dataesr/scientific-paragraphs-categorization huggingface dataset:
Details of the emission profile of PSR B1919+21 The high sensitivity of NenuFAR and its stable gain across the band make it ideally suited to study average profiles in detail. Figure  7  shows the evolution of the observed profile for the historical pulsar B1919+21, discovered by Jocelyn Bell in 1967  (Hewish et al. 1968 ). Using NenuFAR, this pulsar has been detected with a high SNR  (> 2000)  down to 20 MHz. Obtaining good-quality data at such low frequencies is important for modelling profile evolution and constraining emission heights. About three dozen sources have been observed with such frequency resolution as in Fig. 7  in integrations of only one hour  (Bondonneau 2019; Bondonneau et al. 2020).
This translates to an increased precision for the study of DM variations. ations for PSR B1919+21 as measured by NenuFAR and LOFAR FR606 obtained with a 2D template fit (a phasefrequency template extending over 118 channels, built from the longest observation of PSR B1919+21 by NenuFAR, showing a SNR > 6000). The data from both telescopes are comparable, but the NenuFAR data provide a much higher precision (of the order of 10 -5 pc cm -3 ). As the template used for pulsar timing is derived from the data itself, the  procedure only gives relative DM values (sufficient for the analysis of DM variations) rather than absolute DM values  (Donner et al. 2019). Series of DMs are well aligned especially if we look at the simultaneous observation in the left panel in Fig. 11.
Interstellar Scintillation Studies By recording data in waveform mode, an additional filterbank step can be applied to increase frequency resolution (similar to the dynamic spectrum recording mode) before phase-averaging the observations (as in the folded mode). This hybrid combination of observing modes allows dynamic spectra to be constructed of the pulsed SNR with much higher frequency resolution than the standard folded mode allows; and with more sensitivity than the standard  dynamic spectrum mode allows. This is particularly useful for the study of interstellar and interbinary scintillation (Rickett 1990), since the size of scintles is strongly dependent on the observing frequency, causing typical scintillation bandwidths to be much smaller than 195 kHz at NenuFAR frequencies. Figure  12  shows an example of a dynamic spectrum that was obtained with this method, on PSR B0809+74.
Hotan et al. (2004)VE library(Hotan et al. 2004)this code is able to rapidly mitigate RFIs in the observation (using a modified version of the CoastGuard software,Lazarus et al. 2016, https://github. com/plazar/coast_guard)andcreate a quicklook. The source code is available on Github https://github. com/louisbondonneau/NenuPlot12PSRCHIVE: a library for the analysis of pulsar astronomical data. It implements an extensive range of algorithms for use in pulsar timing, scintillation studies, polarimetric calibration, single-pulse work, RFI mitigation, etc. The software is described inHotan et al. (2004).
Acknowledgements. This paper is based on data obtained using the NenuFAR radio-telescope. The development of NenuFAR has been supported by personnel and funding from:  Station de Radioastronomie de Nançay ,  CNRS-INSU, Observatoire de Paris-PSL, Université d'Orléans, Observatoire des Sciences de l'Univers en région Centre ,  Région Centre-Val de Loire, DIM-ACAV and DIM-ACAV+ of Région Ile de France ,  Agence Nationale de la Recherche. We acknowledge the use of the  Nançay Data Center computing facility (CDN -Centre de Données de Nançay). The CDN is hosted by the  Station de Radioastronomie de Nançay  in partnership with  Observatoire de Paris, Université d'Orléans, OSUC  and the  CNRS. The CDN is supported by the  Region Centre Val de Loire, département du Cher. This work was supported by the " Entretiens sur les pulsars " funded by  Programme National High Energies (PNHE)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES.
Hotan et al. (2004)VE library(Hotan et al. 2004)this code is able to rapidly mitigate RFIs in the observation (using a modified version of the CoastGuard software,Lazarus et al. 2016, https://github. com/plazar/coast_guard)andcreate a quicklook. The source code is available on Github https://github. com/louisbondonneau/NenuPlot12PSRCHIVE: a library for the analysis of pulsar astronomical data. It implements an extensive range of algorithms for use in pulsar timing, scintillation studies, polarimetric calibration, single-pulse work, RFI mitigation, etc. The software is described inHotan et al. (2004). 13 ChangeFormat_rescale. py: This code is designed to convert 32-bit PSRFITS data into 8-bit in data while maintaining the signal fidelity. The signal is transformed to 8-bit by decompos- ing it in data, scale and offset (signal=(data×scale)+offset) which allows us to maintain the dynamic range of the signal.
Acknowledgements We thank financial support from the  Federal University of Toulouse/Region Occitanie  (PhD grant of MPB), the  CONACYT  (grants of JEAC and AEC), the  Agence Nationale de la Recherche  ( ANR-19-CE09-0008-01  and the  European Commission  ( H2020-MSCA-RISE-2016 ,  SPINSWITCH , No. 734322 )). We are grateful to  Gaspard Cereza  for his contribution to the construction of the actuator test bench.
The total blank of <5 ng is negligible compared to the 5-20 μg of Cr processed through the columns. The final Cr solution was re-dissolved in 100µL of concentrated HNO3 and then dried again 2-3 times to transform the acid media and to minimize residual organics (i. e. from the cation exchange resin). The 55 Mn/ 52 Cr ratios with an uncertainty of 5% were measured on a Neptune plus multiple-collector inductively-coupled-plasma mass-spectrometer (MC-ICP-MS) following the protocol described by  (Zhu et al. , 2021b)  and previously used in  (Zhu et al. , 2019b; Zhu et al. , 2020b). The Cr isotopic compositions were determined using a Neptune Plus MC-ICP-MS located at the Centre for Star and Planet Formation, Globe Institute, University of Copenhagen. Detailed analytical and data reduction method are described in  (Zhu et al.
, 2021b). Sample solutions with concentration of less than 1 ppm were introduced via an ESI APEX and analyzed at a 52 Cr signal of 30-40 V, except for Norton County, with low-Cr content (180 ppm), that was analyzed at 15 V. Each sample was measured five times by sample-standard bracketing relative to the NIST SRM 979 Cr standard. The 53 Cr/ 52 Cr and 54 Cr/ 52 Cr ratios were normalized to a constant 50 Cr/ 52 Cr ratio of 0. 051859 using an exponential law  (Lugmair and Shukolyukov, 1998)   The measurements in this study are performed following  Zhu et al. (2021b) , and the related elemental doping tests (for Ti, V, Fe, K, and Na), external precision tests and the isotope data of known standard samples for data quality (Allende, DTS-1, PCC-1, DTS-2b) can be also found in  Zhu et al. (2021b).
The final Cr cut was evaporated in concentrated HNO3 drops 3 to 5 times to convert the HCl medium to HNO3 and to remove leftover organics (i. e. , the resin) following which the samples were diluted to a concentration of 1 ppm Cr, in 2% (0. 317 M) HNO3 for isotope analysis. The Cr stable isotope compositions of these purified samples were measured on a Thermo 2), were also measured in this study to test the data quality. Our data are consistent with previous studies  (Bonnand et al. , 2016b; Liu et al. , 2019; Schoenberg et al. , 2016; Sossi et al. , 2018; Wu et al. , 2020; Zhu et al. , 2019c) , which suggests that our δ 53 Cr data are accurate to the stated uncertainties.
Acknowledgements Constructive comments from  Jan Render ,  Royji Tanaka  and  Rick Carlson  greatly improved this manuscript, and comments and editorial handling from the associate editor  Yves Marrocchi  are also appreciated. F. M. acknowledges funding from the  European Research Council  under the  H2020 framework program / ERC Starting Grant  Agreement (# 637503-PRISTINE ) and financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  (# ANR-10-LABX-0023  and # ANR-11-IDEX-0005-02 ), and the  ANR  through a chaire d'excellence Sorbonne Paris Cité. M. B. acknowledges funding the  Carlsberg Foundation  ( CF18-1105 ), the  Danish National Research Foundation  ( DNRF97 ) and the  European Research Council  ( ERC Advanced Grant  Agreement, # 833275-DEEPTIME ). M. S. acknowledges funding from the  Villum Fonden  (# 00025333 ).
Parts of this work were supported by  IPGP  multidisciplinary program PARI, and by  Paris-IdF region SESAME  (# 12015908 ). Pierre Burckel  and  Pascale Louvat  were appreciated for analysis on ICP-MS and MC-ICP-MS at IPGP, respectively. We thank  Ninja Braukmüller ,  Monika Feth ,  Maren Saenz ,  Elis Hoffmann  and  Yogita Kadlag  for technical support at  FUB. Discussions with Paolo Sossi about Cr stable isotope fractionation in S-rich core formation helped us interpret the data. K. Z. thanks an  Alexander von Humboldt fellowship  (for postdoc researchers),  China Scholarship Council (CSC)  for a PhD fellowship (# 201706340161 ) and a TRR 170 fellowship during his stay in Berlin in autumn 2020. H. B. is funded by.
1Figure 1a. 1a Figure 1a. The ε 54 Cr -∆ 17 O diagram for aubrites, enstatite chondrites and other Solar System materials. The area of enstatite meteorites (in red) overlaps with that of Earth-Moon system. The template and dataset (all the data cited are from published literature and no conference abstracts) are fromZhu et al. (2021b), and the data sources are mainly from Schiller et al. (2014 ), Trinquier et al. (2007) , Yamashita et al. (2010) , Qin et al. (2010) , Göpel et al. (2015) , Pedersen et al. (2019) , Mougel et al. (2018) , Clayton and Mayeda (1999) ,Clayton and   Mayeda (1984), Greenwood et al. (2010) , Clayton et al. (1991) ,Clayton et al. (1984), Greenwood et al. (2017) , Newton et al. (2000) , Schrader et al. (2011) , Petitat et al. (2011), Zhu et al. (2020b), Zhu et al. (2020a), Zhu et al.
The BabyIAXO sensitivity line in figure  2  is also the envelope of two data taking campaigns, one in vacuum and one with buffer gas. A 1. 5 year effective exposure is assumed for each of them. As in the case of IAXO, the gas phase exposure is distributed unequally in the various density steps to adjust the sensitivity to the KSVZ axion model g aγ for a relatively large mass range. We refer to  [21]  for more details of these sensitivity calculations. As can be seen from figure  2 , BabyIAXO should reach values of g aγ ∼1. 5 × 10 -11 GeV -1 up to masses m a ∼ 0. 02 eV. In the gas phase, BabyIAXO will extend to higher masses and in particular will probe KSVZ axions in the approximate range of about 0. 06 to 0. 25 eV. IAXO will also be sensitive to non-hadronic axions with a coupling to electrons g ae ∼ 10 Table  1.
All the obtained results could be stored in a database for internal use within the collaboration. • Improvements in radiopurity of components of the detector. An extensive screening program will be essential to identify detector components with the highest activity level, and they could be replaced in detector design updates if more radiopure alternatives are available. • Control of procedures for cleaning and storage of materials. Once the most adequate components in terms of radiopurity are selected, the storage and cleaning of the materials must be controlled in order to avoid possible contaminations for example from radon plate-out. • Measurements of environmental backgrounds. The characterization of the external background components at the experimental site, including radon levels and gamma and neutrons fluxes will allow to adjust the design of the different elements of the active and passive shieldings to optimize its efficiency in the background reduction.
Acknowledgements We want to devote this paper to the memory of our dearest friend, colleague and IAXO enthusiast  Biljana Lakic , who passed away recently. We acknowledge support from the the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , grant agreement  ERC-2017-AdG788781  (IAXO+) as well as ERC-2018-StG-802836 (AxScale), and from the  Spanish Agencia Estatal de Investigación  under grant  FPA2016-76978-C3-1-P , the coordinated grant  PID2019-108122GB , and the  Maria de Maeztu  grant  CEX2019-000918-M. The groups at  Bonn University, Heidelberg University, Mainz University and Siegen University  are supported by  BMBF  contract  05H2018-R&D DETEKTOREN. The groups  IRFU/CEA ,  LIST/CEA  and IJCLAB acknowledge support from the  Agence Nationale de la Recherche (France)   ANR-19-CE31-0024.
The best cross-correlation between the pseudo SJI and the real IRIS SJI was found for a Gaussian FWHM of ∼1. 2 arcsec (panel D of Fig. S1 ). In order to get a better context of the plage target, with IRIS we also carried out dense raster scans before and after the CLASP2 observation. The scan step size was set to 0. 35 arcsec and the FOV covered by the SG was 112 arcsec (perpendicular to the slit) × 175 arcsec (parallel to the slit), taking about 30 min to complete one raster scan. Figure  S3  shows the resulting raster intensity image at the fixed wavelength position of the Mg II k line center, taken at the closest observing time to the CLASP2 observation. These high spatial resolution IRIS data are helpful to understand the structure of the observed plage in the upper chromosphere.
We assume f = 1 in our inferences from the CLASP2 data. 1E. Determination of the photospheric magnetic field from the Hinode/SOT-SP data The photospheric magnetic field in plage regions is generally strong, of the order of kG, and thus the WFA is not generally applicable to infer the magnetic field from the Fe I lines around 630 nm. Therefore, in order to obtain the LOS component of the magnetic field in the photosphere of the observed plage region, we have used the SOT-SP Level 2 information given by the Community Spectro-polarimetric Analysis Center (CSAC) of the High Altitude Observatory (DOI:10. 5065/D6JH3J8D. See also https://www2. hao. ucar. edu/csac). This information stems from the application of the MERLIN Milne-Eddington (ME) inversion code to the SOT-SP Level 1 data (DOI:10. 5065/D6T151QF), which provides the magnetic field vector (field strength B, inclination θ B , and azimuth χ B ), the line-of-sight velocity, the source function, the Doppler broadening as well as the macroturbulence and a stray light factor.
e. , the product of temperature and electron density) shows a high cross-correlation with the magnetic field (see Table  1 ). Figure  S7  shows the product of temperature and electron density along the CLASP2 slit, as derived from the IRIS 2 inversions, in comparison with the CLASP2 magnetic field measurements. The cross correlation values are similar to those of the Mg II k 3 and k 2v intensities shown in Figure  3. The high correlation with the electron pressure provides support for a close relationship between magnetic field strength in the chromosphere and local heating. This is because heating leads to an increase in local temperature, density, and ionization. We note that the IRIS 2 database was created for spectral line profiles at the spatial resolution (0. 4 arcsec) and temporal resolution (2 -30 s) of IRIS.
Despite these inherent limitations (which are not always easy to quantify), tests in which inversion results are compared with "ground truth" from numerical simulations suggest that they can provide valuable information about the atmosphere responsible for the observed emissions  (54, 58). With these caveats in mind, our results suggest that there exists a correlation between two independent "measurements" in the atmosphere: the line-of-sight magnetic field (as derived from the Stokes V CLASP2 measurements), and the product of temperature and electron density (as derived from comparison with STiC inversions in the IRIS 2 database).
01 nm, a temporal resolution of 3. 2 s and a spatial resolution of at least 2 arcsec (26, 27). The resolutions estimated from the pre-flight measurements were ∼1. 1 arcsec and ∼0. 01 nm. A comparison of the spectroscopic data taken by CLASP2 and IRIS during the observation of the plage target suggests that these spatial and spectral resolutions are representative for the CLASP2 flight measurements. Previous space instruments developed for measuring polarization signals in solar ultraviolet lines failed due to in-flight degradation problems (28) , or lacked the polarimetric sensitivity and resolution (spectral, spatial and temporal) needed for accurately determining magnetic fields in the upper solar chromosphere and TR of quiet and active regions (29) (30) (31) (32) (33). The CLASP2 instrument consists of a Cassegrain telescope with an aperture of 27 cm, a slit-jaw (SJ) monitor system, and a spectropolarimeter to measure the Stokes I, Q, U and V profiles in the wavelength window of the Mg II h and k lines over the 196 arcsec covered by the spectrograph's slit.
For each CLASP2 Stokes I spectral line profile of the Mg II h & k lines, we determine the profile in the IRIS 2 database that has the smallest weighted Euclidean distance to the CLASP2 profile. Since we are interested mostly in the chromospheric properties, we place enhanced weighting on the wavelength regions between Mg II k 1v and Mg k 1r and between Mg II h 1v.
Data and materials availability All data needed to evaluate our conclusions are presented in the paper and/or in the Supplementary Materials. The calibrated CLASP2 data will be made publicly available soon through the Virtual Solar Observatory. The Level 2 Hinode/SOT-SP data are available at https://csac. hao. ucar. edu/sp data. php, the IRIS data at https://iris. lmsal. com/data. html, and the SDO data at http://jsoc. stanford. edu.
For each CLASP2 Stokes I spectral line profile of the Mg II h & k lines, we determine the profile in the IRIS 2 database that has the smallest weighted Euclidean distance to the CLASP2 profile. Since we are interested mostly in the chromospheric properties, we place enhanced weighting on the wavelength regions between Mg II k 1v and Mg k 1r and between Mg II h 1v (54)abase(53). T latest version of this database contains 150,000 spectral profiles around the Mg II h & k lines, and the associated model atmospheres (i. e. , temperature, velocity, turbulence, electron density, as a function of optical depth), calculated using the STiC inversion code which includes the effects of radiative transfer with PRD(54).
The extracted values of β 2 /8π are shown for the different data points with the expected values within parenthesis. As an extra check, we compute also the soliton mass (M) as a function of E J 0 /E C 0. This is directly computed from Eq. (  17 ) by eliminating e iβφ/2. The results obtained with infinite DMRG are shown on the right panel (empty circles), together with the analytical values of Eq. (  16 ) (crosses). As is evident, the agreement is reasonable, which gives us confidence that we are indeed probing the qSG regime of the XYZ parameter space. We chose E J /E C 0 = 1. 55 (i. e. , β 2 /8π = 0. 063) and M 0 /2 = E J 0 /E C 0 = 0. 016. The corresponding soliton mass (M), determined using Eq. (  2 ) is 0. 662. The plasmon velocity (u 1. 46) is determined using the Casimir energy as in Fig.
We run the simulations for sufficiently large times until the diffusion constant Dα (t) reaches a plateau (Fig. 1(a) ). We then fit a curve of the form Dα (t) = D α + (D s α -D α ) 1e -(t/τ ) γ from which we can determine the asymptotic value Dα (∞) = D s α. This optimization depends on γ and D s α , but the resulting value of D s α is almost insensitive to γ. We choose γ = 1/2  [53]  for better numerical stability. Results for a tagged particle in a bath (Fig. 1 ) are obtained for c A = 0. 001 in order to collect more data while for mixtures at finite concentration (Fig. 2 ) we increased the volume size V → 2V. We also determined indirect observables from the simulations. For a given parameter set Φ that includes concentrations, temperatures and sizes of the particles, the long-time friction constant is obtained from ζ L α (Φ)/ζ α = D α /D s α (Φ eq ), (S42) where D s α (Φ eq ) = D s α (c A , c B , σ A , σ B ; T A = T B ) corresponds to the equilibrium value of self-diffusion constant.
ACKNOWLEDGMENTS We thank  A. S. Vishen ,  V. Démery  and  M. Delarue  for interesting discussions. We acknowledge support from the  Labex CelTisPhyBio  ( ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 ) and from  Agence Nationale de la Recherche (ANR) , grant  ANR-17-CE11-0004.
Funding. This work was supported by the  National Natural Science Foundation of China  (Grant No. 61605142 ) and the  Open Fund of the State Key Laboratory of High Field Laser Physics  ( Shanghai Institute of Optics and Fine Mechanics ). Dr. François Courvoisier  acknowledges the financial supports of  European Research Council (ERC)   682032-PULSAR , the  EIPHI Graduate School   ANR-17-EURE-0002  and  I-SITE   BFC  project  ANR-15-IDEX-0003. Disclosures. The authors declare no conflicts of interest.
ACKNOWLEDGMENTS We acknowledge fruitful discussions with  E. Pinsolle  and  F. Pistolesi. This work was supported by  ANR-11-JS04-006-01 , the  Canada Excellence Research Chairs program , the  NSERC , the  MEI , the  FRQNT  via the  IN-TRIQ , the  Université de Sherbrooke  via the EPIQ, the  Canada First Research Excellence Fund  and the  Canada Foundation for Innovation.
Acknowledgements M. R. thanks the grant agencies innovation  FRC  for the financial support for the project  Molecular Qudits: Isotopologoues Coordination Chemistry  and the  DFG priority program 1928 "COORNETS"  for generous support. D. S. and P. G. have received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 820391  (Square). A. M. N. and L. J. C. thank the  French Agence National de la Recherche  for financial support ( Neutrino project  no. ANR-16-CE09_0015-02 ). We all thank  Dr. Asato Mizuno  for the structural refinement of [Gd 2 ] complex.
Data availability X-ray crystallographic data of the [Eu 2 ] and [Gd 2 ] complexes, in the form of cif files, discussed in this article can be obtained from the Cambridge Crystallographic Data Centre-CCDC 1863020 [Eu 2 ] and 1961893 [Gd 2 ]. The original data sets corresponding to the experiments can be obtained from the authors upon a reasonable request (contact: mario. ruben@kit. edu).
Acknowledgments: This material is based upon work supported by  NASA  under Contracts  NNM10AA11C  and  NNG12FD66C , issued through the  New Frontiers Program. The  OSIRIS-REx Laser Altimeter  and the Canadian authors were supported by the  Canadian Space Agency. P. M. acknowledges funding support from the  French space agency CNES , from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ) and from  Academies of Excellence: Complex systems and Space , environment, risk, and resilience, part of the IDEX JEDI of the  Université Côte d'Azur. This work used the  Small Body Mapping Tool  (http:// sbmt. jhuapl. edu). We are grateful to the entire OSIRIS-REx Team of engineers, operators, scientists, and administrators for making the encounter with Bennu possible.
Results Brief overview of the method and extracted parameters. Both AFC tip calibration and data processing methods are described in detail in the "Materials and methods" section. However, for a better reading of the manuscript a brief description is given in the following. Indentation curves are acquired using FV mode and processed with a dedicated Matlab program (see Fig. S1  and S2 ). A representation of such an indentation curve (loading and unloading curves in blue and red respectively) is shown in Fig. 2a  with a scheme of the tip-surface interaction during indentation at maximum load (inset). The first step of the AFC method consists in establishing the area function, which describes the evolution of the tip/surface contact area with the indentation depth. Two examples, obtained using a reference sample of known elastic modulus, are given in Fig.