dataesr/scientific-paragraphs-categorization huggingface dataset:
A NA LY S I S For data analysis, we followed the methods, described in SW21. Both TESS and CHEOPS light curves were modelled using the qpower2 transit model, which applies a power-2 limb darkening law  (Maxted & Gill 2019  ). We use it as binary star model including primary and secondary eclipses which is implemented in the python software PYCHEOPS 3   (Maxted et al. 2021 ). The parameters of the binary star model are the orbital period P , the mid-time of the primary eclipse T 0 , the primary and secondary eclipse depths D and L, the impact parameter b , the parameters f c = √ e cos( ω) and f s = √ e sin( ω), which parametrize the eccentricity e and the longitude of periastron ω, the limb darkening parameters h 1 and h 2  (Maxted 2018 ) , and W, which becomes the width of the eclipse for e = 0 and is defined by the stellar radii, impact parameter, and the semimajor axis a (see  Maxted et al.
2021  for details). We used Gaussian priors for f c , f s. These priors were derived from RV measurements of the systems. Orbital parameters from RV measurements for EBLM J0239 -20, EBLM J0540 -17, and EBLM J0546 -18 have been published in  Triaud et al. ( 2017 ). Their eccentricities are reported to be consistent to zero, thus we set those priors to zero for all three systems. For EBLM J0719 + 25 and EBLM J2359 + 44, we used the binary star python code ellc  (Maxted 2016 ) , to model the RV from SOPHIE measurements as well as the two measurements from  Poleski et al. ( 2010 )  for EBLM J2359 + 44. We sampled the posterior probability distribution (PPD) of our model parameters f c , f s , and the semiamplitude K , using the Markov chain Monte Carlo (MCMC) code EMCEE (F oreman-Macke y et al.
CHEOPS light-cur v e analysis CHEOPS light curves were analysed in two steps. First, we analysed every visit separately to derive initial model parameters (see Table  1  for an o v erview of all visits). As described in detail in SW21 , instrumental effects like roll angle, contamination, and background level can be represented using linear correlation parameters or for roll angle φ, sin( φ) , cos( φ) , sin(2 φ), etc. , which were iteratively selected. 4 The PPD of all model and decorellation parameters were sampled simultaneously using EMCEE. We used the same Gaussian priors for f c , and f s as for the TESS data and since we obtained singleeclipse events, we fixed our transit model to accurately measured orbital period P , from the TESS light-curve fit. For secondary eclipses, we used priors on the parameters D , W , and b , as derived from the primary eclipse of each target.
Stellar parameters We used co-added high-resolution spectra to derive the stellar parameters of the primary components ( T eff and [Fe/H]). For EBLM J0540 -17, we used co-added CORALIE spectra, obtained by  Triaud et al. ( 2017 )  and available from the European Southern Observatory (ESO) science archive facility 5  and co-added SOPHIE spectra for EBLM J0719 + 25 and EBLM J2359 + 44. The stellar parameters for these three targets were derived using the equi v alent width method following the same methodology, model atmospheres, and line list as described in  Sousa ( 2014 )  and  Santos et al. ( 2013 ). In here, we applied the ARES code  (Sousa et al. 2015 ) , as well as the MOOG radiative  transfer code  (Sneden et al. 2012 ) , assuming ionization and excitation equilibrium of iron lines. For EBLM J0546 -18, we used co-added CORALIE spectra and applied a wavelet decomposition method where we compare the coefficients from a wavelet decomposition to those from a grid of model spectra.
Those model spectra were synthesized using the code SPECTRUM  (Gray & Corbally 1994 ) , MARCS model atmospheres  (Gustafsson et al. 2008 ) , as well as the atomic line list version 5 of the Gaia ESO surv e y  (Heiter et al. 2015 ). The method is detailed in Gill,  Maxted & Smalley ( 2018 )  and has been found to deliver robust measurements for ef fecti ve temperature and metallicity for spectra with relati vely lo w SNR (SNR 40). For EBLM J0239 -20, we used the SALT spectra and modelled the stellar fundamental parameters using the software SME 6  (Spectroscopy Made Easy;  Valenti & Piskuno v 1996 ; Piskuno v & Valenti 2017  ) that computes synthetic spectra with atomic and molecular line data from VALD 7  (Ryabchikova et al. 2015 ) which are compared to the observations. We chose the stellar atmosphere grid Atlas12  (Kurucz 2013  ) and modelled T eff , log g 1 , abundances and v sin i one parameter at a time.
We find that h 1 agrees on average well with differences of a few per cent, while we find larger discrepancies for h 2 in the order of several 10 per cent similarly in the CHEOPS and TESS data sets. This finding, as well as the derived uncertainties follow the trend from  Maxted ( 2018 ; fig 4) , for h 2 to be about one order of magnitude less constrained than h 1. We find some cases of larger uncertainties in CHEOPS light-curve fits. In the case of EBLM J0546 -18 we derived about 31 per cent uncertainty for h 1 and the derived parameter differs more than 70 per cent from the expectations. This is not surprising, given the large impact parameter which does not allow constraining the limb darkening parameters for this star. We have repeated the CHEOPS and TESS fits for these four targets, using the expected limb darkening parameters as priors, but found that introducing these priors will neither impro v e the fit, nor has it any significant impact on the derived M-dwarf parameters.
These are KIC 1571511B  (Ofir et al. 2012 )  as well as SAO 106989 and HD 24465  (Chaturvedi et al. 2018 ). Populating the low-mass main sequence with M-dwarfs having precise ef fecti ve temperature measurements will help us to constrain possible trends for low-mass dwarfs. This is one of the main goals of our CHEOPS programme. Magnetic activity of the primary star, like spot crossing is not accounted for in our eclipse model, thus, can affect the size determinations of the M-dwarfs. We used the TESS light curves to search for variability linked to magnetic activity, like rotational pattern and flares. No flares have been found in the TESS data set. EBLM J0239 -20 shows a variable modulation of 2-3 per cent close to the orbital period, most probably linked to stellar activity aligned with the rotational period of the G-dwarf.
This research is also supported from the  European Research Council's (ERC)  the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no  803193/BEBOP ). PM acknowledges support from  STFC  research grant number  ST/M001040/1. MIS acknowledges support from  STFC  grant number  ST/T506175/1. SGS acknowledges support from  FCT  through  FCT  contract no. CEECIND/00826/2018  and  POPH/FSE  (EC). SH gratefully acknowledges CNES funding through the grant  837319. YA and MJH acknowledge the support of the  Swiss National Fund  under grant  200020 172746. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund and  MDM-2017-0737 ,  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiolog ía (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
This w ork w as supported by FCT -Funda c˜ ao para a Ci encia e a Tecnologia through national funds and by  FEDER  through  COM-PETE2020 -Programa Operacional Competitividade e Internacionalizac  ˜ ao by these grants:  UID/FIS/04434/2019 ,  UIDB/04434/2020 ,  UIDP/04434/2020 ,  PTDC/FIS-AST/32113/2017 ,  POCI-01-0145-FEDER-032113 ,  PTDC/FIS-AST/28953/2017 ,  POCI-01-0145-FEDER-028953 ,  PTDC/FIS-AST/28987/2017 , and  POCI-01-0145-FEDER-028987. ODSD is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. B-OD acknowledges support from the  Swiss National Science Foundation  ( PP00P2-190080 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES  ; grant agreement No.
724427 ). It has also been carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation  (SNSF). DE acknowledges financial support from the  Swiss National Science Foundation  for project 200021 200726. MF and CMP gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ). DG gratefully acknowledges financial support from the  CRT Foundation  under Grant No. 2018. 2323'Gaseous or rock y? Unv eiling the nature of small worlds'. MG is an F. R. S. - FNRS Senior Research Associate. KGI is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme.
This w ork w as granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. ML acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2 194576. For Italy, CHEOPS activities have been supported by the  Italian Space Agency , under the programs: ASI-INAF n. 2013-016-R. 0  and  ASI-INAF n. 2019-29-HH. 0. This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ). IRI acknowledges support from the  Spanish Ministry of Science and Innovation and the European Re gional Dev elopment Fund  through grant  PGC2018-098153-B-C33 , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
Additional information on the effect of the anthracycline drugs on the packing and the 2D organization of cholesterol monolayers was obtained by grazing incidence X-ray diffraction. The measurements were performed at 30 mN/m. Only one diffraction signal located at Q xy = 1. 09 Å -1 is observed on the diffraction spectrum (Fig. 2 ). The corresponding Bragg rod (Fig. S3 ) shows the maximum intensity of the Bragg peak localized at the horizon at Q z = 0 Å -1  [49, 50]. Therefore, cholesterol forms monolayers with molecules arranged perpendicular to the air-water surface in the hexagonal lattice. The parameters characterizing the crystal lattice obtained for cholesterol monolayers on PBS buffer (Table  3 ) stay in excellent agreement with the literature data  [50, 51]. On pure water, the correlation length L xy , the distance on which we are able to predict the position of a molecule by applying the lattice translation, is estimated from the Full Width at Half Maximum (FWHM) of the peak (see Supporting Info) to be 22 Å.
Acknowledgments This work was financially supported by the  Polish National Science Centre  (Project No. 2016/23/D/ST4/03200 ). The study was partly carried out at the  Biological and Chemical Research Centre , the  University of Warsaw , established within the project co-financed by  European Union  from the  European Regional Development Fund  under the  Operational Program Innovative Economy , 2007-2013. We gratefully acknowledge SOLEIL for provision of synchrotron radiation facilities and support within  CALYPSO program.
Acknowledgements This publication received funding from the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" programme ,  AMX-18-ACE-005  and from the  French National Research Agency  under the contract  ANR-20-CE31-0016. Center de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high performance computing resources.
ACKNOWLEDGMENTS Computation time at CINECA supercomputing centers is gratefully acknowledged. The X-ray absorption measurements were performed on the EPFL/PSI X-Treme beamline at the  Swiss Light Source ,  Paul Scherrer Institut, Villigen, Switzerland , at the BOREAS beamline of the  Alba Synchrotron Radiation Facility, Cerdanyola del Vallés, Spain , and at the ID32 beamline of the  European Synchrotron Radiation Facility, Grenoble, France. We acknowldege funding from the  National Research Council (CNR)  within the  CNR/CAS Cooperative Programme project  " Advanced characterization methods for the study of rare-earth single-ion magnets on oxide substrates ", from the  Czech Academy of Sciences (Mobility Plus  Project No. CNR-19-03 ), and from the  Swiss National Science Foundation  ( 200020_176932  and  200021_175941 ).
ICN2 was funded by the  CERCA Programme/Generalitat de Catalunya  and supported by the  Spanish Ministry of Economy and Competitiveness, MINECO  (grant nos. SEV-2017-0706  a n d  P I D 2 0 1 9 -1 0 7 3 3 8 R B -C 6 5 / A E I / 1 0. 1 3 0 3 9 / 501100011033 ). IMDEA Nanociencia acknowledges support from the  Severo Ochoa Programme for Centres of Excellence in R&D (MINECO , grant  SEV-2016-0686 ). We acknowledge  P. Orgiani  ( CNR-SPIN ) for the loan of some of the Nb-doped STO single crystals used in these studies and  M. Retegan  ( ESRF ) for assistance with the use of Crispy and Quanty.
With Σ SFR ∼ 4200 M e yr -1 kpc -2 , SPT 0346-52 (z = 5. 7) is the most intensely star-forming galaxy discovered by the South Pole Telescope. In this paper, we expand on previous spatially resolved studies, using ALMA observations of dust continuum, [N II] 205 μm, [C II] 158 μm, [O I] 146 μm, and undetected [N II] 122 μm and [O I] 63 μm emission to study the multiphase interstellar medium (ISM) in SPT 0346-52. We use pixelated, visibility-based lens modeling to reconstruct the source-plane emission. We also model the source-plane emission using the photoionization code CLOUDY and find a supersolar metallicity system. We calculate T dust = 48. 3 K and λ peak = 80 μm and see line deficits in all five lines. The ionized gas is less dense than comparable galaxies, with n e < 32 cm -3 , while ∼20% of the [C II] 158 μm emission originates from the ionized phase of the ISM.
ALMA Observations From 2014 September through 2018 September, SPT 0346-52 was observed in ALMA  Bands 6, 7, and 9 (Project IDs 2013. 1. 01231, 2015. 1. 01580, and 2016. 1. 01565; PI: Marrone). The [C II] 158 μm, [N II] 122 μm, and [O I] 63 μm lines were all observed on multiple dates at different resolutions, while [N II] 205 μm and [O I] 146 μm were each observed once. The details of these observations, including dates, observing frequencies, flux and phase calibrators, and resolutions, are listed in Table  1. The data were processed using various pipeline versions of the Common Astronomy Software Applications package (CASA;  McMullin et al. 2007; Petry & CASA Development Team 2012). The [C II] 158 μm, [N II] 205 μm, and [O I] 146 μm were all processed using CASA pipeline version 4. 2. 2, [N II] 122 μm was processed with CASA pipeline version 4.
CLOUDY Modeling In order to understand the physical conditions that explain our observations, we turn to the photoionization code CLOUDY  (version 17. 01; Ferland et al. 2017). CLOUDY simulates the microphysics within a cloud of gas and dust that is heated by a central source. It predicts the physical conditions throughout the cloud, including temperatures, densities, and metallicities, while also computing a predicted observed spectrum.
In order to simplify the model, we do not include a magnetic field component. Following  Cormier et al. (2019) , we assume a constant microturbulent velocity of 1. 5 km s -1. This is consistent with the microturbulent velocities of individual PDRs  (Tielens & Hollenbach 1985; Kaufman et al. 1999 ). The gas cloud is heated by a single-burst stellar population. The starburst spectral energy distribution (SED) was compiled by  Byler et al. (2017)  for use in CLOUDY using the Flexible Stellar Population Synthesis code  (Conroy et al. 2009; Conroy & Gunn 2010). We use the ionizing spectrum from  Byler et al. (2017)  produced by the MESA Isochrones and Stellar Tracks (MIST;  Choi et al. 2016; Dotter 2016). The MIST stellar evolution tracks differ from other models in that they include stellar rotation, which results in harder ionizing spectra and higher luminosities  (Byler et al.
Summary and Conclusions In this work, we present ALMA Bands 6, 7, and 9 observations of [N II] 205 μm, [C II] 158 μm, [O I] 146 μm, and undetected [N II] 122 μm and [O I] 63 μm, as well as the underlying continuum at all five wavelengths, in the z = 5. 7 lensed DSFG SPT 0346-52. We reconstruct the lensed continuum and line data using the pixelated, interferometric lens modeling code RIPPLES in order to study the source-plane structure of SPT 0346-52. We analyze both the galaxyintegrated properties and the spatially resolved properties of SPT 0346-52. We use the photoionization code CLOUDY to model the physical conditions of the ISM in SPT 0346-52. It has lower ionization parameter ( U log 2. 75 ~- ) and hydrogen density ( n log cm 2 H 3 -) than other high-z DSFGs. Based on CLOUDY modeling, we find supersolar metallicity ( Z Z log =  0.
We thank the anonymous referee for the insightful and thorough comments. We also thank  Chris Marslender  for his computational support. The SPT is supported by the  US National Science Foundation (NSF)  through grant  OPP-1852617. K. C. L. , D. P. M. , J. D. V. , K. P. , and S. J. acknowledge support from the  US NSF  under grants  AST-1715213  and  AST-1716127. K. C. L. and S. J. acknowledge support from the  US NSF NRAO  under grants  SOSPA4-007  and  SOSPA5-001 , respectively. J. D. V. acknowledges support from an A. P. Sloan Foundation Fellowship. M. A. acknowledges partial support from  FONDECYT  grant  1211951 ,  ANID+PCI+INSTITUTO MAX PLANCK DE ASTRONOMIA MPG 190030 ,  ANID +PCI+REDES 190194 , and  ANID   BASAL  project  FB210003. N. S. is a member of the  International Max Planck Research School (IMPRS) for Astronomy and Astrophysics at the Universities of Bonn and Cologne.
This research has made use of  NASA 's Astrophysics Data System. This research also uses the Cosmology Calculator by Wright (2006). Cloudy has been supported by  NSF  ( 1816537 ),  NASA  ( ATP 17-ATP17-0141 ), and  STScI  ( HST-AR-15018 ). Software: CASA (v5. 4. 0;McMullin et al. 2007;  Petry & CASA Development Team 2012 ), ripples (Hezaveh et al. 2016), visilens (https://github. com/jspilker/visilens;Hezaveh et al. 2013; Spilker et al. 2016), Cloudy (v17. 01;Ferland et al. 2017), FSPS (Conroy et al. 2009;  Conroy & Gunn   2010 ), MIST (Choi et al. 2016; Dotter 2016).
Background: In 2015, the Global Task Force on Radiotherapy for Cancer Control (GTFRCC) called for 80% of National Cancer Control Plans (NCCP) to include radiotherapy by 2020. As part of the ongoing ESTRO Global Impact of Radiotherapy in Oncology (GIRO) project, we assessed whether inclusion of radiotherapy in NCCPs correlates with radiotherapy machine availability, national income, and geographic region. Methods: A previously validated checklist was used to determine whether radiotherapy was included in each country's NCCP. We applied the CCORE optimal radiotherapy utilisation model to the GLOBOCAN 2020 data to estimate the demand for radiotherapy and compared this to the International Atomic Energy Agency (IAEA) Directory of Radiotherapy Centres (DIRAC) supply data, stratifying by income level and world region.
World regions were defined according to the IAEA. Findings: Complete data (including GLOBOCAN 2020, DIRAC and NCCP) was available for 143 countries. Over half (55%, n = 79) included a radiotherapy-specific checklist item within the plan. Countries which included radiotherapy services planning in their NCCP had a higher median number of machines (1. 68 vs 0. 75 machines/1000 patients needing radiotherapy, p < 0. 001). There was significant regional and income-level heterogeneity in the inclusion of radiotherapy-related items in NCCPs. Low-income and Asia-Pacific countries were least likely to include radiation oncology services planning in their NCCP (p = 0. 06 and p = 0. 003, respectively). Few countries in the Asia-Pacific (18. 6%) had a plan to develop or maintain radiation services, compared to 57% of countries in Europe.
Radiotherapy demand and availability Based on the most recent GLOBOCAN estimates from the International Agency for Research on Cancer (IARC)  [1] , we extracted the number of new cancer cases in 2020, stratified by cancer type for 185 countries and territories. One region (Micronesia) was included in the country data, as an NCCP for this small group of island nations was available. Guam, the only country within Micronesia for which country-level data was available, was therefore excluded. We used previously published estimates of optimal radiotherapy utilisation and machine needs  [14] , estimated as 409 new patients/machine/year for low income (LIC), lower-middle income (L-MIC), upper-middle income (U-MIC) and 288 for high income countries (HIC). We then estimated the number of patients requiring radiotherapy in 2020 by tumour type for each country, and the optimal number of machines (Cobalt and MV Linac) needed to meet these demands (calculated as [Number of patients with indication for radiotherapy per year / 409] for LMICs and [Number of patients with indication for radiotherapy per year / 288] for high income countries).
The estimated demand for radiotherapy machines was then compared with the current number of available radiotherapy centres and radiotherapy machines (Cobalt and MV Linac) per country, as sourced from DIRAC in February 2020  [15]. For any country where DIRAC data was not provided, we assumed machine availability was zero. Finally, for each member state, we calculated the number of available radiotherapy machines per 1000 patients needing radiotherapy as [(Number of.
Statistical analysis We correlated responses to the 14 NCCP checklist items with radiotherapy machine availability per 1000 patients needing radiotherapy. We also examined whether countries had included any of the five radiotherapy specific items in the NCCP and correlated this with radiotherapy machine availability per 1000 patients needing radiotherapy. We then examined the responses to the 14 checklist items, stratified by country income level and geographic location. Categorical data were compared using a Chi squared test. Continuous data with 2 groups were compared using a Student's t-test for parametric data and using Mann-Whitney U for non-parametric data. Where indicated, log transformation was used to normalise the data prior to statistical comparisons, using half-integer correction for zero-inflated data.
Results Complete GLOBOCAN, DIRAC and NCCP data was available for 143 countries. Countries with incomplete data across any of these three databases were excluded, as indicated in Supplemental Table  1. Among the 143 included countries, 55. 2% (n = 79) included information for at least one of the five radiotherapy related questions. The number of radiotherapy related items included in the NCCP for each individual country is illustrated in Fig. 1. Based on the available data, over 2. 5 billion people reside in countries with either no NCCP or where radiotherapy is not mentioned within the existing NCCP. Countries in which the NCCP acknowledged device/machinery needs and maintenance had a higher median number of machines per 1000 patients with an indication for radiotherapy in 2020 (1. 48 vs 1.