We also encountered a wide range of cycles, from as few as 196 to as many as 19176. However, most of the tests we considered had fewer than 500 cycles. This variability posed a potential risk of gradient instability and inconsistent learning in the training process. To mitigate the risk of poor convergence and the possibility of overfitting, we adopted a standardization approach in which all cells were limited to a maximum of 500 cycles, ensuring better balance in the training data and reducing bias, thus increasing reliability.