Based on the mechanical principle of limit equilibrium, a method for calculating the safety factor for a symmetric three-dimensional slope without assuming the inter-column forces is proposed, and the safety factor expression considering the direction and intensity of blast-induced load is derived. The three classic examples are analyzed to quantify the influence of the direction and intensity of blast-induced load on the safety factor of the slopes. The results show that under the action of blast-induced load, the most dangerous action direction of the three-dimensional symmetric slope is consistent with the resultant force direction of the sliding force; as the pseudo-static coefficient of the blast-induced load gradually increases from 0 to 0.3, the slope safety factor gradually decreases, and the safety factors of the three examples in the most unfavorable direction decrease by up to 38.89%, 29.29%, and 42.83% respectively. The proposed method and analysis results provide a reference for the stability analysis and safety risk assessment of homogeneous slopes under weapon strike conditions.

Structural planes, as weak links in rock masses, often determine the overall stability of the rock mass. To reveal the triggering mechanism of structural plane instability, the mechanical and energy characteristics during the formation of sliding surfaces are analyzed. The energy relationship for the progressive instability of structural planes is established, and an energy criterion for structural plane instability under single disturbance is proposed based on a dimensionless energy factor. The main influencing factors of block stability were quantitatively analyzed with a three-block model and a simplified bilinear shear constitutive relation. The analysis revealed that when adopting the bilinear constitutive relation, the threshold of the instability energy factor can be expressed as a function of the initial state of the structural plane and the constitutive characteristics of the material deformation. Its magnitude is proportional to the square of the nominal ultimate strain and the square of the relative magnitude of the initial shear force to the ultimate resistance, while being independent of disturbance parameters. Under single disturbance, higher disturbance frequencies require greater disturbing forces to induce instability. The main conclusions derived from the energy-based theoretical analysis are consistent with the results obtained from limit equilibrium methods and the general patterns revealed by relevant experimental results. The proposed dimensionless energy factor criterion provides a new perspective for preventing engineering disasters induced by structural plane instability.

To investigate the energy absorption characteristics of foam-cored composite fenders reinforced with trapezoidal webs, the full-scale quasi-static compression tests were carried out on the composite fenders of the Yongning Bridge. Based on a hybrid criterion, the theoretical models were derived for the equivalent compressive modulus, strength, and web relative density of a 45° trapezoidal unit cell.The results show that the specimens mainly exhibit three failure modes: web buckling, web-foam debonding, and interlaminar delamination. The ultimate load-bearing capacity reaches 705.68 kN, with an average compressive load of 627.38 kN. In the 10%-20% compression stage, the absorbed energy increases by 116.09%, and the maximum absorbed energy is 2.76×105 J, indicating a stable, stage-wise energy dissipation behavior. The deviation between the theoretical predictions and experimental results is less than 15%, demonstrating that the proposed model has good accuracy and engineering applicability.

To address vehicle entrapment and pavement instability caused by soft ground in tidal flat areas, this research focuses on overcoming key technical bottlenecks in current physical reinforcement methods，namely low construction efficiency, high costs of chemical modification, and insufficient durability of composite materials. A systematic study was conducted by integrating field tests with numerical simulations. Based on time-history data of earth pressure, strain, and displacement measured in a 60-ton tracked vehicle loading test site, combined with parametric modeling in ABAQUS, the load-bearing performance of a composite structure comprising a chemically stabilized layer and an aluminum alloy deck plate was validated. The results demonstrate that the aluminum alloy deck plate maintains an elastic working state under load and, synergistically interacting with a 20 cm-thick chemically stabilized layer, forms a composite foundation solution with low additional stresses. This effectively meets the traffic requirements of heavy-duty vehicles. The findings not only enhance the theoretical framework of layered foundation synergistic bearing mechanisms but also provide a time-efficient and cost-effective engineering solution for military mobility support through the engineering practice of "chemical stabilization combined with aluminum alloy deck paving".

To address the issue of the quadratic growth in simulation time with increasing numbers of Unmanned Aerial Vehicles (UAVs) for multi-UAV combat mission simulations on Central Processing Units (CPUs), a lightweight, GPU-parallel accelerated simulation environment for multi-UAV combat is designed and developed. Unlike existing simulations frameworks that are often limited to single-UAV missions or only parallelize multi-UAV dynamics, the proposed framework, focusing on multi-UAV adversarial scenarios, further implements parallel computation for the relative poses and damage relationships of each UAV within the environment. In response to the limitation that traditional Basic Fighter Maneuvers (BFM) are only well-defined in level flight, an improved set of fully-attitude interpretable basic aerial combat maneuvers is designed. The simulation environment includes a variety of scalable multi-UAV confrontation tasks, which can provide heuristic baseline strategies for various tasks and support proximal policy optimization-based baseline strategies for one-on-one confrontation scenarios. Compared with the multi-threaded simulations running on an 8-core, the 16-thread CPU, the proposed platform improves sampling speed by two to three orders of magnitude. The reinforcement learning training results in both CPU and GPU parallel environments demonstrate that the GPU-accelerated simulation can reduce training time to 1/50, without significantly compromising sampling efficiency due to trajectory fragmentation. The proposed parallel-accelerated multi-UAV combat simulation environment significantly enhances sampling and training speeds, thereby facilitating accelerated research progress in this field.

This paper addresses the challenges in collaborative Simultaneous Localization and Mapping (SLAM) for heterogeneous robot teams in global-navigation-satellite-system-denied environments, including pose alignment errors, communication constraints, and accumulated estimation drift. A multi-stage cooperative SLAM architecture is proposed to overcome these issues. The solution features a two-stage relocalization mechanism that employs Scan Context for coarse alignment and the Generalized Iterative Closest Point (GICP) algorithm for fine registration, enhancing initial pose estimation accuracy across heterogeneous platforms. A lightweight communication framework leveraging high-speed network-address-translation traversal technology is developed, which utilizes point cloud voxel filtering and keypoint extraction for data compression, ensuring real-time transmission in low-bandwidth conditions. Furthermore,the Random Sample Consensus (RANSAC) algorithm is incorporated to optimize multi-robot pose transformations by eliminating outliers, thereby improving global consistency. Multi-source point cloud fusion is subsequently achieved through coordinate transformation.The extensive experiments in underground parking garages and utility tunnels demonstrate the system's robustness and real-time performance, successfully generating complete point cloud maps with relocalization times under 1.58 s, RANSAC optimization under 3.2 ms, and map merging under 1.8 ms. The quantitative results confirm that the collaborative mapping accuracy is improved by over 45% compared with single-robot SLAM, and the integration of RANSAC further boosts relocalization accuracy by more than 15%, significantly enhancing the overall mapping precision. The proposed method offers a valuable reference for multi-robot collaborative perception and navigation.

This study investigates the flexural behavior of adhesive-bonded bridge decks made from large span-to-depth ratio pultruded glass fiber reinforced polymer (GFRP) square tubes, aiming to support the development of modular, lightweight emergency bridges. The three-point bending tests were conducted and numerically simulated with two finite element (FE) models: a solid model accounting for damage evolution and a planar frame model of the directly loaded area. The results reveal that the initial damage occurs at the top flange-web junction near the edge of the directly loaded area. The ultimate load capacity is governed by local buckling of the top flange, with the final failure mode being local buckling of the web. The initiation of structural damage is controlled by localized cross-sectional deformation in the directly loaded area. Specifically, the damage in the edge tubes is caused by combined shear deformation and local transverse bending, while the damage in the central tubes is primarily due to local transverse bending of the top flange. The overall failure mechanism is dominated by mid-span bending moments, leading to successive local buckling of the top flange and web. This process is accelerated by the weakened constraints resulting from the crack initiation at the top flange-web junctions. The designed bridge deck provides a reference for emergency scenarios.

The mechanical behavior of structural planes at various scales within a rock mass determines the stability of the surrounding rock in engineering projects. With the continuous increase in the burial depth of underground engineering projects, the threat posed by time-delay engineering disasters under disturbed environments is becoming more and more prominent. Based on a systematic review of research from field observations, laboratory experiments, and theoretical models, it is concluded that the remote induction of tectonically controlled time-delayed rockbursts and engineering seismic events is essentially the delayed instability of rock mass structural planes triggered by disturbances.A new concept termed the disturbance aftereffect of shear creep on structural planes is proposed. This concept refers to the phenomenon that, when a structural plane undergoing shear creep is subjected to a disturbance, the influence of the disturbance persists even after the disturbance has ceased. As a result, the subsequent creep process of the disturbed structural plane exhibits different states and nonlinear behaviors compared to its undisturbed condition. Based on the typical manifestations of the disturbance aftereffect, the critical transition behavior of shear creep in structural planes and its influencing factors are explored. In addition, this study highlights that the scientific characterization of disturbance intensity, the conditions and mechanisms of delayed instability critical transition, theoretical models of the critical transition process, and cross-scale research of rock-structural plane-rock mass systems are key issues that urgently need to be resolved. The solution of these problems will provide a solid foundation for understanding the mechanisms of remote triggering of earthquakes and the effective prevention of time-delay engineering disasters.

Deep transportation tunnels and coal mine roadways in high geostress and complex geological environments are prone to engineering disasters such as large deformation of surrounding rock, coal bump and rockburst. The traditional cable with insufficient deformation capacity and limited impact resistance cannot adapt to the nonlinear large deformation damage characteristics of the deep buried surrounding rocks. A new type of energy-absorbing anchor cable with a shrinkable tube structure is proposed. This design leverages the plastic deformation energy absorption properties of steel to allow for greater deformation while providing high-strength, stable, and pressure-yielding bearing capacity. To explore its impact resistance characteristics, a series of drop hammer impact tests were conducted on the core pressure-yielding component of the anchor cable under varying impact heights, drop hammer weights, and numbers of impacts. The test results show that the shrinkable-tube energy-absorbing anchor cable can absorb energy up to 103 kJ in a single impact, and the working resistance is basically stabilized between 240~280 kN, which has good impact resistance performance. A field application test was carried out in the roadway of 8302 mining face in Xinjulong coal mine. The results indicate that the shrinkable-tube energy-absorbing anchor cable can achieve 120 mm of pressure-yielding deformation without fracturing. This research provides a new technical approach for controlling large deformation and impact disasters in deep rock masses.

The penetration depth of earth-penetrating projectiles into layered protective structures is a key issue in protective engineering design. In practice, the projectile usually penetrates the target with an incidence angle, and the protective structure is typically composed of multi-layered materials. The relevant theories and empirical formulas for penetration depth are relatively complex, which are not conducive to engineering applications. Based on the motion equation of the projectile, the motion deflection law of the projectile during oblique penetration was analyzed and a safety-biased calculation formula for oblique penetration was presented. The concept of equivalent thickness was introduced to convert the equivalent thickness of multi-layered media through wave impedance, then a calculation method for the penetration depth of multi-layered media was obtained. The results show that the oblique penetration depth can be estimated by multiplying the normal penetration depth by the cosine of the angle of incidence, yielding a conservative result; the penetration depth in different media is inversely proportional to the medium's impedance. The derived formulas for oblique and layered media penetration depths are consistent in form with current specifications, elucidating the physical significance of the empirical formulas in the specifications. Compared with the oblique penetration test in reinforced concrete, the maximum relative error of the theoretical prediction is 21.6% in the range of incidence angle from 0° to 50°. Compared with the numerical simulation of penetration to layered media , the theoretical prediction of the number of penetration layers is consistent with the simulation, and the relative error of penetration depth is 3%, which verifies the applicability of the theoretical calculation method.

Ground penetrating radar (GPR), as an efficient and rapid non-destructive detection technology, is often constrained by environmental noise interference and difficulties in image interpretation during shallow subsurface detection. Particularly in the detection of landmines and explosive remnants of war (ERW) , the false alarm rate remains high. To address these issues, this paper reviews advances in shallow-depth GPR technology focusing on the four critical dimensions: hardware architectures, soil environment, landmine/ERW detection, and signal processing techniques. The technical analyses and experimental investigations were conducted on prevailing shallow subsurface detection challenges, with a special emphasis on exploring the application of deep learning techniques in GPR signal clutter suppression and target recognition. This work provides valuable insights for improving GPR detection efficiency and effectively reducing the false alarm rate, and is expected to offer all-weather technical support for the rapid clearance of unexploded ordnance and military engineering surveys.

To investigate the influence mechanism of liquid filling ratio on the blast resistance of liquid-filled cylindrical shells, this study conducts a systematic examination of the dynamic response of partially filled cylindrical shell structures under near-field underwater explosions through integrated experimental and numerical simulation approaches. The results reveal that the structural response demonstrates three distinct phases: shock wave action stage, stable stage, and bubble pulsation action stage, with the primary deformation occurring during the bubble pulsation action stage. A significant positive correlation exists between liquid filling ratio and blast resistance, evidenced by a 52.7% reduction in central deflection when the filling ratio increases from 0% to 95%. While the internal peak pressure during the shockwave phase increases progressively with elevated filling ratios, the maximum internal pressure during bubble pulsation occurs at the 90% filling ratio. The plasticity of cylindrical shells under explosive loading decreases proportionally with increasing filling ratio, exhibiting reduction trends consistent with central deflection. These findings provide theoretical foundations and engineering insights for blast-resistant design and damage assessment of underwater protective structures.

To investigate the distribution pattern of shock waves in relation to soil damage characteristics under the action of air shock waves generated by ground explosions of TNT charges, static ground explosion tests with 10 kg and 20 kg TNT charges were carried out, and the characteristics of air shock wave overpressure distribution after the explosion of charge were obtained. A simulation model for the pressure field of ground explosion shock waves was established, and its reliability was verified with experimental results.The research findings indicate that a frustum-shaped crater is formed on the soil surface after the charge explosion . The increase in the explosive charge from 10 kg to 20 kg results in an increase in the diameter of the crater from 1.5 m to 1.9 m, an increase in the air shockwave at 5 m from 0.186 MPa to 0.366 7 MPa, and an increase in the reflective pressure on the surface of the wall at 5 m from 0.604 MPa to 1.485 MPa. The shape of the explosive charge has a significant impact on the near-field pressure distribution. The ground reflection effect substantially enhances the pressure values in the near-ground pressure field, with the pressure in the near zone exhibiting a convex profile that gradually degenerates into an ellipsoidal shape at a distance of 5 m from the explosion center. The wall reflection pressure of the shock wave decreases with height, following a quadratic function attenuation pattern.

To solve the problem of low reaction rate and incomplete reaction of metal fuels, a core-shell structured composite energetic material, AP@TiH2, consisting of ammonium perchlorate (AP) particles coated with titanium hydride (TiH2) powder, was prepared by the solvent-nonsolvent method. The microstructure distribution and ignition combustion performance of this composite material were studied by characterization technique and laser ignition experiments. The results show that Ti is uniformly attached to the surface of massive ammonium perchlorate particles, and the AP@TiH2 composite material has brighter flame, larger reaction area, shorter combustion time and faster reaction rate. The addition of AP@TiH2 to solid propellants can effectively improve the ignition characteristics of the propellants, and improve the combustion performance. Compared with the traditional mechanical mixing methods, the temperature rise rate of AP@TiH2/HTPB propellant prepared by the solvent-nonsolvent method is faster, the average temperature of stable combustion stage is higher, the ignition delay time is reduced by about 10%, and the linear combustion rate is increased by about 11%. The coating structure reduced the mass and heat transfer distance between the fuel and oxidizer, and effectively improved the reaction rate and reaction completeness of the fuel. This fine control method is expected to provide a new way for the effective utilization of solid propellant energy.

To address urban traffic congestion and the high demands for traffic collaboration, this study proposes a communication cooperation model-multi-agent deep deterministic policy gradient (CCM-MADDPG) algorithm based on a communication cooperation module (CCM). The core of this algorithm lies in the design of the CCM, which explores the deep-level relationships among neighboring agents through an embedding layer and an information extraction module. It dynamically weights and fuses neighbor information with an attention mechanism to avoid redundancy and information averaging. By embedding the CCM into the Actor network, agents integrate neighborhood information for decision-making, leveraging the locality principle to alleviate joint action space challenges while enhancing system stability and collaborative efficiency. Experiments demonstrate that the CCM-MADDPG significantly outperforms baseline models, such as Qmix and FRAP, on both synthetic and real-world dataset, exhibiting exceptional scalability and adaptability in complex road networks. Specifically, in the Manhattan road network, it reduces average delay by approximately 28.8% and 26.3% compared with Qmix and FRAP, respectively. The ablation studies confirm the necessity of CCM's information extraction and attention mechanisms. This algorithm provides an efficient solution for multi-agent collaborative control, with dual potential for urban traffic optimization and applications. Its core contribution resides in the successful integration of the CCM design with the MADDPG framework.

In the context of modern military operations, the network collaboration capability of distributed unmanned combat clusters is of vital importance. Focusing on such cluster collaborative networks, an inductive graph reinforcement learning framework is established, and a heterogeneous network disintegration method integrating inductive graph representation learning and deep reinforcement learning techniques is proposed. The inductive graph representation learning empowers the method with the ability to rapidly generate disintegration effects on the strategies of emerging networks, and by modeling network disintegration as a Markov decision process and combining it with deep reinforcement learning for solving, the disintegration efficacy is significantly improved while reducing the complexity of the problem. Experiments show that the inductive approach is able to transfer among networks of different scales and achieves a better balance between disintegration effectiveness and time consumption; especially in scenarios where node-attack cost constraints are introduced, it exhibits excellent disintegration efficacy and strategy scalability. This research has significant military application value in reducing the complexity of battlefield cognition and improving the timeliness of dynamic decision-making.

With regard to the new development trends of artillery technology with the support of informationized and intelligent technologies, this paper summarizes the technological progress of artillery in aspects such as dynamic kill chains, long-range high-precision strikes, anti-jamming reliable strikes, high-density firepower strikes, and data-driven intelligent firepower. Furthermore, in combination with typical battlefield applications and new directions of equipment development, it analyzes the positive effects of these advancements on enhancing artillery performance. The research indicates that modern artillery has achieved severalfold increases in performance metrics concerning closed-loop strike time, range, accuracy, and rate of fire. It has also laid the foundation for artillery to adapt to the requirements of intelligent battlefield confrontation in terms of anti-jamming capabilities in complex battlefields and self-sensing/self-controlling functions of artillery systems. On this basis, this paper further explores the future development trends of artillery technology and proposes cutting-edge technological directions such as new-quality launching methods, extended-range technologies, and group firepower capabilities for artillery systems. It also preliminarily analyzes the significant implications of technologies such as high-initial-velocity launching through hydro-oxygen combustion, cannon-launched scramjet propulsion, and intelligent firepower coordination for the new-quality development of artillery, providing a preliminary vision for the future evolution of artillery systems.

Accurate determination of extreme boundary conditions in the forward design of a fuse is a robust guarantee for its safety. The safety margins hold paramount significance in the fuze forward design, where environmental excitations, detonation control components, and other safety factors directly impact fuze reliability. To address these challenges, this study proposes a conceptual framework for the fuze safety margins and establishes an analytical evaluation method for the electromagnetic environment safety margins in the fuze systems. The method specifically addresses two key scenarios: (1) voltage, current, and energy safety margin calculations under known electromagnetic environments, and (2) transient/pulse electromagnetic conditions. Furthermore, the paper systematically elaborates on experimental principles for safety margin verification in fuze detonation control circuits, principles for test point selection, and methods for component parameter selection.These contributions provide references and support for the theoretical analysis and engineering application of fuse safety margins.

Lightning poses a serious threat to the intelligent unmanned tanks in the wild. To clarify the protection requirements for the lightning indirect effects, firstly, the electrostatic simulation model of unmanned tanks with the cloud electrode was established, and the lightning attachment points were analyzed. Secondly, the field distribution and the conduction coupling laws of internal typical single wires and coaxial lines in an unmanned tank subjected to a 200 kA lightning current A-wave impact were investigated through field-circuit co-simulation. The influence of different attachment points, ground conductivity and termination resistances was discussed. Finally, an experimental model of the unmanned tank was constructed proportionally, and the small current equivalent injection test was conducted in accordance with GJB8848. Then the measured coupling currents were linearly extrapolated to verify the validity of the numerical simulation results. The results show that the tank metal shell is more effective in shielding the pulse electric field, but less effective in shielding the low frequency magnetic pulse; the coupling voltage at high impedance port is similar to the differential signal of wave A component and its peak is about 2 kV; the coupling current at low impedance port accords with the wave A component and its peak is about 2 kA.

At present, the refined damage assessment of reinforced concrete structures subjected to penetration explosion mainly relies on numerical simulations and experimental data analysis, both of which suffer from the drawbacks of time-consuming processes and high economic costs. To address these issues, a novel digital-intelligent prediction model for penetration-explosion damage is proposed to simulate the entire dynamics of the penetration-explosion process. Based on the graph neural network and a multi-task learning paradigm, this model can achieve rapid and accurate predictions of building damage morphology and node failures under various penetration positions and explosion yields, with a single penetration-explosion prediction taking only 0.2 seconds. Specifically, during the penetration phase, the structural response field prediction error is less than 2.9%, and the node failure prediction accuracy exceeds 99.5%; during the explosion phase, the structural response field prediction error is lower than 6.5%, and the failure prediction accuracy surpasses 98.8%. The evaluation of 100 penetration-explosion damage scenarios takes only 30 s.The proposed digital-intelligent prediction model demonstrates excellent generalization performance and highly efficient, accurate prediction capability, thus providing a new tool for building structure damage assessment and safety protection design.

Currently, human society is entering an era of Intelligent Internet of Everything (IoE), characterized by the integration of humans, machines, and objects. Information and communication networks, as the crucial information infrastructure supporting the "Intelligent IoE," are undergoing unprecedented transformations. The development of these networks has a profound impact on people's lifestyles, economic growth, and social progress. The deep integration of artificial intelligence and information communication technologies makes intelligent information networks an inevitable trend in the evolution of network intelligence. In response to the diverse application demands of complex scenarios in the intelligent era, networks should have the ability to proactively adapt to changes in both internal and external conditions. Key features such as autonomous learning, optimization, management, and evolution enable networks to enhance the timeliness, effectiveness, and personalization of services, thus meeting the demands of the intelligent age. 

Mobile edge computing (MEC) can effectively address the issues of increasing business demands and greater processing difficulties for mobile users. However, users' service demands have differentiated characteristics in time and space. At the same time, network characteristics such as deep uncertainty in the network environment also restrict the efficiency of network edge task processing and service caching. In response to these challenges, this paper studies the users' service offloading and service caching issues in a multi-user and multi-edge server MEC system. To address the differentiated service requirements of users in both time and space, with the optimization goal of minimizing the average delay and energy consumption cost for all users under the base station, a joint optimization of task offloading and service caching based on the differentiated needs in time and space is established.Taking into consideration the characteristics of the network environment with deep uncertainty, a multi-agent deep reinforcement learning algorithm based on graph neural networks and long short-term memory networks is further proposed for autonomous learning and decision-making of user task offloading, resource allocation, and service caching strategies. Finally, simulations have verified that the proposed algorithm has good convergence and energy efficiency.

Radar signal sorting is a key technology in the electronic warfare system and an essential part of battlefield situational awareness. The development of new system radar technology brings a serious challenge to radar signal sorting in the current complex battlefield electromagnetic environment. For the problem that the traditional K-means clustering algorithm is sensitive to the initial cluster number K and the initial points when performing signal sorting on radar full pulse data, an optimized K-means-based radar signal sorting algorithm is proposed. The combination of water wave center diffusion(WWCD) optimization algorithm and the Canopy algorithm realizes the optimal selection of the distance threshold of the Canopy algorithm, and optimizes the selection of the cluster number K for K-means clustering, effectively reducing the sensitivity of the K-means algorithm to the selection of the initial number of clusters. Three kinds of UCI data and three kinds of frequency-hopping radar pulse data are used to verify the sorting performance of the proposed method, and the clustering effects are also compared with common clustering algorithms such as DBSCAN, OPTICS, and Canopy-K-means. The results show that the proposed method is insensitive to the setting of initial parameters and has a high clustering and sorting accuracy.

Object detection technology has great potential for progress in the field of UAV remote sensing images. In order to design object detection algorithms suitable for remote sensing images, it is necessary to accurately grasp the advantages and disadvantages of the current algorithms and explore new solutions. Firstly, this paper conducts a comprehensive and systematic analysis of object detection detection methods based on traditional techniques and deep learning, and compares the various algorithms. Secondly, it summarizes the commonly used remote sensing image datasets and evaluation metrics for object detection detection algorithms. Thirdly, to address the key challenges in remote sensing image object detection, such as small target size, occlusion, and complex background environments, the paper deeply analyzes the difficulties faced by the existing methods in handling these issues. Finally, considering the background environments and characteristics of remote sensing images, the paper discusses  the potential improvement directions for the current algorithms from five aspects, providing a reference for the development of object detection technology in unmanned aerial vehicle (UAV) remote sensing images.

When the conventional feature matching color transfer method is applied to the fusion of ultra-wide field-of-view infrared and low-light images, the problems such as high mismatch rate and poor fusion effect may occur. Thus, A natural color fusion method based on superpixel features and color clustering joint constraints is proposed. First of all, the superpixel segmentation is carried out on low-light image and space of color reference image, and  low-level, mid-level, and high-level superpixel feature sets are constructed sequentially. Secondly, the superpixel color clustering of space of color reference image is processed by the improved Fuzzy ART network. Thirdly, the initial superpixel matching set is established based on the similarity of superpixel features. Additionally, with the adaptive color transfer, an initial colorized low-light image is generated and then fused with an ultra-wide field-of-view infrared image to obtain an initial color-fused image. Finally, based on the similarity between infrared-only information and infrared information with color, color transfer is applied to the infrared-only information, ultimately obtaining a naturally colored fused image. The experiment results indicate that compared with traditional methods, the proposed method improves fusion metrics and color metrics by over 19.5% and 9.4%, respectively. The fused images obtained not only retain the significant feature information of both infrared and low-light images well, but also exhibit natural and continuous colors. 

The technological innovation and transformation in the new-generation internet domain are pivotal to national security, economic stability, and the enhancement of global competitiveness, serving as a crucial support for China in great power competition. However, the internet system and mechanism constitute a "complex giant system" with two major technological approaches to its innovation research: the evolutionary development route and the revolutionary innovation route. Both have their respective advantages and disadvantages and have played a vital role in addressing significant issues in today's internet. In the future, the further development of the new-generation internet should draw on the strengths of various approaches by integrating diverse new architectures, technologies, and capabilities. This integration will transform the internet from a traditional passive information carrier into a new type of proactive intelligent service facility, achieving leapfrog capabilities in intelligence, security, and diversified service provision. It will provide more intelligent and flexible support for emerging businesses and applications in the future, fostering a new landscape of deep integration and collaborative development between internet technology and various vertical industries.

The pendulum waves in blocky rock masses exhibit nonlinear characteristics, making it difficult to interpret their spectral features and the mechanism of quasi-resonance phenomena. To address this issue, a nonlinear vibration model of pendulum wave that considers cubic terms is established. The approximate analytical solution of multi degree of freedom strong nonlinear vibration is obtained by using the improved L-P method and the mode superposition method. This, to a certain extent, explains the nonlinear phenomenon mechanism of pendulum wave in blocky rock mass observed in the experiments. The results show that the vibration frequency can be expressed as high order polynomials of the input energy and changes non-monotonously as the energy increases. The ratios between the resonance frequencies are related to the system structure and are approximately equal to integer multiples of 2. By establishing a hierarchical structure model for pendulum waves, it is concluded that quasi-resonance serves as an indicator of transitions between pendulum waves across different-sized blocks.

Depth information derived from binocular disparity is a critical cue enabling observers to accurately identify camouflaged targets. Given that stereoscopic vision induced by binocular disparity is effective only within a specific range of distances, its applications are primarily focused on enhancing the probability and accuracy of identifying camouflaged targets. The feasibility of applying stereoscopic vision induced by binocular disparity to camouflage design remains to be explored. This study employs random dot stereograms(RDS) to induce stereoscopic vision in observers and collects their electroencephalogram(EEG) signals. The analysis covers the behavioral data of the observers, EEG fatigue indicators, and changes in dynamic functional networks throughout this process. Behavioral data demonstrate that small disparity RDS can induce stereoscopic vision in participants. Significant differences in fatigue indicators were detected at four electrodes, though the trends varied. Behavioral data also indicate that observers did not experience subjective feelings of fatigue. Dynamic functional network analysis reveals that binocular disparity stimuli induced stronger subnetworks in the α and β frequency bands of the brain functional network. This study confirms that small disparity RDS can effectively induce stereoscopic vision in observers,leading them to pay more attention to erroneous target contours without causing noticeable fatigue. This finding provides a physiological basis for employing binocular disparity in camouflage design, enhancing the stereoscopic effect of flat fake targets and camouflage patterns.

Aiming at the issues of underestimating target reflectivity and difficulties in accurately extracting targets structural features in sparse imaging of synthetic aperture radar (SAR), a sparse SAR imaging algorithm based on non-convex and relative total variation (RTV) regularization is proposed. This algorithm utilizes non-convex penalties to suppress bias effects and leverages RTV to adaptively preserve image structures. Subsequently, under the distributed optimization framework of the alternating direction method of multipliers (ADMM), it achieves coordinated optimization enhancement of multiple regularization terms. Additionally, to further enhance imaging efficiency and reduce memory usage, a measurement matrix constructed using match filter (MF) operators is employed for approximate observations, and quantitative evaluations of the reconstructed SAR image quality are conducted. Both simulation and real-data processing results demonstrate that this method can effectively suppress noise and clutter, significantly improving target reconstruction accuracy and radiometric resolution without compromising spatial resolution.

High-mobility scenarios are known to pose a significant challenge to wireless communications systems due to the resulting doubly-dispersive wireless channel. The orthogonal time frequency space (OTFS) modulation is a two-dimensional modulation method that maps the transmitted signal to the delay-Doppler domain. By converting the doubly-dispersive channel in the time-frequency domain into a flat channel in the delay-Doppler domain, the OTFS modulation can effectively overcome the effects of frequency selective fading and time selective fading. To address the channel parameter estimation problem in multi-user massive multi-input multi-output (MIMO) OTFS systems, firstly, through in-depth analysis of the multi-antenna channel structure characteristics, the channel between users and base stations is modeled as a sparse structure model. Afterwards, the massive MIMO channel is divided into multiple groups, and a pilot pattern suitable for multi-user massive MIMO-OTFS systems is designed. A sparse Bayesian learning channel estimation algorithm based on group block structure and common sparse threshold is proposed. Finally, with the estimated channel state information, a method for estimating channel parameters such as fractional Doppler and angle of arrival is designed to further perceive the users' states. The simulation results show that the proposed channel parameter estimation algorithm outperform the traditional methods in estimation accuracy and system spectral efficiency.

In the UAV-assisted railway moving edge computing (MEC) system, an effective method for computing task unloading and power control is proposed with service delay and task failure execution cost as the performance index, while ensuring the stability of energy consumption of UAVs and the requirement of computing delay constraints. With the Lyapunov optimization theory, the optimization problem based on long-term performance index is transformed into the sub-problems of multiple time slots. A dynamic computational unloading algorithm based on domain adaptive learning is proposed to make unloading decisions for computing tasks in each time slot. Meanwhile, in each time slot, the proposed algorithm also optimizes the transmission power of the service data download when the unloading decision determines that the computing task is executed on the local device. The experimental results show that the proposed algorithm can effectively reduce the service delay and improve the efficiency of task completion.

To address the problems of visual burst and feature redundancy in the classical bilinear pooling model in the field of fine-grained image recognition, this paper proposes a graph bilinear pooling model. This model integrates graph networks into the bilinear pooling framework，leveraging the aggregation capabilities of graph networks to encode differential image features into higher-order features，thereby alleviating the phenomenon of visual burst during the encoding process. The results of the experiments conducted on the three public datasets of CUB, Cars and Aircrafts show that the proposed model achieves accuracies of 87.8%, 93.5% and 89.6%, respectively. Compared with decomposed bilinear pooling, this model's parameter count is only 25% of the baseline model, while the recognition accuracy is improved by 2.4%, 1.7%, and 1.3%, respectively, which fully verifies the effectiveness of the model and can provide a method reference for fine-grained recognition of military targets.

Aiming at the problem of high complexity of the existing algorithms for intelligent reflecting surface (IRS)-assisted cell-free massive multiple-input multiple-output (mMIMO) systems, this paper proposes a low-complexity algorithm based on IRS-assisted cell-free mMIMO system combined with beamforming. To begin with, under the constraints of restoring the signal received, IRS phase shift and transmit power, and aiming at obtaining the optimal transmit beamforming vector, a joint optimization problem of access point (AP) active beamforming and IRS passive beamforming is constructed. Then, the original problem is converted into two tractable sub-problems by the alternating optimization method. Finally, the sub-problems are solved by the Lagrange multiplier method and the artificial bee colony algorithm through the idea of solving non-homogeneous linear equations. The simulation results show that compared with traditional algorithms, the proposed algorithm has lower complexity and lower bit error rate, and can use lower resource consumption such as transmission power and spectrum efficiency to obtain performance comparable to other solutions.

In order to solve the small sample problem of insufficient feature quantity in the laser atmospheric transmission field test dataset, a laser atmospheric transmission model was constructed by the multi-layer phase screen algorithm, and more than 4 000 sets of simulation data were obtained. The simulation data, together with 332 sets of near-infrared laser atmospheric transmission field test data, form the machine learning training data set. With the atmospheric parameters, transmission distance, and other parameters as input items, and four evaluation factors for the target spot light field as output items, the random forest algorithm was used to construct a prediction model of four light field evaluation factors to achieve assessment of laser atmospheric transmission efficiency. The effectiveness of the model was verified using 202 sets of real field data. The research results show that the R2 coefficient of the model constructed by the random forest algorithm is above 0.89, which can better fit the multiple regression relationship between input and output; the average relative error between the predicted value and the experimental value is less than 10%, which indicates that the prediction accuracy of the model is high. The research results can provide certain scientific basis for the application of machine learning algorithms in laser transmission effectiveness evaluation, and provide certain technological support for the evaluation of laser atmospheric transmission effectiveness.

The direct current power supply system with pulse constant power load (PCPL-DCPS) has multiple different operating states as the pulse parameters change. However, the current method for large-signal stability analysis of power systems in a single mode is not applicable to the stability analysis of pulse loads with medium and high frequencies. To address this, a unified large-signal stability analysis method that combines switch theory and mixed potential function method is proposed in the paper. In the method, the fast and slow switch stability domains of switch systems are divided by the dwell time of fast and slow switching, and different principles for large signal stability analysis in different domains to meet different dwell time conditions are proposed. Taking PCPL-DCPS as a simulation experiment, the fast slow switch stability domain boundary division and analysis method are verified to be more reasonable. Furthermore, by correlating the stability of switch system with pulse frequency, a more unified and intuitive characterization formula is obtained. The method described in the paper is suitable for switched power systems throughout the entire time period, and can also be applied to the stability research of alternating current power supply systems with pulse loads, further guiding the selection of pulse loads parameters, such as peak power and pulse frequency.

The storage and computing capabilities of a single mobile edge computing (MEC) server are limited, often unable to meet the demands of heavy computation tasks. Therefore, choosing a collaborative approach among multiple MEC nodes to handle migrated tasks becomes an effective measure. To optimize resource allocation in multi-server collaboration, the game theory is employed, and a resource allocation problem based on the game theory for multi-server collaboration is further proposed. In response to this problem, servers are categorized as either migrators or collaborators based on their status. With the game theory, the profit models for buyers/migrators and sellers/collaborators are proposed respectively. By seeking flexible allocation of computing resources, an equilibrium between buyer server profits and seller server profits is achieved. A dynamic payment bidding mechanism for multi-server computing resources is studied, and an on-demand resource allocation algorithm oriented towards server collaboration is proposed. Simulation results show that the proposed algorithm not only meets the computational needs between different servers but also maximizes system performance.

This paper focuses on the optimal fixed-time leader-following consensus of multi-agent systems (MASs). Firstly, based on the goal of performance optimization, an event-triggered optimal control strategy is designed, which takes into account the fixed-time optimal consistency control objectives and limited system communication and computation resources. Secondly, an adaptive dynamic programming （ADP）online learning algorithm is proposed to approximately solve the solution of Hamilton-Jacobi-Bellman (HJB) equation to obtain the expression of the optimal value function，where the Critic neural network structure is only utilized. Thirdly, combined with the gradient descent method and experience replay approach，the weight vector is updated to approximate the cost function and its gradient at the triggering instants by employing the historical record and current data. Finally，the unmanned swarm systems composed of unmanned ground vehicles (UGVs) are utilized to verify the feasibility of the proposed method.

Armored vehicles are the main targets for reconnaissance and strikes on land battlefields. Due to constraints such as long reconnaissance distance and large detection range, armored vehicles in reconnaissance images often present characteristics of low-resolution and missing details, making their identification extremely difficult. Although existing large models based on deep learning have achieved higher accuracy, they lack consideration for model parameters and computational complexity, making it difficult to implement and apply them. In order to improve computational speed, a low-resolution armored vehicle recognition approach via joint super-resolution and knowledge distillation is proposed. By constructing a lightweight model that integrates super-resolution with classification and designing a learning method for lightweight models based on knowledge distillation, the model's parameter quantity and computational volume have been greatly simplified. The proposed lightweight model demonstrates performance similar to that of the larger model.The effectiveness of the proposed model and method were verified on the three public datasets and a self-built armored vehicle recognition dataset.

The current research on railguns is mainly focused on small-caliber railguns, lacking an understanding of the multi-physical field environment characterization of projectiles during the launching process of large-caliber railguns. Therefore, this paper develops a dynamic simulation model for the integrated projectile launching by large-caliber railgun, fully considering practical factors such as contact characteristics, material property changes and structural deformations among components. The spatial and temporal distribution characteristics of the electromagnetic, temperature, and structural fields of the launching components during the launch process are also discussed. Finally, based on the simulation results, the reliability of the projectile is evaluated. The research results indicate that the 130 mm railgun can accelerate a 26 kg projectile to 1 023 m/s, with a maximum overload of approximately 6 700g. In addition, at the moment when the projectile exits the muzzle, the induced current of the fuse and the stress on the TNT powder charge will sharply increase, posing possible safety hazards to the projectile. However, the temperature field has a relatively insignificant impact on the projectile.

To evaluate the lightning detection performance of the FY-4A, this paper proposes a matching method based on the statistical characteristics of the satellite-ground lightning data. First, a larger time-space window is selected for satellite-ground data matching. Then, the time deviation and distance deviation of the matched data are statistically analyzed to determine the final suitable time-space window. By statistically analyzing the summer lightning data (from June to August) in 2020 from FY-4A and ground-based advanced direction and time-of-arrival detecting system (ADTD) in China, it is finally determined that the time interval is 3.4 s and the spatial latitude and longitude interval is 0.6° as the time-space window for satellite-ground data consistency analysis. Based on this time-space window, a comparative analysis of satellite-ground lightning data was carried out. The results show that the average time deviation of satellite-ground data is 1.3 s, and the average space deviation is 23.73 km. The detection efficiency of FY-4A lightning is 19%. It is found that the detection efficiency of FY-4A has obvious diurnal characteristics, with the detection efficiency during the day significantly lower than that at night. The average detection efficiency at night is 31.1%, while during the day it is 7%. The detection efficiency is only 2.3%, the lowest from 12:00 to 14:00 during the day. The diurnal variation of the light radiation intensity of the FY-4A lightning data positively correlates with the change in the solar height angle. The light radiation intensity is the highest from 12:00 to 14:00 during the day, reaching 1 391 μJ/m2/sr, which is an order of magnitude larger than that at night (124 μJ/m2/sr). This shows that only lightning with strong light radiation can be captured by the FY-4A lightning mapping imager (LMI) during the day, which further explains the reason for the low lightning detection efficiency during the day.