dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , same column, eluents, and mass spectrometer) as described for quantification (see the next section), with some adaptations. In short, 1 μL was injected on a Accela UHPLC system (Thermo Scientific, San Jose, CA, U. S. A. ) equipped with a pump, autosampler, and photodiode array (PDA) detector coupled in-line to a Velos Pro mass spectrometer (Thermo Scientific). UHPLC separation was performed at 35 °C, and the flow rate was 300 μL min -1. The elution program was started by running isocratically at 5% B for 1. 5 min, followed by 1. 5-20 min linear gradient to 100% B, and 20-25 min isocratically at 100% B. Mass spectrometric (MS) data were acquired in negative mode over the m/z range of 150-1500. The source conditions used were a capillary temperature of 400 °C, source heater temperature of 50 °C, source voltage of 3.
The soluble ethyl acetate portion was collected, and 2 mL was dried by SpeedVac. Dried compounds were dissolved in 3 mL of methanol. All dilutions were made in methanol. Naringenin and 8prenylnaringenin were quantified using a Vanquish UHPLC system (Thermo Scientific) equipped with a pump, autosampler, and photodiode array detector. The flow rate was 400 μL min -1 , of which two-thirds (266 μL min -1 ) was directed toward the mass spectrometer by a splitter behind the PDA detector. MS data were collected on a Velos Pro linear ion trap mass spectrometer (Thermo Scientific) equipped with a heated electrospray ionization (ESI) probe (Thermo Scientific). For UHPLC separation, the preheater was set to 45 °C, the column compartment heater was set to 45 °C, and the post-column cooler was set to 40 °C.
The column used was an Acquity UPLC BEH C18 (150 × 2. 1 mm inner diameter, 1. 7 μm) with a VanGuard guard column (5 × 2. 1 mm inner diameter, 1. 7 μm) of the same material (Waters, Milford, MA, U. S. A. ). Eluents used were water (A) and ACN (B), both with 0. 1% (v/v) FA. The elution program was started by running isocratically at 10% B for 1. 09 min, followed by 1. 09-7. 45   2 μm ori, AmpR, HIS3, TEF1 P -tHMG1-TEF1 T , GAP1 P -coSf FPT-CYC1 T this study pUDE188 template for TEF1 T -AmpR-2 μm, GAP P -TEF1 P , 2 μm-HIS3-CYC1 T 26 pUDI069 template for coTAL1 26 its starting composition in 1. 09 min, followed by equilibration for 5. 41 min. Detection wavelengths for ultraviolet-visible (UV-vis) were set to the range of 190-680 nm, and data were recorded at 20 Hz. Full MS data were collected in negative ionization mode over the m/z range of 200-800.
Additionally, selected ion monitoring (SIM) was performed for naringenin (m/z 271) and 8-prenylnaringenin (m/ z 339) with an isolation width of 1. 0. The source conditions used were a capillary temperature of 254 °C, source heater temperature of 408 °C, source voltage of 2. 5 kV, and S-lens RF level of 68. 85. Nitrogen was used as sheath gas (50 arbitrary units), auxiliary gas (13 arbitrary units), and sweep gas (2 arbitrary units). Data were processed using Xcalibur 4. 1 (Thermo Scientific). Naringenin was quantified on the basis of UV absorbance at 280 nm with a calibration curve of the standard ranging from 0. 1 to 50 μg mL -1 in MeOH (R 2 = 0. 9997). 8-Prenylnaringenin was quantified on the basis of SIM with a calibration curve of the standard ranging from 0. 01 to 1. 0 μg mL -1 in MeOH (R 2 = 0.
This relationship is then used to retrieve the density from satellite observations, which are measured by the Advanced Microwave Scanning Radiometer -Earth observing system (AMSR-E) instrument. In situ measurements of snow properties are used as input for the DMRT-ML model. Active microwave observations and in situ measurements of surface snow density are used to validate the method. Section 2 presents remote sensing and in situ data and the microwave emission model. Section 3 presents some elements of the theoretical background of microwave radiative transfer, and Sect. 4 presents the method of retrieving the surface snow density. Section 5 presents the time series of the retrieved surface snow density and its validation and analysis.
Passive microwave data Passive microwave satellite observations were acquired at 18. 7 and 36. 5 GHz by the AMSR-E instrument on-board the Aqua satellite in dual-polarization mode with an observation zenith angle of 54. 8 • over the entirety of the instrument's lifetime, i. e. from 18 June 2002 to 4 October 2011. Daily averaged brightness temperatures (T B ) at Dome C are extracted from the "AMSR-E/Aqua Daily L3 25 km Brightness Temperature & Sea Ice Concentration Polar Grids, Version 3" provided by the National Snow and Ice Data Center (NSIDC,  Cavalieri et al. , 2014). The pixel size is 25 km × 25 km and the total sensor error is around 0. 6 K. The dataset contains, for every day, the mean of the daily averaged ascending orbits and daily averaged descending orbits. This typically represents seven overpasses per day at Dome C.
Active microwave data Two complementary datasets of active microwave observations are used for comparison with the retrieved snow density. The first dataset was acquired by the nadir-looking Radar Altimeter 2 (RA-2) instrument on-board the ENVIronment SATellite (ENVISAT) at 13. 6 GHz (Ku band). The dataset contains the total backscatter power from 12 March 2002 to 8 April 2012 based on along-track data and the Ice-2 retracking algorithm  (Legrésy et al. , 2005; Rémy et al. , 2014). Radar backscatter corresponds to the ratio between the energy emitted by the satellite and the energy reflected by the snow surface and is expressed in decibels. Due to the 35 days of ENVISAT revisit time and the selection of four orbit tracks that are less than 17 km away from Concordia station, temporal sampling of RA-2 observations is typically 8-10 days.
The along-track spatial sampling over the Antarctic Ice Sheet is 330 m, and its typical footprint is of the order of 5-10 km  (Rémy and Parouty, 2009). The sensor precision for determining surface elevation is 47 cm. The second dataset of active microwave observations was acquired by the SeaWinds instrument on-board the Quick SCATterometer (QuikSCAT) at 13. 4 GHz in dualpolarization mode. Zenith viewing angles are 46 and 54. 1 • for horizontal and vertical polarizations, respectively. Normalized radar cross sections σ 0 were extracted at Dome C from the "egg images of SeaWinds on QuikSCAT Enhanced Spatial Resolution Image Sigma-0 Products -version 2" provided by the National Aeronautics and Space Administration -Scatterometer Climate Record Pathfinder (http://www. scp. byu. edu/data/Quikscat/SIRv2/Quikscat_sirV2.
html, last access: 8 April 2019,  Long and Daum, 1998; Early and Long, 2001; Long et al. , 2001). The pixel size is 2. 225 km × 2. 225 km and the relative error is 0. 2 dB  (Spencer et al. , 2000; Early and Long, 2001). The dataset contains the daily averaged backscatter coefficient by combining multiple passes of QuikSCAT (near-polar orbit) from 19 July 1999 to 23 November 2009.
Surface snow density Three time series of snow density were measured at Dome C. The first set of measurements is from the CALVA programme (CALibration-VAlidation of climate models and satellite retrieval, Antarctic coast to Dome C). Surface snow density was measured every 3 to 5 days from 3 February 2010 to 4 October 2011 by using a cylinder cutter (10 cm length and diameter of 5 cm) inserted horizontally so that the cutter top grazes the surface. The value used here is the average of three measurements. The two other sets of measurements are from the project MAPME (Monitoring of Antarctic Plateau by means of Multi-Frequency Microwave Emission) funded by PNRA (Programma Nazionale di Ricerche in Antartide). The second dataset contains the snow density measured from 9 May 2008 to 4 October 2011 by using a cylinder cutter of 25 cm height and a diameter of 4.
5 cm. Measurements were performed at 0. 1 m depth every 15 days in a snow stake network composed of eight poles placed 10 m apart. The value used here is the average of eight measurements. The last dataset contains the snow density measured in snow pits, which were excavated every month from 18 December 2007 to 4 October 2011. Density values were collected every 10 cm in the 0-1 m depth range. A single measurement was collected at each depth of the snow pit. The mean density value is 329, 344, and 321 kg m -3 , respectively, for the CALVA dataset and the two PNRA datasets. The standard deviation is, respectively, 43, 40, and 54 kg m -3. The density is representative of the topmost 5 cm of the snowpack for the CALVA dataset and of the top 10 cm for the two PNRA datasets. Uncertainties associated with the three datasets are equal to or higher (due to the low cohesion of surface snow) than the uncertainty estimated for classic density measurements (11 %,  Conger and McClung, 2009).
Snow density profile The snow density profile (Fig. 1 ) is considered constant and is composed of two different datasets. The first dataset was measured in January 2010 by determining the mass and volume of snow and firn core samples every 5 cm, collected up to 20 m depth. However, the cohesion of the first 0. 3 m of the snow core was very low and impossible to sample correctly. Thus, a second dataset was measured in December 2010 to complete the snow density profile from the surface down to 0. 3 m depth. This second dataset is an average of 13 short density profiles (up to 0. 5 m depth) measured in a snow pit with a dedicated cutter for Antarctic snow  (Gallet et al. , 2011). The snow core and snow pit density profiles below 0. 3 m depth are close together. Indeed, the mean and standard deviation of both profiles (0-0.
5 m deep) are about 340 ± 8 kg m -3 for the snow core dataset and about 355 ± 21 kg m -3 for the snow pit dataset. In addition, the overlapping part (between 0. 3 and 0. 5 m depth) shows similar values. Uncertainties associated with both methods are similar: at least 10 % for measurements of snow and firn core samples  (Arthern et al. , 2010)  and at least 11 % for snow cutter measurements in a snow pit  (Conger and McClung, 2009).
Comparison with in situ measurements Figure  6  shows the time series of ρ sat and the three time series of the in situ surface snow density. We obtain a remarkable agreement even if different daily and weekly variations are observed. The range of observed density is 150-425 kg m -3 for in situ measurements and satellite estimation during the overlap period. From February 2010 to Octo-N. Champollion et al. :  Decrease in the snow density near the surface at Dome C ber 2011 (when all datasets are available), the mean surface snow density is 339. 8 ± 58. 8, 304. 2 ± 48. 7, 346. 4 ± 43. 9 and  303. 4 ± 57. 6 kg m -3 , respectively, for the satellite, CALVA measurements, PNRA stake, and PNRA pit measurements. The datasets are consistent with one another and the mean values are within the uncertainty range.
However, we observe three notable differences: (1) measurements in snow pits are regularly lower by 35-40 kg m -3 than satellite and stake estimates, (2) spatial variability of snow density (41. 6 kg m -3 ) is of the same order of magnitude as the differences between the mean value of datasets (higher difference is 43 kg m -3 ), and (3) satellite density (standard deviation is 63. 5 kg m -3 ) is generally more variable than the in situ measurements (standard deviations are 43, 40, and 54 kg m -3 ). The last observation (3) could be the result of the approximative thickness of the retrieved satellite surface snow density (2-5 cm) compared with the in situ measurements (about 5 cm for CALVA measurements and 10 cm for PNRA measurements). The four time series of surface snow density show a pluriannual decreasing trend.
The linear trends during the common period (February 2010 to October 2011) for the four time series are of the same order of magnitude, between -20 and -40 kg m -3 yr -1. The trend over 10 years of the satellite-retrieved density is -13 kg m -3 yr -1 and the trend over 4 years of the two PNRA in situ datasets are -6 and -8 kg m -3 yr -1.
Uncertainty assessment We use here the signal-to-noise ratio (SNR) to characterize the significance of our results. SNR is the ratio between the mean of the observed data over the standard deviation of the background noise. In our time series of surface snow density, we assume that the standard deviation of quick variations to be noise even though part of it may be a natural signal. That gives an upper limit of the noise. We found a SNR of 5. 9. This value is high enough to conclude that a real signal emerges from the noise, and thus the negative trend of surface snow density is significant at Dome C. Furthermore, the spatial variability of surface snow density (41. 6 kg m -3 ) was measured near Concordia Station, which is smaller than the standard deviation of the retrieved density (63. 5 kg m -3 ).
, 2013; Libois et al. , 2014). The retrieved time series of surface snow density has been compared to data available in the literature and to in situ measurements. All data agreed on the range of surface density values. Hoar crystals have a density between 125 and 178 kg m -3 and, when absent, surface snow density varies between 150 and 520 kg m -3 according to in situ measurements and between 136 and 508 kg m -3 for satellite-retrieved density. In situ measurements show the same negative trend as the satellite observations of about 10 kg m -3 yr -1. The pluri-annual decrease in surface snow density is also visible in active microwave observations (radar backscatter coefficient extracted from ENVISAT-RA-2 and radar polarization ratio calculated from SeaWinds-QuikSCAT). As a result, SeaWinds-QuikSCAT observations also show a probable absence of evolution of the surface roughness.
An overestimation of the decrease in surface snow density can, however, not be totally excluded, especially if surface roughness and/or density stratification in the snowpack increase in parallel with time. Thanks to the passive microwave observations that are allweather, daily, and available for more than 30 years, this method can potentially be applied to the whole Antarctic Ice Sheet, after addressing the following issues: (1) the inclusion of surface roughness, which can be more significant in other regions than at Dome C  (Adodo et al. , 2018) , and the snow at depth in the retrieval method; (2) the inclusion of the regions where melt occurs  (Picard et al. , 2007) ; and (3) the inter-calibration of the different passive microwave satellites. Code and data availability. References describing the individual satellite datasets are provided in the method section.
DMRT-ML model code is available at http://pp. ige-grenoble. fr/pageperso/ picardgh/dmrtml/ (last access: 8 April 2019). The snow property profiles, the surface snow density observations, and the retrieved surface snow density are publicly available online through Zenodo at https://doi. org/10. 5281/zenodo. 2590150 (Champollion, 2019). Author contributions. GP, MF, LA, and NC conceived the study. NC performed the simulation, collected and interpreted QuikSCAT data, carried out the data analysis, interpreted the results, and wrote the manuscript with the important help of GP. GP collected AMSR-E data and is the main developer of the DMRT-ML model. LA and EL measured the snow temperature profiles. LA performed the measurements devoted to the study of the surface snow density variability. NC, GP, LA, GM, and EL participated to the collection of all other in situ measurements.
Acknowledgements. AMSR-E, RA-2 and QuikSCAT data were, respectively, obtained from the  National Snow and Ice Data Center (NSIDC) at the University of Colorado , the  Centre for Topographic studies of the Ocean and Hydrosphere (CTOH)  supported by the  European Space Agency (ESA)   ENVISAT-RA-2  programme, and from the  NASA -sponsored scatterometer climate record pathfinder (SCP) at  Brigham Young University. We are very grateful to  Ilhan Bourgeois ,  Sylvain Lafont , and  Sébastien Aubin  for making snow density measurements at Dome C during the polar winter. Field measurements around Concordia Station were made possible by the joint  French-Italian Concordia Program , which established and currently runs the permanent Concordia station at Dome C. We warmly thank the  French polar institute (Institut Paul Emile Victor, IPEV)  and the  Italian polar programme (Programma Nazionale di Ricerca in Antartide, PNRA).
Finally, this work was supported by the  Programme National de Télédétection Spatiale (PNTS) , the  IPEV programmes (IPEV-CALVA  and  IPEV-GLACIOLOGIE)  and the project  MAPME (Monitoring of Antarctic Plateau by means of Multi-Frequency Microwave Emission ) funded by  PNRA. We are grateful to the two reviewers and the editor, whose comments and discussion improved and clarified the manuscript. The article processing charges for this open-access publication were covered by the  University of Bremen. Review statement. This paper was edited by  Martin Schneebeli  and reviewed by  Zoe Courville  and one anonymous referee.
Acknowledgments. We would like to thank  Dr. Hans Hagdorn  ( Muschelkalkmuseum Ingelfingen, Germany ) for providing the studied samples from the  Maantang Formation. We are extremely grateful to  Dr. Maria-Cristina Cabral  ( University of Lisbon, Portugal ) for her expertise and fruitful taxonomic discussion on Schuleridea. We thank  Prof. Alan Lord  ( Senckenberg Forschungsinstitut, Frankfurt-am-Main, Germany ) and  Dr Emőke Tóth  ( Eötvös Loránd University, Budapest, Hungary ) for their constructive remarks that greatly improved an earlier version of this contribution.
The HRTPs are retrieved with a very good vertical resolution of ∼ 200 m and high precision (random uncertainty) of ∼ 1-3 K for altitudes of 15-32 km and with a global coverage. The best accuracy is achieved for in-orbital-plane occultations, and the precision weakly depends on star brightness. The whole GO-MOS dataset has been processed with the improved HRTP inversion algorithm using the FMI's scientific processor; and the dataset (HRTP FSP v1)  is in open access. The validation of small-scale fluctuations in the retrieved HRTPs is performed via comparison of vertical wavenumber spectra of temperature fluctuations in HRTPs and in collocated radiosonde data. We found that the spectral features of temperature fluctuations are very similar in HRTPs and collocated radiosonde temperature profiles.
It has been shown that the small-scale fluctuations in HRTPs have similar rms values as in collocated radiosonde profiles, for vertical (in orbital plane) occultations of bright stars  (Sofieva et al. , 2009c). In the case of oblique occultations or dim stars, the HRTP fluctuations are of larger amplitude than those of in situ measurements. An analogous study with the method of  Sofieva et al. (2009c)  but applied to much larger datasets of GOMOS HRTP IPF v6 and collocated radiosonde profiles has shown that fluctuations in HRTP v6 are nearly always larger than in the collocated radiosonde data (for both vertical and oblique occultations). Therefore, IPF v6 HRTP data were not recommended for gravity wave analyses. This will be illustrated and discussed in Sect. 4 of our paper. In this paper, we introduce an improved version of the GO-MOS HRTP algorithm and present the whole GOMOS HRTP dataset processed using the FMI's scientific processor, HRTP FSP v1.
The weak singularity of the integrand at the lower limit does not cause problems for a numerical realization: the singularity can be estimated or the midpoint product integration method can be applied. The upper limit should be chosen high enough (∼ 120 km); therefore the refractive angle profile calculated using ECMWF analyses and MSIS90  (Hedin, 1991)  data is used at altitudes above the HRTP range in the processing. Application of Abel integration requires a monotonic impact parameter. This requirement can be violated because the impact parameter is computed using the measured (noisy) refractive angle. In the current implementation, the impact parameter is computed using the smoothed refractive angle and its monotonicity is checked. Real geometric (tangent) altitudes can be determined as h = p n(p) -R, ( 15 ) where R is the local radius of curvature of the Earth surface.
Retrieved HRTPs, their characterization and validation Examples of retrieved GOMOS high-resolution temperature profiles are shown in Figs. 7 and 8 by red lines with 1σ uncertainties (shaded area; all uncertainties presented in our paper are 1σ ). In Fig. 7 , HRTPs are for bright stars in vertical occultations (the best data quality), while in Fig. 8  other occultations are illustrated (oblique or of dim stars). These temperature profiles are collocated with high-resolution radiosonde data from the SPARC data center (https://www. sparc-climate. org/data-centre/data-access/ us-radiosonde/, last access: 21 January 2019). In our illustrations and validation analyses, we use radiosonde data from the US high-resolution radiosonde archive, which contains data from 93 US-operated stations from the years 1998 to 2011.
The uncertainty of the retrievals depends weakly on star brightness; it is only noticeably larger for occultations of dim stars with visual magnitude m > 2. 5. In this paper, we focus on the validation of small-scale fluctuations in HRTPs, as the HRTP vertical resolution allows gravity waves to be probed. Following  Sofieva et al. (2008 Sofieva et al. ( , 2009c)) , we use spectral analysis for validation of the small-scale structure in temperature profiles, as this approach allows measurements to be used that are separated by several hundreds of kilometers and by several hours. This study applies the method by  Sofieva et al. (2009c)  to much larger datasets of HRTPs and collocated radiosonde profiles. In our illustrations, the 1 s data from the SPARC data center archive, which is described above, are used.
However the US 6 s data and other high-resolution radiosonde datasets obtained from NDACC, NILU, SHADOZ and the Sodankylä radiosonde station are also analyzed and they show similar results. The collocated HRTP and radiosonde data are selected using 300 km and a 4 h space-time window. With this criterion, 5023 collocated profiles are found in the 6 s resolution dataset and 1070 in the 1 s resolution data. Among the collocated data, there are occultations of different types. Keeping in mind the results of the previous validation, we compared the rms values of temperature fluctuations in radiosonde temperature profiles with those in HRTPs, in the altitude range 18-30 km, for different occultation types, vertical (|β| < 5 • ), of medium obliqueness (5 • < β < 45 • ) and highly oblique (β > 45 • ), and for stars of different brightness, bright (visual magnitude m < 1), of medium brightness (1 < m < 2.
Illustrations of HRTP application: GW potential energy In this section, we show illustrations of applications of HRTPs for analyses of gravity waves. The spatiotemporal distributions are presented only to demonstrate that the new HRTP dataset provides valuable geophysical information, which is in agreement with analyses using other datasets. The gravity wave potential energy per unit mass is defined as E P = 1 2 g 2 N 2 δT T s 2 , (20) where δT T s is the relative temperature fluctuations with respect to the smooth profile T s , and g is the acceleration of gravity. In our analysis, smooth profile T s is obtained by smoothing HRTPs down to 4 km resolution. The Brunt-Väisälä frequency N 2 is estimated using the smoothed HRTP. The GW potential energy has been evaluated for each temperature profile in the altitude range 20-30 km.