dataesr/scientific-paragraphs-categorization huggingface dataset:
Taking the 12 entries of h, we obtain P est (estimated P), which differs from P by a scale factor k, that can be solved through: k " ||P est M i || a 2i (6) we can choose k as the average value computed from n points. In our case, panoramas are "vertical", which simplifies the extraction of the only non-zero rotation angle κ (using Tait-Bryan angles) and t values from P. Thus, to solve pose estimation, we need a few reliable and well-distributed matching pairs. This can be done manually or automatically using a feature point extraction algorithm and more robust estimating schemes. However, achieving stable results in matching and pose estimates for the whole dataset is tedious, and manual editing Remote Sens. 2015, 7, 16339-16362 requires a dedicated interface. In the next two sections, we propose interfaces for using either 2D maps or 3D as-built CAD models instead of feature point-based methods.
Partial Pose Estimate of Panoramic Images Using 2D Floor Maps A user-oriented approach was implemented to get a first "independent" estimate of parameters (t x , t y ) and (κ) of a vertical panoramic image by using 2D floor maps. Using a user interface with a synchronization of the panoramic view and its position with the camera orientation on the 2D floor map, the procedure followed by the operator is: This method was applied to the entire dataset (1025 panoramic images), and 95% of the images were moved or oriented (970); see Figure  12  Another user-oriented approach was developed to refine the whole pose (including ) of the panoramic images ( , , ) and ( ) given the hypothesis of verticality ( = 0, = 0). Using a user.
Full Pose Estimate of Panoramic Images Using the 3D As-Built Dataset Another user-oriented approach was developed to refine the whole pose (including t z ) of the panoramic images (t x , t y , t z ) and (κ) given the hypothesis of verticality (ω = 0, φ = 0). Using a user interface with a synchronization of the panoramic view and its position with the camera orientation in the 3D model and using switches for overlay and transparency, the procedure followed by the operator consists of independently correcting the four parameters of the pose; see Figure  13:  (1) Move (t z ) panoramic using landmarks of the 3D model that cross the equator of the panoramic in the image (boxes, stairs, guardrail, etc. ); front view;  (2)  Align (κ) panoramic using parallel objects of the 3D model to any meridian of the panoramic in the image (pipes, beams, etc.
); zenith view;  (3)  Move (t x , t y ) panoramic using landmarks on the ceiling of the 3D model to align them to the zenith of the image; zenith view;  (4)  Check horizontally for any issue at +90 ˝and +180 ˝and iterate the first three steps until the best estimate; an incorrect estimate of the vertical when mapping the panorama is the main source of a poor estimate of the pose of the panoramic, and such a panoramic should be corrected. This method was applied to the entire dataset after the first estimate on 2D floor maps (1025 panoramic images). During this process, we moved and oriented 100% of the images (1025). The average processing and control time for one panoramic image is 30 s for low corrections and 1 min 30 s-2 min for more difficult images.
‚ specifying needs, requirements and constraints in detail. On the scale of a building, every misunderstanding or fuzzy specification may have a severe impact on costs, quality or durations; ‚ documenting every step in the process and performing quality monitoring from the beginning, to help both fixing non-conforming data and enriching the dataset for future use; ‚ parallelizing the tasks as much as possible (acquisition and post-processing); when the data production time increases, the number of non-qualities increases significantly. Regarding remaining bottlenecks, we have mentioned several technical brakes on the generalization of current tools and methods for large and complex buildings, regarding raw data acquisition and processing. We have also discussed the three areas of 3D reconstruction: costs, duration and quality.
Since the real requirement is the third one, the first two areas must be viewed as secondary in achieving the dataset in industrial processes. The low level of automation and the low quality of acquisition sensors and tools are the actual brakes on generalization. However, by performing rigorous quality monitoring and control, each dataset can be produced with a high degree of confidence. In such large projects, traceability is one of the keys to quality management. The Figure  2  gives a global overview of the whole framework of as-built data production; we can see that many steps are required, including many tools and human interactions. As shown, also, many control procedures are required to reach and certify a quality level. Indeed, the main drawback for achieving expected quality levels is the difficult inter-validation of data sources.
Developing New Software Applications to Increase the Value of the Dataset In the previous sections, we have detailed the challenges in producing data to represent a complex building. Due to that complexity and the intended uses, multiple data types are required: high resolution panoramic RGB, laser scans, 3D as-built CAD models and 2D floor plans. Once the Remote Sens. 2015, 7, 16339-16362 dataset is complete, issues remain for visualization and exploration, taking into account that users may not be familiar with these various data types.
Developing New Software Applications to Increase the Value of the Dataset In the previous sections, we have detailed the challenges in producing data to represent a complex building. Due to that complexity and the intended uses, multiple data types are required: high resolution panoramic RGB, laser scans, 3D as-built CAD models and 2D floor plans. Once the dataset is complete, issues remain for visualization and exploration, taking into account that users may not be familiar with these various data types.
State of the Art of Multi-Data Visualization for Complex Architectures By its nature, each data source represents only one aspect of the reality of the plant and only meets some needs among many. The co-visualization of the multiple data sources is therefore required to assist and help workers with finding answers in their daily jobs. In recent years, several solutions have been proposed for the problem of multi-source, as-built data visualization; see Figure  14:  • virtual tours with floor plans and panoramic RGB  [39] ; • navigation through several spherical laser views; • navigation and path calculation in 3D environments, for instance in 3D video games or 2. 5D cartography services, such as Google Maps indoors. However, none of these solutions integrates all of the data sources required to represent some complex buildings, such as nuclear reactor buildings, and easily navigate the large dataset.
The third and last scenario consists of helping many recently-hired workers to become familiar with their working environment more rapidly, through dedicated training sessions, including tutoring courses. These few scenarios illustrate the variety of user profiles that should be considered for the common view, which we wish to design. Many of them are not familiar with the handling of as-built datasets, and their use of the tool we developed may be very occasional, which reinforces the need for user-friendly and simple interfaces, as well as taking into account human perception in the design  [40, 41]. To address this problem, several principles have been selected for the development of navigation interaction within our applications, developed using the game engine Unity 3D. The first principle consists of the multi-view synchronization across a multi-source dataset, with the possibility at any time of moving from one view to another without losing user position/orientation (shared, unique position of the user in the environment), as shown in Figure  15.
The first principle consists of the multi-view synchronization across a multi-source dataset, with the possibility at any time of moving from one view to another without losing user position/orientation (shared, unique position of the user in the environment), as shown in Figure  15. The second principle consists of the ability to explore the building in its horizontal and vertical dimensions (through stairs and ladders, as well as trapdoors and removable slatted floors), which is required by tasks, such as handling heavy equipment. The last principle consists of developing all tools and functionalities in a project interface for a better user data management: marks, snapshots, distance measurements with guidance instruction and path computations. For instance, we estimate that in the synchronized, multi-view experience, spherical photographs need to be positioned in the virtual environment with a 2.
58 tolerance of 50 cm and 5°. This accuracy range seems sufficient to help users to focus on their task, by not being disturbed by some inaccuracies in the transitions when switching between views. Furthermore, the verticality of the panoramic images must be known with an error below 3°; otherwise, navigation becomes very uncomfortable. In addition to the navigation features, we provide users with specific interactions with the dataset. Each feature has been designed according to the users' needs. To implement them, we carried out several For instance, we estimate that in the synchronized, multi-view experience, spherical photographs need to be positioned in the virtual environment with a 2. 58σ tolerance of 50 cm and 5 ˝. This accuracy range seems sufficient to help users to focus on their task, by not being disturbed by some inaccuracies in the transitions when switching between views.
Furthermore, the verticality of the panoramic images must be known with an error below 3 ˝; otherwise, navigation becomes very uncomfortable. In addition to the navigation features, we provide users with specific interactions with the dataset. Each feature has been designed according to the users' needs. To implement them, we carried out several iterations with users to overcome technical feasibility issues and create genuinely powerful and smart tools. Among these, we can mention distance measurements on 3D CAD models (perpendicular to the normal unit vectors, vertically constrained or free), annotations and snapshots in every data view, interactive cutaways of walls and user data management and sharing. In all of these developments, a large amount of feedback has been collected in a quick response development process to achieve optimum user benefit.
Conclusions and Future Work The professional use of as-built models has recently increased significantly, resulting in two major challenges: scaling up dense and multi-sensor scanning surveys to a whole building and sharing this dataset with a very large audience. In this paper, we reviewed the state of the art of scanning tools and methods for industrial installations. Then, we proposed a global methodology for acquisition and processing of multi-sensor datasets to represent complex architectures. To face the lack of automation to reach high quality in modelling multi-floor plants, we detailed the optimal contribution of human beings in the process. We then assessed this methodology in a large-scale experiment of modelling the 11 floors of a 1300-megawatt nuclear reactor building. From the acquisition of 1000+ co-stations (RGB panoramic and laser scans) to a 3D.
Conclusions and Future Work The professional use of as-built models has recently increased significantly, resulting in two major challenges: scaling up dense and multi-sensor scanning surveys to a whole building and sharing this dataset with a very large audience. In this paper, we reviewed the state of the art of scanning tools and methods for industrial installations. Then, we proposed a global methodology for acquisition and processing of multi-sensor datasets to represent complex architectures. To face the lack of automation to reach high quality in modelling multi-floor plants, we detailed the optimal contribution of human beings in the process. We then assessed this methodology in a large-scale experiment of modelling the 11 floors of a 1300-megawatt nuclear reactor building. From the acquisition of 1000+ co-stations (RGB panoramic and laser scans) to a 3D as-built reconstruction, the project has involved many contributors for almost a year and a half.
17 Figure 12. 1712 Figure 12. User interface of the partial pose estimation tool. The camera position, orientation and field of view of the panoramic image are synchronized with the floor map (Top). A central cross helps the sighting of landmarks. Key numbers of the corrections using 2D maps (1025 images) (Bottom). 4. 4. Full Pose Estimate of Panoramic Images Using the 3D As-Built Dataset.
Distance algorithms infer phylogenies from matrices of pairwise distances among taxa. These algorithms are fast and have been shown to be fairly accurate using both real and simulated data (e. g. ,  Kuhner and Felsenstein 1994). Moreover, they account for probabilistic modeling of substitutions while estimating evolutionary distances. Even if they are not as accurate as likelihood-based methods, these algorithms are still widely used due to their speed and simplicity, as assessed by the high number of citations for Neighbor Joining (NJ,  Saitou and Nei 1987 ; see also  Studier and Keppler 1988) : Approximately 2,000 in 2014 (Web of Science). NJ is a greedy algorithm that builds trees by iterative agglomeration of taxa. Gascuel and Steel (2006)  showed that the criterion being minimized by NJ is the balanced version of minimum evolution (BME), which estimates the tree length using  Pauplin's formula (2000).
Experiments with real data (both DNA and proteins) show that a substantial gain is obtained, compared with NJ and NJ+NNIs; the best alternative is FastTree1, which (quickly) infers trees that are less fitted than NJ+SPR's regarding minimum evolution, but have similar likelihood value with DNA sequences. Details on our SPR algorithm and these experiments are provided in Supplementary Material online. A number of tree-building algorithms have been added, to infer an initial tree or to improve that tree (or any input tree) with topological moves. These algorithms seek to optimize BME, but also the Ordinary Least Square version of minimum evolution (OLSME; Rzhetsky and Nei 1993), which may be relevant with nonsequence data. These algorithms and their properties are summarized in table 1. The calculation of evolutionary distance matrices from DNA and protein sequences is also available.
Models and options are summarized in table 1. Bootstrapping and analysis of multiple data sets can be performed within a single run. FastME 2. 0 implements Felsenstein's bootstrap, where pseudo trees are built from resampled alignments and compared with the original tree obtained from the input alignment. Users can also submit a unique file containing multiple alignments (e. g. , corresponding to different genes in phylogenomics studies) and launch tree construction for all of them using the same program options. Bootstrapping is a highly parallelizable task. The same holds for distance estimations. FastME 2. 0 provides parallel computing for these two tasks using the OpenMP API. When compiling FastME, users can choose to obtain a mono-thread or a parallel binary. They may then set, on the command line, the number of cores to be used.
Module 1: data and metadata The starting point (upper green box in Fig. 2 ) is a library of co-located atmospheric measurements and their associated uncertainties (X, σ x ) and (Y , σ y ), built up either from existing databases (e. g. , GOME-2A and NDACC total ozone data archives) or from virtual observing systems (e. g. , new concept of satellite or modified network configuration). Each observation has associated with it the set of metadata and ancillary parameters needed to characterize the measurement and its 3-D sensitivity: date and time of the measurement, coordinates and elevation of the station or satellite footprint, measurement mode (e. g. , ground-based direct-sun or zenithsky, satellite nadir or limb), solar zenith and azimuth angles, viewing angle(s) and ground albedo. In particular, the basic properties of the data described in Sect.
Module 2: air mass descriptor The second module associates with each measurement a multi-dimensional description of the air mass contributing to the retrieved information: the so-called observation operator. OSSSMOSE contains a library of generic observation operators for a list of observation techniques and target molecules, including the satellite nadir UV  (Vandenbussche et al. , 2009) , ground-based direct-sun UV and ground-based zenith-sky visible  (Lambert et al. , 1996; Lambert and Vandenbussche, 2011)  total ozone measurement techniques con-sidered as illustrations in the present paper. Resulting from direct and inverse simulations of the remote sensing measurement using ad hoc radiative transfer codes and retrieval tools, a generic observation operator usually consists of a parametrization of the multi-dimensional volume of the air mass contributing to the retrieved atmospheric information, including in some cases a further parametrization of the measurement sensitivity within this volume (e.
The actual air mass contributing to a measurement can differ significantly from either the Field-Of-View (FOV) footprint of a satellite observation or the geolocation of a ground-based instrument. Details of the computation of the specific observation operators are presented for each instrument type in Sect. 2. For nadir satellite TOC measurements the most important information concerns the pixel size and pixel location, and the solar and viewing zenith angles at the time of observation. For a ground-based measurement, required metadata are the location of the station (latitude, longitude and elevation above sea level), the instrument type (Brewer, Dobson, ZSL-DOAS), the observing mode (e. g. direct-sun or zenith-sky, a single exposure or a daily mean), and the SZA.
Module 3: observation simulator The 3rd module of the system simulates each observation by applying the specific air mass descriptor generated by Module 2 into atmospheric fields. Therefore Module 3 includes a library of measured and modelled atmospheric fields at sufficiently high spatial resolution to enable accurate use of the observation operators (centre of the blue box in Fig. 2 ): global gridded data generated by chemical-transport models and data assimilation systems, high resolution measurements over an area taken during an airborne campaign etc. For the intended total ozone illustrations, which target among others seasonal cycles and global statistics, the fixed set-up of high-resolution reanalyses by data assimilation systems make these an appropriate source of global fields. Ideally the atmospheric fields should have quantitative uncertainties associated with them, like systematic and random uncertainty estimates, in order to enable OSSSMOSE to calculate error propagation along its suite of operations.
Un-fortunately, uncertainties on modelled atmospheric fields are difficult to assess and the quality information documented in the literature is usually not of direct use for quantitative error propagation: it consists mainly in comparison results with reference measurements and in other quality diagnostics peculiar to the data assimilation technique. To evaluate the validity of the modelled fields for the intended use, the least that can be done is to test the robustness of the metrology simulations by feeding OSSSMOSE with different (and as independent as possible) modelled fields. Hereafter results are reported for two substantially different atmospheric representations: (1) the MACC-IFS-MOZART reanalysis performed at ECMWF, and (2) the MERRA reanalysis performed by NASA's GMAO. Their general set-up and characteristics are described below.
MERRA The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a reanalysis undertaken by the National Aeronautics and Space Administration (NASA)'s Global Modelling and Assimilation Office (GMAO) with the aim to place observations from NASA's Earth Observation (EO) satellites into a climate context  (Rienecker et al. , 2011) , with a particular emphasis on an accurate representation of the hydrological cycle. MERRA was generated with version 5. 2. 0 of the Goddard Earth Observing System (GEOS) atmospheric model and data assimilation system (DAS). The circulation model is based on finite-volume dynamics and the data ingestion is done with a 3-D variational data assimilation (3DVAR) algorithm, based on the Gridpoint Statistical Interpolation scheme (GSI), using a 6 h update cycle.
A similar level of agreement is obtained using the MERRA reanalysis fields (not shown here, but further elaborated in Sect. 4. 6). The use of the full observation operators (rather than pixel centres or station coordinates) for the averaging of the reanalysis field yields only minimal improvement in observation-model agreement, except for the twilight UV-Vis measurements, where the RMSE can be significantly reduced by using the observation operator (from 5. 3 % down to 4. 2 % in the case of Dumont d'Urville). Use of the satellite observation operator even degrades somewhat the correspondence between GOME and the IFS-MOZART reanalysis fields, but this is not surprising since the GOME data were assimilated in the IFS-MOZART reanalysis without taking into account the dilution of sensitivity towards the sun and satellite.
Smoothing difference errors remain below the combined measurement uncertainty and are thus only a minor component of the total error budget for this particular case. The derivation of the combined measurement uncertainty used here warrants some discussion. Uncertainties provided with NDACC archive data files for the daily mean Dobson measurements represent the uncertainty on the mean of the individual measurements. These are used directly as σ Y. As discussed in Sect. 2. 1, the uncertainties provided with the GODFITv3 satellite data contain only the formal fit uncer-tainty and are known not to represent the full random uncertainty. The random uncertainty derived from sensitivity studies by  Lerot et al. (2014) , i. e. 1. 7-2. 6 %, on the other hand, is found here to be too conservative: using a 1.
GOME/ERS2 vs. SAOZ at Dumont d'Urville At the Antarctic ground station of Dumont d'Urville, the atmospheric dynamics are much more complex, with the ozone-depleted polar vortex either encompassing the station or not, and this on variable time scales. Moreover, the zenithsky ground instrument operated there has a large horizontal area of sensitivity, which can mean that while the station is on one side of the vortex edge, the actual sounded air mass is on the other side. From the right panel in Fig. 6 , it is clear that both the comparison spread and bias are much larger and more structured than for the other two cases. Interestingly, the OSSE manages to qualitatively reproduce this behaviour, both in comparison median and spread, for the better part of the time series. This did require the use of an assumed SAOZ measurement uncertainty of 2 %, which is consider-ably larger than the DOAS fitting uncertainties provided with the NDACC data files (well below 1 %) but far smaller than the 4.
Good agreement between simulated and observed comparison spread was obtained assuming 1. 5 % uncertainty for the Brewer at Uccle, 2. 5 % for the Dobson there, and 1. 5 % for the Dobson at Arosa. These numbers appear realistic. As discussed in Sect. 4, the uncertainty estimate provided with the satellite data takes into account only the formal fitting uncertainty and as such is known to be too optimistic. However, the uncertainty estimate published by  Lerot et al. (2014) , which includes all known sources of random and systematic uncertainty, is confirmed here to be too conservative across the entire NDACC network, as already expected from the case studies in Sect. 4. Indeed, a 1 % satellite random uncertainty suffices at all stations, with the data at the tropical stations requiring only 0.