bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Safety Tunneling Technology of Secondary Outburst Elimination by CO2 Gas Fracturing in High-Outburst Coal Seam
Authors: Xu Zongwei , Zhang Junsheng , Cao Yunxing , Wang Zhenzhi , Zhang Xinsheng
Published: September 2024

The No. 3 coal seam in the Yuxi Coal Mine has a measured maximum gas content of 25.59 m3/t, along with a maximum gas pressure of 2.9 MPa, indicating its high risk to gas and outbursts. To mitigate outburst risks of the coal seam, the 1301 working face has been implemented with gas pre-drainage measures by grid boreholes from underlying roadways. After one year of extraction, it was confirmed that the gas content at all 33 test sites was below 8 m3/t, meeting the outburst prevention standards. However, during subsequent coal tunnel excavation, the gas desorption index K1 value frequently exceeded the standard, resulting in numerous occurrences of abnormal gas emission or small-scale outbursts. To tackle the challenges associated with safe excavation following the first-round regional outburst prevention measures, a research and industrial trial of CO2 gas fracturing (CO2-Frac) technology for secondary outburst prevention and rapid excavation was completed. The results show that the dual-hole and high-pressure (185 MPa) CO2-Frac considerably contributes to outburst prevention. K1 exceedances per hundred meters of tunnel excavations were from an average of 2.54 without CO2-Frac to an average of 0.28 after the new technology was implemented, leading to an eight-fold reduction. Additionally, the monthly excavation footage increased from an average of 81.64 m without CO2-Frac to an average of 162.42 m with CO2-Frac, resulting in a two-fold improvement. The dual-hole and high-pressure CO2-Frac is an advanced technology for safe and efficient excavation for secondary outburst elimination in highly outburst-prone coal seams in the Yuxi Coal Mine, with potential for widespread application in similar coal seam conditions.
The Planck Computer Is the Quantum Gravity Computer: We Live inside a Gigantic Computer, the Hubble Sphere Computer?
Authors: Haug Espen Gaarder
Published: September 2024

Recent developments in the quantization of general relativity theory provide a new perspective on matter and even the whole universe. Already, in 1922, Eddington suggested that a future quantum gravity theory had to be linked to Planck length. This is today the main view among many working with quantum gravity. Recently, it has been demonstrated how Planck length, the Planck time, can be extracted from gravity observations with no knowledge of G, ℏ, or even c. Rooted in this, both general relativity theory and multiple other gravity theories can be quantized and linked to the Planck scale. A revelation from this is that matter seems to be ticking at the reduced Compton frequency, where each tick can be seen as one bit, and one bit corresponds to a Planck mass event. This new speculative way of looking at gravity can also potentially tell us considerably about what quantum gravity computers are and what they potentially can do. We will conjecture that that all quantum gravity and quantum gravity computers are directly linked to the Planck scale and the Compton frequency in matter, something we will discuss in this paper. Quantum gravity computers, we will see, in many ways, are nature’s own designed computers with enormous capacity to 3D “print” real time. So, somewhat speculatively, we suggest we live inside a gigantic quantum gravity computer known as the Hubble sphere, and we even are quantum gravity computers. The observable universe is based on this model, basically a quantum gravity computer that calculates approximately 10104 bits per second (bps).
Temporal Dynamics of Global Barren Areas between 2001 and 2022 Derived from MODIS Land Cover Products
Authors: Eliades Marinos , Neophytides Stelios , Mavrovouniotis Michalis , Panagiotou Constantinos F. , Anastasiadou Maria N. , Varvaris Ioannis , Papoutsa Christiana , Bachofer Felix , Michaelides Silas , Hadjimitsis Diofantos
Published: September 2024

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia).
Multi-Scale Classification and Contrastive Regularization: Weakly Supervised Large-Scale 3D Point Cloud Semantic Segmentation
Authors: Wang Jingyi , He Jingyang , Liu Yu , Chen Chen , Zhang Maojun , Tan Hanlin
Published: September 2024

With the proliferation of large-scale 3D point cloud datasets, the high cost of per-point annotation has spurred the development of weakly supervised semantic segmentation methods. Current popular research mainly focuses on single-scale classification, which fails to address the significant feature scale differences between background and objects in large scenes. Therefore, we propose MCCR (Multi-scale Classification and Contrastive Regularization), an end-to-end semantic segmentation framework for large-scale 3D scenes under weak supervision. MCCR first aggregates features and applies random downsampling to the input data. Then, it captures the local features of a random point based on multi-layer features and the input coordinates. These features are then fed into the network to obtain the initial and final prediction results, and MCCR iteratively trains the model using strategies such as contrastive learning. Notably, MCCR combines multi-scale classification with contrastive regularization to fully exploit multi-scale features and weakly labeled information. We investigate both point-level and local contrastive regularization to leverage point cloud augmentor and local semantic information and introduce a Decoupling Layer to guide the loss optimization in different spaces. Results on three popular large-scale datasets, S3DIS, SemanticKITTI and SensatUrban, demonstrate that our model achieves state-of-the-art (SOTA) performance on large-scale outdoor datasets with only 0.1% labeled points for supervision, while maintaining strong performance on indoor datasets.
Estimating Ground-Level NO2 Concentrations Using Machine Learning Exclusively with Remote Sensing and ERA5 Data: The Mexico City Case Study
Authors: Cedeno Jimenez Jesus Rodrigo , Brovelli Maria Antonia
Published: September 2024

This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model’s adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model’s performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution.
Improved Architecture and Training Strategies of YOLOv7 for Remote Sensing Image Object Detection
Authors: Zhao Dewei , Shao Faming , Liu Qiang , Zhang Heng , Zhang Zihan , Yang Li
Published: September 2024

The technology for object detection in remote sensing images finds extensive applications in production and people’s lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network’s semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network’s overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular.
Coarse-to-Fine Structure and Semantic Learning for Single-Sample SAR Image Generation
Authors: Wang Xilin , Hui Bingwei , Guo Pengcheng , Jin Rubo , Ding Lei
Published: September 2024

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.
Snow Detection in Gaofen-1 Multi-Spectral Images Based on Swin-Transformer and U-Shaped Dual-Branch Encoder Structure Network with Geographic Information
Authors: Wu Yue , Shi Chunxiang , Shen Runping , Gu Xiang , Tie Ruian , Ge Lingling , Sun Shuai
Published: September 2024

Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for IoU_s, F1_s, and MPA, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics.
Evolution Patterns and Dominant Factors of Soil Salinization in the Yellow River Delta Based on Long-Time-Series and Similar Phenological-Fusion Images
Authors: Guo Bing , Xu Mei , Zhang Rui
Published: September 2024

Previous studies were mostly conducted based on sparse time series and different phenological images, which often ignored the dramatic changes in salinization evolution throughout the year. Based on Landsat and moderate-resolution-imaging spectroradiometer (MODIS) images from 2000 to 2020, this study applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm to obtain similar phenological images for the month of April for the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized, and the feature space index models were constructed. Combined with the measured ground data, the optimal monitoring index model of salinization was determined, and then the spatiotemporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed. The main conclusions were as follows: (1) The derived long-time-series and similar phenological-fusion images enable us to reveal the patterns of change in the dramatic salinization in the year that we examined using the ESTARFM algorithm. (2) The NDSI-TGDVI feature space salinization monitoring index model based on point-to-point mode had the highest accuracy of 0.92. (3) From 2000 to 2020, the soil salinization in the Yellow River Delta showed an aggravating trend. The average value of salinization during the past 20 years was 0.65, which is categorized as severe salinization. The degree of salinization gradually decreased from the northeastern coastal area to the southwestern inland area. (4) The dominant factors affecting soil salinization in different historical periods varied. The research results could provide support for decision-making regarding the precise prevention and control of salinization in the Yellow River Delta.
Artificial Intelligence-Based Underwater Acoustic Target Recognition: A Survey
Authors: Feng Sheng , Ma Shuqing , Zhu Xiaoqian , Yan Ming
Published: September 2024

Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing.
A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics
Authors: He Yao , Xu Le , Huo Jincong , Zhou Huaji , Shi Xiaowei
Published: September 2024

Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations.
Assessment of Commercial GNSS Radio Occultation Performance from PlanetiQ Mission
Authors: Zhran Mohamed , Mousa Ashraf , Wang Yu , Hasher Fahdah Falah Ben , Jin Shuanggen
Published: September 2024

Global Navigation Satellite System (GNSS) radio occultation (RO) provides valuable 3-D atmospheric profiles with all-weather, all the time and high accuracy. However, GNSS RO mission data are still limited for global coverage. Currently, more commercial GNSS radio occultation missions are being launched, e.g., PlanetiQ. In this study, we examine the commercial GNSS RO PlanetiQ mission performance in comparison to KOMPSAT-5 and PAZ, including the coverage, SNR, and penetration depth. Additionally, the quality of PlanetiQ RO refractivity profiles is assessed by comparing with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) data in October 2023. Our results ensure that the capability of PlanetiQ to track signals from any GNSS satellite is larger than the ability of KOMPSAT-5 and PAZ. The mean L1 SNR for PlanetiQ is significantly larger than that of KOMPSAT-5 and PAZ. Thus, PlanetiQ performs better in sounding the deeper troposphere. Furthermore, PlanetiQ’s average penetration height ranges from 0.16 to 0.49 km in all latitudinal bands over water. Generally, the refractivity profiles from all three missions exhibit a small bias when compared to ERA5-derived refractivity and typically remain below 1% above 800 hPa.
Mapping Kinetic Energy Hotspots in the Persian Gulf and Oman Sea Using Surface Current Derived by Geodetic Observations and Data Assimilation
Authors: Pirooznia Mahmoud , Voosoghi Behzad , Khalili Mohammad Amin , Di Martire Diego , Amini Arash
Published: September 2024

Harnessing ocean kinetic energy has emerged as a promising renewable energy solution in recent years. However, identifying optimal locations for extracting this energy remains a significant challenge. This study presents a novel scheme to estimate the total surface current (TSC) as permanent surface current by integrating geodetic data and in-situ measurements. The TSC is typically a combination of the geostrophic current, derived from dynamic topography, and the Ekman current. We utilize NOAA’s Ekman current data to complement the geostrophic current and obtain the TSC. To further enhance the accuracy of the TSC estimates, we employ a 3DVAR data assimilation method, incorporating local current meter observations. The results are verified against two control current meter stations. The data-assimilation process resulted in an improvement of 4 to 15 cm/s in the precision of calculated TSC. Using the assimilated TSC data, we then assess the kinetic energy potential and identify six regions with the most significant promise for marine kinetic energy extraction. This innovative approach can assist researchers and policymakers in targeting the most suitable locations for harnessing renewable ocean energy.
Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA
Authors: Li Yongcheng , Xu Huaqiang , Xu Jiawei , Chen Hao , An Qiying , Hou Kangming , Wang Jingjing
Published: September 2024

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.
Quaternary Deformation along the Gobi–Tian Shan Fault in the Easternmost Tian Shan (Harlik Mountain), Central Asia
Authors: Shen Tianyi , Ding Yan , Wang Guocan , Zhang Dehai , Zhao Zihao
Published: September 2024

The Tian Shan is a typical active intracontinental orogenic belt that is driven by the ongoing indentation of India into Eurasia. However, the geological features of Quaternary deformation, especially in the easternmost sector near Harlik Mountain, remain elusive. Field observations, topographic analysis, and Electron Spin Resonance (ESR) dating were employed to comprehensively assess the deformation features and evaluate the deformation pattern for this region during the Quaternary period. The results disclose evidence of deformation in the northern and southern foreland basins of Harlik Mountain. In the Barkol Basin to the north, crustal shortening results in the formation of surface scarps and folds, indicating north-directed thrusting, with a shortening rate of ~0.15 mm/yr. In the Hami Basin, the north-directed thrust elevates the granites, which offset the alluvial fans, with a shortening rate of ~0.18 mm/yr. Together with the shortening along the boundary fault, the aggregated north–south shortening rate is approximately 0.69 mm/yr in the easternmost Tian Shan, corresponding with the differential motion rate between the north and south Harlik Mountain revealed by the GPS velocity. These findings imply that, distal to the collision zone, tectonic strain in the eastern Tian Shan is primarily accommodated through the reactivation of pre-existing strike–slip faults, with crustal shortening concentrated at the overlapping position of parallel northeast-trending left-lateral strike–slip faults.
Development and Comparison of InSAR-Based Land Subsidence Prediction Models
Authors: Zheng Lianjing , Wang Qing , Cao Chen , Shan Bo , Jin Tie , Zhu Kuanxing , Li Zongzheng
Published: September 2024

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.
HRTracker: Multi-Object Tracking in Satellite Video Enhanced by High-Resolution Feature Fusion and an Adaptive Data Association
Authors: Wu Yuqi , Liu Qiaoyuan , Sun Haijiang , Xue Donglin
Published: September 2024

Multi-object tracking in satellite videos (SV-MOT) is an important task with many applications, such as traffic monitoring and disaster response. However, the widely studied multi-object tracking (MOT) approaches for general images can rarely be directly introduced into remote sensing scenarios. The main reasons for this can be attributed to the following: (1) the existing MOT approaches would cause a significant rate of missed detection of the small targets in satellite videos; (2) it is difficult for the general MOT approaches to generate complete trajectories in complex satellite scenarios. To address these problems, a novel SV-MOT approach enhanced by high-resolution feature fusion and a two-step association method is proposed. In the high-resolution detection network, a high-resolution feature fusion module is designed to assist detection by maintaining small object features in forward propagation. By utilizing features of different resolutions, the performance of the detection of small targets in satellite videos is improved. Through high-quality detection and the use of an adaptive Kalman filter, the densely packed weak objects can be effectively tracked by associating almost every detection box instead of only the high-score ones. The comprehensive experimental results using the representative satellite video datasets (VISO) demonstrate that the proposed HRTracker with the state-of-the-art (SOTA) methods can achieve competitive performance in terms of the tracking accuracy and the frequency of ID conversion, obtaining a tracking accuracy score of 74.6% and an ID F1 score of 78.9%.
Changes in the Fatty Acid Composition of Vegetable Oils Affect the Feeding Behavior, Feed Preference, and Thermoregulatory Responses of Sheep
Authors: Andrade Évyla Layssa G. , Pereira Filho José M. , Lucena Kevily Henrique de O. S. de , Barreto Yuri C. S. , Oliveira Ronaldo L. , Sousa Bonifácio B. de , Vaz Antônio Fernando de M. , de Oliveira Juliana Paula F. , Fonseca Mozart A. , Bezerra Leilson R.
Published: September 2024

This research evaluated the effects of energy supplementation on sheep’s feeding behavior, feed preference, and thermoregulatory responses using technical cashew nutshell liquid (CNSL) and different vegetable oils with different unsaturated fatty acid (UFA) compositions. The experiment was completely randomized with five treatments: a mixture of CNSL (0.5%) + vegetable oils [canola (high in monounsaturated fatty acids—MUFA), and corn, soybean, sunflower, or cottonseed oil (high in polyunsaturated fatty acids-PUFA) at 1.5%] based on total diet dry matter, with eight replications. Forty uncastrated male sheep, with an average initial BW of 24.44 ± 1.5 kg, were evaluated for 70 days. The CNSL + vegetable oil blend did not affect DM and neutral detergent fiber (aNDF) intake (p > 0.05). However, diets with canola oil resulted in higher SFA intake (p < 0.05) than other oils. The canola oil + CNSL blend led to a higher intake of UFA and MUFA and lower PUFA intake than other oil blends (p < 0.05). Sheep fed canola oil ruminated fewer boli per day than those fed soybean and sunflower oils. Using three sieves (pef1.18) reflected in higher sheep aNDF intake. Respiratory frequency and surface temperature of sheep were lower before feeding than 3 h after, without effects of the type of oil. Higher serum creatinine and cholesterol levels were observed in sheep fed CNSL with corn and canola oils compared to other oils. Serum calcium was lower in sheep fed CNSL with soybean and canola compared to sunflower and corn. Including CNSL with vegetable oils with different FA compositions did not affect physiological and thermographic variables. However, sheep showed better diet selectivity and lower bolus rumination with higher MUFA (canola oil) content. Including CNSL with canola oil in sheep diets is recommended, as it increases dietary energy content, enhances diet selectivity, reduces PUFA intake, and does not impact animal health.
Radiation Impedance of Rectangular CMUTs
Authors: Khorassany Shayan , Dew Eric B. , Rahim Sobhani Mohammad , Zemp Roger J.
Published: September 2024

Recently, capacitive micromachined ultrasound transducers (CMUTs) with long rectangular membranes have demonstrated performance advantages over conventional piezoelectric transducers; however, modeling these CMUT geometries has been limited to computationally burdensome numerical methods. Improved fast modeling methods, such as equivalent circuit models, could help achieve designs with even better performance. The primary obstacle in developing such methods is the lack of tractable methods for computing the radiation impedance of clamped rectangular radiators. This paper presents a method that approximates the velocity profile using a polynomial shape model to rapidly and accurately estimate radiation impedance. The validity of the approximate velocity profile and corresponding radiation impedance calculation was assessed using finite element simulations for a variety of membrane aspect ratios and bias voltages. Our method was evaluated for rectangular radiators with width:length ratios from 1:1 up to 1:25. At all aspect ratios, the radiation resistance was closely modeled. However, when calculating the radiation reactance, our initial approach was only accurate for low aspect ratios. This motivated us to consider an alternative shape model for high aspect ratios, which was more accurate when compared with FEM. To facilitate the development of future rectangular CMUTs, we provide a MATLAB script that quickly calculates radiation impedance using both methods.
Scintillation in Liquid Xenon for Gamma-Ray Medical Imaging: From Single Time-over-Threshold to Multi-Time-over-Threshold PMT Signal Measurements
Authors: Lainé Quentin , Beaupere Nicolas , Cai Dingbang , Morteau Eric , Seguin Fabrice , Thers Dominique , Lahuec Cyril
Published: September 2024

In this paper, a new light event acquisition chain in a three-gamma liquid xenon prototype for medical nuclear imaging is presented. The prototype implements the Multi-Time-Over-Threshold (MTOT) method. This method surpasses the Single-Time-Over-Threshold (STOT) by precisely determining both the number of vacuum ultraviolet (VUV) photons detected by each photomultiplier tube (PMT) and their arrival times for light signal measurement. Based on both the experimental and simulated results, the MTOT method achieved a 70% improvement in reconstructing photoelectrons (PEs) and enhanced the precision of the arrival time estimation by 20–30% compared with STOT. These results will enable an upgrade of the XEMIS2 (Xenon Medical Imaging System) camera, improving its performance as the imaged activity increases.
Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode Decomposition Interval Threshold
Authors: Xing Chuanxi , Tan Guangzhi , Dong Saimeng
Published: September 2024

To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of −9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.
Construction of Cu2Y2O5/g-C3N4 Novel Composite for the Sensitive and Selective Trace-Level Electrochemical Detection of Sulfamethazine in Food and Water Samples
Authors: Surya Rajendran , Sakthinathan Subramanian , Meenakshi Ganesh Abinaya , Yu Chung-Lun , Chiu Te-Wei
Published: September 2024

The most frequently used sulfonamide is sulfamethazine (SMZ) because it is often found in foods made from livestock, which is hazardous for individuals. Here, we have developed an easy, quick, selective, and sensitive analytical technique to efficiently detect SMZ. Recently, transition metal oxides have attracted many researchers for their excellent performance as a promising sensor for SMZ analysis because of their superior redox activity, electrocatalytic activity, electroactive sites, and electron transfer properties. Further, Cu-based oxides have a resilient electrical conductivity; however, to boost it to an extreme extent, a composite including two-dimensional (2D) graphitic carbon nitride (g-C3N4) nanosheets needs to be constructed and ready as a composite (denoted as g-C3N4/Cu2Y2O5). Moreover, several techniques, including X-ray diffraction analysis, scanning electron microscopy analysis, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and Raman spectroscopy were employed to analyze the composites. The electrochemical measurements have revealed that the constructed g-C3N4/Cu2Y2O5 composites exhibit great electrochemical activity. Nevertheless, the sensor achieved outstanding repeatability and reproducibility alongside a low limit of detection (LOD) of 0.23 µM, a long linear range of 2 to 276 µM, and an electrode sensitivity of 8.86 µA µM−1 cm−2. Finally, the proposed GCE/g-C3N4/Cu2Y2O5 electrode proved highly effective for detection of SMZ in food samples, with acceptable recoveries. The GCE/g-C3N4/Cu2Y2O5 electrode has been successfully applied to SMZ detection in food and water samples.
Visual Deprivation’s Impact on Dynamic Posture Control of Trunk: A Comprehensive Sensing Information Analysis of Neurophysiological Mechanisms
Authors: Sasaki Anna , Nagae Honoka , Furusaka Yukio , Yasukawa Kei , Shigetoh Hayato , Kodama Takayuki , Miyazaki Junya
Published: September 2024

Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion–extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future.
Design and Development of an Automatic Layout Algorithm for Laser GNSS RTK
Authors: Tang Jiazhi , Sun Xuan , Lu Xianjian , Jia Jiguang , Tang Shihua
Published: September 2024

At the current stage, the automation level of GNSS RTK equipment is low, and manual operation leads to decreased accuracy and efficiency in setting out. To address these issues, this paper has designed an algorithm for automatic setting out that resolves the common problem of reduced accuracy in conventional RTK. First, the calculation of the laser rotation center is conducted using relevant parameters to calibrate the instrument’s posture and angle. Then, by analyzing the posture information, the relative position and direction of the instrument to the point to be set out are determined, and the rotation angles in the horizontal and vertical directions are calculated. Following this, the data results are analyzed, and the obtained rotation angles are output to achieve automatic control of the instrument. Finally, a rotating laser composed of servo motors and laser modules is used to control the GNSS RTK equipment to locate the set-out point, thereby determining its position on the ground and displaying it in real-time. Compared to traditional GNSS RTK equipment, the proposed automatic setting out algorithm and the developed GNSS laser RTK equipment reduce the setting out error from 15 mm to 10.3 mm. This reduces the barrier to using GNSS RTK equipment, minimizes human influence, enhances the work efficiency of setting out measurements, and ensures high efficiency and stability under complex conditions.
Determining Carbohydrates for Increasing Safety: GC-FID Quantification of Lactose, Galactose, Glucose, Tagatose and Myo-Inositol in ‘Maturo’ PDO Pecorino Sardo Cheese
Authors: Dedola Alessio Silvio , Caredda Marco , Addis Margherita , Lai Giacomo , Fiori Myriam , Pes Massimo , Mara Andrea , Sanna Gavino
Published: September 2024

Although PDO Pecorino Sardo is one of the oldest traditional cheeses of Sardinia, Italy, data on its nutritional properties and food safety are lacking. In particular, significant amounts of lactose and galactose may be a health concern for consumers. The primary objective of this study is to quantify, using a validated GC-FID method, the residual lactose and galactose content in “maturo” (i.e., ripened for at least two months) Protected Denomination of Origin (PDO) Pecorino Sardo cheese. A statistically representative sampling from seven dairies distributed throughout Sardinia has been selected for this aim. In addition to lactose and galactose, two of their metabolites (i.e., glucose and tagatose, respectively) and a bioactive polyol like myo-inositol were quantified. The concentration of lactose (mean 26 mg kg−1, range 4–90 mg kg−1) was below the strictest limit set in the European Union (i.e., 100 mg kg−1), while the galactose content was found to be in an amount (mean: 76 mg kg−1, range: 10–200 mg kg−1) that even patients afflicted with severe galactosemia, albeit with some circumspection, could consume this cheese. Ripening (two to four months) had no significant effect on the amount of all analytes, while a slight decrease in galactose levels was observed during the manufacturing season. Finally, the amounts of glucose, tagatose, and myo-inositol are constant in the range of a few tens of mg kg−1.
Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education
Authors: Mezak Matijevic Mirela , Pisker Barbara , Dokic Kristian
Published: September 2024

Due to the fast-changing environments caused by artificial intelligence development, the socio-technical challenge in contemporary educational systems focuses on the need for more regulative measures guiding system stakeholders’ behavior. In fulfilling the present legal gap, enacted soft law regulation has been laid out, and a detailed systematic literature review was conducted in the paper presented. The specific methodological approach was selected to deal with two crucial research tasks: to reveal and recommend fundamental governing mechanisms regarding the use and application of generative artificial intelligence; more precisely, large language models in educational systems. Three systematically guided layers of quantitative and qualitative content analysis of central policy, legislation, and regulatory mechanisms in governing AI in education were extracted from the 142 Scopus Database and Web of Science research papers analyzed and presented. These research findings benefit policymakers, regulatory and legislative bodies, and agencies in constructing governing frames for using and applying generative artificial intelligence in education.
Improved Antioxidant Status after Diet Supplementation with Novel Natural-Based Supplement in Combat Athletes
Authors: Tirla Adrian , Vicas Simona Ioana , Sirca Teodora Bianca , Cinezan Corina , Cavalu Simona
Published: September 2024

Background: Intense physical activity is known to generate high levels of oxidative stress, and supplementation with bioactive products with powerful antioxidant effects is often recommended. In this context, the present study aims to evaluate the potential benefits of supplementing the diet of professional combat fighters with a new bioactive formulation based on Aronia melanocarpa (black chokeberry) and bee pollen, a natural combination with a balanced ratio of macro- and micronutrients, rich in proteins and polyphenols. Methods: A total of 31 professional combat fighters were selected to participate in this study. Due to allergic medical history, two were excluded, and the remaining 29 were divided into two groups: 14 in the control and 15 in the supplementation group. The supplemented group consumed daily 40 g of novel supplement based on a combination of dried black chokeberry and bee pollen (1:2 ratio) for 4 weeks. Results: A statistically significant increase in plasma antioxidant capacity was observed in the supplemented group compared to control related to the time and group factors at the end of this study. During this study, a significant increase in neutrophils was recorded in both groups. Supplementation with black chokeberry and bee pollen generated no significant modifications in inflammatory markers, body composition, glycemia, maximum aerobic capacity, blood glycemia, hemoglobin or red cell line. Conclusion: This clinical study pointed out a positive result in terms of plasma antioxidant capacity upon administration of the novel nutraceutical formulation.
A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks
Authors: Yin Qing , Han Chunmiao , Li Ailin , Liu Xiao , Liu Ying
Published: September 2024

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance.