The substantial volume of data generated by Earth observation (EO) satellites poses a significant challenge to the limited-rate satellite-to-ground links. This paper addresses the downlink communication problem of change detection in multispectral satellite images for EO purposes. The proposed method is based on a cohesive strategy capable of eliminating clouds and performing semantic encoding during image processing. This approach is a manifestation of semantic communication, as it encodes vital information for the target application, in the form of changed multi-spectral pixels (MPs) to minimize energy consumption. The proposed method is based on a three-stage end-to-end scoring mechanism, which quantifies the significance of each MP before determining its transmission. Specifically, the sensing image is (1) normalized and passed through a highperformance cloud filtering via the Cloud-SLR model, (2) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them, and forward to the ground station, and (3) reconstructed at ground gateway based on the reference image and received data. The numerical results show the effectiveness of the proposed framework in achieving energy savings of up to 58% while upholding the transmission of high-quality data for satellite-based EO applications.

We consider a Wireless Networked Control System (WNCS) where sensors provide observations to build a DT model of the underlying system dynamics. The focus is on control, scheduling, and resource allocation for sensory observation to ensure timely delivery to the DT model deployed in the cloud. Timely and relevant information, as characterized by optimized data acquisition policy and low latency, are instrumental in ensuring that the DT model can accurately estimate and predict system states. However, optimizing closed-loop control with DT and acquiring data for efficient state estimation and control computing pose a non-trivial problem given the limited network resources, partial state vector information, and measurement errors encountered at distributed sensing agents. To address this, we propose the Age-of-Loop REinforcement learning and Variational Extended Kalman filter with Robust Belief (AoL-REVERB), which leverages an uncertainty-control reinforcement learning solution combined with an algorithm based on Value of Information (VoI) for performing optimal control and selecting the most informative sensors to satisfy the prediction accuracy of DT. Numerical results demonstrate that the DT platform can offer satisfactory performance while halving the communication overhead.