dataesr/scientific-paragraphs-categorization huggingface dataset:
(2009). We show the SDSS designation, redshift, stellar mass  (Kauffmann et al. 2003)  and SFR  (Brinchmann et al. 2004)  and radio fluxes at 150 MHZ taken from  Shimwell et al. (2019) , 1. 4 GHz  (White et al. 1997 ) and at 3 GHz  (Lacy et al. 2020 ). D 25 is the optical diameter at the 25 mag isophote taken from the NASA/IPAC Extragalactic Database (NED) 1. We used the radii computed from the SDSS r-band observation or if available photometric B-band observation. We further present the galaxy inclination calculated from a 2D Sérsic profile fit as described in detail in Sect. 3. 3. at the beginning of the night and at dawn, as well as pointing measurements every 2 hours. The temperature scale we use here is main-beam temperature and the beam size is λ/D = 22 at 2. 8 mm and 12 at 1. 4 mm wavelength with an average beam efficiency of η mb = T * A /T mb = 0.
MEGAFLOW sample Furthermore, we aim to discuss our observations with respect to recent NOEMA observations, conducted by  Freundlich et al. (2021). They measured CO(3-2) and (4-3) detection limits for 6 galaxies at z = 0. 6 -1. 1 which have confirmed in-and outflows in the circumgalactic medium. They succeeded to detect a signal by stacking their observations. These observations were a pilot program of the MusE GAs FLOw and Wind (MEGAFLOW) survey. They aim to test the quasi-equilibrium model and the compaction scenario describing the evolution of galaxies along the main sequence, implying a tight relation between SF activity, the gas content and in-/outflows. This sample will help us to discuss different mechanisms of compaction due to filaments or merger driven inflows, increasing both the molecular gas content and the star formation efficiency which is discussed in Sect.
Acknowledgements. We thank the anonymous referee who helped us to improve our emission line fitting procedure which finally strengthened the arguments in our analysis and discussion. We thank  Anaëlle Halle  for the fruitful discussions on the manuscript and  Susanne Maschmann  for helpful advise on the English language. This work is based on observations carried out under the two project numbers  198-19  and  166-20  with the IRAM 30m telescope at Pico Veleta in Spain. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Sci-A&A  proofs: manuscript no. main ence, and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah.
To still make use of the empirical estimation of P, an analytical pupil sign mask input has to be provided which allows us to determine the sign of P all over the pupil. Such a mask can be obtained by numerically propagating P 0 through the optical system. Figure  2  shows an example of the image I C recorded on the HiCAT bench, normalized by |P| 2 , and the reconstructed wavefront ϕ. Normalizing the image removes some artifacts coming from pupil illumination discrepancies. The full phase reconstruction requires the use of Eqs. (  15 ) or (  16 ), including the estimation of b. Throughout this paper, we use the open-source code pyZELDA (Vigan & N'Diaye 2018) with the updated formalism of  N'Diaye et al. (2013b)  presented here to take into account the FPM filtering. Our code performs phase reconstruction using Eq.
Acknowledgements. R. P. acknowledges PhD scholarship funding from  Région Provence-Alpes-Côte d'Azur  and  Thales Alenia Space. The authors are especially thankful to the extended HiCAT team (over 50 people) who have worked over the past several years to develop this testbed. This work was supported in part by the  National Aeronautics and Space Administration  under Grant  80NSSC19K0120  issued through the  Strategic Astrophysics Technology/Technology Demonstration for Exoplanet Missions Program  (SAT-TDEM; PI:  R. Soummer ). E. H. P. was supported by the  NASA   Hubble Fellowship  grant  HST-HF2-51467. 001-A  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555.
It can thus be used to create airflows across the participants' face, and modulated to produce radial or translational tactile motion with different flow rates and patterns. It is fully controlled by a dedicated software implemented in the Labview environment that enables the generation of spatial and temporal complex patterns. The PAF device can therefore generate controlled airflows with a predefined orientation, moving laterally or horizontally according to the arrangement of the nozzles, or even randomly stimulating different parts of the body like the face at different levels of pressure intensity. We present here technical and experimental tests performed to validate the present device as an MR-compatible tool useful to study tactile motion perception and its neural basis. First, we verified that this device did not affect anatomical or functional images acquired with a 3 T MR scanner.
1A ). According to this constant inflow pressure, the downstream airflow is modulated only by the valve opening, i. e. the driving current. As most of the analog I/O cards can only generate variable voltage outputs, we built a specific electronic board to convert a voltage output into a regulated current driving (Fig. 1B ). Each electro-valve output is connected to a flowmeter (Key Instruments MR3A17BVBN; Fig. 1A  middle part) that allows the precise The pneumatics are controlled using a specific software interface written using the National Instruments LabVIEW © 2017 environment. The program is driving two multifunction National Instruments cards: 1) a PXI-6289 with 4 analog voltage outputs, dedicated to drive the proportional electro-valve' openings, 2) a USB-6002 dedicated to the pressure regulator: an analog voltage output sends the command to the regulator while an analog input allows to control the effective pressure given by the regulator.
Image analysis All acquired MR images in DICOM format were converted into nifti format and stored following the Brain Imaging Data Structure (BIDS) standard (http://bids. neuroimaging. io, Gorgolewski et al. , 2016). For each participant, the images were pre-processed using the fMRIprep procedure (RRID:SCR_016216; Esteban et al. , 2019, available here: 10. 5281/zenodo. 852659) which is a Nipype-based tool (RRID: SCR_002502;  Gorgolewski et al. , 2011 , available here: 10. 5281/zenodo. 581704. ). This procedure includes a step of segmentation of brain tissue into cerebrospinal fluid (CSF), white matter (WM) and grey matter (GM) performed on the brain-extracted T1-weighted (FSL v5. 0. 9, RRID: SCR_002823). Functional images were (1) corrected for magnetic field inhomogeneities using a B0-nonuniformity map (or fieldmap) estimated based on the two echo-planar imaging (EPI) references with opposing phase-encoding directions, with 3dQwarp Cox and Hyde (1997) (AFNI 20160207), (2) realigned to correct for head movements between scans, (3) coregistered to the respective anatomical scan, (4) normalized to the ICBM 152 Nonlinear Asymmetrical template version 2009c (RRID: SCR_008796,  Fonov et al.
, 2007) , which estimates the nuisance signals of tissues that are not expected to exhibit BOLD fMRI signals of neuronal origin, i. e. white matter and CSF, and includes the mean and the 12 principal components of the signal of those tissues in the GLM as additional regressors. The first-level contrast maps of the 20 participants were then entered into a second-level analysis to perform between-subject analysis. A multivariate pattern analysis (MVPA) on all participants was also performed using Nilearn  (Abraham et al. , 2014 , https://nilearn. github. io/), a python module. The pre-processed functional data were modeled to yield one beta per trial for the four conditions of interest (Motion, Flicker, Left, Right), while the non-interest conditions as well as the movement regressors were modeled as 1 beta for the whole run.
fMRI tests 3. 2. 1. Effect of the PAF device on MR images RF noise measurement provided by the MRI manufacturer and separate analysis of SNR measurement on a water MRI phantom placed in the head coil did not show any impact of the device on the MR measurement. Images for the SNR measurements were acquired on the phantom with the same MPRAGE and EPI sequences applied for participants' acquisition: with and without the device being installed near the head coil (similar to the in vivo experiment). As shown in Fig. 5 , we did not detect any artifacts created by the presence of the PAF device on the MPRAGE or EPI images of the phantom. We further computed noise measurement by acquiring signal from the reception chain without any excitation provided to the sample. No difference was observed with the noise measurement for the two conditions, with or without the presence of the device (data not shown).
It is worth noting that this noise measurement is routinely used in our MRI Center before installing any new stimulation device and it was shown to be extremely sensitive to any kind of RF contamination around the Larmor frequency range. For SNR measurements, MRIQC software was used  (Esteban et al. , 2017). MRIQC provides a wide range of calibrated metrics to assess the quality of human brain images. In this case, we chose the "SNR total" metrics to evaluate the impact of the presence of the airflow device. A digression of  3. 2% (SNR=77. 1 without and SNR=74. 6 with the device installed) and 2. 3% (SNR = 2. 3067 without and SNR=2. 3014 with the device installed) were observed on the MPRAGE and EPI scans, respectively. These digression values are within the standard variation of this measurement  (Cheng and Halchenko, 2020; Yang et al.
B. N. , A. K. , J. C-G. : Conceptualization, J. C-G: Acquisition data; J. S. : Adaptation of the dataset in BIDS format; B. N & J. B. : Methodology, conception and development of the PAF device. B. N. : development of a custom program to control the PAF device; J. -L. A. , J. S. : Methodology, optimization of the MRI protocol, tests on phantom; J. C-G, N. C. : Analysing the psychophysical data; J. C-G, J-L. A. , A. K. :Analysing the MRI data; J. C-G, B. N. , A. K. : Writingoriginal draft; A. K. : Funding acquisition & Supervision; all the co-authors revised and approved the final version of the submitted manuscript.
Acknowledgements Funding: This work has received support from the  French government  under the  Programme "Investissements d ′ Avenir", Initiative  d  ′ Excellence d ′ Aix-Marseille Université via AMidex funding  ( AMX-19-IET-004 ). JCG was funded by the  Agence Nationale de la Recherche  grant (nEURo *AMU ,  ANR-11-IDEX-0 0 01-02  grant). This work was performed in the  Center IRM-INT@CERIMED  ( UMR 7289 ,  AMU-CNRS ), platform member of  France Life Imaging network  (grant  ANR-11-INBS-0 0 06 ).
The lack of an expanding shell at one side of a region leading to rapid hot plasma leakage was recently demonstrated for RCW 49 by  Tiwari et al. (2021). Studying the [C II] emission dynamics can also help to constrain the role of stellar feedback in suppressing the star formation of a molecular cloud, as well as triggering a second generation of star formation, which is still highly debated (e. g. ,  Zavagno et al. 2010 Zavagno et al. , 2020;; Dale et al. 2013; Walch et al. 2015). Luisi et al. (2021)  proposed, based on [C II] data taken within the Stratospheric Observatory for Infrared Astronomy (SOFIA) legacy program FEEDBACK 12   (Schneider et al. 2020) , that star formation is triggered in RCW 120. They propose a model in which a wind-driven expanding [C II] shell sweeps up a gas shell that fragments and forms stars on very short timescales (∼0.
In Figure  1 , the Hα absorption in the eastern part of the ring would then suggest that this part of the ring is expanding toward us and that the western part is expanding away from us  (Minier et al. 2013). Multiwavelength observations of RCW 36 have also established the presence of several protostar and young stellar object (YSO) candidates  (Bik et al. 2006; Ellerbroek et al. 2011; Hill et al. 2011; Giannini et al. 2012; Massi et al. 2019) , indicating relatively low mass ongoing star formation in the region, as well as the presence of two Herbig-Haro (HH) jets  (Ellerbroek et al. 2013b). In this paper, we present the first [C II] and [O I] observations of RCW 36 obtained by the SOFIA FEEDBACK Legacy survey in combination with complementary data to acquire a global understanding of the evolution of this region.
Both the [C II] and [O I] lines are PDR tracers, where [O I], with a critical density of 5 × 10 5 cm -3 , is particularly used to trace dense PDRs  (Röllig et al. 2006 ). These FEEDBACK observations unveil new dynamical information that was not available before. Section 2 describes our new [C II], [O I], and CO (3-2) observations, as well as archival X-ray and submillimeter continuum data sets. The results of these observations are presented in Section 3. In Section 4, the expansion energetics and mass ejection parameters are calculated, and in Section 5 the [C II] data are compared with Chandra X-ray observations of RCW 36. In Section 6, these results are combined to propose a scenario for the evolution of RCW 36, and we discuss the results in the global context of stellar feedback on molecular clouds.
SOFIA The FEEDBACK C + Legacy survey observed the [C II] 3 P 3/2 → 3 P 1/2 transition, at 158 μm, and [O I] 3 P 1 → 3 P 2 transition, at 63 μm, simultaneously with the upGREAT instrument  (Risacher et al. 2018 ) on board the airborne SOFIA  (Young et al. 2012)  during a flight on 2019 June 6 from Christchurch, New Zealand. The observation strategy was to carry out [C II] and [O I] on-thefly (OTF) maps and is described in detail in  Schneider et al. (2020). For RCW 36, the planned map within the legacy program has a size of 14 4 × 14 4, indicated in Figure  1. Here we present the lower part of the map (14 4 × 7 2) that has been observed so far. The center of the full map is located at α (J2000) = 08 h 59 m 26 8 and δ (J2000) = -43°44′14 1, and the emission-free OFF position is at α (J2000) = 09 h 01 m 31 7 and δ (J2000) = -43°22′50 0.
Ancillary Data The Chandra X-ray Observatory ACIS camera  (Garmire et al. 2003 ) observed a 17′ × 17′ field centered on RCW 36 for ∼70 ks in 2006. The MYStIX project  (Kuhn et al. 2013)  found 502 X-ray point sources in this field. These were excised, and the remaining data smoothed, to obtain an 0. 5-7 keV diffuse X-ray image of the field  (Townsley et al. 2014). ACIS is an intrinsic spectrometer, tagging every X-ray event with its energy and its position. We will use the Chandra/ACIS data to study the X-ray spectra in a variety of regions in and around RCW 36. The RCW 36 region, being part of the Vela C molecular cloud, was observed with the PACS  (Poglitsch et al. 2010 ) and SPIRE  (Griffin et al. 2010)  bolometers on the Herschel telescope as part of the Herschel imaging survey of OB young stellar objects (HOBYS) program  (Motte et al.
Chandra Chandra observations trace the presence of hot plasma formed in the winds of massive stars. A map of diffuse X-ray emission for RCW 36 was presented in  Townsley et al. (2014). The methods to extract this diffuse X-ray emission are described in detail in  Broos et al. (2010 Broos et al. ( , 2012) )  and  Broos & Townsley (2019). Analyzing the Chandra data will thus allow a more comprehensive view of the feedback processes that are driving the observed dynamics around RCW 36.
3D Structure of RCW 36 RCW 36 consists of a bipolar cavity and a dense central ring surrounding the waist of the H II region created by the central OB cluster. In the cavities, the expanding [C II] shell is only observed at blueshifted velocities, which implies that it is  100 K 4 × 10 2 M e 2 × 10 -2 M e km s -1 yr -1 7 × 10 -4 M e yr -1 250 K 3 × 10 2 M e 1 × 10 -2 M e km s -1 yr -1 5 × 10 -4 M e yr -1 500 K 3 × 10 2 M e 1 × 10 -2 M e km s -1 yr -1 4 × 10 -4 M e yr -1 Note. Based on arguments presented in the text, the errors, which are difficult to quantify for all sources of uncertainty, on these values are easily a factor 2. expanding toward us. Fitting the foreground column to the Chandra data toward the cavities, in particular the eastern one, suggests that most column density, observed with Herschel, is associated with and on the near side of RCW 36 from our point of view.
The Pressure and Energy Budget in RCW 36 Analysis of the Chandra data shows that a hot plasma, created by the stellar winds, pervades RCW 36 and that it has the required energy to drive the observed expanding shells in both cavities. To discuss the role of different physical processes, the associated pressure terms were estimated and are presented in Table  5. Specific assumptions made for these calculations are specified in Appendix E. The thermal pressure from the hot plasma in both cavities is ∼(0. 6-1. 0) × 10 6 K cm -3 , which is slightly larger than the estimated thermal pressure of the photoionized gas, i. e. , (4. 6-5. 0) × 10 5 K cm -3 , derived from the SHASSA Hα observations  (Gaustad et al. 2001). However, as for the hot plasma, if the photoionized gas is confined in a subregion of the cavities, this will increase the estimated pressure.
Conclusions We presented the currently obtained [C II] and [O I] observations from the SOFIA FEEDBACK Legacy program toward RCW 36, a bipolar H II region in the young Vela C molecular cloud. These observations are complemented with 12 CO (3-2) and 13 CO (3-2) observations from APEX, Chandra X-ray observations, Herschel photometric data, and the BLASTpol dust polarization observation at 500 μm. The main results can be listed as follows: 1. The [C II] and 13 CO (3-2) data show that the central molecular ring is expanding with an estimated velocity of 1. 0-1. 9 km s -1 (see Section 3. 3. 2). 2. The [C II] and 12 CO (3-2) data show the presence of several filaments in the ambient cloud that are being swept away with velocities <3 km s -1 (see Section 3. 3. 3). 3. The [C II] line unveils blueshifted expanding shells in the bipolar cavities with a velocity of ∼5.
We thank the  USRA  and  NASA  staff of the  Armstrong Flight Research Center in Palmdale and of the Ames Research Center in Mountain View , as well as the  Deutsches SOFIA Institut  for their work on the observatory. We acknowledge the support by the upGREAT team for operating the instrument, for planning the detailed observing scenarios, and in the calibration of the data. The development and operation of upGREAT were financed by resources from the  MPI für Radioastronomie, Bonn , from  Universität zu Köln , and from the  DLR Institut für Optische Sensorsysteme, Berlin , and by the  Deutsche Forschungsgemeinschaft (DFG)  within the grant for the Collaborative Research Center 956, as well as by the  Federal Ministry of Economics and Energy (BMWI)  via the  German Space Agency (DLR)  under grants  50 OK 1102 ,  50 OK 1103 , and  50 OK 1104.
The  FEEDBACK  project is supported by the  BMWI via DLR , Projekt No. 50 OR 1916 (FEEDBACK). L. B. was supported by a  USRA postdoctoral fellowship , funded through the  NASA SOFIA  contract  NNA17BF53C. N. S. acknowledges supported by the  Agence National de Recherche (ANR/France)  and the  Deutsche Forschungsgemeinschaft (DFG/Germany)  through the project " GENESIS"  ( ANR-16-CE92-0035-01/DFG1591/2-1 ). Facilities: SOFIA (upGREAT), Chandra, APEX, Herschel. Software: astropy (Astropy Collaboration et al. 2013), cygrid (Winkel et al. 2016),.
g. spinal pathology, prostatitis, urinary tract infections, or other medical conditions considered to potentially influence the pain presentation), and prior surgery in the area of the current clinical entity of groin pain or any surgery in the groin region within the previous year. Ethics. Ethical approval was granted by the ethics committee of Clinique du sport de Bordeaux-Merignac, France (#02-2020. 1). Athletes were included in this study after giving informed consent according to the Declaration of Helsinki. Study participants could withdraw at any time point without reporting a reason and without any consequences. Patient information. Demographic data (age, height, weight, type and level of sport/competition, and duration of groin pain) were recorded. Acquisition of pain drawings. Athletes drew their area of pain on a high-resolution and detailed digitalbody chart using the Navigate Pain web application (Aglance Solutions ApS, Denmark) and a computer tablet (iPad Air 1).
To assess whether there was a difference in pain duration between the number of clinical entities, we used the Kruskall Wallis-H-test. We used the Mann-Whitney U-test to determine differences in pain area between unilateral and bilateral clinical entities. We used the chi-square goodness-of-fit test to assess the proportion or relative frequency of pain descriptor intensity for the whole cohort and to determine differences in the proportion of pain descriptors between adductor-, inguinal-, iliopsoas-, and pubic-related groin pain. All analyses were carried out in Statistical Package for Social Sciences (SPSS; version 27, IBM). We used Bonferroni corrections for multiple comparisons, and present only adjusted p-values in the results. Missing data were excluded from analyses.
| Experiment This approach was implemented in a program written in C++ that includes a GUI to set the input variables and visualize the generated solutions. To accelerate the computations, all the processes can run in parallel thanks to an implementation using  OpenMP (n. d. ). The program was run on an Intel Xeon W-2135 CPU @ 3. 70 GHz × 12 with 32 GB RAM. A collection of 30 fields from the real world were chosen to evaluate the proposed approach. These fields range in area from 1. 83 to 13. 20 ha. Twenty fields have a simple shape for which no field decomposition was required, while 10 fields have a complex shape for which at least two different dividing lines were provided to try different field decompositions. To extract the data of these fields, the annotation tool of  Géoportail (n. d.
For instance, the farmer may have used visual clues to help working the land which may have had an influence on the choice of the trajectories, while we did not consider this as a useful factor for a robot. As previously mentioned, the total number of explorations for a field depends on the number of selected entrances and the number of provided dividing lines. Therefore, for each field several explorations were performed. Consequently, in Table  2 , single exploration time contains the average and standard deviation over all explorations performed on all simple and complex fields. In the following, we illustrate the results on a sample of six fields extracted from the data set: four simple and two complex fields. The name of each sample corresponds to its original name in the data set provided in the appendices.
DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. POUR ARAB ET AL. 15564967, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/rob. 22187 by Cochrane France, Wiley Online Library on [10/05/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.