Miracle-dz/UniFAR-FTUs huggingface dataset:
LOLWTC: A Deep Learning Approach for Detecting Living Off the Land Attacks

Living off the Land (LotL) attacks have gained attention in recent years because they are sneaky. These attacks exploit legitimate tools, scripts, and system permissions, making them hard to detect and track. As a result, defense costs increase. However, most research focuses on detecting and classifying malware rather than LotL attacks. This study aims to explore a novel approach that combines machine learning, deep learning, and natural language processing methods for detecting LotL attacks. We propose a deep learning detection framework called LOLWTC. It utilizes word embedding to represent qualitative features in command-line text as two-dimensional matrices. These matrices are then used for classification using deep networks. Experimental results demonstrate the robustness and effectiveness of LOLWTC. It achieves an impressive f1 score of 0.9945 on the test set during 10-fold cross-validation. This showcases its significant potential for detecting LotL attacks and addressing associated security concerns.
Radar Applications of a Prototype Dual-Band Metamaterial and Metasurface Antenna

Current dual-band radars consist of 2 different radar antennas with the same back-end electronics and software. There is great interest in AFRL to develop dual-band radars by using the same antenna, operating at two different frequencies, to achieve the desired efficiency as with traditional systems but without the SWaPC constraints. This article discusses radar applications of a dual-band antenna design made possible by integrating metamaterial and metasurface design elements. The metamaterial component of the design investigates the phase change properties of $VO_{2}$ as a mechanism for switching the equivalent impedance of the proposed antenna. The meta-surface component of the design investigates the size, arrangement and structure of the antenna's unit cells that allow for both dual-band behavior and beam-steering capabilities. This article discusses the application of the proposed dual-band antenna for detection of and tracking of radar targets.
Enhanced Power Management System Powered by Piezo Electric Energy Harvesters for Data Transmission Systems

This paper presents an enhanced power management system (PMS) for data transmission applications. The presented EHS is based on a low power non-inverting buck-boost converter and provides maximum power point tracking and protection features. Compared to previous implementations [1]-[4], the presented PMS improves efficiency by up to 50%, reduces first transmission time by 35% and periodic transmission time by 17%.
Effective Automated Drowsiness Detection System Based on Machine Intelligence Techniques

This paper discusses the development of an effective automated drowsiness detection system using machine intelligence techniques. Drowsy driving is a significant safety concern that can result in accidents, injuries, and even fatalities. To address this issue, we have developed a model that uses machine intelligence techniques such as computer vision and machine learning. The proposed system combines several machine intelligence techniques to detect drowsiness accurately. Computer vision techniques analyze facial features and eye movements to detect signs of drowsiness. The Dataset used was collected by researchers at the University of Texas, Arlington. The system's performance is evaluated using various metrics, including accuracy, sensitivity, roc curve, f1 score, and specificity. The results show that the proposed system is highly effective in detecting drowsiness, with an accuracy of over 80%. Overall, the proposed automated drowsiness detection system is an effective and reliable tool to enhance driving safety. It demonstrates the potential of machine intelligence techniques in addressing real-world problems and improving human life quality.
Modeling of ferroelectric micro-cantilever actuator

This paper proposes a simple modeling and characterizing ferroelectric micro-cantilever beam. The key ingredient of the proposed method is the incorporation of phenomenological hysteresis model into the voltage-induced bending moment of the Euler-Bernoulli beam model. Furthermore, by combining the predicted hysteresis displacement with measurement data, ferroelectric parameters including remanent and saturated polarization, coercive electric field as well as piezoelectric coefficient e31 can be determined simultaneously. The proposed model and characterization approach can be useful for micro-actuator design.
Regulation and cross-talk between environmental triggers of local immune responses in airway epithelial cells

Background: Primary nasal epithelium of house dust mite allergic individuals is in a permanently activated inflammatory transcriptional state. Objective: To investigate whether a deregulated expression of EGR1 and/or DUSP1, two potential negative regulators of pro-inflammatory responses, could contribute to the activation of the inflammatory state. Methods: We silenced the expression of EGR1 or DUSP1 in the airway epithelial cell line NCI-H292. The cell lines were stimulated in a 24-hour time course with house dust mite allergen or poly(I:C). RNA expression profiles of cytokines were established using q-PCR and protein levels were determined in supernatants with multiplex ELISA. Results: The shRNA-mediated gene silencing reduced expression levels of EGR1 by 92 % (p<0.0001) and of DUSP1 by 76 % (p<0.0001). Both mutant cells lines showed an increased and prolonged response to the HDM allergen. The mRNA induction of IL-6 was 4.6 fold (p=0.02) and 2.4 fold higher (p=0.01) in the EGR1 and DUSP1 knock-down respectively when compared to the induced levels in the control cell line. For IL-8, the induction levels were 4.6 fold (p=0.01) and 13.0 (p=0.001) fold higher. The outcome was largely similar, yet not identical at the secreted protein levels. Furthermore, steroids were able to suppress the poly(I:C) induced cytokine levels by 70 - 95 %. Conclusions: Deregulation of EGR-1 and/or DUSP-1 in nasal epithelium could be responsible for the prolonged activated transcriptional state observed in vivo in allergic disease. This could have clinical consequences as cytokine levels after the steroid treatment in EGR1 or DUSP1 knock-down remained higher than in the control cell line.
A simple DC-Link Voltage Balancing Strategy for NPC Three-level Inverters

Three-level neutral point clamped PWM inverters overcome some limitations of two-level inverters in medium voltage applications, leading to lower device ratings and greater efficiency. However, they are burdened by an intrinsic drawback of the neutral point clamped structure, which causes, under some operating conditions, a voltage imbalance of the DC link capacitors. In this paper, such an issue is faced on a system driven by a dual-carrier PWM strategy through a simple hysteretic control. The proposed technique features a very low computational burden and does not need additional power circuits or sensors. It is first presented theoretically, then its performance is evaluated through simulations.
Temperature distribution of 10 kV and 15 kV SiC-MOSFETs with large edge area

Thermal simulations evaluated temperature distribution of 10kV and 15kV SiC-MOSFETs with larger die edge areas. Experimental temperature measurements of the die surface confirmed thermal modeling. The results revealed that larger edges amplified die surface temperature variation compared to 1.2kV SiC-MOSFET. Simulation results also mentioned temperature variation of bond wires and solder during power cycle testing.
Comparative Analysis of Coil Parameters for the Optimization of Coupling Factor

This paper presents a methodology for designing a wireless power transfer (WPT) system, focusing on optimizing the electromagnetic shielding using finite element simulation software. The study evaluated different combinations of coils and shielding systems to identify an optimal design. Results demonstrate the effectiveness of the proposed methodology in achieving an optimal design that maximizes power transfer efficiency while ensuring safe exposure limits to electromagnetic fields. The proposed methodology provides a practical approach to the design of WPT systems with improved performance and safety.
An IGBT Junction Temperature Estimation Method Based On A New On-state Voltage Monitoring Circuit

Insulated gate bipolar transistor (IGBT) modules have been widely used in power electronic systems because of their large current capacity, high withstand voltage and fast switching speed. IGBT is currently the most widely used power device in power converters, and it is also the part with the highest failure rate in the power system. The reliability of IGBT is closely related to junction temperature, it’s an effective mean to improve the reliability if power devices by measuring and controlling the Average and fluctuation range of junction temperature Because of working in steady state, high resolution to parameter, high anti-interference, on-state voltage drop of IGBT (vCE-ON) is one of the most widely used thermal-sensitive parameter. Traditional monitoring circuits use high voltage MOS or diode as the voltage isolating component when IGBT turns off, which is hard to isolate the high voltage or keep the accuracy in high voltage condition. In this paper, a measurement circuit is proposed for on-state voltage drop measurement of IGBT modules with high power levels. This circuit can realize the online measurement of IGBT on-voltage drop under high voltage conditions, and has the advantages of low cost, strong anti-interference ability and good safety. Experimental results verify the effectiveness of this circuit.
Ultra-Wide Voltage Gain Hybrid Modulated CLLC Converter for Electric Vehicle

Improved modulation and control methods for the same topology yield significant efficiency improvements, reduction of passive component dimensions and help to achieve high power density. In this paper, a hybrid modulation scheme is proposed for an isolated CLLC converter for a portable EV battery charging system. Wide output voltage (150 V - 950 V) with decent flat efficiency is achieved without additional power processing stage.
Synergism of riverine and winter storm-related sediment transport processes in Louisiana's coastal wetlands

ABSTRACT The roles of various mechanisms that supply sediments from major sources, including rivers and nearshore shelf, to coastal Louisiana are not well understood or quantified, temporally or spatially. Recent studies reveal that an important association between riverine sediment input and the cyclic passage of winter storms results in a periodic supply of suspended sediments to coastal marshlands. The fact that these two mechanisms coincide in winter and early spring maximizes the availability of particulate matter for counteracting coastal land loss by enhancing substrate accretion and plant productivity. Identification of these optimum periods of suspended sediment activity, when overbank sedimentation, riverine concentrations, flood processes, and cold front passages are synergistic, can maximize the performance of wetland restoration efforts, because mineral matter and nutrients are critical for marsh vitality. When mitigation of coastal land loss is planned, it would be advantageous for weekend restoration to consider this timing when both sediment availability and constructive processes of sediment transport are optimal.
High step-down DC-DC converter and results for 48-to-5 V and 400-to-48 V implementation

This paper presents a novel concept of a DC-DC converter with high step-down of voltage. In the proposed topology the maximum voltage of the inductor is three times lower than in a buck converter. The proposed converter operates with threefold wider duty cycle than the buck at a given voltage gain. Moreover, it provides continuous regulation, low voltage stress across switches, and is adequate for bi-directional GaN transistors use. Its operation, efficiency and power losses analysis are demonstrated at 48-to-5 V and 400-to-48 V case of operation. An effective method for efficiency improvement by the use of an extended converter concept was also demonstrated.
On the Use of Planar Skin Models in High Frequency Dosimetry Assessment

The paper is on the use of a surface integral equation (SIE) based formulation and the conventional method of moments (MoM) in high frequency dosimetry. The investigation is carried out, whether the reduced planar models, could latter be utilized in the assessment of spatial averaging of absorbed power density (APD) at frequencies of 10 and 30 GHz. Some numerical results illustrating the distribution of the surface electric field and the convergence of several electric field measures, including maximum and average values, are presented in the paper.
GPS‐based atmospheric precipitable water vapor estimation using meteorological parameters interpolated from NCEP global reanalysis data

[1] This study for the first time used the Indian GPS network data along with the interpolated National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis meteorological data to estimate the precipitable water vapor (PWV) over the Indian atmosphere for a 4 year period (2001–2004) at 21 Indian GPS and 7 International GNSS Service stations. The site-specific daily average meteorological data required for water vapor estimation at these sites is obtained by performing vertical interpolation on 2.5 × 2.5° gridded NCEP/NCAR global reanalysis data set available at 17 pressure levels and horizontal interpolation on similarly gridded NCEP/NCAR data set available at the surface. The comparison of the site-specific interpolated surface pressure and temperature thus obtained from NCEP/NCAR data with the measured pressure and temperature profiles available at six GPS sites gives biases of <1 mbar and <3°C which exhibits the robustness of the interpolation schemes. The water vapor estimated at these six sites located in different geographical regions using interpolated surface pressure and weighted mean temperature values from NCEP data compare well (bias < 0.5 mm and standard deviation < 0.6 mm) with water vapor estimated using measured surface pressure and temperature values. GPS PWV estimates also compare well (bias < 3 mm) with the PWV estimates from the nearby radiosonde sites. GPS PWV values at all the sites compare well (bias < 5 mm) with the horizontally interpolated NCEP PWV values except for few sites located in the highly undulated terrain. These GPS PWV values of the Indian network (4–35°N) are then used to model the spatial variability of PWV over the Indian subcontinent as a function of zenith wet delay (ZWD). The modeled spatial variability function gives a quick and reliable estimate of near real-time GPS PWV in Indian subcontinent directly from ZWD thus eliminating the need of site-specific weighted mean temperature values.
An Improved Designing of Neuroprosthetics Arm Using LDA

Neuroprosthetics have experienced a remarkable evolution in recent times, particularly with the advent of intelligent neuroprosthetics that leverages artificial intelligence (AI) technology. These devices have the potential to improve both the input and output of neurological systems, allowing individuals to control prosthetic limbs with their thoughts and perform everyday tasks such as picking and placing objects. the key to this innovative technology lies in the ability to collect brain signals through electroencephalography (EEG) devices, which can be then deciphered and utilized by the mechatronic components of the prosthetic limb. However, effectively separating relevant signals from irrelevant ones has been a significant challenge for the design and development of neuroprosthetics, as these signals fall into three distinct categories of brain communication. In this research paper, we explore the utilization of split signals in neuroprosthetics using LDA and how this varies from person to person depending on their mental state. Incorporating LDA methods into these devices seems to be the most viable solution for society to build a system that can efficiently differentiate and utilize brain signals, bringing us one step closer to unlocking the full potential of neuroprosthetics. It is found that with the adaptation of the LDA approach, 91% of the results are properly categorized and are best among the existing literature available to date.
Investigating the Efficacy of In-Process Feedback in Improving Human-Machine Interaction in Autonomous Vehicles

As autonomous vehicles become more prevalent on our roads, the improvement of human-machine interaction (HMI) becomes increasingly critical. In-process feedback has emerged as a promising approach to enhancing HMI in autonomous vehicles, but limited research exists on its effectiveness. The purpose of this study is to determine how effective in-process feedback is at enhancing HMI. To investigate the impact of in-process feedback on HMI, a within-subjects approach and a quasi-experimental design were used. Ninety participants with experience driving autonomous vehicles were recruited and divided into an investigational group receiving in-process feedback and a control group without such feedback. The study measured the efficacy of in-process feedback on HMIs through subjective and objective measures. The subjective measure utilized a questionnaire to assess participants' perceptions of their driving experience, including comfort, confidence, and trust in the autonomous vehicle. The objective measure analyzed participants' driving behavior, encompassing speed, lane keeping, and steering patterns. The experimental group outperformed the control group in terms of comfort, confidence, and trust in the driverless car, according to the results. The objective measure also indicated improved driving behavior in the investigational group, including smoother lane keeping and fewer instances of sudden braking and acceleration. In conclusion, this study supports the effectiveness of in-process feedback as an approach to enhancing HMI in autonomous vehicles. The findings have practical implications for the design and development of autonomous vehicle technology, ultimately contributing to the public's acceptance and adoption of this technology.
Model for Angular Dependency of the Intrinsic Coercivity of Ferrite Permanent Magnets

In internal permanent magnet synchronous machines (IPMSM), the use of ferrite permanent magnets is being studied as an alternative to rare-earth elements-based permanent magnets, such as NdFeB. However, demagnetization measurements of ferrite magnets are rarely published and such information is crucial for an efficient electrical machine design with ferrite magnets. In this paper, we present measurements of partial demagnetization on ferrite permanent magnets subject to inclined external magnetic fields. From the measurements done, mathematical models are developed for Y30 and Y40 samples that defines a relationship between the intrinsic coercivity and the inclination of the external demagnetizing field. Furthermore, from the primary results, the angular dependency of hysteresis losses and relative permeability are also explored, as well as their impact on the design of IPMSM.
Turn-Off Characteristic Monitoring of Power Electronic Devices in Three-Phase Inverters Based on ZYNQ

The voltage rise time (trv) of an insulated gate bipolar transistor (IGBT) is an important condition monitoring parameter. There are some problems in traditional trv measurement methods, such as low security, complex measurement, and difficult engineering implementation, and it is also difficult to locate the IGBT to be tested. To overcome the above problems, based on the analysis of the high-frequency circuit model of the inverter-fed machine system. This paper proposes a research scheme to accurately extract trv from a detection circuit current through a parallel capacitive detection circuit of output terminals of an inverter. Combined with the polarity of the output current of the inverter, the accurate positioning of the IGBT under test is realized. Then, taking trv and inverter output currents as characteristic parameters, the IGBT turn-off characteristic monitoring system based on ZYNQ is built. The experimental results show that the proposed scheme is accurate in trv extraction, low cost, simple operation, and easy to implement. The related turn-off characteristic monitoring system has a stable function, fast trv extraction speed, and high accuracy, maximum absolute error of 10.70ns, and maximum relative error of 8.37%.
Dielectric Function of VO2 determined by Spectroscopic Ellipsometry

The transition metal oxide VO2 exhibits a metal-insulator transition and is of interest for dynamic metamaterial applications. Here the complex dielectric function of VO2, de-posited via magnetron sputtering, is determined below and above the metal-insulator transition temperature using spectroscopic ellipsometry over a wide soectral range.
A Comparative Study in the Design of an ANN Based Controller

The power system stabilizer is based on artificial neural networks and is designed to cope with changing operational circumstances in real-world applications. The parameters of a conventional or supplementary power system stabilizer are generated with changing operating circumstances using a Multi-layer feed-forward with back propagation (MLP) Artificial neural network. As a result, the power system stabilizer's utility becomes more stable. Many researchers included this artificial neural network in their designs; however, information on network creation functions, hidden layer size, non-linear (active) functions employed in output and hidden layers, and Back Propagation (BP) training functions were not studied in detail. In this paper a study is carried out on these aspects to explore the effect of these aspects on the functionality of power system stabilizers.
Face Detection under Complex Background and Illumination

For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.
Comparative Study of DDoS Attacks & Tools and Their Analysis

Distributed denial-of-service or DDoS is one of the most popular attacks and is one of the most widely used attacks which is capable of shutting down a service like web application or even a system completely by supplying it will maliciously file via botnets which makes it harder to trace to the real source and protect against it. DDoS attacks have existed for a long amount of time and made vast improvement. In this paper, the study of various attacks including their Taxonomy has been studied in order to give a brief review about their capabilities and know about their shortcomings. This paper is also focused on the detection, protection and mitigation of attacks and how it can be done in order to keep the system safe. The latter part of the paper gives an analysis on the popular tools that are being used in order to perform these attacks and how their features can make it more effective and what shortcomings come up while using a certain tool for the attack. In conclusion, a summary analysis of the paper has been presented which gives a detailed view of what has been observed which conducting this analysis.