dataesr/scientific-paragraphs-categorization huggingface dataset:
Valuing knowledge from the Floods Directive The Floods Directive helped initiating significant mapping of flooding area for the most vulnerable territories in France and Overseas. 122 Territories of Significant Risks of flooding (TSR) have been defined. Out of the approximately 7700 municipalities covered and warned by the Vigicrues® system, nearly 1,600 are located inside of a TSR (see Fig. 8 before ). Therefore, these areas are covered by 2 or 3 flood maps created for the Floods Directive, including frequent and medium scenarios. These scenarios form a first dataset that can be integrated into VIGInond. Before VIGInond import, a data recover is needed. First, an analysis is required to link Floods Directive scenario to a water height at a hydrometric station, preferably located within a TRI.
Definition of influence zones and VIGInond standard compliant data formatting needs also to be done. Moreover, hydraulic models have mostly been built or reused to produce all or part of three scenarios of the Flood Directive. Using these models on a few extra flood scenarios will be possible if these models are well calibrated. In a challenging public economy, the cost to carry this additional work on TRI will be weighed against the gains for the community in matters of: x rescue team mobilization and flood damage reduction; x crisis preparation and preventive action improvement.
Tools for data visualization Since March 2016, VIGInond data are made available to a first group of crisis management actors, risk services in Regional Directorates of Environment and the FFC, by providing alphanumeric and cartographic web services. Web services make database interoperable with other databases and visualization tools. These web services are operated by applications of the Ministries in charge of Environment and Interior, allowing therefore visualization of flooded areas with different levels of expertise and uses : x a dedicated application and accessible through the government portal Georisques (see Fig. 9  after) on its "Internal" part reserved for state services with a secured extranet. This platform aims to view relatively simply and intuitively flooded areas of data with other geo-referenced data associated to risk.
Conclusion Since 2011, several activities have been performed to implement a new reference dataset : the potential flooded areas. Tasks from pooling resources to collect and valorise observed data to others related to the operational production organisation have been conducted by the SCHAPI and the FFC. Today, these reference data can be generated at national level. Flooding forecasting is made possible thanks to the national database VIGInond, which allows storage and diffusion of flooded areas to ministerial services. As a first step, dissemination to crisis managers is planned during the first semester of 2016, leading to their appropriation of this new information. For the second half of 2016, VIGInond will become operational, opening therefore the potential distribution of these data to other partners impacted by flood hazard.
x Fine topographic data -LIDAR Digital Terrain Model (DTM) As part of the Flood Directive [4], a high resolution DTM acquisition program by LIDAR Technology was conducted between 2011 and 2014. These topographic data cover floodplains on about 80 % of the linear river sections surveyed by hydrological vigilance, providing therefore an essential database for the mapping of future flooded area (see DTM comparison on Fig. 4 after).
Also, it collects network data such as statistics and network logs and keeps them into its database. Based on these data, the orchestrator constructs a holistic network view including the network topology. In order to reinforce and propagate the security policies, it uses an Access Table. It contains all the stateful and stateless security policies specified by the administrator. The orchestrator manages this table to propagate the security policies into the network. The orchestrator can also configure the behavior of the Firewall Applications dynamically. When the latter receives a new configuration, it loads the corresponding module and stops the old one. Such configurations options are the behavior mode: stateful or stateless, the event mode: periodic (according to a timer) or instantaneous (according to a sensor) and the topology discovering mode: static (topology data provided by the user) or dynamic (by learning dynamically the topology).
In the first step, the Orchestrator sends the Universal OF Rule and the settings of the table miss to all the Firewall Application instances. The following program code describes the structure of the universal OF Rule for TCP communications. Through the controller, the Firewall Application is constantly listening to a potential connection of new data plane devices. It reacts to the network events by propagating the received Universal Rule and the settings of the table miss. It also, installs the Openflow Stateful Rules when the state transitions are triggered. The Firewall Application observes networks events according to two modes: 1. Periodic Mode: in this mode (see the Firewall_Periodic_Mode Class) the firewall sets a timer to observe periodically new network events coming from the controller.
Implementation The firewall API (see Figure  2 ) comprises two packages. The orchestrator package runs in the management layer. It offers a General user Interface to manage the security policies and the OF rules. It allows adding, modifying and displaying the security policies and manages the static topology information. It offers to the administrator the possibility to configure many parameters of the firewall such as event modes, behavior modes, etc. It keeps open sockets with all the Firewall Application instances to communicate with them. Through these canals, it sends management commands and collects network events. 6 ) The Sentinel singleton is responsible for the interaction with the controller. It configures also the firewall components and instantiates them. (7) The Engine class expresses the behavior of the firewall in handling all the phases of a stateful connection and in processing the communication between the client and the server.
Test & Results We deploy and configure our test environment (see Figure  3 ) in mininet  [24]. The latter is a Python application to emulate virtual networks. Our mininet environment comprises 3 clients and a HTTP server. All are connected by their virtual network interfaces to a virtual switch (OVS  [25] ). Each client runs a Python script which generates a number of simultaneous queries to request data from the HTTP server. The Ryu controller is remotely connected to the virtual switch via the virtual channel offered by mininet. It offers an execution environment to the Firewall Application and ensures all its interactions with the virtual environment. Besides we run the orchestrator and we connect it with a socket to the firewall Application. Our environment is running under Ubuntu 14, 64 bits, 2 GB of RAM and 2 processors at 2.
, 2015b). Interestingly, environmental parameters, although possibly inducing phytoplankton blooms, were negligible in determining plankton community structure during the weeks that the succession induced by the bloom continued, suggesting that interactions within the community played a much more important role than previously thought  (Needham and Fuhrman, 2016). Similarly, the analysis of the Tara Oceans dataset demonstrated that environment and geography explained up to 18% of community variation. Using a random forest approach it was shown that 95% of the models based on OTU composition alone were more accurate in explaining variation than models incorporating additionally environmental data  (Lima-Mendez et al. , 2015). As far as we know the Tara Oceans study is the only one that uses such a holistic network approach on samples covering a geographical range in the ocean, and not a timeseries on a fixed location.
22 µm membranes, SPA (small particle associated) for bacteria collected on the 3 µm membranes, and LPA (large particle associated) for bacteria collected on the 8 µm membranes. DNA extraction and Illumina sequencing were performed as described  (Milici et al. , 2016b)  and the raw data can be accessed at the ENA database (European Nucleotide Archive) BioProject ID: PRJEB11493.
Taxonomic Classification Taxonomic classification was assigned using SINA aligner (version 1. 2. 11)  (Pruesse et al. , 2012)  employing the reference database SILVA (119 NR)  (Pruesse et al. , 2007). The OTUs were aligned and classified against a maximum of 100 sequences that had a minimum of 97% similarity with the query sequence, using the lowest common ancestor method (LCA). The retrieved results were sorted for further analysis and processed in the following way: all the OTUs assigned to archaea or "unclassified" were excluded from the dataset and not considered further, since in silico analysis of the primer coverage revealed that only 18. 2% of the archaeal sequences present in the SILVA database (123 NR) matched the primers used here. Sequences assigned to "chloroplast" were excluded from the bacterial table and analyzed separately with the PhytoREF database  (Decelle et al.
, 2015)  with BLAST version 2. 2. 28  (Camacho et al. , 2009). All reads that had a similarity below 95% to known 16S sequences were excluded from the dataset. Results of the SINA classification were checked with BLAST  (Camacho et al. , 2009)  version 2. 2. 28 against the SILVA 119 NR database  (Pruesse et al. , 2007). Some reads could be assigned only at the domain or phylum level with SINA (Table  S2 ). For those ambiguous sequences a manual curated taxonomical assignment was performed (Figure  S1 ). We first performed BLAST for the sequences against the SILVA database in order to understand whether the mis-classification was due to the absence of a reference or to misclassified sequences in the database. We found sequences that were assigned to two contrasting phylogenetic lineages of the domain bacteria (e.
g. , OTU_2_FL had 100% similarity both with a Prochlorococcus and OM1 clade sequence). Based on these results we downloaded a reference dataset constructed according to the BLAST results employing the SILVA database. We constructed alignments with MUSCLE and built phylogenetic trees with MEGA 6 using Neighbor-Joining method  (Tamura et al. , 2013). Because of the difference in gene copy number across the different algae and bacterial lineages, read counts of 16S rRNA genes cannot be used to infer cell abundance. Here, we therefore report relative comparisons only using the term "relative abundance. " The taxonomic information and relative abundances of the OTUs identified in this study are reported in Table  S3.
Statistical Analysis and Network Analysis Statistical analyses were performed with PRIMER (v. 7. 0. 6, PRIMER-E, Plymouth Marine Laboratory, Plymouth, UK;  Clarke and Gorley, 2015) , with the add on PERMANOVA+ (v. 1. 0. 6 PRIMER-E, Plymouth Marine Laboratory, Plymouth, UK;  Anderson et al. , 2006)  and the statistical program R (https:// www. r-project. org/, v. 3. 0. 1) with the library vegan: Community Ecology Package (v. 2. 0-8). Rarefaction analysis was performed in R with library vegan, in order to assess if the sequencing effort was sufficient to investigate bacterial communities (Figure  1B ). To infer correlations between OTUs and OTUs and environmental parameters, we first removed OTUs with a relative abundance below 0. 1% of the total number of reads. We also removed all samples that had <7500 reads.
Rare OTUs and samples with low Rarefaction analysis for the three size fractions of bacterioplankton. The number of sequences per sample was limited to 30,000 sequences, the original raw data can be found in Table  S3. For both panels color code distinguishes the six oceanographic provinces. sequencing depth might cause artifacts in the network analysis  (Berry and Widder, 2014; Ju et al. , 2014). Subsequently sample size for each of the three size fraction of the plankton was rarefied to 8,709, 10,125, and 7,780 sequences per sample in FL, SPA, and LPA respectively. The cumulative species dominance is shown in Figure  S2. Co-occurrences were then calculated with SparCC  (Friedman and Alm, 2012)  for each size fraction of the plankton and depth layer  (20, 40, 50-80, 85-120, and 140-200 m)  separately.
, 2012; Klindworth et al. , 2013)  showed a good coverage (86. 4%) of bacterial lineages included in the SILVA database (version 123, NR). Taxonomic classification of the bacterial sequences showed that the majority of the OTUs (59-69%) could be affiliated at the order level, between 35 and 42% of OTUs could be affiliated to a family, and 16-24% were assigned to a genus (Table  S6 ). Because the primers used were universal for the prokaryotic small subunit RNA gene, they inevitably amplified also the plastidial 16S rRNA gene of eukaryotic microalgae. They were not found in the FL community (confirming the efficiency of the filtration), but amounted to ∼6% in the SPA community and were termed SP (small phytoplankton) and ∼21% in the LPA community and were termed LP (large phytoplankton). Seven different classes of microalgae could be identified (Table  S3 ).
An average of 1,094 ± 1,189 sequences for SP and 7,345 ± 10,504 for LP were generated. In silico analysis of the primer coverage of the marine photosynthetic eukaryotes (terrestrial lineages were excluded) included in the PhytoREF  (Decelle et al. , 2015)  database revealed that primer sequences perfectly matched (no mismatch) 68% of the reference sequences (Table  S7 ) and had one mismatch with 88% of the reference sequences (data not shown). Primers coverage differed for the various taxa; e. g. , it was high for diatoms (92%) but low for haptophytes (8%) (Table  S7 ). Although abundant, photosynthetic dinoflagellates are also under-represented in the reference database PhytoREF because of their specific plastidial genome organization  (Decelle et al. , 2015). These biases have to be taken into account when interpreting the data.
Distribution and Taxonomic Composition of Photosynthetic Eukaryotes The sequences from the plastidial 16S rRNA genes of photosynthetic eukaryotes obtained in this study were analyzed using the reference database PhytoREF  (Decelle et al. , 2015)  and have unambiguously been assigned with high percentage similarity (from 95 to 100%) to reference sequences (Table  S3 ). In the LP communities (>8 µm) seven classes of eukaryotic microalgae were identified, from which Bacillariophyceae (diatoms) and Prymnesiophyceae (haptophytes) were numerically the most prevalent (Figure  5 ) and showed a clear biogeographical pattern. Bacillariophyceae were more represented in the marginal provinces (FKLD and NADR) with strong depletion in relative abundances in the more oligotrophic SATL province. By contrast, Prymnesiophyceae were the most abundant group from the BRAZ to the NAG province with peaks in the BRAZ and SATL province.
We show that sequences retrieved with the bacterial primers F807 and R1050  (Bohorquez et al. , 2012)  cover almost 70% of the marine eukaryotic photosynthetic lineages of the PhytoREF database  (Decelle et al. , 2015)  offering the opportunity to investigate the distribution of eukaryotic microalgae. However, interpretation must take into account the primer coverage and the issue of 16S rRNA gene copy number, which varies across taxonomic groups. In our study, the two most abundant classes of eukaryotic microalgae identified were Prymnesiophyceae (haptophytes) and Bacillariophyceae (diatoms). These results are similar to previous studies that used other molecular tools  (Kirkham et al. , 2011 (Kirkham et al. , , 2013;; de Vargas et al. , 2015)  or measurement of accessory pigments via HPLC  (Barlow et al.
ACKNOWLEDGMENTS We thank the crew of RV Polarstern for their excellent support during samples collection. This work was funded by  Deutsche Forschungsgemeinschaft (DFG)  in Transregio-SFB TR51. JD was supported by the  OCEANOMICS  project, funded by the  French Government  and managed by the  Agence Nationale de la Recherche , under the grant agreement " Investissements d'Avenir "  ANR-11-BTBR-0008.
METHOD The model presented hereafter has been developed using MATLAB ® R2014a. All the calculations are wavelength-dependent with rectangular integration on the wavelength between 280 and 1450 nm with a 0. 5 nm step. The efficiencies were calculated for air mass 1. 5 global (AM1. 5G) without concentration using data from the ASTM G173-03 reference spectrum  [17]. The percentage of arsenic x in the top GaAsxP1-x cell can vary from x=0. 55 to 1, representing the direct bandgap domain of GaAsxP1-x  [18]. The main challenge in accurately simulating a bandgap-dependent GaAsxP1-x/Si dualjunction solar cell lies in the lack of data regarding the electronic parameters of GaAsxP1-x for varying percentages of arsenic x. Using the blackbody theory applied to semiconductors and the flow equilibrium in the cells, our model reduces the amount of electronic parameters needed to a very limited number, namely the bandgap Eg(GaAsxP1-x), the densities of states in the conduction and valence bands Nc(GaAsxP1-x) and Nv(GaAsxP1-x), the diffusion coefficient of electrons and holes Dn(GaAsxP1-x) and Dp(GaAsxP1-x) and the relative permittivity εr(GaAsxP1-x).
The MUSICA ground-based products The MUSICA ground-based remote sensing retrieval uses the PROFFIT retrieval code  (Hase et al. , 2004)  and the middle-infrared spectra recorded within the NDACC (www. acom. ucar. edu/irwg/). The NDACC spectra are of very high spectral resolution (0. 005 cm -1 ) and offer H 16 2 O, H 18 2 O and HD 16 O absorption lines of similar strength. For the final MUSICA retrieval version (v2015), we perform an optimal estimation of (ln[H 2 O, the temperature and the interfering species CO 2 , O 3 , N 2 O, CH 4 and HCl. The code PROFFIT uses the root mean square value of the residual (difference between simulated and measured radiances) as noise level for constraining the retrievals. We use HITRAN 2012 parameters  (Rothman et al. , 2013)  optimized for speed-dependent Voigt line parameterization.
We show that the space-and groundbased MUSICA v2015 data are rather consistent on global scale. First, there is no significant bias between both data sets and second, the space-and ground-based products consistently detect extremes in the {H 2 O,δD} distribution at different globally distributed locations. This suggests that the calibrations with respect to H 2 O and δD reference scales and the validations with respect to the reference {H 2 O,δD} pair distributions have global validity. We present examples of seasonal cycles in the NDACC/FTIR {H 2 O,δD} pair distribution and briefly discuss possible links between the seasonality in this distribution and the seasonality in moisture sources and transport processes. Since NDACC/FTIR provides long-term data records, there are good possibilities for studying the {H 2 O,δD} pair distributions in a climatological context.
Data availability The MUSICA NDACC/FTIR data are available via the database of NDACC (ftp://ftp. cpc. ncep. noaa. gov/ndacc/ MUSICA/) and via ZENODO  (Barthlott et al. , 2016b). Details are given in  Barthlott et al. (2016a). In the long run, we also plan to disseminate the MUSICA MetOp/IASI data via a database and in a standard data format. At this stage the data are available as ascii data files and can be requested from the MUSICA team (by email to the leading author of this paper). Correct data usage will be assured by direct contact between the data users and the MU-SICA team. The MUSICA aircraft-based and ground-based in situ water vapour isotopologue observations used as the references in this study are discussed in detail in  Dyroff et al. (2015)  and  González et al. (2016) , respectively.
B2 Satellite-based tropospheric water vapour isotopologue data A brief overview of available products of tropospheric water vapour isotopologues and the respective satellite sensors is given in the Tables  B1  and B2. The thermal nadir sensors TES and IASI have the best sensitivity with respect to the water vapour isotopologues in the middle troposphere (about 2-8 km altitude). In addition to the MUSICA research team, a group at the University of Brussels (ULB) is working on IASI water vapour isotopologue retrievals. The ULB IASI retrieval uses two small spectral microwindows (1193-1223 and 1251-1253 cm -1 ) and fits the proxies for humidity, δD and CH 4 below 10 km altitude as well as ground temperature  (Lacour et al. , 2012). It uses the EUMETSAT Level 2 temperature output for the whole atmosphere and the EUMETSAT Level 2 humidity output for altitudes above 10 km (no fit).
The ULB group uses the same globally uniform a priori data as the MUSICA group. For the ULB retrieval, Type 2 products (a posteriori processed, H 2 O and δD have almost identical sensitivities) and Type 1 products (individual optimal estimation of H 2 O and δD) can be made available. Another tropospheric isotopologue product is generated from AURA/TES spectra. It has first been presented by  Worden et al. (2006) , whereby small spectral microwindows have been fitted (similar to the ULB IASI retrieval). The TES version 5 product used nowadays is discussed in  Worden et al. (2012) , and the respective retrieval set-up is rather similar to the MUSICA IASI retrieval set-up: a broad spectral window, simultaneous fit of proxies for humidity and δD as well as of temperature and the interfering gases throughout the atmosphere.
However, for the TES retrieval the H 2 O a priori assumption comes from the NCEP humidity analyses, and the δD a priori has a latitudinal dependency. The generation of a Type 2 product is theoretically possible, but it is currently not provided. TES measures limb and thermal nadir spectra (the isotopologue data are generated from the nadir spectra). It has a similar spectral coverage to IASI, but a significantly higher spectral resolution and, on the other hand, much sparser daily horizontal coverage. Lacour et al. (2015)  showed a cross-validation of the TES version 5 products and the IASI products generated by the ULB group. Space-based sensors measuring solar short-wave infrared spectra (SWIR) reflected on the Earth's surface theoretically have better sensitivity in the lower troposphere than the thermal nadir sensors.
Concerning temporal variability,  Steinke et al. (2015)  reported good correlation of integrated water vapour (IWV) observations and simulations made within 3 h (see their Fig. 7 ). Vogelmann et al. (2015)  use DIAL (differential absorption lidar) for estimating temporal mismatches in vertical profiles. Their Fig. 6  reports middle tropospheric (4-6 km) H 2 O variabilities within 2 h of about 20 % and within 1 h of about 15 %. This variability increases strongly in the upper troposphere where they found about 60 % within 2 h. When interpreting that study, we have to take into account that the DIAL detects vertically high-resolution profiles, whereas the FTIR and IASI remote sensing data represent deep layers (where variability largely cancels out). Spatial variability can be estimated by spacebased observations or by models.
5 h after sunrise) with the free tropospheric aircraft measurements made between 10:30 and 13:30 UT (see Fig. 3  in Sect. 3). This means that there is a temporal mismatch between the FTIR and the ISOWAT observations between 45 min and 5 h for the measurement pairs we define as optimal coincidences. This increases the mismatch uncertainty by about a factor of 3, i. e. from 10 % and 10 ‰ to 30 % and 30 ‰ (this estimatiuon is based on Fig. 12  of  Schneider et al. , 2010b). Nevertheless, this is what we define as optimal coincidences. Figure  C2  shows the same as Fig. 3 , but for FTIR data that have been measured during the time of the aircraft profile measurements, i. e. in the late morning hours and during midday. The comparison plots show relatively large variability in FTIR data that represent the atmospheric layer just above the island, revealing the large impact of the local diurnal upslope flow on the FTIR observations.