dataesr/scientific-paragraphs-categorization huggingface dataset:
Figures S6-S8 in Supporting Information S1). On the contrary, ITS can more effectively retrieve the 21 November 2016 event (Figure  12g ), likely thanks to the spatiotemporal approach (cf. Figures S9-S14 in Supporting Information S1), while IMG is less well performing. This seems to suggest that the two different image-based data representations carry some particular characteristics coming from the network geometry and the spatiotemporal variability of the data. We also notice that the outlier displacement value north of the epicenter of the 19 July 2008 event (cf. Figure  12d ) is actually an artifact introduced by the linear interpolation performed on the time series in presence of a large data gap. Therefore, either a more efficient method should be set up for the missing data interpolation, or artifacts should be taken into account in the training database.
Accounting for the data gaps is not a trivial task and future developments should focus on this aspect, since, as we saw, the larger the data gaps, the harder the characterization. Figure  12. Displacement fields associated with the eight events of the ISTerre/DD dataset. The deformation fields have been computed by subtracting the deformation at day t c + 1 and t c -1. In each subplot, the focal mechanism from the NIED catalog is shown as well as the magnitude and depth (in each title) with the yellow, brown, and blue points representing the predictions for TS, IMG, and ITS, respectively. It is worth mentioning the performance of the models on the Tohoku event (11 March 2011, M w = 9. 1), which is estimated as a M w ∼ 8. 5 event by IMG and ITS, and as a M w ∼ 9 event by TS on the NGL/PPP dataset and as a M w ∼ 6.
8 event on the ISTerre/DD dataset. Although these results may suggest that TS might perform well on patterns whose magnitude has never been presented to the network before, they also should be taken carefully since we cannot assess the robustness of the methods in case of testing against data having different characteristics. In this study, we generate uniformly distributed events to train the deep learning models, yet we set the focal mechanism to be a thrust. We motivate this mainly by claiming that we are interested in characterizing thrust subduction events and thrust crustal events as well. However, one may ask how the models trained on a variety of focal mechanisms would perform on real data, with respect to the ones trained on thrust events. Results on synthetic data are presented in Figures S15-S19 in Supporting Information S1 and show that the models trained on all focal mechanisms have slightly lower performances and more variability than the models trained on thrust only.
Results on synthetic data clearly evidence a detection threshold associated with GNSS data, which is linked to the SNR, and also dependent on the depth and position of events. This allows us to partition the output space by identifying regions in which the source characterization can be performed with confidence. The performance on real datasets is globally consistent with the results obtained on synthetic data, although it is hard to draw robust statistics due to the small amount of testing data. Image-based methods, that is, IMG and ITS, qualitatively outperform the TS approach in both real datasets, being able to retrieve the source parameters of most of the tested events, with IMG effective in characterizing most of them. The ITS model shows that the proposed spatiotemporal approach is crucial in resolving the location and magnitude of some of the real events where IMG had poor performance.
This result confirms that accounting for the pre-and post-event noise level can lead ITS to a better estimate of the co-seismic offset. However, the noise characterization needs to improved, in order to better account for outliers in GNSS time series, data gaps, and, possibly, common modes. By improving the simulation of the realistic noise, we can produce more and more real-looking synthetic data, possibly having better results on the characterization and a lower SNR threshold. Also, we might expect that the performance on real data would improve if we generated a more realistic synthetic dataset that contains also a post-seismic signal in addition to the co-seismic offset, as shown in Section 3. 6. Nonetheless, the results on real data are promising and could potentially also lead to an effective analysis of slow deformation, which would benefit from the present work as well as from the potential refinements that we have listed before.
Figure11. Actual versus predicted plot on real data from ISTerre/DD and NGL/PPP datasets. Each subplot shows the real versus predicted comparison for the estimated parameters (fault centroid latitude, fault centroid longitude, and magnitude in each row) for each of the three methods (TS, IMG, and ITS in each column). For each scatter plot, circles and squares represent predictions associated with the ISTerre/DD and NGL/PPP datasets, respectively. Filled markers represent events having a trust focal mechanism, while empty markers indicate any other focal mechanism. The solid dashed line shows the line of perfect prediction. The data points are color-coded according to the time of occurrence.
Data Availability Statement All rough data used in this manuscript are available https://doi. osug. fr/public/GNSS_products/GNSS. products. Japan. html (ISTerre/DD) and http://geodesy. unr. edu (NGL). All the software as well as the pre-trained deep learning models are available at: https://gricad-gitlab. univ-grenoble-alpes. fr/costangi/eq-characterization.
Results and Discussion The quantitative results are shown in Table  3 , while Figure  11  shows the performance of the tested methods on the two real datasets. The displacement fields with all the characterizable events in the ISTerre/DD dataset are in 12. From the numerical results listed in Table  3 , associated with the "characterizable" events having a thrust focal mechanism, it is hard to assess the best method they have been obtained on an insufficient number of samples. The IMG model performs better on the ISTerre/DD dataset in terms of median prediction error, with time-series-based models (i. e. , TS and ITS) being more accurate on the NGL/PPP dataset on magnitude and location accuracy, respectively. All the models have a larger error associated with the NGL/PPP dataset, probably.
The appearance of Dimorphos contrasts with impressions from lower-resolution images of the Didymos surface, where regional roughness variations are apparent (Extended Data Fig. 4 ). Note the size of the spacecraft relative to the asteroid. The spacecraft bus was approximately 1. 2 × 1. 3 × 1. 3 m, from which other structures extended, resulting in dimensions of approximately 1. 8 × 1. 9 × 2. 6 m. The spacecraft also had two large solar arrays that were each 8. 5 m long. b, A closer view of the DART impact site showing the outline of the spacecraft bus and solar arrays over the DRACO image. Note the positions of the two solar arrays relative to two large boulders, labelled 1 (6. 5 m long) and 2 (6. 1 m long). This subframe is from an image taken 2. 781 s before impact. c, The spacecraft bus hit between boulders 1 and 2, whereas the solar arrays interacted with these boulders.
Article The final complete DRACO image captured an approximately 880-m 2 area surrounding the impact site at a 5. 5-cm pixel scale. The impact region (Fig. 2b ) exhibits blocky terrain resembling the rest of the observed hemisphere (Fig. 2a , and Extended Data Figs. 4  and 5 ). There is evidence for variation within boulders, 'rocks on rocks' similar to observations on Bennu  31  , and partially buried boulders (Extended Data Fig. 5 ). The longest axes of boulders counted in the final complete image are 0. 16 m to 6. 5 m in length. The impact region has fewer boulders in the 0. 2-0. 5 m size range than expected if the cumulative distribution followed a single power law, even though the pixel scale of the image is sufficient for their identification (Extended Data Fig. 6 ). There is no evidence for expansive smooth deposits (grain size smaller than the image pixel scale) such as those seen on Itokawa  17.
Shape modelling of Dimorphos We built the shape model using stereophotoclinometry (SPC), a technique that has been widely used to model the shapes of small bodies  [40] [41] [42]. Before the impact of DART, the shape modelling team conducted tests to understand the strengths and limitations of the SPC approach given the data expected from DART  43. DART images pose a challenge for any image-based shape modelling technique owing to the single viewing geometry and lighting condition. SPC provides robust results despite these challenges  43. Extended Data Fig. 7  illustrates the steps used to build the Dimorphos shape model. First, we used DRACO images to determine the dimensions of an initial triaxial ellipsoid. The ellipsoid was constrained by the location of the sunlit limb, the location of the limb lit by light scattered off Didymos and the position of the terminator.
Together, the sunlit and Didymos-lit limbs revealed the complete extents of the X and Z axes of Dimorphos (Extended Data Fig. 8 ). The position of the terminator constrained the Y-axis extent. The last DRACO image to contain all of Dimorphos in the field of view was registered to the ellipsoid. Other DRACO images were registered to that image or, in the case of the highest-resolution images of the impact site, to the immediately preceding image. To correctly determine the scale of the shape model, we used the known time of impact and spacecraft speed to set the spacecraft range for each image. Next, we pursued parallel paths (Extended Data Fig. 7 ). The first path used standard SPC processes to construct small digital terrain models (called 'maplets') using an SPC program called lithos  40, 42, 43.
Shape model uncertainties We quantified uncertainties in the global DTM of Dimorphos using techniques employed to estimate the uncertainties associated with the global DTM of Bennu  44. These techniques were also used to assess uncertainties in shape models developed during pre-impact tests done for DART  43. The analyses involved comparing DRACO images against the global DTM when rendered with the same lighting and viewing geometry as the image. We used three analyses described in the above references. The first method, referred to as the limb and terminator method, used image thresholding to identify the limb and terminator in the DRACO image and rendered shape model. The thresholded images were subtracted to reveal mismatches between the image and rendered shape model and any size bias in the model (Extended Data Fig.
9a-e ). The second and third methods relied on analysis of corresponding surface features, or keypoints, in images and the rendered shape model, to understand errors in the overall size of the shape model (Extended Data Fig. 9f-i ). In the second method, referred to as keypoint matching, the rendered image was rotated, translated and scaled to minimize differences in keypoint locations on the DRACO image and rendered shape model. The third method, referred to as keypoint distance, was based on the measured distances between all keypoints in the DRACO image and rendered shape model. The limb and terminator assessments indicated that the model is 75 cm too small in X, Y and Z, with a limb uncertainty of 1. 3-2 m. Most of the mismatch between DRACO images and the rendered shape model occurred along the limb and terminator.
Impact site identification The construction of an SPC shape model produced a set of surface landmarks that were used to determine the location (at the time of each image) of the spacecraft relative to the surface in the Dimorphos body fixed frame. This procedure used the spacecraft's Didymos relative velocity computed by the ground navigation team using a combination of radiometric tracking data (Doppler, range) and optical images of Didymos. We used this information and the shape model of Dimorphos to compute the location of the impact site. The positions of the DART spacecraft from SPC were converted to the inertial J2000 frame and corrected for light time and aberration. The velocity of the spacecraft was estimated by fitting a second-order polynomial function to these positions as a function of time.
This approach is identical to that employed previously  45  to estimate the position of the Hayabusa spacecraft relative to Itokawa. We used the locations from the last 14 complete images collected by DART to determine the spacecraft velocity. These images contained large numbers of landmarks due to their fine pixel scales, which help anchor the spacecraft position for each image. The fit residuals (that is, the difference between the fit of the spacecraft location to that determined from SPC) are <1 m (Extended Data Fig. 10 ). We combined our estimated velocity and the SPC-derived spacecraft positions of the last five images to determine the impact location. Only the last five images were used to locate the impact because heater cycling on the spacecraft introduced cyclic error rates in the inertial attitude knowledge.
These rates were estimated to be small at the time of impact owing to the timing of impact relative to heater cycling but at their highest approximately 30 s before impact. Using only the last few images reduced the influence of this known (and cumulative) error source. At each of these five spacecraft positions, we propagated the velocity vector until it intersected with the surface of the Dimorphos shape model. We took the mean of these positions as the impact location. We also computed the spacecraft state at a few different heights above the intersection point to determine the order in which the solar arrays and bus contacted the surface. The propagation from each of the last five images provided the same impact point to within 1 cm. The uncertainties in the impact point location (reported in Table  1 ) are dominated by the residuals to the fits of the spacecraft position obtained by SPC in the Y and Z directions (Extended Data Fig.
Boulder counting The longest axis of each boulder in the final full DRACO image was identified as a line (as done for asteroid (101955) Bennu  46  ). The length of the longest axis was determined from the line length and image pixel scale, assuming the last full image as a 'flat' scene. The total number of boulders and pebbles identified in the final full DRACO image impact site is 953 and range in size (that is, the length of the longest axis) from 0. 16 m (limit of image resolution, assuming ≥3-pixel sampling  47  ) to 6. 5 m. The resulting size-frequency distribution is shown in Extended Data Fig. 6.
Acknowledgements We thank  P. Boie ,  R. Harvey ,  M. Hill ,  A. Johnson ,  C. Kim ,  J. Kim ,  D. O'Shaughnessy ,  N. Osiander ,  G. Ottman ,  L. Rodovskiy ,  M. Rodriguez ,  A. Smith ,  K. Volland  and all of the other people who made the DART impact possible; and  M. Bruck Syal  and  K. Kumamoto  for their feedback on the manuscript. This work made use of the  Small Body Mapping Tool. This work was supported by the  DART mission ,  NASA  Contract No. 80MSFC20D0004. This work was supported by the  Italian Space Agency (ASI)  within the  LICIACube  project ( ASI-INAF  agreement AC n. 2019-31-HH. 0 ). P. S. and P. P. were supported by the  Grant Agency of the Czech Republic , grant  20-04431S. B. J. B. was funded by the  NASA   DART Participating Scientist Program  # 20-DARTPSP20-0007. S. C.
acknowledges funding from the  Crosby Distinguished Postdoctoral Fellowship Program of the Department of Earth, Atmospheric and Planetary Science, Massachusetts Institute of Technology. G. S. C. was funded by  UK Science and Technology Facilities Council  Grant  ST/S000615/1. F. F. acknowledges funding from the  Swiss National Science Foundation (SNSF) Ambizione  grant No. 193346. M. J. and S. D. R. acknowledge support by the  Swiss National Science Foundation  (project number  200021_207359 ), and from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 870377  (project  NEO-MAPP ). T. K. is supported by  Academy of Finland  project  335595  and by institutional support RVO 67985831 of the  Institute of Geology of the Czech Academy of Sciences.
P. M. acknowledges funding support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 870377  (project  NEO-MAPP ), the  CNRS  through the  MITI interdisciplinary programmes ,  CNES  and  ESA. N. M. and C. Q. R. acknowledge funding support from the  European Commission 's  Horizon 2020 research and innovation programme  under grant agreement no. 870377  ( NEO-MAPP  project) and support from the  Centre National d'Etudes Spatiales (CNES). J. O. has been funded by grant No. PID2021-125883NB-C22  by the  Spanish Ministry of Science and Innovation/State Agency of Research   MCIN/AEI/10. 13039/501100011033and  by ' ERDF A way of making Europe '. S. R. S. acknowledges support from the  NASA   DART Participating Scientist Program , award no. 80NSSC22K0318.
J. K. S. acknowledges support from  NASA  award  80NSSC21K1014. J. M. T. -R. acknowledges financial support from the project  PID2021-128062NB-I00  funded by  Spanish   MCIN/AEI/10. 13039/501100011033. P. B. acknowledges funding support from  Europlanet/University of Edinburgh and Technical University of Kenya. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration.
Data availability The DRACO images shown in this paper, the global digital terrain model of Dimorphos and the local digital terrain model of the impact site are available in a permanent archive associated with this paper in the JHU/APL Data Archive (https://lib. jhuapl. edu/papers/ dart-an-autonomous-kinetic-impact-into-a-near-eart/). All raw and calibrated DRACO images, as well as higher-order products such as digital terrain models, will ultimately be available via the Planetary Data System (PDS) (https://pds-smallbodies. astro. umd. edu/data_sb/ missions/dart/index. shtml) by October 2023.
Acknowledgments: This research has been possible with funds from  CONACYT Ph. D. Grant  610869  for  C. F. Herrera-Vázquez , and this is a contribution of The Waves Group. We acknowledge the support from  CICESE Physical Oceanography Department Graduate Program. Sincere thanks to  Julio Sheinbaum  and  Luis Zavala , for their helpful comments and support. We greatly acknowledge the support and BOMM data availability from CIGoM ( SENER-CONACYT  project  201441. This work has been funded by  Frontiers of Science CONACYT  project  CF-2019-116328 , and by an  IFREMER-ESA  contract to CICESE. The authors are grateful to the anonymous referees for their constructive comments and suggestions.
We hypothesize that plant productivity is strongly linked to plant type, terrain characteristics, and water availability, and that the plant seasonal dynamic is determined by local and upslope snowmelt dynamics. We further hypothesize that the strength in the relationships between state variables, such as electrical conductivity, soil moisture, and vegetation greenness, are time-dependent and can be used for ecosystem understanding, if captured at the optimal time and to constrain spatially continuous estimates of soil moisture dynamics at hillslope or larger scale. Testing these hypotheses is needed to understand specific plant type responses to changes in water availability in snow dominated environments, as well as to develop strategies to constrain estimates of soil property distributions through the merging of various datasets.
To test the above hypotheses, an autonomous acquisition system, including an electrical resistivity tomography (ERT) system and soil moisture sensors, was deployed along a hillslope to floodplain transect in a lower montane environment in the upper East River Watershed of the Upper Colorado River Basin. In addition, the transect was periodically surveyed with an Unmanned Aerial Vehicle (UAV) to map vegetation reflectance and infer green chromatic coordinate (GCC) and normalized difference vegetation index (NDVI), as well as surface elevation to infer changes in plant height. 2 Site description, and data collection and processing.
15 and 1. 5 m, with a mean value of 0. 76 m. The soil thickness shows an increasing trend toward the bottom of the hillslope with largest values in topographic lows and in the floodplain  (Yan et al. , 2021). Meteorological forcing at the site involves snowpack accumulation from November through mid-April, with snowmelt occurring in the spring season (March-June) and peak of snowwater-equivalent in mid-April. Meteorological data obtained from the Butte Snow Telemetry (SNOTEL) station located 2 km from the site and 350 m higher in elevation (https://wcc. sc. egov. usda. gov/ nwcc/site?sitenum=380) indicate a mean daily air temperatures that ranges from -8. 3 °C in December to 11 °C in June. Annual precipitation for 30-year period of record at Butte SNOTEL station is 670 ± 120 mm/year with snow accounting for 66% ± 12% annual precipitation  (Carroll et al.
UAV-based spatiotemporal mapping of vegetation indices and plant height Time-lapse imagery was collected 4 times in 2017 and 4 times in 2018 using a 3DR Solo UAV platform. In both years, the aerial surveys involved the collection of RGB imagery using a Sony 5,100 digital camera. In addition, multi-spectral reflectance imagery was collected with a Micasense Rededge camera in 2017. The UAV surveys were performed across a 300 x 500 m 2 area encompassing the studied hillslope transect. About 50 small PVC targets were deployed and used as Ground Control Points (GCPs) for accurate georeferencing of the UAV-based imagery. The GCPs centers were measured prior to each UAV flights with a Real Time Kinematic (RTK)-GPS. The multi-spectral reflectance data were calibrated using a diffusive reflectance target provided by Micasense.
All georeferenced orthomosaics and digital surface model (DSM) were reconstructed using AgiSoft reconstruction software using structure-from-motion-based reconstruction techniques (e. g. ,  James and Robson, 2012). The resulting georeferenced orthomosaics and DSMs, with a resolution ranging between 2 and 10 cm, were all interpolated to 10 cm pixel resolution for comparison purposes. Comparison of the orthomosaics and DSM at various times with reference GCPs across the surveyed area showed that the uncertainty in x, y, z was in the order of 10 cm. We inferred GCC from the RGB imagery and NDVI from the Rededge multi-spectral imagery. NDVI is defined as (NIR-R)/(NIR + R) and the GCC is defined as [G/(R + G + B)], where B, G, R and NIR represent the blue, green, red, and near-infrared channel, respectively (e.
Digital terrain model, vegetation type map and landsat imagery Airborne LiDAR data were acquired over the study area on 10 August 2015, using a Riegl Q1560 dual-channel LiDAR system and provided a point density with more than 8 pulses/m 2 that was used to create a digital terrain model (DTM), representing the first bare-ground elevation, at a spatial resolution of 0. 5 m  (Falco et al. , 2019a). The DTM was compared with RTK GPS measurements (positioning accuracy within few centimeters) in a vegetated region within the hillslope; the root-mean-square-error of the DEMs was less than 0. 15 m. The DTM was used to infer topographic metrics including slope and topographic wetness index (TWI). A map of the vegetation type was obtained by using the Lidar data, an optical satellite image acquired by the WorldView-2 on 24 September 2015, and ground-based spectral measurements and identification of plant communities  (Falco et al.