dataesr/scientific-paragraphs-categorization huggingface dataset:
The vast majority of VIPERS galaxies are below z < 1. 0, and the CFHTLS imaging does not fully probe the restframe ultraviolet (UV) light emitted by massive stars, which is crucial for an SFR estimate. The VIPERS area has been partially observed with the GALEX survey  (Martin et al. 2005)  in the far-UV (135-175 nm) and near-UV (170-275 nm). However, measuring the GALEX photometry is a complex problem because of the large point-spread function of the telescope (FWHM∼ 5"). The standard SExtractor photometry  (Bertin & Arnouts 1996)  delivered by the GALEX pipeline is not optimal for faint sources because of blending problems. It requires a dedicated software  (Conseil et al. 2011) , as discussed in this paper. With the physical properties of the VIPERS spectroscopic sources derived from the multi-wavelength dataset described in this paper,  Davidzon et al.
This paper is organised as follows: we present the WIRCam-K s and GALEX-UV observations, the data reduction and the creation of the multi-colour catalogue based on the CFHTLS optical catalogue from the final TERAPIX release (T0007). We perform various tests on our photometry and completeness by comparing them with deeper overlapping datasets and simulations. We use the large spectroscopic sample to quantify the quality of our photometric redshifts. As direct applications, we present the selection of a bright BzK sample, and we illustrate the morphology of low-z (z ≤ 0. 25) large galaxies in the (NUVr) vs (r -K) rest-frame colour diagram. Throughout the paper we adopt the following cosmology: H 0 =70 km s -1 Mpc -1 and Ω M = 0. 3, Ω Λ = 0. 7. We adopt the initial mass function of  Chabrier (2003) , truncated at 0.
These steps bring the absolute and relative photometric calibrations over the entire survey to an accuracy level of between 1 to 2%. The final images were stacked with the software SWARP  (Bertin 2006). The photometry was performed with SExtractor  (Bertin & Arnouts 1996)  in dual mode with the source detection based on the gri -χ 2 image  (Szalay et al. 1999). Following  Erben et al. (2013) , we used the isophotal apertures, extracted from the detection images, to measure the galaxy colours. The apertures are smaller than the Kron-like apertures  (Kron 1980) , which provides less noisy estimates of the colours and leads to an improved photometric redshift accuracy  (Hildebrandt et al. 2012). To derive the total magnitudes, we rescaled the isophotal magnitudes (MAG_ISO) to the quasi-total magnitudes (MAG_AUTO) for each source.
Data reduction All the data were reduced at CFHT and TERAPIX. The initial processing with the i'wii pipeline 6  at CFHT removes the instrumental imprints on the individual images (flagging of saturated, bad, and hot pixels, correcting for nonlinearity, bias removal, dome and sky flat-fielding, and guide window masking). CFHT delivers detrended images with a first astrometric solution, photometric zeropoint, and a sky subtraction for each exposure. TERAPIX first uses QualityFITS on detrended images to produce weight-maps (with the WeightWatcher software;  Marmo & Bertin (2008) ), source catalogues and checks the individual image qualities (e. g. , seeing and depth). The validated exposures are then median-combined to produce an initial stack by using the Scamp and Swarp softwares 7   (Bertin 2006).
To improve the sky subtraction and the photometry of faint sources, a two-step strategy is adopted similar to the CFHT-WIRDS  (Bielby et al. 2012 ) and the  ESO-UltraVISTA (McCracken et al. 2012 ) surveys. The initial stack is used to produce a mask with all the faint sources. In a second pass, the mask is applied to the individual exposures and a new sky background is estimated by medianing images inside a sliding window and for images within a time window ∆t < 15 min and angular separation ∆θ < 10. A last quality assessment of the individual sky-subtracted images is performed before producing the final stacks. The final WIRCam products are composed of stack images and weight images resampled on the footprint of the CFHTLS-MegaCam tiles and with the same pixel scale (0. 18 /pix) using the SWARP software with a Lanczos3 resampling kernel.
Comparison with deeper datasets As shown in Fig. 2 , we can use the existing deep near-IR surveys, UKIDSS-Ultra Deep Survey  (Lawrence et al. 2007, UDS)  and VIDEO  (Jarvis et al. 2013) , to estimate the photometric accuracy, depth, and completeness of our K s sample. UDS and VIDEO are significantly deeper than our dataset (K ∼ 24. 5 and 23. 8 respectively). For both fields the relative astrometric accuracy with our sample is σ ≤ 0. 09 with systematic shifts of the same order. We adopted a maximal matching distance of 0. 5. To compare the fluxes, we used the SExtractor MAG_AUTO magnitudes for the VIDEO survey and the petrosian magnitudes for the UDS survey (which should be close to our own magnitudes). Figure  4  shows the magnitude differences as a function of magnitude (on the left) and the dispersions in three magnitude intervals (on the right) after correcting for the systematic shifts (K DEEP -K WIRCam ∼0.
06 and 0. 10 mag for UDS and VIDEO, respectively). For both surveys the mean difference is stable and close to zero down to K s ∼ 22, confirming that no bias due to sky-background subtraction is present in our data. At all magnitudes, the dispersions are reasonably well fitted by a Gaus-Article number, page 4 of 14 sian distribution. We expect to reach σ ∼0. 3 mag (S/N∼ 3) near K s ∼ 22. The dispersions predicted with these external dataset are consistent with those estimated by SExtractor (after the 1. 5 rescaling factor mentioned above). Because we used the gri -χ 2 images for the detection, our completeness is not directly related to the background noise of the WIRCam images. To estimate the completeness, we used all the sources detected in the UDS and VIDEO images and measured the fraction of them detected in our optical-based catalogue.
Only the GALEX PanStarr fields, centred on the VVDS-22hr region, are available in the W4 field, with a small overlap with the VIPERS-W4 area. All the GALEX pointings and their respective integration times are given in Table  2. 3. 1. The total area observed with GALEX (W1 and W4) is ∼7. 8 and ∼22deg 2 in the FUV and NUV bands. The data were reduced with the standard GALEX pipeline. In the specific case of the DIS observations, the large PSF (FWHM∼ 5 ) affects the extraction and photometry of faint UV sources because sources are confused  (Xu et al. 2005). As an alternative to the source extraction of the GALEX pipeline (based on SExtractor software), we developed a dedicated code to perform the UV photometry, EMphot  (Guillaume et al. 2005; Conseil et al. 2011). EMphot uses a Bayesian approach with optical priors based on the CFHTLS (T0007) u * -selected sources.
Comparison with the VIDEO photometric redshifts Half of the three VISTA pointings from the VIDEO survey  (Jarvis et al. 2013 ) overlaps with our WIRCam observations (see Fig. 2 ). We can compare the photometric redshifts derived from the deep VIDEO Y, J, H, K data (reaching K ∼ 23. 5) with our WIRCam photometric redshifts to partially remove the spectroscopic bias discussed above. We first matched the full CFHTLS-T0007 optical catalogue with the NIR VIDEO photometry (within a search radius of 0. 5 ), to which we added the GALEX photometry. We derived the photometric redshifts for this combined catalogue (GALEX-CFHTLS-VIDEO) using the same recipe as described above. In Figs. 13 and 14 we compare our original photometric redshifts (referred to as z wircam ) with VIDEO photometric redshifts (referred to as z vista ) for a K-(K V IS T A ≤ 23.
BzK selection The large area covered by our K-band survey and the optical depth of the CFHTLS Wide data makes our sample unique to select a large sample of bright and massive galaxies at high redshift, z ≥ 1. 4, with the BzK colour selection  (Daddi et al. 2004 ). We adopted the criteria from Arcila-Osejo & Sawicki (2013), who adjusted the selection for the CFHTLS and WIRCam Kband photometry. This colour selection is shown in Fig. 10  (solid lines) and provides a sample of ∼100,000 star-forming and ∼7,800 passive BzKs. The differential number counts are shown   (McCracken et al. 2010). Our results provide robust constraints at the bright end. For the star-forming BzKs, we agree well with previous works in the overlapping range and down to our limit, K s ∼22. For the passive BzKs our result agrees with others at the bright end, but we observe a drop at K s ≥ 20.
Conclusions We presented the multi-wavelength observations collected in the VIPERS region within the W1 and W4 fields of the CFHTLS. The NIR observations were adjusted to provide K s -band photometry of the VIPERS spectroscopic sources (i AB ≤ 22. 5). By comparing them with external deep data, we estimated the depth of our WIRCam to be K s ∼ 22 at 3σ. We combined this dataset with GALEX observations. Because of the large GALEX PSF, we developed a specific procedure to derive GALEX photometry in crowded fields based on u -band optical priors. We estimated the reliability of the method with simulations and found that we reach robust flux measurement down to NUV ∼ 25 at 5σ. With this multi-wavelength catalogue (GALEX, optical, K s ) we estimated new reliable photometric redshifts for more than one million galaxies at z ≤ 1.
5, with σ z ≤ 0. 04 and η ≤ 2% down to i ∼ 23. We also pushed our analysis at higher redshift with the BzK galaxy populations. Despite our native gri detection method, we showed that we can assemble a complete sample of BzK star-forming galaxies down to K s ∼ 22 and BzK passive galaxies down to K s ∼ 20. 7, The deep multi-wavelength dataset combined with the unprecedented large area makes this sample perfectly suited for statistical analysis and constrains the rare massive galaxies. In the companion paper  (Moutard et al. 2016) , we use this sample to determine the massive end of the GSMF for quiescent and starforming galaxies based on photometric redshifts at 0. 2 ≤ z ≤ 1. 5 with high accuracy, while  Davidzon et al. (2013)  performed a similar analysis in between 0. 5 ≤ z ≤ 1. 3 with the VIPERS spectroscopic sample.
Acknowledgements. We gratefully thank  C. Moreau  from  CeSAM/LAM  for her major contribution to the building of the database. We would like to thank  M. Jarvis  and  B. Haeussler  for giving us access to the VIDEO dataset in the XMMLSS field. We also wish to thank  R. Overzier  for providing us semi-analytical simulations, and  L. Arcila-Osejo  for helping us with the BzK compilations. This research is in part supported by the  Centre National d'Etudes Spatiales (CNES)  and the  Centre National de la Recherche Scientifique (CNRS) of France , and the  ANR Spin (e )  project ( ANR-13-BS05-0005 , http://cosmicorigin. org). L. G. acknowledges support of the  European Research Council  through the  Darklight ERC Advanced Research Grant  (#  291521 ). This paper is based on observations obtained with  MegaPrime/MegaCam , a joint project of  CFHT  and  CEA/DAPNIA , and with  WIRCam , a joint project of  CFHT, Taiwan, Korea, Canada and France , at the  Canada-France-Hawaii Telescope (CFHT).
Mechatronic systems are complex systems that could be rightly developed by using the MBSE approach, because they deal with multi-domain and related interactions of effects a Corresponding author: patalano@unina. it due to different existing domains  [4]. Several and different contributions are known in literature and a significant part of them affects both the process-based problems and design data-related problems  [5]. In reference  [6] , authors proposed the use of hierarchical models to support the whole design process of mechatronic systems. In reference  [7] , a multidisciplinary approach using SysML language and aimed to the designing of mechatronic systems is presented. On the other side, along the design datarelated problems, the authors in reference  [8]  present different architectural designs in the context of automation systems domain, for an efficient and effective integration solution related to data model design.
Article published by EDP Sciences In reference  [11] , a co-modelling methodology and co-simulation tools integration framework are presented to improve the co-design process of mechatronic devices by maintaining the domain specific properties of the model components. Furthermore, the implementation of the MBSE approach within the commercial CAD/PDM environment CATIAV6, by Dassault Systemes could be considered the method known as RFLP (Requirement/Functional/Logical/Physical)  [5]. In particular, such method includes multi-domain modelling within Modelica language and, therefore the related software tool is effective in providing integrated-domain design. During the application of a V-cycle approach, the first main objective is that the system level requirements are propagated to component design level in a top-down way, allowing traceability between the models developed with different levels of abstraction.
During this phase the main features and services offered by the analysed system are defined in a specific documentation. The system architect develops the functional (F) architecture of the system in a hierarchical way, defining the functions, the links between them, the types of connections and the flows to be exchanged between functions. Logical (L) modeling is an intermediate step between functional and physical levels. Since several functions at functional level could be allocated to one technological solution, logical modelling allows system architects to define the different possible candidate solutions of the considered system. At this level, behaviour analysis can be implemented to help system architects in the choice of the best solutions. Behaviour can be implemented in logical level in two ways; either by allocating 3D Logical representation to logical components or with 0D Physical (dynamical) behaviour using Modelica language.
3D Logical representation is used to allow spatial analysis of the candidate solutions. 0D Physical modelling through Modelica language allows the analysis of the dynamic behaviour of the candidate solutions. Combining 0D Physical with 3D Logical representation helps system architects in understanding the dynamic behaviour of mechanisms and moving parts. The Physical (P) level is required for the detailed development of the components of a chosen architecture. The detailed 3-D models can therefore be developed with their parametrical relationships. The RFLP approach allows a top-down process from Requirement to Physical. However, a bottom-up approach could also be applied. For example, a 3D logical model can be easily extracted from an existing 3D physical assembly. The 3D logical model can therefore be integrated in the logical level by using appropriate CATIA/Modelica components, which allows system architects to verify (Verification in Fig.
The modelling operations of this phase are preliminarily carried out taking advantage of the software Dymola, which is based on the Modelica environment and is specifically devoted to the simulation of complex and multiphysical systems. Then, the system logical model is integrated into CATIA V6 by means of the dynamic behaviour modeling (DBM) workbench provided by Dassault Systemes  [17]. Each component of the propulsion system is simulated through simplified models, mainly based on the information reported both on the datasheets and in literature. In fact, the main aim of this paper is not devoted to the modelling of specific components. The first component, which is taken into account in the logical analysis of the whole power train, is the energy storage system. On the base of the above hypothesis the model of the vehicle battery pack is simulated with the static resistance single-stack model  [18] , through the 'Electric Energy Storage' Modelica library.
The dynamic brake model is realized using the Dymola mechanical libraries. In particular, the vehicle inertia and road resistance forces are evaluated trough Modelica scripts and reported to the brake rotor shaft. The complete logical block scheme of the propulsion system running on the test-bench is reported in Figure  3. At the end of the Dymola modelling phase, preliminary simulations are performed in order to analyse the behaviour of the whole system running on specific driving cycles. At this point, it is possible to verify the right design of the whole power train, following a specific verification procedure. The flow-chart related to the verification procedure is reported in Figure  4. As described in Figure  4 , after the definition of the logical model, the main simulation parameters are required to be set, taking into account the specific case study.
At this point it is possible to run the simulations and evaluate the related numerical results, in order to verify the designing process through the presented iterative procedure. In fact, the first evaluation is based on the comparison between the actual and the reference vehicle speed, which is imposed by the specific driving cycle. This evaluation is aimed to analyse the performance of the propulsion system in terms of speed, when the vehicle is running on a specific road. If the average percentage error between the actual and reference speed is higher than 15%, the electric drive is not considered suitable to satisfy the dynamic requirements of the driving cycle and a new electric drive is selected from a database. This database has been preliminarily populated with the main characteristics of a large number of electric drives with different rated power and speed.
Observations To test the model performance, we use measurements of POC concentrations performed in the Atlantic and Pacific oceans, whose stations are presented in Fig. 3. They relatively sparsely cover these oceans. Unfortunately, to our knowledge, there are no particle data available in the other ocean basins. Since the POC concentration is an ancillary parameter of GEOTRACES, the number of POC data should considerably increase in the near future. In the Intermediate Data Product (IDP2014) of GEOTRACES released recently  (Mawji et al. , 2015) , only the GA03 transect (the red crosses displayed in Fig. 3  in the North Atlantic Ocean) includes POC observations. Observed POC fluxes are from  Dunne et al. (2005) ,  Gehlen et al. (2006), and Le Moigne (2013). In these datasets, data have been obtained from sediment traps and/or 234 Th.
6 , even though these data are very sparse (see also Fig. S1  in the Supplement). The benthic oxygen demand provides an indirect measurement of the biogenic organic carbon flux to the sediments, at least when anoxic processes are negligible, which is generally true in deep-sea sediments  (Archer et al. , 2002). Figure  7  compares POC fluxes to the sediments derived from a global database of the benthic oxygen demand  (Jahnke, 1996 ; Seiter ) to the sediments reconstructed (a) from oxygen fluxes  (Jahnke, 1996)  and (b) from correlations based on the organic carbon content in surface sediments and the bottom oxygen concentrations  (Seiter et al. , 2005). Panels (c, d) show the corresponding fluxes as predicted in the noRC and RC experiments respectively. et al. , 2005)  with those from our model simulations.
Acknowledgements. The help received from the  Ferret community and especially Ryo Furue ,  Patrick Brockman ,  Ansley Manke , and  Sarah Tavernel  concerning visualisation and related technicalities is strongly appreciated. This work benefited from fruitful discussions with  Laurent Bopp ,  Laurent Mémery , and  Alessandro Tagliabue. The funding of this work was through the European Project  H2020   CRESCENDO. It was also funded by a  Swedish Research Council  grant ( 349-2012-6287 ) within the framework of the French-Swedish cooperation in the  Common Research Training Program in the Climate, Environment and Energy Agreement between VR ,  Formas , and  LSCE , for the project "Particle transport derived from isotope tracers and its impact on ocean biogeochemistry: a  GEOTRACES  project". Edited by: A.
Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refi nements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess causespecifi c mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies.
The inputs that are updated in the second run of the process are shown by patterned boxes in this fl ow chart. Because of the very large and changing eff ects of HIV/AIDS on all-cause mortality in several countries with large HIV epidemics and limited data on all-cause mortality, the estimation of HIV/AIDS and all-cause mortality are closely linked and are presented jointly here. GBD=Global Burden of Disease. 5q0=probability of death from birth to age 5 years. 45q15=probability of death from age 15 to 60 years. ART=antiretroviral therapy. CBH=complete birth histories. CDR=crude death rate. COD=causes of death. DSP=disease surveillance points. ED=educational attainment in years per capita above age 15 years and mother's educational attainment in years per capita for children younger than 5 years.
Level 2 causes consist of 21 cause groups, such as neoplasms and cardiovascular diseases. Levels 3 and 4 consist of disaggregated causes, such as liver cancer and cerebrovascular disease (Level 3), and liver cancer due to hepatitis C and ischaemic stroke (Level 4). GBD=Global Burden of Disease. YLD=years lived with disability. YLL=years of life lost. countries from which both sources are available and found no systematic biases from sibling survival method (methods appendix p 292). 7, 25, 26 We synthesised vital registration data corrected for completeness and adjusted sibling history data into a best time series estimate of adult 45q15 using spatiotemporal Gaussian process regression. Examples of the application of these steps in three types of settings are shown in fi gure 4. The spatiotemporal Gaussian process regression method used to fi t the model to the available data included lag distributed income per capita, educational attainment, and the estimated HIV/AIDS death rate as covariates.
Age-specifi c mortality In demographic estimation, measures of child mortality, adult mortality, or both are used alongside a model life table system to predict age-specifi c mortality. The GBD approach uses three inputs to generate agespecifi c mortality: 5q0, 45q15, and a relevant empirical reference pattern of mortality by age. 7 The reference in the GBD system was selected on the basis of empirical age patterns that are closest to the population in space and time. 7 The reference was developed with a database of 16 507 age patterns of mortality from settings that meet explicit inclusion criteria as described in the methods appendix (pp 34-42). Table  3  shows a summary of the availability of empirical age-sex patterns of mortality in the GBD database. To account for the eff ect of HIV/AIDS on the age pattern of mortality, the GBD model life table system for locations aff ected by HIV/AIDS and without highquality vital registration data used a two-step process whereby we fi rst estimated an HIV-free age pattern of mortality assuming that deaths due to HIV/AIDS were removed.