dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation Reichardt's law GNN model   7  have not been scaled in order to explicitly show that the model can operate on flows that have widely different scales, thanks to the scaling used for the model inputs. In accordance with the instantaneous results, a law-of-the-wall model based on Reichardt's law greatly underestimates the mean WSS in the BFS simulation after x=h s ¼ 2 and overestimates the mean WSS in the N65b simulation upstream of the blade and near the leading edge. In the APG simulation, the mean WSS is overestimated in the region of positive curvature (x=R max < 1) and slightly underestimated in the region of negative curvature (x=R max > 1). In each case, the GNN WSS model improves the average WSS prediction and leads to predictions that are close to the reference value. This demonstrates that the model is able to leverage the instantaneous spatial information to improve the mean WSS prediction, even when generalizing to cases that are not seen in the training dataset.
A parametric study of the influence of the number of message-passing steps N on this ability has been performed. This parameter is related to the capacity of the GNN since it controls the size of the receptive field of the GNN WSS model (4). The results, provided in Appendix A, demonstrate that a number of message-passing steps N ≥ 3 is required to properly predict the WSS in the various flows included in our dataset. This is intuitively understood by the fact that the GNN WSS model needs to use information in a relatively large region to immerse the learning problem in the high dimensional space required to reduce the ambiguities associated with the WSS prediction and discriminate between various flow configurations. The value N ¼ 4 used in the present study is thus suitable. C τ R 2 R 2,Reichardt C τ R 2 R 2,.
A posteriori tests This section investigates the machine-learning wall-shear-stress model a posteriori, that is, by performing simulations implementing the GNN. All simulations presented in this section are performed using a compressible, unstructured and massively parallel flow solver (AVBP;  Schönfeld and Rudgyard, 1999) , coupled to a code dedicated to the inference of the GNN. The coupling approach is described in  Serhani et al. (2022) , but the computational cost of the model is also briefly discussed in Appendix B. At each time step, data are extracted from the fields of the simulation in the vicinity of walls to create near-wall graphs, sent to the GNN using the MPI. The inference is distributed to a number of GPU according to a specific data partitioning, independent of the CPU partitioning.
Backward-facing step The LES of the flow over a BFS using a GNN WSS model is addressed in this section. The configuration is similar to the simulation BFS described in Section 2. 1, which was not included in the training dataset. The expansion ratio ER ¼ L y þ h s À Á =L y of the step, where L y is the inlet channel height and h s the step height, is equal to 1. 2. The Reynolds number based on h s and the inflow bulk velocity is Re h ¼ 5100. The inlet is located 10 h s before the step. At the inlet, the mean streamwise velocity profile of the wall-resolved simulation of  Dupuy et al. (2022)  is imposed with the characteristic nonreflecting boundary condition of  Poinsot and Lele (1992). In addition, isotropic fluctuations are imposed following the turbulence kinetic energy profile of the wall-resolved simulation using the approach of  Kraichnan (1970).
This study shows the value of data-driven efforts toward wallshear-stress modeling in realistic solvers. This justifies further efforts to assemble even more comprehensive and varied databases, which are critical to produce more accurate and robust models. of the blade and near the tripping steps, which induce a small separation region. Averaged flow statistics are computed over 10 inlet flow characteristic times c=U inlet. An overview of the flow is given in Figure  C1. The case is characterized by a complex boundary-layer dynamics on the surface of the blade. While the boundary layer is laminar near the leading edge of the blade, transition to turbulence is induced by rectangular tripping steps which aim to reproduce the effect of the experimental sandpapers. There is a three-dimensional separation region in the corner between the suction side of the blade and the endwall.
Acknowledgments. This work was granted access to the computer resources of IDRIS under the allocations  A0102A06074  and  A0122A06074  made by  GENCI. We acknowledge  PRACE  for awarding us access to Joliot-Curie at  GENCI@CEA, France , through the project CornerLES. We are indebted to the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 814837 ) for producing and making openly available some of the data used in this work. We are grateful to  L. Agostini  and  P. Vincent  for providing us the data for the channel-flow simulation at Re τ ¼ 180, to  O. Lehmkuhl  and  A. Miró  for providing us the data for the 3DD simulation, to  F. C. Massa  and  A. Colombo  for providing us the data for the APG simulation, to  J. Bodet  and  F. Gao  for providing us the data for the stagnation pressure loss coefficient of the NACA 65-009 blade cascade in the experiments at 4°and 7°and in the numerical simulation of Gao et al.
ABSTRACT Background and Aims: Pulsed field ablation (PFA) is a new, non-thermal ablation modality for pulmonary vein isolation (PVI) in patients with atrial fibrillation (AF). The multi -center EU-PORIA (EUropean Real World Outcomes with Pulsed Field AblatiOn in Patients with Symptomatic AtRIAl Fibrillation) registry sought to determine the safety, efficacy, and learning curve characteristics for the pentaspline, multielectrode PFA catheter. Methods: All-comer AF patients from seven high-volume centers were consecutively enrolled. Procedural and follow-up data were collected. Learning curve effects were analyzed by operator ablation experience and primary ablation modality. Results: In total, 1,233 patients (61% male, mean age 66±11years, 60% paroxysmal AF) were treated by 42 operators. In 169 patients (14%), additional lesions outside the PVs were performed, most commonly at the posterior wall (n=127).
Centers All centers involved in this study are high-volume AF ablation centers in Europe (400-1400 AF ablations per year) that participated in the early market release for the Farapulse PFA technology in Europe. This ensures a high number of patients per center as well as adequate follow-up time. All cases, including the initial use of the PFA catheter for each operator, were included in this study. Individual data on AF ablation experience was also collected. Operators were divided into three groups with <2 years, 2-5 years and >5 years of AF ablation experience. Moreover, operators were classified as primarily single shot cryoballoon operators, primarily point-by-point ablation radiofrequency (RF) operators, or both.
Follow-up Follow-up for subjects was based on each institution's standard practice, generally outpatient visits including 24-120h Holter monitoring were performed at 3-, 6-, and 12months follow-up. Data on individual patient follow-up schedule was not recorded. Any episode of atrial tachycardia (AT) or AF lasting more than 30 seconds was considered an arrhythmia recurrence. Major adverse clinical events including tamponade, air embolism, stroke, transient ischemic attack, atrioesophageal fistula, and death were captured. The relevance of each adverse event to the device and/or procedure was determined by the participating center. Moreover, information on antiarrhythmic drugs (AADs) and oral anticoagulation status were collected.
The variables with a p<0. 05 in the univariable model were included in a multivariable binary logistic regression model. Parameters with perfect collinearity were excluded from the logistic regression analysis and are reported descriptively. Multivariate analysis modelled on the probability of a recurrence was performed using a Cox regression model. All p-values are two sided. A p-value of <0. 05 was considered significant. All statistical analyses were performed using SPSS version 28. 0 (IBM SPSS Statistics).
PFA for repeat ablation procedures In three centers, the pentaspline PFA catheter was used for repeat ablation of patients with recurrent tachyarrhythmias after an index thermal ablation. Data on 49 repeat procedures (4% of total cohort) were collected and analyzed. This included 19, 24 and 6 patients with paroxysmal, persistent, and long-standing persistent AF, respectively. In 8/49 (16%) patients, a PVI-only ablation strategy was performed. Extra-PV ablation was carried out in the majority of patients (41/49; 84%). Peri-procedural complications occurred in 2 (4%) patients, including TIA (n=1) and vascular access site complication (n=1), respectively.
Effects of Operator Experience Procedural and outcome data were analyzed according to the operator experience with AF ablation. No significant differences were found for procedural metrics or procedural complications (Table  4 ). When stratifying for the primary ablation modality previously used by each operator, prior cryoballoon users had shorter procedure times and fewer cardiac perforations (Table  5 ). Neither ablation center, operator's AF ablation experience nor the A C C E P T E D M A N U S C R I P T operator's previous primary ablation modality had an influence on the AF/AT free survival during follow-up for patients undergoing an index PFA procedure (Figure  3 ).
39  This real-world dataset provides additional evidence supporting the PVI ablation workflow with the pentaspline PFA catheter. Future studies systematically evaluating the lesion durability will be needed to directly compare across modalities. A C C E P T E D M A N U S C R I P T rate of 91%. In this clinical experience, the use of 3D mapping with the pentaspline catheter did not improve lesion durability. However, future full integration allowing for simulation of the electrical field within the acquired 3D map, may be beneficial.
Limitations EU-PORIA was designed to evaluate the real-world use and adoption of a novel PFA technology for an all-comer AF patient population. No specified inclusion or exclusion criteria were considered. This was a retrospective, observational study, where AF ablation and patient management were all performed according to standard-of-care at each center. In particular, follow-up and arrhythmia recurrence monitoring were performed based on each center's standard practice and was not recorded for each patient. No data monitoring was applied. Several operators utilized 3D mapping for lesion visualization, but at this time, the current PFA system is not fully integrated into a 3D mapping system. Comparison to prospective studies with rigorous monitoring strategies in regard to effectiveness should therefore be carried out with caution since monitoring strategies may differ substantially.
The camera thermoelectrically cools the sensor down to -45 °C (adjustable), and the generated heat is extracted by a closed-loop circulation of water at 16 °C. The QHY411 camera is connected to the control computer through a USB3 interface, and commands are sent through a python wrapper on top of the QHY SDK 2. The data were acquired in exposure mode, not in video mode, which we did not manage to work with from Linux. The tunable electronic bias and amplifier gain of the camera were kept fixed to their respective values of 100 and 60, which delivered a gain on the order of 1. 2 e -ADU -1 , effectively mapping the sensor dynamics 3 over the 16 bits. The test camera is mounted on a two-axis linear stage in an optical test bench, where it can intercept either a wavelength tunable f /10 monochromatic light beam or a flat-field illumination beam.
Monochromatic beam The monochromatic light beam is obtained by imaging the horizontal output slit of an integrating sphere (IS1) on the vertical 1 See e. g. ,  Pace (2021)  for a description of CMOS image sensors shuttering. 2 https://github. com/ebertin/qhpyccd 3 The single pixel full well capacity is expected to be 80 ke - according to the manufacturer website: https://www. qhyccd. com/ scientific-camera-qhy411-qhy461/ entrance slit of a Czerny-Turner monochromator (SP-DK240) using a relay of two 90°off-axis parabolic mirrors (M1 and M2). The output slit of the monochromator is then imaged on an aperture stop in front of the detector plane by a second relay (M3 and M4). The focal length of the monochromator is 240 mm and its turret hosts three 68 mm × 68 mm gratings, accepting input beams with a recommended minimal f number of 3.
Dataset Throughout this study, we performed five different kinds of measurements: dark current and readout noise measurements for which the sensor is protected from any illumination by an aluminum front cover; stability measurements for which the camera is kept fixed in front of the flat-field beam at four different (stabilized) temperatures to study temperature dependence; photon transfer curve and linearity measurements for which the illumination level is varied by changing either the source brightness or the exposure time; uniformity measurements scanning the sensor sensitive surface; and quantum-efficiency measurements for which we interleaved measurements of the brightness of the monochromatic beam with the camera and the reference photodiode. This last measurement is repeated scanning every nanometer in wavelength.
Outside of the sensor calibration bench, we also gathered two supplemental datasets using a simpler illumination source to enable the experimental study of correlations between neighboring pixels as a signature for inter-pixel capacitance or brighterfatter effects (dataset A) and mapping of the individual pixel gain using a large number of images (datasets B and C). In this configuration, the camera was capped with a 3D-printed hollow cone holding a simple 450 nm LED behind a diffusive screen. Switching to an external illumination source allowed us to free the bench for the calibration of another camera while taking these time-consuming measurements. The illumination stability with this alternate source is, however, one order of magnitude worse than what is achieved on the bench. Therefore, we do not rely on these supplemental datasets for our baseline determination of the gain.
Stability of the photometric response The stability of the camera response in time and temperature is obtained from the stability dataset described in Sect. 3. 1. A small trend with sensor temperature is detectable in the camera response. Figure  10  displays the measured response at four different stabilized temperatures between -5 and 10 °C. The measurements are well described by a simple linear trend, with a relative slope of 0. 027% °C-1. The gain estimate derived from the same dataset is in contrast extremely stable and compatible with no gain change at the 2 × 10 -4 level over the 15 °C spanned by the test data. This result suggests that the observed temperature trend is dominated by a change in the sensor quantum efficiency. The test, conceived as a check of the gain stability, was performed at a single wavelength.
Photon transfer curve in 6 × 6 superpixels For each M p and V p in datasets 1-3, we compute the mean variance V of all the N µ superpixels sharing the same M p = µ value as follows: V(µ) = 1 N µ {p,M p =µ} V p -∆ 2 (µ -b) 2 , ( 5 ) where the ∆ term corrects the statistics for the effect of small illumination fluctuations during the measurement (see Appendix A. 2 for details). This procedure allows us to recover the full resolution of the PTC shape, while a simple averaging over the focal plane would smear the features due to nonuniformity in the illumination level. The result is presented in Fig. 11. All three datasets are presented on the same plot and present a rather consistent picture. The most striking feature is a periodic differential nonlinearity (DNL), which is very consistent in all datasets, likely corresponding to a fixed bit width error.
As striking as it appears on the PTC, the DNL likely has few practical consequences. However, it complicates the analysis of the PTC as statistics obtained with slightly different illumination are no longer directly comparable, and a simple model for the shape does not describe the data. When independently fitting a simple linear relation to the three datasets, we recover an average gain value of 1. 2740 ± 0. 0016 e -ADU -1 , where the quoted uncertainty is the RMS among the three datasets. The middle panel in Fig. 11  shows the residuals to this simple model for the three datasets. Fitting instead for the approximate shape of the PTC in presence of BF proposed in  Astier et al. (2019, Eq. (   16 )) gives a significantly different average value of 1. 2636 ± 0. 0023 e -ADU -1 and a 4-σ difference with a moderate preference for a nonzero curvature of the binned PTC: a 00 = -5 ± 2 × 10 -7.
The recovered values for the curvature are not very consistent between the three datasets, and they change when the top of the scale is cut differently (e. g. , at 15 kADU), as can be expected from the poor quality of the fit. A rough modeling of the DNL can be attempted to improve the fit consistency and enable comparison between statistics at different focal plane locations more easily, alleviating the need for matched illumination levels. In Appendix A. 3, we present the tools to numerically compute the relevant statistics with arbitrary digital boundaries. We model the DNL as a sine function with a period of 512 bits; that is to say, we assume that the digital scale admits boundaries between codes D n = n + A DNL cos 2π 512 (nϕ DNL ). Not knowing the details of the architecture of the ADC array, little more than that can be A119, page 9 of 18 Top: the three colors correspond to three independent datasets obtained with different illuminations, exposure times, and numbers of images (see Table  3 ).
The black solid line presents the linear relation from Eq. (A. 9) using the best-fit gain value. Middle: residuals to the linear model. The dashed black lines denote 1% variations of the integral gain. Bottom: residuals to the full model, including the effect of a 512-bit periodical DNL with 0. 65 ADU of amplitude, and provision for curvature of the PTC due to residual influence of a brighter-fatter effect in the binned superpixels. done, but this ad hoc model may be a good enough description of the reality for the sake of the present study. Fitting for A DNL and ϕ DNL along with the readout gain and noise delivers a fairly comprehensive description of datasets 1 and 3, although distinctive features remain visible in the residuals as shown in the bottom panel of Fig. 11. The best-fit values are A DNL = 0.
651 ± 0. 006ADU and ϕ DNL = 44. 4 ± 0. 2. The corresponding reduced chi-squared for the two datasets are 1. 41 and 1. 22. Interestingly, dataset 2, which is obtained from a much higher number of consecutive frames (see Table  3 ) is at odds with the other two datasets with a much less satisfactory fit quality of χ 2 /d. o. f. = 2. 39. The reasons behind this disagreement are for the moment not understood. However, the problem is unlikely to affect the gain determination as all three datasets now deliver a very consistent gain value G = 1. 273 ± 0. 0008e -ADU -1 , where again the quoted uncertainty is the RMS of the three fits. The improved modeling, however, does not entirely solve the inconsistency between the datasets when allowing for curvature. The mean and RMS of the recovered curvature now settles at a 00 = -1.
Correcting the recovered gain value from the expected influence of the IPC measured on hot pixels (see Sect. 4. 1), brings the gain estimate on the full statistics even closer to the value recovered in binned statistics. Both classical nonlinearity and the brighter-fatter effect could cause the observed curvature of the PTC. Behavior similar to the brighter-fatter effect has previously been reported in CMOS  (Greffe et al. 2022 ) and CMOS NIR detectors  (Plazas et al. 2018) V[ADU] 2 1 2 3 -500 0 500 V -(µ-b) G -σ 2 G 2 [ADU] 2 0 10000 20000 30000 40000 50000 µ [ADU] -500 0 500 V -E(V)[ADU] 2 Fig. 12. Photon transfer curve for the IMX411 sensor obtained from statistics build from individual physical pixels over the full scale. Top: the three colors correspond to three independent datasets obtained with different illumination levels and exposure times (see Table  3 ).
The dataset specifically taken to study correlations (A in Table  3 ) provides moderate evidence for the presence of positive correlations between the nearest neighbors in a line (C 01 ) and in a column (C 10 ), in excess of what would be expected from measured IPC alone. The resulting curves are shown in Fig. 13. The shape does not readily comply with expectations for BF-induced correlations, and at this stage we do not have a compelling model for the measured shape. The most obvious caveat in the dataset is the need to subtract for the contribution of quite large illumination fluctuations (∆ ∼ 10 -3 ). This is done using the RMS of the mean of all images in the sequence to build an estimate of the illumination fluctuation (see Appendix A. 2). We note that the proposed technique would allow investigation of the correlations at larger distances C i j ; however, adequate hardware to perform the required comparatively heavy computations in real time would be required.
The flat-field illumination source was built with the idea of providing a direct linearity measurement, but at this stage the linearity of the illumination has not yet been calibrated. The gathered dataset still allows limited investigation of the linearity by interlacing variations of the illumination (through variation of the LED intensity), with variations of the exposure time, and solving for illumination level at a given LED intensity. Doing so gives the linearity check presented in Fig. 14 , which does not detect significant integral nonlinearities in the chain up to 55 000 ADU (70 ke -). We therefore conclude that the curvature observed in the PTC is not attributable to classical nonlinearity and is at least qualitatively compatible with expectations of a small brighter-fatter effect.
Uniformity of the readout gain Determination of the individual pixel gains using this technique requires a very large number of images, but is in principle achievable. We made an attempt to test the concept with the high-statistics datasets denoted (B) and (C) in Table  3. The result is presented in the left panel of Fig. 15. The main visible feature in the recovered gain map is the imprint of the differential nonlinearity due to the smooth change in illumination level in the flat-field images. Simply correcting the measured variance using the ad hoc model from Fig. 11  gives the per-pixel meanover-variance map presented in the right-panel. Residual features in the corrected map show that the applied correction is not fully adequate across the focal plane, but it at least qualitatively describes the phenomenon.
Uniformity of the camera response The camera response (i. e. , the product of the quantum efficiency and the readout gain) has been mapped at the 16 wavelengths delivered by the flat-field light source. Given that the spatial extent of the IMX411 sensor is larger than the area over which the flat-field beam can be considered uniform, we resorted to iteratively solving for the intensity of the illumination pattern and the camera response map. The 25 images in a single wavelength dataset are thus interpreted as the product of the illumination pattern B(x) at this wavelength with the camera response map M(x) for 25 known central positions x i of the camera. Image i therefore follows the following equation: I i (x) = B(x + x i ) × M(x). (6) The illumination pattern over the full area is developed on a cardinal bicubic B-spline basis with 10 × 10 nodes.