Browse Topic: Data acquisition and handling

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Target Positioning Method for Unmanned Aerial Vehicle Tracking Based on Beidou2026-99-235310/30/2026
In order to support successful tracking and monitoring tasks in different environments, drones need to quickly and accurately identify targets. This paper studies a drone combat target positioning method, with a focus on the BeiDou satellite navigation system and its performance under different environmental conditions. A complete system has been designed, including flight control system, environmental sensors, data transmission and processing system, aiming to achieve precise positioning and target tracking of unmanned aerial vehicles. This paper uses on-site testing to explain the coverage range and stability positioning accuracy of ranging signals in different environments. Experimental data shows that the BDS system has good positioning error performance for unmanned aerial vehicles, with an average error of 46.52 cm. BDS positioning time is usually short, with a maximum positioning time of 780ms and a minimum positioning time of 224ms. However, the BDS system still needs improvement in terms of dynamic environmental adaptability, multi-target tracking, anti-interference ability, and adaptability to harsh environmental conditions. Future work will focus on algorithm optimization, hardware technology improvement, and data security to enhance system performance.
Tao, Zhiping, Zhang, Chenghao, Hou, Bo, Song, Juan, Yuan, Peng, Liu, Xianyang
Optimization Method for UAV Scheduling Based on Beidou2026-99-235210/30/2026
Currently, UAVs are increasingly used in remote sensing monitoring, logistics and distribution, emergency rescue and recreational aerial photography, etc., which puts higher requirements on wireless networks, i.e., how to allocate limited wireless resources in real-time, efficiently and fairly to ensure that a large number of UAVs can work together in complex environments and maintain high-quality communication services. This paper investigates an optimisation method for UAV scheduling based on the BeiDou satellite navigation system, which improves the accuracy and efficiency of UAV mission execution by utilising the high-precision positioning and time synchronisation services provided by the BeiDou system. The results of the experiment showed that as the number of missions increased from 10 to 50, the average mission completion time increased from 8.3 to 12.5 minutes, the average speed increased from 30 to 34 km/h, the average energy consumption increased from 0.8 to 1.2 kWh, the average distance flown increased from 12 to 22 km, the communication success rate decreased slightly from 99.2 to 98.2%, the resource utilization rate increased from 85.1% to 87.5%. These results show that the method performs well in terms of task completion time, energy consumption, communication success rate and resource utilisation, which provides strong support for the application of UAVs in the fields of logistics and distribution, emergency rescue, and so on.
Ji, Hongbin, Jiang, Kexin, Zhang, Dongxue, Bai, Yanhui, Liu, He, Zhang, Shuangna
HER-DETR: Lightweight UAV Object Detection Based on High-Frequency Enhancement and Multi-Scale Sparse Fusion2026-99-233610/30/2026
The rapid growth of the low-altitude economy has accelerated the deployment of unmanned aerial vehicle (UAV) technology in a wide range of applications, including power infrastructure inspection and disaster relief operations. These scenarios impose stringent real-time processing demands on onboard vision systems. However, aerial images are characterized by a high density of small and distant targets, which significantly weakens feature representation quality, and current state-of-the-art detection models carry substantial computational overhead that conflicts with the limited hardware resources available on embedded UAV platforms. This paper addresses both challenges simultaneously by proposing a novel lightweight, end-to-end detection framework designated the High-frequency Enhancement Retentive Detection Transformer (HER-DETR). The framework introduces three coordinated technical contributions. First, a lightweight backbone network termed the Cross Stage Partial High-frequency Enhancement Network is constructed. This backbone integrates a cross-stage high-frequency residual enhancement mechanism to alleviate feature attenuation commonly observed in small-target scenarios. Second, a Depthwise Intrascale High-Low Feature Interaction block is designed for intra-scale feature interaction at the highest semantic level, strengthening fine-grained representation through a dual-frequency decoupling strategy. Third, a Retention Feature Fusion module is developed as the core cross-layer feature fusion component, enabling bidirectional multi-scale feature integration via a lightweight sparse attention mechanism. Comprehensive experiments on the VisDrone2019 and HIT-UAV benchmark datasets confirm that HER-DETR achieves state-of-the-art performance. Compared with the RT-DETR-r18 baseline, the proposed framework improves mean average precision at a 0.50 intersection-over-union threshold by 2.0 percentage points on VisDrone2019 and by 1.6 percentage points on HIT-UAV, while simultaneously reducing model parameter count and computational floating-point operations by 41.6% and 35.3%, respectively. These results validate the framework’s capability to balance detection accuracy and computational efficiency for real-time UAV deployment.
Jiao, Minghai, Liang, Yinghan, Zhang, Ende, Kan, Shuang
Research on Failure Mode Verification of Civil Aircraft Based on Reliability Data2026-99-231410/30/2026
To verify the accuracy and completeness of FMEA and FTA, this paper proposes a comprehensive failure mode verification method based on reliability data. This method collects multi-source reliability data and identify actual failure modes occurring in equipment during operation firstly, thereby compiling a list of actual failure modes. Next, this list is compared with the FMEA report from the design phase to verify the failure modes themselves. A deeper qualitative verification of the causes, mechanisms, and impacts of the failures is then conducted, leading to the correction and updating of the FTA logical structure to ensure consistency between the safety analysis model and the actual failure logic.Building upon the qualitative verification, the method proceeds to quantitative verification. A comparative analysis is conducted between the actual failure rates of the failure modes and the estimated values in the FMEA. Using the validated failure rate data, the probability of the FTA top event is recalculated and compared against the safety target probability specified in airworthiness regulations to achieve quantitative verification of the failure condition probability. As a core innovation and enhancement of the method, this paper introduces sensitivity analysis. Through tools such as calculating Birnbaum importance and plotting tornado plots, it quantitatively assesses the sensitivity and impact of uncertainties in the failure rate parameters of each bottom event on the probability of failure condition, thereby identifying the key failure modes. The research findings provide theoretical support and a practical engineering foundation for evaluating the actual reliability level of the aircraft fleet, improving the accuracy of safety analysis during the design phase, and guiding preventive maintenance and design improvements.
Han, Bing, Li, Haiwei
Vibration–Acoustic Emission Fusion and STFT–MFDCNN for Fault Diagnosis of Bearings in the Aero-Engine2026-99-231510/30/2026
Rolling bearings are key components of aero-engine rotor systems, and their degradation can trigger abnormal dynamics and safety risks. Unplanned bearing failures increase downtime and inspection cost; early detection enables condition-based maintenance and reduces conservative replacement. Early faults are weak, intermittent, and often masked by operating variability, so diagnosis based on a single sensing modality is usually incomplete. Vibration signals describe global dynamic behavior and resonance responses, whereas acoustic emission (AE) captures micro-damage processes and short impulsive events. Given the differences in bandwidth and noise sources, combining vibration and AE can provide a fuller description of bearing states. This study proposes an STFT–MFDCNN framework for bearing fault diagnosis using fused vibration–AE information. First, synchronized vibration and AE signals are converted into time– frequency images via the short-time Fourier transform (STFT), preserving spectral content and temporal non-stationarity in a unified representation. Second, a dual-branch multi-scale feature dense convolutional neural network (MFDCNN) is constructed. In each branch, parallel convolutions with different kernel sizes capture fault patterns across receptive fields, and dense concatenation connects all preceding feature maps to improve feature reuse and gradient flow. Third, global average pooling generates compact feature vectors for both branches, which are concatenated in a fusion head and classified by a Softmax layer. Experiments on a vibration–AE dataset built in this work show that the proposed method reaches 97.78% identification accuracy. Compared with dual-branch fusion baselines based on AlexNet, ResNet, and a shallow CNN, accuracy increases by 3.34%, 4.45%, and 5.00%, respectively. Compared with single- modality models (Only-AE and Only-Vib), accuracy improves by 14.71% and 6.77%.
Li, Zhichuang, Wang, Xingfeng, Zhang, Yanpu, Yang, Guoan
Feature Fusion and CNN-Based Acoustic-Emission Diagnosis for Aero-Engine Initial Damage2026-99-231610/30/2026
Acoustic emission (AE) signals generated by incipient aero-engine damage are usually weak, intermittent, and highly dependent on sensor position, which makes diagnosis based on a single channel unreliable. To address this issue, this paper develops a multi-sensor AE diagnosis framework that combines entropy-guided feature fusion with a convolutional neural network (CNN). Time-domain and frequency-domain descriptors are first extracted from AE waveforms collected by multiple sensors. The consistency of each feature type across channels is then evaluated using feature entropy. Features with lower entropy are considered more informative for damage discrimination and are therefore retained for fusion. Based on the normalized channel contributions of these selected features, an adaptive weighted fusion matrix is constructed and used as the input to the CNN. The method is validated on a self-built aero-engine structural test rig equipped with 12 AE sensors. Fifteen representative initial-damage cases are designed, including signal excitation, crack-like excitation simulated by pencil-lead break (PLB), and impact-induced micro-deformation cases. Experimental results show that the proposed framework achieves a best average classification accuracy of 96.80% and provides more balanced class-wise performance than conventional machine-learning and deep-learning baselines. Additional noise-injection tests further indicate that the method maintains good robustness over a wide signal-to-noise-ratio range. These results suggest that entropy-guided multi-sensor AE fusion is a practical solution for intelligent diagnosis of aero-engine initial damage.
Jin, Yucheng, Han, Cong, Zhang, Leixuan, Yang, Guoan
ME-YOLO: Aircraft Corrosion Defect Detection Model Based on Multi-Directional Edge Perception and Multi-Scale Feature Fusion2026-99-232110/30/2026
Detecting surface defects on aircraft is critical to safety in the aerospace field. The current detection methods suffer from low efficiency, high false positive rate and poor adaptability to complex environment. Although the existing target detection algorithms have achieved notable performance in this field, at present, many models in this field still face considerable challenges. For example, due to the constraints of hardware platforms, machines that can be deployed on edge devices such as UAVs and robots need lightweight and easy-to-deploy models, but lightweight designs often a certain degree of accuracy degradation. At the same time, most of the surface defects of the aircraft are small targets, which impose higher demands on the model. In order to solve these limitations, this paper proposes an improved YOLO model ME-YOLO (multi directional edge fusion YOLO) based on edge enhancement and multi-scale fusion. Firstly, the EdgePyramid module is designed, which uses the rich edge responses extracted by the Multi Orientation Attention Sobel (MOAS) module and through the three-layer cascade pool structure to construct the three-scale edge features aligned with the backbone network. Then, the EdgeEnhance module is introduced to adaptively fuse the conventional convolution features and edge features at the corresponding resolution, effectively enhancing the sensitivity of the model to the corrosion boundary. Experimental results show that, compared with the baseline YOLOv11, ME-YOLO improves mAP@0.5 by 2.7% and simultaneously increases Recall by 2.3%, which helps reduce missed detections of corrosion targets. Although the introduction of multi-directional edge priors brings a slight increase in parameters (+3.66M) and a minor decrease in inference speed (--8.6%), the model still maintains a real-time detection capability of 97.54 FPS, meeting the deployment requirements for edge devices such as UAVs. This strategy of trading a modest computational overhead for improved detection rates in safety-critical tasks offers a potentially practical and robust alternative for intelligent operation and maintenance of aviation equipment.
Zhou, Xing, Li, Yuan
Research on Fault Diagnosis of Civil Aircraft Engine Thrust Reverser Deployment Overspeed with Small Samples Based on Semi-Supervised Learning2026-99-231910/30/2026
Civil aircraft engine thrust reversers are easy to have overspeed faults when they are being deployed, therefore this condition is very important for flight safety and maintenance work efficiency. The low sampling frequency, about 1Hz, of onboard data recording systems, for example WQAR, together with external environment interferences such as sudden strong winds, therefore causes a quite large number of wrong and missed alarms in fault data. Therefore, the labeled data that can be obtained are not only few in number but also have low quality. In this article, a fault diagnosis framework which is based on semi-supervised learning is put forward to solve the problems of high labeling expenses and fault diagnosis under the condition of small samples. This framework makes use of a small amount of labeled data and a large amount of unlabeled data. It uses Semi-Supervised Support Vector Machines, Semi-Supervised Neural Networks, and Semi-Supervised Random Forests for dynamic self- training to realize classification of three kinds of states: “Normal State,” “Low-Speed Thrust Reverser Deployment Exceedance,” and “Medium-Speed Thrust Reverser Deployment Exceedance”. The experiment outcomes show that the Semi-Supervised Neural Network model which uses self-training method has the highest classification accuracy on the test set, achieving 94.8%. Further robustness analysis, such as label noise and feature noise disturbance experiments, indicates that this model displays the smallest performance lowering under noisy environments, hence it represents the best overall performance. This research gives an effective method for the diagnosis of engine thrust reverser function problems in an effective and strong way.
Yuan, Xiaojiang, Liu, Nan
High-Temperature Sealed Disc Spring Compression Load-Holding Performance Testing and Material Parameter Calibration2026-99-232210/30/2026
In high-temperature sealed assemblies, disc springs provide sustained preload to suppress leakage. However, at elevated temperatures, material creep and stress relaxation may reduce elastic recovery and accumulate permanent deformation, thereby degrading sealing reliability. Because the material properties of commercially procured disc springs vary with suppliers and production batches, they are difficult to apply directly in the analysis of high-temperature components. This study investigates Inconel-800 disc springs. Sustained compression tests were conducted at 600–900 °C to systematically obtain load–time responses and the temperature dependence of permanent deformation. The results show that, as temperature increases, the rate of force decay accelerates significantly and the residual load decreases markedly. At 800 °C and 900 °C, the permanent deformation approaches the compression limit, indicating that time-dependent material deformation becomes the dominant factor. Based on these results, a finite-element model consistent with the test conditions was established. Material creep parameters were calibrated from component-level compression data by minimizing the discrepancy between simulated and experimental curves. The identified parameters were validated under different temperature conditions and loading cases. The simulation results agree well overall with the experiments, and the errors remain within engineeringly acceptable limits. This study provides baseline data and an engineering method for obtaining material parameters to support material selection and structural optimization of high-temperature sealed disc springs.
Yang, Yuyao, Cai, Xianhui
Study on Interface Design and Functional Tolerance of Structured Composites for Zero Thermal Expansion Performance2026-99-232310/30/2026
Structured zero thermal expansion composites demonstrate significant application potential in thermal deformation control for high-end equipment, particularly aerospace precision instruments, due to their unique ultra-low thermal expansion properties. However, during the transition from theoretical design models to manufacturing models, uncertainties at connection interfaces and manufacturing errors alter the thermal deformation coordination between heterogeneous components within the material, leading to significant degradation of its zero thermal expansion performance. To address this issue, this study focuses on lattice-structured zero thermal expansion composites derived from topology optimization. First, the connection interface design between heterogeneous lattice components was completed. Subsequently, two typical manufacturing errors—dimensional errors and assembly tolerances—were incorporated into the manufacturability model. Using Monte Carlo random sampling, a mapping relationship was established between the random and discrete manufacturing errors and the composite's equivalent thermal expansion performance. Building upon this foundation, a yield rate metric based on functional tolerance concepts was introduced. This metric was used to evaluate how different interface configurations affect the stability of zero thermal expansion performance. The results demonstrate that, under identical manufacturing precision conditions, different interface configurations exhibit significantly varying thermal expansion stability in response to manufacturing errors characterized by structural parameter dispersion. This research provides valuable insights for translating theoretical designs of such functional materials into manufacturable solutions that meet thermal expansion performance requirements.
Yan, Shaojie, Yang, Zihao, Zhang, Yongcun
PINNs-Based Parameter Estimation for Gradient Descent2026-99-230310/30/2026
To address the joint estimation of state and parameters for fixed-wing aircraft in wind gradient environments, this paper proposes a high-precision identification method based on a physics-informed neural network. By integrating wind gradient models with flight dynamics equations, a multi-objective optimization framework combining data-driven approaches with physical constraints is established. Employing polynomial time embedding and a dual-branch network architecture, coupled with a dynamic training strategy, simultaneous estimation of flight state and wind gradient parameters is achieved. Experimental results demonstrate that this method achieves approximately 7% parameter estimation error while accurately reconstructing unobserved attitude information. Requiring only four-dimensional observation data, this approach provides an effective solution for wind gradient glide navigation in low-cost aircraft.
Wu, Ting, Bai, Zhuoyue, Gao, Xianzhong
An Improved ResU-Net for Thermally Grown Oxide Layer Segmentation in Thermal Barrier Coatings2026-99-236110/30/2026
Thermal barrier coatings (TBCs) serve as the critical protective layer for gas turbine components. In complex service environments, the thermally grown oxide (TGO) layer formed between the top coat (TC) and the bond coat (BC), along with the evolution of its microscopic morphology, is a key factor determining the service life of TBCs. To address the complex morphology of TGO layers that challenges conventional segmentation methods, this paper proposes MSF-SA ResU-Net, a multi-scale feature fusion self-attention model based on the ResU-Net architecture, for the image segmentation of thermally grown oxide layers. Building upon residual connections, this model incorporates two key enhancement modules at the bottleneck layer: Multi-Scale Fusion (MSF) and Self-Attention (SA). During the feature processing stage, the MSF module effectively captures the multi-scale morphological features of the TGO layer through receptive fields of different sizes, solving the difficulty of extracting long-strip and irregular morphologies. Subsequently, the SA module recalibrates the fused features by calculating the global dependencies between them, enhancing the network's attention to TGO boundaries and micro-defects. Comprehensive experiments on an in-house dataset of 3,000 SEM images demonstrate that MSF-SA ResU-Net outperforms standard U-Net and contemporary segmentation architectures. Furthermore, due to the feature extraction capability of the ResNet backbone network, the model maintains high accuracy and exhibiting good convergence performance. This makes the architecture an effective solution for the microscopic analysis of thermal barrier coatings.
She, Weicen, Song, Yan
Configuration Design and Implementation of the SuperView Neo 2 Satellite System2026-99-237010/30/2026
With the rapid advancement of spaceborne Synthetic Aperture Radar (SAR) technology, distributed Interferometric SAR (InSAR) has emerged as the definitive method for high-precision global topographic mapping. This study focuses on the SuperView Neo 2 (SVN2) satellite system, developed by China Siwei Surveying and Mapping Technology Co., Ltd., detailing its technical configuration design and operational implementation within the next-generation commercial remote sensing sector. Equipped with X-band high-performance phased-array SAR payloads, the SVN2 system is engineered for the high-efficiency production of global 1:25,000-scale Digital Surface Models (DSM) while providing millimeter-level surface deformation monitoring through Differential InSAR (DInSAR) capabilities. The paper emphasizes a systematic design approach that integrates four coupled elements: mission objectives, system performance, orbit design, and formation geometry. To satisfy the rigorous requirements of interferometric mapping, the system adopts a sun-synchronous, strictly repeating frozen orbit with a 200-meter radius “pipeline” control strategy. By implementing flexible formation configurations for high and low latitudes, SVN2 achieves the triple synchronization of time, space, and phase necessary for sub-meter resolution and high vertical accuracy. Using the SVN2-03/04 cluster as a primary case study, the results demonstrate that through a sequence of over 40 precise orbital maneuvers, the dual-satellite system successfully established a close-range fly-around formation within the 100-meter-level pipeline just seven days after launch. This operational success represents three major technical breakthroughs for Chinese commercial radar satellites: high-precision strictly repeating orbit maintenance, stable close-range formation flying, and fully autonomous inter-satellite cooperative control. The SVN2 constellation provides a robust, all-weather mapping guarantee for the global geographic information framework and supports diverse applications in geological hazard detection and social economic planning.
Tang, Ke, Qiao, Yishi, Huang, Tao, Zhang, Guoliang, Zhao, Di, Zhang, Zhongze
Experimental Study on Two-Phase Flow and Heat Transfer Characteristics in Rectangular Enhanced Channels2026-99-232610/30/2026
With the rapid development of information technology, electronic devices in aerospace, data centers and other fields have faced increasingly severe thermal runaway issues. Two-phase heat transfer technology, with its superior heat dissipation performance, has attracted growing attention and research. A test rig was established to investigate the flow boiling characteristics of R134A within rectangular enhanced flow channels. The test conditions are listed below: heat load ranging from 100W to 3000W, vapor quality from 0.05 to 0.7, and flow rate from 5 L/h to 240 L/h. The results indicate: With the increase of inlet flow rate, The phase change pressure drop gradually increases within the rectangular enhanced flow channels. Similarly, the relationship holds true for phase change pressure drop and cold plate heat load, showing a distinct linear relationship. Under constant heat load, the phase change flow resistance within rectangular enhanced flow channels gradually decreases with increasing the vapor quality. The pressure drop changes dramatically when the vapor quality is below 0.3 and becomes significantly more gradual when the vapor quality exceeds 0.4. Under the test conditions, the maximum density of heat flux is approximately 230 W/cm^2, corresponding to a maximum temperature rise of about 22°C in the heating unit. The equivalent maximum two-phase heat transfer coefficient exceeds 12,000 W/(m^2K). When the cold plate outlet vapor quality is between 0.4 and 0.7, the flow resistance within the rectangular enhanced flow channels is relatively low, and the cooling performance is relatively high. It is recommended that two phase system should be designed within this vapor quality range.
Shao, Yawei, Li, Benke, Li, Li, Qian, Jiyu, Wei, Tao
Multilevel Bayesian Model Updating for Satellite High-Stability Brackets Considering Welding Uncertainties2026-99-292510/19/2026
The high-stability bracket is the primary component in remote sensing satellites responsible for carrying the camera and attitude control equipment; its dynamic behavior directly affects imaging quality and in-orbit service life. Welding-based connections introduce uncertainties during manufacturing and assembly, leading to significant discrepancies between finite element analysis results and experimental data. To quantitatively assess the impact of welding processes on the accuracy of the high-stability bracket’s finite element model(FEM), this study proposes a Bayesian parameter correction method based on Markov Chain Monte Carlo (MCMC) sampling and the Metropolis-Hastings (MH) algorithm. It employs a multilevel correction experimental framework—”material-level, component-level, and system-level”—to progressively refine material parameters and establish a high-fidelity FEM. First, the elastic modulus of the welded and non-welded regions were calculated using stress-strain curves obtained from static tensile tests on welded and non-welded plates; then, targeting the first two natural frequencies from dynamic impact tests on bar specimens, the material densities of the welded and non-welded regions were corrected using the Bayesian method; finally, the effectiveness of the correction method was verified through free vibration tests on the actual high-stability bracket. The results indicate that, compared to the undamaged region, the welded region exhibits a 13.87% loss in elastic modulus and a 55.32% loss in density. The relative errors in the first three natural frequencies of the corrected model were 0.59%, 0.01%, and 0.83%, respectively, verifying the high reliability of the proposed method in modal identification and providing a new technical approach for correcting spacecraft structural models.
Liu, Kun, Guo, Chongchong, Wu, Wenhua
Adaptive Slicing and High-Reliability Transmission Method for Spacecraft On-Orbit Data2026-99-292210/19/2026
Spacecraft On-orbit data are characterized by large volumes, stringent timeliness requirements, and heterogeneous data values, posing significant challenges to traditional satellite-ground transmission systems. To improve transmission efficiency and reliability under dynamic link conditions, this paper proposes an adaptive data slicing and collaborative reliability enhancement method. First, a joint evaluation model integrating Data Impact and Link Health is developed to evaluate data importance and transmission conditions. Based on the evaluation results, the slice size, priority allocation, and redundancy configuration are dynamically optimized. Second, a distributed collaborative verification mechanism combining Reed–Solomon erasure coding and multi-node arbitration is introduced to enhance data integrity and recovery capability. Furthermore, a mission-driven elastic scheduling strategy is designed to facilitate efficient resource allocation in multi-user transmission scenarios. Experiments were conducted on 100 telemetry files with a total volume of 1.9 GB in a simulated satellite-ground communication environment. Experimental results demonstrate that the proposed method improves average throughput by 25.3% and achieves a reconstruction success rate of 99.7% across 1,000 packet-loss reconstruction trials.
Li, Cheng, Liu, Peng, Chen, Xi, Cui, Yan, Li, Han, Li, Xian, Wang, Zhihui, Yi, Xiu
Efficient Extrapolation-Based Dynamic Link Prediction for Large-Scale Satellite Networks2026-99-290610/19/2026
Inter-satellite links (ISLs) in large LEO constellations vary quickly with time, and their rapid change makes future link-state prediction a practical concern. Predicting ISLs with reasonable accuracy benefits routing decisions, resource scheduling, and overall network stability. Existing methods are still limited in several ways. Rule-based and physics-based methods build on hand-crafted assumptions, which become unreliable once links flicker frequently or topologies grow heterogeneous. Static graph neural networks describe the relations within a single snapshot, but their formulation does not represent how ISLs evolve from one moment to the next. Many recent dynamic graph models stay close to interpolation, and they exploit physical and temporal satellite attributes only in part, which weakens both node representations and prediction accuracy. To address these gaps, we develop an extrapolation-oriented dynamic link prediction framework with three contributions: (1) we shift the task from interpolation toward future-state extrapolation, which makes proactive management possible; (2) we extract physics- and temporal-aware node features by combining orbital dynamics with historical ISL information; (3) we restrict prediction to a candidate edge set, so the computation stays manageable while the method remains scalable. In experiments, the proposed method outperforms existing baselines on accuracy, efficiency, and scalability, suggesting that it can serve as a useful tool for managing large-scale LEO networks in practice.
Zhao, Jiayi, Fei, Shihan, Bao, Junpeng
Power Allocation for Uplink RSMA in LEO Satellite Systems2026-99-292110/19/2026
This paper focuses on power allocation for RSMA-enabled uplink transmission in LEO satellite systems. A representative cluster is selected for analysis, where the terminal with the best channel quality serves as the target user and transmits its information by means of two split streams. The remaining users are treated as weak users sharing the uplink access structure. Since satellite-ground links are affected by channel estimation errors and propagation uncertainty, the available channel state information (CSI) may deviate from the actual channel. To improve the reliability of uplink transmission, a bounded CSI error model is adopted, and the decoding conditions for different successive interference cancellation (SIC) stages are formulated in a robust form. On this basis, the target-user rate is optimized under transmit-power and decoding-reliability constraints. The formulated problem is difficult to solve directly because the power variables are coupled with the SINR and rate expressions. Therefore, a successive convex approximation (SCA) method is employed to transform the original problem into a series of solvable convex subproblems. The obtained results confirm that the proposed scheme brings a clear target-user rate gain over the NOMA benchmark under the same simulation setup. The results show the effectiveness of robust RSMA power allocation for uplink interference management in LEO satellite systems.
Yu, Xiaodi, Jin, Hang, Song, Yu, Hong, Tao, Ding, Xiaojin, Zhang, Gengxin
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
Research on Interconnection Technology of Spacecraft in Cislunar Space2026-99-294310/19/2026
Communication interconnection and interoperability in cislunar space have become an important development direction for low earth orbit satellite internet to extend into outer space. To address the challenges such as the difficulty in cross-domain interconnection of heterogeneous spacecraft, insufficient compatibility of protocols and architectures in the ground station,earth orbit, lunar orbit, lunar surface and extended networks, this paper reviews the current development situation of the US and China’s cislunar space networks, and summarizes six challenges in the aspects of link layer and network layer protocol standardization, addressing and routing, gateway design, information security, and multi-system integration. Based on this, this paper proposes constructing a cislunar space network centered on IP protocols based on the USLP link-layer protocol, which adopts a combination of two-layer switching and three-layer routing addressing mechanism, multi-gateway interconnection, hybrid security architecture, and multi-protocol stack compatibility, and clarifies four key technologies: network protocol architecture, standardized routing gateway, electronic data sheet, and network digital modeling and simulation. An integrated network system covering the five domains is constructed, achieving unified standards, fault tolerance, safe and efficient interconnection and interoperability for cislunar space spacecraft, which can provide core support for human lunar exploration, deep space exploration, and the perception and development of the cislunar space.
Jia, Weisong, Wang, Xiang, Liu, Yusheng, Wang, Luyuan, Sun, Yong, He, Xiongwen, Zhao, Kanglian, Xu, Ke
EAU-Net: An Edge-Aware Enhanced Attention U-Net for Robust SAR Near-Shore Segmentation2026-99-291010/19/2026
Synthetic Aperture Radar (SAR) provides important all-weather images. These images help researchers observe the sea surface. However, separating the coast from the ocean in these images is hard. Near-shore areas face three main issues. The first issue is coherent speckle noise. The second issue is geometric distortion caused by the radar’s side-looking geometry. The third issue is feature aliasing caused by multiple scattering from coastal buildings. To solve these problems, this paper proposes the Enhanced Attention U-Net (EAU-Net). This model is a deep learning framework. It adds SAR scattering priors directly into the learning process. The architecture has three main parts. First, the encoder includes an adaptive local signal-to-noise ratio (SNR) module. This module filters multiplicative speckle dynamically. It stops the network from mistaking noise for real features. Second, the network bottleneck uses a lightweight Transformer block. This block learns global shape priors. It helps the network find the continuous coastline even when radar shadows cover parts of the image. Third, the decoder uses an adaptive edge refinement mechanism. A composite edge-aware loss guides this mechanism. It fixes rough edges and corrects pixel boundaries. This step removes jagged artifacts. Tests on a near-shore dataset show the reliability of this method. EAU-Net achieves a Dice Similarity Coefficient (DSC) of 0.9601 and an Intersection over Union (IoU) of 0.9234. The Average Symmetric Surface Distance (ASSD) is 5.7501 pixels. This paper compares EAU-Net with classical convolutional networks, global-attention architectures, and vision foundation models. The results show that EAU-Net preserves boundary precision better. It outperforms state-space models like VM-UNet and large-scale zero-shot models like SAM-Med2D. EAU-Net uses physical inductive biases to separate coastal building interference from marine targets. This provides a realistic foundation for downstream tasks like autonomous ship detection.
Bai, YuanJie, Zhong, Sheng
PI-Unet Rapid Prediction Method for Satellite Transient Temperature Fields Based on Filtering Concept2026-99-294210/19/2026
To address the low computational efficiency problem by numerical calculation and large cumulative error by long-term sequential iterative prediction during the process of predicting the satellite’s transient temperature fields, this paper proposes a Physics-Informed U-Net (PI-Unet) model. Specifically, this PI-Unet model uses multi-branch spatiotemporal features as its inputs and combines hard constraints with a physics-informed loss to ensure the physical consistency of the predicted temperature field. Furthermore, for the error accumulation problem in long-sequence autoregressive prediction models, this paper presents a model training method based on the concept of filtering. Under the guidance of this method, the PI-Unet model can develop a long-term sequence prediction capability while keeping the accuracy of spatial temperature field prediction, achieving high-precision spatiotemporal prediction. By actively injecting prediction errors through a scheduled sampling strategy, along with a multi-step rolling training strategy, the PI-Unet model not only learns the governing physical laws of the spatial distribution of the transient temperature field, but also acquires the filtering capability to mitigate the accumulation of the long-term sequence rolling prediction errors. The experimental results indicate that the PI-Unet model maintains the maximum absolute error of predicting spatial transient temperature field stably below 5.5K and the error of the rolling-predicted transient temperature field in one orbit period less than 6K. In addition, the time for a single-step prediction is reduced to 0.005s, and the time for a total-period prediction is less than 0.5s. Therefore, this research provides a strong and high-efficiency technical support for the real-time monitoring of the satellite’s on-orbit temperature fields.
Li, Wanze, Xiao, Fuhong, Xue, Xiaoguang
Fault Diagnosis Study of MCSECNN Model Integrating MK-MMD and DANN under Variable Operating Conditions2026-99-297210/19/2026
Bearing signals measured under different loads rarely keep the same waveform characteristics. Their amplitudes, spectral components, and statistical properties may change at the same time, which makes a classifier trained on one load condition less reliable on another. This study investigates a cross-load diagnosis method that trains a multi-scale convolutional squeeze-excitation network (MCSECNN) with multi-kernel maximum mean discrepancy (MK-MMD) and a domain-adversarial neural network (DANN). The convolutional branch captures vibration patterns over several temporal ranges, and the SE unit adjusts the importance of the channels generated by the parallel kernels. MK-MMD penalizes the distance between load-dependent feature distributions, whereas DANN weakens the load identity retained in the features. Tests using CWRU bearing data show that the integrated model gives better accuracy than the basic MCSECNN and the two single-adaptation variants. Ablation results indicate that the statistical and adversarial constraints contribute to transfer performance in different ways.
Ming, Mei, Ai, Yumei, Li, Shengnan, Wan, Haozheng, Sang, Shentian, Zhang, Haifeng
An Adaptive Cloud Prediction Network for Supporting Space-Ground Laser Communication Links2026-99-298210/19/2026
Space-ground laser communication links are highly susceptible to interruptions caused by cloud cover. Reliable dynamic scheduling of ground stations and seamless link handover, therefore, requires accurate short-term cloud-layer forecasts. However, cloud evolution is inherently chaotic, and atmospheric conditions exhibit evident seasonal non-stationarity. These factors weaken the prediction stability of existing models and make it difficult to maintain consistent accuracy over extended forecasting horizons. To address these challenges, this paper proposes KFMNet, an adaptive cloud prediction network that integrates Koopman theory with frequency-domain Mamba modeling. Unlike conventional frameworks that mainly process cloud features in the spatial domain, KFMNet embeds a linear state-space modeling mechanism into the frequency-domain feature evolution process. Specifically, an adaptive spectral gating mechanism is used to capture global cloud distribution patterns in the frequency domain, thereby reducing the computational burden associated with high-resolution image sequences. Meanwhile, a Koopman seasonal gating module introduces monthly prior knowledge into the network, enabling the model to adapt to atmospheric drift across seasonal transitions. Within the encoder-decoder backbone, KFMNet jointly incorporates temporal modeling, frequency-domain representation, and physical consistency constraints, providing a more stable framework for short-term cloud prediction. We evaluate KFMNet on multi-season cloud images collected over Nanjing and Beijing. The climate profiles of these two cities are markedly different. Under these conditions, KFMNet achieves competitive accuracy in cloud segmentation and shows a better ability to handle seasonal and regional shifts than several reference models. In essence, our method adapts well to changing atmospheric conditions and provides a new cloud prediction approach for space-ground laser link assurance. This method combines physical interpretability with the actual needs of real-world deployment.
Wu, Xibing, Meng, Xiangxu, Zhong, Yun, Li, Wenfeng, Zhao, Kanglian
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
Adaptive Parameter Optimization for BP/LTP in Deep Space Communication Based on a Deep Q-Network2026-99-299610/19/2026
This study addresses the limited adaptability of Bundle Protocol/Licklider Transmission Protocol (BP/LTP) parameter configuration in deep-space communication environments through a cooperative optimization mechanism that operates under dynamic link conditions and diverse service demands. Conventional BP/LTP parameter settings typically rely on fixed thresholds or empirical rules, assuming relatively stable link states. However, in practical deep-space missions, the bit error rate, propagation delay, and available transmission rate all exhibit significant temporal variability. Moreover, variations in data size and reliability requirements further exacerbate the performance degradation caused by parameter mismatches. Owing to the multi-layer encapsulation and resource coupling of bundle size, block size, segment size, and the number of sessions, inappropriate parameter configurations can result in increased acknowledgment and retransmission overhead, underutilized link capacity, and prolonged end-to-end delivery latency. Traditional approaches are thus inadequate for achieving holistic, real-time, and joint optimization in such dynamic environments, necessitating adaptive parameter tuning mechanisms with link-state awareness. This paper formulates BP/LTP parameter optimization as a sequential decision process and proposes a method based on a Deep Q-Network (DQN) for adaptive parameter configuration. The state space includes transmitted data size, bit error rate, one-way propagation delay, and available transmission rate. Based on this state information, the DQN selects the bundle size and LTP block size from predefined candidate sets. The LTP segment size is derived from a mapping rule related to link conditions, and the number of concurrent sessions is calculated online using an analytical model. The method is evaluated on a satellite–ground simulation platform that consists of a BP/LTP sender, a BP/LTP receiver, and a computing unit for DQN parameter optimization. It is compared with three fixed parameter configurations in 36 experimental scenarios. The results show that, compared with the fixed configurations, the proposed method reduces end-to-end delivery latency, improves link utilization efficiency, and maintains more stable transmission performance under different link conditions and data scales.
Han, Yingxiang, Bian, Mingyue, Chang, Xiang, Cao, Suzhi, Yan, Lei
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
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
Formal Verification of a Generic Algorithm for Universal TDM Communication Using UPPAAL2026-99-298610/19/2026
This paper describes formal modeling and verification of generic algorithm for universal Time Division Multiplexing (TDM) communication, one of the orchestration algorithms used in PTB-FLA (Python TestBed for Federated Learning Algorithms), a light-weight framework for developing distributed applications, developed as a part of the TaRDIS (Trustworthy and Resilient Decentralized Intelligence For Edge Systems) project. Modeling and verification is done using UPPAAL model checker tool. When developing a distributed application, in early development phases a user of PTB-FLA can abstract away low-level details of message passing and some elements of orchestration, by choosing one of the available orchestration algorithms, therefore being able to focus on the actual distributed algorithm at the application level. The generic algorithm for universal TDM communication models peer data exchange between the nodes within a discrete time-slots, a frequent case in inter-satellite links. It is an orchestration algorithm intended to be used in satellite-swarm applications, such as distributed ODTS (Orbit Determination and Time Synchronization). Formal modeling and formal verification contributes significantly to increased reliability of orchestration layer, making development of a distributed algorithm easier by narrowing down possible sources of errors to application level. This paper gives overview of PTB-FLA framework, provides a description of the orchestration algorithm, shows its formal model in UPPAAL, and verifies some important algorithm properties, namely deadlock freedom (a safety property) and successful termination (a liveness property). This paper represents a continuation of one of the research lines related to PTB-FLA, which is formal verification of the framework’s generic orchestration algorithms, and other elements.
Djukic, Miodrag, Popovic, Miroslav, Li, Yuqing
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
Friction Dynamics Modeling and System Simulation of Large-Angular-Momentum Flywheels for Satellites2026-99-296410/19/2026
In response of the stringent requirements of high-resolution optical remote sensing satellites for attitude control stability and micro-vibration environment, this study focuses on the friction dynamics modeling and simulation analysis of large-angular-momentum flywheels across a wide speed range. To quantify the impact of flywheel friction disturbances on satellite attitude pointing accuracy, friction torque characteristic data were first acquired through ground testing over the full speed range, and a high-fidelity static fitting mathematical model was constructed using the least squares method. To further elucidate the nonlinear friction mechanisms in the low-speed regime—particularly during speed zero-crossings—the LuGre dynamic friction model was introduced, and its core parameters were identified and optimized. Comparative results demonstrate that the model exhibits high-precision characterization capabilities, with a deviation of less than 0.0015 Nm from measured data within the critical low-speed range of ±50 rpm. Subsequently, the established friction model was integrated into a satellite attitude control closed-loop system for digital simulation to quantitatively evaluate the disturbance of friction torque on system steady-state accuracy. The results indicate that under the closed-loop control compensation mechanism, the impact of flywheel friction on satellite attitude pointing accuracy is effectively suppressed, with the attitude angle deviation maintained within 0.002°, thereby meeting the engineering requirements for high-precision remote sensing imaging. This research provides a theoretical basis and data support for the optimization design of flywheel systems and the refined design of satellite attitude controllers.
Hu, Yuewei, Liang, Zhulin, Lu, Ming, Zhang, Pengbo, Hu, Xiaozhen
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
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
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
Global Task Graph-Based Multi-Agent Offloading for Dependency-Aware Satellite Edge Computing2026-99-296310/19/2026
Satellite edge computing (SEC) enables low-latency services by leveraging Low Earth Orbit (LEO) satellites. However, emerging applications increasingly generate dependency-aware tasks with complex precedence constraints, leading to challenging multi-DAG (directed acyclic graph) offloading under dynamic satellite environments. To address this issue, we propose a global task graph (GTG)-based multi-agent offloading frame-work named GTG-MARL, for dependency-aware task execution in regional satellite-ground collaborative networks. Specifically, we construct a GTG by merging multiple DAGs to capture both intra-task dependencies and inter-task coupling, thereby achieving unified dependency-aware modeling. Then, a graph neural network (GNN) is lev-eraged to encode the GTG into dependency-aware subtask embeddings. To handle dependency-aware offloading in dy-namic environments, we develop a multi-agent reinforce-ment learning framework based on generalized proximal policy optimization, where each LEO satellite acts as an agent for decision-making while a centralized critic stabilizes offline learning to eliminate real-time on-orbit communication overhead. During the execution phase, only the light-weight trained actors are deployed on satellites for on-orbit inference. Extensive experiments demonstrate that the pro-posed method significantly reduces task completion delay compared to state-of-the-art baselines, validating the practi-cal efficiency and low synchronization cost of the proposed framework for regional SEC deployment.
Geng, Zeao, Zhao, Lu, Jin, Huiying, Jiang, Ping, Cai, Hui, Zhou, Jian
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
Investigation of the Effect of Series and Parallel Line Designs on Temperature and Flow Rate Distribution in Bus Cabin Heating Systems2026-01-508210/16/2026
The aim of this study is to improve cabin temperature homogeneity by comparing serial and parallel coolant line configurations in Anadolu Isuzu’s new midibus model. A one-dimensional hydraulic model was developed in KULI using supplier-provided pressure drop versus flow rate curves for the preheater, defroster, convectors, and pumps. Pipe lengths and inner diameters were obtained from the CATIA vehicle layout. The model was assessed against physical flow rate measurements for both configurations. The maximum deviations were 11.6% for the serial layout and 6.2% for the parallel layout. KULI was used only for coolant flow rate prediction; all temperature results were obtained from physical vehicle tests. At the 30th min, the measured left–right convector outlet temperature difference decreased from 6.6°C in the serial layout to 1.0°C in the parallel layout. For the investigated vehicle and operating condition, the parallel configuration provided a more balanced flow distribution and improved outlet temperature homogeneity.
Birgül, Çağrı Emre
Aircraft Asset Life Cycle and Digital Data Standards OverviewAIR7501A (Current)10/15/2026
This SAE Aerospace Information Report (AIR) reviews technical, operational, and maintenance data being exchanged between key stakeholders in aerospace asset life cycle and data standards available for such exchanges. It identifies gaps and non-availability of data standards in certain areas. The scope of the current document is limited to aircraft operations, maintenance, and disposal stages post-build phase, and does not include the detailed interactions during the aircraft build phase. This updated version reflects all aerospace digital data standards published following the release of the initial document.
G-31 Digital Transactions for Aerospace
Latency-Aware Optimal Scheduling Scheme for Computational Offloading12-10-01-000210/9/2026
Proactive safety and vehicle automation typically requires high energy use from sensors and energy-intensive computing from sensor data processing. For high-quality, reliable perception and localization within a driving environment at the vehicle level, incoming data from multiple sensors need to be fused using advanced computational algorithms, which demand a high compute load. Alternatively, computational offloading of automated driving tasks shifts energy consumption from the vehicle to cloud infrastructure, where renewable energy sources, such as hydropower or solar power, can be utilized more efficiently. Herein, an optimal scheduling strategy for autonomous driving tasks via the cloud layer is formulated as a mixed-integer linear programming (MILP) problem and verified using measurement-informed task graphs. It is shown that cloud-based computational offloading enables energy-efficient operation while maintaining task deadlines to ensure the timely availability of perception and localization information, which is consistent with prior studies in the literature. Simulation results demonstrate that on average, 35.78% of the computational load was offloaded to the cloud, which can achieve significant energy savings for the onboard system. The compute operations achieved an average energy savings of 35.65%, while total system savings ranged from 26.16% to 30.67% under different cloud energy efficiency scenarios, highlighting the advantages of offloading compute-intensive tasks. The framework was further evaluated using multiple directed acyclic graph (DAG) configurations to assess its scalability and adaptability. Critical tasks, such as sensor fusion, were executed exclusively on the vehicle to ensure real-time responsiveness.
Sharma, Sachin, Meyer, Richard, Feinberg, Ben, Asher, Zachary
Development and Validation of Quasi-Dimensional Multi-Zone Combustion Model for Homogeneous Reactivity-Controlled Compression Ignition with Renewable Fuels—Part 1: Fuel and Temperature Stratification Prediction03-19-05-002310/9/2026
As part of the dtec.bw MORE project, serial hybrid powertrains featuring alternative combustion regimes, specifically homogeneous reactivity-controlled compression ignition (hRCCI), are under investigation to maximize thermal efficiency while minimizing NOx and soot emissions. The proposed hRCCI concept utilizes dual-fuel stratification, employing early direct injection (DI) of octanol (high-reactivity fuel) alongside port fuel injection (PFI) of ethanol (low-reactivity fuel). While 0D/1D engine modeling is essential for developing predictive powertrain simulation frameworks, conventional models often lack the robustness required to capture the complexities of low-temperature combustion (LTC). This study addresses this limitation by developing a multi-zone “onion skin” quasi-dimensional model. Since for LTC, ignition and heat release rates are highly sensitive to thermal and chemical stratification, capturing these gradients is critical. 3D computational fluid dynamics (CFD) simulations are capable of capturing thermal and chemical stratifications to a high degree of accuracy, whereas 0D simulation models, in general, do not consider them. The primary contribution of this work is the translation of high-fidelity 3D CFD data into a computationally efficient quasi-dimensional environment. A simplified 3D CFD architecture was utilized to map the effects of various operating parameters on mixture distribution. Using these results, a regression learning model was trained to predict octanol and temperature stratification within the combustion chamber. Validation against simulation data demonstrates that this coupled machine learning and multi-zone approach provides a robust, predictive tool for evaluating advanced LTC concepts within larger system-level simulations.
Sundaram, Pravin Kumar, Grundl, Larissa Michaela, Trapp, Christian Thorsten, Tinschmann, Georg
Aerospace & Defense Technology: Oct 202626AERP1010/8/2026
Terabit Optical Communications in Space: Engineering High-Speed Networks for Data Centers in Orbit Mild Hybrids: A Current-Gen Bridge to Electrifying Military Ground Vehicles The Integrated and Flexible Approach to Large-Scale Metrology: How Integrating Laser Trackers and Modular Arms Creates a 'Unified Nervous System' The Defense Industry Continues to Meet CMMC Phase II Requirements Despite 60-Day Pause In Space, No One Can Hear You Stream AMD to Bring UCIe Connectivity to Select Versal Adaptive SoCs How Special Interface Gages Provide Microscopic Precision for Designing Ballistic Missiles The pristine precision needed to certify the nation's underwater nuclear deterrent is anchored on the talents of civilian scientists at a Navy laboratory in Southern California. Rapidly Testing Deployable Systems Sandia National Laboratories is evaluating portable security architectures that combine radar, drones, ground sensors, cameras and wireless communications networks into a single rapidly deployable system for remote and austere environments. Testing focuses on sensor integration, resilient communications and edge-based data processing to improve situational awareness while operating within real-world bandwidth constraints. Tailored Surface Vibrations to Improve Aerodynamic Performance in Passenger Jets University of Colorado Boulder researchers have demonstrated two new phononic flow-control concepts, super-resonance and scatterless interference, that use engineered subsurface vibrations to interact with boundary-layer airflow across a broader range of turbulence frequencies than previously possible. By delaying flow instabilities without altering a wing's external shape, the technology could reduce aerodynamic drag and improve fuel efficiency in passenger aircraft. New Type of Attack Can Slip Past the Defenses in Your Computer's Processor MIT researchers found a way to exploit a split-second gap in chip security - and used it to acquire a Linux system's password file. Army Tests Safer Electrical Connectors for Ground Vehicle Readiness U.S. Army DEVCOM Ground Vehicle Systems Center engineers are evaluating cadmium- and hexavalent chromium-free electrical connectors designed to reduce hazardous material exposure while maintaining corrosion protection, electrical performance, and long-term vehicle sustainment.
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
Golden Tester Framework for SAE J1939-91C Cybersecurity2026-01-75229/22/2026
The impending formal adoption of SAE J1939-91C creates an urgent need for rigorous, repeatable validation methods that extend beyond functional conformance. This paper presents a structured validation and benchmarking framework, with a focus on performance characterization across dynamic vehicle configurations. Building on prior work in secure network formation, rekeying, and Golden Tester concepts, we define a minimal, transport-agnostic set of cryptographic and protocol test vectors for deterministic validation of secure message authentication, alongside simulation-based methods for evaluating network formation and rekey behavior. The framework integrates performance metrics such as secure message latency, rekey time and throughput, while also introducing cybersecurity-specific diagnostics and logging requirements for gateway module implementation. Security validation scenarios are mapped to explicit detection and response benchmarks. The resulting methodology provides OEMs, Tier-1 suppliers, and research organizations with a practical, reproducible approach to validating J1939-91C implementations, supporting both development-phase evaluation and ongoing lifecycle assurance.
Zachos, Mark, Kulkarni, Prakash
Scene-Level Multi-View Instance Consistency Using Multi-Feature Vision Fusion for Synthetic Ground Vehicle Environments2026-01-75029/22/2026
Maintaining consistent object identities across multiple camera viewpoints is a critical challenge in synthetic perception environments used for autonomous ground vehicle evaluation. This paper presents a scene-level multi-view instance consistency framework that integrates OpenUSD scene composition, Omniverse Replicator synthetic-data generation, and a multi-feature vision fusion pipeline. The proposed approach combines semantic embeddings from CLIP, patch-level descriptors from DINOv2, geometric correspondences from LoFTR, mask-derived shape invariants using Hu moments, and relative-position priors to associate object instances across views, including visually identical objects. A compact composite scoring function fuses these complementary cues to achieve robust cross-view identity assignment while preserving OpenUSD asset modularity through grouped-prim support. Synthetic experiments across 120 multi-camera scenes demonstrate improved Top-1 Match Accuracy and Identity Consistency Rate, with reduced ID-switch occurrences compared to single-cue baselines. The framework supports scalable, repeatable, and traceable digital engineering workflows for defense-oriented perception evaluation.
Bhattacharya, Sambit, Nakamoto, Kyle
Intelligent Optimization Simulation Framework for Test and Evaluation of Autonomous Systems2026-01-75519/22/2026
This paper describes ongoing research and development of an efficient optimization/search–based modeling and simulation framework for rapidly identifying low-performance scenarios in advanced autonomous systems. Ensuring predictable, safe behavior across complex, integrated systems remains a core operational test-and-evaluation challenge. Our goal is to balance rigorous validation with timely deployment. We are developing TEAAS (Test & Evaluation of Advanced Autonomous Systems), a scalable, faster-than-real-time framework designed to uncover critical failure scenarios efficiently. Key features include GPU-accelerated parallel simulation and learning, computational intelligence–based search of optimal parameters, uncertainty quantification for reproducibility, and real-time physics-accurate sensor models. We conducted simulation experiments to evaluate and demonstrate the framework performance for two black-box ground-vehicle autonomous systems. Key results were that adequate uncertainty quantification can be achieved with as few as 10 repeated runs per simulation scenario, sensor realism has a significant effect on failure rate, distinct differences between the two autonomies failure modes were identified, and our efficient optimization/search methods identify critical performance regions in a small fraction of the number of simulations required by a naïve Monte Carlo search.
Snarski, S., Menozzi, A., Persons, B., Lazar, D., Khan, N.
Analyzing Open-Source Data to Inform Military Vehicle Design2026-01-75599/22/2026
The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.
Lynch, Benjamin, Mittal, Vikram
Distributed Intelligence at the Tactical Edge: Enabling Contested Logistics in Degraded Networks2026-01-75589/22/2026
Contested logistics environments expose the limitations of both legacy fragmented systems and emerging Next Generation Command and Control architectures that assume persistent connectivity. In degraded or denied conditions, sustainment operations face latency, bandwidth constraints, and reduced decision velocity. Expanded decision support tools further increase reliance on timely, relevant data exchange. This paper argues that contested logistics requires distributed, mission-aware intelligence at the tactical edge. Low-power onboard compute enables real-time inference, adaptive data conditioning, and connectivity-aware transmission across Radio-Frequency and non-RF pathways. By selectively elevating critical information based on mission context and network state, edge-intelligent architectures improve survivability, bandwidth efficiency, and sustainment effectiveness in degraded networks.
Baumann, Edward, Pardee, Shawn
SOSA Enabled Automotive Technologies Disrupt the Battlefield2026-01-75359/22/2026
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Pilaud, William J.
Streamlined Model Composition for OneSAF for Use in Rapid Trade Study Data Collection and Analysis2026-01-75479/22/2026
This research is meant to enhance the analytic capabilities of OneSAF for usage as a Monte Carlo-style data generator for use in large problem space trade studies. Using novel ground vehicle data such as: RHA armor values, weapon penetration prediction models, and sensor values, new models can be developed in OneSAF for use in data generation and analysis. This process and companion software developed for this purpose enables the rapid construction and evaluation of differing vehicle variants in a fraction of the time of the baseline process, improving the efficiency of using OneSAF as a data analysis tool. This approach facilitates a more comprehensive virtual experimentation approach that can use manufactured data as a part of the process.
Sapunkov, Oleg, Roberts, Bradshaw, Jorgensen, Maxwell
Leveraging Domestic Automotive Software Development Best Practices for MOSA and GCIA/Victory Implementation2026-01-75389/22/2026
ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY
Priemer, Douglas, Sime, Karl
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