BALS-D3QN Training and Evaluation Data

The present datasets present the outcomes occurred during the research and development of the BALS-D3QN mechanism. BALS-D3QN is a balanced and adaptive scheduler based on the D3QN algorithm, designed to decide when the battery data of a DPP should be updated and resubmitted to an external DLT network, through the Age of Incorrect Information (AoII) metric. Also, BALS-D3QN acts as the decision mechanism of our previous ABS-TD3 solution, by reutilizing the latest observed submission latency of the Shimmer DLT to enable latency-exploratory updates. The proposed framework is evaluated using simulated battery trajectories from products listed within the EPREL database of the European Commission (EC), and compared against key baselines, namely consecutive updates, random policies, and periodic decisions, reflecting different types of behaviors. The folder "Battery" consists the battery trajectories that were utilized within the scope of or project. For our solution, a total of 100 battery trajectories were simulated through the PyBaMM tool and utilized for the required training process. Also, 7 additional battery trajectories were simulated as part of the evaluation process of our proposed solution. In both cases, the trajectories consist the data representing their lifecycle up to their 80% State of Health The folder "Evaluation Results" consists of the outcomes that occur based on the functionality of our BALS-D3QN solution, and the selected baselines: Spamming, Random, and Periodic policies. In all cases, and to ensure transparency, we provide all the available generated data, specifically, detailed per-battery trace, global evaluation data, and per-battery evaluation data. The folder "Latencies" consists of the latencies that were collected during the interaction process with the Shimmer DLT, and specifically by using the ABS-TD3 mechanism. Through the submission of a static DPP version, a total of 500 latencies were collected as part of the training process of BALS-D3QN, as well as a total of 1300 latencies as part of its evaluation process. Finally, the folder "Normalization Comparisons" consists of the results that occurred during the selection process of suitable normalization approach for the BALS-D3QN solution. Specifically, the folder presents the data that occurred from the Running Max, Running Mean, and Running Min-Max approaches, including per-seed results, last episode traces, loss history and training summaries. The results suggest that BALS-D3QN successfully minimizes the congestion impact occurring from the aggressive policies, minimizes the AoII compared to sparse periodic decisions, and showcases better adaptive behavior compared to periodic cases of similar ranges.本数据集收录了BALS-D3QN机制研发过程中产生的全部实验结果。BALS-D3QN是一种基于D3QN算法的均衡自适应调度器，旨在通过错误信息年龄（Age of Incorrect Information, AoII）指标，决策DPP的电池数据何时应更新并重新提交至外部分布式账本技术（Distributed Ledger Technology, DLT）网络。此外，BALS-D3QN可作为此前提出的ABS-TD3方案的决策机制，通过复用观测到的Shimmer DLT最新提交延迟，实现基于延迟探索的更新操作。本研究提出的框架依托欧盟委员会（European Commission, EC）EPREL数据库中收录的产品模拟电池轨迹开展评估，并与三类典型基准方法进行对比，即连续更新策略、随机策略与周期性决策策略，以覆盖不同的行为模式。 "Battery"文件夹收录了本项目范围内使用的电池轨迹数据：针对本方案，我们通过PyBaMM工具模拟了共计100条电池轨迹，用于模型训练流程；另有7条额外的电池轨迹用于本方案的评估流程。两类轨迹均涵盖了电池直至80%健康状态（State of Health, SoH）的全生命周期数据。 "Evaluation Results"文件夹收录了基于BALS-D3QN方案及所选基准方法（包括激进更新（Spamming）、随机策略与周期性决策策略）运行产生的全部实验结果。为确保研究透明度，本文件夹提供了所有可获取的生成数据，具体包含单电池详细轨迹数据、全局评估数据以及单电池评估数据。 "Latencies"文件夹收录了与Shimmer DLT交互过程中采集到的延迟数据，具体而言，本次采集工作依托ABS-TD3机制完成：通过提交静态DPP版本，我们在BALS-D3QN的训练阶段共采集到500条延迟数据，在其评估阶段共采集到1300条延迟数据。 最后，"Normalization Comparisons"文件夹收录了为BALS-D3QN方案筛选合适归一化方法过程中产生的实验结果。具体而言，该文件夹包含了滑动最大值（Running Max）、滑动均值（Running Mean）以及滑动最小-最大归一化（Running Min-Max）三种方法对应的实验数据，涵盖各随机种子结果、最终轮次轨迹、损失函数历史曲线以及训练总结数据。 实验结果表明，BALS-D3QN可有效缓解激进型策略带来的拥塞影响，相较于稀疏周期性决策策略可显著降低AoII，且在相近周期范围内的周期性策略中展现出更优异的自适应性能。