The emergence of large vision-language models (VLMs) has significantly enhanced the efficiency and flexibility of geospatial interpretation. However, general-purpose VLMs remain suboptimal for remote sensing (RS) tasks. Existing geospatial VLMs typically adopt a unified modeling strategy and struggle to differentiate between task types and interpretation granularities, limiting their ability to balance local detail perception and global contextual understanding. In this paper, we present SkyMoE, a Mixture-of-Experts (MoE) vision-language model tailored for multimodal, multi-task RS interpretation. SkyMoE employs an adaptive router that generates task- and granularity-aware routing instructions, enabling specialized large language model experts to handle diverse sub-tasks. To further promote expert decoupling and granularity sensitivity, we introduce a context-disentangled augmentation strategy that creates contrastive pairs between local and global features, guiding experts toward level-specific representation learning. We also construct MGRS-Bench, a comprehensive benchmark covering multiple RS interpretation tasks and granularity levels, to evaluate generalization in complex scenarios. Extensive experiments on 21 public datasets demonstrate that SkyMoE achieves state-of-the-art performance across tasks, validating its adaptability, scalability, and superior multi-granularity understanding in remote sensing.

Deep learning-based semantic communications (DLSC) leverage deep neural networks in transmitters and receivers, pushing the boundaries beyond Shannon limit. However, DLSC is extremely vulnerable to malicious physical-layer adversarial attacks due to the openness of wireless channels. Meanwhile, existing defense approaches still suffer from two challenges for robust DLSC. First, most methods require offline DLSC retraining to defend against various attacks, causing interruptions of online service. Second, they struggle to achieve effective defense in real-world time-varying channels, thus limiting DLSC reliability. We propose PBNet, integrating a pluggable protector and an adaptive protector to respectively address the above two challenges. First, the pluggable protector utilizes a novel denoising module to safeguard the transmitted signals, enabling hot-pluggable deployment without interrupting communication. Second, the adaptive protector leverages a novel alternating adaption strategy to achieve effective defense in time-varying channels, ensuring robust performances under real-world dynamic conditions. Evaluations involving symbols, images, texts, and speeches show the efficacy of our PBNet, which has respectively achieved an impressive 72.22% and 73.71% accuracy improvement in defending against unknown$l_{0}$-norm and$l_{2}$-norm attacks on image-based DLSC. Furthermore, we developed two real-world radio systems of PBNet to perform over-the-air signal generation, integrating hardware and software such as FPGA chips and GNU radio. We also implemented an interactive UI of PBNet based on QT5, aiming to demonstrate the effect of attacks and defense visually. This work achieves robust DLSC performances under various attacks and time-varying channels, taking a significant step towards the practical defense scheme for robust DLSC.