Adding MAR climate-model features plus a multi-branch GraphSAGE/temporal-convolution design with adaptive fusion reduces deep-ice-layer thickness RMSE by 21.01% over a no-knowledge multi-branch baseline on the SRED Greenland dataset.
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A beam steering method using an additional element achieves better than 5 μrad and 5 μm precision with >89.8% fiber-to-fiber coupling efficiency stable over 10-40°C temperature range.
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K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
Adding MAR climate-model features plus a multi-branch GraphSAGE/temporal-convolution design with adaptive fusion reduces deep-ice-layer thickness RMSE by 21.01% over a no-knowledge multi-branch baseline on the SRED Greenland dataset.
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Precise and robust optical beam steering for space optical instrumentation
A beam steering method using an additional element achieves better than 5 μrad and 5 μm precision with >89.8% fiber-to-fiber coupling efficiency stable over 10-40°C temperature range.