bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distributed Control System for Agrotechnical Processes Using Customizable Procedural Regulations
Authors: Melnik V , Nikiforov A , Zelensky Al , Zelensky An , Antoshchemkov R 
Published: April 2022

Abstract
Nowadays, the technical and technological capabilities achieved in the field of agriculture permit to implement quickly convertible (adaptive) crop production systems. This, along with increased efficiency, also greatly complicates the management process. Automated control systems (ACS) with adaptive properties are needed. The concept of creating an ACS assumes the implementation of the principle of modularity and flexible changes in the structure of the management hierarchy, powers, tasks and functions for geographically distributed decision-making centers and executors. Key technologies in the design of such ACS are: synthesis of a temporary control loop from geographically distributed elements and synthesis of procedural regulations for a decision-making center included in the temporary control loop. The article formulates the problem of synthesis of procedural regulations in the form of a tensor transformation problem of the original electrical network (library of procedures) into a network with specified properties. A method for obtaining the tensor equation of transformation of the original network is presented based on the requirements for the composition of the input and output data of the synthesized procedural regulations. The solution obtained after tensor transformation has the form of a matrix of connections and a vector of voltages on the intermediate coils of the original electrical network (library of procedures). This forms structural links for the synthesized procedural rules. The demonstrated approach can be extended to solve a wide range of problems of adaptive changes in the structures of organizational management systems.
Theory and Practice of Creating a Two Energy Outputs Magnetron
Authors: Churyumov G , Qiu S , Wang N , Li W 
Published: April 2022

Abstract
This paper presents the methodology for 3-D simulation and design of the magnetron with two energy outputs. The principal functionality of the modified magnetron, both theoretically and experimentally, has been determined. The central distinguishing feature of this magnetron (for example, unlike the conventional magnetron with one output) is the availability of second energy output in the anode block. The mathematical models of both the resonant anode block and the electron-wave interaction are described. The dispersion characteristics of the anode block for cavities various geometries are given. It is supposed that forming the total RF field in the interaction space results from the interference of RF fields excited severally in the resonant anode block consisting of the small and large cavities (resonators). For PIC-simulation of electron-wave interaction in the dual-output magnetron, the non-linear system of equations is stated as a self-consistent system containing the equation of motion (for electron stream), the equation of excitation (for RF field), and Poisson’s equation for calculating the space-charge field. The fundamental feature of the self-consistent system of equations is a new algorithm for determining the Coulomb interaction forces. The implementation of the mathematical model made it possible not only to gain new knowledge about the physical processes in the magnetron but also to determine its output characteristics. On operating frequency of ∼ 13.34 GHz, at an anode voltage of 495 V, a magnetic field of ∼ 0.25 T, and with air cooling of the magnetron, there were obtained the following limiting values: the RF output power of ∼ 14.6 W and the power conversion efficiency of ∼ 40.8%. The use of the second energy output allowed extending the magnetron’s functionality and implementing the modes of the frequency tuning (adjunction) and its stabilization. The simulation results are in good agreement with the experiment.

To the 100th anniversary of magnetron
UCB-DQN based Joint Beamforming and Power Control for Software Defined UAV Swarm Network
Authors: Li Haitao , Zhang Shuai , Fang Zheng , Qiu Qiming 
Published: April 2022

Abstract
In this paper, we address the problem of deep Q-Network (DQN) based joint power control and beamforming for enabling interference suppression in software defined UAV swarm (SD-UAS) network. We first present the optimization model of joint beamforming and power control (JBPC). Then, to solve this joint optimization problem, we make use of UCB exploration in the learning process of DQN. Simulation results validate that the convergence obtained with the proposed UCB-DQN strategy outperform the DQN learning algorithm, which can to raise the exploration efficiency and sequentially speed up the convergence for the JBPC problem. It is benefit to supress interference and enhance the SD -UAS communication performance.
Transmission Shaft Fault Diagnosis Based on Variational Modal Decomposition (VMD) Feature Fusion
Authors: Chong Zezhong , Zhang Mengqian , Liu Sijie , Wu Yafeng 
Published: April 2022

Abstract
It is difficult to achieve an excellent result to accomplish the transmission shaft fault diagnosis with single feature, so thadt a feature fusion fault diagnosis method is proposed. First, the preprocessed vibration signal is decomposed by variational mode decomposition (VMD), and the Intrinsic mode function (IMF) is obtained; Then, singular value, sample entropy and approximate entropy are obtained as features and fused; Finally, the fused feature is used as the classification object of SVM for fault diagnosis. In this paper, the feasibility of this method is verified by the transmission of experimental data. The experiment included five conditions, and the diagnostic accuracy was 99%.
Neural Sliding Mode Tracking Control of Air-breathing Hypersonic Vehicles
Authors: Cui Peng , Gao Changsheng 
Published: April 2022

Abstract
This paper proposes a neural sliding mode control method for the tracking problem of the longitudinal dynamics of air-breathing hypersonic vehicles (ABHV). Considering the input/output feedback linearization, a high-order sliding mode law of the elevator deflection and the fuel equivalence ratio is designed. Moreover, the effect of uncertain model and control input disturbances is approximated with a Radial Basis Function Neural Network (RBFNN). The stability of the closed-loop system is analysed based on Lyapunov theorem. Simulation results shows the good tracking performance of the proposed controller and robustness with parameter uncertainties. All the signals are globally bounded and converged in short time.
Joint Optimization of Resource Allocation and UAVs’ Trajectories in Multi-UAV-Aided MEC Systems
Authors: Wu Runuyang 
Published: April 2022

Abstract
The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has become an up-and-coming technique to provide additional computing support for smart mobile devices with limited battery capacity in the wireless communication network systems. In this paper, a multi-UAV-aided MEC system is modelled where UAVs are equipped with MEC servers and users can execute local computing and computation offloading to UAVs simultaneously. The total amount of processing bits of all users is maximized by the joint optimization of the user-UAV association, local processing frequency, transmit power, bandwidth allocation and UAVs’ trajectories, meanwhile, the energy consumption constraint and the UAVs’ speed constraint are satisfied. Because the formulated problem is not convex, it is challenging to get the global optimal solution directly. To iron out the non-convex problem, it is divided into three subproblems and solved by an alternating optimization algorithm. Simulations show that the algorithm introduced in our paper performs better than the local computing, full offloading and fixed trajectory patterns.
Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm
Authors: Liao Jiayu , Liu Xiaolan , Xie Mengying 
Published: April 2022

Abstract
Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.
A Rail Wear Detection Method Based on Particle Swarm Algorithm
Authors: Chen Zhiyuan , Jiang Xintao , Luo Qingli , Zhang Shubin , Chen Honghui , Chen Xiang 
Published: April 2022

Abstract
In China, the high-speed rail network has developed rapidly in recent years. The wear of rail is a risk to its safe operation. Compared with traditional static detection methods, the methods applying computer vision have become one of the major detection methods due to its non-contact ability, high efficiency, and low cost. However, the accuracy of dynamic detection is affected by the flexible outdoor detection conditions, random vibration during the detection process and other undesirable factors. Thus, in order to improve the accuracy of rail wear detection under complex detection conditions, this paper develops a rail wear detection method based on particle swarm algorithm. The experimental results with line-structured light data show that the proposed method improves the accuracy of rail wear detection and provides technical references for the dynamic detection of rail wear.
A Game Theory Approach in Quality Classification and Secondary Use of Electric Vehicle Used Batteries
Authors: Yao Cailiang , Ma Xinyu , Hu Tong , Gu Xiaoyu 
Published: April 2022

Abstract
At present, electric vehicles have become one of the main directions for the development of today’s automotive industry by virtue of their high energy utilization efficiency and low environmental pollution. With the popularity of electric vehicles, the recycling of their batteries will become a challenge in the future. At present, the recycling system of vehicle batteries is not perfect. How to evaluate different quality of batteries to decide whether to reuse them or to remanufacture them directly is a question worth exploring. The purpose of this paper is to discuss the quality grading strategy of used batteries and make decisions based on the evaluation results by constructing a game theory model that includes four parties: new energy vehicle manufacturers, consumers, battery manufacturers, and recyclers, with the goal of maximizing the utility of the parts in the closed-loop supply chain of electric vehicles. This study is beneficial for the government and managers to better understand the electric vehicle battery closed-loop supply chain, and for the sustainable development of the supply chain as well.
Predicting the mechanical properties of borophene by artificial neural networks
Authors: Moreno J D , López A , Gutierrez E D 
Published: April 2022

Abstract
A material of great relevance in the current research context is borophene, a monolayer nanomaterial composed only of boron atoms with extraordinary electrical and mechanical properties. In the present work, a neural network was designed and trained in order to predict the mechanical properties of this material, such as Young’s modulus, fracture strength and fracture strain. The training data set was obtained through molecular dynamics simulations, with different parameter scenarios in order to analyze the effects of temperature, strain rate and strain direction. The trained machine learning model succeeded in predicting the material’s behavior with high accuracy. Its results reflect a decrease in yield stress with increasing temperature and a slight improvement in the fracture strain with increasing strain rates, as expected. Additionally, a web application with a graphical interface was developed, which uses the trained model, in order to make this tool available to any user. This interface also makes it possible to visualize the approximate stress-strain curve, drawn based on the resulting fracture stress and strain.
Research on thermal characteristics of tapered laser chip
Authors: Wang Hongran , Liao Wenyuan , Zhang Chengwei , Lai Canxiong , lu Guoguang , Yang Shaohua , Wang Xiaohua , Wei Zhipeng 
Published: April 2022

Abstract
The tapered laser with high power and high beam quality is one of the research hotspots in the field of laser. The thermal effect of the tapered laser will reduce the thermal saturation current and produce thermal deformation, which will lead to the decrease of output power. Therefore, the thermal effect of the tapered laser is the main cause of failure. In order to investigate the thermal characteristics of the tapered laser chip, this paper simulates the tapered laser by using COMSOL Multiphysics. The changes of thermal stress under different ridge widths and tapered angles are simulated, and the thermal stress distributions in the ridge and tapered regions are obtained. In addition, a porous heat sink is designed. The simulation results of ordinary radiator and porous radiator are compared, which can be seen that porous heat sink has better heat dissipation capacity than ordinary heat sink. Through the simulation of tapered laser, this paper does research on the thermal characteristics of tapered laser, which can be a useful reference to improving the performance of tapered laser.
Research Status of Amplified Spontaneous Emission Sources based on Doped Materials
Authors: Lei Jiayang , Qiu Shuang , Yang Kuo , Zhao Xiaoyu 
Published: April 2022

Abstract
Amplified spontaneous emission (ASE) was once considered a harmful parasitic effect. This is because ASE will reduce the number of the inverted population in the gain medium, thereby reducing the efficiency of lasers. However, the output characteristics of ASE make it applicable to many studies. Therefore, the experimental settings and beam output characteristics of different doped laser ASE sources are summarized in this review. The summarization begins with the characteristics of ASE, such as threshold condition. Then, two doped fiber lasers are introduced. Based on erbium-doped fiber, an ASE source with high stability is made by measuring the best parameters and selecting a suitable structure. The ytterbium-doped fiber can benefit Q-switch pulses and high-order Stokes generation by suppressing the self-saturation of ASE and offering gain for ASE, respectively. Besides, three different ASE sources based on Nd-doped laser crystals are presented. They are spatially selective ASE sources that achieve high-intensity, narrow-bandwidth output pulses and Q-switching in a cavityless laser. Additionally, the performance of the ASE source can also be improved by doping with gold nanoparticles, Gd3+, and other particles.
Research on dielectric and microwave absorbing properties of TiO2/TiB2/Thermoplastic polyurethanes (TPU) composite materials
Authors: Liu Xiongzhang , Li Xixi , Lei Xue , Zhang Deping , Liu Zhijun , Long Xueyuan , Gong Rongzhou 
Published: April 2022

Abstract
TiO2/TiB2 materials are prepared by the carbothermal method and high-temperature calcination. Further, the phase composition, microstructure, dielectric and electromagnetic (EM) wave absorption characteristics are investigated. The results display that the product is composed of TiO2, TiB2 and impurity phases. The complex permittivity increases at elevated TiO2/TiB2 loading and slowly decreases with rising frequency in 2-18 GHz. When the TiO2/TiB2 content is 45 wt%, the minimum reflection loss (RLmin
) reaches -43.12 dB at 11.68 GHz with a thickness of 1.9 mm. The effective absorption band (EAB, RL<-10 dB) can reach 3.68 GHz at 2.3 mm as the TiO2/TiB2 content is 30 wt%. The enhanced EM wave absorption owes to the strong polarization loss and high conductivity loss. It goes without saying that TiO2/TiB2 materials can acquire important breakthroughs in the development of novel EM wave absorbers with outstanding absorption loss, wider EAB and smaller thicknesses.
A rapid measurement method for ship’s longitudinal and athwartship induced magnetic field
Authors: He Baowei , Zhang Junhong , Liu Shengdao , Li Fengxie 
Published: April 2022

Abstract
Induced magnetic field (IMF) measurement is a key step in the ship’s magnetic field treatment process. In this paper, a new method for rapid measurement of longitudinal and athwartship IMF is proposed, which can improve the traditional measurement method that is either time-consuming and labor-intensive, or dependent on the magnetic field uniformity of geomagnetic simulation coils. Using the geomagnetic simulation coil in the close-wrap magnetic treatment facility, a mapping model between the coil magnetic field and the ship’s magnetized field excited by the coil is constructed in a single-heading. Based on this single-heading-measurement method, numerical simulations and hull experiments are carried out. By combining the regularization technology, the results show that the proposed method can quickly and accurately measure the ship’s IMF, and can suppress the influence of measurement error on obtaining the IMF. Compared with the geomagnetic simulation method, proposed method not only has a great calculation accuracy and strong robustness, but also requires a lower magnetic field uniformity of the geomagnetic simulation coil, and can be used as a fast emergency measurement method for ship’s IMF in engineering.
Hog: handling HDL repository on git
Authors: Biesuz N.V. , Cieri D. , Giangiacomi N. , Gonnella F. , Peck A. 
Published: April 2022

Abstract
Handling HDL project development within large collaborations presents many challenges in terms of maintenance and versioning, due to the lack of standardised procedures. Hog (HDL on git) is a tcl-based open-source management tool, created to simplify HDL project development and management by exploiting git and Gitlab Continuous Integration (CI). Hog is compatible with the major HDL IDEs from Xilinx and Intel-FPGA, and guarantees synthesis and placing reproducibility and binary file traceability, by linking each binary file to a specific git commit. Hog-CI validates any changes to the code, handles automatic versioning and can automatically simulate, synthesise and build the design.
Ion beam tools for nondestructive in-situ and in-operando composition analysis and modification of materials at the Tandem Laboratory in Uppsala
Authors: Ström P. , Primetzhofer D. 
Published: April 2022

Abstract
Ion accelerators have demonstrated tremendous versatility in
their research applications throughout several decades. Starting
predominantly as nuclear physics tools, they subsequently provided
novel insights into fundamental atomic physics. Nowadays small and
medium size accelerators are routinely employed in several branches
of materials science. As research questions increasingly drive the
need for analyses and material modification experiments performed
in-situ and/or in-operando, facilities must adapt to service such a
need. In this work, we highlight capabilities for in-situ and
in-operando composition depth profiling and materials modification,
developed with instrumentation at the Tandem Laboratory at Uppsala
University. An overview of available ion sources and accelerators is
given. We then exemplify the specific capabilities of the available
end stations for materials synthesis, sample modification and device
operation either during simultaneous ion beam based characterization
or with such characterization performed in-situ before and after the
application of various modification steps. Describing specific
recent examples and potential future applications, we illustrate the
impact of the present national research infrastructure.
Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area
Authors: Abolafia-Rosenzweig Ronnie , He Cenlin , Chen Fei 
Published: April 2022

Abstract
This study predicts summer (June–September) fire burned area across the western United States (U.S.) from 1984 to 2020 using ensembles of statistical models trained with pre-fire season climate conditions. Winter and spring climate conditions alone explain up to 53% of the interannual variability and 58% of the increasing trend of observed summer burned area, which suggests that climate conditions in antecedent seasons have been an important driver to broad-scale changes in summer fire activity in the western U.S. over the recent four decades. Relationships between antecedent climate conditions with summer burned area are found to be strongest over non-forested and middle-to-high elevation areas (1100–3300 m). Statistical models that predict summer burned area using both antecedent and fireseason climate conditions have improved performance, explaining 69% of the interannual variability and 83% of the increasing trend of observed burned area. Among the antecedent climate predictors, vapor pressure deficit averaged over winter and spring plays the most critical role in predicting summer fire burned area. Spring snow drought area is found to be an important antecedent predictor for summer burned area over snow-reliant regions in the nonlinear statistical modeling framework used in this analysis. Namely, spring snow drought memory is realized through dry anomalies in land (soil and fuel) and atmospheric moisture during summer, which favours fire activity. This study highlights the important role of snow drought in subseasonal-to-seasonal forecasts of summer burned area over snow-reliant areas.
A trace formula for metric graphs with piecewise constant potentials and multi-mode graphs
Authors: Gnutzmann Sven , Smilansky Uzy 
Published: April 2022

Abstract
We generalize the scattering approach to quantum graphs to quantum graphs with piecewise constant potentials and multiple excitation modes. The free single-mode case is well-known and leads to the trace formulas of Roth (1983 C. R. Acad. Sci., Paris I 296 793–5), Kottos and Smilansky (1997 Phys. Rev. Lett.
79 4794). By introducing an effective reduced scattering picture we are able to propose new exact trace formulas in the more general settings. The latter are derived and discussed in details with some numerical examples for illustration. Our generalization is motivated by both experimental applications and fundamental theoretical considerations. The free single-mode quantum graphs are an extreme idealization of reality that, due to the simplicity of the model allows to understand a large number of generic or universal phenomena. We lift some of this idealization by considering the influence of evanescent modes that only open above threshold energies. How to do this theoretically in a closed model in general is a challenging question of fundamental theoretical interest and we achieve this here for quantum graphs.