bluuebunny/crossref_metadata_2025 huggingface dataset:
Technical Possibilities and Limitations of the DPS-4D Type of Digisonde in Individual Meteor Detections
Authors: Szárnya Csilla , Mošna Zbyšek , Igaz Antal , Kouba Daniel , Verhulst Tobias G. W. , Koucká Knížová Petra , Podolská Kateřina , Barta Veronika
Published: July 2024

During the peak days of the 2019 Leonids and Geminids (16–19 November and 10–16 December), two ionograms/minute and one Skymap/minute campaign measurements were carried out at the Sopron (47.63°N, 16.72°E) and Průhonice (50.00°N, 14.60°E) Digisonde stations. The stations used frequencies between 1 and 17 MHz for the ionograms, and the Skymaps were made at 2.5 MHz. A temporary optical camera was also installed at Sopron with a lower brightness limit of +1 visual magnitude. The manual scaling of ionograms for November and December 2019 to study the behavior of the regular sporadic E layer was also completed. Although the distributions of the stations were similar, there were interesting differences despite the relative proximity of the stations. The optical measurements detected 88 meteors. A total of 376 meteor-induced traces were found on the Digisonde ionograms at a most probable amplitude (MPA) threshold of 4 dB and of these, 40 cases could be linked to reflections on the Skymaps, too. Of the 88 optical detections, 31 could be identified on the ionograms. The success of detections depends on the sensitivity of the instruments and the noise-filtering. Geometrically, meteors above 80 km and with an altitude angle of 40° or higher can be detected using the Digisondes.
A Deep Learning Approach to Segment Coastal Marsh Tidal Creek Networks from High-Resolution Aerial Imagery
Authors: Dutt Richa , Ortals Collin , He Wenchong , Curran Zachary Charles , Angelini Christine , Canestrelli Alberto , Jiang Zhe
Published: July 2024

Tidal creeks play a vital role in influencing geospatial evolution and marsh ecological communities in coastal landscapes. However, evaluating the geospatial characteristics of numerous creeks across a site and understanding their ecological relationships pose significant challenges due to the labor-intensive nature of manual delineation from imagery. Traditional methods rely on manual annotation in GIS interfaces, which is slow and tedious. This study explores the application of Attention-based Dense U-Net (ADU-Net), a deep learning image segmentation model, for automatically classifying creek pixels in high-resolution (0.5 m) orthorectified aerial imagery in coastal Georgia, USA. We observed that ADU-Net achieved an outstanding F1 score of 0.98 in identifying creek pixels, demonstrating its ability in tidal creek mapping. The study highlights the potential of deep learning models for automated tidal creek mapping, opening avenues for future investigations into the role of creeks in marshes’ response to environmental changes.
Assessment of Atmospheric Correction Algorithms for Sentinel-3 OLCI in the Amazon River Continuum
Authors: Valerio Aline M. , Kampel Milton , Vantrepotte Vincent , Ballester Victoria , Richey Jeffrey
Published: July 2024

Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were evaluated against in situ remote sensing reflectance (Rrs) measurements. K-means classification identified four optical water types (OWTs) that are affected by the ARC. Two OWTs showed seasonal differences in the Lower Amazon River, influenced by the increase in suspended sediment concentration with river discharge. The other OWTs in the Amazon River Plume are dominated by phytoplankton or by a mixture of optically significant constituents. The Quality Water Index Polynomial method used to assess the quality of in situ and orbital Rrs had a high failure rate when the Apparent Visible Wavelength was >580 nm for in situ Rrs. OC-SMART Rrs products showed better spectral quality compared to Rrs derived from other AC processors evaluated in this study. These results improve our understanding of remotely sensing very turbid waters, such as those in the Amazon River Continuum.
Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas
Authors: Zakharov Igor , Kohlsmith Sarah , Hornung Jon , Charbonneau François , Bobby Pradeep , Howell Mark
Published: July 2024

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).
Development of an Algorithm for Assessing the Scope of Large Forest Fire Using VIIRS-Based Data and Machine Learning
Authors: Son Min-Woo , Kim Chang-Gyun , Kim Byung-Sik
Published: July 2024

Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest fires must be collected to establish disaster prevention policies and conduct relevant research projects. However, some countries do not share details, including the location of forest fires, which can make research problematic when it is necessary to know the exact location or shape of a forest fire. This non-disclosure warrants remote surveys of forest fire sites using satellites, which sidestep national information disclosure policies. Meanwhile, original data from satellites have a great advantage in terms of data acquisition in that they are independent of national information disclosure policies, making them the most effective method that can be used for environmental monitoring and disaster monitoring. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has worldwide coverage at a daily temporal resolution and spatial resolution of 375 m. It is widely used for detecting hotspots worldwide, enabling the recognition of forest fires and affected areas. However, information collection on affected regions and durations based on raw data necessitates identifying and filtering hotspots caused by industrial activities. Therefore, this study used VIIRS hotspot data collected over long periods and the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to develop ST-MASK, which masks said hotspots. By targeting the concentrated and fixed nature of these hotspots, ST-MASK is developed and used to distinguish forest fires from other hotspots, even in mountainous areas, and through an outlier detection algorithm, it generates identified forest fire areas, which will ultimately allow for the creation of a global forest fire watch system.
Multi-Wave Structures of Traveling Ionospheric Disturbances Associated with the 2022 Tonga Volcanic Eruptions in the New Zealand and Australia Regions
Authors: Li Xiaolin , Ding Feng , Xiong Bo , Chen Ge , Mao Tian , Song Qian , Yu Changhao
Published: July 2024

Using dense global navigation satellite system data and brightness temperature data across the New Zealand and Australia regions, we tracked the propagation of traveling ionospheric disturbances (TIDs) associated with the 15 January 2022 Tonga volcanic eruptions. We identified two shock wave-related TIDs and two Lamb wave-related TIDs following the eruptions. The two shock wave-related TIDs, propagating with velocities of 724–750 and 445–471 m/s, respectively, were observed around New Zealand and Australia within a distance of 3500–6500 km from the eruptive center. These shock wave-related TIDs suffered severe attenuation during the propagation and disappeared more than 6500 km from the eruptive center. Based on the TEC data from the nearest ground-based receivers, we estimated the onset times of two main volcanic explosions at 04:20:54 UT ± 116 s and 04:24:37 UT ± 141 s, respectively. The two shock wave-related TIDs were most likely generated by these two main volcanic eruptions. The two Lamb wave-related TIDs propagated with velocities of 300–370 and 250 m/s in the near-field region. The Lamb wave-related TIDs experienced minimal attenuation during their long-distance propagation, with only a 0.17% decrease observed in the relative amplitudes of the Lamb wave-related TIDs from the near-field to far-field regions.
Diurnal Variation in Surface Incident Solar Radiation Retrieved by CERES and Himawari-8
Authors: Lu Lu , Li Ying , Liang Lingjun , Ma Qian
Published: July 2024

The diurnal variation of surface incident solar radiation (Rs) has a significant impact on the Earth’s climate. Satellite-retrieved Rs datasets display good spatial and temporal continuity compared with ground-based observations and, more importantly, have higher accuracy than reanalysis datasets. Facilitated by these advantages, many scholars have evaluated satellite-retrieved Rs, especially based on monthly and annual data. However, there is a lack of evaluation on an hourly scale, which has a profound impact on sea–air interactions, climate change, agriculture, and prognostic models. This study evaluates Himawari-8 and Clouds and the Earth’s Radiant Energy System Synoptic (CERES)-retrieved hourly Rs data covering 60°S–60°N and 80°E–160°W based on ground-based observations from the Baseline Surface Radiation Network (BSRN). Hourly Rs were first standardized to remove the diurnal and seasonal cycles. Furthermore, the sensitivities of satellite-retrieved Rs products to clouds, aerosols, and land cover types were explored. It was found that Himawari-8-retrieved Rs was better than CERES-retrieved Rs at 8:00–16:00 and worse at 7:00 and 17:00. Both satellites performed better at continental sites than at island/coastal sites. The diurnal variations of statistical parameters of Himawari-8 satellite-retrieved Rs were stronger than those of CERES. Relatively larger MABs in the case of stratus and stratocumulus were exhibited for both hourly products. Smaller MAB values were found for CERES covered by deep convection and cumulus clouds and for Himawari-8 covered by deep convection and nimbostratus clouds. Larger MAB values at evergreen broadleaf forest sites and smaller MAB values at open shrubland sites were found for both products. In addition, Rs retrieved by Himawari-8 was more sensitive to AOD at 10:00–16:00, while that retrieved by CERES was more sensitive to COD at 9:00–15:00. The CERES product showed larger sensitivity to COD (at 9:00–15:00) and AOD (at 7:00–10:00) than Himawari-8. This work helps data producers know how to improve their future products and helps data users be aware of the uncertainties that exist in hourly satellite-retrieved Rs data.
Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery
Authors: Dorbu Freda , Hashemi-Beni Leila
Published: July 2024

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.
Radar Emitter Recognition Based on Spiking Neural Networks
Authors: Luo Zhenghao , Wang Xingdong , Yuan Shuo , Liu Zhangmeng
Published: July 2024

Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods.
Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images
Authors: Nahrstedt Konstantin , Reuter Tobias , Trautz Dieter , Waske Björn , Jarmer Thomas
Published: July 2024

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.
Simulation of Modal Control of Metal Mode-Filtered Vertical-Cavity Surface-Emitting Laser
Authors: Mu Jingfei , Zhou Yinli , Chen Chao , Zhang Xing , Zhang Jianwei , Liu Tianjiao , Zhang Zhuo , Xu Yuehui , Yuan Gaohui , Zhang Jiye , Ning Yongqiang , Wang Lijun
Published: July 2024

In this study, a novel metal-dielectric film mode filter structure that can flexibly regulate the transverse mode inside vertical-cavity surface-emitting lasers (VCSELs) is proposed. The number, volume, and stability of transverse modes inside the VCSEL can be adjusted according to three key parameters—the oxide aperture, the metal aperture, and the distance between the oxide aperture and the metal aperture—to form a flexible window, and a new parameter is defined to describe the mode identification. This study provides a complete simulation theory basis and calculation method, which is of great significance for the optical mode control in VCSELs.
Acoustic Sensors for Monitoring and Localizing Partial Discharge Signals in Oil-Immersed Transformers under Array Configuration
Authors: Wang Yang , Zhao Dong , Jia Yonggang , Wang Shaocong , Du Yan , Li Huaqiang , Zhang Bo
Published: July 2024

Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. “Electric-force-acoustic” multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers.
Sustainable Electromagnetic Prototype for Detecting Internal Deterioration in Building Walls
Authors: Ascona García Papa Pio , Aguirre Camacho Marco Antonio , Silva Barboza Elger Orlando , Ordoñez Carpio Guido Elar , Cuadros-Rojas Emerson
Published: July 2024

The aim of this study was to develop a sustainable electromagnetic prototype to detect the interior deterioration of walls in buildings in order to mitigate uncertainty as it is a challenge to observe the interior state of walls without utilising destructive procedures. The method used was experimental, developmental and quantitative in its approach. The inductance, electric current, modulated frequency and power of the electromagnetic field were used to penetrate the constructed specimens, which were built of materials such as concrete, brick, adobe, plaster and fine sand and had walls with a thickness of less than 300 millimetres. The results show that the optimum value of the magnetic field was 0.18 µT, which was sufficient to penetrate 150 mm with densities between 1.0 and 2.4 g/cm3 and porosities between 11 and 60%. The current and wave each had a coefficient of determination R2 = 0.8914, and the average inductance value was 184 µH, which was established with an air core of radius 9.75 cm and with 19 turns with AWG-25 wire. The frequency-modulated signal ranged in the audible zone between 10 and 22 kHz. The presented prototype detects the interior deterioration of the walls of the building, and the signal is reflected on a metallic guide on the opposite side of the wall with a reading error of 5%. The use of this prototype does not represent a risk to the operator or the environment.
Automated Detection of In-Home Activities with Ultra-Wideband Sensors
Authors: Narkhede Arsh , Gowing Hayden , Vandenberg Tod , Phan Steven , Wong Jason , Chan Andrew
Published: July 2024

As Canada’s population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring.
Improved Particle Filter Algorithm for Multi-Target Detection and Tracking
Authors: Cheng Yi , Ren Wenbo , Xiu Chunbo , Li Yiyang
Published: July 2024

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.
A Joint Network of Edge-Aware and Spectral–Spatial Feature Learning for Hyperspectral Image Classification
Authors: Zheng Jianfeng , Sun Yu , Hao Yuqi , Qin Senlong , Yang Cuiping , Li Jing , Yu Xiaodong
Published: July 2024

Hyperspectral image (HSI) classification is a vital part of the HSI application field. Since HSIs contain rich spectral information, it is a major challenge to effectively extract deep representation features. In existing methods, although edge data augmentation is used to strengthen the edge representation, a large amount of high-frequency noise is also introduced at the edges. In addition, the importance of different spectra for classification decisions has not been emphasized. Responding to the above challenges, we propose an edge-aware and spectral–spatial feature learning network (ESSN). ESSN contains an edge feature augment block and a spectral–spatial feature extraction block. Firstly, in the edge feature augment block, the edges of the image are sensed, and the edge features of different spectral bands are adaptively strengthened. Then, in the spectral–spatial feature extraction block, the weights of different spectra are adaptively adjusted, and more comprehensive depth representation features are extracted on this basis. Extensive experiments on three publicly available hyperspectral datasets have been conducted, and the experimental results indicate that the proposed method has higher accuracy and immunity to interference compared to state-of-the-art (SOTA) method.
The Role of Selected Speech Signal Characteristics in Discriminating Unipolar and Bipolar Disorders
Authors: Kamińska Dorota , Kamińska Olga , Sochacka Małgorzata , Sokół-Szawłowska Marlena
Published: July 2024

Objective:The objective of this study is to explore and enhance the diagnostic process of unipolar and bipolar disorders. The primary focus is on leveraging automated processes to improve the accuracy and accessibility of diagnosis. The study aims to introduce an audio corpus collected from patients diagnosed with these disorders, annotated using the Clinical Global Impressions Scale (CGI) by psychiatrists. Methods and procedures: Traditional diagnostic methods rely on the clinician’s expertise and consideration of co-existing mental disorders. However, this study proposes the implementation of automated processes in the diagnosis, providing quantitative measures and enabling prolonged observation of patients. The paper introduces a speech signal pipeline for CGI state classification, with a specific focus on selecting the most discriminative features. Acoustic features such as prosodies, MFCC, and LPC coefficients are examined in the study. The classification process utilizes common machine learning methods. Results: The results of the study indicate promising outcomes for the automated diagnosis of bipolar and unipolar disorders using the proposed speech signal pipeline. The audio corpus annotated with CGI by psychiatrists achieved a classification accuracy of 95% for the two-class classification. For the four- and seven-class classifications, the results were 77.3% and 73%, respectively, demonstrating the potential of the developed method in distinguishing different states of the disorders.
Optically Transparent Frequency Selective Surfaces with Wideband Capability for IoT Applications: A Polarization-Independent Double-Layer Design
Authors: Gunaydin Omer Faruk , Can Sultan
Published: July 2024

This study proposes wide-band frequency selective surfaces (FSS) with polarization-independent characteristics that are tailored for IoT applications. The design consists of two different layers with band-stop characteristics that target key frequency bands in sub-6 GHz: 3.7 GHz (n77) and 4.5 GHz (n79), offering a 1.39 GHz bandwidth spanning from 3.61 GHz to 5.0 GHz. This study also presents a double-layer structure with a WB property with a fractional bandwidth of 32%. Simulations have been conducted to observe variations in insertion loss across incident and polarization angles ranging from 0 to 60 degrees for both TE and TM modes in the suggested FSS structures. These simulations demonstrate the design’s polarization independence. Transparent polyvinyl chloride with a dielectric constant of 2.77 and a thickness of 1.48 mm has been utilized as the substrate material. The optical transmittance is calculated to be 96.7% for Layer 1, 95.7% for Layer 2, and 92.4% for the double-layer structure, and these calculated optical transmittance values were found to be higher compared to the studies in the literature. The proposed design is well-suited for sub-6 GHz IoT applications due to their high transparency, cost-effectiveness, robust high-performance capabilities in suppression, and polarization-independent features. The results of 3D full-wave simulations were compared with measurement and the equivalent circuit model outcomes, and a good agreement between the results was observed.
Research on Vehicle Pose Detection Method Based on a Roadside Unit
Authors: Ni Juan , Kong Xiangcun , Yan Bingchen , Si Shuzhe , Shi Shuyue , Guo Dong , Wang Pengwei , Wang Lei , Xu Yi
Published: July 2024

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.
Error Correction of the RapidEye Sub-Pixel Correlation: A Case Study of the 2019 Ridgecrest Earthquake Sequence
Authors: Luo Wulinhong , An Qi , Feng Guangcai , Xiong Zhiqiang , He Lijia , Wang Yilin , Jiang Hongbo , Wang Xiuhua , Li Ning , Wang Wenxin
Published: July 2024

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.
Multi-Scale Spatial Attention-Based Multi-Channel 2D Convolutional Network for Soil Property Prediction
Authors: Feng Guolun , Li Zhiyong , Zhang Junbo , Wang Mantao
Published: July 2024

Visible near-infrared spectroscopy (VNIR) is extensively researched for obtaining soil property information due to its rapid, cost-effective, and environmentally friendly advantages. Despite its widespread application and significant achievements in soil property analysis, current soil prediction models continue to suffer from low accuracy. To address this issue, we propose a convolutional neural network model that can achieve high-precision soil property prediction by creating 2D multi-channel inputs and applying a multi-scale spatial attention mechanism. Initially, we explored two-dimensional multi-channel inputs for seven soil properties in the public LUCAS spectral dataset using the Gramian Angular Field (GAF) method and various preprocessing techniques. Subsequently, we developed a convolutional neural network model with a multi-scale spatial attention mechanism to improve the network’s extraction of relevant spatial contextual information. Our proposed model showed superior performance in a statistical comparison with current state-of-the-art techniques. The RMSE (R²) values for various soil properties were as follows: organic carbon content (OC) of 19.083 (0.955), calcium carbonate content (CaCO3) of 24.901 (0.961), nitrogen content (N) of 0.969 (0.933), cation exchange capacity (CEC) of 6.52 (0.803), pH in H2O of 0.366 (0.927), clay content of 4.845 (0.86), and sand content of 12.069 (0.789). Our proposed model can effectively extract features from visible near-infrared spectroscopy data, contributing to the precise detection of soil properties.
Research on Isomorphic Task Transfer Algorithm Based on Knowledge Distillation in Multi-Agent Collaborative Systems
Authors: Bo Chunxue , Liu Shuzhi , Liu Yuyue , Guo Zhishuo , Wang Jinghan , Xu Jinghai
Published: July 2024

In response to the increasing number of agents and changing task scenarios in multi-agent collaborative systems, existing collaborative strategies struggle to effectively adapt to new task scenarios. To address this challenge, this paper proposes a knowledge distillation method combined with a domain separation network (DSN-KD). This method leverages the well-performing policy network from a source task as the teacher model, utilizes a domain-separated neural network structure to correct the teacher model’s outputs as supervision, and guides the learning of agents in new tasks. The proposed method does not require the pre-design or training of complex state-action mappings, thereby reducing the cost of transfer. Experimental results in scenarios such as UAV surveillance and UAV cooperative target occupation, robot cooperative box pushing, UAV cooperative target strike, and multi-agent cooperative resource recovery in a particle simulation environment demonstrate that the DSN-KD transfer method effectively enhances the learning speed of new task policies and improves the proximity of the policy model to the theoretically optimal policy in practical tasks.
Artificial Intelligence in Pancreatic Image Analysis: A Review
Authors: Liu Weixuan , Zhang Bairui , Liu Tao , Jiang Juntao , Liu Yong
Published: July 2024

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel’s workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.
Integrated Wearable System for Monitoring Skeletal Muscle Force of Lower Extremities
Authors: Luo Heng , Xiong Ying , Zhu Mingyue , Wei Xijun , Tao Xiaoming
Published: July 2024

Continuous monitoring of lower extremity muscles is necessary, as the muscles support many human daily activities, such as maintaining balance, standing, walking, running, and jumping. However, conventional electromyography and physiological cross-sectional area methods inherently encounter obstacles when acquiring precise and real-time data pertaining to human bodies, with a notable lack of consideration for user comfort. Benefitting from the fast development of various fabric-based sensors, this paper addresses these current issues by designing an integrated smart compression stocking system, which includes compression garments, fabric-embedded capacitive pressure sensors, an edge control unit, a user mobile application, and cloud backend. The pipeline architecture design and component selection are discussed in detail to illustrate a comprehensive user-centered STIMES design. Twelve healthy young individuals were recruited for clinical experiments to perform maximum voluntary isometric ankle plantarflexion contractions. All data were simultaneously collected through the integrated smart compression stocking system and a muscle force measurement system (Humac NORM, software version HUMAC2015). The obtained correlation coefficients above 0.92 indicated high linear relationships between the muscle torque and the proposed system readout. Two-way ANOVA analysis further stressed that different ankle angles (p = 0.055) had more important effects on the results than different subjects (p = 0.290). Hence, the integrated smart compression stocking system can be used to monitor the muscle force of the lower extremities in isometric mode.
Effects of Fatigue and Unanticipated Factors on Knee Joint Biomechanics in Female Basketball Players during Cutting
Authors: Zhu Aojie , Gao Shunxiang , Huang Li , Chen Hairong , Zhang Qiaolin , Sun Dong , Gu Yaodong
Published: July 2024

(1) This study examined the impact of fatigue and unanticipated factors on knee biomechanics during sidestep cutting and lateral shuffling in female basketball players, assessing the potential for non-contact anterior cruciate ligament (ACL) injuries. (2) Twenty-four female basketball players underwent fatigue induction and unanticipated change of direction tests, and kinematic and kinetic parameters were collected before and after fatigue with a Vicon motion capture system and Kistler ground reaction force (GRF) sensor. (3) Analysis using two-way repeated-measures ANOVA showed no significant interaction between fatigue and unanticipated factors on joint kinematics and kinetics. Unanticipated conditions significantly increased the knee joint flexion and extension angle (p < 0.01), decreased the knee flexion moment under anticipated conditions, and increased the knee valgus moment after fatigue (p ≤ 0.05). One-dimensional statistical parametric mapping (SPM1d) results indicated significant differences in GRF during sidestep cutting and knee inversion and rotation moments during lateral shuffling post-fatigue. (4) Unanticipated factors had a greater impact on knee load patterns, raising ACL injury risk. Fatigue and unanticipated factors were independent risk factors and should be considered separately in training programs to prevent lower limb injuries.
Design and Construction and Energy Consumption Study of a New Electrolyzed Water Cell Generator Prototype for Food Disinfection
Authors: Sanzani Simona Marianna , Catalano Pasquale , Tanucci Gianluca , Giametta Ferruccio , Bianchi Biagio
Published: July 2024

This study explores the feasibility of producing electrolyzed water (EW) for post-harvest treatment of fruits and vegetables as a new substitute for current chemical products. A prototype generator using tap water and NaCl solution was tested for EW’s sanitization efficiency, energy, and economic costs. In vitro tests on Penicillium e., Aspergillus n., Botrytis c., and Alternaria a. assessed EW pH, chlorine concentration, electro-oxidative potential, pathogen contact time, and energy consumption. Optimal results were achieved with a pH of 4.6, electro-oxidative potential of 188 mV, active chlorine concentration of 3.4 mg/L, and a contact time of 1–2 min. The prototype produced 10.0 L of EW in 1 h, consuming 0.11 kWh of electricity. Real-scale energy consumption was 545 kWh/m3 EW, costing 12.51 euro/m3. The study concludes that optimizing EW production can reduce energy consumption, making it a viable alternative for industrial sanitization of fruits and vegetables.
Athlete Maltreatment as a Wicked Problem and Contested Terrain
Authors: Park Haewan , Sam Michael P. , Jackson Steven J.
Published: July 2024

Athlete maltreatment in organized sport has attracted considerable attention from governing bodies, stakeholders and the general public. Despite numerous studies and policy proposals from various countries, the problem remains unresolved due to its inherent complexity. Drawing upon the concept of ‘a wicked problem’ widely utilized in policy analysis, this study first identifies the wicked features of maltreatment, focusing on: (1) the difficulty of establishing a definition of maltreatment; (2) the challenges of identifying its causes; and (3) the impediments to identifying solutions in a context of embedded stakeholders and unintended consequences. To provide further analysis, we compare athlete maltreatment with other issues in sport such as doping and match-fixing, to suggest that lessons can be drawn from other wicked problems in the same contested terrain. Overall, given the complex interplay between maltreatment and the maintenance/legitimization of sport systems, this paper calls for continuing attention and evaluation of existing research/policies and advocates for a more multidimensional view that acknowledges maltreatment as a wicked problem.
“When Is a School Not a School?” Dr. Carrie Weaver Smith, Child Prisons, and the Limits of Reform in Progressive Era Texas
Authors: Harrell Sam
Published: July 2024

This archival study explores the life and work of Dr. Carrie Weaver Smith (1885–1942), a Progressive Era social worker and prison warden. Specifically, I explore the first phase of her career as a House Physician at the Virginia K. Johnson Home in Dallas, Texas (1911–1915) and as the first Superintendent of the Texas State Training School for Girls in Gainesville, Texas (1916–1925). Using archival research, I detail three conflicts that defined Dr. Smith’s superintendency: her fight to reclassify a youth prison as a school, her challenges to a Ku Klux Klan-dominated legislature, and her refusal to cede authority to a State Board of Control. Together, these conflicts led the Board to terminate Dr. Smith’s position, an outcome that would replay twice more before she retired from prisonwork. I argue that when most reformers made significant concessions, compromising their visions to maintain state funding and political allyship, Dr. Smith stood out for her record of refusal. And yet, like other reformers, she left Texas with the capacity to imprison more women and girls than ever before.
Comparing the Anthropometrics, Body Composition, and Strength Performance of Male and Female Italian Breaking Athletes: A Pilot Study
Authors: Ruscello Bruno , Morganti Gabriele , De Fano Antonio , Mancina Flavio , Lunetta Laura , Di Mauro Giuseppe , Cogoni Claudio , Pagano Edilio , Brigati Nicolò Marco , Di Castro Andrea , Gianfelici Antonio , Spada Raffaella , Padua Elvira , Ragona Chiara
Published: July 2024

Breaking is a performative art that has recently undergone a process of sportification, developing into an aesthetic sport included in the 2024 Paris Olympic Games. Despite its growing worldwide popularity, there is a lack of research on Breaking. Accordingly, this pilot study’s aim was twofold: (a) to provide an initial understanding of the anthropometric measures, body composition data, somatotype profiles, and strength performance of male (B-boys) and female (B-girls) Italian Breakers divided into elite (international) and sub-elite (national) levels and (b) to guide further research on the area, providing the methodological approach for future investigations. A total of 24 B-boys (elite n = 5; sub-elite n = 19) and 9 B-girls (elite n = 3; sub-elite n = 6) were included in this study. Descriptive analyses revealed that B-boys and B-girls displayed low height and weight (1.70 m (63.8 kg) and 1.58 m (54.2 kg), respectively), low levels of body fat percentages (10.3% and 17.6%, respectively), and a balanced mesomorph somatotype (2.28–4.64–2.69 and 2.34–5.16–2.38, respectively), revealing a marked development of muscular mass. Due to the small sample size, Welch’s test and correlation analyses did not report any elite vs. sub-elite difference. It was hypothesized that Breakers’ morphological profiles result from the selection procedures and training regimens related to Breaking aesthetic, athletic, and physiological demands.