Transaction
ID: da8fe1130d...e0b2
Inputs (1)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0042 ERG
Tokens:
10.01
Outputs (3)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0021 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Transaction Details
Confirmations: 847,286
Total coins transferred: 0.0042 ERG
Fees: 0.0011 ERG
Fees per byte: 0.00000029 ERG
Raw Transaction Data
{
"id": "da8fe1130d5c1ddf480685ecbd0a95cc3a9f8a579e33b7a4390c3cf67de5e0b2",
"blockId": "c6917df3671b910cb91671da2b91120fb2f8b61a5d9f831e1f02e73f14b5d852",
"inclusionHeight": 917447,
"timestamp": 1673622454908,
"index": 8,
"globalIndex": 4609815,
"numConfirmations": 847286,
"inputs": [
{
"boxId": "e06b87e7e49e29fbc7e5e67faa46ec494f6ddaa5a54b107eaa1ba73b7951680e",
"value": 4200000,
"index": 0,
"spendingProof": null,
"outputBlockId": "c6917df3671b910cb91671da2b91120fb2f8b61a5d9f831e1f02e73f14b5d852",
"outputTransactionId": "6417b0caaf15334af9eddc196c4f1b80e3b86b354a0287f8326ddd97c2302e53",
"outputIndex": 0,
"outputGlobalIndex": 25578192,
"outputCreatedAt": 917443,
"outputSettledAt": 917447,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 1\n3: Coll(119,119,119,-27,5,31,-118,107,-61,8,39,-18,65,-35,57,-32,75,-54,29,-70,53,35,107,-102,38,-76,-108,-32,-70,-56,79,51)\n4: 0\n5: 0\n6: false\n7: 0\n8: 1\n9: 2\n10: 0\n11: 2100000\n12: 1\n13: 100\n14: 1\n15: Coll(48)\n16: 1\n17: 1\n18: false\n19: 0\n20: 6\n21: CBigInt(1000)\n22: CBigInt(177584)\n23: 0\n24: 0\n25: CBigInt(1000000)\n26: 86400000\n27: 2\n28: 1\n29: 86400000\n30: 2\n31: 1\n32: 1\n33: false\n34: 8\n35: false\n36: 14400000\n37: 2\n38: 1\n39: 0\n40: 0\n41: 0\n42: 100\n43: 500\n44: 1000\n45: 2000\n46: 4000\n47: 9000\n48: 13000\n49: 20000\n50: 30000\n51: 40000\n52: 50000\n53: 70000\n54: 110000\n55: 140000\n56: 170000\n57: 210000\n58: 250000\n59: 300000\n60: 500000\n61: 1\n62: 0\n63: 1\n64: 0\n65: 1\n66: CBigInt(4)\n67: 3\n68: CBigInt(10)\n69: CBigInt(0)\n70: 4\n71: 5\n72: 10000\n73: 0\n74: Coll(102,102,102,-116,-29,83,-2,-3,20,109,20,-76,20,-126,-43,38,-21,-14,106,90,125,32,79,54,87,12,89,-59,88,19,107,-125)\n75: 30\n76: 2\n77: 2\n78: 3\n79: 3\n80: 7\n81: false\n82: 2\n83: 4\n84: 0\n85: 2\n86: 2\n87: false\n88: false",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val tuple2 = (Coll[Byte](), placeholder[Long](0))\n val tuple3 = coll1.getOrElse(placeholder[Int](1), tuple2)\n val l4 = tuple3._2\n val tuple5 = coll1.getOrElse(placeholder[Int](2), tuple2)\n val l6 = tuple5._2\n val coll7 = placeholder[Coll[Byte]](3)\n val bool8 = if ((l4 > placeholder[Long](4)) && (l6 > placeholder[Long](5))) {\n ((tuple3._1 == coll7) && (tuple5._1 == SELF.R7[Box].get.id)) && (SELF.propositionBytes == SELF.R7[Box].get.propositionBytes)\n } else { placeholder[Boolean](6) }\n val box9 = if (bool8) { SELF.R7[Box].get } else { SELF }\n val prop10 = box9.R9[SigmaProp].get\n val bool11 = !bool8\n val box12 = OUTPUTS(placeholder[Int](7))\n val coll13 = box12.propositionBytes\n val coll14 = SELF.propositionBytes\n val box15 = OUTPUTS(placeholder[Int](8))\n val coll16 = box9.R8[Coll[Long]].get\n val l17 = coll16(placeholder[Int](9))\n val l18 = CONTEXT.preHeader.timestamp\n val l19 = l17 - l18\n val coll20 = box12.tokens\n val tuple21 = coll20.getOrElse(placeholder[Int](10), tuple2)\n val l22 = tuple21._2\n val l23 = box12.value\n val bool24 = l23 >= placeholder[Long](11)\n val l25 = coll16(placeholder[Int](12))\n val l26 = l25 * placeholder[Long](13)\n val tuple27 = coll20.getOrElse(placeholder[Int](14), tuple2)\n val l28 = tuple27._2\n val coll29 = tuple21._1\n val coll30 = box9.id\n val coll31 = placeholder[Coll[Byte]](15)\n val bool32 = if (coll13 == coll14) {\n (\n (\n (\n ((((bool24 && (coll29 == coll7)) && (l22 >= placeholder[Long](16))) && (tuple27._1 == coll30)) && (l28 >= placeholder[Long](17))) && (\n box12.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get\n )\n ) && (box12.R5[Coll[Byte]].get == coll31)\n ) && (box12.R6[Coll[Byte]].get == coll31)\n ) && (box12.R7[Box].get == box9)\n } else { placeholder[Boolean](18) }\n val coll33 = box9.R5[Coll[Byte]].get\n val bool34 = l18 <= l17\n val tuple35 = box15.tokens.getOrElse(placeholder[Int](19), tuple2)\n val coll36 = tuple35._1\n val l37 = tuple35._2\n val coll38 = prop10.propBytes\n val l39 = coll16(placeholder[Int](20))\n val bi40 = placeholder[BigInt](21)\n val bi41 = placeholder[BigInt](22)\n val bool42 = coll16(placeholder[Int](23)) == placeholder[Long](24)\n val bi43 = placeholder[BigInt](25)\n val bool44 = l18 > l17\n val bool45 = if (bool42) { (bool8 && bool44) && (l18 < l17 + placeholder[Long](26)) } else { bool8 && bool34 }\n prop10 && sigmaProp(((bool11 && (OUTPUTS.size == placeholder[Int](27))) && (coll13 != coll14)) && (box15.propositionBytes != coll14)) || sigmaProp(\n (\n (\n if ((bool11 && (INPUTS.size == placeholder[Int](28))) && (l19 >= placeholder[Long](29))) {\n (\n (\n bool32 && if (coll20.size == placeholder[Int](30)) {\n ((bool24 && (l22 == l4)) && (l28 == l4 - placeholder[Long](31) / l26 + placeholder[Long](32))) && (box12.R7[Box].get == SELF)\n } else { placeholder[Boolean](33) }\n ) && (box15.propositionBytes == coll33)\n ) && (box15.value >= coll16(placeholder[Int](34)))\n } else { placeholder[Boolean](35) } || if (((!(bool34 && (l18 > l17 - placeholder[Long](36)))) && (INPUTS.size == placeholder[Int](37))) && (\n CONTEXT.dataInputs.size > placeholder[Int](38)\n )) {(\n val box46 = CONTEXT.dataInputs(placeholder[Int](39))\n val l47 = l6 - l28\n val l48 = box46.R4[Long].get\n val l49 = max(placeholder[Long](40), l48 - l39 * l25)\n val bi50 = l39.toBigInt\n val coll51 = Coll[Long](\n placeholder[Long](41), placeholder[Long](42), placeholder[Long](43), placeholder[Long](44), placeholder[Long](45), placeholder[Long](\n 46\n ), placeholder[Long](47), placeholder[Long](48), placeholder[Long](49), placeholder[Long](50), placeholder[Long](51), placeholder[Long](\n 52\n ), placeholder[Long](53), placeholder[Long](54), placeholder[Long](55), placeholder[Long](56), placeholder[Long](57), placeholder[Long](\n 58\n ), placeholder[Long](59), placeholder[Long](60)\n )\n val coll52 = coll51.map({(l52: Long) => l52 * l52 }).zip(coll51)\n val i53 = coll52.map({(tuple53: (Long, Long)) => if (tuple53._1 >= l19) { placeholder[Long](61) } else { placeholder[Long](62) } }).indexOf(\n placeholder[Long](63), placeholder[Int](64)\n )\n val tuple54 = coll52(i53 - placeholder[Int](65))\n val bi55 = tuple54._2.toBigInt\n val tuple56 = coll52(i53)\n val bi57 = tuple54._1.toBigInt\n val bi58 = bi55 + tuple56._2.toBigInt - bi55 * l19.toBigInt - bi57 / tuple56._1.toBigInt - bi57\n val bi59 = placeholder[BigInt](66) * coll16(placeholder[Int](67)).toBigInt * l25.toBigInt * bi50 * bi58 / placeholder[BigInt](68) * bi40 * bi41\n val bi60 = l48.toBigInt\n val bi61 = max(placeholder[BigInt](69), bi59 - bi59 * coll16(placeholder[Int](70)).toBigInt * max(bi60 - bi50, bi50 - bi60) / bi40 * bi50)\n val bi62 = if (bool42) { l49.toBigInt + bi61 } else { l49.toBigInt + bi61 + bi61 * coll16(placeholder[Int](71)).toBigInt * bi58 / bi40 * bi41 }\n val bi63 = l47.toBigInt * bi62 - bi62 % placeholder[Long](72).toBigInt\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (box46.tokens(placeholder[Int](73))._1 == placeholder[Coll[Byte]](74)) && (HEIGHT <= box46.R5[Int].get + placeholder[Int](75))\n ) && bool32\n ) && (l23 == SELF.value)\n ) && (l22 == l4)\n ) && (coll36 == coll30)\n ) && (l37 == l47)\n ) && (OUTPUTS(placeholder[Int](76)).propositionBytes == coll38)\n ) && (OUTPUTS(placeholder[Int](77)).value.toBigInt >= max(bi43, bi63))\n ) && (OUTPUTS(placeholder[Int](78)).propositionBytes == coll33)\n ) && (OUTPUTS(placeholder[Int](79)).value.toBigInt >= max(bi43, bi63 * coll16(placeholder[Int](80)).toBigInt / bi40))\n )} else { placeholder[Boolean](81) }\n ) || if (((bool45 && (INPUTS.size == placeholder[Int](82))) && (OUTPUTS.size == placeholder[Int](83))) && (\n CONTEXT.dataInputs.size == placeholder[Int](84)\n )) {(\n val l46 = l4 - l22\n ((((bool32 && (l28 == l6)) && (coll36 == coll7)) && (l37 == l46)) && (OUTPUTS(placeholder[Int](85)).propositionBytes == coll38)) && (\n OUTPUTS(placeholder[Int](86)).value >= l46 / l26 * l39 * l25\n )\n )} else { placeholder[Boolean](87) }\n ) || if (bool44 && (!bool45)) { ((coll13 == coll38) && (coll29 == coll7)) && (l22 == l4) } else { placeholder[Boolean](88) }\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "777777e5051f8a6bc30827ee41dd39e04bca1dba35236b9a26b494e0bac84f33",
"index": 0,
"amount": 1001,
"name": "fakeUSD",
"decimals": 2,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "0e240008cd039ed9a6df20fca487da2d3b58e822cdcc5bcfad4cca794eadf132afa3113f31a6",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd039ed9a6df20fca487da2d3b58e822cdcc5bcfad4cca794eadf132afa3113f31a6"
},
"R6": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
},
"R8": {
"serializedValue": "1109020280b0fed0ef61e807e807d804beeacdfe080a80897a",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1,1,1681430400000,500,500,300,1206499999,5,1000000]"
},
"R7": {
"serializedValue": "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",
"sigmaType": null,
"renderedValue": null
},
"R9": {
"serializedValue": "08cd02c35a808c1c713fc1ae169e33da7492eee8f913a2045a7d56a3ca3103b5525ff3",
"sigmaType": "SSigmaProp",
"renderedValue": "02c35a808c1c713fc1ae169e33da7492eee8f913a2045a7d56a3ca3103b5525ff3"
},
"R4": {
"serializedValue": "0e2e43414c4c5f415f66616b655553445f4552475f313230363439393939395f323032332d30342d31345f7065725f31",
"sigmaType": "Coll[SByte]",
"renderedValue": "43414c4c5f415f66616b655553445f4552475f313230363439393939395f323032332d30342d31345f7065725f31"
}
}
}
],
"dataInputs": [],
"outputs": [
{
"boxId": "bd49b144caa2885cb3f098e249f23fbdecc568f4f7989d9d93da2f19f79bb515",
"transactionId": "da8fe1130d5c1ddf480685ecbd0a95cc3a9f8a579e33b7a4390c3cf67de5e0b2",
"blockId": "c6917df3671b910cb91671da2b91120fb2f8b61a5d9f831e1f02e73f14b5d852",
"value": 2100000,
"index": 0,
"globalIndex": 25578199,
"creationHeight": 917444,
"settlementHeight": 917447,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 1\n3: Coll(119,119,119,-27,5,31,-118,107,-61,8,39,-18,65,-35,57,-32,75,-54,29,-70,53,35,107,-102,38,-76,-108,-32,-70,-56,79,51)\n4: 0\n5: 0\n6: false\n7: 0\n8: 1\n9: 2\n10: 0\n11: 2100000\n12: 1\n13: 100\n14: 1\n15: Coll(48)\n16: 1\n17: 1\n18: false\n19: 0\n20: 6\n21: CBigInt(1000)\n22: CBigInt(177584)\n23: 0\n24: 0\n25: CBigInt(1000000)\n26: 86400000\n27: 2\n28: 1\n29: 86400000\n30: 2\n31: 1\n32: 1\n33: false\n34: 8\n35: false\n36: 14400000\n37: 2\n38: 1\n39: 0\n40: 0\n41: 0\n42: 100\n43: 500\n44: 1000\n45: 2000\n46: 4000\n47: 9000\n48: 13000\n49: 20000\n50: 30000\n51: 40000\n52: 50000\n53: 70000\n54: 110000\n55: 140000\n56: 170000\n57: 210000\n58: 250000\n59: 300000\n60: 500000\n61: 1\n62: 0\n63: 1\n64: 0\n65: 1\n66: CBigInt(4)\n67: 3\n68: CBigInt(10)\n69: CBigInt(0)\n70: 4\n71: 5\n72: 10000\n73: 0\n74: Coll(102,102,102,-116,-29,83,-2,-3,20,109,20,-76,20,-126,-43,38,-21,-14,106,90,125,32,79,54,87,12,89,-59,88,19,107,-125)\n75: 30\n76: 2\n77: 2\n78: 3\n79: 3\n80: 7\n81: false\n82: 2\n83: 4\n84: 0\n85: 2\n86: 2\n87: false\n88: false",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val tuple2 = (Coll[Byte](), placeholder[Long](0))\n val tuple3 = coll1.getOrElse(placeholder[Int](1), tuple2)\n val l4 = tuple3._2\n val tuple5 = coll1.getOrElse(placeholder[Int](2), tuple2)\n val l6 = tuple5._2\n val coll7 = placeholder[Coll[Byte]](3)\n val bool8 = if ((l4 > placeholder[Long](4)) && (l6 > placeholder[Long](5))) {\n ((tuple3._1 == coll7) && (tuple5._1 == SELF.R7[Box].get.id)) && (SELF.propositionBytes == SELF.R7[Box].get.propositionBytes)\n } else { placeholder[Boolean](6) }\n val box9 = if (bool8) { SELF.R7[Box].get } else { SELF }\n val prop10 = box9.R9[SigmaProp].get\n val bool11 = !bool8\n val box12 = OUTPUTS(placeholder[Int](7))\n val coll13 = box12.propositionBytes\n val coll14 = SELF.propositionBytes\n val box15 = OUTPUTS(placeholder[Int](8))\n val coll16 = box9.R8[Coll[Long]].get\n val l17 = coll16(placeholder[Int](9))\n val l18 = CONTEXT.preHeader.timestamp\n val l19 = l17 - l18\n val coll20 = box12.tokens\n val tuple21 = coll20.getOrElse(placeholder[Int](10), tuple2)\n val l22 = tuple21._2\n val l23 = box12.value\n val bool24 = l23 >= placeholder[Long](11)\n val l25 = coll16(placeholder[Int](12))\n val l26 = l25 * placeholder[Long](13)\n val tuple27 = coll20.getOrElse(placeholder[Int](14), tuple2)\n val l28 = tuple27._2\n val coll29 = tuple21._1\n val coll30 = box9.id\n val coll31 = placeholder[Coll[Byte]](15)\n val bool32 = if (coll13 == coll14) {\n (\n (\n (\n ((((bool24 && (coll29 == coll7)) && (l22 >= placeholder[Long](16))) && (tuple27._1 == coll30)) && (l28 >= placeholder[Long](17))) && (\n box12.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get\n )\n ) && (box12.R5[Coll[Byte]].get == coll31)\n ) && (box12.R6[Coll[Byte]].get == coll31)\n ) && (box12.R7[Box].get == box9)\n } else { placeholder[Boolean](18) }\n val coll33 = box9.R5[Coll[Byte]].get\n val bool34 = l18 <= l17\n val tuple35 = box15.tokens.getOrElse(placeholder[Int](19), tuple2)\n val coll36 = tuple35._1\n val l37 = tuple35._2\n val coll38 = prop10.propBytes\n val l39 = coll16(placeholder[Int](20))\n val bi40 = placeholder[BigInt](21)\n val bi41 = placeholder[BigInt](22)\n val bool42 = coll16(placeholder[Int](23)) == placeholder[Long](24)\n val bi43 = placeholder[BigInt](25)\n val bool44 = l18 > l17\n val bool45 = if (bool42) { (bool8 && bool44) && (l18 < l17 + placeholder[Long](26)) } else { bool8 && bool34 }\n prop10 && sigmaProp(((bool11 && (OUTPUTS.size == placeholder[Int](27))) && (coll13 != coll14)) && (box15.propositionBytes != coll14)) || sigmaProp(\n (\n (\n if ((bool11 && (INPUTS.size == placeholder[Int](28))) && (l19 >= placeholder[Long](29))) {\n (\n (\n bool32 && if (coll20.size == placeholder[Int](30)) {\n ((bool24 && (l22 == l4)) && (l28 == l4 - placeholder[Long](31) / l26 + placeholder[Long](32))) && (box12.R7[Box].get == SELF)\n } else { placeholder[Boolean](33) }\n ) && (box15.propositionBytes == coll33)\n ) && (box15.value >= coll16(placeholder[Int](34)))\n } else { placeholder[Boolean](35) } || if (((!(bool34 && (l18 > l17 - placeholder[Long](36)))) && (INPUTS.size == placeholder[Int](37))) && (\n CONTEXT.dataInputs.size > placeholder[Int](38)\n )) {(\n val box46 = CONTEXT.dataInputs(placeholder[Int](39))\n val l47 = l6 - l28\n val l48 = box46.R4[Long].get\n val l49 = max(placeholder[Long](40), l48 - l39 * l25)\n val bi50 = l39.toBigInt\n val coll51 = Coll[Long](\n placeholder[Long](41), placeholder[Long](42), placeholder[Long](43), placeholder[Long](44), placeholder[Long](45), placeholder[Long](\n 46\n ), placeholder[Long](47), placeholder[Long](48), placeholder[Long](49), placeholder[Long](50), placeholder[Long](51), placeholder[Long](\n 52\n ), placeholder[Long](53), placeholder[Long](54), placeholder[Long](55), placeholder[Long](56), placeholder[Long](57), placeholder[Long](\n 58\n ), placeholder[Long](59), placeholder[Long](60)\n )\n val coll52 = coll51.map({(l52: Long) => l52 * l52 }).zip(coll51)\n val i53 = coll52.map({(tuple53: (Long, Long)) => if (tuple53._1 >= l19) { placeholder[Long](61) } else { placeholder[Long](62) } }).indexOf(\n placeholder[Long](63), placeholder[Int](64)\n )\n val tuple54 = coll52(i53 - placeholder[Int](65))\n val bi55 = tuple54._2.toBigInt\n val tuple56 = coll52(i53)\n val bi57 = tuple54._1.toBigInt\n val bi58 = bi55 + tuple56._2.toBigInt - bi55 * l19.toBigInt - bi57 / tuple56._1.toBigInt - bi57\n val bi59 = placeholder[BigInt](66) * coll16(placeholder[Int](67)).toBigInt * l25.toBigInt * bi50 * bi58 / placeholder[BigInt](68) * bi40 * bi41\n val bi60 = l48.toBigInt\n val bi61 = max(placeholder[BigInt](69), bi59 - bi59 * coll16(placeholder[Int](70)).toBigInt * max(bi60 - bi50, bi50 - bi60) / bi40 * bi50)\n val bi62 = if (bool42) { l49.toBigInt + bi61 } else { l49.toBigInt + bi61 + bi61 * coll16(placeholder[Int](71)).toBigInt * bi58 / bi40 * bi41 }\n val bi63 = l47.toBigInt * bi62 - bi62 % placeholder[Long](72).toBigInt\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (box46.tokens(placeholder[Int](73))._1 == placeholder[Coll[Byte]](74)) && (HEIGHT <= box46.R5[Int].get + placeholder[Int](75))\n ) && bool32\n ) && (l23 == SELF.value)\n ) && (l22 == l4)\n ) && (coll36 == coll30)\n ) && (l37 == l47)\n ) && (OUTPUTS(placeholder[Int](76)).propositionBytes == coll38)\n ) && (OUTPUTS(placeholder[Int](77)).value.toBigInt >= max(bi43, bi63))\n ) && (OUTPUTS(placeholder[Int](78)).propositionBytes == coll33)\n ) && (OUTPUTS(placeholder[Int](79)).value.toBigInt >= max(bi43, bi63 * coll16(placeholder[Int](80)).toBigInt / bi40))\n )} else { placeholder[Boolean](81) }\n ) || if (((bool45 && (INPUTS.size == placeholder[Int](82))) && (OUTPUTS.size == placeholder[Int](83))) && (\n CONTEXT.dataInputs.size == placeholder[Int](84)\n )) {(\n val l46 = l4 - l22\n ((((bool32 && (l28 == l6)) && (coll36 == coll7)) && (l37 == l46)) && (OUTPUTS(placeholder[Int](85)).propositionBytes == coll38)) && (\n OUTPUTS(placeholder[Int](86)).value >= l46 / l26 * l39 * l25\n )\n )} else { placeholder[Boolean](87) }\n ) || if (bool44 && (!bool45)) { ((coll13 == coll38) && (coll29 == coll7)) && (l22 == l4) } else { placeholder[Boolean](88) }\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "777777e5051f8a6bc30827ee41dd39e04bca1dba35236b9a26b494e0bac84f33",
"index": 0,
"amount": 1001,
"name": "fakeUSD",
"decimals": 2,
"type": "EIP-004"
},
{
"tokenId": "e06b87e7e49e29fbc7e5e67faa46ec494f6ddaa5a54b107eaa1ba73b7951680e",
"index": 1,
"amount": 11,
"name": "CALL_A_fakeUSD_ERG_1206499999_2023-04-14_per_1",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "0e2e43414c4c5f415f66616b655553445f4552475f313230363439393939395f323032332d30342d31345f7065725f31",
"sigmaType": "Coll[SByte]",
"renderedValue": "43414c4c5f415f66616b655553445f4552475f313230363439393939395f323032332d30342d31345f7065725f31"
},
"R5": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
},
"R6": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
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{
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}
],
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}