domain: spi phys

text:
Valentina Dragos
email: valentina.dragos@onera.fryolène.constable@onera.fr

Yolène Constable

Comparison of Classification Techniques for Extremism Detection in French Social Media

Keywords: online data, classification, extremism detection, natural language processing, machine learning

With social platforms being used by an increasing number of users, the Internet became a perfect place for extremist ideas and opinions to be created and propagated. Detection of extremist content can be an asset for security agencies but it comes with technical challenges and requires semi-automatic approaches, as reading the sheer amount of data released online is an impossible task for analysts. This paper investigates the use of learning models to detect extremist content in French corpora and focuses on right-wing extremist detection. Several learning models have been developed including unsupervised approaches and neural ones. The models were applied to a data set gleaned online. Experiments show that data representation and parameters of models may affect the overall performance and extremist content can be accurately detected when parameters and thresholds are tuned correctly. These results are novel as they contribute to the analysis of social data conveying extremist ideas in French.

I. INTRODUCTION

Social media offers a new vision of social interactions when content is no longer created and broadcast by the few to the many, but rather released on platforms designed to share information among real users and virtual communities. A significant characteristic of social platforms is their ability to disseminate information almost instantly, and it is troublesome that violent extremism, ideologies and ideas can be spread globally with very low costs. The users, applications and communities are inter-connected and extremism has become a supranational phenomenon [START_REF] Srifi | Transnational extremism, social media and the state[END_REF]. In this context, it is helpful to detect extremist content online in order to monitor the outreach of extremist ideas on social media. However automatic detection faces different challenges: the sheer amount of information, making it impossible to analyze by human operators, the many types of extremism, and inherently, challenges of the language itself, including ambiguities and semantic variations.

Detection of extremism-related content and users on social platforms is not new, and several applications addressed the identification of extremist behaviours [START_REF] Scrivens | Exploring radical right-wing posting behaviors online[END_REF] or the spread of extremist propaganda [START_REF] Ferrara | Contagion dynamics of extremist propaganda in social networks[END_REF]. For textual content analysis, monitoring specific keywords or terms is often used to detect online extremism [START_REF] Johansson | Detecting linguistic markers of violent extremism in online environments[END_REF]. Such approaches generate a lot of irrelevant material due to the elusive nature of extremism, which is a multifaceted and often ill-defined concept. This paper presents a comparison of methods developed to detect extremist content in French. The application scenario is the semi-automatic consolidation of an existing knowledge base, where methods for automated classification are applied in order to facilitate manual efforts to grow the data pool of French right-wing extremist content. The approach is intended to support expert's decision regarding the integration of incoming content. In addition, results can also be valuable for flagging and removing extremist content from social platforms.

Extremism detection is often casted as a binary classification task: given a message, a paragraph, or any other type of text, the algorithm will classify it as conveying extremist ideas or not. Although binary categorization of extremismrelated attitudes is an oversimplification and results may be coarse-grained, in the long run this comparison can reduce the workload of human analysts. Binary classification is a suitable task because learning methods often ignore the meaning and interrelationships among data items and the internal characteristics of extremism-related content cannot be well explained.

While online extremism detection is tackled by several research communities, training data and learning models are not easily available for languages other than English. Few studies utilize data sets in French and none of the existing studies controls for both data characteristics and model complexity when analyzing online data.

This paper investigates how the joint analysis of data and learning models improve the quality of extremism detection. Main contributions of the paper are: (i) providing a data set for extremist content analysis in French, which can be widely used for training data for machine learning approaches; (ii) developing several classification methods and evaluate their results thanks to manual validation.

The reminder of the paper is organized as follows: section II discusses related approaches; section III presents the construction of data sets and the general architecture for data processing. Experiments and results are discussed in sections IV and V, respectively, while section VI concludes the paper and sketches directions for future work.

II. RELATED WORKS AND BACKGROUND

This section introduces the notion of extremist content and covers several studies related to extremism detection.

A. Extremist content online

The concept of extremism is complex. Intuitively, extremism is defined as supporting beliefs that are extreme, although extremism covers a broad and complex range of social phenomena [START_REF] Bötticher | Towards academic consensus definitions of radicalism and extremism[END_REF] and refers to attitudes that are unacceptable to the general perception of the society. An important point to note is that defining extremism as a process or concept suffers from a lack of consensus within the sociology field itself [START_REF] Thorburn | Measuring extremism: Validating an alt-right twitter accounts dataset[END_REF]. Several authors pointed in to the fluid nature of extremism, including a verity of discourses from revisionists, racists, skinheads and extremist hooligans, nationalists or identity-based groups, paramilitary, xenophobic or anti-migrant groups [START_REF] Araque | An ensemble method for radicalization and hate speech detection online empowered by sentic computing[END_REF]. For example, the features of right-wing extremism in Europe, change considerably from one country or region to another. In order to avoid the difficulty of explicitly defining the concept of extremism the context of this study was restricted to the analysis of extremism in French blogosphere. Special attention was given to the data set construction. Data collection first consists in selecting several sources acknowledged as extremist and then collecting of data released by those sources. A source can be a user, a hashtag, a keyword or a combination of those three elements.

Data was collected for a short period of time (one year), in an effort to minimize harm to online privacy, For ethical concerns, collected hashtags and URIs are not provided in this paper and the results are presented in a way that avoids reidentification of any URIs, hashtags or keywords that have been used to collect the content. All the collected URIs where deleted after preparing this work.

B. Automated approaches for extremism detection

The literature survey shows that detection of extremist content online has only few peer-reviewed research papers [START_REF] Fernandez | Artificial intelligence and online extremism: Challenges and opportunities[END_REF]. However, detection of extremist users or identification of topics related to extremism attracted more attention, and several data sets, classification methods and tools have been developed [START_REF] Gaikwad | Online extremism detection: A systematic literature review with emphasis on datasets, classification techniques, validation methods, and tools[END_REF]. As an example, Hartung and colleagues [START_REF] Hartung | Identifying rightwing extremism in german twitter profiles: A classification approach[END_REF] developed a method to support the identification of extremist users and rank unknown profiles based on their relative proximity to other users in the vector space. More specifically, the authors detected right-wing extremist content in German Twitter profiles by using a combination of several feature sets, including lexical (word stems), social identity, emotional, and lexico-syntactic (sentence constructions).

Gupta and colleagues [START_REF] Gupta | Detecting and characterizing extremist reviewer groups in online product reviews[END_REF] presented a solution to characterize extremist groups and distinguish them from moderate ones. The approach detected extremists by first observing their actions as a group and identifying a set of features and then using a supervised learning model integrating those features. The solution was applied to find extremist users writing product reviews on Amazon.

Detection of extremist content online was also investigated by adapting methods and techniques for natural language processing [START_REF] Torregrosa | A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges[END_REF]. The analysis of linguistic patterns of extremist supporters is described in [START_REF] Panizo-Lledot | Describing alt-right communities and their discourse on twitter during the 2018 us mid-term elections[END_REF], where the authors used topic extraction algorithms to characterize the discourse of right wing communities on Twitter during the 2018 Mid-term elections in the US. The results showed that communities commonly used racist language and anti-immigration themes, criticized mainstream media and advocated for alternative media sources. Virtual communities of white supremacist were investigated in [START_REF] Brindle | The language of hate: A corpus linguistic analysis of white supremacist language[END_REF] and the authors showed that those users tend to use specific vocabulary, abbreviations, and coded words to express their beliefs.

Several studies examined the joint use of domain specific and agnostic word embedding with deep learning to detect the content released by white supremacists. Among them, [START_REF] Alatawi | Detecting white supremacist hate speech using domain specific word embedding with deep learning and bert[END_REF] used BERT (Bidirectional Encoder Representations from Transformers) [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF], a bidirectional language model trained on very large datasets (i.e., Books corpus and Wikipedia) and contextual representations. The models were built in order to detect terms, abbreviations, and intentional misspellings of white supremacists. The results show that BERT outperforms domain specific approaches, whilst the domain specific approach is able to detect intentional misspellings and common slang from hate community, that the BERT model fails to detect. BERT was also used by [START_REF] Mozafari | A bert-based transfer learning approach for hate speech detection in online social media[END_REF] to detect hateful messages on social media. The authors developed fine-tuning methods based on transfer learning and they showed that the approach achieves considerable performance in terms of precision and recall although the model also captures some biases in data annotation and collection process.

Although the research trend is dynamic, less attention has been given to developing corpora and lexical resources for other languages aside from English [START_REF] Sanguinetti | An italian twitter corpus of hate speech against immigrants[END_REF]. There are only a few ontologies for French [START_REF] Battistelli | Building a formal model for hate detection in french corpora[END_REF] in the more general area of social data analysis [START_REF] Abdaoui | Feel: a french expanded emotion lexicon[END_REF] and translating English resources does not offer good results, as demonstrated by Ghorbel and Jacot [START_REF] Ghorbel | Sentiment analysis of french movie reviews[END_REF]. The authors translated English SentiWordNet entries into French and showed that even if the translation of entries is correct, some parallel words do not always share the same polarities or meaning across both languages, simply due to differences in common usage of words.

The goal of this contribution is to provide the community with means to improve extremism detection online. The paper also contributes to the development of learning models and annotated data sets for French.

III. DATA COLLECTION AND PROCESSING ARCHITECTURE

A. Data collection and annotation

Extremist content were collected by using the following features:

• specific hashtags identified in the French social media;

• a set of representative keywords;

• several sources of extremist groups that are active online. Data was collected from December 2019 to December 2020 and includes streams from Twitter and discussion forums, all of which were merged together. Merging those sets, although provided by distinct sources, allows us to build a homogeneous corpus according to the principle of homophily [START_REF] Oussalah | On detecting online radicalization using natural language processing[END_REF] stating that users within a certain group setting have a tendency to develop a similar use of language when developing the ideas of the group. In spite of using keywords, the subject matter and the homogeneity of the corpora were difficult to establish after the collection step, because online users user can write about any subject.

After collecting rows of online data, several validation and characterization steps were performed on the entire collection to ensure that data sets are relevant and to detect finer categories of extremist content, as shown in Fig. 1. The annotators have solid background on extremism analysis from a social perceptive, and the team adopted an annotation protocol to ensure agreement and homogeneity of annotations. First, the team was provided with a set of symbols, hashtags and notions associated to extremist ideologies. This input was used as a baseline to decide which content is considered extremist and which is not. Then, the annotators carefully studied both the information comprised in texts and about the texts (namely the source or keywords used to collect the tweet) in order to identify textual units carrying relevant extremist or non-extremist content. The length of textual units ranges from one to eight sentences, as one sentence only is often not enough to identify extremist data.

By following the annotation protocol, each textual unit was labeled either as extremist (1), non-extremist (0) or unknown (x) by each annotator. For the final collection, only the extremist and non-extremist textual units were selected. The total number of textual units in the final data set is 1728, out of which 1129 were labeled extremist and the remaining 599 were labeled as non extremist. Table I shows the distribution of categories in the annotated corpus. In spite of selecting data with specific keywords, at the end of this phase, the final collection is still imbalanced. Thus, the two classification categories (extremist and non extremist) are not equally represented.

B. Overview of the generic architecture

The architecture of the framework developed for content classification is presented in Fig. 3. The framework is composed of three major components, implementing preprocessing, machine learning and deep learning models. The comparison of results was done manually.

The first component implements basic preprocessing procedures. The text was lowercased, special and numeric characters removed, and words tokenized. The texts have also been segmented into sentences and tagged for part-of-speech (POS) labels. Regarding the content, hyphens ('-') were replaced with a space to split up the hyphenated term and hashtag symbols ('#') were replaced with a space. The hashtag terms were retained without the symbol, as these often indicate topics of conversation and would be helpful as features for content characterisation.

The second and third components of the architecture implement several learning models described hereafter.

IV. PRELIMINARY EXPERIMENTS

For experiments, several feature-based models were developed and performance measures (recall, precision, accuracy and f-measure) were used to compare those models with CamemBERT [START_REF] Martin | Camembert: a tasty french language model[END_REF], a weakly supervised language model pretrained to process texts in French Data used in the experiments followed standard curation, by filtering out URLs, stop words and the words containing less than 3 characters.

A. Feature-based models

By considering previous approaches in the literature, four sets of features were selected that are relevant to detect extremist content.

• Surface features: presence or absence of specific punctuation marks (exclamation marks) and URLs. • Domain specific features: presence of words from HurtLex, a multilingual lexicon listing hate-specific words [START_REF] Bassignana | Hurtlex: A multilingual lexicon of words to hurt[END_REF]. • Polarity features: positive, negative and neutral polarity of words; • Linguistic features: presence of specific verbs. In all experiments, two representations of data were used: bigrams and trigrams Tf/IDF [START_REF] Aizawa | An information-theoretic perspective of tf-idf measures[END_REF] and bag-of-words (BOW) [START_REF] Brownlee | A gentle introduction to the bag-of-words model[END_REF]. By using those features, three classifiers have been built based on Support Vector Machine (SVM) [START_REF] Mammone | Support vector machines[END_REF], see tab. II. SVM is a supervised machine learning method in which the learning machine is given a set of examples (input) with the associated labels (output values). Based on labeled training data, the algorithm outputs an optimal hyperplane, which categorizes new testing data. SVM was used for this work as it can be easily customized to work on various numerical representations of textual data and it works well with small amount of training data.

Data vectorization was done with Scikit-learn [START_REF] Kramer | Scikit-learn[END_REF] by ignoring stopwords and words having frequency less than 6 and experiments have been conducted with several combinations of the features above.

B. CamemBERT

The last model used in this work is CamemBERT, a language model for French. CamemBERT is a derivation of BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF], a learning model based on the transformer architecture [START_REF] Vaswani | Attention is all you need[END_REF]. BERT was pre-trained with two learning objectives that force the model to learn semantic information within and between sentences. Thanks to this training approach, the BERT model embeds each word based on the surrounding words.

In order to adapt BERT to French, CamemBERT was pretrained on general corpora in French, such as books or Wikipedia pages. In the context of this work, CamemBERT was used for classification as the model consistently outperforms the state-of-the art models on downstream natural language processing tasks. For this work, the word embedding representation uses a French Wikipedia2Vec with 300 dimensional embeddings trained on words from Wikipedia in French.

Tab. III presents hyperparameters used for training and evaluation. In order to optimize the results of CamemBERT, the number of epochs was increased until reaching a convergence of the loss function so that the model makes as few errors as possible when making predictions.

V. RESULTS EVALUATION Experimental protocol. All learning models were trained and used for extremist content detection. Two experiments were carried out by using first an initial data set collected online and validated by researchers in sociology and then an augmented version of this data set, where oversampling was used to cope with imbalanced categories. The goal of the experiments is to compare the results of different approaches according to performance metrics and also to investigate the impact of data oversampling.

The results presented in this paper were obtained by using 10-fold cross validation, since the number of extremist annotations in the data set is rather small, Results using the initial data set. The first experiment was carried out on the initial data set by mixing features and classifiers. Table IV presents the results in terms of accuracy (A), Precision (P), Recall (R) and macro-averaged F-score (F). Maximal values are presented in bold. Results show that the three SVM classifiers provide similar results in terms of Precision and Accuracy, with values ranging from 0.66 to 0.68, while CamemBERT performs better and reaches around 0.75 for both Accuracy and Precision. However, the three SVM classifiers provide belter results on Recall, with values ranging from 0.91 to 0.99 while CamemBERT reaches only 0.91 on this metric.

For this first experiment, CamemBERT provides better detection results: the Precision and Accuracy are both around 75%, which means that the model correctly identifies extremist paragraphs in approximately 75% of cases.

Among the three classifiers, when the SVM model is combined with linguistic characteristics, the classifier is not reliable and shows low performance on new data. Results also show that SVM with BOW and TF/IDF vectors have similar results and are more relevant than the set of linguistic characteristic for extremist content detection.

Results after oversampling. The second experiment was carried out by re-balancing the distribution of classes in the initial data set thanks to an oversampling strategy. Thus, the minority class was over-sampled by creating additional paragraphs in order to fairly represent both classes. Data was rebalanced by using SMOTE (Synthetic Minority Over-sampling TEchnique), [START_REF] Chawla | Smote: synthetic minority over-sampling technique[END_REF] a technique that creates synthetic data by using a strategy based on nearest neighbors. The SMOTE implementation used six nearest neighbors and created 258 new samples for the minority (which is to say non extremist) class. Tab. V shown the new and more balanced distribution of categories after data sampling, First, the results show that after using oversampling, values of Recall are lowered for all classifiers. Values of Precision and Accuracy are improved for CamemBERT (from 0.76 to 0.80 and from to 0.75 to 0.78 respectively).

The values of the same metrics (Accuracy and Precision) are also significantly improved for the SVM model using Tf/IFD. However, Accuracy and Precision are almost unchanged for the SVM classifier using BOW while their values are drastically decreased for the SVM classifier using linguistic features.

More notably, after oversampling, the SVM model using Tf/DF vectors provides results similar to CamemBERT on Accuracy and Precision, while still having higher Recall. With a better overall F-score, this classifier outperforms CamemBERT on this second experiment.

The oversampling technique proved to be efficient in increasing the Recall of the minority class that was re balanced, although it strongly affected the Recall and, for some classifiers, also the Accuracy of the initial majority class. Thus, the oversampling highlights two classifiers having similar results (SVM usinf tf/IDF and CamemBERT) that largely outperform the models using SVM and BOW, and SVM and linguistic features.

General remarks. The analysis of results from both experiments shows that among all the combinations of features and classifiers experimented, CamemBERT performs best for Accuracy and Precision. The only exception is the classification of the augmented data set, where one traditional classifier (SVM with Tf/DF vectors) provides better values for Recall and almost equivalent values for Precision and Accuracy.

Results also show that classifiers using linguistic features have very low values of Accuracy and Precision for both experiments. The same model along with data augmentation has even decreased Accuracy, Precision and Recall.

Regarding the impact of the initial and the augmented data sets, the oversampling improved the values of Accuracy and Precision for all learning models that are not using linguistic features. In addition, the Recall was decreased for all classifiers.

The poor performance of the model using linguistic features can be the consequence of using generic, non-specific features, unable to clarify the distinction between extremist and nonextremist content. Thus, there is a need to provide deeper insights and in-depth domain adaptation in order to improve the quality of results. However, further experimentation with different sets of features are required to investigate this aspect.

VI. CONCLUSION

This paper presents a comparison of classification methods developed for binary classification of extremist content in French. Three feature-based models have been developed by using SVM and a combination of features including Tf/IDF, BOW and shalllow linguistic features. CamemBERT, a pretrained language model for French, was also used.

Experiments were carried out by using realistic data sets gathered online and show that learning models are of variable efficiency when it comes to extremist detection. Data sampling was used to improve the model's performances for the classification task. Comparison of results show that CamemBERT is the best performing model with respect to Accuracy and Precision, although by re-balancing the data set the model using SVM and Tf/IDF vectors achieves similar performance.

As a general conclusion, all the models are still making classification mistakes and this task proved to be challenging for the learning models to achieve ideal results on. This is mainly due to the subjectivity involved in annotating extremist texts and the lack of explicit annotations labeling extremist content. Obviously, the binary annotation (extremist/ non extremist) proves to be of limited relevance when it comes to building efficient classifiers.

For future work, the first direction intends to explore several fine-tuned models and to use additional data sampling and augmentation techniques in order to improve the results of learning models. Another direction will investigate the use of additional features, by considering finer social categories of extremist [START_REF] Dragos | Ontology adaptation for opinion mining in french corpora[END_REF] or emotional annotations [START_REF] Dragos | Angry or sad? emotion annotation for extremist content characterisation[END_REF], and analysing their impact on algorithms.

There are also several limitations of data sets and models presented in this paper, such as ignoring hashtags and emoticons, although they are relevant for online content analysis. Future research will also address some of those limitations.

Fig. 1 .

 1 Fig. 1. Selection and validation of data sets Data sets have been labelled as extremist or non-extremist by a team of three annotators, see Fig.2.

Fig. 2 .

 2 Fig. 2. Extremist and non-extremist paragraphs

Fig. 3 .

 3 Fig. 3. General architecture for extremist content classification

ACKNOWLEDGMENT

This work was supported by ASTRID, a joint research program of ANR (The French National Research Agency) and DGA (The French Defence Procurement Agency).

DATA AVAILABILITY STATEMENT

The annotated data set is available for research purposes upon request.


Daniel Roger
email: daniel.roger@univ-artois.fr

Senior Member, IEEE Sonia Ait-Amar
email: sonia.aitamar@univ-artois.fr

Member, IEEE Ewa Napieralska
email: ewa.napieralskajuszczak@univ-artois.fr

Senior Member, IEEE Piotr Napieralski
email: piotr.napieralski@p.lodz.pl

A Proposition for Improving the Design of Motor Windings for low-Pressure Environment

In low-pressure environment, partial discharges (PDs) appear for lower voltages, shortening strongly the machines life times fed by PWM inverters. A new approach is proposed for designing AC motor windings free of PD at low pressure. The method consists of adding a thin resistive layer on the outer surface of the enameled wire used for winding the machine. Thereby, PDs occur only in critical zones, near the wire connections, rather than randomly in coils. Consequently, a coil design with a small additional quantity of varnish in critical zones can increase strongly the partial discharge inception voltage (PDIV). The paper proposes a theoretical analysis of the improvement based on Paschen's law for its application in non homogenous electrical fields.

I.

INTRODUCTION

In recent airliners, the electrical energy takes a larger and larger place because it brings many advantages comparing to hydraulic and pneumatic ones. For increasing the electric power, the aircraft industry has chosen higher voltages, switching from 115V AC to 540V DC. High power density electric actuators are connected to the 540V DC grid by inverters that provide an efficient machine control and a full reversibility of the power. However, the electronic switches of the inverters impose steep-fronted voltage pulses that cause repetitive short voltage spikes in the machine windings and induce PDs and an earlier aging of the machine Electrical Insulation System (EIS), when their magnitudes exceed the PDIV [START_REF] Maussion | Lifespan and aging modeling methods for insulation systems in electrical machines: A survey[END_REF][START_REF] Lahoud | Electrical Aging of the Insulation of Low-Voltage Machines: Model Definition and Test With the Design of Experiments[END_REF]. For Permanent Magnet Synchronous Motors (PMSM), the winding can be made with concentrated windings (one coil per stator tooth), with a deterministic design of coils. The exact position of each turn is known and the coil design can he made for getting a good distribution of voltage spikes endured by the turn-to-turn insulation [START_REF] Fabiani | Aging acceleration of insulating materials for electrical machine windings supplied by PWM in the presence and in the absence of partial discharges[END_REF][START_REF] Iosif | Voltage stress predetermination for long-life design of windings for electric actuators in aircrafts[END_REF]. Cheaper induction machines are also used but they need distributed windings. The coils of such motors have a random topology. The turn-to-turn voltage may be large because the probability to have the input turn near the output one is not negligible [START_REF] Duchesne | Study of wire distribution in a slot of a motor fed by steep fronted pulses for lifetime extension[END_REF]. The thin insulating layer of the enameled wire and the residual air between them must resist to a high voltage stress and PDs may appear inside coils. The paper proposes technological solution that consists of adding a thin resistive layer on the outer surface of the enameled wire. This layer reduces the electric field in the residual air voids between the turns and concentrates the electric field in the polymer. However, a critical zone appears near the wire connections, where the enameled wire must be stripped. The paper details theoretical investigations able to compute the PDIV in such critical zones.

II. ENVIRONMENT OF AIRCRAFT EMBEDDED MACHINES

Two very different environments physical environments exist for embedded motors; they can be placed in the cabin or outside. For the first case, the air-control of the cabin provides classical temperatures at a pressure that gives comfortable conditions to passengers. In old aircrafts, the pressure corresponds to mountain conditions at an altitude of 2000m and a little less for recent aircrafts (1850m). The cabin pressure is more or less 80% of the sea-level pressure. However electrical machines can be placed outside the cabin where the environmental conditions are very different. Aeronautical engineers work with a standard atmosphere [START_REF][END_REF] that gives temperatures and pressures representatives of any point of the world. Table 1 gives 3 typical values. The lower pressure and temperature influence the performance of the Electrical Insulation System (EIS) of the electric actuator windings. The motor windings are generally made with enameled wire and impregnated with a varnish that provides a mechanical coherence and better insulation characteristics. However, it is practically impossible to guarantee the absence of small airvoids between turns inside the windings. Partial Discharges (PDs) may occur in these air-voids during the voltage spikes caused by the PWM inverter that feeds the machine. When they exist, PDs accelerate the EIS aging by slow erosions, at a microscopic scale, of the thin polymer layer of the enameled wire.

It is well known that PDs apparition conditions depend on temperature and pressures in the small air-voids [START_REF] Liu | Low Pressure Partial Discharge Investigation with FEM Modeling for a Twisted Pair of Insulated Conductors[END_REF]. Two cases must be analyzed because the air-void can be internal, without any contact with the ambient air, or external at ambient pressure. For the first case, the air-void volume is constant. The machine is generally made with the standard 3 phases "vacuum pressure impregnation" (VPI) at standard atmospheric pressure (sea level). During the first step, the liquid varnish penetrates in the motor winding placed in a partial vacuum. The second phase consists in applying a pressure to the whole system when the varnish is still liquid helping its penetration. The third phase consist in polymerizing the varnish in a hoven at ambient pressure. At the beginning of the polymerization process, the varnish is still liquid; the air-void volume change for getting a global pressure balance. The final volume of residual closed airvoids is fixed when the varnish becomes solid. Consequently, the initial data of closed air-voids can be estimated to the pressure in the polymerization oven 𝑝 ! = 1013 hPa and its temperature 𝑇 ! = 120 °C (393°K). The pressure at another temperature can be estimated using the perfect gas law 𝑝 𝑉 = 𝑛 𝑅 𝑇. The product pressure volume depends on the number of gas molecule (n), the constant of the perfect gas (R) and the absolute temperature in Kelvin (T). Considering two absolute temperature T 1 and T 2 , the ratio of the corresponding pressures in a given air-voids is:

𝑝 ! = 𝑝 ! 𝑇 ! 𝑇 ! (1)
Table 2 gives the estimation of gas pressures in closed airvoids at 3 altitudes computed by (1). For opened air-voids the pressure is the atmospheric one at the considered altitude. The comparison of tables 1 and 2 shows that the pressure is much lower for opened air-voids. This case is more stressful for the EIS.

Paschen's formulae (2) results of old experimental works on the breakdown voltage in gas a given pressure p, placed between two flat electrodes at a distance d [START_REF] Paschen | Ueber die zum Funkenübergang in Luft, Wasserstoff und Kohlensäure bei verschiedenen Drucken erforderliche Potential differenz[END_REF]. This formula predicts the avalanche threshold V Th at from the pressure distance product. The Paschen's formula uses the old unit for the pressure (Torr); the constants A and B use these units. 𝐴 = 15𝑇𝑜𝑟𝑟 !! 𝑐𝑚 !! ; 𝐵 = 365𝑉𝑇𝑜𝑟𝑟 !! 𝑐𝑚 !! . 𝛾 = 0.01 is the second Townesend coefficient [START_REF] Townsend | Electric discharges through gases, Ionization of gases[END_REF].

𝑉 !! = 𝐵 𝑝. 𝑑 𝐶 + ln 𝑝. 𝑑 𝑤𝑖𝑡ℎ 𝐶 = 𝑙𝑛 𝐴 ln (1 + 1/𝛾) (2)
Another old works made by Peek improve the Paschen's law taking the gas temperature into consideration [START_REF] Peek | Dielectric phenomena in high Voltage Engineering[END_REF]. Peek propose a correction of Paschen's threshold voltage 𝑉 !!! computed at room pressure (760𝑇𝑜𝑟𝑟) and temperature (293𝐾).

𝑉 !! 𝑝, 𝑇 = 293 𝑇 𝑝 760 𝑉 !!" (3)
Recent experimental works confirm the Paschen's law for the right part of the curve, for distances and pressure over the Paschen's minimum [START_REF] Osmokrovic | Mechanism of electrical breakdown of gases at very low pressure and interelectrode gap values[END_REF][START_REF] Sili | Temperature Dependence of Electrical Breakdown Mechanism on the left of the Paschen Minimum[END_REF]. Another recent work confirms the Peek correction for very cold Nitrogen [START_REF] Massarczyk | Paschen's law studies in cold gases[END_REF].

Figure 1 shows the corresponding curves for temperature and pressure in opened air-voids at 3 altitudes. The horizontal axis is the distance d between the flat electrodes of Paschen's experiences. These curves can be used for estimating the partial discharge inception voltage (PDIV) at high altitude from a measurement made at sea level. The small circles added on this figure illustrate the case of a twisted pair made with a standard enameled wire whose diameter is 0.7mm. The PDIV measured at room temperature and pressure is 1.25kV peak . When the variation the polymer permittivity is neglected and considering the same geometry, Paschen's curve, with Peek's correction, predict a PDIV estimated to 420V peak at 10000m. Consequently, for avoiding PDs in standard machines placed outside the cabin of airliners, the voltage spikes must be much lower. With out any change in the motor design, the supply voltage should be more of less 1/3 of the standard one, which is very disadvantageous for the drive global performances. Let us remind that Paschen's experiences were made with flat electrodes that create uniform fields. In the opened air-voids of motor windings, the electric fields are not uniform. A more detailed analysis of Paschen's hypothesis shows that Paschen's law can be used with a reasonable error [START_REF] Roger | A method to reduce partial discharges in motor windings fed by PWM inverter[END_REF].

III. DRAWBACKS OF PDS IN A MOTOR WINDING

Traditional motor are wound with enameled wires made of a copper conductor insulated by thin polymer layers. The most widely used enameled wires are made with two insulating layers. The first one, in contact with copper, is a polyesterimide (PEI) providing a good adherence on the copper. The outer layer is made of polyamide-imide (PAI), which provides the mechanical and thermal properties [START_REF]W 210 enameled wire[END_REF]. The global thicknesses of the insulating layers depend on the diameter of the wire and its grade [START_REF]Specifications for particular types of winding wires -Part 0-1: General requirements -Enamelled round copper wire[END_REF]. A typical polymer thickness is 40µm for a 0.7mm wire diameter. The enameled wire is manufactured with a complex industrial process, which consists in depositing several thin layers for getting the required global insulation thickness. Consequently, no significant air-void can exist inside the insulation layer of the enameled wire. PDs occur only in air-voids existing between turns or between wires and stator teeth. For both cases PDs cause a slow erosion of the PAI layer of the enameled wire.

Figure 2 shows a microscopy of the cross-section of a twisted pair eroded by a large PD activity; the abrasion near the contact point is clearly visible.

For building machines able to operate during a long lifetime, the windings made with standard enameled wires must be free of PD, despite the low-pressure environment.

IV. PRINCIPLE OF THE PROPOSED IMPROVEMENT

In a motor coil, the outer surfaces of the wires are in contact together. Therefore, at a microscopic scale, an additional resistive layer deposited on each turn will create equipotential surfaces able to reduce strongly electric field in the air-voids between turns. Preliminary investigations are made on twisted pairs covered with a silver conducting paint. Fig. 3 shows the classical twisted pair on the left and the modified one with a conducting paint on the right. With the conducting paint, the electric field is concentrated in the thin PEI-PAI layer that has dielectric strength much higher than air. Figure 4 present an image of the PD activity of both twisted pairs fed by at 3kV peak 50Hz. The pictures were made in a deep darkness with a night vision device called "CoronaFinder" [17] placed at 0.8m. This optical system is able to make a visible image from the UV and IR radiation emitted by the observed objects. For these pictures the voltage was much over the PDIV for getting many PDs.

The left picture shows that PDs are distributed along the twisted pair; the right one shows that they are concentrated on the ends of the conducting paint layer. These pictures show that the conducting layer moves PDs to small zones. The abrasion is now localized in deterministic small areas rather than distributed along the twisted pair. However, electric measurements show a slightly lower PDIV for the modified twisted pair (1.1kV peak rather than 1.25kV peak ). Therefore, at a given voltage over the PDIV, the abrasion due to PDs will be more intense. Without any corrective action, the enameled wire lifetime will be much lower.

V. ANALYSES OF THE ELECTRIC FIELD IN CRITICAL ZONES

A 2D finite-element (FE) linear electrostatic simulation is performed for computing the electric field in the critical zones where PDs may appear. The electric field is computed in the polymer layer and in the ambient air in the critical zone near the end of the silver conducting paint. Figure 5 shows the FE mesh of the electrostatic problem built at the micrometer scale. Figure 6 presents the corresponding color map of the electric field magnitude. The field is computed with a constant polymer relative permittivity of 4 and for 1kV imposed between the boundaries (conducting paint and copper wire). The simulation results can be applied with other voltages because of the linearity of the electrostatic problem. In Fig. 6, the highest field values are removed because the sharp end of the conducting paint is not realistic. The field lines in the critical zone or air, where PDs may appear are plotted in figure 7. The global shape of these field lines is far from the straight lines on the uniform field of the Paschen's experiences. A deeper analysis must be made for applying his results, reminding the hypothesis made by Paschen for interpreting these experiences.

VI. DISCUSSIONS ON PASCHEN'S HYPOTHESIS FOR PREDICTING THE PDIV IN A MOTOR WINDNG

Let us remind that Paschen's works were made more than a century ago. At his time, the physics was mainly based on a corpuscular approach of free electrons accelerated by the Coulomb force 𝐹 = 𝑞𝐸. An electronic avalanche appears when the accelerated free electrons acquire enough energy for ionizing gas molecules when they collide. This electronic avalanche creates more complex phenomena corresponding to PDs or breakdown if no external phenomena stops the discharges. The PDIV corresponds to the electronic avalanche threshold. In Paschen's experiences, the free electrons were accelerated following the straight field lines the speed and the force are collinear vectors. The computation of the acquired kinetic energy is a scalar problem. At a field supposed exactly equal to the avalanche threshold, a collision of an accelerated free electron with a gas molecule absorbed the kinetic energy for creating a new free electron, which is accelerated by the Coulomb force. For a uniform field the free electrons follow the field lines. The trajectory of free electrons is superimposed to the field lines. The distance d between the flat electrodes is the field line length. However, for a non-uniform field, the electrons inertia may change the free electron trajectories, they do not follow exactly the field lines. The computation of free electron energy is more complex because the free electron speed and the Coulomb force are no more collinear vectors. The proposed analysis uses the Paschen's hypothesis but it adds the inertial equation of a mass m placed in a field force 𝐹 = 𝑞𝐸. The influence of the gravity is neglected. The mechanical problem is solved in an electric field map proportional to the electric field in the critical zone computed by the FE simulation at half the experimental PDIV of the modified twisted pair. This value is defined considering a perfect symmetry of the modified twisted pair: the same phenomena occur on each wire covered with a conduction paint. The air of the critical zone is supposed to be made only of nitrogen and oxygen.

The ionization energy of these molecules is 13.6 eV for Oxygen and 14.5 eV for Nitrogen. An average value rounded at 14eV is considered for defining the points where energy of a free electron is absorbed by an ionizing collision. The elastic collisions does not change free electron energies, they are not considered. The mechanical equation is solved with a very short time step of 0.01ps; results are plotted in figure 8. Two voltages 550V and 183V (half the PDIV measured on modified twisted pairs and half the PDIV estimated at 10000m) are considered. This figure shows that the higher voltage create a stronger acceleration. The free electron needs a shorter time to reach the ionization energy level. Figure 8 is plotted for a starting point of the first free electron at the beginning or line field 15 of Fig. 7. For another starting point situated at the beginning of a longer field line, the curves are similar but with weaker accelerations and longer times between two ionizing collisions because the field is lower but the mechanism is the same. The trajectories of successive free electrons are plotted in figure 9 for 2 starting points corresponding to the field lines 10 and 15 in Fig. 7. The small circles added on the curves correspond to the position of free electrons when they get the ionizing energy for collisions. For computing these trajectories, a square mesh of 0.5µm is used. Figure 9 shows that, for this geometry and a voltage equal to half the PDIV at room temperature and pressure, the free electron trajectories (black lines) are very near the corresponding field line (red lines). It is not exactly the case for a lower PDIV (green lines), where the distance between two successive ionizing collisions is larger. The trajectory and field line lengths are computed and compared in table 3. For lines length under 100µm of the considered geometry, the maximum error is more or less 5% when the field line length is considered rather than the actual free electrons trajectories. Therefore, Paschen end Peek theories can be applied for estimating the PDIV of modified motors up to an altitude of 10000m when the Paschen's distance d is the field line length; the voltage is the potential difference between the ends of the considered line. More theoretical details are given in [START_REF] Roger | A method to reduce partial discharges in motor windings fed by PWM inverter[END_REF].

VII. INFLUENCE OF AN ADDITIONAL POLYER DROP IN THE CRITICAL ZONE.

For getting higher PDIV with the modified twisted pairs, il is possible to add a drop on varnish in the critical zones. Figure 10 presents FE simulation results considering the same geometry with an additional varnish drop, which is represented in 2D by a circle arc, which radius is 40µm. The varnish relative permittivity is 𝜀 ! =4. It can be seen that the higher field modulus is now inside the polymer drop; the critical zone in air is now shifted after the polymer drop, with another geometry and weaker fields. The corresponding field lines are plotted in Fig. 11; breaks at the border of the polymer drop are typical of the permittivity change.

The field line length in air and the corresponding potential differences are computed for both simulations and plotted in red on Paschen-Peek curves in figure 12. The "+" signs correspond to the FE simulation made without any polymer drop; the "o" signs ate computed by the second simulation.

The number placed near the signs corresponds to the field line ones. The "+" signs shows that lines 17 and 18 are very near the Paschen-Peek curves at sea level, the applied voltage is just below the PDIV at room temperature and pressure, which corresponds to the experimental measurement. With a slight increase of the peak voltage, PDs will appear between lines 17 and 18 for a distance between 15 and 22µm. At an altitude of 10000m, many PDs will appear but the physical problem is more complex, Paschen-Peek theory cannot be used for studying them.

The positions of the "o" signs show a large security margin at sea level; it is smaller at 10000m. A small voltage increase will initiate PD; is a zone delimited by field lines 4 and 7 that correspond to much longer average distances of more or less 50µm.

VIII. CONCLUSION

A new approach for designing ac motor windings free of partial discharges (PD) is proposed. The method consists of adding a thin additional layer with a non-zero electric conductivity on the outer surface of the enameled wire used for making the machine winding. This additional layer reduces the electrical fields, in the residual air-voids existing between turns in motor windings. However, this additional layer moves the critical zones, where PDs may occur, in critical zones localized near the wire connections. With such deterministic localizations, it is possible to reduce strongly the PD activity by adding small quantities of varnish in the critical zones.

After studying the environment of electrical machines embedded in airliners, the influence of pressure and temperature on the PDIV is explained. Specific investigations on the proposed geometry places in the external environment of airliners show that the classical Paschen's law modified by Peek can be used with an error of about 5%. On this basis, a simple 2D finite element is proposed for estimating the PDIV of the proposed solution in the harsh environment of an airliner.

The experimental investigations were made with a modified twisted pair covered by a silver conducting paint. It is not possible to build motor coils such a modified wire because its fragility its too high conductivity. Indeed, in a motor coil, the outer surfaces of wires in contact form shortcircuited turns, which increase Joule losses due to currents induced by the variations on the magnetic flux. The proposed developments are at the beginning of the technological maturation process; investigations are in progress with thinner additional layers that have a lower conductivity in order to find a balance between the larger PDIV and the extra Joule losses.

Fig. 1 .

 1 Fig. 1. Breakdown voltage in air versus the distance between flat parallel electrodes for 3 altitudes.

Fig. 2 .

 2 Fig. 2. Microscopy of the cross section of a twisted pair aged by a strong PD activity where the polymer layer abrasion is visible.

Fig. 3 .

 3 Fig. 3. Classical twisted pait (left); modified twisted pair (right).

Fig. 4 .

 4 Fig. 4. DPs observed by a corona finder device (left standard twisted pair, right modified twisted pair with a silver conducting paint).

Fig. 5 .

 5 Fig. 5. Mesh of the critical zone and of the polymer layer.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Electric field in the critical zone of air and in the polymer insulation layer.

Fig. 8 .

 8 Fig. 8. Free electron energy between two ionizing collisions gor line fiels 15.

Fig. 9 .

 9 Fig. 9. Trajectories of free electrons and field lines 10 and 15.

Fig. 10 .

 10 Fig. 10. Eclectic field modulus with an additional varnish drop.

Fig. 11 .

 11 Fig. 11. Field lines in the critical zone of air and in the polymer drop.

Fig. 12 .

 12 Fig. 12. PDIV estimation using Paschen and Peek treory and fiend-line length.

TABLE 1 .

 1 PRESSURE

		AND TEMPERATURES DEFINED IN THE STANDARD
		ATMOSPHERE [6]
	Altitude	Altitude (feet)	Temperature	Pressure
	[m]		(°C)	[hPa]
	0 -sea level	0	15	1013
	5000	16404	-17.5	541
	10000	32808	-50	265

TABLE 2 .

 2 ESTIMATION OF THE TEMPERATURES AND PRESSURES IN CLOSED

			AIR-VOIDS
	Altitude (m)	Altitude (feet)	Temperature (°C)	Temperature (°K)	Pressure in closed air-bubbles (hPa)
	0 -sea level	0	15	288	739
	5000	16404	-17.5	255.5	658
	10000	32808	-50	223	574

TABLE 3 .

 3 TRAJECTORIES AND FIELD LINE LENGTH COMPARISONS Field lineNb.

		Fiels line	Tr.length	Difference	Tr.length	Difference
		length	at 550V	(%)	at 183V	(%)
		(µm)	(µm)		(µm)
	10	84.3	84.8	0.6	88	4.3
	15	40	40	0	41	2.5