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With the background of on-orbit repetitive connection and separation of CubeSats, an electromagnetic docking mechanism for repeated docking and separation is proposed. A reusable electromagnetic docking scheme combining lead screw transmission with electromagnets is introduced. The electromagnetic force/torque model and the attitude model of the CubeSat are derived based on the relationship between force and magnetic flux density in a magnetic field. The coil layout and the polarity of magnetic poles are optimized and analyzed, four different layout configurations are proposed, and their mechanical characteristics are analyzed. A multi-body dynamics simulation analysis of the entire mechanism is conducted to evaluate the attitude correction capability of the electromagnetic attraction separation unit. A three-degrees-of-freedom capture and separation test of the electromagnetic attraction separation unit is carried out in a microgravity-simulated environment to investigate the characteristics of capture and separation under different position and attitude deviation conditions of the energized solenoids. The designed electromagnetic docking mechanism has an adaptive attitude adjustment and docking range of a 30° cone. It can achieve low-impact, high-tolerance, and reusable docking and separation. Full article

Efficient data transmission in low Earth orbit (LEO) satellite networks is critical for supporting real-time global communication, Earth observation, and numerous data-intensive space missions. A fundamental challenge in these networks involves solving the maximum flow problem, which determines the optimal data throughput across highly dynamic topologies with limited onboard energy and data processing capability. Traditional algorithms often fall short in these environments due to their high computational costs and inability to adapt to frequent topological changes or fluctuating link capacities. This paper introduces an accelerated maximum flow algorithm specifically designed for dynamic LEO networks, leveraging a prediction-enhanced approach to improve both speed and adaptability. The proposed algorithm integrates a novel energy-time expanded graph (e-TEG) framework, which jointly models satellite-specific constraints including time-varying inter-satellite visibility, limited onboard processing capacities, and dynamic link capacities. In addition, a learning-augmented warm-start strategy is introduced to enhance the Ford–Fulkerson algorithm. It generates near-optimal initial flows based on historical network states, which reduces the number of augmentation steps required and accelerates computation under dynamic conditions. Theoretical analyses confirm the correctness and time efficiency of the proposed approach. Evaluation results validate that the prediction-enhanced approach achieves up to a 32.2% reduction in computation time compared to conventional methods, particularly under varying storage capacity and network topologies. These results demonstrate the algorithm’s potential to support high-throughput, efficient data transmission in future satellite communication systems. Full article

Low Earth Orbit (LEO) satellite communication is gradually becoming the main carrier for satellite communication by virtue of its advantages, such as high landing power, narrow beam, large transmission bandwidth, and small time delay. In the military field, interference with LEO satellites has become a core element in combat, but the existing interference and confrontation methods cannot meet the needs of LEO satellite interference. Aiming at the above problems, this paper proposes an LEO satellite navigation signal multi-dimensional interference optimisation method based on hybrid game theory. Firstly, the method achieves a dynamic classification of jammers within the airspace. Then, an interference effectiveness evaluation function is established, which reflects the time, frequency, and power domain losses, as well as the strategy gains. With the help of hybrid game theory, the optimal resource allocation under Nash equilibrium is achieved, and the distributed interference optimisation problem is effectively solved. The experiment uses a large microwave darkroom as an interference verification scenario. The results indicate that the interference bit error rate (BER) of the algorithm proposed in this paper is on the order of ${10}^{-2}$, under the premise of guaranteeing the full coverage of the area to be interfered. The value of the multidimensional interference utility function, including the power, time, and frequency domains, is improved by at least 0.4993 times compared to other algorithms. Full article

For the satellite orbit pursuit–evasion game problem, this paper proposes a multi-stage optimization-based solution aimed at improving the confrontation strategies between task satellites and target satellites in complex space environments. The approach divides the satellite pursuit–evasion game into two phases: the “approach phase” and the “sustained phase”. It dynamically optimizes the trajectories and strategies of the task and target satellites to achieve adaptive orbit control and behavior optimization. To enhance the global search capability and local convergence of the algorithm, this paper employs the Zebra Optimization Algorithm, introducing a multi-population cooperative evolution mechanism, and integrates differential game theory to improve the stability and reliability of the game strategies. Simulation results demonstrate that the proposed method effectively enhances task efficiency under multiple constraints, dynamically adjusts the strategies of both the pursuer and the evader, and provides an efficient, scalable solution applicable to satellite pursuit–evasion games in complex space environments. Full article

This report examines the successful recovery of a feline that presented with multiple complex fractures and dislocations involving the facial and cranial structures resulting from a traffic accident. Diagnostic CT imaging identified significant injuries, including luxation of the left temporomandibular joint (TMJ), a mandibular symphyseal fracture, a hard palate fracture, and a left orbital fracture accompanied by severe exudate within the nasal cavity, compressing the left orbit and nasal passages. Importantly, no additional injuries were detected in the thoracic or abdominal regions, facilitating a more targeted treatment plan. The management of this case required extensive surgical intervention, including open reduction of the TMJ, stabilization of the mandibular symphysis, repair of the bony palate, and partial maxillectomy. After 20 days of ICU hospitalization, the feline fully recovered. This outcome is particularly noteworthy as the combination of severe injuries observed in this case is unprecedented in the veterinary literature. Consequently, it offers critical insights into both surgical techniques and postoperative management strategies applicable to similarly complex trauma cases. The feline’s full recovery, characterized by the restoration of normal daily functions, highlights the clinical significance of pursuing multiple, complex surgical procedures in cases of severe trauma. It serves as a valuable reference for advancing the understanding and management of severe facial trauma in veterinary practice. Full article

The development of small-molecule drugs targeting growth factors for cancer therapy remains a significant challenge, with only limited successful cases. We attempted to identify hepatocyte growth factor (HGF) inhibitors as novel anti-cancer small-molecule drugs. To identify compounds that bind to the β-chain of HGF and inhibit signaling through HGF and its receptor Met interaction, we performed a hierarchical in silico drug screen using a three-dimensional compound structure library (Chembridge, 154,118 compounds). We experimentally tested whether 10 compounds selected as candidates for novel anticancer agents exhibit inhibition of HGF activity. Compounds 6 and 7 potently inhibited Met phosphorylation in the human EHEMES-1 cell line, with IC₅₀ values of 20.4 and 11.9 μM, respectively. Molecular dynamics simulations of the Compound 6/7–HGF β-chain complex structures suggest that Compounds 6 and 7 stably bind to the interface pocket of the HGF β-chain. MM-PBSA, MM-GBSA, and FMO analyses identified crucial amino acid residues for inhibition against the HGF β-chain. By interfering with the HGF/Met interaction, these compounds may attenuate downstream signaling pathways involved in cancer cell proliferation and metastasis. Further optimization and comprehensive evaluations are necessary to advance these compounds toward clinical application in cancer therapy. Full article

As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data and dynamic noise interference, and the traditional Extended Kalman Filter (EKF) struggles to meet the accuracy and robustness requirements in complex orbital environments. To address these challenges, this paper proposes a Bayesian Adaptive Extended Kalman Filter (BAEKF), which synergistically optimizes track determination through dynamic noise covariance adjustment and Bayesian a posteriori probability correction. Experiments demonstrate that the average root mean square error (RMSE) of BAEKF is reduced by 34.7% compared to the traditional EKF, effectively addressing EKF’s accuracy and stability issues in nonlinear systems. The RMSE values of UKF, RBFNN, and GPR also show improvement, providing a reliable solution for high-precision orbital determination using optical observation. Full article

In the context of the escalating requirement for high-throughput multimedia services, Orthogonal Frequency Division Multiplexing (OFDM) signal systems have become increasingly prevalent in Low Earth Orbit (LEO) satellite communications. In order to promote the judicious, efficient, and cost-effective deployment of satellite spectrum resources, there is a critical need to augment the capabilities of spectrum monitoring technology for uncollaborative LEO satellite OFDM signals. Addressing the complexities inherent in the broad bandwidth, substantial dynamic range, and weak prior knowledge associated with LEO satellite OFDM signal monitoring, this study introduces an innovative methodology. This approach harnesses a broadband parallel flexible filtering variable sampling technique to facilitate the real-time observation of satellite OFDM signals across a wide bandwidth spectrum. Moreover, the research presents a dynamic compensation technique, which utilizes ephemeris information, to mitigate frequency offset and amplitude fading issues within the monitored signals. Post compensation, the study conducts signal identification and parameter analysis, utilizing the intrinsic features of OFDM signals. This technique empowers real-time monitoring and the accurate analysis of satellite broadband OFDM signals, ensuring robust performance in scenarios characterized by weak prior knowledge and significant dynamic variations. Full article

Free-space optical communications have emerged as a powerful solution for inter-satellite links, playing a crucial role in next-generation satellite networks. This paper introduces a comprehensive model that enables the dynamic evaluation of optical power requirements for realistic low Earth orbit satellite constellations throughout the orbital period. Our approach incorporates the constellation architecture, link budget analysis, and optical transceiver design to accurately estimate the power required for sustaining connectivity for both intra- and inter-orbit links. We apply the model considering Walker delta-type constellations of varying densities. We show that in dense constellations, even at high data rates, the required transmission power can be low enough to mitigate the need for optical amplification. Dynamically estimating the power requirements is vital when evaluating energy savings in adaptive scenarios where terminals adaptively change the emitted power depending on the link status. Our model is implemented in Python and is openly available under an open-source license. It can be easily adapted to various alternative constellation configurations. Full article

Synthetic Aperture Radar (SAR) constellations have become a key technology for disaster monitoring, terrain mapping, and ocean surveillance due to their all-weather and high-resolution imaging capabilities. However, the design of large-scale SAR constellations faces multi-objective optimization challenges, including short revisit cycles, wide coverage, high-performance imaging, and cost-effectiveness. Traditional optimization methods, such as genetic algorithms, suffer from issues like parameter dependency, slow convergence, and the complexity of multi-objective trade-offs. To address these challenges, this paper proposes a hybrid optimization framework that integrates chaotic sequence initialization and fuzzy rule-based decision mechanisms to solve high-dimensional constellation design problems. The framework generates the initial population using chaotic mapping, adaptively adjusts crossover strategies through fuzzy logic, and achieves multi-objective optimization via a weighted objective function. The simulation results demonstrate that the proposed method outperforms traditional algorithms in optimization performance, convergence speed, and robustness. Specifically, the average fitness value of the proposed method across 20 independent runs improved by 40.47% and 35.48% compared to roulette wheel selection and tournament selection, respectively. Furthermore, parameter sensitivity analysis and robustness experiments confirm the stability and superiority of the proposed method under varying parameter configurations. This study provides an efficient and reliable solution for the orbital design of large-scale SAR constellations, offering significant engineering application value. Full article

As the primary public source of satellite trajectory data, the Two-Line Element (TLE) dataset offers fundamental orbital parameters for space missions. However, for satellites with poor data quality, traditional neural network models often underperform, hindering accurate orbit predictions and meeting demands in satellite operation and space mission planning. To address this, a federated-learning-based trajectory prediction enhancement strategy is proposed. Satellites with low training efficiency and similar orbits are grouped for collaborative learning. Each satellite uses a Convolutional Neural Network (CNN) model to extract features from historical prediction error data. The server optimizes the global model through the Federated Averaging algorithm, learning more orbital patterns and enhancing accuracy. Experimental results confirm the method’s effectiveness, with a marked increase in prediction accuracy compared to traditional methods, validating federated learning’s advantage. Moreover, the combination of federated learning with basic neural network models like the Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU) is explored. The results indicate that integrating federated learning can greatly enhance satellite prediction, opening new possibilities for future orbital prediction and space technology development. Full article

This study investigates price decisions in a queue with two servers, where customers exhibit retrial behavior. There is no waiting space. Arrival customers have the option to either join the system or balk; when the two servers are occupied, those who decide to enter become repeat customers. Two scenarios where the waiting lines in orbit are unobservable and observable are considered. We analyze customers’ behavior and derive their Nash equilibrium strategies under both cases. Additionally, we examine optimal pricing decisions aimed at maximizing profit and social welfare, respectively. Moreover, we demonstrate that these objectives often lead to divergent outcomes. Compared to a single-server queue, the reduction in customers’ sojourn time is more obvious when the waiting line is unobservable. Under certain conditions—such as a large potential market size, high customer impatience, or a low retrial rate—increasing the number of service personnel can enhance both profit and social welfare. Notably, a profit-maximizing manager is more incentivized to increase servers than the social planner. These findings provide valuable insights for balancing operational efficiency, profitability, and customer satisfaction in queue management systems. Full article

Applying the acoustic orbital angular momentum (AOAM) wave for underwater imaging can yield richer differential target echo information, a consequence of its spiral wavefront phase and multiple mutually orthogonal modes. In broadband AOAM wave imaging, the resolution of conventional beamforming is very low. Although sub-band processing can improve resolution, it cannot handle coherent signal sources. To further enhance the resolution of broadband AOAM wave underwater imaging and address the imaging issue of coherent signals in practice, this paper proposed a modal-domain focusing beamforming method. This paper initially established the echo signal model of broadband AOAM waves based on a uniform circular array. This was followed by the derivation of the beam output signal model. Finally, a new modal-domain focusing transformation matrix was constructed. Numerical results show that the proposed method reduces the background level of the beam pattern to −86dB in simple coherent target source imaging, compared with −40dB for sub-band methods and −70dB for plane wave focusing processing. Furthermore, under different noise conditions, the proposed method achieves high-resolution imaging of complex structures and good imaging of details. Full article

This study addresses chaos control in a Buck–Boost converter using ZAD technique and FPIC. The system analysis identified 1-periodic orbits and observed the occurrence of flip bifurcations, indicating chaotic behavior characterized by sensitivity to initial conditions. To mitigate these instabilities, FPIC was successfully applied, stabilizing periodic orbits and significantly reducing chaos in the system. Numerical simulations verified the presence of chaos, confirmed by positive Lyapunov exponents, and demonstrated the effectiveness of the applied control methods. Steady-state and transient responses of the open-loop model and experimental system were evaluated, showing a strong correlation between them. Under varying load conditions, the numerical model accurately predicted the converter’s real dynamics, validating the proposed approach. Additionally, closed-loop control with ZAD exhibited robust performance, maintaining stable inductor current even during abrupt load changes, thus achieving effective control in non-minimum phase systems. This work contributes to the design of robust control strategies for power converters, optimizing their stability and dynamic response in applications that require precise management of power under variable conditions. Finally, a comparison was made between the performance of the Buck–Boost converter controlled with ZAD and the one controlled by PID. It was observed that both controllers effectively regulate the current, with a steady-state error of less than 1%. However, the system controlled with ZAD maintains a fixed switching frequency, whereas the PID-controlled system lacks a fixed switching frequency and operates with a very high PWM frequency. This high frequency in the PID-controlled system presents a disadvantage, as it leads to issues such as chattering, duty cycle saturation, and consequently, overheating of the MOSFET. Full article

The fully polarimetric aperture synthesis radiometer (FPASR) is capable of acquiring the fully polarimetric brightness temperature (BT), which has become increasingly significant in remote sensing. Antenna pattern errors can introduce significant errors to the reconstructed image of the FPASR. Analyzing and correcting the antenna pattern errors is crucial for obtaining high-quality BT images. In this paper, the antenna pattern errors are analyzed and classified into additive and multiplicative errors. A two-step correction method is proposed to reduce the influence of antenna pattern errors on the reconstructed BT. An end-to-end simulator for FPASR has been developed to assess both the antenna pattern errors and the effectiveness of the correction method. The simulation results show that the two-step correction method can reduce the brightness temperature error caused by the antenna pattern errors by over 70%. The successful image of the flight experiment validates the correction method as well. Full article