dataesr/scientific-paragraphs-categorization huggingface dataset:
026 with an average V -band (DIMM) seeing of ∼ 1. 2. The raw data were reduced using version 1. 6. 4 of the standard MUSE pipeline  (Weilbacher et al. 2014) , automating bias subtraction, wavelength and flux calibration, as well as illumination-, flat-field, and differential atmospheric diffraction corrections. In addition to the sky subtraction automated by the pipeline, which uses a model created from a "blank sky" region of the FoV, we have performed an additional sky subtraction using a Principal Component Analysis (PCA) code by Bernd Husemann and the Close AGN Reference Survey 5  (CARS;  Husemann et al. 2016 Husemann et al. , 2017)). We have also corrected the datacube for Galactic foreground extinction using A V = 0. 082, estimated from the  Schlafly & Finkbeiner (2011)  recalibration of the  Schlegel et al.
While the nebular emission lines in the A2597 MUSE observation were bright enough to be fit at the native (seeinglimited) spatial resolution, the S/N of the stellar continuum was low enough to necessitate spatial binning. We have applied the Voronoi tesselation technique using a Python code kindly provided 6  by Michele Cappellari  (Cappellari & Copin 2003). The MUSE cube was tessellated to achieve a minimum S/N of 20 (per bin) in the line-free stellar continuum. The products from PYPARADISE then enabled creation of spatially resolved flux, velocity, and velocity dispersion maps of those emission lines most relevant to our study, namely Hα, [O I] λ6300 Å, [O III] λ5007 Å, and Hβ, along with Voronoi-binned velocity and FWHM maps for the galaxy's stellar component. We have also created Balmer decrement (Hα / Hβ ratio), color excess (E(B -V )), and optical extinction (A V ) maps by dividing the Hα and Hβ maps and scaling the result by following equation (  1 ) in  Tremblay et al.
ALMA and MUSE Line Ratio Maps We have also created Hα/CO(2-1) flux, velocity, and velocity dispersion ratio maps by dividing the ALMA "masked moment" maps from the corresponding MUSE maps. To accomplish this, we made small WCS shifts in the MUSE maps to match the ALMA CO(2-1) image with the PYRAF imshift and wcscopy tasks, assuming that the CO  and Hα photocentroids in the galaxy center as well as a bright, clearly detected (> ∼ 10σ) "blob" of emission to the northwest in both datasets should be aligned. The needed shifts were minor, and applying them also aligned enough morphologically matching features that we are confident that the alignment is "correct", at least to an uncertainty that is smaller than the PSF of either observation. We then confirmed that the ALMA synthesized beam closely matched the MUSE PSF at Hα (7101 Å and 6563 Å in the observed and rest-frames, respectively), making smoothing unnecessary.
We then resampled the ALMA data onto the MUSE maps' pixel grids in Python using reproject, an Astropy affiliated package 7. Depending on science application, the reprojected ALMA image was then either divided directly from the MUSE map, or divided after normalization or rescaling by some other factor (for example, to convert pixel units). The Python code used to create these maps, along with all MUSE and ALMA data products, is included in this paper's software repository  (Tremblay 2018 ).
Deep Chandra X-ray data Finally, we have combined all available Chandra X-ray Observatory data for A2597, spanning 626. 37 ksec in to- tal integration time across fourteen separate ACIS-S observations. The oldest three of these (see   Tremblay). This new dataset will be analyzed in detail by  Tremblay et al. (in prep). Here, we show only the deep image for the purposes of comparing it with the ALMA and MUSE data. To create this deep image, all fourteen ACIS-S observations were (re)-reduced, merged, and exposure corrected using CIAO version 4. 9  (Fruscione et al. 2006 ) with version 4. 7. 5. 1 of the Calibration Database. All exposures centered the cluster core (and therefore the BCG) on the nominal aimpoint of the back-illuminated S3 chip. We have applied a radially varying gradient filter to the final merged Chandra image using a Gaussian Gradient Magnitude (GGM) technique recently implemented to highlight surface brightness edges in Chandra data by  Sanders et al.
Compare these maps to those for the cold molecular nebula in Fig. 6. We compare the MUSE and ALMA data directly in subsection 3. 6. namical interaction with the radio source, or the buoyant Xray cavities it has inflated. Cospatial with these features,  Oonk et al. (2010)  discovered coherent velocity streams of warm molecular hydrogen (traced by the H 2 1-0 S(3) and Paα lines) similarly hugging the edges of the radio source, at roughly the same line of sight velocity and velocity width as those seen in the MUSE Hα maps. Again, like the molecular nebula, the northern and southern warm ionized filaments are more difficult to interpret. All show narrow velocity structure, and no evidence for freefall. This is the case for a large number of warm nebulae in CC BCGs  (Hatch et al. 2007; Edwards et al.
MUSE and ALMA Comparison Comparing the MUSE and ALMA data directly reveals strong evidence that the warm ionized and cold molecular nebulae are not only cospatial, they are comoving. In Fig. 16  we overplot the Hα+[N II] and CO  profiles extracted from matching 6 diameter apertures centered on the galaxy core in the MUSE and ALMA cubes, respectively. The panels at the sides of Fig. 16  show matching Hα and CO(2-1) morphology at the broadest wings of each line, consis-tent (but not uniquely) consistent with the hypothesis that the two lines stem from largely the same population of clouds. This cannot be true entirely, as the deblended Hα FWHM is 565 ± 25 km s -1 , a factor of ∼ 2 broader than the 252 ± 14 km s -1 FWHM of the CO(2-1) line. This velocity width mismatch is more readily apparent in Fig.
17 , where we plot CO(2-1) line-of-sight velocity and dispersion against the same quantities for Hα. We have smoothed the data points (i. e. , one point for each cospatial spaxel in the registered MUSE and ALMA cubes) with a Gaussian, and show shaded regions indicating ratios of 1:1-2:1 and 2:1-4:1. While the line velocity centroids lie largely along the 1:1 line, the line widths preferentially span the 2:1-4:1 range. The rightmost panels of Fig. 17  show the difference and ratio, respectively, between the MUSE Hα and ALMA CO(2-1) velocity and dispersion maps. In the velocity difference map, bluer colors mean that the CO(2-1) velocity centroid is slightly blueshifted relative to the Hα velocity centroid. The velocity difference map is largely smooth and below ±45 km s -1 , which shows that the Hα and CO(2-1) line velocity centroids track one another closely across the entire overlap region between the molecular and ionized nebulae.
These infalling clouds may join the population we observe in absorption, powering black hole activity once again, and keeping the fountain long-lived. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2012. 1. 00988. S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. We are grateful to the European ALMA Regional Centres, particularly those in Garching and Manchester, for their dedicated end-to-end support of data associated with this paper. We have also received immense support from the National Radio Astronomy Observatory, a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Two AE sensors (Micro 80 model) were used to also perform a linear localization of the acoustic sources (referred to as events). Once the acoustic emission activity within the composite material is recorded, a post processing (cluster analysis) of the data is required to identify the acoustic signature of the different damage mechanisms. This classification aims mainly to identify the cracking mechanism (AE signature) that governs the damage evolution of the composite material. A quasi-static load-unload tensile test was done starting from 30 % of its load to break and increasing load by 5 % (of the load to break) each time. This way we can identify acoustical signature of damage in the samples and re-use these criteria in fatigue tests to show damage evolution.
Modern drone technology provides an efficient means to monitor the response of alpine glaciers to climate warming. Here we present a new topographic dataset based on images collected during ten UAV surveys of the Gornergletscher glacial system (Switzerland) carried out approximately every two weeks throughout the summer of 2017. The final products, available at: https://doi. org/10. 5281/zenodo. 263045610. 5281/zenodo. 1487862 (Benoit et al, 2018) , consist of a series of 10 cm resolution ortho-images, digital elevation models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements. Used on its own, this dataset allows mapping the glacier and monitoring surface velocities over the summer at a very high spatial resolution. Coupled with a classification or feature detection algorithm, it enables extracting structures such as surface drainage networks, debris or snow cover.
The long history of glaciological studies in this area has shown that the mass balance of the Gornergletscher system was stable from the 1930s to the early 1980s, and significantly dropped since then  (Huss et al, 2012). This can be associated with the rise of its Equilibrium Line Altitude (ELA) due to an increase of the local average yearly temperature. The ELA stands nowadays around 3300m according to studies carried out at the neighboring Findelgletscher  (Sold et al, 2016). In this context, the current dataset aims at complementing existing studies about the Gornergletscher system by documenting the behavior of its ablation zone during an entire summer, at a time when this glacial system is thought to be out of equilibrium with a clear trend toward glacial retreat. In particular, this dataset focuses on the monitoring of the glacier surface at high spatial and temporal resolution.
UAV surveys The primary data are RGB images acquired by repeated UAV surveys over an area of 10 km 2. A fully autonomous fixed-wing UAV of type eBee from SenseFly, equipped with a 20 megapixels SenseFly S. O. D. A camera, has been used for image acquisition  (Vallet et al, 2011). The camera was static within the UAV body (no Gimbal) and therefore all pictures are quasinadir (i. e. images are taken with an angle +/-10° from the vertical line). For flight planning and UAV piloting, the eMotion3 software was used. Raw images were acquired with a ground resolution ranging from 7. 3 cm to 8. 8 cm for the glaciated parts of the area of interest. For the requirements of photogrammetric processing, flight plans have been designed to allow for an overlap between images ranging from 70% to 85% in the flight direction, and from 60% to 70% in the cross-flight direction.
Generation of co-registered orthomosaics and DEMs Pictures have been processed separately for each acquisition date with the photogrammetric software pix4DMapper version 3. 1 (  (Vallet et al, 2011) , Fig 2) using default parameters for nadir flights (see the processing reports for details about these parameters). The output resolution has been set to 10 cm/pixel in order to prescribe a constant resolution across all final products. During the photogrammetric processing, the raw pictures are first oriented by bundle adjustment, and then a DEM and an ortho-rectified image (orthomosaic) are generated for each day of interest. Since the only geolocation information included into the bundle adjustment procedure is the trajectory of the UAV derived from code-only GPS data, the initial georeferencing of the orthomosaics and DEMs is limited to a few meters.
4. 1 for details), the geolocation errors related to the absence of GCPs can impair comparisons with other datasets covering the same geographic area. To improve absolute georeferencing and to link our dataset with the Swiss national reference system, Table  2  provides the parameters of the affine transformation between the local reference frame of this dataset and the Swiss CH1903 -LV 03 reference system. This transformation has been estimated from 81 manual tie points identified in (1) the orthomosaic derived from the master bundle adjustment and (  2 ) a.
Surface displacement tracking: generation of Matching Maps Consecutive co-registered orthomosaics enable to quantify horizontal displacements at the surface of the glacier. In the present dataset, this information about ice surface displacements is provided by the Matching Maps (MMs, Fig  3 ). In practice, a MM is an image that pairs the positions of similar ice patches at times t and t+dt (dt being the time span between consecutive acquisitions) (Fig  2 ). The footprint of the MM is the overlap of the footprints of the orthomosaics at times t and t+dt. MMs inherit the spatial resolution of the original orthomosaics (i. e. 10 cm) and can therefore be used either to relate directly any pixel of a given orthomosaic to its counterpart in the following orthomosaic (and therefore easily navigate within the whole dataset), or to estimate surface flow velocities.
This operation is repeated for all possible patches in the reference orthomosaic. The MMs have been calculated using an open source utility called MatchingMapMaker developed as part of this project, and made available along with the dataset (See Sect. 5. 2 for code availability). The MatchingMapMaker tool has been designed to account for the specificities of UAV-based orthoimages, and in particular their very high resolution. To ensure the reliability of this utility, MMs have been benchmarked against horizontal displacement maps calculated using well-established image correlation algorithms, namely Imcorr  (Scherler et al. , 2008)  and CIAS  (Heid and Kääb, 2012). The results of this benchmark (see Sect 5. 2) show a very good agreement between horizontal displacement maps derived from MatchingMapMaker, Imcorr and CIAS.
Bundle adjustment and co-registration A first validation of this dataset can be done by checking the relative orientation of the cameras during the bundle adjustment, as well as the co-registration of orthomosaics and DEMs. Processing reports detailing the quality of the bundle adjustment for each session are available along with the dataset (see Sect. 5. 1). Table  2  displays three indices summarizing the quality of both bundle adjustment steps. First, the mean reprojection error (in pixels) quantifies the mismatch in the raw images between the observed and the modelled position of tie points used during the relative orientation step. The sub-pixel level of errors (Table  2 , column 2) ensures that the orientations of the camera are reliable. Next, the co-registration step is assessed by the mean Root-Mean-Square (RMS) error of manual tie point coordinates.
Structure and availability of the dataset All the data presented in this dataset are available in the following repository (Rep): https://zenodo. org/record/1487862, with the following DOI: https://doi. org/10. 5281/zenodo. 263045610. 5281/zenodo. 1487862 (Benoit et al, 2018). The results of the photogrammetric processing, i. e. the orthomosaics and the DEMs, are available in the compressed folder Rep\Photogrammetric_Products. zip. Within this folder, the products are grouped in sub-folders by acquisition date using the following standard: 2017_mm_dd with 'mm' the month and 'dd' the day of acquisition. Finally, these sub-folders contain the following files: -2017_mm_dd_2017_nn_ee__disp_mask: Contains the mask of reliable displacements after filtering: 1 if the location corresponds to a reliable displacement, 0 otherwise.
Code availability The photogrammetric processing has been carried out using the proprietary software Pix4D Mapper, commercially available at: https://pix4d. com/ (last access 2018/11/16). The Matching Maps have been computed using Matlab routines written by Mathieu Gravey. The related utilities are freely available on the following repository: https://github. com/GAIA-UNIL/MatchingMapMaker. The sub-repository Benchmarking_tests contains the results of benchmarking tests aiming at comparing the displacement maps computed by the MatchingMapMaker utility (i. e. MMs) with displacement maps computed by well-established glacier surface tracking algorithms, namely Imcorr (https://nsidc. org/data/velmap/imcorr. html) and CIAS (https://www. mn. uio. no/geo/english/research/projects/icemass/cias/). The sub-repository Similarity_score_tests contains the results of tests assessing the sensitivity of the MatchingMapMaker output to the similarity score used to define patch matches.
How, then, should we view this third type of simulation used in biology (after Cohen's essentially probabilistic simulation and Honda and Fisher's essentially geometric simulation) in plant morphogenesis? A botanist won over by logical positivism: the "theory of lifecycles" by A. Lindenmayer (1963 Lindenmayer ( -1965) )  During the 1963 university year, the Hungarian botanist Aristid Lindenmayer, who was at that time attached to Queens College of the New York University, spent some time in London with the British embryologist and philosopher of science Joseph Henry  Woodger (1894 Woodger ( -1981)). He had been invited there on a university fellowship funded by the National Science Foundation. Woodger was 69 years old when Lindenmayer visited him, and was by then retired. For many years his presentations had been essentially philosophical in nature.
Furthermore, Woodger considered that biological language was too often used for extra-scientific purposes, including emotional aims, for example. A formally purified artificial language that could be subjected to calculation would neutralize these detrimental subjective tendencies. This was the main reason he advanced in The Axiomatic Method in Biology 2 to justify his adhesion to the logical reconstruction schemes of logicians and philosophers such as Rudolf Carnap. In this work in particular, adopting the extensional and logicist approach of Russell and Whitehead's Principia Mathematica, Woodger started from the possibility of considering any organism or organ as being constituted of temporal and spatial "slices". From these "atoms" of the living being, he developed a general set of axioms for biology in which he demonstrated, in essence, that, contrary to the affirmations of the vitalists, for example, it was possible to formalize a one-to-many relation, or a relation of rising complexity and cellular differentiation, in an organism during ontogenesis.
On the one hand, many mathematized laws possess this dynamic property of temporal resemblance or "over time". On the other hand, we have seen that there are reasons to already give the name "simulation" to the construction of models that produce resemblances that are not essentially dynamic, but rather are visual. Resemblance by dynamic should be distinguished from resemblance of dynamics. The latter is more constraining and is not necessary for every simulation. But for that matter, can resemblance by dynamics itself be defined or constructed without relying on another type of resemblance, or at least a correspondence of terms (even if only for the purposes of defining an order of calculation), and which in that regard precedes it? We will see later that de Reffye's team was able to simulate tree architectures on the computer by means of temporally dissimilar step-by-step simulation stages (AMAPsim software), the results of which were nonetheless visually similar.
By means of these reiterated simulation experiments and by noting the stability of the simulations obtained from a statistical point of view, Blaise was able to conclude that the discretization of space was correctly adapted to the objectives. It was in this sense that Blaise spoke of "experiments". 29 He tested the statistical properties of the simulations with a commonly used software for statistical analysis, SAS, as one would do for real experimental designs. This work was part of the verification stage (the harmlessness of the implementation with regard to the model).
As we have seen, this software undertook to give a realistic growth dynamic, and not just snapshots. It was therefore the growth engine itself that could be evaluated, and not some unspecified image of a plant. Finally, in accordance with the intended objective, this generation of softwareknown as AMAPpara (for "parallelism") -made it possible to simulate the secondary growth, i. e. , growth in thickness, of the trunk and branches through the transport and deposit of assimilates. The software also allowed interactions between the trees and their surroundings to be taken into account. 30 Furthermore, AMAPpara was evolutive, thanks to the relatively complex structuring of its data compared with the earlier version. This complexity was a positive sign according to AMAP's botanical and agronomical researchers, since it was intended to reflect the complexity of the actual plant.
This was called the ACM -SIGGRAPH (Special Interest Group in GRAPHics). 13 Here, GERDAT stands for Gestion de la Recherche Documentaire et Appui Technique (Management of Documentary Research and Technical Support). 14 A procedural programming language is based on variables, data structures and subroutines, i. e. , a series of actions defined by elementary procedures. 15 See Glossary: "object-oriented programming". 16 Smith (A. R. ), "Plants, fractals, and formal languages", Computer  Graphics, 1984, Vol. 18, No. 3, July, p. 2. 17 According to Edelin, some of Hallé's architectural models appeared closer than others and should therefore be regrouped under several "super-models". The so-called "computer" model would make it possible to specify these more appropriately from a botanical point of view.
Conceptual and institutional convergence: the CIRAD/INRA partner laboratory (1995) Following the completion of Blaise's thesis and in the face of the adverse effects of simulation on botanical concepts, a need for clarification and conceptual systematization once again made itself felt. De Reffye noted that each time there was a change of plant, further ad hoc modifications were still necessary in order to make the integrated computer model and its stochastic processes correspond to the new subject under study. In other words, the computer model lacked generality. Furthermore, AMAP suffered from an absence of internal compatibility between its own programs. In order to resolve these problems, CIRAD recruited two mathematics/computer scientists as research engineers, Yann Guédon and Christophe Godin, who began to develop the AMAPmod software (where "mod" stood for "modelling"), and the AML language (for "AMAP Modelling Language").
AMAPmod software was used to systematically estimate the parameters of the stochastic processes involved in a simulation managed by Jaeger's software. The latter had in the meanwhile been renamed AMAPsim (for "simulation"), and had been rewritten by research engineer Jean-François Barczi, who had integrated concepts of physiological age and of Markov processes. 13 AMAPmod used the AML standardized description language, which in turn was based on the concept of automaton or "axis of reference". In this new formal framework, the simulated plant could be conceived as a "multi-level graph" 14 generated by a stochastic process. This process itself could be formally replaced by a "dynamic probabilistic model" 15 along the lines of a Markov state-transition automaton. As a result of this formal rewriting, the renewal theory became invaluable because it then became possible to resolve the inverse mathematics problem, which involves tracing back to the parameters of the processes once their empirical distributions in the field are known.