dataesr/scientific-paragraphs-categorization huggingface dataset:
It is currently computationally too e xpensiv e to produce a finemesh grid in the parameter space of puffy discs, to calculate a table model that could be implemented for public use in the XSPEC software package. For this reason, we cannot at present explicitly fit puffy disc models to observational X-ray spectra. Ho we ver, this is clearly one of the directions that should be pursued in the future. Currently, we do not have puffy disc solutions for the Kerr metric with a * = 0, nor for L < 0. 6 L Edd. The latter solutions are urgently needed, as there as yet exist no known stable solutions for disc accretion at rates corresponding to the luminosity of many BHXB sources.
AC K N OW L E D G E M E N T S The authors thank  Andrew Chael ,  Brandon Curd , and  Aleksander S ądowski  for support with the KORAL code, as well as  Silke Britzen ,  Michal Bursa , and  Monika Mo  ścibrodzka for valuable comments and discussions. The computations in this work were supported by the  PLGrid Infrastructure  through which access to the Prometheus supercomputer, located at ACK Cyfronet AGH in Krak ów, w as provided. This w ork w as supported in part by the  Polish NCN  grant  2019/33/B/ST9/01564  and the  Black Hole Initiative at Harvard University , which is funded by grants from the  John Templeton Foundation  and the  Gordon and Betty Moore Foundation  to Harv ard Uni versity. MA ackno wledges the  Polish NCN  grant  2015/19/B/ST9/01099 , while AR acknowledges the  NCN  grant  2021/41/B/ST9/04110.
Methods Transmission electron microscopy (TEM). A Tecnai F20 (FEI) TEM was used to determine the size and size dispersity of the metal core and to evaluate the encapsulation success. The measurements were performed at an acceleration voltage of 200 kV. For each sample 7 mL of the Au-PNIPAM particle dispersion was dried on carbon-coated, 300 mesh copper grids (Electron Microscopy Sciences). Various TEM images were used for the determination of the gold core size and 200 particles a The nominal molar cross-linker ratio values are the ones used in the synthesis and not the ratios in the final microgel polymer network. were measured by the ImageJ software. 42 Moreover, we used the TEM images to evaluate the encapsulation success of the cores in the PNIPAM shells. UV-Vis spectroscopy. Absorbance spectra were measured with a Specord S 600 spectrometer equipped with a temperature-controlled sample changer (Analytik Jena AG).
154 nm (Cu K-Alpha). The scattering data was collected using a PILATUS3 300 K detector (DECTRIS) with pixel size of 172 Â 172 mm 2. Particle aqueous dispersions (1-5 wt%) were filled into 1 mm round capillaries delivered from WJM Glas, and the exposure time for each measurement was 3600 s. For all measurements, Milli-Q water was used for the background correction. Small-angle neutron scattering (SANS). SANS measurements (DOI:10. 5291/ILL-DATA. EASY-632) were performed at the Institut Laue-Langevin (ILL) in Grenoble (France) using the D11 instrument. The neutron wavelength was 0. 46 nm for the sample-to-detector distances of 1. 4, 8 and 39 m and 10 nm for 39 m. The signals were collected using the  3  He gas detector (CERCA) with an area of 96 Â 96 cm 2 and a pixel size of 3. 75 Â 3. 75 mm 2.
To cover a broad q range, the data for the dilute samples in D 2 O (0. 5 wt%) were collected at sample-todetector distances of 1. 4, 8 and 39 m with acquisition times of 300, 600, and 1200 s, respectively. The SANS measurements of all CS microgels were performed at four different temperatures of 25, 35, 37 and 50 1C. However, due to limited beamtime, we could not record data at low q values at 37 1C. The dilute dispersions were measured in cylindrical quartz glass cells (Hellma Germany, light path 1 mm). The collected data was corrected for D 2 O and empty cell scattering. Scattering data were radially averaged and normalized to absolute intensities using standard routines of the ILL (Lamp). The data reduction considered the sample transmission, sample thickness, detector noise (via a 10 B 4 C absorber), flat field (from measurement of H 2 O) and empty cell subtraction, providing data in absolute scale using water as a secondary standard.
Instrument resolution accounts for the resolution in q based on the experimental direct beam width, wavelength spread (Dl/l = 9% (FWHM)) and detector pixel size. The recorded spectra at different sample-todetector distances were merged and analyzed by SASfit software by Kohlbrecher. 43 efractive index measurements. Refractive indices of particle dispersions were recorded with an Abbemat-WR/MW refractometer (Anton Paar). The measurements were performed at the wavelength of 589. 3 nm at 25 and 50 1C. The concentrations of the particles were kept the same as for the SAXS measurements. 500 mL of highly concentrated PNIPAM solution (1,4-dioxane)  were left to dry in the air in the measuring unit until a constant value was reached to obtain the pure linear PNIPAM film refractive index. FDTD simulations.
Theoretical extinction spectra were calculated using finite difference time domain (FDTD) simulations employing commercial software from Lumerical Solutions, Inc. (FDTD Solutions, Version 8. 18. 1332). Absorption and scattering spectra were simulated in a box in x, y and z-direction with perfectly matched layer (PML) boundary conditions. We used a Total-Field Scattered-Field (TFSF) source and a box of power monitors consisting of the total field and scattered field monitors. The auto shut-off was set at 10 À8 before reaching 1000 fs simulation time. Gold core sizes were simulated with a radius of 6 nm with a mesh of 0. 25 nm. The radius is slightly smaller than obtained from TEM (7. 7 nm) and SAXS (6. 8 and 6. 5 nm) measurements. The wavelengthdependent refractive index of the gold core was taken from literature.
The respective values of R h in the swollen and collapsed state are summarized in Table  1. For a better comparison of the swelling characteristics of the microgels, we look at the deswelling ratios, a, in Fig. 2. a is defined as the ratio of the hydrodynamic volume at any temperature T, V h (T), and the respective hydrodynamic volume in the swollen state V h,swollen : a ¼ V h ðTÞ V h;swollen (11) For our CS microgels, we consider only the volume of the swellable PNIPAM shell to calculate a: V h ¼ 4 3 pðR 3 h À R core 3 Þ: (12) Here, R core corresponds to the radius of the rigid AuNP cores, that do not change their volume in response to temperature in the relevant temperature window. We used data from the  sigmoidal fits to the swelling curves at 25 1C to calculate V h,swollen (see Table  1 ).
In Fig. 2 (a) the deswelling behavior of the three microgels with different shell thicknesses but similar cross-linker contents are compared. The data of the different samples nearly collapse onto a common master curve with very similar VPTTs and only slight fluctuations -mostly at lower temperatures. Furthermore, very similar values of a of approximately 0. 4 are reached at 50 1C. In other words, the shrunken state volume of the PNIPAM shells is only about 40% of the swollen state volume. The overlap of the data for the different CS batches indicates that indeed the degree of cross-linking is very similar for CS1-3. 51 In contrast, Fig. 2(b ) compares the deswelling behavior of the set of samples with different nominal crosslinker contents. The relative volume change between swollen and collapsed state is largest for CS4, which has the lowest nominal cross-linker content.
† Here, we want to focus on the observed increase in absorbance with increasing temperature, i. e. when the shells collapse due to the VPT behavior. Fig. 3(a)-(e)  show measured and normalized absorbances at selected wavelengths as a function of temperature for all CS microgels. For a better direct comparison we normalized all data for each sample using the absorbances measured at l = 300 nm in the fully collapsed state, i. e. at 60 1C. Due to the power law scaling of the wavelength in RDG scattering (pl À4 , see eqn (  5 )), the absorbances at a given temperature decrease with increasing wavelength, i. e. from 300 to 400 to 523 nm, for each sample. We will come back to the experimentally observed power law scaling at a later stage in this work. At a given wavelength all samples show a continuous increase in absorbance with increasing temperature in the region of the VPT, i.
Thus, the CS1 microgels show a slightly different behavior than CS2 and CS3 with similar cross-linker contents. Generally the calculated polymer volume fractions in the collapsed state are in good agreement with values reported in literature,  14, 25  where, for example, Lopez and Richtering estimate a value of 0. 44, independent of the cross-linking density and molar mass. 24 The collapsed state of microgels is often used as the reference state, for example, when the swelling behavior is described by the Flory-Rehner theory. 12, 23, 24 In this case precise knowledge of the polymer volume fraction is crucial. While this value is often estimated rather than precisely measured,  12, 28  we provide an approach to determine the polymer volume fraction with great confidence. Furthermore, the values from our collapsed state SANS data are in good agreement with the results from refractive index measurements (Table  3 ) despite the slightly larger variation between samples for the latter dataset.
However, the scattering is obviously still far from matching the RDG prediction. While the internal structure of PNIPAM microgels appears well understood and the fuzzy sphere model is well accepted, our data reveal the complexity when it comes to understanding simple absorbance properties: during the temperature induced collapse, PNIPAM microgels show an increase in scattering that cannot be explained by the RDG theory for homogeneous refractive index spheres. Even in the collapsed state, where SANS data clearly reveal the form factor of polydisperse hard spheres with a homogeneous polymer distribution, measured absorbances can neither be described with the RDG model nor by Mie theory calculations (see Fig. S12  in the ESI †). This becomes particularly evident when comparing the power law scaling of the wavelength dependence.
Acknowledgements The authors acknowledge the  Melbourne Advanced Microscopy Facility (Bio 21 ,  The University of Melbourne ) for access to electron microscopes and  Heyou Zhang  from the  University of Melbourne  for his support with the TEM measurements. The authors thank the  Institut Laue-Langevin  for allocation of SANS beamtime on D11 via EASY access; data are available on demand (DOI:  10. 5291/ILL-DATA. EASY-632 ). The authors are also grateful for financial support from the  German Academic Exchange Service (DAAD)  through its  Thematic Network Melbourne-Bayreuth Polymer/Colloid Network , sponsored from funds of the  Federal Ministry of Education and Research (BMBF). The authors thank the  Center for Structural Studies (CSS)  that is funded by the  Deutsche Forschungsgemeinschaft  ( DFG Grant  numbers  417919780  and  INST 208/761-1 FUGG ) for access to the SAXS instrument.
Conceptualization, N. T. M. , E. V. A. and F. A. ; methodology, F. A. and N. T. M. ; software, F. A. ; validation, N. T. M. , E. V. A. and F. A; formal analysis, N. T. M. , E. V. A. and F. A. ; investigation, E. V. A. ; resources, N. T. M. ; data curation, E. V. A. ; writing-original draft preparation, E. V. A. , N. T. M. and F. A. ; writing-review and editing, N. T. M. and F. A. ; visualization, E. V. A. ; supervision, N. T. M. and F. A. ; project administration, N. T. M. ; funding acquisition, N. T. M. and F. A. All authors have read and agreed to the published version of the manuscript. Funding: Financial support from the National Science Foundation Award CHE-1940333 (EVA), and by the Computational Chemistry Center: Chemistry in Solution and at Interfaces funded by the U. S. Department of Energy, Office of Science Basic Energy Sciences, under Award DE-SC0019394 (N.
Author Contributions: Conceptualization, N. T. M. , E. V. A. and F. A. ; methodology, F. A. and N. T. M. ; software, F. A. ; validation, N. T. M. , E. V. A. and F. A; formal analysis, N. T. M. , E. V. A. and F. A. ; investigation, E. V. A. ; resources, N. T. M. ; data curation, E. V. A. ; writing-original draft preparation, E. V. A. , N. T. M. and F. A. ; writing-review and editing, N. T. M. and F. A. ; visualization, E. V. A. ; supervision, N. T. M. and F. A. ; project administration, N. T. M. ; funding acquisition, N. T. M. and F. A. All authors have read and agreed to the published version of the manuscript. Funding: Financial support from the National Science Foundation Award CHE-1940333 (EVA), and by the Computational Chemistry Center: Chemistry in Solution and at Interfaces funded by the U.
, J. Geophys. Res. Space Physics, 2017, 122,  7865-7890). At that time, the Cassini spacecraft was fortunately travelling within the solar wind for a few days, and provided a very accurate timing of the ICME structure. A survey of the Cassini data for the same period indicated a significant increase in the SKR emissions, showing a good correlation after the passage of the ICME shock with a delay of ∼13 h and after the magnetic cloud passage with a delay of 25-42 h.
Thus, it has been possible to use in situ data from the Cassini magnetometer (MAG)  (Dougherty et al. , 2004)  and energetic particle instrument (MIMI)  (Krimigis et al. , 2004)  to provide accurate timing for the ICME arrival. The ICME impacted Cassini on 12 November 2014. A survey of the RPWS data for the same period indicated a significant increase in the SKR emissions. This article reports on the correlation between this solar wind disturbance and SKR emissions.
DATA AND TRAJECTORIES In this study, we use several datasets from various instruments: radio frequency data from the High Frequency Receiver (HFR) of Cassini/RPWS  (Gurnett et al. , 2004) ; magnetic field data from the Cassini/MAG (Magnetometer)  (Dougherty et al. , 2004) ; and particle observations from Cassini/MIMI (Magnetosphere Imaging Instrument)  (Krimigis et al. , 2004). The trajectory data is provided by the CDPP (Centre de Données de la Physique des Plasmas) through their AMDA (Automated Multi-Dataset Analysis) and 3Dview tools  (Génot et al. , 2017; Génot et al. , 2021). The Cassini/RPWS/HFR instrument records radio electric signals from 3. 5 kHz to 16 MHz, using three electric antennas. This instrument allows for reconstruction of the absolute flux density and polarization of the observed radio waves  (Cecconi and Zarka, 2005).
In this study, we make use of two datasets from HFR: 1) the Cassini/RPWS/HFR Level 3e (N3e) dataset  (Cecconi et al. , 2017) , i. e. , the "Circular Polarization Mode" goniopolarimetric inversion as described in Section 2. 1. 3. 2 of  Cecconi and Zarka (2005) ; and 2) the Cassini/RPWS/HFR SKR dataset  (Lamy et al. , 2009b)  as described in  Lamy et al. (2008). The Cassini/MIMI instrument is designed for energetic neutral and charged particle detection. MIMI consists of three sensors of which we use the Low Energy Magnetospheric Measurement System (LEMMS) sensor  (Krimigis et al. , 2004). LEMMS is a charged particle telescope with two units separated by 180°in pointing that use solid state detectors and coincidence logic to determine the type of particle (electron or ion) and its energy, as well as magnetic deflection to better separate ions from low energy (≤800 keV) electrons  (Roussos et al.
Desch and Rucker (1983)  reported the disappearance of SKR when Saturn is in Jupiter's magnetotail, hence in a depleted plasma environment. The spacecraft is located on the dawn side of the magnetosphere (∼0700 h LT) throughout this interval. It is expected due to the location of radio sources and the hollow cone beaming pattern of the SKR that radio emissions in the main SKR band should maximise around this viewing location  (Lamy et al. , 2008). Indeed, Figure  4  displays continuous SKR FIGURE 6 | Comparison of the solar wind modelled parameters from the two selected propagation models. From top to bottom: (A) the SKR RH emitted power density integrated over 100-400 kHz and smoothed to a temporal resolution of 6000 s; (B-E) the modelled solar wind density (in cm-3), temperature (in eV), radial velocity (in km/s) and dynamic pressure (in nPa), as provided by the ENLIL code at CCMC (black) and 1D-MHD code at CDPP (grey); (F,G) the modelled (ENLIL) and measured (Cassini/Mag) solar wind magnetic field in RTN cooordinates (Br in red; Bt in blue; Bn in grey; B magnitude in black).
ACKNOWLEDGMENTS The authors thank the developers of: AMDA (Automated Multidataset Analysis Tool) and 3Dview at CDPP (Centre de Données de la Physique des Plasmas); Autoplot at University of Iowa; and TOPCAT (Tools for Operations on Catalogues and Tables) at Bristol. Figures 4, 5, 7, 8 have been produced with Autoplot  (Faden et al. , 2010). The Cassini/RPWS data displayed on the figures have been retrieved from PADC (Paris Astronomical Data Centre) using the das2 (Piker, 2017) interfaces of the MASER (Measurements, Analysis, and Simulation of Emission in the Radio range) team  (Cecconi et al. , 2020). The Cassini/MAG is distributed by University of Iowa, also through a das2 interface. Figure  6  have produced with TOPCAT  (Taylor, 2005) , after conversion of the ENLIL original tabular data into VOTable  (Ochsenbein et al.
The knowledge of TMDs, in particular of TMD FFs, would be greatly improved by using data from electron-positron annihilations into two almost back-to-back hadrons  [10]. Unfortunately, this data is presently not available. There are measurements for the inclusive production of single hadrons  [11]  but, in this case, transverse momenta need to be defined with respect to the thrust axis: a careful description of the latter is non trivial, and a rigorous factorization theorem for this process has been discussed only recently (see, e. g. , refs. [12] [13] [14]  and references therein). Therefore, for TMD extractions we currently talk about a global fit when data from SIDIS and DY processes are included. In the last ten years, several extractions of TMDs have been presented  [5, 7, [15] [16] [17] [18] [19] [20] [21] [22] [23] [24].
Most of them suffered some shortcomings: they were either obtained in a parton-model framework without QCD corrections, or they took into account only a limited set of data, or did not perform a full fit. TMDs were also studied in a different framework, the so-called parton-branching approach  [25] [26] [27]. At present, only two works have reached the stage of combining SIDIS and DY data in a full-fledged global TMD fit: the above mentioned extraction of ref. [5] , henceforth named PV17, and the extraction of ref. [22] , henceforth named SV19. The PV17 extraction reached the NLL accuracy, was based on the calculation of observables at mean kinematics in each bin, and did not manage to describe the normalization of all datasets. In this work, we push the accuracy of the analysis to what we will refer to as N 3 LL -(only NNLO collinear FFs are currently missing in order to reach full N 3 LL accuracy).
1 Apart from the increase in perturbative accuracy, in this work we also include many measurements published after 2017. The SV19 extraction reached the same perturbative accuracy and included essentially the same datasets. Our work has crucial differences in the selection of specific data points JHEP10(2022)127 (we include a much larger number of points), the implementation of TMD evolution, the choice of nonperturbative components, and the handling of normalization for SIDIS data. As we will describe in detail, we found it particularly difficult to describe in a satisfactory way the normalization of SIDIS data obtained in fixed-target experiments at moderate to low scales. When the analysis is performed at NLL accuracy, we can describe well shape and normalization of SIDIS data, but the description of high-energy DY data is very poor.
Data selection In this section we describe the experimental data included in our global analysis. We consider a large number of datasets related to DY lepton pair production and SIDIS, for the observables discussed in section 2. 1 and section 2. 2. The coverage in the x-Q 2 plane spanned by these datasets is illustrated in figure  3. The majority of datasets analyzed in the present work was already included in the global analysis of SIDIS and DY data in ref. [5]  and in the fit of DY data discussed in ref. [7]. For more details, we refer the reader to those references. The new datasets included in the present analysis are: • DY di-muon production from the collision of a proton beam with an energy of 800 GeV on a 2 H fixed target from E772 ( √ s = 38. 8 GeV)  [76] ; • DY di-muon production from the PHENIX Collaboration  [77] ; • DY data at 13 TeV from the CMS Collaboration  [78]  and the ATLAS Collaboration  [79].
Drell-Yan Our analysis is based on TMD factorization, which is applicable only in the region |q T | Q. Therefore, in agreement with the choices of refs. [7, 22]  we impose the following cut Table  2  summarizes all the DY datasets included in our analysis. For some DY datasets the experimental observable is given within a fiducial region. This means that kinematic cuts on transverse momentum p T and pseudo-rapidity η of the single final-state leptons are enforced (values reported in the next-to-last column of table  2 ). For more details we refer the reader to ref. [7]. The second column of table 2 reports, for each experiment, the number of data points (N dat ) that survive the kinematic cuts. The total number of DY data points considered in this work is 484. Note that for E605 and E288 at 400 GeV we have excluded the bin in Q containing the Υ resonance (Q 9.
5 GeV). |q T | < 0. 2 Q. ( 3 As can be seen in table  2 , the cross sections are released in different forms: some of them are normalized to the total (fiducial) cross section while others are not. When necessary, the required total cross section σ is computed using the code DYNNLO  [35, 36]  with the MMHT14 collinear PDF set, consistently with the perturbative order of the differential cross section (see also table  1 ). More precisely, the total cross section is computed at NLO for NNLL accuracy, and NNLO for N 3 LL -accuracy. The values of the total cross sections at different orders can be found in table 3 of ref. [7]. For the ATLAS dataset at 13 TeV, the value of the fiducial cross section is 694. 3 pb at NLO and 707. 3 pb at NNLO.
JHEP10(2022)127 Experiment N dat Observable Channels Q [GeV] x z Phase space cuts Ref. HERMES 344 M (x, z, |P hT |, Q) p → π + p → π - p → K + p → K - d → π + d → π - d → K + d → K - 1 - √ 15 0. 023 < x < 0. 6 (6 bins) 0. 1 < z < 1. 1 (8 bins) W 2 > 10 GeV 2 0. 1 < y < 0. 85 [74] COMPASS 1203 M (x, z, P 2 hT , Q) d → h + d → h - 1 -9 (5 bins) 0. 003 < x < 0. 4 (8 bins) 0. 2 < z < 0. 8 (4 bins) W 2 > 25 GeV 2 0. 1 < y < 0. 9 [68] Total 1547 Table  3. Breakdown of the SIDIS datasets included in this analysis. For each dataset, the table includes information on: the number of data points (N dat ) surviving the nominal cut on |P hT |, the observable delivered, the SIDIS channel, the range(s) in photon invariant mass Q, the ranges in the kinematic variables x and z, possible cuts on the single final-state lepton, and the public reference (when available).
The total number of SIDIS data points amounts to 1547. In order to restrict ourselves to the SIDIS current fragmentation region and interpret the observables in terms of parton distribution and fragmentation functions, we apply a cut in the kinematic variable z by requiring 0. 2 < z < 0. 7. The lower limit is the same used in the study of collinear fragmentation functions  [29, 96]. We used a slightly more restrictive upper limit, to avoid contributions from exclusive channels and to focus on a region where the collinear fragmentation functions have small relative uncertainties. For what concerns the cut on transverse momentum, our baseline choice is |P hT | < min min[c 1 Q, c 2 zQ] + c 3 GeV, zQ , (3. 2) with fixed parameters c 1 = 0. 2, c 2 = 0. 5 and c 3 = 0. 3. This choice is more restrictive than a similar one made in ref.