Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
本文提出了一种基于Radon点扩散函数的方法，用于解决空间碎片图像因平台抖动和姿态调整导致的帧间仿射错位问题，无需迭代优化或特征提取。
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Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness
This work addresses the challenges of transferring large language models (LLMs) to the domain of space situational awareness (SSA), which stem from task-structure misalignment, lack of higher-order cognitive supervision, and inconsistencies between data and engineering standards. To overcome these issues, the authors propose the BD-FDG framework, which—drawing on Bloom’s taxonomy for the first time in domain-adaptive data generation—constructs a continuous gradient of samples spanning nine question types and six cognitive difficulty levels. Domain knowledge is organized via a knowledge-tree structure, and a multidimensional automated quality assessment pipeline yields a high-quality SSA-SFT dataset comprising 230,000 samples. The resulting SSA-LLM-8B, fine-tuned from Qwen3-8B, achieves a 144% (without chain-of-thought) and 176% (with chain-of-thought) improvement in BLEU-1 on in-domain evaluation, attains an arena win rate of 82.21%, and preserves strong general-purpose capabilities.
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Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
本文提出了一种基于Radon点扩散函数的方法，用于解决空间碎片图像因平台抖动和姿态调整导致的帧间仿射错位问题，无需迭代优化或特征提取。
0 citationsRead paper
Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness
This work addresses the challenges of transferring large language models (LLMs) to the domain of space situational awareness (SSA), which stem from task-structure misalignment, lack of higher-order cognitive supervision, and inconsistencies between data and engineering standards. To overcome these issues, the authors propose the BD-FDG framework, which—drawing on Bloom’s taxonomy for the first time in domain-adaptive data generation—constructs a continuous gradient of samples spanning nine question types and six cognitive difficulty levels. Domain knowledge is organized via a knowledge-tree structure, and a multidimensional automated quality assessment pipeline yields a high-quality SSA-SFT dataset comprising 230,000 samples. The resulting SSA-LLM-8B, fine-tuned from Qwen3-8B, achieves a 144% (without chain-of-thought) and 176% (with chain-of-thought) improvement in BLEU-1 on in-domain evaluation, attains an arena win rate of 82.21%, and preserves strong general-purpose capabilities.
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