Miracle-dz/UniFAR-FTUs huggingface dataset:
SWaP Optimised L -Band Power Amplifier for Airborne Applications

This paper discusses the design and development of a SWaP (size, weight and power) optimized L-Band Power Amplifier for Ground and Airborne applications. GaN transistors the main active high-power element in this amplifier designs for high efficiency. Keysight Advance Design System (ADS) simulation tool is used to design and optimize the input and output matching along with transient & EM simulation of power amplifier. The 3-stage amplifier has been designed using two driver stages and one power stage amplifier. The bias sequencing circuit for GaN is designed using P-channel, N-channel MOSFETs and AND gates. The amplifier has provision of reverse polarity protection and switching on/off by digital controls. This amplifier works with single power supply which can range from 8V-60V and its output can be switched between two antenna ports. With optimized matching networks, more than 4-watt output power, power gain of 15 dB and power added efficiency (PAE) of more than 50% are obtained at carrier frequency of 1200 MHz and band width of 100 MHz of main power stage. The isolation between the two antenna ports is more than 50 db. The amplifier has been designed with thermal compensation for power output of more than 4 watt in the temperature range of - 20°C to +55°C. The hardware realized using mixed four-layer PCB with the total thickness of 1.6 mm on Rogers 4350B and FR-4substrate. The top Layer 20 mil Rogers 4350B is used for RF routing. Components are populated on both side of the PCB due to size constraint. The SWaP optimized amplifier has been realized in mechanical housing with the size of 124 mm x 86 mm x 31.5 mm (LxWxH) and weight of 480 gm with extra mounting studs for fitment.
Parallel Gibbs Sampling for Computing and Propagating Large Covariance Matrices

The Gibbs sampler for inverting large matrices of normal equations and for propagating the resulting covariance matrices is modified for parallel computing. The matrix times vector multiplications for generating the error vectors of the unknown parameters are replaced by matrix times matrix multiplications so that the computationally efficient BLAS and ATLAS subroutines can be applied. To judge the accuracy of the computed covariance matrix a criterion is derived which gives information about the significant digits obtained during the process of the parallel computing. Finally, based on the blocking technique for reducing correlations between the generated error vectors a storage scheme is used which allows the inversion of matrices of normal equations of any size. The parallel Gibbs sampler has been applied for the inversion of normal equations as they result from the data analysis for the new satellite gravity missions. With a parallel computer consisting of 15 nodes the speed of the computations could be increased by a factor of 7.4 in comparison to a single computer.
(Partial) Program Dependence Learning

Code fragments from developer forums often migrate to applications due to the code reuse practice. Owing to the incomplete nature of such programs, analyzing them to early determine the presence of potential vulnerabilities is challenging. In this work, we introduce NeuralPDA, a neural network-based program dependence analysis tool for both complete and partial programs. Our tool efficiently incorporates intra-statement and inter-statement contextual features into statement representations, thereby modeling program dependence analysis as a statement-pair dependence decoding task. In the empirical evaluation, we report that NeuralPDA predicts the CFG and PDG edges in complete Java and C/C++ code with combined F-scores of 94.29% and 92.46%, respectively. The F-score values for partial Java and C/C++ code range from 94.29%-97.17% and 92.46%-96.01%, respectively. We also test the usefulness of the PDGs predicted by NeuralPDA (i.e., PDG*) on the downstream task of method-level vulnerability detection. We discover that the performance of the vulnerability detection tool utilizing PDG* is only 1.1% less than that utilizing the PDGs generated by a program analysis tool. We also report the detection of 14 real-world vulnerable code snippets from StackOverflow by a machine learning-based vulnerability detection tool that employs the PDGs predicted by NeuralPDA for these code snippets.
Sooty Tern Optimization Technique based 2DOFPID Controller for Stability of Multisource Power System with Plug-in Electric Vehicles Integration

In this work, the stability of dual area multisource (DAMS) interconnected power system (IPS) is assessed subjected to the perturbation in step load (SLP) of 10% in area-1. For this purpose, a two-degree-of-freedom (DOF) PID (2DOFPID) controller optimized with the newest algorithm of the sooty tern optimization technique (STOT) is enacted as the secondary regulator. However, the STOT-based 2DOFPID controller efficacy is validated with the widely accepted controllers available in the literature. The communication time delays (CTDs) are taken with the DAMS system to perform the realistic investigation. CTDs’ impact on DAMS performance is disclosed and justified. Additionally, the plugin electric vehicles (PEVs) have been incorporated into area-1 of the DAMS system for attaining system performance improvement.
A Medium to Long-Term Multi-Influencing Factor Copper Price Prediction Method Based on CNN-LSTM

Non-ferrous copper prices exhibit high noise, non-smoothness, and non-linearity, which pose significant challenges to accurate price prediction. One of the current methods for predicting copper prices is multi-influencing factor analysis, which typically relies on traditional optimization or neural network methods to identify factors that affect copper prices. However, extracting attribute features and high-level semantics from raw data using these conventional methods can be difficult, which may necessitate revision of the selected influencing factors and final results. This paper proposes a CNN-LSTM-based approach that leverages the feature extraction capabilities of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. After analyzing the fluctuation features of copper prices and their qualitative relationships with factors such as supply and demand, energy costs, alternative metals, global macroeconomic conditions, and national policies, we selected 11 influencing factors for copper price fluctuation as explanatory variables using scatter plots, Pearson correlation coefficients, and heat maps. These variables are then fed into a CNN-LSTM network as a two-dimensional multivariate time series, along with historical copper price data, for monthly price forecasting. Experimental results show that our proposed method outperforms other existing methods by utilizing the attribute space feature extraction capability of CNNs and the temporal feature extraction of LSTMs.
Enhancing Network Intrusion Detection: An Online Methodology for Performance Analysis

Machine learning models have been extensively proposed for classifying network flows as benign or malicious, either in-network or at the endpoints of the infrastructure. Typically, the performance of such models is assessed by evaluating the trained model against a portion of the available dataset. However, in a production scenario, these models are fed by a monitoring stage that collects information from flows and provides inputs to a filtering stage that eventually blocks malicious traffic. To the best of our knowledge, no work has analysed the entire pipeline, focusing on its performance in terms of both inputs (i.e., the information collected from each flow) and outputs (i.e., the system’s ability to prevent an attack from reaching the application layer). In this paper, we propose a methodology for evaluating the effectiveness of a Network Intrusion Detection System (NIDS) by placing the model evaluation test alongside an online test that simulates the entire monitoring-detection-mitigation pipeline. We assess the system’s outputs based on different input configurations, using state-of-the-art detection models and datasets. Our results highlight the importance of inputs for the throughput of the NIDS, which can decrease by more than 50% with heavier configurations. Furthermore, our research indicates that relying solely on the performance of the detection model may not be enough to evaluate the effectiveness of the entire NIDS process. Indeed, even when achieving near-optimal False Negative Rate (FNR) values (e.g., 0.01), a substantial amount of malicious traffic (e.g., 70%) may still successfully reach its target.
MirrorTaint: Practical Non-intrusive Dynamic Taint Tracking for JVM-based Microservice Systems

Taint analysis, i.e., labeling data and propagating the labels through data flows, has been widely used for analyzing program information flows and ensuring system/data security. Due to its important applications, various taint analysis techniques have been proposed, including static and dynamic taint analysis. However, existing taint analysis techniques can be hardly applied to the rising microservice systems for industrial applications. To address such a problem, in this paper, we proposed the first practical non-intrusive dynamic taint analysis technique MirrorTaint for extensively supporting microservice systems on JVMs. In particular, by instrumenting the microservice systems, MirrorTaint constructs a set of data structures with their respective policies for labeling/propagating taints in its mirrored space. Such data structures are essentially non-intrusive, i.e., modifying no program meta-data or runtime system. Then, during program execution, MirrorTaint replicates the stack-based JVM instruction execution in its mirrored space on-the-fly for dynamic taint tracking. We have evaluated MirrorTaint against state-of-the-art dynamic and static taint analysis systems on various popular open-source microservice systems. The results demonstrate that MirrorTaint can achieve better compatibility, quite close precision and higher recall (97.9%/100.0%) than state-of-the-art Phosphor (100.0%/9.9%) and FlowDroid (100%/28.2%). Also, MirrorTaint incurs lower runtime overhead than Phosphor (although both are dynamic techniques). Moreover, we have performed a case study in Ant Group, a global billion-user FinTech company, to compare MirrorTaint and their mature developer-experience-based data checking system for automatically generated fund documents. The result shows that the developer experience can be incomplete, causing the data checking system to only cover 84.0% total data relations, while MirrorTaint can automatically find 99.0% relations with 100.0% precision. Lastly, we also applied MirrorTaint to successfully detect a recently wide-spread Log4j2 security vulnerability.
A review of visible single target tracking based on Siamese networks

Visible single-target tracking is an important research direction in computer vision. Recently, twin networks have shown strong performance in target tracking. Although there have been many single-target tracking reviews, there are few summaries of single-target twin networks. In order to better clarify the development status of twin networks, 17 popular trackers are divided into five categories, and the performance of these trackers in five data sets, such as LASOT, OTB and VOT, is discussed and analyzed—finally, the development of single target tracking in twin network prospects.
Experimental Comparison of UWB and Magnetic Ranging Systems in Robotics Applications

This paper presents an experimental comparison of an Ultra Wide Band (UWB) and a magnetic ranging system, with the goal of developing a new tool for distance estimation targeting robotic applications. To characterize the performance, compatibility, and complementarity of the two ranging systems, three experiments were performed. These experiments demonstrate that these two technologies can coexist. Also, analyzing the standard deviation of the distance estimation error, it was observed that up to 1.5 meters the magnetic ranging system achieves better performance, while for greater distances the UWB ranging system represents a better choice. In addition, it has been shown from experiments with obstacles, thus in non-line-of-sight conditions, that the UWB ranging system can be affected by metallic obstructions. In contrast, the magnetic system does not show significant variations in these situations. With one of the experiments, the absence of substantial interference between the magnetic system and a UAV (Unmanned Aerial Vehicle) was also verified.
Hardware processors for pairing-based cryptography

Bilinear pairings can be used to construct cryptographic systems with very desirable properties. A pairing performs a mapping on members of groups on elliptic and genus 2 hyperelliptic curves to an extension of the finite field on which the curves are defined. The finite fields must, however, be large to ensure adequate security. The complicated group structure of the curves and the expensive field operations result in time consuming computations that are an impediment to the practicality of pairing-based systems. The Tate pairing can be computed efficiently using the ηT method. Hardware architectures can be used to accelerate the required operations by exploiting the parallelism inherent to the algorithmic and finite field calculations. The Tate pairing can be performed on elliptic curves of characteristic 2 and 3 and on genus 2 hyperelliptic curves of characteristic 2. Curve selection is dependent on several factors including desired computational speed, the area constraints of the target device and the required security level. In this thesis, custom hardware processors for the acceleration of the Tate pairing are presented and implemented on an FPGA. The underlying hardware architectures are designed with care to exploit available parallelism while ensuring resource efficiency. The characteristic 2 elliptic curve processor contains novel units that return a pairing result in a very low number of clock cycles. Despite the more complicated computational algorithm, the speed of the genus 2 processor is comparable. Pairing computation on each of these curves can be appealing in applications with various attributes. A flexible processor that can perform pairing computation on elliptic curves of characteristic 2 and 3 has also been designed. An integrated hardware/software design and verification environment has been developed. This system automates the procedures required for robust processor creation and enables the rapid provision of solutions for a wide range of cryptographic applications.
A Review on Fake News Detection Techniques

In the modern era, digital communication has become an integral part of our lives. The way we consume news has also evolved, with the internet providing various platforms to access news content such as websites, applications, and web page pop-ups. However, with the increased availability of news sources, it has become more challenging to determine which sources are trustworthy and which ones are not. Some news outlets are recognized and trusted by millions of people around the world, while others disseminate fake news with the intent of creating disharmony in society. Spreading false information can have negative effects on public sentiment and decision-making. Therefore, many researchers have developed ways to detect and segregate news based on its truthfulness. These techniques aim to differentiate between fake news and genuine news. This review paper summarizes some of the methods developed over the years that can help verify news and distinguish between fake and original or real news. The paper provides a useful overview of the tools available for individuals to verify news content and make informed decisions about the credibility of news sources.
Acquiring Photoplethysmography (PPG) Signal Without LED

Photoplethysmography (PPG) sensors should use the least amount of power possible when integrated into wearables as battery life is one of the main concerns in wearable area. In this context, we propose a system which can collect PPG signals under ambient light conditions without turning on any LED of the PPG sensor (NO-LED mode). The system consists of an efficient analog front end, a photodiode and a microprocessor. Results show that PPG signals recorded with the proposed system under different ambient light conditions can be placed in reliable or acceptable category. Moreover, heart rate estimated from NO-LED mode PPG signal shows less than 6% error when compared with heart rate estimated from a typical PPG signal acquired with LED on. Since LED is the most power consuming part of the PPG sensor, the proposed system has potential for increasing the battery life of PPG sensor-based wearables.
Highly Sensitive Miniaturized DS-CSRR Sensor for Complex Permittivity Measurements

This work presents the simulation study of a novel square Double Split Complementary Split Ring Resonator (DS-CSRR) resonating at 2470 MHz of the ISM band to be used as microwave-based planar resonator sensor for estimating the complex permittivity of unknown chemical samples. Polynomial curve fit equations were derived from the simulated data obtained using High Frequency Structure Simulator (HFSS). Using this dielectric constant (ε‘) and dielectric loss (ε”) of unknown liquid sample were determined. The developed fit equations are based on the performance of the sensing element for the six Liquid Under Tests (LUTs) spanning over 1–111 of permittivity values. The found average and normalized sensitivity of the proposed sensor are 42 MHz per unit dielectric constant and 1.7 respectively which is much higher in comparison to recently published works. The applicability of the sensor in material characterization can be assured as it can accurately predict the real and imaginary parts of complex permittivity of unknown sample with less than 3.4% difference.
Artificial Intelligence (AI) and Machine Learning (ML)-based Information Security in Electric Vehicles: A Review

The use of artificial intelligence (AI) and machine learning (ML) in electric vehicles (EVs) is gaining popularity as a means of improving information security. However, there is a lack of research on the specific ways in which Artificial intelligence (AI) and machine learning (ML) are being used in this context. This review aims to provide an overview of the current state of Artificial intelligence (AI) and machine learning (ML)-based information security in EVs. We conducted a systematic literature search to identify relevant studies and articles and analyzed them to identify common themes and trends. Our findings show that Artificial intelligence (AI) and machine learning (ML) are being used in a variety of ways to improve information security in EVs, including in the areas of authentication, intrusion detection, and attack prevention. In particular, we found that the use of ML algorithms such as deep learning and neural networks is becoming increasingly prevalent in these applications. Additionally, we found that there is a growing interest in the use of blockchain technology in combination with Artificial intelligence (AI) and machine learning (ML) for EV information security. Our research gathered that about 75% of the studies in the field are focused on intrusion detection, 20% on authentication, and 5% on attack prevention. The majority of the studies (70%) are based on the use of deep learning, 15% of them use neural networks, and the rest of the studies use other algorithms.
Breakdown at Medium Frequencies of Thermally-Aged Polyester Films

This article investigates the evolution of electrical properties of polyethylene terephthalate (PET) with thermal aging. Electrical tests are performed within a wide range of frequencies in order to simulate the application conditions of insulating materials in inverter-fed machines. In particular, for the aging conditions considered, the real part of permittivity showed to be correlated with the concentration of the degradation species, investigated through FTIR. On the contrary, the dielectric breakdown at high frequencies showed to be independent from the corresponding values of the dissipation factor (tanδ). This behavior would imply that thermal runaway is not the principal phenomenon leading to the dielectric failure.
A Survey of Incentive Mechanism in MCS: Types and Characteristics

Mobile Crowd Sensing (MCS) represents a sensing paradigm in which users utilize sensor-rich mobile devices to collect and contribute data that would otherwise be difficult to gather. This data is leveraged to enable diverse applications. Users are invited to offer data services on an MCS platform, and several requesters compete for the services they want. Designing incentive systems to provide users with benefits is essential due to the costs users incur when providing crowd sensing services. It also helps motivate mobile users to participate in crowd sensing tasks. In this work, we discuss an MCS system, its evolution, architecture, phases and where an incentive mechanism comes into play. We then discuss the types of incentive mechanisms, broadly monetary and non-monetary. We state the characteristics a successful incentive mechanism should possess and review the existing mechanisms. We conclude by emphasizing the importance of maximum resource utilization by achieving an optimal combination of monetary and non-monetary incentives and identify areas of future work.
Alternative method to characterize the electrical tree propagation in micaceous insulating materials

The research and development of innovative insulation systems for High Voltage Rotating Machines demand a deeper understanding and characterization of the failure mechanisms involved in the new technology. When only the electrical stress is considered, the literature describes that the breakdown of the insulation is determined by the electrical tree propagation through the material. Hence, it is important to have techniques to characterize the path of electrical tree through insulation after its rupture. Albeit some disruptive technologies are being proposed, most of the insulation still consists of mica, a carrier material, and a binder resin. Such insulation is considerably rigid, making difficult to separate one layer from the other and to follow the damage path left by the breakdown once the mica layers are broken during this process. This work describes an alternative method to characterize this electrical failure path, which consists of submitting the sample to a calcination process at high temperature before peeling it. The calcination process extracts all organic materials from insulation. In this condition, it is much easier to peel the insulation layer by layer and the carbonization path produced by electrical discharge can be clearly observed and characterized. This work illustrates this proposed technique by analyzing samples extracted from stator bars previously submitted to voltage endurance test up to electrical failure.
Structural Integrity Analysis of a Two-Pole Synchronous Reluctance Machine with Non-circular Shaft

This paper investigates the structural strength of a 6-inch diameter, two-pole, 4 kW line start synchronous reluctance machine (LS-SynRM) designed with a new noncircular shaft structure that serves as a pump motor. Flux paths on the rotor are widened while narrowing down the shaft of the motor on the q-axis to improve the motor efficiency. By using this method, a wider path is created for the flux in the d-axis. As a result, the inductance in the d-axis, the ratio of inductance between the d-axis and q-axis (referred to as saliency ratio), and the difference in inductance between the d-axis and q-axis are all amplified. To evaluate the structural strength of the machine, a series of analyses are conducted, including modal, harmonic, and static examination on the rotor using ANSYS Structural. The findings indicate that to prevent redundant deformations and undesirable vibrations because of resonance, the maximum safe limit for shaft size reduction is determined as 8 mm.