dataesr/scientific-paragraphs-categorization huggingface dataset:
Normalized power versus irradiance plot for EURAC dataset with applied Filter #3; blue, raw data; green, threshold filter; red, final filter [Colour figure can be viewed at wileyonlinelibrary. com] 4. 4. 2 | EURAC system Looking at Figure 6, one can see that similar observations can be made to a certain extent for the EURAC plant. Again, higher irradiance thresholds tend to yield lower PLR. Additionally, low irradiance thresholds (e. g. , Filter #10 and #2) give, in certain circumstances, accurate PLR results. As we deal here with real performance data, outlier accountability seems to play an important role as well. Filter #7, #4, #10, #3, #1, and #2 yield PLR very close to the mean reference. Four of these six filters are in relatively narrow intervals, excluding power-irradiance pairs which are not representing the nearly linear relationship between both variables.
PV systems have been modeled, two plants with 5 years of repeating weather data and two PV systems with 4 years of satellite data (location: Rennes in the west of France) followed by a fifth colder year. The benchmarking results of the digital plants serve as a reference since the real PLR values are known for these systems, which is not the case for the real datasets. Two definitions of PLR are indicated, the loss in P mpp at STC and the constant absolute loss in energy from year to year. Both approaches are presented in the results. From a practical point of view, the parameter of interest is the degradation in energy.
Figure8shows the results for the digital plants with simulated degradation. Here, the indicated degradation values (orange and green lines) vary distinctively from one another. Among other things, this has to do with the irradiance distribution for this particular simulated site. In FigureA1, the yearly POA irradiation per irradiance interval is shown for the dataset measured at the test site in Rennes, France. It is visible that more than 60% of the irradiation energy is provided from instantaneous irradiance values below 600 W/m 2. Thereby, a large amount of PV energy is produced under irradiance conditions far.
In Figure10A, a minimum of three calculated PLR was set for a statistical method to be included in the benchmark evaluating seven different PV systems. These datasets are considered as being of high quality without serious data quality issues apart from the minor ones reported in Section 4. 2. It can be seen that the majority of applied filter-metric-method approaches have results in the first and second isoband with a relative average difference from the mean PLR of up to 17% and a corresponding standard error (se) of 1% to 6%. According to the results evaluating seven different PV systems, YbY1, STL4, STL8, LS-LR3, and HW1 provide the most accurate results (all in the first isoband). YbY1 has been applied to three PV systems, and the remaining three methods have been applied to all seven PV systems considered in the study.
High irradiance thresholds tend to lower the overall PLR, especially when modules/systems are experiencing degradation in low light performance due to a decreasing shunt resistance. Additionally, filtering approaches including statistical filter, which remove a large share of power-irradiance data pair outliers, together with low to medium irradiance thresholds seem to provide the most reliable datasets for further treatment and consequently result with the highest accuracy. T A B L EA 1 Description of datasets used in the PLR benchmarking exercise Irradiance data from HelioClim for Rennes, France. c A, tropical; B, arid, C, temperate; D, continental; E, polar climates; f, no dry season; m, monsoon, s, dry summer; w, dry winter; S, steppe; W, desert; a, hot summer; b, warm summer; c, cold summer; d, very cold summer; h, hot; k, cold.
For the US DOE systems, the mean PLR corresponds to the average PLR of the methodologies STL1 and YoY2. That is because both system datasets were subject to data shifts at the beginning of operation (see Section 4. 2. 1). This shift should have been detected and excluded for the PLR calculation. This was done only with statistical method STL1 and YoY2. For NREL3, the mean PLR corresponds to the average PLR of the methodologies F I G U R E A 2 Calculated PLR of EURAC system, FOSS system, RSE CdTe & pc-Si systems, Pfaffstaetten A, B & C systems [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E A 1 Irradiance distribution for digital power plant (location: Rennes, France). Plane-of-array irradiance distribution per 100-W/m 2 interval [Colour figure can be viewed at wileyonlinelibrary.
com] SCSF1, YoY2, and STL1. YoY2 and STL1 used the provided modeled clear-sky irradiance data series as an input, and the methodology SCSF1 is not based on any irradiance data series. The remaining approaches used the faulty measured irradiance dataset as input the corresponding results deviate thereby substantially from the "true" PLR. F I G U R E A 3 Calculated PLR of NREL systems; mean PLR * for NREL3 corresponds to average PLR of SCSF1, STL1 and YoY2 [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E A 4 Calculated PLR of US DOE systems; mean PLR* for US DOE wca0c5m and z0aygry corresponds to average PLR of STL1 and YoY2 [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E A 5 Data quality issues US DOE systems: Energy (red)/power (blue) over time for US DOE datasets: (A) US DOE c10hov6-initial inverter clipping and missing data; (B) US DOE luemkoynegative power values and initial inverter clipping; (C) US DOE lwcb907-negative power values and inverter clipping; (D) US DOE t3pg1sv-inverter clipping; (E) US DOE wca0c5m-data shift after 1 year and inverter clipping; and (F) US DOE z0aygry-data shift after 1 year and initial inverter clipping [Colour figure can be viewed at wileyonlinelibrary.
com] F I G U R E A 6 Data quality issues NREL systems: Power over plane-of-array irradiance for NREL datasets: (A) measured irradiance-NREL1numerous outlier detected; (B) measured irradiance-NREL2-numerous outlier detected; (C) modeled irradiance-NREL3-extreme outlier and inverter clipping detected; and (D) modeled irradiance-NREL4-extreme outlier and inverter clipping detected [Colour figure can be viewed at wileyonlinelibrary. com].
ACKNOWLEDGEMENTS The authors kindly acknowledge the data provided by  Karl Berger (AIT)  for the Pfaffstaetten system and  Giosué Maugeri  ( RSE ) for the  RSE datasets. The research has received funding from the  European Union 's  Horizon 2020 program  under  GA. No. 721452-H2020-MSCA-ITN-2016. This material is based upon work supported by the  U. S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE)  under the  Solar Energy Technologies Office  Award Number  34366. Sandia National Laboratories  is a multimission laboratory managed and operated by  National Technology & Engineering Solutions of Sandia, LLC , a wholly owned subsidiary of  Honeywell International Inc. , for the  U. S. Department of Energy's National Nuclear Security Administration  under contract  DE-NA0003525.
F I G U R E 4 4 Normalized power versus irradiance plot for EURAC dataset with applied Filter #3; blue, raw data; green, threshold filter; red, final filter [Colour figure can be viewed at wileyonlinelibrary. com] 4. 4. 2 | EURAC system Looking at Figure 6, one can see that similar observations can be made to a certain extent for the EURAC plant. Again, higher irradiance thresholds tend to yield lower PLR. Additionally, low irradiance thresholds (e. g. , Filter #10 and #2) give, in certain circumstances, accurate PLR results. As we deal here with real performance data, outlier accountability seems to play an important role as well. Filter #7, #4, #10, #3, #1, and #2 yield PLR very close to the mean reference. Four of these six filters are in relatively narrow intervals, excluding power-irradiance pairs which are not representing the nearly linear relationship between both variables.
Four PV systems have been modeled, two plants with 5 years of repeating weather data and two PV systems with 4 years of satellite data (location: Rennes in the west of France) followed by a fifth colder year. The benchmarking results of the digital plants serve as a reference since the real PLR values are known for these systems, which is not the case for the real datasets. Two definitions of PLR are indicated, the loss in P mpp at STC and the constant absolute loss in energy from year to year. Both approaches are presented in the results. From a practical point of view, the parameter of interest is the degradation in energy.
b High irradiance thresholds tend to lower the overall PLR, especially when modules/systems are experiencing degradation in low light performance due to a decreasing shunt resistance. Additionally, filtering approaches including statistical filter, which remove a large share of power-irradiance data pair outliers, together with low to medium irradiance thresholds seem to provide the most reliable datasets for further treatment and consequently result with the highest accuracy. T A B L EA 1 Description of datasets used in the PLR benchmarking exercise Irradiance data from HelioClim for Rennes, France. c A, tropical; B, arid, C, temperate; D, continental; E, polar climates; f, no dry season; m, monsoon, s, dry summer; w, dry winter; S, steppe; W, desert; a, hot summer; b, warm summer; c, cold summer; d, very cold summer; h, hot; k, cold.
Four more statistical methods have been tested using the same metric and filter. It is interesting to note that two of these use STL as a statistical method, the only difference is that one func- tion was taken from Python (STL8) while the other one was taken from R (STL7). The latter exhibits the highest se in Figure 10B. A cross-comparison of STL7 and STL8 shows that both approaches yield very similar results, except for system NREL1. Here, STL7 overestimates the mean PLR substantially, whereas STL8 results in a PLR lower than the mean value, but with a lower deviation. This overestimation explains the highest standard error across all tested methodologies. Overall, a "perfect" combination of filter, metric, and PLR calculation method probably does not exist, since there are complex interactions of filters, metrics, and methods with the characteristics of the datasets.
The state-of-the-art methods to process datasets in polarimetry, apart from the calibration, are "step-by-step" methods. First, the data are transformed with the required translations and rotations to be easier to process and the bad pixels are interpolated. Such interpolations introduce correlations that are not taken into account in the following processing. Second, the interpolated data are reduced to the Stokes parameters which are directly related to the different polarization states. These reductions can be done with the double difference or the double ratio (Tinbergen 2005;  Avenhaus et al. 2014). If the double ratio takes into account the possibility of multiplicative instrumental effects, none of these methods deal with the noise statistics. This results in some limitations in sensitivity in the case of low Signal-tonoise Ratio (SNR).
Polarimetric data During a sequence of observations with SPHERE/IRDIS in DPI mode, the HWP is rotated several times along a given cycle of angles α ∈ {0 • , 45 • , 22. 5 • , 67. 5 • }. Besides, the two polarizers of the analyzer set of SPHERE/IRDIS are imaged on two disjoint parts of the same detector. The resulting dataset consists in K frames composed of two, left and right, sub-images, each with a different position of the HWP. Typical values of K can go from 32 to more than 512 depending of the observed target. After pre-processing of the raw images to compensate for the bias and the uneven sensitivity of the detector and to extract the two subimages, the available data are modeled by: d j,k,m ≈ I det j,k,m (12) where I det j,k,m is given in Eq. (  9 ) for k ∈ 1, K the index of the acquisition, j ∈ {1, 2} indicating the left/right polarizer of the analyzer, and m ∈ 1, M the pixel index in the corresponding left/right sub-image.
(  13 ). The VMLM-B algorithm is a quasi-Newton method with limited memory requirements and able to account for separable bound constraints. VMLM-B is applicable to large size problems and only require to provide the bounds and a numerical function to compute the objective function and its gradient. In the following, we compare the performances of RHAPSODIE for the polarimetric parameters X = (I, Q, U ) and X = (I u , Q, U ) on simulated synthetic datasets. We refer to linear RHAPSODIE when we reconstruct X = (I, Q, U ) and to non-linear RHAPSODIE when we reconstruct X = (I u , Q, U ). Both parametrizations allow us to access the parameters of interest I p and θ using Eq. (  6 ). The best choice of polarimetric parameters is then used to process astrophysical datasets.
2019 ) that these neutral density filters do not affect the PSF shape and thus its calibration. This instrumental calibration leads to the estimation of the PSF modeled through the operators A k. The PSF model does not include the spiders to remain rotation invariant (see Fig. 3 ). In the case of synthetic observations, the assumed 2D PSF is extracted from the real data RY Lup (e. g. similar to the top left image on Fig. 3 ). For astrophysical observations, we fit a 2D PSF model on pre-reduced PSF data for each observed target (e. g. Gaussian, Airy, and Moffat fits on Fig. 3 ). During the coronographic observing sequence, the star point spread function peak is hidden by the coronagraphic mask and its position was determined using a special calibration (STAR-CENTER) where four faint replicas of the star image are created by giving a bi-dimensional sinusoidal profile to the deformable mirror (see  Beuzit et al.
After computing the double difference, IRDAP uses a Mueller matrix model of the instrument carefully calibrated using real on sky data to correct for the polarized intensity I p (created upstream of the HWP) and crosstalk of the telescope and instrument to compute the model-corrected Q and U images. Using IRDAP, we estimate the instrumental transmission parameters (ν j,k,l ) j∈{1, 2},k∈ 1,K , ∈ 1,L from the Mueller matrix to calibrate the instrumental polarization in our datasets. IRDAP also determines the corresponding uncertainty by measuring the stellar polarization for each HWP cycle individually and by computing the standard error of the mean over the measurements. Finally, IRDAP creates an additional set of Q and U images by subtracting the measured stellar polarization from the model-corrected Q and U images.
Application on synthetic data The performance of the linear and non-linear methods are first evaluated on synthetic datasets, without (resp. with) the deconvolution displayed on Fig. 4 , Fig. 5  and Fig. 6  (resp. Fig. 7 , Fig. 8  and Fig. 9 ). These datasets are composed of unpolarized residual stellar flux, mixed with unpolarized and polarized disk flux. We produce several synthetic datasets following steps given in Appendix A, for different ratios of the polarization of the disk, called τ disk and defined in the equation (A. 1). The results of the RHAPSODIE methods are compared to the results obtained with the classical Double Difference method  (Tinbergen 2005; Avenhaus et al. 2014). The Double Difference is applied on recentered and rotated datasets with the bad pixels interpolated. For the comparison with deconvolution, the results of the Double Difference are deconvolved after the reduction.
Astrophysical data RHAPSODIE was applied to several IRDIS datasets dedicated to protoplanetary, transition and debris disks, to test the efficiency of our method and to compare it to the stateof-the-art method. For each reconstruction, we present the     4 : Information of the datasets used for the target reconstruction: the name of the target, the date of the observation, the filter used, the exposition time ∆ t for one acquisition, the number of cycles of HWP, the number of frame missing or removed K miss , the total exposition time of the observation ∆ tot t considering K miss , the seeing, and observation conditions τ 0. The total number of frames in each dataset is given by K = ∆ tot t / ∆ t + K miss. Target λ no-dec I u λ no-dec Q+U µ no-dec I u µ no-dec Q+U λ dec I u λ dec Q+U µ dec I u µ dec Q+U PSF TW Hydrae 10 4.
The instrumental polarization in this dataset introduces a polarization rotation and an attenuation (loss of polarization signal) of the intensity with varying time during the observations which can be monitored easily because the disk is face-on. If uncorrected, the combination of data from multiple polarimetric cycles will result in very poorly constrained polarimetric intensity measurements. When we correct the instrumental polarization (see Fig. 10c ) by using the IR-DAP method, these effects are compensated and the disk reconstructed is more accurate  (Fig. 10c (iii). As a result, the contamination of the U φ signal from cross-talk is decreased and becomes negligible as seen in Fig. 10c (iii). The comparison with  van Boekel et al. (2017); de Boer et al. (2020)  shows that our method is less impacted by the bad pixels and improves the disk SNR in the area where the signal is low.
In all cases, the deconvolution sharpens the polarized intensity image, helps to recover the polarization signal lost by averaging over close-by polarization signals with opposite sign, and does not introduce artificial structures that are not present in the original source (see Fig. 13  (RY Lup)). As mentioned before the hyperparameters could be further tuned to adjust the smoothing of the deconvolved images according to the required noise/angular resolution trade-off. For several of these disks (IM Lup, MY Lup on Fig. 11 ), the outer edge of the disk and thus the lower disk surface have been detected in  Avenhaus et al. (2018)  and are confirmed by our method. The deconvolution of these datasets allows to further highlight these fainter features, to enhance or reveal the midplane gaps in the case of T Cha (on Fig.
13 ) which was not identified by  Pohl et al. (2017). The reason is that without deconvolution, the PSF smears light from the disk upper and lower sides into the midplane gap. Except for Au Mic, the debris disk presented on known East-West brightness asymmetry of the disk, which was detected in total intensity  (Kalas et al. 2015; Lagrange et al. 2016). Thanks to the deep dataset and good reconstruction, we also detect the backward-scattering far side of the disk to the west of the star, just south of the brighter near side of the disk. This feature is further highlighted by the deconvolution. The reader may remark that the RHAPSODIE deblurred reconstructions of RXJ 1615 (on Fig. 13 ) and HD 106906 (on Fig. 15 ) seems noisier than without deconvolution. This is due to hyperparameters purposely chosen so as to achieve the best angular resolution with the side effect of slight noise amplification.
Thus the noise is not negligible in the reconstruction. As shown in Fig. 2 , increasing the regularization weight to smooth the background would lead to a loss of the thin structures. We also analyzed two datasets (HD 61005 and Au Mic) taken under very bad conditions. For these two datasets, our method also proves to be efficient in using incomplete polarization cycles to recover the disk polarized signal, and to deconvolve this signal despite strong artifacts produced in addition by the rotating spiders (i. e. , unmasked by the Lyot stop) when observing in field stabilized. The difference in the spider position during the polarimetric cycle results in an artificial polarimetric signal when using standard data reduction techniques. It is worth noticing that the strength of our method to deal with these artifacts comes from its ability to use weighted maps to account for them.
tre, jointly operated by OSUG/IPAG (Grenoble), PYTH-EAS/LAM/CeSAM (Marseille), OCA/Lagrange (Nice), Observatoire de Paris/LESIA (Paris), and Observatoire de Lyon (OSUL/CRAL). This work is supported by the French National Programms PNP and the Action Spécifique Haute Résolution Angulaire (ASHRA) of CNRS/INSU co-funded by CNES. The authors are grateful to the LABEX Lyon Institute of Origins (ANR-10-LABX-0066) of the Université de Lyon for its financial support within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) of the French government operated by the National Research Agency (ANR). This paper is based on observations made with ESO Telescopes at the La Paranal Observatory under programme ID: 598. C-0359, 095. C-0273, 0102. C-0916, 096. C-0523, 097. C-0523, 096. C-0248.
(1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a highresolution scale on HPC infrastructures for both preclinical and clinical research.
Furthermore, modern optical microscopes have increased the difficulty of exhaustive analysis by allowing the digitization of histological whole slide images-WSI-at a sub-micrometric in-plane resolution. Resulting images reveal small biological objects-such as individual cells-but consequently their sizes have dramatically increased from megapixels (Mp) to terapixels (Tp). In this context, the automated quantification of histological images is a prerequisite to speed up data processing and to reduce human workload. Such automation increasingly relies on supervised machine learning Part I: material and methods Histological datasets. Ethical statement. All experimental protocols were approved by CETEA (Comité d' éthique en expérimentation animale) n°44 and the Ministry of higher education, research and innovation (MESRI).
The datasets used were histological images of macaque central nervous system sections. Four animals were euthanized by injection of a lethal dose of pentobarbital (Dolethal, Vetoquinol, France). Their brains (n = 3) and brainstem (n = 1) were extracted according to European ethics rules. All animal studies were conducted according to French regulations (EU Directive 2010/63-French Act Rural Code R 214-87 to 131). The animal facility is authorized by veterinarian inspectors (authorization n° B 92-032-02) and complies with Standards for Humane Care and Use of Laboratory Animals of the Office of Laboratory Animal Welfare (OLAW-n°#A5826-01). The study is reported in accordance with ARRIVE guidelines. Dataset description. Three brains of 9, 6. 5 and 5 years old healthy male macaques were cut into 8 series of 40-µm-thick coronal Sects.
30, 31. For the first brain, one series of 133 sections was produced and stained with DAB-Ni Neuronal Nuclei (NeuN) using a standardized protocol ensuring reproducible staining among sections. Only one section was produced and stained for the two other macaque brains. All the sections were digitized using a Whole Slide Imaging (WSI) bright field virtual scanner (Axio Scan. Z1, Zeiss), with a × 20 magnification factor (in-plane image resolution of 0. 22 × 0. 22 µm). Each digitized slice weighed approximately 40 Gigapixels (Gp) and exhibited various levels of neuronal density (Fig. 1a ). Three ground-truth datasets were created by extracting a hundred sample images (512 × 512 pixels) in a predetermined number of anatomical regions of the brain spread through the middle sections for each macaque.