Miracle-dz/UniFAR-FTUs huggingface dataset:
TWO-PHASE HEAT TRANSPORT SYSTEMS FOR THERMAL CONTROL APPLICATIONS

The contents of this paper constitute the first lecture of a series of three lectures by the author on two-phase flow and heat transfer in terrestrial and aerospace-related thermal control system developments. These lectures were presented during the ICHMT Course on Passive Thermal Control (PTC-03) in Antalya, Turkey, October 22 25, 2003. This first lecture gives a general overview of several issues of two-phase flow and heat transfer in various gravity environments. The second lecture pertains to oscillating/pulsating heat transfer devices. The third lecture concerns mechanically pumped two-phase thermal control loops. NOMENCLATURE & ACRONYMS APS Absolute Pressure Sensor ATLID Atmospheric Lidar CL(H) Capillary Link (Header) CCPL Cryogenic CPL CPL/CAPL Capillary Pumped Loop DPS Differential Pressure Sensor EMP Equipment Mounting Plate EOS Earth Observation Satellite ESA European Space Agency GAS Get Away Special LFM Liquid Flow Meter LHP Loop Heat Pipe LHPFX Loop Heat Pipe Flight Experiment NASA National Aeronautics & Space Administration TEEM Two-Phase Extended Evaluation in Microgravity TPF Two-Phase Flow TPHTS Two-Phase Heat Transport System TPX Two-Phase Experiment VQS Vapour Quality Sensor A area (m Bo boiling number (-) Cp specific heat at constant pressure (J/kg.K) D diameter (m) d diameter of curvature (m) Eu Eulernumber(-) Fr Froude number (-) g gravitational acceleration (m/s) H enthalpy (J/kg) h heat transfer coefficient (W/m.K) hlv latent heat of vaporisation (J/kg) j superficial velocity (m/s) L length (m) Ma Mach number (-) Mo Morton number (-) m. mass flow rate (kg/s) Nu Nusselt number (-) p pressure (Pa = N/m Pr Prandtl number (-) Q power (W) q heat flux (W/m) Re Reynolds number (-) S slip factor (-) T temperature (K) or (°C) t time (s) v velocity (m/s) We Weber number (-) X vapour quality (-) z axial or vertical co-ordinate (m) α vapour/void fraction (volumetric) (-) β constant in eq. (13) (-) δ surface roughness (m) ∆ difference, drop (-) μ viscosity (N.s/m) σ surface tension (N/m) ! thermal conductivity (W/m.K) π dimensionless number (-) ρ density (kg/m) ν angle (with respect to gravity) (rad) Subscripts a acceleration c condenser f friction g gravitation l liquid m momentum, model o reference condition p pore, prototype t total tp two-phase v vapour w water
Ownership structure , leverage and firm value

This study analyzes the effect of ownership structure on leverage and firm value of French firms from 2006 to 2012. In particular, we classified ownership on three groups: High, medium and low concentration structure. High ownership concentration incite firms to uses more debt and then increases leverage. We find that the effect of the ownership is positive on leverage and firm value. The result confirms the cost agency hypothesis; high leverage therefore improves the firm performance. This effect is more important for firms characterized by dispersed ownership structure.
Effective Viscosity of Magnetic Suspensions

The effect of a homogeneous magnetic field on the viscosity of a suspension whose solid particles possess intrinsic magnetic moments is investigated. The orienting field impedes rotation of the particles in a vorticalliquid flow, the result being an increase of the effective viscosity. Brownian motion and hydrodynamic forces exert a disorienting effect on the magnetic moments. The influence of the aforementioned factors on the rotation of suspended particles is taken into account macroscopically within the framework of the hydrodynamics of a homogeneous liquid with an internal angular momentum. The theoretical results are in good agreement with the experimental data on the dependence of the viscosity of magnetic suspensions on the field strength.
Chapter 5 North America

Abstract The colonial history of North America presents a contrast between Mexico and the two predominantly English-speaking countries, the United States and Canada. In Mexico, indigenous and other local communities own considerable forested lands, a consequence of the Mexican Revolution of the early twentieth century. In the United States, forest land is now primarily in private or federal hands, while in Canada forest land is primarily managed by the provinces. In all three countries, traditional knowledge had little effect upon forestry until the end of the twentieth century. In Mexico and the United States, the central government retained control over forested lands ostensibly held by communities. Policy changes in those two countries have
Embedding procedure for cluster calculations of ionic crystals

An embedding procedure suitable for Hartree–Fock studies of ionic systems is presented and implemented on the semiempirical level. In this approach model clusters are embedded in finite fields of pseudoatoms with average self‐consistent orbital occupation numbers or fixed orbital occupation numbers for best charge equilibration of cluster atoms. These pseudoatoms are of the same kind and described with the same set of parameters as the real cluster atoms. The implementation in the semiempirical self‐consistent field molecular orbital method SINDO1 is described. Calculations are performed for the systems TiO2 and MgO and the influence of the embedding on geometry and electronic structure is discussed.
Twistronics: a turning point in 2D quantum materials

Moiré superlattices—periodic orbital overlaps and lattice-reconstruction between sites of high atomic registry in vertically-stacked 2D layered materials—are quantum-active interfaces where non-trivial quantum phases on novel phenomena can emerge from geometric arrangements of 2D materials, which are not intrinsic to the parent materials. Unexpected distortions in band-structure and topology lead to long-range correlations, charge-ordering, and several other fascinating quantum phenomena hidden within the physical space between the (similar or dissimilar) parent materials. Stacking, twisting, gate-modulating, and optically-exciting these superlattices open up a new field for seamlessly exploring physics from the weak to strong correlations limit within a many-body and topological framework. It is impossible to capture it all, and the aim of this review is to highlight some of the important recent developments in synthesis, experiments, and potential applications of these materials.
Small Solar System objects on highly inclined orbits

Context. Less than one percent of the discovered small Solar System objects have highly inclined orbits (i > 60°), and revolve around the Sun on near-polar or retrograde orbits. The origin and evolutionary history of these objects are not yet clear. Aims. In this work we study the surface properties and orbital dynamics of selected high-inclination objects. Methods. BVRI photometric observations were performed in 2019–2020 using the 2.0 m telescope at the Terskol Observatory and the 2.6 m telescope at the Crimean Astrophysical Observatory. Additionally, we searched for high-inclination objects in the Sloan Digital Sky Survey and Pan-STARRS. The dynamics of the selected objects was studied using numerical simulations. Results. We obtained new photometric observations of six high-inclination objects (468861) 2013 LU28, (517717) 2015 KZ120, 2020 EP, A/2019 U5 (A/PanSTARRS), C/2018 DO4 (Lemmon), and C/2019 O3 (Palomar). All of the objects have similar B−V, V −R, R−I colours, which are close to those of moderately red TNOs and grey Centaurs. The photometric data that were extracted from the all-sky surveys also correspond to moderately red surfaces of high-inclination objects. No signs of ultra-red material on the surface of high-inclination asteroids were found, which supports the results of previous works. The comet C/2018 DO4 (Lemmon) revealed some complex morphology with structures that could be associated with particles that were ejected from the cometary nucleus. Its value of the parameter Afρ is around 100 cm for the aperture size of 6000 km. The value of Afρ for the hyperbolic comet C/2019 O3 (Palomar) is much larger, and is in the range from 2000 to 3700 cm for the aperture sizes from 25 000 to 60 000 km. For objects 2013 LU28, 2015 KZ120, and 2020 EP we estimated future and past lifetimes on their orbits. It appears that the orbits of considered objects are strongly chaotic, and with the available accuracy of the orbital elements no reliable predictions can be made about their distant past or future. The lifetimes of high-inclination objects turned out to be highly non-sensitive to the precision of the orbital elements, and to the Yarkovsky orbital drift.
Correlations of sieve weights and distributions of zeros

In this note we give two small results concerning the correlations of the Selberg sieve weights. We then use these estimates to derive a new (conditional) lower bound on the variance of the primes in short intervals, and also on the so-called `form factor' for the pair correlations of the zeros of the Riemann zeta function. Our bounds ultimately rely on the estimates of Bettin--Chandee for trilinear Kloosterman fractions.
Surface Electromyography as a Natural Human–Machine Interface: A Review

Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the last two decades, with significant advances in both the hardware and signal processing methods used to collect and analyze sEMG signals. While early work focused mainly on medical applications, there has been growing interest in utilizing sEMG as a sensing modality to enable next-generation, high-bandwidth, and natural human-machine interfaces. In the first part of this review, we briefly overview the human skeletomuscular physiology that gives rise to sEMG signals followed by a review of developments in sEMG acquisition hardware. Special attention is paid towards the fidelity of these devices as well as form factor, as recent advances have pushed the limits of user comfort and high-bandwidth acquisition. In the second half of the article, we explore work quantifying the information content of natural human gestures and then review the various signal processing and machine learning methods developed to extract information in sEMG signals. Finally, we discuss the future outlook in this field, highlighting the key gaps in current methods to enable seamless natural interactions between humans and machines.
Model-based cellular kinetic analysis of SARS-CoV-2 infection: different immune response modes and treatment strategies

Increasing number in global COVID-19 cases demands for mathematical model to analyze the interaction between the virus dynamics and the response of innate and adaptive immunity. Here, based on the assumption of a weak and delayed response of the innate and adaptive immunity in SARS-CoV-2 infection, we constructed a mathematical model to describe the dynamic processes of immune system. Integrating theoretical results with clinical COVID-19 patients' data, we classified the COVID-19 development processes into three typical modes of immune responses, correlated with the clinical classification of mild & moderate, severe and critical patients. We found that the immune efficacy (the ability of host to clear virus and kill infected cells) and the lymphocyte supply (the abundance and pool of naive T and B cell) play important roles in the dynamic process and determine the clinical outcome, especially for the severe and critical patients. Furthermore, we put forward possible treatment strategies for the three typical modes of immune response. We hope our results can help to understand the dynamical mechanism of the immune response against SARS-CoV-2 infection, and to be useful for the treatment strategies and vaccine design.
Quantum control in open and periodically driven systems

ABSTRACT Quantum technology resorts to efficient utilization of quantum resources to realize technique innovation. The systems are controlled such that their states follow the desired manners to realize different quantum protocols. However, the decoherence caused by the system-environment interactions causes the states deviating from the desired manners. How to protect quantum resources under the coexistence of active control and passive decoherence is of significance. Recent studies have revealed that the decoherence is determined by the feature of the system-environment energy spectrum: Accompanying the formation of bound states in the energy spectrum, the decoherence can be suppressed. It supplies a guideline to control decoherence. Such idea can be generalized to systems under periodic driving. By virtue of manipulating Floquet bound states in the quasienergy spectrum, coherent control via periodic driving dubbed as Floquet engineering has become a versatile tool not only in controlling decoherence, but also in artificially synthesizing exotic topological phases. We will review the progress on quantum control in open and periodically driven systems. Special attention will be paid to the distinguished role played by the bound states and their controllability via periodic driving in suppressing decoherence and generating novel topological phases.
Holographic thermodynamics requires a chemical potential for color

The thermodynamic Euler equation for high-energy states of large-$N$ gauge theories is derived from the dependence of the extensive quantities on the number of colors $N$. This Euler equation relates the energy of the state to the temperature, entropy, number of degrees of freedom and its chemical potential, but not to the volume or pressure. In the context of the gauge/gravity duality we show that the Euler equation is dual to the generalized Smarr formula for black holes in the presence of a negative cosmological constant. We also match the fundamental variational equation of thermodynamics to the first law of black hole mechanics, when extended to include variations of the cosmological constant and Newton's constant.
Measurement of the flux-averaged inclusive charged-current electron neutrino and antineutrino cross section on argon using the NuMI beam and the MicroBooNE detector

We present a measurement of the combined $\nu_e$ + $\bar{\nu}_e$ flux-averaged charged-current inclusive cross section on argon using data from the MicroBooNE liquid argon time projection chamber (LArTPC) at Fermilab. Using the off-axis flux from the NuMI beam, MicroBooNE has reconstructed 214 candidate $\nu_e$ + $\bar{\nu}_e$ interactions with an estimated exposure of 2.4$\times10^{20}$ protons on target. Given the estimated purity of 38.6\%, this implies the observation of 80 $\nu_e$ + $\bar{\nu}_e$ events in argon, the largest such sample to date. The analysis includes the first demonstration of a fully automated application of a dE/dx-based particle discrimination technique of electron and photon induced showers in a LArTPC neutrino detector. We measure the $\nu_e + \bar{\nu}_e$ flux-averaged charged-current total cross section to be ${6.84\pm\! 1.51~\textrm{(stat.)}\pm\! 2.33~\textrm{(sys.)}\! \times\! 10^{-39}~\textrm{cm}^{2}/~\textrm{nucleon}}$, for neutrino energies above 250 MeV and an average neutrino flux energy of 905 MeV when this threshold is applied. The measurement is sensitive to neutrino events where the final state electron momentum is above 48 MeV/c, includes the entire angular phase space of the electron, and is in agreement with the theoretical predictions from \texttt{GENIE} and \texttt{NuWro}. This measurement is also the first demonstration of electron neutrino reconstruction in a surface LArTPC in the presence of cosmic ray backgrounds, which will be a crucial task for surface experiments like those that comprise the Short-Baseline Neutrino (SBN) Program at Fermilab.
Estimating Galactic Distances From Images Using Self-supervised Representation Learning

We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as well as an application-specific augmentation accounting for galactic dust. We find that the resulting visual representations of galaxy images are semantically useful and allow for fast similarity searches, and can be successfully fine-tuned for the task of redshift estimation. We show that (1) pretraining on a large corpus of unlabeled data followed by fine-tuning on some labels can attain the accuracy of a fully-supervised model which requires 2-4x more labeled data, and (2) that by fine-tuning our self-supervised representations using all available data labels in the Main Galaxy Sample of the Sloan Digital Sky Survey (SDSS), we outperform the state-of-the-art supervised learning method.
Counting and localizing defective nodes by Boolean network tomography

Identifying defective items in larger sets is a main problem with many applications in real life situations. We consider the problem of localizing defective nodes in networks through an approach based on boolean network tomography (BNT), which is grounded on inferring informations from the boolean outcomes of end-to-end measurements paths. {\em Identifiability} conditions on the set of paths which guarantee discovering or counting unambiguously the defective nodes are of course very relevant. We investigate old and introduce new identifiability conditions contributing this problem both from a theoretical and applied perspective. (1) What is the precise tradeoff between number of nodes and number of paths such that at most $k$ nodes can be identified unambiguously ? The answer is known only for $k=1$ and we answer the question for any $k$, setting a problem implicitly left open in previous works. (2) We study upper and lower bounds on the number of unambiguously identifiable nodes, introducing new identifiability conditions which strictly imply and are strictly implied by unambiguous identifiability; (3) We use these new conditions on one side to design algorithmic heuristics to count defective nodes in a fine-grained way, on the other side to prove the first complexity hardness results on the problem of identifying defective nodes in networks via BNT. (4) We introduce a random model where we study lower bounds on the number of unambiguously identifiable defective nodes and we use this model to estimate that number on real networks by a maximum likelihood estimate approach
Effective uniform approximation by $ L$-functionsin the Selberg class

Recently, Garunkštis, Laurinc̆ikas, Matsumoto, J. & R. Steuding showed an effective universality-type theorem for the Riemann zeta-function by using an effective multidimensional denseness result of Voronin. We will generalize Voronin’s effective result and their theorem to the elements of the Selberg class satisfying some conditions.
Javanese Volcanic Allophane Adsorbent as Heavy Metal Adsorber to Improve the Quality of Drinking Water in Surakarta

The present study is concern with the identification, characterization and activation of natural allophane as an adsorbent of heavy metal ions Cr, Fe, Cd, Cu, Pb and Mn. The purposes of this study were to identify and characterize the natural allophane of Javanese volcanoes (Papandayan, Arjuna and Wilis), and to determine the optimum conditions of adsorption and adsorption isotherms that happened. The identification of natural allophane was performed by using NaF, pH, FTIR, and XRD. The characterization of the acidity of allophane was analyzed by using ammonia adsorption. The activation was done in the NaOH solution of 1 N and 3 N, and in the soaking time of 1, 3, and 5 hours. The adsorption was done according to batch method with the variations of contact time of 30, 60, 90 and 120 minutes. The adsorption isotherms performed by using the variations of the amount of adsorbate. Atomic Absorption Spectroscopy (AAS) was used in the analysis to determine the concentration of heavy metal ions in solution. The results show that the analysis of NaF, pH, FTIR and XRD indicate the presence of allophane in the volcanic samples. The optimum conditions of activation and adsorption of the metal ions of Cr and Cd are in the activation of NaOH 3N for 3 hours within 120 minutes contact time, those of metal ions of Fe are in the activation of NaOH 3N for 1 hour within 30 minutes contact time, those of the metal ions of Pb and Mn are in the activation of NaOH 3N for 5 hours within 90 minutes contact time, and those of metal ions of Cu are in the activation of NaOH 3N for 5 hours within 60 minutes contact time. The adsorption isotherms performed to all kinds of metals follow Freundlich equation which suggests that physical interactions occur. Thus, an activation with NaOH can increase the acidity of allophane causing to improve adsorption capacity to the metals by about 50-100%.
Assessing the accuracy of cosmological parameters estimated from velocity – density comparisons via simulations

A promising method for measuring the cosmological parameter combination f σ8 is to compare observed peculiar velocities with peculiar velocities predicted from a galaxy density field using perturbation theory. We use N-body simulations and semi-analytic galaxy formation models to quantify the accuracy and precision of this method. Specifically, we examine a number of technical aspects, including the optimal smoothing length applied to the density field, the use of dark matter halos or galaxies as tracers of the density field, the effect of noise in the halo mass estimates or in the stellar-to-halo mass relation, and the effect of finite survey volumes. We find that for a Gaussian smoothing of 4h−1 Mpc, the method has only small systematic biases at the level of 5%. Cosmic variance affects current measurements at the 5% level due to the volume of current redshift data sets.
Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects

Abstract This study proposes an econometric framework to interpret and empirically decompose the difference between instrumental variables (IV) and ordinary least squares (OLS) estimates given by a linear regression model when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference in weights on the treatment levels, and the difference in identified marginal effects that arises from endogeneity bias. Applications of this framework to return-to-schooling estimates demonstrate the empirical relevance of this distinction in properly interpreting the IV-OLS gap.
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set

Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from the largest Romanian e-commerce platform. We employ two sentiment classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (bag-of-word-embeddings generated by clustering word embeddings with k-means). As an additional contribution, we replace the k-means clustering algorithm with self-organizing maps (SOMs), obtaining better results because the generated clusters of word embeddings are closer to the Zipf’s law distribution, which is known to govern natural language. We also demonstrate the generalization capacity of using SOMs for the clustering of word embeddings on another recently-introduced Romanian data set, for text categorization by topic.