dataesr/scientific-paragraphs-categorization huggingface dataset:
The MeteoIO experiment employs a forcing dataset obtained through the spatial interpolation of data provided by the neighbouring stations (see Sect. 3. 2 and Appendix B). GLDAS, ERA5 and ERAI experiments use different reanalysis products described in Sect. 3. 3, namely GLDAS-2. 1, ERA5 and ERA-Interim. Both MeteoIO and reanalysis data had to be rearranged and interpolated to 30 min resolution in order to be used as forcings for snow models. In the case of ERA-Interim, for example, forecasts are initialized only twice a day at 00:00 and 12:00 UTC, and accumulated fluxes of total precipitation, surface solar and thermal downward radiation are available as forecasts at 3 h intervals for the following 12 h. From these forecasts we derive the average fluxes over 3 h intervals and we assume the fluxes to be constant during each interval.
For the other ERA-Interim parameters, namely 2 m temperature, dew-point temperature, surface pressure, and 10 m U and V wind components, we consider the analyses at 00:00, 06:00, 12:00, and 18:00 UTC and the forecasts at +3 h. These data are linearly interpolated in time to the integration time step (30 min) of the snow models. Some calculations are necessary to obtain all the variables required by the models. For example, ERA-Interim does not directly provide relative humidity, which we derive using the Magnus formula from the dew-point temperature and the 2 m air temperature  (Lawrence, 2005). The last two experiments, ERAI-LR and ERAI-BIAS, investigate whether bias-correcting (some of) the reanalysis drivers improves the snow model performance. To this end, we bias-correct air temperature (and indirectly the ratio of solid to total precipitation that depends on temperature) while keeping all other variables unchanged.
The idea is to test whether (i) the adjustment of air temperature (and the rainfall-snowfall partition) only can improve model performances and to what extent and whether (ii) very simple biascorrection methods can be sufficient or more sophisticated ones are necessary. In the ERAI-LR experiment we take into account the fact that ERA-Interim has a smoothed topography and the altitude of the grid point closest to the Torgnon station is 680 m lower than the actual elevation of the station. In the ERAI-LR experiment we adjust the temperature data assuming a fixed moist lapse rate of -6. 5 • C km -1. This correction results in a cooling of 4. 4 • C with respect to the original temperature data. In the ERAI-BIAS experiment we correct ERA-Interim air temperature using the difference in the climatological averages between ERA-Interim data and the Torgnon station observations, which was found to be 2 • C.
RAD-ERAI, SWIN-CLS -model sensitivity to the radiation input A typical problem occurring in case of snowfall is that when the radiation sensors get covered with snow, they record inaccurate data. To take into account this issue and test how it affects snow simulations, in experiment RAD-ERAI we use incoming longwave and shortwave radiation data from the Torgnon station except in case of snowfall, when we employ external LWIN and SWIN data derived from ERA-Interim. In the other experiment, SWIN-CLS, we replace observed incoming shortwave radiation data with the external data described in Sect. 4. The results of these simulations are reported in Table  4. Although the difference between external data and Torgnon data can be high at the time step of the model (not shown), their overall impact on snow simulations is low.
5 • C, with the effect of reducing the amount of the solid precipitation by 10 %. We investigate the impact of these biases on the snow simulations in the following. Figure  4  represents, for all the models, the simulated snow depth and SWE when input data are sampled (or accumulated, in the case of total precipitation) at 3, 6 and 12 h and then interpolated (or equally distributed for precipitation) over 30 min time steps, compared to the simulated snow depth obtained with the original 30 min resolution forcing (CTL) and compared to observations. The TIME-12h experiment employs the incoming shortwave radiation estimated with the potential radiation method; Appendix C reports the corresponding experiment employing the incoming shortwave radiation estimated with linear interpolation of the station measurements.
In addition, Table  4  reports the RMSE associated with all these simulations. The model response to the degradation of the temporal resolution of the forcing depends on the model and season. A common feature of the models is the small (or null) difference in terms of RMSE between TIME-3h and CTL simula-  tions, indicating that using forcing data at 3 h temporal resolution generates snow depth simulations almost as accurate as in the case of 30 min resolution input data. A second common feature is the general worsening of model performances when using input data at 6 h temporal resolution, reflected in an increase in the RMSE values. TIME-6h simulations are usually very close to the CTL in the accumulation period up to the snow peak. Afterwards, in the melting period, some models, mainly SNOWPACK, UTOPIA and, to a lesser extent HTESSEL and SMASH, slightly overestimate snow mass/depth in selected seasons, contributing to an increase in the model RMSE.
MeteoIO, GLDAS, ERA5, and ERA-Interimmodel sensitivity to the spatial resolution and bias in the forcing We consider a rather standard case for which no station measurements are available for the area of interest, and one has to rely on gridded datasets, which are generally characterized by lower resolution and lower accuracy with respect to station measurements. To explore a representative range of possible alternatives, we employ datasets with different characteristics: the MeteoIO dataset, based on the interpolation of data from neighbouring stations, and the GLDAS, ERA5 and ERA-Interim reanalyses at 25, 30 and 80 km respectively. An overview of the comparison between the meteorological forcing provided by these datasets and the observations in Torgnon is shown in Fig. 3. The MeteoIO forcing is in fairly good agreement with observations.
Impact of the bias adjustment of ERA-Interim air temperatures We test the effect of two very simple bias-correction techniques applied to the ERA-Interim air temperature. In the first approach, in the ERA-LR experiment, we take into account the difference in elevation between the ERA-Interim grid point at Torgnon and the true elevation of this site, applying a lapse rate correction, i. e. subtracting 4. 4 • C from the original ERAI data. Alternatively, in the ERAI-BIAS experiment, we remove the average bias of ERA-Interim data at the Torgnon site with respect to the station measurements, i. e. subtracting 2 • C from the original ERAI data. The lapse rate correction excessively reduces ERA-Interim temperatures: the average temperature bias shifts from +2 to -2. 4 • C and the snowfall bias increases from -32 % to +15 % (Fig.
As a general remark, it is preferable to apply a temperature correction based on local temperature observations or even just climatology, when available, as the correction based on the lapse rate does not ensure a better agreement with the reference data. Spatial interpolations of neighbouring station data, such as the MeteoIO interpolation used here, can be another valid alternative in the absence of in situ observations. In our experiment the models' RMSE values for snow water equivalent and snow depth are generally higher than those obtained using the Torgnon data at lower temporal resolution. GLDAS and ERA5 are affected, on average, by a large temperature bias and a moderate precipitation bias at the Torgnon grid point, probably owing to difficulties of these datasets in simulating processes in high-elevation regions.
The GEOTOP model provides the best snow depth estimates when forced by MeteoIO, ERA5 and ERAI-LR. However, all these forcing datasets have a cold temperature bias, and GEOTOP is affected by a systematic underestimation error in snow depth. These errors offset each other, with the effect that the RMSE in snow depth simulations is smaller than for the other models. Conversely, when using ERA-Interim forcing, GEOTOP performances are the worst owing to the positive temperature bias of the reanalysis dataset, which increases the underestimation of snow depth simulations. In this set of experiments GEOTOP shows weaknesses in reproducing the snow density and depth, thus calling for a check of its snow scheme. The UTOPIA and HTESSEL models perform as well as the most sophisticated SNOWPACK with optimal forcing, but they require fewer input data: for example, they do not need ground temperature.
These models can be employed when no information on snowpack internal structure and stratification is needed. UTOPIA and HTESSEL provide good performances also with low temporal resolution forcings up to 6 h and with ERA-Interim forcing. However, lower skills are found when employing the MeteoIO low-accuracy input dataset, suggesting that UTOPIA and HTESSEL can be sensitive to the bias in the meteorological forcing. In agreement with former studies (e. g. Essery et al. , 2013) , also in our analysis the best-performing models have (i) an explicit representation of the meltwater retention and refreezing in the snowpack and (ii) an intermediate-complexity representation of the snow albedo as a function of at least the surface temperature and snow age. According to our results, the representation of the snowpack as a medium with multiple layers alone does not guarantee improved results compared to models with single-layer snow schemes but is able to take into account meltwater infiltration and refreezing within the snowpack.
e. temperature and total precipitation, compared to reference datasets (e. g. E-OBS version 13,  Haylock et al. , 2008)  over the entire greater Alpine region (GAR, 4-19 • E, 43-49 • N). The time-averaged biases found at the Torgnon site are spatially consistent with those found at the mountain range scale, with the magnitude of the bias slightly varying across the region and with elevation. This analysis broadens the perspective beyond the specific case of the Torgnon site and provides guidance on the exportability of our experiment results to other areas in the Alpine region.
In order to address the issue of the exportability of the methods and results of the reanalysis-driven experiments to other areas of the Alps, we evaluated the biases of the reanalyses in reproducing the main drivers of the snowpack processes, i. e. temperature and total precipitation, compared to observational data. The aim is to evaluate the spatial distribution of the temperature and precipitation biases and their consistency at the mountain range scale. ERA5, ERA-Interim and GLDAS temperatures have been averaged over the months October-June and over the years 1980-2014 (except for GLDAS, which has been available since 2000 only, so the averages have been calculated over the period  [2000] [2001] [2002] [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012] [2013] [2014]  and then compared to the E-OBS version 13 observational dataset  (Haylock et al.
, 2008)  over the greater Alpine region (GAR, 4-19 • E, 43-49 • N). E-OBS is a daily gridded dataset at 0. 25 • resolution, based on the European Climate Assessment and Data set station measurements. ERA5 and GLDAS temperature biases are large and negative over the entire GAR (Fig. D1 ). GLDAS bias is especially strong, and it exceeds -4 • C in most of the region. ERA5 bias is large at high elevations and decreases towards the lowlands. Compared to ERA5 and GLDAS, ERA-Interim temperature is in better agreement with observations, with mainly negative biases across the region and values close to zero (both positive and negative values) at the mountain ridges in the western Alps. All these results are consistent with those found at the Torgnon site (Fig. 3 ), so the biases at the point scale are reflected at the mountain range scale.
Regarding precipitation, it is well known that standard surface station gauges have problems in capturing snowfall, and thus they underestimate total precipitation in mountain areas. Similarly, observational-based datasets such as E-OBS have also been found to suffer from underestimation of precipitation at high elevations  (Turco et al. , 2013). To overcome this problem, instead of using observation-based datasets as a reference, we evaluate precipitation ratios with respect to one of the reanalyses (ERA-Interim), since reanalyses inherently take into account orographic effects. Figure  D2  shows the ERA5 and GLDAS October-to-June accumulated precipitation ratios relative to ERA-Interim over the periods  1980 -2014 and 2000 -2014 respectively (GLDAS has been available since 2000)). Also in this case the ERA5 spatial pattern is homogeneous over the Alpine range, with ERA5 showing consistently more precipitation than ERA-Interim in the mountain areas.
Acknowledgements. We thank  Richard L. H. Essery  and one anonymous referee for their comments which helped us to improve the manuscript. Financial support. This research has been supported by the  Italian Project of Interest NextData of the Italian Ministry for Education, University and Research , by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements no. 641762  (ECOPOTENTIAL) and no. 641816  (CRESCENDO), and by the  European Union 's  ERA4CS  project under grant agreement no. 690462  (MEDSCOPE). Review statement. This paper was edited by  Daniel Viviroli  and reviewed by  Richard L. H. Essery  and one anonymous referee.
Context. Groups are the most common association of galaxies in the Universe, found in different configuration states such as loose, compact and fossil groups. Aims. We have studied the galaxy group MKW 4s, dominated by the giant early-type galaxy NGC 4104 at z = 0. 0282. Our aim was to understand the evolutionary stage of this group and to place it within the framework of the standard ΛCDM cosmological scenario. Methods. We have obtained deep optical data with CFHT/Megacam (g and r bands) and we have applied both the galfit 2D image fitting program and the IRAF/ellipse 1D radial method to model the brightest group galaxy (BGG) and its extended stellar envelope. We have also analysed publicly available XMM-Newton and Chandra X-ray data. From N-body simulations of dry-mergers with different mass ratios of the infalling galaxy, we could constrain the dynamical stage of this system.
5-7. 0 keV] by the script merge_obs from CIAO. The XMM-Newton data reduction was done with SAS version 20160201 and calibration files updated in September 2016. Background flares were identified and rejected by applying a 1. 8σ clipping to the high-energy count rate histogram. We used the resulting "cleaned" exposure files to detect and exclude point sources from our analysis. For both Chandra and XMM-Newton datasets, the spectral analysis was performed in the [0. 5-7. 0 keV] band. For the spectral fit, we applied XSPEC v12. 9 and χ 2 statistics, adopting an absorbed single temperature plasma model MEKAL  (Kaastra & Mewe 1993; Liedahl et al. 1995) , fixing the redshift and letting all the other parameters (temperature, metallicity, normalisation, and hydrogen column density) vary as free parameters.
Galaxy velocity distribution We analysed SDSS data (DR12), selecting galaxies in a 4. Best result of the 2D fit using galfit. Left: original r-band image with the positions of the various shells labelled from 1 to 6, with "s" or "n" referring to the southern and northern shells, respectively. The shells were drawn as circular arcs, symmetric with respect to the centre of NGC 4104. The dashed lines correspond to the shells that we could not measure the magnitude, see Table  7. Middle: best fit model (with a flat background included), simultaneously taking into account the three brightest galaxies in the group. The isocontours from the original r-band image, corresponding to 24 and 26 mag/arcsec 2 (shown in red and green, respectively) are displayed for reference. Right: residue, i. e. original image minus model, with the positions of the shells superimposed.
Conclusions We have measured the surface brightness radial profile along the major axis using the ellipse task from IRAF. We fitted it with a single Sérsic profile taking into account the PSF, which was modelled by a Moffat function, that allowed us to consider data points (pixels) inside the PSF length scale (of about 1 arcsec). This method, however, cannot fit properly the extended stellar envelope. Therefore, we used galfit, a 2D fitting program, to model NGC 4104 plus the second and third brightest galaxies inside the BGG envelope. The residual image clearly shows the shell features that were probably created by the merger of the second brightest galaxy with NGC 4104, as corroborated by our N-body simulation results. From the X-ray data, MKW 4s could be classified either as a rich group or a poor cluster, with virial temperature ∼ 2.
Acknowledgements. We thank the referee for constructive suggestions that helped improve the clarity of this paper. GBLN is grateful for the financial support from  FAPESP  (grant  2018/17543-0 ) and  CNPq , and thanks the hospitality of IAP. TFL acknowledges financial support from  FAPESP  and  CNPq  (through grants  18/02626-8  and  303278/2015-3 , respectively). REGM acknowledges support from  CNPq  through grants  303426/2018-7  and  406908/2018-4. F. D. acknowledges long-term support from  CNES. NM acknowledges support from a fellowship of the  Centre National d'Etudes Spatiales (CNES). This work made use of the  Laboratory of Astroinformatics (IAG/USP, NAT/Unicsul ), whose purchase was made possible by the  Brazilian agency FAPESP  (grant  2009/54006-4 ) and the  INCT-A. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France , and the  University of Hawaii  (observation Program  13AF002 ).
Discussion Schreiber Beach samples, which are composed of thin, discontinuous carbonaceous cell envelopes (sheaths or cell walls) immersed in a silica matrix are among the most challenging structures for imaging methods in terms of their minimal contrast against the matrix phase. When structures have dimensions below the pixel size of the dataset, they are not resolved since each 3D pixel (voxel) comprises a mixture of kerogenous material and the surrounding silica. This also occurs at the border or interface of different materials and generates an intermediate pixel intensity, depending on the proportion of each material. This phenomenon, called partial-volume effect, has implications for the quantification of electron density and spatial resolution (See Material and Methods and Fig. S4  for a detailed discussion).
For this we split the projections into two independent datasets, each with double angular spacing, by taking either the even or odd angular projections. With this we then compute two independent tomograms. The FSC between these two tomograms is calculated and the point where the FSC intersects the 1/2 bit threshold defines the resolution (Supplementary Fig. S1 )  21, 48. Tomographic data treatment. Segmentation, 3D rendering and analysis were performed using Avizo 9. 5 (Thermo Scientific). The intensity of the pixels was the major criterion for the segmentation and no filter was applied to the images to maintain their original electron density infomation. For electron density quantification, a different segmentation was performed in order to avoid pixels at the border of material phases, which may contain a mixture of contributions from the studied object and its surroundings.
For this, after thresholding segmentation, segmented selections were shrunken by 3 pixels in every direction. This shrinking value was estimated by considering the minimum size of a spherical particle of radius R that present a number of voxels within the volume (n V ) higher than within its surface area (n A ), i. e. 4π/3(R 3 /l) > 4π (R 2 /l), therefore R > 3 l, where l is voxel width. The segmented materials were exported as TIFF masks which were used for the extraction of the intensity histogram using the Fiji software.
Mass density quantification. The pixel values of the phase tomogram provide the real part δr of the complex refractive index of the sample, n(r) = δ(r) + iβ(r). The electron density n e (r) was retrieved from the δr values using the relation n e (r) = 2πδ(r)/λ 2 r 0 , where r 0 denotes the classical electron radius and λ the wavelength of the radiation, as described in detail in Diaz et al. 22. After the segmentation of the material phases and extraction of intensity histograms with Fiji software, Gaussian functions were made using the MagicPlot 2. 8 software (Magic Plot Systems, LCC). The peak center of each Gaussian function was then used to estimate center distribution of the electron density of each material, including air for normalization. In all cases, the range of distribution was taken as the standard deviation of the fitted Gaussian functions.
Mass density was estimated based on the electron density using the formula ρ = n e A/N A Z, where A is the molar mass, Z is the number of electrons and N A is Avogadro's number. We used the A and Z values of the candidate minerals  26, 27, 34. For quartz (SiO 2 ) A/Z = 2, and for hematite/maghemite and magnetite (Fe 3 O 4 , Fe 2 O 3, ), A/Z = 2. 1. For kerogen, we used a composition of C 175 H 102 O 9 N 4 S 2, estimated by Ungerer, Collell, and Yiannourakou 2015  49  , which results in a ratio of A/Z ratio of 1. 92 and is in agreement with typical values for this ratio in other biological compounds  22. The calculated mass densities were compared with mineral candidates using the density values found in the mineral database Mindat. org.
Magnetic analysis. Experiments were performed in a 2 G Enterprises model 755 superconducting quantum interference device (SQUID) with in-line alternating field coils and direct field (DC) in z-axis. The SQUID magnetometer is housed in a magnetically shielded room (magnetic intensity lower than 200 nT) and nominal sensitivity of 10 -12 Am². For anhysteretic remanent magnetization (ARM), we used an alternating field of 100 mT and a direct field of 0. 1 mT, whereas a stepwise isothermal remanent magnetization (IRM) with a maximum field of 1. 2 T was obtained in a Vibrating Sample Magnetometer (VSM). The magnetic domain states were obtained with the first-order reversal curve (FORC) diagrams in a VSM, with maximum field of 1. 4 T and smoothing factor of 6, the data was processed using the software FORCinel 50.
Acknowledgements We acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for provision of synchrotron radiation beamtime at cSAXS beamline of the  Swiss Light Source (SLS) ,  Joakim Reuteler  ( ScopeM ) for the preparation of the samples using FIB, the  Centro Brasileiro de Pesquisas Físicas (CBPF)  for support in sample preparation and SEM analysis, and  Sylvain Janiec  ( ISTO, Orléans ) for preparing the thin sections. LM was funded by  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES) -Finance Code 001  and by  Fundação de Amparo à Pesquisa do Estado de São Paulo  ( FAPESP grants   2015/21810-6  and  2016/25681-9 ), which included being hosted at the IPANEMA laboratory. DG acknowledges  FAPESP  ( 2016/06114-6 ),  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq ,  424367/2016-5  and  301263/2017-5 ).