Miracle-dz/UniFAR-FTUs huggingface dataset:
SMIB Power System Dynamic Modeling and Simulation with TCSC Using PSO

In terms of improving the power system's stability, this study describes a method for designing, simulating and fine-tuning the variables of such a Thyristor Controlled Series Compensator (TCSC) controller. The TCSC controller design challenge is framed as an optimisation problem, then Particle Swarm Optimization (PSO) is used to find the best TCSC controller settings. The system's stability is improved by minimising a time-domain dependent objective function that encompasses the generator's asynchronous rotor speed variation.
A Realistic Failure Propagation Model for Smart Grid Networks

A smart grid connects components of power systems and communication networks through complex and interdependent relationships. The heterogeneous nature of these systems and interdependencies between their elements make them vulnerable to cyber attacks. Hence, reliable approaches should be used to secure the smart grid. In this paper, we propose a novel cyberattack failure propagation model in a smart grid environment. Our Realistic Failure Propagation (RFP) model addresses the system's heterogeneity by assigning different roles to its components. We define rules and interdependencies for failure propagation and propose a new process to study the behavior of cascading failures. The RFP model implements power flow analysis to ensure that all transmission lines work under their capacity and remove lines that exceed the capacity.
A Modified H-plane Horn Antenna In PCB Technology With Improved Efficiency

This paper presents a technique to enhance the antenna efficiency of a printed circuit board (PCB) based modified H-plane horn antenna. Improvement in antenna efficiency is obtained by correcting the phase error on the radiating aperture using a modified feed geometry. It also helps to improve input matching. This modification does not increase the overall rectangular dimension of the antenna when compared to a conventional H-plane horn antenna. As an example, a modified H-plane horn is presented for the 24-24.25 GHz ISM band. It increases the antenna efficiency by at least 30% over the conventional PCB-based horn. The best |S11| for a conventional horn in the same substrate which could be achieved is − 6 dB. In comparison, the modified horn provides a 10 dB matching bandwidth of at least 516 MHz. Other advantages are low side lobe levels and small 3 dB beamwidth.
A Series Combiner-Fed Patch Array for Energy Harvesting Applications

A series combiner fed antenna array for wireless energy harvesting applications is proposed here. The isolation resistor of the Wilkinson power combiner is replaced by a series combiner and the difference power flow due to unequal received power at the antenna is used for simulatenous dynamic alignment. A dual port microstrip loop is used here as the series combiner and also as a feed to the aperture coupled rectenna elements that are placed in two planes with an angle of 120° in between. The structure was simulated in 3D EM solver of ANSYS HFSS and a gain of 7.16 dBi was observed when the feeds are separately excited. The series combiner action is also validated.
Investigation of segmentation methods for highlighting areas of interests on lung X-rays

The article presents various segmentation approaches implemented using the Scikit-Learn library and the Python programming language. A comparative analysis of their effectiveness has been carried out to improve the accuracy of identifying areas of interest in the task of diagnosing lung diseases. The data set used for the study was obtained from the database of patients of the clinic “Road Clinical Hospital at Samara station of JSC Russian Railways”.
Reconstructing vehicle tracks from a fish-eye lens dataset

The task of localization is central to autonomous driving. Simultaneous Localization And Mapping (SLAM) and Structure-from-Motion (SfM) algorithms provide localization in and reconstructions of an environment from images. In this thesis, two SfM pipelines are tested on images from the IPLT dataset, a dataset containing images taken by two fish-eye lens cameras, front and rear, on a vehicle moving trough a parking area. A comparison was made between COLMAP and hloc, two pipelines capable of sparse reconstruction. SIFT, used by COLMAP for feature matching, had difficulty matching features from different perspectives, in two images taken by different cameras. SuperPoint and SuperGlue, part of the hloc pipeline for feature extraction and matching, proved valuable for reconstructions involving opposing perspectives in the IPLT dataset. The feature matches generated by SuperPoint and SuperGlue proved more reliable in producing reconstructions with geometrically correct angles. The Unified Camera Model was implemented for use in the pipelines, allowing known IPLT intrinsic camera parameters to be used in the reconstruction process. The effect of the Unified Camera Model implementation was compared with existing camera models in the pipeline.
Assessing the sustainability of biofuel production from camelina in Spain, results of the ITAKA project – analysis of GHG emissions

Purpose With the rapid growth and increased consumption of biofuels worldwide, and the multitude of policy decisions supporting this expansion, growing concerns about the biofuels sustainability have arisen. Therefore, the European project “ITAKA”, aiming at supporting the development of aviation biofuels in an economically, socially and environmentally sustainable manner, has devoted considerable effort to take sustainability into account, in a quantitative and qualitative manner. This paper aims to calculate a robust assessment of a life-cycle greenhouse gas (GHG) for the entire ITAKA value chain. Design/methodology/approach The calculation for the produced bio jet fuel has been set up using the roundtable on sustainable biomaterials (RSB) European Union (EU) renewable energy directive (RED) methodology, through the online RSB tool. This pathway includes feedstock production, feedstock processing, biofuel production, biofuel distillation and all transport steps involved. Findings A significant reduction in GHG emissions has been demonstrated, up to 66 per cent emission reduction if one considers a mature pathway for the entire ITAKA biofuel chain. Practical implications The camelina oil produced can be sustainable according to RSB and RSB EU RED schemes if the practices defined in the project are applied. Originality/value Application of different frameworks (actual vs theoretical) to the ITAKA value chain has aimed at testing and demonstrating the commercial application of the sustainability standards in Europe and the readiness of biofuels in Europe as a major means to decrease GHG emissions in aviation.
Particle Swarm Optimization (PSO) for Maximum Power Point Tracking

In this paper particle swarm optimization (PSO) algorithm has been presented for the maximum power point tracking (MPPT) method for photovoltaic systems under partial shading conditions. PSO is one of the bio-inspired algorithms. In this algorithm, only an objective function is needed and gradient and any differential form of the objects are not required. The performance of the proposed method has been verified with the help of several simulations carried out in a MATLAB environment for various shading patterns. Using simulation results, the performance and effectiveness of the PSO-based MPPT techniques are evaluated.
A Transfer Learning Image Classification Method Using Self-Supervised Information

Research on intelligent diagnosis and treatment is a major frontier issue in the current era of medical big data. For the global health crisis COVID-19, the radiological imaging techniques CT can provide useful and important information thus widely preferred due to its merit and three-dimensional view of the lung. However, to classify the CT-slices to assist in diagnosis, due to the annotation by radiologists is a highly subjective task, tedious and time-consuming work often influenced by individual bias and clinical experiences. Moreover, the current image classification methods cannot work well on the massive real-time totally unlabeled CT scans. To address these challenges, we proposed a transfer learning method using self-supervised information to classify the unlabeled CT images, using an auxiliary task of segmentation to improve classification efficiency. We classified the totally unlabeled CT scans from Huoshenshan Hospital into ordinary, severe and critical cases, and the accuracy rate reached 86%. The experimental results show that the use of small-sample semi-supervised transfer learning algorithm can be used in insufficient CT images. Our framework can improve the learning ability and achieve a higher performance. Extensive experiments on real CT volumes demonstrate that the proposed method outperforms most current models and advances the state-of-the-art performance.
Stacked Language Models for an Optimized Next Word Generation

Next word prediction task is the application of a language model in natural language generation that deals with generating words by repeatedly sampling the next word conditioned on the previous choices. This paper proposes a stacked language model for optimized next word generation using three models. In stage I, the meaning of a word is captured through learn embedding and the structure of the text sequence is encoded using a stacked Long Short Term Memory (LSTM). In stage II, a Bidirectional Long Short Term Memory (Bi-LSTM) stacking on top of the unidirectional LSTM encodes the structure of the text sequences, while in stage III, a two-layer Gated Recurrent Unit (GRU) is used to capture text sequences of data. The proposed system was implemented using Python 3.7, Tensorflow 2.6. 0 with Keras and a Nvidia Graphical Processing Unit (GPU). The proposed deep learning models were trained using the Pride and Prejudice corpus from the Project Gutenberg library of ebooks. The evaluation was performed by predicting the next 3 words after considering 10 sets of text sequences. From the experiment carried out, the accuracy of the two-layer LSTM model measured 83%, the accuracy of the Bi-LSTM stacking on unidirectional LSTM model measured 79%, and the accuracy of the two-layer GRU model measured 81%. Regarding predictions, the two-layer LSTM predicted the 10 sequences correctly, the Bi-LSTM stacking on unidirectional LSTM predicted 8 sequences correctly and the two-layer GRU predicted 7 sequences correctly.
Knowledge Based Power Sharing in DC-Homes and Power Injection in Single Phase Grid

Solar PV sources is one of the abundantly available resource to the mankind. Nowadays, almost all the applications are integrated with solar PV for energy efficient design. In such a way DC-Homes are the future trending domain, in which almost all the homes will be powered by solar PV modules. In such cases, the power generated from the solar PV should be effectively used without wasting the conserved energy or the converted electrical energy. Hence, grid tied inverters emerged for integrating the solar PV with that of the utility grid. However, for low power domestic applications, grid tied concept will not work out due to the snags present in the complexity and harmonic distortions. This paper proposes a methodology of power sharing among the DC-Home units with lkW load and single phase 230V, 50Hz surplus power injection into a single phase grid. The proposed design and methodology is simulated in MatLab / Simulink and the prototypical setup is designed in hardware-in-loop with dSPACE MicroLabBox. The results obtained from the design validate the performance of the power sharing among the DC-Homes and power injection into grid.
Influence of Nonlinearity of Magnetic Material on Skin Effect in Tubes

The skin effect will be considered on the basis of solving both the wave and integral equations of the skin effect in which the magnitudes of the current density within the conductor are unknown. Both equations are derived under the assumption that the conduction current prevails over the dielectric displacement current in the conductor and that the effect of propagation along the conductor is negligible, so that it is a plane-parallel quasi-stationary field. The obtained approximate numerical results were compared with the exact analytical ones and with results obtained by FEM and very good agreement was found. A massive tube filled with a nonlinear material in the magnetic sense, whose hysteresis loop is approximated by an ellipse, is considered in this paper.
Application of Multilevel Inverter in Power Generation Using Renewable Resources

The generation of electricity from renewable energy sources like the sun, wind, and hydrothermal is growing increasingly common as demand grows. Renewable energy systems are attaining much more consideration in present times and most nations are attempting to put themselves out of the dependency on conventional energy resources. An increase in demand from the high power sectors also made an increment in demand in a power grid. Power electronics are an important component of solar energy, and the demand for efficient power electronic converters is growing. Researchers are increasingly interested in multilevel inverters over two-level inverters because of their ability to deliver improved efficiency and higher DC link voltages. The paper explains the MLI based on their application, development, and demands with practical recommendations for utilizing them in renewable energy systems.
A Square Shape Notched UWB Antenna for GPR Applications

A square shaped patch Ultra Wide Band (UWB) antenna with defected ground plane is reported. The proposed antenna consisting of a microstrip line feed with rectangular slot in the middle and notched corners etched to the square structured patch along with DGS is utilized to achieve the bandwidth ranging from 3.11-12.24 GHz for UWB applications. The proposed antenna characteristics such as S11, stable radiation patterns, high peak gain of 6.92 dBi, high radiation efficiency in frequency domain and a flat group delay variations of <0.3 nsec, isolation of $S_{21} < -35\ \ \text{dB}$, and linear phase characteristics in time domain makes it suitable for portable UWB and high gain ground penetrating radar wireless communications.
Hyper-parameter Tuning for Long Short-Term Memory (LSTM) Algorithm to Forecast a Disease Spreading

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are just a few of the technologies that can help healthcare organizations support real-time decision-making to control the spread of a pandemic. This study aims to investigate hyper-parameter tuning for Long Short-Term Memory (LSTM) to forecast SARSCoV2 daily infection cases in the Russian Federation by selecting the best loss function, activation function, number of epochs, number of neurons in a cell, and optimizer to minimize the error in addition to a good fit for the model on both the training and validation sets. In the meanwhile, we use LSTM, LSTMs (stacked LSTM), bidirectional LSTM, BILSTMs (stacked BILSTM), convolution neuron networks (Conv), ConvLSTMs and other forecasting models to forecast the daily SARSCoV2 infection cases one by one. We analyzed and compared the results obtained by these models. We discovered that BiLSTM can efficiently extract features from the data, which are the items from the previous day. With the extracted feature data, we used this model to forecast daily infection cases. We used BiLSTM to forecast SARSCoV2 spreading in the Russian Federation for one month. Hence, the Bidirectional LSTM can accurately predict daily SARSCoV2 infection cases in Russia, according to experiments.
Tests for Departure from Normality

A fastener comprises a pin member and a socket member for receiving the pin member. The members are so contoured that when the pin member is inserted into the socket member in a particular orientation and then rotated by at least a given amount, the pin member is locked into the socket member and is restrained from either axial or rotational movement. However, a forced counter rotation permits the separation of the members.
Impedance Modeling and Stability Analysis of Direct Drive Permanent Magnet Synchronous Generators

Sub-synchronous oscillation (SSO) problems exist in wind power gathering areas as northwest China. To make clear mechanism of SSO induced by large capacity direct drive permanent magnet synchronous generators (D-PMSG), linear impedance model of grid side converter (GSC) is built and verified by simulation. Closed-loop transfer functions are deduced, and stability of D-PMSG connected to weak network, together with the influencing factors, are analyzed by impedance method, which helps to analyze and solve SSO problems in wind farms.
Damage Detection and Smart Warning for Eventual Structure Failures in Mechatronic Systems

Structural damages could appear in any mechanical parts. New methods for their structural monitoring would be a good way to estimate structural strength and, consequently, its capacity to efficiently assist people in need, even for health recovery (surgery, severe illness cure), or for improving the lives of the visually impaired. Mechanical damages mean irreversible changes in structural characteristics. Damage when appear, may cause excessive deflection, buckling, fracture, changes in vibration eigenvalues and eigenmodes, etc. Techniques for damage detection give us warnings at the right moment on duty before any dangerous failure of the structure is triggered. Accurate localization of the damaged zone would help to act after warning so that to avoid structural failure. This study presents the concept of a smart warning system based on damage detection and damage localization techniques, as well as an intelligent control system. The studied models are mechatronic systems used in healthcare. A new parameter was developed to explore accurate information related to the parts damaged that have a higher probability of failure, referenced to the system’s users and to the medical procedures.
Prediction of Bipolar Disorder Using Machine Learning Techniques

bipolar disorder may be an advanced disorder that affects variant individuals across the world. We assume that with the utilization of huge information with machine learning we will facilitate every patient as well as doctors to perform a much better designation of this sickness. Paper aims to use different Machine learning algorithms to predict the variants of bipolar disorder. The prediction model would help the psychiatrists fordiagnosing whether the patients are having a depression or mania episode, or staying in an exceedingly euthymic state. It also aims at developing a prophetic model with an appropriate level of confidence, it's essential to own each associate understanding of the information that's getting used and also thetheory relating to every algorithmic rule that's applied, similarly as having enough information for the algorithms to figure with.
Closed-Loop Control of Advance DC-DC Converters for BLDC Motor Driven SSWPS

In this paper, the design for control and implementation of standalone solar water pumping system (SSWPS), using speed control of BLDC motor drive which is being fed from SPV array via advanced DC-DC converters (ADDC's), namely Luo, single-ended primary-inductance converter (SEPIC) and ZETA converters have been carried out. For achieving the desired performance of BLDC motor, there is an immense requirement of proper speed controller and an input supply having low ripple value. Thus, by integrating PI controller with an ADDC, an attempt has been made to implement a better speed control system considering BLDC motor drive. The specifications of the controller have been fine-tuned to optimize motor performance. The modeling and performance using comparative analysis for studying the effectiveness of the intended system is authenticated by carrying out a relative simulation study in Matlab/Simulink software.
Autonomous Flight Control Design Based on Multi-Sensor Fusion for a Low-Cost Quadrotor in GPS-Denied Environments

This paper presents a fully autonomous and low-cost quadrotor helicopter system in GPS-denied environments. An extended Kalman filter (EKF) framework is employed to fuse the raw data from inertial measurement unit (IMU), optical flow, ultrasonic rangefinder to achieve real-time state estimation of the quadrotor. An improved cascaded proportional-integral-derivative (PID) control scheme is proposed to achieve high-precision flight control for the quadrotor. A lightweight, powerful app of DroneKit-Python is integrated to directly guide the flight mission of the quadrotor on an onboard computer. The onboard computer which communicates with the flight controller using the MAVLink protocol augments the performance of the autopilot by a low-latency link. Finally, the autonomous quadrotor system is justified by real-world experiments, and the experimental results show that the quadrotor can realize high-precision autonomous localization in an indoor environment.
Anisotropic Metasurface Inspired Circularly-polarized Monopole Antenna for OFF Body Communications

This paper presents a novel approach for achieving circular-polarization (CP) in the 2.4 GHz ISM-band for OFF-body communication. The behaviour is modelled by the incident of $x$-and $y$-polarized waves generated from a 45°-tilted monopole structure to an anisotropic metasurface. The reflected waves from the metasurface are then combined with the direct wave in the far-field, resulting in an equalization of the orthogonal component's amplitude with a 90° phase difference. The monopole structure is designed first at 2.45 GHz, and then the metasurface is designed according to the axial ratio (AR) characteristics. The metasurface is made up of $3\times 3$ ring-type unit cells. The proposed metasurface not only generates CP but also aids in the reduction of backward radiation and provides miniaturization compared to the conventional rectangular unit-cell. The design is accomplished using CST Microwave Studio. The proposed metasurface-based antenna produced an impedance bandwidth of 330 MHz, an AR bandwidth of 74 MHz, a peak gain of 6.6 dBi, and a radiation efficiency of 69 % in free space (FS). On the body, its performance remains the same as in free space and provides a SAR value of 0.09 and 0.04 W/Kg under the 1 g and 10 g standards, respectively.
Metallic Object Detection Inside Human Stomach With Antipodal Vivaldi Antenna Utilizing Microwave Imaging Technique

An Ultra-Wideband (UWB) Antipodal Vivaldi Antenna (AVA) is introduced here for microwave imaging applications. The AVA accomplishes high transmission capacity from 1.2–14 GHz with a maximum gain of 13 dBi at 10 GHz. The antenna is utilized for the identification of a metal object located inside a human stomach cross-section modelled in the simulator. A suitable imaging algorithm is devised to identify the material inside the stomach. The algorithm can locate the exact coordinates of the metallic objects along with their actual dimensions used in the simulations. Further, the resolution of the image has been improved.