bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel polarization converter based on the band-stop frequency selective surface
Authors: Liao Kun , Sun Shining , Zheng Xinyuan , Shao Xianxian , Kong Xiangkun , Liu Shaobin 
Published: August 2021

Abstract
A dual-passband single-polarized converter based on the band-stop frequency selective surface (FSS) with a low radar cross-section (RCS) is designed in this article. The unit cell of the proposed converter is formed by a polarization layer attached to the band-stop frequency selective surface. The simulation results reveal that the co-polarization reflection coefficients below −10 dB are achieved in 3.82–13.64 GHz with a 112.4% fractional bandwidth (the ratio of the signal bandwidth to the central frequency). Meanwhile, a polarization conversion band is realized from 8.14 GHz to 9.27 GHz with a polarization conversion ratio which is over 80%. Moreover, the 1 dB transmission window is obtained in two non-adjacent bands of 3.42–7.02 GHz and 10.04–13.91 GHz corresponding to the relative bandwidths of 68.9% and 32.3%, respectively. Furthermore, the radar cross-section of the designed structure can be reduced in the wideband from 2.28 GHz to 14 GHz, and the 10 dB RCS reduction in the range of 4.10–13.35 GHz is achieved. In addition, the equivalent circuit model of this converter is established, and the simulation results of the Advanced Design System (ADS) match well with those of CST Microwave Studio (CST). The archetype of the designed converter is manufactured and measured. The experiment results match the simulation results well, which proves the reliability of the simulation results.
Tunable terahertz transmission behaviors and coupling mechanism in hybrid MoS2 metamaterials
Authors: Deng Yuwang , Zhou Qingli , Liang Wanlin , Zhang Pujing , Zhang Cunlin 
Published: August 2021

Abstract
A hybrid metamaterial with the integration of molybdenum disulfide (MoS2) overlayer is proposed to manipulate the terahertz (THz) wave. The simulated results indicate that the introduction of MoS2 layer could significantly modify the resonant responses with large resonance red-shift and bandwidth broadening due to the depolarization field effect, especially for the structure on the small permitivity substrate. Additionally, the wide-band modulator in off-resonant region and a switch effect at resonance can be achieved by varying the conductivity of MoS2 layer. Further theoretical calculations based on the Lorentz coupling model are consistent with the simulated results, explicating the response behaviors originate from the coupling between MoS2 overlayer and the metastructure. Our results could provide a possibility for active control THz modulator and switchable device based on the MoS2 overlayer and advance the understanding of the coupling mechanism in hybrid structures.
A Permissioned Blockchain System for Secure Multiparty Computation
Authors: Garg S , Vashisht R 
Published: August 2021

Abstract
Permissioned blockchain is the blockchain network that requires access to be part of the network. Participant’s actions are governed by the control layer that runs on top of the blockchain. This type of blockchains is preferred by individuals who need role description, identity, and security within the blockchain.
Secure multi-party computation (MPC) is a part of cryptography that involves the modeling of procedures for two or more participants who want to work together. These participants involve sharing of input and required computational data for a particular function without sharing their confidential data actively to each other and achieving a common goal which is beneficial to both as the outcome achieved is only revealed to the participants and is highly required for their functional purpose.
In this work, SPDZ (Speedz) implementation is explored leveraging additive secret sharing on the private blockchain (Hyperledger fabric). SPDZ protocol is chosen over any other computational protocol as it is highly secured from any active deceptive n-1 participant among the n participants. In this work, a backend is developed that uses a fabric SDK node.js library that interacts with the Hyperledger Fabric network. The proposed solution is shown through a demonstration. This paper concludes that for business-to-business scenarios, using SPDZ protocol on permissioned blockchain provides more security against adversaries as permissioned blockchain provides transparency over the participants of the network. As a result, permissioned blockchain is a more secure choice for enterprises to compute confidential data rather than permissionless blockchain.
Assessment of Optimizers impact on Image Recognition with Convolutional Neural Network to Adversarial Datasets
Authors: Vidushi  , Agarwal Manisha , Rajak Akash , Shrivastava Ajay Kumar 
Published: August 2021

Abstract
In Artificial Intelligence, the machine modeling technique means to behave in the manner of human reflects indistinguishable. To automatizes the development of rational model for data evaluation, machine learning mechanism of artificial intelligence, is used. Deep learning is the machine learning discipline, having objective to imbide the system and to discover pattern from input. In pattern recognition, deep learning has paramount importance and different advance powerful model’s architecture. The most effective, vital, and influential innovation in computer vision discipline is one of the architectures of deep learning called convolutional neural network. In this neural network various optimizers can be used for model molding into its appropriate form by weights futzing. Aiming to overcome the problem in getting the optimized result, the research used various algorithms of weight optimization. The article elaborates convolutional network concept as well as the idea behind the use of optimizers. Furthermore, the detailed study of optimizers is also presented in this paper. Along with it, experimental comparison and result of different learning paradigm optimizers is shown in the document. Considering the different image datasets, including MNIST, and CIFAR 10 dataset, the accuracies of convolutional model with different optimizers are verified.
Efficient Construction of Successive Cancellation Decoding of Polar Codes using Logistic regression Algorithm
Authors: Albert raj A. , Judson D. , Beno A. 
Published: August 2021

Abstract
In recent days, polar codes have attracted the attention of Industry and Academia. It is one of a linear block error correction codes. Polar codes are one of the capacity achieving algorithms over a wide range of channels, making them very attractive from a theoretical perspective. Polar codes are shown to be instances of concatenated codes. It has been shown that the effect of a polar code can be enhanced by showing the multistage decoding algorithm with log-likelihood based Successive List decoding (SCL). However, SCL decoders are no longer optimal in terms of frame error rate. It is believed that the machine learning based algorithm can improve the FER for the decoded bit. Thus, the logistic regression algorithm is proposed in this paper. The proposed method offers an optimized solution to the decoded bits. The simulation result shows that with sufficient training, this method can provide a lower frame error rate for different code lengths.
Privacy-preserving Search over Encrypted Personal Health Record in Multi-Source Cloud
Authors: Hema Latha M , Suhasini Snigdha A , Vijaya Lakshmi B , Padmavathi K , Satish M 
Published: August 2021

Abstract
The biggest domain which needs a cloud platform in this smart world is the medical domain, where hospitals have a huge amount of data regarding the patient’s records. When these personal health records are stored in the cloud, the system needs some encryption mechanism to secure the records. In this paper, an enhanced Attribute-Based Encryption (ABE). The health records of the patient are decrypted with the help of private keys. In this model, the patient doesn’t need any certificate for authorization, so the model uses the details of the patients as attributes, and these details are encrypted. A threshold value is defined as t, if the number of encrypted attributes is matched with the threshold value then only the data owner can decrypt the data. A hierarchical mechanism helps the model to have smooth access to the data. The traditional algorithms are good at handling a small amount of data but this algorithm provides security as well as scalable property. The enhanced mechanism includes the unsupervised machine learning approach to label the patients based on their disease. In the cloud, a group of clusters are formed to separate the patients and allocate the doctors based on their disease treatment. The process of labeling is major drawback in case of attribute based encryption mechanism.
Compressing Biosignal for Diagnosing Chronic Diseases
Authors: Ithayarani P. , Jainul Fathima A. 
Published: August 2021

Abstract
Electrocardiogram, electromyogram, electroencephalogram are the foremost required vital signs for diagnosing chronic diseases like sleep disorder, mood disorder, epilepsy etc., which demands long-term monitoring. A sensor based wearable system which is enabled with internet technology, supports the continuous recordings of these vital signs without troubling the patient’s daily activities. And the wearable hub is responsible for collecting the readings of bio signals from multiple micro-sensor nodes deployed around the body which creates the short range of communication and forward to the observer. These continuous monitoring increases the signal transmission cost and declines the battery life of wearables. So, the observed multiple bio signals can be compressed jointly than individually before sending, at an edge level. This paper proposes transfer learning based multimodal convolutional denoising autoencoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with better quality score than by randomly initialized weights. The proposed work achieves compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in case of consuming multiple sensors. And the experimental result proves that the computation cost is low in multimodal compression than in unimodal compression.
Bottle Line Detection using Digital Image Processing with Machine Learning
Authors: Anush Ch , Yashwanth K. , Shashank S. , Venkat Reddy M. , Kumar Ashwani 
Published: August 2021

Abstract
Image processing is often used in industrial applications. The automatic visual inspection of a product before it is packaged and dispatched to the client is one of the most common uses for image processing. Using these types of systems in production facilities helps the avoidance of situations where a faulty or sub-standard product is shipped to the customer. The quantity of the fluid in each bottle differs with the accuracy level of the machine and furthermore due to mistakes done by employee during the underlying arrangement. Inappropriate filling during a bottle leads the makers to dispose of whole bunches of an item resulting in huge capital loss. A Economical mechanized review structure for inline packaging which detects oddities has been examined. The system are ending up being to be assign with checking the degree of the fluid filled inside each bottle as the container goes through the assessment framework introduced round the current packaging line. It will help to keep up the product quality and quantity as well and furthermore maintains the accuracy of the filling. The bit of the space between the outside of the fluid inside the bottle and container will be used to observe the differences like underfilling and overfilling. The proposed work is accomplished using image processing techniques in Machine Learning using python platform. Here we use ANN algorithm. Process of detecting the liquid level in bottle as follow as a group of pictures were given, taken under close to consistent lighting conditions in the manufacturing plant. These arrangement of pictures contains both underfilled and overfilled bottles which the packaging organization expects that you should use to identify certain issues as a component of the filling, covering and naming tasks before the final packing stage. The distance for each dataset must be same. We need to provide threshold value to detect if the bottle is overfilled or underfilled.
Improvisation of RSA Algorithm in Respect to Time and Security with the Proposed (AEA) Algorithm
Authors: Taneja A , Shukla R K , Shukla R S 
Published: August 2021

Abstract
The security of digital information has become increasingly vital in the digital world, which is currently evolving and changing at such a quick speed. With the rapid growth of hidden contact and communication around the world, the need for communication security is becoming increasingly important. The main motive of cloud service providers is to collect the information and details of the material uploaded on their cloud servers. One of the major concern is security of the contents uploaded on the cloud servers. Although, they challenge about the security provided by them to the individuals but they are the owner of servers and the information on it. The same has been observed when one of our server crashed and we consulted about the incident with the customer care department of our CSP. We use to back up our university servers backup on cloud for which we pay some amount as decided by our management as well as CSP. The aim of this research is that the cloud owners cannot have access to data of the users. I proposed a new robust encryption technique in this paper to improve the security of the key generating algorithm. To achieve this, an algorithm has been implemented which performs much better than renowned algorithm i.e. RSA algorithm. This algorithm achieves better response time than RSA algorithm which is described and proved in this paper. This paper uses ASCII and EBCDIC codes while encryption and decryption.
Intelligent System for Skin Disease Prediction using Machine Learning
Authors: Elngar Ahmed A. , Kumar Rishabh , Hayat Amber , Churi Prathamesh 
Published: August 2021

Abstract
Skin is an extraordinary human structure. It frequently suffered from many known and unknown disease. Therefore, diagnosis of human skin diseases is the most uncertain and complicated branch of science. It has been observed that most of the cases remain unnoticed because of the lack of better medical infrastructure and facilities. This paper is devoted to solve this challenge. Therefore, this paper effectively proposed (CNN-SVM-MAA) system which combines Convolutional Neural Network with Support Vector Machine classifier to develop a Mobile Android Application. Thus, to evaluate the performance of the proposed system several experiments are conducted on our dataset. This dataset consists around 3000 images which collected from a lot of sources like Beni-Suef University Hospital, Cairo University Hospital and various websites as well to be more accurate and realistic. A comparative study of applying different Feature extraction algorithms with different classifiers was accomplished. The results obtained showed the adequacy of the proposed (CNN-SVM-MAA) system how many skin diseases images have been detected from skin disease dataset. Which lead to detect skin disease and provide the user with the disease name and treatment related prescription with high accuracy.
System analysis methodology for decision making in the design problems of new generation nuclear reactors
Authors: Okunev V S 
Published: August 2021

Abstract
The main goal of the article is to summarize the results of many years of work on the creation of a unified methodology for designing new generation nuclear reactors that meet the requirements for generating electricity on a given scale, economic efficiency, inherent safety, and fuel supply. The methodology is based on the methods of systems analysis and operations research. Accidents with unacceptable releases of radioactive substances outside the NPP in new generation reactors can be eliminated by using core materials with specified (obtained within the same methodology) properties. The research methods are based on solving mathematical programming problems in different formulations, solving a number of multicriteria problems, using the non-formalized theory of conflict and informal procedures. As a result of the research, decision-making algorithms have been proposed; software has been developed and modernized to solve the problem of designing nuclear reactors. The practical significance of research in substantiating the fundamental possibility of creating safe nuclear power based on high-power reactors with lead cooling, cermet fuel and innovative structural materials.
Big Data technology: assessing the quality of the educational environment
Authors: Denisova O A 
Published: August 2021

Abstract
The paper shows the prospects for the development of digital technologies of Big Data, which are proposed to be used as a means of qualitative assessment of education, as well as its effectiveness. The use of intelligent systems that work at the level of the best teacher in the future will be available in any educational organization. This will make it possible to overcome the “educational inequality” and reduce educational barriers for people with disabilities. If the methodology of traditional teaching is created based on the personal experience of the teacher, then the methodology based on big data analysis technologies is the result of the experience of a huge number of users. With the help of big data, you can create methods that are adapted to a large number of students; personalize content; choose learning modes, that is, learning becomes adaptive and personality-oriented. The educational program is transformed from the traditional approved text format to the format of personalized online content, which dynamically changes depending on requests. In addition, approaches to monitoring and evaluating both the educational process itself and the results of education are changing. As a result of the application of Big Data technologies (multidimensional statistical analysis), new methods of forecasting are proposed for use, when a combination of known data predicts the desired unknown; structure identification and clustering; network analysis. Big data is a huge volume of information, big data analysis methods are the systematization of this information, but they can never replace the teacher who gives his own and “reads” the emotions of the student.
Study on the Effect of Cement Sheath on the Stress of Gas Storage Well
Authors: Song Chengli , Liu Xinbao , Li Guangshan , Wang Shuai 
Published: August 2021

Abstract
Compressed natural gas (CNG) has been widely used as an automotive fuel in china, which the service security of CNG storage well (CSW), the main storage equipment in Chinese filling station, is becoming more and more prominent. In order to analyze the stress of CSW and its influencing factors, the mechanical model of CSW + cement sheath + stratum (CCS) processed by both elastic mechanics and finite element method was studied in this work. Using the most common well of Φ177.8× 10.36mm as a calculation case to obtain the analytic solutions and numerical solutions of CCS. The results indicated that the analytic solutions and numerical solutions are very close with relative deviation less than 3%, which verified their reliability each other. The calculation case can prove that the stress of CSW reduced evidently due to the effect of well cementation, which equivalent to the value of circular and axial stress of CSW strengthens to 18% and 20%. And when increasing the elastic modulus of cement sheath, the stress of CSW decreased that the support and potentiation to CSW by cement sheath becomes stronger.
Examinations of Errors in a D/Pd Gas-loading System when Using a Heat-flow Calorimeter
Authors: Wang Xingye , Shen Bingjun , Jin Lihong , Zhou Dan , Tian Jian 
Published: August 2021

Abstract
Measurement of a reaction between deuterium (hydrogen) gas and materials using a calorimeter which is calibrated by nitrogen will show erroneous excess heat power production due to the structure of the reaction chamber and the difference of thermal conductivity of the gases used in the calibration and triggering experiments, which leads to the energy generated in the chamber of the calorimeter has not been totally measured. The error is not obvious at low applied power, and it increases significantly with the increase of applied power. This insidious error is discussed by using a stable heat-flow (Seebeck) calorimeter and procedures to reduce error are put forward. And the authenticity of Rossi’s patent is questioned.
Transient Hydrodynamic Analysis of Ball Mill Multi-Pocket Pivoted Pad Hydro-static Bearing
Authors: Li Miaomiao , Ma Xizhi 
Published: August 2021

Abstract
Huge ball mill bearings experience a transient period when they are fed with raw material or their basis is impacted. The behavior of such bearings during the transient period is significant to the safety of the machine. In the present paper, the step load is used to simulate this process. Transient hydrodynamic lubricating models of a multi-pocket pivoted pad hydro-static bearing used in ball mill are established. The hydrodynamic and squeeze effects are take into account, and the models are solved using nonlinear numerical methods. The journal center trajectory, time response of the pad tilting, oil pressure in the pockets, and minimum pad film thickness during the transient period are presented. The oil flow rate evolutions out of the main pockets in all pads are exhibited. The effects of step load on bearing performance are discussed. It was found that the decrease in minimum film thickness can be quite large under the step load impact, which may lead to the damage of the bearing.
Design of Intelligent Robot Flexible Sorting Center
Authors: Ran Wenxue , Bai Haixia , Ma Ruijin 
Published: August 2021

Abstract
In order to make up for the shortcomings of traditional robot sorting flexibility and limited operation efficiency, the concept of intelligent robot flexible sorting center is proposed in this paper. We firstly conceive its characteristics, then construct its structure, which consists of a robot with changeable claws, a claw storage, an automatic setback stereoscopic warehousing system, an order goods conveying system, a visual recognition information system. Thus make the intelligent sorting intensive system with more functions enables the robot to adapt to a variety of picking methods, showing higher flexibility, higher order sorting quality, higher equipment utilization rate, relatively stable sorting capacity and great adaptability, its application scope can be continuously expanded to meet the needs of more diversified goods.