bluuebunny/crossref_metadata_2025_split huggingface dataset:
The study of the X17 anomaly with the PADME experiment
Authors: Gianotti Paola ,   PADME collaboration
Published: September 2023

Abstract
In dark matter studies, the absence of experimental evidences other than the astrophysical observations, has triggered new approaches. Nowadays, many particle physics experiments at accelerators are trying to contribute by looking for signals of hidden particles postulated by different theoretical extensions of the Standard Model. This results in a vaste hunting for new particles with a wide range of properties with the intention to also give reason to other unexplained particle physics phenomena. Within this scenario is inserted the Positron Annihilation into Dark Matter Experiment (PADME) ongoing at the Laboratori Nazionali di Frascati of INFN that is looking for signals of hidden particles by studying the annihilations of a positron beam with the electrons of a fixed target. PADME had, up to now, two data taking periods devoted to the search of a dark photon signal, but its setup turned out to be also suited to explore the existence of a protophobic new boson of mass 17 MeV/c
2 postulated to explain an anomalous effect observed by a Hungarian group while studying nuclear excited states. In this paper it is reported an overview of the PADME experiment and of the modifications implemented to allow a dedicated data taking at 282 MeV beam energy, meant to produce the new particle at resonance.
Ion-source development at the off-line LERIB test-facility at iThemba LABS
Authors: Segal S T , Bark R A , Abraham J , Anderson H , Baard S , Crombie A , Ellis C , Duckitt W , Cornelius B , Kohler I , Shirinda O , Van Schalkwyk P 
Published: September 2023

Abstract
The Low Energy Radioactive Ion Beam (LERIB) facility [1] will be used to produce low-energy radioactive-ion beams (RIBs) with energies up to 60 keV. Radioactive reaction products will be created by a 66 MeV proton-beam impinging on a target made of carbide disks, such as SiC [2]. These reaction products will then be ionized in a target-ion-source (TIS) and extracted as beam. The TIS design allows three ion-sources: a surface ion-source [3], a forced electron-beam induced arc-discharge (FEBIAD) ion-source [4], and a resonance-ionization laser ion-source, or RILIS. The surface-ionization source was commissioned with stable beams in October 2021. The production of ions from Group-1 elements was accomplished with beams of 39K+, 41K+ and 23Na+ where currents were measured in the μA range. This source may be advantageous for producing stable pilot-beams for future radioactive-beam experiments. The FEBIAD is still in development at present.
INFN-LNS facility upgrade for the Nuclear Physics Renaissance
Authors: Gammino S. , Tumino A. , Greco V. , Celona L. , La Cognata M. , Di Pietro A. , Musumeci M. S. , Russo A.D. 
Published: September 2023

Abstract
A broad range of Nuclear Physics research activities have been carried out at INFN-LNS until the summer 2020, when the accelerators were stopped for the upgrade. The upgrade of LNS is a project mainly funded by a PON-FESR (National Program for Research and Innovation) strategic line for boosting the research infrastructures, having its own goals, time-schedule and deadlines. In addition to such an action promoted by the Italian Ministry of Research, further funds have been made available from INFN budget. The end of the phase supported by the PON for procurement and tenders is currently set for the end of 2023. A series of actions will therefore be implemented to improve scientific opportunities for users. In particular, the focus is on the commissioning of the Tandem and Superconducting Cyclotron with the new set-up, completed by the renewal of the experimental areas and the commissioning of the new fragment separator FRAISE, also financed under the PON. The high-intensity program, including the determination of the nuclear matrix elements (NME) for the double beta decay and the study of EOS for nuclear matter with large neutron content, will be made feasible by these improvements to accelerators, beamlines and detectors. Some highlights of the whole activity as well as of the Applied Physics perspectives and the Astroparticle Physics multi-messenger program, strictly connected to the Nuclear Physics program, are given.
Young Professionals Mentoring Programme in The Nuclear Industry
Authors: Bellingan Nancy , Steinmetz Hans-Jürgen 
Published: September 2023

Abstract
The nuclear industry has been facing issues linked directly to the mixed feelings of the public opinion and national governments towards the use of nuclear energy for electricity production among others.
Different institutions are aware of it and published reports on the challenges identified in this field, including the IAEA (International Atomic Energy Agency) and ESK (German Commission on Waste Management).
In this paper is presented an analysis of said challenges and a possible solution, the network NANCY (Nuclear Applications newcomers CommunitY), an initiative currently in phase of development at the focus field Nuclear Waste Management, in the university of applied sciences FH Aachen, Germany.
Prospective appraisal of a formative nuclear and radiation physics at Nairobi
Authors: Angeyo HK , Kaniu IM , Mustapha AO , Massop AD 
Published: September 2023

Abstract
We provide a prospective appraisal of the evolution of the graduate nuclear and radiation physics program at the University of Nairobi underscoring its unique growth in the framework of relevant knowledge networks and a nascent nuclear power program. Due to the distinctive characteristics of the nuclear fuel cycle, there arose in Kenya especially nuclear security educational requirements for a proliferation-free nuclear power program as well as the need for provision of a broad spectrum of technical capacities to plan, regulate, and safely handle a nuclear power infrastructure. We detail how the graduate program centres on our newly established research line in nuclear forensics. Learning from this experience we propose, as a result, moving into nuclear photonics enabled by accelerators so as to play more key roles in accelerating capacity building embracing both nuclear and laser physics.
An introduction to computational complexity and statistical learning theory applied to nuclear models
Authors: Idini Andrea 
Published: September 2023

Abstract
The fact that we can build models from data, and therefore refine our models with more data from experiments, is usually given for granted in scientific inquiry. However, how much information can we extract, and how precise can we expect our learned model to be, if we have only a finite amount of data at our disposal? Nuclear physics demands an high degree of precision from models that are inferred from the limited number of nuclei that can be possibly made in the laboratories.
In manuscript I will introduce some concepts of computational science, such as statistical theory of learning and Hamiltonian complexity, and use them to contextualise the results concerning the amount of data necessary to extrapolate a mass model to a given precision.
Systematics of mean resonance spacing and average radiative width from random forest regression
Authors: Bormans J P , Wyngaardt S M , Wiedeking M , Malatji K L 
Published: September 2023

Abstract
The interaction of a nucleus with photons plays a key role in understanding a competition in nuclear reactions. The photon strength function (PSF) gives the average response of a nucleus to an electromagnetic probe. In this study, an estimation is performed of the mean resonance spacing and average radiative width of 22 nuclei in the range A = 46 – 90. The calculations for this analysis are done in TALYS. In addition, a machine-learning driven approach for determining experimentally obtained model parameters from the neutron/proton and mass number is given. A reasonable agreement within the uncertainties is achieved between the model dependent calculations, using TALYS, and results from random forest regression.
Enhanced Security Contraband Detection through Integration of Attention Mechanism in R3Det
Authors: Ye Chao , Ding Jiangang , Wang Huaizhi , Yang Ming , Tian Aojia , Li Wei 
Published: September 2023

Abstract
Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network’s ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced the spatial and channel attention mechanism during the connection between the backbone and the Feature Pyramid Network (FPN), enabling the model to focus on significant features while ignoring complex background information, then the following Feature Refinement Module to achieve feature alignment in a pixel-by-pixel manner. Our approach successfully achieved rotating target detection in the background of complex X-ray images. Through end-to-end training, our proposed method achieves a 2.6% improvement over the original detector, with a mean Average Precision (mAP) of 86.7%. Notably, our approach showed remarkable results in detecting sensors, pressure, and firetrackers. Now, we have deployed our proposed method on actual security machines for hazardous material detection tasks.
A Framework for False Image Detection with Sample-Oriented Intelligent Adversarial
Authors: Jun Li , Jiajie Zhang , Yongqiang Feng , Yanzhao Liu 
Published: September 2023

Abstract
To develop algorithms capable of automatically detecting and evaluating the authenticity of images and videos, researchers have focused on false image detection algorithms. These algorithms aim to identify the authenticity of images by distinguishing between real images and forged ones generated using false generation algorithms. In this paper, the main focus is on implementing a single-frame authenticated image detector using the concept of migration learning. The detector utilizes Inception ResNet v2, a target classification network pre-trained on a self-built military scene dataset. To enhance the dataset, a series of graphical enhancement algorithms are employed, enabling the classification network to learn the crucial differences between real and forged images. Additionally, Focal Loss is introduced to balance the dataset for various GAN-based image generation algorithms. As a result, the final forged target image detector achieves an impressive classification accuracy of 0.8908 on a large-scale sample test set.
Flame Detection Based on Improved YOLOv7
Authors: Wu Lumeng , Wang Yingfan 
Published: September 2023

Abstract
A flame detection algorithm with improved YOLOv7 is proposed to address the problems that small targets in flame detection are easy to be missed and misdetected, and are easily disturbed by bright objects during detection. In the improved algorithm, the SE attention module is introduced to enhance the perception of the model on the channel; ConvNeXt is used to retain the features of small target flames for small targets among flame images; Employing K-means ++ clustering algorithm to gain the anchor points that match the flame size more to enhance the detection accuracy.The results testify that the improved algorithm we proposed in this paper is higher than the YOLOv7 in terms of average accuracy and precision.
Low illumination image enhancement based on attention mechanism and global illumination estimation
Authors: Shen Linshuai , Wei Weimin , Chen Tao 
Published: September 2023

Abstract
Aiming at the problems of insufficient or excessive local brightness enhancement, color distortion, and excessive noise in the existing low-light image enhancement algorithms, a low-light image enhancement method combining attention mechanism and global illumination estimation is proposed. First, the illumination distribution map of low illumination image is obtained through the illumination distribution estimation network coupled with the attention gate mechanism. Then, the weight of the light distribution map is learned in the feature attention module. Finally, the image details are fused by the detail reconstruction module to create improved image. According to the experimental results, this method may effectively improve image brightness, contrast, and color in subjective visual effects while also enhancing objective evaluation indicators like PSNR, SSIM, and MSE when compared to some conventional methods.
Single Image Rain Removal Algorithm Based on U-Net and Vision Transformer
Authors: Cui Hua , Yang Zhiqiang , Shi Yanzi 
Published: September 2023

Abstract
Rain will significantly lower image quality, which will undoubtedly have an impact on how well outdoor computer vision systems operate, such as autonomous driving. This paper proposes a two-branch deep neural network consisting of an attention guided U-Net and Vision Transformer, which could capture intra-field details and cross-patch relationships to obtain good global and local deraining effects. In order to ensure both branches pose positive effects on the target derain task, we specifically design a patch boot image module to achieve complemented feature fusion, which adaptively selects informative intra-field regions guided by the patch-wise importance map. Moreover, Wasserstein distance between the prediction and reference image is applied as the objective function to pursue better image quality measurement. In comparison to existing algorithms, the suggested method may more effectively remove rain and restore the background details, according to qualitative and quantitative results on the public datasets Rain200L and Rain200H. On the aforementioned datasets, the peaks of the structural similarity (SSIM) and signal-to-noise ratio (PSNR) were 32.55/0.9476 and 26.12/0.8826, respectively.
FCA-Net: A Fast Inference and Channel Attention Based Network for Hyperspectral Image Classification
Authors: Ji Bo , Bai Lin , Wang Haijuan , Shen Jiaming , Chen Guoqiang , Jin Ying 
Published: September 2023

Abstract
Hyperspectral imaging (HSI) is a competitive remote sensing technique used in various fields such as land cover mapping and environmental monitoring. Each hyperspectral imaging (HSI) scene is comprised of numerous narrow and contiguous spectral bands, rendering the extraction of information from HSI data cubes a challenging and computationally intricate endeavor. Convolutional neural networks (CNNs) have garnered widespread adoption for HSI classification due to their impressive performance. Nevertheless, the substantial number of internal parameters within CNNs engenders high computational and memory requirements, resulting in inefficient floating-point operations per second (FLOPS), particularly when faced with frequent memory access and an abundance of operators. To address this issue, this paper proposes a novel framework named Fast Inference and Channel Attention-based Network (FCA-Net). Specifically, the framework introduces a lightweight convolutional layer and a channel attention mechanism (CAM) to enhance the extraction of spatial and spectral information within the network. The proposed FCA-Net significantly reduces computational costs while maintaining reliable classification results and can perform fast processing on GPU or even CPU, making it a promising option for embedded systems. Furthermore, the optimized global computational cost, including reduced demand for compute power and memory, results in lower energy consumption, which has previously been proven advantageous for improving deep model performance.
Similar Graph Face Clustering Based on Graph Convolutional Neural Network
Authors: Fang Mingming , Li Shengen , Wang Jian 
Published: September 2023

Abstract
Face clustering is primarily a method for grouping a large number of face images and has important applications in the fields of label-free face image annotation and image management. Traditional machine learning clustering algorithms do not work well on face image data and are unable to learn effectively on complex face image features. Recent research has turned to the use of graph convolutional neural networks (GCNs) to learn contextual information from neighbourhood features between face images for inference, which can significantly improve performance. Unlike the conventional link prediction of face data points and confidence prediction between vertices by using GCNs, for this paper a similarity graph based face clustering algorithm is proposed. Construction of subgraphs by K-nearest neighbour algorithm, Learn the similarity between the k-nearest neighbor subgraphs of each face instance by constructing subgraphs with the k-nearest neighbor algorithm. Using a bottom-up clustering strategy to merge subgraphs to complete the clustering, this algorithm improves the clustering results on complex face features by 20% and 7% compared to traditional machine learning methods such as K-means and DBSCAN respectively, improving the clustering accuracy and reducing the computational complexity.
Research on image quality evaluation and its application in image fusions
Authors: Xin Hongqiang , Li Chao , Li Yunchao , Wei Zhuo , Wang Jian , Zhang Jie , Qiu Chun 
Published: September 2023

AbstractIn view of the digital image in the process of acquisition, storage, processing and transmission due to the camera equipment, compression degree and transmission bandwidth caused by various distortion problems, this paper summarizes the image quality evaluation methods. Firstly, two methods of image quality evaluation, namely subjective evaluation and objective evaluation, are introduced, and then the relationship between subjective evaluation and objective evaluation is analyzed. Finally, the image quality evaluation method is successfully applied to the image fusion processing test, which provides ideas for the subsequent research on image quality evaluation methods.
A time-frequency method for induction motor fault feature extraction using instantaneous power signals
Authors: Sun A L , Duan C D 
Published: September 2023

Abstract
To solve the issue that the characteristic frequencies of the broken rotor bar fault (BRBF) are always submerged by the fundamental component during the stator current spectrum analysis in light loads, this paper proposes an effective approach based on the instantaneous power signal and the local maximum synchrosqueezing transform (LMSST). By multiplying the current and voltage, the instantaneous power signal can provide more fault features and enhance fault component amplitudes. The LMSST method is used to obtain a time-frequency analysis with more concentrated energy, and more fault characteristics are extracted from the reconstructed signal, which helps to accurately identify the motor BRBF.
ERNIE-based Named Entity Recognition Method for Traffic Accident Cases
Authors: Cheng Zibin , Duan Zongtao , Bu Nana , Liu Yuexia , Cao Jianrong , Gao Zhengge , Zuo Pengfei 
Published: September 2023

Abstract
Traffic accident case named entity recognition, which helps mine key information in traffic accident texts, plays a vital role in downstream tasks such as the construction of knowledge graphs in road traffic and intelligent policing. In this paper, we construct a named entity recognition model based on the EDE (Entity Data Enhancement)-ERNIE-Bidirectional Gated Recurrent Unit Network (BiGRU)-Conditional Random Field (CRF) to address the current situation of low traffic accident case data and poor recognition of long-text entities. First, the amount of accident case data is enhanced using the entity random substitution method. Next, the text data of traffic accident cases are characterized as a dynamic word vector using the ERNIE pretraining model. Then, the BiGRU network learns the long-distance dependency relationship in the text to enhance the effect of the model on long-text entity recognition. Finally, the result sequence is constrained by the CRF layer to realize the named entity recognition model. The experimental part uses data related to real traffic accident cases in a domestic area. The data enhancement method increases the data volume three times compared to the original data volume. Experimental results show that the EDE-ERNIE-BiGRU-CRF model achieves better F1 values, recall and precision achieved better performance than the entity recognition methods of BERT-BiGRU-CRF, ERNIE-BiGRU-CRF, ERNIE-BiLSTM-CRF, ERNIE-CRF, ERNIE, BiGRU-CRF, ROBERTA-wwm-ext-BiGRU-CRF and verify its effectiveness for entity recognition in traffic accident cases.
Spatial-temporal Traffic Flow Prediction Model Based on the GAT and BiGRU
Authors: Gao Yunmeng , Zhao Liang , Du Jin , Wang Junnan 
Published: September 2023

Abstract
Real-time and accurate traffic flow prediction is crucial for improving the safety, stability, and efficiency of intelligent transportation system. Considering that traffic flow prediction methods rarely analyze from the perspective of the road network, in this paper, a spatial-temporal traffic flow prediction model based on the combination of graph attention network (GAT) and bidirectional gated recurrent unit (BiGRU) neural network is proposed. Firstly, GAT is used to analyze the complex topology of the road network, effectively obtaining the spatial features of the road network. Secondly, BiGRU is used to learn the dynamic changes of traffic flow data, effectively obtaining the temporal features. Thirdly, the obtained spatial-temporal features are output by the fully connected layer to complete the prediction of future traffic flow. Finally, the model is validated and evaluated on the California highway dataset. The experimental results show that the accuracy of GAT-BiGRU model is better than other benchmark models in predicting future traffic flows transformation, especially in long-term prediction.
Design and implementation of regional integrated environmental measurement for China space station
Authors: Yi Yusheng , Fan Gaojie , Bai Linhou , Wei Bangyou , Zheng Yi , Qi Xin , Ying Le , Zhang Shuqi 
Published: September 2023

Abstract
The monitoring of cabin air pressure, temperature, humidity, microgravity and other environmental parameters is very important for the long-term and reliable operation of large manned spacecraft. Based on the characteristics of 1553B bus, a design method of regional integrated environmental measurement for the space station was proposed, which realized the high precision and reliable measurement of environmental parameters such as pressure, temperature, humidity and microgravity of china space station. The measurement data can accurately reflect the environmental changes of astronauts’ in-orbit living, working and extravehicular activities, which verifies the effectiveness of the design. It provides the necessary new principle application verification for higher precision pressure measurement in lunar landing missions.
Digital twin design of pneumatic classifier and its key technology
Authors: Yu Y , Gao S C , Cao Y N , Liu J X , Jiao Z W 
Published: September 2023

Abstract
The design scheme for a digital twin of the pneumatic classifier is proposed, which is associated with the physical entity. The key technologies such as visualization design and data acquisition are analysed in order to realize the digital management of the pneumatic classifier and operating process and monitor flow field characteristics and particle motion based on data analysis according to the digital twin. It aims to realize real-time monitoring, simulation and prediction of the classification performance of the complex pneumatic classification systems, and provide practical guidance for the optimal design of pneumatic classifier.