Space-air-ground integrated networks (SAGINs) augmented with mobile edge computing (MEC) provide a unified yet heterogeneous substrate for latency-sensitive services. Deploying service function chains (SFCs) over satellites, aerial platforms, and ground nodes, however, is difficult due to hierarchical resource heterogeneity, time-varying network states, and stringent end-to-end (E2E) delay requirements. In this paper, we study online SFC embedding in a three-layer SAGIN-MEC architecture under coupled computing and networking constraints. We model the deployment as a two-stage process: (i) placing each virtual network function (VNF) onto feasible nodes subject to computing-capacity constraints, and (ii) mapping inter-VNF traffic onto feasible paths subject to bandwidth and delay constraints. To achieve adaptive decisions under dynamic states, we cast the problem as graph reinforcement learning by jointly encoding the substrate topology and each SFC into a unified graph state, and propose a structure-aware PPO agent that combines graph convolution with domain features and an action-masking mechanism to eliminate infeasible placement/routing actions. Extensive experiments in dynamic large-scale scenarios show that the proposed method consistently outperforms competitive baselines, improving the acceptance rate by 6.991% under high load, reducing the average E2E delay by 13.556%, and increasing the long-term revenue-to-cost ratio by 27.763% on average.

Linear servomotors (LSM) are widely used in industries. A LSM requires a mover positioning tool for precise control. A Vision-based digital image grating (DIG) measurement for LSM mover positioning is proposed in this study with advantages of simple system, low cost, and good robustness to environments. It estimates the LSM's rough position according to four mark-lines and obtains accurate LSM's positions by a fast least-square (FLS) image-matching algorithm. As a result, it can measure the LSM's position accurately in a wide range. The FLS proposed here represents the least-square cost function by the transformation parameters explicitly, and then calculates the parameters directly. Therefore, the image resampling process, which is inevitable for the existing least-square methods is not necessary. Simulations prove that the FLS is approximately 4.4 times faster than the classic inverse compositional Gauss–Newton (IC-GN) algorithm, and that the FLS is more accurate and robust than the IC-GN. Experiments show that the proposed DIG method can measure the LSM mover positions with an accuracy of 0.75μm mean absolute error.

Spatial target pose estimation with deep learning has garnered increasing attention in recent years. However, the existing methods in this field suffer from poor generalization. In this study, we propose a robust and reliable pose estimation method for spatial targets. The method aims to achieve keypoints filtrating. It involves a detection network tasked with identifying the target area, while the subsequent stage employs a classification network to regress keypoints from the detected target area. To improve the accuracy of pose estimation, we leverage spatial target geometric constraints to formulate 2-D–3-D keypoints equations for an initial pose. Then, we create a nonlinear optimization equation based on the confidence of 2-D keypoints and accomplish nonlinear refinement. We conduct extensive experiments on commonly used datasets and demonstrate the effectiveness of the proposed method. Furthermore, thanks to the effectiveness of keypoints filtrating and nonlinear refinement, the proposed method is robust with challenging scenarios and domain bias.