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Blockchain technology has revolutionized the management of Unmanned Aerial Vehicle (UAV) networks by enhancing security, enabling decentralized control, and improving operational efficiency. This study assesses the efficiency of private blockchain architectures in UAV networks, specifically examining important performance metrics such as throughput, latency, scalability, and packet size. Furthermore, we evaluate the effectiveness of UAV networks when integrating private blockchain technologies, focusing particularly on key performance indicators such as area, altitude, and data rate. The scope of our work includes extensive simulations that employ a private blockchain to assess its impact on UAV operations. In the blockchain network, throughput decreased as the number of UAVs and transactions increased, while delay remained constant up to a certain point. In contrast, the UAV network saw improved throughput but increased delay with more UAVs and transactions. Changes in area and altitude had little impact on the blockchain network but increased delays in the UAV network. Higher data rates enhanced the UAV network by reducing latency and improving throughput, though this effect was less pronounced in the blockchain network. The aforementioned results highlight the potential and limitations of private blockchains in enhancing the durability and efficiency of UAV networks.
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http://dx.doi.org/10.3390/s24237813 | DOI Listing |
Neural Netw
September 2025
School of Automation, Southeast University, Nanjing, 210096, China; Advanced Ocean Institute of Southeast University Nantong, Nantong, 226010, China. Electronic address:
Unmanned Aerial Vehicle (UAV) tracking requires accurate target localization from aerial top-down perspectives while operating under the computational constraints of aerial platforms. Current mainstream UAV trackers, constrained by the limited resources, predominantly employ lightweight Convolutional Neural Network (CNN) extractor, coupled with an appearance-based fusion mechanism. The absence of comprehensive target perception significantly constrains the balance between tracking accuracy and computational efficiency.
View Article and Find Full Text PDFChemosphere
September 2025
Azerbaijan National Academy of Sciences, Institute of Geography, Baku, AZ1073, Azerbaijan.
This study presents the first integrated assessment of plastic pollution at the Kura River delta, where the river enters the hydrologically enclosed Caspian Sea. We applied a modular toolbox comprising four complementary components: high-resolution hydrodynamic modeling to predict debris convergence zones, UAV-based mapping to survey shoreline conditions, automated object-based image analysis for debris detection and classification, and standardized field monitoring by trained community participants for ground-truthing and source identification. Using this framework, we identified debris accumulation hotspots and developed a replicable approach for assessing plastic pollution in semi-enclosed systems.
View Article and Find Full Text PDFISA Trans
August 2025
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, PR China; School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, PR China; Laboratory for Microwave Spatial Inte
Failures in long-term tracking have been frequently reported, posing significant challenges for the practical implementation of UAV tracking systems. Previous research has often employed a metric based on the current tracking state to assess reliability, coupled with a time-consuming re-detection network designed to recover the lost target. However, this approach lacks sufficient robustness and flexibility when dealing with unknown factors present in complex tracking scenarios.
View Article and Find Full Text PDFFront Plant Sci
August 2025
College of Pharmacy, Inner Mongolia Medical University, Hohhot, China.
Background: Water and nitrogen are essential elements prone to deficiency during plant growth. Current water-fertilizer monitoring technologies are unable to meet the demands of large-scale cultivation. Near-ground remote sensing technology based on unmanned aerial vehicle (UAV) multispectral image is widely used for crop growth monitoring and agricultural management and has proven to be effective for assessing water and nitrogen status.
View Article and Find Full Text PDFSci Rep
August 2025
The School of Information, Yunnan Normal University, Kunming, 650500, Yunnan, China.
Most existing small object detection methods rely on residual blocks to process deep feature maps. However, these residual blocks, composed of multiple large-kernel convolution layers, incur high computational costs and contain redundant information, which makes it difficult to improve detection performance for small objects. To address this, we designed an improved feature pyramid network called L Feature Pyramid Network (L-FPN), which optimizes the allocation of computational resources for small object detection by reconstructing the original FPN structure.
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