Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation

This paper deals with a specific approach to fault detection in transformer systems using the extended Kalman filter (EKF). Specific faults are investigated in power lines where a transformer is connected and only the primary electrical quantities, input voltage, and current are measured. Faults can occur in either the primary or secondary winding of the transformer. Two EKFs are proposed for fault detection. The first EKF estimates the voltage, current, and electrical load resistance of the secondary winding using measurements of the primary winding. The model of the transformer used is known as mutual inductance. For a short circuit in the secondary winding, the observer generates a signal indicating a fault. The second EKF is designed for harmonic detection and estimates the amplitude and frequency of the primary winding voltage. This contribution focuses on mathematical methods useful for galvanic decoupled soft sensing and fault detection. Moreover, the contribution emphasizes how EKF observers play a key role in the context of sensor fusion, which is characterized by merging multiple lines of information in an accurate conceptualization of data and their reconciliation with the measurements. Simulations demonstrate the efficiency of the fault detection using EKF observers.
Evaluation of Reinforced Concrete Structures with Magnetic Method and ACO (Amplitude-Correlation-Offset) Decomposition

The magnetic method is one of the very few nondestructive testing (NDT) techniques that provide the possibility to conduct area tests of reinforced concrete (RC) structures in a fast, cheap, and straightforward way. This paper aims to present a new approach to the simultaneous identification of rebars’ diameter, alloy class, and thickness of the concrete cover tested with this method. Since rebars from different manufacturers may have different electromagnetic properties (standardization only for mechanical properties), preparing an effective and universal database is impossible. In this work, ACO decomposition is proposed, a new attributes extraction method designed to identify object parameters, even if it is impossible to collect a suitable training database (by pattern recognition and analysis of the deviation). Conducted tests prove that the ACO method enables accurate reflection of the waveform shape and limitation of attributes number to three or fewer (avoiding the curse of dimensionality). These properties, combined with the ability to analyze spatial components of magnetic induction (which only magnetic sensors provide), make the complex task of identification of three parameters more straightforward and the separation between the results received for different classes larger. This article presents the measurement results and the whole identification process.
RESEARCH CAMPAIGN: The Convective and Orographically Induced Precipitation Study

Abstract The international field campaign called the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007 in southwestern Germany/eastern France. The overarching goal of COPS is to advance the quality of forecasts of orographically-induced convective precipitation by four-dimensional observations and modeling of its life cycle. COPS was endorsed as one of the Research and Development Projects of the World Weather Research Program (WWRP), and combines the efforts of institutions and scientists from eight countries. A strong collaboration between instrument principal investigators and experts on mesoscale modeling has been established within COPS. In order to study the relative importance of large-scale and small-scale forcing leading to convection initiation in low mountains, COPS is coordinated with a one-year General Observations Period in central Europe, the WWRP Forecast Demonstration Project MAP D-PHASE, and the first summertime European THORPEX Regional.
Modeling Software Reliability with Learning and Fatigue

Software reliability growth models (SRGMs) based on the non-homogeneous Poisson process have played a significant role in predicting the number of remaining errors in software, enhancing software reliability. Software errors are commonly attributed to the mental errors of software developers, which necessitate timely detection and resolution. However, it has been observed that the human error-making mechanism is influenced by factors such as learning and fatigue. In this paper, we address the issue of integrating the fatigue factor of software testers into the learning process during debugging, leading to the development of more realistic SRGMs. The first model represents the software tester’s learning phenomenon using the tangent hyperbolic function, while the second model utilizes an exponential function. An exponential decay function models fatigue. We investigate the behavior of our proposed models by comparing them with similar SRGMs, including two corresponding models in which the fatigue factor is removed. Through analysis, we assess our models’ quality of fit, predictive power, and accuracy. The experimental results demonstrate that the model of tangent hyperbolic learning with fatigue outperforms the existing ones regarding fit, predictive power, or accuracy. By incorporating the fatigue factor, the models provide a more comprehensive and realistic depiction of software reliability.
Neural Augmentation-Based Saturation Restoration for LDR Images of HDR Scenes

There are shadow and highlight regions in a low-dynamic-range (LDR) image which is captured from a high-dynamic-range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this article, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the new model-based approach. It relaxes the requirement of mapping integers to integers and improves the modeling accuracy. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the highlight regions. Both synthetic images are then refined via one single exposedness-aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multiscale exposure fusion (MEF) algorithm. Experimental results indicate that the proposed algorithm outperforms existing algorithms in terms of HDR-VDP-3. The proposed algorithm can be embedded in any smartphones or digital cameras to produce an information-enriched LDR image.
Cell throughput analysis for downlink multi-user MIMO transmission with radiation pattern reconfigurable antennas

Reconfigurable antennas can provide superior performance over legacy antennas, since they can dynamically reshape the channels by configuring the optimal operation mode. In this paper, we focus on the effectiveness of applying reconfigurable antennas in an industrial multi-user MIMO system. Firstly, we propose a new reconfigurable antenna structure that can realize dual-polarization independent control of operation modes. Then, we capture the relationship between the cell throughput performance with the operation modes of the reconfigurable antennas, based on which a mode selection problem is formulated. Additionally, we propose an efficient downlink transmission scheme for the reconfigurable MIMO system and design a near-optimal but low-complexity joint mode selection and user scheduling algorithm. Numerical results validate the superiority of reconfigurable MIMO systems over legacy MIMO systems, and also demonstrate the effectiveness of the proposed algorithm.
UMA METODOLOGIA PARA A IDENTIFICAÇÃO DE PERDAS NÃO TÉCNICAS EM GRANDES CONSUMIDORES RURAIS

A irrigacao de grandes areas agricolas representa uma porcao significativa do consumo de energia eletrica em paises produtores de alimentos. A potencia das bombas de irrigacao em lavouras de arroz, por exemplo, pode ser superior a 800 CV, sendo que esses sistemas sao utilizados continuamente no periodo da safra. No Brasil, as perdas nao tecnicas neste tipo de consumidor geralmente sao devidas a fraudes, erros em medidores de energia e ligacoes clandestinas. Esse tipo de problema e de dificil fiscalizacao devido a grande extensao das linhas rurais, alem da dificuldade de acesso, em muitos casos. Este trabalho apresenta uma proposta para a identificacao de perdas nao tecnicas em alimentadores rurais contendo sistemas de bombeamento para irrigacao de lavouras de arroz. A metodologia proposta e baseada na correlacao dos padroes de consumo de energia eletrica, das caracteristicas da area irrigada e das condicoes climaticas do periodo de irrigacao. A metodologia emprega a tecnica de Rede Neural Artificial, e tem como entrada um conjunto de dados de precipitacao pluviometrica, temperatura, incidencia solar, umidade do ar, carga instalada e area de solo irrigado caracteristico do cultivo de arroz. O resultado final das analises indica o risco percentual de cada conjunto de dados e inconsistencias que possam implicar em perdas nao tecnicas. Os resultados do trabalho foram obtidos e validados a partir de uma base de dados reais de safras do periodo entre 2009 e 2014, de lavouras do Estado do Rio do Grande do Sul.
Subsurface Radar Imaging by Optimizing Sensor Locations in Spatio-Spectral Domains

This article deals with subsurface radar imaging (SRI) for a 2-D scalar setting consisting of a two-layered background medium imaged via a multifrequency, multimonostatic configuration. The objective is to reduce data for a subsurface imaging problem without performance degradation by determining the optimal sensor locations in both spatial and frequency domains. In this regard, we present a sampling method that effectively extends the maximal projection on minimum eigenspace (MPME) algorithm to tackle the semidiscrete inverse problem associated with subsurface imaging. Compared to the state-of-the-art technique, we significantly reduce the required samples for imaging. Numerical and experiment results, the latter concerning a buried water pipe, are reported to demonstrate the effectiveness of the proposed sampling strategy. In particular, for the considered cases, the proposed sampling method shows a data reduction of more than 50% compared to other literature sampling methods.
Using speech synthesis to explain automatic speaker recognition: a new application of synthetic speech

Some speech synthesis systems make use of zero-shot adaptation to generate speech based on a target speaker. These systems produce speaker embeddings in the same way that speaker embeddings (often called ‘x-vectors’) are produced in automatic speaker recognition systems. This commonality between the two technologies could lower barriers that constrain the use of automatic speaker recognition systems in forensic speech analysis casework. A key barrier to the use of automatic speaker recognition in the forensic context is the issue of explainability, including what information about the voice a system uses in order to arrive at conclusions. This paper sets out a new approach that could be used to effectively communicate this type of information to audiences in the legal setting. Specifically, it is proposed that exposing listeners to synthetic speech produced by a zero-shot adaptation system could illustrate what aspects of the voice an automatic speaker recognition system captures.
Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines

In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software.
Theoretical Considerations on the Impact of Nearby Tags on the Wireless Power Transfer From the Reader to the Target Tag in Passive UHF RFID

Radio frequency identification (RFID) technology, and in particular the passive UHF (ultra-high frequencies) standard, is developing strongly with the emergence of cognitive sensor networks and especially the Internet-of-Things. A significant indicator of this evolution is that tags are increasingly equipped with new capabilities: in addition to the identification functionality, there are, for example, those of sensor or actuator. In this context, the energy requirement at the tag level, known as augmented tag, is clearly increasing. Consequently, on the one hand, reading distances can be limited (the energy received is shared between the different components of the augmented tag) and on the other hand, to overcome the problem, semi-passive solutions (on-board battery to power the active electronic circuits) are proposed at the expense of the decisive advantage of the passive nature of the tags. It is therefore necessary to find alternative solutions to optimize the energy transfer between the reader and the tags. The concept proposed here is to target a particular tag, the one being queried, and to exploit the tags in its vicinity as reflectors in order to converge a maximum of power transmitted by the reader on the “target” tag. The paper uses an adapted mathematical formalism to evaluate the total power that can be received by a target tag as a function of the tags in its neighborhood: the impact of their relative position and of the load impedance of the reflector tags is analyzed and illustrated by simulations.
Defect Mapping and Densification in Self-Assembled Monolayers of Octadecyltrichlorosilane on SiO2

Self-assembled monolayers (SAMs) can be used for surface functional control to assist with pattern collapse prevention and as a protective layer to enable Area Selective Deposition (ASD). To be successful, these applications require the formation of a high-density, defect-free, so-called well-packed SAM at the nm scale. In this paper, we describe a method to map the nm scale defects of octadecyltrichlorosilane (ODTS) SAMs using a post-etching AFM analysis of the surface of the substrate and used this technique to develop a process to form high-density, defect-free SAM layer at the nm scale. This was achieved by optimizing the water concentration in the solvent for the precursor solution and annealing after SAM formation.
Quantitative Insights: Unveiling the Interplay Between Mathematics and Physics in College Education

This study investigates the intricate interplay between mathematics and physics in college education. Through a mixed-methods approach involving surveys, interviews, and classroom observations, the research delves into students' perceptions, instructional challenges, and effective pedagogical strategies. The findings reveal that while many students recognize the relationship between the disciplines, challenges persist in bridging mathematical concepts with physics principles. Instructors employ interactive simulations and inquiry-based methods to address these challenges. The study's insights hold implications for curriculum development and teaching practices, aiming to enhance the integration of mathematics and physics education and provide a more enriched learning experience for students
Sulphur and boron influences soil quality indicators in a typical entisol

Sulphur and boron are the two essential nutrients having well defined roles to play in the production phenology of oil seed crops. These nutrients often act synergistically in improving the soil characteristics as well as the yield and yield attributing characters of the crop. Synergistic interaction between these two nutrients in enhancing soil and plant health was reported by Das (2007). Sahrawat et al. (2008) reported widespread deficiency of S and B in the semi arid regions of India. The present paper focuses the impact of S and B on soil quality indicators in a typical entisol of Kerala with sesame as the test crop.
Characterization of stellar companions from high-contrast long-slit spectroscopy data. The EXtraction Of SPEctrum of COmpanion (Exospeco) algorithm

High-contrast long-slit spectrographs can be used to characterize exoplanets. High-contrast long-slit spectroscopic data are however corrupted by stellar leakages which largely dominate other signals and make the process of extracting the companion spectrum very challenging. This paper presents a complete method to calibrate the spectrograph and extract the signal of interest. The proposed method is based on a flexible direct model of the high-contrast long-slit spectroscopic data. This model explicitly accounts for the instrumental response and for the contributions of both the star and the companion. The contributions of these two components and the calibration parameters are jointly estimated by solving a regularized inverse problem. This problem having no closed-form solution, we propose an alternating minimization strategy to effectively find the solution. We have tested our method on empirical long-slit spectroscopic data and by injecting synthetic companion signals in these data. The proposed initialization and the alternating strategy effectively avoid the self-subtraction bias, even for companions observed very close to the coronagraphic mask. Careful modeling and calibration of the angular and spectral dispersion laws of the instrument clearly reduce the contamination by the stellar leakages. In practice, the outputs of the method are mostly driven by a single hyper-parameter which tunes the level of regularization of the companion SED.
On the Control of Elemental Composition, Macro-, and Microstructure of Directionally Solidified Additive Products from Nickel-Based Alloy

The present work establishes the influence of heat input and methods of heat removal at the wire-feed electron beam additive manufacturing (EBAM) process on the structure of an additive product made of a nickel-based alloy. The following printing approaches are considered: changes in heat input, 3D printing strategy, and heat removal conditions due to (1) heating of the substrate, (2) partial suppression of radiative heat dissipation, and (3) thermal insulation of the substrate. It is shown that epitaxial growth of dendrites occurs in each case. However, in the case of an increase in speed and a change in the 3D printing strategy, the directed dendritic growth is interrupted. Preheating of the substrate and subsequent maintenance of the temperature reached during the EBAM process, as well as partial suppression of the radiative component of heat removal, allow to obtain the most uniform directional structure.
Rapid temperature measurement BOTDR technology based on frequency agility technology

We propose a scheme of high-speed Brillouin optical time-domain reflectometer (BOTDR) for dynamic strain measurements, which implements by frequency-agile techniques. The spontaneous Brillouin gain spectrum can be demodulated online in the time domain using band-pass filters and envelope detection for truly distributed, single-ended access and dynamic strain measurements. The proposed sensor achieves a strain sampling rate of 62.5 Hz with 100 sweeps and 64 averages over 172 m of single-mode fiber. The vibration frequencies of 6.82 Hz and 14.77 Hz are measured in the experiment with a spatial resolution of 2 m and a dynamic range of 4000 $\mu\varepsilon$. The measurement accuracy is $\pm 30\mu\varepsilon$, calculated by the standard deviation of the static fiber Brillouin frequency shift.
Automated Multiple Sclerosis Screening Based on Encoded Speech Representations

Multiple Sclerosis (MS) is a chronic disease affecting over 2.5 million people worldwide. Its early detection is crucial for the management and treatment of the disease. Here we present an approach for automatic MS screening based on encoded speech representations. Our methods rely on Wav2Vec2 models to extract relevant traits from speech recordings of patients, which are then fed into a Support Vector Machine. Besides employing Wav2Vec2 models pre-trained on large public corpora, we also fine-tune them on 85 hours of the target language (Hungarian) in two distinct ways: for ASR and for speaker identification. Both variations outperformed the original models and conventional methods (ComParE functionals, x-vectors, and ECAPA-TDNN). Our findings suggest that fine-tuning for the actual speaker provides more advantages than the typical approach of fine-tuning for ASR purposes. Still, we improved our best MS discrimination performance when we fused features from our two fine-tuned models.
Differential effective medium schemes for investigating the relationship between high‐frequency relative dielectric permittivity and water content of soils

Differential effective medium (DEM) theory is presented and used to calculate the effective HF dielectric permittivity κ of unsaturated soils considered as mixtures of solid and two fluid phases. In the case of coarse‐grained soils for which dielectric losses are negligible, a good agreement with the empirical equation of Topp et al. [1980] is obtained using only a spherical grain shape. Moreover, results of DEM schemes have shown that ohmic losses induced by salinity modify significantly the relationship between the apparent dielectric permittivity at 120 MHz and the volumetric water content θ, for NaCl concentration greater than 0.045 mol/L. In the case of clayey soils, simulations based on the same theoretical approach show that the available data can not be modeled by considering only the geometrical effect associated with the “platy” units and the ohmic losses associated with the surface conductivity.
Coupled Line Wilkinson Combiner-Antenna Integrated Design for 300-GHz Arrayed UTC-PDs

We designed and analyzed the performance of a 2 by 1 coupled line Wilkinson combiner-antenna device for arrayed photomixers at 300 GHz. Simulation results showed that there is a 2.59 dB increase in total radiated THz-wave power when the coupled line Wilkinson combiner is used to integrate two identical UTC-PDs and feed their output power into an antenna, compared to a single-port feed for a single UTC-PD. Additionally, there is a -21.5 dB isolation between the feed ports and a realized gain of 4.8 dBi in the +z (theta = 0) direction. Furthermore, the quarter wavelength lines for the designed combiner covers a half area of $0.1 \times 0.055 \ \mathrm{mm}^{2}$ compared to the conventional Wilkinson combiner.
Neural Speech Synthesis with Enriched Phrase Boundaries

Prosodic phrasing is one of the factors influencing the nat-uralness of synthesized speech. In this paper, we enrich the phonetic representation for neural speech synthesis with additional markers denoting the strength of phrase breaks between words. These markers are assigned to the training data automatically, using our previously introduced model for audio-based phrase boundary detection. We tested the approach with two different levels of resolution for the break indices – either ten distinct levels (P10) or only “ToBI-like” four levels (P4). Listening tests with two different speaker voices show a statistically significant preference among listeners for P10 or P4 over the baseline speech synthesis without these markers (P0), although which version is judged as better depends on the voice.