dataesr/scientific-paragraphs-categorization huggingface dataset:
(2021b)  in comparing VLT/MUSE data with FP data obtained with GHASP. At higher spatial frequencies, fluctuations in the velocity dispersion are observed on scales comparable to or larger than the one set by the seeing. These fluctuations are smoother in the GHASP data than in the PUMA ones. This is due to the difference in the method used to compute the map (Gaussian smoothing versus Voronoi tessellation, respectively). Panels (c) and (e) of Fig. C. 3 show intensity-velocity dispersion diagnostic diagrams and panels (a) and (d) the histograms for the intensities and of the velocity dispersions. These diagnostic diagrams are generally used to characterise the dynamics of the ISM in extragalactic giant HII regions (Munoz-Tunon et al. 1996)  and also to investigate the dynamic of HII galaxies (Moiseev & Lozinskaya 2012; Carvalho & Plana 2018).
Acknowledgements. Based on observations collected at the  Observatorio Astronómico Nacional at San Pedro Mártir, Baja California, México (OAN -SPM). We thank the daytime and night support staff at the  OAN-SPM  for facilitating and helping obtain our observations. Also, based on observations taken with the GHASP and MISTRAL spectrographs at the  Observatoire de Haute Provence (OHP, France ), operated by the  French CNRS. The authors warmly thank  Olivier Boissin  from  LAM  and the  OHP team  for its technical assistance before and during the observations, namely the night team:  Jean Balcaen ,  Stéphane Favard ,  Jean-Pierre Troncin ,  Didier Gravallon  and the day team led by  François Moreau  as well as  Dr. Auguste Le Van Suu , the  Head of Observatoire de Haute Provence-Institut Pythéas.
We are grateful to the whole CFHT team who assisted us in the preparation and in the execution of the observations and in the calibration and data reduction:  Todd Burdullis ,  Daniel Devost ,  Bill Mahoney ,  Nadine Manset ,  Andreea Petric ,  Simon Prunet ,  Kanoa Withington. We acknowledge financial support from " Programme National de Cosmologie and Galaxies" (PNCG)  funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France ,and from  "Projet International de Coopération Scientifique" (PICS)  with  Canada  funded by the  CNRS, France. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration and of the GOLDMine database  (http://goldmine.
New Target-Ion Source Systems combining a target and a catcher material are developed in the radioactive beam community, in particular at GANIL, in order to maximise the yield of very short lived atoms by minimizing the atom-to-ion transformation time. The aim of this study is to characterize the release properties of 81 Rb collected on two graphite catchers and two carbon nanotube catchers. The release fractions were measured at various catcher-heating temperatures and then compared to the analytical expressions relevant to each catcher. This comparison led to the extraction of the pre-exponential factor (D 0 ) and the activation energy (E act ) involved in the diffusion coefficient of Rb for three carbon microstructures. All these data allowed to define an ideal catcher which could be made of aligned carbon nanotubes of small diameter and oriented in order to collect all the 81 Rb atoms produced by the target but also to release them efficiently.
Determination of the release time and efficiency In order to compare the release properties of the studied catchers, release efficiencies are more convenient than released fractions. Both depend on the diffusion coefficient and can be calculated in the frame of models describing diffusion in specific media: in a sheet, a cylinder, a sphere or a fibre  [12] -  [14]. The choice of the medium in which the diffusion takes place will be made according to the structural properties of the different catchers. In the case of diffusion in a sheet, the profile and depth of implantation must also be known. Profile and implantation depth were calculated in the frame of the LISE++ software (version 15. 13. 7)  [15]. Then the relevant analytical expressions linking the diffusion coefficient to the released fractions and to the release efficiency for a radioactive isotope are indicated.
The simulation results are in good agreement with the experimental measurements: 43 % in the CNT h layers and 57 % in the Papyex® sheet. For the CNT v catcher, LISE++ indicates that 100 % of the 81 Rb are implanted in the CNT v catcher and 0 % in its quartz support, which is not in agreement with the experimental data (69 %, see section 4). However, the LISE++ code assumes that the materials have a uniformly distributed density, which is not the case for the CNT v catcher that is architecturally designed with the carbon nanotubes aligned perpendicularly to their support. The 81 Rb beam is emitted in a truncated cone with a vertex angle of 74 • inside which 34 % of the beam is collinear with the direction of the carbon nanotubes. Under these conditions, taking into account the diameter of the CNT v and of the Rb atom as well as the number of CNT v per unit area, only ~ 8 % of the 81 Rb have a probability of encountering a carbon nanotube and implanting itself.
Thanks to the advent of specific ALMA observations of prominences and filaments, we can review here not only the procedures leading to ALMA prominence observations and the diagnostic potential of ALMA data, but also the results of the first analyses of a quiescent prominence  (Heinzel et al. , 2022; Labrosse et al. , 2022)  and filament (da Silva  Santos et al. , 2022)  observations. The first prominence observations were obtained only in Band 3 but, thanks to a dedicated coordinated observing campaign, are accompanied by Hα and Mg II h&k spectra. The first observations of an (active region) filament were also obtained in a single ALMA band (Band 6), accompanied by simultaneous Mg II h&k spectral observations. In the frame of this review, we address in detail the difficulties encountered in the ALMA observations of prominences and discuss various aspects of ALMA data interpretation.
Of particular interest is the development of relevant multi-spectral inversion techniques. The paper is organized as follows. In Section 2, we present a historical overview of prominence observations in mm/sub-mm domain. In Section 3 we provide a brief description of ALMA with the emphasis on solar observations. Section 4 describes the formation mechanisms of the ALMA continua under the conditions of solar prominences. The issues of the visibility of quiescent prominences in the ALMA bands are addressed in Section 5. Section 6 is devoted to a thorough discussion of the plasma diagnostic capabilities of ALMA. In Section 7 we present the set-up of the current ALMA prominence observations, together with a discussion of the complexity of the data reduction of ALMA off-limb observations and the results of the analysis of these first ALMA prominence observations.
These effects can be included in the synthetic T b maps thanks to the simulation toolkit contained in the standard CASA (Common Astronomy Software Applications, see http://casa. nrao. edu/) package. The simulated degraded visibilities can be obtained from ideal T b maps using the simobserve() task. Afterwards, the visibility synthesis and image analysis (namely the comparison with the initial ideal model and the fidelity estimation) can be performed by simanalyze(). The results are presented in Figure  1. The left part shows the ideal T b map at 100 GHz (i. e. , 3 mm) used as an input model for visibility simulations. It has been computed using the observed Hα integrated intensity as described in  Heinzel et al. (2015). Its resolution is ≈ 1 -2 arcsec. Overlaid are the pointings (white crosses) of ALMA 12 m array for a mosaic that is able to sample the displayed part of the prominence and field of view for the single pointing (a white circle).
ALMA prominence observations 7. 1 Observational setup and data reduction The feasibility of prominence observations above the limb has been demonstrated by CASA simulations (see Section 5) and demonstrated during the Solar ALMA observing campaigns in 2014 and 2015  (Shimojo et al. , 2017; White et al. , 2017; Bastian et al. , 2018). The ALMA observing setup for prominences typically includes a small mosaic (as a result of the trade-off between full-scan cadence and the FOV), sometimes accompanied by a high-cadence single-FOV observations performed as close as possible to the mosaic (so called backto-back execution blocks). The latter serves for studies of fast dynamic phenomena on small spatial scales. The observations may be performed at a single ALMA frequency band or in multiple bands, again, in a back-to-back manner.
This plot was constructed from the used grid of models (small kinks on some curves are due to finite separation between the models in the grid). The individual curves correspond (from below) to E(Hα) from 10 4 up to 10 6 erg cm -2 s -1 sr -1. Reproduced from  Heinzel et al. (2022). the TP-antenna primary beam at a given AzEl coordinate. This means that the TP scan produces direct low-resolution image in the absolute brightness temperature scale. The INT and TP observations have to be combined during the data-reduction process to get the complete reconstruction of the brightness map. The basic data reduction of ALMA observations is performed at ALMA Regional Centers (ARCs) as part of the Quality Assurance level 2 procedure (QA2). The standard procedures for solar INT and TP data are described in Shimojo et al.
Conclusion In this review we addressed various aspects of the prominence physics from the point of view of radio mm/submm observations. Historically, such observations have been carried out at rather low spatial resolution which did not allow the study of prominence fine structures, contrary to high-resolution observations available in optical and UV. When ALMA was going to be operational during the last decade, a hope arose that we will be ultimately able to resolve the fine structures in solar prominences and filaments in the radio domain. Based on data-driven simulations  (Heinzel et al. , 2015)  and other modeling work  (Rodger and Labrosse, 2017) , several proposals have been submitted to facilitate prominence observations with ALMA. As a result, one prominence was observed still at the moderate ALMA resolution of 1-2 arcsec and the data was recently analyzed -we provide an overview of obtained results.
R. S. is supported by the  Office of Naval Research  and by  NASA  under  PSP/WISPR  grant  NNG11EK11I  and  HSR  grant  80HQTR21T0106. S. M. and T. P. work was supported by the  Programme National PNST of CNRS/INSU  co-funded by  CNES  and  CEA. S. M. , T. P. , and P. W. thank the  Alliance Hubert Curien Program  funded by the  Ministère de l'Europe et des Affaires Etrangères and the Ministère de l'Enseignement Supérieur et de la Recherche  and managed by  Campus France. A. K. H. acknowledges support from the  GSFC   Heliophysics Internal Scientist Funding Model competitive work package program. Aleida K. Higginson  https:/ /orcid. org/0000-0003-1380-8722Roger Scott https:/ /orcid. org/0000-0001-8517-4920Sophie Masson https://orcid. org/ 0000-0002-6376-1144Theo  Pelegrin-Frachon https:/ /orcid.
Computational details In all simulations, Ewald sum method with accuracy of 10 -5 is deployed to cope with long-range electrostatics; and tail corrections, for long-range Lennard-Jones interactions. A timestep of 1 fs is adopted. The atomic configurations equilibrated at various temperatures and RH obtained from ref. [39]  are first equilibrated in a 0. 1 ns NVT run (using Nosé-Hoover thermostat and damping parameter of 100 timesteps). Then, the system is equilibrated during 0. 1 ns in the microcanonical ensemble (NVE). Next, in the production run, the MSDs are obtained from NVE simulations. NVE simulations are used in production since the fictitious forces associated with the thermostat (or barostat if NPT is used) may affect the dynamics  [42]. After equilibration, the MSD data is collected during a 1 ns run, being recorded each 1 fs.
Data reduction used the Mantid software  [20] , with normalised diffraction patterns from banks 1,2,3 used in structure refinement (average 2 scattering angles approx. 10°, 26°, 52° respectively). Crystal structure refinement by Rietveld profile analysis used the Fullprof suite of programs  [21]. The neutron scattering lengths used were those included in the software, and a cylindrical sample absorption correction was applied during the refinements to account for the large wavelength-dependent neutron absorption cross-section of Sm in samples with y  0 (a ~5900 barns at  =1. 8Å). The magnetic data were collected using a superconducting quantum interference device magnetometer (MPMS, Quantum Design), while the heat capacity and resistivity measurements were carried out by means of a multi-purposes instrument (PPMS, Quantum Design).
The dose was tallied as dose-to-water, and data were normalized to the target dose (physical dose) to report on absorbed doses per target dose (mSv/Gy) or neutron dose equivalent per target dose (mSv/Gy), while also an estimate of the total organ dose was provided for a target dose of 50. 4 Gy(RBE). Out-of-field doses showed absorbed doses that were 5 to 6 orders of magnitude lower than the target dose. The discrepancy between TLD data and the corresponding scored values in the Monte Carlo calculations involving proton and gamma contributions was on average 18%. The comparison between the neutron equivalent doses between the Monte Carlo simulation and the measured neutron doses was on average 8%. Organ dose calculations revealed the highest dose for the thyroid, which was 120 mSv, while other organ doses ranged from 18 mSv in the lungs to 0.
Finally, data were expressed as thermal neutron dose equivalent data (mSv), which were normalized to the physical target dose (Gy) and expressed as [mSv/Gy]. The estimated uncertainty was 100%  (8). Uncertainties on the out-of-field absorbed dose were assessed considering dosimeter reproducibility (1. 8%), batch reproducibility (1. 9%), Co-60 calibration uncertainty (2. 4%) (9), and background uncertainties, which were dependent on the measured dose and reached up to 11% (coverage factor k = 1) for the farthest positions. For the energy response of MTS-7 detectors, the energy dependence for both photons  (10)  and protons  (11)  was considered. For the energy dependence of photons, a characteristic spectrum was used in the study of  (12). Photon energies ranged between 30 keV and 10 MeV. Assuming a flat energy dependence above 1 MeV (10, 13), the calculated uncertainty was below 1% (k = 1).
Bubble Detectors Bubble detectors for personal neutron dosimetry (BD-PNDs) (Bubble Technology Industries, BTI, Chalk River, ON, Canada) were used to measure neutrons of energies above 50 keV. These cylindrical detectors are 15 cm in length and 2 cm in diameter, but the sensitive part, where bubbles are created, is only 7 cm in length and 1. 6 cm in diameter (see Figure  1 ). BD-PNDs were calibrated with a Cf-252 source to obtain neutron dose equivalent by applying fluence-to-dose equivalent conversion factors derived from kerma factors k(E) and a quality factor as a function of neutron energy (Q(E)) for ICRU tissue, as described previously  (14). The final data were expressed in neutron dose equivalent per target dose (physical) [mSv/Gy]. Uncertainties of BD-PNDs are estimated to be on average 20% (k = 1).
Monte Carlo Framework The well-established Geant4  (15) (16) (17)  wrap-up MC code TOPAS v3. 6 (Geant4)  (18) , in conjunction with the Matlab (The Mathworks, Inc. , Natick, MA, USA)-based matRad v2. 10. 1  (19)  project to create a DICOM-based dose verification system, was used to simulate the out-of-field absorbed dose distribution. For this purpose, matRad was extended by including the possibility to process DICOM RTIon files. With this feature, it was then possible to create the TOPAS input files with the treatment room-specific radiation parameters employing matRad as the TOPAS syntax parser. The simulations for the determination of the neutron equivalent dose at a point and the proton and gamma out-of-field dose could then be conducted.
Beam Model To simulate the anthropomorphic phantom irradiation, it was necessary to run TOPAS simulations that reproduce the commissioned beam. Mean energy and spread have been adjusted to reproduce the measured depth dose curves following the methods of  (20, 21) , and (  22 ) in 5 MeV steps from the lowest energy available (that is, 100. 0 MeV) up to the highest energy available (that is, 226. 7 MeV). The commissioned beam data for the corresponding reference values were yielded by measurements with the plane parallel Bragg peak chamber (PTW, Freiburg, Germany)  (23). Simulated and measured depth doses agreed within ±0. 01 cm at R 80. In addition, the Fermi-Eyges parameters from the beam model implemented in RayStation were used to fully characterize the proton pencil beam. Furthermore, an MU/ion calibration was performed to determine the number of protons in TOPAS corresponding to the respective MUs.
For this purpose, reference fields, consisting of 1,681 spots with 0. 25-cm spacing arranged in a symmetrical square around the isocenter, were simulated with 5 × 10 5 protons per spot for the 27 different energies that make up the beam parameter database. The protons started 50 cm upstream of the isocenter at the nozzle exit of the treatment head. The Fermi-Eyges parameters were back-projected in vacuum to the nozzle exit  (24) (25) (26). The method of (  27 ) was applied to obtain the spot positions of the protons, taking into account the deflection of the protons at the two foci from the scanning magnets of the pencil beam dedicated nozzle. Downstream, a water tank with a volume of 50 × 50 × 50 cm 3 was created. The isocenter was at 3-cm depth of the water tank. Simulation conditions thereby correspond to the conditions for beam-monitor calibration at the WPE, generally following reference dosimetry according to TRS 398 and DIN 6801-1  (28, 29).
RT Integration in TOPAS The matRad code was extended to read DICOM RTIon files including both RT Plan and RT Struct. The RS used was considered in the matRad configuration files with the 4. 44-cmthick material lucite comprising a mean excitation energy of 74. 0 eV, a density of 1. 19 g/cm 3 , and material composition of 8. 05% H, 59. 98% C, and 31. 96% O, corresponding to the material definition as included in the PSTAR database given by the National Institute of Standards and Technology (NIST). Since RayStation specifies spot positions at the isocenter, these were back-calculated analogously to the procedure for MU/ion calibration. Based on the machine parameter file, the required beam data were linearly interpolated starting from the energies given by the RayStation RT Plan file for the individual energy layers.
0 × 7. 0 × 20. 0 m 3. The world material in these simulations was altered from vacuum to air to account for ionization occurring in the air relevant for thermal neutrons and gammas. The physical properties of air were modeled according to  (31). However, to ensure that the initial protons hit the upstream side of the RS as accurately as possible with respect to the Fermi-Eyges parameters, the initial proton transport started in a vacuum box with the lateral dimensions of the RS, and a longitudinal extension of 50 cm upstream of the isocenter to the upstream side of the RS. The additional energy loss due to air scattering downstream the RS, up to the surface of the anthropomorphic phantom, was accounted for via a slight increase in the mean energy of each spot. This increase was calculated by an interpolation employing the data provided in the PSTAR database for the continuous slowing down approximation in air based on a logarithmic cubic spline fit and the predicted track length of the protons.
MCP-6 and MCP-7 Thermoluminescent Detectors Thermal neutron dose was obtained by subtracting MCP-7 from MCP-6 detectors. The neutron dose equivalent per target dose ranged between 20. 3 and 0. 08 mSv/Gy for 7. 7 to 50 cm from the isocenter, respectively (see Figure  2 ). It should be noted that the uncertainty (k = 1) on the calibration factor is 100%, which is due to a number of different contributions as described in  (8). These data can also be expressed in gamma equivalent neutron doses (not plotted), as it is sometimes referred to by other groups to quantify the thermal neutron dose in terms of gamma dose equivalent. This easy approach is obtained by MCP-6 minus MPC-7 doses as calibrated in Co-60 dose in water (D W ). Hence, gamma equivalent neutron doses in this study ranged between 1846.
On the contrary, when neglecting the primary protons and only considering secondary protons, the gamma dose was on average 30% and 70% close to the field and far out-of-field, respectively. It should be noted that the uncertainty bars become wider the further the dose has been simulated out-of-field. This is related to the decreased particles in these regions hitting the small TLD volumes. To avoid biasing the data, no variance reduction techniques have been applied in the simulations. Neutron dose equivalent considering all neutrons and only thermal neutrons are shown in the right plot in Figure  4. Data demonstrate a neutron dose equivalent of 528 ± 41 mSv/Gy at a 7. 8-cm distance, which decreases to 11. 2 mSv/Gy at 50 cm from the isocenter. Clearly, the contribution from thermal neutrons to the total is very small and remained below 1%.
Nevertheless, previous data using BD-PNDs in PT have shown good agreement with H p  (10)  reference measurements  (39). Still, an underestimation was measured in some positions, which could be assigned to the use of PMMA slabs instead of the anthropomorphic materials containing tissue materials. The discrepancy is the largest, and up to 50% lower for measured data compared to simulated data, in the area where there is lung material. As the TOPAS model was based on the phantom's CT and did not model the PMMA slabs and the exact material densities during measurements, this could be the cause of the discrepancy. Although this could be considered a limitation of the study, the dose data, namely, tissue dose, as determined in the TOPAS simulations are highly relevant for translation to the clinic and epidemiology.
FUNDING The presented research has been funded by the  HARMONIC  project. The  HARMONIC  project  (Health effects of cArdiac fluoRoscopy and MOderN radIotherapy in  paediatriCs) has received funding from the  Euratom  research and training program  2014-2018  under grant agreement No  847707. MR acknowledges funding from the  Sistema Nacional de Investigacioń de Panama. IT-C acknowledges support from the  Spanish Ministry of Science and Innovation and State Research Agency  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S ) and support from the  Generalitat de Catalunya  through the  CERCA Program.
ACKNOWLEDGMENTS This article comprises results of the doctoral theses of O. Kaleja and B. Anđelić and is based on data obtained during online beam times in 2018 and offline measurements up to 2019 to study the systematic uncertainties. This experiment was carried out as part of FAIR-Phase-0 at the beam line Y7/SHIP at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany. We thank the GSI ion source and accelerator staff for providing intense ion beams. In addition, we thank D. Racano, H. -G. Burkhard, and J. Maurer (GSI) for their supportive engineering work. We acknowledge financial support by the German BMBF (Grants.