Electronics

30 pages, 59872 KiB

Open AccessArticle

Advancing 3D Seismic Fault Identification with SwiftSeis-AWNet: A Lightweight Architecture Featuring Attention-Weighted Multi-Scale Semantics and Detail Infusion

by Ang Li, Rui Li, Yuhao Zhang, Shanyi Li, Yali Guo, Liyan Zhang and Yuqing Shi

Electronics 2025, 14(15), 3078; https://doi.org/10.3390/electronics14153078 (registering DOI) - 31 Jul 2025

Abstract

The accurate identification of seismic faults, which serve as crucial fluid migration pathways in hydrocarbon reservoirs, is of paramount importance for reservoir characterization. Traditional interpretation is inefficient. It also struggles with complex geometries, failing to meet the current exploration demands. Deep learning boosts [...] Read more.

The accurate identification of seismic faults, which serve as crucial fluid migration pathways in hydrocarbon reservoirs, is of paramount importance for reservoir characterization. Traditional interpretation is inefficient. It also struggles with complex geometries, failing to meet the current exploration demands. Deep learning boosts fault identification significantly but struggles with edge accuracy and noise robustness. To overcome these limitations, this research introduces SwiftSeis-AWNet, a novel lightweight and high-precision network. The network is based on an optimized MedNeXt architecture for better fault edge detection. To address the noise from simple feature fusion, a Semantics and Detail Infusion (SDI) module is integrated. Since the Hadamard product in SDI can cause information loss, we engineer an Attention-Weighted Semantics and Detail Infusion (AWSDI) module that uses dynamic multi-scale feature fusion to preserve details. Validation on field seismic datasets from the Netherlands F3 and New Zealand Kerry blocks shows that SwiftSeis-AWNet mitigates challenges like the loss of small-scale fault features and misidentification of fault intersection zones, enhancing the accuracy and geological reliability of automated fault identification. Full article

(This article belongs to the Special Issue Advances in Image Recognition, Image Segmentation, Image Fusion, and Singal Processing)

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28 pages, 4107 KiB

Open AccessArticle

Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network

by Kevin Verdezoto Moreno, Pablo Lupera-Morillo, Roberto Chiguano, Robin Álvarez, Ricardo Llugsi and Gabriel Palma

Electronics 2025, 14(15), 3077; https://doi.org/10.3390/electronics14153077 (registering DOI) - 31 Jul 2025

Abstract

This paper introduces a theoretical large-scale radio channel model for the downlink in cellular systems, aimed at estimating variations in received signal power at the user terminal as a function of device mobility. This enables applications such as direction-of-arrival (DoA) estimation, estimating power [...] Read more.

This paper introduces a theoretical large-scale radio channel model for the downlink in cellular systems, aimed at estimating variations in received signal power at the user terminal as a function of device mobility. This enables applications such as direction-of-arrival (DoA) estimation, estimating power at subsequent points based on received power, and detection of coverage anomalies. The model is validated using real-world measurements from urban and suburban environments, achieving a maximum estimation error of 7.6%. In contrast to conventional models like Okumura–Hata, COST-231, Third Generation Partnership Project (3GPP) stochastic models, or ray-tracing techniques, which estimate average power under static conditions, the proposed model captures power fluctuations induced by terminal movement, a factor often neglected. Although advanced techniques such as wave-domain processing with intelligent metasurfaces can also estimate DoA, this model provides a simpler, geometry-driven approach based on empirical traces. While it does not incorporate infrastructure-specific characteristics or inter-cell interference, it remains a practical solution for scenarios with limited information or computational resources. Full article

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22 pages, 6436 KiB

Open AccessArticle

Low-Resolution ADCs Constrained Joint Uplink/Downlink Channel Estimation for mmWave Massive MIMO

by Songxu Wang, Yinyuan Wang and Congying Hu

Electronics 2025, 14(15), 3076; https://doi.org/10.3390/electronics14153076 (registering DOI) - 31 Jul 2025

Abstract

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a [...] Read more.

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

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20 pages, 2649 KiB

Open AccessArticle

GreenRP: Task-Aware Discharge-Resilient Routing for Sustainable Edge AI in Satellite Optical Networks

by Huibin Zhang, Dandan Du, Kunpeng Zheng, Yuan Cao, Lihan Zhao, Yongli Zhao and Jie Zhang

Electronics 2025, 14(15), 3075; https://doi.org/10.3390/electronics14153075 (registering DOI) - 31 Jul 2025

Abstract

Research in on-orbit processing enables edge AI deployment over satellite optical networks. However, these operations induce frequent battery discharge cycles, particularly depth-of-discharge (DoD) events, which accelerate degradation and curtail satellite longevity. To address this, we propose green task-aware routing planning (GreenRP), a task-aware [...] Read more.

Research in on-orbit processing enables edge AI deployment over satellite optical networks. However, these operations induce frequent battery discharge cycles, particularly depth-of-discharge (DoD) events, which accelerate degradation and curtail satellite longevity. To address this, we propose green task-aware routing planning (GreenRP), a task-aware routing framework that achieves sustainable edge AI through dynamic task offloading and discharge-resilient path orchestration. GreenRP employs a novel battery aging model explicitly coupling DoD effects with laser inter-satellite link dynamics under AI workloads, enhancing system sustainability. Comprehensive evaluation on a 1152-satellite constellation demonstrates that GreenRP extends network lifetime by 176% over shortest-path routing while meeting latency and completion rate targets. This work enables reliable edge AI via sustainable satellite resource utilization. Full article

(This article belongs to the Special Issue Security and Privacy in Emerging Edge AI Systems and Applications)

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18 pages, 1910 KiB

Open AccessArticle

Hierarchical Learning for Closed-Loop Robotic Manipulation in Cluttered Scenes via Depth Vision, Reinforcement Learning, and Behaviour Cloning

by Hoi Fai Yu and Abdulrahman Altahhan

Electronics 2025, 14(15), 3074; https://doi.org/10.3390/electronics14153074 (registering DOI) - 31 Jul 2025

Abstract

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central [...] Read more.

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

(This article belongs to the Special Issue Reinforcement Learning: Sample Efficiency, Generalisation, and AI Applications)

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18 pages, 4863 KiB

Open AccessFeature PaperArticle

Evaluation of Explainable, Interpretable and Non-Interpretable Algorithms for Cyber Threat Detection

by José Ramón Trillo, Felipe González-López, Juan Antonio Morente-Molinera, Roberto Magán-Carrión and Pablo García-Sánchez

Electronics 2025, 14(15), 3073; https://doi.org/10.3390/electronics14153073 (registering DOI) - 31 Jul 2025

Abstract

As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not [...] Read more.

As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not direct attacks—to evaluate and compare explainable, interpretable, and opaque machine learning models. Through advanced preprocessing and feature engineering, we examine the trade-off between model performance and transparency in the early detection of suspicious connections. We evaluate explainable ML-based models such as k-nearest neighbours, fuzzy algorithms, decision trees, and random forests, alongside interpretable models like naïve Bayes, support vector machines, and non-interpretable algorithms such as neural networks. Results show that neural networks achieve the highest performance, with a macro F1-score of 0.8786, but explainable models like HFER offer strong performance (macro F1-score = 0.6106) with greater interpretability. The choice of algorithm depends on project-specific needs: neural networks excel in accuracy, while explainable algorithms are preferred for resource efficiency and transparency, as stated in this work. This work underscores the importance of aligning cybersecurity strategies with operational requirements, providing insights into balancing performance with interpretability. Full article

(This article belongs to the Special Issue Network Security and Cryptography Applications)

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Graphical abstract

15 pages, 608 KiB

Open AccessArticle

A Personal Privacy-Ensured User Authentication Scheme

by Ya-Fen Chang, Wei-Liang Tai and Ting-Yu Chang

Electronics 2025, 14(15), 3072; https://doi.org/10.3390/electronics14153072 (registering DOI) - 31 Jul 2025

Abstract

User authentication verifies the legitimacy of users and prevents service providers from offering services to unauthorized parties. The concept is widely applied in various scenarios, including everyday access control systems and IoT applications. With growing concerns about personal privacy, ensuring user anonymity has [...] Read more.

User authentication verifies the legitimacy of users and prevents service providers from offering services to unauthorized parties. The concept is widely applied in various scenarios, including everyday access control systems and IoT applications. With growing concerns about personal privacy, ensuring user anonymity has become increasingly important. In addition to privacy, user convenience is also a key factor influencing the willingness to adopt a system. To address these concerns, we propose a user authentication scheme that ensures personal privacy. The system consists of a backend server, multiple users, and multiple control units. Each user is issued or equipped with an authentication unit. An authorized user can be authenticated by a control unit, with assistance from the backend server, without revealing their identity to the control unit. The scheme is suitable for applications requiring privacy-preserving authentication. Furthermore, to enhance generality, the proposed design ensures computational efficiency and allows the authentication unit to adapt to specific application requirements. Full article

(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)

23 pages, 1422 KiB

Open AccessArticle

Large Vision Language Model: Enhanced-RSCLIP with Exemplar-Image Prompting for Uncommon Object Detection in Satellite Imagery

by Taiwo Efunogbon, Abimbola Efunogbon, Enjie Liu, Dayou Li and Renxi Qiu

Electronics 2025, 14(15), 3071; https://doi.org/10.3390/electronics14153071 (registering DOI) - 31 Jul 2025

Abstract

Large Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture that combines text prompting with exemplar-image processing for cattle herd detection in [...] Read more.

Large Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture that combines text prompting with exemplar-image processing for cattle herd detection in satellite imagery. Our approach introduces a key innovation where an exemplar-image preprocessing module using crop-based or attention-based algorithms extracts focused object features which are fed as a dual stream to a contrastive learning framework that fuses textual descriptions with visual exemplar embeddings. We evaluated our method on a custom dataset of 260 satellite images across UK and Nigerian regions. Enhanced-RSCLIP with crop-based exemplar processing achieved 72% accuracy in cattle detection and 56.2% overall accuracy on cross-domain transfer tasks, significantly outperforming text-only CLIP (31% overall accuracy). The dual-prompt architecture enables effective few-shot learning and cross-regional transfer from data-rich (UK) to data-sparse (Nigeria) environments, demonstrating a 41% improvement over baseline approaches for uncommon object detection in satellite imagery. Full article

(This article belongs to the Topic Next-Generation IoT and Smart Systems for Communication and Sensing)

18 pages, 74537 KiB

Open AccessArticle

SDA-YOLO: Multi-Scale Dynamic Branching and Attention Fusion for Self-Explosion Defect Detection in Insulators

by Zhonghao Yang, Wangping Xu, Nanxing Chen, Yifu Chen, Kaijun Wu, Min Xie, Hong Xu and Enhui Zheng

Electronics 2025, 14(15), 3070; https://doi.org/10.3390/electronics14153070 (registering DOI) - 31 Jul 2025

Abstract

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper [...] Read more.

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults. Full article

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18 pages, 3506 KiB

Open AccessReview

A Review of Spatial Positioning Methods Applied to Magnetic Climbing Robots

by Haolei Ru, Meiping Sheng, Jiahui Qi, Zhanghao Li, Lei Cheng, Jiahao Zhang, Jiangjian Xiao, Fei Gao, Baolei Wang and Qingwei Jia

Electronics 2025, 14(15), 3069; https://doi.org/10.3390/electronics14153069 (registering DOI) - 31 Jul 2025

Abstract

Magnetic climbing robots hold significant value for operations in complex industrial environments, particularly for the inspection and maintenance of large-scale metal structures. High-precision spatial positioning is the foundation for enabling autonomous and intelligent operations in such environments. However, the existing literature lacks a [...] Read more.

Magnetic climbing robots hold significant value for operations in complex industrial environments, particularly for the inspection and maintenance of large-scale metal structures. High-precision spatial positioning is the foundation for enabling autonomous and intelligent operations in such environments. However, the existing literature lacks a systematic and comprehensive review of spatial positioning techniques tailored to magnetic climbing robots. This paper addresses this gap by categorizing and evaluating current spatial positioning approaches. Initially, single-sensor-based methods are analyzed with a focus on external sensor approaches. Then, multi-sensor fusion methods are explored to overcome the shortcomings of single-sensor-based approaches. Multi-sensor fusion methods include simultaneous localization and mapping (SLAM), integrated positioning systems, and multi-robot cooperative positioning. To address non-uniform noise and environmental interference, both analytical and learning-based reinforcement approaches are reviewed. Common analytical methods include Kalman-type filtering, particle filtering, and correlation filtering, while typical learning-based approaches involve deep reinforcement learning (DRL) and neural networks (NNs). Finally, challenges and future development trends are discussed. Multi-sensor fusion and lightweight design are the future trends in the advancement of spatial positioning technologies for magnetic climbing robots. Full article

(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)

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22 pages, 3409 KiB

Open AccessArticle

Short-Term Prediction Intervals for Photovoltaic Power via Multi-Level Analysis and Dual Dynamic Integration

by Kaiyang Kuang, Jingshan Zhang, Qifan Chen, Yan Zhou, Yan Yan, Litao Dai and Guanghu Wang

Electronics 2025, 14(15), 3068; https://doi.org/10.3390/electronics14153068 (registering DOI) - 31 Jul 2025

Abstract

There is an obvious correlation between the photovoltaic (PV) output of different physical levels; that is, the overall power change trend of large-scale regional (high-level) stations can provide a reference for the prediction of the output of sub-regional (low-level) stations. The current PV [...] Read more.

There is an obvious correlation between the photovoltaic (PV) output of different physical levels; that is, the overall power change trend of large-scale regional (high-level) stations can provide a reference for the prediction of the output of sub-regional (low-level) stations. The current PV prediction methods have not deeply explored the multi-level PV power generation elements and have not considered the correlation between different levels, resulting in the inability to obtain potential information on PV power generation. Moreover, traditional probabilistic prediction models lack adaptability, which can lead to a decrease in prediction performance under different PV prediction scenarios. Therefore, a probabilistic prediction method for short-term PV power based on multi-level adaptive dynamic integration is proposed in this paper. Firstly, an analysis is conducted on the multi-level PV power stations together with the influence of the trend of high-level PV power generation on the forecast of low-level power generation. Then, the PV data are decomposed into multiple layers using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and analyzed by combining fuzzy entropy (FE) and mutual information (MI). After that, a new multi-level model prediction method, namely, the improved dual dynamic adaptive stacked generalization (I-Stacking) ensemble learning model, is proposed to construct short-term PV power generation prediction models. Finally, an improved dynamic adaptive kernel density estimation (KDE) method for prediction errors is proposed, which optimizes the performance of the prediction intervals (PIs) through variable bandwidth. Through comparative experiments and analysis using traditional methods, the effectiveness of the proposed method is verified. Full article

(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)

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23 pages, 978 KiB

Open AccessArticle

Emotional Analysis in a Morphologically Rich Language: Enhancing Machine Learning with Psychological Feature Lexicons

by Ron Keinan, Efraim Margalit and Dan Bouhnik

Electronics 2025, 14(15), 3067; https://doi.org/10.3390/electronics14153067 (registering DOI) - 31 Jul 2025

Abstract

This paper explores emotional analysis in Hebrew texts, focusing on improving machine learning techniques for depression detection by integrating psychological feature lexicons. Hebrew’s complex morphology makes emotional analysis challenging, and this study seeks to address that by combining traditional machine learning methods with [...] Read more.

This paper explores emotional analysis in Hebrew texts, focusing on improving machine learning techniques for depression detection by integrating psychological feature lexicons. Hebrew’s complex morphology makes emotional analysis challenging, and this study seeks to address that by combining traditional machine learning methods with sentiment lexicons. The dataset consists of over 350,000 posts from 25,000 users on the health-focused social network “Camoni” from 2010 to 2021. Various machine learning models—SVM, Random Forest, Logistic Regression, and Multi-Layer Perceptron—were used, alongside ensemble techniques like Bagging, Boosting, and Stacking. TF-IDF was applied for feature selection, with word and character n-grams, and pre-processing steps like punctuation removal, stop word elimination, and lemmatization were performed to handle Hebrew’s linguistic complexity. The models were enriched with sentiment lexicons curated by professional psychologists. The study demonstrates that integrating sentiment lexicons significantly improves classification accuracy. Specific lexicons—such as those for negative and positive emojis, hostile words, anxiety words, and no-trust words—were particularly effective in enhancing model performance. Our best model classified depression with an accuracy of 84.1%. These findings offer insights into depression detection, suggesting that practitioners in mental health and social work can improve their machine learning models for detecting depression in online discourse by incorporating emotion-based lexicons. The societal impact of this work lies in its potential to improve the detection of depression in online Hebrew discourse, offering more accurate and efficient methods for mental health interventions in online communities. Full article

(This article belongs to the Special Issue Techniques and Applications of Multimodal Data Fusion)

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17 pages, 460 KiB

Open AccessArticle

Efficient Multi-Layer Credential Revocation Scheme for 6G Using Dynamic RSA Accumulators and Blockchain

by Guangchao Wang, Yanlong Zou, Jizhe Zhou, Houxiao Cui and Ying Ju

Electronics 2025, 14(15), 3066; https://doi.org/10.3390/electronics14153066 (registering DOI) - 31 Jul 2025

Abstract

As a new generation of mobile communication networks, 6G security faces many new security challenges. Vehicle to Everything (V2X) will be an important part of 6G. In V2X, connected and automated vehicles (CAVs) need to frequently share data with other vehicles and infrastructures. [...] Read more.

As a new generation of mobile communication networks, 6G security faces many new security challenges. Vehicle to Everything (V2X) will be an important part of 6G. In V2X, connected and automated vehicles (CAVs) need to frequently share data with other vehicles and infrastructures. Therefore, identity revocation technology in the authentication is an important way to secure CAVs and other 6G scenario applications. This paper proposes an efficient credential revocation scheme with a four-layer architecture. First, a rapid pre-filtration layer is constructed based on the cuckoo filter, responsible for the initial screening of credentials. Secondly, a directed routing layer and the precision judgement layer are designed based on the consistency hash and the dynamic RSA accumulator. By proposing the dynamic expansion of the RSA accumulator and load-balancing algorithm, a smaller and more stable revocation delay can be achieved when many users and terminal devices access 6G. Finally, a trusted storage layer is built based on the blockchain, and the key revocation parameters are uploaded to the blockchain to achieve a tamper-proof revocation mechanism and trusted data traceability. Based on this architecture, this paper also proposes a detailed identity credential revocation and verification process. Compared to existing solutions, this paper’s solution has a combined average improvement of 59.14% in the performance of the latency of the cancellation of the inspection, and the system has excellent load balancing, with a standard deviation of only 11.62, and the maximum deviation is controlled within the range of ±4%. Full article

(This article belongs to the Special Issue Connected and Autonomous Vehicles in Mixed Traffic Systems)

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17 pages, 4557 KiB

Open AccessArticle

Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection

by Sergio Pallas Enguita, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic and Richard Lebel

Electronics 2025, 14(15), 3065; https://doi.org/10.3390/electronics14153065 (registering DOI) - 31 Jul 2025

Abstract

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, [...] Read more.

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral signatures along a wavelength range of 400-1000nm (visible and near infrared). Preliminary findings from laboratory tests are presented, demonstrating the basic feasibility of HSI for differentiating surface conditions (corrosion, coatings, bare metal) and relative coating thickness, alongside LiDAR’s capability for detailed geometric capture. Although these results do not represent a deployable system, they highlight how LiDAR and HSI could address key limitations of current practices and suggest promising directions for future research into integrated sensor-based corrosion assessment strategies. Full article

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34 pages, 2642 KiB

Open AccessArticle

Strengths and Weaknesses of LLM-Based and Rule-Based NLP Technologies and Their Potential Synergies

by Nikitas Ν. Karanikolas, Eirini Manga, Nikoletta Samaridi, Vaios Stergiopoulos, Eleni Tousidou and Michael Vassilakopoulos

Electronics 2025, 14(15), 3064; https://doi.org/10.3390/electronics14153064 (registering DOI) - 31 Jul 2025

Abstract

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier [...] Read more.

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier years, and they have performed well in certain areas of Natural Language Processing (NLP). Today, an arduous task that arises is how to estimate the quality of the produced text. This process depends on the aspects of text that you need to assess, varying from correct grammar and syntax to more intriguing aspects such as coherence and semantical fluency. Although the performance of LLMs is high, the challenge is whether LLMs can cooperate with rule-based NLG/NLU technology by leveraging their assets to overcome LLMs’ weak points. This paper presents the basics of these two families of technologies and the applications, strengths, and weaknesses of each approach, analyzes the different ways of evaluating a machine-generated text, and, lastly, focuses on a first-level approach of possible combinations of these two approaches to enhance performance in specific tasks. Full article

(This article belongs to the Special Issue Natural Language Processing Based on Neural Networks and Large Language Models)

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22 pages, 2738 KiB

Open AccessArticle

Mitigation of Solar PV Impact in Four-Wire LV Radial Distribution Feeders Through Reactive Power Management Using STATCOMs

by Obaidur Rahman, Duane Robinson and Sean Elphick

Electronics 2025, 14(15), 3063; https://doi.org/10.3390/electronics14153063 (registering DOI) - 31 Jul 2025

Abstract

Australia has the highest per capita penetration of rooftop solar PV systems in the world. Integration of these systems has led to reverse power flow and associated voltage rise problems in residential low-voltage (LV) distribution networks. Furthermore, random, uncontrolled connection of single-phase solar [...] Read more.

Australia has the highest per capita penetration of rooftop solar PV systems in the world. Integration of these systems has led to reverse power flow and associated voltage rise problems in residential low-voltage (LV) distribution networks. Furthermore, random, uncontrolled connection of single-phase solar systems can exacerbate voltage unbalance in these networks. This paper investigates the application of a Static Synchronous Compensator (STATCOM) for the improvement of voltage regulation in four-wire LV distribution feeders through reactive power management as a means of mitigating voltage regulation and unbalance challenges. To demonstrate the performance of the STATCOM with varying loads and PV output, a Q-V droop curve is applied to specify the level of reactive power injection/absorption required to maintain appropriate voltage regulation. A practical four-wire feeder from New South Wales, Australia, has been used as a case study network to analyse improvements in system performance through the use of the STATCOM. The outcomes indicate that the STATCOM has a high degree of efficacy in mitigating voltage regulation and unbalance excursions. In addition, compared to other solutions identified in the existing literature, the STATCOM-based solution requires no sophisticated communication infrastructure. Full article

(This article belongs to the Special Issue Power Electronics and Renewable Energy System)

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28 pages, 2174 KiB

Open AccessArticle

Validating Lava Tube Stability Through Finite Element Analysis of Real-Scene 3D Models

by Jiawang Wang, Zhizhong Kang, Chenming Ye, Haiting Yang and Xiaoman Qi

Electronics 2025, 14(15), 3062; https://doi.org/10.3390/electronics14153062 (registering DOI) - 31 Jul 2025

Abstract

The structural stability of lava tubes is a critical factor for their potential use in lunar base construction. Previous studies could not reflect the details of lava tube boundaries and perform accurate mechanical analysis. To this end, this study proposes a robust method [...] Read more.

The structural stability of lava tubes is a critical factor for their potential use in lunar base construction. Previous studies could not reflect the details of lava tube boundaries and perform accurate mechanical analysis. To this end, this study proposes a robust method to construct a high-precision, real-scene 3D model based on ground lava tube point cloud data. By employing finite element analysis, this study investigated the impact of real-world cross-sectional geometry, particularly the aspect ratio, on structural stability under surface pressure simulating meteorite impacts. A high-precision 3D reconstruction was achieved using UAV-mounted LiDAR and SLAM-based positioning systems, enabling accurate geometric capture of lava tube profiles. The original point cloud data were processed to extract cross-sections, which were then classified by their aspect ratios for analysis. Experimental results confirmed that the aspect ratio is a significant factor in determining stability. Crucially, unlike the monotonic trends often suggested by idealized models, analysis of real-world geometries revealed that the greatest deformation and structural vulnerability occur in sections with an aspect ratio between 0.5 and 0.6. For small lava tubes buried 3 m deep, the ground pressure they can withstand does not exceed 6 GPa. This process helps identify areas with weaker load-bearing capacity. The analysis demonstrated that a realistic 3D modeling approach provides a more accurate and reliable assessment of lava tube stability. This framework is vital for future evaluations of lunar lava tubes as safe habitats and highlights that complex, real-world geometry can lead to non-intuitive structural weaknesses not predicted by simplified models. Full article

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22 pages, 645 KiB

Open AccessArticle

Asymptotic Solution for Skin Heating by an Electromagnetic Beam at an Incident Angle

by Hongyun Wang, Shannon E. Foley and Hong Zhou

Electronics 2025, 14(15), 3061; https://doi.org/10.3390/electronics14153061 (registering DOI) - 31 Jul 2025

Abstract

We investigate the temperature evolution in the three-dimensional skin tissue exposed to a millimeter-wave electromagnetic beam that is not necessarily perpendicular to the skin surface. This study examines the effect of the beam’s incident angle. The incident angle influences the thermal heating in [...] Read more.

We investigate the temperature evolution in the three-dimensional skin tissue exposed to a millimeter-wave electromagnetic beam that is not necessarily perpendicular to the skin surface. This study examines the effect of the beam’s incident angle. The incident angle influences the thermal heating in two aspects: (i) the beam spot projected onto the skin is elongated compared to the intrinsic beam spot in a perpendicular cross-section, resulting in a lower power per skin area; and (ii) inside the tissue, the beam propagates at the refracted angle relative to the depth direction. At millimeter-wavelength frequencies, the characteristic penetration depth is sub-millimeter, whereas the lateral extent of the beam spans at least several centimeters in applications. We explore the small ratio of the penetration depth to the lateral length scale in a nondimensional formulation and derive a leading-term asymptotic solution for the temperature distribution. This analysis does not rely on a small incident angle and is therefore applicable to arbitrary angles of incidence. Based on the asymptotic solution, we establish scaling laws for the three-dimensional skin temperature, the skin surface temperature, and the skin volume in which thermal nociceptors are activated. Full article

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9 pages, 1953 KiB

Open AccessArticle

Planar Hall Effect and Magnetoresistance Effect in Pt/Tm₃Fe₅O₁₂ Bilayers at Low Temperature

by Yukuai Liu, Jingming Liang, Zhiyong Xu, Jiahui Li, Junhao Ruan, Sheung Mei Ng, Chuanwei Huang and Chi Wah Leung

Electronics 2025, 14(15), 3060; https://doi.org/10.3390/electronics14153060 (registering DOI) - 31 Jul 2025

Abstract

Spin transport behaviors in heavy metal/ferromagnetic insulator (HM/FI) bilayers have attracted considerable attention due to various novel phenomena and applications in spintronic devices. Herein, we investigate the planar Hall effect (PHE) in Pt/Tm₃Fe₅O₁₂ (Pt/TmIG) heterostructures at low temperatures; [...] Read more.

Spin transport behaviors in heavy metal/ferromagnetic insulator (HM/FI) bilayers have attracted considerable attention due to various novel phenomena and applications in spintronic devices. Herein, we investigate the planar Hall effect (PHE) in Pt/Tm₃Fe₅O₁₂ (Pt/TmIG) heterostructures at low temperatures; moment switching in the ferrimagnetic insulator TmIG is detected by using electrical measurements. Double switching hysteresis PHE curves are found in Pt/TmIG bilayers, closely related to the magnetic moment of Tm³⁺ ions, which makes a key contribution to the total magnetic moment of TmIG film at low temperature. More importantly, a magnetoresistance (MR) curve with double switching is found, which has not been reported in this simple HM/FI bilayer, and the sign of this MR effect is sensitive to the angle between the magnetic field and current directions. Our findings of these effects in this HM/rare earth iron garnet (HM/REIG) bilayer provide insights into tuning the spin transport properties of HM/REIG by changing the rare earth. Full article

(This article belongs to the Section Electronic Materials, Devices and Applications)

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Electronics

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