The HAPNet-CD, a new change detection method, is proposed in this paper to solve the problems of noise misalignment, object boundary ambiguity and low change detection rate of small targets in the processes of encoding and decoding with the existing methods. On the one hand, the encoder of HAPNet-CD adopts siamese branches, in which HRNetV2 is used as the backbone network, and the alignment-and-perturbation-aided difference module is embedded to extract the variation features and difference information. As a result, the high-resolution feature representation can always be maintained in the process of feature extraction, so that the obtained features are more accurate in space. On the other hand, the decoder of HAPNet-CD uses the change features and difference information to construct a hybrid decoder and a differential decoder for decoding. By designing a loss function based on label smoothing, the network pays more attention to the variations of object boundaries and small targets, so that the change detection accuracy of object boundaries and small targets can be improved. Tests were carried out on the public data sets DSIFN-CD and LEVIR-CD, and the experimental results are as follows. Compared with the other 9 mainstream methods, the HAPNet-CD has improved the metrics of Precision, Recall, F1, and IoU by 2.55%,4.58%, 3.59%, and 5.9%, respectively, on the DSIFN-CD dataset. On the LEVIR-CD dataset, the Precision metric is improved by 0.54%, while the metrics of Recall, F1, and IoU are all close to the most advanced level.

With the widely use of embedded software，test towards embedded software has become a hot spot in software test area. Traditional interface test methods aim at embedded software depend on artificial data，which is not in support of general utilization. This paper focus on the design and generation methods of interface test data, and come up with a universal modeling tool towards protocol frame format to help testers completing the building process of data frame format. In order to achieve the automatic generation of test data，we designs the rules of data disturbance based on modeling tool. Experiment indicates that the method proposed by this paper to generate the interface test data of embedded software can improve the efficiency and accuracy of test，ascending the coverage of bugs.

The efficient solution of UAV path planning problem is a key to ensure UAV flight efficiency and flight safety. To efficiently solve the problem of UAV 3D path planning, a method based on improved football team training algorithm is proposed. With the actual requirements of UAV flight path considered, the fitness function of UAV 3D path planning model under multiple constraints is constructed, which takes path cost, height constraint and turn constraint into account. Setting a typical 3D path planning problem as the solution object, the effects of three parameters, which are stochastic probability, learning probability and communication probability, are studied on the efficiency of the algorithm considering three evaluation indexes, which are path costs, iteration times and algorithm running time, respectively. Finally, a comparison experiment is conducted with the whale optimization algorithm, and the results show that the improved algorithm has a better solving efficiency.

In the future, the semantic communications will be widely used in cellular wireless networks. This paper focuses on the issue of the on-demand semantic communications in cellular networks in the context of device-to-device (D2D) content recommendation scenarios. A semantic transmission mode is proposed in this paper, which takes the target user's quality of experience (QoE) as the indicator. The mode allows the cluster head's target user to adjust the semantic compression ratio and select the access link based on the requirements of delay and image fidelity, and further designs a resource optimization problem from the perspective of semantic compression ratio and channel allocation. To solve this problem, a ratio compression factor is designed for the semantic encoding and decoding process in image semantic communications, and a small batch model training method is proposed to train sub-semantic models by randomly selecting batches, taking into account the model performance and training complexity. In addition, the game theory approach is introduced to solve the problems of channel allocation and semantic compression factor selection. The problem is modeled as a potential game problem, and utility functions and potential functions are designed, and the existence of Nash equilibrium is proved. Finally, a parameter correction space adaptive algorithm is proposed to achieve Nash equilibrium. The simulation results demonstrate the effectiveness of the proposed method on the overall QoE of the target user.

The shear deformation characteristics of joints usually determine the overall stability of the surrounding rock mass. To investigate the shear instability characteristics of joints, disturbance shear tests under different initial stresses were carried out on symmetrical regular dentate specimens with various undulation angles and materials. According to the basic parameters easily obtained from the engineering site, such as peak shear strength and displacement, the joints can be classified into 3 types, that is, “low strength, low brittleness and high ductility”, “high strength, high brittleness and high ductility” and “high strength, high brittleness and low ductility”. The peak shear strength of the dentate joints under disturbance conditions decreases significantly, but the peak shear displacement does not differ much, compared with the quasi-static condition. The “low strength, low brittleness, high ductility”joints are the non-catastrophic types with no significant stress dropping under different stress paths. The “high strength, high brittleness, low ductility” joints exhibit higher peak strength, smaller cumulative irreversible displacement and larger post-peak dropping, which are more prone to instability than the “high strength, high brittleness, high ductility” joints.

To address the issues such as the immobility, small killing radius, and significant post-war hazards of traditional landmines, a type of intelligent loitering landmine system deployed by rockets has been proposed. The combat effectiveness analysis model of the loitering landmine was established based on the Monte Carlo algorithm, used to explore the influences of some parameters like the lateral and longitudinal firing dispersion of the rocket launcher, the quantity, the perception capability and flight capability of loitering landmines. The results indicate that compared with traditional landmines, the operational efficiency of the intelligent loitering landmine has been significantly enhanced, and there exists an optimal matching effect between the strike probability and the firing dispersion of the rocket launcher. Additionally, the combat effectiveness is affected by performance parameters such as the mobility of the target, the flight speed and perception range of the loitering landmine. Enlarging the perception radius of the loitering landmine and increasing the flight speed are conducive to improving the blocking and control effect of the intelligent loitering landmine. The findings of this paper can provide technical support for the index demonstration, scheme design, effectiveness evaluation and operational application of the loitering landmine.

The metal airbag has become a research hot issue in the design of dynamic deformation and shock reduction structures of various spacecraft because of its high strength, good air tightness and environmental adaptability, as well as better shape stability, This paper mainly studies the key parameters that lead to fracture or fold instability during the inflation process of airbags, and also analyzes the influences of the location of the air inlet and the thickness of the balloon edge on the swelling effect. With the finite element simulation method, the five groups of commonly used metal material parameters are selected as the basic data to carry out the orthogonal experiments on material density, Young's modulus, Poisson's ratio and yield limit. The simulation results show that the maximum expansion height of the metal airbag is most affected by the material density, and the range is 8.26 mm. The maximum fold depth is most affected by the yield limit, with a range of 5.48 mm. The number of folds is least affected by the yield limit, and the range is only 0.8. The location of the air inlet will affect the uniformity of the balloon expansion, but will provide a larger expansion height in a specific area. Increasing the edge thickness will enhance the strength of the airbag, but reduce the maximum expansion height of the airbag. This conclusion can provide theoretical reference for engineering design of metal airbag.

The hollow-core fiber reinforced polymer (FRP)-concrete-steel (HC-FCS) column is a new type of concrete composite column, which is composed of an outer FRP tube, an inner steel tube and a concrete filling between the two tubes. This study numerically investigated the behavior of HC-FCS column against the close-in blast load. The arbitrary Lagrangian Eulerian (ALE) algorithm was used to establish the numerical model. The accuracy of the numerical model was first validated using the existing test results, the transmission process of blast wave, damage development of concrete, circumferential strain of FRP tube and residual mechanical properties after explosion are analyzed. The results demonstrate that the FRP tube is able to effectively confine the infilled concrete in the blast region during the close-in blast. Both the column's axial load capacity and axial stiffness are significantly decreased after the close-in blast. Changing the FRP thickness, steel tube thickness, hollowness ratio can improve the blast resistant capacity of HC-FCS column, with the effects of increasing FRP tube thickness and steel tube thickness more significant.

The basalt fiber reinforced polymer (BFRP) bar is a new material that can be used in the field of civil engineering instead of steel, and it is urgent to carry out an in-depth study on the failure mechanism of BFRP bar-concrete bond and bond strength. In this paper, the basic theories of elasto-plastic mechanics and concrete fracture mechanics are utilized to describe the bond damage process and failure mechanism of BFRP bar-concrete, and to establish a semi-empirical and semi-theoretical method for calculating the bond strength of BFRP bar-concrete. The method adopts the corresponding calculation method for the damage mode based on the logistic regression model, and comprehensively considers the effects of concrete strength, BFRP bar diameter, concrete protective layer thickness, and loading rate, and finally verifies the reliability and prediction accuracy of the calculation method through the relevant test data. The results show that the bond strength of BFRP bar-concrete is closely related to the damage mode; the tensile strength of concrete is the primary factor affecting the bond strength; the cracking state of concrete depends on the diameter of the BFRP bar; the thickness of the protective layer of concrete and the loading rate directly affects the bond strength of BFRP bar-concrete. The calculation results of this method are in good agreement with the test results, and the prediction error of the proposed method is 0.9%—23.7%,which provides an effective method for predicting the bond strength of BFRP bar-concrete. 

Low earth orbit (LEO) satellites are capable of conducting rapid geolocation of global navigation satellite system (GNSS) interference sources with Doppler measurement data. However, the highly dynamic characteristics of LEO satellites limit the quantity of acquired Doppler observations, consequently restricting the localization accuracy achievable through conventional least squares algorithms. To enhance the accuracy, a weighted least squares localization algorithm based on Doppler measurement Cramér-Rao bound (CRB) is proposed in this paper. This method incorporates a signal-to-noise ratio (SNR) lookup table for Doppler sampling instances to ascertain the Cramér-Rao bound (CRB), thereby standardizing the noise across various Doppler observation moments through weighting. This adjustment ensures that the least squares solution for the interference source location is made close to the optimal estimation. The effectiveness of the proposed algorithm was verified by simulation under different sampling rates, SNRs and sampling durations. The FPGA-based GNSS interference acquisition and localization system was also constructed and tested in real scenarios. The simulation and experiment results show that the proposed algorithm significantly improves the localization accuracy by 17.43%, compared with the least squares algorithm.

This paper addresses a wireless transmission system with simultaneous wireless information and power transfer (SWIPT) assisted by an active reconfigurable intelligent surface (RIS). To tackle the issue of insufficient fairness in energy harvesting performance caused by multiplicative fading, a fairness-aware energy harvesting resource allocation algorithm is proposed. Firstly, a power splitting (PS) architecture is employed to facilitate simultaneous information and energy transmission for active RIS-aided SWIPT networks. The objective function is formulated to maximize the minimum harvested energy among all users, taking fairness into consideration. This leads to a joint resource allocation problem with constraints, including user signal-to-interference-plus-noise ratio, active RIS parameters, base station transmission power, and PS factor. Secondly, employing techniques such as alternating optimization, semi-definite relaxation, continuous convex approximation, and penalty functions, the original non-convex problems that cannot be directly solved are transformed into the standard convex problems. Subsequently, an alternating iterative fairness-aware energy harvesting algorithm is proposed. Finally, numerical simulations validate that the proposed optimization algorithm significantly enhances the energy harvested at the user with the minimum energy resource allocation, ensuring fairness in energy resource allocation within the communication network.

Comparison of intuitionistic fuzzy numbers (IFNs) is always the core issue of decision-making theory with intuitionistic fuzzy sets (IFSs). We are aiming to improve the comparison methods of the IFNs in this work, which have been known for lacking differentiating ability or being too complex in the structure. We propose a novel score function of the IFNs from a new perspective and define a new possibility degree formula between two IFNs, which can quantitatively measure the discrimination degree between any two IFNs, to expand the applicability of the proposed score function. Then, we develop a novel multi-attribute decision-making (MADM) method based on the proposed possibility degree formula and further apply it to solve some practical MADM problems under the intuitionistic fuzzy environment. Theoretical analysis and experiment results indicate that: the proposed score function has refined Atanassov's partial order and can construct a more reasonable admissible weak order among all IFNs, and the proposed possibility degree formula employs some nice properties and can effectively expand the usage of the proposed score function of the IFNs. Moreover, the proposed intuitionistic fuzzy MADM method, based on the possibility degree matrix, has a strong self-checking capability in the decision-making process and can ensure the validity of the final decision-making results by guaranteeing the calculation process, and is applicable for large-scale decision-making.

In order to study the injectable characteristic of coral sand materials from South China Sea islands and reefs, a series of orthogonal experiments on its injectable characteristic were carried out with superfine Portland cement slurry. The influence of factors such as compactness of coral sand, the water-cement ratio of the slurry, grouting pressure and pressure holding time on the stone rate of grouting was 0analyzed. The test results show that the sensitivity order of superfine Portland cement to the effect of coral-sand grouting stone rate is as follows: grouting pressure, water-cement ratio, pressure holding time and compactness. Multiple linear regression analysis method was used to establish the mathematical model between grouting stone rate and influencing factors, and the linear correlation of the model was good.

Vertical cylindrical steel storage tanks, which are usually used as oil storage facilities, are prone to overall deformation or local damage failure in the event of blast or other strong dynamic loads, which will result in significant losses. In order to address this issue, this paper proposes a rapid processing method in which the polyurea spraying is applied to enhance the blast resistance of existing steel tanks, and some blast resistance experiments on sprayed composite tanks have been conducted. The experimental results demonstrate that the polyurea layer can effectively absorb a portion of the energy of the blast products under the action of repeated explosion loads, resulting in a reduction of 12.3% in the transverse width of the concave yield surface of the coated tank and a reduction of 12.5% in the maximum deflection value. A numerical simulation with LS-DYNA was employed to reproduce the experiment. The numerical simulation results were then compared with the experiment in terms of the shape of the concave yield surface and the depth of the tank wall collapse. The result of this comparison demonstrates that the numerical simulation is able to accurately reproduce the experiment, thereby providing evidence of its reliability. The further investigations reveals that the polyurea layer decreases and diffuses the localized strong impact of the repeated blast loads, reduces the local impact compression capacity of the shock wave on the tank wall, and attenuates the stress concentration on the tank wall and weld joints, which can prevent parts of the tank wall from entering the plastic phase and producing local failure. Furthermore, the polyurea layer can reduce the peak kinetic energy of the tank by 27.8% under the action of repeated explosive loads, reduce the overall degree of collapse of the tank, and protect the structural integrity of the tank.

In order to solve the problem that the infrared and visible image fusion algorithm based on convolutional neural networks（CNNs） does not consider the long-range dependencies of the original image in the fusion process, a fusion algorithm for infrared and visible light images based on the Siamese Swin Transformer is proposed, and a twin network model is constructed. First of all, the latent low-rank representation（LatLRR） decomposition method is used to decompose the original image into the detail layer image and the base layer image, and then the detail layer image and the base layer image were fused respectively to improve the accuracy of image fusion. Secondly, in the feature extraction stage, the Swin Transformer is used to extract the features of the detail layer image and the base layer image respectively which includes the long-range dependencies of the image. Thirdly, in the weight generation stage, the L1-norm regularization method is used to normalize features, and the Softmax method is adopted to obtain the weights of the detail layer image and the base layer image respectively. Finally, the fusion image of the detail layer and the base layer is reconstructed by a linear weighting method, and then the final fusion image is obtained by a linear summation. The proposed algorithm is evaluated on the VIFB. The experimental results show that the proposed algorithm has excellent qualitative performance compared with 20 other fusion algorithms in the qualitative performance comparison; the proposed algorithm obtains 3 best values in the 13 metrics, which exceeds the most other fusion algorithms in the quantitative performance comparison; the running time of the proposed algorithm is less than that of most other fusion algorithms. Overall, compared with other fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.

In response to the practical requirement of measuring the absolute spatial orientation of the artillery axis under the condition of an uneven artillery chassis, a dual theodolite spatial angle measurement method based on the solution of the direction cosine matrix is proposed. This method integrates the Beidou dual-antenna orientation and the dual theodolite measurement of three marked points to obtain the absolute spatial orientation of the artillery axis.With the horizontal and vertical angles of these three marked points determined by the theodolites as known inputs, the azimuth and elevation angles of the artillery barrel in the geographical coordinate system are derived through vector correlation and direction cosine matrix calculations, following the principle of double vector attitude determination. The accuracy of spatial angle measurement with dual theodolites is analyzed through the Monte Carlo simulation method, revealing that the azimuth angle measurement precision exceeds 57.6″ and the elevation angle measurement precision surpasses 28.8". An experimental test system, constructed with a high-precision two-axis turntable, demonstrates the azimuth measurement accuracy of 65.5″ and the elevation angle measurement accuracy of 51.1″, accounting for Beidou orientation errors. Both the simulation and experimental results confirm the effectiveness of this method for measuring the spatial orientation of the artillery axis.

Hyperspectral image target detection technology has important application value in many fields. In order to fully exploit the rich spatial and spectral information in hyperspectral images, a hyperspectral image target detection method called Gabor-CEM, which combines Gabor filtering with constrained energy minimization (CEM), is proposed. This method fully combines the advantages of the Gabor filtering and the CEM algorithm. The Gabor filtering is utilized to effectively extract the spatial texture and orientation features from hyperspectral images, providing abundant spatial information for target detection. Meanwhile, the CEM algorithm is leveraged for efficient utilization of target spectral information, enabling target localization and identification. Experiments were conducted using hyperspectral images captured by a field imaging spectrometer, and the results showed that compared with the traditional methods, the Gabor-CEM method can more accurately detect targets, reduce false positives and false negatives, and has significant advantages in target detection tasks, providing a new and effective approach for hyperspectral image target detection.

Existing code similarity detection models primarily focus on constructing encoders, with limited research on loss functions in deep learning. To address the overlooked issue of evaluating embedded binary function vectors, this paper proposes an angular-margin-based binary code contrastive learning framework (AngCLF). By optimizing the objective function of contrastive learning, the model's accuracy and convergence speed are enhanced. Besides, the study analyzes the reasons for the model's effectiveness and introduces multiple metrics for evaluating binary code vector spaces. The experimental results validate the accuracy of the AngCLF. The AngCLF surpasses six models including the jTrans model in accuracy, and has faster convergence speed and obvious advantages in alignment and uniformity metrics.

To enhance survival capabilities in modern battlefields, airborne multifunction radars exhibit the characteristics such as complex signal patterns, highly variable parameters, irregular on/off switching, and reduced radiation times, posing certain challenges to radar operating mode recognition based on traditional methods. By referring to sample data generated from typical characteristic parameters of common operating modes of multifunction radars, multiple model parameters are optimized with the water wave center diffusion (WWCD) algorithm, and an adaptive weighting strategy is employed to improve the performance of ensemble learning algorithms for multifunction radar operating mode recognition. Experiments were conducted using genetic algorithms, particle swarm optimization algorithms, differential evolution algorithms, and the WWCD algorithm to optimize single model parameters, and different ensemble learning strategies such as soft voting, hard voting, and adaptive weighting were used for operating mode recognition.The results demonstrate that the proposed algorithm achieves higher accuracy, compared with traditional algorithms. Furthermore, the performance of the algorithm in recognizing radar operating modes under small sample conditions was also tested, verifying the feasibility and high recognition efficiency of this algorithm.

The variation of draft of a belt floating bridge will lead to the variation of its waterplane, which will lead to the change of elastic foundation stiffness. The existing calculation methods based on the elastic foundation beam model usually satisfy the assumption of equal stiffness of the elastic foundation, which has a large calculation error. In this paper, a calculation method of belt floating bridge considering the variation of elastic foundation stiffness is established. The calculation model of the floating bridge is divided into multiple sections for theoretical modeling and solving, and the calculation results of vertical displacement, deck slope, span bending moment and shearing force of the floating bridge can be obtained. Compared with the traditional method, the calculation result of this method is more accurate. Combined with a case, the calculation results of different elastic foundation stiffness are compared and analyzed. The comparison results show that under different loads, the error between the average waterplane method and the subsection method is the smallest. The calculation process of the subsection method is more complicated, and the average waterplane method can be used to calculate and analyze the belt floating bridge when the requirement of calculation accuracy is not high.

Ammunition reserve and scheduling, an important link of ammunition support, has a bearing on whether or not the ammunition support task can be completed in a timely, accurate, and reliable manner. In order to solve the problem of joint optimization of two-level ammunition reserve and scheduling, with the optimization objectives of minimizing the total delay time, the minimum total cost and the maximum actual quantity delivered, considering the uncertainty of scheduling time, a multi-objective joint optimization model of ammunition reserve and scheduling is established, and the multi-objective model is transformed into a single-objective model by the linear weighted sum method, and then solving it using a particle swarm optimization (PSO) algorithm with a penalty function. Finally, the effectiveness of the model and solution algorithm is verified through a numerical example. Compared with a single-objective optimization strategy, the objective function constructed in this paper can effectively reflect the weight coefficients obtained from the expert evaluation method, and can balance all optimization objectives. In conclusion, this study can provide valuable references for the scientific development of ammunition reserve and scheduling.

Based on the fact that the existing calibration methods for imaging systems cannot effectively calibrate and fix the supe wide field-of-view infrared gaze imaging system. this research proposes a new indirect corner detection algorithm as well as an improvement scheme to address the problem of local optimization in the iterative computation of the Scaramuzza calibration model. We use morphological operations to detect the edges of the board calibration board at the pixel level, and then using interpolation techniques to refine the edges so that the edges have sub-pixel level accuracy, obtaining the four board unit corners near the real corner points, and indirectly obtaining the real corner coordinates by averaging the coordinates of the four corners, so that the corner detection correctness reaches 100%, which is much higher than that of general algorithms, and is more in line with the location of the real corner points. The SSRE is collected during each iteration by using specific circular area sampling grid points for the whole area, and then all the collected data are minimized to take the value, which overcomes the local optimum case.It improves the localization accuracy, and greatly reduces the iterative computation volume and the number of iterations. It compensated the existing general calibration algorithms to meet the shortcomings of the ultra large field-of-view infrared gaze imaging system.

Hyperspectral imaging technology enables the comprehensive utilization of spectral and spatial information, providing a viable solution for precise target identification. It is widely used in areas such as military target characteristic analysis and classification of camouflaged targets. In order to solve the problems of high spectral similarity and low classification and detection accuracy between camouflaged objects and real targets, a camouflage target classification method based on hyperspectral imaging and extreme learning machine (ELM) is proposed. This method first searches for the "window band" for identifying camouflage by studying the spectral characteristics between the camouflage object and the real target. Then, the spectral dimensionality reduction method is used to find the most suitable features for camouflage target classification, and the extracted features are used as input to the ELM multi-classification model to complete the classification task. Taking the classification of camouflage materials in grassland backgrounds as an example, experiments were conducted using hyperspectral images captured in the field. The experimental results show that the classification accuracy of the proposed method reaches 97.27%, which is superior to the classification performance of other comparative classification algorithms. The proposed method provides a valuable reference for future camouflage target classification.

Aiming at the problem that the optimization of energy efficiency (EE) and spectrum efficiency (SE) in low earth orbit (LEO) satellites cannot maintain a consistent growth trend, a method for optimizing the trade-off between EE and SE in LEO satellites is proposed. This method models the LEO satellite resource allocation scenarios, simplifies the dynamic model by dividing time slots, and optimizes the throughput by adjusting sub-carrier power, thereby optimizing EE and SE. In addition, EE and SE are weighted by introducing weight factors and unifying the units of EE and SE, so as to achieve the maximum balance between them. In order to deal with the large state action space problem, the Dueling DQN algorithm is used to achieve a better control strategy. Simulation results show that compared with other deep reinforcement learning (DRL) algorithms, the proposed algorithm converges faster and the convergence value is increased by 10.1% and 18.2% higher, respectively. When the noise power changes, the SE obtained by Dueling DQN is increased by 15.6%, compared with other DRL algorithms.

The planning of surveying and mapping tasks by small aircraft has significant research value. Unlike traditional multiple traveling salesman problems, each surveying target requires more than one surveying payload for surveying, and there is also a time interval between multiple surveying, which is called path-crossing multiple traveling salesman path planning problem. For point target surveying task scenarios, the goal of solving the minimum number of aircraft is transformed into solving the shortest navigation path. On the basis of solving the shortest path by genetic algorithm, combined with detection load constraints, detection task duration constraints, and other conditions, the optimal scheduling scheme is further planned . For the planning of point target and regional target mapping tasks, taking the balance of the workload of each airport's mapping tasks into consideration, the mapping workload index is defined as the number of mapping target points and target roads completed by each airport. The demodulation strategy and the number of aircraft are first calculated, and then the equilibrium is solved to ensure that the number of target points and target roads detected by the three airports is equivalent.The results indicate that the proposed scheme balances both the surveying and mapping costs and the equity of task allocation.

In order to evaluate the protective effect of polyurea on liquid-filled containers, a combination of experimental and numerical simulation methods was used to conduct detailed analysis of the dynamic response behavior of polyurea coated liquid-filled cylindrical shell structure under explosive loading. The macro/micro damage characteristics and the dynamic response process of polyurea coated liquid storage containers were obtained. The results showed that, under close-range explosion loading, thin-walled cylindrical liquid storage shells primarily experience localized damage, and polyurea can effectively enhance the explosion resistance of these cylindrical liquid storage shells. When the stand-off distance Z is 0.134 m/kg1/3, the polyurea absorbs relatively more internal energy at the initial stage of the shock impact from explosion products, and the subsequent process mainly converts and absorbs energy from the kinetic energy of the liquid medium and the internal energy of aluminum tube. As the thickness of polyurea increased from 0 mm to 6 mm, the total amount of energy absorbed by polyurea significantly increased, while the energy absorbed by aluminum tubes shows a decreasing trend, indicating that polyurea can share some of the energy and reduce the energy absorbed by aluminum tubes to increase the protective capacity of the container. The energy absorption per unit thickness of polyurea shows an increasing trend with the increase of polyurea thickness. The synergistic deformation ability of polyurea and substrate is also the principal factor for improving blast protection performance.

Efficient and reliable multi-hop broadcasting algorithms can effectively mitigate the impact of traffic anomalies. However, due to the high-speed movement of vehicles and frequent disruptions in communication links the on internet of vehicles, designing such algorithms presents a significant challenge. To address this issue, a multi-hop broadcasting algorithm based on the crow search algorithm and genetic optimization algorithm has been proposed. The first step involves dividing the areas affected by the traffic anomaly. Next, to ensure stable and reliable messages can be forwarded to the affected areas, an evaluation model is designed that takes multiple factors into account. Different forwarding strategies are then developed for different road areas to accommodate the complex environment of connected vehicles. An optimization algorithm based on the crow search and genetic algorithms is also designed to address the issue of relay node selection. Finally, the OMNeT++ tool is used for simulation. Results show that, compared with other algorithms, this approach achieves varying degrees of improvement in commonly used metrics such as average delay per hop, redundancy rate, and packet delivery rate.

Plateau cold regions are characterized with harsh climate conditions and challenging transportation. According to the climate characteristics and indoor load demands in such regions, a cross-seasonal energy storage compound heating system composed of solar energy, step-change energy storage device and a diesel boiler as auxiliary heat source is proposed. To explore the practical performance and feasibility of this new heating system at a system level, the building and supporting system models were developed with simulation software. The annual operation process of the system was simulated based on Ali region's climatic conditions to analyze the changes in relevant operating parameters to identify the feasible design methods for the system. Furthermore, the feasibility of the system under dynamic climate conditions was verified. Results indicate that the solar collector can operate for more than 2 000 hours annually, providing sufficient solar heat storage capacity. Additionally, at the end of the energy storage period, the phase change water tank temperature can reach 82 ℃. However, when utilizing high-temperature water end radiators with tertiary phase change material heat release causing a drop of water tank temperature to 42 ℃, it becomes difficult to achieve preset indoor temperatures. The low-temperature radiant heating system is better suited to the thermal energy and water temperature fluctuations of the solar cascade phase change storage system, in comparison to the auxiliary diesel boiler used for maintaining water tank temperature and end radiator water supply temperature.

Given the complexity of bulldozer operation and the difficulty of assessing the construction effect, the real-time position of the bulldozer is determined by means of the Real Time Kinematic Differential Positioning (RTK) of the Beidou System (BDS) and kinematic equations. The method of using the elevation difference between the bulldozed earth and the designed plane for evaluating the quality of leveling construction is proposed, which can intuitively evaluate the construction effect. A driving guidance device has been developed that can display the status of bulldozers and construction progress in real time.The engineering application demonstrates that this device's absolute positioning error is less than 5 cm, achieving centimeter-level positioning accuracy to fulfill the demand for precise bulldozer construction; and the vehicle-mounted display terminal, via RS232 communication, can accurately obtain the bulldozer's own state parameters and construction data, such as coordinates, speed, heading, and so on. In actual construction scenarios, this system can effectively reduce the number of reworks and the driver's labor intensity while also improving construction efficiency to fulfill the goal of auxiliary construction.