dataesr/scientific-paragraphs-categorization huggingface dataset:
The only difference between the analysis of the field campaign and the wind tunnel datasets lies in the shattering treatment as this artefact removal feature has been turned off for the wind tunnel measurements. While most shattering should be minimized by the anti-shattering tips, the shattering treatment in regions of large effective diameters and high IWC further corrects for shattering artefacts  (Korolev et al. , 2013). Shattering of large particles is a function of IWC; see  Field et al. (2006). Since hardly any particles were larger than the full array width (see the results in Sect. 4. 6. 4) we argue that large particles that would have caused shattering artefacts are not present in the icing wind tunnel. Therefore, the shattering treatment was turned off.
Acknowledgements. The presented work was conducted within the framework of the  DLR@Uni programme SuLaDI , which is founded by the  Helmholtz Association. The ice-crystal-icing capability was achieved by funding from the  European Union 's  Seventh Framework Programme  in research, technological development and demonstration under grant agreement no. ACP2-GA-2012-314314. The improved spray system was funded by the  German Federal Ministry of Economic Affairs and Energy , grant number  Drífa-FKZ0325842A. Some of the contributed computational results were obtained thanks to  Wagdi Habashi  ( McGill university, Canada ) and the funding of the  German Research Foundation ,  DFG  grant  BA4953/2-1. Christiane Voigt  and  Tina Jurkat-Witschas  have been funded by the  Helmholtz Society  under contract no.
[22]  shows very similar values to our Saharan dust sample in the first Fe-O shell (1. 92-2. 04 Å), while the Fe-Fe bond distance is higher for the urban sample (3. 01-3. 45 Å) with respect to the present desert dust aerosol sample. While in urban aerosols the dominating phase for iron is ferrihydrite, Saharan dust contains not only Fe oxy(hydr)oxides but also aluminosilicates where the six oxygen atoms in octahedral geometry are maintained but neighboring iron atoms could exhibit different distances. Table  3. EXAFS: quantitative data analysis results for Saharan dust (SH_Dec2014), Steel production emission (AST_E45) and mixed aerosol samples collected in the Terni urban area (TR_mix1 and TR_mix2). Errors in the last significant figure are indicated in brackets. Legend: o: octahedral site, t: tetrahedral site.
Introduction Estimation of trace gas emission from confined source areas on a local scale (i. e. , receptor (measurement) distance to sources less than 500 m) using the combination of inverse dispersion modelling with either concentration or flux measurements is a widespread method, especially in the agricultural sector (e. g. ,  [1] [2] [3] [4] [5] [6] ). In particular, the combination of concentration measurements with backward Lagrangian stochastic (bLS) modelling is a convenient way of emission estimation that has spurred its utilization in the past decade  [7]. Flesch et al. [8]  present details of a surface-layer bLS model that has been embedded in the framework of the frequently used software 'WindTrax' (http://www. thunderbeachscientific. com/). A variety of trace gases, such as, e.
Structure and Implementation of the bLSmodelR The bLS model by Flesch et al. [8]  together with the deposition algorithm was implemented into an R  [18]  package called bLSmodelR (Supplement S1, current version available at https://www. agrammon. ch/documents-to-download/blsmodelr/). The deposition algorithm was included in the bLSmodelR package as a post-processing function that allows the modelling of the dry deposition additionally to the dispersion modelling. It provides reduced C/Q ratios due to the user defined dry deposition velocity, where the bLS model needs to be run first in normal mode, i. e. , without the dry deposition mechanism included. The bLSmodelR package makes use of several different available R packages. A complete list of all packages that bLSmodelR depends on can be found on the above-mentioned homepage.
Acknowledgments: We thank  Markus Jocher  ( Agroscope INH, Zürich, Switzerland ) for technical support with the equipment on site,  Bernhard Märki  and  Alfred Burri (Witzwil farm, Gampelen, Switzerland)  for providing the experimental site and their support during the measurement period. The  Swiss Federal Office for the Environment FOEN  (contract  06. 9115. PZ/L225-3604 ) and the  Swiss Federal Office for Agriculture FOAG  (contract  09. 0084. PJ/K233-1881 ) are acknowledged for funding this study.
The final data matrix comprised 4143 bp of 113 species from 51 genera. DNA extraction, PCR amplification, and sequencing. The specimens used in the study were collected alive and preserved in 96% ethanol in the field. DNA was extracted from whole specimens or from one leg with a standard phenol-chloroform extraction  [34]  or the DNeasy Tissue Kit (Qiagen GmbH, Hilden, Germany). Voucher specimens are stored in CINJ and CNHM, and DNA aliquots are kept in the tissue collections of Naturalis Biodiversity Center. We amplified six fragments of five genes, three mitochondrial and two nuclear: (i) two non-overlapping sections of mitochondrial cytochrome c oxidase subunit 1, the 5' and 3' halves, which we here term COIa and COIb, respectively; (ii) the 5' end of the mitochondrial large ribosomal unit plus the Leucine transfer RNA plus the 3' end of NADH dehydrogenase subunit 1 (rrnL-nad1); (iii) an internal fragment of cytochrome b (cob); (iv) the 5' end of the small ribosomal unit, 18S rRNA (SSU); (v) an internal fragment of the large ribosomal unit, 28S rRNA (LSU).
Primers used are given in Table  1. Each 25 μl PCR mixture included 1 μl (10 pmol) of each primer, 2. 5 μl 10x PCR buffer, 0. 5 μl dNTPs, 0. 25 μl Taq-polymerase, 18. 8 μl ddH2O and 5 μl template DNA. PCR cycles were run at the following conditions: 3 min at 94 ˚C, followed by 40 cycles of 15 s at 94 ˚C, 30 s at 54 ˚C and 40 s at 72 ˚C, and finally, 5 min at 72 ˚C. PCR fragments were sequenced directly and sequencing was performed by BaseClear (www. baseclear. com). Sequences were assembled and edited using Geneious version 8. 0. 5  [35]. DNA sequences obtained for each genetic marker were aligned separately using MAFFT version 7  [36]. Phylogenetic analysis. DNA sequences from the current study (S1 Table ) and a selection of data from Ribera et al. [3]  were combined using Geneious v9. 1.
2 [35]  as a workbench. For each genetic marker, an alignment was made using MUSCLE  [37]. For LSU and SSU, in some regions, alignment was ambiguous due to large indels; these regions were removed from the final data matrix. Translations of coding genes were checked for consistency and validity. We have refrained from nucleotide substitution model fitting for two reasons. First, model-fitting software such as jModelTest  [38]  takes an expected initial tree against which the software fits the model, and that model is then used to fit the phylogenetic reconstruction. This may introduce a degree of circularity in the procedure. Second, our method of choosing a general model allows the Bayesian MCMC algorithm to fit the model and the tree at the same time, which in our opinion is a more objective method.
Prior to the analysis in BEAST, we performed Bayesian phylogenetic analyses on the entire dataset, to clarify the taxonomic status of all taxa included in the analyses. The analysis was conducted using MrBayes 3. 2. 2 [39]  on the CIPRES web portal  [40] , while data for all markers were linked and the most general substitution model (gamma, with all substitutions possible) was set. Two replicates of 10 x 10 6 generations each were run, sampling values and trees every 5000 generations. Convergence diagnostics were run using Tracer version 1. 5  [41] , where ESS values for all parameters were >>200. After discarding a 25% burn-in, the resulting majorityrule consensus tree was visualized using FigTree version 1. 4  [42]. Genetic distances among subspecies in three Anthroherpon species (A. taxi, A.
Reconstruction of the ancestral range The ancestral geographic range of Anthroherpon was inferred using the R package BioGeo-BEARS 0. 2. 1  [48, 49]. This model approach directly tests the fit of commonly used biogeographical inference models: dispersal-extinction-cladogenesis model (DEC)  [50] , maximum likelihood versions of dispersal-vicariance analysis (DIVALIKE)  [51] , and Bayesian biogeographical inference (BAYAREALIKE)  [52]. The ultrametric tree from the Bayesian relaxed molecular clock analysis, which includes one member per (sub)species, was used as input tree. We identified 16 different geographic areas, corresponding to mountain ranges or caves, inhabited by Anthroherpon species. We could not include a larger number of areas due to computational limitations. Each species was coded as being present or absent in each of these areas (Table  2 ), and a maximum number of areas occupied by a single species was set to 3.
Linear morphometrics and three-dimensional geometric morphometrics Data collection. Specimens of the genus Anthroherpon were pin-mounted on a rectangular piece cardboard affixed to the ventral side of the body and labelled. A total of 102 specimens belonging to 16 species and 20 subspecies of Anthroherpon were measured for morphometric analyses (S3 Table ). All of these species except A. matulici were included in the phylogenetic analysis. Specimens are stored at the CINJ and at the MNHN. To exclude sex-specific characters (males have one additional protarsomere), measurements data were only collected from male beetles. Linear and 3D morphometric measurements were obtained with a Micro-Vu Vertex 251HC (https://www. microvu. com/, St. Wendel, Germany), three-dimensional set-up, using Inspec Metrology Software (https://www.
Exploration of shape evolution within Anthroherpon To quantify the habitus shape variation within the genus Anthroherpon (s. str. ), we analyzed shapes of the three main body parts (head, pronotum, and elytra), using geometric morphometrics. For each body part, each species' or subspecies' shape was estimated by the shape of the specimen closest to the mathematical mean shape of the taxon. The data were subjected to character evolution analyses by testing for phylogenetic signal in each of the three body part datasets separately, using the K of Blomberg  [60, 61]. Tests were performed using 10 000 permutations, and a risk alpha of 5%. We then used our phylogeny to retrace the shape evolution in the phylomorphospace using ancestral state reconstruction according to a Brownian motion model of evolution  [62].
Acknowledgments We are especially grateful to  Petar Kosovac  for accompanying the first author on numerous field trips and helping to locate caves and collect cave beetles. We also thank  Roman Ozimec ,  Pier Mauro Giachino ,  David Čeplik ,  Roman Lohaj  and  Eric Que  ´innec for providing specimens from their institution or their private collections. We are very grateful to  Marjan Komnenov ,  Ivo Karaman ,  Jasminko Mulaomerović ,  Una Tulić , Z ˇeljko  Madz ˇgalj ,  Nenad Grković ,  Blaz ˇo Grković ,  Dubravko Kurtović ,  Predrag Milos ˇević ,  Vanja Kukurić , and numerous local people from Bosnia and Herzegovina, and Montenegro for their help in the field. Special thanks to  Jelena C ´alić ,  Dejan Vučković  and  Milan Rabrenović  for their help regarding the geology and geomorphology of Dinaric Mountains.
Acknowledgements We thank  Thierry Bernard  for technical support, and  Hervé Bercegol  and  Vadim Nikolayev  for fruitful discussions. We also thank  Francois Daviaud ,  Hugues Chaté ,  Francois Ladieu ,  Cindy Rountree  and  Julien Scheibert  for the careful reading of the manuscript. Funding through  ANR  project  MEPHYSTAR  ( ANR-09-SYSC-006-01 ) and by  "Investissements d'Avenir" LabEx PALM  ( ANR-10-LABX-0039-PALM ) is also gratefully acknowledged.
Trace elements in MIs were analysed by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) (GeoLasPro 193 nm ArF Excimer laser ablation (LA) system, connected with an Agilent 7500ce quadrupole ICP-MS at Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Palermo) using a Laser repe-tition rate of 10 Hz, a spot size between 24 and 32 µm, corresponding to a Fluency between 14 and 15. 5 J cm - 2 , respectively, and a flux of He in the ablation cell ranging between 0. 82-0. 79 L/min. Data acquisition was accomplished in peak-jumping mode with a dwell time of 10 ms. Plasma conditions were adjusted to oxide formation <0. 2%, monitored using the 248/232 mass ratio. For each melt inclusion, one or two spots (where there was enough space), were performed, each spot analysis lasting 2 minutes, including 60sec for background acquisition and 60 sec for the analysis.
Acknowledgements We would like to thank  Emmanuel ROBERT  ( Codirector of Geological Collections, University Lyon 1, France ) for his hospitality during our investigations of the collections of the  Claude Bernard University , and both  Keith P. MINOR  ( University of Texas at Austin, USA ) and  Stephen CAREY  ( Federation University, University of Ballarat, Australia ) for the improvement of the English text. We also warmly thank our reviewers  Miguel COMPANY  ( Granada University, Spain ) and  Zdenĕk VAŠIČEK  ( Academy of Sciences, Czech Republic ) for their constructive remarks.
Acknowledgements This work was financially supported though a JSPS international research fellowship PE 14721 (to MWB) and  JSPS KAKENHI  grant numbers  JP 26287139  and  JP15KK0150  (to HS). The work of DAZ and ALR was supported by  Russian science foundation  ( 16-17-10067 ). RB acknowledges funding from the  NERC  ( NE/M000427/1 ). SM acknowledges funding from the  NERC  ( NE/PO12167/1 ). We also appreciate the constructive comments from  Yaakov Weiss  and an anonymous reviewer, which contributed greatly to our manuscript. Editor:  Helen Williams.
The spatial and temporal variation of energy dissipation rates in breaking waves controls the mean circulation of the surf zone. As this circulation plays an important role in the morphodynamics of beaches, it is vital to develop better understanding of the energy dissipation processes in breaking and broken waves. In this paper, we present the first direct field measurements of roller geometry extracted from a LiDAR data set of broken waves to obtain new insights into wave energy dissipation in the inner surf zone. We use a roller model to show that most existing roller area formulations in the literature lead to considerable overestimation of the wave energy dissipation, which is found to be close to, but smaller than, the energy dissipation in a hydraulic jump of the same height. The role of the roller density is also investigated, and we propose that it should be incorporated into modified roller area formulations until better knowledge of the roller area and its link with the mean roller density is acquired.
An example of the final LiDAR data set is also shown in Figure  2c  and illustrates the spatial resolution and extent of the data set (0. 1 m cross-shore grid). In addition to the scanners, the full experimental setup included three RBR pressure transducers (PT) and three Nortek Acoustic Doppler Velocimeters (ADVs), sampled at 2 and 16 Hz, respectively (Figure  2c ). As a consequence of the macrotidal environment in this part of the North Sea, every phase of the nearshore wave transformation could be measured: from propagating bores in the inner surf zone during flood or ebb phases to shoaling and breaking waves during high tides. The present study focuses on the inner surf zone, where broken waves propagate as fully developed bores after the transition point  (Basco & Yamashita, 1986; Kweon & Goda, 1996; Nairn et al.
Wave-by-Wave Analysis: Extraction of Roller Properties The surface roller properties presented in this paper are extracted from individual broken waves that are tracked in the inner surf zone using the algorithms developed in  Martins et al. (2016 Martins et al. ( , 2017a Martins et al. ( , 2017b Martins et al. ( , 2017c)). The tracking works by detecting the wave crests as maxima in the surface elevation time series. Individual wave heights H are then computed as the vertical distance between the crest and preceding trough elevations (h c and h t , respectively, see Figure  1 ), and the wave period T is defined as the time elapsed between the passage of the two troughs either side of a crest at a given cross-shore location. In the LiDAR data set, we define the surface roller as the part of the wave profile from the wave crest, through the breaking region (where @g=@x < 0) to the roller toe.
Concluding Remarks In this paper, we present a high-resolution LiDAR data set from which the geometrical properties of surface rollers (h and L r ) are extracted. This data set constitutes the first direct measurements of these properties from field experiments. We report roller angle values up to 6 times greater than the value of 5. 78 typically used in energy balance-based numerical models that use the parameterization of  Duncan (1981)  to model the energy dissipation in broken waves (equations (  1 ) and (  11 )). Future deployment of LiDAR scanners at different field sites will enable this data set to be extended for a range of wave conditions and beach types, and will potentially allow the parameterization of L r and h as a function of wave and beach parameters. These novel measurements reduce the number of unknowns in the parameterization of D81 (equation (  11 )) to the roller area A and the mean roller density q r , which are two parameters linked through the definition of A.
Acknowledgments The authors acknowledge the financial assistance provided by the  Engineering and Physical Sciences Research Council  grant  EP/N019237/1 , Waves in  Shallow Water , awarded to  Chris Blenkinsopp. K evin Martins  was supported by the  University of Bath , through a  University Research Services (URS) scholarship. Assistance during the field experiments from  Jack Puleo ,  Brittany Bruder , and  Aline Pieterse  ( University of Delaware, USA ) is greatly appreciated. Jos e Beya  ( Universidad de Valpara ıso, Chile ) and  James Duncan  ( University of Maryland, MD, USA ) are greatly acknowledged for fruitful exchanges. We thank two anonymous reviewers for their detailed and constructive comments which made the manuscript improve. The data used in this research are available from the  University of Bath  Research Data Archive (https://doi.
Acknowledgments: This paper is a contribution of the  ERC Advanced Research Grant RHEOLITH  (grant agreement No. 290864 ), of  Institut Universitaire de France and Labex VOLTAIRE. We are grateful to  Total SA and ION Geophysical  for providing access to the seismic profile of the  Uruguay Margin. PS thanks the  SNSF   Ambizione grant  ( PZ00P2_168113/1 ).
This classification is iterated several times with random initial conditions following the procedure of  Yiou et al. (2008)  in order to obtain weather regimes that are stable. The choice of four weather regimes is to be consistent with the seminal paper of  (Cassou et al. 2005). For comparison, we classify different reanalysis datasets with the NCEP weather regimes. All the reanalysis data are interpolated onto the NCEP grid (2. 5 • × 2. 5 • ). The SLP data classifications of all reanalyses are obtained by determining the minimum of the Euclidean distances to the four NCEP summer weather regime centroids. This is achieved without further EOF truncation. The NCEP summer weather regimes are shown in Fig. 1a-d  To ensure that there are no inhomogeneities in the method, we have verified that the root mean square error (RMSE) between the reference period and the other periods/datasets is small (Fig.
Seasonal scale: weather regimes during the warmest summers We carried out a statistical analysis of the hottest summers of the period 1851-2014 using 56 members and the Ensemble Mean (EM) of the 20CRv2c. In Western-Europe [10 • W -7. 5 • E; 35 -50 • N], the 24 warmest summers (Fig. 1e-f ) are defined in each dataset as the ones having the highest average temperature anomalies with respect to the climatology. Figure  1e  shows the probability to have a dominant weather regime, which is the one with highest anomalous frequency, in each summer detected. From the selection of the 24 warmest summers for each member we detect 52 different summers. In order to show the agreement between the members, we calcule the probability to have a summer dominated by each weather regime (Fig. 1e ). We divide the number of members that detects a weather regime dominating that summer by the total number of members that has detected that summer as a warm summer.
Those findings are consistent with the results of  Horton et al. (2015) , although we consider heatwaves on a finer spatial scale (Western-Europe). The analysis of Hoffmann (  2017 ) is also complementary to ours, albeit on another region (Postdam, Germany), whose temperature does not respond to the same atmospheric patterns. However, he found (Figure  11 ) an increment of two new dominant wave-like patterns with more meridional oscillation, as we have seen for Western Europe at seasonal (Fig. 1 , Fig. S2  and Fig. S4 ) and subseasonal scales (Fig. 5 ). The robustness of our results is demonstrated by the use of 20CRv2c 56 ensemble members and other reanalysis datasets (see supplementary material) where we have found similar results. The dynamical analysis also suggests that there is an increase of negative correlations between warmest summers and the NAO-regime.
• Table  S1 : Absolute root mean square error by period and weather regime. Correspondence and requests for further materials should be addressed to M. C. A-C (email: carmen. alvarez-castro@lsce. ipsl. fr). S1 ). was the dominant weather regime (Same frequencies of BLO and AR for NCEP during this summer). g-i, Summer of 1955, where BLO was the dominant weather regime. j-l, Since there is not a common summer for all the reanalysis datasets with NAOas a dominant weather regime, here we show the most warmest summer  (1872, 1904, 2015)  where NAO-was the dominant weather regime in each reanalysis dataset (20CR, ERA20C, NCEP). 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990   1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010  Temperature ERA20C b  1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990.
A-C. was supported by the Swedish Research Council grant No. C0629701 (MILEX). D. F. was supported by the ERC grant No. 338965-A2C2. P. Y. was supported by the European Unions Seventh Framework Programme grant No. products (20CR, ERA20C, NCEP). • Figure S6: Dynamical representation of the warmest summers for ERA20C during 1900-2010 in regimes AR-NAO-and BLO-AL.
607085-EUCLEIA. The authors thank G. Compo, for the help supported with data from members of 20CR reanalysis. 20CR and NCEP Reanalysis data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, and retrieved from https://climatedataguide. ucar. edu/climate-data/noaa-20th-century-reanalysis-version-2-and-2c and http://www. esrl. noaa. gov/psd/. ERA-20C reanalysis data provided by the ECMWF (European Centre for Medium-Range Weather Forecasts), Reading, UK, from their website at http://apps. ecmwf. int/datasets/data/era20c-daily/. The authors declare that there is no conflict of interest regarding the publication of this paper.
, 2015b). The black y axis on the right represents radionuclide concentrations, measured or simulated, in bottom waters of the central Labrador Sea. On these axes are the blue and red lines that represent two versions of the radionuclide input function at 60 • N after dilution (DF = 50 and 30) and application of a time delay (6 and 14 years) that corresponds to the transport from 60 • N to the central Labrador Sea. The blue and red simulations include background concentrations for 129 I (2. 5 × 10 7 at kg -1 ;  Edmonds et al. , 2001 ) and 236 U (10 × 10 6 at kg -1 ; e. g. Christl et al. , 2012)  due to the global fallout. On the black y axis are also the radionuclide concentrations measured in 2014 that correspond to the bottom sample collected in GEOVIDE station 69. Earlier data on 129 I concentrations at similar locations were reported for 1993 by  Edmonds et al.