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Linked challenge: AI-Powered SSA: Anomaly Detection with Gemma and Explainable AI

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Linked challenge

AI-Powered SSA: Anomaly Detection with Gemma and Explainable AI

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Design a high-level system architecture for the AI-powered SSA anomaly detection and explanation system. Detail how telemetry data flows through the system, from ingestion to anomaly detection, and finally to explanation generation via the Langroid agent and Anthropic API. Specify the key components, data interfaces, and the role of the LLM components in decision support.

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Linked challenge

AI-Powered SSA: Anomaly Detection with Gemma and Explainable AI

The increasing congestion of orbital space necessitates advanced Space Situational Awareness (SSA) capabilities to identify anomalous object behavior, predict potential collisions, obtain insights into mission profiles, and assess potential threats. This challenge focuses on developing an intelligent system that uses synthetic or real-world space object telemetry to detect and categorize anomalous events. Participants will design, implement, and evaluate a machine learning pipeline capable of identifying unusual orbital maneuvers, conjunction events, or non-standard satellite operations. The system should go beyond simple detection by leveraging large language models to provide actionable, explainable insights into the nature and potential implications of detected anomalies, crucial for both civil and defense space tracking efforts. The solution will integrate a robust anomaly detection model, potentially built with or informed by Gemma 2 principles, processing diverse sensor data (e.g., TLEs, radar, optical observations). Furthermore, it will utilize the Langroid framework to build an interactive agent that queries the anomaly detection system and uses the Anthropic API to generate rich, context-aware explanations for identified anomalies. This will enable human operators to quickly understand complex situations and make informed decisions, directly supporting enhanced Space Situational Awareness and threat assessment capabilities.

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