bluuebunny/crossref_metadata_2025 huggingface dataset:
Thin and Large Depth-Of-Field Compound-Eye Imaging for Close-Up Photography
Authors: Cheng Dewen , Wang Da , Yao Cheng , Liu Yue , Dai Xilong , Wang Yongtian
Published: January 2024

Large depth of field (DOF) and stereo photography are challenging yet rewarding areas of research in close-up photography. In this study, a compound-eye imaging system based on a discrete microlens array (MLA) was implemented for close-range thin imaging. A compact imaging system with a total length of 3.5 mm and a DOF of 7 mm was realized using two planar aspherical MLAs in a hexagonal arrangement. A new three-layer structure and discrete arrangement of sublenses were proposed to suppress stray light and enable the spatial refocusing method, which restores image information at different object depths. The system is successfully fabricated, and the system performance is carefully investigated. Our system offers a large depth of field, high resolution, and portability, making it ideal for close-up photography applications requiring a short conjugate distance and small device volume, while also addressing the issue of crosstalk between adjacent channels.
Flow Characteristics Analysis of a 1 GW Hydraulic Turbine at Rated Condition and Overload Operation Condition
Authors: Liu Chengming , Wu Siyuan , Huang Xingxing , Zhang Shuai , Zhou Lingjiu , Hu Tianli , Wang Zhengwei
Published: January 2024

Flow stability is extremely important for hydraulic turbines, especially for 1 GW hydraulic turbines, and has a strong impact on mesh stability. However, turbines often operate under non-design conditions, and current research on this aspect is still lacking. So a model of the fluid domains of a high-quality installed 1 GW Francis turbine was established to investigate the flow characteristics of the turbine and fluid domains. CFD simulations of a 1 GW Francis turbine under rated load and overload operation conditions were performed. According to simulation results, when the turbine is under the overload operation condition, the internal flow stability of the 1 GW hydraulic turbine can be obviously different from that of the rated load. In the overload condition, the flow field is more turbulent and a large number of vortices are generated in the draft tube, resulting in significant changes in pressure, flow rate, and output. In order to improve calculation accuracy, a pure clearance model containing only clearances and pressure balance pipes was established. The results of the full flow channel and pure clearance were compared. It was found that under the rated operating condition and the overload condition, compared with the pure clearance model, the axial force of the runner calculated by the full flow channel model is approximately 2–7% biased, the radial force is biased by approximately 7–8%, and the leakage flow is smaller.
Effects of Viscosity Law on High-Temperature Supersonic Turbulent Channel Flow for Chemical Equilibrium
Authors: Zhao Shuo , Chen Xiaoping , Yang Yuting , Huang Dengsong
Published: January 2024

Direct numerical simulations of temporally evolving high-temperature supersonic turbulent channel flow for chemical equilibrium were conducted with a Mach number of 3.0, a Reynolds number of 4880, and a wall temperature of 1733.2 K to investigate the influence of the viscosity law. The mean and fluctuating viscosity for the mixture rule is higher than that for Sutherland’s law, whereas an opposite trend is observed in the mean temperature, mean pressure, and dissociation degree. The Trettel and Larsson transformed mean velocity, the Reynolds shear stress, the turbulent kinetic energy budget, and the turbulent Prandtl number are insensitive to the viscosity law. The semilocal scaling that take into account local variation of fluid characteristics better collapses the turbulent kinetic energy budget. The modified strong Reynolds analogies provide reasonably good results for the mixture rule, which are better than those for Sutherland’s law. The streamwise and spanwise coherencies for the mixture rule are stronger and weaker than those for Sutherland’s law, respectively. The relationship between viscosity and species components can help to identify the traveling wave packet.
Heteroatom-Doped Carbon-Based Catalysts Synthesized through a “Cook-Off” Process for Oxygen Reduction Reaction
Authors: Zhang Ruiquan , Liu Qiongyu , Wan Ming , Yao Zhenhua , Hu Maocong
Published: January 2024

The development of efficient and low-cost non-metallic catalysts is of great significance for the oxygen reduction reaction (ORR) in fuel cells. Heteroatom-doped carbon-based catalysts are one of the popular candidates, although their preparation method is still under exploration. In this work, single (CS)-, double (NCS)-, and triple (NBCS)-heteroatom-doped carbon-based catalysts were successfully prepared by a “cook-off” process. The morphology, elemental composition, and bonding structure of the catalysts were investigated by SEM, TEM, Raman spectra, BET, and XPS. ORR catalytic performance measurements suggested an activity trend of CS < NCS < NBCS, and NBCS demonstrated better methanol resistance and slightly higher stability than the commercial Pt/C catalyst, as evaluated with both rotating disk electrode (RDE) and rotating ring-disk electrode (RRDE) systems. The mechanism for the promoted performance was also proposed based on the conductivity of the catalysts. In this paper, the heteroatoms N, B, and S were co-doped into activated carbon using a simple, fast, and efficient preparation method with high electrical conductivity and also increased active sites, showing high electrocatalytic activity and good stability. This work provides a new approach to preparing highly active non-Pt catalysts for oxygen reduction reactions.
Multi-Mode Control of a Hybrid Transformer for the Coordinated Regulation of Voltage and Reverse Power in Active Distribution Network
Authors: Xu Xiao , Zhang Teng , Qiu Ziwen , Gao Hui , Yu Haicheng , Ma Zongxiong , Zhang Ruhai
Published: January 2024

The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction of protection relays, etc. To ensure the reliable and flawless operation of active distribution networks, an electrical device enabling active network management is necessary, and a hybrid distribution transformer offers a promising solution. This study introduces a novel hybrid transformer topology and multi-mode control strategy to achieve coordinated voltage and reverse power regulation in active distribution networks. The proposed hybrid transformer combines conventional transformer windings with a partially rated SiC-MOSFET-based back-to-back converter, reducing additional investment costs and enhancing system reliability. A multi-mode control strategy is proposed to facilitate the concurrent reverse power control and voltage violation mitigation of the presented hybrid transformer, allowing a smooth transition between the P–Q control mode and the V–f control mode. The control mode switching can be activated manually or autonomously in response to voltage violations or reverse power overloading. The effectiveness of the proposed hybrid transformer configuration and its control mode transition mechanism are examined through comprehensive case studies conducted in the PSCAD/EMTDC environment. The proposed HT design has been confirmed to achieve a voltage regulation range of ±20% of the nominal voltage and effectively regulate bidirectional active power flow within a range of −25% to 25% of the rated power.
Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications
Authors: Albanwan Hessah , Qin Rongjun , Liu Jung-Kuan
Published: January 2024

Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.
Three Decades of Oasis Transition and Its Driving Factors in Turpan–Hami Basin in Xinjiang, China: A Complex Network Approach
Authors: Zhang Qinglan , Yan Min , Zhang Li , Shao Wei , Chen Yiyang , Dong Yuqi
Published: January 2024

As a predominant ecosystem-providing area and distinctive landscape in arid regions, an oasis plays an important role in maintaining land stability, human production, and daily activities. Studying the dynamics of oasis and its driving factors is vital to supporting arid regions’ sustainable development. As a typical mountain–desert–oasis landscape, the Turpan–Hami (Tuha) Basin, located in Xinjiang, China, includes complex interactions among different land types. For this study, we revealed the spatio-temporal patterns and transition processes of the oasis using a complex network method between 1990 and 2020 in the Tuha Basin. In the oasis transition network, the degree value, betweenness centrality, and average path length were calculated to express the transition relationship, key oasis type, and oasis structural stability, respectively. Six factors related to climate change and human actives were selected to investigate the driving forces behind oasis transitions, including the average temperature and precipitation in the growing season, the total power of agricultural machinery (TAMP), the production of raw coal (PRC), the total output value of the plantation industry (TPI), and the population (Pop). Our results show that the oasis area of the Tuha Basin, including the natural oasis and artificial oasis, all grew from 1990 to 2020, with the natural oasis expanding more than the artificial oasis. The transitions between oasis types became more frequent as the area of oasis land types increased throughout the study period. Grassland acted as the most important oasis type in the network, with the highest betweenness centrality, but its importance declined due to the increasing complexity of the oasis transition network from 1990 to 2020. The transitions between oasis types became simpler, and the oasis structural stability decreased. Through driving force analysis, the oasis changes showed a positive correlation with the temperature (p-value < 0.05, r = 0.88), and urbanization and industrialization factors prompted transitions to built-up areas and cropland from grassland and shrubland. In summary, our results suggest that to create a harmonious symbiotic relationship between the natural environment in dryland and human activities, preventing grassland degradation and excessive reclamation of land cover is an available way. Meanwhile, the protection of shrubland and water resources is also important. This study provided reference and theory support for promoting the sustainable development of oases.
Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion
Authors: Wilken Dennis , Mercker Moritz , Fischer Peter , Vött Andreas , Erkul Ercan , Corradini Erica , Pickartz Natalie
Published: January 2024

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.
Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise
Authors: Cheng Guorui , Liu Jingang , Song Shenmin
Published: January 2024

This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system.
Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning
Authors: Huber Florian , Inderka Alvin , Steinhage Volker
Published: January 2024

Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.
Remote Heart Rate Estimation Based on Transformer with Multi-Skip Connection Decoder: Method and Evaluation in the Wild
Authors: Othman Walaa , Kashevnik Alexey , Ali Ammar , Shilov Nikolay , Ryumin Dmitry
Published: January 2024

Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos. Our method is based on the skin color variation caused by the change in blood volume in its surface. The presented heart rate estimation framework consists of three main steps: (1) the segmentation of the facial region of interest (ROI) based on the landmarks obtained by 3DDFA; (2) the extraction of the spatial and global features; and (3) the estimation of the heart rate value from the obtained features based on the proposed method. This paper investigates which feature extractor performs better by captioning the change in skin color related to the heart rate as well as the optimal number of frames needed to achieve better accuracy. Experiments were conducted using two publicly available datasets (LGI-PPGI and Vision for Vitals) and our own in-the-wild dataset (12 videos collected by four drivers). The experiments showed that our approach achieved better results than the previously published methods, making it the new state of the art on these datasets.
An Intelligent Ball Bearing Fault Diagnosis System Using Enhanced Rotational Characteristics on Spectrogram
Authors: Seong Gyujin , Kim Dongwan
Published: January 2024

Faults in the ball bearing are a major cause of failure in rotating machinery where ball bearings are used. Therefore, there is a growing demand for ball bearing fault diagnosis to prevent failures in rotating machinery. Although studies on the fault diagnosis of bearing have been conducted using temperature measurements and sound monitoring, these methods have limitations, because they are affected by external noise. Therefore, many researchers have studied vibration monitoring for bearing fault diagnosis. Among these, mel-frequency cepstral coefficients (MFCCs) and 2D convolutional neural networks (CNNs) have attracted significant attention in vibration monitoring schemes. However, the MFCC in existing studies requires a high sampling rate and an expansive frequency band utilization. In addition, 2D CNNs are highly complex. In this study, a rotational characteristic emphasis (RCE) spectrogram process and an optimized CNN were proposed to solve these problems. The RCE spectrogram process analyzes a narrow frequency band and produces low-resolution images. The optimized CNN was designed with a shallow network structure. The experimental results showed an accuracy of 0.9974 for the proposed system. The optimized CNN model has parameters of 5.81 KB and FLOPs of 1.53×106. We demonstrate that the proposed ball bearing fault diagnosis system can achieve high accuracy with low complexity. Thus, we propose a ball bearing fault diagnosis scheme that is applicable to a low sampling rate and changing rotation frequency.
Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs
Authors: Rodriguez-Vazquez Javier , Prieto-Centeno Inés , Fernandez-Cortizas Miguel , Perez-Saura David , Molina Martin , Campoy Pascual
Published: January 2024

Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses on detecting the vertices of solar panels, which provides a richer granularity than traditional approaches. Drawing inspiration from CenterNet, our architecture is optimized for embedded platforms like the NVIDIA AGX Jetson Orin, achieving close to 60 FPS at a resolution of 1024 ×1376 pixels, thus outperforming the camera’s operational frequency. Such a real-time capability is essential for efficient robotic operations in time-critical industrial asset inspection environments. The design of our model emphasizes reduced computational demand, positioning it as a practical solution for real-world deployment. Additionally, the integration of active learning strategies promises a considerable reduction in annotation efforts and strengthens the model’s operational feasibility. In summary, our research emphasizes the advantages of keypoint-based object detection, offering a practical and effective approach for real-time solar farm inspections with UAVs.
Embedded Spatial–Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification
Authors: Xu Fang , Zhu Ming , Lin Mengxue , Wang Maosen , Chen Lei
Published: January 2024

Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial–temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial–temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.
Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM
Authors: Wu Jiaju , Kong Linggang , Kang Shijia , Zuo Hongfu , Yang Yonghui , Cheng Zheng
Published: January 2024

As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly applied to raw monitoring data without the need for additional algorithms to extract fault degradation features. It fully leverages the advantages of 1DCNN in extracting local features along the spatial dimension and incorporates CBAM, a channel and spatial attention mechanism. CBAM could assign higher weights to features relevant to fault categories and make the model pay more attention to them. Subsequently, it utilizes BiLSTM to handle nonlinear time feature sequences and bidirectional contextual feature information. Finally, experimental validation is conducted on the publicly available CMAPSS dataset from NASA, categorizing fault modes into three types: faultless, HPC fault (the single fault), and HPC&Fan fault (the mixed fault). Comparative analysis with other models reveals that the proposed model has a higher classification accuracy, which is of practical significance in improving the reliability of aircraft engine operations and for Remaining Useful Life (RUL) prediction.
Monitoring Off-Shore Fishing in the Northern Indian Ocean Based on Satellite Automatic Identification System and Remote Sensing Data
Authors: Li Jie , Xing Qianguo , Li Xuerong , Arif Maham , Li Jinghu
Published: January 2024

Satellite-derived Sea Surface Temperature (SST) and sea-surface Chlorophyll a concentration (Chl-a), along with Automatic Identification System (AIS) data of fishing vessels, were used in the examination of the correlation between fishing operations and oceanographic factors within the northern Indian Ocean from March 2020 to February 2023. Frequency analysis and the empirical cumulative distribution function (ECDF) were used to calculate the optimum ranges of two oceanographic factors for fishing operations. The results revealed a substantial influence of the northeast and southwest monsoons significantly impacting fishing operations in the northern Indian Ocean, with extensive and active operations during the period from October to March and a notable reduction from April to September. Spatially, fishing vessels were mainly concentrated between 20° N and 6° S, extending from west of 90° E to the eastern coast of Africa. Observable seasonal variations in the distribution of fishing vessels were observed in the central and southeastern Arabian Sea, along with its adjacent high sea of the Indian Ocean. Concerning the marine environment, it was observed that during the northeast monsoon, the suitable SST contributed to high CPUEs in fishing operation areas. Fishing vessels were widely distributed in the areas with both mid-range and low-range Chl-a concentrations, with a small part distributed in high-concentration areas. Moreover, the monthly numbers of fishing vessels showed seasonal fluctuations between March 2020 and February 2023, displaying a periodic pattern with an overall increasing trend. The total number of fishing vessels decreased due to the impact of the COVID-19 pandemic in 2020, but this was followed by a gradual recovery in the subsequent two years. For fishing operations in the northern Indian Ocean, the optimum ranges for SST and Chl-a concentration were 27.96 to 29.47 °C and 0.03 to 1.81 mg/m3, respectively. The preliminary findings of this study revealed the spatial–temporal distribution characteristics of fishing vessels in the northern Indian Ocean and the suitable ranges of SST and Chl-a concentration for fishing operations. These results can serve as theoretical references for the production and resource management of off-shore fishing operations in the northern Indian Ocean.
Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks
Authors: Andrade-Hoz Jimena , Wang Qi , Alcaraz-Calero Jose M.
Published: January 2024

In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation.
Spatial Galloping Behavior of Iced Conductors under Multimodal Coupling
Authors: Cui Fujiang , Zheng Kaihong , Liu Peng , Wang Han
Published: January 2024

In this study, the coupled ordinary differential equations for the galloping of the first two modes in iced bundled conductors, including in-plane, out-of-plane, and torsional directions, are derived. Furthermore, through numerical analysis, the critical conditions of this modal galloping are determined in the range of wind speed–sag parameters, and the galloping patterns and variation laws in different parameter spaces are analyzed. The parameter space is then divided into five regions according to the different galloping modes. Under the multimodal coupling mechanism of galloping, the impact of single and two kinds of coupled mode galloping on the spatial nonlinear behavior is explored. The results reveal that the system exhibits an elliptical orbit motion during single mode galloping, while an “8” motion pattern emerges during coupled mode galloping. Moreover, two patterns of “8” motion are displayed under different parameter spaces. This research provides a theoretical foundation for the design of transmission lines.
Design of Preamplifier for Ultrasound Transducers
Authors: Choi Hojong
Published: January 2024

In diagnostic ultrasound imaging applications, preamplifiers are used as first-stage analog front-end amplifiers for ultrasound transducers because they can amplify weak acoustic signals generated directly by ultrasound transducers. For emerging diagnostic ultrasound imaging applications, different types of preamplifiers with specific design parameters and circuit topologies have been developed, depending on the types of the ultrasound transducer. In particular, the design parameters of the preamplifier, such as the gain, bandwidth, input- or output-referred noise components, and power consumption, have a tradeoff relationship. Guidelines on the detailed design concept, design parameters, and specific circuit design techniques of the preamplifier used for ultrasound transducers are outlined in this paper, aiming to help circuit designers and academic researchers optimize the performance of ultrasound transducers used in the diagnostic ultrasound imaging applications for research directions.
An Impedance-Loaded Surface Acoustic Wave Corrosion Sensor for Infrastructure Monitoring
Authors: Devkota Jagannath , Greve David W. , Diemler Nathan , Pingree Richard , Wright Ruishu
Published: January 2024

Passive surface acoustic wave (SAW) devices are attractive candidates for continuous wireless monitoring of corrosion in large infrastructures. However, acoustic loss in the aqueous medium and limited read range usually create challenges in their widespread use for monitoring large systems such as oil and gas (O&G) pipelines, aircraft, and processing plants. This paper presents the investigation of impedance-loaded reflective delay line (IL-RDL) SAW devices for monitoring metal corrosion under O&G pipeline-relevant conditions. Specifically, we studied the effect of change in resistivity of a reflector on the backscattered signal of an RDL and investigated an optimal range through simulation. This was followed by the experimental demonstrations of real-time monitoring of Fe film corrosion in pressurized (550 psi) humid CO2 conditions. Additionally, remote monitoring of Fe film corrosion in an acidic solution inside a 70 m carbon steel pipe was demonstrated using guided waves. This paper also suggests potential ways to improve the sensing response of IL-RDLs.
Development of Self-Sensing Asphalt Pavements: Review and Perspectives
Authors: Gulisano Federico , Jimenez-Bermejo David , Castano-Solís Sandra , Sánchez Diez Luis Alberto , Gallego Juan
Published: January 2024

The digitalization of the road transport sector necessitates the exploration of new sensing technologies that are cost-effective, high-performing, and durable. Traditional sensing systems suffer from limitations, including incompatibility with asphalt mixtures and low durability. To address these challenges, the development of self-sensing asphalt pavements has emerged as a promising solution. These pavements are composed of stimuli-responsive materials capable of exhibiting changes in their electrical properties in response to external stimuli such as strain, damage, temperature, and humidity. Self-sensing asphalt pavements have numerous applications, including in relation to structural health monitoring (SHM), traffic monitoring, Digital Twins (DT), and Vehicle-to-Infrastructure Communication (V2I) tools. This paper serves as a foundation for the advancement of self-sensing asphalt pavements by providing a comprehensive review of the underlying principles, the composition of asphalt-based self-sensing materials, laboratory assessment techniques, and the full-scale implementation of this innovative technology.
Three-Dimensional Reconstruction and Deformation Identification of Slope Models Based on Structured Light Method
Authors: Chen Zhijian , Zhang Changxing , Tang Zhiyi , Fang Kun , Xu Wei
Published: January 2024

In this study, we propose a meticulous method for the three-dimensional modeling of slope models using structured light, a swift and cost-effective technique. Our approach aims to enhance the understanding of slope behavior during landslides by capturing and analyzing surface deformations. The methodology involves the initial capture of images at various stages of landslides, followed by the application of the structured light method for precise three-dimensional reconstructions at each stage. The system’s low-cost nature and operational convenience make it accessible for widespread use. Subsequently, a comparative analysis is conducted to identify regions susceptible to severe landslide disasters, providing valuable insights for risk assessment. Our findings underscore the efficacy of this system in facilitating a qualitative analysis of landslide-prone areas, offering a swift and cost-efficient solution for the three-dimensional reconstruction of slope models.
Optimization of Covert Communication in Multi-Sensor Asymmetric Noise Systems
Authors: Qiao Sen , Zhu Ruizhi , Ji Xiaopeng , Zhao Junjie , Ding Huihui
Published: January 2024

This work investigates wireless covert communication in a multi-sensor asymmetric noise scenario. We adopt KL (Kullback–Leibler) divergence as the covertness constraint metric and mutual information as the transmission rate metric. To accurately approximate KL divergence and mutual information in covert communication, we employ the Taylor series expansion technique. Analytical expressions for KL divergence and mutual information in covert communication are derived, and we optimize the amplitude gain and phase angles based on these analytical expressions. Our findings underscore the importance of phase angle selection in covert communication within asymmetric noise systems. We propose an effective method for optimizing the transmission amplitude gain and phase angles in scenarios with asymmetric noise. Numerical results validate the effectiveness and superiority of our proposed method.
Ternary Nanostructure Coupling Flip-Flap Origami-Based Aptasensor for the Detection of Dengue Virus Antigens
Authors: Hasan Mohd. Rahil , Singh Saumitra , Sharma Pradakshina , Rawat Chhaya , Khanuja Manika , Pilloton Roberto , Narang Jagriti
Published: January 2024

There is currently a lot of interest in the construction of point-of-care devices stemming from paper-based origami biosensors. These devices demonstrate how paper’s foldability permits the construction of sensitive, selective, user-friendly, intelligent, and maintainable analytical devices for the detection of several ailments. Herein, the first example of the electrochemical aptasensor-based polyvalent dengue viral antigen detection using the origami paper-folding method is presented. Coupling it with an aptamer leads to the development of a new notation known as OBAs, or origami-based aptasensor, that presents a multitude of advantages to the developed platform, such as assisting in safeguarding the sample from air-dust particles, providing confidentiality, and providing a closed chamber to the electrodes. In this paper, gold-decorated nanocomposites of zinc and graphene oxide (Au/ZnO/GO) were synthesized via the chemical method, and characterization was conducted by Scanning Electron Microscope, Transmission Electron Microscope, UV-Vis, and XRD which reveals the successful formation of nanocomposites, mainly helping to enhance the signal and specificity of the sensor by employing aptamers, since isolation and purification procedures are not required. The biosensor that is being demonstrated here is affordable, simple, and efficient. The reported biosensor is an OBA detection of polyvalent antigens of the dengue virus in human serum, presenting a good range from 0.0001 to 0.1 mg/mL with a limit of detection of 0.0001 mg/mL. The reported single-folding ori-aptasensor demonstrates exceptional sensitivity, specificity, and performance in human serum assays, and can also be used for the POC testing of various viral infections in remote areas and underdeveloped countries, as well as being potentially effective during outbreaks. Highlights: (1) First report on origami-based aptasensors for the detection of polyvalent antigens of DENV; (2) In-house construction of low-cost origami-based setup; (3) Gold-decorated zinc/graphene nanocomposite characterization was confirmed via FESEM/UV-Vis/FTIR; (4) Cross-reactivity of dengue-aptamer has been deduced; (5) Electrochemical validation was conducted through CV.
A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol
Authors: Souissi Rihab , Sahnoun Salwa , Baazaoui Mohamed Khalil , Fromm Robert , Fakhfakh Ahmed , Derbel Faouzi
Published: January 2024

Indoor localization of a mobile target represents a prominent application within wireless sensor network (WSN), showcasing significant values and scientific interest. Interference, obstacles, and energy consumption are critical challenges for indoor applications and battery replacements. A proposed tracking system deals with several factors such as latency, energy consumption, and accuracy presenting an innovative solution for the mobile localization application. In this paper, a novel algorithm introduces a self-localization algorithm for mobile targets using the wake-up media access control (MAC) protocol. The developed tracking application is based on the trilateration technique with received signal strength indication (RSSI) measurements. Simulations are implemented in the objective modular network testbed in C++ (OMNeT++) discrete event simulator using the C++ programming language, and the RSSI values introduced are based on real indoor measurements. In addition, a determination approach for finding the optimal parameters of RSSI is assigned to implement for the simulation parameters. Simulation results show a significant reduction in power consumption and exceptional accuracy, with an average error of 1.91 m in 90% of cases. This method allows the optimization of overall energy consumption, which consumes only 2.69% during the localization of 100 different positions.
Designing a Surveillance Sensor Network with Information Clearinghouse for Advanced Air Mobility
Authors: Dulia Esrat Farhana , Shihab Syed A. M.
Published: January 2024

To ensure safe, secure, and efficient advanced air mobility (AAM) operations, an AAM surveillance network is needed to detect and track AAM traffic. Additionally, a cloud-based surveillance data collection, monitoring, and distribution center is needed, where AAM operators and service suppliers, law enforcement agencies, correctional facilities, and municipalities can subscribe to receiving relevant AAM traffic data to plan and monitor AAM operations. In this work, we developed an optimization model to design a surveillance sensor network for AAM that minimizes the total sensor cost while providing full coverage in the desired region of operation, considering terrain types of that region, terrain-based sensor detection probabilities, and meeting the minimum detection probability requirement. Moreover, we present a framework for the low altitude surveillance information clearinghouse (LASIC), connected to the optimized AAM surveillance network for receiving live surveillance feed. Additionally, we conducted a cost–benefit analysis of the AAM surveillance network and LASIC to justify an investment in it. We examine six potential types of AAM sensors and homogeneous and heterogeneous network types. Our analysis reveals the sensor types that are the most profitable options for detecting cooperative and non-cooperative aircraft. According to the findings, heterogeneous networks are more cost-effective than homogeneous sensor networks. Based on the sensitivity analysis, changes in parameters such as subscription fees, the number of subscribers, sensor detection probabilities, and the minimum required detection probability significantly impact the surveillance network design and cost–benefit analysis.
Remote Sensing Evaluation Drone Herbicide Application Effectiveness for Controlling Echinochloa spp. in Rice Crop in Valencia (Spain)
Authors: Bautista Alberto San , Tarrazó-Serrano Daniel , Uris Antonio , Blesa Marta , Estruch-Guitart Vicente , Castiñeira-Ibáñez Sergio , Rubio Constanza
Published: January 2024

Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L’Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.
Examining Loneliness: A Comparative Analysis of Face-to-Face, Telephone, and Online Communication among Japanese Young Adults
Authors: Tanaka Yuko , Iwata Yuka , Oe Nanami , Tadaka Etsuko
Published: January 2024

(1) Background: Loneliness is inherently linked to social connections, with interpersonal communication playing a pivotal role. Despite this connection, limited research exists on the relationship between loneliness and communication among young adults. This study investigates the correlation between face-to-face, telephone, and online communication frequencies and loneliness among individuals in their 20s. (2) Methods: Using a dataset from a nationwide survey conducted by the Japanese Cabinet Office, this study focuses on 1812 respondents aged 20–30, selected from a random sample of 20,000 individuals aged 16 and older across Japan. (3) Results: A Multivariate Logistic Regression Analysis reveals a significant association between communication frequency and loneliness, even after adjusting for demographic characteristics. Notably, decreased communication frequency across all modalities correlated with increased loneliness. Online communication exhibited the highest impact, followed by face-to-face communication, with phone call communication ranking last. (4) Conclusions: This study emphasizes the importance of seamlessly integrating social networking service (SNS)-based communication with various forms of social interaction. A well-balanced integration of these approaches is crucial for mitigating loneliness among young individuals and promoting positive mental health outcomes.
Digital Communication Studies during the Pandemic: A Sociological Review Using Topic Modeling Strategy
Authors: Taboada-Villamarín Alba , Torres-Albero Cristóbal
Published: January 2024

The health crisis triggered by COVID-19 has exerted a profound influence on both conventional communication methods and the manifestations of interaction within the virtual sphere. Gradually, studies on digital communication have taken on an increasingly prominent role in various social science disciplines that address determinants such as the crisis of misinformation or digital interaction in contemporary societies. This study aims to analyze the key research topics that sociology has addressed in relation to the pandemic, along with the level of innovation in the utilization of digital sources and analytical methodology. The analysis is grounded in the hypothesis that the effects of the pandemic have led the discipline of sociology to reassess and more fully integrate studies on digital communication. On this premise, a systematic review of studies sourced from the Web of Science (WoS) and Scopus databases was executed. Innovative computational methodologies were employed for the categorization of articles and the elucidation of principal research topics. Furthermore, this research scrutinized the principal digital platforms utilized in these investigations and assessed the extent of methodological innovation applied to data analysis. The outcomes unveiled a pronounced ascendancy in the prominence of communication studies during the pandemic. Nevertheless, it is noteworthy that the utilization of digital data sources in research remains surprisingly limited. This observation highlights a potential avenue for further exploration within the domain of sociological research, promising a more profound and contemporaneous comprehension of social phenomena amid times of crisis.