dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank an anonymous reviewer for helpful comments. GS and LL thank  Jens Bölke  and  Kevin Küpfer  for technical support and  Axel Heuer  for the beam profiler. GS thanks  Corinna Kallich  for help with image processing and  Manuel Harder ,  Sergio Speziale ,  Alexander Laskin ,  Angelika Rosa ,  Nicolas Guignot ,  Thomas Gaumnitz ,  Norbert Gulde ,  Gabor Csucs ,  Leonid Dubrovinsky ,  Innokenty Kantor  and  Lutz Decker  for fruitful exchange. We acknowledge the  Deutsches Elektronen-Synchrotron DESY in Hamburg, Germany , a member of the  Helmholtz Association HGF , for the provision of experimental facilities during beamtime  11008255  (proposal I-2019100), and we thank the staff of beamline  P01  for their support and exchange. CS and MW acknowledge support from the  BMBF  project  05K10PEC , CA acknowledges financial support through  DFG  project  STE 1079/4-1 (CarboPat --FOR2125 ) and RS acknowledges financial support through  DFG  project  STE 1079/2-1.
Mapping and Petrological Methods In order to derive the key parameters that control the geometry and progressive widening of eclogite-facies shear zones, we conducted detailed structural and lithological mapping of two representative outcrops with varying eclogite abundance to extract the geometry of the shear zone network. The outcrops were segmented into a grid with 2 × 2 m 2 sectors and first-order structural elements (e. g. , foliation, lineation, fold axial planes, shear-sense, and fracture-sets), geometries of the eclogite-facies shear zones, and the occurrence of static eclogitization were documented in detail. Outcrops A and B cover 140 m 2 (35 sectors) and 320 m 2 (80 sectors), respectively. To support the structural maps, image data of a DJI Phantom 4 drone from the Centre of Earth Evolution and Dynamics at the University of Oslo was used complementary to the mapping.
Furthermore, 14 drill core samples were taken (length: 7-15 cm, diameter: 4. 5 cm) out of which eight samples (unaltered, least affected by amphibolite-facies overprint) were selected for quantitative measurements of the major element composition of the rock-forming minerals. The measurements were performed using the JEOL JXA 8800 SuperProbe electron microprobe microanalyzer at the Freie Universität Berlin with an accelerating voltage of 15 kV, a beam current of 200 nA and a beam diameter of 1 μm for all analyzed minerals. Additionally, X-ray fluorescence analyses (XRF) of one pristine sample of each lithology were made with a Panalytical Axios Advanced spectrometer at the GeoForschungsZentrum Potsdam to obtain representative chemical bulk data for major and trace elements (Table  S1 ). This data was used to verify that the exposed lithologies are representative in the regional context.
Acknowledgements. The authors thank  Christian Ethe  and  Claude Talandier  for their help in adapting the model configuration. The authors are grateful for stimulating discussions with  Dan Whitt. Financial support. This research has been supported by the  Agence Nationale de la Recherche (SOBUMS  (grant no. ANR-16-CE01-0014 )) and the  European Commission 's  Horizon 2020 Framework Programme (CRESCENDO  (grant no. 641816 )). Review statement. This paper was edited by  Akihiko Ito  and reviewed by  Scott C. Doney  and  Christopher Sabine.
Numerical model The numerical approach used in this paper is based on the thermo-hydro mathematical formulation implemented in the finite element software FEFLOW c ⃝, with the 1D elements to represent the pipework also available in the same software. 33, 34 This approach was validated for the case of energy walls by Ref. 22, by comparing against the experimental results provided by Ref. 26. A full description of the model, including governing equations is given in Ref. 22. The following sections focus instead on the details of this specific case.
Acknowledgements This work was carried out in the framework of the  Cost Action GABI TU 1405, European network  for shallow geothermal energy applications in buildings and infrastructures. The authors are also grateful for additional funding from the  Royal Academy of Engineering  in the UK,  EPSRC  (grant reference  EP/S001417/1 ) and from the  Politecnico di Torino, Italy  ( Assegnazioni di Ateneo per la ricerca di base ). The laboratory  3SR  is part of the  LabEx Tec 21 (Investissement d'avenir -grant agreement n. ANR-11-LABX-0030 ).
Determination of the 3D shape Reconstruction of the 3D shape of our targets has always followed a well-defined scheme. Our SPHERE data along with light curve data have been used to constrain the rotational parameters (spin axis and period) and have allowed us to reconstruct a first 3D shape of every target with the ADAM model  (Viikinkoski et al. 2015a ). We used all available "dense-in-time" light curves from the DAMIT 1  database  ( Ďurech et al. 2010 ) and supplemented them with additional data either from a different source (such as ALCDEF 2  ) or based on our new observations. All lightcurve data that were used for the reconstruction and that are not included in DAMIT are listed in Table  A. 3. We excluded the "sparse-in-time" data from surveys such as the Catalina Sky Survey for their redundancy as rich dense datasets are usually available.
A large fraction of the listed data are based on observations from the SuperWASP survey that were processed by  Grice et al. (2017). Photometry for several asteroids was obtained by the TRAPPIST telescopes  (Jehin et al. 2011 ) and through the Gaia-GOSA 3 observing campaign. The strength of the ADAM model is that it makes use of all types of data (light curves but also stellar occultations when available), not just AO data. Given that the SPHERE data provide only a partial surface coverage for most of our targets, light-curve data were often necessary to constrain the uncovered aspect angles. We note that the SAGE model  (Bartczak & Dudziński 2018) , improved over the course of our large programme, has now the same type of strength as the ADAM model as it can constrain the spin axis and reconstruct the 3D shape of an asteroid using not only light curves but also AO data  (Dudziński et al.
2020). We note that the SAGE model was key in constraining the rotational parameters of two targets (128 Nemesis and 324 Bamberga), illustrating the benefit of having multiple reconstruction methods. The 3D shape produced with the ADAM model was subsequently used as input to the MPCD model  (Capanna et al. 2013). The strength of the MPCD method with respect to the ADAM or SAGE models is that it tries to capture small topographic details from the high-resolution SPHERE images. Unlike the ADAM and SAGE approaches, the MPCD method was designed for space missions and uses only SPHERE data along with a decimated version of the ADAM model as input. It is therefore not surprising that the images generated with the MPCD models using the OASIS software  (Jorda et al. 2010 ) usually show the smallest residuals with respect to the SPHERE images (Appendix B).
Acknowledgements. We warmly thank the  ESO  staff at Paranal and in particular  Henri Boffin  for providing us precious support and advice throughout the entire observing programme. P. V. , A. D. , M. F. , L. J. and B. C. were supported by  CNRS/INSU/PNP. The work of T. S-R. was carried out through grant  APOSTD/2019/046  by  Generalitat Valenciana (Spain). This work was supported by the  MINECO (Spanish Ministry of Economy ) through grant  RTI2018-095076-B-C21  ( MINECO/FEDER, UE). TRAPPIST  project is funded by the  Belgian Fund for Scientic Research (Fond National de la Recherche Scientique, FNRS)  under the grant  PDR T. 0120. 21. E. J. is a FNRS Senior Research Associate. F. M. is supported by the  National Science Foundation  under Grant No. 1743015. A56 , page of 48 P. Vernazza et al.
An accurate knowledge of its value is therefore important for the computation of the electron diffusion and scattering performed in the next section. For instance,  Albert et al. (2020)  found that the lifetimes for 1-2 MeV electrons for L = [1. 5,2] was a factor of 4-5 times greater using a low-density model than a high-density model. Because plasma density influences so greatly diffusion coefficients, it is a source of a great amount of uncertainty in electron flux prediction (e. g. Camporeale et al. , 2016). The plasma density for our event period is derived by procedure that combines Van Allen Probes data and models when data are lacking. The density is first either derived directly from the upper hybrid resonance frequency  (Kurth et al. , 2015)  measured by EMFISIS, or when EMFISIS density is unavailable, derived from spacecraft floating potential data calibrated against plasma densities determined from the upper hybrid resonance frequency  (Thaller et al.
, 2015). Spacecraft floating potential is measured by the EFW instrument. Combining both techniques allows to obtain the density the majority of the time (as shown and discussed below and in Fig. 3 ). Nevertheless, if the density from either technique is unavailable, the procedure automatically replaces the unknown density by the statistical model of  Albert (1999)  within the plasmasphere and the  Sheeley et al. (2001)  expression outside. In these cases, the plasmapause location is needed to differentiate between the two models. This is defined from the 100 #/cc level extracted from both EMFISIS and EFW database or, if the 100 #/cc level ( ±30%) is also undefined, we use the empirical plasmapause model of  Carpenter and Anderson (1992)  with the knowledge of the Kp* index in order to separate the dense plasma from the low plasma density.
Diffusion coefficients The computation of the bounce-and drift-averaged equatorial pitchangle diffusion coefficient is performed for each 8 h interval at each Lshell (0. 1 L resolution) based on the observed wave properties described in section 2. Mathematical expressions of the diffusion coefficients can be found in  (Lyons et al. , 1972; Albert, 1999; Albert, 2005; Glauert and Horne, 2005)  and are not recalled here. Diffusion coefficients are computed for 60 energies (E varying from 0. 07 MeV to 6 MeV) at every node (t, L), i. e. , 75 600 times (43L × 30t × 60E). A grid of 256 pitch angles in [0, 90 • ] and n cyclotron harmonics is used, with the harmonic number determined by an estimate  (Mourenas and Ripoll, 2012). A massively parallel supercomputer is required to generate this database of 19.
6% of the database). It is evident that the diffusion coefficients are highly variable from one day to the next. This is due to the variability of the wave properties and the plasma density. Typically, the wave normal angle influences the diffusion profile; a low wave-normal angle produces cyclotron diffusion which increases with pitch angle, whereas an intermediate wave-normal angle produces cyclotron diffusion that is approximately constant with respect to pitch angle. We observe higher diffusion coefficients, for instance on the 07/01 and on 07/05, which coincides with non-perturbed times. It is important to note that wave properties are correlated together as well as correlated with the plasma density as previously discussed (and in  Malaspina et al. , 2016 Malaspina et al. , , 2018;; Ripoll et al.
Acknowledgments This work was performed under the auspices of an agreement between CEA/DAM and NNSA/DP on cooperation on fundamental science. The authors acknowledge the  International Space Sciences Institute (ISSI)  and the participants in a 2020 ISSI workshop. Part of the research presented in this article was supported by the  Laboratory Directed Research and Development program  of  Los Alamos National Laboratory  under project number  20180731ER. Work at Iowa is performed under the support of  JHU/APL  contract  921647  under  NASA  Prime contract  NAS5-01072. Work at  CU/LASP  is supported by  NASA  grant  80NSSC18K1034. O. S. acknowledges support from the  Czech Academy of Sciences Praemium Academiae  award and from the  MEYS  grant  LTAUSA17070. Processing and analysis of the [HOPE, MagEIS, REPT, or ECT] data was supported by  Energetic Particle, Composition, and Thermal Plasma (RBSP-ECT ) investigation funded under NASA's Prime contract no.
88×10 -7 m 2 s -3 and its standard deviation 7. 61×10 -7 m 2 s -3 ; ϵ E = 3. 28×10 -7 m 2 s -3 and its standard deviation 3. 85×10 -7 m 2 s -3. The time-mean depth profile of the dissipation rate varies in the range [10 -8 -10 -6 ] m 2 s -3 with smaller values for the Thorpe method at the pycnocline oscillating in the range 75-120 m (see Fig. 2f ), which may result from a bias in the detection of overturns for the strongest stratification. These values characterize strong dissipation rates in the pycnocline, which can be compared to the highest thermocline dissipation rate found in the Halmahera Sea during the INDOMIX Program  (Bouruet-Aubertot et al. , 2018). The discrepancy between the Thorpe and Ellison methods is larger when we consider the lower bound inferred from the GM background dissipation rate estimate that results from the much less frequent detection of overturns in the CTD profiles than that of in the temperature-thermistor time series profile.
Acknowledgment This observation was funded by the Indonesian Endowment Fund Scholarship Program (LPDP), Indonesian Ministry of Finance; the program of CNRS LEFE INSU "Ondes solitaires et turbulence dans le detroit de Lombok" as well as the PHC Nusantara Project, a joint program between Campus France and Indonesian Ministry of Research and Fig. 5. (a) Spatiotemporal evolution of the dissipation rate estimated using Ellison scale (ϵ E ) at several mooring depths (scaled in log 10 , in colored contours; note that the depth of the dissipation estimate follows the sensors displacements), the strain (scaled in log 10 , in blue-scaled contours), and the low pass filtered (>3h) isotherms (black contours); (b) mean over the depth covered by the mooring of the dissipation rates inferred using Thorpe scale patch and that considering the GM-background dissipation rate (black dotted line and blue dotted line, respectively), and using Ellison scale (dashed line for the mean and shaded red area for the confidence interval assuming the Gaussian distribution shown in Figure  A3b  for the error made on the estimate of the local temperature gradient.
A reference fitted spectrum S R (f) is then built by a power-law fitting of the temperature spectrum S(f) omitting points in the possibly contaminated range S R (f)df of the variance of the signal in the surface waves frequency band between the fitted and observed spectrum is computed. By visual inspection of the spectra S(f) and corresponding time series, we determined that surface wave contamination was not detectable for r > 0. 6, with this criteria, 20% of the data were rejected. Figure  A1  shows an example of the rejected spectrum and the accepted spectrum. An important source of error is the estimation of dθ/dz. Indeed, we estimate the mean temperature gradient from the temperature difference between successive temperature sensors. Since these sensors are separated by a large distance of 40 m on average, this estimate may not represent the local gradient.
With this description in hand, the experimental transient reflectivity can be fitted by a least-square procedure. The parameters at each time delay are ∆E(t), T holes (t), and T elec (t), plus the two time-independent factors a elec and a holes , resulting in a total of N t + 2 independent variables, where N t is the number of time points. We perform the least-square minimization on the 2D matrix (time × energy) using the parallel implementation of the lsqnonlin routine in MATLAB 2018a. We used many randomly selected starting points as well as checked that lower and upper bounds did not influence the result. Finally, once the optimum was found, we computed the Jacobian at the solution point which was then used to obtain 95% confidence intervals, shown together with the optimal solution in Fig.
The energy shift of the DOS arising due to this cross-term is most likely negligible in our case because of rather high carrier temperatures, and correspondingly wide Fermi-Dirac distributions, compounded with experimental and core-hole lifetime broadening. This might not be the case for narrower distributions. Here we directly compare the atomic motion measured in our experiment (Fig. 4 ) with existing data in the literature. In particular, Giret et al. [31]  developed a thermodynamical model wherein most of the parameters were obtained from ab initio calculations. The agreement between their result and experimental measurements of   [31] , dashed lines are our experimental results (same as in Fig. 4  of the manuscript). The sample temperature before excitation is 0 and 300 K for theory and experiment, respectively.
ACKNOWLEDGEMENTS We thank  H. -T. Chang  for helpful discussions on the analysis and fitting of transient XUV data. Investigations at  UC Berkeley  were supported by the  Defense Advanced Research Projects Agency PULSE Program  Grant  W31P4Q-13-1-0017  (concluded), the  U. S. Air Force Office of Scientific Research Nos. FA9550-19-1-0314 ,  FA9550-20-1-0334 ,  FA9550-15-0037  (concluded), and  FA9550-14-1-0154  (concluded), the  Army Research Office  No. WN911NF-14-1-0383 , and the  W. M. Keck Foundation  award No. 046300-002. I. T. acknowledges funding from the  Swiss National Science Foundation (SNSF) , through Grant No. 200021-179138 , and its  National Centre of Competence in Research (NCCR) MAR-VEL. Computer time was provided by  CSCS  ( Piz Daint ) through Project No. s836. R. G. acknowledges support from " Investissements d'Avenir "  LabEx PALM  ( ANR-10-LABX-0039-PALM ).
Ac knowledgements We thank the Ma yor of Gonfa ron for authorizations to collect fossils, the  Ma ison de la Nature  ( Les Ma yons ) for accommodations,  I. van Waveren  ( Natural History Museum Leiden ),  J. Fortuny  (ICP),  Stanislav Stamberg  ( Eastern Bohemian Museum Czech Republic ),  G. Lemaitre  ( Phylogenia Association ),  G. Ga  nd ( Dijon University ),  M. Durand  ( Nancy University ),  T. Arbez  ( Ottawa University ), and the excavation team ( M. Raymond ,  M. Ughetto ,  X. Pouly ,  V. Blondel ,  M. Denis ) for their help in the field. We thank  E. Fara  and  J. Thomas  for the access to the  Dijon University collection. This research wa s supported by the interdisciplinary programs RedPerm I and II, ATM MNHN, LabEx, and  Projet Fédérateur MNHN  attributed to J-S. S. , R. G, and A.
1 ). In this study, we address the problem of crustal attenuation and exhumation of pre-rift midcrustal granulites based on the analysis and comparison of the offshore Le Danois High and the onshore Labourd Massif. Both settings show mild contractional reactivation of extensional structures, display evidence of extensional detachment systems, and preserve granulites, which are locally reworked in tectono-sedimentary breccias of Aptian to Cenomanian age. Based on new seismic interpretations and field observations, combined with published geological and geochronological datasets, we describe and compare the syn-rift architecture preserved in the Le Danois High and the Labourd Massif. We discuss key structural and stratigraphic observations and first-order analogies reported at the two sites, as well as their implications for hyperextension models in the Pyrenean and Biscay hyperextended rifts.
However, data is scarce and may not provide robust general constraints. New dating, including in-place dredges and larger datasets, are necessary to provide strong time constraints, which enable to investigate further crustal exhumation processes. Comparison between seismic and field derived observations and interpretations and available thermochronological data allow us to propose that the main stage of granulite exposure, leading to exhumation of mid-to lower crustal rocks to very shallow depths in the Biscay and Pyrenean rift systems occurred most likely from Aptian to Cenomanian under a brittle regime.
Conclusions In this work, we show a detailed analysis and comparison of the Le Danois High and the Labourd Massif that are part of the Biscay and the Pyrenean rift segments, respectively. New reported seismic and field observations indicate that the northdipping Le Danois and North Mauléon extensional detachment systems correspond to major and analogous structures, which developed from Aptian to Cenomanian, in the context of hyperextension within the Biscay and Pyrenean rifts. Seismic and field-derived interpretations, together with the integration of previous geological and thermochronological datasets suggest that these structures were responsible for crustal thinning and the exhumation of pre-rift mid-crustal rocks to the seafloor. This combined offshore-onshore study supports granulite exhumation in a brittle regime during Aptian to Cenomanian.
Acknowledgements. The  OROGEN  Project, a partnership between the  INSU-CNRS ,  TOTAL R&D , and the  BRGM , supported this work. The CS01 seismic data have been purchased through the Project MARCAS, Reference CTM2009-11522, funded by the  Ministry of Economy and Competitiveness of Spain. P. Cadenas acknowledges the  University of Oviedo  for providing a workspace during short visits. We thank  David Pedreira  and  Yoann Dènele  for fruitful comments and reviews and  Editors Olivier Lacombe  and  Romain Augier  for handling the manuscript.
The obtained data points of the different aliquots display the same changes in a K-Ar isochron diagram (Figure  1 ): those of samples L7 and L8 move in opposite directions, whereas the H 2 O leachate of the L10 aliquot yields a significant decrease in the age value due to an increase in the K 2 O content. In turn, the K-Ar data reflect the various contents of the major elements, namely the three glauconite samples are not identical in their chemical composition and, therefore, not in their mineralogical composition, due to the presence of contaminant minerals. In the case of sample L10, the dissolution of a soluble mineral from untreated aliquot, even after a preliminary routine H 2 O treatment, modified the Rb-Sr data, which is unusual e. g. ,  [25]. Such behavior is most probably due to the dissolution of a mineral rich in Sr and depleted in Rb and K.
Comparison of the K-Ar and the Previously Published Rb-Sr Data The Rb-Sr data of Pasteels et al. [6]  that were obtained on the same samples as the K-Ar and geochemical data generated here (Table  4 ), outline a high Rb-Sr age for the sole untreated L10 sample with a very high Sr content. It is clear that the difference is not due to an analytic aspect as the two other samples yield identical Rb-Sr and K-Ar ages. Clearly abnormal, the Rb-Sr age spectra at 42. 1 ± 1. 6, 24. 2 ± 1. 3 and 28. 7 ± 1. 1 Ma for the three analyzed L10 aliquots with an initial 87 Sr/ 86 Sr ratio set conventionally at 0. 7086, are clearly above the corresponding K-Ar values. These high values are also visible in the isochron diagram (Figure  4A ), where the untreated aliquot has the lowest 87 Rb/ 86 Sr ratio of all analyzed aliquots, while plotting above the drafted isochron.
In summary, the database points towards a specific mineral composition that clearly affects the Rb-Sr system and not its K-Ar equivalent. The Rb contents remain quite constant for the untreated and leached aliquots of sample L10, the difference needs to relate to the higher Sr content of the untreated sample, which reduces its Rb/Sr ratio to a significant lower value than those of the other aliquots. This is also visible in the diagram that compares the 87 Sr/ 86 Sr ratios with the corresponding Sr content (Figure  4B ). Interestingly, when ultrasonically leached, the L10 aliquots yield Rb-Sr age data that are much younger, even within analytical uncertainty, than those of the K-Ar data. In turn, as the differences between the Rb-Sr and K-Ar data of sample L10 obviously relate to the occurrence of blödite, a mineral enriched in Sr and lacking K and Rb, in the untreated L10 aliquot.