bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Dimensional Spatial and Temporal Variations of Ecosystem Service Values in the Li River Basin, 1990–2020
Authors: Hu Jinlong , Qiu Sicheng , Luo Nan , Qing Guo , Huang Chunbo
Published: June 2023

Changing landscape patterns would alter ecosystem components and functions, affecting the supply of ecosystem services. Understanding the spatial and temporal variations of ecosystem services is an important basis for ecosystem management and planning decisions and is of great significance for the realization of regional sustainable development. Based on Landsat TM/OLI remote sensing images from 1990, 2000, 2010, and 2020 in the Li River Basin, we explored the spatial and temporal variabilities of ecosystem services in the Li River Basin over the past 30 years, from both horizontal and vertical dimensions, using modified equivalence factor method and spatial autocorrelation analysis. The research findings are as follows: (1) Forestland has consistently been the dominant landscape type in the Li River Basin, with its area continuously increasing, while farmland, water bodies, and grassland have decreased, and construction land and bare land have increased. (2) The value of ecosystem services in the Li River Basin exhibited an initial increase followed by a decrease trend, with a net increase of 9.20 × 108 yuan. Forestland contributed the most to the value of ecosystem services. (3) Hydrological regulation and climate regulation are the dominant functions of the Li River Basin’s ecosystems, accounting for over 50% of the total contribution. (4) The value of ecosystem services per unit area increases with increasing slope and elevation. The segments with slopes ranging from 15 to 25 degrees and elevation zones between 200 and 500 m have the highest total value of ecosystem services. (5) The overall level of ecosystem services in the Li River Basin is relatively high and continues to rise, but areas with a low ecosystem service value are gradually concentrated. (6) The Moran’s I values for ecosystem services in all four periods are greater than 0, indicating a significant positive spatial autocorrelation. The overall pattern of ecosystem services is relatively stable, but there are significant spatial variations, which are characterized by lower values in the central area and higher values in the surrounding areas. The research findings provide a scientific basis for watershed ecological environment construction, optimal allocation of land resources, and sustainable landscape management.
MT-FANet: A Morphology and Topology-Based Feature Alignment Network for SAR Ship Rotation Detection
Authors: Liu Qianqian , Li Dong , Jiang Renjie , Liu Shuang , Liu Hongqing , Li Suqi
Published: June 2023

In recent years, ship target detection in synthetic aperture radar (SAR) images has significantly progressed due to the rapid development of deep learning (DL). However, since only the spatial feature information of ship targets is utilized, the current DL-based SAR ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale, rotations, or complex backgrounds. To address these issues, in this paper, a novel deep-learning network for SAR ship rotation detection, called a morphology and topology-based feature alignment network, is proposed which can better exploit the morphological features and inherent topological structure information. This network consists of the following three main steps: First, deformable convolution is introduced to improve the representational ability for irregularly shaped ship targets, and subsequently, a morphology and topology feature pyramid network is developed to extract inherent topological structure information. Second, based on the aforementioned features, a rotation alignment feature head is devised for fine-grained processing as well as aligning and distinguishing the features; to enable regression prediction of rotated bounding boxes; and to adopt a parameter-sharing mechanism to improve detection efficiency. Therefore, utilizing morphological and inherent topological structural information enables a superior detection performance to be achieved. Finally, we evaluate the effectiveness of the proposed method using the rotated ship detection dataset in SAR images (RSDD-SAR). Our method outperforms other DL-based algorithms with fewer parameters. The overall average precision is 90.84% and recall is 92.21%. In inshore and offshore scenarios, our method performs well for the detection of multi-scale and rotation-varying ship targets, with its average precision reaching 66.87% and 95.72%, respectively.
Construction of the Global Reference Atmospheric Profile Database
Authors: Guo Yuhang , Li Xiaoying , Cheng Tianhai , Li Shenshen , Zhang Xinyuan , Lu Wenjing , Fang Weifang
Published: June 2023

Atmospheric profiles are important input parameters for atmospheric radiative transfer models and atmospheric parameter inversions. The construction of regionally representative reference atmospheric profiles can provide basic data for global atmospheric and environmental research. Most reference atmospheric profile databases commonly used lag behind in updating frequency. These databases usually have limited spatial and temporal resolution and differ greatly from the real atmospheric state. To present the real atmospheric state, this article constructs the Global Reference Atmospheric Profile Database (GRAP) based on ACE-FTS satellite products of 2021 and 2022, AIRS satellite products and ERA5 reanalysis data of 2022 u6sing a random forest regression model and a hierarchical mean algorithm. The radiance spectrum of FY-3E HIRAS-II using different profile databases was simulated and compared with the measured spectrum. The results show that GRAP spectral simulations fit better with the measured HIRAS-II spectrum. Comparing the CO2, CH4, O3 and N2O profiles of GRAP, AFGL, MIPAS, RTTOV and NDACC ground station profiles in equatorial, mid-latitude summer and polar winter, the results show that GRAP has high spatial and temporal resolution and better fits the current real atmospheric state. Comparing the temperature profiles of eight regions in China, the results illustrate that GRAP is a better representation of the state of the atmosphere in the Chinese region. GRAP can provide fundamental atmospheric data for radiative transfer studies and atmospheric parameter inversions.
Sentinel-1 SAR Images and Deep Learning for Water Body Mapping
Authors: Pech-May Fernando , Aquino-Santos Raúl , Delgadillo-Partida Jorge
Published: June 2023

Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.
Using NDT Data to Assess the Effect of Pavement Thickness Variability on Ride Quality
Authors: Plati Christina , Georgouli Konstantina , Loizos Andreas
Published: June 2023

Pavement condition largely determines its long-term behavior and is of paramount importance for rehabilitation and maintenance management. The use of non-destructive testing (NDT) systems to assess pavement condition has gained much popularity. Often, well-known NDT systems are combined to take full advantage of the capabilities of each system. Combining independent NDT systems to optimize the assessment process is a scientific challenge. With this in mind, the purpose of this paper is to investigate the extent to which data from two independent NDT systems can be combined: pavement thickness obtained with ground penetrating radar (GPR) and roughness data obtained with a road surface profiler (RSP). In particular, the objective of this study is to determine whether the expected variations in asphalt layer thickness, due to the construction process and the different pavement cross sections along the same road/highway road, may have an impact on pavement roughness as expressed in International Roughness Index (IRI) values. GPR and roughness data are collected, processed, and analyzed. The analysis results show that thickness variations are reflected in pavement roughness. The greater the variation in asphalt layer thickness, the greater the IRI values. Furthermore, it is argued that the GPR capabilities can be used for an initial assessment of the expected pavement quality.
Evaluating the Effect of Training Data Size and Composition on the Accuracy of Smallholder Irrigated Agriculture Mapping in Mozambique Using Remote Sensing and Machine Learning Algorithms
Authors: Weitkamp Timon , Karimi Poolad
Published: June 2023

Mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques is challenging due to its small and scattered areas and heterogenous cropping practices. A study was conducted to examine the impact of sample size and composition on the accuracy of classifying irrigated agriculture in Mozambique’s Manica and Gaza provinces using three algorithms: random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Four scenarios were considered, and the results showed that smaller datasets can achieve high and sufficient accuracies, regardless of their composition. However, the user and producer accuracies of irrigated agriculture do increase when the algorithms are trained with larger datasets. The study also found that the composition of the training data is important, with too few or too many samples of the “irrigated agriculture” class decreasing overall accuracy. The algorithms’ robustness depends on the training data’s composition, with RF and SVM showing less decrease and spread in accuracies than ANN. The study concludes that the training data size and composition are more important for classification than the algorithms used. RF and SVM are more suitable for the task as they are more robust or less sensitive to outliers than the ANN. Overall, the study provides valuable insights into mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques.
Determination of the Stability of a High and Steep Highway Slope in a Basalt Area Based on Iron Staining Anomalies
Authors: Qian Lihui , Zang Shuying , Man Haoran , Sun Li , Wu Xiangwen
Published: June 2023

In recent years, geological disasters have frequently occurred on basarlt highway slopes. Studying the stability of highway slopes in this type of area is of great significance for traffic safety. However, due to the high cost and low efficiency of traditional monitoring and experimental methods for slope engineering, these methods are not conducive to the quick and comprehensive identification of regional slope stability. Due to the high iron content of basalt, iron staining anomalies in the ore prospecting field are reinterpreted from an engineering perspective in this study. Taking the S3K section of a highway in Changbai County, China, as an example, Landsat8 remote sensing (RS) images from 2014, 2016, 2018, 2020, and 2021 are selected, and principal component analysis is used to extract iron staining anomalies in the region. Combined with field investigation and evidence collection, the corresponding rock mass fragmentation is distinguished via iron staining anomalies. Then, according to previous research results, eight indexes including annual rainfall, slope, topographic relief, surface roughness, vegetation index, leaf area index (LAI), root depth of vegetation, and human activity intensity are selected for investigation. The artificial neural network–cellular automata (ANN-CA) model is established, and the rock fragmentation classification data obtained based on iron staining anomalies are used to simulate the area. Next, the calculation formula of slope stability is determined based on the simulation results, and the stability of a high and steep slope in the area is calculated and analyzed. Finally, a comparison with an actual field investigation shows that the effect of the proposed method is good. The research findings reveal that it is feasible to judge the stability of a high and steep slope in a basalt area via the use of iron staining anomalies as an indicator. The findings are tantamount to expanding the application scope of RS in practical engineering.
Deriving the Vertical Variations in the Diffuse Attenuation Coefficient of Photosynthetically Available Radiation in the North Pacific Ocean from Remote Sensing
Authors: Chen Lei , Zhang Jie , Pan Xiaoju , Shi Peng , Zhang Xiaobo
Published: June 2023

Diffuse attenuation coefficient of photosynthetically available radiation (PAR), KPAR, is a key product of ocean color remote sensing. Current ocean color algorithms generally detect only the average KPAR within one optical depth, KPARRS. Due to the marked vertical variations of KPAR, knowledge of KPARRS is insufficient to accurately evaluate the submarine light field. By using field in situ observations, a two-step approach, based on the development of an ocean color algorithm for KPARRS and the relationships between KPARRS and the average KPAR from the surface down to depth Z (K¯PARZ), was developed to remotely estimate the vertical variations in K¯PARZ in the North Pacific from the MODerate-resolution Imaging Spectrometer (MODIS) imagery. The root mean square difference of log(K¯PARZ) in depths within the euphotic zone was around ±0.059 (in unit of m−1 for K¯PARZ), which corresponded to a deviation of ±15% for the estimated K¯PARZ and the penetration depths of PAR. Our study may provide a promising approach to detect the vertical variations of K¯PARZ and underwater PAR distributions in the North Pacific Ocean.
Estimation of the Soil Water Content Using the Early Time Signal of Ground-Penetrating Radar in Heterogeneous Soil
Authors: Lu Qi , Liu Kexin , Zeng Zhaofa , Liu Sixin , Li Risheng , Xia Longfei , Guo Shilong , Li Zhilian
Published: June 2023

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content.
SAFF-SSD: Self-Attention Combined Feature Fusion-Based SSD for Small Object Detection in Remote Sensing
Authors: Huo Bihan , Li Chenglong , Zhang Jianwei , Xue Yingjian , Lin Zhoujin
Published: June 2023

SSD is a classical single-stage object detection algorithm, which predicts by generating different scales of feature maps on different convolutional layers. However, due to the problems of its insufficient non-linearity and the lack of semantic information in the shallow feature maps, as well as the fact that small objects contain few pixels, the detection accuracy of small objects is significantly worse than that of large- and medium-scale objects. Considering the above problems, we propose a novel object detector, self-attention combined feature fusion-based SSD for small object detection (SAFF-SSD), to boost the precision of small object detection. In this work, a novel self-attention module called the Local Lighted Transformer block (2L-Transformer) is proposed and is coupled with EfficientNetV2-S as our backbone for improved feature extraction. CSP-PAN topology is adopted as the detection neck to equip feature maps with both low-level object detail features and high-level semantic features, improving the accuracy of object detection and having a clear, noticeable and definitive effect on the detection of small targets. Simultaneously, we substitute the normalized Wasserstein distance (NWD) for the commonly used Intersection over Union (IoU), which alleviates the problem wherein the extensions of IoU-based metrics are very sensitive to the positional deviation of the small objects. The experiments illustrate the promising performance of our detector on many datasets, such as Pascal VOC 2007, TGRS-HRRSD and AI-TOD.
Use of Landsat Satellite Images in the Assessment of the Variability in Ice Cover on Polish Lakes
Authors: Sojka Mariusz , Ptak Mariusz , Zhu Senlin
Published: June 2023

Despite several decades of observations of ice cover in Polish lakes, researchers have not broadly applied satellite images to date. This paper presents a temporal and spatial analysis of the variability in the occurrence of ice cover on lakes in the Drawskie Lakeland in the hydrological years 1984–2022 based on satellite data from Landsat missions 4, 5, 7, 8, and 9. The range of occurrence of ice cover was determined based on the value of the Normalised Difference Snow Index (NDSI) and blue spectral band (ρλblue). The determination of ice cover extent adopted ρλblue  values from 0.033 to 0.120 as the threshold values. The analysis covered 67 lakes with an area from 0.07 to 18.71 km2. A total of 53 images were analysed, 14 and 39 out of which showed full and partial ice cover, respectively. The cluster analysis permitted the designation of two groups of lakes characterised by an approximate range of ice cover. The obtained results were analysed in the context of the morphometric parameters of the lakes. It was evidenced that the range of the ice cover on lakes is determined by the surface area of the lakes; their mean and maximum depth, volume, length, and width; and the height of the location above sea level. The results of analyses of the spatial range of ice cover in subsequent scenes allowed for the preparation of maps of probability of ice cover occurrence that permit the complete determination of its variability within each of the lakes. Monitoring of the spatial variability in ice cover within individual lakes as well as in reference to lakes not subject to traditional observations offers new research possibilities in many scientific disciplines focused on these ecosystems.
Tracking Geomagnetic Storms with Dynamical System Approach: Ground-Based Observations
Authors: Alberti Tommaso , De Michelis Paola , Santarelli Lucia , Faranda Davide , Consolini Giuseppe , Marcucci Maria Federica
Published: June 2023

Using a dynamical systems approach, we examine the persistence and predictability of geomagnetic perturbations across a range of different latitudes and levels of geomagnetic activity. We look at the horizontal components of the magnetic field measured on the ground between 13 and 24 March 2015, at approximately 40 observatories in the Northern Hemisphere. We introduced two dynamical indicators: the extremal index θ, which quantifies the persistence of the system in a particular state and the instantaneous dimension d, which measures the active number of degrees of freedom of the system. The analysis revealed that during disturbed periods, the instantaneous dimension of the horizontal strength of the magnetic field, which depends on latitude, increases, indicating that the geomagnetic response is externally driven. Furthermore, during quiet times, the instantaneous dimension values fluctuate around the state-space dimension, indicating a more stochastic and thus less predictable nature system.
A Self-Organizing Multi-Layer Agent Computing System for Behavioral Clustering Recognition
Authors: Qian Xingyu , Yuemaier Aximu , Yang Wenchi , Chen Xiaogang , Liang Longfei , Li Shunfen , Dai Weibang , Song Zhitang
Published: June 2023

Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented toward behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns through binary encoding and completes motion pattern summarization using a similarity comparison algorithm. Furthermore, in the face of unknown behavioral video data, a self-organizing structure with layer-by-layer accuracy progression is used to achieve motion law summarization using a multi-layer agent design approach. Finally, the real-time feasibility is verified in the prototype system using real scenes to provide a new feasible solution for unsupervised behavior recognition and space-time scenes.
Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model
Authors: Liu Yang , Dong Chen , Qin Xiaoqi , Xu Xiaodong
Published: June 2023

In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized.
Cybersecurity Analysis of Wearable Devices: Smartwatches Passive Attack
Authors: Silva-Trujillo Alejandra Guadalupe , González González Mauricio Jacobo , Rocha Pérez Luis Pablo , García Villalba Luis Javier
Published: June 2023

Wearable devices are starting to gain popularity, which means that a large portion of the population is starting to acquire these products. This kind of technology comes with a lot of advantages, as it simplifies different tasks people do daily. However, as they recollect sensitive data, they are starting to be targets for cybercriminals. The number of attacks on wearable devices forces manufacturers to improve the security of these devices to protect them. Many vulnerabilities have appeared in communication protocols, specifically Bluetooth. We focus on understanding the Bluetooth protocol and what countermeasures have been applied during their updated versions to solve the most common security problems. We have performed a passive attack on six different smartwatches to discover their vulnerabilities during the pairing process. Furthermore, we have developed a proposal of requirements needed for maximum security of wearable devices, as well as the minimum requirements needed to have a secure pairing process between two devices via Bluetooth.
Calibration of a Structured Light Imaging System in Two-Layer Flat Refractive Geometry for Underwater Imaging
Authors: Zoraja Domagoj , Petković Tomislav , Forest Josep , Pribanić Tomislav
Published: June 2023

The development of a robust 3D imaging system for underwater applications is a crucial process in underwater imaging where the physical properties of the underwater environment make the implementation of such systems challenging. Calibration is an essential step in the application of such imaging systems and is performed to acquire the parameters of the image formation model and to enable 3D reconstruction. We present a novel calibration method for an underwater 3D imaging system comprising a pair of cameras, of a projector, and of a single glass interface that is shared between cameras and projector(s). The image formation model is based on the axial camera model. The proposed calibration uses a numerical optimization of a 3D cost function to determine all system parameters, thus avoiding the minimization of re-projection errors which require numerically solving a 12th order polynomial equation multiple times for each observed point. We also propose a novel stable approach to estimate the axis of the axial camera model. The proposed calibration was experimentally evaluated on four different glass interfaces, wherein several quantitative results were reported, including the re-projection error. The achieved mean angular error of the system’s axis was under 6∘, and the mean absolute errors for the reconstruction of a flat surface were 1.38 mm for normal glass interfaces and 2.82 mm for the laminated glass interface, which is more than sufficient for application.
A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
Authors: Zhou Yuke , Liu Jiang , Zhang Ran , Ouyang Man , Huang Tao
Published: June 2023

Thanks to their wide coverage and relatively low latency compared to geosynchronous satellites, Low Earth Orbit (LEO) satellite networks have been regarded as one of the most promising solutions to provide global broadband backhaul for mobile users and IoT devices. In LEO satellite networks, the frequent feeder link handover invokes unacceptable communication interruptions and affects the backhaul quality. To overcome this challenge, we propose a maximum backhaul capacity handover strategy for feeder links in LEO satellite networks. To improve the backhaul capacity, we design an available backhaul capacity ratio to jointly consider feeder link quality and the inter-satellite network in handover decisions. In addition, we introduce a service time factor and handover control factor to reduce the handover frequency. Then, we propose the handover utility function based on the designed handover factors and propose a greedy-based handover strategy. Simulation results show that the proposed strategy outperforms conventional handover strategies in backhaul capacity with low handover frequency.
Design of a Reconfigurable Ultra-Wideband Terahertz Polarization Rotator Based on Graphene Metamaterial
Authors: Ding Guowen , Zhou Yanjun , Zhang Shuyang , Luo Xinyao , Wang Shenyun
Published: June 2023

In this work, a reconfigurable ultra-wideband transmissive terahertz polarization rotator based on graphene metamaterial is proposed that can switch between two states of polarization rotation within a broad terahertz band by changing the Fermi level of graphene. The proposed reconfigurable polarization rotator is based on a two-dimensional periodic array of multilayer graphene metamaterial structure, which is composed of metal grating, graphene grating, silicon dioxide thin film, and a dielectric substrate. The graphene metamaterial can achieve high co-polarized transmission of a linearly polarized incident wave at the off-state of the graphene grating without applying the bias voltage. Once the specially designed bias voltage is applied to change the Fermi level of graphene, the polarization rotation angle of linearly polarized waves is switched to 45° by the graphene metamaterial at the on-state. The working frequency band with 45-degree linear polarized transmission remaining above 0.7 and the polarization conversion ratio (PCR) above 90% is from 0.35 to 1.75 THz, and the relative bandwidth reaches 133.3% of the central working frequency. Furthermore, even with oblique incidence at large angles, the proposed device retains high-efficiency conversion in a broad band. The proposed graphene metamaterial offers a novel approach for the design of a terahertz tunable polarization rotator and is expected to be applied in the applications of terahertz wireless communication, imaging, and sensing.
Naturalistic Hyperscanning with Wearable Magnetoencephalography
Authors: Holmes Niall , Rea Molly , Hill Ryan M. , Boto Elena , Leggett James , Edwards Lucy J. , Rhodes Natalie , Shah Vishal , Osborne James , Fromhold T. Mark , Glover Paul , Montague P. Read , Brookes Matthew J. , Bowtell Richard
Published: June 2023

The evolution of human cognitive function is reliant on complex social interactions which form the behavioural foundation of who we are. These social capacities are subject to dramatic change in disease and injury; yet their supporting neural substrates remain poorly understood. Hyperscanning employs functional neuroimaging to simultaneously assess brain activity in two individuals and offers the best means to understand the neural basis of social interaction. However, present technologies are limited, either by poor performance (low spatial/temporal precision) or an unnatural scanning environment (claustrophobic scanners, with interactions via video). Here, we describe hyperscanning using wearable magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs). We demonstrate our approach by simultaneously measuring brain activity in two subjects undertaking two separate tasks—an interactive touching task and a ball game. Despite large and unpredictable subject motion, sensorimotor brain activity was delineated clearly, and the correlation of the envelope of neuronal oscillations between the two subjects was demonstrated. Our results show that unlike existing modalities, OPM-MEG combines high-fidelity data acquisition and a naturalistic setting and thus presents significant potential to investigate neural correlates of social interaction.
Direction-of-Arrival (DoA) Estimation Performance for Satellite Applications in a Multipath Environment with Rician Fading and Spatial Correlation
Authors: Hasib Mutmainnah , Kandeepan Sithamparanathan , Rowe Wayne S. T. , Al-Hourani Akram
Published: June 2023

Direction-of-arrival (DoA) estimation methods are highly versatile and find extensive applications in satellite communication. DoA methods are employed across a range of orbits, from low Earth orbits (LEO) to geostationary Earth orbits (GEO). They serve multiple applications, including altitude determination, geolocation and estimation accuracy, target localization, and relative and collaborative positioning. This paper provides a framework for modeling the DoA angle in satellite communications with respect to the elevation angle. The proposed approach employs a closed-form expression that incorporates various factors, such as the antenna boresight angle, satellite and Earth station positions, and the altitude parameters of the satellite stations. By leveraging this formulation, the work accurately calculates the Earth station’s elevation angle and effectively models the DoA angle. To the authors’ knowledge, this contribution is unique and has not been previously addressed in the available literature. Furthermore, this paper studies the impact of spatial correlation in the channel on well-known DoA estimation techniques. As a significant part of this contribution, the authors introduce a signal model incorporating correlation in satellite communication. Although selected studies have presented spatial signal correlation models in satellite communications to analyze the performance metrics, such as the bit error or symbol error probability, outage probability, and ergodic capacity, this work stands out by presenting and adapting a correlation model in the signal specifically for studying DoA estimations. Accordingly, this paper evaluates DoA estimation performance using root mean square error (RMSE) measurements for different satellite communication link conditions (uplink and downlink) through extensive Monte Carlo simulations. The simulation’s performance is evaluated by comparing it with the Cramer–Rao lower bound (CRLB) performance metric under additive white Gaussian noise (AWGN) conditions, i.e., thermal noise. The simulation results demonstrate that incorporating a spatial signal correlation model for DoA estimations significantly improves RMSE performance in satellite systems.
Verification of Commercial Near-Infrared Spectroscopy Measurement and Fresh Weight Diversity Modeling in Brix% for Small Tomato Fruits with Various Cultivars and Growth Conditions
Authors: Ino Masazumi , Ono Eiichi , Shimizu Yo , Omasa Kenji
Published: June 2023

The use of non-destructive commercial near-infrared (NIR) spectroscopy to estimate Brix% was verified using all samples of cherry tomato ‘TY Chika’, currant tomato ‘Microbeads’, and the M&S or market-purchased and supplemental local source tomatoes. Additionally, the relationship between fresh weight and Brix% of all samples was examined. These tomatoes had a diversity of cultivars, growing methods, harvest timing, and production locations and varied widely from 4.0% to 14.2% for Brix% and 1.25 g to 95.84 g for fresh weight. Regardless of the diversity of all samples, it was revealed that the refractometer-based Brix% (y) was practically estimated from the NIR-derived Brix% value (x) using a relationship of y = x (RMSE = 0.747 Brix%) after only a one-time calibration for the NIR spectrometer offset. An inverse relationship between fresh weight and Brix% could be modeled using a hyperbolic curve fit, and the model showed an R2 of 0.809 except for ‘Microbeads’. The Brix% of ‘TY Chika’ was highest on average (9.5%) and had a large difference from 6.2 to 14.2% among the samples. Data distribution of cherry tomato groups such as ‘TY Chika’ and M&S cherry tomatoes was closer, indicating a roughly linear correlation between fresh weight and Brix%.
Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
Authors: Yi Jinwang , Tang Jie , Yuan Fei , Qiao Guanhao , Dai Dongping
Published: June 2023

Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a “hot zone” problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms.
A Joint Acoustic Emission Source Localization Method for Composite Materials
Authors: Wang Xiaoran , Yin Fang , Wan Zhishuai
Published: June 2023

Damage localization methods for composite materials are a popular research topic at present. The time-difference-blind localization method and beamforming localization method are often individually utilized in the localization of the acoustic emission sources of composite materials. Based on the performances of the two methods, a joint localization method for the acoustic emission sources of composite materials is proposed in this paper. Firstly, the performance of the time-difference-blind localization method and the beamforming localization method were analyzed. Then, with the advantages and disadvantages of these two methods in mind, a joint localization method was proposed. Finally, the performance of the joint localization method was verified using simulations and experiments. The results show that the joint localization method can reduce the localization time by half compared with the beamforming localization method. At the same time, compared with the time-difference-blind localization method, the localization accuracy can be improved.
Variable Rate Point Cloud Geometry Compression Method
Authors: Zhuang Lehui , Tian Jin , Zhang Yujin , Fang Zhijun
Published: June 2023

With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Learned point cloud compression has attracted much attention for its excellent rate distortion performance. However, there is a one-to-one correspondence between the model and the compression rate in these methods. To achieve compression at different rates, a large number of models need to be trained, which increases the training time and storage space. To address this problem, a variable rate point cloud compression method is proposed, which enables the adjustment of the compression rate by the hyperparameter in a single model. To address the narrow rate range problem that occurs when the traditional rate distortion loss is jointly optimized for variable rate models, a rate expansion method based on contrastive learning is proposed to expands the bit rate range of the model. To improve the visualization effect of the reconstructed point cloud, a boundary learning method is introduced to improve the classification ability of the boundary points through boundary optimization and enhance the overall model performance. The experimental results show that the proposed method achieves variable rate compression with a large bit rate range while ensuring the model performance. The proposed method outperforms G-PCC, achieving more than 70% BD-Rate against G-PCC, and performs about, as well as the learned methods at high bit rates.
Scapular Resting Posture and Scapulohumeral Rhythm Adaptations in Volleyball Players: Implications for Clinical Shoulder Assessment in Athletes
Authors: Pascoal Augusto Gil , Ribeiro Andrea , Infante Jorge
Published: June 2023

Volleyball players develop shoulder sports-related adaptations due to repetitive overhead motions. It is essential to differentiate between these sports-related adaptations and pathological patterns in clinical assessments, particularly on scapular resting posture and scapulohumeral rhythm. Using an electromagnetic tracking system, the 3D shoulder kinematics of 30 male elite asymptomatic volleyball players and a matching control group were recorded at rest and in eight humeral elevation positions, in 15-degree increments from 15 to 120 degrees. The results indicated that the dominant scapular resting posture of the volleyball group was more anteriorly tilted than the control group (Volleyball: mean = −12.02°, STD = 4.16°; Control: mean = −7.45°, STD = 5.42°; Mean difference = 4.57°; STD = 6.85°; CI95% = 2.1° to 7.1°). The scapulohumeral rhythm in the volleyball group showed greater scapular internal rotation (Volleyball: mean = 41.60°, STD = 9.14°; Control: mean = 35.60°, STD = 6.03°; mean difference = 6.02°, STD = 1.47°; CI95% = 4.80° to 7.25°) and anterior tilt (Volleyball: mean = −9.10°, STD = 5.87°; mean = −2.3°, STD = 9.18°; mean difference = 6.88°, STD = 0.66°; CI95% = 6.34° to 7.43°). These findings suggest that volleyball players have developed a sports-related scapular adaptive pattern. This information may be valuable for clinical assessment and rehabilitation planning in injured volleyball players and may aid in the decision-making process for determining a safe return-to-play after a shoulder injury.
Assessing the Traveling Risks Perceived by South African Travelers during Pandemic Outbreaks: The Case of COVID-19
Authors: Plank Pia Antoinette , Gomes Luís Filipe , Caldas Paulo , Varela Miguel , Ferreira Diogo Cunha
Published: June 2023

The unprecedented global health crisis caused by COVID-19 is undoubtedly having a major impact on international tourism for two reasons. While the imposed travel restrictions have discouraged people from traveling, travelers are struggling with growing anxiety in coping with the new travel environment. We address the changing risk perceptions of travelers in the wake of the COVID-19 pandemic. Our primary objective is to identify and weigh significant emerging travel risks and develop a Risk Score Index to measure destination performance and strategic interventions for South African travelers. In this case, we used MACBETH and web-Delphi to construct that index with the help of 32 experts in the field. We found that the risks perceived by tourists are multifaceted and encompass categories, such as additional costs, exchange rates, and reimbursement-related factors. These three criteria are most important to the general perception of travel risk. We applied the developed risk assessment index to five destinations to assess their performance relative to the identified risks. The UK was the best-performing country.
Power of eWOM and Its Antecedents in Driving Customers’ Intention to Revisit: An Empirical Investigation on Five-Star Eco-Friendly Hotels in Saudi Arabia
Authors: Salah Maha Hassan Ahmed , Abdou Ahmed Hassan , Hassan Thowayeb H. , El-Amin Maha Abdul-Moniem Mohammed , Kegour Adam Basheer Adam , Alboray Hanem Mostafa Mohamed , Mohamed Amal Salah Darder , Ali Howayda Said Ahmed Mohamed , Mohammed Elham Farouq Ali
Published: June 2023

In recent years, electronic word-of-mouth (eWOM) has greatly impacted the hotel industry, as social media and online review platforms have given customers many opportunities to share their personal experiences with other individuals. Hence, this study aimed to empirically investigate several relationships within the setting of five-star eco-friendly hotels in Saudi Arabia. These included: (1) the direct impact of perceived quality (PQ), positive emotional experience (PEE), and customer satisfaction (CS) on promoting positive eWOM; (2) the potential intermediary role of CS in the connections between PQ, PEE, and eWOM; (3) the direct influence of eWOM on intention to revisit (ITR); and (4) the potential intermediary role of eWOM in the PQ–ITR, CS–ITR, and PEE–ITR relationships. To achieve these objectives, an online questionnaire was developed and directed to a sample of Saudi individuals who have stayed in five-star eco-friendly hotels, particularly in Riyadh City. Based on their previous interactions with these hotels on the TripAdvisor website during the last six months and their willingness to participate in the field study, a convenience sampling technique was utilized to select participants for data collection. The study mainly focused on Saudi individuals to investigate their behaviors and attitudes toward eco-friendly hotels, which have been considered a growing trend in the country in recent years. Participants were emailed individually, and a total of 423 acceptable forms were gathered and subjected to analysis using the PLS-SEM method. The research’s findings revealed that PQ had the strongest predictive power for promoting positive eWOM, leading to a greater increase in ITR than PEE and CS. Moreover, the study identified the significant mediating influence of CS in exploring how PEE and PQ contribute to customers’ intention to spread positive eWOM. Additionally, the results showed the substantial role of eWOM in mediating the relationships between PQ, PEE, CS, and ITR. Based on these findings, the study suggests practical implications for hotel operators and marketers looking to promote positive eWOM and increase revisit intentions.
Exploring the Interplay between Sustainability and Debt Costs in an Emerging Market: Does Financial Distress Matter?
Authors: Al Barrak Thamir , Chebbi Kaouther , Aljughaiman Abdullah A. , Albarrak Mohammed
Published: June 2023

Embracing corporate sustainability has emerged as a crucial strategy for companies to bolster their competitive edge and reputation. This research delves into the connection between environmental, social, and governance practices (ESG) and the cost of debt, as well as the moderating role of financial distress within this connection. By analyzing data from Saudi-listed firms between 2013 and 2021, we discovered that ESG practices have a notable negative impact on borrowing costs. This implies that organizations with increased transparency in their ESG disclosure gain access to external financial resources under more favorable terms. Additionally, we observed that the effect of ESG on the cost of debt is significantly and negatively moderated by the financial distress encountered by a firm. To bolster the credibility of these findings, dynamic generalized method of moments (GMM) models were utilized to address any potential endogeneity concerns, thereby enhancing the strength and resilience of the outcomes. The findings of this paper hold substantial value for investors, lenders, corporate management, and policymakers when considering the implementation and significance of a company’s ESG practices.