LLM based expert AI agent for mission operation management

[1] Ahn H. et al.: Deep generative models-based anomaly detection for spacecraft control systems. Sensors 20(7), 2020, 1991.
  Google Scholar

[2] Berquand A. et al.: Artificial intelligence for the early design phases of space missions. 2019 IEEE Aerospace Conference, IEEE, 2019.
  Google Scholar

[3] Chen B. et al.: Unleashing the potential of prompt engineering in large language models: a comprehensive review. arXiv, 2310.14735, 2023.
  Google Scholar

[4] Cuéllar S. et al.: Explainable anomaly detection in spacecraft telemetry. Engineering Applications of Artificial Intelligence 133, 2024, 108083.
  Google Scholar

[5] Edge D. et al.: From local to global: A graph rag approach to query-focused summarization. arXiv, 2404.16130, 2024.
  Google Scholar

[6] Ferreira J. J., de Souza Monteiro M.: Do ML Experts Discuss Explainability for AI Systems? A discussion case in the industry for a domain-specific solution. arXiv, 2002.12450, 2020.
  Google Scholar

[7] Florin et al.: The power of noise: Redefining retrieval for rag systems. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024.
  Google Scholar

[8] Furano G. et al.: AI in space: Applications examples and challenges. IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). IEEE, 2020.
  Google Scholar

[9] Hei Z. et al.: Dr-rag: Applying dynamic document relevance to retrieval-augmented generation for question-answering. arXiv, 2406.07348, 2024.
  Google Scholar

[10] Herrmann L. et al.: Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry. CEAS Space Journal 16(2), 2024, 225–237.
  Google Scholar

[11] Janjanam D. et al.: Design of an expert system architecture: An overview. Journal of Physics: Conference Series 1767(1), 2021.
  Google Scholar

[12] Jin H. et al.: A comprehensive survey on process-oriented automatic text summarization with exploration of LLM-based methods. arXiv, 2403.02901, 2024.
  Google Scholar

[13] Josphineleela R. et al.: Exploration beyond boundaries: Ai-based advancements in rover robotics for lunar missions space like chandrayaan. Int. J. Intell. Syst. Appl. Eng 11(10s), 2023, 640–648.
  Google Scholar

[14] Ke Y. et al.: Development and Testing of Retrieval Augmented Generation in Large Language Models--A Case Study Report. arXiv, 2402.01733, 2024.
  Google Scholar

[15] Liu D. et al.: Fragment anomaly detection with prediction and statistical analysis for satellite telemetry. IEEE Access 5, 2017, 19269–19281.
  Google Scholar

[16] Liu S. et al.: Towards a robust retrieval-based summarization system. arXiv, 2403.19889, 2024.
  Google Scholar

[17] Muhammad H. H. et al.: A review on optimization-based automatic text summarization approach. IEEE Access 12, 2023, 4892–4909.
  Google Scholar

[18] Murdaca F. et al.: Knowledge-based information extraction from datasheets of space parts. 8th International Systems & Concurrent Engineering for Space Applications Conference 2018.
  Google Scholar

[19] Nalepa J. et al.: Evaluating algorithms for anomaly detection in satellite telemetry data. Acta Astronautica 198, 2022, 689–701.
  Google Scholar

[20] Obied M. A. et al.: Deep clustering-based anomaly detection and health monitoring for satellite telemetry. Big Data and Cognitive Computing 7(1), 2023, 39.
  Google Scholar

[21] O'Meara C. et al.: Applications of deep learning neural networks to satellite telemetry monitoring. 2018 Spaceops Conference.
  Google Scholar

[22] Ostaszewski K. et al.: Pattern recognition in time series for space missions: A rosetta magnetic field case study. Acta Astronautica 168, 2020, 123–129.
  Google Scholar

[23] Pilastre B. et al.: Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning. Signal Processing 168, 2020, 107320.
  Google Scholar

[24] Purwar A.: Evaluating the efficacy of open-source llms in enterprise-specific rag systems: A comparative study of performance and scalability. arXiv, 2406.11424, 2024.
  Google Scholar

[25] Sahoo P. et al.: A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv preprint arXiv, 2402.07927, 2024.
  Google Scholar

[26] Schefels C. et al.: To Catch Them All: A Generic Approach for Pattern Detection in Time Series Satellite Telemetry Data. 2021.
  Google Scholar

[27] Raj Mathav J. et al.: Fine tuning llm for enterprise: Practical guidelines and recommendations. arXiv, 2404.10779, 2024.
  Google Scholar

[28] Waisberg E. et al.: Generative pre-trained transformers (GPT) and space health: a potential frontier in astronaut health during exploration missions. Prehospital and Disaster Medicine 38(4), 2023, 532–536.
  Google Scholar

[29] Wang Y. et al.: A deep learning anomaly detection framework for satellite telemetry with fake anomalies. International Journal of Aerospace Engineering 1, 2022, 1676933.
  Google Scholar

[30] Zeng Z. et al.: Satellite telemetry data anomaly detection using causal network and feature-attention-based LSTM. IEEE Transactions on Instrumentation and Measurement 71, 2022, 1–21.
  Google Scholar