dataesr/scientific-paragraphs-categorization huggingface dataset:
At present the data provided are largely for two-body reactions and hence are appropriate for low-pressure plasmas, but this will change in the future. Given sets of reactions, QDB then assembles these sets in chemistries for important plasma mixtures. If there are suitable experimental data available, these chemistries can be validated. The following section gives an overview of QDB, with a technical specification of the data model given in the appendix. Section 3 catagorises the process types included in the database while Section 4 summarises the data sources used. A list of the reactions with a complete set of references is given as supplementary data to this article. Section 5 explains our chemistry construction and validation procedure; this is illustrated for two chemistries, those comprising SF 6 /CF 4 /O 2 and SF 6 /CF 4 /N 2 /H 2 , respectively.
Overview QDB provides reaction rates, cross sections and chemistries. The basic data item is the species, which can be state-specified, e. g. N( 4 S), or not, e. g. N 2. At present QDB considers three generic species: the electron, the photon, and M , the third body in three-body reactions, plus 405 other atomic and molecular species. This total rises to 904 species when state-specified species are counted separately. Species can undergo a series of processes called reactions. This includes, for example, elastic scattering or momentum transfer that are not generally regarded as chemical reactions. These processes are considered in more detail below. At present the database contains data on 4096 distinct reactions, comprising 2861 energy-dependent cross sections and 2259 temperature-dependent rate coefficients in Arrhenius form.
Note that QDB allows multiple datasets for the same reaction, and for some reactions we have distinct data which are available as either cross sections or rates coefficients. Many of these reactions are compiled into chemistries. Currently QDB has 28 chemistries, which are tabulated and discussed below. These chemistries can be validated, provided appropriate experimental data are available. Currently QDB contains 8 chemistries with some degree of validation; two of these chemistries are considered in detail below. Notes on the validation procedure are provided in the form of a datasheet for each validated chemistry. Chemistries are awarded a star rating that reflects how far they have been shown to satisfy the criteria of being complete, consistent, and correct. QDB is structured as a MySQL relational database; the data model used is discussed in the Appendix to this article.
Users can upload new data using the interface on the QDB website Comparison between various electron-impact ionisation (EIN, EDI, ETI, see Table  1 ) cross sections for methane: data from Song et al  [16] , and Janev and Reiter  [17]. (www. quantemoldb. com) and download data using a choice of file formats, which is being expanded. Currently supported formats are comma-separated text for each reaction, provided as a zip file or in qdat format, which facilitates input for Kushner's Hybrid Plasma Equipment Model (HPEM)  [14, 15]  code. The zip format contains all the cross sections, as individual comma-separated text files, and the rate coefficients that are needed for the specific chemistry. It also includes a manifest file listing all the files provided in the zip archive, a readme file, and a set of citations in bibtex format.
Process types Each reaction dataset in QDB is classified as containing cross sections or rate coefficients. The latter can be generated from the cross sections. The rate coefficients are expressed in Arrhenius form stored in the form of three parameters (A, n, and E), which can be used to compute the rate coefficients at the desired temperatures. For electron-impact reactions the Arrhenius formula is A T e 1 eV n exp - E T e (1) where T e is the electron temperature in eV and E is the activation energy in eV. For heavy-particle reactions the Arrhenius formula employed is A( T g 300 ) n exp( -E T g ), (2) where T g is the gas temperature in K and E is the activation energy in K. In both cases, A is the Arrhenius coefficient whose units depend on the order of the reactions. First-order reactions such as photodissociation and photoexcitation are expressed in s -1 ; secondorder reactions, such as electron-impact reactions or two-body heavy-particle reactions, are expressed in cm 3 s -1 ; and three-body reactions use cm 6 s -1.
Cross sections, e. g. for electron-neutral-molecule scattering, are given in units of cm 2 as a function of electron energy in eV. Each reaction is classified according to the process considered. These processes are listed in Tables  1, 2 , and 3 which consider electron collision processes, heavy-particle reactions and processes involving photons, respectively. Note that some heavy-particle processes, in particular HAS and HIR, also involve a third body, generically denoted M in the database. The process label does not depend on the presence of M which is therefore not included in the process description.
Chemistry construction and validation The chemistry sets are assembled starting from reactions already present in QDB; missing reactions are then extracted from the literature and added to QDB where possible. In cases where important reactions have not been previously studied, the missing reaction data are calculated using appropriate methods, such as Quantemol-N  [36]  for electron-molecule scattering reactions, or by scaling laws, or estimated to provide the necessary data. These data are added to QDB. This allows us to provide complete and self-consistent chemistry sets that form the starting point for validation. Figure  2  illustrates the network of 196 reactions assembled to characterise the chemistry of SF 6 and O 2. Table  7  lists the chemistries avaible in QDB as of November 2016.
The self-consistency of each chemistry set is checked using a range of models including Kushner's zero-dimensional GlobalKin  [293, 294, 30, 33]  as implemented in Quantemol-P. GlobalKin couples molecular data (i. e. reaction probabilities) with plasma models to determine plasma properties, such as equilibrium concentrations of species. A variety of plasmas can be simulated using this software, such as etching and atmospheric pressure plasma reactors. HPEM, as implemented in Quantemol-VT, is also used. Validation is achieved by using a chemistry set as the basis for the modelling of different industrial reactors. Comparison of the model output with measurement is the principal means by which validation is achieved. For higher-dimensional simulation, the behaviour of the species and the surface parameters across the wafer, such as etching or deposition rates, can also be used for comparison.
Chemistry sets are given a reliability rating on the basis of these comparisons; see Table  8. We now illustrate this process using two chemistry sets: those for SF 6 /CF 4 /O 2 and SF 6 /CF 4 /N 2 /H 2 gas mixture etching Si. As discussed below, these sets comprise subsets of mixtures, which provide important chemistries as well. These were also validated as part of the validation process. The chemistries were validated using GlobalKin and compared with experimental data provided by Infineon. Unfortunately the data available for complex reactions sets such as the ones considered is often very limited making the validation at best partial for these cases. We note that the primary goal of these examples is to illustrate the verification process in a simple form, not to reflect the highest verification rating.
In fact, we consider the examples to only achieve the lowest level of validation -that is, agreement in the general trends between simulation and experiment for one discharge. The Infineon tool consists of two parts. The first part is a coaxial microwave plasma discharge. In this part the electromagnetic wave propagates along the interface between the plasma column and the surrounding dielectric tube and the plasma column is sustained by electromagnetic energy. Free radicals are formed in this chamber with high efficiency. This chamber is connected to a larger vessel where the flux of particles propagates and where the remote wafer to be etched is located. Our GlobalKin models only attempted to model the second, larger chamber. GlobalKin performs a spatially homogeneous plasma chemistry simulation which are coupled with surface reaction modules.
Our GlobalKin simulations only provide global average values and therefore cannot provide an absolute quantitative comparison. For validation of our global model simulation we therefore compare trends. In particular, we compared the trends in the etching rate with measurements provided by Infineon Technologies, who studied the effect of both power variation and pressure variation. According to these measurements, the etch rate increases with increasing power but decreases with increasing pressure. Figure  3  illustrates the effect of varying power and pressure on the silicon etch rate for a mixture of SF 6 /CF 4 /O 2. Good agreement in the trends is observed between the results of our simulation and the experimental data of Infineon. By increasing the power in the measurements and simulation we observe an increase in the Si etching rate but the rate drops as the pressure is raised.
Future developments The process of adding both more reactions and chemistries to QDB is continuous and ongoing. We will also progressively improve the validation status of current  chemistries and validate more chemistries, although these activities require appropriate experimental data to be available for us to validate against. We have developed an initial rating system for these chemistries, and we plan to develop this further by allowing users to also submit ratings. At the same time we plan to implement more formal uncertainty quantification (UQ) procedures  [298]. The processes currently covered by QDB are listed in Tables  1, 2 , and 3. These lists do cover all possible low-pressure gas phase processes. For example, inclusion of vibrationally-resolved reactions for molecules, such as in electron collisions with CO 2  [81] , is important for a number of plasma studies.
QDB has the capability to hold such data but more work on data input and processes considered will be required to make it fully functional. At present QDB does not include processes that occur on surfaces and has only limited data for processes involving a third body. Both of these will be included in the database in the future. The inclusion of three-body reactions will extend the coverage to atmospheric pressure plasmas. At present the data can be downloaded in two formats: a generic one and one that is appropriate for HPEM. With increasingly large datasets and sophisticated modelling programs, it is desirable for data to be transferred directly from the database to the model using an application program interface (API). We plan to develop APIs for commonly used plasma modelling programs in order to facilitate the use of QDB.
Conclusions One of the challenging problems when modelling plasmas is the lack of reliable chemistry data. For this purpose, we have developed the Quantemol Database (QDB), which aims to provide a platform for the exchange and validation of reactions that are important in plasmas and plasma chemistry datasets. The database provides data on both electron scattering and heavy-particle reactions, and it aims to facilitate and encourage peer-topeer data sharing by its users. QDB currently includes almost 5000 reactions and 28 complete sets of chemistries; so far 8 of these sets have undergone some sort of validation. The set of reactions includes more than 2800 cross sections and more than 2100 sets of reaction rate coefficients in Arrhenius format for more than 980 species in different states.
Appendix: The QDB Data Model The Quantemol database (QDB) is implemented using the MySQL relational database management system (RDBMS). An overview of the principal tables and their relations is given in Figure  6. Each collision or reaction is considered to take place between one or more reactants to give one or more products. Each of the reactants and products may be an atom, ion, molecule, molecular ion, or particle (such as a photon or an electron), perhaps in a specified quantum state. These species are represented by their chemical formula according to a standard notation (for example, Ar, H2O, NH2+ for atoms and molecules, e-for electrons hν for photons). State information is attached as a number of text strings matching a defined pattern, which can be parsed according to the type of state being considered.
A list of some of the state types with examples is given in Table  11. Each reactive or collisional process may be described by more than one DataSet: an experimental measurement or theoretical prediction of rate data relating to the process. There are two principal types of DataSet. Cross sections are represented as a table of (electron energy, cross section value) pairs, stored in an external resource referenced by filename or URL. Rate data expressed according to an Arrhenius-like expression, see Eqs. (  1 ) and (  2 ), are represented by storing separate parameters A, n, and E. The parameters and the columns of any tabular data have associated metadata (name, units, and description) in a linked relational database table. Different reactive and collisional processes are identified by a three-letter code (process type) (for example: EDR = dissociative recombination).
The codes employed are an extended version of those defined in the IAEA document of Humbert et al. [299]. A list of codes is given in Tables 1, 2 and 3; more extensive descriptions and examples are given on the QDB website at http://quantemoldb. com/reactions/processes/. The structure of the data model allows the user interface to perform searches of the collisions by species (reactant or product), process type, and citation. Furthermore, additional fields within the DataSet table allow for evaluation comments, quality RP An atomic, molecular or ionic species, or particle such as an electron, optionally with attached state information, which can act as a reactant or product to a reaction or collisional process. assessment rating, and validity and usage notes to be stored. QDB Chemistries are self-contained collections of collisional and reactive processes describing the properties of a plasma under some set of conditions.
Acknowledgements We thank  Prof. Uwe Czarnetzki ,  Dr. Nobuyuki Kuboi ,  Prof. Satoshi Hamaguchi ,  Prof. Mark Kushner  and  Prof. Leanne Pitchford  for helpful discussions during this project. Partial funding was received from the  UK STFC  under grants  ST/M007774  and  ST/K004069 , and from the  Powerbase  project. Powerbase has received funding from the  Electronic Component Systems for European Leadership Joint Undertaking  under grant agreement No  662133. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and   Austria, Belgium, Germany, Italy, Netherlands, Norway, Slovakia, Spain, United Kingdom.
Two-dimensional deformations Here we outline a two-dimensional plane stress approach. This was implemented within finite element analyses using the package ABAQUS, with the material model incorporated via a 'UMAT' user-defined subroutine. The kinematics are similar to those used previously for high temperature stretching of polypropylene  (Sweeney et al. 2009) , polypropylene and polycarbonate  (Sweeney et al. 2007 ) and fracture of polyethylene  (Naz et al. 2010). At each computed point the deformation is input to the subroutine in the form of the deformation gradient tensor F defined in global 1-2 axes. This strain corresponds to the total for the model of Fig. 1. There have been a number of approaches to the analysis of elasticplastic behavior at large deformation, a useful summary of which has been made available by  Figiel and Buckley (2009).
Images of the specimens were taken through the window of the environmental chamber using a PixeLINK model PL-D722MU-T video camera operating at capture rates up to 75 frames/s. After testing, the images were analyzed using the software Fiji, which is based on ImageJ  (Schindelin et al. 2012 ), together with an in-house plugin. The plugin selects triplets of neighboring dots to act as the vertices of constant large-strain triangles, analyzing the triangles exactly to give principal extension ratios. The greatest principal extension ratio is then associated with the axial extension ratio, so that values derived for the latter are independent of the precise orientation of the specimen within the field of view. Testing speeds were in the range 0. 54 to 320 mm/min, with testing temperatures 323, 333, 341 and 346 K.
Acknowledgements F. Boutenel acknowledges an Erasmus Programme Scholarship. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Eco-design Documents Altermaker is a young start-up specialized in software development to support design for sustainability. The company led analysis on industrial materials and processes used in mechanical engineering. The results are stocked as MsPowerpoint documents in which several elements are defined for each material or process: advantages, disadvantages, short description and specific comparisons. In these documents (0), several features are interesting to consider and other ones need more analysis. Our study aims at analysing these documents in order to define the concepts that show the main features of given materials and processes.
Characteristics Classifications Repetition of characteristics is then used in order to classify them. On one hand, our classification aims at emphasizing the impact of process and materials on the environment and, on another hand, at helping designers to deal with process and materials in ecodesign. For instance, the main process characteristics are identified as: cost, consummation, pollution, technicality, etc. (0). For materials, we identify: Resistance, compacity, disassembling, modification, etc. (0). Then, for each process type (assembling, drilling, welding,…) values are associated to these classifications. These identified characteristics and classifications allowed us to build a database of processes and materials with technical, organisational and economic data. These characteristics are then linked to environmental ones and ready to be implemented in the "ECODESIGN STUDIO" 1 software to support decision making of environmental experts.
Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in order to obtain the best usage of the resources, providing their fair usage and partitioning for the users. In contrast, current cloud schedulers are normally based on the immediate allocation of resources on a first-come, first-served basis, meaning that a request will fail if there are no resources (e. g. OpenStack) or it will be trivially queued ordered by entry time (e. g. OpenNebula). Moreover, these scheduling strategies are based on a static partitioning of the resources, meaning that existing quotas cannot be exceeded, even if there are idle resources allocated to other projects. This is a consequence of the fact that cloud instances are not associated with a maximum execution time and leads to a situation where the resources are under-utilized.
Today the efficient management of Cloud resources is therefore an open issue. The INDIGO project is addressing it through several, complementary approaches that will be explained in the upcoming sections. In Section 2 we will explain how we implemented fair-share scheduling strategies in both OpenStack and OpenNebula. In Section 2. 3 we present an implementation of preemptible instances for OpenStack clouds. In Section 3 we describe the Partition Director, a tool that esas the transition of computing nodes between different infrastructures.
The priority of the queued requests is periodically recalculated in order to increase the priority of older requests. To prevent any possible interaction issue with the existing OpenStack clients, no new states have been added so that from the client point of view the queued requests remain in "Scheduling" state till the compute resources are available. Whenever applicable Synergy has been developed by adopting the full OpenStack style according to the specific development guidelines, including the coding style and the technologies to be used: the source code is in launchpad [6, 7] and the OpenStack Continuous Integration system is used. Synergy is a full python service accessed by clients through a well defined RESTful API which provides either management commands for handling the plugged managers (e.
FaSS: A fair-share scheduler for OpenNebula The current OpenNebula scheduler is first-in-first-out (FIFO) and it is based only on static resources partitioning among the projects, which is not efficient for a scientific datacenter. FaSS grants fair access to dynamic resources prioritizing tasks assigned according to an initial weight and to the historical resource usage, irrespective of the number of tasks running on the system. The software was designed to be fully synchronized with the ONE authentication, quota, monitoring and accounting systems, without being intrusive in the ONE code and keeping to the minimum the dependencies on the ONE implementation details. In such a way, the code is fairly independent on future ONE releases. The ONE original scheduler is kept to match the resources to requests and FaSS is structured as a self-contained module interacting only with the ONE XML-RPC interface.
Architecture The architecture of the FaSS service is depicted in Figure  3. It has been conceived following the ONE conceptual design  [8]. In the figure, white blocks are native ONE components, while the newly developed components are shown in blue. New tools are pictured in green, and interfaces/API in gray. Purple arrrows represent internal component interactions. Blue arrows represent the API-Sunstone GUI interaction. The main component of FaSS is the Priority Manager. Its task is to calculate periodically priorities for queued jobs. It receives input data from the ONE core through XML-RPC calls, and from the Priority Database through a no-SQL interface. This choice has been made in order to avoid storing large XML strings, which could easily be corrupted in a single SQL entry. The Priority Database holds the modules internal data: initial priority values, historical information on resource usage and recalculated priority values.
The Priority Manager interacts with a set of pluggable algorithms, such as MultiFactor  [5]  and FairTree  [9] , to calculate priorities. It exposes an XML-RPC interface, independent from the one exposed by the ONE core, and uses the Priority Database as data back-end. The second fundamental component of FaSS is the XML-RPC interface of the Priority Manager, which replaces the ONE XML-RPC interface in the communication with the FIFO scheduler. The queue of pending jobs to be processed must be the one ordered according to the priorities calculated by the Priority Manager. The XML-RPC server, then, catches the scheduler calls, provides a reordered queue and redirects unknown methods to the core of ONE XML-RPC server. The Sunstone web-based GUI service is extended to allow monitoring and operating the FaSS module functionalities.