bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Operational Global Ocean Wave Forecasting System of China
Authors: Wu Mengmeng , Wang Juanjuan , Cai Qiongqiong , Wang Yi , Wang Jiuke , Wang Hui
Published: September 2024

Based on the WAVEWATCH III wave model, China’s National Marine Environmental Forecasting Center has developed an operational global ocean wave forecasting system that covers the Arctic region. In this study, in situ buoy observations and satellite remote sensing data were used to perform a detailed evaluation of the system’s forecasting results for 2022, with a focus on China’s offshore and global ocean waters, so as to comprehensively understand the model’s forecasting performance. The study results showed the following: In China’s coastal waters, the model had a high forecasting accuracy for significant wave heights. The model tended to underestimate the significant wave heights in autumn and winter and overestimate them in spring and summer. In addition, the model slightly underestimated low (below 1 m) wave heights, while overestimating them in other ranges. In terms of spatial distribution, negative deviations and high scatter indexes were observed in the forecasting of significant wave heights in semi-enclosed sea areas such as the Bohai Sea, Yellow Sea, and Beibu Gulf, with the largest negative deviation occurring near Liaodong Bay of the Bohai Sea (−0.18 m). There was a slight positive deviation (0.01 m) in the East China Sea, while the South China Sea exhibited a more significant positive deviation (0.17 m). The model showed a trend of underestimation for the forecasting of the mean wave period in China’s coastal waters. In the global oceanic waters, the forecasting results of the model were found to have obvious positive deviations for most regions, with negative deviations mainly occurring on the east coast and in relatively closed basins. There were latitude differences in the forecasting deviations of the model: specifically, the most significant positive deviations occurred in the Southern Ocean, with smaller positive deviations toward the north, while a slight negative deviation was observed in the Arctic waters. Overall, the global wave model has high reliability and can meet the current operational forecasting needs. In the future, the accuracy and performance of ocean wave forecasting can be further improved by adjusting the parameterization scheme, replacing the wind fields with more accurate ones, adopting spherical multiple-cell grids, and data assimilation.
On a Correlation Model for Laser Scanners: A Large Eddy Simulation Experiment
Authors: Kermarrec Gaël
Published: September 2024

Large Eddy Simulations (LES) allow the generation of spatio-temporal fields of the refractivity index for various meteorological conditions and provide a unique way to simulate turbulence-distorted phase measurements as those from geodetic sensors. This approach enables a statistical quantification of the von Kármán model’s adequacy in describing the phase spectrum and the assessment of the validity of common assumptions such as isotropy or the Taylor frozen hypothesis. This contribution shows that the outer scale length, defined using the Taylor frozen hypothesis as the saturation frequency of the phase spectrum, can be statistically estimated, along with an error fit factor between the model and its estimation. It is found that this parameter strongly varies with height and meteorological conditions (convective or wind-driven boundary layer). The simulations further highlight the linear dependency with the variance of the turbulent phase fluctuations but no dependency on the local outer scale length as defined by Tatarskii. An application of these results within a geodetic context is proposed, where an understanding and solid estimation of the outer scale length is mandatory in avoiding biased decisions during statistical deformation analysis. The LES presented in this contribution support derivations for an improved stochastic model of terrestrial laser scanners.
Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes
Authors: Fendereski Forough , Creed Irena F. , Trick Charles G.
Published: September 2024

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.
Calibration Methods for Large-Scale and High-Precision Globalization of Local Point Cloud Data Based on iGPS
Authors: Han Rui , Dunker Thomas , Trostmann Erik , Xu Zhigang
Published: September 2024

The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are generally small in spatial extent, it is necessary to accurately globalize the local point cloud to measure large components. In this paper, the method of using an iGPS (indoor Global Positioning System) as an external measurement device to realize high-accuracy globalization of local point cloud data is proposed. Two calibration models are also discussed for different application scenarios. Verification experiments prove that the average calibration errors of these two calibration models are 0.12 mm and 0.17 mm, respectively. The proposed method can maintain calibration precision in a large spatial range (about 10 m × 10 m × 5 m), which is of high value for engineering applications.
Analysis of Road Roughness and Driver Comfort in ‘Long-Haul’ Road Transportation Using Random Forest Approach
Authors: Ajayi Olusola O. , Kurien Anish M. , Djouani Karim , Dieng Lamine
Published: September 2024

Global trade depends on long-haul transportation, yet comfort for drivers on lengthy trips is sometimes neglected. Rough roads have a major negative influence on driver comfort and increase the risk of weariness, distracted driving, and accidents. Using Random Forest regression, a machine learning technique well-suited to examining big datasets and nonlinear relationships, this study examines the relationship between road roughness and driver comfort. Using the MIRANDA mobile application, data were gathered from 1,048,576 rows, including vehicle acceleration and values for the International Roughness Index (IRI). The Support Vector Regression (SVR) and XGBoost models were used for comparative analysis. Random Forest was preferred because of its ability to be deployed in real time and use less memory, even if XGBoost performed better in terms of training time and prediction accuracy. The findings showed a significant relationship between driver discomfort and road roughness, with rougher roads resulting in increased vertical acceleration and lower comfort levels (Road Roughness: SD—0.73; Driver’s Comfort: Mean—10.01, SD—0.64). This study highlights how crucial it is to provide smooth surfaces and road maintenance in order to increase road safety, lessen driver weariness, and promote long-haul driver welfare. These results offer information to transportation authorities and policymakers to help them make data-driven decisions that enhance the efficiency of transportation and road conditions.
Design and Simulation of High-Performance D-Type Dual-Mode PCF-SPR Refractive Index Sensor Coated with Au-TiO2 Layer
Authors: Ding Xin , Lin Qiao , Wang Mengjie , Liu Shen , Zhang Weiguan , Chen Nan , Wang Yiping
Published: September 2024

A novel surface plasmon resonance (SPR) refractive index (RI) sensor based on the D-type dual-mode photonic crystal fiber (PCF) is proposed. The sensor employs a side-polished few-mode PCF that facilitates the transmission of the fundamental and second-order modes, with an integrated microfluidic channel positioned directly above the fiber core. This design minimizes the distance to the analyte and maximizes the interaction between the optical field and the analyte, thereby enhancing the SPR effect and resonance loss for improved sensing performance. Au-TiO2 dual-layer material was coated on the surface of a microfluidic channel to enhance the penetration depth of the core evanescent field and tune the resonance wavelength to the near-infrared band, meeting the special needs of chemical and biomedical detection fields. The finite element method was utilized to systematically investigate the coupling characteristics between various modes and surface plasmon polariton (SPP) modes, as well as the impact of structural parameters on the sensor performance. The results indicate that the LP11b_y mode exhibits greater wavelength sensitivity than the HE11_y mode, with a maximum sensitivity of 33,000 nm/RIU and an average sensitivity of 8272.7 nm/RIU in the RI sensing range of 1.25–1.36, which is higher than the maximum sensitivity of 16,000 nm/RIU and average sensitivity of 5666.7 nm/RIU for the HE11b_y mode. It is believed that the proposed PCF-SPR sensor features both high sensitivity and high resolution, which will become a critical device for wide RI detection in mid-infrared fields.
A Statistical Approach for Functional Reach-to-Grasp Segmentation Using a Single Inertial Measurement Unit
Authors: Dotti Gregorio , Caruso Marco , Fortunato Daniele , Knaflitz Marco , Cereatti Andrea , Ghislieri Marco
Published: September 2024

The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject’s wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols.
Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness
Authors: Miladinović Aleksandar , Accardo Agostino , Jarmolowska Joanna , Marusic Uros , Ajčević Miloš
Published: September 2024

Brain–computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1–2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.
Integrated Approach for Human Wellbeing and Environmental Assessment Based on a Wearable IoT System: A Pilot Case Study in Singapore
Authors: Salamone Francesco , Sibilio Sergio , Masullo Massimiliano
Published: September 2024

This study presents the results of the practical application of the first prototype of WEMoS, the Wearable Environmental Monitoring System, in a real case study in Singapore, along with two other wearables, a smart wristband to monitor physiological data and a smartwatch with an application (Cozie) used to acquire users’ feedback. The main objective of this study is to present a new procedure to assess users’ perceptions of the environmental quality by taking into account a multi-domain approach, considering all four environmental domains (thermal, visual, acoustic, and air quality) through a complete wearable system when users are immersed in their familiar environment. This enables an alternative to laboratory tests where the participants are in unfamiliar spaces. We analysed seven-day data in Singapore using a descriptive and predictive approach. We have found that it is possible to use a complete wearable system and apply it in real-world contexts. The WEMoS data, combined with physiology and user feedback, identify the key comfort features. The transition from short-term laboratory analysis to long-term real-world context using wearables enables the prediction of overall comfort perception in a new way that considers all potentially influential factors of the environment in which the user is immersed. This system could help us understand the effects of exposure to different environmental stimuli thus allowing us to consider the complex interaction of multi-domains on the user’s perception and find out how various spaces, both indoor and outdoor, can affect our perception of IEQ.
A Novel Topology of a 3 × 3 Series Phased Array Antenna with Aperture-Coupled Feeding
Authors: Yang Guang , Lee Choon Sae , Zhang Linsheng
Published: September 2024

This paper presents a novel 3 × 3 phased array antenna optimized for 4 GHz operation, achieving a realized gain of 13.2 dBi and enabling 30-degree beam steering with a minimal capacitance variation of 1.5 pF. The design features a series aperture-coupled feeding mechanism that not only reduces the antenna’s size but also simplifies the fabrication process, making the device both cost-effective and compact. Integrating cost-efficient quadrature-hybrid phase shifters and novel power splitters with cascaded quadrature hybrids ensures uniform power distribution and precise beam steering. The innovative use of these components addresses common challenges in phased array systems, such as space constraints, high costs, and complex power distribution.
Experimental Investigation on the Effect of Coil Shape on Planar Eddy Current Sensor Characteristic for Blade Tip Clearance
Authors: Zhao Lingqiang , Lyu Yaguo , Liu Fulin , Liu Zhenxia , Zhao Ziyu
Published: September 2024

Given the increasing application of eddy current sensors for measuring turbine tip clearance in aero engines, enhancing the performance of these sensors is essential for improving measurement accuracy. This study investigates the influence of coil shape on the measurement performance of planar eddy current sensors and identifies an optimal coil shape to enhance sensing capabilities. To achieve this, various coil shapes—specifically circular, square, rectangular wave, and triangular wave—were designed and fabricated, featuring different numbers of turns for the experiment at room temperature. By employing a method for calculating coil inductance, the performance of each sensor was evaluated based on key metrics: measurement range, sensitivity, and linearity. Experimental results reveal that the square coil configuration outperforms other shapes in overall measurement performance. Notably, the square coil demonstrated a measurement range of 0 mm to 8 mm, a sensitivity of 0.115685 μH/mm, and an impressive linearity of 98.41% within the range of 0 mm to 2 mm. These findings indicate that the square coil configuration enhances measurement capabilities. The conclusions drawn from this study provide valuable insights for selecting coil shapes and optimizing the performance of planar eddy current sensors, thereby contributing to the advancement of turbine tip clearance measurement techniques in aero engines.
Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies
Authors: Kifor Claudiu Vasile , Popescu Aurelian
Published: September 2024

Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.
Multilayer Fused Correntropy Reprsenstation for Fault Diagnosis of Mechanical Equipment
Authors: Deng Qi , Zhao Guanhui , Jiang Weixiong , Wu Jun , Dai Tianjiao
Published: September 2024

Fault diagnosis is vital for improving the reliability and safety of mechanical equipment. Existing fault diagnosis methods require a large number of samples for model training. However, in real-world environments, mechanical equipment usually operates under healthy conditions during most of its service life, resulting in a scarcity of fault samples. To solve this problem, a novel multilayer fusion correntropy representation method combined with a support vector machine is proposed for the fault diagnosis of mechanical equipment. First, the monitoring signal is expanded into multilayer signal components using wavelet packet decomposition. Then, the correlation between the signal components of each layer is expressed by correntropy, and the corresponding correntropy matrix is constructed. After performing the matrix logarithm operator, all correntropy matrices composed of correntropy values are fused into a vector, which is viewed as a feature of the signal. Finally, a support vector machine is established using small samples to realize fault classification. The effectiveness of the proposed method is validated on four public datasets. The results indicate that compared with other methods, the proposed method has advantages in terms of diagnosis accuracy and noise immunity ability.
Blockchain 6G-Based Wireless Network Security Management with Optimization Using Machine Learning Techniques
Authors: Chinnasamy Ponnusamy , Babu G. Charles , Ayyasamy Ramesh Kumar , Amutha S. , Sinha Keshav , Balaram Allam
Published: September 2024

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end–end delay, and 94% packet delivery ratio.
Gas Sensing Properties of Indium–Oxide–Based Field–Effect Transistor: A Review
Authors: Liang Chengyao , Cao Zhongyu , Hao Jiongyue , Zhao Shili , Yu Yuanting , Dong Yingchun , Liu Hangyu , Huang Chun , Gao Chao , Zhou Yong , He Yong
Published: September 2024

Excellent stability, low cost, high response, and sensitivity of indium oxide (In2O3), a metal oxide semiconductor, have been verified in the field of gas sensing. Conventional In2O3 gas sensors employ simple and easy–to–manufacture resistive components as transducers. However, the swift advancement of the Internet of Things has raised higher requirements for gas sensors based on metal oxides, primarily including lowering operating temperatures, improving selectivity, and realizing integrability. In response to these three main concerns, field–effect transistor (FET) gas sensors have garnered growing interest over the past decade. When compared with other metal oxide semiconductors, In2O3 exhibits greater carrier concentration and mobility. The property is advantageous for manufacturing FETs with exceptional electrical performance, provided that the off–state current is controlled at a sufficiently low level. This review presents the significant progress made in In2O3 FET gas sensors during the last ten years, covering typical device designs, gas sensing performance indicators, optimization techniques, and strategies for the future development based on In2O3 FET gas sensors.
Machine Learning-Based Gesture Recognition Glove: Design and Implementation
Authors: Filipowska Anna , Filipowski Wojciech , Raif Paweł , Pieniążek Marcin , Bodak Julia , Ferst Piotr , Pilarski Kamil , Sieciński Szymon , Doniec Rafał Jan , Mieszczanin Julia , Skwarek Emilia , Bryzik Katarzyna , Henkel Maciej , Grzegorzek Marcin
Published: September 2024

In the evolving field of human–computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments.
A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications
Authors: Shokri Reza , Koolivand Yarallah , Shoaei Omid , Caviglia Daniele D. , Aiello Orazio
Published: September 2024

Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain–machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm2. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.
Page Turning Using Assistive Robot with Low-Degree-of-Freedom Hand
Authors: Ikeda Hidetoshi , Mizukami Yuta , Sakamoto Masahiro , Saeki Takumi , Lee Hokyoo , Hori Masakazu
Published: September 2024

This paper proposes a page-turning strategy using an assistive robot that has a low-degree-of-freedom robotic hand. The robotic hand is based on human object handling characteristics, which significantly reduces the number of fingers and joints required to handle various objects. The robotic hand has right and left planar fingers that can transform their shape to handle various objects. To turn a page, the robot uses the planar fingers to push the surface of the page and then rotates the fingers. The design concept, mechanism, sensor system, strategy for page turning, and control system of the robotic hand are presented. The experimental results show that the robot can turn pages using the proposed method; however, it sometimes failed to turn the page when the robotic hand height was too low and too close to the book because the rotation of the fingers was stopped by the book. When the hand detects excessive force during page turning, the control system changes the shape of the fingers and releases the force from the book. The experimental results show the effectiveness of the control system.
An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments
Authors: Slingsby James , Scott Beth E. , Kregting Louise , McIlvenny Jason , Wilson Jared , Helleux Fanny , Williamson Benjamin J.
Published: September 2024

Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success and behaviour, with the potential for animal interactions with MRE devices. Uncrewed aerial vehicles (UAVs) offer a novel tool for the fine-scale data collection of surface turbulence features and animals, which is not possible through other techniques, to provide information on the potential environmental impacts of anthropogenic developments. However, large imagery datasets are time-consuming to manually review and analyse. This study demonstrates an experimental methodology for the automated detection of turbulence features within UAV imagery. A deep learning architecture, specifically a Faster R-CNN model, was used to autonomously detect kolk-boils within UAV imagery of a tidal stream environment. The model was trained on pre-existing, labelled images of kolk-boils that were pre-treated using a suite of image enhancement techniques based on the environmental conditions present within each image. A 75-epoch model variant provided the highest average recall and precision values; however, it appeared to be limited by sub-optimal detections of false positive values. Although further development is required, including the creation of standardised image data pools, increased model benchmarking and the advancement of tailored pre-processing techniques, this work demonstrates the viability of utilising deep learning to automate the detection of surface turbulence features within a tidal stream environment.
Impedance Spectroscopy Study of Charge Transfer in the Bulk and Across the Interface in Networked SnO2/Ga2O3 Core–Shell Nanobelts in Ambient Air
Authors: Krawczyk Maciej , Korbutowicz Ryszard , Suchorska-Woźniak Patrycja
Published: September 2024

Metal oxide core–shell fibrous nanostructures are promising gas-sensitive materials for the detection of a wide variety of both reducing and oxidizing gases. In these structures, two dissimilar materials with different work functions are brought into contact to form a coaxial heterojunction. The influence of the shell material on the transportation of the electric charge carriers along these structures is still not very well understood. This is due to homo-, hetero- and metal/semiconductor junctions, which make it difficult to investigate the electric charge transfer using direct current methods. However, in order to improve the gas-sensing properties of these complex structures, it is necessary to first establish a good understanding of the electric charge transfer in ambient air. In this article, we present an impedance spectroscopy study of networked SnO2/Ga2O3 core–shell nanobelts in ambient air. Tin dioxide nanobelts were grown directly on interdigitated gold electrodes, using the thermal sublimation method, via the vapor–liquid–solid (VLS) mechanism. Two forms of a gallium oxide shell of varying thickness were prepared via halide vapor-phase epitaxy (HVPE), and the impedance spectra were measured at 189–768 °C. The bulk resistance of the core–shell nanobelts was found to be reduced due to the formation of an electron accumulation layer in the SnO2 core. At temperatures above 530 °C, the thermal reduction of SnO2 and the associated decrease in its work function caused electrons to flow from the accumulation layer into the Ga2O3 shell, which resulted in an increase in bulk resistance. The junction resistance of said core–shell nanostructures was comparable to that of SnO2 nanobelts, as both structures are likely connected through existing SnO2/SnO2 homojunctions comprising thin amorphous layers.
A High Gain Circularly Polarized Slot Antenna Array for 5G Millimeter-Wave Applications
Authors: He Wei , Hong Jun , Ren Yongmei , Deng Yuanxiang , Wang Xiaohu , Fang Xiaoyong
Published: September 2024

An air-filled substrate-integrated waveguide (AF-SIW) circularly polarized (CP) 1 × 8 mm wave antenna array is presented for fifth-generation (5G) applications. The presented slot antenna array consists of three layers of PCB and one layer of aluminum, which serve as the AF-SIW feeding network and the metal cavity radiation element, respectively. The CP characteristic is achieved by the use of an S-shaped aluminum radiation cavity on the top of the AF-SIW feeding network. The air-filled substrate-integrated waveguide technique is unitized to achieve high radiation efficiency. A wide input impedance bandwidth of 18.4% is obtained for the proposed antenna scheme, ranging from 34.5 GHz to 41.5 GHz, with a peak gain of 18 dBic. As for CP characteristic, the proposed antenna possesses a wide 3 dB axial ratio (AR) bandwidth, which is 16.4% (36.5 GHz to 43 GHz). The antenna scheme is fabricated and measured to verify the potential application as well as the promising performance. The measured results of the 1 × 8 antenna array shows that the wide AR as well as the input impedance are simultaneously achieved, which coincide well with the simulated results. Also, the measured results indicate that the proposed antenna scheme might be a good candidate for future mobile applications.
Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station
Authors: Babu Gurujukota Ramesh , Gokuldhev Mony , Brahmanandam P. S.
Published: September 2024

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall’s correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, ‘Bayesian optimization with KNN’, was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.
Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization—Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm
Authors: Han Daoqi , Li Honghui , Fu Xueliang
Published: September 2024

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time.
IV-YOLO: A Lightweight Dual-Branch Object Detection Network
Authors: Tian Dan , Yan Xin , Zhou Dong , Wang Chen , Zhang Wenshuai
Published: September 2024

With the rapid growth in demand for security surveillance, assisted driving, and remote sensing, object detection networks with robust environmental perception and high detection accuracy have become a research focus. However, single-modality image detection technologies face limitations in environmental adaptability, often affected by factors such as lighting conditions, fog, rain, and obstacles like vegetation, leading to information loss and reduced detection accuracy. We propose an object detection network that integrates features from visible light and infrared images—IV-YOLO—to address these challenges. This network is based on YOLOv8 (You Only Look Once v8) and employs a dual-branch fusion structure that leverages the complementary features of infrared and visible light images for target detection. We designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to effectively integrate multimodal features, reducing errors from feature redundancy and extracting fine-grained features for small object detection. Additionally, we developed a Shuffle-SPP structure that combines channel and spatial attention to enhance the focus on deep features and extract richer information through upsampling. Regarding model optimization, we designed a loss function tailored for multi-scale object detection, accelerating the convergence speed of the network during training. Compared with the current state-of-the-art Dual-YOLO model, IV-YOLO achieves mAP improvements of 2.8%, 1.1%, and 2.2% on the Drone Vehicle, FLIR, and KAIST datasets, respectively. On the Drone Vehicle and FLIR datasets, IV-YOLO has a parameter count of 4.31 M and achieves a frame rate of 203.2 fps, significantly outperforming YOLOv8n (5.92 M parameters, 188.6 fps on the Drone Vehicle dataset) and YOLO-FIR (7.1 M parameters, 83.3 fps on the FLIR dataset), which had previously achieved the best performance on these datasets. This demonstrates that IV-YOLO achieves higher real-time detection performance while maintaining lower parameter complexity, making it highly promising for applications in autonomous driving, public safety, and beyond.
HubNet: An E2E Model for Wheel Hub Text Detection and Recognition Using Global and Local Features
Authors: Zeng Yue , Meng Cai
Published: September 2024

Automatic detection and recognition of wheel hub text, which can boost the efficiency and accuracy of product information recording, are undermined by the obscurity and orientation variability of text on wheel hubs. To address these issues, this paper constructs a wheel hub text dataset and proposes a wheel hub text detection and recognition model called HubNet. The dataset captured images on real industrial production line scenes, including 446 images, 934 word instances, and 2947 character instances. HubNet is an end-to-end text detection and recognition model, not only comprising conventional detection and recognition heads but also incorporating a feature cross-fusion module, which improves the accuracy of recognizing wheel hub texts by utilizing both global and local features. Experimental results show that on the wheel hub text dataset, the HubNet achieves an accuracy of 86.5%, a recall of 79.4%, and an F1-score of 0.828, and the feature cross-fusion module increases the accuracy by 2% to 4%. The wheel hub dataset and the HubNet offer a significant reference for automatic detection and recognition of wheel hub text.
Consumer Satisfaction with the Quality and Availability of Gluten-Free Products
Authors: Knežević Nada , Karlović Sven , Takács Krisztina , Szűcs Viktória , Knežević Sara , Badanjak Sabolović Marija , Brnčić Suzana Rimac
Published: September 2024

The aim of this study is to investigate consumer attitudes toward the quality and availability of gluten-free products. For this purpose, quantitative research was conducted using a questionnaire completed by 107 respondents who consumed gluten-free products. The results of the research showed that most consumers prepared their own meals, paid attention to ingredients, and strictly adhered to a gluten-free diet. About 10% of the respondents declared that they occasionally did not follow a gluten-free diet. Respondents were generally not satisfied with the price and availability of gluten-free products on the market. An additional aggravating circumstance for them when eating out was that restaurants generally do not have a sufficient selection of gluten-free dishes in their daily offer. Although most of them stated that they buy basic, well-known gluten-free products, they want to try new products with different, new flavors. This demand offers food manufacturers the opportunity to capitalize on the rapidly growing gluten-free market by developing creative and sustainable products that not only meet dietary requirements but also exceed consumer expectations. By focusing on innovation and variety, companies can participate in a profitable market while contributing to the growth of a more sustainable economy. The results of this study pointed to consumer preferences that could be used to influence the improvement and development of new gluten-free products, which are on the rise and will have a better nutritional quality and thus had a positive impact on health.
Experimental Study on Optimization of Consolidation Parameters of Silty Clay Based on Response Surface Methodology: A Case Study on the Protection and Restoration of the Ming and Qing Dynasty Hangzhou Seawall Site
Authors: Ye Liang , Chen Zhenyan , Wu Liquan , Zou Baoping
Published: September 2024

The preservation of the ancient seawall site is a focal point and challenge in the protection of historical relics along Hangzhou’s Grand Canal in China. This endeavor holds significant historical and contemporary value in uncovering and perpetuating Hangzhou’s cultural heritage. Researchers investigating the Linping section of the seawall site aimed to address soil site deterioration by selecting environmentally friendly alkali-activated slag cementitious materials and applying the response surface method (RSM) to conduct solidification experiments on the seawall soil. Researchers used the results of unconfined compressive strength tests and microscopic electron microscopy analysis, considering the comprehensive performance of soil solidification mechanisms and mechanical properties, to establish a least-squares regression fitting model to optimize the solidification material process parameters. The experimental results indicate that the optimal mass ratio of lime, gypsum, and slag for achieving the best solidification process parameters for the seawall soil, with a 28-day curing period, is 1:1.9:6.2. This ratio was subsequently applied to the restoration and reconstruction of the seawall site, with parts of the restored seawall exhibited in a museum to promote the sustainable conservation of urban cultural heritage. This study provides theoretical support and practical guidance for the protection and restoration of soil sites.
Developing a Conceptual Model for Promoting Risk Management for Public–Private Partnerships Projects
Authors: Chen Yin , Kho Mei , Mohamed Othman
Published: September 2024

Over the past decade, there has been significant growth in the literature addressing risk issues associated with Public–Private Partnerships (PPPs). However, few studies have attempted to comprehensively map and visualize research in this domain. This study aims to fill this gap through a systematic literature review conducted in four stages, utilizing knowledge mapping analysis. More than 400 peer-reviewed articles published between 1990 and September 2023 were examined, making this study the first to analyze such a substantial sample of papers in this field. The research identifies three distinct phases of growth trends in PPP risk management studies. To minimize arbitrariness and subjectivity in analyzing research focuses, this study employed a scientometric approach using VOSviewer software (version 1.6.19), which provides insights into the current state and trends of research on PPP risk management, including high-frequency keywords, contributions of core authors, and distribution across countries, regions, institutions, and journals. In addition to the scientometric analysis, a qualitative review identified six primary research interests, various research perspectives, and diverse methodologies present within the literature. Based on these findings, this study culminates in the development of a conceptual model for risk management in PPPs, offering a structured framework for future research and practical application. This study contributes to the literature by providing the first large-scale, comprehensive mapping and analysis of PPP risk management research, presenting a novel conceptual model that connects theoretical insights with practical implications, and highlighting key areas for future exploration.
A Comparative Study of Microwave and Resistance Heating for the Efficient Thermal Desorption of Mineral Oil from Contaminated Soils
Authors: Xu Jun , Liu Songtao , Chen Chuanmin
Published: September 2024

The contamination of soils by mineral oils presents a significant environmental challenge, particularly due to the widespread use of petroleum products in various industrial sectors. This study investigates the efficiency of microwave heating compared to conventional resistance heating for the thermal desorption of mineral oil from contaminated soils. The experimental results demonstrated that microwave heating offers superior performance in terms of pollutant removal efficiency, energy consumption, and the preservation of soil’s physical and chemical properties. This study further conducted a kinetic analysis of the desorption process, revealing that microwave heating follows a first-order kinetic model and requires lower activation energy than conventional methods. The findings suggest that microwave-assisted thermal desorption is a highly effective and energy-efficient technology for soil remediation, providing a potential alternative to conventional techniques. This research offers valuable insights into optimizing thermal desorption processes for environmental remediation, with implications for broader applications in soil treatment.