Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameterized ionospheric model: A global ionospheric parameterization based on first principles models

We describe a parameterized ionospheric model (PIM), a global model of theoretical ionospheric climatology based on diurnally reproducible runs of four physics based numerical models of the ionosphere. The four numerical models, taken together, cover the E and F layers for all latitudes, longitudes, and local times. PIM consists of a semianalytic representation of diurnally reproducible runs of these models for low, moderate, and high levels of both solar and geomagnetic activity and for June and December solstice and March equinox conditions. PIM produces output in several user selectable formats including global or regional latitude/longitude grids (in either geographic or geomagnetic coordinates), a set of user specified points (which could lie along a satellite orbital path), or an altitude/azimuth/elevation grid for a user-specified location. The user selectable output variables include profile parameters (ƒ0F2, hmF2, total electron content, etc.), electron density profiles, and ion composition (O+, NO+, and O2+).
Classifier of Liver Diseases According to Textural Statistics of Ultrasound Investigation and Convolutional Neural Network

Liver disease is a significant threat to human life and requires accurate and rapid diagnosis. Traditional methods either carry additional health risks (biopsy) or are not accurate and fast enough (manual analysis of ultrasound diagnostics). This paper proposes a classifier of liver diseases based on ultrasound images. The uniqueness of this approach to solving the problem of disease classification is a combination of classical methods of texture analysis, radiomics and modern convolutional neural networks. The resulting architecture demonstrates higher indicative accuracy compared to radio-based models that do not use convolutional neural networks both in the classification among all types of diseases (growth from 77% to 87%) and in the classification in the approach "one against all" or the norm / disease (increase from 85% to 92%).
A rapidly deployable chemical sensing network for the real-time monitoring of toxic airborne contaminant releases in urban environments

We present findings of the DYCE project, which addresses the needs of military and blue light responders in providing a rapid, reliable on-scene analysis of the dispersion of toxic airborne contaminants following their malicious or accidental release into a rural, urban or industrial environment. We describe the development of a small network of ad-hoc deployable chemical and meteorological sensors capable of identifying and locating the source of the contaminant release, as well as monitoring and estimating the dispersion characteristics of the plume. We further present deployment planning methodologies to optimize the data gathering mission given a constrained asset base.
Subset-Class Relation, Common Pitches, and Common Interval Structure Guiding Estimations of Similarity

THE PRESENT STUDY EXAMINED THE importance of subsetclass relation for closeness estimations. Two experiments used set-class pairs consisting of a pentad class and a tetrad class represented by block chords, and approximately half of the pairs accomplished the subset-class relation. In the first experiment the number of common pitches was systematically varied. In the second experiment the intervals between the lowest pitch and the other pitches were either similar in both chords (except for the interval missing from the tetrachord) or dissimilar, but the chords shared no common pitches. The degree of set-class consonance was also varied. The results of the experiments indicated that the number of common pitches and the common interval structure were important factors guiding closeness estimations. The subset-class relation was important since it enabled a maximum number of common pitches, maximally similar intervals, or highly uniform voice-leading between chords.
Measurement of neutron yields from a water phantom bombarded by 290 MeV/u carbon ions

Nobuhiro Shigyo, Yusuke Uozumi, Haruhiko Uehara, Tomoya Nishizawa, Keiichi Hirabayashi, Daiki Satoh, Toshiya Sanami, Yusuke Koba, Masashi Takada and Naruhiro Matsufuji Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0935, Japan; Japan Atomic Energy Agency, 2-4 Shirakata, Tokai-mura, Naka-gun, Ibaraki-ken, 319-1195, Japan; High Energy Accelerator Research Organization, 1-1 Oho, Tsukuba, Ibaraki, 305-0801, Japan; National Institute for Radiological Sciences, 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan
A 133.3 dB Dynamic Range Pulse Oximeter Front-End with Low-Noise Area-Efficient Offset Cancellation Current DAC

This paper introduces a pulse oximeter front-end with low-noise area-efficient offset cancellation current digital-to-analog converter (DAC). The proposed offset cancellation current DAC is implemented to cancel the input DC current to improve the effective dynamic range (DR). The current DAC is designed to have low-noise and area-efficient characteristics by applying the dynamic element matching technique and reducing the number of idle current mirror transistors. It can generate a current of up to 256 μA. Due to the offset cancellation of the current DAC, the proposed pulse oximeter has a signal-to-noise ratio of 77.4 dB and an effective DR of 133.3 dB. The pulse oximeter front-end is designed in 0.13 μm CMOS and consumes 307.1 μW from a 1.65 V supply. The figure-of-merit is 0.81 pJ/Sample.
Analyzing Success Factors of E-Learning Effectiveness at Higher Education in Indonesia: A Review

E-learning is a student-centered education system which enables students to study anything from anywhere and anytime according to their learning goals. Since the COVID-19 pandemic constrained people to commute, e-learning has been massively implemented to enable educational activities. This study aims to investigate the e-learning success factors and its influence on e-learning effectiveness in higher education. The systematic Literature Review (SLR) has taken into account in order to identify e-learning success factors. Through analyzing the selected literatures, extraction of e-learning success factor domains has been attempted based on the influencing factors and its impact. As a result, seven success factors related to technology, learner, instructor, content, and institution's support, are identified as influential in the effectiveness of e-learning implementation.
Post-Fault Power Flow Control for Multi-Terminal Bipolar HVDC Systems Connecting Offshore Wind Farms

With increasing transport capacities required for offshore wind integration, bipolar HVDC systems are expected to replace state-of-the-art monopolar systems. Bipolar systems with a dedicated metallic return offer a 50% redundancy after single-pole DC faults and might therefore shorten outage times of offshore wind farms. To further extend this redundancy, and increase interconnection capacities, these bipolar links might to evolve towards multi-terminal HVDC networks. However, to utilize the full redundancy of bipolar systems, an adapted post-fault load flow control is required according to conceptual analyses in recent literature. AC-side coupling of the P- and N-pole offshore wind farms would allow to distribute power among the poles, but poses a challenge on the offshore wind farm control. Therefore, in this paper, a control concept is developed to redistribute power after faults in multi-terminal HVDC systems and avoid overload situations. A droop-based grid-forming control is adapted for the AC-side coupled offshore HVDC converters and validated via EMT simulations. It is shown for a 4-terminal network that the developed control concept allows to fully utilize the remaining transmission capacity up to a wind infeed of 75% without curtailment of the offshore wind power. Without the developed concept, curtailment is required starting at 50%.
Model Predictive Control Based Maximum Power Point Tracking Algorithm for Solar PV Module

This paper analyzed the effect of various maximum power point tracking (MPPT) algorithms applied on the solar PV module and describes the importance of Model Predictive Control (MPC) algorithm applied for MPPT of solar PV. Perturb & Observe (P&O) and Incremental Conductance (InC) based tracking algorithms are the conventional and most popular tracking algorithms for MPPT of solar PV module, but they are lacking due to low efficiency with slower tracking speed of maximum power point (MPP) and higher oscillations. DC-DC boost converter has been used to scale the low-level voltage into high-level voltage. The complete setup inclusive of PV module, DC-DC boost converter and tracking algorithms has been designed and analysed on MATLAB/Simulink Software and the simulated results shows the performance of the applied algorithms on the PV module setup and suffice that MPC based tracking algorithm is well suited for solar PV MPPT setup.
Modulated Predictive Current Control of Back-to-Back NPC Converter in PMSG Wind Energy System with Reduced Computational Burden

This paper proposes a novel modulated predictive current control with a reduced computational burden for a three-phase back-to-back connected neutral-point clamped (NPC) converter in a permanent magnet synchronous generator (PMSG) based wind energy system. An algorithm has been developed to determine the optimal sector number based on measured generator- and grid-side voltages and currents. The eight voltage vectors in the optimal sector are then used to determine the optimal triangular region (among 6) that produces the minimum cost function value for NPC rectifier and inverter. The three voltage vectors in the optimal triangular region are selected and applied to the rectifier and inverter via seven segment switching scheme. The dwell times of medium voltage vectors in the switching sequence are adjusted dynamically to balance the DC capacitor voltages. The proposed approach produces constant switching frequency, minimal steady-state errors, and fast transient response. The developed control scheme is tested on a 3 MW, 3 kV PMSG wind energy system through MATLAB simulations.
Torque Ripple Reduction in Six-phase Induction Motor Drive using DTC Technique

This work presents a Direct Torque Control (DTC) technique for a six-phase asymmetrical induction motor fed with a two-level six-phase inverter for torque ripple minimization. By increasing the levels in conventional torque hysteresis controller, high torque ripples (which is a major problem in basic DTC) can be minimized and dynamic torque response can be improved. But in practice, the inevitable delay existing in the implementation of hysteresis controllers will cause undesirable overshoot and undershoot in the torque response resulting in high ripples. Also, the torque decreasing rate is comparatively higher than that of torque increase which further boosts the torque ripples and degrades the steady-state performance. A five-level torque controller with modified output status is presented here by taking into account the above two limitations. The auxiliary subspace which is inherently present in a six-phase machine causing stator current distortion is also controlled by generating virtual voltage vectors. The resulting DTC drive is validated using MATLAB/Simulink software.
Enhanced Power Management and Control of a PVWind- BES Based Microgrid System

In this work, the design, control, and power management scheme of a solar, wind and battery energy storage (BES) based microgrid (MG) system is presented. The major power source in the proposed system is the PV, while the secondary power source is the wind. and battery energy storage system provide the backup power supply. The system can operate in “ off-grid” and“on-grid” modes, based on the power management control setup. The grid synchronization of the system is achieved by employing the power balance (PB) theory based control algorithm. This microgrid system is managed and controlled by an improved power management (IPM) algorithm to enhance the power quality, stability, and efficiency. The effectiveness of the suggested system is evaluated under numerous condition of uncertainties of weather conditions and instability of the utility grid power distribution system.
Digitalization Strategy through Gamification and Innovation Culture to Enhance Heavy Equipment Practitioner's Performance

The effects of gamification on education have been the subject of several research, but there haven't been many in-depth investigations into how gamification affects the productivity of heavy equipment practitioners in Indonesian coal mining organizations. In some literature it is stated that integrating gamification with big data analytics can improve performance, but what impact does this have on employee performance, especially heavy equipment practitioners in mining contractor companies, is one of the research questions in this study. Big data technologies are only one illustration of how Predictive maintenance, uses big data analytics as a decision-maker since it has three basic dimensions: variety, velocity, and volume. However, there are currently few studies that investigate the connection between employee performance and the gamification strategy used in big data analytics. The implementation of an innovation culture within the company is one way to create a conducive atmosphere for the integration of big data analytics and gamification techniques. Therefore, the goal of this study is to comprehend how innovation culture and gamification in big data analytics affect heavy equipment practitioners in Indonesian coal mining organizations. In this study, a simple random sampling approach was used to develop the sampling methodology based on the likelihood of selection. determining the number of samples using the Slovin principle. The sample consisted of 119 heavy equipment practitioners who were scattered throughout various regions of Indonesia. The examination, which examines both the outer and inner models, is conducted using the smartpls 3.2. 9 program. Indicators of the instrument's reliability and validity, such as Cronbach's Alpha, Composite Reliability (Pc), Average Variance Extracted (Pvc), and Discriminant Validity, are evaluated on the outer model while keeping an eye on the inner model testing and outer loading. Gamification and Innovation Culture on Employee Performance are found to be significantly correlated with a T statistic of 9.128 for Innovation Culture and 2.515 for Gamification. The assessment's results show that gamification has a P-Value of 0.000 and innovation culture has a P-Value of 0.012. The test results reveal a significant relationship between gamification in big data analytics and innovation culture, which impacts the performance of heavy equipment practitioners. From the test, it was discovered that employee performance increased in direct proportion to the practitioners' level of gamification in big data analytics and innovation culture.
Detection Performance Analysis based on EEG Signal for Visual BCI

The brain computer interfaces (BCIs) offer a possibility of communication for people with computer. Event related potential (ERP) and steady-state visual evoked potential (SSVEP) studies using simple images were mainly conducted to observe brain response characteristics to visual stimuli. In this paper, we present a Visual BCI dataset according to the presence or absence of a target in a still image and analyze the performance of various feature extraction and classification algorithms. Throughout various experiments, the proposed variance and mean ratio (VMR) based on Takens' delay embedding showed the best average accuracy from 69.23 to 76.40%.
Radiation-Hardened Processing-In-Memory Crossbar Array With Hybrid Synapse Devices for Space Application

This paper presents a multilayer perceptron (MLP) that offers excellent accuracy for classifying MNIST handwritten images considering radiation-induced bit failures. By introducing a stochastic model for radiation effect on ideal error-free MLP, the performance degradation of the neural network on space application is inevitable. Radiation-hardened processing in memory (PIM) should be developed with minimum hardware additives to utilize edge devices more practically in space. In the previous studies on digital synaptic devices to overcome radiation-related side effects, as the number of transistors in the unit storage device increases, more tolerance to radiation is expected. However, when all weight devices are replaced with bulky ones, the overall volume of the processor increases. This work proposes a digital hybrid synaptic device that only uses a larger device on the most significant bit (MSB) when the radiation effect is considered. With minimum hardware overhead for synapses, improved performance in the classification of MNIST is obtained. From the Neurosim framework with a single hidden layer, the accuracy is dramatically improved while sacrificing 1-bit weight information.
An Effective Encoderless Control for PMSM Drives: an Enhanced Active Flux Observation Technique

Permanent magnet synchronous motor drives with encoderless control techniques will simplify servo system de-sign, reduce cost, and improve reliability, which has therefore attracted decades of research in both the areas of academia and industry. Owing to its simple structure and reduced application requirements, model-based encoderless control methods have been widely used. Active flux model based technique shows good robustness thanks to its low parameter dependence. However, inverter non-linearity, dead time, and IGBT voltage drop will in-evitably cause mismatches between the desired and actual voltage outputs, which considerably affects the active flux estimation, and hence deteriorates the encoderless control performance. In this work, we propose a simple extended state observer based method to accurately estimate the active flux. Experiments confirm that the proposed technique achieves both good steady and dynamic performances at a wide speed range.
A Combined S-transform and Ensemble of DT based Protection scheme for six-phase transmission line

The current transmission infrastructure must be able to transmit more electricity due to the alarming increase in energy consumption. Instead of relying on the constraints of three-phase double-circuit lines to meet the growing power needs, a new generation of six-phase transmission systems has emerged as a potential solution. The protection challenges with six-phase transmission systems are exceptionally challenging, however, because of the enormous number of probable flaws. Given the advantages of six-phase systems over three-phase systems in meeting rising power demands and the dearth of existing protection schemes for six-phase lines, there is room for the development of a consistent protection scheme for a six-phase system that can satisfactorily perform the proposed protection tasks under a wide range of operating contingencies. To that end, this research proposes a novel protection strategy based on an ensemble of decision trees and the S-transform. To train the fault detector/classifier, the estimated frequency and phase of the current signals in real time have been utilised as input characteristics, and the S-transform has been used to do so. With a one-cycle moving window, it is feasible to estimate the input characteristics in a recursive fashion. To ensure the anticipated scheme is applicable to a six-phase system, it has been subjected to extensive simulations of a variety of fault conditions with a broad range of fault characteristics. The proposed relaying approach can reliably detect and classify issues in a single cycle across all test cases.
Errata: Reflection and transmission coefficients in T.I. media

As the two papers that will be dealt with here, Daley and Hron (1977) [paper 1] and Graebner (1992) [paper 2], involve essentially the same topic, placement of typographical and other errors within these papers in a common location would seem appropriate. Both papers deal with particle displacement reflection and transmission coefficients at a solid/solid interface between two transversely isotropic (T.I.) media. The axes of rotational invariance in both the upper (1) and lower (2) media, separated by a plane interface, are aligned perpendicular to the plane interface. The paper by Daley and Hron (1977) was written both to obtain reflection and transmission coefficients for this medium type for a fairly specific purpose and to show that plan-wave coefficients are associated with the zero-order approximation of an infinite asymptotic series. The 1992 paper published by Graebner, presents a much more general and possibly useful plane-wave approximation for the reflection and transmission coefficients in terms of the horizontal slowness, p, in a manner similar to those derived for isotropic media by Aki and Richards (1980). No analytic expressions for individual coefficients are given in paper 2 for the solid/solid case. Rather, the problem is set up in a manner to be solved by numerical methods using block partitioning of the original 44 × matrix. This allows the generally complex value of a specified coefficient of the 16 possible reflection and transmission coefficients at this type of interface to be obtained over a range of slownesses. In this report, apart from correcting the typographic errors in paper 1, the derivation of the polarization vectors in a T.I. in paper 2 will be re-examined.
Transformer Leakage Inductance Impact on Switching Behavior in a Full-Bridge Forward Converter

This paper presents how the leakage inductance influences the switching behavior of a hard-switched isolated full-bridge forward converter. Equations describing the transformer current and drain-source voltage across the primary switch are derived. The described waveforms are verified through measurements. The comparison shows excellent accuracy between calculated and measured parameters, with a maximum difference of 6.67% for the waveform parameters.
An Extensive Comparison of a Modular Multi-Level Converter Topology with different number of Sub-Modules

An increase in distributed generation and renewable energy in the modern grid has made modular multi-level converters (MMC) very popular. However, renewables demand more energy storage to provide power during faults and uneven energy production from sources like PV and wind. This paper describes an MMC scheme that connects renewables to the grid with minimal filter requirements. The PWM scheme used generates a multi-level voltage output with equal utilization of all the submodules, low harmonic distortion, and design flexibility. The later part of the paper compares the MMC topology with a different number of sub-modules. Then, it focuses on energy storage in capacitors with different numbers of sub-modules under different conditions. The stored energy can be useful for fault ride-through during grid fault conditions.
Development of Short-Term Wind Power Forecasting Methods

This paper presents new approaches for short-term wind power forecasts developed by the authors. Day-ahead and hours-ahead wind power forecasts were derived from the wind speed forecast data generated by High Resolution Deterministic Prediction System (HRDPS) Model at the Environment and Climate Change Canada (ECCC). Following a statistical analysis to verify the accuracy of the ECCC wind speed forecasts themselves, a power curve transfer model was developed to offer day-ahead wind power forecasts by converting the ECCC wind speed forecasts to wind power forecasts. An hours-ahead wind speed forecasting method was developed using a fusion approach to predict wind power for look-ahead times ranging from 30 minutes to six and a half hours with 5-min time steps to meet forecast delivery requirements of utilities and system operators. Using operational data over several years from six wind farms in different locations in Canada, the forecasting methodologies were validated for their good performance on the basis of statistical metrics and error distribution analyses.