Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation Analysis of the influence of electromagnetic Pulse Power on the performance degradation of CMOS Inverter

The strong electromagnetic pulses generated by nuclear explosions and electromagnetic pulse weapons can interfere radar system by damage the integrated circuit in the system. With the increasing of integrated circuit integration and the continuously reducing of the device feature size, the damage effect of electromagnetic pulse on the integrated circuit becomes more and more obvious. The electromagnetic pulse damage effect of integrated circuit is mainly reflected in the thermal field in the integrated circuit. Aiming at the electromagnetic pulse damage law of integrated circuits, CMOS inverter is researched. The device simulation software Sentaurus is then used to establish the inverter model. Then the effects of the power and pulse width of the injected electromagnetic pulse on the performance of the inverter are studied. The simulation results show that when the power is low, the performance of the inverter is only temporarily interfered, and its operation can resume normal when the EMP no longer acts. When the power reaches a certain threshold, the performance of the inverter will be permanently damaged. The larger the pulse width, the smaller the damage threshold of the inverter.
Channel estimation for OTFS system over doubly spread sparse acoustic channels

This paper addresses sparse channels estimation problem for the generalized linear models (GLM) in the orthogonal time frequency space (OTFS) underwater acoustic (UWA) system. OTFS works in the delay-Doppler domain, where time-varying channels are characterized as delay-Doppler impulse responses. In fact, a typical doubly spread UWA channel is associated with several resolvable paths, which exhibits a structured sparsity in the delay-Doppler domain. To leverage the structured sparsity of the doubly spread UWA channel, we develop a structured sparsity-based generalized approximated message passing (GAMP) algorithm for reliable channel estimation in quantized OTFS systems. The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm. In addition, the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance. Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.
A Systematic Literature Review of Virtual Reality Utilization in Tourism During COVID-19 Pandemic

Virtual reality (VR) is an innovation that is increasingly applied in many fields of study. Although VR is adapted in most of simulation works like aviation, cargo movement, surgery, and phobia therapy, the application in tourism has different intention compared to other contexts, which is for promotion purpose. Therefore, this study provides a systematic literature review of how VR has been applied to help promoting tourism industry during the pandemic of COVID-19, when most people in the world are restricted from traveling, and ordered to stay at home. A research question that would address application of VR in tourism is identified. A comprehensive literature search has been carried out resulting 87 journals and proceeding papers are filtered. Data extraction and synthesize is performed on the selected studies based on the research question. From the SLR process, we have found that VR is utilised in tourism for promotion purposes, and to evaluate the attitude and emotion of users towards the application of VR in virtual tourism during the pandemic. Our result suggests that tourism industry players need to pay more attention and widen the intention in utilizing VR in the future to confirm the sustainability of tourism industry.
Mitigation of Rumours in Social Networks via Epidemic Model-based Reinforcement Learning

While detection of rumours in online social networks has been intensively studied in the literature, mitigation of the spread of rumours has only recently gained attention and remains a challenging task. Some studies developed user opinion models to find top influential users as debunkers to spread the truth to counter rumour spread. Other studies designed an intervention framework to optimize the mitigation activities for given debunkers. The issue of optimizing the selection of debunkers in a dynamic environment where users’ beliefs and behaviour change remains under investigated. This paper addresses this issue by proposing a rumour mitigation approach based on the deep reinforcement learning framework. In particular, we model the changes in users’ beliefs with an epidemic model. We further employ deep reinforcement learning to train an agent to learn a multi-stage policy for selecting the optimal debunkers to inject truthful information under a budget constraint. Our model selects debunkers to inject truthful information at multiple stages with an overall objective to maximize the number of users who will believe in the true information (a.k.a number of recovered nodes), such that the spread of rumours is minimized. Our experiments on synthetic and real-world social networks show that our proposed method for rumour mitigation can effectively minimize the spread of rumours.
Voltage Regulation of DC-DC Buck Converters Feeding CPLs via Automatic Curriclum Learning

Curriculum Reinforcement Learning (CRL) conducts agent learning through predefined training courses, improving the learning speed and stability of the agent. However, predefined courses rely too much on the quality of prior experience and ignore feedback to learners. To solve the stability and load uncertainty problems of DC-DC buck converters with constant power load. First, sampling goal on the boundary where an agent can reach a set of targets, then the method will provide a stronger learning signal compared to the target of random sampling. Therefore, we import the Goal Proposal Module to consider more boundary goals and automatically generate effective courses. In the state of relatively large conversion of the constant power load. The simulation results of control strategy based on automatic curriculum learning in reference to PI. The results show that the automatic curriclum learning has higher dynamic performance and learning speed and can track.
IRAS OBSERVATIONS OF THE DIFFUSE INFRARED BACKGROUND

IRAS data reveal bright emission from interplanetary dust which dominates the celestial background at 12, 25, and 60 microns except near the galactic plane. At 100 microns, interplanetary dust emission is prominent only near the ecliptic plane; diffuse galactic emission is found over the rest of the sky. At the galactic poles, the observed brightness implies that A(v) is likely to be of order 0.1 mag. The angular variation of the zodiacal emission in the ecliptic plane and in the plane at elongation 90 deg, and an annual modulation of the ecliptic pole brightness, are generally consistent with previously determined interplanetary dust distributions.
Acoustic-perceptual correlates of whispered Mandarin consonants

This study aims to explore the acoustic-perceptual correlates of whispered Mandarin consonants. The acoustic features of whispered consonants in Mandarin in comparison to their phonated counterparts and their perceptual correlates were analyzed. The acoustic measurements included word duration, consonant duration, closure duration, and frication duration of plosives and affricatives as well as the intensity and spectral moments. Perceptual experiments examined identification accuracy and confusion matrices. The acoustic analyses showed that whispered consonants have longer durations. The intensities of consonants, especially nasals and semivowels, were lower in whispered conditions but the relative intensity of most whispered consonants became higher, except for nasals. The spectral moments of whispered fricatives and affricatives did not change significantly, and the duration distinction was preserved, although narrowed. The perceptual results showed that listeners had lower but still high consonant recognition accuracy (above 75%), possibly owing to the preserved spectral moments, duration distinction of aspiration and other acoustic features. The perceptions of nasal and aspiration distinctions were substantially influenced by whispering, which could be attributable to the lower intensity, the absence of vocal cord vibration, and the narrower duration distinction of aspiration. These findings enrich our understanding of the production and perception mechanisms of whispered consonants.
DDoS Detection by Using Information Gain-Naïve Bayes

Advances in technology and the rapid development of the internet have led to more information and data that needs to be protected because there are many ways that hackers can do to get information or data. The vulnerability of a network is also caused by the more open knowledge about hacking. Distributed Denial of Service (DDoS) is an attack in which more than one attacker floods with packets to the server, so the server is busy serving many packet requests and makes server performance decrease. Many studies have been done to detect DDoS attacks. However, the study conducted still uses old datasets that have not followed the development of DDoS attack trends. This study classifies the CICIDS2018 DDoS attack dataset with feature selection using information gain to look for features that have a big influence on determining whether a packet sent is a DDoS attack or not. The Naïve Bayes method is used to build a prediction model. The use of information gain to perform feature selection on the CICIDS2018 dataset resulted in nine optimal features, including src_ip, dst_ip, flow_duration, flow_iat_max, fwd_iat_max, and bwd_iat_tot. The results show the accuracy of DDoS attack detection for the Naïve Bayes method without feature selection is 65% and 69.6% with feature selection.
Machine Learning Technique for Classification of Internet Firewall Data Using RapidMiner

A firewall system is a type of security system that controls how data packets enter and leave a network. It does this to improve cyber defence and decide what to do with harmful packets. Traffic packets are checked against criteria to stop possible cyber threats from getting into the network. This study demonstrates the classification of internet firewall data using a public dataset containing 65,532 records and 11 features, and a unique machine-learning technique. No use has been made of any personally identifying information in the filtered data. The action characteristic is selected from among these features as the class label. The action class now supports the options “allow,” “deny,” “drop,” and “reset-both. ” The study proposes an intelligent classification model that firewall systems can use to determine what to do with each received packet using a machine learning algorithm and the RapidMiner tool to look at a packet’s properties. We classified the data using the Decision Tree (DT) and K-Nearest Neighbor (K-NN) methods. The highest accuracy was achieved using the Decision Tree model.
Multi-layer Multi-head Self-attention Model for Radar HRRP Target Recognition

Radar automatic target recognition (RATR) based on high-resolution range profile (HRRP) has attracted much attention in recent years. We present a Multi-head Attention model to characterizes the long-term temporary dependency across the range cells of HRRP, which utilizes stacked attention modules to aggregate features with weights obtained by aligning range cells in a single HRRP, resulting in more discriminative latent representations. We further develop two efficient pooling strategies to obtain the final HRRP representations for the downstream tasks. Experiments on measured data show that the proposed method brings better performance over the corresponding models.
Cramer-Rao bound of joint estimation of target location and velocity for coherent MIMO radar

The optimal estimation performance of target parameters is studied. First, the general form of Cramer-Rao bound(CRB)for joint estimation of target location and velocity is derived for coherent multiple input multiple output(MIMO) radars. To gain some insight into the behavior of the CRB, the CRB with a set of given orthogonal waveforms is studied as a specific case. Second, a maximum likelihood(ML) estimation algorithm is proposed. The mean square error(MSE) of the ML estimation of target location and velocity is obtained by Monte Carlo simulation and it approaches CRB in the high signal-to-noise ratio(SNR) region.
Incoherent Change Detection Method Based on Image Stack for UWB SAR Image

This paper presents a novel incoherent change detection method based on image stacks for the ultra-wideband synthetic aperture radar (UWB SAR) images. This method includes the image preprocess and change detection process. The image preprocess adopted the bilinear relative radiometric correction method for the radiometric correction. The change detection process firstly obtains the multiple difference images by subtracting a surveillance image with three reference images. And then, a Gaussian probability density function is used to model the distribution of the amplitude of the difference image. Finally, the generalized likelihood ratio test (GLRT) is used for the change detection. Compared with existing change detection methods, the proposed method in this paper shows the better detection performance on the CARABAS-II dataset. Under a specific configuration, the proposed method achieves a detection probability of 97% with a false alarm rate of 0.16/km2.
Simulation on Bone Porosity Estimation Using Pulse Echo Technique Ultrasound

The precision of conventional quantitative ultrasonography, which uses parameters such as attenuation and velocity to assess bone health, is still unsurpassed by Dual X-ray absorptiometry, which completely exploits the interaction of ultrasound with bone. The accuracy of ultrasonic assessment using the transmission approach to forecast fracture has improved with the discovery of fast and slow waves that can travel through cancellous bone, although it is still confined to a specific region of the skeletal site. Pulse echo approach is presented to tackle the problem based on previous simulation research by conducting a simulation and experiment to study the interaction of rapid and slow waves with various microstructures of cancellous bone models. Bone models with varying porosity will be investigated using two types of transmissions are used for this study namely Through-Transmission (TT) technique and Pulse-Echo (PE) technique. The received wave is captured and filtered. The mix of fast, and slow wave characteristics, such as signal amplitude, and attenuation are then calculated. These parameter values will be plotted against the porosity of bone models to study their correlation. The performance of the correlation between fast and slow wave parameters acquired using the pulse echo technique will be compared to the performance of the correlation between fast and slow wave parameters obtained using the transmission approach. Overall, the amplitude value for both involved approaches decrease as porosity increases. In most cases, a high correlation value is also seen, demonstrating how strongly the distribution of amplitude depends on the bone porosity percentage.
Effect of Speaker-Microphone Proximity on Pop Noise: Continuous Wavelet Transform-Based Approach

The problem of Voice Liveness Detection (VLD) has been explored recently, wherein pop noise is used as the discriminating acoustic cue between live and spoofed speech. In this work, we propose an algorithm to capture pop noise energy using Bump wavelet, wherein we test our proposed algorithm to analyse the effect of distance (between the speaker and the microphone) on the strength of pop noise captured by the microphone. Furthermore, the type of phoneme uttered also plays an important role, when dealing with pop noise. Therefore, in this work, we have analyzed the variability of pop noise strength with distance, for each of the phoneme types. To the best of the authors’ knowledge and belief, this is the first work of its kind to take into account the distance as well as the phoneme type for the analysis of pop noise event in the speech signal. We have compared our proposed CWT-based approach with STFT-based approach using trendline equations. Our analysis shows that CWT-based approach is better suited for VLD, and the strength of the pop noise for phoneme classes, such as liquids and nasals is the least affected by distance. On the other hand, pop noise due to plosives and fricatives is the most affected by distance variability.
Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection

Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and evaluations for treatment. DFU patient population is on the rise and will soon outpace the available health resources. Autonomous monitoring and evaluation of DFU wounds is a much-needed area in health care. In this paper, we evaluate and identify the most accurate feature extractor that is the core basis for developing a deep learning wound detection network. For the evaluation, we used mAP and F1-score on the publicly available DFU2020 dataset. A combination of UNet and EfficientNetb3 feature extractor resulted in the best evaluation among the 14 networks compared. UNet and Efficientnetb3 can be used as the classifier in the development of a comprehensive DFU domain-specific autonomous wound detection pipeline.
The equilibrium theory of inhomogeneous polymers

1. Introduction 2. Ideal Chain Models 3. Single Chains in External Fields 4. Models of Many-Chain Systems 5. Self-Consistent Field Theory 6. Beyond Mean-Field Theory A. Fourier Series and Transforms B. Gaussian Integrals and Probability Theory C. Calculus of Functionals D. Complex Langevin Theory
Optimal FCL Placement and Sizing Incorporate DOCR Settings to mitigate Escalated Fault Stresses in Distribution Network

Despite providing increased reliability and power quality in meeting the energy demand based on continuous network growth, the high penetration of integrated distributed generations (DGs) has changed the fault current levels and direction. Consequently, some buses are exposed to critical conditions, violate the circuit breakers (CB) handling capacity, and affect the relay coordination operation. Considering this, the distribution network operators inhibit the connection of DGs at high-fault buses to avoid the adverse impact on the switch gears. To mitigate these adverse impacts, fault current limiters (FCL) can be a potential solution. Due to its expensive cost, it is crucial to ensure optimal FCL placement and sizing while maintaining the effectiveness of the protective relay performance during network operations. This paper proposes a combined optimization strategy for optimal deployment and sizing of FCL along with DOCR settings to mitigate the negative impact of DGs in maintaining proper protective device coordination. The proposed combined optimization technique is tested on a Canadian radial Distribution Network (DN). The results show the effectiveness in optimizing the FCL size with the least cost and reducing the overall relay operating time under different grid operating scenarios (On/Off).
Nonlinear Sheath Admittance, Currents, and Charges Associated With High Peak Voltage Drive on a VLF/ELF Dipole Antenna Moving in the Ionosphere

We consider a bare antenna consisting of two line elements driven by a VLF source and immersed in the ionosphere. Nonlinear effects associated with high voltages up to 1000 volts and with an additional induced v×B·1 voltage are included. The applied voltage is visualized as a dc effect. For various instantaneous voltage values, we obtain the distribution along the antenna of voltage and resis'ive current, sheath size, and charge per unit length. At the feed points, waveforms for the charge and both resistive and displacement currents are illustrated, assuming an applied cosine voltage form. Averaging over a cycle yields, as a function of peak bias voltage, the average power dissipated in collection of particles from the medium, the average work done in moving charge between the two half-antennas, and the average sheath conductance and capacitance. Numerical results are given for three altitudes and two antenna lengths.
Analysis of PAPR and BER in MIMO OFDM 5G Systems with different Modulation and Transform Techniques

Peak to average power ratio (PAPR) has been reduced using a number of different ways. For MIMO- OFDM system Selected Mapping (SLM) is one of the most impressive PAPR reducing techniques. This work looks into a useful peak to average power ratio reduction method for orthogonal frequency division multiplexing (OFDM) signals. The proposed scheme employs a combination of different modulation techniques and different transforms for the SLM technique with random phase sequences and centring matrix to reduce the PAPR and BER. Here 2X2 LTE-Multiple Input and Multiple Output (MIMO) technique is used for reducing PAPR and Bit Error Rate (BER). For the proposed method i.e. Differential Amplitude Phase Modulation (DAPM)-Discrete Wavelet Transform (DWT) combination PAPR and BER is less compared to all other combinations of modulation techniques such as Quadrature Amplitude Modulation (QAM), Pulse Amplitude Modulation (PAM) and transforms such as Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT). For MIMO OFDM technique, PAPR for the combination of DAPM-DWT is 3.433 at 1$0^{-1}$ CCDF and BER is 0.4006 at SNR of 20db and for LTE-MIMO, PAPR3.446 at 1$0^{-1}$CCDF and BER is 0.4285 at SNR of 20db.
A note on the instantaneous streamlines, pathlines and pressure contours for a cavitation bubble near a boundary

Abstract Instantaneous streamlines, particle pathlines and pressure contours for a cavitation bubble in the vicinity of a free surface and near a rigid boundary are obtained. During the collapse phase of a bubble near a free surface, the streamlines show the existence of a stagnation point between the bubble and the free surface which occurs at a different location from the point of maximum pressure. This phenomenon exists when the initial distance of the bubble is sufficiently close to the free surface for the bubble and free surface to move in opposite directions during collapse of the bubble. Pressure calculations during the collapse of a cavitation bubble near a rigid boundary show that the maximum pressure is substantially larger than the equivalent Rayleigh bubble of the same volume.
Enhancing Individual Fairness through Propensity Score Matching

The central idea of individual fairness is based on an auspicious yet intuitive assertion: similar individuals should be treated similarly. Nevertheless, the fulfillment of individual fairness is hindered by three major obstacles. First, one needs to determine individuals who should receive similar treatment. Second, seamlessly formulating the notion of individual fairness in an ML learning process is another challenge. Third, effectively evaluating the notion of similar treatment in probabilistic classifiers is another challenge. To overcome these challenges, we propose a novel framework called FairMatch. Our proposed framework offers a new approach to pairing similar and dissimilar individuals using a causal analysis method called propensity score matching. Moreover, we formulate individual fairness as a representation learning problem where we incorporate similar and dissimilar pairs in a triplet-based loss function. Eventually, we devise a novel metric to evaluate individual fairness that captures the notion of similar treatment in probabilistic classifiers in a better way. Experimental results on four real-world datasets verify the superiority of FairMatch to existing solutions where we demonstrate it can deliver fairer decisions without scarifying the predictive performance.