Update method/Readme.md

method/Readme.md

@@ -8,7 +8,7 @@ Our analysis indicates that among the four machine learning models evaluated, DN
 
 
 ### Data Selection
-<p>We query Ethereum’s data using Google BigQuery. The raw data contains information on timestamps, block numbers, hash, parent hash, transaction, etc. Since our research aims to predict gas used in the next block, we only keep the relevant features, including time stamp, block number, gas limit, gas used, and base fee. Notably, the NFT-airdrop can substantially boost recipients' and non-recipients' engagement levels in the transactions. As a result, high volatility in gas used will occur and lead to subsequent base fee alternation. Hence, our research is structured around two distinct periods. The first period spans the apex of the ARB airdrop, recognized as the most substantial in 2023, from March 21 to April 1, encompassing 78290 blocks. The second period pertains to the month devoid of significant NFT-airdrop activities, spanning from June 1st, 2023, to July 1st, 2023, and containing 213244 blocks.</p>
+<p>We query Ethereum’s data using Google BigQuery. The raw data contains information on timestamps, block numbers, hash, parent hash, transaction, etc. Since our research aims to predict gas used in the next block, we only keep the relevant features, including time stamp, block number, gas limit, gas used, and base fee. Notably, the token-airdrop can substantially boost recipients' and non-recipients' engagement levels in the transactions. As a result, high volatility in gas used will occur and lead to subsequent base fee alternation. Hence, our research is structured around two distinct periods. The first period spans the apex of the ARB airdrop, recognized as the most substantial in 2023, from March 21 to April 1, encompassing 78290 blocks. The second period pertains to the month devoid of significant token-airdrop activities, spanning from June 1st, 2023, to July 1st, 2023, and containing 213244 blocks.</p>
 
 <p>We also query the discord data.