dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We wish to thank our anonymous Referee whose comments helped us to improve the manuscript. This work has made use of data from the  European Space Agency  (ESA) mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/ consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Italian participation in DPAC has been supported by  Istituto Nazionale di Astrofisica (INAF)  and the  Agenzia Spaziale Italiana (ASI)  through grants  I/037/08/0 ,  I/058/10/0 ,  2014-025-R. 0 ,  2014-025-R. 1. 2015  and  2018-24-HH. 0  to INAF ( PI M. G. Lattanzi ). The Swiss participation by the  Swiss State Secretariat for Education, Research and Innovation  through the " Activités Nationales Complémentaires ".
UK community participation in this work has been supported by funding from the  UK Space Agency , and from the  UK Science and Technology Research Council. This work was supported in part by the  French Centre National de la Recherche Scientifique (CNRS) , the  Centre National d'Etudes Spatiales (CNES) , the  Institut des Sciences de l'Univers (INSU)  through the  Service National d'Observation (SNO)   Gaia. This research was supported by the ' SeismoLab '  KKP-137523   Élvonal  grant of the  Hungarian Research, Development and Innovation Office (NKFIH) , and by the  LP2018-7   Lendület  grant of the  Hungarian Academy of Sciences. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. It is a pleasure to thank  M. B. Taylor  for developing the TOPCAT software, which was very useful in carrying out this work.
To realize the projected increase in world-wide demand for floating offshore wind, numerical simulation tools must capture the relevant physics with a high level of detail while being numerically efficient. This allows engineers to have better designs based on more accurate predictions of the design driving loads, potentially enabling an economic breakthrough. The existing generation of offshore wind turbines is reaching a juncture, where traditional approaches, such as the blade element momentum theory and actuator disc models, are becoming inadequate due to the increasing occurrence of substantial blade deflections. QBlade is a tool that includes a higher fidelity aerodynamic model based on lifting-line theory, capable of accurately modeling such scenarios. In order to enable the simulation of offshore conditions in QBlade, and to make use of this aerodynamic capability for novel offshore wind turbine designs, a hydrodynamic module called QBlade-Ocean was developed.
In the present work, this module is validated and verified with two experimental campaigns and two state-of-the art simulation frameworks on three distinct floating offshore wind turbine concepts. The results confirm the implementation work and fully verify QBlade as a tool to be applied in offshore wind turbine simulations. Shortcomings reported by comparable tools in the non-linear response to irregular wave excitation are confirmed by QBlade and reiterate the need for further research in this field. A code-to-code comparison with the industry-designed Hexafloat concept highlights the coupled interactions on floating turbines that can lead to large differences in motion and load responses in otherwise identically behaving simulation frameworks.
source panel code XFOIL  (Drela, 1989) , the graphical user interface (GUI) XFLR5  (Deperrois)  and an in-house developed steady BEM solver. The code has been expanded in its capabilities ever since. Today, QBlade features two time domain aerodynamic solvers. The lower fidelity one is based on the Blade Element Momentum theory. Briefly summarized, the BEM algorithm iterates a set of equations for the tangential and axial induction stemming from the momentum balance and the blade element theory until a converged value for the axial and tangential induction is found. The method has been expanded with several engineering correction methods such as the Prandtl tip loss correction and dynamic inflow filter. The BEM method implemented in QBlade uses a polar grid for the azimuthal discretization of the induction factors, following the approach laid out by  Madsen et al.
(2020). In this method, the rotor disc is divided into a polar grid that enables the computation of the local velocity at each intersection taking the local induced velocity and inflow conditions into account. The implementation of the blade element model accurately resolves the velocity at curved stations (e. g. prebend or deflection) by a transformation of the velocity vector into the local coordinate system of the station. The higher fidelity method, the Lifting-Line-Free-Vortex-Wake (LLFVW) solver, applies the Lagrangian vortex theory and follows the implementation of  van Garrel (2003). The structural model used in QBlade relies on the finite element analysis (FEA) module of the Project::Chrono multi-physics engine  (Tasora et al. , 2016). The application in QBlade is such that the structural model of the turbine consists of multiple body objects for the blades and the tower.
DeepLines Wind TM DeepLines Wind TM (Principia) is a module of the commercial integrated software solution DeepLines TM developed by Principia and IFP Energies Nouvelles. It is primarily known as a software solution to calculate in-place and installation analyses for offshore structures such as flexible risers, power cables and mooring systems. Due to the increased market share of wind energy in the offshore environment, the DeepLines Wind TM module was developed in 2011 and is now able to carry out fully coupled dynamic finite element analysis. Multiple BEM models are implemented and can be chosen from an external. dll library. Like QBlade, DeepLines Wind TM can model horizontal and vertical axis wind turbines. Within this study, a dynamic inflow model was activated while no unsteady blade aerodynamics were applied.
Simulation Models and Modeling Approaches This section briefly introduces the three simulation models used for the validation and verification of QBlade. First, the properties and characteristics of each of the three FOWTs are discussed together with their respective modeling approaches. Second, the areas where the three simulation suites differ from another in the representation of the physics are highlighted and discussed regarding their possible influence on results. Third, the chosen test cases for validation and verification purposes are introduced. A more extensive description of the three QBlade model definitions can be found in Perez-Becker and Behrens de Luna (2022). Additionally, each turbine database is available on the Zenodo online platform. The corresponding DOIs are provided at the end of this work.
OC5 Model The first model of a floating offshore wind turbine used for the verification and validation is the MARIN Stock Wind Turbine mounted on the DeepCwind substructure, henceforth referred to as the OC5 model. This FOWT was thoroughly investigated within the Offshore Code Comparison, Collaboration, Continued, with Correlation project (OC5)  (Robertson et al. , 2014). The OC5 project was operated under the International Energy Agency Wind Task 30 and builds on previous code-to-code comparison efforts (OC3 and OC4). In the OC5 collaboration, a large number of institutions applied various modeling tools with the goal to simulate test cases that were carried out experimentally and relate differences to certain modeling approaches. (2014). Similar to the approach undertaken in the OC5 project, the numerical models are formulated utilizing properties that have been upscaled to match the dimensions of the full-scale size.
, 2013)  for DeepLines Wind TM. The authors acknowledge that OpenFAST includes the OLAF solver in its newest AeroDyn v15 release. OLAF is a higher fidelity lifting line solver for aerodynamics similar to the one implemented in QBlade  (Shaler et al. , 2020). Moreover, OpenFAST includes the BeamDyn structural model that allows for the computation of full geometric non-linearity and large deflections of the blades due to its exact beam theory formulation. The application of both modules was not included in this study. The amount of simulations for the different turbine designs was large and the simulations where performed on desktop workstations. Using OLAF and BeamDyn for the OpenFAST calculations would have rendered the evaluation time for these simulations unacceptably large. Finally, NREL released OpenFAST v3.
(2022). Table  2  shows the natural frequencies as they are extracted from the time series of the decay test for the three FOWT models. The blank spaces indicate that no results of the corresponding test was obtainable. In the case of the OC5 model for instance, the result of the experiment decay test was not publicly available. Thanks to NREL however, the time series of surge and pitch decays were shared upon request. Given that the decay tests of the Softwind model were solely conducted for the surge, heave and pitch DoFs, the numerical simulations were exclusively performed for these corresponding cases. As noted previously for the Hexafloat model, QBlade will only be verified with the commercial software DeepLines Wind TM in a code-to-code comparison since for this model no experimental data is available.
Hence, for this turbine, the thrust coefficient was the main point in the comparison. In each software package, the thrust force applied on the rotor shaft was used to calculate the thrust coefficient. A description of the test cases can be found in Table  3. The average value for C p and C t for each rotor speed of the OC5 turbine sweep is displayed in Fig. 4. The reference results that are displayed stem from the study of  Goupee et al. (2015) , in which the polars of an OpenFAST model were calibrated to resemble the aerodynamic behavior of the OC5 turbine. Two curves can be seen for each coefficient. Test case 2. 1 ranges from a tip speed ratio (TSR) of 2. 8 up to close to 9 and represents a rotor speed-sweep at constant, close to rated, wind speed with 0. 89°p itch angle. Test case 2. 2 ranges from a TSR of 1.
The PI parameters of the OO-Star 10 MW FWT are used, since the pitch natural frequency between both the OO-Star and Softwind platforms is nearly identical  (Yu et al. , 2018). In case of the Hexafloat model, the ROSCO v 2. 4. 1 controller (NREL, 2021) was selected due to its ability to include a velocity feedback damping loop designed to reduce pitching motion and thereby increase stability of the floating substructure. Unfortunately, this feature had to be disabled for this code-to-code comparison after difficulties were found in the communication between the ROSCO controller and DeepLines Wind TM. Leaving this feature active in QBlade alone would have impeded a consistent comparison. Therefore, the controller gains were de-rated to prevent negative damping effects instead. The Softwind model is analyzed at above-rated conditions, with a mean wind speed of 18 m/s that requires continuous blade pitch actuation by the DTU wind turbine controller.
Coil features Fig. 1  shows an 8-channel ModFlex consisting of two identical flexible coil modules  [32]. In each module, four single-gap coaxial coils  [8, 14]  with 8 cm loop diameter and %1. 4 mm cable thickness  [16]  (Molex 047SC-2901, Lisle, Illinois, USA) are embedded in three textile layers. The coaxial coils are operated close to their self-resonance, leading to a homogeneous current density distribution at their outer conductor  [8]. A compact biocompatible 3D-printed housing (lasersintered polyamide PA2200) holds two stacked printed circuit boards with all electrical components for fine-tuning to the Larmor frequency of 123. 2 MHz (L T ), matching (C M1 , C M2 ) and active detuning with a pair of PIN (positive intrinsic negative) diodes (MACOM, Lowell, MA, USA) and RF chokes (Coilcraft, Cumbernauld, UK).
The coils were fine-tuned and matched on a volunteer subject. The broadband active detuning circuitry in each receive-only coil element has the function to block induced currents on the ModFlex coil during RF transmission with the body coil. As an additional safety measure, a fuse with 315 mA current rating is located in the outer conductor at the coil port in case the coil's active detuning switching network should fail. If the fuse interrupts the connection of the outer conductor, i. e. creates an open circuit, the resonant transmission line behavior will be eliminated, preventing the formation of high currents on any coil conductor. To avoid induced currents on the cable shields, a floating shield current suppression trap  [33]  (>25 dB attenuation at 123. 2 MHz) is placed on each module's cable strand.
2. Button colors enable the identification of modules at the MR console and (de-)activation of those 4channel modules needed to image the target FOV. In this study, the coil configurations used during phantom tests were a and d (on the abdominal region of a torso phantom). For in vivo experiments, the 8-channel ModFlex in configuration a was used for neck imaging and different 16-channel configurations were used for spine, hip and ankle imaging (f, g, a+c and separate module, see also Figs. 3  and 4 ). or two ModFlex coils (8 or 16 coil channels) include e. g. , the breast, shoulder, knee, elbow or abdomen. During preliminary studies  [34, 35]  the proposed coaxial coil unit was compared to a custom-built 4-channel surface loop coil array of the exact same dimensions fabricated out of stranded copper wire.
e. , between the patient table and the volunteer. The MRI protocol consisted of sequences targeting the technical coil performance evaluation which were measured per volunteer and per coil (ModFlex and corresponding reference coil): after a localizer scan, a 3D T 1 -weighted gradient echo (GRE) sequence with 1. 6 Â 1. 6 mm 2 (ankle and spine) or 2. 1 Â 2. 1 mm 2 (neck and hip) in plane resolution (TR/TE = 3. 7/2. 5 ms, 3°flip angle, 2. 0-2. 2 mm slice thickness, 1490-1570 Hz/px), and a noise-only scan (without any transmit RF pulse or imaging gradients) were acquired. These data were used for SNR evaluation in a volume of interest and to determine the noise correlation, i. e. , coupling between individual coil channels. In parallel imaging, the socalled geometry-factor (g-factor) which depends on the signal correlation between individual coil channels and is spatially variable, decreases the SNR in addition to the square root of the acceleration factor R.
Data post-processing In MATLAB 2021b (The Mathworks, Inc. , Natick, MA, USA) we calculated SNR maps based on the pseudo multiple replica method from 3D in vivo GRE and noise only data, and extracted the normalized noise correlation matrix  [38]. A GRAPPA reconstruction  [39]  framework and 2D GRE together with noise-only data was used for the calculation of SNR maps with a fully sampled or undersampled kspace, and thereof, g-factor maps which were smoothed with a 3 Â 3 mean filter. Acceleration factors R ranging from 2 to 6 and different slice orientations for each anatomical target region were simulated and maximum and mean g-factors extracted for the slice covering the regions of interest (ROIs). To visualize deviations between FA maps acquired with the body coil or a detuned ModFlex coil placed on the phantom, relative FA difference maps were calculated.
Funding This research is supported by the  Austrian Science Fund  ( FWF  project  I-3618  " BraCoil " and project  P-35305  " AquaLactEMISM "), the  Agence Nationale de Recherche (ANR) :  ANR-17-CE19-0022 , and the  CITRUS  project that has been funded by the  European Union 's  Horizon Europe research and innovation programme  under Grant Agreement no  101071008.
Analyzing confounding and causal paths, we demonstrate that this integration allows for causal inference on microbiome-metabolite relations, even in the presence of unmeasured confounders. nature of both types of omics datasets. 11  Moreover, from the viewpoint of causal statistics, extracting causal models from observational data requires full information on all relevant confounding variables. 12  However, measuring and conceptualizing all relevant confounders poses conceptual and practical problems for concrete microbiome-metabolome study, partly due to limited knowledge on relevant confounding factors. 13  Hence, results of statistical hypothesis-free screening approaches are often difficult to interpret and embed into the knowledge already gathered about microbial biology. 13, 14  Integrating the genetic content of the microbial community with knowledge of microbial biology while respecting basic laws of nature, such as conservation of mass and charge, constraint-based reconstruction and analysis (COBRA),  15  allows a fine-graded mapping of metabolic functions of microbial communities.
Empirical results For testing the theoretical framework outlined above, we utilized fecal metabolome and metagenomic data from Yachida et al. 7  First, we mapped the measured fecal metabolome data, reporting the absolute concentrations for 450 metabolites, onto the AGORA collection of 818 microbial genomescale reconstructions. 4  We found that 106 metabolites were measured by the metabolome data and had a corresponding metabolite exchange reaction in at least one microbial reconstruction. From these 106 metabolites, 54 had values above the limit of detection in the fecal metabolome for at least 50% of all samples and, thus, they were included in the subsequent analyses (Figures  4A  and 4B ). Second, we mapped the relative metagenomic quantifications that had been reported by Yachida et al.
DISCUSSION The microbiome contributes essentially to the human metabolism by providing essential nutrients, such as vitamins and short-chain fatty acids, which would be inaccessible otherwise. 29  Numerous studies have been conducted to shine a light on the complex metabolic host-microbiome interplay, generating insights from a diverse range of paradigms spanning experimental work,  [30] [31] [32]  computational and theoretical studies,  11, 33  and statistical interrogation into the dependence patterns of observational multi-omics datasets. 13, 34  Herein, we bridge the gap between population statistics and in silico COBRA microbial community modeling to facilitate causal inference in metabolome-microbiome observational data. For identifying causal relationships in observational data, either full information on relevant covariates  35  or plausible instrumental variables (e.
g. , as utilized in Mendelian randomization  36  ) must be available. In the case of microbiome-metabolome  association studies, information on relevant covariates, such as diet, is typically missing and often difficult to assess reliably. 13  Moreover, while certain genetic variants have been shown to be associated with the microbiome,  37  the host genetic signature is modest at best 38 making the identification of suitable genetic instrument variables to perform Mendelian randomization difficult. Therefore, metabolome-microbiome associations are challenging to interpret in causal terms and, indeed, simulation studies of microbial communities suggest that naive interpretation would lead to a high level of false positives. 11  However, we demonstrate that we can overcome these difficulties by systematically integrating in silico COBRA community modeling into statistical analyses of metabolome-microbiome datasets.
Significance of pattern is determined by a significant slope of the regression line and significant sign (dis)agreement through permutation testing. while working reasonably well for a range of metabolites, the chosen operationalization of flux variability analysis  15, 39  may not be universally useful for quantifying metabolite-species relations across all metabolic functions. However, the general methodology of in silico in vivo association pattern analysis is not constrained to flux variability analysis. In contrast, it can be performed with any plausible readout of microbiome community models on the level of fluxes. We analyzed both species presence as well as species abundance. On the surface, this is contra-intuitive, since information on the presence of a species is represented in the abundance data as well.
Conclusions We presented a theoretical framework integrating population statistics with COBRA microbial community modeling for causal inference on microbiome-metabolome relations. We then showed the feasibility and validity of our approach on an empirical dataset, consisting of 347 individuals with fecal metabolome and metagenomics data. Conceptually, we validated thereby a methodological framework to incorporate formalized knowledge about microbial biology in the form of genomescale models into statistical association analyses, bridging two major paradigms of systems biology. The successful identification of significant in silico in vivo association patterns for 26 metabolites highlights the validity of COBRA microbial community models as a theoretical model for actual microbial metabolic activity.
Study sample The study sample consisted of the Japanese colorectal cancer cohort data obtained from Yachida et al. ,  7  which included for 347 individuals (220 colorectal cancer cases and 127 healthy controls) shot-gun sequencing data for fecal metagenomics and mass spectrometric metabolome data including quantifications for 450 metabolites. Sequencing reads and taxonomic assignments had been performed using the MetaPhlAn2 pipeline. 28  Furthermore, meta-data on age, sex, and BMI were available and were included in the re-analyses of the data. The details on metagenomic and metabolomic measurements, can be found in the stem publication of the data of Yachida et al. 7  The raw metabolome data and the microbiome abundance data can be found in the supplementary material of the stem paper (https://static-content.
Construction of sample-specific gut microbiota models Relative abundances on the species level for the 347 samples were obtained from the supplementary material (https://staticcontent. springer. com/esm/art%3A10. 1038%2Fs41591-019-0458-7/MediaObjects/41591_2019_458_MOESM3_ESM. xlsx) of Yachida et al. 7  In the first step, the quantified species were mapped onto the reference set of 818 microbial metabolic reconstructions (AGORA). 4  Next, COBRA microbial community modeling was done using mgPipe of the Microbiome Modelling Toolbox. 16, 25  The input data were relative abundances of the present microbes of each sample and their genome-scale reconstructions obtained from AGORA. To use higher-level taxonomical data, pan-reconstructions were created using the ''createPanModels'' function of the Microbiome Modeling Toolbox.
FVA resulted in values for minDiet nj , maxDiet nj ; minFaecal nj , and maxFaecal nj. The maximum production maxProd nj and uptake capacity maxUpt nj were calculated as maxProd nj = maxFaecal nj + minDiet n j ; (Equation  14 ) and maxUpt nj = minFaecal nj + maxDiet n j : (Equation  15 ) The maximal net secretion flux (maxProd nj ), which corresponds to the absolute value of the difference between the maximal flux through the fecal secretion exchange reaction and the minimal flux through the corresponding dietary uptake exchange reaction, were calculated subsequently for each metabolite. This has been done for each personalized model. All simulations were performed in MATLAB (Mathworks, Inc. ) version R2018b with IBM CPLEX (IBM) as the linear programming solver. The simulations were carried out using the COBRA Toolbox  15  and the Microbiome Modelling Toolbox.
ACKNOWLEDGMENTS This study was funded by grants from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 757922 ) to I. T. , the  National Institute on Aging  (grant nos. 1RF1AG058942  and  1U19AG063744 ), and the  Science Foundation Ireland  (grant no. 12/RC/2273-P2 ). J. H. received additional funding from the  Deutsche Forschungsgesellschaft (DFG)  (grant no. HE 9198/1-1 ).