Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating and Planning Flexibility in Sustainable Power Systems

Power systems have traditionally been designed to provide flexibility in a context where demand is met by bulk generation. The integration of variable and uncertain renewable generation sources, such as wind, increases the flexibility needed to maintain the load-generation balance. This paper aims to provide a systematic approach to evaluate the flexibility level and investigate the role of flexibility in generation planning and market operation. An “offline” index is proposed to estimate the technical ability of both the individual generators and the generation mix to provide the required flexibility. A dedicated unit construction and commitment (UCC) algorithm, able to consider plant investment and operation costs, is developed to determine the optimal investments in flexible generating units. Market simulation models are then implemented to determine the profits obtained from providing flexibility in different market designs. An adapted RTS-96 system is used to test the proposed models and the flexibility metric. Results demonstrate the validity of the UCC algorithm and the coherence of the proposed flexibility index. The analysis of the profits of flexibility shows that the market design plays an important role in the efficient deployment and subsequent profitability of flexibility resources.
Spectrum of a Rotating Cylindrical Cavity Resonator

The effects of changing the parameters of the electromagnetic field in rotating cavities are applied in various experimental facilities in industry and science. For rigorous interpretation of the results, the understanding of the behavior of electromagnetic field in rotating non-inertial reference frames under the influence of equivalent gravitational field is required. The problem of excitation of a rotating cylindrical cavity resonator by an arbitrary distribution of currents and charges is set up and solved rigorously in the paper using covariant form of Maxwell's equations. The influence of rotation on the spectrum of a rotating cylindrical cavity resonator, which is excited with arbitrary current and charge distribution, is studied. Frequency responses of an excited rotating cavity resonator illustrated relativistic effect of the resonator eigenfrequency splitting under rotation.
Briófitos como bioindicadores de calidad botánica en zonas áridas del SE español: Sierras de Filabres, Cabrera, Alhamilla y Cabo de Gata (Almería, España)

Briofitos como bioindicadores de calidad botanica en zonas aridas del Sudeste espanol: Sierras de Filabres, Cabrera, Alhamilla y Cabo de Gata (Almeria, Espana). Basado en el estudio de la flora y vegetacion briofiticas de las sierras de Filabres, Cabrera, Alhamilla y Cabo de Gata, se ha realizado la evaluacion fitobiologica de las mismas aplicando un coeficiente de calidad botanica a una serie de localidades elegidas en el seno de estos sistemas montanosos. De este modo se han cartografiado las zonas con diferentes grados de conservacion de la flora y vegetacion briofitica en el area de estudio.
Revision of the ``Catalog of Large Martian Impact Craters''

The original “Catalog of Large Martian Impact Craters” (henceforth “Catalog 1.0”) was completed using Viking 1:2M-scale photomosaics during the 1980’s by the author [1]. It contains location, diameter, preservation, geologic unit, and ejecta and interior morphology information for 42,283 craters generally ≥5-km-diameter across the entire Martian surface. The database served as the primary source of data for Martian impact crater studies for many years. With the advent of higher resolution global datasets, it become clear that a revision of the databased was warranted, and this was begun about 10 years ago using THEMIS daytime IR image data (100 m/px resolution). We are nearing completion of this revised dataset and here present some comparisons of the new catalog (henceforth “Catalog 2.0”) with Catalog 1.0 and the Robbins Mars crater dataset [2]. Comparison of Catalogs 1.0 and 2.0
Attacks on tomorrow’s virtual world

The gaming industry is booming, as is the size of its market, which has seen a growth surge. This increase goes hand in hand with the growing popularity of online games, including massively multiplayer ones, a trend that is continuing and is expected to grow even more in the coming years. These gaming environments lay the foundations of what is likely to be tomorrow’s Metaverse. Whatever systemic flaws or vulnerabilities they have will most likely be carried over, if not addressed properly now. Inspired by a class of attacks that disrupt the game synchronization between players, we propose a thought-provoking reflection, claiming that these issues cannot be addressed by the classical body of knowledge in dependable or secure distributed systems. We offer ideas for research plans to move forward, using the games as a concrete experimentation platform but keeping in mind the broader impact of these issues.
An IoT Intensive AI-integrated System for Optimized Surface Water Quality Profiling

Surface water is heavily exposed to contamination as this water is the ubiquitous source for the majority of water needs. This situation is exaggerated by excessive population, heavy industrialization, rapid urbanization, and ad-hoc monitoring. Comprehensive measurement and knowledge extraction of surface water pollution is therefore pivotal for ensuring safe and hygienic water use. However, current processes of surface water quality profiling involve laboratory-based manual sample collection and testing, which is tardy, expensive, error-prone, and untraceable. This paper, therefore presents the design and development of an IoT integrated water quality profiling system with a novel plug-and-play physical layer for the sensor actuation, and an AI powered fog computing based cloud application layer for remote water quality parameter measurement and data acquisition, remote data logging, monitoring and control, with data analytic for critical reasoning and decision making.
Spoken Digit Recognition using Machine and Deep Learning-Based Approaches

Recognition of spoken words is one of the fields that attract researchers due to its importance. It involves in various applications as many applications can be fed with data in voice format. One of vocal recognition fields is Spoken Digit Recognition (SDR) which aims to translate the input spoken audio file to its relative numerical value. Machine Learning (ML) and Deep Learning (DL) are used in various digital signal processing applications due to their ability to extract useful features from input data which enhances the accuracy of the target models as well as their ability to adapt with wide range of applications. This paper proposes two approaches to tackle the problem of spoken digits recognition. First approach is to recognize the spoken digits using wavelet time scattering and Support Vector Machine (SVM) classifier. While the second aims to solve this problem using Mel-frequency spectrograms and Deep Convolutional Neural Networks (DCNN). Experiments are performed with Free Spoken Digit Dataset (FSDD) as a training and testing dataset. Although the second approach outperforms the first one by an average increase of 1.1% in terms of accuracy metric, results shows that both approaches are suitable for such an application.
Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS?

The number of papers on network intrusion detection based on machine and deep learning is growing at an unprecedented rate. Most of these papers follow a well-consolidated pattern: (i) proposal of an intrusion detection system based on machine (deep) learning, (ii) learning-testing with one (more) public intrusion dataset(s), (iii) achievement of outstanding detection performance. Is the intrusion detection problem solved? Unfortunately, no. This paper shares a deep reflection on the major limitations of public intrusion datasets and related machine learning experiments, which greatly diminish the findings documented by the literature. At the end of the day, in spite of the academic hype and the increasingly-complex machine and deep learning exercises around, the role of public datasets in advancing intrusion detection of real-world networks remains questionable. The way existing intrusion datasets are collected, released and used by the community should be approached with extreme caution. This paper provides concrete hints for the construction of future intrusion detection datasets and more rigorous machine learning experiments.
Enhancing Honeypot Fidelity with Real-Time User Behavior Emulation

Honeypot is an effective tool widely adopted in security systems, providing rich information to lure attackers. However, honeypots are not foolproof and can be detected by sophisticated attackers via specific features, e.g., the lack of real user activities. In this paper, we propose HoneyMustard, a real-time application-level user behavior emulation framework to enhance the fidelity of honeypots. HoneyMustard can emulate GUI-based user activities in the honeypots by a remote desktop connection. Because attackers can only observe the remote connection during the emulation, HoneyMustard can conceal the emulator as a normal service so that it achieves real-time user emulation without being detected. The user activities are emulated by reproducing real user operations or converting application manuals, ensuring that attackers can observe logical activities at an application level. We implement a prototype of HoneyMustard and evaluate the decoy effectiveness and overhead. The experimental results show that our solution can effectively improve the fidelity of honeypots with a low overhead.
A Comparative Study of User Pairing Techniques of MIMO-NOMA System in different channel models

In 6G wireless communication systems, Nonorthogonal multiple access (NOMA) can solve the problem of extra consumption power and bandwidth to enhance the spectral-efficiency (SE) of the system. Multiple-users can share the same bandwidth by using user pairing techniques (UPT) which superimposes their signals with different power levels. Multiple-input multiple-output (MIMO) NOMA system can be used to improve the performance of systems, especially for massive internet of things (IOT) devices where a large number of devices need to be connected with low latency and high energy efficiency. Therefore, MIMO NOMA system is a promising technology for 6G internet of things. In this paper, the performance of different UPT in power-domain downlink MIMO-NOMA system at multi-scattering channel (MSC) and the Rayleigh fading channel (RFC) is studied. It notices that the enhancement of UPT is obvious at lower signal to noise ratio (SNR) in MSC rather than RFC. The UPT is compared at several condition of MSC with different number of antenna MIMO. The improvement of using UPT decreases, when the number of antennas decrease and the number of scatters increase. So, we can reduce the complexity of selecting suitable type of UPT at rich scattering channel and at simple MIMONOMA systems.
Sleep Behavior Classification Based on Clusters of Sleep Quality

Sleep is a significant activity that can influence livelihoods. The critical part of sleep is recovery, repairing cells physically, and preparing energy for the beginning of the next living days. Good sleep can refer to strong health and mental health, which is capably measured by sleep quality. Normally, many works used the whole dataset to train models. But we believe that each person has a different sleeping pattern. So, in this paper, we presented a classification of sleep behavior based on a cluster of sleep quality. We first clustered people who have similar sleep patterns using the Principal Component Analysis technique and K-means algorithm. Then, we used Logistic Regression and Random Forest algorithm to classify sleep behavior. We performed models from the analysis with Leave-one-out cross-validation. The results showed that the accuracy given by Random Forest algorithm models in every group was better than Logistic Regression models between 2.1% and 7.6%.
Superconducting Integrated Microcircuits Based on NbTiN/Al Transmission Lines Operating at Frequencies Above 1 THz

The results of the development and testing of superconducting integrated microcircuits based on NbTiN/Al transmission lines at the frequencies up to 1.1 THz are presented. The integrated circuits consist of a slot planar antenna made of thin film of NbTiN coupled to a NbTiN/Al microstrip line, and a superconductor-insulator-superconductor (SIS) junction based on Nb/AlN/NbN operating as a terahertz (THz) detector. Two different designs having the operating range of 0.9-1.2 THz were numerically simulated, fabricated and experimentally tested. A strong pumping of SIS detector by a signal of the backward wave oscillator (BWO) around frequency of 1.05 THz was observed, which demonstrated the applicability of fabricated transmission lines at frequencies higher than 750 GHz, where the traditionally used Nb/Nb transmission lines cannot operate.
University-wide Implementation of Blended Learning through Systematic MOOC Integration

More and more universities across the globe are transforming their teaching strategies by adopting innovative technology to equip their students with 21st century skills, help them gain an international perspective and increase their employability. Many higher education institutions which started to make use of Massive Open Online Courses (MOOCs) during the pandemic have recognized their many benefits and plan to continue incorporating them into their teaching strategies. This paper describes the process of MOOC integration in higher education based on available research and on the experience of Alamein International university, Egypt, undertaking a university-wide MOOC-based initiative of blended learning in collaboration with Coursera for Campus platform. In addition, an evaluation of the integration is presented using analytics from the MOOC dashboard during more than a year as well as results of student and instructor survey responses reflecting on their experience. We hope our experiment can serve as a reference for similar endeavors in the region and help in a successful wider adoption worldwide.
NetLoiter: A Tool for Automated Testing of Network Applications using Fault-injection

The reliability of a network is a crucial requirement for applications and systems such as IoT (Internet-of-Things), cloud-based solutions, client-server, or peer-to-peer architectures. Unfortunately, real networks cannot be assumed to be fault-free, especially when considering various hardware problems, performance issues, or even malicious attacks. Testing network applications should include the evaluation of fault-tolerance of a system under various network conditions. The paper introduces a tool, NetLoiter, which helps developers and verification&validation practitioners easily analyse their network application’s behaviour in unexpected network situations. The tool is based on man-in-the-middle attacks and aims at network nodes communicating directly using a single network interface or indirectly via a single network active component (such as a switch or router). NetLoiter implements a fault-injection method; supported faults and attacks are inspired by the real world, including lossy channels, network jitter, data corruption, or disconnections.
Compact Band-Pass Filter Made Using SIW Technology

Ahstract- The paper presents the possibility of reducing the weight and dimensions by bending the filter. In the course of the work, an electrodynamic analysis and synthesis of a third-order bandpass filter were carried out. These filters are made using SIW technology, which has gained great popularity due to the possibility of reducing the weight and size of the device compared to the classic rectangular waveguide. The amplitude frequency characteristics of the computer model and the manufactured filter are compared. The longitudinal size was reduced by 5 times compared to the prototype filter.
Multiclass Classification of Gastric Cancer using Artificial Neural Network

Gastric Cancer (GC) remains on the top of the list in death troll due to cancer worldwide and has a high mortality due to its vague symptoms. With the lack of effective biomarkers and absence of symptoms, it is an urgent need to develop a noninvasive, rapid, and highly accurate technique for GC screening at early-stage detection, prognostics, and diagnostics. Breath shows a promising multi-constituent oral fluid with a noninvasive source. The proposed solution is based on the unsupervised learning model that is capable to classify the cancer patient in different categories based on the spread of the disease. The proposed model can distinguish the level of disease as healthy person, moderate spread and acute spread. We have three hundred saliva samples of volunteers which comprise 102 EGC, 156 AGC, and 110 Healthy persons. This study has shown pivotal information in distinguishing EGC, AGC from healthy persons, which have a great clinical translational prospect. The developed Artificial Neural Network (ANN) based predictive model has produced an accuracy of 81.4%, 68.6% and 92.5 % for AGC, EGC and Healthy persons.
CarFASE: A Carla-based Tool for Evaluating the Effects of Faults and Attacks on Autonomous Driving Stacks

This paper presents CarFASE, an open-source carla-based fault and attack simulation engine that is used to test and evaluate the behavior of autonomous driving stacks in the presence of faults and attacks. Carla is a highly customizable and adaptable simulator for autonomous driving research. In this paper, we demonstrate the application of CarFASE by running fault injection experiments on OpenPilot, an open-source advanced driver assistance system designed to provide a suite of features such as lane keeping, adaptive cruise control, and forward collision warning to enhance the driving experience. A braking scenario is used to study the behavior of OpenPilot in the presence of brightness and salt&pepper faults. The results demonstrate the usefulness of the tool in evaluating the safety attributes of autonomous driving systems in a safe and controlled environment.
Pneumonia Detection Method based on Convolutional Neural Network

This paper attempts to apply a neural network to classify pneumonia images. The classification model is built based on thousands of actual X-ray images that belong relatively to both pneumonia patients and normal people. Classification neural networks are a popular approach to machine learning that will serve as an aid to diagnose pneumonia and perform pre-screening. In this paper, we analyze and include the details of the algorithm we used. By analyzing the structure of the network, we propose several useful ways to improve the model, leading to a better model structure and application for classification. Our proposed new structure is demonstrated in this paper. The final training accuracy is more than 85%.
Minimizing Energy Loss for Multiple Load Power Flow Assignment in Smart Homes

Nowaday, it is a great challenge to design the Energy storage system (ESS) with the renewable energy (RE) resources in the distributed energy systems. The intermittent nature of energy makes that the RE resources are unstable energy resources and becomes very difficult to use in the house level environment. In this paper, we consider a distributed power flow system (DPFS), which consists of uncontrollable power generators, unpredictable usage of power loads, and multiple ESSs, in which their different combinations of logical power connections to minimize the energy loss. We propose a multiple load power flow assignment (MPFA) with load shifting algorithm to deal the logical power connection via single ESS or multiple ESSs to mitigate the possibility of energy loss reduction and to improve the energy efficiency of DPFS in the smart home domain. Simulation results reveal that the proposed algorithms can significantly reduce the energy loss in winter and summer seasons. Numerically, the proposed MPFA algorithms with load shifting algorithm can reduce more energy loss by about 11.4% and 13.8% during winter and summer, respectively. Our numerical results also show that the highest energy loss is in spring compared to other three seasons.
A Comparative FEM Approach of Nanoparticles-Mediated Thermal Therapy of Breast Cancer

Breast cancer (BC) affects women at any age after puberty, with rising incidence in later life. Curing from BC can be very efficient, with 90% or greater survival rates when the illness is detected early. Hyperthermia (HT) has been studied as a cancer effective treatment modality to weaken or even kill cancerous tissues. In the present study, we investigated the cytotoxic effect of various types of nanoparticles (NPs) to enhance HT treatment of BC tissues using a circular inset-feed microstrip patch antenna array operating at 2.45 GHz. Finite Element Modeling of specific absorption rate and temperature distribution for BC and surrounding tissues with aluminum, silver, and gold NPs at the frequency 2.45 GHz showed rise in internal BC tissues to 40.63, 42.44, and $44.26^{\circ}\mathrm{C}$, while keeping the surrounded normal breast tissues unharmed at $39^{\circ}\mathrm{C}$.
Real-Time Wheelchair Controller Based on POF-Based Pressure Sensors

The design of electrical wheelchair controllers remains a challenge for the scientific community. In recent years, Polymer Optical Fiber (POF)-based sensors have been used for biomedical applications, however, the application of these types of sensors for the control of assistive devices has been little explored. For this reason, this work proposes the design and validation of a fuzzy logic controller that uses information from an array of POF-based sensors located in a pillow, which is used for wheelchair control in four directions (right, left, forward and stop) by using neck movements. The results allow concluding that the system is fast, accurate, and reliable. This work presents a contribution related to the design of assistive devices based on optical fiber sensors and focuses on people with upper- and lower-limb mobility restrictions. Future studies will address the evaluation of the system for people with disabilities.
Non-iterative parameter extraction method based on the single diode model (SDM).

Currently, the analysis of photovoltaic devices that convert light energy into electrical energy is of great importance, as efforts are being made to reduce global pollution. In this regard, various tools such as software, models, and simulators have been developed that allow for the understanding and improvement of the efficiency of these devices. Such tools are a valuable resource for research and development in the field of photovoltaic solar energy. This paper presents a model that allows for the analysis of the I-V and P-V curves’ behavior under different radiation and temperature conditions, as well as the influence of parameters that describe the single diode model. For this purpose, the commercial RTC France solar cell and the Kyocera 200GT solar panel, which are among the most reported in the literature, were used as study objects. The results obtained through the proposed model contribute to the understanding of the factors that affect the efficiency of photovoltaic devices and can be of great utility for the design and optimization of solar energy systems.