bluuebunny/crossref_metadata_2025 huggingface dataset:
Wang Yangming’s Concept of Oneness from the Perspective of Mysticism
Authors: Liu Linna
Published: September 2024

Wang Yangming’s emphasis on the concept of oneness establishes a significant connection between his philosophy and mysticism. This comparative study draws upon his mystical encounters and conceptual framework. Although “all things form one body” appears to be a typical formula of extrovertive mysticism, his doctrine is more inclined to be classified as a type of introvertive mysticism because of the special definition of “thing” and the deep involvement of heart–mind. The realm of oneness is based on the mode of a heart–mind pervading the entirety of the universe and all things, with the core concept of liangzhi as both pure consciousness and the base of the world. The adoption of a mystical philosophical perspective highlights Wang’s theory of oneness as a spiritual philosophy, transcending general ethical claims, thus enriching our understanding of Wang’s philosophy of mind.
‘The Hidden Present’: Time and Eschatology in Jean-Yves Lacoste
Authors: Turcan Nicolae
Published: September 2024

This article explores the phenomenology of time and eschatology in the thought of Jean-Yves Lacoste, including his recent book on the philosophy of history. Lacoste’s idea of “the hidden present” is examined within the context of his broader theological and philosophical framework, with a particular focus on the way it addresses the intersection of temporality and eternity. Human temporality is characterized by finitude and death, which are interpreted both philosophically—under the influence of Heidegger’s philosophy—and theologically. Using Husserlian and Heideggerian concepts, Lacoste proposes a theologically inspired conceptual network: phenomenological reduction versus theological reduction, world versus creation, death versus resurrection, care (Sorge) versus eschatological restlessness, and time versus eschaton. All of these describe the liturgical experience of man before God and the possibility of an eternity which, from the point of view of the world and of our experience in the world, can only take on the ever-provisional figure of anticipation. The present article argues for the existence of a theological paradox of eschatology in the writings of the French phenomenologist: even if eschatology is only anticipated, the liturgical man, situated before God (coram Deo), experiences it in an incomplete and apophatic manner.
Monitoring and Law Analysis of Secondary Deformation on the Surface of Multi-Coal Seam Mining in Closed Mines
Authors: Liu Xiaofei , Wang Jiangtao , Du Sen , Deng Kazhong , Chen Guoliang , Qin Xipeng
Published: September 2024

A large number of mines have been closed due to resource depletion, failure to meet safety production requirements, and other reasons. To effectively ensure the safety of the ecological environment above these closed mines along with the safety of engineering construction, it is necessary to monitor the secondary deformation of closed mines. Based on TerraSAR-X, Sentinel-1A data, and InSAR technology, this study obtained high-density secondary surface deformation data on the Jiahe Coal Mine and Pangzhuang Coal Mine in the western Xuzhou area. Combining mining geological data, we analyzed the spatiotemporal variation patterns and mechanisms of secondary deformation in multi-seam mining of closed mines. It was found that when mining multiple seams involves large interlayer spacing, the secondary deformation pattern shows a “W” shape. In this situation, the deformation can be divided into five stages: subsidence, uplift, re-subsidence, re-uplift, and relative stability. This study provides technical support for the evaluation and prevention of secondary deformation hazards in closed mines.
Stepwise Construction and Integration of Ecological Network in Resource-Based Regions: A Case Study on Liaoning Province, China
Authors: Wang Shaoqing , Zhao Yanling , Ren He , Zhu Shichao
Published: September 2024

Ecological networks are an effective strategy to maintain regional ecological security. However, current research on ecological network construction in areas with large-scale resource extraction is limited. Moreover, classic ecological network construction methods do not perform satisfactorily when implemented in heavily damaged mining landscapes. Taking the example of Liaoning Province, China, a framework for stepwise renewal of ecological networks was proposed, which integrates basic ecological sources and other sources that include mining areas. The framework was based on multi-source ecological environment monitoring data, and all potential ecological sources were extracted and screened using an MSPA model and the area threshold method. Further, ecological sources were classified into two types and three levels based on the influence of abandoned mines and the characteristics of ecosystem services in the ecological sources. Ecological corridors were extracted using the MCR model. An ecological corridor optimization process based on combining the gravity model with addition and removal rules of corridors was proposed. The results indicated that the basic ecological network in Liaoning Province included 101 ecological sources and 162 ecological corridors, and the supplementary ecological network included 28 ecological sources and 67 ecological corridors. The ecological sources were divided into two types, and corridors were divided into three types. The basic ecological network exhibited a spatial distribution of discrete connections in the west and close connections in the east. Changes in ecological network topological indicators indicated that a supplementary ecological network strengthened the structural performance of the regional ecological network, expanding spatial coverage, filling hollow areas, and enriching local details of the regional ecological network. Regulation strategies were proposed for ecological sources with different connection modes. The number of ecological sources implementing restrictive development, pattern optimization, and protective development were 101, 12, and 16, respectively. This paper provides a constructing framework of ecological networks adapted for resource-based regions. This method can support decisions for the environmental governance of mines, thus contributing to a balance between resource exploitation and ecological protection in regions.
InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area
Authors: Yan Liangxuan , Xiong Qianjin , Li Deying , Cheon Enok , She Xiangjie , Yang Shuo
Published: September 2024

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.
Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery
Authors: Liang Zhipeng , Gabrieli Fabio , Pol Antonio , Brezzi Lorenzo
Published: September 2024

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological surveys due to its low-cost, ease of use, and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide, we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a newly developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views, we can quickly detect the occurrence of collapse events, determine their location, and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we conducted preliminary tests of the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining the collapse information proposed in this paper can extend the potential of landslide monitoring systems based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.
On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
Authors: Schunck David , McCallum Lucia , Molera Calvés Guifré
Published: September 2024

The upcoming European Space Agency (ESA) satellite mission GENESIS is an Earth-orbiting satellite carrying instruments of all four space geodetic techniques. The onboard transmitter for Very Long Baseline Interferometry (VLBI) will allow the observation of the satellite with VLBI radio telescopes. The objective of this study is to investigate the integration of VLBI observations of GENESIS into the operations of the VLBI Global Observing System (VGOS). Based on both current and foreseeable modern VGOS antenna networks, we consider the realistic observability of both geodetic radio sources and GENESIS. We conduct a comprehensive scheduling and perform extensive simulations of the VLBI observations. We assume that observations of GENESIS are scheduled within regular, geodetic experiments. The integration of GENESIS as an additional source in the scheduling results in a minimal degradation in the geodetic parameter estimation of station positions and dUT1 of less than 0.09 mm and 0.06 μs, respectively. The results suggest to schedule scans of GENESIS at intervals of about 5 min to limit the decrease in the number of observations of geodetic sources to less than 5% with respect to schedules containing only geodetic radio sources. The schedules for 24 h experiments comprise about 150 to 200 scans and 1000 to 5000 observations of GENESIS, depending on the size of the utilized network. The frame tie accuracy between the VLBI and GENESIS frames is assessed in the form of station positions, which are solely estimated from observations of GENESIS. Multiple 24 h experiments are simulated over 52 weeks with assumed session cadences of two to three experiments per week. By stacking the normal equations from three months of experiments, we obtain station position estimates with a precision of less than 10 mm. After 12 months, the repeatabilites are reduced to less than 5 mm.
The Response of Stratospheric Gravity Waves to the 11-Year Solar Cycle
Authors: Wang Cong , Mi Qianchuan , He Fei , Guo Wenjie , Zhang Xiaoxin , Yang Junfeng
Published: September 2024

Atmospheric gravity waves are one of the important dynamic processes in near space and are widely present in the atmosphere. They play a crucial role in the transfer of energy and momentum between different regions of the atmosphere. The Sun, as the ultimate source of gravity wave energy, significantly influences the intensity of gravity wave disturbances through its activity variations. This paper utilizes data from the Global Navigation Satellite System Occultation Sounder (GNOS) onboard the Fengyun-3C (FY-3C) satellite to invert global stratospheric gravity wave disturbances. It provides the global stratospheric gravity wave distribution from 2015 to 2023, nearly covering one solar activity cycle, and focuses on analyzing the response of gravity waves at different latitudes, altitudes, and wavelengths to the solar activity cycle. We found that short-wavelength gravity waves respond more noticeably to solar activity compared to long-wavelength gravity waves. Through analyzing the intensity of stratospheric gravity wave disturbances across different latitude bands, we found that in high-latitude regions, stratospheric gravity wave disturbances are most sensitive and respond most quickly to variations in solar activity. Furthermore, the Southern Hemisphere exhibits a stronger response to the current year’s solar activity changes compared to the Northern Hemisphere. In the mid-latitude and equatorial regions, the response to changes in solar activity intensity is delayed. The correlation gradually strengthens with this lag, reaching a very strong level after a 2-year lag. Additionally, the correlation between the Southern Hemisphere and solar activity is generally higher than that of the Northern Hemisphere.
Incoherent Detection Performance Analysis of the Distributed Multiple-Input Multiple-Output Radar for Rice Fluctuating Targets
Authors: Miao Zhuo-Wei , Wang Jianbo
Published: September 2024

Utilizing spatial diversity, the distributed multiple-input multiple-output (MIMO) radar has the potential advantage of improving system detection performance. In this paper, the incoherent detection performance of distributed multiple-input multiple-output (MIMO) radars is investigated for Rice fluctuating targets. To calculate the incoherent detection probability, the moment generating function (MGF) of the Rice variable is expanded as the infinite series form. By inverting the product of MGFs of multiple independent Rice variables, new closed-form expressions for the probability density function (PDF) of the sum of independent and weighted squares of Rice variables are proposed. The proposed PDF expression for the sum of independent, non-identically distributed (i.n.i.d.) Rice variables involves an infinite series in terms of the confluent Lauricella function. Specially, the PDF for the sum of independent identically distributed (i.i.d.) Rice is expressed as the confluent hypergeometric function-based infinite series. In addition, the uniform convergence of the proposed PDF expression is also validated. Using this proposed expression, the closed-form and approximate expressions of the incoherent detection probability of MIMO radar are derived, respectively. Numerically evaluated results are illustrated and compared with Monte Carlo (MC) simulations to validate the accuracy of the derivations.
UVIO: Adaptive Kalman Filtering UWB-Aided Visual-Inertial SLAM System for Complex Indoor Environments
Authors: Li Junxi , Wang Shouwen , Hao Jiahui , Ma Biao , Chu Henry K.
Published: September 2024

Precise positioning in an indoor environment is a challenging task because it is difficult to receive a strong and reliable global positioning system (GPS) signal. For existing wireless indoor positioning methods, ultra-wideband (UWB) has become more popular because of its low energy consumption and high interference immunity. Nevertheless, factors such as indoor non-line-of-sight (NLOS) obstructions can still lead to large errors or fluctuations in the measurement data. In this paper, we propose a fusion method based on ultra-wideband (UWB), inertial measurement unit (IMU), and visual simultaneous localization and mapping (V-SLAM) to achieve high accuracy and robustness in tracking a mobile robot in a complex indoor environment. Specifically, we first focus on the identification and correction between line-of-sight (LOS) and non-line-of-sight (NLOS) UWB signals. The distance evaluated from UWB is first processed by an adaptive Kalman filter with IMU signals for pose estimation, where a new noise covariance matrix using the received signal strength indicator (RSSI) and estimation of precision (EOP) is proposed to reduce the effect due to NLOS. After that, the corrected UWB estimation is tightly integrated with IMU and visual SLAM through factor graph optimization (FGO) to further refine the pose estimation. The experimental results show that, compared with single or dual positioning systems, the proposed fusion method provides significant improvements in positioning accuracy in a complex indoor environment.
A Spatial Reconstruction Method of Ionospheric foF2 Based on High Accuracy Surface Modeling Theory
Authors: Wang Jian , Han Han , Shi Yafei
Published: September 2024

The ionospheric F2 critical frequency (foF2) is one of the most crucial application parameters in high-frequency communication, detection, and electronic warfare. To improve the accuracy of spatial reconstruction of the ionospheric foF2, we propose a high-accuracy surface (HAS) modeling method. This method converts difficult-to-solve differential equations into more manageable algebraic equations using direct difference approximation, significantly reducing algorithm complexity and computational load while exhibiting excellent convergence properties. We used seven stations in Brisbane, Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville, with one station as a validation station and six as training stations (e.g., Brisbane as a validation station and the other stations—Canberra, Darwin, Hobart, Learmonth, Perth, and Townsville—as training stations). The training stations and the HAS method were used to train and reconstruct the validation stations at different solar activity periods, seasons, and local times. The predicted values of the validation stations were compared with the measured values, and the proposed method was analyzed and validated. The reconstruction results show the following. (1) The relative root mean square errors (RRMSEs) of HAS method prediction in different solar activity epochs were 13.67%, 7.74%, and 9.19%, respectively, which are 13.57%, 7.41%, and 6.41% higher than the prediction accuracy of the Kriging method, respectively. (2) In the four seasons, the RRMSEs of the HAS method prediction are 9.27%, 13.1%, 8.81%, and 8.09%, respectively, which are 10.83%, 11.73%, 4.25%, and 12.00% higher than the prediction accuracy of the Kriging method. (c) During the daytime and nighttime, the RRMSEs of HAS method prediction were 9.23% and 11.17%, which were 5.92% and 11.99% higher than the prediction accuracy of the Kriging method, respectively. (d) Under the validation dataset, the average predictive RRMSE of the HAS method was 10.29%, and the average predictive RRMSE of the IRI prediction model was 12.35%, with a 2.06% improvement in the predictive accuracy of the HAS method. In general, the prediction effect of the HAS method was better than that of the Kriging method, thus verifying the effectiveness and reliability of the proposed method. In summary, the proposed reconstruction method is of great significance for improving usable frequency prediction and enhancing communication performance.
Modeling and Forecasting Ionospheric foF2 Variation Based on CNN-BiLSTM-TPA during Low- and High-Solar Activity Years
Authors: Xu Baoyi , Huang Wenqiang , Ren Peng , Li Yi , Xiang Zheng
Published: September 2024

The transmission of high-frequency signals over long distances depends on the ionosphere’s reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models.
Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
Authors: Ji Qing , Liu Nana , Yu Mengqin , Zhang Zhiming , Xiao Zehui , Pang Xiaoping
Published: September 2024

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change.
ConvMambaSR: Leveraging State-Space Models and CNNs in a Dual-Branch Architecture for Remote Sensing Imagery Super-Resolution
Authors: Zhu Qiwei , Zhang Guojing , Zou Xuechao , Wang Xiaoying , Huang Jianqiang , Li Xilai
Published: September 2024

Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant size variations in ground objects. Moreover, recovering lost details from low-resolution remote sensing images with complex and unknown degradations, such as downsampling, noise, and compression, remains a critical issue. To address these challenges, we propose ConvMambaSR, a novel super-resolution framework that integrates state-space models (SSMs) and Convolutional Neural Networks (CNNs). This framework is specifically designed to handle heterogeneous and complex ground features, as well as unknown degradations in remote sensing imagery. ConvMambaSR leverages SSMs to model global dependencies, activating more pixels in the super-resolution task. Concurrently, it employs CNNs to extract local detail features, enhancing the model’s ability to capture image textures and edges. Furthermore, we have developed a global–detail reconstruction module (GDRM) to integrate diverse levels of global and local information efficiently. We rigorously validated the proposed method on two distinct datasets, RSSCN7 and RSSRD-KQ, and benchmarked its performance against state-of-the-art SR models. Experiments show that our method achieves SOTA PSNR values of 26.06 and 24.29 on these datasets, respectively, and is visually superior, effectively addressing a variety of scenarios and significantly outperforming existing methods.
The Spatiotemporal Surface Velocity Variations and Analysis of the Amery Ice Shelf from 2000 to 2022, East Antarctica
Authors: Ma Yuanyuan , Wang Zemin , Zhang Baojun , An Jiachun , Geng Hong , Li Fei
Published: September 2024

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation in the surface velocity was observed between 2000 and 2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017 and 2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS.
Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems
Authors: Castaño-Martín Francisco Manuel , Gómez-Gutiérrez Álvaro , Pulido-Fernández Manuel
Published: September 2024

Extensive livestock farming plays a crucial role in the economy of agrosilvopastoral systems of the southwestern Iberian Peninsula (known as dehesas and montados in Spanish and Portuguese, respectively) as well as providing essential ecosystem services. The existence of livestock in these areas heavily relies on the effective management of natural resources (annual pastures and water stored in ponds built ad hoc). The present work aims to assess the water availability in these ponds by developing equations to estimate the water volume based on the surface area, which can be quantified by means of remote sensing techniques. For this purpose, field surveys were carried out in September 2021, 2022 and 2023 at ponds located in representative farms, using unmanned aerial vehicles (UAVs) equipped with RGB sensors and survey-grade global navigation satellite systems and inertial measurement units (GNSS-IMU). These datasets were used to produce high-resolution 3D models by means of Structure-from-Motion and Multi-View Stereo photogrammetry, facilitating the estimation of the stored water volume within a Geographic Information System (GIS). The Volume–Area–Height relationships were calibrated to allow conversions between these parameters. Regression analyses were performed using the maximum volume and area data to derive mathematical models (power and quadratic functions) that resulted in significant statistical relationships (r2 > 0.90, p < 0.0001). The root mean square error (RMSE) varied from 1.59 to 17.06 m3 and 0.16 to 3.93 m3 for the power and quadratic function, respectively. Both obtained equations (i.e., power and quadratic general functions) were applied to the estimated water storage in similar water bodies using available aerial or satellite imagery for the period from 1984 to 2021.
Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading
Authors: Naoum Maria C. , Papadopoulos Nikos A. , Sapidis George M. , Voutetaki Maristella E.
Published: September 2024

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.
Mapless Path Planning for Mobile Robot Based on Improved Deep Deterministic Policy Gradient Algorithm
Authors: Zhang Shuzhen , Tang Wei , Li Panpan , Zha Fusheng
Published: September 2024

In the traditional Deep Deterministic Policy Gradient (DDPG) algorithm, path planning for mobile robots in mapless environments still encounters challenges regarding learning efficiency and navigation performance, particularly adaptability and robustness to static and dynamic obstacles. To address these issues, in this study, an improved algorithm frame was proposed that designs the state and action spaces, and introduces a multi-step update strategy and a dual-noise mechanism to improve the reward function. These improvements significantly enhance the algorithm’s learning efficiency and navigation performance, rendering it more adaptable and robust in complex mapless environments. Compared to the traditional DDPG algorithm, the improved algorithm shows a 20% increase in the stability of the navigation success rate with static obstacles along with a 25% reduction in pathfinding steps for smoother paths. In environments with dynamic obstacles, there is a remarkable 45% improvement in success rate. Real-world mobile robot tests further validated the feasibility and effectiveness of the algorithm in true mapless environments.
Performance Evaluation of Carrier-Frequency Offset as a Radiometric Fingerprint in Time-Varying Channels
Authors: Albehadili Abdulsahib , Javaid Ahmad Y.
Published: September 2024

The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers.
RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping
Authors: Xin Rui , Guo Ningyan , Ma Xingyu , Liu Gang , Feng Zhiyong
Published: September 2024

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.
Multi-Index Driver Drowsiness Detection Method Based on Driver’s Facial Recognition Using Haar Features and Histograms of Oriented Gradients
Authors: Quiles-Cucarella Eduardo , Cano-Bernet Julio , Santos-Fernández Lucas , Roldán-Blay Carlos , Roldán-Porta Carlos
Published: September 2024

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver’s state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.
Optimal Sensor Placement for Enhanced Efficiency in Structural Health Monitoring of Medium-Rise Buildings
Authors: Saeed Salman , Sajid Sikandar H. , Chouinard Luc
Published: September 2024

Output-only modal analysis using ambient vibration testing is ubiquitous for the monitoring of structural systems, especially for civil engineering structures such as buildings and bridges. Nonetheless, the instrumented nodes for large-scale structural systems need to cover a significant portion of the spatial volume of the test structure to obtain accurate global modal information. This requires considerable time and resources, which can be challenging in large-scale projects, such as the seismic vulnerability assessment over a large number of facilities. In many instances, a simple center-line (stairwell case) topology is generally used due to time, logistical, and economic constraints. The latter, though a fast technique, cannot provide complete modal information, especially for torsional modes. In this research, corner-line instrumented nodes layouts using only a reference and a roving sensor are proposed, which overcome this issue and can provide maximum modal information similar to that from 3D topologies for medium-rise buildings. Parametric studies are performed to identify the most appropriate locations for sensor placement at each floor of a medium-rise building. The results indicate that corner locations at each floor are optimal. The proposed procedure is validated through field experiments on two medium-rise buildings.
Evolving Dynamics of Neck Muscle Activation Patterns in Dental Students: A Longitudinal Study
Authors: Almeida Manuel Barbosa de , Moreira Marion , Miranda-Oliveira Paulo , Moreira José , Família Carlos , Vaz João R. , Moleirinho-Alves Paula , Oliveira Raúl
Published: September 2024

Cervical pain has been linked to increased motor unit activity, potentially associated with the initiation and progression of chronic neck pain. Therefore, this study aimed to compare the time-course changes in cervical superficial muscle activation patterns among dental students with and without neck pain throughout their initial semester of clinical training. We used an online Nordic Musculoskeletal Questionnaire for group allocation between neck pain (NP) (n = 21) and control group (CG) (n = 23). Surface electromyography (sEMG) of the sternocleidomastoid and upper bilateral trapezius was recorded before starting their clinical practice and after their first semester while performing a cranio-cervical flexion test (CCFT) in five increasing levels between 22 mmHg and 30 mmHg. After the first semester, both the CG (p < 0.001) and NP (p = 0.038) groups showed decreased sternocleidomastoid activation. The NP group exhibited a concomitant increase in upper trapezius coactivation (p < 0.001), whereas the muscle activation pattern in asymptomatic students remained unchanged (p = 0.980). During the first semester of clinical training, dental students exhibited decreased superficial flexor activity, but those with neck pain had increased co-contraction of the upper trapezius, likely to stabilize the painful segment. This altered activation pattern could be associated with further dysfunction and symptoms, potentially contributing to chronicity.
ZnO Hexagonal Nano- and Microplates Modified with Nanomaterials as a Gas-Sensitive Material for DMS Detection—Extended Studies
Authors: Suchorska-Woźniak Patrycja , Teterycz Helena
Published: September 2024

The detection of dimethyl sulphide (DMS) at levels between ppb and ppm is a significant area of research due to the necessity of monitoring the presence of this gas in a variety of environments. These include environmental protection, industrial safety and medical diagnostics. Issues related to certain uncertainties concerning the influence of high humidity on DMS measurements with resistive gas sensors, e.g., in the detection of this marker in exhaled air, of the still unsatisfactory lower detection limit of DMS are the subject of intensive research. This paper presents the results of modifying the composition of the ZnO-based sensor layer to develop a DMS sensor with higher sensitivity and lower detection limit (LOD). Improved performance was achieved by using ZnO in the form of hexagonal nano- and microplates doped with gold nanoparticles (0.75 wt.%) and by using a well-proven sepiolite-based passive filter. The modification of the layer composition with respect to the authors’ previous studies contributed to the development of a sensor that is highly sensitive to 1 ppm DMS (S = 11.4) and achieves an LOD of up to 406 ppb, despite the presence of a high water vapour content (90% RH) in the analysed atmosphere.
A Novel Methodology for GB-SAR Estimating Parameters of the Atmospheric Phase Correction Model Based on Maximum Likelihood Estimation and the Gauss-Newton Algorithm
Authors: Li Xiheng , Liu Yu
Published: September 2024

Atmospheric phase error is the main factor affecting the accuracy of ground-based synthetic aperture radar (GB-SAR). The atmospheric phase screen (APS) may be very complicated, so the atmospheric phase correction (APC) model is very important; in particular, the parameters to be estimated in the model are the key to improving the accuracy of APC. However, the conventional APC method first performs phase unwrapping and then removes the APS based on the least-squares method (LSM), and the general phase unwrapping method is prone to introducing unwrapping error. In particular, the LSM is difficult to apply directly due to the phase wrapping of permanent scatterers (PSs). Therefore, a novel methodology for estimating parameters of the APC model based on the maximum likelihood estimation (MLE) and the Gauss-Newton algorithm is proposed in this paper, which first introduces the MLE method to provide a suitable objective function for the parameter estimation of nonlinear far-end and near-end correction models. Then, based on the Gauss-Newton algorithm, the parameters of the objective function are iteratively estimated with suitable initial values, and the Matthews and Davies algorithm is used to optimize the Gauss-Newton algorithm to improve the accuracy of parameter estimation. Finally, the parameter estimation performance is evaluated based on Monte Carlo simulation experiments. The method proposed in this paper experimentally verifies the feasibility and superiority, which avoids phase unwrapping processing unlike the conventional method.
A Pressure Sensing Device to Assist in Colonoscopic Procedures to Prevent Perforation—A Case Study
Authors: Kim Se-Eun , Kang Young-jae , Jung Chang-ho , Jeon Yongho , Jung Yunho , Lee Moon Gu
Published: September 2024

Colonoscopy has a limited field of view because it relies solely on a small camera attached to the end of the scope and a screen displayed on a monitor. Consequently, the quality and safety of diagnosis and treatment depend on the experience and skills of the gastroenterologist. When a novice attempts to insert the colonoscope during the procedure, excessive pressure can sometimes be applied to the colon wall. This pressure can cause a medical accident known as colonic perforation, which the physician should prevent. We propose an assisting device that senses the pressure applied to the colon wall, analyzes the risk of perforation, and warns the physician in real time. Flexible pressure sensors are attached to the surface of the colonoscope shaft. These sensors measure pressure signals during a colonoscopy procedure. A simple signal processor is used to collect and process the pressure signals. In the experiment, a colonoscope equipped with the proposed device was inserted into a simulated colon made from a colon extracted from a pig. The processed data were visually communicated to the gastroenterologist via displays and light-emitting diodes (LEDs). The device helps the physician continuously monitor and prevent excessive pressure on the colon wall. In this experiment, the device appropriately generated and delivered warnings to help the physicians prevent colonic perforation. In the future, the device is to be improved, and more experiments will be performed in live swine models or humans to confirm its efficacy and safety.
Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection
Authors: Balhareth Ghaida , Ilyas Mohammad
Published: September 2024

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient’s health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network’s edge. The system’s performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model’s performance empirically in real-world IoMT scenarios.