dataesr/scientific-paragraphs-categorization huggingface dataset:
Correction from the OSM Database The OSM database is modeled as an additional sensor (see Equation (  16 )). The prediction of the map measurement Z OSM t+1|t+1 is done from the unscented transform (Equation (  17 )). This prediction is matched with the OSM road network and the matched segment is used to correct the estimation of the ground-location and direction of the vehicle. In  [24] , the author lists various approaches for map-aided positioning of a vehicle and most of them concerns geometric methods. We propose to solve the map matching step through a statistical approach that relies on the computing of the Mahalanobis distance  [25]  between the UKF-predicted map measurement and all available OSM road descriptors. Knowing that the digital road map is described by geolocated and interconnected nodes whose main drawback is their random sampling grid, we have refined the map modeling.
In the matching step, we introduce as inputs the orthogonal projections of the UKF-predicted map measurement onto each nearby OSM road segment if possible, otherwise the nearest line segment extremity is used instead of the projection. This step leads to the identification of the position of the closest candidate for each road segment of the map database. At time instant t + 1, we get the set of orthogonal projections Z OSM m : Z OSM m = proj ⊥,m ( Z OSM t+1|t+1 ) (21) m = {1,. , n} and n describes the maximum projections number. Map descriptor includes the road direction. So, the Mahalanobis distance d m = d(Z OSM m , Z OSM t+1|t+1 ) can be evaluated in the cases of a two-way road (θ 1 , θ 2 ) or a one-way road (θ 1 ): Z OSM, θ 1 m = (x OSM m , y OSM m , θ OSM m ) T Z OSM, θ 2 m = (x OSM m , y OSM m , θ OSM m + π) T (22) The Mahalanobis distances can be derived from the previous equation: (d θ k m ) 2 = (Z OSM, θ k m -Z OSM t+1|t+1 ) T ( P GPS t+1|t+1 ) -1 (Z OSM, θ k m -Z OSM t+1|t+1 ) with k = {1, 2} (23) The innovation process has a Gaussian distribution.
Correction from DEM Database A DEM database is used to take into account the elevation in the multisensor processing. A first solution has been proposed in  [26]  and was based on the minimization of Mahalanobis distances between the nearest horizontal point of elevation and the filter estimation. In area of low elevation change, this approach may be sufficient. In areas with higher elevation, the use of raw DEM data can generate significant errors. In Section 4, we develop our main contribution that is relative to a new modeling of DEM data based on triangulations. The last step concerns the correction with elevation measurements from the ASTER terrain surface. The goal is to bound with DEM corrections  [27] , the well-known altitude errors of GPS measurements  [28]. The position and elevation errors of the DEM data are modeled by Equation  (10)  and the unscented transform is used to derive the predicted measurement.
Road Slope Computing The final objective of this work is to propose an enrichment of OSM database with the inclinations of the roads. This step is done from the estimated elevation (z-component of the state vector in Equation (  24 )) and the matched orthogonal projection on the OSM road network (see Equation (  21 )). The last step relies then on the estimation of the inclination parameters. The following equation details the relationship between the elevation E s,t and the radial distance ρ s,t of the projected estimation at t onto the line segment #s to the extremity (x s,0 , y s,0 ): E s,t = a s ρ s,t + z s,0 (25) where a s denotes the slope of the line segment #s and z s,0 its intercept. For the n s projections onto the same line segment #s, Equation (  25 ) can be written as follows: E s = Hu =    ρ s,1 1.
ρ s,n s 1    a s z s,0 (26) where the radial distance ρ s,t is described by: ρ s,t = (x OSM s,t -x s,0 ) 2 + (y OSM s,t -y s,0 ) 2 , ∀t (27) H is a n s × 2 matrix. The parameter n s changes for each line segment #s and depends on several parameters (vehicle speed, length of the line segment,. ). For each segment, a least squares method is used to estimate the road inclination: âs ẑs,0 = H T H -1 H T E s ( 28 ) This statistical estimation of the inclination is used to automatically enrich the road map database.
Scenario Context The objective is to estimate the roads inclinations. Theses roads are extracted from the OSM database. The experimentation context is shown in Figure  3. A car is driven on this part of OSM road network composed of motorways mainly. It is fitted with a dual-GPS system that relies on two u-blox EVK-6T receivers that collect the 3-D vehicle positionings. The receiver manufacturer announces the position accuracy as 2. 5 m CEP (Circular Error Probable) and 2 m CEP if SBAS is enabled. These precisions may be more or less degraded depending on the experimental conditions (multipaths, etc. ). The GPS antennas are located on the car roof and aligned with the driver side. The spacing distance is D = 1. 75 m. Their height from ground is H = 1. 55 m. The improved SBAS positioning (EGNOS) has been enabled on each receiver whose GPS navigation rate has been set to 1 Hz.
The ASTER GDEM2 informs on the elevation levels of the experimentation area. They are plotted in Figure  4a, b. This dataset is composed of a grid of 14,560 MSL-elevation points that are spaced by a known horizontal distance: ∆x DEM = ∆y DEM = 1 arcsecond (or 30 m) in a LLH geographical datum; these values must be recomputed if a horizontal UTM datum is used. In addition, it must be noticed that some voids and artifacts (step, pit-in-bump, mole-run, etc. ) remain in this version of dataset, although they announced as decreased in the northern area or almost disappeared. The ASTER GDEM2 is provided with a quality parameter that informs on the measurement accuracy of the elevation. This information can be used in the matching procedure of DEM to reject the measure, if needed. Note that the u-blox GPS receivers directly deliver MSL-elevations in the EGM84 vertical datum while the ASTER GDEM V2 use the geoid of EGM96.
Map-Matching Procedure Our OSM dataset is composed of 73 polylines that represent various roads as primary or secondary roads, motorways, etc. These polylines are modeled by nodes and road segments whose total number is 417 as shown in Figure  5a. The 2-D location of MSL-elevation points are also plotted (light grey) on this figure. The main objective is here to estimate the slope of each road segment on which the car is driven. Therefore, the UKF-based algoritm allows to realize the fusion of GPS/OSM/ASTER data for estimating the car path that is plotted in black squares here. Selected enlargements of the estimated path are available in Figure  5b-d  Once the two GPS positioning fused, the OSM-matching step allows to identify the road segment on which the car is located. This procedure relies on the computing of Mahalanobis distances whose the time-evolution is plotted on Figure  6 , highlighting ε that is the OSM-matching threshold.
Road Inclination Estimation The proposed approach fuses the GPS-based altitudes and the matched ASTER-based elevations to refine the estimated altitude of the car as shown in Figure  7  where the time-evolution is plotted in blue circles. This figure shows also the number of all matched OSM road segments-during the defined scenario-that are plotted in black squares. To estimate the slope of each road segment, the proposed least squares method needs the UKF-estimated altitudes and the matched map data as input. As detailed in Equation (  25 ), the road segment needs to be traveled during 2 GPS time-instants to estimate the inclination but in order to improve the estimation, the minimum number of needed GPS readings is set to 4. Figure  8  shows the results of roads inclination percentage. They concern only the OSM road segments that the car could traveled during the test scenario.
Conclusions This work states a general approach to estimate the inclinations of OSM road segments. It relies on the collected data from a dual-GPS positioning, an OSM road network and an ASTER GDEM2 terrain surface that are fused following a centralized scheme. The proposed method takes into account the topology of the geographical area and allows a new computation of the elevation point from the terrain models. The map-matching procedure allows to select the used OSM road segments and the triangulation of DEM leads to a refinement of the ASTER MSL-elevation. Therefore, it becomes possible to enhance the OSM database with the estimated slope of each matched road segment automatically. Then, the road inclination parameter is available for further more reliable route planning applications, for instance those of electric vehicles whose autonomy is yet limited for trips.
Based on the OSM concept, our method is designed for a collaborative enhancement of the database. This general and theoretical framework is suitable to the implementation of other kinds of GNSS, DEM, etc. It provides the basis for a 3-D modeling of the road segments in a map database. Cases of GPS outages and non-existing OSM roads are not considered in this work. The next step is now to study multi-GNSS positioning systems and alternative roadmap databases.
Acknowledgments We would like to thank  Dr. Roberto Zorzin  and  Dr. Anna Vaccari  for access to the specimens housed in the  Museo Civico di Storia Naturale di Verona, Italy. We are also grateful to  Severin Morel  ( CR2P ,  UMR CNRS 7202 , MNHN) and to  Sabine Riffault  (iPHEP,  UMR CNRS 7262 , UniversitePoitiers) for their technical and artistic contributions. Finally we are grateful to  Juan Marcos Mirande ,  Matt Friedman  and anonymous reviewer for their valuable comments in our manuscript. None of the authors declare any competing interests.
, 2014)  gave a comprehensive audit of the status of organic aerosol schemes in global models, brought together several benchmark observational datasets and intercompared and evaluated the OA simulated by a large number of global aerosol models against them. Results indicate that simulated OA greatly varies between models in terms of POA emissions, SOA formation and complexity of OA parameterizations, but the amount of OA remains under-predicted. In the latest EURODELTA III (ED-III) European model intercomparison, seven different regional models were applied in the European domain during different periods  (Bessagnet et al. , 2016). All models under-predicted the total measured organic fraction during the February-March 2009 winter episode mainly due to the uncertainties in SOA representation  (Bessagnet et al.
, 2015). Nonmethane organic gases with a proton affinity greater than that of water were measured using a high-resolution proton transfer reaction mass spectrometer (PTR-ToF-MS 8000, Ionicon Analytik G. m. b. H. ). The PTR-ToF-MS was operated with hydronium ([H 2 O + H] + ) as reagent, a drift tube pressure of 2. 2 mbar, a drift tube voltage of 543 V and a drift tube temperature of 90 • C leading to a ratio of the electric field (E) and the density of the buffer gas (N ) in the drift tube (E/N ) of 137 Townsend. The analysis of PTR-ToF-MS data and the identification of the precursors' chemical nature are described in  Bruns et al. (2016). The elemental composition of the detected gases was analysed using the Tofware post-processing software (version 2. 4. 5), running in the Igor Pro 6. 3 environment  (version 6.
26 and 4. 56 (Table  1 ). Thus, the model implicitly accounts for the addition of 1. 1 to 1. 5 oxygen atoms and the loss of 2. 75 to 4. 25 carbon atoms, with one oxidation step. Set 3 was constrained based on the proton transfer reaction mass spectrometer (PTR-MS) data. The measurements suggested an average NTVOC carbon and oxygen number of about 7 and 1, respectively. Based on reported molecular speciation data (e. g. Kleindienst et al. , 2007) , we expect the products of C 7 compounds to have a C 5 -C 6 carbon backbone. These products were placed in set 3 following a kernel function based on the distribution of naphthalene oxidation products. At least two oxygen atoms were added to the NTVOC mixture upon their oxidation (Table  1 ). The overall O : C ratio in the whole space roughly spans the range of 0.
H vap functions and OM sv. Vol. dist were tested. In Table  2 , the measured OA t=0 for all the 11 experiments, which ranges from 6. 0 to 22. 6 µg m -3 , are reported. The OM sv values that match the measured OA t=0 are shown as an example for the cases when OM sv. H vap = {85 000 -4000 × log(C * )} J mol -1 and Table  2. Modelled and experimental data for 11 wood-burning experiments. The OM sv mass at the beginning of each chamber experiment is reported together with the measured OA t=0 and the initial NTVOCs concentration. NTVOCs / OM sv is the ratio between the measured NTVOCs and the calculated OM sv mass at the beginning of each experiment for the two different OM sv. H vap functions: a OM sv. H vap = {85 000 -4000 × Log(C * )} J mol -1 and b OM sv. H vap = {70 000 -11 000 × Log(C * )} J mol -1 , used in combination with OM sv.
g. NTVOCs / OM sv and NTVOC composition) might be expected depending on the different burning conditions and biomass used. Therefore, special care is required when extrapolating the results to a global scale, including more detailed emission information related to the wood-burning habits of the different countries. An application of the proposed scheme, limited to the European scale, can be found in  Ciarelli et al. (2017). Code and data availability. The VBS box model was written in Fortran and was compiled using the Portland group compilers. Simulations were performed under Scientific Linux SL distribution using C shell scripts in order to brute force the chemical and physical parameters. Model output was written in tab-separated text format. Routines to perform the oxidation reactions of SOA precursors and partitioning between particle and gas-phase organic material were taken from the CAMx model (available at http://www.
The French collection of Plan-Reliefs, scale models of fortified towns, constitutes a precious testimony of the history of France. The aim of the URBANIA project is the valorisation and the diffusion of this Heritage through the creation of virtual models. The town scale model of Strasbourg at 1/600 currently exhibited in the Historical Museum of Strasbourg was selected as a case study. In this paper, the photogrammetric recording of this scale model is first presented. The acquisition protocol as well as the data postprocessing are detailed. Then, the modelling of the city and more specially building blocks is investigated. Based on point clouds of the scale model, the extraction of roof elements is considered. It deals first with the segmentation of the point cloud into building blocks. Then, for each block, points belonging to roofs are identified and the extraction of chimney point clouds as well as roof ridges and roof planes is performed.
Data post-processing The post-processing of the photographs was performed with the commercial software Photoscan (Agisoft). The exterior orientation of the 5000 photographs is first calculated based on the matching of points of interest. This step includes the calibration of the digital camera. The result of the positioning of the photographs is shown Figure  3a. The scaling of the model is then realized thanks to five distances measured on the borders of the model. The local position of the digital camera (position in the grid with a 20 cm step) is set up for each photograph in the software and is also used for the scaling. A total error of 1. 3 cm is observed for the positioning of the cameras. Once the exterior orientation and the scaling are carried out, point clouds of more or less high density are generated.
The models of the parametric library are described with graphical schemes as explained in  (Chevrier, 2016) ; -The orientation of the building is retrieved  (Chevrier, 2015)  and the modelling can be performed from the graphical schemes of the library parametric model as suggested in  (Chevrier, 2016) ; -The buildings are then textured from the photographs with the Photoscan software; -Chimneys and walls, which are represented with polylines, and vegetation elements, which are represented with points, are computed. Figure  10  shows the result of the 3D parametric modelling of city block 2. Figure  10. Result of the parametric modelling for city block 2.
Field Activity At each of the chosen biogeochemical stations (486 to 517 in Figure  1a ), a full set of physical and biogeochemical parameters was collected. In this paper, however, we will focus on the basic physicochemical and biological parameters. To support the discussion on the ice cover properties, we will also use the data set from physical transects performed by the ice physics team on board. We briefly expose how these were collected below. First, the ship was anchored to an ice floe selected in accordance with plans from all the AWECS teams. Care was taken to set up the trace metal clean biogeochemical sampling site upwind of the ship and at a reasonable distance from it (300-500 m). A 10 3 10 m area was then flagged, in which access was only permitted to operators wearing clean suits to prevent contamination  (Lannuzel et al.
2). Stable isotopes data also help in better understanding the dynamic of the winter sea ice brine system during AWECS. Snow samples span a large range of negative values (-8 to 225 &), reflecting different temperatures of formation or cloud history during the precipitation phase of the hydrological cycle. Sea ice samples, resulting from the freezing of sea water with d 18 O values very close to 0& (-0. 27&), are enriched in heavy isotopes and show steadily increasing d 18 O values with depth (Figure  5b ), under the combined effect of snow ice contribution in the surface layers  (Jeffries et al. , 1994 (Jeffries et al. , , 1997;; Lange et al. , 1990)  and of a decreasing freezing rate downward  (Eicken, 1998; Souchez et al. , 1988). Snow ice, as mentioned before, shows negative values, due to the contribution of snow in various proportions.
A Changing Antarctic Sea Ice in the Weddell Sea? Data on winter Antarctic sea ice properties are scarce, and, for the Weddell Sea, only three winter cruises are available, including the AWECS cruise described in this paper. As pointed out in the description of Figure  2  in the results section, interesting contrasts however seem to emerge. The 2013 snow depth distribution is clearly shifted toward higher thickness classes as compared to 1986, a situation that seems to initiate in 1992 already (Figure  2  left, Table  1 ). While the ice thickness median is significantly higher in 1992 and 2013 as compared to 1986, no significant difference is seen between the median values of these 2 years. This slight increase in ice thickness could potentially reflect an increase in snow ice formation, as the snow depth globally increased in the last decades and thin (0.
Although the number of previous reference winter cruises is obviously too low to describe trends, our results show that the 2013 cruise is characterized by higher snow depth, lower (and more negative) freeboard, and higher Chl-a median values compared to the 1986 and 1992 surveys. This result is consistent with the outputs of the NEMO-LIM3 sea ice model, showing a clear trend of increasing snow thickness, mean ice temperature, and mean relative brine volume from 1986 to 2013, and reasonably good agreement with our data set for 2013. Because snowfall is forced from climatology, the increasing trend in snow depth simulated by the model can only be associated with changes in the sea ice state. In addition,  Toyota et al. (2016)  see no trend in net precipitation in the Weddell Sea for the period of concern, based on ERA-Interim atmospheric reanalysis.
Toyota et al. (2016)  suggest increasing snow depth, could be associated with reduced loss of snow to open water due to increasing ice concentration. The observed increase in the sea ice season duration  (Stammerjohn et al. , 2007 (Stammerjohn et al. , , 2012) )   (2012) for the future Arctic. A more complete analysis of the simulated snow mass balance, including interannual precipitation forcing, would shed more light on these alternatives. Clearly, our AWECS data set challenges the traditional assumption that sea ice winter biogeochemical activity is restricted by the very low ambient temperatures and light levels. It suggests that previous estimates of annual budgets of climatically significant gas fluxes across the ocean-sea ice-atmosphere interfaces, essentially based on Spring-Summer observations, will need to also take winter processes into account.
Acknowledgments The authors wish to warmly thank the  Captain  and crew of the RV Polarstern and the  Chief Scientist Peter Lemke  for their unconditional logistic and scientific support during the AWECS cruise. This work is a contribution to the  Belgian Science Policy Office  project  BIGSOUTH  (contract  SD/CA/ 05A ). The work of SS was funded by the  EU  7th  Framework  project  SIDARUS  ( FP7-SPACE-2010-1 , grant agreement  262922 ). S. M. is supported by the  Australian Research Council's Special Research Initiative for Antarctic Gateway Partnership  (Project  ID   SR140300001 ) and postdoctoral researcher with the FRS-FNRS. D. N. was supported by  JSPS KAKENHI  grant  12J04175  and  17H04715. The work of C. U. was funded by a  Postdoctoral Fellowship of the Universit e Libre de Bruxelles  and the  Alfred-Wegener-Institute.
The work of A. M. L. was supported by  Walter  and  Andr ee de Nottbeck Foundation ,  Onni Talas Foundation , and  Finnish Antarctic Research Program FINNARP. J. J. was supported by the  Quantitative Antarctic Science Program  jointly led by the  University of Tasmania  and the  Australian Antarctic Division. G. C. benefited from a  Belgian Fonds National de la Recherche Scientifique (FRS-FNRS)  research grant (contract  A 4/5 -MCF/DM -2657 ). B. D. is a research associate with the F. R. S-FNRS. Data Base: PANGEA under doi. pangaea. de/10. 1594/PANGAEA. 834492Journal of Geophysical Research: Oceans.
g. AEWs, vortices), and (d) discuss impacts on rainfall, clouds and atmospheric composition. This way the paper aims to fulfil a similar role as  Janicot et al. (2008)  for AMMA. The analysis will build on and expand some of the concepts introduced in this section and provide a consistent framework for the detailed analysis of DACCIWA field campaign data in following years. From an atmospheric dynamics and chemistry perspective, SWA is of particular interest and has not been studied much in the past. AMMA had fewer stations in SWA and only a few publications covering this region. During GATE, data quality (e. g. from radiosondes and satellites) and data assimilation (e. g. use of cloud motion vectors) had not evolved enough to allow a reliable analysis of 850 hPa streamlines for example, especially over the Gulf of Guinea (e.
Data For the investigation of atmospheric dynamics, analysis and reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used. Most of the analyses are based on the ERA-Interim (hereafter ERA-I) reanalysis at about 0. 7 • grid spacing  (Dee et al. , 2011) , which allows the computation of background climatologies back to 1979. For investigations focusing on the campaign period in 2016 alone, the higher-resolution operational analyses (native resolution of ∼ 9 km; model version Cy41r2; see www. ecmwf. int) are employed. As there was no change to the operational system during the study period, these data can be regarded as homogeneous, in contrast to longer time spans of operational data. The majority of radiosondes launched during the DACCIWA field campaign were distributed through the Global Telecommunication System and were assimilated at ECMWF.