bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electric field dependence of spin qubit in a Si-MOS quantum dot
Authors: Ma 马 Rong-Long 荣龙 , Ni 倪 Ming 铭 , Zhou 周 Yu-Chen 雨晨 , Kong 孔 Zhen-Zhen 真真 , Wang 王 Gui-Lei 桂磊 , Liu 刘 Di 頔 , Luo 罗 Gang 刚 , Cao 曹 Gang 刚 , Li 李 Hai-Ou 海欧 , Guo 郭 Guo-Ping 国平 
Published: March 2024

Abstract
Valley, the intrinsic feature of silicon, is an inescapable subject in silicon-based quantum computing. At the spin–valley hotspot, both Rabi frequency and state relaxation rate are significantly enhanced. With protection against charge noise, the valley degree of freedom is also conceived to encode a qubit to realize noise-resistant quantum computing. Here, based on the spin qubit composed of one or three electrons, we characterize the intrinsic properties of valley in an isotopically enriched silicon quantum dot (QD) device. For one-electron qubit, we measure two electric-dipole spin resonance (EDSR) signals which are attributed to partial occupation of two valley states. The resonance frequencies of two EDSR signals have opposite electric field dependences. Moreover, we characterize the electric field dependence of the upper valley state based on three-electron qubit experiments. The difference of electric field dependences of the two valleys is 52.02 MHz/V, which is beneficial for tuning qubit frequency to meet different experimental requirements. As an extension of electrical control spin qubits, the opposite electric field dependence is crucial for qubit addressability, individual single-qubit control and two-qubit gate approaches in scalable quantum computing.
First Digit Distributions of Gamma-Ray Bursts
Authors: Lai Hou-Yu , Wei Jun-Jie 
Published: March 2024

Abstract
The occurrence of the first significant digits from real world sources is usually not equally distributed, but is consistent with a logarithmic distribution instead, known as Benford’s law. In this work, we perform a comprehensive investigation on the first digit distributions of the duration, fluence, and energy flux of gamma-ray bursts (GRBs) for the first time. For a complete GRB sample detected by the Fermi satellite, we find that the first digits of the duration and fluence adhere to Benford’s law. However, the energy flux shows a significant departure from this law, which may be due to the fact that a considerable part of the energy flux measurements is restricted by lack of spectral information. Based on the conventional duration classification scheme, we also check if the durations and fluences of long and short GRBs (with duration T
90 > 2 s and T
90 ≤ 2 s, respectively) obey Benford’s law. We find that the fluences of both long and short GRBs still agree with the Benford distribution, but their durations do not follow Benford’s law. Our results hint that the long–short GRB classification scheme does not directly represent the intrinsic physical classification scheme.
Comparison of the activation level in the sensorimotor cortex between motor point and proximal nerve bundle electrical stimulation
Authors: Yuan Rui , Peng Yu , Ji Run , Zheng Yang 
Published: March 2024

Abstract

Objective. Neuromuscular electrical stimulation (NMES) is widely used for motor function rehabilitation in stroke survivors. Compared with the conventional motor point (MP) stimulation, the stimulation at the proximal segment of the peripheral nerve (PN) bundles has been demonstrated to have multiple advantages. However, it is not known yet whether the PN stimulation can increase the cortical activation level, which is crucial for motor function rehabilitation. Approach. The current stimuli were delivered transcutaneously at the muscle belly of the finger flexors and the proximal segment of the median and ulnar nerves, respectively for the MP and PN stimulation. The stimulation intensity was determined to elicit the same contraction levels between the two stimulation methods in 18 healthy individuals and a stroke patient. The functional near-infrared spectroscopy and the electromyogram were recorded to compare the activation pattern of the sensorimotor regions and the target muscles. Main Results. For the healthy subjects, the PN stimulation induced significantly increased concentration of the oxygenated hemoglobin in the contralateral sensorimotor areas, and enhanced the functional connectivity between brain regions compared with the MP stimulation. Meanwhile, the compound action potentials had a smaller amplitude and the H-reflex became stronger under the PN stimulation, indicating that more sensory axons were activated in the PN stimulation. For the stroke patient, the PN stimulation can elicit finger forces and induce activation of both the contralateral and ipsilateral motor cortex. Conclusions. Compared with the MP stimulation, the PN stimulation can induce more cortical activation in the contralateral sensorimotor areas possibly via involving more activities in the central pathway. Significance. This study demonstrated the potential of the PN stimulation to facilitate functional recovery via increasing the cortical activation level, which may help to improve the outcome of the NMES-based rehabilitation for motor function recovery after stroke.
Machine learning-enhanced model-based scenario optimization for DIII-D
Authors: Morosohk S. , Leard B. , Rafiq T. , Schuster E. 
Published: March 2024

Abstract
Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.
Dynamics of inertial particles under velocity resetting
Authors: Olsen Kristian Stølevik , Löwen Hartmut 
Published: March 2024

Abstract
We investigate stochastic resetting in coupled systems involving two degrees of freedom, where only one variable is reset. The resetting variable, which we think of as hidden, indirectly affects the remaining observable variable via correlations. We derive the Fourier–Laplace transforms of the observable variable’s propagator and provide a recursive relation for all the moments, facilitating a comprehensive examination of the process. We apply this framework to inertial transport processes where we observe the particle position while the velocity is hidden and is being reset at a constant rate. We show that velocity resetting results in a linearly growing spatial mean squared displacement at later times, independently of reset-free dynamics, due to resetting-induced tempering of velocity correlations. General expressions for the effective diffusion and drift coefficients are derived as a function of the resetting rate. A non-trivial dependence on the rate may appear due to multiple timescales and crossovers in the reset-free dynamics. An extension that incorporates refractory periods after each reset is considered, where post-resetting pauses can lead to anomalous diffusive behavior. Our results are of relevance to a wide range of systems, such as inertial transport where the mechanical momentum is lost in collisions with the environment or the behavior of living organisms where stop-and-go locomotion with inertia is ubiquitous. Numerical simulations for underdamped Brownian motion and the random acceleration process confirm our findings.
Ground fault detection in substation DC system based on improved differential current method
Authors: Li Zhanghongtai , Zhang Lutong , Shen Chuanqi , Yue Luansong 
Published: March 2024

Abstract
DC system is the main electrical equipment and control signal system in the substation, and its stability and reliability are directly related to whether the power system can operate safely. This paper firstly introduces the common methods of insulation detection of DC power system in substation and its advantages and disadvantages, and proposes the differential current method based on unbalanced bridge, i.e., using the combination of unbalanced bridge and leakage current sensor to detect the insulation condition of DC system, which makes up for the defects of the traditional differential current method that can only detect the faults of simple branch circuits, and realizes the detection of multi-branch grounding under complex situations. Finally, the feasibility of this method is verified by building a simulation model through simulink.
GPS/Beidou fusion positioning technology for transmission line grounding line
Authors: Wang Wei , Gao Bo , Li Gang , Li Guang , Zhao Xu , Shi Yuxin , Wang Ziming , Yang Yang 
Published: March 2024

Abstract
The grounding wire plays a role in buffering high-voltage electricity in power transmission lines. However, during operation, it is difficult for personnel to obtain real-time information about the location of the grounding wire. The existing positioning technology for grounding wires in power transmission lines can prevent accidents caused by missing grounding wires or transmitting electricity through grounding wires. However, due to the vast scale of power transmission lines, there are often limited visible satellites and poor satellite signal quality, resulting in insufficient accuracy of satellite positioning and navigation. This paper first discusses the necessity of online monitoring of grounding wires in power transmission lines. Then, based on this, it introduces the positioning methods of GPS and Beidou positioning systems. Finally, it focuses on the GPS/Beidou fusion positioning technology. Through experimental verification, the GPS/Beidou fusion positioning technology is shown to greatly enhance the reliability of positioning by coordinating and complementing various navigation systems.
Research on Convolutional Neural Network-Based Compression Methods for Multispectral Images
Authors: Wei Kegang , Liu Haiyang , Yu Peixu , Ren Yushuai , Gao Guang Min , Wen Changzhe 
Published: March 2024

Abstract
Considering the traits of multispectral images, which include numerous bands, high spatial and spectral redundancy, and a large data volume, there is a proposed investigation on compression methods based on convolutional neural networks to reduce the storage space consumed by an individual image and enhance compression effectiveness. This paper first introduces the development of image compression algorithms and deep learning in recent years. Based on these two structures, a framework for lossy compression of multispectral images utilizing an end-to-end convolutional neural network is proposed. A self-encoding structure is used to process three-dimensional hyperspectral images, extracting local spectral features and fusing spectral information using large convolutional kernels. Residual layers are employed to preserve spectral information. Rate-distortion optimization is performed to jointly optimize image distortion and compression bitrate. Finally, a comparison with the traditional JPEG method is conducted experiments to assess the efficacy of the proposed algorithm. The MS-SSIM is improved by nearly 0.08, and the compressed images exhibit no noticeable distortion.
Research on prediction method of tool wear degree based on SVM
Authors: Han Peng-hui , Zhu Can-shi 
Published: March 2024

Abstract
Tool wear of machine tools not only directly affects the processing quality, but also leads to the life of processing equipment and production costs. It is of great significance to correctly identify, classify and predict the state of tool wear. In this paper, through the collection of machine tool operation data, using machine learning modeling, using the model to identify the tool wear state, and then predict and classify the tool wear state, using the classification results to determine whether the tool can continue to use. After simulation verification, the results show that the model can identify and predict the wear state more realistically, and has strong practicability.
Thermal Energy-Kinetic Energy Conversion Efficiency Analysis Method Based on Grey Cluster Analysis
Authors: Min Hao , Zhang Yalin , Xu Juntao , Wang Xiaochen 
Published: March 2024

Abstract
At this stage, although thermal power generation is still the main method of power generation in my country’s power plants, due to more and more energy consumption, the reserves of resources are gradually beginning to be stretched. Application in the power generation and production process of thermal power plants has become a road that more and more power plants must explore for development. In this paper, through the grey cluster analysis, the power plant can fully improve the power generation efficiency of the power plant in the process of utilizing the existing energy, so as to achieve the purpose of energy saving and environmental protection. Combined with the connotation of thermal energy-kinetic energy conversion, this paper establishes a theoretical framework for the analysis method of thermal energy-kinetic energy conversion efficiency based on grey cluster analysis. On the basis of theoretical research, using descriptive statistical tools, the main performance of thermal energy-kinetic energy conversion to promote economic growth is summarized. The realization method of gray cluster analysis theory is to identify and process the external features of gray information, capture effective information, and quantify the features (ie, whitening process), so as to complete the evaluation or decision-making, and verify the effect of these five mechanisms on economic growth. The final results of the study show that when K=1, 2, 3 and 4, the proportion of heat transfer is 41.63%, 42.19%, 49.63% and 52.13%, and the proportion of heat convection is 37.56% and 41.33%, respectively., 29.69% and 26.78%, the heat transfer and heat convection have the greatest influence on the thermal energy-kinetic energy conversion efficiency.
Partial Discharge Detection Method of Electrical Equipment Based on UHF Method
Authors: Guo Xirong 
Published: March 2024

Abstract
Partial discharge is an early symptom of insulation defects. In the field application, the detection method can not accurately identify those noise signals mixed in the partial discharge signal, which limits the detection to a certain extent. Based on UHF method, a partial discharge detection method for electrical equipment is designed. When partial discharge occurs, the exchange between charges, the radiation of electromagnetic waves and the consumption of energy will occur at the discharge position, causing the change of electrode potential. For the phase distribution spectrum, the signal characteristics produced by partial discharge are extracted from three aspects: statistical information, time domain information and frequency domain information. During detection, UHF sensor is placed on the basin insulator, and then UHF signal is received. Insulation defects are identified according to various differential discharge patterns formed by the discharge characteristics of different defects. Test results show that the signal spectrum detected by UHF partial discharge roughly matches the typical discharge spectrum, and it is determined that there is partial discharge in this area. This method has high positioning accuracy and meets the detection requirements of electrical equipment.
A Robust Controller Deployment Algorithm under Multiple Constraints for SDON
Authors: Zhang Tao , Liu Guqing , Ran Jinzhi , Guo Chaoping , Xie Qingsong , Liang Zhenhua , Ding Deqiang 
Published: March 2024

Abstract
The deployment of controller for SDON (Software Defined Optical Networks) is an NP-hard multi-objective optimization problem, and it is difficult to find an optimal solution that satisfies all objectives. Therefore, to improve the performance of control plane, the model we proposed is constructed based on three constraints on the survivability, delay, and control redundancy, which has three advantages. first, the survivability is improved by not sharing the risk between the main control channel and the protection control channel, which also improves the utilization of network resources. Second, the accuracy of delay calculation is improved by using a delay optimization model, and a threshold value is set to constrain the delay. Finally, the greedy strategy idea is used to achieve the full network coverage of the control range and improve the convergence speed. Simulation results show that the algorithm improves the fault recovery probability by an average of 15% compared with the algorithm of KSP computed routing. In addition, the algorithm in this paper achieves the same survivability as the C-MPC algorithm while using an average 11.1% reduction in the number of controllers.
Operation Fault Detection of Electrical and Mechanical Equipment Based on Kalman Filter
Authors: Nie Qiong 
Published: March 2024

Abstract
In order to realize the remote collaborative diagnosis of electromechanical equipment fault in integrated energy system, a design method of remote collaborative diagnosis system of electromechanical equipment fault in integrated energy system based on Kalman filter is proposed. The hardware of the diagnosis system designed in this paper includes main control module, fault information parameter identification module, dynamic sensor acquisition module, fault signal filtering and detection module, adaptive control module, output interface module and so on. The collected fault information of electromechanical equipment in the system is fused by multi-source sensors, the fault information feature extraction is completed by Kalman filter, the information clustering processing is completed, and the algorithm design of electromechanical equipment operation fault diagnosis is realized. The fault diagnosis algorithm based on Kalman filter is loaded into the main control module of the motor equipment fault diagnosis system to realize the identification of motor equipment fault parameters and complete the development and design of the diagnosis system. The test results show that the text diagnosis system has good adaptability, high reliability and accuracy.
Improved Kalman Filter Based Accurate Pseudo-Range Positioning Method for BeiDou
Authors: Cui Wendong , Luo Xueyu , Zhou Zilong , Cheng Sijin , Zhao Guanglu , Qi Entie 
Published: March 2024

Abstract
In response to the issue of poor performance in traditional Beidou pseudorange positioning, this paper proposes a Beidou pseudorange precise positioning method that combines weighted least squares and adaptive Kalman filtering based on moving window covariance estimation. This method utilizes the fast convergence speed of the weighted least squares and the high accuracy of Kalman filtering. On this basis, the Kalman filter is modified with a moving window to ensure the accuracy and global convergence of the positioning process. Through experimental simulation and comparison, the effectiveness of this algorithm is demonstrated, showing its ability to improve positioning accuracy and meet certain positioning requirements.
Monitoring method for power equipment operation condition based on data warehouse
Authors: Chen Anxin , Li Xiaoqing , Yin Linlin 
Published: March 2024

Abstract
in the process of monitoring the operation state of power equipment, it is easy to be affected by the external environment, resulting in a large amount of data of the operation state of power equipment and the monitoring effect. A monitoring method of power equipment operation condition based on data warehouse is proposed. By analyzing the constraints of power equipment operation condition monitoring, a data warehouse is established; The transition probability and stability probability are determined by Markov chain model, and the characteristic values of operation state level variables are obtained by calculating the comprehensive correlation value and sensitivity; The entropy weight coefficient method calculates the weight of each performance index of the monitoring object, constructs the operation state monitoring model of power equipment, and realizes the monitoring of the operation state of power equipment. The experimental results show that the improved method can effectively improve the monitoring efficiency and shorten the monitoring time.
Magnetic biocomposite scaffold based on decellularized tendon ECM and MNP-deposited halloysite nanotubes: physicochemical, thermal, rheological, mechanical and in vitro biological evaluations
Authors: Koç-Demir Aysel , Elçin Ayşe Eser , Elçin Yaşar Murat 
Published: March 2024

Abstract
The development of new three-dimensional biomaterials with advanced versatile properties is critical to the success of tissue engineering (TE) applications. Here, (a) bioactive decellularized tendon extracellular matrix (dECM) with a sol-gel transition feature at physiological temperature, (b) halloysite nanotubes (HNT) with known mechanical properties and bioactivity, and (c) magnetic nanoparticles (MNP) with superparamagnetic and osteogenic properties were combined to develop a new scaffold that could be used in prospective bone TE applications. Deposition of MNPs on HNTs resulted in magnetic nanostructures without agglomeration of MNPs. A completely cell-free, collagen- and glycosaminoglycan- rich dECM was obtained and characterized. dECM-based scaffolds incorporated with 1%, 2% and 4% MNP-HNT were analysed for their physical, chemical, and in vitro biological properties. Fourier-transform infrared spectroscopy, x-ray powder diffractometry and vibrating sample magnetometry analyses confirmed the presence of dECM, HNT and MNP in all scaffold types. The capacity to form apatite layer upon incubation in simulated body fluid revealed that dECM-MNP-HNT is a bioactive material. Combining dECM with MNP-HNT improved the thermal stability and compressive strength of the macroporous scaffolds upto 2% MNP-HNT. In vitro cytotoxicity and hemolysis experiments showed that the scaffolds were essentially biocompatible. Human bone marrow mesenchymal stem cells adhered and proliferated well on the macroporous constructs containing 1% and 2% MNP-HNT; and remained metabolically active for at least 21 d in vitro. Collectively, the findings support the idea that magnetic nanocomposite dECM scaffolds containing MNP-HNT could be a potential template for TE applications.