dataesr/scientific-paragraphs-categorization huggingface dataset:
The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images.
Then level-3 data are higher-level products such as thematic maps or time series results (obtained by merging or concatenation of several individual images) or similar operations. Finally, users can apply additional interactive processing steps on their own or exploit available software/platform concepts  [4]. This principle of ordered value-adding requires well-established techniques for data management, batch processing and databases, local and distributed (cloud) processing, understanding of the information flow, experience with learning principles, knowledge extraction from image and library data, and discovery of image semantics. At present, typical data sources with easy access are publicly available scientific image data provided by the European Copernicus mission with its Sentinel satellites  [5, 6]  as well as high-resolution remote sensing images  [7, 8].
The European Sentinel satellites comprise among others a constellation of SAR imagers (i. e. , Sentinel-1A/ Sentinel-1B providing typically large radar images, with a ground sampling distance of 20 meters and selectable horizontal and vertical polarizations), and a constellation of optical imagers (i. e. , Sentinel-2A/Sentinel-2B delivering typically large multispectral images with 13 different bands and a ground resolution-depending on the bands-of 10-60 m). This space segment of the Copernicus mission is complemented by systematic level-1 and level-2 image data processing on the ground and by support environments that serve as comfortable platforms for further data handling and interpretation covering all aspects of applied data science. These approaches then pave the way for deeper semantic data analysis and understanding as typically required in Earth observation for crop yield predictions, atmospheric research, etc.
Currently, we see much progress by "deep" techniques (e. g. , deep learning  [15, 16] ). An important point is the selection of reliable reference data for traceable validation and verification of the methods. • "Artificial intelligence" describes how machine learning results are exploited for further use. Typically this includes recognizing and being aware of typical situations, making decisions based on the recognized high-level parameters, and predicting future developments. To this end, one can profit from external databases complementing machine learning results. • "Data science" covers the entire field of comprehensive data management and tools, machine learning, and artificial intelligence. This includes topics like distributed processing, monitoring of workflows, visualization techniques, and performance monitoring.
The CANDELA platform CANDELA's main objective is the creation of additional value from Copernicus data through the provisioning of modeling and analytics tools provided that the DOI: http://dx. doi. org/10. 5772/intechopen. 86886 tasks of data collection, processing, storage, and access will be carried out by the Copernicus Data Information and Access Service  [18]. The corresponding flowchart is presented in Figure  2  and in [17]. In the end, after the integration of all components, CANDELA will be deployed on top of DIAS. The CANDELA platform [17] allows prototyping of EO applications by applying efficient data retrieval, data mining augmented with machine learning techniques, as well as interoperability in order to fully benefit from the available assets and to add more value to the satellite data.
Data set description for CANDELA Our main data sets extracted from different instruments are Earth's surface images of the European Copernicus Programme (e. g. , Sentinel-1 and Sentinel-2). Sentinel-1 is a twin satellite synthetic-aperture radar configuration, while Sentinel-2 is also a twin satellite configuration, each carrying a multispectral imager  [22, 23]. There are three reasons why we are selecting and using Sentinel-1 and Sentinel-2 images. Firstly, we can recognize different target area details in overlapping radar and optical images complementing each other with rapid succession. Secondly, individually selectable Sentinel-1 and Sentinel-2 images can be rectified and co-aligned by publicly available toolbox routines offered by ESA allowing a straightforward image comparison or image fusion.
Thirdly, all Sentinel instruments are totally openly available to the EO community. Many publications (dedicated conferences  [1, [24] [25] [26] ) already describe newly discovered Earth's surface characteristics derived from the individual instruments. Furthermore, the long-term operations of the Sentinel satellites allow the interpretation of image time series or even the combination of time series data with external supplementary data via additional data mining and data fusion tools  [1, 25, 26]. Besides these data sets, we include other third-party EO mission data sets as specified by CANDELA users (e. g. , TerraSAR-X and WorldView).
Third-party mission data From the available third-party mission data sets, we selected for demonstration four pairs of multi-sensor images of TerraSAR-X and WorldView-2  [27]. TerraSAR-X is a German radar satellite launched in June 2007, followed by its TanDEM-X twin in 2010. Both operate in X-band and are side-looking SAR instruments that offer a wide selection of operating modes and product generation options  [7]. TerraSAR-X has a revisit cycle of 11 days on the Earth's equator. We selected high-resolution spotlight mode images because they provide the highestresolution data of the target areas. As for the product generation options, we took enhanced ellipsoid corrected (EEC) and radiometrically enhanced (RE) data. Finally, we took horizontally polarized (HH) or vertically polarized (VV) images, as this option is most frequently used.
Data fusion by machine learning Currently, what exists in the field of data fusion is a collection of routines/algorithms that can be linked and embedded for various applications. A very well-known open-source toolbox is Orfeo [38] which provides a large number of state-of-the-art algorithms to process SAR and multispectral images for different applications. Another one is Google Earth [31] that includes a large image database and an expandable number of algorithms that can be used for image processing. In our case, we need to recognize different target area details in overlapping SAR and multispectral images. For doing this, we selected a number of cities from all over the world. The cities are Bucharest in Romania, Munich in Germany, Venice in Italy, and Washington in the USA. The selection criteria of these cities were the simultaneous availability of these cities covered by the two satellites and the variety of categories that can be found.
The objective is the knowledge extraction from the sensor signal of the physically meaningful parameters or Earth's surface cover categories. The process is divided in two steps: ○ The first step is an automated batch process to manage the satellite image product files, i. e. , to extract the image data and to select the relevant metadata, to perform a spatial breakdown of the image into patches, to estimate for each image patch the particular signatures or primitive descriptors, and to further structure the extracted information in a database. ○ In a second step following interactive machine learning paradigms, the extracted information is transformed into semantic entities attached to each image location. The process is a combination of querying, browsing, and active learning. Using positive examples, i.
e. , training samples for the categories of interest and complemented by negative examples to enhance the accuracies of each class, a user can define the image semantics adapted to a particular application. • The second perspective is based on the value-adding logic (Figure  20 ). Based on these procedures, value-adding is an iterative process. The satellite data are generally multi-mission data, e. g. , multispectral and SAR data that are restructured in a common database, which becomes the data source. The data preparation component is generating the Analysis-Ready Data (ARD) ensuring the least and mandatory processing and organizational steps that enable a direct analysis, thus minimizing the user interaction at the data level. Among them are the generation of radiometrically and geometrically calibrated data cubes.
Specific evaluation paradigms are needed to build trust in the obtained results, to be used to make predictions. The process is iterative, and when new data are acquired, they will be analyzed further. • The third perspective is the implementation architecture logic (Figure  21 ). The implementation of these paradigms requires a concept of integrating artificial intelligence with software (SW) system architectures enabling interactive multiuser operations in real time relative to the user reaction times. End users will be able to work on shared user scenarios, results of their analyses, or information extraction procedures. The central component is a data index (DI) which is a very specific database model for very fast, real-time management, processing, and distribution of large Artificial Intelligence Data Science Methodology for Earth Observation DOI: http://dx.
Future work During the next years, we expect a wide variety of new satellite image data that can be easily downloaded, handled, and analyzed by individual users. We also think that a number of new geophysical databases and browse tools will become available so that each user has easy access to numerous additional satellite data sources together with auxiliary geophysical data from common libraries and data management tools supporting in-depth image data analyses and their interpretation. Innovative application fields (such as autonomous driving based on machine learning and artificial intelligence) will bring us still more data handling tools and new data archives becoming available via the Internet. In addition, we also suppose that these new tools will be supplemented by management and support environments, for instance, for system testing and performance monitoring.
Acknowledgements Part of this work was supported by  CANDELA-the Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator -a  H2020 research and innovation project  under grant agreement no. 776193. Another part of the work was supported by EOLib-the Earth Observation Image Librarian-an  ESA  technological project. The TerraSAR-X image data being used in this study were provided by the  TerraSAR-X Science Service System (Proposal MTH   1118 ), while the WorldView-2 image data were provided by the  European Space Imaging (EUSI).
So I think this story had been sort of put safely to bed. I'm not sure why there wasn't more written about it. Certainly Sam Kula 13 wrote two articles but they're basically identical articles for different publications. And they don't say that much. They talk about his experience of going there and finding them. Then they talk about the most well-known stars, the people whose names have really survived, who were in the collection. If you go to the library and archives in Canada there is a data base but that's not publicly available. There's also an internal memo that was chock full of errors. You know, just absolutely complete --even from page one to page seven there's errors and inconsistencies. It's a mystery to me why even just in a scholarly way it wasn't written about. In some ways it's curious that no one made a film just because there was a lot of film material there.
Taxon sampling Taxon sampling included 165 placental mammal species, of which 135 are from assembled genomes in public databases, 13 are from unassembled sequence reads in the Sequence Read Archive (SRA) or European Nucleotide Archive (ENA), eight are from a combination of assembled genomes and unassembled sequence reads, and nine are from new Illumina HiSeq2500, HiSeq4000, HiSeqX10, or NovaSeq data (Additional file 1: Table  S1 ). Taxon sampling included 12 taxa from Afrotheria, ten taxa from Xenarthra, 61 taxa from Euarchontoglires, and 82 taxa from Laurasiatheria. Among the afrotherians, three taxa (Mammut americanum [American mastodon], Mammuthus primigenius [woolly mammoth], Palaeoloxodon antiquus [straight-tusked elephant]) are extinct. Toothless taxa included two vermilinguans (anteaters), two pholidotans (pangolins), and seven mysticetes (baleen whales); taxa that lack enamel on their teeth included one tubulidentate (aardvark), two folivorans (sloths), and six cingulatans (armadillos).
BLAST searches and alignments Genomic sequences encoding ODAM were downloaded from NCBI for representatives of Euarchontoglires (Homo sapiens), Laurasiatheria (Bos taurus), and Afrotheria (Trichechus manatus). These sequences were employed in BLAST searches against other placental mammals in NCBI's 'RefSeq Genome' and 'Whole-genome shotgun contigs' databases. We used megablast for highly similar sequences and the more sensitive blastn for less similar sequences when megablast searches failed to return results. Complete coding sequences and intervening introns were imported into Geneious 11. 1. 5 [31]  and aligned with MAFFT  [32, 33]  with minor adjustments by eye. For five xenarthrans (Cabassous unicinctus, Tolypeutes matacus, Euphractus sexcinctus, Choloepus didactylus, Tamandua tetradactyla), we imported Discovar de novo (https://software.
broadinstitute. org/software/discovar/blog/) genome assemblies into Geneious  [12]  and performed local BLAST searches (discontiguous megablast) using reference xenarthran sequences (Dasypus novemcinctus, Choloepus hoffmanni). With a few exceptions (see below), exon-intron boundaries are conserved among placental mammals. In cases where there were suspected assembly gaps with missing exons, we used sequences from the closest available taxon (exon plus ~100 bp of each flanking intron) to perform additional BLAST searches with megablast against the Sequence Read Archive (SRA). Sequences from SRA BLAST searches were imported into Geneious and mapped to the closest reference sequence to assemble missing exons and their flanking intron sequences. A similar strategy was employed to retrieve complete protein-coding sequences for ODAM in taxa with unassembled genomes in the SRA.
We also used a mapping to reference approach in Geneious to retrieve ODAM sequences from unpublished Illumina data for Choeropsis liberiensis (pygmy hippopotamus) and three xenarthrans. For C. liberiensis, we mapped reads to the ODAM sequence for Hippopotamus amphibius (river hippopotamus). For the three xenarthrans (Chlamyphorus truncatus, Calyptophractus retusus, Cyclopes didactylus), we imported short reads into Geneious and mapped them to various xenarthran reference sequences. Individual exon sequences (and flanking introns) from each SRA taxon were concatenated into a single sequence with Geneious and aligned to the global alignment.
Inactivating mutations We inspected the final alignment (165 taxa) for inactivating mutations including exon deletions, frameshift mutations, altered start and stop codons, premature stop codons, and splice site mutations. We also used the alignment program MACSE v2  [34, 35]  to crosscheck the results of manual inspections for frameshifts and premature stop codons in coding sequences. Parsimony optimizations with delayed transformation (deltran) were performed with PAUP* 4. 0a150  [36]  to map inactivating mutations to branches of the phylogeny of placental mammals.
We assumed fossil calibrations from previous studies  [44, 46, 47]  and extracted life history traits from PanTHERIA  [48]. We set the prior on the root node to 97 Ma with a standard deviation of 20 Ma following the dating of Meredith et al. [44]. We ran two independent MCMC for a total of 5000 cycles sampling parameters every cycle. After checking for convergence by monitoring the effective sample size of the different parameters using the tracecomp command, we excluded the first 500 points of each MCMC as burnin, and made inferences from the remaining 4500 sampled points of each chain. Additional dN/dS analyses were performed with the codeml program of PAML 4. 4  [49]. Codeml does not employ the autocorrelative model of Brownian motion  [50]  that is incorporated into Coevol. For computational efficiency and tractability, codeml analyses were performed with 96 taxa after pruning all but one representative for mammalian families with ODAM sequences that are intact for all exemplars included in our study.
] , Monodontidae [1 sp. ], and Delphinidae  [2 spp. ] ). The number of inactivating mutations among different odontocete species ranges from one in Physeter macrocephalus (splice site mutation) to four in the three phocoenid species (Phocoena phocoena, Neophocaena phocaenoides, N. asiaeorientalis). Beyond these odontocetes, additional taxa with enamel-capped teeth had putative inactivating mutations. The phocid (seal) Neomonachus schauinslandi has one 2-bp frameshift deletion, one premature stop codon, an acceptor splice site mutation (intron 2 acceptor, AG = > AA), and a putative deletion of exon 5. However, genomic Illumina data are not available for Neomonachus, and we were unable to validate these mutations. By contrast, the New World primate Callithrix jacchus has a premature stop codon in exon 5 and an acceptor splice site mutation (AC) in intron 4, both of which are confirmed by SRA data.
5 ). Beyond its inactivation in toothless and enamelless forms, we also discovered inactivating mutations or exon deletions in taxa with enamel-capped teeth. Two of these (odontocetes, some proboscideans) are discussed in separate sections below. Among the remaining taxa with mutations in ODAM, the phocid Neomonachus has four separate mutations (2 bp frameshift deletion, premature stop codon, splice site mutation, exon 5 deletion) and is the best candidate for inactivation of this gene among taxa with enamel-capped teeth. However, short read data for Neomonachus are not available in the SRA and we were unable to validate these mutations. Another phocid, Leptonychotes weddellii, also has problems with the annotated gene sequence, and like Neomonachus is missing exon 5 (as well as exons 6-8).
SRA data are available for Leptonychotes and we were able to reconstruct all of the missing exons for this taxon. It remains unclear if the missing exon and other problems with Neomonachus ODAM are real or instead are assembly or annotation errors. Enhydra lutris has a 1 bp frameshift in exon 1 of the annotated sequence, but three different individuals with SRA data have both intact and frameshifted versions of this exonic region, which leaves open the possibility that the mutated form of exon 1 represents a second allele, or more likely, a paralogous locus. Callithrix jacchus (common marmoset) has an acceptor splice site mutation in intron 4, a premature stop codon in exon 5, and an elevated dN/dS ratio (~0. 85-0. 90). Curiously, marmosets exhibit a loss of lingual enamel and a hypertrophy of buccal enamel in their mandibular incisors  [75].
ODAM deletion and functionality in Proboscidea? Our taxon sampling included six proboscideans, of which five (Loxodonta africana, L. cyclotis, Elephas maximus, Mammuthus primigenius, Palaeoloxodon antiquus) belong to Elephantidae and one belongs to Mammutidae (Mammut americanum). Exon 6 is putatively missing from four of these taxa based on the absence of sequencing reads that map to this region (Fig. 3 ). The assembled genome sequence for L. africana is also missing exon 6, but we were able to assemble this exon and its flanking intronic regions from SRA data where the Sanger sequencing-based assembly contains a string of Ns (assembly gap). The presence of exon 6 in L. africana and E. maximus, but not in other proboscideans, is unexpected given the phylogenetic relationships of these taxa.
Acknowledgments We thank  B. de Thoisy ,  M. Superina ,  M. Nachman  ( Museum of Vertebrate Zoology, University of California, Berkeley, USA ),  G. Haszprunar , and  M. Hiermeier  ( The Bavarian State Collection of Zoology, Munich, Germany ) for providing xenarthran tissue samples and  G. Amato  (formerly at  NYZS ) for providing pygmy hippopotamus DNA. We also thank  J. Johnson  ( Broad Institute, Cambridge, USA ) for granting access to unpublished xenarthran Discovar assemblies and  C. Buell  for providing paintings of mammals.
Funding This work was supported by  NSF  grant  DEB-1457735  to J. G. and M. S. S, a grant from the  Leibniz Association  ( SAW-2016-SGN-2 ) to M. H. , a  PRESTIGE Marie-Curie post-doctoral fellowship  to C. A. E. , and  "Investissement d'Avenir" of the "Agence Nationale de la Recherche " (CEBA:  ANR-10-LABX-25-01 ) and  European Research Council  (ConvergeAnt:  ERC-2015-CoG-683257 ) grants to F. D. This is contribution ISEM  2019-005  of the  Institut des Sciences de l'Evolution de Montpellier. These agencies provided funding for generating new data and provided support for personnel.
Study design This study was a physiological randomized, crossover interventional study involving male volunteers. It took place in 2013 in the Department of Medical Intensive Care of the University Hospital of Angers, France. This study was approved by the leading hospital ethics committee (Comité de Protection des Personnes Ouest II) under the reference 2012/35. All participants gave informed consent prior to their inclusion in the study. The study was registered as NCT01818219.
c. Connection of the recording systems The Servo-I ® ventilator was connected through a RS 232 cable to a laptop equipped with a dedicated software (Servo-tracker ® software version 4. For all the conditions, Eadi-time curve was recorded using the ventilator-dedicated software (Servotracker ® software version 4. 2, Getinge, Solna, Sweden). Esophageal pressure was recorded using an analog-to-digital converter (MP150, Biopac Systems, Goleta, CA, USA) connected to a pressure transducer and to a laptop computer. Sampling rate for all the recordings was 100 Hz. During assisted ventilation, flow-time curve was recorded using both the ventilator-dedicated software and the analog-to-digital converter (equipped with a flow module connected to a flow sensor placed at the airway opening). The two recording systems were started simultaneously, and data were stored for offline analyses.
Offline analyses Flow, Eadi and esophageal pressure signal analyses were performed on 25 consecutive breaths after 120 s have elapsed. If an ineffective effort, an esophageal spasm/contraction, an autotriggering or a double triggering  [26]  was diagnosed by visual inspection, the corresponding respiratory cycle was excluded from the analysis. The Acknowledge software version 4. 2 (Biopac Systems, Goleta, CA, USA) was used to analyze Eadi-time and flow-time curves. Measurements derived from esophageal pressure were taken using the SR program (Sistema Respiratorio, a semi-automated research software used in previous works  [10, 27] ). As illustrated in Fig. 2 , for each analyzed breath, inspired tidal volume (VT), pneumatic inspiratory time (Tip)defined as the duration of inspiratory flow-, and its ratio to total duration of a respiratory cycle (Tip/Ttot) were measured from the flow-time curves.