Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumption based-only load forecasting for individual households in nanogrids: a case study

Electricity load forecasting plays an important role in planning and a vital role in the operational management of an electric power system based on smart grids. In this work, several data-driven approaches are used to forecast the individual electricity demand for the subsequent hour of three Italian households in a nanogrid context. For each user, the tested prediction models exploit only the historic series of total electricity consumption. The results show similar performances in all models implemented. Despite a widespread delay, the predictions follow the measurement trend well, while also highlighting the particular difficulty of predicting peak values.
Absorption Spectra of High-Temperature Solid Propellant Flames

Abstract : A new, high-temperature, infrared radiation source was used in conjunction with a rapid-scanning spectrometer to obtain absorption spectra at an 800 percent rate from composite solid propellant flames during both constant and varying pressure. The propellants studied were AP-HTPB composites. The 3.17 micron H2O, 4.26 micron CO2, and 4.72 micron CO absorption bands were calibrated so that the time-dependent variations in the gas phase concentration of these combustion products could be measured. High-frequency oscillations (200-300Hz) were present even in constant pressure tests and are believed to be associated with inhomogenieties along the optical path length in the flame. During forced oscillation pressure tests at frequencies of 20 to 100 Hz. , the composition and temperature of the propellant flame oscillated in phase with the pressure. A study of the variation in the absorption spectra for the major combustion products indicates that prior interpretations of flame emission data during transient pressure conditions were incorrect; however, a principal conclusion of this earlier study was confirmed. Significant gas phase composition variations are produced by pressure changes.
IoTFinder: Efficient Large-Scale Identification of IoT Devices via Passive DNS Traffic Analysis

Being able to enumerate potentially vulnerable IoT devices across the Internet is important, because it allows for assessing global Internet risks and enables network operators to check the hygiene of their own networks. To this end, in this paper we propose IoTFinder, a system for efficient, large-scale passive identification of IoT devices. Specifically, we leverage distributed passive DNS data collection, and develop a machine learning-based system that aims to accurately identify a large variety of IoT devices based solely on their DNS fingerprints. Our system is independent of whether the devices reside behind a NAT or other middleboxes, or whether they are assigned an IPv4 or IPv6 address. We design IoTFinder as a multi-label classifier, and evaluate its accuracy in several different settings, including computing detection results over a third-party IoT traffic dataset and DNS traffic collected at a US-based ISP hosting more than 40 million clients. The experimental results show that our approach allows for accurately detecting many diverse IoT devices, even when they are hosted behind a NAT and their traffic is “mixed” with traffic generated by other IoT and non-IoT devices hosted in the same local network.
A Fine-Grained Analysis on the Inconsistent Changes in Code Clones

Existing studies report that inconsistent changes in code clones can introduce bugs or inconsistencies in a software system’s code-base. However, inconsistent changes can often be intentional and these might not lead to bugs. Thus, it would be beneficial if we could have an automatic mechanism for proactively determining which inconsistent changes are likely to introduce bugs in a code-base. With this focus, in our research we investigate the underlying factors affecting the possibility that an inconsistent change made to code clones will lead to bugs. We extract the evolutionary history of the clone fragments in open-source software systems and analyze whether clone-types, former clone evolutionary patterns, and clone proximity have impacts on the bug-proneness of the inconsistent changes to clones. We perform our investigation on six subject systems written in three different programming languages (Java, C, and C#) and find that inconsistent changes in Type 3 clones have the highest possibility of introducing bugs among all three clone-types (Type 1, 2, and 3). Moreover, similarity preserving inconsistent changes are significantly more likely to introduce bugs compared to diverging inconsistent changes. Proximity as well as granularity of code clones have significant impacts on their possibilities of experiencing bug-fixes after having inconsistent changes. Findings from our research can be important for minimizing bugs and inconsistencies in software systems during their evolution.
How (Not) to Find Bugs: The Interplay Between Merge Conflicts, Co-Changes, and Bugs

Context: In a seminal work, Ball et al. [1] investigate if the information available in version control systems could be used to predict defect density, arguing that practitioners and researchers could better understand errors "if [our] version control system could talk". In the meanwhile, several research works have reported that conflict merge resolution is a time consuming and error-prone task, while other contributions diverge about the correlation between co-change dependencies and defect density. Problem: The correlation between conflicting merge scenarios and bugs has not been addressed before, whilst the correlation between co-change dependencies and bug density has been only investigated using a small number of case studies—which can compromise the generalization of the results. Goal: To address this gap in the literature, this paper presents the results of a comprehensive study whose goal is to understand whether or not (a) conflicting merge scenarios and (b) co-change dependencies are good predictors for bug density. Method: We first build a curated dataset comprising the source code history of 29 popular Java Apache projects and leverage the SZZ algorithm to collect the sets of bug-fixing and bug-introducing commits. We then combine the SZZ results with the set of past conflicting merge scenarios and co-change dependencies of the projects. Finally, we use exploratory data analysis and machine learning models to understand the strength of the correlation between conflict resolution and co-change dependencies with defect density. Findings: (a) conflicting merge scenarios are not more prone to introduce bugs than regular commits, (b) there is a negligible to a small correlation between co-change dependencies and defect density—contradicting previous studies in the literature.
Transport thermique dans des membranes très minces de SiN amorphe

Afin de comprendre les mecanismes de transport de la chaleur dans des films tres minces des materiaux amorphes, nous avons propose et demontre experimentalement une nouvelle technique de mesure des proprietes thermiques de membranes tres minces. Cette technique consiste a coupler la methode 3 omega avec la geometrie Volklein (membrane suspendue allongee). L'echantillon d'interet est alors monte dans un pont de Wheatstone specifique afin d'eliminer le signal electrique 1 omega. Cette technique permet de mesurer avec une tres haute sensibilite le signal thermique 3 omega et donc les proprietes thermiques des membranes. Le nitrure de silicium etudie dans ce travail constitue un materiau amorphe typique. Nous avons ete interesses par l'etude du transport thermique dans un tel systeme de dimensions reduites en fonction de la temperature et du stress intrinseque qui presente dans les films. Afin d'atteindre cet objectif, les membranes de nitrure de silicium de stress eleve et de faible niveau de stress ont ete mesurees respectivement pour une epaisseur de 50 nm et 100 nm. Le comportement global de la conductivite thermique mesuree est une croissance quand la temperature augmente, une tendance generalement constate pour un materiau amorphe. Le data de membrane de 50 nm presente une conductivite thermique inferieure a celle du 100 nm, ce qui est en accord avec l'effet des dimensions reduites. La chaleur specifique mesuree s'ecarte sensiblement de la loi en T3 de Debye. Cela est particulierement important en dessous de 100 K ou la chaleur specifique est plus eleve que celle prevue par la modele Debye. Ces resultats experimentaux sont en excellent accord avec les previsions d'un model theorique qui tient en compte de l'effet TLS (Two Level System) qui presente dans le materiaux amorphe. Il a ete montre experimentalement que le stress n'a pas d'effet sur la chaleur specifique de nitrure de silicium. De plus, nous avons demontre que le stress n'affecte pas la dissipation dans nitrure de silicium, et la dissipation par dilution semble etre la cause de la reduction de la dissipation. Par consequent, le stress ne devrait pas affecter la conductivite thermique du nitrure de silicium, ce qui est coherent avec les resultats experimentaux. En terme d'application de la methode 3 omega-Volklein, nous avons demontre que la membrane de SiN peut etre utilisee comme capteur thermique specifique pour caracteriser un autre materiau deposee sur la face arriere de la membrane. Nous avons teste ce modele pour mesurer les proprietes thermiques d'un film de 200 nm de Bi2Te3. Les resultats obtenus sont en excellent accord avec la litterature. Comme le SiN est un materiau isolant, ce modele est capable de mesurer des films tres minces quelle que soit sa nature, isolant, semi conducteur ou metallique.
Three-Pole Magnetic Bearing Design and Actuation

The three-pole magnetic bearing has recently gained interest within the magnetic suspension research community due to its simple structure that relies on only three coils and ability to be operated by three-phase power converters. However, the three-pole bearing is substantially more complicated to control. Control strategies based on linearized current models result in parasitic coupling between <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$y$</tex-math></inline-formula> radial forces, yielding significant force vector errors. Research initiatives have developed distinct three-pole geometries with varying capabilities and operating principles. This article reviews the various research initiatives on the three-pole bearing, develops a generalized framework for considering the forces of any three-pole bearing configuration, exercises this framework to provide insights into fundamental design topology choices, and proposes a method to calculate exact control currents and an optimal bias field. This article shows that using the exact control current calculations with the proposed optimal bias field yields a force density improvement of up to 15.5% while requiring 23.4% fewer ampere turns as compared to using conventional bias levels. Experimental validation of the force calculation framework is provided.
Technology of Production of Refractory Composites for Plasma Technologies

On the basis of theoretical and experimental studies of intensive processes of vibration and hydrodynamic drawing, the technological scheme of production of bimetallic composites of the “zirconium or hafnium-zirconium core copper shell” system is improved, substantiated experimentally, instrumentally drafted and implemented in production quantities. The scheme comprises hot pressing of the assembled work piece on a bimetallic rod and further drawing with inter pass annealing using the vibration of the drag and the hydrodynamic introduction of lubricant.
Learning Based Resource Allocation Under Limited Backhaul Capacity of Millimeter Wave Networks

Millimeter wave (mmwave) communication, which is an ideal candidate for meeting the high data rate requirements, are prone to random and dynamic blockages due to the small wavelength used. Backhaul communication between base station and core network, on the other hand, provides a stable connection, but have limited capacity when compared with increasing data rate requirements at the user side. Efficient backhaul resource allocation is required to strike a balance between the limited capacity, stable backhaul link and high capacity, unstable mmwave links. We propose a combination of stochastic geometry and analytical channel model to bring in real world mmwave blockage effect. A neural network based backhaul resource allocation is proposed to maximize the system utility under limited backhaul capacity of a mmwave network. Simulation results prove that the proposed method outperforms the existing solutions.
Using ASR Posterior Probability and Acoustic Features for Voice Disorder Classification

Dysphonia can be caused not only by the frequent use voice, but many other reasons, including environmental noise, environmental pollution and dry environment. Dysphonia can serve as an indicator for several serious and less serious diseases. Therefore a system that models the cognitive decision making processes of an expert would be of great value in order to make reliable and quick decisions to help physicians in diagnosing dysphonia. This paper focuses on the front-end of such a system, and evaluates acoustic features measured in different phonetic classes and ASR posterior probability values in two classification model schemes, with SVM and a DNN, for the classification of healthy and disordered voices in Hungarian-speaking patients. When the combination of the two features is used the classification accuracy increases to 89 %. While this is better than just using ‘acoustic’ features as an input for the DNN (88 %), we did not find significant impact of using ASR posterior probability values. Based on our results, it can be concluded that it is not worthwhile to calculate ASR phone posterior, as it has no significant impact, but it can greatly complicate and slow down a diagnosis support system.
Macroscopic Traffic Stream Variable Prediction with Weather Impact using Recurrent Learning Approach

Accurate prediction of the macroscopic traffic stream variables such as speed and flow is important for traffic operation and management in an intelligent transportation system. The accurate prediction of these variables is challenging because of the non-linear and complex characteristics of the traffic stream. With recent computational technologies and huge data availability, such a problem is solved using data-driven approaches. Traditional data-driven approaches use shallow architecture which ignores the hidden influencing factor and proved to have limitations in a high dimensional traffic state. Adverse weather conditions like fog, wind, rainfall, and snowfall affect the visibility of the driver, mobility of vehicle and road capacity. We examine the effect of rainfall on traffic stream variable prediction. The deep learning approaches use a layered architecture to extract the inherent features in the data. In this paper, recurrent neural network(RNN) and its variant long short term memory(LSTM) are used and their accuracy of predicting the traffic stream variables with and without rainfall variable is studied. To validate model performance, traffic sensor data from an arterial road and rainfall data from weather stations in San Diego are used for model training and testing. The test experiments show that with the combination of traffic data and rainfall data, recurrent learning models give better prediction accuracy over the model without rainfall data. Also, the LSTM outperforms other deep learning models in the presence of rainfall data.
A Wideband and Low-Loss Spatial Power Combining Module for mm-Wave High-Power Amplifiers

We present a low-loss power combiner, providing a highly integrated interface from an array of mm-wave power amplifiers (PAs) to a single standard rectangular waveguide (WG). The PAs are connected to an array of parallel and strongly coupled microstrip lines that excite a substrate integrated waveguide (SIW) based cavity. The spatially distributed modes then couple from the cavity to the rectangular WG mode through an etched aperture and two stepped ridges embedded in the WG flange. A new co-design procedure for the PA-integrated power combining module is presented that targets optimal system-level performance: output power, efficiency, linearity. A commercial SiGe quad-channel configurable transmitter and a standard gain horn antenna were interfaced to both ends of this module to experimentally demonstrate the proposed power combining concept. Since the combiner input ports are non-isolated, we have investigated the effects of mutual coupling on the transmitter performance by using a realistic PA model. This study has shown acceptable relative phase and amplitude differences between the PAs, $i.e$ . within ±15° and ±1 dB. The increase of generated output power with respect to a single PA at the 1-dB compression point remains virtually constant (5.5 dB) over a 42% bandwidth. The performed statistical active load variation indicates that the interaction between the PAs through the combiner has negligible effect on the overall linearity. Furthermore, the antenna pattern measured with this combiner shows negligible deformation due to non-identical PAs. This represents experimental prove-of-concept of the proposed spatial power combining module, which can be suitable for applications in MIMO array transmitters with potentially coupled array channels.
Analysis of Sound Imagery in EEG with a Convolutional Neural Network and an Input-perturbation Network Prediction Technique

Sound imagery has been studied in past decades with techniques such as fMRI, PET, MEG, or tDCS. However, sound imagery phenomenon in EEG signal has not been widely studied. Use of deep learning in EEG applications is increasing in popularity due to the ability to learn EEG data without rich data pre-processing. In contrast to typical classification models, with the input -perturbation network prediction technique used here, we visualized the learned features from the trained model in terms of the correlation between the change in input frequency and the change in network prediction to better understand the features the model used for decision making. In this study, we recorded EEG signals from three subjects who were asked to perform a sound imagery task. In the first phase, subjects were asked to listen to and remember a generated sound; in the second phase, subjects were asked to imagine a sound of the same pitch. One-fourth of trials had no sound generation; EEG signals were labeled with the no imagery class. EEG signals from the remaining trials were labeled with the sound imagery class for model training. The best accuracy of 71.41% was obtained by the shallow model for subject 1, and an average accuracy of 61.00% was achieved between subjects. The model’s decision to classify EEG data into the sound imagery class was based on decreases in the delta, theta, and low beta bands in the frontal lobe and corresponding increases in the in the right temporal lobe of the brain.
Temporal Prediction Model for CO and CO2 Pollutants using Long Short Term Memory

Forest and land fires in Indonesia cause smoke haze pollution in the Southeast Asian region. This situation has resulted in the air quality around the forest and peatland fires location being tripled from the hazardous to health to exceed 1000 in the Pollutant Standard Index (PSI). This study aims to create a predictive model of CO and CO2 concentration data from forest and land fires using the Long Short Term Memory method. The data used in this study are the HYSPLIT simulation results with data input are Sumatra’s hotspot data in 2019, and meteorological data in 2019. This research was carried out through several stages, namely data collection, selection of hotspots, dispersion simulations, merging data, data processing, data sharing, creating prediction models for CO and CO2 pollutant concentrations, and evaluation of the model with RMSE and R. This study produces a predictive model of CO pollutants that had the best R and RMSE values of 0.156977610 and 2.882038×10−8 respectively. Simultaneously, the CO2 pollutant prediction model has the best R and RMSE values of 0.6164270 and 1.815969×10−6, respectively.
Decolourization of synthetic dyes by laccase enzyme produced by Kluyveromyces dobzhanskii DW1 and Pichia manshurica DW2

Industrialization has come with environmental challenges. Industries like paper, printing, textile, leather and so on widely use chemical dyes whose waste treatment or degradability is difficult. Among various methods employed, the use of microbial enzymes is the most effective. The study aimed at producing laccase from identified yeast strains for potential industrial use in dye decolourization. Laccase produced by Kluyveromyces dobzhanskii DW1 and Pichia manshurica DW2 were purified and immobilized up to 65.2 and 73.1%, respectively. The crude, purified and immobilized forms of the enzymes were used to decolourize malachite green and methyl red dyes each at concentrations of 0.05 and 0.1 g/L. The highest percentage decolourization by K. dobzhanskii DW1 was 81.50% (immobilized) and 87.50% (purified), respectively for malachite green and methyl red dyes while P. manshurica DW2b (crude) had 84.40 and 76.89%, respectively. The Fourier transform infrared (FTIR) spectrum of the dyes was collected within a scanning range of 4000 to 400 cm-1. The spectrum of methyl red dye by the purified K. dobzhanskii DW1 laccase showed disappearance of some chemical groups (peak), while the crude P. manshurica DW2 laccase removed the main azo-group, alcohol/phenol and higher alkane (1487.17) groups. The spectrum of untreated malachite green also showed 25 peaks with one disulphide, 2 aliphatic halogens, 2 thio ethers, 3 sulphones, 4 imino groups among other chemical groups. The decolourization of the dye with the immobilized K. dobzhanskii DW1 laccase showed a spectrum of 17 peaks with the removal of the disulphide (420.5), one aliphatic halogen (C-I), thio ether, 2 sulphone, 4 imino and 2 higher alkanes, while the crude P. manshurica DW2 laccase removed the 2 aliphatic halogen, 4 imino, 1 amine, 2 alkane and 2 of the 3 alcohol/phenol chemical groups. The removal of the main components (azo chemical group) of the dyes proved their effectiveness in decolourisation and bioremediation of the textile wastes. Key words: Decolourisation, azo-dye, laccase, immobilized, FT-IR spectra.
Low Complexity Model Predictive Flux Control for Three-Level Neutral-Point Clamped Inverter-Fed Induction Motor Drives Without Weighting Factor

Tuning of weighting factors is a very complex task in the implementation of conventional finite set predictive torque control (FS-PTC) for three-level neutral-point-clamped (3L-NPC) inverter fed IM drive. The choice of these weighting factors strongly influences the control objectives such as flux, torque, and neutral point voltage (NPV). Moreover, the computational burden is high as all 27 switching states are enumerated for the evaluation of a cost function involving multiple objectives. To reduce the complexity of the conventional FS-PTC, this article proposes a high-efficient single-vector-based predictive flux control for an IM drive fed by a 3L-NPC inverter. The proposed cascaded predictive flux and NPV control evaluates 13 vectors for outer predictive flux control and two switching states for the inner predictive NPV control. It significantly reduces the computational load as the number of enumerations is reduced by more than 50%. The nontrivial process of weighting factor calculation is no longer required. The complexity reduction and the elimination of weighting factors are achieved without sacrificing the transient and steady-state performances in terms of torque and stator flux responses, total harmonic distortion of stator currents, NPV variation, and average switching frequency, compared to the conventional method as verified by experimental results.