dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015; Heinzeller et al. , 2016; Hewitt et al. , 2016; Janssen et al. , 2013). From a technical point of view, different approaches can be followed to implement the coupling between existing model components. A first approach is fully embedded coupling, i. e. assembling components into one executable in a hardcoded way, using or not classes and methods from coupling toolkits such as the Model Coupling Toolkit (MCT,  Larson et al. , 2005). One example is the Coupled Ocean-Atmosphere-Wave-Sediment-Transport system (COAWST,  Warner et al. , 2010)  that uses directly the MCT library and the Spherical Coordinate Remapping Interpolation Package (SCRIP,  Jones, 1999)  to assemble into one executable the Weather Research and Forecasting (WRF,  Skamarock et al. , 2008)  atmospheric model, the Regional Ocean Modeling System (ROMS,  Shchepetkin and McWilliams, 2005)  oceanic model, the Simulating WAves Nearshore (SWAN,  Booij et al.
For example, if the components are sequentially coupled (i. e. one component cannot do any work while the other is running to produce its coupling field and vice versa), running concurrently on different sets of resources will lead to some waste of resources. This paper presents the development of a standard coupling interface in the SURFEX surface modelling platform  (Masson et al. , 2013)  based on OASIS3-MCT to couple atmospheric models with a variety of hydrological, ocean, wave and sea-ice models. SURFEX is open-source software that represents the evolution of surface-atmosphere fluxes considering four surface types (land, town, ocean and inland waters). As SURFEX is a fully externalized surface model, it can be used in stand-alone mode, i. e. driven by a prescribed atmospheric state, or embedded in an atmospheric model.
Interfacing of the SURFEX surface modelling platform with OASIS3-MCT The standard coupling interface with OASIS3-MCT has been part of the SURFEX open-source code suite since release v8. 0, except for the additional code for exchanges with a wave model which will soon be available with version v8. 1. Including OASIS3-MCT subroutine calls in SURFEX v8. 0 source code (in the subroutines indicated in italics between parentheses) was done as follows: 1. initialization (sfx_oasis_init) and namelist reading (sfx_oasis_read_nam); 2. multi-process partition definition and listing of the exchanged fields (sfx_oasis_define); 3. receiving (sfx_oasis_recv) and sending (sfx_oasis_send) of the coupling fields; 4. finalization (sfx_oasis_end). Steps 1, 2 and 4, which correspond to preparation and closure of the coupling, are either called by SURFEX itself when SURFEX runs stand-alone or by the atmospheric model when it integrates SURFEX (because the parallelization information to be sent to OASIS3-MCT is then managed by the atmospheric model).
Some of the hydrological models, such as CTRIP, also simulate floodplains. These processes are strongly coupled to the land surface water and energy budget, and feedback to the ocean. For this purpose, the CTRIP river routing model has been coupled to SURFEX via OASIS3-MCT. In SURFEX, the continental water and energy budgets are computed using the ISBA scheme. The soil moisture and heat vertical transports are explicitly solved using a multi-layer scheme  (Boone et al. , 2000; Decharme et al. , 2011). This scheme has been validated over many local field datasets  (Boone et al. , 2000; Habets et al. , 2003; Decharme et al. , 2011)  or regional studies  (Decharme et al. , 2013 (Decharme et al. , , 2016)) , improving confidence in the model's ability to consistently represent a variety of environmental conditions in different climate regimes.
ORAS4 is used as the Atlantic boundary condition for the ocean model (Balmaseda   (Berrisford et al. , 2009)  is used for the atmosphere. Spectral nudging is applied in addition over the inner atmospheric model domain toward the ERA-Interim reanalysis. The solar penetration on the upper ocean is prescribed using monthly mean maps of chlorophyll-a concentration (Ocean Colour Climate Change Initiative dataset, European Space Agency, available online at http://www. esa-oceancolour-cci. org/). The amplitude of the SST diurnal cycle simulated by the model is compared to the Météo-France Lion buoy observations in the north-western Mediterranean over the 2009-2013 period (Fig. 4 ). The amplitude of the diurnal cycle of the SST is computed as the difference between the maximum of the hourly mean SST, between 09:00 and 17:00 UTC, minus the minimum between 18:00 UTC the day before and 08:00 UTC.
AROME-NEMO WMED This application gives an example of the new coupling of a NWP limited-area model to an ocean model over a fraction of its marine domain. The newly developed AROME-NEMO coupled system aims at better representing the ocean-atmosphere coupled processes at a fine scale and at assessing the impact of the coupling on short-range forecast of severe weather in the Mediterranean region. This coupled system combines the NEMO ocean model and the NWP system of Météo-France AROME which belongs to the common ARPEGE/ALADIN/AROME model software suite. There is no ICE, no WAV and no HYD component. It is applied over the western Mediterranean Sea (Fig. 2c ) and involves, as an ATM model, the 2. 5 km resolution AROME-WMED configuration  (Fourrié et al. , 2015)  in version cy38t1. Its surface scheme is SURFEX v7.
The horizontal resolution of MESONH is 2 km and its initial conditions are provided by AROME operational analyses. MARS3D is run with a horizontal resolution of 500 m and its initial and boundary conditions come from coastal operational oceanography system Prévimer  (Lazure et al. , 2009)  using the 2. 5 km Bay of Biscay configuration. WW3 is used with a spatial resolution of about 1. 5 km and its spectral resolution corresponds to 32 frequencies ranging from 0. 0373 to 0. 7159 Hz and 24 points for the propagation direction (every 15 • ). At open boundaries, WW3 is forced by 3-hourly energy spectra from the HOMERE hindcast database  (Boudière et al. , 2013). The atmospheric roughness length is estimated from the Charnock parameter supplied by WW3 (Eq. 9). The coupling frequency between the three models is 100 s.
This greatly facilitates the development of new coupled systems, even if caution must always be taken. For example, even if there is no specific work to be done for the coupling in itself, changing the region for limited-area coupled systems necessitates adaptation of each component and careful definition of the corresponding grids and masks for OASIS3-MCT (as mentioned in Sect. 2. 3). Also, the OASIS3-MCT namelist (namcouple) must always be carefully edited by the user, notably for the exchange field names and for the interpolation methods. Finally, the ability of SURFEX to run in stand-alone mode (driven by atmospheric forcings) offers new opportunities for model evaluation. Ocean models are usually run either forced by fluxes or forced by near-surface atmospheric fields. In the last case, bulk formulae are embedded in the ocean code to calculate turbulent fluxes.
The comparison with coupled runs is not straightforward in this case as bulk formulae used in the ocean code are not the same as those used in the coupled system. Using SURFEX on top of the ocean model in forced mode would resolve this issue as the fluxes would be calculated in SURFEX with the exact method used in coupled mode. A SURFEX-OCE-HYD configuration also allows us to run the whole surface hydrological water cycle forced by an atmospheric observed forcing. It is often hard to clearly isolate the pure coupling effect in existing applications, and it is not the purpose of this paper. In most cases, comparison of coupled and uncoupled simulations rather shows the benefit of using a more detailed SST field in terms of space and time variations than the pure coupling effect. Nevertheless, for operational purposes, coupled models appear to be very promising tools able to represent and take into account the rapid SST evolution in coherence (balance) with the atmosphere.
Acknowledgements. The authors acknowledge the  MIS-TRALS/HyMeX program  and the  ANR-2012-BS06-0003  ASICS-Med funding. The authors acknowledge the  DGA (Direction Générale de l'Armement) , a part of the  French Ministry of Defense , for its contribution to  Romain Rainaud 's PhD, as well as the coastal part of the  Copernicus  project co-funded by the  MEDDE (French Ministry of Ecology, Sustainable Development and Energy)  and  INSU (National Institute of Sciences and Universe). The authors acknowledge  Edmée Durand  and  Yann Drillet  from Mercator Océan technical support. Simulations of the MESONH-SYMPHONIE coupled system were performed using HPC resources from CALMIP (grants  P09115  and  P1325 ). The M2W calculation was performed with the  HPC facilities of "Pôle de Calcul Intensif pour la Mer , PCIM", http://www.
ifremer. fr/pcim. OASIS3-MCT development was supported by the  ESi-WACE H2020   European  project, grant agreement no. 675191  (www. esiwace. eu),the IS-ENES2 FP7 European project, contract number  312979  (https://verc. enes. org/ISENES2),and the  CON-VERGENCE  project funded by the  French National Research Agency ,  ANR-13-MONU-0008. Edited by: Astrid Kerkweg Reviewed by: three anonymous referees.
Code availability. The OASIS3-MCT-SURFEX interface is available in version 8 of SURFEX (http://www. cnrm-game-meteo. fr/ surfex). The SURFEX code is freely available (Open-SURFEX) using a CECILL-C Licence (a French equivalent of the L-GPL licence; http://www. cecill. info/licences/Licence_CeCILL-C_V1-en. txt), excepted for the gaussian grid, the LFI and FA I/O formats, and the dr HOOK tool. OASIS3-MCT can be downloaded at https://verc. enes. org/oasis/download. The public may copy, distribute, use, prepare derivative works and publicly display OASIS3-MCT under the terms of the Lesser GNU General Public License (LGPL) as published by the Free Software Foundation, provided that this notice and any statement of authorship are reproduced on all copies. The NEMO model can be downloaded at http://www.
Conclusion and acknowledgement We introduced a new Python package for the modelling of atmospheric showers and their imaging with and array of ground based Cherenkov telescopes. This software does not intend to replace Monte-Carlo but to substitute them for quick, simple and flexible modelling of showers. Arrays configuration (including telescopes positions and pointing) can quickly be tested and resulting images produced. ps 2 chitt! may already be downloaded  [9]  and used to generate fake images stereoscopically coherent, testing arrays configurations as well as for teaching and public dissemination and outreach. We acknowledge support from the ASTERICS project supported by the European Commission Framework Programme Horizon 2020. Research and Innovation action under grant agreement n. 653477 On the left, all the denoised camera images have been stacked to retrieve the direction of the shower in the sky.
Methodological approach of MarCons MarCons will revolve around six main themes (Fig. 2 ) that cover major gaps in European marine conservation. While each of the themes has a specific focus, they will be conducted in parallel, with coordination and collaboration. As such, the initiative facilitates a type of integration often lacking in ecosystem-based management and MSP  (Portman 2011). The thematic working groups will capitalize upon existing knowledge, adapt and evolve conservation planning tools implemented in other regions of the world, and develop new methodological and computational tools, databases and background information to support the decisionmaking process for marine policies in the region. Adopting a multidimensional and flexible methodological context, MarCons will employ an approach, which is: 1.
Modelling approach A 2D orthogonal cutting model based on a Lagrangian approach has been employed in this analysis as it appears as the most relevant method to simulate cutting of heterogeneous materials. Built from the work of Mabrouki et al. [15] , this Finite Element model is divided into three parts: the chip, the tooltip passage zone and the workpiece (Fig. 1 ). Coupled thermomechanical simulations have been conducted in the commercial code Abaqus/Explicit c. The model consists of a deformable workpiece and a rigid cutting tool with both solids meshed using 4-node plane strain thermally coupled quadrilateral elements (CPE4RT). The main drawback of this approach is that it requires a separation criteria such as a damage initiation and evolution for the elements deletion in the passage zone in order to form the chip.
Microstructural considerations The microstructure has been taken into account only within the chip, as shown in Fig. 1. In order not to use a simple square or rectangular shape and insure a good mesh quality compared to spherical ones, grains have been assumed to be hexagonal as in  [14]  with the grain size corresponding to two times the side length. A Python script has been developed to automatically divide the chip geometry into grains and properly affect the properties of the ferrite or pearlite to model to different microstructure morphologies. Three microstructural parameters have been investigated in this work: grain size, phase distribution and ferrite-pearlite ratio. Table  1  summarises the different simulated configurations. Examples of phase distribution are given in Fig. 1. A mesh sensitivity study has been performed to identify the proper mesh size in each part of the model.
g. , water and DNA). In general, MCTS codes devoted to the charged particle track modeling in biological matter include input databases taken from different standards (see for example  [2] [3] [4] [5] ), mainly based on experimental data and/or semi-empirical approaches. Moreover, it is worth noting that the vast majority of existing numerical codes to model proton-induced damage and its derivatives (radiotherapy, dosimetry, medical imaging, etc. ) uses water as tissue-equivalent medium with -for the most sophisticated -the modeling of the chemical species and the free radical creation and diffusion to estimate the indirect radiation effect on DNA and/or the inclusion of the molecular DNA structure, the latter being generally superimposed on the ion track-structure in water (see for example Ref.
The second sampling refers to the collision type that occurs at the selected position. Whether it is for primary protons or secondary electrons, the latter is randomly chosen according to the relative magnitude of the individual total cross section of each collisional process (elastic as well as inelastic). The collision type k is simply given by k-1 i=1 P i Γ k i=1 P i with P i = σ i σ T. ( 3 ) If the selected interaction is ionizing, a third sampling procedure is performed in order to select the molecular subshell impacted by considering the contribution of each target subshell to the total cross section of the selected process, namely, by using the partial cross sections. Finally, random samplings are performed in order to quantify the full kinematics of the selected interaction. This step is obviously the most time consuming since it requires parsing in a large database (>10 Gb memory size) needed for describing the numerous collisional processes cited above.
[14]  where all the necessary coefficients and quantum numbers are reported). Regarding DNA, we recently proposed in reference  [15]  an ab initio approach to express the molecular wave function of each DNA component at the RHF/3-21G level  [16]  with the GAUSSIAN09 software. In this context, each Mulliken population analysis and their sum for each occupied MO is very close to 2, since only atomic shells with very small population have been discarded. Besides, the computed ionization energies of the occupied MOs of the biological targets investigated here were scaled so that their calculated Koopmans ionization energy, i. e. the ionization energy of their HOMO, coincides with experimental values available in the literature (for more details, we refer the interested reader to our previous study  [15] , where all the quantum numbers and coefficients needed for expressing the target molecular wave functions as well as the corresponding ionization energy are reported).
Irrespective of the biomolecular targets investigated, TILDA-V is based on a cross section database, which refers to isolated molecules and to living matter components in vapor state. In this context, the present work clearly differs from existing studies on condensed matter (water or DNA), where the energy-loss function of realistic biological components was extracted from experimental data and interpolated for use in cross section calculations (see for example Ref. [17]  and the recent series of works by Abril and co-workers  [18, 19]  and Emfietzoglou et al. [20] ). However, whether it is for water or DNA, the available data -cross sections as well as macroscopic outcomes like ranges and stopping power -are exclusively measured in vapor phase and comparisons with the liquid homologous have been only rarely reported.
The electron-induced double ionization will be implemented in a near future into the TILDA-V database by means of the perturbative model provided by Oubaziz et al. [56]. Those authors reported on a quantum mechanical approach providing a quantitative description of the electron-induced double ionization process on isolated water molecules for impact energies ranging from the target ionization threshold up to ∼10 keV. Furthermore, the electron tracking will be completed by elastic scattering description in DNA. The chosen approach will be based on a partial-wave treatment similar to that reported above for water, where the interaction potential will account for a static potential numerically deduced from the molecular wave functions as well as exchange and correlation-polarization contributions mentioned above.
Conclusions Whether it is for radiotherapy or radioprotection purposes, a fine understanding of the underlying physics of radiations in living matter is an essential prerequisite of any research at the physics and biology frontier. In this context, Monte Carlo simulations are now accepted as the most suitable and powerful tool for investigating the radioinduced interactions. We have here detailed our TILDA-V code, which refers to a transport code aiming at describing the trackstructure of protons and its secondaries in living matter that includes both water and biological species. All the collisions are described at the nanometric scale by means of a large database including total cross sections as well as a variety of differential cross sections ranging from triply to singly differential ones.
However, CLEAN and most of the algorithms that belong to the CLEAN family are not tailored for large-scale data. For this reason, a lot of effort has gone into designing scalable methods based on convex optimization  [8] -  [14]. These algorithms, which unrestrictedly operate in the image or visibility plane, adopt sparse models in an adequate domain, and This work was partly supported by the Agence Nationale pour la Recherche, France, (MAGELLAN project, ANR-14-CE23-0004-01). perform deconvolution by minimizing a data fidelity term and one or more regularizations. The scalability of these algorithms comes from the fact that computationally intensive steps are performed in parallel. In the mono-frequency case  [8] ,  [9] , parallel computations rely on splitting the data into multiple blocks and exploiting randomization over data blocks at each iteration.
V. SIMULATIONS We simulate the PSF of MeerKAT 1  observations using the HIinator package 2  based on the MeqTrees software  [30]. We simulated a cube with 100 frequency bands and a size of 256 × 256 pixels. Figure  1  shows the PSF at the 100-th wavelength band which corresponds to a Fourier coverage produced by a total observation time of 8 hours. The sky corresponds to the radio emission of an HII region in the M31 galaxy. The sky cube for this real sky image is computed using first order power-law spectrum models. The 256 × 256 map of spectral indices is constructed following the procedure detailed in  [31] : for each pixel, the spectral index is a linear combination of an homogeneous Gaussian field and the reference sky image. For Ws and W λ , we considered a union of 8 Daubechies wavelet bases for the spatial regularization and a cosine We tested self-tuned MUFFIN on the simulated data set.
III. How does climate change influence the provision of ecosystem services supporting fisheries and aquaculture? The complex interactions between different impacts of climate change and the lack of observation data make it challenging to clearly predict the implications on the provision of goods and ecosystem services from Arctic seas (see, e. g. ,  Post et al. 2009). Results from the ACCESS project reported below provide a more complete picture. Model predictions  (Eide 2017 ) indicate a 10% increase in carrying capacity and a larger distributional area in the Barents Sea for demersal species (with seasonal variation) but the key fishing areas remain, allowing ''business as usual''. While aquaculture is currently limited mainly to Norwegian Atlantic salmon farming, temperature increase will open new areas for farming primarily in north Norway and the Kola Peninsula.
The complex array of environmental and socio-economic changes facing northern communities requires an inclusive and integrated multistakeholder approach to aquaculture governance. Reviews of, for example, existing licensing, animal health, and construction of facilities regulations will be necessary in the light of climate change effects  (NERC 2015). ACCESS research illustrates that the main governance challenges facing marine transport, are the unification of the application and enforcement of ship rules. The new International Maritime Organization Polar Code fills many of the earlier gaps in shipping legislation in polar environments. However, it does not cover all polar marine safety and environmental protection issues and barely addresses the impacts of climate change. An International Convention for the Control and Management of Ships' Ballast Water and Sediments will enter into force in September 2017.
Such data should be collected in ways and at spatiotemporal intervals such that it can be used jointly with biogeophysical data in a meaningful way. This would allow a better understanding of social-ecological and cross-sectoral interactions and improve forecasting capacity in all domains where human-nature interactions matter. Ideally a socio-economic data observing system should be part of the initiatives already discussed for biogeophysical data just mentioned (e. g. , SAON). Other data needs concern quantification and understanding of the provision of ecosystem services and data with high enough resolution and number of observations to help anticipate and analyze potential abrupt changes and tipping points in all domains. • Decision making based on state of the art scientific knowledge and advice requires more quantified and specific approaches to assess impacts.
Acknowledgements The research leading to these results has received funding from the  EU  under Grant Agreement n°265863: ACCESS (http://www. access-eu. org):within the  Ocean of Tomorrow call of the European Commission Seventh Framework Programme. We thank the ACCESS participants and partners for their valuable contributions during the project; in particular, the work put into the project synthesis. We found substantial inspiration and information in synthesis inputs provided by  Angela Benn ,  Anna Stammler Gossmann ,  Lindsay Parson  for aspects of the projects for which the results are not published other than in the project reports. 16.