Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Dynamically triggered seismicity on a tectonic scale:A review

Earthquakes triggered by dynamic disturbances have been confirmed by numerous observations and experiments.In the past several decades,earthquake triggering has attracted increasing attention of scholars in relation to exploring the mechanism of earthquake triggering,earthquake prediction,and the desire to use the mechanism of earthquake triggering to reduce,prevent,or trigger earthquakes.Natural earthquakes and large‐scale explosions are the most common sources of dynamic disturbances that trigger earthquakes.In the past several decades,some models have been developed,including static,dynamic,quasi‐static,and other models.Some reviews have been published,but explosiontriggered seismicity was not included.In recent years,some new results on earthquake triggering have emerged.Therefore,this paper presents a new review to reflect the new results and include the content of explosion‐triggered earthquakes for the reference of scholars in this area.Instead of a complete review of the relevant literature,this paper primarily focuses on the main aspects of dynamic earthquake triggering on a tectonic scale and makes some suggestions on issues that need to be resolved in this area in the future.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

含夹层钛/铝爆炸焊接复合材料的界面特征及力学性能

通过爆炸焊接技术制备了含夹层的TC1/AA1060/AA6061复合材料,并对界面形貌和界面元素分布进行了研究。通过电子背散射衍射方法研究界面晶粒分布,并通过数值模拟得到的界面温度来解释晶粒分布。通过力学性能试验得到复合材料的拉伸和剪切力学性能。界面微观组织结构和力学性能的测试结果表明:TC1/AA1060/AA6061复合材料具有较好的焊接质量。两个界面形貌与数值模拟结果一致,数值模拟得到的界面温度可以解释界面和涡旋区域的晶粒分布。元素在TC1/AA1060界面的扩散宽度为12.3μm,未检测到金属间化合物。在两个涡流区只检测到Al和O元素。此外,还在界面附近不同区域进行了纳米压痕试验,TC1/AA1060界面的纳米硬度介于两侧材料之间,而AA1060/AA6061界面的纳米硬度小于两侧材料。

Effect of diagenetic variation on the static and dynamic mechanical behavior of coral reef limestone

Coral reef limestone at different depositional depths and facies differ remarkably on the textural and mineralogical characteristics,owing to the complex sedimentary diagenesis.To explore the effects of pore structure and mineral composition associated with diagenetic variation on the mechanical behavior of reef limestone,a series of quasi-static and dynamic compression tests along with microscopic examinations were performed on the reef limestone at shallow and deep burial depths.It is revealed that the shallow reef limestone(SRL)is classified as a porous aragonite-type carbonate rock with high porosity(55.3±3.2)%and pore connectivity.In comparison,the deep reef limestone(DRL)is mainly composed of dense calcite-type calcium carbonate with low porosity(4.9±1.6)%and pore connectivity.The DRL strengthened and stiffened by the tight grain framework consistently displays much higher values of the dynamic compressive strength,elastic modulus,brittleness index,and specific energy absorption than those of the SRL.The gap between two types of limestone further increases with an increase in strain rate.It appears that the failure pattern of SRL is dominated by the inherent defects like weak bonding interfaces and growth lines,revealed by the intricate fracturing network and mixed failure.Likewise,although the preexisting megapores in DRL may affect the crack propagation on pore tips to a certain distance,it hardly alters the axial splitting failure of DRL under impacts.The stress wave propagation and attenuation in SRL is primarily controlled by the reflection and diffusion caused by plenty mesopores,as well as an energy dissipation in layer-wise pore collapse and adjacent grain crushing,while the stress wave in DRL is highly hinged on the insulation and diffraction induced by the isolated megapores.This process is accompanied by the energy dissipation behavior of inelastic deformation resulted from the pore-emanated microcracking.

Mechanical properties and energy evolution of Beishan shallow-layer granite under different unloading paths

Rock has mechanical characteristics and a fracture damage mechanism that are closely related to its loading history and loading path. The mechanical properties, fracture damage features, acoustic emission(AE) characteristics, and strain energy evolution of the Beishan shallow-layer granite used in triaxial unloading tests were investigated in this study. Three groups of triaxial tests, namely, conventional triaxial compression test(Group Ⅰ), maintaining deviatoric stress synchronously unloading confining pressure test(Group Ⅱ), and loading axial pressure synchronously unloading confining pressure test(Group Ⅲ), were carried out for the cylindrical granite specimens. AE monitoring device was utilized in these tests to determine the degree to which the AE waves and AE events reflected the degree of rock damage. In addition, the crack stress thresholds of the specimens were determined by volumetric strain method and AE parameter method, and strain energy evolution of the rock was explored in different damage stages. The results show that the shallow-layer granite experiences brittle failure during the triaxial loading test and unloading test, and the rock has a greater damage degree during the unloading test. The crack stress thresholds of these samples vary greatly between tests, but the threshold ratios of all samples are similar in the same crack damage stage. The Mogi-Coulomb strength criterion can better describe the unloading failure strength of the rock. The evolution of the AE parameter characteristics and strain energy differs between the specimens used in different stress path tests. The dissipative strain energy is the largest in Group Ⅱ and the smallest in Group Ⅰ.

Blockchain-Based MCS Detection Framework of Abnormal Spectrum Usage for Satellite Spectrum Sharing Scenario

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

Resource allocation for D2D-assisted haptic communications

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments,e.g.,teleoperation and autonomous driving.Considering the strict transmission requirements on reliability and latency,Device-to-Device(D2D)communications is introduced to assist haptic communications.In particular,the teleoperators with poor channel quality are assisted by auxiliaries,and each auxiliary and its corresponding teleoperator constitute a D2D pair.However,the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation,especially facing the sporadic packet arrivals.First,the contentionbased access scheme is applied to achieve low-latency transmission,where the resource scheduling latency is omitted and users can directly access available resources.In this context,we derive the reliability index of D2D pairs under the contention-based access scheme,i.e.,closed-loop packet error probability.Then,the reliability performance is guaranteed by bidirectional power control,which aims to minimize the sum packet error probability of all D2D pairs.Potential game theory is introduced to solve the problem with low complexity.Accordingly,a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium.Experimental results demonstrate the superiority of the proposed learning algorithm.

Equipment damage measurement method of wartime based on FCE-PCA-RF

As the“engine”of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy’s attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation(FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85%of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.

An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.

Ballistic performance of spaced multi-ply soft fabrics: Experimental and numerical investigation

It has been reported that the ply gap influences the ballistic resistance of spaced multi-ply fabric systems,but its working mechanism was not well-understood. This paper reports the experimental and numerical approaches and results of an investigation on the mechanisms that enable the improved ballistic performance of spaced multi-ply systems. Penetration tests were performed over a range of impact velocities ranging from 200 m/s to 400 m/s. The results confirmed that the ply gap is beneficial to the energy absorption capability of the systems. This is because the front plies tend to absorb more energy when they are not immediately constrained by the rear plies. During a ballistic event, the gap relieves the reflection of the compressive pulse, prolonging the projectile engagement time with the front plies;on the other hand, the rear plies become increasingly less active in dissipating energy as the gap increases.When the gap is sufficiently widened to avoid any interference between the plies before the failure of the front ply, the responses of the whole system no longer vary. It was also found that the ballistic performance of the spaced systems is influenced by ply thickness, impact velocity, and the stacking order of the ply gap.

Joint User Association and Satellite Selection for Satellite-Terrestrial Integrated networks

In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage for users in the densely populated areas.To deal with the dynamic satellite backhaul links and backhaul capacity caused by the satellite mobility, severe co-channel interference in both satellite backhaul links and user links introduced by spectrum sharing,and the difference demands of users as well as heterogeneous characteristics of terrestrial backhaul and satellite backhaul, we propose a joint user association and satellite selection scheme to maximize the total sum rate. The optimization problem is formulated via jointly considering the influence of dynamic backhaul links, individual requirements and targeted interference management strategies, which is decomposed into two subproblems: user association and satellite selection. The user association is formulated as a nonconvex optimization problem, and solved through a low-complexity heuristic scheme to find the most suitable access point serving each user. Then, the satellite selection is resolved based on the cooperation among terrestrial relays to maximize the total backhaul capacity with the minimum date rate constraints. Finally,simulation results show the effectiveness of the proposed scheme in terms of total sum rate and power efficiency of TRs' backhaul.

Non-dimensional analysis on blast wave propagation in foam concrete:Minimum thickness to avoid stress enhancement

Foam concrete is a prospective material in defense engineering to protect structures due to its high energy absorption capability resulted from the long plateau stage.However,stress enhancement rather than stress mitigation may happen when foam concrete is used as sacrificial claddings placed in the path of an incoming blast load.To investigate this interesting phenomenon,a one-dimensional difference model for blast wave propagation in foam concrete is firstly proposed and numerically solved by improving the second-order Godunov method.The difference model and numerical algorithm are validated against experimental results including both the stress mitigation and the stress enhancement.The difference model is then used to numerically analyze the blast wave propagation and deformation of material in which the effects of blast loads,stress-strain relation and length of foam concrete are considered.In particular,the concept of minimum thickness of foam concrete to avoid stress enhancement is proposed.Finally,non-dimensional analysis on the minimum thickness is conducted and an empirical formula is proposed by curve-fitting the numerical data,which can provide a reference for the application of foam concrete in defense engineering.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Anti-explosion performance and dynamic response of an innovative multi-layer composite explosion containment vessel

An innovative multi-layer composite explosion containment vessel(CECV)utilizing a sliding steel platealuminum honeycomb-fiber cloth sandwich is put forward to improve the anti-explosion capacity of a conventional single-layer explosion containment vessel(SECV).Firstly,a series of experiments and finite element(FE)simulations of internal explosions are implemented to understand the basic anti-explosion characteristics of a SECV and the rationality of the computational models and methods is verified by the comparison between the experimental results and simulation results.Based on this,the CECV is designed in detail and a variety of FE simulations are carried out to investigate effects of the sandwich structure,the explosive quantity and the laying mode of the fiber cloth on anti-explosion performance and dynamic response of the CECV under internal explosions.Simulation results indicate that the end cover is the critical position for both the SECV and CECV.The maximum pressure of the explosion shock wave and the maximum strain of the CECV can be extremely declined compared to those of the SECV.As a result,the explosive quantity the CECV can sustain is up to 20 times of that the SECV can sustain.Besides,as the explosive quantity increases,the internal pressure of the CECV keeps growing and the plastic deformation and failure of the sandwich structure become more and more severe,yielding plastic strain of the CECV in addition to elastic strain.The results also reveal that the laying angles of the fiber cloth's five layers have an impact on the anti-explosion performance of the CECV.For example,the CECV with fiber cloth layered in 0°/45°/90°/45°/0°mode has the optimal anti-capacity,compared to 0°/0°/0°/0°/0°and 0°/30°/60°/30°/0°modes.Overall,owing to remarkable anti-explosion capacity,this CECV can be regarded as a promising candidate for explosion resistance.

A Practical Approach for Missing Wireless Sensor Networks Data Recovery

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.

Modified Sadowski formula-based model for the slope shape amplification effect under multistage slope blasting vibration

Blasting operations,which are crucial to open-pit mine production due to their simplicity and efficiency,require precise control through accurate vibration velocity calculations.The conventional Sadowski formula mainly focuses on blast center distance but neglects the amplification effect of blasting vibration waves by terraced terrain,from which the calculated blasting vibration velocities are smaller than the actual values,affecting the safety of the project.To address this issue,our model introduces the influences of slope and time into Sadowski formula to measure safety through blast vibration displacement.In the northern section of the open-pit quartz mine in Jinchang City,Gansu Province,China,the data of a continuous blasting slope project are referred to.Our findings reveal a noticeable vibration amplification effect during blasting when a multi-stage slope platform undergoes a sudden cross-sectional change near the upper overhanging surface.The amplification vibration coefficient increases with height,while vibration waves within rocks decrease from bottom to top.Conversely,platforms without distinct crosssectional changes exhibit no pronounced amplification during blasting.In addition,the vibration intensity decreases with distance as the rock height difference change propagates.The results obtained by the proposed blast vibration displacement equation incorporating slope shape influence closely agree with real-world scenarios.According to Pearson correlation coefficient(PPMCC)analysis,the average accuracy rate of our model is 88.84%,which exceeds the conventional Sadowski formula(46.92%).

Planning,monitoring and replanning techniques for handling abnormity in HTN-based planning and execution

A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

Joint Optimization for on-Demand Deployment of UAVs and Spectrum Allocation in UAVs-Assisted Communication

To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles(UAVs),a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed,which is modeled as a mixed-integer non-convex optimization problem(MINCOP).An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated annealing.The MINCOP is then decomposed into three sub-problems based on the block coordinate descent method,including the spectrum allocation of UAVs,the association between UAVs and ground users,and the deployment of UAVs.Specifically,the optimal spectrum allocation is derived based on the interference mitigation and channel reuse.The association between UAVs and ground users is optimized based on local iterated optimization.A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs deployment.Simulation results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation.