用于检测卫星遥测异常的OPS-SAT基准
The OPS-SAT benchmark for detecting anomalies in satellite telemetry
AI总结：
本文提出AI就绪的卫星遥测异常检测基准数据集OPSSAT-AD，并给出30种经典与深度机器学习算法的基线结果及建议质量指标，以推动公平、可复现的异常检测验证。
摘要：
检测卫星遥测中的异常事件是太空作业中的一项关键任务。然而，这项任务极其耗时、容易出错且依赖人工，因此自动化的数据驱动异常检测算法一直在稳步涌现。但目前没有公开可用的、带有真值标注的真实卫星遥测数据集，可用于训练和验证有监督的异常检测模型。在本文中，我们针对这一研究空白，引入了AI就绪的基准数据集（OPSSAT-AD），其中包含在OPS-SAT上获取的遥测数据——OPS-SAT是一颗由欧洲航天局运营的CubeSat任务，该任务已于2024年5月22日至23日夜间（中欧夏令时）结束。该数据集附带了使用30种有监督和无监督的经典及深度机器学习异常检测算法获得的基线结果。这些算法使用本文引入的训练-测试数据集划分方式进行训练和验证，并且我们提出了一组建议的质量指标，在利用OPSSAT-AD对比新的异常检测算法时，应始终计算这些指标。我们相信，这项工作可能成为构建公平、可复现且客观的验证流程的重要一步，该流程可用于以无偏且完全透明的方式量化新兴异常检测技术的能力。
英文摘要：
Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT -- a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22--23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.
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。
