diff --git a/scripts/derive_galileo_gas_parameter/Pipfile b/scripts/derive_galileo_gas_parameter/Pipfile
new file mode 100644
index 0000000000..4f21733fe0
--- /dev/null
+++ b/scripts/derive_galileo_gas_parameter/Pipfile
@@ -0,0 +1,18 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
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+[packages]
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+rlp = "*"
+zstandard = "*"
+requests = "*"
+async_timeout = "*"
+
+[dev-packages]
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+[requires]
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diff --git a/scripts/derive_galileo_gas_parameter/Pipfile.lock b/scripts/derive_galileo_gas_parameter/Pipfile.lock
new file mode 100644
index 0000000000..551b0dac16
--- /dev/null
+++ b/scripts/derive_galileo_gas_parameter/Pipfile.lock
@@ -0,0 +1,2166 @@
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+                "sha256:9174f4ed06f790a6869b41cba05b43eeb9a35f8993c4422ab853b705e8112bbd",
+                "sha256:9300d02ea7c6506f00e627e287e0492a5eb0371ec1670ae852fefffa6164b072",
+                "sha256:933b65d7680ea337180733cf9e87293cc5500cc0eb3fc8769f4d3c88d724ec5c",
+                "sha256:9654dbc012d8b06fc3d19cc825af3f7bf8ae242226df5f83936cb39f5fdc846c",
+                "sha256:98750a309eb2f020da61e727de7d7ba3c57c97cf6213f6f6277bb7fb42a8e065",
+                "sha256:99c0c846e6e61718715a3c9437ccc625de26593fea60189567f0118dc9db7512",
+                "sha256:a1a4ae2dec3993a32247995bdfe367fc3266da832d82f8438c8570f989753de1",
+                "sha256:a3f79487c687b1fc69f19e487cd949bf3aae653d181dfb5fde3bf6d18894706f",
+                "sha256:a4089a10e598eae6393756b036e0f419e8c1d60f44a831520f9af41c14216cf2",
+                "sha256:a51ff14f8017338e2f2e5dab738ce1ec3b5a851f23b18c1ae1359b1eecbee6df",
+                "sha256:a5a419712cf88862a45a23def0ae063686db3d324cec7edbe40509d1a79a0aab",
+                "sha256:a9ec8c642d1ec73287ae3e726792dd86c96f5681eb8df274a757bf62b750eae7",
+                "sha256:aaf21ba8fb76d102b696781bddaa0954b782536446083ae3fdaa6f16b25a1c4b",
+                "sha256:ab85470ab54c2cb96e176f40342d9ed41e58ca5733be6a893b730e7af9c40550",
+                "sha256:b9af1fe743828123e12b41dd8091eca1074d0c1569cc42e6e1eee98027f2bbd0",
+                "sha256:bfc4e20784722098822e3eee42b8e576b379ed72cca4a7cb856ae733e62192ea",
+                "sha256:bfd06b1c5584b657a2892a6014c2f4c20e0db0208c159148fa78c65f7e0b0277",
+                "sha256:c19bcdd826e95671065f8692b5a4aa95c52dc7a02a4c5a0cac46deb879a017a2",
+                "sha256:c2ba942c94e0691467ab901fc51b6f2085ff48f2eea77b1a48240f011e8247c7",
+                "sha256:c8e167d5adf59476fa3e37bee730890e389410c354771a62e3c076c86f9f7778",
+                "sha256:ca54090275939dc8ec5dea2d2afb400e0f83444b2fc24e07df7fdef677110859",
+                "sha256:d7541afd73985c630bafcd6338d2518ae96060075f9463d7dc14cfb33514383d",
+                "sha256:d8c56bb4e6c795fc77d74d8e8b80846e1fb8292fc0b5060cd8131d522974b751",
+                "sha256:da469dc041701583e34de852d8634703550348d5822e66a0c827d39b05365b12",
+                "sha256:daab68faadb847063d0c56f361a289c4f268706b598afbf9ad113cbe5c38b6b2",
+                "sha256:e05ab82ea7753354bb054b92e2f288afb750e6b439ff6ca78af52939ebbc476d",
+                "sha256:e09bb6252b6476d8d56100e8147b803befa9a12cea144bbe629dd508800d1ad0",
+                "sha256:e29f0cf06974c899b2c188ef7f783607dbef36da4c242eb6c82dcd8b512855e3",
+                "sha256:e59fdc271772f6686e01e1b3b74537259800f57e24280be3f29c8a0deb1904dd",
+                "sha256:e7360eae90809efd19b886e59a09dad07da4ca9ba096752e61a2e03c8aca188e",
+                "sha256:e96594a5537722fdfb79951672a2a63aec5ebfb823e7560586f7484819f2a08f",
+                "sha256:ea9d54cc3d8064260114a0bbf3479fc4a98b21dffc89b3459edd506b69262f6e",
+                "sha256:ec996f12524f88e151c339688c3897194821d7f03081ab35d31d1e12ec975e94",
+                "sha256:f27662e4f7dbf9f9c12391cb37b4c4c3cb90ffbd3b1fb9284dadbbb8935fa708",
+                "sha256:f373da2c1757bb7f1acaf09369cdc1d51d84131e50d5fa9863982fd626466313",
+                "sha256:f5aeea11ded7320a84dcdd62a3d95b5186834224a9e55b92ccae35d21a8b63d4",
+                "sha256:f604efd28f239cc21b3adb53eb061e2a205dc164be408e553b41ba2ffe0ca15c",
+                "sha256:f67e8f1a324a900e75b5e28ffb152bcac9fbed1cc7b43f99cd90f395c4375344",
+                "sha256:fd7a5004eb1980d3cefe26b2685bcb0b17989901a70a1040d1ac86f1d898c551",
+                "sha256:ffef5a74088f1e09947aecf91011136665152e0b4b359c42be3373897fb39b01"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.9'",
+            "version": "==0.25.0"
+        }
+    },
+    "develop": {}
+}
diff --git a/scripts/derive_galileo_gas_parameter/claude.md b/scripts/derive_galileo_gas_parameter/claude.md
new file mode 100644
index 0000000000..5d2c380fbc
--- /dev/null
+++ b/scripts/derive_galileo_gas_parameter/claude.md
@@ -0,0 +1,70 @@
+# Project Description
+
+This is a Scroll L2 blockchain gas fee parameter calculation project for deriving Scroll gas fee parameters.
+
+# Environment Setup
+
+Claude is executed within a pipenv shell environment, so all packages should already be installed.
+
+# Guidelines
+
+- All Python script comments must be in English
+- When executing `derive_galileo_gas_parameter.py`, output should be redirected to a fixed log file
+  - Use the `-u` option to disable output buffering for real-time logging
+  - Always use `/tmp/derive_galileo_gas_parameter.log` as the log file
+  - Example: `python -u derive_galileo_gas_parameter.py --mode collect --n-batches 5 > /tmp/derive_galileo_gas_parameter.log 2>&1 &`
+
+# Python Script Execution
+
+## Command Format
+
+```bash
+python -u derive_galileo_gas_parameter.py [OPTIONS] > /tmp/derive_galileo_gas_parameter.log 2>&1 &
+```
+
+## Options
+
+- `--mode {collect,load}` (required): Operation mode
+  - `collect`: Collect new data from blockchain
+  - `load`: Load data from cached pickle file
+
+- `--n-batches N`: Number of batches to collect (default: 30)
+  - Only used in `collect` mode
+
+- `--start-batch N`: Start batch index (required for `load` mode)
+
+- `--end-batch N`: End batch index (required for `load` mode)
+
+- `--target-penalty FLOAT`: Target penalty at P95 (default: 0.1 = 10%)
+
+- `--penalty-multiplier FLOAT`: Fixed penalty multiplier (optional, will be calculated from P95 if not specified)
+
+## Examples
+
+Collect data for 3 batches:
+```bash
+python -u derive_galileo_gas_parameter.py --mode collect --n-batches 3 > /tmp/derive_galileo_gas_parameter.log 2>&1 &
+```
+
+Load cached data:
+```bash
+python -u derive_galileo_gas_parameter.py --mode load --start-batch 12345 --end-batch 12347 > /tmp/derive_galileo_gas_parameter.log 2>&1 &
+```
+
+Collect with custom target penalty:
+```bash
+python -u derive_galileo_gas_parameter.py --mode collect --n-batches 30 --target-penalty 0.15 > /tmp/derive_galileo_gas_parameter.log 2>&1 &
+```
+
+## Monitoring Execution
+
+Check log output in real-time:
+```bash
+tail -f /tmp/derive_galileo_gas_parameter.log
+```
+
+View complete log:
+```bash
+cat /tmp/derive_galileo_gas_parameter.log
+```
+
diff --git a/scripts/derive_galileo_gas_parameter/derive_galileo_gas_parameter.py b/scripts/derive_galileo_gas_parameter/derive_galileo_gas_parameter.py
new file mode 100644
index 0000000000..32e262effa
--- /dev/null
+++ b/scripts/derive_galileo_gas_parameter/derive_galileo_gas_parameter.py
@@ -0,0 +1,1462 @@
+"""
+Derive Galileo Gas Parameters from on-chain data
+
+This script derives the L1 fee parameters for Galileo upgrade:
+- commit_scalar
+- blob_scalar
+- penalty_multiplier
+
+Using a two-stage algebraic approach:
+1. Stage 1: Set penalty_multiplier based on P95 transaction size
+2. Stage 2: Solve commit_scalar and blob_scalar using simple algebraic division
+"""
+
+from web3 import Web3
+import pandas as pd
+import numpy as np
+import pickle
+import zstandard as zstd
+import rlp
+import math
+import requests
+import time
+import os
+from pathlib import Path
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from multiprocessing import Pool, cpu_count
+
+# ============================================================================
+# CONSTANTS
+# ============================================================================
+
+# Read RPC endpoints from environment variables
+mainnet_url = os.getenv('MAINNET_URL', '')
+scroll_url = os.getenv('SCROLL_URL', 'https://rpc.scroll.io')
+
+# Validate that environment variables are set
+if not mainnet_url:
+    raise ValueError("MAINNET_URL environment variable is not set. Please set it to your Ethereum mainnet RPC endpoint.")
+if not scroll_url:
+    raise ValueError("SCROLL_URL environment variable is not set. Please set it to your Scroll RPC endpoint.")
+
+# EtherFi contract addresses (lowercase for comparison)
+ETHERFI_SPEND_ADDRESS = "0x7ca0b75e67e33c0014325b739a8d019c4fe445f0"
+ETHERFI_SWAP_ADDRESS = "0x4deaa5f2e1cd1a792304d1649edfa35d565f9346"
+
+# ============================================================================
+# ABIs
+# ============================================================================
+
+rollup_abi = [
+    {
+        "anonymous": False,
+        "inputs": [
+            {"indexed": True, "internalType": "uint256", "name": "batchIndex", "type": "uint256"},
+            {"indexed": True, "internalType": "bytes32", "name": "batchHash", "type": "bytes32"}
+        ],
+        "name": "CommitBatch",
+        "type": "event"
+    },
+    {
+        "anonymous": False,
+        "inputs": [
+            {"indexed": True, "internalType": "uint256", "name": "batchIndex", "type": "uint256"},
+            {"indexed": True, "internalType": "bytes32", "name": "batchHash", "type": "bytes32"},
+            {"indexed": False, "internalType": "bytes32", "name": "stateRoot", "type": "bytes32"},
+            {"indexed": False, "internalType": "bytes32", "name": "withdrawRoot", "type": "bytes32"}
+        ],
+        "name": "FinalizeBatch",
+        "type": "event"
+    },
+]
+
+fee_oracle_abi = [
+    {
+        "type": "event",
+        "name": "L1BaseFeeUpdated",
+        "inputs": [{"name": "l1BaseFee","type": "uint256","indexed": False,"internalType": "uint256"}],
+        "anonymous": False
+    },
+    {
+        "type": "event",
+        "name": "L1BlobBaseFeeUpdated",
+        "inputs": [{"name": "l1BlobBaseFee","type": "uint256","indexed": False,"internalType": "uint256"}],
+        "anonymous": False
+    },
+]
+
+# ============================================================================
+# WEB3 SETUP
+# ============================================================================
+
+w3 = Web3(Web3.HTTPProvider(mainnet_url))
+scroll_w3 = Web3(Web3.HTTPProvider(scroll_url))
+
+rollup_contract_address = Web3.to_checksum_address("0xa13BAF47339d63B743e7Da8741db5456DAc1E556")
+rollup_contract = w3.eth.contract(address=rollup_contract_address, abi=rollup_abi)
+
+l1_fee_oracle_contract_address = Web3.to_checksum_address("0x5300000000000000000000000000000000000002")
+l1_fee_oracle_contract = scroll_w3.eth.contract(address=l1_fee_oracle_contract_address, abi=fee_oracle_abi)
+
+# ============================================================================
+# UTILITY FUNCTIONS
+# ============================================================================
+
+def reverse_make_canonical(input_bytes, n=31):
+    """Reverse effect of make_canonical"""
+    output_bytes = bytearray()
+    for i in range(0, len(input_bytes)):
+        if i % (n+1) != 0:
+            output_bytes.extend(input_bytes[i:i+1])
+    return output_bytes
+
+
+def kzg_to_versioned_hash(kzg_commitment):
+    """Given kzg_commitment returns versioned_hash of version 0x01"""
+    from hashlib import sha256
+    return "0x01"+sha256(bytes.fromhex(kzg_commitment[2:])).hexdigest()[2:]
+
+
+def latest_finalized_event_block(width=5):
+    """Find the latest L1 block with a FinalizeBatch event"""
+    finalized_l1_head = -1
+
+    to_block = w3.eth.block_number
+    from_block = to_block - width
+
+    while finalized_l1_head == -1:
+        event_filter = rollup_contract.events.FinalizeBatch.create_filter(fromBlock=from_block, toBlock=to_block)
+        events = event_filter.get_all_entries()
+
+        if len(events) > 0:
+            finalized_l1_head = events[-1]['blockNumber']
+            break
+
+        to_block = from_block - 1
+        from_block = from_block - width - 1
+
+    return finalized_l1_head
+
+
+def beacon_head(l1_head, mainnet_beacon_url):
+    """Get beacon chain slot number for given L1 block"""
+    latest_block_number = w3.eth.block_number
+    url = f"{mainnet_beacon_url}/eth/v1/beacon/headers/head"
+    headers = {'accept': 'application/json'}
+    response = requests.get(url, headers=headers)
+    return int(response.json()['data']['header']['message']['slot']) - (latest_block_number - l1_head)
+
+
+def to_bytes_helper(value):
+    """Convert various types to bytes"""
+    if isinstance(value, bytes):
+        return value
+    elif isinstance(value, int):
+        # Handle integer values (convert to bytes)
+        if value == 0:
+            return b''
+        return value.to_bytes((value.bit_length() + 7) // 8, 'big')
+    elif hasattr(value, 'hex'):
+        # Handle HexBytes and similar objects
+        try:
+            hex_str = value.hex()
+            # Check if hex_str contains only valid hex characters
+            hex_clean = hex_str[2:] if hex_str.startswith('0x') else hex_str
+            # Validate hex string before conversion
+            if all(c in '0123456789abcdefABCDEF' for c in hex_clean):
+                # Ensure hex string has even length (pad with leading zero if odd)
+                if len(hex_clean) % 2 == 1:
+                    hex_clean = '0' + hex_clean
+                return bytes.fromhex(hex_clean)
+            else:
+                # Invalid hex, try direct bytes conversion
+                return bytes(value)
+        except (ValueError, AttributeError):
+            # If hex() method exists but produces invalid output, try direct bytes conversion
+            return bytes(value)
+    elif isinstance(value, str):
+        hex_clean = value[2:] if value.startswith('0x') else value
+        # Validate hex string before conversion
+        if all(c in '0123456789abcdefABCDEF' for c in hex_clean):
+            # Ensure hex string has even length (pad with leading zero if odd)
+            if len(hex_clean) % 2 == 1:
+                hex_clean = '0' + hex_clean
+            return bytes.fromhex(hex_clean)
+        else:
+            raise ValueError(f"Invalid hex string: {value}")
+    else:
+        try:
+            return bytes(value)
+        except TypeError:
+            # Last resort: convert to int first then to bytes
+            int_val = int(value)
+            if int_val == 0:
+                return b''
+            return int_val.to_bytes((int_val.bit_length() + 7) // 8, 'big')
+
+
+def get_raw_transaction_from_structured(tx):
+    """
+    Convert structured transaction to raw transaction bytes
+    Supports: Legacy (type 0), EIP-2930 (type 1), EIP-1559 (type 2), L1 message (type 126), EIP-7702 (type 4)
+    """
+    tx_type = tx.get('type')
+
+    if tx_type == 126:
+        queue_index = tx['queueIndex']
+        if hasattr(queue_index, 'hex'):
+            queue_index = queue_index.hex()
+
+        gas_val = tx['gas']
+        value_val = tx['value']
+
+        to_addr = tx['to']
+        if hasattr(to_addr, 'hex'):
+            to_addr = to_addr.hex()
+
+        sender_addr = tx['sender']
+        if hasattr(sender_addr, 'hex'):
+            sender_addr = sender_addr.hex()
+
+        elements = [
+            bytes.fromhex(queue_index[2:].rjust(6, "0")),
+            gas_val.to_bytes(max(1, math.ceil(gas_val.bit_length() / 8)), 'big'),
+            bytes.fromhex(to_addr[2:]),
+            value_val.to_bytes(max(1, math.ceil(value_val.bit_length() / 8)), 'big') if value_val > 0 else b'',
+            to_bytes_helper(tx['input']),
+            bytes.fromhex(sender_addr[2:])
+        ]
+        return b'~'+rlp.encode(elements)
+
+    elif tx_type == 2:  # EIP-1559 Transaction
+        to_address_bytes = bytes.fromhex(tx['to'][2:]) if tx.get('to') else b''
+
+        access_list_rlp = []
+        for item in tx.get('accessList', []):
+            storage_keys = [to_bytes_helper(sk) for sk in item['storageKeys']]
+            access_list_rlp.append([bytes.fromhex(item['address'][2:]), storage_keys])
+
+        elements = [
+            tx['chainId'],
+            tx['nonce'],
+            tx['maxPriorityFeePerGas'],
+            tx['maxFeePerGas'],
+            tx['gas'],
+            to_address_bytes,
+            tx['value'],
+            to_bytes_helper(tx['input']),
+            access_list_rlp,
+            tx['yParity'],
+            to_bytes_helper(tx['r']),
+            to_bytes_helper(tx['s'])
+        ]
+        return b'\x02' + rlp.encode(elements)
+
+    elif tx_type == 1:  # EIP-2930 Access List Transaction
+        to_address_bytes = bytes.fromhex(tx['to'][2:]) if tx.get('to') else b''
+
+        access_list_rlp = []
+        for item in tx.get('accessList', []):
+            storage_keys = [to_bytes_helper(sk) for sk in item['storageKeys']]
+            access_list_rlp.append([bytes.fromhex(item['address'][2:]), storage_keys])
+
+        elements = [
+            tx['chainId'],
+            tx['nonce'],
+            tx['gasPrice'],
+            tx['gas'],
+            to_address_bytes,
+            tx['value'],
+            to_bytes_helper(tx['input']),
+            access_list_rlp,
+            tx['yParity'],
+            to_bytes_helper(tx['r']),
+            to_bytes_helper(tx['s'])
+        ]
+        return b'\x01' + rlp.encode(elements)
+
+    elif tx_type == 0 or tx_type is None:  # Legacy Transaction
+        to_address_bytes = bytes.fromhex(tx['to'][2:]) if tx.get('to') else b''
+
+        elements = [
+            tx['nonce'],
+            tx['gasPrice'],
+            tx['gas'],
+            to_address_bytes,
+            tx['value'],
+            to_bytes_helper(tx['input']),
+            tx['v'],
+            to_bytes_helper(tx['r']),
+            to_bytes_helper(tx['s'])
+        ]
+        return rlp.encode(elements)
+
+    elif tx_type == 4:  # EIP-7702 Transaction
+        to_address_bytes = bytes.fromhex(tx['to'][2:]) if tx.get('to') else b''
+
+        access_list_rlp = []
+        for item in tx.get('accessList', []):
+            storage_keys = [to_bytes_helper(sk) for sk in item['storageKeys']]
+            access_list_rlp.append([bytes.fromhex(item['address'][2:]), storage_keys])
+
+        authorization_list_rlp = []
+        for auth in tx.get('authorizationList', []):
+            authorization_list_rlp.append([
+                auth['chainId'],
+                bytes.fromhex(auth['address'][2:]),
+                auth['nonce'],
+                auth['yParity'],
+                to_bytes_helper(auth['r']),
+                to_bytes_helper(auth['s'])
+            ])
+
+        elements = [
+            tx['chainId'],
+            tx['nonce'],
+            tx['maxPriorityFeePerGas'],
+            tx['maxFeePerGas'],
+            tx['gas'],
+            to_address_bytes,
+            tx['value'],
+            to_bytes_helper(tx['input']),
+            access_list_rlp,
+            authorization_list_rlp,
+            tx['yParity'],
+            to_bytes_helper(tx['r']),
+            to_bytes_helper(tx['s'])
+        ]
+        return b'\x04' + rlp.encode(elements)
+
+    else:
+        raise ValueError(f"Unsupported transaction type: {tx_type}")
+
+
+def get_transaction_byte_length(tx):
+    """Get transaction size in bytes"""
+    raw_tx = get_raw_transaction_from_structured(tx)
+    return len(raw_tx)
+
+
+def compress_transaction(tx):
+    """
+    Compress transaction using zstd level 3
+    Returns compressed size (with min to handle compression failure)
+    """
+    raw_tx = get_raw_transaction_from_structured(tx)
+    compressed = zstd.compress(raw_tx, level=3)
+    # Handle compression failure: use min of compressed and original
+    return min(len(compressed), len(raw_tx))
+
+
+def process_transaction_worker(tx_dict):
+    """
+    Worker function for parallel transaction processing
+    Takes a dict representation of transaction and returns sizes
+
+    Args:
+        tx_dict: Dictionary representation of transaction
+
+    Returns:
+        (tx_hash, size, compressed_size)
+    """
+    size = get_transaction_byte_length(tx_dict)
+    compressed_size = compress_transaction(tx_dict)
+    return (tx_dict['hash'].hex() if hasattr(tx_dict['hash'], 'hex') else tx_dict['hash'],
+            size,
+            compressed_size)
+
+
+# ============================================================================
+# BLOB AND BATCH PARSING
+# ============================================================================
+
+# Global cache for blob data
+indexed_blob = {}
+
+
+def get_blob_data_v2(blob_hash, block_number, mainnet_beacon_url, cur_slot, cur_block):
+    """
+    Fetch blob data from beacon chain
+    Returns: blob data, updated cur_slot, updated cur_block
+    """
+    global indexed_blob
+
+    if blob_hash in indexed_blob:
+        return indexed_blob[blob_hash], cur_slot, cur_block
+
+    cur_slot = cur_slot - (cur_block - block_number) + 1
+    cur_block = block_number
+
+    found_the_blob = False
+    while not found_the_blob:
+        cur_slot -= 1
+        url = f"{mainnet_beacon_url}/eth/v1/beacon/blob_sidecars/{cur_slot}"
+
+        headers = {'accept': 'application/json'}
+        try:
+            response = requests.get(url, headers=headers, timeout=5)
+        except Exception as e:
+            print(f"[warn] request error at slot {cur_slot}: {e}")
+            continue
+
+        if response.status_code != 200:
+            print(f"[warn] non-200 from beacon at slot {cur_slot}: {response.status_code}")
+            continue
+
+        if 'data' in response.json().keys():
+            for blob in response.json()['data']:
+                hash = kzg_to_versioned_hash(blob['kzg_commitment'])
+                if blob_hash == hash:
+                    found_the_blob = True
+                indexed_blob[hash] = blob['blob']
+
+    return indexed_blob[blob_hash], cur_slot, cur_block
+
+
+def parse_batch(blob_hash, block_number, mainnet_beacon_url, cur_slot, cur_block):
+    """
+    Parse batch from blob data to get L2 block range
+    Returns: (initial_L2_block_number, num_blocks, cur_slot, cur_block)
+    """
+    ZSTD_MAGIC = b'\x28\xb5\x2f\xfd'
+
+    batch_data, cur_slot, cur_block = get_blob_data_v2(blob_hash, block_number, mainnet_beacon_url, cur_slot, cur_block)
+    batch_data = reverse_make_canonical(bytearray.fromhex(batch_data[2:]), 31)
+
+    version = int(batch_data[0])
+    payload_N = int.from_bytes(batch_data[1:4], 'big')
+    flag = batch_data[4]
+    payload = batch_data[5:5+payload_N]
+    if flag == 1:
+        payload = zstd.decompress(ZSTD_MAGIC+payload)
+    initial_L2_block_number = int.from_bytes(payload[64:64+8], 'big')
+    num_blocks = int.from_bytes(payload[72:72+2], 'big')
+
+    return (initial_L2_block_number, num_blocks, cur_slot, cur_block)
+
+
+# ============================================================================
+# DATA COLLECTION FUNCTIONS
+# ============================================================================
+
+def collect_batch_data(n_batches=30, width=5, start_time=None):
+    """
+    Collect batch data from L1 (commits and finalizations)
+
+    Strategy: First find finalized batches, then collect their commit data
+
+    Args:
+        n_batches: Number of batches to collect
+        width: Block range width for event filtering
+        start_time: Script start time for elapsed time calculation
+
+    Returns:
+        batch_df: DataFrame with columns:
+            - index (batch_index)
+            - versioned_hash
+            - initial_L2_block_number
+            - num_blocks
+            - commit_cost
+            - finalize_cost
+            - blob_cost
+    """
+    print("=" * 60)
+    print(f"COLLECTING BATCH DATA ({n_batches} batches)")
+    print("=" * 60)
+
+    # Get L1 head
+    mainnet_beacon_url = "https://eth-mainnetbeacon.g.alchemy.com/v2/Lzj9QLupql91nuRFYAosy"
+    l1_head = latest_finalized_event_block(width)
+    beacon_head_slot = beacon_head(l1_head, mainnet_beacon_url)
+
+    print(f"L1 HEAD: {l1_head}")
+    print(f"Beacon HEAD: {beacon_head_slot}")
+
+    # Step 1: Collect FinalizeBatch events until we have enough batches
+    print(f"\nStep 1: Collecting FinalizeBatch events...")
+    finalize_events = []  # List of (batch_index, finalize_cost)
+    to_block = l1_head
+    from_block = to_block - width
+    total_batches_covered = 0
+    prev_event_count = 0
+
+    # Keep scanning until we have enough batches
+    # We need at least one extra event to properly calculate the range
+    while total_batches_covered < n_batches or len(finalize_events) < 2:
+        event_filter = rollup_contract.events.FinalizeBatch.create_filter(fromBlock=from_block, toBlock=to_block)
+        events = event_filter.get_all_entries()
+
+        for event in events:
+            batch_index = event['args']['batchIndex']
+            receipt = w3.eth.get_transaction_receipt(event.transactionHash)
+            finalize_cost = receipt['gasUsed'] * receipt['effectiveGasPrice']
+            finalize_events.append((batch_index, finalize_cost))
+
+        # Calculate how many batches we have covered so far
+        if len(finalize_events) >= 2:
+            finalize_events.sort(key=lambda x: x[0], reverse=True)
+            latest_batch = finalize_events[0][0]
+            # To properly calculate coverage, we need the range from the second-to-last event
+            # to the latest event, because we'll select N batches ending at latest_batch
+            if len(finalize_events) >= 2:
+                second_oldest = finalize_events[-2][0]
+                # The batches we can properly account for are from second_oldest+1 to latest_batch
+                total_batches_covered = latest_batch - second_oldest
+
+        # Only print when we find new events
+        if len(finalize_events) > prev_event_count:
+            print(f"  Found {len(finalize_events)} FinalizeBatch events, covering ~{total_batches_covered} batches")
+            prev_event_count = len(finalize_events)
+
+        to_block = from_block - 1
+        from_block = from_block - width - 1
+
+        if from_block < 0:
+            break
+
+    if len(finalize_events) == 0:
+        raise RuntimeError("Could not find any FinalizeBatch events")
+
+    # Sort events by batch index (descending - newest first)
+    finalize_events.sort(key=lambda x: x[0], reverse=True)
+    print(f"\nFinalizeBatch events found:")
+    for batch_idx, cost in finalize_events:
+        print(f"  Batch {batch_idx}: {cost:,} wei")
+
+    # Calculate which batches each FinalizeBatch event covers
+    # and compute per-batch finalize costs
+    batch_finalize_costs = {}  # batch_index -> finalize_cost
+    all_finalized_batches = set()
+
+    for i, (batch_index, finalize_cost) in enumerate(finalize_events):
+        # Determine the range this FinalizeBatch covers
+        if i == len(finalize_events) - 1:
+            # This is the oldest event - we don't know where it starts
+            # For now, assume it only finalizes this single batch
+            start_batch = batch_index
+        else:
+            # This event finalizes from (next_older_batch + 1) to current_batch
+            next_older_batch = finalize_events[i + 1][0]
+            start_batch = next_older_batch + 1
+
+        end_batch = batch_index
+        num_batches = end_batch - start_batch + 1
+        per_batch_cost = finalize_cost / num_batches
+
+        print(f"\n  FinalizeBatch({batch_index}) covers batches {start_batch}-{end_batch} ({num_batches} batches)")
+        print(f"    Total cost: {finalize_cost:,} wei")
+        print(f"    Per-batch cost: {per_batch_cost:,.2f} wei")
+
+        # Record the finalize cost for each batch in this range
+        for b in range(start_batch, end_batch + 1):
+            batch_finalize_costs[b] = per_batch_cost
+            all_finalized_batches.add(b)
+
+    # Select the latest N batches
+    latest_batch = max(all_finalized_batches)
+    finalized_batch_indices = list(range(latest_batch - n_batches + 1, latest_batch + 1))
+
+    print(f"\n  Selected {len(finalized_batch_indices)} consecutive batches: {finalized_batch_indices[0]} - {finalized_batch_indices[-1]}")
+
+    # Verify all selected batches have finalize costs
+    missing_batches = [b for b in finalized_batch_indices if b not in batch_finalize_costs]
+    if missing_batches:
+        print(f"  Warning: {len(missing_batches)} batches missing finalize data: {missing_batches[:10]}...")
+        # Need to scan further back
+        print(f"  Scanning further back to find more FinalizeBatch events...")
+        # Continue scanning...
+        while missing_batches and from_block >= 0:
+            to_block = from_block - 1
+            from_block = from_block - width - 1
+
+            event_filter = rollup_contract.events.FinalizeBatch.create_filter(fromBlock=from_block, toBlock=to_block)
+            events = event_filter.get_all_entries()
+
+            for event in events:
+                batch_index = event['args']['batchIndex']
+                receipt = w3.eth.get_transaction_receipt(event.transactionHash)
+                finalize_cost = receipt['gasUsed'] * receipt['effectiveGasPrice']
+                finalize_events.append((batch_index, finalize_cost))
+
+            if len(events) > 0:
+                # Recalculate with new events
+                finalize_events.sort(key=lambda x: x[0], reverse=True)
+                batch_finalize_costs = {}
+
+                for i, (batch_index, finalize_cost) in enumerate(finalize_events):
+                    if i == len(finalize_events) - 1:
+                        start_batch = batch_index
+                    else:
+                        next_older_batch = finalize_events[i + 1][0]
+                        start_batch = next_older_batch + 1
+
+                    end_batch = batch_index
+                    num_batches = end_batch - start_batch + 1
+                    per_batch_cost = finalize_cost / num_batches
+
+                    for b in range(start_batch, end_batch + 1):
+                        batch_finalize_costs[b] = per_batch_cost
+
+                missing_batches = [b for b in finalized_batch_indices if b not in batch_finalize_costs]
+                print(f"    Still missing {len(missing_batches)} batches...")
+
+                if not missing_batches:
+                    break
+
+    # Step 2: Now collect commit data for these specific batches
+    print(f"\nStep 2: Collecting commit data for finalized batches...")
+    batch_data_dict = {}  # batch_index -> [versioned_hash, initial_L2_block_number, num_blocks, commit_cost, blob_cost]
+    to_block = l1_head
+    from_block = to_block - width
+    cur_slot = beacon_head_slot
+    cur_block = l1_head
+    prev_batch_count = 0
+
+    # We need to find commits for all batches in finalized_batch_indices
+    target_batches = set(finalized_batch_indices)
+    last_tx_hash = None
+    blob_index = 0
+
+    while len(batch_data_dict) < n_batches:
+        event_filter = rollup_contract.events.CommitBatch.create_filter(fromBlock=from_block, toBlock=to_block)
+        events = event_filter.get_all_entries()
+
+        for event in events:
+            batch_index = event['args']['batchIndex']
+            tx_hash = event.transactionHash
+
+            # Track blob index for multiple batches in same transaction
+            # This must be done BEFORE filtering, so we count all batches in the tx
+            if last_tx_hash == tx_hash:
+                blob_index += 1
+            else:
+                last_tx_hash = tx_hash
+                blob_index = 0
+
+            # Only process if this batch is in our finalized list
+            if batch_index not in target_batches:
+                continue
+
+            # Skip if we already have this batch
+            if batch_index in batch_data_dict:
+                continue
+
+            tx = w3.eth.get_transaction(event.transactionHash)
+            receipt = w3.eth.get_transaction_receipt(event.transactionHash)
+
+            block_number = tx.blockNumber
+            num_batches_in_tx = len(tx.blobVersionedHashes)
+            blob_hash = tx.blobVersionedHashes[blob_index].hex()
+
+            # Calculate commit cost (execution gas only, amortized across all batches in this tx)
+            commit_cost = (receipt['gasUsed'] * receipt['effectiveGasPrice']) / num_batches_in_tx
+
+            # Parse batch to get L2 block range
+            initial_L2_block_number, num_blocks, cur_slot, cur_block = parse_batch(
+                blob_hash, block_number, mainnet_beacon_url, cur_slot, cur_block
+            )
+
+            # Calculate blob cost (amortized across all batches in this tx)
+            blob_cost = (receipt['blobGasPrice'] * receipt['blobGasUsed']) / num_batches_in_tx
+
+            batch_data_dict[batch_index] = [blob_hash, initial_L2_block_number, num_blocks,
+                                              commit_cost, blob_cost]
+
+        # Only print when count changes
+        if len(batch_data_dict) > prev_batch_count:
+            print(f"  Collected commit data for {len(batch_data_dict)}/{n_batches} batches")
+            prev_batch_count = len(batch_data_dict)
+
+        to_block = from_block - 1
+        from_block = from_block - width - 1
+
+        # Stop if we've searched far enough
+        if from_block < 0:
+            break
+
+    # Step 3: Combine commit and finalize data
+    print(f"\nStep 3: Combining commit and finalize data...")
+    batch_list = []
+    for batch_index in sorted(batch_data_dict.keys(), reverse=True):
+        if batch_index not in batch_finalize_costs:
+            print(f"  Warning: Batch {batch_index} has commit but no finalize data (skipping)")
+            continue
+
+        blob_hash, initial_L2_block_number, num_blocks, commit_cost, blob_cost = batch_data_dict[batch_index]
+        finalize_cost = batch_finalize_costs[batch_index]
+
+        batch_list.append([
+            batch_index,
+            blob_hash,
+            initial_L2_block_number,
+            num_blocks,
+            commit_cost,
+            finalize_cost,
+            blob_cost
+        ])
+
+    # Create DataFrame
+    column_names = ['index', 'versioned_hash', 'initial_L2_block_number', 'num_blocks',
+                    'commit_cost', 'finalize_cost', 'blob_cost']
+    batch_df = pd.DataFrame(batch_list, columns=column_names).astype(object)
+    batch_df = batch_df.set_index('index').sort_index()
+
+    print(f"  Successfully collected {len(batch_df)} complete batches")
+    print(f"  Batch range: {batch_df.index[0]} - {batch_df.index[-1]}")
+    if start_time:
+        print(f"\n✓ Batch data collection completed in {time.time() - start_time:.1f}s")
+
+    return batch_df
+
+
+def collect_transaction_data(batch_df, start_time=None):
+    """
+    Collect L2 transaction data for all batches
+
+    Args:
+        batch_df: DataFrame with batch information
+        start_time: Script start time for elapsed time calculation
+
+    Returns:
+        tx_df: DataFrame with columns:
+            - hash
+            - block_number
+            - batch_index
+            - size
+            - compressed_tx_size
+            - l1_base_fee
+            - l1_blob_base_fee
+            - L1_fee
+            - gas_price
+            - gas_used
+            - base_fee_per_gas
+            - to
+    """
+    print("=" * 60)
+    print("COLLECTING TRANSACTION DATA")
+    print("=" * 60)
+
+    # Calculate L2 block range (batches may not be in order)
+    first_block = int(batch_df['initial_L2_block_number'].min())
+    last_block = int((batch_df['initial_L2_block_number'] + batch_df['num_blocks']).max())
+
+    print(f"L2 block range: {first_block} - {last_block}")
+    print(f"\n  Fetching L2 blocks...")
+
+    # Fetch all blocks in parallel
+    indexed_block = {}
+    total_blocks = last_block - first_block
+
+    def fetch_block(block_number):
+        """Fetch a single block with full transactions, with retry on rate limit"""
+        max_retries = 5
+        retry_delay = 2  # seconds
+
+        for attempt in range(max_retries):
+            try:
+                return block_number, scroll_w3.eth.get_block(block_number, full_transactions=True)
+            except Exception as e:
+                if '429' in str(e) or 'Too Many Requests' in str(e):
+                    wait_time = retry_delay * (attempt + 1)
+                    print(f"    [WARN] Rate limit hit (429) for block {block_number}, retrying in {wait_time}s (attempt {attempt + 1}/{max_retries})")
+                    if attempt < max_retries - 1:
+                        time.sleep(wait_time)  # Exponential backoff
+                        continue
+                    else:
+                        print(f"    [ERROR] Max retries reached for block {block_number}")
+                raise
+
+    # Use ThreadPoolExecutor for parallel RPC calls
+    # With paid endpoint, can use more workers
+    max_workers = 20  # Higher limit for paid RPC endpoint
+    print(f"    Starting parallel fetch with {max_workers} workers...")
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = {executor.submit(fetch_block, bn): bn for bn in range(first_block, last_block)}
+
+        completed = 0
+        start_fetch = time.time()
+        for future in as_completed(futures):
+            block_number, block = future.result()
+            indexed_block[block_number] = block
+            completed += 1
+
+            # More frequent progress updates (every 50 blocks or major milestones)
+            if completed % 50 == 0 or completed == total_blocks:
+                elapsed = time.time() - start_fetch
+                rate = completed / elapsed if elapsed > 0 else 0
+                eta = (total_blocks - completed) / rate if rate > 0 else 0
+                print(f"    Fetched {completed}/{total_blocks} blocks ({completed*100//total_blocks}%, {rate:.1f} blocks/s, ETA: {eta:.0f}s)")
+
+    fetch_blocks_elapsed = time.time() - start_fetch
+    print(f"\n  ✓ Block fetching completed in {fetch_blocks_elapsed:.1f}s")
+
+    # Get initial L1 fee oracle values
+    print(f"\n  Recording L1 fee oracle updates...")
+    l1_base_fee = 0
+    l1_blob_base_fee = 0
+    cur_block_num = first_block
+
+    while l1_base_fee == 0:
+        block = scroll_w3.eth.get_block(cur_block_num, full_transactions=True)
+        for tx in reversed(block['transactions']):
+            if tx.to == '0x5300000000000000000000000000000000000002' and tx.input.hex()[:10] == '0x39455d3a':
+                l1_base_fee = int.from_bytes(bytes.fromhex(tx.input.hex()[2:])[-64:-32], 'big')
+                l1_blob_base_fee = int.from_bytes(bytes.fromhex(tx.input.hex()[2:])[-32:], 'big')
+        cur_block_num -= 1
+
+    # Process transactions in parallel
+    print(f"\n  Processing transactions in parallel...")
+
+    # Collect all transactions with their metadata first
+    all_txs = []
+    for block_number in range(first_block, last_block):
+        block = indexed_block[block_number]
+        for tx in block['transactions']:
+            # Convert AttributeDict to regular dict for pickling
+            tx_dict = dict(tx)
+            all_txs.append((block_number, tx_dict))
+
+    print(f"  Total transactions to process: {len(all_txs)}")
+
+    # Parallel processing of transaction sizes
+    tx_sizes = {}  # tx_hash -> (size, compressed_size)
+    num_workers = min(cpu_count(), 8)  # Limit to 8 workers to avoid overhead
+
+    start_process_tx = time.time()
+    with Pool(processes=num_workers) as pool:
+        # Process in chunks for progress reporting
+        chunk_size = 500
+        processed = 0
+
+        for i in range(0, len(all_txs), chunk_size):
+            chunk = [tx_dict for _, tx_dict in all_txs[i:i+chunk_size]]
+            results = pool.map(process_transaction_worker, chunk)
+
+            for tx_hash, size, compressed_size in results:
+                tx_sizes[tx_hash] = (size, compressed_size)
+
+            processed += len(chunk)
+            print(f"    Processed {processed}/{len(all_txs)} transactions...")
+
+    process_tx_elapsed = time.time() - start_process_tx
+    print(f"\n  ✓ Transaction processing completed in {process_tx_elapsed:.1f}s")
+
+    # Fetch transaction receipts in parallel to get gas_used and L1_fee
+    print(f"\n  Fetching transaction receipts...")
+    tx_receipts = {}  # tx_hash -> receipt
+
+    def fetch_receipt(tx_hash_str):
+        """Fetch a single transaction receipt with retry"""
+        max_retries = 5
+        retry_delay = 2
+
+        for attempt in range(max_retries):
+            try:
+                return tx_hash_str, scroll_w3.eth.get_transaction_receipt(tx_hash_str)
+            except Exception as e:
+                if '429' in str(e) or 'Too Many Requests' in str(e):
+                    wait_time = retry_delay * (attempt + 1)
+                    if attempt < max_retries - 1:
+                        time.sleep(wait_time)
+                        continue
+                raise
+
+    start_fetch_receipts = time.time()
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        tx_hashes = [tx_dict['hash'].hex() if hasattr(tx_dict['hash'], 'hex') else tx_dict['hash']
+                     for _, tx_dict in all_txs]
+        futures = {executor.submit(fetch_receipt, tx_hash): tx_hash for tx_hash in tx_hashes}
+
+        completed = 0
+        for future in as_completed(futures):
+            tx_hash, receipt = future.result()
+            tx_receipts[tx_hash] = receipt
+            completed += 1
+
+            if completed % 500 == 0 or completed == len(tx_hashes):
+                print(f"    Fetched {completed}/{len(tx_hashes)} receipts ({completed*100//len(tx_hashes)}%)")
+
+    fetch_receipts_elapsed = time.time() - start_fetch_receipts
+    print(f"\n  ✓ Receipt fetching completed in {fetch_receipts_elapsed:.1f}s")
+
+    # Build rows with oracle fee tracking
+    print(f"\n  Building transaction dataframe...")
+    rows = []
+    for block_number, tx_dict in all_txs:
+        tx_hash = tx_dict['hash'].hex() if hasattr(tx_dict['hash'], 'hex') else tx_dict['hash']
+        size, compressed_tx_size = tx_sizes[tx_hash]
+
+        # Get receipt for gas_used and L1_fee
+        receipt = tx_receipts.get(tx_hash)
+        gas_used = receipt['gasUsed'] if receipt else 0
+        L1_fee = receipt.get('l1Fee', 0) if receipt else 0  # L1 fee from receipt (Scroll-specific field)
+
+        # Get gas_price from transaction
+        gas_price = tx_dict.get('gasPrice', 0)
+        if gas_price == 0 and 'maxFeePerGas' in tx_dict:
+            # For EIP-1559 transactions, use maxFeePerGas if gasPrice not available
+            gas_price = tx_dict.get('maxFeePerGas', 0)
+
+        # Get base_fee_per_gas from block
+        block = indexed_block[block_number]
+        base_fee_per_gas = block.get('baseFeePerGas', 0)
+
+        # Get the 'to' address
+        to_address = tx_dict.get('to', None)
+        if to_address and hasattr(to_address, 'lower'):
+            to_address = to_address.lower()
+
+        rows.append([
+            tx_hash,
+            block_number,
+            size,
+            compressed_tx_size,
+            l1_base_fee,
+            l1_blob_base_fee,
+            L1_fee,
+            gas_price,
+            gas_used,
+            base_fee_per_gas,
+            to_address
+        ])
+
+        # Update L1 fee oracle values if needed
+        if tx_dict.get('to') == '0x5300000000000000000000000000000000000002':
+            input_hex = tx_dict['input'].hex() if hasattr(tx_dict['input'], 'hex') else tx_dict['input']
+            if input_hex[:10] == '0x39455d3a':
+                l1_base_fee = int.from_bytes(bytes.fromhex(input_hex[2:])[-64:-32], 'big')
+                l1_blob_base_fee = int.from_bytes(bytes.fromhex(input_hex[2:])[-32:], 'big')
+
+    tx_df_columns = ['hash', 'block_number', 'size', 'compressed_tx_size', 'l1_base_fee', 'l1_blob_base_fee',
+                     'L1_fee', 'gas_price', 'gas_used', 'base_fee_per_gas', 'to']
+    tx_df = pd.DataFrame(rows, columns=tx_df_columns)
+
+    # Classify transaction types based on 'to' address
+    def classify_tx_type(to_address):
+        """Classify transaction type based on destination address"""
+        if to_address == ETHERFI_SPEND_ADDRESS:
+            return 'etherfi_spend'
+        elif to_address == ETHERFI_SWAP_ADDRESS:
+            return 'etherfi_swap'
+        else:
+            return 'other'
+
+    tx_df['tx_type'] = tx_df['to'].apply(classify_tx_type)
+
+    # Add batch_index to each transaction
+    print(f"\n  Mapping transactions to batches...")
+
+    def block_to_batch(block_num):
+        for idx, row in batch_df.iterrows():
+            if row['initial_L2_block_number'] <= block_num < row['initial_L2_block_number'] + row['num_blocks']:
+                return idx
+        return None
+
+    tx_df['batch_index'] = tx_df['block_number'].apply(block_to_batch)
+
+    print(f"\n  Collected {len(tx_df)} transactions")
+    print(f"  Transaction size stats:")
+    print(f"    Mean: {tx_df['compressed_tx_size'].mean():.0f} bytes")
+    print(f"    Median: {tx_df['compressed_tx_size'].median():.0f} bytes")
+    print(f"    P95: {tx_df['compressed_tx_size'].quantile(0.95):.0f} bytes")
+
+    # Report EtherFi transaction counts
+    etherfi_spend_count = (tx_df['tx_type'] == 'etherfi_spend').sum()
+    etherfi_swap_count = (tx_df['tx_type'] == 'etherfi_swap').sum()
+    if etherfi_spend_count > 0 or etherfi_swap_count > 0:
+        print(f"\n  EtherFi transaction counts:")
+        print(f"    EtherFi Spend: {etherfi_spend_count}")
+        print(f"    EtherFi Swap:  {etherfi_swap_count}")
+
+    if start_time:
+        print(f"\n✓ Transaction data collection completed in {time.time() - start_time:.1f}s")
+
+    return tx_df
+
+
+# ============================================================================
+# DATA PERSISTENCE
+# ============================================================================
+
+def save_data(tx_df, batch_df, start_batch, end_batch):
+    """
+    Save collected data to pickle file with batch range in filename
+
+    Args:
+        tx_df: DataFrame of transactions
+        batch_df: DataFrame of batches with L1 cost data
+        start_batch: First batch index
+        end_batch: Last batch index
+    """
+    filename = f"galileo_data_batch_{start_batch}_{end_batch}.pkl"
+    data = {
+        'tx_df': tx_df,
+        'batch_df': batch_df,
+        'start_batch': start_batch,
+        'end_batch': end_batch
+    }
+
+    with open(filename, 'wb') as f:
+        pickle.dump(data, f)
+
+    print(f"\n✓ Data saved to {filename}")
+    print(f"  Batches: {start_batch} - {end_batch} ({end_batch - start_batch + 1} batches)")
+    print(f"  Transactions: {len(tx_df)}")
+
+
+def load_data(start_batch, end_batch):
+    """
+    Load data from pickle file
+
+    Args:
+        start_batch: First batch index
+        end_batch: Last batch index
+
+    Returns:
+        tx_df, batch_df
+    """
+    filename = f"galileo_data_batch_{start_batch}_{end_batch}.pkl"
+
+    if not Path(filename).exists():
+        raise FileNotFoundError(f"Data file not found: {filename}")
+
+    with open(filename, 'rb') as f:
+        data = pickle.load(f)
+
+    print(f"\n✓ Data loaded from {filename}")
+    print(f"  Batches: {data['start_batch']} - {data['end_batch']} ({data['end_batch'] - data['start_batch'] + 1} batches)")
+    print(f"  Transactions: {len(data['tx_df'])}")
+
+    return data['tx_df'], data['batch_df']
+
+
+# ============================================================================
+# STAGE 1: CALCULATE PENALTY MULTIPLIER
+# ============================================================================
+
+def calculate_penalty_multiplier(tx_df, target_penalty=0.1):
+    """
+    Calculate penalty_multiplier based on transaction size distribution
+
+    Sets penalty_multiplier such that P95 transactions get target_penalty (default 10%)
+    from the quadratic term.
+
+    Args:
+        tx_df: DataFrame with 'compressed_tx_size' column
+        target_penalty: Target penalty ratio at P95 (default 0.1 = 10%)
+
+    Returns:
+        penalty_multiplier
+    """
+    sizes = tx_df['compressed_tx_size'].values
+
+    p50 = np.percentile(sizes, 50)
+    p95 = np.percentile(sizes, 95)
+    p99 = np.percentile(sizes, 99)
+
+    # For P95 transaction: quadratic_term / linear_term = target_penalty
+    # (P95^2 / penalty_multiplier) / P95 = target_penalty
+    # penalty_multiplier = P95 / target_penalty
+    penalty_multiplier = p95 / target_penalty
+
+    print("=" * 60)
+    print("PENALTY MULTIPLIER CALCULATION")
+    print("=" * 60)
+    print(f"Total transactions: {len(sizes):,}")
+    print(f"\nTransaction size distribution:")
+    print(f"  P50 (median): {p50:.0f} bytes")
+    print(f"  P95:          {p95:.0f} bytes")
+    print(f"  P99:          {p99:.0f} bytes")
+    print(f"\nTarget penalty at P95: {target_penalty*100:.0f}%")
+    print(f"=> penalty_multiplier = {penalty_multiplier:.0f}")
+    print("=" * 60)
+
+    return penalty_multiplier
+
+
+# ============================================================================
+# STAGE 2: AGGREGATE BATCH DATA
+# ============================================================================
+
+def aggregate_batch_data(tx_df, batch_df, penalty_multiplier):
+    """
+    Aggregate transaction data by batch with given penalty_multiplier
+
+    Calculates effective_size and equation coefficients for each batch.
+
+    Args:
+        tx_df: DataFrame of transactions
+        batch_df: DataFrame of batches with L1 costs
+        penalty_multiplier: Fixed penalty multiplier
+
+    Returns:
+        batch_agg_df: DataFrame with columns:
+            - batch_index
+            - sum_l1_base_effective: Σ[l1_base_fee × effective_size]
+            - sum_blob_base_effective: Σ[l1_blob_base_fee × effective_size]
+            - commit_cost: L1 commit cost
+            - finalize_cost: L1 finalize cost
+            - blob_cost: L1 blob cost
+    """
+    print("=" * 60)
+    print("AGGREGATING BATCH DATA")
+    print("=" * 60)
+
+    # Calculate effective_size for all transactions
+    tx_df['effective_size'] = (tx_df['compressed_tx_size'] +
+                                tx_df['compressed_tx_size']**2 / penalty_multiplier)
+
+    # Calculate coefficients
+    tx_df['l1_base_effective'] = tx_df['l1_base_fee'] * tx_df['effective_size']
+    tx_df['blob_base_effective'] = tx_df['l1_blob_base_fee'] * tx_df['effective_size']
+
+    # Aggregate by batch
+    batch_agg = tx_df.groupby('batch_index').agg({
+        'l1_base_effective': 'sum',
+        'blob_base_effective': 'sum'
+    }).rename(columns={
+        'l1_base_effective': 'sum_l1_base_effective',
+        'blob_base_effective': 'sum_blob_base_effective'
+    })
+
+    # Merge with batch L1 costs
+    batch_agg_df = batch_agg.join(batch_df[['commit_cost', 'finalize_cost', 'blob_cost']])
+
+    print(f"Aggregated {len(batch_agg_df)} batches")
+    print(f"\nSample batch data:")
+    print(batch_agg_df.head())
+
+    return batch_agg_df
+
+
+# ============================================================================
+# STAGE 3: SOLVE FOR SCALARS
+# ============================================================================
+
+def solve_scalars(batch_df):
+    """
+    Solve for commit_scalar and blob_scalar using simple algebraic division
+
+    Sum all batches and divide:
+    - commit_scalar = Σ(commit_cost + finalize_cost) / Σ(sum_l1_base_effective)
+    - blob_scalar = Σ(blob_cost) / Σ(sum_blob_base_effective)
+
+    Args:
+        batch_df: DataFrame with columns:
+            - sum_l1_base_effective
+            - sum_blob_base_effective
+            - commit_cost
+            - finalize_cost
+            - blob_cost
+
+    Returns:
+        commit_scalar, blob_scalar
+    """
+    total_commit_finalize_cost = (batch_df['commit_cost'] + batch_df['finalize_cost']).sum()
+    total_sum_l1_base_effective = batch_df['sum_l1_base_effective'].sum()
+
+    total_blob_cost = batch_df['blob_cost'].sum()
+    total_sum_blob_base_effective = batch_df['sum_blob_base_effective'].sum()
+
+    commit_scalar = total_commit_finalize_cost / total_sum_l1_base_effective
+    blob_scalar = total_blob_cost / total_sum_blob_base_effective
+
+    print("=" * 60)
+    print("SCALAR CALCULATION")
+    print("=" * 60)
+    print(f"Commit/Finalize:")
+    print(f"  Total cost: {total_commit_finalize_cost:,.0f} wei")
+    print(f"  Total sum(l1_base × effective_size): {total_sum_l1_base_effective:,.0f}")
+    print(f"  => commit_scalar = {commit_scalar:.2f}")
+    print()
+    print(f"Blob:")
+    print(f"  Total cost: {total_blob_cost:,.0f} wei")
+    print(f"  Total sum(blob_base × effective_size): {total_sum_blob_base_effective:,.0f}")
+    print(f"  => blob_scalar = {blob_scalar:.2f}")
+    print("=" * 60)
+
+    return commit_scalar, blob_scalar
+
+
+# ============================================================================
+# VALIDATION & ANALYSIS
+# ============================================================================
+
+def analyze_results(commit_scalar, blob_scalar, penalty_multiplier, tx_df, batch_df):
+    """
+    Analyze the quality of the derived parameters
+
+    Args:
+        commit_scalar: Derived commit scalar
+        blob_scalar: Derived blob scalar
+        penalty_multiplier: Fixed penalty multiplier
+        tx_df: Transaction DataFrame
+        batch_df: Batch DataFrame
+
+    Returns:
+        Analysis results
+    """
+    print("\n" + "=" * 60)
+    print("RESULTS ANALYSIS")
+    print("=" * 60)
+
+    # Calculate effective_size
+    tx_df['effective_size'] = (tx_df['compressed_tx_size'] +
+                                tx_df['compressed_tx_size']**2 / penalty_multiplier)
+
+    # Calculate predicted L1 fees per transaction
+    tx_df['predicted_l1_fee'] = ((tx_df['l1_base_fee'] * commit_scalar +
+                                   tx_df['l1_blob_base_fee'] * blob_scalar) *
+                                  tx_df['effective_size'])
+
+    # Aggregate by batch
+    batch_predicted = tx_df.groupby('batch_index')['predicted_l1_fee'].sum()
+    batch_actual = batch_df['commit_cost'] + batch_df['finalize_cost'] + batch_df['blob_cost']
+
+    # Calculate errors
+    batch_errors = batch_predicted - batch_actual
+    batch_relative_errors = batch_errors / batch_actual * 100
+
+    # Statistics
+    rmse = np.sqrt(np.mean(batch_errors**2))
+    mae = np.mean(np.abs(batch_errors))
+    mape = np.mean(np.abs(batch_relative_errors))
+    max_error = np.max(np.abs(batch_errors))
+    max_relative_error = np.max(np.abs(batch_relative_errors))
+
+    print(f"\nPer-Batch Error Statistics:")
+    print(f"  RMSE:                  {rmse:,.0f} wei ({rmse/1e18:.6f} ETH)")
+    print(f"  MAE:                   {mae:,.0f} wei ({mae/1e18:.6f} ETH)")
+    print(f"  MAPE:                  {mape:.2f}%")
+    print(f"  Max absolute error:    {max_error:,.0f} wei ({max_error/1e18:.6f} ETH)")
+    print(f"  Max relative error:    {max_relative_error:.2f}%")
+
+    # Total recovery ratio
+    total_predicted = batch_predicted.sum()
+    total_actual = batch_actual.sum()
+    recovery_ratio = total_predicted / total_actual
+
+    print(f"\nTotal Cost Recovery:")
+    print(f"  Predicted total: {total_predicted:,.0f} wei ({total_predicted/1e18:.6f} ETH)")
+    print(f"  Actual total:    {total_actual:,.0f} wei ({total_actual/1e18:.6f} ETH)")
+    print(f"  Recovery ratio:  {recovery_ratio:.4f} ({recovery_ratio*100:.2f}%)")
+
+    return {
+        'rmse': rmse,
+        'mae': mae,
+        'mape': mape,
+        'recovery_ratio': recovery_ratio,
+        'batch_errors': batch_errors,
+        'batch_relative_errors': batch_relative_errors
+    }
+
+
+def analyze_etherfi_penalties(tx_df, penalty_multiplier):
+    """
+    Analyze penalties for EtherFi transactions
+
+    Calculates the penalty component (quadratic term) for specific transaction types:
+    - EtherFi Spend (to: 0x7Ca0b75E67E33c0014325B739A8d019C4FE445F0)
+    - EtherFi Swap (to: 0x4dEAa5f2e1CD1A792304d1649EdfA35D565F9346)
+
+    The penalty is the quadratic component: size^2 / penalty_multiplier
+
+    Args:
+        tx_df: Transaction DataFrame with 'tx_type' and 'compressed_tx_size' columns
+        penalty_multiplier: Penalty multiplier parameter
+
+    Returns:
+        Dictionary with penalty statistics by transaction type
+    """
+    print("\n" + "=" * 60)
+    print("ETHERFI PENALTY ANALYSIS")
+    print("=" * 60)
+
+    # Filter for EtherFi transactions
+    etherfi_spend_txs = tx_df[tx_df['tx_type'] == 'etherfi_spend'].copy()
+    etherfi_swap_txs = tx_df[tx_df['tx_type'] == 'etherfi_swap'].copy()
+
+    if len(etherfi_spend_txs) == 0 and len(etherfi_swap_txs) == 0:
+        print("\nNo EtherFi transactions found in dataset.")
+        print("=" * 60)
+        return None
+
+    results = {}
+
+    # Analyze EtherFi Spend transactions
+    if len(etherfi_spend_txs) > 0:
+        # Calculate penalty (quadratic term) and linear term for each transaction
+        etherfi_spend_txs['linear_term'] = etherfi_spend_txs['compressed_tx_size']
+        etherfi_spend_txs['penalty_term'] = (etherfi_spend_txs['compressed_tx_size']**2 / penalty_multiplier)
+        etherfi_spend_txs['penalty_ratio'] = (etherfi_spend_txs['penalty_term'] /
+                                               etherfi_spend_txs['linear_term'])
+
+        print(f"\nEtherFi Spend Transactions (to: {ETHERFI_SPEND_ADDRESS}):")
+        print(f"  Count: {len(etherfi_spend_txs)}")
+        print(f"\n  Transaction Size:")
+        print(f"    Mean:   {etherfi_spend_txs['compressed_tx_size'].mean():.0f} bytes")
+        print(f"    Median: {etherfi_spend_txs['compressed_tx_size'].median():.0f} bytes")
+        print(f"    Min:    {etherfi_spend_txs['compressed_tx_size'].min():.0f} bytes")
+        print(f"    Max:    {etherfi_spend_txs['compressed_tx_size'].max():.0f} bytes")
+        print(f"\n  Penalty Term (size² / penalty_multiplier):")
+        print(f"    Mean:   {etherfi_spend_txs['penalty_term'].mean():.2f} bytes")
+        print(f"    Median: {etherfi_spend_txs['penalty_term'].median():.2f} bytes")
+        print(f"    Min:    {etherfi_spend_txs['penalty_term'].min():.2f} bytes")
+        print(f"    Max:    {etherfi_spend_txs['penalty_term'].max():.2f} bytes")
+        print(f"\n  Penalty Ratio (penalty / linear_term):")
+        print(f"    Mean:   {etherfi_spend_txs['penalty_ratio'].mean()*100:.2f}%")
+        print(f"    Median: {etherfi_spend_txs['penalty_ratio'].median()*100:.2f}%")
+        print(f"    Min:    {etherfi_spend_txs['penalty_ratio'].min()*100:.2f}%")
+        print(f"    Max:    {etherfi_spend_txs['penalty_ratio'].max()*100:.2f}%")
+
+        results['etherfi_spend'] = {
+            'count': len(etherfi_spend_txs),
+            'size_mean': etherfi_spend_txs['compressed_tx_size'].mean(),
+            'penalty_mean': etherfi_spend_txs['penalty_term'].mean(),
+            'penalty_ratio_mean': etherfi_spend_txs['penalty_ratio'].mean()
+        }
+
+    # Analyze EtherFi Swap transactions
+    if len(etherfi_swap_txs) > 0:
+        # Calculate penalty (quadratic term) and linear term for each transaction
+        etherfi_swap_txs['linear_term'] = etherfi_swap_txs['compressed_tx_size']
+        etherfi_swap_txs['penalty_term'] = (etherfi_swap_txs['compressed_tx_size']**2 / penalty_multiplier)
+        etherfi_swap_txs['penalty_ratio'] = (etherfi_swap_txs['penalty_term'] /
+                                              etherfi_swap_txs['linear_term'])
+
+        print(f"\nEtherFi Swap Transactions (to: {ETHERFI_SWAP_ADDRESS}):")
+        print(f"  Count: {len(etherfi_swap_txs)}")
+        print(f"\n  Transaction Size:")
+        print(f"    Mean:   {etherfi_swap_txs['compressed_tx_size'].mean():.0f} bytes")
+        print(f"    Median: {etherfi_swap_txs['compressed_tx_size'].median():.0f} bytes")
+        print(f"    Min:    {etherfi_swap_txs['compressed_tx_size'].min():.0f} bytes")
+        print(f"    Max:    {etherfi_swap_txs['compressed_tx_size'].max():.0f} bytes")
+        print(f"\n  Penalty Term (size² / penalty_multiplier):")
+        print(f"    Mean:   {etherfi_swap_txs['penalty_term'].mean():.2f} bytes")
+        print(f"    Median: {etherfi_swap_txs['penalty_term'].median():.2f} bytes")
+        print(f"    Min:    {etherfi_swap_txs['penalty_term'].min():.2f} bytes")
+        print(f"    Max:    {etherfi_swap_txs['penalty_term'].max():.2f} bytes")
+        print(f"\n  Penalty Ratio (penalty / linear_term):")
+        print(f"    Mean:   {etherfi_swap_txs['penalty_ratio'].mean()*100:.2f}%")
+        print(f"    Median: {etherfi_swap_txs['penalty_ratio'].median()*100:.2f}%")
+        print(f"    Min:    {etherfi_swap_txs['penalty_ratio'].min()*100:.2f}%")
+        print(f"    Max:    {etherfi_swap_txs['penalty_ratio'].max()*100:.2f}%")
+
+        results['etherfi_swap'] = {
+            'count': len(etherfi_swap_txs),
+            'size_mean': etherfi_swap_txs['compressed_tx_size'].mean(),
+            'penalty_mean': etherfi_swap_txs['penalty_term'].mean(),
+            'penalty_ratio_mean': etherfi_swap_txs['penalty_ratio'].mean()
+        }
+
+    print("=" * 60)
+
+    return results
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+def main():
+    """Main execution logic"""
+    import argparse
+
+    start_time = time.time()
+
+    parser = argparse.ArgumentParser(description='Derive Galileo Gas Parameters')
+    parser.add_argument('--mode', choices=['collect', 'load'], required=True,
+                        help='Mode: collect new data or load from cache')
+    parser.add_argument('--n-batches', type=int, default=30,
+                        help='Number of batches to collect (default: 30)')
+    parser.add_argument('--start-batch', type=int,
+                        help='Start batch index (for load mode)')
+    parser.add_argument('--end-batch', type=int,
+                        help='End batch index (for load mode)')
+    parser.add_argument('--target-penalty', type=float, default=0.1,
+                        help='Target penalty at P95 (default: 0.1 = 10%%)')
+    parser.add_argument('--penalty-multiplier', type=float,
+                        help='Fixed penalty multiplier (if not specified, will be calculated from P95)')
+
+    args = parser.parse_args()
+
+    print("\n" + "=" * 60)
+    print("GALILEO GAS PARAMETER DERIVATION")
+    print("=" * 60)
+
+    # Step 1: Collect or load data
+    if args.mode == 'collect':
+        print(f"\nMode: COLLECT DATA ({args.n_batches} batches)")
+        print("=" * 60)
+
+        # Collect batch data
+        batch_df = collect_batch_data(n_batches=args.n_batches, start_time=start_time)
+
+        # Collect transaction data
+        tx_df = collect_transaction_data(batch_df, start_time=start_time)
+
+        # Save data
+        start_batch = batch_df.index[0]
+        end_batch = batch_df.index[-1]
+        save_data(tx_df, batch_df, start_batch, end_batch)
+
+    else:  # load mode
+        print(f"\nMode: LOAD DATA (batches {args.start_batch}-{args.end_batch})")
+        print("=" * 60)
+
+        if args.start_batch is None or args.end_batch is None:
+            raise ValueError("--start-batch and --end-batch required for load mode")
+
+        tx_df, batch_df = load_data(args.start_batch, args.end_batch)
+
+    # Step 2: Use fixed penalty_multiplier if provided, otherwise calculate it
+    if args.penalty_multiplier is not None:
+        penalty_multiplier = args.penalty_multiplier
+        print("=" * 60)
+        print("USING FIXED PENALTY MULTIPLIER")
+        print("=" * 60)
+        print(f"penalty_multiplier = {penalty_multiplier:.0f}")
+        print("=" * 60)
+    else:
+        penalty_multiplier = calculate_penalty_multiplier(tx_df, target_penalty=args.target_penalty)
+
+    # Step 3: Aggregate batch data
+    batch_agg_df = aggregate_batch_data(tx_df, batch_df, penalty_multiplier)
+
+    # Step 4: Solve for scalars
+    commit_scalar, blob_scalar = solve_scalars(batch_agg_df)
+
+    # Step 5: Analyze results
+    results = analyze_results(commit_scalar, blob_scalar, penalty_multiplier, tx_df, batch_df)
+
+    # Step 6: Analyze EtherFi penalties
+    etherfi_results = analyze_etherfi_penalties(tx_df, penalty_multiplier)
+
+    # Final summary
+    print("\n" + "=" * 60)
+    print("FINAL RESULTS")
+    print("=" * 60)
+    print(f"\nDerived Parameters:")
+    print(f"  commit_scalar:        {commit_scalar:.2f}")
+    print(f"  blob_scalar:          {blob_scalar:.2f}")
+    print(f"  penalty_multiplier:   {penalty_multiplier:.0f}")
+    print(f"\nFit Quality:")
+    print(f"  MAPE:                 {results['mape']:.2f}%")
+    print(f"  Recovery ratio:       {results['recovery_ratio']*100:.2f}%")
+    print("=" * 60)
+
+    return {
+        'commit_scalar': commit_scalar,
+        'blob_scalar': blob_scalar,
+        'penalty_multiplier': penalty_multiplier,
+        'tx_df': tx_df,
+        'batch_df': batch_df,
+        'batch_agg_df': batch_agg_df,
+        'results': results,
+        'etherfi_results': etherfi_results
+    }
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/derive_galileo_gas_parameter/show_batches.py b/scripts/derive_galileo_gas_parameter/show_batches.py
new file mode 100644
index 0000000000..f67312e64e
--- /dev/null
+++ b/scripts/derive_galileo_gas_parameter/show_batches.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+"""
+Show batch IDs in a saved data file
+"""
+
+import pickle
+import sys
+from pathlib import Path
+
+def show_batches(filename):
+    """Show batch IDs in the data file"""
+
+    if not Path(filename).exists():
+        print(f"Error: File not found: {filename}")
+        return
+
+    try:
+        with open(filename, 'rb') as f:
+            data = pickle.load(f)
+
+        batch_df = data['batch_df']
+        tx_df = data['tx_df']
+
+        print(f"\n{'='*60}")
+        print(f"File: {filename}")
+        print(f"{'='*60}")
+        print(f"\nBatch range: {data['start_batch']} - {data['end_batch']}")
+        print(f"Total batches: {len(batch_df)}")
+        print(f"Total transactions: {len(tx_df)}")
+
+        print(f"\nBatch IDs:")
+        batch_ids = sorted(batch_df.index.tolist())
+
+        # Print batch IDs in a compact format
+        for i in range(0, len(batch_ids), 10):
+            batch_subset = batch_ids[i:i+10]
+            print(f"  {', '.join(map(str, batch_subset))}")
+
+        # Show detailed information for each batch
+        print(f"\n{'='*60}")
+        print("BATCH DETAILS")
+        print(f"{'='*60}")
+
+        for batch_id in sorted(batch_df.index.tolist()):
+            batch_row = batch_df.loc[batch_id]
+            print(f"\n{'─'*60}")
+            print(f"Batch {int(batch_id)}")
+            print(f"{'─'*60}")
+            print(f"  versioned_hash: {batch_row['versioned_hash']}")
+            print(f"  commit_cost: {batch_row['commit_cost'] / 1e9:.9f} gwei")
+            print(f"  blob_cost: {batch_row['blob_cost'] / 1e9:.9f} gwei")
+            print(f"  finalize_cost: {batch_row['finalize_cost'] / 1e9:.9f} gwei")
+
+            # Get transactions for this batch
+            batch_txs = tx_df[tx_df['batch_index'] == batch_id]
+            print(f"\n  Transactions ({len(batch_txs)} total):")
+
+            if len(batch_txs) > 0:
+                for idx, tx in batch_txs.iterrows():
+                    print(f"\n    Transaction {idx}:")
+                    print(f"      hash: {tx['hash']}")
+                    print(f"      block_number: {tx['block_number']}")
+                    print(f"      size: {tx['size']}")
+                    print(f"      compressed_tx_size: {tx['compressed_tx_size']}")
+                    print(f"      l1_base_fee: {tx['l1_base_fee'] / 1e9:.9f} gwei")
+                    print(f"      l1_blob_base_fee: {tx['l1_blob_base_fee'] / 1e9:.9f} gwei")
+            else:
+                print("      (No transactions)")
+
+        print(f"\n{'='*60}\n")
+
+    except Exception as e:
+        print(f"Error reading file: {e}")
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python show_batches.py <data_file.pkl>")
+        print("\nExample: python show_batches.py galileo_data_batch_491125_491136.pkl")
+        sys.exit(1)
+
+    show_batches(sys.argv[1])