dataesr/scientific-paragraphs-categorization huggingface dataset:
Further improvement in the performance of the CAD software might be achieved by modifying it to focus on the   [32, 33]. The present study has several limitations. First, this was a singlecenter retrospective study with inherent limitations regarding its generalizability. Second, patients with breast cancer undetected on ABUS were excluded from the statistical analyses; however, this was due to the limitations of the ABUS device itself. Third, the radiologists who reviewed ABUS in our study were not blinded to the fact that they were assessing BI-RADS category 6 lesions. Fourth, all the ABUS scans were performed after core needle biopsy. Thus, the interpretation of ABUS might have been influenced by the findings of HHUS performed during the core needle biopsy. Furthermore, the biopsy itself may affect the margin and shape of lesion and may influence the detection rate of CAD software.
Fifth, it is possible that interobserver variability in the lesion description may have influenced our results and may have decreased their reproducibility. Finally, we only included the largest mass in patients with multifocal or multicentric breast cancers in our analyses. In conclusion, the CAD software showed a promising detection rate of breast cancer. However, radiologists should judge whether a CAD software-marked lesion is a true-or false-positive lesion, considering its PPV and high FPR. Moreover, it would be helpful for radiologists to keep in mind the characteristics associated with falsenegative outcomes when reading ABUS with CAD. Fig. 3. A representative false-negative case (a 69-year-old woman with invasive ductal carcinoma detected by routine screening). The lesion was confirmed as invasive ductal carcinoma through ultrasound-guided core needle biopsy.
However, the CAD software did not point to the lesion appropriately. Subsequent left breast-conserving surgery was performed, and the final pathology report revealed a 1. 3cm invasive ductal carcinoma (pT1c). A. The coronal view of automated breast ultrasonography (ABUS) shows a 1. 2-cm irregular angular isoechoic mass (arrow) in the 9. 5 o'clock direction, 3. 2 cm from the nipple (asterisk) of the left breast. B. In the axial view of ABUS, the lesion was located in the posterior third of the left breast. B, C. In both axial and coronal views of ABUS, no definite architectural distortion was observed around the lesion (arrows). Asterisk represents the location of the nipple. A B C.
These results are extended by adding Lorentz-boosted top quarks with collimated and overlapping decay products. This is the first time that both the resolved and boosted techniques are used in a combined analysis. Techniques for the boosted and resolved reconstructions of t h and t are developed or improved, where t h (t ) represents a top quark decaying into a b quark and a W boson with a subsequent hadronic (leptonic) decay of the W boson. The differential cross sections are extracted by performing a combined fit to various categories that are defined by the reconstruction methods, the lepton flavors, and the three years of data taking. The combination of the different categories provides constraints on the systematic uncertainties and results in an improved precision with respect to previous measurements.
The simulations include multiple pp interactions per bunch crossing (pileup). The simulated distribution of the number of pileup interactions corresponds to the distribution in data for each year. Finally, the same reconstruction algorithms that are applied to the data are used for the simulated events. This POWHEG+PYTHIA simulation is taken as the default to obtain all corrections for the extraction of the differential cross sections. To estimate systematic uncertainties, several variations in the default simulation are used, including simulations with m t varied by ±1 GeV and variations of the UE parameters representing the uncertainties in the tunes. In other simulations, the parton shower matching scales h damp = (1. 58 +0. 66  -0. 59 )m t (CUETP8M2T4) and (1. 38 +0. 92 -0. 51 )m t (CP5) are varied within their uncertainties, which are obtained from the UE tunes.
The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of about 4 µs  [56]. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage  [57]. For this measurement events are selected using.
Another background contribution is single top quark production, which contributes about 2. 5% in the 2t and 1t1l categories. It is subtracted during the cross section fits using SM expectations obtained from the simulation, where the following dominant uncertainties are included: variations of µ R and µ F , jet energy scales, and b tagging efficiency. The variations of µ R and µ F are treated independently from the corresponding variations of the tt simulation, whereas the experimental uncertainties are fully correlated among all processes. and predictions (colored histograms) obtained for the parton-level measurement. Contributions of the various reconstruction categories are obtained from the POWHEG+PYTHIA tt simulation. The contribution of multijet, DY, and W boson background events in the 2t and 1t1l categories are extracted from the data (cf.
p T (t h ), and |∆y t/t | vs. m(tt ) are not well described by any of the tested predictions. The corresponding distributions are shown in Figs. 27 -32. The one-dimensional distributions of p T (t h ), m(tt ), and p T (tt) are consistent with the predictions at the level of two standard deviations, which calls for further investigations to improve the understanding of the kinematic relations between these variables. In addition, at the particle level the kinematic distributions in bins of jet multiplicity are not well described. The measurements at the parton level are compared to the POWHEG+PYTHIA (CP5), POWHEG+HERWIG, MG5 aMC@NLO+PYTHIA, and the MATRIX predictions. For the particlelevel measurements, the MATRIX prediction is replaced by POWHEG+PYTHIA (CUETP8M2T4). In general, when comparing the distributions in data and simulation, very similar trends are observed at both levels.
A small modulation is observed in the p T (tt) distribution, i. e. , the data are underestimated at about 300 GeV and overestimated at very high values. A discrepancy also occurs at high values of |y(tt)|, where all simulations predict a higher cross section than observed. Despite these small deviations, there is no significant dis- Figure  14 : The Z-scores from the corresponding χ 2 tests comparing the measured differential cross sections for the shown variables at the parton (upper) and particle (lower) levels to the predictions of POWHEG+PYTHIA (P8) for the CP5 and CUETP8M2T4 (T4) tunes, POWHEG+HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The Z-scores are truncated at an upper limit of six. The uncertainties in the measurements and the predictions are taken into account in the χ 2 calculation.
The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels. The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels.
Of special note in Fig. 39  is the softer p T ( ) spectrum in data, compared to the prediction, a quantity that is not directly affected by the modeling of jets. Also, the number of additional jets with p T > 30 GeV and |η| < 2. 4 is higher in data compared to most of the predictions. Only POWHEG+PYTHIA with the CUETP8M2T4 tune makes a correct prediction of the jet multiplicity. The distributions of H T and m evt are well described. Figure  40  shows that the measured p T (t h ) spectrum is only softer than the predictions if there is no or, to a smaller extent, one additional jet. For the higher multiplicities the spectrum is better described. Good agreement between data and predictions is observed for the differential cross sections as a function of m(tt ) and p T (tt) measured for different numbers of additional jets, as given in Figs.
Summary Measurements of differential and double-differential top quark pair (tt) production cross sections have been presented as a function of many kinematic properties of the top quarks and the tt system at the parton and particle levels, where the latter reduces extrapolations based on theoretical assumptions. In addition, the number of additional jets and kinematic variables in bins of jet multiplicities have been measured at the particle level. The data correspond to an integrated luminosity of 137 fb -1 recorded by the CMS experiment at the LHC in proton-proton collisions at √ s = 13 TeV. The tt cross sections are measured in the e/µ+jets channels with a single electron or muon and jets in the final state. For the first time the full spectra of differential cross sections are determined using a combination of resolved and boosted tt topologies, in which the tt decay products can be either identified as separated jets and isolated leptons or as collimated and overlapping jets, respectively.
A softer top quark p T spectrum is observed compared to most of the next-to-leading-order predictions. Deviations between the predictions and data are observed when the top quark p T is measured in bins of the tt invariant mass and p T. The POWHEG+HERWIG and MG5 aMC@NLO+PYTHIA simulations do not give a good description of the observed jet multiplicities and related observables such as the scalar p T sum of additional jets. The total tt production cross section is measured to be σ tt = 791 ± 25 pb. ( 13 ) When breaking down the uncertainty into different sources, we find σ tt = 791 ± 1 (stat) ± 21 (syst) ± 14 (lumi) pb, (14) where the last uncertainty comes from that in the integrated luminosity. The measured value of σ tt is in good agreement with the standard model expectation.
Figure15: Differential cross sections at the parton level as a function of p T (t h ), p T (t ), p T (t high ), p T (t low ), and S T. The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels.
Figure19: Differential cross sections at the parton level as a function of kinematic variables of the tt system. The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels.
Normalized double-differential cross section at the parton level as a function of p T (tt) vs. p T (t h ). The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels.
Normalized double-differential cross section at the parton level as a function of |∆y t/t | vs. m(tt ). The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels.
Normalized double-differential cross section at the parton level as a function of p T (tt) vs. p T (t h ). The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels. 137 fb -1 (13 TeV) 137 fb -1 (13 TeV) ] ] -10 -10 h ) [GeV -2 (t T 4 3 T 0 < p CMS t (t ) < 50 GeV Particle level +jets µ e/ Data Syst. Stat. POWHEG P8 (CP5) stat. ⊕ POWHEG H7 (CH3) -10 -10 h ) [GeV -2 (t T 4 3 CMS 50 < p T +jets Particle level µ e/ ) < 120 GeV t (t Data Syst.
Normalized double-differential cross section at the parton level as a function of |∆y t/t | vs. m(tt ). The data are shown as points with gray (yellow) bands indicating the statistical (statistical and systematic) uncertainties. The cross sections are compared to the predictions of POWHEG combined with PYTHIA (P8) or HERWIG (H7), the multiparton simulation MG5 aMC@NLO (MG)+PYTHIA FxFx, and the NNLO QCD calculations obtained with MATRIX. The ratios of the various predictions to the measured cross sections are shown in the lower panels. [GeV ) t | dm(t t t/ y ∆ d| norm σ σ 2 d 1 10 -norm σ 1 -10 -10 -10 -5 4 3 2 1 10 ] -1 [GeV ) t | dm(t t t/ y ∆ d| σ 2 d y ∆ 0 < | CMS y ∆ 0 < | CMS t/ t/ | < 0. 6 t Parton level +jets µ e/ | < 0. 6 t Particle level +jets µ e/ (13 TeV) stat. -1 137 fb Data ⊕ Syst.
SAXS and WAXS patterns were collected using a PILATUS 1M and a PILATUS 300K-W detector, respectively, with an acquisition time of 30 s. The detectors were positioned, respectively, at 5000 and 280 mm away from the sample for SAXS and WAXS analyses, respectively. The WAXS patterns were collected in the q-space region from 6 to 20 nm -1 , while the SAXS patterns were recorded in the q-space region from 0. 1 to 1 nm -1. A background pattern was acquired using an empty cell, and it was subtracted from the sample patterns before the reduction of the acquired data to 1D profiles using the python-based Bubble program  32  available at the beamline. The q-scale was calibrated using the known peak position from a standard silver behenate sample for SAXS and from a standard Al 2 O 3 powder for WAXS.
Moreover, a background spectrum was registered using an empty aluminum pan/lid system. All spectra were normalized to the intensity of the band at 1456 cm -1 (deformation vibration of CH 3 groups), which is not influenced by the degree of crystallinity and by the degree of conformational order of the polymer chains,  35  and subsequently, the band area was integrated using a fitting routine. The area of each diagnostic band was evaluated after baseline subtraction to determine the conformational changes during the meso-to-α transition. Both for X-rays and Raman, data analysis was conducted using the MATLAB software, and plots were generated using the Origin software.
The persistency of some of the bands (400, 973, 998, and 1300 cm -1 ) after melting is consistent with the preservation of a certain degree of conformational order in iPP after transition to the melting state. 43 esides tracing the thermal transitions, the simultaneous acquisition of SAXS, WAXS, and Raman data allows us to perform a quantitative description of the structural and conformational changes occurring during the transitions and enables us to elucidate the influence of the presence of the ethylene co-units on the meso-to-α phase transition, which we discuss in the next section. (a-f): Summary of the results simultaneously obtained during the in situ analysis of (left panels) an iPP homopolymer (HOMO) and (right panels) a random poly(ethylene propylene) copolymer (RACO). (a,b) WAXS intensity profiles as a function of temperature for HOMO and RACO, respectively; the thicker profiles denote the onset of the meso-to-α phase transition (c,d) Lorentz corrected SAXS I(q)q 2 profiles as a function of temperature for HOMO and RACO, respectively; (e,f) selected Raman spectra as a function of temperature for HOMO and RACO, respectively.
Criteria on stellar characteristics In Paper I, we have shown that any stellar activity or characteristics (multiplicity, binarity, variability, etc. ) may impact the calibration of SBCRs. We therefore implemented a set of criteria to select a correct sample of early-type stars. We restricted the calibration of our SBCR to V and K photometries, as it is the set of color which provides the lowest dispersion  (Kervella et al. 2004 ). We started the selection from the SIMBAD Astronomical Database 2. We first selected early-type stars, thus O, B, and A stars with V -K < 1 mag. We then considered only subgiants and dwarfs. From  Challouf et al. (2015) , we know that the projected rotational velocity affects the surface brightness of the star. They show that a rotational velocity lower than 85% of the critical velocity has an impact of at most σ F V = 0.
003 mag on the RMS of the SBCR. Alternatively, if one cannot access the critical velocity of the star, they demonstrate that considering stars with projected rotational velocity V rot sin i lower than 100 km/s also results in a dispersion of 0. 003 mag. Therefore, to reach a precision of ∼2% on the angular diameter estimate, we made the choice to consider only stars with V rot sin i lower than 75 km/s. We excluded all known binary stars in our sample. The SEDs of all the stars were checked with the VO Sed Analyzer (VOSA) software 3. We have shown in Paper I that a variability above 0. 1 mag could significantly affect the SBCR. Following this strategy, we searched for information about the variability of the stars in  Samus' et al. (2017). We then rejected variable stars with a variability above 0.
VEGA/CHARA interferometric measurements We observed the sample of 18 early-type stars from 23 February 2019 to 16 December 2020. The calibrated oifits files are available on OIdB 5. The data were processed using the standard VEGA pipeline  (Mourard et al. 2009 ) and the squared visibilities were calibrated using reference stars selected with the Search-Cal tool 6   (Bonneau et al. 2006). The list of the calibrators is included in Table  1. The uniform-disk angular diameter in the R band θ UD [R] is taken from the JMMC Stellar Diameters Catalogue version 2  (Bourges et al. 2017, JSDC2) , but we consider the uncertainties from the JDSC version 1  (Lafrasse et al. 2010) ,  Salsi et al. :  Progress on the calibration of surface brightness-color relations for early-and late-type stars Table  1.
Name Sp. Type Notes. From left to right: the name of the star, its spectral type taken from the SIMBAD Astronomical Database, the V magnitude  (Kharchenko & Roeser 2009) , the visual interstellar extinction  (Lallement et al. 2014; Capitanio et al. 2017) , the (V -K) 0 color corrected from the extinction, the reference used for the infrared K photometry, the limb-darkening coefficient, the limb-darkened angular diameter, and the reduced chi-squared. V A V (V -K) 0 K-ref u R θ LD ±σ θ LD χ 2 r [mag] [mag] [mag] [ (*) Variable stars.
In this work, and as a first step, we considered only dwarfs and subgiants stars. We also improved SBCRs for late-type stars presented in Paper I by converting the photometric data into the same system, and also by correcting the calculation of the extinction in the Gaia band. The lack of a large set of homogeneous angular diameter interferometric measurements remains a major problem in the implementation of SBCRs. With the future SPICA instrument  (Mourard et al. 2018 ) that will be installed at the CHARA array, we expect to derive the angular diameter of 800 stars all over the HR diagram, with a 1% precision level. CHARA/SPICA will also study rotation, multiplicity, wind, and environment by performing images in the visible domain. That should therefore reinforce our knowledge of SBCRs for early-type stars and in particular help to better understand the impact of stellar activity.
Acknowledgements. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The  CHARA Array  is supported by the  National Science Foundation  under Grant No. AST-1636624  and  AST-1715788. Institutional support has been provided from the  GSU College of Arts and Sciences  and the  GSU Office of the Vice President for Research and Economic Development. This work made use of the JMMC Measured stellar  Diameters Catalog  ( Duvert 2016 ). This research made use of the SIMBAD and VIZIER 8 databases at  CDS, Strasbourg (France)  and the electronic bibliography maintained by the  NASA /ADS system. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos.
In this framework, many researchers, such as Yamada-Sun  [40] , Hart-Smith  [41] , and Hashin  [42] , proposed different models to capture varying degrees of these damage mechanisms. Hashin's criterion uses more than one stress component to evaluate the different failure modes. This work has been focused on examining the change of the structural properties and damage generated on large wind turbine blade under different impact scenarios. The problem of the design of wind turbine blades has been also treated using ABAQUS software based on the finite element method taking into account aerodynamic, centrifugal, and inertial loads under the conditions of service of the blade. The effect of the impactor geometry, weight, and impact velocity on the dynamic response and damage kinetics inducted to the wind turbine blade in service where they are investigated to improve the long-term structural integrity of composite wind turbine blades.
• Gravity load is computed by ABAQUS FEA software using the layup parameter of the blade. • Fig. 8  shows the centrifugal force which is calculated using Eq. (  1 ). It depends on the weight and the rotational velocity of the blade. where ω is the rotational velocity and r is the distance of the blade element from the center of rotation. Equation (2) was used to calculate the centrifugal force on each element along the blade's length. Figure  9a  shows the blade under all loads where the green arrows are for centrifugal load and the purple ones are for (1) the aerodynamic load. Figure  9b  is an enlargement of a section of the blade. The general specifications used during the study are listed in Table  5. F c = 1 2 m 2 r (2) F c = 2 2 ∫ m s rdr For a good design of the wind turbine blade, and to understand its behavior under dynamic loading, we conducted impact tests.