dataesr/scientific-paragraphs-categorization huggingface dataset:
Our tests, conducted with some basic MathML elements, prove that only the mobile version of Firefox web browser provides complete satisfaction. In Figure  3  and Figure  4  the MathML script is natively interpreted by the Firefox browser mobile version. Other tested browsers required for the same task additional resources as shown in Figure  2  for each tested mobile browser. Tests (Figure  3  and Figure  4 ) also show the natively compatibility of SVG scripts for vector graphics visualization for all tested browsers. This point is important and may replace in certain cases the unwieldy raster graphics. The SVG script is XMLbased and fully integrated in the web page in the same manner as MathML. In this HTML5 collection, we can insert a bitmap image or any other binary data using Base64 encoding method to produce an ASCII sequence natively interpreted by latest versions of mobile browsers.
ORCHIDEE-CCDAS concept We have designed a CCDAS around the ORCHIDEE land surface model (ORCHIDEE-CCDAS, later also referred to as ORCHIDAS for simplicity) that combines a state-of-the-art description of the driving biogeochemical processes within the model with multiple observational constraints in a robust statistical framework, in order to improve the simulation of land carbon fluxes and stocks. The system allows us to retrieve the best estimate, given the observations and prior information, of selected parameters (see Sect. 2. 3. 3) as well as to evaluate their uncertainty. It relies on a stepwise assimilation of a comprehensive set of three C cycle-related observations that are representative of small (100 m) to large (continental) scales (see Sect. 2. 2): step 1: satellite measurements of vegetation activity using the Normalized Difference Vegetation Index (NDVI) from the MODIS instrument over the 2000-2008 period for a randomly selected set of sites for boreal and temperate deciduous vegetation types; step 2: in situ eddy covariance net CO 2 and water (latent heat) flux measurements from the FLUXNET database for a large set of sites, spanning seven different vegetation types; step 3: in situ monthly atmospheric surface CO 2 concentration measurements from the GLOBALVIEW-CO2 database over 3 years  (2002) (2003) (2004).
ORCHIDEE land surface model In this study we use the ORCHIDEE process-oriented land surface model  (Krinner et al. , 2005) , which computes water, carbon and energy balances at the land surface on a halfhourly time step, using a mechanistic description of the physical and biogeochemical processes (see, http://labex. ipsl. fr/ orchidee/). The model describes the exchange of carbon and water at the leaf level, the allocation of carbon within plant compartments (leaves, roots, heartwood and sapwood), the autotrophic respiration, the production of litter, the plant mortality and the degradation of soil organic matter (CEN-TURY model;  Parton et al. , 1988). The hydrological processes for the soil reservoir rely on a double bucket scheme  (Ducoudré et al. , 1993). The link between the water and carbon modules is via photosynthesis, which is based on the leaf-scale equations of  Farquhar et al.
, 2004). The carbon pools are brought to equilibrium (spin-up procedure) for both site and global-scale simulations by cycling the available meteorological forcing over several millennia, to ensure that the long-term net carbon flux is close to zero. For the global simulation in the third step, we spun-up the model recycling the 1989-1998 meteorology and then used a transient simulation from 1990 to 2001 with changing climate (ERA-Interim) and increasing CO 2 , before starting the optimization with atmospheric data over 2002-2004. For the site simulations (i. e. , the assimilation of flux data), we recycled the available in situ meteorological forcing to spinup the model, with present-day CO 2. Note that the use of soil carbon data, such as from the Harmonized World Soil Database (as well as aboveground biomass data), to initialize the model is not straightforward and represents a challenge to keep the internal model consistency, given that the three soil carbon reservoirs of the CENTURY model are in balance with all components of the model, in particular the input through the different litter pools.
The prior standard deviation for each parameter is equal to 40 % of the parameter range (lower and higher boundaries) prescribed for each parameter following  Kuppel et al. (2012). The parameter ranges were specified following expert judgment of their meaning in the ORCHIDEE equations and based on literature reviews or databases (such as the global database of plant traits, TRY;  Kattge et al. , 2011). 2. 4 System description: a stepwise approach 2. 4. 1 Stepwise assimilation of three data streams The ORCHIDAS system relies on a stepwise assimilation of the three data streams described in Sect. 2. 2. Figure  2  illustrates the flow of information in this sequential approach: Step 1 -assimilation of MODIS-NDVI: four parameters related to the seasonal cycle of the vegetation (phenology) are optimized for the temperate and boreal deciduous PFTs (TeBD: temperate broadleaf deciduous, BoND: boreal needleleaf deciduous, BoBD: boreal broadleaf deciduous and NC 3 ; see caption of Table  2 ).
These four deciduous PFTs alone are considered in step 1 in this ORCHIDAS version because the tropical deciduous phenology modules in OR-CHIDEE require further modifications to improve the func-tions that control leaf growth and fall in response to water availability  (MacBean et al. , 2015). Evergreen PFTs were also not considered, as there are no phenology modules related to these PFTs in the model. The procedure is similar to that described in detail in  MacBean et al. (2015)  and therefore only briefly recalled here. A simple linear relationship between the modeled FAPAR and MODIS-NDVI observations is assumed, based on studies such as  Knyazikhin et al. (1998). Given that considerable discrepancies exist between so-called "high-level" satellite products such as LAI or FAPAR when considering their magnitude  (D'Odorico et al.
The computation of pCO SW 2 is performed using feedforward artificial neural networks, i. e. , a multiLayer perceptron (MLP;  Rosenblatt, 1958)  that maps a set of spatiotemporal variables (input) onto observed pCO SW 2 data. We use a two-step approach: the first step to derive a monthly mean pCO SW 2 climatology and the second step to correct for the year-to-year variations. The pCO SW 2 observations come from the global surface pCO 2 (Lamont-Doherty Earth Observatory, LDEO) Database  (Takahashi et al. , 2009). The inputs are a series of variables connected to the spatial and temporal evolution of pCO SW 2 : (i) sea surface temperature (SST), sea surface salinity (SSS) and mixed layer depth (MLD) as a proxy of the physical processes (these fields come from a reanalysis of the Nucleus for European Modelling of the Ocean (NEMO) ocean model  (Madec et al.
Consistency for MODIS-NDVI First, we notice again the significant RMSE reduction between the prior and step 1, as discussed in Sect. 3. 1. The fit to MODIS-NDVI (normalized data) for steps 2 and 3 shows only a significant degradation (increased RMSE) for temperate broadleaf deciduous (TeBD) forest, which decreases the improvement achieved in step 1 (compared to the prior) by a factor of 2. A marginal degradation for natural C 3 (NC 3 ) grassland is obtained after step 3: the RMSE increases slightly from 0. 24 to 0. 26, but is still lower than the prior value of 0. 3. There is no degradation for boreal needleleaf deciduous (BoND) trees, but a surprising small decrease of the RMSE (i. e. , improvement in the model-data fit) for boreal broadleaf deciduous forests (BoBD; from 0. 26 to 0. 23). In this latter case, the use of additional parameters in steps 2 and 3 (see Sect.
e. , the white zone). For PFT-dependent parameters, there are nine sub-plots corresponding to PFTs that were optimized (except for K pheno,crit with only five PFTs). For each parameter, there are three estimated values for the three successive steps: step 1: assimilation of MODIS-NDVI data (green symbol); step 2: adding FLUXNET data (blue symbol); step 3: adding atmospheric CO 2 data (red symbol). The parameter values are depicted with the symbols and the estimated uncertainties with the vertical line (±σ ). average, in order to decrease the heterotrophic respiration (see  Kuppel et al. , 2014  for additional discussion). In step 3 the same scalars that were defined for an ensemble of large regions (K soilC,reg ) have decreased in the Southern Hemisphere (less than 10 %; see Fig. A2 in Appendix A3 ) and slightly increased in the Northern Hemisphere (around 1 %), to achieve a better match to the atmospheric CO 2 growth rate and north-south gradient.
12 PgC yr -1 and a decrease of the tropical sink by 0. 05 PgC yr -1 (Fig. 10 ). Step 2 (assimilation of FLUXNET data) does not significantly change the net C sink from step 1, with only a small increase in the tropical sink by 0. 1 PgC yr -1. The assimilation of atmospheric CO 2 data in step 3 provides a large-scale constraint, as already discussed, and increases the total land sink to 2. 2 PgC yr -1 , a value in much closer agreement with the estimate by the GCP. A larger tropical NEE uptake is responsible for the large increase of the terrestrial biosphere C sink (from 0. 3 PgC yr -1 in step 2 to 1. 7 PgC yr -1 ) while the sink in the north increases by less than 0. 1 PgC yr -1. The comparison with the GCP number should be taken with caution. The ORCHIDAS estimated sink includes all effects (natural and anthropogenic), since we used atmospheric CO 2 as a global constraint.
Discussion and conclusions In this paper we have described a first global carbon cycle data assimilation system that assimilates three major carboncycle-related data streams, namely MODIS-NDVI observations of vegetation activity at 60 sites, FLUXNET NEE and LE measurements, at more than 70 sites, and atmospheric CO 2 concentrations at 53 surface stations over 3 years in order to optimize the C cycle parameters of the ORCHIDEE process-based LSM (ORCHIDEE-CCDAS). The study details the concept, the implementation and the main results of a stepwise assimilation approach, where the data streams have been assimilated in three successive steps (including a propagation of the retrieved posterior parameter distributions from one step to the next). The assimilation of MODIS-NDVI (60 grid-cell points, step 1) improved the phenology of ORCHIDEE with a significant reduction of the growing season length and thus a direct impact on the GPP.
The results are similar to those presented in  MacBean et al. (2015) , who describe the impact of such optimization on the global FAPAR simulations and the improvement in the bias of the calculated leaf onset and senescence dates in more detail. The optimization with FLUXNET data (78 sites, step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes, constraining primarily the photosynthetic processes. Some discrepancies remain due to site heterogeneity (i. e. , different species and edaphic conditions) that the model does not fully capture, and due to missing processes in the model (see  Kuppel et al. , 2014  for a more thorough discussion). However, without the assimilation of atmospheric CO 2 concentrations, the global (and continental) net carbon balance after step 2 was still clearly outside the admitted range (as reported by the GCP in Le  Quéré et al.
, 2015) , which highlights the importance of assimilating a data stream such as this that provides information at larger scales (constraining large-scale respiration fluxes). The use of atmospheric CO 2 concentration as an overall constraint in step 3 was technically challenging as it required the coupling of ORCHIDEE with an atmospheric transport model in forward and reverse mode (i. e. , to com-pute the cost function and its gradients at each step of the minimization process). As a result of the final step, we were able to fit the atmospheric CO 2 growth rate and thus to derive a land C sink compatible with current best estimates from the GCP. The assimilation of CO 2 data also slightly changed the seasonality of the NEE, which improved the fit to the atmospheric CO 2 seasonal cycle. Note that assimilating only CO 2 data would lead to a similar global land C sink but with a different model parameter set not compatible with the information provided by MODIS-NDVI and FLUXNET data.
The consistency of the stepwise approach has been evaluated with back-compatibility checks after the final step (step 3: assimilation of atmospheric CO 2 concentration). The optimized model with the final set of parameters does not degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps (only minor changes of the RMSEs occur; see Fig. 8 ). This result has two important consequences. Most importantly it suggests that current state of the art LSMs (at least ORCHIDEE) have reached a level of development where consistent assimilation of multiple data streams is finally possible. This overcomes the most important limitation noted by  Rayner (2010)  to the widespread use of CCDAS systems. At a more technical level it suggests that stepwise assimilation is a valid and feasible approach.
Although we only carried the estimated parameter uncertainties from one step to the next (as a first more simple approach), and not the full error variance-covariance matrix, we were able to propagate enough information to maintain an optimal model-data fit after the last step for the three data streams, as confirmed with the back-compatibility checks. MacBean et al. (2016a) provided a more specific analysis of this issue. However, not propagating the covariance terms may have a larger impact for the reduction of the inferred parameter uncertainties (see for instance the large parameter/flux error reduction in Fig. 9 /Fig. 11 ). The order of the different steps was dictated by the number of parameters we choose to expose to each data stream, from only a few phenology parameters for NDVI up to the largest set for atmospheric CO 2.
Recall that under the fundamental theory the order of assimilation is unimportant. Testing whether our system meets this criterion is an important check on the robustness of the method but is not technically feasible with the full-blown system; it is currently being tested with some smaller models. Most of the optimized parameter values have significantly changed compared to their prior values, with a large error reduction for most (Fig. 9 ) that results in a strong constraint on the simulated fluxes (Fig. 11 ). In the last step, the assimilation of atmospheric CO 2 data mainly led to the optimization of respiration-related parameters, especially the regional soil carbon multipliers (K soilC,reg ). Note that this was also the case for the BETHY-CCDAS, as described in  Rayner et al. (2005)  (see their Table  2 ).
Finally, one can mention new approaches based on remote sensing data to account for site level differences in productivity potential due to edaphic variability (soil quality and slope/drainage) within the same vegetation type, as illustrated for high latitudes in North America  (Ise and Sato, 2008). To conclude, this work is a step forward in terms of multiple data streams assimilation that opens new perspectives for a better understanding of the carbon cycle and better predictions of the fate of the land carbon sink in the 21st century as a consequence of anthropogenic changes. As ORCHIDEE is part of the IPSL Earth system model the impact of the optimization on future climate change predictions will be assessed in a future study. However, we first need to run the ORCHIDAS with a longer atmospheric CO 2 record (i.
Code and data availability The ORCHIDEE model code and the run environment are open source (http://forge. ipsl. jussieu. fr/orchidee) and the associated documentation can be found at https://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation. Note that the tangent linear version of the ORCHIDEE model has been generated using commercial software (TAF; http://www. fastopt. com/products/taf/taf. shtml). For this reason, only the "forward" version of the ORCHIDEE model is available for sharing. The optimization scheme (in Python) is available through a dedicated web site for data assimilation with OR-CHIDEE (http://orchidas. lsce. ipsl. fr/). Nevertheless readers interested in running ORCHIDEE are encouraged to contact the corresponding author for full details and latest bug fixes. Finally, the source code of the LMDz atmospheric transport model can be downloaded from the following site: http:// lmdz.
Figure9. Prior and posterior parameter values and uncertainties for a set of optimized parameters (nine PFT dependent and four non-PFT dependent). The prior value corresponds to the horizontal black line and the physical allowed range of variation to the "y" range (i. e. , the white zone). For PFT-dependent parameters, there are nine sub-plots corresponding to PFTs that were optimized (except for K pheno,crit with only five PFTs). For each parameter, there are three estimated values for the three successive steps: step 1: assimilation of MODIS-NDVI data (green symbol); step 2: adding FLUXNET data (blue symbol); step 3: adding atmospheric CO 2 data (red symbol). The parameter values are depicted with the symbols and the estimated uncertainties with the vertical line (±σ ).
Acknowledgements. This work was mainly funded by the  EU   FP7   CARBONES  project (contracts  FP7-SPACE-2009-1-242316 ), with also a small contribution from  GEOCARBON  project ( ENV. 2011. 4. 1. 1-1-283080 ). This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (U. S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program ;  DE-FG02-04ER63917  and  DE-FG02-04ER63911 ),  AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux ,  Fluxnet-Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada, and NRCan), Green-Grass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, Université Laval  and  Environment Canada and US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California-Berkeley, University of Virginia.
) and machine tools; 5. Geometry of the machining part and stock (volumes, planes, islands, contours, curves, etc. ); 6. Milling strategies (axial, radial, profile), and styles (spiral, back and forth, one way, inward helical, etc. ). By defining and simulating all milling sequences in CAM modules like Prismatic Machining in PLM system CATIA  [6] , user generate P. P. R. structure of the process and manufacturing program, easy to edit in the future if needed (Fig. 4 ). In order to obtain the final shape of the part, we combined facing, pocketing, profile contouring and curve following operations in just one program. Some of the milling sequences are given in Table  1.
System description As the tracking framework we are using UbiTrack  [7]. UbiTrack is an open source, general purpose tracking framework for Augmented Reality developed by CAMPAR group (TUM Munich, Germany). UbiTrack has been successfully adopted to Microsoft Windows, Linux, Mac OS and Android  [9]. For the software engine we are using Unity3D  [8]  for the ease of use and its platform independency. This allows us to deploy our application to all desktop systems and Android devices without any need to change the source code of the application. To fulfill all requirements for an AR system, the key step is to estimate the position and orientation of the camera in respect to the axis of the scene. To do this we employ a technique called "marker tracking", in the case of QR marker based augmented reality system  [10] ,  [11].
Acknowledgments. This work is supported by project " Application of Biomedical Engineering in Preclinical and Clinical Practice ", supported by the  Serbian Ministry of Education, Science and Technological Development  ( III-41007 ). The work of  Ionut Ghionea  was supported by the  Sectorial Operational Programme Human Resources Development 2007-2013 of the Ministry of EU Funds through the Financial Agreement   POSDRU/159/1. 5/S/138963.
Homology modeling The F. graminearum longiborneol synthase coded by the locus FGSG_10397 (ACY69978. 1) [12] is described in the Uniprot database as being encoded by the CML1 gene (see http://www. uniprot. org/uniprot/D1M8S2) and belongs to the fungal sesquiterpene synthase superfamily of enzymes. To identify possible templates for building a three dimensional (3D) model of this protein, it was first necessary to search protein databases in order to identify proteins that could be suitable templates for obtaining a robust and convincing homology 3D model. Our selection was based on close relatednesses in both structure and function within all members of the family. We focused mostly on the isoprenoid domain characterized by a well-conserved structural organization that is mostly α-helical with a core bundle of antiparallel α-helices.
The superposition of 33 structures of this family shows clearly the conservation of this structural organization within the whole family (http://www. cathdb. info/version/latest/superfamily/1. 10. 600. 10). We checked carefully that this structural signature had been conserved during our homology modeling process. Once the protein class was identified and the possible orthologs detected, we searched for similar structures at the Protein Data Bank (PDB),  [16]  to look for the most suitable template for homology modeling. This step is crucial as the quality of any homology model depends strongly on the percentage of similarity, identity and sequence coverage. When low sequence identities are detected, as in this work (less than 20 %), the modeling task is not trivial and requires a suitable strategy to improve the quality of the proposed models.
fr web server set PhyML3. 1/3. 0 aLRT with default parameters (Substitution model: WAG for proteins, aLRT test: SH-like, number of substitution rate categories: 4, gamma parameter: estimated, proportion of invariable sites: estimated and the transition/transversion ratio: 4; the tree branch support was evaluated by an approximate likelihoodratio test)  [22]. O P O P O - O O O - O - + H 2 O (2E , The closest template was retained to perform the homology modeling task using the MODELLER program with its default settings  [23]  (see the MODELLER manual and tutorial). Additionally, the automatic loop refinement method available in MODELLER was used. The crude longiborneol synthase 3D 0 model was achieved via this route. The model was validated by the server SAVES  [24].
Molecular dynamics Starting from the homology built 3D 0 model obtained from the previous step, we refined the model and investigated its behavior in a physiological medium. The modeled protein was embedded in a box of 100 Å × 100 Å × 100 Å with TIP3P explicit water molecules  [25]. Next, Na + ions were added to ensure electrostatic neutrality. The NAMD program version 2. 6 was employed in conjunction with the CHARMM27 force field  [26, 27]  in order to simulate the ensemble of the 94,678atom system. The initial state for dynamics was generated from the model after 64,000 steps of conjugate gradient minimization. The conformational behavior of the protein, water and counter ions system was obtained by running 100 ns of MD. The simulations were carried out in the isobaricisothermal ensemble, maintaining pressure and temperature at 1 atm and 300 K, respectively, using Langevin dynamics (damping parameter of 1 ps -1 ) and piston approaches.