Mihai Dascalu
email: mihai.dascalu@cs.pub.ro

Stefan Trausan-Matu
email: stefan.trausan@cs.pub.ro

Philippe Dessus
email: philippe.dessus@upmf-grenoble.fr

Cohesion-based Analysis of CSCL Conversations: Holistic and Individual Perspectives

Although Computer Supported Collaborative Learning (CSCL) technologies have gained an increasing role in educational environments, there are few automatic systems that address involvement, knowledge-building and collaboration in order to support tutors in the time consuming process of analyzing conversations. We propose a cohesion-based analysis model integrating multiple natural language techniques, an intervention scoring mechanism and a comprehensive collaboration assessment method, derived from social knowledgebuilding, reflected at utterance level through cohesion. Furthermore, by combining a holistic perspective of the entire conversation with a more fine grained view focused on each participant, we obtain a thorough evaluation of chat conversations with focus on topics modeling, participant interaction and collaboration. In order to sustain our model, we have performed a preliminary validation study that proves that our analysis is consistent with tutor evaluations.

Introduction

Learners collaborating through forums or chats use language as a mediator for building knowledge, more specifically for developing one's view about the material, for negotiating meaning with each other and for defining new cultural artifacts [START_REF] Stahl | Group cognition. Computer support for building collaborative knowledge[END_REF]. The activity of reading is the cornerstone of every CSCL situation: learners are typically engaged in reading to acquire new pieces of knowledge; to be aware of the way their peers get acquainted about this knowledge; to understand what is required by their teachers or tutors. They can even carefully re-read their own utterances before and after posting.

Cohesion, i.e., relations of meaning within a text, is one of the most important features driving the understanding of read texts [START_REF] Tapiero | Situation models and levels of coherence[END_REF]. Cohesion gaps within textual materials, e.g., online conversation, may yield comprehension difficulties, mostly for low-knowledge learners [START_REF] Mcnamara | Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text[END_REF], but also be viewed as topic change along discussion threads (Dascalu et al., 2010a). However, the cohesion-based assessment of learning materials or learners' utterances is a very demanding task and systems that automatically analyze utterances during learning sessions are of particular interest [START_REF] Fujita | Automating the analysis of collaborative discourse: Identifying idea clusters[END_REF][START_REF] Trausan-Matu | Visualisation of learners' contributions in chat conversations[END_REF]. Such systems often make an intensive use of Latent Semantic Analysis [START_REF] Landauer | A solution to Plato's problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge[END_REF], which has been proven to capture cohesion-based textual relationships [START_REF] Graesser | Coh-Metrix: Analysis of text on cohesion and language[END_REF].

In this paper we introduce ReaderBench, a multi-purpose system that proposes several cohesion-based views on the learners' contributions to the discussion and presents them upon two perspectives: individual, centered on each participant, and holistic, covering the entire discussion. The first perspective allows learners to individually understand the extent to which they contribute to a given discussion, while the latter is useful for teachers and tutors to assess its fruitfulness in terms of knowledge-building and collaboration.

Moreover, our aim is to provide a comprehensive approach for assessing collaboration throughout a multi-participant chat conversation, as a refinement of the initial information gain assessment of collaboration (Dascalu et al., 2010b;[START_REF] Trausan-Matu | A System for the Automatic Analysis of Computer-Supported Collaborative Learning Chats[END_REF]. Our implemented model automatically identifies intense collaboration zones and extrapolates the concepts of personal and social knowledge-building [START_REF] Bereiter | Education and Mind in the Knowledge Age[END_REF][START_REF] Scardamalia | Collective cognitive responsibility for the advancement of knowledge[END_REF][START_REF] Stahl | Group cognition. Computer support for building collaborative knowledge[END_REF] to a finer grained dimension, more specifically to each utterance. In this particular context, cohesion is the bridge between utterances, enabling information transfer and cumulative knowledge-building from both personal (utterances from the same speaker) and social perspectives (utterances from different speakers). Additionally, our model of analysis goes one step further towards defining an implemented polyphonic model of a discussion [START_REF] Trausan-Matu | Visualisation of learners' contributions in chat conversations[END_REF] by also considering an analogy to key dialogic concepts [START_REF] Bakhtin | Speech genres and other late essays[END_REF]. In order to provide a quick equivalence that will be detailed later on, topics emerging from the conversation and pertaining to different speakers are actual voices, whereas cohesion induces the echo cancelation effect of each utterance upon later interventions within the discussion.

The following section provides an insight into our analysis model, with emphasis on building the cohesion graph and on the multi-layered scoring mechanism. Afterwards, the individual assessment section is centered on the identification of topics and modeling the participants' interaction throughout the conversation. Later on, we shift the point of interest towards the assessment and evolution of collaboration. Eventually, a preliminary validation experiment is presented and the relevance of the results is debated.

Cohesion-based Conversation Analysis

Cohesion, seen as the relationship at lexical and semantic levels between two segments of texts, is defined within our model in terms of recency, lexical proximity and semantic distances. Therefore, cohesion is a symmetric function that combines: 1/ the inverse distance between the analyzed textual elements within a fixed window of analysis that defines a local context, 2/ lexical proximity as a relaxed condition of having identical stems within both text fragments and 3/ sematic distances determined from three perspectives.

The first semantic dimension of cohesion is represented by semantic distances within lexicalized ontologies. As ReaderBench was designed to cover both English and French, we used besides WordNet [START_REF] Miller | WordNet: A Lexical Database for English[END_REF], a transposed and serialized version of WOLF (Wordnet Libre du Français, http://alpage.inria.fr/~sagot/ wolf.html) in order to measure similarity between concepts. Our experiments showed that Wu-Palmer semantic distance performs best as it scales correspondingly to the [0; 1] interval and it works properly for both English and French languages. Although WOLF is rather incomplete and mixes word glosses, expressing them partially in English and partially in French, it is currently the best alternative for having a multi-lingual analysis platform.

Going deeper within our cohesion estimation function, the other two semantic dimensions are represented by Latent Semantic Analysis (LSA) [START_REF] Landauer | A solution to Plato's problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge[END_REF] and Latent Dirichlet Allocation (LDA) [START_REF] Blei | Latent Dirichlet Allocation[END_REF]. Whereas LSA is used for measuring similarity between discourse fragments through cosine similarity after projecting concepts in a pre-trained vector space, LDA exploits the Jensen-Shannon dissimilarity [START_REF] Manning | Foundations of Statistical Natural Language Processing[END_REF] of posterior topic distributions in order to grasp inconsistencies or discrepancies between texts. Therefore, from a computational point of view, we have combined Information Retrieval specific techniques, mostly reflected in word repetitions and normalized number of occurrences, with semantic models (LSA and LDA) in order to provide a comprehensible and measurable value of cohesion between textual fragments.

After defining the cohesion function between different analysis units within the discourse, the next step consists of automatically building the cohesion graph, which plays a central role within our analysis. We propose a multi-layered graph consisting of the following hierarchy: document (entire chat conversation or reading material) > block (utterance or paragraph within the initial text) > sentence. Within the previous structure, we enforce hierarchical links determined by inclusion functions (sentences within a block, blocks within the document) and mandatory links obtained from: 1/ explicit links defined by participants within the user interface -in our case, ConcertChat -or 2/ adjacent blocks of text. Later on, cohesion is determined between all sentences of each block (intra-block cohesion) and between blocks within a predefined sliding window of analysis (inter-block cohesion measurements that within our experiments considered a window of 20 utterances). The resulting links were pruned after the sum of mean and standard deviation of cohesion values in order to keep only relevant links within the discourse model.

Our aim was to provide a generalized and customizable model for assessing different types of discourses in terms of cohesion: multi-participant chat conversations, on one hand, and texts in general, on the other. More specific to CSCL conversations, we opted for Dong's perspective (1995) of separating utterances based on turn-taking events between speakers. Although most participants' utterances consist of one sentence (in some cases, elliptical sentences are quite frequent), we preferred to create a scalable model, which can be later on easily adjusted to forums, for example, in which interventions entangle multiple sentences.

An important component in the evaluation process of each participant and of collaboration throughout the conversation is our bottom-up intervention scoring method. Firstly, specific natural language techniques are applied on each utterance: tokenizing, splitting, part of speech tagging, parsing, stop words elimination, dictionary-only words selection, stemming, lemmatizing, named entity recognizer and co-reference resolution [START_REF] Manning | Foundations of Statistical Natural Language Processing[END_REF]. Afterwards, each sentence is assigned an individual score of each remaining concept equal to its term frequency -inverse document frequency [START_REF] Manning | Foundations of Statistical Natural Language Processing[END_REF] multiplied by its cosine similarity from the LSA vector space to the corresponding sentence, denoting the local significance of each word. Furthermore, cohesion with a generic title or with a list of predefined key concepts is also measured and added to the score of each sentence. Due to the nature of our experiments conducted in an academic environment, each conversation had a specific set of concepts, defined by the tutor, which had to be covered by the participants. In other words, we measure through cohesion the degree in which each participant addresses each concept and whether the conversation stays on-topic.

Secondly, at utterance or block level, individual sentence scores are weighted by cohesion measures and summed up in order to define the intra-block score. By going further into our discourse decomposition model (document > block > sentence), inter-block cohesion values are used to augment the previous intra-block scores by also considering all the links from the previously defined cohesion graph. In the end, all block scores are combined in order to determine the overall conversation score. Figure 1 depicts the scores for each intervention in brackets, immediately after each participant's intervention. The proposed scoring mechanism is effective because it considers the local importance of each intervention and it also combines these individual scores through cohesion to other semantically related elements within the discourse structure.

Individual Participant Assessment

Our aim regarding the individual assessment of participants is to focus on covered topics and interaction modeling. Topics, seen as key concepts from the conversation, play a leading role in obtaining a view of the overall discussion, but also in observing each participant's points of interest. Tightly connected to the cohesion graph and the previous scoring mechanism, topics are extracted per entire conversation and per participant. The relevance of each concept mentioned within the discussion is determined by combining a multitude of factors: 1/ individual term frequency -inverse document frequency of each word, 2/ semantic similarity through LSA to the entire conversation, corresponding utterances and sentences 3/ cosine similarity between the distribution of the word in the predetermined topics from LDA and the conversation's overall distribution over the same classes and 4/ the scores of each sentence and utterance in which the word is present. Within this evaluation, the initial natural language processing, with emphasis on lemmatization, played an important role in standardizing the inputs. Moreover, the scores of each concept are normalized by the length of the lexical chains in which they occur; the previous lexical chains are determined from the disambiguation graph modeled through semantic distances from WordNet and WOLF [START_REF] Galley | Improving Word Sense Disambiguation in Lexical Chaining[END_REF]. In addition, as an empirical improvement, filtering the previous list of topics by corresponding parts of speech and selecting solely nouns provided more accurate results in most cases, as nouns tend to better grasp the conceptualization of the discussion.

In this context, key topics together with their corresponding lexical chains can be considered voices that spread throughout the conversation, while cohesion simulates echoes of voices to other inter-linked utterances. Moreover, by changing the focus on a specific participant, we can observe the strength of a voice as being directly proportional to the relevance of previously identified topics.

Figure 1 presents the main user interface from ReaderBench in which a chat conversation has been loaded from the XML file exported from ConcertChat, the scores of each intervention are displayed in brackets and the automatically identified topics for a specific participant are presented in the right sidebar. Following the transition from a global view of discourse to a user centered perspective, the visualization component of the conceptual space for each chat participant as a mind-map is based on cosine similarities computed within our LSA vector space. Terms central to a given discussion may not appear in any utterance but, nonetheless, be worth displaying for comprehension's sake. We thus enriched the previously identified topics list with inferred concepts, not mentioned within the text, for which three measures are computed and summed in the end: 1/ the similarity to the entire conversation vector, 2/ the similarity to the weighted topics vector determined from the previous list of concepts displayed within the user interface and 3/ the similarity to the closest concept within the text, because tight correlation to a concept already present within the text increases the proximity to the overall discussion. These three dimensions have as empiric argumentation the method in which experts selected inferred concepts from manually assessed conversations: similarity to a concept or idea from the text, but also to the whole text (and the entire text is represented as the document vector and as an extrapolation of the already identified topics).

Figure 2a presents the conceptualization space of a user including initial topics and the automatically inferred concepts. From a computational point of view, we determine all similarities between the previous words, considered nodes within the network, and select as edges solely the ones that are above the threshold selected from the interface (30% in this particular case). The actual size of each node is directly proportional to a Social Network Analysis metric derived from the previous graph (e.g. betweenness). All multidimensional scaling and visualization of the graph as a force-based layout are performed by Gephi [START_REF] Bastian | Gephi: an open source software for exploring and manipulating networks[END_REF]. Besides the identification of topics for each participant in the discussion, ReaderBench also supports participant interaction modeling covering two major dimensions: a preliminary quantitative one, generated by the actual number of interactions per participant, and a deeper qualitative dimension, obtained after summing up intervention scores. Internally, an interaction graph is built with participants as nodes and the weight of links equal to the sum of interventions scores multiplied by the cohesion function with the referred element of analysis, extracted from the cohesion graph. Therefore, by performing social network analysis on the previous participant interaction graph, the scale of analysis is shifted towards an individual perspective (see Figure 2b).

Collaboration Assessment

In order to achieve genuine collaboration, which can be seen as true polyphony in terms of Bakhtin's dialogic theory (1984), the conversation must be generated by a dense intertwining of voices, derived from key concepts of the conversation and covering all participants [START_REF] Trausan-Matu | Polyphonic Inter-Animation of Voices in VMT[END_REF]. Moreover, by enforcing a personal and social knowledge-building process [START_REF] Bereiter | Education and Mind in the Knowledge Age[END_REF][START_REF] Scardamalia | Collective cognitive responsibility for the advancement of knowledge[END_REF][START_REF] Stahl | Group cognition. Computer support for building collaborative knowledge[END_REF] at utterance level and by using cohesion as a bridge between utterances seen as an overlap of voices, the actual information transfer through cohesion between units of analysis obtains two valences. Firstly, a personal dimension emerges by considering utterances with the same speaker, therefore modeling an inner voice or a continuation of the discourse. Secondly, inter-changed utterances having different speakers define a social perspective that models collaboration as a cumulative effect.

Therefore, each intervention now has its previously defined importance score and a knowledgebuilding (KB) effect, both personal and social. The personal effect is initialized as the intervention's score, whereas the social effect is zero. Later on, by considering all the links from the cohesion graph, each dimension is correspondingly augmented: if the link is between utterances with the same speaker, the previously built knowledge (both personal and social) from the referred utterance is transferred through the cohesion function to the personal dimension of the current utterance; otherwise, if the pair of utterances is between different participants, the social knowledge-building dimension of the currently analyzed utterance is increased with the same amount of information (previous knowledge multiplied by the cohesion measure). In other words, continuation of ideas or explicitly referencing utterances of the same speaker builds an inner dialogue or personal knowledge, whereas the social perspective measures the interaction with other participants, encourages sharing of ideas, fostering creativity for working in groups (Trausan-Matu, 2010b) and influencing the other participants' points of view during the discussion, thus enabling a truly collaborative discussion.

In this manner we can actually measure collaboration through the sum of social knowledge-building effects, starting from each intervention's score corroborated with the cohesion function. Moreover, personal knowledge-building addresses individual voices (participant voices or implicit/alien voices covering the same speaker), while social knowledge-building, derived from explicit dialog (that by definition is between two entities), sustains collaboration and highlights external voices. By referring to the dialogic model of discourse analysis, besides voices that are derived from the topics of the conversation in correlation to each participant's point of view, echoes are reflected by cohesion in terms of the information transferred between utterances, whereas the attenuation effect diminishes the strength of the cohesion link with the increase in distance between the analysis elements.

Nevertheless, we must also consider the limitations of our implemented model in terms of personal knowledge-building. Collaboration clearly emerges from social knowledge transfer through cohesion as the influence of one's intervention over other participants' discourse. In contrast, the approximation of personal knowledge-building rather represents an upper bound of the explicitly expressed information transfer between one's personal interventions. Similarly to the gain-based approach (Dascalu et al., 2010b;[START_REF] Trausan-Matu | A System for the Automatic Analysis of Computer-Supported Collaborative Learning Chats[END_REF], we use a quantifiable approximation of inner dialogue, although limited in terms of underlying cognitive processes. Personal knowledge-building is seen as a reflection of one's thoughts expressed explicitly within the ongoing conversation as cohesive links between interventions of the same chat participant. But this reflection does not necessarily induce personal knowledge-building, only a cohesive discourse. Therefore, we can consider that the computed value of personal knowledge-building is a maximum value of the explicit personal knowledge-building effect, modeled during the discourse through cohesive links.

In addition to the estimation of personal and social knowledge-building effects for each utterance and the modeling of their corresponding evolution throughout the conversation (Figure 3), ReaderBench automatically identifies intense collaboration zones that are intervals of utterances in which participants are actively involved, collaborate and generate new ideas related to the ongoing context of the discussion. The first step within our greedy algorithm for detecting intense collaboration zones consists of identifying social knowledge-building peaks as maximum local values. Afterwards, each peak is expanded sideways within a predefined slack (experimentally set at 2.5% of the number of utterances). This slack was important due to our focus on the macro-level analysis of collaboration and due to the possible intertwining of multiple discussion threads. In the end, only zones above a minimum spread of 5 utterances are selected as intense collaboration zones. In other words, after identifying the utterances with the greatest social knowledge-building effect, the algorithms expands each zone to the left and to the right, in a non-overlapping manner to previously identified zones, by considering solely utterances above the mean social knowledge-building value and that are in the previously defined slack. If in the end, the zone covers more than the specified minimum spread, it is considered an intense collaboration zone. From a different point of view and highly related to social knowledge-building, cohesion binds utterances within an intense collaboration zone in terms of on-topic relatedness. From a holistic perspective, three factors were implemented in order to best characterize the overall collaboration within the discussion. Firstly, quantitative collaboration is determined as the percentage of links from the cohesion graph having different speakers in comparison to the number of links automatically identified. Although rough as estimation, this measurement provides good insight with regards to the actual information exchange between participants. Secondly, the overall social knowledge-building score is compared to the overall knowledge-building effect. Thirdly, the ratio between the overall social knowledge-building score and the overall intervention importance scores is computed for highlighting the amount of information that is transferred through collaboration in comparison to what was withheld initially within each utterance. Low values for the latter factors are normal due to the fact that cohesion, after normalization in the [0, 1] interval, usually integrates low similarity values between utterances of a chat conversation in terms of LSA and LDA.

First Validation Study

Preliminary experiments were conducted in order to validate the dialogic model used for evaluating chat conversations, with emphasis on participant involvement and collaboration assessment. Three chat conversations conducted in an academic environment, with students from the 4 th year undergoing the Human-Computer Interaction course and debating on CSCL technologies, were manually assessed by 4 tutors. More specifically, each student had to focus on a CSCL technology (chat, wiki, blog or forum), to present and debate on its benefits in specific use case scenarios generated throughout the conversation. These three conversations (Team 4, Team 34 and Team 36) were selected for detailed analysis after an overview of approximately 50 discussions engaging more than 200 students. Although high discrepancies were noticed in terms of the quality of the content, the involvement and the collaboration of its participants, these conversations were considered representative for the entire sample and the preliminary evaluations were conducted only on these conversations due to the high amount of time it takes to manually assess a single chat conversation (2 to 4 hours for a deep understanding of involvement and of collaboration).

Additionally, the time evolution modeling interface depicted in Figure 4 was developed in order to facilitate the manual evaluation of chats in terms of intense collaboration zones. In this context, the presentation of the conversation follows the timeline and models the inter-twining of utterances, based on the cohesion graph. This component is useful for manually identifying: 1/ breaks within the conversation, zones with limited or no collaboration, due to the fact than within a specific time-frame we have a monologue of a participant, without any interventions from other users, and 2/ zones with high collaboration due to the dense interanimation of utterances between different participants. In the particular case presented in Figure 4, all utterances with identifiers between 30 and 50 belong a single user, within a limited time-frame, therefore making the social knowledge-building effect zero. Afterwards, as multiple participants get involved in the ongoing discussion, collaboration increases. Table 1 presents the correlation between different evaluation factors extracted from ReaderBench and the final grades assigned by the experts. Although the participant's identifiers coincide, each conversation had different students attending it. Moreover, in order to ensure the equitability of our analysis, the correlations between the factors automatically determined by the system and the average values of the grades manually assigned by the experts were computed after combining the participants' scores from all conversations.

As an interpretation of the results presented in Table 1, we can observe in Team 4 conversation a discrepancy, as the involvement of the participants from a personal point of view was good, while the actual collaboration throughout the conversation is highly unbalanced. Team 34 conversation has the lowest scores in all the factors, whereas Team 36 conversation, that was considered the best by the tutors in terms of both quality and involvement, has the highest scores assigned by the system.

By analyzing each factor's correlation, it becomes quite clear that the tutors emphasized on the quality of the interventions, not on the mere number of utterances. As the Intraclass Correlation Coefficient (ICC) was .61 on single measures and .86 on average, results in terms of intervention importance scores and social knowledge-building correlate extremely well with the average expert grades. Additionally, the personal knowledge-building dimension is better correlated with the expert's grades; this sustains the empiric observation that the personal effect reflected in each participant's involvement has a higher impact in the expert's evaluation than the social dimension, through which collaboration is attained. In other words, it is easier to grasp one's active participation than his social interaction from the perspective of collaboratively building knowledge.

Table 1: Correlation between manual and automatic participants' evaluations.

In terms of intense collaboration zones, manual annotations and automatically identified zones are presented in Table 2. By analyzing all previous results, besides the global evaluation factors that correlate well with the human evaluations, the following indicators of bad collaboration (mostly in Team 34 conversation) were observed: the high number of automatically identified zones containing 1 to 3 utterances which were not considered intense collaboration zones in the end, the low average value of social knowledge-building effects and no automatically identified collaboration zone with a wide spread (over 50 utterances, although it was the shortest conversation of the three). In contrast, conversations with good collaboration (namely Team 36 which had the best overall collaboration) have more balanced collaboration zones, higher average values of social knowledge-building and a better distribution and coverage over the entire conversation.

Further investigation and analysis of results is required on an extended sample of discussions, but by taking into consideration the subjectivity of the tasks at hand, preliminary results are quite promising and prove that our implemented dialogic model provides measures relevant in terms of the evaluation.

Conclusions

ReaderBench has been designed to support tutors and learners in various educational scenarios that involve the use of chats or forum discussions for solving different tasks, with emphasis on collaborative problem solving or debating. Based on the results of our first validation study, we strongly believe that ReaderBench can be used to support tutors in the time consuming process of manually assessing chat conversations. Overall, starting from a dialogic model of discourse, our integrated analysis system can be used to identify cohesion gaps between utterances, to analyze participants' involvement and to evaluate collaboration individually and holistically, through the process of social knowledge-building. The collaboration assessment model can be considered a cornerstone as it induces dialogistic approach and emphasizes the social knowledgebuilding perspective of collaboration in environments. In extent, by combining two different perceptions of cohesion, CSCL participants can use this system to assess to what extent utterance cohesion reflects group cohesion, as an outcome of collaboration depicted from the interaction graph.

Figure 1 .

 1 Figure 1. ReaderBench Main User Interface.

Figure 2

 2 Figure 2. a. Concepts Visualization and b. Individual View of the Interaction Graph.

Figure 3 .

 3 Figure 3. Collaboration Assessment and its Evolution in Time.

Figure 4 .

 4 Figure 4. Time slice of a conversation highlighting cohesion links and a monologue within the conversation.

Table 2 :

 2 Overlap between manual and automatic identification of intense collaboration zones.

	Conversation	Number of	Manually annotated	Automatically identified collaboration zones
		utterances	collaboration zones
	Team 4	389	[90; 160], [320; 360]	[19; 43], [72; 146], [157; 166], [240; 280], [290; 317],
				[329; 379]
	Team 34	190	[90; 120], [170; 178]	[51; 67], [74; 78], [94; 121], [127; 131], [158; 166],
				[181; 195]
	Team 36	297	Relatively uniform	[38; 58], [69; 122], [136; 157], [202; 221], [286; 290]
			distribution

Acknowledgments

The research presented in this paper was partially supported by the project No. 264207, ERRIC-Empowering Romanian Research on Intelligent Information Technologies/FP7-REGPOT-2010-1.


Klaus Paasch
email: klauspaasch@aol.com

On the Calculation of Elementary Particle Masses

Keywords: Hypothetical particle physics models, Composite models, Cosmology

With an exponential model including the gravitational constant as a time dependent parameter a mass function is derived where elementary particle masses are combined and related. The proton mass m p is derived as a function of the electron mass m e and fine structure constant α as m p =1,672621•10 -27 kg (1,672622•10 -27 kg, Δm/m=6•10 -7 ), the measured mass and the relative deviation of both are in brackets. The neutron mass then is calculated as a function of m p and α as m n =1,67492745 •10 -27 kg (1,67492747 •10 -27 kg, Δm/m≈10 -8 ). The tau mass is expressed as a function of m p and m e resulting in m τ = ,

•10

-27 kg ( , •10

-27 kg, Δm/m≈10 -5 ). The results for the neutron and tau are within the estimated standard deviation of the experimental values.

Introduction

The precise prediction and calculation of elementary particle masses still is not covered by any theory, e.g. the standard model. Even though the mechanisms that provide the specific particle masses are thought to be understood and confirmed by the discovery of the Higgs boson, a clue to why elementary particles have their specific mass values would be of great importance for our understanding of quantum objects and matter. A correlated unresolved issue is why and how nature offers such a wide scale of masses from neutrinos to galaxies and the observable universe itsself. This paper attempts to provide an approach to elementary particle masses by constructing an exponential model that covers the mass scale of the observable universe and allows to calculate the proton and neutron masses with an accuracy of 6 resp. 8 decimal digits. As a premise the gravitational constant G is assumed to be a function of time G(t) proportional to 1/t, compatible with Dirac´s conclusions from the Large Number Hypothesis LNH [START_REF] Dirac | Cosmological Models and the Large Numbers Hypothesis[END_REF]. As a result a set of mass dependend integers is observed and introduced. The relation of particle masses and integer values has already been pointed out by the formula of Koide [2] for lepton masses. It is compared with the result for the tau mass calculated with the appoach of this paper.

The Exponential Model

Estimations based on measurements of the mass and radius of the observable universe result in a ratio of these values that about matches the conditions of a black hole. Here the mass relates to the so far observable ordinary baryonic matter, thus not including dark matter nor dark energy. Within a space of a given radius the smallest rest mass greater zero that can be observed corresponds to a compton wavelength that is of the order of that radius. In this model the observable mass m U of the universe and the such defined smallest mass m γ inside are fitted by an exponential approach. The largest possible wavelength of a particle with mass m γ thus is of the order of the radius of the universe, which is proportional to m U . Assuming that the reduced compton wavelength r C of m γ is equal to the gravitational radius r G of the universe, then

= ℏ = = (2.0a) = √ ℏc , = √ ℏ → • = (2.0b)
where m pl, r pl are the Planck mass and lenght resp. with the gravitational constant G, Planck´s constant ħ and the speed of light c [START_REF]CODATA Recommended Values[END_REF]. The observable horizon of the universe is the Schwarzschild radius r S =2r G . The initial mass of the exponential model is the Planck mass. To obtain an exponential scaling the Planck mass is multiplied by successive factors to obtain first the masses for the stable particles proton resp. electron.

The first step results in the proton mass m p = followed by the electron mass m e = and finally the smallest mass m

γ = = (2.1a)
where n is an integer and m x a mass to be determined. For m U it follows with Eq. (2.0b)

= → = ( ) (2.1b)
For m x =m p and n=2 the result for m U and the approximate age t 0 =r S /c of the observable universe is

= , • ≈ , = , • , ± , •
This is in agreement with measurements of the mass and radius resp. the age [START_REF]Planck 2015 results. XIII. Cosmological parameters[END_REF] of the observable universe, where the measured values are in brackets. Now m U and m γ are defined as

= ( (2.2a) = ( (2.2b)
The model mass m U is assumed to always fulfil the mass radius relation of Eq. (2.0a). Provided the radius of the universe increases with time, m U is less for a smaller radius in the past and there is no singularity at an initial radius r 0 that exceeds the mass radius relation of Eq. (2.0a). But with Eq. (2.2b) this implies that the Planck mass cannot be a time independent constant, when assuming that the particle masses m e and m p are constant.

Here it is assumed that the gravitational constant G is a parameter G(t) that was larger in the past. Thus the Planck mass m pl (G(t)) and m U (m pl ) were smaller. The time dependencies of G(t) and m U (t) are compatible with the conclusions from the LNH, see Appendix C. For m U (t) beeing smaller in the past, there is eventually a condition reached when it is equal to the smallest observable mass m γ (t), which is referred to as the state of equilibrium. This state is derived and analysed.

State of Equilibrium

The state of equilibrium is defined by m E =m γ =m U . Since the smallest observable mass m γ cannot exceed the mass of the universe m U , it is the initial state of the model. The equilibrium values are:

= , • , = , • - , = , • - (3.3)
The equilibrium mass m E is smaller than the muon, between the electron and proton masses. To roughly estimate the ratio of the strength of gravitational to electromagnetic interaction at equilibrium, the interaction of two masses m E /2 is compared with the interaction of two elementary charges e:

= ℏ = ≈ ,
At equilibrium the gravitational and electromagnetic interaction converge and the spatial size of the model mass m U is with r S =2r E of the order of a proton.

Composition of the Equilibrium Mass

When m U is of the order of m E and thus exhibits the mass and size properties of an elementary particle, the value for G(t) is too large to neglect binding energy effects. It is assumed that within the space of m U there is a constituent mass m b as well as it´s self resp. binding energy m S c 2 . Futher it is assumed that there is an energy resp. mass contribution m Q from an elementary charge e distributed over the sphere of m U . Thus a general approach for the energy resp. mass content of m U is made. With Eq. (2.2b) it follows:

= = + + +
Inserting this into Eq. (4.4) results in

= ( ) ( = , • - (4.7a)
which is about half the muon mass. Eq. (4.4) is rewritten by defining

= = = ( ) = , • - (4.7b)
resulting in

= + + (4.7c)
The general approach to particle masses is the equilibrium mass in Eq. (3.0). To solve Eq. (4.7c) for the proton mass m p the equilibrium mass will be generalized.

The Generalized Equilibrium Mass

The equilibrium mass in Eq. (3.0) is utilized for finding dependencies between elementary particle masses. Expressing it in units of the electron mass m e results in

( = ( ) = ,
Replacing the proton by the neutron mass results in

= ( ) = ,
Thus both values group around the integer value 150. The relation of particle masses and integer values have already been pointed out by other authors such as Koide [2] with his pure empirical though astonishing precise and profound formula for lepton masses (see Appendix B). To evaluate whether the integer value for the proton resp. neutron mass is a random result, the electron resp. proton mass in Eq. (3.0) are replaced by other elementary particle masses. Here the lepton and meson masses smaller than the proton, the proton p, neutron n and the tau τ are applied, since this will be sufficient for the derivation of the proton mass. The elementary particles smaller than the proton are the electron e, muon μ, pions π0+, kaons k+0, the eta η, rho ρ, omega ω and K * +0, see Appendix A and [START_REF]CODATA Recommended Values[END_REF][START_REF]PDG Particle Data Group 7[END_REF]. The equilibrium mass is generalized as

´ , = (

(5.0a)

Every particle combination i, j is assigned a mass m´. The results for setting j=n and j=p resp. then relate the proton, neutron, pion, kaon,ωand η masses, where the measured values are in brackets:

+ = 1 3 = , • - , • - (5.0b) = + = , • - , • - = = , • - , • -
It is notable that the kaons and the adjacent pions are related pairwise and that ω is the equilibrium mass of the adjacent η with j=p. This approach is now investigated for i=e. Every elementary particle j is assigned a mass m´(e,j) and a mass number N(j), which is the ratio of m´(e,j) and the electron mass. The results are shown in Fig. 1. where the μ, pion π+, the symmetric grouping of the kaons, K * + and p, n build up an integer scheme. Thus the particles values N(j) are supposed to group around a scheme of integers and are assigned these integers:

N(μ) = 35
The proton is assigned N(p)=150 i.e. a mass m´(e,p) which is equal to the equilibrium mass m E in Eq. (3.0). In addition there is an exponential scaling for particle masses by an factor f N to be applied in the calculation of the neutron mass. For the range of particles from pion to tau:

= ´ ,
´ , + ⁄ = ,

(5.3a)

Then within an error of ≈10 The reason for this exponential behavior can be approached by assigning each particle a generalized equilibrium mass m´(i,e) with the properties

  independent of the definition of the Planck mass in Eq. (2.0b), which may vary depending on it´s derivation. Inserting m E for m pl into Eqs. (2.2a) and (2.2b) yields

( 4 .

 4 0a) where m α resembles a second order correction term. The binding energy m S c 2 is = , = ℏ then with Eqs. (2.0a) and (2.0b) ξ is a constant to be determined later. Then m U is for m b is defined by the square root within Y, hence Inserting Y into Eq. (4.3a) is a straightforward calculation and yields = ( ) ( (4.5) For the condition of equilibrium (Eq. (3.1)) the Planck mass in Eq. (4.5) is set equal to the equilibrium mass m E in Eq. (3.0):

Fig. 1 .

 1 Fig.1. N(j) for elementary particles from electron to neutron

- 3 :

 3 N(π0+) f N = 64,18 = N(g), which is within the gap between π´s and K´s(5.3b)N(g) f N = N(K+0) N(K+0) f N = N(p,n) N(p,n) f N = N(τ) = 229,52

Successive ratios of an M-value and twice the preceeding M-value approach with values between 0,61786 and 0,61859 the golden ratio phi=0,618034 as shown in Fig. 2, which is the perfect ratio of resonances. These perfect proportions already have been found in experiments observing other quantum mechanical systems e.g. quantum phase transitions atomic chains [7]. Then the ratio of M(i)´s is f M ≈2phi and with Eq. (5.4)

which is consistent with Eq. (5.3a). With Eq. (5.1) particles m´ resp. N ratios are expressed as integer ratios which can be related to each other:

where u is a rational resp. integer number. An obvious example for the μ, the π´s and p,n is

With these approaches and results the proton mass can be calculated as a function of the electron mass.

Calculation of the Proton Mass

In Chapter 4 the mass m b in Eq. (4.7c) has been derived, which is a component of the equilibrium mass and now defined as a mass m´(e,j). Then according to Eqs. (5.1) and (5.2) m b is assigned a mass number N(b)=7n.

Thus Eq. (4.7c) becomes

The closest value m´ of a particle to m b in Eq. (4.7a) is the η with n=15:

and

The next possible mass number is N(b)=7n, n=16, thus

The second order contribution m c ξα 2 is solved in a seperate approach independently of Eq. (5.0). Considering Eq. (5.1), then Eq. (5.6) is rewritten as

where n 1 and n 2 are integers. Since m c ξα 2 also is assumed to be a mass m´(e,j), the approach is = (6.2b) and

where the right side of the equation is a second order correction term with a constant ξ´ to be determined. The number u is assumed to be an integer similar to common fine structure corrections which are of the order (uα) 2 .

With the results of Eq. (5.1), m e is considered a constituent mass of the generalized equilibrium masses m´(e,j). The left side of Eq. (6.2c) is assumed to be proportional to a self energy contribution of the electron, which is proportional to 3/5 and to m e . This is evident when replacing m b by m e in Eq. (4.0b) and since the Planck mass in the self energy term is proportional to m e .

Then from Eq. (6.2c) it follows

Solving Eq. ( 6.3) by inserting m c from Eq. (4.7b) results in ξ´u 2 =101,02. In a first approach the combination with the best fitting integer

is utilized to solve Eq. ( 6.3). The second order term in Eq. (6.0) now is = (6.5) which yields

Inserting N(b) and n 3 yields

Besides the integers in Eq. (4.4) resulting from the solution of Eq. (4.2) the proton mass depends on two integers N(b)=112 and n 3 =10 as a result of the quantisation approach in chapter 5. Solving Eq. (6.6) for m p with Eq. (4.7b) yields

and with defining

where the measured mass is in brackets, see Appendix A.

Calculation of the Neutron Mass

The factor f N in chapter 5 provides an exponential scaling of particle masses but no precise results as for the proton mass in chapter 6. But the assumption is that this is a function of the mass difference involved, i.e. that for small scales resp. mass differences the precision increases. To verify this the splitting of the neutron and proton mass is now approached with the principles previously deployed. With Eqs. (4.0c) and (6.0) it is assumed that the splitting is proportional to α and m p . Then with Eq. (6.2a) a related approach is

where according to Eqs. (5.2) and (5.6) n 1 and n 2 are assumed to be from the set of mass numbers and integers in the previous chapters, thus the number of possible combinations is limited. The factor f N from Eq. (5.3a) has been adjusted with the correct exponent, since it relates to ratios of m´ and thus of N(j), but masses m are applied here. Then with Eq. (5.1) it follows

Considering the previous results, then a supposable combination from the set of integers resp. mass numbers is n 1 =n 3 from the solution for the proton mass in Eq. (6.4) and n 2 =N(e)=1, resulting in

yielding a mass formula that with f N relates the pion, proton, neutron and tau masses

Then solving for the neutron mass and inserting f N and n 3 =10 yields

The factor f N from Eq. (5.3a) is the best fit for Eq. (5.3b). Replacing it by the theoretical value from the observation presented in Fig. 2 and Eq. (5.5) results in

with an accuracy of seven decimal digits. Solving Eq. (7.2) for m p yields

The results of Eqs. (7.2) and (7.4) are within the standard deviation of the measured masses, see Appendix A.

Calculation of the Tau Mass

The results of Eq. (5.1) shown in Fig. 1. can be written as:

´ , = ( = (8.0)

Here N(J) is the ratio of an equilibrium mass and the electron mass. The ratio of an equilibrium mass and the proton mass then is approached with

´ , = ( = (8.1)

since m´(e,j) is smaller than m p in the considered mass range, where I(j) are integers. For j=τ an accurate result for the tau mass m τ is obtained:

Inserting m p from Eq. (6.8) yields

which is within the standard deviation of the measured mass in brackets, see Appendix A. For a comparison the formula of Koide in Appendix B is used to calculate the tau mass [5] as a function of the muon and electron mass with the result m τ =3,16773•10 -27 kg. The accuracy of both results in principle allows to equate the tau mass in Eq. (8.2c) and in the Koide formula to relate the proton, electron and muon masses.

Discussion

The proton, neutron and tau as well as meson masses in the range probed can be calculated by defining a generalized equilibrium mass as a combination of two particle masses. For particular equilibrium masses which are a function of the electron mass, quantum structures i.e. mass numbers can be observed which allow to relate elementary particle masses. The proton and neutron masses then are a function of the finestructure constant, the electron mass and integers resp. an exponential factor, which does not seem to be accidentally due to the accurate results of six resp. eight decimal digits. The exponential dependencies of mass numbers can be traced back to the golden ratio, but their origin remains unresolved. This result is similar to the empirical formula of Koide (Appendix B), which relates the e, μ, and τ masses with two integers only with the accuracy of four to five decimal digits. Zenczykowski [8] is pointing out that this does not seem to be an 'accident' and thus strongly suggests an algebraic origin of mass. For the exponential model of mass scales which is needed for the calculations, the gravitational constant becomes a parameter G(t) that was larger in the past and results in a strength converging with electromagnetic interaction at the proton sized elquilibrium state. The results suggest that the origin of the observed elementary particle masses is linked to fundamental constants, an inherent algebraic structure and that the constancy of the gravitational constant has to be questioned.

Appendix A

Elementary particle masses in kg from [START_REF]CODATA Recommended Values[END_REF][START_REF]PDG Particle Data Group 7[END_REF] The values in brackets are the estimated standard deviations of the last digits of the measured masses.

Appendix B

The formula of Koide [2]:

The value of Q should be a random number, but it is exactly halfway between 1/3 when all lepton masses would be equal, and 1 when two masses would be neglectible, which suggests an unresolved physical meaning of the formula.

Appendix C

The time dependencies of G and m u derived by Dirac from the LNH [START_REF] Dirac | Cosmological Models and the Large Numbers Hypothesis[END_REF], resulting in G proportional 1/t and m u proportional t 2 , are solutions of G(t) and m u (G) in Eqs. (2.0b) and (2.2b). In the following m pl (G 0 ) and G 0 are the present values of the Planck mass and gravitational constant, whereas m pl (G x ) is the value for a specific G x .

When m U was smaller by a factor x, then with Eq. (2.2b) The solutions for G(t) thus depend on r(t). With Eq. (A1) and the approximation for the ages of the observable universe t 0 =2r G /c and t x =2r x /c resp. we get

Inserting G from Eq. (A2) into Eqs. (2.0b) and (2.2b), then the mass of the universe is:

These are the time dependencies of the gravitational constant and the mass of the universe concluded from LNH.


Zied Aloui

Nawfal Ahamada

Julien Denoulet

Francine Pierre

Martin Rayrole

Marc Gatti

Bertrand Granado

Embedded real-time monitoring using SystemC in IMA network

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

It is well known that avionics is a very restricting domain for obvious safety reasons. Along with miniaturization comes the idea of integration. More functionality on one spot requires a good management of privacy and congestion on shared platforms. This is why determinism is one of the keywords of avionics works. This led to protocols like ARINC653 [START_REF] Spitzer | ARINC Specification 653, Avionics Application Software Standard Interface[END_REF] assuring that, multitask embedded programs respect a predictable policy applied by the operating system (OS). Another key protocol is ARINC664, which guarantees that multiple communicating systems efficiently share the network. These two protocols are pillars of the Integrated Modular Architecture (IMA) concept [START_REF] Paul | ARINC 653 role in integrated modular avionics (IMA)[END_REF]. IMA concept consists of multitask module hosting ARINC653 OS, interconnected with ARINC664 data network. Compared to federated avionics architecture, it considerably reduces the overall weight and power consumption for aircraft, reduces development expenses and design cycle times as well as maintenance costs. With the intention to step forward with this concept, the CORAC (The Council for Civil Aeronautics Research) develops a technological demonstration platform (PDT) called Extended Modular Avionic (AME) [START_REF] Corac | COnseil pour la Recherche Aéronautique Civile[END_REF]. Therefore, as partner of the project, we work on a project dedicated to monitor the system.

In this paper, we propose to use a new kind of monitoring based on embedded simulation. This simulator implemented in SystemC, monitors data traffic generated by key aviations in order to detect suspicious behavior such as missing data, unexpected communication of simply incoherent data. In the next sections, we first introduce our method and its related tools, most notably the SystemC language and the modifications required to make this language compliant with avionic constraints. Then we introduce a use case which will validate our method. Finally, we will conclude..

Method

Method presentation

Considering the predictability and determinism of applications software ruled by the protocol ARINC653 and their windows of communication in ARINC664, one can predict part of the aircraft data traffic. Some verification within the communication protocol already exist concerning the integrity of the data transport but none can analyze the content itself to determine whether one or another application is really supposed to send a value, or if a communication disappeared or if a value is simply incoherent. Obviously simulating the whole communication flow to determine if it is coherent would be too much time expensive in simulation. The idea is to target specific applications, or specific suspect behaviors (missing material, erroneous values) we could watch over during the flight. Knowing what we're looking for, we could then create a simplified functional timed model of applications as communication providers. On the basis of ARINC664 and ARINC653 configurations values (major frame, bandwidth allocation gap ..), we could predict communication by simulation and compare it with the real traffic. A privileged place to implement a model simulation is the switch modules where the CPU only manages message traffic and have available time. In Figure 1, we present the principle of our method. We consider an avionic architecture featuring core processing modules (CPM) implementing several applications and generating data traffic and avionics switch modules (ASM) which route data packets to their destination CPM.

As an example, CPM1 in Figure 1 features three partitions, each one hosting an application dedicated respectively to GPS, Speed, and Angle estimation. Through an ARINC664 communication End System, data generated by these applications are sent through several Virtual Links (VL) of the data network. While performing data traffic management, the ASM also implements a simulator that runs a timed model of the expected communication traffic, considering the OS and network parameters. The simulator performs two types of verification: temporal consistency which checks whether communication occurs at the expected time, according to the system scheduling, and data consistency which analyses N consecutive data values to determine if their evolution is coherent or if an error can be assumed to have occurred.

To achieve such a goal, we have chosen the SystemC [START_REF] Initiative | IEEE standard SystemC language reference manual[END_REF] language as an appropriate candidate to model as well software (application) and hardware system (processors and communication modules) under time constraints (defined by ARINC653 and ARINC664). The next subsections present the SystemC language specifications, as well as SystemCASS, a SystemC simulation kernel that can meet avionics 7/20/2015 requirements. We finally present the modifications we introduced to the SystemCASS kernel to meet these constraints.

SystemC

SystemC is a C++ class library based on object-oriented design concept (OOD) providing common Hardware Description Language (HDL) features. As such, it allows hardware description alongside with software development. The concurrency of hardware behavior is simulated by the way simulation time is being managed by the simulator.

Hardware components are modeled using the sc_module class and are interconnected to each other with sc_port class objects. Module internal registers are represented by sc_signals, and module behavior by processes, which can be described as functions triggered by the update of ports or signals that are registered in a sensitivity list. A SystemC program usually consists in an elaboration phase where all the elements of the described system are declared and assembled, and where all processes are listed. Then comes the simulation phase, which is initiated by the sc_start method, which is a function of the simulator. Finally, the cleanup phase ends simulation, by cleaning objects and structures. The role of a SystemC simulator is to manipulate the timestamp to simulate the concurrency of hardware behavior. It determines in which order processes must be executed, and when values of ports and signals must be updated. The Accelera Systems Initiative (ASI) provides an event driven simulator with the language library. The simulator operates according to Figure 2.

During initialization, all processes are put in a state of being executed, which is done at the beginning of the simulation phase. It features three steps: Evaluation, in which the simulator checks which processes must be executed, according of their sensitivity list. The simulator then executes these processes. When this is done, the second step, Update, updates the values of ports/signals according to the previous processes executions. If signal or ports updates trigger a process sensitivity list again, then we go back to the evaluation step. When no process is triggered anymore, the simulation timestamp is updated in the Time Elapse step.

The ASI simulator, as it is implemented, features memory dynamicity, which avionic constraints don't allow. Furthermore, process scheduling at each timestamp is dynamic and nondeterministic [START_REF] Schumacher | SCandal: SystemC analysis for nondeterminism anomalies[END_REF]. This doesn't affect the result of the simulation, but can be an issue in an avionic context, considering execution time.

SystemCASS

SystemCASS (SystemC Accurate System Simulator) [START_REF] Buchmann | Fast cycle accurate simulator to simulate event-driven behavior[END_REF] is a SystemC simulator that establishes a static scheduling of processes, which is made at the start of simulation, based on the considered design (Fig. 3).

To do so, SystemCASS requires describing all component models as CFSM (Communicating Finite State Machine) using a CABA (Cycle Accurate Bit Accurate) abstraction level. Furthermore, a single clock must drive all modules. When calling the sc_start method, SystemCASS creates depending graphs that generate the static scheduling of processes, which will be used throughout the simulation phase. This implementation ensures a deterministic behavior of the simulation. Which is why SystemCASS is more suitable to avionic constraints than a dynamic event driven simulator. As we use gcc compiler, SystemCASS current implementation features dynamic memory allocation during the creation of the depending graph after the elaboration phase, and right before the simulation phase. So we have worked to remove these dynamic allocation. To do so, we have first used a static version of gcc compiler and second we have identified in run-time all the encountered dynamic memory allocations and replaced it with static memory allocations.

3 Case study

Case study presentation

To realize a proof-of-concept scenario, we use PolyORB Kernel (POK), a partitioned operating system compliant with ARINC653 avionic standard [START_REF] Delange | POK, an ARINC653-compliant operating system released under the BSD license[END_REF]. POK ensures enforcement of safety and security requirements at run-time. Along with the operating system, POK also provides some example of avionics applications. One of these applications is the Flight Management (see Figure 3) that handles speed, angle and GPS coordinate. Our testbed system will be composed of two parts, transmission and reception. This testbed is represented in Figure 4.

7/20/2015

The transmission part has two subsystems: the first one, POK OS Application generates application data (GPS, speed, angle) while the second one, End System ARINC664, encapsulates data to be compliant with the ARINC 664 standard.

On the other hand, the reception part, SystemC Module Frame Analyzer, verifies the received ARINC664 frames by performing data consistency based on physical variation laws and temporal consistency by simulating the behavior of the transmission part.

Transmission part

ARINC653 guarantees space partitioning (meaning that memory of partition is protected) and also guarantees time partitioning (meaning that only one partition at a time is executed). These properties are also ensured in POK OS. As can be seen in Figure 5, POK will manage three partitions (one for each application) and generates the applications data (speed, angle and GPS). The execution of each partition is handled by a static scheduler (as we can see in Figure 6) and is defined by the system integrator. Each partition (P1, P2 and P3) has a set of execution windows (T1, T2, T3) and this set of windows is repeated in time (T4, T5, T6 and so on…) and at the same order, which guarantees that each partition has access to the system resources once in a MAF (Major Frame). Once that data is generated by POK, they are put in the Queuing Port or Sampling Port and are then sent to the End System with the order defined by the scheduler. Queuing Port can be seen as a buffer and the Sampling Port as a FIFO. The End System then encapsulates the data in an ARINC664 frame with the specification of the Virtual Link (BAG, Frame Size, Jitters) that has been defined by the system integrator (see Figure 7). A Virtual Link defines an unidirectional logical connection from one source End-system to one or several destination End-System(s). Each partition has a dedicated Virtual Link (VLi is dedicated to the data of the Partition i).

Reception part

The reception part is a SystemC module that analyzes the ARINC664 frames coming from the transmitter part. It performs data and temporal consistency.

The data consistency consists in analyzing the payload of the ARINC664 frame that contains data of each application (GPS, Speed, angle). In order to do so, a verification of the physical variation law between two data values T and T+1 for each application is performed. For example, the verification of the value of the partition P1-SEQ1 and P1-SEQ2 is performed as shown in Figure 8. On the other hand, temporal consistency consists in verifying that the execution order of each partition is consistent with the scheduling defined by the transmitter part Figure 9 shows an example of the temporal consistency verification. In this article we have presented a new method to monitor in real time temporal consistency of data circulating ARINC664 frames. The goal of this monitoring is to checks whether communication occurs at the expected time, according to the system scheduling, and to validate data consistency. To realize this monitoring we use SystemC language and SystemCASS simulator to simulate a timed model of a part of the avionic system. To validate our system, we are on going to construct a demonstrator based on two Qoriq T2080 design board which has a PowerPC E6500 processor. One board will perform the transmitter part (CPM module) and the second board will perform the reception part (ASM module) as resume the Figure 10.

Each Qoriq T2080 Board (Transmission and Reception) will host the operating system POK and will integrate the SystemC engine. In transmission part (CPM) POK OS will handle the flight management application (GPS, Speed and Angle) and it the same time handle the End System ARINC 664 module that will be developed in SystemC. In Reception part (ASM) POK will handle the SystemC module that performs data and temporal consistency.

Figure 1 :

 1 Figure 1: Monitoring methodology

Figure 2 :

 2 Figure 2: SystemC flow

  SystemCASS modules can include three types of processes: Transition: triggered by the clock rising edge, it sets the new values of registers, depending on their actual values as well as input port values. Moore Generation: triggered by the clock falling edge, it sets the new values of output ports, depending on register values only. Mealy Generation: triggered by the clock falling edge, it determines the new values of output ports depending on register values and input port values.

Figure 3 :

 3 Figure 3: POK Flight Management Application

Figure 4 :

 4 Figure 4: Testbed Structure

Figure 5 :

 5 Figure 5: Data Generation and Space Partitionning

Figure 6 :

 6 Figure 6: Partitionning Scheduling

Figure 7 :

 7 Figure 7: ARINC 664 Frame at the Output of the End System

  Figure 8: Data Consistency

Figure 9 :

 9 Figure 9: Temporal Consistency Verification

Figure 10 :

 10 Figure 10: T2080 demonstrator

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