Browse Topic: Vehicles and equipment

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Behind-Helmet Blunt Trauma Deformation Geometry under Different Helmet Materials09-14-03-001610/26/2026
Modern combat helmets improve wearer survivability through preventing ballistic penetration via delamination or deformation of the shell material. However, this deformation may impinge upon the skull and present a risk of behind-helmet blunt trauma (BHBT). While legacy aramid helmets from the late 1970s–1980s inform current NIJ testing standards, the advent of lower areal density ultra-high molecular weight polyethylene (UHMWPE) and modern suspensions may incur unknown changes that present an unknown effect on BHBT risk. This study investigates whether current standards relying on a peak acceleration threshold and residual clay deformation may not reflect changes in deformation geometry seen in newer shell materials and suspension designs. Thirty-eight helmets categorized into three groups, older aramid, newer aramid (with new suspension), and prototype UHMWPE (with old suspension), were mounted on Hybrid III ATD head-necks and impacted on the temporal surface with 9 mm NATO projectiles at velocities between 388 and 482 m/s. Flash x-ray imaging captured deformation geometry. Deformations were digitized to measure maximum depth, volume, and surface area recruited. Multiple linear regression identified the influence of shell material and suspension on geometry. Results indicated that shell material significantly affected deformation volume and area (p < 0.05). Notably, UHMWPE exhibited a qualitatively more “pointed” deformation profile compared to aramid, recruiting material differently as depth increased. In contrast, suspension type and helmet design did not significantly influence deformation geometry. These findings suggest that the BHBT geometry in UHMWPE helmets differs fundamentally from aramid helmets. As UHMWPE helmets encounter higher-energy threats, current acceleration threshold or depth-based standards may not fully capture the risk of skull fracture. To future-proof helmet validation, testing standards should evolve beyond current standards to include area recruitment and other geometric measures.
Pang, Derek Y., Salzar, Rober S., Boggess, Brian M., Bass, Cameron R.
Influence of Blast Directionality, Intensity, and Combat Helmet Use on Head Surface Pressure Responses09-14-03-001710/26/2026
The widespread use of explosive weapons in recent military conflicts has contributed to an increased incidence of traumatic brain injuries. Although current infantry helmets provide effective protection against fragmentation, ballistic impact, and blunt trauma, they are not designed to mitigate primary blast loading. Previous studies suggest that blast waves may infiltrate the space between the head and helmet, leading to localized increases in pressure on the head surface, a phenomenon referred to as the underwash effect. However, experimental validation of the underwash effect remains limited, particularly for blast scenarios representative of buried-charge detonations, despite their potential to increase underwash-related loading across large regions of the cranial surface. This study experimentally evaluated the effects of combat helmet use, blast orientation, and blast intensity on head surface loading to assess helmet protective performance across various blast scenarios. A human surrogate headform instrumented with four flush-mounted pressure sensors was exposed to blast overpressures of approximately 16 and 24 psi using a large advanced blast simulator. Tests were conducted at 0° and 45° head orientations (rotation about the transverse axis), both with and without a combat helmet, with three repetitions per combination of experimental conditions. In the 0° orientation, helmet use reduced loading at the forehead and anterior head but increased loading at the posterior region. In the 45° orientation, helmet use led to increased peak pressure and total impulse at all measured locations. Increasing the blast intensity resulted in similar trends for both orientations and helmet conditions but with greater peak pressures and total impulses at each measured location. These findings suggest that certain blast orientations exacerbate the underwash effect and amplify cranial loading beneath the helmet. This work advances understanding of orientation-dependent blast loading, highlights limitations in current helmet performance, and offers guidance to support the development of future protective technologies.
Nelson, Allison J., Burt, Ethan T., Hamilton, Joseph M., Espinoza, Kenny J., Magallanes, Joseph, VandeVord, Pamela J.
Suppression of Traffic Oscillations by Vehicular Platoons with Periodic Control Configuration12-10-01-000310/21/2026
Traffic oscillations degrade the operational efficiency and safety of vehicular platoons. This study proposes a predecessor-following platoon with periodically assigned feedback gains and investigates its frequency-selective disturbance attenuation mechanism. An auxiliary second-order undamped periodic model is first established, and the Bloch–Floquet formulation is used to derive the nominal dispersion relation and bandgap boundaries. Closed-loop disturbance attenuation is then evaluated separately using spacing-error transfer matrices that retain positive velocity feedback and actuator-response lag. Frequency-domain transmission analysis and time-domain simulations show that, under nominal homogeneous and fully controllable connected and automated vehicle (CAV) assumptions, the periodic gain configuration creates tunable attenuation bands and provides stronger downstream attenuation than a homogeneous-gain configuration. The results clarify the relationship among periodic gain assignment, bandgap formation, periodic-cell string stability, and finite-length traffic-wave suppression. The effects of vehicle heterogeneity, sensing delay, and mixed traffic remain topics for future investigation.
Yang, Xiujian, Fan, Yanwen, Zhuang, Qingyuan, Qiao, Feiyu
Vehicle Attitude Control via Load Resistance Regulation of a Hydraulic–Electric Energy-Harvesting Suspension10-11-01-000710/21/2026
Vehicle dynamics during autonomous driving can cause passenger discomfort as well as motion sickness. This article introduces an energy-efficient vehicle attitude control method through load resistance control in a hydraulic–electric energy-harvesting suspension. A thorough study on how external load resistance affects a vehicle’s pitch, roll, and vertical acceleration has been conducted without requiring additional energy input. To analyze the effects that the variation of load resistance has on vehicle attitude, a 10 DOF vehicle model and a hydraulic–electric energy-harvesting suspension have been developed. The relationship between load resistance and vehicle attitude is defined and experimentally demonstrated in slalom tests under real-world conditions. The results demonstrate that reducing the load resistance from 70 Ω to 30 Ω reduces peak roll angle by 33.9% and peak pitch angle by 30.4%, while the RMS values are reduced by 46.2%, 28.6%, and 28.6% for roll angle, pitch angle, and vertical acceleration, respectively. A frequency-domain analysis shows that these improvements are achieved without shifting vibrational energy into the 4–8 Hz range most perceptible to humans. This systematic approach provides an energy-efficient way to improve vehicle stability and ride comfort, with potential implications for reducing motion sickness in autonomous driving systems. The quantitative relationship between load resistance and vehicle attitude provides a new understanding of the dynamics of a vehicle with energy-harvesting suspension systems.
Fan, Zhengwu, Liu, Wenbo, He, Mengfan, Xu, Guangzhao, Wu, Zhifei
Letter from the Guest Editors10-10-04-002710/20/2026
Letter from the Guest Editors
Ivanov, Valentin, Gunji, Daisuke, Savitski, Dzmitry, Zhu, Yueying
Fine-tuned Large Language Model Enhanced Nonlinear Model Predictive Control for Spacecraft Attitude Stabilization2026-99-296610/19/2026
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Guo, Zili, Gao, Sihong, Liu, Yuhan, Wang, Pengyu, Guo, Yanning
Trajectory Reconstruction Method for Earth-Moon Low-Energy Transfer2026-99-298010/19/2026
In Earth-Moon low-energy transfers, the strict geometric constraints among the Sun, Earth, and Moon often complicate trajectory redesign when the original transfer window is missed. To address this issue, this paper presents a nominal-trajectory-based reconstruction method for Earth-Moon low-energy transfers. Using the original mission trajectory as the reference, the method fixes the middle arc and performs segmented reconstruction that combines position targeting with velocity matching, so that only the escape and lunar insertion phases are locally corrected. This approach enables rapid trajectory reconstruction, efficient search for feasible launch windows, and evaluation of the corresponding velocity increment. Following this strategy, the existence and distribution of feasible solutions are examined on the hour, day, month, and year timescales. Simulation results show that feasible transfer windows with relatively low cost still exist within approximately four to six hours before or after the nominal window. Once the departure time shifts by one day, the total velocity increment rises sharply, and local reconstruction is no longer engineering-feasible. After waiting for about one synodic month, feasible windows reappear, and the required velocity increment falls back within the 200 m/s threshold. Within a one-year range around the nominal epoch, the feasible windows exhibit an overall periodic pattern that repeats approximately every 27 days. The proposed method provides a reference for trajectory reconstruction after a missed transfer window.
Wang, Yuxia, Li, Xiangyu, Zhou, Xingyu, Wang, Shen
The Emerging Strategies of Energy Management System for Fuel Cell Hybrid Aircraft in the Low-Altitude Economy2026-99-291910/19/2026
The low-altitude economy has been established as a strategic emerging industry, driving the development of various new aircraft types, including electric vertical take-off and landing (eVTOL) vehicles and industrial unmanned aerial vehicles (UAVs). In this development, proton exchange membrane fuel cell (PEMFC)/lithium-battery hybrid propulsion systems face challenges in balancing fuel economy, stack durability, and implementability under stochastic aerodynamic loads. Six energy management strategies (EMS) are systematically evaluated in this study, namely Thermostat, Fuzzy Logic, State Machine (SM-EMS), Power Following (PF-EMS), Dynamic Programming (DP-EMS), and a proposed load-disturbance-based method (LD-EMS). These strategies are tested on three distinct platforms, including a 2 kW fully simulated system, a 2 kW hardware-based setup incorporating a real PEMFC stack and Li-FePO4 battery, and a 30 kW hybrid testbed. Experiments were conducted under synthetic stochastic load profiles representative of gust-induced power transients. The results indicate that LD-EMS achieves a total equivalent hydrogen consumption of 2.71 g (close to the DP-EMS optimum of 2.64 g and 19 % lower than SM-EMS) while reducing fuel cell state-of-health degradation to only 0.0005 % over the test cycle, which is an order of magnitude better than PF-EMS and SM-EMS. On the 30 kW platform under two distinct challenging profiles, LD-EMS maintains fuel cell efficiency above 90 %, keeps the stack operating in high-efficiency zone, and limits power switching events to 97 and 212 respectively over test cycles, demonstrating robust scalability and transient suppression capability without requiring prior mission knowledge. A five-dimensional evaluation framework is introduced to compare the six EMS. In accordance with this framework, platform-specific selection guidelines are established. Thermostat EMS is recommended for small industrial UAVs where hardware simplicity dominates; LD-EMS is the optimal choice for passenger eVTOL under unpredictable gust conditions; and SM-EMS is preferred for commuter/regional aircraft operating on fixed, repetitive routes.
Gao, Zibo, Chigan, Dongdong, Xiao, Yizhuo, Li, Yuansheng, Ji, Guoming, Han, Qing, Li, Zhi-peng
Simulation Study on the Influence of Acceleration on Mass Flow Distribution of Supercritical Carbon Dioxide inside Complex Capillary Pipeline Structures2026-99-296110/19/2026
Supercritical carbon dioxide (sCO2) power cycles present promising application prospects in spacecraft and mobile energy systems owing to their outstanding advantages including high thermal efficiency and compact system structure. As a key component of sCO2 heat exchange systems, the complex capillary pipeline structure significantly influences heat transfer performance and system stability through its internal flow distribution uniformity. In this study, a multi-physics numerical simulation approach is employed to establish a complex capillary pipeline model with four compartments labeled A, B, C and D, aiming to explore the effects of different acceleration (overload) conditions on internal flow distribution. Various acceleration cases are considered, such as zero gravity, horizontal placement (0, 1g, 0), vertical placement (-1g, 0, 0), (0.15g, -0.7g, 0), (5g, -3g, 0) and (8g, -3g, 0). Meanwhile, the regulation effect of orifice plates on flow distribution is analyzed. The results demonstrate that both the magnitude and direction of acceleration exert remarkable impacts on the flow distribution ratio within the pipeline. Under vertical acceleration, flow distribution among the different branches becomes more uneven. Under high acceleration conditions, compartment A is prone to obvious flow deviation, which deteriorates overall flow uniformity. Orifice structures adjust flow distribution under acceleration pretty well—they work by changing the flow resistance characteristics. The present research provides reliable theoretical foundation and data support for the optimal design of complex capillary pipeline structures under variable acceleration environments.
Chen, Weihua, Wei, Xiaorui, Yang, Tianyu, Wang, Cong, Qin, Jiang
An Integrated Fault Diagnosis and Prediction Method for Planetary Gears Based on Ensemble Learning2026-99-294410/19/2026
Planetary gears are key transmission components in spacecraft drive systems and are prone to failures under long-term operation in harsh environments. To address the problem that fault diagnosis and life prediction are treated separately in existing research, this paper proposes an integrated diagnosis-prediction method. First, order tracking and time synchronous averaging are used to preprocess vibration signals, eliminating speed fluctuations and noise interference. Then, fault features are extracted via VMD/EEMD decomposition, and the gear health condition is identified through a deep convolutional neural network. Finally, ensemble learning prediction models are established separately for different fault states to realize life prediction. Experimental results show that the VMD+CNN method achieves higher diagnostic accuracy than traditional methods for gears in healthy, cracked, spalled, and pitted conditions. The ensemble prediction models matched with different states can further reduce the life prediction error. This method provides effective technical support for condition-based maintenance of spacecraft planetary gears.
Liu, Yanyan, Gao, Tongxin, Zhu, Yangsong
Coking Inhibition Characteristics of Dimethyl Disulfide during Supercritical Pyrolysis of n-Decane2026-99-299310/19/2026
Inhibition of coking from pyrolysis of hydrocarbon fuel is critical for ensuring the safe operation of high-speed aircraft. In this work, the inhibitory effects of dimethyl disulfide (DMDS) on coke during the supercritical pyrolysis of n-decane were investigated. Experiments using an electrically heated micro-reactor tube were conducted to study the influences of presulfidation and cofeeding methods with varying DMDS concentrations. The pyrolysis products were analyzed via gas chromatography, while pressure drop and coke mass were measured. The composition and morphology of coke were further characterized by energy-dispersive spectroscopy and scanning electron microscopy. The results show that the influence of DMDS on coke formation depends on its application method and concentration. At a DMDS concentration of 500 ppm, the coke inhibition rate reached 85.5% with presulfidation and 81.7% with cofeeding. However, with increasing DMDS concentration beyond 500 ppm, the amount of coke formation increases. Coke formation during supercritical pyrolysis of n-decane primarily involves metal-catalyzed coking and bulk-phase coking processes. The H2S and HS radicals generated from pyrolysis of DMDS react with transition metals such as Fe and Ni, thereby inhibiting the formation of metal-catalyzed filamentous coke. The presulfidation process forms a sulfide layer earlier than the cofeeding process, more effectively suppressing the generation of filamentous coke. However, excessively high DMDS concentrations can lead to its decomposition into sulfur-containing radicals such as thiol radicals (CH3S·), which incorporate into the coke matrix, enhance its reactivity, and promote coke formation. This work offers valuable insights for the application of DMDS as a coke inhibitor in endothermic hydrocarbon fuels.
Liao, Yan, Yao, Yixuan, Yang, Can, Huang, Qin, Chen, Yu
Deep Reinforcement Learning for Downlink User Scheduling in LEO Satellite Massive MIMO Systems2026-99-299010/19/2026
Low earth orbit (LEO) satellite communication system is expected to play a key role in the 6G wireless networks. Due to the limited communication resource and inter-user interference (IUI), the user equipments (UEs) must be well scheduled to maximize spectral efficiency. However, rapid channel variations and high mobility in LEO system make the conventional user scheduling strategies relying on instantaneous channel state information impractical. To address this challenge, this paper proposes a deep reinforcement learning (DRL) framework based on batch normalization in CrossQ algorithm to optimize the downlink user scheduling in a LEO satellite massive multi-input multi-output (MIMO) system. The scheduling task is modeled as a Markov decision process (MDP), where the observation space integrates high-dimensional features including large-scale fading (LSF) coefficients, channel matrix, and temporal features capturing the orbital progress of the satellite. Unlike traditional DRL methods, the CrossQ-based algorithm applies batch normalization (BN) within the critic networks to eliminate the need for slowly-moving target networks, fostering training stability and sample efficiency. A continuous priority score vector is mapped to discrete scheduling decisions through a top-z selection logic, allowing the agent to effectively identify spatially orthogonal user subsets to suppress the IUI. To guide the agent toward the true optimization direction, a differential reward mechanism is designed. Simulation results demonstrate that the proposed CrossQ-based scheduler consistently achieves nearly optimal performance at different signal-to-noise ratios (SNR), reaching at least 91% and 94% of the Monte Carlo search ceiling under different user densities. Moreover, the trained model provides scheduling strategy in millisecond inference time, making it a practical and robust solution for future 6G non-terrestrial networks.
Deng, Chaojun, Zhong, Yun, Meng, Xiangxu, Li, Wenfeng, Zhao, Kanglian
Emergence and Regulation of Scale-Free Correlation in UAV Swarm under Communication Energy Constraints2026-99-298310/19/2026
This paper studies how scale-free correlations, a key feature for efficient group response, emerge in collective motion under communication energy constraints. A two-dimensional Boids model integrated with a point-to-point wireless communication energy consumption model is used. The model dynamically determines, based on a limited communication power budget, an individual’s interactive neighbors, where neighbors are selected based on two different mechanisms: distance-based and probability-based, until the total communication cost reaches the budget. Simulations of flock sizes ranging from 30 to 230 individuals confirm that scale-free correlation persists, even under strict power constraints, which is shown by a correlation length ξ proportional to the flock’s linear size L. Notably, the ratio ξ/L, which reflects the robustness of collective correlation, displays a non-monotonic dependence on the available communication power. Initially, increasing power enhances ξ/L by expanding effective interaction ranges and improving information transmission. However, beyond a threshold, further power increase causes a slow drop in ξ/L, likely due to signal interference from excessively many or distant connections. This finding demonstrates that communication power is a critical, tunable engineering parameter regulating collective coordination, analogous to interaction range or neighbor number in biological models. The results imply that for engineered swarms, maximizing communication resources is not always beneficial; instead, an optimal power allocation exists to maximize coordination performance, balancing connectivity with efficient, localized interactions.
Zhao, Shiping, Gan, Qingzhong, Liu, Jinglei, Liu, Zhihao, Zhang, Liang
LASERAgent: Toward Highly Automated and Reliable Reproduction of LEO Networking Experiments2026-99-299110/19/2026
It is still difficult to reproduce the low-Earth-orbit (LEO) networking experiments since the experimental settings are scattered and the Large Language Model (LLM) assisted workflow is laborious and needs to be refined by manually crafting the prompt, transferring the code and debugging. Existing retrieval-assisted pipelines still demand substantial human effort for parameter inference and code repair, particularly given the precise alignment required for LEO topology, constellation, and simulator configurations. This paper presents LASERAgent, a highly automated agent system for LEO networking experiment reproduction. Unlike retrieval-assisted pipelines that mainly support evidence retrieval and code generation, LASERAgent organizes the downstream workflow into a bounded pipeline of retrieve - extract - generate - check - execute, integrating structured parameter extraction, template-guided generation, deterministic validation, controlled execution, and limited retry. Preliminary results show that LASERAgent reduces required human interventions by roughly half while improving result consistency over prior retrieval-assisted workflows, and that the validation stage plays an important role in reliable automation.
Tian, Siyu, Wei, Guangyu, He, Xiongwen, Liu, Donghua, Ma, Yue, Zhan, Yuheng, Xia, Lang
A Deep Learning Approach for Space Target Pose Estimation Based on Multi-Task Joint Training2026-99-298110/19/2026
The growing number of space objects raises collision risks and demand for on-orbit maintenance. Since most servicing tasks require observing the target, relative pose estimation is key for debris removal and satellite servicing. The complex orbital environment poses significant challenges for conventional feature extraction networks.. To address these challenges, this paper presents a multi-task convolutional neural network for space target pose estimation. The network uses a MobileNetV3-based encoder to extract image features and an Adaptively Spatial Feature Fusion (ASFF) module for adaptive multi-scale feature fusion. Although MobileNetV3 and ASFF have rarely been combined in the aerospace field, their combination offers significant advantages in low parameter count and reduced computational load, showing strong potential for spacecraft pose estimation. Two parallel prediction heads are employed: an EfficientPose head consisting of classification, bounding box regression, rotation matrix regression, and translation vector regression, together with a Heatmap head. This design leads to more robust and accurate pose estimation. The loss function follows a multi-prediction-head joint approach, where each component is weighted to form the final loss for multi-task optimization. The network is trained, validated, and tested on a simulation dataset of the DAWN satellite built with Unity. The dataset includes illumination variations, large distance changes, and multi-view target appearances, thus capturing the complex scenes typical of vision-based recognition tasks. Experimental results show that after 40 training epochs, the model achieves an average position error of about 0.05 m and a rotation error of about 0.8° on the test set. These results demonstrate the effectiveness and accuracy of the proposed network for space target pose estimation.
Wang, Wenzhi, Zhang, Zexu, Zhang, Fan, Song, Zhuo
Multivariate Satellite Telemetry Data Compression with Spatial Reduction Based on Linear Correlation-Guided Channel Selection and Receiver-Side Recovery2026-99-298910/19/2026
Satellite telemetry data compression is of great importance for preserving the integrity and effectiveness of telemetry information. As the number of telemetry parameters gradually increases, satellite states can be characterized from more comprehensive data dimensions. However, it is difficult to maintain long-term high-sampling-rate transmission for all channels constrained by telemetry bandwidth. This condition weakens the capability of telemetry data to capture satellite state variations in a sufficiently detailed manner. Therefore, it is still a challenge for selecting more representative telemetry channels and recovering the remaining dimensions. To address this issue, this paper proposes a data compression method based on linear correlation-guided channel selection and receiver-side Long Short-Term Memory Variational Autoencoder recovery, in which satellite telemetry data compression is achieved through spatial reduction. First, the Pearson correlation matrix of all channels is computed from telemetry data, and the structure among telemetry channels is analyzed based on a prior on pairwise linear relations. Second, a linear correlation-guided iterative channel selection strategy is developed under a fixed transmission budget. Candidate channels are scored by the residual correlation coverage matrix over the unselected channels, and the initially selected subset is further refined through local channel swapping to obtain a more globally representative transmitted subset. Finally, the transmitted channels are fed into the reconstruction model to recover the untransmitted channels, so as to ensure the transmission of telemetry information. Experiments are conducted on real multivariate satellite telemetry data under predefined transmission budgets. Results on 84-dimensional real operational satellite telemetry data show that the proposed method exhibits favorable data compression capabilities. Representative RMSE comparisons under different transmission budgets further showed favorable reconstruction performance, demonstrating the feasibility of the proposed method for telemetry data compression under limited communication resources.
Wang, Zhipeng, Zhang, Kuibin, Song, Yuchen, Peng, Yu
Research on Network Attack and Defense Technology in LEO Large-Scale Constellation Networking2026-99-298710/19/2026
With the rapid development of LEO large-scale constellations, space networks evolved into a critical domain of cyber confrontation. The heterogeneous architecture, open inter- satellite links and limited on-board processing capability introduce serious security risks. Aiming at advanced persistent threat (APT), data poisoning and spear phishing, this paper analyzes new attack mechanisms in LEO constellation networking, satellite-ground collaboration and communication - computation convergence scenarios. A satellite-ground-link collaborative active defense model is proposed to improve the security and stability of LEO constellation systems.
Guan, Mengmeng, Xia, Ming, Fang, Liang, Li, Ruxue, Lv, Xing
Research on Testing Methods and Practices of on Board Data Handling Subsystem Based on Spacecraft2026-99-294710/19/2026
Subsystem testing, as a crucial engineering phase in the development process of the On Board Data Handling (OBDH) subsystem to ensure product quality, is increasingly valued by engineering organizations. The OBDH subsystem, apart from its characteristics of embedded software and FPGA with strong real-time performance and close hardware relations, also features multiple external interfaces, large communication data volumes, and frequent software changes. These factors highlight the significant challenges in testing the OBDH subsystem, making it essential to develop a theoretical framework and methods to guide the testing process. Following the principles of the Director’s Order No. 7 for full-cycle and multi-dimensional work, and based on the testing characteristics of the OBDH subsystem, a full-cycle testing model for the OBDH subsystem has been established. This paper presents comprehensive research and practical implementation of OBDH subsystem testing from three aspects: test environment establishment, test method implementation, and test experience summarization.
Ye, Zhiling, Zhang, Tao
Transformer-Based Short-Arc Relative Motion Pattern Recognition for Non-Cooperative Space Objects2026-99-295310/19/2026
To meet the demands of on-orbit monitoring and space situational awareness, this study proposes a Transformer-based relative motion pattern recognition method based on the self-attention mechanism, addressing the issues of difficulty in accurately characterizing the relative motion patterns of non-cooperative space targets under short-arc observation conditions, strong dependence on prior knowledge in traditional methods, and insufficient stability under noise interference. Firstly, this study constructes a non-cooperative spacecraft motion pattern action data set based on the Clohessy–Wiltshire (C-W) relative motion equation. By designing typical initial conditions of relative motion, trajectory samples including fixed-point following, hovering following, oscillating following, coplanar circling, non-coplanar circling, approaching and receding, etc. of multiple motion patterns are generated, and the relative position and relative velocity sequences in the local orbital coordinate system are used as model inputs. Subsequently, an motion pattern recognition network composed of an input representation module, a Transformer encoding module, and a classification output module is constructed. The trajectory sequence representation is achieved through linear embedding and position encoding, and the multi-head self-attention mechanism is used to capture the global dependencies and key dynamic features between different time steps, thereby completing the discrimination of the target motion pattern category. Finally, model training and testing are carried out based on the constructed data set, and experimental analysis is conducted from three aspects: training convergence, classification performance in the absence of noise, and robustness in the presence of noisy observations. The results show that the proposed method can effectively learn the discriminative features in short arc segment relative motion, and performs well in terms of accuracy, Macro-F1, and confusion matrix; in the presence of Gaussian noise interference, it outperforms the Multi-Layer Perceptron (MLP) baseline model while still maintaining clear category boundaries and high recognition stability.
Mao, Yicheng, Zhang, Zexu, Li, Yingshuo, Song, Zhuo, Zhang, Fan, Liang, Jintang
A Wide-Angle Beam Scanning Algorithm for Reconfigurable Liquid Crystal Phased Arrays in Low Earth Orbit Satellite Acquisition2026-99-294910/19/2026
To satisfy the real-time beam tracking requirement of low earth orbit (LEO) satellite ground terminals under highly dynamic pass conditions, this paper proposes a three-state hierarchical state-machine beam-control algorithm for liquid crystal phased array antennas (LCPAs). The tracking process is divided into three stages. S1 coarse nine-point acquisition is used to rapidly cover a wide pointing-uncertainty region. S2 fine nine-point refinement introduces quadratic-surface fitting to obtain local peak estimation at sub-step resolution. S3 predictive tracking adopts an interacting multiple model Kalman filter (IMM-KF) based on the constant-acceleration (CA) model and the Singer model, and designs an adaptive covariance regulation mechanism for both measurement and process noise. Combined with explicit delay compensation for liquid-crystal response time, the method suppresses pointing lag and error accumulation in high-dynamic intervals. A dual-threshold hysteretic loss-of-lock criterion and a filter-reset mechanism are also integrated to guarantee autonomous link recovery. Simulation results show that, in an LEO pass scenario with a maximum angular rate of about 2 °/s, the proposed method reduces the median 2D RMSE by about 52%, reduces the 90% quantile error by about 54%, and lowers the median average power loss from 0.14 dB to 0.03 dB compared with the traditional scheme.
Tian, Lingrui, Guo, Xiaoxu, Xu, Yao, Chen, An, Zhu, Yaowei, Zhang, Zhizhong
Low-Cost Ground Station Network Siting for Geostationary Space-to-Ground Laser Communication2026-99-295110/19/2026
The deployment of multi-link ground station networks for geostationary space-to-ground laser communication is constrained not only by local weather conditions but also by infrastructure accessibility and engineering cost. Existing ground station siting studies have mainly emphasized cloud-related meteorological indicators and site diversity, while giving insufficient attention to deployment-oriented cost factors. As a result, meteorologically favorable solutions may still be expensive to implement in practice. To address this issue, this paper investigates the low-cost siting problem of a ground station network under a target network availability constraint and proposes a two-stage method for a multi-link, single-telescope scenario. Under the adopted assumption, each ground station can provide at most one available link at any given time, so the number of available network links is equal to the number of clear-sky available stations. The network availability is therefore defined as the probability that at least a required number of links are simultaneously available. Using half-hourly cloud mask data from the Himawari 8 geostationary meteorological satellite for the year 2023, site-neighborhood cloud metrics and inter-site weather correlation metrics are constructed. A data-center clustering and distance-decay model is then introduced to build an infrastructure impact cost field. On this basis, nationwide optimization is carried out through candidate construction and final subset selection. A baseline method without data-center factors is also evaluated under the same total-cost definition. The results show that, under the same target network availability requirement, the proposed method can effectively reduce total deployment cost. This indicates that incorporating data-center factors during siting improves the engineering tradeoff of multi-link geostationary laser communication ground station networks and provides a reproducible route for low-cost planning under service availability constraints.
Zhang, Chi, Zhao, Juntong, Li, Wenfeng, Zhao, Kanglian
ARET-ALSTM: Intelligent Beam Prediction and Dynamic Compensation Network for Aerial Base Stations under Non-Stationary Turbulent Conditions2026-99-295410/19/2026
With the unprecedented surge in demand for flexible deployment and ultra-high throughput in next-generation wireless networks, airborne base stations (ABS), particularly unmanned aerial vehicles (UAVs), have become emerged as indispensable mobile communication infrastructure. To facilitate high-gain directional transmission within the millimeter-wave band, ABS frequently employ massive MIMO antenna arrays. However, under random disturbances caused by non-stationary turbulence, the system faces a severe physical bottleneck: weak anti-interference capability due to narrow beamwidth, which can easily lead to a sharp drop in link capacity. Addressing the two major shortcomings of existing deep learning prediction schemes—susceptibility to sensor noise interference and negative capacity performance shifts during stable periods, and response lag due to memory inertia during sudden turbulence periods—this paper proposes an ARET-ALSTM active compensation network based on SNR-aware multimodal attention, agile reset and adaptive event-triggered mechanisms. This network incorporates an event-triggered mechanism governed by a dynamic physical variance threshold, adaptively executing compensation during stable intervals to prevent excessive overshoot and redundant computational overhead. Simultaneously, it employs an agile feature reset mechanism based on an adaptive sliding window for concept drift detection, forcibly discarding old memories and reconstructing attention weights during sudden wind shear, achieving millisecond-level ultra-fast adaptation and directly driving network optimization with the goal of maximizing communication capacity. Simulation experiments show that, under different array configurations ranging from 2×2 to 16×16 and complex flow conditions, ARET-ALSTM successfully overcomes the physical capacity collapse limitation under large antenna scales, and its tracking agility and robustness are significantly better than benchmark algorithms such as Transformer, RNN and traditional Kalman filtering.
Tu, Jiajun, Zhou, Jihua, Li, Yanhang, Zhao, Tao, Huang, Hua, Wen, Yuanbin, Guo, Ziwen
Research on Satellite Constellation Selection Method for GEO Multi-Satellite Relay Systems2026-99-295010/19/2026
Geostationary Earth Orbit (GEO) satellites, as a crucial infrastructure for global communication and emergency support systems, are highly valued due to their wide coverage, long communication distances, and large transmission capacity. However, the scarcity of orbital slots and the difficulty in quickly identifying available satellites in actual user scenarios have become the main bottlenecks restricting the communication efficiency of multi-satellite relay systems in geostationary orbit. To address these issues, this paper proposes an accurate and efficient method for selecting the set of available satellites for multi-satellite relay applications. We construct a spatial coverage model based on the H3 hierarchical discrete global grid system and a geometric model that characterizes the visible orbital arc of the user station. By comprehensively considering the geographic locations of gateway stations and user stations, satellite beam coverage boundaries, and the visibility constraints imposed by user terminal antenna pointing, the proposed method can accurately screen out qualified satellite sets that satisfy the end-to-end link condition of “gateway–satellite–user station”. Simulation experiments are conducted in the Ku-band to validate the effectiveness of the proposed scheme, with performance metrics including coverage probability, accessible satellite quantity distribution, and selection efficiency evaluated. The results show that the H3-based modeling approach effectively reduces computational complexity while ensuring high accuracy in visibility judgment. The proposed method significantly improves the accuracy and efficiency of satellite selection, and supports dynamic updates in mobile user scenarios. This work presents a reliable satellite selection algorithm, which can be used to support efficient resource scheduling and provide a strong guarantee for GEO multi-satellite relay communication.
Yu, Xinyue, Jiang, Yanzi, Lu, Yi, Hou, Yucheng, Su, Xingyu, He, Wenxi, Zhang, Jie
A Hybrid Routing and Domain-Boundary Authentication Framework for Heterogeneous Multi-Operator GEO-LEO Satellite Networks2026-99-295510/19/2026
Heterogeneous multi-operator GEO-LEO satellite networks serve as key infrastructure for next-generation global broadband communications. However, existing routing and security frameworks fail to resolve two fundamental challenges. First, orbital layers display inherent topological asymmetry: LEO intra-domain topologies suit fixed routing, while GEO-LEO links change dynamically. This dynamism makes pre-planned SR paths across the GEO layer unfeasible. Second, multi-operator networks introduce cross-domain trust boundaries without pre-established PKI. Centralized packet authentication is impractical for large constellations with intermittent ground contact. Existing solutions either use uniform SR or ground-assisted authentication, neither maintaining correctness in unstable polar regions. This paper proposes H-SID, a hybrid routing and authentication framework for multi-operator GEO-LEO networks. H-SID adopts a dual-layer architecture: SRv6 uSID enables deterministic routing in LEO domains, and a lightweight dynamic protocol handles real-time GEO forwarding. We design uSec, a 16-bit uSID instruction with VER and AuthType fields, allowing GEO nodes to adjust forwarding independently in polar instability. BG nodes perform SR-to-IP conversion and IBC-based authentication for cross-domain flows. GEO satellites act as offline IBE PKGs, issuing long-lived keys to BG nodes without data-plane operations. H-SID concentrates overhead at boundaries and works without cross-operator PKI, ensuring deployability.
Gao, Yuan, Li, Congcong, Ma, Hongxiao, Zhang, Shuo, Yan, Jingshan, Liu, Huiyong
A Dynamic Primary/Backup Path Algorithm Based on Multi-Level Feedback Queue for Differentiated Services in LEO Satellite Networks2026-99-297710/19/2026
With the accelerated deployment of Low Earth Orbit (LEO) mega-constellations, such as Starlink and OneWeb, satellite networks are transitioning from providing best-effort connectivity to supporting stringent, mission-critical services, including autonomous driving coordination, ultra-high-definition video streaming, and real-time financial transactions. This paradigm shift imposes highly heterogeneous Quality of Service (QoS) requirements regarding latency, bandwidth, and reliability. However, traditional unified scheduling and static routing mechanisms are inadequate for handling the dynamic topologies and bursty traffic inherent in LEO environments, often leading to service starvation or excessive packet loss. To address these challenges, this paper proposes an integrated primary-backup path guarantee framework that tightly couples a Multi-Level Feedback Queue (MLFQ)-based dynamic scheduling algorithm with a Dynamic Weight Adaptive Routing Algorithm (DWARA). The MLFQ scheduler employs an aging-based priority promotion mechanism to dynamically adjust time slices, effectively preventing low-priority traffic starvation while ensuring ultra-low latency for Class A services. Concurrently, DWARA models QoS as a three-dimensional vector (Throughput, Delay, Reliability) and utilizes the eigenvector method to derive service-aware weights for path selection. To validate the efficacy of the proposed scheme, comprehensive simulations were conducted using STK and Python, emulating a 64-node polar constellation under varying fault conditions. The results demonstrate that the integrated framework significantly outperforms baseline algorithms. Specifically, it reduces the average blocking rate by up to 37% under scenarios with 10 faulty satellites compared to Dijkstra, while lowering end-to-end latency for delay-sensitive services to approximately 28 ms at a traffic arrival rate of 500. Furthermore, the framework achieves a 92% bandwidth satisfaction rate for high-throughput services and maintains near-zero packet loss for high-reliability transmissions, offering a scalable and robust QoS assurance solution for next-generation space-air-ground integrated networks.
Zhang, Hongyuan, Zhang, Pengfei, Zhou, Qihao, Gao, Zhao
Lightweight Compression Networks for Efficient RGBD-IR Imagery Transmission on Resource-Constrained Edge UAV Platforms2026-99-297810/19/2026
Due to the limitation of unstable wireless links and resource-constrained edge computing device, it is challenging to transmit the unmanned aerial vehicles (UAVs) captured images with large data volume in real-time. To address this issue, this paper proposes a lightweight hybrid compression method named Jpeg Plus Plus Network (JppNet) for efficient multimodal image transmission. Different from existing methods that mainly focus on RGB image compression for offline storage and often use large networks for coding and reconstruction, the proposed JppNet focuses more on edge deployment under limited computing resources. At the encoder side, JPEG is first used to perform high-ratio compression on the RGB, D, and IR channels. Then, a lightweight convolutional encoder network extracts supplementary binary features for progressive transmission. At the decoder side, a medium-scale reconstruction network fuses the supplementary code with the JPEG reconstruction to restore image details. To verify the effectiveness of the proposed method, this paper built a multimodal data acquisition prototype and collected 20,000 high-quality RGBD-IR image samples for training and testing. Experimental results show that JppNet combines the high coding efficiency of JPEG with the detail recovery ability of convolutional neural networks. With low computational cost, JppNet obtains much lower reconstruction errors than JPEG on the RGB, D, and IR channels, especially at very low compression ratios. This supports the efficient transmission of UAV captured multimodal images under limited communication conditions.
Pi, Haoran, Ao, Yile, Li, Zhi, Tang, Gang, Jiang, Bowu
Methodological Study on Fluid-Structure Interaction Dynamics of Spacecraft in Martian Rarefied Atmosphere Aerobraking2026-99-293210/19/2026
During aerobraking, spacecraft deploy large flexible solar arrays to obtain enough aerodynamic drag. In the rarefied Martian atmosphere, rapidly varying dynamic pressure causes low-frequency elastic vibrations of these appendages, leading to coupled translation-rotation-vibration fluid-structure interaction (FSI) that affects both aerodynamic characteristics and attitude stability. Traditional high precision methods such as DSMC are difficult to apply to long-duration simulations owing to their high computational cost. To solve this problem, this paper proposes a one-way FSI framework, which conbines a cross-regime panel method for aerodynamic calculation with a reduced-order multibody dynamic model. The simulation results show that the framework can reduce the computational burden while retaining the main physical characteristics required for the analysis. The aeroelastic response of a representative spacecraft configuration is analyzed, and the results can support configuration design, aerobraking corridor constraints, and control system development.
Zhou, Tianhao, Cui, Hutao
Multi-Objective Optimization Design Method for MEO Constellations Oriented towards LEO Constellation Coverage2026-99-293110/19/2026
Aiming at the challenges faced by Low Earth Orbit (LEO) constellations, such as difficulties in cross-shell communication, complexity in global management and control, and highly dynamic network topologies, this paper evaluates the introduction of a Medium Earth Orbit (MEO) constellation as a management layer to optimize the coordination and servicing of the underlying LEO constellation. The core contributions of this work are twofold. First, a coverage performance analysis method based on Voronoi diagrams and Delaunay triangulation is adopted to evaluate the coverage of MEO constellations over LEO constellations. Second, a four-dimensional multi-objective fitness function is constructed, comprehensively considering launch cost, communication delay, coverage performance, and space security. On this basis, a multi-objective genetic algorithm is employed to optimize the key parameters of a Walker-type MEO constellation, including orbital altitude, orbital plane count, and satellites per plane. Simulation results show that, under the condition of a LEO constellation altitude of 600 km, highly fit MEO configuration parameters are obtained. STK simulations verify that this configuration can achieve complete and dynamic coverage of the underlying LEO constellation. This study provides a systematic theoretical framework and an efficient algorithmic tool for solving the configuration optimization problem of the MEO layer in the "LEO-MEO" architecture.
Li, Ruiyao, Cui, Hutao
Pose Tracking and Formation Keeping Control of Spacecraft Formations under High-Order Control Barrier Functions2026-99-297610/19/2026
Aiming at the constraint issues of adjacent spacecraft solar panel collision avoidance and formation keeping in the pose tracking control of spacecraft formation, this paper proposes a distributed relative position and attitude cooperative control strategy on the basis of high-order control barrier functions. First, nominal control strategies for relative position and attitude tracking using the sliding mode control method to achieve high-precision tracking of relative motion are de-signed. Then, high-order control barrier functions suitable for solar panel collision avoidance and spacecraft formation keeping constraints are constructed. The control strategy constraints satisfying safety specifications are derived based on the forward invariant set theory. Finally, a quadratic programming optimization framework is established by combining the nominal control strategies and constraint conditions and then we obtain the optimal control strategy by solving this framework. Numerical simulations verify that the designed control scheme achieves precise tracking of relative position and attitude for spacecraft formation, avoids collisions between solar panels of adjacent spacecraft and prevents excessive loosening of the formation configuration.
Zhu, Ziqi, Qin, Wenyu, Lyu, Yueyong
Adaptive Optimization of Fault Diagnosis Models Based on Reinforcement Learning and Robustness Quantification Feedback2026-99-299510/19/2026
Spacecraft operate in a highly complex in-orbit environment. Telemetry data from power systems is often plagued by multi-source disturbances such as plasma noise, electromagnetic interference, and communication packet loss, which places very strict demands on fault diagnosis model reliability. Using dynamic disturbance test manifolds and the comprehensive robustness quantification index (RQI) for evaluation, we found a critical vulnerability: baseline models such as the joint loss enhanced global sparse attention transformer (JLE-GSA-Transformer) perform well in resisting continuous signal disturbances, but their performance drops sharply when temporal structures are disrupted due to data missingness. This finding highlights that static diagnostic models have fragile robustness boundaries when operating under extreme non-stationary conditions. To solve this problem, this paper puts forward an adaptive optimization method that combines deep reinforcement learning (RL) with quantitative robustness feedback. By making use of the dynamic disturbance manifold, this method embeds the RQI into the reward function of the RL algorithm. Through continuous updates to the agent’s policy, it drives the adaptive adjustment of key parameters in the JLE-GSA-Transformer, mainly including the coding structure of global sparse attention (GSA) and the discriminative boundaries of temporal cosine-center joint loss. This closed-loop mechanism can significantly improve the stability, adaptability, and long-term reliability of the model when it works in complex environments with multi-source disturbances.
Liu, He, Wei, Cheng, Zhang, Zexu
Robust Cross-Modal UAV Detection via Bidirectional Collaborative Energy Field under Weak Calibration2026-99-299210/19/2026
The precise detection and localization of 'low-slow-small' (LSS) unmanned aerial vehicles (UAVs) pose significant challenges in terminal defense scenarios. These non-cooperative targets have extremely sparse representations in radar point clouds because of their minimal radar cross-sections and appear as textureless, blurred patches in visual images. Furthermore, traditional multi-modal fusion paradigms rely heavily on rigid geometric projection matrices, which frequently fail under the mechanical vibrations, thermal deformations, and extrinsic perturbations inherent in rapid deployment environments, which often results in severe feature misalignment and contamination. To overcome these critical bottlenecks, we present an end-to-end cross-modal detection framework built on Bidirectional Collaborative Energy Fields (Co-Field). By redefining the 3D object detection task as an energy field reconstruction process within a latent multimodal feature space, we entirely remove the need for absolutely precise extrinsic parameters. Our framework uses a novel bidirectional mutual-guided interaction mechanism. Specifically, a vision-guided radar branch employs deformable cross-attention to adaptively aggregate highly discrete radar geometric features around visual semantic centers, achieving a feature-level soft alignment that implicitly compensates for calibration drift. Concurrently, a radar-corrected vision branch utilizes radar-derived geometric occupancy probabilities as depth anchors to gate and calibrate visual features, effectively suppressing the inherent divergence of monocular depth estimation. Ultimately, the framework regresses the target's precise 3D states and fine-grained categories directly from the extrema of the reconstructed collaborative energy field, circumventing complex heuristic post-processing. Experiments on the curated MMAUD dataset show that the proposed Co-Field method achieves better robustness. It remains highly effective even under severe calibration errors up to 10 degrees and achieves a 74.2% recall rate at extreme sparse distances ranging from 70 to 100 meters, and consistently outperforms state-of-the-art rigid and soft fusion baselines.
Peng, Chao, Shao, Peng, Ding, Qiao, Chen, Xiaoqing, Han, Aojia, Xu, Yuelei
Study of On-Orbit Rigid-Flexible-Thermal Coupling Dynamic Response and Attitude Control of Large Spaceborne Deployable Parabolic Truss Antenna2026-99-290910/19/2026
Modern space exploration relies more and more on vehicles that carry expansive antenna arrays, a trend driven by steady progress in the field of space engineering. The study centers on constructing a coupled model that accounts for structural rigidity, flexibility, and thermal effects in a sizable orbital unfurling parabolic antenna, along with designing its orientation regulation system. A blended coordinate formulation and a heat-based discretization approach are employed to derive the motion and temperature-governing equations of the assembly. A unified pointing control method is then developed by merging classical feedback control principles with torque compensation control. Computational experiments involving a spacecraft fitted with such a dish antenna are executed. The findings reveal that sudden heat fluctuations, exemplified by the transition out of Earth’s umbra, trigger oscillatory behavior in the antenna framework and disrupt the satellite’s angular position. The designed orientation regulator successfully counteracts these thermally driven perturbations, even when the system representation contains inaccuracies.
Zhou, Shijie, Liu, Xiang, Cai, Guoping
Analysis of MIMO Channel Characteristics under Large Spatio-Temporal Scales Based on Distribution Formula2026-99-296210/19/2026
In recent years, the global demand for high-rate, high-reliability, and wide-coverage wireless communications has continued to grow. Traditional satellite communications are faced with bottlenecks such as spectrum scarcity, insufficient single-satellite capacity, and poor adaptability to highly dynamic channels. Relying on the advantages of wide-area coverage and multi-satellite cooperation, low Earth orbit (LEO) satellite constellations can construct distributed satellite MIMO systems and form virtual large-scale antenna arrays, significantly improving system capacity and reliability. However, such systems are characterized by large spatiotemporal scales, and their channels exhibit strong heterogeneity in dimensions such as delay, frequency, and polarization, which are difficult to accurately evaluate using existing methods. To this end, this paper proposes a large-spatiotemporal-scale channel characteristic analysis method for distributed LEO satellite MIMO systems. A satellite-terrestrial full-link channel model is established, and quantitative characterization of multi-dimensional heterogeneity is realized based on statistical boundary analysis. Combined with the H3 global grid system, large-scale simulations are carried out to statistically analyze the distribution laws of delay differences, frequency offsets, and polarization angle deviations under different user configurations. The unique value of this analysis method lies in that its output results can be used to determine the upper limit of buffer capacity required for the receiver to complete coherent combining of multi-channel signals and the frequency offset correction range required for signal acquisition, provide a quantitative basis for optimizing satellite set selection, and offer a solid theoretical model and data support for performance evaluation and parameter design of multi-satellite cooperative systems.
Lu, Yi, Hou, Yucheng, Su, Xingyu, Yu, Xinyue, Jiang, Yanzi, Zhang, Jie, He, Wenxi
Move Blocking Event Triggered Model Predictive Control of Spacecraft Formation2026-99-295610/19/2026
This paper addresses the problem of formation reconfiguration for deputy spacecraft flying in a spatial circular formation around a chief spacecraft. To improve the real-time applicability of formation control under onboard computational limitations, a reconfiguration method is proposed by integrating Tschauner–Hempel (T-H) relative dynamics, Event-Triggered Model Predictive Control (ET-MPC), and a Move Blocking strategy, while explicitly considering collision-avoidance constraints. Based on the T-H relative dynamic model, an MPC framework is established to generate control inputs for the reconfiguration process. To reduce the computational burden of conventional MPC, an event-triggering mechanism is introduced so that the optimization problem is solved only when necessary, rather than at every sampling instant. Meanwhile, a Move Blocking strategy is employed to reduce the number of online decision variables by constraining successive control moves over predefined intervals. In addition, to simultaneously address inter-spacecraft collision avoidance and limited-thrust constraints, an optimization model with binary variables is formulated. To evaluate the proposed approach, four simulation cases, namely MPC, ET-MPC, MB-MPC, and MBET-MPC, are conducted under the same reconfiguration scenario. Simulation results show that all four methods can successfully accomplish the formation reconfiguration task while satisfying the minimum safe-distance requirement. Moreover, the methods incorporating Move Blocking reduce the computation time required for solving each individual optimization problem, while the event-triggered schemes significantly reduce the number of optimization updates compared with the corresponding time-triggered methods, thereby further lowering the overall computational cost. The results demonstrate that the proposed method provides an effective and feasible solution for real-time formation reconfiguration and control on resource-constrained spacecraft platforms.
Zhang, Yuning, Zhang, Zexu, Liang, Jintang
Coupled Vibration Analysis and Vibration Suppression for Spacecraft Multi-Panel Structures by Stiffness-Tunable Hinges2026-99-292410/19/2026
Spacecraft hinged-panel structures feature a multi-hinged configuration, substantial dimensions, low stiffness, and low damping, making them susceptible to sustaining low-frequency vibrations under multi-source excitations. To address this challenge, a novel stiffness-tunable hinge is proposed for low frequency vibration attenuation. Firstly, the hinge structural geometry and stiffness-tunable principle are presented. Subsequently, FEM models of multi-panel structures incorporating stiffness-tunable hinges are developed. The natural frequencies and mode shapes of the system are investigated. Simulation results indicated that the multi-panel structures reveal bending vibrations, torsional vibrations, and coupled bending–torsional vibrations of the hinge-panel structure. Finally, a microgravity experimental platform was developed, this experimental platform offers several advantages, including extremely low friction, unconstrained horizontal motion, a compact structure, a small footprint, and robust reliability. The gravitational load of the hinged-panel is nearly counterbalanced, thereby enabling its unrestricted, omnidirectional translation across the horizontal plane. The experimental results rigorously validated the link between hinge stiffness-tuning and system eigen-frequency migration. Furthermore, vibration suppression at low frequencies of 0.29 Hz (for three-panel) and 0.31 Hz (for four-panel) was realized using non-contact magnetic excitation. This approach achieved amplitude reductions of approximately 9.70 dB at 0.29 Hz and 12.53 dB at 0.31 Hz. These findings demonstrate a novel strategy for suppressing low-frequency vibrations in spacecraft structures.
Bi, Zhiwei, Lin, Hui, Hu, Wei, Wang, Yufeng, Zhang, Ce, Yu, Ruijiang
Rapid Trajectory Optimization for Mars Aerobraking Campaigns2026-99-294010/19/2026
Aerobraking is a key technology for reducing propellant consumption in deep space exploration missions. Targeting the Mars aerobraking trajectory optimization problem, this paper proposes a rapid trajectory optimization method based on a high-precision analytical approximate solution. First, a Mars aerobraking dynamics model is established. Based on the assumptions of a linear flight path angle and a linear altitude rate, a high-precision analytical approximate solution for Mars aerobraking trajectories is derived. Simulation results verify that the proposed analytical solution satisfies the accuracy requirements for Mars aerobraking trajectory optimization. Based on this analytical solution, the state transition function of the aerobraking trajectory optimization problem is reconstructed, and the Sequential Quadratic Programming (SQP) algorithm is employed for the optimization. Numerical simulation results demonstrate that the proposed method requires less than one minute to optimize an aerobraking campaign consisting of 80 atmospheric passes. Compared with traditional optimization methods, it exhibits significant advantages in computational efficiency when dealing with aerobraking trajectory optimization problems characterized by long durations and multiple velocity pulses.
Ding, Yuancheng, Dong, Jie, Cui, Hutao, Chen, Zheng
OpenClaw-Enabled Real-Time Diagnosis of Inter-Satellite Links in Decentralized Satellite Network Emulation2026-99-291510/19/2026
With the continued expansion of low Earth orbit (LEO) satellite constellations, inter-satellite links (ISLs) have become a critical infrastructure for network connectivity and end-to-end forwarding capability. Existing studies often show a gap between the physical layer and the system layer: physical factors such as link budget, pointing error, and Doppler mismatch are frequently simplified, while link performance analysis is difficult to validate in a closed loop using a real protocol stack. In addition, dynamic topology and time-varying link states make fault detection and localization highly complex.To address these problems, this paper constructs a decentralized satellite network emulation platform and proposes a decentralized addressing and connectivity verification method for full inter-satellite link interfaces. At the physical layer, an optical inter-satellite link (OISL) model is developed by incorporating free-space path loss, Doppler mismatch, and BER-FEC-PLR mapping, with its dynamic evolution driven by orbital parameters. At the system layer, based on a one-satellite-one-container architecture, OpenClaw is introduced to perform link detection and log analysis, thereby generating diagnostic reports.Experimental results show that the proposed method supports interface-level link detection, identifies dynamic link faults such as transient impairments caused by Doppler mismatch in real time, and generates corresponding diagnostic reports.
Zhao, Jiangtong, Luo, Jiangtao, Zhang, Jinshuo, Ran, Yongyi
A CTCM-ALA Adaptive Embedded Fusion Optimization Method for UAV Path Planning in Complex Medical Rescue Environments2026-99-290510/19/2026
To improve the accuracy and safety margin deficiencies of unmanned aerial vehicles in autonomous medical emergency search-and-rescue missions, this paper develops an adaptive embedded fusion optimizer named CTCM-ALA for rescue UAV path planning. The proposed algorithm hybridizes the tribe-competition member-cooperation strategy and artificial lemming algorithm to construct hierarchically adaptive initial populations, speed up convergence and prevent local convergence traps. Tests on 12 CEC2017 functions prove its better optimization performance. Real-world applications in dense urban and complicated topographic medical rescue cases verify its reliability and precision.
Chen, Yuhan, Xiong, Junhui, Li, Yuesheng, Zhou, Yifan, Yao, Junjie, Li, Rujun
Switched Adaptive Flight Control of UAV Carrier Subject to Transient Separation Disturbances2026-99-295210/19/2026
A switched adaptive flight control architecture is proposed for fixed-wing carrier-sub UAVs to handle abrupt mass and inertia variations, as well as parameter uncertainties induced by transient aerodynamic interference, during sequential deployment. The separation process is rigorously modeled as a switched linear system, and a combined direct and indirect adaptive control scheme featuring an online Recursive Least Squares (RLS) modification is designed to compensate for model uncertainties. Furthermore, a dwell-time admissible switching law is established by solving Linear Matrix Inequalities (LMIs), ensuring the stability of the switching process through theoretically guaranteed Lyapunov energy drops at switching instants. Numerical simulations verify that transient tracking error spikes are significantly suppressed, and high-frequency oscillations are effectively attenuated compared to baseline controllers.
Chi, Haiyi, Yuan, Shuai
A Genetic Algorithm-Based Autonomous Mission Planning Method for Limited-Thrust Modes2026-99-294810/19/2026
To meet the requirements of rapidity, economy and efficiency for space debris removal missions and space debris governance missions, this study designs an autonomous mission planning scheme under finite thrust conditions, to achieve the disposal of multiple target space debris within a single orbital period. The scheme decomposes the planning problem into two sub-problems: mission object sequence optimization and transfer orbit design, and establishes mathematical models from the perspectives of global mission scheduling and local trajectory optimization respectively. An improved genetic algorithm is adopted for solution, to ensure the optimization of resource utilization under fuel constraints. This study systematically elaborates the theoretical basis, optimization indicators and constraint modes of the planning method, constructs a complete technical framework, and verifies the correctness and effectiveness of the method through numerical simulations, providing methodological guidance for the autonomous implementation of in-orbit rendezvous missions.
Yu, Dateng, Guo, Peng, Li, Ziyi
Trajectory Planning for Heterogeneous Multi-Satellite Cooperative Proximity Operations2026-99-291410/19/2026
This study addresses the challenges in heterogeneous multi-satellite cooperative proximity operations, including functional heterogeneity, strong temporal coupling, and difficulties in unified planning. A functionally layered framework composed of observation, support, and servicing satellites is constructed. Considering the distinct mission characteristics of different satellite types, differentiated decision variables, objective functions, and constraints are designed, and a particle swarm optimization algorithm is employed for solution. Simulation results demonstrate that the proposed method can generate feasible cooperative trajectories satisfying constraints such as safety distance, temporal coordination, communication links, and mission windows, while maintaining system stability and operational safety. Parametric analysis indicates that mission duration, minimum access distance, and support effectiveness significantly affect system performance. Through functional decomposition and cooperative optimization, the system achieves an effective trade-off among different preferences, providing theoretical support for the mission design and control of heterogeneous multi-satellite proximity operations.
Xiao, Jinzhao, Zhang, Jin
Research on the Flow Characteristics of the Airflow Field in an Open Fan Array2026-99-297910/19/2026
Small aerial vehicles operating at low altitude are frequently exposed to wind fields with strong spatial gradients and rapid temporal variations. Although conventional closed-section wind tunnels provide well-controlled test conditions, they are generally unable to reproduce the large-scale non-uniformity typical of urban low-altitude environments. Open multi-fan arrays provide a promising alternative because they can generate programmable inflow conditions over a relatively large test area. In this study, a 9 × 9 open fan array is investigated using the momentum-source method to reproduce representative inflow conditions, including uniform flow and shear flow. The numerical results reveal a persistent edge-dissipation effect: velocity decays more rapidly and turbulent kinetic energy is higher near the outer fans than near the array center. In the uniform-flow case, a relatively stable low-turbulence corridor develops downstream of the array. In the shear-flow case, the imposed asymmetry is preserved, while edge-induced losses become more pronounced near the perimeter. These results clarify the main flow structures generated by open multi-fan arrays and provide a physical basis for future wind-environment testing of aerial vehicles.
Lu, Yuexuan, Tu, Zhan, Li, Daochun, Li, Huadong, Liu, Yiliang
Distributed Cooperative Task Allocation for Many-to-Few On-Orbit Servicing Missions2026-99-293010/19/2026
This paper investigates cooperative task allocation for multi-satellite clusters under onboard functional payload constraints in many-to-few low Earth orbit non-cooperative servicing scenarios involving heterogeneous space targets. Although the servicing satellite cluster has a numerical advantage, the effectiveness of a single satellite is limited by orbital maneuver cost and the stringent requirements of onboard functional payload systems on rendezvous accuracy and interaction timing. As a result, multiple satellites are generally required to cooperate to establish local numerical superiority for effective task execution. Meanwhile, differences in object strategic importance and resistance capability may lead to excessive resource concentration on high-value objects, thereby degrading overall operational efficiency. To address these issues, an existing multi-satellite cooperative task planning framework is adopted to characterize the matching relationship between resource supply and task demand by jointly considering maneuver cost, target value heterogeneity, and coalition-size requirements. On this basis, a distributed cooperative mechanism is introduced to enable the satellite cluster to adaptively form coordinated task coalitions matched to object value. Simulation results show that, in representative many-to-few LEO servicing scenarios, the resulting allocation scheme can establish local cooperative resource concentration while satisfying full target coverage and no-idle-satellite constraints. It also suppresses excessive competition for high-value objects and improves the balance of resource utilization, thereby demonstrating the effectiveness of the proposed approach for cooperative task allocation in multi-satellite LEO servicing scenarios.
Liu, Jiawei
Dual-Quaternion Based Nonlinear Model Predictive Control for Quadrotor Leader-Follower Formation2026-99-297110/19/2026
To effectively address the challenges of cooperative control for quadrotor formations in complex environments, this paper extends the dual-quaternion based nonlinear model predictive control (DQ-NMPC) framework to leader-follower formation. Unlike conventional methods that treat translation and rotation separately, the dual-quaternion based model achieves a unified and compact representation of rigid-body motion using only eight real numbers without singularities, enabling global description on SE(3). Through the logarithmic map, the pose error is mapped from the Lie group to its Lie algebra, resulting in a quadratic cost function that naturally resolves the dimensional inconsistency between position and attitude errors, thereby simplifying weight tuning and improving numerical stability. Building on this formulation, a UAV formation controller is developed, where the relative motion between leader and follower is concisely described using dual quaternions, and a minimum safe distance constraint is explicitly incorporated to avoid collisions and maintain safe separation. In formation scenarios, the follower autonomously accomplishes chasing, rendezvous, and formation keeping under various trajectories, including straight-line pursuit and Lissajous curves. Moreover, the stability of formation control is validated under non-ideal conditions such as communication delay, mass mismatches, and constant external disturbances. Furthermore, the optimal weight combination is determined through grid search, which further improves formation tracking accuracy and control smoothness. In addition, the proposed framework inherently supports time-varying formation patterns, such as dynamic switching between different relative configurations and adaptive scaling. Computational efficiency analysis shows that the average solution time per control cycle is far below the sampling interval, validating real-time feasibility. Overall, the DQ-NMPC framework provides a unified, robust, and efficient solution for quadrotor formation control under both ideal and perturbed environments.
Li, Jiaye, Zhang, Zexu, Liu, Jingzong
Lightweight and Fast Reconstruction Algorithm for 5D Spatio-Temporal-Frequency Situation Based on Kronecker Compressive Sensing2026-99-298410/19/2026
Large-scale UAV clusters make real-time reconstruction of joint spatio-temporal-frequency (STF) situations difficult because the monitored state is high-dimensional and strongly coupled. This paper proposes a lightweight reconstruction algorithm based on Kronecker Compressive Sensing (KCS). First, a Multi-Channel Random Demodulation (MCRD) sensing model is built, and a Spatio-Temporal-Frequency 5D Grid (STF-5D Grid) is used to represent space, frequency, and time resources in a unified form. Next, a Tensor Orthogonal Matching Pursuit (T-OMP) algorithm is developed under the KCS framework. By using the separability of Kronecker products, the method replaces storage and multiplication of a global high-dimensional sensing matrix with dimension-wise operations. Two simulation studies are conducted to evaluate computational feasibility and reconstruction quality. In the city-scale case with 62.91 million grid cells, the factorized implementation avoids constructing the global sensing matrix and completes the tested reconstruction in 9.44 s. The reported memory value is interpreted as additional algorithmic storage for the factorized operators rather than total process peak memory, and should be rechecked with full tensor input/output and runtime overhead included. In noise-free, highly sparse proxy tests, several reconstruction errors approach the numerical precision floor; these results are therefore treated as ideal upper-bound evidence rather than practical field accuracy. The simulations suggest that KCS-based T-OMP can reduce storage and computational pressure for STF reconstruction, while its engineering performance still requires validation under noisy observations and realistic platform constraints.
Tian, Jinghao, Wang, Qianzhe, Di, Shen, Wei, Tan, Song, Bowen
Gradient-Driven Point Allocation for Depth-Guided 3D Gaussian Splatting in Indoor Scenes2026-99-299410/19/2026
High-precision 3D environmental perception is crucial for autonomous systems to navigate and interact effectively within complex indoor environments. However, achieving high-granularity 3D reconstruction and high-fidelity rendering remains challenging. Although 3D Gaussian Splatting (3DGS) enables efficient photorealistic rendering, its geometric inconsistency limits its ability to reconstruct high-accuracy scenes. Standard 3DGS relies on sparse Structure-from-Motion (SfM) initialization, which often leads to uneven point distributions and floaters, particularly in weak-texture areas like plain walls. Furthermore, existing methods based on depth regularization or geometric constraints usually fail to address the fundamental sparsity of the initialization. To address these issues, we propose a lightweight gradient-driven point allocation strategy for depth-guided Gaussian splatting that bypasses traditional SfM. By utilizing RGB-D priors, we obtain a high-quality, structurally continuous initial Gaussian distribution through multi-view depth back-projection and texture-aware density control. Our method incorporates a bidirectional re-projection check to eliminate transient noise and occlusions, ensuring geometric consistency. We introduce a dual-grid allocation scheme that adaptively distributes Gaussians based on spatial frequency, mandating density in high-gradient regions while enforcing sparsity in flat areas. Quantitative and qualitative experiments on the ScanNet++ dataset demonstrate that our method achieves superior reconstruction accuracy and rendering quality. Notably, our approach achieves state-of-the-art performance with a significantly reduced Gaussian count, decreasing from 1.70M to 0.39M in representative scenes, thereby mitigating unnecessary point cloud bloat and reducing memory overhead without sacrificing structural integrity.
Shang, Yuhao, Qu, Zhenshen, Rong, Guanyi, Gao, Qiushuang, Yu, Zhiwei, Bakar, Elmi Bin Abu, Junos, Mohammad Haniff Bin
Performance Prediction of Hall Thrusters with Deep Learning Algorithms2026-99-298810/19/2026
Accurate performance prediction is essential for improving the reliability and service life of Hall thrusters in space applications. Because conventional scaling-based prediction methods are often limited when the operating state and magnetic-field structure become complex, this study develops a deep-learning framework for rapid prediction of key Hall-thruster performance indicators. Three representative neural-network architectures, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM), are constructed and comparatively evaluated. Among them, BiLSTM achieves the best overall performance, with a Mean Absolute Percentage Error (MAPE) of 6.44% on the test set. Additional validation based on real Hall-thruster experimental data gives a MAPE of 3.884%, indicating that the proposed model has good generalization ability and can provide an effective data-driven tool for Hall-thruster performance prediction.
Sun, Chang-You, Tian, Bin, Yu, Yun-Chao, Wang, Ke, Li, Shu-Feng, Xie, Kan, Yang, Su-Lan
A Health State Assessment Method Based on Autoencoder and Analytic Hierarchy Process for Spacecraft Components2026-99-297310/19/2026
Health assessment of complex systems mainly relies on expert experience or a data-driven health assessment model trained by a large volume of historical condition monitoring data. However, only relying on expert experience to handle the health information contained in a large amount of telemetry data is very time-consuming and labor-intensive for spacecraft, and it does not make full use of a large amount of historical data. Moreover, due to limitations such as space-ground data transmission resources, it is also difficult to obtain all the condition monitoring parameters at each system level for different health states. In response to these challenges, this paper proposes a health assessment framework combining expert experience and data-driven strategy to achieve a multi-level quantitative assessment for spacecraft components. Specifically, a deep autoencoder is trained to construct the state change curves of the key condition monitoring parameters, and the quantitative score map is constructed for each parameter. Then, by analyzing the matrix of pairwise importance for parameters based on expert experience, the health status change curve at a second level of the component is constructed. Finally, a quantitative score map of the component status is provided using the health status change curve to realize the multi-level quantitative health assessment. A case study on health assessment for an attitude control actuator is conducted to show the overall procedure and effectiveness of the health assessment framework. The results demonstrate that the proposed method can effectively integrate expert knowledge and data characteristics, providing a feasible framework for the multi-level health assessment of spacecraft components.
Dong, Yunjia, Li, Yuqing
Collaborative Design of Low Earth Orbit Multi-Layer Constellations for Coverage and Networking Performance2026-99-290710/19/2026
With the growth of global communication demand, multi-layer Low Earth Orbit (LEO) satellite constellation has become an important part of the next generation network. The multi-layer hybrid constellation can effectively improve the coverage performance and network connectivity through the cooperative work of satellites with different orbital heights. In this paper, a dynamic model of constellation topology is established for multi-layer LEO constellations. The index system of coverage and networking is constructed by using the strategy of fixed neighbor inter-satellite link (ISL) in the same layer and the nearest neighbor dynamic inter-layer link (ILL) between layers. Through simulation, the performance of single-layer and multi-layer constellation configurations is compared. The influence of design parameters such as orbital altitude, number of ILLs and inter-layer satellite resource allocation on the indexes of multi-layer constellation is analyzed. The simulation results show that the multi-layer constellation is superior to the single-layer constellation in terms of coverage continuity and network capacity with a fixed total number of satellites. As the number of ILLs increases from 1 to 5, the average shortest path length (ASPL) and the average inter-satellite propagation delay decrease, and the network capacity is significantly improved. Reasonable inter-layer resource allocation and link configuration can effectively improve the networking performance of multi-layer constellations.
Wu, Kejie, Chen, Quan, Li, Linhua, Liu, Erjiang
Nonlinear Dynamic Modeling and Active Control of a Large-Scale Two-Dimensional Planar Phased Array Antenna2026-99-290110/19/2026
A new method for dynamic modeling and active control of the large-scale, on-orbit assembled two-dimensional planar phased array antenna is proposed in this study. As the demand for high imaging accuracy and scanning coverage in satellite antennas increases, there is a shift toward larger apertures. However, large-scale phased array antennas face challenges such as low rigidity and significant nonlinear vibration during attitude maneuvers, which can degrade pointing accuracy and shape precision. To address these issues, this study proposes a dynamic modeling framework that incorporates geometric nonlinearity, employing finite element method (FEM) and a polynomial fitting-based model order reduction technique. A rigid-flexible coupling dynamic model is then developed based on the velocity variation principle. In control design, the paper integrates an active vibration control law using cable actuators with a computation torque algorithm based on control moment gyroscopes (CMGs) to design an integrated attitude-vibration control system. This system aims to suppress nonlinear vibration while achieving high-precision attitude control. Simulation results demonstrate the effectiveness of the proposed integrated control approach in both vibration suppression and attitude maneuvering, offering valuable insights for its practical application in future space missions.
Ma, Yihao, Liu, Xiang, Jin, Chaochen, Cai, Guoping
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