A demodulation method for tunable diode laser absorption spectroscopy (TDLAS) second harmonic based on the Hilbert transform is proposed in this paper. The second harmonic of the TDLAS signal can be easily obtained without a reference signal. The TDLAS signal is firstly processed by band-pass filtering, then the envelope of the processed signal is obtained with Hilbert transform. And finally, the second harmonic is extracted from the 1f component of the envelope. The validity of the proposed method is confirmed by simulation and verified by experiment, and the result shows that the error is acceptable under the cases of weak absorbance, which indicates that the proposed method is applicable to practical trace gas detection.

Piezoelectric micromechanical ultrasonic transducers (pMUTs) are new types of distance sensors with great potential for applications in automotive, unmanned aerial vehicle, robotics, and smart homes. However, previously reported pMUTs are limited by a short sensing distance due to lower output sound pressure. In this work, a pMUT with a special dual-ring structure based on scandium-doped aluminum nitride (ScAlN) is proposed. The combination of a dual-ring structure with pinned boundary conditions and a high piezoelectric performance ScAlN film allows the pMUT to achieve a large dynamic displacement of 2.87 μm/V and a high electromechanical coupling coefficient ($k_t^2$) of 8.92%. The results of ranging experiments show that a single pMUT achieves a distance sensing of 6 m at a resonant frequency of 91 kHz, the farthest distance sensing registered to date. This pMUT provides surprisingly fertile ground for various distance sensing applications.

Dynamically modeling the flexible characteristics of large-scale jointed composite spacecraft is challenging. In this study, a dynamic modeling method for rigid–flexible composite spacecraft is proposed based on the absolute nodal coordinate formulation (ANCF). First, the spacecraft in the jointed composite is simplified as a rigid body, and the docking mechanisms between spacecraft are approximated using the fully parameterized beam model. Next, regarding the constraints between the beam and the rigid body, the beam’s absolute nodal coordinates are converted into rigid body coordinates. This allows the dynamic equations to be simplified using independent coordinates, reducing the model dimension. Finally, system damping is increased through the mean stress noise reduction method, which suppresses high-frequency components in the dynamic model and further reduces the rigidity of the dynamic equations for the composite body. This modeling method decreases the complexity of the composite body dynamics and avoids the difficulty of solving algebraic–differential equations exhibited by Lagrange multiplier methods, facilitating numerical simulations. The proposed method is applicable to both tree and mesh topologies. MATLAB simulations demonstrate that the proposed dynamic model alleviates the dimensionality disaster caused by conventional algorithms, significantly reducing computation time. The simulation results are consistent with ADAMS. The proposed model exhibits displacement errors less than 1 mm, highlighting its efficiency and accuracy.

To address the limitations of inadequate real-time performance and robustness encountered in estimating the pose of non-cooperative spacecraft during on-orbit missions, a novel method of feature point distribution selection learning is proposed. This approach utilizes a non-coplanar key point selection network with uncertainty prediction, pioneering in its capability to accurately estimate the pose of non-cooperative spacecraft, thereby representing a significant advancement in the field. Initially, the feasibility of designing a non-coplanar key point selection network was analyzed based on the influence of sensor layout on the pose measurement. Subsequently, the key point selection network was designed and trained, leveraging images extracted from the spacecraft detection network. The network detected 11 pre-selected key points with distinctive features and was able to accurately predict their uncertainties and relative positional relationships. Upon selection of the key points exhibiting low uncertainty and non-coplanar relative positions, we utilized the EPnP algorithm to achieve accurate pose estimation of the target spacecraft. Our experimental evaluation on the SPEED dataset, which comes from the International Satellite Attitude Estimation Competition, validates the effectiveness of our key point selection network, significantly enhancing estimation accuracy and timeliness compared to other monocular spacecraft attitude estimation methods. This advancement provides robust technological support for spacecraft guidance, control, and proximity operations in orbital service missions.

In large-scale satellite constellations, the efficiency of inter-satellite communication is paramount. Traditional topology control strategies, such as the Manhattan configuration, provide stable links but can result in indirect communication paths, affecting the efficiency of information transfer. This paper addresses this issue by proposing an innovative “3 + 1” dynamic topology control scheme. The scheme retains three static links determined by the relative angular velocity and acceleration while introducing a dynamic link based on distance and angular velocity constraints to optimize the link duration and overall network communication efficiency. To address the complexity of matching dynamic links, this paper introduces an elite strategy-based maximum weighted matching algorithm for general graphs. Compared to traditional greedy algorithms, our proposed algorithm significantly improves the link duration and topological stability. Through simulation experiments comparing communication delays between Xiamen and Los Angeles, our results show that the proposed dynamic link scheme substantially reduces the average delay, enhancing the efficiency and flexibility of inter-satellite communication. This research not only extends the duration of inter-satellite links but also provides new perspectives and methodologies for further studies on inter-satellite topology control strategies.

This paper investigates the problem of angular momentum control and planning for control moment gyroscope(CMG) arrays in rigid spacecraft attitude control systems using deep reinforcement learning (DRL). Specifically, a DRL-based angular momentum control strategy is proposed for spacecraft attitude control systems employing multiple CMGs as actuators. The twin-delayed deep deterministic policy gradient (TD3) algorithm is used to perform online learning and policy updates based on environmental feedback. This approach eliminates the need for precise mathematical models and iterative parameter tuning. This enables the CMG system to perform angular momentum planning and facilitates rapid and high-precision spacecraft attitude maneuvers and control through angular momentum exchange. Simulations were performed to analyze spacecraft attitude maneuvers and stabilization under various scenarios, focusing on the angular momentum control process of a pyramidal single gimbal CMG (SGCMG) array. The results demonstrate that the proposed method effectively achieves large-angle attitude maneuvers and stable attitude maintenance, both in ideal conditions and in the presence of nonlinear disturbances. During large-angle maneuvers, the spacecraft’s attitude estimation using MRPs converges in less than 1 min, and the convergence accuracy during attitude-holding reaches the order of 10⁻³. Moreover, the approach fully leverages the output characteristics of the CMG system and achieves robust performance and accuracy, even under conditions with significant noise and disturbances.