Synthetic aperture radar (SAR), with its all-weather and all-day active imaging capability, has become indispensable for geoscientific analysis and socio-economic applications. Despite advances in deep learning–based object detection, the rapid and accurate detection of small objects in SAR imagery remains a major challenge due to their extremely limited pixel representation, blurred boundaries in dense distributions, and the imbalance of positive–negative samples during training. Recently, vision–language models such as Contrastive Language-Image Pre-Training (CLIP) have attracted widespread research interest for their powerful cross-modal semantic modeling capabilities. Nevertheless, their potential to guide precise localization and detection of small objects in SAR imagery has not yet been fully exploited. To overcome these limitations, we propose the CLIP-Driven Adaptive Tiny Object Detection Diffusion Network (CDATOD-Diff). This framework introduces a CLIP image–text encoding-guided dynamic sampling strategy that leverages cross-modal semantic priors to alleviate the scarcity of effective positive samples. Furthermore, a generative diffusion-based module reformulates the sampling process through iterative denoising, enhancing contextual awareness. To address regression instability, we design a Balanced Corner–IoU (BC-IoU) loss, which decouples corner localization from scale variation and reduces sensitivity to minor positional errors, thereby stabilizing bounding box predictions. Extensive experiments conducted on multiple SAR and optical remote sensing datasets demonstrate that CDATOD-Diff achieves state-of-the-art performance, delivering significant improvements in detection robustness and localization accuracy under challenging small-object scenarios with complex backgrounds and dense distributions.

空间环境温度变化会使空间天线支撑结构产生热变形,影响其使用性能,因此进行天线热稳定性设计及热变形分析具有重要的意义.基于代表性体积单元(Representative Volume Element,RVE)方法对空间天线结构进行热稳定性设计与热变形分析.通过建立纤维随机分布并含有材料孔隙的RVE,得到纤维热膨胀系数.对M40/TDE85单向复合材料的热膨胀性能进行实验测试,计算结果与测试结果吻合良好,验证了RVE模型的正确性与准确性.建立了复合材料圆管参数化模型,根据计算得到的热膨胀系数及优化算法,对天线支撑结构进行热稳定性优化设计,并对优化后的天线结构进行热致变形分析,结果表明优化后的结构具有很高的热稳定性.