dataesr/scientific-paragraphs-categorization huggingface dataset:
Baseline Characterization. Baseline characterization consisted of collecting datasets prior to the oxygen injection (February-March 2014). 3. 1. 1. Geophysical Data. Figure  3  shows the results for the permanent profile in March 2014. This profile went through the barrier (metric point 51 m) and was within close proximity to several piezometers (from north to south: Pz12, P5, Pz7, P3, Pz24, P1, and Pz4) whose positions are plotted on Figure  3. The temporary profile was located around 8 m downstream the barrier and it overlapped with two piezometers (from west to east: Pz24 and P2, Figure  4 ). The electrical resistivity tomography from the permanent profile showed resistivities between 40 and 80 Ω⋅m, over the 6 first meters, which correspond to the sandy-loamy layer of the aquifer. From 6-meter depth, resistivity was much smaller (<20 Ω⋅m) and this may be associated with a sandy clay level.
One Year of Electrical Monitoring. Before barrier activation, it was possible to localize a zone of natural biodegradation on the permanent and the temporary profile (Figures  3  and 4 ). Figure  7  shows the evolution of the electrical data over a year of enhanced biodegradation, for the permanent profile. The resistivity model sections were obtained after time-lapse inversion of 12 datasets, spaced about a month, acquired between March 2014 and April 2015. The convergence of iterative inversion is excellent (<1% for resistivity and <2 for normalized chargeability) and it is comparable with the noise level of the resistivity meter (1%, according to Iris Instruments). In order to compare the electrical data taking into account the impact of temperature and water saturation on electrical resistivity and chargeability, the inversed data were considered only in the saturated zone and adjusted at 25 ∘ C thanks to a linear law  [52, 53] : 𝜌 25 𝜌 𝑇 = 𝛼 * (𝑇 -25) + 1, (2) where   is resistivity at temperature  ( ∘ C),  25 is resistivity at 25 ∘ C, and  ( ∘ C -1 ) is a temperature compensation factor.
Acknowledgments The authors acknowledge the  French Research Agency  (Project  BIOPHY ,  ANR-10-ECOT-014 ) and  LABEX VOLTAIRE (Excellence Laboratory for Atmosphere, Resources and Environmental Interactions Study, University of Orleans, France)  ( ANR-10-LABX-100-01 ) for financial support. They also thank all the cooperation partners of the  BIOPHY  project:  BRGM ,  LPC2E ,  Serpol , and  Total. The LPC2E technical research team is gratefully acknowledged for their contribution to the development of the SPIRIT instrument. Technical support from  BRGM  is greatly appreciated as well. And they thank  P. Gaudry ,  S. Williams , and  G. Belot  ( LPC2E  master's students) for their help during in situ measurement campaigns, as well as  E. Verardo  ( BRGM Ph. D. student ) for her contribution.
A similar technique was used in our study, utilizing the voxels of the segmented HRCT images to generate a much denser seed dataset. In a later validation paper, medical imaging data from a single human volunteer were used instead of the generalized lung atlas information employed in the first paper  [31]. Recently, Bordas et al. [32]  proposed a work pipeline for the generation of such models and validated them using measured morphometric data and calculated airway resistances in healthy and asthmatic patients. The current study proposes an alternative work-flow pipeline to that of  [32]. The algorithm created by Tawhai et al. [28]  was implemented with certain modifications. Algorithm expansion parameters were formulated and described using morphological information from the literature and implemented with medical image data from human volunteers to provide a set of airway tree models for healthy humans.
Medical Image Acquisition The HRCT images were acquired as part of a study at the University Hospital Southampton NHS Foundation Trust (clinical trail EudraCT #2007-003563-43). The study is described in detail elsewhere  [19, 33]. Approval was obtained from the local research ethics committee and the UK Administration of Radioactive Substances Advisory Committee. In brief, the study included seven healthy subjects and six patients with moderate persistent asthma (GINA guidelines 2005) free from exacerbation for at least four weeks. All subjects were male, nonsmokers between the ages of 20 and 58. The healthy subject demographic information can be observed in Table  1. A Sensation 64 slice HRCT scanner (Siemens, Erlangen, Germany) was utilized to acquire 3D images from the mouth to the base of the lungs.
The acquisition was performed with the subject in the supine position during slow exhalation against a resistance after a tidal breath. This maneuver was intended to image the glottal aperture in the open position. The average scan time was 12. 3 s. The imaging parameters were 120 kVp, 120 mAs and a pitch of 1. 0. The average effective dose of radiation for each subject was estimated to be 4. 5 mSv. The captured 3D images have slice thickness of 0. 75mm, with pixel spacing varying between 0. 55 and 0. 65mm and slice separation of 0. 5mm. The data are stored in DICOM format, with image matrices of typical size 512x512x650.
Software for Bronchial and Lung Geometry Acquisition The initial information used for the development of the airway tree model were the geometric characteristics of the "real" bronchial tree as determined from the medical images obtained in the clinical study. The Pulmonary Workstation 2 (PW2) software package (VIDA Diagnostics, Coralville, IA) was used for the segmentation and measurement of the airway tree bronchi down to the most distal distinguishable generation. This was typically generation 3-4 in the upper lobes and generation 6-7 in the lower lobes. The measurements include all the morphometric attributes of the airways. These are essential for the reconstruction of the bronchial tree as a network of interconnected straight tubes and include average lumen diameters, airway lengths, branching and rotation angles, branch start and end coordinates, etc.
358), suggesting that most of the difference in the symmetry of the tree is produced by the utilization of medical imaging data. Finally, the number of Horsfield (u hMAX = 26, avg. 25) and Strahler (u sMAX = 13, avg. 12) orders predicted by the model are lower) and higher respectively as compared to  [23]  (u hMAX = 28),  [28]  (u hMAX = 26) and  [31]  (u sMAX  = 11). Since an airway tree begins at the trachea and ends at the TB, for a meaningful comparison between the different datasets a normalization of range of orders needs to be made. Therefore, for each subject in our model an AGR of 28  (Horsfield)  and 12  (Strahler)  was considered and a theoretical number of branches was interpolated to fit the new AGR. The result is displayed in Fig 4A  and 4B. The data are given as mean ± standard deviation (where available) for our model and the following studies, in chronological order of publication: (Anatomical studies) Weibel  [22] , Horsfield & Cumming  [23].
Diameter The average diameter for our model and three more models in the literature is plotted on log scale against generation in Fig 5A. It can be observed that the model is in good agreement with all three published datasets, especially between generations 5-10. Some differences exist in the first 4 generations where the greatest structural variability exists in the tree. Between generations 10-15 our model predicts slightly lower values. The difference with the literature is less than one standard deviation, with the diameters in the model remaining stable after generation 15, where the conducting part of the tree is usually considered to be replaced by the gas-exchange part. The rate of decline of diameter with depth, given by the HR, was computed to be HR = 0. 789±0. 194, almost identical to the theoretical value calculated by  [42, 44]  and confirmed experimentally in  [22, 31]  but a bit lower than the one suggested by  [56] , where HR = 0.
Both Horsfield and Strahler ordering systems are viable options when the deeper lung is concerned but the large asymmetry in the central tree suggests that a normalization in the order range is necessary prior to data comparison. Therefore, even though the branching, diameter and length ratios (RB, RD, RL) are good measures for the prediction of the basic geometric characteristics of the human lung (including symmetric properties), they cannot give us the entire story. This is evident through the discrepancy of the two plots in Fig 4B , where the RB S between datasets is similar. The algorithm implemented in this study is based on the algorithm proposed by  [28]  and has many common features with the work of  [32]. However, some marked differences also exist. The diameter of the generated airways was assigned using a relationship with the airway length instead of either a parent-child relationship or an assignment based on lung depth (generation or order).
Supporting Information S1 File. Model Morphology Data. zip. A set of two files per subject containing the morphology and connectivity information of the airway tree (both "real" and "model" data). The _morphology. txt file includes the total lung volume of the subject lung in cm 3 and the %volume per lobe. It also includes the following fields in each row (one row per airway): (i) Vida_Name: The name assigned to each airway by the Vida PW2 software (0 if none exists), (ii) CenterLine-Length: The distance between airway start and end coordinates in mm, (iii) Diameter: The airway average diameter in mm, (iv) Branching_Angle: the airway branching angle in degrees, (v) VidaGeneration: The generation assigned to each airway by the Vida PW2 software, vi) xs, ys, zs, xe, ye, ze: The coordinates of the start and end node of each airway in voxels, (vii) Adept_Names: The individualized naming scheme for each airway; in-house terminology is used, (viii) Generation: The airway generation assigned to each airway using a Boyden-based scheme, (ix) RootFlag: A flag indicating whether the airway is a "leaf" in the tree (1) or just a tree branch (0), (x) SpaceVolume: The volume of the space in the lung supplied by each airway in mm 3.
Instrumentation BC was characterized with the dual-spot Aethalometer (Magee Scientific Aethalometer model AE33) with realtime loading compensation  (Drinovec et al. , 2015). The inlet air stream of the AE33 is split and the sample is collected on two filter spots concurrently within the instrument. The flow through each of the two spots is different so that loading rates on the respective sample spots are different. Different loading rates cause the accumulation of the sample to be different between the two spots, resulting in the different magnitude of FLE between the spots. Measurement of FLE enables the compensation of the data -using the parametrization described in  Drinovec et al. (2015) , the compensation parameter k can be derived. This parameter describes the linear decrease of the instrumental sensitivity with loading of the spot with the absorbing sample for each wavelength of light at which the measurement is performed.
However, for the measurements of NR-PM 1 species in ambient air,  Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2016 -285, 2016  Manuscript under review for journal Atmos. Meas. Tech. Published: 13 October 2016 c Author(s) 2016. CC-BY 3. 0 License. a good agreement is commonly observed between data from these two instruments  (Crenn et al. , 2015; Fröhlich et al. , 2015). Individual aerosol particles were analyzed using field emission scanning electron microscope (FEG SEM, JEOL JSM 7100F TTLS) coupled with energy dispersive X-ray (EDX) system (X-Max Large Area Analytical Silicon Drift detector, Oxford Instrument). Aluminum foils used for sampling of aerosol particles (sampling described in section 2. 2) were mounted directly on the sample holder by double-sided conductive carbon tape. SEM-EDX acquisitions were performed under high vacuum at 5 keV accelerating voltage.
s. l. )  during January and February 2013. During winter ambient aerosols are dominated by wood burning emissions from household heating. The laboratory biomass burning profile was obtained during an experiment performed at the Paul Scherrer Institute  (Bruns et al. , 2015). Laboratory diesel exhaust was characterized at Aerosol d. o. o. in Ljubljana from a EURO 3 diesel engine. Exhaust was transferred into the smog chamber, where it was diluted and analyzed. Ljubljana roadside measurements were conducted during November 2012 in the city center, close to a heavy traffic road. Paris and Payerne data were collected during summer intensive campaigns (see above). The Reno laboratory experiment was conducted at Desert Research Institute (Reno, Nevada, USA). Pure mustard oil was burned in a simple ceramic bowl with a cotton wick.
Aerosol age and origin influence the filter loading effect Influence of the aerosol age was indirectly investigated by using potential source contribution function (PSCF). We divided the dataset into two subsets distinguished by k 6 being bigger or smaller than the campaign average of 415 0. 002. The analysis was carried out for the summer period, when the planetary boundary layer is thicker compared to the winter with stronger influence of long-range transport than in winter, and good vertical mixing to the ground, where the measurement station is positioned. In Paris, the compensation parameter k is very well correlated with aerosol age: high values indicate local 420 sources while values close to zero indicate a regional contribution (Figure  9 ). For low k values it seems that the sources are distributed over France, part of Great Britain, and the Atlantic Ocean.
Acknowledgements The work described herein was supported by: -EUROSTARS grant E!4825 FC Aeth -JR-KROP grant  3211-11-000519 550 -JR-RK  grant  3330-14-509063 -ACTRIS EU-FP7  grant  n°262254  -' PREQUALIF ' project (ADEME contract  n°1132C0020 ),  DIM R2DS (AAP 2010 ) -' PARTICUL'AIR ' project, and institutions  CNRS ,  CEA  and FEON. -the  National Science Foundation  under Cooperative Support Agreement No. EPS-0814372 555 -NASA EPSCoR  under Cooperative Agreement No. NNX14AN24A -NASA ROSES  under Grant No. NNX15AI48G  - National Science Foundation (NSF)  under Grant No. AGS-1544425 -We thank the  Slovenian Environmental Agency  and AMES d. o. o. for the use of their measurement sites.
Velocity is thus estimated from the head field as follow: V = - K ρg ∇P ( 1 ) where V is the 2D (vertical plane) velocity field, ρ the water density and g the gravity acceleration. The hydraulic conductivity K is estimated at 0. 016 cm. s -1 using a series of falling head tests  [42]  from 3 differents samples taken at the sand surface along the studied area. In order to analyse the overall groundwater pressure and velocity fields from discrete measurements points, a triangulation-based natural neighbour interpolation is applied on the pressure data  [43]. The choice of a proper interpolation method is not straightforward, in particular in the present case where the vertical spacing between sensors is smaller than the horizontal one. A series of tests has been carried out to compare the interpolated fields provided by four interpolation methods: linear, natural neighbour and cubic triangulations-based and bi-harmonic spline interpolations (MATLAB R 4 method).
In order to highlight the evolution of the groundwater pressure field in response to the global offshore shifting of the swash zone, we select three characteristic 3-hour long periods referred to as Phases (grey block in Fig. 4 ). Phases 1 and 3 are representative of typical groundwater states associated to high and low swash zones, respectively. They are largely depicted herein after. Phase 2 illustrates a transitional state between phase 1 and phase 3. Head timeseries for G1t (swash top sensor) and G5t (swash bottom sensor) sensors are presented in Fig. 4. Panels B and C show the entire measurement dataset while panels D and E focus on selected 40 s period for G1t and G5t sensors, respectively. These zoomed plots, which are detailed below, help to build up a better idea of the collected groundwater pressure data.
Time-averaged dynamics Fig. 5  depicts the time-averaged head and velocity fields computed over the three selected phases (see Fig. 4 ). Data are time-averaged over 3h acquisition periods. Maximal velocity magnitudes are 1. 5 10 -5 , 1. 4 10 -5. and 2. 4 10 -5 m/s for the top, middle and bottom plots, respectively. Sand bed elevation measured by LiDAR and tacheometer on December 13 are shown as well as the locations of buried pressure sensors. The cross-shore position of the swash zone for the selected time period can be found in Fig. 4, F. The measurements presented in Fig. 5  indicate the presence of a rather consistent groundwater circulation pattern under the swash zone, which shifts offshore as the swash zone retreats with the lowering of MWL. Phase 1 (Fig. 5 , top plot) is characterized by a very high swash zone.
Discussion For the first time from an in-situ dataset, the present study demonstrates the presence of a time-averaged seaward directed circulation cell under the swash zone, which is mainly driven by both wave activity and water level fluctuations (wave setup) at the beachface. These results are consistent with existing prototype-scale laboratory  [25]  and numerical  [20]  datasets. In addition to good qualitative agreement with these studies, the  groundwater velocities calculated from in-situ buried pressure sensors range in the same (non-dimensional) orders of magnitude. A key issue is how and to what extent this swash groundwater circulation would be affected by backbarrier lagoon water level fluctuations which induce, in nature, watertable gradients of smaller amplitude than those imposed by swash runups on the beach face  [48].
a circulation cell with infiltration at the upper swash and exfiltration at the lower swash, regardless the artificially imposed seaward-or landward-directed hydraulic gradients across the barrier. However, an important disagreement exists between those studies on the possible controlling influence of the landward groundwater conditions on the swash-driven flow: Turner et al. [25]  observed a swash groundwater circulation nearly isolated, from a hydrodynamic point of view, from back-barrier fluctuations while Li and Barry  [20]    the cross-shore location of the divergence point in the groundwater velocity field at the top of the swash zone. While the present dataset does not allow to conclude in one way or the other, it is interesting to note that, if both laboratory experiments and numerical simulations predict the presence of a flow division near the uprush limit, it has not been observed in our field experiment.
The present in-situ measurements rather reveals the presence of a nearly hydrostatic groundwater region beyond the uprush limit. This should likely be attributed to, (i), the effect of a high inland watertable in this storm decay context inhibiting the formation of landward gradients beyond the swash top and, (ii), to the spatial limitation of the present dataset. Furthermore, this can explain the difference observed between our field data and the experiments of Turner et al. [25]  or the simulations of Li and Barry  [20]  about the shape of the groundwater circulation. Indeed, taking a closer look at the time-averaged groundwater velocity field measured here, one notes that the inflow and outflow occur (Fig. 5 , Phase 1) are more symmetric than the laboratory or numerical ones, which can again be explained by the role played by the inland watertable as suggested by Li and Barry  [20].
At the swash event scale, the present data allows to carry out a simple but original comparison between the discharges observed above and under the sand bed. A specific swash event with a moderate amplitude has been selected during phase 1 in order to monitor the complete event with the present setup. The swash discharge (per beach meter) is calculated dividing the swash volume at the maximal uprush location by the time between the swash arrival in the measurement zone and the time for maximal uprush. It is about 0. 75 m 2 /s for the considered event. The groundwater horizontal and vertical discharges are averaged over the whole swash event through horizontal (Z = 0. 1 m) and vertical (X = 7. 5 m) cross-sections, respectively. This leads to negative downward vertical discharge about 7. 10 -6 m 2 /s while horizontal discharge are seaward and about 2.
Conclusion and prospects This paper aims to present original field data of groundwater dynamics underneath the swash zone. The 24h swash zone dataset was recorded during a larger field experiment on the microtidal Rousty sandy beach, Camargue, France. Both offshore and inner surf zone measurements are provided to quantify the incoming wave forcing. Continuous high-frequency swash zone measurements are performed thanks to, (i), a 2-dimensional laser scanner (LiDAR) to follow free surface and sand bed dynamics and, (ii), a cross-shore array of 15 buried pressure sensors to study groundwater properties. By contrast to, for example,  [29]  the focus is mainly made here on the permanently saturated zone under the swash zone and the presented data are, to the best of the authors knowledge, a first field evidence of groundwater circulation under the swash zone.
Figure4: A: 20-min averaged head at each sensor (colored lines) and surf zone MWL (black flakes), with black arrows indicating high (H. T. ) and low (L. T. ) tide. B and C: G5t and G1t instantaneous head and LiDAR bed level over the entire dataset (light red line corresponds to measurements performed above the watertable, see text). D and E: G5t and G1t instantaneous head and LiDAR bed level for selected 40-min periods. Note the sparser LiDAR measurements due to rarer drying backwash events. F: probability density for the cross-shore position of the swash front for the three selected periods (vertical arrows indicate the cross-shore position of pressure sensors).
Acknowledgements This study was sponsored by the  Direction Departementale Territoriale et Maritime 13  and the  ANR  Grant No. ANR-13-ASTR-0007. We thank the  Parc Naturel Rgional de Camargue ,  Reserve Naturelle de Camargue  and the municipality of Saintes-Maries-de-la-Mer for technical assistance and autorization to access the beach. The GLADYS group (www. gladys-littoral. org)supported the experimentation. We are grateful to all the contributors involved in this experiment. The authors are particularly indebted to  Sébastien Marguerite  whose efforts were essential to the deployment.
A specimen (HNHM 6899) was bisected and the middle part sectioned to observe musculature. Three specimens (Crevada 6, Clauzetto 2 and Ragogna 2) were sent to Professor Javier Alba-Tercedor of the University of Granada for micro-tomographic scanning with a Bruker-Skyscan 1172. This technique examined features of specimens without dissection, in particular the intestinal typhlosole shape and extent. LOMBRI software  [18]  was used for identification confirmed by scientific literature (listed in synonymy) using family and species systematics of Blakemore  [19, 20].
Unpurified PCR amplicons were sequenced in both directions using M13 tailed primers, their products subsequently purified using Agencourt CleanSEQ protocol and processed using Big-Dye version 3. 1 on an ABI 3730 DNA Analyzer (Applied Biosystems). Sequences were assembled and edited with Sequencher 4. 5 (GeneCode Corporation, Ann Arbor, MI, USA). Alignments used BIOEDIT version 7. 0. 5. 3  [25]. Sequences are publicly available on GenBank (KT352925-KT352978) and on BOLD in the dataset [DS-NEO1] through the following DOI: dx. doi. org/10. 5883/DS-NEO1. Data analysis. Distance analyses were performed with MEGA6  [26] , using a Neighbor-Joining  [27]  algorithm with the Kimura-2 parameter model  [28]  to estimate genetic distances. The robustness of nodes was evaluated through bootstrap re-analysis of 1000 pseudoreplicates.