Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilience and Security on Enterprise Networks: A Multi-Sector Study

The rate of cyber-attacks on enterprise network is increasing daily, accounting for losses in billions of dollars yearly. Professor Soren Morgan once asserted that if it were measured as a country, then the damages from cybercrime alone estimated at $6 trillion globally in 2021, would be the world's third-largest economy after the U.S. and China. This study examines the implications of cyber resilience on the security of enterprise network operations with a view to ascertaining how organizations in different economic sectors adopt cyber resilience strategies towards protecting their network resources against data and privacy violations. Twenty organizations in three different sectors were studied. The findings showed that organizations in the two sectors: oil and gas, and finance and banking, demonstrate better cyber security infrastructure and the adoption of cyber resilience strategies respectively, whereas, organizations in the higher education sector did not show remarkable commitment to the adoption of good cyber security practices. Overall, all organizations appear rather slow in the implementation of multi-level resilience strategies.
Fast Tree Model for Predicting Network Security Incidents

Network security personnel are expected to provide uninterrupted services by handling attacks irrespective of the modus operandi. Multiple defensive approaches to prevent, curtail, or mitigate an attack are the primary responsibilities of a security personnel. Considering the fact that, predicting security attacks is an additional technique currently used by most organizations to accurately measure the security risks related to overall system performance, several approaches have been used to predict network security attacks. However, high predicting accuracy and difficulty in analyzing very large amount of dataset and getting a reliable dataset seem to be the major constraints. The uncertain behavior would be subjected to verification and validation by the network administrator. KDDD CUPP 99 dataset and NSL KDD dataset were both used in the research. NSL KDD provides 0.997 average micro and macro accuracy, having average LogLoss of 0.16 and average LogLossReduction of 0.976. Log-Loss Reduction ranges from infinity to 1, where 1 and 0 represent perfect prediction and mean prediction respectively. Log-Loss reduction should be as close to 1 as possible for a good model. Log-Loss in the classification is an evaluation metrics that characterized the accuracy of a classifier. Log-loss is a measure of the performance of a classifier where the prediction input is a probability value between “0.00 to 1.00”. It should be as close to zero as possible. This paper proposes a FastTree Model for predicting network security incidents. Therefore, ML.NET Framework and FastTree Regression Technique have a high prediction accuracy and ability to analyze large datasets of normal, abnormal and uncertain behaviors.
Bandwidth Dependence of the Propagation Channel in Circular Metallic BAN Environments

In this paper, the bandwidth dependence of the propagation channel for Body Area Networks (BANs) in circular metallic environments is addressed and models are proposed to evaluate the appropriate short-term fading margins that should be considered as a function of the system bandwidth. The deployment of BANs in metallic indoor environments, such as ships, factories, warehouses and other similar environments, involves additional challenges compared to other indoor environments due to the specific propagation effects in this type of environments (i.e., with strong reflections). No studies of this kind and for this type of environment can be found in literature, giving the motivation for this work. Bandwidth dependent values of delay spread are also presented and discussed. It is observed that for a system bandwidth up to 100 MHz the fading depth is composed of three main stages, with the transitions between these stages being associated with the system ability to discriminate arriving rays at the receiver. Average values of fading depth are 16.4, 13.2 and 11.0 dB for stages 1, 2 and 3, respectively, the difference between consecutive stages ranging between 2.2 to 3.2 dB. For a system bandwidth larger than 100 MHz, the fading depth decreases with an increasing system bandwidth, with an average decay rate close to 3 dB per 100 MHz bandwidth, being about 2 dB for system bandwidths above 400 MHz.
Two ResNet Mini Architectures for Aircraft Wake Vortex Identification

The identification of aircraft wake vortex is an essential issue in the operation of airspace utilization ratio. In particular, accurately identifying wake vortex in fine classification is helpful to guide separation standards under realistic airport conditions that consist of various complex operation scenarios. To stress this issue and improve the efficiency at the same time, we developed two mini architectures with each network of 10 layers by modifying deep residual neural network (ResNet) and describe the results of a study to evaluate the performances for identifying wake vortex in fine classification. For this purpose, we built the wake vortex dataset measured with pulsed Doppler LiDAR at Chengdu Shuangliu International Airport from Aug 16, 2018, to Oct 10, 2018. To support wake vortex identification in fine classification, the classification indices that consider the background wind speeds, wake vortex evaluation and aircraft types were included in the learning and identification tasks. We compared the performance of the two ResNet mini architectures with other lightweight networks by using wake vortex dataset. The experimental results demonstrate that the developed two ResNet mini architectures contribute to competitive wake identification modeling in terms of accuracy and parameter number.
Integration of multidimensional parameters of polarimetric synthetic aperture radar images for land use and land cover classification

Abstract Diverse parameters that are decomposed from quad polarimetric synthetic aperture radar (PolSAR) imagery become the important basis in the target recognition and classification. The selection of effective parameters is a very important research topic. This work aims to explore the algorithm of parameter selection based on the parametric statistics and multidimensional analysis. The proposed algorithm merges the parameters from different decomposed algorithms and the optimal parameters describing the backscattering characters of the targets are explored. The difference of parameters’ locations in three-dimensional spaces is the important basis of target differentiation. Based on the selected parameters, PolSAR images are classified using the object-oriented analysis and decision tree method. The experimental results indicate that the overall accuracy and Kappa coefficient of the classification using the integrated multidimensional parameters were higher than those using Freeman and H / A / α decomposed parameters. The advantage of this algorithm is to select optimal parameter combinations in multidimensional space by integrating many parameters from different decomposed algorithms.
ASQ-FastBM3D: An Adaptive Denoising Framework for Defending Adversarial Attacks in Machine Learning Enabled Systems

Machine learning has made significant progress in image recognition, natural language processing, and autonomous driving. However, the generation of adversarial examples has proved that the machine learning system is unreliable. By adding imperceptible perturbations to clean images can fool the well-trained machine learning systems. To solve this problem, we propose an adaptive image denoising framework Adaptive Scalar Quantization (<italic>ASQ-FastBM3D</italic>). The <italic>ASQ-FastBM3D</italic> framework combines the <italic>ASQ</italic> method with the <italic>FastBM3D</italic> algorithm. The adaptive scalar quantization is the improvement of scalar quantization, which is used to eliminate most of the perturbations. <italic>FastBM3D</italic> is proposed to improve the quality of the quantified image. The running time of <italic>FastBM3D</italic> is 50% less than that of <italic>BM3D</italic>. Compared with some traditional filter methods and some state-of-the-art neural network methods for recovering the adversarial examples, the accuracy rate of our <italic>ASQ-FastBM3D</italic> method is 99.73% and the F1 score is 98.01%, which is the highest.
Prediction of Aqueous Solubility of Drug Molecules by Embedding Spatial Conformers Using Graph Neural Networks

Aqueous solubility prediction of drug molecules is essential in drug design pipelines. Due to the availability of a vast amount of high-quality data, deep learning based methods of molecular property prediction methods are gaining more and more attention every day and have achieved outstanding results. Graph Neural Networks is one of the most successful classes in deep learning for this specific task, which can be because of the graph-like nature of molecules. In this paper, we proposed to use a new GNN model called ALIGNN, which has achieved the state of the art performance on QM9 dataset tasks by introducing the line graph concept. Training ALIGNN on the Delaney dataset, we have achieved an RMSE of 0.511.
Spectral Characteristics of DC Short Glow Discharge Plasma With Grid Electrodes

The characteristics of post-cathode plasma maintained by a self-made DC short glow-discharge plasma generator with grid electrodes were measured by optical emission spectroscopy. The discharge existed in an abnormal glow discharge mode at a voltage of 250–400 V and a helium pressure of 3–15 Torr and a discharge gap of 3 mm. The results indicate that the intensity distribution of helium atom spectral lines in the post-cathode space differs from that in the post-anode space. The temperature of electron excitation in the post-cathode plasma reaches its peak of 1500–3000K at 5–6 mm from the grid cathode. The difference in the intensity distributions of helium atom spectral lines in the post-cathode and post-anode spaces is associated with electron generation mechanism in these cases.
A Pathway on 5G EMC Testing: A Tutorial

The fifth-generation wireless system (5G) is becoming more present in today's reality. Understanding the electromagnetic compatibility (EMC) requirements is critical for preparing future device designs to conform to the upcoming standards. This article provides an overview of EMC's role in the certification process and provides an updated standardization guide, comparing emissions and immunity tests of the latest 3GPP/ETSI and ITU recommendations. We discuss the test methodologies and challenges when setting up EMC test facilities, especially for OTA at the FR2 band. We also provide an example of equipment selection as a guide for 5G 3GPP RF conformance tests for User Equipment (UE) and Base Station (BS). Finally, thinking about the future, we dive into the next-generation wireless communication world to provide insights on how 6G technology advancements would impact EMC.
Design of the Wideband and Low-Height Omnidirectional Cylindrical Dielectric Resonator Antenna Using Arced-Apertures Feeding

This paper mainly discusses the design of wideband and low-height omnidirectional cylindrical dielectric resonator antenna (DRA). It was theoretically found that the TM-mode category of the cylindrical DRA is suitable for the wideband and low-height design using mode-merging method (TM<inline-formula> <tex-math notation="LaTeX">$_{01 \delta }$ </tex-math></inline-formula> and TM<inline-formula> <tex-math notation="LaTeX">$_{02 \delta }$ </tex-math></inline-formula> mode). An improved apertures-fed technique is proposed to excite the TM-mode DRA. The feeding consists of four arc-shaped apertures, and each aperture was fed by the in-phase signal. Based on the E-field distribution, the higher-order TM<inline-formula> <tex-math notation="LaTeX">$_{02 \delta }$ </tex-math></inline-formula> mode of the cylindrical DRA can be excited by adjusting the position of the apertures, and it fuses together with the fundamental TM<inline-formula> <tex-math notation="LaTeX">$_{01 \delta }$ </tex-math></inline-formula> mode to achieve a wideband antenna. Besides, the aperture mode further widen its impedance bandwidth. To demonstrate the idea, a wideband and low-height omnidirectional cylindrical glass DRA was designed for WLAN 5.2-GHz and 5.8-GHz applications. The experimental results show that the proposed low-height DRA (<inline-formula> <tex-math notation="LaTeX">$h $ </tex-math></inline-formula> = <inline-formula> <tex-math notation="LaTeX">$0.108\lambda _{0}$ </tex-math></inline-formula>) can achieve an impedance bandwidth of 25.2 % (without aperture mode) and 34 % (with aperture mode), respectively. The bandwidth enhancement of the proposed design is significant, compared to other reported low-height TM-mode DRAs.
Acoustic Scene Classification for Bone-Conducted Sound Using Transfer Learning and Feature Fusion

The air-conducted (AC) sound is usually used in the task of acoustic scene classification (ASC). Compared with the AC sound, bone-conducted (BC) sound has the unique advantage of shielding background noise. However, the amount of information contained in BC sound is far less than that in the AC sound due to its limited frequency bandwidth. In this paper, an acoustic scene classification method for BC sound is proposed with a small BC dataset. Firstly, the prosodic features are combined with the spectral features to capture more information, and feature fusion is adopted. Secondly, in order to deal with the small BC dataset, transfer learning is used with a large AC dataset. Finally, a deep learning network based on local residual learning is proposed. The experimental results show that the proposed method achieves the superior performance over the reference models.
Desynchronization Waves in Power Grids Described by a Kuramoto-like Model

A network of coupled Kuramoto-like oscillators is studied to model the dynamics in modern power systems. While previous works focus on the criteria of synchronization, we in this paper study the development of desynchronization, which may lead to power outrage and system breakdown in real power systems. Instead of numerically simulating detailed component-level models, we use a simplified network-reduced model which captures essential features of a real system, including spread of desynchronization waves under disturbances. Some statistical results about the relation between the maximum disturbance and coupling strength are also found.
Association Between Serum Trace Heavy Metals and Liver Function Among Adolescents

Higher level of serum Zn and Hg were associated with elevated ALT in adolescents, respectively. Serum cholesterol may be the intermediate pathway for the ALT increase caused by serum metals. This study can provide support for sex-based adverse reaction analysis of heavy metals exposure in the future. Background Exposure to metals has been associated with liver-related disease. Few studies have explored the effect of sex stratification on adolescent liver function. Method From the National Health and Nutrition Examination Survey (2011–2016), 1143 subjects aged 12–19 years were selected for analysis. The outcome variables were the levels of alanine aminotransferase (ALT), aspartate aminotransferase, and gamma-glutamyl transpeptidase. Results The results showed a positive association between serum zinc and ALT in boys (odds ratio [OR], 2.37; 95% confidence interval [CI], 1.11–5.06). Serum mercury was associated with an increase in ALT level in girls (OR, 2.73; 95% CI, 1.14–6.57). Mechanistically, the efficacy mediated by total cholesterol accounted for 24.38% and 6.19% of the association between serum zinc and ALT. Conclusions The results imply that serum heavy metals were associated with the risk of liver injury, possibly mediated by serum cholesterol, in adolescents.
A new way to characterize autostereoscopic 3D displays using Fourier optics instrument

Auto-stereoscopic 3D displays offer presently the most attractive solution for entertainment and media consumption. Despite many studies devoted to this type of technology, efficient characterization methods are still missing. We present here an innovative optical method based on high angular resolution viewing angle measurements with Fourier optics instrument. This type of instrument allows measuring the full viewing angle aperture of the display very rapidly and accurately. The system used in the study presents a very high angular resolution below 0.04 degree which is mandatory for this type of characterization. We can predict from the luminance or color viewing angle measurements of the different views of the 3D display what will be seen by an observer at any position in front of the display. Quality criteria are derived both for 3D and standard properties at any observer position and Qualified Stereo Viewing Space (QSVS) is determined. The use of viewing angle measurements at different locations on the display surface during the observer computation gives more realistic estimation of QSVS and ensures its validity for the entire display surface. Optimum viewing position, viewing freedom, color shifts and standard parameters are also quantified. Simulation of the moire issues can be made leading to a better understanding of their origin.
Accuracy of quasistationary analysis of highly dynamic speech signals

In this paper, the accuracy of the analysis of rapidly varying formants using spectrogram and linear prediction is assessed. Analysis of various dynamic signals shows that, when a long analysis window, like 25 ms, is used, the quality of the representation may be impoverished. Obvious unwanted effects are staircaselike formant tracks, flattening‐off of formants close to voicing onset, and bending of the formant towards a strong energy concentration in the release burst. The parameters that have the largest influence on the quality of the representation are the length of the analysis window, the transition rate of the formant, the fundamental frequency, and the position and energy of the release burst. It is shown that the most accurate analysis using a quasistationary method is made when windows are positioned pitch synchronously. Finally, a quantitative analysis of the influence of the mentioned parameters provides evidence that no deviations due to the quasistationarity assumption occur when the effecti.
Multi-harmonic electron cyclotron instabilities

We present parametric studies of the loss cone instability of multi-harmonic electron cyclotron instabilities in a plasma consisting of a hot component with loss cone free energy source and a cold thermal component. We find that the cold upper hybrid frequency must exceed the wave frequency in order that a non-convective instability exist. When NC/NH ∼ 0.5, at least the first three harmonic bands can be simultaneously non-convective. However, the first harmonic band has large spatial growth rates over the widest parameter range; it should therefore be the most commonly observed harmonic, and except in certain circumstances should normally accompany the higher harmonics when they are observed.
Efficient identification of uncertain parameters in a large‐scale tidal model of the European continental shelf by proper orthogonal decomposition

The adjoint method can be used to identify uncertain parameters in large‐scale shallow water flow models. This requires the implementation of the adjoint model, which is a large programming effort. The work presented here is inverse modeling based on model reduction using proper orthogonal decomposition (POD). An ensemble of forward model simulations is used to determine the approximation of the covariance matrix of the model variability and the dominant eigenvectors of this matrix are used to define a model subspace. An approximate linear reduced model is obtained by projecting the original model onto this reduced subspace. Compared with the classical variational method, the adjoint of the tangent linear model is replaced by the adjoint of a linear reduced forward model. The minimization process is carried out in reduced subspace and hence reduces the computational costs. In this study, the POD‐based calibration approach has been implemented for the estimation of the depth values and the bottom friction coefficient in a large‐scale shallow sea model of the entire European continental shelf with approximately 106 operational grid points. A number of calibration experiments is performed. The effectiveness of the algorithm is evaluated in terms of the accuracy of the final results as well as the computational costs required to produce these results. The results demonstrate that the POD calibration method with little computational effort and without the implementation of the adjoint code can be used to solve large‐scale inverse shallow water flow problems. Copyright © 2011 John Wiley & Sons, Ltd.
Azimuthal electromagnetic surface waves in a rod dielectric magnetized plasma waveguide and their excitation by an annular relativistic rotating electron beam

The dispersion relation of azimuthal electromagnetic surface waves in a rod dielectric magnetized plasma waveguide is obtained. It is investigated that these waves in E-type can be excited by a thin annular relativistic rotating electron beam (TARREB). The effects of the rotating velocity of beam, radius TARREB, on the frequency spectra and growth-rate coefficient are presented.
Hydrogen induced donor‐type Si/SiO2 interface states

Study of donor‐type Si/SiO2 interfaces states shows that these centers anneal at room temperature when neutral but are stable when charged positively. Moreover, the anneal process is accompanied by the release of H. We propose that the donor states are related to H attached to interfacial network sites, most likely O atoms, constituting electrically active complexes. When positively charged, H is strongly bonded; when neutral H is only weakly attached. In the latter case it can escape and dimerize so that the states disappear. Similar complexes tying down H in a positively charged state would constitute small cross‐section hole traps in the bulk of the oxide.
A Systematic Review of the Adherence to Home-Practice Meditation Exercises in Patients with Chronic Pain

Mindfulness-, compassion-, and acceptance-based (i.e., “third wave”) psychotherapies are effective for treating chronic pain conditions. Many of these programs require that patients engage in the systematic home practice of meditation experiences so they can develop meditation skills. This systematic review aimed at evaluating the frequency, duration, and effects of home practice in patients with chronic pain undergoing a “third wave” psychotherapy. A comprehensive database search for quantitative studies was conducted in PubMed, Embase, and Web of Sciences Core Collection; 31 studies fulfilled the inclusion criteria. The reviewed studies tended to indicate a pattern of moderately frequent practice (around four days/week), with very high variability in terms of time invested; most studies observed significant associations between the amount of practice and positive health outcomes. Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy were the most common interventions and presented low levels of adherence to home practice (39.6% of the recommended time). Some studies were conducted on samples of adolescents, who practiced very few minutes, and a few tested eHealth interventions with heterogeneous adherence levels. In conclusion, some adaptations may be required so that patients with chronic pain can engage more easily and, thus, effectively in home meditation practices.