dataesr/scientific-paragraphs-categorization huggingface dataset:
TLS data processing The transmittance T of a medium made-up of randomly distributed vegetation elements is commonly described as following an exponential attenuation along a path of length δ  (Nilson, 1971; Ross, 1981) : T = e -λ δ (1)  where λ (m -1 ) is the attenuation coefficient of the medium. The plant area density (PAD) (m 2 m -3 ) is related to λ as follows: PAD = λ/G (2) where G is the projection coefficient function of unit plant area on a plane perpendicular to the light incident direction. The PAD for all transects was calculated using a voxel-based approach (Fig. 2b ). All transects were divided into 1 m 3 voxels, and the attenuation calculated for each of these voxels. This procedure was done in the LIDAR data voxelization software AMAPVox v 1. 6. 1  (Vincent et al. , 2017). To compute PAD from attenuation, G was taken equal to 0.
Statistical assessment of edge effects on canopy structural traits Each canopy structural trait was modelled as a function of distance from the edge. We compared linear mixed models containing distance from the edge with nonlinear mixed models containing an asymptotic component that represents the saturation of the canopy metric with distance from the edge. An asymptotic component in the model is more ecologically significant to investigate the edge effects on forest structure and canopy metrics  (Nunes et al. , 2021). We developed hierarchical models using the lme4 package in R  (Bates et al. , 2015) ; and included the spatial levels of region of study, forest fragment size (1, 10 and 100 ha fragments) within region of study, and transect within fragment size treated as random effects to account for the nested spatial nonindependence of transects.
We delineated the hypervolumes using one-class support vector machine (SVM) method, which is considered insensitive to outliers and better suitable for highdimensional hyperspaces. All calculations were done using the hypervolume package in R  (Blonder, 2019). For more details on the SVM approach for delineating hypervolumes, please refer to  (Blonder et al. , 2018). To evaluate the changes in structural diversity caused by edge effects, we divided the combined data from all transects into slices of 5 m, from the edge to 50 m inside the forest fragments. The n-dimensional hypervolume was then calculated independently for each slice using the same dimensionality and parameters to allow a direct comparison of the hypervolumes. We then evaluated the similarity between the hypervolume of each forest slice and the hypervolume obtained at a distance of 50 m from the edge, using the Jaccard similarity index and the Sorensen similarity index, which are used to describe the pairwise overlap between hypervolumes.
GEDI has currently collected thousands of samples throughout the Amazon basin. The sampling characteristics of GEDI (i. e. along track beams spaced by 60 m), as well as its footprint of 25 m, do not allow a transect-based design as presented in this study. However, the multiple overpasses are likely to cover a large range of representative conditions, including different distances from edges and degrees of disturbances. Given that the retrieval of forest structural traits with TLS can be done without carrying out a floristic inventory of the forest, this approach has potential to be upscaled to other areas in Amazonian forests. In our study, the region where the data was collected, and the size of the fragments, accounted for a small fraction of the variability in our results (less than 3% on average) (Table  S2  in the supplementary material).
Acknowledgments This study was funded by the  Academy of Finland  (decision numbers  318252 ,  319905  and  340175 ). This publication is number 833 of the  Technical Series of the Biological Dynamics of Forest Fragment Forest (BDFFP). Y. M. M. was supported by a research grant from the  Royal Society  through the  Newton International Fellowship  funding, grant number  NF170036  and  HPC-Europa3  (process n:  HPC17TA3RL ) supported by  European Commission   H2020. KC was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  835398. GZ was funded by the  Danish Council for Independent Research -Natural Sciences  (# 9040-00136B ). G. V. received support from " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref.
All statistical analyses and graphs were performed with R software v3. 6. 0 and SAS software 9. 4 TS Level 1M6  52. To find out whether CG had a different effect from the effect of control on clinical variables and to assess if the difference was statistically significant, we performed a linear mixed model for repeated measures, with Toeplitz or autoregressive structure (AR) as covariance structure. In order to account for variability between subjects and to adjust for any non-specific differences, subjects were included as random effects. To quantify the effect of CG compared to the effect of control, we estimated the difference in terms of change ETD = After Fiber -Before Fiber -(After Control -Before Control) to which the p value of the interaction treatment*time is associated. For gut microbiota analysis, relative abundances performed in Qiime2 were calculated in R for each taxa and ASV.
Methods: The study included all patients prospectively enrolled in EpSSG RMS 2005 study under 21 years of age with localized HN ARMS and diagnosed between 2005 and 2016 in France. Medical data including imaging, surgical report, and radiation therapy planes were analyzed. Results: Forty-eight patients (median age 6 years; range 4 months-21 years), corresponding to 30 parameningeal and 18 non-parameningeal ARMS, were included. There were 33 boys (69%). Tumor locations included the following: orbit (n = 7) among which four cases had bone erosion, paranasal sinuses and nasal cavity (n = 16), deep facial spaces (n = 10), nasolabial fold (n = 8), and other non-parameningeal HN sites (n = 7). A fusion transcript of PAX3-FOXO1 or PAX7-FOXO1 was expressed in 33 of the 45 cases (73%) with molecular analysis.
Population This multicenter study included all French patients prospectively enrolled in the EpSSG RMS 2005 protocol under 21 years of age with localized HN-ARMS diagnosed between 2005 and 2016 (17). Patient's consent or his/her legal representative's was collected. Analyses were performed on the data derived from EpSSG RMS 2005 study. Additional data, particularly those concerning modalities of LN staging, surgery, and radiotherapy, were retrieved from medical center files. Based on initial RMS2005 criteria, pathologists should consider ARMS diagnosis when tumor showed any focal alveolar pattern histology. However, since RMS2005 trial ran from 2005 to 2016, it was next recommended in the pathologist community to consider ARMS if alveolar pattern was predominant. The location of the primary tumor was determined by imaging at the time of diagnosis and classified into three sites: orbit, non-parameningeal (non-PM), and PM sites.
Survival curves were calculated by the Kaplan-Meier method. The 5-year OS and the 5-year event-free percent survival were expressed with their confidence interval. The log-rank test was used for univariate analysis of survival data. For all statistical tests used, results were considered significant for a p ≤ 0. 05. Data were analyzed using GraphPad Prism software (version 8. 00 GraphPad Software, La Jolla, CA, USA; www. graphpad. com).
Data Retrieval The Academic Track Twitter Application Programming Interface (API) was used to retrieve tweets from the 24-hour range 2021-09-17T00:00:0 to 2021-09-18T00:00:00Z (Greenwich Mean Time), which corresponds to the day that Squid Game was officially released on Netflix. As opposed to using a longitudinal data collection method, we strategically adopted a cross-sectional data collection approach at the critical stage of the show's popularity  (Rindfleisch et al. , 2008). This approach has helped us capture the social activities and underlying network dynamics, marking Squid Game's phenomenal popularity right after its release. Furthermore, previous academic research studying health campaigns has also examined 24-hour time periods  (Ahmed et al. , 2018). The keyword 'Squid Game' was used to retrieve tweets that would also capture any mentions of 'SquidGame' or #SquidGame.
There were a total of 13,727 Twitter users within the network that had 12,722 unique relationships such as mentions, retweets, replies and quotes. The data from Squid Game were compared with the data of adult animated comedy series Chicago Party Aunt that was released on the same date as Squid Game. This was selected because it provided a comparison point to another recently released show. Data were retrieved for Chicago Party Aunt using the same API and date range provided earlier for Squid Game.
Data Analysis SNA was utilized to analyse the relationships between Twitter users, and NodeXL (version 1. 0. 1. 447) was used to perform this analysis. Influential users were identified, drawing upon the betweenness centrality algorithm. More specifically, SNA was applied to two Netflix shows, one that had much higher popularity (high impact) and one with less popularity (low impact). NodeXL, drawing upon Natural Language Processing (NLP), can identify co-word correlations among clusters of users. NodeXL also imports tweets with their retweet and favourite count, among other meta-data. This data was systematically observed by one of the authors in order to identify a number of over-arching themes, which are described in Table  1. Researchers unfamiliar with SNA methods may wish to consult  Smith et al.
Influencer Characteristics Table  1  provides insight into the Twitter users that were most influential based on their calculated betweenness centrality. Based on the values presented, we classified these users as key opinion leaders within the network. The group column within Table  1  corresponds to individual group characteristics presented within Figure  3 , highlighting the strategic location of opinion leaders within the network. A thorough investigation of the data presented in Figure  3  and Table  1  has helped us understand that opinion leaders were scattered around the network and stirred further conversations and discussions on four key areas (plot discussion, reference to other shows, anticipation and recommendation). In Figure  3 , users that have not replied to, been replied to, retweeted or mentioned other users, or been retweeted or mentioned themselves have been removed.
Limitations The study exclusively focuses on a cross-sectional (yet large) dataset collected at the introductory stage of the show. Our motive behind such an approach was to study why the show became such a phenomenal success across the globe without pre-audience awareness or active marketing programmes. More extensive and longitudinal data gathered from social media could help understand the evolving nature of audience opinion and influencers deploying differential e-WOM strategies at different stages of the viral phenomenon. Another important limitation of the study lies in the nature of the collected data. Twitter is a microblogging-based social platform that is effective in creating instant hype and anticipation towards a sociocultural or political topic. Data from this platform is an excellent source for understanding the influencer network characteristics as intended in this study  (Arora et al.
, 2019). However, the larger variety and veracity of opinion collected from more in-depth reviews posted on platforms such as the Internet Movie Database (IMDB) and Reddit can act as a further eye-opener towards consumer sentiment and acceptance of the programme. Such investigations can further aid our understanding of the role of renowned popular culture critiques towards amplifying or de-amplifying the Squid Game viral phenomenon. SNA comparison against Chicago Party Aunt also does not provide a common ground for comparison. The show was selected based on its proximity of release date to Squid Game, but these two programmes belong to very different genres of popular culture aimed at different audiences.
In the context of global change, up-to-date land use land cover (LULC) maps is a major challenge to assess pressures on natural areas. These maps also allow us to assess the evolution of land cover and to quantify changes over time (such as urban sprawl), which is essential for having a precise understanding of a given territory. Few studies have combined information from Sentinel-1 and Sentinel-2 imagery, but merging radar and optical imagery has been shown to have several benefits for a range of study cases, such as semantic segmentation or classification. For this study, we used a newly produced dataset, MultiSenGE, which provides a set of multitemporal and multimodal patches over the Grand-Est region in France. To merge these data, we propose a CNN approach based on spatio-temporal and spatio-spectral feature fusion, ConvLSTM+Inception-S1S2.
In a previous work  [19] , we experimented with feature fusion methods between Sentinel-2 single date optical imagery and spectral and textural indices for mapping urban areas in five thematic classes and obtained very promising results. To our knowledge, very few works attempt to classify urban areas with so many classes. Thus, the objective of this work is to explore the combination of multitemporal and multimodal imagery for urban fabric (UF) mapping using semantic segmentation networks. We make the hypothesis that (1) multitemporal optical and SAR imagery and (2) balancing the dataset can improve UF classification. The rest of the paper consists of four parts: the choice of study sites and preprocessing methods in Section 2, the deep learning architecture used in Section 3, the analysis of the results in Section 4.
MultiSenGE dataset MultiSenGE  [36]  is a multitemporal and multimodal dataset developed over the Grand-Est region (Figure  1 ) in France. It covers 14 Sentinel-2 tiles over one of the biggest regions in France (57,433 km 2 ). The dataset contains 8157 multitemporal patches of 256 × 256 pixels for the Sentinel-1 and Sentinel-2 sensors for 2020. A reference data, preprocessed from a Land Use Land Cover database (BDOCGE2), is included with each Sentinel-1 and Sentinel-2 patch to form data triplet. The global process of MultiSenGE construction can be found in  [36]. This dataset is one of the first providing multitemporal and multimodal imagery using Sentinel sensors for LULC applications. Furthermore, the reference data typology offers a diversity, especially for UF in 5 classes (see semantic classes typology in Figure  1 ).
Optical and SAR Multitemporal Patches Selection MultiSenGE  [36]  provides a set of functions to extract multiple Sentinel-1 and Sentinel-2 time series information for each patch. For example, it is possible to extract all patches that have at least one Sentinel-2 image associated for several months. Thus, we chose to explore the dataset to find the best compromise between temporal and spatial diversity. From Figure  2 , we note that the dataset contains few images for winter and early spring. Therefore, we decided to select patches for July (07), August (08), September (09), and November  (11)  to have the highest number of patches (Figure  2 ). To obtain images with a regular time-lapse, a constraint on the number of days between two consecutive months is applied to select the patches. To help in the choice of the best configuration of number and spatial diversity on a large region, we propose a web-page (http://romainwenger.
Reference Data Typology The reference land cover dataset of MultiSenGE is described in 14 semantic classes. Following the choice of the different sets by geographical stratification, some classes are not homogeneously distributed in all the sets; for instance Orchards (8), Groces and Hedges  (10) , Open Spaces, Mineral (12), and Wetlands (13) represent mostly under 1% for the total dataset surface (Table  2 ).
Implementation Details Due to the imbalance of the dataset (Table  2 ), we chose to implement a Weighted Categorical Cross Entropy allowing us to assign a higher weight to the less balanced classes. The weight of each class is defined as the inverse of the frequency of the class  [19, 27]  and is commonly used in multiclass remote sensing classification  [43, 44]. As demonstrated by  [45] , Categorical Cross Entropy loss performs better than some other loss functions for semantic segmentation tasks. This method allows forcing the network to pay more attention to the less represented classes in the reference data (e. g. , Built-Up (Class 1), Specialized However, Vegetative Areas (Class 4), or Large Scale Networks (  5 )). Furthermore, Adam is selected as an optimizer  [46]  as it performs better in remote sensing data than all others  [47] [48] [49] [50].
The Sentinel-1 and Sentinel-2 data are normalized to the multi-temporal information of each band using the following formula: n = b -b σ b (4) where n represents the normalized spectral band, b the reflectance values of each multitemporal spectral bands, b the mean of the multitemporal reflectance values, and σ b the standard deviation of the multitemporal reflectance values. We chose to implement three different methods of data augmentation based on either 90, 180, or 270-degree rotation up/down flips and left/right flips. The training dataset is augmented to 75%. EarlyStopping, present in the Keras (https://www. tensorflow. org/ api_docs/python/tf/keras accessed on 27 May 2022) library, is used with a patience of 20 epochs to avoid any overfitting. Adam optimizer is used with a LR of 10 -3 and we reduce it by a factor of 0.
Results This section presents the results obtained for the two methods developed over the test dataset. The test data set is independent of the training set and the validation set and includes all available patches for the T31UEQ tile, as seen in Section 2. 2. First, we present the results for six LULC classes in Section 4. 1 then the results for 10 LULC classes in Section 4. 2 and finally we compare UFs classification results between 6 and 10 LULC classification methods for UFs in Section 4. 3.
ConvLSTM-S1 ConvLSTM-S2 The addition of multitemporal and multimodal data for the extraction of spatiotemporal features (ConvLSTM-S1S2 method) allows obtaining a large part of the best Recall score and F1 Score , especially for Dense Built-Up (1), Sparse Built-Up (2) and Large Scale Networks  (5). The addition of the Inception module for the extraction of spatio-spectral features (ConvLSTM+Inception-S1S2) does not allow a significant improvement of the classes, even if in terms of Weighted-F1 Score , it is very close to the ConvLSTM-S1S2 method with 0. 8875 against 0. 9018 for the latter (Table  5  and Figure  A1 ). On the other hand, ConvLSTM+Inception-S1S2 obtains the best Weighted-Precision and Weighted-F1 Score with 0. 9591 and 0. 9018. The confusion matrix (Figure  A2 ) informs us about a strong confusion between Specialized However, Vegetative Areas (4) and Non-urban areas  (6) , probably due to the imbalanced dataset because Non-urban areas (6) covers 92.
46% of the dataset. Furthermore, it is complex to differentiate it from other natural classes as Non-urban areas  (6)  is the aggregation of all other natural areas. Moreover, we notice a strong confusion between Dense Built-Up (1) and Sparse Built-Up (2) which are complex classes to differentiate because their texture and spectral signature are very close at 10m. The only difference between these two UF classes is the portion of vegetation between buildings  [7, 19] , which are close to none for Dense Built-Up  (1)  and are restricted to the personal garden for Sparse Built-Up (2). We can see that Recall values are systematically higher than the Precision Values whatever the method. However, the Precision value is also always higher (classes Sparse Built-Up (2) and Specialized Built-Up Areas (3)) or very similar (classes Dense Built-Up (1), Specialized However, Vegetative Areas (4) and Large Scale Networks (  5 )) with ConvLSTM+Inception-S1S2 than other methods.
Discussion In this paper, we developed a semantic segmentation method taking as input multitemporal and multimodal imagery from the MultiSenGE dataset. We propose a network consisting of two extractors, one for spatio-spectral features and the other for spatio-temporal features. The latter provides the best results compared to the other tested approaches. Moreover, discriminating an over-represented class improves the classification results for the studied object, in our case the UFs, reducing both intra and inter-classes confusion.
Application on UF Mapping For UF mapping, results showed higher quantitative metrics at 10 classes using SAR and optical time series and an Inception module allowing the extraction of spatio-spectral features in addition to spatio-temporal features. This latter approach allows reducing the strong confusion between classes coming from imbalanced datasets during the 6 LULC classification. This was particularly true for Figures  8  and 9  where Vineyards and Orchards (7) were strongly confused with Specialized but Vegetative Areas (4), a class that also includes scattered trees in urban parks or squares. Moreover, the results presented allowed us to see the contribution of Sentinel-1 SAR imagery thanks to the better detection of natural surfaces in the periphery of the UF. Without this acquisition mode, the confusion between the natural classes and Specialized but Vegetative Areas (4) would probably have been more important and would not have allowed a better detection of these areas.
Comparison with a State of the Art LULC Product Compared to existing products such as OSO  [1] , our method is better to describe UF classes on most of the class. Indeed, using semantic segmentation instead of classical machine learning approaches (e. g. , Random Forest) reduces the salt and pepper effect. Furthermore, we include a fifth class, Specialized but Vegetative Areas (4), which is almost never mapped in existing works using 10 m spatial resolution imagery (e. g. , Sentinel). In fact, OSO only derives UFs in four classes, Dense Built-Up, Sparse Built-Up, Specialized Built-Up, and Large Scale Networks. For natural areas, OSO has 13 semantic classes, which is slightly higher than our approach. However, we are only experimenting with a specific region in France and a regional LULC semantic segmentation dataset, which reduce the possibilities to extend the number of classes.
Network Performance Semantic segmentation networks, and more specifically the encoder-decoder like structures, allow us to obtain a map with the spatial extent of each class (assigning to each pixel a label and taking into account the spatial context of the image). Thus, we can note a lack of precision in the border of the classes, in particular those of the UF. Moreover, the complexity and density of UF make the distinction between classes difficult, especially at 10 m spatial resolution. The geographical area, being anisotropic in nature, differences in the distribution and frequency of the classes over the territory also make the classification more complex and challenging. This could be assessed by doing pixel-wise classification and balancing each class in the dataset. Through this technique, salt and pepper noise, which was almost erased with semantic segmentation, could happen again.