dataesr/scientific-paragraphs-categorization huggingface dataset:
There is extremely little data on Sphagnum LAI and none on its seasonal change. We monitored Sphagnum mosses LAI phenology in a mountainous peatland site (altitude of 1,343 m) from June to December 2021 using two methods: 2D scans of monthly Sphagnum moss samples and analysis of Sentinel-2 images. LAI derived from field campaigns and the remote sensing approach show a strong seasonality, with high peak values reaching 10 and 7 m 2 •m -2 , respectively. The Sentinel-2 images were also used to derive common vegetation indices. The moisture soil index effectively discriminates Sphagnum-dominated areas in the peatland. Satellite-derived LAI of Sphagnum mosses is directly correlated to gross primary production monitored by gas exchange measurements (R 2 = 0. 83) but also to physical drivers of the environment such as air temperature (R 2 = 0.
The comparison is more difficult to establish with in situ measurements  (Kganyago et al. , 2020). Field measurements correspond to "true LAI" measurements, whereas satellite measurements are "effective LAI" (LAIe) measurements. The effective LAI assumes the hypothesis of a random distribution of leaves of the vegetation studied, which is not necessarily the case. The clumping index makes it possible to go from the true LAI to the effective LAI, taking into account the density of the vegetation and therefore any shading effects  (Weiss et al. , 2020). A recent study  (Brown et al. , 2021)  focused on the validation of the SL2P algorithm, for calculating the LAI according to the  Weiss et al. (2020)  method shows good performance in the prediction of effective LAIe. We also learn that these algorithms have been incorporated into the Google Earth Engine (GEE) application, thus facilitating the use of the algorithm via GEE without going through the SNAP software.
Satellite LAI Retrievals and Vegetation Index Calculation We used remote sensing data (Copernicus Sentinel data) from the Sentinel-2 satellites to derive the evolution of LAI from 2017 to 2021, establish the reflectance spectrum of the peatland vegetation and calculate commonly used vegetation indices over the peatland. The two Sentinel-2 satellites carry a multi-spectral imager sensor providing a set of 13 spectral bands spanning from the visible (VIS) and near-infrared (NIR) to the shortwave infrared (SWIR)  (443-2,190 nm). Four bands have a spatial resolution of 10 m (VIS and NIR bands), six bands of 20 m (red-edge and SWIR), and three bands at resolution of 60 m. Snow cover periods have not been covered. The annual cycles presented, therefore, go from April to November (included). The LAIs were calculated using the LEAF toolbox developed by  Fernandes et al.
(2021). The LEAF toolbox is an open-source application, available on GGE, and allows to perform various Level 2 Vegetation Biophysical Products processing. LEAF is a better implementation of the SL2P algorithm  (Weiss et al. , 2020). Indeed, the SNAP (ESA's SNAP) "Biophysical Processor" had an incorrect implementation of the sampling used to define the calibration database (https://github. com/rfernand387/LEAF-Toolbox/wiki/FAQ). Here, the python API was used to run the SL2P algorithm at 10 m resolution to calculate LAIs at 10 m resolution. The primary purpose of this tool is to generate LAI maps on regions of interest but not time series, we slightly modified the available python API to calculate the LAI on the Bernadouze area over the 2017-2021 period. The biophysical processor determines the LAI using a set of backpropagation artificial neural networks (ANNs) trained by worldwide distributed simulations from a radiative transfer model (PROSAIL, http://teledetection.
ipgp. jussieu. fr/prosail). The ANN requires as inputs: reflectance bands (B3-B7, B8a, B11, and B12) and viewing zenith, solar zenith, and relative azimuth angles characterizing the satellite orbit  (Weiss et al. , 2020). Here, the Sentinel-2 S2-SR (Sentinel-2 reflectance) harmonized images data set was used as input. We computed commonly used vegetation indices (see Table  1 ) sensible to vegetation activity (NDVI), vegetation greenness (GCC), CI, and vegetation moisture content (MSI). The vegetation indices were calculated thanks to the use of the GEE code editor, with the S2-SR harmonized images data set. S2 SR harmonized data set is available on GEE, and provides atmospherically corrected surface reflectance images. A filter of clouds at 30% on the peatland area has been applied to all the data (LAI and vegetation indices).
Due to the presence of snow from November to February, LAI and indices have not been calculated over these months. After removing the cloudy images, we were able to use an average of four images per month, or approximately one image every 7 days. The months of June-August often benefit from more favorable atmospheric conditions, with clear skies, allowing us to exploit an average of six images per month for the latter. Considering only snow-free and cloud-free images, we obtained a data set of 202 images over 5 years. LAIs and vegetation indices were then averaged over two areas of 20 m × 15 m, one dominated by Sphagnum moss (orange area in Figure  1 ), and the other one by herbaceous plants (blue area in Figure  1 ). Finally, linear interpolations were applied to vegetation indices and LAI data to obtain time series of daily data.
Reflectance Spectra We calculated the reflectance spectra of Sphagnum using the SNAP toolbox. We used an image acquired on 21 July 2021, at a time when sphagnum growth was high. The image was downloaded from the ESA PEPS platform (https://peps. cnes. fr). It is an L1C top-of-the-atmosphere product. An atmospheric correction was then applied to the L1C product, using the ESA Sen2Cor module, to obtain the L2A top of the canopy product. We then chose a "subset" of the image, corresponding to the Bernadouze peatland, and resampled the spectral bands (B3-B7, B8a, B11, B12) at a resolution of 10 m. The same two distinct vegetation zones as above were selected to plot the spectrum (orange and blue areas in Figure  1 ).
Time Series and Statistical Analysis To make statistical analyses, we first built daily time series. The hourly WTD measurements from the nine piezometers were averaged out to get one daily time series. The daily time series of PAR was built using only values between 11 and 13 local time to coincide with the timing of the CO 2 flux measurements. We then calculated the mean annual cycle of WTD and PAR over the 2017-2021 period and applied a 10-day rolling mean to smooth out the signal. For the GPP flux, to compensate for the small number of data per year (once a month), we used the measurements from the 5 years to build one annual cycle. We then applied a B-spline function (degree 3) to obtain a time series of daily midday GPP values. We obtained an annual cycle including the 5 years. Finally, the Pearson correlation coefficient was evaluated over all the annual cycles in a correlation matrix (not shown here).
The ground LAI (2D scans) corresponds to the green LAI. However, green parts of Sphagnum, especially when the latter has fully grown, may not see the light because they remain hidden in the shadow of the dense carpet of Sphagnum. The green LAI, therefore, corresponds to a measure of living, green biomass capable of photosynthesizing. However, for part of the green plant, no photosynthesis will occur because of the lack of light. The satellite LAI, therefore, seems to better fit with the leaf surface responsible for photosynthesis and not the total green surface of the plant. However, as explained by  Weiss et al. (2020) , the satellite-derived LAI is defined as an effective LAI not taking into account the clumping effect. Thus, as effective LAI is lower than true LAI, correcting the data by taking it into account could lead to higher values of satellite-derived Sphagnum LAI.
Data Availability Statement Data are available at https://doi. org/10. 5281/zenodo. 7554537  (Garisoain, 2023). To generate the LAI with the SL2P10 algorithm, the code available on the GitHub project https://github. com/rfernand387/LEAF-Toolbox/ wiki/FAQ was used  (Fernandes et al. , 2021). The S2M data set is freely available on the AERIS data center on the following https://doi. org/10. 25326/37#v2020. 2  (Vernay et al. , 2023). The S2M data are provided by Météo-France-CNRS, CNRM, Centre d'Études de la Neige, through AERIS.
The three test-cases -a spar-buoy, a semisubmersible and the innovative two-part floater concept; Hexafloat, recently proposed by Saipem -are simulated in a variety of Design Load Cases (DLCs), including both power-production and parked conditions, as well as wind gusts. The test-cases are simulated using three offshore codes, OpenFAST (OF), DeepLines Wind (DL) and QBlade-Ocean (QB), which was recently extended to enable offshore simulations within the H2020 project FLOATECH. The latter code includes higher-fidelity modeling features such as LLFVW wake aerodynamics and explicit buoyancy calculation, as illustrated in  (Behrens De Luna et al. , 2023). The predicted dynamics are compared in terms of extreme loads, fatigue loads and statistics. Time series are also compared in https://doi. org/10.
5194/wes-2023-107 Preprint. Discussion started: 6 September 2023 c Author(s) 2023. CC BY 4. 0 License. detail to give more insight into the differences in dynamics. The entire input conditions and compared datasets are available open-access and can act as validation databases for other offshore codes or as a benchmark for future modeling improvements. An extensive comparison, involving three different models with different substructure designs, three different numerical codes and multiple DLCs that include hundreds of simulations is an important point of novelty of this study and does not come without challenges. In fact, comparing coupled simulations that are aero-hydro-servo-elastic in nature such as in this study makes isolating the potential sources of any differences challenging. Nonetheless, it offers the unique opportunity of evaluating the trade-off between computational time and accuracy of the modeling theories in terms of their impact on the final design load predictions in a realistic scenario.
European Met-Ocean Conditions Design classes are not currently prescribed for any type of offshore wind turbine as they are for onshore wind turbines, in favor of standardization. Although the need for such standardization is acknowledged and encouraged in the DNVGL-SST-0119 design standard (DNVGL, 2018), the designer is currently required to verify the turbine and substructure combination of choice for specific installation sites. As discussed in the following sections, standards require the definition of specific wind conditions, normally grouped in "models" such as the Normal Turbulence Model (NTM), and sea condition, generally grouped in "sea states". Some databases containing such met-ocean data can be found in previous work -for a comprehensive literature review see  (Papi and Bianchini, 2023)  -however if we restrict our research to Europe, we did not find suitable met-ocean conditions https://doi.
org/10. 5194/wes-2023-107 Preprint. Discussion started: 6 September 2023 c Author(s) 2023. CC BY 4. 0 License. for this analysis. Therefore, an open-source procedure to obtain and prepare long-term environmental data so it can be used in a design load calculation of an offshore wind turbine was developed. The procedure that is detailed in  (Papi et al. , 2022b)  and is available open-access for others to use and improve upon (10. 5281/zenodo. 6972014). Data is obtained from the Copernicus re-analysis database ERA5. Environmental data is available on a 30x30 km grid, therefore the procedure can be applied to a generic world-wide offshore site. In this study, hourly records of wind speed, wind direction, significant wave height, wave direction and peak spectral period from 1979 to 2000 for a site located west of the Scottish island of Barra are used.
This location was chosen because of its particularly harsh environment, expected to increase non-linearities and differences in the examined models, and because it is also used in other EU-funded projects such as LifeS50+ (Antonia  Krieger et al. , 2015)  and CoreWind  (Vigara et al. , 2020). The open-source Python tool Virocon  (Haselsteiner et al. , 2019)  is leveraged to build a joint probabilistic model of the dataset, able to describe the long-term probability of the four environmental variables that are considered: wind speed ( ), significant wave height ( ), peak spectral period ( ) and wind-wave misalignment ( ). The model is then used to find the most likely combination of and for a given , defining the Normal Sea State (NSS), and to define environmental contours: extreme conditions with 50-year recurrence period that are used to define the Extreme Sea State (ESS) and the Severe Sea State (SSS).
1. As shown in Tab. 1, only the simulations in DCL 1. 2 are used to compute DELs. MExtremes is used to compute ultimate loads on the structure. In this case, DLCs 1. 3, 1. 4, 1. 6, 6. 1, 6. 2 and 6. 3 are used. To obtain a conservative estimate of ultimate loads in accordance with IEC 61400-1 annex I (Internationale Elektrotechnische Kommission, 2019), an averaging approach is used when computing ultimate loads, as explained in  (Buhl, 2015). This work leverages some of the authors' past experience and as such many of the same modeling techniques as described in  (Behrens De Luna et al. , 2023)  are used, where a more complete description of the employed methods can be found. Three   et al. , 2022; Behrens De Luna et al. , 2023). The main numerical models in each code are described in this section.
Structural Models Structural dynamics are modeled with a modal-based linear superposition approach in OF through the submodule ElastoDyn. One limitation is that blade torsion is not modeled in ElastoDyn. In QB and DL on the other hand, a higher fidelity finite-element approach is used, whereby the structural dynamics are modeled with a multi-body representation that uses Euler-Bernoulli beam elements in a co-rotational formulation  (Marten, 2020; Le Cunff et al. , 2013). Within OF a more sophisticated blade structural model exists that is able to account for blade torsion. Nonetheless, it was chosen to use ElastoDyn in this study for two reasons. The first reason is to speed up the OF calculations, as ElastoDyn requires less computational resources to run. The second reason is that by using a simpler structural model in OF, the impact of structural modeling accuracy can be better evaluated.
Data Availability The simulation results used in this study are publicly available at 10. 5281/zenodo. 7254241. The met-ocean conditions are also available at doi. org/10. 1088/1742-6596/2385/1/012117. The QBlade-Ocean models upon which the models tested herein are based are available at 10. 5281/zenodo. 6397352 (OC5), 10. 5281/zenodo. 6397358 (SW), 10. 5281/zenodo. 6397313 (HX) and the modifications required to align them with the models tested herein are detailed in 10. 5281/zenodo. 7817707.
Introduction NEOs are asteroids and comets whose orbital perihelion distance is q < 1. 3 au. Asteroids, which represent the great majority of NEOs on short-period orbits, are the main focus here, but we also include comets with a < 4. 2 au. The goal is to develop an accurate model of the orbital and absolute magnitude distribution of NEOs that can be used to understand the observational incompleteness, design search strategies, and evaluate the impact risk. We aim at setting up a flexible scheme that can easily be updated when new observational data become available (from the Vera C. Rubin Observatory, NEO Surveyor, etc. ). We closely follow the methodology developed in previous studies  (Bottke et al. 2002; Granvik et al. 2018 ; also see  Greenstreet et al. 2012) , and attempt to improve it whenever possible.
The model optimization with MultiNest is described in Section 5. The final model, hereafter NEOMOD, synthesizes our current knowledge of the orbital and absolute magnitude distribution of NEOs (Section 6). It can readily be upgraded as new NEO observations become available. We provide the NEOMOD Simulator  13  -an easy-to-operate code that can be used to generate user-defined samples of model NEOs. Planetary impacts are discussed in Section 7. Section 8 considers several modifications of our base model. In Section 9, we drift mainbelt asteroids toward source resonances to test whether the flux of bodies into resonances is consistent with the results inferred from the NEO modeling.
The orbital distribution and number of JFCs produced in the model were calibrated to the known population of active comets. We refer the reader to  Nesvorný et al. (2017)  for further details. Here we use the model from  Nesvorný et al. (2017)  to set up the orbital distribution of comets in the NEO region. The comet production simulations from  Nesvorný et al. (2017)  were repeated to have better statistics for q < 1. 3 au. Specifically, every body that evolved from the scattered disk to q < 23 au was cloned 100 times, and the code recorded all orbits with q < 1. 3 au and a < 4. 5 au (with a 100 yr cadence). This data represents our model for cometary NEOs. The model includes the population of long-period comets but does not account for the long-period comet fading  (Vokrouhlický et al. 2019).
3 au) at t = 100 Myr were collected, and their integration was continued to t = 500 Myr. We tested the contribution of longlived NEOs for t > 500 Myr and found it insignificant. The orbits of model NEOs were recorded with a 1000 yr cadence. This is good enough-with the large number of test bodies per source-to faithfully represent the orbital distribution from each source. For the ν 6 and 3:1 resonances, we also 15 The double Gaussian distribution is given here by ⎜ ⎟ ⎜ ⎟ ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ ⎤ ⎦ ⎥ ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ ⎤ ⎦ ⎥ ( ) ( ) f x w x w x exp 1 2 exp 1 2. 1 1 1 1 2 2 2 2 2 m s m s = - - + - - tested the high-cadence sampling, with the orbits being recorded every 100 yr, and verified that the results were practically the same. The high-cadence sampling, however, generated data files that were too large to be routinely manageable with our computer resources (hundreds of gigabytes per source).
Observations The Mt. Lemmon (IAU code G96) and Catalina (703) telescopes of CSS  (Christensen et al. 2012 ) produced nearly 22,000 NEO detections and redetections during the 8 yr long period from 2005-2012. The two surveys were complementary to each other, with the 1. 5 m G96 telescope providing the narrow-field and deep limiting magnitude observations and the 0. 7 m 703 telescope providing the wide field and shallow limiting magnitude observations. The survey has a carefully recorded pointing history, amounting to well over 100,000 fields of view (FOVs) for each site (for the 2005-2012 period), and a well-characterized detection efficiency  (Jedicke et al. 2016). The orbital and magnitude distributions of NEOs detected by CSS were reported in  Jedicke et al. (2016). Here we use new detections and accidental redetections of NEOs by CSS-4510 individual NEOs in total.
Geometric Probability To account for (i), we use the publicly available object-sInField 21  code (oIF) from the Asteroid Survey Simulator (AstSim) package  (Naidu et al. 2017). The oIF code inputs several parameter sets: (1) the list of survey exposure times (MJD), (2) the pointing direction for each exposure, as defined by the R. A. and decl. of the field's center, (3) the sky orientation in the focal plane (the angle between sky north and the "up" direction in the focal plane), (4) the FOV size and shape (rectangular or circular), and (5) the observatory code as defined by the Minor Planet Center. 22  The user needs to generate a database (. db) file, for example, with the help of the DB Browser for SQLite, 23  containing all inputs. We refer the reader to the GitHub documentation of the oIF code for further details.
The oIF code inputs the orbital elements of a body at a reference epoch, propagates it over the duration of the surveyusing the OpenOrb 24  package  (Granvik et al. 2009 ) and NASA/JPL's Navigation and Ancillary Information Facility (NAIF) utilities 25  -and outputs the list of survey's FOVs in which the body would appear. To speed up the calculation, oIF uses a series of nested steps where the body's position relative to a specific FOV is progressively refined. The orbital propagation can use the Keplerian or N-body methods.
Trailing Loss The trailing loss stands for a host of effects related to the difficulty of detecting fast moving objects. If the apparent motion is high, the object's image (a streak) is smeared over many CCD pixels, which diminishes the maximum brightness and decreases the signal-to-noise ratio. Long trails may be missed by the survey's pipeline (due to streaking), the object may not be detected in enough images of an FOV set (as required for a detection), or the streaks in different images may not be linked together. The trailing loss is especially important for small NEOs; they can only be detected when they become bright, and this typically happens when they are moving very fast relative to Earth during a close encounter. The oIF code provides the rate of motion (w in deg day -1 ) for each FOV where the test object was detected.
CSS Bias as a Function of a, e, i, and H The detection probability of CSS, ( ) a e i H , , , , needs to be computed as a function of a, e, i, and H. As we described in Section 3, the model NEO orbits are binned (Table  2 ). We therefore need to compute ( ) a e i H , , , in each bin. For each bin, we generated a large number (N obj = 10,000; the required number was determined by convergence tests) of test objects with a uniformly random distribution of a, e, and i within the bin boundaries. The mean, perihelion, and nodal longitudes were randomly chosen between 0°and 360°. The oIF code was then used to determine the CSS geometric detection probability (or the detection rate). For each H bin, we assigned the corresponding absolute magnitude to 10,000 test NEOs and propagated the information to compute the photometric detection efficiency ò P (V ) (Equation (3)), individually for every FOV, and the trailing loss ò T (w, V ).
We typically use N ref = N(H ref ) with H ref = 17. 75 (diameter D = 1 km for the reference albedo p V = 0. 14). The normalization constant and slope parameters are used to compute ( ) N H log 10 at the boundaries between segments; cubic splines are constructed from that result  (Press et al. 1992 ). The splines assure that N(H) smoothly varies with H. This representation has the desired properties: it is accurate, flexible, and can easily be generalized by adding more segments. 28 Optionally, we can use additional constraints to inform the MultiNest fits. For example, the known sample of NEOs with H < 15 is complete, and there are ;50 such objects in the JPL Small Bodies Database. 29  We can therefore fix N(15) = 50 and compute the γ 1 slope such that this additional constraint is satisfied.
The Base NEO Model Our base NEO model accounts for n s = 12 sources. Each source has a magnitude-dependent contribution (Section 5. 1) and the source weights α j (15) (for H = 15) and α j (25) (for H = 25) therefore represent 2(n s -1) model parameters (the last source's contribution is computed from Equation (10)). There are four parameters related to the magnitude distribution, N ref and γ j , 2 j 4 (15 H 25). The γ 1 parameter is fixed such that N(15) = 50 (Section 5. 2). In addition, the q 0 * and δq * parameters define the disruption model. This adds to 28 model parameters in total. We used uniform priors for all parameters (see Section 5. 1 for the multivariate uniform distribution of α j (15) and α j (25)). The CSS fits were executed with the MultiNest code running on 2000 Ivy Bridge cores of the NASA Pleiades Supercomputer.