dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. CONCLUSION Nowadays, social media for education uses Web-based technologies to turn learning into interactive dialogue. Much like Web-based social networking, mobile social networking occurs in virtual communities through a mobile phone. The main challenge of mobile networking is the context-aware content adaptation. As a result: (1) many mobile applications are based on Cloud services such as location service and messaging service, (2) turning a speech-production service into a product may consist in writing a script to invoke the proper SOAP methods that will enable to send text and generate speech files on the Speech Cloud (SaaS) server. On the other hand, as suggested in this paper for the next generation of LCMS we can consider scenarios where it will be possible among different systems to exchange not only data but also applications.
Google Earth (GE) provides very high resolution (VHR) natural-colored (red-green-blue, RGB) images based on commercial spaceborne sensors over worldwide coastal areas. GE is rarely used as a direct data source to address coastal issues despite the tremendous potential of data transferability. This paper describes an inexpensive and easy-to-implement methodology to construct a GE natural-colored dataset with a submeter pixel size over 44 km 2 to accurately map the water depth, seabed and land cover along a seamless coastal area in subtropical Japan (Shiraho, Ishigaki Island). The valuation of the GE images for the three mapping types was quantified by comparison with directly-purchased images. We found that both RGB GE-derived mosaic and pansharpened QuickBird (QB) imagery yielded satisfactory results for mapping water depth (R 2 GE = 0.
71 and R 2 QB = 0. 69), seabed cover (OA GE = 89. 70% and OA QB = 80. 40%, n = 15 classes) and land cover (OA GE = 95. 32% and OA QB = 88. 71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (Z Water depth = 6. 29, Z Seabed = 4. 10, Z Land = 3. 28, α two-tailed < 0. 002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OA GE = 99. 17% and OA QB = 97. 80%). Implications and limitations of our findings provide insights for the use of GE VHR data by stakeholders tasked with integrated coastal zone management.
5 US$•km -2 ) has heavily impaired the study of large-scale areas. In 2005, Google Inc. released Google Earth (GE), a freely available version of Earth Viewer 3D that enabled all personal computer users to visualize superimposed landscapes derived from satellite and aircraft imageries based on a geographic information system (GIS) environment. GE offers images based on VHR satellite data as an open source platform, which has proven to be an asset for qualitatively validating global mangrove forests  [18]  and referencing wetland changes in China  [19] , for example. Excluding a limited number of studies assessing GE horizontal accuracy  [20] , removing shadows from GE  [21]  and mapping specific land use/land covers  [22] , GE images have rarely been used as the primary material for VHR coastal mapping.
Indeed, a transferrable and easy method of exploiting GE images will provide a common and robust framework upon which a large panel of scientists and stakeholders might tackle coastal issues. A pioneer team endeavored to use VHR GE images as a direct source for mapping  [22] ; however, their methodology suffered from several limitations (i. e. , non-English-based software used to download GE images, GE altitude-resolution relationship, comparison date of GE images versus native images, etc. ), which significantly hindered the reproducibility of their results and, therefore, the transferability of their method to the community. With the goal of serving as many people as possible involved in geospatial management, an easy-to-implement and transparent method should be built so that any person equipped with a computer, internet access, a GE professional license and GIS software might study the coastal seamless landscapes at VHR.
Remotely Sensed Datasets Unlike the freely available license, the GE professional license ($US 399 per year) authorizes displayed RGB images (8-bit radiometric resolution) to be saved in the form of a Premium uncompressed JPEG file (4800 × 4153 pixels) with no copyright watermarks precluding any informational mapping. The construction of the GE spatial dataset relies on successive steps that can be summarized in a flowchart (Figure  2 ). (1) Once the area of interest (ranging from local to regional scales) has been selected on GE (here, 44 km 2 Shiraho area), an acquisition date should be specified (here, 2 July 2007) using the historical imagery button included in the GE toolbar. (2) Embedded at the bottom of the image is the company name of the source sensor (here, DigitalGlobe) following "Image © year".
The 8-bit imagery is composed of four multispectral images (blue, green, red and infrared) at 2. 4 m spatial resolution and one panchromatic image at 0. 6 m spatial resolution. The spatial resolution of the multispectral images can be scaled up to the resolution of the panchromatic using the pixel-level fusion technique pansharpening  [27]. Based on previous results  [14] , this procedure was applied using a simulation of the 2. 4 m panchromatic image that accounts for the optical transmittance of the QB sensor, Gram-Schmidt sharpening transformation, and cubic convolution resampling. As a result, the source dataset featured RGB images (deletion of the infrared) with 0. 6 m spatial resolution, thus paralleling those of GE. Initially referencing the UTM 52 N projection, the source dataset was further registered (RST warping and nearest-neighbor resampling) based on the previous 45 GCP (RMSE < 0.
Bathymetry and Topography An acoustic survey was conducted to measure the water depth of the Shiraho lagoon area. Sounding was performed from a small fishing boat equipped with a dual frequency (50/200 kHz) transducer (single beam) and 12-channel GPS antenna (Lowrance LCX-15MT). Data were recorded at 1 Hz to a multimedia card (MMC), and the boat followed transects across the lagoon at an average speed of 1 m•s -1 and spanned a large variability of water depth and seabed cover. The horizontal and vertical spatial accuracy delivered by the acoustic system were estimated to reach 1 m and 0. 03 m, respectively  [28]. A total of 22,481 individual measurements were recorded and corrected for the tide level by coupling the GMT and Ishigaki Port tide prediction heights. (http://www1. kaiho. mlit. go.
jp/KANKYO/TIDE/tide_pred/index_e. htm). The corrected dataset will assist in the quantification of the accuracy of further Digital Relative Depth Models (DRDMs). To compute a DRDM using a simple but proficient methodology based only on RGB images, we opted for the calibrated ratio transform  [29, 30] , solving for the water depth as follows: = 1 ln ( i ) ln ( j ) -0 (1) where DN i and DN j refer to the digital numbers of the wavebands i and j, respectively; m 1 and m 0 are the slope and intercept of the fitted linear model; n is a fixed constant to ensure that the natural logarithm is positive. According to the increasing absorption of light by water from the blue to red bands, we tested three possible spectral combinations for each remotely sensed dataset. The resulting six DRDM were compared with the water depth measurements and specifically corrected for the tide level bound to the satellite acquisition time.
Topographical data, which were not derived from the remotely sensed datasets, were employed to verify whether it improves the mapping results associated with terrestrial cover types. For the sake of affordability, freely available elevation data were sourced from the Advanced Spaceborne Thermal Emission And Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 2, provided at one arcsecond (approximately 30 m at the equator) and 8 m of horizontal and vertical resolution (http://www. jspacesystems. or. jp/ersdac/GDEM/E/4. html). A digital elevation model scaled up at 0. 6 m spatial resolution was created by interpolating to regular grid Delaunay-triangulated GDEM data points (Figure  5A ). A relevant combination of the GDEM and GE natural-colored (Figure  5B ) data consists of calculating an elevation-based 3D point cloud (Figure  5C ) over which the GE image can be draped (Figure  5D ).
Relying on the percentage of dominant seabed type (>90%), 10 so-called pure images by seabed type were selected and considered as representatives of their inherent type. By matching the locations of the pure images and corresponding pixels, a spectrally even buffer area was grown around each pixel so that a series of 100 pixels was assigned to each seabed type. Land reference data were composed of a mix of in situ georeferencing and image interpretation. A panel of 11 land types (Table  1 ) was selected as the representative cover types in Shiraho. For sparse but noteworthy types, such as sugar cane fields and mangrove forests, the geographic coordinates of the target perimeter were attained using a handheld Garmin eTrex 20 GPS. For sufficiently contrasted spatial and spectral information, the pixels tied to the remaining land types (n = 9, Table  1 ) were assigned through a visual inspection by a single analyst.
For each land type, 100 pixels were selected either between or around the core pixels (i. e. , matching locations of the surveyed and mapped data) to determine a dataset that is balanced within a seamless coastal landscape. The classification of the study area was performed for each of the remotely sensed datasets to test the reliability of GE for coastal mapping. Ground-truth pixels characterizing the 26 classes were divided into two independent clusters: the training cluster, which helped define the class-specific spectral signatures required for classification, and validation cluster, which was used to assess the accuracy of the classification process. Out of the 100 pixels representative of each class, 67 and 33 were randomly attributed to the training and validation clusters, respectively.
The widely spread maximum likelihood (ML) algorithm (QGIS) is a pixel-based, supervised procedure fueled by the 67 pixels of the 26 classes. Prior to launching the ML classifier, a curve boundary separating the land from water was manually delineated over the wet/dry sand demarcation line. The ML procedure was conducted over a marine area devoid of land based on the RGB and RGB + bathymetry datasets and was also applied over a terrestrial area devoid of sea based on the RGB and RGB + topography datasets. The validation cluster was required to construct a confusion matrix so that the accuracy of each classification (n = 8) could be quantified. This technique relies on the proportion of pixels that are correctly classified in the resulting image based on the match between the class identity of the validation and classified pixels.
Comparison of the Water Depth Retrieval Using the uncalibrated ratio transform, three Digital Relative Depth Models (DRDM) were produced for both the Google Earth (GE)-derived very high resolution (VHR) mosaic and pansharpened QuickBird (QB) imagery (Figure  6 ). For both datasets, the ratio transforms involving the blue and red spectral bands (see Figure  6B, E ) were closely followed by the green-red combination (see Figure  6C ,F) and provided satisfactory measures of agreement between the modeled and actual water depths (R 2 ≈ 0. 69). Interestingly, the blue-red combination (most accurate) reached higher measures of agreement for GE compared to QB (R 2 GE = 0. 7134 versus R 2 QB = 0. 6862, Z = 6. 29, α two-tailed < 0. 002). This performance difference between GE and QB occurred systematically in favor of GE across the three ratio transforms (Table  2 ).
For both datasets, the blue-red DRDM were calibrated to a digital depth model (DDM) using the linear fitting model (associated with the R 2 value indicated in Figure  6B, E ) to test whether and the extent to which the seabed mapping may be improved by merging the DDM to RGB bands. Mimicking the elevation-related Figure  5 , the calibrated DDM (Figure  7A ) may be exploited to create a 3D point cloud (Figure  7C ) over which the GE natural-colored image (Figure  7B ) can be draped (Figure  7D ).
Comparison of the Seabed Cover Mapping The marine portion of the study area was classified into 15 classes for both the GE-derived VHR mosaic and pansharpened QB imagery, which were devoid of and provided with the associated DDM (Figure  8 ). Satisfactory to very satisfactory overall accuracies (OA) ranging from 80%-90% were found for all of the datasets, and the GE-derived seabed maps were better matched to the validation data than those stemming from QB (OA GE = 89. 70% versus OA QB = 80. 40%, Z = 4. 10, α two-tailed < 0. 002). Although the GE-derived mapping displayed no UA and a single PA below 60% (i. e. , PA Seagrass = 57. 58%), QB-related accuracies below 60% included two UAs (UA Seagrass = 58. 33% and UA Blue coral = 55. 56%) and two PAs, of which the seagrass attained a very low performance (PA Hard coral bommie = 54.
55% and PA Seagrass = 21. 21%) (Table  3 ). Including the water depth data with the RGB datasets, did not modify the GE-derived OA (OA GE = 89. 70%) and slightly but insignificantly improved the QB-derived OA (from OA QB = 80. 40% to OA QB_DDM = 81. 21%, Z = 0. 32, α two-tailed = 0. 749). Despite the unchanged OA, the water depth information produced permutations among the GE-derived UA and PA that were beneficial to seagrass (PA = +15. 15%) and all three of the coral classes (PA Hard coral bommie = +18. 18%, UA Hard coral thicket = +15. 25%, UA Blue coral = +10. 88%) and detrimental to mud (PA = -15. 15%) and blue coral (UA = -10. 22%). The modest improvement of QB mapping by including the water depth data appeared to be partly reflected by an enhanced seagrass discrimination (UA = +11. 67%) as well as pebble discrimination (PA = +9.
Comparison of the Land Cover Mapping The land portion of the coastal area was categorized into 11 classes for both the GE-derived VHR mosaic and pansharpened QB imagery that were devoid of and provided with the associated DEM (Figure  9 ). Across the band modalities, the 11 targeted features benefited from a successful classification that spanned the highest deciles of both measures of agreement. Once again, the GE-based land maps produced better results than the corresponding QB-based maps (OA GE = 95. 32% versus OA QB = 88. 71%, Z = 3. 28, α two-tailed < 0. 002). Contrary to the GE RGB dataset, the QB dataset showed six UA and PA performing below 84%, of which one bottomed at 48. 48% (PA Mangrove forest ) (Table  4 ). The combination of elevation information and RGB datasets significantly increased the GE and QB classification accuracy by 3.
As a reflection of the synergistic collaboration between forest researchers and GE scientists  [34] , it would be highly relevant to apply the method described in this paper to coastal areas leveraging GE VHR data to assist researchers and stakeholders in addressing urgent coastal issues. Based on a substantial sampling (N GE = 22,478 and N QB = 22,481), all of the accuracy measures were significantly distinct (α two-tailed < 0. 002), and the three GE-based DRDM remarkably outperformed those of the QB (see Figure  6  and Table  2 ). These original findings are surprising because the GE-derived VHR mosaic is sourced from QB data that are provided with the exact same time and location. We previously indicated that the significant differences between the two datasets are a result of either a divergence in the image processing technique, such as the pansharpening procedure, image assimilation technique, such as the JPEG format conversion, or a combination of both factors.
The visual examination of both datasets at the highest resolution reveals a segregation in the image texture (Figure  10 ). Although we applied the resampling technique to produce the smoothest results (cubic convolution, see  [14] ) in the QB pansharpening procedure, the variation of GE pixel values with distance is obviously lacking high spatial frequency compared to that of QB. Therefore, it is reasonable to assume that GE scientists have applied a customized pansharpening algorithm to QB images and assimilated the images in the form of JPEG files to yield smoother output VHR images and that this smoother image better matched the spatial accuracy of the acoustic survey (e. g. , GPS horizontal accuracy). Other spectral sharpening methods, such as the Color Normalized or Principal Component algorithms, should be tested in order to elucidate the GE products.
Seamless Seabed and Land Cover Mapping The coastal zone was successfully mapped in a seamless manner at a VHR by both GE and QB datasets, with approximately 26 cover types resolved and typified by a lowest OA equaling 80. 40% (i. e. , OA QB_Seabed ). The very high classification accuracies have to be contextualized on the basis of the proximity of the ground-truth pixels. Although randomly sampled, the training and validation pixels may suffer from a spatial autocorrelation, incurring a likely overestimation of the accuracy. Both datasets were composed of the three blue, green and red bands (the infrared band was excluded) and overarched sufficiently rich spectral information to discriminate primary cover features in a spatially complex environment. Consistent with the water depth results, the findings related to the classification accuracy showed that the GE dataset produced more reliable coastal cover maps for both marine and terrestrial realms than did the QB dataset.
Specifically, the hard coral bommie, blue coral, dark and mangrove forests were much better classified based on GE data than on QB data. Presupposing the GE-driven smoother texture image, we can hypothesize that the pansharpened QB imagery featured a texture sharp enough that the sun glint occurring over very shallow coral colonies and leaf-induced speckles over dense tree canopies impeded a robust classification. The integration of freely available elevation data into the RGB GE and QB datasets significantly enhanced the land cover classification accuracy, particularly for (1) human infrastructure, (2) dark and mangrove forests and, to a lesser degree, roads and rivers, respectively. The GDEM is derived from the stereoscopic sensor ASTER, which captures and renders 3D landscapes that include cover features  [35].
Consequently, the GDEM maps the top of the human infrastructure and tree canopies, which explains the greater classification consistency for land cover types demonstrated in our study. Despite the initial spatial resolution of one arcsecond, we recommend combining the GDEM-nested elevation data that are rasterized at the appropriate VHR with the RGB GE-derived mosaic to optimize the land cover mapping. However, including the water depth information retrieved from the RGB datasets to either of the datasets did not significantly improve the marine cover mapping. This result may be explained by the absence of a water column correction to account for the RGB light by water, which is intricately linked with the water depth. Ongoing research on the radiometric calibration of GE data that is based on the sensitivity of the source sensor and sun-scene-sensor trigonometry has a strong potential to provide water-leaving reflectances that are capable of retrieving seabed reflectances when merged with calibrated DDMs.
Conclusions Focused on the seamless coastal area of Shiraho (Ishigaki, Japan), we found that both RGB GE-derived mosaic imagery and RGB pansharpened QB imagery yielded satisfactory to very satisfactory results for the water depth (R 2 GE = 0. 71 and R 2 QB = 0. 69), seabed cover (OA GE = 89. 70% and OA QB = 80. 40%, n = 15 classes) and land cover (OA GE = 95. 32% and OA QB = 88. 71%, n = 11 classes) mapping. In addition, we showed for the first time that the GE dataset significantly outperformed the QB dataset for all three mappings (Z Water depth = 6. 29, Z Seabed = 4. 10, Z Land = 3. 28, α two-tailed < 0. 002). The integration of freely available elevation (GDEM) data into both RGB datasets significantly improved the land cover classification accuracy (OA GE = 99. 17% and OA QB = 97. 80%) compared to that of the water depth.
Acknowledgments This study was fully supported by  Grant-in-Aid for JSPS Fellows  (No. 2402800 ) of the  Japan Society of the Promotion of Science (JSPS)  and partly supported by  Grant-in-Aid for Scientific Research (A)  (No. 24246086 ,  25257305 ) of  JSPS. The first author also acknowledges  Hiroyuki Takamiyagi , who nimbly drove the boat along the full-range of lagoon water depths. This paper was significantly improved by the comments and suggestions of two reviewers who neatly underlined points to be clarified and contextualized.
Electron cryo microscopy and tomography data collection 2. 4. 1. Electron cryo microscopy Electron cryo microscopy was performed at 300 keV, using a F30 'Polara' electron microscope (FEI). Low-dose, zero-loss images were acquired with SerialEM  [31]  in EFTEM mode with a Gatan 964 Quantum energy filter operated with a 20 eV wide energy selecting slit. Images were recorded at nominal magnifications of 5600 Â , 9300 Â and 41,000 Â sampling the specimens at calibrated pixel sizes of 3. 65 nm, 2. 10 nm and 0. 72 nm, respectively. Low magnification montages (LMM) and high magnification montages (HMM) were collected as standard procedure in SerialEM  [31]  via the 'navigator' GUI. Initially, LMM grid scans were recorded at a nominal magnification of 480 Â in EFTEM mode using the SerialEM 'grid full montage' option, with an overlay of 15%.
Accuracy of correlation calculation 2. 5. 1. Correction of multi-channels fluorescent cryo microscopy images and determination of accuracy of alignment (s A ) In order to correct the shift between the images of the different channels due to mechanical instabilities or chromatic shifts caused by the optics of the microscope, we used TetraSpeck microspheres, which are fluorescent in all three channels, as reference markers. To identify and determine the positions of the Tetraspeck fluorescent microspheres (fiducials) in all three channels with very high accuracies, we used algorithms which were originally developed for localisation microscopy (for details see Appendix A)  [34, 35]. An automated procedure for identifying objects that are fluorescent in all 3 channels was established and implemented in MATLAB (MathWorks).
Fluorescent fiducial based coordinates transformation and determination of its accuracy (s C ) The coordinate transformations based on fluorescent microsphere fiducials were assigned as described previously  [24, 25]. This procedure was performed using a MATLAB script based on software kindly provided by Martin Schorb (EMBL, Heidelberg, Germany). As inputs we used the two drift-corrected cryoLM images (blue and green channel), and the 2D projection EM images acquired at 5600 Â or 9300 Â nominal magnifications with calibrated pixel sizes of 3. 65 nm and 2. 10 nm (4. 20 nm binned). Between 3 and 10 Fluo-Spheres were picked in each image. The precise coordinates of the signals of the fluorescent fiducials were determined using a 2D Gaussian fit, which was carried out on a fluorescence image after high-pass-filtering with a smooth cut-off of 70 pixels.
The MATLAB control point selection tool was then used to determine the transformation between the FM and EM image based on the picked fiducials in both the FM and EM image. For the calculation of the correlation accuracy s C was determined as described previously  [24, 25]. The position of one fiducial was left out for finding the transformation between the FM and the EM image. The fiducial position is then compared with the predicted position, and the displacement is measured. This is repeated for all fiducials selected in the images. The mean value of the displacements gives an estimate for the correlation accuracy s C.