bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel method for telescope polarization modeling based on an artificial neural network
Authors: Peng Jian-Guo , Yuan Shu , Ji Kai-Fan , Xu Zhi 
Published: August 2021

AbstractThe polarization characteristics of an astronomical telescope is an important factor that affects polarimetry accuracy. Polarization modeling is an essential means to achieve high precision and efficient polarization measurement of the telescope, especially for the alt-azimuth mount telescope. At present, the polarization model for the telescope (i.e., the physical parametric model) is mainly constructed using the polarization parameters of each optical element. In this paper, an artificial neural network (ANN) is used to model the polarization characteristics of the telescope. The ANN model between the physical parametric model residual and the pointing direction of the telescope is obtained, which reduces the model deviation caused by the incompleteness of the physical parametric model. Compared with the physical parametric model, the model fitting and predictive accuracy of the New Vacuum Solar Telescope (NVST) is improved after adopting the ANN model. After using the ANN model, the polarization cross-talk from I to Q, U, and V can be reduced from 0.011 to 0.007, and the crosstalk among Q, U, and V can be reduced from 0.047 to 0.020, which effectively improves the polarization measurement accuracy of the telescope.
DeepSun: machine-learning-as-a-service for solar flare prediction
Authors: Abduallah Yasser , L. Wang Jason T. , Nie Yang , Liu Chang , Wang Haimin 
Published: August 2021

Abstract
Solar flare prediction plays an important role in understanding and forecasting space weather. The main goal of the Helioseismic and Magnetic Imager (HMI), one of the instruments on NASA’s Solar Dynamics Observatory, is to study the origin of solar variability and characterize the Sun’s magnetic activity. HMI provides continuous full-disk observations of the solar vector magnetic field with high cadence data that lead to reliable predictive capability; yet, solar flare prediction effort utilizing these data is still limited. In this paper, we present a machine-learning-as-a-service (MLaaS) framework, called DeepSun, for predicting solar flares on the web based on HMI’s data products. Specifically, we construct training data by utilizing the physical parameters provided by the Space-weather HMI Active Region Patch (SHARP) and categorize solar flares into four classes, namely B, C, M and X, according to the X-ray flare catalogs available at the National Centers for Environmental Information (NCEI). Thus, the solar flare prediction problem at hand is essentially a multi-class (i.e., four-class) classification problem. The DeepSun system employs several machine learning algorithms to tackle this multi-class prediction problem and provides an application programming interface (API) for remote programming users. To our knowledge, DeepSun is the first MLaaS tool capable of predicting solar flares through the internet.
Physical and geometrical parameters of CVBS XIV: the two nearby systems HIP 19206 and HIP 84425
Authors: Al-Wardat Mashhoor A. , Abu-Alrob Enas , Hussein Abdallah M. , Mardini Mohammad K. , Taani Ali A. , Widyan Hatem S. , Yousef Zahraa T. , Al-Naimiy Hamid M. , Yusuf Nihad A. 
Published: August 2021

Abstract
Data release 2 (DR2) from the Gaia mission was of great help in precise determination of fundamental parameters of Close Visual Binary and Multiple Systems (CVBMSs), especially masses of their components, which are crucial parameters in understanding formation and evolution of stars and galaxies. This article presents the complete set of fundamental parameters for two nearby close visual binary systems (CVBSs), which are HIP 19206 and HIP 84425. We utilised a combination of two methods; the first one is Tokovinin’s dynamical method to solve the orbit of the system and to estimate orbital elements and the dynamical mass sum, and the second one is Al-Wardat’s method for analysing CVBMSs to estimate the physical parameters of the individual components. The latest method employs grids of Kurucz line-blanketed plane parallel model atmospheres to build synthetic Spectral Energy Distributions (SEDs) of the individual components. Trigonometric parallax measurements given by Gaia DR2 and Hipparcos catalogues are used to analyse the two systems. The difference in these measurements yielded slight discrepancies in the fundamental parameters of the individual components, especially masses. So, a new dynamical parallax is suggested in this work based on the most convenient mass sum as given by each of the two methods. The new dynamical parallax for the system HIP 19205 of 22.97 ± 0.95 mas coincides well with the trigonometric one reported recently (in December 2020) by Gaia EDR3 of 22.3689 ± 0.4056 mas. The positions of the components of the two systems on the evolutionary tracks and isochrones are plotted, which suggest that all components are solar-type main sequence stars. Their most probable formation and evolution scenarios are also discussed.
A novel stellar spectrum denoising method based on deep Bayesian modeling
Authors: Kang Xin , He Shi-Yuan , Zhang Yan-Xia 
Published: August 2021

Abstract
Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model includes a prior distribution for each stellar subclass, a spectrum generator and a flow-based noise model. Our method takes into account the noise correlation structure, and it is not susceptible to strong sky emission lines and cosmic rays. Moreover, it is able to naturally handle spectra with missing flux values without ad-hoc imputation. The proposed method is evaluated on real stellar spectra from the Sloan Digital Sky Survey (SDSS) with a comprehensive list of common stellar subclasses and compared to the standard denoising auto-encoder. Our denoising method demonstrates a superior performance to the standard denoising auto-encoder, in respect of denoising quality and missing flux imputation. It may be potentially helpful in improving the accuracy of the classification and physical parameter measurement of stars when applying our method during data preprocessing.
A method for pulsar searching: combining a two-dimensional autocorrelation profile map and a deep convolutional neural network
Authors: Wang Long-Qi , Jin Jing , Liu Lu , Shen Yi 
Published: August 2021

Abstract
In pulsar astronomy, detecting effective pulsar signals among numerous pulsar candidates is an important research topic. Starting from space X-ray pulsar signals, the two-dimensional autocorrelation profile map (2D-APM) feature modelling method, which utilizes epoch folding of the autocorrelation function of X-ray signals and expands the time-domain information of the periodic axis, is proposed. A uniform setting criterion regarding the time resolution of the periodic axis addresses pulsar signals without any prior information. Compared with the traditional profile, the model has a strong anti-noise ability, a greater abundance of information and consistent characteristics. The new feature is simulated with double Gaussian components, and the characteristic distribution of the model is revealed to be closely related to the distance between the double peaks of the profile. Next, a deep convolutional neural network (DCNN) is built, named Inception-ResNet. According to the order of the peak separation and number of arriving photons, 30 data sets based on the Poisson process are simulated to construct the training set, and the observation data of PSRs B0531+21, B0540-69 and B1509-58 from the Rossi X-ray Timing Explorer (RXTE) are selected to generate the test set. The number of training sets and the number of test sets are 30 000 and 5400, respectively. After achieving convergence stability, more than 99% of the pulsar signals are recognized, and more than 99% of the interference is successfully rejected, which verifies the high degree of agreement between the network and the feature model and the high potential of the proposed method in searching for pulsars.
The research objectives and observational possibilities for fast moving near-Earth asteroids
Authors: Pomazan Anton , Tang Zheng-Hong , Maigurova Nadiia , Tang Kai , Yu Yong , Mao Yin-Dun 
Published: August 2021

Abstract
The paper describes observations of fast-moving near-Earth asteroids (NEAs) made with the small ground-based telescopes of National Time Service Center of Chinese Academy of Science (NTSC of CAS) and Research Institute “Mykolaiv Astronomical Observatory” (RI MAO) by the rotating-drift-scan CCD (RDS CCD) technique. This technique is used to obtain the point images of both the studied objects and reference stars. The results of ongoing follow-up observations of NEAs are discussed. The residual differences (O – C) between the observed and calculated positions from JPL ephemeris were generally small for these asteroids. The standard deviations of these differences were typically ±(0.2″ – 0.3″) in both coordinates for objects with apparent velocity which substantially exceed FWHM for the given exposure time. The results of comparative statistics for such observations from the MPC database show that this is a good level of precision for NEAs. Moreover, the telescopes with the RDS CCD technique implemented can observe the NEAs that closely approach the Earth and with enough observations can improve the precision of determining their orbital elements and impact predictions.
The deep and low-mass-ratio contact binary CSS J022914.4+044340 with a luminous additional companion
Authors: Liu Liang , Li Xu-Zhi 
Published: August 2021

AbstractThe firstB-,V-,Rc- andIc-band light curves of CSS J022914.4+044340 are presented and analyzed. It is found that CSS J022914.4+044340 is a low mass ratio (0.198 ± 0.005) deep (63.7 ± 7.9%) contact binary, indicating that it is already at the end evolutionary stage of tidally-locked evolution via magnetized wind. Because of the totally eclipsing character, the photometric solutions are reliable. The temperature and metallicity are determined from the spectroscopic data asT= 5855 ± 15 K and [Fe/H] = –0.842 ± 0.031, respectively. Based on the parallax ofGaiaEDR3, the physical parameters of CSS J022914.4+044340 are estimated asM1=1.44−0.22+0.25M⊙,M2=0.29−0.05+0.05M⊙,R1=1.26−0.06+0.08R⊙,R2=0.65−0.04+0.03R⊙,L1=1.718−0.191+0.186L⊙andL2=0.416−0.050+0.039L⊙. Combining the fraction of light from the third body via the photometric solution (54%), the luminosity of the third body is estimated as 2.705L⊙. The third body may be inferred to be a subgiant. Thus, why the primary component of CSS J022914.4+044340 has higher mass compared to similar systems is explained, as well as why its metallicity is so poor.
A low-cost and high-performance technique for adaptive optics static wavefront correction
Authors: Ren De-Qing , Zhang Tian-Yu , Wang Gang 
Published: August 2021

Abstract
Non-Common Path Error (NCPE) is one of the factors that limit an Adaptive Optics (AO) system from delivering ultra-high performance. To correct the NCPE associated static aberration, we propose a simple but robust and high-performance pupil-plane based wavefront measurement and correction technique, which can copy a single-mode fiber generated perfect wavefront to the AO system via an iteration optimization process, and the NCPE can be effectively corrected by directly commanding the Deformable Mirror (DM) of the AO system. Compared with the previous focal-plane based approach that uses focal plane based Point Spread Function (PSF) for correction evaluation, the pupil-plane based approach can be reliably and rapidly converged to a global optimization result and provides better performance, in particular for an AO system with a large initial static wavefront error. This technique we proposed can be implemented in astronomical AO systems where extremely high performance is required.
Ultra-low noise L-band cryogenic astronomical receiver for the FAST telescope
Authors: Liu Hong-Fei , He Chuan , Wang Jin , Jiang Peng , Wang Sheng-Wang , Wu Yang , Zhang Hang , Song Jin-You , Shi Xiang-Wei , Guo Ming-Lei 
Published: August 2021

Abstract
This paper presents an ultra-low noise L-band radio astronomical cryogenic receiver for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) telescope. The development of key low noise microwave parts of coupling low noise amplifier (Coupling-LNA) and conical quad-ridge orthogonal mode transducers (OMT) and reasonable system integration achieve outstanding performance of receiver. It covers the frequency range of 1.2 GHz to 1.8 GHz. Novel cryogenic Coupling-LNAs with low noise, large return loss, high dynamic range and the function of coupling calibration signals are developed for the proposed receiver. Amplification and coupling function circuits are integrated as a single Coupling-LNA with full noise temperature of 4 K at the physical temperature of 15 K. Its return loss is more than 18 dB, and output 1 dB compression power is +5 dBm. A cryogenic dewar is fabricated to provide 55 K and 15 K cryogenic environment for OMT and Coupling-LNAs, respectively. The receiver’s system noise temperature is below 9 K referred to feed aperture plane. Benefiting from optimal design and precise mechanical treatment, good scattering performance of OMT and equalized radiation patterns of horn are achieved with an antenna efficiency above 75%.
Predicting the 25th solar cycle using deep learning methods based on sunspot area data
Authors: Li Qiang , Wan Miao , Zeng Shu-Guang , Zheng Sheng , Deng Lin-Hua 
Published: August 2021

Abstract
It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-short-term memory (LSTM) and neural network autoregression (NNAR) deep learning methods to predict the upcoming 25th solar cycle using the sunspot area (SSA) data during the period of May 1874 to December 2020. Our results show that the 25th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.
Axisymmetric Drop Shape Analysis using a low-cost home-made setup
Authors: Guilizzoni Manfredo , Sapienza Jessica 
Published: August 2021

Abstract
Surface science and engineering gained an increasing importance in recent years, due to the potential benefit they can offer in many applications. An important parameter in this field is the surface wettability, that is in general evaluated by the contact angle. One of the most accurate techniques to measure this quantity is the axisymmetric drop shape analysis, based on the fitting of the theoretical Laplace-Young profile to the contour of experimental drops. In this paper the performance of a simple, low-cost setup – that can be built “at home” – to apply this technique is assessed, also including a detailed analysis about the influence of the most important parameters to set in the procedure. The latter aspect was evaluated by using computer-generated drop profiles and pictures, to have “realistic” images, but for drops of known properties. The experimental setup was built using a desk, a table lamp, a medical syringe, a support for the samples and a “bridge” camera. Measurements were performed on smooth and rough textile surfaces and the results were compared with previous measurements taken with a professional setup. From the comparison it can be affirmed that the performance of the home-made setup is very satisfactory.
Vision based quality control of composite components: considerations about measurement accuracy
Authors: D’Emilia Giulio , Gaspari Antonella , Natale Emanuela , Ubaldi Davide 
Published: August 2021

Abstract
In this paper a methodology is discussed concerning the measurement of yarn’s angle of a reinforced polypropylene matrix used in the production of automotive components. The measurement method is based on a vision system and advanced processing of images in order to evaluate the geometrical parameters of interest; the accuracy of measurement is a mandatory requirement, in order to assess the simulation approach for thermoplastic process optimization. Many aspects influencing the whole accuracy of the method have been identified and their effect evaluated, of both geometrical and optical type, allowing to perform angle measurements of the fiber angle with a whole accuracy in the order of a few degrees. By this way both local and extended defects can be identified in a reliable way also with reference to components of complex geometry. According to these results, accurate measurements of angle allows us to both validate the simulation of the thermoplastic process and to give suggestions for process improvement of fiber glass components of complex geometry.
Dancing with Multiple Stub, Discontiguous Areas, and routing protocols of OSPF in IXP
Authors: Oo Tun Tun , Africa Aaron Don M. , Navea Roy Francis 
Published: August 2021

Abstract
This Research paper implemented the stability of OSPF LSAs-types in IXP by utilizing the complexity area with a high-speed data rate. The multiple stubs, discontiguous areas, and multiple routing protocols are deployed to be intended for ISP/IXP. When the stubby which managed the entering external or internal information database of LSA are analyzed and changed the LSAs-types, router types, and routing protocol. And then, the redistribution or External summary are applied to communicate different Routing Information protocol as (IGP, EGP). The RIPv2 and EIGRP routing protocol are configured to examine the external routing protocol. Furthermore, Totally NSSA and the Totally stubs operations are described as a coherent system in this system. The end-to-end throughputs are better improved, after using a stub, NSSA. And then, the security of routing information is safe and some IP was been the hidden system for each section. The OSPF algorithm with SPF is link update and boost routing information protocol table, database system.
Neural Network-Based Ridesharing Policy for Reducing Rider Transportation Cost
Authors: Obias Karl , Magsino Elmer R. , Sybingco Edwin 
Published: August 2021

Abstract
With the increasing demand for taxis during peak hours, adding more vehicles is not always the optimal solution since this will also contribute to the increasing traffic congestion, noise, and air pollution. In this study, we propose a neural network-based ridesharing policy that will utilize the available seats in an occupied taxi to accommodate other queueing passengers. The proposed policy allows a single taxi to transport more people at the same time and reduces the passenger taxi fare. The ridesharing policy is extensively verified by studying empirical mobility trace datasets to determine its capability of reducing transportation costs for riders. Test results show that the proposed ridesharing policy can reduce the cost by approximately 120% with the implementation of ridesharing.
Autonomous Vehicle: Introduction and Key-elements
Authors: Halin Hafiz , Khairunizam Wan , Ali Hasimah 
Published: August 2021

Abstract
The development of autonomous vehicles is undergoing extensive research because the autonomous system must ensure passenger safety. Consumers are concerned about vehicle safety, data privacy, system safety, and the autonomous vehicle's legal liability. Autonomous develop based on several key elements; perception, data processing, path planning, and control system. Comfort and a safe autonomous system can be achieved by creating a controller that can imitate human intelligence and decision-making ability. The proposed controller will be developed from an analysis of the human driving characteristic. The Allied Research Market forecast the autonomous vehicle industries can generate a lot of revenue in the future.
Fusion wind and solar generation prototype design with Neural Network
Authors: Al Asbahi M M Y M , Mansor M N , Manan M R , Aziz M A A , Kawi R Md , Mohd Faudzi F H 
Published: August 2021

Abstract
Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore, power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction with neural network was proposed to support a small microgrids system. All the system performance measurement such as sensitivity, specificity and accuracy give higher than 90%.
Five-phase multilevel inverter with output voltage waveform optimized using soft-computing methods
Authors: Yong W V , Chew W T , Ong S L , Leong J H 
Published: August 2021

Abstract
In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) are employed to optimize the switching angles for 5-phase 15-level asymmetrical cascaded H-bridge multilevel inverter (ACHBMLI). Both optimization algorithms are applied in selective harmonic minimization pulse width modulation (SHMPWM) based switching-angle calculation technique that aims to maintain the desired fundamental harmonic while minimize the undesired harmonics. The performance of GA- and PSO-SHMPWM are compared in term of switching angles, objective function, and cumulative distribution function. For fair comparison, both GA- and PSO-SHMPWM are implemented and analyzed in MATLAB with the same population size and total number of iterations. Furthermore, a PSIM simulation model of 5-phase 15-level ACHBMLI has also been developed to validate the effectiveness of GA- and PSO-SHMPWM.
FPGA-based Design of Multiple Models for Industry 4.0 Cyber Security
Authors: Al-Salman Husham Ibrihem Mahdi , Ehkan Phaklen , Al-Doori Muataz Hamed 
Published: August 2021

Abstract
Industry 4.0 computing infrastructure depends on traditional processing platforms, and all these platforms perform well and achieving acceptable throughput. Two significant problems confront with these platforms are the delay in data processing and security hazard for potential threats. A design coding has been written using VHDL based on behaviour temporal parallelism for Industry 4.0 cybersecurity model. The models are divided into three namely, single, dual and quad models. Every model has three main components include UART, FIFO and DES algorithm for data encryption and decryption. Encryption is achieved more than one block at a time and over the number of designs to find the most suitable application for data processing. A DES algorithm based efficient implementing the design of 16-round multiple parallel models for low-cost, scalable, and robust encryption solution of a maximum clock frequency of 227MHz and it is capable of encrypting or decrypting data blocks at a rate of throughput 58,288Mbps.