dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Investigations of floras, databases and collections allowed us to recognise for the Comoros 21 species of Hymenophyllaceae belonging to seven genera to be compared to previous studies recognising respectively 13 and 17 species. These three checklists are compared in Table  1. The most diversified genera in the archipelago are Didymoglossum (7 spp. ) including the single Comorian endemic species D. kirkii (Hook. ) Ebihara & Dubuisson, Hymenophyllum (6 spp. )  and Crepidomanes (4 spp. ). Abrodictyum, Polyphlebium (newly reported for the archipelago), Trichomanes and Vandenboschia are each represented by a single species. Diversity of the overall morphology is illustrated by representative species as observed in situ (Fig. 3 ).
A precise investigation of these specimens reveals in fact that they are not D. cuspidatum but rather the distinct D. erosum (Willd. ) J. P. Roux. Didymoglossum cuspidatum would be absent in Comoros but confusion with D. erosum as detailed here-after remains easy. This explains why we, however, chose to maintain D. cuspidatum in the Xper database, in order to allow its potential identification in future collects. Didymoglossum erosum (Willd. ) J. P. Roux Didymoglossum erosum (Willd. ) J. P. Roux, Strelitzia 22: 212 (2008).
obs. ). Such 'dwarf forms' are often lacking in collections and we decided here to integrate them in our Xper database as a distinct item. Hymenophyllum hirsutum (L. ) Sw. Hymenophyllum hirsutum (L. ) Sw. , Journal für die Botanik 1800: 99 (1801   Roux (2009)  as synonym of H. capillare, but it is clearly H. hirsutum, explaining why we consider here this species as synonym of the latter one; it is also the case of the P isotype but in the specimen noted B, the second frond from the right belongs to H. capillare. Sphaerocionium ciliatum (Sw. ) C. Presl, Eine botanische Abhandlung: 34 (1843). Sphaerocionium hirsutum (L. ) C. Presl, Eine botanische Abhandlung: 34 (1843). Hymenophyllum ciliatum (Sw. ) Sw. var. boryanum (Willd. ) Mett. ex  Kuhn, Filices Africanae: 39 (1868). Hymenophyllum ciliatum (Sw.
Diversity of Comorian Hymenophyllaceae, distribution and collect effort Grande Comore is the largest island with the highest elevational gradient and potentially the highest diversity in terms of habitats. Therefore this island potentially could host more species than the three others. We show here that the most diversified island is Anjouan with 16 reported species while Grande Comore would host only 13 taxa. These strong contrasts could be due to the contrasts in habitats but ecological data and preferences for every taxon (e. g. , the type of forests and elevational range) are strongly lacking or parcellar and do not currently allow to precisely study relationships between species and habitat diversity. Precise habitat for each taxon must often be extracted from literature and observations on other localities, like Madagascar and Mascarenes.
Prospective works In addition to a first revision of the diversity of the Hymenophyllaceae for the Comoros, our study allowed evidencing some taxonomic problems (especially concerning the complex H. capense/ fumarioides/kuhnii, plus H. inaequale) that need additional taxonomic investigations involving phylogenetic approaches at least at the western Indian Ocean and continental African scale, in addition to new investigations in the field as we developed here-above. We also provide, with the Xper database, powerful tools for identification. This database built for Comorian Hymenophyllaceae could be a first step toward similar databases extended to or focused on neighbouring areas. This explains that some descriptors, such as '15. Frond orientation' (see Appendix 1), which are not useful to discriminate Comorian taxa, can help identifying Abrodictyum meifolium present in Madagascar and Mascarenes.
In addition, as discussed above, the presence of additional taxa in Comoros as A. meifolium is possible and such taxa could easily be added in the database. An Xper database is indeed easily updated and modified/adapted by adding (or deleting or correcting) new items (taxa) and descriptors and states. Epiphytic: Growing on another plant; i. e. , the whole life cycle is performed fixed on another plant without connection to the soil and without parasitism. The host is named phorophyte.
Six descriptors are continuous; the 74 others are qualitative or discrete. The total number of states defined for the discrete descriptors is 209, whose minimum is 2 (binary descriptor) and maximum is 12 states (multi-state descriptor). All descriptors and states are defined and described if necessary in the database. In addition, 77 descriptors and 163 states are illustrated. The Xper2 database is available on request; the Xper3 version is available online with a free access at http://www. xper3. fr/xper3GeneratedFiles/Somerepresentative descriptors are illustrated in Fig. 2. The list of descriptors, states with definition and descriptions are also reported in Appendix 1.
Acknowledgments This paper is dedicated to the memory of  Professor Jean-Noël Labat  ( MNHN, Paris ) who strongly contributed to developing French-Comorian collaboration in botanical research during the last two decades. This work mostly resulted from the  Ahamada H. Saïd 's international Master thesis that was supported by the program ' Sud Expert Plant' (SEP)  funded by the  French Ministry of Foreign Affairs , such international Masters program being also supported by ' Institut de Recherche pour le Développement' (IRD). SEP promoted research and training in botany in southern emerging countries. Most fieldwork in the Comoros was part of the program 'Connaître pour conserver: le patrimoine naturel caché des Comores' supported by the  Fondation de la Recherche sur la Biodiversité (FRB). The authors would like to thank  France Rakotondrainibe  ( MNHN, Paris ) for fruitful discussions on filmy fern diversity and taxonomy in the western Indian Ocean area.
Six descriptors are continuous; the 74 others are qualitative or discrete. The total number of states defined for the discrete descriptors is 209, whose minimum is 2 (binary descriptor) and maximum is 12 states (multi-state descriptor). All descriptors and states are defined and described if necessary in the database. In addition, 77 descriptors and 163 states are illustrated. The Xper2 database is available on request; the Xper3 version is available online with a free access at http://www. xper3. fr/xper3GeneratedFiles/Somerepresentative descriptors are illustrated in Fig. 2. The list of descriptors, states with definition and descriptions are also reported in Appendix 1. Taxa Anjouan Grande Comore Mayotte Mohéli Abrodictyum pseudorigidum Bauret & Dubuisson X X X Crepidomanes bipunctatum (Poir.
) Mett. ex Kuhn X X X Polyphlebium borbonicum (Bosch) Ebihara & Dubuisson X X Trichomanes boivinii Bosch X Vandenboschia gigantea (Bory ex Willd. ) Pic. Serm. X X X Total species 16 13 11 11 45 out of the 111 (40. 5%)Comorian specimens deposited at P, while Grande Comore and Mayotte have provided 27 (24. 3%)and23 (20. 7%) specimens, respectively, and Mohéli only three (2. 7%) specimens. Furthermore, at least 13 out of 111 (11. 7%) specimens (from the oldest collects) do not provide any other locality information than Comoros. Since 2008, 45 additional specimens have been collected representing 28. 8% of the total Hymenophyllaceae collects and illustrate a significant inventory effort: seven for Anjouan, 19 for Grande Comore and 19 for Mohéli. Xper DatabasesFor the Xper databases, we defined 80 descriptors (characters) for 23 items (or valid species if we consider Hymenophyllum kuhnii C.
Chr. distinct from H. capense Schrad. , and by including Didymoglossum cuspidatum (Willd. ) Ebihara & Dubuisson, unknown from the archipelago so far, see the discussion section, in addition to dwarf forms of H. capillare Desv. as distinct item). Descriptors were classified into 6 groups: habitat (1 descriptor), rhizome and roots (11 descriptors), whole frond (4 descriptors), stipe (14 descriptors), lamina (38 descriptors), and sorus (12 descriptors). The database is complete. Among the 80 descriptors, 20 are without dependencies (i. e. , their presence is not conditioned by the presence of another character/descriptor), 16 are mother-descriptors and 50 depend from mother-descriptors. publish/identification/-147806907205992046/mkey. html. If this link does not work, you can request for access from the authors at dubuisson@mnhn.
(2004) ;  Puig et al. (2009) ;  Butler et al. (2005) ;  Pleau et al. (2005). Nevertheless, the majority of RTC applied in CUDN have only focused on hydraulic model and control objectives without considering the polluting quality load inside the carried water, e. g. Cembrano et al. (2004). The few references which consider quality models during the control process mostly use simulation tools to overcome dealing with the complexity of modelling water quality directly. In  Rathnayake (2015) , control of CUDNs minimizing CSO is achieved by using a non-sorted genetic algorithm (NSGA) linked with storm water management model (SWMM) 5. 0. Butler et al. (2005)  optimizes integrated modelling of the operation and control of an integrated urban drainage system by developing a simulation package SYNOPSIS which integrates the sewer system (simulated by KOSIM), treatment plant (simulated by IAWPRC) and river model (simulated by DUFLOW) using different softwares.
After that, a new linear delayed expressions of TSS and water volume can be defined as: Using these modelling methodologies, simplified models of TSS inside a sewer are created, which allow on-line model calibrations and real-time control of sewer networks. For each sewer, the parameters c, c 1 , c 2 , c vc , d, t vc and e p need to be calibrated by GAMS using historic or real-time data from telemetry systems. Emphasized on TSS modelling approach, c, c 1 , c 2 , c vc , d and e p are will be calibrated. Model 3 T S S out (k) = c vc T S S in (k -d) + e p (6a) Q out (k) = t vc Q in (k -d ) ( 6b.
MODEL CALIBRATION AND VALIDATION The real case study of the sewer network of Perinot in Louis Fargue catchment of Bordeaux Metropole covers a total area of 260 ha that is mainly residential. The sewer length is 3 km with an average slope of 0. 007, quite constant over the whole catchment. It includes a retention tank separated in three hydraulically connected bodies for a total storage volume of 35000 m 3. Even if the slope is generally low, there is no sediment issues on the sewer reported from the operators. Rain scenarios used for calibration and validation come from the real rainfall measured at France in the year of 2007 as show in Figure  6. Rain scenarios T 1, T 2 in different time stage of the year 2007 which represent different rain densities are selected to produce TSS training data using SWMM-TSS in order to calibrate the proposed simplified models.
CONCLUSIONS This paper proposed simplified conceptual modelling approaches for representing dynamic behaviors of TSS in sewer networks, which are oriented to be involved into the integrated RTC controller of CUDN and WWTP for environmental protection. The SWMM is used to produce realistic simulated data for calibration and validation. The Perinot sewer network, which is a real life example, is used as a case study. The application and validation results of the proposed modelling approaches have proved that, the simplified conceptual model.
Pharmacokinetic Population Modeling A non-parametric model was developed in Pmetrics R. Pmetrics R is a free access Software developed by the Laboratory of Applied PharmacoKinetics and Bioinformatics (LAPK) in Los Angeles CA in the United States. The PK profiles were best described by a one-compartment model with first order elimination. The absorption kinetics was fashioned with 2 distinct but parallel routes of oral absorption, both following a gamma pattern. This model has been previously described and validated in 2 independent cohorts to model immediate and delayedrelease form of Tac in lung and renal transplant patients, respectively  (Saint-Marcoux et al. , 2005 , 2010). Details are given in Supplemental Data 1. For the error model, to weight the concentrations by the reciprocal of their variances in the fitting process, we used a polynomial error of the form SD = 0.
Statistical Analysis Statistical analyses other than for PK model development were performed using JMP R 12. 2. 0 Pro for Windows (SAS Institute Inc. , Cary, NC, United States). Baseline characteristics were summarized as mean and the corresponding standard deviation (SD). CYP3A genotype clustering was executed as previously established  (Elens et al. , 2011). Groups were compared using non-parametric tests. To compare two groups, we used the Mann-Whitney U-test, and to compare several groups, the Kruskal-Wallis test was applied. For association between categorical data, we used Pearson's Chi Square test or Fisher's exact test, as appropriate. In all cases, p-values of less than 0. 05 were considered statistically significant.
In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1. 5 years of satellite data (July 2015-November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0. 087 m 3 /m 3 and 0. 059 m 3 /m 3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.
The first proposed method is based on the interpretation of backscattering statistics from Sentinel-1 observations, using the minimum and maximum values of this parameter throughout the full period of observation, whereas the second method is based on the analysis of backscattering differences on two consecutive acquisition days. With both methodologies, the Sentinel-1 data is combined with the normalized difference vegetation index (NDVI) computed from Sentinel-2 data. Site calibration is not mandatory for these two methods. Our paper is organized as follows: in Section 2, the studied site and database are presented. Section 3 describes the two proposed methodologies. Section 4 presents our validation methodology, the ground measurements, and the resulting soil-moisture maps. Finally, our discussion and conclusions are presented in the last section.
With both methodologies, the Sentinel-1 data is combined with the normalized difference vegetation index (NDVI) computed from Sentinel-2 data. Site calibration is not mandatory for these two methods. Our paper is organized as follows: in Section 2, the studied site and database are presented. Section 3 describes the two proposed methodologies. Section 4 presents our validation methodology, the ground measurements, and the resulting soil-moisture maps. Finally, our discussion and conclusions are presented in the last section.
Ground Measurements In-situ soil moisture measurements were acquired continuously (5 min sampling frequency) over a period of several months, in two demonstration fields belonging to the new irrigation district: Foradada and Agramunt (Figure  1 ). Each measurement point was analyzed at different depths. The precipitation data comes from the nearest meteorological station to the demonstration field. For Foradada field, the Baldomar station, which is about 6 km away, is taken, while for Agramunt field, the Tornabous station, which is about 11 km, is considered. Table  1  lists the measured soil moisture and texture characteristics of the two test fields. The Sentinel-1 satellites are equipped with C-band Synthetic Aperture Radar (SAR) instruments, providing data in dual or single polarizations.
Sentinel-1 provides data with a spatial resolution of 10 m and a temporal resolution of 12 days, in both VV and VH polarizations. In the present study, signals recorded in the VV polarization were used to compute the soil moisture estimations. Only one ground track (110) was considered, for which the incidence angle was approximately 40. 3 •. Previous studies have shown that VH data has only a limited potential for the estimation of soil moisture, in particular as a consequence of its high sensitivity to volume scattering, which depends strongly on the geometrical alignment and characteristics of the vegetation  [75] [76] [77]. The Sentinel-1 satellite database corresponds to the period from July 2015 to November 2016 (Table  2 ). All of the Sentinel-1 data was pre-processed using the Sentinel-1 Toolbox, in three steps: -Thermal noise removal -Radiometric calibration -Terrain correction using SRTM DEM at 30 m.
The last step is needed to average the data over 100 m pixels or cells. As discussed above, the methodologies proposed in this study were developed on the basis of this spatial resolution, which also has the advantage of eliminating speckle effects in the radar data. The Sentinel-2A satellite was launched in June 2015, and was followed by Sentinel-2B in March 2017. It is a wide-swath, high-resolution, multi-spectral imaging mission, and is designed to provide full and systematic coverage of the Earth's land surfaces  [78]. The Sentinel-2 database corresponds to the period from July 2015 to November 2016 (Table  3 ). The Sentinel-2 data corresponds to images recorded in 13 spectral bands, with a spatial resolution of 10 m. In the present study, band 4 (Red) and band 8 (NIR) are used to calculate the NDVI: NDVI = (NIR -Red) (NIR + Red) (1) Band "QA60", which is a bit-mask band containing cloud mask information, is applied in order to remove areas covered by cloud.
The Sentinel-2 data corresponds to images recorded in 13 spectral bands, with a spatial resolution of 10 m. In the present study, band 4 (Red) and band 8 (NIR) are used to calculate the NDVI: (NIR -Red) NDVI = (NIR + Red) (1) Band "QA60", which is a bit-mask band containing cloud mask information, is applied in order to remove areas covered by cloud. Figure  2   - The first of these corresponds to dry (non-irrigated) land, revealing an NDVI cycle that occurs between April and July, with a low NDVI for the remaining periods of the year. This trend is confirmed for all of the pixels observed at this location. - The second site corresponds to an irrigated area, which is characterized by a broad range of spatial and temporal variations in NDVI. In order to develop suitable soil moisture algorithms, a mask is used to remove high density vegetation areas with an NDVI > 0.
As our radar database covers a period of only 1. 5 years (due to the later launch date of the Sentinel-2 satellite, i. e. , June 2015), it was not possible to retrieve this relationship for each value of NDVI. We thus consider NDVI classes for the computation of σ 0 dry,(i,j),NDVI , using intervals of 0. 1 (0. 1-0. 2, 0. 2-0. 3, 0. 3-0. 4, etc. ). In the present case, the NDVI over the studied agricultural site ranges between a minimum of 0. 1 and a maximum of 0. 8. Various experimental studies have shown that a linear relationship exists between radar signal differences and changes in soil moisture  [19, 80] , in the case of bare soils and vegetation-covered surfaces. For a given NDVI, the radar signal difference, ∆σ NDVI , can thus be written as: ∆σ NDVI = α(NDVI)∆Mv ( 4 ) where ∆Mv is the change in soil moisture between the date d and the date when the soil was at its driest.
The backscattering difference calculations were carried out for all cells and all S1 acquisition dates (over a period of approximately two years). ∆σ NDVI max is modeled as [15]: ∆σ NDVI max = f(NDVI) = a NDVI + ∆σ bare max ( 7 ) When NDVI = 0, ∆σ NDVI max = ∆σ bare max , which corresponds to the maximum value of backscattering difference under the driest, bare-soil conditions. In order to minimize the influence of noise when estimating f(NDVI), for each selected value of NDVI, we excluded the upper 1% of the corresponding values of radar signal difference, as well as all data points having a radar signal lower than -15 dB, since these are known to correspond to water  [46, 82]  (Figure  4 ). As our radar database covers a period of only 1. 5 years (due to the later launch date of the Sentinel-2 satellite, i.
In order to minimize the influence of noise when estimating   f NDVI , for each selected value of NDVI, we excluded the upper 1% of the corresponding values of radar signal difference, as well as all data points having a radar signal lower than -15 dB, since these are known to correspond to water  [46, 82]. (Figure  4 ). The soil moisture for each pixel can thus be retrieved using the following function:           NDVI i,j max min min Δσ Mv i, j, NDVI, d = Mv -Mv + Mv i, j, d f NDVI (8) SMOS low-resolution moisture products (SMOS Level 3 daily product), corresponding to the two-year period of S1 acquisitions, were used to estimate max Mv and min Mv , since the ground measurements were recorded for a limited period of time. The mean S1 radar signal is estimated over a SMOS pixel (40 km × 40 km).
Acknowledgments: This study is part of the REC project partially funded by the European Commission Horizon Acknowledgments: This study is part of the  REC  project partially funded by the  European Commission Horizon 2020 Programme for Research and Innovation (H2020)  in the context of the  Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action  under grant agreement No. 645642  and TOSCA/CNES ASCAS.
and astrophysics constraints, and a consistent treatment of systematic uncertainties. In this paper we describe its main functionality, provide a guide to getting started quickly, and show illustrative examples for results obtained with DarkBit (both as a standalone tool and as a GAMBIT module). This includes a quantitative comparison between two of the main dark matter codes (DarkSUSY and micrOMEGAs), and application of Dark-Bit's advanced direct and indirect detection routines to a simple effective dark matter model.
In order to extract the maximal amount of information and narrow down the properties of a given DM candidate, or exclude it, it is mandatory to combine these measurements in a statistically rigorous way. With this article we introduce DarkBit, a new numerical tool for tackling this task. DarkBit calculates DM observables and likelihoods in a comprehensive and flexible way, making them available for both phenomenological DM studies and broader Beyond-the-Standard Model (BSM) global fits. In particular, the first release of DarkBit contains up-to-date limits and likelihoods for indirect DM searches with gamma rays and neutrinos, for the spin-dependent and spin-independent cross-sections relevant to direct detection, and for the relic density. In order to increase the efficiency of observable and likelihood calculations by reusing as much code as possible, DarkBit relies on highly flexible data structures that can easily accommodate the specific needs of most particle models.
Examples include the Process Catalogue (Sec. 6. 3. 1) , which contains all relevant particles and interaction rates, a general halo model, and a fully model-independent framework to calculate the relic density. DarkBit is designed as a module within the GAM-BIT framework  [10] [11] [12] [13] [14]. Where we introduce key terms with specific meanings in the context of GAMBIT, we highlight and link them to the glossary of standard GAMBIT terms at the end of this paper. GAMBIT defines a series of physics modules, each consisting of a collection of module functions. Each module function is able to compute an observable, a likelihood or some intermediate quantity required in the calculation of other observables or likelihoods. At runtime, the user informs GAMBIT of the observables they want to compute, the theoretical model and parameter ranges over which those observables should be calculated, and how they would like GAMBIT to sample the parameter space.