A diffractive imaging system consisting of two satellites is analyzed in view of dynamics. The mathematical model of rigid and flexion couples is studied to describe the relative motion of diffractive satellites and imaging satellites. Based on an integrated dynamics model with dual quaternion, a fixed-time non-singular terminal sliding mode controller is designed to meet the requirements of Earth observation. Finally, introducing the non-singular terminal sliding mode as the control group, a comparative simulation of relative motion and control is implemented to verify the controller and dynamics model.

Optically pumped magnetometers (OPM) are core quantum payloads for geomagnetic remote sensing. Among them, the spin-exchange relaxation-free (SERF) OPM with aT-level ultimate sensitivity stands as mainstream. While enlarging the alkali-metal vapor cell of the SERF OPM enhances sensitivity, it triggers complex atomic spin relaxation, notably intensified magnetic field gradient relaxation. To address the dilemma of atomic spin relaxation regulation and the engineering requirements of ultra-high-sensitivity SERF magnetometers, this paper constructs an analytical model of the total relaxation rate that comprehensively considers wall-collision relaxation, spin-destruction collision relaxation, and longitudinal/transverse magnetic field gradient relaxation, etc. The analytical relationship between buffer gas pressure and total relaxation rate for a commonly used spherical vapor cell is derived, revealing the intrinsic correlation among cell size, atomic spin relaxation, and optimal pressure. Based on the theoretical model, the filling parameters of the vapor cell are optimized, and experimental measurements are carried out. The theoretical relaxation results are highly consistent with the experimental ones, realizing the precise optimization of buffer gas pressure. The optimization method proposed in this paper provides a theoretical basis and parameter guidance for the engineering preparation of alkali-metal vapor cells for high-sensitivity SERF magnetometers in remote sensing applications.

This work studies trustworthy use of large language models for remote sensing satellite downlink scheduling. Rather than accepting a generated optimization model at face value, we organize the workflow into three guarded steps: candidate generation, benchmark-based validation, and fallback exact solving. The core technical component is a global time-slicing validator that converts visibility windows into atomic intervals; so, mutual exclusion at the ground-station side, mutual exclusion at the satellite side, and per-satellite download caps can be checked in a physically faithful manner. Results on a prototype instance indicate that LLM-based modeling can be integrated into a dependable scheduling pipeline when external verification and recovery are built into the loop.