dataesr/scientific-paragraphs-categorization huggingface dataset:
Geolocation Uncertainty Validation Geolocation performance is assessed both at level 1B and at level 1C with or without geometric refinement using the GRI. All results are presented in the following paragraphs. Geometric refinement being not activated in the operational processing chain at the manuscript-writing time, the results related to refined products are obtained off-line by CNES using the Ground Processor Prototype Two different GCPs database are used for the various geolocation validations:  A dedicated high-resolution ortho-images database with geolocation accuracy better than 5m is used by Thales Alenia Space in the frame of MPC, see world distribution in Figure  44.  Pleiades HR database including more than 500 images with accuracy better than 5 m is used by CNES. These ground control points have to be as accurate as possible and on flat areas to limit parallax effects, shadows, etc.
Level-2A Calibration Dataset A calibration dataset of Sentinel-2 Level-1C products is constituted and regularly updated, covering different land cover types (e. g. , snow, rocks, desert, urban, vegetation, grass, forest, cropland, vineyard, irrigated crops, rivers, lakes, sand, costal area, wetlands, ocean water) and different atmospheric conditions (e. g. , cloud cover, aerosol optical thickness, water vapour content). The calibration dataset is worldwide including different latitudes to cover various solar angles and seasons. The 24 Level-2A calibration sites were selected above an active AERONET  [45]  site. Their geographical locations are shown on Figure  60. Their characteristics are further defined in  [46].
Several issues have been identified, regarding the SRTM geolocation itself, the DTED processing and the handling of DEM NoData values. For each of these issues a recommendation has been formulated and a solution proposed for implementation in the next release of Sen2Cor, version 2. 3. A number of algorithm evolutions are foreseen for the atmospheric correction module of Sen2Cor: improvements of cirrus correction and AOT retrieval as well as the implementation of an AOT fallback estimation based on ECMWF aerosol information when AOT retrieval fails in case of missing DDV pixels.
Method The procedure for the calibration of the Cloud Screening and Scene Classification algorithm is performed as follows: 1. Sen2Cor processor (with the option for scene classification only) runs on the Level-2A Calibration dataset using all thresholds by default from the current Level-2A Processing Baseline, producing for each S2 scene (100 km × 100 km) a scene classification map and 2 quality indicators maps (Cloud confidence and Snow confidence). These outputs are stored with the objective to be used later in the calibration procedure as reference.
5. When the tuning activity is finished, a new processing baseline is provided with a new set of updated SC parameters delivered in an updated Sen2Cor calibration file "L2A_CAL_SC_GIPP. xml". 6. Sen2Cor processor (with the option for scene classification only) runs again on the Level-2A Calibration dataset using the updated Level-2A Processing Baseline, producing for each S2 scene (100 km × 100 km) a new scene classification map and 2 new quality indicators maps (Cloud confidence and Snow confidence). 7. A comparison exercise is performed between the SC results with the updated thresholds and the SC results with the standard baseline to assess the impact of the changes. Through visual assessment, the pixel class transitions are evaluated on a large range of land cover types, atmospheric conditions, solar and viewing conditions.
Method The SCL validation is limited due to the lack of "ground truth" data sets. The applied validation method performs stratified random sampling followed by establishment of a reference database by visual inspection of the samples, and finally establishment of an error matrix allowing calculation of accuracy statistics. Each scene classification product from selected test sites contains more than 30 million pixels. As the validation is performed manually in a pixel based manner, a representative subset of these data has to be selected. Stratified random sampling guarantees statistical consistency (validity) and avoids exclusion of spatially limited classes from the validation. Visual, pixel-based manual inspection of the samples is supported by RGB band composition image (Bands 4,3,2), Colour Infra-Red composition image to highlight vegetation features (bands 8A,4,3) or snow (bands 12, 11, 8A), shape of the spectral curve, snow confidence Quality Image and cloud confidence Quality Image.
Results and Analysis Ground-truth classification images were created and compared to corresponding pixels of the Sen2Cor classification images for four scenes at four different locations. One example of data analysis is shown in Figure  62. Remote Sens. 2017, 9, 584 73 of 80 The SCL validation is limited due to the lack of "ground truth" data sets. The applied validation method performs stratified random sampling followed by establishment of a reference database by visual inspection of the samples, and finally establishment of an error matrix allowing calculation of accuracy statistics. Each scene classification product from selected test sites contains more than 30 million pixels. As the validation is performed manually in a pixel based manner, a representative subset of these data has to be selected.
Stratified random sampling guarantees statistical consistency (validity) and avoids exclusion of spatially limited classes from the validation. Visual, pixel-based manual inspection of the samples is supported by RGB band composition image (Bands 4,3,2), Colour Infra-Red composition image to highlight vegetation features (bands 8A,4,3) or snow (bands 12, 11, 8A), shape of the spectral curve, snow confidence Quality Image and cloud confidence Quality Image. After the Scene Classification results are compared against the reference database, an error matrix (confusion matrix) is calculated. Overall, user's and producer's accuracies are then computed as validation measures.
Results and Analysis Ground-truth classification images were created and compared to corresponding pixels of the Sen2Cor classification images for four scenes at four different locations. One example of data analysis is shown in Figure  62. The image contains clouds, cloud shadows, vegetation, not-vegetated and water pixels. Table  15  contains the corresponding confusion matrix. The diagonal in bold shows the number of correctly classified pixels in the validation set. In this example, vegetation, not-vegetated, water, high probability clouds and cloud shadow classes have been correctly classified for a very large portion of The image contains clouds, cloud shadows, vegetation, not-vegetated and water pixels. Table  15  contains the corresponding confusion matrix. The diagonal in bold shows the number of correctly classified pixels in the validation set.
Method The validation is performed comparing Sen2Cor outputs with surface reflectance reference data for a 9 km × 9 km subset around the sunphotometer location. Reference data are computed by a second run of Sen2Cor using AOT550 information provided by collocated sunphotometer measurements as input. The resulting "sunphotometer-corrected" surface reflectance data are considered to provide the surface reflectance "truth", since the greatest uncertainty in atmospheric correction comes from the aerosol characterization.
Results and Analysis Processing results with 60 m, 20 m or 10 m spatial resolution give consistent results. Example for BOA-SR validation at test site Belsk (Poland) is typical for locations with enough DDV pixels in the image (Figure  64 ). Example spectra were extracted at different locations in the reference image and in the image to validate. Example spectra for dark and bright soils, for forest and for different other vegetated locations show the expected spectral dependency and agree within the target accuracy of measurements from previous years. The agreement is better in case of more turbid water giving higher reflectance. This may be due to sun-glint effects not considered in Sen2Cor. Sun glint has a larger influence on lower signals coming from the water. Another result is that water parameter retrievals on basis of band ratio algorithms performed better with TOA-data than with BOA-data.
Results for European Block of Global Reference Image The coverage percentage of each orbit of the final GRI over Europe is given by Figure  56. Moreover, 66 km of image are missing on orbit 123, due to the acquisition of the 29/09/2015 split in two segments. Those parts of orbits should be filled later to complete the GRI over Europe, when cloud-free products will be available. The geometrical model of the 65 European GRI products was refined by space triangulation using tie points between images and GCPs. The location precision of the final GRI was validated using Pleiades database (references with a location precision better than 5 m). Figure  57  presents the area of each European GRI product according to the number of Pleiades references available, with the following legend of colours:.
Results and Analysis Processing results with 60 m, 20 m or 10 m spatial resolution give consistent results. Example for BOA-SR validation at test site Belsk (Poland) is typical for locations with enough DDV pixels in the image (Figure  64 ). Example spectra were extracted at different locations in the reference image and in the image to validate. Example spectra for dark and bright soils, for forest and for different other vegetated locations show the expected spectral dependency and agree within the target accuracy of relative 5% for BOA-reflectance. The SR differences up to 0. 04 between Sen2Cor results and reference lead to a Normalized Density Vegetation Index (NDVI) uncertainty up to 0. 06. These results are consistent with other investigations. Sentinel-2 Level-2A data processed with Sen2Cor were compared to atmospherically-corrected Landsat-8 data achieving an overall Root Mean Square Error (RMSE) of 0.
Ferran Gascon is the ESA technical officer of the MPC activity and the Sentinel-2 data quality manager. Catherine Bouzinac is the coordinator of the MPC Calibration and Validation activities, replacing Olivier Thepaut. Sebastien Clerc is the technical manager of the MPC. Stephane Massera is responsible for the GRI generation. Mathieu Jung is the leader of the L1 calibration activities. Bruno Lafrance is responsible for the radiometric calibration analyses. Benjamin Francesconi is the leader of the L1 validation activities. Francoise Viallefont is responsible for the MTF validation. Bahjat Alhammoud is responsible for the vicarious validation. Laetitia Pessiot is the service manager of the MPC. Vincent Lonjou is the CNES radiometry expert for Sentinel-2. Angélique Gaudel-Vacaresse and Florie Languille are the CNES geometry experts for Sentinel-.
FEA models Plane models of 11 mandibles of Cingulata (Table  1 ), each one corresponding to a different species, were created according to the methodology summarized by  Fortuny et al. (2012). The models were created using the ANSYS FEA Package (Ansys Inc. ) v. 15 for Windows 7 (64-bit system) to obtain the von Mises stress distribution. Two main masticatory muscles (i. e. , temporalis and masseter) were included in the model as a vector between the centroid of the muscular attachment in the mandible and the centroid of the equivalent muscle attachment in the skull following the modelling approach used in  Serrano-Fochs et al. (2015). To compare the models, a scaling of the values of the forces was applied according to a quasi-homothetic transformation in the FEA models  (Marcé-Nogué et al.
Quantitative stress data Von Mises stress values were obtained for all the elements of each finite element model. Values of stress (and strain) can be obtained for each element because FEA mathematically solves stress (and strain) inside each element, while displacements are computed at the nodes  (Zienkiewicz, Taylor & Zhu, 2013). ANSYS provides a complete data file with these results that can be easily manipulated in spreadsheets and other software. This means that the number of stress data points for each mandible is the same as the number of elements of the mesh. The new methodology proposed here divides the values of stress into N equal intervals (Fig. 1 ). Therefore, each interval will contain all the elements within a certain threshold, each one being defined by a lower threshold T lower and an upper threshold T upper.
ACKNOWLEDGEMENTS We would like to acknowledge to  Eileen Westwig ,  Dr. Marcelo Reguero ,  Dr. Mariano Merino  and  Dr. Frieder Mayer , who provided S. D. E. T. access to the collections and kindly help with us with the information about specimens. The authors are grateful to  Robert Brocklehurst  for his comments, corrections and help during the preparation of this manuscript and also want to acknowledge the comments of three anonymous reviewers and the editor  Philip Cox  that helped to improve the quality of the present manuscript. This research is publication no. 94 of the  DFG Research Unit 771 'Function  and performance enhancement in the mammalian dentition-phylogenetic and ontogenetic impact on the masticatory apparatus'. This research paper is a contribution to the  CERCA programme (Generalitat de Catalunya ).
The present paper takes advantage of the recent development of such harmonized microsimulation models for Ecuador and Colombia, ECUAMOD and COLMOD. On this basis, we provide one of the first comparative microsimulation studies for Latin American countries. These microsimulation models are based on rich representative household microdata for each country. They are developed as a multi-country microsimulation embedded in the modeling structure of EUROMOD. This means that every redistributive instrument is defined according to the same core calculation and definition, using similar data treatment for source income and household characteristics, hence guaranteeing a harmonized framework for international comparisons (see Sutherland and Figari 2013). 7 Armed with this tool, we assess the distributional effects of the Ecuadorean and Colombian tax-benefit systems by "swapping" tax-benefit rules between the two countries.
The effect is particularly small compared to European economies, where the tax-benefit system reduces income inequality by around 20. 9 points on average (from 50. 1 to 29. 2). 9 One of the main issues is the lack of targeting of non-contributory benefits, only half of it reaching the poor on average (Robles et al. 2015). Ecuador Ecuador is an interesting example of a country that has achieved a large decrease in income inequality and poverty. According to data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), the Gini coefficient of household income per capita in Ecuador fell from 53. 6% in 2004 to 45. 2% in 2014. 10 Over the same period, Ecuador also experienced an important decrease in poverty, with the absolute poverty headcount falling from 48. 0% in 2004 to 23.
6% in 2014. 11  8 Other types of benefits represent a larger share of direct transfers for the working-age population in the EU: family benefits (2. 4% of GDP), unemployment benefits (1. 4% of GDP), housing benefits (0. 6% of GDP). See Eurostat social protection statistics: http://appsso. eurostat. ec. europa. eu/nui/show. do?dataset=spr exp sum& lang=en 9 See EUROMOD statistics on Distribution and Decomposition of Disposable Income, accessed at http:// www. euromod. ac. uk/using-euromod/statistics 10 Note that differences between SEDLAC figures and our results are on account of the use of different surveys in the analysis. SEDLAC statistics for Ecuador are based on the National Survey of Employment, Underemployment and Unemployment (Encuesta Nacional de Empleo, Desempleo y Subempleo). Statistics for Colombia are based on the Integrated Household Survey (Gran Encuesta Integrada de Hogares).
Moreover, SELDAC figures are based on the concept of market income augmented with cash transfers, i. e. taxes and social insurance contributions are not deducted as in general no information is available in household surveys. For more information see: http://sedlac. econo. unlp. edu. ar/eng/index. php. 11 SEDLAC figures on poverty as well as those extracted from Lustig (2017) are obtained using a poverty line of USD 4. 00 PPP per day. Note that the 23. 6% figure for 2014 seems still overstated compared to our estimations (18%). As explained later, the differences are driven by (i) different datasets (the data we use contains much more detailed income information than the yearly survey used by SEDLAC (see Jara et al. 2017, and confirmations by the national statistical institute), (ii) different income concepts (we use household disposable income while SEDLAC uses market income plus cash transfers, as explained in footnote 12).
). The imputation approach directly uses the information on taxes and transfers available from the data while it imputes the rest from other sources. 15 Using this approach, Lustig (2017) confirms the best performances of country like Argentina, Uruguay and Brazil while Colombia is among the countries redistributing the least. Ecuador ranks 5th (out 16 countries) in terms of redistributive effect of the tax-benefit system. The results further highlight that countries with similar levels of market income inequality (for instance Brazil, Honduras and Colombia) achieve different levels of redistribution. In terms of income poverty, direct taxes and benefits reduce extreme poverty in 12 out of the 16 countries analyzed, however to very different extents. The effect of tax-benefits policies on poverty reduction is about twice larger in Ecuador than in Colombia.
Data and simulations Data Our results are based on representative household survey data from Ecuador and Colombia. Data from Ecuador comes from the National Survey of Income and Expenditures of Urban and Rural Households (Encuesta Nacional de Ingresos y Gastos de Hogares Urbanos y Rurales, ENIGHUR) 2011-2012. Data from Colombia comes from the Quality of Life National Survey for 2014 (Encuesta Nacional de Calidad de Vida, ENCV). Both surveys contain detailed information on household and personal characteristics, employment, earnings, income from capital and property, private transfers, income from remittances, cash transfers, pensions, and expenditures. Income concepts have been harmonized in both datasets with the aim to achieve comparability in the simulation results (see Jara et al. 2017 and Rodriguez 2017).
The sample for our simulations contains 153,341 individuals for Ecuador and 67,332 individuals for Colombia. Tax-benefit simulations Our study makes use of the newly developed tax-benefit microsimulation models ECUAMOD and COLMOD. 16 ECUAMOD and COLMOD combine detailed country-specific coded policy rules with cross-sectional micro-data in order to simulate direct taxes and social insurance contributions, as well as cash transfers for the household population of Ecuador and Colombia. The models have been implemented on the EUROMOD software, which enables users to analyze the effect of tax-benefit policies on the income distribution in a comparable manner across countries. Both, ECUAMOD and COLMOD, are static models in the sense that tax-benefit simulations abstract from behavioral reactions of individuals and no adjustments are made for changes in the population composition over time.
Our analysis takes 2014 policies (as on June 30th) in Ecuador and Colombia as the starting point. In the case of Ecuador, market incomes and non-simulated tax-benefit variables in the 2011-12 data are adjusted to 2014 levels using source-specific updating factors (Jara et al. 2017). Table  1  provides a summary of the tax-benefit instruments simulated in ECUAMOD and COLMOD for the purpose of our exercise. 16 ECUAMOD has been developed as part of UNU-WIDER's project on 'SOUTHMOD-simulating tax and benefit policies for development' in which tax-benefit microsimulation models have being built for selected developing countries. Simulation results for ECUAMOD have been validated both at the micro and macro level (see Jara et al. 2017). For more information about SOUTHMOD see: https://www. wider.
Scope of the simulations Our analysis focuses on the concept of disposable income, that is, market income after income tax and SICs deductions and after receipt of cash benefits. Indirect taxes and in-kind benefits are not considered but the implications of including them in the analysis are discussed in our conclusions. ECUAMOD and COLMOD simulations cover the most important direct taxes and cash transfers present in Ecuador and Colombia. Table  2  provides a list of all income components used in our simulation models. Due to data limitations, some tax-benefit instruments cannot be simulated and are taken directly from the data. Therefore, the effect of these policies when applied to the other country in our counterfactuals cannot be assessed. This includes contributory public pensions and severance payments, which cannot be simulated given the lack of information on contribution records in the surveys, most disability benefits (which are then partially simulated) because of insufficient information on the degree of disability, and property taxes and motor vehicle taxes due to the absence of information about property and vehicles value in the data.
Tables  A3  and A4  in the Online appendix present a comparison of poverty and inequality results from ECUAMOD and COLMOD, with those obtained directly from ENIGHUR and ECV, and with respect to official statistics. Poverty and inequality estimated with ECUAMOD and COLMOD only slightly underestimate those obtained from ENIGHUR and ECV data. The underestimation is more important with respect to official statistics. The reason behind the discrepancies is that official statistics are based on different surveys than those used in our models. In particular, for Ecuador, the National Survey of Employment, Underemployment and Unemployment (ENEMDU) is used to produce official estimates of income poverty and inequality. The discrepancy is mainly driven by the fact that ENIGHUR, on which ECUAMOD input data are based, contains much more detailed income information than ENEMDU (Jara et al.
Social insurance contributions are clearly progressive with respect to disposable income in Ecuador and represent around 8. 8% for the top income decile. In Colombia, SICs increase with disposable income but at a lower rate, and the presence of SIC payments in the bottom decile group is explained by the existence of a floor on SIC payments. As mentioned in the previous section, the data used in our analysis allows us to identify individuals who are affiliated to social security. SIC payments are simulated only for those affiliated to social security in Ecuador and Colombia under the baseline and counterfactual scenarios presented in the next section. In both countries, personal income tax plays a minor role and is noticeable only for the top income decile group, representing around 5% of disposable income in Ecuador and 4% in Colombia.
Acknowledgments This work was supported by the  Economic and Social Research Council (ESRC)  through the  Research Center on Micro-Social Change (MiSoC)  at the  University of Essex , grant number  ES/L009153/1. The results presented here are based on ECUAMOD v1. 0 and COLMOD v1. 0. ECUAMOD is developed, maintained and managed by  UNU-WIDER  in collaboration with the EUROMOD team at  ISER  ( University of Essex ),  SASPRI (Southern African Social Policy Research Institute ) and local partners in selected developing countries ( Ethiopia, Ghana, Mozambique, Tanzania, Zambia, Ecuador and Viet Nam ) in the scope of the  SOUTHMOD  project. The local partner for ECUAMOD is  Instituto de Altos Estudios Nacionales (IAEN). We are indebted to the many people who have contributed to the development of SOUTHMOD and ECUAMOD.