bluuebunny/crossref_metadata_2025_split huggingface dataset:
The benefits of CMB delensing
Authors: Hotinli Selim C. , Meyers Joel , Trendafilova Cynthia , Green Daniel , van Engelen Alexander 
Published: April 2022

Abstract
The effects of gravitational lensing of the cosmic microwave
background (CMB) have been measured at high significance with
existing data and will be measured even more precisely in future
surveys. Reversing the effects of lensing on the observed CMB
temperature and polarization maps provides a variety of benefits.
Delensed CMB spectra have sharper acoustic peaks and more prominent
damping tails, allowing for improved inferences of cosmological
parameters that impact those features. Delensing reduces B-mode
power, aiding the search for primordial gravitational waves and
allowing for lower variance reconstruction of lensing and other
sources of secondary CMB anisotropies. Lensing-induced power
spectrum covariances are reduced by delensing, simplifying analyses
and improving constraints on primordial non-Gaussianities. Biases
that result from incorrectly modeling nonlinear and baryonic
feedback effects on the lensing power spectrum are mitigated by
delensing. All of these benefits are possible without any changes
to experimental or survey design. We develop a self-consistent,
iterative, all-orders treatment of CMB delensing on the curved sky
and demonstrate the impact that delensing will have with future
surveys.
The Potential of Detecting Radio-flaring Ultracool Dwarfs at L band in the FAST Drift-scan Survey
Authors: Tang Jing , Tsai Chao-Wei , Li Di 
Published: April 2022

Abstract
The Five-hundred-meter Aperture Spherical radio Telescope (FAST) completed its commissioning and began the Commensal Radio Astronomy FasT Survey (CRAFTS), a multi-year survey to cover 60% of the sky, in 2020. We present predictions for the number of radio-flaring ultracool dwarfs (UCDs) that are likely to be detected by CRAFTS. Based on the observed flaring UCDs from a number of unbiased, targeted radio surveys in the literature, we derive a detection rate of ≥3%. Assuming a flat radio spectrum ν
L

ν
∝ ν

β+1 with β = −1.0 for UCD flares, we construct a flare luminosity function

dN

/

dL
∝

L

−
1.96
±
0.45

(here L = ν
L

ν
). CRAFTS is found to be sensitive enough for flares from UCDs up to ∼180 pc. Considering the Galactic thin disk, we carry out a 3D Monte Carlo simulation of the UCD population, which is then fed to mock CRAFTS observations. We estimate that ∼170 flaring UCDs would be detected through transient searches in circular polarization. Though only marginally sensitive to the scale height of UCDs, the results are very sensitive to the assumed spectral index β. For β from 0 to −2.5, the number of expected detections increases dramatically from ∼20 to ∼3460. We also contemplate the strategies for following up candidates of flaring UCDs, and discuss the implications of survey results for improving our knowledge of UCD behavior at L band and dynamos.
Global saturation physics of ion temperature gradient turbulence in finite normalized pressure tokamaks
Authors: Masui H. , Ishizawa A. , Imadera K. , Kishimoto Y. , Nakamura Y. 
Published: April 2022

Abstract
Nonlinear saturation mechanism of ion-temperature-gradient turbulence at finite normalized pressure is identified by analysis of the nonlinear entropy transfer in global gyrokinetic simulations of the turbulence. Turbulence at such finite normalized pressure is electromagnetic and often exhibits non-saturation due to a lack of zonal flows by the influence of magnetic fluctuations of the turbulence in local gyrokinetic simulations. The present study identifies a new saturation mechanism caused by global entropy transfer due to turbulent E × B flow convection in real space. The convection of the entropy associated with the turbulence in the radial direction produces global zonal flows at the both sides of the most active region of the turbulence to avoid the effect of the magnetic fluctuations, and then global zonal-flow excitation is not suppressed, leading to a steady state of the turbulence.
Gyrokinetic modeling of anisotropic energetic particle driven instabilities in tokamak plasmas
Authors: Rettino B. , Hayward-Schneider T. , Biancalani A. , Bottino A. , Lauber Ph. , Chavdarovski I. , Weiland M. , Vannini F. , Jenko F. 
Published: April 2022

Abstract
Energetic particles produced by neutral beams are observed to excite energetic-particle-driven geodesic acoustic modes in tokamaks. We study the effects of anisotropy of distribution function of the energetic particles on the excitation of such instabilities with ORB5, a gyrokinetic particle-in-cell code. Numerical results are shown for linear electrostatic simulations with ORB5. The growth rate is found to be sensitively dependent on the phase-space shape of the distribution function. The behavior of the instability is qualitatively compared to the theoretical analysis of dispersion relations. Realistic neutral beam energetic particle anisotropic distributions are obtained from the heating solver RABBIT and are introduced into ORB5 as input distribution function. Results show a dependence of the growth rate on the injection angle. A qualitative comparison between the numerical results and experimental measurements is presented. An explanation for the differences is advanced.
Conceptual design and optimization of an ITER-type ICRF antenna on CFETR
Authors: Zhang W. , Liang Q.C. , Qin C.M. , Zhang X.J. , Wang Y.S. , Yang H. , Liu L.N. , Zhu G.H. , Mao Y.Z. , Yuan S. , Cheng Y. 
Published: April 2022

Abstract
The Chinese Fusion Engineering Testing Reactor (CFETR) plans to use an ITER-type antenna and couple ∼30 MW ion cyclotron range of frequencies (ICRF) power to the plasma. In this paper, the physical design of a CFETR antenna in the midplane port is carried out. Parameter scans were performed to study the optimized toroidal and poloidal numbers of straps as well as the optimized geometric sizes of the straps. The coupling resistance, power spectrum, maximum voltage in the resonant transmission line of the strap and parallel electric field in the antenna vicinity are used to determine the performance of the studied antennas. It is shown that four poloidal substraps (i.e. quadruplets) and six toroidal strap columns arranged in half of the antenna allows the antenna to have the best coupling capability. To improve the coupling capability of the proposed antenna model, local gas puffing methods, as well as various antenna phasings, are studied. It is indicated that the coupling resistance can be increased by a factor of three for all studied antenna phasings when applying the midplane gas puffing with a gas puff rate in the order of 4 × 1023 el s-1. The toroidal phasings suitable for heating include (0, pi, 0, pi, 0, pi) and (0, pi, pi, 0, 0, pi).
Divertorlets concept for low-recycling fusion reactor divertor: experimental, analytical and numerical verification
Authors: Saenz F. , Sun Z. , Fisher A.E. , Wynne B. , Kolemen E. 
Published: April 2022

Abstract
The ‘divertorlets’ concept is a potential non-evaporative liquid metal solution for heat removal at low recycling regime. A toroidal divertorlets prototype was built and tested in LMX-U at Princeton Plasma Physics Laboratory to evaluate the performance of this configuration. In this paper, details of the design, experimental results, comparison with analytical theory and MHD numerical simulations of toroidal divertorlets are covered. Experiments, analytical model and simulations showed agreement and allowed the projection of operation properties at higher magnetic flux densities (reactor-like operation), proving the concept to be a compelling solution for divertor applications.
A lightweight convolutional network for infrared object detection and tracking
Authors: Xu Yin , Fan Qiang 
Published: April 2022

Abstract
As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a method for target detection and tracking, with a deeper and lightweight MobileNet V2 structure as the backbone network. In the end, the tracker is tested on various datasets. Result shows that the tracker can get a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.
Towards efficient quantized inference for convolutional neural network on edge system
Authors: Tan Hai , Wang Nan , Lei Bo 
Published: April 2022

Abstract
Convolutional Neural Networks (CNN) have made splendid achievements in various object detection tasks. To extend the applications of CNN detection models, the implementation of model inference on edge platforms, such as ASIC, FPGA and other embedded systems, has been intensively investigated in recent years. However, the huge model size and its enormous overhead constrain the deployment of detection model on edge platform which always has limited computational capability. Quantized inference of CNN model is one of the efficient approach to running model on edge platform. In this paper, we develop a hardware-friendly quantized inference scheme of detection model that is used for efficient inference on embedded FPGA systems. The proposed method contains several techniques that are able to optimize the quantized inference of detection model on FPGA device. The experimental results demonstrate that not only make the detection model quantized inference more efficient but also maintain the accuracy of object detection.
A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors
Authors: Elamir Mohamed Shawki , Gotzig Heinrich , Zöllner Raoul , Mäder Patrick 
Published: April 2022

AbstractIn this paper, a deep learning approach is presented for direction of arrival estimation using automotive-grade ultrasonic sensors which are used for driving assistance systems such as automatic parking. A study and implementation of the state of the art deterministic direction of arrival estimation algorithms is used as a benchmark for the performance of the proposed approach. Analysis of the performance of the proposed algorithms against the existing algorithms is carried out over simulation data as well as data from a measurement campaign done using automotive-grade ultrasonic sensors. Both sets of results clearly show the superiority of the proposed approach under realistic conditions such as noise from the environment as well as eventual errors in measurements. It is demonstrated as well how the proposed approach can overcome some of the known limitations of the existing algorithms such as precision dilution of triangulation and aliasing.
Application and Research of Elevator Internet of Things-Lifting Robot Using Elevator Scheme
Authors: Zhang Pengzhao , Chen Hu , Lv Qin , Shen XiaYang 
Published: April 2022

Abstract
To satisfy the demand of the handing robot using elevator in automatic workshop, we had finished the communication between the control system RCS of the elevator and the handling robot based on Modbus TCP protocol, so as to realize the interaction between the control robot and the Elevator Internet of Things system. The research of this paper provides a solution for the access and application of the handling robot in the Elevator Internet of Things, which solves the problem of the handling robot in the elevator up and down, and provides a new way for the material transportation efficiently in the three-dimensional industrial plant.
Multi-material 3D-Printing Soft Robot Functional Component
Authors: Ma Kaiqi , Zhang Yin , Jin Guoqing 
Published: April 2022

Abstract
—3D printing has a wide range of applications and addresses many challenges inherited from conventional manufacturing techniques. This paper presents a dual-extrusion 3D printing platform capable of printing soft materials for soft robot functional components. Dow Corning-737 and Dragon Skin-0030 materials are used. Single-line experiment is used to determine the optimization parameters of extrusion pressure, printing speed, and layer thickness of the developed multi-material 3D printing platform. A soft valve consisting of two soft materials has been fabricated, and the printed soft valve is verified by a performance experiment. The results indicate that the developed 3D printing platform can fabricate soft robot functional components at high speed with high accuracy.
A potential fault of circuit breaker found using SF6 decomposition products detection
Authors: Fu Lijun , Guan Yanling , Zhang Liang , Zhang Dewen , Wang Han , Chi Jingyuan , Wei Bing 
Published: April 2022

Abstract
A 126kV circuit breaker was found to have latent fault by SF6 decomposition product detection method. It was found that the sulfur hexafluoride gas decomposition products of the circuit breaker contained a large amount of CO gas, a certain amount of SO2 and H2S, and a small amount of HF gas. The characteristic components and their contents of the decomposition products were analyzed, it is preliminarily concluded that there is insulation material fault in the circuit breaker, and the potential fault is further confirmed by the disassembly inspection that the MoS2 lithium base grease is overcoated at the metal tie rod joint.
Design and Implementation of WSN-based Warehouse Temperature Sensor Monitoring System
Authors: Wang Qijian , Xia Mengqin , Chen Yehui , Yu Yuting 
Published: April 2022

Abstract
China is a large agricultural country. As an important part of the national economic foundation, the scientific storage of grain is particularly important in the process of grain production. This system uses STC89C52, nRF24L01, DHT11, etc. to form a WSN-based wireless temperature and humidity sensor monitoring system. The system monitors the humidity and temperature in the granary in real time. If the set point is exceeded, it will give an alarm. The timeliness and accuracy of environmental monitoring data on granary management will be improved, and human resources will be saved.
Simulation Analysis and Fabrication of a Silicon Carbide-Based Piezoresistive Accelerometer
Authors: Zhai Yanxin , Li Haiwang , Tao Zhi , Yang Chunhui , Cao Xiaoda , Che Zhizhao , Xu Tiantong 
Published: April 2022

Abstract
This paper proposes a Micro Electro Mechanical Systems piezoresistive accelerometer based on a whole SiC substrate. Compared with Si-based sensors, SiC-based sensors have stronger mechanical advantages and unique advantages for applications in ultra-high temperature environments. The characteristics of the accelerometer are designed and numerically simulated, and the accelerometer is evaluated in terms of stress load and working frequency band. An innovative design is carried out to eliminate the stress concentration phenomenon in the corner area of the sensor, which guarantees the working safety of the fragile structure of SiC. After fabrication, packaging and vibration experiment, it is found that the sensor’s working sensitivity can reach 0.21mv/g, and its linearity can reach 98%.
Research on Manufacturing Technology of New Energy Vehicle Based on NDT
Authors: Wang Yiyun , Fan Guangfa , Feng Ling 
Published: April 2022

Abstract
For the detection of laser welding defects of new energy vehicles, the traditional detection methods are inefficient and have limited accuracy. The existing visual detection methods mainly use area array cameras and image processing algorithms for detection. The image acquisition efficiency of the area array camera is low. The traditional image processing methods are vulnerable to the strong reflection on the surface of new energy vehicles, and the recognition speed and accuracy are limited. Therefore, in this paper, the linear array high-resolution camera is used for two-dimensional image acquisition, and the deep learning method is used for the recognition of weld defects. Based on the single-stage target detection network, the YOLO network is optimized according to the particularity of weld defects. The weld data set and defect data set are prepared, and the data set is used to train the optimized YOLO model. The feasibility of the method is verified by experiments, which can meet the mainstream weld length detection standards in the automotive industry.
Seismo-acoustic signal extraction from shallow sea seabed seimic wave field based on F-K method
Authors: Tang Kun , Cheng GuangLi , Liu Bao 
Published: April 2022

Abstract
Aiming at the numerical simulation of shallow sea seismic wave field, the finite element theory is used to establish the sea water and seabed models respectively, and then the boundary conditions of the sea water and the seabed fluid-solid coupling are constructed according to the fluid-solid coupling theory. This method can be used to quickly predict the shallow sea seismic wave field, based on this method, the vertical displacement of seabed seismic wavefield is carried out. On this basis, adopt F-K(frequency-wavenumber) method, one kind of two-dimesional Fourier transforn, to analyze wave components. Lastly, according to the principle of apparent velocity filtering, the seismo-acoustic signal (Scholte wave) in shallow sea seismic wave field is extracted.
Based on Arduino Design of Intelligent Exploration Vehicles
Authors: Liu Zhijun , Yang Chenglong , Xi Wen 
Published: April 2022

Abstract
This paper details the design and implementation of the hardware circuit of the intelligent cart motion control system, and uses a variety of virtual reality and visual sensing technologies to design an environmental detection and search and rescue robot under complex conditions. Infrared technology is used to achieve the function of automatic obstacle avoidance of the trolley, ultrasonic technology is used to achieve the function of distance measurement, Bluetooth is used to achieve the function of wireless remote control of the trolley, GPS technology is used to display the geographical position of the trolley, a homemade bionic robotic arm is installed, and the robotic arm servo can be controlled by PC upper computer, Bluetooth and homemade body remote sensing controller to work to achieve the removal of obstacles and unknown environment and dangerous areas The information is collected through a PC, Bluetooth and a home-made body telemetry controller.
Pipeline Laser Methane Detector Based on TDLAS Technology for In-situ Direct Measurement
Authors: Wang Yao 
Published: April 2022

Abstract
In order to quickly detect methane gas in real time, solve the defects of the traditional “extraction method” methane concentration measurement of gas pipeline based on pump suction or differential pressure sampling, and ensure the sensor’s performance requirements for resolution, a pipeline laser methane detector for in-situ direct measurement, based on TDLAS technology, has been developed. By studying effect of different chamber length on the resolution of different concentrations of methane gas, the gas chamber with length in the range of 4∼6cm can meet the performance requirements as much as possible, and the detector combine with the processing circuit designed with low power consumption for concentration demodulation. Test has shown that the measurement error in the range of 0%∼10% is less than ±0.05%CH4, and the measurement error is less than ±5% of the true value in the range of 10%∼100%.
Research on excavation and identification method of typical interference signal during transformer partial discharge test
Authors: Liu Rui , Lu Yangze , Huang Meng , Xie Qijia , Tu Yalong 
Published: April 2022

Abstract
—In order to remove interference signal and external discharge signal during partial discharge test and get the effective signal of partial discharge, this paper carries out the research of typical interference signal mining and identification method for transformer partial discharge test. Firstly, based on the experience of previous researchers in partial discharge research, 47 feature parameters including statistical, texture and shape features are extracted, and the original feature space is constructed based on them, which provides the data basis for the optimization of the feature space and signal classification identification later. Next, the random forest algorithm is used to measure and rank the importance of the feature parameters, and the original feature space is optimized according to the ranking results to obtain the optimized feature space. Finally, the optimized feature space is used as the input of linear discriminant analysis algorithm and K-nearest neighbor algorithm to classify and identify the local discharge signal and typical interference signal, and the recognition accuracy of these two classification and identification algorithms is compared and analyzed, and the K-nearest neighbor algorithm is selected as the final classification and identification algorithm.
A logical reasoning-based method for sensing the operating status of equipment
Authors: Li YongSheng , Duo XiangYang , Yang Ming , Liu ShengNan , Yu Yang , Zhou Ming 
Published: April 2022

Abstract
Grid equipment monitoring is to achieve comprehensive perception, rapid analysis and accurate disposal of grid operation status. As the scale of the grid continues to expand, many problems such as low efficiency in single point-based alarming, complicated and difficult process, irregular work norms, etc., have occurred in the current operating mode, which greatly restricts the improvement of management level of monitoring operation. Based on the correlation between substation monitoring information and monitoring equipment operation, we establish the correspondence between primary equipment such as main transformer, busbar, line and capacitor and the corresponding telemetry such as current, voltage, active power, reactive power and frequency. Then we analyze the occurrence mechanism of power grid equipment operation events, sense the equipment operation status, and realize timely and accurate sense and push of equipment operation defects and faults, thereby improving operational efficiency of monitoring devices.
Highly Efficient Low Noise Solutions in ECG Signals
Authors: Liu Fengru , Xu Yijie , Yao Yuan 
Published: April 2022

Abstract
This paper aims at helping researchers develop low-noise ECG monitors and analyze existing solutions for eliminating noise in ECG signals. A brief introduction of ECG signal, main kinds of noise in ECG signal and a summary of different kinds of low noise solutions is given. The components of the ECG signal and the origin of noise in the ECG signal are analyzed first. In order to find optimized solutions for eliminating the noise, highly effective solutions are listed and analyzed. Solutions are divided into three main directions, which are circuit design, signal processing and machine learning. Specific practical examples from former research and analyses for each direction help the reader understand and select different ways of eliminating noise in ECG signals. After introducing three directions, a summary and further improvement are given to help readers have a horizontal comparison between solutions.