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RAMA integrates episodic memory into multi-agent DRL to cut mean end-to-end latency by 13.4% and 99th-percentile latency by 66.7% in a 140-satellite Kepler constellation.
RT-LSTM reports 1.405 ms mean absolute error, cutting error versus LSTM, StarNet, and ASTM.
STR-Agent, an LLM-driven framework, translates natural-language service requests into adaptive QoS-aware routing for LEO satellite networks, cutting end-to-end delay by up to 60%.
HYDRA models link-flooding attacks on LEO satellite constellations as botnet minimization, matching prior disruption with 34% fewer bots and 23% less traffic.
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