dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors would like to acknowledge the  Engineering and Physical Sciences Research Council (U. K. ) [EPSRC(GB) ] (Grant Nos. EP/L016982/1 ,  EP/M003132/1 ,  EP/P015719/1 ,  EP/P006973/1 , and  EP/M015181/1 ) for financial support. J. Bruckbauer  acknowledges the support of  SUPA  through a  PECRE travel award. The data associated with this research are available at https:// doi. org/10. 15129/c71c237d-a544-4bf5-b7d3-1432ea22608eor from the corresponding author.
The second step is to perform this comparison with the same instruments at different latitudes and longitudes (intensity and Dopplergrams). Finally, the third step consists of the extension over the solar cycle with SDO data for the full Sun component. In this paper, we investigate the properties of the exploders through the observations of the ground-based THEMIS telescope, IRIS, SDO, and the Hinode satellites, and finally the simulation. Section 2 describes the data selection and reduction. The "manual" analysis of coordinated observations of exploding granules by THEMIS, IRIS, and SDO is presented in section 3. Section 4 is devoted to the "statistical" analysis of exploding granules from independent long temporal sequences. The results and conclusion are given in section 5.
, 2014] , and the ground-based THEMIS telescope (Observatorio del Teide, Tenerife). A coordinated campaign of observations was performed in mid-September 2019 (9 to 14) involving the THEMIS (Tenerife), IRIS, and SDO/HMI satellites. We obtained two sequences of simultaneous observations of the same solar region with THEMIS, IRIS, and SDO/HMI on 10 Sept 2019 with moderate seeing, and on 14 Sept 2019 with good seeing with THEMIS and SDO/HMI. On 10 Sept 2019 the common sequences occurred from 12:27 to 12:53 UT and on 14 Sept 2019 from 08:41 to 09:34 UT at disk center. For the statistical study we added the sequence of Hinode observations (29 August 2007) described in  Roudier et al. [2016] , and the sequence of simulation results presented in  Roudier et al. [2019]  and obtained with the 3D magneto-convection code  [Stein and Nordlund, 1998 ].
The characteristics of all sequences are summarized in Tables  1  and 2. The THEMIS imagery instrument was used to observe the solar granulation at the disk center at λ = 6500Å. On 10 Sept 2019, the moderate seeing (the adaptive optics and image derotator were off) gave us the possiblity to observe together with IRIS and SDO the same field of view (FOV), but we only obtained a few frames from THEMIS allowing comparison to space-borne observations. On 14 Sept 2019, we obtained a time sequence with very good seeing at the disk center, but only in coordination with SDO/HMI. The mean time step for that date is 37. 4 seconds. Prior to restoration, all images were pre-treated for dark and flat-field corrections. We then applied speckle imaging restoration software to the THEMIS data in order to compensate for image degradation due to turbulence.
Our software relies on classical Knox-Thompson as-  sumptions  [Knox and Thompson, 1974] , and was largely inspired by the KISIP phase iteration algorithm  [von der Luehe, 1993]. The quality of such image restoration has been successfully tested against images from space experiments  [Wöger and von der Lühe, 2008]. The final resolution of restored THEMIS images (Figure  1 ) is 0. 2 in the best cases, although the quality is not homogeneous across the whole data set. The restored images were derotated, aligned, destretched, and filtered for p-modes in the k-ω space  [Title et al. , 1989 ] (threshold phase velocity = 6 kms -1 ). We used the IRIS slit jaw images to get the temporal evolution of the granulation at λ = 2832Å with a time step of 20. 8 seconds  [Wülser et al. , 2018]. By temporal interpolation, we removed the images of the slit on each frame allowing a perfect 2D field.
The restored images were then deconvolved by the IRIS transfer function, and finally filtered for p-modes in the k-ω space (threshold phase velocity = 6 kms -1 )). The Helioseismic Magnetic Imager (HMI) on board SDO provides uninterrupted observations over the entire disk. This gives a unique opportunity for extracting solar granulation characteristics during a long period of time with uniform observations. We used SDO/HMI data observed simultaneously with THEMIS at the disk center. The original pixel size of images is close to 0. 5. Each image was deconvolved by the HMI transfer function  [Couvidat et al. , 2016]  and rebined by a factor of two (pixel of 0. 25). This last operation allows an increase in the number of pixels for each granule and allows better temporal tracking. We also used data sets from the Solar Optical Telescope (SOT) on board the Hinode mission.
Manual analysis of exploding granules of simultaneous observations and simulation In the manual approach each granule area is extracted by using the watershed method. It requires adjusting some parameters, like the size of the opening morphological disk and intensity threshold, for each sequence to get the best cell contour of the granule (to the eye). The watershed method allows us to define the cell of the granule, taking into account the part of the granule up to its neighbors. This step cannot be automated and homogenized for all sequences, which is why we only selected a small number of exploding granules for each sequence (five on 10 Sept and ten on 14 Sept). After the segmentation the final step is to label each granule in space and time. In order to compare the images from the different telescopes, we chose to interpolate the data to the pixel size of THEMIS.
We find a good agreement in the area     evolution of that granule (shown in Figure  6 ) computed from the three instruments at different spatial resolutions. The time derivative of the area provides an estimation of the expansion velocity, but is too noisy to be plotted and to be conclusive. The correspondence of the area evolution between the three instruments is available for the five selected granules. Figures  7  and 8  give an example the 3D evolution (x,y,t) of the same granule between 0 and 1500 s showing a great similarity between segmentations applied to IRIS and SDO data; SDO areas appear more noisy due to the lower spatial and temporal resolutions. Figure  9  confirms the very good correlation (R=0. 92) of the measured radius of the granule on both IRIS and SDO data. Although the SDO data have a lower spatial resolution (by a factor of 2), the combined effects of the oversampling just after the deconvolution and the segmentation processing, which is sensitive to the intensity gradients, provide area measures slightly larger than IRIS when all images are interpolated to the THEMIS pixel size.
Local exploding granules observed by THEMIS and SDO on 14 Sept 2019 On 14 Sept 2019 we selected ten exploding granules from the 53 min sequence at the disk center from THEMIS and SDO simultaneous observations. In that sequence the starting area we selected is between 2 and 3. 0 Mm 2 (radius 800 and 980 km) and grows to 5. 5 Mm 2 (radius 1260 km) in the first 600 s. The larger area on that date relative to the 10 Sept 2019 measurement for the THEMIS data is due to the higher seeing quality. The segmentation parameters of SDO are fixed, for all sequences, to get the best granule areas close to the THEMIS values. The expansion velocity, however, is found to be approximately the same as on 10 Sept 2019. It is around 1 kms -1 in the first minute and decreases to 0. 5 kms -1 at 600 s. Figure  10  gives an example of the evolution of such a granule, observed by THEMIS and SDO.
Hence, the method provides statistics of the formation and expansion of parent granules at the origin of the largest families, in terms of area (Figure  15 ) and expansion speed (Figure  16 ). The results suggest that the mean initial expansion speed is in the range 2 -3 kms -1 for the multi-wavelength observations and simulation, and decreases in a few minutes to about 1 kms -1 or less. The mean area of parent granules exhibits a similar behavior both for observations and simulation. It should be noted, however, that sequences are much shorter for THEMIS (Table  1 ). Sequences also have different wavelengths, bandpass, optical resolutions (Table  2 ), and contrasts (Table  1 ). The IRIS continuum forms above the other observations (Table  1 ). The area growth rate is higher in THEMIS data probably due to the the phase diversity processing.
We think that the area growth rate for THEMIS data is due to the seeing effects and to our reduction processing. Data issued from free seeing space-borne instruments show a slower area expansion. We do not observe any relationship between area expansion and the relative altitude of observations. As parent granules are detected earlier than can be done with the manual method described in the previous section, the two methods provide complementary results, at different development stages. The characteristics of expansion velocities are summarized in Table  3.
This work was granted access to the HPC resources of CALMIP under the allocation 2011-[P1115]. Thanks to SDO/HMI , IRIS , and Hinode/SOT teams. The THEMIS telescope is supported by the French Centre National de la Recherche Scientifique (CNRS -INSU). We thank all the team of THEMIS for adjusting the telescope during our observing campaign. All of the authors would like to thank R. Stein for the use of the 96 Mm x 96 Mm x 20 Mm simulation. We thank the anonymous referee for his/her careful reading of our manuscript and his/her many insightful comments and suggestions.
Data calibration was performed using the Common Astronomy Software Application (CASA, see  McMullin et al. 2007 ) version 4. 5. 3 for Cycle 3 observations and 5. 1. 1 for the Cycle 5 observations. Flux calibrators were manually checked and the flux calibration uncertainty was estimated to be 20%. For each individual exposure, phase selfcalibration was computed on the high signal-to-noise continuum and applied to both the continuum and 12 CO data. The Cycle 5 continuum peak (centered at α J2000 : 04 h 27 m 02 s. 573, δ J2000 : +26 • 05 30. 170) was defined as a phase reference. Relative phase shifts between all different exposures from the three configurations were automatically corrected by the self-calibration procedure. Final calibrated data sets from the three different configurations were combined with their default weights.
Acknowledgements. The authors would like to thank the referee, whose comments helped improve the quality of the paper. We acknowledge  Ana Lopez-Sepulcre  for crucial help in the data reduction process at the  IRAM-Grenoble ALMA ARC  node. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2015. 1. 01108. S, ADS/JAO. ALMA# 2017. 1. 01605. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. Part of the data reduction presented in this paper were performed on the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is partly supported by the  Equipe@ Meso project  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
This work was supported by the  Programme National de Physique Stellaire (PNPS)  and the  Programme National de Physique et Chimie du Milieu Interstellaire (PCMI)  of  CNRS/INSU  co-funded by  CEA  and  CNES. L. A. Z acknowledge financial support from  DGAPA, UNAM , and  CONACyT , México. F. L acknowledges the support of the  Fondecyt program  n  • 3170360.
The phase calibrator was J0635-7516 (0. 46-0. 53 Jy at 230. 5 GHz during the time range of observations). J0635-7516 was also used as bandpass calibrator in all but one execution which used J0538-4405 (1. 2 Jy at 230. 5 GHz). The amplitude calibrator was J0519-454 (0. 66-1. 0 Jy at 230. 5 GHz). Data were calibrated using the ALMA Calibration pipeline version Cycle3R1 included in Common Astronomy Software Applications (CASA; http://casa. nrao. edu) v. 4. 3. 1 (McMullin et al. 2007 ). Calibrated visibilities were subsequently continuum-subtracted in the uv domain and deconvolved using clean, tclean in CASA 4. 5, and a prototype version of the auto-multithresh automasking routine now included in CASA (since v. 5. 1. 0 Kepley et al. 2020). Visibilities from both projects were included in the deconvolution.
2008). For clump segmentation, we use the quickclump python implementation (https://github. com/vojtech-sidorin/quickclump/) to which we added a I minpk parameter, the minimum peak intensity required for a valid clump (https://github. com/indebetouw/ quickclump). This allows the fainter envelopes of bright clumps to be included without keeping faint noise peaks. ), magenta contours: 13 CO 2-1 beak brightness at 0. 04,0. 06,0. 08,. 1 Jy bm -1 (cube rms = 2. 2mJy bm -1 ). H2O masers are marked with yellow circles of radius=1 , the quoted positional uncertainty  (Imai et al. 2013 ). An OH maser is marked similarly with a cyan circle  (Brogan et al. 2004 ). Properties (moments, fitted sizes, etc) of clumps are calculated with a python translation of the moments calculations in cpropstoo (https://github.
com/akleroy/cpropstoo). For dendrograms we use the python implementation at https://github. com/dendrograms/astrodendro. Somewhat more recently it has become popular to identify elongated regions of emission and refer to them as filaments. We use filfinder  (Koch & Rosolowsky 2015)  and its python implementation (https://github. com/ indebetouw/FilFinder) which allowed for easy modification, testing, and incorporation into other analysis scripts. Filaments are initially identified in the (2D) peak intensity image -if there is not an overabundance of sightlines with multiple velocity components, this should work well, and indeed for this data, the method produces a visually very satisfactory result (Figure  4 ). Manual examination and searching of this data cube only finds one position at which there are two bright structures at different velocities, and fewer than 10 positions with a bright structure at one velocity, and structure wings or faint emission at another velocity.
By contrast, clumpfind is highly biased towards selecting clumps in a narrow range of sizes. The relation is fit to a power-law with the Levenberg-Marquardt least-squares fitter in the Kapteyn package  (Terlouw & Vogelaar 2015). The slope of 0. 75±0. 05 is somewhat steeper than the theoretical slope of 0. 5 for a medium dominated by turbulent shocks  (Ballesteros-Paredes et al. 2011, and refs therein) , and on the higher side but within 3σ of values measured in other clouds (see references and discussion of 30Dor-10 at lower angular resolution in  Nayak et al. 2016). N159, a pair of massive star forming clouds just to the south of 30 Doradus, has been observed with ALMA at similar angular resolution to the data presented here  (Saigo et al. (2017)   2019 ): resolution of 0. 28 ×0. 25 ) dendrogram structures in N159 have a size-linewidth slope of 0.
This underscores the need to use the same segmentation method when comparing different datasets. It also suggests that if parts of clouds segmented with dendrograms are analyzed, the relative boundedness between two different clouds is a robust comparison, but the absolute value of that boundedness relative to the theoretical lines of stability should be interpreted cautiously. As a final test, we calculated the sizelinewidth-mass relations for structures identified with clumpfind but using a higher and higher noise floor or cutoff, raising it gradually up to 20× the noise level in the cube. Raising the cutoff floor causes the assigned structures' properties to smoothly move over to the location of the dendrogram-assigned structures, as expected. 4. 6. Structure analysis result: Core mass function A fundamental question in star formation is whether the stellar initial mass function (IMF) is predetermined by the molecular cloud structure.
Software: CASA (v4. 3. 1;v4. 5; v5. 1. 0McMullin et al. 2007), Kapteyn (Terlouw & Vogelaar 2015), filfinder (Koch & Rosolowsky 2015), quickclump (https://github. com/vojtech-sidorin/quickclump/),clumpfind (Williams et al. 1995(Williams et al. , 2011)), cprops ( Rosolowsky & Leroy  2006, 2011), dendrograms (Rosolowsky et al. 2008) This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2011. 0. 00471. S, ADS/JAO. ALMA# 2013. 1. 00346 ,ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
The presence of nitrogen-enriched stars in globular clusters provides key evidence for multiple stellar populations (MPs), as has been demonstrated with globular cluster spectroscopic data towards the bulge, disk, and halo. In this work, we employ the VVV Infrared Astrometric Catalogue (VIRAC) and the DR16 SDSS-IV release of the APOGEE survey to provide the first detailed spectroscopic study of the bulge globular cluster UKS 1. Based on these data, a sample of six selected cluster members was studied. We find the mean metallicity of UKS 1 to be [Fe/H]= -0. 98 ± 0. 11, considerably more metal-poor than previously reported, and a negligible metallicity scatter, typical of that observed by APOGEE in other Galactic globular clusters. In addition, we find a mean radial velocity of 66. 1 ± 12. 9 km s -1 , which is in good agreement with literature values, within 1σ.
2011 ). More recently,  Baumgardt et al. (2019)   smaller heliocentric distance of ∼7. 8 kpc and a cluster mass of ∼ 0. 8 × 10 5 M by comparing the cluster density profile from the most recent Gaia DR2 data  (Gaia Collaboration et al. 2018 ) to a large suite of direct N-body star cluster simulations. There is also a discrepancy regarding the cluster metallicity, that is to say, integrated infrared photometry indicates metallicities of [Fe/H] ∼ -1. 2 and -1. 18  (Bica et al. 1998) , while echelle spectra covering the range 1. 5 -1. 8 µm estimate an intermediate metallicity of [Fe/H]= -0. 78  (Origlia et al. 2005 ). In addition, there is no age estimate available for UKS 1. Therefore, a detailed and reliable physical characterization of this cluster is still lacking  (Cohen et al. 2018).
estimated a The main purpose of the present paper is to explore the near-IR high-resolution spectroscopic observations from the APOGEE survey towards UKS 1, combined with an updated version of the VVV Infrared Astrometric Catalogue (VIRAC). These help us obtain a better view of the properties of the stellar population in the inner regions of UKS 1, as well as to produce a much improved CMD, helping to roughly estimate the cluster age by adopting a Bayesian statistical approach (see, e. g. Souza et al. 2020). In Section 2, we briefly describe the observations. In Section 3, the data and selection of the potential members of UKS 1 are described. In Section 4, we describe the adopted atmospheric parameters, followed by a discussion of the chemical abundances of our candidate stars in Section 5.
Membership We identified a sample of potential stellar members of UKS 1 in the APOGEE DR16 database. Table  1  summarizes the main properties of the likely members of UKS 1. Radial velocities are taken from the APOGEE catalogue  (Ahumada et al. 2020) , while the absolute proper motions and near-IR magnitudes (J, K s ) are retrieved from VIRAC and 2MASS, respectively. We selected probable cluster members based on their radial velocities, absolute proper motions, metallicity, and position in the near-IR CMD. First, we restrict our sample to stars within 6. 32 from the GC centre, that is, inside the nominal GC tidal radius given in the new catalogue of fundamental properties of Galactic GCs  (Baumgardt & Hilker 2018; Baumgardt et al. 2019; Hilker et al. 2020). In addition, for the stars selected above, we manually validated their metallicities ([Fe/H]) from the strength of selected iron (Fe I) lines, by adopting the same methodology as described in  Fernández-Trincado et al.
Spectroscopic parameters To determine the chemical abundances of the selected members from UKS 1, we adopted the uncalibrated stellar atmospheric parameters (T eff , log g, and [M/H]) computed from the ASPCAP pipeline  (García Pérez et al. 2016). With the fixed T eff , log g, and first-guess [M/H], the first step consisted in determining the metallicity from selected Fe I lines, the micro-turbulence velocity (ξ), and the convolution parameter with the BACCHUS code  (Masseron et al. 2016). Thus, the metallicity provided is the average abundance of selected Fe lines, while the micro-turbulence velocity is obtained by minimizing the trend of Fe abundances against their reduced equivalent width, and the convolution parameter stands for the total effect of the instrument resolution. We proceed in the same manner as in  Hawkins et al.
(2016) , i. e. we derive a single global convolution value per spectrum, based on the average broadening of Fe lines, by assuming a Gaussian convolution profile. Once [Fe/H], ξ, and the convolution parameters are determined, O, C, and N abundances are estimated from selected 16 OH, 12 C 16 O, and 12 C 14 N molecules  (Smith et al. 2013). Once those elements are measured, the full process is iterated until convergence (see, e. g. Fernández-Trincado et al. 2016 , 2017 , 2019a,b,c,d). Finally, for each chemical species and each line, the abundance ratios are determined with the BACCHUS code following the procedure as described in  Hawkins et al. (2016) , and briefly summarized here for guidance. (i) a spectrum synthesis, using the full set of lines from the internal APOGEE DR14 atomic/molecular linelist (linelist 20150714) to find the local continuum level via a linear fit; (ii) cosmic and telluric rejections are performed; (iii) the local S/N is estimated; (iv) a series of flux points contributing to a given absorption line is automatically selected; and (v) abundances are then derived by comparing the observed spectrum with a set of convolved synthetic spectra characterized by different abundances.
Chemical properties of UKS 1 The present study adds a substantial contribution to the chemical characterization of UKS 1. In comparison,  Origlia et   We proceed to compare our results with the homogeneous sample from  Mészáros et al. (2020) , as that data set has been carefully examined with the same code and similar methodology as adopted in this paper. We avoid any comparison with GCs only based in the ASPCAP APOGEE pipeline as they may exhibit larger systematic offsets (see, e. g. Nataf et al. 2019).
Age Deriving the age for the UKS 1 GC is not an easy task because, as mentioned before, it is in a region with very high extinction (see, e. g. Minniti et al. 2011). Through isochrone fitting, we try to provide a rough estimation of the age. To accomplish that, we employ the SIRIUS code  (Souza et al. 2020) , which applies a statistical Bayesian Markov Chain Monte Carlo method. For the isochrone fitting, we adopt the Darthmouth Stellar Evolutionary Database (DSED;  Dotter et al. 2008 ) with an αenhancement of +0. 4 and canonical helium (Y∼ 0. 25) models. The DSED isochrones are available in the 2MASS photometry system, and they were converted to the VVV photometry system. Since we do not have the entire CMD available, in particular, the turn-off region, we imposed Gaussian distribution priors for the metallicity of [Fe/H]= -0.