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Distributed Denial of Service (DDoS) attacks,
particularly those executed by bots, significantly impair the
Quality of Service (QoS) for legitimate users. While network-level
DDoS attacks have been largely mitigated through decades of
research, application-level DDoS attacks remain a challenge due
to the difficulty in distinguishing malicious from legitimate traffic.
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To establish a versatile RSFM adaptable to diverse tasks, RingMoE requires a comprehensive and diverse pre-training dataset that accounts for significant variations in imaging modalities, spatial resolutions, temporal dynamics, geographic regions, and scene complexities. To meet this challenge, we curate RingMOSS, a large-scale multi-modal RS dataset comprising 400 million images from nine satellite platforms, covering a broad spectrum of Earth observation scenarios.
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