dataesr/scientific-paragraphs-categorization huggingface dataset:
The studied events In this work, the ANTILOPE quantitative precipitation estimates (QPEs) are used for precipitation estimation  (Champeaux et al. , 2009). The ANTILOPE QPEs are based on a fusion between the radar data provided by the operational radar network ARAMIS  (Tabary, 2007)  and the measurements at rain gauges, spatialized by the kriging method. ANTILOPE QPEs are available at hourly time step and 1 km × 1 km resolution. The criticized observed discharges at the outlet of the two catchments are taken from the hydrometric French database (http://www. hydro. eaufrance. fr, last access: 15 March 2021). Table  1  presents the characteristics of the studied events. Three flash flood events are considered for each catchment over the 2017-2019 period. This period is chosen because it corresponds to the period of availability of the LDAS-Monde at fine scale (2.
Parametrization and precipitation forcing The MARINE model requires the definition of (i) the digital elevation model (DEM), (ii) soil survey data to compute the hydraulic and storage properties of the soil, and (iii) landuse data to configure the surface roughness parameters. The IGN-25 m DEM is used in this work. The soil depths and soil texture maps are taken from the INRA soil database for the Ardeche and Languedoc-Roussillon regions (Robbez-  Masson et al. , 2000). The parameters of the pedotransfer function are computed based on the USDA soil classification  (Spaargaren and Batjes, 1995). Land cover is provided by the Corine Land Cover 2006 database (Aune-Lundberg and Strand, 2010). This study uses the calibration of MARINE provided by  Garambois et al. (2015b)  for the Orbieu catchment and by  Douinot et al.
The soil layer empties faster with the base model, leading to a simulated SSD significantly lower than the SSF and SSF-DWF models. For the simulated events overall, the simulated SSD and the SMOSMA-NIA measurements appear to be better correlated when using the SSF-DWF model than the base model or SSF model. The soil physics used in the SSF-DWF model, i. e. , the use of the volumetric soil water content rate and the vertical discretization into two layers, allows us to enhance the SSD simulation for the surface layer with respect to in situ measurements. This point will be developed by considering the catchment average of simulated SSD in the next section. In addition, the SSD output of the SSF-DWF model are generally larger than the output of the base and SSF models. This behavior can be explained by the fact that for the SSF-DWF model, the depths of the upper layer are taken from the INRA soil database, whereas for the base and SSF models, a multiplicative, calibrated coefficient greater than 1 is applied.
Consequently, lateral flows based on the water height gradients are larger than lateral flows based on the volumetric soil water gradients. In addition, the dynamics as well as the amplitudes of the SSD simulated in the SSF model and for the upper layer in the SSF-DWF model are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Considering that the dynamics of the LDAS-Monde HU surf are of satisfying accuracy, this assessment leads to the conclusion that the SSF-DWF model improves the simulation of the dynamics of the surface layer moisture, compared to both the SSF and base models. This result appears to be particularly reliable, since it is observed both at the point measurement scale and at the catchment scale. In the SSF-DWF model, the simulation of the moisture in the deep layer is also compared to LDAS-Monde moisture data provided for deeper layers.
(  2019 ) and represent about 5 % of the SMOS-IC SSM values. 4. The ESA CCI (European Spatial Agency, Climate Change Initiative) product provides surface soil mois-ture datasets at daily temporal time step and 25 km spatial resolution. In this product, the AMI-WS and MetOp ASCAT C-band data are merged with several radiometer soil moisture products along the algorithm presented by  Wagner et al. (2012). The uncertainties associated with the ESA CCI SSM product are considered the variance of the dataset, estimated through triple collocation analysis. Uncertainties represent about 3% of the ESA CCI SSM values. Figure  A1  jointly displays the catchment average for these products over the studied events. The impact of the spatial resolution on the spatially averaged values can be clearly noticed.
In this product, the number of informative pixels per catchment for the studied cases is greater than 14 % of the catchment area. Consequently, in this work, the CGLS SWI product is taken into account to perform the comparison with the soil moisture simulated in MARINE. Nevertheless, this literature exploration of the data available for soil moisture description illustrates the difficulty estimating surface soil moisture based on satellite data at small catchment scales (∼ 100 km 2 ). Table  A1. Investigated satellite derived soil moisture products and their main characteristics. Shown are the data producer, Soil Water Index (SWI) or superficial soil moisture (SSM), spatial resolution, and the satellite imagery used.
Data availability. Hydrometric data come from the national network of operational measurements (HydroFrance databank http: //www. hydro. eaufrance. fr /,Ministère de l'Ecologie, du Développement Durable et de l 'Energie , 2015). Rainfall data and SIM outputs are provided by  Météo-France  (https://publitheque. meteo. fr/,MeteoFrance, 2021). The THEIA-Land VHSR data are broadcasted through the prism platform (https://www. theia-land. fr/en/homepage-en/, Theia, 2020). Acknowledgements. This work is a contribution to the  HyMeX program. The authors would like to thank  CNRM-Météo France  for providing the ANTILOPE radar reanalysis, the  Services de Prévision des Crues  ( Méditerranée Ouest et Grand Delta ) for providing the discharge data, and  Jean-Pierre Wigneron ,  Merez Zribi , and  Nicolas Baghdadi  for providing the SMOS-IC and THEAI-Land VHSR data, respectively, and for feeding the discussion on these products.
12 m/z in each scan. In each scan the top speed (most intense) ions were fragmented by CID and detected in the linear ion trap. The ion count target values for survey and MS/MS scans were 400,000 and 10,000, respectively. Target ions already selected for MS/MS were dynamically excluded for 15 s. Spray voltage in the NanoMate source was set to 1. 60 kV. RF Lens was tuned to 30%. Minimal signal required to trigger MS to MS/MS switch was set to 5,000. The spectrometer was working in positive polarity mode and singly charge state precursors were rejected for fragmentation. Database searching was done with Proteome Discoverer software v2. 1. 0. 81 (Thermo) using Sequest HT search engine and SwissProt Human release 2018 01 and manually introduced contaminants database and user proteins. Searches against targeted and decoy database were used for determining the false discovery rate (FDR).
ATR-FTIR Spectroscopy For FTIR analysis, biofilm samples (before and after plasma exposure) were dehydrated by using a moisture analyzer (Sartorius MA30). The coupons were heated at 45 °C until reaching constant weight. The spectra of bacterial cells without matrix were obtained by analyzing dehydrated pellets of overnight microbial cultures. The extracellular glucan polymer fraction from L. citreum NRRL B-1299 was produced by enzymatic reaction from sucrose and purified as previously described by Bounaix et al. [21]. The FTIR spectra were recorded between 4000 and 800 cm -1 on a Spectrum 65 spectrometer equipped with the Spectrum software (Perkin Elmer). Each spectrum was an average of 32 scans with a resolution of 4 cm -1. For all spectra, baselines were corrected. Spectra were normalized on the 1280 cm -1 peak, which corresponds to a distinctive peak from the membrane, and with low abundance for bacterial cells and biofilm spectra.
Data Availability Statement: Not applicable. Funding: This research was funded by by grants from the Toulouse University Paul Sabatier and Occitanie Regional Council (grants no. 14055695, no. 12050475). Maritxu Labadie was supported by a fellowship from the Occitanie Regional Council (ALDOCT-000268/2017 001895), IUT 'A' Paul Sabatier ad Auch ad Gers Local Concils.
Aims. In this work we study how to employ the upcoming Legacy Survey of Space and Time (LSST) data, from the Vera C. Rubin Observatory, to constrain physical properties of normal, star forming galaxies (so-called, the main sequence galaxies). Since the majority of observed LSST objects will have no auxiliary data, we use simulated LSST data and existing real observations to test the reliability of estimations of the physical properties of galaxies, such as their star formation rate (SFR), stellar mass (M star ), and dust luminosity (L dust ). We focus on normal star-forming galaxies, as they form the majority of the galaxy population in the universe and therefore are more likely to be observed by the LSST. Methods. We perform a simulation of LSST observations and uncertainties of 50 385 real galaxies within redshift range 0 < z < 2.
5. In order to achieve this goal, we used the unique multi-wavelength data from the Herschel Extragalactic Legacy Project (HELP) survey. Our analysis focus on two fields: ELAIS N1 and COSMOS. To obtain galaxy physical properties we fit their Spectral Energy Distributions (SEDs) using the Code Investigating GALaxy Emission (CIGALE). We simulate the LSST data by convolving the SEDs fitted by employing the multi-wavelength observations. We compare the main galaxy physical properties, such a SFR, M star , and L dust obtained from the fit of the observed multi-wavelength photometry of galaxies (from ultraviolet to far infrared) to the ones obtained from the simulated LSST optical measurements only. Results. We present the catalogue of simulated LSST observations for 23 291 main sequence galaxies in the ELAIS N1 field and 9 093 in the COSMOS field, available on the HELP virtual observatory.
These potentially large datasets will raise multifold questions such as: how can we use only LSST optical observations to obtain estimates of the main physical properties of galaxies? How realistic and reliable would they be? In this paper we investigate the aforementioned topics by performing a simulation of LSST observations of main-sequence (hereafter MS) galaxies which form a nearly linear relation (in log-log space) between their stellar mass and SFR  (Noeske et al. 2007 , Elbaz et al. 2010 , Rodighiero et al. 2011 , Speagle et al. 2014 , Schreiber et al. 2015 , Whitaker et al. 2015 , Pearson et al. 2018). Main sequence galaxies constitute the dominant population in deep fields such as COSMOS and ELAIS N1 that can reach very faint optical magnitudes (∼ 29-30 mag). LSST is expected to expand, among others, the observed MS population of galaxies to other fields which are not currently covered by deep field surveys.
For this reason, we decided to focus on the MS galaxies. The paper is organised as follows. In Section 2 we describe the data and the HELP project. In Section 3 we present the sample selection, outliers and starburst, and the methodology used for this work. In Section 4 we discuss the simulated LSST magnitude and errors. The same Section, together with Section 5 and 6, presents the results. Our conclusions are presented in Section 7. Throughout this paper we use WMAP7 cosmology  (Komatsu et al. 2011) : Ω m = 0. 272, Ω Λ = 0. 728, H 0 = 70. 4 km s -1 Mpc -1.
Data The HELP collaboration provides extremely valuable multiwavelength data over the HerMES  (Oliver et al. 2012)  and the H-ATLAS survey fields  (Eales et al. 2010 ) and other relevant Herschel fields. The total area of HELP is 1 269. 1 deg. 2  (Oliver et al. in prep, Shirley et al. 2019). Herschel was equipped with two imaging instruments, the Photodetector Array Camera and Spectrometer (PACS;  Poglitsch et al. 2010) , that observed the FIR at 100 and 160 µm, and the Spectral and Photometric Imaging Receiver (SPIRE;  Griffin et al. 2010 ) that covered 250, 350, and 500 µm wavelength ranges. Surveys that combine a wide range of wavelengths have particular identification issues due to the different spatial resolution of the sources in different bands. To correct this issue HELP builds a master list catalogue of objects as complete as possible for each field and uses the NIR sources of this catalogue as prior information to deblend the Herschel maps.
Detailed description can be found in  Shirley et al. (2019). The tool developed to obtain the photometry of Herschel sources, XID+  (Hurley et al. 2017) , is a probabilistic de-blending algorithm which extracts source flux densities from photometry maps that suffer from source confusion. It uses Bayesian inference to explore the posterior probability distribution and provide probability density function (PDFs) for all prior sources, and thus flux and uncertainties can be estimates. A detailed description can be found in  Hurley et al. (2017). The whole procedure is described in Notebook and stored in a GitHub repository 1. In the following paper, we use two HELP fields, due to the wealth of multi-wavelength data available within their wide field coverage: the European Large Area ISO Survey North 1, hereafter ELAIS N1  (Oliver et al.
2000)  and COSMOS field  (Laigle et al. 2016). Besides data from two PACS and three SPIRE maps, we used available sets of photometric data at shorter wevelenghts for both fields (listed in Table  1  and described below in Sections 2. 1 and 2. 2). Based on true galaxy observations from that fields we evaluate the 'LSST-like' observations used for further analysis.
ELAIS N1 field According to the HELP strategy, all sources detected in any of the Spitzer IRAC bands were used as a prior for XID+ to obtain FIR fluxes. XID+ was run on the Spitzer MIPS 24 µm and Herschel PACS and SPIRE maps. The flux level at which the average posterior probability distribution of the source flux becomes Gaussian is 20 mJy for MIPS, 12. 5 and 17. 5 mJy for 100 µm and 160 µm PACS bands respectively, and 4 mJy for all three (250, 350 and 500µm) SPIRE bands (see for more details  Hurley et al. 2017 ; indicating that the information from data dominates over the prior). In the de-blending procedure, the priors used for computing the fluxes satisfied two criteria: they must have an IRAC 1 band detection and they must have been detected in either the optical or near IR (NIR) wavelengths to eliminate artefacts.
2006 , Dalton et al. 2006)  and UKIDSS-LAS (WFCAM bands J, H, K) catalogues. The merging strategy is the same as for ELAIS N1 and is described in detail in  Shirley et al. 2019). The list of filters used for COS-MOS survey is shown in Table  1. Detailed description of both fields (the area, mean depths in different filters, all raw files and ancillary data and many others) can be found on the http://hedam. lam. fr/HELP/ dataproducts/dmu31/dmu31_Field_overviews/ webpage.
Total sample As part of the HELP database, both fields catalogues include photometric redshifts generated using a template fitting method, based on a Bayesian combination approach, described in  Duncan et al. (2018). They investigated the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties, over the NOAO Deep Wide Field Survey Bootes and COSMOS fields. The three template sets used are: (1) default EZY reduced galaxy set  (Brammer et al. 2008) , (2) "XMM COSMOS" templates  (Salvato et al. 2009) , and (3) atlas of Galaxy SEDs  (Brown et al. 2014). The total sample includes 39 329 objects for the ELAIS N1 survey and 14 864 for COSMOS, with FIR detections in at least two photometric bands with signal-to-noise ratio (S /N) 3.
To obtain the starting MS sample of galaxies with the correct physical properties necessary to compare with those obtained with LSST only, we run CIGALE on the ELAIS N1 and COSMOS samples with the physical modules and parameters reported in Table  2. We did not use the AGN module (see App. B and Sec. 3. 5). As shown in  Małek et al. (2018) , this set of parameters corresponds to the one that best fits a large sample of IR detected galaxies in the 23 HELP fields, for the redshift range 0<z<6. We use a SFH modelled as delayed exponential function with an additional exponential burst to select and remove starburst galaxies from our sample in order to only retain MS galaxies. We perform the SED fitting by employing the modified version of  Charlot & Fall (2000)  attenuation law, as already employed in  Małek et al.
(2018)  for a large sample of multiwavelength HELP data. We use the Draine et al. (  2014 ) dust emission module. A detailed description of each module can be found in  Boquien et al. (2019)  and  Małek et al. (2018). To improve the quality of our selection, from the full sample we only select objects with redshift lower than 2. 5. Our cut in redshift is not related with the LSST redshift range, as the photometric redshifts for LSST will be applied and calibrated over the range 0 < z < 4 for galaxies to r∼27. 5 (LSST Science  Collaboration et al. 2009 ). The z<2. 5 is related to the redshift distribution in the ELAIS N1 and COSMOS fields used in this analysis, and also it restricts us to high quality data and so maximize the accuracy of the estimation of physical properties. Moreover, we removed all the objects recognized as possible stars by GAIA (flag GAIA>0 in the database).
2019). To obtain LSST fluxes we run CIGALE by fitting the photometric measurements and providing to the code the LSST filter response curves. We use a CIGALE module (called fluxes) specifically designed to estimate the fluxes in the defined filters. We compute LSST fluxes from the best-fit model of each object. We include in our sample all the galaxies that will be detected in all bands at the depth of the 10 year survey: u <26. 1, g <27. 4, r <27. 5, i <26. 8, z <26. 1, y <24. 9. In this way we discard 8 645 objects (23% of the to-tal sample) from ELAIS N1 and 2 623 (19% of the total sample) from COSMOS. To incorporate an LSST-like observational uncertainty into our catalogue, we must take into account random phenomena that could occur during a real observation, such as change in the sky seeing, number of visits etc.
and the low quality of GALEX observations for higher redshift sources. To confirm this statement, we simulate GALEX NUV and FUV observations for the whole sample (from now we refer to it as GALEX true ), again using the CIGALE module fluxes , and employ it togheter with LSST to estimate the physical properties. We decide to cut objects with z > 1. 5 as, after this limit, both NUV and FUV GALEX bands are probing emissions below the Lyman break. Fig. C. 1 shows the comparison of the SFR evaluated in this way and the one evaluated using LSST observation only. We find a clear correction of the overestimation, highlighting the great impact that UV observations have on the SFR estimation. We confirm that the overestimation is partially due to the lack of the direct tracer of the young stellar population.
Lower left panel: Same as in the other panels, but adding of GALEX observations. We obtain a slight decrease in the overestimation, but not enough to completely remove it, obtaining also an underestimation in the central redshift bins. For this comparison we consider the ∼ 3 000 objects that have counterpart in the GALEX database. Lower right panel: M dust ratio evaluated with LSST observations only (red) and with LSST+SPIRE observations (blue). The results are consistent with the 'real' one with a very low scatter. ones obtained with the  Charlot & Fall (2000)  prescription at low redshift but change in shape going to higher z. The overestimation with the  Calzetti et al. (2000)  curve is more constant along the redshift range, while it decreases faster when using the Charlot & Fall 2000 prescription.
Acknowledgements. We would like to thank  William Pearson  for his help in understanding our object's behaviour in the SFR-M star diagram. GR, KM, AN, MH and AP acknowledges support from the  National Science Centre  ( UMO-2018/30/E/ST9/00082 ,  UMO-2018/30/M/ST9/00757  and  UMO-2020/38/E/ST9/00077 ). Authors are grateful for the support from  Polish Ministry of Science and Higher Education  through a grant  DIR/WK/2018/12. We also thank the anonymous referee who has helped clarify and improve various aspects of this article.
Resumo Estudos recentes evidenciam que soluções arquitetônicas simples e não invasivas possibilitam recuperar o potencial de ventilação natural na reabilitação de edifícios em clima temperado. No entanto, a eficiência destas soluções em meios urbanos densos e clima quente e úmido ainda não foi investigada. Este artigo objetiva avaliar os benefícios para o conforto térmico decorrente da ventilação natural na reabilitação de um edifício de escritórios situado no centro do Rio de Janeiro. Simulações computacionais térmicas e fluidodinâmicas (CFD) são realizadas com os softwares Designbuilder e Ansys CFX para avaliar a circulação internas dos ventos antes e após reabilitação. O diagnóstico do cenário atual indica que o entorno bloqueia uma parcela significativa dos ventos, comprometendo o conforto térmico na maior parte do ano, sobretudo nos andares mais baixos.
Materials and methods The methodology included two stages: diagnosis and retrofit. The diagnosis consists of assessing the air flow within the urban fabric and the building in the current scenario. Based on the diagnosis, a retrofit proposes an architectural solution to enhance building natural ventilation potential. The two stages included CFD and thermal simulations, considering the building itself and its surrounding buildings. In the diagnosis stage, urban CFD simulations were performed to identify the impact of surrounding obstacles on the air flow around the building. Based on the air pressure on facades (Pa), indoor CFD simulations were carried out to calculate air flow and air change rate within the building before and after the retrofit. The output data was used in thermal simulations to estimate operative temperatures on each floor (Figure  1 ).
Urban CFD simulations Through the commercial CFD code Ansys CFX 14. 5, three-dimensional and stationary simulations of a single-phase incompressible flow were performed. The choice among CFD methods considered a compromise between accuracy and computational demand. We adopted the standard k-ε turbulence model since it has proven to be quite stable for urban simulations  (COST, 2007). It is known that the standard k-ε turbulence model overestimates the k value in the impinging region and cannot capture accurate separation and reattachment. Nonetheless, it requires less computational resources  (SEIFERT et al. , 2006). In this study, Navier-Stoke equations were discretized by the first-order method. The geometry was modeled on CAD format and exported to Ansys CFX. To render simulation feasible, the urban fabric was simplified by gathering buildings set side by side in a single block.
Indoor thermal simulations Thermal simulations were carried out using DesignBuilder 4. 5, a software that performs building energy analysis, considering local climatic data and building attributes such as construction materials, occupancy, and operation. For the thermal simulations, we modeled a geometry composed by Galeria Building and nearby obstacles. Surrounding buildings were taken into account only to consider the shading effects in Galeria facades. Therefore, they were indicated as adiabatic blocks. To simulate both scenarios -before and after retrofit -all construction, occupancy, and operation parameters (Table  2 ) were kept constant, varying only the design of the openings and the air change rate in each zone, based on the results of indoor CFD simulations. Since natural ventilation is the only cooling strategy in the building, HVAC systems were all disabled.