dataesr/scientific-paragraphs-categorization huggingface dataset:
Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D-1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d'assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform.
However, current applications of urban 2D/1D models focus on applying increasingly detailed remote sensing data  [1, 2]. Few studies have attempted to quantify the gains made by using high-resolution data compared with only using free access data sources (for example, low-resolution topographic data and coarse land-use information). A reference for the economical use of data collection efforts and human resources is required for urban stormwater governors and practitioners. Such work is crucial for recognizing the real needs of collecting remote sensing data for improving urban stormwater modeling. In this study, the 2D-surface TREX (Two-dimensional Runoff, Erosion, and Export model) model  [15]  and the 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d'assainissement) model  [16]  are integrated in TRENOE platform.
TREX is an open source code, and both the TREX and CANOE models are well-documented, with robust numerical schemes. That makes these models suitable for modifications and adaptations in an urban context. The coupling between TREX and CANOE models is designed to be able to simulate the 2D overland flows and the 1D sewer network routing. Four different types of remote sensing (RS) and geographic information system (GIS) data are applied to the modeling platform: (i) high resolution topographic data derived from LiDAR; (ii) low-resolution digital terrain model (DTM) data obtained from the free access database; (iii) detailed land-use data proceeded from multiple data sources; (iv) coarse land-use information extracted from the free access database. The influences of using such RS and GIS data on urban stormwater 2D-1D modeling are analyzed for a small urban catchment near Paris (Le Perreux sur Marne, Val de Marne, France).
Besides, as most roofs are directly connected to the sewer network in the studied catchment, the grid cells of roofs that are linked to the same sewer node (the nearest) are assembled as virtual sub-basins in the CANOE model. The areas of these sub-basins are equal to the total area of the connected roof cells. The non-linear reservoir method is then applied to simulate the rainfall-runoff relations for each conceptual sub-basin. These sewer nodes are the connecting points between the 2D surface module and the 1D network module in the TRENOE platform, and are called "junction nodes". At each time-step, TRENOE removes water from the grid cells of "manholes" (sewer inlets, sewer drain grills) and simultaneously makes it enter into the corresponding "junction nodes". Additionally, the flow routing process in the sewer networks is computed by the 1D kinematic wave approximation of the shallow water equations and solved by a four-point implicit finite-difference scheme due to Preismann  [18]  (Equation (  1 )).
Rainfall Event Data A tipping-bucket rain gauge is installed on the roof of a building close to the urban catchment (see Figure  2 ). The rain gauge has a resolution of 0. 1 mm. As the study area is quite small, rainfall is considered as homogeneous within the basin. The monitoring was performed between 20 September 2014 and 27 April 2015. In our previous work  [19] , 56 rainfall events were identified during this study period, in which more than 88% of the events had a rain depth of less than 8 mm, nearly 89% of the events had a mean intensity smaller than 3 mm/h, and 87% of the events had a duration shorter than 7 h. In this study, six typical rainfall events were selected for model assessment. These rainfall events can be considered as representative of various hydrological situtations on the catchment.
LiDAR and Topographic Data In Europe, the DTM databases are accessible for public research centres (Directive 2007/2/EC of the European Parliament and of the Council). As for France, the National Institute of Geographic and Forestry Information (IGN) provides the large-scale reference database (RGE ® , http://professionnels. ign. fr/rge) that contains DTM data of 25 m resolution for the whole country. Figure  3a  shows a part of this data on our study site. This dataset is easy to achieve and demands no extra cost, hence have evident value for operational purposes. In addition to this available DTM data, a mobile mapping system (MMS) called Stereopolis  [20]  has been applied over the study area in order to produce a 3D point cloud of the road topography with centimetric resolution. Whereas the measured 3D cloud not only contains the topography, but also objects such as trees, urban furniture, and moving objects such as vehicles and pedestrians.
A semi-automatic modeling approach was then developed in order to filter out the non-terrain point  [21]. At the end, a 20 cm resolution topographic data of road and sidewalks was accomplished (Figure  3b ). Forestry Information (IGN) provides the large-scale reference database (RGE ® , http://professionnels. ign. fr/rge) that contains DTM data of 25 m resolution for the whole country. Figure  3a  shows a part of this data on our study site. This dataset is easy to achieve and demands no extra cost, hence have evident value for operational purposes. In addition to this available DTM data, a mobile mapping system (MMS) called Stereopolis  [20]  has been applied over the study area in order to produce a 3D point cloud of the road topography with centimetric resolution. Whereas the measured 3D cloud not only contains the topography, but also objects such as trees, urban furniture, and moving objects such as vehicles and pedestrians.
Landuse Information In order to obtain more finely detailed information regarding urban land-use, different land-use classes can be identified through combining multiple data sources such as aerial orthophotos, LiDAR data, and a publicly accessible database. This is the first configuration of the input land-use data for the TRENOE platform (Figure  5a ), which is noted as "detailed land-use". In detailed land-use, the road surfaces are traced by using aerial orthophotos; as a result, the sidewalks can be identified automatically as the areas between buildings and roads. Then, inside every cadastral plot, the complement of buildings was considered. Orthophoto identification was applied in order to classify those parts into different classes such as bituminous surface, lawn, and vegetation. LiDAR data was used for recognizing low vegetation from trees.
Moreover, an automatic image segmentation tool  [22]  was applied to aerial orthophotos for recognizing different types building roofs, such as flat, slopping, and tiled roofs. As for the overlapped areas within different data sources, a specific priority order of land-uses is defined by considering the hydrologic connectivity in urban landscapes, followed by roads, sidewalks, parking lots, various types of roofs, trees, grass, and others. Additionally, since the manholes have a key role in the coupling between the 2D surface and 1D network models, they are represented as a specific class of land-use in the TRENOE model. The second configuration of the input land-use data is simply extracted from the BD-TOPO ® database, which is free access and much easier to achieve than the multiple data sources; this data is noted as "coarse land-use" (Figure  5b ).
Configuration of Parameters for Urban Areas In order to reduce the calibration efforts, several parameter values are determined from bibliographic works. For instance, the initial loss for different urban land-uses are defined by considering the findings of Xiao et al. [23] ; besides, in accordance with the propositions of Rossman  [24] , we fixed two parameters for the Green and Ampt method  [17]  (suction head at the wetting front and the residual soil moisture content), as well as three parameters used in the sewer network module, including the initial loss of roof wetting, the Manning's N value for conduits, and virtual sub-basins. The trial and error procedure is performed for calibrating the Manning's N value and the saturated conductivities (Ksat) by using the DTM + LiDAR topographic data and the detailed landuse information.
Configuration of Parameters for Urban Areas In order to reduce the calibration efforts, several parameter values are determined from bibliographic works. For instance, the initial loss for different urban land-uses are defined by considering the findings of Xiao et al. [23] ; besides, in accordance with the propositions of Rossman  [24] , we fixed two parameters for the Green and Ampt method  [17]  (suction head at the wetting front and the residual soil moisture content), as well as three parameters used in the sewer network module, including the initial loss of roof wetting, the Manning's N value for conduits, and virtual sub-basins. The trial and error procedure is performed for calibrating the Manning's N value and the saturated conductivities (Ksat) by using the DTM + LiDAR topographic data and the detailed land-use information.
On the other hand, the results surprisingly demonstrate that the gaps between the reference simulations and simulations using free access topographic data (DTM and DTM_dug) are not significant. The simulated water flows at the sewage outlet are hence not sensitive to the use of different resolutions of topographic data. This result is mainly related to the precisely defined and closely located manholes in the studied urban catchment. Since the distances between manholes are very short (generally less than 30 m), stormwater runoffs enter rapidly into the sewer networks. Thus, the precision of the road topography does not notably influence the transfer time, as this time is very short in any case. Moreover, as the building roofs are directly connected to the sewer networks in the TRENOE platform, the effects of using precise road topographic data has no effect on these land-uses.
Of course, this result needs to be confirmed by applications of this modeling approach to other urban catchments with different shapes and slopes. It is important to notice that the resolution of road topographic data may not be a key factor for urban 2D/1D modeling (excepted urban flood modeling). As presented in the above sections, by assessing the performance of the newly developed 2D/1D TRENOE platform on an urban catchment at the city district scale, detailed land-use information is required for accurate simulations of water flows at sewer outlets. On the contrary, it is not recommended for urban stormwater managers to apply ad hoc RS techniques for measuring high-resolution altimetry data. Using only easily achieved GIS topographic data with artificially dug streets seems to be a reasonable compromise to obtain acceptable simulations.
Conclusions In this paper, we presented for the first time a rigorous discussion on the use of free access GIS data and ad hoc RS data for urban 2D-1D stormwater modeling. The coupled 2D-surface and 1D-Sewer TRENOE model was applied to a small urban catchment near Paris (Le Perreux sur Marne, 0. 12 km 2 ). Four different types of RS and GIS data have been applied in the modeling platform, including (i) high-resolution topographic data derived from LiDAR; (ii) low-resolution DTM data obtained from the free access database; (iii) detailed land-use data proceeded from multiple data sources; and (iv) coarse land-use information extracted from the free access database. According to the results, the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations; however, using the very high resolution LiDAR data is not equally significant for water flow simulations at sewage outlets.
Acknowledgments: The research work of student  Yi Hong  was financed by  ANR - Trafipollu  project ( ANR-12-VBDU-0002 ) and  Ecole des Ponts ParisTech. The authors would like to thank the experimental team of  ANR Trafipollu  project for all collected necessary for this work, in particular  David Ramier  (CEREMA),  Mohamed Saad (LEESU)  and  Philippe Dubois (LEESU).
Phylogenetic analysis revealed that the virus (European Nucleotide Archive [ENA] accession number LT630606) belonged to the Sierra-Leone 3 (SL3) lineage and clustered with strains of the large Conakry-Dubréka sublineage that were circulating between May and July 2015 in these prefectures (Figure  1B )  [1]. We identified a unique genomic signature consisting of 3 nucleotide polymorphisms at positions 6027, 12290, and 15660, which were not found elsewhere in the EBOV database. Due to EVD confirmation in the child, the parents received the recombinant vesicular stomatitis virus (rVSV)-based vaccine expressing the glycoprotein of Zaire Ebola virus (rVSV-ZEBOV) vaccine on 27 August (Figure  1A )  [2]. On 30 August, both tested positive for EBOV-specific immunoglobulin G but not immunoglobulin M in enzyme-linked immunosorbent assay, which was interpreted as a sign of past EBOV infection and raised the suspicion of asymptomatic virus carriage.
Therefore, body fluids of the parents were tested for EBOV RNA using the RealStar ZEBOV RT-PCR kit (Altona, Germany) on a Rotor-Gene instrument (Qiagen)  [3]. On 9 September, the breast milk tested EBOV RNA positive with a cycle threshold (Ct) value of 23. 3, whereas urine and whole blood were negative (Figure  1A ). Sequencing and phylogenetic analysis demonstrated that the viruses from child and breast milk (ENA accession number LT630561) are closely related and share 2 of the unique single-nucleotide polymorphisms (Figure  1B ). In the phylogenetic tree, the virus from the breast milk appears ancestral to that of the infant. On 15 September, a semen sample from the father tested EBOV RNA positive with a Ct value of 35. 6, whereas urine and whole blood were negative (Figure  1A ). The presence of EBOV RNA in the semen sample was confirmed by repeat RNA extraction and RT-PCR testing as well as genomic sequencing.
The virus sequenced from the semen (ENA accession number LT630562) was also part of the SL3 lineage, but ancestral to the Conakry-Dubréka cluster and without close link to the viruses of mother and child (Figure  1B ). On 7 October, the father's semen tested negative for EBOV. Epidemiological investigation could not reveal source, locality, or time period of EBOV infection in the parents. They did not report a severe febrile illness and denied contact with known EVD cases or family deaths, or attending funerals since the beginning of the Ebola outbreak. The father, a 28-year-old nurse, had worked as community healthcare worker in the district of Mali in the northern part of Guinea from February 2014 to March 2015. Only 5 EVD cases have been reported in this district throughout the outbreak.
European Nucleotide Archive accession numbers are LT630562 for the father's semen, LT630561 for the mother's breast milk, and LT630605 for the child's swab. The length of the branches roughly corresponds to the number of single-nucleotide polymorphisms in the respective strain(s) that distinguish them from the ancestral virus. Abbreviations: Ct, cycle threshold; EBOV, Ebola virus; IgG, immunoglobulin G; IgM, immunoglobulin M; rVSV-ZEBOV, recombinant vesicular stomatitis virus (rVSV)-based vaccine expressing the glycoprotein of Zaire Ebola virus; SNP, single-nucleotide polymorphism. medical episode except chronic headaches. She traveled daily between Dubréka and her school in Conakry by collective taxi. The described family cluster highlights peculiarities of EVD, which are poorly understood and difficult to study, including EBOV infection in the absence of classical risk factors and virus persistence in patients with a mild or asymptomatic course of EVD associated with risk of virus transmission.
The cases of mother and father show that EBOV may persist in breast milk and seminal fluid in survivors with mild disease or asymptomatic infection. This raises the possibility of virus transmission by individuals who were not diagnosed with the disease (and thus do not know they had EVD) and are not listed in the national survivors databases, like the 2 parents. The closely related EBOV sequences in mother and child, the ancestral position of the mother's virus relative to the child's virus in the phylogram, the epidemiological link between mother and child, and the absence of contact of the child to other EVD cases in the community or the hospital suggest that the mother transmitted the virus to the child via breastfeeding. Our findings are in line with the case of an asymptomatic mother whose breast milk tested positive and who may have transmitted EBOV to her 13-month-old child  [7].
European Nucleotide Archive accession numbers are LT630562 for the father's semen, LT630561 for the mother's breast milk, and LT630605 for the child's swab. The length of the branches roughly corresponds to the number of single-nucleotide polymorphisms in the respective strain(s) that distinguish them from the ancestral virus. Abbreviations: Ct, cycle threshold; EBOV, Ebola virus; IgG, immunoglobulin G; IgM, immunoglobulin M; rVSV-ZEBOV, recombinant vesicular stomatitis virus (rVSV)-based vaccine expressing the glycoprotein of Zaire Ebola virus; SNP, single-nucleotide polymorphism.
vestigated location. The Weibull distribution has become a widely used standard in wind energy application due to its simplicity. It depends on two easily estimated parameters and there are simple analytic expressions for the moments. It is a reference used in wind energy softwares such as the Wind Atlas Analysis and Application Program (WAsP), and it is included in regulations such as the IEC 61400-12 on wind turbine power performance testing. Many other aspects of the resource assessment methodology can lead to bad estimates, such as the wind measurement accuracy, the vertical extrapolation of wind measurements, their temporal extrapolation with measure-correlate-predict techniques, the wind flow and wake modeling or the use of inaccurate power curves. Each issue is complex enough to be the subject of many dedicated research articles, but this paper is aimed at evaluating the impact of the very step of wind-speed statistic modeling that uses a Weibull distribution.
To address this issue, the present study is based on a large wind dataset of 89 weather stations, covering different sub-climatic regions of France. This enables the discovery of some systematic behaviors: systematic over-or underestimations depending on the wind characteristics at the location and the fitting method. It enables emphasis on the link between the goodness of fit of the distribution and the production estimate errors (thus completing the work of  Carta et al. , 2008). Section 2 presents the data and the methodology used to fit the distributions and evaluate the errors in energy content and production arising from the statistical modeling. Section 3 presents the resulting errors at all stations and Sect. 4 discusses these results. Finally, Sect. 5 concludes the study.
Wind-speed data In this article we use wind surface measurements (10 m a. g. l) from the global NOAA ISD Lite database  (Smith et al. , 2011) , already used in  Drobinski et al. (2015)  and  Vautard et al. (2010). This compilation of observations from operational weather stations is the best publicly available dataset over the considered region. We use 4 years of measurements made between 1 January 2010 and 31 December 2013. The 10 min averaged wind speeds are recorded every hour. We select the stations located in France that present a data availability greater than 97 % over the 4 years, with a minimum of 85 % for each month to ensure a good representation of the seasonal cycle. We also keep only the stations with the best precision in the measurements. We therefore keep 89 stations. Calm winds correspond to wind speeds equal to zero in the dataset.
They represent 5. 2 % of the entire dataset, with disparities among the stations of course. The calm winds are removed before fitting the distributions since they are not taken into account in the Weibull distribution. The wind speeds are binned with intervals of about 0. 514 m s -1 because the data were previously recorded with bins of 1 knot. We add a small random noise to the wind-speed data in order to remove the effect of this sampling. The added noise is a continuous uniform distribution between -0. 5 and +0. 5 knot (arising negative values are set to zero). We also tested a Gaussian noise with a standard deviation of 1/3 knot, which led to the exact same results. Throughout the article the wind speed is noted as w and the series of hourly observations is noted as (w i ) n i=1 , where n is the number of observations, which would be 35 064 for a complete set over the years 2010 to 2013, but is fewer due to the missing values and removed calms.
Sensitivity of the results The figures above are given for the whole dataset (years 2010 to 2013). The results are similar when limiting the wind series to only 1 year, with only small differences due to the interannual wind variability. For the computation of the production, the power curve has an important role. We tested other shapes, among which were simpler power curves with a ramp between cut-in and rated wind speed as a linear function of either w or w 3. They all led to similar results. Choosing a different capacity factor, such as 25 or 35 %, to adjust the a parameter of the power curve has also a small impact. The errors tend to be smaller when using a larger capacity factor, except in the case of the MLE method where the errors tend to increase. When the capacity factors are much lower, the errors of the Weibull MLE switch to negative values.
Today computers can handle very long wind series without any problem, limiting the need for any modeling when there are actual measurements. Indeed, the measurements are much more precise and contain much more information than any two-or four-parameter model. This study benefits from the use of a large dataset, covering all regions of France. The drawback of this dataset is that they are surface measurements, often located in areas not actually adapted to wind project development. The results apply to all the studies using surface measurements, and could be followed by more precise studies at precise locations, using real wind project measurements at higher levels above ground. A question is whether or not the wind distribution varies a lot with the altitude and whether the shapes are closer to the Weibull distribution higher up.
Bacterial strain and growth conditions A free-living S. symbiotica strain, CWBI-2. 3 T , isolated from a natural Aphis fabae collected in Belgium in 2009 was routinely maintained on 863 agar at 20 • C, containing 1% glucose, 1% yeast extract, 1% casein peptone, and 1. 7% agar. A draft version of the S. symbiotica CWBI-2. 3 T genome is available under the GenBank accession number CCES01000000. This draft contains 3,664 predicted protein-coding sequences  (Foray et al. , 2014). To prepare protein extracts, triplicate cultures were grown in 400 mL of 863 medium under vigorous agitation at 20 • C  (Sabri et al. , 2011)  and were collected during the exponential growth phase when reaching an optical density (OD) of 0. 4-0. 6 at 600 nm. Cells were harvested by centrifugation at 3,200 × g for 10 min at 20.