bluuebunny/crossref_metadata_2025_split huggingface dataset:
Methodological approaches to integrate gender analysis in One Health perspective at human-animal-ecosystem interface
Authors: Cataldo Claudia 
Published: November 2024

Abstract
The One Health approach considers the interdependence between environmental aspects, human and animal health, and promotes a multi-sectoral collaboration of policies, systems and processes that should be taken into account to prevent and mitigate emerging risks at human-animal-ecosystem interface. Gender is defined as the sum of roles and behaviors determined by the social context learned and adopted by men and women which lead to different identities and roles considered suitable for each of them. Men and women are systematically exposed to several factors that can affect their well-being; this exposure is influenced by gender, social rules and behaviors. It follows that gender analysis is an essential element to maximize the potential of One Health, and it is a lens to analyze, prevent and mitigate risks at the human-animal-ecosystem interface, more effectively, identifying in detail the target groups involved in specific contexts in a socio-ecological perspective.

One Health impact statement
The One Health approach to public health is a holistic approach that considers environmental sustainability, animal health, and human health together. Several factors including gender, age groups, cultures and social conditions may affect significantly the risk of diseases and their clinical evolution. Supplementing the One Health perspective with a gender analysis to study the Influence of social norms, activities, and risk behavior on exposure to infections, chemical pollution, and the consequences of climate change, could help to formulate targeted interventions and maximize the effectiveness of intersectoral approach at the human-animal-ecosystem interface. However, it is often ignored, but this inclusion is critical to the ongoing development of the One Health approach in prevention and preparedness to global threats in line with the recent socio-ecological principles.
Gravitational reheating in Starobinsky inflation
Authors: Dorsch Gláuber C. , Miranda Luiz , Yokomizo Nelson 
Published: November 2024

Abstract
We investigate the possibility of achieving post-inflationary reheating exclusively through the gravitational interaction in Starobinsky inflation, which itself assumes nothing but gravity. We consider the possibility that the reheating sector couples to gravity via a non-minimal coupling. Our analysis is performed both in a perturbative and in a non-perturbative approach, where particle production is computed from Bogoliubov coefficients.
Our findings indicate that, for a minimal coupling, a reheating temperature T
reh ∼ 108 GeV is obtained, with a reheating duration of approximately 21 e-folds. We also show that non perturbative gravitational production during preheating can lead to maximum temperatures of the order of 1012 GeV. This shows that the gravitational interaction could be the sole responsible for reheating the Universe after inflation, without the need to assume other ad hoc inflaton interactions.
K-mouflage at high k: extending the reach of Hi-COLA
Authors: Gupta Ashim Sen , Fiorini Bartolomeo , Baker Tessa 
Published: November 2024

Abstract
The Hi-COLA (https://github.com/Hi-COLACode/Hi-COLA) code is an efficient dark matter simulation suite that flexibly handles the Horndeski family of modified gravity models. In this work we extend the scope of Hi-COLA to accommodate Horndeski theories with K-mouflage screening, allowing for the computation of matter power spectra in the non-linear regime in these models. We explore the boost of the dark matter power spectrum relative to GR-ΛCDM in K-mouflage gravity, and also discuss how large-scale structure computations change between the Einstein and Jordan frames. A dissection of the relative contributions of the modified background, linear growth, fifth force, and the conformal factor (a new inclusion to Hi-COLA) to the boost factor is presented. The ability of Hi-COLA to run with general Horndeski models and multiple screening mechanisms makes it an ideal tool for testing gravity with upcoming galaxy survey data.
CNN based approach for root zone soil moisture prediction by ground penetrating radar
Authors: Liu Kexin , Lu Qi , Guo Shilong , Liang Wenjing 
Published: November 2024

Abstract
Traditional soil moisture prediction methods often face challenges in providing spatially and temporally comprehensive root zone soil moisture (RZSM) with complex soil environment. In this paper, a novel method is proposed for predicting RZSM using images obtained from ground penetrating radar (GPR) data and processed with the convolutional neural network (CNN). The CNN encompasses two major stages: classification and regression. Initially, a CNN is tailored to classify tree root images obtained by GPR into distinct water content categories. Subsequently, through transfer learning, the pre-trained classification network is adapted for regression to predict continuous soil moisture levels. Then, the field experimental results demonstrate the efficacy and robustness of our approach, offering promising avenues for enhancing root zone water management and sustainability.
Deep learning-based cross-hole GPR travel time tomography
Authors: Cheng H , Hu S F , Zhao Y H 
Published: November 2024

Abstract
As a non-destructive geophysical detection method that is not limited by the target depth, cross-hole radar can accurately identify the positions and properties of underground cavities. Traditional cross-hole radar methods face significant challenges in stability, accuracy, and computational efficiency. In recent years, with the rapid development of deep learning and its remarkable achievements in the field of imaging, it has been applied in various fields. As a deep learning network structure for image segmentation, the Unet model can realize “end-to-end” image output. Therefore, the Unet model of convolutional neural network is introduced into cross-hole radar travel time tomography in this paper. A deep-learning trave time tomography method based on the nonlinear relationship between travel time data and models is proposed. Firstly, the models with cavities of different permittivities are constructed. The 2D traveltime images of transmitting and receiving pairs are calculated based on the GPR travel time fast-picking algorithm. Then, a Unet model is built to learn the nonlinear mapping relationship between the 2D travel time images and models with cavities. Finally, the intelligent travel time tomography method based on the Unet model of underground cavities is realized. Experiment results of models demonstrate the effectiveness of the proposed method.
GPR Image Retrieval and Localization Based on Spatial Prior Constraints
Authors: Bi Beizhen , Chen Yuwei , Shen Liang , Zhang Pengyu , Huang Xiaotao , Jin Tian 
Published: November 2024

Abstract
Localizing Ground Penetrating Radar (LGPR) offers the distinct advantage of being unaffected by weather and light conditions changes. As a novel auxiliary driving localizing system, LGPR enhances the robustness of the auto-drive system. Traditional ground penetrating radar localizing technologies rely heavily on high-quality map data, exhibiting optimal performance in environments with complete, high-precision maps. However, deviations from the mapped trajectory can compromise localization accuracy. We introduce a novel approach: a ground penetrating radar image retrieval and localization method based on spatial prior constraints of array antennas to address this challenge. This method aims to mitigate mismatches in ground penetrating radar navigation and localization encountered under incomplete map coverage. We performed a comparative analysis with the LGPR baseline and then validated them experimentally on our dataset. The results show that the proposed method achieved significant enhancement in accuracy. In particular, the superiority of the proposed method is demonstrated in low overlap GPR image matching and localisation.
Enhanced Scoring Instance Segmentation Network for Subsurface Targets Recognition from GPR B-Scans
Authors: Hou Feifei , Yin Xue , Liu Hai , Qiao Boxuan 
Published: November 2024

Abstract
Ground Penetrating Radar (GPR) is a non-destructive technique used for detecting subsurface structure and space. However, the interpretation of field GPR data is quite labour-intensive and time-consuming. This paper develops a deep learning-based approach for the detection and segmentation of subsurface targets based on GPR data. Since existing deep learning methods do not always accurately reflect the true quality of segmentation masks and exhibit locality when handling details in complex scenes, the proposed method employs the Mask Scoring R-CNN (MS R-CNN) framework as its primary framework and makes additional modifications to its backbone and neck architecture. Experimental results on GPR bridge dataset show notable improvements in loss metrics, segmentation accuracy, and bounding box precision. These enhancements have effectively reduced overall losses and enhanced the performance of Average Precision index of the enhanced MS R-CNN, demonstrating the effectiveness of our approach in the accurate identification of underground objects under complex environments.
A holographic subsurface imaging radar with high plane resolution: principle and application
Authors: Zhihua He , Tao Liu , Xiaoji Song , Cheng Chen , Guanghu Jin , Chunlin Huang , Yi Su 
Published: November 2024

Abstract
Holographic subsurface imaging radar uses the penetration of electromagnetic waves into non-metallic media to detect and image the interior of the media, which can intuitively reflect the structure and abnormal distribution of objects in the media in the form of images. By adopting the design methods of integrated radar RF front-end, fast high-density spatial sampling and autofocus imaging processing, a holographic subsurface imaging radar with high plane resolution is realized. The system weighs only 2.5 kg and achieves a plane resolution of 2.5mm, which has high detection efficiency and high portability. The application tests of imaging resolution, penetration of different materials, penetration of biological tissues and non-destructive detection of materials were carried out, which verified the feasibility and effectiveness of the design scheme.
Research on ground penetrating radar and Muon detection method in Xixia Mausoleum
Authors: Lu Jiayu , An Baizhou , Bai Yadong , Zeng Zhaofa , Ren Xiufen , Zhou Shuai , Sun Boyang , Yan Zhaotao 
Published: November 2024

Abstract
Xixia Mausoleum is an important historical site in China, which has important research value and archaeological significance. Non-destructive detection technology is of great significance for the detection of Xixia Mausoleum and its surrounding architectural sites. In this paper, GPR and other methods were used in comprehensively archaeological exploration of Mausoleum No. 2. The mausoleum pagoda, central platform and underground in the mausoleum area showed obvious GPR abnormal characteristics which reflects the underground structure. For the mausoleum pagoda and the central platform, GPR and cosmic ray probe were used to image the density structure inside the mausoleum, and the range-ranging GPR was used to conduct regional detection in the mausoleum area and the external wall area to study the characteristics of the underground structure in this area. The solid loess body structure of the mausoleum pagoda and the central platform was found in this detection, and there were certain differences in underground structure between the inner and outer walls, which was inferred to be an unknown ancient building buried. The detection results will be combined with other geophysical methods and technologies in the future to complete the imaging of the internal structure characteristics of Xixia Mausoleum site, and provide innovative and reliable basis for the archaeological exploration of Xixia Mausoleum.
Spatiotemporal Localization of GPR Sequence Images With 3DResNet18
Authors: Hu Yuchen , Zhang Pengyu , Bi Beizhen , Chen Yuwei , Xin Qin , Huang Xiaotao 
Published: November 2024

Abstract
Localizing Ground Penetrating Radar can provide a reliable support for current autonomous navigation and localization. Based on the characteristics of ground penetrating radar images, sequence-based matching and localization can effectively improve the problem of a large number of mismatched candidates in single frame image matching. Convolutional neural network is a deep model that can act directly on the original input, 2D convolutional neural network mainly extracts spatial information, we use 3DResNet18 model to extract features from both spatial and temporal dimensions by 3D convolution, so as to capture information in multiple neighbouring frames, to generate multiple channels of information from input frames, and finally combine the information from all the channels together to get a high-level feature for matching. We use the model for localizing of ground penetrating radar sequence images and it achieves better performance compared to the baseline approach.
Experiment on Measuring Lake Ice Thickness Using a SFCW Ground Penetrating Radar System
Authors: Zheng Hongxing , Huang Chunlin , He Zhihua , Liu Tao , Song Xiaoji 
Published: November 2024

Abstract
To ensure transportation safety and for other purposes, it becomes necessary to measure the thickness of the ice layer on natural water bodies during winter. As an efficient and non-destructive subsurface detection tool, Ground Penetrating Radar (GPR) can achieve rapid and accurate measurements of ice thickness. For this experiment, we utilize a stepped frequency continuous wave (SFCW) GPR system based on a Vector Network Analyzer (VNA) system to measure the thickness of ice layers. Additionally, based on the scenario of detecting ice layer structures with ground-penetrating radar, the formula for calculating the thickness of ice layers was revised to achieve more accurate measurements of ice thickness. Finally, we compared the calculated results with the ice thickness detection results from ice layer drilling experiments to verify the effectiveness of the system in detecting ice layer thickness.
A Ground Penetrating Radar Array Antenna Designed for Small Targets Detection
Authors: Shi Xinghua , Li Shaolong , Yin YueMeng , zhang Zhao 
Published: November 2024

Abstract
The detection of small targets, especially those made of non-metallic materials, presents significant challenges due to their small size and the difficulty of being detected by traditional metal detectors. Additionally, environmental factors and soil conditions further complicate their detection. Traditional Ground Penetrating Radar (GPR) operate with a single-line detection method, resulting in a low number of scan lines and inefficient detection. This approach makes it difficult to generate slice images and can lead to high missed detection. A miniaturized GPR array antenna suitable for handheld and unmanned platforms has been developed. Utilizing a planar dipole antenna with multiple resistive and capacitive loading, a horizontal oblique polarization array comprising five transmitters and six receivers has been constructed, achieving dual-reception from a single transmission across ten channels, significantly improving upon the limitations of single antenna systems in terms of data volume and sampling interval. Experimental tests demonstrate that the designed system can successfully detect the echo signals of metallic and non-metallic small targets like buried up to 20 cm deep in typical soil conditions, and generate clear slice images, proving its capability for high-resolution imaging detection of shallow buried small targets.
A Comprehensive Detection and Evaluation Method for the Internal Health Status of Highways
Authors: Feng W Y , Cheng D D , Wang C H , Cheng X , Yin Y J , Geng H Y 
Published: November 2024

Abstract
It is necessary to regularly diagnose highway health status. The current evaluation system pays less attention to internal damages, which makes the evaluation results not comprehensive and accurate enough. Therefore, Multi frequency array ground penetrating radar and falling weight deflectometer are adopted to detect the thickness of structural layers, underground disease, and structural bearing capacity. Establish evaluation criteria, quantify and grade the detection results, comprehensively evaluate health status of highways. Experiment results demonstrate the feasibility and effectiveness of this method.
Accurate real-time ANN classification of cavities and utilities using 3D GPR data in the MFCC domain
Authors: Ogunbo J N , Baek S H , Kim S W 
Published: November 2024

Abstract
Real-time identification of serviceable utilities and cavities is critical for the safety of urban settlements because delay has been reportedly costly and destructive. For utilities, maintaining their integrity is the goal, while for cavity imaging, prevention or mitigation becomes the focus. Several existing research has recommended some methodology for classification of these utilities and cavities, but their approaches are either geometrically restrictive or cumbersome. We therefore present a practical methodology that provides accurate real-time classification of cavity, box, manhole, patch, and pipe. 3D GPR data is reshaped into 1D supertrace and a 2D mel-frequency cepstrum coefficients (MFFC) feature are computed, serving as an input pattern, paired up with a utility class. The ANN is subsequently trained and tested during training to avoid overfitting of data. 99% model testing accuracy was achieved after 100 epochs, translating to only 3 misclassifications out of 1000 3D GPR data inputs used for computing a confusion matrix.
GPR circumferential survey line radius’s effect on shield machine tunnel look-ahead detection
Authors: Duan W , Xie X , Zhu T , Yang Y , Wu H , Pei Y 
Published: November 2024

Abstract
In southern cities of China shield machines often encounter obstacles such as large boulders in complex strata which may cause more abrasion to the shield machine’s cutterhead and even make the shield machine stuck. Therefore, it is important to know distribution of obstacles in front of excavation face in advance. Ground penetrating radar (GPR) is an efficient and accurate detection method to ahead detect large boulders when GPR antennas are put in the cutter head. Synthetic simulations of GPR look-ahead detection are carried out based on finite difference time domain mothed. Antennas are put at two different radii of 4 meters and 7 meters within a 15-meter diameter cutter head. GPR response of boulders in various positions can be clearly found in B-scan images and effect of survey line radius is discussed. Reflection wave in circumferential survey line has continuous sinusoidal wave feature which is different from hyperbola curve feature in conditional straight survey line. The reflection waves of boulders in GPR B-scan images show same characteristics at two different radii of the survey line. Numerical simulation results provide evidences for GPR in accurate tunnel forward-looking detection in complex strata.
Antenna Comparison of Different Antennas in Through-Wall Radar Human Vital Signs Recognition
Authors: Liu Tieyu , Li Jing , Zeng Zhaofa , Liu Hai 
Published: November 2024

Abstract
Through wall radar (TWR) is an essential technology in the post-disaster rescue of buried individuals. The human vital signs analysis of breathing, heartbeat, and other periodic motions is the information used to identify and classify the human target. In the TWR system, selecting a suitable antenna and processing weak signals due to wall clutter and weak human vital signals is essential. In this abstract, we compare the performance of different antennas in TWR human vital feature recognition. We introduce a threshold pixel-connectivity signal processing method to locate the human body efficiently and accurately and extract the human respiratory frequency. The conclusion is that the Horn antenna has strong directionality, radiation energy, and a high resolution for recognizing the vital features of stationary human targets.
Analysis of characteristics of underground pipeline detection by co-polarization and cross-polarization of ground penetrating radar
Authors: Xu Zeshan , Xiao Min , Chen Jie 
Published: November 2024

Abstract
The polarization mode of ground penetrating radar includes co-polarization mode and cross-polarization mode. The co-polarization mode is more commonly used, mainly because of its good polarization matching effect and good detection effect, so the importance of cross-polarization is ignored. In order to achieve better detection effect in underground pipeline detection, this paper compares the co-polarization and cross-polarization experiments, The results show that when the surface roughness of the pipeline is low, the detection effect of VV polarization of the co-polarization mode is better, and when the surface roughness of the pipeline is high, the detection effect of cross polarization mode is better.
Multi-frequency electromagnetic sensor for detecting NaCl concentration in water - experimental study
Authors: Wang Haoyu , Zeng Zhaofa , Li Zhilian 
Published: November 2024

Abstract
Clean water resources have become the focus of global attention. The rapid detection of water pollution and the rapid evaluation of NaCl content in water is an important evolution the quality of water or soil in environment and agriculture. The main method analysis the content in water or soil is based on laboratory chemical tests at present. In this paper, we used a method based on a non-uniform microwave sensor array to determine the concentration of NaCl in water. Based on the experiment data, empirical formulas obtained between the concentration of NaCl in water and different electromagnetic frequency which can be used for NaCl content estimation. The results of the study demonstrated the feasibility of microwave sensors in detecting chemical content in water, and as basic experiment for further study for water pollution.
Multi-sensor fusion estimation subject to random sensor failures under binary encoding scheme: A federated-filtering-based method
Authors: Li Na , Zou Lei , Wang Chenxi 
Published: November 2024

Abstract
This paper explores the issue of fusion estimation in multi-sensor systems experiencing random sensor failures via a binary coding scheme (BCS). The occurrence of sensor failures is modeled using random variables with predetermined probability distributions. To avert the potential signal distortions during network-based communication, the BCS is utilized to transform the measurement signals into bit strings. A novel federated-filtering-based fusion estimation approach is developed to obtain the desired state estimates. The optimal estimator parameters are achieved by solving a pair of recursive difference equations, taking into account the impacts of bit errors and probabilistic quantization. Additionally, the ultimate boundedness of the estimation error covariance for the fusion estimates is guaranteed by a sufficient condition that we establish. Finally, the utility of the introduced fusion estimation method is illustrated through a simulation example.