dataesr/scientific-paragraphs-categorization huggingface dataset:
Since its first release in 2014, the software has been widely used for ocean engineering applications, such as the design of wave-energy converter devices  [5] [6] [7] [8] , floating offshore wind turbine platforms  [9, 10]  and hybrid wind-wave energy converter devices  [11, 12]. The hydrodynamic database from NEMOH is often used as an input for time domain body dynamic solvers as reported in  [13]. NEMOH can compute the hydrodynamic interaction of multi-bodies and can be coupled with a wave propagation model for studying the far-field effect of the interaction as reported in  [14]  for wave energy converter arrays. Performance of the previous NEMOH version (v2. 0) has been reported in comparison studies against the commercial software WAMIT  [15]  in  [5, 7] , and against the open-source software HAMS  [16]  in  [7].
In the previous release version, NEMOH v2. 0 was available only for computing first-order hydrodynamic coefficients without the irregular frequency removal method. The studies reported that NEMOH had good accuracy in most applications with limitations in computational cost, irregular frequencies, and documentation. NEMOH v2. 0 had poor accuracy when dealing with a thin structure, i. e. heave plate, due to the accuracy of the look-up table and the interpolation method on the free-surface Green function as noted in  [4]  and reported in  [7]. This paper reports the release of NEMOH v3. 0. In this version, an extended boundary integral method for irregular frequency removal is implemented, new linear system solvers are added to enhance the computational efficiency, and an extended module to compute second-order wave load quadratic transfer functions (QTFs) is also available.
NEMOH v3. 0 has better accuracy than v2. 0 with finer interpolation points of the free surface Green function computation and the influence coefficients are now obtained by applying the Gauss-quadrature integration method. As in the pre-vious version, NEMOH v3. 0 can deal with multi-bodies interaction. NEMOH v3. 0 can be extended for solving diffraction and radiation problems of deflection modes of a flexible body. This new feature is expected to be added in the updated v3. 0 version. The QTF module in NEMOH v3. 0 is a new feature and probably the only open-source software that provides the module. The first development of the QTF module was reported in  [17]. Extensive verification of recent developments of the QTF module, against the commercial software developed by Bureau Veritas HYDROSTAR  [18] , for uni-directional QTFs, was reported in  [19]  and for bidirectional QTFs in  [20].
NEMOH software is available for downloading and contributions on the Gitlab platform  [21]. The code is developed and redistributed under the terms of the GNU General Public License as published by the Free Software Foundation. Along with the source code, documentation that describes the compilation and execution of the source files is also distributed. Four floating platforms are considered for verification cases; a truncated cylinder, a truncated cylinder with a thin heave plate, a spar-buoy (SOFTWIND) and a semi-submersible (OC4). The NEMOH results are compared with WAMIT  [15] , HYDROSTAR  [18]  and DIODORE  [22]  software. This paper is organized as follows. Section 2 describes theoretical and numerical aspects of the open-source NEMOH. Section 3 describes program documentation of the source files, compilation, execution of the codes, input/output files, and test cases.
Mesh preprocessor NEMOH mesh preprocessor, the executable file mesh, is used to generate the NEMOH mesh file with a given geometry input file and an input Mesh. cal file. This mesh is not a meshing code but allows the user to refine an existing mesh and to calculate properties such as displacement, buoyancy center, and hydrostatic stiffness. It also makes estimates of masses and the inertia matrix. The concept with this program is to write by hand a coarse description of the body under consideration in a GeomInput file and to have the mesh preprocessor make the refined mesh for the NEMOH calculations.
NEMOH1: 1st-order solver NEMOH1 solves the first-order potential flow problem. There are four modules: preProc, hydrosCal, solver and post-Proc, described as follows. • preProc: processes the input mesh file and generates the body condition for each calculation case (diffraction and radiation). The outputs are used as input for solver. • hydrosCal: computes hydrostatic parameters, i. e. stiffness matrix and inertia matrix. The output file will be used in the postProc to compute the RAOs. • solver: solves the boundary value problems for each problem, diffraction and radiation, defined in the file Normalvelocities. dat, provided by the preProc. • postProc: post-processes the solver's output files. The results are the excitation forces, added mass, and damping coefficients. Optionally, the program computes the impulse response functions of radiation damping and excitation force, the Kochin coefficient, the free-surface elevation, and the motion response amplitude operator.
Compilation NEMOH uses two external libraries, BLAS and LAPACK, that have to be installed before the compilation. For the compilation, two tools are required; i) a Fortran compiler (e. g. , gfortran or ifort on Unix/Linux platforms), ii) a cross-platform tool, CMake, for building and testing the software package. The compilation produces all the executables for each module described above. The program can be compiled on any computer architecture. It has been tested on Windows and Unix/Linux platforms. Note that the Gitlab releases provide pre-compiled executable files for Windows and Linux platforms.
Running NEMOH NEMOH has been developed for command-line runs. Matlab wrapper files are also provided for running NEMOH in the Matlab environment. To run NEMOH, some input files have to be userprovided. Section 3. 3. 1 describes the required input files and also the output files. Test cases for verifying the NEMOH results are provided with the reference data from commercial BEM solvers such as AQUAPLUS (which is the ancestor of NEMOH) and HY-DROSTAR  [18] , described in Section 3. 3. 2.
Test cases There are 13 test cases, 8 test cases for NEMOH1 and 5 test cases for NEMOH2, provided in the software package. These test cases enable the user to test and verify all the features in NEMOH. The test cases cover: i) different body inputs, such as single body or double bodies, symmetrical or non-symmetrical body, ii) the different solver options, iii) the post-processing options, Kochin function and free-surface elevation, iv) with and without the removal of irregular frequencies, v) sum-and difference-frequencies QTFs, vi) uni-or bi-directional QTFs, vii) geometry complexities from a simple cylinder, a spar-buoy (OC3-HYWIND or Softwind) floating wind-turbine platform and an OC4-semisubmersible platform. In the test cases, the NEMOH results can be compared with the reference data from AQUAPLUS and HYDROSTAR provided in the package.
For both floating platforms, the difference-frequency QTFs of the surge, heave, and pitch are computed without the free-surface integrals. The sumfrequency full QTFs, including the free-surface integrals, are computed for the SOFTWIND platform. In most of the applications, the free surface terms are negligible in the difference-frequency QTFs but are important for the sum-frequency QTFs  [34, 35]. The computed results of NEMOH are compared with the results obtained using one or more of the following commercial software, i) WAMIT  [15] , ii) DIODORE  [22] , iii) HYDROSTAR  [18]  developed by Bureau Veritas. The QTFs results are compared only with HY-DROSTAR results. For a direct comparison with the different software, the same mesh is used in the computations. The irregular frequency is applied in the computations of all the test cases except if it is mentioned as not applied.
2  NEMOH is an open-source code, redistributed under the terms of the GNU General Public License (version 3) as published by the Free Software Foundation. Along with the source code, documentation  [30]  is provided which makes compilation and execution of the code easy. In addition, various test cases with reference data are provided in the software package to verify the results. To our knowledge, this NEMOH release will be the only open-source software that provides the QTF module.
Acknowledgement This work was done within the framework of the  FLOAT-ECH  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  101007142. We thank  Moran Charlou  from  LHEEA, ECN  for his help in finalizing the NEMOH v3. 0 release in Gitlab. We thank  Dr. W. Sheng  from  Lancaster University  for providing the WAMIT computation results of the truncated cylinder with a thin heave plate. We are grateful to the reviewers for their useful suggestions.
Experimental data used for parameterisation and evaluation Measured data from five two-year experiments (Exp1, Exp2, Exp3, Exp4, Exp5) in five countries (Australia, Argentina, Canada, China and Ireland) were used for model development and evaluation. Exp1 was conducted under controlled conditions (Mt Pleasant Laboratories, Launceston, Tasmania, Australia) with four waterlogging treatments using six contemporary Australian barley genotypes differing in their waterlogging tolerance from 2019 to 2020 (see refs. 39, 40 ). In Exp2, barley yields were measured under five waterlogging treatments in the greenhouse and field conditions at the School of Agronomy, University of Buenos Aires, Argentina in 2010 (see ref. 68  ). In Exp3, barley genotypes were evaluated for waterlogging tolerance in controlled field conditions at Brandon Research and Development Centre, Brandon, Manitoba, Canada from 2016 to 2017.
Waterlogging treatments were initiated at tillering by adding the water to heights of 0. 5-1 cm above the soil surface (see ref. 69  ). In Exp4, barley yields were measured in field conditions carried out at Oak Park, Carlow, Ireland from 2017 to 2018. Waterlogging treatments were initiated at the tillering stage using a boom irrigator (see ref. 70  ). In Exp5, field experiments were conducted in 2003-2004 and  2005-2006 at Zhejiang University, Hangzhou, China. Waterlogging treatments were imposed at tillering (see refs. 71, 72 ). All experiments were carefully managed to provide adequate nutrition and control of biotic pressures. Original datasets used for model evaluation (e. g. , yield under ambient conditions and those subject to waterlogging) were compiled from a range of environments, including field experiments and environmentally controlled experiments.
French scientific institutions organized to monitor the activity with an improved land network and through campaigns at sea, which lead to the creation of the REVOSIMA (Réseau de surveillance volcanologique et sismologique de Mayotte) in 2019. Thanks to this effort, several seismic stations were installed on land and on the ocean bottom to better characterize the seismicity  [Saurel et al. , 2022]. Before this work and its operational implementation  [Retailleau et al. , 2022] , the seismicity was identified and located mostly manually, which limited catalogs to the strongest activity [usually events greater than M 3;  Saurel et al. , 2022]. Automatic picking procedures are widely used to analyse large, continuous datasets that can not be easily analysed though hand picking. Automated methods have been extremely useful for real-time detection, when it is not possible for an analyst to continuously pick events, and for reanalysis of long time series.
We analyse their characteristics in the Mayotte system with a focus on LP behavior. We also note the observation of Very Long Period events  [VLP or VLF, Cesca et al. , 2020 , Lemoine et al. , 2020 , Feuillet et al. , 2021 , Laurent et al. , 2020]. These events have monochromatic signatures and do not have common earthquake characteristics. For this reason, PhaseNet does not detect VLP events, and we do not present their analysis; however, we discuss their observation and their links to other events. We analyze two years of continuous data recorded by the stations installed on the island of Mayotte using the machine learning-based method PhaseNet  [Zhu and Beroza, 2019]. We assess the robustness of P-and S-phase arrival picking by comparing with manual picks from two different catalogs. We associate events when enough phases are detected, and then proceed to locate them.
Automatic detection in continuous time series with PhaseNet Only one seismic station was installed on the island of Mayotte before the seismic crisis started [RA. YTMZ RESIF, 1995] ; however, the seismic network was progressively extended on the island to record a more extensive dataset, improve the detection level, and increase the location precision of earthquakes  [Saurel et al. , 2022]. To monitor the early part of the crisis, the regional stations on Grande Comore and Madagascar islands had to be used to locate events, which did not allow precise locations  [Lemoine et al. , 2020]. Since March 2019, the permanent continuous real-time seismic network has been extended with 2 accelerometers, 4 broadband stations (flat response down to 20 or 120 s), and 2 short-period low-cost stations (flat response down to 1 Hz).
Since then, the network aperture has been wide enough to allow stable and robust automatic detection and location of the seismicity with at least 4 stations. Figure  1a  shows the stations used on the island of Mayotte for this study and Figure  1b  shows data availability for each station throughout the period of interest. We use the PhaseNet algorithm to identify Pand S-phase arrivals on the continuous archived data. PhaseNet is a deep-neural-network-based method trained on numerous earthquakes in northern California to pick and identify phases on 3-component seismograms  [Zhu and Beroza, 2019]. It generalizes well enough to have been successfully used in diverse tectonic contexts, including: the Ridgecrest earthquake sequence  [Liu et al. , 2020] , induced seismicity in Arkansas  [Park et al.
, 2020] , and a year of seismicity in the Appenines  [Tan et al. , 2021]. We first filter the data with a 0. 4-45 Hz frequency band filter, remove the instrument response, then resample at 100 Hz and split into 30 s windows with a 50% overlap. Finally, this data is sent to PhaseNet for pick estimation. For the vertical-only station (AM. RAE55), we add two 0-filled vectors of data as dummy horizontal channels before sending the data to PhaseNet. We extract all the picks of the different stations. An event is declared when at least 10 P and S picks are within the expected time window for events in the area (2 s time window after the first arrival for the P waves and 8 s time window starting 4. 5 s after the first P arrival for the S waves).
Pick quality estimated from manual catalogs PhaseNet was trained on tectonic events. To test how well it generalizes for events that occur in a volcanic environment, we compare its arrival time measurements to reference arrival time measurements performed by skilled analysts. In principle, such a comparison between several observations quantifies uncertainties of arrival-time determination of hand-picked data  [Diehl et al. , 2012]. It has also been used for performance assessment of various automatic picking procedures  [Dai and Macbeth, 1995 , Leonard, 2000 , Di Stefano et al. , 2006]. For their analysis of the Mayotte seismo-volcanic crisis,  Cesca et al. [2020]  and  Lemoine et al. [2020]  built seismic catalogues through manual picking using the local station YTMZ. Consequently, we have access to a dataset of events with two independent reference manual estimations of P and S onsets besides PhaseNet picks on YTMZ data.
The first catalog of manual picks (from BRGM) contains 1347 P onsets and 1326 S onsets hand-picked by several expert analysts for events of magnitudes above M 3. 5 detected between May 10th, 2018 and December 5th, 2019  [Lemoine et al. , 2020]. The second catalog (from GFZ institute, GeoForschungsZentrum, Germany) contains 5999 P and 5999 S hand-picked onsets realized by a single expert analyst for events occurring between May 10th, 2018 and February 28th, 2019  [Cesca et al. , 2020]. We assess PhaseNet's performance and accuracy by comparing the arrival times automatically picked on YTMZ continuous data by PhaseNet from the beginning of the seismic crisis with the manually picked arrival times for the events of the two manual catalogs. PhaseNet identifies the correct arrival for 99. 6% of P onsets and 98.
4% of S onsets from the BRGM catalog, forming a comparative dataset of 1342 common P-picks and 1305 common S-picks. Similarly, PhaseNet identifies 97. 7% of P onsets and 98. 8% of S onsets from the GFZ catalog, forming a comparative dataset of 5858 common P picks and 5924 common S picks. Figure  2  show the time differences between the 3 catalogs and their statistical distributions, for the P-picks and S-picks, excluding outliers. We estimate that two picks are consistent if the difference in arrival-time between them is less than 0. 5 s for P phases and less than 0. 8 s for S phases. Outliers above the defined threshold mainly result from event or phase mis-identifications. Among the 1342 P and 1305 S arrivals in common with BRGM, 22 (1. 6%) and 102 arrivals (7. 8%) are outliers, respectively.
Among the 5858 P and 5924 S arrivals in common with GFZ, 277 (4. 7%) and 506 arrivals (8. 5%) are outliers, respectively. The two manual catalogs have 915 common P arrivals, including 21 (2. 3%) outliers; and 904 common S arrivals, including 23 outliers (2. 5%). Hence, manually hand-picked datasets Lise  Retailleau et al. show less incoherent picks for S arrivals. In the following analysis, we only consider the arrival time differences that have not been flagged as outliers according to our criteria. Arrival-time differences between independently hand-picked data display a non-Gaussian distribution of pick time differences. To assess the dispersion of pick time differences, we compare the median value and the interquartile range (i. e, containing the closest 25% of the distribution around the median) as it is more suitable for describing non-Gaussian distributions.
01 s and 0. 1 s (Figure  2b , middle panel). These values are smaller than the pick time differences observed between the two manual catalogs. PhaseNet S picks are more consistent with the manual picks from GFZ, even though this catalog eventuates smaller magnitudes and presents a larger population. On average, GFZ S picks arrive a few milliseconds sooner than BRGM picks. Similarly, PhaseNet S picks arrive a few milliseconds sooner than BRGM picks but a few milliseconds after GFZ S picks. These results are a representation of the picking precision among the different methods when a phase is correctly identified. The distribution of arrival differences for S onsets without removing outliers above 0. 8 s shows a recurrence of a systematic picking difference at +1. 2 s for approximately 44 picks (3%) for the first comparative dataset BRGM-PhaseNet and 188 picks (3%) for the second GFZ-PhaseNet comparative dataset (Supplementary Figure  9 ).
Such a large pick difference suggests a misidentification of the S onset with a precursory seismic arrival. Indeed, the Mayotte seismic sequence occurs within a complex volcanic and underwater environment, which may generate complex seismic waveforms, including P-to-S or a S-to-P phase conversion which are actually expected to arrive approximately one second before the S phase arrival time  [Garmany, 1989]. Here, PhaseNet automatic S picks are confused a few times with those precursory arrivals but the confusion has also been seen on some S picks in the catalogs picked manually by expert analysts. It is also possible that the manual training data contained misidentified picks, thus generating these erroneous picks. Further neural training could possibly help correct the confusion between a precursory arrival and an S pick that sometimes happens with PhaseNet.
Event location An advantage of PhaseNet over other auto-picking algorithms is its ability to pick and identify both P and S waves, when picking methods usually only identify the P-wave arrivals. The addition of S waves allows much more precise locations and event depth constraints, which is crucial for accurate hazard assessment. In Mayotte, stations have a limited azimuthal coverage, which makes earthquake location, and particularly depth resolution, particularly challenging. Each declared event with a set of P and S picks is located with NonLinLoc  [Lomax, 2008]  using a local 1D velocity model for the east of Mayotte  [Lavayssière et al. , 2022]. This model was developed with the code VELEST  [Kissling et al. , 1995]  using the 813 most robust earthquake locations (i. e. , events with at least 30 phases recorded, azimuthal gap < 180°; horizontal error < 2 km and vertical error < 5 km).
[2022]  estimated station corrections to account for lateral heterogeneity and variations in the velocity structure at shallow depth beneath the stations. The velocity model and station corrections obtained hence represent the best 1D approximation of the 3D subsurface structures of the region, which is essential for accurate earthquake location. Using the P-and S-arrival times identified by PhaseNet with the new velocity model and station corrections, we locate the events using the nonlinear probabilistic earthquake-location program NonLinLoc  [Lomax, 2008] , which calculates a maximum-likelihood hypocenter that represents a global minimum misfit for the spatial location and the origin time of each event. We represent in Figure  1a  the 50,512 events we detected and located accurately. We only keep the events with a final RMS lower than 0.
Acknowledgments Since June 2019, Mayotte eruption monitoring is funded by  le Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI), le Ministère de la Transition Ecologique (MTE) and le Ministère des Outremers (MOM)  with the support of  le Ministère de l'Intérieur (MI) and le Ministère des.
, 2018; Irani and Anandan, 1998 ) that classical, geometry-based techniques are better suited to the "motionbased" parts of the problem, while deep networks are more suitable for modeling appearance. In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly. We present a novel probabilistic model to estimate the camera's rotation given the motion field. We then rectify the flow field to obtain a rotation-compensated motion field for subsequent segmentation. This strategy of first estimating camera motion, and then allowing a network to learn the remaining parts of the problem, yields improved results on the widely used DAVIS benchmark as well as the recently published motion segmentation data set MoCA (Moving Camouflaged Animals).
A 30/20 training/validation split is provided. Our model is evaluated on the validation set. Ground truth segmentations of the most prominent moving object are provided for each frame. DAVIS has been widely used for general video segmentation as well as motion segmentation. MoCA (Moving camouflaged animals) (Lamdouar et al. , 2020) comprises a set of 141 videos depicting 67 different animals. The data set is split into three motion types describing the animals motion locomotion, static and deformation. Following the procedure of  (Lamdouar et al. , 2021; Yang et al. , 2019)  we evaluate on the locomotion split, which forms the largest part of the dataset with 88 video sequences in total. Annotations are provided in form of bounding boxes. An evaluation script is provided by the authors of MoCA.