dataesr/scientific-paragraphs-categorization huggingface dataset:
4 1. 4×10 5 3. 7×10 3 HN 13 C(1-0) 4. 2 9. 6×10 4 - N 2 H + (1-0) 4. 5 6. 1×10 4 1. 0×10 4 Notes. ( a) E u /k are from the Cologne Database for Molecular Spectroscopy (CDMS,  Müller et al. 2001 Müller et al. , 2005;; Endres et al. 2016 ). ( b) n crit are from  Shirley (2015)  assuming T k =10 K, except for 13 CO(1-0) and HN 13 C(1-0), which were calculated following the method described by  Shirley (2015). We also assume HN 13 C(1-0) shares the same collision rate coefficients as HNC. The relevant data used for the calculation are from  Yang et al. (2010)  and  Dumouchel et al. (2010) , and the Python script can be found at https://github. com/ZhiyuZhang/ critical_densities. ( c) n eff assuming T k =10 K are from  Shirley (2015). tor, and the results are listed in Table  2. Comparing to the 13 CO emission, these typical dense gas tracers cover between 6% and 28% of the area of the 13 CO emission, depending on the tracer (Table  2 ).
As shown in Fig. 7  where the power-law indices are plotted as a function of the virial parameters, the virial parameters of all regions and tracers are around or smaller than two. There is no clear correlation between the virial parameters and the power-law slope (Fig. 7 ). Among all four molecular Notes. ( a) Five σ threshold for the N-PDFs and the thick white contours in Fig. 4. (b) The mean column density above 5σ and used to normalize the column density for the N-PDFs. ( c) The optimal column density determined by the program Powerlaw. N 2 H + Fig. 5. Probability density functions of the 13 CO, HCO + , HCN, HNC, and N 2 H + column density for the whole filament. The dashed vertical lines in each panel mark the column density threshold (5σ level), and the solid vertical lines mark the mean column densities.
Acknowledgements. Y. W. , H. B. , S. B. , and J. D. S. acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Consolidator Grant   CSF-648505. H. B. also acknowledges support from the  Deutsche Forschungsgemeinschaft in the Collaborative Research Center  ( SFB 881 ) "The  Milky Way System " (subproject  B1 ). F. B. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 726384/"EM-PIRE" ). N. S. acknowledges support from the  ANR/France  and  DFG/Germany  project ' GENESIS'  ( ANR-16-CE92-0035-01/DFG1591/2-1 ) and from the  DFG  project  SFB 956. C. B. gratefully acknowledges support from the  National Science Foundation  under Award No.
) 25 The initial process started with determining the computational domain and boundary condition for the current cases. Meshing procedure was performed using ANSYS ICEM CFD software to select the best meshing for the simulation process. In the simulation setup using FLUENT software, there are several crucial parameters need to be determined: a) time step size, b) mesh convergence study, and c) simulation with different turbulence model. Based on these parameters, the best meshing and turbulence model were selected to continue for different incidence angles  [11] [12]. Fig. 3  illustrates the flow methodology used for the present study.
, ∆ 2 S k-1 , ∆S k ) T ∈ R k. Finally, once β (k) is computed, the coefficients γ k = S 0 + k-1 ∑ j=0 θ j Q j As a numerical test, we consider the linear system of tensor equations given by (31) where A is a random tensor N = 3, I 1 = I 2 = 20, I 3 = 10, J 1 = 20, J 2 = 10 and J 3 = 5 as in  [19]. The exact solution is the tensor X whose elements are all one and the right hand side B is given by B = A * N X. The computations are carried out using MATLAB 7. 4 with machine epsilon about 2 • 10 -16. We took m = 10, p = 1 and stopped the iteration when the relative error norm of the residual was less than 10 -6. Then after k = 6 iterations, Algorithm 4 for the tensor RRE method, gives the relative residual norm R k R 0 = 8. 5 × 10 -7 where the initial guess is X 0 = O. We notice that the convergence of the original sequence (S n ) is very slow and at n = 200, we get a relative residual R(S n )/R 0 of 10 -1.
WASP Photometry WASP-186 was observed by SuperWASP beginning in 2006 and ending in 2014 with a total of 81,014 30-second exposures. The star was originally flagged for further review in July 2016 after inspection of the lightcurve. WASP-187 was observed from 2004 until 2011 with a total of 15,278 observations. In 2014, the target was flagged for further observations. The ORCA-TAMTFA transit-search run, which includes multiseason data on 480 fields in the region around the celestial equator that was observed by both SuperWASP and WASP-South, 716 fields from SuperWASP only and 553 from WASP-South only, is the largest and most homogeneous dataset available for WASP. As such, the photometry from this run was used as the basis for analysis by the machine learning method described in  Schanche et al.
TESS photometry In October and November 2019, both candidates were observed in Sector 17 of the TESS mission and given the designations as TESS Objects of Interest (TOI)-1494. 01 and TOI-1493. 01 with four transits observed for each target. However, a momentum dump at the  beginning of the observation run coincided with the first transit of WASP-186 leading the affected transit to appear deeper. We therefore remove the first transit from further analysis. Although the event did not disrupt a transit for WASP-187, we remove the data for this time frame to remove any impact on the out-of-transit measurement. Additionally, the data surrounding spacecraft perigee was removed for both lightcurves. x The Full Frame Images (FFIs) have a cadence of 30 minutes, with each image comprising a stack of 2 second exposures over that time frame.
√  cos  and √  sin ; the fit to the orbit of WASP-187 was, however, found to be consistent with an eccentricity of 0. Therefore the final MCMC held these values constant. Finally, the transit depth of WASP-187 was underestimated when WASP data were included in the fit. There are no contaminating stars within 114 arcsec. The most likely explanation is a slight dilution of the signal from the detrending of the WASP data. To account for the dilution, we fit an additional positive, constant flux offset to the WASP fluxes for this star, thereby allowing the TESS data to dominate the depth determination while retaining the timing information provided by WASP. Initial fits for the depth, width, impact parameter, period, and epoch for the photometric datasets were done using the Transit Model in the PyCHEOPS v0.
ACKNOWLEDGEMENTS NS acknowledges the support of  NPRP  grant # X-019-1-006  from the  Qatar National Research Fund  (a member of  Qatar Foundation ). ACC acknowledges support from the  Science and Technology Facilities Council (STFC)  consolidated grant number  ST/R000824/1  and  UKSA  grant  ST/R003203/1. CAH and UCK are supported by  STFC  under Consolidated Grant  ST/T000295/1. DLP, RGW and PJW have been supported by  STFC  consolidated grants  ST/P000495/1  and  ST/T000406/1. This work was supported by the  Swiss National Science Foundation (SNSF). This paper includes data collected by the TESS mission. Funding for the TESS mission is provided by the  NASA   Explorer Program. This research made use of Lightkurve, a Python package for Kepler and TESS data analysis (Lightkurve Collaboration, 2018).
Data collection In view of study design and available resources, a convenience sample was used, and the overall number of participants was limited to those who could attend on that day. A topic guide was developed prior to the study and reviewed by the research team (ESM Appendix 1). It consisted of questions around the main topics of exploring attitudes towards frailty, perceptions of PHC professionals' training needs regarding the identification and management of frailty, and what are the main features of a training programme, including learning objectives, format, and frequency. Two focus groups with PHC professionals were conducted in parallel and each focus group was co-facilitated by two researchers, with an additional researcher who kept field notes. Focus groups were audio recorded with consent, transcribed verbatim and identifiable data was anonymised.
Data analysis Transcripts were read by all members of the research team. Thematic analysis  [21]  was used to identify key emergent themes and their meaning. The analysis team (all authors) identified a preliminary thematic framework. A coding framework was developed, agreed and applied to all transcripts. NVivo software (version 11, QSR International) was used to facilitate data management. The coding framework was applied to the transcripts and refined iteratively. The overall interpretation of meaning and implications for practice were considered by the research team, who brought expertise in general practice, geriatrics, frailty, medical education and qualitative research methods. Selected illustrative quotes (translated from Greek to English) are presented.
Acknowledgements We would like to thank the health professionals who kindly gave up their time to take part in this study and all the members of the AUTH. PHC. RN for their help and guidance throughout this study. The  Aristotle University of Thessaloniki Primary Health Care Research Network  (AUTH. PHC. RN. ) was founded in October 2017 in Thessaloniki, in Greece. Our primary aims are to promote research and improve the quality of care in Primary Health Care and General Medicine through the collaboration of primary care structures with the  Aristotle University of Thessaloniki. Coordinators:  Gavana M. , Haidich  A. B. , Smyrnakis E. Members:  Andreou M. ,  Avgerinou Ch. ,  Birtsou Ch. , Rev. Dandoulakis M. ,  Kokkali St. , Kanoutas V. ,  Kotsani M. ,  Manolaki Ch. , Moraiti E. , Papageorgiou D.
Patient selection, target definition and dose prescription A retrospective treatment planning study was performed on a cohort of 20 patients; 10 cases were selected with positive lymph nodes (N+) and 10 without positive nodes (N-) from the institutional trial database. Target delineation was performed according to the definitions and guidelines of the ongoing international clinical trial for gynaecological IG-IMRT  [21]. In summary, target delineation was performed after fusing contrast-enhanced computed tomography (CT) images with a resolution of 1. 2 × 1. 2 × 2. 5 mm 3 and T2 weighted magnetic resonance images (MRI, resolution of 5x5x3 mm 3 ) registered to those images. The gross tumour volume (GTV) was delineated as the intermediate signal intensity region in the cervix and vagina on T2 MRI.
Plans for more extended targets could be optimised with more manual procedures (requiring the use of sequential optimisations). Another potential limitation in the study derives from the fact that dose calculations were performed with 2. 5 mm resolution for Halcyon and 5 mm in the x-y plane for Helical Tomotherapy. Although this might be a potential source of inconsistency in the comparison of the two datasets, we appraised its relevance on a subset of 5 patients recalculating the dose distributions in Halcyon with a 5 mm resolution. It resulted that the median percentage difference, computed over all the metrics used in the study, was 0. 5% (the mean was 0. 7%) ranging from 0. 0 to 4. 1%. The reason for this derives from the large volumes involved in the study for both targets and OARs, the fact that the voxel size in the HT calculations was < 0.
Materials and measurements A questionnaire was developed from the literature review regarding the behaviors of antibiotic use and laboratory testing on antibiotic resistance. The questionnaire consisted of seven parts. In part one, 6 questions were used to collect sociodemographic characteristics, such as sex, age, highest education level, and marital status. In part two, 9 questions were used to collect data on the experiences of antibiotic use within the past 12 months, such as the source of antibiotic assessment, frequency, reason for taking antibiotics, and source of antibiotic knowledge. In part three, 10 questions were used to collect knowledge on infectious diseases. Those who scored 60. 0% and over were de ned as having good knowledge. Those who had scores of 59. 0% and lower were de ned as having poor knowledge.
Statistical analysis Data were double-entered into Excel spreadsheets and were checked for any errors before being transferred into SPSS version 24 (SPSS, Chicago, IL) for further analysis. Descriptive statistics were used to describe the participants' characteristics in both categorical and continuous data. Chi-square tests and logistic regressions were used to identify the factors associated with antibiotic resistance among the participants at the signi cance level of α = 0. 05. In the logistic regression steps, the Hosmer-Lemeshow chi-square test was used to assess the overall model prediction before and after the variables were included into the model. The tness of the model was determined by the Cox-Snell R 2 and Nagelkerke R 2 before the results were interpreted.
The FTIR system was absolutely calibrated by means of a continuous Langley-Plot analysis over the 1-year period. An important temporal drift of the calibration constant was observed as a result of the environmental exposure of the FTIR's external optical mirrors (linear degradation rate up to 1. 75% month -1 ). The cross-validation of AERONET-FTIR databases documents an excellent agreement between both AOD products, with mean AOD differences below 0. 004 and root-mean-squared errors below 0. 006. A rather similar agreement was also found between AERONET and FTIR convolved bands, corroborating the suitability of low-resolution sunphotometers to retrieve high-quality AOD data in the NIR and SWIR domains. In addition, these results demonstrate that the methodology developed here is suitable to be applied to other FTIR spectrometers, such as portable and low-resolution FTIR instruments with a potentially higher spatial coverage.
The spectral AOD dependence for the seven FTIR micro-windows have been also examined, observing a spectrally flat AOD behaviour for mineral dust particles (the typical atmospheric aerosols presented at IZO). A mean AE value of 0. 53 ± 0. 08 for pure mineral dust in the 1020-2314 nm spectral range was retrieved in this paper. A subsequent cross-validation with the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package has ensured the reliability of the FTIR dataset, with AE values between 0. 36 to 0. 60 for a typical mineral dust content at IZO of 100 cm -3 and water-soluble particle (WASO) content ranging from 600 to 6000 cm -3. The new database generated in this study is believed to be the first long-term time series (1-year) of aerosol properties generated consistently in the NIR and SWIR ranges from ground-based FTIR spectrometry.
Aerosol models provide a general description of aerosol properties using precalculated optical and microphysical properties of particles. Optical Properties of Aerosols and Clouds (OPAC)  [14]  and the Global Aerosol Database (GDAS)  [15]  are two of the most widely used models in the literature, both applying aerosol chemistry data sets to derive aerosol radiative properties. Recently, the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package has been developed by  [16] , improving the existing aerosol shape characterisation through spheroids and a small set of irregularly shaped dust particles. All these models provide reference information to estimate the aerosol radiative effect, but a few direct observations in the long-wave spectral range still exist for their validation.
The joint information on AOD and AE allows us to discriminate aerosols with similar solar extinction features but with different sizes  [47, [51] [52] [53]. This pioneering study has been developed at the subtropical high-mountain Izaña Observatory (IZO), where high-resolution FTIR measurements have been coincidentally carried out with standard sun photometry in the framework of the AERONET network during one year (May 2019-May 2020). The co-located AERONET-FTIR database will allow the new FTIR products to be comprehensively validated in the common spectral range covering the NIR and the SWIR. The spectral dependence of FTIR aerosol products will be further assessed by comparison to MOPSMAP model estimations. To do so, this paper is structured as follows. The test site is presented in Section 2, while the description of the FTIR technique and the measurements used in this study are given in Section 3.
Section 4 describes the ancillary data considered to assess the quality and consistency of the new FTIR IR aerosol products: CE318-AERONET database and MOPSMAP package. Section 5 gives a detailed description of the methodology to select the FTIR micro-windows for the spectral AOD calculation (Section 5. 1), the approach used for AOD (Section 5. 2) and AE estimation (Section 5. 3), the quality control implemented on the FTIR observations (Section 5. 4), and the main limitations for FTIR AOD retrievals (Section 5. 5). The results are tackled in Section 6, in terms of the FTIR absolute calibration (Section 6. 1), AOD (Section 6. 2), and AE (Section 6. 3). Finally, a summary and conclusions of the present work are provided in Section 7.
The IZO monitoring activities routinely participate in multiple international networks and databases, highlighting its role as an absolute calibration site for two of the most important ground-based photometer aerosol networks: NASA-AERONET and GAW precision-filter radiometers (GAW-PFR). In this sense, IZO is commonly used for optimal application of the Langley-Plot technique to a large number of activities, such as absolute direct sun/moon photometric calibration  [30, 56, 58, 59]  or the retrieval of reference top-of-atmosphere (TOA) solar spectrum from the ground at different spectral ranges  [60, 61]. Refer to  [55]  for more details about IZO and its atmospheric monitoring programmes. Another important feature of IZO is related with its proximity to the African continent (∼300 km), where the Earth's largest sources of mineral dust are located  [62, 63].
Level 1. 5 version 3 AOD and AE products from the AERONET database has been used in this study. This data selection ensures cloud-screened and quality-controlled AOD data. AOD in the NIR and SWIR (at 870, 1020, and 1640 nm) will be also used to retrieve the AE parameter in the IR and, therefore, to compare these values to those AE extracted from FTIR observations.
MOPSMAP The MOPSMAP package has been developed in  [16]  as a dataset of precalculated optical properties of single aerosol particles to retrieve the full set of optical properties of user-defined aerosol ensembles under a high variety of atmospheric conditions. Different aerosol components can be selected (e. g. , mineral nucleation/accumulation/coarse modes or water-soluble) as well as up to 10 aerosol types (e. g. , continental clean, urban, or maritime polluted), consisting of a combination of aerosol components. The main advantage of this package in comparison to the previous OPAC versions is that MOPSMAP uses a user-friendly web interface and a freely available FORTRANcode and includes more aerosol features such as spheroids and a small set of irregularly shaped dust particles. This new model is very efficient in term of computation time and is accessible via an interactive web interface (https://mopsmap.
g. ,  [35] [36] [37] 40] ). Only the humidity vertical profiles have been varied among simulations for the example measurement of Figure  1 , considering the actual column integrated PWV value of 2. 5 mm retrieved from TCCON records and two high PWV values, 15 mm and 25 mm, which could be typical values at higher latitudes. It should be noted that these two high PWV amounts are extreme values at IZO given the predominantly dry atmospheric conditions of the observatory. Considering the TCCON FTIR PWV time series in the 2007-2019 period, the annual average of PWV is only 4. 8 mm (standard deviation, σ, of 2. 8 mm), with maximum PWV amounts reached in summer (mean and σ of 7. 1 and 3. 3 mm, respectively)  [77]. All remaining inputs for the simulations have been kept identical. The vertical profiles of atmospheric gaseous concentrations are taken from the TCCON database, which are daily generated at each TCCON site by a set of empirical functions that are optimized to fit different input observations (in situ measurements from balloon-borne platforms, aircraft, and space-based data)  [39].
Spectral Two of the seven FTIR selected spectral bands are near-coincident with the CE318-AERONET channels (highlighted in bold in Table  1 ) and with those in the space-based sensor OLI (1609 and 2200 nm), VIIRS (1240, 1378, 1610, and 2257 nm), or MODIS (1240, 1630, and 2130 nm). Therefore, this new FTIR AOD database offers us a valuable tool to perform cross-validations with standard AOD products, such as AERONET or space-based platforms.
FTIR Cloud Mask and Quality-Control Procedure A cloud mask and a quality-control procedure have been developed to remove cloud-contaminated data and/or instrumental issues from the FTIR dataset. It is composed of three different flags: 1. Cloud mask: 1-min measurements of co-located global and diffuse short-wave downward radiation (SDR) have been used to screen clouds following  [94]. This cloud-screening method is based on the approach suggested by  [95] , with four individual tests applied to the normalized global and maximum diffuse SDRs in addition to the temporal change in global SDR and the normalized diffuse variability in SDR.
FTIR AOD Retrievals FTIR AOD has been derived in a total of 156 days (25,590 instantaneous AOD values for the seven spectral bands) within the 1-year period using Equation  (2). The cloud screening and quality-control procedure described in Section 5. 4 has been applied to this dataset, therefore obtaining a final FTIR AOD database quality-controlled with 24,293 instantaneous AOD values for the seven spectral bands. A smooth temporal evolution between the calibration dates has been adopted to calculate AOD in the whole period, as explained in the previous section. The time series of the daily-mean spectral AOD for the seven FTIR bands is displayed in Figure  5. A gradual decrease in AOD is observed at longer FTIR wavelengths in those conditions with medium and high aerosol loading. This spectral behaviour, determined by the combined effect of the aerosol size distribution, chemical composition, mixing state, and shape of the aerosols present, follows the expected λ-AOD dependence described by the Ångström power law in Equation (  5 ).
074, N = 21317). Figure 6. Spectral dependence of the FTIR AOD calculated at IZO for mineral dust conditions (AOD AE,1640 >= 0. 074, N = 2976) and for pristine conditions (AOD AE,1640 < 0. 074, N = 21,317): lower and upper boundaries for each box are the 25th and 75th percentiles, the solid line is the median value, and hyphens are the maximum and minimum values. The regression fit to a power law is also included for each AOD range. The AOD temporal evolution of near-coincident FTIR B4 and AERONET AOD at 1640 nm from May 2019 to May 2020 is shown in Figure  7 , where the good agreement between the two databases becomes evident. The evolution of the two products during a sequence of 11 days in July 2019 is also displayed in the zoom. Two dust outbreaks at IZO (AOD AE,1640 up to 0. 18) and a sequence of six days with pristine conditions (AOD AE,1640 ranging from 0 and 0.