dataesr/scientific-paragraphs-categorization huggingface dataset:
The surface elevation of the wave was measured at the turbine rotor plane (z = 3. 88 m) which gave a clear indication of the wave surface profile and allowed a direct comparison between the wave elevation and the induced turbine loadings. Fig. 19  and Fig. 20  show the wave surface elevation over a 5 s time period for the experimental and CFD model results of P1W1 and P2W2, respectively. Experimentally, Figs. 19 and 20 show a 5 s snapshot of the total data set; however, both the wave frequency and wave height were measured over 7 to 10 different tests and an average was obtained across the multiple runs to get a more representative average of the sample conditions. Table  18  shows the target and measured wave characteristics for the experimental results as well as the CFD model comparison.
CFD comparison of transient turbine loadings: current-only and wave-current Blade, hub and total turbine thrust and torque loadings were investigated using CFD simulations as individual component loadings were not measured during the experimental testing. It is therefore assumed that the data presented by the CFD models would be representative of the experimental thrust and torque data as a result of prior validation using alternative parameters. The average thrust and torque loadings show good similarity between each of the five types of flow condition tested (U, P1, P2, P1W1, P2W2), with small variations between cases due to differences in the calculated W vol. Table  23  shows how these average thrust and torque loadings are not significantly affected by the different wave and current characteristics.
Conclusions This study presents an experimental test campaign, using a 3-bladed, 1:20 scale, HATT performed at the IFREMER wave-current flume, which is used to validate numerical models under current-only and wave-current conditions. The average and transient turbine thrust, torque and out of plane bending moment were measured both experimentally and numerically, along with the flow velocity and wave height. These measurements allowed the turbine performance characteristics and loads to be calculated. The main conclusions from the study are: • The turbine was fully characterised under uniform flows, experimentally and numerically, providing a base case of the simplest flow conditions for comparisons against more complex flow investigations. • The mode of the streamwise velocity was found to accurately represent the predominant flow conditions for all experimental data distributions.
Acknowledgements The author acknowledges support from  SuperGen UK Centre for Marine Energy  [ EP/N020782/1 ] as well as  Cardiff University  for providing funding. The experimental testing carried out at IFREMER was funded by  EPSRC Impact Acceleration Account (IAA)  funding [ EP/ R51150X/1 ] and the  Dynamic Loadings on Tidal Turbine Arrays (DyLoTTA)  project  [EP/N020782/1 ]. The author would also like to thank the staff at  IFREMER  for their expertise and support during the test campaign. In addition, this research was performed using the supercomputing facilities at  Cardiff University  operated by  Advanced Research Computing at Cardiff (ARCCA) on behalf of the Cardiff Supercomputing Facility  and the  HPC Wales and Supercomputing Wales.
A comprehensive European dataset on monthly atmospheric NH 3 , acid gases (HNO 3 , SO 2 , HCl), and aerosols (NH + 4 , NO - 3 , SO 2- 4 , Cl -, Na + , Ca 2+ , Mg 2+ ) is presented and analysed. Speciated measurements were made with a low-volume denuder and filter pack method (DEnuder for Long-Term Atmospheric sampling, DELTA ® ) as part of the EU NitroEurope (NEU) integrated project. Altogether, there were 64 sites in 20 countries (2006)(2007)(2008)(2009)(2010), coordinated between seven European laboratories. Bulk wet-deposition measurements were carried out at 16 co-located sites (2008)(2009)(2010). Inter-comparisons of chemical analysis and DELTA ® measurements allowed an assessment of comparability between laboratories. The form and concentrations of the different gas and aerosol components measured varied between individual sites and grouped sites according to country, European re-gions, and four main ecosystem types (crops, grassland, forests, and semi-natural).
, 2009)  and with the network of main CarboEurope-IP CO 2 flux monitoring sites (http://www. carboeurope. org/ ceip/, last access: 5 January 2020)  (Flechard et al. , 2011 (Flechard et al. , , 2020)). Two of the UK sites in the NEU DELTA ® network are existing UK NAMN  (Tang et al. , 2018a)  and AGANet sites  (Tang et al. , 2018b). These are Auchencorth Moss (UK-Amo) and Bush (UK-EBu), located in southern Scotland. Monthly gas and aerosol data at the two sites, made as part of the UK national networks, were included in the NEU network. NEU network N r data were used, together with a range of dry-deposition models, to model dry-deposition fluxes  (Flechard et al. , 2011)  and to assess the influence of N r on the C cycle, potential C sequestration, and the greenhouse gas balance of ecosystems using CO 2 exchange data from the Figure  1.
Following data checks against defined quality protocols  (Tang et al. , 2009) , the finalized dataset was uploaded to the NEU database (http://www. nitroeurope. ceh. ac. uk/, last access: 29 July 2020). Establishment of the network, including the first year of measurement results on N r com-Y. S. Tang et al. :  Pan-European rural monitoring network shows dominance of NH 3 gas ponents, is reported in  Tang et al. (2009). Information on co-located measurements and agricultural activities at each of the sites was also collected and is accessible from the NEU website (http://www. nitroeurope. ceh. ac. uk/, last access: 29 July 2020).
3 ). The laboratories were CEAM (all three Spanish sites); INRAE (French Renon site); and SHMU, designated the main laboratory responsible for all other sites. A full suite of precipitation chemistry analyses were carried out that included pH, conductivity, NH + 4 , NO - 3 , SO 2- 4 , PO 3- 4 , Cl -, Na + , K + , Ca 2+ , and Mg 2+. Rain volumes and precipitation chemistry data were submitted in a standard template to UKCEH for checking and then uploaded to the NEU database (http://www. nitroeurope. ceh. ac. uk/, last access: 29 July 2020). Samples with high P (> 1 µg L -1 PO 3- 4 ), high K + , and/or NH + 4 values that are indicative of bird contamination were rejected. Annual wet deposition (e. g. kg N ha -1 yr -1 ) was estimated from the product of the species concentrations and rain volume.
European emissions data With the exception of Russia and Ukraine, official reported national emissions data on SO 2 , NO x , and NH 3 are avail-able for all other 18 countries in the NEU network from the European Environment Agency (EEA) website  (EEA, 2020). Emissions data for the period 2007 to 2010 were extracted, and the emission densities of each gas (t km -2 yr -1 ) in each country were derived by dividing the 4-year-averaged total emissions by the land area (km 2 ). Gridded emissions data (at 0. 1 • × 0. 1 • resolution) for SO 2 , NO x , and NH 3 are available from the EMEP emissions database (EMEP, 2020). The 0. 1 • × 0. 1 • gridded data for the period 2007 to 2010 were downloaded and were used to estimate national total emissions (sum of all grid squares in each country) and 4-yearaveraged emission densities (t km -2 yr -1 ) for Russia and Ukraine.
As a check, total emissions for the other 18 countries were also calculated by this method and were the same as the national emission totals reported by the EEA (EEA, 2019). et al. , 2018a, b). The UK approach is a high-density network with low-time-resolution (monthly) measurements, combining an implementation of the DELTA ® method used in the present NEU DELTA ® network and a passive ALPHA ® method  (Tang et al. , 2001)  to increase network coverage in NH 3 measurements  (Sutton et al. , 2001b; Tang et al. , 2018a). Monthly and annual data for the overlapping period of the project were extracted from the UK-AIR website (https://uk-air. defra. gov. uk/, last access: 25 November 2019) and nested with the NEU network data for analysis in this paper.
The occurrence of outliers in some of the individual monthly values indicates that caution needs to be exercised in the interpretation of these data points in the intercomparison. To average out the influence of a few individ-ual outliers, the mean concentrations from each of the seven laboratories for each of the four field sites were calculated and compared with averaged median estimates of all laboratories for each site. A summary of the mean concentrations and the percentage difference from the median is presented in Table  2. Since the INRAE laboratory did not join the NEU network until 2008, averaged median values from the 2008 and 2009 inter-comparisons are used to compare with the INRAE results, included in the table for clarity. The mean concentrations between laboratories are broadly comparable.
Inter-comparisons : SO 2 , SO 2- 4 Six laboratories provided slopes within 12 % of the median values in the regression analysis for SO 2 (Fig. 6 ). The smaller R 2 values were from two laboratories (CEAM and SHMU; R 2 < 0. 7), with data points both above and below the 1 : 1 line. For INRAE, the larger slope of 1. 6 (R 2 = 9) was due to a single high SO 2 reading reported for Auchencorth of 2. 0 µg SO 2 m -3 , compared with the median of 1. 4 µg SO 2 m -3. When the mean SO 2 concentrations measured by INRAE are compared with the median, the difference was on average 13 %, providing acceptable agreement, which suggests that the high reading may just be an outlier. There was more scatter in the inter-comparison for SO 2- 4 , although the majority of points are still close to the 1 : 1 line (Fig.
Inter-comparisons: base cations (Na + , Ca 2+ , Mg 2+ ) Measurements of Ca 2+ and Mg 2+ were the most uncertain, with the largest scatter in the inter-comparisons (Fig. 6 ). Despite the trace levels of these base cations at all field sites, four laboratories (INRAE, UKCEH, SHMU, vTI) provided data close to the 1 : 1 line, demonstrating close agreement between these laboratories. The clear outliers above the 1 : 1 line are from CEAM, MHSC, and NILU, with slopes > 2. While MHSC over-read Ca 2+ and Mg 2+ , its results for Na + were in better agreement with other laboratories, with a slope of 0. 9 (R 2 = 0. 5) (Fig. 6 ). There was a lot of scatter in the data however, with outlier points both above and below the 1 : 1 line, suggesting measurement uncertainties in their base cation measurements.
For NILU, the only base cation results reported by the laboratory were for the 2008 DELTA ® inter-comparisons at Auchencorth and Braunschweig. This accounts for the low number of data points (n = 4) from the NILU laboratory. The median concentrations of Ca 2+ and Mg 2+ at both field sites were very low (< 0. 1 µg m -3 ), which makes comparison with the few data reported from NILU highly uncertain. Like NILU, CEAM also did not report base cation results for all of the DELTA ® inter-comparison. Base cation results provided by CEAM were for 2007-2009 only.
9b ). HNO 3 concentrations on the European continent were generally higher (0. 2-0. 4 µg HNO 3 -N m -3 ). In the UK, HNO 3 data are also available on a wider spatial scale from the AGANet (Sect. 2. 7. 2; Tang et al. , 2018b). The 4-year-average concentrations of HNO 3 from 30 sites in the AGANet are plotted alongside the NEU HNO 3 data from the four UK sites in its network in Fig. 9b. The UK HNO 3 data on the UK-AIR database (https://uk-air. defra. gov. uk/, last ac-cess: 25 November 2019) have been corrected for HONO interference with a 0. 45 correction factor (see  Tang et al. , 2018b). For consistency in Fig. 9b , the UK raw uncorrected HNO 3 data are used for the present comparison. The 30-site mean (0. 17 µg HNO 3 -N m -3 ) was higher than from just 4 UK sites in the NEU network (0. 10 µg HNO 3 -N m -3 ).
HNO 3 The seasonal distribution in HNO 3 is similar between the different ecosystem groups, varying only in magnitude of concentrations (Fig. 14c ), and reflects the secondary nature of this component that is formed from oxidation of NO x  (Fahey et al. , 1986; ROTAP, 2012). Since the HNO 3 data are actually the sum of HNO 3 and HONO, with a small contribution from NO 2 (see Sect. 2. 2. 3), the temporal patterns seen are likely to be the superimposed profiles of both HNO 3 and HONO. In the studied region, NO 2 is predominantly from vehicular emissions, which are not expected to show large seasonal variations and should therefore exert a negligible effect on the temporal patterns in HNO 3. With this caveat in mind, HNO 3 concentrations in the crops group are up to 2 times larger than the grassland group, while the smallest concentrations are in the semi-natural group.
Bulk wet-deposition measurements Annual mean wet deposition of chemical species measured at the NEU bulk sampling sites was estimated by combining measured concentrations with annual precipitation. Site changes also occurred during the operation of the bulk wetdeposition network, with some sites closed and new sites added. At Mitra (PT-Mi3), contamination of the rain samples from bird strikes resulted in the rejection of a large proportion of the monthly data, and this site was excluded from the data analysis. In total, 12 sites provided 2 years of monthly data, with a further 5 sites providing 1 year of monthly data over the period 2008 to 2010. Due to differences in start and end dates for bulk measurements between the sites, the annual mean data derived are for 12-month periods or 2 × 12month periods and not from calendar years.
Contributions by the other base cations Ca 2+ and Mg 2+ gave a further 20 % (range = 8 %-41 %, n = 17) (Table  S14 ). The wet-deposition data on NH + 4 and NO - 3 , combined with a wider precipitation chemistry dataset (e. g. from EMEP and other national precipitation networks), were used to estimate total N r deposition to a site  (Flechard et al. , 2011 (Flechard et al. , , 2020)). Together, the dry (DELTA ® network) and wet N r estimates (NEU bulk network, combined with data from other national precipitation chemistry networks) are used to compare with EMEP models and to examine the interactions between N r supply and greenhouse gas exchange at the NEU DELTA ® sites, presented in a separate paper by  Flechard et al. (2020). The wet-deposition measurements in this paper highlight where DELTA ® and bulk wet-deposition data are co-located and provide parallel information on gas and aerosol concentrations (for dry-deposition modelling) and wet deposition at those sites.
Conclusions The NitroEurope DELTA ® network has provided for the first time a comprehensive quality-assured multi-annual dataset on reactive gases (NH 3 , HNO 3 , SO 2 , HCl) and aerosols (NH + 4 , NO - 3 , SO 2- 4 , Cl -) across the major gradients of emission densities, ecosystem type, and climatic zones of Europe. By sharing the method and protocol with several European laboratories and developing synergies with established infrastructure (e. g. CarboEurope network and EMEP field sites), it has proven possible to establish a large-scale network within a relatively short timescale and with low costs. Key elements were a harmonized methodology and the implementation of quality protocols that included regular lab-oratory and field inter-comparisons to monitor and improve performance. At the same time, the concurrent measurement of the gas and aerosol components permitted an assessment of the atmospheric composition and the spatial and seasonal characteristics in the gas and aerosol phase of these components.
The dataset has also been used to develop estimates of sitebased N r dry-deposition fluxes across Europe, including supporting the development and validation of long-range transport models. Combined with estimates of wet deposition (from NEU bulk wet-deposition network and other networks such as EMEP), an assessment of the interactions between N supply and greenhouse gas exchange was addressed in a separate paper by  Flechard et al. (2020)  using N r and CO 2 flux data from the co-location of the NEU DELTA ® with Car-boEurope Integrated Project sites. Two key features have emerged in the data. The first is the dominance of NH 3 as the largest single component at the majority of sites, with molar concentrations exceeding those of HNO 3 and SO 2 combined. As expected, the largest NH 3 concentrations were measured at cropland sites, in intensively managed agricultural areas dominated by NH 3 emissions.
Acknowledgements. The contributions by UKCEH scientists were further supported by the  UK Natural Environment Research Council (NERC) National Capability  award  NE/R016429/1 , as part of the  UK-SCAPE programme delivering National Capability  (https: //www. ceh. ac. uk/ukscape, last access: 1 November 2020). Atmospheric measurements in the  UK National Ammonia Monitoring Network (NAMN)  and  Acid Gas and Aerosol Monitoring Network (AGANet)  were funded by the  UK Department for Environment, Food and Rural Affairs (Defra)  and devolved administrations. Fundación CEAM is partly supported by  Generalitat Valenciana ,  Bancaja , and the programme CONSOLIDERINGENIO 2010 (GRAC-CIE). The authors gratefully acknowledge support and contributions by (1) the large network of dedicated local site contacts, field teams, and host organizations at NEU DELTA ® and bulk wet-deposition sites; (2) all personnel involved in the sample preparations and chemical analyses from the chemical laboratories; (3) RIVM for hosting the DELTA-AMOR inter-comparisons at Vredepeel; and (4)  Jan Vonk  at  RIVM  for providing links to access NH 3 and SO 2 data from the  Dutch national network LML (Landelijk Meetnet Luchtkwaliteitl ).
Geodetic Data In this study, we exploit multiple synthetic aperture radar (SAR) datasets to measure the co-eruptive ground displacement (Table  S2 ). A joint analysis of differential In-SAR and Multiple Aperture Interferometry from ALOS-2, and pixel offset tracking from COSMO-SkyMed (CSK), allows us to decompose the 3D co-eruptive displacement field. We use the 3D displacements to interpret the deformation signal, determining the possible location and nature of the active deformation sources.
3D Decomposition Following the method of T. J. Wright et al. (2004) , we invert for the 3D co-eruptive displacement field using the InSAR LOS measurements, pixel offsets and MAI measurements. This results in an overdetermined system of equations, and we perform a least squares inversion to solve for the horizontal (NS and EW) and vertical components of displacement (see Figure  2 ). Each dataset was weighted equally in the inversion, which assumes similar accuracy for all measurements. InSAR has a higher accuracy than the other datasets, but the equal weighting ensures that contributions from the pixel offsets and MAI, such as displacement along the satellite azimuth, are not under-prioritized in the 3D decomposition  (Grandin et al. , 2018). Notable characteristics of the 3D displacement field allow us to hypothesize which deformation sources were active during this event.
Data Subsampling and Covariance Matrix To compare modelled and measured surface displacements, measurements must first be subsampled to include a reasonable number of datapoints in the inversion, while still retaining enough information to robustly estimate the relevant model parameters. We include and subsample both the ascending and descending InSAR measurements in the inversion. Neither the SAR pixel offsets, nor the MAI displacements measurements, are included due to their low signal-to-noise ratio. However, we project modelled surface displacements into the LOS for all available datasets a posteriori (Fig. S4 ). The data is subsampled using an adaptive quadtree decomposition algorithm  (Walstead, 1999; Jónsson et al. , 2002) , as described in the Supporting Information Text S5, Table  S4  and Fig.
Test inversions also show that the depth cannot be adequately constrained, and we therefore rely on independent datasets (e. g. ,  Shreve et al. (2019) ;  Hamling et al. (2019) ;  Hamling and Kilgour (2020) ;  Allard et al. (2015) ;  Legrand et al. (2005)  for reservoir depth) and post-processing (see Section 6. 2. 2) to obtain reasonable values. Again, we invert for normal and shear stress changes on the dike and fault as nonlinear parameters. We approximate the reservoir as a thin oblate spheroid as opposed to a horizontal fracture because the nonnegativity constraint restricts closing on all fractures in the inversion. Instead of closing of a horizontal fracture, we impose a uniform negative pressure change boundary condition on the spheroid. For a given ∆V res , reservoir volume V and change in pressure ∆P res are inversely proportional  (Amoruso & Crescentini, 2009) , resulting in the high ∆P res obtained by the inversion given the thin reservoir.
the exact timing of onset of ring-fault slip, 2. the amount of stress change on the ring-fault from prior eruptions, reservoir processes, and/or earthquakes, 3. the frictional parameters of the fault, 4. the geometry of the system, especially the connection between the fault and the reservoir, and 5. the reservoir volume's unknown upper bound. Continued geodetic monitoring of Ambrym, in conjunction with widespread observations at basaltic calderas around the world, will allow us to further constrain how moderatesized eruptions contribute to basaltic caldera development. of the VOLCPLUME ANR project (ANR-15-CE04-0003-01). This project has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665850.
The authors also thank Yosuke Aoki, one anonymous reviewer and the associate editor for their constructive evaluation of this paper. The authors declare no conflict of interests. Data are available on Zenodo with the DOI https://doi. org/10. 5281/zenodo. 4609496. This is IPGP contribution number 4216. The dike trace represents the upper edge of the quadrangle ("Dtop" in Fig. S7 ). An extraction of less than 7% of magma from the reservoir is sufficient to unclamp the western portion of Ambrym's caldera ring-fault (light green rectangle). During the 2015 eruption, there was an average of 44 cm of slip on the fault plane. We hypothesize that the fault was prestressed by a previous event associated with a depressurizing reservoir (due to an eruption, degassing, etc. ), and the reservoir deflation in 2015 brought the fault to failure.
Acknowledgments We thank the  Italian Space Agency  ( ASI Science  project  No 702 ) and the  Japan Aerospace Exploration Agency  (JAXA 6 th Research Agreement No 3245) for providing access to the radar imagery used in this study. A portion of the ALOS-2 data was provided under a cooperative research contract between Geospatial Information Authority of Japan and JAXA. The ownership of ALOS-2 data belongs to JAXA. The Pléiades data was provided through Dinamis Project 2018-147-Sci, ©CNES (2014, 2015, 2017) and Airbus DS (2017), all rights reserved. We thank  Francisco Delgado  and  Yves Moussallam  for discussions which contributed to developing the ideas within this manuscript. Several calculations used the S-CAPAD cluster of  IPGP. D. Smittarello ,  V. Cayol , and V. Pinel acknowledge the support of projet  ANR-18-CE92-0037.
Participants We recruited a total of 145 participants across four groups: 40 adults without limb differences (Adult-NLD), 35 adults with limb differences (Adult-LD), 45 children with no limb differences (Child-NLD), and 25 children with limb differences (Child-LD). LD and NLD participants were well matched for age  sd: 1. 84; sd: 1. 74; sd: 15. 5; sd: 15. 2 ). We extensively liaised with two UK charities for supporting children with a limb difference and their families, as well as using our ties with the limb-difference community and existing volunteer databases to recruit as many children and adults with   1  Proportion of children and adults with limb differences who have an affected right or left arm, or both arms, and associated ages of groups in years. congenital upper limb differences as possible over the research period.