Yuanfang Chen
email: yuanfang.chen@etu.upmc.fr

Lei Shu
email: lei.shu@lab.gdupt.edu.cn

Antonio M Ortiz
email: antonio.ortiztorres@telecom-sudparis.eu

Noel Crespi
email: noel.crespi@mines-telecom.fr

Lin Lv

Lv Lin

Locating in Crowdsourcing-based DataSpace: Wireless Indoor Localization without Special Devices

Introduction

Mobile indoor localization and navigation have become very popular in recent years [START_REF] Gu | A survey of indoor positioning systems for wireless personal networks[END_REF][START_REF] Varshney | Mobile commerce: framework, applications and networking support[END_REF]. Some mobile indoor localization systems have been successfully used in a great number of applications such as the location detection of products stored in a warehouse, the location detection of medical personnel or equipment in a hospital, the location detection of firemen in a building on fire, the location detection of police dogs trained to find explosives in a building, and finding tagged maintenance tools and equipment scattered all over a plant. However, a challenging issue remains for mobile indoor localization. A number of existing approaches require infrastructures (e.g., indoor beacons) to achieve reliable accuracy or extensive pre-training before system deployment (e.g., WiFi signal fingerprinting).

With the dramatic increase of the size and frequency of mass events and the potential functionality of mobile devices, the study of crowd dynamics based on Mobile Networks has become an important research area [START_REF] Gupta | Mobisoc: a middleware for mobile social computing applications[END_REF][START_REF] Kjaergaard | Mobile sensing of pedestrian flocks in indoor environments using wifi signals[END_REF]. Firstly, as a base of crowdsourcing-based technology, the crowd behavior of human is worthy to be deeply studied, whether in the virtual world or in the real world. By analyzing and studying the crowd behavior, we can extract some useful conclusions about how humans behave when they are in large groups. For the crowdsourcing-based technologies such as Quora, Yahoo Answers and Google Answers, using the extracted conclusions at group and community levels [START_REF] Zhang | The emergence of social and community intelligence[END_REF] (a group or a community can be considered as a crowd) will be helpful to develop and improve this type of technology. Furthermore, predicting the formation of a crowd is helpful in some emergency situations, e.g., evacuation route control; and even the prediction is also beneficial for studying and improving the performance of pub-lic infrastructures, e.g., network usage during a mass event. Secondly, with the increasing ubiquity of sensing the real world context with smart mobile devices (e.g., the physical location of a mobile device) [START_REF] Győrbíró | An activity recognition system for mobile phones[END_REF][START_REF] Tang | Towards context-aware ubiquitous transaction processing: a model and algorithm[END_REF], we investigate the rising opportunities for mobile crowdsourcing to obtain real-time physical data. Based on the data, a bridge between the virtual world and the real world can be built. It also means that the crowdsourcing is extended from the virtual world to the real world to help solve many real-world problems. Hence, the crowdsourcing technology can be used for the location estimation problem in the physical world, and can solve the challenging problem of mobile indoor localization, to achieve "special-infrastructure-free" 1 and even "pre-training-free".

In this article, we first focus on recognizing human crowd behavior by analyzing the data measured by internet-accessible mobile phones from a location-aware online social network. By crowd behavior recognition, we understand that the movement of a large number of individuals has a pattern and can be attributed, depending on relevant parameters such as the friendship between individuals2 and check-in locations (with time) of these individuals.

Then, we propose LiCS, an indoor localization algorithm that considers trace data from individuals' mobile devices and a location estimation model. The trace data includes the following information: MAC addresses of devices, MAC addresses of signal transmitters and corresponding RSSIs (generally, the RSSI (Received Signal Strength Indication) can be used to indicate the value of RSS). With LiCS, mobile devices periodically report their trace data to a Data Analysis Center (DAC). The DAC runs a machine learning algorithm that accepts the wireless-based trace data as features of user mobility patterns, and periodically estimates the locations of mobile devices in real time. Since we use wireless information obtained from the social environment around us, LiCS can achieve fine-grained localization.

To validate this design, we implement a prototype system to conduct long-term experiments in two research laboratories and a corridor of a middle-size academic building covering over 39, 725m 2 . Experimental results show that LiCS achieves comparable location accuracy to previous approaches even without a site survey. Moreover, LiCS provides a room-level localization service.

The main contributions of this work are summarized as follows:

1. We design a crowd recognition model. One of the main challenges in crowd behavior recognition is to infer the most likely crowd behavior using the data collected from a set of persons. We use check-in times and locations (T ime and Location id; we convert each latitude/longitude coordinate of the earth into a unique Location id) to quantify the trace of each individual. Then, a clustering algorithm3 is used to find the likely crowds. 2. We design LiCS, an indoor localization algorithm for social environments based on the target's trace data. LiCS utilizes automatic selftraining for target trace data without any specific configuration for mobile devices. 3. We implement the prototype system of LiCS on Android devices, and perform an extensive set of experiments.

The rest of this article is organized as follows. Section 2 introduces related work. Section 3 describes our system model. Section 4 shows the existence of human crowd behavior in our daily lives and how to recognize it from the trace data of individuals. The "Locating in Crowdsourcing-based DataSpace" (LiCS) algorithm is presented in Section 5, while the evaluation of LiCS algorithm and the analysis of results are shown in Section 6. The article concludes with a brief summary in Section 7.

Related Work

In this study, the related work concerns two main aspects: (i) mobile-device-based indoor localization; (ii) crowdsourcing-based technology and crowdsourcingbased indoor localization (crowd-sourced data can be used to improve the performance of indoor localization).

Wireless Indoor Localization

In the mobile computing community, a user can carry a sensing device (such as a smart phone) to move randomly or within a field of fixed sensors [START_REF] Chen | Body area networks: A survey[END_REF]. In either case, the knowledge of user's location can be useful for some applications (called Location-Based Services (LBS) [START_REF] Dey | Locationbased services[END_REF]). Outdoor localization is well solved by GPS, but indoor localization remains a challenge in many cases. For the indoor localization issue, a number of algorithms have been proposed in the past two decades. Generally, these algorithms fall into two categories: (i) some works are based on sensors or beacons which are installed throughout the environment [START_REF] Savvides | Dynamic finegrained localization in ad-hoc networks of sensors[END_REF][16] [START_REF] Borriello | Walrus: wireless acoustic location with room-level resolution using ultrasound[END_REF]; so special hardware is required to run these algorithms; (ii) in recent approaches, the location is determined by observing RSS. Two techniques are widely used in this kind of algorithms: fingerprinting-based and model-based technique. A large body of indoor localization algorithms adopt fingerprint matching as the basic scheme of location determination. The main idea of this technique is to fingerprint the surrounding signatures at some locations in the area of interest and then build a fingerprint database. The location of a user is then estimated by matching the new measured fingerprint of the user against the database. Many kinds of signatures have been exploited by researchers such as WiFi signals [START_REF] Wu | Fila: finegrained indoor localization[END_REF] (e.g., LiFS algorithm [START_REF] Yang | Locating in fingerprint space: wireless indoor localization with little human intervention[END_REF]), Radio Frequency (RF) signals [START_REF] Bahl | Radar: An in-building rfbased user location and tracking system[END_REF] [START_REF] Youssef | The horus wlan location determination system[END_REF] and Frequency Modulation (FM) radio signals [START_REF] Chen | Fmbased indoor localization[END_REF]. The considerable manual costs and efforts, in addition to the inflexibility to environmental dynamism, are the main drawbacks of fingerprinting-based algorithms. The model-based technique calculates locations based on geometrical or statistical models rather than searches for best-fit signatures from pre-built reference databases [START_REF] Roos | A statistical modeling approach to location estimation[END_REF]. For instance, the prevalent Log-Distance Path Loss (LDPL) model [START_REF] Jung | Wi-fi fingerprint-based approaches following log-distance path loss model for indoor positioning[END_REF] builds up a semi-statistical function between RSS values and RF propagation distances. However, these model-based algorithms trade the measurement efforts at the cost of decreasing localization accuracy.

Crowdsourcing-based Technology

Crowdsourcing is one specific form of harvesting wisdom and contributions from individuals of crowds; based on this harvesting scheme, some applications or services are developed and the performance of some algorithms can be improved: it can be called crowdsourcingbased technology. Some researchers explore crowdsourcing based on the use of mobile devices. Moreover, as an important basic service, wireless mobile-device-based indoor localization technologies such as GSM-based, Bluetooth-based or WiFi-based localization, have been extended by using crowdsourcing data to support and improve the performance of location estimation [START_REF] Rai | Zee: Zero-effort crowdsourcing for indoor localization[END_REF] [START_REF] Yang | Locating in fingerprint space: wireless indoor localization with little human intervention[END_REF]. Current crowdsourcing-based localization approaches depend on calibration of the space of interest. Such cal-ibration tends to be onerous, because it has to be repeated for every new space and each time there is a significant change in a given space (e.g., a change in the placement of signal transmitter). LiCS is aimed at eliminating the need for such explicit calibration effort and the need for any kind of map concerning the space of interest. LiCS exploits the advantage of model-based technique (versatility and conciseness) and avoids its drawback (accuracy loss for localization) by training a localization model using real-time trace data of individuals in crowds from the physical world. Figure 1 shows an example for the motivation of applying LiCS and the system architecture of LiCS.

System Model

The prototype system is developed on Android smartphones and follows mobile-based network-assisted architecture (Fig. 1(b)). Our system model can be considered as a wireless network where there are N fixed signal transmitters T = {t 1 , t 2 , ..., t i , ..., t N } and M mobilizable signal receivers R = {r 1 , r 2 , ..., r i , ..., r M }. The parameter p(t) denotes the estimate of a mobile terminal location at time t. And the parameter p (t) is used to denote the real location of a mobile terminal at time t. Moreover, the parameter d(t) is the measured distance between the estimated location p(t) and the real location p (t), and we use the "step" to measure the distance between p(t) and p (t) as the localization error at time t in our evaluation experiments (Section 6).

The problem of crowdsourcing-based location estimation can be defined as an identification procedure. The matched fingerprints can be identified from the model-assisted fingerprint database. And the model learns the real environment with wireless signals (RSS) and can be trained in real time with the wireless information that is submitted by mobile terminals.

Crowd Behavior Recognition

As the basis of crowdsourcing-based location estimation, we need to confirm the existence of crowd behavior; and it can be recognized from wireless mobile data of people's daily lives. In this section we formalize a series of processing steps which can be used to infer crowd behavior from location-based wireless information. We also present a mathematical model for recognizing the crowd behavior of a population.

First, based on the collected data from a locationbased mobile social network using its public API [START_REF] Cho | Friendship and mobility: user movement in location-based social networks[END_REF] the relationships between the check-in time, locations, friend-we confirm whether the crowd behavior of individuals can be identified. Figure 2 shows the results of data clustering with F riendship and Location id attribute, respectively (these two attributes are evaluation classes of clustering), using the Expectation-Maximization (EM) algorithm [START_REF] Do | What is the expectation maximization algorithm?[END_REF]. From Fig. 2, the crowd behavior (we define "crowd" in Definition 1) has been recognized using the processed data (in our study we process the original collected data). Moreover, we can find that different evaluation classes have different clustering accuracy levels, so different attributes have different impacts on crowd behavior. Figure 3 shows the number of users for each cluster corresponding to Fig. 2 (Fig. 2 only shows the users who are "1-hop" friends, but in Fig. 3, the users who are multihop friends are also counted for each cluster). For Fig. 3, more detailed explanations are necessary: why some clusters are composed by thousands of users when we use "Friendship" attribute as the class of clustering evaluation? Because (i) we use "multihop" friends in the clustering evaluation; that is to say, if A is a friend of B within the range r and B is a friend of C within the range r, A, B and C all will belong to a same crowd; (ii) the error of clustering evaluation is existent; (iii) we use the evaluation dataset which is from one special month, e.g., some data of the dataset is from a major celebration (a great number of users are gathered together), to show the impact of attributes on crowd behavior 5 .

ship and crowd behavior of users in 772, 966 distinct places. The data consists of 58, 228 nodes (users) and 214, 078 friend edges (friendship is directed between any two nodes). 5 Even if the dataset is incomplete, it still can be used to show that "the impact of attributes (friendship and check-in locations) is existent on crowd behavior".

Definition 1 A crowd can be defined as follows: a group of individuals at a "same" physical location (the range radius r for each user is 10m) and at the "same" time (the time range for a crowd is set to 15 minutes; namely, if the check-in time difference between two users is within 15 minutes, we consider that they are "at the same time").

The characteristics of crowd behavior for each single person can therefore be inferred from his/her check-in records. We refer to this as their "individual behavior". This shows which individuals participate in a specific crowd. From the EM algorithm, a given record belongs to each cluster with certain probabilities. Moreover, likelihood is a measurement of "how good" a clustering process is and it increases in the successive iterations of EM algorithm. It is worth mentioning that the higher the likelihood, the better the model fits the data.

Second, the clustering process (crowd behavior recognition model) is described as follows. We define two parameters: (i) the user u's check-in data S u which is a sequence of activity observations for user u; (ii) a set of unknown values θ (i.e., the serial numbers of clusters). These two parameters are used along with a Maximum Likelihood Estimation (MLE): L(θ; S u ) = p(S u |θ). Our purpose is to seek the MLE of marginal likelihood. In other words, we need to find the most probable θ which the user u belongs. The EM algorithm iteratively applies the following two steps to achieve our purpose:

1. Expectation step (E step): calculate the expected value of the log-likelihood function under the current established clusters (θ (t) ):

Q(θ|θ (t) ) = E S u ,θ (t) [log L(θ; S u )];
2. Maximization step (M step): find the appropriate value of parameter θ, which maximizes this quantity:

θ mle = arg max θ Q(θ|θ (t) ).
MLE estimates θ by finding a value of θ that maximizes Q(θ|θ (t) ), and the estimation result can be flagged as:

θ mle .
Further, based on our recognition model, we add the spatio-temporal pattern of crowd into crowd behavior (clustering). We use a triple q = (θ, p i , t i ) to replace θ. Then the expectation-maximization process becomes:

1. Expectation step: Q(q|q (ti) ) = E S u ,q (t i ) [log L(q; S u )],
where p i is the position characteristic of location measurement m i , and t i is the timestamp of m i . Moreover, q (ti) is a set of current established clusters with their locations and timestamps;

2. Maximization step: choose q to maximize Q(.), q mle = arg max q Q(q|q (ti) ).

Finally, in order to preserve the integrity of our model, the recognition accuracy must be measured. In our crowd behavior recognition model, the value of the log-likelihood can be used to measure the accuracy. For instance, using F riendship attribute as the evaluation class, based on one-month data, the log likelihood of crowd identification is: -16.42186, and using Location id attribute as the evaluation class, the log likelihood is: -16.87742. Their accuracy is different, and F riendship attribute is more effective for improving the recognition ability of the model.

Locating in Crowdsourcing-based DataSpace

As an effective measurement, RSS is easily available from various wireless signals which are from most offthe-shelf wireless equipment such as WiFi-or Bluetoothcompatible devices. And a large number of RSS-based indoor localization algorithms are proposed. However, considering RSS as a database to support indoor localization (e.g., RSS fingerprint space), it is time-consuming and labor-intensive. Especially, from extensive experiments, we observe that the RSS is vulnerable due to environmental dynamism (an example is shown in Fig. 4(a)). How to avoid these weaknesses to improve the performance of RSS-based indoor localization? It is worth noting that the trend of RSS change is obvious between different locations (Figure 4(b)). Location number RSS from WiFi Router 3 (dBm) (a) An example: the fluctuation range of RSS for some locations. For example, at the location 60, the RSS is not a value, it is a range of values. So the fingerprint of a location cannot be denoted by the absolute value of RSS. To try to avoid these weaknesses, we present LiCS and the details are shown as follows.

Input: Signal triples6 from individuals. First, at time t + 1, the value p(t + 1) can be estimated based on "observed locations" p(t), p(t-1), p(t-2), ..., p(t -k + 1). Moreover, the relationship between the output p(t + 1) and the input p(t), p(t -1), p(t -2), ..., p(t -k + 1) has the following mathematical representation (Eq. 1):

p(t + 1) = α 0 + q j=1 α j g(β 0j + p i=1 β ij p(t -i + 1)) + , (1)
where α j (j = 0, 1, 2, ..., q) and β ij (i = 0, 1, 2, ..., p; j = 1, 2, 3, ..., q) are the connection weights between time series, p is the number of "observed locations", q is the number of nodes of hidden layer 7 and is noise of the estimation. The logistic function g(x) = 1 1+e -x is used as a hidden-layer transfer function. In this study, an optimal location estimation model is built by training the model with the wireless data collected from the real physical space around us (the collected data can be denoted as signal triples). The training steps are shown as follows:

Step 1: Cluster the signal triples. Partition all triples into several clusters, using an Expectation-Maximization (EM) clustering algorithm. Moreover a cluster center can be obtained for each cluster. Each cluster is given a unique number as its location.

Step 2: Input some selected time-serial signal triples with corresponding locations of clusters into Eq. 1 for learning the optimal configuration of parameters, α j , β ij and . A location estimation model with optimal parameter configuration can be obtained. Moreover, the growth of logistic function g(.) satisfies: the initial stage of growth is approximately exponential; then, as saturation begins, the growth slows, and at maturity, the growth stops. So if the training time is long enough, the parameter configuration of Eq. 1 will gradually converge to the optimal solution.

Then, a target can be located with the optimal location estimation model. (i) Give the target a start location p(0). Calculate the Euclidean distances between a received new signal triple and all cluster centers (the new signal triple is from the target). If the shortest Euclidean distance is relative to the cluster k, p(0) = k. (ii) Calculate the location at time t + 1. Using the trained Eq. 1 as "optimal location estimation model", from the start location p(0), we can obtain time-serial locations. And based on "observed locations", [p(t), p(t -1), ..., p(0)], p(t + 1) can be calculated.

The real-time location of a target is obtained by our algorithm. Moreover, (i) the optimal location estimation model is periodically trained by new signal triples; and (ii) if more signal transmitters can be detected by receivers, it will help to distinguish different locations more effectively for achieving higher localization accuracy.

Output:

The real-time location (cluster number) of a target. For some special applications, if the absolute coordinates of clusters are available, the absolute physical location of target will be known. Note that Eq. 1 is the core of LiCS. From above descriptions of the algorithm LiCS, we can find that these parameters will affect the accuracy of LiCS: the connection weights between time series, α j and β ij . The parameter α j reflects the importance of hidden-layer transfer for the location estimation of time t+1. In other words, it denotes the degree of correlation between different time series. The parameter β ij reflects the importance of the j th node in the hidden layer, when the hidden layer transfers the influence of observed location p(t -i + 1) to p(t + 1). The appropriate values of these parameters for the model of location estimation at time t + 1 will be helpful to improve the accuracy of localization.

The variables that are used in LiCS are summarized in Tab. 1.

Evaluation

We develop the prototype system of LiCS on the increasingly popular Android OS which supports WiFi and Bluetooth. We conduct long-term experiments in two laboratories (84m 2 and 53m 2 , respectively) and a corridor (Fig. 5) of a middle-size academic building where a number of WiFi routers without location information have been installed. Moreover, in each experimental site, we install three Bluetooth transmitters (laptop-embedded Bluetooth transmitters are used in our experiment, so they are not special devices; the signal of Bluetooth is full-coverage for each experimental site). The experiment lasts one month using 9 volunteers. We measure WiFi signals and Bluetooth signals. We use the variable to denote the location of a mobile terminal at time t. α j

The variable is the connection weight between time series, and it reflects the importance of hidden-layer transfer for the location estimation of time t + 1.

β ij
The variable is the connection weight between time series, and it reflects the importance of j th node in the hidden layer, when the hidden layer transfers the influence of observed location p(t -i + 1) to p(t + 1).

Experimental Setup

Each volunteer carries a mobile phone and can take any activity in any area of experiment. The trace data of each volunteer is recorded every 30 seconds during working hours (from 9:00 a.m. to 10:00 p.m.). Moreover, the trace data from volunteers covers most of the areas of experiment.

In our experiments, for LiCS, we use WiFi and Bluetooth signals. Bluetooth is a wireless technology standard for exchanging data over short distances, so Bluetooth signals attenuate more rapidly with distance compared with WiFi signals.

We compare our algorithm with LiFS [START_REF] Yang | Locating in fingerprint space: wireless indoor localization with little human intervention[END_REF] under the same experimental conditions. LiFS is an RSS-based indoor localization algorithm (using WiFi signals). The key idea behind LiFS is that human motion can be applied to connect previously independent radio fingerprints under certain semantics. In LiFS, absolute values of RSS are used to establish a fingerprint database. When a user sends a location query with his/her current RSS fingerprint, LiFS retrieves the fingerprint database and returns the matched fingerprints as well as the corresponding locations.

Performance Evaluation

In this section, we show the comparative results of LiFS and LiCS. We estimate 248 location queries, and cumulate all localization errors of these queries (Cumulative Distribution Function (CDF) 8 ) for both algorithms, respectively. The results are shown in Fig. 6 and Fig. 7. For comparing the overall performance of three experimental sites for both algorithms, we calculate the average results of three experimental sites for LiCS (Bluetooth), LiCS (WiFi) and LiFS. From Fig. 7, we can find that our LiCS is better than LiFS for the average of three experimental sites. For example, the localization errors of 95% queries are less than 6m for LiC-S (Bluetooth), 69% queries are less than 6m for LiCS 8 Cumulative Distribution Function describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. It can be formulated as F X (x) = P (X ≤ x).

(WiFi) and 60% for LiFS. LiCS uses the model training with real-time data, so the localization accuracy for location queries is improved compared with LiFS. Moreover, why the localization accuracy of LiCS (Bluetooth) is higher than LiCS (WiFi)? Based on the attenuation characteristics of Bluetooth signals and WiFi signals, the differences of RSS between different locations for Bluetooth are greater than the differences for WiFi. So using the RSS of Bluetooth to distinguish different locations is more accurate than using the RSS of WiFi. If we can distinguish different locations more clearly, we can obtain higher localization accuracy based on an RSS-based fingerprint database. Furthermore, from the experimental results of Fig. 6, we can find that: (i) on average, for localization accuracy, LiCS is better than LiFS, in the three different experimental sites. For example, the localization errors of 80% queries are less than 2.4m for LiCS (Bluetooth), and 70% for LiFS, in the laboratory covering over 84m 2 ; (ii) the localization accuracy of LiCS (Bluetooth) is better than LiCS (WiFi) and LiFS. For example, the localization errors of 50% queries are under 2.4m for LiCS (Bluetooth), while about 30% for LiCS (WiFi) and about 25% for LiFS, in the laboratory covering over 53m 2 . Bluetooth improves the average localization error up to 39% compared with LiCS (WiFi), and up to 55% compared with LiFS. Because the signal strength of Bluetooth is changed sharply between locations, which makes the distinction of signal strength between different locations more remarkable (the "remarkable" is conducive to improving the accuracy of localization). Moreover, LiFS is based on a priori database (some human intervention is necessary in the build phase of a database). LiCS is crowd-sourced, so only wireless information is required, which is received by humans in their daily lives. And the locating process of LiCS is automatic and priori-database-free. LiCS is based on WiFi and Bluetooth information which is readily available. The above-mentioned features of LiCS make the rapid deployment of system possible.

Conclusion

Crowdsourcing is a distributed problem-solving scheme that has emerged in recent years. It exploits the potential and wisdom of crowds to support various applications and to improve the performance of various algorithms in a cost-effective fashion. In our study, we investigate the existence of crowd behavior in the real world and it can be recognized using location-aware wireless mobility information of people's daily lives. Furthermore, we present a model for crowd behavior recognition. On this basis, by sensing and collecting WiFi and Bluetooth information from the social surroundings around us, we propose a time-serial location estimation model. Using this model, we design and implement LiC-S, an indoor target localization algorithm based on two aspects, (i) mobile devices carried by individuals, and (ii) a location estimation model which is trained by the collected data from individuals about WiFi and Bluetooth information. The experimental results show that LiCS achieves competitive location accuracy without any special infrastructure. This work sets up a novel perspective to crowd-sourced indoor localization algorithms.

4 ,

 4 (a) Motivation example. The location-finding ability of indoor localization systems heavily depends on the broadcasts from wireless equipment. For LiCS, any individual can use the trained model to estimate his/her location, and then his/her RSS can be used to train the model again. With the crowd-sourced RSS, the model is trained to make its parameters accordant with the practical situations. (b) System architecture. As an improvement, LiCS exploits the advantage of model-based technique (versatility and conciseness) and avoids its drawback with the training for a location estimation model: crowd-sourced data is used to train the model and the model is installed in distributed servers.

Fig. 1

 1 Fig. 1 Motivation example and system architecture.

Fig. 2 (

 2 Fig. 2 (a) Clustering result with F riendship attribute. (b) Clustering result with Location id attribute. For clearness, we only show one-month clustering result using F riendship and Location id attribute as the classes of clustering evaluation, respectively. 5 clusters can be found for the two clustering processes, and we use different colours to distinguish different clusters.

Fig. 3

 3 Fig. 3 Number of users for each cluster. (a) Number of users for each cluster using F riendship attribute as the class of clustering evaluation. (b) Number of users for each cluster using Location id attribute as the class of clustering evaluation.

  Changing trend of RSS. location number = 0 is the location of a signal transmitter; as a signal receiver moves away from the transmitter, the RSS is decreasing.

Fig. 4

 4 Fig. 4 Instability of RSS and changing trend between different locations.

  Variables Explanations [RSS, M AC T , M AC R ] A triple consists of three elements, where RSS is Received Signal Strength received by a mobile terminal, M AC T is the MAC address of corresponding signal transmitter and M AC R is the MAC address of corresponding signal receiver. The triple is used to train the location estimation model and structure the fingerprint database. p(t)

Fig. 5

 5 Fig. 5 Floor plans of experimental sites. (a) Laboratory covering over 84m 2 . (b) Laboratory covering over 53m 2 . (c) Corridor covering over 302m 2 .

Fig. 6

 6 Fig. 6 CDFs of localization errors for both algorithms in three different experimental sites. (a) CDF of localization errors in the laboratory covering over 84m 2 . (b) CDF of localization errors in the laboratory covering over 53m 2 . (c) CDF of localization errors in the corridor covering over 302m 2 .

Fig. 7

 7 Fig. 7 CDF of localization errors for the average of three experimental sites.

Table 1

 1 Variables and explanations

  The unit of the estimated error for localization is step ≈ 1.2m. The average localization error of LiCS with Bluetooth signals is 2.6575m, LiCS with WiFi signals is 4.35m, and LiFS is 5.95m. The maximum localization error of LiCS with Bluetooth signals is 33.6m, LiCS with WiFi signals is 34.8m and LiFS is 40.8m.

A "special infrastructure" means that the infrastructure consists of customized equipment. LiCS is based on Received Signal Strength (RSS) that exists in any wireless equipment, so LiCS can be directly supported by existing wireless infrastructures around us.

[START_REF] Borriello | Walrus: wireless acoustic location with room-level resolution using ultrasound[END_REF] For a location-aware online social network, if B is in the friend list of A, we consider that there is friendship between A and B, and the relationship is directed.

An Expectation-Maximization (EM) clustering algorithm[START_REF] Do | What is the expectation maximization algorithm?[END_REF] is used in this article. The EM assigns a probability distribution for each trace record (instance), which indicates the probability of each instance belonging to each of the clusters. The EM can automatically decide how many clusters to create.

The collected data with anonymous mobile devices from Brightkite is used to correlate, model, evaluate and analyze

A triple can be denoted as [RSS, M AC T , M AC R ]. For the specific RSS of a location, M AC T is the MAC address of corresponding signal transmitter and M AC R is the MAC address of corresponding signal receiver.

In machine learning, using the hidden layer enables greater processing power and system flexibility. The nodes of hidden layer are named as hidden nodes. Hidden nodes are the nodes that are neither in the input layer nor the output layer. These nodes are essentially hidden from view, and their number and organization can typically be treated as a black box to people who are interfacing with the system.

Acknowledgment

Lei Shu's work is supported by the Guangdong University of Petrochemical Technology Internal Project (2012RC0106).


M Atgie

O Masbernat

K Roger

Isabelle Borget

Emulsions Stabilized by Gum Arabic: Composition and Packing within Interfacial Films

Gum arabic is a heterogeneous natural hydrocolloid commonly used in the agro-food industry to provide metastability to oil-in-water emulsions. Since aqueous solutions of gum arabic contain a complex mixture of protein/polysaccharide conjugates, the composition of interfacial films is expected to differ from the bulk composition. Here, we investigate the composition of interfacial films in oil/ water emulsions stabilized by gum arabic at various concentrations, pH and salinity. Using both size exclusion and hydrophobic interaction chromatography separations, we show that the interface is enriched in protein-rich species displaying a broad range of sizes. These species are irreversibly adsorbed as monolayers at the oil/water interface. We observe that the surface coverage density, or packing, of the adsorbed species at oil/water interfaces drastically increases with both the increasing gum concentration and decreasing ionic repulsions, through increasing the ionic strength or decreasing the pH. Strikingly, these packing changes correspond to only minor composition changes in the adsorbed layer. We thus conclude that the key parameter modified in different formulations is the conformation of the adsorbed species rather than their composition distribution. These findings can be readily used to adjust the amount of gum arabic necessary to produce metastable emulsions.

■ INTRODUCTION

Gum arabic, or acacia gum, is an exudate originating from acacia trees in sub-Saharan countries, which has been used for centuries for its binding and stabilizing properties. [START_REF] Sanchez | Acacia Gum: History of the Future[END_REF] Nowadays, it is mainly used to stabilize dispersed systems such as oil-inwater emulsions, in particular, flavored beverages. [START_REF] Mirhosseini | Effect of Arabic Gum, Xanthan Gum and Orange Oil Contents on ζ-Potential, Conductivity, Stability, Size Index and PH of Orange Beverage Emulsion[END_REF] This natural hydrocolloid, highly soluble in water, is composed of a complex mixture of proteins and polysaccharides. Many studies have been devoted to elucidate the chemical composition and colloidal structure of gum arabic. In contrast, very few studies have investigated the actual composition of adsorbed gum arabic films and its interplay with formulation parameters and emulsion metastability.

Randall et al. analyzed the aqueous phase from a gumstabilized emulsion by size exclusion and observed that the high molecular weight macromolecules preferentially adsorbed at the interface. Using the same procedure, Padala et al. showed that all classes of molecular weights could adsorb. [START_REF] Padala | Adsorption of Gum Arabic, Egg White Protein, and Their Mixtures at the Oil-Water Interface in Limonene Oil-in-Water Emulsions[END_REF][START_REF] Randall | The Role of the Proteinaceous Component on the Emulsifying Properties of Gum Arabic[END_REF] By preparing emulsions using recovered gum arabic fractions from size-exclusion chromatography (size) or hydrophobic interaction (protein content), it was shown that using a combination of both small and high molecular weight species provided the best emulsion metastability. [START_REF] Ray | Functionality of Gum Arabic. Fractionation, Characterization and Evaluation of Gum Fractions in Citrus Oil Emulsions and Model Beverages[END_REF] Studies were conducted to evaluate the surface concentration of gumstabilized droplets but without evaluating the film composition. It was observed that physico-chemical parameters such as lower pH or higher salinity promoted interfacial adsorption [START_REF] Padala | Adsorption of Gum Arabic, Egg White Protein, and Their Mixtures at the Oil-Water Interface in Limonene Oil-in-Water Emulsions[END_REF][START_REF] Castellani | Hydrocolloids with Emulsifying Capacity. Part 2 -Adsorption Properties at the n-Hexadecane-Water Interface[END_REF][START_REF] Nakauma | Comparison of Sugar Beet Pectin, Soybean Soluble Polysaccharide, and Gum Arabic as Food Emulsifiers. 1. Effect of Concentration, PH, and Salts on the Emulsifying Properties[END_REF] but no systematic study was performed to evaluate the related composition changes at oil/water interfaces.

In the present work, we present a method to systematically characterize the interfacial composition in molecular weight and hydrophobicity, in respect to the corresponding bulk solutions. Surface coverage densities of gum arabic-stabilized emulsions as a function of emulsion formulation (gum concentration, pH and ionic strength) are compared. Compositions of the interface as a function of these parameters are then discussed. Finally, we show how these findings can lead to a more rational design of gum arabic-stabilized emulsions and to a notable reduction of its amount in the formulation.

■ RESULTS AND DISCUSSION

Chromatographic Characterization of Gum Arabic Solutions. In this section, we sum up the main results of a chromatographic analysis from a previous study dedicated to gum arabic in solutions that are relevant to the present work. [START_REF] Atgie | Gum Arabic: Composition and Multi-Scale Structures in Solution[END_REF] Figure 1A displays the size-exclusion chromatogram of our gum arabic sample (Caragum International) using both UV detection at 210 nm, which is mostly sensitive to the peptide bond, [START_REF] Kobayashi | Intensive UV Absorption of Dextrans and Its Application to Enzyme Reactions[END_REF][START_REF] Kuipers | Prediction of Molar Extinction Coefficients of Proteins and Peptides Using UV Absorption of the Constituent Amino Acids at 214 Nm To Enable Quantitative Reverse Phase High-Performance Liquid Chromatography-Mass Spectrometry Analysis[END_REF] and refractive index detection, which is proportional to the concentration. Here, we suppose a constant refractive index increment (dn/dc) for all gum arabic species. We verified this hypothesis by comparing the weight fractions for fractions A, B, and C obtained through a calculation from the refractive index chromatogram and from the direct measurement using preparative chromatography. The two methods lead to similar figures within the experimental error margin (FA: 20 vs 22 wt. %, FB: 55 vs 51 wt.%, FC: 25 vs 27 wt.%), which validates the hypothesis and confirms that the refractive index chromatogram is a good measure of relative concentrations.

This chromatogram is similar to previously published gum arabic chromatograms, [START_REF] Idris | Characterisation of Gum from Acacia Senegal Trees of Different Age and Location Using Multidetection Gel Permeation Chromatography[END_REF][START_REF] Renard | Acacia Senegal Gum: Continuum of Molecular Species Differing by Their Protein to Sugar Ratio, Molecular Weight, and Charges[END_REF][START_REF] Padala | Adsorption of Gum Arabic, Egg White Protein, and Their Mixtures at the Oil-Water Interface in Limonene Oil-in-Water Emulsions[END_REF] which ensures the representability of our gum arabic batch. We observe that gum arabic displays a broad range of molecular weight ranging from 6 kg•mol -1 to more than 1000 kg•mol -1 . From the refractive index detection, we observed that the gum is mainly composed of intermediate molecular masses, whereas the higher molecular masses (peak α 1 and α 2 ) represent around 15% w/w of the gum. This is consistent with the weight fractions of the different gum arabic fractions obtained through a similar preparative chromatographic separation (A-D from Figure 1). The protein weight fractions were also determined and display variations ranging from 2.9 to 19 wt.% between the different fractions.

The protein content measured in the four fractions (from size-exclusion separation: A-D) suggests that the various species of gum arabic may display amphiphilicity. We have then used hydrophobic interaction chromatography to probe the hydrophobicity of gum arabic, which corresponds to its interaction with a phenyl grafted column. Unlike previous works, [START_REF] Ray | Functionality of Gum Arabic. Fractionation, Characterization and Evaluation of Gum Fractions in Citrus Oil Emulsions and Model Beverages[END_REF][START_REF] Renard | Acacia Senegal Gum: Continuum of Molecular Species Differing by Their Protein to Sugar Ratio, Molecular Weight, and Charges[END_REF][START_REF] Randall | Fractionation and Characterization of Gum from Acacia Senegal[END_REF] a continuous gradient of sodium chloride concentration was imposed along the column height to avoid artefacts during abrupt salinity changes. The resulting chromatogram is displayed in Figure 1B. The first peak is actually the convolution of several peaks as shown in Figure 3D, with the first convoluted peak corresponding to species that are not retained by the phenyl grafted column and the following convoluted peaks to species that are only weakly retained. This indicates the presence of a rather homogeneous hydrophilic population in terms of amphiphilicity. In contrast, the rest of the chromatogram displays a broad elution profile, which emphasizes the heterogeneity of the gum as a continuum of species of increasing hydrophobicity. refractive index detection. Weight percentages of the recovered fraction using a preparative size-exclusion column (same column filling, larger dimensions) are indicated as well as the protein content for these fractions. Scale for molecular weights as a function of elution volumes is displayed on the top of the chromatograms. (B) Hydrophobic interaction chromatogram of a 30 g•L -1 aqueous solution of gum arabic using a UV detection at 210 nm. Less hydrophobic species of the gum are not retained by the column and species eluted last are the most hydrophobic ones.

We then performed a two-dimensional chromatographic separation consisting of, first, a size-exclusion chromatography separation, followed by a hydrophobic interaction chromatography separation, using the same columns as for the data displayed in Figure 1. [START_REF] Atgie | Gum Arabic: Composition and Multi-Scale Structures in Solution[END_REF] We evidenced that fractions from either method encompassed a diversity of families and that these two properties are thus not correlated. Also, in contrast with previous interpretations based on the finite protein amount within each fraction, we showed that some families include none or a negligible protein amount and should not display amphiphilicity. These hydrophilic populations correspond to species of intermediate molecular weight (elution volume between 5.5 and 9 mL).

Is the Adsorption of Gum Arabic Species an Equilibrium Process? When an aqueous solution of amphiphilic species is placed in contact with an oil phase, some species are expected to adsorb at the interface. This adsorption may proceed along a thermodynamic pathway and is then controlled by adsorption equilibrium constants, which yields adsorption isotherms. This is typically the case for short chain surfactant molecules. [START_REF] Fennell Evans | The Colloidal Domain: Where Physics[END_REF] Adsorption may alternatively proceed through a kinetic pathway, which will depend on both adsorption equilibrium constants and transport properties during the interface formation. This corresponds for instance to the case of particle-stabilized emulsions. [START_REF] Binks | Particles as Surfactantssimilarities and Differences[END_REF][START_REF] Arditty | Materials Based on Solid-Stabilized Emulsions[END_REF] In both cases, the interfacial composition is expected to differ from the bulk composition when species of different amphiphilicity and sizes are present in water. However, distinguishing between thermodynamically and kinetically controlled adsorption is crucial to determine the interfacial composition. To this purpose, we prepared an oil/water emulsion, which was centrifuged to collect a concentrated cream. We then rinsed this cream several times and measured, after each rinsing step, the amount of gum arabic species that swept away from the cream (Figure 2). Around 1.4% of the initial gum amount is collected after the first rinsing, which corresponds to species that were trapped in the aqueous phase in between oil droplets. Subsequent rinsing yields negligible quantities of gum arabic. Yet, we observed through ultra-centrifugation experiments that rinsed emulsions displayed an even higher metastability than the original emulsion, demonstrating that the adsorbed species are not leached out by dilution as would be expected for an equilibrium adsorption process. Removing both water and oil from the rinsed emulsion yields the total mass of gum arabic adsorbed, which corresponds to 15.8% of the total gum arabic before rinsing and to a constant value of 13.8% after the second rinsing. We can thus conclude that oil/water interfaces of gum arabic-stabilized emulsions are mostly populated by irreversibly adsorbed species. This property is routinely used in the beverages industry, where arabic gum is used to stabilize concentrated aroma oil/water emulsions that will be highly diluted in the final beverage.

However, we also observed that upon intense centrifugation conditions, droplet coalescence could occur, leading to a partial desorption of some species. Reducing the interfacial area may thus force some species to desorb, which is consistent with microscopy observations showing that emulsion droplets remain spherical. From a thermodynamic standpoint, this means that the adsorption energy of some adsorbed species is smaller than the gain in relaxing droplet's shape upon coalescence. The average area occupied by gum species is thus bound by a maximal value.

How Does the Film Composition Compare with the Bulk Composition? Since the adsorption of gum arabic species can be considered as irreversible, the interfacial composition will be preserved upon dilution. Taking advantage of this property, we have designed an experimental method to determine the interfacial composition of gum arabic-stabilized emulsions (as shown in scheme in Figure 2). From a given emulsion, the nonadsorbing species were collected by recovering the emulsion aqueous phase. The remaining emulsion cream was rinsed several times to recover all species that are still potentially present in the aqueous phase of the cream and not strongly adsorbed. These rinsing phases were added to the emulsion aqueous phase. After dialysis and freezedrying, a dry extract containing the nonadsorbed species was Figure 3A shows that the chromatogram of the adsorbed species is much more intense than the chromatogram of gum arabic with UV detection at 210 nm. This trend is also observed at 280 nm (see Figure 3 in the Supporting Information). This contrasts with the refractive index signal which gives the same area for both samples, which is expected since both solutions have the same concentration in gum species. The difference in absorbance between the adsorbed molecules and gum arabic shows that for a given elution volume several species with different molar extinction coefficient ε are present. This is consistent with our previous results from two-dimensional chromatographic separation of gum arabic. Indeed, species collected from a given elution volume in the size-exclusion separation yielded a continuum of elution volumes in hydrophobic interaction separation. Interestingly, the area ratio between the two chromatograms using UV detection at 210 nm, and displayed in Figure 3A, equals to 4.1, which is similar to the protein content ratio of 3.7 between the two samples. This is consistent with the sensitivity of UV detection at 210 nm to proteins via the peptide bond. We can thus conclude that the interface is largely enriched in the protein-rich species. To assess the size distributions of the adsorbed species compared to the bulk solution, we normalized both chromatograms to ease peak comparison, as displayed in Figure 3B. Strikingly, the overall features are similar, which means that the interface remains populated by species of diverse sizes, a result in agreement with the study of Padala and co-workers but contradicting the wellspread idea that only the largest species adsorb at the interface. [START_REF] Randall | The Role of the Proteinaceous Component on the Emulsifying Properties of Gum Arabic[END_REF][START_REF] Padala | Adsorption of Gum Arabic, Egg White Protein, and Their Mixtures at the Oil-Water Interface in Limonene Oil-in-Water Emulsions[END_REF] However, large differences between the native solution and the interface are visible on the refractive index signal, as shown in Figure 3C. The predominant fraction of gum arabic, which corresponds to intermediate elution volumes, and thus molar masses, is significantly depleted at the interface. Indeed, this fraction contains the majority of the most hydrophilic species that do not display amphiphilicity. On the contrary, the interface is enriched in species corresponding to high molecular masses (>1000-600 kg• mol -1 , elution between 4.5 and 7 mL) and small molecular masses (250-45 kg•mol -1 , elution around 9.5 and 11.5 mL). As shown previously, these fractions contain most of the protein amount of gum arabic. As mentioned in the Materials and Methods section, refractive index detection could not be performed after an elution volume of 10.5 mL. In Figure 4C, the mass percentage corresponding to the peak at 11.5 mL on the UV chromatogram was thus not detected. Nevertheless, its absorbance at 210 nm (and 280 nm) is not negligible and is amplified in the case of the adsorbed species. The preparative separation of gum arabic has confirmed that this peak represents a very low weight percentage with a non-negligible UV absorption. The fact that its intensity increases in the case of the adsorbed species, thus indicates a very high protein content (above 50 wt %). We can estimate that it represents around 1-2 wt % of the adsorbed species.

According to the molecular weight universal calibration and the concentration repartition from the refractive index profile, we estimated that in our emulsions, the oil droplet surface was populated of approximately 19-32% in the number of the smallest species (45-6 kg•mol -1 ), 37-43 and 12-16% in the number of intermediate molecular weight macromolecules (of respective molecular weight of 250-45 and 600-250 kg• mol -1 ) and 18-22% of the largest species (>1000-600 kg• mol -1 ). This estimate shows that gum arabic-stabilized droplets are not primarily covered by large proteinpolysaccharide conjugate moieties as discussed in previous studies, [START_REF] Randall | Fractionation and Characterization of Gum from Acacia Senegal[END_REF] but by a larger proportion of smaller molecular weight macromolecules from the gum.

Comparing the hydrophobic interaction chromatograms of gum arabic and recovered species from the interface exhibits important differences as shown in Figure 3D. The first important observation from this comparison is that the species recovered from the interface present a chromatogram of much higher intensity than the gum arabic at the same concentration. This observation is in accordance with the trend observed in size-exclusion chromatography and can be ascribed to the larger protein content of this fraction compared to that of native gum arabic. The second observation is that the intensity of the first peak, corresponding to the less hydrophobic species, which are not retained by the column, is less intense in the case of the species adsorbing at the interface but not negligible. Since the emulsion cream was rinsed, this shows that even weakly hydrophobic species may adsorb irreversibly at the interface. The third observation is that the interface is composed of a broad range of hydrophobicity, which corresponds to a broad range of elution volumes on the hydrophobic interaction chromatogram.

To summarize, we showed that the interfacial adsorbed film is heterogeneous in molecular mass and hydrophobicity. The high and small molecular masses preferentially adsorb at the interface and these species present a broad range of hydrophobicity. We suggest that the interface is populated by species that all possess a polypeptide chain, whereas pure polysaccharides remain in solution. It can be concluded that the interfacial composition largely differs from the bulk solution but is far more complex than what is routinely assumed, for instance, a layer of large arabinogalactan-protein conjugates (AGP).

Can We Define a Mean Area per Adsorbed Species in This Adsorption Process? The chromatographic analysis has shown that in the emulsification conditions investigated, amphiphilic species are in large excess. Chromatograms of the nonadsorbed species show that the different classes of preferentially adsorbing macromolecules are far from being fully consumed. In a classical adsorption process, for instance involving a surfactant solution, it is expected that a maximal surface density, which corresponds to a mean area per stabilizer, is reached. We decided to measure this quantity as a function of several physico-chemical parameters used in an emulsion formulation (pH or ionic strength, gum concentration). In practice, these parameters are coupled to one another, since gum arabic polysaccharides are weak polyelectrolytes, due to glucuronic moieties. Therefore, varying the gum concentration will also result in changing both pH and ionic strengths. Simple comparisons thus require to maintain two parameters approximately constant.

Surface densities were calculated from the known amount of gum adsorbed at the droplet surfaces (from the interfacial separation procedure) and the volume/surface diameter of the emulsion (Sauter diameter), which are presented in Table 1. They are of the same order of magnitude as values from previous studies in which emulsions were not rinsed. [START_REF] Padala | Adsorption of Gum Arabic, Egg White Protein, and Their Mixtures at the Oil-Water Interface in Limonene Oil-in-Water Emulsions[END_REF] Estimates of mean area per head are also given using a mean molar mass of 670 kg•mol -1 from the refractive index chromatograms for the adsorbed species. However, these values should be read with caution as averages over a very disperse species distribution.

Padala et al. [START_REF] Padala | Adsorption of Gum Arabic, Egg White Protein, and Their Mixtures at the Oil-Water Interface in Limonene Oil-in-Water Emulsions[END_REF] argued that such large values of the surface densities compared to small proteins could be explained either by conformational changes or multilayers. The multilayer hypothesis would require a strong self-association of gum species to withstand rinsing. Although self-association has indeed been observed by us and others, [START_REF] Atgie | Gum Arabic: Composition and Multi-Scale Structures in Solution[END_REF][START_REF] Wang | Solution Properties of Conventional Gum Arabic and a Matured Gum Arabic (Acacia (Sen) SUPER GUM)[END_REF][START_REF] Dror | Structure of Gum Arabic in Aqueous Solution[END_REF] rheological studies also indicate that such structures do not resist to shear and collapse under moderate mixing conditions. [START_REF] Li | Rheological Study of Gum Arabic Solutions: Interpretation Based on Molecular Self-Association[END_REF][START_REF] Li | Rheological Properties of Gum Arabic Solution: From Newtonianism to Thixotropy[END_REF] Therefore, we expect that the rinsing procedure removes any Protein contents were calculated from measured nitrogen content using a 7 conversion factor based on the amino acid composition of the gum (see the Materials and Methods section). potential multilayer structure. Differences in surface densities can thus be ascribed to conformational changes.

C GA [g•L -1 ] pH C NaCl [molc] ionic strength [mol•L -1 ] protein content [wt %] a M adsorbed [mg] d 32 [μm] Γ [mg•m -2 ] a 0 [
From Table 1, we can observe that the surface density depends on both the amount of gum and pH and ionic strength. Comparing emulsions C and D yields the effect of ionic strength, whereas comparing emulsions B and D yields the effect of pH. Overall, we can conclude that the surface concentration value is driven by the magnitude of ionic repulsions. The surface density thus increases when the ionic strength increases and the pH decreases, which corresponds to decreasing ionic repulsions. From emulsion B to emulsion A, the important increase in the surface concentration corresponds to a simultaneous increase in the ionic strength and a decrease in the pH, both induced by the increase in the gum concentration. These results are consistent with interfacial tension measurements, in which decreasing pH and increasing salinity lead to a decrease in the interfacial tension (see Figure 4 in the Supporting Information). The droplet diameter evolution for emulsions A and B is presented in Supporting Information Figure 5. The droplet diameter was constant throughout the rinsing procedure for emulsions A and B.

Three scenarios can be proposed to interpret these surface coverage density variations: (i) The emulsification conditions correspond to a defect of species with interfacial properties rather than an excess of them. In addition to the chromatographic analysis, which does not support this hypothesis (adsorbing species are far from being fully consumed), comparing emulsions B-D shows that even with the same gum concentration, surface concentrations are different. Additionally, we did not observe a limited coalescence regime when using gum arabic as an emulsion stabilizer. Indeed, there was no linear evolution between the volume/surface diameter of the emulsion droplets and the inverse of gum concentration, indicating that we are not in default of adsorbing species. (ii) There are differences in film composition induced by different conditions, leading to packing changes at the interface since species of various sizes are adsorbed. (iii) There are conformational changes with the different conditions, leading to changes in surface density resulting from the variation of the mean area per adsorbed molecule, similarly to what is observed with adsorbed polymer brushes. [START_REF] Balastre | A Study of Polyelectrolyte Brushes Formed from Adsorption of Amphiphilic Diblock Copolymers Using the Surface Forces Apparatus[END_REF][START_REF] Zhao | Polymer Brushes: Surface-Immobilized Macromolecules[END_REF] Film Composition as a Function of Physico-Chemical Parameters. To test the second hypothesis (corresponding to the surface concentration changes induced by composition changes), chromatographic analysis of the adsorbed species for the different conditions of Table 1 was performed.

Firstly, we investigated the effect of increasing gum arabic bulk concentration on the interfacial composition. As observed from Figure 4A, the molecular weight distributions for both film compositions are similar in shape. For both SEC and HIC chromatograms, the area ratio between the two samples corresponding to emulsions A and B correlates well with the ratio of their protein content (SEC area ratio: 5.2/4.85 = 1.07, HIC area ratio 6.29/5.16 = 1.22, protein content: 12.2/10.8), within the precision limits of each technique (0.3% SEC, 6% HIC).

This small protein amount difference is consistent with the observation that more amphiphilic species (hydrophobic) are adsorbed upon increasing the surface density. Similar observations were made from refractive index detection (data not shown here). The hydrophobic interaction chromatograms support consistently that more hydrophobic species are present upon increasing the surface density.

Secondly, we examined the effect of ionic strength and pH changes at a constant gum bulk concentration. As observed in Figure 5, all chromatograms corresponding to these different formulations are fairly similar. Weak variations in protein content are observed by direct analysis or by 210 and 280 nm SEC chromatograms (see Figure 3 in the Supporting Information). These analyses do not give information on polysaccharides. However, we have observed, through HIC chromatography, all classes of adsorbed species except for the most hydrophilic ones. This suggests that there is no free polysaccharide at the interface after rinsing, and therefore all polysaccharide chains are linked with proteins. The similarity of the three chromatograms thus suggest similar interfacial compositions. 1). Chromatograms were obtained by subtraction of the nonadsorbed species chromatograms from the total gum arabic chromatogram and rescaled to a concentration of 30 g•L -1 to compare the molecular mass composition for each adsorbed film.

Overall, the composition changes seem too small to provide an explanation for the variation of the surface concentration change (hypothesis (ii)).

Significant differences in surface coverages and mean area per species are observed between the more concentrated emulsion (9.6 mg•m -2 /116 nm 2 ) and the less concentrated emulsion (6.6 mg•m -2 /169 nm 2 ), and can therefore be ascribed to conformational changes at the interface (hypothesis (iii)).

Weakly Hydrophobic Species Desorb upon Reducing the Interfacial Area. Emulsion B was centrifuged at 10 000g prior to recovering the species from the interface and the aqueous phase of the emulsion. It was observed that the Sauter diameter of the emulsion increases after a one-minute centrifugation. This reduction in the interfacial area leads to an increase in the surface coverage, as indicated by the total amount recovered from the interface.

Comparison of the size-exclusion chromatograms in Figure 6A shows that the interface is enriched in the high molecular weight macromolecules and some of the smaller ones, and therefore further depleted in the intermediate molecular masses. Figure 6B exhibits a major difference between the hydrophobic interaction chromatograms of both samples. Indeed, the first eluted peak completely disappeared in the case of the emulsion that was centrifuged and subjected to coalescence events. A forced reduction of the interfacial area, here achieved by centrifugation and subsequent coalescence, can cause the desorption of the more hydrophilic species, a result consistent with the observation of a much larger protein content after centrifugation (14.7 wt %) than before (10.8 wt %). Interestingly, the emulsion size distribution was modified only during the first centrifugation step, whereas further centrifugation did not lead to further evolution. This observation, suggests that the desorption of hydrophilic species results from conditions where droplets are forced to coalesce to a given extent, above which emulsions reach a high metastability.

Formulating Emulsions with Gum Arabic Using Physico-Chemical Variations. Although we leave a mechanistic description of gum arabic stabilization of oil/water emulsions for a following structural work, the present results readily suggest some formulation keys.

Gum arabic is widely used in the food industry, in particular, for the stabilization of beverage emulsions. These emulsions are stored as a concentrate that is diluted in the final product. Both concentrated and dilute emulsions need to remain metastable for several months or years to meet industrial specifications. In this work, we have shown that species from gum arabic were irreversibly adsorbed at oil/water interfaces, which contrasts with, for instance, common surfactants. This observation thus rationalizes the use of gum arabic for applications involving large emulsion dilutions. Another important constraint in using gum arabic is the use of a large excess in beverage formulation (>20% w/w), since gum arabic has the reputation to contain a limited amount of surface-active species. This results in an important concentration of the non-adsorbed species in the aqueous phase, which may cause unwanted effects such as depletion, osmotic stress, pH, and viscosity changes. However, high concentration of gum arabic also corresponds to a high ionic strength, which, as we showed, promotes adsorption. We have demonstrated that an accurate control of physico-chemical parameters such as pH and ionic strength was an efficient lever to enhance the surface coverage of emulsion droplets, which may be used to counterbalance a concentration decrease. A rational formulation design would thus consist of decreasing the gum arabic amount and increase the salt concentration or decrease the pH. Importantly, once the emulsion is formed, the adsorbed layers are extremely resilient to further changes and will not relax their conformation. This resilience can be utilized to subsequently modify the concentration and pH and ionic strength, as required for the final application, since these parameter values are only critical during emulsification.

Following this prediction, a proof-of-concept experiment was performed using a very low concentration of gum arabic to stabilize hexadecane-in-water emulsions. One sample was emulsified without additional ingredients, whereas in the other two, either the pH was decreased or sodium chloride was added. The three samples were compared with an emulsion stabilized with ten times more gum arabic. Size distributions of the emulsions, after one hour of aging, are presented in Figure 7.

It is important to mention that right after emulsification all the emulsions displayed the same size distribution. After 1 h, no noticeable change can be observed with the emulsion made with a solution concentrated in 30 g•L -1 in gum at a pH of 3.5 (black curve). Dividing the gum concentration by 10 at the same pH leads to a significant coarsening of the emulsion (green curve). Increasing the pH of the solution to 5.8 leads to a fast destabilization of the emulsion (red curve), whereas adding salt significantly compensates the pH increase, leading to the size distribution slightly unchanged compared to the initial state (orange curve). These observations further underline the importance of the physico-chemical parameters employed in a gum arabic-stabilized emulsion formulation. The simple example displayed here highlights alternative formulation pathways to reduce the amount of gum arabic in formulations, which is cost-efficient and should prevent any issues stemming from depletion effects.

■ CONCLUSIONS

Gum arabic solutions contain a diversity of polysaccharides and polysaccharide/protein conjugates of various sizes and hydrophobicities. When such solutions are placed in contact with an oil phase, interfacial adsorption occurs. In this work, we quantify the composition difference between the bulk solution and the interface. The main observation is a large enrichment in proteinaceous species of the interface. The interface is also enriched in the minority fractions of gum arabic corresponding to the larger and smaller sizes but depleted in the predominant species corresponding to the intermediate sizes.

Still, the interface remains populated with a large variety of species of contrasting size and hydrophobicity. The adsorption process is out of thermodynamic equilibrium, so that most species present at the interface, except for the most hydrophilic ones, can be considered as irreversibly adsorbed. This explains the wide use of arabic gum as a stabilizer for beverage emulsions, which must withstand extensive dilutions.

A significant increase in the interfacial concentration of gum species with a decrease in ionic repulsions is reported, despite a large excess of gum arabic in the aqueous phase. The ionic repulsions could be varied simultaneously through changes in the gum concentration or separately by changing pH or ionic strength. The composition of the interfacial films corresponding to these different interfacial coverages only weakly varies, which suggests that the interfacial concentration changes are not related to compositional changes. Rather, these changes stem from structural modifications within the interfacial layer.

We suggest that the systematic control of this interfacial density is a key parameter for a rational design of formulations using gum arabic. For instance, decreasing the pH or increasing the salt concentration prior to emulsification allows the formulation of metastable emulsions with much lower amounts of gum arabic, which is cheaper and hinders depletion effects.

■ MATERIALS AND METHODS

Materials. Spray-dried powder of gum arabic (Acacia senegal) was a gift from Caragum International (Marseille, France) and the composition was 2.5 wt % of proteins (Nx7), 11 wt % moisture, 3.16 wt % ash, and nearly no lipid (CG 103). This leads to a protein rate of 2.9 wt % of proteins (Nx7) for the dry gum arabic without ash. Sodium chloride (>99.5% BioXtra), hexadecane (99% ReagentPlus), pentane, and hydrochloride acid were purchased from Sigma Aldrich. Distilled water was used for all experiments.

Emulsion Ionic Strength. The ionic strength of a solution can be calculated using eq 1

I C z 1 2 i i i 2 ∑ = (1)
C i represents the concentration of each ion in solution and z i represents its valence. In the case of gum arabic-stabilized emulsions, all the ions present in eq 2 need to be considered. These include the carboxylic functions present via the glucuronic acid moieties of the polysaccharides, the gum cations, the protons in solution, and additional ions during the emulsion formulation. The protein content of the gum arabic being around 2.9 wt %, the contribution of NH 3 + and COO -ions of the gum proteins can be neglected. Ionic strength reads

I 1 2 ( COO 2 Mg 2 Ca K H Na Cl ) 2 2 2 2 = [ ] + [ ] + [ ] + [ ] + [ ] + [ ] + [ ] - + + + + + - (2)
The amount of glucuronic acid (mol) is calculated from the percentage of glucuronic acid (molar mass 194 g•mol -1 ) in gum arabic taken from the literature (around 14% w/w). [START_REF] Idris | Characterisation of Gum from Acacia Senegal Trees of Different Age and Location Using Multidetection Gel Permeation Chromatography[END_REF] The concentration of deprotonated carboxylic functions is given by eqs 3 and 4 n n

uronic acid COOH = (3) n V COO % COOH COO aqueous [ ] = × - Φ - (4)
Only the deprotonated form of the carboxylic acid moieties will play a role in the ionic strength of the emulsion. The percentage of deprotonated carboxylic acid is dependent on the pK a of glucuronic acid and the pH of the aqueous phase. Glucuronic acid pK a is taken equal to 3.2. [START_REF] Fernandes Diniz | Determination of Weak Acids over a Large PH Range[END_REF] COO COOH 10

K pH p a [ ] [ ] = - - (5)
Thus, for pH = 5 there is approximately 98% of carboxylic acid at deprotonated form, whereas at pH = 3.5 the percentage is approximately 67%. The concentration of gum cations can be calculated from eq 6 (illustrated with the concentration of magnesium). The weight percentage of each cation in the gum was determined using ion chromatography (

[K] = 0.8% w/w, [Mg] = 0.16% w/w, [Ca] = 0.55% w/w). m M V Mg wt % 2 Mg GA Mg aqueous [ ] = × × + Φ (6)
Concentrations of the sodium and chloride ions are dependent on the amount of sodium chloride (NaCl) and/or hydrochloric acid (HCl) added to the aqueous phase. Finally, the concentration of free protons is provided by the pH value.

H 10 pH [ ] = + - (7)
Emulsion Preparation. Oil-in-water emulsions stabilized by gum arabic were prepared using a rotor/stator device (Ultra-Turrax). Alkane oil (pentane or hexadecane) was used as the organic phase. We showed that the nature of the oil did not influence the composition of the interface (see Figure 1 in the Supporting Information). Emulsions of 100 mL were formulated as follows: 40% v/v of oil was dispersed in gum arabic aqueous solution. Gum arabic solutions were prepared at different concentrations and centrifuged under 19 000g to remove insoluble materials. Then, the pH or the salt concentration of each solution was adjusted by adding small amounts of HCl at 1 mol•L -1 or NaCl at 3 mol•L -1 . Both phases were then mixed for 1 min at 10 000 rpm.

Interfacial Separation. Gum arabic amphiphilic species were separated through an emulsion mediated separation technique. Emulsions of pentane in water stabilized by arabic gum were formed following the aforementioned protocol. Emulsions were then placed in a separatory funnel, diluted with distilled water saturated with pentane (to avoid pentane to diffuse from oil droplets into the aqueous phase) and allowed to cream. The aqueous phase was recovered and the emulsion cream was rinsed again to remove all species that could be trapped between the droplets without being strongly adsorbed at an interface. The aqueous phase containing all the species that did not adsorb was then dialyzed against water and lyophilized. The final cream was dispersed in water and freeze-dried.

A mass balance on the recovered species was carried out on each batch to calculate the surface concentration of the droplets.

Drop Size Distribution. Droplet size distributions of emulsions were measured using a static light scattering instrument (Mastersizer 3000, Malvern). The laser wavelength was of 633 nm. An emulsion sample was dispersed in recirculated water until an obscuration value between 1 and 10% was obtained to avoid multiple scattering. The average droplet diameter was determined as the volume-surface mean diameter d [3, 2]. Each measurement was repeated in triplicate at room temperature.

Size-Exclusion Chromatography. Size-exclusion chromatography was used to separate arabic gum species as a function of their relative hydrodynamic volume. A 7.8 mm × 300 mm BioSuite 450 Å SEC column (Waters) packed with 8 μm porous silica beads was used. The average pore size of the silica beads is 450 Å.

The separation was performed on an Alliance HPLC unit (Waters 2695 separations module), a 0.5 mol•L -1 NaCl aqueous solution at 25 °C was used as the eluent phase at a flow rate of 0.8 mL•min -1 . Each sample was filtrated with a nylon 0.2 μm membrane and a volume of 50 μL was injected. UV detection was performed at 210 and 280 nm using a Waters 2487 UV detector. Refractive index detection was performed to measure the mass percentage of each eluted species (Waters 410 differential refractometer). Refractive index detection was performed only until 10.5 mL of elution due to a negative peak appearing from the difference in the refractive index between the sodium chloride of the eluent phase and water from the injected sample. The column was calibrated using branched dextran standards (Waters) (see Figure 6 in the Supporting Information). [START_REF] Kato | Comparison of Poly(Ethylene Oxide), Pullulan and Dextran as Polymer Standards in Aqueous Gel Chromatography[END_REF] Variation coefficients for each detection were calculated from three injections of the same sample. For UV detection at 210 nm a 0.3% variation coefficient was measured. It was of 0.5% for UV detection at 280 nm and of 13% for refractive index detection.

UV detection at 280 nm is more sensitive to the amount of aromatic amino acid present within the gum polypeptidic moieties (tyrosine and phenylalanine). Aromatic amino acids, however, are not the predominant amino acid moieties in gum arabic, as already observed in the literature. [START_REF] Ray | Functionality of Gum Arabic. Fractionation, Characterization and Evaluation of Gum Fractions in Citrus Oil Emulsions and Model Beverages[END_REF][START_REF] Randall | Fractionation and Characterization of Gum from Acacia Senegal[END_REF][START_REF] Goodrum | Gum Arabic Glycoprotein Contains Glycomodules of Both Extensin and Arabinogalactan-Glycoproteins[END_REF] UV detection at 210 nm is mostly sensitive to π → π* transitions from the CO bond of the amide linkages between amino acid moieties and from those of carboxylic moieties present on the polysaccharide backbones and of reducing sugar units at the end of polysaccharide chains.

Hydrophobic Interaction Chromatography. Hydrophobic interaction chromatography was used to separate arabic gum fractions as a function of their hydrophobic properties. A 7.5 mm × 75 mm 10 μm Biosuite Phenyl column (Waters) consists of phenyl groups bonded to a metacrylic ester-based polymeric resin. The average pore size of the column is 1000 Å to accommodate macromolecules with high molecular weights. A gradient of salt concentration is required to create a "salting out" effect and progressively desorb the hydrophobic species. The less hydrophobic species are eluted first with the highest salt concentration, whereas the more hydrophobic species are the last one eluted.

The separation was performed on an Alliance HPLC unit (Waters 2695 separations module). The mobile phase was composed of a solution of NaCl at a constant flow rate of 0.5 mL min -1 . A continuous time gradient of salt concentration between 4 mol•L -1 and 0 was applied for 22 min followed by distilled water for 12 more minutes (see Figure 7 in the Supporting Information). Unlike other studies, [START_REF] Ray | Functionality of Gum Arabic. Fractionation, Characterization and Evaluation of Gum Fractions in Citrus Oil Emulsions and Model Beverages[END_REF][START_REF] Renard | Acacia Senegal Gum: Continuum of Molecular Species Differing by Their Protein to Sugar Ratio, Molecular Weight, and Charges[END_REF][START_REF] Randall | Fractionation and Characterization of Gum from Acacia Senegal[END_REF] our experiments have been performed with a continuous gradient of salt concentration to avoid artefacts during abrupt salinity changes. Each sample was filtrated with a nylon 0.2 μm membrane and a volume of 30 μL was injected. UV absorbance was measured at 210 and 280 nm (Waters 2487 UV detector). We observed that UV absorbance (especially at 210 nm) was very sensitive to NaCl concentration. The baseline was not constant along the separation time. To correct this deviation, the chromatogram of distilled water injected in the same elution conditions was subtracted from each measured chromatogram.

Variation coefficients for each detection were estimated from comparison of three injections of the same sample. A 6% (resp. 11%) variation coefficient was observed at 210 nm (resp. 280 nm).

Interfacial Film Composition. The surface concentration (in mg•m -2 ) in emulsions was determined using eq 8. The amount of recovered mass (m ads ) was determined from the mass balance from the interfacial separation procedure.

m d V 6 ads 3,2 oil Γ = [ ] (8)
Chromatograms of the adsorbed species at interfaces were determined using the following procedure: a native gum arabic sample and the species recovered from the aqueous phase of the emulsion (nonadsorbed species) were solubilized in water at a concentration of 3% w/w. The same volume for each solution was injected into both chromatographic columns (SEC and HIC). For both chromatographic techniques, the total area of chromatograms has been found to be proportional to the species concentration of each sample (see Figure 2 in the Supporting Information). Knowing from the interfacial separation procedure, the mass percentage of macromolecules from the gum that did or did not adsorb, the chromatogram of the species recovered from the aqueous phase was multiplied by its mass percentage. To obtain the chromatogram of the adsorbed species, the chromatogram of the nonadsorbed species was subtracted from that of the gum arabic. Finally, to compare each chromatogram between them, the resulting chromatogram was multiplied by 100 divided by its mass percentage determined from the interfacial separation procedure (percentage of gum macromolecules that adsorbed from the amount of gum used in the emulsion).

Nitrogen Content Analysis. Nitrogen content analyses were performed at the Laboratoire de Coordination Chimique (Toulouse, France). A Perkin-Elmer 2400 CHN series II was operated at 1050 °C with oxygen as carrier gas. Each sample was analyzed in duplicate. Protein fraction content was deduced from the nitrogen content through a protein conversion number calculated from the gum arabic amino acid distribution from Renard, Sanchez and coworkers 11 % % 7

protein nitrogen = × (9)
It is worth noting that this value slightly differs from the common value of 6.6 taken from the majority of gum arabic publications. However, we must stress that the 6.6 value does not correspond to the measured amino acid distribution of gum arabic, and is therefore incorrect.

■ ASSOCIATED CONTENT

* S Supporting Information

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.8b02715. Supporting Information is included with seven ( 7) figures (PDF)

S1

Supporting information. This supporting information sections contains: Small differences in interfacial composition may arise from the fact that although both emulsions exhibit similar Sauter diameters, their size distribution are slightly different, with the emulsion with pentane being more polydisperse. Nevertheless, their interfacial composition is nearly the same. According to interfacial tension measurements between gum Arabic solutions and hexadecane, physico-chemical parameters appeard to influence the adsorption of gum Arabic species at the interface. This observation might be either the results of an increase in the amount of gum amphiphilic species adsorbed (isotherms of adsorption), or the difference in the type of macromolecules adsorbed (chromatographic analysis) or a combination of both. Figure S7: NaCl elution gradient used for hydrophobic interaction chromatographic separation.

Figure 1 .

 1 Figure 1. (A) Size-exclusion chromatogram of a 30 g•L -1 aqueous solution of gum arabic. Thick line: UV detection at 210 nm. Dotted line:refractive index detection. Weight percentages of the recovered fraction using a preparative size-exclusion column (same column filling, larger dimensions) are indicated as well as the protein content for these fractions. Scale for molecular weights as a function of elution volumes is displayed on the top of the chromatograms. (B) Hydrophobic interaction chromatogram of a 30 g•L -1 aqueous solution of gum arabic using a UV detection at 210 nm. Less hydrophobic species of the gum are not retained by the column and species eluted last are the most hydrophobic ones.

Figure 2 .

 2 Figure 2. (A) Scheme of the rinsing procedure. (B) Evolution of gum arabic mass percentage in the emulsion cream (compared to the amount of gum in the emulsion, 100% is the amount used in the emulsion formulation) when rinsing an emulsion cream as a function of the number of rinsing steps. 0 corresponds to the amount of gum in the emulsion cream without rinsing.

Figure 3

 3 displays the comparison of gum arabic with adsorbed species chromatograms rescaled to the same weight fraction in water.

Figure 3 .

 3 Figure 3. Comparison of gum arabic species (black line) and gum arabic species adsorbing at oil-water interface (red line). (A) Size-exclusion chromatograms with UV detection at 210 nm. (B) Both chromatograms from (A) are normalized by the highest intensity to compare intensity ratios between different peaks. Numbers are peak ratios with respect to the highest peak. (C) size-exclusion chromatograms with a refractive index detection and (D) hydrophobic interaction chromatograms with a UV detection at 210 nm. Adsorbed species chromatograms are obtained by subtraction of the nonadsorbed species chromatograms from the total gum arabic chromatogram and rescaled to a concentration of 30 g•L -1 to compare the molecular mass composition with that of native gum arabic solutions (same area on refractive index chromatograms figure (C)).

Figure 4 .

 4 Figure 4. (A) Size-exclusion chromatograms and (B) hydrophobic interaction chromatograms of the adsorbed species as a function of the amount adsorbed at oil/water interfaces with UV detection at 210 nm (emulsions A and B from Table1). Chromatograms were obtained by subtraction of the nonadsorbed species chromatograms from the total gum arabic chromatogram and rescaled to a concentration of 30 g•L -1 to compare the molecular mass composition for each adsorbed film.

Figure 5 .

 5 Figure 5. Size-exclusion chromatograms of the adsorbed species as a function of the amount adsorbed at oil/water interfaces. Chromatograms were obtained by subtraction of the non-adsorbed species chromatograms from the total gum arabic chromatogram and rescaled to a concentration of 30 g•L -1 to compare the molecular mass composition for each adsorbed film.

Figure 6 .

 6 Figure 6. (A) Size-exclusion chromatograms and (B) hydrophobic interaction chromatograms of the adsorbed species after a (10 000g) or without (1g) centrifugal acceleration, with UV detection at 210 nm. Chromatograms were obtained by subtraction of the nonadsorbed species chromatograms from the total gum arabic chromatogram and rescaled to a concentration of 30 g•L -1 to compare the molecular mass composition for each adsorbed film.

Figure 7 .

 7 Figure 7. Size distribution of hexadecane-in-water emulsion (20% v/ v) stabilized by gum arabic one hour after emulsification with variations of physico-chemical parameters employed in the formulation (pH and salt concentration).

Figure

  Figure S1: (A) Emulsion size distribution with either pentane or hexadecane as the oil phase using the same formulation, (B) Same emulsions aqueous phase size exclusion chromatograms. ____________________________2

Figure S2 :

 S2 Figure S2: Size exclusion chromatogram of gum Arabic at 30g/L by varying the injected volume. (A) UV detection at 210nm, (B) UV detection at 280nm.

Figure S3 :

 S3 Figure S3: Comparison of gum Arabic (black line) and gum Arabic species adsorbing at oil water interface (red line) size exclusion chromatograms with a UV detection at 280nm. (A): Adsorbed species chromatograms was obtain by subtraction of the non adsorbed species chromatograms to the total gum Arabic chromatogram and brought backto a concentration of 30g/L in order to compare the molecular mass composition with gum Arabic. (B): Both chromatogram from (A) were normalized by the higher intensity in order to compare intensity ratios between different peaks.

Figure S5 :

 S5 Figure S5: Evolution of the droplets diameters for emulsions A and B during the rinsing procedure

Table 1 .

 1 Protein Content, Total Mass of Gum Adsorbed, Surface Concentration Γ and Mean Area per Adsorbed Species a 0 as a Function of pH and Ionic Strength

Emulsions stabilized by gum Arabic: composition and packing within interfacial films

  Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France *to whom correspondence should be addresse: kevin.roger@ensiacet.fr

	M. Atgié, O. Masbernat, K.Roger*

The authors would like to thank Agence Nationale de la Recherche (ANR) for financial support of Laboratoire Commun SOpHy (Projet-ANR-14-LAB3-0011), a partnership between