dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank  M. Watson  and  V. Realmuto  for their helpful reviews and comments, which greatly improved the quality and focus of this manuscript. We also thank  Hilary Morgan  for completing the content analysis of BV and JVGR that generated Table 1. Funding for MR was made possible through the  NASA Earth System Science Research Program  (grants  NNG04GO69G  and  NNX08AJ91G ), the  NSF   Petrology and Geochemistry Program  ( EAR-1019558 ) as well as the support of the  ASTER Science Team. AH is supported by la  Région Auvergne , of which this is Laboratory of Excellence ClerVolc contribution no. 21.
Introduction While SysML  [41] , the Systems Modeling Language derived from UML  [36] , has been widely adopted for Systems Engineering applications, the specificities of Systems of Systems (SoS) fostered the creation of further customizations. The Unified Profile for DoDAF and MoDAF (UPDM)  [37] , based on the US and UK military architectural framework, is one of them and is used on a regular basis in SoS Engineering. Specific extensions of SysML/UPDM are considered in DANSE  [13] , one of the first European project aiming at developing a methodological and technical framework for SoS Engineering with associated tool support. This framework shall support the SoS architect from the modeling activities to the analysis phase (abstraction, simulation, formal verification), especially by providing concrete solutions to address common SoS issues: constant evolution of a large-scale SoS and its stakeholders' needs, unexpected emergent behaviors, limited awareness of the global situation.
In the frame of DANSE, we extend the language of SysML/UPDM to add formalized requirements for an SoS. Formalizing the SoS goals makes it possible to verify them automatically (with an adjustable probability) using a statistical model checker such as Plasma-Lab  [24, 28]  in combination with a simulation platform such as DESYRE. The challenge is to propose a high-level formal language that is directly usable by an SoS architect, while being still automatically translatable to the expressive low-level specification of the model checker, in a similar way to editors like IBM Rhapsody that could make an executable specification (FMI) out of a high-level formalism (SysML/UPDM behavioral diagram). For our purpose, the low-level specification is the Bounded Linear Temporal Logic (B-LTL), an extension of the Linear Temporal Logic in which each temporal operator is bound by a temporal constant.
This logic is expressive enough to cover a large set of properties and to write static as well as behavioral SoS goals. But this logic is defined using the standard temporal operators, which are quite low-level: defining complex properties often requires to interlock several layers of nested operators. Writing or understanding such formulas is difficult, thus error-prone, and does not fit at all with our target of a clear and simple specification language. So we propose in this paper the very first contract language for SysML/UPDM, defined using a strong B-LTL based semantics, but close to hand written English requirements for SoS on the surface. This language of goal formalisation, which is developed in the scope of the DANSE project, is called the Goal and Contract Specification Language (GCSL).
GCSL makes use of the Object Constraint Language (OCL), a formal language by the Object Management Group (OMG)  [34]  used to describe static properties on UML models, thus also on SysML/UPDM ones. OCL can be used for a number of different purposes, but especially as a model-based query language and for writing expressions, which perfectly suits our needs here. GCSL also reuses the Contract Specification Language (CSL)  [43] , developed in the previous SPEEDS European project  [44] , which comes with convenient temporal patterns. The three key elements required for the formalization of behavioral goals and the way we address them in our approach are  (1)  being able to refer to model elements: use of the same names as in the SysML/UPDM model, (2) being able to write static properties about them: use of OCL and (3) being able to integrate these expressions inside behavioral patterns: use of CSL patterns.
System of Systems Modeling Overview of SysML/UPDM SysML  [41]  is a general-purpose modeling language defined as an extension of a subset of the Unified Modeling Language (UML)  [36]  using UML's profile mechanism. SysML is used for Systems Engineering applications, whereas UML is more targeted towards object-oriented Software Engineering. A large set of diagrams is provided with SysML to model a system's requirements, structure (e. g. block definition diagram, internal block diagram), behavior (e. g. state machine, activity diagram), etc. Using the same UML's profile mechanism, another language built on top of UML/SysML has de facto become a standard for SoS architects: UPDM. This profile is the result of the unification effort of the US Department of Defense and the UK Ministry of Defense architecture frameworks and associated metamodels.
It adds a layer of new meta-objects that are typically (but not exclusively) used in the context of military SoS, as well as a significant amount of predefined views (e. g. system views, operational views, capability views) which help splitting the whole modeling activity in smaller tasks. The executable part of a UPDM modeling can be compiled into a program based on the Functional MockUp Interface (FMI)  [40]  that defines a standardized interface used in simulations of complex systems. In DANSE, the SoS is compiled into FMI program and executed by the simulation engine DESYRE  [2]. This whole FMI program can be considered as a state transition system, e. g. the formal semantics on which we will use to propose our language. The states denote the global states of the SoS, e. g. the result of collecting the internal states of each constituent in the SoS.
They have been introduced to enable the user to reason about event triggering that are equivalently replaced in DANSE by property satisfaction. The properties handled by these patterns are about the state of a SoS. We use OCL to specify these state properties. This language allows to build some behavioral properties to express some temporal relations about facts or events of the system denoted by the state properties. It is sufficiently powerful to describe precisely a state of a SoS. Here, we will only consider a subset of the OCL language, but it is not unrealistic to consider a larger subset of OCL to describe the requirements. We restrict the language here to express some properties that can be verified using the SMC techniques applied to SoS's. We briefly recall the notion of Collection that we will use in the rest of the paper.
State properties in OCL Originaly introduced to supplement UML, the Object Constraint Language (OCL)  [35]  is particularly adapted to describe the internal state of a component. The Object Constraint Language is a rather simpleto-write, yet formal text language that provides constraint and object query expressions based on any meta-model, so for instance the SysML/UPDM ones. It has a concise notation for accessing, collecting, filtering and evaluating model elements. More generally, it allows to write invariants on a model, that we use in our approach to write the static properties that we insert in the behavioral contracts. As we will see in the following paragraphs, we also pushed the concept further by sometimes embedding a CSL pattern inside an OCL-like expression, when we want to state that the pattern shall hold for some or all elements in a set.
"Any district cannot have more than 1 fire station, except if all districts have at least 1" SoS. itsDistricts→exists(district | district. containedFireStations→size() > 1) implies SoS. itsDistricts→forAll(district | district. containedFireStations→size() ≥ 1) "The mean city area under fire shall be less than 0. 01%" mean(SoS. itsDistricts. fireArea→sum()) ≤ 0. 0001 "The fire fighting cars hosted by a fire station shall be used all simultaneously at least once in 6 months" SoS. itsFireStations→forAll  (  The proposed language ,composed by 11 SPEEDS patterns, is sufficient expressive to formalize the behavior from 15 requirements identified in CAE incubator. This list of patterns can be easily extended for the future needs, but the experiments conducted in SPEEDS and DANSE show it covered all the requirements to be expressed.
It has already successfully experimented in biology area  [10, 29, 39] , software engineering  [9]  as well as industrial area  [6]  More recently, in DANSE  [13] , we adapt the SMC techniques to treat large heterogeneous systems like Systems of Systems. Among them, one finds systems integrating multiple heterogeneous distributed applications communicating over a shared network. We proposed to extend UPDM specification -the SoS specification -with some requirements that the SoS must satisfy. These requirements, are specified with the contract language we specially designed for the SoS's. These goals are viewed as behavioral objectives that support the SoS architect in assessing different strategies and finding the best ones. As shown in Figure  2 , these contracts are compiled into B-LTL formulas that are verified against the SoS (whose constituent systems are compiled into FMI executables) using the Statistical Model Checker Plasma-Lab  [24]  combined with the efficient simulation engine DESYRE developed by Ales  [2].
Component Figure  2:  The SMC process in DANSE The main algorithm we used in DANSE is the Monte Carlo algorithm. This algorithm estimates the probability that a system Sys satisfies a B-LTL property P by checking P against a set of N random executions of SyS. The estimation p is given by p = ∑ N 1 f (ex i ) N where f (ex i ) = 1 if ex i |= P, 0 otherwise Using the formal semantics of B-LTL, each execution trace is monitored in order to check if P is satisfied or not. The accuracy of the estimation increases with a bigger number of monitored simulations. Plasma-Lab  [24]  implements a set of tools from the statistical area to perform the SMC. It provides some engines for simulating biologic models, models written in the Prism language  [39] , but it has.
In the frame of the CAE, all behaviors are abstracted in state machines using IBM Rhapsody, but it would be possible to use any other language and tool as long as it is compliant with the FMI export format. The following figure shows the overall architecture of the CAE incubator as well as the behavior of one of the constituent types: a fire fighting car. We attached to the CAE incubator the following requirement, written accordingly to our proposed formalism: "The mean city area under fire shall be less than 0. 01%" mean(SoS. itsDistricts. fireArea→sum()) ≤ 0. 01 % As described in the paper, we were able to translate this requirement to the low-level B-LTL specifi-cation for the statistical model checker Plasma-Lab and use it in conjunction with the simulation platform DESYRE to assess the probability that this goal is met in the specified time range (the simulation time for each run was 4 months).
Conclusion This papers presents the results of the very first contract-based language for UPDM/SysML model of SoS we developed in the DANSE project. The SoS model used in the project remains rather simple, but powerful enough to capture behaviors and requirements of a CAE case study developed in collaboration with EADS. Also, we are the first to study the relation between a modeling language used in industry (UPDM) and a verification approach developed by academic. As a future work, we plan to offer more dynamicity, which we will do by exploiting and extending the work done on adaptative systems  [48, 20, 8, 19]. This will also requires to adapt the UPDM framework. Another interesting future work will be to add more quantitative information directly in the patterns assumption and guarantee.
The algorithm commonly used by us and others calculates the filter in an iterative fashion and is based on the work of Anderson  [22]. Each iteration extends the AR model by one. The number of coefficients in the prediction error filter employed to construct the spectrum is hence not fixed and requires some care in its choice. If a number that is too low is chosen, resolution and important detail can be lost. On the other side of the coin, if the number of coefficients is run up too high, there may be spurious peaks  [21]. An objective method has been developed using the methods of Akaike  [21] , based on information theory. The filter length chosen is consistent with the most amount useful information that is being extracted as each iteration extends the length of the filter. This is used in the MESA software employed in our work, but we also commonly set a minimum filter length of about N/4 for biological rhythm analyses to ensure adequate representation of any long period cycles in the presence of noise, which can be considerable.
Data conditioning Biological signals can be notoriously non-stationary and noisy. This variation can take the form of linear or nonlinear trends in amplitude, variations in period and the waveform. As with any signal analysis system, MESA output can be improved by conditioning the signal. It should be noted, however, that MESA is robust in the face of such problems from the start. Incorporated into our MESA program is a detrending step which fits a line by regression and subtracts it. This eliminates linear trend and removes the mean. Mean removal is highly recommended, as this DC component can obscure the rhythmic one if it is excessive  [12]. Removal of nonlinear trend can be accomplished by high-pass filtering by numerous methods and will not be discussed here as it is beyond the scope of this work.
Stimuli Faces from 16 different individuals were selected from the Karolinska Directed Emotional Face database  [65]  under their fearful and neutral frontal view versions. An averted gaze version was obtained for each of these stimuli, by manually modifying the eye positions of the faces, using Adobe Photoshop 7. 0. 1. This resulted in four experimental conditions in a 262-factorial design with emotion (fearful or neutral) and gaze direction (direct or averted) as orthogonal factors. All pictures were set to gray level, resized and cropped to an oval shape. Global luminance and contrast of each stimulus (measured inside the oval shape) were modified using Adobe Photoshop 7. 0.
Procedure Participants were comfortably seated inside an electromagnetically shielded MEG room in front of a translucent screen placed at a viewing distance of 82 cm. Stimuli were back projected onto the screen through a video projector placed outside of the room and two mirrors inside the MEG room. Stimulus presentation was controlled by a computer equipped with OmniStim, a home-made software, and connected to the MEG data acquisition computer through the parallel port. Each trial started with a central fixation point displayed for 700 to 900 ms. Then a face stimulus was displayed for 500 ms, followed by a blank screen presented for 1 to 2 seconds before the next trial started (Figure  1 ). There were six blocks of 64 trials (total = 384 trials). In each block, the 16 different faces were seen once under each of the 4 experimental conditions of gaze (direct/ averted) and emotion (fearful/neutral).
Event-related Magnetic Fields (ERFs) Trials contaminated by eye movements, blinks or muscular activity were rejected manually upon visual inspection of the MEG and EOG signals. The mean number of trials included in the ERF averages did not differ across conditions (mean number of trials6SEM averaged: 81. 462. 8, 80. 863. 1, 82. 162. 9 and 80. 963. 0 for fearful faces with direct gaze, fearful faces with averted gaze, neutral faces with direct gaze, and neutral faces with averted gaze respectively; all F(1,14),1. 5, all p. 2). Event-related magnetic fields (ERFs) were then averaged for each condition between 2200 ms and +600 ms (0 ms = face onset). Finally, data were baseline corrected according to the 200 ms preceding face onset, and digitally low-pass filtered at 40 Hz.
Source Localization Forward problem. For each subject, a tessellated envelope of the neocortex was obtained with The Anatomist/brainVISA free software solutions (http://brainvisa. info). Moreover, the amygdala was segmented from each individual MRI with the recently developed method of  [52]. This method is based on a competitive region-growing approach and was operated in the brainVISA environment. It provides tessellated surfaces of the amygdala and hippocampus. Thus, we obtained three tessellated surfaces that were used to distribute the elementary equivalent current dipoles (ECD) of our source imaging model (Figure  2 ). ECDs were evenly distributed perpendicularly to the surfaces of the neocortex and of the hippocampus, in order to model the macrocolumns of pyramidal cells in these structures.
Data Measurements and Statistical Analyses We measured the mean amplitude of the amygdala activity in two time ranges selected on the basis of the peaks of activity obtained across subjects: i) between 130 and 170 ms, to encompass the early prominent peak of amygdala response across subjects, and ii) in four consecutive 40-ms time windows between 190 and 350 ms, to encompass the secondary peaks of amygdala observed and that resulted in a sustained response from about 200 ms across subjects. These data were analyzed using analyses of covariance (ANCOVA) with emotional expression (fearful/neutral), gaze (direct/averted), and hemisphere (left/right) as within-subject factors, and the participant's anxiety score as a covariate. The window of measurement (190-230 ms/230-270 ms/270-310 ms/310-350 ms) was introduced as an additional withinsubject factor for the analysis of the mean amplitude of amygdala activity between 190 and 350 ms.
Notably, hippocampal responses were much attenuated in comparison with amygdala responses (see Figure  S1 ). As these structures have similar depth, this is a good hint to a focus of activity in the amygdala. On a secondary note, it may be reminded here that amygdala responses to faces, and to emotional faces and gaze in particular, have been reported using other brain imaging modalities such as fMRI and intracerebral EEG recording. Taken together with the histological and functional arguments raised in our Introduction, and with the above data from neighboring regions, it forms converging lines supporting the view that we discerned actual amygdala responses. In sum, we provided a new method for the estimation of amygdala activity from MEG signals using an imaging approach of the inverse modeling problem  [50]  that included the individual cortical brain surface and the individually segmented amygdala volume  [52]  as the source space.
Flux data The observations of CO 2 surface fluxes used in this study have been made with the eddy-covariance technique. This method uses the covariance between fluctuations of turbulent vertical wind (eddies) and the scalar of interest (e. g. , CO 2 mixing ratio) to calculate vertical fluxes of carbon, water and energy within atmospheric boundary layers  (Aubinet et al. , 2000). The measurements have a footprint of a few hectares and notably rely on the assumption of a well-mixed, fully turbulent boundary layer above a flat landscape. At present, flux towers are considered as the reference standard for CO 2 surface flux measurement, and there has been an effort to develop a global network of them across representative biomes in the FluxNet database  (Baldocchi et al. , 2001; Baldocchi, 2008).
Data assimilation system The site-scale inversions of the ORCHIDEE parameters are independently performed at each of the sites listed in Table A1, with a variational data assimilation system. The optimised parameters are listed in Table  B1 , while a more detailed description of the assimilation system can be found in  Kuppel et al. (2012). Briefly, we define a Bayesian cost function J , J (x) = 1 2 y o -H (x) T R -1 y o -H (x) + x -x prior T B -1 x -x prior , (5) where all the terms have been defined in the previous section. The cost function is minimised iteratively using a gradientbased approach, and the boundaries can be explicitly prescribed for each parameter in the minimisation algorithm. At each iteration, the gradient of the cost function is computed with respect to all parameters, using the tangent linear version of the model -except for threshold phenological parameters where a finite-differences approach is used.
(2007). We consider two types of observations. The first one is the measurement of mixing ratios of CO 2 collected in flask air samples or performed in situ by automatic analysers. We use the observation time-space sampling of the database gathered by  Chevallier et al. (2011)  for year 2010, which includes 91 stations over all oceans and continents. The second type of measurement is the column-averaged dry air mole fractions of CO 2 (hereafter X CO 2 ) retrieved from surface stations  (Wunch et al. , 2011)  or from space (e. g. ,  Crisp et al. , 2012). We use the time-space sampling of the quality-controlled retrievals from the Japanese Greenhouse gases Observing SATellite (GOSAT) made by NASA's Atmospheric CO 2 Observations from Space (ACOS) project  (O'Dell et al. , 2012; Crisp et al. , 2012).
Projection in the space of concentrations Assuming similar model-data mismatch across biomes with the ORCHIDEE model (see, for example,  Wang et al. , 2012) , the characteristics of the observation error R diagnosed in temperate deciduous broadleaf forests (e-folding lengths of 500 km and 1 day for space and time correlation, respectively) are prescribed at the global scale, and we project the inferred Rprior in the space of atmospheric concentrations using the LMDZ transport model (see Sect. 2. 5). For the surface air sample measurements, we find that the median of the standard deviation of the observation error is 1. 3 ppm across the 91 sites with a maximum of 8 ppm (at station UTA, Wendover, Utah, USA) and a minimum of 0. 1 ppm (at station SMO, Tutuila, American Samoa). No specific spatial structure of this error is seen.
Acknowledgements. This work has been supported by the  French Agence Nationale pour la Recherche  (project  MSDAG ) and by the  CARBONES  project within the  EU's 7th Framework Programme for Research and Development. The Ph. D. programme of  S. Kuppel  has been funded by the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA). The sites PIs of  DE-Hai, DK-Sor, FR-Fon, FR-Hes, JP-Tak, UK-Ham, US-Bar ,  US-Ha1 ,  US-LPH, US-MOz, US-UMB  and  US-WCr  are thanked for making their data available. The authors would like to acknowledge the work of the  NASA ACOS  project to create the X CO 2 data. This work was performed using HPC resources from  DSM-CCRT  and [CCRT/CINES/IDRIS] under the allocation 2012-t2012012201 made by  GENCI  ( Grand Equipement National de Calcul Intensif ) and by local computer resources of LSCE.
CLASS To further adapt the use of COMET to our context, we added a level of precision to the solution code in the argument class (see gray lines on Table  1 ). Initially in COMET, the solution code should be attributed to segments in which a participant elicits a solution answering the design problem. With our adaptation, solutions can be 1) relative to the model's concepts, or 2) out of the model scope. In order to analyze and understand the use of the model's concepts during the session, four possible values, representative of the concepts expressed in the model used, have been considered for the solution codes: metaphor, entity, attribute and channel. Table  1  above summarizes all the codes composing our final coding scheme. 2 , both sessions combined, our analysis has been conducted on more than 2700 segments, which represent more than 26000 words.