dataesr/scientific-paragraphs-categorization huggingface dataset:
It is critical that negative flight height records like these are maintained, even if, instead of a fully interpretable high-resolution sequence like in this example, there are just a few isolated negative flight height records in a low-resolution dataset. In addition, if we only kept the records with positive flight height, we would obtain a biased sample of the distribution of flight height. Both in simulations and in the raptor case studies, discarding negative flight height records led to the overestimation of the mean flight height in the remaining dataset, the underestimation of the variance in flight height, the introduction of a right skew in the distribution of flight height, and the overestimation of the collision risk (Fig. 3 ). The latter result was because negative records mostly occurred when the bird was flying below the collision zone, and thus removing negative records led to under-sample safe periods of time.
The mean flight height is not sufficient to describe the distribution of flight heights Flight height datasets are often reduced to a single summary metric, the mean flight height and its variation with environmental and individual covariates  [29, [46] [47] [48] [49]. This decision is mostly based on the ease of implementing spreadsheets, linear models, moving averages, or spline models. In this section, we instead call for approaches that describe the full distribution of flight heights in the aerosphere, not only the mean flight height. To justify this call, we again focus on collision risk estimation. Indeed, if the variance in flight height is large enough, a proportion of time may be spent in the collision zone even if the mean flight height is outside the collision zone. In simulations, the proportion of time spent in the collision zone indeed depended on both the mean and the variance in flight height (Fig.
4a, b ). In the raptor datasets, the estimated probability of flying in the collision zone did not decrease much for the individuals whose mean flight height was estimated above the collision zone (Fig. 4c ). Similarly, the individuals that had an estimated mean flight height well below the collision zone were predicted to spend about 20% of their time in the collision zone (Fig. 4c ). We strongly recommend that collision risk forecasts should not be based on the fixed effects of linear models, but instead on the full distribution of flight heights-a recommendation that will likely hold for all studies into vertical airspace use.
Indeed, even when in theory the model is estimable, sometimes only a subset of the parameters of a state-space model are separately estimable, a phenomenon called "weak identifiability" that occurs when the sampling variance largely exceeds the process variance. An example of weak identifiability is when the difference between two classes of individuals are larger than the differences within the classes  [55]. In addition, there are large statistical correlations between variance parameters in a movement model  [52] , making it extra difficult to accurately separate movements and errors in sparse datasets. In that context, unambiguously erroneous records, such as negative flight heights, represent an additional source of information  [20]. They can help separate the process and sampling variances  [10]  and solve issues of weak identifiability.
This algorithm is fast, but requires all the model processes to be Gaussian or approximately Gaussian (no truncation or constraint, no excess extreme values, no excess kurtosis or skew). • The TMB package for R  [60]  approximates the likelihood of the state-space model using the automatic differentiation algorithm with Laplace approximation. That approach makes computing times shorter than the next option, while still allowing for flexible modelling such as non-Gaussian errors  [26] , custom link functions  [10] , or multiple data streams. • The Monte Carlo Markov Chain Bayesian framework  [61] [62] [63]  generates parameter distributions that iteratively converge towards the solution. This option is the most flexible in terms of nonlinearities and non-Gaussian features, such as truncated distributions  [20] , but the computing time can be prohibitive for large datasets.
Acknowledgements A. Scharf  and  H. Wehner  ( MPI ) flew the drone and assisted with data collection. M. Quetting flew the ultra-light aircraft and  E. Lempidakis  assisted with data collection. We warmly thank everyone involved in the collection and management of the raptor tracking data, and in particular C. Itty for the eagle data, and the organizations Asters and  Vautours des Baronnies  for the help with field experiments.
The high species richness, coupled with high proportion of endemism, makes the Mediterranean one of the world's 'biodiversity hotspots'. However, the continuous increase in fisheries in the last few decades has led to the overexploitation of their main commercial stocks. Using fishery-independent data collected under the framework of the MEDITS trawl surveys carried out over the last 20 years, we study the demersal fish diversity pattern in the Mediterranean at a large spatial and temporal scale to determine whether it is being affected by the general fishing overexploitation of the demersal resources. The detected diversity trends are compared with the spatio-temporal variation in bottom trawl fishing effort in the Mediterranean. Our results show a stability and even recovery of demersal fish diversity in the Mediterranean together with higher diversity values on the continental shelves of the Balearic Islands, Sardinia, Sicily and the Aegean Sea.
The aim of this work is to study the demersal fish diversity pattern in the Mediterranean at a large spatial and temporal scale and to assess whether this pattern is being affected by the general fishing overexploitation of demersal resources in the area. To do so, we used fishery-independent data collected under the framework of the MEDITS trawl surveys carried out during the last 20 years. The detected trends were compared with the spatio-temporal variation in bottom trawl fishing effort in the Mediterranean Sea.
Data Demersal fish were collected during MEDITS bottom trawl surveys conducted from 1994 to 2015 in 14 geographical sub-areas (GSAs) along the European coasts of the Mediterranean Sea. Some GSAs have gaps in their sampling years: i) GSA 5 started sampling in 2001; ii) there are no data in 2002 for GSA 8 (technical problem of the boat); and iii) there are no data for  2002 , 2007 , 2009 -2013 and 2015 for GSAs 20, 22 and 23. for GSAs 20, 22 and 23. Sampling was performed during spring-summer in daylight hours using the GOC73 experimental gear, whose efficiency for catching demersal species has been tested by  Fiorentini et al. (1999)  and  Dremière et al. (1999). For more details about the sampling strategy and protocol see  Bertrand et al. (2002)  and the MEDITS hand-book, instruction manual version 9 (MEDITS Working Group 2017).
Fish assemblages and diversity Cluster analysis was used to analyse the structure of demersal fish assemblages and to identify different assemblages according to depth strata in each GSA. Rela-  tionships among samples were detected by hierarchical agglomerative clustering with group-average linkage after a forth root transformation of the data. The distance used to make groups was the Bray-Curtis similarity. These analyses were performed using PRIMER 7  (Clarke et al. 2014). The calculus of diversity indices explained below was made taking into account the groups of samples obtained from the cluster analysis. The N 90 diversity index was calculated following the procedure described by  Farriols et al. (2015). It is the mean number of species contributing up to 90% of within-group similarity calculated from abundance data expressed as N km -2 and assigned a priori to groups.
Fishing effort Information on annual fishing effort was collected from the working group reports of the GFCM (http:// www. fao. org/gfcm/data/safs/en/) and the Scientific, Technical and Economic Committee for Fisheries (STECF, https://stecf. jrc. ec. europa. eu/reports/medbs). Fishing effort data were compiled by trawl fleet targeting different species. The units vary between the different reports, being mainly provided in terms of number of vessels, kilowatts per days at sea and gross tonnage per days at sea (see Table  S1 ). To estimate fishing effort in each depth stratum obtained from cluster analysis, the strata were associated with the main target species of the fleets. Because target species varied between GSAs, we considered i) Mullus barbatus or Mullus surmuletus for the continental shelf; ii) Nephrops norvegicus or Parapenaeus longirostris for the shelf break/upper slope; and iii) Aristeus antennatus or Aristaeomorpha foliacea for the lower slope.
e. variance explained). These analyses were carried out with R, version 3. 1. 1 (R Core Team 2014). In order to observe spatial differences in diversity by GSA, time series of mean values and standard deviation of each diversity index (see Data section for years included in each GSA) were plotted. For those series with a significant temporal trend, the diversity values at the beginning and the end of the time series were plotted instead of mean values and standard deviation. SIMPER analysis for each group of samples from N 90 was also performed to see differences in species composition in each GSA. The percentage of contribution of each species to within-group similarity was calculated as the mean value of species contributions to similarity, taking all groups of observations by year and stratum for each GSA into account.
ACKNOWLEDGEMENTS The present study could not have been done without the work of all participants and crew in the MEDITS scientific surveys, funded by the  European Union Data Collection Framework for the Common Fisheries Policy , the funding projects supporting this research ( ECLIPSAME  Project  CTM2012-37701  and  CLIFISH  project  CTM2015-66400-C3-1-R MINECO/FEDER ) and the  FPI Fellowship  ( BES-2013-065112 ) from the  Spanish Ministry of Economy and Competitiveness  granted to MTF. GSA 1 M. barbatus Number of vessels 2005-2007 SAC GFCM 2008a M. barbatus kW*days at sea (*), GT*days at sea 2009-2013 STECF 2015a P. longirostris kW*days at sea (*) 2003-2015 STECF 2016b GSA 5 A. antennatus Number of vessels (*) 2011 SAC GFCM 2012a A. antennatus Number of vessels (*) 1998-2009 SAC GFCM 2010a A.
, 2019; Boniardi et al. , 2019; Liu et al. , 2019; Jones et al. , 2020). When street or street-network models are used, emission data are often modified and/or are coupled to observational data to improve modelled concentrations. For example,  Brasseur et al. (2015)  used a street model over two street canyons in Brussels. To achieve the high BC concentrations observed in the streets, a correction factor of 3. 0 was applied to traffic emissions calculated with COPERT IV, a database that provides traffic emissions factors for European vehicles  (Ntziachristos and Samaras, 2018). This under-estimation is also suggested in studies using more complex local-scale models for BC dispersion. For example,  Tong et al. (2011)  simulated BC concentrations using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) in an urban region at South Bronx, New York.
Simulations setup This section describes the input data and the model configuration of the SinG simulations, at both the regional and local scales. Six particle size sections are employed, with bound diameters 0. 01, 0. 0398, 0. 1585, 0. 4, 1. 0, 2. 5 and 10 µm. Simulations were run from 15 March to 15 May 2014. Model to data comparisons are performed from 12 April to 15 May, where BC concentrations were measured at the air monitoring station operated by Airparif during the TRAFIPOLLU project (http://www. agence-nationale-recherche. fr/?Project= ANR-12-VBDU-0002).
Local scale At the local scale, SinG simulates the street network represented in Figure  2 , containing 577 streets including the "Boulevard Alsace Lorraine", where the measurements were performed. Meteorological data above each street are extracted from the same WRF simulation as employed at the regional scale. EF bc s,v = EF tsp s,v × f pm10 s × S s (u v ) × f bc s. ( 10 ) where s is the non-exhaust source (tyre, brake or road wear, indicated by ty, bk and rd respectively), v is the vehicle type (v = LDV or HDV), EF tsp s,v is the total suspended particles (TSP) wear emission rate for a determined source s and vehicle v, f pm10 s the PM 10 fraction in each wear source s, and S s the correction factor according to vehicle speed u v. S s varies according to the wear emission source, as shown in Equations (  11 ), (  12 ) and (  13 ): S ty (u v ) =            1.
Thus, the DTW distance matrix is computed for every point of the series and used as a similarity measure for a chosen clustering algorithm. In general, DTW distance matrix has a high computational cost. To this end, the analysis of large datasets at pixel level may be extremely time-consuming and, hence, unreasonable. To deal with this issue, several object-based DTW clustering approaches have been proposed  [13] [14] [15]  to analyze the data both at temporal and spatial dimension. In these methods, spatio-temporal segments (in a form of a 2D map) are extracted for the whole SITS, then, the temporal sequences constructed for segment descriptors are clustered. Therefore, the object-based SITS analysis has drastically reduced computational cost and ensured more homogeneous results of clustering algorithms compared with the pixel-based approaches.
Second, we perform a preliminary segmentation of the SITS on its two most representative images. Then, we correct the preliminary segmentation by using the encoded feature image. Finally, we regroup the obtained objects with hierarchical clustering algorithm  [28]  using the encoded features as descriptors. The proposed approach showed us good results in the two real-life datasets and outperformed the concurrent methods, including the ones based on the DTW measure. We summarize our contributions as follows: • We propose a fully unsupervised approach of SITS clustering using deep learning approaches.
Methodology Our proposed approach is developed for segmentation and clustering of a SITS. Let R S be a time series of S co-registered images Im 1 , Im 2 ,. , Im S acquired at timestamps T 1 , T 2 ,. , T S. The framework is composed of several steps which are the following: 1. We start by relative normalization of all the images of the SITS using an algorithm described in Reference  [29]  and correction of saturated pixels. 2. We deploy a two-branch multi-view 3D convolutional AE model in order to extract spatio-temporal features and compress the SITS. 3. Then, we perform a preliminary SITS segmentation using two farthest images of the dataset taken in different seasons. 4. We correct the preliminary change segmentation using the compressed SITS. 5. Finally, we perform the clustering of extracted segments using their spatio-temporal features as descriptors.
Therefore, the 3D convolution network extracts both spatial and temporal features. The deployment of an AE type of model ensures the extraction of robust spatio-temporal features in an unsupervised manner without using any reference data. In classic AEs, the model firstly encodes the input data in some compressed latent representation and then decodes it back to its initial self. In image processing, the encoding pass is usually composed of convolutional and pooling layers for feature maps (FM) extraction that are followed by some fully-connected (FC) layers for feature compression. The decoding pass is often symmetrical to the encoding one. Once the model is trained, the extracted compressed representation is used to describe the data under the study. The encoder-decoder model allows us to compress the whole dataset in an uniform way.
The second row displays the segmentation of a road. We can observe that the road is over-segmented and its borders are shifted at the same time. To tackle this problem, we propose to perform a two steps segmentation that includes the correction of the preliminary segmentation in respect to all objects borders of the time series. The preliminary segmentation is performed on two most representative concatenated images of the SITS. To obtain the maximum of coherent spatio-temporal objects in the preliminary segmentation Seg pr , the chosen images should be as far apart as possible (e. g. , the first and the last image) and correspond to different seasons. For all image segmentations, the MeanShift  [31]  algorithm available in Orfeo ToolBox software (www. orfeo-toolbox. org) under QGIS interface was chosen.
Data We evaluate the proposed approach on two real-life publicly available time series issued from SPOT-5 and Sentinel-2 missions. Both SITS are taken over the same geographical location (Montpellier area, France), but, however, differ in terms of spectral and temporal resolution. While the first SITS contains 12 images that are irregularly samples over 6 years, the second one contains 24 images taken over 2 years with more regular temporal resolution. SPOT-5 dataset was taken between 2002 and 2008 and belongs to the archive Spot World Heritage (Available on https://theia. cnes. fr/). We have filtered out cloudy and flawed images from all the images acquired by the SPOT-5 mission over the considered geographic area and obtained 12 exploitable images with irregular temporal resolution (minimum temporal distance between two consecutive images is 2 months, maximum-14 months, average-6 months).
Distribution of dataset images is presented in Table  1. All SPOT-5 images provide green, red, NIR and SWIR bands with 10-meters resolution. Sentinel-2 dataset was taken between January 2017 and December 2018 (Available on https://earthexplorer. usgs. gov/). After deleting unexploitable images as well as the images that were less than 15 days apart from the previous images, we have obtained 24 images with more regular temporal resolution (minimum temporal distance between two consecutive images is 15 days, maximum-2. 5 months, average-1 month). Distribution of the dataset images is presented in Table  2. Sentinel-2 images provide multiple spectral bands of different spectrum and spatial resolution, however, it was decided to keep only 10-meters resolution spectral bands-blue, green, red, NIR.
The original images of both datasets are clipped to rectangular shapes of 1600×1700 pixels and transformed to UTM zone 31N: EPSG Projection. The clipped image extent corresponds respectively to the following latitude and longitude in WGS-84 system: • bottom left corner: 43 The pre-processing level of both datasets is 1C (orthorectified images, reflectance at the top of atmosphere). For this reason, both SITS were radiometrically normalized with the aim to obtain homogeneous and comparable spectral values over each dataset. For the image normalization, we have used an algorithm introduced in Reference  [29]  that is based on histogram analysis of pixel distributions. The ground truth (GT) for both datasets was taken from an open data website of Montpellier agglomeration (http://data. montpellier3m.
fr/) and correspond to landcover maps which we have manually modified to keep only distinguishable classes and merged the look-alike classes. While for the SPOT-5 dataset we have used Corina Land Cover (CLC) map of the 2008 year, for the Sentinel-2 dataset CLC of the 2017 year was taken. We have defined 9 well-distinguished GT classes: 1. urban and artificial area, 2. wooded area (include forests, parks, family gardens etc. ), 3. natural area (not wooded), 4. water surface, 5. annual crops, 6. prairies, 7. vineyards, 8. orchards, 9. olive plantation. For both datasets, the olive plantation class is very small, so we choose 8 reference classes for our clustering algorithm. The GT olive plantation class will be ignored during the evaluation. Note that it is difficult to create a GT for a multi-annual SITS analysis as some objects may go through changes and it is impossible to detect all these changes manually.
For this reason, for the SPOT-5 dataset, we use the GT that corresponds to the last year of the SITS. The SPOT-5 dataset was taken over 6 years and contains many change processes, mostly such as different constructions and permanent crop rotations. The study for change detection in the SPOT-5 dataset is presented in Reference  [6]. As these changes are less numerous, they will be considered by most of clustering algorithms as outliers, hence, they will be mixed with "stable" classes. However, some of these changes are only several timestamps long, so we still perform the clustering of the whole SITS instead of only free-change areas. Thus, the change areas will be regrouped with no change areas with the most similar temporal behavior or even make their proper clusters. At the same time, we consider that the Sentinel-2 dataset does not have any or has very few change areas as it is spread over only two years.
Experiments All the algorithms were tested on 6 cores Intel(R) Core(TM) i7-6850K CPU 3. 60 GHz with 32 GB of RAM computer with a NVIDIA Titan X GPU with 12 GB of RAM and developed in Python programming language using PyTorch 1. 3 library on Ubuntu 16. 4. For segmentation, we used Orfeo ToolBox 6. 6. 1 under QGIS 2. 18. tslearn library  [32]  was used to calculate DTW distance matrices for the concurrent approaches.
Time Series Encoding As it was mentioned in Section 2, we have the same AE model for both datasets with the parameters that depend on the time series length. Thereafter, we set different sizes of the encoded feature vector f for our datasets that is proportional to the SITS length. f values were obtained empirically and correspond to f = 10 and f = 20 for the SPOT-5 and Sentinel-2 datasets respectively (for 12 and 24 images in the datasets). During the model training, we equally add Gaussian noise with 0. 25 factor to the input patches of the original images to extract more robust and generalized features. We do not add any noise to the NDVI patches as it reduces model capacities to differentiate some minor variations in the vegetation. After several trials to assess the best parameters values, we set learning rate to 0.
0001 and batch size to 150 to ensure the most optimal model converging during the training. We use all SITS patches during the training of the model. We train the model for both datasets for 2 epochs until it is stable. The number of epochs was obtained after the analysis of loss trend and the visual analysis of the encoded images. However, if one does not dispose of a sufficient graphic memory for 2 epochs model training, it can be trained for only one epoch without significant loss in accuracy.
Preliminary Segmentation As it was mentioned earlier, we perform the preliminary segmentation of two concatenated images of the dataset that should be as far apart as possible and belong to two different seasons. For the SPOT-5 dataset, these images were the first and the last images of the dataset (taken on 2002-10-05 and 2008-08-21), for the Sentinel-2-the fifth and the last image (taken on 2017-06-12 and 2018-12-29). The chosen segmentation parameters for the MeanShift algorithm are presented in Table  3 , other parameters are used by default in the Orfeo ToolBox software, including Reg min = 100. For the choice of range and spatial radius, note that pixel values of SPOT-5 images do not exceed 475, while the Sentinel-2 images have 4096 maximum pixel value (after the elimination of saturated pixels).