Miracle-dz/UniFAR-FTUs huggingface dataset:
ON THE FLARE-INDUCED SEISMICITY IN THE ACTIVE REGION NOAA 10930 AND RELATED ENHANCEMENT OF GLOBAL WAVES IN THE SUN

A major flare (of class X3.4) occurred on 2006 December 13 in the active region NOAA 10930. This flare event has remained interesting to solar researchers for studies related to particle acceleration during the flare process and the reconfiguration of magnetic fields as well as fine-scale features in the active region. The energy released during flares is also known to induce acoustic oscillations in the Sun. Here, we analyze the line-of-sight velocity patterns in this active region during the X3.4 flare using the Dopplergrams obtained by the Global Oscillation Network Group (GONG) instrument. We have also analyzed the disk-integrated velocity observations of the Sun obtained by the Global Oscillation at Low Frequency (GOLF) instrument on board the Solar and Heliospheric Observatory spacecraft as well as full-disk collapsed velocity signals from GONG observations during this flare to study any possible connection between the flare-related changes seen in the local and global velocity oscillations in the Sun. We apply wavelet transform to the time series of the localized velocity oscillations as well as the global velocity oscillations in the Sun spanning the flare event. The line-of-sight velocity shows significant enhancement in some localized regions of the penumbra of this active region during the flare. The affected region is seen to be away from the locations of the flare ribbons and the hard X-ray footpoints. The sudden enhancement of this velocity seems to be caused by the Lorentz force driven by the “magnetic jerk” in the localized penumbral region. Application of wavelet analysis to these flare-induced localized seismic signals shows significant enhancement in the high-frequency domain (5 <ν < 8 mHz) and a feeble enhancement in the p-mode oscillations (2 <ν < 5 mHz) during the flare. On the other hand, the wavelet analysis of GOLF velocity data and the full-disk collapsed GONG velocity data spanning the flare event indicates significant post-flare enhancements in the high-frequency global velocity oscillations in the Sun, as evident from the wavelet power spectrum and the corresponding scale-average variance. The present observations of the flare-induced seismic signals in the active region in context of the driving force are different as compared to previous reports on such cases. We also find indications of a connection between flare-induced localized seismic signals and the excitation of global high-frequency oscillations in the Sun.
Point Clouds Downsampling Based on Complementary Attention and Contrastive Learning

This paper presents a novel method for point clouds down-sampling. We formulate the sampling task as an optimal permutation problem and develop two techniques, the com-plementary attention module and contrastive learning mech-anism, to enable it. The complementary attention module assigns large weights to point-wise features to emphasize fea-tures related to selected and discarded points. We optimize the network by introducing the contrastive learning mecha-nism, which minimizes feature discrepancy of the discarded points while maximizing feature separation between the se-lected points. It is evaluated on ModelNet40 and ShapeNet-Core datasets for classification and reconstruction tasks and achieves promising results.
A stochastic modelling of electrical vehicle load and its impacts on a Swedish distribution network

The increasing penetration of intermittent renewable energy sources and the emergence of various types of loads introduce new operational challenges to future distribution network operators. These challenges become a more prominent issue when a system deals with high penetration of uncontrolled mobile loads like electric vehicles. This paper aims to present a stochastic modelling approach based on Markov chain Monte Carlo simulation to model the aggregated EV load profile. The stochastic EV load profile is used further to calculate the overloading probability of the distribution transformers. In this regards, the historical data of a distribution transformer load of a northern Swedish distribution network has been used. The proposed stochastic modelling approach could help the system operator determine the probability of not supplying EV loads due to the overload of the distribution transformer.
THE SPECTRALLY RESOLVED Lyα EMISSION OF THREE Lyα-SELECTED FIELD GALAXIES AT z ∼ 2.4 FROM THE HETDEX PILOT SURVEY

We present new results on the spectrally resolved Lyα emission of three Lyα-emitting field galaxies at z ∼ 2.4 with high Lyα equivalent width (>100 Å) and Lyα luminosity (∼1043 erg s−1). At 120 km s−1 (FWHM) spectral resolution, the prominent double-peaked Lyα profile straddles the systemic velocity, where the velocity zero point is determined from spectroscopy of the galaxies' rest-frame optical nebular emission lines. The average velocity offset from systemic of the stronger redshifted emission component for our sample is 176 km s−1 while the average total separation between the redshifted and main blueshifted emission components is 380 km s−1. These measurements are a factor of ∼2 smaller than for UV-continuum-selected galaxies that show Lyα in emission with lower Lyα equivalent widths. We compare our Lyα spectra to the predicted line profiles of a spherical “expanding shell” Lyα radiative transfer grid that models large-scale galaxy outflows. Specifically, blueward of the systemic velocity where two galaxies show a weak, highly blueshifted (by ∼1000 km s−1) tertiary emission peak, the model line profiles are a relatively poor representation of the observed spectra. Since the neutral gas column density has a dominant influence over the shape of the Lyα line profile, we caution against equating the observed Lyα velocity offset with a physical outflow velocity, especially at lower spectral resolution where the unresolved Lyα velocity offset is a convoluted function of several degenerate parameters. Referring to rest-frame ultraviolet and optical Hubble Space Telescope imaging, we find that galaxy–galaxy interactions may play an important role in inducing a starburst that results in copious Lyα emission as well as perturbing the gas distribution and velocity field, both of which have strong influence over the Lyα emission line profile.
Explaining the unusual line profiles of SN 2006gy

Origin of enigmatic line profiles of extremely luminous type IIn supernova SN~2006gy on day 96 is explored. Among conceivable possibilities the most preferred is the model that suggests holes in the optically thick cool dense shell (CDS). The line radiation emitted at the inner side of the opaque CDS escapes through the holes thus producing unusual line profile with the emission shifted redward. The holes could emerge as a result of a vigorous Rayleigh-Taylor instability leading to the CDS fragmentation. The model light curve with the CDS fragmentation is shown to be consistent with the SN~2006gy bolometric light curve.
THE GRAVITATIONAL POTENTIAL NEAR THE SUN FROM SEGUE K-DWARF KINEMATICS

To constrain the Galactic gravitational potential near the Sun (∼1.5 kpc), we derive and model the spatial and velocity distributions for a sample of 9000 K-dwarfs with spectra from SDSS/SEGUE, which yield radial velocities and abundances ([Fe/H] and [α/Fe]). We first derive the spatial density distribution for three abundance-selected sub-populations of stars accounting for the survey's selection function. The vertical profiles of these sub-populations are simple exponentials and their vertical dispersion profile is nearly isothermal. To model these data, we apply the “vertical” Jeans equation, which relates the observable tracer number density and vertical velocity dispersion to the gravitational potential or vertical force. We explore a number of functional forms for the vertical force law, fit the dispersion and density profiles of all abundance-selected sub-populations simultaneously in the same potential, and explore all parameter co-variances using a Markov Chain Monte Carlo technique. Our fits constrain a disk mass scale height ≲ 300 pc and the total surface mass density to be 67 ± 6 M☉ pc−2 at |z| = 1.0 kpc of which the contribution from all stars is 42 ± 5 M☉ pc−2 (assuming a contribution from cold gas of 13 M☉ pc−2). We find significant constraints on the local dark matter density of 0.0065 ± 0.0023 M☉ pc−3 (0.25 ± 0.09 GeV cm−3). Together with recent experiments this firms up the best estimate of 0.0075 ± 0.0021 M☉ pc−3 (0.28 ± 0.08 GeV cm−3), consistent with global fits of approximately round dark matter halos to kinematic data in the outskirts of the Galaxy.
The GALEX Arecibo SDSS Survey – IV. Baryonic mass–velocity–size relations of massive galaxies

We present dynamical scaling relations for a homogeneous and representative sample of �500 massive galaxies, selected only by stellar mass (> 10 10 M⊙) and redshift (0.025 < z < 0.05) as part of the ongoing GALEX Arecibo SDSS Survey. We compare baryonic Tully-Fisher (BTF) and Faber-Jackson (BFJ) relations for this sample, and investigate how galaxies scatter around the best fits obtained for pruned subsets of disk-dominated and bulge-dominated systems. The BFJ relation is significantly less scattered than the BTF when the relations are applied to their maximum samples (for the BTF, only galaxies with Hi detections), and is not affected by the inclination problems that plague the BTF. Disk-dominated, gas-rich galaxies systematically deviate from the BFJ relation defined by the spheroids. We demonstrate that by applying a simple correction to the stellar velocity dispersions that depends only on the concentration index of the galaxy, we are able to bring disks and spheroids onto the same dynamical relation — in other words, we obtain a generalized BFJ relation that holds for all the galaxies in our sample, regardless of morphology, inclination or gas content, and has a scatter smaller than 0.1 dex. We compare the velocity-size relation for the three dynamical indicators used in this work, i.e., rotational velocity, observed and concentration-corrected stellar dispersion. We find that disks and spheroids are offset in the stellar dispersion-size relation, and that the offset is removed when corrected dispersions are used instead. The generalized BFJ relation represents a fundamental correlation between the global dark matter and baryonic content of galaxies, which is obeyed by all (massive) systems regardless of morphology.
A Deep Curriculum Learner in an Active Learning Cycle for Polsar Image Classification

The integration of deep learning and active learning has achieved great success in polarimetric synthetic aperture radar (PolSAR) image classification. However, the training samples provided by the active learning approach are inade-quate to promote the performance of deep learning methods. Also, in the initial learning stages, querying a small amount of informative and complex samples, which are plagued by significant speckle noise, not only increases the risk of overfitting, but also makes the further annotations of less importance. To alleviate these problems, by utilization of curriculum learning (CL), we propose a novel classification method for PolSAR images, considering the complexity of informative samples before applying them to the deep learning model. Furthermore, we develop a new lightweight 3D convolutional neural network with high-level feature extraction ability while having a very low computational cost. Experimental results with the two PolSAR benchmark data sets of AIRSAR Flevoland and ESAR Oberpfaffenhofen indicate our proposed method achieved the state-of-the-art classification results with a significantly smaller amount of training data.
Development and application of HINT2 to helical system plasmas

The three-dimensional (3D) magnetohydrodynamic (MHD) equilibrium solver HINT is improved as a new HINT code, ‘HINT2’ having various useful features by introducing modern computing techniques. By using the algorithm inherent to HINT2, the treatment of the peripheral plasma corresponding to the experimental results is addressed in the Large Helical Device (LHD).
Lorentz Invariance Violation Test from Time Delays Measured with Gravitationally Lensed GRB Candidates 950830 and 200716C

The spectral lag features in gamma-ray bursts (GRBs) have been widely used to investigate possible Lorentz invariance violation (LIV). However, these constraints could depend on the unknown source-intrinsic time delays in different energy bands. Biesiada & Piórkowska theoretically proposed that gravitational lensing time delays in a strongly lensed GRB can become a tool for testing LIV free from the intrinsic time lag problem. Recently GRB 950830 and GRB 200716C have been proposed to be lensed by an intermediate-mass black hole. They should still be considered as candidates of strongly lensed bursts, since no angular offset (i.e., the evidence for multiple images) was detected, but only a double peak structure in the light curve. The redshift of the burst z s and of the lens z l have not been measured in either case; hence we assumed a reasonable guess of z l = 1.0, z s = 2.0 for GRB 950830 and z l = 0.174, z s = 0.348 for GRB 200716C. Bearing all this in mind, we attempted to constrain LIV theories in a prospective way based on the two GRBs by considering time delays between two pulses in different energy channels. By directly fitting the time delay data of GRBs 950830 and 200716C we obtained the following limits on LIV energy scale: E QG,1 ≥ 3.2 × 109 GeV and E QG,1 ≥ 6.3 × 109 GeV, respectively. Sensitivity analysis regarding the (unknown) redshifts leads to the most conservative estimate, E QG,1 ≥ 1.5 × 108 GeV for GRB 950830 and E QG,1 ≥ 4.8 × 108 GeV for GRB 200716C, when they would be located at z s ∼ 5.
Nonlinear Evolution of Streaming Instabilities in Accreting Protoplanetary Disks

The streaming instability (SI) is one of the most promising candidates for triggering planetesimal formation by producing dense dust clumps that undergo gravitational collapse. Understanding how the SI operates in realistic protoplanetary disks (PPDs) is therefore crucial to assess the efficiency of planetesimal formation. Modern models of PPDs show that large-scale magnetic torques or winds can drive laminar gas accretion near the disk midplane. In a previous study, we identified a new linear dust-gas instability, the azimuthal drift SI (AdSI), applicable to such accreting disks and is powered by the relative azimuthal motion between dust and gas that results from the gas being torqued. In this work, we present the first nonlinear simulations of the AdSI. We show that it can destabilize an accreting, dusty disk even in the absence of a global radial pressure gradient, which is unlike the classic SI. We find the AdSI drives turbulence and the formation of vertically extended dust filaments that undergo merging. In dust-rich disks, merged AdSI filaments reach maximum dust-to-gas ratios exceeding 100. Moreover, we find that even in dust-poor disks the AdSI can increase local dust densities by 2 orders of magnitude. We discuss the possible role of the AdSI in planetesimal formation, especially in regions of an accreting PPD with vanishing radial pressure gradients.
Stellar mass estimation based on IRAC photometry for Spitzer SWIRE-field galaxies

We analyze the feasibility of estimating the stellar mass of galaxies by mid-infrared luminosities based on a large sample of galaxies cross-identified from Spitzer SWIRE fields and the SDSS spectrographic survey. We derived the formulae to calculate the stellar mass by using IRAC 3.6 μm and 4.5 μm luminosities. The mass-to-luminosity ratios of IRAC 3.6 μm and 4.5 μm luminosities are more sensitive to the star formation history of galaxies than to other factors, such as the intrinsic extinction, metallicity and star formation rate. To remove the effect of star formation history, we used g - r color to recalibrate the formulae and obtain a better result. Researchers must be more careful when estimating the stellar mass of low metallicity galaxies using our formulae. Due to the emission from dust heated by the hottest young stars, luminous infrared galaxies present higher IRAC 4.5 μm luminosities compared to IRAC 3.6 μm luminosities. For most of type-II AGNs, the nuclear activity cannot enhance 3.6 μm and 4.5 μm luminosities compared with normal galaxies. Star formation in our AGNhosting galaxies is also very weak, almost all of which are early-type galaxies.
SCTRANS: A TRANSFORMER NETWORK BASED ON THE SPATIAL AND CHANNEL ATTENTION FOR CLOUD DETECTION

Cloud detection is an important preprocessing step for remote sensing image processing and analysis. The current deep-learning-based cloud detection methods are mostly based on Convolutional Neural Network (CNN) which pay more attention to local information. To make more use of the global information, in this article, we propose a transformer-based cloud detection method (SCTrans) based on the spatial and channel attention mechanism. The experiment results show that when using only three-band images on the Landsat7 dataset, the mIoU of the validation set reaches 85.92% and the mIoU of the test set reaches 87.86%. The experimental results show that the proposed network has a higher mIoU and F1 score than Fmask and other networks.
PULSAR WIND MODEL FOR THE SPIN-DOWN BEHAVIOR OF INTERMITTENT PULSARS

Intermittent pulsars are part-time radio pulsars. They have higher slow down rates in the on state (radio-loud) than in the off state (radio-quiet). This gives evidence that particle wind may play an important role in pulsar spindown. The effect of particle acceleration is included in modeling the rotational energy loss rate of the neutron star. Applying the pulsar wind model to the three intermittent pulsars (PSR B1931+24, PSR J1841−0500, and PSR J1832+0029) allows their magnetic fields and inclination angles to be calculated simultaneously. The theoretical braking indices of intermittent pulsars are also given. In the pulsar wind model, the density of the particle wind can always be the Goldreich–Julian density. This may ensure that different on states of intermittent pulsars are stable. The duty cycle of particle wind can be determined from timing observations. It is consistent with the duty cycle of the on state. Inclination angle and braking index observations of intermittent pulsars may help to test different models of particle acceleration. At present, the inverse Compton scattering induced space charge limited flow with field saturation model can be ruled out.
Improved Detection of Lunar Water Ice Using Supervised Machine Learning Approach

Mini-SAR data for characterization of lunar craters with water ice has been done by the use of enhanced radar Circular Polarization Ratio (CPR) as an indicator of water ice. In this study, we have examined the ability of supervised Machine Learning (ML) technique to classify craters having anomalous high CPR in the cold traps of water ice in polar region. Since elevated CPR values alone, can be an ambiguous signature, caused by wavelength scale corner reflectors and presence of low volatiles such as water ice, study attempts to recognize dominance of anomalous class inside craters rim. In addition to CPR - a key indicator of frozen volatiles, considering backscattering coefficient, surface roughness and surface temperature as input parameters to support vector machine algorithm. The results obtained from supervised ML classification has enabled detection of additional 14 anomalous craters including Cabeus A, having favorable factors of surface temperature less than 120K, low surface roughness and low backscattering coefficient (SI) ~ −21.1 dB, Thereby enhancing detection of craters with water ice.
Towards a Deep Learning Fractional Woody Vegetation Cover Monitoring Framework

Savannahs cover 50% of the African continent and 20% of the global land surface. African savannahs are increasingly threatened by over-exploitation, deforestation, woody thickening and encroachment and, consequently, land degradation. In South Africa, savannah degradation is acute and accelerating, threatening the ecosystem services provided to some of the country's most vulnerable populations. Here, we devise a methodology for the accurate mapping and monitoring of the fraction of the woody component of savannah vegetation and apply it to the South African Northwest Province. Our approach involves the use of aerial photography for training and validation; the entire dry-season Landsat archive over the last three decades, and deep learning segmentation and classification techniques. Our results are able to identify areas of significant woody densification and encroachment, as well as areas with declining trends.
PHOSPHORUS-BEARING MOLECULES IN MASSIVE DENSE CORES

Phosphorus is a crucial element for the development of life, but so far P-bearing molecules have been detected only in a few astrophysical objects; hence, its interstellar chemistry is almost totally unknown. Here, we show new detections of phosphorus nitride (PN) in a sample of dense cores in different evolutionary stages of the intermediate- and high-mass star formation process: starless, with protostellar objects, and with ultracompact H ii regions. All detected PN line widths are smaller than ≃5 km s−1, and they arise from regions associated with kinetic temperatures smaller than 100 K. Because the few previous detections reported in the literature are associated with warmer and more turbulent sources, the results of this work show that PN can arise from relatively quiescent and cold gas. This information is challenging for theoretical models that invoke either high desorption temperatures or grain sputtering from shocks to release phosphorus into the gas phase. Derived column densities are of the order of 1011–12 cm−2, marginally lower than the values derived in the few high-mass star-forming regions detected so far. New constraints on the abundance of phosphorus monoxide, the fundamental unit of biologically relevant molecules, are also given.
Global and Multi-Scale Feature Learning for Remote Sensing Scene Classification

Although the convolutional neural network (CNN)-based and vision transformer (ViT)-based methods have achieved effective remote sensing scene classification results in the past few years, the CNN's inductive bias and the ViT's single spatial scale limit the further improvement of accuracy. To address these problems, in this paper, we proposed a novel multi-scale vision transformer (MS-ViT) for remote sensing scene image classification, consists of two different spatial scale information streams that extract spatial multi-scale information, a hybrid attention module that fuses the information and a classifier to predict the scene category. We evaluated the effectiveness of our proposed method on two different remote sensing datasets, namely NWPU-RESISC45 and AID. The experimental results also show that our method outperforms CNN-based methods and the original ViT-based method in performance.