dataesr/scientific-paragraphs-categorization huggingface dataset:
The Two-Fluid Model The two-fluid Model is computationally more expensive than the mixture Model as it was illustrated in figure  2. The isothermal two-fluid model  [27]  will be described in this section (as general description for multiphase flows) as described in the commercial software Siemens Star-CCM+, intended to simulate the vertical liquid-solid JICF. The main objective is to investigate the potential of the two-fluid model in commercial CFD code, with a modified-(kε) RANS turbulence model, to predict (at which accuracy compared to the mixture Model new solver in OpenFOAM ? and at which computational cost ?) the liquid-solid jet's bend as observed experimentally.
It is given as the following: C D = 24 Re d 1 + 0. 15 Re d 0. 687 i f 0 < Re d ≤ 1000 0. 44 i f Re d > 1000 (22) with Re d is the effective Reynolds number given by: Re d = ρ c |U r |d e f f µ c ( 23 ) where µ c is the dynamic viscosity of the continuous phase, and d e f f is the particles effective diameter. It is important to note that several C D drag coefficient models exist in the literature depending on the volume fraction, geometry and type of application (droplets in gas or liquid, solid particles in gas or liquid, etc). Furthermore, CFD commercial and open source codes implement differently similarly-named drag coefficient models (e. g. see  Greifzu et al. 2016 [44] ). Due to the fact that there is no access to modify directly the C D value inside the commercial software Siemens Star-CCM+ library, the Gidaspow drag model paremeters n and l cd " were modified instead.
Using the new solver mixture model developed in OpenFOAM (for a total physical time of 4 seconds), the solution required 22. 08 hours as computational time, solved in OpenFOAM in parallel on a cluster of 32 intel processors (each 2. 3 GHz). The new solver via the mixture model in OpenFOAM required a computational time that is less by a factor of the order of O(~10), compared to the commercial twofluid model in Star-CCM+. The concentration profiles for the liquid-solid jet's bend are shown in figure  6. It can be clearly observed that the numerical results are very close to the experimental data at the different times 400, 800 and 2500 ms. The new solver developed in Open-FOAM as a mixture model predicted slightly-better the jet's bend or jet's trajectory at t = 400 ms and t = 800 ms compared to the two-fluid model as shown from the isosurface contour-lines of figures 6-(g,h).
Acknowledgment This work was carried out within the framework of Research on Ground Transports and Mobility by the  French National Scientific Research Center CNRS , in coordination with the  ELSAT2020  project supported and financed by the  European Community , the  French Ministry of Higher Education and Research , the  Hauts-de-France Regional Council. The authors gratefully acknowledge and appreciate the support of all these institutions. It was also supported by  ALSTOM SA , which funds a part of the present research work.
This "best result" simulation was achieved by reducing the original SST by 3 °C. The corresponding simulation is denoted as reference run in the following. For the analysis, we used model output on model and z levels and times are given in UTC (CET = UTC + 1). Sunrise was at 0530 UTC and sunset at 16:49 UTC. To prevent boundary effects for the 500 m grid spacing run, the analysis is confined to a smaller area with about 50 km distance to the upstream (western) boundaries. In order to obtain information on the flow, i. e. whether the flow is around the island or over the mountain range, we used the online trajectory module implemented in COSMO  (Miltenberger et al. , 2013)  and started trajectories hourly from 0600 UTC on 09 October onwards at 8. 5°E between 41. 4°N and 42. 7°N with 0. 01°increments and from levels between 500 and 3000 m MSL.
Acknowledgments This work is a contribution to the HyMeX programme. We would like to thank  Veronique Ducrocq  from  METEO-FRANCE  and  Evelyne Richard  and  Dominique Lambert  from the  University of Toulouse/CNRS  for their support in performing the measurements of KITcube on the island of  Corsica during HyMeX. We are grateful to the whole IMK team for deploying and running the KITcube on the island of Corsica and would like to thank  Miriam Bentke  for plotting some of the figures.
Study Design The research follows a hybrid methodology, combining a quantitative component with a questionnaire completed prior to a telephone in-depth interview (qualitative component) among the same respondents. It was conducted in France, Germany, Spain, and the UK among oncology healthcare providers (HCPs, i. e. , head nurse, oncologist, pharmacist, safety manager, risk manager, pharmacovigilance manager, quality manager, medical information manager within oncology centers). In Germany, oncologists' offices were included to reflect the specifics of the local oncology management. The data collection period was January 2018 to April 2018.
Quantitative Analysis The questionnaires were self-completed and returned prior to the interview. They included information on the characteristics of the respective center environment and a description of the infusion pathway from risk assessment to infusion monitoring. The analysis of the quantitative component was undertaken by Kantar Health using Qlikview software version 11. 20. 12904. 0 SR12 developed by QlikTech (Pennsylvania, USA). Due to the nature of the study, the analysis is only descriptive. Any categorical variables (when available) were summarized using frequency and percentage. Continuous data have been presented with the mean, median, minimum, and maximum.
Qualitative Analysis All telephone interviews were audio-recorded with permission. HCPs were asked 25 questions covering processes to prevent and manage SIRs in the respective HCPs clinic. HCPs were also asked to recall the most striking case of SIR (grade 3 or 4) that had occurred during the past 5 years and to spontaneously describe patient characteristics, agents received, SIR symptoms observed, steps taken to manage the SIR, and outcome on patient, staff, and hospital processes. No computer-assisted qualitative data analysis software (CAQDAS) was used in this process -only transcripts, audio-recording and content-analysis grid. Data were analyzed based on grounded theory method. Data were coded in terms of basic psycho-social processes, based on how participants acted in response to different contexts.
" (Safety Manager, Cancer Center, France). Nurse training on new products to increase personal awareness and improve patient education should include minor and major SIR warning signs, drug-drug interactions, and dietary supplements and herbal products. Better tools for the identification of high-risk patients, including predictive biomarkers, alternative treatments with lower risk of SIRs, more efficacious and safer premedication, and an improved, tailored desensitization protocol are needed. Post-event, more time should be devoted to full rootcause analysis and debrief. National databases to document events and share data analyses were also proposed. National guidelines should be updated, and hospital standard operating procedures aligned accordingly, including benchmarking efforts and internal audits.
Proposed measures were: (a) an improved prescription software, featuring mandatory pre-infusion checklists and a warning in case of documented previous allergic reactions, with improved identification of patients at high risk of developing SIRs, such as predictive biomarkers; (b) the establishment of an international database of documented SIR events including data analysis; (c) educational efforts to better understand treatments and their potential to produce SIRs. Taken together, such efforts could substantially improve the scientific knowledge base on the incidence of SIRs, the circumstances of their occurrence, and their best management, and could thus be synthesized to inform future international guidelines. This study raises some important questions to be addressed in future research, such as estimating the rate of SIRs overall and on a center level; estimating the impact of the level of authorization of the nurses to respond to the SIRs in legislations where they can immediately respond on a nurse level compared to legislations where they have to wait for doctors' instructions.
PFGE. We genotyped all the isolates by determining the macrorestriction profile of their total DNA (hereafter called pulsotype) by PFGE (CHEF DRIII, Bio-Rad) after restriction with DraI (Roche Diagnostics), according to a technique previously validated in the laboratory  [14]. The software Bionumerics (Applied Math, Kortrijk, Belgium) built a DNA similitude matrix based on calculating the Dice coefficient for pairwise isolate comparisons. The dendrogram was built using the UPGMA (unweighted pair group using arithmetic averages) hierarchical algorithm. We used the Staphylococcus aureus NCTC 8325 strain as a reference to allow comparison between gels. The pulsotypes were defined using a similarity coefficient of 80%, as described in a previous study  [15].
Whole Genome Sequencing. Bacterial DNA was isolated from an overnight culture on Mueller-Hinton agar (Bio-Rad, Marne la Coquette, France) and extracted with a QIAmp DNA Minikit (Qiagen). DNA was sequenced on an Illumina NextSeq, which generates 150-bp paired-end reads with a mean coverage of 80X. Reads were assembled using SPAdes v. 3. 13. 0. We determined the cgMLST with the homemade pipeline pyMLST (https://github. com/bvalot/pyMLST) using a core genome of 3,831 previously defined genes  [16]. A minimum spanning tree (MST) was built with GrapeTree  [17]  using distances calculated from genes present in all strains (n = 3,491). Isolates for which the genomes had ≤ 20 alleles of difference were considered to epidemiologically linked.
Identification of hypermutators. Hypermutators are defined by a high rate of mutation, often due to a defective mismatch repair (MMR) system. We identified hypermutator candidates in silico by searching for non-synonymous mutations, insertions, or deletions in the MMR genes (mutS, mutL, or uvrD) relative to the PAO1 reference strain genome (NCBI accession number NC_002516). We confirmed that isolates defective in the MMR system were hypermutators by comparing their mutation frequency with that of close isolates with an intact MMR system and that of the PAO1 reference strain using a previously described procedure  [18]. We used Student's unpaired t-tests to determine the statistical significance of comparisons of the emergence frequencies of mutants resistant to rifampicin between ST395 isolates and the reference strain PAO1.
aeruginosa to test the ability of PFGE to differentiate isolates of the same clonal group that are not epidemiologically linked and test the stability of the method. Here, the accuracy of PFGE is illustrated by the five cgMLST clusters matching those found with PFGE and by the fact that PFGE correctly differentiated genetically distant isolates collected in the same ICU over the same time period (Table  I ) in the Lyon and Montpellier Hospitals (pulsotypes C, E, and H). The genetic distance between the genomes of the isolates belonging to pulsotypes A and D led us to test whether they were hypermutators. Hypermutators are characterized by a defective MMR system, which involves the genes mutL, mutS, and uvrD  [21]. We then compared the nucleotide sequences of these three genes of all the genomes of our dataset (n = 65) with that of the reference strain P.
aeruginosa PAO1 and found that the five genetically distant isolates of pulsotype D (but not the two pulsotype A isolates, data not shown) specifically harbored a defective MMR system. Pulsotype D isolates were collected throughout a 13-year outbreak at the University Hospital of Besancon  [11]. The isolate DHS15 was retrieved in February 2002 and had a non-synonymous mutation in mutS, leading to an amino-acid substitution in the protein MutS (Y433D). The four other isolates (210690101, 210690111, 210690101, B168) were retrieved in May 2009 and had the same mutation in mutL, creating a stop codon in MutL (R535X). We compared the mutation frequency of the five MMR-defective isolates with that of two isolates of pulsotype D with a wildtype MMR system and the reference strain PAO1 to confirm the phenotypic consequence of these mutations (Supplemental Figure  1 ).
Overall, our results show that pulsotypes B, D, E, F, and G were consistent with cgMLST clustering, confirming that local outbreaks caused by P. aeruginosa can be investigated with confidence with PFGE. PFGE results are easily interpreted since pulsotype clustering is based on the similitude coefficient  [15]. The interpretation of cgMLST data is more complicated, for which the number of differences depends on the species, with no threshold defined for P. aeruginosa. Here, we set the threshold to 20 alleles of difference to delimit cgMLST clusters. A previous study chose a threshold of 14 alleles of difference for P. aeruginosa  [16]. However, this threshold was not suitable for our study as it would have created too many clusters, especially for pulsotype D (data not shown). Indeed, it has been suggested that epidemiological data (e.
This makes it difficult to associate the observed H variability to physical parameters, in order to perform a prediction of the topside ionosphere variability based on theoretical expectations  3, 4, 28. The goal of this work is to shed some light on the connection between modeled H and theoretical VSH (as derived by plasma ambipolar diffusion theory) through mathematical deductions, modeling approaches, and measured topside datasets. Recently, Pignalberi et al. 13  have proposed a method to derive a fully analytical expression of the effective topside scale height based on the analytical inversion of the semi-Epstein function (applied by both International Reference Ionosphere 29 (IRI) and NeQuick 30 models to represent the topside profile) as a function of the electron density and F-layer peak parameters.
They applied the proposed methodology to a selected reliable dataset of COSMIC/FORMOSAT-3 (hereafter, COSMIC for brevity) RO profiles by highlighting a very distinct linear increasing trend of H for the lowest part of the topside ionosphere. By linearly fitting calculated H, they were able to obtain the H's intercept and slope values for each COSMIC topside profile. These coefficients allow to model H, hence the topside electron density profile, in the topside region probed by COSMIC satellites: from the height of the F-layer peak to about 800 km. In this study, the connection between the fully analytical scale height H of Pignalberi et al. 13  and the theoretical VSH (from plasma ambipolar diffusion theory) is mathematically studied. This is done to link empirical modeled effective parameters to theoretical physical quantities.
Firstly, effective and theoretical scale heights are linked through a mathematical relation by showing that they tend to each other in the topside ionosphere. Secondly, their connection is preliminarily demonstrated by calculating effective scale height values from the entire COSMIC/FORMOSAT-3 radio occultation dataset. Thirdly, a possible connection between the vertical gradient of the topside scale height (as obtained by COSMIC/FORMOSAT-3 satellites) and the electron temperature (as obtained by ESA Swarm B satellite) is studied by highlighting corresponding similarities in the diurnal, seasonal, solar activity, and latitudinal variability. In the context of the Space Weather, the ionosphere plays a fundamental role as the medium coupling the external forcing given by solar radiation, solar wind, and magnetosphere, to the inner Earth's atmosphere.
A preliminary application based on COSMIC/FORMOSAT-3 radio occultation and ESA Swarm satellites data The mathematical findings obtained in the section "Linking the Epstein layer effective vertical scale height to the plasma ambipolar diffusion theory" are here substantiated by applying them to actual measured data. For this purpose, the very reliable and wide dataset of topside electron density vertical profiles given by COSMIC RO is exploited. The Pignalberi et al. 13  approach is first applied to the entire COSMIC dataset to retrieve effective scale height parameters on a global basis and for different helio-geophysical conditions. After that, a preliminary (14) lim z→∞ H Epstein (z) VSH = lim z→∞ tanh z 2H Epstein (z) = 1 ⇒ H Epstein (z) ---→ z→∞ VSH. ( 15 ) ∂VSH ∂z = ∂H Epstein( z) ∂z tanh z 2H Epstein( z) - H Epstein (z) -z ∂H Epstein( z) ∂z 2H Epstein (z) sinh 2 z 2H Epstein( z).
(16) lim z→∞ ∂VSH ∂z = lim z→∞ ∂H Epstein( z) ∂z tanh z 2H Epstein( z) - H Epstein (z) -z ∂H Epstein( z) ∂z 2H Epstein (z) sinh 2 z 2H Epstein( z) = ∂H Epstein (z) ∂z. ( 17 )    VSH| z=n•2H Epstein( z) = H Epstein( z) tanh (n) , ∂VSH ∂z � � z=n•2H Epstein( z) = ∂H Epstein( z) ∂z � n tanh (n)+sinh 2 (n) tanh (n) sinh 2 (n) � - 1 2 sinh 2 (n). Table  1. Numerical values of VSH and ∂VSH ∂z as a function of H Epstein (z) and ∂H Epstein (z) ∂z , respectively, and related numerical coefficients defined in Eq. (  17 ), for different values of n = z/2H Epstein (z). Deriving the effective vertical scale height from radio occultation data. Pignalberi et al. 13  performed a study based on a selected dataset of 382 COSMIC/FORMOSAT-3 RO profiles matching simultaneously measured F2-layer peak parameters (NmF2 and hmF2) by co-located ionosondes.
This allowed to work on a very reliable dataset of topside electron density profiles  35. The Pignalberi et al. 13  technique is here applied to the entire COSMIC RO dataset, recorded from 2006 to 2018, to retrieve preliminary information on the effective vertical scale height dependence on diurnal, seasonal, solar activity, and spatial variabilities. COSMIC/FORMOSAT-3 was a six microsatellites constellation launched on 15 April 2006 and deployed into a circular orbit (with 72° of inclination) at about 800 km of altitude (reached at the end of 2007) and a separation angle of 30° in longitude between neighboring satellites  36. The mission was a collaborative project between the National Space Organization in Taiwan and the University Corporation for Atmospheric Research in the United States.
Each satellite carried a Global Positioning System (GPS) RO receiver, composed by four antennas, capable of measuring the phase delay of radio waves from GPS satellites as they are occulted by the Earth's atmosphere, thus providing an accurate determination of the ionospheric vertical electron density profile. COSMIC RO data (ionPrf files) were downloaded from the COSMIC Data Analysis and Archive Center (CDAAC, https ://cdaac -www. cosmi c. ucar. edu/cdaac /produ cts. html). n tanh (n) VSH| z=n•2HEpstein(z) n tanh (n)+sinh 2 (n) tanh (n) sinh 2 (n) 1 2 sinh 2 (n) ∂VSH ∂z z=n•2HEpstein(z) 0 0 ∞ ∞ ∞ ∞ 1 0 In this work, all available COSMIC ionPrf files from 22 April 2006 to 31 December 2018 were used, a total of 3,626,729 COSMIC retrieved electron density profiles. For the selection of reliable COSMIC topside profiles, a specific filtering process was developed to remove profiles affected by issues related to the failure of the spherical symmetry assumption in the Abel inversion procedure  37.
The interested reader can refer to the section "Methods" for a detailed description of the filtering process. After filtering, the COSMIC dataset used in this study reduced to 1,791,676 topside profiles (49. 4% of the total). In Pignalberi et al. 13  , H Epstein (z) values retrieved by COSMIC RO topside electron density values through Eq. (  9 ) were linearly fitted to calculate corresponding slope and intercept values. The topside effective scale height thus obtained, called H Linear (z), is function of the reduced height z: In Eq. (  18 ), ∂H Linear ∂z and H 0, Linear are the slope and intercept values obtained after applying the linear fit procedure of Pignalberi et al. 13. The slope represents the gradient of the modeled topside scale height ∂H Linear ∂z , while the intercept H 0, Linear represents the value of H Linear (z) at the F2-layer peak (h = hmF2).
This procedure is here applied to each of the 1,791,676 selected COSMIC topside profiles, thus allowing for compiling a very large dataset of ∂H Linear ∂z and H 0,Linear. Some examples of application of Eq. (  18 ) are shown in Figs. 1, 2 , and 3 for three COSMIC RO profiles recorded at high (Fig. 1 ), mid (Fig. 2 ), and low (Fig. 3 ) latitudes. In both examples, H Linear (z) values are obtained applying the linear fit of Eq. (  18 ) to H Epstein (z) values obtained through Eq. (  9 ). Modeled topside electron density values are thus calculated replacing H Epstein (z) with H Linear in Eq. (  8 ), and are represented by the red curve in the upper left panels of Figs. 1, 2,  and 3. Figures  1, 2 , and 3 show how H Epstein (z) values retrieved from COSMIC N e measurements exhibit a clear linear trend with height; while only a slight departure from this behavior is visible for the few first tens of kilometers above the F2-layer peak.
Figures  1, 2 , and 3 testify that it is possible to relate in a very accurate way effective scale height values to theoretical VSH ones for a wide range of altitudes from some hundreds of kilometers above the F2-layer peak to the plasmasphere domain. The comparison made by Pignalberi et al. 13  between electron density values measured by COSMIC and those modeled by using H Linear (z) showed that it is possible to accurately reproduce the ionospheric topside electron density profile by using a semi-Epstein layer with a topside scale height linearly dependent on the height, at least from hmF2 to about 800 km, considering a limited dataset of only 382 RO profiles. For each of the 1,791,676 selected COSMIC topside profiles, the topside total electron content (tTEC) is calculated by integrating the topside electron density values from hmF2 to the satellite height.
On the contrary, tTEC values outside the linear fit in the scatter plot of Fig. 4  (the dark blue bins just above the fit line ) cannot be explained as above. These profiles do not exhibit a linear trend of the scale height with altitude. They can be considered as outliers and they are not visible in the histogram of Fig. 4  because outside the displayed axis range. Anyhow, these outliers affect very little the full statistics because they are relatively few (tens of topside profiles). Figure  5  shows the normalized occurrence of residuals percentage between modeled and measured electron density values, for different reduced heights, for the entire COSMIC selected dataset. Notably, it is expected that (20) the spreading of data increases with altitude, because all profiles are constrained at the peak (z = 0).
Nevertheless, up to z = 500 km the profiles are remarkably well reproduced. Specifically, Fig. 5  highlights that most of the occurrences lie in a narrow range between ± 5%. As a consequence, topside electron density values modeled by the H Linear (z) approximation (Eq. (  18 )) allow for a reliable description of the topside profile in the altitude range probed by COSMIC satellites. Results of Figs. 4  and 5  confirm what preliminarily found by Pignalberi et al. 13  , i. e. , the scale height retrieved from COSMIC RO profiles exhibits a clear linear trend for the lower topside region, the one from the F2-layer peak to about 800 km of height. The COSMIC RO dataset used for this work includes different diurnal, seasonal, and solar activity conditions spanning from equatorial to auroral latitudes.