dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This work was partially supported by the  RHU EPINOV, A* MIDEX  project ( ANR-17-RHUS-0004 ) funded by the  'Investissements d'Avenir' French Government  and by a  FLAG ERA/HBP grant  from  Agence Nationale de la recherche  " SCALES "  ANR-17-HBPR-0005 , by  France Life Imaging network  (grant  ANR-11-INBS-0006 ), and  ILCB convergence institute   ANR-16-CONV-0002.
(2018). Such an approach includes a nuclear structure part modeled by quasi-particle random phase approximation-(QRPA-) based transition densities, and a reaction part via distorted wave Born approximation (DWBA) cross section calculations. Here, we provide for the first time the experimental cross section angular distribution data and discuss their comparison with the theoretical calculations. Contextually, the elastic and inelastic scattering of 18 O beam on 40 Ca target at the same bombarding energy were measured in a wide momentum transfer range and studied to extract the optical potential to be used in the SCE calculations. The importance of experimental constraints coming from the elastic scattering data to the determination of the ion-ion interaction was already stressed by  Lenske et al.
EXPERIMENT AND DATA REDUCTION The 40 Ca( 18 O, 18 F) 40 K charge exchange reaction was measured at INFN-Laboratori Nazionali del Sud in Catania (Italy). A beam of 18 O 4+ ions, extracted by the K800 Superconducting Cyclotron accelerator, bombarded a 280 ± 14 µg/cm 2 natural calcium (96. 9% 40 Ca) target at an incident energy of 275 MeV. The calcium material for the target was evaporated on a carbon backing of 25 µg/cm 2 thick, and a further carbon layer of 15 µg/cm 2 was evaporated on the top of the calcium layer to reduce oxidation processes. The 18 O + 40 Ca elastic scattering and inelastic scattering were measured in a dedicated run using a 250 ± 12 µg/cm 2 thick calcium target evaporated on 47 µg/cm 2 carbon backing. A Faraday cup located inside a scattering chamber and 15 cm downstream of the target was used to collect the beam charge.
CONCLUSIONS The 40 Ca( 18 O, 18 F) 40 K charge exchange reaction, together with the elastic scattering and inelastic scattering of 18 O on 40 Ca, has been studied at an incident energy of 275 MeV and at forward angles using the MAGNEX spectrometer. The availability of this complete set of data, where the absolute cross section at different angles has been measured with high precision, has allowed a constrained and reliable description of the direct reaction mechanism for the charge exchange process. Charge exchange cross section calculations have been performed in DWBA using a CCEP tested against the elastic and inelastic scattering data and form factors extracted from double folding of a nucleonnucleon isovector interaction with QRPA transition densities. Such full quantum-mechanical calculations with microscopic nuclear structure inputs describe the order of magnitude and shape of the observed cross sections.
FUNDING This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  (Grant Agreement No. 714625 ). Partial funding by the  European Union 's  Horizon 2020 research and innovation program  under Grant No. 654002  is acknowledged. The grants  DGAPA-UNAM IN107820 ,  AG101120 , and  CONACyT 314857 ,  Mexico ,  FAPESP  ( Fundação de Amparo à Pesquisa do Estado de São Paulo , proc. 2019/07767-1 ), and  INCT :  Instituto Nacional de Ciência e Tecnologia-Física Nuclear e Aplicações (INCT-FNA , research project  464898/2014-5 ), Brazil are also acknowledged. HL acknowledges support by  INFN  and  DFG , Grant  Le439/16.
Acknowledgments We are grateful to  Yu-tin Huang ,  Henrik Johansson ,  Ian Low ,  Nic Pavao ,  Radu Roiban ,  Oliver Schlotterer ,  Aslan Seifi ,  Bogdan Stoica , and  Ingrid Vazquez-Holm  for interesting discussion of related subjects. We are additionally grateful for especially helpful comments by  Oliver Schlotterer  on an earlier draft. This work is supported by the  Department of Energy  under Award Number  DE-SC0021485. LR is supported by  Taiwan Ministry of Science and Technology  Grant No. 109-2811-M-002-523. SZ would like to acknowledge the  Northwestern University Amplitudes  and Insight group,  Department of Physics and Astronomy , and  Weinberg College  for support.
Acknowledgments We acknowledge  A. Miyake ,  J. Béard ,  F. Hardy ,  J. -P. Brison ,  K. Ishida ,  Y. Tokunaga ,  Y. Yanase , and  H. Harima  for useful discussions. This work at the LNCMI was supported by the " Programme Investissements d'Avenir " under the project  ANR-11-IDEX-0002-02  (reference  ANR-10-LABX-0037-NEXT ). We acknowledge the financial support of the  Cross-Disciplinary Program on Instrumentation and Detection of CEA , the  French Alternative Energies and Atomic Energy Commission , and  KAKENHI  ( JP15H05882 ,  JP15H05884 ,  JP15K21732 ,  JP16H04006 ,  JP15H05745 ,  JP19H00646 ).
(2018). Rotationally inelastic collisions with electrons were also taken into account by means of rate coefficients for 10 < T < 4000 K and the rotational transitions J -J = 2, 2 J 5, derived from the empirical excitation cross sections of  England et al. (1988) ; charge neutrality requires that n(e -) = n(H + ). The rates of these transitions, induced by heating and cooling processes are taken into account, with proper allowance for radiative transfer in the ultraviolet lines. 4 Proton-exchange collisions with other species, such as H + 3 , can also play a role, particularly in the interstellar medium. electrons, become comparable with their rates, induced by protons, for T ∼ > 1000 K; but, at these high temperatures, collisions with H and/or H2 tend to dominate. For kinetic temperatures in the above ranges, the rate coefficients, q (cm 3 s -1 ), for de-excitation of H2 in collisions with H and H + were evaluated by means of cubic-spline interpolation of the data points; the rate coefficients for excitation were derived from the detailed-balance relation, g I g J q(v J → vJ) = gI gJ q(vJ → v J ) exp - (EvJ -E v J ) kBT where gI = 2I +1 is the nuclear-spin statistical weight (3 for odd J and 1 for even J) and gJ = 2J + 1 is the rotational degeneracy.
ACKNOWLEDGMENTS This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 811363 ). We acknowledge the  Programme National Physique et Chimie du Milieu Interstellaire (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES. FL acknowledges financial support from the  Institut Universitaire de France. TG-L acknowledges support from Project No. FIS2017-83157-P (MINECO/AEI/FEDER , UE). DRF acknowledges support from  STFC  ( ST/L00075X/1 ), including provision of local computing resources.
org/10. 24414/wvj6-nm32 May-2020. Both the images were created with the use of JHelioviewer software  (Müller et al. 2017). In addition to EUI data, we used EUV images from SDO/AIA. AIA provides full-disk solar images in seven different EUV channels. The AIA images have 0. 6 pixels and 12 s temporal cadence. For our analysis, we mainly used  304, 171, 193, and 211  Å images, because we were able to recognize the EUI events clearly in these four channels. We found that campfires are rarely visible in hotter AIA channels (e. g. 335 and 94 Å). We also checked that they are hardly visible in AIA's UV channels (1600 and 1700 Å). To investigate the photospheric magnetic field setting of campfires, we used line of sight magnetograms, of 45s series, from SDO/HMI. The HMI has 0. 5 pixels, 45 s temporal cadence and a noise level of about 7 G  (Schou et al.
2012; Couvidat et al. 2016). Two consecutive magnetograms have been summed at each time step to enhance the visibility of weak field regions. We downloaded two hours of AIA and HMI data for 20-May-2020 and an hour data for 30-May-2020, using JSOC data cutout service 2. The AIA and HMI data sets have been co-aligned using SolarSoft routines  (Freeland & Handy 1998). It is important to note that EUI and AIA observed the Sun from different heliocentric distances. Solar Orbiter was at 0. 612 AU and 0. 556 AU from the Sun on 20-May-2020 and 30-May-2020, respectively. Therefore, the events would appear 3. 22 minutes and 3. 68 minutes earlier in EUI images than in the AIA images on 20-May-2020 and 30-May-2020, respectively. We have created movies, using EUI, AIA, and HMI data, to follow the evolution of campfires together with their underlying magnetic field.
The movies and their corresponding figures display the same field of view. The contours, of ±15 G, of HMI magnetograms are overplotted in the movies and figures. The Differential Emission Measure (DEM;  Cheung et al. 2015)  has been computed, using six AIA channels  (171, 193, 211, 131, 335, and 94 Å) , to investigate the emission of campfires in different temperature bins. We inspected the full-resolution HRI EUV data and manually identified 52 campfires (from 44 different locations). We looked for those events that show similarities with the campfires in the Solar Orbiter/EUI press release images  3. After selecting the campfires in HRI EUV 174 Å, we looked for the same campfires in the AIA 171 Å images. Then we co-aligned, using solar soft routines, the HMI line of sight magnetograms with respect to the AIA images, both onboard SDO.
Therefore, the alignment between AIA images and HMI magnetograms is accurate in order to study the photospheric magnetic field of the campfires. Our selected campfires are also consistent with the categories of campfires (loop-like, dot-like, and complex) in  Berghmans et al. (2021). In addition, we find several campfires to be jet-like in their appearance. All of our selected events are listed in Table  1. We find that some of the campfires occur more than once from the same location. We mainly obtained the duration, length, and width of the campfires from the HRI EUV images. We used AIA data to estimate the duration of those campfires that start/end a few minutes before/after the EUI-time coverage. In Table  1 , the measured length is the integrated distance of each campfire along their longer-extension, whereas the estimated width is the cross-sectional distance of the campfire.
Attosecond transient absorption spectroscopy (ATAS) is used to observe photoexcited dynamics with outstanding time resolution. The main experimental challenge of this technique is that highharmonic generation sources show significant instabilities, resulting in sub-par sensitivity when compared to other techniques. This paper proposes edge-pixel referencing as a means to suppress this noise. Two approaches are introduced: the first is deterministic and uses a correlation analysis, while the second relies on singular value decomposition. Each methods is demonstrated and quantified on a noisy measurement taken on WS2 and results in a fivefold increase in sensitivity. The combination of the two methods ensures the fidelity of the procedure and can be implemented on live data collection but also on existing datasets.
However, because this requires additional hardware and reduces the available XUV flux, it is not readily applicable to any experiment. Here we take a different approach and explore two different post-processing procedures based on edgereferencing  [17, 18]. The methods use a general noise suppression scheme taking advantage of strong correlations in the probe spectra, which are first characterized on a calibration dataset. We present two complementary methods and demonstrate their efficiency in an attosecond transient reflectivity experiment, measured on a sample of WS 2. The edge-referenced data obtained using either methods shows a drastic fivefold increase in sensitivity which in turn allows for the observation of features previously hidden by spectral fluctuations. Our approach is significant in four respects: (1) it radically filters out the correlated part of the noise without requiring any new hardware, (2) it does not require sacrificing a part of the XUV flux for reference detection, and is therefore applicable to virtually any ATAS experiments, (3) the combination of the two methods ensures that no artefacts are introduced during the edge-referencing and (4) the noise calibration step can be constructed from information contained in existing datasets, which makes it valuable to analyze already measured data.
II. 2. Experiment To demonstrate our method, we use a dataset of attosecond pump-probe measurement obtained in bulk WS 2. Carrier-envelope phase stabilized few-cycle (5 fs) pulses of visible/near-infrared light are used to photoexcite the sample and to generate the broadband XUV radiation which serves as probe. Using krypton as a generating medium, the XUV spectrum is optimized to overlap with the 4f (31. 4/33. 6 eV) and 5p (36. 8 eV) core-levels of tungsten, as shown on Fig. 1(a). The XUV pulse probes the transitions from these core-levels to the valence and conduction band regions. The sample was produced by plasma-enhanced atomic layer deposition of 40 nm thick tungsten oxide on a silicon wafer capped with 250 nm thick SiO 2 , and subsequently converting the tungsten oxide to WS 2 by reacting the deposited tungsten oxide with H 2 S and Ar as buffer gas at 550 • C  [20].
39 eV as well as two weaker signals at slightly lower photon energies. The atomic-like narrow linewidth of the signal and the fact that it starts appearing at -10 fs suggests a core-excitonic nature  [22] [23] [24]. Indeed, at negative delays, the role of the pump and probe pulses are reversed: signals in these regions are due to an XUV-triggered dynamics probed by the NIR field. At positive delays on the other hand, we expect signals triggered by the NIR pulse, which has an energy of ω pump = 1. 2 -2. 5 eV. This is enough to photoinject carriers across the ∼1. 35 eV bandgap  [25]  of WS 2. Such carriers would give signals in the valence and conduction band regions  [21, 26]  which here lie in the 36-41 eV range. The experimental noise of this dataset is too large to distinguish these features.
III. 2. First approach: correlation matrix In this approach, a correlation matrix is used to map the XUV fluctuations in the edge-pixels onto the signalpixels. We note n the number of pixels in the spectrum, and m the number of pixels chosen as edge-pixels, with m < n. If N t is the number of pump-probe delays, N t pairs of pump-on and pump-off spectra are obtained in the experiment, yielding the raw transient absorption ∆OD measured which is a (N t × n) matrix. ∆OD edge measured is a (N t × m) matrix -a subset of ∆OD measured -used to correct the noise in the entire measurement region: ∆OD referenced = ∆OD measured -∆OD edge measured • B (2) B is the (m × n) correlation matrix that is measurement specific and needs to be constructed for each experiment. The first step is to compute B. To this end, we need a calibration dataset which is a series of probe spectra acquired while blocking the pump beam.
Pairing these spectra two by two yields p "blank" transient spectra, resulting in the (p × n) calibration matrix ∆OD calib. We follow Refs. [18, 27]  to obtain the B matrix which minimizes the residual noise after referencing. It can be written in the following matrix form: B = ∆OD edge calib T • ∆OD edge calib -1 • ∆OD edge calib T • ∆OD calib , (3) where -1 denotes the matrix inversion operation. Since ∆OD edge calib is a real, (p × m) matrix, we have rank(∆OD edge calib T • ∆OD edge calib ) = rank(∆OD edge calib ) ≤ min(p, m). Therefore, the inversion can be performed only if p > m. In other terms, the calibration dataset needs more observations as there are edge-pixels for Equ. 3 to be used. If this condition is met, computing B is extremely efficient. Finally we note that Equ. 3 can also be formulated using covariances (as done in Ref.
[27] ) using the fact that both ∆OD calib and ∆OD edge calib have an expectation value of zero. For experiments with long acquisition times and possibly varying noise structures, this calibration dataset can be updated periodically during the measurement. The transient data of Fig. 1  was taken without such calibration step and as such this approach is unavailable. However, the data was collected by alternating between pump-off and pump-on measurements. We can therefore use all the pump-off spectra as our calibration dataset for building the B matrix. This approach will not be as accurate as an experiment where the B matrix is periodically computed, but it has the advantage of being applicable to any dataset at the post-treatment stage. In our case, pairing the pump-off spectra two by two yields a total of p = 1220 measurements for the matrix ∆OD calib.
Nonetheless, the improvement of the data quality demonstrates the strength of the edge-referencing method. Its performance is now assessed quantitatively as a function of both the size of the calibration dataset p and the number of edge-pixels n. The remaining noise level is defined as the root-mean square of the optical densities measured at negative delays (t < -10 fs). Figure  2(c-d ) illustrate the evolution of the noise when varying the number of edge-pixels (darker blue shaded areas indicate less edge-pixels) and restricting the number of calibration ∆OD spectra in ∆OD calib. The trend is very similar to the one identified in Ref. [18] : larger edge-pixels regions increase the performance of the method, but require more calibration measurements to reach the lowest noise. Robben et al.
For these reasons, we present a variant of the edgereferencing method which is less optimal to correct source noise but can be applied in a more controlled manner. Instead of computing B, we perform a singular-value decomposition (SVD) of the calibration dataset. This yields the singular vectors, together with their respective singular values, which describe the structure of the noise. The number of significant singular values, i. e. values clearly higher than noise-related ones, helps to identify the dominant noise components. At the correction step, only the components corresponding to the N highest singular values are fitted to the edge-pixels by performing a least-squares minimization. Results from the fit are then subtracted to ∆OD measured in order to remove noise in the signal region.
Investigations at UC Berkeley were supported by the W. M. Keck Foundation award No. 046300-002, the Army Research Office Multidisciplinary University Research Initiative (MURI) grant No. WN911NF-14-1-03 and the U. S. Air Force Office of Scientific Research No. FA9550-19-1-0314. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U. S. Department of Energy under Contract No. DE-AC02-05CH11231. H. J. B. M. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sk lodowska-Curie grant agreement No 801459 -FP-RESOMUS -and the Swiss National Science Foundation through the NCCR MUST. R. G. acknowledges funding from the LabEx PALM (ANR-10-LABX-0039-PALM).
FUNDING Investigations at UC Berkeley were supported by the W. M. Keck Foundation award No. 046300-002, the Army Research Office Multidisciplinary University Research Initiative (MURI) grant No. WN911NF-14-1-03 and the U. S. Air Force Office of Scientific Research No. FA9550-19-1-0314. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U. S. Department of Energy under Contract No. DE-AC02-05CH11231. H. J. B. M. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sk lodowska-Curie grant agreement No 801459 -FP-RESOMUS -and the Swiss National Science Foundation through the NCCR MUST. R. G. acknowledges funding from the LabEx PALM (ANR-10-LABX-0039-PALM).
Acknowledgements This work was written during a visit at  UC Berkeley , under the direction of  S. Dyatlov  and  M. Zworski. We thank this institution and  S. Dyatlov  and  M. Zworski  for their help, suggestions and comments. We also thank  C. Guillarmou  for helpful comments on a first version of the paper. Partial support form the  National Science Foundation  grant  DMS-1500852  is also acknowledged.
, 2013). Orbit information is then available over the whole orbit of the probes around the Moon, thus allowing for a highly accurate recovery of the lunar gravity field on both sides of the Moon (see, e. g. ,  Lemoine et al. , 2014; Konopliv et al. , 2014; S. Goossens et al. , 2020). Moreover, the Ka-Band Range Rate (KBRR) link also allows for an independent check of the quality of the recovered orbits for both probes. In this paper, we present the planetary extension within the development version of the Bernese GNSS Software (BSW,  Dach et al. , 2015). We use it to provide independent solutions for the orbits of GRAIL-A and GRAIL-B and for the gravity field of the Moon up to degree and order (d/o) 350 in spherical harmonic expansion based on data from the GRAIL Primary Mission  (PM, 01-Mar-2012 to 29-Apr-2012).
Impact of KBRR data In the case of GRAIL, orbits are further improved based on accurate and continuous inter-satellite KBRR measurements. We extend the analysis presented in section 3. 1 to the combined processing of Doppler and KBRR data. For these preliminary analysis, the two datasets are weighted with a fixed ratio of 1 : 10 8 in favor of KBRR, as suggested by the relative accuracy of Doppler (∼ 0. 1mm/s) and KBRR (∼ 0. 03 µm/s) data (see, e. g. ,  Lemoine et al. , 2013). In section 4. 2, we discuss more advanced weighting scenarios. As KBRR data enter the OD process in a combined least-squares fit with Doppler data, we cannot use KBRR residuals to independently evaluate the orbit quality (more estimated parameters will automatically result in lower residuals). We then recur to the analysis of orbit overlaps: GRAIL-A/B orbits are propagated for 25h from the daily set of estimated arc-parameters, resulting in overlapping orbit position batches of 1 h length.
We hence compute VCE weights for a reduced setup, with orbits parametrized by only 6 keplerian elements per arc and by empirical accelerations in along-track (constant) and radial (1-cpr) directions. Fig. 8  compares several solution strategies, eventually including VCE, in terms of difference degree amplitudes. Relative weights used in these solution for individual NEQs in each phase are given in Table  4. Estimated VCE weights were compared and validated against the GEODYN II software  (Pavlis & Nicholas, 2017). Letting VCE determine relative weights between both observables and phases ("VCE, all NEQs") results in a clear improvement against fixed weights, especially at low degrees. Another possible approach would be to first combine Doppler and KBRR data in each NEQ with a fixed 1 : 10 8 weight, and then estimate relative weights for the 3 different sub-phases.
Even then, VCE estimated weights for this data were significantly lower than others phases (see Table  4 ). After 9 iterations, we converge on solution AIUB-GRL350A. As shown in Fig. 10 , AIUB-GRL350A shows a very good consistency with GRGM900C up to d/o 50, where the missing information in terms beyond degree 350, and subsequent omission error, cause increased differences. Further improvements would require an extended parameter space, which is out of our current computational capabilities and of the scope of this work. On top of difference degree amplitudes (which assume GRGM900C as a reference solution), we also evaluate the improvement along the iterative process by checking KBRR residuals and by independent metrics such as the correlation of our gravity fields with topography-induced gravity.