dataesr/scientific-paragraphs-categorization huggingface dataset:
The results showed a wrong detection of the shape, even with the lower value of "expansion", a parameter used to select similar geometry. Putting this value on one, the lowest rate, the software selected also a part of the epistyle of the temple (Figure  15a ). By increasing the value, the results were worst (the algorithm selected also the pillars in the back and a bigger part of the epistyle) even if the number of columns selected is higher (Figure  15b ). This software works fine on CAD models with well-defined parts but not with 3D reality-based meshes. One can redefine and clean the automatic selection but then the export of the segmentation is done only with a proprietary format (extension *. seg) and can be opened only with SimSelect. This difficulty makes the software useless for the goal of the segmentation on reality-based models.
ACKNOWLEDGEMENTS The authors want to thank  Dr Anthony Pamart  from  CNRS MAP -Marseille  for permitting the use of his 3D point clouds of the Cambodian buildings and  Dr Adeline Manuel  from  the same Laboratory  for her help in understanding the segmentation algorithms. For the mesh of the Neptune temple in Paestum, thanks are essential to the researchers of the 3DOM unit of FBK who processed all the images and gave the result to the authors, and to the  Superintendence of Paestum  for allowing to use its material. The authors wish also to acknowledge the support of the  European Union Joint Programming Initiative on Cultural Heritage (JPICH) , for funding the research in the framework of the project  Cultural Heritage Through Time  ( CHT2 ).
The results showed that using an intermediate resolution of 4 km led to improved results than when ASTER was used for disaggregating SMOS directly to the targeted (500 m) resolution. That is notably because MODIS reduced the non-linearity influences across scales between the soil evaporation rate and soil moisture  [23]. Note that the temporal resolution of the observations used for disaggregation is of great importance for providing disaggregated soil moisture at suitable time intervals. Since MODIS data are available twice a day, the availability (on clear sky days) of the 1 km resolution DISPATCH product over time depends solely on the SMOS revisit time, which is three days. ASTER and Landsat data can be used for disaggregating the 1 km DISPATCH products to finer resolution  [23, 25]. However, the availability of ASTER and Landsat data over time  (16 days)  makes the use of the disaggregated soil moisture in agriculture questionable.
In fact, more frequent (few days) disaggregated soil moisture estimates are required in order to monitor the water availability for plants and, hence, to schedule the time and water amount of irrigation  [26]. The C-band backscatter data acquired by the synthetic aperture radar (SAR) sensor aboard Sentinel-1 satellite is a potential candidate for disaggregating the 1 km DISPATCH products to 100 m resolution due to the shorter revisit time and the free availability to public users. In this context, this work aims at employing the C-SAR data acquired every six days by Sentinel-1 for disaggregating SMOS products to 100 m resolution using the 1 km DISPATCH product as a reference at an intermediate resolution. In this work, three different methods are investigated: (1) the 100 m to 1 km resolution backscatter ratio is used as an indicator for soil moisture spatial variability; (2) the regression form of the near linear relationship between soil moisture and backscatter is used for disaggregation; and (3) the cumulative probability (CDF) of the backscatter is used for estimate 100 m resolution soil moisture given the maximum and minimum soil moisture difference captured by the 1 km DISPATCH product.
DISPATCH Soil Moisture Product DISPATCH soil moisture maps were produced over the study area for the period between 1 January 2016 and 11 October 2016. The SMOS level-3 soil moisture data were disaggregated to 1 km resolution using the new SMOS level-4 processor (C4DIS) developed as part of the French ground segment for SMOS level-3 and level-4 data (Centre Aval de Traitment des Donnees SMOS). Details about the C4DIS processor and product can be found in Molero et al. [29]. Briefly, the C4DIS processor implements the DISPATCH method  [23, 24, 30]  at the quasi global scale for each selected MODIS tile separately. The DISPATCH method expresses the 1 km resolution soil moisture as a function of the ~40 km resolution SMOS level-3 soil moisture and the soil evaporation efficiency estimated at 1 km resolution from MODIS LST and NDVI data.
SAR Data Forty-six level-1 Ground Range Detected (GRD) C-band SAR scenes acquired by Sentinel-1 between 1 January 2016 and 11 October 2016 in ascending pass direction at a nominal time of 18:33 UTC are employed for this study. Note that the descending pass direction never matches the DISPATCH product over the study area. Sentinel-1 has four operating modes; however, over land, it is nominally in the interferometric wide swath (IW) mode and measures at dual (VV and VH) polarization with a 250 km swath at a 5 m by 20 m spatial resolution. The VH data are not used for this study since a significantly better correlation between radar and in situ soil moisture was found for VV than for VH over the study area  [28]. However, the VH data can be employed for quantifying the vegetation contribution to the backscatter acquired by the satellite since VH is more sensitive to the vegetation properties  [31].
In Situ Measurements The Sidi Rahal site was equipped by a monitoring station (Figure  1b, c ) in December 2013 in the frame work of the Joint International Laboratory TREMA (a French acronym for Remote Sensing and Water Resources in the Semi-Arid Mediterranean  [32, 33]. The monitoring station includes: (1) micro-meteorological instruments for estimating the latent and sensible heat fluxes at the soil-vegetation-atmosphere interface; (2) sensors for measuring rainfall, global radiation, temperature, relative humidity, and wind speed at a half-hourly time step; and (3) probes for measuring soil water content at different depths and at the time interval of 30 min. For this study, only the in situ measurements of the topsoil (5 cm) moisture content are used. Data were extracted on the 28 dates when both DISPATCH and C-SAR Sentinel-1 data were available.
Methodology Processing the C-band SAR data is an essential step before applying the proposed methods. In particular, the data processing consisted of:(1) converting radar reflectivity into physical units (calibration); (2) reducing the spatial resolution by producing square pixels to improve the radiometric resolution (noise reduction); (3) using a median filter of a 7 by 7 window to alleviate the speckle effect (speckle filtering); and (4) converting the data from ground range geometry to backscatter using the Shuttle Radar Topography Mission (SRTM) 30 m digital elevation model (terrain correction). It is worth mentioning that regions covered by evergreen trees were employed for testing various speckle filters since the backscatter show temporal stability over these trees. The median filter resulted in the most stable backscatter over time and, hence, it was selected for alleviating speckle in this study.
Data Analysis Given that the applied approaches differ in considering the spatial and temporal variability of the soil properties, it is necessary to investigate the relevance between the used data (DISPATCH and SAR data) and in situ measurements as an essential step for investigating the use of C-SAR data for disaggregation as well as the performance of each method. Three main aspects are analyzed focusing on: (1) the accuracy of the 1 km DISPATCH product; (2) the sensitivity of backscatter to in situ measurements; and (3) the dependence between backscatter and the 1 km DISPATCH product. Figure  3  is a graphical evaluation of the 1 km DISPATCH product in comparison to the in situ measurements. The figure shows that some of the 1 km DISPATCH product highly deviates from the measurements causing a wide spread of the matchups among the fitted line.
033 m 3 m -3. In addition, the calculated overall bias is negligible (0. 0003 m 3 m -3 ). Similarly, the sensitivity of the 1 km resolution aggregated backscatter to the in situ measurements is graphically investigated by Figure  4a. The correlation is slightly increased (R 2 = 0. 335) in comparison to the correlation detected in Figure  3 , indicating that the vertical polarized backscatter could be used for improving the spatial resolution of DISPATCH soil moisture. The spread of the data among the fitted line is lower but has similar pattern to that observed between the 1 km DISPATCH product and the measurements indicating that the spatial scale difference influences the 1 km backscatter and DISPATCH product similarly. Evaluating the sensitivity of the 100 m resolution aggregated backscatter to the in situ measurement (Figure  4b ) shows relatively higher R 2 (~0.
36). The matchups show high consistency among the fitted line reflecting that the spatial pattern of the moisture content is captured by Sentinel-1 data. The dependence between the backscatter and the 1 km DISPATCH product is also investigated. Figure  5a  shows that the 1 km resolution backscatter has a very weak dependence on the 1 km DISPATCH product with R 2 of 0. 253 while there is no dependence found between the DISPATCH product and the 100 m resolution backscatter (Figure  5b ). The no dependence between the backscatter and DISPATCH data and the correlation between both data and the in situ measurements confirm that Sentinel-1 data area new source of information by can be used for disaggregation.
Evaluation of Disaggregated Soil Moisture Data Sets The disaggregated soil moisture obtained from each method is assessed through the comparison with the in situ measurements. The graphical evaluation presented by Figures  6 7 8 depicts that R 2 values vary across the range 0. 292-0. 515. Figure  6  shows that the weight method gives the highest R 2 value (0. 515) improving the R 2 value resulted from evaluating the 1 km DISPATCH product against the in situ measurements (Figure  3 ). The spread of the matchups among the fitted line shows that soil moisture are relatively well estimated. However, the figure shows that disaggregated soil moisture corresponding to in situ measurements of ~0. 12 m 3 m -3 has higher dispersion along y axis. The detected dispersion is a direct result of the disaggregation method where the spatial variability of the backscatter (100 m resolution) is the main influencing factor.
Consistency with DISPATCH Data at the 1 km Resolution As an inception for analyzing their spatial pattern, the consistency between DISPATCH and 100 m resolution disaggregated products is evaluated. This consistency evaluation is performed over pixels of the study area representing bare soil conditions only since considering the vegetation contribution to the C-SAR backscatter is not a straight forward operation  [31]. Figure  11  is scatter plots comparing between the disaggregated soil moisture by each method and the 1 km DISPATCH products where values of all pixels of all maps; 46 pairs of maps of both disaggregated soil moisture and DISPATCH products are used with excluding those values estimated over vegetated regions. Figure  11a  shows that the weight method is the only one that provides disaggregated estimates consistent with the DISPATCH product at all soil moisture levels.
Discussion A relatively poor correlation is detected between the DISPATCH product and in situ measurements. This is probably due, at least in part, to the mismatch in the spatial extent between the 1 km resolution DISPATCH pixel and the soil moisture probes representative of a tiny soil volume (several squared decimeters). The input datasets used by the DISPATCH algorithm are the main constraints of the spatial and temporal resolution of DISPATCH data. Indeed, the DISPATCH product is obtained from the daily global maps of soil moisture, land surface temperature and vegetation index and its associated parameters over a three-day moving window  [29]  while the in situ measurements are obtained from the instantaneous measurements of the dielectric constant. The weight method leads to high improvement in the correlation between sm estimates and measurements because of linking sm variability in space to the backscatter spatial variability regardless roughness effect.
The backscatter sensitivity to soil moisture is slightly stronger than both the DISPATCH product-backscatter and DISPATCH product-soil moisture correlations (Figures  3 4 5 ) indicating that Sentinel-1 data area new source of information. Nonetheless, the 100 m sm product resulted from all methods based on the backscatter-sm near linear relationship do not show significant improvement. The spatial and temporal soil moisture patterns provided by the weight method are similar to those of the DISPATCH product. This is due to the fact that the weight method relies on the instantaneous backscatter-soil moisture relationship only, while the other methods are based on the near-linear (temporal) relation between backscatter and soil moisture time series.
Conclusions The 40 km resolution SMOS level 3 product is first disaggregated to 1 km resolution using C4DIS processor, which employs the DISPATCH algorithm and ancillary 1 km resolution MODIS data. Then, different methods are applied for disaggregating the 1 km DISPATCH product to 100 m resolution using Sentinel-1 data as an independent source of information. The methods are identified as weight, regress km , regress 100m , CDF all and CDF every methods, and evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The first (weight) method focuses on the spatial variability of soil moisture while the second and third (regress km and regress 100m ) accounts for the soil roughness as constant over time. The fourth and fifth (CDF all and CDF every ) methods are based on employing the cumulative probabilities of the backscatter to calculate soil moisture through building cumulative distribution functions.
Based on the obtained results, we firstly conclude that DISPATCH soil moisture weakly correlates with the in situ measurements, notably due to the spatial scale mismatch between the 1 km resolution DISPATCH pixel and the representativeness scale (several squared decimeters) of in situ sensors, despite the satisfying accuracy of the DISPATCH product (RMSD = 0. 033 m 3 m -3 ). Secondly, C-SAR data are an independent source of information where the backscatter-soil moisture correlation is slightly larger than the DISPATCH product-soil moisture correlation, while the DISPATCH product-backscatter correlation is very low. Among the applied methods, the weight method leads to superior results preserving the soil moisture spatial and temporal patterns over the study area. Moreover, one significant aspect of the validation exercise is that the correlation between 100 m resolution disaggregated and in situsoil moisture is much improved compared to the 1 km resolution DISPATCH data case, with an associated lower error (RMSD = 0.
Acknowledgments: This work is a contribution to the  REC  project funded by the  European Commission Horizon 2020 Programme for Research and Innovation (H2020)  in the context of the  Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action  under grant agreement no:  645642. In addition, this work has been partially funded by a public grant of  Ministerio de Economía y Competitividad  ( DI-14-06587 ) and  AGAUR-Generalitat de Catalunya  ( DI-2015-058 ).
The gradient was (A/B): 95/5 at 0 min, 95/5 at 1 min, 0/100 at 13 min, 0/100 at 24 min (A = water, B = methanol, both with 0. 1% formic acid) at a flow rate of 300 μL/min. The MS/MS peak lists were extracted with RMass-Bank  [8]  using the ion mass and a retention time window of 0. 4 min around the expected retention time and reported as absolute ion intensities. To obtain highquality spectra, the data was cleaned and recalibrated to within 5 ppm using known subformula annotation  [8] , all other peaks without a valid subformula within 5 ppm of the recalibrated data were removed. All substances with double chromatographic peaks, different substances with identical spectra (detected via the SPectraL hASH (SPLASH)  [9, 10] ), MS/MS containing only one peak or with a maximum intensity below 1 × 10 5 were excluded from the datasets.
Training and challenge datasets The MS/MS spectra and corresponding candidates were split into training and challenge datasets, according to the spectral similarity to MassBank spectra (as many substances were already in MassBank). Challenge spectra were those where no MassBank spectrum was above 0. 85 similarity (calculated with MetFusion  [14] ); all spectra where there was a match in MassBank above 0. 85 were included in the CASMI training set. There were two exceptions: Alizarin, similarity 0. 88 to laxapur (FIO00294), and anthrone, similarity 0. 86 to phosphocreatine (KO003849), to ensure a sufficient number of natural products remained as challenges for Category 1 (see below). Many of the natural products in the mixes did not ionize well with the experimental setup used. The challenge dataset consisted of 208 peak lists from 188 substances, 127 obtained in positive mode (all [M+H] + ) and 81 in negative mode (all [M-H] -).
The retention times for each substance was provided in a summary CSV file. The training dataset consisted of 312 MS/MS peak lists (from 285 substances), of which 254 were obtained in positive mode (all [M + H] + ) and 58 negative mode (all [M-H] -). The identities and retention times of the substances in the training dataset were provided in a summary CSV file. All files were uploaded to the CASMI website  [15]. Participants were asked to contact the organizers if they required additional formats. To allow a comparison with manual approaches, Challenges 10-19 in Category 1 were a (re-named) subset of the dataset in Categories 2 and 3. The corresponding challenge numbers are given in Table  1. Information about the full scan (MS1) data was not originally provided for CASMI 2016, but was provided retrospectively for Challenges 10-19 in Category 1 upon request and post-contest for Categories 2 and 3 for another publication  [16].
Participant methods Team Allen (Felicity Allen, Tanvir Sajed, Russ Greiner and David Wishart) processed the provided candidates for Category 2 using CFM-ID  [18]. CFM-ID uses a probabilistic generative model to produce an in silico predicted spectrum for each candidate compound. It then uses standard spectral similarity measures to rank those candidates according to how well their predicted spectrum matches the challenge spectrum. The original Competitive Fragmentation Model (CFM) positive and negative models were used, which were trained on data from the METLIN database  [19]. Mass tolerances of 10 ppm were used, the Jaccard score was applied for spectral comparisons and the input spectrum was repeated for low, medium and high energies to form the CFM_orig entry. The CFM_retrain entry consisted of a CFM model trained on data from METLIN and the NIST MS/ MS library  [20]  for the positive mode spectra.
This new model also incorporated altered chemical features and a neural network within the transition function. Mass tolerances of 10 ppm were used, and the DotProduct score was applied for spectral comparisons. This model combined the spectra across energies before training, so only one energy exists in the output. The negative mode entries were the same as for CFM_orig. CFM-ID was also used to submit entries for Category 3, by combining the above CFM-based score with a database score (DB_SCORE). For each hit in the databases HMDB  [21] , ChEBI  [22] , FooDB  [23] , DrugBank  [24]  and a local database of plant-derived compounds, 10 was added to DB_SCORE. The CFM_retrain+DB and CFM_orig+DB submissions were formed by adding the DB_SCORE for each candidate to the CFM_retrain and CFM_orig entries from Category 2, respectively.
To assess the generalization of their method, the classifiers were retrained on the same training set, plus CASMI training spectra, but with these top scoring candidates removed. As this entry was "guesswork" and did not affect the contest outcomes, upon request Team Dührkop resubmitted a true "leave out" entry postcontest where all CASMI challenge compounds were removed from their training set (not just their "guess" based on top scoring candidates) prior to retraining and calculating the CSI:FID_leaveout results. For the sake of interpretation, only these updated "leave out" results are presented in this manuscript. Team Kind (Tobias Kind, Hiroshi Tsugawa, Masanori Arita and Oliver Fiehn) submitted entries to Category 3 using a developer version (1. 60) of the freely available MS-FINDER software  [35, 36]  combined with MS/MS searching and structure database lookup for confirmation (entry MS-FINDER+MD).
MS-FINDER was originally developed to theoretically assign fragment substructures to MS/MS spectra using hydrogen rearrangement (HR) rules, and was subsequently developed into a structure elucidation program consisting of formula prediction, structure searching and structure ranking methods. For CASMI, an internal database was used to prioritize existing formulas from large chemical databases over less common formulas and the top 5 molecular formulas were regarded for structure queries. Each formula was then queried in the CASMI candidate lists as well as an internal MS-FINDER structure database. A tree-depth of 2 and relative abundance cutoff of 1% as well as up to 100 possible structures were reported with MS-FINDER. The final score was calculated by the integration of mass accuracy, isotopic ratio, product ion assignment, neutral loss assignment, bond dissociation energy, penalty of fragment linkage, penalty of hydrogen rearrangement rules, and existence of the compound in the internal MS-FINDER structure databases (see Additional file 1 for full details).
MS-FINDER uses ion intensities in the relative abundance cutoff and isotopic ratio calculations, but not in candidate scoring. Secondly, MS/MS search was used for further confirmation via the NIST MS Search GUI  [37]  together with major MS/MS databases such as NIST  [20] , MassBank of North America (MoNA)  [38] , ReSpect  [39]  and Mass-Bank  [33]. The precursor was set to 5 ppm and product ion search tolerance to 200 ppm. Around 100 out of the 208 candidates had no MS/MS information. For these searches, a simple similarity search without precursor information was also used, or the precursor window was extended to 100 ppm. Finally, those results that gave overall low hit scores were also cross-referenced with the STOFF-IDENT database of environmentally-relevant substances  [40, 41]  to obtain information on potential hit candidates.
The default formula finder settings were used, except the mass tolerance, which was set to ±5 ppm mass accuracy as given by the CASMI organizers. MS-FINDER typically retrieves candidates from an Existing Structure Database (ESD) file compiled from 13 databases, but this was disabled as candidates were provided. Instead, one ESD was created for each of the 208 challenges, containing the information from the candidate lists provided by the CASMI organizers. A batch search of the challenge MS/MS against the challenge candidate list (in the ESD) was performed on the top 500 candidates, to avoid long computational run times. Up to 500 top candidates structures were exported as a text file from MS-FINDER. Scores for automatically matching experimental to virtual spectra were ranked based on mass error, bond dissociation energy, penalties for linkage discrepancies, or violating hydrogen rearrangement rules.