dataesr/scientific-paragraphs-categorization huggingface dataset:
PIAF Very recently the nowcasting department of Météo-France has developped a new nowcasting system PIAF (for "Prévision Immédiate Agrégée Fusionnée" in French,  Moisselin et al. , 2019)  which is a data fusion product between radar extrapolation and numerical prediction (the rainfall forecast by AROME-NWC). For the extrapolation of radar quantitative precipitation estimation, radar data are proccessed as follows: rainy cells are identified by windows surrounding areas of connected pixels above a given threshold, the displacement of each cell is determined using the previous image (highest correlation), a gridded motion field is computed from the movement vectors of the cells with different threshold values and applied to the cells to extrapolate them in the future. PIAF is based on a sequential aggregation of these two predictors (radar extrapolation and numerical prediction) and the results of blending is a linear compound of both.
For lead times between 2h and 3h, the performance of AROME-NWC is higher or equivalent to that of PIAF in general. This evaluation points out the strengths and weaknesses of the nowcasting system types during extreme events, that should be considered before selecting the best method or combination for future studies or operational purposes. Depending on the accuracy of the initial radar rainfall estimation and the spatial distribution and intensity of the precipitation, radar extrapolation can provide valuable nowcasting information in the very short-term forecast. However due to dynamical evolution of precipitation, especially in convective situations, there is a rapid decrease in accuracy with forecasting lead time. The above results show that blending the extrapolation of radar data with numerical prediction forecasts allows to improve the nowcasting accuracy and to extend the lead time beyond the characteristic one-hour lead time of extrapolation methods.
The FSS ranges from 0 to 1, close to 1 for a good forecast and close to 0 in the opposite case. In general, as the spatial tolerance increases, the FSS increases, and as the intensity thresholds increases, the FSS decreases. Acknowledgements. This research was performed in the framework of the HyMeX programme (MISTRALS grants) and is a contribution to the French project PICS (ANR -17 -CE03 -0011). The authors would like to express their gratitude for access to useful data and discussions with the members of the nowcasting team at Météo-France, and especially Céline Jauffret, Philippe Cau and Nicolas Merlet. Table  3. Best anticipation of the rise in flow, peak value and peak timing simulated with ISBA-TOP driven by the AROME-NWC rainfall forecast for the Fresquel river at Pezens on 14-15 October 2018.
Design of experiments DoE was used to obtain the correlation models for calculating process performance indicators as a function of extraction conditions. The models were second-degree polynomials, which allow an accurate representation of non-linear systems. S1 ). The polynomial equations describing each process performance (Y x, Eq. 1) were expressed with both process conditions (X 1 and X 2 ) and intercept (b 0 ), linear (b i ), quadratic (b ii ) and interaction (b ij ) coefficients: Y x = b 0 + 2 i=1 b i X i + 2 i=1 b ii X 2 i + 2 i<j=1 b ij X i X j (1) MODDE 12. 0. 1 software (Umetrics, Sartorius Stedim Biotech, Sweden) was used to fit the experimental data to the polynomial equations with adapted transformation if necessary. The regression coefficients were obtained by multivariable regression.
An ANOVA was used to evaluate the statistical significance of these coefficients (p-value < 0. 05). Final correlation models were obtained by suppressing the non-significant terms (p-value > 0. 05). In the case where the coefficient of linear effect was non-significant, but at least one of the coefficients of cubic effect or interaction was significant, both coefficients were kept in the model. The coefficient of determination (R 2 ), the relative standard deviation (RSD), the reproducibility and the lack of fit were evaluated using the MODDE software to assess the robustness of each model (Y 1 and Y 2 ).
Pareto's domain determination The second step of the MCDM optimization is to obtain of all possible Pareto-efficient solutions using the domination concept. To do this, a routine developed internally upon Matlab software (MathWorks, Natick, Massachusetts, USA) using a genetic-evolutionary algorithm was applied  (Muniglia et al. , 2004). This optimization tool is meant to identify the Pareto front for both simple and complex problems, i. e. with millions of possible solutions, in which case a genetic algorithm (or any stochastic algorithm) is needed for optimization. This algorithm was based on the principles of biological evolutionary processes and used them as rules for optimization problems. The diploid version of the genetic-evolutionary algorithm was used since it was more efficient and faster than the haploid version  (Fonteix et al.
Albumin content in extract RA was quantified in the extracts by size exclusion high performance liquid chromatography (SE-HPLC) according to the method developed by  Defaix et al. (2019). A Biosep-SEC-s2000 (300 × 7. 8 mm, 5 m) from Phenomenex (Torrance, CA, USA) was used, connected to a Shimadzu model LC20 system (Shimadzu Corporation, Kyoto, Japan). An isocratic elution was used to separate the samples at 0. 6 mL min -1 with a water/acetonitrile/trifluoroacetic acid (TFA): 54. 9/45/0. 1 (v/v) solvent. A 5 L of filtered sample were injected onto the column and the elution was performed during 30 min. The UV signal was recorded at 214 nm. Data were collected and treated using LabSolutions software (Shimadzu Corporation, Kyoto, Japan). RA content in the extract is the proportion of this protein fraction in relation to the total soluble proteins in the extract (i.
Statistical analysis All experiments were performed at least in triplicate (n = 3) and data were expressed as means with standard deviation (± SD). The statistical analysis of the models was carried out with the DoE software MODDE 12. 0. 1 software (Umetrics, Sartorius Stedim Biotech, Sweden) as described in Section 2. 1. The other analyses were performed using the freeware R (3. 4. 1. ) and the Analysis ToolPak of Excel (Microsoft Office, Redmond, Washington, USA).
This installation is located in North-Western France (oceanic climate, quite windy, around 1 MWh/m 2 /year of solar energy). It is important to note that in our case, the "real system" is in fact simulated with physical models and experimental data. This approach allows to test different hyperparameters and to compare the trained strategy to the basic one. Concerning data, we rely on three years of data (2019-2021). It is important to note that the same dataset is used both during the sizing and the policy training: the studied microgrid should therefore offer little room for maneuver. Consumption data are extracted from the IHOGA software  [41] , electricity prices from EPEX spot, and meteorological data from Ref. [42] , using a tool recalculating (and publishing freely) weather conditions worldwide using real data.
Electrolyser The electrolyser is characterized by its efficiency η el : η el = ṁel HHV P el ( 2 ) where ṁel is the mass flow rate of produced hydrogen and P el the power consumption. In our previous work, the electrolyser was modeled with a static efficiency. In this work, we chose to use a dynamic value of efficiency relying on experimental data. The different values are presented in Figure  4 : the efficiency rises quickly from 0 to 5% of relative power usage to reach 76% of efficiency. Then, the efficiency decreases slowly to 65% when the electrolyser works at full power. We took data from Ref. [44]  and interpolated it with the dedicated function of the Scipy Python library.
Sizing of the System The sizing of the system, realized with the Skopt Python library, has two steps: a simplex optimization that is followed by a Bayesian optimization. Bayesian optimization  [45]  uses a prior belief about the objective function f obj and updates the prior with samples drawn by an acquisition function with direct sampling in towards an area where an improvement over the current best observation is likely. The skopt package is used by default for the acquisition function an expected improvement. -EI(x) = -E[ f obj (x) -f obj (x best )] (4) Other acquisition functions are possible, such as the lower confidence bound and the probability of improvement. Bayesian optimization also uses an surrogate model, often a Gaussian process, to approximate the objective function. The Bayesian optimization quickly converges near the optimum but, depending on its hyper-parameters, we found that the final steps can be hard.
• Energy produced: given the meteorological state (irradiation, wind speed, temperature, ambient pressure), it returns the renewable energy production; • H 2 production: the H 2 production for the total electricity consumed (electrolyser and compressor); • Electrolyser consumption: for a total electricity used to produce H 2 , this digital twin returns the consumption of the electrolyser branch. It is separated from the previous digital twin as it is needed to know which quantity to send in the electrolyser branch and in the compressor branch separately. We used the Pygam Python library to fit the digital twins. This library is an implementation of GAMs in Python. GAMs are a type of extension of linear models: they try to build a model with more classical functions than polynomial ones. Pygam proposes splines, exponential terms, and a bi-variate product.
• Energy available: the quantity of energy produced minus the consumption, i. e. , the energy to be distributed between H 2 production and electricity selling; • Electricity price: the average electricity price over the sequence; • H 2 mass stored: the quantity of H 2 available at the beginning of the session. Last, the chosen clustering method is a classical least square k-means from the SciPy python library. Once the different clusters are obtained, a difficulty persists: each cluster represents a sequence, i. e. , several consecutive values of available energy and prices while their centers are only scalar values. Thus, we cannot directly use cluster centers to train the policy. Instead, for each cluster, we use the closest sequence to each center: the distance is obviously calculated with the least square, as it is the norm for clustering.
Note that the zero point in Fig. 2  is zero in order to demonstrate the relative constancy of the ratio. We performed differential photometry using two different pipelines. The first pipeline was an in-house Southampton University pipeline and the second is the HiPERCAM pipeline. 2 Both pipelines use very similar methods, centroiding on the objects of interest and then performing aperture photometry using a background annulus surrounding the object. The results were very similar. Here we present the results using the HiPERCAM pipeline, in large part because this pipeline is well established and well documented and the code is already publicly available thus allowing future researchers to check our results more easily.
Overall we conclude that the lags listed in Table  3  are a fair reflection of the true lags. In Table  3  we also give the effective wavelength of each band for these particular filters as used on HiPERCAM on the GTC and taking account of atmospheric transmission. We derived these values, which are the 'Bandpass Average Wavelengths', using the pysynphot package 6  which is specifically designed for HiPERCAM. We note that the values derived here are actually very similar to the central wavelengths listed on the HiPERCAM filter web site 7  , i. e. 3526, 4732, 6199, 7711 and 9156 Å respectively for us, gs, rs, is and zs for which slightly simpler assumptions are made. Here we use the effective, or Bandpass Average Wavelengths, given in Table  3.
CONCLUSIONS We present three nights (∼ 15hr) of u, g, r, z and two nights (∼ 12hr) of u, B, g imaging of the low mass AGN NGC 4395 on the robotic LT. These data provide lightcurves which show clear correlated variations on few hour timescales but with interband lags too short to be measured accurately given the ∼ 200s sampling. A follow-up 6 hr observation with HiPERCAM on the 10. 4m GTC provided superb simultaneous 5-band (us, gs, rs, is and zs) imaging, with 15s sampling in us and 3s in all other bands, the fastest yet reported for an AGN. The observation is dominated by two smooth, almost sinusoidal, variations, similar to those seen in the LT observations. Very clear correlated variability is seen in all HiPER-CAM bands and wavelength dependent lags between bands are measured relative to the us-band with much higher precision than in any other AGN.
We observe that when the source is brighter it is bluer and also the lags, at least at short wavelengths, are longer. These results are in fairly good agreement with the expectation of disc reprocessing with a higher illuminating luminosity, around a factor 2 or 3. Variations in the X-ray luminosity by even larger factors on comparable timescales are commonly seen in X-ray observations  (Vaughan et al. 2005)  and could easily explain such variations. We hope to repeat and test this analysis with higher quality HiPERCAM data. We have combined the HiPERCAM lags with lags measured previously between the X-ray, UV and g band (M c  Hardy et al. 2016)  and have fitted the combined lag spectra with truncated accretion disc models using lags derived from the KYNxilrev code and also from our own inhouse models.
ACKNOWLEDGEMENTS We dedicate this paper to the memory of our friend and colleague  Tom Marsh  who very sadly died whilst this paper was being refereed. As well as making excellent contributions in many other areas of astronomy, Tom was a critical member of the team that developed the HiPERCAM instrument, on which this paper is based. He will be greatly missed. We thank the referee,  Patrica Arévalo , for an extremely helpful and constructive response which greatly improved the quality of the paper. This paper is based on observations made with the  Gran Telescopio Canarias (GTC)  and with the  Liverpool Telescope (LT). Both the GTC and the LT are installed at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias , on the island of  La Palma. The LT is operated by  Liverpool John Moores University  with financial support from the  UK Science and Technology Facilities Council.
We thank  Michal Dovciak  for assistance in getting the KYNxilrev software to run properly. We also thank  Iossif Papadakis ,  Ari Laor  and  Hagai Netzer  for useful discussions over a number of disc, BLR and black hole mass issues. The design and construction of HiPERCAM was funded by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) under ERC-2013-ADG Grant Agreement No. 340040  (HiPERCAM). VSD and HiPERCAM operations are supported by  STFC  grant  ST/V000853/1. IMcH and FMV thank  STFC  for support under grant  ST/J001600/1  and EB thanks  STFC  for support under grant  ST/S000623/1. TRM thanked  STFC  for support under grant  ST/T000406/1. MB and MV thank  STFC  for studentships under numbers  ST/S505705/1  and  ST/W507428/1  respectively.
EK acknowledges financial support from the  Centre National d'Etudes Spatiales (CNES). JHK. acknowledges financial support from the  State Research Agency (AEI-MCINN)  of the  Spanish Ministry of Science and Innovation  under the grant "The structure and evolution of galaxies and their central regions" with reference  PID2019-105602GB-I00/10. 13039/501100011033 , from the  ACIISI ,  Consejería de Economía ,  Conocimiento y Empleo del Gobierno de Canarias  and the  European Regional Development Fund (ERDF)  under grant with reference  PROID2021010044 , and from  IAC  project  P/300724 , financed by the  Ministry of Science and Innovation, through the State Budget  and by the  Canary Islands Department of Economy , Knowledge and Employment, through the  Regional Budget of the Autonomous Community.
g. ,  Ferrarese et al. 2012; Duc 2020) , the VLT Survey Telescope (VST; e. g. ,  Venhola et al. 2017; Iodice et al. 2021) , or the Subaru telescope (e. g. ,  Alabi et al. 2020; Jackson et al. 2021). Once the deep images are acquired, the LSB structures need to be identified, a task traditionally done with a visual inspection by one or a handful of contributors. This inspection can be performed on the images resulting from the basic data reduction, possibly adjusting the scaling and dynamics of the image intensity to enhance the LSB structures, or on images with an advanced processing, such as residual images obtained by subtracting the light profile model of the target (using softwares like GALFIT  (Peng et al. 2002)  for example). This technique discloses the inner most tidal debris as asymmetries that are not well fit by symmetric models of galactic light (e.
Although other tools such as the Zooniverse platform also enable citizen scientists to delineate morphological features, such as spiral arms or bars  (Masters et al. 2021) , our interface provides new functionalities. It offers a larger variety in the annotation shapes used to delineate features, and the flexibility to switch between bands, which are facilities that are well suited for LSB structures annotations. Given the complexity of the precise annotation task and focus, our tool is more adapted to expert users. The delineated tidal features are stored in a database, from which we can then determine their distribution of shapes, sizes and surface brightness. Delineation is the first step toward exploiting the full 2D profiles of the individual features, which gives additional constraints on the merger that created them.
Deep images The images and surveys we used in this paper are briefly described here. We used data from the 3. 6-m Canada-France-Hawaii Telescope (CFHT), with the wide-field optical imager, MegaCam. In particular, we utilised images from two CFHT Large Programs: the Canada-France Imaging Survey (CFIS 2  ) and the Mass Assembly of early-Type GaLAxies with their fine Structures survey (MATLAS 3  ). MegaCam offers a wide field of view of 1 • × 1 • with a resolution of 0. 18 arcsecond per pixel. Images were processed by the Elixir-LSB dedicated pipeline optimized for the detection of LSB structures  (Cuillandre, priv. comm. , Duc et al. 2015). The limiting surface brightness reaches 28. 3 mag arcsec -2 for CFIS (Cuillandre, priv. comm. ) and 28. 9 mag arcsec -2 for MATLAS in the r-band. While MATLAS targets nearby massive galaxies, especially of early-type  (Duc et al.
In addition, g -r colormaps were computed from the surface brightness maps for a subsample of 177 ETGs and 53 LTGs from MATLAS with available g-band images, and exploited to further characterize the LSB strutures, as described in Sect. 4. 6. Finally, we also considered for our annotations shallower true-color images from the Data Release 1 (DR1) of the Panoramic Survey Telescope and Rapid Response System (PanSTARRS) 6  (see Sect. 3), an imaging survey that covered the entire sky north of Dec = -30 deg in five bands (g, r, i, z and y)  (Chambers et al. 2016).
In comparison with the Zooniverse interface, our tool offers several drawing options, an easy navigation through the image, the possibility to display images from other surveys and a simple way to verify the annotations once they have been drawn. The annotation process, described in detail in Sect. 3. 2, relies on the visualization of astronomical images thanks to an online tool that uses the Aladin Lite 8  facility developed by the Centre de Données astronomiques de Strasbourg (CDS). It enables the visualization of sky regions, overlaid with object information from astronomical databases such as the Set of Identifications, Measurements and Bibliography for Astronomical Data (SIMBAD). Data from various surveys, such as PanSTARRS DR1, can be displayed and explored, but custom images can also be added, provided that the images are in the Hierarchical Progressive Surveys (HiPS, see  Fernique et al.
2015)  format. The HiPS format enables the representation of large astronomical datasets as the resolution increases when the users zoom on a part of the image. It relies on the hierarchical partitioning of a sphere into smaller and smaller Fig. 1. Annotation interface with its main facilities: drawing buttons (label 1), classification menu (label 2), examples of already drawn annotations (label 3) and summary table (label 4). The background is a CFIS asinh-scaled image. diamonds as the order of the partitioning increases, each diamond being identified by a unique index and order. Hence, we added our own HiPS images, whose origin is detailed in Sect. 2.
Annotation process The annotation process can be divided in several steps: the selection of the galaxy to annotate, the annotation itself, its classification and the verification or modification of the annotation. All users are previously identified and logged in to record their annotations. The annotation is made directly from the image navigation interface. The latter allows us to zoom in and out in the images, navigate through them and switch between different layers, that is images with different intensity scalings or from various surveys. Catalogs from for instance the SIMBAD database can be overlaid to display pieces of information about the objects in the image, including their velocity when known. Drawing tool buttons are used to make the annotations. The interface is displayed in Fig.
One of the goals of this paper is to precisely characterize each type of structures and retrospectively assess the relevance of the classification. The annotation of contaminants such as cirrus, ghost reflections, or high background is essential as they might pollute the stellar structures of interest and either make complicated their detection or skew their annotation. Examples of galaxies with tidal features from CFIS are presented in Fig. 3 , while Once the user has finished annotating a galaxy, the shape parameters, positions (in pixel coordinates and in right ascension and declination) and labels of each annotation are stored in a database hosted by a server. These can be used to redraw the annotations on the images or plot them as thumbnails, as it will be seen later. Examples of annotated galaxies are visible in Fig.
2014) through skeletonization. At the end of this step, the skeletons can have several branches but only the longest possible path was kept. To this purpose, the FilFinder package (Koch & Rosolowsky 2015) was used. An example of the previous steps is shown in Fig. 6. A limit of this method arises for round polygons, that is to say whose shape tends to be circular. In such cases, the topological skeleton tends to be shorter and shorter, up to the limit case of a circle where the skeleton is reduced to the center of the circle. This leads to underestimate the estimated lengths. To tackle this issue, we adapted our method for round polygons and approximate them by the maximum volume inscribed ellipse. The length is then taken as that of the major axis. We also modify the value of the area defined in Sect.
Surface brightness measurements The assessment of the surface brightness (SB) values of each type of tidal feature is important to make comparison with simulations but also determine whether they will be detectable in surveys to come, such as Euclid or the Vera Rubin Observatory. With our annotation tool, such measurements can be indirectly retrieved. Indeed, since the coordinates of the contours of the annotations are available, it is possible to retrieve them to create masks that may then be attached to the surface brightness maps. To do so, the RA, Dec coordinates of the contours are converted into pixel coordinates of the Flexible Image Transport System (FITS) image on which we want to create the mask, using Astropy World Coordinate System functions 12. The interior of that boundary in pixel coordinates is then filled to have the mask, using OpenCV-Python 13  functions.