dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment This work has been funded by the  ESA Cryo-SeaNice Project. All the SAMOSA+ data-sets presented in this paper have been computed with the SARvatore processing chain the ESA GPOD platform (https://gpod. eo. esa. int/). Close collaboration with the GPOD team has resulted in mutual progress that should benefit the entire community. We would like to thank them warmly, and especially  Giovanni Sabatino  who has very patiently responded to all our intensive requests. We also thanks the anonymous reviewers who have provided essential comments and guidelines to improve the content and the fluidity of this paper.
The method developed in this paper adapts this procedure. Its novelty lies in relying on information from remote global datasets rather than on local observational data to compute each indicator. The approach allows to compute each element of the Cowan's formula and therefore estimate the value of n. While land cover maps are extensively used to derive most of those terms, other sources of data are also used when more appropriate. The general output format for our roughness coefficients would be 2-dimensional maps for roughness coefficients in the floodplain and 1-dimensionnal along-centerline data point for roughness coefficients in the main channel. Indeed, while only raster-based products are used to derive roughness coefficients in the floodplain, vector-based products are also employed to https://doi.
Land cover maps The processing chain IOTA 2  (Inglada et al. , 2017)  allows the fully automatic generation of land cover maps at country scale and high resolution. The chain relies on satellite optical imagery (e. g. Landsat-8, Sentinel-2) and on existing land cover databases (e. g. Corine Land Cover, Randolph Glacier Inventory). Satellite imagery is collected over a complete year and pre-processed with temporal gapfilling and resampling techniques. Three spectral indices (NDVI, NDWI, brightness) are computed at every date to build the feature vector. The land cover databases are cleansed, filtered and merged to generate a reference dataset, that is defined with the targeted number of land cover classes. A first part of the reference dataset is used to train a supervised classifier, namely a Random Forest, while the second part is used to validate the land cover maps obtained with the trained classifier.
The chain has been successfully applied over France to generate land cover maps for multiple years already. Given the reference database used to train the IOTA 2 chain, the land cover map nomenclature will be different. Our methodology relies on 8 broader classes detailed in Table  2. In future applications, land cover maps should be aggregated to this reduced nomenclature to be later used in our chain. SoilGrids  (Poggio et al. , 2019)  is a system for global soil mapping. SoilGrids250m, its main product, provides, for 6 different depth-levels, global maps of soil properties at a resolution of 250 m. It allows to access at the pixel-level an estimation of the fraction of silt, sand and clay that constitutes the soil. The Global River Widths from Landsat or GRWL  (Allen and Pavelsky, 2018)  was built to characterize the global coverage of rivers and streams wider than 30 m.
One of the products offers a vector-based dataset of river centerlines, each segment being characterized by several properties including an estimation of the river width at the mean annual discharge. 3. Indicators and values of n b (bed material) for both the main channel and the floodplain. GlobalDatasetForRivers  (Frasson et al. , 2019)  took advantage of GRWL and the Shuttle Radar Topography Mission (SRTM) digital elevation model to create a global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge and is also distributed as a vector-based dataset of river centerlines. The next sections detail how the above products are used to derive each term of the Cowan formula.
Computation of n b n b represents the friction only due to the surface onto which water flows, for both the channel and the floodplain. Arcement and Schneider (1989)  directly relates the value of n b to the materials that constitute the soil surface or soil type, e. g. sand, concrete, firm soil, gravel, cobble, etc. Since SoilGrids250m provides the fraction of sand, silt and clay that composes the ground at the surface level, n b is derived from this database. The value of n b for each type of soil are then given in Table  3 , taken from  Arcement and Schneider (1989). The value of n b at any given location is computed as the weighted average of the adopted values of n b given the fraction of corresponding soil type. There are a few locations where SoilGrids provide no data. In this case n b is computed as the average of the three adopted values of n b values, equal to 0.
Method applicability at global scale This method aims at being appliable globally. This may be possible as most of the required data are publicly available and have global coverage, see Table  1. However we should first point out that the IOTA 2 products are currently only available over France. Thus generating land cover maps from the IOTA 2 chain on its own is mandatory for study areas outside France. Runnning the IOTA 2 chain is manageable but requires both training and availability of the required inputs. From our experiments (e. g. Po river, see Section 2. 2. 2 https://doi. org/10. 5194/hess-2021-551 Preprint. Discussion started: 3 December 2021 c Author(s) 2021. CC BY 4. 0 License. and Brahmaputra river, see Section 3. 2), it may be difficult to find cloud-free Sentinel 2 images which are required to compute Land-cover maps using the IOTA 2 chain.
Still, cloud-free Sentinel 2 images can be produced using, for example, the MAJA software  (Baetens et al. , 2019). The methodology also requires cross-sectionnal profiles to compute n 1 in the main channel. Such profiles are generally obtained locally from field surveys. Still, one could reconstruct profiles down to the lowest observed water level from remotelysensed products; by combining, for example, measurements of elevations -from nadir altimetry -simultaneously with watermasks -from optical imagery or even watermask processing chains such as SURFWater (Peña Luque et al. , 2021) -from which river widths can be extracted, similarly to approaches used in  Hostache et al. (2010) ;  Desrochers et al. (2020). Others approaches are possible, see the application over the Brahmaputra river in Section 3.
Garonne domain The study area consists in a 75-km-long reach of the Garonne river, from Toulouse to Castelsarrasin, see Figure  1. The Garonne river is the largest river in South-West of France, it drains an area of about 10,000 km 2 in Toulouse. The studied segment has an average slope of 0. 9 m/km, the main channel width ranges from 100 to 300 m and the floodplain width can reach 4 km. The topography of the reach is described by a set of 145 cross-sections. Each cross-section is the compound product of a local topographic survey achieved during the early 2000s (main channel) and an extraction from the French national Digital Elevation Model (DEM) RGE ALTI (floodplain). Discharge data has been collected over the year 2019 at multiple gauges, located either directly on the Garonne or on its main tributaries.
Water level data has also been collected over the same time period at a single gauge located in the middle of the reach (Verdun-sur-Garonne). Land cover maps of France obtained from the IOTA 2 chain are already freely available. The map latest version classifies land use in 23 different categories. This native nomenclature was deemed too high for developing the quasi-automatic procedure to estimate roughness coefficients. A new land cover map was therefore created by aggregating the original 23 classes down to the 8 macro-classes introduced in Tab 2. Table  10  shows how the original classes were mapped to the aggregated classes while.
Po domain The study area consists of a 96-km-long reach, from Cremona to Borgoforte, see Figure  5. The Po river is the largest river in Italy, it drains an area of about 50,000 km 2 in Cremona. The studied segment has an average slope of 0. 15 m/km and the main channel width ranges from 150 to 500 m. The floodplain is marked by the presence of two continuous dike systems, whose width ranges from 400 m to 4 km. The topography of the reach is described by a set of 91 cross-sections whose data points have been extracted from a 2005 2m-resolution DEM that was made available by the Po River Basin Authority (AdBPo). This DEM was obtained by a fusion of Lidar data for the floodplain and underwater sonar and ground survey data for the main channel. Discharge and water level data has been collected over the period 2016-2019 at multiple in situ gauges from https://simc.
arpae. it/dext3r/. Water level data has also been collected over the same time period at three virtual stations located on or very close to the Po river. This data has been downloaded from the online platform Hydroweb that makes available water level time series of continental waters (large lakes, reservoirs, major rivers) obtained after processing nadir altimetry measurements. Land cover maps generated by the IOTA 2 chain were not available for this study domain. The chain was therefore directly run to create a land cover map, using Corine Land Cover (CLC) 2018 as reference data for training. CLC was however not used as is, its classes were aggregated into the 8 meta-classes described in Table  2  (see the details of the aggregation in Appendix A). This resulted in the creation of a land cover map consistent with the one available for the Garonne study domain, see Figure  6.
Approach The roughness coefficient of a river portion is not a quantity that can be measured on the field. Because of that, the results generated by the method developed in this paper cannot be compared to any existing dataset. The method, however, generates roughness coefficients that can be used as input to design a hydraulic model, whose river-related outputs can be compared to observed data. The approach adopted to validate the method consists of two steps. First, a hydraulic model is required over a study domain whose characteristics are within the scope of SWOT. This model, either constructed for the sake of validation or obtained from another source, serves as a reference model. Its roughness coefficients must be set after a calibration procedure, whose complexity should be limited to reflect the global scope of the method.
Applications These new remote-data-fusion-based friction can serve various applications in hydrology. A first and obvious field of application would be to use these friction coefficients in one-dimensional shallow water models (such as HEC-RAS, Mascaret or DassFlow-1D amont others). A second field of application could be fine-scale 2D shallow water models for surveys or research on flood prediction. Indeed our chain does not only provide friction coefficients in the main channel and in the near floodplain, but could also be extended to the entire floodplain using the 2D maps of IOTA 2 and ancillary data (SoilGrids, etc. ). In this case the values computed by our chain could be used as-is or would require a small tuning. One must recall that the friction coefficients in shallow water models encompass the modeling errors and so may vary from on model to another and also to take into account modeling differences between one-dimensionnal and two-dimensional models.
Zone RMSE Z [m] Exp   14. Real-data experiment results before (left-hand side of the slash character) and after (right-hand side of the slash character) assimilation per zone. Performances are evaluated by RMSE for Z at observation gauges: free run RMSE (left-hand side of the slash character) are compared to analysis run RMSE (right-hand side of the slash character). the observations, as it would be complicated to assess the quality of updated roughness coefficient over unobserved part of the study domain. Better performances could be surely obtained by better tunning and setting the DA framework but such setup is out of the scope of the present study. Despite these conditions, the physically-based roughness coefficients present as interesting priors parameters to initialize such DA experiments.
SWOT Discharge Algorithm A second case of application will consist in assessing the benefits of using the remote-data-fusion-based friction coefficients as a first guess for the assimilation of synthetic SWOT observations in the HiVDI discharge algorithm  (Larnier et al. , 2021; Larnier and Monnier, 2020). The performance of such applications will be compared to the current implementation that uses a rough estimation of friction coefficients from the river width. As SWOT has not been launched yet, the discharge algorithms have been benchmarked so far on synthetic SWOT observations, namely the PEPSI challenges' datasets  (Durand et al. , 2016; Frasson et al. , 2021). These datasets consist in synthetic SWOT observations of river portions with various flow regimes, computed using calibrated shallow water models.
Here we realized the comparison on three cases of the PEPSI challenges. The first two cases are the Garonne river and the Po river cases. These cases were selected as the friction coefficients for these rivers have already been estimated in this study. The third case is the Brahmaputra case (India). This last case has been selected based on the availability of input data for running the IOTA 2 chain. For this case, the Copernicus land-cover at 100m resolution  (Buchhorn et al. , 2020)  was used for the calibration of the IOTA 2 classifier. The PEPSI dataset does not contain any information about the separation between main channel and the floodplain. But as the objective of these datasets was to benchmark algorithms on "nominal conditions", namely natural rivers with no or very little flooding, we conducted our experiments under the asumption that the flow is restricted to the main channel.
Thus we only used friction coefficients estimated for the main channel. Moreover, no cross-sectional profiles were previously available. As introduced in Section 2. 1. 9, we combined simultaneous observations of width and water surface elevations, provided by the PEPSI dataset, to reconstruct each cross-sectionnal profile individual shape. Using a handcrafted routine, we then draw the profiles along the riverline such that they remain quasi-orthogonal to the riverline while never crossing each other. For the Garonne River and Po river cases, friction coefficients already computed in our study were spatially interpolated using curvilinear abscissae in the PEPSI 1 datasets. For the Brahmaputra river, the friction coefficients were directly computed at the location of the cross-sections in the PEPSI 2 dataset using our new method.
For the Brahmaputra river, the results obtained with the new method slightly underperform those obtained with the former method with an 3 % increase of errors. However the results are comparable. These results show the potential benefits of this new method to better constrain the SWOT discharge algorithms. esting aspect of such a chain is that it only uses remotely-sensed, publically available datasets and/or existing pre-processing chains. It can therefore be applied at global-scale for a wide range of applications. Among the input data, land cover maps generated by the IOTA 2 chain are at the core of the methodology to derive roughness coefficients in the floodplain. Such maps are currently only available yearly over France but the IOTA 2 chain itself can be run over different region given any adapted training data.
During the present project, we used the already-available French land cover maps over a 75-km-long reach of the upstream Garonne river near Toulouse in France and we run by ourselves the IOTA 2 chain over two additional domain: a 96-km-long reach along the middle Po river in Italy and a 250-km-long portion of the Brahmaputra river. The method was first tested over the Garonne and Po domains and the third one was only used in applications. The interesting aspect with using successively these three domains is the diminution of available local -and field were acquired only for the Garonne and Po domain while profiles had to be reconstructed and simulated data had to used for the Brahmaputra river. Therefore, we were able to apply our methodology over both well-gauged and ungauged domains. Our chain aims at estimating physically-based roughness coefficients, therefore, for specific modeling applications, our coefficients still require some further tuning and calibration to match the model physics.
The HiVDI algorithm, one of the official in-development SWOT dicharge algorithm, is applied over the Brahmaputra domain, one of the PEPSI dataset domains, using our coefficients as first guess for the friction parameters. To evaluate the benefits of our roughness ceofficients, the performances of the experiment were compared to the usual implementation using the mean width to estimate the roughness coefficients. Results were very promising as experiments using our roughness coefficients performed better or slightly worse (for the Brahmaputra only) than the classical implementation. Given that it has been shown that the overall performances of SWOT discharge algorithms highly depend on the quality of the priors, such results show the potential benefits of this new methodology to infer roughness coefficients and better constrain SWOT discharge algorithm.
(2019) or the SWOT a priori river database could be used to set the spatial coverage and resolution of the resulting product. Roughness would be easily derived in the floodplain given that only global-scale raster products are used. 1. A prediction step, during which the control vector is propagated from time t k to t k+1 using the numerical model M k,k+1 -here Mascaret. 2. An analysis step, generating the analysis control vector denoted x a k , with a standing for "analysis", updated given the prior control vector, the available observations y o k and their associated uncertainies, gathered in matrices B k for the control vector and R k for the observations. Moreover, when the control variables are of a different nature of the observations, as in our current experiment where control varaibles are Strickler coefficients and the observations are water surface elevations, an observation operator denoted H k (and H k for its matrix form) is used to map the control variables 515 in the observation space, giving, the Kalman-type analysis equation: Note that in our application, we chose n e = 100 to ensure an large-enough ensemble regarding sample errors but still reasonable to avoid long execution time.
Acknowledgements. This paper relays the work led by  CS GROUP-France  team for the  CNES R&T  project entitled "Parameters extraction to estimate friction coefficients from land cover maps generated by the IOTA 2 chain". It was supported by  CNES  within the framework of the  SWOT-AVAL  project. The authors would like to thank  Mannaig L'Haridon  and  Catherine Bouzinac  for their work and support at the.
Code and data availability. The IOTA 2 chain is available through the framagit platform at https://framagit. org/iota2-project/iota2. The toolbox used to build cross-sectionnal profiles over the Brahmaputra river domain are hosted on Kevin Larnier's private github account, please directly contact Kevin Larnier's (kevin. larnier@csgroup. eu). To access the roughness coefficient extraction chain, please directly contact co-authors Santiago Pena Luque (santiago. penaluque@cnes. fr) or Charlotte Emery. All ancillary databases used for this study are freely available online. The GRWL vector product in available at https://zenodo. org/record/1297434#. YJOrbSY68UE. The World-DatabaseRivers database is available at https://zenodo. org/record/2582500#. YNismTo69hF. The RGE ALTI French DEM is available at https://geoservices.
ign. fr/documentation/diffusion/telechargement-donnees-libres. html. French land cover maps are available yearly at https: //www. theia-land. fr/ceslist/ces-occupation-des-sols/. Insitu river data were downloaded from the French Banque Hydro website (http://www. hydro. eaufrance. fr/). The Po basin DEM has been downloaded from https://adbpo. gov. it/carta-del-po-e-dtm/. Finally, when available, altimetry water surface elevations from virtual stations were downloaded from the Theia-Hydroweb website at http://hydroweb. theia-land. fr/.
Challenges and new directions While the current state-of-the-art facilities have allowed for interesting chemical and physical processes happening in molecular systems to be scrutinized, scientists in this area are forever pushing the boundaries forward in an attempt to understand the very first steps in molecular reactions. In order to do so, it is imperative that FEL sources are able to routinely provide ≤10 fs of time resolution, even as short as attoseconds, with faster repetition rates, and sequences of FEL pulses. This would at the same time require that detectors and measurement devices are able to keep up with the upgrades to efficiently collect shot-to-shot data and analysis techniques evolved to disentangle these complicated datasets. In order to provide a full picture, the new experimental Fig.
Unravelling the role of reactions involving charged species permits to tackle fundamental questions in astrochemistry, organic and inorganic reaction mechanisms, biochemistry and catalysis. Beyond the fundamental interest on reaction dynamics, the quantitative outputs from these experiments, namely branching ratios, kinematic constants or cross sections and thermochemical information (tabulated in the form of extensive databases) are beneficial to models describing the behavior and evolution of plasmas of all sorts, from astrophysical media (e. g. ,  [81]  and refs therein), to flames or laboratory and industrial plasmas. It is beyond the scope of this short section to provide an exhaustive review of the field, but we wish to mention here the main tools that have been developed to study the so-called low energy ion-neutral reactions, i.
g. , with the Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential  [129]  implemented in the LAMMPS software package  [130]. In addition to LAMMPS, several molecular dynamics codes are available, one of them being NAMD  [131] -an open-source software designed for high-performance simulations of large biomolecular systems. It has been shown that under certain experimental conditions, ion impact can induce bottom-up processes of molecular growth by species produced in a cluster fragmentation covalently bonding with other molecular fragments or intact monomers. Such reactions are of great importance among others in the fields of astrophysics and the atmospheric sciences because they constitute possible ways of formation of PAHs and fullerenes in space or formation of aerosols in the atmosphere.
DFTB simulations reproduced the classical simulations results quite well, but at higher temperatures DFTB molecular dynamics simulations predicted easier bond cleavages than simulations of classical molecular dynamics. One interesting modification of classical molecular dynamics simulations adequate to study complex molecular systems over nanosecond timescales consists in the introduction of random and local modifications of the classical force fields to account for chemical transformations, i. e. , bond cleavages/formations, dangling bonds and changes in molecular topology of the system. Such approach allows to study irradiation driven chemistry-a family of chemical modifications induced by irradiation with external fields. This methodology called Irradiated Driven Molecular Dynamics (IDMD)  [108]  has been implemented into the MBN Explorer software package  [132]  and applied to the study of focused electron beam deposition of tungsten hexacarbonyl W(CO) 6 precursor molecules on a hydroxylated SiO 2 surface demonstrating its potential to describe complex dynamics and nanostructure formation and growth  [109].