Author

S. Sim

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Preprint Aug 2026

ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink

ELMZip formulates the fitting process as a convex least-squares problem using random-feature single-layer networks, thereby eliminating the need for computationally expensive backpropagation and achieves significant compression efficiency while maintaining high reconstruction fidelity.

Woojin Cho, Junghwan Park, S. Sim et al. · 0 citations

Preprint Aug 2026

Summarize First, Download Later: Onboard VLMs for Bandwidth-Efficient Earth Observation

This work proposes a "Summarize First, Download Later" paradigm that exploits recent advances in onboard edge computing and Vision-Language Models (VLMs) and substantially reduces bandwidth consumption while accelerating time-to-insight for time-sensitive missions.

Junghwan Park, S. Sim, Woojin Cho et al. · 0 citations