Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of high velocity molecular outflows with an up-to-date sample

A statistical study of the properties of molecular outflows is performed based on an up-to-date sample. 391 outflows were identified in published articles or preprints before February 28, 2003. The parameters of position, morphology, mass, energy, outflow dynamics and central source luminosity are presented for each outflow source. Outflow lobe polarity is known for all the sources, and 84% are found to be bipolar. The sources are divided into low mass and high mass groups according to either the available bolometric luminosity of the central source or the outflow mass. The pace of discovery of outflows over the past seven years has increased much more rapidly than in previous periods. Surveys for outflows are still continuing. The number of high-mass outflows detected (139) has considerably increased, showing that they are commonly associated with massive as well as low mass stars. Energetic mass ejection may be a common aspect of the formation of high mass as well as low mass stars. Outflow masses are correlated strongly with bolometric luminosity of the center sources, which was obtained for the first time. There are also correlations between the central source luminosity and the parameters of mechanical luminosity and the thrust or force necessary to drive the outflow. The results show that flow mass, momentum and energy depend on the nature of the central source. Despite their similarity, there are differences between the high mass and low mass outflows. Low mass outflows are more collimated than high mass outflows. On average, the mass of high mass sources can be more than two orders of magnitude larger than those of low mass outflows. The relation between flow mass and dynamical time appears to differ for the two types of outflows. Low mass sources make up 90% of outflows associated with HH objects while high mass outflows make up 61% of the sources associated with H 2 O masers. Sources with characteristics of collapse or infall comprise 12% of the entire outflow sample. The spatial distribution of the outflow sources in the Galaxy is presented and the local occurrence rate is compared with the stellar birth rate.
Anisotropy of the Fracture of Single Crystal Silicon Studied by Nonlinear Surface Acoustic Waves

Due to the elastic nonlinearity of the material, high-amplitude surface acoustic waves (SAWs) undergo nonlinear sharpening during propagation, which may introduce damage or produce modification of material microstructure. For single crystal silicon, the nonlinear evolution of SAWs and the resulting dynamic fracture depend on the free-surface orientation and direction of propagation. To investigate the effect of the geometries on behavior of SAWs, we performed molecular dynamics simulations of the nonlinear evolution of SAWs on the single crystal silicon surface. The second-order nonlinear coefficient is used to quantify the degree of nonlinear evolution of SAWs. In addition, the critical strength of the {111} plane of silicon is evaluated based on the SAW nonlinear evolution method.
A Comparative Machine Learning Approaches for Patient Flow Forecasting in an Emergency Department during the COVID-19

The Corona Virus Disease 2019 (COVID-19) has impacted numerous areas of the health system. In fact, it made the world work remotely during several months and created an assorted uncertainty for medical service recipients. Thus, anticipating novel everyday patient income in relation to the COVID-19 has become pivotal for clinical, political, and different authorities who handle on a daily basis, COVID-19 related planned operations. Current machine learning draws near, in an attempt to get dynamic results. This work intends to demonstrate the wayan Emergency Department (ED) is able to use machine-learning approaches during the daily patient flow forecasting for better management in an emergency department. Thus, it is essential to test five different supervised machine-learning approaches by evaluating their coefficient of determination (R2) to figure the everyday patient flow income for better management.
Gamma rays from clumpy wind-jet interactions in high-mass microquasars

The stellar winds of the massive stars in high-mass microquasars are thought to be inhomogeneous. The interaction of these inhomogeneities, or clumps, with the jets of these objects may be a major factor in gamma-ray production. Our goal is to characterize a typical scenario of clump-jet interaction, and calculate the contribution of these interactions to the gamma-ray emission from these systems. We use axisymmetric, RHD simulations to model the emitting flow in a typical clump-jet interaction. Using the simulation results we perform a numerical calculation of the high-energy emission from one of these interactions. The radiative calculations are done for relativistic electrons locally accelerated at the jet shock, and the synchrotron and IC radiation spectra are computed for different stages of the shocked clump evolution. We also explore different parameter values, such as viewing angle and magnetic field strength. We generalize phenomenologically the results derived from one clump-jet interaction to multiple interactions under different wind models, estimating the clump-jet interaction rates, and the resulting luminosities in the GeV range. If particles are efficiently accelerated in clump-jet interactions, the apparent gamma-ray luminosity through IC scattering with the stellar photons can be significant even for rather strong magnetic fields and thus efficient synchrotron cooling. Moreover, despite the standing nature or slow motion of the jet shocks for most of the interaction stage, Doppler boosting in the postshock flow is relevant even for mildly relativistic jets. For clump-to-average wind density contrasts $\gtrsim 10$, clump-jet interactions could be bright enough to match the observed GeV luminosity in Cyg~X-1 and Cyg~X-3 when a jet is present in these sources, with required non-thermal-to-total available power fractions $\sim 0.01$ and $0.1$, respectively.
A Differential Difference Amplifier With A Combined Input Pairs for Neural Signal Recordings

This paper presents a differential difference amplifier (DDA) with a combined symmetrical and complementary input pairs for neural signal recording. The front-end amplifier determines the fidelity, signal-to-noise ratio, power consumption and form size for the future multi-channel neural signal recording systems and applications. The proposed DDA has three input transconductors, the first one is connected to the target neural signal, to ensure a high input impedance. The second one stabilizes the dc quiescent point and determines the cut-off frequency. The third one provides the common mode feedback (CMFB) and gain calibration. The circuits are targeted at IC realization, designed in a 0.18-μm CMOS technology. The post layout simulation achieves an input impedance of circa 1GΩ at 100 Hz, a CMRR of 128 dB. The proposed CIP-DDA (two channels) with a system CMFB achieves a die area of 0.23 mm2 and consumes a power consumption of 51 μW (single channel of DDA consumes 3.6μW).
Analysis of Low-Latency Virtual Network Resource Reservation for LEO Satellite Network

For beyond 5G and 6G communications, the satellite terrestrial integrated network (STIN) is expected to provide diverse services with seamless coverage. The first step for implementing the STIN is to make the satellite network capable of supporting advanced functions that the terrestrial counterpart is providing. In this paper, network virtualization with network slices, which is actively studied in the terrestrial network, is analyzed for the satellite network. The main difference between satellite and terrestrial networks is the mobility of satellites. Since the slice services require end-to-end connectivity, the satellite network topology change due to the mobility of satellites can give a huge impact to the slices. The latency is analyzed with time-varying satellite topology with an assumption that the virtual network resource for slice is reserved for low latency. For simulations, the minimum number of handovers is assumed and the end-to-end latency is analyzed for its initial latency, average latency, minimum latency, and maximum latency during the service time of slices in the satellite network.
Optical appearance of a compact binary system in the neighbourhood of supermassive black hole

Optical appearance of a compact binary star in the field of a supermassive black hole is modeled in a strong field regime. Expressions for the redshift, magnification coefficient and pulsar extinction time are derived. By using the vierbein formalism we have derived equations of motion of compact binary star in the external gravitational field. We have analysed both the evolution of redshift of the optical ray from the usual star or white dwarf, and the times of arrival of pulses of pulsar. The results are illustrated by a calculation for a model binary system for the case of external gravitational field of a Schwarzschild black hole. The obtained results can be used for fitting timing data from the X-ray pulsars that moves in the neighbourhood of the Galactic Center (Sgr A*).
Adaptive Scheduling Algorithm Based on Time Aware Shaper

At present, Time-Sensitive Networking has attracted extensive attention in automatic driving, Industrial 4.0, 5G and so on. TSN is used to guarantee the real-time transmission of critical traffic with bounded delay, which is realized by a variety of traffic scheduling mechanisms. One important mechanism of research is Time Aware Shaper mechanism, but current researches on this mechanism usually have the following problems: (a) It takes a long time to get the solution of the algorithm; (b) It’s difficult to schedule burst traffic and the algorithm depends on time synchronization; (c) There is a waste of bandwidth. The core idea of TAS mechanism is time division multiplexing, and the solution of transmission time is the key of traffic scheduling. This paper proposes an adaptive scheduling algorithm based on TAS to avoid above problems. First, the worst-case scheduling problem of actual traffic is seen as the scheduling problem of continuous traffic, and the transmission time of continuous traffic of the worst-case delay is analyzed; Second, the concept of queue scheduling is proposed to optimize scheduling, which achieves the adaptive scheduling of actual traffic, does not depend on time synchronization, and improve bandwidth utilization; Finally, the worst-case delay of the algorithm in actual traffic scheduling is analyzed.
Sensing-based Two-timescale Channel Estimation for RIS-assisted Hybrid Millimeter Wave Systems

In this paper, we propose a sensing-based two-timescale channel estimation algorithm for reconfigurable intelligent surface (RIS) assisted multi-user hybrid millimeter wave systems. The proposed channel estimation algorithm aims to separately estimate the base station (BS)-RIS and RIS-user channels instead of estimating the cascaded BS-RIS-user channel with a limited number of radio frequency chains equipped at the BS. In particular, we first cooperatively acquire parameters of the BS-RIS channel, e.g., the line-of-sight component and the equivalent channel state information (CSI) of the BS-RIS channel, via sensing methods by both transmitting and receiving sensing signals at the BS once over the large timescale. Then, users transmit orthogonal training sequences to the BS while RIS elements are sequentially turned on for obtaining CSIs of time-varying RIS-user channels over the small timescale. Our analytical and simulation results show that the proposed channel estimation algorithm can effectively estimate RIS-user channels with hybrid architecture at the cost of a reasonable training overhead.
Modified Noisy Gradient Descent Bit-Flipping Decoding Algorithms for LDPC Codes

In this paper, we propose modified multi-bit noisy gradient descent bit flipping (M-NGDBF) algorithms for decoding low-density parity-check codes. To simplify the decoder design, we eliminate the use of Gaussian noise generators at the decoder and replace them with received signals after simple transformations. We then improve the convergence rate by removing the randomness in the M-NGDBF algorithm during the first few iterations. Subsequently, we construct a tabu-list to record bits that are flipped in the current iteration and allows these bits to be flipped in the next iteration only with a very small probability. Simulation results show that our proposed algorithms outperform the original M-NGDBF algorithm in terms of both bit error rate and convergence rate.
Reheating and Leptogenesis after Pati-Salam F-term Subcritical Hybrid Inflation

In this paper, we extend the analysis of a Pati-Salam subcritical hybrid $F$-term inflation model, proposed by two of us, by studying the reheating and the baryogenesis (via leptogenesis) of the model. This SUSY GUT model is able to fit low energy electroweak precision data, LHC data, $b$-physics data, in addition to inflation observables such as the tensor-to-scalar ratio and the scalar spectral index. The reheating mechanism of this model is instant preheating due to the bosonic and fermionic broad parametric resonance, while the baryon-to-entropy ratio is obtained from the CP asymmetric right-handed (s)neutrinos decay. The phases in the neutrino Yukawa matrices are fixed by fitting to the low energy observables. With these phases, the heaviest right-handed (s)neutrinos decay to produce a lepton asymmetry with the correct sign, while the two lighter right-handed (s)neutrinos decay to produce the wrong sign. Consequently, the baryogenesis analysis is necessarily performed by including all three families of the right-handed (s)neutrinos.
Depletion of Heavy Nitrogen in the Cold Gas of Star-forming Regions

We investigate nitrogen-isotope fractionation in forming and evolving molecular clouds using gas-ice astrochemical simulations. We find that the bulk gas can become depleted in heavy nitrogen (15N) due to the formation of 15N-enriched ices. Around the chemical transition from atomic nitrogen to N2, N15N is selectively photodissociated, which results in the enrichment of 15N in atomic nitrogen. As 15N-enriched atomic nitrogen is converted to ammonia ice via grain surface reactions, the bulk gas is depleted in 15N. The level of 15N depletion in the bulk gas can be up to a factor of two compared to the elemental nitrogen-isotope ratio, depending on the photodesorption yield of ammonia ice. Once the nitrogen isotopes are differentially partitioned between gas and solids in a molecular cloud, the condition should remain in the later stages of star formation (e.g., prestellar core) as long as the sublimation of ammonia ice is inefficient. Our model suggests that all of the N-bearing molecules in the cold gas of star-forming regions can be depleted in 15N, which is at least qualitatively consistent with the observations toward prestellar core L1544. In our models, icy species show both 15N and deuterium fractionation. The fractionation pattern within ice mantles is different between 15N and deuterium, reflecting their fractionation mechanisms; while the concentration of deuterium almost monotonically increases from the lower layers of the ice mantles to the upper layers, the concentration of 15N reaches the maximum at a certain depth and declines toward the surface.
Anyon condensation and a generic tensor-network construction for symmetry-protected topological phases

We present systematic constructions of tensor-network wavefunctions for bosonic symmetry protected topological (SPT) phases respecting both onsite and spatial symmetries. From the classification point of view, our results show that in spatial dimensions $d=1,2,3$, the cohomological bosonic SPT phases protected by a general symmetry group $SG$ involving onsite and spatial symmetries are classified by the cohomology group $H^{d+1}(SG,U(1))$, in which both the time-reversal symmetry and mirror reflection symmetries should be treated as anti-unitary operations. In addition, for every SPT phase protected by a discrete symmetry group and some SPT phases protected by continous symmetry groups, generic tensor-network wavefunctions can be constructed which would be useful for the purpose of variational numerical simulations. As a by-product, our results demonstrate a generic connection between rather conventional symmetry enriched topological phases and SPT phases via an anyon condensation mechanism.
Radon transforms of projection functions

We show that a centrally symmetric convex body is determined, up to translation, by any Radon transform of one of its projection functions. It is also proved that all Radon transforms are injective when restricted to projection functions of polytopes. Both results were known previously in the case of Radon transforms Ri,j with i<j. Here we use harmonic analysis on Grassmann manifolds to show that these results extend to all Radon transforms.
Closed geodesics in Lorentzian surfaces

We introduce class A spacetimes, i.e. compact vicious spacetimes (M; g) such that the Abelian cover (M; g) is globally hyperbolic. We study the main properties of class A spacetimes using methods similar to the one introduced in [19] and [3]. As a consequence we are able to characterize manifolds admitting class A metrics completely as mapping tori. The set of class A spacetimes is shown to be open in the C0-topology on the set of Lorentzian metrics. As an application we prove a coarse Lipschitz property for the time separation of the Abelian cover.
Cosmological Fluctuations of Small Wavelength

This paper presents a completely analytic treatment of cosmological fluctuations whose wavelength is small enough to come within the horizon well before the energy densities of matter and radiation become equal. This analysis yields a simple formula for the conventional transfer function T(k) at large wavenumber k, which agrees very well with computer calculations of T(k). It also yields an explicit formula for the microwave background multipole coefficient Cℓ at very large ℓ.
Radially anisotropic systems with r-α forces - II: radial-orbit instability

We continue to investigate the dynamics of collisionless systems of particles interacting via additive $r^{-\alpha}$ interparticle forces. Here we focus on the dependence of the radial-orbit instability on the force exponent $\alpha$. By means of direct $N$-body simulations we study the stability of equilibrium radially anisotropic Osipkov-Merritt spherical models with Hernquist density profile and with $1\leq\alpha 0.3$, so they are never flatter than an E7 system.
Research on MPC Control of a 6 DOF Stewart Platform

This paper proposes an algorithm to solve inverse kinematics problem for Stewart Platform which is based on a parallel mechanism and can provide 6 degrees of freedom. After modeling of the Stewart Platform's motions as ball-and-plate system, the 6–3 spherical-prismatic-spherical (SPS) and the 6–6 SPS, equations for Stewart platform dynamics are presented. A model predictive control (MPC) is put forward to solve the optimal control problem by calculating the applied input on-line over affixed prediction horizon into the future.
Bloch functions, asymptotic variance, and geometric zero packing

abstract:Motivated by a problem in quasiconformal mapping, we introduce a problem in complex analysis, with its roots in the mathematical physics of the Bose-Einstein condensates in superconductivity. The problem will be referred to as {\it geometric zero packing}, and is somewhat analogous to studying Fekete point configurations. The associated quantity is a density, denoted $\rho_{\Bbb C}$ in the planar case, and $\rho_{\Bbb H}$ in the case of the hyperbolic plane. We refer to these densities as {\it discrepancy densities for planar and hyperbolic zero packing}, respectively, as they measure the impossibility of atomizing the uniform planar and hyperbolic area measures. The universal asymptotic variance $\Sigma^2$ associated with the boundary behavior of conformal mappings with quasiconformal extensions of small dilatation is related to one of these discrepancy densities: $\Sigma^2=1-\rho_{\Bbb H}$. We obtain the estimates $3.2\times 10^{-5}<\rho_{\Bbb H}\le0.12087$, where the upper estimate is derived from the estimate from below on $\Sigma^2$ obtained by Astala, Ivrii, Per\"al\"a, and Prause, and the estimate from below is much more delicate. In particular, it follows that $\Sigma^2<1$, which in combination with the work of Ivrii shows that the maximal fractal dimension of quasicircles conjectured by Astala cannot be reached. Moreover, along the way, since the universal quasiconformal integral means spectrum has the asymptotics ${\rm B}(k,t)\sim{1\over 4}\Sigma^2 k^2|t|^2$ for small $t$ and $k$, the conjectured formula ${\rm B}(k,t)={1\over 4}k^2|t|^2$ is not true. As for the actual numerical values of the discrepancy density $\rho_{\Bbb C}$, we obtain the estimate from above $\rho_{\Bbb C}\le0.061203\ldots$ by using the equilateral triangular planar zero packing, where the assertion that equality should hold can be attributed to Abrikosov. The value of $\rho_{\Bbb H}$ is expected to be somewhat close to that of $\rho_{\Bbb C}$.
Stadium Detection From Alsat-2 and Google-Earth Multispectral Images using YOLOv5 and Mask R-CNN

Deep Learning (DL) has recently shown promise performance in remote sensing (RS) field. Object detection is one of the hottest research and challenging topic in RS due to the large variant in object distributions, complex object geometry, sun angle, scales, weather conditions, etc. In this paper, stadium detection approach based on YOLOv5 and Mask R-CNN models is proposed and tested on two multispectral datasets; Alsat-2 and Google-Earth imageries in three different scenarios. The proposed framework provides a comparative study of multi-source and single source training, considering the trade-off between the detection accuracy and the generalization capacity where the experimental results show that the average detection accuracy of the proposed technique for the merged training samples is the highest against single training source.
Point Cloud Map Generation and Localization for Autonomous Vehicles Using 3D Lidar Scans

Autonomous vehicles are the future intelligent vehicles, which are expected to reduce the number of human drivers, improve efficiency, avoid collisions, and become the ideal city vehicles of the future. To achieve this goal, vehicle manufacturers have started to work in this field to harness the potential and solve current challenges to achieve the desired results. In this sense, the first challenge is transforming conventional vehicles into autonomous ones that meet users’ expectations. The evolution of conventional vehicles into autonomous vehicles includes the adoption and improvement of different technologies and computer algorithms. The essential task affecting the autonomous vehicle’s performance is its localization, apart from perception, path planning, and control, and the accuracy and efficiency of localization play a crucial role in autonomous driving. In this paper, we describe the implementation of map-based localization using point cloud matching for autonomous vehicles. The Robot Operating System (ROS) along with Autoware, which is an open-source software platform for autonomous vehicles, are utilized for the implementation of the vehicle localization system presented in this paper. Point cloud maps are generated based on 3D lidar points, and a normal distributions transform (NDT) matching algorithm is used for localizing the test vehicle through matching real-time lidar measurements with the pre-built point cloud maps. The experiment results show that the map-based localization system using 3D lidar scans enables real-time localization performance that is sufficiently accurate and efficient for autonomous driving in a campus environment. The paper comprises the methods used for point cloud map generation and vehicle localization as well as the step-by-step procedure for the implementation with a ROS-based system for the purpose of autonomous driving.