dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. MODIS data are available from the Level 1 and Atmosphere Archive & Distribution System (LAADS) at https://ladsweb. nascom. nasa. gov (last access: 12 March 2018). Aura OMI and MERRA-2 reanalysis data are available from the Mirador NASA Goddard Earth Sciences Data and Information Center (GES DISC) (https://mirador. gsfc. nasa. gov, last access: 12 March 2018). CALIPSO data are available from the NASA Atmospheric Science Data Center (https://eosweb. larc. nasa. gov, last access: 12 March 2018). Planetary boundary layer height and air mass back trajectories are retrieved from Global Data Assimilation System (GDAS) archives hosted at the NOAA Air Resource Laboratory (https://ready. arl. noaa. gov, last access: 12 March 2018). MODIS fire products are obtained from the Fire Information for Resource Management System (FIRMS) (https://firms.
Radar data We use several airborne ice-penetrating radar surveys collected in the Dome C region by the University of Texas at Austin Institute for Geophysics (UTIG) and the Australian Antarctic Division (AAD) as part of the ICECAP project (International Collaborative Exploration of the Cryosphere F labels a 1. 5 million-year-old ice new candidate site (see companion paper). The background is bedrock elevation in m a. s. l. and combines Bedmap2 bed elevations  (Fretwell et al. , 2013)  as well as a recompilation based on the OIA radar bed elevations  (Young et al. , 2017)   through Airborne Profiling,  Cavitte et al. , 2016)  and the Oldest Ice candidate A (OIA) survey flown by ICECAP in January 2016 (Fig. 1 ,  Young et al. , 2017). All surveys use the same center frequency of 60 MHz and the same bandwidth of 15 MHz; radar isochrones can therefore be easily matched from one season to the next.
3). There is a very good agreement in the magnitude of accumulation values between the two datasets and in their north-south accumulation gradient on large-scales (100s km), with accumulation decreasing with distance from the coast. White lines outline the same areas of small-scale high accumulation as in Fig. 3. Background is the same as in Fig. 1. detrended paleoaccumulations for each isochrone-bounded layer (Fig. 6 ). 3. 5 Slope and curvature in the prevailing wind direction (SPWD and CPWD) In Sect. 5, we discuss the importance of surface slope in the prevailing wind direction (SPWD) and curvature in the prevailing wind direction (CPWD). We use ECMWF 5 year average wind directions  (Simmons et al. , 2007)  and  Bamber et al. (2009)  surface elevations to calculate SPWD and CPWD values over a 3 km radius in the survey region (Fig.
, 2016; Urbini et al. , 2008)  combined with the presence of areas of subglacial melting in the region  (Young et al. , 2017) , creating additional draw-down of the surface. Although we cannot yet explain the mechanisms causing the small-scale paleoaccumulation variability we observe in the Dome C region, which is beyond the scope of this manuscript, our observations have important ramifications for better understanding the region's stability through time. In the future, we hope to improve our paleoaccumulation rate reconstructions, and in particular go back further into the last glacial cycle with a full 3-D model. Further GPR data was recently collected over the LDCM, and strain nets and various other instruments have been deployed. These new measurements will add to the existing data set and provide important constraints if we hope to develop 3-D inversions.
Figure5. Historical average accumulation rates a 100 yrs along the radar lines superimposed on ECMWF ERA40 estimated presentday surface accumulation rates (see Sect. 3. 3). There is a very good agreement in the magnitude of accumulation values between the two datasets and in their north-south accumulation gradient on large-scales (100s km), with accumulation decreasing with distance from the coast. White lines outline the same areas of small-scale high accumulation as in Fig. 3. Background is the same as in Fig. 1.
Acknowledgements. We would like to first thank  Kenny Matsuoka  for his constructive editing advice, and the three anonymous reviews who greatly improved this manuscript. Special thanks to  Olivier Passalacqua  for fruitful discussions. This publication was generated in the frame of  Beyond EPICA-Oldest Ice (BE-OI). The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 730258  ( BE-OI CSA ). It has received funding from the  Swiss State Secretariate for Education, Research and Innovation (SERI)  under contract number  16. 0144. It is furthermore supported by national partners and funding agencies in  Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland , the  Netherlands  and the  UK. Logistic support is mainly provided by  AWI ,  BAS ,  ENEA  and  IPEV.
We acknowledge the support of  Kenn Borek Airlines. Additional support was provided by the  French ANR Dome   A  project ( ANR-07-BLAN-0125 ), the  CNRS/INSU/LEFE fundings "IceChrono"  and " CO2Role " and the  Grenoble-Alpes University AGIR  funding "Oldest Ice". The opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency, the Swiss Government or other national funding bodies. This is BE-OI publication number 3. This is UTIG contribution  3116. Edited by: Kenny Matsuoka Reviewed by: three anonymous referees.
Moreover, only data gathered in off-axis mode with SZA < 75º were used in this work. Similarly, the O4 differential slant column densities (dSCDs) were also retrieved (337-370 nm spectral window) in order to invert the vertical profile of the aerosols extinction coefficient (AEC) and therefore to characterize the scattering properties of the atmosphere and the light path of the photons reaching the detector. The reliability of the aerosol vertical information retrieved by MAX-DOAS observations has already been demonstrated under different visibility conditions (e. g. ,  Frieß et al. , 2016). This retrieval is based on the concept that the concentration O4 is known and stable in the atmosphere. Hence, a variation 5 in the O4 dSCDs is usually related to a change of the optical path, generally due to the presence of aerosols (e.
Ancillary Data In order to interpret the aimed bromine results, additionally to the spectra gathered by the MAX-DOAS measurements, near surface O3 vmr measured at both stations were also compiled. At both sites, the surface O3 was measured with ozone analyzers  (Thermo Environmental Instrument, Thermo Fisher Scientific, model 49; i. e. , TEI49). The operation principle of this in situ instrumentation consists in the attenuation of an ultraviolet light beam (254 nm) by an air sample containing ozone and has a 5 manufactured sensitivity and limit of detection of 1 nmol mol -1. In the case of Marambio station, which contributes to the GAW network (Global Atmosphere Watch, WMO-World Data Centre for Greenhouse Gases WDCGG), the measurements of surface O3 were carried out by the National Meteorological Service of Argentina (SMN) and can be retrieved from the WMO -World Data Centre for Greenhouse Gases (https://ds.
data. jma. go. jp/gmd/wdcgg/). These data from the TEI49 from Marambio is compared every year against the regional standard (WMO, RCC-BsAs, TEI49PS). At the research site of  10 Belgrano, the year-round surface O3 is measured by INTA since February 2007 (e. g. ,  Jones et al. , 2013). At this site, the inlet of the analyzer, protected from rain, snow and dust, is placed 0. 85 m above the roof of the base in the cleanest area of the station, being free of pollution from the research site. Additionally, the weather information was obtained from the observations performed by the SMN at Marambio (WMO station 89055) and by INTA at Belgrano. In the case of Marambio, the weather station is installed in the so-called Scientific Pavilion 15 of the Marambio Antarctic Station together with an Automated Met Station (AMS), which measures the temperature, humidity, precipitation, wind speed and direction besides the atmospheric pressure.
DOAS observations: BrO VCD2km and AOD2km Herein we present the AOD and the BrO VCD measured with the DOAS technique during the sunlit period of 2015. This period lasted for about 8. 5 months in Marambio and 7. 5 months in Belgrano (Fig. 3 y 4 ). Due to instrumental issues, there were missing data at the beginning of the year at both stations. Note, however, that the polar sunrise was well covered at both sites and therefore the peak season of the bromine activation (e. g. ,  Simpson et al. , 2007). 35  As mentioned in Sect. 2. 2. 1, the sensitivity of the MAX-DOAS observations decreases with altitude. Hence, aiming also at comparing both stations, here we refer to the AOD and BrO VCD inferred in the first 2 km of the troposphere at each site (i. e. , AOD2km and VCD2km, respectively). Figure  3  shows the AOD2km retrieved at both stations.
Acknowledgements 5 Sea ice concentration maps are obtained from the website of the  Sea Ice Remote Sensing group of the University of Bremen  (https://seaice. uni-bremen. de/start/data-archive/). This work was supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the projects HELADO ( CTM2013-41311-P ),  VIOLIN  ( CGL2010-20353 ) and the  Spanish contribution  to  ORACLE-O3  ( POL2006-00382 ). Authors would also like to acknowledge the work done by the different technicians at the stations of Belgrano and Marambio.
Results Temperature trends. Figure  1  shows the linear trends of the yearly-mean TAS series retrieved from the global datasets and the regional experiments (WRF CTRL configuration, see Table  1  The differences between the VAR and CTE experiments and between these and the global datasets are also provided. The Supplementary Material (Supplementary Figs  1 2 3 4 5 6 7 8 9 ) provides the same analysis for the DJF and JJA-mean TAS series and for maximum (Tx) and minimum (Tn) temperatures. All the global and regional datasets and experiments usually depict positive trends, which are more marked over land than sea. The spatial patterns are very similar for TAS, Tx and Tn, with the largest signals (up to 6 K century -1 at the annual scale) appearing over central and northern Europe for the recent past, and also along the south-western Mediterranean strip for the future.
By seasons, the signals increase to 8 K century -1 in winter (north-eastern areas, past period) and summer (south-western areas, future period). Both WRF experiments (VAR and CTE) reproduce the most outstanding features of the spatial patterns retrieved from their respective global driving datasets, while showing some important discrepancies. For instance, at the annual scale, both ERA-driven WRF experiments provide weaker TAS trends in Mediterranean Europe and north-eastern areas compared to the results from ERA (Fig. 1 , first row). This is more marked in the CTE experiment than in the VAR, the differences between them being statistically significant over both such areas, with higher values from the VAR experiment, up to 1 K century -1 higher (which involves doubling the signal), than from the CTE one (Fig.
5 °C global warming future period (2011-2040), compared to the historical period (1971-2000), as retrieved from GCM and GCM-driven WRF simulations, both CTE and VAR. Analogous plots (with very similar appearances) for Tx and Tn are provided in the Supplementary Material (Supplementary Figs  10 11 12 ). Increases over 2 K, up to 3 K at most, are projected from all the datasets and seasons (annual, winter, and summer scales), with the patterns from the regional experiments resembling the overall structure of the patterns from their driving GCM (Fig. 2 , first, second, and fourth columns). The differences between the former and the latter are negative (positive) in southern (north-eastern) areas, generally smaller (larger) for the VAR experiment than for the CTE but for summer, (Fig. 2 , third and fifth columns).
Methods Experiments. The 3. 6. 1 version of the WRF model  13  was used to run the regional climate simulations over Europe with a spatial resolution of 0. 44°in latitude and longitude, using the EURO-CORDEX compliant domain  14. For the historical period (1951-2005), both the ERA20C reanalysis  32, 33  , which assimilates observations of surface pressure and surface marine winds and has an horizontal resolution of approximately 125 km, and the GCM CMIP5-experiment  16, 17  r1i1p1 MPI-ESM-LR historical run  30  with an approximate horizontal resolution of 200 km over land (hereafter referred to as ERA and GCM data, respectively) were used to initialize and drive (without nudging) the WRF model. Both global datasets incorporate the year-to-year varying annual concentrations of the CO 2 , CH 4 , and N 2 O GHG (as estimated from inventory data  28  ), which show a continuous positive trend  1.
Based on previous results  40  , these configurations yield results that cover a wide range of the model spread. Simulations other than the CTRL one span only the periods 1971-2000 and 2011-2040, which are enough to assess impacts under 1. 5 °C global warming. Also, the GCM-driven CTRL experiments were replicated for these periods over a smaller area domain, with latitude ranging from 44. 8°to 53. 5°and longitude from 16. 2°to 30°(see Supplementary Fig. 13 ). In practice, the simulated periods were split into 5-year periods that were then continuously run with a spinup period of 4 months (time-slice approach). Updates of the boundary conditions, from either ERA or GCM, were performed every 6 h at the borders of the regional domain. The outputs were recorded at one-hour intervals. Each WRF realization was run twice: considering static concentrations of CO 2 , CH 4 , and N 2 O GHG, fixed to their values in 2005 (the by-default option in WRF; CTE experiments), and considering their evolution, their annual estimated values, along the simulated periods as it was included in the driving global datasets (VAR experiments).
Data analysis. The analysis first deals with the temporal trends of TAS over both historical and scenario periods: differences between the VAR and CTE experiments, and between these and their driving global datasets (ERA and GCM). The temporal trends were computed as the linear trends of the yearly-mean series of TAS at either annual or seasonal scales (winter: December to February, DJF, averages; summer: June to August, JJA, averages). The daily maximum and minimum TAS (Tx and Tn, respectively) series were also yearly or seasonally averaged and included in the analysis. The linear trends were computed using the Theil-Sen estimator, a nonparametric method that defines the slope as the median among all possible lines through pairs of points. This approach is less sensitive to outliers than the classic leastsquares method and is more accurate in case of non-normal data.
Trend significance was estimated applying the standard Mann-Kendall trend test. Statistical significance of trend differences (i. e. , between datasets) was equally estimated but using the difference time series (i. e. , series of TAS, Tx or Tn differences between datasets). This method reduces noise levels by subtracting the variability common to both datasets being faced  47. In all cases, we imposed the threshold p < 0. 1. Second, the analysis deals with climate change regional impacts on TAS, Tx and Tn, as depicted by the VAR and CTE experiments under 1. 5 °C global warming above pre-industrial levels. For that, following the common procedure  6  , change patterns were constructed by subtracting the averaged values of TAS, Tx and Tn over the 30year future period centered in the year Y when global mean temperature rises 1.
Acknowledgements This study was funded by the  Spanish government  and the  Fondo Europeo de Desarrollo Regional (FEDER)  through the project  REPAIR  ( CGL2014-59677-R ). S. J. acknowledges the  Portuguese Foundation for Science and Technology (FCT)  for its support through the  FCT Investigator Programme 2015  ( IF/01142/2015 ). M. T. was supported by the  Spanish Juan de la Cierva Programme  ( IJCI-2015-26953 ). Special thanks to  Samuel Somot  for the helpful discussions on this study.
3Dnow is developed with the idea of encouraging users to upload their own sets of images that can be acquired with any camera device and then generate 3D content (downloadable as point clouds or meshes). Differently from other 3D reconstruction web services (e. g. ARC3DWeb, Recap), 3Dnow targets both experts and non-experts, offering them a host of functionalities, all embedded inside a friendly and freely available web service. No additional software needs to be installed other than a standard web browser. The image processing and 3D reconstruction modules are based on the open source pipeline COLMAP (Schonberger, 2016), which have been customised with new analysis features and editing algorithms. The innovative aspects of 3Dnow are: • Fully automatic and web-based 3D digitisation pipeline, from images to simplified meshes; • Intuitive web viewer for 3D reconstruction; • Fully-automated cleaning procedure of point cloud and meshes; 1 Website: http://3dnow.
Back end The back end integrates a request manager that schedules and handles the processes of 3D reconstruction and evaluation, data pre-processing for visualisation, account information fetching and data storage. The back end is multi-GPU and implemented in Django, an open-source Python-based web framework that uses MVT (Model-View-Template, 2018) architectural pattern. MVT is a development framework that promotes code maintenance and expansion. Because multiple users can launch processes simultaneously (e. g. reconstruction, automatic editing, mesh simplification), we integrated a First In First Out (FIFO) multi-queue scheduling strategy to handle concurrent reconstruction processes. We separate queues for CPU and GPU processes. Based on the number of available CPU cores and GPUs on the server, we can evenly distribute the resources for each user.
The original (full resolution) version of the meshed reconstruction is retained in the database. Downloadable point clouds and meshes are ply and obj files, respectively, while the camera poses and processing parameters are nvm and metadata file. Fig. 4  shows an example of meshing performed using Poisson reconstruction with default parameters (surface trim value equal to 10) and with personalised parameters (surface trim value equal to 5). Because a larger trim values discard a larger number of octree nodes, we can observe that the inner surfaces, affected by the casted shadow, have been filtered out in (a). On the other hand, the inner surfaces are maintained in (b), but the noisy 3D points outside the target object lead to noisier surfaces.
RECONSTRUCTION USE-CASES To evaluate the performances and reliability of 3Dnow, we processed several datasets. Tab. 1 reports the reconstructed scenes with their main characteristics, while Fig. 8 , 10 and 9 show screenshots of the reconstructed scenes. The 3D reconstruction tests were also performed using different server configurations to provide figures about the computational time. The reconstruction algorithm embedded in 3Dnow has been numerically evaluated on a few use-cases in  (Nocerino et al. , 2017 , Knapitsch et al. , 2017) , where it showed state-of-the-art performances.
Experiment analysis We run several experiments with different scales of observation and using images that were captured from diverse types of cameras, such as professional, compact, mobile phones and aerial (Tab. 1). Acquisitions range small (Buddha Thailand -Fig. 8a, b ) to large objects (Dortmund -Fig. 8c ), and single monuments (Chigi -Fig. 8d ) to large squares (Trento -Fig. 8e ). The reconstruction, editing and visualisation algorithm embedded in 3Dnow showed evidence to handle the tested cases, even when images characterised by different scales or multiple cameras (e. g. terrestrial and UAV images for the Neptune Paestum dataset Fig. 10 ). Noisy points and unwanted artifacts, such as the sub-clusters of points separated from the main "body" of Sarcophagus, can be eliminated through the automatic editing procedure embedded in 3Dnow (Fig.
Computational performance We performed the dense reconstruction on two different server configurations in order to test the performances using the multi-GPU architectures. The dataset we used for this experiment contained 55 images of size 5472×3648 pixels. In Tab. 2 we can observe   that the computational time decreases dramatically as the number of GPUs increases. The steps that benefit the most from the use of GPUs are feature matching and dense reconstruction, and this is because the operations performed during these steps are mostly linear operations. This suggests that for this type of processing, architectures for parallel processing can be more effective also to handle concurrent sessions from multiple accounts.
CONCLUSIONS The paper presented the developments of a new web server for the 3D reconstruction of various scenarios from set of images. The server offers high performance processing capabilities for the orientation of any type of image block and the generation of dense point clouds. With respect to existing solutions, the main features of 3Dnow include: user friendly GUI, selection of computation parameters, 3D visualisation of generated point clouds and meshes, filtering and cleaning of 3D data, statistics about processing steps. The reported examples span from small to large datasets, from smartphone cameras to professional SLR and aerial cameras, from urban to heritage and architectural scenarios. As future work, we are exploring mechanisms to predict (and suggest) processing parameters in order to generate more precise results with the automatic reconstruction process.
10. However, the two-dimensional ice shape distant to the tunnel wall, which covers 80 % of the wing span, remains unaffected. The second case of wind-tunnel wall interaction is an inviscid effect. The distant walls that are not connected to the test model will influence the streamlines around the model. Thereby, both pressure and shear stress distribution along the model surface are altered. Interestingly, at low speeds and sufficiently large droplet sizes (MVD = 15 µm), the ice accretion is nearly unaffected, when tunnel walls are introduced; see the computational results of the icing code TAUICE in Fig. 11. TAUICE is developed by the German Aerospace Center. It solves the Reynolds-averaged Navier-Stokes equations using a finite volume approach, calculates droplet trajectories with a Lagrangian scheme and finally determines the ice accretion using a model of  Messinger (1953).
It is a linear fit, a constant feed-rate has been proven by weighing, as indicated by the red data points. The blue dots and the solid blue line represents masses of ice weighed at the outlet of the sieving machine. This curve shows a very linear behaviour as well. One can conclude that after about 30 s the ice particle massflow injected into the wind tunnel is temporally constant. Weighing of ice mass passed by the sieving machine has been repeated several times, also for lower feed-rates and it turned out that after about 30 s the provided ice massflow is stable. Correlations between the injected ice particle massflow ṁice,inj and IWC inside the test section are discussed in Sect. 4. 6. The lower left part of Fig. 16  shows a microscopic image of two ice particles which have been captured at the exit of the sieving machine.