dataesr/scientific-paragraphs-categorization huggingface dataset:
2001) , given this is the approach recommended in guidance to CBD parties and widely used to assess progress toward achieving Aichi Target 11  (Woodley et al. 2012; CBD 2014; Juffe-Bignoli et al. 2014; Tittensor et al. 2014; Venter et al. 2014; Watson et al. 2014). We projected all spatial data into Mollweide equal area projection, and processed in vector format using ESRI ArcGIS v10, calculating PA coverage through spatial intersections of PAs and conservation features (Tables  S3  and S4 ). For all terrestrial coverage statistics, we followed established practice (e. g. ,  Juffe-Bignoli et al. 2014; Venter et al. 2014 ) by excluding the Antarctic ecoregions "Marielandia Antarctic tundra" and "Maudlandia Antarctic desert"  (Olson et al. 2001). We estimated temporal trends in PA coverage using data on the year of PA establishment recorded in the January 2013 version of the World Database on Protected Areas.
As this was unknown for 14. 3% of terrestrial and 8. 6% of marine PAs, we randomly assigned a year from another PA within the same country, or for countries with less than five PAs with known year of establishment, from all terrestrial or marine PAs, and then repeated this procedure 1,000 times, and plotted the median and 95% confidence intervals. To identify the extent of additional land requiring conservation to meet different target-setting scenarios in the terrestrial environment, we built on the approaches of previous studies (e. g. ,  Pressey et al. 1993; Faith et al. 2001; Rodrigues et al. 2004a; Watson et al. 2010; Pouzols et al. 2014; Venter et al. 2014) , using the Marxan conservation planning software  (Ball et al. 2009). This uses a simulated annealing approach to identify near-optimal portfolios of planning units that meet the specified conservation feature targets while minimizing costs  (Ball et al.
We used the 1-km resolution Global Rural-Urban Mapping Project (GRUMPv1) data set  (CIESIN et al. 2011)  in ArcGIS to calculate for each planning unit the total human population size, and the human population size within PAs and within unprotected IBAs or AZEs. Finally, we used these data to produce two conservation planning systems. The first listed for each planning unit the unprotected area of each conservation feature and the human population size on this unprotected land. It also included one "protected" planning unit that listed the total area of each conservation feature falling within the global PA network, and the total human population size found in the global PA network. This "protected" planning unit was set as automatically selected in Marxan, so the software would identify additional planning units that met the specified targets by complementing the existing PA network.
Acknowledgments We are grateful to the many individuals and organizations who contribute to the IUCN Red List of Threatened Species, WDPA, or to identification of IBAs or AZEs. We thank  A. Bennett  for help with data collation and  N. Dulvy ,  W. Laurance , and  D. Faith  for helpful comments on an earlier draft. This work was supported by the  Cambridge Conservation Initiative Collaborative Fund and Arcadia.
Thus, the study could aid the development of more focused or locally adapted public policies and planning choices. For the purpose of this research, data on active commuting and explanatory factors were collected from a large sample of French adults and an innovative local-targeted regression technique was applied. Associations between active commuting behaviors and social support (e. g. [9, 10] ), different physical environment dimensions, e. g. walkability and bikeability, land use, public transportation availability, safety, aesthetics, etc. , in residential and/or work neighborhoods are documented in the literature (e. g. [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] ). However, previous studies have largely been based on the implied and strong assumption that the relationship between individual/ environmental factors and active commuting is spatially homogeneous, i.
e. that pertinent factors operate in a similar manner everywhere. This is a necessary condition for the use of global regression models. Yet, non-stationarity, referring to the variation in relationships across space  [25, 26] , is a very common phenomenon in any geographical dataset like place-level factors. For instance, a global positive association can be found between walking to work and overall walkability, but is this really the case everywhere throughout a city or a neighborhood? In recent years, an innovative local-based regression technique has been gaining popularity for exploring spatial non-stationarity among data: the Geographically Weighted Regression model (GWR,  [25] ). GWR allows the parameter estimates to vary locally, unlike in global models where they remain constant. GWR fits local regression at each location by applying a weighting scheme (based on a kernel function) which gives more weight to neighboring locations  [27, 28].
(i) Land use and facilities Land use included the percentage of area covered by individual housing, collective housing and vegetation, respectively, as well as proximity facilities. Data on housing and vegetation were provided as shapefiles (polygons) by the Paris Region Urban Planning & Development Agency (IAU Île-de-France, 2008, scale: 1:5000). Data on proximity facilities were given as the number of 26 different types of facilities, e. g. banks, bakeries, drugstores, restaurants, etc. , by the IRIS Census unit. IRIS areas (acronym for "Aggregated Units for Statistical Information"), provided by the French National Institute of Statistics and Economic Studies (INSEE, www. insee. fr), represent the smallest unit for dissemination of French infra-municipal data. They include an average of 2000 residents per unit and are homogeneous in terms of housing and socio-economic conditions.
Data on land use and facilities were then disaggregated into a grid of 200 × 200 m cells, in order to obtain a spatially homogeneous data net. Finally, these data were linked to each individual within a Euclidian buffer of 500 m around the residential address (mean values of the cells included in the buffer), as this distance is commonly used in accessibility studies  [42, 43]. (ii) Level of bikeability Bikeability (positive expected association with active commuting), including bike-sharing facilities and bike path densities, was assessed by fixed kernel density estimation (KDE) with a bandwidth of 500 m, which is equivalent to a Euclidian buffer for polygon-shaped variables. KDE is a smoothing geostatistical technique to transform a point or a line pattern into a continuous surface map of density (raster), with an estimated value for each cell.
(iv) Socioeconomic environment The socioeconomic level of the residential environment has been considered since it is expected to be positively associated with overall physical activity, including active commuting  [47]. Eight neighborhood-level socioeconomic variables, from the Census database (www. insee. fr), were included and disaggregated into the 200-m square grid (following the same procedure as used for land use and facilities) (see Figure  2 ): percentage of foreign residents, unemployed, part-time workers, university graduates, homes occupied by their owners, car owners, households with a parking space, and median income.
In this study, the number of neighbors minimizing the AICc was 800 (after testing down from 4,000 to 50, every 50). This amounted to an adaptive bandwidth size varying from 3,150 m to 19,250 m and a mean distance of 5,650 m, knowing that the study area spreads over approximately 30,000 m. AICc was also used to compare the performance of both global and local models. The model with the smallest AICc should be selected as an optimal model called MAICE (minimum AIC estimator, see  [30]. A difference in AICc values of more than 2 is considered substantial. Global models were performed with SAS 9. 3 software (SAS Institute Inc. , Cary, NC, USA) and the academic piece of software GWR 4. 0 was used to calibrate and run geographically weighted models. This software is a tool for modelling varying relationships among variables by calibrating GWR and Geographically Weighted Generalized Linear Models (GWGLM) with their semi-parametric variants (https://geodacenter.
asu. edu/gwr_software, see Nakaya et al. [51]  and Nakaya  [52]  for additional details). Maps with continuous values were based on an interpolation procedure (inverse distance weighting) and were processed with ArcGIS 10. 1 (ESRI Inc. , Redlands, CA, USA). Finally, a measure of statistical significance (pseudo t-value) of GWPR estimates was added visually as isolines for each local term map. A value greater than |1. 96| indicates a p-value < 0. 05. Mapped GWPR estimates are log-odds, with negative and positive values meaning negative and positive relationships, respectively.
Implications of the findings The non-stationarity of spatial datasets has been demonstrated in other studies dealing with health-related outcomes, such as obesity  [37] , cardiovascular mortality  [34] , and health care system organization  [35]. Such observations lead to questions about the relevance of using global models, which tend to smooth the effects of one or another variable across the entire area, whereas these effects are in fact area-specific. Considering local models over global ones has potential implications for public health policies. As emphasized by Yang and Matthews  [35] , GWR-based analyses in health-related research could be used as a tool for place-specific targeting and/ or tailoring of public health interventions. For example, potential interventions by local authorities regarding bike safety in traffic, e.
Acknowledgments This work is part of the  ACTI-Cités  project (coordinator:  J. M. Oppert ) carried out with financial support from the  French National Cancer Institute (Institut National du Cancer, INCa)  through the Social sciences and humanities and public health programme (2011-1-PL-SHS-10). The NutriNet-Santé cohort study is funded by the following public institutions:  Ministère de la Santé, Institut de Veille Sanitaire (InVS) ,  Institut National de la Prévention et de l'Education pour la Santé (INPES) ,  Fondation pour la Recherche Médicale (FRM) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Paris 13 University. The authors also wish to thank the two anonymous referees for their constructive comments.
The present work focuses on the dry months of 2006 in order to avoid clouds and to maximize the probability of encountering mixtures of biomass-burning and mineral dust aerosols. Experiments were conducted with the SIRAMix approach with the replacement of MACC-II aerosol data by more accurate AERONET (Aerosol Robotic Network) measurements as input. The use of accurate aerosol data is crucial to any study of the impact of aerosol composition on DSSF, as inaccurate AOD and aerosol composition may give rise to misleading conclusions. Two approaches were used to calculate global, direct, and diffuse DSSF. The first considered dynamic AOD and a fixed aerosol type. The second included the aerosol composition variations during the day. Improvements in global DSSF estimation obtained for each case were compared to results from the LSA-SAF product according to aerosol activity.
For example, they are part of the AERONET network of sun photometers  [21] , which perform direct sun measurements every 15 min, with a 1. 2° field of view at the nominal wavelengths of 440, 675, 870, 940, and 1020 nm. They provide accurate, real-time information on the (aerosol and gaseous) composition of the atmosphere, including AOD, aerosol size distribution, ozone and water vapor content and aerosol absorption and refractive indexes as described in  [22]. In the work presented here, we used AERONET data corresponding to the cloud-screened and quality-checked level 2. 0. Measurements of global, direct, and diffuse DSSF are also available in Niamey from the Atmospheric Radiation Measurement (ARM) station  [23]. A Radar-Lidar Mobile Facility (AMF) was also deployed at the Niamey station in 2006 as part of the AMMA field experiment  [12, 24].
The AMF is a portable facility equipped with cloud-dedicated instrumentation (vertically pointing 94-GHz radar and lidar) that provides continuous cloud profile documentation, radiosounding results, and surface budget measurements. The operations performed during the AMMA campaign  [13]  in Dakar provided similar flux measurements. The downward broadband shortwave radiative flux is measured in the spectral range of 200 to 3600 nm using a Kipp and Zonen CM22 pyranometer with a time step of 2 min. For diffuse radiation measurements, the direct solar radiation is intercepted by a 60 mm diameter sphere shadower tracking the Sun. The solar direct radiative flux, in the 200 to 4000 nm spectral range, is measured by a Kipp and Zonen CH1 pyrheliometer. These data were used differently in the present study.
Data Base of Aerosol Properties A database of aerosol optical properties was used for the SIRAMix1 and SIRAMix2 approaches. These data were necessary to accurately simulate incoming solar radiation. Several data bases  [30] [31] [32] [33] , each containing the optical properties of a given aerosol layer have been proposed in recent decades. The calculation of aerosol radiative properties is quite challenging since different aerosol types are related to distinct optical properties  [29]  and the properties of aerosol mixtures are generally poorly documented. This is particularly the case for the combination of mineral dust, biomass burning and sea salt found in West Africa  [4]. The present work used the OPAC (Optical Properties of Aerosols and Clouds) package, which provides optical properties for 10 elementary aerosol components  [33].
in  [14]  was used as the keystone of the second and third methods. The advanced physical parameterization (analytical formulae) employed in this method allows it to retrieve the direct and diffuse DSSF through the use of time-evolving aerosol properties (load and type) as input. The global DSSF is obtained by simply adding the two surface fluxes. Multiple values of aerosol transmittances and albedo are pre-computed using the radiative transfer code libRadtran for several default aerosol components and types, and are stored in a look up table (LUT). The LUT is accessed for each direct or diffuse DSSF calculation in order to obtain the aerosol radiative quantities corresponding to each instant of time. We refer the reader to the Appendix for more details about the SIRAMix approach. Two configurations of SIRAMix regarding the definition of the aerosol layer were used in the present work.
The observed improvement is quite remarkable given the high aerosol load throughout the day under study (average AOD > 1. 5), which generally results in large estimation errors. It is worth noting here that a small time shift can be seen between the ground and estimated surface fluxes values for Niamey, which comes from the use of aerosol data from Banizoumbou. In contrast to the mixture day analyzed in Section 4. 1. 3, the aerosol loading varied quite a lot during the day under study and therefore the 50 km between the two stations became significant. Nonetheless, the estimation of surface fluxes also improved thanks to the use of the SIRAMix-based methods and especially to SIRAMix2.
Scores for All Selected Days Table  6  summarizes the average scores considering the 20 selected clear-sky dates (see Section 2. 2). The results show that the LSA-SAF global DSSF was generally improved by the fact that the two configurations of SIRAMix took the aerosol content (SIRAMix1) and the aerosol composition (SIRAMix2) into account. The average RMSE for all day types decreased from 92. 7 W/m 2 for the LSA-SAF product to 42. 9 W/m 2 for the SIRAMix1 configuration and 32. 6 W/m 2 for SIRAMix2. The same tendency was observed in Niamey, with values of 58. 8 W/m 2 , 45. 8 W/m 2 , and 25 W/m 2 , respectively. Again, scores at Niamey are significant given the use of aerosol data from Banizoumbou. The importance of considering the properties of the two predominant aerosol components with SIRAMix2 is clear.
The present study may be useful for circumventing the enhanced absorption in the OPAC desert aerosol model, that has been found in some studies using aerosol observations in the Sahara desert as a reference. Most of the aerosol databases show higher absorption in the visible spectrum  [52, 53]  than are found from in situ  [54, 55]  and satellite  [37]  observations. These differences come from the difficulty of predicting the absorption of mineral dust because of its complex composition  [43, 56, 57]. Also, the presence of soot particles coming from biomass burning may, in some cases, explain the higher absorption, which in turn may be compensated for by considering the two predominant aerosol types following the SIRAMix2 method. Although this work shows the importance of considering the proper aerosol optical properties, the accurate estimation of incoming solar radiation in satellite-oriented projects such as LSA-SAF remains difficult.
AERONET aerosol observations are only available at reduced spatial scales and therefore other data bases of aerosol properties at larger scale must be considered (e. g. , MACC-II). Unfortunately, model-based data bases such as MACC-II are often related to decreased accuracy of the aerosol data. For instance, Ceamanos et al. [14]  have reported biases of 76. 2 and 62. 0 W/m 2 for direct and diffuse DSSF using SIRAMix with MACC-II data over the North African station of Tamanrasset (with aerosol conditions similar to those at the stations studied here). These errors are much higher than those obtained by SIRAMix with AERONET aerosol load and type data in the work reported here (30. 6 and 22. 1 W/m 2 for direct DSSF and 30. 7 and 25. 1 W/m 2 for diffuse DSSF for Dakar and Niamey, respectively).
Ray aer atm A A A + ≈ (A10) where ARay is set to 0. 0685. Aerosol particles in SIRAMix are characterized by the radiative quantities Taer,dir, Taer,dif and Aaer. For SIRAMix1, the three terms are obtained from a pre-computed LUT built by the radiative transfer code libRadtran using the OPAC desert aerosol model. The LUT allows choosing the aerosol transmittance and albedo values that correspond to the actual geometrical and atmospheric conditions. For SIRAMix2, the previous aerosol quantities are calculated as the combination of the individual contributions of the two predominating aerosol modes following the model in  [15]  , , , where exponents f and c are used to distinguish aerosol quantities related to the fine aerosol mode from those corresponding to the coarse aerosol mode. The total aerosol optical depth (δ) is the sum of the fine (δf) and coarse (δc) contributions.
Acknowledgments Mamadou S. Dramé  carried out this research during his stay at CRNM as a visiting scientist. Both Mamadou S. Dramé and Xavier Ceamanos were supported by  EUMETSAT  through the  LSA-SAF  project. We thank the  AERONET team  for the dissemination of the aerosol data. Thanks are also due to the people involved in the AMMA and ARM programs for sharing the solar radiation data. Finally, we are most grateful to  Fleur Couvreux  and  Dominique Bouniol  for sharing the cloud mask in Niamey with us.
The paper will present the framework of fully automated two/three dimensional ice accretion simulation package NSMB3D-ICE, with emphasis on the remeshing step. The NSMB3D-ICE Navier-Stokes code, coupled to an Eulerian droplet module and iterative Messinger thermodynamic model, can perform multi time-steps ice accretion simulations via an automated multi-block elliptic/parabolic grid generation code (NSGRID3D). Attention is paid to the efficiency and robustness of the numerical procedure especially for complex 3D glaze ice simulation. The new automated multi time-step icing code NSMB3D-ICE/NSGRID3D is validated and verified using several icing case studies such as the GLC305-3D rime and glaze ice cases. The Navier-Stokes flow solver NSMB3D is a finite volume three-dimensional multiblock Euler/Navier-Stokes flow solver developed by J.
The NSMB solver is also used within the multi-time steps framework of icing Simulation CANICE2D-NS  [10] [11]. The new development in NSMB3D made the code capable of ice accretion simulations, albeit with only a single time step approach. The NSMB3D-ICE icing simulation includes 4 main steps: RANS flow simulation, Eulerian droplet computation, iterative Messinger thermodynamic module solved with a Newton-Krylov solver and ice growth computations  [12]. To perform multi-time steps 3D ice accretion simulation especially for complex glaze ice formation, NSMB3D-ICE has now been coupled with the elliptic/parabolic multi-block grid generation code NSGRID3D. NSGRID-2D/3D is a two and three multi-block multi-grid elliptic/parabolic grid generation code developed at the research laboratory of Professor Eric Laurendeau at École Polytechnique Montreal.
Wikipedia is a huge global repository of human knowledge, that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix, for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, we perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time.
To apply PageRank and 2DRank methods, we consider each edition as the network of articles of the given edition connected by hyper-links among the articles (see the details of ranking algorithms in Section Methods). The full list of considered Wikipedia language editions is given in Table  1. Table  2  represents the top 10 historical figures by PageRank and 2DRank in the English Wikipedia. Roughly speaking, top PageRank articles imply highly cited articles in Wikipedia and top 2DRank articles imply articles which are both highly cited and highly citing in Wikipedia. In total, we identified 2400 top historical figures for each ranking algorithm. However, since some historical figures such as Jesus, Aristotle, or Napoleon appear in multiple Wikipedia editions, we have 1045 unique top PageRank historical figures and 1616 unique top 2Drank historical figures.
We should note that the extraction of persons and their information from a Wikipedia edition is not an easy task even for the English edition, and much more complicated for certain other language editions. Therefore, the above automatic method based on 1. 1 million English names and their corresponding names seems to us to be the most adequate approach. Of course, it will miss people who do not have a biographical article on the English Wikipedia. Cross-checking investigation is done for Korean and Russian Wikipedia, which are native languages for two authors, by manually selecting top 100 persons from top lists of all articles ordered by PageRank and 2DRank in both Wikipedia editions. We find that our automatic search misses on average only 2 persons from 100 top persons for these two editions (the missed names are given in SI).