bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of the relationship between light intensity and electrical power of corona discharges
Authors: Riba Jordi-Roger 
Published: November 2024

Abstract
This work analyzes the intensity of the light emitted by corona discharges using a smartphone camera, which is compared to the electrical power associated with the corona discharges. The raw images from the camera provide essential information for the experiment. Data at different atmospheric pressures are also provided to help students understand that at lower pressures, due to the increased mean free path of the electrons, they are more efficient at ionizing neutral air molecules. An analysis of the data obtained also makes it possible to estimate the values of the energy of the electrons involved in the discharge process, the mean free path of the electrons and the cross section for electron collision, using much simpler equipment compared to other studies. The data and approach presented in this paper can be adapted for a group project or a guided practical session for both undergraduate and graduate courses.
Opportunities and challenges of eutectic electrolytes for advanced zinc-iodine batteries
Authors: Han Mingming , Hu Jun , Xian Jinglin , Cai Jie , Wang Bo , Yang Peihua 
Published: November 2024

Abstract
Aqueous zinc–iodine batteries have garnered extensive attention due to their low cost, high safety and favorable voltage characteristics. However, their performance is limited by the severe shuttle effect and side reactions, which impede operability, energy density and cycling stability. This perspective begins by outlining the configuration of zinc–iodine batteries, detailing their chemical redox mechanism and associated challenges. It then shifts focus to zinc salt-based eutectic electrolytes, exploring their fundamental properties and potential applications in zinc batteries. The role and functions of eutectic electrolytes in zinc–iodine batteries are emphasized in following, highlighting their achievements in enhancing stability and energy density. Given that research on eutectic electrolytes is still in its infancy, remaining challenges and future directions are also discussed, offering insights for the rational design of advanced eutectic electrolytes and zinc–iodine batteries.
Terahertz six-channel metasurface for the dynamic modulation of OAM
Authors: Guo Kaiyu , Xin Jinhao , Song Zhengyong 
Published: November 2024

Abstract
Dynamic wavefront modulation enables the performance enhancement of optical devices. Few reseachers have achieved the dynamic wavefront modulation of orbital angular momentum (OAM). In this work, a dynamic modulation of six-channel OAMs is realized. When VO2 is metallic, the metasurface realizes OAM with a mode of l = − 3 in the left-handed circularly polarized (LCP) copolarized reflection channel. In the right-handed circularly polarized (RCP) copolarized reflection channel, a vortex beam is generated with a mode of l = − 1. In CP cross-polarized reflection channels, OAMs are generated with a mode of l = − 2. Relatively, as VO2 is insulating, OAM is presented with a mode of l = 1 in the LCP copolarized reflection channel. In the RCP copolarized reflection channel, a vortex beam is achieved with a topological charge of l = 3. In CP cross-polarized reflection channels, the obtained OAMs have a mode of l = 2. As a result, our design has flexible multichannel characteristics. Furthermore, it shows considerable application value in beam shaping and optical encryption.
Experimental and numerical study on surface streamer evolution in SF6/N2 gas mixtures
Authors: Wang Wei , Wang Xinyan , Yang Xin , Wu Zhenyu , Xu Hao , Meng Yongpeng , Lv Zepeng , Wu Kai 
Published: November 2024

Abstract
The SF6/N2 gas mixture is a promising alternative to reduce SF6 usage in the power industry, with surface streamers being the dominant discharge form. This study develops an electrical-optical system based on the Pockels effect to measure the spatiotemporal morphology of surface streamers in pure N2 and SF6/N2 gas mixtures. In pure N2, surface streamers are filamentary with bifurcations. In contrast, in SF6/N2 gas mixtures, the morphology of surface streamers initially appears filamentary and transforms into clusters under alternating current applied voltage. The formation of clustered channels may be explained in terms of the lateral drift and diffusion of the net space charge. Two fluid models analyze the experimental results. The fluid simulation reveals a sheath between the surface streamer and the dielectric surface. The propagating surface streamer floats above the dielectric, with a net space charge layer lying at its bottom, sustaining its propagation through the net-charge-induced field. Due to the presence of the sheath, the surface streamer’s morphology and properties are similar to those of volume streamers. This study enhances the understanding of surface streamer properties in insulating gases.
Right-handed neutrino dark matter in U(1)XSSM
Authors: Liu Ming-Yue , Zhao Shu-Min , Gao Song , Ruan Long , Feng Tai-Fu 
Published: November 2024

Abstract
There is strong evidence for the existence of dark matter in a number of current experiments. We study dark matter using the U(1)
X
SSM obtained from the U(1)
X
extension of the minimal supersymmetric standard model. In the U(1)
X
SSM, we use the right-handed neutrino as a dark matter candidate, whose lightest mass eigenstate has cold dark matter features. In this paper, the relic density of right-handed neutrino as dark matter is investigated. For dark matter scattering, both spin-independent and spin-dependent cross sections are studied. In the final numerical results obtained, some parameter spaces can satisfy the constraints of the relic density and dark matter direct detection experiments.
Exploring moiré skyrmions in twisted double bilayer and double trilayer

Cr

I

3
Authors: Jabakhanji Bilal , Ghader Doried 
Published: November 2024

Abstract
Moiré magnets have emerged as intriguing platforms for hosting exotic magnetic states due to the competing interactions within these materials. Recent experiments have reported noncollinear magnetic states in moiré

Cr

I

3

, particularly focusing on twisted double bilayer (tDB) and double trilayer (tDT) configurations. However, atomistic simulations of moiré

Cr

I

3

have largely been limited to the bilayer case. Here, we use stochastic Landau–Lifshitz–Gilbert simulations to investigate skyrmion nucleation in tDB and tDT

Cr

I

3

and to estimate the threshold Dzyaloshinskii–Moriya interaction (th-DMI) required for their stabilization. Our findings show that the th-DMI decreases significantly with reduced twist angles. Above this threshold, three skyrmions nucleate at the monoclinic regions of the moiré supercell, randomly distributing across the interfacial layers and imprinting onto adjacent layers. Additionally, a substrate-induced DMI further reduces the th-DMI without affecting skyrmion localization. These results suggest that small twist angles and substrate-induced DMI can facilitate skyrmion formation in moiré structures.
Evolution of tunable energy bandgap and magnetic anisotropy in Mn substituted ferrimagnetic nickel-chromates
Authors: Datta A , Sarkar T , Roy-Chowdhury M , Tiwari P , Singh H , Dey J K , Thota S 
Published: November 2024

Abstract
We report a detailed study on the composition (x) dependence of structural, electronic, magnetic, and optical studies of nickel chromate spinel (NiCr2O4) at various levels of Mn substitution at B sites. No significant structural distortion from cubic symmetry Fd-3m was noticed for all the compositions in the range 0 ⩽ x⩽ 1 of Ni(Cr1−x
Mn
x
)2O4. However, there is significant alteration in the bond angles ∠B–O–B (90.51°-93.86°) and ∠A–O–B (122.48°–124.90°) (both of which follow completely opposite trend with increasing x) and bond lengths A–O (1.82–1.94 Å) and B–O (2.02–2.08 Å). The corresponding lattice parameter (a) follows Vegard’s law (8.32 ± 0.001 Å ⩽ a ⩽ 8.45 ± 0.001Å). The electronic structure determined from the x-ray photoelectron spectroscopy reveals the divalent nature of Ni (with spin–orbit splitting energy Δ ∼ 17.62 eV). While the Cr and Mn are stable with trivalent electronic states having Δ =8 and 11.7 eV, respectively. These results are in consonance with the cationic distribution (Ni)A[(Cr1−x
Mn
x
)2]BO4 obtained from the Rietveld refinement analysis. Interestingly, the current series shows a direct bandgap (E
G) semiconducting nature in which E
G varies from 1.16 to 2.40 eV within the range of x = 0.85–0. Such variation of E
G (x) is consistent with the compositional variation of the crystal structure data with anomalous change between x = 0.25 and 0.6. Beyond this range, the E
g mode (140 cm−1) in Raman spectra arising from Mn–O octahedral decreases continuously and vanishes at higher Mn concentrations. Our analysis shows that all the investigated compounds show long-range ferrimagnetic ordering below the Néel temperature, T
FN due to the unequal magnetic moments of the cations. However, both the ordering temperature T
FN and saturation magnetization (M
S) increases progressively from 73.3 K (1500 emu mol−1) to 116 K (3600 emu mol−1) with increasing the Mn content from 0 to 1, yet the maximum anisotropy (HK
~4.5 kOe, K
1~2.5 × 104 erg cc−1) shows an opposite trend with x. Such variation is ascribed to the altered magnetic superexchange interactions between the cations located at A and B sites following the trend J
BB > J
AB > J
AA, (J
BB/k
B =13.36 K).
Three-dimensional colour reconstruction of aviation spiral bevel gear tooth surface through fusion of image and point cloud information
Authors: Zhou Hanbin , Chen Longting , Xia Fujia , Li Danni , Tang Jinyuan , Feng Ke 
Published: November 2024

Abstract
Contact pattern measurement is commonly applied to assess gear transmission and meshing performance in aviation spiral bevel gear (ASBG) manufacturing enterprises. However, these patterns cannot be directly segmented and obtained from point clouds or images because each tooth of the spiral bevel gear has complex 3D spiral surface and texture information. Moreover, research on the high-precision 3D colour reconstruction of spiral gear tooth surfaces is relatively rare. Aimed at these problems, the current work proposed a high-precision 3D colour reconstruction method of spiral bevel gear. This method is based on the fusion of the image and point-cloud information. It includes two core steps to satisfy the practical demands of high precision and high efficiency. First, dense point clouds of the 3D calibration block are converted to a plane using the bird’s eye view method for corner detection. Second, the corners are quickly and accurately detected using a two-stage corner detection algorithm based on the inherent geometric features of the calibration block. The identified corners are then used to solve the transformation matrix from the pixel coordinate system to the line laser coordinate system. Image textures can be precisely projected and fused onto a 3D geometry for the coloured reconstruction of the gear tooth surface on the basis of this transformation matrix. The proposed method was validated by applying it to the 3D colour reconstruction of tooth surfaces of 10 distinct types of ASBGs. The experimental results demonstrated that the re-projection error of the proposed method is less than 0.1 millimetres. The minimal margin of error enables high-precision 3D colour reconstruction of the spiral bevel gear tooth surface. Compared to other classic 3D colour reconstruction methods, the proposed method achieves superior accuracy and efficiency. An experimental device was also developed for the 3D colour reconstruction of the tooth surface of ASBGs, as illustrated in figure 1.
Alarm data mining in complex industrial facilities using adaptive discretization based on time clustering and unified pattern fusion mining
Authors: Hu Tao , Liu Zhiping , Shao Wenjun , Guo Zhen , Zeng Yuhao 
Published: November 2024

Abstract
Alarm systems in complex industrial facilities are crucial for ensuring operational efficiency and safety. However, the intricate interconnections and collaborations among facility components often lead to fault propagation, resulting in alarm redundancy and thereby increasing the difficulty of addressing genuine faults. One effective way to address this issue is to mine meaningful alarm patterns, which can be used to optimize alarm management. Existing alarm data mining techniques lack effective discretization methods for unlabeled continuous time-series alarm data during the data preprocessing stage, compromising the accuracy of mining results. Moreover, these methods typically identify only single association rules or sequential patterns, making it challenging to comprehensively reveal the interactions and dependencies among complex events—a critical factor for effective alarm management. To address these challenges, this paper proposes an adaptive discretization based on time clustering (ADTC) method, which transforms historical alarm data into transaction sets while effectively preserving the key features of the original data. Building on this, a unified pattern fusion mining (UPFM) method is developed to mine co-occurrence relationships and sequential dependency rules of alarm events, utilizing directed graphs for visualization. This approach enhances the accuracy of analyzing interactions and dependencies among complex equipment. To validate the effectiveness of the proposed methods, experiments were conducted on both synthetic alarm datasets and historical alarm data from a port transshipment system in northern China. Compared to discretization methods with different time window sizes and the density clustering discretization method based on DBSCAN, the ADTC method outperformed other methods in both support and confidence metrics. Additionally, the UPFM method successfully mined sequential dependencies and co-occurrence relationship rules of alarm events, demonstrating its effectiveness and accuracy in data mining within complex industrial environments.
Research on a multi-sensor information fusion method for wind turbine gearbox fault diagnosis based on the BLSCFN model
Authors: Wang Chengyu , Wan Shuting , Zhang Xiong , Wang Xuan 
Published: November 2024

Abstract
The internal structure of wind turbines is complex, and their operating conditions vary widely. When a gearbox failure occurs, complex coupling effects emerge among different parts of the turbine, causing collected signals to be easily interfered with by other components. Traditional methods relying on a single signal for wind turbine gearbox fault diagnosis often result in low accuracy. This paper proposes a wind turbine gearbox fault diagnosis method based on the bilinear sensor complementary fusion network model with multi-sensor information fusion to address this issue. First, the collected gearbox vibration signals are processed using fast Fourier transformation, and the frequency spectrum of the vibration signals is used as the training input for a convolutional neural network. Simultaneously, the stator current signals are input into a Bi-directional long short-term memory network to capture the temporal relationships of the stator current from both forward and backward directions. Then, a cross-attention mechanism is introduced to calculate the attention scores between the stator current features and the gearbox vibration features. Using a designed linear weighted fusion strategy, information interaction and fusion of the two different source feature signals are conducted to obtain the relevant parts of the input features. Experimental results show that compared to other commonly used methods and single-sensor gearbox fault diagnosis techniques; the proposed method achieves superior diagnostic performance. This multi-sensor fusion approach effectively improves accuracy and reliability of wind turbine gearbox fault diagnosis.
A model-based and efficient parameters correction method for low-cost MEMS IMU
Authors: Xu Tongxu , Xu Xiang , Ye Hualong , Zhang Lingling 
Published: November 2024

Abstract
With the wide use of intelligent mobile terminals, inertial measurement unit (IMU) made by micro-electromechanical system (MEMS) has become an important part of their positioning systems. MEMS IMU has advantages of low cost, low power consumption and small size. However, the scale factor error, nonorthogonality error, and bias affect the measurement accuracy of MEMS IMU. In this paper, the output data of MEMS IMU at the six-position were analyzed and modelled. A parameter correction method based on the model was proposed for obtaining more accurate scale factor, nonorthogonality parameters and bias. The proposed method has low-cost and the obtained parameters’ accuracy are consistency. Ten groups of two MEMS IMUs parameter correction experiments show that the parameters obtained by the proposed method have small fluctuation and high consistency. The rotation navigation test shows that the navigation errors of the corrected data of one MEMS IMU reduce to 15.2 %, 9.3% and 5.1% in the east, north and up directions compared with the position errors of the original data, which proves the effectiveness of the proposed method.
A novel fault diagnosis model based on deep feature fusion network under imbalanced data: towards railway dual-switch machines traction occasion
Authors: Xiao Shuai , Feng Qingsheng , Hu Xiaoxi , Song Yakun , Cong Guanglin , Yao Zeyuan , Li Hong 
Published: November 2024

Abstract
Fault diagnosis of railway switch machines is crucial for ensuring safe and efficient train operations, as well as for the maintenance of intelligent Switching & Crossing systems. Current methods primarily focus on single-switch machine traction modes, often overlooking the challenges of effectively utilizing multi-source data and comprehensively representing fault information. This limitation results in restricted applicability and suboptimal recognition accuracy. To address these challenges, we propose a novel fault diagnosis model based on a deep feature fusion network (DFFN) specifically designed for railway dual-switch machines in traction occasion, particularly under imbalanced data conditions. First, we introduce an improved synthetic minority oversampling technique that integrates clustering technology with neighbor-based strategies to balance the experimental data and mitigate training bias. Second, we incorporate a cross-branch convolutional collaborative self-attention mechanism network and an adaptive weight learning network into the DFFN, facilitating the extraction of multi-scale fault feature correlations and promoting efficient fusion. Experimental results, based on multiple vibration sensing signals, demonstrate an average diagnostic accuracy of 96.66% and an F1-score of 96.85% in real railway environments. Comparative analyses with other state-of-the-art methods confirm that our approach achieves superior diagnostic performance.
Parametric model of resonant differential MEMS pressure sensors: enabling rapid structural optimization and automated layout design
Authors: Chen Aobei , Gao Ge , Li Dapeng , Na Rui , Zheng Dezhi 
Published: November 2024

Abstract
This paper aims to enhance the efficiency of sensor structural optimization design and achieve rapid performance evaluation and automatic layout drawing. To this end, we propose a parameterized model of a resonant differential MEMS pressure sensor (PRDMP). It can complete sensitivity calculations within 1 ms, far faster than traditional finite element analysis (FEA) methods. Additionally, compared to FEA results, its accuracy exceeds 90

%

. Furthermore, we reconstructed the uncertainty analysis part of PRDMP based on an error model. Compared to the Monte Carlo method used in existing studies, our method is faster and yields more stable results. Benefiting from the speed and accuracy of PRDMP, we achieved, for the first time, the multi-parameter collaborative automatic optimization of this sensor. Optimization results show that sensitivity increased by 36.2

%

while uncertainty decreased by 15.8

%

. Finally, we developed a sensor performance analysis and automatic layout drawing tool based on PRDMP, further enhancing design efficiency.
LSYOLO: an algorithm for linear scan PCB defect detection
Authors: Chen Rui , Yu Zehuan 
Published: November 2024

Abstract
Aiming at the current problems that different defects in linear scan PCB have scale differences and some defects have high similarity with the background, which are difficult to localize and classify, an expanded receptive field PCB defect detection algorithm is proposed to be applied to the defect detection of linear scan circuit boards. The expanded receptive field module (ERFM) is used in the backbone of YOLOv8 to replace C2f, which can avoid information loss and gridding artifacts while obtaining better contextual information to improve the detection performance of defects with high background similarity. Then, the spatial selective feature pyramid (SSFPN) is used as the FPN to enhance the model’s ability to detect defects at different scales while reducing the model performance requirements by utilizing the information of the spatial dimensions of the feature maps to fuse the feature maps at different scales. Wise-intersection over union is used as the bounding box loss function, and slide loss is used as the classification loss function to enhance the model’s focus on difficult-to-localize and difficult-to-classify samples. Comparison experiments are conducted on a linear scan printed circuit board dataset, and the experimental results show that the improved model obtains a significant improvement in the detection performance of defects with high detection difficulty; the average precision of the overall defects is improved by 9.6%, the number of model parameters is reduced by 40%, the amount of computation is reduced by 20%, and the size of the model weights file is only 3.64MB. Detecting defects in linear scan PCB is more efficient and lighter than other algorithms.
Friction-enhanced formation of Cu microwire on Si wafer
Authors: Liu Chenxu , Song Yang , Chai Zhimin , Zeng Hongbo , Tian Yu , Meng Yonggang 
Published: November 2024

Abstract
Tribological printing is emerging as a promising technique for micro/nano manufacturing. A significant challenge is enhancing efficiency and minimizing the need for thousands of sliding cycles to create nano- or microstructures (2018 ACS Appl. Mater. Inter. 10 335–47, 2019 Nanotechnology 30 302). This study presents a rapid approach for forming Cu microwires on Si wafers through a friction method during the evaporation of an ethanol-based lubricant containing Cu nanoparticles. The preparation time is influenced by the volume of the lubricant added, with optimal conditions reducing the time to 300 s (600 sliding cycles) for producing Cu microwires with a thickness of 200 nm. Key aspects include the lubricating effect of ethanol on the friction pairs and the role of ethanol evaporation in the growth of Cu microwires. Successful formation requires a careful balance between microwire thickening and wear removal. The resulting Cu microwires demonstrate mechanical and electrical properties that make them suitable as micro conductors. This work provides a novel approach for fabricating conductive microstructures on Si surfaces and other curved surfaces, offering potential applications in microelectronics and sensor technologies.
On the rigidity of uniformly rotating vortex patch near the Rankine vortex
Authors: Huang Yupei 
Published: November 2024

Abstract
The uniformly rotating vortex patch solutions(also known as V-states) are a class of important periodic solutions for the 2D incompressible Euler equations. In this paper, we perform the bifurcation analysis and give a rigidity result of the V-states near the Rankine vortex in some weak topology.