Sébastien Mustière
email: sebastien.mustiere@ign.fr

Chantal Reynaud
email: chantal.reynaud@lri.fr

Brigitte Safar
email: brigitte.safar@lri.fr

Nathalie Abadie
email: nathalie-f.abadie@ign.fr.

Same words? Same worlds? Comparing ontologies underlying geographic data

Keywords: geographic database, data integration, semantics, ontology alignment, data specifications

Assessing how much two geographic databases reflect the same point of view is a key issue for data integration. We argue that this task requires developing ontologies revealing the point of view of each piece of information, and neglecting the technical choices behind the organization of information in data schemas. These ontologies need then to be aligned and globally compared. In this paper, we describe techniques allowing doing that in the fields of ontology alignment and natural language processing. We illustrate those points through results from experiments made on actual data with a semi-automated analysis of their specifications.

Assessing the point of view of geographic information

Geography is a scientific field that requires combining numerous information about human as well as environmental phenomena and processes. It is essentially a major field of application of multi-criteria analysis [START_REF] Malczewski | GIS-based multicriteria decision analysis: a survey of the literature[END_REF]. Hopefully, more and more geographic information are nowadays available, each one reflecting a special point of view about the same geographic "real world". This is a great opportunity for geographic analysis, and this amount of information could lead to significant knowledge. Unfortunately, those pieces of information are usually independently produced and managed. Many efforts are still necessary in the research area to help the integration of heterogeneous data, dealing with their diversities and complementarities but also their imprecision, uncertainty, incompleteness, redundancies and inconsistencies [START_REF] Egenhofer | Evaluating inconsistencies among multiple representations[END_REF][8] [START_REF] Sheeren | A data-mining approach for assessing consistency between multiple representations in spatial databases[END_REF].

One of the main challenges for such integration is to be able to assess and compare the points of view behind pieces of geographic information to be integrated. Indeed, each geographic database reflects the particular conceptualization of the world of its producer [START_REF] Aerts | A practical example of semantic interoperability of large-scale topographic databases using semantic web technologies[END_REF]. As a consequence, a single phenomenon of the real world will have different interpretations and descriptions, reflecting the "semantic heterogeneity" of geographic data [START_REF] Partridge | The role of ontology in integrating semantically heterogeneous databases[END_REF]. Understanding the semantic heterogeneity of different geographic datasets is the key to assess their complementarities and redundancies. In other words, it is the key to answer the questions: is there any meaning of integrating those two pieces of information? If yes, how should it be done?

There is a common agreement of this issue: semantics of given information would gain a lot to be explicitly represented [START_REF] Partridge | The role of ontology in integrating semantically heterogeneous databases[END_REF], and ontologies are tools to do that. In this paper we address two related questions: how to build ontologies underlying some given geographic datasets and, once this is done, how to compare those ontologies?

2 Discovering the ontology underlying geographic databases

The gap between schema and ontology

The first element that may explicit the semantics of a dataset is the data schema. Every database conceptual schema is made with a certain goal, and then is based on an underlying ontology [START_REF] Partridge | The role of ontology in integrating semantically heterogeneous databases[END_REF]. A conceptual schema whose elements would correspond exactly to the concepts of the ontology would be called an ontological schema. However, due to technical and historical reasons, geographical schemas are far from being ontological schemas.

One explanation of that originates from the history of GIS softwares. Most, if not all, geographical schemas separate classes of objects holding surfacic, punctual and linear geometries. This originates from the fact that the main challenges for first GIS softwares was the management of geometric properties. Thus, geometric types have been defined and were the basis for defining the schema: an old and still current view is that geographical data are first of all geometries associated to some properties. A more ontological approach should be to rely only on geographic (not geometric) concepts to define the schema, and then to consider geometries as one property of features among others. This consideration explains some gaps between schemas and an ontology. For example, we find in some data schema the independent classes "surface building", "point building" and "linear building", while the underlying ontology would better contain only the concept of "building", may be with some properties like "shape" or "height".

Another explanation originates also from the nature of geographic data: most meaningful relations between data may be spatial relations like "to be near" / "to follow" / "to lead to", which can be derived from the geometric properties of features, at least interactively when visualising the data. For this reason, geographic schema explicit very few relations between classes, if any, compared to most database schemas in other fields. In the opposite, ontologies intend to explicit those relations.

Another, and certainly deeper explanation, originates from the mapping history of geographic databases. Most geographic databases have been defined to produce maps, or at least by people and organisations with a strong mapping background. Classes of geographical schemas may then group objects based on cartographic habits rather than ontological considerations. In a caricatured manner, a class may contain all objects appearing in blue in a map. More reasonably, we encounter databases with a class representing all together the concepts of "beach", "summit", valley", "cave", "peninsula" and "crest". The main reason is that all these geographic concepts are considered in those databases as background information to be displayed only as a toponyms. The underlying ontology of this database would gain to contain all these disjoint concepts, associated with the property "geographical name".

Finally, the design of geographic databases requires a complex conceptualization process where selection, aggregation and splitting operations are performed. The geographic space is far too complex to be represented entirely. Thus, two designers with the same goal may design two different database schema. If this can be said in any thematic domain, this is particularly true for geography because of the complexity of the geographic world. It is thus very difficult to separate differences between schemas originating from design choices from thus originating from actual differences in conceptualizations of the world.

However, we do not claim that geographical schemas should be redesigned with ontological principles because, first, we need to handle existing databases and, more importantly, these schemas are usually very adapted to most user needs among which mapping is important. Nevertheless, for all the reasons explained above, schemas are not rich enough and organised to be used as ontological references to assess the point of view of a database.

Textual specifications as sources of semantics

Most of the knowledge reflecting the database design and instantiation process is compiled in textual documents, known as database specifications. Specifications of topographic databases precisely describe the meaning of the database content through the description of the data capture process. They are paper documents, usually covering hundreds of pages, highly structured but containing a lot of informal information expressed in natural language. They are used as guidelines for data capture, as well as data description for data management and data usage.

If differences can appear between specifications from different data producers, all of them globally contain the same information (see example in Fig. 1). After some general information about the database, themes and feature classes are described one by one. After the name of the class, a definition explains the meaning of the represented concept in a few sentences. Then, selection criteria are usually detailed, i.e. the conditions that a real world object must observe to be part of the database. Attention is also paid to information related to the geometry of the feature class. Other attributes are finally listed and described, usually by textual definitions, the list of their possible values, and again a textual definition for each possible value. All this information, be it formal or textual, is very rich. It is the best available source of knowledge describing the point of view followed when designing the database. We may thus reasonably think that these documents could be sources of knowledge to explicit the ontology underlying a geographical database, and even gaps between this ontology and the schema [START_REF] Gesbert | Etude de la formalisation des spécifications de bases de données géographiques en vue de leur intégration[END_REF].

In order to experiment this idea, we analysed two different specifications documents from IGN, the French mapping agency. By means of natural language processing tools including morpho-syntactic parsing, we searched for geographic concepts expressed in the textual parts of the specification, like in definitions of classes. The overall process was to identify all nominal groups in sentences, then filter them with some external corpuses of non geographic documents, and organise the retained concepts in a hierarchy according to their location in the document [START_REF] Abadie | Constitution d'une taxonomie géographique à partir des spécifications de bases de données[END_REF]. Based on results of this first automated step, an interactive filtering and reorganisation has been definitely necessary to complete the work. For each database and its related specification, this process led us to a taxonomy of concepts encountered in the specification, a first step toward a more formal ontology1 (see extract in Fig. 2). Insights from these experiments were manifold. First, our experiments have shown that a highly assisted process to identify relevant geographic concepts from textual specifications is possible. If this is not fully automated, a limited amount of interactive work is enough to reach reasonable results. We believe that this approach should then be extended to enhance the process and to take one step forward to a more complete ontology with properties, relations, definitions… Second, if schemas of the studied databases contain a few tenths of classes (respectively 40 and 35), their respective taxonomies contain hundreds of concepts (respectively 580 and 500), which clearly shows the richness of the specifications in terms of semantics: each class is described by one term in the schema (e.g. 'river'2 ), while it actually refers to more than ten different geographic concepts in average (e.g. 'river', 'waterway', 'stream', 'canal', 'watercourse, 'rivulet'…).

Third, a more specific insight is that we discovered many geographic concepts in all classes related to toponymy. Without generalizing too much, this may be due to the fact that what is named is in general well known and then well described.

Forth, difficulties encountered may be interesting for an extension of such a work. Some difficulties where linked to the detection of concepts. The most difficult question we had to solve was: when do we consider that a nominal group is a geographic concept in its entirety (e.g. 'terraced house') or is a geographic concept qualified by an attribute (e.g. 'big house')? The answer is difficult and a matter of philosophy. For example, if we encounter the nominal group 'protestant church', we may choose to introduce in our ontology the concept of 'church', with an associated property (religion). However, if we rather encounter the term 'temple', we may introduce it in its entirety in the ontology, with the synonym 'protestant church'. We believe there are no universal answer to that question, and that the only reasonable one in our context is to follow the principles followed by the developers of the database (did they separate or not 'protestant church' from 'catholic church' in the data?). Some other difficulties were linked to the hierarchisation of concepts. For example, some definitions of classes encountered in specifications are real definitions (e.g. "cape: prominent part of the shoreline…"), while some other definitions are more selection principles like (e.g. "hamlet: hamlet with a name'). As another example, some concepts encountered in definitions are real subtypes of the general concept corresponding to the name of the class, while some are not. For example in 'river = watercourse even in town', 'watercourse' can be thought of as a subtype of 'river', while 'town' of course not.

Comparing ontologies for assessing differences and commonalities

Once ontologies underlying two geographic databases have been determined, from the analysis of specifications as explained above or by any other mean, an important question needs to be answered: in what extent do these ontologies reflect the same conceptualization of the world? In other words, the question is: are the differences between the databases somehow artificial and only due to technical or terminological choices; or conversely, are they deeper differences that actually reflect different conceptualizations of the world? The answer is a key to assess how tractable, useful and meaningful is the integration of those databases, and how to do it.

Visser et al. [START_REF] Visser | An analysis of ontological mismatches: Heterogeneity versus interoperability[END_REF] make a very useful distinction between ontology mismatches, identifying two types of basic mismatches: conceptualization mismatches which are mismatches between two conceptualizations of a domain, and explication mismatches which are mismatches in the way a conceptualization is specified. Considerable efforts have been devoted to the development of algorithms and tools that attempt to identify and resolve ontology mismatches in the field of ontology matching in general [START_REF] Euzenat | Ontology Matching[END_REF], and in the geographic context in particular [START_REF] Egenhofer | Evaluating inconsistencies among multiple representations[END_REF]. Comparing vocabularies related to geography from different cultures also received attention in the literature [START_REF] Mark | Does the Semantic Similarity of Geospatial Entity Types Vary Across Languages and Cultures?[END_REF]. These works usually focus on the analysis of differences between concepts, terms and definitions. However, as far as we know, few works exist on the global comparison of ontologies for gaining a compiled overview of differences and commonalities between them. In this section we first introduce works related to ontology matching, a necessary first step before a global comparison. Based on actual experimentations, we then exemplify which insights could be discovered from ontology matching. We finally introduce initial ideas towards a more global comparison of ontologies.

Ontology Matching

Due to the development of an ever-growing number of ontologies, ontology matching algorithms or techniques occupy a key role in facilitating the design of ontology-based applications. The matching process aims at finding an alignment between two ontologies which express correspondences between their entities found according to a particular matching algorithm. Matching algorithms primarily provide equivalence relationships (isEq) meaning that the matched objects are the same or are equivalent. It is also possible to obtain more specific relationships (isA) meaning that a class is a sub class of another, disjointness when two classes are supposed to be disjoint or semantically related relations (isClose) for a link between two classes considered as related but without a specific typing of the relationship.

More formally, the matching process can be seen as a function f which, from a pair of ontologies to match O and O', an input alignment A which can be completed, a set of parameters p (e.g. weights, thresholds), and a set of external resources r, returns an alignment A' between these ontologies [START_REF] Euzenat | Ontology Matching[END_REF]: A' = f (O, O', A, p, r). All the parameters (A, p, r) are optional. Their use depends on the matching techniques performed by matcher tools. These techniques can be performed according to different approaches. We can distinguish individual algorithms [START_REF] Giunchiglia | Semantic Matching[END_REF][23] and combinations of the individual algorithms, either hybrid [START_REF] Madhavan | Generaic Schema Matching with Cupid[END_REF] when several individual algorithms are synthesized into a new one or composite solutions [START_REF] Doan | Ontology Matching: a machinelearning approach[END_REF] allowing an increased user interaction.

Whatever the approach is, elementary techniques or algorithms that are used for solving the ontology matching problem exploit various types of ontology information, e.g. element names, data types, structural properties as well as characteristics of data instances. Based on the classification according to the kind of input described in [START_REF] Euzenat | Ontology Matching[END_REF], the following techniques can be distinguished: terminological, structural, extensional and semantic. Terminological techniques work on strings. Terms can be either considered as sequences of characters or be interpreted as linguistic objects. Structural techniques exploit ontology structures. This can be done at two levels, either by considering the internal structure of entities, e.g. attributes and their types, or by considering the relationships between entities. Extensional techniques work on data instances. Semantic techniques work on models. They require some semantic interpretation of the ontology and usually use some semantically compliant reasoner to deduce the correspondences. Furthermore, these current matching techniques can be complemented by using additional descriptions, called background knowledge. Some works assume that ontology matching can rely on a unique and predefined ontology that covers a priori all the concepts of the ontologies to be matched. Conversely, other works suppose that there does not exist a priori any suitable ontology. Hence, their idea is to dynamically select online available ontologies.

We illustrate now some of these matching techniques through TaxoMap [START_REF] Hamdi | TaxoMap in the OAEI 2008 alignment contest[END_REF] which has been used to match the two taxonomies built from the textual databases specifications mentioned in section 2. TaxoMap makes the assumption that most semantic resources are based essentially on classification structures which contain rich lexical information and hierarchical specification without describing specific properties or instances. Indeed, in practice, actual and available ontologies are mainly hierarchies of concepts, even if they could be much richer and more complex in theory. Hence, to find mappings in this context, we can only use the following available elements: labels of concepts and hierarchical structures. In TaxoMap, an ontology is considered as a pair (C, H C ) consisting of a set of concepts C arranged in a subclass hierarchy H C . A concept c is defined by two elements: a set of labels and subclass relationships. The labels are terms that describe entities in natural language and which can be an expression composed of several words. A subclass relationship establishes links with other concepts.

The matching process in TaxoMap is oriented from a source O S to a target ontology O T . It aims at finding one-to-many mappings between single concepts and establishing three types of relationships, equivalence, more specific and semantically related relationships.

TaxoMap mainly relies on terminological techniques. It performs a linguistic similarity measure between labels of concepts. The measure takes into consideration categories of words which compose a label. The words are classified as functional (verbs, adverbs or adjectives) and stop words (articles, pronouns) thanks to the use of TreeTagger [START_REF] Schmid | Probabilistic Part-of-Speech Tagging Using Decision Trees[END_REF], a tool for tagging text with part-of-speech and lemma information. Stop words categories enable to ignore these words in similarity computation. Functional words have less power than all the others (noun, etc.). The position of a word in the label is also of importance, a common word between two labels is less important after a preposition than a word that is a head.

Eight different matching techniques are implemented in TaxoMap, applied in a sequential way. The technique T i will be performed on a concept C S in O S only if no correspondence with a concept C T in O T has been discovered with the techniques previously applied. Let C S be a concept in O S for which a correspondence has to be found and C Tmax , C T2 and C T3 the three concepts in O T having the best similarity measures with C S , here is an overview of these techniques with some illustrations:

-Equivalence relationships identification technique (T 1 ): An equivalence relationship (C S isEq C Tmax ) is generated when the similarity measure between one label of C S and one label of C Tmax is greater than a given threshold.

-Techniques based on label inclusion (T 2 ,T 3, T 7 ): These techniques consider inclusion of label words. According to T 2 , (C S isA C Tmax ) is proposed when one label of C Tmax is included in one label of C S without being behind a determiner. That way, "Route départementale" isA "Route" 3 is generated (cf. Fig. 3). Inversely, (C S isClose C Tmax ) is proposed by T 3 when one label of C S is included in one label of C Tmax . T 7 is not performed immediately. Its aim is to identify hidden label inclusions. -Techniques based on relative similarity (T 4 ,T 5 ,T 6 ): These techniques are applied on C S when no correspondence has been generated by the techniques T 2 and T 3 and when the similarity measure of C Tmax is significantly higher than the measure of C T2 . For example, "Chaîne de montagne" isClose "Montagne" 4 is proposed according to T 4 (cf. Fig. 4). "Montagne" is included into "Chaîne de Montagne" but it is situated behind "de" denoting that this word is not of first importance in the expression "Chaîne de montagne".

-Techniques based on structure (T 8 ): This technique is applied after all those presented above. It is performed on C S for which the similarity measure of C Tmax , C T2 and C T3 is not very high (although greater than a given threshold) and when at least two of the concepts C Tmax , C T2 and C T3 have a common father. In that case the relationship (C S isA CommonFather) is generated, for example "Equipement de sport d'hiver" " isA "Entité topographique artificielle"5 in Fig. 5.

Insights from the analysis of alignments

The two taxonomies mentioned in section 2 have been aligned by means of techniques explained above. Concretely, we used the TaxoMap tool [START_REF] Hamdi | TaxoMap in the OAEI 2008 alignment contest[END_REF]. Some typical examples of what can be learned from this alignment illustrate its interest.

First, some effective alignments did map similar concepts expressed by different labels in the two taxonomies. These differences are examples of purely labelling differences and not conceptualization differences. This is the case for example for 'wooded area' mapped to 'clump'. Some groups of concepts, existing in one taxonomy but not mapped in the other one, also illustrates the peculiarities of one taxonomy against the other one:

-This may emphasize the thematic choices behind the respective databases. For example, we identify such groups of concepts related to tourism ('tourist information office', 'seaside resort', 'seafront boardwalk' or 'historical downtown'), hydrographical details ('rivulet' or 'inlet'), or land use ('shrub area', 'rice swamp', or 'banana plantation').

-This may also emphasizes that databases adopt different global approaches. In our experiments one database adopts a more functional point of view describing relations between features, compared to the other one that adopts a topographic point of view describing what is seen. Indeed, elements related to the description of network nodes appear more often in one taxonomy than in the other one (like 'diffluence' or 'cul-de-sac').

-The same type of analysis shows the difference of spatial level of detail between the databases: some high-level concepts may appear only in one taxonomy (like 'mountain range', 'town' or 'state').

A detailed analysis of the mapped concepts brings also some information about the different conceptualizations. Let us take a focused but significant example, the geomorphologic concept of 'cluse' (transverse valley, see fig. 6) exists in both taxonomies. However, in one taxonomy it is a subconcept of 'gorge', while it is more closely related to 'mountain pass' in the other one. This certainly originates from difficulties to classify such specific concepts. However, this may also be explained by the topographic point of view (a cluse is usually stip-sided valley, like a gorge) against the functional point of view (a cluse is a pass between valleys).

Fig. 6. Typical geomorphological shape of 'cluse'

We can hardly generalize too much from those focused examples. In order to make such a generalisation and assess the differences between conceptualizations underlying the two studied databases, some methods for a more systematic and global comparison of ontologies are needed or, in other words, distances between ontologies should be defined. Some directions for such distances are expressed in the next section.

Toward a global comparison of ontologies

Two main groups of distances between ontologies could be defined: the one relying on ontologies previously mapped, and the other ones [START_REF] Euzenat | Quelques pistes pour une distance entre ontologies[END_REF]. Some distances, for example based on extensions of distances between concepts developed in the field of ontology alignment, are proposed and analysed according to their theoretical properties in [START_REF] Euzenat | Quelques pistes pour une distance entre ontologies[END_REF]. Many measures can be defined. One major difficulty may be to interpret their meaning according to the intended use of the distance. For example: which measures could be used to assess if ontologies follow the same conceptualization but with different levels of detail? Which measures could be used to assess that ontologies are compatible or not? We hereafter develop some general ideas for meaningful and interpretable measures of differences and commonalities between ontologies, based on various fields of research on ontologies.

Partition of ontologies.

Works on ontology partitioning can help to compare ontologies, especially when the partitions are built by taking the alignment objective into account. In [START_REF] Hamdi | Partitionnement d'ontologies pour le passage à l'échelle des techniques[END_REF], two methods which transform the two ontologies to be aligned into two sets of blocks of a limited size are proposed (see fig. 7). Partitioning a set E consists in finding disjoined subsets E 1 , E 2 , …, E n , of elements semantically close i.e. connected by an important number of relations. The realization of this objective consists in maximizing the relations within a subset and in minimizing the relations between the different subsets. The proposed partitioning methods are partially inspired by co-clustering techniques which consist in exploiting, besides the information expressed by the relations between the concepts within one ontology, the information which corresponds to the inter-ontology relations between concepts such as equivalence relationships. The partitioning process brings together the concepts that have relations between them in blocks. Both ontologies are partitioned one after the other. Blocks of the second ontology are built around sets of concepts whose label is equivalent to the label of concepts belonging to the same block in the first partition. That way, we obtain blocks in the two ontologies that correspond, e.g. containing concepts with equivalent labels and semantically close. An analysis of the two partitions and of the pairs of blocks can allow answering the following questions. Given two ontologies O and O', what is the corresponding part in O' of the block B i in O? Are there parts with no correspondent? Are the parts similar according to the number of concepts with an equivalent label? Indeed, a high number of equivalent concepts may correspond to parts which are very closely related. Are corresponding parts different according to the number of concepts or to the depth of the concepts hierarchy? Is a description more refined or more precise than the other one? Fig. 7. Partition of ontologies in a matching context A partitioning experiment has been done with the two ontologies mentioned in section 2. A preliminary analysis of the results attests that such an approach can contribute to improve the understanding of the ontologies and to show a number of differences or common points. More precisely, the partitioning approach summarizes the themes described by one of the ontology (the first one which is partitioned) and allows answering the following question: are the same themes described in the second ontology? In our experiment, the first ontology has been decomposed into five blocks, each block having its correspondent in the second one. This forms five pairs of blocks grouping concepts related to: (1) natural topological entities (maritime space, element of the relief, ground hydrography), (2) administration buildings, entities with industrial vocation, sport equipments, … (3) infrastructures of transport, (4) industrial buildings, entities with agricultural vocation, equipments with military vocation, (5) cemetery, religious buildings, elements of the patrimony, equipments of leisure. These five blocks are indicators of five main topics described by both ontologies.

The number of concepts contained in the first three pairs of blocks is very similar, which means that the themes are equally described. Some blocks as that concerning the natural topological entities contain a high number of concepts linked by an equivalence relationship. The descriptions in both ontologies may be very close. They may correspond to knowledge expressed by the same point of view.

On the opposite, the number of concepts in the last two pairs of blocks differs. The corresponding themes are less prominent in the second ontology. Some blocks in the second ontology have no correspondent in the first, as that concerning the sea links. Some concepts are isolated as urban centre or natural obstacle. The observation of such isolated blocks or concepts leads to an analysis at various levels of granularity, either at a theme level or at a concept level. Furthermore, note that the maximal size of the blocks is a parameter in the partitioning process. Successive experiments can be made with different values. We can also apply the partitioning process on the whole ontologies and then reapply it on the pairs of blocks. This allows an analysis at various levels of detail and can lead to a more precise understanding of the geographical coverage of both ontologies. Small parts of models are easier to understand.

Evaluation of ontologies.

Works on comparison of ontologies may benefit from those concerning the evaluation of ontologies which need to assess all their important features. Despite works in that domain lack of automatic, well grounded, methodologies, it seems to us important to briefly present them in order to identify a number of focus areas for future research. Ontologies may be assessed from different angles. Ontology evaluation can include aspects of ontology validation and verification, i.e. structural, functional, and usability issues [START_REF] Gangemi | A theoritical framework for ontology evaluation and validation[END_REF]. An ontology can be evaluated against criteria based on its content coverage, for example by using a corpus describing the domain of interest. Some ontology search engines adopt a Page-Rank-like method to evaluate and rank ontologies by analysing links and referrals between the ontologies in the hope of identifying the most popular ones. Other systems for ranking ontologies are based on a number of measures that assess the ontology in terms of how well it represents the concepts of interest expressed by users. Such analysis metrics could be useful for our comparison purpose. For example, four ranking measures are applied in AKTiverank [START_REF] Alani | Ranking Ontologies with AKTiveRank[END_REF] in order to evaluate different representational aspects of the ontology and calculate its ranking. One of the measures applied is the density measure (DM). It includes how well the concept is further specified (the number of subclasses), the number of properties associated with that concept, number of siblings, etc. DM is intended to approximate the representational-density or information-content of classes and consequently the level of knowledge detail. Another measure is the Centrality Measure (CEM) which aims to assess how representative a class of an ontology is. It assumes that the more central a class is in the hierarchy, the more likely it is for it to be well analysed and fully represented. These measures could be used to compare ontologies.

Ontologies and fitness for use. Other works aiming at detecting and retrieving relevant ontologies need means for measuring the similarity between ontologies. So, in [START_REF] Maedche | Measuring Similarity between Ontologies[END_REF] a set of measures that capture the similarity between ontologies at two different levels, the lexical and the conceptual levels, is proposed. Those similarity measures describe the coverage of one ontology specification by another. At the lexical level, labels of concepts are compared; their similarity based on the Levenshtein minimum string distance is computed in order to determine the best measure for each label. The average of the similarity measure of all the labels, AVG(SM(L 1 ,L 2 )) determines the coverage of the vocabulary L 1 of an ontology O 1 by the vocabulary L 2 of the other ontology O 2 . This is an asymmetric measure. When L 2 contains all the strings of L 1 , but also plenty of others, then AVG(SM (L 1 ,L 2 )) = 1 but AVG(SM (L 2 ,L 1 )) may approach zero. The conceptual similarity is based on the intentional semantics of a concept C in an ontology O, IS(C,O), defined as the set of all its super-and subconcepts in O. When a concept C belongs to two ontologies, one can define the taxonomic overlap (TO) between O 1 and O 2 for this concept, denoted TO(C, O 1 , O 2 ) and defined as the ratio between the number of common elements in the intentional semantics of C in O 1 and in O 2 and the total number of elements belonging to the union of these two sets. If a concept C is in O 1 but not in O 2 , an optimistic approximation of TO(C,O 1 ,O 2 ) is defined as the maximum overlap obtained by comparing IS(C,O 1 ) to the intentional semantics of all the concepts in O 2 . The average of the taxonomic overlaps allows comparing semantic structures of the two ontologies O 1 and O 2 .

Visualisation of ontologies. Works on visualisation and understanding of very large and complicated ontologies can be helpful for comparison of ontologies because their objective is to provide global views. The main feature of the visualization approach described in [START_REF] Tu | Tpwards Imaging Large-Scale Ontologies for Quick Understanding and Analysis[END_REF] is that it presents a large-scale ontology by a holistic "imaging" which is semantically organized for quick understanding of the subject and the content. Furthermore, the approach has to assess the importance of classes because when displaying the layout, only the most important classes that fall into the screen are labelled. The importance is computed based on the class hierarchy by a formula which first part gives more importance to the classes higher in the hierarchy, while the second part gives more importance to the classes with more descendants. In [START_REF] Wu | Identifying Potentially Important Concepts and relations in an Ontology[END_REF], CARRank, an automatic ranking algorithm, which can be integrated in existing ontology visualization tools, is described. It is a tool to help understand ontologies based on the identification of potentially important concepts and relations userindependently. The importance of concepts and the weights of relations reinforce one another in CARRank in an iterative manner.

Analogy between conceptual and physical worlds. In order to compute and interpret distances between ontologies, an analogy could be made between spatial networks, like road networks, and ontologies that also rely on a graph structure. Indeed, an analogy between the geographic space and the conceptual space could be easily understood, at least for geographers and spatial analysts. In the field of spatial analysis, there exists a lot of works to analyse (like works studying accessibility or vulnerability of networks [START_REF] Gleyze | Using structural approach to understand transportation networks vulnerability[END_REF]), simplify (like works on generalisation of networks for mapping [START_REF] Thomson | Generalisation of Geographical Networks: In Generalisation of Geographic Information: Cartographic Modelling and Applications[END_REF]) or compare spatial networks (like works on network matching (cf. fig. 8, [START_REF] Mustière | Matching networks with different levels of detail[END_REF]). These works use measures similar to the one previously mentioned about ontologies, or more specific measures taking advantage of the geometric aspect of spatial networks. We think that both fields of research, spatial analysis and ontology analysis, would gain from a mutual comparison and enrichment, for developing new methods. [START_REF] Mustière | Matching networks with different levels of detail[END_REF]. Examples where a roundabout (on the left) or a square (on the right) can be mapped to a single node in a less detailed network, like some groups of concepts in an ontology could be matched to a single concept in another one.

Conclusion

In this paper we argue that assessing how much two geographic databases reflect the same point of view is a key issue for data integration. This necessitates distinguishing in data schemas between differences originating from actual conceptual choices from those originating from technical reasons. We have shown in some experiments that ontologies, or at least taxonomies, reflecting the point of view of a database can be derived from textual documents like data specifications. The derivation can possibly be assisted by means of natural language processing techniques. These ontologies can then be linked and compared by means of ontology matching techniques. Some of our experiments show that interesting information, like differences between the thematic points of view or the conceptual levels of detail could be derived from the analysis of ontology matching. We then claim that methods to globally compare ontologies are needed, and different field of researches could be fruitful sources of inspiration for this, from ontology visualization to spatial network matching.

We are convinced that such methods for comparing ontologies should have two main properties, even more important than being precise and efficient: the first one is the possibility to make a meaningful interpretation of the results of the methods; the second one is to be adapted to 'light' but actually existing ontologies, which are more or less hierarchical taxonomies.

Fig. 1 .

 1 Fig. 1. Excerpt from textual specifications of IGN-France.

Fig. 2 .

 2 Fig. 2. Excerpt from the obtained taxonomy (in French)

Fig. 3 .

 3 Fig. 3.: Illustration of the T 2 technique.

Fig. 4 .

 4 Fig. 4.: Illustration of the T 4 technique

Fig. 5 .

 5 Fig. 5.: Illustration of the T 8 technique.

Fig. 8 .

 8 Fig.8. Matching of networks with different levels of detail, from[START_REF] Mustière | Matching networks with different levels of detail[END_REF]. Examples where a roundabout (on the left) or a square (on the right) can be mapped to a single node in a less detailed network, like some groups of concepts in an ontology could be matched to a single concept in another one.

We use the word 'ontology' in this paper in order to designate a formal conceptualization of the world, be it a simple taxonomy, without any reference to a particular modeling of it, like for example in OWL format with the formal concepts of properties, relations, definitions…

All examples in this paper originate from actual databases and specifications from IGN-France. For the sake of clarity, even if the work has been done on texts in French, most examples in this paper are given in English, except in section

3.1 describing label comparison techniques, where a translation would have been meaningless. We are aware of imprecision due to this translation or adaptation; however we consider it as sufficiently insignificant for illustrative examples.

In Enlish: "B-Road" isA "Road",

In English: "Mountain range" isClose "Mountain"

In English : "Winter sport equipment" isA "Artificial topographic entity"

Acknowledgement

This research is partly funded by the French Research Agency, through the GeOnto project ANR-O7-MDCO-005 on 'Creation, Comparison and Exploitation of Heterogeneous Geographic Ontologies' (http://geonto.lri.fr/).


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Claire Naude

Cécile Bujon

Salah Boussen

Thierry Serre

Frédérik Bélot

Comparison of kinetic changes during helicopter medical evacuations: civilian

Keywords:

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Introduction

In recent armed conflicts, 12-23% of military personnel evacuated by helicopter were critically injured patients [START_REF] Carfantan | Forward medevac during Serval and Barkhane operations in Sahel: A registry study[END_REF][START_REF] Simonet | Épidémiologie des évacuations sanitaires héliportées des blessés français au combat en Afghanistan de 2010 à 2012[END_REF]. War wounds, often hemorrhagic injuries requiring urgent surgical treatment, result in most deaths within the first few minutes of patient care. Helicopter evacuation plays a key role in improving patient survival by reducing evacuation times [START_REF] Pham | Faster on-scene times associated with decreased mortality in Helicopter Emergency Medical Services (HEMS) transported trauma patients[END_REF][START_REF] Butler | Is it the H or the EMS in HEMS that has an impact on trauma patient mortality? A systematic review of the evidence[END_REF]5].

While the notion that the initial 60 minutes following trauma is pivotal for transporting patients to a trauma center has been a foundational principle of trauma systems, national guidelines, and clinical practice, empirical evidence establishing a direct correlation between time to treatment post-injury and clinical outcomes remains inconclusive. Despite anecdotal clinical instances highlighting the importance of timely care, the evidence supporting a one-size-fits-all "golden hour" is not definitive. Identifying the subset of trauma patients who would most benefit from minimized transport times is an ongoing area of investigation. Extensive research has aimed to elucidate the relationship between out-of-hospital time and outcomes after injury [START_REF] Sampalis | Trauma care regionalization: a process-outcome evaluation[END_REF][START_REF] Sampalis | Impact of on-site care, prehospital time, and level of in-hospital care on survival in severely injured patients[END_REF][START_REF] Mccoy | Emergency medical services out-of-hospital scene and transport times and their association with mortality in trauma patients presenting to an urban Level I trauma center[END_REF][START_REF] Stiell | The OPALS Major Trauma Study: impact of advanced life-support on survival and morbidity[END_REF][START_REF] Pepe | The relationship between total prehospital time and outcome in hypotensive victims of penetrating injuries[END_REF][START_REF] Petri | The effect of prehospital transport time on the mortality from traumatic injury[END_REF][START_REF] Lerner | Is total out-of-hospital time a significant predictor of trauma patient mortality?[END_REF][START_REF] Pons | Eight minutes or less: does the ambulance response time guideline impact trauma patient outcome?[END_REF][START_REF] Di Bartolomeo | Are pre-hospital time and emergency department disposition time useful process indicators for trauma care in Italy? Injury[END_REF][START_REF] Osterwalder | Can the "golden hour of shock" safely be extended in blunt polytrauma patients? Prospective cohort study at a level I hospital in eastern Switzerland[END_REF][START_REF] Newgard | Emergency medical services intervals and survival in trauma: assessment of the "golden hour" in a North American prospective cohort[END_REF]. Although a minority of studies suggest that shorter pre-hospital times may be associated with improved survival, the majority of studies have not found a conclusive link. The reliability of these studies is often compromised by methodological limitations, including biases arising from the severity of injuries, unaccounted confounding variables, retrospective designs, and constrained analytical methodologies. In the specific context of military medicine, the "golden hour" principle has been a tactical mainstay since 2009, shaping medical and operational strategies, particularly within the U.S. Army. Conversely, French military health support doctrine prioritizes the deployment of mobile surgical teams close to potential injury sites rather than strictly adhering to the "golden hour" rule. This distinction is critical as military operations can often predict where casualties may occur, allowing for the strategic positioning of medical support. In contrast, civilian trauma incidents are less predictable, necessitating a system where patients are rapidly transported to the nearest trauma center.

In the past decades, many studies have shown that helicopter transport of patients with severe trauma improves survival compared to ground transport [START_REF] Champion | A Profile of Combat Injury[END_REF][START_REF] Sullivent | Reduced Mortality in Injured Adults Transported by Helicopter Emergency Medical Services[END_REF][START_REF] Tsuchiya | Outcomes after helicopter versus ground emergency medical services for major trauma--propensity score and instrumental variable analyses: A retrospective nationwide cohort study[END_REF]. Initially, it was thought that this better prognosis was due to faster transport time to an adequate hospital facility. However, other data showed that better survival of patients transported by helicopter was independent of the total duration of transport. Some studies even showed that this improved prognosis remained when the duration of helicopter transport was greater than that of ground transport [START_REF] Brown | Helicopter transport improves survival following injury in the absence a time-saving advantage[END_REF][START_REF] Chen | Speed is not everything: Identifying patients who may benefit from helicopter transport despite faster ground transport[END_REF][START_REF] Desmettre | Impact of emergency medical helicopter transport directly to a university hospital trauma center on mortality of severe blunt trauma patients until discharge[END_REF]. A second hypothesis was that helicopter crews composed of physicians were more experienced than ground transport crews [START_REF] Galvagno | Helicopter emergency medical services for adults with major trauma[END_REF]. However, a French study found that survival was still improved even when ground and helicopter crews had the same experience and skills in trauma care [START_REF] Ageron | Association of helicopter transportation and improved mortality for patients with major trauma in the northern French Alps trauma system: An observational study based on the TRENAU registry[END_REF]. Therefore, there is some indication that other mechanisms may contribute to a better prognosis for patients transported by helicopter. One of these mechanisms could be the impact of accelerations on the patient during transport. Indeed, during ground transport, patients are in reclining position and undergo repeated accelerations: linear accelerations and decelerations due to traffic, lateral accelerations due to turns and changes in direction and vertical accelerations due to road layout and defects [START_REF] Serre | Vehicle Dynamics Endured by Patients during Emergency Evacuation-Ambulance versus Helicopter[END_REF]. Newton's second law of motion states that the force applied to an object is equal to the product of its mass and its acceleration. Hence, the force applied to the blood mass is directly influenced by acceleration, and kinetic changes can lead to hemodynamic changes. This theory is supported by experimental studies in rats that have shown that such linear accelerations are responsible for hemodynamic changes with an increase in blood pressure during vertical and anterior accelerations [START_REF] Yakushin | The response of the vestibulosympathetic reflex to linear acceleration in the rat[END_REF][START_REF] Yates | Cardiovascular responses elicited by linear acceleration in humans[END_REF]. Similar effects have been found in human subjects [START_REF] Zhu | Linear acceleration-evoked cardiovascular responses in awake rats[END_REF]. Loring et al. [29] also showed that axial accelerations had a short and transient impact on lung mechanics. Finally, an experimental study showed that healthy volunteers had an increased intracranial pressure during helicopter flight when in the reclining position. This effect disappeared when the head was elevated [START_REF] Maissan | Helicopter Transportation Increases Intracranial Pressure: A Proof-of-Principle Study[END_REF].

Overall, patient positioning and kinetic factors during helicopter flight could influence hemodynamic stability and intracranial pressure in severe trauma patients, explaining in part why helicopter flights improve prognosis in these patients compared to ground transport. Serre et al [START_REF] Serre | Vehicle Dynamics Endured by Patients during Emergency Evacuation-Ambulance versus Helicopter[END_REF] compared the acceleration levels experienced by patients transported by ambulance and civilian helicopter and found that ambulance transport results in more abrupt changes in the longitudinal and transverse directions, but less in the vertical direction. Military helicopter evacuations are considered more demanding because of the tactical and combat implications, and therefore exposure to acceleration is probably higher than in civilian evacuations. The objective of this study is to quantify accelerations during military medical evacuations and compare them to civilian evacuations. The goal is to identify potential factors causing harmful effects of flight on patients, necessitating improvements in patient positioning in the aircraft.

Material and methods

Vehicle dynamics data were recorded with an inertial measurement system during medical evacuations on a civilian EC135 T1 helicopter (Airbus helicopters, Marignane, France) and during training flights on military helicopters, Puma SA330 (Aerospatiale, Paris, France) and Caiman NH90 TTH (NHindustries, Aix-en-Provence, France). Table 1 gives their main manufacturer's specifications. For the civilian helicopter, 29 trips were recorded in the Marseille area at the APHM (Assistance Publique des Hôpitaux de Marseille) over a period of three months, under real conditions of patient repatriation to a hospital, and with the same pilot. The data collected were recorded on a voluntary basis, the recording being deactivated by default. Only the actual flying phases of a patient repatriation to the hospital were kept. For each trip, the severity of the patient's condition was unknown.

For military helicopters, recorded flights were training flights simulating tactical medevac missions conducted by the Army Light Aviation School (EALAT) in Le Luc, France. Three Puma and 2 Caiman were instrumented for a period of one month (Figure 1), which allowed the recording of 19 Puma trips and 14 Caiman trips, with several different pilots, apprentices or instructors. Only the "return" part of the intervention was kept for the study.

The inertial unit used was self-powered and fixed on the floor, near the center of gravity of the aircraft. This system integrates a tri-axis accelerometer, gyroscopic sensors and a GPS. The sensors do not emit electromagnetic signals and therefore cannot interfere with the flight instruments. Longitudinal, lateral and vertical accelerations as well as roll, pitch and yaw rates are recorded at a frequency of 100 Hz, and the GPS speed at a frequency of 1 Hz.

Figure 1: Instrumentation of a civilian and five military helicopters

The recorder was installed horizontally in the helicopter, and an offset procedure was performed once installed so that in this position (helicopter on the ground) the three accelerations measured are the longitudinal acceleration along X, the lateral acceleration along Y and the vertical acceleration along Z (Figure 1). The acceleration of gravity (9.81 m/s²) has been removed from the acceleration along Z so that in the absence of speed variation and in horizontal position, the three accelerations are close to 0. When the helicopter is in stabilized flight with an angle, it is no longer in the horizontal starting position and neither is the recorder. As a consequence, the acceleration of gravity, still vertical, is partially transferred to the X axis. In steady state, this angle between the longitudinal axis of the helicopter and the horizontal plan is called pitch attitude. The greater the pitch attitude, the greater the transfer. This does not change the overall acceleration experienced by the helicopter and the patient. Due to the aircraft pitch attitude, acceleration and braking are characterized by longitudinal acceleration, but also partly by vertical acceleration. In the case of turning, the acceleration is partly lateral and vertical, as the helicopter leans to the side. It is therefore interesting to compare the three accelerations of the three types of helicopters but also the magnitude of the accelerations in order to get an overall idea of the differences in stresses endured.

Filtering techniques were employed to eliminate vibratory motion artifacts, enabling quantification of the magnitude and intensity of kinetic changes experienced by patients during flight. A specific study of vibrations was carried out, the details of which can be found in [START_REF] Serre | Vehicle Dynamics Endured by Patients during Emergency Evacuation-Ambulance versus Helicopter[END_REF] and resulted in 30-point moving average filter applied to the accelerations. The study conducted a detailed analysis of the transit period from the injury site to the treatment center, with a particular focus on the takeoff phases of MEDEVAC helicopter flights. This specific focus on takeoff was driven by its critical nature in both military and civilian medevac operations. Takeoff often occurs in potentially hostile or unstable areas, where rapid and sometimes abrupt maneuvers are necessary, leading to more significant and potentially impactful accelerations compared to other phases of the flight. This can be especially true in military scenarios where the urgency and risk factors are heightened. To gain a comprehensive understanding of the kinetic changes during these flights, the study calculated the distributions of acceleration. This involved quantifying the time spent at each level of acceleration, using intervals of 1 m/s², for all trips made by each type of helicopter.

Patient involvement

Patients were not involved in the design and conduct of this research. No patient data were collected.

Results

The recorded data includes 29 civilian helicopter flights, 19 Puma flights and 14 Caiman flights, representing 12, 6 and 4 flight hours, and around 2500, 1200 and 700 kilometers travelled. The characteristics of trip duration, distance, and average speed are compared in Table 2. Due to a technical incident affecting GPS positioning, the flight speed is correctly recorded only for one Puma flight out of 19. However, the data analysis allows to estimate that the speed of the Caiman (mainly between 250 and 345 km/h) was significantly higher than that of the Puma (105-205 km/h), while the speed of the civilian helicopter was between the two (210-250 km/h). Figure 2 shows the speed of the single recorded Puma flight, and the speed from a representative flight for the other two helicopters. For the three types of helicopters, the magnitude of accelerations, which refers to the overall acceleration experienced by the helicopter and its occupants, was compared for the whole flights, including the take-offs (Table 3, columns "Whole trip"). During Puma flights, the acceleration was less than 1 m/s² for 88.2% of the time, and during Caiman flights, it was less than 1 m/s² for 84.4% of the time. The total acceleration exceeded 2 m/s² for only 1.3% of Puma flights and 1.9% of Caiman flights.

In comparison, civilian helicopter flights had a total acceleration of less than 1 m/s² for only 40.3% of the time, and it exceeded 2 m/s² for 3.1% of the flight time. Generally, for all three types of helicopters,

6
the acceleration rarely exceeded 3 m/s². However, civilian helicopter flights frequently experienced acceleration between 1 and 2 m/s², which was much rarer in military helicopters. The longitudinal, lateral, and vertical accelerations of the different types of helicopters are shown in Figure 3." The analysis showed that all helicopters exhibited similar flight patterns when it came to extreme forces. The most extreme accelerations were caused by vertical loads, with maximum accelerations ranging from -9 to 12 m/s² (not shown in Figure 3 due to their rarity), while longitudinal accelerations ranged from -3 to 3 m/s² and transverse accelerations from -4 to 5 m/s². These extreme accelerations were found to be rare and lasted for less than a second. Furthermore, the study revealed that longitudinal acceleration was mostly negative, occurring approximately 66% of the time for Pumas, 58% for Caimans, and up to 91% for the civilian helicopter. This can partly be attributed to the attitude of the helicopters to tilt to move forward, and a transfer of stresses on the vertical axis can be observed, with more time spent with a vertical acceleration between 1 and 2 m/s² than between -2 and -1/s². The projection of the gravity vector on longitudinal axis of the sensors fixed to the helicopter causes a negative longitudinal acceleration when the helicopter's attitude is tilted forward For the helicopter to be balanced in stabilized flight, the resultant of gravity and drag must be equal and opposite to lift (Figure 4). Finally, by specifically studying the take-off phases (Table 3), it can be seen that the stress undergone is lower than in the other flight phases for all the helicopters. During take-off, military helicopters tend to endure more pronounced acceleration stress compared to the civilian helicopter. Firstly, there are fewer instances of acceleration greater than 2 m/s² during civilian flights as opposed to military flights. Secondly, military helicopters experience acceleration peaks that exceed 3 m/s² and even 4 m/s² for a very short period of time, between 0.1% to 0.2% of the take-off time. In contrast, there are no such peaks of acceleration observed in civilian helicopters during take-off.

Discussion

Contextualizing the study: Addressing a critical gap in current research

Medevac flights are critical for providing timely medical intervention to patients in remote or conflict areas. Current research has predominantly focused on the physiological impacts of fixed-wing air medical transport, highlighting issues such as the exacerbation of traumatic injuries due to altitudeinduced hypobaric conditions [START_REF] Teichman | International Aeromedical Evacuation[END_REF][START_REF]ABC of Transfer and Retrieval Medicine -Google Livres[END_REF] or noise and vibration [START_REF]Quantifying Patient Vibration Patterns During Aeromedical Evacuation (AE) Aboard the UH-60L Equipped with the Interim Medevac Mission Support System (IMMSS)[END_REF]. However, there is a paucity of data concerning the kinetic stress experienced by patients during helicopter medevac operations which are more commonly used in difficult terrain and rapid response scenarios. Despite the operational prevalence of helicopters in medical evacuations, little is known about the specific acceleration forces encountered during these flights and their potential impact on different medical conditions. This represents a significant gap in our understanding, as the optimization of patient care and outcomes during air transport requires detailed knowledge of these forces. Our study fills this critical gap by providing a comprehensive analysis of acceleration patterns in both civilian and military helicopter medevac flights. By comparing different helicopter models and examining various flight phases, this research offers new insights into the kinetic stress patients may endure. This information is vital for developing guidelines for patient positioning and care during air transport, particularly in helicopters.

Interpretation of results

The study did not reveal any significant differences in kinetic stress between military and civilian medevac flights. However, it is worth noting that the accelerations in military aircraft, particularly the Pumas, were slightly less intense than those in civilian helicopters. This difference could be attributed to the varying flight environments of these two types of flights. Civilian flights typically occur at lower altitudes in urban areas, while military flights spend more time in stabilized flight and have fewer constraints once the combat zone has been evacuated. In addition, the mechanical variations between the Caiman and the Puma may have contributed to the slight differences in kinetic stress observed between Caimans and the Pumas. Given that the Caiman is a newer and faster helicopter, it is possible that it has updated mechanical features that influence the levels of kinetic stress experienced during flight. Therefore, future studies could focus on evaluating the specific mechanical differences between these helicopters to better understand how they affect the health and well-being of passengers during medevac flights.

Take-off phases, which were thought to be the most sensitive, especially in military flights since they consist of extraction from a combat zone, ultimately showed less intensity in accelerations than other flight stages. However, as expected, kinetic stress in military take-offs was significantly more important than in civilian settings. Once again, the kinetic stress is higher in the more powerful aircraft. Several factors could contribute to this observed phenomenon:

-Pilot Experience and Training: Some military helicopters in the study were piloted by trainees, who may have less experience in executing smooth take-offs compared to their civilian counterparts. Trainee pilots, while skilled, might not yet have developed the nuanced control over acceleration and maneuvering that comes with more flight hours and experience. This could result in more abrupt take-offs, leading to higher acceleration stresses. -Operational Context and Urgency: Military operations, by their nature, often occur under more demanding and unpredictable conditions. The need for rapid deployment in potentially hostile environments can necessitate quicker, more aggressive take-offs, contributing to higher acceleration stresses. In contrast, civilian pilots, even in emergency situations, are more likely to operate in controlled environments where the urgency is balanced with the need for patient safety and comfort. This could explain why civilian helicopters tend to have smoother take-offs with fewer instances of high acceleration. -Helicopter Design and Performance Characteristics: Military helicopters are often designed for agility and rapid response, which might translate into more dynamic take-off capabilities. This design aspect could inherently lead to higher acceleration stresses during take-off.

Civilian helicopters, on the other hand, might be optimized for smoother operations, prioritizing patient comfort and stability, especially during medical evacuations. -Flight Protocols and Procedures: Military and civilian flight operations follow different protocols and procedures, which can influence pilot behavior during take-off. Military protocols might prioritize speed and responsiveness, while civilian protocols could emphasize smoothness and stability, especially in the context of medical evacuations. -Psychological Factors: The mindset of a pilot in a military operation, often characterized by a heightened sense of urgency and alertness due to the nature of their missions, might unconsciously influence their piloting style, leading to more pronounced acceleration. Civilian pilots, while also operating under pressure in emergency situations, might be more focused on ensuring a stable and gentle take-off to safeguard patient well-being.

In conclusion, the observed differences in acceleration stress during take-off between military and civilian helicopters are likely multifactorial, influenced by pilot training and experience, operational context, helicopter design, flight protocols, and psychological factors. Each of these elements contributes to the unique flight characteristics observed in the study.

Our data align with those of manufacturers, particularly in terms of flight speed. The cruising speed of the Puma is 240 km/h, the Caiman is 300 km/h, and the civilian helicopter is 260 km/h. However, it is important to note that the recordings of military flights were obtained during medevac training flights rather than in actual combat zones. This was done to maintain confidentiality, and because obtaining patient consent was not feasible. As a result, it is possible that the flight stress experienced during real military flights in combat may be underestimated since the unique constraints and stresses of combat situations could not be fully accounted for in the training exercises. Nevertheless, the fact that the military take-off phases showed greater acceleration than those of civilian helicopters suggests that the training exercises were reasonably realistic simulations of real medevac evacuations.

Implications of findings

This study provides insights into the flight acceleration patterns observed during medevac flights, which can help optimize patient positioning based on their medical condition. Notably, the most extreme accelerations were vertical, while longitudinal accelerations were predominantly negative due to the pitch attitude of the aircraft. These findings offer valuable information for optimizing patient positioning during medevac flights based on their medical condition. It is well-established that vasoconstriction alone is not sufficient to maintain venous return during hypovolemic shock, and that shifts in position can lead to reduced venous return to the heart and decreased cardiac output [START_REF] Rothe | Physiology of Venous Return: An Unappreciated Boost to the Heart[END_REF].

Based on Newton's second law of motion, positioning the patient with their head towards the back of the aircraft may theoretically improve venous return to the heart and cardiac output. However, for patients with head trauma or acute neurological injuries, a 30° head elevation is recommended to decrease intracranial pressure and improve cranial perfusion pressure [START_REF] Ledwith | Effect of Body Position on Cerebral Oxygenation and Physiologic Parameters in Patients With Acute Neurological Conditions[END_REF][START_REF] Ng | Effects of Head Posture on Cerebral Hemodynamics: Its Influences on Intracranial Pressure, Cerebral Perfusion Pressure, and Cerebral Oxygenation[END_REF]. Conversely, negative longitudinal accelerations could be harmful if patients with head injuries were positioned with their heads at the rear of the aircraft, potentially increasing intracranial pressure. Thus, patients with head injuries would benefit from being positioned with their head towards the front of the aircraft or with a slight head elevation, as suggested by a previous study [START_REF] Maissan | Helicopter Transportation Increases Intracranial Pressure: A Proof-of-Principle Study[END_REF]. It is important to note that these recommendations for patient positioning remain theoretical, and further studies should be conducted to validate or invalidate these assumptions. The data presented here yields valuable insights into how flight accelerations may affect patient outcomes. It underscores the importance of positioning patients carefully during medevac flights. By examining the acceleration patterns identified in this study, healthcare professionals can take steps to minimize the risk of complications and improve patients' overall wellbeing.

Study limitations

While this study advances our understanding, it is not without limitations. The extrapolation of training flight data to combat scenarios is speculative and may not capture the full spectrum of stresses encountered in actual combat conditions. Additionally, the study's conclusions are based on a limited data set, predominantly from one type of civilian and military helicopter, potentially limiting the generalizability of the findings.

The current study lacks a direct quantitative analysis linking acceleration levels during MEDEVAC flights to specific injury outcomes. The complex relationship between acceleration levels and patient outcomes in MEDEVAC, influenced by factors like injury type, overall health, exposure duration, and individual variability, requires a more thorough investigation. To this end, ongoing research aims to provide concrete data on this matter, by conducting a detailed analysis of patient outcomes concerning specific acceleration levels during different MEDEVAC flight phases. While our study offers valuable insights into acceleration profiles, further research is essential to quantify the clinical implications.

Conclusion

This exploration introduces a novel perspective to the medevac operational narrative, dissecting the intricacies of flight dynamics and their biomedical implications. It reveals that while kinetic stresses are an inherent aspect of medevac flights, they are predominantly within tolerable limits for both civilian and military helicopters. The insights gleaned advocate for nuanced patient positioning strategies to mitigate flight-induced risks, particularly for those with specific medical conditions. Going forward, the integration of these findings into medevac protocols can bolster patient outcomes, inspiring a new trajectory for both aeronautical engineering and emergency medical systems.

Figure 2 :

 2 Figure 2: Comparison of speed during a single flight

Figure 3 :Figure 4 :

 34 Figure 3: Distribution of longitudinal, lateral and vertical accelerations

Table 1 : Helicopters main characteristics Helicopter Puma SA330 Caiman NH90 TTH Civilian EC135T1

 1

	Maximum mass (kg)	7000	10600	2630
	Empty mass (kg)	3800	6400	1370
	Length (m)	18.18	19.56	12.20
	Fuselage length (m)	14.06	16.09
	Cabin length (m)	6.05	4.80	3.06
	Cabin width (m)	1.80	2.00	1.49
	Height (m)	5.14	4.20	3.62
	Cabin height (m)	1.55	1.58
	Cruising speed (km/h)	248	260	262
	Maximum speed (km/h)	257	295	287
	Total motor power (hp)	2600	2500	590

Table 2 : Trip characteristics (*Estimation)

 2

		Helicopter	Puma	Caiman	Civilian Helicopter
		Number of crafts	3	2	1
		Number of trips	19	14	29
		Total duration (h)	6	4	12
		Total distance (km)	1200*	693	2465
		Minimum	6	8	10
	Trip duration (min)	Average Maximum	19 40	17 45	25 60
		Standard deviation	8.5	9.3	11.5
		Minimum		28	21
	Trip distance (km)	Average Maximum	(one trip: 49)	50 112	85 209
		Standard deviation		28	43
		Minimum		48	139
	Trip speed (km/h)	Average Maximum	(one trip: 142)	168 237	204 238
		Standard deviation		48	20

Table 3 : Proportion of time spent in different levels of acceleration during whole trips and during take-offs

 3

	Total	Puma		Caiman	Civilian
	acceleration					Helicopter
		Whole trip Take-off Whole trip Take-off Whole trip	Take-off
	< 1 m/s²	88.2%	98.9%	84.4%	98.1%	40.3%	98.3%
	[1 -2] m/s²	10.5%	0.7%	13.7%	1.4%	56.6%	1.6%
	[2 -3] m/s²	1.0%	0.2%	1.4%	0.3%	2.7%	0.1%
	[3 -4] m/s²	0.2%	0.1%	0.4%	0.1%	0.3%	0.0%
	> 4 m/s²	0.1%	0.1%	0.1%	0.1%	0.1%	0.0%


Davide Rizzo

Christine Leclercq

Elisa Marraccini

David Grandgirard

Olivier Scheurer

Alicia Ayerdi Gotor

Co

Co-Design to Identify Lack Of On-Farm Agronomic References

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

Co-Design to Identify Lack Of On-Farm Agronomic References

Why ?

Produicing our own references

Adapted from Kneebone, S., 2012.

Hypothesis 1: the reason why farmers do not use some techniques, except farm machinery availability, is that they need more references in their combination of cropping system (CS) and soil.

Hypothesis 2:

The references that they need are not obvious enough to be identified through a simple survey such as "what do you want /need to know to be able to improve your CS?"

Hypothesis 3:

Wondering and arguing about introducing a new technique that could improve a CS is a relevant way to detect the reason(s) why it is not already used.

Hypothesis 4:

Several types of references may be needed about a new technique to judge if it can introduce in a given combination of CS and soil:

• the technique efficiency according to its objective(s),

• the conditions for which the technique is efficient and feasible,

• its potential side effects.

How ?

• The second step of the STEPHY approach, initially developed to co-design CS for low pesticide use, was enlarged to other farmers' objectives such as reducing nitrates leaching or avoiding resistance development to herbicides in weeds population => data on suitable techniques were compiled according to the types of references for each potential objective.

• main objectives of each farmer were identified.

• each farmer argued the reasons why it has not or do not consider introducing each proposed technique in a chosen CS.

Which results ?

Open or closed ended questions such as: "does sunflower introduction need to be combined with mechanical weeding to reduce herbicide use in Rapeseed/ Wheat/ Winter barley/ Pea/ Wheat rotation on a chalky soil?" Those questions have been presented to the farmers' group as a feedback for complements and validation.

Objective

D

  . Rizzo 1 , C. Leclercq 1 , E. Marraccini 1 , O. Scheurer 2 , D. Grandgirard 1 , A. Ayerdi Gotor 3 1 UP INTERACT 2018.C102 UniLaSalle, Beauvais (France), 3 UniLaSalle (Beauvais, France), 4 UP AGHYLE 2018.C101 UniLaSalle, Beauvais (France) 1 Kneebone, S. 2012. Extension systems and change facilitation in DARNHOFER, I., GIBBON, D., DEDIEU, B. (Eds). Farming Systems Research into the 21st Century : The New Dynamic. Dordrecht Heidelberg New York London : Springer. p212 2 RS = rapeseed, WW = winter wheat, WB = winter barley, SP = spring pea, SB = spring barley Davide RIZZO