH9tnMma4rAhDLCIAAIs7zbHmzbt0s0Jx0ki0Hl5zsurLolZxs%2B%2Bikk2yfdetm%2B5B5HAAcDAIggIjauFG66CKpf38pP9%2BmfvnXv6QIzjYTs1JTbV9deqntu/79bV9u3Oi6MgCxhgAIICI8T3r5ZalVK2nqVFvV4rHHpClTJBYVKLvKlW2fPfaY7cOpU22fvvwyo4EAyo4ACCDsFi%2B2Q5aXXWbr2rZqJX35pTRokBQIuK4u9gQCtu%2B%2B/NL25YYNtm%2B7dbN9DQAHQgAEEDY//yz17Su1bi19%2BKGUlCQNHSp9%2B63Urp3r6mJfu3a2L4cOtX374Ye2r/v2tX0PAKVhKTgAeznUpeB%2B%2BUV65BFp9Ghp50772gUXSI8%2BKjVrFuJiIUn68Ufpllukt96yzytXlm68URo8WKpTx21tAKIPI4AAQmblSummm6TGje0ctZ07bVWPWbOkN98k/IVTs2a2j2fNsn2%2Bc6f9Dxo3tv/JypWuKwQQTQiAAA6J50mff25Xph5xhPTkk9KuXVKHDjZtyX/%2BI518susq/ePkk22fv/uu/Q927bL/yRFH2P/o88%2B5WAQAh4AB/NFvvyn4z38qrVcv5axYodTGjfd5t23b7GrUp5%2BW5s4t%2BXrXrtKQIdLpp3OBh2ueJ330kS0h9%2BGHJV8/9ljphhvswpFq1Up58KZN0ief2D%2BxSxepevVIlAwgQgiAAExRkXTbbVJ2toI7dihNUk5SklL79rWT9ypUkOfZ6NL48dKrr1oIlGyS4ssukwYOtIsQEH0WLJCeeMJC%2B65d9rVq1aRLLpGuuko64YT/BfaCAjuZ8NlnS%2B5YpYrUrx8LNQNxhACI6FdQIOXlWSeE8Bk82IKepKBkAVBSqqSNl/XXE02f1KRJe55LduSR0rXXWoCoVSvyJaP8Nm2yAD92rPTDDyVfb9xY6tVL%2BuuP/VXr5ex9P3jwYOnhhyNSp2/t2CFVqiRVqOC6EsQ5AiCi13ffSffdZ5c1FhTY0NKgQVKfPgd8aPGzmkOQZbR5s1S//u4Rnz8GwDxVVIbWaIPqqFo16eKLpT//2ZYlYx/HJs%2BTPv1UmjBBeu01G82to/VarYaqpPx9P6hyZemnn6SaNSNaa6wqVzs0YYL0j3/YUG3FinbZ/N13S8ccE84S4WMEQESnb7%2B1846KjzH%2B3h136KcbHtRXX0nz5klLltio1Pr10pYt9ga6qMjumpRkh7lq1JBq15bq1pUyMqRGjeyqySOOsI9JSZH846LQa6/ZscD/%2BWMAlKQnjn1B6bdeqfPOYzA23uzYIU2fLq1/%2BEUNnHuAN1ivvmrvAHwsN9em3Vm2zD6uWiWtWWNzL/76q7VD27bZ/SQ7al6lirVD6ek22tqihdS2rXT88VKD0XdIw4fv/YtSUqQZM6T27SP698EfCICITl262HwW%2B1CoBDXTj1qlxiH5VQkJUtOmtqJCZqZ9POYYqWVLOxITr3bskL74Qpo5Uwq8%2Boru%2B6Hn7u/tKwBq/Hgb9kP8mjDBjufvxz1HvizvkkvVpYtNNxPPbwby8qT//tcORixaZKusLFokLV9e8ibzUDXWCi3TEUpUKT%2BwSxcLgUCIEQARfVavtiG6/bgncL/eaXuX2rWzwNakiVSvnh2ZqlLFTp/xPDuiuW2bvSPfsEFau9beqa9YUfIOfuvWff%2BOChXsXfrRR%2B95a9JESkwMw98dRkVF9vfOni199ZUtITZnjh1Zl/Y%2B9LdXAExMtF6vYUM3fwAiY/Vqe4KXkm5%2BfyqAZK%2BRdu2kjh1tJCsry0bUY%2B06kcJCaxMWLtzztmRJyWvkj1JSSo4gNGliRxbq15cOP9xG%2BqpVs4ujAgH7GTt22JkW69bZ71q0yF6D58x7QPd5d%2B%2B/wNWr7RcAIcRZpog%2Bmzcf8C533bhZ9z116L/K8%2Bywzfffl7y7L278c3Ls80WLpFdeKXlMUpIFw6OOso/Nm9utWTO7EMLlOXGeZ4fClyyxv2nhQjulaP78fQfdBg2kU06RTj01XbkfXaVKU8bs%2Bwf36kX484OGDe1//dJL%2B/x27uV/0bDT6mjGDBug/%2Bkn6euv7VYsJUVq08ZO2T366JLXSXq6%2B9fGpk32RmjpUrsVv06WLCk5XPtHaWklb/6KjxIcdZSdThKKvyev3yZp9AHutHkzARAhxwggosq6ddKDQ4J6eGJdVdWO0u/43HPSX/4Stjo8zzq3777bc0Tg%2B%2B9LZsbYl5QUO7%2BnYUMLV/XrW8dXp46dg1irlo0OpKaW77zDwkILcL/9Zp3Yr7/acmvr1tmo5urVdh7k8uXS9u37/hnJydYxd%2BhgIzadO1utu%2BXl2eRwL7ygYGGhjQAGAkrt1csuGU1OLnvBiF27dtml3ZMnl4wEJibaxVdPP73HeRErV9rE0l9%2BaSFw/vzSXx9Vq9qpFsWvj/r1bdT%2B96%2BN6tXtNVSeEfbcXCkYtFH%2B37821q%2B318ZPP5W8Pkob7Zfs6X3UUXuO9h9zjL2Owxpcn3tOuuaaUr%2B9XVV06xU/684RqapXL4x1wHcIgIgKBQXSyJG2qP22bdIzul7Xq5TRqJo17Tiug5OPCgutI/n%2Bezs3aMkSm0pj2TILY%2BVRsaL9CcnJJbM%2BFHc0hYVSfr51bjt2lKynWxYJCXZIqmVLG61o3dqCX8uW9jsPaPVqBadNU9pNNylnwQKlchWiP61YYbNHBwJSt25lGgHOz7fXxfz5NvK8eLF9vmJF%2Bc6Zq1zZXhtJSfacLQ6EnlcyK9SuXfbayC/lguXS1Ktnh26PPNJGJlu2tODXuLGjUzt27LDRvVKOfDyj63WDnlG1atY%2B3nwzM8QgNAiAcO677%2Bzagjlz7POOHaUn7t%2Bq44eeZbMO/15qql2ueMopEa/zQHbutKsBV660EYe1ay0U/vyznX/466/WxgeDB/87kpJspOSww2zkpG5dG0nJyLAOrGlTC3%2BHevFKMBhUWlqacnJylJqaeuAHAPuRl2chcPlye32sWWOvj59/ttG6jRtt9K60w7BlkZpq7w1r17bz8OrWtbBXv75l18aN7dTiypVD9VeF0KxZ0nnn7d04nHCCvvr7%2Bxp4VzV9%2BaV9qV07u1aH92U4VARAOON5tjLBbbdZB1GjhvTII3YRYkKC7K3%2BtGk2RcmOHZYMr7nGWvcYVnw4d9s2%2B7N27bK/v6CgZN6wxEQb%2BUhKsg6rWjU7NBapo7AEQLiwa1fJa2PnTguE%2Bfn2mpFsMLJCBXuDk5xso4TFr41YuzBrLxs2SOPG2fH0KlVsqp3zz5cqVFBRkV2EP3iwHequVMkWZRk4kHk4cfAIgHBi82bpyittwXpJOuccacwYe9cO9wiAQPT5%2BWfpuuukd96xz88%2BW3rxReblxsGJsYv1EQ8WLJCOO87CX1KSNHq0HdUl/AFA6erWtbYyO9vaznfftbZ0wQLXlSEWEQARUdOm2dWnK1bYuWpffmkXnnIYAwAOLBCQbrzR2s4mTawt7dzZ2lagPAiAiJhRo6Tu3W2akq5dpW%2B%2BsaWQAADl07attaFdu1qb2r27tbFAWREAEXaeJw0ZYtMXeJ7Ut6/03nuctwIAh6JmTWtLr7/e2tabb7a2ljP7URYEQIRVYaGdtPzww/b58OF2zh/zWAHAoatQwebnHj7cPn/4YWtzi6%2BcBkpDAETYFBTY4gHjxtm0Ls89Z1O%2BcL4fAIROIGBt63PPWVs7bpy1vaWtYwxIBECESUGBTfMyaZK9Q3355bCu3AYAvveXv1hbW6GCtb1XXkkIROk4EIeQKyqyhmjKFGuIXntNuuAC11UBQPy7%2BGKbRP7ii0va4AkT/je5PvA7PCUQUp5nUxS89JI1PK%2B%2BSvgDgEi64AJrexMTrS2%2B8UYuDMHeCIAIqTvukJ591s5Jeeklm5oAABBZ3btLEydaW/zss9Y2A79HAETIjBxp61NKtqxbz55u6wEAP%2BvZ09piydrmkSPd1oPoQgBESLz2mi1MLknDhknXXOO2HgCAtcXDhtn2X/9qbTUgEQARAp9/LvXubdv9%2Btl0BIhN2dnZyszMVFZWlutSAITIbbdZ2%2Bx51lZ//rnrihANAp7HqaE4eMuXS8cfL23cKJ13njR1qp14jNgWDAaVlpamnJwcpaamui4HwCEqLJR69JCmT5cOO0z6%2BmtbSxj%2BxQggDlowKJ17roW/du2kyZMJfwAQjRITrY1u187a7HPOsTYc/kUAxEEpKpKuuEJavFiqW9feVVat6roqAEBpqla1trpuXWu7r7jC2nL4EwEQB%2BXee6W335aSkqS33pLq13ddEQDgQOrXtzY7Kcna8KFDXVcEVwiAKLdp06QHHrDtsWOlDh3c1gMAKLsOHaztlqT777c2Hf5DAES5LF1q60tK0k03lVz9CwCIHb17WxsuWZu%2BdKnbehB5BECU2c6d0kUX2YnDJ54oPfqo64oAAAfr0UelE06wNv2ii6yNh38QAFFmAwZICxZIhx8uvfKKLTgOAIhNFSvamsGHH25t%2B4ABritCJBEAUSaTJknPPWfrSk6eLNWr57oiAMChqlfP2vRAwNr4SZNcV4RIIQDigJYulfr2te177pFOP91tPQCA0Dn9dGvbJWvrOR/QH1gJBPuVl2fniHzzjXTKKdJHHzHZsx%2BwEgjgL4WFFgRnzZKOO076z3%2BkSpVcV4VwYgQQ%2B3X33Rb%2BataUJk4k/AFAPEpMtDa%2BZk1r8%2B%2B%2B23VFCDcCIEo1Y4b0yCO2PW6c1KCB23oAAOHToIG19ZK1/TNnOi0HYUYAxJ527pS2b9dvv0l9%2BkieJ11zjdS9u%2BvCAADh1r27tfmeZ/MD/vabpO3bmSMmDhEAYWbOlLp2tcUiq1XTxuad1HbNdDVrJv3jH66LQ1nl5%2BdryJAhOuaYY1S1alXVq1dPV155pdatW%2Be6NAAx4h//kJo1k45dM00bm3eSqlWzvuGMMxgWjCNcBAJbB%2BjCC%2B0s4D/4cfAzavbw9Q6KwsHIycnRRRddpGuvvVZt2rTRli1bNHDgQBUUFOibb74p88/hIhDA33689Vk1e6Tv3t%2BoUEF6/XXp/PMjXxRCigDod0VFUpMm0urV%2B/5%2Baqq0dq29A0RMmj17tjp06KBVq1apYcOGZXoMARDwsW3bbILArVv3/f1GjaTly6UEDiLGMv57fvfJJ6WHP8nWCJo%2BPXL1IORycnIUCARUvXr1Uu%2BTm5urYDC4xw2AT02bVnr4k6RVq6RPP41cPQgLAqDfbd4cmvsgKu3atUu33XabLr/88v2O5A0fPlxpaWm7bxkZGRGsEkBUoV/wBQKg3x199IHvc8wx4a8DB2XSpEmqVq3a7tunv3tXnp%2Bfr549e6qoqEijR4/e78%2B5/fbblZOTs/u2Zs2acJcOIFodqM0PBMrWdyCqcQ4gpDPPlP79731/7%2Bijpe%2B%2Bi2w9KLOtW7fql19%2B2f15/fr1VblyZeXn5%2BuSSy7R8uXL9fHHH6tWrVrl%2BrmcAwj43DHHSAsX7vt73bpJ//pXZOtByFVwXQDc%2B%2Bzq8ar5767K1Pd7fqNBA%2Bm119wUhTJJSUlRSkrKHl8rDn9Lly7VjBkzyh3%2BAECvvWbTvvz00x5fXqyjtPnq8TrRUVkIHUYAfW7nTql1a2nNsl169rRX1afWOzYdTNeuUu/eXP0bYwoKCnThhRdqzpw5euedd1SnTp3d36tZs6YqlXFxT0YAAWjbNumll6QPP5QSE/XCpnN03ceXquERSVqwQKpc2XWBOBQEQJ%2B76y7pwQftiv/Fi6W0NNcV4VCsXLlSTZo02ef3ZsyYoS5dupTp5xAAAfxRTo6UmSmtW2d9x/33u64Ih4IA6GPffy%2B1aSPl50tvvCH16OG6IkQLAiCAfZk61dYNqFhRmj9fOuoo1xXhYHEVsE95nnTDDRb%2BzjmHtX4BAAfWvbv1Gfn51ocwhBS7CIA%2BNXGiNGuWncPx5JN2VT8AAPsTCFifUbmy9SGTJrmuCAeLAOhDOTnS4MG2fffdUuPGTssBAMSQxo2t75CkW26xPgWxhwDoQ/fcI/3yi9SihfS3v7muBgAQa/72N%2BtDfvlFuvde19XgYBAAfea776TsbNt%2B6impjLOCAACwW6VKdihYsr6E9QJiDwHQRzxPuukmm%2BavRw%2Bb6g8AgINxxhnWlxQWWt/CBSGxhQDoI6%2B/Ls2cKSUnS48/7roaAECse%2Bwx61NmzrQ%2BBrGDAOgTO3faybqSNGSI1KiR23oAALGvcWPrUyS7uHDnTqfloBwIgD7x2GPS6tVSRoZ0662uqwEAxItbb7W%2BZdUq62sQGwiAPrBunfTQQ7Y9YoRUpYrbegAA8aNKFetbJOtr1q1zWw/KhgDoA3feKW3fLnXqJPXs6boaAEC86dnT%2Bpjt263PQfRjLeA4N3eu1L69XZ315ZfS8ce7rgixgLWAAZTXV19JHTvaaiHffisde6zrirA/jADGMc%2BzCz88T7rsMsIfACB8jj/e%2Bprf9z2IXgTAOPbee9LHH0tJSdKwYa6rQSzIzs5WZmamsrKyXJcCIAYNG2aTRH/8sfVBiF4cAo5TBQVS27bSokX2TuyRR1xXhFjCIWAAB2vwYOnRR6VWraT586XERNcVYV8YAYxTL7xg4a9mTemOO1xXAwDwizvukGrUsD7ohRdcV4PSEADj0M6dJYtz33mnvRABAIiEGjVKrgS%2B5x4mh45WBMA49OST0tq1ttrHjTe6rgYA4Df9%2BkkNG1pf9OSTrqvBvhAA48yWLdLw4bb997/bGo0AAERScrJ03322PXy49U2ILgTAOPPww9Jvv9nJt1dc4boaAIBfXXGF9UW//WZ9E6ILATCOrF8vjRxp28OGceUVAMCdxETpwQdte9Qo66MQPQiAcWTYMDvZtmNH6dxzXVcDAPC7886zCaJ37GA%2B2mjDPIBxYtUq6cgjpbw86aOPpNNOc10RYhnzAAIIlY8/lk4/3SaI/uEHu0AR7jECGCfuv9/C36mnEv4AANHjtNOsb8rLkx54wHU1KMYIYBz48UepRQupsFD6z3%2Bkzp1dV4RYxwgggFD6/HPphBPsvMAlS6RmzVxXBEYA48B991n4O%2Bsswh8AIPp07iz93/9ZX1U8PQzcYgQwxv3wg3TUUVJRkfT111JWluuKEA8YAQQQarNnSx06SAkJ0vff23nrcIcRwBh3//0W/s45h/AHAIheWVnWVxUVWd8FtxgBjGFLlkiZmfZi%2BuYbqX171xUhXjACCCAcvv1WOu44GwVcvNjOX4cbjADGsAcftPB37rmEPwBA9Gvf3vqsoqKSSaLhBiOAMWrZMnvnVFRk51Ucd5zrihBPGAEEEC7ffGOHgxMTpf/%2BVzriCNcV%2BRMjgDFq2DALf3/6E%2BEPABA7jjvO%2Bq7CQlYHcYkRwBi0cqXUvLlUUCB98YUt/QaEEiOAAMLpyy%2BlTp2kChWkpUulxo1dV%2BQ/jADGoBEjLPydcQbhD6GVnZ2tzMxMZXFJOYAw6thR6trV%2BrKHH3ZdjT8xAhhj1q2TmjSxJXVmzpROOcV1RYhHjAACCLdZs6QuXWyN4BUrpHr1XFfkL4wAxpjHHrPwd%2BKJhD8AQOw65RTry/LyrG9DZBEAY8imTdKzz9r2HXe4rQUAgENV3Jc9%2B6z1cYgcAmAMefJJaft26dhjbU1FAABi2f/9n/Vp27dbH4fIIQDGiG3bSl4ct90mBQJu6wEA4FAFAtanSdbHbdvmth4/IQDGiLFjpc2bbcLMCy90XQ0AAKFx4YXWt23ebH0dIoMAGAPy8qTHH7ftW2%2B12dMBAIgHiYnS4MG2/fjjUn6%2B23r8ggAYA6ZMkX76SapbV%2Brd23U1AACE1pVXSunp1tdNnuy6Gn8gAEY5z5MeecS2b75ZSk52Ww8AAKGWnCwNHGjbjzxifR/CiwAY5d57T1q0SEpJka6/3nU1AACEx/XXS9WqWZ/33nuuq4l/BMAoVzz6d911UvXqbmsBACBcqlcvGego7vsQPiwFF8W%2B%2BUbKyrLFsleskBo0cF0R/IKl4AC4sGaN1LSprRE8e7Z03HGuK4pfjABGseKlcXr2JPwBAOJfRob1eRLLw4UbI4BRatUqqVkzqbBQmjtXatvWdUXwE0YAAbgyb56tDpKYKC1fLjVs6Lqi%2BMQIYJR68kkLf6efThKAsw8AAAfISURBVPgDAPhH27bSaadZHzhqlOtq4hcBMAoFg9KYMbY9aJDbWgAAiLS//c0%2Bjh1rfSJCjwAYhZ5/Xtq6VWrZ0hbKBgDAT/7v/6wPDAal8eNdVxOfCIBR5vdD3gMHSgn8hwAAPpOQUDIx9MiR1jcitIgXUWb6dJvypWZNln1D5GVnZyszM1NZWVmuSwHgc717W1%2B4YoX09tuuq4k/BMAo88QT9vH666UqVdzWAv/p16%2BfFi9erNmzZ7suBYDPValiiyBIJX0jQodpYKJI8aXvTPwM15gGBkA0%2BOknqXFjOwQ8b57Upo3riuIHI4BRpPjcvwsvJPwBANCggXTRRbY9cqTbWuINI4BR4tdfbQb03Fzp88%2BlTp1cVwQ/YwQQQLT44gupc2cpKcmWiqtd23VF8YERwCgxdqyFv%2BOOkzp2dF0NAADRoWNH6xtzc6Vx41xXEz8IgFGgoEB6%2BmnbHjBACgTc1gMAQLQIBKxvlKTRo63PxKEjAEaBadPsRNfataVLLnFdDQAA0eWSS6yP/Okn6zNx6AiAUeCpp%2BzjdddJycluawEAINokJ0vXXmvbxX0mDg0XgTi2cKF0zDFSYqK0ciVX/yI6cBEIgGjz%2BylhFi6UWrVyXVFsYwTQsexs%2B3j%2B%2BYQ/AABK06CBdN55tl3cd%2BLgEQAdCgaliRNtu18/t7UAABDt%2Bve3jy%2B9ZH0oDh4B0KGXXpK2bZNatpROPdV1NQAARLdTT7U%2Bc9u2kgEUHBwCoCOeVzL1y403MvULAAAHEghYnynZlDBcxXDwCICOfPqptGiRLXbdu7fragAAiA29e1vfuWiR9aU4OARAR555xj5efrlUvbrbWhC/rr/%2BegUCAT3xxBOuSwGAkKhe3fpOqaQvRfkRAB3YsEF6/XXbvuEGt7Ugfr311lv66quvVK9ePdelAEBIFfedr79ufSrKjwDowPjxUn6%2BlJUltWvnuhrEo7Vr16p///6aNGmSKlas6LocAAipdu2sD83Ptz4V5UcAjLCiImnMGNu%2B/nq3tSA%2BFRUVqXfv3ho8eLBaMVMqgDhV3IeOGWN9K8qHABhhH30kLV8upaVJPXu6rgbxaMSIEapQoYJuuummMj8mNzdXwWBwjxsARLOePa0vXb7c%2BlaUDwEwwp591j5ecYVUtarbWhD7Jk2apGrVqu2%2BzZo1SyNHjtSECRMUKMfcQsOHD1daWtruW0ZGRhirBoBDV7Wq1KuXbRcfWUPZsRZwBP3yiy1lU1AgzZ8vtW7tuiLEuq1bt%2BqXX37Z/flrr72mO%2B%2B8UwkJJe/tCgsLlZCQoIyMDK1cuXKfPyc3N1e5ubm7Pw8Gg8rIyGAtYABRbcECqU0bqUIFWyu4Th3XFcWOCq4L8JPx4y38HX884Q%2BhkZKSopSUlN2fX3fddTr33HP3uM%2BZZ56p3r1766qrrir15yQlJSkpKSlsdQJAOLRubX3qV19JEyZIQ4a4rih2EAAjpKhIGjfOtrn4A%2BFSq1Yt1apVa4%2BvVaxYUenp6WrRooWjqgAgfK67zgLguHHSrbeyslZZcQ5ghMycKf34o5SSIl1yietqAACID5dean3rsmXW16JsCIARUjz6d/nlXPyByFq5cqUGDhzougwACIuqVUtWBhk71m0tsYSLQCJg82apbl0pL0%2BaPVs67jjXFQH7FwwGlZaWxkUgAGLCN9/YxNBJSdK6dVLNmq4rin6MAEbAxIkW/tq2ldq3d10NAADxpX1762Nzc63PxYERAMPM86TnnrPtq6/m5FQAAEItELA%2BVrI%2Bl2ObB0YADLNvv7V5ipKSSiasBAAAoXX55dbXLlhgfS/2j2lgwqxxY2nECOm336QaNVxXAwBAfKpZU/rrX62vbdzYdTXRj4tAAOyFi0AAIL5xCBgAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARDAbtnZ2crMzFRWVpbrUgAAYcRKIAD2wkogABDfGAEEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIggN2ys7OVmZmprKws16UAAMIo4Hme57oIANElGAwqLS1NOTk5Sk1NdV0OACDEGAEEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnmAcQwF48z9PWrVuVkpKiQCDguhwAQIgRAAEAAHyGQ8AAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAD4/3brQAAAAABAkL/1IBdFADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMCCAAwI4AAADMBOpCAlOOgzpAAAAAASUVORK5CYII%3D'}