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Effective waterfowl management relies on the collection of relevant demographic data to inform land-management decisions; however, some types of data are difficult to obtain. For waterfowl, brood surveys are difficult to conduct because wetland habitats often obscure ducklings from being visually assessed. Here, we used Unoccupied Aerial Systems (UAS) to assess what wetland habitat characteristics influenced brood abundance in Suisun Marsh, California, USA. Using a thermal-imaging camera, we surveyed 17 wetland units that encompassed 332ha of flooded area on the premises of seven waterfowl hunting clubs during the waterfowl breeding season. Additionally, using a combination of multi-spectral imagery collected from the UAS flights and LiDAR data from the previous year, we mapped habitat composition within each unit to relate to brood observation counts. From June 3–7, 2019, we identified 113 individual broods comprising 827 ducklings. We found a positive relationship between the number of broods observed and the proportion of the unit that was flooded. We also found a positive relationship between the number of broods observed and the area of effective habitat—a metric of flooded habitat within two times the 95th-percentile Euclidean distance that all broods were observed from any vegetated cover. Brood surveys using UAS could complement the traditional Breeding Population Survey and provide local managers with fine-scale and timely information about shifts in brood abundance in the region.

In response to the issues of multiple operators and significant safety hazards in traditional underwater elevation measurement methods during the monitoring and inspection process of river training works, taking the No.12th dam-No. 20th dam of Shendi river training works as an example, non-manned survey technologies such as unmanned aerial vehicles and unmanned boats were used to simultaneously measure the topography of the river channel and underwater terrain. The slope factor was introduced to improve the regional growth method, and the forest area identified by visual interpretation method was used as a benchmark to analyze and compare the recognition accuracy of the regional growth method and the regional growth method considering slope. The underwater terrain data was interpolated by using the natural neighbor- hood method and fused with the above-water terrain data to obtain the water surface and underwater Digital Elevation Model (DEM) of Shendi river training works, which was compared with the three-dimensional view of the project. The results show that the use of unmanned aerial ve- hicles and unmanned boats for integrated mapping to generate DEM results can meet the requirements of relevant technical specifications and have higher accuracy. The region-growing method considering slope is used to identify the wave-proof forest and roadside forest of the river training works, with a recognition accuracy of 92.47%. [ABSTRACT FROM AUTHOR]

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments. [ABSTRACT FROM AUTHOR]

With the advancement of modern UAV technology, UAVs have become integral to creating traffic management monitoring systems. Additionally, UAV-based traffic monitoring systems can predict traffic flow by integrating machine learning methods. Specifically, traffic flow data contains both spatial and temporal information, which can be effectively processed by graph neural networks (GNNs). However, GNNs often face the challenge of oversmoothing, which hinders their ability to capture complex structures in the data. The Spatio-Temporal Graph Ordinary Differential Equations (STGODE) model addresses this issue by introducing Neural Ordinary Differential Equations (NODEs) to construct deeper GNNs. Despite this, STGODE relies on initially predefined semantic neighborhood matrices, which do not adapt well to the dynamic nature of traffic information. To overcome this limitation, we propose an evolutionary graph neural network for traffic prediction, capable of continuously updating the semantic adjacency matrix throughout the training process. This dynamic evolution of the semantic adjacency matrix allows it to adapt to the features and semantic relations of the current data, enhancing its ability to capture the complexity and variability of traffic patterns. We validate our approach through experiments on several real-world datasets, demonstrating that our method outperforms state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

The use of computer vision and deep learning in boundary documentation for land registration stems from the ongoing demand for appropriate mapping approaches of unregistered land rights to eradicate the global challenge of tenure insecurity. Previous research has yielded promising results towards automated extraction of photo‐visible cadastral boundaries from high‐resolution imagery. Nonetheless, the extraction of invisible cadastral boundaries is still a challenge. This study investigates the place of sensor/s on‐board unmanned aerial vehicles and deep learning algorithms in detecting cadastral boundaries. It develops a participatory boundary marking procedure using low‐cost markers to bring monument to previously invisible and ill‐defined cadastral boundaries. After that, the researchers trained and tested the accuracy of a convolutional neural network, namely single shot multi‐box detector (SSD) based on Residual Neural Network (ResNet) and Visual Geometry Group (VGG) backbone networks to automatically detect cadastral boundary markers from unmanned aerial vehicle imagery. SSD based on ResNet34 performed best with 0.88 precision, 0.92 recall and 0.91 F measure or (F1) score. VGG19‐based SSD yielded a precision of 0.47, recall of 0.53 and F1 score of 0.50. The horizontal accuracy of the cadastral map generated varied from 0.089 to 0.496 m per parcel, with a standard deviation of 0.120 m. Results show that this approach is practical for cadastral mapping in rural areas. [ABSTRACT FROM AUTHOR]

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies. [ABSTRACT FROM AUTHOR]

Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species. [ABSTRACT FROM AUTHOR]

Tea leaf blight (TLB) is a common disease of tea plants and is widely distributed in tea gardens. Although the use of unmanned aerial vehicle (UAV) remote sensing can help to achieve a wider scale for TLB detection, the blurring of UAV images, overlapping of tea leaves, and small size of TLB spots pose significant challenges to the task of detection. This study proposes a method of detecting TLB in UAV remote sensing images by integrating super-resolution (SR) and detection networks. We use an SR network called SERB-Swin2sr to reconstruct the detailed features of UAV images and solve the problem of detail loss caused by the blurring in UAV images. In SERB-Swin2sr, a squeeze-and-excitation ResNet block (SERB) is introduced to enhance the models' ability to extract the target details in the images, and the convolution stem replaces the convolution block in order to increase the convergence rate and stability of the network. A detection network called SDDA-YOLO is applied to achieve precise detection of TLB in UAV remote sensing images. In SDDA-YOLO, a shuffle dual-dimensional attention (SDDA) module is introduced to enhance the feature fusion capability of the network, and an Xsmall-scale detection layer is used to enhance the detection ability of small lesions. Experimental results show that the proposed method is superior to current detection methods. Compared with a baseline YOLOv8 model, the precision, mAP@0.5, and mAP@0.5:0.95 of the proposed method are improved by 4.2%, 1.6%, and 1.8%, and the size of our model is only 4.6 MB. [ABSTRACT FROM AUTHOR]

Rice straw is used as livestock feed and compost. Ferimzone and tricyclazole, common fungicides for rice blast control, can be found in high concentrations in rice straw after unmanned aerial vehicle (UAV) spraying, potentially affecting livestock and human health through pesticide residues. In this study, an optimized method for the analysis of the two fungicides in rice straw was developed using the improved QuEChERS method. After the optimization of water and solvent volume, extraction conditions including ethyl acetate (EtOAc), acetonitrile (MeCN), a mixed solvent, and MeCN containing 1% acetic acid were compared. Different salts, including unbuffered sodium chloride, citrate, and acetate buffer salts, were compared for partitioning. Among the preparation methods, the MeCN/EtOAc mixture with unbuffered salts showed the highest recovery rates (88.1–97.9%, RSD ≤ 5.1%). To address the severe matrix effect (%ME) of rice straw, which is characterized by low moisture content and cellulose-based complex matrices, samples were purified using 25 mg each of primary–secondary amine (PSA) and octadecylsilane (C18), without pesticide loss. The developed method was validated with a limit of quantification (LOQ) of 0.005 mg/kg for target pesticides, and recovery rates at levels of 0.01, 0.1, and 2 mg/kg met the permissible range (82.3–98.9%, RSD ≤ 8.3%). The %ME ranged from −17.6% to −0.3%, indicating a negligible effect. This optimized method was subsequently applied to residue studies following multi-rotor spraying. Fungicides from all fields and treatment groups during harvest season did not exceed the maximum residue limits (MRLs) for livestock feed. This confirms that UAV spraying can be safely managed without causing excessive residues. [ABSTRACT FROM AUTHOR]

Many drones work together in an ad hoc manner to form flying ad hoc networks. While these networks have opened up new possibilities for a wide range of applications like the commercial, and residential, they have also presented several problems, including high-speed nodes, limited density, and abrupt dynamic topology. Therefore, routing is a complex problem in such networks. The optimized link state routing protocol served as an inspiration for this plan. This article proposes a delay-conscious routing protocol for flying ad hoc networks and offers a new method for calculating the link lifespan among two unmanned aerial vehicles based on factors such as their distance apart, relative speed, and the direction in which they travel. An approach is presented in which the emperor penguin colony algorithm is used to select multi-point relay nodes. A node's ability to serve as a multi-point relay node is based on its remaining energy, link lifespan, neighboring degree, and eagerness. In sum up, the suggested approach generates paths between nodes taking energy and lifespan into account. The performance evaluation of the proposed routing is done against ML-OLSR and MP-OLSR. At a minimum, a 15% and 32% increase in latency and energy consumption were achieved by implementing the proposed technique. [ABSTRACT FROM AUTHOR]

In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60–200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs. [ABSTRACT FROM AUTHOR]

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Unmanned aerial vehicle (UAV) clusters are increasingly deployed in military and civilian applications, necessitating efficient and reliable communication networks for cooperative operations. However, heterogeneous UAVs pose significant challenges for data transmission within and outside the cluster due to their high mobility, rapid topology changes, and limited node energy. For the routing planning problem of transmitting data from autonomous, heterogeneous UAVs to the cloud, existing methods struggle to accommodate the heterogeneity of UAVs and the communication bandwidth requirements, given the NP‐hard nature of the problem. To address this issue, this paper first constructs an exact model based on mixed‐integer linear programming to describe the heterogeneous UAVs, comprehensively searches the solution space, and generates a reasonable routing scheme for the heterogeneous UAVs, guiding the cluster communication network system design. Secondly, a heuristic algorithm is proposed that leverages density–based spatial clustering and matching game theory. The algorithm calculates relay nodes based on local density and relative distance. It generates an approximate optimal data transmission path for each UAV through a multilevel matching process, effectively reducing communication delay within the cluster. Finally, the paper conducts simulation experiments in randomly generated regional scenarios to validate the efficiency and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with a miniaturized sensor package were developed for aerial observations, which realizes aerial observations affordable to scientists in atmospheric science and achieves aerial measurements in high spatial resolution. UAVs are deployed to a variety of aerial detecting tasks in different scientific scenarios including chemical industry parks (CIPs) with hazardous gases emissions, and some places difficult for humans to reach. In this study, UAV sensing technology was deployed to detect air pollutants in a suburb, a CIP and a natural gas plant, respectively. The effects of atmospheric conditions such as the atmospheric boundary layer height, long-distance transport and atmospheric stability on the spatiotemporal variations of the air pollutants vertical profiles were investigated by the UAV. The UAV with the sensor package was deployed to capture the methane (CH4) leakages in a natural gas plant. The spatiotemporal variations of CH4 in both vertical and horizontal directions studied by UAV were employed to calculate accurate CH4 emissions, which is crucial to reducing the emissions of greenhouse gases. The low-cost UAV sensing technology for air pollutants was developed by Dr. Xiaobing Pang, who was funded by the Newton Fellowship in 2009 and worked in the University of York. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system's high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry. [ABSTRACT FROM AUTHOR]

This paper considers UAVs as edge computing nodes and investigates a novel network resource allocation method for federated learning within a three-layer wireless network architecture containing cloud, edges (UAVs), and clients. To address the issue of fair bandwidth resource allocation among clients participating in federated learning, a contribution calculation strategy based on the Shapley value (SV) used as the weight for model aggregation is proposed. On this basis, a client selection and wireless resource allocation method based on model contribution is further designed. By reducing the training and aggregation frequency of the low-contribution clients during the asynchronous aggregation phase, the limited bandwidth resources are allocated to high-contribution clients, thus improving the convergence speed and accuracy of the global model. Simulation experiments demonstrate that the proposed method can significantly reduce the system delay and total energy consumption with gains between 15% and 50% while also improving the final accuracy of the global model by 0.3% and 2% on both short-term and long-term perspectives, respectively. [ABSTRACT FROM AUTHOR]

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator's "LMT" JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided. [ABSTRACT FROM AUTHOR]

The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the study of the usage of a solution employing a proxy transmission unit for air-to-ground delivery of video streaming multiplexed with sensor data in the UAV-IoT system when using the WebRTC protocol stack. The comparative experiments were carried out for two cases employing the 802.11ac network with WebRTC: the first scenario (S1) without an external transceiver and the second scenario (S2) with an external transceiver working as a proxy of the ground receiver. The presented results compare the transmission conditions without (scenario S1) and with (scenario S2) the external transceiver in terms of the RSSI, the available data rate, and total throughput of transmission of multimedia data (video stream from the UAV camera and bursty data coming from employed sensors. The usefulness of the external transceiver used in a wide range of transmission conditions is clearly proven. [ABSTRACT FROM AUTHOR]

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments. [ABSTRACT FROM AUTHOR]

This study proposes a UAV-based remote measurement method for accurately locating pedestrians and other small targets within small reservoir dams. To address the imprecise coordinate information in reservoir areas after prolonged operations, a transformation method for converting UAV coordinates into the local coordinate system without relying on preset parameters is introduced, accomplished by integrating the Structure from Motion (SfM) algorithm to calculate the transformation parameters. An improved YOLOv8 network is introduced for the high-precision detection of small pedestrian targets, complemented by a laser rangefinder to facilitate accurate 3D locating of targets from varying postures and positions. Furthermore, the integration of a thermal infrared camera facilitates the detection and localization of potential seepage. The experimental validation and application across two real small reservoir dams confirm the accuracy and applicability of the proposed approach, demonstrating the efficiency of the proposed routine UAV surveillance strategy and proving its potential to establish electronic fences and enhance maintenance operations. [ABSTRACT FROM AUTHOR]

In order to solve the security risks caused by UAV “ black flight”, aiming at the problems of large model parameters and long training time of the existing UAV target detection algorithm based on deep learning, a lightweight real-time object detection algorithm for UAV based on YOLOv5 (ED-YOLOv5s) was proposed. Firstly, the feature extraction part of YOLOv5 backbone network was reconstructed by combining the lightweight model efficient model (EMO) . Secondly, normalized gaussian wasserstein distance (NWD) and scale factor were introduced to calculate the similarity between candidate boxes to partially replace IoU. Then, SimAM was introduced to optimize the weight distribution and improved the detection accuracy, so as to achieved the effect of optimizing the Backbone and Head of YOLOv5 network. Finally, the model size is 4. 57 M. ED-YOLOv5s lightweight UAV detection algorithm with floating point computation of 26. 1 (GFLOPs). The experimental data shows that the improved algorithm improves the detection accuracy and realizes the model lightweight. The proposed algorithm achieves 96. 7% AP@ 50 value on the DUT Anti-UAV dataset, and the detection speed reaches 37 frame / s on the RTX2060 GPU. [ABSTRACT FROM AUTHOR]

In order to alleviate the energy shortage problem and realize green communication, we propose a multiple unmanned aerial vehicles (UAV) enabled energy harvesting (EH) system based on time division multiple address (TDMA) and frequency division multiple address (FDMA). Taking energy efficiency as an objective, we jointly optimize the time allocation scheme and power control in multi-UAV EH network. Before solving the optimization problem, node assignment scheme which determines the connection relationship between nodes and each UAV needs to be obtained first. In TDMA mode, we obtain the node assignment scheme and the trajectory of UAVs based on genetic algorithm. In FDMA mode, the node assignment scheme is obtained based on K-means clustering algorithm, and the hover points' coordinates of UAV are optimized according to the result of node assignment. We design a time and power joint optimization algorithm (TPJOA) based on Lagrange duality method and discuss three possible cases of the optimal solutions. Simulation results show that our TPJOA method performs better than the water-filling algorithm in energy efficiency. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost‐ and time‐efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone‐based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent‐based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems. [ABSTRACT FROM AUTHOR]

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. [ABSTRACT FROM AUTHOR]

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones' industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area's temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research. [ABSTRACT FROM AUTHOR]

Aiming at the task assignment problem of heterogeneous UAV performing multi-type fixed tasks on the ground, a task allocation method based on Cooperative Multiple Task Allocation Problem (CMTAP) and Modified Chimp Optimization Algorithm (MChOA) is proposed. Firstly, the CMTAP model is established, and the combat scene is depicted by setting the elements in the quad, and a multi-objective evaluation function with the highest total return, the lowest threat cost, and the shortest total flight range is established. Secondly, the MChOA algorithm is used to complete the model solving: the chaotic reverse learning strategy is adopted to improve the diversity of the intial population distribution. In the iteration stage, the global and local search capabilities of the algorithm are better balanced by adopting the dynamic adjustment strategy of preemptive behavior. Comparative experiments show that compared with the original algorithm, the resulting task allocation scheme reduces the average total voyage in the two scale scenarios by 22.6% and 21.1%, respectively, while maintaining a higher task gain and a lower threat cost. [ABSTRACT FROM AUTHOR]

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI – Green Optimized Soil Adjusted Vegetation Index, MCARI2 – Modified Chlorophyll Absorption Index-Improved, NDRE – Normalized Difference Red Edge, NDVI – Normalized Difference Vegetation Index, and OSAVI – Optimized Soil Adjusted Vegetation Index and WDVI – Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error – MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy's variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations. [ABSTRACT FROM AUTHOR]

Background: The body weight (BW) of animals at various growth stages is an important piece of information for the decision-making process. In the current "livestock 4.0" or precision livestock farming it becomes necessary to know if body measurements obtained from the dorsal view of an animal are related to its BW. Objective: To evaluate the relationship between BW and dorsal area (DA) of water buffaloes (Bubalus bubalis) reared in southeastern Mexico. Methods: The BW (340 ± 161.68 kg), hip width (HW), thorax width (TW), and body length (BL) were measured in 215 female Murrah buffaloes aged between 3 months and 5 years. The DA (m2) was calculated using the mathematical formulae for the area of a trapezoid, considering HW, TW, and BL in the calculation. The relationship between BW and DA was assessed with correlation and regression models. Results: The correlation coefficient between BW and AD was 0.96 (p<0.001). The linear equation had the highest determination coefficient (R2 = 0.94) along with the lowest mean square error (MSE = 1716.86), root MSE (RMSE = 41.43), Akaike Information Criterion (AIC = 1603.36), and Bayesian Information Criterion (BIC = 1610.10). Conversely, the allometric equation exhibited the highest values of MSE, RMSE, AIC, and BIC. Based on the quality of fit by the k-folds technique, the three proposed equations showed consistent adjustments, with more than 90% accuracy (R2 = 0.92). The quadratic equation exhibited the lowest mean squared prediction error and mean absolute error. Conclusion: The DA can be used as a good predictor of BW in buffaloes, especially when incorporated into first and second-degree linear equations. [ABSTRACT FROM AUTHOR]

Accurately monitoring the change of mine area in the mining process is beneficial to mine safety management. This paper briefly introduces the collection of remote sensing images by unmanned aerial vehicles (UAVs) and its application in measuring surface mining subsidence and surrounding vegetation in the mining area. A case study was carried out in some mining areas of Nanshan Mountain, Yuncheng City, Shanxi Province. The surface mining subsidence value and vegetation-related parameters were measured by comparing the digital elevation model and multi-spectral images collected on May 12 and June 12, 2023. The validity experiment verified that the UAV image data could be used to measure the mining subsidence and vegetation parameters. Moreover, it was found that mining underground coal could lead to significant ground subsidence and pollute the surrounding environment, reducing vegetation. The innovation of this article lies in using UAV-collected remote sensing images instead of manually collecting ground elevation data and vegetation distribution data, providing effective references for safe mining in mining areas. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) communication has been proposed as an effective solution in both military and civilian scenarios, with low cost, high efficiency, high flexibility, and on‐demand deployment. Network simulation is an economically efficient method for validating new ideas in UAV communication. While some simulation tools have been proposed for UAV communications, there is a lack of a state‐of‐the‐art review to guide newcomers in this research field. In existing surveys, the discussion of network simulators is not comprehensive and the content discussed is outdated. There has also been no discussion of open‐source UAV simulation tools. To fill these research gaps, this survey presents an updated and comprehensive survey of UAV network simulation tools, including a unique collection of open‐source tools. Research challenges and opportunities have been proposed to inspire follow‐up studies. [ABSTRACT FROM AUTHOR]

This paper proposes a novel controller design using adaptation based modified super twisting control to facilitate trajectory tracking and hovering maneuvers for the quadrotor. The controller gains of the existing modified super twisting control require bounds on the disturbance for trajectory tracking and hovering of the quadrotor. In this paper, the controller gains are adapted using the proposed dynamic adaptation law without knowing the actual disturbance or their upper bounds. The controller is designed within a nonlinear framework without performing linearization of quadrotor dynamics, which enables the proposed controller to remain effective even when the states deviate significantly from their nominal values. The performance of trajectory tracking and hovering of the quadrotor in the presence of disturbance is demonstrated using numerical simulations. In order to assess the effectiveness of the controller, the performance of the adaptation based modified super twisting control is compared to the existing modified super twisting control, and the proposed controller outperforms the existing one. • Adaptive modified super twisting control for robust trajectory tracking and hovering. • Dynamic adaptation law relaxes the need for an upper bound on disturbance. • Numerical simulations show the validity of the proposed approach. • Performance is compared with the existing modified super twisting control. • Efficacy is demonstrated with Dryden wind and sinusoidal disturbances. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have been widely applied in the mechanical field, these methods often fail to integrate multi-source information and overlook the importance of system prior knowledge. As a result, this study employs a spatial-temporal difference graph convolutional network (STDGCN) for the fault diagnosis of UAV sensors, where the graph structure naturally organizes the diverse sensors. Specifically, a difference layer enhances the feature extraction capability of the graph nodes, and the spatial-temporal graph convolutional modules are designed to extract spatial-temporal dependencies from sensor data. Moreover, to ensure the accuracy of the association graph, this research introduces the UAV's dynamic model as prior knowledge for constructing the association graph. Finally, diagnostic accuracies of 94.93%, 98.71%, and 92.97% were achieved on three self-constructed datasets. In addition, compared to commonly used data-driven approaches, the proposed method demonstrates superior feature extraction capabilities and achieves the highest diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is of the utmost importance for every country to monitor and control maritime pollution within its exclusive economic zone (EEZ). The European Maritime Safety Agency (EMSA) has developed and implemented the CleanSeaNet (CSN) satellite monitoring system to aid in the surveillance and control of hydrocarbon and hazardous substance spills in the ocean. This system's primary objective is to alert European Union (EU) coastal states to potential spills within their EEZs, enabling them to take the necessary legal and operational actions. To reduce operational costs and increase response capability, the feasibility of implementing a national network (NN) of unmanned vehicles (UVs), both surface and aerial, was explored using a Portuguese case study. The following approach and analysis can be easily generalized to other case studies, bringing essential knowledge to the field. Analyzing oil spill alert events in the Portuguese EEZ between 2017 and 2021 and performing a strengths, weaknesses, opportunities, and threats (SWOT) analysis, essential information has been proposed for the optimal location of an NN of UVs. The study results demonstrate that integrating spill alerts at sea with UVs may significantly improve response time, costs, and personnel involvement, making maritime pollution combat actions more effective. [ABSTRACT FROM AUTHOR]

Glaciers and ice caps surrounding the Greenland Ice Sheet are found to be sensitive to warming climate thus the knowledge of their thickness and internal structure is substantial to determine their future impact on sea level and local environment. Still, in situ glaciological measurements of such glaciers are very scarce. Here, we present the results of ground penetrating radar (GPR) and uncrewed aerial vehicle surveys conducted on the two southern outlet glaciers of Qaanaaq Ice Cap in NW Greenland. GPR measurements reveal up to 170 m thick ice and the lack of englacial hyperbolae indicating no developed en/subglacial drainage system. The glaciers consist mainly of radar transparent facies characteristic for cold ice, while limited scattering facies appear closer to the glacier's terminus beneath the thinnest ice and are attributed to debris inside the ice. Results show that the glaciers flow into narrow V‐shaped valleys suggesting spatio‐temporally limited subglacial erosion and restricted possible distribution of temperate ice in the past. The comparison of the ice thickness measurement data with global ice thickness model estimates shows considerable discrepancies emphasising the need of modelling improvements in the case of narrow valley and outlet glaciers. [ABSTRACT FROM AUTHOR]

In this work, we determine the physical layer security (PLS) metrics for ground-to-unmanned aerial vehicle (UAV) link in the presence of a suspicious UAV, which intends to overhear confidential transmission. The ground node sends data up to the UAV, which is positioned exactly above the transmitter and is able to spot an aerial eavesdropper according to the predefined horizontal and/or vertical guard distance. However, the attacker tries to intercept the channel transmission outside of marked zone. The main and wiretap channel are both assumed to be subjected to Fisher-Snedecor fading process. Under such system/channel scenario, the average secrecy capacity, lower bound of secrecy outage probability, intercept probability and non-zero secrecy capacity are characterized in terms of mathematical tractable forms. In addition, numerical and simulation results are presented to verify the correctness of theoretical ones. The impact of different positions of UAVs (their heights and mutual distances) and various conditions over channels on the secrecy transmission is analysed and discussed in details. The proposed PLS scenario can be utilized in Internet of Things environments, with UAV as a data collector, to enhance the security of energy-aware or disaster-stricken transmissions. All obtained results can be helpful in prediction of positioning UAV to achive low probability of interception of confidential communication. [ABSTRACT FROM AUTHOR]

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks. [ABSTRACT FROM AUTHOR]

In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions. [ABSTRACT FROM AUTHOR]

In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection. With regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering, multi-UAV mission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstream schemes are tested, and the results show that the present method has a huge advantage in terms of space and time complexity, and at the same time, the accuracy for data extraction is relatively improved by 10.46% and 12.71%. Finally, by eliminating the clustering process and the subtask assignment process, the AoI performance decreases by 3.46× and 4.45×, and the energy performance decreases by 3.52× and 4.47×. This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations. [ABSTRACT FROM AUTHOR]

Our work aimed to compare the chip pile volumes calculated by laser ground scanning, UAV technology, and laser ground measurement and also to determine the accuracy, speed, and economic efficiency of each method. The large chip pile was measured in seven different ways: band measurement, laser measurement with Vertex, global navigation satellite system, handheld mobile laser scanner, terrestrial laser scanner, drone, and smartphone with a light detection and ranging sensor. All the methods were compared in terms of accuracy, price, user-friendliness, and time required to obtain results. The calculated pile volume, depending on the method, varied from 2588 to 3362 m3. The most accurate results were provided by the terrestrial laser scanning method, which, however, was the most expensive and the most demanding in terms of collecting and evaluating the results. From a time and economic point of view, the most effective methods were UAVs and smartphones with LiDAR. [ABSTRACT FROM AUTHOR]

The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis , thereby enhancing management efficiency. [ABSTRACT FROM AUTHOR]

Building instance extraction and recognition (BEAR) extracts and further recognizes building instances in unmanned aerial vehicle (UAV) images, holds with paramount importance in urban understanding applications. To address this challenge, we propose a unified network, BEAR-Former. Given the difficulty of building instance recognition due to the small area and multiple instances in UAV images, we developed a novel multi-view learning method, Cross-Mixer. This method constructs a cross-regional branch and an intra-regional branch to, respectively, extract the global context dependencies and local spatial structural details of buildings. In the cross-regional branch, we cleverly employed cross-attention and polar coordinate relative position encoding to learn more discriminative features. To solve the BEAR problem end to end, we designed a channel group and fusion module (CGFM) as a shared encoder. The CGFM includes a channel group encoder layer to independently extract features and a channel fusion module to dig out the complementary information for multiple tasks. Additionally, an RoI enhancement strategy was designed to improve model performance. Finally, we introduced a new metric, Recall@(K, iou), to evaluate the performance of the BEAR task. Experimental results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs' battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir's secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions. [ABSTRACT FROM AUTHOR]

In order to solve the problems of IspecHyper multi-rotor UAV hyperspectral imaging system, such as lack of data processing supporting software, large error in collecting multi-band hyperspectral data, lack of coordinates, and inability to automatically stitch. Taking the citrus planting base of Jiaolong Village, Taiping Town, Wuming District as the research area, this paper studies the hyperspectral image processing method of IspecHyper multi-rotor UAV. Firstly, the IspecHyper-VM200 imaging system was used to obtain high-definition photos and multi-band hyperspectral image data of the study area. Secondly, using high-definition photos as data sources, high-resolution UAV orthophotos were formed by PXI4D Mapper software preprocessing and ENVI software image geometric correction. Finally, the ENVI software was used to cut the distorted boundary data of multi-band hyperspectral images, and the geometric correction was completed based on the UAV orthophoto image, and then the hyperspectral reflectance image product was formed through image mosaic and spectral conversion calculation. The experimental results show that the technical method formed in this paper can effectively solve the problems of hyperspectral image processing of IspecHyper multi-rotor UAV, and provide technical reference for hyperspectral image processing of UAV. [ABSTRACT FROM AUTHOR]

A real-time wind velocity vector and parameters estimation and wind model identification approach using a machine learning technique is addressed in this paper. The proposed method uses only the state measurements of an aircraft and does not require control commands, air data systems, or satellite-based data. Small unmanned aerial vehicles (UAVs) can benefit from this method, since it relies solely on measurement results from the common sensors as an attitude and heading reference system. The independence of external sources of information made estimations resistant to intentional errors. This algorithm uses long short-term memory neural networks (LSTM NNs) in a two-step deep learning process involving classification and regression. A classification NN was trained with four different labeled wind models, while individual regression NNs were trained to estimate the velocity vector and parameters of each wind model. The linear acceleration, angular velocity, and Euler angle measurements were used as the inputs of trained networks. The algorithm suggests in its first step identifying the exact wind model, and in its second step estimating the wind velocity vector and parameters using a properly assigned estimation from a trained network. A nonlinear six-degree-of-freedom simulation of straightforward and level turn maneuvers of a fixed-wing UAV in the presence of different wind models served as the dataset in the learning process. Monte Carlo simulations proved the accuracy and rapidity of the proposed algorithm in identifying the wind model and estimating three-dimensional wind velocity vector and parameters. [ABSTRACT FROM AUTHOR]