dataesr/scientific-paragraphs-categorization huggingface dataset:
Aladin is also the astronomical Virtual Observatory image portal, giving access to all data provided in VO-enabled services and able to interact with the other VO tools thanks to the VO interoperability framework. A major recent evolution is the usage of Healpix sky tessellation  (Gorski, Hivon, Banday, Wandelt, Hansen, Reinicke, et al. , 2005) , which provides a hierarchical view of data with fast zooming capabilities and is used by the Planck and Gaia projects. This allows a new way of using the tool, also adapted to building views of the full or a part of the sky from data obtained by individual projects. These are offered the possibility to build a local Healpix database, which can be opened for usage by all Aladin users, by their collaborators, or kept for themselves  (Fernique, Oberto, Boch & Bonnarel, 2009).
The system also allows navigation between publication and databases  (Ochsenbein, Bertout, Lequeux, & Genova, 2003). R&D is thus a fundamental activity for medium/long term sustainability, and it has to be maintained in spite of the heavy constraints linked to the core data centre role (inclusion of the ever increasing data flow in the databases, software development and maintenance, operations). At CDS this is an in-house activity, which takes a significant fraction of engineers' and "instrumentalist" researchers' time. R&D actions have to be properly focused: they are driven by the data centre needs and not technology driven, i. e. , new technologies are assessed only when there is a serious promise that they could improve CDS services or functioning and not because they are trendy. Relevant technologies have to be implemented early enough to fulfil users' expectations, but one critical requirement is that they be "sustainable enough" for usage for a certain number of years in a technology landscape where buzz and bandwagon effects tend to be dominant and highly praised technologies can disappear within a few years.
Collected data by each vehicle should be delivered to a third-party to be used for continuous monitoring of the disaster area. This has led to the appearance of a new challenge namely Data Collection. This concept has snatched some interest in the VANET research community and some architectures and communication standards are proposed in the field. These architectures depend on the application constraints and the collection can be either decentralized where vehicles are autonomously organized or centralized where a static fixed third-party is responsible for the network organization. The most investigated type of data collection architectures is decentralized, but in case of emergency scenarios where connectivity and communication between vehicles is not guaranteed, these solutions have proven inefficiency.
II. RELATED WORK Since the emergence of data collection concept, many architectures were defined to get useful data from vehicles and reuse them depending on the application. In fact, these architectures can be categorized into two classes: a self-organizing decentralized or a centralized architecture which we expose in details in the first subsection. In both classes, data is periodically collected by regular vehicles from the roads where they pass and hence give only a local overview of the monitored area. However, in a case of a disaster we need to have a global overview of the affected area. So, deploying a number of vehicles namely harvesters that are able to explore the entire monitored area and collect data, unlike the previous proposals, is a promising solution that guarantees a clear and global overview about what happens in such an area and facilitates later decisions by a data center.
A. Data Collection architectures for VANETs A car can be defined, nowadays, as a set of non limited capabilities sensors that are able to detect and treat a huge variety of data. The collected data is exchanged between cars at each encounter while moving in a road topology using the newly standardized technique IEEE802. 11p or cellular capabilities such as UMTS or LTE. Data collection is one of the most emerging treated fields in VANET community. Many architectures have been defined to support data exchange between vehicles. One of the most popular solutions are based on self-organizing architectures which can be either decentralized organization where the network is autonomous and vehicles are organizing themselves without using any external infrastructure or centralized where the vehicles organization is delegated to a third-party fixed infrastructure (e.
They assume that each vehicle is equipped with both 802. 11p and LTE capabilities. The 802. 11p is used for the communication and data exchange between vehicles and LTE is used to interface with the eNodeB manager. In the proposed framework named LTE4V2X, each eNodeB manages the vehicles in its range by initially giving to each vehicle an ID, then it organizes the nodes into clusters and designates a cluster head. The eNodeB is responsible of the periodic update of its clusters in case of an incoming or a living node. Data is periodically collected by each cluster head, aggregated and sent to the responsible eNodeB. In  [11] , the same authors augment the LTE4V2X with the dissemination capabilities where they treat the case of tunnels where the eNodeB coverage is limited. They use the multi-hop forwarding between cluster heads to send data.
B. Optimization of Data Harvesters Deployment Optimizing harvesters in an urban area is a nested problem that contains two main fields to be treated which are the optimization of each harvester movement circuit and the optimal number of these harvesters. As far as we know, we are the first to treat this problem in VANET. However, many works were proposed treating location optimization problems and the most known are facilities location, covering and Vehicle Routing Problem (VRP). In facilities location problem  [2] , each facility should be optimally placed in order to distribute goods to clients at a minimum cost. [8]  treats a problem of locating the charging stations to serve electricity demands of electric vehicles. A Genetic algorithm is used to minimize the investment and transportation costs.
II , Fig. 2 , 3 and 4 we notice that our algorithm is able to deliver an optimal solution at a very reasonable time even if the size of the topology to be monitored is very large or the time-constraint increases. These results prove the higher performance of the given algorithms. In Table  II , we also conclude that the Best Insertion is able to give an approximative optimal solution especially for a high value of T MAX which means that we can minimize the CPU time by limiting calculation to the first initial solution. V. C ONCLUSION Data collection is a very challenging field in the VANET research community. This concept defines how data is being retrieved from vehicles in a geographic area to a third-party of interest. Many self-organizing centralized or decentralized architectures were defined to support data collection.
Introduction In recent years, a variety of initiatives have been funded with the aim of producing software tools or environments of a type variously known as virtual research environments, research infrastructures, or cyberinfrastructures. These initiatives vary in their scale, specialization, scope, and level of funding. One issue that they face in common, however, is that of sustainability: how can the continued-and useful-existence of a system or tool be guaranteed, or at least facilitated, once a project's funding has been spent? In this paper, we examine how such sustainability has been enabled, in the particular case of infrastructures for textual scholarship, in the context of three international projects: TextGrid, 1 TEXTvre, 2 and DARIAH 3. Firstly, we will address the inter-project collaboration and crossfertilization between TextGrid and TEXTvre, including architectural decisions and shared data infrastructures, and investigate how the projects benefited from the exchange.
Working Together on Tools As discussed above, TextGrid is built on a distributed architecture and offers its users access to an ecosystem of different services and content; these tools will be the focus of this section. TextGridLab, TextGrid's Eclipse-based user interface, integrates a set of interactive tools such as the XML Editor, the Text-Image Link Editor (German "Text-Bild-Link-Editor", TBLE) and the Text-Text Link Editor (discussed in section 6). The XML Editor is one of the core functionalities of TextGridLab, allowing users to switch easily between a more technical view with tags and attributes and a structural view that is oriented towards standard text-editing applications. The Unicode Character Table enables the user to search, copy, and insert symbols from the Unicode character set.
For more specialized publishing tasks, a module called XML Print 14 is integrated into TextGrid to allow printing of scholarly texts with complex typesetting layout requirements based on XML data, with a particular view towards the needs of critical editions and dictionaries. Finally, the Bibliography Tool can import bibliographical data from existing data inventories and be used to capture, process, and administer bibliographies. Users can also export bibliographical data into certain standard formats (for example, TEI and MODS). source framework GATE (Cunningham, Maynard, and Bontcheva 2011; Cunningham et al. 2002). <oXygen/> is the XML editor of preference for DDH projects, so the TEXTvre team considered accommodating this to be an important candidate for TEXTvre. The <oXygen/ > Eclipse plugin, which supports all of <oXygen/>'s basic XML editing functionality,  16  was integrated into TextGridLab, allowing users to create TEI files with <oXygen/> instead of the TextGrid editor.
Data Infrastructure Collaborations The second main component of TextGrid is the TextGridRep, which provides access to a repository infrastructure for long-term preservation of data based on grid technology. Researchers can decide how and with whom their data will be shared by using sophisticated rights management services. Findings and research data can be published directly from the TextGridLab to the TextGridRep via a publishing process that guides researchers in preparing the data for long-term accessibility. The TextGridRep middleware consists of various components for handling files in the data grid, for rights management, for metadata management in an XML database, and for relations in an RDF triple store. TextGrid is not a closed system but rather an open platform that enables scholars to adapt the environment to their needs.
This in itself was relatively straightforward, and is illustrated in figure  1. An additional complication is that edition projects as a whole are not actually modelled as objects in TextGrid (i. e. , they are not managed by TG-CRUD); instead, they are LDAP constructs referenced in TextGrid relationships. As there are no objects in TextGrid corresponding to projects, an attempt to store the relationship between an object and a project in Fedora fails as the target object does not exist. Other issues arose from TextGrid's approach to versioning. In TextGrid, different versions of an object can be created, and each version is maintained as an independent object and can be edited and changed at any time; however, no change management is available on the individual objects. TextGrid maintains the concept of a current version of an object, and the "current" version number is kept as a field in the object's metadata in the eXist database.
23 • As projects do not exist as objects in TextGrid, an explicit check is made for "inProject" relationships, and a dummy project object is created in Fedora (if it does not already exist). Each time that a relationship is added to Fedora, the component checks that both of the objects in the relationship already exist in Fedora. If either or both of the objects do not exist, placeholder objects are created and the relationship is stored referencing these. A user searching via Fedora can easily see whether an object referenced in a relationship is versioned or is an unversioned placeholder. This part of the project led to additional funding from the Software Sustainability Institute (SSI),  24  an EPSRC-funded 25 body that helps to increase the sustainability of open-source research software.
The experience of the collaboration between TEXTvre and TextGrid can be seen as exemplary in this context. While variety is to be expected at this level, the diversity of local and national data services will be brought together into a common European data layer through a toolkit of lightweight services that allows, for example, cross-referencing and discovery across various data repositories. The existing DARIAH Persistent Identification (PID) service is a good example of such a lightweight solution (Blanke et al. 2011). DARIAH recognized early on that the use of PIDs within the new infrastructure is imperative; when a researcher cites an article or dataset, whether digitally or in hardcopy, they need to be assured that the citation itself will always lead to the original resource cited. The creation of relationships between resources, for example between a researcher and the articles they have published, requires a similarly persistent mechanism.
New Services: Embedding Intertextual Links Extensibility in the face of new requirements is an important attribute of any infrastructure, and both TextGrid and TEXTvre have given rise to follow-on projects that addressed specific research questions. One such set of requirements for additional functionality arose from the needs of the SAWS (Sharing Ancient WisdomS) project  28   (Jordanous et al. 2012), which addressed the digital publication of medieval wisdom literatures in Greek and Arabic. These texts, also termed gnomologia, are anthologies of "wise sayings", encapsulating moral and social guidance or philosophical ideas. Gnomologia also formed a key route via which ideas were disseminated over a large area, and across different cultures, often over the course of many centuries. While the publication of such collections using TEI was one objective of the SAWS project, a distinguishing characteristic of these texts-which both makes them of particular interest to scholars and raises particular challenges for existing models of digital publication-is that the sayings or excerpts from which they are constituted are borrowed, modified, and recombined in different ways as new manuscripts are written on the basis of older ones.
Collaboration thus cannot just mean reuse of complex software application but rather needs to function as a genuine exchange of tools, expertise, and experience so that customized solutions that work in different national and institutional environments can be developed and taken forward. Therefore, as far as data infrastructure is concerned, DARIAH has rejected the idea of providing a single data layer, and will instead focus on supporting a range of more diverse services, which are difficult to realize locally as they require a more sustainable environment than project-based funding can provide. TEXTvre has reached the end of its funding, while TextGrid has achieved a third round of funding. During this period, TextGrid is establishing a national working group on the coordination of sustainability and information infrastructure in the humanities (Koordination Geisteswissenschaften Verstetigung/Informationsinfrastruktur [KGW]) with the support of the BMBF.
The IPCC has published guidelines and good practices for GHG accounting  (IPCC 2006 ) and various tools have been developed to help those performing GHG assessment within these guidelines. These tools provide a framework for the assessments and a database of emission factors  Denef et al (2012)  classified these tools into calculators, protocols, guidelines and models. This review focuses on calculators (automated web, Excel or other software-based calculation tools) that have been developed for quantifying GHG emissions and removals by AFOLU. This letter provides guidance for potential users in selecting the most appropriate calculator for their aims and requirements. The discussion covers the major methodological differences between these calculators and considers comparability issues. This letter also sets out to improve transparency in GHG and carbon analysis, improve the analysis of results by end users and provide ideas for further development of GHG and carbon calculators.
g. the area, the number of animals, the mass or volume of fuel). For CO 2 emissions related to LUC (a change from one IPCC category to another-forest land, cropland, grassland, wetlands, settlements, other land-IPCC 2006) or due to change of management within one category (e. g. tillage to no-tillage for cropland), IPCC recommends a mass balance approach based on carbon stock changes over time for 5 compartments: above-ground biomass, below-ground biomass, litter, dead wood and soil. Global calculators tend to use the Tier 1 approach, whereas regional calculators tend to combine Tiers 1 and 2. Some regional calculators use Tier 3 with a modelling approach (e. g. Cool Farm Tool, US cropland calculator). The calculators require pedo-climatic and management inputs. Each calculator has a slightly different input dataset depending on its features, the default data provided and the tier selected.
The principal aim of the calculators is to compare different products rather than assessing the emissions from an area and they are, therefore, designed to provide a GHG assessment for a single product. They reveal the relationship between production levels and emissions. Usually these calculators include processing and transport. Only 2 of the 18 calculators evaluated are in this category: Cool Farm Tool and Diaterre R. Most current LCA calculators with their associated databases (e. g. SimaPro-Rice et al 1997) are in this category.
01  (IPCC 2006). This is the average value for all managed soils, N fertilizers, climates, soil management strategies, etc. Each of these affects the nitrification-denitrification processes and, therefore, the N 2 O emitted. According to the IPCC, the uncertainty range for this emission factor is from 0. 003 to 0. 03 and so the uncertainty is over 100% for this source of N 2 O. Using biophysical models such as NOE or Ngas could greatly reduce the uncertainty  (Groffman et al 2000 , Hénault et al 2004). For landscape-scale assessment, such models would need to be used with large geographical databases which are not available at the moment. Researchers and calculator developers should focus on identifying easily measurable parameters that would make it possible to estimate the main sources of emissions more accurately.
The ATLAS and CMS collaborations have also announced the discovery of a new resonance at a mass of 125 ÷ 126 GeV, that has similar properties as the SM Higgs boson, thus confirming the hints present in the 2011 dataset. Some interesting discrepancies are present in the measured rates of various channels, mainly photons and tau ones, however more statistics will be necessary in order to derive any clear indication on the nature of the resonance. However, even if the discovery were finally associated with the SM Higgs and the absence of new physics signals were to persist, the interest in extensions of the SM would not fade. In fact, it will take much more effort to determine if the resonance discovered by the experiments is the SM Higgs or not. Furthermore, the discovery of new particles in the messy hadronic environment at the LHC experiments can be more challenging than we hoped for: new physics may well require more statistics or more sophisticated search strategies.
Monte Carlo implementation We implemented this model in the FeynRules  [16]  package of Mathematica, which allows to interface the model with Monte Carlo generators like CalcHEP, MadGraph4 and 5. The implementation should contain the full tiers (1, 0), (0, 1), (2, 0), (0, 2) with ξ-dependent mass corrections and mixing between the two even tiers. The tier (1, 1) may also give rise to interesting phenomenology, however there is a dependence on the unknown coefficients of the localised Lagrangians, which allow for the direct decays into a SM pair: an example of a spectacular 4-top signal has been studied in  [17]. In the following we will not consider this tier any further. The full implementation would contain a huge number of new states and interactions. Following the above discussion of the three physical regimes, however, we can simplify the model by considering two separate limits: the case of asymmetric radii and the degenerate case.
Z → l + l -searches The most promising search is for a resonance in the dilepton channel, which is one of the cleanest channels at hadronic colliders like the LHC. Both CMS  [18]  and ATLAS  [19]  have published the final analysis of the full 2011 dataset at 7 TeV, while only CMS has published preliminary results with the 8 TeV run data  [20]. Both experiments search for final states with at least two energetic electrons or muons with opposite sign, and reconstruct the invariant mass of the pair. The electron channel is slightly more efficient than the muon one, because the total energy can be measured in the electromagnetic calorimeter. On the other hand, the muon momentum can only be reconstructed by measuring the curvature of the track in the muon chambers, and this procedure becomes more difficult for energetic muons.
05% to 0. 3% in the symmetric case. Nevertheless, the total cross section is still quite large and comparable to the experimental bounds. The results of our calculation are shown in Figure  8 , where the black lines show the total cross section while the dashed (dot-dashed) ones show the component from pair (single) production channels only. The experimental bounds, in green for ATLAS and yellow for CMS, refer to the combined results with 5 fb -1 of data at 7 TeV, where we can see that at large masses the CMS bound prevails. In the asymmetric case, the best bound is at m KK ∼ 575 GeV, which is in a mass region where the pair and single production channels are comparable. In the symmetric case (right plot), we see that the pair production channel is highly suppressed because of the heavier mass, while the single channel is suppressed to a smaller degree due to the smaller branching ratios.
The net result is a bound at around m KK ∼ 440 GeV, dominated by pair production. The CMS collaboration has also published preliminary results on an analysis performed on the 2012 data at 8 TeV, however no combined electron-muon results is reported. The best quoted bound comes from a combination of the 7 and 8 TeV datasets, which is done by rescaling the signal at 7 TeV to 8 TeV. As this rescaling is model dependent, we cannot directly use this result here. Nevertheless, in the Figure  8 , we show for comparison the expected cross section at 8 TeV in blue.
W → lν searches The second most promising analysis is the search for a W in the leptonic channel. Both CMS  [21]  and ATLAS  [22]  collaborations have published their final searches with the 2011 7 TeV dataset, while only CMS  [23]  has preliminary results available for the 2012 8 TeV dataset. This search is based on the requirement of a single high transverse momentum p T electron or muon in association with missing transverse momentum (which may derive from the neutrino). The main observable used to isolate the signal is the transverse mass M T , defined as  where p l T is the transverse momentum of the electron, p miss T is the missing transverse momentum in the event and δφ is the azimuthal angle between the lepton and missing transverse momenta. A signal from a massive W → lν would produce a Jacobian peak in the M T distribution at the W mass, therefore a lower cut on M T is imposed somewhat below the W mass used in the simulated signal.