Miracle-dz/UniFAR-FTUs huggingface dataset:
Constant false alarm rate detection of slow targets in polarimetric along‐track interferometric synthetic aperture radar imagery

To improve the detection performance for ground moving targets with small radar-cross-section and low velocity in Along-Track Interferometric Synthetic Aperture Radar (AT-INSAR) system, this study explores new methods to elevate the target-to-clutter ratio by using full Polarimetric AT-INSAR (AT-POLINSAR). Three novel detectors that can achieve Constant False Alarm Rate (CFAR) detection are put forward for AT-POLINSAR at first, namely, the Suboptimal Interferometric Coherence Detector, the Generalized Polarimetric Whitening Filter and the Dual Detector. Secondly, the analytical expressions of their false-alarm rates are derived under the Gaussian distribution assumption of the six-dimensional polarimetric interferometric vector, then the estimation methods of distribution parameters and the calculating methods of detection thresholds as well as the specific implementation schemes of CFAR detection are given. Finally, the proposed detectors are compared with single-polarimetric AT-INSAR detectors and full polarimetric log-likelihood-ratio test through both the Monte Carlo detection experiments and the scene detection experiments of simulated AT-POLINSAR imagery, where the results indicate that new methods have rather higher CFAR detection performance and velocity estimation accuracy than current single-polarimetric detectors.
Bayesian Inference for Stochastic Volatility Models

Stochastic volatility (SV) models provide a natural framework for a representation of time series for financial asset returns. As a result, they have become increasingly popular in the finance literature, although they have also been applied in other fields such as signal processing, telecommunications, engineering, biology, and other areas. In working with the SV models, an important issue arises as how to estimate their parameters efficiently and to assess how well they fit real data. In the literature, commonly used estimation methods for the SV models include general methods of moments, simulated maximum likelihood methods, quasi Maximum likelihood method, and Markov Chain Monte Carlo (MCMC) methods. Among these approaches, MCMC methods are most flexible in dealing with complicated structure of the models. However, due to the difficulty in the selection of the proposal distribution for MetropolisHastings methods, in general they are not easy to implement and in some cases we may also encounter convergence problems in the implementation stage. In the light of these concerns, we propose in this thesis new estimation methods for univariate and multivariate SV models. In the simulation of latent states of the heavy-tailed SV models, we recommend the slice sampler algorithm as the main tool to sample the proposal distribution when the Metropolis-Hastings method is applied. For the SV models without heavy tails, a simple Metropolis-Hastings method is developed for simulating the latent states. Since the slice sampler can adapt to the analytical structure of the underlying density, it is more efficient. A sample point can be obtained from the target distribution with a few iterations of the sampler, whereas in the original MetropolisHastings method many sampled values often need to be discarded. In the analysis of multivariate time series, multivariate SV models with more general specifications have been proposed to capture the correlations between the innovations of the asset returns and those of the latent volatility processes. Due to some
Data-Driven-Based Distributed Fuzzy Tracking Control for Nonlinear MASs Under DoS Attacks

In this paper, the distributed fuzzy tracking control problem is investigated for high-order fuzzy nonlinear multiagent systems (MASs) under denial-of-service attacks. The proposed method is distinct from existing approaches in that it can accommodate scenarios where the model parameters of the reference systems are unknown, and the MAS is high order and nonlinear. Specifically, a data-driven algorithm is first introduced to learn the unknown reference system matrix. Based on the learned matrix, a distributed resilient observer and an improved observer are, respectively, designed to guarantee that both observers can observe the reference system state and that the high-order derivative of the improved observer state exists. By using the improved observer state and its high-order derivatives, a decentralized adaptive fuzzy controller is designed for each agent based on the backstepping technique. It is shown that the distributed resilient tracking can be achieved by the proposed method. Finally, a simulation example is proposed to show the effectiveness of the developed method.
Au-Nanorods Supporting Pd and Pt Nanocatalysts for the Hydrogen Evolution Reaction: Pd Is Revealed to Be a Better Catalyst than Pt

Ordered thin films of Au nanorods (NRs) on Ti/Au/Si heterostructure substrates are electrodeposited in thin film aluminum oxide templates and, after template removal, serve as supports for Pd and Pt nanocatalysts. Based on previous work which showed a better electrocatalytic performance for layered Au/Pd nanostructures than monolithic Pd, electrodeposited 20 nm Pd discs on Au-NRs are first investigated in terms of their catalytic activity for the hydrogen evolution reaction (HER) and compared to monolithic 20 nm Pd and Pt discs. To further boost performance, the interfacial interaction area between the Au-NRs supports and the active metals (Pt and Pd) was increased via magnetron sputtering an extremely thin layer of Pt and Pd (20 nm overall sputtered thickness) on the Au-NRs after template removal. In this way, the whole NR surface (top and lateral) was covered with Pt and Pd nanoparticles, ensuring a maximum interfacial contact between the support and the active metal. The HER performance obtained was substantially higher than that of the other nanostructures. A Salient result of the present work, however, is the superior activity obtained for sputtered Pd on Au in comparison to that of sputtered Pt on Au. The results also show that increasing the Au-NR length translates in a strong increase in performance. Density functional theory calculations show that the interfacial electronic interactions between Au and Pd lead to suitable values of hydrogen adsorption energy on all possible sites, thus promoting faster (barrier-free diffusion) hydrogen adsorption and its recombination to H2. A Volmer–Heyrovsky mechanism for HER is proposed, and a volcano plot is suggested based on the results of the Tafel plots and the calculated hydrogen adsorption energies.
DIGITAL HISTORIC STRUCTURE REPORT (DHSR): A BUILDING INFORMATION MODELLING (BIM) UTILIZATION STRATEGY IN HISTORIC PRESERVATION

Abstract. To allow a more comprehensive, adaptable, and accessible Historic Structure Report (HSR), an approach to transform HSR to Digital HSR (DHSR) utilizing Building Information Modelling (BIM) will be evaluated in this paper. HSR is the type of historic documentation ruled by the National Park Service (NPS), a U.S. Department of the Interior bureau. It is directed to owners of historic buildings to help with decisions on the structure and provide an information resource for future uses. BIM’s ability to enhance the current HSR process by closing the disconnection between visual and information could suggest its potential utility in complementing or even substituting physical HSR. This research will be conducted in two approaches to support the proposal for a standard application of DHSR. The first approach is by analyzing the current guideline of HSR, which will include the evaluation of the NPS’s “Preservation Brief. ” The second approach is to evaluate the BIM features, potential, and challenges for Digital HSR by analyzing the software as a user. The result of this study would be a proposed protocol of the DHSR, as an application guideline.
Preconditioning for Mimetic Spectral Element Method: A Preliminary Study Applied on Elliptic Equations

In order to solve linear system of equations obtained from numerical discretisation fast and accurate, preconditioning on the coefficient matrix is needed. In this thesis research, a preliminary study on preconditioning techniques suitable for Mimetic Spectral Element Method (MSEM) will be presented. The spectral limit change of some important discrete operators that appear in MSEM are studied, with respect to the discretisation order change. Some of the limits are proven by theorems and some are estimated by reasoning. Preconditioners and preconditioning techniques are developed and performed on a Poisson problem using both p-version and hp-version MSEM. Lastly in conclusion, the cause of ill-conditioning of MSEM-related systems are discussed; a common framework of preconditioning study for MSEM is proposed, which will be helpful for future studies and application on different problem types.
Parameterization analysis and inversion for orthorhombic media

Accounting for azimuthal anisotropy is necessary for the processing and inversion of wide-azimuth and wide-aperture seismic data because wave speeds naturally depend on the wave propagation direction. Orthorhombic anisotropy is considered the most effective anisotropic model that approximates the azimuthal anisotropy we observe in seismic data. In the framework of full wave form inversion (FWI), the large number of parameters describing orthorhombic media exerts a considerable trade-off and increases the non-linearity of the inversion problem. Choosing a suitable parameterization for the model, and identifying which parameters in that parameterization could be well resolved, are essential to a successful inversion. In this thesis, I derive the radiation patterns for different acoustic orthorhombic parameterization. Analyzing the angular dependence of the scattering of the parameters of different parameterizations starting with the conventionally used notation, I assess the potential trade-off between the parameters and the resolution in describing the data and inverting for the parameters. In order to build practical inversion strategies, I suggest new parameters (called deviation parameters) for a new parameterization style in orthorhombic media. The novel parameters denoted d, ηd and δd are dimensionless and represent a measure of deviation between the vertical planes in orthorhombic anisotropy. The main feature of the deviation parameters consists of keeping the scattering of the vertical transversely isotropic (VTI) parameters stationary with azimuth. Using these scattering features, we can condition FWI to invert for the parameters which the data are sensitive to, at different stages, scales, and locations in the model. With this parameterization, the data are mainly sensitive to the scattering of 3 parameters (out of six that describe an acoustic orthorhombic medium): the hori5 zontal velocity in the x1 direction, 1 which provides scattering mainly near the zero offset in the x1-x3 vertical plane, and d, which is the ratio of the horizontal velocity squared in the x1 and x2 direction. Since, with this parameterization, the radiation pattern for the horizontal velocity is azimuth independent, we can perform an initial VTI inversion for two parameters (velocity and 1), then use d to fit the azimuth variation in the data. This can be done at the reservoir level or any region of the model.
Technique to deliver dsRNA in Bemisia tabaci nymphs and its validation through silencing of juvenile hormone esterase gene

RNA interference (RNAi) is a technique employed to investigate the functions of various genes in whitefly and other insects. The effectiveness of the RNAi technique depends upon the delivery of dsRNA into the insects and it has been achieved through microinjection, oral feeding, egg soaking, transfection, topical application, spraying with nanoparticles, leaf‐mediated dsRNA feeding or plant‐mediated/ transgenic plants. In Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae), most of the studies on RNAi were conducted on the adult through either injection or oral feeding. There is a need to develop a system to deliver dsRNA in whitefly nymphs because the first instar nymphs after hatching find a suitable place on the leaf and feed on the same place till adult emergence. So, we evaluated nine different techniques using different sponges, tubes, and, Petri plates for feeding nymphs. The detached leaf survived for maximum number of days (8.25 days) with minimum nymphal mortality (12.01%) in the Whitefly Nymphal Feeding System‐1 (WNFS‐1) treatment; therefore, WNFS‐1 was selected for the delivery of dsRNA to B. tabaci nymphs. In WNFS‐1, blue food dye was used to confirm that nymphs were being fed, and the results were evidenced by blue‐coloured bodies. The juvenile hormone esterase (jhe) gene was silenced in order to successfully deliver dsRNA to nymphs using WNFS‐1 and resulting in much higher nymphal mortality (68.36%). Therefore, WNFS‐1 is a reliable method of delivering dsRNA for the purpose of silencing the genes in the nymphal stages of whiteflies. The WNFS system may also be used extensively to evaluate any chemistry (insecticides, hormones, biopesticides, etc.) in nymphs of whitefly.
A Comprehensive Review of Multi-Port DC/DC Converters for The Off-Grid System Integration with Renewable Energy Resources

Appropriate design of Multi-Port Converter (MPC) topology helps to overcome the difficulties of combining numerous renewable energy sources (RES). In this article, a comprehensive analysis of these MPCs in terms of their topologies, operating principles, various dependability, and overall efficacy is offered. There are two main types of MPCs which are non-isolated and isolated MPCs being coupled in parallel and series configurations to function as multi-input converters. These MPCs can flow the load power in one or both directions for the RES or BESS (battery energy storage system) applications. The non-isolated MPCs are most used in the latest years but they have some limitations such as small voltage gain, and failure in isolation. The isolated MPC is more effective than the non-isolated ones in terms of isolation between the input power stage and output but has some drawbacks such as the high cost, and large size of the high-frequency transformer (HFT). In this review paper, a high-efficiency voltage-regulator/battery energy storage system (VR-BESS) was presented as a multi-port DC-DC converter for the standalone PV (photovoltaic) array. This converter has fewer switches, is cheaper, and is more dependable than its counterparts.
AraBERTopic: A Neural Topic Modeling Approach for News Extraction from Arabic Facebook Pages using Pre-trained BERT Transformer Model

: Topic modeling algorithms can better understand data by extracting meaningful words from text collection, but the results are often inconsistent, and consequently di ffi cult to interpret. Enrich the model with more contextual knowledge can improve coherence. Recently, neural topic models have emerged, and the development of neural models, in general, was pushed by BERT-based representations. We propose in this paper, a model named AraBERTopic to extract news from Facebook pages. Our model combines the Pre-training BERT transformer model for the Arabic language (AraBERT) and neural topic model ProdLDA. Thus, compared with the standard LDA, pre-trained BERT sentence embeddings produce more meaningful and coherent topics using di ff erent embedding models. Results show that our AraBERTopic model gives 0.579 in topic coherence.
Space-variant motion compensation for synthetic aperture radar under complicated flight path

Abstract. Motion compensation (MOCO) is an essential procedure for synthetic aperture radar under a complicated flight path. Wide swath and low-precision navigation data limit traditional MOCO algorithms. We present a space-variant MOCO method to solve two problems: (1) estimation of the deviations of radar during data acquisition and (2) range envelope correction (REC) before range cell migration correction. In the first problem, we apply phase-gradient autofocus with correlate input and weighted total least square jointly. In the second one, the chirp-scaling-based (CS) REC is proposed. Finally, both simulation and real data experiments validate the proposed method.
Higher education student responses to the use of online assessments on biology material

The integration of technology into learning assessment activities makes it easy for educators to carry out online assessments which provide convenience for students and an effective assessment. The purpose of this research is to find out the response of students in the Biology Education Study Program towards online assessment platforms for Biology materials and learning. This typed of researched was quantitative descriptive used a surveyed method which had been carried out in the odd semester of 2021/2022 with the researched subjects being active students of Biology Education Study Program, Faculty of Teacher Training and Education, University of Muhammadiyah Malang, a total of 114 students. The data collection technique used a surveyed method used Google Form and the data was analyzed descriptively. The results of the studied showed that of the 5 assessment platforms used in the researched, Quizizz and Google Form platforms was the preferred assessment platforms by students The results of this study indicate that in the future we need a product that can meet the needs of the assessment and according to the needs of the respondents. This product corresponds to the advantages and disadvantages of each online assessment platform. Thus, creating a more effective and efficient online assessment platform.
An Analysis of Students’ Speaking Anxiety in Virtual Learning Classroom During the COVID-19

This study aimed to find (1) the level of students’ speaking anxiety; and (2) factors causing the anxiety among students during virtual learning. This research used descriptive qualitative method, with the aim of analyzing data taken from 30 students of second semester students of English Education Department at Syiah Kuala University in academic year of 2021/2022. In conducting this study, the researcher used PSCAS (Public Speaking Class Anxiety Scale) questionnaire by Yaikhong and Usaha (2012). The results revealed that most of students (67%) were in medium level which experienced by 20 out of 30 students. Meanwhile, the factors that most influenced student anxiety is lack of preparation factors (73%). Then, the next factors are grammatical error, motivation, friend/classmate, embarrassment, lack of confidence, fear of making mistakes, shyness and limited vocabulary. Based on these findings, it can be concluded that speaking in the virtual classroom can make students feel anxious and pressured. Anxiety can negatively affect students' speaking achievement; therefore, it is important for lecturers to be concerned with students' anxiety and find the best learning methods to reduce their anxiety, also by making the class atmosphere more comfortable and not monotonous.
Exploratory study of the prevalence of high risk for coronary events in the brazilian population: evidence from two population-based surveys

Circulatory system diseases are the leading causes of death and disability in the Brazilian’s population. To carry out an exploratory analysis of temporal changes in the prevalence of high risk coronary events and associated factors in the Brazilian population based on the first phase of risk stratification of the Brazilian Guidelines on Cardiovascular Risk. This cross-sectional study was carried out in panels to assess temporal changes in the prevalence of high risk coronary events in Brazilian adults according to the first phase of risk stratification of the Brazilian Guidelines for Cardiovascular Prevention based on self-reported information from two national household surveys. Proportional differences tests and generalized linear models (Poisson Regression) were used to consider the complex survey samplings, namely NHS 2013 and NHS 2019. There was an increase in the proportion of high risk coronary events between the two surveys (10.05 vs 12.11, p<0.001). There was a higher prevalence of high risk coronary events in residents from the Center-South region of Brazil (PR=1.40; 95% CI 1.31-1.50), with advancing age (PR=4.87;95% CI 3.85-6.15), male (1.15 ; 95% CI 1.09-1.21), with regular, poor, or very poor health self-assessment (PR=2.63;95% CI 2.44-2.85), a smoking history (PR=1.12; 95% CI 1.06-1.17), underlying health conditions factors such as hypertension (PR=1.82 ; 95% 1.70-1.95) and high cholesterol (PR=1.31; 95% CI 1.08-1.60) and overweight/obesity (PR=1.42; 95% CI 1.32-1.56). There was an increase in the prevalence of high risk coronary events in the five years in the Brazilian population. The higher prevalence of this outcome was associated with sociodemographic factors, underlying health conditions factors, and a history of smoking and being overweight and obese.
Effects of magnetic fields on an unsteady mixed convective boundary layer flow of an electrically conducting fluid with temperature dependent properties

A mixed convective boundary layer flow of an electrically conducting fluid with temperature dependent properties over an inclined plate is investigated. The magnetohydrodynamic boundary layer governing equations are derived by using Boussinesq and boundary layer approximations. The equations are transformed to similarity form using a similarity transformation variable and the resulting boundary value problem is solved numerically. The effects of magnetic fields, unsteadiness, mixed convection and variable fluid properties on velocity and temperature in the boundary layer are analysed. The effects of pertinent parameters on skin friction and heat transfer are also analysed. Keywords: Magnetohydrodynamics; Mixed convection; Boundary layer; Temperature dependent fluid properties.
Carbon Conversion Using High Voltage Plasma Method Based on Mangrove Wood Charcoal

This study investigates the conversion of mangrove wood charcoal into graphene using high voltage plasma technology through arc discharge. The experiment involves heating carbon with high voltage plasma generated from rod and plate electrodes. The variables examined are the electrode distance and carbon treatment time. The results demonstrate the successful conversion of mangrove wood charcoal into graphene. The generated plasma is influenced by the electrode distance, with a 1 cm gap producing stronger bluish-orange plasma. Varying the treatment time also affects the graphene yield, with a 3-minute treatment generating more graphene compared to 2 minutes, and 2 minutes yielding more graphene than 1 minute. XRD analysis reveals characteristic peak shifts indicative of graphene presence. SEM analysis confirms the graphene structure with porous features and sub-micrometer sizes. SEM images and diameter data further validate the successful conversion of carbon into graphene. These findings provide a foundation for the development of high voltage plasma-based production of graphene from mangrove wood charcoal. The utilization of a 10kV Neon Power Supply transformer enables the generation of high voltage plasma for the carbon-to-graphene conversion process. The electrode distance in the transformer plays a crucial role, as greater distances result in higher voltages, while shorter distances lead to lower voltages. This research significantly contributes to expanding the knowledge and application of graphene in various scientific and engineering fields. Moreover, the understanding of how electrode distance affects the generated voltage using a Neon Power Supply transformer is an important finding for optimizing the performance of this type of transformer.