The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

There are four recursive algorithms used in the computation of the fully normalized associated Legendre functions (FNALFs): the standard forward column algorithm, the standard forward row algorithm, the recursive algorithm between every other degree, and the Belikov algorithm. These algorithms were evaluated in terms of their first relative numerical accuracy, second relative numerical accuracy, and computation speed and efficiency. The results show that when the degree

This paper presents a vision-guided nonlinear model predictive control (MPC) framework for coordinated dual-manipulator pre-contact acquisition of tumbling targets. Temporal monocular estimation supplies pose, motion, and uncertainty; rigid-body prediction generates fixture trajectories; and model-based or diffusion candidates initialize exact Denavit–Hartenberg (DH) refinement before clearance-checked execution. Synthetic tests give mean position and attitude errors of 0.0170–0.0182 m and 1.05–1.19°. With initial-state uncertainty and $\pm 6\%$ inertia mismatch, 3 s fixture-prediction root-mean-square error is 0.0822 m with 94.5% covariance coverage. In 30 paired stress scenarios, original-criterion safe capture is 19/30 for exact-DH MPC versus 14/30 for linearized MPC (exact McNemar $p=0.180$). A separate reviewer-motivated post hoc sensitivity imposes three-step angular-rate matching uniformly across five methods: safe capture is 19/30 versus 8/30 (Holm-adjusted $p=0.0171$). Diffusion and uncertainty inflation add no safe captures. Actual-cycle analysis finds a small 1.15% iteration reduction with diffusion, without an end-to-end timing benefit. Sequential mean update times are 1.234 s without diffusion and 1.392 s with diffusion, exceeding the 0.10 s simulated update interval. The evidence concerns delay-free, fixed-base kinematic supervisory planning with target-specific synthetic vision, not real-time hardware control or flight readiness.

Accurate attitude measurement of non-cooperative space targets is essential for on-orbit servicing, active debris removal, and autonomous rendezvous missions. To address the challenges associated with unknown geometry, rapid tumbling motion, and the limited observability of single-view systems, this study proposes a cooperative multi-view measurement framework for three-dimensional reconstruction and attitude estimation. Multiple spacecraft are deployed to form a stable observation configuration, and multi-view image sequences are acquired to strengthen geometric constraints. A learning-based multi-view stereo reconstruction module is used to estimate depth information and reconstruct point clouds, which are further processed through iterative closest point (ICP) registration to derive inter-frame attitude variations. An extended Kalman filter (EKF) is then introduced to improve temporal consistency and suppress measurement noise. Validation is conducted in a numerical simulation using a simplified Fengyun-1 (FY-1) satellite model under a three-spacecraft cooperative fly-around scenario. The simulation results demonstrate that the proposed method achieves high-precision attitude estimation, with attitude errors below $0.3°$ and positional errors within $0.05\mathrm{m}$. Comparative experiments show that the method maintains stable measurement performance under varying observation distances and viewing configurations. The proposed framework provides a reliable and robust measurement solution for dynamic attitude determination of free-tumbling space targets.