Guillaume Razongles

Lionel Sicot

Maryline Joanny

Eric Gerritsen

Paul Lefillastre

Silke Schroder

Philippe Lay

Bifacial photovoltaic modules: measurement challenges

Keywords: Photovoltaic performance, Bifacial modules, Measurement

. But the lack of standardization [2] and feedback on large systems seems to limit the proliferation of bifacial modules. There are no Standard Test Conditions defined for their measurement, and no available commercial simulator that can predict their energy production. As a result, investors are still reluctant to choose bifacial technologies as an alternative to the standard monofacial ones. In this paper, we analyse three different approaches for bifacial module performance measurements. The first approach consists in measuring both sides independently with a standard solar simulator and build an equation to extrapolate the contribution of back side illumination to the front-side power value. The second approach consists in illuminating both sides simultaneously with a specific double illumination characterization setup [3] that verifies the results of the first approach. Finally, the third approach compares outdoor and indoor behaviour of the modules. The results presented in this paper show how the different approaches are complementary to help building up Standard Test Conditions and outdoor simulation tools for bifacial modules.

The challenge

The main challenge of photovoltaic performance characterization is to accurately predict the energy yield of a future system. The challenge has been tackled for several years, with characterization and modelling of:

The solar resource The incident light on the module plane The module performance For monofacial modules, the international community already agreed on the performance Standard Test Conditions. For bifacial ones, the process of standardization officially started in 2015 for SEMI and in 2016 for IEC (new project approved in February 2016). Bifaciality brings new challenges because performance depends on the module surroundings (albedo, shadowing etc.), as well as on the irradiation type (direct/diffuse). As a result, the challenge is strongly linked with both i) the front and back side contributions to the power values of the module which need to be standardized, and ii) the expected outdoor back side irradiance, which is very sensitive to the geometry of the system, that can be simulated [START_REF] Reise | Realistic yield expectations for bifacial PV systems -an assessment of announced, predicted and observed benefits[END_REF].

The different approaches

First approach: independent measurement of both sides and extrapolations

To measure independently both sides, we first need to minimize the contribution of the photons coming from the side opposite to the light source. But bifacial modules are always semitransparent to the incident photons (between the cells, and through the cells), so back reflections are inevitable (Fig. 1).

The state of the art enlightens two methods:

SUPSI experiment [START_REF] Corbellini | Modelling of bifacial PV modules. 4th PV Performance modeling collaborative workshop[END_REF] (Fig. 1. a):

Characterization of the back irradiation over the back surface of the module during flash tests Extraction of the minimum reflected irradiation value, because it's the one which limits the total power (0.44% of front irradiance in that case) Correction of measured front and back power using this minimum irradiation value and the power bifacial ratio (power back/power front)

Common solution (Fig. 1. b & c), which consists in:

Fixing a dark screen on the back side of the module Considering back reflections as negligible. The characterisation of a very low irradiation in the first solution has a large uncertainty, depending on the exact shunt quality of the reference cell, and which is varying a lot between each cells.

The last solution is commonly used, easier and faster to implement. The deviation is certainly the lowest one, even if it is difficult to characterize. It is the solution CEA applies for this first measurement approach.

The first approach consists in measuring one face at a time, and to build equations to extrapolate the contribution of a back side illumination to the electrical values of bifacial modules. This approach implies two hypotheses: Isc (front or back) is proportional to the irradiation Pmax of the module can be expressed as a unique function of Isc, equation (1), wherever the photogeneration takes place at the front or at the back side.

The first hypothesis is a commonly agreed approximation, whereas the second needs to be assessed. If equation ( 1) is commonly used for monofacial modules, we need to verify if it is correct for bifacial modules.

) ( max Isc g P ( 1 )
The use of the module efficiency and the normalization of this equation is common and enables the comparison of module behavior, equation (2).

) (

STC STC Isc Isc f ( 2 )
Fig. 2 shows the normalized efficiencies measured at INES on different modules, of very different physical characteristics. These efficiencies are not linear functions of the irradiation as it could be approximated for Isc. Most of PV laboratories already built their own equation to describe this function, in order to simulate performance (inter-comparisons have already been benchmarked in the projects PERFORMANCE Europe and SOPHIA). One challenge is also to reduce the number of flashes and characterization steps to extrapolate a relevant model.

CEA developed and applies the 'MotherPV' model [START_REF] De Montgareuil | A new tool for the MotherPV method: modeling of the irradiance coefficient of photovoltaic modules[END_REF], as described in equation ( 3), ( 4) and [START_REF] De Montgareuil | A new tool for the MotherPV method: modeling of the irradiance coefficient of photovoltaic modules[END_REF].

STC Isc Isc S ( 3
) ) ²( ln )² 1 ( ) ln( ) 1 ( 1 S d S c S b S a f ( 4 ) ) ( max ) max( S f S P S P STC ( 5 )
To verify that the power of the module depends on Isc, whatever the proportion of current generated at the front and at the back of the module, we need to compare the normalized efficiency of the front and the back of bifacial modules.

Based on indoor flash measurements on 4 bifacial modules (manufactured at CEA-INES on one hand and commercial product on the other hand), we applied the MotherPV model (Fig. 3). The meaning of Fig. 3 is that the general behavior of the modules is close to what is modeled whichever side is tested. Contributions of the series resistance and shunt resistance to front and back performance looks similar from both sides.

For the absolute values (Pmax and Isc front are different from Pmax and Isc back), the precise difference of modeled Pmax front and modeled Pmax back is calculated for every fixed Isc (Fig. 4).

The results are shown in Fig. 4. Relative difference ranges from -1% to +2%, and weighted average values ranges from -0.7% to +1%. These values are not negligible, but moderated by the fact that rear side contribution to the general behavior is low. Considering that 200 W/m² on the back only generate around 1/6 of the total current, the overall behavior should be very close to the front one, especially for these 4 modules that have deviations around 1%. However, until we have more data and understanding, this deviation represents a 1% added uncertainty on our extrapolations.

The deviation of modeled Pmax front and back are weighted by the yearly irradiances that occur at the INES PV platform in Cadarache, a South of France city with a Mediterranean climate. For these 4 modules, if we make the approximation that Pmax of the module is not modified by the different front and back contribution to total Isc, the interpretation of such a hypothesis can be described in equation ( 6 With the translation equation (7) and the previous equations ( 4), ( 5) and [START_REF] Joanny | Bifacial PV modules: measurement challenges[END_REF], we are able to extrapolate Pmax(S) for all x and y values, which is necessary for simulating the annual energy yield with a software. 1000 Gfront S ( 7 )

In conclusion, providing data to energy yield simulators is possible from the set of flashes described in table 1(a). More flashes can be performed in order to have a more accurate model. However, the aim of Standard Test Conditions is not to describe Pmax for all x and y values, but to provide a single reference value per module that can be easily compared with other modules. Inter alia, this reference value is used as one of the inputs of the energy yield simulators, and as an input to size the system wires and the inverters current and voltage limits. The simplicity and the speed are also of importance in the choice of the method. For example, the number of flashes and the ability to perform the test with a standard flash test has to be taken into account.

The back-side of bifacial modules can provide in certain conditions up to 35% of added power, so it is relevant to build a standard close to the STC, with 1000 W/m² on one side, and up to 350 W/m² on the other side. Furthermore, in this limited range of back irradiation, the normalized efficiency looks easier to model (see the efficiency close to 1000 W/m² in Fig. 2. b). So we could do a lower number of flashes, and use linear, polynomial, or exponential models.

Second approach: simultaneous measurement of both sides

Simultaneous measurement of both sides of a bifacial module can be performed with a double-sun simulator on large size modules or with a tilted mirrors setup. Eternal Sun proposes to perform measurements with two identical sun simulators on large size modules (Fig. 5.a), while our laboratory at CEA-INES applies the tilted mirrors approach on 4 cell mini-modules (Fig. 5.b). Here we used a specific double illumination setup for bifacial 4 cells mini-modules [START_REF] Soria | A study of the annual performance of bifacial photovoltaic modules in the case of vertical façade integration[END_REF]. The bifacial module is placed between two aluminium mirrors, and metallic grid filters with several pitches are used to attenuate the light on the backside of the module in order to mimic different albedo conditions.

We used this setup to assess if the approximations of the first approach are reliable. 8 flashes have been done on 6 bifacial modules, described in Table 2.b.

If we use a linear model starting at the value of the flash 5, based on Pmax bifaciality, then we obtain the deviations described in Fig. 6.a.

As we supposed in the first approach, a linear approximation can provide a good accuracy close to 1000 W/m² front and 0 to 200 W/m² back, but it is unable to be precise beyond 200 W/m² back.

The 3 first flashes of Table 3 are not enough to draw a complete MotherPV model (because it's a 4 parameter model). So we can use other simple models, for example, we model the Voc with a power function equation and the FF with a linear equation, and we managed to get a new set of extrapolated values that are closer to the measured values: Fig. 6.b shows that the deviations are lower, below 2% on all the back irradiance values.

Other models like polynomial should lead to much lower deviations and should be less dependent on the module characteristics. Logarithm models like MotherPV are more accurate but they need a larger set of flashes.

As a conclusion, performance trends models close to 1000 W/m² should be sufficient to assess bifacial performance standardization. Next step would be to assess this point with bifacial measurement of 60 cells modules on tilted mirrors setup or 2 solar simulators setup, for different technologies of bifacial modules.

Third approach : Outdoor measurement

CEA-INES is evaluating outdoor performance of PV modules since 1983, and outdoor bifacial measurements are running since 2014 [START_REF] Soria | A study of the annual performance of bifacial photovoltaic modules in the case of vertical façade integration[END_REF]. A new setup has been built in 2015, described in Fig. 7, with the study of: Two 60 cells bifacial modules with a black mask on the back side, for reference use Two 60 cells bifacial modules with a very reflective membrane fixed to the ground to maximize the back side gain (>25% according to Fig. 8).

The gain of the bifacial module compared to the monofacial is larger during cloudy days (see Fig. 8). The rear side contribution to the total power increases with an increasing part of diffuse irradiation in the total irradiation.

For the front irradiations, energy prediction software already include numerous equations and models that convert meteo data into irradiation data on the tilted front plan of the module.

The back irradiation is much more dependent on the geometrical configuration of the system and the position of the Sun rays. So ray tracing simulations and new algorithms will need to be accomplished. Now we need to compare the performance indoor and outdoor, we normalize with the same Isc STC value, then we see a difference between the indoor and outdoor characterization of the same module (Fig. 9). This Fig. 9 shows that indoor and outdoor behaviors cannot be superposed. This displaced curve is due to dispersion of photo-generation through the module and the presence of by-pass diodes. The back side irradiation is much more inhomogeneous than the front one. This results in a step distortions in the IV-curve, like in Fig. 10. Linking Isc to Pmax could be done with the estimation of the minimum string current. But this approach needs a complete ray tracing simulation tool.

For a power plant, this could be done with a geometrical determination of what is the limiting string and to simulate this string as the one which generates the additional back-irradiation contribution.

Conclusions

In this paper, we have shown that, for the bifacial modules studied: For a fix Isc value, bifacial modules behave similar when current is generated on front or rear-side From a 6 flashes set, Pmax could be extrapolated to any front and back irradiation, and a standard can be built from less flashes, depending on the accuracy that can reach models. Finally, the results presented in this paper show how the different approaches are complementary to help building up Standard Test Conditions and outdoor simulation tools for bifacial modules. A fast, easy and reliable standard will need deep study of the module type, comprising back and front sides analysis, and simultaneous bifacial irradiation measurement. Indoor characterization cannot be used for outdoor prediction without a realistic ray tracing simulation to evaluate the minimum back irradiance.

Fig. 1 .

 1 Fig. 1. (a) A standard flash room brings parasitic reflections, which can be characterized [4]. (b) Adding a black screen on the back of the module brings the reflections to a minimum. (c) Zoom on the (b) fixed screen setup: there is still a little deviation, which is more difficult to quantify

Fig. 2 .

 2 Fig. 2. (a) Normalized efficiencies of different modules measured at CEA-INES and corrected to 25°C. (b) Schematic curve of normalized efficiencies for different modules

Fig. 3 .

 3 Fig. 3. Normalized efficiencies of 4 bifacial modules (results of indoor flash measurements and equivalent MotherPV models)

Fig. 4 .

 4 Fig. 4. Modeled Pmax deviations between front and back for every fixed Isc and their weighted average values

Table 1 .

 1 (a) Minimum set of flashes to extrapolate all possible front and back contributions to the module power. (b) Mirror setup set of flashes a

Fig. 5 .

 5 Fig. 5. (a) Eternal Sun double-sun-simulator setup (b) CEA-INES setup for 4 cells modules, with 2 tilted mirrors, mesh filters and a standard flash test [3].

Fig. 6 .

 6 Fig. 6. (a) Deviation between a linear approximation of the power and the measurement (Gfront=1000 W/m²) (b) Deviation between a non-linear model of the power and the measurement (Gfront=1000 W/m²)

Fig. 7 .

 7 Fig. 7. Description of the CEA-INES outdoor bifacial setup

Fig. 9 .

 9 Fig. 9. Normalized efficiency of the same module indoor and outdoor

Fig. 10 .

 10 Fig. 10. Bifacial module experiment at INES. Partial shadowing on the back face of the module generates disruptive steps into the IV curve that disable the link between Isc and Pmax of the module.

Acknowledgements

This work was performed within the frame of the BIPPP project, supported by the French program "Investing for the Future". Stefan Roest and Pepijn Veling of ETERNAL SUN are acknowledged for technical discussions and data sharing.


Valerio Basile

Elena Cabrio

Claudia Schon

KNEWS: Using Logical and Lexical Semantics to Extract Knowledge from Natural Language

We present KNEWS, a pipeline of NLP tools that accepts natural language text as input and outputs knowledge in a machinereadable format. The tool outputs frame-based knowledge as RDF triples or XML, including the word-level alignment with the surface form, as well as first-order logical formulae. KNEWS is freely available for download. Moreover, thanks to its versatility, KNEWS has already been employed for a number of different applications for information extraction and automatic reasoning.

Introduction

Machine Reading [START_REF] Etzioni | Machine reading at web scale[END_REF] is the task of extracting formally encoded knowledge from natural language text. It subsumes Natural Language Understanding: after resolving the ambiguities in the text, the information extracted is grounded by linking it to a knowledge base. A complete machine reading tool is a step towards the construction of large repositories of general knowledge without having to rely on human-built resources. Such system can automatically learn, for instance, that the Knife is used as Instrument to Cut the Bread, where Knife, Instrument, Cut and Bread are all entries in some knowledge base on the Web. Moreover, a machine reading component can play an important role in other environments: in many automated reasoning applications, it is necessary to use large ontologies as background knowledge. Machine reading helps to disambiguate predicate names. This supports selection methods like SInE [START_REF] Hoder | Sine qua non for large theory reasoning[END_REF] to select very focused background knowledge.

Several tools have been published to solve parts of this complex task, notably the extraction of entities [START_REF] Rao | Entity Linking: Finding Extracted Entities in a Knowledge Base[END_REF][START_REF] Ratinov | Design challenges and misconceptions in named entity recognition[END_REF], relations [START_REF] Mintz | Distant supervision for relation extraction without labeled data[END_REF], semantic roles [START_REF] Màrquez | Semantic role labeling: An introduction to the special issue[END_REF], and more. FRED [START_REF] Presutti | Knowledge extraction based on discourse representation theory and linguistic frames[END_REF] is a notable example, proposing a complete machine reading pipeline that extracts RDF triples comprising every aspect of the semantics of the input text.

In this demo we present KNEWS, a complete and versatile text-to-knowledge pipeline for machine reading, that tries to overcome some of the limits of existing systems. In particular, in comparison with FRED, from which we drew the initial inspiration, KNEWS is configurable to use different external modules, provides different kind of meaning representations as output, and, last but not least, its source code is freely available and not bound to online APIs. KNEWS is available at https://github.com/ valeriobasile/learningbyreading, a demo is at http: //gingerbeard.alwaysdata.net/knews/.

NLP Pipeline for Knowledge Extraction

KNEWS is a pipeline system. The main components are a semantic parser and two modules for word sense disambiguation and entity linking. KNEWS works by running these components separately on a text, then it aligns the output of the the semantic parser to the output of the other two modules (Figure 1). Semantic parsing The semantic parsing module must process the input text and output a complete formal representation of its meaning. To this aim, KNEWS employs the C&C tools and Boxer. The C&C tools [START_REF] Curran | Linguistically motivated large-scale nlp with c&c and boxer[END_REF] are a pipeline of statistical NLP tools including a tokenizer, a lemmatizer, named entity and part-of-speech tagger, and a parser that creates a Combinatorial Caregorial Grammar representation of the natural language syntax. Boxer [START_REF] Bos | Wide-coverage semantic analysis with boxer[END_REF] is a rule-based system that builds an abstract meaning representation on top of the CCG analysis. Such structures contain, among other information, predicates representing the roles of the entities with respect to the detected events, e.g., event(A), entity(B), agent(A,B) to represent B playing the role of the agent of the event A.

Word sense disambiguation and Entity Linking In order for the semantic representations to be useful in contexts such as knowledge representation and automatic reasoning, the predicates need to be linked to a knowledge base. KNEWS uses WordNet [START_REF] Miller | Wordnet: A lexical database for english[END_REF] to represent concepts and events, DBpedia4 for named entities, and FrameNet's frames [START_REF] Baker | The berkeley framenet project[END_REF] to represent events, integrating the mapping with the WordNet synsets provided by FrameBase [START_REF] Rouces | Framebase: Representing n-ary relations using semantic frames[END_REF]. The inventory of thematic roles used by Boxer is taken from VerbNet [START_REF] Schuler | Verbnet: A Broad-coverage, Comprehensive Verb Lexicon[END_REF], while KNEWS employs the mapping provided by SemLinks [START_REF] Palmer | SemLink: Linking PropBank, VerbNet and FrameNet[END_REF] to link them (whenever possible) to FrameNet roles. By linking the discourse referents representing concepts in a DRS to WordNet synsets, entities to DBpedia and events to FrameNet frames, KNEWS is able to extract complete meaning representations from natural language text linked to Linked Open Data knowledge bases.

Frame-based Semantics

The first output mode of KNEWS is frame instances, sets of RDF triples that contain a unique identifier, the type of the frame, the thematic roles involved in the instance, and the concepts or entities that fill the roles. The format follows the scheme of FrameBase [START_REF] Rouces | Framebase: Representing n-ary relations using semantic frames[END_REF], which offers the advantage of interoperability with other resources in the Linked Open Data cloud. An example of frame instance, extracted from the sentence "A robot is driving the car." is given by the following triples5 : fb:fi-dc59afa6 a fb:frame-Operate_vehicle-drive.v . fb:fi-dc59afa6 fb:fe-Driver dbr:Robot . fb:fi-dc59afa6 fb:fe-Vehicle wn:02961779-n .

This output mode of KNEWS has been employed in [START_REF] Valerio Basile | Building a general knowledge base of physical objects for robots[END_REF] to create a repository of general knowledge about objects.

Word-aligned Semantics

The second output mode of KNEWS is similar to the previous one (frame instances) with the difference that it contains as additional information the alignment with the text. We exploit the Discourse Representation Graph [START_REF] Basile | From logic to language: Natural language generation from logical forms[END_REF] output of Boxer to link the discourse referents to surface forms, i.e., span of the original input text, resulting in a word-aligned representation as: Such surface forms can be complete (e.g., "A robot") or incomplete (e.g., "x1 is driving x2") and can be composed to recreate the original, as well as lexicalizations for new knowledge [START_REF] Basile | A repository of frame instance lexicalizations for generation[END_REF].

<frameinstances>
First-order Logic In the third output mode, KNEWS is able to generate first-order logic formulae representing the natural language text given as input. For example the input "She won a spelling bee." leads to the following first-order logic formula: Providing the predicates as Wordnet synsets has the advantage that this information can be exploited to select background knowledge in a much more focused manner, as proposed in [START_REF] Furbach | Commonsense reasoning meets theorem proving[END_REF]. In the example above, the use of KNEWS ensures that "spelling bee" is distinguished from the insect bee. This can be used to avoid selecting background knowledge on insects. The syntax of the formula returned by

Figure 1 .

 1 Figure 1. Architectural Scheme of KNEWS.

  ∃A, B, C(r1Theme(A, C)∧ r1Agent(A, B) ∧ 201102550-v(A)∧ 200940051-v(C) ∧ r1of (C, B) ∧ 107907011-n(B) ∧ n2female(B))corresponding to a disambiguated version of the following one:∃A, B, C(r1Theme(A, C) ∧ r1Agent(A, B) ∧ win(A)∧ bee(C) ∧ r1of (C, B) ∧ n1spelling(B) ∧ n2female(B))

  Universität Koblenz-Landau, Germany {schon@uni-koblenz.de}

	1	Université	Côte	d'Azur,	Inria,	CNRS,	I3S,	France,
		{valerio.basile@inria.fr}
	2	Université	Côte	d'Azur,	Inria,	CNRS,	I3S,	France,
		{elena.cabrio@unice.fr}
	3

https/dbpedia.org

The instance id and namespaces are abbreviated for readability.KNEWS is very similar to the well-known TPTP format[START_REF] Sutcliffe | The TPTP Problem Library and Associated Infrastructure: The FOF and CNF Parts, v3.5.0[END_REF] -in fact we plan to provide TPTP syntax directly as output of KNEWS.


Valerio Basile
email: valerio.basile@inria.fr

A Repository of Frame Instance Lexicalizations for Generation

Robust, statistical Natural Language Generation from Web knowledge bases is hindered by the lack of text-aligned resources. We aim to fill this gap by presenting a method for extracting knowledge from natural language text, and encode it in a format based on frame semantics and ready to be distributed in the Linked Open Data space. We run an implementation of such methodology on a collection of short documents and build a repository of frame instances equipped with fine-grained lexicalizations. Finally, we conduct a pilot stody to investigate the feasibility of an approach to NLG based on said resource. We perform error analysis to assess the quality of the resource and manually evaluate the output of the NLG prototype.

Introduction

Statistical Natural Language Generation, generally speaking, is based on learning a mapping between natural language expressions (words, phrases, sentences) and abstract representations of their meaning or syntactic structure. In fact, such representations vary greatly in their degree of abstraction, from shallow syntactic trees to fullfledged logical formulas, depending on factors like downstream applications and the role of the generation module in a larger framework.

In order to be useful for statistical generation, the abstract representation needs to be aligned with the surface form. Depending on the format, the level of abstraction and the target degree of granularity of the alignment, it may be more or less straightforward to produce a collection of pairs <abstract representation, surface form>. Moreover, statistical methods typically need a large number of examples to properly learn a mapping and generalize efficiently.

While several resources have been successfully employed as training material for statistical NLG (see the related work section), they lack a direct link with world knowledge. Linked Open Data resources, in particular general knowledge bases such as DBpedia1 , on the other hand, are not straightfoward to use as a basis for generation, while at the same time they are rich in extralinguistic information such as type hierarchy and semantic relations. Having the entities and concepts of an abstract meaning representation linked to a knowledge base allows a generator to use all the information coming from links to other resources in the LOD cloud. Such kind of input to a NLG pipeline is therefore richer than word-based structures, although its increased level of abstraction makes the generation process more complex.

Shifting the level of abstraction, the representation format must be changed accordingly. In the case of many formats proposed in the literature (e.g., the format of the Surface Realization shared task), the input for NLG is made of structures closely resembling sentences. The notion of sentence, however, might not be adequate anymore when the abstract representation of meaning aims to be fit for the standards of the Web. A good compromise is a representation based on frame semantics [START_REF] Fillmore | Frame semantics[END_REF]. A frame is a unit of meaning denoting a situation of a particular type, e.g., Operate vehicle. Attached to the frame there are a number of frame elements, indicating roles that the entities involved in the frame can play, e.g., Driver or Vehicle. [START_REF] Rouces | Framebase: Representing n-ary relations using semantic frames[END_REF] proposes a LOD version of frame semantics implemented in the resource called FrameBase, essentially a scheme for representing instances of frames and frame ele-ments in a Web-based format. The FrameBase project also produced a repository of instances created by automatically translating existing Web resources. Moreover, they made available a large set of (de)reification rules, that is, bidirectional rules to convert between binary relations and framebased representations. For instance, the binary relation drivesVehicle can be transformed by a reification rule into a Operate vehicle frame with the two members of the original relation filling in the roles of Driver and Vehicle. The reification mechanism provides an interesting use case for NLG: if a system is able to generate natural langauge from a frame instance, then it is also able to generate from the corresponding binary relation.

In this paper, we present an ongoing work towards the construction of a domain-agnostic, LOD-compliant knowledge base of semantic frame instances. Frames, roles and entities are aligned to natural language words and phrases that express them, extracted from a large corpus of text. Thanks to this alignment, the resource can be used to create lexicalizations for new, unseen configurations of entities and frames.

Related Work

Several resources exists have been used to train a statistical generator to learn lexicalizations for various types of representations The Surface Realization Shared Task [START_REF] Belz | The first surface realisation shared task: Overview and evaluation results[END_REF], for instance, provides a double dataset of shallow and deep input representations obtained by preprocessing the CoNNL 2008 Shared Task data [START_REF] Surdeanu | The conll-2008 shared task on joint parsing of syntactic and semantic dependencies[END_REF]. Resources used for NLG include including the Penn Treebank [START_REF] Marcus | Building a large annotated corpus of english: The penn treebank[END_REF] for Probabilistic Lexical Functional Grammar [START_REF] Cahill | Robust pcfg-based generation using automatically acquired lfg approximations[END_REF] and CCGBank [START_REF] Hockenmaier | Ccgbank: A corpus of ccg derivations and dependency structures extracted from the penn treebank[END_REF] for Combinatory Categorial Grammar syntax trees [START_REF] White | Towards broad coverage surface realization with ccg[END_REF]. More recently, the Groningen Meaning Bank [START_REF] Valerio Basile | Developing a large semantically annotated corpus[END_REF] has been proposed as a resource for NLG from abstract meaning representations, leveraging the fine-grained alignment between logical forms and their respective surface forms given by the Discourse Representation Graph formalism [START_REF] Basile | Aligning Formal Meaning Representations with Surface Strings for Wide-coverage Text Generation[END_REF].

The process of generating natural language from databases of structured information, including ones following Web standards, has been studied in the past, although often in specific application-oriented contexts. [START_REF] Bouayad-Agha | From ontology to nl: Generation of multilingual user-oriented environmental reports[END_REF] propose an architecture as a basis for generation made of three RDF/OWL ontologies, separation the domain knowledge from the communication knowledge. [START_REF] Gyawali | Surface realisation from knowledge-bases[END_REF] propose a statistical approach to NLG fro mknowledge bases based on tree adjoining grammars. WordNet is relatively less used for generation purposes. Examples of the use of Wordnet in the context of NLG include the methods to address specific NLG-related tasks proposed by [START_REF] Jing | Usage of wordnet in natural language generation[END_REF] and the algorithm for lexical choice of [START_REF] Basile | A lesk-inspired unsupervised algorithm for lexical choice from wordnet synsets[END_REF].

3 Aligning Text and Semantics [START_REF] Basile | Aligning Formal Meaning Representations with Surface Strings for Wide-coverage Text Generation[END_REF] devise a strategy to align arbitrary natural languages expressions to formal representation of their meaning, encoded as Discourse Representation Structures (DRS, [START_REF] Kamp | From Discourse to Logic. Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory[END_REF]). DRSs are logical formulas comprising predicates and relations over discourse referents. For the English language, we are able to obtain DRSs for a given text using the C&C tools collection of linguistic analysis tools [START_REF] Curran | Linguistically motivated large-scale nlp with c&c and boxer[END_REF], which includes Boxer [START_REF] Bos | Wide-coverage semantic analysis with boxer[END_REF], a rule-based system that builds DRSs on top of the CCG parse tree produced by the C&C parser. Boxer implements Neo-davidsonian representations of meaning, that is, formulas centered around events to which participant entities are connected by filling thematic roles. Figure 1 shows an example of DRS for the sentence "A robot is driving the car" as produced by Boxer. In this example the Neo-davidsonian semantics is evident: the ROBOT is the AGENT of the event DRIVE, while the CAR is the THEME. The alignment method proposed by [START_REF] Basile | Aligning Formal Meaning Representations with Surface Strings for Wide-coverage Text Generation[END_REF] is based on a translation of format from DRS into a Discourse Representation Graph (DRG), where the semantic information is preserved but expressed in a flat, non recursive formalism. The surface form is then aligned at the word level to the appropriate tuples. Figure 2 shows the DRG corresponding to the DRS in Figure 1, where the alignment with the surface form is contained in the two rightmost columns. For the details of how the alignment is encoded we refer the reader to the aforementioned paper [START_REF] Basile | Aligning Formal Meaning Representations with Surface Strings for Wide-coverage Text Generation[END_REF].

e1 x1 x2 ROBOT(x1) DRIVE(e1) CAR(x2) AGENT(e1, x1) THEME(e1, x2)
k1 referent x1 1 [A] k1 referent e1 k1 referent x2 1 [the] k1 event DRIVE k1 concept ROBOT k1 role AGENT k1 concept CUSTOMER k1 role THEME ROBOT instance x1 2 [robot] DRIVE instance e1 2 [is, driving] AGENT internal e1 1 AGENT external x1 CAR instance x2 2 [car] THEME internal e1 3 THEME external x2
Figure 2: DRG aligned with the surface form, representing the meaning of the sentence "A robot is driving the car".

In order for the semantic representations, and their alignment to the surface, to be useful in contexts such as knowledge representation and automatic reasoning, these logical forms need to be linked to some kind of knowledge base. Otherwise, the predicate symbols in a DRG like the one depicted in Figure 2 are just interchangeable symbols (although Boxer uses lemmas for predicate names) devoid of meaning.

Popular resources in the LOD ecosystem are well-suited for serving as knowledge bases for grounding the symbols: WordNet [START_REF] Miller | Wordnet: A lexical database for english[END_REF] can be used to represent concepts and events, while DBPedia has a very large coverage for named entities. FrameNet [START_REF] Baker | The berkeley framenet project[END_REF], an inventory of frames and frame elements inspired by Fillmore's frame semantics [START_REF] Fillmore | Frame semantics[END_REF], has a structure that superimposes easily to the neo-Davidsonian semantics of Boxer's DRGs. The inventory of thematic roles used by Boxer is taken from VerbNet [START_REF] Schuler | Verbnet: A Broadcoverage, Comprehensive Verb Lexicon[END_REF]. By linking the discourse referents representing concepts in a DRG to WordNet synsets, entities to DBpedia and events to FrameNet frames we are able to extract complete representations of frames from natural language text linked to LOD knowledge bases.

Collecting Frame Lexicalizations

We developed a pipeline of NLP tools to automatically extract instances of frames from the text. The pipeline comprises the C&C tools and Boxer, a module for word sense disambiguation and a module for entity linking. The two latter modules can be configured to use different external software to perform their task.

The analysis of a text consists in the following steps:

1. Run the C&C tools and Boxer, saving both its XML and DRG output. The XML output of Boxer contains, for each predicates of the DRS that has been constructed, a link to the part of the surface form that introduced it.

2. Run the WSD and entity linking components, preserving the same tokenization. The software then uses the links to the text provided by Boxer to map the word senses and DBpedia entities to the DRS predicates.

3. The word senses corresponding to events are mapped to FrameNet frames, using the mapping provided by [START_REF] Rouces | Framebase: Representing n-ary relations using semantic frames[END_REF]. The VerbNet roles are converted into FrameNet roles using the mapping provided by [START_REF] Loper | Combining lexical resources: Mapping between propbank and verbnet[END_REF].

4. The partial surface forms in the DRG output of Boxer are attached to the frames, semantic roles and frame elements. This pipeline is implemented in the KNEWS system, available for download at https://github.com/valeriobasile/ learningbyreading.

In the following paragraphs we describe the internal details of the components of KNEWS.

Semantic parsing

The semantic parsing module employs the C&C tools and Boxer to process the input text and output a complete formal representation of its meaning. The C&C pipeline of statistical NLP tools includes a tokenizer, a lemmatizer, named entity and part-of-speech tagger, and a parser that creates a Combinatorial Caregorial Grammar representation of the natural language syntax. Boxer builds a DRS on top of the CCG analysis. The predicates of a DRS are expressed over a set of discourse referents representing entities, concepts and events. Such structures contain, among other information, predicates representing the roles of the entities with respect to the detected events, e.g., event(A), entity(B), agent(A,B) to represent B playing the role of the agent of the event A.

Word sense disambiguation and Entity Linking KNEWS uses WordNet to represent concepts and events, DBpedia for named entities, and FrameNet's frames to represent events, integrating the mapping with the WordNet synsets provided by FrameBase. The inventory of thematic roles used by Boxer is taken from VerbNet [START_REF] Schuler | Verbnet: A Broadcoverage, Comprehensive Verb Lexicon[END_REF], while KNEWS employs the mapping provided by SemLinks [START_REF] Palmer | SemLink: Linking PropBank, VerbNet and FrameNet[END_REF] to link them (whenever possible) to FrameNet roles. KNEWS can be configured to use either UKB [START_REF] Agirre | Personalizing pagerank for word sense disambiguation[END_REF] or Babelfy [START_REF] Moro | Entity Linking meets Word Sense Disambiguation: a Unified Approach[END_REF] to perform the word sense disambiguation, and DBpedia Spotlight [START_REF] Daiber | Improving efficiency and accuracy in multilingual entity extraction[END_REF] or Babelfy for entity linking.

Output modes KNEWS's default output consists of frame instances, sets of RDF triples that contain a unique identifier, the type of the frame, the thematic roles involved in the instance, and the concepts or entities that fill the roles. The format follows the scheme of FrameBase, which offers the advantage of interoperability with other resources in the Linked Open Data cloud, as well as the possibility of using FrameBase's (de)reification rules to automatically generate a large number of binary predicates. An example of frame instance, extracted from the sentence "A robot is driving the car." is given in Figure 4. This output mode of KNEWS has been employed in [START_REF] Valerio Basile | Building a general knowledge base of physical objects for robots[END_REF] to create a repository of general knowledge about objects.

For the purpose of NLG, we extended KNEWS with a new output mode, similar to the previous one (frame instances) with the difference that it contains as additional information the alignment with the text. We exploit the DRG output of Boxer to link the discourse referents to surface forms, i.e., span of the original input text, resulting in the word-aligned representation shown in Figure 5. This new output mode of KNEWS consist of an XML list of frameinstance elements. Each frame instance is equipped with its complete lexicalization (the instancelexicalization tag), the incomplete surface form associated with the event (the framelexicalization tag) and a sequence of frameelements. A frameelement represent a role in the frame instance. The concept tag contains a DBpedia or Wordnet resource (depending on the output of the disambiguation module), a lexicalization of the role filler (the conceptlexicalization tag), and the incomplete surface form obtained by composing the surface forms of the role filler and the frame. In the next section we describe an automatically built resource created by parsing text with this configuration of KNEWS.

KNEWS has also an additional output mode: First-order Logic.

With this output mode, KNEWS is able to generate first-order logic formulae representing the natural language text given as input. The symbols for the predicates are Wordnet symbols, allowing the output of KNEWS to be integrated with a reasoning engine, e.g., to select background knowledge in a much more focused manner, as proposed in [START_REF] Furbach | Commonsense reasoning meets theorem proving[END_REF].

Evaluation

In order to test our approach to knowledge extraction, we parsed a corpus of short texts, taken from the ESL Yes website of material for English learners. 2 We find this data particularly apt in the more general context of extracting general knowledge from text, being made of short, clear sentences about simple and generic topics. The corpus comprises 725 short stories, that we divided into 14,140 sentences. Parsing the ESL Yes corpus with KNEWS we collected 30,217 frame instances (420 unique frames), 1,455 concepts (1,201 Word-Net synsets and 254 DBpedia entities) filling in 41,945 roles (161 unique roles). 29,409 role instances could not be mapped to FrameNet, so they are expressed by one of 18 VerbNet roles.

We evaluate the information extraction methodology by assessing the quality of this automatically produced resource. For each frame instance, @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix fb: <http://framebase.org/ns/> . @prefix dbr: <http://dbpedia.org/resource/> . @prefix wn: <http://wordnet-rdf.princeton.edu/wn31/> . fb:fi-Operate_vehicle_dc59afa6 rdf:type fb:frame-Operate_vehicle-drive.v . fb:fi-Operate_vehicle_dc59afa6 fb:fe-Driver dbr:Robot . fb:fi-Operate_vehicle_dc59afa6 fb:fe-Vehicle wn:02961779-n . if all the information is present and complete, it should be possible to recreate the instance lexicalization by applying the composition method of [START_REF] Basile | Aligning Formal Meaning Representations with Surface Strings for Wide-coverage Text Generation[END_REF]. The incomplete surface forms corresponding to the frame and the frame elements are automatically composed and compared to the original frame lexicalization. We ran this evaluation procedure on the resource and found 7,366 instances are correctly regenerated, that is, about one in four instances. Of the remaining instances, 11,996 present incorrect instance lexicalizations, usually containing variables instead of being complete surface forms. These occurrences are caused by misalignments in the representation produced by Boxer, so that the composition algorithm cannot recreate the original surface form. For instance, for the sentence "The mother gave her baby a red apple", the lexicalized DRG produced by Boxer, when the composition algorithm is applied to it, produces "The mother gave k5:x3 baby k4:x2". We also found that in 5,211 cases the presence subordination prevents the realization algorithm from working correctly, because no lexicalization is found for the discourse referent corresponding to the subordinate clause. In 1,865 cases, issues are caused by the presence of phrasal verbs (e.g. "He picked up his clothes") or adverbs, which are analyzed by Boxer using the relation manner between the event and the adverb or proposition, thus like in the previous case no lexicalization is found for all the discourse referents. Finally, 3,779 instances failed the test due to a variety of reasons, e.g., failure of the entity linking module or wrong syntactic analysis. Table 1 summarizes the findings exposed so far, also broken down by the number of frame elements in the frame instances.

When increasing number of frame elements per frame instance, the issues with subordinate constructions dramatically decreases: they amount to 24% of the cases with one frame elements, 3% and 1% with two and three frame elements respectively. Conversely, wrong realizations due to representation misalignments tends to get worse, involving from 30% of the instances with one frame elements to 56% with two, to 39% with three.

The first and most obvious use for the resource presented here in the context of NLG is given by the set of lexicalizations it provides for concepts and entities. In the example in Figure 5, for instance, the DBpedia entity Robot is lexicalized as "A robot" and the synset 02961779-n as "the car". Moreover, the frame is also given the lexicalization with two open variables "x 1 is driving x 2 ". Indeed, the surface forms provided by the DRG can be incomplete, that is, containing variables that can be used to compose a full surface form from the single ones corresponding to the discourse referents, e.g., x 1 :"A robot" and e 1 :"x 1 is driving x 2 " compose to form e 1 :"A robot is driving x ", and so on.

This composition mechanism gives us the opportunity to devise a simple method to produce new frame lexicalizations. Given new concepts or entities with the respective lexicalizations and roles (e.g., Driver: "Valentino Rossi", Vehicule: "the motorbike"), they can be replaced in the appropriate frame instance so that the variables x 1 and x 2 are linked respectively to "Valentino Rossi" and "the motorbike". A subsequent step of composition will then yield the new frame lexicalization "Valentino Rossi is driving the motorbike".

We developed a simple prototype in order to test this approach to NLG from frame instances. This prototype is based on the resource described in Section 5, restricted to the instances with exactly two frame elements and associated with a complete surface form. The procedure we use to evaluate the system is the following:

1. For each frame instance, produce four new frame instances by replacing one or both frame elements, either with similar concepts or with randomly chosen concepts.

2. Generate the lexicalization of the new frames by composing the frame lexicalization structure with the new concept lexicalizations.

3. For each of the four scenarios, select randomly one hundred instance lexicalization for the evaluation.

4. Manually inspect the selected lexicalizations according to three possible classes of fluency: nonsensical (the sentence is not grammatical and it does not make sense), informative (the grammar contains mistakes but the information is clearly transmitted), and fluent (the lexicalization correctly conveys the input knowledge).

When we replace one frame element or both of them with similar concepts, we rely on the WUP similarity defined by [START_REF] Wu | Verbs semantics and lexical selection[END_REF] for pairs of WordNet synsets, a measure of path distance weighted according to the depth of the WordNet taxonomy. We compute the WUP similarity for each pair of concepts in our colelction and replace one or both frame elements with their most similar concepts. For example, the frame elements corresponding to the Vehicle in the frame instance in Figure 5 is associated with the concept http://wordnet-rdf.princeton.edu/ wn31/02961779-n (car, automobile). This concept could be replaced, for the sake of the evaluation, by the similar concept (according to the WUP metric) http://wordnet-rdf. princeton.edu/wn31/104497386-n (truck), if this is also in the collection. A new lexicalization is then produced by composition "A robot is driving the truck". The lexicalization for the replaced concepts is chosen as the most frequent lexicalization of that particular concept, to minimize the occuprrence of awkward realizations like "A robot is driving of the truck".

Note that we only judge fluency. An evaluation of adequacy or other content-oriented metrics should also take into account the input and would be more difficult to evaluate in this setting, since here the input is artificially produced by replacing elements of the frame instances.

The manual inspection of the produced frame instance lexicalizations resulted in the figures shown in Table 2. As expected, replacing both frame elements instead of just one leads to more errors in the realizations. This problem can be mitigated by increasing the coverage of the resource. With a larger collection, the chance of retrieving a frame instance with at least one frame element in common with the new input is higher, thus there will be more cases where only one frame element is new. Interestingly, the choice of concepts to generate with respect to the frame (similar vs. random) does not seem to influence the outcome. The result of this pilot study are encouraging in that a sufficiently large number of correct realizations are produced by a simple mechanism. However, a more thourough evaluation is needed, especially with respect to the coverage (and thus the scalability) of our approach.

Conclusion and Future Work

In this paper we introduced a novel methodology to extract knowledge from text and encode it in formal structures compatible with the standards of the Web. Such structures are essentially instances of frames with their frame elements linked to concepts in Wordnet or DBpedia. This methodology is implemented in the freely available software package KNEWS. Next, we presented a collection of frame instances aligned with natural language, automatically created by parsing text for English learners. Finally, we propose a pilot study on how to use this resource to generate natural language from new frame instances.

In terms of future direction for this work, the low hanging fruit is the enlargement of the resource, which will lead to a higher number of "good" instances to use for direct generation (as shown in Section 6) and more data to use for a statistical approach to generation. Since the resource is produced automatically by parsing raw text with KNEWS, and natural language is abundant on the Web, this is a direction we intend to take in the foreseeable future.

The approach to NLG based on the collection of lexicalized frame instances introduced in NLG is at the preliminary work stage, and many refinements can be made to the algorithm. Given a new frame instance to generate, its frame elements could be matched to the lexicalization in the resource with more sophisticated methods, e.g., using distributional similarity.

As a possible extension to the resource, information such as lemma and number could be included in the lexicalization of concepts. With such information in place, the NLG algorithm could be interfaced with the SimpleNLG surface realization library [START_REF] Gatt | Simplenlg: A realisation engine for practical applications[END_REF] to produce more fluent lexicalizations.

The main selling point of a large knowledge base aligned with text is that its size allows researchers to develop statistical methods to learn a mapping between the formaly encoded knowledge and natural language. While this could be a very challenging enterprise, as highlighted by the work presented in [START_REF] Basile | From logic to language: Natural language generation from logical forms[END_REF], this work of constitutes a first step in this direction.

Figure 1 :

 1 Figure 1: DRS representing the meaning of the sentence "A robot is driving the car"

Figure 3 :

 3 Figure 3: Architectural Scheme of KNEWS.

Figure 4 :

 4 Figure4: RDF triples extracted by KNEWS from the sentence "A robot is driving the car", constituting one frame instance.

Figure 5 :

 5 Figure5: XML output of KNEWS describing a frame instance extracted from the sentence "A robot is driving the car".

Table 1 :

 1 Error analysis of the automatically produced, text-aligned frame instance collection, broken down by number of frame elements.

	roles	1	2	3	all
	correct	4,774 2,374 218	7,366
	subordination	4,824	368	19	5,211
	adverb	1,288	561	16	1,865
	realization	5,885 5,508 603 11,996
	other	2,672 1,009	98	3,779
	total	19,443 9,820 954 30,217

Table 2 :

 2 Result of the manual evaluation of the NLG prototype based on the collection of lexicalized frame instances.

	Replaced frame	Judgment
	elements	nonsensical/informative/fluent
	1, most similar	23/33/44
	2, most similar	24/53/23
	1, random	23/35/42
	2, random	54/23/23

http://dbpedia.org

http://www.eslyes.com/


N Fedorczak
email: nicolas.fedorczak@cea.fr

J P Gunn

N Nace

A Gallo

C Baudoin

H Bufferand

G Ciraolo

Th Eich

Ph Ghendrih

P Tamain

Width of turbulent SOL in circular plasmas: a theoretical model validated on experiments in Tore Supra tokamak

The relation between turbulent transport and scrape off layer width is investigated in circular plasmas toroidally limited on the inner wall. A broad range of experimental observations collected in the Tore Supra scrape off layer are detailed and compared to turbulent interchange models. Blob velocities agree reasonably well with an analytical model derived for isolated blobs. A set of 2D isothermal turbulence simulations are used to derive a scaling law for the density width. Quantitative agreement is found between this model scaling and experimental density width measured in Tore Supra, over a large set of plasma conditions. The sensitivity to control parameters (major radius, safety factor and normalized Larmor radius) is roughly explained by the sensitivity of the blob velocity model. The predictions are also extended to power decay length in limited plasma configurations. For ITER start-up phases, the predicted power decay length fall in the range of extrapolations based on multi-machine regressions.

Introduction

Prediction of scrape off layer (SOL) properties in future tokamak reactors is a fundamental step to prepare and design several aspects of their operation. The average SOL power width λ q sets the local average heat loads on plasma facing components. The average density width λ n sets the particle flux and retention on wall elements, but also coupling properties of electromagnetic systems like lower hybrid current drive or ion cyclotron resonance heating [1]. It also sets the level of impurity produced from their plasma interfaces, due to waves interaction with SOL particles [2]. Additionally, SOL density fluctuations can scatter propagating waves, resulting in wave spectra alteration [3], intermittent acceleration of electron beams in a broad SOL volume [4], etc. Generally fluctuations of large amplitudes, as commonly observed in SOL plasmas, will tend to complexify the prediction capabilities of transport codes, plasma interaction with electromagnetic waves, wall components or neutrals in the divertor [START_REF] Manz | the AS-DEX Upgrade Team. Nuclear Materials and Energy[END_REF]. In this contribution, an attempt is made to clarify the predictability of the SOL state in relatively simple plasma scenarios already well documented [START_REF] Gunn | [END_REF]: high field side (HFS) toroidally limited L-mode discharges performed in Tore Supra tokamak. Motivations are twofold. First, there are clear evidences that turbulence dominate the radial transport in this configuration. Convection of plasma filaments (blobs) around the outer midplane is found to dominate the global radial particle flux in the SOL [7]. These fluctuations measurements are supported by the systematic observation that SOL widths are much narrower in low field side (LFS) than in HFS limited configurations [8], which is explained by a strong enhancement of the radial particle flux at the LFS outer midplane. Second, large SOL widths found in HFS limited circular L-mode plasmas allow more precise and more diversified diagnostic measurements. For instance, reciprocating probes consisting in arrays of small collectors -to study turbulence-can be more safely used than in diverted configurations where SOL are thinner and heat fluxes more intense. Also, lower gradients characterizing these SOL regimes mean that shear flows are weak with respect to turbulence intensity, which simplify the physical system to model. Also, circular geometry simplifies theoretical models. In facts, the possible influence of a magnetic X-point on edge turbulent transport is still under investigation [START_REF] Galassi | Nuclear Materials and Energy[END_REF]. Recent experiments also suggest that the SOL width is sensitive to divertor geometry, which questions the basic understanding that only main SOL transport matters [START_REF] Gallo | Nuclear Materials and Energy[END_REF]. Reasons why SOL profiles are generally thinner in diverted than in limited configurations are possibly related to the influence of magnetic expansion, magnetic shear on turbulence and large scale flows, but mechanisms are still debated. First, proving that transport in circular geometry can be understood is a necessary step before extending to more complex situations. In a first section, the experimental setup in Tore Supra is briefly described. Then, an analyses is made on the link between blob dynamics and SOL profile, based on experimental observations. It is shown that the SOL width is proportional to the blob velocity weighted by an intermittency parameter. In the third section, comparison with theoretical transport model is made. First, blob velocities measured in Tore Supra SOL are found to agree with an analytic blob model. Then, variations of density SOL width over a large range of plasma parameters are compared to a model prediction built from 2D turbulent simulations. Model predictions are shown to agree with experimental density SOL width, both in amplitude and trend. A discussion is finally proposed on the link between density and heat channel width. Predictions are proposed for ITER start-up limiter phases, in agreement with recent multi-machine extrapolations.

Experimental characterization of profiles and turbulence

The following discussion on SOL width and turbulence will be illustrated by measurements performed in the Tore Supra (TS) tokamak during Ohmic limited phases, by means of 2 reciprocating probe systems located at the plasma top [START_REF] Gunn | [END_REF]. One is equipped with a Mach head with tunnel collectors allowing calibrated measurement of parallel flows , electron density and temperature (using IV characteristics) across SOL profiles.

Ion temperature is not systematically measured in Tore Supra SOL, but dedicated experiments using a retarding field analyzer suggest that T i ≈ 2T e is an acceptable guess for the plasma conditions investigated here [12]. The second reciprocating system is equipped with a poloidal rake of small collectors measuring floating potential and saturation current (J SAT ) at a rate of 1MHz across the full SOL profile [13]. On these fluctuating time traces, the choice is made to define blob events as local maxima of J SAT exceeded the local mean value (J b > J loc ).

Blobs are often defined as time events exceeding 2 to 3 standard deviations, and found to carry roughly 50% of the radial particle flux [14]. On the other hand, precise comparisons between density profiles and local electrostatic turbulence in Tore Supra SOL plasmas showed that about 100% of the radial particule flux is carried by electrostatic fluctuations [7; 13], result obtained without any restriction on the fluctuations amplitude. For that reason, a less restrictive definition of blobs is adopted. Normalized blob amplitude will be defined as n b /n ≡ J b / J loc . For every of these time points, a radial drift velocity v b (referred to as blob velocity) is estimated from the spatial difference of floating potential measured by probes surrounding saturated collectors. Blob widths is calculated based on the cross correlation between poloidally separated saturated collectors. Then, statistical averages are made upon collection of blobs belonging to the same spatial location: averaged blob amplitude, width, velocity and duty cycle, as illustrated in Fig1c)d)e). Additionally, the turbulent radial flux transport coefficient will also be estimated by averaging directly the product of saturation current and poloidal electric field fluctuations, as detailed in [13]. Additionally, the mean flow profiles measured by the Mach probe will be used to estimate the local radial particle flux responsible for the onset of parallel flows, based on a tailoring flow experiment as detailed in [7]. Finally, SOL width analyses will focus on a database of about 100 reciprocations performed with the Mach probe to study the SOL properties in ITER-like start-up phase [START_REF] Gunn | [END_REF]: ohmic plasmas limited at the high field side. Slow ramp of plasma current are performed over approximately 10 seconds at constant toroidal magnetic field allowing about 7 probe reciprocations per shot, up to the separatrix, during slowly varying plasma conditions. Gas fueling was adjusted shot to shot to decouple plasma density and plasma current variations. Detachment or MARFE phases are removed from the dataset. Conditions are summarized in Fig. 3c.

On the link between SOL width and blob dynamics

As illustrated in Fig1a, the SOL density profile exhibits a wide dynamical envelope characterized by local maxima 2 to 3 times larger than local minima. On the other hand, the fluctuation level, defined as the standard deviation of the fluctuations, do not exceed 15% of the time average, denoting what is often called large intermittence. It involves, as it is commonly considered, local density filaments or blobs, evolving in 3D: in the periphery of the outer midplane, curvature polarization generates potential vortices around density blobs that drift them outward in average. These blobs extend along magnetic field lines, while beeing immersed in poloidal flows of larger scales. In low confinement regimes of limited configurations, the radial convection of matter and energy associated with their outward motion participates to the onset of SOL width, in balance with losses toward target (including parallel and poloidal flows) and target recycling (considered small in limited configuration [7]). In the following, poloidal flows will be neglected, or assimilated to parallel flows with respect to their similar role as particle sink to target. In simple transport theory, an average density width λ n is the result of a balance between parallel and radial transport, expressed as a divergence free of the flux: ∂ r Γ r + ∂ Γ = 0. In the assumption of radial convection, the radial flux is defined as Γ r = nv r where n is the local time averaged density and v r the effective radial transport coefficient. The radial derivative introduces the SOL width : ∂ r Γ r ≡ -nv r /λ n . Equivalently, the parallel term simplifies into ∂ Γ = nv /L , where v is the typical parallel flow velocity and L the parallel connection length. Thus, a common SOL balance relation is obtained: v r /λ n = v /L . This relation can also be understood as an equality of radial (τ r ≡ λ n /v r ) and parallel (τ ≡ L /v ) transit times in the SOL consequence of the particle flux balance. In general, parallel particle flows are in the range of Bohm values from around the midplane to targets (v ≤ c S with c S the ion sound speed), which gives roughly fractions to one millisecond for τ (L ≈ 60m and v ≈ 60km/s for TS case). Then, SOL density widths of a few centimeters (λ n ≈ 4cm TS case) would require radial transport coefficients in the range of v r ≈ 40m/s, as shown in Fig1b. Interestingly, the SOL width estimated from the radial speed of individual filaments (about 300m/s close to separatrix of TS, Fig1c) would be about 10 times larger. This obviously questions a statistical approach of SOL profile recently proposed, which directly links SOL density width to blob velocity [START_REF] Garcia | the KSTAR Project Team[END_REF]. To compile with the averaged width, which is a physical consequence of particle flux balance, an expression of the radial particle flux carried by an assembly of successive blobs needs to be derived. For simplicity, blobs will be considered identical, of density n b , radial drift velocity v b , and local duty cycle about 10% (Fig1e) and normalized blob amplitudes in the range of 1.3 (Fig1d), this transpot coefficient effectively falls in the range of several tens of meter per seconds, or a fraction of the filament drift velocity. The local turbulent particle flux estimated from the non linear average of potential and density fluctuations (done without blob identification) agrees in ordering with the later, which is not totally surprising since they refer to the same fluctuations, but treated differently. Finally, a third estimate of the local particle flux (at probe location) -obtained from a tailoring experiment on parallel flows -also falls in the range of turbulent values. These estimates of transport coefficients measured at the top poloidal position of the plasma are in relatively good agreement, at least close to the separatrix, to the coefficient required to explain the SOL width (≈ 40m/s). Considering that the radial particle flux is strongly inhomogeneous around the poloidal section of the plasma [7], this agreement could be fortuitous. In facts, it rather shows that the poloidal average of the radial transport -that sets the global SOL width -coincides with the local transport amplitude at the top probe position.

Comparison with an interchange model

The above mentioned transport properties from Tore Supra SOL are in qualitative agreement with interchange mechanisms: over dense blobs propagating outward, transport enhanced around the outer midplane precisely where curvature driven polarization is destabilizing. In order to match these observations with quantitative numbers, 3D simulations of SOL dynamics are in principle needed [START_REF] Tamain | [END_REF], precisely because of the parallel dynamics entering poloidal asymmetries of turbulence. On the other hand, the simple circular geometry of Tore Supra SOL could benefit to an attempt to compare simplified models with experimental results. A class of SOL interchange models are 2D (radial poloidal) [17; 18], which means that parallel dynamics is simplified into control parameters. TOKAM2D is such code [17], with the simplification of isothermal plasma. The set of equation consists of:

∂ t n = -{φ, N } + D∆n -σ ne Λ-φ + S n ∂ t ω = -{φ, ω} + ν∆ω + σ 1 -e Λ-φ -g ∂yn n (1)
, where t is normalized to mi ZeB , space is normalized to

ρ L = c S ωi = mi(Ti+ZTe) B 2 Ze
where m i is the ion mass in kg, T e is the electron temperature in eV, T i = 2T e , Z is the ion charge number, e is the unitary charge in C, B is the magnetic field strength in T, g is the curvature

term g = αgρ L R
where R is the plasma major radius in m, Λ is the sheath potential drop, again normalized to T e , σ = ασρ L q cyl R is the parallel loss rate with q cyl the cylindrical safety factor, ω = ∆φ is the plasma vorticity. D and ν are diffusion and viscosity coefficients normalized to the Bohm value (ρ 2 L ω i ), usually small (< 10 -2 ). The system is driven by a particle source S n localized in the radial direction. Plasma density n is normalized to arbitrary value and plasma potential is normalized to T e . The direction y corresponds to the poloidal direction oriented along the electron diamagnetic direction, assumed normal to the magnetic field and x is the direction along the minor radius, directed outward. The Poisson bracket reads {A, B} ≡ ∂ x A∂ y B -∂ y A∂ x B. Note that both g and σ are defined up to a multiplication factor that can be adjusted to account for a varying degree of poloidal asymmetries. In the following, unitary values are adopted, to account for the outer midplane enhancement of the transport: α g = 1 suggests an effective polarization rate in-between outermidplane maximum (α g = 2) and plasma top (α g = 0), and α σ = 1 suggests a parallel loss rate slightly larger than the average over the entire field line.

Before presenting a comparison of experimental SOL widths with the scaling given by such model, a blob analysis is proposed. Considering isolated blob immersed in a background density, a blob drift velocity can be analytically derived from the vorticity equation by balancing source and sink terms, each written using blob parameters. Details can be found in [START_REF] Krasheninnikov | [END_REF], where different blob regimes are introduced based on the above mentioned vorticity equation. Note that the model can be enriched with additional pieces of physics, such as parallel resistivity, inclusion of X-point and divertor field-lines [20], or ion temperature perturbation in blobs [21]. Based on the simplest model, the blob velocity reads:

V m b = 1 2 σ δ 3 b   1 + δ 0 δ b 5 -1   c S
, where δ 0 = 5 4gδn b nσ 2 is a model scale length (whose value is shown in Fig2b), and δn b = n b -n is the blob density perturbation above background. As seen in Fig2a, this analytical expression agrees reasonably well with the blob velocity measured in TS SOL. Coincidentally or not, the blob size is also found to be relatively close to the model scale length, which normally sets the transition between regimes of propagation. The agreement suggests that blob dynamics in TS SOL can be described by such simple blob model, which would encourage to derive an analytical expression for the SOL width based on blob velocity. That said, such an approach would require to make several important guesses concerning blob size (what fraction of δ 0 ), blob amplitude δn b /n and blob duty cycle f b τ b , which would certainly lead to poor predictability. On the other hand, it might simply help to give a qualitative explanation for the influence of the main control parameters. Assuming that blob size is a fixed fraction of the model scale length δ b ∝ δ 0 , the blob velocity simply reads V b ∝ σ -0.2 g 0.6 c S , which leads to a SOL width scaling : λ n ∝ σ -1.2 g 0.6 ρ L . Comparison with full turbulence simulations is made by spanning different parallel loss and polarization rates in the simulations: σ ∈ 1 • 10 -4 , 5 • 10 -4 and g ∈ 8 • 10 -4 , 40 • 10 -4 . Once regime of saturated turbulence is reached (pictured in Fig3c), the time averaged SOL density decay length is measured (far fro particle source location) and a regression is applied on the dataset to extract the dependence with the two control parameters. As illustrated in Fig3d, the following expression is found λ n = σ -0.75 g 0.3 ρ L . The dependences built from the blob velocity is not so far from this numerical scaling, in the sens that the sensitivity of the control parameter is at least qualitatively matched. Comparison between the full TOKAM2D model (Eq1) and experimental evidences is applied to a large dataset built on Tore Supra for the ITER startup preparation [START_REF] Gunn | [END_REF]. It consists in about 100 reciprocations with a Tunnel Mach probe, spanning different magnetic field, plasma current and density values during ohmic discharges. Plasma shape and plasma parameters are summarized in Fig3a. As shown in Fig3b, the density decay length varies primarily with the plasma current with no or weak dependence with the toroidal magnetic field. Comparison between the numerical scaling and experimental data set of Tore Supra is shown in Fig3d, which reveals a quantitative agreement both in amplitude and parametric variation. Note that the asymmetry factors α g and α σ are maintained to unity, which could be better adjusted to improve the agreement. But this not the purpose of this comparison: it shows that a simplified turbulent transport model can explain the trend found for the SOL width across a wide set of conditions.

Discussion

Scrape off layer properties have been investigated in specific tokamak conditions: toroidally limited L-mode plasmas performed in Tore Supra Tokamak. Mean fluid quantities across SOL width were measured with a calibrated Mach probe system, and fluctuations of potential and density were collected with a poloidal array of small collectors. At the top of the plasma section where these reciprocating probe systems operate, the radial particle flux is found to be dominated by the E × B convection of density fluctuations: the amplitude of this turbulent transport is in agreement with the width of the mean density profile. Now, this radial transport is generally conceived as a result of isolated density blobs propagating radially outward. The averaged velocity of the blobs measured in Tore Supra SOL is in the range of v b ≈ 300m/s, thus an order of magnitude larger than the transport coefficient needed to construct a SOL width of a few centimeters (v eff r ≈ 40m/s). In facts, the blob velocity needs to be weighted by their duty-cycle, of about 10% for the experimental conditions shown here. Comparison with theoretical model is made in two steps. First, blob velocities measured in the SOL of Tore Supra are compared to a simple blob model, and a good agreement is found. This model, following further assumptions on blob size, amplitude and duty-cycle, predicts a SOL width varying as λ n ∝ Rq 1.2 cyl ρ L R 0.4 . Second, a wide dataset of density SOL widths collected in Tore Supra inner limited scenarios is compared to a model prediction issued from full turbulence simulations made with an isothermal 2D code. The model prediction reads λ n = Rq 0.75 cyl ρ L R 0.55 (m), which shows a relatively good agreement with experimental density decay lengths (rms deviation ≤ 24%), both in amplitude an trend. Consequences are twofold: (i) the isolated blob model returned the qualitative sensitivity of the main control parameters (positive sensitivity with normalized Larmor radius, safety factor and major radius), but is unlikely to predict a quantitative estimate without a fair guess about the blob duty cycle. (ii) the parametric dependence of Tore Supra SOL density width is consistent with a physical model of turbulent transport, which should improve the confidence for extrapolations toward ITER start-up conditions. To address this point, a model of heat load decay length is required. From the Tore Supra data set, a rough relation is found between the density and temperature decay length: λ Te ≈ 1.4(±1)λ n . Assuming these limited discharges are in the sheath limited regime, the parallel heat flux simply reads q = γn e c S T e , which trans-

lates into λ q = λ n 1 + 3 2 λn λ T -1
≈ 0.5λ n . Therefore, a model prediction for the heat load decay length in inner limited circular configuration becomes

λ q = 0.5Rq 0.75 cyl ρ L R 0.55 (m) (2)
As this prediction law includes the electron temperature at separatrix, an additional model for T e would be required to be fully predictive. On the other hand, the sensitivity of the decay length with T e is relatively weak, since λ q ∝ T 0.27 e . Across Tore Supra database, the separatrix temperature is about T e = 30 ± 10eV, with no correlation with plasma current or heating power (shown in Fig. 3a).

Figure 4: SOL power decay length measured in the Tore Supra SOL (blue squares and red circles), using two probe systems (tunnel probe and RFA) versus ohmic power. Details are found in [START_REF] Gunn | [END_REF]. The predictions made with Eq.2 are given by the grey diamonds, fixing a unique Te value.

The temperature dispersion around 30eV gives a dispersion of the predicted λ q value of 20%, which motivates the approximation to fix T e = 30eV in the predictive law, for Tore Supra. Comparison between the predicted λ q value and the experimantal scaling of the heat load decay length estimated by Gunn [START_REF] Gunn | [END_REF] is shown Fig. 4. A fairly good agreement is found over the database.

Extrapolation to ITER start-up phases in inner limited configuration can be proposed, in comparison to a recent ITPA analysis [22]. Using parameters given in Table 2 of the cited paper, the normalized Larmor radius is assumed to be in the range of ρ L /R ≈ 4 × 10 -5 for B T = 5.5T and R = 6m. Note that it corresponds to T e ≈ 50eV with T i = 2T e . Predictions from Eq.2 are summarized in Table 1, together with the average extrapolations made in [22]. Predictions with the above model are in good agreement with the extrapolations. I P (MA) λ q (mm) Eq.2 λ q (mm) ITPA 2.5 62 69 ± 18 5 44 54 ± 14 7.5 29 44 ± 11

Table 1: comparison of ITER start-up power decay length predictions based on the model (Eq.2) and average extrapolations made from ITPA multi-machine regression(Table 4 of [22]).

To conclude, this paper presents a physical model for the main SOL width in inner limited plasma configuration. The model is based on turbulent radial transport manifested by radial propagation of blobs. A physics based scaling law is obtained for the width of the density and power decay-length, which follows roughly the sensitivity with control parameters of a blob drift velocity model. It is validated on a broad dataset from Tore Supra, including blob dynamics and average SOL width. Predictions for ITER start-up phases are made, in agreement with multimachine predictions. In this state, the model is limited to specific plasma geometry. Extension should include effects of (i) collisionality in SOL plasma, (ii) magnetic topology on turbulent transport( magnetic shear, inclusion of Xpoint and diverted SOL volumes), and (iii) inclusion of non-ambipolar SOL currents and E × B shear impact on turbulence, in order to approach the narrow SOL feature in L-mode and H-mode physics.

Acknowledgment

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the European Union Horizon 2020 research and innovation program under grant agreement number 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.

Figure 1 :

 1 Figure 1: SOL properties in Tore Supra 44635 discharge. a) Saturation current profile from turbulence RCP: mean profile (thick curve) and min-max envelope (blue area). The radial decay length of the profile is mentioned. b) Transport coefficient built from the profile decay length (dark area), mean flow analyses (thick black curve), turbulence estimate (green squares), or estimated from blob averaged parameters (blue dots). c) averaged blob velocity in the radial direction, d) normalized blob amplitude e) blob duty cycle across the SOL profile.

  f b τ b (where f b is the blob occurrence frequency and τ b the local transit time over its own size). Then, the time averaged particle flux can be expressed as the particle flux carried by one individual filament (n b v b ) weighted by their duty cycle: Γ r blob t = f b τ b n b v b . This allows to express an effective radial transport coefficient characteristic of the radial transit time v r ≡ n b /nf b τ b v b , which is referred to as blob transport coefficient in Fig1b. For duty cycles in the range of

Figure 2 :

 2 Figure 2: a) Blob velocity estimated experiment (blue dots) along the SOL profile, compared to analytical blob model (red curve). b) Blob width measured along the SOL profile compared to the model scale length δ 0 separating inertial and sheath limited blob regimes.

Figure 3 :

 3 Figure 3: a) Cross section of the Tore Supra plasma used for ITER start-up database. The table summarizes the main plasma parameters: magnetic field, plasma current, correlation coefficient between density and current, separatrix temperature and current. b) Dependence of the SOL density decay length with poloidal magnetic field. c) Comparison of the normalized SOL decay length with the numerical scaling.