dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation Details. The proposed algorithm is implemented in Matlab and executed on a 3. 30 GHz Intel5 Core6 i5-4590 processor with 8 GB RAM. The structure of the program can be divided into two parts: kernel determination and image filtering. For each pixel  in retinal image, kernel determination aims to specify an optimal kernel with the best matching orientation and scale. For image filtering, each pixel in the filtered image is computed by using  (7). A pseudocode example of our algorithm is illustrated in Algorithm 1.
DRIVE Database. The DRIVE database (http://www. isi. uu. nl/Research/Databases/DRIVE/) contains a total of 40 RGB color retinal images. These retinal images were captured by a Canon CR5 nonmydriatic 3-CCD camera (Canon, Tokyo, Japan) with a 45-degree field of view (FOV). The size of each image is 768 × 584 pixels. For all these images, manual marks of the vessels are provided and were treated as the ground truth for vessel detection in our experiments. Our experiments were conducted on the green channel of the original RGB retinal images because it preserves the largest vessel-background contrast, as described in  [36, 40, 41]. 6. 3. Qualitative Evaluation 6. 3. 1. Synthetic Vessel Images. We compared our method with BLF by observing their results generated from the synthetic vessel images and found that our method outperforms BLF in the following four aspects.
Real Retinal Images. Another qualitative evaluation is based on the observation that a good retinal image filtering method should not blur/ruin the vascular structures while eliminating noise. In Figure  8 , we show the green channel of a retinal image randomly chosen from the DRIVE database and the results obtained using three image denoising algorithms including Gaussian filter, BLF, and our method. From the results, we can see that the classical Gaussian filter causes blur at the vicinity of the vascular structure. By contrast, BLF performs better in capturing the crisp vascular structure. However, when compared with our method, thin vessels with low image contrast are corrupted in the process of image denoising of BLF. Obviously, our method generates results superior   to both the Gaussian filter and BLF especially in preserving thin vessels.
Real Retinal Images. In the following experiments, we took advantage of the images from the DRIVE database to demonstrate that the proposed method is able to improve the accuracy of vessel detection. To carry out the experiment, the state-of-the-art vessel detection method FR  [19]  was adopted. We ran BLF and our method as a preprocessing  step for the vessel detection process. The detection results of different strategies are given in Figure  9. From these results, we can see that FR's results still contain a few isolated points (pixels belong to background as shown in Figure  9 (c)). These points are caused by the isolated noise in the original image. To tackle this problem, we use BLF to reduce the isolated noise in the original retinal image before vessel detection. Figure  9 (e) gives the result of this strategy, which shows that the noise has been reduced.
Main characteristics The core OTB library is written in C++ and based on the Insight Toolkit (ITK)  [4]. ITK is an open-source software toolkit for medical imaging, including registration and segmentation. As ITK, OTB implementation style is referred to as generic programming (C++ templates). This means that the code is highly efficient, and that many software problems are discovered at compile-time, rather than at run-time during program execution. It also enables to work with different types of images (different number of bands, dimensions, pixel type, etc. ). OTB algorithms covers a large number of features needed to process remote sensing images from basic preprocessing to high performance analysis. OTB's mission statement is to provide a free software end-to-end solution for the Earth Observation image information extraction pipeline based on a generic, high performance C++ library.
Code reuse Remote sensing image processing often leads to the combination of specialized methods available in dedicated software. The idea in OTB is to provide a common interface to these software libraries. Therefore, OTB depends on Geospatial Data Abstraction Library (GDAL)  [5]  for raster and vector formats support; OSSIM for geometric sensor modeling; libSVM  [6] , OpenCV  [7]  and Shark  [8]  for machine learning; and MuParser  [9]  for instance for high performance mathematical expression parsing. Furthermore, OTB also tries to keep track of up-to-date research about the latest developments and integrate reference implementation of algorithms after their publication. For instance Morphological profiles, dimensionality reduction methods (PCA,NA-PCA,ICA,etc. ), Line Segment Detector (LSD)  [10]  fast implementation of Haralick textures and SURF keypoints matching.
The PSC provides a central point of contact for the project and arbitrates disputes. It is also a stable base of institutional knowledge to the project and tries its best to involve more developers and guarantees that OTB remains open and company neutral. The OTB PSC was inspired by similar structures that exist in other geospatial software projects like GRASS GIS  [15]  or QGIS. Finally, OTB is currently in the incubation stage of being part of the OSGeo foundation  [16]. Within the Orfeo ToolBox community we act respectfully toward others in line with the OSGeo Code of Conduct  [17]  and we hope to be able to complete the incubation process in 2017.
From desktop to high performance computing Orfeo ToolBox is designed to accommodate both interactive computing on users' desktops, and terabyte scale processing on many core architectures. This is achieved with a modular software architecture: the so-called OTB sandwich illustrates in Fig. 2. At the core of OTB is the C++ API, which implements the pipeline. This core model of processing supports multi-threading, streaming and message passing. Thus all applications and filters can process images and scale to the available memory and CPU resources. With additional features like in memory application chaining (to minimize disk I/O), porting code from a development machine and scaling up to a high performance cluster is often trivial.
Monteverdi Orfeo ToolBox ships with Monteverdi, a lightweight image rendering and processing tool written in Qt and OpenGL. Monteverdi makes use of the Ice rendering engine  [18] , also available in Orfeo ToolBox which offers: • Smooth navigation in very large datasets using GDAL overviews capabilities, • Reactive local and global rendering tools such as local contrast enhancement or color-mapping, based on advanced OpenGL features such as floating point textures and OpenGL Shading Language (this work was inspired by the authors of pvflip  [19] ), • Multiple image display with on-the-fly rough registration of any image whose coordinate reference system is understood by Orfeo ToolBox, which include both ground projected and sensor geometry images. Monteverdi is a day-to-day tool for fast visualization of processing results, which can display images in sensor geometry.
In short they are: select samples in a reference image to be used for learning (and how to select them), extract pixel values from the image, and train Fig. 2  The difference software layers in Orfeo ToolBox: dependencies, C++ API, applications, and GUI. a. k. a. the "OTB sandwich" a supervised classifier (Support Vector Machines, Random forests, etc. ). This way, control is given to the user over each critical step in the design and tuning of a classification pipeline. This is achieved without an excessive increase in complexity, to maintain both rapid prototyping capability and high performance. Another important design characteristic of applications consists in the indipendency of their code and user interface. This is achieved with the so-called Application Framework. The framework allows multiple interfaces to be provided for each application without code duplication.
Today, OTB applications are available through the following interfaces: • The command-line, • A GUI interface based on Qt, • Python for high level programming and connection with NumPy array, • Monteverdi for interactive processing of viewed images, • QGIS via the Processing plugin, • Zoo-Project  [20]  through a Web Processing Service (WPS). Here is one example of how to use Python to run the Smoothing application, changing the algorithm at each iteration: Additionally, the OTB applications are available in QGIS via a Python plugin which is built on top of the QGIS processing framework  [21]  making spatial analysis tasks more productive and easy to accomplish. Two additional features work together to decrease friction between the multiple interfaces: saving application parameters to XML files, and automatic conversion of GUI parameters to Bash for easy copy-pasting.
Scaling up The same Orfeo ToolBox applications that are available for desktop remote sensing will scale up seamlessly to process larger datasets. Whenever possible, algorithms implemented in Orfeo ToolBox are performing piecewise processing, which means that if the data are larger, they will take longer to process, with a constant memory budget. This piecewise processing is a key aspect of the ITK processing pipeline on which Orfeo ToolBox strongly relies (piecewise processing is sometimes called streaming in the ITK world). This ability is retained even when combining several algorithms in the pipeline. When there is no obvious way of computing some algorithms piecewise, Orfeo ToolBox also offers adapted versions of these methods, such as for MeanShift segmentation  [22]  or large scale region growing segmentation  [23]  which ensures # Example on the use of the Smoothing application # # We will use sys.
argv to retrieve arguments from the command line. # Here, the script will accept an image file as first argument, # and the basename of the output files, without extension. from sys import argv # The python module providing access to OTB applications is otbApplication import otbApplication # otbApplication. Registry can tell you what application are available print "Available applications : " print str( otbApplication. Registry. GetAvailableApplications() ) # Let's create the application with codename "Smoothing" app = otbApplication. Registry. CreateApplication("Smoothing") # We print the keys of all its parameter print app. GetParametersKeys() # First, we set the input image filename app. SetParameterString("in", argv  [1] ) # The smoothing algorithm can be set with the "type" parameter key # and can take 3 values : 'mean', 'gaussian', 'anidif' for type in ['mean', 'gaussian', 'anidif']: print "Running with " + type + " smoothing type" # Here we configure the smoothing algorithm app.
SetParameterString("type", type) # Set the output filename, using the algorithm to differentiate the outputs app. SetParameterString("out", argv  [2]  + type + ". tif") # This will execute the application and save the output file app. ExecuteAndWriteOutput() stable results with piecewise or tile-wise computation processing illustrated in Fig. 4. However, being able to accommodate a memory budget regardless of the size of the data is only a part of the problem. Users usually also want to take advantage of modern CPU architectures as well as High Performance Computing infrastructures (HPC), and Orfeo ToolBox will also do that for them. Whenever possible, algorithms implementation are threaded, mostly using OpenThreads  [24] , even if specific parts of the code will also use OpenMP  [25]  directives.
Orfeo ToolBox will seamlessly use all cores of the CPU. But as the dataset gets bigger, applications generally moves to an HPC architecture sharing many similar nodes with a shared high-bandwidth storage, and Orfeo ToolBox can also do that. It allows for MPI  [3]  parallel processing, meaning that the whole pipeline will be replicated across nodes which will produce a piece of the resulting image  [26]. An example of use of this capability is the pan-sharpening of a whole Pleiades image (1. 6 gigapixels). Spread across 560 mono-threaded nodes, the processing time is cut down to 4. 3 min. One last capability that is very useful to scale up to your data is the inner pipeline connection between applications. Building a processing chain from several applications is very convenient, and will scale-up nicely.
ESA Sentinel-2 ground segment Sentinel-2 mission offers a systematic global coverage with a high spatial resolution, a high revisit and a wide range of spectral bands from visible to short wave infrared. Therefore, the flow of data to process is quite huge (1. 2 TBytes of compressed raw data per day) and requires to design an operational Payload Data Ground Segment (PDGS) with efficient image processing capabilities to produce end users Level 1C products (orthorectified data at Top of Atmosphere level) in near real time. In order to develop the image processing module of the PDGS, European Space Agency (ESA) has selected OTB library as main component of the Instrument Processing Facility (IPF) module. More precisely, OTB filters have been used for all radiometric corrections (denoising, defective pixel detection and correction, quality mask and TOA conversion) and resampling operations.
With good knowledge of the aerosol optical thickness (AOT) and of the aerosol model, and using radiative transfer codes, one can correct for the aerosol effects and convert the satellite top-of-atmosphere (TOA) reflectances into surface reflectances  [30]. Multi-temporal Atmospheric Correction and Cloud Screening (MACCS) is a processing chain  [31]  which implements all these steps. It was designed at CNES and the operational system was entirely developed based on the OTB. MACCS is used in the The French Theia Land Data Centre (THEIA)  [32]  to produce and distribute in nearreal time the Sentinel-2 data acquired on an area of 5Mkm 2. The processing starts with France and is progressively extended to the other selected regions at the end of 2016 and beginning of 2017.
Theia OSO land cover product A detailed and accurate knowledge of the land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable  [33]. This accurate knowledge needs a frequent update of the information. In addition to the distribution of surface reflectance products, the French Theia Land Data Centre has also set up a Scientific Expertise Centre whose aim is to implement an operational fully automatic land cover map production system using mostly Sentinel-2 image time series. The product will be updated once a year and will contain 20 thematic classes mapped at 10 meter resolution. The open source system  [34]  based on OTB developed in the Centre d'Etudes Spatiales de la Biosphere lab (CES-BIO) allows to produce these types of maps in a consistent and reproducible way.
Geoinformation for sustainable development (GEOSUD) Spatial information of ecosystems, agricultural systems and territories is crucial for environmental and agronomic research, as well as public policies. GEOSUD aims to transfer methods for environment and territories management, and ease the access of spatial information to scientific community and public actors  [38]. Currently, this access is guaranteed by a geospatial data infrastructure delivering very high resolution remote sensing products (RapidEye, Pléiades, Spot 6 and 7). It is achieved through the use of Open Geospatial Consortium (OGC) Web Services for data access, visualization and catalog. The incoming extension of the infrastructure implementing the OGC Web Processing Service (WPS) standard  The full map can be found at  [39]  enables the interactive on-demand remote processing of geospatial data.
Sentinel-2 for agriculture Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. As previously described, the Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring. In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture"  [39]  project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. These processors are based on OTB applications framework and library to efficiently generate Level 2A data from Sentinel-2 (and LandSat 8): • Cloud-free Reflectance Composite, • Vegetation Status Indicators (LAI, NDVI and phenology metrics), • Dynamic Cropland mask, • Crop Type map.
Conclusion In ten years of its life, Orfeo ToolBox has evolved from a C++ only, VHR specific library to a versatile toolkit addressing most of remote sensing imagery needs. Orfeo ToolBox is now part of several large, operational projects while still being a tool of choice for the day-to-day image processing in labs and a great environment to write new algorithms and methods. Scalability, versatility and openness are the key assets of Orfeo ToolBox. The software is constantly evolving to enhance performances, to offer more state-of-the-art algorithms and to provide the best interoperability between them. Orfeo ToolBox roadmap is user-driven, and its young open project steering committee ensures that everyone willing to get involved will receive equal attention. In the short to medium term, next OTB versions will include a better integration of non supervised classification algorithms in the applications framework as well as improvements of the integration of applications in Quantum GIS.
Domain framework for implementation of open IoT ecosystems The current Internet-of-Things (IoT) hype, pushed by the unprecedented rate of the technological enablers' innovation is threatening to leave behind some major, not so obvious, unresolved issues. IoT platforms will extend existing Enterprise Information Systems (EIS) infrastructures to encompass cross-domain sensing and actuating capabilities, thus introducing additional complexity and major risks to the implementation. Furthermore, IoT platforms are typically driven by models of the trivial complexity; they support very simple data structures and almost no business logic implementation. Finally, IoT systems are today managed centrally, which often means less openness, less flexibility and greater change management costs. In this paper, we provide the overview of the scientific disciplines which could contribute to the resolution of the IoT implementation problem, namely requirements engineering, change management/continuous improvement, modelbased systems engineering, system architecture design, interoperability and policy and regulatory aspects.
A unique collection of oceanic samples was gathered by the Tara Oceans expeditions (2009-2013), targeting plankton organisms ranging from viruses to metazoans, and providing rich environmental context measurements. Thanks to recent advances in the field of genomics, extensive sequencing has been performed for a deep genomic analysis of this huge collection of samples. A strategy based on different approaches, such as metabarcoding, metagenomics, single-cell genomics and metatranscriptomics, has been chosen for analysis of size-fractionated plankton communities. Here, we provide detailed procedures applied for genomic data generation, from nucleic acids extraction to sequence production, and we describe registries of genomics datasets available at the European Nucleotide Archive (ENA, www. ebi.
Background & Summary Systems-level studies of the functional biodiversity of marine ecosystems are becoming crucial for understanding and managing ocean resources. During the Tara Oceans expeditions (2009-2013), original and innovative strategies were used to gather the largest modern-day collection of marine plankton in combination with an extensive suite of environmental data  1. The worldwide sampling strategy and methodology are presented in Pesant et al. 2. Here we focus on the description of procedures applied for genetic analysis of samples collected during the Tara Oceans campaigns. During the last decade, rapid advances in sequencing technology has been a major force in the rise of studies aimed at deciphering genetic and functional biodiversity in complex environmental samples  3, 4.
Because marine eukaryotes are only poorly represented in databases  17  , further efforts were made to produce reference genomes for some uncultured unicellular organisms. This was possible through single cell isolation by flow cytometry followed by whole genome amplification and de novo sequencing. A first wave of analyses performed using a subset of Tara Oceans samples generated high quality data leading to several publications  [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]  that shed light on the extraordinary biodiversity of marine ecosystems. Among the most significant results, metagenomic data obtained from prokaryote-and virusenriched samples were used to establish an ocean microbial reference catalog with more than 40 million genes  23. In parallel, viral metagenomes combined with morphological datasets were used to assess viral community patterns and structure, providing a map of global dsDNA viral diversity in the surface-and deep ocean  20, 29  whereas for protists and metazoans, 18S rRNA metabarcoding allowed to profile eukaryotic diversity in the photic zone  21.
However, the utility of Tara Oceans metadata have been only partially explored until now and further publications will help disentangling one of the largest and most complex ecosystems on Earth. This paper aims to gather together all procedures used to generate sequencing data, from nucleic acids (DNA/RNA) extraction to sequence production. Experimental methods, mostly already published in Tara Oceans related papers, are here described extensively, adding detailed information about quality control processes applied to each experimental step and to generated datasets. Most importantly, the associated Metadata Record allows straightforward linking of the described methods with genomics datasets already available openly. This paper is an important contribution to better assess already published work, and to support further analyses of genomics data generated by the Tara Oceans expedition.
Methods The generation of information-rich data from marine plankton samples presents unique challenges that are inherent to the particular sampling conditions at sea and the wide spectrum of organisms included in that environment. All processing steps, including biomass collection, sample preservation, nucleic acids extractions and sequencing library preparation, are critical and require specific protocols and robust methods in order to ensure comparability of results and limit potential biases  [30] [31] [32]. Our methods were either developed specifically for Tara Oceans samples or carefully selected among existing ones in order to meet the requirements of our sequencing strategy and to produce optimized datasets for downstream bioinformatics analyses, as for example the production of overlapping reads from metagenomics libraries to facilitate assembly.
They are presented in five sub-sections, starting with a brief description of how samples were handled between the research vessel and the processing laboratories (Subsection 1). Sub-section 2 reports on DNA and RNA extractions procedures for -omics analyses, including the generation of amplified genomic DNA from uncultured isolated unicellular eukaryotes. The generation of 18S and 16S rRNA amplicons from DNA of specific size-fractions is described in subsection 3 and Illumina libraries preparation in subsection 4. Sequencing procedures and post-sequencing data processing are described in subsection 5. For details on the onboard sampling protocols, see Pesant et al. 2. [1] Handling of genomics samples Genomics samples were transferred on average every 6 weeks from a port of call to the European Molecular Biology Laboratory (EMBL, Heidelberg) in Germany.