dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments. This work was supported by the French Space Agency (CNES) under Contract RTD-0167-CN. We greatly acknowledge the Nobeyama Radio Observatory (Nobeyama, Japan) and the Dominion Radio Astrophysical observatory (Penticton, Canada) for making their high quality solar observations publicly available. The Nobeyama Radio Polarimeters are operated by the Nobeyama Radio Observatory, a branch of the National Astronomical Observatory of Japan. Finally, we thank the anonymous referees for helping improve the manuscript. The editor also thanks anonymous referees for their assistance in evaluating this paper. In the "observation" files the final data from 01/11/ 1951 (start of the Japanese measurements) to the end of month M-3 or M-2, M being the current month, and the provisional data range from the beginning of month M-2 or M-1 to the eve of the current day.
In both cases, L-dips separated by 28 mÅ and for the L-dips separated by 56 mÅ, the midpoint between the two dips in the pair practically coincides with the unperturbed wavelength λ 0. This is a strong indication that the quasistatic field F was dominated by the LET. If the LET would be absent, then according to Eq. (  1 ), the mid-point of the pair of the L-dips separated by 28 mÅ should have been shifted by 5. 8 mÅ to the red with respect to λ 0 and the mid-point of the pair of the L-dips separated by 56 mÅ would have been similarly shifted by 10. 7 mÅ to the red with respect to λ 0 , these shifts being due to the spatial non-uniformity of the ion microfield reflected by the 2nd term in Eq. (1). Another strong indication of the presence of the LET observed from the above result comes from the analysis of the broadening of this spectral line that was performed using code FLYCHK  [45].
This code, which does not take into account the Stark broadening by the LET (or the presence of L-dips), yielded N e = 0. 9x10 23 cm -3 , i. e. , almost three times higher than the actual N e = 3. 6x10 22 cm -3 (the best fit to the experimental profile by code FLYCHK is shown in Fig. 2(a). This is yet another strong indication of an additional Stark broadening by the LET (not accounted for by FLYCHK). At the initial laser intensity at the surface of the target estimated as 1. 01x10 21 W/cm 2 , the relativistic critical density would be N cr = 2. 3x10 22 cm -3 according to Eq. (  4 ). However, due to several physical effects, the actual intensity of the transverse electromagnetic wave in the plasma can be significantly higher than the intensity of the incident laser radiation at the surface of the target.
So, the would-be L-dips, corresponding to |q|s = 1 could not form for either or both reasons. The mid-point between the two dips in the pair practically coincides with the unperturbed wavelength λ 0. This is again a strong indication that the quasistatic field F was dominated by the LET. If the LET would be absent, then according to Eq. (  1 ), the mid-point of this pair of the L-dips should have been shifted by 6. 1 mÅ to the red with respect to λ 0. Another strong indication of the presence of the LET in shot 89 comes from the analysis of the broadening of this spectral line is following from the modeling, which was performed using code FLYCHK  [45]. An electron density of N e = 3x10 23 cm -3 , was obtained, i. e. 17 times higher than the experimentally verified N e = 1. 74x10 22 cm -3 (the best fit to the experimental profile by code FLYCHK is shown in Fig.
This value of N e is much lower than N e obtained from the same experimental spectrum by code FLYCHK that did not include the L-dips phenomenon and the spectral line broadening by LET, and therefore had significant discrepancies with the experimental profile at the locations of the L-dips. This spectrum exhibits two pairs of L-dips: one pair -at ± 16. 8 mA from the line center, another pair -at ± 33. 6 mA from the line center. The L-dips are very pronounced: for example, for the L-dips at ± 16. 8 mA from the line center, the intensity of the bump nearest to the center of the dip exceeds the intensity at the center of the dip by 15% for the L-dip at + 16. 8 mA and by 11% for the L-dip at -16. 8 mA. Just by itself, such a strong modulation of the line profile clearly indicates that it is not a noise.
It was attributed to the hottest plasma produced during the highest laser intensity. After 700fs there was no significant signal that would come from colder plasma. So, we interpreted our experimental results (in this regard) the same way as in paper  [44]. The idea of employing these two different codes simultaneously should be applied to other spectroscopic experiments in laser-produced plasmas showing identifiable L-dips. The identification of L-dips in the experimental line profiles provides a very accurate determination of the electron density N e independent of the broadening of the spectral lines. Then if a code like FLYCHK, ignoring the LET and the L-dips, yields a much higher value of N e than the one deduced from the L-dips, this would be an indication of the development of the LET in those other experiments.
Check on the oscilloscope screen that data are correctly recorded and saved after each experiment. 5. Set a suitable trigger for the acquisition system. For example, trigger the oscilloscope when the signal coming from the laser potentiometer trespasses a certain threshold, corresponding to the onset of the first high-power pulse sent to the sample and set with the help of the oscilloscope software. 1. Verify that, as the oscilloscope is triggered, it starts to record the signals coming from the laser potentiometer and from the two pyrometer channels and also sends a signal that triggers acquisition in the spectro-pyrometer. 6. Connect the oscilloscope to a PC. Directly insert software calibration equations 2, 3 and 4 so that the recorded intensity can be plotted directly as temperature-versus-time curves (thermograms) on the PC screen.
Use this test to also check that the function of the trigger system arranged in step 5. 5 is correct. 7. If all checks are successful, deactivate the red pilot laser and switch on the high-power beam. 8. Release all safety switches and start the laser irradiation program on the sample. 9. At the end of the laser-heating and cooling cycles (typically the pre-heating stage plus three or four high-power pulses), verify the appearance of the sample, indicating whether it is totally or partially melted, quenched, broken, still intact, etc. 10. If the sample is still intact, repeat several laser-heating cycles on it and check for result repeatability. NOTE: In successful cases, over forty shots can be repeated on the same sample. Such large datasets can be treated, yielding average values for the phase transition points supported by a sound statistical analysis of the measurement uncertainty.
Data analysis 1. Qualitative thermogram analysis 1. Check the quality and features of experimental thermograms (one per laser shot) recorded by the pyrometer. Verify that, if the maximum temperatures reached were high enough, thermal arrests corresponding to solidification appear on the cooling parts of the thermograms. NOTE: Similar thermal arrests are usually hardly visible on the heating flank because the fast laser heating provides more energy than the melting enthalpy and thermodynamic equilibrium conditions are mostly not realized in this part of the experiment 11. 2. If the maximum temperatures were too low, repeat the laser-heating cycle with higher-power pulses. 3. Repeat the laser-heating/cooling cycles on a new sample if thermograms are too irregular or aberrant (e. g. , the apparent heating and cooling of the sample do not follow the laser pulses), in which case, the sample probably broke, cracked, or vaporized during the experiment.
2. Uranium dioxide is the essential component of the most common fuel in current NPPs. Its oxidation to various oxygen hyper-stoichiometry levels can occur in normal and off-normal reactor conditions 12. With the current method, it was shown that UO 2 oxidation can result in a dramatic decrease of its melting/solidification point by up to 700 K. In this case, experiments had to be carried out under a rather high inert gas pressure (He at 10 MPa) in order to suppress the highly non-congruent vaporization at high temperatures. 4  shows pyrometer (straight line) and spectro-pyrometer thermograms recorded on a plutonium dioxide sample laser heated under an oxidizing atmosphere (compressed air at 0. 3 MPa). Also, PuO 2 is an essential nuclear fuel component. In the same figure, two radiance spectra measured by the spectro-pyrometer at different temperatures are also displayed in the insets, together with curves fitting experimental data and the corresponding T and ε λ values.
Figure4shows pyrometer (straight line) and spectro-pyrometer thermograms recorded on a plutonium dioxide sample laser heated under an oxidizing atmosphere (compressed air at 0. 3 MPa). Also, PuO 2 is an essential nuclear fuel component. In the same figure, two radiance spectra measured by the spectro-pyrometer at different temperatures are also displayed in the insets, together with curves fitting experimental data and the corresponding T and ε λ values. Thanks to the present study, the PuO 2 melting/freezing temperature was reassessed to be 3,017 K ± 28 K, over 300 K higher than previously indicated by more traditional heating methods. Those methods yielded results certainly affected by extensive high-temperature interactions between the sample and containment, an issue that has been largely solved with the present remote heating approach.
QCD predictions and systematic uncertainties QCD predictions for the reduced charm cross sections are obtained at NLO in pQCD (O(α 2 s )) using the OPENQCDRAD package  [22]  as available in HERAFitter 1  [20, 23]. These predictions are based on the ABM implementation  [24]  of charm cross-section calculations in the 1 Recently renamed xFitter. 3-flavour FFNS. The renormalisation and factorisation scales are always taken to be identical. In the calculations, the same settings and parametrisations are chosen as those used for the earlier measurement of m c (m c )  [14]. In addition, scale variations were applied as in Ref. [15]. For all explicit calculations of charm-quark mass running, an implementation of the one-loop formula  [25] , Eq. (3), is used, which is consistent with that used implicitly in OPENQCDRAD.
Results In order to minimise the correlated contribution from inclusive data to the charm-mass determinations, and in particular from the implicit charm-mass scale dependence therein, a set of PDFs in the 3-flavour FFNS is extracted from a QCD fit to inclusive DIS HERA data  [20]. This extraction uses exactly the same setup as that used in a previous publication  [14] , but allows for different charm-quark masses. The charm-quark mass as a function of scale is then extracted from a fit to the charm data only. When this analysis was originally performed  [10, 11] , the inclusive HERA II DIS data  [21]  were not yet available. The use of the earlier inclusive data  [20]  has been retained for several reasons. Firstly, all systematic uncertainties can be treated exactly as in the corresponding previous global m c (m c ) determination  [14].
1997). While each of these datasets are valuable in their own right, significant differences between the sensitivity, location, and operational profiles of each instrument present challenges when attempting to construct and/or constrain an overall picture of the dust influx to planetary atmospheres. One method for estimating the dust fluxes to planetary atmospheres in the outer solar system is to extrapolate the  Grün et al. (1985)  interplanetary dust flux model, or its subsequent updates  (Dikarev et al. 2004 (Dikarev et al. , 2005)) , from 1 AU to the desired heliocentric distance. In the absence of sufficient in situ measurements in the outer solar system, this methodology is perhaps the only recourse; however, there is no a priori reason to justify the assumption that dust dynamics at 1 AU hold throughout the outer solar system, especially given the strongly perturbing presence of the giant planets (e.
Support for this comes from observations of Fe and Cl by X-ray Fluorescence Spectroscopy, made by the VENERA 14 and VEGA landers. The Pioneer Venus probe performed the first and only in situ measurements of cloud particle properties within the cloud layers  (Knollenberg and Hunten 1980). This showed that the Venus cloud particle size distributions exhibit two modes of sulfuric acid droplets (mode 1: r ∼ 0. 2 µm; mode 2: r ∼ 1 µm) throughout the clouds  (Knollenberg and Hunten 1980). A mode 3 was deduced from the Pioneer Venus measurements as well, but this remains controversial since the measurements could be explained by a large (r ∼ 3-4 µm), potentially crystalline particle mode, or by a misinterpretation of the data due to instrument calibration  (Toon et al. 1984). The presence of crystalline particles and exotic trace substances such as P 4 O 10 and Fe 2 Cl 6 has also been suggested  (Andreychikov et al.
Other Terrestrial Planets The contributions of dust and meteorites to the surfaces of the other terrestrial planets, Venus and Mercury, have not been studied in any detail. However, the presence of a dense atmosphere on Venus should result in similar conditions for deceleration and accretion to those on Earth (Fig. 2 ). The global influx of cosmic dust in Venus' atmosphere was recently estimated to be 32 tonnes per Earth day  (Frankland et al. 2017 ). Impact craters have been identified on Venus, but little or no modelling has been done on the meteoritic contribution to the surface of the planet. Thus far, the soil and rock composition data from the Venera landers (e. g. Surkov et al. 1988)  has not included any of the siderophile elements critical for assessing the meteoritic component. Mercury, lacking a substantial atmosphere, is more similar to the Moon, however the elements detected by the orbiting MESSENGER spacecraft (Mg, Al, Si, S, Ca, Ti and Fe) did not provide a direct measurement of the meteoritic component.
Summary This review has described the very diverse impacts that interplanetary dust can have throughout a planet's atmosphere and at its surface. Significant progress has been made recently in quantifying the dust flux throughout the solar system. Models such as those by  Nesvorný et al. (2010 Nesvorný et al. ( , 2011a Nesvorný et al. ( , 2011b) )  and  Poppe (2016)  are necessary to connect in situ measurements of dust densities and fluxes at selected positions in the solar system with an overall picture of dust distributions throughout the solar system. In the inner solar system, continued comparison of dynamical models to available datasets will further refine the available models; although, a dedicated orbital mission around Earth for quantifying the terrestrial meteoric input function and resolving existing discrepancies between Earth-and space-based observations  (Plane 2012 ) is crucial for constraining the overall magnitude and variability of the meteoric influx to Earth.
Analysis of existing datasets, especially from the Cassini Cosmic Dust Analyzer and the New Horizons Student Dust Counter, will continue to yield information and constraints on the outer solar system distributions. NASA's forthcoming Europa Clipper mission contains within its payload the Surface Dust Mass Analyzer (SUDA)  (Kempf et al. 2015) , a powerful dust instrument that, while primarily focused on dust ejected from Europa and the other Galilean moons, will hopefully yield ancillary information regarding the interplanetary dust distribution from its vantage point at Jupiter. The new laboratory experimental systems for simulating meteoric ablation  (Bones et al. 2016; Thomas et al. 2017 ) are important for testing and improving ablation models, leading to more secure predictions of the fraction of incoming dust that ablates in a planetary atmosphere, the altitudes where different constituents are injected, and the fraction of unablated material that reaches the surface in the form of micrometeorites and cosmic spherules  (Carrillo-Sánchez et al.
Acknowledgements We thank  David Nesvorný  ( Southwest Research Institute ) and  Petr Pokorný  (The  Catholic University of America ) for supplying output from the ZoDy astronomical dust model that was used in Fig. 2. J. M. C. P. and J. D. C. S. are supported by the  European Research Council  (project number  291332-CODITA ). G. J. F. is supported by  NASA   Emerging Worlds  grant # NNX17AE59G. A. R. P. is supported by the  NASA   Planetary Atmospheres Program , grant # NNX13AG55G. A. M. and C. L. are supported by  CNES , the  Programme National de Planétologie , and the  ESA mission Mars Express. C. L. also acknowledges  CNES  for a postdoctoral fellowship. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons.
ACKNOWLEDGMENTS The authors would like to thank  Jean-Philippe Banon  for valuable discussions and contributions to the presented work. The research of Ø. S. H. and I. S. was supported in part by  The Research Council of Norway  Contract No. 216699. In addition, I. S. acknowledges financial support from the  French National Research Agency (ANR)  under contract  ANR-15-CHIN-0003-01. This research was supported in part by  NTNU  and the  Norwegian metacenter for High Performance Computing (NOTUR)  by the allocation of computer time.
Using the literature value for the correction factor  [46]  (g = 1. 50), and data from Figure  5a , the order parameter was obtained (S = 0. 60, in agreement with the reported value in literature [46], S ¼ 0:55 at 25 8C) using Equation (2). Furthermore,f rom Equation (3) the tilt angle (q) of the LC (assuming au nimodald elta distribution aroundt he tilt angle) was determined to 31. 2 degrees. [47] ¼h P 2 cosq ðÞ i¼ ( 3cos 2 q À 1 2 ) ð3Þ Inserting this value in Equation (1) for both the parallel and perpendicular configurations, gives ap redicted ratio of the Rabi splitting betweent he two cases [Eq. (  4 )]. " hW R k " hW R ? ¼ mk Á E 0 ! m? Á E 0 ! ¼ cos q ðÞ cosð p 2 À qÞ n k n ? ¼ 1:81 ð4Þ The ratio of the measured Rabi splitting for the two configurations is 1. 78 (Figure  4d ), thus in very good agreement with the predicted one.
Acknowledgements K. B. gratefully acknowledges the financial support of the  Swedish Foundation for StrategicR esearch  ( ICA14-0018 ) and the  RagnarS çderberg Foundation. T. E. acknowledges financial support from  Agence Nationale de Recherche. Bo Albinsson  is acknowledgedf or providing access to FT-IR spectrophotometer and MyFab for free cleanroom access.
Acknowledgements EB and CC are grateful to the  IMAFI institute in Talca-Chile  for hospitality and financial support during the course of this work. MH thanks the  LabEx P2IO of Paris Saclay  for financial support during the initial stages of this work. EB and  CC acknowledge support from the research program ,  Programme national de cosmologie et Galaxies of the CNRS/INSU, France  and from the project  DEFI InFIniTI 2017. EB was supported in part by  Russian Foundation for Basic Research  Grant No. RFBR 15-02-05038. MH is partially supported by grant  1130423  from  FONDECYT  and from  CONICYT, Departamento de Relaciones Internacionales Programa Regional   MATHAMSUD 13 MATH-05. This project is also partially funded by  Proyectos CONICYT-Research Council   UK DPI20140053. CC thanks  Chiara Caprini  and  Christos Tsagas  for interesting discussions on cosmological magnetic fields and  Lavinia Heisenberg  for making some critical remarks in the introduction.
Most XENON1T subsystems were designed such that they can also support a significantly larger dark matter detector, with a target of ∼6 t. This phase of the project, XENONnT, is being prepared during XENON1T data taking to allow for a rapid exchange of the larger instrument after the science goals of XENON1T will have been reached. XENONnT aims at improving the spin-independent WIMP sensitivity by another order of magnitude compared to XENON1T  [18]. The article is structured as follows: in section 2, the XENON1T experiment with all its subsystems is introduced in detail. Section 3 presents selected results from detector commissioning and from science run 0, and section 4 provides an outlook.
The XENON1T Experiment This section describes the XENON1T detector. The dualphase TPC (section 2. 1) is installed inside a double-walled vacuum cryostat (section 2. 1. 4) in the center of a large water tank. The tank serves as passive shield as well as a Cherenkov muon veto (section 2. 2. 1). A three-floor building accommodates all auxiliary systems. These include the systems to cool (section 2. 1. 4), store, and purify the xenon gas (section 2. 1. 5), the cryogenic distillation column for krypton removal (section 2. 2. 3), the data acquisition system (section 2. 4) as well as the control and monitoring systems for the entire experiment (section 2. 5). The TPC calibration systems are installed on the purification system as well as on the top of the water shield (section 2. 3).
1. 5), via the distillation column, into the evacuated cryostat. This procedure would last for ∼3 weeks, for a total Xe amount of 3. 2 t. However, to allow for data acquisition with a fully operational dual-phase TPC while at the same time reducing the Kr concentration, the XENON1T collaboration has successfully established the online removal of Kr. To this end, 7% of the purification gas flow was routed through the distillation column and the Kr-enriched gas (0. 07% of the total flow) was removed from the system. After continuously operating in this mode for 70 days, with an initial measured nat Kr/Xe concentration of 60 ppb, a final concentration of (0. 36 ± 0. 06) ppt was measured by RGMS. This concentration is the lowest ever achieved in a LXe dark matter experiment. Being only a factor of ∼2 above the XENON1T design goal, the concentration was sufficient for a first science run  [20].
The time-stamped digitized pulses are stored in a MongoDB noSQL database, along with some basic quantities of each pulse (time, channel). The sum signal of all bottom PMTs, generated by adding the individual signals by means of linear fan-in/fan-out modules, is continuously read by another computer ("acquisition monitor") together with additional veto/busy information. The latter is used to precisely determine the measurement deadtime. To reduce the input data rate during TPC calibration, a veto module based on a Skutek DDC-10 was developed. Depending on the size of the bottom array sum-signal or the relative amount of signal in the outer detector region, it issues a real-time veto signal which blocks the entire PMT data stream from being digitized. The digitizer firmware delays the incoming data for the required amount of time.
The trigger decision whether a particle interaction has occurred in the TPC is made in real-time by a software eventbuilder running on three server-grade machines (Fujitsu). It scans the MongoDB database for relevant signals, groups the data into causally connected events and stores them in a file. While a variety of trigger algorithms may be adapted to specific use cases, the standard dark matter and calibration S2 trigger is based on the time-clustering of pulses in individual PMT channels. A >99% trigger efficiency is achieved at 200 PE (≈7 e -). Meta-data on the trigger decision is stored with the raw data. It is available for online monitoring of the eventbuilder performance and offline analysis. The muon veto employs a classical coincidence trigger, managed by a custom-programmed CAEN V1495 VME unit, which requires at least N pmt PMT signals in coincidence within a certain time window.