dataesr/scientific-paragraphs-categorization huggingface dataset:
Both the R 2 and RMSE are obtained from the k-fold cross validation of the models. The GLAS footprints did not exactly intersect any inventory plots that were acquired over French Guiana, so we chose a maximum distance between the in situ AGB estimates and the GLAS footprints to relay the GLAS metrics to the AGB. The distance was selected based on the performance of a linear regression model for AGB estimation using the GLAS metrics. Each dataset of this model represented GLAS data from a different maximum distance from the in situ data. The selected maximum distance must maximize the number of available in situ AGB estimates while retaining the highest possible coefficient of determination. Six maximum distances were tested: 100, 200, 250, 300, 350, and 400 m. Choosing a distance of 50 m was not possible because of the very low number of available in situ data (9 AGB plot estimates).
The results showed that the linear regression model produced an R 2 of 0. 63 when using a distance of 100 m, with only 21 in situ AGB estimate plots available. This R 2 decreased to 0. 56 (41 plots), 0. 53 (89 plots), 0. 39 (166 plots), 0. 37 (281 plots), and 0. 11 (374 plots) for distances of 200, 250, 300, 350, and 400 m, respectively (Figure  5 ). Hence, a distance of 250 m was chosen as the best trade-off between an R 2 of 0. 53 and a number of points of 89. Finally, the linear regression model was applied to the remainder of the GLAS footprints (these 39825 GLAS AGB estimates will be used later as calibration data in the wall-to-wall mapping algorithm).
Mapping wall-to-wall AGB We used the ~40000 GLAS AGB estimates (AGBGLAS) from section 3. 1 as AGB calibration data to test two models for AGB wall-to-wall mapping over all of French Guiana: one with the linear regression model (LM) and the other with the random forests (RF) algorithm. The random forests (RF) technique is a performance regression method that is becoming widely used by the remote sensing community for biomass estimation and other studies (e. g. ,  Baccini et al. , 2012 , Baghdadi et al. , 2014). The main advantage of random forests is its incorporation of continuous or qualitative predictors without making assumptions regarding their statistical distribution or covariance structure  ( Breiman, 2001). First, all the auxiliary variables were used in the LM and RF models for AGB estimation.
Next, we used stepwise regression that was based on BIC for the LM model and the variable importance test for the RF model, and the best variables that explained the AGB were selected. The best model between LM and RF was chosen by using the highest R 2 and lowest RMSE. Finally, the independence of the residuals of the selected model was tested (Figure  6 ). Because the RF model failed to guarantee the spatial independence of the residuals, a spatial component (residual Kriging) was added to the AGB estimates map. This technique, which combines a regression model and Kriging, is known as regression Kriging (RK) (Figure  6 ). RK was developed primarily to consider the correlation between environmental variables and the unsatisfactory goodness of fit of the spatial variance model of the dataset  (Sun, Minasny and McBratney, 2012).
Semivariograms have three main parameters: (1) the nugget, which is the semivariance at a lag distance close to zero; (2) the sill, which is the semivariance where no spatial correlation exists at long distances; and (3) the range, which is the distance at which the sill is reached. After plotting the empirical semivariogram, which describes the spatial autocorrelation of a given dataset, a mathematical function is fitted to this empirical semivariogram to represent the spatial structure of the residuals. Thus, the dataset's empirical variogram can now be represented by using a function. Ordinary Kriging is used after the model fitting of the sample semivariogram, which estimates the values of ê at location s0 by using the following equation: ê(𝑠 0 ) = ∑ 𝜆 𝑖 𝑒(𝑠 𝑖 ) 𝑛 𝑖=1 (4) where ê( 0 ) is the predicted value at an unvisited location s0 (in this case, the GLAS AGB estimate residual at location s0), and λi are the weights of n neighboring samples  ( Goovaerts, 1997).
This process was conducted by using either Fisher's test (p-value) for the linear regression model or the increase in the mean square error of the predictions (%IncMSE, estimated with out-of-bag cross validation) from the variable importance test for the random forests model (average and standard deviations of 50 repetitions) (Figure  8 ). Model results showed that the precision of the modelled AGB estimates from the LM were 10% lower in comparison to the modelled AGB estimates from the RF model. Since the RF model performed better, it was used to obtain an initial 1000-m AGB map. The same procedure for variable selection, model calibration and AGB mapping was used to create two additional AGB maps at 500-and 2000-m grid sizes. The precision of the three maps was compared to the in situ AGB estimates validation dataset (half the original in situ AGB estimates dataset).
The exponential equation for the 1000-m AGB map was presented by using a range of 3123 m, a sill of about 5500 and a nugget of about 9700. Furthermore, the residuals were kriged using the exponential equation, and the Kriging of the residuals was added to the original AGB map. The resulting map is presented in Figure  10. A comparison of the new AGB maps with the in situ AGB estimates showed an increase in the precision of the new maps, with an RMSE of 72. 8 Mg/ha (R 2 =0. 12), 50. 2 Mg/ha (R 2 =0. 66), and 43. 2 Mg/ha (R 2 =0. 63) for the 500-, 1000-, and 2000-m AGB maps, respectively (Figures 9d, 9e and 9f). to theoretically produce a more precise map than both with decreased bias  (Avitabile et al. , 2015). The bias removal method consisted of adding the average difference between the input map (Baccini or Saatchi maps) and the reference data for each stratum to the final product.
The map was compared to our AGB in situ reference dataset in the same manner used to validate our own product. Results showed that Avitabile's map overestimated AGB by 73. 0 Mg/ha compared to the in situ AGB dataset (Figure  11 ). Moreover, the precision of Avitabile's map over French Guiana was in the order of 104 Mg/ha (R 2 of 0. 06).
Discussion In this study, we produced an AGB map of French Guiana with a medium resolution (1 km) by using a mix of spaceborne LiDAR, optical and radar data together with additional environmental datasets. This approach allowed us to capture the large variations in AGB from permanent environmental effects more local contrasts from long-term forest dynamics. The mean absolute percentage error of the AGB estimates at the country level was approximately 12%. Additionally, a strong correlation between in situ AGB estimates and GLAS metrics was present. Although important spatial (250 m distance between the in situ AGB estimates and the GLAS footprints) and temporal (in situ AGB dataset that was created decades before the GLAS dataset) shifts occurred between the GLAS dataset and the corresponding in situ AGB estimates, the precision of our map was better than main carbon benchmarks at this scale (Table  1 ).
To date, researchers have preferred to select a small number of plots to guarantee the quality of the terrain data (inventory with botanical determinations, including trees with small DBH, etc. ) to calibrate biomass estimation models, which poses a risk of over-representing the most complex vegetation. We assume that calibrating with data that might be less precise but more abundant is more beneficial, which allows us to better comprehend the regional variations. Therefore, using a maximum number of forest-census field data to calibrate AGB spatial models, not only field plots that exactly matched the locations of remote-sensing footprints, seems to be a good strategy to improve the accuracy of AGB maps. A limitation of the approach in this study is the unavailability of new GLAS measurements since the satellite ceased operations in 2009.
However, implementing the approach in this study for different forest sites is possible because of the large global database of GLAS footprints (hundreds of thousands to millions of footprints available across almost every country). Although none of the methodological steps that we developed were entirely novel, the combination of various types of datasets allowed us to produce AGB maps of a dense tropical forest with much more detail than existing maps. Indeed, we obtained similar overall precision than a previous AGB map that was produced over French Guiana with a grid size of 1 km by using only environmental variables without remote-sensing data  (Guitet et al. , 2015). However, while both quantitative precisions were similar, the qualitative precision of our map appeared to be higher. Indeed, the environmental variables very successfully distinguished large-scale variations at the landscape scale but insufficiently captured AGB variations over short distances because of the natural forest dynamics.
The bias that was estimated by computing the average difference between the input map (Baccini or Saatchi's maps) and the reference data for each stratum was added to the final product  ( Avitabile et al. , 2015). The small number of reference points in Avitabile's study over French Guiana (average AGB of ~420 Mg/ha), caused the AGB to be overestimated by ~73 Mg/a. Comparison results of AGB from Tong Minh et al. , 2014 with our in situ AGB estimates were far better in comparison to the global map, with a precision on the AGB estimates of 30. 2 Mg/ha (R 2 of 0. 71). The low-frequency radar (P-band) is sensitive to the dielectric constant of woody elements, which is linked to the biomass of these elements for a canopy structure Family (dense, sparse, tropical, boreal, etc. ). These relationships are less 'variables' as those that bind the height to biomass; we can therefore expect it to work more globally when we have separated the family structures.
the other are AGB estimates over two small sites in French Guiana by Tong Minh et al. , 2014 using P-band radar data. The comparison was done against the in situ AGB estimates. Results showed very large discrepancies for the global map, while the AGB maps precision from P-band radar were slightly at par with our product. The comparison to the AGB map of Avitabile et al. , 2015 , which was created using a data fusion of Baccini and Saatchi's maps, showed poor precision compared to our in situ AGB dataset. The RMSE of the AGB estimates was on the order of 100 Mg/ha, mostly because of the low frequency of available calibration points over the dense neo-tropical forests that were used inAvitabile et al. ,    2015 AGB estimation model. This model, which is calibrated with few reference points, tends to be more sensitive to outliers.
Acknowledgments The authors wish to thank the  French Space Study Center (CNES ,  DAR 2014 TOSCA ) for supporting this research. The authors acknowledge the  National Snow and Ice Data Center (NSDIC)  for the distribution of the ICESat/GLAS data. The authors also acknowledge the  French Geological Survey (BRGM)  and, in particular,  José Perrin  for providing the low-density LiDAR dataset. The authors wish to thank  Lilian Blanc  (Cirad) and  Grégoire Vincent (IRD)  for providing the high-density LiDAR dataset. We would also like to thank  Noveltis  and  Airbus Defense and Space  for their financial support. We extend our thanks to the  French Forest Agency (ONF)  and the  Guianese National Park (PAG)  for providing field data with funds from the  French Ministry of the Environment 's  ECOTROP program  and the  European Union 's  PO-FEDER program  HABITATS.
Conclusions An approach for AGB mapping over the tropical forest of French Guiana was presented. This approach was based on the merging of in situ AGB estimates, GLAS AGB estimates and ancillary data (optical, radar and environmental data). First, the AGB was estimated by using GLAS data and calibrated in situ AGB points that were distanced 250 m (at maximum) from the GLAS footprints. The choice of this distance was based on a compromise between the number of calibration points and their correlation to the AGB. Next, a LM was created to estimate the AGB with the most important GLAS metrics. This model showed an RMSE for the estimation of AGB of 48. 3 Mg/ha (R 2 of 0. 54). These results showed that neither a temporal mismatch between the acquisition dates of GLAS and in situ AGB data nor a spatial shift (maximum of 250 m between the GLAS and in situ AGB data) seemed to be a problem for AGB spatial modelling.
2009) , and gravitational instability may also form gas giant planets  (Nayakshin & Fletcher 2015). Thus the line between gas giants and brown dwarfs is a blurred one. It is argued that the distinction between these objects should be linked with formation mechanisms  (Chabrier et al. 2014) , and these different formation scenarios are almost certainly responsible for the brown dwarf desert rather than some observational bias  (Ma & Ge 2014). In this paper we report the discovery of a new transiting brown dwarf, EPIC201702477b (V =14. 57), for which we can measure a precise mass and radius. In Section 2 we outline the photometric data from the Kepler space telescope and the LCOGT 1 m network. We also describe the spectroscopic observations used to measure the radial velocities of EPIC201702477 and to spectroscopically characterize the host star.
In such circumstances, further ground-based photometry is very important in order to help fully characterize the system. Of the 37 candidates presented by  Foreman-Mackey et al. (2015) , EPIC201702477 has the longest orbital period, with the exception of EPIC201257461, which has been shown to be a false candidate  (Montet et al. 2015). The reported planet/star radius ratio of EPIC201702477b is R P /R star = 0. 0808, indicating a gas giant exoplanet assuming a solar-type host. We downloaded the K2 pixel data for EPIC201702477 from the Mikulski Archive for Space Telescopes (MAST) * and used a modified version of the CoRoT imagette pipeline  (Barros et al. 2015)  to extract the light curve. We computed an optimal aperture based on signal-to-noise of each pixel. The background was estimated using the 3σ clipped median of all the pixels in the image outside the optimal aperture and re- * archive.
The first transit event for EPIC201702477b monitored by the TECH project was on 2015 March 15 from CTIO. We observed the target from 01:00 UT to 08:13 UT using a Sinistro in the r-band. The exposure times were 240 s, the observing conditions were photometric, and the airmass ranged from 2. 3 to 1. 2. We detected a full transit of EPIC201702477b-with a depth and duration consistent with that seen in the K2 data. The next transit event occurred 40 days later on 2015 April 28, and was observable from SAAO. EPIC201702477 was monitored between 17:00 UT to 22:50 UT using an SBIG camera, again in the r-band. The exposure times were 180 s, the observing conditions were again photometric, and the airmass ranged from 1. 8 to 1. 2. These data show the first half of a transit event consistent with the previous events.
To better determine the stellar properties we obtained a spectrum of the star with Keck/HiReS  (Vogt et al. 1994)  on 2015 June 30. The instrument was configured to the standard setup for the California Planet Search  (Howard et al. 2010). We collected a single 7 min exposure using the C2 (14x0. 861) decker for a spectral resolution of R∼45000 and signal-to-noise ratio of ∼ 25 per pixel at 5500 Å. We used the software specmatch (Petigura 2015) to determine the stellar properties. The resulting parameters are listed as initial spectroscopic information in Table  2. Following the methodology described in  Sozzetti et al. (2007)  we used these initial spectral parameters from Keck as priors for the global fitting (see Section 3), determined a new log g, and then used this as a prior for a second iteration of specmatch.
Joint analysis We analyzed the radial velocity and photometric data of EPIC201702477 with the Markov Chain Monte Carlo (MCMC) algorithm of the pastis software, which is fully described in  Díaz et al. (2014a). We modelled the radial velocities with a Keplerian orbit and the photometric data with the JKTEBOP package  (Southworth 2011)  and references therein. We chose as a prior for the stellar parameters the values derived from the Keck spectroscopy (Section 2. 3). We used the Dartmouth stellar evolution tracks of  Dotter et al. (2008)  to derive the stellar fundamental parameters (mass, radius, age) in the MCMC, in particular the stellar density which was used to constrain the transit parameters given the eccentricity constrained by the radial velocities, as in  Santerne et al. (2014). We ignore pre-main sequence solutions as there is no evidence that this is a young star and the pre-main sequence stage is extremely short in duration.
We ended with more than 3000 independent samples of the posterior distribution that we used to derive the value and 68. 3% uncertainty of each parameters that we report in Table  4. We also modelled the system independently (but with the same datasets) using the exofast software  (Eastman et al. 2013). We find parameters and uncertainties in close agreement with those that were derived using pastis, and therefore we only report the pastis results.
DISCUSSION With a period just over 40 days, EPIC201702477b is the second longest period transiting brown dwarf discovered to date. The discovery of long-period transiting systems from the K2 data is encouraging, as such systems are extremely difficult to find from ground-based surveys; HATS-17b  (Brahm et al. 2016)  being the current record at 16. 3 days. Long-period systems will remain difficult to discover even when the TESS mission is operating  (Ricker et al. 2014)  as most fields in this survey will only be monitored for 27 days. EPIC201702477b also demonstrates that like the Kepler mission, some fraction of the K2 validated planets may turn out not to be planets, even at radii down to 0. 75 R J , due to confusion with brown dwarf companions.
Acknowledgments -This work has been carried out within the framework of the  National Centre for Competence in Research "PlanetS"  supported by the  Swiss National Science Foundation (SNSF). This paper includes data collected by the K2 mission. Funding for the K2 mission is provided by the  NASA Science Mission directorate. This paper makes use of data and services from  NASA   Exoplanet Archive  (Akeson et al. 2013), which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program. We are grateful to our colleagues who have performed some of the observations presented here with the HARPS spectrograph:  F. Motalebi , A.
Now the first constraint obtained from BOP data structure for forward error correction assignment is as follows: S ¼ S k þ X l i¼1 S i ¼ S k þ X l i¼1 r i k i ð6Þ Each Group Of Pictures (GOP) can be packed into a fixed number of block of pictures. In our proposed scheme, one GOP is equal to one BOP. In SVC bit-steam different layers have different priorities; therefore, SVC-based encoded video explicitly requires an unequal error protection scheme, yielding another restriction for forward error correction assignment as follows: 0 6 k 1 6 k 2 6 Á Á Á 6 k l 6 n ð7Þ We elucidate four different adaptive assignment schemes in this section for scalable video transmission over error-prone wireless networks and finally suggest the best one through experimental results in Section 7. The four schemes are as under:  1.
Scalable video content server The content server covers different sub-modules including SVC encoder, SVC streamer, video data base, packet/stream buffers, sending controller, QoS measurement probes and protection module. The SVC encoder encodes offline all video sequences and are stored in video data base. The SVC encoder used in test platform is an optimized version of Joint Scalable Video Model (JSVM). The experimental validation shows considerable improvement in encoding time as compared to original JSVM. The requested bit-streams are moved to the stream buffer and the streamer which accepts commands from the sending controller, segments each bit-stream into video packets. The video packets are put into the packet buffer as the RTP payload. The sending controller interacts with the receiving controller to create a media session for video transmission then server transmits pre-encoded SVC video packets over RTP/UDP.
Video clients The video clients or customer's terminals cover different submodules including SVC decoder, stream/packet buffer, receiving controller, QoS measurement probes and video display (HD and Mobile). At the client side we used Joint Scalable Video Model (JSVM) decoder in order to improve its speed and experimental validation shows considerable improvement in decoding time. The decoder aims to provide a real-time fast and robust decoding framework for SVC. The buffers are used to smooth out the video stream due to changes in bandwidth, packet losses or jitters. Video frames are segmented in different size packets according to channel conditions which arrive at the receiver with an inter-arrival rate. All packets are decompressed and assembled to form a frame again and stored in a buffer and waits there until they are fetched by a display process.
III. 2 Model calibration and inference for real data Daily DST temperature and depth series from a representative fish (tag A05392) was used as a real case study. The model parameters were estimated using the SEM procedure. The algorithm converged after about 50 iterations (Figure  R3 ). The estimated model parameters were D = 27. 3, σ surface = 0. 367, and σ bottom = 1. 859. The reconstructed tracks according to the different criterion (MAP, MPM and mean tracks) looked consistent to one another, i. e. close to each other over the time series (Figure  R3 ). In addition, the reconstructed tracks seemed plausible relative to the sea bass ecology. Thus, over a year of data (383 days), the tagged fish spent some time in the Iroise Sea, then undertook a migration toward the south-east in the Bay of Biscay, stayed there for a while, and moved back to the Iroise Sea, thus completing a annual migration cycle.
In addition, the fish went less South and more toward the West. The use of observations (satellite-based SST) over model outputs (MARS-based SST) were eventually favored as the satellite data are more precise, and the reconstructed tracks with satellite based SST were more coherent (MAP, MPM and mean track close to each other) than with models outputs. Given this analysis, the two zones where the fish stayed longer were interpreted as feeding and spawning grounds, whereas the rapid move between zones were interpreted as fish migration.
The proportion of days spent in 0, 1 and 2 layers can be used an index to specify the quality of the reconstruction. For the representative tags considered for this study, these proportions were respectively 0. 8%, 61. 3% and 37. 9% for the tag A05392, and 2. 8%, 30. 0% and 67. 2% for the tag A06226. Although there were some undetermined days, the number of days with 1 or 2 layers visited was high enough to achieve a coherent reconstruction, as stressed by the sensitivity analysis. Furthermore, we pointed out that the type of reference geophysical fields (satellite based SST vs. MARS based SST) has an impact on the reconstruction. As stated earlier, observation data should be favored over model outputs. In the proposed implementation, we considered a simple Gaussian observation model. More advanced statistical models may be investigated to better account for the different types of noise and uncertainties (e.