bluuebunny/crossref_metadata_2025_split huggingface dataset:
First-principles simulation of optical emission spectra for low-pressure argon plasmas and its experimental validation
Authors: Jenina Arellano Fatima , Gyulai Márton , Donkó Zoltán , Hartmann Peter , Tsankov Tsanko V , Czarnetzki Uwe , Hamaguchi Satoshi 
Published: November 2023

Abstract
Various spectral line emissions are often used for the experimental characterization of low-temperature plasmas. For a better understanding of the relation between the plasma characteristics and optical emission spectra, first-principle numerical simulations for low-pressure radio-frequency driven capacitively-coupled plasmas (CCPs) of argon have been performed by coupling one-dimensional particle-in-cell/Monte Carlo collision (PIC/MCC) simulations with a global collisional-radiative model (CRM). The only ionization and excitation mechanisms included in the PIC/MCC simulations of this study are the electron-impact ionization and excitations of the ground-state Ar atoms, as done commonly, whereas the electron-impact ionization of metastable states and other ionization mechanisms are also included in the CRM to account for the optical emission spectra. The PIC/MCC coupled CRM provides the emission spectra, which are then compared with experimental data obtained from the corresponding Ar CCPs with a gas pressure ranging from 2 Pa to 100 Pa. The comparison has shown good agreement for pressures up to about 20 Pa but increasingly notable deviations at higher pressures. The deviation is ascribed to the missing consistency between the PIC/MCC simulations and CRM at higher pressures, where the ionization from the metastable states is more dominant than that from the ground states, indicating a significant change in the electron energy distribution function due to the electron collisions with excited Ar atoms at higher pressures.
Collision integrals of electronically excited atoms in air plasmas. I. N–N and O–O interactions
Authors: Zhao Wensheng , Hong Qizhen , Yang Chao , Sun Quanhua , Hu Yuan 
Published: November 2023

Abstract
The current work presents the collision integral data for N(4
S)–N(4
S, 2
D, 2
P) and O(3
P, 1
D, 1
S)–O(3
P, 1
D, 1
S) interactions in the temperature range of 500–50 000 K. The collision integrals are calculated based on high-quality potential energy curves (PECs) obtained from fitting the high-level

a
b

i
n
i
t
i
o

calculation data in a wide energy range to the neural network (NN) functions. In the construction of PECs, the diabatic PECs are adopted when avoided crossings exist because the diabatic paths are much more likely to be followed for such situations. Moreover, the nonadiabatic transition effects are estimated to be negligible for PECs crossings. The accuracy of traditional analytical formulas to fit PECs are also examined. It is found that the collision integral calculations are sensitive to the accuracy of PECs and the NN based PECs overwhelm the others. The contribution of inelastic excitation exchange processes to the diffusion collision integrals are also computed by using an accurate evaluation of the differences of PECs for gerade and ungerade pairs of excited atoms. Finally, based on the new collision integral data, we calibrate the collision model parameters suitable for the widely used particle simulation methods. The collision integrals and collision models developed in this work can be used to support high-confidence simulations of weakly ionized air plasma problems.
Design, simulation, and experimental investigation on a novel multi-drive pattern three-degree-of-freedom rotary piezoelectric motor
Authors: Pan Zhiyong , Wang Liang , Jin Jiamei , Qiu Jianmin , Feng Haoren , Yang Ying 
Published: November 2023

Abstract
Three-degree-of-freedom (3-DOF) rotary piezoelectric motors often suffer from low positioning accuracy, complex excitation signals, and uneven preloading, limiting their application in precision drive systems. To address these issues, this study proposes a novel multi-drive pattern 3-DOF rotary piezoelectric motor, comprising a sandwich-type hollow cylindrical stator, a spherical rotor, and a pre-tightening structure. The proposed motor can operate in both inertial and traveling wave drive patterns. In the inertial drive pattern, the first-order and the third-order bending vibration modes of the stator are simultaneously excited by a sawtooth wave signal, generating a saw-tooth displacement on its driving feet to rotate the rotor around the x or y-axis. In the traveling wave drive pattern, two mutually orthogonal first-order bending vibration modes of the stator are simultaneously stimulated by two sinusoidal signals, generating a traveling wave on its driving feet to rotate the rotor around the z-axis. Initially, finite element analysis is used to simulate the operating principle of the stator and determine its geometric dimensions. Subsequently, a prototype of the sandwich-type hollow cylindrical stator is fabricated, and its vibration characteristics are tested to confirm the validation of the proposed operating principle and the correctness of the finite element simulation. Finally, a prototype of the proposed 3-DOF rotary piezoelectric motor is assembled, and its mechanical output characteristics are experimentally evaluated. Experimental results indicate that when the excitation voltage is 200 Vpp, the no-load rotary velocities of the motor prototype in three rotation directions are 79 r min−1, 76 r min−1, and 101 r min−1, respectively, start/stop response times are 10 ms/8.6 ms, 13.4 ms/6.2 ms, and 15.5 ms/7.7 ms, respectively, and the angular displacement resolutions are 7.4 μrad, 8 μrad, and 11.4 μrad, respectively. The proposed motor exhibits high mechanical integration, 3-DOF rotation, few excitation signals, adjustable pre-tightening force, and high positioning accuracy advantages, holding the potential applications in fields such as robotic technology and space pointing mechanisms.
Development and testing of a three-period, subsize 2G AIMI MgB2 planar undulator
Authors: Garg T , Rochester J , Majoros M , Kovacs C , Peng X , Rindfleisch M , Xue S , Tomsic M , Doll D , Collings E W , Sumption M D 
Published: November 2023

Abstract
Compared to Nb3Sn- and NbTi-wound superconducting undulators (SCUs), MgB2-wound SCUs are of interest for future electron synchrotron beam light sources owing to their higher temperature operating margin and associated stability. In this study, a three-period undulator consisting of twelve racetrack coils wound with 2nd generation (2 G) multifilamentary advanced-internal-magnesium-infiltration MgB2 strands were fabricated and tested in liquid and gaseous helium (He) over a temperature range of 4.2 K–20 K. The coil winding cross sections (in each coil) were 5 mm wide and 4.8 mm thick. At 4.2 K, a critical current (I
c) of 325.7 Amps produced a maximum undulator bore field of 1.19 T. It should be noted that the short, 3-period nature of the coil led to an asymmetry in the field profile (the maximum positive field was 1.19 T, the maximum negative was −0.25 T), suggesting a peak field of 0.72 T in the absence of end effects. Finite element modeling (FEM) results of simulations for a one meter long undulator of otherwise identical design gave 0.85 T (larger because of higher currents enabled by the lower field). But in any case, the I
c value coil reached is 94% of that of the short sample (dictated by the 1.19 T positive field for the coil as tested). FEM was performed to study the magnetic field profile, which was validated experimentally. The magnetic field was measured using a Hall probe which was translated along the beam axis during measurement to explore the spatial field variation along the beam travel direction. The spatially alternating field was asymmetric, and the maximum field was more prominent in the positive direction than in the negative direction, the difference being due to broken symmetry, that is, short coil end effects. In this work, we show useful fields are possible for MgB2 undulators; the use of such conductors can allow a larger thermal margin and enable conduction-cooled operation.
Effect of anisotropic interactions on the heat conduction of one-dimensional chains
Authors: Wan Lei , Tao Huang , Tian Yafang , Zeng Chunhua , Li Baowen 
Published: November 2023

Abstract
One-dimensional (1D) chain models are frequently employed to investigate heat conduction in various systems. Despite their widespread use, there has been limited exploration of 1D chain models with anisotropic interactions. In this study, we examine heat conduction in a 1D chain model with orientation–position coupled interaction, namely the compressible XY model, and compare it with isotropic Fermi-Pasta-Ulam-Tsingou β (FPUT-β) systems. At low temperatures, the local temperatures in the translational and rotational degrees of freedom differ due to the difference in the contact thermal resistance in the two degrees of freedom. The system maintains orientational order, and the orientation–position-coupling effect lowers thermal conductivity in translational degrees of freedom. As the temperature rises above a transition point, the rotation of particles switches from oscillation to diffusion, leading to a solid-like to fluid-like transition in the rotational degree of freedom. The anisotropic interactions become negligible under time averaging, making the system’s properties similar to those of isotropic FPUT-β systems. Additionally, we investigate the system’s orientational structure to elucidate this transition. These findings will enhance our understanding of the dynamics of nanoscale anisotropic systems, such as the heat conduction of spin chains.
A tapered feed UWB-MIMO antenna with high characteristics based on fractal method
Authors: Sediq Hiwa Taha 
Published: November 2023

Abstract
A miniaturized antenna based on fractal method and Tapered-feeder technique for ultra-wideband (UWB) applications is developed in this research. The structure of the component cell monopole antenna is designed by using a half circle-shaped fractal method and merging material implementation with the L-stub shape. These techniques provide a wide impedance bandwidth for the innovative wireless antenna with high characteristics, which operates from 2.73 to 12.21 GHz. The planned module cell dimensions are 20 by 16 square millimetres (

0.181

λ

L

×
0.145

λ

L

), where the wavelength (

λ

L

) is 110 mm. This low-profile antenna has then been employed to design an array of 1 × 2 Multi Input Multi Output (MIMO) antennas with an overall size of 20 ×34

mm

2

.

Two symmetric geometries are arranged in the designed miniaturized MIMO system with an inverted T-shaped decoupling copper between them. Measurement results show that the dual antenna with multiple diversity metrics has great characteristics for MIMO applications due to providing low mutual coupling value, lower than 0.33 bits per s./Hz of channel capacity loss, minor envelope correlation coefficient of fewer than 0.005, lower −11 dB of total active reflection coefficient, greater than 9.93 dB of diversity gain and proper mean effective gain (MEG ratio≈1, MEG ≤ 0.5). A high peak gain magnitude of 5.42 dBi is provided at an operating frequency of 10.5 GHz for the recommended single antenna.
A new fractional-order 5D memristive chaotic system with special extreme multistability and its application to image encryption
Authors: Yan Shaohui , Jiang Defeng , Zhang Hanbing , Zhang Yuyan , Cui Yu , Li Lin 
Published: November 2023

Abstract
Introducing memristor into the chaotic system can enrich the dynamic behaviors of the chaotic system. We propose a symbolic function memristor model and introduce it into a chaotic system to construct a fractional-order 5D memristor chaotic system. Through dynamic analysis of equilibrium point, Lyapunov exponents, phase diagram and bifurcation diagram, it is found that the system has abundant dynamic behaviors, for example, the change of equilibrium point type with parameters, transient chaos, offset-boosting and a special kind of extreme multistability. And with the change of parameters, the attractor state and shape will appear rich changes. Then the correctness of the system is verified by circuit simulation. The chaotic system is introduced into the process of image encryption, and an encryption system is constructed, which is composed of Zigzag scrambling, Hilbert curve scrambling, DNA encryption and GF257 diffusion algorithm. Finally, through a variety of security verification, the results show that the encryption system has good security and can resist many kinds of attacks effectively.
Synchronization gauge field, standing waves and one-way-speed of light
Authors: Bhadra Arunava , Chakraborty Abhishek , Ghose Souvik , Raychaudhuri Biplab 
Published: November 2023

Abstract
The absolute nature of many fundamental predictions of the theory of special relativity, including the relativity of simultaneity, has been questioned in the literature owing to the choice of distant clock synchronization process in the theory. Here we discuss the consequences of Anderson-Vetharaniam-Stedman (AVS) conventionality synchronization gauge, which reflects the choice of synchronization convention, on the standing wave observable. We found that although the position of the node(s) is gauge invariant and remain the same as in the standard case of the stationary wave formation following the Einstein synchronization, the anti-node(s) becomes a gauge dependent (conventional) element and the resulting wave travels between two nodes, contrary to the experimental observation. The experimental detection of standing wave substantiates that the one-way velocity is equal to the round-trip velocity implying the uniqueness of the Einstein synchronization convention. The present analysis thus eliminates the (unphysical) synchronization gauge freedom of special relativity.
Effects of the magnetic field and the off-center displacement on the third-order nonlinear optical susceptibility of inverted core/shell spherical one-electron quantum dots
Authors: Negoukoumé Abdoulaï A , Melingui Melono R L , Motapon O 
Published: November 2023

Abstract
The third order nonlinear optical susceptibility (TONOS) of an inverted ZnS/CdSe core–shell spherical quantum dot embedded in SiO
2-matrix with or without impurity, and subjected to an external magnetic field is investigated. Within the framework of effective mass approximation and using B-splines basis functions, the energy levels, the dipole matrix elements and the TONOS are computed. The dielectric mismatch in the interface dots as well as the effective mass dependence on the core and shell regions are taken into account. It is revealed that, simultaneously the shell size, magnetic field and off-center displacement have a noticeable effect on the positions and the heights of the peaks of the TONOS. Moreover, we have shown that, due to the magnetic field and the off-center displacement effects, the TONOS takes different profiles associated with the polarization direction (parallel and perpendicular). In the perpendicular polarization, two peaks of the nonlinear optical properties appear for each off-center displacement value corresponding to the two transversal allowed transitions. The shift towards higher energies of the peak position and the reduction of the intensity of the peak when the magnetic field increases are recorded in the transversal transition case. Furthermore, in the longitudinal transition, as increasing the magnetic field strength, the intensity of the peak of the TONOS grows and the position of the peak is shifted towards lower frequencies. The dielectric constant, effective mass and conduction band offset of the materials that constitute the core and shell are equally found to modify significantly the main features of the susceptibility.
U2-MNet: An Improved Neural Network for Breast Tumor Segmentation
Authors: Li Baoqing , Su Bo , Zhang Xiaoqian 
Published: November 2023

Abstract
In recent years, UNet and its variants have achieved excellent performance. However, since the convolution kernel in UNet focuses solely on local pixels, these models struggle to model long-range dependencies. This issue has been addressed by recently proposed segmentation models based on transformer architecture. The internal self-attention mechanisms included in these systems capture global contextual information to improve segmentation effects. However, the results are often not ideal without pre-training on a large-scale dataset. Therefore, we designed a model (U2-MNet) to overcome the limitations of convolutional kernels, enabling the model to achieve high-accuracy segmentation without pre-training. This approach adopts a multi-layer framework, which can effectively describe multi-level channel information, employing a window-based channel MLP (WCM) block. It utilizes a sliding window and an MLP to capture information about details in local features. In addition, multi-level channel cross mixing (MCCM) block is included in each skip connection to reduce noise after aggregation of low-level and high-level features. The proposed model was trained from scratch and tested on Breast UltraSound Images (BUSI) dataset. Our model achieved 82.76%, 73.17%, and 86.24% on the Dice, IOU, and Precision indicators.
Fusion Neural Network for Gas Concentration Prediction in Mixed Gas Environments
Authors: Li Yiran , Li Fei , Wu Fan , Peng Xiaoyan 
Published: November 2023

Abstract
Due to the inherent complexity and nonlinearity of mixed gas data, existing pattern recognition algorithms utilized in electronic noses often encounter difficulties in accurately predicting gas concentrations. Addressing this issue, we propose a fusion neural network that merges Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), which we denote as the LSTM-TCN fusion model. The LSTM module effectively captures long-term dependencies in time-series data, while the TCN targets local correlations, thereby enhancing the prediction accuracy for complex gas concentrations. Experimental validation was conducted using a mixed gas dataset comprising ethylene and carbon monoxide. When compared with traditional models, including LSTM, TCN, and GRU, the proposed LSTM-TCN model demonstrated superior performance, achieving an R2 value as high as 0.9922. This research holds considerable practical significance and shows promising application prospects, contributing novel insights and methods to the study and application of electronic nose technology.
Research and Optimization of Aircraft Remaining Oil Control System based on Fuzzy Algorithm
Authors: Liu Chaohui , Liu Taihui , Tan Gangtong , Zhang Wei 
Published: November 2023

Abstract
Aiming at the poor accuracy and stability of the existing aircraft remaining oil control system, an amount of oil remaining system for aircraft based on a fuzzy algorithm is proposed based on capacitance detection and mechanical detection, with the introduction of fuzzy algorithm optimized PID control technology. The system can form flight variables control system according to the error between the actual remaining oil and the ideal remaining oil and realize the stable control of the aircraft remaining oil. Furthermore, through targeted case analysis, the deviation of aircraft residual fuel before and after optimization is compared so as to further verify the reliability and feasibility of the designed aircraft residual fuel control system based on a fuzzy algorithm. In conclusion, the fuzzy algorithm is efficient in the optimization of the aircraft remaining oil control system.
COVID-19 Intelligent Detection System Based on Cloud-Edge Fusion Recognition Model
Authors: Xie Jiangjian , Li Datong , Zhou Libo , Ren Jixiong , Li Chao , Wang Ziting 
Published: November 2023

Abstract
Analyzing cough sounds can help with the quick detection of COVID-19. A cloud-edge deep learning fusion-based intelligent detection system for COVID-19 is proposed in this paper. In the cloud-side, a COVID-19 detection model based on ResNet18 is employed, with log-Mel-spectrum characteristics used as inputs. In the edge-side, a COVID-19 detection model based on TCNN is developed using raw audio inputs. To improve the detection accuracy, result fusion is carried out in the cloud-side after getting the recognition results from both models. On the test dataset, the fusion model attained a sensitivity of 0.8012, an AUC of 0.8251, and a specificity of 0.7255. According to comparative testing results, the fusion model outperforms the other models in classification performance and is less prone to false-positive errors. It provides a novel way to COVID-19 recognition and performs well as an auxiliary detection method.
Outlier Detection Based on Autoencoder Ensembles with Denoising layer and Attention Mechanism
Authors: Ma Wenhao , Yu Jiong , Du Xusheng 
Published: November 2023

Abstract
In the field of outlier detection, two common challenges have persisted. Firstly, outlier detection datasets are often small in size, which can lead to overfitting issues when using deep learning models such as autoencoders. Secondly, as the dimensionality of datasets increases, many dimensions may be irrelevant or noisy, which can adversely affect the model’s ability to learn meaningful features. This phenomenon is known as “the curse of dimensionality.” To address these challenges, this study proposes a solution using an ensemble of autoencoders with denoising layers to mitigate overfitting. Additionally, a novel attention mechanism is introduced to predict the importance of each feature, thereby addressing the curse of the dimensionality problem. The proposed approach is evaluated on five datasets, including BreastW and Vowels, and compared with existing methods. Experimental results demonstrate that the proposed method outperforms existing methods on four out of the five datasets, showcasing its effectiveness.
End-to-end Remote Sensing Image Aircraft Target Detection and Fine-grained Recognition Framework
Authors: Mao Jiaxing , Wang Yifan , Guo Zihao , Zhang Xinyu , Yang Yu 
Published: November 2023

Abstract
In view of the practical application of target detection and recognition tasks for remote sensing, an end-to-end aircraft target detection and fine-grained recognition framework is proposed. It can accurately and quickly implement detection and recognition in an end-to-end way. The main network of the framework adopts the design ideas of target detection and fine-grained recognition methods using candidate region extract and visual attention, making sure the accuracy of detection and recognition. Then, to solve the problem of high false detection rate and missed detection rate of densely arranged targets, we propose the re-detection mechanism. To minimize the large amounts of calculations of deep networks and improve real-time performance, we introduce depthwise separable convolution to optimize networks. Finally, a weight mapping idea based on transfer learning is adopted to solve the problem of data labeling and also helps the detection and fine-grained recognition. The results prove that the proposed framework has good robustness, versatility, and efficiency in aircraft detection and fine-grained recognition tasks.