Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Number Systems: Application for Calculations in Galois Fields

Hybrid-base numbering systems are proposed; each digit of a number recorded in the system corresponds to some prime number. Subdivisions using for recording of a number in the frames of proposed approach correspond to specific Galois fields that correspond to the prime numbers that form the base of the coding system. Division of a digit into subunits is based on the theory of algebraic rings, which allows the number to be represented as a linear combination of idempotent elements with coefficients corresponding to the elements of the Galois fields. A specific example of binary-ternary logic where operations are performed in a number system with a base equal to six is considered. It is shown that operations in the decimal number system can similarly be reduced to operations in binary-ternary logic. The issues of practical use of algorithms based on hybrid number systems are discussed. In particular, a scheme of an adder operating with binary-ternary logic is proposed. The advantages of the proposed approach in terms of the possibility of its use in computing machinery on innovative element base are discussed.
Study of Different Gate Materials on Performance of Si Based MOSFET

In this paper, Silicon-based MOSFET has been investigated with different gate oxides for knowing which oxide shows the best result. Silicon-di oxide (SiO<inf>2</inf>), gallium oxide (Ga<inf>2</inf>O<inf>3</inf>), and aluminum oxide (Al<inf>2</inf>O<inf>3</inf>) have been used as gate materials for observing the sensitivity, threshold voltage, and drain current of the MOSFET in this study. Al<inf>2</inf>O<inf>3</inf> gate material gives the highest terminal current and faster turn-on voltage (0.7 V) of the MOSFET compared to the other two. Al<inf>2</inf>O<inf>3</inf> delivers the best performance; and hence, it could be a promising material for the gate layer. In addition, electron and hole concentrations of those gate oxides and electric potential have been compared. In this paper, COMSOL Multiphysics® software has been used to simulate the obtained results.
The Structure and Properties of an Intermediate-Velocity Cloud in the Direction of the Globular Cluster M13

A study of an intermediate-velocity cloud (IVC; VLSR ≈- 70 km s–1) detected in the foreground of the globular cluster M13 has been made using H I spectra obtained with the Lovell Telescope, Jodrell Bank (FWHP beamwidth 12′) and H I spectral maps derived from Dominion Radio Astrophysical Observatory Synthesis Telescope observations (FWHP synthesised beamwidth ≈2′.8 × 2′.0). The radio data complement the optical absorption line spectroscopy of M13 cluster stars reported by Bates et al. The H I data obtained with the Lovell Telescope show extended emission from a broad-velocity component (FWHM ≈ 30 km s–1) of Hi column density ≳ 7 × 1018 cm–2. Two cloudlets are revealed in the H I observations lying on either side of M13 and having a two-component structure; the broad component (FWHM ≈ 32 km s–1) has an Hi column density of ≈ 5 × 1019 cm–2, while the narrow component (FWHM ≈ 8 km s–1) has a column density of ≈ 6 × 1018 cm–2 as seen in the Synthesis Telescope observations. The cloudlets have an angular size ≈2′ and are only partially resolved in the synthesis telescope data. Fine-scale gas structure is also evident from the cluster star spectroscopy with variations in Na I column density of a factor of ≳10 observed across the several arcminutes of the cluster face; there is also evidence for structure on an angular scale of several arcseconds. A column density ratio Na I/H I ≈ 4 × 10–8 is measured for a cloudlet in the foreground of the cluster near the strongest Na I detection. Estimates are made of cloudlet gas density that are comparable with those reported for high-velocity cloud (HVC) concentrations. However, the present observations emphasize further the need for higher spatial resolution H I studies to provide firmer estimates of cloud properties.
Chaotic analyses for space series of gold grade

A new powerful tool, chaotic theory, has been used to study mineralization through chaotic analysis for space series of gold grade in this paper. Both of the most important chaotic measures, Largest Lyapunov exponent (LLE) and fractal dimensional, for space series of gold grade in one gold deposit are computed. The positive LLE suggests that the space series of gold grade are chaotic series. When the phase space dimension approach 8~10, a chaotic attractor appears and their fractal dimension values vary from 1.94 to 3.99. It indicates that the evolution of ore-forming fluid and the enrichment and deposition of gold element are chaotic dynamic process.
A Design of Honeycomb-shape Coil for Wireless Charging of Leadless Cardiac Pacemaker

In this paper, a novel transmitting coil for magnetic resonance wireless power transfer is designed, which is called honeycomb-shape coil. It surrounds the human body and can form a stronger magnetic field than the traditional coil. Take the leadless cardiac pacemaker as an example, suppose it is on the left side of the heart and design the LCC-C compensation topology circuit to build an energy transmission system working at 13. 56MHz, the simulation results show that the transmission efficiency is approximately 0.9%. When the received power is 1mW, the maximum average specific absorption rate (SAR) of the system is 0. 240W/kg, which accords with the EN62209-1 human electromagnetic radiation standard.
Enhancing Classification Performance for Android Small Sample Malicious Families Using Hybrid RGB Image Augmentation Method

With the improvement of computer computing speed, many researches use deep learning for Android malware detection. In addition to malware detection, malware family classification will help malware researchers understand the behavior of the malware families to optimize detection and prevent However, the new malware family has few samples, which lead to bad classification results. GAN-based method can improve the classification results, but minor data will still lead to the unstable quality of the data generated by the deep learning augmentation method, which will limit the improvement of classification results. In the study, we will propose a hybrid augmentation method, first extracting malware features and converting them into RGB images, and then the minor families will augment by the gaussian noise augmentation method, and then combined with the deep convolutional generative adversarial network (DCGAN) which have better effect on image augmentation, and finally input to CNN for family classification. The experimental results show that using the hybrid augmentation method proposed in the study, compared to no augmentation and augmentation with only using the deep convolutional generative adversarial network, the F1-Score increased between 7%~34% and 2%~7%.
Cost-Optimization for Win-Win P2P Energy Systems

Attracted by a cheaper bill, more and more energy customers invest in local electricity generation and storage. Recent research advocates Peer-to-Peer (P2P) energy sharing as a way to make the most of the local energy resources, further reducing the bill of each participating end-user. However, optimizing the usage of the distributed resources is either directed to reduce costs for the customers (such as consumers and prosumers) or the grid company, and it is challenging to make them both win. We develop and analyze in this work a realistic model to implement P2P energy sharing systems that are attractive both to customers and to the grid companies. Considering that all actors are only benefits-driven, we provide mechanisms for achieving win-win P2P energy communities by developing adapted optimization procedures. Through an experimental study, we show that win-win configurations are reachable under a variety of possible scenarios in terms of distribution of energy resources. Our findings include precise figures following the current energy landscape and advocate that all actors can profit from more distributed renewable resources, making them a key driving force of tomorrow's energy systems.
The Fe XXI lambda1354 line in solar flares observed from Skylab and its implications on ionization equilibrium calculations

We have measured the line width of the forbidden line Fe XXI lambda1354 in flare spectra observed from Skylab. The measured line widths are examined using three sets of ionization equilibrium calculations by Jordan, Summers, and Jacobs et al. The results indicate that the ionization equilibrium temperature of Fe XXI is more likely to be approx.1. 0 x 10/sup 7/ K, a value given by Jacobs t al. and Jordan, rather than the higher value of approx.2. 0 x 10/sup 7/ K given by Summers.
A Review: Data Security in Cloud Computing Using Machine Learning

In today's IT industry, cloud computing, and the broader cloud services sector have gained widespread acceptance. To put it simply, cloud services provide cloud computing, which enables on-demand access to IT resources through the internet and employs a pay-per-use pricing model based on subscription authorization and centralized hosting. The need for public cloud services is increasing at an unprecedented rate as the 21st century becomes more digital. Cloud computing makes it easy to access servers, storage, databases, and various application services via the internet. The cloud environment supports a variety of advantages, but it also has a few drawbacks. Regardless of the highest industry certifications and security requirements implemented by cloud service providers, there are always risks when storing sensitive data on third-party service providers. Security and privacy must play a significant role when discussing data security, especially when handling sensitive data. Many solutions have been created to deal with this problem. It is necessary to discover, classify, and analyze the significant existing work because there is a shortage of analysis among the current solutions. This paper relates and briefly analyses the top methods for safely exchanging and safeguarding data in a cloud setting. The discussion of each specific technique covers its role in data protection, prospective and reliable solutions in the field, scope, future directions, etc. The applicability and integrity of the methodologies are then explored regarding the demands and results.
Energy-Efficient Reinforcement Learning for Motion Planning of AUV

The accuracy of mapping results depends on the Autonomous Underwater Vehicles (AUVs) navigation errors. However, signals are attenuated drastically in water, which makes it difficult for AUVs to receive signals underwater. As a result, traditional navigation methods may become unreliable. In this paper, a terrain-aided navigation method that takes into account distance and energy consumption is proposed, which does not rely on a precise positioning system. Given the complexity of 3D terrain, this paper formulates the problem as a Markov decision process (MDP) and aims to minimise the energy cost function, where a motion planning method based on soft actor-critic (SAC) is formulated. Then a 3D energy consumption calculation method is developed for the AUV which is accurate for each time slot during the training process. Finally, experiments on the Gym platform were carried out to verify the effectiveness of the proposed method.
Understanding Behavioral Intention to Use Social Media Technology: Two Comparing Model, TAM and UTAUT

This study has purpose to investigate behavioral intention to use social media technology using two model TAM and UTAUT. The proposed models were examined by 326 sample data from university students and reveal that all variables on TAM and UTAUT show their significantly direct effect among variables in the models. Hedonic motivation as main adding variable in the two model also proves the significantly direct effect on behavioral intention. By way of investigating the strong variables on previous related studies and employing the variables on the proposed model, the study reveals the prominent variables in user acceptance of social media technology including perceived usefulness, perceived ease of use, social influence, facilitating condition, and hedonic motivation. This study contributes to people who interests to the result of this study in revealing the prominent variables in research on user acceptance of social media. The recommendation of this study is the reducing or increasing user intention to use social media can be done by reducing or increasing the influence of factors or variables related to it.
One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach

We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-norm regularisation (<inline-formula> <tex-math notation="LaTeX">$p \geq 1$ </tex-math></inline-formula>) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.
Thin-film thickness profile measurement using a Mirau-type low-coherence interferometer

White-light interferometry has been spotlighted for years in the field of microelectronics as a 3D profiling tool but its application was limited to only opaque surfaces. Recently many approaches using white-light extended sources have been performed to measure the top and bottom surfaces of a thin-film structure simultaneously. When the film thickness is less than the coherence length of the light source, two waves reflected from the top and bottom surfaces of the film overlap and the interference signal become more complicated than for an opaque surface. Thus, it is an essential issue to cleanly separate the film thickness and surface height information from the complex interferograms. In this paper, we describe a Mirau-type low-coherence interferometer for measurements of the film thickness and top surface height profile with a simple measurement procedure. Our proposed method is verified by simulating the measurement errors according to the film thickness and measuring a SiO2 patterned film structure.
Recent Management Trends Involved With the Internet of Things in Indian Automotive Components Manufacturing Industries

The development of smart manufacturing systems is being driven by a variety of diverse needs for the dependability of equipment and the prediction of quality. In order to accomplish this objective through the use of machine learning, a wide range of approaches are being investigated. The management and protection of one’s company’s data presents yet another challenging aspect of doing business. In order to cope with fraudulent datasets, machine learning and internet of things technologies were utilized. These technologies were used to protect system transactions and manage a dataset. Because of this, we were able to find solutions to the problems that we had previously discussed. The gathered information was organized and examined with the help of big data techniques. The Internet of Things system was constructed using the Hyperledger Fabric platform, which is a private computer network. In addition, a hybrid prediction strategy was utilized for the defect diagnostic as well as the defect forecasting. The latest machine learning techniques were utilized in order to model the complexity of the environment and estimate the genuine positive ratio of the quality control system. The quality control of the system was evaluated using these pieces of data.
Mirror and azimuthal drift frequencies for geomagnetically trapped particles

For charged particles trapped in the geomagnetic field, the frequencies of the mirror oscillations ωm and the azimuthal drift ωd are defined as appropriate averages over the helical motion around the field lines and the mirror motion between reflection points in the two magnetic hemispheres. These integrals for ωm and ωd are evaluated numerically. Results are tabulated, illustrated, and represented by approximate analytical expressions.
Preparation and electrical properties of Bi2S3 whiskers

Bi2S3 whiskers ∼10 mm long were synthesized from vapor phase. The electrical conductivities of the resulting whiskers (along the longitudinal direction) were ∼101 S cm−1 at around room temperature and their temperature coefficient was negative. The growing direction was in the direction of the crystallographic c axis, which is parallel to the direction of running chains composed of (Bi4S6)n. The measurements of electrical conductivities in sulfur‐free and sulfur‐loading atmosphere strongly suggest that carrier electrons are generated through the formation of sulfur deficiencies.
The chemical evolution of star-forming viscous discs

Les equations determinant l'evolution chimique d'un disque visqueux sujet a la formation stellaire sont formulees en tenant compte de l'effet des ecoulements radiaux et de la diffusion dans le disque. La distribution de la metallicite est determinee dans les disques stellaires finaux. Un accord satisfaisant est trouve entre les resultats des calculs et les donnees d'observation de la distribution de metallicite dans un vieux disque galactique
The Efficient Generation of Random Orthogonal Matrices with an Application to Condition Estimators

This paper presents a method for generating pseudo-random orthogonal matrices from the Haar distribution for the group of orthogonal matrices. The random matrices are expressed as products of $n - 1$ Householder transformations, which can be computed in $O(n^2 )$ time. The technique is used in an empirical study of two methods for estimating the condition number of a matrix.
Analytical construction of peaked solutions for the nonlinear evolution of an electromagnetic pulse propagating through a plasma

Abstract We obtain analytical solutions, by way of the homotopy analysis method, to a nonlinear wave equation describing the nonlinear evolution of a vector potential of an electromagnetic pulse propagating in an arbitrary pair plasma with temperature asymmetry. As the method is analytical, we are able to construct peaked structures which propagate through the pair plasma, analogous to peakon solutions. These solutions are obtained through a novel matching of inner and outer homotopy solutions. In order to ensure that our analytical results are valid over the whole real line, we also discuss the convergence of the analytical results to the true solution, through minimization of the residual errors resulting from an approximate analytical solution. These results demonstrate the existence of peaked pulses propagating through a pair plasma. The algebraic decay rate of the pulses are determined analytically, as well. The method discussed here can be applied to approximate solutions to similar nonlinear partial differential equations of nonlinear Schr¨odinger type.
A wave equation approach to ultrasound elastography

Tissue elastography is a field of medical imaging which deals with obtaining images of tissue mechanical properties, in particular tissue elasticity. Every elastography imaging system has three major components: an exciter, an imaging system, and an image processing software module. The principle of operation of these systems is that softer and harder tissues react differently to a mechanical excitation. The imaging system captures images of the tissue as the mechanical exciter deforms the tissue. The image processing software module then computes the tissue motion from the acquired images, and solves an inverse problem to obtain tissue elasticity from tissue displacements. In ultrasound elastography all three components of the displacement are not available, and the measurement is carried out only on the imaging plane. The problem of inversion using partial measurements is an inherently challenging problem. To solve this problem the phase speed is estimated in the first place. This thesis studies the process of estimating tissue elasticity from tissue displacements, using the phase speed estimation. It is shown that, in general, the elasticity cannot be inferred from the phase speed, but under certain boundary and excitation conditions, a relationship exists between the phase speed and the elasticity. However the relationship depends on the boundary and excitation conditions. Based on the developed results, a novel rheometry method is proposed for visco-elastic characterization of tissue samples. To increase the resolution of the elasticity images it is of interest to use higher frequencies of excitation. However the inherent low frame rate of ultrasound systems posed a limitation. A novel high-framerate ultrasound system is introduced which is capable of tracking motions of up to 500Hz. The high-frame-rate system is used in conjunction with the inversion algorithms to form an elastography system. The performance of the system is tested experimentally on tissue mimicking material having hard and soft inclusions.
An approach to recording on tilted media

A simple model for recording on media with a tilted anisotropy axis is proposed. The model combines the slope theory (Williams and Comstock model) with vectorial aspects of the head fields, their corresponding gradients and the angular dependence of the switching fields of the medium. In the case of obliquely deposited metal evaporated tape, the model is shown to predict the experimental data correctly. >