Self-Supervised Temporal Pattern Mining for satellite anomaly response operations for low-power autonomous deployments

Self-Supervised Temporal Pattern Mining for satellite anomaly response operations for low-power autonomous deployments Introduction: The Learning Journey That Sparked This Exploration It was during a late-night debugging session with a CubeSat telemetry stream that I had my breakthrough realization. I was analyzing anomalous power consumption patterns from a student-built satellite deployed in low Earth orbit, and the traditional supervised learning approaches kept failing. The problem was fundamental: we had plenty of telemetry data but almost no labeled anomalies. The satellite's limited power budget meant we couldn't afford to run complex neural networks continuously, and the communication latency made real-time ground control intervention impossible for critical anomalies. While exploring self-supervised learning papers from the computer vision domain, I realized the same principles could revolutionize how we handle satellite anomaly detection. The key insight came when I was exper