dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, a map of the buried substratum around the Poitou High was proposed on the base of detailed structural analysis and drilling information  (Rolin and Colchen, 2001). Because we now benefit from recent high resolution aeromagnetic data on the entire southwestern part of the Paris Basin, we are now able to address the litho-structural pattern of its buried substratum with an unprecedented resolution. Complemented by a characterization of the field petrophysical properties of the various litho-structural units, our work ascertain cartographic interpretations of the geophysical signatures. Our methodology emphasizes the processing of interpretation of potential field data to derive a highresolution structural map. Combining the latter with field petrophysical properties, lithologies were then interpreted in each litho-structural unit, leading to a new geological map of the Paris Basin substratum.
The surveys overlap with each other on a 3 to 5 km band at their periphery. In order to get rid of punctual artifacts related to human activities, the data were upward continued to an elevation of 600 m. This removed the short wavelength cultural noise without significantly smoothing the data, with regards to the aims and regional extent of the study. Magnetic anomaly 250 m regular grids were produced using the minimum curvature gridding method  (Taylor and Mason, 1972) , separately, for each survey. In addition three grid transforms were applied to emphasize various properties of the buried substratum: -The reduction to the pole (RTP; Fig. 3b ): it contributes to simplify the magnetic signal interpretation  (Blakely, 1996). Taking into account the Earth field direction, this operator relocates the magnetic anomaly on top of its causative body.
Processing of gravity data Gravity data derive from the compilation of ground gravity surveys conducted in France since the middle of the 20th century and compiled in the Banque Gravimétrique de la France (BGF)  (Martelet, et al. , 2009). In the study area, the average station coverage is about 1 station/km². Data are tied to the CGF65 base station network. In order to derive the Bouguer anomaly, all standard corrections are included, with a reference density of 2600 kg/m3, and terrain corrections computed to a distance of 167 km  (Martelet et al. , 2002). Taking into account the accuracy of 1) the network, 2) the gravity measurements and their positioning and 3) the terrain corrections, the RMS error on the Bouguer anomaly is 0. 32 mGal in the study area. The Bouguer anomaly map presented in Fig.
Geophysical signatures of the Paris Basin substratum The previous studies investigating the substratum of the Paris Basin using magnetic and gravity data manually outlined the main geophysical anomalies. They were interpreted with simplified lithological attributions, based on the substratum nature documented in some boreholes as well as some rock property data  (Weber, 1973; Debeglia and Weber, 1980). More recently,  Martelet et al. , (2013)  proposed a map of petrophysical signatures of the substratum of the south-central part of the Paris Basin, based on a numerical classification combining gravity and magnetic data. We used the same approach to achieve a simplified magnetic-gravity signature of the substratum of the study area. We agree with previous studies that considered the magnetic effect of the Meso-Cenozoic sedimentary cover of the Paris Basin, almost "transparent" for the magnetic field  (Weber, 1973) ; therefore the magnetic map mostly features the buried substratum.
Summary and Conclusion Our paper outlines the benefit of a joint interpretation of potential fields (high-resolution aeromagnetic and gravity data) and petrophysical characterization (magnetic susceptibility and density measurements on rock samples), in order to derive reliable lithological and structural mapping of a buried substratum. In order to propose a geological map of the hidden substratum of the southwestern part of the Paris Basin, our methodology, is divided into five successive stages: i) the potential field data were processed, with the aim to get specific information (geophysical contrasts, structural features…), ii) magnetic susceptibilities and densities were measured on field rock samples along the eastern border of the Armorican Massif and the northern border of the Massif Central, leading to a petrophysical library of lithologies, iii) using selected magnetic and gravity maps, a map of geophysical signatures was synthetized using an unsupervised classification, featuring 6-levels of magnetic/gravity intensities, iv) the combined analysis and interpretation of magnetic and gravity trends with the synthetized geophysical signatures, allowed extending the Variscan litho-structural units below the Paris Basin sedimentary pile, v) relating the geophysical signatures to the petrophysical characteristics (density and magnetization) within each litho-structural unit, allowed interpreting a geological map of the substratum.
Transparency and Verification Comparing the intended NDCs from countries to emission inventories and pathways available in academic studies leads to more questions than answers regarding data sources, quality, and accounting methods. Extending to non-Annex 1 countries the obligation to submit appropriate national greenhouse gas inventories in line with IPCC guidelines will generate a more transparent and accurate emission dataset at the national scale. However, transparency should not be seen merely as a technical issue, but also as a full-fledged research topic both in natural and social sciences. In the social sciences, the cost/benefit of an increased effort to strengthen MRV needs to be carefully assessed: higher MRV stringency can be detrimental if they hamper possible mitigation actions  (14).
Since January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly highspatial-resolution surface current data in the southeastern corner of the Bay of Biscay. The temporal resolution of the HF radar surface currents permits simulating drifter trajectories with the same time step as that of real drifters deployed in the region in 2009. The main goal of this work is to compare real drifter trajectories with trajectories computed from HF radar currents obtained using different methods, including forecast currents. Open-boundary modal analysis (OMA) is applied to the radar radial velocities and then a linear autoregressive model on the empirical orthogonal function (EOF) decomposition of an historical data series is used to forecast OMA currents. Additionally, the accuracy of the forecast method in terms of the spatial and temporal distribution of the Lagrangian distances between observations and forecasts is investigated for a 4-yr period.
Introduction Since January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly highspatial-resolution surface current data (;5 km) in the southeastern corner of the Bay of Biscay. The study area is situated between 43830 0 -44840 0 N and 18-3830 0 W (Fig. 1 ), off the coast of the Basque Country, Spain, AZTI Contribution Number 772. a Current affiliation: DeustoTech Energy, Bilbo, Spain. and southwestern France. The coast is oriented roughly east-west along the Spanish coast and north-south along the French coast, and this discontinuity delineates the main bathymetric features of the study region. Figure  1  shows that the French shelf becomes progressively wider to the north, while the Spanish shelf is much narrower with a constant width (30-40 km) over the entire study area.
The primary circulation pattern in the southeastern corner of the Bay of Biscay is cyclonic in winter and anticyclonic in summer. The analysis of low-pass-filtered currents shows that a key component of this variability is associated with the surface signature of the slope current [Iberian Poleward Current (IPC)], which is more intense over the upper part of the slope  (Solabarrieta et al. 2014). In winter, the IPC flows eastward along the Spanish coast and northward along the French coast, affecting the upper water column from the surface to 300-m depth (Le Cann and Serpette 2009) and is associated with warm surface waters along the northern Spanish slope  (Pingree and Le Cann 1990). From the joint analysis of ADCP, HF radar, and North Atlantic Oscillation (NAO) index databases, eastward (northward) surface currents of 70 cm s 21 were observed along the Spanish (French) slope, related to the winter IPC  (Solabarrieta et al.
a. Data A main component of the Basque Country's in situ operational oceanography observational network includes two long-range CODAR Ocean Sensors Sea-Sonde HF radar systems, owned by the Directorate of Emergency Attention and Meteorology of the Basque government's Security Department. The radar antennas are located at Cape Machichaco and Cape Higuer (Fig. 1 ) and emit at a central frequency of 4. 5 MHz and a 40-kHz bandwidth. The range coverage of radial data is close to 180 km, with 5-km radial resolution. Hourly HF radar data within the period 2009-12 are used in this study. Basque HF radar validation exercises with suitable results have been performed previously by several authors and they can be checked in  Solabarrieta et al. (2014)  and  Rubio et al. (2011). HF radar technology is more detailed in  Paduan and Rosenfeld (1996) ,  Paduan and Graber (1997) , and  Paduan and Washburn (2013).
From April to September 2009, different campaigns launched several drifter buoys within the Bay of Biscay and 20 of them were used in this study. More information about the buoys can be obtained in  Charria et al. (2013). All the buoys had similar characteristics, with a surface float linked to a long (;10 m long 3 ;1 m wide) holey-sock drogue by a thin (;5 mm) cable and centered at 15-m depth. The position was transferred by an Argos localization system every hour. The lifetimes of the 20 real buoys used in this study ranged from several days to more than three months. The spatial distribution of the drifters is plotted in Fig. 1  and the temporal distribution these data within the period April-September 2009, and conditioned to the availability of HF radar data, can be checked in  Solabarrieta et al.
(2014)  (Fig. 2 ). The drifters covered distances from a few kilometers up to 443 km, with speeds between 6. 78 and 37. 95 cm s 21 (with a mean value of 18. 69 cm s 21 and a standard deviation of 9. 08 cm s 21 ). As the buoys were linked to a drogue centered at 15-m depth, it is important to highlight that the deployments took place during mostly stratified months. The same drifter dataset was used for Eulerian comparisons with HF radar velocities in  Solabarrieta et al. (2014). The differences observed between drifter-derived velocities and HF radar within the HF radar footprint area showed RMS values of approximately 13 cm s 21. These values were in agreement with those obtained through a comparison with current data at 12 m measured by the ADCPs located in two moored buoys over the slope during stratified conditions.
The differences observed between HF radar data and moored ADCP velocities in stratified conditions for a longer time series  were similar and higher than those observed in well-mixed periods. As already discussed in  Solabarrieta et al. (2014) , when analyzing the comparisons between the drifters used here and the HF radar data, it has to be kept in mind that the effective averaging depth for surface current measurements by HF radars has been estimated as 5%-16% of the wavelength of the backscattering surface waves  (Barrick 1977; Fernández et al. 1996; Stewart and Joy 1974). For the Basque Country system (4. 5 MHz), the measurements made are expected to integrate currents vertically within the first ;2 m of the water column. Since the nominal depth of the available drifter trajectories ranges from 10 to 20 m and most of the trajectories are obtained during stratified conditions, part of the differences in the drift between real and simulated trajectories can be related to the vertical shear of the current [detailed in Fig.
b. Methods The received signal, an averaged Doppler backscatter spectrum received every 20 min, was processed to radial current using the Multiple Signal Classification (MUSIC) algorithm  (Schmidt 1986)  as applied by the manufacturer, CODAR Ocean Sensors. The radial velocity fields obtained were stored hourly after applying a running-average smoother (centered on a 3-h window). For this work, the radial velocities were quality controlled and converted to total fields, using the HFR_Progs MATLAB package (https://cencalarchive. org/ ProgsRealTime/), based on  Gurgel (1994)  and Lipa and Barrick (1983, see section 2. 6). To obtain total currents gridded into a 5-km-resolution regular orthogonal mesh (see Fig. 1 ), a least-mean-square algorithm (spatial interpolation radius of 10 km) was applied.
This enables the estimation of the variability of the current field during the simulation period and offers a reference for evaluating the benefits of using forecast currents instead of persistence fields for operational purposes. For the comparison with the in situ trajectories, and in order to have more than one comparison trajectory for each real drifter track (20 real drifters in total), one trajectory was generated every 6 hours starting along the corresponding positions of each real track, obtaining more than 4500 starting points to simulate trajectories for comparison exercises. Each particle was then advected during 48 hours using the LPTM and the currents of the differently processed datasets. In this way, the number of comparison tracks increased substantially (see Fig. 3 , top and middle, as an example of the comparison track obtained for one particle).
Analysis of the forecast model spatiotemporal performances The cumulative separation distances between simulated trajectories using OMA and those obtained using persistence and forecast currents are examined for two different seasons for the period 2009-12 (Fig. 5 ). The separation between the simulated trajectories using OMA and the ones using forecast and persistence currents are similar during the first six hours of simulation (around 0. 44 km h 21 ). The separation rates vary seasonally from 0. 54 km h 21 in summer to 0. 3 km h 21 in winter. In both cases, and for both datasets, the separation rates for time horizons of 24-48 hours are lower: around 0. 41 and 0. 33 km h 21 for persistence and forecast, respectively, in summer, and 0. 24 and 0. 22 km h 21 for persistence and forecast, respectively, in winter.
Discussion The skills of different HF radar-derived products to reproduce observed trajectories have been analyzed using real drifter data available within the radar footprint area in 2009. Further analyses have been performed using the complete HF radar dataset available for the study area in order to investigate the temporal and spatial variability of the performances of the forecast products. The differences between real trajectories and simulated trajectories obtained using HF radar total currents show values between 2. 85 and 6. 59 km after 6 and 24 hours of simulation, respectively. Similar to what was discussed in  Solabarrieta et al. (2014)  and  Rubio et al. (2011)  in terms of Eulerian differences, different aspects can contribute to the differences between the real and HF radar-derived trajectories obtained here.
Also, the use of HF radar data in combination with other datasets, like current fields derived from ocean forecast systems and forecast wind fields, could help improve forecast skills in the short term (after 12 hours of simulation). The benefits of using wind fields are discussed in  Frolov et al. (2012). Better results could also be expected for a higherresolution dataset. The benefits of a long-range HF radar system reside in extended footprints, but the resolution in space is reduced. For some operational needs (i. e. , search and rescue operations), higher-resolution fields could be more appropriate. New analyses using the data closer to the antennas, where refined grids for total velocities can be obtained due to a denser coverage of radials, are needed to evaluate further the possibility of improved forecasts.
In any case, it has to be highlighted that for all the analyses performed, the forecast method is always equal to or better than the persistence fields and very similar to those of the total fields. This result confirms the utility of the forecast method for both short time lags and for wide forecast windows up to 48 hours for which the observed errors are under 15 km. The results obtained in our experiments prove the adequacy of the HF radar used here to provide data for operational monitoring of the coastal areas. Indeed, the use of real-time HF radar data and derived products, such as the short-term forecast, has proven to have significant impact on the reduction of operational search radii in real situations (e. g. ,  Roarty et al. 2010; Breivik et al. 2013). Other examples of the application of these data to different issues have been detailed in several publications  (Paduan and Washburn 2013; Wyatt 2014).
Conclusions The skills to reproduce observed trajectories of realtime and forecast HF radar-derived products have been analyzed using a set of drifter data available in the radar footprint area in 2009. Then, the skills of the shortterm forecast model  (Frolov et al. 2012)  used to obtain a forecast from hourly current fields are analyzed in terms of spatial and temporal distributions, for a longer time period. This paper provides quantitative evaluation of the capability of a coastal longrange HF radar data system to generate trajectories for real situations, with reasonable accuracy and low computational costs. Besides, the forecast model used here is proved to provide accurate forecast of real trajectories within an operational window of 48 hours. The benefits of the forecast methodology in absolute terms and with respect to the persistence fields are shown to be variable in time and space in relation to the modulation of the different physical processes governing the local ocean circulation.
Acknowledgments. The work of  L. Solabarrieta  in this study has been supported by the doctoral grant from  Fundación Centros Tecnológicos Iñaki Goenaga. The work of  A. Rubio  and  J. Mader  was partially supported by the  JERICO_NEXT  project, funded by the  European Commission 's  Horizon 2020 Programme  (Contract  65 4410 ). The authors thank the  Directorate of Emergency Attention and Meteorology of the Basque government  for providing the radial HF radar data. The HF radar processing toolbox HFR_Progs was provided by  D. Kaplan ,  M. Cook ,  D. Atwater , and  J. F. Gonzlez. Finally , we also thank the sampling staff of the  Marine Research Unit of AZTI-Tecnalia.
Placing new videos: the prediction problem When uploading a new video, copies of this video should ideally be placed in storage locations close to where it might be most consumed. This is because the viewing patterns of many videos in UGC services present clear geographic trends  [7] , which are strongly correlated with a video's tags  [11]. for instance how the tags "bollywood " and "favela" follow clearly distinctive geographic distributions in a Youtube dataset analyzed in an earlier work  [11]. Correctly predicting the geographic distribution of a video's views is particularly important in decentralized systems that often lack the caching infrastructure of large integrated services. In Fig. 2  for instance, Dave must decide whether to store his new video in the USA or in France. This decision should be driven by the video's likely future popularity in both countries, which can be estimated as the sum of all user affinities in each country.
We then use this polynomial to compute the values associated with the users that are not among the samples. We evaluate Mignon on two distinct datasets. The first consists of an adaptation of the YouTube dataset we introduced in our previous work  [10, 18]. It contains 590, 897 videos, each associated with a set of tags -11. 18 per video on average, with a total of 705, 415 distinct tags-and with a popularity vector that provides an estimated number of views per country. We extracted videos and tags directly from YouTube, while we computed the number of views for videos and tags by crossing YouTube data with information from Alexa Internet Inc. 3  as described in  [10] , with the following equation. views(v)[c] pyt [c] × pop(v)[c] γ∈World pyt [γ] × pop(v)[γ] × tot_views(v) (1) Where views(v)[c] is the number of views of video v in country c, p yt [c] is the proportion of Youtube views in country c at the time our data set was collected, and pop(v)[c] is a popularity vector issued from our ground hypothesis in  [10] , i.
e. a number proportional to the share of video v's views in country c. To evaluate Mignon, we "reinterpreted" this dataset by considering each country as if it was a single user. Our modified dataset therefore consists of 257 users in a single country. Our second dataset, MovieLens, consists of a trace from a movie recommendation system 4. It contains a set of movies, each associated with a vector of ratings (1 to 5 integers) by a subset of the users, and a set of n pairs, each consisting of a tag and a real-valued relevance score. The rating R u (m) expresses the interest of a user u in movie m, while the relevance r m (t) score expresses the importance of a tag t for a given movie m. Based on this information, we compute the interest score u t of a user u for a tag t as follows. u t = 1 n n m=1 (r m (t) * R u (m)) (2) Since we want to evaluate Mignon's ability to estimate the aggregation of a score value, we consider a synthetic set of new "videos", whose profile only comprises a single tag taken from the dataset.
For each such video v, we first select the set of users in its KNN and RPS views, and then compute its affinity with these users. We use this sample of affinity values to produce an estimate (noted âv ) of the video's aggregated affinity with all the users in the system (which we note a v ). To assess the performance of different estimation techniques, we define an estimation ratio: ER v = âv av. We evaluate ER v in a variety of configurations on each of our datasets. Let n be the number of tags in a dataset (and hence of synthetic videos), we present the distribution of ER v , its mean ER = 1 Figure  6  exemplifies the affinity score distribution of particular tags (interpreted as videos) in each of the two dataset. The curve depicts the affinity score of each user for the tag in decreasing order, while the vertical bars represent the data available in the KNN and RPS views.
Sensitivity Analysis Now that we have shown the effectiveness of Mignon's estimation approach on multiple datasets, we analyze how the KNN and RPS views impact its performance. We present our results in the form of whisker plots in Figures  8  and 9. Each box in the plot covers the values between the lower and the upper quartiles; the point in the box represents the mean, while the line the median. The endpoints of the whiskers represent the lowest datum still within 1. 5 * InterQuartile Range (IQR) of the lower quartile, and the highest datum still within 1. 5 * IQR of the upper quartile, while the points outside the whiskers represent outliers. views slightly tend to overestimate the total affinity, while large RPS views slightly tend to underestimate it, with the best performance being achieved with a KNN view of 15 and an RPS view of 10.