dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016; Bett et al. , 2018a). We have also assessed more directlyprobabilistic statistics, looking at reliability and ROC diagrams, and their corresponding Brier and ROC skill scores  (Wilks, 2011; Jolliffe and Stephenson, 2012). We have performed a preliminary skill assessment of the climate variables described above, using the retrospective forecast data sets available from ECMWF as part of the EUROSIP multi-model seasonal forecasting system: from ECMWF  (Molteni et al. , 2011) , Météo-France  (Voldoire et al. , 2013) , and the Met Office  (MacLachlan et al. , 2015). For the final C3S ECEM Demonstrator, we will be using new seasonal retrospective forecast data available from the C3S Climate Data Store, which is being developed in parallel to C3S projects like ECEM. Using three different dynamical forecasting systems, based on different coupled climate models, provides a good overview of the current status of seasonal forecasting skill.
Projection period Climate change projections used in C3S ECEM have been derived from Global Climate Models (GCMs) and Regional Climate Model (RCMs). GCMs are available from the CMIP5 (fifth Coupled Model Inter-comparison Project) ensemble of simulations, but their spatial resolution is relatively coarse. RCMs, which use GCMs (from CMIP5 in our case) as their boundary conditions, provide more detailed spatial resolution (comparable to our historical bias-adjusted datasets). In C3S ECEM, it was decided to base the projections on RCM output for our European domain, EURO-CORDEX, rather than global climate projections. This is because: -EURO-CORDEX provides a higher spatial and temporal resolution, typically one order of magnitude higher; Following EURO-CORDEX, output based on the two most common Representative Concentration Pathways (RCPs), RCP4.
5 and RCP8. 5, have been used in C3S ECEM. Since it would be useful to consider low-carbon scenarios from the EU perspective (as used in e. g. the e-Highway2050 project), it could be argued that also lower RCPs should be considered, as for instance RCP2. 6. One issue is that output from EURO-CORDEX for RCP2. 6 is sparse, and it would not therefore be possible to meaningfully compare this output with that of RCP4. 5 and RCP8. 5. In any case, the two RCPs considered here provide a useful reference for other RCPs. Data from both the GCMs and RCMs are available via the Earth System Grid Federation (ESGF). Over ten RCM simulations are available for the European domain, but some are closely-related variants of other simulations, so less useful than simulations which are more independent of each other.
C4E assessed this similarity and independence, together with the model skill, and determined the best choices of GCM and RCM configurations to use if resources were only available to work with seven, five or three. The rationale behind the C4E choices is that the main determinant driving the variability within an RCM is the underlying GCM providing the boundary conditions. Their choices of 3 and 5 all contain different driving GCMs. For further detail please see C4E report Climate projection dataset release for the energy sector 5. In C3S ECEM we chose to use the 7-member sub-ensemble. The C4E bias-adjusted variables (TA, TP, WS and GHI) were obtained from the relevant ESGF node 6. These variables are listed under the project CORDEX-Adjust. The other variables (RH, MSL and SD) were obtained from the ESGF CORDEX repository 7.
Data were extracted at the best temporal resolution available. This is generally 3-hourly but for some variables it can be daily. The temporal resolution (together with GCM and RCM configuration, ECV and start and end dates) can be found in the corresponding Variable Fact Sheet (VFS) in the C3S ECEM Demonstrator. The data are available for the period from 1979-2100, and for the two RCPs (4. 5 and 8. 5). The switch between historic forcing and projections occurs at year 2005, so the first part of the simulations from 1979 5 Available at: http://clim4energy. climate. copernicus. eu/ sites/default/files/C4E_ClimateProjection4Energy_dataset_v4_ summary. pdf (last access: 25 February 2018). 6 https://esgf-node. ipsl. upmc. fr/search/c3s-energy/ (last access: 25 February 2018). 7 https://esgf-data.
dkrz. de/projects/esgf-dkrz/ (last access: 25 February 2018), (other nodes are also available). Note that C4E obtained the data for one of the CORDEX models directly from the model developer's as it is not in the CORDEX archive. This means that RH, MSL and SD are missing for this RCM. Also, for another model: RH was not available and SD was not used as it contained many small negative values. Given the issues with these SD projection data, these were not used in the Demonstrator in the end. for the two RCPs are the same. The data from each RCM was re-gridded onto the standard C3S ECEM grid (0. 5 • by 0. 5 • latitude/longitude) and the C3S ECEM domain (  27 • -72 • N by 22 • W-45 • E) was retained. From the cluster and country averages, an ensemble mean has been calculated and is available for the monthly, seasonal and annual timescale.
Energy variables A substantial amount of work has gone into collecting and harmonising energy data from various sources including ENTSO-E 8 , eHW2050, EUROSTAT 9 , World Bank, national Transmission System Operators (TSOs) data not contained in the ENTSO-e dataset 10 , the WindPower database 11 , data from EMHIRES 12 , and the Ninja project (Staffell and Pfenninger, 2016). These data have been critical in testing conversion models (from climate to energy variables) developed for the historical period, as described in this section, which are then applied for the seasonal forecast and projection period 13. Based on a literature review, project partners' expertise and stakeholder interaction, the energy variables provided by C3S ECEM are: -Electricity demand (DEM) -Electricity supply from: -Wind power generation (WPG) -Onshore only (data from offshore wind power is much less accessible, than for onshore; it is also less widespread than onshore) 12 European Meteorological derived high resolution renewable energy source generation time series (https://ec.
Electricity demand model Based on EDF expertise, daily DEM is modelled using a regression approach, the Generalized Additive Model (GAM), a generalization of Linear Models. Introduced by  Hastie and Tibshirani (1990) , GAM has been improved by  Wood (2006)  and many others since then. This is a well known method for load forecasting  (Pierrot and Goude, 2011; Fan and Hyndman, 2012; Goude et al. , 2013) , and has been successfully used in the Global Energy Forecasting Competition 2012 (GEFCOM2012) forecasting competition  (Nédellec et al. , 2014). Its major advantages are that it is simple to interpret, it is fast to run and it can be easily adapted to different datasets. DEM is characterized by significant trends, due to population increase and economic activity variability. Across Europe, in particular, the 2008 financial crisis had noticeable impacts on energy demand.
2017 ), in which the total PV power produced in a region is calculated from meteorological data using a physical model. In this model, the characteristics of PV plants such as module orientation angles and power curve are first evaluated on the basis of a statistical analysis of a database containing meta-information of several thousands of PV plants. Assumptions about the installed PV capacity are necessary to aggregate the PV power generation evaluated for each cell in order to compare the model output with TSO data at country level. The model was trained over Germany and tested over France. For Germany, a database of more than 1. 5 million PV plants has been used. For France, the information provided on a yearly basis on the total PV capacity installed on each of the more than 36 000 French zip-code areas has been collected.
Wind Power Generation model Wind power modelling in C3S ECEM has been performed using both physical and statistical models, with the two intercompared in order to select the approach with the more accurate representation of wind power capacity factor. The physical model computes the capacity factor by adopting the standard conversion relationship (e. g. Brayshaw et al. , 2011)  tested for several wind turbine types and several hub heights. Computing the capacity factor at individual wind farm level requires: a. Improving the estimation of individual turbine height when not available in the WindPower database; this has been done by using the closest windfarm data (closest in terms of distance and commissioning date); b. Improving the choice of power curves for individual farms; an analysis has been carried out to identify the most frequent turbine types as a function of the hub height, and a selection has been made to use different power curves associated to the hub height.
The Demonstrator: the visible part of the iceberg As mentioned throughout this paper, the ultimate target of the C3S ECEM project has been the development of the C3S ECEM Demonstrator. This is an interactive visual on-line tool, which allows users to view and explore energy supply and demand profiles, and climate variables, for each European country and subnational cluster, in map and time series format (Fig. 2 ). As discussed, the C3S ECEM Demonstrator has been developed in accordance with user needs, and integrates the energy and climate variable data produced in C3S ECEM on historical, seasonal forecasting and future climate projection timescales. Time series and map data for countries and clusters can also be downloaded directly from the Demonstrator, whereas bulkier gridded data, in the form of netCDF files, are available through the project ftp server, which is also readily accessible via the Demonstrator.
The C3S ECEM Demonstrator has been designed to be relatively simple, with pre-calculated data displayed as both maps and time series plots, while allowing for interactive changes to plotted maps and time series. Although the horizontal resolution displayed is not as high as the original underlying gridded data, the visual simplicity of the C3S ECEM Demonstrator makes it accessible to climate and energy specialists (to carry out initial investigations) and to less experienced users such as policy makers or even high-school students. A wide variety of help and guidance is provided with the Demonstrator, including key messages and event case studies which illustrate the types of information that the Demonstrator offers, and how it can be used and interpreted to benefit the energy sector. Documentation (including variable fact sheets) is provided on all the data sets embedded in the Demonstrator to ensure transparency and that users have appropriate information to judge the quality and reliability of these data for their own particular applications.
It is important to note that in order to handle the large amount of data and files generated by the C3S ECEM Demonstrator, conventions were discussed and agreed as pre-sented in the C3S ECEM Data Management Plan 15. Such conventions will be key elements in the development of any future operational phase of the service. While the Demonstrator functionality is intended to be broadly frozen during the current pre-operational phase, feedback is continually encouraged, for instance though the feedback tab on the right-hand-side of the Demonstrator page. Interested prospective users of the C3S Demonstrators are therefore encouraged to regularly visit the project and Demonstrator web pages, and to test the Demonstrator: http://ecem. climate. copernicus. eu/ demonstrator/ (last access: 3 July 2018).
Conclusions and perspectives The Demonstrator has been promoted and demonstrated at various venues, including the European Meteorological Society Conference in September 2017 in Dublin. It is now available for public access and users are encouraged to explore and exploit it, and to provide ongoing feedback. A number of new features, such as energy variables at the climate projection time scale, are being produced for inclusion in the Demonstrator. Energy companies and operators, such as TSOs, and energy policy makers will benefit from the codesigned climate service Demonstrator as it will provide a set of tools to better assess energy mix options over Europe. Importantly, by bringing together climate and energy data for different time frames -historical, seasonal forecasting and future projections -that are consistent in space and time, well documented, and easy accessible, the C3S ECEM Demonstrator will: -Ensure that estimates of the country energy mixes properly reflect climate conditions including their variability, and it will therefore allow end-users to better assess the optimal supply mix that can meet demand in the most cost effective manner; -Offer a coherent approach for the climate variables and energy indicators used in power demand and supply balance, an added value with respect to current practice in the sector, where climate data and derived energy variables are not always physically homogeneous and/or in balance As well as providing a critical building block for an operational energy service, we envisage that the Demonstrator and its associated data sets could be used in a variety of future research projects (such as European H2020 projects), as it offers a unique approach: it provides a Europe-wide, multivariable, multi-timescale view of the climate and energy sys-.
Figure6. Country-average wind power capacity factor for C3S ECEM wind power models, and JRC (EMHIRES, green) and Ninja (salmon) datasets. In model mix1 and noc1 the known installed capacities from the WindPower database were used; in mix1 a mix of bias-adjusted 100 m wind speed were used (i. e. bias-adjusted over land and non-bias-adjusted over the ocean), in model noc1 the original ERA-I extrapolated to 100 m wind speed were used. In PhM03_wbc, the bias-adjusted 100 m wind speed data were applied to a fixed wind turbine type at each 0. 5 • by 0. 5 • grid cell. Finally, Stat is the statistical model using the support vector machine approach.
The generation of high-power electromagnetic waves is one of the major applications in the field of high-intensity pulsed power. The conventional structure of a pulsed power generator contains a primary energy source and a load separated by a power-amplification system. The latter performs time-compression of the slow input energy pulse and delivers a high-intensity power output to the load. Usually, either a Marx generator or a Tesla transformer is used as a power-amplifier. In the present case a system termed MOUNA (French acronym for 'Module Oscillant Utilisant une Nouvelle Architecture'), uses an innovative and very compact resonant pulsed transformer to drive a dipole antenna. The paper describes the ultra-compact multiprimary winding pulsed transformer developed in common by Université de Pau and Hi Pulse Company that can generate voltage pulses of up to 0.
Firstly, simple techniques are established to estimate analytically these essential parameters, followed by their benchmarking against very precise, but time consuming, calculations using a state-of-the-art software package (CST). Finally, an optimization process is described that helps to improve the transformer characteristics that are then used with a circuit solver (LTSpice) to simulate the final electrical performance of the pulsed transformer. The transfer time (or time-to-peak, 0% to 100%) of the transformer output voltage can be expressed as: 𝑡 𝑝𝑒𝑎𝑘 = 𝜋√𝐿 𝑝 𝐶 𝑝 𝑛²𝐶 𝑠 𝐶 𝑝 + 𝑛²𝐶 𝑠 (3) As easily noticed the transfer time depends on L p which includes, apart from the leakage self-inductance L σ , other parasitic self-inductances related to wires, capacitors, switches, connections, etc.
LTspice modeling The numerical simulation of the resonant pulsed transformer was carried out using the LTspice free software (Figure  14 ), having as load a capacitor simulating the antenna, with a specially developed voltage probe  [27]  mounted in parallel. The resistance of one secondary winding is about 0. 85  for a DC current, but raises to 2. 33  for a pulsed current injected at the transformer's resonant frequency. This last value, divided by 2 to take into account the parallel coupling of the two secondary windings, was used in numerical simulations. The results show the resistance lowers the peak voltage value by only 0. 05 %. It is therefore reasonable to be neglected from the electrical equivalent circuit. The model for the hysteresis cycle of the transformer core magnetic material is adopted from  [28].
Results obtained in repetitive mode It is well-known that the high-intensity electric stress issue becomes much more demanding in a high PRF mode and therefore it was necessary to limit the time duration the dielectrics are stressed by performing the tests under conditions closer to those of the MOUNA prototype. In order to do this, a pressurized gas spark gap was installed to crowbar the output voltage impulse on a 50 Ω resistor, when the output voltage impulse reached a value close to its peak. It is important to note that the crowbar also eliminates the dangerous polarity reversal. Fig. 21  presents data obtained from a burst of 100 pulses at a PRF of 20 Hz. After crowbarring, the measurement is unfortunately not very accurate, because the frequency of the resulting oscillations is higher than the 50 MHz bandwidth limit of the 0.
Comparison between experimental data and thoretical predictions Figure  22  compares the experimental results for an initial charging voltage of 9 kV with predictions made using the LTspice modelling. The small differences noticed after 1 µs are due to the imperfect modelling of the magnetic core losses, but this issue is not important for the present project as it happens after the peak voltage is reached and when most of the energy is already transferred to the antenna. The peak output voltage impulse is 560 kV, with a rise time of 265 ns and a time-to-peak of 440 ns, well inside the values of parameters required by the MOUNA project.
The black line is the best fit coefficient 4. 3 obtained from only our experimental data -data from the literature  30  were later added on top. The gray area illustrates the prediction of our model based on the species we studied. (b) Ratio of EI x vs GJ for our species and species from Vogel  30. The gray area indicates the prediction from our model based on the leaf shapes tested in the lab. Our simple model is based on wind-induced bending and twisting stress, yet we are not able to capture the detailed dynamics of leaf motions. In particular, we are aware that in nature the bending and twisting of leaves are not static, but more dynamic (e. g. fluttering). While we focused on torsion and traction tests, our approach can also be extended with bending measurements to some extent. Our samples were collected during spring/summer when trees grow their leaves and produce food through photosynthesis.
GWML2 has been developed by a group of international groundwater data providers collaborating within the Groundwater Standards Working Group of the Open Geospatial Consortium (OGC). It comprises the groundwater component of the WaterML2 suite of hydrologic data standards and best practices (WaterML2 2017), and consists of data structures and encoding guidelines for groundwater data. GWML2 has been successfully implemented and tested by a variety of major groundwater data providers from North America, Europe, and Australasia, and it is expected to complement emerging data standards for both surface water and atmospheric water to facilitate data exchange for the complete water cycle. This paper describes the motivation behind the development of GWML2, the development methods, the representation itself, its testing and evaluation, its relation to other standards and relevant work, and a summary including possible future directions.
The technologic scenario determines compatibility with other hydrogeological data representations such as database schemas and exchange formats, via conversion to and from GWML2. This is particularly important to enable data interoperability (1) within a groundwater data network, by converting between local databases and GWML2, or (2) between different data networks, by converting between GWML2 and local data formats such as the European-wide INSPIRE  (INSPIRE 2013a,b)  or North American GWML1  (Boisvert and Brodaric 2012). Involved are all entities from the previous scenarios.
& Usability: the information delivered by GWML2 satisfied the primary needs of the usage scenarios, as demonstrated by the successful delivery of all key entities from disparate databases. While efficiency was not measured quantitatively, in all cases the implementations returned results in times acceptable to human users, as indicated in ad-hoc reports. Overall usability, including satisfaction of several usage scenarios, is further demonstrated by the consumption of GWML2 data by various web applications. Figure  4  illustrates satisfaction of the commercial scenario as GWML2 water well information is visualized from the Canadian (Fig. 4a ) and US (Fig. 4b ) groundwater data networks  (Brodaric et al. 2016 ) via the Canadian web portal. This visualization also demonstrates satisfaction of an important aspect of the technological Fig.
Related work on groundwater data representation Although data representations in hydrology are numerous, those focusing on groundwater are less common and can be categorized as (1) database schemas, (2) ontologies, and (3) data exchange formats. Groundwater database schemas provide a structure for organizing and managing groundwater information in data repositories, either for restricted scope (e. g. Chesnaux et al. 2011; Gogu et al. 2001; Steward and Bernard, 2006; Wojda et al. 2010; Oulidi et al. 2009) , or for broad use  (Kisters 2017; Nešetřil and Šembera 2014; Strassberg et al. 2007). Ontologies are formally arranged vocabularies used by data networks to help find and use distributed data, with a focus on hydrology in general (Atkinson and GWML2 clearly falls into this third category of data transfer format, but it is conceptually differentiated from representations in all categories by its adaptation of the water container pattern (from Boisvert and Brodaric 2012; see section 'Results: the GWML2 design'), which distinguishes containers (e.
The total area shown is approx. 12 km × 16 km, with distance in metres indicated along each axis groundwater information from significant data providers to meet key usage scenarios. In this process, has already impacted the design groundwater databases maintained by these and is being incorporated into their operational data delivery mechanisms. It is further informing the nascent development of ontology-based knowledge structures for the hydrological domain  (Hahmann et al. 2016). Next steps for GWML2 involve its possible submission to the World Meteorological Organization for endorsement as a global hydrological standard, in concert with its WaterML2 siblings, to complement its current standing as a global geospatial standard. Possible future technical directions include the development of additional GWML2 encodings in languages other than XML, as well as the enhancement of standard content.
Other potential work involves a Blite^version, to lower the entry barrier to GWML2, as well as advancement of the scientific modeling usage scenario to further test the efficacy of GWML2 in supplying data to, and between, groundwater modeling tools. Augmentation of the technologic usage scenario is also a possibility, to determine alignment with important standards such as with the ArcHydro Groundwater standard database schema  (Strassberg et al. 2007) , and with emerging surface water schemas such as HY_Features  (Atkinson and Dornblut 2012). It is anticipated that these and other future directions for GWML2 will continue to be coordinated by the OGC Groundwater Standards Working Group in collaboration with related activities in industry, academia, and government, to promote GWML2 adoption and further the overall aim of enabling groundwater data exchange and interoperability.