dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Statistical analyses and calculations were conducted using the software R (v4. 0. 3) in  Rstudio (v1. 1. 414; Ihaka and Gentleman 1996). Analysis of variances (ANOVA) and Tukey test, were performed on the different parameters by grouping the station by their position (SI Table  1 ). Seawater density was calculated using r package oce v1. 3. 0 (Kelley, 2018) and mixed layer maximum depth was determined as the depth at which a change from the surface density of 0. 125 has occurred  (Levitus, 1982). Section plots were realized using Ocean Data View  (Schlitzer, 2020). Other packages used in this study include corrplot v0. 84  (Dray, 2008 ) and ggplot2 v3. 3. 3 (Wickham, 2016). Depth integrated values were calculated using the midpoint rule.
Acknowledgments We thank the captain and the crew of the  R/V Meteor  for their support during the M156 cruise. We thank  J. Roa ,  T. Klüver  and  L. Scheidemann  for sampling on board. We thank  J. Roa  and  S. Golde  additionally for the analysis of dissolved organic matter and  T. Klüver  for cell counting, bacterial and phytoplankton activities analyses. We thank  B. Domeyer  and R. https://doi. org/10. 5194/bg-2022-49Preprint. Discussion started: 21 February 2022 c Author(s)  2022. CC BY 4. 0 License.
In this work, we apply the previously developed Deep Single-Point Estimation of Bathymetry (DSPEB) method  [Al Najar et al. , 2021]  and showcase its ability to reconstruct bathymetry using real-world data. We create a supervised dataset for bathymetry inversion using publicly available Sentinel-2 imagery  [Drusch et al. , 2012]  and a number of bathymetry surveys obtained from the French Naval Hydrographic and Oceanographic Service (SHOM). We present two different satellite image pre-processing techniques to augment wave kinematics and color information as inputs to two DSPEB models. We train our models in two different sites and compare the performance of color-based and wave kinematics-based DSPEB to one of the current state-of-the-art bathymetry inversion models based on wave kinematics, Satellite to Shores (S2Shores)  [Bergsma et al.
, 2021 , Baba et al. , 2021]. The layout of this article is as follows. In Section 2, we present the DSPEB and S2Shores approaches for SDB. We then describe our dataset creation methodology and the datasets used in our experiments. In Section 3, we present our results and compare our DSPEB models to the physical method's performances in two application sites. Section 4 concludes the work with a discussion of the results presented, highlighting possible further research paths. In the appendices, we include additional supporting results from the different models presented in the article, our first steps towards a hybrid approach to SDB using deep learning that makes use of the physical characteristics of surface waves in addition to water color to estimate depth, in addition to a full list of the Sentinel-2 images used in order to facilitate reproducibility.
Data and Methods In this section, we first present the DSPEB approach for bathymetry estimation using deep learning, as well as the physics-based method S2Shores, which we use as a reference for comparison. We then describe our data setup and input data pre-processing for both wave kinematics-based and color-based DSPEB. We give an overview of the sites used in this study and the final supervised datasets used to train our DSPEB models. Finally, we present a summarized description of the functioning of DSPEB as a complete workflow.
The inputs to both neural networks are 40 × 40 × 4 px satellite images. We noted that model training was sensitive to different dates with varying meteorological conditions. To construct a training dataset, we select dates based on high estimation correlation from the S2Shores method, which depends on visible waves. We provide a list of all images used in this work in Appendix A. While the focus of this work is to develop on our previous work on DSPEB, a less expensive and more general date selection method is a topic of on-going work. The first step in our pre-processing workflow is cloud detection. We make use of a simple cloud detector based on the percentage of blue pixels in each subtile by looking at the RGB Sentinel-2 bands. We discard all subtiles where the percentage of blue pixels is less than 80%, allowing for a margin of noise in the input data.
5. Next, a subtile is extracted from each of these images for each depth measurement point, such that the depth point is situated in the center of the extracted subtiles. These subtiles are then passed through our pre-processing chain, described in Section 2. 3, subtiles are either pre-processed or recorded on disk to be used for model training. A test dataset is created following the same methodology using a different set of initial Sentinel-2 images with different dates. The DSPEB model is then trained on the prepared training dataset following the method further described in Section 3. 1. After model training, the DSPEB model is used to estimate bathymetry from a Sentinel-2 image using a sliding-window technique (Figure  6b ), where each subtile is treated according to the previously described pre-processing scheme (Section 2), resulting in an estimate profile from a single date.
Limitations While the results we present show that DSPEB is capable of reconstructing bathymetry, there are limitations to the current method. Specifically, we highlight the limitations of data pre-selection based on dates and the requirement to train on application sites. The accuracy of W-DSPEB was found to be sensitive to the dates selected, as was S2Shores, indicating that the necessary wave kinematics information was lacking for certain dates. Currently, S2Shores is used as a date-selection method to dictate which images can be used for training W-DSPEB at a certain site, requiring large amounts of computing time before dataset construction. A possible solution to this limitation could be the use of a CNN as a binary classifier to dictate whether a Sentinel-2 image contains the necessary information for W-DSPEB.
While we present H-DSPEB as our first steps in this direction by engineering a hybrid model, other possibilities exist including traditional and/or deep learning-based data assimilation techniques for example  Arcucci et al. [2021]. A fundamental direction for future research in deep learning-based SDB is the development of a singular model which can be applied directly to sites without training. Such a model would require the inclusion of multiple sites in a single training dataset (mixed-site training), which should minimize the model's ability to overfit to any site-specific features, and consequently increase the model's ability to generalize to previously unseen sites. We also expect that it would need to include both wave kinematic information and color information, as proposed above.
The Deep Single-Point Estimation of Bathymetry method [Al Najar et al. , 2021]  is a deep learning-based bathymetry inversion method that operates on 40 × 40 × 4 px multi-spectral input subtiles (corresponding to the blue (B2), green (B3), red (B4), and near-infrared (B8) bands of the Sentinel-2 satellite constellation Drusch et al. [2012]  at 10 m resolution) to estimate the water depth corresponding to the center of each input subtile. The neural network input is an image of 40 × 40 × 4 px input channels conforming to our dataset of 40 × 40 × 4 satellite subtiles; and a single output neuron with a Rectified Linear Unit (ReLU) activation, corresponding to the average depth beneath the imaged area. Figure1presents the different steps of the DSPEB method.
Sentinel-3 Retrievals Satellite retrievals of snow and ice (SICE) albedo are from the EU Copernicus Sentinel-3A Ocean Land Color Instrument (OLCI). These SICE albedo retrievals agree within 5% with the PROMICE AWS observations  (Kokhanovsky et al. , 2019 (Kokhanovsky et al. , , 2020)). Cloud identification employs Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) data after  (Metsämäki et al. , 2015). The SICE algorithms and data appear in open access repositories  (Box et al. , 2020; Wehrlé et al. , 2020).
AMSR Wet Snow Mapping Advanced Scanning Microwave Radiometer (AMSR) AMSR-E and AMSR2 satellite 19 GHz passive microwave sensors are used to map the presence of snowpack water twice-daily (13 Universal Time Coordinate (UTC) ascending and 01 UTC descending passes) with 20 km spatial resolution after  (Picard & Fily, 2006; Torinesi et al. , 2003). Bare ice melting below the snowline is sometimes not captured by this algorithm and is ignored, leading to underestimates of the total ice sheet melt extent by up to 6%. The interannual frequency of wet snow is evaluated using the data span from years 2003-2021.
This research was funded by the European Space Agency EO Sci for Society ESRIN CCN 4000125043/18/I-NB. GEUS field observations of rainfall have been supported by Greenland Integrated Observing System (GIOS) under the Danish Agency for Higher Education and Science, the INTAROS project under the European Union's Horizon 2020 research and innovation program under grant agreement No. 727890 and the Danish Ministry of Climate, Energy and Utilities via The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-Net). AGU data policy is followed with access to related data appearing in the data availability statement. We gratefully acknowledge PROMICE and GC-Net engineers: Alan Pedersen; Jakob Jakobsen; Chris Shields, field workers: Dirk van As; Andreas Ahlstrøm; Chris Shields; Nanna Karlsson; Alan Pedersen; Derek Houtz and Øyvind Winton and the Swiss Federal Institute for Forest, Snow and Landscape Resarch (WSL) data scientists: Ionut Iosifescu and Rebecca Kurup.
BDP and Black Death mortality Here, we pioneer an alternative approach (discussed in detail in Methods). BDP allows us to evaluate the Black Death's mortality across Europe using quantitative palaeoenvironmental datasets, which can be employed for spatial statistical comparisons (Fig. 1 ). Our dataset consists of fossil pollen counts from 261 radiocarbon-dated sediment cores from 19 present-day European countries (Fig. 2 ; Supplementary Data 1). Pollen data can be used to assess past demographic dynamics as human pressure on the landscape in the pre-industrial period was directly dependent on the availability of rural labour. We focus on the period between 1250 and 1450 ce (comparing 100 years before and after the Black Death; 100 years representing roughly four generations, a time period during which pre-industrial populations could not recover from potentially high plague mortality  23  ) for which 1,634 pollen-analysed sediment samples are available (Supplementary Data 2).
Four major scenarios of landscape and demographic change. Having validated our approach, we analysed the pollen dataset for all of Europe. We focused on contrasting the four summary pollen indicator values on a regional scale for subperiods of 100 years before and after the Black Death. While we discuss the results of this analysis here (Fig. 4 ), our complimentary data presents 50-year (1301-1350 to 1351-1400) and 25-year (1325-1350 to 1351-1375) period analyses (Supplementary Figs. 1  and 2 ). For cereals, they returned the same results in terms of direction of change for 20 out of 21 regions for 50-year and 16 out of 19 regions for 25-year analyses, confirming the robustness of our conclusions (Extended Data Fig. 3 ); the results for other indicators are also highly similar between different periods of analysis (Extended Data Figs.
BDP data collection. Existing online palynological databases (the European Pollen Database (EPD)  62  www. europeanpollendatabase. net, and the Czech Quaternary Palynological Database (PALYCZ)  63  , https://botany. natur. cuni. cz/palycz/), as well as personal contacts of the study authors and a systematic publication search were employed to identify palynological sites in Europe reaching the required chronological and resolution quality for the study of the last millennium. In order to enable statistical analysis, we included only sites clustered in well-defined historical-geographical regions, excluding isolated sites even if the quality of a site's data was very good. Data of sufficient quality and amount from regions for which the Black Death is well-studied, notably central and northern England and the Low Countries, is not presently available; to the best of our knowledge, for each of these regions there currently is not more than a single isolated site  56  , which does not allow for the application of statistical approaches.
Cichorioideae of the EPD and PALYCZ nomenclatures, which primarily refers to the fenestrate pollen of the Cichorieae tribe  64. Ericaceae groups Arbutus unedo, Calluna vulgaris, Vaccinium and different Erica pollen types, whereas deciduous Quercus comprehends both Q. robur and Q. cerris pollen types  65. Rosaceae refers to both tree and herb species of the family. Finally, Rumex includes R. acetosa type, R. acetosella, R. crispus type, Rumex/Oxyria and Urtica groups U. dioica type and U. pilulifera. BDP summary pollen indicators. In order to connect changes visible in the pollen data to human demographic trajectories, we assembled four summary pollen indicators that describe specific landscapes related to human activity. They reflect different degrees of demographic pressure on the landscape (cereal cultivation, pastoral activities, which are less-labour intensive than cereal cultivation, abandonment and rewilding) as well as different durations of land abandonment that might have occurred post-Black Death.
BDP analytical statistical and spatial methods. To control for local specificity, pollen percentages of every taxon from each pollen site were standardized. From the taxa percentage in a given year the arithmetic mean calculated for the observations from the period 1250-1450 was subtracted and the result divided by the standard deviation for the 1250-1450 period. Standardized taxa results were assembled for each site into four BDP summary indicators. Since each indicator has different numbers of taxa, the sum of standardized taxa values calculated for a given year and site was divided by the number of taxa in the indicator. For the purposes of replication, this standardized pollen dataset, comprising the four indicators for each sample from each site, is available as Supplementary Data 2. This dataset has been analysed in two ways, statistically and spatially.
Acknowledgements The authors acknowledge the following funding sources:  Max Planck Independent Research Group, Palaeo-Science and History Group  (A. I. , A. M. and C. V. );  Estonian Research Council  # PRG323 ,  PUT1173  (A. Pos. , T. R. , N. S. and S. V. );  European Research Council  # FP7 263735  ( A. Bro. and  A. Plu. ), # MSC 655659  (A. E. ); Georgetown Environmental Initiative  (T. N. );  Latvian Council of Science # LZP-2020/2-0060  (N. S. and N. J. );  LLNL-JRNL-820941 (I. T. ) ;  NSF  award # GSS-1228126  (S. M. );  Polish-Swiss Research Programme # 013/2010  CLIMPEAT (M. Lam. ), # 086/2010  CLIMPOL (A. W. );  Polish Ministry of Science and Higher Education  # N N306 275635  (M. K. );  Polish National Science Centre  # 2019/03/X/ST10/00849  (M. Lam. ), # 2015/17/B/ST10/01656  (M.
Of note, such a velocity reduction is expected to have implications on other aspects, such as sediment transport and/or other geochemical processes that are functions of a higher power of the velocity magnitude  [6, 46, 47]. Thus, blockage corrections need to be considered when assessing the impact of tidal-stream turbines on the environment. Our results are, however, subject to several limitations. First, the Telemac3D model with actuator disks has been validated with laboratory measurements in the wake of a scaled turbine  [4]. It is, therefore, important to bear in mind that, for operational applications, a model validation relying on measurements acquired in the wake of a full-scale turbine deployed in real sea conditions would be required. Unfortunately, such a dataset is not publicly available for the Alderney Race.
Acknowledgements This work has been funded by the  ANR OLD  and the  Fondation ARS Cuttoli Paul Appell  (the latter to C. G. ). It is part of the research program  Omo Group Research Expedition (OGRE) , also funded by the  French Ministry of Europe and Foreign Affairs , the  Fyssen Foundation , and the  CNRS (INEE). It has been supported by the  ARCCH (Ethiopian Authority for Research and Conservation of Cultural Heritage) , the  CFEE  ( USR CNRS 3137 ), and the  Embassy of France  to Ethiopia. K. T. U. was supported by funding from the  Vetlesen Foundation  and a fellowship from  Columbia University's Center for Climate and Life. This work is based on Omo specimens collected by the  IORE (International Omo Research Expedition)  and the OGRE. We are grateful to all people who took part to fieldwork with the IORE and OGRE, to the staff of the  National Museum of Ethiopia/ARCCH at Addis Ababa , especially the curators in paleontology  Tomas Getachew ,  Getahun Tekle Yemanebirihan ,  Sahle Selassie , and  Gemechis Getane  for their help in the collections, as well as to  Dawit Seifu  for specimen preparation.
The latter relied on-satellite estimates, also using synthetic aperture radar (SAR) acquisition on 30 October, showing 180-185 mph winds at that time, matching Goni's increasing intensity. In the absence of the direct in-field measurements, satellite observations remain the most powerful and reliable for extreme winds/waves monitoring and forecasting, taking advantage of satellite data accessibility, regularity, wide swath, and now improved spatial resolution. To this end, the case of hurricane Goni corresponds to quasi-synchronously overlapping measurements from CFOSAT SWIM/SCAT and Sentinel-1 SAR measurements. For this case, very accurate and high resolution wind information is thus available to help compare wave model outputs and two-dimensional wave information. The analysis further benefits from other available satellite data, e.
The standard 2DVAR algorithm  [11, 41]  was applied to reduce wind vector ambiguities due to high wind speed gradients and precipitation impacts typically observed in zones of tropical storms. On October 29th, tracks of Sentinel-1A and CFOSAT closely crossed the TC Goni eye within less than two hours, at 20:57 to 22:11, respectively. These two operating instruments complement each other. SAR data are fine-resolved on a swath of hundreds of km, and CFOSAT SCAT covers a ∼1000 km swath but at medium 25 km resolution. For TC Goni, R m is of the order of 10 km. Thus, not only considering precipitations possibly affecting the Kuband CFOSAT SCAT measurements, the CFOSAT SCAT resolution is too coarse to provide accurate wind information in the wave-generation region. Note, C-band measurements are much less impacted by heavy precipitations.
TC Goni Surface Waves 2. 2. 1. Data Significant wave heights, wavelengths, and wave directions can all mostly be derived from the CFOSAT SWIM measurement. The SWIM instrument is a Ku-band radar with nadir and near-nadir scanning beam geometry designed to measure the spectral properties of surface ocean waves  [29]. Directional wave spectra are recovered from scanning radar measurements, obtained by rotating beams pointing at 6 • , 8 • , and 10 • mean incidence angles. On ground, a chosen beam will display a cycloid-like pattern, Figure  2a , for the 10 • incidence. SWIM nadir products include Hs and the normalized radar cross-section interpreted in terms of surface wind speed, with accuracy similar to standard altimeter missions  [45]. Other altimeter data come from satellites Jason-3, Sentinel-3A,B, and AltiKa.
In this study, another approach, an explanatory wave-ray model, was first properly tested together with multi-mission satellite observations. The wave-tracing method offers a relative computation simplicity and robustness to also build on a fully consistent system of physical equations and empirical self-similar laws for wave development. Estimations using both the direct model (KYCM) and TCW GMF predictions give reliable results in the vicinity of maximum winds in the forward TC sector but they are less accurate in the backward one. In all comparisons with altimeter measurements, a small underestimation is revealed with bias of the order of 10 cm, which is comparable to the obtained root mean square error. However, the peak wave heights are predicted accurately. Joint analysis of the model results and the data from the recently launched satellite CFOSAT revealed totally new aspects and questions, particularly concerning the wave spectral distributions inside tropical cyclones.
In the future, observed and predicted sea state parameters, i. e. , Hs, wavelengths, and directions, can then be anticipated to be more easily combined to rapidly assess the total wave field developments for assimilation and practical applications. The present study largely benefits from new capabilities that the CFOSAT SWIM instrument can bring. The CFOSAT SWIM instrument is a Ku-band rotating near-nadir real aperture radar to help infer directional wave information, with 15 • azimuthal resolution. In the absence of synchronous wind field information, generalized analytical selfsimilar solutions (TCW GMF) are also demonstrated to provide robust first-guess estimates of surface wave field characteristics (Hs, wavelength, direction) from the Best Track Data. The present study thus demonstrates promising perspectives to rapidly and efficiently combine multi-satellite data from the two CFOSAT instruments (SWIM and SCAT) with satellite high-resolution radar SAR scenes and altimeters with KYCM/TCW GMF outputs.
As anticipated, this results from a resonance between the TC heading translation velocity and surface wave group velocity in the right TC sector, i. e. , the wave-trapping effect. Estimates from both the direct model (KYCM) and TCW GMF predictions agree well with altimeter measurements in the vicinity of maximum winds. In the far zone, the local wind sea is found to dominate. In this region, it can well be reproduced using CFOSAT SCAT data as the wind model input. Results are found to be consistent with predictions using the Pierson-Moskovitz formulation for fully developed wind sea. These assessments are further supplemented with estimates of swell systems outrunning in the TC center region. Comparisons of wave directional properties using CFOSAT SWIM offnadir measurements also confirm qualitative and quantitative agreements between satellite estimates and model predictions.
The 2D dimensionless universal functions Φ e , Φ λ , and Φ ϕ in (A2) describe radial (scaled by L r cr ) and azimuth distributions of the energy, wavelength, and direction of the primary wave system relative to the corresponding values for stationary TC. Shapes of these universal functions are shown in Figure  18  from  [23]  and are also available as numerical matrices at https://zenodo. org/record/4609996#. YGmoDD9n2Ul, accessed on 20 April 2022. From numerical experiments, two regimes are realized: "slow" TC corresponding to the group velocity quasi-resonance, R m /L cr > 1, or "fast" TC, R m /L cr < 1. Here, L cr is the critical fetch defined for maximum wind speed and its radius: L cr = c cr (u 2 m /g)(u m /2V) 1/q. In the latter case ("fast" TC), the TC heading velocity is too large, and all waves "slide down" to the backward TC sector.
Acknowledgments: CFOSAT SWIM and SCAT measurements were provided by  IFREMER Wind and Wave Operational Center  (IWWOC, http://iwwoc. ifremer. fr,accessed on 15 March 2021), co-funded by  CNES  and  IFREMER. ESA CYMS (https://www. esa-cyms. org/,accessed on 25  May 2021 ) project  4000129822/19/I-DT  for the SHOC initiative allowing for SAR data ordering and processing and ESA MAXXS (https://www. maxss. org,accessed on 25  May 2021 ) project  4000132954/20/I-NB  for SAR-derived vortex products are also acknowledged.