bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distribution of radon in large workplaces: an analysis performed on radon levels measured in UK schools
Authors: Kouroukla E , Gooding T D 
Published: September 2024

Abstract
Radon is a radioactive, carcinogenic gas formed by the radioactive decay of uranium and radium that occur naturally in small amounts in all rocks and soils. It is the largest single source of radiation exposure to the UK population, contributing to more than 1 100 lung cancer deaths each year according to an analysis conducted in 2005. Regulations exist to protect employees (and other persons) where radon concentrations exceed the reference level of 300 Bq m−3. Once the reference level is exceeded, annual doses of more than the public dose limit of 1 mSv a−1 are considered to be excessive. A radon measurement campaign for schools, which started in 2009, generated a large dataset, including those with high numbers of simultaneous radon measurements. Radon data between buildings (e.g. homes) have been shown to correspond broadly to the lognormal distribution, after the additive contribution of outside air has been removed. However, there are fewer studies of the distribution of radon levels within a single, large property. Radon data collected from 533 UK schools with at least 20 valid, simultaneous results were analysed against several statistical models. In approximately 50% of schools the radon levels could be represented by the lognormal distribution and in 60% by the loglogistic lognormal distribution, the latter being a better fit probably owing to its lower sensitivity to the tails of the distribution. Qualitatively, the lognormal and the loglogistic probability plots appeared to be indistinguishable. These findings indicate that the lognormal and loglogistic might be appropriate models to characterise the distribution of radon in most large workplaces. For each statistical model, the two distribution parameters can be used to provide a better estimate of the average dose to the occupants. However, caution is required when assessing doses, since the average estimator of the radon concentration does not predict the highest value and may significantly underestimate or overestimate the dose in specific areas.
Measurement method of flexible component pose based on discrete motion actuators
Authors: Wu Zhengtao , Li Shuanggao , Chu Wenmin , Huang Xiang 
Published: September 2024

Abstract
In order to address the limitations of traditional large discrete motion actuator mechanisms in realizing high-precision inter-axis relationship calibration in manufacturing environments, this paper proposes a new convolutional neural network-based attitude mapping estimation method, component pose using convolutional neural network (CPCNN). The method implicitly encodes the inter-axis relation matrix into the weight parameters of the training neural network, which results in a high degree of integration with existing large discrete motion actuator mechanisms. The CPCNN-based method directly obtains the attitude change of the adjustment cabin by reading the change of each axis of the current motion actuator mechanism in its own coordinate system. This method can overcome the limitations of the experimental process in the traditional calibration methods and improve the accuracy of attitude mapping under the influence of self-weight by selecting better motion parameters through redundant degrees of freedom. The application of this new method will provide an effective solution for the high-precision inter-axis relationship calibration of large discrete motion actuator mechanisms in manufacturing environments, offering new possibilities for improving production efficiency and product quality.
A comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder and XGBoost algorithm
Authors: Chi Huashan , Wei Yuanyu , Yuan Bo , Sun Qingchao , Shu Liming 
Published: September 2024

Abstract
Accurately forecasting the remaining useful life (RUL) stands as a pivotal and formidable task within the realm of prognostics and health management. However, there is limited research that considers integrating early fault diagnosis during the bearing’s lifecycle with the prediction of its RUL. In this article, a comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder (AE) and XGBoost algorithm is proposed. To achieve this, an unsupervised piecewise function-based deep stacked convolutional AE was designed to construct the health indicator (HI) of the bearing for reducing the dependency on prior knowledge and furnishing a dynamic foundation for predicting RUL. The 4σ criterion based on HI’s increment was proposed for determining the fault occurrence time (FOT) of the bearing’s operational process. Subsequently, an XGBoost algorithm model was utilized to predict the RUL of faulty bearings. The efficacy of the bearing prognosis framework was validated by two real bearing test datasets. Results indicate the employed criterion of construct HI can flexibly adjust to various operational conditions and accurately pinpoint the bearing’s FOT. Furthermore, the findings demonstrate the proposed bearing prognosis framework achieves superior performance compared with several conventional anomaly detection methods.
Vibration displacement measurement of bridge structural models using image super-resolution reconstruction and visual object detection network
Authors: Wang Sen , Yang Rongliang , Chen Mingfang , Lin Sen , Wang Qingjian 
Published: September 2024

Abstract
Visual vibration measurement has emerged in the field of structural health monitoring in recent years, but it still has some shortcomings in terms of resolution, recognition rate and real-time performance. Considering the three aspects of recovering high-frequency image details, improving the compactness of the target bounding box, and reducing the computational time, we use the constructed image super-resolution reconstruction model and target detection model to measure the vibration displacement of the bridge structural model. First, we integrate the Transformer module into the Unet network with a simple structure. The Swin and Global Transformer Unet (SGTU) module constructed in this form can reduce the computational cost while reconstructing the large-resolution feature map target, and it can sharply edge information of the vibration target. We use the framework of the YOLOv5 algorithm as the backbone, and use the GhostBottleneck (GB) module to reduce the time for convolution operations to generate similar features. In addition, the proposed DWCBottleneck (DWCB) fusion module is also able to achieve high-level semantic fusion and network depth expansion with minimal computational cost. Finally, the center point offset of the bounding box predicted by the model can be used to obtain the displacement offset of the object in the image sequence. The position information of the target in the first frame image is used as the reference frame for calculating the offset, and the vibration displacement of the flexible structure in the image coordinate system is obtained by calculating the deviation of the displacement between the remaining frames and the first frame. We perform qualitative and quantitative comparisons in three aspects: video super-resolution reconstruction, visual detection robustness, and sensor vibration measurement displacement using a homemade vibration image dataset. The time–frequency domain displacement curves regressed by the visual vibration measurement algorithm are compared with the curves acquired after accelerometer acquisition, indicating the necessity of super-resolution reconstruction in visual vibration measurement.
In-flight pixel degradation of the Sentinel 5 Precursor TROPOMI-SWIR HgCdTe detector
Authors: van Kempen Tim A , Lobanova Marina , van Hees Richard , Masarotto Valentina , Tol Paul , Kurchaba Solomiia , Hoogeveen Ruud W M 
Published: September 2024

Abstract
The TROPOMI-SWIR HgCdTe detector on the Sentinel-5 Precursor mission has been performing in-orbit measurements of molecular absorption in Earth’s atmosphere since its launch in October 2017. In its polar orbit the detector is continuously exposed to potentially harmful energetic particles. Calibration measurements taken during the eclipse are used to inspect the performance of this detector. This paper explores the in-orbit degradation of the HgCdTe detector. After five years, the detector is still performing within specifications, even though pixels are continuously hit by cosmic radiation. The bulk of the impacts have no lasting effects, and most of the damaged pixels (95%) appear to recover on the order of a few days to several months, attributed to a slow spontaneous recovery of defects in the HgCdTe detector material. This is observed at the operational temperature of 140 K. The distribution of the observed recovery times has a mean around nine days with a significant tail towards several months. Pixels that have degraded have a significant probability to degrade again. The location of faulty pixels follows a Poissonian distribution across the detector. No new clusters have appeared, revealing that impacts are dominated by relatively low energetic protons and electrons. Due to the observed spontaneous recovery of pixels, the fraction of pixels meeting all quality requirements in the nominal operations phase has always been over 98.7%. The observed performance of the TROPOMI-SWIR detector in-flight impacts selection criteria of HgCdTe detectors for future space instrumentation.
Hair-YOLO: a hair follicle detection model based on YOLOv8
Authors: Zhu Zhibo , Wei Guoliang , Wu Junke , Liu Shuting , Chen Bo , Zhang Zhenyu , Li Qimin 
Published: September 2024

Abstract
Hair follicle detection technology has developed rapidly in recent years. Traditional manual detection methods are labor-intensive and inefficient. To address this problem, we propose a real-time hair follicle detection model called Hair-YOLO based on YOLOv8. This model focuses on accurately identifying the number of hairs within each follicle, providing precise data that help doctors assess hair density and follicle health of patients. First of all, we incorporate the re-parameterization Ghost module into the backbone, reducing the parameters and computational load. Then, the deformable convolution v3 operator is integrated into the neck network, enhancing adaptation to follicle shapes. Next, we propose a novel multi-scale feature perception separated and enhancement attention (Multi-SEAM) module, building upon the SEAM module, to address complex scalp scenarios. Furthermore, we enhance bounding box regression by replacing the standard complete intersection over union loss, with a modified point distance intersection over union loss. Finally, we construct a new hair follicle dataset and use it for a comparative analysis of Hair-YOLO and established models. Our model shows excellent performance, with a 14.26% increase in mAP@0.5:0.95, a 2.98% increase in Recall, and a 4.31% increase in Precision compared to the baseline.
A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification
Authors: Jing Hutao , Yuan Shenfang , Chen Jian , Meng Yixing 
Published: September 2024

Abstract
As a promising structural health monitoring technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1 mm to 1.6 mm, marking a significant improvement.
Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer
Authors: Wang Siyu , Jia Zhen , Li Yang , Yang Qiqi 
Published: September 2024

Abstract
As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.
Applications of deep learning-based denoising methodologies for scanning electron microscope images
Authors: Shin Seoleun , Lee In-Ho , Park Byong Chon , Song Joo-Hyun 
Published: September 2024

Abstract
In this paper, we use five types of deep-learning algorithms for denoising scanning electron microscope (SEM) measurement data. Denoising of SEM images is an important task since the images often suffer from noise, which can make it difficult to accurately interpret the data. We also investigate realistic SEM denoising characteristics using a variety of metrics to assess the quality of denoised images. Overall, we find that the trained generative models provide superior denoising performance and that it is crucial to objectively quantify the performance, just like in the scanning process itself. It is anticipated that the deep-learning based technique can accelerate image measurements, which can be utilized for very fast analytical investigations. We also demonstrate that the success of a generative model may depend on the appropriate assessment of noise characteristics in the specific image data analysis of interest. Moreover, it is addressed that denoising performance can be properly evaluated when a relevant metrics that aligns well with human visual systems.
Three-dimensional reconstruction of bubble geometry from single-perspective images based on ray tracing algorithm
Authors: Song Yuchen , Huang Guangyuan , Yin Junlian , Wang Dezhong 
Published: September 2024

Abstract
An improved shadow-based imaging method combined with a ray-tracing algorithm is proposed for three-dimensional (3D) reconstruction of bubble mesostructures. The 3D micro-scale interface measurement of bubbles categorized as transparent objects is challenging at the forefront of research in the field of multiphase flows. In the study, a reconstruction algorithm is developed using a single high-speed camera under a small-angle diffused backlight source based on Helmholtz reciprocity, Gaussian optics, and Snell’s law. The accuracy of the algorithm is assessed by comparing the reconstruction results of the rendered images generated from the 3D model using Blender. Standard models include a transparent hollow sphere model and several irregular bubble models. The accuracy of the algorithm is determined by analyzing and comparing the contour lines along the edges of the bubble models. The error of the method in the paper can be improved below 5% with the ray tracing optimization. The average uncertainty U
c is 2.8%, according to the evaluation of the models in Blender. The extreme deformation of the bubbles in a turbulent flow field enhanced by ultrasonic is performed to evaluate the 3D reconstruction algorithm. Prior to the fragmentation of bubbles into micro/nanobubbles by ultrasonication, the bubble surface undergoes severe deformation under continuous bombardment by small-scale turbulence eddies. The proposed algorithm exhibited high accuracy and easily performing in experiments.
A new method for thermal runaway warning of electric vehicle charging
Authors: JiaQi Zhang , DeXin Gao , YuanMing Cheng , Qing Yang 
Published: September 2024

Abstract
With the large-scale development of electric vehicle (EV) in China, the frequent occurrence of EV fire accidents has attracted the attention of insiders to the fault monitoring and early warning during the charging process of EV. According to data from the National Emergency Management Ministry, there were over 3,000 EV fire accidents nationwide in 2023, underscoring the urgency for enhanced fault monitoring and early warning measures. In response to these issues, this article proposes an EV thermal runaway early warning method based on Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) by combining the two technologies. The method aims to improve EV safety by monitoring battery status for early thermal runaway warning. First, the historical normal charging data of the battery are preprocessed and feature extracted using the TCN, and then features fed into GRU for time-series modeling and forecasting. This combined model not only demonstrates high prediction accuracy and stability but also swiftly responds to abnormal conditions during charging, effectively preventing thermal runaway accidents and ensuring charging safety. Furthermore, this model possesses excellent generalization capabilities and can adapt to different types and specifications of EV battery systems.
A review on recent advances in g-C3N4-MXene nanocomposites for photocatalytic applications
Authors: Subha N , Nagappagari Lakshmana Reddy , Ravi Sankar A 
Published: September 2024

Abstract
The solutions for environmental remediation and renewable energy generation have intensified the exploration of efficient photocatalytic materials. Recently, the composites of g-C3N4 and MXene have gained considerable interest for their potential applications in photocatalysis. In the g-C3N4-MXene composite, the g-C3N4 possesses unique physical, chemical, and optical properties to increase visible light absorption. At the same time, MXene improves conductivity, adsorption of reactant molecules or the active sites, and charge transfer properties. Combining the unique physico-chemical properties of MXene and g-C3N4, the resulting composite exhibits superior photo-responsive behavior and is critical in photocatalytic reactions. Furthermore, the g-C3N4-MXene composite exhibits stability and recyclability, making it a promising candidate for sustainable and scalable photocatalytic material in environmental remediation. This review offers an in-depth analysis of the development and design of g-C3N4-MXene composites through diverse synthesis procedures and a comprehensive analysis of their application in carbon dioxide (CO2) reduction, photocatalytic degradation, water splitting processes, mainly hydrogen (H2) generation, H2O2 production, N2 fixation, and NO
x
removal. The charge transfer mechanism of g-C3N4-MXene composite for photocatalytic application has also been discussed. This review provides insights into the photocatalytic capabilities of g-C3N4-MXene composites, showing their potential to address current environmental challenges and establish a robust foundation for sustainable energy conversion technologies.
Recent advances in scanning electrochemical microscopy for energy applications
Authors: Wang Qi , Tang Qianlin , Li Peipei , Bai Xiaoxia 
Published: September 2024

Abstract
Scanning electrochemical microscopy (SECM) is a scanning probe technique capable of imaging substrate topography and measuring the local electrochemical reactivity of interfaces. Since introduced by Allen J. Bard and co-workers in 1989, it has expanded into a wide variety of fields, such as nanomaterial characterization, energy, kinetics, electrocatalysis, metal anti-corrosion, biology and instrumental development. SECM uses an ultra-microelectrode as the probe to record redox current during probe scanning across sample surfaces to obtain local topography and electrochemical reactivity of samples. Specifically, three main topics are reviewed and discussed: (1) the working principles and operating modes of SECM; (2) the recent developments in the application of SECM in energy science, including solar cell, rechargeable batteries, fuel cells and supercapacitors, with an emphasis on the last five years (2019–2023); (3) the perspectives and outlook of SECM in various energy devices. We anticipate that a wider adoption of SECM by the energy community will allow for the operando characterization of many types of reactions, and hold the potential to provide new insights into the structure/activity and composition/activity relationships.
Coherent random fiber laser emission from CdSe/ZnS quantum dots
Authors: Ye Lihua , Cheng Zhixiang , Zhang Ziang , Hong Shaoqiang , Zhao Qing 
Published: September 2024

Abstract
We experimentally demonstrate the coherent random laser emission by combining CdSe/ZnS colloidal quantum dots with hollow optical fiber. Through the localized surface plasmon resonance induced by Ag nanoparticles (NPs), well-distinguished discrete spikes are observed from the Ag modified hollow fiber loaded with CdSe/ZnS QDs solution. In addition, coherent random laser action with low threshold is also realized in the hollow optical fiber filled with high packing-density CdSe/ZnS QDs even if the Ag NPs is not introduced. The self-assembled clusters of CdSe/ZnS QDs serve as the optical gain media as well as the strong scattering centers. The angle measurement experiments show that the directional emission of random laser can be adjusted by using different pumping manners. This facile, inexpensive, low pump threshold random laser could be widely used in photonic devices and display imaging.