bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of electronic correlation on topological properties of Kagome semimetal Ni3In2S2
Authors: Das P , Saha P , Singh M , Kumar P , Patnaik S 
Published: August 2024

Abstract
Kagome metals gain attention as they manifest a spectrum of quantum phenomena such as superconductivity, charge order, frustrated magnetism, and allied correlated states of condensed matter. With regard to electronic band structure, several of them exhibit non-trivial topological characteristics. Here, we present a thorough investigation on the growth and the physical properties of single crystals of Ni3In2S2 which is established to be a Dirac nodal line Kagome semimetal. Extensive characterization is attained through temperature and field-dependent resistivity, angle-dependent magnetoresistance (MR) and specific heat measurements. The central question we seek to address is the effect of electronic correlations in suppressing the manifestation of topological characteristics. In most metals, the Fermi liquid behaviour is restricted to a narrow range of temperatures. Here, we show that Ni3In2S2 follows the Fermi-liquid behaviour up to 86 K. This phenomenon is further supported by a high Kadowaki–Woods ratio obtained through specific heat analysis. Different interpretations of the magneto-transport study reveal that MR exhibits linear behaviour, suggesting the presence of Dirac fermions at lower temperatures. The angle-dependent magneto-transport study obeys the Voigt–Thomson formula. This, on the contrary, implies the classical origin of MR. Thus, the effect of strong electron correlation in Ni3In2S2 manifests itself in the anisotropic magneto-transport. Furthermore, the magnetization measurement shows the presence of de-Haas van Alphen oscillations. Calculations of the Berry phase provide insights into the topological features in the Kagome semimetal Ni3In2S2.
Anti-matching effect in a two dimensional driven vortex lattice in the presence of periodic pinning
Authors: P Akhilesh M , Joseph Toby 
Published: August 2024

Abstract
The dynamics of a driven superconducting vortex lattice in a two-dimensional (2D) periodic potential of square symmetry is studied using Brownian dynamics simulations. The range and strength of the vortex-substrate interaction are taken to be of the same order as that of the vortex-vortex interaction. The matching effect in a driven vortex lattice in the presence of a periodic array of pinning centers refers to the enhanced resistance to the vortex lattice motion when the ratio of the number of vortices to the number of pinning centers (called the filling fraction) takes simple fractional values. In particular, one expects a pronounced matching effect when the filling fraction is one. Contrary to this expectation, a drop in the vortex lattice mobility is observed as the filling fraction is increased from value one. This anti-matching effect can be understood in terms of the structural change in the vortex lattice as the filling fraction is varied. The dip observed in vortex mobility as a function of temperature when the filling fraction equals one (Joseph T 2020 Physica A 556 124737), is studied for other values of filling above and below one. The behavior is found to persist for other fillings as well and is associated with the melting of the vortex lattice. The temperature at which the lattice melts is found to increase with drive and explains the shift in the temperature at which mobility is a minimum, locally.
A transformer cascaded model for defect detection of sewer pipes based on confusion matrix
Authors: Yu Zifeng , Li Xianfeng , Sun Lianpeng 
Published: August 2024

Abstract
Urban sewerage systems are critical to urban infrastructure. However, they are often subject to defects that threaten their operational reliability and efficiency. Some different types of sewer defects often have similar features. Recently, a number of deep learning models have emerged to automatically identify sewer failures, but these models often fail to accurately distinguish between them. In this paper, we propose a cascaded model to address this problem. Our work is based on the confusion matrix, which is obtained from a baseline model. With the confusion matrix, we can group confusable defects for better processing. We then design a Transformer cascaded model consisting of two steps. The first step performs coarse-grained defect detection to predict either a specific type of defect, or a group of confusable defects. In the later case, we perform a fine-grained defect detection to further distinguish the specific type of defect with the sub-model specifically trained for that group. The experimental results show that this cascaded model achieves a significant performance improvement by improving the mean average precision from 0.767 to 0.818 with our sewer object detection dataset. This method paves the way for accurate detection of defects in sewer systems.
DFMC SBAS service performance analysis of multi-GNSS based on BDS-3 in different regions
Authors: Wang Ziwei , Wang Le , Xie Wei , Huang Guanwen , Yang Weicong , Tian Yaozhao 
Published: August 2024

Abstract
The dual-frequency multi-constellation satellite-based augmentation system (DFMC SBAS) plays a crucial role in many fields, such as civil aviation and other safety-critical applications. To analyze the performance of the third generation of the BeiDou navigation satellite system (BDS-3) dual-frequency SBAS services in global major regions and the enhancement of navigation service performance by multi-Global Navigation Satellite System DFMC SBAS, four schemes are designed to conduct experiments using user end positioning in China (CHN), Europe (EUR), and Australia (AUS), and the results are analyzed under Approach with Vertical guidance I (APV-I) and Category I Precision Approach (CAT-I). In terms of the number of enhanced satellites, user range error, positioning accuracy, and DFMC SBAS service integrity, the results show that the positioning performances of the BDS-3 DFMC SBAS in the horizontal and vertical directions are 0.60 and 1.12 m for the CHN region, 0.87 and 1.39 m for the EUR region, and 0.64 and 1.11 m for the AUS region. In the CHN and AUS regions, the horizontal and vertical accuracies are comparable and better than those in the EUR region. Under the APV-I performance index, except for stations at the edge of the service range in the CHN and AUS regions, the availability and continuity of all stations were 100% and over 99% in the EUR region. The BDS-3 + Galileo + GPS DFMC SBAS service can significantly improve the positioning accuracy compared to a single constellation. Under the APV-I performance index, the availability and continuity of all stations in the three regions reached 100%, which also satisfied the minimum requirement of CAT-I service availability (99%).
Remaining useful life prediction of rolling bearings based on time convolutional network and transformer in parallel
Authors: Tang Youfu , Liu Ruifeng , Li Chunhui , Lei Na 
Published: August 2024

Abstract
Remaining useful life prediction (RUL) is crucial for maintaining the reliability and safety of industrial equipment. Recently, the Transformer has been widely used due to its ability to effectively extract global feature information in rolling bearing RUL prediction. However, the Transformer is weak in acquiring local feature information and cannot extract temporal features from the degradation process. Conversely, a temporal convolutional network (TCN) can effectively extract local features but is weak in global feature extraction. Therefore, to address the above problems, this paper proposes a prediction method based on the parallel combination of TCN and Transformer. The method first extracts the time domain, frequency domain, and time-frequency domain features from the original vibration signals. After screening through the evaluation indices, the features are fused using K-means clustering and principal component analysis (PCA) to construct health indicators (HI) that characterize the degradation of rolling bearings. Then, a TCN-Transformer parallel network model is constructed to extract both local and global features, and a feed-forward neural network (FNN) is used for the prediction of RUL. Finally, experimental validation is carried out on the PHM2012 bearing dataset, and the results show that the method achieves higher RUL prediction accuracy compared to other methods.
Classification and detection of noise in IoT based MQ gas sensors using artificial neural network
Authors: Dutta Nimisha , Mazumdar Rajdeep 
Published: August 2024

Abstract
IoT-based Metal Oxide Semiconductor (MOS) gas sensors have potential applications in health, industrial, and agriculture sectors. MQ gas sensors are easy to use, have a large detection range, high sensitivity, and efficiency, and can be interfaced with Arduino for effective use. However, like other any other sensors, the MQ gas sensors are also not able to escape the effect of noise that affects the sensitivity and selectivity causing the unwanted wrong classification and identification of gases, etc finally leading to a wrong justification of results and data. Hence, the study of noise and its removal is very crucial for greater accuracy in its analysis. The main aim of our work is to check whether one can classify different types of noises or noisy signals accurately using even simple ANNs so that after testing the different filtration techniques for the signals we can obtain an independent system specifically for the MQ gas sensors, that can classify as well as filter the noisy signals based on the category to which they are classified. The best and most effective noise filtration method is then obtained. During the study Narrow Neural Network model and Medium Neural Network demonstrated high accuracy in validation and testing, with ROC curves indicating their efficiency and effectiveness.
Temporal convolutional network with soft threshold and contractile self-attention mechanism for remaining useful life prediction of rolling bearings
Authors: Ma Hao , Wang Jinrui , Han Baokun , Zhang Zongzhen , Bao Huaiqian 
Published: August 2024

Abstract
Remaining useful life (RUL) prediction is an effective approach to prevent system failures and reduce maintenance expenditures. Due to the wide receptive field and the avoidance of future information leakage, the temporal convolutional network (TCN) is widely applied for RUL estimation of bearings. However, the predictive performance of TCN is limited by the loss of degradation features and the breakdown of continuity in timing information. To overcome the above defects, a hybrid temporal convolutional network with soft threshold and contractile self-attention mechanism (HTCN-SC) is proposed. Firstly, the adaptive threshold is determined by the contraction self-attention mechanism with higher interpretability, which captures the contribution of different features to the estimation of RUL. Then, the soft threshold is employed to activate the degraded features. On the one hand, the degeneracy features endowed by the dilated causal convolution with obvious negative values are fully preserved. On the other hand, the noise components that are given low weights are completely suppressed compared to the original TCN. Finally, parallel branch composed of one-dimensional convolutional networks are used to supplement the continuity of time series. Degradation signals from different working conditions and bearings are employed to verify the performance of the HTCN-SC. The results indicate that HTCN-SC with accurate RUL estimation and generalization ability is an effective tool for rolling bearing health monitoring.
Real Time Measurement of Electron Temperature using Coupled Center Tapped Emissive and Langmuir Probe (CCTELP)
Authors: Sanyasi Amulya Kumar , Awasthi Lalit M , Srivastava Pankaj Kumar , Adhikari Ayan , Sugandhi Ritesh 
Published: August 2024

Abstract
Conventional electrostatic probes are commonly used for measuring the plasma parameters like plasma density, electron temperature, plasma potential and floating potential respectively in a laboratory plasma to unveil physical processes of relevance to magnetosphere and fusion plasmas. With such probes, real-time measurement of electron temperature and its fluctuating AC component becomes very difficult. These measurements are significant as they directly give the measure of energy flux and hence can be utilized in estimating the turbulence-induced energy loss in any plasma system, especially useful for electron temperature gradient plasmas. Although Triple Langmuir Probe (TLP) diagnostics provide information on real-time measurement of electron temperature, this involves many complications. These complications of TLP are suitably overcome by the proposed diagnostic, which makes use of a combination of a closely separated centre tap emissive probe (CTEP) and Langmuir probe (LP) installed on a single probe head. The coupled centre-tapped emissive and Langmuir probe (CCTELP) provides direct measurement of electron temperature (T_e) and its fluctuations (T ̃_e). The design of the proposed diagnostic is very simple and it is almost free of rigorous control electronics and post-data analysis otherwise needed to estimate the electron temperature. These features make this diagnostic an additional and simple candidate for real-time measurement of electron temperature and its fluctuation. The paper will highlight details on this novel diagnostics, called CCTELP for the measurement in Maxwellian plasmas.
Automated body measurement of beef cattle based on keypoint detection and local point cloud clustering
Authors: Yang Lisha , Jiang Tao , Gui Xiaofei , Duan Qingling 
Published: August 2024

Abstract
Body size parameters of beef cattle are crucial for assessing growth status and breeding value. In actual farming environments, the various postures of beef cattle and complex backgrounds can affect the accuracy and stability of non-contact body measurement methods. Therefore, this paper proposes a novel method called the cattle body measurement method (CBMM), which combines keypoint detection with local point cloud clustering. First, a keypoint detection model based on YOLOv8-SimBiFPN is constructed. This model enhances the feature extraction and fusion capabilities of YOLOv8-pose by introducing SimAM and BiFPN into the backbone and neck networks, respectively, and realizes 2D keypoint detection for beef cattle in various postures. Second, a 3D keypoint-locating algorithm based on Density-based spatial clustering of applications with noise (DBSCAN) is proposed. This algorithm utilizes 2D keypoints, depth maps and camera parameters to generate local point clouds, which are then clustered using DBSCAN to segment cattle body point clouds, thereby relocating the 3D keypoints based on their positional features. Finally, body size parameters are calculated based on the 3D keypoints and distance formulae. In our experiment, the mean average precision (mAP@0.5) of YOLOv8-SimBiFPN reached 99.1% on an Angus beef cattle keypoint detection dataset. The mean absolute percentage errors for measuring beef cattle withers height, hip height, body depth, body length, and oblique body length using the CBMM were 4.37%, 4.96%, 6.47%, 4.84%, and 4.14%, respectively. In summary, our method can achieve non-contact body measurement for beef cattle in a free-moving state with high accuracy and stability.
A novel approach on calibration of a laser line sensor’s position in the machine coordinate system
Authors: Mei Ning , Li Du , Zhen Xiaotong , Pan Jianzhou , Zhao Zhengcai 
Published: August 2024

Abstract
On-machine measurement facilitates real-time monitoring and feedback of the machining status of parts, thereby preventing secondary positioning errors in the offline measurement of parts. Calibrating spatial position of the measuring sensor is crucial for ensuring the accuracy of on-machine measurements. This paper introduces a novel approach to on-machine calibration of line laser displacement sensors, based on the fitted sphere-center method and iterative position in both height and width directions along the line laser. By constructing a transformation matrix and measuring multiple sets of data to determine the unknowns in the matrix, the spatial position of the laser displacement sensor can be calibrated. The results showed that the polar deviation of the standard ball radius was 0.006 73 mm, confirming the feasibility and accuracy of the method.
Modified Universal Kriging-based clearance error optimization for orthogonal robot
Authors: Liu Wei , Zhang Qi , Xu Chunjie , Wan Yidong 
Published: August 2024

Abstract
Aiming at the problem that the clearance error of the traditional tire laser engraving robot is too high, and thus the resulting positioning accuracy cannot meet the accuracy requirements needed for engraving at this stage, this paper takes the homemade orthogonal robot as the object of research, and researches the optimization method of the positioning error of the orthogonal robot based on the modified Kriging. In this paper, we use spinor theory to construct a robot clearance error model, establish an objective function with orthogonal robot clearance error as the optimization objective, and use a particle swarm algorithm to modify Universal Kriging (MUK) to optimize the positioning error. The optimization method is simulated and analyzed, and MUK is selected for comparison with other optimization methods. The simulation results show that 52% relative reduces the number of iterations of the MUK to the UK model, 79% relative to the particle swarm optimization, and 81% relative to the ordinary kriging model, which makes the algorithm better applicable in comparison. At the same time, a tire laser engraving orthogonal robot experimental platform was built for physical verification experiments, and the experimental results show that the maximum error before and after the optimization of the integrated error of orthogonal robot positioning accuracy has been reduced by 89.91%; the average positioning error has been reduced by 86.90%; and the root-mean-square error has been reduced by 83.01%, which further proves the practicability of the proposed method.
Nijenhuis geometry IV: conservation laws, symmetries and integration of certain non-diagonalisable systems of hydrodynamic type in quadratures
Authors: Bolsinov Alexey V , Konyaev Andrey Yu , Matveev Vladimir S 
Published: August 2024

AbstractThe paper contains two lines of results: the first one is a study of symmetries and conservation laws ofgl-regular Nijenhuis operators. We prove the splitting theorem for symmetries and conservation laws of Nijenhuis operators, show that the space of symmetries of agl-regular Nijenhuis operator forms a commutative algebra with respect to (pointwise) matrix multiplication. Moreover, all the elements of this algebra are strong symmetries of each other. We establish a natural relationship between symmetries and conservation laws of agl-regular Nijenhuis operator and systems of the first and second companion coordinates. Moreover, we show that the space of conservation laws is naturally related to the space of symmetries in the sense that any conservation laws can be obtained from a single conservation law by multiplication with an appropriate symmetry. In particular, we provide an explicit description of all symmetries and conservation laws forgl-regular operators at algebraically generic points. The second line of results contains an application of the theoretical part to a certain system of partial differential equations of hydrodynamic type, which was previously studied by different authors, but mainly in the diagonalisable case. We show that this system is integrable in quadratures, i.e. its solutions can be found for almost all initial curves by integrating closed 1-forms and solving some systems of functional equations. The system is not diagonalisable in general, and construction and integration of such systems is an actively studied and explicitly stated problem in the literature.
Online MR-guided proton and ion beam radiotherapy: investigation of image quality
Authors: Paul K , Dorsch S , Elter A , Beyer C , Naumann J , Hansmann T , Feldmeier E , Haberer T , Karger C P , Debus J , Klüter S 
Published: August 2024

Abstract

Objective. Magnetic resonance (MR) images free of artefacts are of pivotal importance for MR-guided ion radiotherapy. This study investigates MR image quality for simultaneous irradiation in an experimental setup using phantom imaging as well as in-vivo imaging. Observed artefacts are described within the study and their cause is investigated with the goal to find conclusions and solutions for potential future hybrid devices. Approach. An open MR scanner with a field strength of 0.25 T has been installed in front of an ion beamline. Simultaneous magnetic resonance imaging and irradiation using raster scanning were performed to analyze image quality in dedicated phantoms. Magnetic field measurements were performed to assist the explanation of observed artifacts. In addition, in-vivo images were acquired by operating the magnets for beam scanning without transporting a beam. Main Results. The additional frequency component within the isocenter caused by the fringe field of the horizontal beam scanning magnet correlates with the amplitude and frequency of the scanning magnet steering and can cause ghosting artifacts in the images. These are amplified with high currents and fast operating of the scanning magnet. Applying a real-time capable pulse sequence in-vivo revealed no ghosting artifacts despite a continuously changing current pattern and a clinical treatment plan activation scheme, suggesting that the use of fast imaging is beneficial for the aim of creating high quality in-beam MR images. This result suggests, that the influence of the scanning magnets on the MR acquisition might be of negligible importance and does not need further measures like extensive magnetic shielding of the scanning magnets. Significance. Our study delimited artefacts observed in MR images acquired during simultaneous raster scanning ion beam irradiation. The application of a fast pulse sequence showed no image artefacts and holds the potential that online MR imaging in future hybrid devices might be feasible.