dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary of MUNICH characteristics The concept of the street-network model MUNICH is close to the one used in SIRANE to represent concentration at the street level. We have introduced several parametrizations for the vertical turbulent flux and the average wind speed. It is however not possible to definitively advocate a specific choice for these parametrizations with the set of observations available within the framework of the TRAFIPOLLU project (http://www. agence-nationale-recherche. fr/?Project= ANR-12-VBDU-0002  ity of representing a complex chemistry in the street. It also allows the interactive connection with a Eulerian chemistry transport model.
(2015). However, for the current work the SymuVia outputs were averaged and combined with COPERT 4 emission factors to generate hourly emission rates for each street segment. The emission rates depend on the averaged vehicle speed and composition of the vehicle fleet. This latter was determined through video monitoring  (André et al. , 2017). It is however important to notice that the vehicle fleet composition appears to be a sensitive input data  (Carteret et al. , 2014; Chen et al. , 2017). Two typical days (25 March for a weekday and 30 March for the weekend) were chosen to be simulated with the traffic model and used to represent the traffic emission over the whole period. The traffic model estimates the vehicle flow for each traffic direction of a two-way street. The traffic emissions of a two-way street were then merged to obtain one emission rate for each simulated street segment, the basic input data needed by MUNICH.
Geographic data Traffic lane widths and building heights were obtained from the BD TOPO database (http://professionnels. ign. fr/bdtopo). Total street width includes the lane width, the sidewalk width, or the highway shoulder width (the A86 highway passes through the modeling domain). For minor surface roads, a width of 3 m was used for sidewalks by default, which corresponds to two sidewalks (the minimum sidewalk width in France is 1. 4 m). For the A86 highway, 20 m was added to the lane width including two shoulders (4 m), a median strip (1. 5 m), and two urban train lanes (4 m). Street widths and building heights of the 15 major streets were explicitly estimated. For the other streets, average street width (7. 5 m) and building height (6. 9 m) estimated for the modeling domain were used.
It is also important to note that, on average, over the street network the morning peak of emissions data occurs 1 h later than in Boulevard Alsace-Lorraine. It means that the delay in simulated concentrations is introduced by a transport process (advection in the street network or turbulent exchange with the background atmosphere). In addition to NO 2 concentrations, NO x concentrations (NO 2 equivalent) were measured at the monitoring stations at Boulevard Alsace-Lorraine. The comparison of the measured and simulated concentrations with MU-NICH shows a large underestimation in the NO x concen- a FB (fractional bias), NMSE (normal mean square error), MFE (mean fractional error), VG (geometrical mean squared variance), MG (mean geometrical bias), FAC2 (fraction in a factor of 2), and R (correlation coefficient)  (Chang and Hanna, 2004; Yu et al.
Simulation domains and input data SinG is used to estimate the pollutant concentrations in both the 3-D gridded domain and the street network. Four simulation domains are used from the continental scale to the urban scale (see Fig. 4 ). Domain 1 covers western Europe with a horizontal resolution of 0. 5 •. Domains 2 and 3 cover northern-central France (0. 15 • resolution) and the Île-de-France region (0. 04 • resolution), respectively. The urbanscale domain 4 covers the eastern Paris suburbs (0. 01 • resolution) including the area where the street network is located. The horizontal resolution of domain 4 corresponds to about 1 km. The street network neighborhood is covered by 12 grid cells of domain 4 and corresponds to about 1 % of the domain 4 area. The vertical resolution consists of 10 levels up to 6 km with the lowest level at 15 m.
For Polair3D, boundary conditions for the outer domain 1 were obtained from data simulated by the MOZART 4 global CTM  (Emmons et al. , 2010). Meteorological data were obtained from WRF simulations for all domains  (Thouron et al. , 2017). Anthropogenic emissions were calculated using the European Monitoring and Evaluation Programme (EMEP) inventory for domains 1 and 2 (EMEP/CEIP 2014 present state of emissions as used in EMEP models) and the Airparif inventory for domains 3 and 4. Biogenic emissions were calculated with MEGAN v2. 04  (Guenther et al. , 2006). For MUNICH, which here is the urban canopy model embedded into Polair3D, the input data presented in Sect. 4 were used, except for boundary conditions over rooftops, which were obtained from the lowest layer of Polair3D in the SinG simulation.
Providing the background concentrations modeled by Polair3D to MUNICH improves the simulation results for NO 2 concentrations. The NO x concentrations are also improved with SinG; however, both MU-NICH and SinG simulated NO x concentrations are largely underestimated. This underestimation could be partly explained by uncertainties in NO x emissions or an overestimation of NO x transport into the overlying atmosphere at rooftop. For the latter it would be of interest to further investigate, with the support of appropriate observation data, the relative contribution of the uncertainties in the meteorological data and of the model assumption. The impact of the horizontal resolution of meteorological data on SinG simulations also needs to be studied. For this case study, using a comprehensive chemistry within the street canyon does not notably influence the NO x concentrations.
Consequently, computational costs can be reduced by using the Leighton photostationary state within the urban canopy. However, this test would need to be renewed for new applications. The photostationary assumption cannot hold in condition with high VOC emissions. Further studies are needed to extend the model to simulate primary and secondary particulate matter in an urban canopy. The observation database built within the framework of the TRAFIPOLLU project was focused at the street level. We have not been able to evaluate the ability of the new model to represent background concentrations in comparison to a traditional Eulerian chemistry-transport model. An application of SinG to larger urban domains would allow this type of analysis and would complete the evaluation for street-level concentrations.
Acknowledgements. This work was funded by  EDF R&D  and  EDF R&D China. The authors acknowledge our colleagues  Luc Musson-Genon , CEREA/EDF R&D, and  Jiesheng Min , EDF R&D China, for helpful discussions during the model development. We also thank  Airparif  for providing the emission inventory and the measured concentration data,  Laëtitia Thouron  for providing the WRF meteorological outputs, and the  TrafiPollu ANR  project for making data available for the model application and evaluation. Edited by: Jason Williams Reviewed by: three anonymous referees.
Mas2tering Project (Multi-Agent Systems and Secured Coupling of Telecom and Energy Grids for Next Generation Smartgrid Services) The main goal of Mas2tering (2014-2017, FP7 project) is to develop an automatic system that enables to adapt the residential consumption to the local production. The Mas2tering software platform uses the users' flexibility to perform a three-level optimization of electric grid management. The home-level optimization maximizes self-consumption or electricity purchase at cheapest tariffs to reduce consumer's energy bill, whereas respecting the user's preferences. The local community optimization enables to balance the local grid by trading local generation surplus between local community's prosumers at tariffs more interesting than those proposed by the corresponding prosumer's supplier.
Authentication, Authorization & Accounting: from Multi-Agent System to Grid Security In order to build Flexible Demand Response (DR), connectivity of a growing number of assets is a key enabler. However, increasing asset connectivity, and building cyber physical systems brings cyber threats into the real world. Recent Cyber-Attack consequences, such as the blackout in Kiev in Christmas 2016, show that cybersecurity is a key function to guarantee the resilience of Cyber Physical System. Cyber-attack could have critical impact in terms of safety, cost and even reputation. In Mas2tering, the cybersecurity challenge was tackled by building a secure-by-design technical architecture, taking into account the main threats of Cyber Physical System. A 3-step security analysis was used: Identification of relevant threats regarding use cases (based on threats databases and ISO 27002); Risk analysis based on ISO/IEC 27005 methodology; Identification of security requirements to implement.
Conclusions The demand-side management of residential and tertiary prosumers, organized in local communities supervised by a local aggregator, is an efficient solution to manage the green transition. Flexibility management relies on the capacity to motivate consumers by valorizing the membership to a green community, valuing their flexibility from social and economic aspects  [5] , and enabling savings on their electricity bills. The software platform, developed by Mas2tering and Drive projects, includes forecasting, optimization, cybersecurity and fast-response capabilities modules. The platform will be implemented in five pilot sites across three countries, to prove its capacity to reduce peak-demand, introduce more intermittent renewable energies in the grid, provide economic benefits for both the consumers and the utilities and deliver ancillary services such as frequency regulation for DSOs.
We observe today a large diversity of proof systems. This diversity has the negative consequence that a lot of theorems are proved many times. Unlike programming languages, it is difficult for these systems to co-operate because they do not implement the same logic. Logical frameworks are a class of theorem provers that overcome this issue by their capacity of implementing various logics. In this work, we study the STT∀ βδ logic, an extension of Simple Type Theory that has been encoded in the logical framework Dedukti  [6]. We present a translation from this logic to OpenTheory [8], a proof system and interoperability tool between provers of the HOL family. We have used this translation to export an arithmetic library containing Fermat's little theorem to OpenTheory and to two other proof systems that are Coq [13]  and Matita  [2].
STT∀ βδ vs OpenTheory One may notice that STT∀ βδ and OpenTheory are quite similar. However, there are some differences that makes the translation from STT∀ βδ and OpenTheory not so easy: • Terms in OpenTheory are only convertible up to α conversion while in STT∀ βδ it is up to α, β, δ conversion • All the connectives of OpenTheory are defined from the equality symbol, while in STT∀ βδ they are defined from ∀ and ⇒ connectives • Prenex polymorphism in OpenTheory is implicit: All free type variables in OpenTheory are implicitly quantified while in STT∀ βδ all quantifications are explicit These differences lead to three different proof transformations: • Encode the ∀ and ⇒ connectives using the equality of OpenTheory • Explicit each application of the conversion rule • Finally, get rid of the type quantifier OpenTheory is a classical logic while STT∀ βδ is intuitionistic.
Encoding ∀ and ⇒ using the equality A first idea to encode STT∀ βδ proofs in OpenTheory would be to axiomatize all the rules of STT∀ βδ and then translate the proofs using these axioms. But translating a rule to an axiom in OpenTheory requires the use of implication. Since OpenTheory does not know what an implication is, such axioms would not be usable since it would not be possible to use the modus ponens eliminate the implication itself. Therefore, one must find an encoding of the ∀ and ⇒ connectives such that the rules of STT∀ βδ are admissible. Such encoding is already known from Q 0  [1]. This encoding is presented below and uses two other connectives that are ⊤ and ∧ that can be defined as axiom in OpenTheory: ⊤ = λx. x x ⇒ y = (x ∧ y) = x x ∧ y = λf. f x y = λf. f ⊤ ⊤ ∀x. P = λx. P = λx.
8. Also presented as an ancillary tool to this paper is a Mathematica based code that allows one to obtain a truncated expression for the three mass master sunset integrals when the level of precision and values of the input meson masses are provided. This allows lattice practitioners, amongst others, to obtain analytic approximations for the sunsets for any given set of lattice inputs. These can then be used to construct relatively compact analytic expressions for easy comparison with lattice or experimental data. 9. A numerical study is done in Section 5 for m K , F K , m η and F η to provide a breakup of the relative numerical contributions to the NNLO part of their different constituents. This shows that the sunset integral contribution is significant. 10. In Section 5, we also numerically justify the use of our GMO-simplified expressions by showing that the error on various components constituting the NNLO contribution due to the use of the GMO relation does not exceed 5% in most cases, and that the final error on the NNLO contribution is effectively zero for the kaon mass, and very small for the kaon and eta decay constants.
Acknowledgements SF thanks  David Greynat  for helpful discussions and correspondence. JB is supported in part by the  Swedish Research Council  grants contract numbers  2015-04089  and  2016-05996  and by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  668679 ). BA is partly supported by the  MSIL Chair of the Division of Physical and Mathematical Sciences, Indian Institute of Science.
Landscape and orchard dynamics Orchard data were collected in a peach-growing area in southeastern France. The resulting database includes 34 years of information on 553 patches (i. e. pieces of land); the distance between the centroids of neighbouring patches ranges from 16 to 496 m (median: 55 m). On these patches, 597 peach orchards (i. e. homogeneous crop of a single cultivar with the same planting date) have been continuously cultivated (i. e. removed orchards are replanted during the next winter). In our model, we use the real landscape of 553 patches (electronic supplementary material, figure  S1 ) which has been used previously to estimate most parameters of the model  [27]. However, we simulate new orchard dynamics on the patches. To stabilize the mean age of the orchards at year a 0 = 1, an initial orchard planting year is uniformly drawn between -39 and -20 for each patch.
Then the following algorithm is iterated: (i) the orchard duration (after which the orchard is removed) is drawn from a Poisson distribution whose mean is ψ = 15 years; (ii) the year after orchard removal gives the planting year of the subsequent orchard on the same patch. For each orchard, a planting density is randomly drawn from the empirical distribution of the planting densities of the 597 orchards in the database.
Software The model was written in R and C languages. Within the R software v. 3. 0. 3 [42] , Sobol's sequences and indices were obtained using the packages fOptions (v. 3010. 83) and sensitivity (v. 1. 11) , respectively. The stepwise model-selection algorithm used the function regsubsets of the package leaps (v. 2. 9). A simulation takes approximately 4 s with a regular computer (Intel ® Core™ i7-4600M). The overall exploration of the model was carried out on a computer cluster, which enabled the parallelization of the 50 stochastic replicates for each sensitivity analysis, each replicate requiring roughly one day to explore the 15 000 parameter combinations.
Acknowledgements. The authors thank  G. Labonne  and  S. Grizard  for constructing the reference database, SRAL RA, and FREDON 30 (especially  J. Aymard ) for providing surveillance data and for their expertise in prunus cultivation,  F. Bonnot  for stimulating statistical discussions,  L. Barrett  for reviewing the manuscript,  A. Bouvier  for technical support with CaliFloPP, the  INRA BioSP bioinformatics platform  and particularly  L. Houde  for providing support.
In Ecuador and Peru, geochemical information from Pacific coastal rivers is limited and scarce. Here, we present an unedited database of major element concentrations from five HYBAM observatory stations monitored monthly between 4 and 10 years, and the discrete sampling of 23 Andean rivers distributed along the climate gradient of the Ecuadorian and Peruvian Pacific coasts. Concentration (C) vs. discharge (Q) relationships of the five monitored basins exhibit a clear dilution behavior for evaporites and/or pyrite solutes, while the solute concentrations delivered by other endmembers are less variable. Spatially, the annual specific fluxes for total dissolved solids (TDS), Ca 2+ , HCO 3 À , K + , Mg 2+ , and SiO 2 are controlled on the first order by runoff variability, while Cl À , Na + and SO 4 2À are controlled by the occurrence of evaporites and/or pyrite.
g. ,  Morera et al. , 2017; Rau et al. , 2017a). Therefore, through the study of concentration vs. discharge relationships (''C-Q relationships''), the sensitivity of dissolved matter production to changes in hydroclimatological conditions can be determined. In the present study, we present inorganic dissolved matter fluxes in the Andean Pacific basins based on discrete sampling for solute measurements at 23 stations along the Pacific coast, from Ecuador to southern Peru. Moreover, we describe the TDS variability during the hydrologic cycle based on monthly samples for solute concentrations acquired between 2007 and 2010 at five gauging stations (SNO-HYBAM database). Based on this unedited database, we identified the main factors controlling TDS production as a function of time and space discharge variability in Andean rivers along the Pacific coast.
Material and methods We present the TDS and major element concentrations measured in 28 rivers. 23 rivers were sampled between 1 and 11 times and 5 rivers (Esmeraldas, Tumbes, Santa, Can ˜ete, and Ocon ˜a Rivers) were monitored by the HYBAM monitoring program framework following a monthly sampling frequency between 4 and 10 years (Table  S1 ). All data presented here are available for free download on the SNO-HYBAM website (http://www. ore-hybam. org). The description of the hydroclimate data source, of the hydrochemistry acquisition method, of the flux calculation and of the atmospheric correction methods are reported in supplementary material S1.
Hydrology Annual runoff increases from 188 to 1382 mm yr -1 along the Pacific coast following a south-north gradient (Table  S1 ; Fig. 1 ). According to the database, the 28 sampled rivers produced a total discharge of approximately 59 km 3 Áyr À1 , which corresponds to $49% of the Ecuadorian and Peruvian Pacific rivers' total discharge ($89% of the Peruvian R. discharge and $32% of the Ecuadorian R. discharge) (Autoridad Nacional del Agua, 2012;  Milliman and Farnsworth, 2011 ; Table  S1 ). All gauging rivers respond to homogenous unimodal seasonality, with a higher discharge between December and May (approximately 80% of the annual river discharge) and a low water stage during the June-November period. The seasonality index (SI = (Q m maxÀQ m min)/Q m mean, with Q m the monthly discharge) varies from 1.
Control of TDS budget Spatially, we observed a north-south increase in TDS concentration with decreasing discharge value (Fig. 1  and S3 ). This gradient may be attributed to an increase in evaporites (either neogenous or unreported by the lithological map), because the SO 4 2À + Cl À concentration increase together with the TDS concentration (Fig. S5 ). However, the magnitude of the TDS concentration is lower than that of the annual discharge (and annual runoff) of sampled rivers. Spatial discharge (and spatial runoff) distribution is consequently the first-order parameter that controls the TDS flux (and spatial specific flux) variability along the Pacific coast (Fig. 3 , Fig. S5A ). The range of the specific TDS flux is large and varies from 5 to 182 tÁkm -2 Áyr À1. This range is comparable to the values reported by  Milliman and Farnworth (2011;  based on the Meybeck, 1994 database) for wet young tropical mountainous rivers.
Acknowledgments We thank the two anonymous reviewers for their constructive recommendations under the review process. This work was funded by the  French ''Institut de recherche pour le developpement'' (IRD) , the  French ''Institut des sciences de l'univers'' (INSU), and the ''Observatoire Midi-Pyrenees'' (OMP)  through the  HYBAM Observatory (Hydrogeodynamics of the Amazon basin). We especially thank  Pascal Fraizy ,  Philippe Vauchel ,  Elisa Armijos ,  William Santini ,  Nore Arevalo , the  SENAMHI (''Servicio Nacional de Meteorologı ´a e Hidrologı ´a'', Lima, Peru) , the  INAMHI (''Instituto Nacional de Meteorologı ´a e Hidrologı ´a'', Quito, Ecuador) , the  UNALM (''Universidad Nacional Agraria de La Molina'', Lima, Peru) , as well as all members of the  Observatory for Environmental Research HYBAM , for providing hydrological, suspended solids, and water chemistry data.
Simulation and results In this paper, the proposed approach is tested in the Matlab/Simulink environment with a benchmark model. The detailed the parameters of wind turbine system are given after  Odgaard et al. (2009) , and the main parameters of wind turbine system are R = 57. 5, n g = 95, J t = 55×10 6 kg•m 2 J g = 390 kg•m 2 , K s = 2. 7×10 9 Nm/rad, B s = 9. 45 Nm/(rad/s), ω n = 11. 11, ζ n = 0. 6, τ 1 = 0. 1. Using the Matlab/Yalmip toolbox (Lofberg, 2004) to solve the LMIs, the matrices of the UIO can be obtained; see Eqns. (  46 )-(  51 ). The baseline controller is given after Odgaard et al. (2009). The parameters of the controller for the pitch angle are k p = 4, T i = 4. In the simulation, a random wind speed is selected and shown in Fig. 3. In the following, two cases are considered to verify the proposed approach.
Acknowledgment This work was partially supported by the  National Natural Science Foundation of China  ( 61304077 ), the  International Science & Technology Cooperation Program of China  ( 2015DFA01710 ), the  Natural Science Foundation of Jiangsu Province  ( BK20170094 ), and the  Jiangsu Province "Project Blue": Young Academic Leaders. This work was also supported by  Campus France  through the  Program Partenariats Hubert Curien: "Cai Yuanpei " (201404490146).
Background: One of the approaches to detect genetics variants affecting fitness traits is to identify their surrounding genomic signatures of past selection. With established methods for detecting selection signatures and the current and future availability of large datasets, such studies should have the power to not only detect these signatures but also to infer their selective histories. Domesticated animals offer a powerful model for these approaches as they adapted rapidly to environmental and human-mediated constraints in a relatively short time. We investigated this question by studying a large dataset of 542 individuals from 27 domestic sheep populations raised in France, genotyped for more than 500,000 SNPs. Results: Population structure analysis revealed that this set of populations harbour a large part of European sheep diversity in a small geographical area, offering a powerful model for the study of adaptation.