bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR
Authors: Li Shujie , Wu Xiaochuan , Chen Siming , Deng Weibo , Zhang Xin
Published: August 2024

The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method.
Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
Authors: Giannattasio Fabio , Pignalberi Alessio , Tozzi Roberta , De Michelis Paola , Mestici Simone , Consolini Giuseppe , Coco Igino , Pezzopane Michael
Published: August 2024

The study of the physical properties of the topside ionosphere is fundamental to investigating the energy balance of the ionosphere and developing accurate models to predict relevant phenomena, which are often at the root of Space Weather effects in the near-Earth environment. One of the most important physical parameters characterising the ionospheric medium is electrical conductivity, which is crucial for the onset and amplification of ionospheric currents and for calculating the power density dissipated by such currents. We characterise, for the first time, electrical conductivity in the direction perpendicular to the geomagnetic field, namely Pedersen and Hall conductivities, in the topside ionosphere at an altitude of about 450 km. For this purpose, we use eight years of in situ simultaneous measurements of electron density, electron temperature and geomagnetic field strength acquired by the Swarm A satellite. We present global statistical maps of perpendicular electrical conductivity and study their variations depending on magnetic latitude and local time, seasons, and solar activity. Our findings indicate that the most prominent features of perpendicular electrical conductivity are located at low latitudes and are probably driven by the complex dynamics of the Equatorial Ionisation Anomaly. At higher latitudes, perpendicular conductivity is a few orders of magnitude lower than that at low latitudes. Nevertheless, conductivity features are modulated by solar activity and seasonal variations at all latitudes.
MLF-PointNet++: A Multifeature-Assisted and Multilayer Fused Neural Network for LiDAR-UAS Point Cloud Classification in Estuarine Areas
Authors: Ren Yingjie , Xu Wenxue , Guo Yadong , Liu Yanxiong , Tian Ziwen , Lv Jing , Guo Zhen , Guo Kai
Published: August 2024

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F1-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas.
A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information
Authors: Zhang Wenyi , Zhang Haoran , Zhang Xisheng , Shen Xiaohua , Zou Lejun
Published: August 2024

Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority.
BCMFIFuse: A Bilateral Cross-Modal Feature Interaction-Based Network for Infrared and Visible Image Fusion
Authors: Gao Xueyan , Liu Shiguang
Published: August 2024

The main purpose of infrared and visible image fusion is to produce a fusion image that incorporates less redundant information while incorporating more complementary information, thereby facilitating subsequent high-level visual tasks. However, obtaining complementary information from different modalities of images is a challenge. Existing fusion methods often consider only relevance and neglect the complementarity of different modalities’ features, leading to the loss of some cross-modal complementary information. To enhance complementary information, it is believed that more comprehensive cross-modal interactions should be provided. Therefore, a fusion network for infrared and visible fusion is proposed, which is based on bilateral cross-feature interaction, termed BCMFIFuse. To obtain features in images of different modalities, we devise a two-stream network. During the feature extraction, a cross-modal feature correction block (CMFC) is introduced, which calibrates the current modality features by leveraging feature correlations from different modalities in both spatial and channel dimensions. Then, a feature fusion block (FFB) is employed to effectively integrate cross-modal information. The FFB aims to explore and integrate the most discriminative features from the infrared and visible image, enabling long-range contextual interactions to enhance global cross-modal features. In addition, to extract more comprehensive multi-scale features, we develop a hybrid pyramid dilated convolution block (HPDCB). Comprehensive experiments on different datasets reveal that our method performs excellently in qualitative, quantitative, and object detection evaluations.
Seasonal Dynamics of the Land-Surface Characteristics in Arid Regions Retrieved by Optical and Microwave Satellite Data
Authors: Tian Ying , Ackermann Kurt , McCarthy Christopher , Sternberg Troy , Purevtseren Myagmartseren , Limuge Che , Hagiwara Katsuro , Ogawa Kenta , Hobara Satoru , Hoshino Buho
Published: August 2024

Establishing a quantitative relationship between Synthetic Aperture Radar (SAR) data and optical data can facilitate the fusion of these two data sources, enhancing the time-series monitoring capabilities for remote sensing of a land surface. In this study, we analyzed the Normalized Difference Vegetation Index (NDVI) and Shortwave Infrared Transformed Reflectance (STR) with the backscatter coefficients in vertical polarization VV (σ0VV) and cross polarization VH (σ0VH) across different seasons. We used optical and microwave satellite data spanning from the southern Gobi Desert region to the steppe region in northern Mongolia. The results indicate a relatively high correlation between the NDVI derived from Sentinel-2 and σ0VH (RVH = 0.29, RVH = 0.44, p < 0.001) and a low correlation between the NDVI and σ0VV (RVH = 0.06, RVH = 0.14, p < 0.01) in the Gobi Desert region during summer and fall. STR showed a positive correlation with both σ0VH and σ0VV except in spring, with the highest correlation coefficients observed in summer (RVV = 0.45, RVV = 0.44, p < 0.001). In the steppe region, significant seasonal variations in the NDVI and σ0VH were noted, with a strong positive correlation peaking in summer (RVH = 0.71, p < 0.001) and an inverse correlation with σ0VV except in summer (RVV = −0.43, RVV = −0.34, RVV = −0.13, p < 0.001). Additionally, STR showed a positive correlation with σ0VH and σ0VV in summer (RVH = 0.40, RVV = 0.39, p < 0.001) and fall (RVH = 0.38, RVV = 0.09, p < 0.01), as well as an inverse correlation in spring (RVH= −0.17, RVV= −0.38, p < 0.001) and winter (RVH = −0.21, RVV = −0.06, p < 0.001). The correlations between the NDVI, STR, σ0VH, and σ0VV were shown to vary by season and region. In the Gobi Desert region, perennial shrubs are not photosynthetic in spring and winter, and they affect backscatter due to surface roughness. In the steppe region, annual shrubs were found to be the dominant species and were found to photosynthesize in spring, but not enough to affect the backscatter due to surface roughness.
Improvement of the Estimation of the Vertical Crustal Motion Rate at GNSS Campaign Stations Based on the Information of GNSS Reference Stations
Authors: Jiang Jiazheng , Ding Kaihua , Lan Guanghong
Published: August 2024

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas.
Trade-Off and Synergy Relationships and Driving Factor Analysis of Ecosystem Services in the Hexi Region
Authors: Xiao Sijia , Xia Haonan , Zhai Jun , Jin Diandian , Gao Haifeng
Published: August 2024

The Hexi region, located in a sensitive and fragile ecological zone in northwest China, requires a scientific assessment of ecosystem services and their interactions. Identifying the main factors influencing spatial distribution is crucial for the sustainable development and effective management of the region. This study evaluates key ecosystem services, including regulating services (water conservation, soil conservation, carbon storage) and provisioning services (NPP), using Spearman’s correlation and pixel-by-pixel spatial analysis to calculate spatial trade-offs and synergies. Geographic detectors were used to uncover the underlying driving mechanisms. The results show that: (1) From 2000 to 2020, soil conservation, NPP, and carbon storage showed fluctuating growth, while water conservation declined. Spatially, high-value areas of water conservation, carbon storage, and NPP were concentrated in the central and southern areas, while high values of soil conservation services were mainly in the northwest and southeast regions. (2) The trade-offs and synergies among various ecosystem services exhibit temporal shifts, along with spatial scale effects and heterogeneity. In the study area, the proportion of pixels showing a trade-off relationship between water conservation and soil conservation, and between water conservation and NPP, accounts for 48.21% and 21.42%, respectively. These trade-offs are mainly concentrated in the central and southeastern regions, while the northwestern counties predominantly exhibit synergies. (3) Precipitation was the dominant factor for water conservation, carbon storage, and NPP, as well as for the trade-offs among these services. Among natural factors, climatic factors were significantly more influential than socio-economic factors, and the interaction between two factors had a greater explanatory power than single factors.
A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing
Authors: Zhang Mingle , Yang Mingyu , Xie Hongyu , Yue Pinliang , Zhang Wei , Jiao Qingbin , Xu Liang , Tan Xin
Published: August 2024

Hyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or employ convolutional neural networks (CNNs), which overlook the non-local correlations of materials and spectral characteristics. Furthermore, current research mainly focuses on linear mixing models, which limits the feature extraction capability of deep encoders and further improvement in unmixing accuracy. In this paper, we propose a nonlinear unmixing network capable of extracting global spatial-spectral features. The network is designed based on an autoencoder architecture, where a dual-stream CNNs is employed in the encoder to separately extract spectral and local spatial information. The extracted features are then fused together to form a more complete representation of the input data. Subsequently, a linear projection-based multi-head self-attention mechanism is applied to capture global contextual information, allowing for comprehensive spatial information extraction while maintaining lightweight computation. To achieve better reconstruction performance, a model-free nonlinear mixing approach is adopted to enhance the model’s universality, with the mixing model learned entirely from the data. Additionally, an initialization method based on endmember bundles is utilized to reduce interference from outliers and noise. Comparative results on real datasets against several state-of-the-art unmixing methods demonstrate the superior of the proposed approach.
Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework
Authors: Ke Junmin , Liu Furong , Xu Guofeng , Liu Ming
Published: August 2024

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process.
Gait and Balance Assessments with Augmented Reality Glasses in People with Parkinson’s Disease: Concurrent Validity and Test–Retest Reliability
Authors: van Bergem Jara S. , van Doorn Pieter F. , Hoogendoorn Eva M. , Geerse Daphne J. , Roerdink Melvyn
Published: August 2024

State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study’s objective was to evaluate concurrent validity and test–retest reliability for common clinical gait and balance tests in people with Parkinson’s disease: Five Times Sit To Stand (FTSTS) and Timed Up and Go (TUG) tests. Position and orientation data were collected in 22 participants with Parkinson’s disease using HoloLens 2 and Magic Leap 2 AR glasses, from which test completion durations and durations of distinct sub-parts (e.g., sit to stand, turning) were derived and compared to reference systems and over test repetitions. Regarding concurrent validity, for both tests, an excellent between-systems agreement was found for position and orientation time series (ICC(C,1) > 0.933) and test completion durations (ICC(A,1) > 0.984). Between-systems agreement for FTSTS (sub-)durations were all excellent (ICC(A,1) > 0.921). TUG turning sub-durations were excellent (turn 1, ICC(A,1) = 0.913) and moderate (turn 2, ICC(A,1) = 0.589). Regarding test–retest reliability, the within-system test–retest variation in test completion times and sub-durations was always much greater than the between-systems variation, implying that (sub-)durations may be derived interchangeably from AR and reference system data. In conclusion, AR data are of sufficient quality to evaluate gait and balance aspects in people with Parkinson’s disease, with valid quantification of test completion durations and sub-durations of distinct FTSTS and TUG sub-parts.
Electrochemical Aptasensor with Antifouling Properties for Label-Free Detection of Oxytetracycline
Authors: Kourti Dimitra , Geka Georgia , Nemtsov Lidia , Ahmadi Soha , Economou Anastasios , Thompson Michael
Published: August 2024

Oxytetracycline (OTC) is a widely employed antibiotic in veterinary treatment and in the prevention of infections, potentially leaving residues in animal-derived food products, such as milk, that are consumed by humans. Given the detrimental effects of prolonged human exposure to antibiotics, it has become imperative to develop precise and sensitive methods for monitoring the presence of OTC in food. Herein, we describe the development and results of a preliminary label-free electrochemical aptasensor with antifouling properties designed to detect OTC in milk samples. The sensor was realized by modifying a gold screen-printed electrode with α-lipoic acid–NHS and an amine-terminated aptamer. Different electrochemical techniques were used to study the steps of the fabrication process and to quantify OTC in the presence of the Fe(CN)64−/Fe(CN)63− redox couple The detectable range of concentrations satisfy the maximum residue limits set by the European Union, with an limit of detection (LOD) of 14 ng/mL in phosphate buffer (BP) and 10 ng/mL in the milk matrix, and a dynamic range of up to 500 ng/mL This study is a steppingstone towards the implementation of a sensitive monitoring method for OTC in dairy products.
Spatial–Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model
Authors: Ma Ju , Zhao Juan , Hou Yao
Published: August 2024

Most current methods use spatial–temporal graph neural networks (STGNNs) to analyze complex spatial–temporal information from traffic data collected from hundreds of sensors. STGNNs combine graph neural networks (GNNs) and sequence models to create hybrid structures that allow for the two networks to collaborate. However, this collaboration has made the model increasingly complex. This study proposes a framework that relies solely on original Transformer architecture and carefully designs embeddings to efficiently extract spatial–temporal dependencies in traffic flow. Additionally, we used pre-trained language models to enhance forecasting performance. We compared our new framework with current state-of-the-art STGNNs and Transformer-based models using four real-world traffic datasets: PEMS04, PEMS08, METR-LA, and PEMS-BAY. The experimental results demonstrate that our framework outperforms the other models in most metrics.
Increasing the Beam Width and Intensity with Refraction Power Effect Using a Combination of Beam Mirrors and Concave Mirrors for Surgical-Fluorescence-Emission-Guided Cancer Monitoring Method
Authors: Park Jina , Seo Jeongmin , Yoon Kicheol , Lee Sangyun , Kim Minchan , Ryu Seung Yeob , Kim Kwang Gi
Published: August 2024

The primary goal during cancer removal surgery is to completely excise the malignant tumor. Because the color of the tumor and surrounding tissues is very similar, it is difficult to observe with the naked eye, posing a risk of damaging surrounding blood vessels during the tumor removal process. Therefore, fluorescence emission is induced using a fluorescent contrast agent, and color classification is monitored through camera imaging. LEDs must be irradiated to generate the fluorescent emission electromotive force. However, the power and beam width of the LED are insufficient to generate this force effectively, so the beam width and intensity must be increased to irradiate the entire lesion. Additionally, there should be no shaded areas in the beam irradiation range. This paper proposes a method to enhance the beam width and intensity while eliminating shadow areas. A total reflection beam mirror was used to increase beam width and intensity. However, when the beam width increased, a shadow area appeared at the edge, limiting irradiation of the entire lesion. To compensate for this shadow area, a concave lens was combined with the beam mirror, resulting in an increase in beam width and intensity by more than 1.42 times and 18.6 times, respectively. Consequently, the beam width reached 111.8°, and the beam power was 13.6 mW. The proposed method is expected to be useful for observing tumors through the induction of fluorescence emission during cancer removal surgery or for pathological examination in the pathology department.
Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds
Authors: Ashraf Walid , Moussavi Zahra
Published: August 2024

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800–2199) Hz and (2200–2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice.
Green Intrusion Detection Systems: A Comprehensive Review and Directions
Authors: Roy Swapnoneel , Sankaran Sriram , Zeng Mini
Published: August 2024

Intrusion detection systems have proliferated with varying capabilities for data generation and learning towards detecting abnormal behavior. The goal of green intrusion detection systems is to design intrusion detection systems for energy efficiency, taking into account the resource constraints of embedded devices and analyzing energy–performance–security trade-offs. Towards this goal, we provide a comprehensive survey of existing green intrusion detection systems and analyze their effectiveness in terms of performance, overhead, and energy consumption for a wide variety of low-power embedded systems such as the Internet of Things (IoT) and cyber physical systems. Finally, we provide future directions that can be leveraged by existing systems towards building a secure and greener environment.
Double Type Detection of Triiodide and Iodide Ions Using a Manganese(III) Porphyrin as Sensitive Compound
Authors: Anghel Diana , Epuran Camelia , Fringu Ionela , Fratilescu Ion , Lascu Anca , Macsim Ana-Maria , Chiriac Vlad , Gherban Mihaela , Vlascici Dana , Fagadar-Cosma Eugenia
Published: August 2024

A paramagnetic A3B-type Mn(III)-porphyrin was synthesized and characterized by physical–chemical methods (UV-Vis, FT-IR, 1H-NMR spectroscopy). The obtained compound was tested as a sensitive material for the spectrophotometric and potentiometric detection of iodine species. Using UV-Vis spectroscopy, the triiodide anions could be detected with high precision in the concentration interval of 1.02 × 10−5 to 2.3 × 10−5 M, with an LOD of 9.44 × 10−6 M. The PVC-based electrode using DOP as a plasticizer showed a sensitivity toward iodide in a wide concentration range of 1.0 × 10−5 to 1.0 × 10−1 M, with an LOD of 8.0 × 10−6 M. Both methods are simple, low-cost, and efficient for the detection of iodine species in synthetic samples and pharmaceuticals.
Intelligent Gesture Recognition Based on Screen Reflectance Multi-Band Spectral Features
Authors: Lin Peiying , Li Chenrui , Chen Sijie , Huangfu Jiangtao , Yuan Wei
Published: August 2024

Human–computer interaction (HCI) with screens through gestures is a pivotal method amidst the digitalization trend. In this work, a gesture recognition method is proposed that combines multi-band spectral features with spatial characteristics of screen-reflected light. Based on the method, a red-green-blue (RGB) three-channel spectral gesture recognition system has been developed, composed of a display screen integrated with narrowband spectral receivers as the hardware setup. During system operation, emitted light from the screen is reflected by gestures and received by the narrowband spectral receivers. These receivers at various locations are tasked with capturing multiple narrowband spectra and converting them into light-intensity series. The availability of multi-narrowband spectral data integrates multidimensional features from frequency and spatial domains, enhancing classification capabilities. Based on the RGB three-channel spectral features, this work formulates an RGB multi-channel convolutional neural network long short-term memory (CNN-LSTM) gesture recognition model. It achieves accuracies of 99.93% in darkness and 99.89% in illuminated conditions. This indicates the system’s capability for stable operation across different lighting conditions and accurate interaction. The intelligent gesture recognition method can be widely applied for interactive purposes on various screens such as computers and mobile phones, facilitating more convenient and precise HCI.
Fit Entropy-Based Dynamic Communication Resource Slicing-Optimization Method in Smart Distribution Grids on the Medium–Low-Voltage Side
Authors: Li Haiming , Zhu Xiaorong
Published: August 2024

With the rapid development of new energy and smart technology, the demand for inter-device communication in medium–low-voltage smart distribution grids has sharply increased, leading to a surge in the variety and quantity of communication services. To meet the needs of diverse and massive communication services, deploying service function chains to flexibly combine virtual resources has become crucial. This paper proposes an optimization method based on fit entropy and network utility to address the limited communication network resources in medium–low-voltage smart distribution grids. This was conducted by modeling the distribution grid as a three-domain model consisting of a service domain, a logical domain, and a physical domain and transforming it into a hierarchical bipartite hypergraph-matching problem, which is a complex combinatorial optimization problem. This paper introduces two matching optimization algorithms: “business domain–logic domain–physical domain integration” and “service domain–logic domain, logic domain–physical domain two-stage”, which effectively address this problem based on fit entropy and utility. The simulation results demonstrate that these algorithms significantly improve service success rates and resource utilization, enhancing overall network utility.
Survey on 5G Physical Layer Security Threats and Countermeasures
Authors: Harvanek Michal , Bolcek Jan , Kufa Jan , Polak Ladislav , Simka Marek , Marsalek Roman
Published: August 2024

With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations—the machine-learning-based, localization-based, and behavior-based methods.
Honneth’s Theory of Recognition and Material Poverty
Authors: Schweiger Gottfried
Published: August 2024

This paper explores the intersection of poverty and recognition theory, grounded in Axel Honneth’s framework, to offer a novel perspective on poverty as a multifaceted social phenomenon. It argues that poverty should be understood not only as a lack of material resources but also as a significant deficit in social recognition, encompassing respect and social esteem. By situating poverty within the institutionalized order of recognition, the paper highlights how poverty both stems from and contributes to a lack of recognition, leading to social exclusion, shame, and stigmatization. The theoretical approach is complemented by selected empirical studies that illustrate the lived experiences of poverty, emphasizing the emotional and psychological impacts that extend beyond material deprivation. While the paper advances the theoretical understanding of poverty, it also identifies gaps in the current research, particularly the need for more empirical studies to substantiate these claims. Future research could expand upon these insights through cross-cultural studies and empirical investigations that further explore the connection between recognition and poverty. This work lays the groundwork for a deeper exploration of poverty as a social phenomenon that transcends economic metrics, advocating for a more holistic approach to poverty research.
To Reform the Child Protection System in Portugal—Stakeholders’ Positions
Authors: Diogo Elisete , Silva Joana Véstia , Sacur Bárbara Mourão
Published: August 2024

Notwithstanding the legal milestones observed in the Portuguese child protection system (CPS), several concerns call for political action to ensure the effective guarantee of children’s rights. In this context, it is imperative to discuss proposals for reforming the CPS. Agreement on these proposals by the stakeholders in the system is key to validating and improving the system. Therefore, the main research questions of the present study are (1) what is the level of agreement among stakeholders in the CPS on certain proposals for child protection reform? and (2) what are the positions on those proposals? Mixed methods were conducted based on an online survey (n = 292), supported by Qualtrics, and eight focus groups involving stakeholders as former beneficiaries, families, practitioners and academics (n = 18). Findings show a harmonious alignment with the proposals, justifying an urgent reform of the CPS and the need to strengthen a child-centred system. Three themes emerged: promotion of quality family-based care and promotion of adoption; development of child-friendly terminology; and improvement of the CPS administration. The conclusions highlight implications for policy and practice.
Peer Attachment and Self-Control: Implication on Social Anxiety in Young Adults
Authors: Mota Catarina Pinheiro , Mateus Diogo , Relva Inês Carvalho , Costa Mónica
Published: August 2024

Peers emerge as emotionally present figures in the lives of young adults, capable of protecting them from the challenges that can cause the development of social anxiety. In the context of anxiety, self-control highlights a relevant potential, which is positively correlated with mental health and academic path. The present study aims to understand the role of peer attachment and self-control in the development of social anxiety in young adults in the university context. The sample comprised 407 young adults aged between 18 and 25 (M = 20.90; SD = 2.32). Self-report instruments were used: the Inventory of Parent and Peer Attachment (IPPA), the Scale of Anxiety and Avoidance in Performance and Social Interaction Situations (EAESDIS) and the Low Self-Control Scale. This study concludes that alienation from peers plays a positive role in anxiety and avoidance, as does short temper, with risk-taking and egocentricity negatively predicting social anxiety. The results are discussed, considering the importance of peer relationships in young university students’ adaptive processes and mental health.
Using the Isometric Mid-Thigh Pull to Predict Three-Repetition Maximum Squat Values in Female Athletes
Authors: Pasfield Keely , Ball Nick , Chapman Dale Wilson
Published: August 2024

Prescribing correct training loads in strength- and power-based sports is essential to eliciting performance improvements for athletes. Concurrently, testing strength for the prescription of training loads should be accurate and safe with minimal disruption or fatigue inducement to the athlete. The purpose of this study was to develop a prediction equation in female athletes for the three-repetition maximum (3RM) squat using the isometric mid-thigh pull and basic anthropometric assessments that could be practically applied to support training prescriptions. Female athletes (n = 34) were recruited from netball, volleyball, basketball, and soccer across a spectrum of competitive standards. Each athlete’s weight, standing height, seated height, arm span, and biacromial breadth were recorded, and then, on separate occasions separated by at least 48 h, each athlete completed a 3RM squat test and an isometric mid-thigh pull (IMTP) assessment. IMTP variables of peak force and time-dependent force at 50, 100, 150, 200, and 250 ms, as well as anthropometric measures, were used to develop a prediction equation. Squat strength was low-to-moderately correlated with peak force (r = 0.386); force at 100 ms (r = −0.128), 150 ms (r = −0.040), and 200 ms (r = −0.034); standing height (r = 0.294); and biacromial breadth (r = −0.410). Stepwise multiple regression significantly (p < 0.05) explained 26% of the 3RM squat strength variation using peak force and force at 100 ms, resulting in the following equation: Predicted 3RM squat (kg) = [6.102 + (Peak Force × 0.002) − (Force@100 ms × 0.001)]2. The reported equation’s predictive accuracy was tested using the same testing protocols following 6–8 weeks of training in a sub-cohort of athletes (n = 14). The predicted and actual recorded 3RM values were not significantly (p = 0.313) different, supporting the use of the IMTP as a test that contributes informative values for use in a predictive equation for training prescription and thus reducing the testing and fatigue-inducing impost on female athletes. However, the 95% CI (−4.18–12.09) indicated predicted values could differ in excess of 10 kg. This difference could lead to an excessive load prescription for an athlete’s training program, indicating caution should be taken if using the described method to predict 3RM squat values for programming purposes.
Spatial Distribution Characteristics of Leisure Urban Spaces and the Correlation with Population Activity Intensity: A Case Study of Nanjing, China
Authors: Li Xinyang , Kozlowski Marek , Ismail Sumarni Binti , Salih Sarah Abdulkareem
Published: August 2024

The spatial distribution of Leisure Urban Spaces (LUSs) is closely linked to urban sustainability and residents’ quality of life. This study uses the Central Urban Area of Nanjing as the study area. Using POI and AOI data, the locations of LUS were precisely identified and categorized, including parks, squares, waterfront spaces, and leisure blocks. GIS spatial analysis methods, the nearest neighbor index, standard deviation ellipse, and kernel density estimation were used to analyze these spaces’ form, directivity, and density. Population activity intensity (PAI) data at various time points, collected by Baidu heat map, are correlated with LUS distribution through multiple linear regression analysis. (1) Parks and squares exhibit significant clustering tendencies, whereas waterfront spaces show weaker clustering, and leisure blocks are randomly distributed; (2) The central points of all types of LUS are located in the city center, extending from southeast to northwest, with parks and squares offering a broader range of services; (3) The overall LUS layout shows a ‘core and multiple points’ structure, with varying density patterns across different spaces, indicating concentrated and dispersed leisure areas; (4) The correlation between LUS distribution and PAI strengthens throughout the day and is greater on weekends than weekdays. Leisure blocks significantly enhance activity intensity, while parks have a limited effect, and waterfront spaces often show a negative correlation due to their remote locations. These results provide insights for future urban planning in Nanjing and underscore patterns in residents’ leisure activities.
Twenty-First Century Competencies; about Competencies for Industry 5.0 and the Opportunities for Emerging Economies
Authors: Ikenga Godwin Uzoamaka , van der Sijde Peter
Published: August 2024

The philosophy of Industry 5.0 recognises the transformative power of the industrial sector to add value to society and employees. Industry 5.0 is distinct from its predecessors because it relies on a tripod of sustainability, human centricity, and resilience. These three thematic principles aim to improve work safety, research, and innovation and strengthen industrial resilience and competitiveness. However, a perfect storm is brewing for the future of work, brought about by the concerning trends of displacement and low skill levels of workers’ competencies in handling the new technologies of Industry 5.0 in emerging economies. This conceptual paper will describe Industry 5.0 and provide historical insights on the importance of acquiring the desired twenty-first-century competencies needed in the workforce for Industry 5.0 and how these competencies can be cultivated. Two research questions guided the study in identifying these twenty-first-century competencies, how these competencies can be cultivated and taught in the education curriculum and policies of emerging economies, and the crucial importance of adapting Industry 5.0 to these emerging economies. The study concluded that emerging economies must play an active role in shaping the future by creating policies that will advance social stability, resource preservation, and climate objectives, which will enable more productive production processes with lower waste and energy usage.
Bus Route Sketching: A Multimetric Analysis from the User’s and Operator’s Perspectives
Authors: Jang Junyong , Cho Yongbin , Park Juntae
Published: August 2024

The purpose of this study is to develop an optimal bus route search algorithm that considers both the user’s and supplier’s perspectives. The process of providing bus route service involves route network design, route allocation, and operation and management in sequence. Among these, establishing the optimal rationality for route network design in practical applications is challenging, and route modifications often occur during the operation process. To minimize these practical difficulties, this study proposes the Bus Route Sketch (BRS) methodology. This methodology, designed for network-level optimization, distinguishes itself from existing bus route setting methodologies by minimizing travel costs while taking user needs into account. This study yielded positive results, with the evaluation score improving from 8.83 to 9.50 from the supplier’s perspective and from 7.13 to 9.89 from the user’s perspective. This BRS methodology, developed to suit both route planning and operation processes, is expected to be utilized in the practical evaluation, adjustment, and design of bus routes.
Waste as a Sustainable Source of Nutrients for Plants and Humans: A Strategy to Reduce Hidden Hunger
Authors: Rodríguez-Espinosa Teresa , Voukkali Irene , Pérez-Gimeno Ana , Almendro Candel María Belén , Hernández-Martich J. David , Zorpas Antonis A. , Lucas Ignacio Gómez , Navarro-Pedreño Jose
Published: August 2024

Worldwide, over half of all preschool-aged children and two-thirds of non-pregnant women of reproductive age suffer from hidden hunger. This situation may worsen due to the expected increase in the world population and the effects of climate change. The objective of this paper is to conduct a review of the relationship between soil, plants, and humans at the nutritional level, factors that affect the availability of nutrients, and sustainable strategies to reduce hidden hunger from an organic waste utilization point of view. Nutritional deficiency in people begins with nutrient-deficient soil, followed by crops that do not meet humans’ nutritional needs. According to previous studies, most agricultural soils are deficient in nutrients; however, organic residues containing high concentrations of minerals are present in the non-edible parts that are discarded. New opportunities (based on the circular economy strategy) are opening up to take advantage of the nutrient pool of organic residues, such as the preparation of substrates (technosols) or amendments. Their incorporation into the soil may consider various circumstances to ensure the mineralization and bioavailability of nutrients for crops. Several agronomic practices and methods to monitor soil and crop nutrient depletion can be considered among the best strategies to mitigate and reduce hidden hunger through determining which foods and which parts should be ingested, and how to process them to ensure mineral bioavailability.