bluuebunny/crossref_metadata_2025 huggingface dataset:
Failure Study of BFRP Joints with Two Epoxy Adhesives under Hygrothermal Coupling
Authors: Niu Ruitao , Yang Yang , Lin Yinghao , Liu Zhen , Fan Yisa
Published: October 2023

Basalt Fibre Reinforced Polymer (BFRP)-bonded structures are lightweight, high strength, economical, and environmentally friendly, which is very advantageous in the civil sector. The aim of this paper is to provide a comprehensive account of the hygrothermal degradation and failure mechanisms of BFRP-bonded structures by comparing the residual properties of two epoxy adhesive BFRP single-lap joints after ageing for 240 h, 480 h, and 720 h in an extreme hygrothermal environment with pure water at 80 °C. The hydrophilicity and thermal stability of the two adhesives were firstly compared by water absorption and Thermogravimetric Analysis (TGA) tests, and the hygrothermal degradation of the molecular chains and the reduction in Tg were characterised by Fourier Transform Infra-Red (FTIR) spectroscopy and Differential Scanning Calorimetry (DSC) curves. The failure strength and load-displacement curves of the two joints were then compared, and it was found that the strength and stiffness had different trends, while the paired t-test was used to demonstrate the correlation between the failure strength and the adhesive Tg, as well as the difference in the failure mechanisms of the two joints caused by the water absorption rate. The analysis of macrosections and Scanning Electron Microscope (SEM) images summarised the process and reasons for the transition of the failure mode from fibre tearing to hybrid failure, and finally, the changes in elemental concentration and O/C values were analysed by Energy Dispersive X-ray Analysis (EDX), which proved that the degree of hydrolysis could not be used as a judgement of the degradation degree of the joint alone, and provided data support for the application of the BFRP-bonded structure in the humid and hot environment.
Monitoring the Thermal Activity of Kamchatkan Volcanoes during 2015–2022 Using Remote Sensing
Authors: Girina Olga , Manevich Alexander , Loupian Evgeny , Uvarov Ivan , Korolev Sergey , Sorokin Aleksei , Romanova Iraida , Kramareva Lubov , Burtsev Mikhail
Published: October 2023

The powerful explosive eruptions with large volumes of volcanic ash pose a great danger to the population and jet aircraft. Global experience in monitoring volcanoes and observing changes in the parameters of their thermal anomalies is successfully used to analyze the activity of volcanoes and predict their danger to the population. The Kamchatka Peninsula in Russia, with its 30 active volcanoes, is one of the most volcanically active regions in the world. The article considers the thermal activity in 2015–2022 of the Klyuchevskoy, Sheveluch, Bezymianny, and Karymsky volcanoes, whose rock composition varies from basaltic andesite to dacite. This study is based on the analysis of the Value of Temperature Difference between the thermal Anomaly and the Background (the VTDAB), obtained by manual processing of the AVHRR, MODIS, VIIRS, and MSU-MR satellite data in the VolSatView information system. Based on the VTDAB data, the following “background activity of the volcanoes” was determined: 20 °C for Sheveluch and Bezymianny, 12 °C for Klyuchevskoy, and 13–15 °C for Karymsky. This study showed that the highest temperature of the thermal anomaly corresponds to the juvenile magmatic material that arrived on the earth’s surface. The highest VTDAB is different for each volcano; it depends on the composition of the eruptive products produced by the volcano and on the character of an eruption. A joint analysis of the dynamics of the eruption of each volcano and changes in its thermal activity made it possible to determine the range of the VTDAB for different phases of a volcanic eruption.
Photogrammetric Monitoring of Rock Glacier Motion Using High-Resolution Cross-Platform Datasets: Formation Age Estimation and Modern Thinning Rates
Authors: Meng Tyler M. , Aguilar Roberto , Christoffersen Michael S. , Petersen Eric I. , Larsen Christopher F. , Levy Joseph S. , Holt John W.
Published: October 2023

The availability of remote sensing imagery at high spatiotemporal resolutions presents the opportunity to monitor the surface motion of rock glaciers, a key constraint for characterizing the dynamics of their evolution. In this paper, we investigate four North American rock glaciers by automatically measuring their horizontal surface displacement using photogrammetric data acquired with crewed and uncrewed aircraft along with orbital spacecraft over monitoring periods of up to eight years. We estimate vertical surface changes on these rock glaciers with photogrammetrically generated digital elevation models (DEM) and digitized topographic maps. Uncertainty analysis shows that the imagery with the highest resolution and most precise positioning have the best performance when used with the automated change detection algorithm. This investigation produces gridded velocity fields over the entire surface area of each study site, from which we estimate the age of rock glacier formation using along-flow velocity integration. Though the age estimates vary, the ice within the modern extent of these landforms began flowing between 3000 and 7000 years before present, postdating the last glacial maximum. Surface elevation change maps indicate present-day thinning at the lower latitude/higher elevation sites in Wyoming, while the higher latitude/lower elevation sites in Alaska exhibit relatively stable surface elevations.
Performance Evaluation of China’s First Ocean Dynamic Environment Satellite Constellation
Authors: Qin Dan , Jia Yongjun , Lin Mingsen , Liu Shanwei
Published: October 2023

China’s first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites’ data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites’ single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China’s first dynamic environment satellite constellation was comparable to that of the six satellites.
Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
Authors: Li Jun , Zhu Huizhong , Lu Yangyang , Zhang Mingze , Xu Aigong
Published: October 2023

Network RTK (NRTK), one of the primary means of high-precision real-time kinematic positioning (RTK), has been widely used. The key to providing highly accurate positioning is the ambiguity of the reference station being correctly fixed, but the atmospheric errors must be handled carefully, which seriously affects the efficiency of ambiguity fixing. This paper aims to improve the efficiency of ambiguity fixing by studying the time-varying characteristics of atmospheric errors. Once reasonable constraints are imposed on atmospheric parameters in the uncombined observation model, it can better fix ambiguity. Atmospheric parameters are estimated by random walk at the reference station, and the power spectral density (PSD) of atmosphere is determined by real-time observations, instead of using empirical values or empirical models that do not consider atmospheric variations. The experimental results showed that the real-time estimated PSD can improve the ambiguity fixing time by 18.4% and the ambiguity fixing success rate for the reference station by 11.7%, compared with using empirical PSD for atmospheric parameters. Unlike general NRTK positioning based on differential error correction values, undifferenced NRTK estimates the integer ambiguity and undifferenced error correction value at a single reference station, ensuring the independence of the error correction value of each reference station, and it can be easily broadcast and received through the network, which is more convenient for realizing high-precision RTK positioning for users.
Exploiting Temporal–Spatial Feature Correlations for Sequential Spacecraft Depth Completion
Authors: Liu Xiang , Wang Hongyuan , Chen Xinlong , Chen Weichun , Xie Zhengyou
Published: October 2023

The recently proposed spacecraft three-dimensional (3D) structure recovery method based on optical images and LIDAR has enhanced the working distance of a spacecraft’s 3D perception system. However, the existing methods ignore the richness of temporal features and fail to capture the temporal coherence of consecutive frames. This paper proposes a sequential spacecraft depth completion network (S2DCNet) for generating accurate and temporally consistent depth prediction results, and it can fully exploit temporal–spatial coherence in sequential frames. Specifically, two parallel convolution neural network (CNN) branches were first adopted to extract the features latent in different inputs. The gray image features and the depth features were hierarchically encapsulated into unified feature representations through fusion modules. In the decoding stage, the convolutional long short-term memory (ConvLSTM) networks were embedded with the multi-scale scheme to capture the feature spatial–temporal distribution variation, which could reflect the past state and generate more accurate and temporally consistent depth maps. In addition, a large-scale dataset was constructed, and the experiments revealed the outstanding performance of the proposed S2DCNet, achieving a mean absolute error of 0.192 m within the region of interest.
Honduran Reef Island Shoreline Change and Planform Evolution over the Last 15 Years: Implications for Reef Island Monitoring and Futures
Authors: Husband Emi , East Holly K. , Hocking Emma P. , Guest James
Published: October 2023

Assessing the vulnerability of low-lying coral reef islands is a global concern due to predictions that climate and environmental change will increase reef island instability and cause reef island populations to be among the first environmental refugees. Reef islands in the Pacific and Indian Oceans are highly dynamic environments that morphologically adjust to changing environmental conditions over annual-decadal timescales. However, there is a paucity of reef island shoreline change data from the Caribbean where sea-level rise, ecological and environmental disturbance and hydrodynamic regimes are considerably different than in other oceans globally. Here we present shoreline change analysis of 16 reef islands in northern Honduras, at the southern end of the Mesoamerican Barrier Reef. Satellite imagery from a maximum period of 12.4 years from Utila (2006–2019), and 2.4 years from Cayos Cochinos (2018–2021) was analysed to quantify island shoreline change and planform morphological adjustments. We identified accretion as the dominant island behaviour in Utila, where 5 of 7 islands increased in area and 61.7% of shorelines accreted, contributing to an overall net area increase of 9.4%. Island behaviour was more variable in Cayos Cochinos, where 55.7% of shorelines eroded, 5 of 9 islands remained stable, and net island area change was insignificant (2%). Conversely, the 4 smallest Cayos Cochinos islands (all <1500 m2) experienced significant shoreline change, potentially highlighting a new size threshold for considering reef island evolution. Across both sites, reef islands demonstrated a range of modes of planform change, including lateral accretion and erosion, and migration. Consequently, we provide the first empirical evidence of the dynamic nature of Caribbean reef islands during a period coincident with sea-level rise and highlight the heterogeneous nature of reef island evolution between and within two neighbouring sites at timescales relevant for island adaptation efforts.
A Fourier Frequency Domain Convolutional Neural Network for Remote Sensing Crop Classification Considering Global Consistency and Edge Specificity
Authors: Song Binbin , Min Songhan , Yang Hui , Wu Yongchuang , Wang Biao
Published: October 2023

The complex remote sensing image acquisition conditions and the differences in crop growth create many crop classification challenges. Frequency decomposition enables the capture of the feature information in an image that is difficult to discern. Frequency domain filters can strengthen or weaken specific frequency components to enhance the interclass differences among the different crops and can reduce the intraclass variations within the same crops, thereby improving crop classification accuracy. In concurrence with the Fourier frequency domain learning strategy, we propose a convolutional neural network called the Fourier frequency domain convolutional (FFDC) net, which transforms feature maps from the spatial domain to the frequency spectral domain. In this network, the dynamic frequency filtering components in the frequency spectral domain are used to separate the feature maps into low-frequency and high-frequency components, and the strength and distribution of the different frequency components are automatically adjusted to suppress the low-frequency information variations within the same crop, enhancing the overall consistency of the crops. Simultaneously, it is also used to strengthen the high-frequency information differences among the different crops to widen the interclass differences and to achieve high-precision remote sensing crop classification. In the test areas, which are randomly selected in multiple farms located far from the sampling area, we compare our method with other methods. The results demonstrate that the frequency-domain learning approach better mitigates issues, such as incomplete crop extractions and fragmented boundaries, which leads to higher classification accuracy and robustness. This paper applies frequency-domain deep learning to remote sensing crop classification, highlighting a novel and effective solution that supports agricultural management decisions and planning.
Interferometric Calibration Based on a Constrained Evolutionary Algorithm without Ground Control Points for a Tiangong-2 Interferometric Imaging Radar Altimeter
Authors: Li Lanyu , Tan Hong , Wang Bingnan , Xiang Maosheng , Wang Ke , Wang Yachao
Published: October 2023

The interferometric imaging radar altimeter (InIRA), mounted on the Tiangong-2 space laboratory, utilizes a small incidence and a short interferometric baseline to achieve altimetry for wide swathes of ocean surface topography and inland water surface elevation. To obtain a high-precision digital elevation model (DEM), calibration of the interferometric system parameters is necessary. Because InIRA utilizes the small-incidence interference system design, serious coupling occurs between the interferometric parameters. Commonly used interferometric calibration methods tend to fall into the local optimal solution for InIRA. Because evolutionary algorithms have a stronger robustness and global search ability, they are better suited to handling the solution space structure under the coupling of complex interferometric parameters. This article establishes an interferometric calibration optimization model for InIRA by utilizing the relative flatness of the lake surface as an inequality constraint. Furthermore, an adaptive penalty coefficient constraint evolutionary algorithm is designed to solve the model. The proposed method was tested on actual InIRA data, and the results indicate that it efficiently adjusts interferometric parameters, enhancing the precision of measurements for Qinghai Lake elevation.
Multi-Feature Dynamic Fusion Cross-Domain Scene Classification Model Based on Lie Group Space
Authors: Xu Chengjun , Shu Jingqian , Zhu Guobin
Published: October 2023

To address the problem of the expensive and time-consuming annotation of high-resolution remote sensing images (HRRSIs), scholars have proposed cross-domain scene classification models, which can utilize learned knowledge to classify unlabeled data samples. Due to the significant distribution difference between a source domain (training sample set) and a target domain (test sample set), scholars have proposed domain adaptation models based on deep learning to reduce the above differences. However, the existing models have the following shortcomings: (1) insufficient learning of feature information, resulting in feature loss and restricting the spatial extent of domain-invariant features; (2) models easily focus on background feature information, resulting in negative transfer; (3) the relationship between the marginal distribution and the conditional distribution is not fully considered, and the weight parameters between them are manually set, which is time-consuming and may fall into local optimum. To address the above problems, this study proposes a novel remote sensing cross-domain scene classification model based on Lie group spatial attention and adaptive multi-feature distribution. Concretely, the model first introduces Lie group feature learning and maps the samples to the Lie group manifold space. By learning features of different levels and different scales and feature fusion, richer features are obtained, and the spatial scope of domain-invariant features is expanded. In addition, we also design an attention mechanism based on dynamic feature fusion alignment, which effectively enhances the weight of key regions and dynamically balances the importance between marginal and conditional distributions. Extensive experiments are conducted on three publicly available and challenging datasets, and the experimental results show the advantages of our proposed method over other state-of-the-art deep domain adaptation methods.
Fault Extension Characteristics of the Middle Section of Shanxi Graben System and the Seismogenic Environments of the Hongdong and Linfen Earthquakes
Authors: Xie Xingbing , Zhan Yan , Zhou Lei , Peng Simeng
Published: October 2023

The Shanxi Graben System exhibits high seismic activity and significant destructive potential. Previous studies in this region have primarily focused on geological methods such as seismology, geological surveys, and trench excavations, with limited research in the field of geophysics. In this study, we collected two magnetotelluric profiles crossing the central segment of the Shanxi Graben System, including the fault system and the Hongdong–Linfen seismic zone. Through qualitative analysis and inversion calculations, we constructed a three-dimensional electrical model of the study area. The seismogenic environment was studied by integrating the deformation field, recent seismic geological data, and geophysical survey results of the study area and its surroundings. The Luoyunshan Piedmont Fault and the Huoshan Piedmont Fault are likely two main faults in the central segment of the Shanxi Graben System. These faults span the entire crustal scale and serve as basement faults separating the Ordos Block and the Taihangshan Block. They exhibit patterns of activity. The western side of the Shanxi Graben System, represented by the Ordos Block, exhibits a stable tectonic environment, while the eastern side, represented by the Taihangshan Block, experiences severe lithospheric destruction and thinning. The results of the magnetotelluric (MT) survey support the 1303 Hongdong earthquake as an event that occurred on the Huoshan Piedmont Fault and provide geophysical evidence for the possible dominance of the Luoyunshan Piedmont Fault in the 1695 Linfen earthquake. Multiple factors controlled the seismogenic environments of these two earthquakes. The continuous upwelling of asthenospheric material in the lower-middle crust on the eastern side of the Linfen Basin possibly leads to the regional extension of the North China Block. This, in turn, triggers inclined sliding along the Huoshan Piedmont Fault and the Luoyunshan Piedmont Fault, which may be the main controlling factors for major earthquakes in the region.
Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspectral Imagery
Authors: Liu Jinxiang , Wang Tiejun , Skidmore Andrew , Sun Yaqin , Jia Peng , Zhang Kefei
Published: October 2023

Convolutional neural networks (CNNs) have recently been demonstrated to be able to substantially improve the land cover classification accuracy of hyperspectral images. Meanwhile, the rapidly developing capacity for satellite and airborne image spectroscopy as well as the enormous archives of spectral data have imposed increasing demands on the computational efficiency of CNNs. Here, we propose a novel CNN framework that integrates one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) CNNs to obtain highly accurate and fast land cover classification from airborne hyperspectral images. To achieve this, we first used 3D CNNs to derive both spatial and spectral features from hyperspectral images. Then, we successively utilized a 2D CNN and a 1D CNN to efficiently acquire higher-level representations of spatial or spectral features. Finally, we leveraged the information obtained from the aforementioned steps for land cover classification. We assessed the performance of the proposed method using two openly available datasets (the Indian Pines dataset and the Wuhan University dataset). Our results showed that the overall classification accuracy of the proposed method in the Indian Pines and Wuhan University datasets was 99.65% and 99.85%, respectively. Compared to the state-of-the-art 3D CNN model and HybridSN model, the training times for our model in the two datasets were reduced by an average of 60% and 40%, respectively, while maintaining comparable classification accuracy. Our study demonstrates that the integration of 1D, 2D, and 3D CNNs effectively improves the computational efficiency of land cover classification with hyperspectral images while maintaining high accuracy. Our innovation offers significant advantages in terms of efficiency and robustness for the processing of large-scale hyperspectral images.
Anthropic Constraint Dynamics in European Western Mediterranean Floodplains Related to Floods Events
Authors: Turconi Laura , Bono Barbara , Faccini Francesco , Luino Fabio
Published: October 2023

Numerous riverbeds and floodplains in the Western Mediterranean Area (WMA) have been affected by anthropogenic modifications during the last centuries. In recent decades, an increase in floods in the coastal WMA has been observed. Variations in the rainfall regime and anthropisation have influenced the relevant geomorphological processes. The coastal floodplains analysed include those in Italy, France, and Spain. Geomorphological and land use changes that occurred in the last two centuries were examined using historical and recent maps, historical data, and European big data since the 1800s for 65 basins, for which over 670 flood events and more than 1300 victims were identified. Anthropogenic activities have changed the patterns of floodplains. In most cases, narrowing of the riverbeds, especially in the lower river sections, has been observed. The riverbeds have also changed from braided- to single-channel morphologies. GIS analysis shows reductions in the coastal watercourse widths ranging from 10% to 95%, with an average of 55%. Other changes are related to the deviation in the watercourses, with trends that did not respect the natural river flow. In some cases, the watercourses were covered and have vanished from recent maps. This aspect has reduced or eliminated the perception of the risk not only for the residents but also for land planners.
Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization
Authors: Guo Rui , Zhao Xiaopeng , Zuo Guanzhong , Wang Ying , Liang Yi
Published: October 2023

The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is proposed which optimizes the semantic segmentation metric intersection over union (IOU) instead of using the traditional cross-entropy loss. Meanwhile, making use of the advantages of the dual-path structure, the network extracts feature through the spatial path (SP) and the context path (CP) to achieve a balance between efficiency and accuracy. Aiming at a polarimetric SAR (PolSAR) image, the proposed network is conducted on the PolSAR datasets for terrain segmentation. Compared to the typical dual-path network, which is the bilateral segmentation network (BiSeNet), the proposed LoSARNet can obtain better mean intersection over union (MIOU). And the proposed network also shows the highest evaluation index and the best performance when compared with several typical networks.
Long-Term Dynamics of Sandy Vegetation and Land in North China
Authors: Wang Zhaosheng
Published: October 2023

Owing to the lack of long-term, continuous, large-scale, and high-resolution monitoring data and methods, we still cannot accurately understand the detailed processes of sand change in northern China. To some extent, this hinders the scientific implementation of sand prevention and control actions. To gain a more accurate and detailed understanding of the process of sandy land change, we conducted an investigation using a reconstructed, long-term, continuous, 250 m-high spatial resolution normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data from 1982 to 2018 to examine vegetation changes in sandy land in northern China. This study revealed that vegetation activity (NDVI slope = 0.011/a, R2 = 0.148) and vegetation coverage (FVC slope = 0.011/a, R2 = 0.080) in the northern sandy land (NSL) have slowed the desertification trend. The NSL desertification and reverse areas show decreasing and increasing trends, respectively, indicating an improvement in the degree of desertification from 1982 to 2018. Furthermore, we employed a newly proposed sandy classification method to investigate the area changes in mobile, semi-mobile, semi-fixed, and fixed sandy lands. Over the past 37 years, the total NSL area has shown a significantly weak decreasing trend (slope = −0.0009 million km2/year, r = −0.374, p = 0.023), with relatively small changes in the total area. However, the distribution area of large mobile sandy lands has significantly decreased, whereas the area of fixed sandy lands has significantly increased. Additionally, a survey of changes in the location of sandy lands revealed that 71.86% of the distribution of sandy land remained relatively fixed between 1982 and 2018, with only 28.14% of the distribution remaining in an unstable state. Stable mobile and fixed sandy lands accounted for 85.40% and 82.41% of the total area of mobile and fixed sandy lands, respectively, whereas there were more unstable sandy land distribution areas in the semi-mobile and semi-fixed sandy lands. These results indicate the alleviation of NSL desertification. The new sandy classification and monitoring methods proposed in this study will help improve the remote sensing monitoring of large-scale sand dynamics and offer new ideas for monitoring desertification on a large scale using remote sensing techniques.
Sensitive and Compact Evanescent-Waveguide Optical Detector for Sugar Sensing in Commercial Beverages
Authors: Buzzin Alessio , Asquini Rita , Caputo Domenico , de Cesare Giampiero
Published: October 2023

This work presents a compact and sensitive refractive index sensor able to evaluate the concentration of an analyte in a sample. Its working principle leverages on the changes in the optical absorption features introduced by the sample itself on the evanescent waves of a light beam. The device’s high compactness is achieved by embedding the sample–light interaction site and the detector in a 1 cm2 glass substrate, thanks to microelectronics technologies. High sensitivity is obtained by employing a low-noise p-i-n hydrogenated amorphous silicon junction, whose manufacture process requires only four UV lithographic steps on a glass substrate, thus ensuring low production costs. The system’s capabilities are investigated by sensing the sugar content in three commercial beverages. Sensitivities of 32, 53 and 80 pA/% and limits of detection of 47, 29 and 18 ppm are achieved. The above performance is comparable with state-of-the-art results available in the literature, where more complex optical setups, expensive instrumentation and bulky devices are used.
Protection of NOx Sensors from Sulfur Poisoning in Glass Furnaces by the Optimization of a “SO2 Trap”
Authors: Naddour Carole , Rieu Mathilde , Boreave Antoinette , Gil Sonia , Vernoux Philippe , Viricelle Jean-Paul
Published: October 2023

Electrochemical NOx sensors based on yttria-stabilized zirconia (YSZ) provide a reliable onboard way to control NOx emissions from glass-melting furnaces. The main limitation is the poisoning of this sensor by sulfur oxides (SOx) contained in the stream. To overcome this drawback, an “SO2 trap” with high SOx storage capacity and low affinity to NOx is required. Two CuO/BaO/SBA-15 traps with the same CuO loading (6.5 wt.%) and different BaO loadings (5 and 24.5 wt.%, respectively) were synthetized, thoroughly characterized and evaluated as SO2 traps. The results show that the 6.5%CuO/5%BaO/SBA-15 trap displays the highest SO2 adsorption capacity and can fully adsorb SO2 for a specific period of time, while additionally displaying a very low NO adsorption capacity. A suitable quantity of this material located upstream of the sensor could provide total protection of the NOx sensor against sulfur poisoning in glass-furnace exhausts.
Development of Real-Time Monitoring System Based on IoT Technology for Curing Compound Application Process during Cement Concrete Pavement Construction
Authors: Baek Soon Ho , Lee Kang In , Kim Seong-Min
Published: October 2023

Among the construction processes of Portland cement concrete pavement (PCCP), the curing compound spraying process is one of the most important processes. If the curing compound spraying amount does not meet the standard or if the curing compound is not applied evenly, distresses occur at the early age of construction, ultimately causing deterioration in concrete pavement performance. The purpose of this study is to develop a real-time monitoring system for a curing compound spraying process based on the Internet of Things (IoT) and sensing technologies to improve the construction quality of concrete pavement. To achieve the goal of this research, we conducted various laboratory and field experiments. The curing compound spraying amount and sprayed status were measured and analyzed using flowmeters, image acquisition sensors, and an image processing program, and the data were provided to workers in real time and simultaneously transmitted to the IoT cloud to form a database. From this study, it is confirmed that the IoT-technology-based curing compound spraying amount and sprayed status monitoring systems can be successfully established to manage construction quality related to the curing of concrete pavement.
Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
Authors: Wang Zhifeng , Li Longlong , Zeng Chunyan , Yao Jialong
Published: October 2023

A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom environments have distinct limitations. These include accommodating a substantial student population, grappling with object detection challenges due to obstructions, and encountering accuracy issues in recognition stemming from varying observation angles. To address these limitations, this paper proposes an innovative data augmentation approach designed to detect distinct student behaviors by leveraging focused behavioral attributes. The primary objective is to alleviate the pedagogical workload. The process begins with assembling a concise dataset tailored for discerning student learning behaviors, followed by the application of data augmentation techniques to significantly expand its size. Additionally, the architectural prowess of the Extended-efficient Layer Aggregation Networks (E-ELAN) is harnessed to effectively extract a diverse array of learning behavior features. Of particular note is the integration of the Channel-wise Attention Module (CBAM) focal mechanism into the feature detection network. This integration plays a pivotal role, enhancing the network’s ability to detect key cues relevant to student learning behaviors and thereby heightening feature identification precision. The culmination of this methodological journey involves the classification of the extracted features through a dual-pronged conduit: the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Empirical evidence vividly demonstrates the potency of the proposed methodology, yielding a mean average precision (mAP) of 96.7%. This achievement surpasses comparable methodologies by a substantial margin of at least 11.9%, conclusively highlighting the method’s superior recognition capabilities. This research has an important impact on the field of teaching evaluation system, which helps to reduce the burden of educators on the one hand, and makes teaching evaluation more objective and accurate on the other hand.
Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks
Authors: Rihan Shaza Dawood Ahmed , Anbar Mohammed , Alabsi Basim Ahmad
Published: October 2023

The significant surge in Internet of Things (IoT) devices presents substantial challenges to network security. Hackers are afforded a larger attack surface to exploit as more devices become interconnected. Furthermore, the sheer volume of data these devices generate can overwhelm conventional security systems, compromising their detection capabilities. To address these challenges posed by the increasing number of interconnected IoT devices and the data overload they generate, this paper presents an approach based on meta-learning principles to identify attacks within IoT networks. The proposed approach constructs a meta-learner model by stacking the predictions of three Deep-Learning (DL) models: RNN, LSTM, and CNN. Subsequently, the identification by the meta-learner relies on various methods, namely Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). To assess the effectiveness of this approach, extensive evaluations are conducted using the IoT dataset from 2020. The XGBoost model showcased outstanding performance, achieving the highest accuracy (98.75%), precision (98.30%), F1-measure (98.53%), and AUC-ROC (98.75%). On the other hand, the SVM model exhibited the highest recall (98.90%), representing a slight improvement of 0.14% over the performance achieved by XGBoost.
A Fast Method for Multidimensional Joint Parameter Estimation of Polarization-Sensitive Arrays
Authors: Zhang Zheqi , Lan Xiang , Wang Xianpeng
Published: October 2023

The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton’s method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom’s method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness.
Resampling-Detection-Network-Based Robust Image Watermarking against Scaling and Cutting
Authors: Li Hao-Lai , Zhang Xu-Qing , Wang Zong-Hui , Lu Zhe-Ming , Cui Jia-Lin
Published: October 2023

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness.
Human Micro-Expressions in Multimodal Social Behavioral Biometrics
Authors: Wahid Zaman , Bari A. S. M. Hossain , Gavrilova Marina
Published: October 2023

The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems.
Intelligent Early Fault Diagnosis of Space Flywheel Rotor System
Authors: Liao Hui , Xie Pengfei , Deng Sier , Wang Hengdi
Published: October 2023

Three frequently encountered problems—a variety of fault types, data with insufficient labels, and missing fault types—are the common challenges in the early fault diagnosis of space flywheel rotor systems. Focusing on the above issues, this paper proposes an intelligent early fault diagnosis method based on the multi-channel convolutional neural network with hierarchical branch and similarity clustering (HB-SC-MCCNN). First, a similarity clustering (SC) method is integrated into the parameter-shared dual MCCNN architecture to set up as the basic structural block. The hierarchical branch model and additional loss are then added to SC-MCCNN to form a hierarchical branch network, which simplifies the problem of fault multi-classification into binary classification with multi-steps. Based on the self-learning characteristics of the proposed model, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. The results of the experiments for comparing the abilities between the proposed method and several advanced deep learning models confirm that on the established early fault dataset of the space flywheel rotor system, the proposed method successfully achieves the hierarchical diagnosis and presents stronger competitiveness in the case of insufficient labeled data and missing fault types at the same time.
Remote Sensing Provides a Rapid Epidemiological Context for the Control of African Swine Fever in Germany
Authors: Bergmann Hannes , Czaja Eva-Maria , Frick Annett , Klaaß Ulf , Marquart Ronny , Rudovsky Annett , Holland Diana , Wysocki Patrick , Lehnau Daike , Schröder Ronald , Rogoll Lisa , Sauter-Louis Carola , Homeier-Bachmann Timo
Published: October 2023

Transboundary disease control, as for African swine fever (ASF), requires rapid understanding of the locally relevant potential risk factors. Here, we show how satellite remote sensing can be applied to the field of animal disease control by providing an epidemiological context for the implementation of measures against the occurrence of ASF in Germany. We find that remotely sensed observations are of the greatest value at a lower jurisdictional level, particularly in support of wild boar carcass search efforts.
Transient Thermal Analysis of Concrete Box Girders: Assessing Temperature Variations in Canadian Climate Zones
Authors: Nassar Musab , Amleh Lamya
Published: October 2023

This study examines the temperature distributions and thermal-induced responses in reinforced concrete bridge elements, focusing on the Canadian climate regions. The Canadian Highway Bridge Design Code (CHBDC) currently utilizes a fixed thermal gradient profile that does not account for regional climatic variations. Historical environmental data determines the effective maximum temperatures in the CHBDC. In order to investigate temperature behaviors and distributions, a transient finite element (FE) model is developed using recorded and calculated 3-month thermal loads data for representative cities in different climate regions. The results indicate that the predicted daily maximum effective mean temperatures and extreme daily positive vertical thermal gradients do not align. A linear correlation exists between the daily maximum effective mean temperature and the daily maximum air temperature, with a coefficient of determination (R2) of 0.935. The proposed effective mean temperatures obtained from the FE thermal analysis are higher than the CHBDC recommendations. New thermal gradient profiles are proposed for Canadian climate zones, consisting of two straight lines and a linear gradient at the top and bottom sections. A comparison between the proposed profiles and the CHBDC and AASHTO specifications reveals that a single fixed thermal gradient profile is inadequate to account for the variation in thermal gradients across Canadian climate regions.
Real-Time Belt Deviation Detection Method Based on Depth Edge Feature and Gradient Constraint
Authors: Xu Xinchao , Zhao Hanguang , Fu Xiaotian , Liu Mingyue , Qiao Haolei , Ma Youqing
Published: October 2023

Aiming at the problems of the poor recognition effect and low recognition rate of the existing methods in the process of belt deviation detection, this paper proposes a real-time belt deviation detection method. Firstly, ResNet18 combined with the attention mechanism module is used as a feature extraction network to enhance the features in the belt edge region and suppress the features in other regions. Then, the extracted features are used to predict the approximate locations of the belt edges using a classifier based on the contextual information on the fully connected layer. Next, the improved gradient equation is used as a structural loss in the model training stage to make the model prediction value closer to the target value. Then, the authors of this paper use the least squares method to fit the set of detected belt edge line points to obtain the accurate belt edge straight line. Finally, the deviation threshold is set according to the requirements of the safety production code, and the fitting results are compared with the threshold to achieve the belt deviation detection. Comparisons are made with four other methods: ultrafast structure-aware deep lane detection, end-to-end wireframe parsing, LSD, and the Hough transform. The results show that the proposed method is the fastest at 41 frames/sec; the accuracy is improved by 0.4%, 13.9%, 45.9%, and 78.8% compared to the other four methods; and the F1-score index is improved by 0.3%, 10.2%, 32.6%, and 72%, respectively, which meets the requirements of practical engineering applications. The proposed method can be used for intelligent monitoring and control in coal mines, logistics and transport industries, and other scenarios requiring belt transport.
Study of the Protection and Energy Transmission Modes of One Phase Short Circuit to Ground in Inverters
Authors: Chen Dezhi , Zhang Zhixiang , Zhang Shichong , Sun Yun , Zhao Wenbo , Zhao Wenliang
Published: October 2023

Research indicates that phase-to-ground short-circuits in a frequency converter can subject the rectifier diode and IGBT to excessive voltage and current, potentially causing damage if the component selection margin during hardware design is insufficient. In order to solve the above problems, this paper studies the design of the LCL filter and ground short circuit problem of the hundred-kilowatt inverter. Firstly, an analytical method for calculating the DC bus capacitance and reactor of the inverter is proposed. The interaction between the DC bus capacitance and the reactor parameters and performance is considered in the implementation process. The parameters of the DC bus capacitor and reactor are given. Secondly, the one-to-ground short circuit of the inverter is studied, and the energy flow mode and mathematical expression of the double boost circuit, considering the influence of the leakage inductance of the power transformer, are given. Based on the above analysis, a method for determining the rectifier diode and IGBT, considering the one-to-ground short circuit of the inverter, is proposed. Finally, a one-hundred-kilowatt inverter is developed, and the corresponding experiments are carried out. The feasibility of the proposed scheme is verified by simulation and experiment.