bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Non-Singular Terminal Sliding Mode Trajectory Tracking Control of Robotic Manipulators Based on Disturbance Observer Under Unknown Time-Varying Disturbance
Authors: Yin Song , Shi Zhenjie , Liu Yanjun , Xue Gang , You Huaqiang
Published: January 2025

We address the problems in the trajectory tracking control of a multi-joint robotic arm, including uncertainty in the internal modeling of its significant external unknown time-varying disturbances. With the control objective of tracking the desired trajectories of each joint stably and accurately, this paper proposes an adaptive non-singular terminal sliding mode control method based on a nonlinear disturbance observer (NDO). In order to speed up the convergence of the tracking error and avoid the singular value problem existing in the traditional terminal sliding mode (TSM), a non-singular terminal sliding mode (NTSM) surface is proposed in this paper. Given the system modeling uncertainty and external unknown time-varying disturbances, a nonlinear disturbance observer is used to observe the total set disturbance for feed-forward compensation. Secondly, the switching gain adaptive law is used to estimate the observation error of the total set disturbance and allow for the online adjustment of the switching gain weights. A robust gain adaptive law is also designed to reconverge quickly to the non-singular sliding mode surface when transient significant disturbances shock the system’s state variables out of the sliding phase. A simulation analysis of the spatial two-link robotic arm is carried out in Simulink. The results show that the adaptive non-singular terminal sliding mode control strategy based on the nonlinear disturbance observer proposed (PNDOANTSM) in this paper can reduce the average steady-state errors of robotic arm joints 1 and 2 by 36.58%, 38.40%, 44.68%, and 42.86%, compared with those observed in switching term adaptive law non-singular terminal sliding mode control based on a nonlinear disturbance observer (NDOANTSM) and non-singular terminal sliding mode control based on a nonlinear disturbance observer (NDONTSM). This paper provides a practical and feasible trajectory tracking control method for multi-joint robotic arms.
Low-Carbon Transformation of Polysilicon Park Energy Systems: Optimal Economic Strategy with TD3 Reinforcement Learning
Authors: Hu Shurui , Zhao Chengwenxuan , Wu Jialu , Bian Haiyang , Liu Yongkai , Li Mingtao
Published: January 2025

To achieve the low-carbon transition in polysilicon production, this study proposes and validates a low-carbon economic dispatch strategy for a renewable hydrogen production and storage system in polysilicon parks based by TD3 algorithm. The study uses XGBoost to construct a surrogate model that reflects the nonlinear physical characteristics of the electrolyzer. Through a comparative analysis of operating strategies in five scenarios and sensitivity assessments of key parameters, complemented by comparisons with dispatch results from the DDPG and DQN algorithms, the effectiveness of the coupled operating strategy for electrolyzers, energy storage, and hydrogen storage devices is fully validated. This highlights the critical role of the TD3 algorithm in strengthening the robustness of the energy system under double-end source-load uncertainties. The results show that batteries flexibly adjust to the time-of-use electricity price, and the coordinated operation of the hydrogen storage devices as well as electrolyzers stabilize the electrolyzer efficiency, reducing the total system cost by 0.027% compared to fixed condition equipment models. The TD3 algorithm shows significant advantages in optimized dispatch, reducing the average daily operating cost by 0.6% and 1.2%, respectively, compared to the DDPG and DQN algorithms, and reducing the carbon emission cost by 2.0% and 12.0%, respectively. A comprehensive analysis shows that the proposed model reduces daily carbon emissions by 29.3% compared to the original system, but also introduces cost pressure, mainly due to the high operating costs of renewable energy equipment such as solar panels. This study provides a practical solution for renewable energy management.
Effect of Centrifugal Load on Residual Stresses in Nickel-Based Single-Crystal Substrate and Thermal Barrier Coating System
Authors: Yu Liming , Zhang Yifei , Zhao Rujuan , Wang Yi , Yu Qingmin
Published: January 2025

Thermal barrier coatings (TBCs) and air film-cooling technology have been extensively utilized in nickel-based, single-crystal turbine blades to enhance their heat resistance. However, structural complexity and material property mismatches between layers can affect residual stresses and potentially lead to coating failure. In this study, a three-dimensional finite element model with atmospheric plasma-spraying thermal barrier coatings (APS-TBCs) deposited on air-cooled, nickel-based, single-crystal blades was established to investigate residual stress character under centrifugal load, considering the effect of temperature, crystal orientation deviation angle, oxide layer thickness, and the number of cycles. The results show that when the centrifugal load is increased from 300 MPa to 700 MPa, the absolute value of the residual stress at the crest of the interface between Top Coat (TC) and Thermally Grown Oxide (TGO) increases by only 8.5%, whereas in the region of compressive to tensile stress conversion, residual stress decreases by 100.9%. As the crystal orientation deviation angle increases, the absolute value of the residual compressive stress increases and the absolute value of the residual tensile stress decreases, but the performance is more special in the valley region, where the absolute value of the residual stress increases with the increase in the deviation angle. Special attention is required, as the increase in temperature leads to a rise in the absolute value of residual stress. For example, at the trough of the TC–TGO interface, when the temperature increases from 910 °C to 1100 °C, the residual stress increases by 9.8%. The effect of the number of cycles on residual stress is relatively weak. For instance, at the wave crest of the TC–TGO interface, the residual stress differs by only 0.6 MPa between one cycle and three cycles. The effect of oxide layer thickness on residual stress in the TBCs after a single cycle is nonlinear. When the oxide layer thickness is 0, 4, and 7 μm, the residual stress undergoes a transition between tensile and compressive directions at different locations. The exploration of these results has yielded some valuable laws that can provide a reference for the study of the damage mechanism of TBCs, as well as a guide for the optimization of nickel-based turbine blades in the manufacturing and use processes.
An Enhanced LiDAR-Based SLAM Framework: Improving NDT Odometry with Efficient Feature Extraction and Loop Closure Detection
Authors: Ren Yan , Shen Zhendong , Liu Wanquan , Chen Xinyu
Published: January 2025

Simultaneous localization and mapping (SLAM) is crucial for autonomous driving, drone navigation, and robot localization, relying on efficient point cloud registration and loop closure detection. Traditional Normal Distributions Transform (NDT) odometry frameworks provide robust solutions but struggle with real-time performance due to the high computational complexity of processing large-scale point clouds. This paper introduces an improved NDT-based LiDAR odometry framework to address these challenges. The proposed method enhances computational efficiency and registration accuracy by introducing a unified feature point cloud framework that integrates planar and edge features, enabling more accurate and efficient inter-frame matching. To further improve loop closure detection, a parallel hybrid approach combining Radius Search and Scan Context is developed, which significantly enhances robustness and accuracy. Additionally, feature-based point cloud registration is seamlessly integrated with full cloud mapping in global optimization, ensuring high-precision pose estimation and detailed environmental reconstruction. Experiments on both public datasets and real-world environments validate the effectiveness of the proposed framework. Compared with traditional NDT, our method achieves trajectory estimation accuracy increases of 35.59% and over 35%, respectively, with and without loop detection. The average registration time is reduced by 66.7%, memory usage is decreased by 23.16%, and CPU usage drops by 19.25%. These results surpass those of existing SLAM systems, such as LOAM. The proposed method demonstrates superior robustness, enabling reliable pose estimation and map construction in dynamic, complex settings.
Agent Addition to Coal Slurry Water Using Data-Driven Intelligent Control
Authors: Deng Jianjun , Liu Wentong , Zheng Cheng , Wang Chuanzhen
Published: January 2025

The sedimentation process of coal slurry water is influenced by numerous factors and has complex mechanisms. Its nonlinear and large hysteresis characteristics pose great challenges to process optimization control, making it a current research hotspot. This paper takes the typical slime water treatment process of a coal preparation plant as the object, and, on the basis of selecting raw coal quantity, flocculation dosage, coagulation dosage, overflow turbidity, raw coal ash content, underflow concentration, and slime quantity as the key variables, establishes a quality control method for process detection data consisting of data acquisition → data anomaly detection → data filling and noise reduction; subsequently, different machine-learning algorithms are used to predict the performance of coal-slurry-settling agents. It was found that Long Short-Term Memory shows the highest prediction accuracy for coagulants, with corresponding root mean square errors of 2.72% and 6.23%. Finally, using iFix software (version 5.5), an intelligent control system for the settling process of coal slurry water was constructed, which reduced the usage of coagulants by 31.56% and 37.21%.
Study on the Bubble Collapse Characteristics and Heat Transfer Mechanism of the Microchannel Reactor
Authors: Zheng Gaoan , Xu Pu , Wang Tong , Yan Qing
Published: January 2025

Microreactors have the advantages of high heat and mass transfer efficiency, strict control of reaction parameters, easy amplification, and good safety performance, and have been widely used in various fields such as chip manufacturing, fine chemicals, and biomanufacturing. However, narrow microchannels in microreactors often become filled with catalyst particles, leading to blockages. To address this challenge, this study proposes a multiphase flow heat transfer model based on the lattice Boltzmann method (LBM) to investigate the dynamic changes during the bubble collapse process and temperature distribution regularities. Based on the developed three-phase flow dynamics model, this study delves into the shock dynamic evolution process of bubble collapse and analyzes the temperature distribution regularities. Then, the flow patterns under different particle density conditions are explored. The study found that under the action of shock wave, the stable structure of the liquid film of the bubble is destroyed, and the bubble deforms and collapses. At the moment of bubble collapse, energy is rapidly transferred from the potential energy of the bubble to the kinetic energy of the flow field. Subsequently, the kinetic energy is converted into pressure waves. This results in the rapid generation of extremely high pressure in the flow field, creating high-velocity jets and intense turbulent vortices, which can enhance the mass transfer effects of the multiphase flows. At the moment of bubble collapse, a certain high temperature phenomenon will be formed at the collapse, and the high temperature phenomenon in this region is relatively chaotic and random. The pressure waves generated during bubble collapse have a significant impact on the motion trajectories of particles, while the influence on high-density particles is relatively small. The results offer a theoretical basis for understanding mass transfer mechanisms and particle flow patterns in three-phase flow. Moreover, these findings have significant practical implications for advancing technologies in industrial applications, including chip manufacturing and chemical process transport.
A Compact Review of Current Technologies for Carbon Capture as Well as Storing and Utilizing the Captured CO2
Authors: Thiedemann Tim M. , Wark Michael
Published: January 2025

With the consequences of climate change becoming more urgent, there has never been a more pressing need for technologies that can help to reduce the carbon dioxide (CO2) emissions of the most polluting sectors, such as power generation, steel, cement, and the chemical industry. This review summarizes the state-of-the-art technologies for carbon capture, for instance, post-combustion, pre-combustion, oxy-fuel combustion, chemical looping, and direct air capture. Moreover, already established carbon capture technologies, such as absorption, adsorption, and membrane-based separation, and emerging technologies like calcium looping or cryogenic separation are presented. Beyond carbon capture technologies, this review also discusses how captured CO2 can be securely stored (CCS) physically in deep saline aquifers or depleted gas and oil reservoirs, stored chemically via mineralization, or used in enhanced oil recovery. The concept of utilizing the captured CO2 (CCU) for producing value-added products, including formic acid, methanol, urea, or methane, towards a circular carbon economy will also be shortly discussed. Real-life applications, e.g., already pilot-scale continuous methane (CH4) production from flue gas CO2, are shown. Actual deployment of the most crucial technologies for the future will be explored in real-life applications. This review aims to provide a compact view of the most crucial technologies that should be considered when choosing to capture, store, or convert CO2, informing future researchers with efforts aimed at mitigating CO2 emissions and tackling the climate crisis.
Design of Ejectors for High-Temperature Heat Pumps Using Numerical Simulations
Authors: Unterluggauer Julian , Buruzs Adam , Schieder Manuel , Sulzgruber Verena , Lauermann Michael , Reichl Christoph
Published: January 2025

Decarbonization of industrial processes by using high-temperature heat pumps is one of the most important pillars towards sustainable energy goals. Most heat pumps are based on the standard Carnot cycle which includes an expansion valve leading to irreversible dissipation and energetic losses. Especially for high-temperature applications, these losses increase significantly, and a replacement of the conventional throttle valve with an ejector, which is an alternative expansion device, for partial recovery of some of the pressure lost during the expansion, is investigated in this paper. However, designing such a device is complicated as the flow inside is subject to multiphase and supersonic conditions. Therefore, this paper aims to streamline an approach for designing ejectors for high-temperature heat pumps using numerical simulations. To showcase the application of the design procedure, an ejector, which is used to upgrade a standard cycle high-temperature heat pump with the synthetic refrigerant R1233zdE, is developed. To design the ejector heat pump, an interaction between a fast 1D design tool, a 1D heat pump cycle simulation, and a 2D CFD simulation is proposed. An ejector is designed for a sink temperature of 130 °C, which can potentially increase the COP of the heat pump by around 20%. Preliminary measurements at off-design conditions at 100 °C sink temperature are used to validate the design procedure. The pressure distribution inside the ejector is well captured, with relative errors around 4%. However, the motive nozzle mass flow was underpredicted by around 30%. To summarize, the presented approach can be used for designing ejectors of high-temperature heat pumps, although the numerical modeling has to be further developed by validation with experiments to improve the prediction of the motive mass flow.
Effects of Parental Nurturing Attitudes, Peer Victimization, and Depressive Rumination on Anxiety in Japanese Adults
Authors: Masuya Jiro , Kikkawa Masayuki , Ono Miki , Morishita Chihiro , Ito Shunichiro , Nibuya Rintaro , Tamada Yu , Takashio Osamu , Honyashiki Mina , Inoue Takeshi
Published: January 2025

Parental attitudes and peer victimization experiences in childhood influence anxiety. Depressive rumination is associated with these factors, but the interrelationship between them remains unclear. In this study, we hypothesized that “inadequate parental attitudes” and “peer victimization” experienced in childhood worsen “depressive rumination” and “state anxiety”, and that “depressive rumination” is an intermediate factor that worsens anxiety symptoms in adulthood. We verified this interrelationship by structural equation modeling. A cross-sectional questionnaire study was conducted on 576 adult volunteers, who gave written consent. Demographic data and scores from the Parental Bonding Instrument, Childhood Victimization Rating Scale, Ruminative Responses Scale, and state anxiety of State-Trait Anxiety Inventory Form Y were collected. Data were analyzed by multiple regression analysis and structural equation modeling. In the study models, inadequate parental attitudes (low care and high overprotection) and depressive rumination directly worsened anxiety. Inadequate parental attitudes and peer victimization experienced in childhood indirectly worsened anxiety via depressive rumination. Furthermore, inadequate parental attitudes experienced in childhood indirectly worsened depressive rumination via peer victimization in childhood. Our results indicate that depressive rumination influences the relationship between inadequate parental attitudes, peer victimization, and adulthood anxiety. Therefore, assessing the quality of parental attitudes and peer victimization experienced in childhood and depressive rumination may help to clarify the antecedents of anxiety and how to intervene effectively.
Introduction of the Special Issue “Incorporating the Sacred in Counselling”
Authors: Mutter Kelvin F.
Published: January 2025

It is understandable if readers of this Special Issue of Religions ask the following: “Why this topic [...]
Air Travel-Triggered Tension Pneumocephalus Caused by a Frontal Sinus Osteoma: Case Report
Authors: Djurdjevic Aleksandar , Lepic Milan , Djurdjevic Jovana , Stankovic Svetozar , Pavlicevic Goran
Published: January 2025

Background and Clinical Significance: Pneumocephalus, an accumulation of air within the cranial cavity, typically arises from trauma or iatrogenic causes. However, spontaneous occurrences of this are rare and linked to various pathologies affecting the paranasal sinuses, the ear, or the skull base. The impact of air travel on individuals with pneumocephalus remains uncertain despite ongoing research. We report a unique case of spontaneous tension pneumocephalus attributed to a frontal sinus osteoma during air travel. Case Presentation: A 55-year-old man presented with headache and dizziness, initiated during a nine-hour international flight two weeks prior. The symptoms abated after landing but recurred on his return flight, accompanied by confusion the following day. A neurological examination revealed no deficits. CT and MRI scans indicated the presence of intraparenchymal air collection in the right frontal lobe, attributed to a frontal sinus osteoma causing a dural tear. Surgical intervention included duroplasty and osteoma removal, with postoperative recovery free of complications. Conclusions: Frontal sinus osteoma-induced tension pneumocephalus is exceedingly rare, with only limited cases reported in the literature. This case shows that air travel may exacerbate intracranial gas dynamics that lead to development of tension pneumocephalus with a potentially fatal outcome for patients.
The Role of Graciloplasty in the Treatment of Obstetric Anal Sphincter Injury with Subsequent Fecal Incontinence and Recurrent Low Recto-Vaginal Fistula: A Case Report
Authors: Bergna Alessandro , Megevand Jacques , Mori Giacomo , Lenisa Leonardo , Rusconi Andrea
Published: January 2025

Background and Clinical Significance: Recto-vaginal fistulae (RVF) and fecal incontinence (FI) pose significant challenges for colorectal surgeons. Various therapeutic options have been proposed for each condition over time. Despite its procedural complexity and the risk of complications, graciloplasty remains a viable therapeutic option for both conditions, with favorable long-term results. To our knowledge, this is the first report of a case where the need to treat both conditions concurrently arose. Case Presentation: We report the case of a 54-year-old woman with severe FI and repeatedly operated on recurrent recto-vaginal fistula. The patient underwent graciloplasty to provide healthy tissue with an adequate vascular supply to both enhance the healing process of the fistula and reshape the anal canal with a circular muscular structure. Following the procedure, the patient experienced prompt symptom resolution and good clinical and functional recovery at a 1-year follow-up evaluation. Conclusions: This case report highlights the safety and effectiveness of an overlooked procedure for the treatment of large sphincter defects and concurrent recto-vaginal or recto-vaginal tears.
Evaluating Transition Rules for Enhancing Fairness in Bonus–Malus Systems: An Application to the Saudi Arabian Auto Insurance Market
Authors: Alyafie Asrar , Constantinescu Corina , Yslas Jorge
Published: January 2025

A Bonus–Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder’s claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition rules dictating the movements of policyholders within the system, and the relativities used to determine premium adjustments. This paper explores the impact of modifications to these three elements on risk classification, assessed through the mean squared error. The model parameters are calibrated with real-world data from the Saudi auto insurance market. We begin the analysis by focusing on transition rules based solely on claim frequency, a framework in which most implemented BMSs work, including the current Saudi BMS. We then consider transition rules that depend on frequency and severity, in which higher penalties are given for large claim sizes. The results show that increasing the number of levels typically improves risk segmentation but requires balancing practical implementation constraints and that the adequate selection of the penalties is critical to enhancing fairness. Moreover, the study reveals that incorporating a severity-based penalty enhances risk differentiation, especially when there is a dependence between the claim frequency and severity.
Evaluation of the Impact of Morphological Differences on Scale Effects in Green Tide Area Estimation
Authors: Wu Ke , Xie Tao , Li Jian , Wang Chao , Zhang Xuehong , Liu Hui , Bai Shuying
Published: January 2025

Green tide area is a crucial indicator for monitoring green tide dynamics. However, scale effects arising from differences in image resolution can lead to estimation errors. Current pixel-level and sub-pixel-level methods often overlook the impact of morphological differences across varying resolutions. To address this, our study examines the influence of morphological diversity on green tide area estimation using GF-1 WFV data and the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index at a 16 m resolution. Green tide patches were categorized into small, medium, and large sizes, and morphological features such as elongation, compactness, convexity, fractal dimension, and morphological complexity were designed and analyzed. Machine learning models, including Extra Trees, LightGBM, and Random Forest, among others, classified medium and large patches into striped and non-striped types, with Extra Trees achieving outstanding performance (accuracy: 0.9844, kappa: 0.9629, F1-score: 0.9844, MIoU: 0.9637). The results highlighted that large patches maintained stable morphological characteristics across resolutions, while small and medium patches were more sensitive to scale, with increased estimation errors at lower resolutions. Striped patches, particularly among medium patches, were more sensitive to scale effects compared to non-striped ones. The study suggests that incorporating morphological features of patches, especially in monitoring striped and small patches, could be a key direction for improving the accuracy of green tide monitoring and dynamic change analysis.
Assessment of the Ground Vulnerability in the Preveza Region (Greece) Using the European Ground Motion Service and Geospatial Data Concerning Critical Infrastructures
Authors: Basiou Eleftheria , Castro-Melgar Ignacio , Kranis Haralambos , Karavias Andreas , Lekkas Efthymios , Parcharidis Issaak
Published: January 2025

The European Ground Motion Service (EGMS) and geospatial data are integrated in this paper to evaluate ground deformation and its effects on critical infrastructures in the Preveza Regional Unit. The EGMS, a new service of the Copernicus Land Monitoring Service, employs information from the C-band Synthetic Aperture Radar (SAR)-equipped Sentinel-1A and Sentinel-1B satellites. This allows for the millimeter-scale measurement of ground motion, which is essential for assessing anthropogenic and natural hazards. The study examines ground displacement from 2018 to 2022 using multi-temporal Synthetic Aperture Radar Interferometry (MTInSAR). The Regional Unit of Preveza was selected for study area. According to the investigation, the area’s East–West Mean Velocity Displacement varies between 22.5 mm/y and −37.7 mm/y, while the Vertical Mean Velocity Displacement ranges from 16 mm/y to −39.3 mm/y. Persistent Scatterers (PSs) and Distributed Scatterers are the sources of these measurements. This research focuses on assessing the impact of ground deformation on 21 school units, 2 health centers, 1 hospital, 4 bridges and 1 dam. The findings provide valuable insights for local authorities and other stakeholders, who will greatly benefit from the information gathered from this study, which will lay the groundwork for wise decision-making and the creation of practical plans to strengthen the resistance of critical infrastructures to ground motion.
Estimating Subcanopy Solar Radiation Using Point Clouds and GIS-Based Solar Radiation Models
Authors: Buchalová Daniela , Hofierka Jaroslav , Šupinský Jozef , Kaňuk Ján
Published: January 2025

This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches—r.sun and the Point Cloud Solar Radiation Tool (PCSRT)—in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments.
Soil Organic Carbon Estimation and Transfer Framework in Agricultural Areas Based on Spatiotemporal Constraint Strategy Combined with Active and Passive Remote Sensing
Authors: Qian Jiaxin , Yang Jie , Sun Weidong , Zhao Lingli , Shi Lei , Shi Hongtao , Liao Lu , Dang Chaoya , Dou Qi
Published: January 2025

Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active and passive remote sensing for SOC estimation modeling in agricultural areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polarization and C-band dual-polarization), multi-spectrum (MS) data, and brightness temperature (TB) data. The performance of five advanced machine learning regression (MLR) models for SOC modeling was assessed, focusing on spatial interpolation accuracy and cross-spatial transfer accuracy, using two field observation datasets for modeling and validation. Results indicate that the SOC estimation accuracy when using MS data alone is comparable to that of using TB data alone, and both perform slightly better than SAR data. Radar cross-polarization ratio index, microwave polarization difference index, shortwave infrared reflectance, and soil parameters (elevation and soil moisture) demonstrate high correlation with the measured SOC. Incorporating temporal features, as opposed to single-phase features, allows each regression model to reach its upper limit of SOC estimation accuracy. The spatial interpolation accuracy of each MLR algorithm is satisfactory, with the Gaussian process regression (GPR) model demonstrating optimal modeling performance. When SAR, MS, or TB data are used individually in modeling, the estimation errors (RMSE) for SOC are 0.637 g/kg, 0.492 g/kg, and 0.229 g/kg for the SMAPVEX12 sampling campaign, and 0.706 g/kg, 0.454 g/kg, and 0.474 g/kg for the SMAPVEX16-MB sampling campaign, respectively. After incorporating soil moisture and topographic factors, the above RMSEs for SOC are further reduced by 57.8%, 35.6%, and 3.5% for the SMAPVEX12, and by 18.4%, 8.8%, and 3.4% for the SMAPVEX16-MB, respectively. However, cross-spatial transfer accuracy of the regression models remains limited (RMSE = 0.866–1.043 g/kg and 0.995–1.679 g/kg for different data sources). To address this, this study reduces uncertainties in SOC cross-spatial transfer by introducing terrain factors sensitive to SOC (RMSE = 0.457–0.516 g/kg and 0.799–1.198 g/kg for different data sources). The proposed SOC estimation and transfer framework, based on active and passive remote sensing data, provides guidance for high-resolution regional-scale SOC mapping and applications.
ThermalGS: Dynamic 3D Thermal Reconstruction with Gaussian Splatting
Authors: Liu Yuxiang , Chen Xi , Yan Shen , Cui Zeyu , Xiao Huaxin , Liu Yu , Zhang Maojun
Published: January 2025

Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. Current TIR image-based 3D reconstruction methods primarily focus on static conditions, which only capture the spatial distribution of thermal radiation but lack the ability to represent its temporal dynamics. The absence of dedicated datasets and effective methods for dynamic 3D representation are two key challenges that hinder progress in this field. To address these challenges, we propose a novel dynamic thermal 3D reconstruction method, named ThermalGS, based on 3D Gaussian Splatting (3DGS). ThermalGS employs a data-driven approach to directly learn both scene structure and dynamic thermal representation, using RGB and TIR images as input. The position, orientation, and scale of Gaussian primitives are guided by the RGB mesh. We introduce feature encoding and embedding networks to integrate semantic and temporal information into the Gaussian primitives, allowing them to capture dynamic thermal radiation characteristics. Moreover, we construct the Thermal Scene Day-and-Night (TSDN) dataset, which includes multi-view, high-resolution aerial RGB reference images and TIR images captured at five different times throughout the day and night, providing a benchmark for dynamic thermal 3D reconstruction tasks. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the TSDN dataset, with an average absolute temperature error of 1 °C and the ability to predict surface temperature variations over time.
Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation
Authors: Wu Zhen , Ye Runqing , Huang Jue , Fu Xiaolin , Chen Yao
Published: January 2025

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions.
Structural Analysis and 3D Reconstruction of Underground Pipeline Systems Based on LiDAR Point Clouds
Authors: Lai Qiuyao , Xin Qinchuan , Tian Yuhang , Chen Xiaoyou , Li Yujie , Wu Ruohan
Published: January 2025

The underground pipeline is a critical component of urban water supply and drainage infrastructure. However, the absence of accurate pipe information frequently leads to construction delays and cost overruns, adversely impacting urban management and economic development. To address these challenges, the digital management of underground pipelines has become essential. Despite its importance, research on the structural analysis and reconstruction of underground pipelines remains limited, primarily due to the complexity of underground environments and the technical constraints of LiDAR technology. This study proposes a framework for reconstructing underground pipelines based on unstructured point cloud data, aiming to accurately identify and reconstruct pipe structures from complex scenes. The Random Sample Consensus (RANSAC) algorithm, enhanced with parameter-adaptive adjustments and subset-independent fitting strategies, is employed to fit centerline segments from the set of center points. These segments were used to reconstruct topological connections, and a Building Information Model (BIM) of the underground pipeline was generated based on the structural analysis. Experiments on actual underground scenes evaluated the method using recall rate, radius error, and deviation between point clouds and models. Results showed an 88.8% recall rate, an average relative radius error below 3%, and a deviation of 3.79 cm, demonstrating the framework’s accuracy. This research provides crucial support for pipeline management and planning in smart city development.
Development of Deep Intelligence for Automatic River Detection (RivDet)
Authors: Lee Sejeong , Kong Yejin , Lee Taesam
Published: January 2025

Recently, the impact of climate change has led to an increase in the scale and frequency of extreme rainfall and flash floods. Due to this, the occurrence of floods and various river disasters has increased, necessitating the acquisition of technologies to prevent river disasters. Owing to the nature of rivers, areas with poor accessibility exist, and obtaining information over a wide area can be time-consuming. Artificial intelligence technology, which has the potential to overcome these limits, has not been broadly adopted for river detection. Therefore, the current study conducted a performance analysis of artificial intelligence for automatic river path setting via the YOLOv8 model, which is widely applied in various fields. Through the augmentation feature in the Roboflow platform, many river images were employed to train and analyze the river spatial information of each applied image. The overall results revealed that the models with augmentation performed better than the basic models without augmentation. In particular, the flip and crop and shear model showed the highest performance with a score of 0.058. When applied to rivers, the Wosucheon stream showed the highest average confidence across all models, with a value of 0.842. Additionally, the max confidence for each river was extracted, and it was found that models including crop exhibited higher reliability. The results show that the augmentation models better generalize new data and can improve performance in real-world environments. Additionally, the RivDet artificial intelligence model for automatic river path configuration developed in the current study is expected to solve various problems, such as automatic flow rate estimation for river disaster prevention, setting early flood warnings, and calculating the range of flood inundation damage.
A High-Temperature and Wide-Permittivity Range Measurement System Based on Ridge Waveguide
Authors: Xiong Rui , Hu Yuanhang , Xia Anqi , Huang Kama , Yan Liping , Chen Qian
Published: January 2025

Potential applications of microwave energy, a developed form of clean energy, are diverse and extensive. To expand the applications of microwave heating in the metallurgical field, it is essential to obtain the permittivity of ores throughout the heating process. This paper presents the design of a 2.45 GHz ridge waveguide apparatus based on the transmission/reflection method to measure permittivity, which constitutes a system capable of measuring the complex relative permittivity of the material under test with a wide temperature range from room temperature up to 1100 °C. The experimental results indicate that the system is capable of performing rapid measurements during the heating process. Furthermore, the system is capable of accurately measuring dielectric properties when the real part of the permittivity and the loss tangent vary widely. This measurement system is suitable for high-temperature dielectric property measurements and has potential applications in microwave-assisted metallurgy.
Zero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing
Authors: Lu Zikui , Chang Zixi , He Mingshu , Song Luona
Published: January 2025

With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification. AG-ZSL primarily learns two mapping functions: one that captures traffic behavior embeddings from burst-based traffic interaction graphs, and the other that learns attribute embeddings from traffic attribute descriptions. Then, the framework minimizes the distance between these embeddings within the shared feature space. The gradient rejection algorithm and K-Nearest Neighbors are introduced to implement a two-stage method for general traffic classification. Experimental results on IoT datasets demonstrate that AG-ZSL achieves exceptional performance in classifying both known and unknown traffic, highlighting its potential for enhancing secure and efficient traffic management at the network edge.
The SPICE Modeling of a Radiation Sensor Based on a MOSFET with a Dielectric HfO2/SiO2 Double-Layer
Authors: Marjanović Miloš , Ilić Stefan D. , Veljković Sandra , Mitrović Nikola , Gurer Umutcan , Yilmaz Ozan , Kahraman Aysegul , Aktag Aliekber , Karacali Huseyin , Budak Erhan , Danković Danijel , Ristić Goran , Yilmaz Ercan
Published: January 2025

We report on a procedure for extracting the SPICE model parameters of a RADFET sensor with a dielectric HfO2/SiO2 double-layer. RADFETs, traditionally fabricated as PMOS transistors with SiO2, are enhanced by incorporating high-k dielectric materials such as HfO2 to reduce oxide thickness in modern radiation sensors. The fabrication steps of the sensor are outlined, and model parameters, including the threshold voltage and transconductance, are extracted based on experimental data. Experimental setups for measuring electrical characteristics and irradiation are described, and a method for determining model parameters dependent on the accumulated dose is provided. A SPICE model card is proposed, including parameters for two dielectric thicknesses: (30/10) nm and (40/5) nm. The sensitivities of the sensors are 1.685 mV/Gy and 0.78 mV/Gy, respectively. The model is calibrated for doses up to 20 Gy, and good agreement between experimental and simulation results validates the proposed model.
TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements
Authors: Fang Wenhui , Chen Weizhen
Published: January 2025

Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model’s size. Additionally, the coordinate attention (CA) mechanism is incorporated to mitigate interference from irrelevant factors, thereby improving mean accuracy. Furthermore, the SIOU_Loss (SCYLLA-IOU_Loss) function and the Dynamic Sample(DySample)up-sampling operator are implemented to accelerate convergence and enhance both average precision and detection accuracy. The experimental results show that compared to the YOLOv8n model, the TBF-YOLOv8n model has a 3.7% increase in accuracy, a 1.1% increase in average accuracy, a 44.4% reduction in gigabit floating point operations (GFLOPs), and a 13.4% reduction in the total number of parameters included in the model. In comparison experiments with a variety of lightweight detection models, the TBF-YOLOv8n still performs well in terms of detection accuracy while remaining more lightweight. In conclusion, the TBF-YOLOv8n model achieves a commendable balance between efficiency and precision, offering valuable insights for advancing intelligent tea bud harvesting technologies.
Zero-Trust Access Control Mechanism Based on Blockchain and Inner-Product Encryption in the Internet of Things in a 6G Environment
Authors: Nie Shoubai , Ren Jingjing , Wu Rui , Han Pengchong , Han Zhaoyang , Wan Wei
Published: January 2025

Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption. By using smart contracts for automated access control, a reputation-based trust model for decentralized identity management, and inner-product encryption for fine-grained access control, the framework ensures data security and efficiency. Firstly, smart contracts are employed to automate access control, and software-defined boundaries are defined for different application domains. Secondly, through a trust model based on a consensus algorithm of node reputation values and a registration-based inner-product encryption algorithm supporting fine-grained access control, zero-trust self-sovereign enhanced identity management in the 6G environment of the Internet of Things is achieved. Furthermore, the use of multiple auxiliary chains for storing data across different application domains not only mitigates the risks associated with data expansion but also achieves micro-segmentation, thereby enhancing the efficiency of access control. Finally, empirical evidence demonstrates that, compared with the traditional methods, this paper’s scheme improves the encryption efficiency by 14%, reduces the data access latency by 18%, and significantly improves the throughput. This mechanism ensures data security while maintaining system efficiency in environments with large-scale data interactions.