dataesr/scientific-paragraphs-categorization huggingface dataset:
The set of shapes that can be described by the feature space is limited to the deformation modes derived from the included cohort. Therefore, there is no guarantee that a feature vector can accurately represent every new given anatomy. A second significant limitation concerns the linear growth rate hypothesis; exponential growth rate models  [71]  seem to be more accurate but often require at least three exams over time to be validated  [17, 72]  while the dataset selected for this work includes patients with only two acquisitions. Uncertainty is then introduced by using both MRI images and CT-Scans. However, we tried to mitigate it by including the resolution criterion and segmenting the intra-luminal aortic region to derive the shape parameters  [73]. Moreover, we included patients with non-gated acquisitions, which reduced the accuracy of the results  [74].
This is why an exclusion criterion of 6 months was set so that variations were more likely to be attributed to the growth of the vessel rather than the variations that could emerge between systole and diastole. Furthermore, we did not consider if the patients, during the follow-up period, had taken drugs such as beta-blockers to slow down the aneurysm growth  [75] , a phenomenon that could alter the calculation of the growth rate. In performing the regression, a robust study should include a testing dataset but, given the small number of patients available, we preferred to use only the training and validation set. Furthermore, for local shape features and PCA-based global shape features, we have only reported SVM-based regression results. Other regression models should be tested in order to compare the prediction performances.
02 TeV  →  and  →  events. The differences between the efficiencies measured in data and simulation samples were studied as functions of lepton  T and , and the activity around the lepton. Applying these shifts to leptons in simulated  t events gave per-lepton efficiency changes of up to +0. 4% for electrons and -0. 5% for muons. Given the small sizes of these shifts, no corrections were applied to the nominal simulation, and the largest shifts were taken as uncertainties in the efficiencies and background estimates. The electron-trigger efficiency was measured using  →  events in low-pileup datasets recorded at both √  = 5. 02 TeV and √  = 13 TeV, using the method described in  [33]. The muon-trigger efficiency was measured using  →  events in high-pileup √  = 13 TeV data recorded immediately before the low-pileup datasets  [34].
Category  constraints from LHC data, while the last two are PDF sets that use some LHC data but no  t cross-section measurements. Figure  9 (a) compares the measured cross-section at √  = 5. 02 TeV with the predictions from the aforementioned PDF sets, as well as the MSHT20  [88] , NNPDF4. 0  [89] , ABMP16  [90] , CT18  [91]  and CT18A  [91]  PDFs, and the combination of the CT10, MSTW2008, and NNPDF2. 3 PDFs using the  TeV with NNLO+NNLL theoretical predictions  [5]  calculated using T ++  [17]  with the PDF4LHC prescription for PDF and  s uncertainties  [87] , and   = 172. 5 GeV. The middle panel shows the ratios of the measurements and predictions to the central value of the prediction using PDF4LHC PDFs. The total uncertainties when using the individual NNPDF2. PDF4LHC recipe. The result is also compared with the recent ATLAS 21 PDF fit  [92] , which includes a diverse set of ATLAS Collaboration measurements at √  = 7, 8 and 13 TeV (including  t differential cross-section measurements at √  = 8 and 13 TeV), together with deep-inelastic scattering data from   collisions at the HERA collider  [93].
Conclusion The inclusive  t production cross-section   t has been measured in   collisions at √  = 5. 02 TeV using 257 pb -1 of data recorded by the ATLAS experiment at the LHC in 2017 using events from both the dilepton and single-lepton channels. The combined result is   t = 67. 5 ± 0. 9 (stat. ) ± 2. 3 (syst. ) ± 1. 1 (lumi. ) ± 0. 2 (beam) pb, where the four uncertainties are due to the size of the data sample, experimental and theoretical systematic effects, and imperfect knowledge of both the integrated luminosity and the LHC beam energy. The total uncertainty is 2. 7 pb, or 3. 9%. The result is consistent with the NNLO+NNLL QCD prediction of 68. 2 +5. 2 -5. 3 pb and with a previous measurement by the CMS Collaboration, but has a total uncertainty that is almost a factor of two smaller.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRI, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ; MOST, Taiwan; TENMAK, Türkiye; STFC,  United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  PRIMUS 21/SCI/017  and  UNCE SCI/013 , Czech Republic; COST,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway;  NCN  and  NAWA, Poland ;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden ;  The Royal Society and Leverhulme Trust ,  United Kingdom.
2006 ) , the source has a relatively low Eddington ratio, 1 per cent  (Keck et al. 2015a  ). In the following, we present the spectral and spectropolarimetric analyses of the combined data from IXPE , XMM-Newton , and NuSTAR , providing the most complete view to date of the inner accretion flow in NGC 4151. The paper is organized as follows. In Section 2 , we describe the IXPE , XMM-Newton , and NuSTAR observations and data reduction. In Section 3 , we report on the spectral and spectropolarimetric analyses. In Section 4 , the results are discussed.
O B S E RVAT I O N S A N D DATA R E D U C T I O N IXPE  (Weisskopf et al. 2022  ) observed NGC 4151 starting on 2022 December 8 with its three detector units (DU), for a net exposure time of about 632 ks. The data were calibrated with a standard IXPE pipeline from the Science Operation Center. 1 The pipeline mainly contains the correction processes on the photoionization events and the track reconstruction process following a standard moment analysis  (Bellazzini et al. 2003 ; Fabiani & Muleri 2014 ; Di Marco et al. 2022 ). In addition, variations on gain properties that are caused by susceptibility of gas status (e. g. temperature and pressure) inside the Gas Pixel Detector  (Costa et al. 2001 ; Bellazzini et al. 2007 ; Fabiani et al. 2012 ; Baldini et al. 2021  ) and non-uniformity of the charging on the Gas Electron Multiplier material are accounted for.
The onboard calibration data were utilized to deal with the small time-scale variations  (Ferrazzoli et al. 2020 ). The spurious modulation was also taken into account in this process  (Rankin et al. 2022 ). The scientific analysis was performed using the IXPEOBSSIM software version 30. 2. 1  (Baldini et al. 2022 ). Source and background data were extracted centred on the source position in the detector frame that co v ered the entire source emission and source-free regions, respectiv ely. F or the re gion selection criteria, we applied a 72 arcsec circle for the source and an annulus with inner and outer radii of 150 and 240 arcsec, respectively, for the background (Di  Marco et al. 2023 ). In order to estimate the polarization properties, we created (1) the polarization cube ( PCUBE ) based on the  Kislat et al.
2023 ; Serafinelli et al. in preparation). The NuSTAR  (Harrison et al. 2013  ) observation started on 2022 December 16 simultaneously to XMM and IXPE pointings, with both coaligned X-ray telescopes with Focal Plane Module A (FPMA) and B (FPMB). The Nupipeline task and the latest calibration files available in the data base (CALDB 20221229) were used to produce and calibrate cleaned event files. In this case, the source and background extraction radii are 2 and 1. 22 arcmin, respectively. The net exposure time for the FPMA and FPMB resulted to be 97 and 96. 3 ks, respecti vely. Significant de viations from the pn spectrum were still present in the NuSTAR spectra below 4 keV, even after applying the correction mentioned abo v e. Therefore, we will consider NuSTAR data only abo v e 4 keV (see e.
For the reflection component, we used BORUS  (Balokovi ć et al. 2018 ; Balokovi ć, Garc ía & Cabral 2019 ) , which models reprocessing from a torus with variable covering factor, self-consistently illuminated by a nthcomp spectrum with the same photon index and electron temperature as the primary continuum. In view of the joint spectropolarimetric fit with the IXPE data presented in Section 3. 3 , we separated the reflection component from the corresponding fluorescent lines with the two dedicated BORUS tables, linking all the parameters (including the normalization). Since they are not constrained by the fit, we fixed the cosine of the inclination and the co v ering factor of the torus to 0. 6 (appropriate for an intermediate Seyfert galaxy) and 0. 5 (default value in BORUS ), respectiv ely.
Moreo v er, in order to account for the modest broadening of the iron line as reported abo v e, we convolv e the BORUS tables with a gsmooth of 28 + 15 -17 eV. The X-ray spectrum of NGC 4151 is well known to be strongly affected by complex absorption. Similarly to  Keck et al. ( 2015a ) , we used two partial-co v ering neutral absorbing layers (PC, modelled by zpcfabs ) and a warm absorber (WA, modelled with zxipcf and co v ering factor fix ed at 1). The presence of a second WA phase at lower ionization, which has been observed in some prior studies (e. g. Keck et al. 2015a ; Zoghbi et al. 2019 ; Szanecki et al. 2021 ) , is not required by the data ( χ 2 = -3 for 2 d. o. f. less). Ho we ver, it is plausible that one of the neutral PCs may already mimic a lowionization WA. Finally, as suggested by the presence of emission lines at ∼0.
Spectropolarimetric analysis: XMM-Newton , NuSTAR , and IXPE We added the IXPE data ( I , Q , and U spectra of the three detectors) to the XMM-Newton + NuSTAR best-fitting presented abo v e, with all the spectral parameters linked to the other instruments, allowing only an inter-calibration constant (found to be of the order of ∼0. 80) for each detector to vary. We then add separate polconst multiplicative models to account for the polarization of each additive component of the global model. The polarization degree and angle are set to 0 for the BORUS component producing the emission lines, since they are expected to be intrinsically not polarized, as well as for the CLOUDY component, which does not contribute at all in the IXPE energy band (see the previous section). On the other hand, the primary Comptonized continuum and the reflection component associated with the other BORUS table have and free to vary.
The best fit gives χ 2 /dof = 1433/1264, with no appreciable variations in the spectral parameters with respect to the one without IXPE , being indeed dominated by the much higher sensitivity, spectral resolution and broad band co v erage of XMM-Ne wton and NuSTAR. Considering the complex X-ray spectrum of NGC 4151 with multiple components Downloaded from https://academic. oup. com/mnras/article/523/3/4468/7191850 by University College London user on 28 June 2023 in absorption and emission, the cross-calibration uncertainties among the se veral dif ferent instruments used, the v ariability of the source and the much longer exposure time of IXPE with respect to XMM-Newton and NuSTAR , we find this fit, if not ideal, acceptable given the goal of the paper. We note that the fit applied only to the IXPE data gives a significantly better fit ( χ 2 /dof = 672/612), confirming that the model is a good representation of the data in the 2-8 keV band, where all the polarimetric information is present.
Moreo v er, the polarimetric parameter themselves are only sensitive to the IXPE data, and are not directly affected by the spectroscopic fit on the other data sets. In this configuration, we get loose constraints for the polarimetric parameters, i. e. < 5 per cent and unconstrained angle for the primary continuum, and > 38 per cent and = 96 • ± 16 • for the reflection component. We thus linked the angles of the two components, either to be equal or to be differ by 90 •. In both cases, we still obtain that the polarization is dominated by the reflection component, and the polarization degree of the primary emission is an upper limit. We therefore fixed the polarization properties of the reflection component to physically moti v ated v alues, 15, 20, 30 per cent (as found, for example, for the reflection-dominated Circinus galaxy;  Ursini et al.
The seed photons for Comptonization in this case may come from the outer cold disc or be internal synchrotron photons  (Veledina, Vurm & Poutanen 2011 ). It is very difficult to distinguish between these two scenarios, because in both cases photons undergo many scatterings before they reach the IXPE energy band. We followed the approach of  Ursini et al. ( 2022 ) , performing various simulations with the two geometries, using the general relativistic Monte Carlo radiative transfer code MONK  (Zhang, Dov čiak & Bursa 2019 ). We have also cross-checked these results with those obtained with an iterative radiation transport solver  (Poutanen & Svensson 1996 ; Veledina & Poutanen 2022 ). In our simulations, we assumed a BH mass of 4. 57 × 10 7 M  (Bentz et al. 2006  ), a spin a = 0. 998, and an Eddington ratio L Bol / L Edd = 1 per cent.
B , V , R, and I broad-band polarimetry of NGC 4151 was obtained contemporaneously to the IXPE observation using the Perkins Telescope Observatory. Those new measurements are in all respects similar to the archi v al data and will be presented in a future publication compiling recent optical polarimetric data from changing-look AGNs. Downloaded from https://academic. oup. com/mnras/article/523/3/4468/7191850 by University College London user on 28 June 2023.
Microclimate buffering is the dampening of macro-climatic uctuations due to local conditions (e. g. topography and vegetation cover), such that these uctuations still exist at the microclimatic scale, but have lower intensity and a more limited effect on organisms  34. Microclimate heterogeneity can buffer the impacts of macroclimate change if individuals are able to move between neighbouring sites characterized by microclimatic differences  35, 36, 37, 38. Detailed information on microclimate and snow cover is thus pivotal to understand the consequences of climate change on organisms living in mountain ecosystems and the potential buffering effect favoured by microclimatic heterogeneity, still this requires global scale, high-resolution analyses that were so far lacking. Here, we use a unique dataset of near-subsurface soil temperatures collected in 175 stations from polar, equatorial and alpine glacier forelands to produce a high-resolution, global reconstruction of monthly average soil temperatures during the snow-free season in high mountains and proglacial environments (Fig.
83 vs 0. 52-0. 68) and mean absolute error (wMAE = 1. 45°C vs 1. 98 to 2. 52°C; 1:1 line). Extended Data Fig. 2 : Comparison between the performance of our model and alternative approaches to the estimation of local temperature. Due to reduced temporal extent of the Chelsa dataset, the observations from 2020 were excluded from all the comparisons, and the recorded soil temperature was regressed against a) the predictions obtained from the leave-one-out approach, b) the downscaled macroclimate and the estimates obtained with the widely used climate products c) TerraClimate and d) Chelsa. The dashed black lines mark the perfect t (1:1 line), while the red lines represent the ts from the weighted linear regression; shaded red areas represent the 95% con dence interval of the average estimates.
g. texture, composition) hampers the modelling of soil water content at regional and global scale  40. As far as we know, the dataset we assembled is the most complete collection of soil temperature recordings in high-mountain areas. Despite we tried to cover areas at different latitudes and with diverse climatic conditions, we acknowledge that data used to develop our model are not truly global, thus may not be fully representative of the conditions in other mountain ranges with very different climatological characteristics. For instance, in some regions glaciers currently are not retreating, owing to the combination of locally stationary temperatures, increasing precipitation and / or heavy debris cover (e. g. Karakoram 52 ); in others they are considerably retreating, but following local dynamics (e.
g. the southern Himalayas, conditioned by the Indian monsoon). A further extension of the dataset and its implementation with other initiatives (e. g. 41) might improve the global representation of the overall highelevation soil temperature dynamics, still our analysis provide much better information on the microclimate of high mountain environments than any currently available product (Extended Data Fig. 2 ), and enables an unprecedented view of the ne-scale heterogeneity of climate change. During the last decades we boosted our understanding of the drivers of microclimate  24. A huge amount of information is already available at the macro-and meso-scales for long time-series (e. g. monthly soil moisture, shortwave radiation or temperatures from TerraClimate), and other can be retrieved using remotely-sensed data.
Declarations (1500 m) was set up for Morteratsch and Dammagletscher forelands in order to include part of the glacier tongue within the ROI. Macroclimate information for the sampled months and years was retrieved from the medium-resolution climate product TerraClimate 42 (150 arcsec). Monthly mean temperatures were calculated as the midpoint between TerraClimate monthly minimum and maximum To account for the adiabatic decrease of temperature we applied the standard and xed environmental lapse rate of -0. 0065 °C/m  55. The high-resolution digital elevation data needed for downscaling macroclimate were retrieved from the ASTER GDEM v3 (resolution: 1 arcsec, i. e. approx. 30 m at the equator; latitudinal extent: 82° N to 83° S) via the NASA Earthdata interface (https://doi. org/10. 5067/ASTER/ASTGTM.
5, 1, 1. 5, 2, 2. 5 and 3°C), and calculated the corresponding number of days with no snow on the ground for each of these thresholds. These estimates were compared with the ones obtained, for the same months and years, using the NDSI-derived coarse-grained MODIS Terra 500m daily fractional snowcover  57  (available at https://developers. google. com/earthengine/datasets/catalog/MODIS_006_MOD10A1). Fractional snowcover was converted to snow occurrence using the conservative threshold of 40%  58  , and the monthly frequency of snow-free days was estimated. Whatever the threshold, we found an excellent match between the number of days under the snow estimated from sensor and from the MODIS data (Pearson's r ranging from 0. 918 to 0. 942); to calculate the proportion of snow-free days, the highest correlation was obtained for a threshold value of 1.
5°C (r: 0. 942). Therefore the proportion of snow-free days was calculated on the basis of this threshold. The presence of nearby glaciers represents a further driver of local temperature, for instance because of katabatic winds. To account for this cooling effect, we measured the distance between each sampling station and the glacier front, under the assumption of decreasing cooling effects with distance  59. For each glacier, the most recent outline was retrieved from Marta et al. 60  , checked against glacier position between 2015 and 2019 using USGS Landsat 8 imagery (available at https://developers. google. com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_TOA) and eventually updated to include other nearby glaciers or more recent outlines. Outlines were transformed to polygons, rasterized, and distance maps at 30 m resolution were calculated using the function gridDistance from the raster R package  61.
Three categorical variables were calculated to account for the effect of i) tree cover, ii) permafrost occurrence and iii) differences in the depth of logger burying. Tree shading can reduce soil temperature, thus we used the 30-m resolution Hansen Global Forest Change v1. 8 62 (available at https://developers. google. com/earthengine/datasets/catalog/UMD_hansen_global_forest_change_2020_v1_8) to assess if a logger was in a tree-covered area or not. Data on permafrost extent were retrieved from Gruber  63  ; given the coarse-grained resolution of this dataset (30 arcsec), we calculated the spatially weighted mean over each ROI after disaggregating at the 30 m resolution. To convert mean permafrost extent over the ROI to occurrence of continuous / extensive discontinuous permafrost we used the conservative 0.
g. air temperature, solar radiation, cloud cover) during seasons with snow. To remove the effects of this decoupling, while reconstructing soilT during the snow-free season, we i) classi ed sfd in 10 intervals between 0 and 100%; ii) run iteratively the model retaining only records with sfd > 10%, 20%,. , and iii) plotted tted values vs residuals to evaluate the residual structure at each step. With sfd > 30% the effect of decoupling was almost completely erased. Consequently, all the months with sfd ≤ 30% were discarded, and the resulting dataset included 1,258 monthly average temperatures from 26 glacier forelands. Before running the nal model, dg was square-root transformed to linearize the relationship with the response variable and all the continuous predictors were scaled to zero mean and unit variance.
Linear mixed models were run using the lme4  64  and lmerTest 65 R packages. Model residuals approximated a normal distribution (Shapiro-Wilk test; W = 0. 996), and the variance in ation factor was low (GVIFadj max = 1. 81, including interaction terms), indicating that multicollinearity did not pose major issues. Model performances were evaluated using Nakagawa and Schielzeth 66 R 2 , as implemented in MuMIn R package  67. The amount of variance explained by single model terms was quanti ed by calculating the semi-partial R 2 using the partR2 R package  68  , with 1,000 bootstrap replicates. partR2 iteratively removes predictors, and compares the change in variance of the linear predictor to the variance explained by the full model; higher the difference between the two values, higher the amount of variance explained uniquely by a given predictor.