Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Distributed Attack Penetration Testing Automation Using Dynamic Infrastructure Framework Axiom on Web-Based Systems

The increasing use of the internet is attributed to the growing reliance on web-based systems, as nearly every aspect of present-day life utilizes such systems. According to the HackerOne 2021 report, there has also been a rise in cyberattacks, particularly targeting web-based systems. One method of identifying vulnerabilities in web-based systems is through penetration testing. The conducted research involves the implementation of distributed penetration testing on web-based systems using the Dynamic Infrastructure Framework Axiom as a tool for constructing distributed infrastructure. The researchers tested three vulnerabilities: Cross-site Scripting (XSS), SQL Injection, and Arbitrary Code Injection. These three vulnerabilities are among the top ten most frequently identified vulnerabilities according to the HackerOne 2021 report. The results of the research reveal a significant difference in testing times between distributed and non-distributed automated penetration testing. Distributed automated penetration testing was notably faster compared to both non-distributed and non-automated penetration testing.
Multi-objective Design Optimization and Selection of Bidirectional DC-DC Converters for Solid Oxide Fuel Cells

This paper presents a multi-objective design and optimization approach to find the optimal design for DC-DC converters used to interface a 16 kW bidirectional solid oxide fuel-cells (SOFCs). Both charging and discharging cycles of the fuel cells were considered in the optimization strategy. Three candidate topologies, including buck-boost converter, two-channel and three-channel interleaved buck converters were evaluated based on the achieved power density and efficiency. The optimization strategy was automated in MATLAB using prebuilt libraries of various semiconductor and passive components. The optimization process generated three two-dimensional Pareto fronts, comparing efficiency and weight, for the three compared topologies. The optimization concluded the superior performance of the two-channel interleaved buck converter used as a bidirectional interface converter for SOFCs.
Hexagon Trajectory Flux Weakening Control for Permanent magnet assisted SynRM

The synchronous reluctance motor has a simple structure and smaller torque ripple, making it more robust and reliable. However, it does not have a wide speed range and the output torque is not fully utilized. The flux weakening (FW) control is helpful to improve voltage utilization of synchronous reluctance motor (SynRM) drives in high-speed region. This paper is based on the d-q axis equivalent circuit of SynRM and derives the voltage and current constrain for $FW$ process. Deriving the modulation algorithm and applying it to the weak magnetic operation of permanent magnet assisted SynRM to achieve higher utilization of DC bus voltage. The effectiveness of the proposed method will be verified through simulation.
An Intelligent RL-Based Scheduler to Control Flooding in a Renewable Energy Powered Automatic Water Dam Control System

The use of non-renewable energy to operate large-scale systems is proving detrimental to the environment through pollution as well as leading to its depletion. As an alternative, the focus of industries, both private and public, is shifting towards the profitable use of renewable energy sources where it is available in abundance for such purposes. Water dams have been built above rivers to tap this natural resource to supply water to the nearby population. However, in the rainy season, excessive flooding wreaks havoc in the surrounding areas, leading to crop failures, damage to households and dam infrastructure. To counter this problem, this paper proposes an intelligent scheduler for an efficient renewable-energy powered automatic water dam control system which is modelled as a non-convex optimization problem. The scheduler uses the Soft Actor Critic with Emphasized Recent Experience and Prioritized Experience Replay Scheduler (SEPS), as the automatic Reinforcement Learning (RL) based scheduling algorithm to manage flooding in the control system through irrigation, hydroelectricity generation and reservoir storage such that it can sufficiently satisfy the needs of domestic households while also ensuring its efficient working. Simulated sensor readings record the current state of the environment and the proposed scheduler can make decisions based on these environmental variables with less human oversight and great accuracy. Experimental results clearly indicate that the SEPS scheduler outperforms its RL counterpart Proximal Policy Optimization Scheduler (PPOS) and the Natural Evolution Strategies Scheduler (NESS) in terms of average rewards obtained for managing floods efficiently and using renewable energy to run the control system.
Analysis of Energy Consumption in Smart Edge Processing Device

This work presents an analysis of the energy consumption for a setup developed with embedded artificial intelligence to recognize the characters of an analogic hydrometer and transmitting the data via LoRa. The results showed that the developed device with image edge computing is capable of capturing the image, recognizing the specific characters in the image and transmitting. An estimated battery lifetime is 5 years and 3 months using a battery with a capacity of 10,000 [mAh], based on a daily transmission of 5 characters from the hydrometer.
A Taguchi-LHS-RSM Double-Staged Approach for Design Optimization of Self-Ventilated Cooling Systems Utilized in PMSMs

Thermal management is critical for high-power-density permanent magnet synchronous machines (PMSMs). To realize the efficient and effective design optimization of self-ventilated air cooling systems for PMSMs, in this paper, a Taguchi-preconditioned Latin hypercube sampling (LHS) and response surface method (RSM) combined approach is proposed. First, as the optimization variable numbers are mostly large when considering simultaneously the heat-sink and air-fan influences, the Taguchi method is employed to decouple the variables based on comprehensive fluidic-thermal calculations. The values of the less interconnected variables are optimized directly in this stage while the two most interconnected ones are selected and optimized in the next stage. Secondly, LHS is used to generate the sample points of the two variables with the values centered at the initially optimized results in the first stage. The RSM surrogate model is established with the LHS points, and the finally optimized values are then decided based on analytical analysis of the surrogate model. The proposed double-staged method is utilized in optimizing the self-ventilation system of a 2000-rpm outer rotor PMSM, and the optimization effectiveness is validated by comparing the cooling efficiency and effectiveness of the results at each optimization stage.
Multi-tenant, Decentralized Access Control for the Internet of Things

The Internet of Things (IoT) involves diverse devices exchanging data and impacting the real world through actuators, often unattended. IoT data and actuation are accessed by many and diverse stakeholders, emphasizing the need for security and privacy and in particular for effective, efficient, usable, and fine-grained access control solutions. Unfortunately, most of the conventional access control approaches have been proven inefficient or ineffective for the IoT. Novel access control mechanisms have been designed, specifically for the IoT, with some of them taking advantage of Distributed Ledger Technologies (DLTs). DLTs and smart contracts provide increased availability, automation, transparency, and decentralization. Here, we first review the related literature and then we develop IoT access control requirements and solutions. We argue that DLT-based access control systems can address the key requirements for the IoT and we validate this claim by designing a realistic access control solution for a smart city use case.
Recommender Systems for Students’ Performance in Higher Educations: A Review

Student success is a primary strategic goal for most higher education institutions. With budget cuts and rising operating costs, higher education institutions are focusing more on retaining student enrollment in their programs while preserving educational quality. This motivates academics to develop the best way for predicting students' future success based on Recommender System, which aids students in their academic achievement. As a result, the primary goal of this study is to identify the most often investigated elements influencing student performance. In this research, 15 research articles were critically reviewed and analyzed. This review found that from the small amount of research, most studies use machine learning algorithms to find solutions to the problems. However, this article will look to find a method that may prove to be more effective. Finally, this article contributes to students' academic achievement via a recommender system and identifies the most successful machine learning methods.
Image contour edge segmentation and fitting based on Anny-Gaussian edge test operator

Image edge analysis and application is a classic problem in image analysis and an important preprocessing method in image follow-up processing. In order to use subpixel edge extraction method to extract edge pixels and establish automatic curve segmentation and fitting model, this paper first smooths the image through Gaussian filter and sharpened the image to highlight the contour features. Then subpixelation is carried out, the optimized inverse Gaussian edge test operator is established, and the edge contour curve is obtained. Then the curvature of each point is calculated by the gradient algorithm of the discrete contour points, and the module segmentation is completed by the input of the module segmentation state machine, the curve parameters are determined, and the image contour is vectorized. In the complex environment, this research carries out contour recognition and vector information extraction on bitmaps taken by cameras, which has important reference value in the field of industrial machine vision to improve the accuracy of photo contour recognition and reduce the space occupied by pictures. Moreover, the segmentation fitting of the second step can provide clues for further detection of whether there is a bump in industrial parts.
Communication Latency Assessment for an Interoperability Interface Prototype Applied to Power Converters in Laboratory Microgrids

One of the most vital aspects behind performing laboratory-scale studies on the interoperability of power electronic converters within the scenario of smart microgrids is the implementation of communication interfaces. Having the flexibility to support bidirectional communication technologies and presenting easy configuration settings are usually desired features of such interfaces, mostly because they can be used to emulate different data exchange behaviors, which are commonly targeted on the assessment of how power converters impair microgrid operation. This paper presents the implementation layout of an interoperability interface based on an off-the-self microprocessor platform (i.e., Raspberry Pi model), which can be applied to small-scale microgrid laboratory facilities, in which a local area communication network is used to achieve interoperability among power converters. In addition, an experimental assessment on the communication latency of such interoperability system is presented in this paper, considering a TCP/IP-based communication layout using the IEEE 802.3 protocol. The results show that data packet exchange between two communication interfaces can occur as fast as 0.118 ms, with a maximum latency of 1.173 ms, which is generally attractive to the targeted scenario of study.
Wise use: using ecological models to understand and manage aquatic ecosystems

Using ecological-response models to understand and improve management of aquatic ecosystems is increasingly common. However, there are many questions about reliability and utility that can make the use of ecological modelling fraught. One critical question is how ecological-response models translate to what happens in practice. Many models purport to improve management by simulating ecological response to changing conditions. This suggests that tangible benefits (e.g. increased biodiversity) should flow when recommendations for action are implemented. But testing these links is rare and there are implications if those links are tenuous. One problem leading to a lack of congruence between models and reality can be a lack of ecological data for the system being modelled. Incomplete understanding, erroneous assumptions about drivers or degree of variability, and uncritical use of expert opinion can all result in models that may be more likely to mislead than inform. Explicit validation of models, sensitivity testing and ongoing development of novel solutions to deal with incomplete data can all assist. So, wise and critical use of ecological models provides one mechanism to increase our ability to quantify adverse effects on, and project future trajectories of, aquatic ecosystems.
Failure Control Scheme of Network Automatic Transfer Switching Unit in Ring Network

Reliability of power transmission lines ensures the reliability of power supply to consumers. The accident rate and the number of power supply outages depend on the condition and quality of operation of power lines. At the same time, agricultural networks are characterized by the greatest length and low reliability. Consumers of all reliability categories are connected to distribution networks. Low reliability and high deterioration of agricultural distribution networks adversely affect the reliability of power supply to consumers. Improving power supply reliability for consumers of the agro-industrial complex will reduce interruptions in power supply, reduce direct economic and technological damage as well as the losses of power supply organizations as a result of decreasing the undersupply of electricity. From the foregoing, it follows that consumers and producers of electricity are interested parties in improving the reliability of power supply to consumers of the agro-industrial complex. The proposed circuit for monitoring failures of right triggering the automatic transfer switch unit will reduce the damage from undersupply of electric energy and increase the reliability of power supply to consumers of the agro-industrial complex. The implementation of the developed scheme will reduce the economic and technological damage to agricultural consumers.
An Improved Model Predictive Current Control of Permanent Magnet Machine with Integrated MRAS-based Online Parameter Identification

Finite control set model predictive control (FCS-MPC) is a promising method for permanent magnet machine control for its merits in dynamic performance and multi-objective control. However, the parameter mismatch issue and computation burden have restricted its application. An improved model predictive current control of permanent magnet machine with integrated MRAS-based online parameter identification is proposed in this paper, which can deal with the parameter mismatch issue without significantly increasing the computation burden. Different from existing methods, an integrated design is adopted to combine the FCS-MPC and the model reference adaptive system (MRAS) together, which can reuse some prediction results in both the FCS-MPC and the reference model of the MRAS. Experimental results validate the effectiveness of the proposed method in parameter identification and computation burden releasing.
Automated Detection and Response to Cyberbullying Using Machine Learning

Cyberbullying poses a significant threat to the mental and emotional well-being of individuals, particularly in today’s digital age, where online communication and social media are pervasive. Current automated content moderation solutions often struggle to effectively detect and filter offensive messages. In this paper, we present an innovative bot designed to combat cyberbullying by detecting and reporting insults in both voice and text messages. Our bot utilizes OpenAI’s API, speech recognition technology and machine learning models to detect cyberbullying. We discuss the challenges related to detecting and preventing cyberbullying, the technologies and methodologies employed in our bot, and the results of its performance in detecting and filtering offensive messages. The proposed solution demonstrates its effectiveness and potential for real-world applications, offering an accessible and powerful tool for promoting a safe and respectful online environment.
Distribution of Iridium and Other Elements near the Cretaceous/Tertiary Boundary in Hole 465A: Preliminary Results

Very significant enhancements of the element iridium have been observed in association with the Cretaceous/Tertiary boundary in marine sediments laid down 65 m.y. ago and subsequently uplifted above the ocean's surface. If our hypothesis for the origin of the iridium and the cause of the Cretaceous/Tertiary life extinctions (the asteroid-impact theory) (Alvarez et al., 1980) is correct, the Ir anomaly should be associated with the Cretaceous/Tertiary boundary region wherever it is intact. The present work was undertaken to search for the Ir anomaly in a deep-sea-drilling core, in order to check this aspect of the asteroid-impact theory.
Investigation on Peak Wavelength Tracking in FBG Sensor

Fiber Bragg Grating (FBG) is one kind of Optical fiber sensors which can be used for the applications of both strain and temperature monitoring in structural health monitoring. An FBG is actually an reflective narrow-band optical filter while FBG interrogator fastly scans the reflective FBG spectrum from distributed FBG sensors. FBG interrogator offers the shifted Bragg wavelength data of the FBG, and furthermore to determine and assess the physical performance of the external environment. Spectral resolution and noise mainly limit the accuracy of finding the peak wavelength of the FBG sensor when scanning a spectrum, Here we reviewed and discussed several algorithms for peak wavelength tracking, and our experiment verified that both Centroid and 2nd polynomial fitting methods are better for fast and accurate FBG interrogation.
Personalized Federated Learning with Genetic Algorithm

Privacy concerns have heightened due to the risks associated with data breaches and surveillance, emphasizing the need for personal autonomy and trust in digital interactions. Federated Learning (FL) is a promising solution to this problem as it enables multiple parties to collaborate without disclosing their data. Nevertheless, the presence of non-identically and independently distributed (non-IID) user data can exert adverse effects on the convergence speed and efficacy of training. To address this predicament, this article proposes an evaluation mechanism known as "Genetic evaluation" which endeavors to strike a delicate balance between personalism and globalism while concurrently enhancing client personalism. The " Genetic evaluation " mechanism entails a preliminary genetic search conducted on the parameters derived from stochastic gradient descent (SGD). Subsequently, the deviation between the offspring and the global parameters is computed to quantify their disparities. By judiciously weighing the performance of each offspring and its respective deviation, a specific number of exceptional offspring are identified and selectively preserved in the repository. Finally, this article introduces a novel personalized FL algorithm called FedGA. Empirical evaluations convincingly demonstrate that the proposed algorithm attains superior convergence rates, as evidenced by a remarkable tenfold performance enhancement over FedAvg, a threefold improvement over Per-FedAvg, and a twofold improvement over PFedMe on the widely used CIFAR-10 dataset. Furthermore, experimental results show that FedGA outperforms preceding FedAvg and Per-FedAvg in terms of accuracy.
Modeling of the Output Admittance for the Grid Connected Three-Level T-Type Power Converter with LCL Filter

The output admittance of the grid connected voltage source converter (VSC) is a measure of interactivity or passivity of the VSC at a point of connection (PCC) to the grid. In this paper the modeling of the output admittance is conducted on a four wire 3-level T-type VSC. The modeling is done analytically, with real-time simulation and experimentally. Analytical model is used to show effects of the current control on the output admittance of the VSC. Hardware in the loop (HIL) as well as experimental results are presented to verify the analytical model results and to provide additional insight on harmonics.
A Simplified Modeling and Analysis Method for Interturn Short-Circuit Fault of Permanent Magnet Synchronous Motor

The modeling and mechanism analysis of interturn short-circuit fault (ISCF) of permanent magnet synchronous motors are significant for fault diagnosis and fault-tolerant control. Accurate models increase the complexity of computation and the specificity of the ISCF, while simple modeling is more suitable for developing universal features and theories. Therefore, based on the equivalent circuit model, this paper further simplifies the permanent-magnet flux linkage harmonics and short-circuit current of the motor, and thoroughly analyzes the modeling process and fault mechanism of the ISCF. A surface-mounted permanent magnet synchronous motor with ISCF was modeled and simulated in MATLAB/Simulink and ANSYS Maxwell, and the fault mechanism was verified. Finally, an experimental prototype is built, and the experimental results show that the motor current sequence analysis method is not necessarily effective when there is an inherent imbalance in the motor.
A Digital Controlled Desaturation Detection with Adjustable Blanking Time for IGBT Driver

In this paper, a desaturation detection structure with a digital controlled adjustable blanking time for IGBT driver is proposed. It consists of a desaturation Filter a Leading Edge Blanking module (LEB) and a LOGIC control module. The LEB time and Fault time can be adjusted by users through an SPI in LOGIC module in order to be suitable for different kinds of IGBTs. The circuit is designed based on the SMIC 180 nm BCD Process.
A Hybrid Flux Estimator for Detection of Inter-Turn Short Circuit Fault in Traction Motors

To diagnose the inter-turn short circuit fault of traction motors, this paper proposes a fault diagnosis method based on a voltage-current hybrid model flux estimator. The fault characteristic signals are acquired using the PI module in the flux estimator, which is designed to compensate for flux errors related to the pure integrator and stator resistance measurements at low speeds, thereby improving performance under the full speed range. First, a mathematical model of the traction motor is developed to describe the fault. Second, to understand the fault characteristic signals in the PI module, the closed-loop flux estimator transfer function is derived. Finally, the fault signals are extracted from the compensation voltage generated by the PI module for fault diagnosis using the Fast Fourier Transform. The proposed prognosis method is validated by simulation and is found to effectively detect faults under different operating conditions.