dataesr/scientific-paragraphs-categorization huggingface dataset:
2016). BIM also provides the base for further structural analysis and data management. Therefore, the BIM environment combines the geometry modelling, element parameterization, dynamic information management and structural analysis together, which avoids the transformation among diverse platforms and data formats. Currently, BIM software (typically Autodesk Revit) has provided a platform for manual semantic modelling on reference of realitybased data, such as measurement, total station points, imagery and laser scanning. Once the elements have been parametrically created, they are simultaneously linked together with strict spatial relationships. The spatial relationships are fixed, even if the sizes or shapes of the elements change. However, the manual elements segmentation and parameterization process is time-consuming, especially when addressing complex structures.
Revit API development Autodesk Revit has been widely used to aid the reality-based parametric modelling process, by directly manual family creation  (Garagnani and Manferdini, 2013)  or commercial plugins  (Klein et al. , 2015)  such as Scan-to-BIM and Leica CloudWorx. Yet the plugins are expensive and tend to regular buildings and the manual creation becomes time-consuming with the complex 3D space distribution of the elements. The Revit Application programming interface (API) combines the BIM parametric modelling and programming functions (Table  1 ). The interacting programming methods offer designers the ability to interactively design and manipulate Revit elements using algorithms and computational logic. Revit can provide the UI platform, serves as the basic view platform and database, and parametrically represents the element and builds the relationship automatically.
The program can reduce the manual operation, and realize automatic and batch processing aiming at specific functions. Besides, the existing algorithms and libraries can be directly introduced and conducted. Therefore, they can simultaneously automate the element segmentation and parametric representation procedure in BIM environment by specific functions. Bassier et al. (2016)  noted the important role of BIM from scan data to structural analysis model for heritage timber roof structures. They utilized BIM technique to connect the geometry model (SolidWorks ScanTO3D) and structural analysis (ANSYS) (Figure  1 ). The goal of our research is to explore the potential of such uniform platform for heritage documentation and management (Figure  2 ). Utilizing Revit API, we developed a plugin with a workflow that consists of automatic geometry reconstruction and element parametric modelling.
Compared to datasets based on point clouds acquired by terrestrial laser scanning, the number points recorded by total station is very low but each point is significant. Total stations deliver highly accurate single-points, often used as 3D surface reconstruction reference points or control points for other techniques. Although limited number of measured points does not allow a detailed study of the structure, it is still easy to rebuild undistorted rectangular beams. To construct the beams in Revit, we can either manually create beams in 3D space, or firstly draw the lines in CAD and then apply a transfer into Revit IFC format. Revit 3D modelling tools focus primarily on delivering 2D documents with the added 3D capability. When creating complicated geometry and handle large amounts of data in 3D space directly, the user's freeform design is limited.
Total station based beam construction Although the field work based on the total station points was time consuming, the beam construction process could be finished fast with a limited number of accurate points. It is expected to transfer the disordered points to parameters describing the cuboid beams. A cuboid beam can be described by a central point, three directions and extensions (Figure  8 ), which is the basis to create the beam element in Revit. As the points are not always the corners, the two parameters of the beam are not totally confirmed: one is the circle point and another is the height. A Python program has been developed to calculate the beam parameters based on the following algorithm workflow (Figure  9 ). Firstly, we can obtain the direction of height (h), considering that collected total station points are located in a set of parallel edges.
CONCLUSION In this paper, we discussed the potential of as-built BIM environment for the historic beam frame conservation utilizing interactive Revit API development. The tool does not only automate the parametric modelling, but also integrate together the traditionally separate geometry modelling, parametric elements management and structural analysis. BIM software, typically as Autodesk Revit, is increasingly supportive of reality-based data, normally called Scan-to-BIM process. The current element parametric procedure is generally manual, and it ignores the existing semantic modelling and point cloud processing algorithms and libraries. The Revit API provides interactive environment to utilize Revit functions and develop specific algorithms simultaneously. Meanwhile, the API development manipulates the geometric/semantic modelling and structural analysis directly in BIM environment.
For example, beam elements are traditionally constructed firstly, and their joint and interconnected relationship need to be judged and analyzed further. While the relationship is the core for BIM technique and related functions are available within API development. Thus, the integrated environment not only avoids the data exchange, but also reduces some inefficient and repetitive work. In conclusion, current reality-based modelling and structural analysis technique and platforms are important for verifying the actual load-bearing capacity and structural safety of historic timber roof structures, and the BIM provides the possibility to combine the separate processing, the software incompatibility and data homogeneity to the uniform platform. The BIM based parametric modelling of a timber roof structure can provide: (i) an accurate complete survey on the geometry aspect; (ii) attribute, material and relationship information of the sub-elements; (iii) possible deformations and changes over time; and (iv) loadbearing and structural analysis.
We present the results of a multi-wavelength investigation of the very X-ray luminous galaxy cluster  hereafter MACSJ0553). Combining high-resolution data obtained with the Hubble Space Telescope and the Chandra X-ray Observatory with groundbased galaxy spectroscopy, our analysis establishes the system unambiguously as a binary, post-collision merger of massive clusters. Key characteristics include perfect alignment of luminous and dark matter for one component, a separation of almost 650 kpc (in projection) between the dark-matter peak of the other subcluster and the second X-ray peak, extremely hot gas (kT >15 keV) at either end of the merger axis, a potential cold front in the East, an unusually low gas mass fraction of approximately 0. 075 for the western component, a velocity dispersion of 1490 +104 -130 km s -1 , and no indication of significant substructure along the line of sight.
2008)  in November 2016 for program  GO-14096 (PI: Coe)  in the F105W and F140W filters. We used the ASTRODRIZZLE v2. 1. 0 software package to remove geometric distortions, background variations, and cosmic rays, and to create combined images with a pixel size of 0. 03 (we set pixfrac=0. 7 as recommended by ISR ACS 2015-1). The resulting ACS colour image (Fig. 2 ) covers approximately the same area as shown in Fig. 1  and confirms the bimodal nature of the system already apparent in the groundbased image, anchored by two approximately equally luminous brightest cluster galaxies (BCGs), separated by almost 450 kpc in projection.
5 Å. We used the XIDL and DEEP2  (Cooper et al. 2012; Newman et al. 2013)  pipelines to reduce the LRIS and DEIMOS data, respectively. Redshifts were measured from the one-dimensional spectra using elements of the SpecPro software package  (Masters & Capak 2011). A comparison with Fig. 1  shows that our ACS observation covers essentially all of the area within with X-ray emission was detected with Chandra. Insets (15 ×15 ) show the brightest cluster galaxies (BCGs); note that the third-brightest galaxy (top left) is a foreground object (see Section 6. 1). The projected distance between the BCGs corresponds to 446 kpc at the cluster redshift. The red circle at the eastern edge of the field marks a binary star used to ensure astrometric alignment with the X-ray data (see Section 4).
X-ray MACSJ0553 was observed with the Chandra X-ray Observatory's ACIS-I detector in 2005 (ObsID 5813: PI Ebeling) and again in 2011 (ObsID 12244, PI: Ebeling) for exposure times of 9. 9 and 71. 9 ks, respectively 1. Both observations were performed in Very Faint mode. We reprocessed the datasets from either observation with the standard CIAO analysis tools and Chandra Calibration Database 4. 7. 2. Periods of flaring background were identified and removed with the help of the lc clean script, before projection of the shorter observation onto the longer one. Fig. 3  shows the X-ray emission from MACSJ0553 in the Chandra broad band (0. 5-7 keV) after adaptive smoothing of the merged dataset. We use ASMOOTH  (Ebeling et al. 2006)  with the default setting of 3σ for the significance with respect to the local background of any features in the adaptively smoothed image.
Blank-sky background To avoid these complications we here follow the alternative path of determining the X-ray background from separate calibration files, provided by the CALDB for the time of our observation via the acis bkgrnd lookup tool. As a first step, we project the blanksky and particle-background 2  datasets thus obtained to the pointing coordinates and orientation of each of our two Chandra observations of MACSJ0553. Following  Hickox & Markevitch (2006)  we then account for the time variability of the particle background by scaling the archival particle background such that it matches our observations in the 9-12 keV range. The required scaling factor is calculated as: A = c(9-12 keV) -0. 0132 × c readout (9-12 keV) c particle (9-12 keV) (1) where c and c particle are the count rates in the 9-12 keV range recorded in the photon events files for a given ACIS observation and for the time-matched archival particle-background observation, respectively, while c readout is the readout contribution to the background, provided by the CIAO tool readout bkg.
Properly scaled, the particle background A × f particle can then be subtracted across the full spectrum within any given region. Since our analysis works with fluxes, rather than counts, we need to ensure that differences in exposure time as well as the telescope and instrument response are accounted for. We use the CIAO specextract tool to extract spectra from our science data set and to compute the corresponding auxiliary response and redistribution matrix files (ARF and RMF). For the blank-sky and particlebackground data sets ARFs and RMFs are not available. We therefore apply the respective ARF and RMF from the science observation to blank-sky and particle-background spectra generated with dmextract, and then scale both by appropriate factors to match the exposure time of the science observation.
Photon background model The photon background is not subtracted, but modeled as the sum of thermal emission from the Galactic bubble  (Kuntz & Snowden 2000; Lumb et al. 2002 ) and a power-law contribution that accounts for unresolved emission from distant AGN. We add a multiplicative exponential term to account for photoelectric absorption by the Galaxy. This model is then fit to the blank-sky data set with all model parameters frozen at their literature values except for their normalisations and the equivalent column density of neutral hydrogen which we set to the value determined from 21-cm observations in the direction of each target  (Kalberla et al. 2005). By fitting both blank-sky data sets simultaneously (one each for ObsIDs 5813 and 12244) and requiring their respective normalizations to match the ratio of the exposure times, we obtain a universal, fully determined model for the photon background that, properly scaled to the area under consideration, can be included in the spectral fits to the data for any region of our science dataset.
Spectral model Prior to fitting, spectra from both data sets (ObsIDs 5183 and 12244) are combined using specextract; the same procedure is applied to the corresponding particle-background data sets. We then group the spectra, requiring 20 counts per bin, except for fits to the global emission (see Section 6. 2) where we require 30 counts per bin. After subtraction of the particle-background component we fit a thermal-plasma model for the cluster emission (plus a fixed photon-background contribution) modified by absorption at the observer. In SHERPA terms, our model is thus described as xswabs× (xsapec. sc+xsapec. bg1+xsapec. bg2+powlaw1d. bg3), where the final three components describe the photon background (see Table  2 ) and are scaled to the area of the region of interest.
Spatial modelling Although the heavily disturbed X-ray morphology of MACSJ0553 (individual features are discussed in Section 6. 2. 1) and the limited photon statistics of the existing Chandra observations prevent a physically meaningful modeling of the observed surface brightness distribution (let alone any deprojection analysis), we nonetheless attempt to quantify and compare the large-scale X-ray emission from the ICM of the two apparent cluster components anchored by the system's two BCGs. To this end we bin the X-ray data within a 6. 3 × 6. 3 arcmin 2 region (approximately 1 Mpc on the side) into 3 pixels, and then remove the irregularly shaped X-ray peak in the far East of the system (see Fig. 3 ) by replacing the pixel values in this area with the azimuthal average observed on either side, adopting the western X-ray peak as the centre, and adding noise commensurate with the photon statistics.
1 and 1. 3 from the detection of the [O II] emission line), and for system 5 (from the identification of strong Lymanα emission for image 5. 3). We show the relevant feature from the spectra of these three images in Fig. 7. Our spectroscopic observations also targeted all images of system 4 and 7, as well as the multiple-image family members 2. 1, 2. 2, 8. 1, and 8. 3 without detecting spectral features in any of them. For systems 2, 3, 4, 7, 8, 9, and 10, we therefore use the ACS photometry in three bands to estimate a crude photometric redshift with the HYPERZ code  (Bolzonella et al. 2000) ; the results then refine the redshift prior for the Lenstool model as follows. We adjust the spectral energy distribution of each multiple image with spectral templates at solar metallicity  (Bruzual & Charlot 2003) , varying the reddening within 0 < AV < 4 and the redshift within 0.
Galaxy distribution We list, in Appendix A, the positions and redshifts of all galaxies successfully targeted in our spectroscopic follow-up observations (Section 3. 1. 2 and Table  1 ). Their location on the sky is shown in Fig. 8. We find no significant substructure along the line of sight, as evidenced by the unimodal redshift distribution shown in Fig. 9. The overlaid Gaussian, derived from the redshifts of 81 cluster members using the ROSTAT package  (Beers et al. 1990) , is characterised by a velocity dispersion of σ=1490 +104 -130 km s -1. Adopting the M -σ scaling relation for dark matter derived by  Evrard et al. (2008) , and assuming that galaxies are unbiased tracers of dark matter, this high value of σ implies a very high dynamical mass of M200 = 4. 2 +0. 9 -1. 0 × 10 15 M. The two BCGs of MACSJ0553, highlighted in Fig.
We have attempted to explain the violation of this cardinal rule in MACSJ0553 as the consequence of the effective capture of part of one cluster's ICM by the other; however, much better observational data as well as dedicated hydrodynamical simulations of the MACSJ0553 collision will be needed to test the plausibility of the scenario proposed by us here. Both of these (in particular much deeper X-ray observations) will also be critical to solve the puzzle of the survival of the apparent cold front (Section 6. 2. 1 and Fig. 12 ) in the absence of gravitational support from an associated dark-matter concentration. Finally, we note that, if improved observational constraints (from additional spectroscopy of both cluster galaxies and multiple-image systems, as well as deeper X-ray and radio observations) combined with numerical simulations of the collision confirm the scenario schematically outlined in Fig.
ACKNOWLEDGEMENTS We thank an anonymous referee whose insightful comments greatly improved this paper, to the extent that a co-authorship would be warranted. Support for program GO-12362 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. Support for this work was also provided by the  National Aeronautics and Space Administration  through  Chandra Award Number   GO1-12153X  issued by the Chandra X-ray Observatory Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of the  National Aeronautics Space Administration  under contract  NAS8-03060. JR acknowledges support from  ERC  starting grant  336736-CALENDS.
We introduce the method ADAMONT v1. 0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short-and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands.
D. Verfaillie et al. :  The method ADAMONT v1. 0 for statistical adjustment of climate projections (meteorological conditions) or in order to drive specific impact models. Various downscaling and adjustment methods have been developed  (Maraun et al. , 2010; Teutschbein and Seibert, 2012, 2013). They all require an observation dataset which (i) meets the data requirements of the application and (ii) is sufficiently long and reliable to be used to infer the relationships between the observations and the raw climate projections during the observation time period. Several approaches, such as the analog method, search for relationships between observed large-scale predictors (generally from reanalyses) and observed local-scale predictands  (Vrac et al. , 2007a; Dayon et al. , 2015). In contrast, model output statistics approaches calibrate model outputs against observations, with various levels of complexity, such as scaling methods (linear scaling, local intensity scaling, variance scaling, etc.
g. the analog method. Furthermore, the performance level of quantile mapping methods is sensitive to the observation dataset used and the detailed characteristics of their implementation, which requires specific attention. Climate projections in mountainous regions, which are motivated by a broad range of geophysical, environmental and societally relevant scientific challenges  (Martin et al. , 1994; Beniston, 1997; Jomelli et al. , 2009; Castebrunet et al. , 2014; Piazza et al. , 2014; Schmucki et al. , 2014; Lafaysse et al. , 2014; Boulangeat et al. , 2014; Thuiller et al. , 2014; Castebrunet et al. , 2014; Francois et al. , 2015; Spandre et al. , 2016)  are particularly sensitive to the quality of the adjustment method. Indeed, RCM resolutions typically between 10 and 50 km are not sufficient to capture the fine-scale processes and thresholds at play.
Here we introduce the ADAMONT v1. 0 method to adjust climate model projections in order to provide hourlyadjusted meteorological conditions for past and future conditions based on climate model output and observational datasets. Although it could be applied for GCM output, it was primarily designed to process RCM output. Indeed, raw regional climate projection data are increasingly made available, e. g. the World Climate Research Program (WCRP) Coordinated Regional Downscaling Experiment (CORDEX;  Giorgi et al. , 2009) , whose aim is to improve and distribute regional climate modelling worldwide. Its European branch, EURO-CORDEX  (Jacob et al. , 2014 ), gathers regional climate simulations over Europe from 30 different modelling groups at 50 km (EUR-44) and 12. 5 km (EUR-11) resolutions. On the observation side, the use of surface meteorological reanalysis is a powerful alternative to station observation data to provide the necessary observational dataset  (Berg et al.
Similarly,  Addor et al. (2016)  demonstrated the sensitivity of quantile mapping adjustment to circulation biases over the alpine domain. Additionally, the frequency of weather regimes may change in a changing climate  (Boe et al. , 2006; Cattiaux et al. , 2013). To improve the stationarity of our method in a changing climate, weather regimes are thus taken into account, i. e. quantile adjustment functions are computed and applied depending on the weather regime. Assuming the availability of a gap-free meteorological observational dataset at hourly time resolution consisting of one or several geographical locations considered sharing similar large-scale meteorological conditions, and daily RCM model outputs covering the geographical domain of interest, the statistical adjustment method ADAMONT consists of the following steps: 1.
On one hand, this number is a compromise between accuracy of the correction and robustness of the percentile estimation (more regimes can be used, such as in  Ummenhofer et al. , 2017). On the other hand, this relatively small number of regimes ensures a sufficiently large size of the datasets used for quantile mapping (which are, as described below, further partitioned into four seasons: DJF, MAM, JJA and SON). Figure  1  represents the different regimes used in this study. 3. Aggregation from hourly to daily observations: the observational data are aggregated from hourly to daily time resolution, depending on the variable considered (see Table  1 ). For temperature, the daily minimum and maximum values (from 06:00 to 06:00 UTC the next day) are selected (RCMs generally offer daily minimum and maximum temperature); for wind speed and humidity, the last value of each day (at 06:00 UTC) is selected (in order to be comparable to an instantaneous value), and for precipitation and radiation, the daily mean (06:00 to 06:00 UTC) is used.
4. Computation of quantile distributions: the quantile values (the 99 percentile values as well as the 0. 5 and 99. 5 % quantile values) of the observational dataset and corresponding RCM grid point distributions are calculated for each variable, each season (DJF, MAM, JJA, SON) and each of the four weather regimes for a reference (also referred to as learning) time period when both datasets are available. 5. Quantile mapping: quantile mapping is then applied to the entire RCM dataset for the application time period,  taking into account the season and the weather regime. A linear interpolation is used for quantile values between the quantile values specifically computed (the 99 percentile values as well as the 0. 5 and 99. 5 % quantile values). For RCM values greater than the 99. 5 % quantile, a constant adjustment based on the value of this last quantile is applied in order to allow for new extremes.