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UAV-NIDD: A Dynamic Dataset for Cybersecurity and Intrusion Detection in UAV Networks

IEEE Transactions on Network science and engineering 2025年

作者： Hadi, Hassan Jalil Cao, Yue Khan, Muhammad Khurram Ahmad, Naveed Hu, Yulin Fu, Chao Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430073 China King Saud University Center of Excellence in Information Assurance Riyadh Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh Saudi Arabia Wuhan University School of Electronic Information China Information and Communication Technology Co. LtdChina China

Unmanned Aerial Vehicles (UAVs) are necessary for numerous tasks but are vulnerable to cyber threats due to their widespread use and connectivity. The lack of a comprehensive dataset necessitates the development of effective detection and mitigation solutions. Our work introduces UAV-NIDD, a new dataset that addresses the gaps in understanding and countering both cyber and physical threats in UAV networks. It includes three distinct attack scenarios: compromised UAV initiating a network-wide attack, access point compromised network-wide intrusion, and compromised Ground Control Station (GCS) establishing a network-wide attack. We develop a real-time testbed for creating UAV-NIDD (Unmanned Aerial Vehicles-Network Intrusion Detection Dataset), incorporating UAV devices, data collection tools, and controllers. Our testbed facilitates cyberattack execution and data gathering under normal and attack conditions. Our dataset covers various cyber-attacks like Scanning, Reconnaissance, DoS, DDoS, GPS Jamming & Spoofing, MITM, Replay, Evil Twin, Brute-Force, and Fake Landing packet attacks. Additionally, UAV-NIDD presents a valuable resource for AI and ML solutions, strengthening UAV networks against evolving cyber threats. Moreover, we offer open access and cooperative innovation in terms of long-term updating of dataset. © 2013 IEEE.

关键词： Network intrusion

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Ore genesis and critical metal enrichment using deep learning algorithms in Gunga Pb-Zn deposit, Southern Pakistan: Constraints from sphalerite geochemistry and isotopic compositions

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Journal of Geochemical Exploration 2025年 275卷

作者： Gul, Muhammad A. Zhang, Huishan Yang, Yu Ren, Guangli Sun, Chao Zhao, Xiaojian Yousuf, Tahir Bin Shah, Ahmed Awan, Rizwan Sarwar Faisal, Mohamed Yang, Xiaoyong CAS Key Laboratory of Crust-Mantle Materials and Environments University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China MNR Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits Xi'an Center of Geological Survey CGS Xi'an710054 China Department of Computer Science and IT Virtual University Lahore Pakistan Center of Excellence in Mineralogy University of Baluchistan Quetta Pakistan School of Environment and Resources Engineering Hefei University of Technology Hefei China Department of Geology Faculty of Science Suez Canal University El-Ismailia41522 Egypt School of Geosciences and Info-Physics Central South University Changsha410083 China

The Kirthar Fold and Thrust Belt in southern Pakistan is widely recognized as a major metallogenic province owing to the extensive presence of Pb-Zn deposits within the Jurassic sedimentary shelf sequence. One of these deposits, the Gunga Pb-Zn deposit, is a significant economic ore deposit situated within the Anjira Formation and comprises two mineralization zones, the Upper Mineralization Zone (UMZ) and Lower Mineralization Zone (LMZ). Despite the scarcity of documentation on the metallogenic source, origin, mineralization temperature, and other physicochemical conditions, our research is the first to explore the metallogenic differentiation and ore genesis of the deposit by examining minor and trace elements in sphalerite and isotopic analyses (S-Pb). The Gunga Pb-Zn deposit, amounting to 6.86 Mt. with 2.1 % and 11.4 % Pb and Zn, respectively, is linked to the interbedded limestone and shale unit of the Anjira Formation, and approximately below the depth of 200–250 m of the Jurassic-Cretaceous contact, the mineralization is believed to occur in the upper section of the Formation. Sphalerite is the dominant ore mineral, galena is a subordinate mineral, and pyrite is present in the main ore minerals. Sphalerite in the LMZ was found in a vein style, whereas it was disseminated in the UMZ and enriched in (medians) Ge (166 ppm), Cd (541 ppm), Pb (139 ppm), Ag (42 ppm), and Hg (68 ppm), lacking In (0.17 ppm), Co (6 ppm), and Mn (19). Based on trace element concentrations, the LMZ has a reduced environment, higher sulfur fugacity, and higher mineralization temperature (180–200 °C) than the UMZ, which has a lower mineralization temperature (130–180 °C). The presence of black shales, low temperatures, and high sulfur fugacity are the key parameters for Ge enrichment in the Gunga deposit. The δ34S values range from −10 to +5.6 ‰, suggesting that reduced sulfur was produced by both the BSR and TSR mechanisms, with basinal brines and seawater being the main sources of sulfur

关键词： Sulfur

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Liquid crystal variable retarder with microchannels for high-power continuous laser application

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Journal of Molecular Liquids 2025年 419卷

作者： Wang, Jundong Zhao, Yuan-an Tao, Chunxian Wang, Jianguo Chen, Yi Wang, Fajia Zhao, Yadi Liu, Xiaofeng Hong, Ruijin Zhang, Dawei Shao, Jianda School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Laboratory of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics No.390 Qinghe Road Jiading District Shanghai201800 China Key Laboratory of Materials for High Power Laser Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Ultra-Intense Laser Science Chinese Academy of Sciences Shanghai201800 China

A liquid crystal variable retarder (LCVR) experiences significant thermal effects when exposed to high-power continuous-wave lasers, leading to the degradation of its phase delay. When the temperature surpasses the clear point temperature of the liquid crystal material, the device loses its phase delay performance. To address this issue, we designed an LCVR based on microchannel water cooling and heat dissipation. A microchannel fabricated within the substrate of the LCVR in an annular shape can transmit a large-aperture beam. We evaluated the phase delay degradation under continuous-wave laser loading with a spot diameter of 15 mm (1/e2 diameter) at a wavelength of 1070 nm, as well as the phase delay variation under voltage control. By integrating simulation models, we determined the usable area of the microchannel LCVR where the phase distortion on the device is less than λ/50 under continuous-wave laser irradiation at varying power densities. Our results indicate that the microchannel LCVR can maintain a usable area with a diameter of 4.8 mm under continuous-wave laser irradiation at 46 W/cm2. Furthermore, the microchannel LCVR can still achieve a phase delay of 90° by setting the voltage to 7.2 V. The maximum power density tolerated by the LCVR without a microchannel is 22 W/cm2, which is 40 W/cm2 lower than that of the microchannel LCVR. As long as the power density of the irradiation does not exceed 62 W/cm2, the microchannel LCVR can be used in high-power laser systems. © 2024 Elsevier B.V.

关键词： Irradiation

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Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

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Medical Image Analysis 2025年 103卷 103580页

作者： Manzano-Patrón, J.P. Deistler, Michael Schröder, Cornelius Kypraios, Theodore Gonçalves, Pedro J. Macke, Jakob H. Sotiropoulos, Stamatios N. Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham United Kingdom Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen & Tübingen AI Center Germany School of Mathematical Sciences University of Nottingham United Kingdom Belgium Department of Computer Science and Department of Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI. © 2025 The Authors

关键词： Monte Carlo methods

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Recurrent neural network-based automated early detection of pandemic-prone diseases through symptoms analysis

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Discover Social science and Health 2025年第1期5卷 1-24页

作者： Tungal, Aditika Singh, Kuldeep Singh, Prabhsimran Rehman, Ateeq Ur Sood, Sandeep Kant, Vishnu Kumar, Anand Hussen, Seada Hamam, Habib Computer Engineering & Technology Guru Nanak Dev University Amritsar India Department of Electronics Technology Guru Nanak Dev University Amritsar India School of Computing Gachon University Seongnam-si 13120 South Korea Center for IT Solutions Guru Nanak Dev University Amritsar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Electrical Power Adama Science and Technology University Adama 1888 Ethiopia Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

A pandemic is a disease outbreak that affects an alarmingly high percentage of the population and spreads over a large geographic area. Some diseases are more likely to start pandemics because they are contagious and can cause severe illness or death. The most recent lethal disease to emerge and cause global devastation among all other pandemics is COVID-19. Since the COVID-19 disease breakout in December 2019, it has become a global pandemic that has caused millions of people throughout the planet. Modern information technology applications have emerged as a result of the pandemic’s heightened demand for healthcare products and services. To effectively manage future pandemic-prone diseases and slow their vigorous spread, their early detection is crucial. New-age technologies, such as artificial intelligence, internet of things, cloud computing, etc., are therefore a major asset in the fight against such terrible diseases. Therefore, the current study employs the proposed architecture of a recurrent neural network (RNN) for the accurate identification of patients infected with COVID-19 disease through analysis of its major symptoms. Before classification, extra trees-based feature selection was used to collect the most significant features that accurately characterize COVID-19 positive or COVID-19 negative classes. This work also takes into account several deep learning models viz., RNN, Bi-LSTM, GRU, LSTM along with other machine learning models such as Logistic Regression to identify the COVID-19 pandemic. The thorough analysis of results shows that, the RNN classifier appears to perform better than the other classifiers in the early diagnosis of this fatal disease, with an accuracy of 98.70%, sensitivity of 97.81%, specificity of 95.55%, precision value equal to 98.97%, F1-score of 96.16%, and false discovery rate of 1.03% only. Therefore, the proposed RNN-based approach can help identify fatal infections caused by pandemic-prone diseases at an early stage, enabl

关键词： Artificial intelligence COVID-19 Deep learning Internet of things Machine learning Pandemic-prone diseases RNN

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A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

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Photoluminescence from metal nanostructures - dependence on size

arXiv

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arXiv 2025年

作者： Kalyan, Imon Un, Ieng Wai Rosolen, Gilles Shitrit, Nir Sivan, Yonatan School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter School of Physics South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou510006 China Micro and Nanophotonic Materials Group Research Institute for Materials Science and Engineering University of Mons Mons7000 Belgium

For decades, there have been multiple seemingly contradicting experimental reports on the dependence of the photoluminescence from metal nanostructures on their size. We reconcile these reports using a simple analytic formula which is found to match well photoluminescence measurements for a range of structures and illumination conditions. Our expression requires only knowledge of the local electric field and temperature, and it points to the physical quantities that determine the emission strength and its dependence on size. © 2025, CC BY.

关键词： Photoluminescence

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Enhancement of sensitivity near exceptional points in dissipative qubit-resonator systems

arXiv

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arXiv 2025年

作者： Han, Pei-Rong Wu, Fan Huang, Xin-Jie Wu, Huai-Zhi Zou, Chang-Ling Yi, Wei Zhang, Mengzhen Li, Hekang Xu, Kai Zheng, Dongning Fan, Heng Wen, Jianming Yang, Zhen-Biao Zheng, Shi-Biao School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fuzhou350108 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Institute of Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States Department of Physics Kennesaw State University MariettaGA30060 United States

Dissipation usually plays a negative role in quantum metrological technologies, which aim to improve measurement precision by leveraging quantum effects that are vulnerable to environment-induced decoherence. Recently, it has been demonstrated that dissipation can actually be used as a favorable resource for enhancing the susceptibility of signal detection. However, demonstrations of such enhancement for detecting physical quantities in open quantum systems are still lacking. Here we propose and demonstrate a protocol for realizing such non-Hermitian quantum sensors for probing the coupling between a qubit and a resonator subjecting to energy dissipations. The excitation-number conversion associated with the no-jump evolution trajectory enables removal of the noisy outcomes with quantum jumps, implementing the exceptional point (EP), where the Rabi splitting exhibits a divergent behavior in response to a tiny variation of the effective coupling. The sensitivity enhancement near the EP is confirmed by both theoretical calculation and experimental measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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