RoBo6：用于火箭箭体分类的标准化MMT光变曲线数据集
RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification
AI总结：
针对火箭箭体分类缺乏综合基准的问题，本文引入源自Mini Mega Tortora数据库的RoBo6光变曲线数据集（含6类箭体、5676训练/1404测试样本），经数据处理后评估模型，Astroconformer表现最佳，为该任务建立通用基准。
摘要：
空间碎片对未来空间任务的可持续性构成严峻挑战，凸显了对稳健且标准化识别方法的需求。然而，火箭箭体分类的综合基准仍然缺失。本文通过引入基于光变曲线的RoBo6火箭箭体分类数据集填补了这一空白。该数据集源自Mini Mega Tortora数据库，包含六种火箭箭体类别的光变曲线：CZ-3B、Atlas 5 Centaur、Falcon 9、H-2A、Ariane 5和Delta 4。它有5676个训练样本和1404个测试样本，使用重采样、归一化和滤波技术解决数据不一致问题。研究评估了包括CNN和基于Transformer的方法在内的多种机器学习模型，其中Astroconformer报告了最佳性能。该数据集为火箭箭体分类任务的未来比较和进展建立了通用基准。
英文摘要：
Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curves. The dataset, derived from the Mini Mega Tortora database, includes light curves for six rocket body classes: CZ-3B, Atlas 5 Centaur, Falcon 9, H-2A, Ariane 5, and Delta 4. With 5,676 training and 1,404 test samples, it addresses data inconsistencies using resampling, normalization, and filtering techniques. Several machine learning models were evaluated, including CNN and transformer-based approaches, with Astroconformer reporting the best performance. The dataset establishes a common benchmark for future comparisons and advancements in rocket body classification tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
