bluuebunny/crossref_metadata_2025_split huggingface dataset:
Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance
Authors: Wang Chaoge , Tian Xinyu , Zhou Funa , Wang Ran , Wang Lijun , Tang Xiaojing 
Published: October 2024

Abstract
In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a self-weighted graph attention networks based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using stacked autoencoders, and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.
MFHARFNet: multi-branch feature hybrid and adaptive receptive field network for image segmentation
Authors: Li Meng , Yun Juntong , Jiang Du , Tao Bo , Liu Rong , Li Gongfa 
Published: October 2024

Abstract
Accurate segmentation of medical images is crucial for disease diagnosis and understanding disease changes. Deep learning methods, utilizing encoder-decoder structures, have demonstrated cutting-edge performance in various medical image segmentation tasks. However, the pooling operation in the encoding stage results in feature loss, which makes the network lack the ability to fuse multi-scale information at different levels, hinders its effective perception of multi-scale information, and leads to poor segmentation performance. Drawing inspiration from the U-shaped network, this study introduces a multi-branch feature hybrid attention and adaptive receptive field network (MFHARFNet) for medical image segmentation. Building upon the encoder-decoder framework, we initially devise a multi-branch feature hybrid attention module (MFHAM) to seamlessly integrate feature maps of varying scales, capturing both fine-grained features and coarse-grained semantics across the entire scale. Furthermore, we redesign the skip connection to amalgamate feature information from different branches in the encoder stage and efficiently transmit it to the decoder, providing the decoder with global context feature maps at different levels. Finally, the adaptive receptive field (ARF) module is introduced in the decoder feature reconstruction stage to adapt and focus on related fields, ensuring the model’s adaptation to different segmentation target features, and achieving different weights for the output of different convolution kernels to improve segmentation performance. We comprehensively evaluate our method on medical image segmentation tasks, by using four public datasets across CT and MRI. Remarkably, MFHARFNet method consistently outperforms other state-of-the-art methods, exceeding UNet by 2.1%, 0.9%, 6.6% and 1.0% on Dice on ATLAS, LiTs, BraTs2019 and Spine and intervertebral disc datasets, respectively. In addition, MFHARFNet minimizes network parameters and computational complexity as much as possible. The source codes are in https://github.com/OneHundred99/MFHARFNet.
Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers
Authors: He Wen , Qin Limu , Lu Yazhong 
Published: October 2024

Abstract
Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum FSC blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.
Precision and uncertainty in natural gas calorific value estimation: advanced combinatorial predictive models for complex pipeline systems
Authors: Zhou Jun , Fu Wenqi , Liang Guangchuan , Zhang Daixing , Ma Junjie 
Published: October 2024

Abstract
In recent years, natural gas pipelines have been characterized by multiple intake points, the mixing of different gas sources, and significant variations in gas quality. The existing volumetric measurement system is no longer suitable for the development and operation of natural gas pipelines in China. To ensure accurate and equitable implementation of natural gas measurement, there is a gradual shift towards energy measurement. However, due to numerous measurement interfaces, installing chromatographs at all measurement stations would lead to substantial investment costs. Therefore, predicting the calorific value of natural gas is a key technology for energy measurement. In view of the issues existing in the current methods of obtaining natural gas calorific value, different prediction models of natural gas calorific value are constructed. According to the influencing factors of natural gas calorific value, the uncertainty evaluation is carried out, and the accuracy of different calorific value prediction models is analyzed by case data. The maximum error of regression equation, response surface analysis, BP neural network and genetic algorithm prediction model is 2.29%, 0.43%, 0.59% and 0.45% respectively. However, the combined calorific value (CCV) prediction model is 0.41%. The results indicate that the predicted value calculated by the CCV prediction model is closer to the actual value and has higher prediction performance, which provides a new method for the calorific value prediction and measurement management of natural gas.
Potential for suprathermal chain reactions in degenerate deuterium plasmas at high densities
Authors: Robinson A P L 
Published: October 2024

Abstract
It is argued that fusion chain reactions in the D-D system are feasible with supra-thermal deuterons in the MeV regime, with new generations of deuterons being generated either via neutron-deuteron and proton-deuteron collisions, provided that fuel densities in excess of 1000 gcm−3 are reached. The propagation of supra-thermal deuterons in an infinite, dense deuterium target was studied using a Monte Carlo method that includes multiple nuclear reactions, electron and ion stopping, along with neutron and proton knock-ons. Prior studies into this system are extended and it is shown that : (a)dense pure deuterium may be more supportive of chain reactions via refinements to the ion-ion stopping number, (b) such chain reactions can be supported even when the target is electron degenerate, and (c) the D-T reaction plays a critical role in sustaining these chain reactions.
Critical current characteristics and ampere force distribution of a novel HTS cable with different transposition lengths
Authors: Zhang Heng , Chang Shaonan , Yang Jingyi , Pi Wei 
Published: October 2024

Abstract
Distribution characteristics of the current, ampere force and magnetic flux density (B [T]) of a novel high temperature superconducting (HTS) cable woven by transposed REBCO tapes under different transposition lengths are proposed, and these characteristics of the cable stacked with 3 REBCO tapes are also carried out in this paper for comparison. The objective is to enhance the current density and the compactness of the HTS tapes combination, and uniform the current, ampere force and magnetic flux density (B [T]) distributions. A three-dimensional (3D) finite element model, based on Maxwell’s equations without consideration of the displacement current, is established to describe the state of the system in the liquid nitrogen condition. The distributions characteristics, maximum and minimum values of current, ampere force and magnetic flux density (B [T]) show significant differences in the simulation results between the novel HTS cable woven by 3 transposed REBCO tapes and the cable stacked with 3 REBCO tapes. The simulated results show that the distribution characteristics are improved, with a more uniform distribution characteristic than that of the cable stacked with REBCO tapes. This is due to fully transposed REBCO tapes. In addition, the experimental E–I curve was also reached. The experimental results show that using a shorter transposition length in the cable can improve the critical current. However, a too small transposition length can cause significant damage to the HTS tape, resulting in deformation of the HTS tape and a reduction in the electromagnetic properties of the HTS tape. This affects the critical current of the HTS tape and, ultimately, the critical current of the cable. From the point of view of whether the production process is convenient, meets the requirements of economy and reduces costs, we need to find a suitable transposition length. The measured optimal transposition length is 95 mm for the cable woven by 3 REBCO tapes.
Importance of magnetic shape anisotropy in determining triaxial magnetic anisotropy of ferromagnetic semiconductor CrSCl monolayer
Authors: Tu Yufei , Zhang Jiawen , Tian Zhuang Zhuang , Du Hailong , Zhao Long , Jia Minglei , Wang Bing 
Published: October 2024

Abstract
Magnetic anisotropy (MA) is pivotal for stabilizing long-range magnetic order in two-dimensional (2D) systems against thermal fluctuations. Here, we conduct a comprehensive investigation of the electronic and magnetic properties of CrSCl monolayer using first-principles methods and Monte Carlo (MC) simulations. Our results reveal that CrSCl monolayer exhibit a direct band gap ferromagnetic semiconductor (FMS) with a high Curie temperature (TC, 143 K). Notably, we identify triaxial magnetic anisotropy in this monolayer, characterized by the easy magnetization axis along the y-axis, intermediate axis along the x-axis, and hard axis along the z-axis. This anisotropy arises from a combination of magnetocrystalline anisotropy and shape anisotropy, in which shape anisotropy dominating over weak magnetocrystalline anisotropy. Orbital projection analysis shows that the major contribution of magnetic anisotropy energy comes from the d orbital of Cr atom. These findings provide some insights into the strain response of MA and suggest that studies of other FM monolayers may uncover future contenders for strain-switchable and ultra-compact spintronics devices.
A highly sensitive and selective ZnO-based ammonia sensor: Fe- doping effect
Authors: Chaudhary Dinesh Kumar , Joshi Surya Kumari , Shrestha Rajesh 
Published: October 2024

Abstract
This study investigates the gas sensing capabilities of ZnO and Fe-doped ZnO films prepared via a cost-effective spin coating method. The films were characterized optically, structurally, and morphologically. X-ray diffraction revealed good crystal quality with a polycrystalline nature, and wurzite structure, though crystal quality and crystallite size decreased with Fe doping. Optical measurements indicated an increased optical band gap from 3.205 ± 0.002 to 3.220 ± 0.002 eV after Fe doping. Scanning electron microscope images confirmed spherical grainy structures with reduced grain sizes after Fe doping. Gas sensing measurements at room temperature (RT) at an exposure of vapors of various toxic chemicals (acetone, ethanol, propanol, methanol, and ammonia), demonstrated a highly selective nature of ZnO and Fe-ZnO towards the ammonia. The Fe-doping into ZnO improved the ammonia sensing capability of ZnO film. The Fe-ZnO film exhibited a gas response of 388.8 ± 5.5 at 400 ppm ammonia exposure, which was nearly 10 times larger than that of ZnO film with a response/recovery time of 22/51 s, good stability, and good repeatability. The Fe-ZnO film’s higher response is attributed to its smaller grain sizes and surplus of charge carriers after Fe doping which promote the adsorption of extra oxygen ions onto the film’s surface and the subsequent interaction between the adsorbed oxygen ions ammonia molecules. It could detect up to the lower limit of 1 ppm ammonia with a response of 24.2 ± 0.9, which is better than the previous reports. These results reveal the Fe-ZnO film as a viable material for developing a cost-effective and efficient ammonia sensor at room temperature.
Investigating the photocatalytic efficacy of a porous cuprous gallate (CuGa2O4) thick film
Authors: Huseynzade Fatima , Akbaba Yesim , Shawuti Shalima , Can Musa Mutlu 
Published: October 2024

Abstract
The study aimed to investigate the photocatalytic activity of porous copper gallate (CuGa2O4) thick film with significant wall dimensions. The thick film was meticulously fabricated using tape casting systems, with CuGa2O4 nanoparticles synthesized by Sol–Gel technique and subsequently transformed into gels for the tape casting process. Rietveld refinements were employed to elucidate the crystal structure and lattice parameters of the CuGa2O4 spinel oxide lattice. Moreover, electronic energy configurations and optical transmittance measurements were acquired through UV-visible spectrophotometry. The correlations between the crystal structure, band gap change and formation of electronic energy levels in relation to the photocatalytic performance of CuGa2O4 were explored. The comprehensive examination provides valuable insights into the complex interaction between material properties and photocatalytic behavior, offering a nuanced understanding of the potential applications of porous CuGa2O4 thick films in various technological fields.
Positron interactions with vinyl acetate from 0.1 eV to 5 keV
Authors: Kaur Savinder , Arora Ajay Kumar , Bharadvaja Anand , Baluja Kasturi Lal 
Published: October 2024

Abstract
Scattering cross sections from positron impact on vinyl acetate are explored in the energy region 0.1 eV to a 5 keV, employing a cc-pVTZ basis set. The optimized molecular wavefunction of the target was obtained through a multi-center expansion of Gaussian-type orbitals in the Hartree–Fock self-consistent field framework. The elastic cross sections are computed using the single-centre-expansion formalism. Two distinct models were employed to address the long-range effects associated with the target's polar nature and yielded almost identical corrections. The Born-corrected elastic cross sections align more strongly with the existing experimental corrected data than the results reported from the independent-atom-model approximation. The differential and momentum transfer cross sections after applying Born-correction are also reported. The direct ionization cross sections are obtained using the binary-encounter-Bethe model for positrons. The cross sections obtained by summing elastic and ionization cross sections align closely with ‘forward angle corrected’ experimental total cross sections across a significant energy range. The agreement significantly improves beyond 30 eV, suggesting that the omission of excitation and positronium formation cross sections becomes less significant. A brief analysis of the electron interaction with the target is also made.
The electronic structure, optical, and transport properties of novel SrScCu3M4 (M = Se, Te) semiconductors
Authors: Khan Muhammad Salman , Gul Banat , Mohamed Abdelhay Salah , Abbas Faheem 
Published: October 2024

Abstract
The density functional theory is used to investigate the complex relationships between the physical properties of the novel quaternary SrScCu3M4 (M = Se, Te) semiconductors. The computed negative formation energy values of these materials demonstrate their stable nature. The distribution of ELF around chalcogens and Cu atoms shows substantial localization, indicating strong covalent bonding. The phonon dispersion curves show that the materials have good structural stability with no negative frequencies. The s/p states of Se and s/p/d of Te play minor roles, while Cu-d states have a considerable influence on the valence band region. The computed energy gap values without SOC for SrScCu3Se4 and SrScCu3Te4 are 1.29, and 0.90, respectively. The predicted energy gap values with SOC for SrScCu3Se4 and SrScCu3Te4 are 1.35, and 0.87, respectively. SrScCu3Se4 is a harder and more compressible material than SrScCu3Te4, as confirmed by its higher bulk modulus. The ε
1(ω) values decrease and ultimately become negative, which suggests these materials are reflective. SrScCu3Te4 exhibits plasmon resonance at a high energy domain as compared to SrScCu3Se4, resulting in a greater loss function. The current study can establish the potential efficiency of these materials in cutting-edge optoelectronic devices.
New scheme of cooperative compressed spectrum sensing
Authors: Benzater Hadj Abdelkader , Lassami Nacerredine , Teguig Djamal 
Published: October 2024

Abstract
This study addresses key challenges in sparse signal recovery and compressed spectrum sensing (CSS), focusing on low signal-to-noise ratios (SNR), and the computational complexity of cooperative systems. Motivated by the need for faster and more accurate recovery techniques, we first investigate and generalize the Reduced-Set Matching Pursuit (RMP) algorithm, which overcomes the speed and accuracy limitations of conventional greedy algorithms. Secondly, we propose a novel spatial averaging technique that enhances detection performance by exploiting data from multiple users to counteract low SNR. Lastly, we integrate cooperation into CSS, further improving the detection capabilities during the recovery process. Compared to existing techniques like Joint Sparse Recovery (JSR) and CoSaMP, which face computational and accuracy constraints in real-time applications, the RMP algorithm, combined with the Virtual method (data transformation) and AND fusion rule, delivers superior performance than JSR methods. Moreover, spatial averaging significantly increases the probability of cooperative detection Q

d
, with SNR increasing linearly by a factor of L − 1 per channel. The results are validated through the implementation of SDR. These findings demonstrate the potential of RMP and cooperation to overcome current limitations in CSS, advancing the state-of-the-art in spectrum sensing for collaborative networks.
Zinc cobalt sulfide with carbon (graphene oxide and CNT) based nanocomposite as positive electrode for asymmetric coin cell supercapacitors
Authors: Ravisankar D , Geetha D , Ramesh P S 
Published: October 2024

Abstract
The world dependence on portable electronic devices has increased the demand for high-performance energy storage devices. The use of transition metal sulfides as faradaic electrode materials for electrochemical energy storage is rapidly increasing due to their high energy density. Herein Zinc Cobalt Sulfide (ZCS) with graphene oxide (GO) and carbon nanotubes (CNT) were used to create an interconnected ZCS composite network using a solvothermal technique. The materials were characterized by utilizing XRD, FT-Raman, TGA, FESEM/EDX, XPS, and BET. The electrochemical performance of the materials was examined using cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS). The prepared electrodes exhibited both pseudocapacitor behavior and double-layer capacitor behavior, indicating the hybrid nature. Furthermore, All the electrode ZCS, ZCS/GO, ZCS/CNT, and ZCS/GO/CNT electrodes demonstrated higher capacitance behavior, with values of 420, 551, 585 and 811 F g−1 at 1 A/g. Among these ZCS/GO/CNT electrode exhibits outstanding electrochemical properties, with a notable retention of 81.08% at 10 Ag−1 because Combining ZCS nanoparticles with interconnected GO and CNT provides excellent electronic conductivity and stability. The assembled ZCS/GO/CNT//graphene oxide asymmetric coin cell (ACC) supercapacitor showed a high energy density of 33.3 Wh kg–1 at a power density of 624 W kg–1. The 3D nanostructure of ZCS/GO/CNT/Graphene oxide has great potential for developing foldable energy storage devices.
Using linear and nonlinear entanglement witnesses to generate and detect bound entangled states on an IBM quantum processor
Authors: Gulati Vaishali , Singh Gayatri , Dorai Kavita 
Published: October 2024

Abstract
We investigate bound entanglement in three-qubit mixed states which are diagonal in the Greenberger-Horne-Zeilinger (GHZ) basis. Entanglement in these states is detected using entanglement witnesses and the analysis focuses on states exhibiting positive partial transpose (PPT). We then compare the detection capabilities of optimal linear and nonlinear entanglement witnesses. In theory, both linear and nonlinear witnesses produce non-negative values for separable states and negative values for some entangled GHZ diagonal states with PPT, indicating the presence of entanglement. Our experimental results reveal that in cases where linear entanglement witnesses fail to detect entanglement, nonlinear witnesses are consistently able to identify its presence. Optimal linear and nonlinear witnesses were generated on an IBM quantum computer and their performance was evaluated using two bound entangled states (Kay and Kye states) from the literature, and randomly generated entangled states in the GHZ diagonal form. Additionally, we propose a general quantum circuit for generating a three-qubit GHZ diagonal mixed state using a six-qubit pure state on the IBM quantum processor. We experimentally implemented the circuit to obtain expectation values for three-qubit mixed states and compute the corresponding entanglement witnesses.