dataesr/scientific-paragraphs-categorization huggingface dataset:
Ryugu's Visible to Near-infrared Spectral Properties The NIRS3 spectral data set can be compared with both the ONC color and ground-based observations. The normalized near-infrared observations of  Moskovitz et al. (2013) and Le Corre et al. (2017)  are compared to a subset of the 38-40 m footprint resolution NIRS3 opposition spectra (Figure  14 ). All spectra are normalized to unity at 2. 0 μm and show a similar spectral shape in the wavelength range of overlap between the three data sets. Even though the spectra overlap within the noise of the ground-based observations, the NIRS3 spectra suggest a shallower slope at longer wavelengths. The groundbased observations show differences in the spectral slope at shorter wavelengths approaching the visible and do not overlap below ∼1. 4 μm. Examination of the  Moskovitz et al.
Comparisons of Photometric Properties The ONC-T approach observations were modeled by  Tatsumi et al. (2020)  using the Hapke set of equations. Their results are compared to those from this study of the NIRS3 opposition data set. It is important to note that the ONC-T study included both disk-integrated and disk-resolved observations. The ONC values are derived from disk-integrated measurements constrained by disk-resolved measurements, but the NIRS3 values are derived solely from disk-resolved values. The ONC-T data set also covered a larger range of phase angle, which include diskintegrated opposition measurements. The surface roughness parameter from the ONC-T study was 28°± 6°  (Tatsumi et al. 2020 ) whereas the NIRS3-based value is 29°± 3°, both identical within the error bars. The NIRS3 study found a uniform value of 0.
The single-particle scattering function is less strongly backward scattering in the NIRS3 modeling results, which results in a darker reflectance, thus the absolute reflectance differences between the visible (ONC) and near-infrared (NIRS3) are accounted for by the model in the single-particle scattering function. The opposition parameter values from the ONC-T study were assumed constant with wavelength over the instrument's band range (0. 40-0. 95 μm), because the wavelength-to-wavelength variation was minor  (Tatsumi et al. 2020). The opposition parameter values from the ONC-T study are based on visible diskintegrated opposition observations, in contrast to the NIRS3 opposition data set that consists of near-infrared disk-resolved measurements. Thus, the ONC parameters describe the globally averaged surface, whereas the NIRS3 parameters describe the near-equatorial region they observe.
2002)  utilized two-term single-particle scattering functions that allow for combinations of forward and backward scattering. The single-particle scattering function can affect the reflectance observed in the opposition region (α < 5°), oftentimes in subtle ways especially if the data set is limited in geometric angle coverage. Comparisons of Ryugu's opposition parameter values (Figure  17 ) with those from studies of other solar system objects show that, in general, Ryugu's opposition width is broader but its amplitude overlaps with the opposition characteristics of Eros, Itokawa, and some early studies of the lunar The values shown for comparison are predominately from visible wavelength observations, whereas the NIRS3 results are from near-infrared observations. The different wavelength regions could be a contributor to the differences seen between Ryugu and these other objects.
2020; Tsuda et al. 2020)  along with the thermal inertia measurements  (Shimaki et al. 2020)  suggest that fine grains exist in some regions as a portion of Ryugu's regolith. This is commensurate with the photometric behavior of the spectral slope we observe in the NIRS3 data set. The phasereddening behavior with both phase and incidence angle, in comparison with laboratory measurements of carbonaceous meteorites, indicates a granular regolith with a fine-grained (<45 μm) component. In 2020 December, the samples collected from Ryugu's surface were returned to Earth. Insight into the properties of Ryugu's regolith will be revealed in the analyses of these samples.
Image Acquisition The concentration field of FS is measured optically from the intensity of its emitted light at wavelengths around em = 521 nm , when excited by a light source at ex = 494 nm (e. g. ,  Anna et al. 2014; Jiménez-Martínez et al. 2015 , 2017; Jougnot et al. 2018; Sjöback et al. 1995). To reduce tracer bleaching occurring during FS exposure to light (e. g. ,  Imamura and Koizumi 1955) , we use a flash lamp, placed about 60 cm above the cell, that is acti- vated by an electronic signal (transistor-transistor logic gate, TTL). The latter is triggered from a computer via the open-source software Micromanager, used also to control a CCD camera (Ximea, MD120MU-SY), placed about 85 cm below the cell. The flash lamp is combined with a parabolic umbrella with a reflective internal surface focusing the emitted light towards the flow cell and the camera below it.
g. ,  Balluffi et al. 2005) : where D is the diffusion coefficient and the weights b n are the Fourier coefficients of the series, given by For 2-D diffusion, we consider a confined domain of length L = 590 mm and width w = 74 mm (as in the experimental cell, Sect. 2. 1), with a no-flux condition along all the boundaries. Different initial conditions for the modelled NaCl concentration field are considered (Sect. 5. 2. 1) and the resulting boundary-value problem is numerically solved using the finite difference implementation included within the groundwater solute transport simulator package MT3D-USGS  Bedekar et al. (2016).
Acknowledgements We would like to thank  Ludovic Baron  for his help in setting up the electrical monitoring system used for this experiment. Funding Open Access funding provided by  Université de Lausanne. ENIGMA ITN  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No  722028. This work has received funding from the  Swiss National Science Foundation  under Grant  200020-184574. PdA acknowledges funding for laboratory equipment offered by the  Swiss National Science Foundation  under grant  200021_172587.
2013; Fromang et al. 2013). In this paper, we focus on the zero-net-flux configuration (without a mean vertical field) and examine the effect of ambipolar diffusion on the gravito-turbulent dynamo. We performed 3D MHD stratified shearing-box simulations with self-gravity and vertical stratification using two different codes, PLUTO and Athena++. (The Hall effect and Ohmic dissipation are omitted. ) Broad agreement between the code results is achieved, demonstrating their weak dependence on the implementation of self-gravity and on other numerical details (time-stepping methods, orbital advection algorithm, etc. ). We explore different cooling prescriptions (namely, linear cooling and T 4 cooling) and consider a wide range of ambipolar Elsasser numbers (Am) from 1 to 200. Given the size of the box necessary to capture GI and thus our limited resolution per H, it is not possible to probe the regime of yet larger Am.
Initial flows: hydrodynamical simulations A customary way to study the dynamo process in simulations is to compute the (unmagnetized) hydrodynamic state of fully developed turbulence, introduce a zero-net flux magnetic seed field, and see whether that field grows or decays over time. Following this scheme, we first simulate purely hydrodynamical gravito-turbulence, with the aim of comparing two different cooling laws (linear and T 4 ) as well as both codes, PLUTO and Athena++ (a similar code comparison was done by  Riols et al. (2017a)  using PLUTO and RODEO). The key parameters that control the strength of the turbulence are the cooling times τ0 (when T 4 cooling is used; see eqn 9) and τc (when linear cooling is used; see eqn 10). For T 4 cooling, we use the fiducial τ0 = 10Ω -1 (runs AT-GHydro and PL-GHydro).
Resolution To study whether our fiducial resolution is sufficient to capture all of the relevant dynamics, we re-perform the set of zero-net-flux, T 4 runs at double resolution (512 × 512 × 288) for ideal MHD, Am = 100 and Am = 10 with Athena++, and Am = 100 with PLUTO. In Athena++, we restart the simulations from an MHD quasi-steady turbulent state at fiducial resolution and interpolate the magnetic field so that ∇ • B = 0 is preserved. (Because the code stores magnetic fluxes through cell interfaces, a simple linear interpolation of magnetic flux density onto new interfaces guarantees that the total magnetic flux through a cell remains zero when doubling resolution. ) In PLUTO, we start from a gravito-turbulent hydrodynamical state and add a strong toroidal magnetic perturbation in the midplane of the form By = 0.
ACKNOWLEDGMENTS AR and GL have received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 815559  (MHDiscs)). High-performance computing resources were provided by the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is supported by Grenoble research communities; and the  PICSciE-OIT TIGRESS High Performance Computing Center and Visualization Laboratory at Princeton University.
Observations The NOrthern Extended Millimeter Array (NOEMA) large program "Fragmentation and disk formation during high-mass star formation -CORE" is a high angular resolution survey (∼0. 4 at 1. 37 mm) designed to study the fragmentation, kinematic, and chemical properties of a homogeneous sample of 18 HMSFRs. The observations consist of spectral line and continuum data in Band 3  (1 mm ). An overview of the project and results of the analysis of the dust continuum and fragmentation properties are presented in  Beuther et al. (2018). In combination with this paper, we release all data products of the CORE project with and without self-calibration conducted with GILDAS. The data can be found on the CORE collaboration website: https://www. mpia. de/core. The CLEANed data products as well as the calibrated uv-tables are available.
7 kpc and 5. 5 kpc. Table  1 : Overview of the CORE sample. The isotopic ratios are taken from  Wilson & Rood (1994) , further explained in Sect. 3. 2, Sect. 4. 2, and Sect. 5  Table  2 : Overview of the CORE data products. The noise of the continuum data σ cont is computed in a 3. 5 × 3. 5 rectangle in an emission-free region. I peak is the continuum peak intensity. The average map noise of the merged (combined NOEMA + IRAM 30m) spectral line data σ line,map is computed in a line-free range from a rest-frequency of 219. 00 GHz to 219. 13  GHz with a channel width of 3 km s -1 (2. 2 MHz) within the full primary beam. (Beuther et al. 2018 ): while some regions contain mainly a single isolated core (e. g. , AFGL 2591), other regions fragment into up to 20 cores (e. g. , IRAS 21078). Individual case-studies were carried out on some of the regions, e.
The dashed orange line shows the systemic velocity of the region LSR and the dash-dotted blue lines show the lower and upper velocity limits ( LSR ± 10 km s - 1  ) used for the integrated intensity map shown in the top panel. multi-configuration NOEMA data set. The pilot studies have no corresponding D array observations, so less uv-coverage, and could hence not be accurately compared. However, a detailed individual chemical analysis of the pilot regions is already presented in  Feng et al. (2016).
NOEMA Observations The sample was observed from 2014 to 2017 in the A (extended), the old B or new C (intermediate), and D (compact) array configurations. Two regions each were observed in track-sharing pairs to reduce calibration time. Spectral line data were obtained with the broad-band WideX correlator at a rest-frequency range of 217. 2 -220. 8 GHz with a spectral resolution of ∼2. 7 km s -1. In addition, eight narrow-band units were placed within this frequency range in order to obtain high spectral resolution data (∼0. 4 km s -1 ) of kinematically interesting lines such as CH 3 CN (a summary of the high spectral resolution units is summarized in  Table 2 and 3 in Ahmadi et al. 2018). The interferometric data were calibrated with the CLIC package in GILDAS 1. The 1. 37 mm continuum data were extracted from the broad-band WideX data by carefully selecting line-free channels in each region.
IRAM 30m Observations Interferometers spatially filter the extended emission. The shortest baseline of the NOEMA array is ∼20 m, thus spatial scales larger than 16 are not recovered by the interferometric observations at 1. 37 mm. All CORE regions were therefore observed with the IRAM 30m telescope using the Eight MIxer Receiver  (EMIR, Carter et al. 2012 ) in order to recover the missing flux in the spectral line data due to spatial filtering. EMIR has four basebands and each have a width of ∼4 GHz and a spectral resolution of 200 kHz corresponding to ∼0. 3 km s -1 at 1. 37 mm). The lower inner baseband (LI) of our EMIR spectral setup covers the same spectral range as the NOEMA observations with the broad-band WideX correlator. The half power beam width (HPBW) of the IRAM 30m telescope is 11.
While GILDAS standard calibrated data already reveals the complex structure of the region, the noise is high throughout the primary beam with many negative features. Applying the CASA and GILDAS self-calibration significantly lowers the noise and increases the peak intensity. The continuum noise and peak intensity are summarized for all regions in Table  2. The mean continuum noise σ cont is 0. 74 mJy beam -1 in the standard calibrated data, while for the CASA and GILDAS self-calibrated data, the mean noise σ cont is lowered by ∼25% to 0. 53 mJy beam -1 and 0. 56 mJy beam -1 , respectively. In general, σ cont is higher toward the strong continuum sources S106, CepA HW2, and W3 H2O. The mean S /N ratio of the GILDAS standard calibrated continuum data is 74, while for the CASA and GILDAS self-calibrated continuum data, the mean S /N is improved by a factor of two to 130 and 132, respectively.
Even though both CASA and GILDAS self-calibration methods improve the data quality, due to the different techniques to define a source model (interactive masking and defining num-ber of clean components, respectively), there are differences in the resulting images. In Fig. 1 , a faint structure toward the NW of the continuum peak is seen in the GILDAS standard calibrated and self-calibrated image, but it is not recovered in the CASA selfcalibrated image. Comparing the self-calibration results (Table  2 ), the peak intensities are higher in the GILDAS self-calibrated data, while a lower noise is achieved in the CASA self-calibrated data. For very faint structures and a complex source morphology, a careful interpretation of the self-calibrated data is recommended, but overall all the main features are recovered with both self-calibration methods.
Data products and public release The data merging of the interferometric and single-dish data and imaging is done in the GILDAS/MAPPING package. The WideX and the corresponding EMIR spectral-line data are smoothed to a common spectral resolution of 3. 0 km s -1. The narrow-band and the corresponding EMIR spectral line data are smoothed to a common spectral resolution of 0. 5 km s -1. In order to merge the NOEMA and IRAM 30m data, the task UVSHORT converts the short spacings into a pseudo uv-table and combines them with the NOEMA data. The deconvolution of the NOEMA continuum, NOEMA spectral line and merged (combined NOEMA + IRAM 30m) spectral line data is performed in GILDAS/MAPPING using the Clark CLEAN algorithm (Clark 1980) adopting three different weightings: robust weighting with a robust parameter of 0.
A comparison between the Clark and SDI algorithms for imaging of the W3 IRS4 region is presented in  Mottram et al. (2020). Final data products of the merged broadband spectral line data of molecular lines with potential largescale emission ( 13 CO 2 -1, SO 6 5 -5 4 , H 2 CO 3 0,3 -2 0,2 , H 2 CO 3 2,2 -2 2,1 , H 2 CO 3 2,1 -2 2,0 ) CLEANed using the SDI algorithm with robust weighting (robust parameter of 3, θ ∼ 0. 6) with a stopping criterion of n iter = 25 000 are provided as well. Primary beam correction is applied to the continuum and spectral line data products. In this study we use the primary beam corrected NOEMAonly continuum and merged (NOEMA + IRAM 30m) broadband spectral line data. Both data products are imaged with the Clark algorithm and a robust parameter of 0. 1 resulting in the highest angular resolution (θ ∼ 0.
Continuum emission A detailed analysis of the continuum data is presented in  Beuther et al. (2018). The updated GILDAS self-calibrated continuum data are shown in contours in Fig. 3. While the sample was selected to be largely homogeneous in luminosity, at an angular resolution of ∼0. 4 a variety of structures can be identified. While some regions appear as isolated single objects (IRAS 23151, AFGL 2591), others show spatially separated cores (IRAS 23033, G108. 75, G139. 9091, S106, CepA HW2, W3 H2O). There are regions in which fragmentation is observed within an embedded envelope (IRAS 23385, IRAS 21078). Many of the regions have a complex morphology such as filamentary structures and extended envelopes (G084. 9505, G094. 6028, G100. 38, G138. 2957, G75. 78, NGC7538 IRS9, S87 IRS1, W3 IRS4).
In this study we aim to analyze the physical properties and chemical variation across the regions, and have therefore selected a number of positions throughout the different regions. Deriving the column density of all detected species in every single spectrum in each region is computationally expensive and we restrict this method to the analysis of the H 2 CO and CH 3 CN temperature maps (Sect. 3. 2). In order to study the cores and envelopes around forming protostars we select positions which have a clear spherically symmetric core-like morphology in the 1. 37 mm continuum data which is the case for most regions toward the continuum peak position, but also multiple spherically symmetric objects within a region can be identified (e. g. , toward IRAS 23033). In addition, we select positions in the extended envelopes to study potential differences in the chemical abundances.
(  3 ). The core mass M core is calculated according to Eq. (  5 ). The estimate of the chemical timescale τ chem is explained in detail in Sect. 5  Notes. a(b) = a×10 b. Fit results for r in , r out , T kin (r in ), and q derived from either the H 2 CO or CH 3 CN radial temperature profiles (Fig. Fig. 5 : Histogram of the temperature power-law index q. The red line shows a Gaussian fit to the data points for q = 0. 1 -0. 6. the calculation of the Gaussian line profiles optical depth effects are included. For each molecule, the properties of all transitions are taken from an embedded SQlite3 database containing entries from the Cologne Database for Molecular Spectroscopy (CDMS,  Müller et al. 2005 ) and the Jet Propulsion Laboratory (JPL,  Pickett et al. 1998 ) using the Virtual Atomic and Molecular Data Centre (VAMDC,  Endres et al.
As HMSFRs have high densities n > 10 5 cm -3 toward the locations of the protostars, LTE can be assumed here  (Mangum & Shirley 2015). The critical densities n crit = A ul C ul for the CH 3 CN and H 2 CO lines are ∼4×10 6 cm -3 and ∼3×10 6 cm -3 , respectively (A ul is the Einstein A coefficient and C ul is the collisional rate coefficient). Here, we use C ul measured at 140 K taken from the Leiden Atomic and Molecular Database (LAMDA,  Schöier et al. 2005). The H 2 CO and CH 3 CN temperature maps of each region are shown in Fig. C. 1. The H 2 CO lines trace the extended low temperature gas at 10 -100 K. The CH 3 CN maps mostly show spatially compact emission tracing the higher temperature gas at >100 K. For each selected position (Table  A. 1), we extract the H 2 CO and CH 3 CN radial temperature profile which are binned in steps of half a beam (∆r).
Density structure In contrast to the merged (NOEMA + IRAM 30m) spectral line data, the 1. 37 mm continuum NOEMA data suffer from missing flux due to the lack of short-spacing information. Therefore, it is difficult to reliably derive radial intensity profiles in the final images that are required to determine the density structure. This issue can be minimized by analyzing instead the complex visibilities of the 1. 37 mm continuum data in the uv-plane (Adams 1991;  Looney et al. 2003; Beuther et al. 2007b; Zhang et al. 2009). Assuming spherical symmetry, the power-law index of the complex visibility profile α is related to the density powerlaw index of the radial density distribution p by  (Looney et al. 2003) : α = p + q -3. ( 3 ) For each core position, the phase center is shifted to this location.
The remaining cores within a region are fitted as a point source + circular Gaussian (to account both for the compact and the extended emission) and subtracted using the UV_FIT task in GILDAS. With this approach blending of nearby cores in the visibility profiles within a single region can be minimized. The azimuthally averaged complex visibilities computed using the UVAMP task in MIRIAD  (Sault et al. 1995)  are binned in steps of 20 kλ. The radial visibility profiles are shown in Fig. 6. The uv distances range from ∼ 20 -800 m corresponding to spatial scales of ∼ 10 3 -10 5 au at distances of a few kpc. We apply a power-law fit to the data in order to derive the power-law index α. The density power-law index p is calculated according to Eq. (  3 ) taking into account the temperature power-law index q derived in Sect.
3. 2. The results for α and p for all cores are summarized in Table  3. Most of the visibility profiles (Fig. 6 ) can be described by a single power-law. The higher scatter at large uv distances is due to the fact that less data is available at long baselines. For IRAS 23385, the visibility profiles of core 1 and 2 do not follow a simple power-law. Toward smaller spatial scales (< 10 4 au), the profiles are steeper. This could be due to the fact, that even though the contribution of nearby cores is minimized by subtracting a point source + circular Gaussian, in the case of IRAS 23385 it is not possible to clearly distinguish the contribution of both cores, which are embedded within a common dust envelope. A histogram of the density index p is shown in Fig. 7. Using the observationally derived values of q, the power-law index ranges from 1.
Framework Programme via the  ERC Consolidator Grant   CSF-648505. D. S. acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : "Building a  Habitable Earth  ( SE 1962/6-1 )". R. G. -M. acknowledges support from  UNAM-PAPIIT  project  IN104319. R. K. acknowledges financial support via the  Emmy Noether Research Group on Accretion Flows and Feedback in Realistic Models of Massive Star Formation  funded by the  German Research Foundation (DFG)  under grant no. KU 2849/3-1  and  KU 2849/3-2. A. S. -M. research is partially carried out within the  Collaborative Research Centre 956  (subproject  A6 ), funded by the  Deutsche Forschungsgemeinschaft (DFG)  -project  ID 184018867. A. P. acknowledges financial support from  CONACyT  and  UNAM-PAPIIT IN113119  grant, México.
ATLAS detector The ATLAS Collaboration A search for pair production of third-generation scalar leptoquarks decaying into a top quark and a -lepton is presented. The search is based on a dataset of   collisions at √  = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb -1. Events are selected if they have one light lepton (electron or muon) and at least one hadronically decaying -lepton, or at least two light leptons. In addition, two or more jets, at least one of which must be identified as containing -hadrons, are required. Six final states, defined by the multiplicity and flavour of lepton candidates, are considered in the analysis. Each of them is split into multiple event categories to simultaneously search for the signal and constrain several leading backgrounds.