dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The ISTO group thanks  I. Di Carlo  for the technical support with  SEM  and  EMPA  and  Daniel Andrade ,  Silvana Hidalgo ,  Patricia Mothes  and  Mario Ruíz  for scientific discussions about  Tungurahua. Reviews  from  I. Smith  and two anonymous reviewers improved the quality of the manuscript. We also thank for the comments and Editorial handling of editors  J. Gamble  and  M. Wilson.
Spectra were measured on a confocal LabRam300 Horiba Jobin Yvon spectrometer using a 532 nm laser produced by a diode-pumped solidstate laser with incident power of 100 mW. Measurements were collected with a 300 μm confocal hole size and used an Olympus 50× objective with a holographic grating of 1800 mm -1 , coupled to a Peltier-cooled front-illuminated CCD detector over the range from 150 to 1600 cm -1 with a 2 μm spot size. Depth profile analysis was used to estimate a penetration depth of ∼22 μm in such a configuration. Spectra were analyzed using PeakFit software, and the CaMoO 4 characteristic bands were fit with pseudovoigt profiles. Three sites were probed per sample, and average values were used for peak analysis. EPR was used to describe the defect structure in the bulk of both crystalline and amorphous phases.
Nevertheless, the amount and diversity of radar data available for each eruption make it possible to retrieve the 3-D displacement components with an unprecedented accuracy, as discussed later in the paper. 2. 1. 2. Interferograms and 3-D displacement computations 2. 1. 2. 1. Interferogram computation. Our interferograms were produced with the DIAPASON© software (CNES/Altamira-Information, 1996) using the two-pass method described by  Massonnet and Feigl (1998). The contribution of the orbital trajectories to the interferograms was modeled and removed using the precise orbit state vectors provided by Space Agencies. A 5 m Lidar DEM, produced by the French Geographic Institute (IGN) in 2008-2009, has been used to model and remove the topographic contribution. The IGN Lidar DEM was also used to provide a common geographic frame (UTM-WGS84) for the interferometric products so that all interferograms have exactly the same spatial coverage.
The estimated variances range between 1. 4 × 10 -5 m 2 and 2. 4 × 10 -3 m 2 with a mean value of 2. 3 × 10 -4 m 2 (Table  1 ) and the covariances range between 7. 8 × 10 -6 m 2 and 2. 9 × 10 -5 m 2. V is calculated on the same areas as for the polynomial surface calculation at the de-trending step. It is used to weight the inversion. It also allows producing an associated standard error map for each displacement map, as estimated from the square root of the diagonal elements of the posterior covariance matrix (Appendix B;  Strang, 1986). For each map, the displacement components were inverted on a pixel-by-pixel basis, after eliminating, for each interferogram, pixels whose coherence was below a fixed threshold and, for each whole interferometric dataset, pixels whose coherence standard deviation was above a fixed threshold.
This coherence standard deviation is also calculated on a pixel-by-pixel basis for each interferogram dataset. The thresholds are fixed empirically (with coherence threshold = 0. 33 and coherence standard deviation threshold in the range of 0. 25 to 0. 35). As a result, the number of independent LOS displacement data retained for the 3-D component inversion can differ between adjacent pixels. In order to limit this difference, all pixels with less than m-2 data available for inversion after thresholding were eliminated (i. e. m = 9 for the February eruption, m = 10 for the May eruption, m = 8 for the July and m = 7 for the August eruption; Table  1 ; Fig. 2 ; Appendix C). Finally, the missing areas, whose sizes do not exceed 2 pixels, were interpolated by computing the median of nearest neighbors in a 5 × 5 pixel window.
2 ; Table  2 ). The receivers used during the 2015 surveys were the multi-frequency, multi-constellation ProMark 800 GNSS Surveying System from Ashtech. Due to the large number of benchmarks the acquisition time at each point is now limited to 3 min with a rate of 1 measurement per second. These data are post-processed with the Spectra Precision Survey Office software and referenced to the permanent GITG station located just outside of the north-western part of the Enclos Fouqué caldera (~4 km from the summit; Fig. 1 ). Data precision measured by this rapid-static mode is about 2. 5 cm horizontally and 4 cm vertically (versus a few mm horizontally and 1-2 cm vertically on the permanent recordings), which is good enough to study the large deformation linked to dike injections.
Consistency between the GNSS and InSAR methods To evaluate the consistency between the GNSS and InSAR datasets, we retrieved the 3-D displacements derived from InSAR at the exact location of each GNSS stations or benchmarks by interpolating InSAR data with a Kriging regression. We plotted the GNSS-InSAR residual values of the EW, NS and vertical displacements associated with the four 2015 eruptions on Fig. 9. The best results are found for the EW component, where, depending of the event, 79 to 100% of the data have GNSS-InSAR residual values b2. 5 cm, and 96 to 100% of the data have GNSS-InSAR residual values b5 cm (Fig. 9 ; Table  3 ). By contrast, the largest discrepancies for all events are observed on the NS component, with 6% to 51% (depending of the events) of the data showing GNSS-InSAR residual values b2.
5 cm (Fig. 9 ; Table  3 ). The worst results of the comparison are observed for the NS component of the August 2015 dike injection, with a mean discrepancy of 6. 4 and 9. 7 cm between displacements deduced from InSAR and permanent and re-iterated GNSS data (i. e. static-rapid GNSS surveys), respectively. For the vertical component, the mean   1  and 2  for the precise dates of the satellite acquisitions and static-rapid GNSS field campaigns, respectively. For the permanent GNSS, the displacements have been calculated for the mean period covered by the interferograms. The lava field emplaced during this eruption is outlined in red, and the previous 2015 one is outlined in white. Low-coherence areas outside the Enclos Fouqué caldera are masked. Coordinates in kilometers (WGS84, UTM 40S). 1  and 2  for the precise dates of the satellite acquisitions and static-rapid GNSS field campaigns, respectively.
For the permanent GNSS, the displacements have been calculated for the mean period covered by the interferograms. The lava field emplaced during this eruption is outlined in red, and the previous 2015 ones are outlined in white. Low-coherence areas outside the Enclos Fouqué caldera are masked. Coordinates in kilometers (WGS84, UTM 40S). discrepancies between the two methods ranged between 1. 7 and 4. 3 cm for the February and May 2015 events, and between 0. 9 and 2. 1 cm for the July and August 2015 events. As for the two other components, the comparison results for the vertical component are better with the permanent GNSS data than with the rapid-static GNSS data, with a mean discrepancy of 0. 9-2. 9 cm and 2. 1-4. 3 cm, respectively. Furthermore, a systematic shift between the InSAR data and the permanent GNSS data for February, May and August is observed, i.
e. a westward (InSAR b GNSS EW displacements) and upward shift (InSAR N GNSS NS displacements; Fig. 9 ). This is not as clear on the staticrapid GNSS data. For August, a clear southward shift between the InSAR data and the static-rapid GNSS data is also observed (InSAR b GNSS vertical displacements). For the points located on the terminal cone, the GNSS-InSAR residual values seem to correlate with elevation (Fig. D1  in Appendix D). Such discrepancies between the two datasets could be attributed to the errors associated with each method. Contrary to GNSS, there is no standard procedure for InSAR uncertainty mitigation since the uncertainties have various origins and are specific to each case. We present below, in more detail, the derived-InSAR displacement uncertainty obtained for our 3D displacement calculation.
InSAR displacement standard error maps The InSAR displacement standard error maps, obtained as output of the 3-D component inversion procedure (Appendix B), provide an estimation of the displacement uncertainty derived from InSAR. They reflect all the inconsistencies between the interferograms used for each eruption, weighted by the data variance-covariance matrix. The EW displacement standard error maps are shown, for each eruption, in Fig. 10. The NS and UD standard error maps are linearly proportional to the EW ones and will not be described further here. The largest standard errors occur in areas covered by recent lava flows. For instance, the lava field emplaced in May 2015 is especially well visible on the July 2015 EW displacement standard error map (Fig. 10c ), and even remains visible on the August 2015 one (Fig.
Geometric dilution of precision In order to quantify the influence of the InSAR dataset geometry on the precision of the inverted 3-D displacement components, we also calculated, for each eruption, the geometric dilution of precision (GDOP) for different configurations of the dataset using the following formulation  (Strang and Borre, 1997; Wright et al. , 2004; Table 4 ; Table  E1  in Appendix E): ∑ u ¼ σ 2 P τ P Â Ã -1 ð2Þ where the GDOP values for the EW, NS and UD components are given by the square root of the diagonal terms of Σ u when σ is set to 1. σ is the   1  and 2  for the precise dates of the satellite acquisitions and static-rapid GNSS field campaigns, respectively. For the permanent GNSS, the displacements have been calculated for the mean period covered by the interferograms.
The lava field emplaced during this eruption is outlined in red, and the previous 2015 ones are outlined in white. Low-coherence areas outside the Enclos Fouqué caldera are masked. Coordinates in kilometers (WGS84, UTM 40S). standard deviation of errors in the independent co-eruptive interferograms. P is the matrix containing the LOS unit vector for the independent co-eruptive interferograms. Not surprisingly, the precision of the inverted 3-D displacement components correlates approximately with the GDOP values (i. e. the lowest the GDOP, the highest the precision of the inverted 3-D displacement components). The three last eruptions of 2015 have significantly lower GDOP than that of the February eruption (Table  C1  in Appendix E). These differences in GDOP are linked to the presence of ALOS-2 data, acquired with left-looking radar antenna, in the three datasets used for the inversion of the May, July and August 3-D displacement components, while all the data used for February were only acquired with right-looking radar antennas.
Influence of the distinct periods considered for each datasets Various sources of errors can explain the discrepancy between GNSS and derived-InSAR data. The most obvious is the distinct periods covered by each dataset (Fig. 2 ; Tables  1, 2 ). Combining interferograms covering different periods is already an intrinsic source of uncertainty in InSAR measurements. Inter-eruptive displacements in 2015 were low (often b2 cm of cumulated displacement in the month preceding an eruption;  Peltier et al. , 2016)  compared to the co-intrusive ones, so most of the displacements mapped on Figs. 5, 6, 7, 8 were generated during the dike injection toward the surface, that preceded the eruption by a few minutes/hours. However, we used the permanent GNSS stations to test the influence of the different periods on the results by comparing InSAR-derived data with different periods of GNSS data (minimum extreme dates, maximum extreme dates, mean dates of the InSAR dataset used to process the displacement maps, Table  1 ).
Our results confirm that the integration of a few days/weeks of intereruptive period does not affect the quality of our comparison (see Appendix F). Note that the GNSS-InSAR residual values are most often lower for the GNSS permanent data than for the GNSS survey data (Table  3 ). Our test shows that this is most probably linked to differences in precision rather than to the distinct periods covered by the two datasets; because of the short time acquisitions of the measurements during the static-rapid GNSS surveys (only 3 min compared to continuous acquisition for daily solutions from the permanent stations), the precision is lower (a few cm) than that for the GNSS permanent station measurements (a few mm). Thus, having excluded the hypothesis on the distinct periods covered by the two datasets, the error sources must be attributed to the methods themselves.
This concerns mainly uncorrected atmospheric effects in InSAR (see below) and larger errors on the vertical GNSS component (most of the GNSS-InSAR residuals are b5 cm on the vertical component, whereas most are b 2. 5 cm on the EW component; Table  3 ). Due to the limited perspective of the satellites from which the height is measured, the vertical component of GNSS data is often two to five times less accurate than the horizontal one. In the same way, the accuracy of the vertical component deduced from InSAR is always lower because the ground surface is only viewed from above.
Origins of InSAR displacement uncertainty For a given eruption, the interferograms can cover different time periods resulting in at least two possible sources of inconsistency: (i) inter-eruptive displacement recorded over different time periods (even if inter-eruptive displacement was low during the considered periods, Fig. 4 ), and (ii) different uncorrected atmospheric artifacts. In addition, the interferograms can have different sensitivity to topography, depending on their perpendicular baseline  (Massonnet and Feigl, 1998). This effect induces inconsistencies between interferograms in the same dataset and thus a strong standard error on the 3-D displacement components. As a result, any topographic error in the DEM will induce an inconsistency in the interferometric dataset. The correlation between InSAR displacement standard errors and the local slope (Fig.
Possible improvements The use of InSAR data with multiple acquisition geometries, and in particular left-looking data, allowed us to obtain precision on the NS component close to that of the rapid-static GNSS data (standard error of between 2 and 4 cm for the NS displacements calculated with the dataset including left-looking data, compared to between 4 and 6 cm without this additional data, Table  4 ). However, the precision on the NS InSAR component remains about 4 to 6 times lower than that on the EW and UD components, respectively. The pixel offset tracking method, by SAR amplitude image correlation, has been demonstrated to be efficacious in the determination of the NS component in the case of large displacements  (Wright et al. , 2006; Grandin et al. , 2009). In our case, however, where the maximum NS displacements are about a few tens of cm, the theoretical accuracy of this technique -in the order of one tenth of the spatial resolution, i.
As overlap regions represent about 10% of the burst length,  Grandin et al. (2016)  had to interpolate between successive burst overlaps to obtain a continuous along-track displacement field. This approach only characterizes the largest wavelengths of the displacement field. In our case, since Piton de la Fournaise is entirely covered by only one to two Sentinel-1 bursts (depending the pass), burst overlap interferometry is clearly not adapted to measure coeruptive displacement, which the spatial distribution typically ranges from few hundred of meters to some kilometers. The ALOS-2 data could also be used more optimally than we have done here to determine the near-field displacement. In our current processing chain, we have used a unique coherence mask for all the interferograms in each dataset.
This leads to loss of the near-field displacement, an area in which the ALOS-2 data provides exceptional information, but the X-band and C-band data are generally poorly coherent or at least difficult to unwrap properly. One solution could be to perform a more adaptive masking of the data and/or to use the information provided by ALOS-2 data to improve the unwrapping of X-band and C-band interferogams in the near-field. Another way to improve determination of the 3-D displacement components is obviously by trying to reduce the atmospheric component in InSAR data, especially on the terminal cone. The usual approach, consisting of removing the correlated part of the atmospheric component using an empirical relation between phase and elevation  (Remy et al. , 2003; Lin et al. , 2010; Shirzaei and Burgmann, 2012) , is not useful here since there is no other relief on Piton de la Fournaise with the same elevation range as that of the terminal cone that could be used to adjust the empirical phase-elevation relation.
An alternative solution is to exploit the data provided by the dense GNSS permanent network to perform 3-D tomography of the atmospheric phase delay (e. g. Wadge et al. , 2002; Webley et al. , 2002). Although this solution is an obvious way of making the best synergistic use of the GNSS and InSAR data at Piton de la Fournaise, it has not yet been implemented into a fully operational procedure. It appears as particularly promising to assess atmospheric artifact on the terminal cone, where the GNSS network is relatively dense with a mean distance of about 2 km between stations. In contrast, at the scale of the whole Piton de la Fournaise edifice, the mean distance between GNSS stations is ~7. 3 km, so that one may expect to be able to correct by this way only the largest wavelengths of the atmospheric component.
For instance, in May 2015 at Piton de la Fournaise most of the permanent stations and reiterated benchmarks were outside the deformation pattern, except the SE quarter of the terminal cone (Fig. 6 ). Such a pattern of huge deformation outside the field networks was already observed in April 2007, when an eastward motion of up to 1. 4 m was detected by InSAR on the eastern flank, which was not equipped at that time with permanent GNSS receivers  (Clarke et al. , 2013; Froger et al. , 2015). The high spatial resolution of InSAR data is particularly useful to create a reliable numerical model of the displacement source. However, InSAR data generally provides a final view of the displacement that does not offer the possibility to assess how the source grew and propagated to the surface. By contrast, thanks to their high and continuous acquisition frequency, the permanent GNSS data provide clues as to the dynamics of magma transfer to the surface.
Conclusions This paper is one of the first detailed studies to assess the reliability and consistency of the InSAR and GNSS techniques for measuring 3-D ground displacements on volcanoes during rapid changes. Our study confirms the consistency and the complementarity of the two methods to characterize (i) the ground deformation distribution at high spatial resolution (InSAR), and (ii) the dynamism (GNSS) associated with eruptive activity. Our evaluation carried out on the Piton de la Fournaise volcano allows us: -to improve the precision of the InSAR-resulting 3-D data by the use of InSAR data with multiple acquisition geometries, and in particular left-looking data; -to describe in detail the deformation pattern associated with the four 2015 eruptions, i. e. decimeter ground displacements asymmetrically distributed along the dike path, evidencing a preferential eastward motion; -to quantify the discrepancies between InSAR and GNSS: apart from the NS component, InSAR and GNSS data are in overall agreement (most of the GNSS-InSAR residuals are b 2.
Acknowledgments This study was supported by  INSU/CNRS  ( Institut National des Sciences de l'Univers, CNRS ), through the  National Service for Volcanological Observations , by  ANR  ( Agence Nationale de la Recherche)  through the  GEOSUD-ANR-10-EQPX-20  project and by  CNES  ( Centre National d'Etudes Spatiales)  through the  Kalideos  project. We thank  DLR  and  JAXA  for providing the TerraSAR-X/TanDEM-X and the ALOS-2 data, respectively, through the LAN # 0237  project and the 4th ALOS RA # 1287  project. We thank  SEAS-OI (Survey of Environment Assisted by Satellite in the Indian Ocean ) receiving station that provided RADARSAT-2 images for free (www. seas-oi. org). We thank the Sentinel-1 ESA team, especially  P. Potin  and  Y. -L. Desnos  for having made possible routine Sentinel-1 stripmap acquisitions on La Réunion.
A 4 meters pole and a remote control are used to get the most favourable viewpoints, and the shots are treated with Agisoft Photoscan. The ortho-images computed are likely the most important products for this specific mission, as they can be imported in a CAD software to draw the excavation map. Multiscale works can also be realised easily through this technique, as for example drawings of individual bricks that can be inserted directly in the georeferenced CAD file. By following this process several types of documents can be produced: 3D models (possibility to create videos), ortho-images, excavation maps, etc. The production of the archaeological documents has benefited from that technology, and so has their exploitation. The realization of a 3D model in a sector constitutes a digital copy of an incomparable precision/resolution, while remaining a very simple tool that can be used by non-experts of 3D modeling.
In addition, when the set-back distance is so short that the photographer cannot shoot a whole scene within a single frame, he can apply a photogrammetric acquisition protocol and produce a photomontage with higher quality and better precision than the photomerge tool on photoshop software. As an example, it is possible to make a photograph of an 4,5 meters high mast-grooves with only a 2 meters gap to take the photos (Figure  1 ). The process that the CFEETK employs to create orthophotographs is the same than in archaeology but it doesn't require GCP, as the visual results are satisfying as they are for the epigraphic study. To solve the orthograph's orientation unknowns without GCP, a couple of points can be indicated on the horizontal or vertical lines that are often present on the Egyptian scenes.
It is hoped to be able some day to cover the whole area with such a tool. Integrating a global 3D model in which the documents in the Projet Karnak would georeference each of the historical findings would be a major advance in the digital conservation of Karnak. The amount of data accumulated will likely, at some points, become so massive that it will require too much workforce to be analysed, exactly as the 2 km² area is complicated to fully study on the field. The developments in the image recognition and classification fields could help to automatically extract semantic elements, as presented by  (Franken & van Gemert, 2013 , Nederhof, 2015). Projet Karnak's database, designed to index every inscriptions on the walls of the temple, is expected to continue its growth upward the current 8500 documents.
ACKNOWLEDGEMENTS This project is supported by  LabEx ARCHIMEDE  from " Investissement d'Avenir" program   ANR-11-LABX-0032-01. The authors would like to thank the  Ministry of Egyptian Antiquities ,  Abd Al Sattar Badri  and  Christophe Thiers , codirectors of the CFEETK and  Amin Ammar , General Director of the temples of Karnak for their daily support and help. Special thanks to  Mrs Chiara Salvador  (doctoral researcher at the  University of Oxford ) and to  Mr Hervé Lançon  ( CEO of Sites SAS ).