bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inter-machine plasma perturbation studies in EU-DEMO-relevant scenarios: lessons learnt for prediction of EM forces during VDEs
Authors: Sias G. , Minucci S. , Lacquaniti M. , Lombroni R. , Fanni A. , Calabrò G. , Cannas B. , Pisano F. , Siccinio M. , Ramogida G. , Giorgetti F. , Fanelli P. , Maviglia F. , the EUROfusion MST1 Team  , JET EFDA Contributors  
Published: February 2022

Abstract
To support the deployment of the DEMO wall protection strategy, the development of comprehensive analyses is essential to understand the implications of transient perturbations on plasma shape control and on vertical stability, some of the most critical aspects to be considered in elongated plasmas. The design activities of the DEMO limiter structures require a deep understanding of the effects induced by transient plasma perturbations coupled with one of the most severe load conditions to occur in tokamaks, the vertical displacement event (VDE). Since electromagnetic (EM) loads during VDE phases are among the DEMO limiter’s design drivers, this study focuses on predictive simulations of the final plasma position and of EM loads following a VDE. For this purpose, a multi-tokamak study, supported by the construction of an inter-machine database containing experimental transient plasma perturbations and VDEs from JET and ASDEX Upgrade (AUG), has been carried out. It aims to characterize some transient plasma perturbations that may lead to high control efforts by the vertical stability system in terms of variations of the plasma’s internal parameters and vertical displacements. Consequently, such experimental transient plasma perturbations have been properly scaled to DEMO reference geometries with different magnetic configurations, to be simulated in terms of plasma dynamical behaviour by means of MAXFEA code. Finally, initial predictive EM loads on DEMO limiter structures are discussed in the case of VDEs following plasma perturbations.
Exogenous and endogenous price jumps belong to different dynamical classes
Authors: Marcaccioli Riccardo , Bouchaud Jean-Philippe , Benzaquen Michael 
Published: February 2022

Abstract
Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to individual volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.
Design and Research of Stepped Wind Turbine Foundation Structure
Authors: Wu Xiaoqing , Jin Junwu , Zhan Fan , Sun Xinjian 
Published: February 2022

Abstract
This paper introduces a design and research of stepped wind turbine foundation and compares it with circular spread foundation and octagonal raft foundation in engineering quantity and constructability. It shows that the stepped wind turbine foundation has clear mechanical behaviour, efficient in design and analysis. It is a good solution in terms of investment and constructability in wind power projects. Finally, a practical application of this stepped foundation design in a wind farm in Pakistan is presented, efficient construction, and good economic benefits are obtained.
Ultrafast Microwave Assisted Synthesis of Three-Dimensional Graphene Material and its Excellent Performance for Supercapacitors
Authors: Wang Wenjian , Shi Xingyu , Xue Weidong , Zhao Rui 
Published: February 2022

Abstract
Ultrafast microwave assisted synthesis of three-dimensional graphene via one-step microwave irradiation method has been reported. The milligram-grade highly conductive acetylene black as the power conversion center, rapidly converts microwave energy into heat and forms local instantaneous high temperature, resulting in partial detachment of functional groups and promoting the recovery of the conductive network of the carbon layers within a few seconds. Interestingly, the nano-scaled acetylene black was found to be tightly bound to the lamella of the 3D graphene, the 3D graphene containing the few acetylene black exhibited a typical double-layer behavior with outstanding specific capacitance of 279.8 F g−1 at 0.5 A g−1 and high capacitance retention over 85% after 20000 cycles. A symmetric supercapacitor assembled in organic electrolyte shown a wide voltage window (0 - 2 V). These satisfactory results provide a potential solution for the manufacture of high-capacity electrochemical energy storage.
Novel and Green Fe3O4@Sio2-L-Cys Magnetic Absorbent for Efficient Removal of Pb (II) from Aqueous Solution
Authors: Su X D , Huang E , Cheng Z , Su Y , Zhong Y H , Deng X 
Published: February 2022

Abstract
In this study, Fe3O4@Sio2-NH2 was synthesized by the modification of Fe3O4 particles with tetraethyl orthosilicate(TEOS) and 3-aminopropyl-Triethoxysilane (APTES), and then the cysteine was grafted with glutaraldehyde as cross-linking agent.The material was characterized by XRD,FIIR and TEM. The effects of temperature, time, initial concentration of Pb(II) and pH value on the adsorption rate of Pb(II) were investigated, and the adsorption mechanism and the regeneration performance of the adsorbent were discussed.The results indicated that when the amount of adsorbent was 0.4g/L, the temperature was 303K, the adsorption time was 2.5h and the pH was 5.02, the adsorption rate of Pb(II) in water can reach up to 98.25%.The adsorption kinetics of Pb(II) on the material accorded with Lagergren quasi-second-order kinetic equation, which belonged to the chemisorption process.Adsorption isothermal curve studies showed that the adsorption of Pb(II) on the material complied with the Langmuir isothermal adsorption model and was a monolayer adsorption.The maximum adsorption capacity of Pb(II) is 120.77mg/g. Adsorption thermodynamics studies showed that the whole system was endothermic reaction,and warming was conducive to the adsorption,which happens spontaneously.After 4 times of the adsorption recycling,the adsorption rate can still reach 92.7%.
Calculation Model for the Selection of Physical Dimensions of a New Type of Heating Curing Building
Authors: Yu N , Shen Z , Wu J S , Kong X R 
Published: February 2022

Abstract
The spatial pattern of the new type of heating curing building directly affects the energy consumption of the PC component and the passive utilisation efficiency of solar energy. Based on the thermal design theory and building energy consumption simulation analysis method, this study constructed the heating-curing energy consumption model and determined the evaluation index of building space shape parameters. Based on the energy consumption model and Energy Plus simulation software, the calculation case—the best spatial shape characteristic parameters of the new-type heating and curing building in the Shijiazhuang area was given. Compared with the traditional steam warming curing, the carbon emission per unit building area was reduced by 33.4% each year, and the carbon emission of one ton of PC component was reduced by 0.97t.
Optimal Design of Energy Supply System Based on River Water Source Heat Pump in Load Intensive Area
Authors: Xiao Jing , Qiu Zejing , Jiang Cheng , Hu Wenbo , Xu Jingzhou 
Published: February 2022

Abstract
Aiming at the contradiction between supply and demand of cooling, heating and electric load in load intensive area, an energy supply system suitable for load intensive area is proposed, and the mathematical model of the system is constructed. Secondly, considering the economic, energy consumption and environmental performance of the system, the evaluation index of system is established, and thus, a multi-objective optimization model is constructed. The weight of different optimization indexes is determined by using analytic hierarchy process. Then, four types of buildings, including residential buildings, offices, hotels and shopping malls are employed and hourly cooling, heating and electric load are simulated using DeST software. Finally, the design optimization of the system is carried out.
Thermodynamic Analysis of Space Regenerative Orc (Srorc) System for Automotive Waste Heat Recovery
Authors: Tong D H , Kong X H , Li M , Liu X , Huang G L , Wang X P 
Published: February 2022

Abstract
In this paper, the space regenerative ORC (SRORC) system is proposed. In SRORC system, the superheated working fluid is divided into two parts after it works completely in the expander. The performance of SRORC system is studied, the performance of ORC system and SRORC system under different conditions are compared and analyzed. From the perspective of latent and sensible heat, the mechanism of using space regeneration to improve the performance of SRORC system is explored. The models of ORC system and SRORC system are built by Aspen plus software. R245fa is chosen as the working fluid because of its excellent performance. The simulation results show that compared with the evaporation temperature, the evaporation pressure has greater influence on the performance of SRORC system under B75 condition. Under C25, B50, A75, B75, and B100 conditions, compared with ORC system, the net output power of SRORC system is increased by 30.7%, 30.3%, 34.4%, 33.2% and 34%, the thermal efficiency is increased by 4.3%, 4.36%, 4.97%, 4.79% and 4.89%, the exergy efficiency is increased by 11.13%, 10.28%, 10.36%, 10.69% and 10.02%, the recovery efficiency of engine power is increased by 3.92%, 2.55%, 2.9%, 2.91% and 3.36%, respectively. The heat recovery rate of IHE-ORC system and SRORC system are 13.7kW and 24.8kW under B75 condition. In SRORC system, liquid working fluid can recover sensible and latent heat of superheated working fluid, and the latent heat accounts for most of the heat recovered.
Influence of Dry Sorbent Injection System on Emission Characteristics of Condensable Particulate Matter in Coal-fired Power Plant
Authors: Zhang Ziwei , Zhao Bing , Xi Yan , Wang Haigang , Zhou Baozhu , Zhao Qi , Dong Libin , Li Xukai , Jin Xuliang , Zhang Ziwen , Zhang Meng , Ma Lin , Li Shendong , Jia Jia , Xu Chao 
Published: February 2022

Abstract
To study the effect of dry sorbent injection (DSI) system on the emission characteristics of condensable particulate matter (CPM) in a 600MW unit of an ultra-low emission coal-fired power plant in China, the EPA Method 202A is used to sample the flue gas from its total exhaust. Through sample analysis, the CPM emission concentration and water-soluble ion composition corresponding to the two unit loads and whether the DSI is running are obtained. The results show that the use of DSI system reduces CPM emissions at 600MW and 300MW unit loads, and the CPM emission concentration is reduced by 32% and 9.7%, respectively. For the CPM reduction caused by DSI system under these two loads, SO4
2- and Cl− together contributed 65.7% and 87.3%, respectively. The DSI system achieves the removal of CPM mainly by removing the SO3 and HCl, which are gaseous precursors of SO4
2- and Cl− in CPM. At the same time, water-soluble ions are the main component of CPM. The analysis of its composition shows that the masses of NH4
+, Cl− and SO42- ions are relatively large, and they together account for 95.8%-96.8% of all water-soluble ions mass.
Research on Risk Early Warning of New Energy Consumption Based on Weight of Differentiated Indicators
Authors: Cui Z W , Li M , Gan Y Q , Li X W , Chen C 
Published: February 2022

Abstract
Considering the pressure of peak shaving, difficulty of local absorption and high rate of wind abandonment caused by large-scale new energy absorption, this paper proposes a new energy absorption risk early warning method based on differential index weight. The purpose is to solve the problems caused by large-scale new energy access from the aspects of per-planning, construction and access system of new energy projects. The principles of risk early warning system construction for new energy planning and construction are put forward, and the risk early warning index system is constructed based on the risk of absorptive capacity, networking conditions and project completion rate. The risk early warning mechanism is established, and the weight coefficients of each index are differentiated by using the analytic hierarchy process. A risk early warning evaluation model suitable for different power grid environments is obtained. Taking a power grid as an example, the proposed model and method are verified. The results show that the proposed method is accurate in evaluating the new energy absorption capacity of 14 cities, and has validity, practicability and application value.
Fuzzy Comprehensive Evaluation of the Suitability of Offshore Sunken and Submerged Oil Containment Technology in a Specific Scenario
Authors: Jianping Zhao , Wei An , Qingfan Zhang , Weiwei Jin , Baozhan Liu 
Published: February 2022

Abstract
In this paper, multiple evaluation means including the fuzzy evaluation method, the analytic hierarchy process and the expert evaluation method are used to evaluate the suitability of sunken and submerged oil containment technology in a specific scenario. The results show that the containment technology has a good effect of disposal of submerged oil and floating oil, especially when they may affect sensitive areas.
Experimental Study on Humidifier Performance in a Humidification-Dehumidification System
Authors: Ge Hongyu , Cai Yisong , Kang Peisen , Liu Jiawei , Liu Xiaohua 
Published: February 2022

Abstract
The characteristic of Humidification-dehumidification seawater desalination technology is flexible scale. It is suitable for dispersive fresh water demand and easy to couple with low-grade energy. In this study, an experimental platform of humidifier performance is designed to investigate the influence of spray flow, spray liquid temperature, air flow, hole diameter, pitch of holes and the number of sieve plates on moisture content of humid air at outlet and evaporation capacity. The results show that, when the spray flow and temperature increase, the moisture content of outlet air and evaporation capacity increase. The evaporation capacity increases when the extent of the air flow increasing is more than that of the moisture content decreasing. When the spray flow is 0.025kg/s, the evaporation capacity is the highest when the sieve plate with the hole diameter of 3.0mm and the holes pitch of 9.0mm is used. For other flow conditions, the evaporation rate is the highest when the sieve plate with the hole diameter of 2.5mm and the hole pitch of 7.5mm is used. The evaporation capacity with double sieve plates is higher than that with single sieve plate. The research of this paper provides guidance for practical application of the HDH seawater desalination system.
Research on Modulation Recognition Method in Low SNR Based on LSTM
Authors: Zhang Beiming , Chen Guoping , Jiang Chun 
Published: February 2022

Abstract
Modulation mode recognition of radio signal is a committed step between signal detection and signal demodulation. At present, quite a lot studies have fully proved that deep learning algorithms can effectively identify the modulation pattern of radio signals. However, the sudden decline of recognition accuracy under the condition of low signal-to-noise ratio needs to be continuously studied and solved. Inspired by the excellent performance of recurrent neural network in signal recognition, this article optimizes and improves the existing system methods, realizes the noise reduction processing of low signal-to-noise ratio signals, and further solves the problem of low recognition accuracy. Through a large number of experimental tests using RML public dataset, the effectiveness of this paper is verified. The results show that the accuracy of modulation pattern recognition of low signal-to-noise ratio signals reaches an average of 27.2%. At last, the paper analyzes the existing problems and optimization points, and looks forward to the further research of relevant contents in the future.
Hyperspectral remote sensing image classification based on spectral-spatial feature fusion and PSO algorithm
Authors: Song Xiaonan , Wang Cailing 
Published: February 2022

Abstract
Hyperspectral images(HSI) have rich spectral information and spatial information. In the classification of hyperspectral images, the combination of spectral information and spatial information has become an effective means to obtain good classification results. Specifically, firstly, PCA algorithm is introduced to extract the spectral information of the image. Secondly, a bilateral filter is introduced for each band to extract the spatial information of the image. Thirdly, the image is classified by using SVM to get the final classification result. Considering that the performance of SVM depends on the choice of parameters, particle swarm optimization algorithm (PSO) is combined with the proposed method to find the optimal penalty parameters and kernel parameters. We find that the classification accuracy is effectively improved by using PCA, bilateral filtering and optimizing SVM-PSO parameters on this basis. Experimental results based on one real hyperspectral dataset show that the proposed algorithm can achieve higher classification accuracy in a shorter time than other algorithms.
Pseudo-random noise generator based on FPGA
Authors: Yang TanZheng , Zhang Qin , Li PengHui 
Published: February 2022

Abstract
This paper proposes a novel pseudo-random noise generator based on FPGA. The system architecture is written by Verilog, and each sub-module is designed in Vivado software. Modelsim is employed to co-simulate the sub-modules and the saved data is imported to Matlab for spectrum analysis. Consequently, the proposed design can be applied to practical noise generating with great scalability and flexibility.
Research on Three-phase Imbalance Treatment of Low-Voltage Distribution Transformer Based on User Phase Sequence Recognition
Authors: Sun Liyao , Xianyu Jianchuan 
Published: February 2022

Abstract
Abstract:The current management of three-phase imbalance of distribution transformers mainly relies on distribution operation and inspection personnel to determine the phase sequence switching of load according to their own experience, which is a large workload and low precision of phase sequence adjustment, and cannot meet the increasingly expanding scale of power grid. There is an urgent need for a research solution that can accurately judge the phase of station users and timely adjust the station users with heavier phase sequence to another phase sequence with lighter load for power supply, so that the three-phase load can be balanced. To this end, a solution is proposed to manage the three-phase imbalance of distribution transformers by intelligently identifying the phase sequence of station users through genetic algorithm. The example shows that the method is easy to use and can effectively guide the distribution operation and inspection personnel to accurately determine the phase sequence of station users and reduce the three-phase imbalance of distribution transformers.
Deep neural network based concentration prediction model of dry electrostatic precipitator with a modified PID optimizer
Authors: Fu Yao , Su Zhigang 
Published: February 2022

Abstract
The dust control technology of thermal power units plays an important role in reducing the emission of air pollutants and improving the ecological environment. Electrostatic precipitator is not only an important equipment to reduce dust emission from coal-fired power plants, but also a high energy consuming equipment. Establishing the model of electrostatic precipitator is of great significance for energy saving of electrostatic precipitator and emission reduction of power plant. In this paper, a deep neural network based concentration prediction model of dry electrostatic precipitator of 600MW coal-fired unit is established and trained through running data. In the process of model training, an improved PID-based optimizer is used. The modified optimizer is proved to have the effect of accelerating convergence and improving accuracy in experiments. The modeling results demonstrate that the trained neural network model can effectively predict the outlet concentration of dry electrostatic precipitator under wide unit load.
Boosting the Transferability of Adversarial Examples with More Efficient Data Augmentation
Authors: Gao Chenxiang , Wu Wei 
Published: February 2022

Abstract
Currently, Deep Neural Networks has achieved excellent results on many tasks. But recent studies have shown that these networks are easily influenced by adversarial examples, which are artificially crafted by adding perturbation to original image. Moreover, most of the models to which we can access are black-box, we don’t know the internal structure and parameters of the model. Thus, it is more practical and more challenging to study how to attack these models. In this article, we propose a cam(class activation map)-guided data augmentation attack method, which can improve the transferability of adversarial examples. Specifically, first use the trained network to get the class activation maps for an input image, then binarize the cam to get the mask, finally implement the data augmentation attack method on the masked area of the image. Experiments based on ImageNet prove that our proposed method can generate more transferable adversarial examples, and the attack success rates of our method have a certain improvement compared with the latest methods.
Technique for suppression of background cascades produced by atmospheric muon bundles in the Baikal-GVD
Authors: Allakhverdyan V.A. , Avrorin A.D. , Avrorin A.V. , Aynutdinov V.M. , Bannasch R. , Bardačová Z. , Belolaptikov I.A. , Borina I.V. , Brudanin V.B. , Budnev N.M. , Dik V.Y. , Domogatsky G.V. , Doroshenko A.A. , Dvornický R. , Dyachok A.N. , Dzhilkibaev Zh.-A.M. , Eckerová E. , Elzhov T.V. , Fajt L. , Fialkovski S.V. , Gafarov A.R. , Golubkov K.V. , Gorshkov N.S. , Gress T.I. , Katulin M.S. , Kebkal K.G. , Kebkal O.G. , Khramov E.V. , Kolbin M.M. , Konischev K.V. , Kopański K.A. , Korobchenko A.V. , Koshechkin A.P. , Kozhin V.A. , Kruglov M.V. , Kryukov M.K. , Kulepov V.F. , Malecki Pa. , Malyshkin Y.M. , Milenin M.B. , Mirgazov R.R. , Naumov D.V. , Nazari V. , Noga W. , Petukhov D.P. , Pliskovsky E.N. , Rozanov M.I. , Rushay V.D. , Ryabov E.V. , Safronov G.B. , Shaybonov B.A. , Shelepov M.D. , Šimkovic F. , Sirenko A.E. , Skurikhin A.V. , Solovjev A.G. , Sorokovikov M.N. , Štekl I. , Stromakov A.P. , Sushenok E.O. , Suvorova O.V. , Tabolenko V.A. , Tarashansky B.A. , Yablokova Y.V. , Yakovlev S.A. , Zaborov D.N. 
Published: February 2022

Abstract
Baikal-GVD (Gigaton Volume Detector) is a neutrino telescope located in pure water of Lake Baikal. At the current stage (season 2021), detector is composed of 2304 optical modules arranged in 8 clusters. In searching for neutrino cascade events, light patterns produced via discrete stochastic energy losses along muon tracks create the most abundant background. Methods to separate cascade-like events from tracks and neutrino cascades in a single cluster have been developed and optimized. One of the method tries to find the maximum number of track hits amongst cascade hits, which are present in the muon bundle event. Other ones rely on the distributions of charges and positions of hits on optical modules associated with cascade events. All suppression methods were optimized by the Monte Carlo simulation datasets.