dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the low computational cost makes it possible to use ensemble weather forecasts to predict the significant wave height and a confidence interval, thus reinforcing the value of the prediction made and its usability for marine applications. The proposed methodology could be useful to extend the available history of sea state conditions at lo-cations where such information is sparse. For example, one could be interested in estimating the energy producible by a WEC  (Payne et al. , 2021)  or estimating weather windows for the operation and maintenance of offshore structures  (Walker et al. , 2013). Code and data availability. Python scripts and data used in Sect. 4 are available online at https://doi. org/10. 5281/zenodo. 5524370 (Michel et al. , 2021). Author contributions. SO proposed the project of using DL to predict H s and did the preliminary analyses with projected winds.
MM developed the Python codes and all the analysis, along with the oceanographic interpretation of the results. NR eased the access to the dataset and helped with the workflow. VM and PA brought expert advice on neural networks. VM, PA, and NR provided ideas for the development of the method. MM wrote the paper and all co-authors contributed. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Application to in situ data and comparison with RSCODE In the previous sections, the HCNN neural network was only trained on the outputs of the HOMERE numerical model. It is also interesting to evaluate the ability of the network to learn from in situ data. Here, the reference data are from station no. 62001 (https://www. ndbc. noaa. gov/station_page. php?station=62001, last access: 5 October 2021), which is located at the bottom of the continental slope in the Bay of Biscay (45. 2 • N, 5 • W). The data were collected via the Copernicus Marine In Situ Thematic Assembly Centre (http://www. marineinsitu. eu/dashboard/, last access: 5 Oc- tober 2021). The depth is more than 4500 m. This buoy is owned and maintained by the UK Met Office in coopera- tion with Météo-France. As this buoy is located outside the HOMERE mesh, but within the RSCODE mesh, the latter dataset is used to evaluate HCNN.
Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a large range in spatial scale, mass scale, and morphology. In this work, we address the problem with a novel multiscale method based on wavelets. We tested our method on simulated Hubble Space Telescope (HST) imaging data of strong lenses containing the following different types of mass substructures making them deviate from smooth models: (1) a localized small dark matter subhalo, (2) a Gaussian random field (GRF) that mimics a nonlocalized population of subhalos along the line of sight, and (3) galaxy-scale multipoles that break elliptical symmetry.
g. ,  Blumenthal et al. 1986; Zubovas & King 2012) , poses a challenge in explaining observations (e. g. ,  Vogelsberger et al. 2020). For instance, predictions from the cold DM model lead to overly cuspy central density profiles (the so-called cusp-core problem,  Moore 1994; de Blok 2010) , too many low-mass ( 10 8 M ) dwarf galaxies (the missing satellites problem,  Moore et al. 1999; Klypin et al. 1999 ) and intermediate-mass (∼10 10 M ) galaxies populated by too few stars (the too-big-to-fail problem,  Boylan-Kolchin et al. 2011; Papastergis et al. 2015). Movies associated to Fig. 4  are available at https://www. aanda. org All methods presented here are publicly available in our new Herculens package, https://github. com/austinpeel/ herculens One path to reconcile theory with observations is to examine alternative DM models that assume different properties of the DM particle.
g. ,  Gu et al. 2022 ). We leave this optimization improvement to future work. We use the automatic differentiation Python library jax  (Bradbury et al. 2018)  to construct a fully differentiable model and loss function. Wavelet transforms are implemented using convolutions, making them straightforwardly differentiable (as in convolutional neural networks). We also take advantage of efficient compilation features of jax to speed up computations A155, page 6 of 24 during evaluation of the loss function and its derivatives. All of our modeling methods and algorithms are implemented in the Python software package Herculens, which we make publicly available 3. The code structure and part of the modeling routines of Herculens are based on the open-source modeling software package Lenstronomy  (Birrer & Amara 2018; Birrer et al.
( 18 ) 3 https://github. com/austinpeel/herculens 4 https://github. com/aymgal/wavelet-lensing-papers The FIM can then be used to approximate the covariance matrix of the MAP solution through matrix inversion: C η ≈ I(η MAP ) -1. ( 19 ) This covariance directly gives a lower bound on the uncertainty for each parameter 5. We note that this (first-order) approximation of C η becomes exact if the loss function locally behaves quadratically or, equivalently, follows a Gaussian distribution. We expect this to be the case for simple, smooth models described with analytical profiles, as shown in  Vernardos & Koopmans (2022). We find that while for fully analytical models we can rely on the above first-order approximation of parameter covariance matrix, a sampling-based exploration of the highly nonlinear parameter space using HMC is warranted.
g. ,  Shu et al. 2017 ). The smooth lens potential is described by a singular isothermal ellipsoid (SIE) for the main deflector, embedded in an external shear to simulate the net influence of neighboring galaxies. All analytical profiles used in this work are defined in Appendix B. To create the mock data, we use an algorithm that is entirely independent of our modeling code, in order to prevent the occurrence of artificially advantageous minima during optimization. This also has the advantage to closely mimic a real-world situation, as the data never exactly corresponds to any model generated by the modeling code itself. We use the software package molet  (Vernardos 2022)  to simulate typical observations of strongly lensed galaxies as observed with HST and the Wide Field Camera 3 (WFC3) instrument, in the near infrared (F160W filter).
The initial learning rate is set in order to obtain a smooth decrease of the loss function until convergence, coupled with an exponential decay of the learning rate. We use the opti-6 https://docs. scipy. org/doc/scipy/tutorial/optimize. html mization library Optax  (Hessel et al. 2020 ) that implements the AdaBelief algorithm. For fully analytical models, we find that using the fast estimation from the FIM leads to a parameter covariance matrix almost indistinguishable from the one obtained via sampling methods. Therefore we only rely on the FIM for these simple models. However, for the more complex models that include a pixelated component, we find that HMC sampling of the parameter space is warranted to obtain reliable estimates of the posterior distributions. We use the python package BlackJAX 7  that provides an implementation of HMC well-integrated with jax, that we run using the "No U-Turn Sampler" algorithm (NUTS, Hoffman & Gelman 2011) to dynamically adapt the step size, and their "Stan's adaptation window" feature to improve sampling efficiency.
Their results remain relevant and are applicable in the context of our work as well. This is a promising path towards the modeling of the tens of thousands strong lenses that will be discovered in the era of Vera Rubin and Euclid survey telescopes  (Collett 2015). Moreover, highly flexible yet computationally efficient models will be essential to handle the even higher resolution data sets soon delivered by the James Webb Space Telescope and future extremely large telescopes. where θ E is the Einstein radius, q m is the axis ratio of the elliptical profile, and the corresponding position angle is φ m. In practice, we do not optimize the axis ratio and position angle, but rather ellipticity components {e 1,ψ , e 2,ψ } to prevent sampling issues with the periodicity of φ m (particularly for small values of q m ).
Acknowledgements. We thank the anonymous referee for helpful comments that improved this manuscript. We thank  Luca Biggio ,  Martin Millon ,  Eric Paic  and  Simona Vegetti  for useful feedback and discussion on the present work. We thank  Simon Birrer  for useful discussion, and for making the modeling software package Lenstronomy open-source, which made the development of Herculens much easier. This programme is supported by the  Swiss National Science Foundation (SNSF)  and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (COSMICLENS: grant agreement No. 787886 ). G. V. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodovska-Curie  grant agreement No.
g. in remote catchments locations, for time and/or money constraints). The literature is therefore lacking means to account for the potentially high spatial and temporal variability of soil surface properties and to represent runoff dynamics in agricultural catchments. Accordingly, the goal of our research was to develop and evaluate a parametrization software producing runoff and erosion model inputs: the PREMACHE (Parameterization of Runoff and Erosion Models in Agricultural CatcHmEnts) software. It provides users with an easy approach when using models to address the complex hydro-sedimentary behavior of agricultural catchments. This approach is based on the use of soil surface state as proxies of soil hydrodynamic properties the validity of which was demonstrated the literature. A simple parameterization of soil surface state dynamics over common crop types is proposed, and evaluated in an agricultural catchment located in the European loess belt.
Study area The monitoring campaigns were performed in the Bourville catchment, located in Upper Normandy, France (Figure  1a ), within the European loess belt, defined as t -USDA classification applied to the dataset proposed by  Ballabio et al. (2016). This site is a 1045 ha catchment which was mainly covered, during the monitoring period of almost 9 years (September, 26 th 2007 -May, 31 th 2016) with cropland (72%), grassland (18%), urban (6%) and forested areas (4%). The main crops were, relative to the total crops area, wheat (42%), flax (16%), rapeseed (13%), sugar beet (7%), winter barley (7%), potatoes (6%) and maize (6%). The catchment is mainly covered with Neoluvisol and Brunisol soils. According to the USDA soil textural classification, soils are referred to as silts and silt loams, associated with a low structural stability.
These soils developed on well-drained thick soils, overlying karstic geological formation. Silt and silt loam corresponded to more than 9% of the surface area of the European soil texture dataset proposed by  Ballabio et al. (2016) , indicating that the studied catchment soils are representative of cultivated soils across the continent. These soil types have been described as sensitive to surface crusting, affecting the soil s hydrodynamic properties. Indeed, an increase in crusting results in a decrease of the infiltration capacity  (Boiffin et al. , 1988; Le Bissonnais et al. , 1998)   (1988)  and  Ludwig (1992). Two micro-plots (50 cm x 35 cm) were delimited in each plot, photographs were taken and observations were performed on the field to evaluate crop cover and surface roughness. The specific procedures described in  Bresson and Boiffin (1990)  were used to define the crusting stage.
Additional soil surface state data To increase the database robustness, the 204 observations on plots cultivated with wheat presented by  Delmas et al. (2012)  were used in the current research to generate the parameterization proposed in section 3. 2 and 3. 3. In the current research, results will be presented only for the main winter (i. e. for validation. This compilation relied on observations made across three different catchments and contrasted monitoring periods, corresponded to three years of monitored data. It is therefore expected that this compilation would produce results that can be extrapolated to other catchments, as it included various rainfall depths, intensity, kinetic energy, as well as variations in temperatures and soil textures.
Monitoring stations and data processing Rainfall and water discharge were measured in the Bourville catchment. Measurement of water discharge was contemporary to rainfall period. Rainfall was monitored with automatic rain gauges at a 6-minutes time step (Précis Mécanique 3029) from September 2007 to May 2016. Mean annual rainfall was ranging from 629 mm to 974 mm, with a mean of 769 mm over the monitoring period. The mean long-term  annual rainfall recorded at the nearby Le Havre station is 790 mm with a mean monthly rainfall ranged from 52 mm (February) to 89 mm (December). The monitored rainfall is therefore representing average conditions, including both dry and wet years. Water discharge was measured at four locations in the catchments, including nested measurements in sub-catchments. In this study, we only used data from the station located at the catchment outlet.
Discharge was measured using a calibrated flume, using water height probes (INW PT12) measuring water height at a high frequency, and recorded using a ISCO 2105G data logger. The monitoring frequency ranged from one to six minutes, depending on the monitoring period. Gauging was performed using a velocimeter (Valport 801 flat) or the salt dilution method, depending on the discharge range. Gauging was combined with water height levels to establish rating curves  (Richet et al. , 2021) , resulting in high-frequency discharge monitoring at each station. The rating curve fitted well with the 13 measurements; the determination coefficient was 0. 99 at the catchment outlet. Measured discharge ranged from 0. 04 m 3. s -  to 2. 7 m 3. s -. 95. 7% of the values recorded during the monitoring period were included in this range, indicating its representativity.
Model summary The objective of the PREMACHE software was to generate soil hydrodynamic property maps that can be readily used as runoff and erosion models inputs. The following model inputs can be created at a n coefficient. Additional variables related to erosion modelling are also provided (sheet erosion concentration and soil erodibility), based on the data proposed by  Cerdan et al. (2002b). However, in this study, results will focus on infiltration capacity, as it is one of the main runoff model requirements. To create a spatial distribution of these parameters (maps), the soil surface state is modeled by PREMACHE at a daily time step, on each plot across the catchment. For each plot, PREMACHE initialized the crop cover, crusting and roughness based on empirical data depending on the crop type, previous shallow tillage operations and potential chemical crop destruction.
Required inputs The required inputs are: A raster providing the expected resolution and extent, such as the Digital Elevation Model (DEM). A shapefile corresponding to the catchment plots. Each plot should be associated with a plot (arbitrary) number. As the plot sizes and locations may change over time, multiple shapefiles can be used to reflect the land use temporal evolution. Monitored data, national databases providing annual maps or statistics can be used to fill in this spreadsheet. A spreadsheet file indicating the land use (including crop type and farming operations) at each measurement period, with the associated plot numbers. A two-column file including the rain gauge records. Rainfall records should be provided with a daily time step. If a higher resolution is available, the toolbox can be used to decrease the resolution to a daily time step in order to avoid high frequency variations while conserving an adequate temporal resolution regarding the timescales involved in the control of soil surface state evolution.
Limitations and adaptations In the toolbox, crops are assumed to grow independently from rainfall and temperature. Our dataset, and the corresponding parameterization, should therefore need additional calibration for catchments undergoing severe dry or wet periods. We are also aware that process-based approaches were proposed in the literature  (Peñuela et al. , 2018; Boas et al. , 2021) , for instance to model crop growing at various scales. However, the goal of the current research was to provide measurement data and a simple parameterization to obtain reliable estimates of soil surface evolution, based on limited input requirements. Moreover, the software made use of simple spreadsheets; values can therefore be easily modified according to the knowledge, or using dedicated measurements or more detailed crop growing modelling.
The current study took advantage of extensive field measurements obtained with the active cooperation of landowners. Consequently, a unique long-term monitoring of crop types and shallow tillage operations was available for this study. At large scales, such data are usually not available, but interesting approaches such as crop rotation simulations  (Schönhart et al. , 2011; Sietz et al. , 2021)  may contribute to improve such shortcomings. The proposed database from our study may be used to validate such approaches. Crop cover was divided into 5 segments (0% to 100% in 20% increments) to allow for a smoother transition between classes, particularly for crusting and roughness evolutions (see section 3. 3 below).
Evaluation of the parameterization extrapolation abilities In addition to parameterization evaluation as performed in sections 3. 2 and 3. 3, we assessed whether the PREMACHE framework could be applied to other catchments by using the 109 observations of the Blosseville catchment. Results from the Blosseville catchments are presented in supplementary material. For the 20 monitored plots covered with wheat (7 plots), flax (2 plots), peas (3 plots) and winter barley (3 plots), the predicted crop cover was good. The modeled values corresponded to measurements in 91% of the cases. Errors were observed regarding the occurrence of intermediate crop cover (C2) on fields planted with peas and wheat. For crusting, only data for fields cultivated with flax and peas were available, and the parameterization matched the observations in 87% of the cases, with the few errors occurring regarding the prediction of the structural crusting stage (F1).
Implications for runoff modelling Adequately representing the variability of soil hydrodynamic properties, such as infiltration capacity, is a long-standing issue for runoff modelers. Moreover, in agricultural catchments, the significance of shallow tillage operations can dramatically change these properties within a very short period of time  (Martin et al. , 2004). The PREMACHE software proposes an alternative method to account for these variations in runoff modelling, which may be crucial in understanding catchments hydrological behavior  (Wagner et al. , 2019). As an illustrative example of the approach, the Bourville catchment mean infiltration capacity was modeled over two entire crop cycles. Calculations were performed from 1 st September 2014 to 1 st September 2016. For readability purpose, results are presented for 1 st September 2014 to 1 st June 2016, corresponding to a total of 253 rainfall events, as no significant runoff event was recorded after 1 st June 2016.
Runoff events, as defined in section 2. 3 (i. e. based on a threshold on runoff volume and discharge peak), occurred mainly when rainfall increased crusting, resulting in a mean infiltration capacity below 20 mm. h -1. The PREMACHE software may therefore be used to provide inputs for runoff and erosion models and to increase their performance by taking into account the fast (e. g. progressive crusting in winter and tillage operations) and long-term (cycles over multiple years) dynamics of soil infiltration capacity. It therefore offers a possibility to quantify the infiltration-runoff partition and could therefore complement existing modelling approaches such as that including curve number variations in models  (Mehdi et al. , 2015). This may be useful in agricultural catchments, as both infiltration-excess and saturation-excess may be involved in generating flood events in such environments  (Saffarpour et al.
Conclusions In the current research, existing knowledge on soil surface state and a unique database were compiled. The database included the monitoring of crop types and soil surface state, as well as the highresolution measurement of rainfall and runoff. Although the monitored sites corresponded to loamy soils sensitive to surface crusting under temperate climatic conditions, they may be representative of the conditions observed in other cultivated regions where hydrological processes are dominated by infiltration-excess runoff. A framework describing the soil surface state dynamics was developed and included in a software When applied to the studied catchment, results demonstrated the high variability of soil infiltration capacity between crop types, depending on the sequences of tillage operations and rainfall dynamics.
The variations in infiltration capacity at the catchment scale and for various time scales, from the rainfall event to the inter-annual scale, and its strong implications for runoff modelling were illustrated. The proposed approach allows representing this variability in runoff models by creating runoff model inputs, based on a large database proposed along with the manuscript. It may therefore be useful for applications in unmonitored agricultural areas in general and more specifically on loamy soils, susceptible to crusting. It will help representing the temporal and spatial variability of soils hydrodynamic properties for different crops and a sequence of hydrological years, which will ultimately contribute to a better understanding of runoff pathways and hydrological connectivity at the catchment scale.
PREMACHE is licensed under GPL V3. 0. It was developed by Rosalie Vandromme and Thomas Grangeon (r. vandromme@brgm. fr and t. grangeon@brgm. fr) in early 2021 under Python 3. 8. 5, using Windows 10, a 2. 5 GHz i5-7300HQ CPU with 16 Go memory. Using this configuration, the model processed the dataset proposed with the manuscript (a shapefile including approximately 1200 polygons monitored over 9 years) and exported rasters at a 5 m resolution for a subset of 50 rainfall events in approximately 15 minutes.
Acknowledgments: The general idea of this manuscript, based on soil surface state, largely originated from the pioneering work of late  Yves Le Bissonnais. We would particularly like to thank the landowners for their involvement in the data collection and for granting access to their fields, making this long-term work possible:  Mrs. Baret ,  Bazire, Bouclon, Burel, Cabot, Constantin, Cordier, Delafontaine, Delamare, Delaunay, Grindel, Laguerre, Larcher, Lefrançois, Martine, Mignot, Moonen van Meer, Olivier, Pesqueux, Petit, Planchon, Poulet, Rossignol, Roussel, Terrier and Voisin. The assistance of  Mathieu Saulnier  in field measurements is acknowledged. Thomas Grangeon would like to thank  Farid Smai  and  Theophile Guillon  for their help in model formatting and  GitHub deposit. The two anonymous reviewers provided constructive and detailed comments that helped improving the manuscript quality.