dataesr/scientific-paragraphs-categorization huggingface dataset:
The architecture of the DESYRE framework is shown in Fig. 5. To run the simulation, the system descriptor files are provided to the DESYRE simulation kernel. The kernel parses the files and invokes the DESYRE model builder that instantiates the required modules, interconnects them and configure their parameters. As soon as the model has been completely instantiated, the SystemC simulation kernel is invoked and the simulation starts. During the simulation, the simulator collects logs and traces that can be used later on to create diagrams or analyze system performances. The main features of the simulator are: • Simulation of heterogeneous components (capability to import models from other tools or defined with other languages, such as: MATLAB/Simulink  [21] , Modelica  [4] , Rhapsody/SysML  [12] , FMI  [26] ); • Hardware and software in the loop simulation (capability to simulate a system model connected to real physical devices and/or interacting with some real target software components); • Fault Injection Capabilities to support fault tolerance analysis; • Tracing mechanisms; • Monitor synthesis generation (capability to generate executable monitors starting from contract specifications); • Support for debugging of code executed on the simulated system; • Support for distributed simulation using the HLA standard  [30].
FMI support in DESYRE DESYRE supports the import and simulation of functional mockup units (Fig. 6 ) for both Model Exchange and Co-Simulation version of the standard. In the context of the DANSE EU project, the FMI standard version 1. 0 for Model Exchange will be used. DESYRE API provides a service ("des importFMU(String fmuURI)") that allows the user to import an FMU into the DESYRE workspace and automatically generates the DESYRE module descriptor necessary to enable the DESYRE model builder to instantiate and interconnect the new module into a system model. Imported FMUs are wrapped with an FMI Wrapper module that allows the DESYRE simulator to set and get FMU inputs, outputs and parameters and to correct-ly solve the FMU model equations. FMU I/O are redirected on a DESYRE module I/O ports.
To interconnect two or more FMUs it is enough to connect their I/O ports using the DESYRE XML or SLang specification language. When an FMU model is instantiated, the DESYRE simulator automatically regis-ters it to the DESYRE FMI Scheduler. The DESYRE FMI scheduler is an FMI 1. 0 compliant scheduler that requires the FMUs execution according to the FMI standard protocol rules.
Conclusion In this paper we have presented a statistical model checking framework for systems of systems, developed with support from the DANSE EU FP7 project. The framework focuses on performance constraints and non-critical structural and temporal constraints. We have shown how the extensible UPDM/SysML language for systems of systems can be successfully extended to represent stochastic behavior and complex structural and temporal contract specifications. We have presented an innovative tool-chain that integrates two successful technologies, namely PLASMA, developed at INRIA, and DESYRE, developed at ALES S. r. l. , to effectively provide scalable performance verification techniques for systems of system.
Dynamic Voltage and Frequency Scaling (DVFS) Today, all processors integrate dynamic frequency scaling (also known as CPU throttling) to adjust frequency at runtime. The system service which adapts frequency in the operating system is called a governor. Different governors can be implemented with different policies regarding frequency management. In the Linux kernel, the Ondemand governor changes frequency depending on CPU utilization. It changes frequency between the lowest level (when CPU utilization is less than 20%  [22] ) and the highest level (when CPU load is higher). The Performance governor always keeps the frequency to the highest value while the Powersave governor keeps it at the lowest level. The Conservative governor decreases or increases frequency by one level through a range of values supported by the hardware, according to the CPU load.
Environment Our experiments were performed on a DELL Optiplex 755, with an Intel Core 2 Duo 2. 66GHz with 4G RAM. We run a Linux Debian Squeeze (with the 2. 6. 32. 27 kernel) in a single processor mode. The Xen hypervisor (in his 4. 1. 2 version) is used as virtualization solution. The evaluation described below were performed with two applications: when we aim at measuring an execution time, we use an application which computes an approximation of π. This application is called π-app. when we aim at measuring a CPU load, we use a web application (a Joomla CMS server) which receives a load generated by httperf  [14]. This application is called Web-app. The Joomla server consists of Joomla 1. 7, relying on Apache 2. 2. 16, PHP 5. 3. 3 installed into Apache with modphp5 and MySQL 5. 1. 49. This application is called Web-app in the following.
Background Archie Cochrane, director of the MRC Epidemiology Unit in Cardiff, Wales, UK, at the time of writing the above evaluation of the National Health Service (UK), clearly articulated the need for applied scientific evidence over the expert opinion of clinicians  [1]. In 1979 he suggested that 'It is surely a great criticism of our profession that we have not organized a critical summary, by specialty or subspecialty, adapted periodically of all relevant randomized controlled trials'  [2]. This spurred Sir Iain Chalmers  [3]  and others to take the steps needed to set up, in 1993, The Cochrane Collaboration, which began with a meeting of 77 people from nine different countries. Since then, The Cochrane Collaboration has grown substantially. It is now an international network of more than 28,000 voluntary contributors from over 120 countries  [4] , which publishes full systematic reviews and their protocols in The Cochrane Database of Systematic Reviews (CDSR).
This has published reviews online since 1996, via The Cochrane Library. The CDSR currently includes more than 2,300 review protocols and more than 5,600 full reviews have been published. The Library also contains, amongst other databases CENTRAL, the world's largest repository of records for randomized trials, with more than 700,000 as of September 2013. The Cochrane Collaboration has been at the forefront of systematic review methodology, pioneering a rigorous approach that standardizes a highly structured systematic review model. Key elements of the model are transparency and reproducibility of research methods. These include title registration, publication of a protocol and periodic updating of the subsequent published systematic review. The Cochrane Collaboration has drawn in an international spectrum of individuals to support and develop the methods for systematic reviews over this time.
The Centre for Reviews and Dissemination (CRD) part of the National Institute of Health Research (NIHR) in the UK, is one of a number of producers of systematic reviews and health technology assessments for the NIHR, but has also developed the international prospective register of systematic reviews (PROSPERO)  [8]  and continues its work to produce The Database of Abstracts of Reviews of Effects. Also based in the UK is the Evidence for Policy and Practice Information and Co-ordinating Centre that produces systematic reviews and develops review methods in social science and public policy. The Cochrane Collaboration has many ties with these organizations and members that work across these different agencies. It also has partnerships with organizations which cite, use or collaborate on systematic reviews and other initiatives.
The future of methods development in Cochrane The ongoing drive for The Cochrane Collaboration is to ensure that it produces high quality, relevant and up to date systematic reviews. In keeping with the need to continue to improve the quality of reporting  [20] [21] [22] [23] , The Cochrane Collaboration has produced a set of standards. These standards  [24]  cover the conduct and reporting of reviews, including the reporting of protocols and the updating of reviews. The Cochrane Collaboration is committed to pioneering research that will lead to further improvements in the methods used for Cochrane Reviews. As an example, the Cochrane Methods Innovation Fund has supported six methods related projects since 2012. These projects are investigating priority topics for Cochrane and include: methods of searching for unpublished trials, extensions to the Cochrane 'Risk of bias' tool to assess risk of bias in randomized trials with non-parallel-group designs and non-randomized studies, enhancing the acceptance and implementation of 'Summary of findings' tables, the assessment of complex interventions, addressing missing trial participant data and methods (network meta-analysis) for comparing multiple interventions.
, 2010; Claeyman et al. , 2011b). This paper is structured in the following way. In Sect. 2, we describe the dataset used, for both the full RT and the approximated POs. In Sect. 3. 1 we test the capability of the approximations to replicate the variability of the AKs of the full RT calculations and related quantities. In Sect. 3. 2 we discuss how the approximated POs can replicate the LT ozone spatio-temporal distributions, in particular in the presence of an ozone pollution event. Finally, conclusions are given in Sect. 4. A list of the abbreviations used in this paper is in Appendix A.
Datasets description Our exercise is targeted on ozone retrievals at the lowest altitudes, i. e. at altitudes important for air quality  (Amann et al. , 2005). With this aim, we have produced both full RT and approximated POs of surface-6 km and surface-3 km ozone partial columns. The effect of clouds is not considered in our study. In the following, we describe the common baseline of the full and approximated simulations, and then the different datasets.
The most relevant case is a strong gradient of these two quantities, following the East-West direction in the North-West quadrant (approximately between the Atlantic Ocean, west of Ireland, and the North Sea, between UK and Scandinavia), which is not replicated by the approximations. As for test criterion 2, we studied the evolution of a LT ozone plume, developing between 19 and 21 August 2009, and affecting the region between Southern France and the Scandinavian peninsula, on different days. The most important feature is the overoptimistic description of this ozone pollution phenomenon done by the two approximated POs datasets. The plume is not detected by the full RT POs, while the approximations see a contiguous and resolved plume. This effect is strongly related to the overestimated sensitivity, found by test criterion 1, over the North Sea area affected by the ozone plume crossing on the 20 August.
Here, we use a general process-based global vegetation model to simulate postfire forest carbon dynamics. After fire, the model establishes a new forest cohort with explicit stand structure, and simulates forest dynamics such as selfthinning and snag decomposition. Carbon flux and carbon stock data from 13 flux sites across North American boreal forests are used for comprehensive model calibration and er-ror characterization. The role of historical climate and atmospheric CO 2 trend in driving the postfire carbon flux trajectory is explored by model manipulation in combination with in situ measurements. Specifically, the objectives of this study are: -to perform a calibration of the model by adjusting its parameters simultaneously against CO 2 fluxes (gross primary production or GPP, net ecosystem production or NEP, total ecosystem respiration or TER), and on carbon stocks (total and aboveground biomass carbon stock, forest floor carbon stock, woody debris carbon, mineral soil carbon), and forest stand structure (basal area, stand density, mean diameter at breast height, or DBH); -to evaluate the model performance at different sites across different soil drainage conditions; -to attribute the effects of climate and atmospheric CO 2 trends in driving the postfire trajectory carbon fluxes at the sites examined; -to quantify the uncertainty in CO 2 fluxes and the residual model biases which cannot be reduced by calibration, in order to assess the modeled carbon balance uncertainty for regional-scale applications.
, 1997; Liu et al. , 2005; Goulden et al. , 2006). Permafrost occurs at the Alaska and Manitoba sites but is absent for the top 2 m of soil for all the Saskatchewan sites  (Gower et al. , 1997). The model was evaluated against various measurements collected from a range of published studies, or retrieved by the authors. Variables evaluated comprise GPP, NEP, TER, leaf area index (LAI), total (aboveground) biomass carbon, woody debris carbon, forest floor carbon, mineral soil carbon, forest DBH, individual density, and basal area. The detailed data source information is provided in Table  S1.
Model description of ORCHIDEE-FM For our research objectives, the global general purpose, process-based vegetation model ORCHIDEE  (Krinner et al. , 2005)  is used. Brought to steady equilibrium state for vegetation and soil carbon pools after a long spinup, ORCHIDEE originally represents an "average" mature forest in a bigleaf approximation. To explicitly account for forest stand structure and processes related with forest age, a forest management module (FM) was developed  (Bellassen et al. , 2010). The version of ORCHDEE with FM module is hereinafter referred to as "ORCHIDEE-FM", and the original ORCHDEE (without FM) will be referred to as "the standard ORCHIDEE". ORCHIDEE-FM includes several processes that are important in simulating forest stand development: (1) agerelated stand dynamics, including the decline in NPP in old forests, age limitation of LAI growth in young stands, and age-dependent allocation of woody NPP among stems and coarse roots; (2) a woody litter pool to account for the slow decomposition of that material; and (3) an explicit description of forest stand structure (stand density, tree diameter distribution, etc.
Climate forcing data Two sets of climate data are used for climate forcing. Composite Monthly Climate Data (hereafter referred to as CMCD) include monthly temperature and precipitation from meteorological stations close to the evaluation sites with the missing fields being filled by CRU3. 1 data. Half-hourly climate data (hereafter referred to as HHCD) include gapfilled in situ meteorological measurements from the eddycovariance sites (see Supplement Sect. 2 for more detailed description).
Simulation protocol Our simulations are conducted in a way that does not require specific biometric measurement inputs (such as LAI, initial biomass, mineral soil carbon etc. ). Rather, these variables are determined from climate forcing data and the model equations in a prognostic way, by starting with a spinup simulation followed with transient simulations. Boreal forests are known to experience recurrent fire disturbances, and similarly, our simulation protocol is designed to allow a gradual ecosystem carbon pool accumulation under recurrent fires until equilibrium state, before the most recent fire event is simulated at each site (Fig. 1 ). As we did not know the exact history of fire disturbance for each evaluation site, the fire return interval (FRI) for all the sites was set uniformly to be 160 yr.
e. , 20 successive "fire rotations" with assumed stand-replacing fires occurring every 160 yr (each 160 yr period is called a "fire rotation"). This second spinup allows all forest ecosystem carbon pools (especially the mineral soil carbon pool) to reach a long-term equilibrium state in the presence of recurrent fire disturbance. Finally, the most recent fire event is simulated for the year of burning. The model is then driven with actual climate forcing data during the postfire period of forest regrowth, so that the model output can be compared to and evaluated against field measurements. We define a "climate forcing history" which was used by most of the simulations. The monthly CMCD climate data were used in this climate forcing history. The rationale of the climate forcing history was to ensure that, for the historical spinup, the average state of historical climate was used, and for the period before and after the most recent fire event, actual observed climate data were used.
Method for simulation-measurement comparison Due to the differences in the scope of the forest floor, woody debris and mineral soil carbon as measured in the field and as modeled, a scheme was developed to match the model output with field measurements for these variables (refer to Supplement Sect. 3 for more details). Three metrics were used to quantitatively evaluate the simulation-measurement agreement in a comprehensive way: 1. Metric 1. Linear regression was used to examine the overall model-measurement agreement. Simulation data were regressed against measurements with the regression line forced through the origin (regression through origin; RTO) as it was assumed when the measurement value is zero, the simulation value should be zero as well. The RTO model used is Y i = slope • X i + ε i , (2) where X i are measurement data, Y i are simulation data, and ε i is random error.
3. Metric 3. Measurement-simulation uncertainty overlap ratio was used to characterize measurement-simulation agreement with uncertainties in both being considered. First, we collected for each evaluation variable the measurement standard error or 90 % confidence interval, when they are reported in the source literature, and treated them as the measurement uncertainty. Then we calculated the number of data points where the simulation uncertainty and the measurement uncertainty overlapped. Finally we calculated the ratio of this overlapped number to the total number of measurement data points  (Bond-Lamberty et al. , 2006). We considered one overlap between model and measurement uncertainty to indicate that the model was able to simulate the measurement correctly. The model simulation uncertainty was constructed by pooling together simulation data for all the evaluation sites at the same site cluster, and the minimum-maximum range among model outputs was treated as simulation uncertainty.
This is mainly due to the restriction in the availability of historical climate data from the meteorological station. This assumption may lead to underestimated varying climate effects as the climate trend before 1968 is not taken into account. For clarity, we focus on the site CA-NS1 and then briefly discuss the sites CA-SF1 and US-Bn1. We find that the temporal pattern and magnitudes of postfire CO 2 fluxes at the Manitoba sites (CA-NS1 to CA-NS7) over the past 150 yr are greatly driven by the fertilization effect of increasing atmospheric CO 2. The EC-measured magnitudes of postfire GPP for different ages of forest after fire are much higher than the simulation result with fixed CO 2 (CO 2 FIX-CLIMVAR). The GPP measurements can only be reproduced by the model if the effect of increasing CO 2 is accounted for (GPPCAL-CMCD, Fig.
Discussion In the preceding sections, the ORCHIDEE-FM-BF model is evaluated against chronosequence measurements of carbon www. biogeosciences. net/10/8233/2013/ Biogeosciences, 10, 8233-8252, 2013 -2. 0 5. 0 * Climate effect was calculated as difference between simulations of CO 2 FIX-CLIMVAR and CO 2 FIX-CLIMFIX, and CO 2 effect was calculated as difference between simulations of GPPCAL-CMCD and CO 2 FIX-CLIMVAR, and combined effect as difference between simulations of GPPCAL-CMCD and CO 2 FIX-CLIMFIX. fluxes, ecosystem carbon pools and stand structure, with focus on the capability of the model to reproduce the temporal evolution of each variable as a function of time-after-fire. To our knowledge, this is the first study that tries to evaluate a global process-based vegetation model for fire disturbances against multiple sites and multiple observation data sets.
Acknowledgements. This study was supported by the fire_cci project (http://www. esa-fire-cci. org/),funded by the  European Space Agency. The authors would like to thank  S. Yi  for providing the climate data for the meteorological stations of  Delta Junction and Fairbanks, Alaska. We thank  B. D. Amiro  for providing the CO 2 flux data and the constructive comments and  M. L. Goulden  for providing carbon flux data from the Manitoba sites. We also thank the two anonymous reviewers for providing constructive reviews of previous versions of this paper. This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program  ( DE-FG02-04ER63917  and  DE-FG02-04ER63911 )),  AfriFlux ,  AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada, and NRCan), Green-Grass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC.
We acknowledge the financial support to the eddy covariance data harmonization provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, Université Laval  and  Environment Canada and US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California -Berkeley, University of Virginia. Edited by: P. Stoy The publication of this article is financed by  CNRS-INSU.
In this paper, we propose EZ a new gateway compiler for the z2z language [2] that relies on the event paradigm. We choose the z2z language as it has already proven to be adequate to describe gateways. To solve performance issues EZ defines a workflow of handlers, which communicate with events. Further, to take into account software heterogeneity, EZ is able to compile the z2z language to both C and Java code, ready to be plugged into respective runtimes. The remainder of this paper is structured as follows. Section 2 introduces our approach to generate from high level specifications our next generation gateways for either C or Java environment. Section 2. 2 is focused on the internal design of our next generation gateways. In particular, it presents our new asynchronous runtime system for building scalable gateways in either C or Java along with EZ.
EZ approach In a way similar to z2z, zebu  [2, 6] , our approach is based on generative programming. More specifically, EZ reuses the z2z domain specific language to specify generated gateways (Figure  1 , ❶). However, EZ introduces a new compiler enabling the generation of asynchronous code in either C or Java to be linked into an adequate and efficient event-based oriented runtime system (Figure  1, ❷, ❸ ) that fulfills the requirements of IoT. In other terms, developers only have to replace the z2z compiler by the EZ one to generate event-based gateways. These gateways use exclusively non-blocking system calls to perform asynchronous I/O network operations. Fig. 1. EZ approach to generate asynchronous multi-platform gateway.
Z2z domain specific language Our EZ approach has been design especially to be fully compliant with the z2z language that has been proved to be adequate to describe gateways in high level manner by hiding to developers low-level network and system codes. Z2z language. The z2z language provides facilities for defining three types of modules: the Protocol Specification (PS) modules used to describe the network protocol behaviors, the Message Specification (MS) modules used to describe message structures, and the Message Translation (MT) modules used to describe the message translation logic. More precisely, a PS module provides information about various properties of the interaction with the network, such as the transport protocol used, whether requests are sent in unicast or multicast, and whether responses are received synchronously or asynchronously.