dataesr/scientific-paragraphs-categorization huggingface dataset:
From a total of 340 spectra collected in the laboratory, 84 represent Li minerals, 196 correspond to the outcropping lithologies of the Fregeneda-Almendra area that can be used as training areas for satellite image classification, and 60 spectra were collected from samples in Li pegmatite falsepositive areas identified in previous satellite image classification attempts  [13]. Additionally, 75 field spectra are presented in the spectral database (35 measurements of Li minerals and 40 measurements of distinct Li pegmatites). As mentioned in Table  1 , two spectrometers were used for spectral measurements. For the data acquired with the SR-6500 equipment, the UTF-8 text files are composed of a 28 line-header containing information about the equipment and acquisition settings and two columns, the first with the wavelength (in nanometers or nm) and the second with the measured reflectance (in percentage).
Spectra Acquisition Two field campaigns were carried out in February and July of 2020 to collect the data to build the spectral library. At the time of the first campaign, there was no portable spectrometer available in the University of Porto to collect spectra in situ, therefore representative samples from 25 locations were taken to the GeoRessources laboratory (University of Lorraine). The spectral measurements were performed with the SR-6500 equipment. Due to difficulties in accessing pegmatites containing spodumene on the field, samples from an existing Li mineral collection at the University of Porto were also analyzed. In the second field survey, favorable weather conditions (absence of clouds) allowed collection of spectra from several Li pegmatites and the different Li minerals in 35 locations dispersed along the Fregeneda-Almendra pegmatite field.
Spectra Curation and Treatment The spectra acquired with ASD FieldSpec 4 were post-processed using the ASD ViewSpec Pro ™ software  [29]. The first step included averaging each of the five spectra collected for each spot. To the averaged spectrum, a splice correction was applied to remove the offset between detectors that is caused by differences in their sensibility  [29]  (Figure  2 ). Finally, the final spectra were exported to UTF-8 text files. In the case of the spectra collected using the SR-6500, a simple conversion from SED to the UTF-8 text file was necessary. The SpectraGryph software  [30]  was used to analyze each spectrum individually to exclude the ones with measurement errors or with noticeable noise caused by the presence of vegetation, for example. collected for each spot.
To the averaged spectrum, a splice correction was applied to remove the offset between detectors that is caused by differences in their sensibility  [29]  (Figure  2 ). Finally, the final spectra were exported to UTF-8 text files. In the case of the spectra collected using the SR-6500, a simple conversion from SED to the UTF-8 text file was necessary. The SpectraGryph software  [30]  was used to analyze each spectrum individually to exclude the ones with measurement errors or with noticeable noise caused by the presence of vegetation, for example. The VNIR and SWIR2 ranges are adjusted using the endpoints (~1000 to 1800 nm) and shape of the curve of the SWIR1 range since this detector is more stable to temperature changes  [29]. To allow a more detailed analysis of each spectrum, a hull quotient procedure was applied to remove the continuum and normalize the spectra (Figure  3 ).
This was accomplished in a Python environment using the pysptools library  [31]  based on the technique proposed by Clark and Roush  [2]. First, a convex hull was fitted to the spectrum (Figure  3a ) connecting the hull points (corresponding to local maxima)  [32]. Afterwards, the convex hull (continuum) was removed through a hull-quotient process and the measured reflectance values were divided by the convex hull  [2, [33] [34]. The continuum removal process is of great importance because it not only allows the comparison of the data obtained with different spectrometers but also allows the enhancement and correct determination of the absorption features  [32] [33] [34]. The pysptools library was used to automatically extract the main absorption features (Figure  3b-d ) and to calculate their associated statistics according to the work of Kokaly  [35].
To avoid the extraction of non-significant The VNIR and SWIR2 ranges are adjusted using the endpoints (~1000 to 1800 nm) and shape of the curve of the SWIR1 range since this detector is more stable to temperature changes  [29]. To allow a more detailed analysis of each spectrum, a hull quotient procedure was applied to remove the continuum and normalize the spectra (Figure  3 ). This was accomplished in a Python environment using the pysptools library  [31]  based on the technique proposed by Clark and Roush  [2]. First, a convex hull was fitted to the spectrum (Figure  3a ) connecting the hull points (corresponding to local maxima)  [32]. Afterwards, the convex hull (continuum) was removed through a hull-quotient process and the measured reflectance values were divided by the convex hull  [2, 33, 34].
User Notes This spectral database has the advantage of presenting field and laboratory spectra for 52 analyzed spots, allowing comparisons and evaluation of the influence of the acquisition conditions on the final spectra. Additionally, field spectra are expected to better match the spectral signatures acquired by airborne sensors. However, as can be seen in Figure  4 , field spectra present additional sharp bands around 1400 and 1800 nm caused by atmospheric water that should be removed before additional processing. A final note on the usage of the automatically extracted features that are available for all spectra acquired in the laboratory. As aforementioned, a baseline value was set to determine which absorption features to extract. Therefore, setting a different baseline may produce different results and change the features' statistics.
Additionally, different results can be obtained if the features are extracted considering the whole spectrum or spectra subset matching a given region of interest  [34]. According to the user's objectives, one of the two methods may be more appropriate. This spectral database has the advantage of presenting field and laboratory spectra for 52 analyzed spots, allowing comparisons and evaluation of the influence of the acquisition conditions on the final spectra. Additionally, field spectra are expected to better match the spectral signatures acquired by airborne sensors. However, as can be seen in Figure  4 , field spectra present additional sharp bands around 1400 and 1800 nm caused by atmospheric water that should be removed before additional processing. A final note on the usage of the automatically extracted features that are available for all spectra acquired in the laboratory.
Funding: The authors are grateful for the financial support provided by  FCT-Fundação para a Ciência e a Tecnologia, I. P. , through the  ERA-MIN/0001/2017 - LIGHTS  project and also the  869274-GREENPEG-H2020-SC5-2018-2019-2020  project. The work was also supported by  National Funds  through the  FCT  project  UIDB/04683/2020-ICT  ( Institute of Earth Sciences ). Joana Cardoso-Fernandes  and  Filipa Dias  are financially supported within the compass of their respective Ph. D. theses, ref. SFRH/BD/136108/2018  and ref. 2020. 05534. BD, by national funds from  MCTES  through  FCT , and cofinanced by the  European Social Fund (ESF)  through  POCH-Programa Operacional Capital Humano-and NORTE 2020 regional program. The  Spanish Ministerio de Ciencia, Innovacion y Universidades  (Project  RTI2018-094097-B-100 , with ERDF funds) and the  University of the Basque Country (UPV/EHU)  (grant  GIU18/084 ) also contributed economically.
In other words, no huge manually annotated data are required for automatic annotation nor for segmentation. After that we propose an algorithm to classify the type of the segmented sign using the automatically generated annotations. In the next section we present some of the works done in the SL processing field. Sections 3, 4 and 5 describes our proposed method. Then, Section 6 discusses the evaluation method, the dataset on which we worked on and the results. In the last section we present our conclusions and the perspective of our work.
RELATED WORK Nowadays, most of the works dedicated to the SL processing are interested in the recognition of lexical signs. Many attempts of SL recognition are conducted on isolated signs  (Rastgoo et al. , 2020) ,  (Lim et al. , 2019). However, in a natural speech of SL, it is often difficult to find precisely the beginning and the end of each sign because of the problems of coarticulation we mentioned before. In addition, most of these works focus on specific datasets, made in controlled environments (uniform background, signer with dark clothes) and dealing with a specific topic, such as weather  (Koller et al. , 2015). But the real challenge in SL recognition remains in identifying dynamic signs, i. e. signs in continuous SL speech, and most importantly independently of the signer  (Liang et al.
Controlled Vocabulary Once we get the coordinates of the different body and face landmarks, we proceed to calculate further features such as mouthing, gaze, etc. In manual annotation software, the ensemble of these features are called controlled vocabulary. Till this date, there is no one standard controlled vocabulary list defined. Each linguist annotates the events that are relevant for his studies. With our automatic annotation system, we annotate the features that are frequently annotated in the literature: mouthing, gaze/head direction, bi-manual motion, signing space and hands-head distance. To annotate mouthing, we calculated the isoperimetric ratio (or circularity) of the interior of the lips using the coordinates provided by OpenFace and the formula: IR = 4πa p 2 where a is the area of the interior of the lips and p is its perimeter.
The higher the ratio is, the more the mouth is open, which is an indication of a mouthing. The hands-head distance is simply calculated using the coordinates of both wrists ((x W Right , y W Right ),(x W Le f t , y W Le f t )) and nose (x Nose , y Nose ) (center of the face). For the bi-manual motion, we measure the speed and direction of each hand ((V W Right ,D W Right ),(V W Le f t ,D W Le f t )). The correlation of these two information between the two hands indicates whether these latter are symmetrical or moving in an opposite motion. To determine the location of the sign in the space, we compare the abscissa of the neck (x Neck ) to the abscissas of both wrists (x W Right , x W Le f t ) to decide if the sign is centered or not. As for the gaze/head direction, we use OpenFace directly for the task.
TEMPORAL SIGN SEGMENTATION Signs can be produced sequentially, separated by transitions and pauses. In order to study the grammar of the SL or to automatically recognise them, one possible strategy consists in segmenting the video before analyzing the resulting portions of videos. The temporal segmentation of a sign means finding its boundaries, that is when the sign starts and when it ends. In the manual annotation software, the linguists segment the signs manually, which takes hours of work. In addition, the resulting segmentation is, as mentioned previously, non-reproducible since it depends on the subjectivity of the annotator and his/her experience. We propose an algorithm that segments the signs automatically using only hands motions, pauses and hand shapes. The algorithm does not need any learning phase thus annotated data are not required.
Dataset The dataset used in our work is a portion of MOCAP 1 dataset: a collection of 2D RGB videos in French Sign Language. The dataset includes 49 videos with 4 different signers filmed from hip up face view and equipped with motion detectors for 3D representation (figure. 8) used other study purposes since this dataset was not produced specifically for our work. We note that the presence of the motion detectors did not prevent the detection of face, hands and body keypoints in our 2D RGB image study. The length of the videos varies from 15 to 34 seconds with an average of 24 seconds, and the dataset contains a total 19. 63 min of videos. In each video, the signer was asked to describe an image that represents a scene such as a living room (figure. 9) or a forest. 25 images were chosen delicately to have a variation between lexical signs and HIS.
Evaluation As described before, our algorithm starts by annotating features such as mouthing, head direction, location of sign. Then, using hand motions, the frames are scanned to find the boundaries of each sign segment. After that, the automatically generated features and a portion of manual annotations provided by an expert are used to create a probabilistic model. The latter classifies the segmented signs into lexical and non lexical signs. In this section, we measure the performance of the algorithm to locate the lexical signs and find its boundaries in comparison to the ground truth. Since our ground truth is based on manual annotations which are dependent on the annotators knowledge and experience, the evaluation results have to be taken with caution. As we described in section 6. 1, our dataset is divided into training data and testing data.
For the evaluation, we compare the automatically generated annotation files to the rest of the manual annotations which were not used in the learning phase to create the classifier. Using the testing dataset, we count the number of true positives (TP) of the detected signs, the false positive (FP), the true negative (TN) and the false negative (FN) ones. We consider that a lexical sign is correctly detected when 3 consecutive frames classified as lexical fall in the range of the manually annotated sign (figure. 10). Then we compute the TP and TN rates (TPR and TNR), i. e. the sensitivity and the specificity, the positive prediction value (PPV) (or precision) and F1-score: We calculated the same measures with frame by frame comparison were each frame from the automatic annotations is compared to the corresponding frame in the manual annotations to determine whether it's a TP, FP, TN, or FN frame to give an approximate measure on the precision of the classification in terms of segmentation.
Results The results of our method are evaluated for each signer individually and then combined to check if the classification is independent of the signer. quences. The segmentation was carried out by considering only the information coming from the hands, which makes our method applicable to all sign languages. We first used the hands motion calculated using body landmarks provided by OpenPose. Then we corrected the segmentation using the hands shapes. Once we got all signs isolated individually, we built a probabilistic model using a portion of MOCAP dataset which was annotated manually by an expert. The model was used as a classifier to distinguish lexical and non lexical signs. To evaluate our algorithm of classification we used the sensitivity, the specificity, the precision and the F1 score metrics.
Sequencing and Phylogenetic Analysis The positive PCR products were purified using the Wizard SV Gel and PCR clean-up system kit (Promega, Dane County, WI, USA) according to the manufacturer's instructions. The purified PCR products were sequenced at LGC Genomics (Berlin, Germany). The sequence data were assembled using Vector NTI 11. 5 software (Invitrogen, Carlsbad, CA, USA). The generated sequences were submitted to GenBank under accession numbers MK302070 to MK302091 and MK302092 to MK302113 for the RPO30 and GPCR genes, respectively, and MN161848 for the partial EEV glycoprotein of LSDV Embu/B338/2011. Nucleotide sequences were aligned using the MUSCLE algorithm and the codon option implemented in MEGA software version 7. 0. 26 [21]. The complete RPO30 and GPCR gene sequences of 11 additional LSDVs from East Africa (Table  1 ), 17 LSDVs from other regions, 7 GTPVs, and 4 SPPVs retrieved from GenBank were included for comparative analyses.
Bayesian phylogenetic inference was performed with BEAST v1. 8. 4  [22]  using the HKY+G nucleotide substitution and a UPGMA starting tree option  [22]. The Markov chain Monte Carlo method was run with BEAST for 10,000,000 generations, with a sample taken every 10,000 generations. The maximum clade credibility (MCC) was produced using TreeAnnotator after discarding the 10% burn-in determined using the TRACER program. The tree was visualized with the associated meta-data using the ggtree package in R  [23]. A section of the alignment of the GPCR, between nucleotide positions 80 and 120, was visualized together with the GPCR tree  [23].
Similarly, we also found noticeable differences between LSDV Embu/B338/2011 and the South African vaccine-related field LSDVs. Hence, two synonymous nucleotide changes occurred on the NTPase gene (A/G and G/A at positions corresponding to 75,253 and 75,325, respectively, in NC_003027) and five occurred on the N1R/p28-like protein gene (A/G, G/A, G/T, A/T, and G/A, at positions corresponding to 133,035, 133,056, 133,108, 133,126, and 133,137, respectively, in NC_003027). LSDVs retrieved from GenBank. The variable sites appear as letters, and identical nucleotides appear as dots.
Discussion In this study, we analyzed LSDV isolates collected in East Africa before the appearance of LSD in the Middle East and its incursion into Europe. The East African LSDV field isolates covered in this study (newly sequenced and recovered from public databases) were collected between 1950 and 2012. Overall, except for LSDV Embu/B338/2011, the East African field isolates in this study were highly similar to each other and to the previously sequenced East African field isolates of LSDV for the RPO30 and the GPCR genes. Likewise, the East African isolates were also highly similar to LSDV field isolates encountered elsewhere, including the Middle Eastern and European isolates. As the LSDV genome is stable, limited variability is likely to have occurred following the escape of the virus from Africa to the Middle East and then to Europe.
For instance, LSDV NI-2490 and LSDV Kenya, two historical field isolates collected in Kenya in 1950 and 1958, respectively, also contain a 12-nucleotide insertion in the GPCR gene  [4, 30]. Several reports have suggested that the occurrences of LSDV in vaccinated flocks were because of vaccination failures or the potential residual virulence of vaccines such as LSDV KS1  [18, [31] [32] [33]. However, our data show that LSDV KSGP O240 and LSDV Embu/B338/2011 are genetically different. It is also interesting to note that LSDV isolates carrying the 12-nucleotide insertion in their GPCR gene were previously rare. For instance, LSDV Kenya occurred in 1950, followed by LSDV RSA/54 in 1954, LSDV NI-2490 in 1958, LSDV Neethling vaccine LW-1959 in 1958 and 1959, LSDV KS-1 in 1976, and the LSDV Embu/B338/2011 of this study in 2011.
Sampling Collection of samples took place during a single field campaign in November-December 2018. Ten plant samples were collected. If possible, replicate plant species were sampled at various sampling sites within the system. The different species were selected based on the occurrence of large cohorts in the field or based on high reported occurrences in previous studies  (Stormanns, 1987; Neal, 2001 ; see also Fig. 3 ), but not all major plant communities mentioned could successfully be sampled during the field campaign. Sampled species include aquatic plants from the Nymphaeaceae family (n = 2) as well as the aquatic plant Phragmites australis (n = 2) growing on both dry and flooded soils; two species of wetland grasses, namely Vossia cuspidata (n = 2) and Cynodon dactylon (n = 2); and two representatives of the Cyperaceae family, namely Cyperus papyrus (n = 1) and Cyperus alternifolius (n = 1).
The CO 2 reference gas (δ 13 C = 34. 17 ‰ ± 0. 1 ‰ vs. Vienna Pee Dee Belemnite (VPDB), 5. 0 V ± 0. 5 V at m/z 44) was calibrated using IAEA-CH-6 international standards. Quantification of total nitrogen and organic carbon was achieved by external standard calibration using peak areas that yielded a linear five-point calibration curve. Repeated analysis of an internal laboratory standard cross-referenced to the certified IAEA-CH-6 international standard yielded a precision and accuracy of 0. 2 ‰ for each. Sample concentration data are reported blank corrected. Thermal analyses were performed at the IFP Energies Nouvelles lab (Rueil-Malmaison, France) using a Rock-Eval ® 6 device (Vinci Technologies). About 80 mg of freezedried ground sample was pyrolyzed in an inert atmosphere (N 2 ) by heating from 200 to 650 • C at 25 • C min -1 ; then residual carbon was combusted in air from 300 to 850 • C at 20 • C min -1  (Espitalie et al.
Statistical analyses All statistical analyses were performed using R software (R version 4. 0. 3 and RStudio version 1. 4. 1103). To test whether statistically significant differences occurred between the subenvironments, the Kruskal-Wallis test was performed using the stat_cor_mean() function of the "ggpubr" package (version 0. 4. 0). When the p value indicated that differences (p < 0. 05) between sub-environments were evident, the test was supplemented with a pairwise Wilcoxon rank sum test (base package "stats") to determine which sub-environments had significant differences between them. The Benjamini and Hochberg "BH" method was used to adjust p values. Correlation coefficients were calculated by using the stat_cor function with the default Pearson method of the ggpubr package (version 0.
4. 0). Box-and-whisker plots were generated using the "ggplot2" system (version 2. 3. 3. 3) of the "tidyverse" package (version 1. 3. 0) and ggpubr (version 0. 4. 0) and the geom_boxplot() function implemented here. Here, the median of the respective data is shown as a solid line and the lower and upper hinges correspond to the first (25th percentile) and third (75th percentile) quartiles. The lower/upper whisker extends from the lower/upper hinge to the smallest/largest value if it is not smaller/larger than 1. 5 times the interquartile range from the hinge. Otherwise, the respective data point is drawn as a single point representing an outlier. For the principal component analysis (PCA) performed, missing values of the data set were first imputed using the impute. PCA() function and applying the regularized iterative PCA algorithm of the "missMDA" package (version 1.
Acknowledgements. We would sincerely like to thank  Letitia Pillay ,  Archibold Buah-Kwofie , and  Lucky  for their expertise, support, and energetic assistance in obtaining the material studied. Additionally, we particularly thank  Lucky  for his assistance in communicating with the local landowners. We thank  Marcus Elvert  and  Hendrik Reuter  for their contributions to previous versions of the paper. This study would also not have been possible without the help of technicians  Ralph Kreutz  and  Jenny Wendt  of  MARUM -Center for Marine Environmental Sciences. We thank them for their support in chemical analyses and thank student assistant  Abdullah Saeed Khan  for assisting in bulk organic matter analyses. We thank  Marion Bamford  and  Frank Neumann  for their support and help with identifying the plant samples.