Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid
development of Internet of Things (IoT) and cloud technologies together with recent …

Abstract Space anomaly detection plays a critical role in safeguarding the integrity and
reliability of space systems amid the rising tide of threats. This survey aims to deepen …

The field of time series anomaly detection is constantly advancing, with several methods
available, making it a challenge to determine the most appropriate method for a specific …

Compressed Neural Networks have the potential to enable deep learning across new
applications and smaller computational environments. However, understanding the range of …

Satellite telemetry data plays an ever-important role in both the safety and the reliability of a
satellite. These two factors are extremely significant in the field of space systems and space …

As spacecraft missions become more complex and ambitious, it becomes increasingly
important to track the status and health of the spacecraft in real-time to ensure mission …

Time series anomaly detection is a critical task with applications in various domains. Due to
annotation challenges, self-supervised methods have become the mainstream approach for …

As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT
devices are also diversifying. However, the proliferation of IoT devices, often intertwined with …

Space anomaly detection is of paramount importance in ensuring the safety and reliability of
space systems, particularly in the face of increasing threats. This comprehensive survey …