bluuebunny/crossref_metadata_2025 huggingface dataset:
The Religious Leaders’ Perspectives on Corona Survey: Methods and Key Results
Authors: Öhlmann Philipp , Sonntag Ekkardt A.
Published: December 2024

Religious leaders are highly influential actors in many societies across the globe. In the singular global crisis brought upon by the COVID-19 pandemic, their perspective on the pandemic itself but also the post-pandemic future is of high relevance. Against this background, we conducted the Religious Leaders’ Perspectives on Corona Survey, a comprehensive survey of 1200 religious leaders globally, in 2020/2021. Its aim was to investigate the role of religious communities and religious leaders during the COVID-19 pandemic. This article provides an in-depth outline and discussion of the survey methodology and the resultant dataset, thereby paving the way for future research using the survey data. Moreover, the first set of key results is highlighted. It emerges that the COVID-19 pandemic was not primarily a health crisis. Rather, in terms of its consequences, COVID-19 had the characteristics of a primarily economic crisis in the Global South and a primarily psychosocial crisis in the Global North. Moreover, the pandemic has had a fundamental impact on religious practice across the globe. This impact, however, seems to be highly unequal between the Global South and North. Religious communities are shown to have had an important role as civil society actors in the pandemic, providing both psychosocial and material support. Regarding the post-pandemic world, religious leaders envision a more equitable society and emphasize the need for environmental sustainability.
A Rare Case of a Large Composite Endometrioma–Mature Cystic Teratoma: The Importance of Surgical Treatment and Pathologic Diagnosis
Authors: Song Jun , Martin Caitlin
Published: December 2024

Endometriosis is a common benign gynecologic disorder associated with infertility and pelvic pain, affecting 6–11% of reproductive-age females, and can frequently lead to the formation of ovarian endometriomas. Mature cystic teratomas are benign ovarian tumors comprising 10–25% of ovarian tumors. Both pathologies are common individually but rarely coexist. The case presented here describes a 49-year-old female presenting with a large composite endometrioma–mature cystic teratoma, a rare occurrence with few documented cases. The patient had a 24 cm × 17 cm × 15 cm adnexal mass identified via imaging, which was surgically removed. Pathology confirmed a composite tumor, with the teratoma encased within the endometrioma. This case underscores the importance of surgical management in complex adnexal masses to obtain tissue for definitive diagnosis and to exclude malignancy. Given the rarity of such coexistence and the challenges in preoperative diagnosis, surgical intervention is crucial for accurate diagnosis and effective management.
Classifying Serrated Tussock Cover from Aerial Imagery Using RGB Bands, RGB Indices, and Texture Features
Authors: Pham Daniel , Gautam Deepak , Sheffield Kathryn
Published: December 2024

Monitoring the location and severity of invasive plant infestations is critical to the management of their spread. Remote sensing can be an effective tool for mapping invasive plants due to its capture speed, continuous coverage, and low cost, compared to ground-based surveys. Serrated tussock (Nassella trichotoma) is a highly problematic invasive plant in Victoria, Australia, as it competes with the species in the communities that it invades. In this study, a workflow was developed and assessed for classifying the cover of serrated tussock in a mix of grazing pastures and grasslands. Using high-resolution RGB aerial imagery and vegetation field survey plots, random forest models were trained to classify the plots based on their fractional coverage of serrated tussock. Three random forest classifiers were trained by utilising spectral features (RGB bands and indices), texture features derived from the Grey-Level Co-occurrence Matrix, and a combination of all the features. The model trained on all the features achieved an overallaccuracy of 67% and a kappa score of 0.52 against a validation dataset. Plots with high and low infestation levels were classified more accurately than plots with moderate or no infestation. Notably, texture features proved more effective than spectral features for classification. The developed random forest model can be used for producing classified maps to depict the spatial distribution of serrated tussock infestation, thus supporting land managers in managing the infestation.
Identification of a Potential Rare Earth Element Deposit at Ivanpah Dry Lake, California Through the Bastnäsite Indices
Authors: Gadea Otto C. A. , Khan Shuhab D.
Published: December 2024

A groundbreaking remote sensing approach that uses three Bastnäsite Indices (BI) to detect rare earth elements (REEs) was initially developed using ore samples from the Sulfide Queen mine in California and later applied to various well-studied ground-based, drone-based, airborne, and spaceborne imaging spectrometers across a wide range of scales, from micrometers to tens of meters. In this work, those same innovative techniques have revealed the existence of a potential site for extracting REEs. Data from AVIRIS-NG, AVIRIS-Classic, HISUI, DESIS, EnMAP, EO-1 Hyperion, PRISMA, and EMIT were utilized to map Ivanpah Dry Lake, which is located fourteen kilometers northeast of the Sulfide Queen mine. Although this area was not previously associated with REE deposits, BI maps have indicated the presence of a site that has remained enriched in REEs for decades, suggesting an opportunity for further exploration and mining. Historically, a pipeline transported wastewater from facilities at the Sulfide Queen mine to evaporation ponds on or near Ivanpah Dry Lake, where wastewater may have contained concentrated REEs. This research highlights imaging spectroscopy not only as a valuable tool for rapidly identifying and efficiently extracting REEs, but also as a means of recovering REEs from supposed waste.
An Efficient Process for the Management of the Deterioration and Conservation of Architectural Heritage: The HBIM Project of the Duomo of Molfetta (Italy)
Authors: Nieto-Julián Enrique , Bruno Silvana , Moyano Juan
Published: December 2024

The work developed aims to present an innovative methodology to execute the heritage conservation processes in a collaborative and interdisciplinary Building Information Modeling (BIM) project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with an architectural survey using terrestrial laser scanning (TLS) and terrestrial photogrammetry software, Structure from Motion (SfM), studying study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the Cyclone 3DR post-processing software, which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it was possible to develop an efficient Scan-to-HBIM workflow, where the Heritage BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, and quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural heritage, focusing on the deformations and deterioration of the masonry in columns and pilasters. To make multidisciplinary conservation work more flexible, specific properties have been created for the identification and analysis of the degradation detected in the structures, with the HBIM project constituting a manager of the control and inspection activities. The restoration technician interacts with the determined 3D element to mark the “type decay”, managing the properties in the element’s own definition window. Interactive schemes have been defined that incorporate the items for the mapping of the elements, as well as particular properties of a conservation process (intervention, control, and maintenance). All listed parametric elements have links to be viewed in 2D and 3D views. Therefore, the procedure has facilitated the auscultation of the scanned element as it is semantically delimited, the parametric modeling of it, the analytical study of its materials and deterioration, and the association of intrinsic parameters so that they can be evaluated by all the intervening agents. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment.
Evaluations of Microwave Sounding Instruments Onboard FY-3F Satellites for Tropical Cyclone Monitoring
Authors: Wang Zhe , Weng Fuzhong , Han Yang , Hu Hao , Yang Jun
Published: December 2024

Fengyun-3F (FY-3F) satellite was launched in 2023 with a MicroWave Temperature Sounder (MWTS) and a MicroWave Humidity Sounder (MWHS) onboard. This study evaluates the in-orbit performances of these two instruments and compares them with similar instruments onboard FY-3E and NOAA-20 satellites. It is found that the polarization of FY-3F MWHS at channel 1 is different from FY-3E from the quasi-horizontal to quasi-vertical, whereas the rest of the channels are revised to quasi-horizontal polarization. FY-3F MWTS performance at the upper air channels is, in general, better than FY-3E MWTS, with 0.3 K smaller in biases (O-B) and 0.13 K lower in standard deviation. The striping noise between FY-3E and 3F MWHS is similar in magnitude for most of the channels. The FY-3F can form a satellite constellation with the FY-3E and NOAA-20, enabling better monitoring of many weather events, such as typhoons and hurricanes, through the use of all three satellites. Using the Global-Scene Dependent Atmospheric Retrieval Testbed (GSDART), Typhoon Yagi warm cores are retrieved from both MWTS/MWHS and ATMS. It is shown the warm core structures of Typhoon Yagi are consistent with the three satellites in terms of their magnitudes and locations.
Quantifying Methane Emissions Using Satellite Data: Application of the Integrated Methane Inversion (IMI) Model to Assess Danish Emissions
Authors: Vara-Vela Angel Liduvino , Rojas Benavente Noelia , Nielsen Ole-Kenneth , Nascimento Janaina Pinto , Alves Rafaela , Gavidia-Calderon Mario , Karoff Christoffer
Published: December 2024

After stabilizing in the mid-2000s, atmospheric methane (CH4) levels have accelerated over the past decade. In response, satellite-based inversion techniques have been employed to meet the increasing demands of the climate community. In this study, the Integrated Methane Inversion (IMI) model, a novel approach based on the TROPOspheric Monitoring Instrument (TROPOMI), is used to quantify CH4 emissions across Denmark. Over 900,000 TROPOMI observations from spring to early autumn of 2018–2022 were used to inform the inversions. Overall, TROPOMI CH4 concentrations within the inversion domain showed an upward trend of approximately 12.71 ppb per year, reflecting the global trend. Excluding 2022, which included only four months of data, the inversions suggest an underestimation of emissions by 190(160–215) × 103 tonnes, or 66(56–75)% of prior estimates. Northern and southern Jutland, along with the Copenhagen metropolitan area, were identified as key sources of CH4 emissions. Additionally, the inversions indicated a decline in emissions during the COVID-19 pandemic, despite stable activity data. This study demonstrates the feasibility of using the IMI model to monitor CH4 emissions in small countries like Denmark, offering a satellite-based perspective to better identify and mitigate these emissions.
Improving Atmospheric Correction Algorithms for Sea Surface Skin Temperature Retrievals from Moderate-Resolution Imaging Spectroradiometer Using Machine Learning Methods
Authors: Luo Bingkun , Minnett Peter J. , Jia Chong
Published: December 2024

Satellite-retrieved sea-surface skin temperature (SSTskin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SSTskin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SSTskin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SSTskin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research.
Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
Authors: Singh Jagdeep , Dhurandher Sanjay Kumar , Woungang Isaac , Chao Han-Chieh
Published: December 2024

In opportunistic IoT (OppIoT) networks, non-cooperative nodes present a significant challenge to the data forwarding process, leading to increased packet loss and communication delays. This paper proposes a novel Context-Aware Trust and Reputation Routing (CATR) protocol for opportunistic IoT networks, which leverages the probability density function of the beta distribution and some contextual factors, to dynamically compute the trust and reputation values of nodes, leading to efficient data dissemination, where malicious nodes are effectively identified and bypassed during that process. Simulation experiments using the ONE simulator show that CATR is superior to the Epidemic protocol, the so-called beta-based trust and reputation evaluation system (denoted BTRES), and the secure and privacy-preserving structure in opportunistic networks (denoted PPHB+), achieving an improvement of 22%, 15%, and 9% in terms of average latency, number of messages dropped, and average hop count, respectively, under varying number of nodes, buffer size, time to live, and message generation interval.
Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults
Authors: Robles Cruz Diego , Lira Belmar Andrea , Fleury Anthony , Lam Méline , Castro Andrade Rossana M. , Puebla Quiñones Sebastián , Taramasco Toro Carla
Published: December 2024

Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in older adults and explore the relationship between GPS- and accelerometer-derived metrics and fall risk. Methods: A total of 129 older adults, with and without a history of falls, were monitored over an 8 h period using GPS and accelerometer data. Three experimental conditions were evaluated: GPS data alone, accelerometer data alone, and a combination of both. Classification models, including Random Forest (RF), Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN), were employed to classify participants based on their fall history. Results: For GPS data alone, RF achieved 74% accuracy, while SVM and KNN reached 67% and 62%, respectively. Using accelerometer data, RF achieved 95% accuracy, and both SVM and KNN achieved 90%. Combining GPS and accelerometer data improved model performance, with RF reaching 97% accuracy, SVM achieving 95%, and KNN 87%. Conclusion: The integration of GPS and accelerometer data significantly enhances the accuracy of distinguishing older adults with and without a history of falls. These findings highlight the potential of sensor-based approaches for accurate fall risk assessment in community-dwelling older adults.
Motion Planning and Control with Environmental Uncertainties for Humanoid Robot
Authors: Jiang Zhiyong , Wang Yu , Wang Siyu , Bi Sheng , Chen Jiangcheng
Published: December 2024

Humanoid robots are typically designed for static environments, but real-world applications demand robust performance under dynamic, uncertain conditions. This paper introduces a perceptive motion planning and control algorithm that enables humanoid robots to navigate and operate effectively in environments with unpredictable kinematic and dynamic disturbances. The proposed algorithm ensures synchronized multi-limb motion while maintaining dynamic balance, utilizing real-time feedback from force, torque, and inertia sensors. Experimental results demonstrate the algorithm’s adaptability and robustness in handling complex tasks, including walking on uneven terrain and responding to external disturbances. These findings highlight the potential of perceptive motion planning in enhancing the versatility and resilience of humanoid robots in uncertain environments. The results have potential applications in search-and-rescue missions, healthcare robotics, and industrial automation, where robots operate in unpredictable or dynamic conditions.
A Study on the Timing Sensitivity of the Transient Dose Rate Effect on Complementary Metal-Oxide-Semiconductor Image Sensor Readout Circuits
Authors: Fu Yanjun , Peng Zhigang , Dong Zhiyong , Li Pei , Wei Yuan , Zhang Dongya , Zuo Yinghong , Zhu Jinhui , Niu Shengli
Published: December 2024

Complementary Metal-Oxide-Semiconductor (CMOS) image sensors (CISs), known for their high integration, low cost, and superior performance, have found widespread applications in satellite and space exploration. However, the readout circuits of pixel arrays are vulnerable to functional failures in complex or intense radiation environments, particularly due to transient γ radiation. Using Technology Computer-Aided Design (TCAD) device simulations and Simulation Program with Integrated Circuit Emphasis (SPICE) circuit simulations, combined with a double-exponential current source fault injection method, this study investigates the transient dose rate effect (TDRE) on a typical readout circuit of CISs. It presents the variations in the photoelectric signal under different dose rates and at different occurrence moments of the TDRE. The results show that, under low dose rates, the CIS readout circuit can still perform data acquisition and digital processing, with the photoelectric signal exhibiting some sensitivity to the occurrence moment. At high dose rates, however, the photoelectric signal not only remains sensitive to the occurrence moment but also shows significant discreteness. Further analysis of the CIS readout circuit sequence suggests that the occurrence moment is a critical factor affecting the circuit’s performance and should not be overlooked. These findings provide valuable insights and references for further research on the TDRE in circuits.
Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery
Authors: Luo Ruikang , Song Yaofeng , Ye Longfei , Su Rong
Published: December 2024

Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog.
The Minimum Number of Strides Required for Reliable Gait Measurements in Older Adult Fallers and Non-Fallers
Authors: Commandeur Drew , Klimstra Marc , Yoshida Kaya , Hundza Sandra
Published: December 2024

While the value of walking gait metrics collected using pressure-sensing walkways has shown promise for fall risk assessment, there is no consensus on the minimum number of strides required to obtain reliable metrics. This study aimed to determine the minimum stride count required for reliable single-task (ST), dual-task (DT), and difference score (DS) measurements of the spatio-temporal parameters of gait in older adult fallers and non-fallers. Forty community-dwelling older adults (74.6 ± 3.5 years) performed 10 ST and 10 DT walking passes (~100 strides total) across a GAITRite™ pressure mat. Nine truncated datasets (1–9 passes) were created from the original for each walking condition to assess agreement using two-way random effects, absolute agreement, and single-rater intraclass correlations (ICCs). ICCs demonstrated that a minimum of one pass (~10 strides) is sufficient for reliable mean gait metrics for ST and DT conditions and 10–30 strides for DS, while 10–80 strides are needed for reliable gait variability measures, depending on the metric. This study provides stride count recommendations to ensure reliable gait measurement in older adult populations, highlighting that as few as 10–30 strides are necessary for mean metrics, while variability metrics may require up to 80 strides to ensure reliability.
Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition
Authors: Ray Ausrukona , Uddin Md. Zasim , Hasan Kamrul , Melody Zinat Rahman , Sarker Prodip Kumar , Ahad Md Atiqur Rahman
Published: December 2024

Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize background-subtracted silhouette sequences to extract gait features. While effective and easy to compute, these methods are susceptible to variations in clothing, carried objects, and illumination changes, compromising the extraction of discriminative features in real-world applications. In contrast, model-based approaches using skeletal key points offer robustness against these covariates. Advances in human pose estimation (HPE) algorithms using convolutional neural networks (CNNs) have facilitated the extraction of skeletal key points, addressing some challenges of model-based approaches. However, the performance of skeleton-based methods still lags behind that of appearance-based approaches. This paper aims to bridge this performance gap by introducing a multi-biometric framework that extracts features from multiple HPE algorithms for gait recognition, employing feature-level fusion (FLF) and decision-level fusion (DLF) by leveraging a single-source multi-sample technique. We utilized state-of-the-art HPE algorithms, OpenPose, AlphaPose, and HRNet, to generate diverse skeleton data samples from a single source video. Subsequently, we employed a residual graph convolutional network (ResGCN) to extract features from the generated skeleton data. In the FLF approach, the features extracted from ResGCN and applied to the skeleton data samples generated by multiple HPE algorithms are aggregated point-wise for gait recognition, while in the DLF approach, the decisions of ResGCN applied to each skeleton data sample are integrated using majority voting for the final recognition. Our proposed method demonstrated state-of-the-art skeleton-based cross-view gait recognition performance on a popular dataset, CASIA-B.
A Three-Stage Cellular Automata Model of Complex Large Roundabout Traffic Flow, with a Flow-Efficiency- and Safety-Enhancing Strategy
Authors: Liang Xiao , Xie Chuan-Zhi Thomas , Song Hui-Fang , Guo Yong-Jie , Peng Jian-Xin
Published: December 2024

Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud–vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario. To address this, we proposed the Three-Stage Cellular Automata (TSCA) model based on empirical observations, dividing the vehicle journey over roundabouts into three stages: entrance, following, and exit. Furthermore, four optimization strategies were developed based on empirical observations and simulation results, using the traffic efficiency, delay time, and dangerous interaction frequency as key evaluation indicators. Numerical tests reveal that dangerous interactions and delays primarily occurred when the roundabout Road Occupancy Rate (ρ) ranged from 0.12 to 0.24, during which times the vehicle speed also decreased rapidly. Among the strategies, the Path Selection Based on Road Occupancy Rate Recognition Strategy (Simulation 4) demonstrated the best overall performance, increasing the traffic efficiency by 15.65% while reducing the delay time, dangerous interactions, and frequency by 6.50%, 28.32%, and 38.03%, respectively. Additionally, the Entrance Facility Optimization Strategy (Simulation 1) reduced the delay time by 6.90%. While space-based optimization strategies had a more moderate overall impact, they significantly improved the local traffic efficiency at the roundabout by approximately 25.04%. Our findings hold significant practical value, particularly with the support of onboard sensors, which can effectively detect non-compliance and provide real-time warnings to guide drivers in adhering to the prescribed traffic management strategies.
Analysis of Cell Dielectrophoretic Properties Using Isomotive Creek-Gap Electrode Device
Authors: Tada Shigeru , Sato Noriko
Published: December 2024

Various types of dielectrophoresis (DEP) cell separation devices using AC electric fields have been proposed and developed. However, its capability is still limited by a lack of quantitative characterization of the relationship between frequency and force. In the present study, this limitation was addressed by developing a method capable of fast and accurate quantification of the dielectric properties of biological cells. A newly designed Creek-gap electrode device can induce constant DEP forces on cells, realizing the isomotive movement of cells suitable for DEP analysis. The real number part of the Clausius–Mossotti (CM) factor of cells, Re(β), was obtained by simple cell velocimetry together with the numerical three-dimensional (3D) electric field analysis. Human mammary cells, MCF10A, and its cancer cells, MCF7 and MDAMB231, were used as model cells to evaluate the capability of the proposed device. The estimation of Re(β) using the Creek-gap electrode device showed good agreement with previously reported values. Furthermore, the thermal behavior of the Creek-gap electrode device, which is crucial to cell viability, was investigated by adopting micro laser-induced fluorescence (LIF) thermometry using Rhodamine B. The temperature rise in the device was found to be approximately several degrees Celsius at most. The results demonstrate that the proposed method could be a powerful tool for fast and accurate noninvasive measurement of the DEP spectrum and the determination of the dielectric properties of biological cells.
HyperKAN: Kolmogorov–Arnold Networks Make Hyperspectral Image Classifiers Smarter
Authors: Firsov Nikita , Myasnikov Evgeny , Lobanov Valeriy , Khabibullin Roman , Kazanskiy Nikolay , Khonina Svetlana , Butt Muhammad A. , Nikonorov Artem
Published: December 2024

In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov–Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neural networks with their KAN-based counterparts. Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). Experiments conducted using seven publicly available hyperspectral datasets demonstrated a substantial improvement in classification accuracy across all the networks. The best classification quality was achieved using a KAN-based transformer architecture.
YOLO-Dynamic: A Detection Algorithm for Spaceborne Dynamic Objects
Authors: Zhang Haiying , Li Zhengyang , Wang Chunyan
Published: December 2024

Ground-based detection of spaceborne dynamic objects, such as near-Earth asteroids and space debris, is essential for ensuring the safety of space operations. This paper presents YOLO-Dynamic, a novel detection algorithm aimed at addressing the limitations of existing models, particularly in complex environments and small-object detection. The proposed algorithm introduces two newly designed modules: the SC_Block_C2f and the LASF_Neck. SC_Block_C2f, developed in this study, integrates StarNet and Convolutional Gated Linear Unit (CGLU) operations, improving small-object recognition and feature extraction. Meanwhile, LASF_Neck employs a lightweight multi-scale architecture for optimized feature fusion and faster detection. The YOLO-Dynamic algorithm’s performance was validated on real-world images captured at Antarctic observatory sites. Compared to the baseline YOLOv8s model, YOLO-Dynamic achieved a 7% increase in mAP@0.5 and a 10.3% improvement in mAP@0.5:0.95. Additionally, the number of parameters was reduced by 1.48 M, and floating-point operations decreased by 3.8 G. These results confirm that YOLO-Dynamic not only delivers superior detection accuracy but also maintains computational efficiency, making it well suited for real-world applications requiring reliable and efficient spaceborne object detection.
Intelligent Transducer for Temperature Measurement with Two-Wire or Three-Wire Platinum RTD
Authors: Miczulski Wiesław , Krajewski Mariusz , Sienkowski Sergiusz , Kawecka Elżbieta , Perec Andrzej
Published: December 2024

The article presents an intelligent temperature transducer (ITT), which can work with a two-wire or a three-wire platinum resistance temperature detector (RTD). The ITT design allowed for compensation of the RTD’s lead wire resistance. The ITT used the author’s auto-calibration procedure, which minimized linearity errors of the ITT and RTD processing characteristics, ITT offset and gain errors, and errors resulting from changes in the ITT operating conditions concerning the nominal conditions. The presented results of a simulation and experimental studies confirmed the high effectiveness of this procedure. The determined uncertainty of temperature measurement using the Monte Carlo method and the obtained experimental results confirmed the possibility of measuring temperatures in the range of 0–200 °C with an expanded uncertainty of 0.02 °C at a 99% confidence level.
1H NMR Sensor for Nondestructive Characterization of Organic and Inorganic Materials
Authors: Díaz-Díaz Floriberto , Cano-Barrita Prisciliano F. de J. , León-Martínez Frank M. , Acevedo-Arzola Víktor
Published: December 2024

Nuclear magnetic resonance relaxation of the proton spins of liquid molecules and their evolution during processes such as drying, fluid flow, and phase change of a sample can be monitored in a nondestructive way. A unilateral 1H NMR sensor made with a permanent magnet array, inspired by the NMR MOUSE, with an RF coil tuned to 11.71 MHz was developed. This creates a sensitive homogeneous measuring volume parallel to the sensor surface and located 14 mm from its surface, allowing contactless measurements from the sample’s interior. As this sensitive volume is moved across the sample using a semi-automatic linear displacement mechanism with millimetric precision, spatial T2 lifetime and signal intensity 1D profiles can be obtained. To characterize the sensor’s sensitive volume, eraser samples were used. To evaluate the sensor’s ability to characterize different materials, cement paste samples containing ordinary and white Portland cement were prepared and measured at seven days of age. In addition, measurements were made on organic samples such as a Hass avocado and beef steak. Based on the results, a 1 mm spatial resolution of the sensor was achieved. The sensor was able to detect differences in T2 lifetimes in eraser specimens composed of layers of three different erasers. Also, a clear difference in T2 lifetimes and signal intensities was observed in cement pastes composed of white and ordinary Portland cement. On the other hand, it was possible to obtain signals from the peel and pulp of the avocado fruit, as well as from the fat and meat in a beef steak in a nondestructive way. The T2 lifetimes of the different materials agreed with those obtained using a commercial NMR spectrometer.
Microwave Sensors and Their Applications in Permittivity Measurement
Authors: Liu Changjun , Liao Chongwei , Peng Yujie , Zhang Weixin , Wu Bo , Yang Peixiang
Published: December 2024

This paper reviews microwave sensors and their applications in permittivity measurement. The detection, diagnosis, classification, and monitoring without contact and invasion have been the subject of numerous studies based on permittivity characteristics tracking. This review illustrates many new types of research in recent years. Firstly, the application background is briefly introduced, and several main measurement methods are presented. An overview of measurement technology in various applications is compiled and summarized based on numerous typical examples. Exciting applications are compared and presented separately, combining resonator sensors with strong electric fields. Furthermore, differential signals represent trends for future applications with strong environmental immunity, an alternative option to expensive measuring equipment. With the alternation of metamaterials, microfluidics technologies, cross-technology, algorithms, and so on, sensors play an exceptionally prominent role in practical and low-cost applications.
Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset
Authors: Matuzevičius Dalius
Published: December 2024

Facial analysis is an important area of research in computer vision and machine learning, with applications spanning security, healthcare, and user interaction systems. The data-centric AI approach emphasizes the importance of high-quality, diverse, and well-annotated datasets in driving advancements in this field. However, current facial datasets, such as Flickr-Faces-HQ (FFHQ), lack detailed annotations for detecting facial accessories, particularly eyeglasses. This work addresses this limitation by extending the FFHQ dataset with precise bounding box annotations for eyeglasses detection, enhancing its utility for data-centric AI applications. The extended dataset comprises 70,000 images, including over 16,000 images containing eyewear, and it exceeds the CelebAMask-HQ dataset in size and diversity. A semi-automated protocol was employed to efficiently generate accurate bounding box annotations, minimizing the demand for extensive manual labeling. This enriched dataset serves as a valuable resource for training and benchmarking eyewear detection models. Additionally, the baseline benchmark results for eyeglasses detection were presented using deep learning methods, including YOLOv8 and MobileNetV3. The evaluation, conducted through cross-dataset validation, demonstrated the robustness of models trained on the extended FFHQ dataset with their superior performances over existing alternative CelebAMask-HQ. The extended dataset, which has been made publicly available, is expected to support future research and development in eyewear detection, contributing to advancements in facial analysis and related fields.
Development of Image-Based Water Level Sensor with High-Resolution and Low-Cost Using Image Processing Algorithm: Application to Outgassing Measurements from Gas-Enriched Polymer
Authors: Lee Ji Hun , Jung Jae Kap
Published: December 2024

A high-resolution and low-cost image-based water level sensor was developed using an image processing algorithm. The sensor measures water levels in six channels simultaneously. The image processing algorithm automatically identifies water level images and determines the water levels by analyzing the brightness of the images. The measured water levels were verified by comparison with the calibrated water levels using known length standards. The performance test results of the developed water level sensor were compared with those of commercial water level sensors, demonstrating a superior resolution of 0.06 mm and an inexpensive cost of USD 80. In addition, the developed sensor demonstrated an accuracy of 0.9%, a stability of 0.3%, an adjustable measurement range, and an instantaneous response time. In conclusion, the image-based water level sensor that was developed provides a reliable method for real-time visual monitoring of water levels in six channels simultaneously.
Plug-and-Play PRNU Enhancement Algorithm with Guided Filtering
Authors: Liu Yufei , Xiao Yanhui , Tian Huawei
Published: December 2024

As a weak high-frequency signal embedded in digital images, Photo Response Non-Uniformity (PRNU) is particularly vulnerable to interference from low-frequency components during the extraction process, which affects its reliability in real-world forensic applications. Previous studies have not successfully identified the effective frequency band of PRNU, leaving low-frequency interference insufficiently suppressed and impacting PRNU’s utility in scenarios such as source camera identification, image integrity verification, and identity verification. Additionally, due to differing operational mechanisms, current mainstream PRNU enhancement algorithms cannot be integrated to improve their performance further. To address these issues, we conducted a frequency-by-frequency analysis of the estimated PRNU and discovered that it predominantly resides in the frequency band above 10 Hz. Based on this finding, we propose a guided-filtering PRNU enhancement algorithm. This algorithm can function as a plug-and-play module, seamlessly integrating with existing mainstream enhancement techniques to further boost PRNU performance. Specifically, we use the PRNU components below 10 Hz as a guide image and apply guided filtering to reconstruct the low-frequency interference components. By filtering out these low-frequency components, we retain and enhance the high-frequency PRNU signal. By setting appropriate enhancement coefficients, the low-frequency interference is suppressed, and the high-frequency components are further amplified. Extensive experiments on publicly available Dresden and Daxing digital device forensics datasets confirm the efficiency and robustness of the proposed method, making it highly suitable for reliable forensic analysis in practical settings.
Identification of Game Periods and Playing Position Activity Profiles in Elite-Level Beach Soccer Players Through Principal Component Analysis
Authors: Vaccaro Benet Pau , Ugalde-Ramírez Alexis , Gómez-Carmona Carlos D. , Pino-Ortega José , Becerra-Patiño Boryi A.
Published: December 2024

Beach soccer has gained increasing interest for study in the sports sciences. In this sense, the analysis of activity profiles is important for training design and load individualization. Therefore, the aims of this study were to identify the most important variables to assess the activity profile and to compare them according to the playing position and game periods in international beach soccer matches. A total of 19 matches of the Spanish national beach soccer team were analyzed during their participation in different international competitions during the 2021–2022 season. A Principal Component Analysis (PCA) was applied to objectively select the external load variables that best explain the data. Kaiser–Meyer–Olkin values of 0.705 and Bartlett’s Sphericity (p < 0.01) were obtained. Kruskal–Wallis and Friedman tests was performed for playing positions and game period comparisons, respectively. The PCA showed seven components that grouped a total of 20 variables, explaining 66% of the total variance. Only PC1 and PC2 explained more than 15% (23% and 17%, respectively). Differences were found between playing positions (H > 22.73; p < 0.01) and between game periods (X2 > 16.46; p < 0.01). A significant decrease was found throughout the game, with the highest demands in period 1 and the lowest in period 3. The highest workloads were performed by wingers and the lowest by goalkeepers. The differences between positions and game periods were found in the following: Total Distance (m/min), HIBD (m/min), High Acc (m/s), High Dec (m/s), Acc 1–2 m/s2 (m), Acc 2–3 m/s2 (m), Imp 4–5G (n), Imp 5–6G (n), Sprint (n), and Dec 10–6 m/s2 (m) (p < 0.01). In conclusion, physical and tactical demands faced by elite-level beach soccer players will be influenced by playing positions and game periods. Coaches needs to develop position-specific training programs and optimize substitution strategies for enhancing overall team performance.
Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images
Authors: Cambay Veysel Yusuf , Barua Prabal Datta , Hafeez Baig Abdul , Dogan Sengul , Baygin Mehmet , Tuncer Turker , Acharya U. R.
Published: December 2024

This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. Using ResNet50*, a gastrointestinal image dataset was trained, and an explainable deep feature engineering (DFE) model was developed. This DFE model comprises four primary stages: (i) feature extraction, (ii) iterative feature selection, (iii) classification using shallow classifiers, and (iv) information fusion. The DFE model is self-organizing, producing 14 different outcomes (8 classifier-specific and 6 voted) and selecting the most effective result as the final decision. During feature extraction, heatmaps are identified using gradient-weighted class activation mapping (Grad-CAM) with features derived from these regions via the final global average pooling layer of the pretrained ResNet50*. Four iterative feature selectors are employed in the feature selection stage to obtain distinct feature vectors. The classifiers k-nearest neighbors (kNN) and support vector machine (SVM) are used to produce specific outcomes. Iterative majority voting is employed in the final stage to obtain voted outcomes using the top result determined by the greedy algorithm based on classification accuracy. The presented ResNet50* was trained on an augmented version of the Kvasir dataset, and its performance was tested using Kvasir, Kvasir version 2, and wireless capsule endoscopy (WCE) curated colon disease image datasets. Our proposed ResNet50* model demonstrated a classification accuracy of more than 92% for all three datasets and a remarkable 99.13% accuracy for the WCE dataset. These findings affirm the superior classification ability of the ResNet50* model and confirm the generalizability of the developed architecture, showing consistent performance across all three distinct datasets.
Electromyographic Comparison of Traditional Fitness Machines, Outdoor Fitness Equipment Without Load Selectors, and Outdoor Fitness Equipment with Load Selectors in a Seated Chest Press Exercise in Trained Young Men
Authors: Abelleira-Lamela Tomás , Marcos-Pardo Pablo Jorge , Abraldes J. Arturo , González-Gálvez Noelia , Espeso-García Alejandro , Esparza-Ros Francisco , Vaquero-Cristóbal Raquel
Published: December 2024

Outdoor fitness equipment (OFE) are strength training installations comparable to those found in indoor gyms but are located outdoors with greater accessibility. However, the scientific evidence supporting their effectiveness remains limited. The objective of this study was to analyze and compare the electromyographic (EMG) activity of upper limb muscle groups during the use of a traditional seated chest press (SCP) machine, a classic OFE SCP (OFE-SCP), and a new OFE-SCP featuring a load selector system (BIOFIT-SCP). The sample was composed of 34 active young men. EMG activity of five muscle groups was analyzed: the anterior deltoid (AD), the clavicular (CP) and sternal (SP) heads of the pectoralis major, and the lateral (LHTB) and long heads of the triceps brachii (LongHTB), under different intensities. The OFE-SCP showed significantly lower EMG activity compared with the SCP and BIOFIT-SCP in all muscles and phases (p < 0.001). Significantly lower EMG activity for the SP in all three phases was found on the BIOFIT-SCP compared with the SCP (p < 0.001), but it was significantly higher for the LHTB and AD in the BIOFIT-SCP compared with the SCP during the full and concentric phases (p < 0.05 to p < 0.001). In conclusion, training with the OFE-SCP generates less EMG activity than traditional machine training, while, in general, BIOFIT-SCP proved effective for strength training comparable to the SCP.