nils-herrmann/clean_scirepeval huggingface dataset:
Three-dimensional magnetic field analysis of novel asymmetric AMF contacts for vacuum interrupters

An axial magnetic field (AMF) applied to the vacuum arc column in the vacuum tube can make the vacuum arc keeping in diffusion state, even if it is interrupting the short circuit current. In this paper a novel AMF contact with asymmetric structure has been present. In principle, the necessary axial magnetic field would be created by two coils in series behind the static contact plate. The axial magnetic field and eddy field are analyzed based on novel axial magnetic field contact design with finite element method. At zero current, the generation of eddy currents by 31.5kA, 50Hz excitation is also considered. The results of axial magnetic field and eddy field analysis such as distribution of the axial magnetic field are described. It has also studied whether the parameters such as the coil height, the coil thickness and the coil diameter have an effect on the axial magnetic field and to what extent they do.
Central Author Mining from Co-authorship Network

Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.
Data Preprocessing Method of ECG Indicators When Applied ECG in Driver Mental Workload Research

Electrocardiogram (ECG) indicators are widely used in driver mental workload studies. Because ECG is always recorded continuously in experiments, ECG indicator data often have trend. To make follow-up data analysis faster and more reliable, ECG indicator data should be preprocessed to remove or extract the trend according to the aim of the experiment. However, most researchers tend to ignore this point. Based on time series theories, this paper proposed a set of methods to preprocess ECG indicator data series. In this paper, the data preprocessing method was described in detail. And an example was given to illustrate and prove the validity of the method.
Study of optically controlled active frequency selective surfaces with organic semiconductor

Active Frequency Selective Surface (FSS) with tunable response has been the focus of many researches. In this work, a new optically controlled approach which exploits variable dielectric property of organic semiconductor under the optical illumination is studied. P3HT and PTAA which are two potential organic polymers with tunable dielectric properties under optical illumination are investigated in this work. The tunable responses of two different freestanding FSS are simulated.
Analysis on the gas flow characteristic of high voltage SF6 circuit breaker with novel energy separated nozzle

In this paper, the high voltage SF6 circuit breaker (HV SF6CB) was taken as the research object, an axial vortex arc quenching chamber structure with a novel “X-type” guide blade on the nozzle upstream is presented to effectively control the kinetic energy of the free vortex formed by the gas flow and to exchange the energy in the radial direction. During the interruption, the temperature gradient along the radial direction can be generated. In the process of the internal energy exchange, because the outer spin flow of the turbulence gains more kinetic energy than the internal energy loss, which makes the total temperature of the outer rotary flow increase, and the internal gas flow temperature decrease accordingly. In this paper, the novel nozzle is proposed and applied to achieve energy separated vortex movement of the high pressure gas flow to reduce the axial temperature and increase the gas flow capability. And a 550kV SF6 CB was taken as the research object in this paper. By using finite volume method (FVM), the 3D flow field mathematical model under the small capacitive current interruption is proposed and the influence of the X type guide blade structure on flow field is simulated. By solving the gas flow field with complex flow path, supersonic nozzle, viscous, compressible and variable boundaries, the influences of different axial vortex nozzle structure on the velocity and pressure distributions have been achieved. In addition, the comparisons of gas flow distributions in the chamber with and without X type guide blade nozzle were obtained and analyzed.
Construction of distributed LDoS attack based on one-dimensional random walk algorithm

This paper designs a distributed low-rate DoS (LDoS) attack using random walk algorithm; random walk algorithm can be used in generating power-law distribution which complies with normal network behavior feature. In this attack pattern, the behavior of each distributed attack flows are normal and nonperiodic, while the superposed effect has the same damage degree as traditional square wave LDoS attacks. As the distributed attack flow satisfies the network traffic behavior feature, it is more difficult to detect and trace attack sources. The study on the feature of this attack provides reference for distributed low-rate DoS attack countermeasures.
Nonparametric background model based clutter map for X-band marine radar

In a radar system, clutter means any echoes which are not scattered by the wanted target. Usually, the radar clutter map stores an average level for each point or cell in the range-azimuth coordinates as the reference value. A target is then detected in a range-azimuth region if the new echo value in that region exceeds the average background level. In visual computing domain, background model is employed for foreground segmentation, motion detection, salient feature detection, etc. A few kinds of background models are built on each pixel of the sequential images, including the recently popular non-parametric model. In this paper, we proposes a non-parametric background model to implement the radar clutter map. A set of intensity values, which are selected in the past in each pixel location, are stored as the initial clutter model. Then, the new value of a pixel is classified as foreground stemming from a moving target, if the value was stronger than those of the reference samples in the recorded set Clutter model updating is based on randomly choosing in temporal and substituting background pixel values in spatial. Proposed model is proved to be efficient and effective in a moving vehicle detecting application. Also it is compared to a broadly used clutter map method which employs averaging in temporal.
A Variational Approach to Medical Image Inpainting Based on Mumford-Shah Model

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic services. Medical image inpainting is an important research field in CAD, which can be used to retouch and remove speckle noise and conceal spots and cracks in organic images. When processing damaged medical image, because the amount of data and computing of image is very large and traditional inpainting algorithms have early convergence problems, the quality of image restoration is poor. This paper discussed the problem of medical image inpainting by approaching a modified Mumford-Shah paradigm from a numerical approximation perspective. Within this framework an adaptive iterative algorithm for the numerical solving of the inpainting model was presented. First, we proposed a new nonparametric Mumford-Shah model for medical image inpainting. It improves the robustness and effectiveness by imposing some explicit smooth constrains of connection on the formation of discontinuous edges. Thus, the unknown inpainted curves in the model would be computed and identified as the smooth transition regions by several order parameters. Finally, the minimization was achieved by computing the gradient of energy evolving for the inpainting model. Experimental results on noisy medical images demonstrate the efficacy of the proposed algorithm.
Pose-Invariant Face Alignment with a Single CNN

Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNNs, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted features and slow training speed. To address these issues, we propose a new layer, named visualization layer, which can be integrated into the CNN architecture and enables joint optimization with different loss functions. Extensive evaluation of the proposed method on multiple datasets demonstrates state-of-the-art accuracy, while reducing the training time by more than half compared to the typical cascade of CNNs. In addition, we compare across multiple CNN architectures, all with the visualization layer, to further demonstrate the advantage of its utilization.
A method for shadow invariant image

The lighting variance brought great trouble for computer vision and has been extensively researched. In this paper, we propose a new method that uses a RGB image to generate a grayscale image that is not sensitive to shadows. We first roughly detect shadows from an image, then we used the detected shadow to generate paired regions of shadows and non-shadows. Finally we used these paired regions to estimate the parameters of the proposed shadow invariant algorithm. Our method does not use any other prior knowledge except just a RGB image. It is simple and effective.
Design of polarization-independent Electromagnetically Induced Transparency structure in terahertz band

Metamaterial Electromagnetically Induced Transparency (EIT) structures have attracted much attention for their significant applications in terahertz rotation sensing, filters, storage and temperature sensing elements. However, most researches on terahertz metamaterial EIT are polarization dependent, which limits their application. In this work, we proposed a terahertz EIT structure with polarization-independence. The unit cell of the structure consists of two layers, the first layer is a square ring with two gaps which can be deposited on the crystal substrate by photolithographic technology. The transmission frequency response characteristics of the structure can be modeled as an infinite array in CST Microwave software. To analyze physical characteristic of the EIT structure, the bright and the dark modes occurring in the structure are investigated based on surface current distributions On the other hand, the influence of the substrate on the transmission frequency and magnitude of the EIT are discussed.
Multi-stage convergent-divergent nozzle structure and their effects on gas flow in high voltage SF/sub 6/ circuit breaker

A novel combined improved total variation diminish scheme (TVD) with finite volume method (FVM) has been proposed and applied for calculating the N-S and K-epsi equation of flow field in SF6 circuit breaker under no-load breaking in this paper. And during the computing, the influence of actual nozzle structure is taken into consideration. Through the computation and analysis of combined electric field and flow field, the effect of gas flow on the dielectric recovery characteristic with different multi-stage convergent-divergent nozzle structure and their effects on gas flow in high voltage SF6  circuit breaker have been obtained and analyzed
Domain Coupling of Multi-Physical Parameters for EHV SF6 Circuit Breaker

Coupling of multi physical parameters in extra high voltage SF6 circuit breaker (EHV SF6 CB) has been investigated. A 2D combined mathematical model of electric field, flow field and ε − k turbulent flow was developed, and the coupling condition of multi physical field of CB was established. And the computation has been carried out for a 500kV 1-break SF6 CB. The multi physical parameters and the electrical characteristics varying with the interrupting course have been investigated. In addition, the dielectric recovery characteristics (DRC) of the 500kV 1-break SF6 CB has been obtained, and the visualization of the breaking course has been realized. Keywords-coupling field, domain decomposition, numerical simulation, puffer CB
The EEG changes during night-time driver fatigue

Driver fatigue is one of the main causes of traffic accidents. Many researchers believe that electroencephalography is very accurate and promising to detect driving fatigue, but so far, the research results have been variable. Through simulation experiments, this paper found when driving at night, delta band varies significantly with the degree of fatigue. The results have implications for real-time automatic detecting driver fatigue at night and are helpful for future development of driver fatigue countermeasures.
An Automated System for Plant-Level Disease Rating in Real Fields

Cercospora leaf spot (CLS) is the most serious disease in sugar beet plants that significantly reduces the sugar yield throughout the world. Therefore the current focus of the researchers in agricultural domain is to find sugar beet cultivars that are highly resistant to CLS. To measure their resistance, CLS is manually observed and rated in a large variety of sugar beet by different human experts over a period of a few months. Unfortunately, this procedure is laborious and subjective. Therefore, we propose a novel computer vision system, CLS Rater, to automatically and accurately rate CLS of plant images in the real field to the "USDA scale" of 0 to 10. Given a set of plant images captured by a tractor-mounted camera, CLS Rater extracts multi-scale super pixels, where in each scale a novel histogram of importances feature representation is proposed to encode both the within-super pixel local and across-super pixel global appearance variations. These features at different super pixel scales are then fused for learning a bagging M5P regress or that estimates the rating for each plant image. We test our system on the field data collected over a period of two months under different day lighting and weather conditions. Experimental results show CLS Rater to be highly consistent with a rating error of 0.65, which demonstrates higher consistency than the rating standard deviation of 1.31 by the human experts.
Design and experimental verification of wire grid polarizer on dielectric substrate for quasi-optical network system

The quasi-optical network (QON) system generally requires that it has a feature of multi-frequency and multi-polarization for transmitting or receiving more channels in a small space to reduce the satellite load. The wire grid polarizer (WGP) can be mainly used into separating double different signals with horizontal polarization or vertical polarization. Due to its frequency irrelevant attribute, the most obvious advantage of WGP is that has much greater working bandwidth comparing with the frequency selective surface (FSS) during the millimeter wave (MMW) and sub-millimeter wave (sub-MMW) frequency. A novel WGP with a wide working band from 1MHz to 500GHz is presented and meanwhile measured from 75GHz to 325GHz in the BUPT, which is printed on the dielectric substrate. Its design principle and the analysis process of experimental data by the Time Domain Analysis technology are given simultaneously. The multipath and stray transmission components are analyzed and filtered to obtain the polarization isolation during the whole working bandwidth. What's more, the WGP is adopted into a dual-channel 3D quasi-optical network system (QON) to separate 183GHz and 324GHz. The experimental near-field planar results in the output position have also been obtained, which further demonstrates the validity of system design.
Demonstration of a fast all-fiber acousto-optic tunable filter based variable optical attenuator

An all-fiber AOTF-based VOA with a compact size (/spl sim/3 cm), low insertion loss (<0.2 dB), fast response time (<15 /spl mu/s), broad bandwidth (FWHM bandwidth /spl sim/172 nm), and extinction ratio up to 12 dB is demonstrated on etched cladding single mode fiber. The device is a promising candidate for fast dynamic channel power management in fiber-optic networks.
Chaotic analysis of high voltage SF6 circuit breaker during breaking process Part II: Gas flow chaos under small capacitive current interruption

By calculating the flow field of 2D high voltage SF6 circuit breaker (HV SF6 CB) under the small capacitive current interruption, a 252kV HV SF6 CB is taken as the investigation example. By modeling and calculating, the flow parameters have been simulated under the small capacitive current interruption condition and the variation of the internal flow parameters, such as the velocity, pressure, density, temperature and Mach number have been numerically simulated and analyzed. There exist strong nonlinear variation of the flow parameters during the breaking process, therefore the chaos theory is applied to determine the internal chaotic characteristic. By using the CC method and the Wolf algorithm, the maximum Lyapunov exponent (LEmax) of the above main flow parameters has been obtained, and the internal factors affecting the chaos phenomenon of the SF6 CB have been further analyzed.
A feasible graph partition framework for random walks implemented by parallel computing in big graph

Graph partition is a fundamental problem of parallel computing for big graph data. Many graph partition algorithms have been proposed to solve the problem in various applications, such as matrix computations and PageRank, etc., but none has pay attention to random walks. Random walks is a widely used method to explore graph structure in lots of fields. The challenges of graph partition for random walks include the large number of times of communication between partitions, too many replications of the vertices, unbalanced partition, etc. In this paper, we propose a feasible graph partition framework for random walks implemented by parallel computing in big graph. The framework is based on two optimization functions to reduce the bandwidth, memory and storage cost in the condition that the load balance is guaranteed. In this framework, several greedy graph partition algorithms are proposed. We also use five metrics from different perspectives to evaluate the performance of these algorithms. By running the algorithms on the big graph data set of real world, the experimental results show that these algorithms in the framework are capable of solving the problem of graph partition for random walks for different needs, e.g. the best result is improved more than 70 times in reducing the times of communication.
An analytical method for determining comprehensive impact of Rayleigh backscattering on performance of systems with distributed Raman amplifiers

An analytical method to determine the impact of amplified spontaneous Raman scattering and double Rayleigh backscattering noise in distributed Raman amplifiers (DRAs) is derived. By this method, two applications of DRAs are discussed. Firstly, DRAs are used as preamplifiers before receivers. Secondly, DRAs are used as preamplifiers of EDFAs in repeated systems. In both cases, calculations show that there is an optimal Raman pump power at which the system has the best performance.
Illuminating Pedestrians via Simultaneous Detection and Segmentation

Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection. When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector. Using this approach, we find weakly annotated boxes to be sufficient for considerable performance gains. We provide an in-depth analysis to demonstrate how shared layers are shaped by the segmentation supervision. In doing so, we show that the resulting feature maps become more semantically meaningful and robust to shape and occlusion. Overall, our simultaneous detection and segmentation framework achieves a considerable gain over the state-of-the-art on the Caltech pedestrian dataset, competitive performance on KITTI, and executes 2 × faster than competitive methods.
Numerical simulation of electric field with open boundary using intelligent optimum charge simulation method

For improving the calculation precision of the electric field, a novel combination of genetic algorithms (GA) and charge simulation method (CSM) is proposed. In which, the allocations and quantities of the optimized charges can be obtained using GA, and higher calculation precision can be achieved. The combined CSM and the hybrid conventional and optimized CSM were applied for computing the electric field of the demonstration examples. The comparison of the relative error between the conventional and optimized CSM has been obtained. The feasibility, practicability and validity of the proposed optimized CSM have been verified
A Case Study of Software Security Test Based on Defects Threat Tree Modeling

Due to the increasing complexity of software applications, traditional function security testing ways, which only test and validate software security mechanisms, are becoming ineffective to detect latent software security defects (SSD). However, the most of vulnerabilities result from some typical SSD. According to CERT/CC, ten defects known are responsible for 75% of security breaches in today software applications. On the base of threat tree modeling, we use it in the integrated software security test model. For introducing the usefulness of the method, we use the test model in M3TR software security test.
Pathways core: a data model for cross-repository services

As part of the NSF-funded pathways project, we have created an interoperable data model to facilitate object re-use and a broad spectrum of cross-repository services. The resulting pathways core data model is designed to be lightweight to implement, and to be widely applicable as a shared profile or as an overlay on data models currently used in repository systems and applications. We consider the data models underlying the Fedora, Dspace and aDORe repository systems, and a number of XML-based formats used for the representation of compound objects, including MPEG-21 DIDL, METS, and IMS/CP