Data Preprocessing

Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts.

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Summary: The archive of DCLN project (https://sourceforge.net/projects/dcln/) is provided.
Code & Script: Written in C/C++, run Shell scripts on Linux system. Mature DCLNv2 package available for download. Check './dcln.sh' for usage info.
Document: Details of hyperparameters tuning, data preprocessing and code compiling are given.
Data: Four nonlinear simulation datasets are provided (Fig. 2 of the main paper). Each study has ~2000 training samples and ~2000 test samples.

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