For online trajectory optimization of the near-space vehicle in computational guidance, a trajectory optimization method based on actual dynamic equations is proposed via control vector parameterization. Firstly, a trajectory optimization problem with initial and terminal state constraints is developed by discretizing the nonlinear dynamic equations using the flipped-Radau pseudospectral approach. Secondly, control vector parameterization reduces complexity and boosts computational efficiency by converting the optimization problem for control quantities at each discrete point into an optimization problem for multiple parameter vectors. Thirdly, to deal with parameter amplitude constraints, a trajectory optimization method based on convex programming techniques is proposed via control vector parameterization. Finally, given that the midcourse guidance mission is insensitive to terminal flight time, the optimization problem is transformed from being time-dependent to downrange-dependent, thereby further reducing the computational time. Simulation results demonstrate that the proposed method satisfies the high-efficiency and high-precision requirements for trajectory optimization in computational guidance.

This research proposes an adaptive avoidance decision-making mechanism that combines safety assessment with pigeon-inspired optimization (PIO) to enhance the collision avoidance capabilities and coordination efficiency of unmanned aerial vehicle (UAV) swarms operating in complicated environments. A multidimensional safety evaluation index system is constructed by analyzing both local conflict risks and the overall coordination level of the swarm. Principal component analysis is employed to derive a comprehensive safety score, based on which a three-stage avoidance control strategy is formulated. An artificial potential field function with adaptively tuned parameters is designed according to the safety score, enabling responsive and autonomous individual-level control. Furthermore, to improve global cooperative avoidance under high-risk scenarios, the improved PIO algorithm is embedded to jointly optimize the velocity direction and avoidance parameters of swarm members. The effectiveness and resilience of the suggested strategy in dynamic contexts are validated by simulation findings, which show that it dramatically lowers the likelihood of local conflicts while preserving swarm cohesion.

The traditional fixed optical axis observation approach reduces positioning accuracy and reliability in the starlight refraction indirect horizon-sensing positioning method by producing a small number of refracted stars with an uneven distribution. Therefore, a high-precision starlight refraction celestial navigation observation method is proposed. Based on the geometric relationship between the stratospheric refraction band and the star sensor’s field of view (FOV), a relation model is established between the FOV coverage area of the refraction band and the star sensor’s elevation angle. By maximizing the FOV coverage area and combining the flight parameters, the optimal elevation angle is determined to increase the observable refracted stars. Additionally, a quantitative relationship model is developed between positioning precision and the spatial arrangement of refracted stars. Based on the minimum position dilution of precision (PDOP) criterion, the optimal spatial geometric configuration of refracted stars is derived, thereby obtaining the optimal azimuth angle of the optical axis. This enables continuous and uniform observation for refracted starlight, so as to improve the positioning accuracy of celestial navigation. Simulation results demonstrate that compared with the conventional method, the proposed method achieves 75.7% and 66.7% improvements in position and velocity estimation accuracy, respectively.

To address the conflict between the limited payload capacity of a single launch vehicle and the substantial deployment demands of large-scale target payloads, this paper employs a two-phase manned lunar landing mission architecture, where on-orbit refueling connects Earth ascent and space transfer. Mission scale analysis and calculations were performed for manned lunar landing utilizing on-orbit refueling. Dynamic models for the four main phases—launch, rendezvous, Earth-Moon transfer, and lunar landing—were established, and overall mission design simulation analysis was completed. A mass estimation method was employed to compare the total mission mass and the scale of each stage between the traditional mode and the on-orbit refueling mode. In order to define phase-specific limitations and develop a mass model that takes into account in-orbit evaporation losses of cryogenic propellant, a mission model covering the four phases of launch, rendezvous and docking, Earth-Moon transfer, and lunar landing was built. Subsequently, mission windows were calculated, and full-process orbital simulations were conducted to analyze trajectory parameters and propellant consumption for each phase. Genetic algorithms were used to optimize parameters for the lunar landing phase. Mass estimation results show that, compared to the thousands of tonnes launch mass required by the traditional mode, the total launch mass for the on-orbit refueling scheme is below 500 tonnes, and the space transfer stage mass is on the order of tens of tonnes. The full-process simulation findings provide crucial references for engineering design, further elucidating the propellant consumption for each stage of the on-orbit refueling strategy.

To achieve an objective assessment of driving comfort, this study developed and systematically compared multiple machine learning models based on multimodal physiological signals. 64 individuals had their electrocardiograms (ECGs), surface electromyograms (sEMGs), muscle oxygen saturation (SmO₂), and arterial blood flow signals recorded while they drove. Using comfort scores as labels, six types of models, including support vector machine (SVM), random forest (RF), and adaptive Boosting (AdaBoost), were constructed and optimized with principal component analysis, synthetic minority oversampling technique (SMOTE), and grid search. Results showed that multimodal signal fusion significantly improved classification performance. The SVM model achieved optimal performance with the feature set containing arterial blood flow parameters, reaching an average area under the curve (AUC) of 0.97 and an accuracy of 0.90. RF models consistently exceeded 0.95 AUC with fused signals. Practical signal combinations for portable monitoring were identified: "sEMG + ECG" dual-signal combination (average AUC is 0.90) and "SmO₂ + ECG + sEMG" triple-signal combination (average AUC is 0.92). This study offers empirical support for model selection and signal setup in objective driving comfort evaluation, providing insightful information for the development of in-car health monitoring systems.

The rapid development of spaceflight has significantly advanced science and technology, but it also poses non-negligible environmental challenges. This study focuses on major emissions from rocket launches, including CO₂, black carbon (BC), H₂O, Al₂O₃, and chlorine species (Cl_y). To assess the atmospheric influence of space activities, their horizontal and vertical spatial distributions are examined using worldwide launch data from 2020 to 2024. The results indicate that gaseous emissions are dominated by CO₂ and H₂O, with total quantities of approximately 2.71×10⁸ kg and 1.44×10⁸ kg, respectively. In contrast, particulate emissions consist mainly of BC and Al₂O₃, with estimated releases of 6.70×10⁶ kg and 7.80×10⁶ kg, respectively. Horizontally, the emissions exhibit strong regional concentration, with the United States and China accounting for the majority of CO₂, H₂O, and BC emissions. Al₂O₃ and Cl_y emissions are mainly concentrated in China, France, the United States, and India. Vertically, CO₂ emissions are concentrated in the troposphere, while BC emissions peak in the stratosphere and mesosphere. Due to the persistence of BC in the upper atmosphere, a near twofold increase in net radiative forcing can be observed in these regions. This suggests that because of their high-altitude BC emissions, space launch operations are more dangerous to the climate than aircraft and conventional industrial sources that are limited to the troposphere.

In this paper, an adaptive hierarchical control method based on a regulation-triggered batch identifier is proposed. This approach addresses the challenges in stability analysis and control design of fixed-wing UAVs, which are complicated by the coupling of underactuated characteristics and parametric uncertainty. In order to deal with the underactuated problem, the original fixed-wing UAV model is transformed into a virtual integrator and linear parameterized model based on the unicycle model. Backstepping is then combined with fixed-time control methods to create an equivalent flight tracking controller. By analyzing the mechanism of parameter uncertainty on the motion state of the UAV, a linear regression equation is constructed by spectral decomposition technology to quantitatively collect excitation information, which ensures the richness of the collected excitation information and the boundedness of the linear regression equation. A regulation-triggered batch identifier is created to update the parameter estimation using the gathered excitation data when the trigger function satisfies specific requirements in order to remove the impact of parametric uncertainty. Compared with the existing hierarchical control strategies for fixed-wing UAVs, the proposed framework achieves the unity of error convergence, excitement richness and parameter identification accuracy.