dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation in terms of Data Availability All the three models considered in the current study rely on time series of images to retrieve soil moisture. It is thus desirable to know how the number of images available impact the retrieval accuracy. Soil moisture is retrieved with different window size ranging from 3 to 30 at an interval of 3. Similar window size is used to retrieve soil moisture from all the three models considered and for all the four polarization. Figure  9  shows the RMSE for the three model considered and for all the four polarization. Delta Index (DI) model performed relatively poor for all the four polarizations. Change detection (CD) and CT model showed similar performance at higher window size (more than 12 images). CT model outperformed the CD method for smaller window size (less than 12), which could be due to the fact that for CD only two data points are used for normalization while for CT entire time series is used for normalization.
Validation at SMOS Scale The soil moisture retrieved from RADARSAT-2 is up-scaled to the coarse scale to compare with the soil moisture from SMOS. The up-scaling is performed for the eight strategies mentioned in Table  2. The correlation coefficient and RMSE for the comparison are shown in Figure  13. To analyze the impact of SMOS pass direction (ascending/descending) the comparison is performed separately for only ascending pass data, for only descending pass data and for all the passes. The descending pass of SMOS showed a better comparison (a relatively higher correlation coefficient and a relatively lower RMSE) for all the eight strategies. Strategy number 3, 4, 7 and 8 outperformed the other strategies (a relatively higher correlation coefficient and a relatively lower RMSE) and the difference among these four best strategies (3, 4, 7 and 8) is very minimal.
The impact of various source of spatial heterogeneities (soil texture, land cover and mean antenna foot print) is tested on the soil moisture aggregation from the scale of RADARSAT-2 to the scale of SMOS. It is found that there is significant impact of heterogeneity on the aggregation. Up-scaling with land-cover is impacting most with RMSE of approximately 0. 05 and correlation coefficient of approximately 0. 9. This study highlights the importance of testing various spatial heterogeneities on the aggregation scheme. The impact of land cover on the aggregation can not be generalized for other regions as it depends on several factors. The quality of the coarse scale microwave data from SMOS depends on many effects like the presence of Radio frequency interference, the surface of water bodies in the area,the freezing of the soil, the presence of snow, the distance from the coast and the density of the vegetation cover  [40, 68].
Acknowledgments RADARSAT-2 data procured under RISAT-1 (Radar Imaging SATellite) utilization programme of ISRO is gratefully acknowledge. The authors acknowledges  European Space Agency  for the SMOSL2 dataset. The authors are also thankful for partial financial support received under the IISc- STC  project  (ISTC/CCE/MS/214 ). This work was also supported by the  VIGISAT-CLS  project which provided the Radarsat-2 satellite dataset from 2012 to 2014. The funding from  CEFIPRA  project  4700-W1  for salary of project personnel and field studies is acknowledged. Further funding from ISRO0198 for equipment and field measurements is also acknowledged. The authors would like to thank the help provided during field campaigns by the project staff  Mr. P. Giriraj  and by the farmers.
The transmitter-receiver distance was determined with measuring tape, and recording was triggered by the direct air wave. The latter is not ideal, and can be improved by using fiber-optic cables. Processing of the radar data included horizontal alignment of the first arrivals (a. k. a. t 0 correction), dewow filtering, Ormsby bandpass filtering, and the application of a depth-variable gain. Because triggering was done with the direct air wave, a static time shift was added to each trace to account for the delayed arrival of the air wave for increasing offsets. In multi-offset surveys, the traveltime of internal reflectors increases hyperbolically with increasing offset (e. g. ,  Dix, 1955) , while the surface wave (traveling in the firn column directly from transmitter to receiver) has a linear moveout.
The WARR data presented here were collected with a 10 MHz radar. The disadvantage of this low frequency is that fewer reflectors above the firn-ice transition can be picked at this low resolution, relative to higher-frequency data sets (cf. Eisen et al. , 2002  who derived an 8 % velocity error with a 25 MHz radar versus a 2 % error with 200 MHz radar). We found that the method applied here can cope with the picking uncertainties at 10 MHz, whereas using Dix inversion frequently resulted in interval densities much larger than the pure ice density. The advantage of using a 10 MHz radar is that the entire ice column is illuminated, including the unambiguous basal reflector. This opens up the possibility for more sophisticated radar-wave velocity models including ice anisotropy originating from aligned crystal orientation fabric below the firn-ice transition  (Drews et al.
Acknowledgements. This paper forms a contribution to the  Belgian Research Programme on the Antarctic (Belgian Federal Science Policy Office) , project  SD/SA/06A "Constraining ice mass changes in Antarctica" (IceCon) , as well as the  FNRS-FRFC  ( Fonds de la Recherche Scientifique ) project  IDyRA. We thank the  InBev Baillet Latour Antarctica Fellowship  for financing the RBIS fieldwork and the  International Polar Foundation  for providing all required logistics in the field. We thank in particular  A. Hubert ,  K. Moerman ,  K. Soete , and  L. Favier  for support in the field. Morgane Philippe is partially funded through a grant from the " Fonds David et Alice Van Buuren ". The comments of two reviewers and the editor  R. Bingham  have improved the initial version of this manuscript. A version of the code is accessible on request and on GitHub (https://github.
The aim of the present study is to estimate the export fluxes of major dissolved species at the scale of the Amazon basin, to identify the main parameters controlling their spatial distribution and to identify the role of discharge variability in the variability of the total dissolved solid (TDS) flux through the hydrological cycle. Data are compiled from the monthly hydrochemistry and daily discharge database of the " Programa Climatologico y Hidrologico de la Cuenca Amazonica de Bolivia" (PHICAB) and the HYBAM observatories from 34 stations distributed over the Amazon basin (for the 1983-1992 and 2000-2012 periods,  respectively). This paper consists of a first global observation of the fluxes and temporal dynamics of each geomorphological domain of the Amazon basin. Based on mean interannual monthly flux calculations, we estimated that the Amazon basin delivered approximately 272 × 10 t year (263-278) of TDS during the 2003-2012 period, which represents approximately 7 % of the continental inputs to the oceans.
AQ7 The present paper is a complementary study of  Moquet et al. ( 2011 )  and  Sanchez et al. ( 2015 )  and includes the database previously used by these authors. These studies estimate the weathering budget of the Andean basins  (Moquet et al. 2011  ) and report estimates of dissolved load exports at the scale of the main tributaries  (Sanchez et al. 2015 ). In the present study, we expanded our analysis to all the available contents of the Geodynamical, hydrological and biogeochemical control of erosion/alteration and material transport in the Amazon, Orinoco and Congo basins-Environmental Research Observatory ( HYBAM observatory) and Programa Climatologico y Hidrologico de la Cuenca Amazonica de Bolivia (PHICAB) databases (>3,000 samples analysed for major elements over 34 gauging stations) at the outlet of the main geomorphological domains of the Amazon basin.
1982 ; Konhauser et al. 1994 ). The hydrological network of the Amazon basin is organized according to five main tributaries: the Negro, Solimões, Madeira, Tapajós and Xingu Rivers. The following water budget and areas are compared with the corresponding number after the confluence of the Amazon, Tapajos and Xingu Rivers. It is based on the 2003-2012 discharge at the HYBAM observatory database at the outlet stations of these rivers reported in Table  2  and by  Getirana et al. ( 2010 )  for the Negro River budget  (1997-2006 period). The Negro River, near Manaus City (~17 % of the Amazon discharge and 13 % of its area;  Southern American Monsoon System (SAMS). It depends on the seasonal variation of continent-ocean contrast in temperature and the seasonal migration of the intertropical convergence zone (ITCZ) southward (northward) during austral summer (winter)  (Marengo 2004 ; Marengo et al.
1997 ; Tardy et al. 2005 ; Bustillo et al. 2011 ). For example,  Tardy et al. ( 2005 )  showed that the mean half-lives of water in the Amazon basin are approximately 22. 2, 32. 6 and 60. 5 days for superficial runoff, hypodermic and groundwater reservoirs which annually contribute 29. 5, 47. 8 and 22. 7 % of the Amazon discharge, respectively. Recently, based on hydrological models,  Paiva et al. ( 2013 )  showed that surface waters govern most of the monthly terrestrial water storage changes (56 %), followed by soil water (27 %) and ground water (8 %). In the channel, as the velocity of surface water ranges between 1 and 2 m s (SO-HYBAM gauging database), the mean transit time from the Andes to the outlet of the basin (approximately 6,000 km) can be morehigher than 1 month.
Material and methods In this study, we used the available data of the PHICAB (Programa Climatologico y Hidrologico de la Cuenca Amazonica de Bolivia) and HYBAM observatory (Geodynamical, hydrological and biogeochemical control of erosion/alteration and material transport in the Amazon, Orinoco and Congo basins-Environmental Research Observatory) frameworks (the HYBAM data are freely available at the HYBAM observatory website, http://www. ore-hybam. org , and the PHICAB database is available in  Guyot 1993 ). The PHICAB framework collected hydrochemical data at various gauging stations of the Upper Madeira basin between 1983 and 1992. The HYBAM framework has collected hydrochemical data since 2000. In the present study, we use the available data corresponding to the 2000-2012 period in the Solimões basin, at three stations in the Madeira basin and at Brazilian stations on the Negro, Amazon and Tapajós Rivers (Table  2  ).
Hydrological data Water levels were collected daily or twice daily using a conventional hydrologic method. During the PHICAB period  (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) , gauging was accomplished using punctual velocity measurements with a mechanical current metre over the whole river section  (Guyot 1993 ). For the 2000-2012 period, within the framework of the HYBAM programobservatory ( http://www. ore-hybam. org ), gauging was accomplished using a 600-kHz (Peru, Bolivia and Brazil) or 1,200-kHz (Ecuador) acoustic Doppler current profiler (ADCP). The daily discharge series was then calculated from rating curves (discharge-water level relationship) using the Hydraccess software  (Vauchel 2005  ; free download at the SO-HYBAM website).
The samples collected during the PHICAB program were analysed according to the analytical procedure described in  Guyot ( 1993 ) : alkalinity was measured by acid titration, Cl by mercuric thiocyanate colorimetry, SO by barium sulfate nephelometry, Ca and Mg by atomic absorption, Na and K by flame spectrometry and Si by ammonium molybdate colourimetry. The samples collected during the HYBAM program were analysed according to the procedure described in  Cochonneau et al. ( 2006 ). Cl and SO concentrations were measured by ion chromatography, and Na , Ca , Mg , K and Si were analysed by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Alkalinity was determined by acid titration. The TDS concentration corresponds to the sum of the cations (Ca, Mg, Na and K), the anions (HCO , SO and Cl) and SiO concentrations, all expressed in milligram per litre.
Based on the analyses of geostandards, the analytical error is less than 10 % for both sampling periods. We observed that TDS and conductivity are significantly correlated (r = 0. 86; p values < 0. 01; N = 1,782), considering the whole database, with the exception of one outlier (when conductivity or TDS > average + 3σ; ABA station): Here, the error is defined as the mean averagebsolute error (MAE) of the relationship. In the present study, we use the conductivity as a proxy for TDS concentration to describe the concentration (C) vs discharge (Q) relationship because of the higher frequency of conductivity acquisition (10 days), allowing a precise characterization of the total solute dynamics throughout the hydrological year.
Annual flux calculation The Amazon TDS flux corresponds to the sum of the inputs of the Amazon River at the OBI station, the Tapajós River at the ITA station and the Xingu River at the ALT station. To calculate the annual flux of each solute at each station, we first estimated the monthly flux based on the multiplication of the instantaneous concentrations obtained at a monthly period by the monthly discharge calculated from the sum of daily discharges (method M1C in Table  S1  of the supplementary material). We then calculated the interannual average of monthly fluxes. We finally added the 12 monthly flux averages to evaluate the mean annual flux. At two stations (the OBI and ITA stations), we also performed three other annual flux calculations for TDS (Table  S1  ) to compare the effect of the flux calculation methods based on the same dataset in the period 2003-2012.
The results are presented in Table  S2. The method M1A corresponds to the mean concentration of the overall data multiplied by the mean monthly discharge; then, we added the 12 monthly flux averages to evaluate the mean annual flux. The method M1B corresponds to the monthly concentration interannual average multiplied by the mean monthly discharge interannual average; then, we added the 12 monthly flux averages to evaluate the mean annual flux. The method M2A corresponds to the application of the C = aQ relationship (see the following section) based on the daily discharge database. The daily flux is then calculated based on the multiplication of Cj and Qj, and the annual flux corresponds to the sum of the daily fluxes. The error associated with these estimates corresponds to the sum of the analytical error (<10 %) and the error associated with the discharge measurements.
Hydrochemical characteristics The description presented hereafter is based on the PHICAB and HYBAM monitoring networks and partly summarizes results already reported in  Moquet et al. ( 2011 ) , for the Andean tributaries, and in  Sanchez et al. ( 2015 ) , for some of the downstream stations. Even if slight differences in the mean concentration values are observed between the present study and the previous ones due to the actualization of the database, the values measured in both studies are commensurate. Hydrochemical characteristics are reported in Table  3  and are illustrated in Fig. 3. The pH of the river water samples ranges between 4. 8 and 7. 9 (median value for each gauging stations). The highest value is recorded in the semi-arid basin of the Grande River (ABA station, Upper Madeira River), which is characterized by significant carbonates and Neogene evaporites  (Moquet et al.
Annual flux calculation The database allows us to estimate the solute fluxes from the different domains of the Amazon basin. The Amazon basin (Amazon flux at OBI + Tapajós Basin at ITA + Xingu basin at ALT) produced approximately 272 (±54) × 10 t TDS year during the period 2003-2012. Using out four calculation methods (Table  S1  ) based on the same database, results range between 263 and 278 × 10 t TDS year (Supplementary material S2 ). The Andean tributaries (Napo, Marañon, Ucayali, Madre de Dios, Beni and Mamoré Rivers) contribute approximately 64 % of the Amazon TDS flux for only 26 % of the discharge and 25 % of the basin area (Fig. 7  ). The central plain (37 % of the basin area) and the shield tributaries (37 % of the basin area) contribute approximately 25 and 11 % of the TDS flux, respectively (Table  4  and Fig.
Acknowledgments AQ35 We especially thank  Daniel Ibarra  ( Stanford University ) for his constructive recommendations under the review process. We also especially thank  Dr. Julien Bouchez  ( CNRS-IPGP ) for insightful discussions and his help to improve the manuscript. This work was funded by the  French Institut de Recherche pour le Développement (IRD)  and the  French Institut des Sciences de l'Univers (INSU)  through the  SO-HYBAM Observatory. We especially thank  Pascal Fraizy ,  Philippe Vauchel ,  William Santini ,  Elisa Armijos , Francis.
Experimental data and simulations 2. 1 Experimental setup The data analysed here using beams of positively charged hadrons were collected at CERN in 2007 and at FNAL in 2009. The CALICE setup at CERN is described in detail in Ref. [13]  and comprised the silicon-tungsten electromagnetic calorimeter (Si-W ECAL), the Fe-AHCAL, and the scintillator-steel tail catcher and muon tracker (TCMT). Beams of positive hadrons in the momentum range from 30 to 80 GeV were delivered with the CERN SPS H6 beam line. The CALICE setup during the test beam campaign at FNAL is described in detail in Ref. [15]  and comprised the Fe-AHCAL and TCMT, without any electromagnetic calorimeter in front of the Fe-AHCAL. The data at FNAL were collected for positive hadrons with initial momenta of 10 and 15 GeV from the MTest beam line.
Data collected with normal incidence of the beam with respect to the calorimeter front plane are used for the current analysis. The Si-W ECAL is a highly granular sampling electromagnetic calorimeter  [16]  comprised of 30 layers and constructed from three sections with different absorber thicknesses. The transverse size of its active zone is 18×18 cm 2. The Si-W ECAL has a depth of one nuclear interaction length at normal incidence and a very fine transverse segmentation equivalent to 1×1 cm 2 cells. The Fe-AHCAL is a sampling structure of 38 active layers interleaved with absorber plates (21 mm of stainless steel per layer). The full transverse size of the calorimeter is 90×90 cm 2. Each active layer is assembled from 5 mm thick scintillator tiles of varied transverse sizes: 3×3 cm 2 in the central part, 6×6 cm 2 in the surrounding region, and 12×12 cm 2 in the peripheral region.
For the positive hadron test beam data analysed here, the gas pressure was set between the pion and proton thresholds. The information from the Cerenkov counter was not used in the trigger decision during the data acquisition but was recorded for each event in the data set. This information was used for offline discrimination between pions and protons on an event-by-event basis. The visible signal in each calorimeter cell is obtained in units of minimum-ionising particle (MIP). Only cells with a signal above 0. 5 MIP were considered for further analysis; a cell above threshold is called hit. The spatial position of each calorimeter cell is defined in the right-handed Cartesian coordinate system with the z-axis oriented along the beam direction, that is perpendicular to the calorimeter front plane, and the y-axis pointing up when looking along the beam direction.
Monte Carlo simulations Simulations were done using the physics lists QGSP_BERT and FTFP_BERT from GEANT4 version 9. 6 patch 1  [14, 18]. The physics list QGSP_BERT is widely used for simulation in the LHC experiments and has demonstrated the best agreement with data in earlier versions, for instance in the version 9. 2  [19]. The QGSP_BERT physics list is maintained due to its wide use. The physics list FTFP_BERT was significantly improved in version 9. 6 and is now recommended for HEP simulations by the GEANT4 collaboration  [20]. The QGSP_BERT physics list employs the Bertini cascade model (BERT) below 9. 5 GeV, the quark-gluon string precompound model (QGSP) above 25 GeV, and the low energy parametrised model (LEP) in the intermediate energy region. The transition regions between models are from 9.
5 to 9. 9 GeV and from 12 to 25 GeV. The FTFP_BERT physics list uses the Bertini cascade model for low energies and the Fritiof precompound model (FTFP) for high energies with a transition region from 4 to 5 GeV. Separate samples of single pion and single proton events were simulated. The simulated samples were digitised taking into account the SiPM response, light crosstalk between neighbouring scintillator tiles in the same layer, and calorimeter noise extracted from data. The digitisation was validated using the electromagnetic response of the Fe-AHCAL  [21]. The test beam profile and its position on the calorimeter front face in each data run were reproduced in simulations.
The fraction of such events in the CERN test beam data analysed here was significantly smaller than in the FNAL data samples. A cross-check of the FNAL data with simulations and the CERN data for negative pions of the same energy showed that an additional selection is necessary to reject events with several incoming particles. The following additional constraints were applied to the FNAL data samples: events were rejected with more than 165 and more than 220 hits for 10 and 15 GeV samples, respectively. These selections reject less than 0. 2% Table  1. The total number of collected events; the fraction of rejected muon, positron, and double particle events; the number of selected pion and proton events for data samples used in the analysis; and the estimated purity of the selected proton samples.
Calorimeter response and p/π ratio The total energy deposited by a particle is reconstructed at the electromagnetic scale and expressed as E event = E HCAL + E TCMT for FNAL data, E track ECAL + E HCAL + E TCMT for CERN data, (3. 1) where E track ECAL , E HCAL , and E TCMT are the energies deposited in the Si-W ECAL, in the Fe-AHCAL, and in the TCMT, respectively. The deposited energies are obtained by multiplying the visible signal in units of MIP by a suitable conversion factor from MIP to GeV for each detector section. The dash-dotted lines correspond to the effective nuclear interaction lengths, calculated for the Fe-AHCAL using the material properties data from the PDG tables  [22]. The energy deposited in the Si-W ECAL is that of a minimum ionising particle, as the events are selected with the shower start at the beginning of the Fe-AHCAL.
Therefore, the measurable energy is different for pions and protons and corresponds to the total particle energy in the case of mesons and to the kinetic energy in the case of baryons E proton available = p 2 beam + m 2 proton -m proton , (3. 2) where p beam is the beam momentum and m proton is the proton rest mass. Figure  3  shows the ratios of the mean reconstructed energy to beam momentum and to the available energy for data and simulations with the FTFP_BERT and QGSP_BERT physics lists. The mean reconstructed energy is corrected for the contamination bias as described in Section 2. 5. The difference between positive pion and proton response to the available energy remains at the level of ∼5%, in agreement with the difference observed for the Sc-Fe Tile ATLAS calorimeter  [11]. The FTFP_BERT physics list gives better predictions of the response for pions than QGSP_BERT and very good predictions for protons.