Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of a Circumterrestrial Disk and Formation of a Single Moon

Abstract We investigate the evolution of a circumterrestrial disk of debris generated by a giant impact on Earth and the dynamical characteristics of the moon accreted from the disk by using high-resolution N -body simulation. We find that in most cases the disk evolution results in the formation of a single large moon on a nearly circular orbit close to the equatorial plane of the initial disk just outside the Roche limit, which is consistent with the previous work by S. Ida et al. (1997, Nature 389 , 353–357). The efficiency of incorporation of disk material into a moon is 10–55%, which increases with the initial specific angular momentum of the disk. These results hardly depend on the initial condition of the disk as long as the mass of the disk is 2 to 4 times the present lunar mass and most mass of the disk exists inside the Roche limit. The timescale of the disk evolution is determined mainly by the surface density of the disk because mass transfer to the outside of the Roche limit and formation of lunar seeds are regulated by collective behavior of disk particles. The evolution of the disk is summarized as follows: The disk contracts through collisional damping. As the velocity dispersion of disk particles decreases, particle clumps grow inside the Roche limit. The clumps become elongated due to Keplerian shear, which forms spiral arm-like structure. Particles are transfered to the outside of the Roche limit through the gravitational torque exerted by the spiral arms. When a tip of a spiral arm goes beyond the Roche limit, it collapses into a small moonlet. The rapid accretion of these small moonlets forms a lunar seed. The seed exclusively grows by sweeping up particles transfered over the Roche limit. When the moon becomes large enough to gravitationally dominate the disk, it pushes the rest of the inner disk to Earth. The formation timescale of the moon is of the order of 1 month if a particulate disk is assumed and the effect of melting/vaporization is not included.
Effect of Synthesized Titanium Dioxide Nanofibers Weight Fraction on the Tribological Characteristics of Magnesium Nanocomposites Used in Biomedical Applications

Biomedical applications, such as artificial implants, are very significant for the disabled due to their usage in orthopedics. Nevertheless, available materials in such applications have insufficient mechanical and tribological properties. The current study investigated the mechanical and tribological properties of a biomedical metallic material, magnesium (Mg), after incorporating titanium dioxide nanofibers (TiO2) with different loading fractions. The TiO2 nanofibers were synthesized using the electrospinning technique. The ball-milling technique was utilized to ensure the homogenous distribution of TiO2 nanofibers inside the Mg matrix. Then, samples of the mixed powder with different loading fractions of TiO2 nanofibers, 0, 1, 3, 5, and 10 wt. %, were fabricated using a high-frequency induction heat sintering technique. The physicomechanical and tribological properties of the produced Mg/TiO2 nanocomposites were evaluated experimentally. Results showed an enhancement in mechanical properties and wear resistance accompanied by an increase in the weight fraction of TiO2 nanofibers up to 5%. A finite element model was built to assess the load-carrying capacity of the Mg/TiO2 composite to estimate different contact stresses during the frictional process. The finite element results showed an agreement with the experimental results.
Speech Enhancement Algorithm Based on a Convolutional Neural Network Reconstruction of the Temporal Envelope of Speech in Noisy Environments

Temporal modulation processing is a promising technique for improving the intelligibility and quality of speech in noise. We propose a speech enhancement algorithm that constructs the temporal envelope (TEV) in the time-frequency domain by means of an embedded convolutional neural network (CNN). To accomplish this, the input speech signals are divided into sixteen parallel frequency bands (subbands) with bandwidths approximating 1.5 times that of auditory filters. The corrupted TEVs in each subband are extracted and then fed to the 1-dimensional CNN (1-D CNN) model to restore the TEVs distorted by noise. The method is evaluated using 2,700 words from nine different talkers, which are mixed with speech-spectrum shaped random noise (SSN), and babble noise, at different signal-to-noise ratios. The Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) metrics are used to evaluate the performance of the 1-D CNN algorithm. Results suggest that the 1-D CNN model improves STOI scores on average by 27% and 34% for SSN and babble noise, respectively, and PESQ scores on average by 19% and 18%, respectively, compared to unprocessed speech. The 1-D CNN model is also shown to outperform a conventional TEV-based speech enhancement algorithm.
Methodological Aspects for the Assessment of Social Behaviour and Avoidance Distance on Dairy Farms

Some aspects of on-farm assessment of social behaviour and avoidance distance were investigated on 20 Austrian dairy farms. The avoidance distance of at least 75% of cows was assessed. Social behaviour of the cows was observed for one hour and the number of animals standing was recorded every 10 min. Lameness of each animal was scored, and Spearman correlations were calculated. Generally, the avoidance distance of the cows was very low. Lameness did not correlate significantly with avoidance distance. The number of agonistic interactions with body contact per cow correlated negatively with the percentage of lame animals (rs = -0.49; P = 0.029). However, this correlation was no longer found (rs = -0.22; not significant) when calculated on the basis of standing animals only. The total number of social interactions correlated highly with the number of social interactions when interactions in the feeding rack were disregarded. The present study suggests that lameness confounds the assessment of social behaviour but not that of avoidance distance of cows, and that social interactions of animals standing in the feeding racks can be disregarded without decreasing the reliability of the assessments.
Byte-Pair Encoding for Classifying Routine Clinical Electroencephalograms in Adults Over the Lifespan

Routine clinical EEG is a standard test used for the neurological evaluation of patients. A trained specialist interprets EEG recordings and classifies them into clinical categories. Given time demands and high inter-reader variability, there is an opportunity to facilitate the evaluation process by providing decision support tools that can classify EEG recordings automatically. Classifying clinical EEG is associated with several challenges: classification models are expected to be interpretable; EEGs vary in duration and EEGs are recorded by multiple technicians operating various devices. Our study aimed to test and validate a framework for EEG classification which satisfies these requirements by transforming EEG into unstructured text. We considered a highly heterogeneous and extensive sample of routine clinical EEGs (n = 5785), with a wide range of participants aged between 15 and 99 years. EEG scans were recorded at a public hospital, according to 10/20 electrode positioning with 20 electrodes. The proposed framework was based on symbolizing EEG signals and adapting a previously proposed method from natural language processing (NLP) to break symbols into words. Specifically, we symbolized the multichannel EEG time series and applied a byte-pair encoding (BPE) algorithm to extract a dictionary of the most frequent patterns (tokens) reflecting the variability of EEG waveforms. To demonstrate the performance of our framework, we used newly-reconstructed EEG features to predict patients' biological age with a Random Forest regression model. This age prediction model achieved a mean absolute error of 15.7 years. We also correlated tokens' occurrence frequencies with age. The highest correlations between the frequencies of tokens and age were observed at frontal and occipital EEG channels. Our findings demonstrated the feasibility of applying an NLP-based approach to classifying routine clinical EEG. Notably, the proposed algorithm could be instrumental in classifying clinical EEG with minimal preprocessing and identifying clinically-relevant short events, such as epileptic spikes.
Influenza Treatment: Limitations of Antiviral Therapy and Advantages of Drug Combination Therapy

Influenza infection is serious and debilitating for humans and animals. The influenza virus undergoes incessant mutation, segment recombination, and genome reassortment. As a result, new epidemics and pandemics are expected to emerge, making the elimination challenging of the disease. Antiviral therapy has been used for the treatment of influenza since the development of amantadine in the 1960s; however, its use is hampered by the emergence of novel strains and the development of drug resistance. Thus, combinational therapy with two or more antivirals or immunomodulators with different modes of action is the optimal strategy for the effective treatment of influenza infection. In this review, we describe current options for combination therapy, their performance, and constraints imposed by resistance, calling attention to the advantages of combination therapy against severe influenza infections. We also discuss the challenges of influenza therapy and the limitations of approved antiviral drugs.
Hyperspectral imaging through partially transparent media

The operation of hyperspectral imaging systems in industrial environments can be a challenge. In the nuclear industry, partially transparent elements such as gloveboxes or panels are often used to cover samples for protection against the risk of contamination. In practical terms, this means that the hyperspectral sensors can only capture data through partially transparent media, which interferes the vision between sensor and sample. Representative examples of these media are Polymethyl Methacrylate (PMMA) or acrylic and Polycarbonate (PC). In this work, we evaluate the effect that the transparent media can have on the data when captured under these conditions, where transparent materials are placed between sensor and sample. Experiments include hyperspectral images of the same samples captured with and without panel obstruction for a direct comparison of spectral responses, suggesting potential artificial intelligence techniques and methods to identify these effects and mitigate them.
Order parameter in complex dipolar structures : Microscopic modeling

Microscopic models have been used to reveal the existence of an order parameter that is associated with many complex dipolar structures in magnets and ferroelectrics. This order parameter involves a double cross product of the local dipoles with their positions. It provides a measure of subtle microscopic features, such as the helicity of the two domains inherent to onion states, curvature of the dipolar pattern in flower states, or characteristics of sets of vortices with opposite chirality (e.g., distance between the vortex centers and/or the magnitude of their local dipoles).
Inelastic transport of tunnel and field-emitted electrons through a single atom

The desorption of individual hydrogen atoms from a Ge~111! surface with the tip of a scanning tunneling microscope is used as a prototype system for investigating the inelastic transport of electrons through a single atom. A method based on the analysis of the desorption time averaged over the spatial extent of the hydrogen site has enabled the localization and the efficiency of the inelastic electron interaction to be studied. Both the localization and the efficiency show very different features in the tunnel and field emission regimes.
Revisiting the Santa Barbara sense of direction scale, mental rotations, and gender differences in spatial orientation

Angular direction estimation to landmarks of varying distance in the physical environment was utilised to investigate the ecological validity of the Santa Barbara sense of direction scale (SBSOD). Two- and three-dimensional MR measures were included to enable further the scale applicability. Results showed a moderate correlation between SBSOD and angular deviation from landmarks in the immediate landscape, but not with local or distant landmarks. Moreover, the findings suggest that skills which underlie three-dimensional MR better relate to pointing accuracy (PA) of distant landmarks and the cardinal direction, North. Results also showed a gender-related systematic biases in landmark estimation.
Über einige risikotheoretische Fragestellungen

Abtsract I . Die zuerst von Dr. F. Lundberg [6–10] gegrundete und spater auch von anderen skandinavischen Verfassern ausgebildete, sogenannte kollektive Risikotheorie hat in den letzten Jahren auch das Interesse anderer europaischen Forscher geweckt. Die Tatsache, dass in dieser Weise neue Beitrage zur Beleuchtung der hinzugehorigen Fragen erschienen sind, muss von den skandinavischen Bearbeitern des risikotheoretischen Gebietes begrusst werden, denn dadurch ist eine Kritik und Gegenkritik entstanden, die auf die Theorie nur fordernd wirken kann. So hat zum Beispiel Dr. G. Ottaviani in der Giornale dell'Istituto Italiano degli Attuari fur April 1940 eine Arbeit [11] publiziert, die eine scharfe Kritik gegen die kollektive Risikotheorie enthalt und dadurch teilweise zu diesem Aufsatz Anlass gegeben hat.
High Adsorption of Hazardous Cr(VI) from Water Using a Biofilter Composed of Native Pseudomonas koreensis on Alginate Beads

Most conventional methods to remove heavy metals from water are efficient for high concentrations, but they are expensive, produce secondary pollution, and cannot remove low concentrations. This paper proposes a biological system to remove Cr(VI) from aqueous solutions; the biofilter is composed of a native Pseudomonas koreensis immobilized in calcium alginate beads. Lab experiments were conducted in batch reactors, considering different operating conditions: Cr(VI) concentration, temperature, pH, and time. At 30 °C and a pH of 6.6, the immobilized bacteria achieved their optimal adsorption capacity. In the chromium adsorption system, saturation was reached at 30 h with a qmax = 625 mg g−1. By adjusting the experimental data to the Langmuir and Freundlich models, it is suggested that P. koreensis forms a biofilm with a homogeneous surface where Cr(VI) is adsorbed and that the bacteria also incorporates the metal in its metabolism, leading to a multilayer adsorption. On the other hand, using Fourier transform infrared spectroscopy, it was inferred that the functional groups involved in the adsorption process were O-H and C=O, which are a part of the P. koreensis cell wall.
A machine learning approach for prediction of seasonal lightning density in different lightning regions of India

Lightning is one of the most severe weather events causing significant loss of human lives and resources. Increasing number of lightning fatalities due to recent climatic changes is emerging out to be a serious concern for India during last few years. Proper characterization and parameterization of the same, therefore, is extremely crucial. However, lightning is an extremely dynamic phenomenon having enormous spatio‐temporal inhomogeneity especially over such a vast country like India with varied topographic and climatological features. Therefore, proper parameterization of lightning activity over India needs consideration of different lightning climatologies. This study has attempted to resolve the issue by regionalizing Indian subcontinent in different lightning climatologies based on lightning density and associated atmospheric variables that is, CAPE, specific humidity at different pressure levels, temperature, k index and cloud particle size and identified seven distinct lightning climatologies over India. A regression model is proposed for estimating the annual and seasonal (monsoon and pre‐monsoon) lightning activities over the seven resulting lightning zones based on the said atmospheric variables using machine learning techniques. Four machine learning models have been tested among which Random forest has shown the best accuracy. The regression model has shown an R‐squared score of 0.81 during monsoon season and 0.71 during the pre‐monsoon. The atmospheric features based on their influences on the lightning activity in these seven climatologies has been ranked which presented the evidences of largely varied interplay between different atmospheric variables and lightning over different parts of the country and during different seasons.
Assessment of degradation of VIRGO radiometers on board SOHO

The determination of the total solar irradiance (TSI) from the SOHO/VIRGO experiment is made by first correcting the time series for all a priori known influences, and then correcting the data for instrumental degradation, using the back-up instruments PMO6-VB and DIARAD-R. The long-term behaviour of PMO6-VA shows an exponential decrease in its sensitivity with a time constant of τ = 390 days and a 1/e amplitude of 528 × 10-6, whereas DIARAD-L exhibits a general increase in its sensitivity of about 0.25 × 10-6/day combined with an early exponential decrease of 405 days and an 1/e amplitude of 65 × 10-6. After correcting PMO6- VA and DIARAD-L for their degradation, the VIRGO TSI shows a minimum around July 1996 and has been increasing since then by about 0.4 W m-2.
Study of 'Sticking and Restarting Phenomenon' in Electropneumatic Positioning Systems

This paper explains the possible occurrence of the "sticking and restarting phenomenon" observed with electropneumatic positioning systems. This is carried out from the notion of partial equilibrium, with the analysis of the model which incorporates two parallel phenomena which are used to generate a pressure force subjected to dry friction forces. Also, an experimental result has been studied in a particular pressure force plane which shows the origin of the problem more explicitly. The theoretical results give a necessary and sufficient condition for the restarting phenomenon not to occur and, if this condition is not validated, there is an estimation of the restarting time. Understanding this undesirable phenomenon will be the basis for further work which will attempt to find solutions to a void its occurrence.
Calculation of Gauss quadrature rules

Most numerical integration techniques consist of approximating the integrand by a polynomial in a region or regions and then integrating the polynomial exactly. Often a complicated integrand can be factored into a non-negative ''weight'' function and another function better approximated by a polynomial, thus $\int_{a}^{b} g(t)dt = \int_{a}^{b} \omega (t)f(t)dt \approx \sum_{i=1}^{N} w_i f(t_i)$. Hopefully, the quadrature rule ${\{w_j, t_j\}}_{j=1}^{N}$ corresponding to the weight function $\omega$(t) is available in tabulated form, but more likely it is not. We present here two algorithms for generating the Gaussian quadrature rule defined by the weight function when: a) the three term recurrence relation is known for the orthogonal polynomials generated by $\omega$(t), and b) the moments of the weight function are known or can be calculated.
Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.
Noise and error rate performance of semiconductor laser amplifiers in PCM-IM optical transmission systems

Applications of semiconductor laser amplifiers in intensity modulated digital optical transmission systems were studied theoretically. An optical linear amplifier repeater between electronically regenerating terminal repeaters and an optical linear preamplifier in front of a photodetector in an electronically regenerating repeater are discussed. Both traveling-wave type and Fabry-Perot cavity type laser amplifiers are considered. The noise and error rate performance in these systems are evaluated using formulations for semiconductor laser amplifiers. The mean and variance in the optical amplifier output photons calculated by the photon master equation [1] is used to obtain the worst case variance in the equalized output voltage [2] for these systems. The required receiving power reduction from direct detection scheme by a preamplifier system and the repeater spacing expansion between two electronically regenerating terminals by an optical linear amplifier repeater system are delineated.
Effective parameters of resonant negative refractive index metamaterials : interpretation and validity

We present a numerical study of resonant negative refractive index (NRI) metamaterials in which their effective parameters are calculated. For a periodic media consisting of split-ring resonators and metallic wires the effective refractive index is compared by means of two different methods. The first one is an inversion procedure in which the effective refractive index is calculated from the reflection and transmission coefficients of a finite structure and the second one consists its calculation from the phase velocity issued from the dispersion diagram. A significant difference between the two cases is highlighted in the frequency interval of interest (NRI regime) and for the finite media, counterintuitive observations are made. These anomalous features are observed in a frequency range in which there is a non-negligible contribution of the higher-order modes to propagation inside the periodic metamaterial. Hence in this particular frequency interval, the media cannot be described by an effective refra.