Transaction
ID: 2a82c2710f...cf12
Inputs (5)
Spent
Output transaction:
Settlement height:
Value:
0.673198031 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
49.85
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0005 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.208701966 ERG
Tokens:
49.75
Outputs (3)
Spent
Spent in transaction:
Settlement height:
Value:
0.8 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.082399997 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.002 ERG
Transaction Details
Confirmations: 510,917
Total coins transferred: 0.884399997 ERG
Fees: 0.002 ERG
Fees per byte: 0.000000709 ERG
Raw Transaction Data
{
"id": "2a82c2710fc933bf904f8a10938d632e4bb90e665da8b6725f3e185aa0e8cf12",
"blockId": "b7c0c82e5a8384f289126256ca59b7a66373e40b0c573efeeee1e4bf3a73c2a5",
"inclusionHeight": 1255393,
"timestamp": 1714620098822,
"index": 8,
"globalIndex": 7129500,
"numConfirmations": 510917,
"inputs": [
{
"boxId": "dbb993455ac6165bcbfe096b6174dfbbb3121031d36f0370274a8445ad739597",
"value": 673198031,
"index": 0,
"spendingProof": "f6efd5d19555737114a3759e74ffff6fbfbb9b0d3ca43d1ab93a4b96512f7b6a4c8d291d916893a98eb225f55f513096823b0c56adcd3011",
"outputBlockId": "4647c953956dc6af0f5e06255c8b30128c37b02acfd864f0a4b73f6b2da056b4",
"outputTransactionId": "c384eb91e6985328afe63ed1a10ad194d3d90ef452993db8ee10cf1fc47cb85c",
"outputIndex": 1,
"outputGlobalIndex": 37222362,
"outputCreatedAt": 1208599,
"outputSettledAt": 1208601,
"ergoTree": "10420400060101040004020400058084af5f058084af5f040404020400058084af5f058084af5f058084af5f0e20b533a477f97e8cb51b65e79fbb0ee7dcb232f62bb9b1f4057789d60109daab02040004000e203d2878c9b6ac700d3ae0dc6dc9b940acba75dc5b826aaacf0457603bf495d48f04000e20003bd19d0187117f130b62e1bcab0939929ff5c7709f843c5c4dd158949285d0040004060480ade20404d00f0404040406016404809bee020580897a04000400040204000580897a04020e20e63a1afefdc042f7c779e29e7b6148be8fde14036e5cfcf4b4e00a8390725ed80480897a0580897a0402040004060480ade20404d00f04040404060164040404000400010004020406040004060480ade20404d00f0404040406016404809bee020580897a0400058084af5f040a058084af5f01000100d825d601b2a4730000d602e4c6a70807d603c2a7d604db6501fed605e4c6a70659d6068c720501d6077301d608db6308a7d609b27208730200d60a8c720902d60b7e720a06d60cb27204730300d60de4c6720c0411d60eb1720dd60fe4c6a70558d6108c720f01d611b27204730400d612e4c672110411d613b0b472128c720f02b172127305d90113599d9c8c7213018c7213027306d614b27204730700d615e4c672140411d6167e9593720e72107213d802d6169a72107308d617b0b472157309b172157213d90117599d9c8c7217018c721702730a9593720e72167217b0b4720d7216720e7217d90118599d9c8c7218018c721802730b06d617730cd6189a72079d9c720b72167e721706d619e4c6a7040ed61ae4c6a7070ed61be4c6a70959d61c8c721b01d61d8c721b02d61e730dd61f938cb2db63087211730e0001721ed62093e4c6721106047210d621938cb2db6308720c730f00017310d622ed938cb2db6308721473110001721e93e4c672140604720ed623c1a7d6247312d6258c72090195937201a7d809d626b2a5731300d627b27204731400d628db63087227d629c17226d62a997e7229067e731506d62b7ee4c67227040406d62c7ec1722706d62d7316d62ee4c672260959ea02cd7202d1edededededededed93c272267203ed919d9c9c7e8cb272287317000206722a722b9a9c9a722c9d9c722c7e73180673197e722d069c722a722b9d9c72187e7206067e722d069272299a7e731a05731b93b2db63087226731c007209edededededed93e4c67226040e721993e4c672260558720f93e4c672260659720593e4c67226070e721a93e4c6722608077202938c722e01721c938c722e02721d721f72207221938cb27228731d0001721a72229593b2a4731e00a7d80fd626b2a5731f00d627c27226d628c17226d6299272289972237320d62ab2a5732100d62b93cbc2722a7322d62c92c1722a9a7e7323057324d62ddb6308722ad62eb2722d732500d62f938c722e017224d6308c722e02d631b2722d732600d6329372317209d633db63087201d634db63087226d1ececededededededededed93722772197229722b722ced722f917e72300672187232721f7220722172229592b172337327d808d635997e7223067e732806d6367ee4c67201040406d6377ec1720106d6387329d6399d9c9c7e8cb27233732a000206723572369a9c9a72379d9c72377e732b06732c7e7238069c72357236d63a9972397218d63b7e8c72050206d63c9d9c723a997e723806723b7e723806ededededededededededeced9072399d9c72187e7206067e723806927ea305721d917ea305721c722b722f722c958f723c7207927e7230067239d802d63db2a5732d00d63eb2db6308723d732e00ededed927e7230069a72189d9c723a723b7e723806927e8c723e0206723c93c2723d7219938c723e0172247232938cb27233732f0001721a721f722072217222733095ed92b1723473318fb1db630872017332d80fd635e4c672260959d6368c723502d6379372277203d638b27234733300d6398c723802d63a7e723906d63bededededed93e4c67226040e721993e4c672260558720f93e4c672260659720593e4c67226070e721a93e4c6722608077202938c723501721cd63cb27204733400d63ddb6308723cd63e997e7228067e733506d63f7ee4c6723c040406d6407ec1723c06d6417336d642ed929d9c9c7e8cb2723d7337000206723e723f9a9c9a72409d9c72407e73380673397e7241069c723e723f9d9c9a72079d9c723a72167e7217067e7206067e7241069272289a7e733a05733bd643938cb2723d733c0001721a95937236733dededededededededededededededed72379272287223938c723801722592723a99720b9d9c7e7230067e7217067216723b93721d7236722b722c722fed938c72310299720a7239938c7231017225721f72207221722272427243edededededededededededef7242723772299372347208723bed9172367ea3058f72367e9aa3733e0593721d733f721f72207221724372227340d17341",
"ergoTreeConstants": "0: 0\n1: CBigInt(1)\n2: 0\n3: 1\n4: 0\n5: 100000000\n6: 100000000\n7: 2\n8: 1\n9: 0\n10: 100000000\n11: 100000000\n12: 100000000\n13: Coll(-75,51,-92,119,-7,126,-116,-75,27,101,-25,-97,-69,14,-25,-36,-78,50,-10,43,-71,-79,-12,5,119,-119,-42,1,9,-38,-85,2)\n14: 0\n15: 0\n16: Coll(61,40,120,-55,-74,-84,112,13,58,-32,-36,109,-55,-71,64,-84,-70,117,-36,91,-126,106,-86,-49,4,87,96,59,-12,-107,-44,-113)\n17: 0\n18: Coll(0,59,-47,-99,1,-121,17,127,19,11,98,-31,-68,-85,9,57,-110,-97,-11,-57,112,-97,-124,60,92,77,-47,88,-108,-110,-123,-48)\n19: 0\n20: 3\n21: 5000000\n22: 1000\n23: 2\n24: 2\n25: CBigInt(100)\n26: 3000000\n27: 1000000\n28: 0\n29: 0\n30: 1\n31: 0\n32: 1000000\n33: 1\n34: Coll(-26,58,26,-2,-3,-64,66,-9,-57,121,-30,-98,123,97,72,-66,-113,-34,20,3,110,92,-4,-12,-76,-32,10,-125,-112,114,94,-40)\n35: 1000000\n36: 1000000\n37: 1\n38: 0\n39: 3\n40: 5000000\n41: 1000\n42: 2\n43: 2\n44: CBigInt(100)\n45: 2\n46: 0\n47: 0\n48: false\n49: 1\n50: 3\n51: 0\n52: 3\n53: 5000000\n54: 1000\n55: 2\n56: 2\n57: CBigInt(100)\n58: 3000000\n59: 1000000\n60: 0\n61: 100000000\n62: 5\n63: 100000000\n64: false\n65: false",
"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val ge2 = SELF.R8[GroupElement].get\n val coll3 = SELF.propositionBytes\n val coll4 = CONTEXT.dataInputs\n val tuple5 = SELF.R6[(Long, Long)].get\n val l6 = tuple5._1\n val bi7 = placeholder[BigInt](1)\n val coll8 = SELF.tokens\n val tuple9 = coll8(placeholder[Int](2))\n val l10 = tuple9._2\n val bi11 = l10.toBigInt\n val box12 = coll4(placeholder[Int](3))\n val coll13 = box12.R4[Coll[Long]].get\n val i14 = coll13.size\n val tuple15 = SELF.R5[(Int, Int)].get\n val i16 = tuple15._1\n val box17 = coll4(placeholder[Int](4))\n val coll18 = box17.R4[Coll[Long]].get\n val l19 = coll18.slice(tuple15._2, coll18.size).fold(placeholder[Long](5), {(tuple19: (Long, Long)) => tuple19._1 * tuple19._2 / placeholder[Long](6) })\n val box20 = coll4(placeholder[Int](7))\n val coll21 = box20.R4[Coll[Long]].get\n val bi22 = if (i14 == i16) { l19 } else {(\n val i22 = i16 + placeholder[Int](8)\n val l23 = coll21.slice(placeholder[Int](9), coll21.size).fold(l19, {(tuple23: (Long, Long)) => tuple23._1 * tuple23._2 / placeholder[Long](10) })\n if (i14 == i22) { l23 } else { coll13.slice(i22, i14).fold(l23, {(tuple24: (Long, Long)) => tuple24._1 * tuple24._2 / placeholder[Long](11) }) }\n )}.toBigInt\n val l23 = placeholder[Long](12)\n val bi24 = bi7 + bi11 * bi22 / l23.toBigInt\n val coll25 = SELF.R4[Coll[Byte]].get\n val coll26 = SELF.R7[Coll[Byte]].get\n val tuple27 = SELF.R9[(Long, Long)].get\n val l28 = tuple27._1\n val l29 = tuple27._2\n val coll30 = placeholder[Coll[Byte]](13)\n val bool31 = box17.tokens(placeholder[Int](14))._1 == coll30\n val bool32 = box17.R6[Int].get == i16\n val bool33 = box12.tokens(placeholder[Int](15))._1 == placeholder[Coll[Byte]](16)\n val bool34 = (box20.tokens(placeholder[Int](17))._1 == coll30) && (box20.R6[Int].get == i14)\n val l35 = SELF.value\n val coll36 = placeholder[Coll[Byte]](18)\n val coll37 = tuple9._1\n if (box1 == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](19))\n val box39 = coll4(placeholder[Int](20))\n val coll40 = box39.tokens\n val l41 = box38.value\n val bi42 = l41.toBigInt - placeholder[Int](21).toBigInt\n val bi43 = box39.R4[Int].get.toBigInt\n val bi44 = box39.value.toBigInt\n val i45 = placeholder[Int](22)\n val tuple46 = box38.R9[(Long, Long)].get\n proveDlog(ge2) && sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (box38.propositionBytes == coll3) && (\n (\n coll40(placeholder[Int](23))._2.toBigInt * bi42 * bi43 / bi44 + bi44 * placeholder[Int](24).toBigInt / placeholder[BigInt](\n 25\n ) * i45.toBigInt + bi42 * bi43 > bi24 * l6.toBigInt / i45.toBigInt\n ) && (l41 >= placeholder[Int](26).toLong + placeholder[Long](27))\n )\n ) && (box38.tokens(placeholder[Int](28)) == tuple9)\n ) && (\n (\n (\n (\n (((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (\n box38.R7[Coll[Byte]].get == coll26\n )\n ) && (box38.R8[GroupElement].get == ge2)\n ) && (tuple46._1 == l28)\n ) && (tuple46._2 == l29)\n )\n ) && bool31\n ) && bool32\n ) && bool33\n ) && (coll40(placeholder[Int](29))._1 == coll26)\n ) && bool34\n )\n )} else { if (INPUTS(placeholder[Int](30)) == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](31))\n val coll39 = box38.propositionBytes\n val l40 = box38.value\n val bool41 = l40 >= l35 - placeholder[Long](32)\n val box42 = OUTPUTS(placeholder[Int](33))\n val bool43 = blake2b256(box42.propositionBytes) == placeholder[Coll[Byte]](34)\n val bool44 = box42.value >= placeholder[Int](35).toLong + placeholder[Long](36)\n val coll45 = box42.tokens\n val tuple46 = coll45(placeholder[Int](37))\n val bool47 = tuple46._1 == coll36\n val l48 = tuple46._2\n val tuple49 = coll45(placeholder[Int](38))\n val bool50 = tuple49 == tuple9\n val coll51 = box1.tokens\n val coll52 = box38.tokens\n sigmaProp((((((((((((coll39 == coll25) && bool41) && bool43) && bool44) && (bool47 && (l48.toBigInt > bi24))) && bool50) && bool31) && bool32) && bool33) && bool34) || if (coll51.size >= placeholder[Int](39)) {(\n val bi53 = l35.toBigInt - placeholder[Int](40).toBigInt\n val bi54 = box1.R4[Int].get.toBigInt\n val bi55 = box1.value.toBigInt\n val i56 = placeholder[Int](41)\n val bi57 = coll51(placeholder[Int](42))._2.toBigInt * bi53 * bi54 / bi55 + bi55 * placeholder[Int](43).toBigInt / placeholder[BigInt](44) * i56.toBigInt + bi53 * bi54\n val bi58 = bi57 - bi24\n val bi59 = tuple5._2.toBigInt\n val bi60 = bi58 * i56.toBigInt - bi59 / i56.toBigInt\n ((((((((((((bi57 <= bi24 * l6.toBigInt / i56.toBigInt) && (HEIGHT.toLong >= l29)) || (HEIGHT.toLong > l28)) && bool43) && bool47) && bool44) && if (bi60 < bi7) { l48.toBigInt >= bi57 } else {(\n val box61 = OUTPUTS(placeholder[Int](45))\n val tuple62 = box61.tokens(placeholder[Int](46))\n (((l48.toBigInt >= bi24 + bi58 * bi59 / i56.toBigInt) && (tuple62._2.toBigInt >= bi60)) && (box61.propositionBytes == coll25)) && (tuple62._1 == coll36)\n )}) && bool50) && (coll51(placeholder[Int](47))._1 == coll26)) && bool31) && bool32) && bool33) && bool34\n )} else { placeholder[Boolean](48) }) || if ((coll52.size >= placeholder[Int](49)) && (box1.tokens.size < placeholder[Int](50))) {(\n val tuple53 = box38.R9[(Long, Long)].get\n val l54 = tuple53._2\n val bool55 = coll39 == coll3\n val tuple56 = coll52(placeholder[Int](51))\n val l57 = tuple56._2\n val bi58 = l57.toBigInt\n val bool59 = (((((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (box38.R7[Coll[Byte]].get == coll26)) && (box38.R8[GroupElement].get == ge2)) && (tuple53._1 == l28)\n val box60 = coll4(placeholder[Int](52))\n val coll61 = box60.tokens\n val bi62 = l40.toBigInt - placeholder[Int](53).toBigInt\n val bi63 = box60.R4[Int].get.toBigInt\n val bi64 = box60.value.toBigInt\n val i65 = placeholder[Int](54)\n val bool66 = (coll61(placeholder[Int](55))._2.toBigInt * bi62 * bi63 / bi64 + bi64 * placeholder[Int](56).toBigInt / placeholder[BigInt](57) * i65.toBigInt + bi62 * bi63 >= bi7 + bi58 * bi22 / l23.toBigInt * l6.toBigInt / i65.toBigInt) && (l40 >= placeholder[Int](58).toLong + placeholder[Long](59))\n val bool67 = coll61(placeholder[Int](60))._1 == coll26\n if (l54 == placeholder[Long](61)) { ((((((((((((((bool55 && (l40 >= l35)) && (tuple56._1 == coll37)) && (bi58 >= bi11 - l48.toBigInt * l23.toBigInt / bi22)) && bool59) && (l29 == l54)) && bool43) && bool44) && bool47) && ((tuple49._2 == l10 - l57) && (tuple49._1 == coll37))) && bool31) && bool32) && bool33) && bool34) && bool66) && bool67 } else { (((((((((((!bool66) && bool55) && bool41) && (coll52 == coll8)) && bool59) && ((l54 > HEIGHT.toLong) && (l54 < HEIGHT + placeholder[Int](62).toLong))) && (l29 == placeholder[Long](63))) && bool31) && bool32) && bool33) && bool67) && bool34 }\n )} else { placeholder[Boolean](64) })\n )} else { sigmaProp(placeholder[Boolean](65)) } }\n}",
"address": "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",
"assets": [
{
"tokenId": "2c65cafcdf0a466fb6578f74d614d81b6296b04a56a8b902d88c364545b13e53",
"index": 0,
"amount": 1000,
"name": "Borrow Token SigRSV-0e",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "58069401",
"sigmaType": "(SInt, SInt)",
"renderedValue": "[3,74]"
},
"R6": {
"serializedValue": "59c413d804",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1250,300]"
},
"R8": {
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"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: CBigInt(1)\n2: 0\n3: 1\n4: 0\n5: 100000000\n6: 100000000\n7: 2\n8: 1\n9: 0\n10: 100000000\n11: 100000000\n12: 100000000\n13: Coll(-75,51,-92,119,-7,126,-116,-75,27,101,-25,-97,-69,14,-25,-36,-78,50,-10,43,-71,-79,-12,5,119,-119,-42,1,9,-38,-85,2)\n14: 0\n15: 0\n16: Coll(61,40,120,-55,-74,-84,112,13,58,-32,-36,109,-55,-71,64,-84,-70,117,-36,91,-126,106,-86,-49,4,87,96,59,-12,-107,-44,-113)\n17: 0\n18: Coll(0,59,-47,-99,1,-121,17,127,19,11,98,-31,-68,-85,9,57,-110,-97,-11,-57,112,-97,-124,60,92,77,-47,88,-108,-110,-123,-48)\n19: 0\n20: 3\n21: 5000000\n22: 1000\n23: 2\n24: 2\n25: CBigInt(100)\n26: 3000000\n27: 1000000\n28: 0\n29: 0\n30: 1\n31: 0\n32: 1000000\n33: 1\n34: Coll(-26,58,26,-2,-3,-64,66,-9,-57,121,-30,-98,123,97,72,-66,-113,-34,20,3,110,92,-4,-12,-76,-32,10,-125,-112,114,94,-40)\n35: 1000000\n36: 1000000\n37: 1\n38: 0\n39: 3\n40: 5000000\n41: 1000\n42: 2\n43: 2\n44: CBigInt(100)\n45: 2\n46: 0\n47: 0\n48: false\n49: 1\n50: 3\n51: 0\n52: 3\n53: 5000000\n54: 1000\n55: 2\n56: 2\n57: CBigInt(100)\n58: 3000000\n59: 1000000\n60: 0\n61: 100000000\n62: 5\n63: 100000000\n64: false\n65: false",
"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val ge2 = SELF.R8[GroupElement].get\n val coll3 = SELF.propositionBytes\n val coll4 = CONTEXT.dataInputs\n val tuple5 = SELF.R6[(Long, Long)].get\n val l6 = tuple5._1\n val bi7 = placeholder[BigInt](1)\n val coll8 = SELF.tokens\n val tuple9 = coll8(placeholder[Int](2))\n val l10 = tuple9._2\n val bi11 = l10.toBigInt\n val box12 = coll4(placeholder[Int](3))\n val coll13 = box12.R4[Coll[Long]].get\n val i14 = coll13.size\n val tuple15 = SELF.R5[(Int, Int)].get\n val i16 = tuple15._1\n val box17 = coll4(placeholder[Int](4))\n val coll18 = box17.R4[Coll[Long]].get\n val l19 = coll18.slice(tuple15._2, coll18.size).fold(placeholder[Long](5), {(tuple19: (Long, Long)) => tuple19._1 * tuple19._2 / placeholder[Long](6) })\n val box20 = coll4(placeholder[Int](7))\n val coll21 = box20.R4[Coll[Long]].get\n val bi22 = if (i14 == i16) { l19 } else {(\n val i22 = i16 + placeholder[Int](8)\n val l23 = coll21.slice(placeholder[Int](9), coll21.size).fold(l19, {(tuple23: (Long, Long)) => tuple23._1 * tuple23._2 / placeholder[Long](10) })\n if (i14 == i22) { l23 } else { coll13.slice(i22, i14).fold(l23, {(tuple24: (Long, Long)) => tuple24._1 * tuple24._2 / placeholder[Long](11) }) }\n )}.toBigInt\n val l23 = placeholder[Long](12)\n val bi24 = bi7 + bi11 * bi22 / l23.toBigInt\n val coll25 = SELF.R4[Coll[Byte]].get\n val coll26 = SELF.R7[Coll[Byte]].get\n val tuple27 = SELF.R9[(Long, Long)].get\n val l28 = tuple27._1\n val l29 = tuple27._2\n val coll30 = placeholder[Coll[Byte]](13)\n val bool31 = box17.tokens(placeholder[Int](14))._1 == coll30\n val bool32 = box17.R6[Int].get == i16\n val bool33 = box12.tokens(placeholder[Int](15))._1 == placeholder[Coll[Byte]](16)\n val bool34 = (box20.tokens(placeholder[Int](17))._1 == coll30) && (box20.R6[Int].get == i14)\n val l35 = SELF.value\n val coll36 = placeholder[Coll[Byte]](18)\n val coll37 = tuple9._1\n if (box1 == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](19))\n val box39 = coll4(placeholder[Int](20))\n val coll40 = box39.tokens\n val l41 = box38.value\n val bi42 = l41.toBigInt - placeholder[Int](21).toBigInt\n val bi43 = box39.R4[Int].get.toBigInt\n val bi44 = box39.value.toBigInt\n val i45 = placeholder[Int](22)\n val tuple46 = box38.R9[(Long, Long)].get\n proveDlog(ge2) && sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (box38.propositionBytes == coll3) && (\n (\n coll40(placeholder[Int](23))._2.toBigInt * bi42 * bi43 / bi44 + bi44 * placeholder[Int](24).toBigInt / placeholder[BigInt](\n 25\n ) * i45.toBigInt + bi42 * bi43 > bi24 * l6.toBigInt / i45.toBigInt\n ) && (l41 >= placeholder[Int](26).toLong + placeholder[Long](27))\n )\n ) && (box38.tokens(placeholder[Int](28)) == tuple9)\n ) && (\n (\n (\n (\n (((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (\n box38.R7[Coll[Byte]].get == coll26\n )\n ) && (box38.R8[GroupElement].get == ge2)\n ) && (tuple46._1 == l28)\n ) && (tuple46._2 == l29)\n )\n ) && bool31\n ) && bool32\n ) && bool33\n ) && (coll40(placeholder[Int](29))._1 == coll26)\n ) && bool34\n )\n )} else { if (INPUTS(placeholder[Int](30)) == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](31))\n val coll39 = box38.propositionBytes\n val l40 = box38.value\n val bool41 = l40 >= l35 - placeholder[Long](32)\n val box42 = OUTPUTS(placeholder[Int](33))\n val bool43 = blake2b256(box42.propositionBytes) == placeholder[Coll[Byte]](34)\n val bool44 = box42.value >= placeholder[Int](35).toLong + placeholder[Long](36)\n val coll45 = box42.tokens\n val tuple46 = coll45(placeholder[Int](37))\n val bool47 = tuple46._1 == coll36\n val l48 = tuple46._2\n val tuple49 = coll45(placeholder[Int](38))\n val bool50 = tuple49 == tuple9\n val coll51 = box1.tokens\n val coll52 = box38.tokens\n sigmaProp((((((((((((coll39 == coll25) && bool41) && bool43) && bool44) && (bool47 && (l48.toBigInt > bi24))) && bool50) && bool31) && bool32) && bool33) && bool34) || if (coll51.size >= placeholder[Int](39)) {(\n val bi53 = l35.toBigInt - placeholder[Int](40).toBigInt\n val bi54 = box1.R4[Int].get.toBigInt\n val bi55 = box1.value.toBigInt\n val i56 = placeholder[Int](41)\n val bi57 = coll51(placeholder[Int](42))._2.toBigInt * bi53 * bi54 / bi55 + bi55 * placeholder[Int](43).toBigInt / placeholder[BigInt](44) * i56.toBigInt + bi53 * bi54\n val bi58 = bi57 - bi24\n val bi59 = tuple5._2.toBigInt\n val bi60 = bi58 * i56.toBigInt - bi59 / i56.toBigInt\n ((((((((((((bi57 <= bi24 * l6.toBigInt / i56.toBigInt) && (HEIGHT.toLong >= l29)) || (HEIGHT.toLong > l28)) && bool43) && bool47) && bool44) && if (bi60 < bi7) { l48.toBigInt >= bi57 } else {(\n val box61 = OUTPUTS(placeholder[Int](45))\n val tuple62 = box61.tokens(placeholder[Int](46))\n (((l48.toBigInt >= bi24 + bi58 * bi59 / i56.toBigInt) && (tuple62._2.toBigInt >= bi60)) && (box61.propositionBytes == coll25)) && (tuple62._1 == coll36)\n )}) && bool50) && (coll51(placeholder[Int](47))._1 == coll26)) && bool31) && bool32) && bool33) && bool34\n )} else { placeholder[Boolean](48) }) || if ((coll52.size >= placeholder[Int](49)) && (box1.tokens.size < placeholder[Int](50))) {(\n val tuple53 = box38.R9[(Long, Long)].get\n val l54 = tuple53._2\n val bool55 = coll39 == coll3\n val tuple56 = coll52(placeholder[Int](51))\n val l57 = tuple56._2\n val bi58 = l57.toBigInt\n val bool59 = (((((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (box38.R7[Coll[Byte]].get == coll26)) && (box38.R8[GroupElement].get == ge2)) && (tuple53._1 == l28)\n val box60 = coll4(placeholder[Int](52))\n val coll61 = box60.tokens\n val bi62 = l40.toBigInt - placeholder[Int](53).toBigInt\n val bi63 = box60.R4[Int].get.toBigInt\n val bi64 = box60.value.toBigInt\n val i65 = placeholder[Int](54)\n val bool66 = (coll61(placeholder[Int](55))._2.toBigInt * bi62 * bi63 / bi64 + bi64 * placeholder[Int](56).toBigInt / placeholder[BigInt](57) * i65.toBigInt + bi62 * bi63 >= bi7 + bi58 * bi22 / l23.toBigInt * l6.toBigInt / i65.toBigInt) && (l40 >= placeholder[Int](58).toLong + placeholder[Long](59))\n val bool67 = coll61(placeholder[Int](60))._1 == coll26\n if (l54 == placeholder[Long](61)) { ((((((((((((((bool55 && (l40 >= l35)) && (tuple56._1 == coll37)) && (bi58 >= bi11 - l48.toBigInt * l23.toBigInt / bi22)) && bool59) && (l29 == l54)) && bool43) && bool44) && bool47) && ((tuple49._2 == l10 - l57) && (tuple49._1 == coll37))) && bool31) && bool32) && bool33) && bool34) && bool66) && bool67 } else { (((((((((((!bool66) && bool55) && bool41) && (coll52 == coll8)) && bool59) && ((l54 > HEIGHT.toLong) && (l54 < HEIGHT + placeholder[Int](62).toLong))) && (l29 == placeholder[Long](63))) && bool31) && bool32) && bool33) && bool67) && bool34 }\n )} else { placeholder[Boolean](64) })\n )} else { sigmaProp(placeholder[Boolean](65)) } }\n}",
"address": "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",
"assets": [
{
"tokenId": "2c65cafcdf0a466fb6578f74d614d81b6296b04a56a8b902d88c364545b13e53",
"index": 0,
"amount": 1000,
"name": "Borrow Token SigRSV-0e",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "58069401",
"sigmaType": "(SInt, SInt)",
"renderedValue": "[3,74]"
},
"R6": {
"serializedValue": "59c413d804",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1250,300]"
},
"R8": {
"serializedValue": "0703b4cd9192bc073e8ce0d54e69d408024ff80bf6dc865cda6d46f4c87e9716a865",
"sigmaType": "SGroupElement",
"renderedValue": "03b4cd9192bc073e8ce0d54e69d408024ff80bf6dc865cda6d46f4c87e9716a865"
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"R7": {
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"sigmaType": "Coll[SByte]",
"renderedValue": "1d5afc59838920bb5ef2a8f9d63825a55b1d48e269d7cecee335d637c3ff5f3f"
},
"R9": {
"serializedValue": "59e6c39b018084af5f",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1274099,100000000]"
},
"R4": {
"serializedValue": "0e240008cd03b4cd9192bc073e8ce0d54e69d408024ff80bf6dc865cda6d46f4c87e9716a865",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd03b4cd9192bc073e8ce0d54e69d408024ff80bf6dc865cda6d46f4c87e9716a865"
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"spentTransactionId": "c1d6623ae783813935d21dbc302999ee2aab3ab373593c9558b0b65894f9f0a9",
"mainChain": true
},
{
"boxId": "35e49c0f4bb9de33764c9a9eacde55807bcf2c440fda6f4cbcbf6578db626c6f",
"transactionId": "2a82c2710fc933bf904f8a10938d632e4bb90e665da8b6725f3e185aa0e8cf12",
"blockId": "b7c0c82e5a8384f289126256ca59b7a66373e40b0c573efeeee1e4bf3a73c2a5",
"value": 82399997,
"index": 1,
"globalIndex": 39748042,
"creationHeight": 1255391,
"settlementHeight": 1255393,
"ergoTree": "0008cd03b4cd9192bc073e8ce0d54e69d408024ff80bf6dc865cda6d46f4c87e9716a865",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(b4cd91,73915b,...)))}",
"address": "9hqS4X3keu6fS5z1wnbAkdzTzkSdYVcPp4M3aDnRmaA2Sid7XwZ",
"assets": [
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}