PhD, IRT Jonathan Villain
email: jonathan.villain@railenium.eu

Virginie Deniau
email: vir-ginie.deniau@ifsttar.frchristophe.gransart@ifsttar.fr

Eric Anthony Fleury
email: anthony.fleury@imt-lille-douai.fr

Pierre Simon

Christophe Gransart

Raouf Kousri
email: raouf.kousri@railenium.eu

Member, IEEE Eric Pierre Simon
email: eric.simon@univ-lille.fr

1n communication networks

Keywords: IEMI, Intentional ElectroMagnetic Interference, Classification, Wlan, Wi-Fi, communication network journal, IEEE 802.11n. I

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dummy commands for the denials of service etc. Different approaches can be studied to protect these wireless communications from such attacks. In this article, we consider that the first step to countermeasure attacks consists in detecting them and recognizing the type of attack in order to adapt the action to envolve.

Intrusion Detection Systems (IDS) [START_REF] Rowland | Intrusion detection system[END_REF] can detect an abnormal activity on an analyzed target. There are three major IDS families. The Network Intrusion Detection System (NIDS) [START_REF] Sun | Network intrusion detection system[END_REF] which monitors the security state at the network level, the HostBased Intrusion Detection System (HIDS) [START_REF] Vokorokos | Host-based intrusion detection system[END_REF] which monitors the security state at the host level and an hybrid IDS which combines NIDS and HIDS. The major difference between NIDS and HIDS is that HIDS is particularly effective in determining whether a host is contaminated whereas a NIDS can monitor an entire network. However, IDS mainly work on the upper layers of the OSI model and do not protect the wireless communication links. Moreover, the intrusion detection principles used need to be deployed on all terminals. In this study, we work on a solution which outsources the attack detection function by analyzing the wireless electromagnetic (EM) activity. It consists in taking data from antennas and receivers perfectly independent from the protected communication networks, then applying classification algorithms on the data.

In [START_REF] Van Aubel | Side-channel based intrusion detection for industrial control systems[END_REF], the authors have already considered improving IDS approaches through EM measurements with the same objective of outsourcing the monitoring. However, the context was different as their work dealt with the analysis of processor EM emissions with the view to detecting software compromise in the context of protecting industrial control systems.

In [START_REF] Xu | The feasibility of launching and detecting jamming attacks in wireless networks[END_REF], the detection of jamming attacks on wireless link networks was studied by analyzing the Received Signal Strength Indication (RSSI) received by a station. The RSSI is used by the IEEE 802.11 standard to measure the relative quality of the received signal. In [START_REF] Bhojani | An integrated approach for jammer detection using software defined radio[END_REF], the detection is based on a synchronization indicator together with an adaptive signal to noise plus jammer power ratio. In our case, as the monitoring solution is outsourced, we are not limited to the indicators of the standard. We selected the EM spectra collected by an independent bench, since they provide more information than the RSSI for the monitoring of the physical link.

Our article focuses on the Wi-Fi system. Nowadays Wi-Fi is not only used to access to internet. There are growing numbers of applications using Wi-Fi, including critical applications in terms of security. For instance, in the railway sector, Wi-Fi is increasingly used to ease the maintenance. Certain trains are now equipped with on-board systems that provide maintenance checks and report to a center via Wi-Fi transmissions. Hence, a monitoring system with attack detection functions can help strengthen the Wi-Fi network when used for critical applications. [START_REF] Deniau | IEEE 802.11n Communications in the Presence of Frequency-Sweeping Interference Signals[END_REF] recently analyzed the impact of jamming attacks on the performance of a Wi-Fi 802.11n transmission but it did not consider the detection and the classification of attacks. In [START_REF] Grimaldi | An svm-based method for classification of external interference in industrial wireless sensor and actuator networks[END_REF] the authors studied different EM interference sources but they did not consider attacks. However, in [START_REF] Grimaldi | An svm-based method for classification of external interference in industrial wireless sensor and actuator networks[END_REF], the interference signals were classified, using a combination of SVM binary classifications to determine if a channel is free from the interference source, if a microwave oven is active during the sensing period or if another network is overlapping the channel.

In our article, the considered attack scenarios correspond to attackers who would use jammers to provoke denial of services and/or who would send deauthentication frames on a public access point. The attack by deauthentication frames is generally applied to disconnect a station from a licit access point in order to benefit from all of the Wi-Fi ressource or to force a station to connect to an illicit access point in order to intercept private data. The attacks by deauthentication frames correspond to protocol-based attacks. In this work, we want to develop a single approach able to detect both jamming attacks and protocol-based attacks, and to distinguish them.

To achieve this goal, the proposed system first collects EM spectra of the frequency band of interest via an antenna. Then, the classification based on these extracted data from the spectra is carried out. Moreover, we propose the following two steps based on classification protocol: the first step uses the Support Vector Machine (SVM) for the classification, and the second step applies a time correction using the nearest neighbors. This time correction takes advantage of the fact that the attacks range on several spectra. In this way, both dimensions, the frequency dimension via the spectra, and the time dimension via the corrections are exploited. To sum up, the contributions of the paper are :

• The original approach for detecting the attacks based on the physical layer via the spectra -this approach makes it possible to outsource the monitoring system. • The classification protocol adapted to this context -both the ability to detect jamming attacks as well as protocolbased attacks. This paper is organized as follows. Section II presents the considered EM attack experimentation configuration. Section III reports the state of the art about the SVM and the nearest neighbour approaches. Section IV is a detailed analysis of the spectra in a classification perspective. Finally, Section V gives classification results and the conclusion is drawn in Section VI.

II. EM ATTACK EXPERIMENTATION CONFIGURATION

The considered communication protocol is the IEEE 802.11n, which employs an OFDM modulation scheme. We

s(t) = A cos 2π f 2 -f 1 2T t + f 1 t , 0 < t < T, (1)
where A is the interference signal amplitude. Here, the jamming signal that we consider sweeps the [2.4 GHz, 2.5 GHz] frequency band in T = 10 µs A time-frequency representation of the jamming signal is given Fig. 3.

b) Protocol attack: The attack by deauthentication frames uses management frames defined into the IEEE 802.11 standard. In a network infrastructure composed of several access points, when a client station (STA) is moving, the power of the Wi-Fi signal evolves. As for cellular networks (3G, 4G), a roaming principle has been specified in the standard. When a STA is connected to an Access Point (AP) and moves away from this AP, the Wi-Fi received signal power decreases. Due to the moving of the STA, it can detect the Wi-Fi beacon signal from another AP with an increasing power. In that case, a roaming procedure is launched. The roaming procedure consists in disconnecting the STA from the first AP and in reconnecting the STA to the second AP using IEEE 802.11 management frames deauthentication and authentication. The attack by deauthentication sends to a STA a frame of deauthentication even if the STA is not moving. Then, the STA executes the command and is disconnected from the wireless network. This attack can be easily implemented using tools like aircrack-ng (airplay-ng) [START_REF] Vinek | BackTrack 5 Wireless Penetration Testing Beginners Guide[END_REF]. Using particular parameters, the attacker creates a crafted frame that contains the real MAC address of the AP and the MAC address of the STA target. This attack is called directed deauthentication. For directed deauthentications, aireplay-ng sends out a total of 128 packets for each specified deauthentication. 64 packets are sent to the AP and 64 packets are sent to the client. After the attack, according to the configuration of the STA, the client can stay offline and the user has to reconnect the computer manually to the Wi-Fi network or the device can try by itself to reconnect after a few seconds. Both attacks with jamming signals or deauthentication frames, are generally the first step of attacks aiming the interception of data. Indeed, these attacks permit to disconnect the STA from a licit access point in order to reconnect it to a fake access point.

c) Device setting:

To study the detection and the distinction of the different attack modes by using a classificationbased approach, a Wi-Fi communication is set-up in an anechoic chamber by installing a server, an access point and a client computer. The Wi-Fi channel 1, centered on the 2.412 GHz frequency, is used. We include a monitoring antenna nearby the client. The monitoring antenna is connected to a spectrum analyzer which is outside the chamber. A 40 MHz frequency band, centred on 2.412 GHz, is monitored by the spectrum analyzer. The classification approach is performed on the collected spectra by the spectrum analyzer. Another antenna connected to an arbitrary waveform generator is placed in the chamber to emit the jamming signal. For the protocolbased attack, another computer is present in the anechoic chamber to send the deauthentication frames.

d) Attack configuration:

To evaluate the performances of the classification approach, we implement 6 distinct configurations. The first configuration is without attack: spectra acquisitions are carried out with a Wi-Fi communication only. Three jamming attack configurations are tested: one configuration with a low powerful jamming signal that has no impact on the communication quality. The bit rate is still at the maximal level (about 95 Mbits/s) and the noise on the channel does not significantly change with or without the jamming signal. A second configuration uses a jamming signal power level which sligthly degrades the communication quality. The bit rate is reduced at about 75 Mbits/s. The third configuration uses a jamming signal power level 1 dB inferior to the required power to totally interrupt the communication. So, the three configurations whith three different jamming signal power levels represent a jammer which would be at three different distances from the client and the AP. Another configuration consists in degrading the communication quality but without any attack. For that, electromagnetic absorbing materials are placed around the access point in order to degrade the signal quality. Finally, the last configuration corresponds to the deauthentication attack for which a dedicated computer has been used. This dedicated computer sends deauthentication requests to the STA to force it to leave the network. In this work, these different attacks were applied permanently during the acquisitions.

e) Acquisition: To illustrate the different configurations, we represent 99 spectra (see Fig. 2) collected by the spectrum analyzer from the monitoring antenna which is nearby the client. The spectrum analyzer configuration is as follows: a 40 MHz frequency span, 2.412 GHz center frequency, a 100 kHz resolution bandwidth and 1601 points. The sweep time of the spectrum analyzer is 38.2 µs. By observing the figure, we notice different curves for each attack configuration.

III. STATISTICAL APPROACH

For the classification of data, it is essential to know the relationships that bind a variable (here the type of attack) that one seeks to classify with some observable variables called explanatory variables (in this work the spectra corresponding to the attacks).

The purpose of the statistical modeling is to predict an output from explanatory variables. In our work, this output is the state (attack or not and if attack, which one) of the communication network at a precise time. To perform this task, we first learn to model the six profiles of communication presented in figure 2.

A. Support Vector Machine (SVM) Basics

In 1964, Vapnik and Chervonenkis [START_REF] Vapnik | The nature of statistical learning theory[END_REF] set up a method to determine an optimal margin separator hyperplane for the separation of two classes in a Hilbertian space [START_REF] Halmos | Introduction to Hilbert space and the theory of spectral multiplicity[END_REF] associated with a scalar product denoted by ., . . To begin with, let us consider a binary classification problem. The multi-class problem is investigated at the end of this section. For two classes, the goal is to find a classifier that will separate the data by maximizing the distance between the margin and the closest points of each class. a large variety of possible hyperplanes, but SVM chooses the optimal one. The intuitive idea is to look for an hyperplane for which the minimal distance in the learning data is maximal. To separate two classes, the greater the distance, the easier the class separation is. This distance is named "margin", as we seek to maximize it, it is then called a wide margin separator.

b) Separating Hyperplane: We have the following training data set : n couples {(x i , y i ), i = 1, . . . , n} where the label y i ∈ {-1, 1} indicates to which class the data x i ∈ R p belongs. A canonical separating hyperplane H is given by: w, x + b = 0.

(

We can choose w and b such that the closest point to H satisfies:

w, x + b = 1 if w x i + b ≥ 0, -1 if not. ( 3
)
we deduce that w x 1 + b > 0 and w x 2 + b < 0 as:

w, x 1 -x 2 = w, x 1 -w, x 2 = (1 -b) -(-1 -b) = 2, (4)
and consequently the margin towards H, denoted by M is given by:

M = w w , x 1 -x 2 = 2 w , (5)
where w = w 2 1 + . . . + w 2 p . So, smaller w is, bigger the margin is. For this reason, in order to find the hyperplane which offers the best separation of the data, we must find the one that respects the conditions of a canonical hyperplane and for which w is minimal. In order to find this hyperplane, we need to minimize:

1 2 w 2 (6)
under the constraints

y i ( w, x i + b) ≥ 1, i = 1, . . . , n (7)
which ensures that the hyperplane separates well the data and that it is canonical. c) Decision function: The decision function determines to which class belongs x i . It is based on the sign of w, x i +b. The mathematical function that extracts the sign is noted sgn and returns the values 1 or -1. For i = 1, . . . , n, y i ( w, x i + b) > 0 only if sgn (y i ( w, x i + b)) = y i and so if x i is in the right side of the hyperplane. Then, we can show that if w,

x i + b ≥ 0, then y i = 1 is assigned to x i , if w, x i + b ≤ 0, y i = -1. The hyperplane is said canonical. A property of this problem is that f (w) = w 2 = w 2 1 + . . . + w 2 p ( 8
)
is a convex function. That ensures the absence of local minimum and the presence of an unique solution. d) Lagrangian: In the current context, the problem of maximization becomes a problem of maximization under constraints. The minimization problem of (6) under the constraints (7) can be solved with the Lagrange multipliers. We denote, for i = 1, . . . , n, α i ≥ 0 the Lagrange's multipliers associated to the constraints. The Lagrangian function is given by:

L(w, b, α) = 1 2 w 2 - n i=1 α i (y i ( w, x i + b) -1) . (9)
e) Dual problem: Integrating the constraints, using Lagrange multipliers, the minimization problem can be formulated in its dual form. Thus, we try to minimize L(w, b, α) with respect to the primal variables w, b and to maximize it with respect to the dual variables α i . When we reach the optimal point of this minimization problem, we have ∂L(w,b,α) ∂b = 0 and ∂L(w,b,α) ∂w=0 and so n i=1 α i y i = 0 and n i=1 α i y i x i = w. By replacing these values in the Lagrangian, we obtain the dual problem:

max αi n i=1 α i - 1 2 n i=1 n j=1 α i α j y i y j x i , x j (10)
under constraints α i ≥ 0 and n i=1 α i y i = 0. In the dual problem, the b coefficient does not appear. It is known that when w, x + b > 1 the coefficient α i = 0 and that w, x + b = 1 for the support vector.

f) Resolution of the dual problem: To solve the dual problem, an average of these parameters is then calculated for the carrier vectors to estimate

M = 2 w = i∈SV α i -1/2
where SV (Support Vector) is the set of the support vectors. The decision boundary is then written:

w, x + b = i∈SV α i y i x i , x + b (11)
g) Non-linear hyperplane: Separation of classes by a linear hyperplane is a special case. Generally, the definition of the boundary between the different classes requires to determine a non linear hyperplane. The definition of a such hyperplane leads to the introduction of a release variable ξ i for the constraints. The release variable ξ i penalizes the slacking in the objective function. The problem then becomes min w,b,ξi

1 2 w 2 + C n i=1 ξ i (12)
under constraints y i ( w, x i + b) ≥ 1 -ξ i and ξ i ≥ 0 with C the penalisation term which has to be defined. The Lagragian is then given by

L(w, b, ξ i , α, ν) = 1 2 w 2 + C n i=1 ξ i - n i=1 α i (y i ( w, x i + b) -1 + ξ i ) - n i=1 ν i ξ i . (13)
Under optimal conditions we have

n i=1 α i y i = 0, w = n i=1 α i y i x i = 0 and C -α i -ν i = 0 for i = 1, . . . , n.
By replacing these values in the Lagrangian, we obtain the following dual problem:

max αi n i=1 α i - 1 2 n i=1 n j=1 α i α j y i y j x i , x j (14)
under constraints 0 ≤ α i ≤ C and n i=1 α i y i = 0. We consider now a nonlinear separation. In this case, the complexity of the separator hyperplane used in the linear case is not sufficient to correctly classify the data. In order to overcome this problem, we consider a nonlinear transformation φ(.) which projects the data in a larger dimension space so that the data in the transformed space are linear. The separating hyperplane is then written:

w, φ(x) + b. (15)
Replacing the data by the transformation φ(.) in the Lagrangian we obtain:

L(w, b, ξ i , α, ν) = 1 2 w 2 + C n i=1 ξ i - n i=1 α i (y i ( w, φ(x i ) + b) -1 + ξ i ) - n i=1 ν i ξ i . (16)
under constraints n i=1 α i y i = 0, w = n i=1 α i y i x i = 0 and C -α i -ν i = 0, i = 1, . . . , n we deduce:

max αi n i=1 α i - 1 2 n i=1 n j=1 α i α j y i y j φ(x i ), φ(x j ) (17)
under constraints 0 ≤ α i ≤ C and n i=1 α i y i = 0.

h) Kernel trick: It is difficult to build the hyperplane in the transformed space (φ(.)). In 1992, Boser et al. [START_REF] Boser | emphA training algorithm for optimal margin classifiers[END_REF], using Mercer theorem [START_REF] Mercer | Functions of positive and negative type, and their connection the theory of integral equations[END_REF] have found a way to build the optimal hyperplane in the projection space without using an explicit form of it. Applying this theorem, the problem can be written:

max αi n i=1 α i - 1 2 n i=1 n j=1 α i α j y i y j K(x i , x j ) (18)
under constraints 0 ≤ α i ≤ C and n i=1 α i y i = 0. In this study, we consider the Radial Basis Function kernel (RBF), given by:

K(x i , x j ) = exp - x i -x j 2 2 σ 2 , ( 19
)
where 1 2σ set the width of the bell-shaped curve which should be chosen a priori. The larger the value of 1 2σ , the narrower the bell is.

i) Multi-class problem: In this study, the problem is a multi-class classification case. Indeed, the problem is not limited to one class for "attack" and one for "normal communication". Firstly, we consider different kinds of attacks in order that the classification allows us to identify the type of attacks. Secondly, we consider the possibility to have configurations in which the communication quality is degraded due to bad propagation conditions. We want to be able to distinguish this last case in order to avoid false positive attack detections. In a multi-class classification case, two stategies can be deployed to adapt the SVM algorithm. The first is One-against-one strategy and the second is One-against-all strategy. In the following, the strategy selected to classify the spectra is the One-againstall strategy [START_REF] Liu | One-against-all multi-class SVM classification using reliability measures[END_REF]. One-against-all strategy involves training a single classifier per class, with the samples of that class as positive samples and all other samples as negative. To build the L-class classifiers, it is common to construct binary classifiers f 1 , f 2 , . . . , f L and combine them. The combination of these classifiers is carried out by ajusting the maximal output before applying it to the function sgn. The combination is then given by argmax j=1,...,L l i=1

y i α j i K(x, x i ) + b j . ( 20
)
This value can also be used as a rejecting decision when we consider the difference between the two largest values and as a confidence-building-measure in the classification of x.

B. Nearest Neighbour Approach

The nearest neighbor classifier does not require any preprocessing of the labelled samples prior to its use. The crisp nearest-neighbor classification rule assigns an input sample vector ŷ to the class of its nearest neighbor [START_REF] Peterson | K-nearest neighbor[END_REF]. The vector ŷ contains the labels predicted by the SVM model. This idea can be extended to the K-nearest neighbors with the vector ŷ being assigned to the class that is represented by a majority amongst the K-nearest neighbors. When more than one neighbor is considered, we can have a tie among classes with a maximum number of neighbors in the group of K-nearest neighbors. One simple way of handling this problem is to restrict the possible values of K. For example, given a two-class problem, if we restrict K to odd values only, no tie will be possible. Of course, when more than two classes are possible, this technique is not useful. A way of handling the occurrence of a tie is as follows. If these classes are tied, the sample vector is assigned to the class for which the sum of distances from the sample to each neighbor in the class is a minimum. This can still lead to a tie. In that case, the assignment is to the last class encountered. Consequently, there are cases where a classification vector becomes an arbitrary assignment, no matter what additional procedures are included in the algorithm.

C. General protocol

In this section, we describe the protocol which is applied to classify attacks by focusing on the relationship between the attack and EM spectra (classification) as well as the time correlation (correction). To estimate the profile of the attack (and as a consequence learn to recognize it), the protocol contains two main parts.

a)

Step 1: The first part of the protocol (Figure 5) performs a learning step of a classification based on SVM algorithm [START_REF] Vapnik | The nature of statistical learning theory[END_REF] (see Section III-A) with a radial basic function [START_REF] Park | Approximation and radial-basis-function networks[END_REF] as kernel. The SVM classification is performed using an One-against-all classification approach [START_REF] Liu | One-against-all multi-class SVM classification using reliability measures[END_REF]. In order to choose the parameter σ, we minimize the validation error for a sigma value included in a selected interval obtained with the procedure developed by Caputo and al. [START_REF] Caputo | Appearance-based object recognition using SVMs: which kernel should I use?[END_REF]. To estimate C, we considered the approach of Cherkassy and Ma [START_REF] Cherkassky | Practical selection of SVM parameters and noise estimation for SVM regression[END_REF].

b) Step 2: The second step predicts the class of a new data using two steps. In a first time, using the model estimated in step 1, a prediction is made for the new data. In a second time, with a lag of k/2, a correction is applied on the predicted class using K nearest temporal neighbors. The correction is possible due to the duration of the attacks over time which cannot be focused on a single spectrum [START_REF] Peterson | K-nearest neighbor[END_REF]. In the figure 5, Step 2 presents the general architecture of the proposed system that allows us to test the quality of the prediction.

IV. DATA ANALYSIS

A. Frequency band selection

To analyse the importance of the frequencies, we study the linear relationship between the frequencies of a spectrum. From the spectra obtained previously, we aim to find models that could distinguish classes corresponding to known attacks or to a normal use of the network.

The spectrum acquisitions are performed over a 40 MHz frequency band. The Wi-Fi channel bandwidth being 20 MHz, the spectrum characteristics before and after the 20 MHz channel may significantly vary if there are other access points or not in the vicinity. Thus, it is preferable for the classification to only exploit data belonging to the frequency band of the channel so that the detection is less dependent on the electromagnetic activity over the other channels. In learning algorithms, before any modeling we try to reduce the number of variables and to only select variables in the 20 MHz channel frequency band, for example by deleting the ones providing identical information. In the case of the spectra, due to the type of acquisition and the set up of the spectrum analyzer, we need to check the correlation between the different frequencies composing the spectra (see Fig. 6). Figure 6 highlights the high level of correlation between the frequencies in the interval [2.402; 2.422] GHz as well as the frequencies in the interval [2.392; 2.402[U ]2.422; 2.432] GHz. In addition, the central frequency corresponding to the zone D is more than 60% correlated with the whole band. Thus, only the data included in the band 2.402 and 2.422 GHz, corresponding to the zones B, D and E, are retained to perform a classification.

B. Principal Component Analysis

A way to check if the different classes can be separated is to compute a Principal Component Analysis (PCA) on all data, i.e. 99 spectra for the 6 configurations [START_REF] Wold | Principal component analysis[END_REF]. These components correspond to the axes obtained from the eigenvectors constructed from the spectra. Projecting the spectra on the two components associated to the eigenvector possessing the higher eigenvalues (see Fig. 7), we check if the different configurations can be discriminated by classification. In this new representation, each point corresponds to one spectrum. The six different colors represent the spectra collected in the six different configurations: Wi-Fi communication alone in blue, Wi-Fi in the presence of absorbers in yellow, Wi-Fi with jamming signal without effect in red, Wi-Fi with jamming signal with lightly effect in green, Wi-Fi with jamming signal at the limit of the break in purple and Wi-Fi communication with a deauthentication attack in brown. Fig. 7: Representation of the spectra on the two first components of the PCA.

In Fig. 7, we notice that the attack by deauthentication is significantly separated from the other classes. This result is very satisfying because this attack is a protocol-based attack and the nature of the signal is not really different from a normal communication. This result is encouraging to develop a detection approach able to distinguish a large number of attacks. Nevertheless, the separation is less obvious between the Wi-Fi communication alone, the jamming without impact and with moderate impact. To improve the separation between these three classes, we have to take into account other components beyond the first two and we must adopt a nonlinear separation. As a consequence, the relevance of the retained variables (zones B, D et E in Fig. 6) has to be verified in the case of a non linear analysis.

C. Identification of the most discriminating frequencies

The relevance of the retained variables has been demonstrated in a linear analysis. While conducting a nonlinear analysis, it must be verified that the most discriminative frequencies belong to sections B, D and E of Fig 6 .  To identify the most discriminating frequencies in the spectrum, we use sensitivity response aggregation functions [START_REF] Cortez | Using sensitivity analysis and visualization techniques to open black box data mining models[END_REF]. The sensitivity method works by varying an input variable x a through its range with S levels. This method works by Fig. 8: Weight of the 20 first relevant frequencies. considering a given baseline vector d of size S which generally contains the statistical values (mean, median, quartile, . . . ) of each input. Then, it cycles through all {x a : a ∈ 1, . . . , p} inputs. For each input, S inputs are built using all d values except {x aj : j ∈ {1; . . . ; S}}. We denote the respective model responses by ŷa = { ŷ aj : j ∈ {1; . . . ; S}}, where ŷ aj represents the response for x aj . Based on these values, we compute a sensitivity measure of the input. Thus, highest is the score obtained by a variable, the more it discriminates the studied cluster. To study the discrimination capacity of the different frequencies, we use these sensitivity response aggregation functions. Fig. 8 represents the 20 most discriminating frequencies, with in abscissa, their respective weight issued from the sensitivity measure. These most discriminating frequencies are located between 2.4 GHz and 2.42 GHz (see red points in Fig. 9) and the best frequency is 2.412025 GHz. Therefore, the central 20 MHz frequency band is sufficient to discriminate the spectra associated to the different types of attack, even using a non linear analysis. In the following, only the central frequency band of the spectrum is considered. Moreover, in this paper the acquisitions are performed in anechoic chamber, using a single Wi-Fi channel. However, in realistic conditions, the adjacent Wi-Fi channels can be used or not. Therefore, the spectra over 40 MHz can significantly evolve according to the activity of the Wi-Fi network. As a consequence, centering the analysis on the 20 MHz of the used channel can make the classification results less dependent on the activity of the adjacent communication channels.

The identification of the most discriminative frequencies is also interesting to optimize the acquisition process. Indeed, the perspective of this work, is also to develop attack detection tools on Software Defined Radio (SDR) platforms. The acquisition process could be optimized by focusing on a reduced

V. CLASSIFICATION RESULTS

The classification is then performed only on the central 20 MHz frequency band. We want to identify whether or not the communication network is facing an attack at a precise time using the spectra corresponding to this time. We have to identify 6 attack profiles presented in section II. We estimate the attack profile using the protocol presented in Fig. 5. In the second step of the protocol, to perform the K-nearest neighbors on the SVM predictions, we use the 10 nearest neighbors. To verify the quality of the prediction, the general architecture of the proposed system is tested on sampling data constructed as presented in Fig. 5.

The learning phase is the phase where the SVM model learns the separating hyperplanes. The learning data set is composed of 49 spectra for each configuration (294 spectra in total). The validation phase is a verification of the parametrization of the SVM model. The validation data set is composed of 29 spectra for each configuration (174 spectra). Finally, the testing phase is the phase in which we compute and evaluate the correction. The real time data is replaced by a testing data set composed of 126 spectra organized in a temporal way simulating a succession of configurations (see figure 10). validation, the testing sets and by a cross-validation. Crossvalidation is a validation technique to assess how the results of a statistical analysis can be generalized to an independent data set. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset and validating the analysis on the other subset. The number of folds determines the number of rotations used to estimate the combined measures. In the following, we use the cross-validation on the combined training, validating and testing sets to estimate the classification error using a three-fold validation.

Table II represents the confusion matrix between the predicted classes and the real one (the predicted classes are obtained by cross-validation). 1) corresponds to a Wi-Fi communication only, 2) corresponds to a communication in the presence of absorbers, 3) corresponds to a communication in the presence of jamming without effect, 4) corresponds to a communication with a light impact jamming, 5) corresponds to a communication in the presence of jamming that provokes short communication interruptions and 6) corresponds to a communication with a deauthentication attack (see Table II).

Observing the confusion matrix, the majority of the errors are concentrated on two different cases : (1) between normal communication and the presence of absorbing materials, and (2) between strong jamming creating short connection losses and deauthentication attacks. It is important to note that there is no confusion between a degraded propagation situation by the presence of absorbing materials and the presence of an attack signal. This shows that the degraded propagation situation is not likely to produce a false alarm. The errors between normal communication and the presence of absorbing materials can be explained by the proximity in their spectra profiles as it can be observed in Fig. 7. The confusion between strong jamming creating short connection losses and deauthentication attacks, comes from restarts on the Wi-Fi communication after interruptions. To overcome this confusion, we compute a correction using the 9 nearest neighbors which permits to take into account the proximity in time of the spectra to be analysed. To evaluate this correction, we apply it on the testing set.

In Table III, to analyze the weakness of the initial model and the potential relation between the attacks, we also analyze the confusion matrix established over the test data respectively without and with correction by the nearest neighbors.

Here, the correction is based on the fact that an attack cannot be implemented on only one spectrum, it lasts a certain time and covers several spectra. In our configuration, this correction suppresses all the errors. But, the correction does not work when an error occurs on a transition phase (transition between two states of the network). Moreover, the lack of good prediction in a transition phase can create a new error in the case where the classification correctly predicts the attack or produces a wrong correction of the error. The choice of the K parameter is crucial for the correction. On one hand, it is important to take a sufficiently large value to have a good correction. On the other hand, higher the value is, later the prediction arrives.

VI. CONCLUSION

This paper focuses on the conception of a monitoring system able to detect and classify jamming and protocol based attacks. To achieve this goal, we propose to outsource the attack detection function from the network to protect and we use an antenna to monitor the spectrum over the time.

In this study, the Wi-Fi network and the attacks are carried out in an anechoic chamber to avoid disturbing other Wi-Fi communication networks in the vicinity.

A study of the spectra highlights that the frequencies of interest belong to the communication channel between 2.402 GHz and 2.422 GHz. Focusing the analysis on this 20 MHz frequency band permits to construct a classification model to overcome the problems induced by the utilization of the adjacent channels which can be or not occupied by other Wi-Fi communications.

On these frequencies, the proposed estimation model shows good results in the prediction of attacks. In addition, the correction using the K spectra nearest in time permits to correct most of the miss classification.

In our future work, we plan to verify the behavior of our model on data acquired outside of the anechoic chamber, in realistic situations. Another important point is knowing how our model can evolve in the case where unknown attack occurs. Finally, as new (unpresented/unlearned) attacks can appear very quickly, we aim to use machine learning techniques. These techniques include adaptive classification algorithms, able to change the models after their creation and the number of classes over time. By learning upstream the standard behavior of the communication, the algorithm analyzes the data as they arrive and try to classify them as one standard communication or not. If the communication is not standard, a new class is created and considered by defaults as an unknown attack.

Fig. 1 :

 1 Fig. 1: Experimentation with a 802.11n communication in the presence of attack

Fig. 2 :Fig. 3 :

 23 Fig. 2: Spectra of IEEE 802.11n communication plus attacks collected by the monitoring antenna

  Fig. 4: Example of Separating hyperplane and support vectors in 2 dimensions.

Fig. 5 :

 5 Fig. 5: Schematization of the Protocol.

Fig. 6 :

 6 Fig. 6: Frequencies correlation.

Fig. 9 :

 9 Fig. 9: Best Frequency.

Fig. 10 :

 10 Fig. 10: Test set organization.

TABLE I :

 I Classification error

		model error (%)	sample size n
	Cross-validation	15	468
	Training	7	294
	Validation	9	174
	Test	14	126

TABLE II :

 II Confusion matrix obtained by cross-validation between predicted classes and real configuration.

					predicted attack
			1	2	3	4	5	6
		1: Wi-Fi alone	80 19	0	0	0	0
	real attack	2: With absorbers 3: Low jamming 4: Moderate jamming 5: Strong jamming	25 74 0 0 0 0 0 0	0 97 4 0	0 2 95 0	0 0 0 63 36 0 0 0
		6: Deauthentication	0	0	0	0	2	97

TABLE III :

 III The confusion matrix obtained on the testing set. In each line of the table, the two results correspond respectively to the result without and with correction

ACKNOWLEDGMENT

This work was performed in the framework of the X2Rail-1 project (Shift2Rail Joint Undertaking) and in the framework of the ELSAT2020 project which is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council.


J.-M Cadet
email: mauricejean.cadet@univ-reunion.fr

INTER-COMPARISON CAMPAIGN OF SOLAR UVR INSTRUMENTS AT RÉUNION ISLAND (21.0°S, 55,5°E): FINDINGS AND RECOMMENDATIONS

Reunion Island (21.0°S, 55.5°E) is a French and European territory situated in the Indian Ocean in the tropics where solar ultraviolet radiation (UVR) levels are high almost all-year round. This is mainly due to intense incident solar radiation combined with weak stratospheric ozone columns. Yet, very few UVR and ozone measurements are available or operational in these regions, especially in the south-west Indian Ocean countries. Since 2009, the Reunion University, a French and European university, started a research program based on ground-based solar UVR and ozone observations. Continuous UVR measurements require instrument monitoring and calibration processes in terms of wavelength and intensity on regular intervals and made with regular comparison against a reference instrument. Several UV instruments comparison has already been done [1], but a comparison at high UV level can highlight a different instrument trend. In the framework of the NDACC (Network for the Detection of Atmospheric Composition Change) and in collaboration with the LOA (Laboratoire d'Optique Atmosphérique, University of Lille, France) a Bentham DM300 spectrometer (BT) is operated at Reunion Island. We recently implemented an inter-comparison campaign between the Bentham spectroradiometer and four UV radiometers: a Kipp&Zonen UVS-E-T (KZ), a Solar Light 501 (SL), a SGlux UV COS (SG) and a Davis radiometer (DV), with the Bentham spectrometer as a reference. It should be noted that the Kipp&Zonen and Solar Light 501 radiometers were calibrated during the International UV filter Radiometer Comparison in summer 2017 organised by PMOD/WRC at the WCC-UV in Davos [2]. In order to identify clear sky conditions, an all-sky camera recording cloud fraction (CF) has been operating at the observation site.

Clear sky filtering

For manually selected clear sky day, the all sky camera values are between 2 and 10%. Figure 1

Reference Conclusion

 Figures 3 and4 show a good agreement between the KZ and the SG to the BT. The bias is similar to the instrument accuracy, within ±5%.

 A slight dependence on SZA is identified on KZ data, from +5% at low SZA to -5% at high SZA  The SL over estimates the UVI about 30% to the BT. This bias does not depend on SZA  Figure 4 highlights a strong solar zenith angle dependence on the DV data, but the bias is low for low solar zenith angle (0 to 30°)

 The cloud condition doesn't affect the bias between the instruments. The effect of the cloud cover on the UVI is the same on all the co-localised instruments  KZ: Good coherence with BT. KZ measurements seem to be SZA-dependent but overall bias remain below ±7%, as stated in the specification. Nevertheless, the reason of this SZA dependency have to be clarified

Recommandations

The SL501 shift can be corrected by using a new calibration factor according to the bias founded, 0.69 W.m -2 .V -1 instead of 0,8686 W.m -2 .V -1 . A new calibration can also be performed to solve this issue.

Figure 1 :

 1 Figure 1: Example of comparison between UV index from ground-based instruments and cloud fraction from all-sky camera at Saint-Denis on 11/04/2018

Figure 2 :

 2 Figure 2: Bias between BT and KZ as function of solar zenith angle and cloud fraction. The legend table indicates the maximum threshold of cloud fraction.

Figure 3 :

 3 Figure2shows no significant difference as function of cloud fraction. Indeed, the bias is the same from clear sky condition (CF=0,1) to overcast sky condition (CF=1)

Figure 4 :

 4 Figure 4: Bias as function of solar zenith angle. Bias computed with clear sky data by using a maximum threshold of 10% of cloud fraction.



  SL: There is no SZA dependency as the SL was calibrated in order to take into account effects of SZA and ozone absorption during Davos inter-comparison campaign [2]. However there is a constant positive bias of 30%. This shift can be corrected by using a lower calibration factor  SG: Good agreement with the BT: low and constant bias  DV: The UVI are clearly function of SZA. The bias is low, within 5% for low solar zenith angle All data are interpolated on a 1 degree grid of solar zenith angle. The comparison is performed by calculating bias per SZA, MAPE, correlation and RMSE [1] de Paula Corrêa, M., Godin-Beekmann, S., Haeffelin, M., Brogniez, C., Verschaeve, F., Saiag, P., Pazmino, A. & Mahé, E. (2010). Comparison between UV index measurements performed by research-grade and consumer-products instruments. Photochemical & Photobiological Sciences, 9(4), 459-463. [2] International UV Filter Radiometer Comparison PMOD/WCC-UV, Davos, Switzerland, Summer 2017 http://projects.pmodwrc.ch/bb2017/media/UVC-II_report.pdf [3] Lamy, K., Portafaix, T., Brogniez, C., Godin-Beekmann, S., Bencherif, H., Morel, B., Pazmino, A., Metzger, J.-M., Auriol, F., Deroo, C., Duflot, V., Goloub, P. & Long, C. N. (2018). Ultraviolet radiation modelling from ground-based and satellite measurements on Reunion Island, southern tropics. Atmospheric Chemistry and Physics, 18(1), 227-246.


Bassam Moujalled
email: bassam.moujalled@cerema.fr

Yacine Aït Ouméziane

Sophie Moissette

Marjorie Bart

Christophe Lanos

Driss Samri

Experimental and numerical evaluation of the hygrothermal performance of a hemp lime concrete building: a long term case study

Keywords: Hemp concrete, Building envelope, Hygrothermal behavior, In-situ measurement, heat and moisture transfer model

Hemp-lime concrete (HLC) is a bio-based material which is currently undergoing a growing development. HLC is a low embodied energy material and an excellent hygrothermal regulator. Its thermal, hygric and mechanical properties are well known, and its capacity to reduce energy needs and to improve hygrothermal comfort is demonstrated across many laboratory and numerical studies. However, there are few works about its hygrothermal performance in real climatic conditions on the scale of a building. In order to address this issue, a long term in-situ measurement is carried out to analyze the hygrothermal performance of a HLC individual dwelling-house during 4 years. The analysis of the hygrothermal behavior of a wall is achieved by comparing measurements and numerical simulations results. In this study, two simulation tools are used and compared. The first tool

Introduction

In a context of sustainable development, one of the concerns in building construction is the choice of environmentally friendly materials. In fact, it has some impacts on exhaustion of natural resources, energy consumption and polluting emission. In this framework, bio-based materials appear as a good solution to address these issues [START_REF] Pretot | Life cycle assessment of a hemp concrete wall: Impact of thickness and coating[END_REF][START_REF] Maalouf | An energy and carbon footprint assessment upon the usage of hemp-lime concrete and recycled-PET façades for office facilities in France and Italy[END_REF][START_REF] Mazhoud | Hygric and thermal properties of hemplime plasters[END_REF][START_REF] Collet | Experimental highlight of hygrothermal phenomena in hemp concrete wall[END_REF][START_REF] Costantine | Experimental and numerical investigations of thermal performance of a Hemp Lime external building insulation[END_REF]. This study deals with Hemp-Lime Concrete (HLC) made of hemp shiv (renewable raw and fast-growing bio-based material) and of lime which has a lower embodied energy than cement [START_REF] Boutin | Etude des caractéristiques environnementales du chanvre par l'analyse de son cycle de vie[END_REF]. The use of hemp in construction was demonstrated to be a promising opportunity to radically decrease the carbon footprint of buildings [START_REF] Pittau | Fast-growing bio-based materials as an opportunity for storing carbon in exterior walls[END_REF]. Depending on its composition and manufacturing process of compaction, HLC can be used for several applications: wall, floor and roof. HLC is characterized by a very high porosity (more than 70% for a ''wall' mixture") and a low dry density around 400 kg.m -3 for a "wall mixture" [START_REF] Collet | Experimental highlight of hygrothermal phenomena in hemp concrete wall[END_REF][START_REF] Costantine | Experimental and numerical investigations of thermal performance of a Hemp Lime external building insulation[END_REF][START_REF] Collet | Caractérisation hydrique et thermique de matériaux de génie civil à faibles impacts environnementaux[END_REF][START_REF] Cerezo | Propriétés mécaniques, thermiques et acoustiques d'un matériau à base de particules végétales : approche expérimentale et modélisation théorique[END_REF][START_REF] Samri | Analyse physique et caractérisation hygrothermique des matériaux de construction[END_REF][START_REF] Chamoin | Optimisation des propriétés (physiques, mécaniques et hydriques) de bétons de chanvre par la maîtrise de la formulation[END_REF][START_REF] Collet | Comparison of the hygric behaviour of three hemp concretes[END_REF]. It shows low compressive strength and is consequently mainly used as filling material associated with a wooden frame [START_REF] Collet | Experimental highlight of hygrothermal phenomena in hemp concrete wall[END_REF][START_REF] Elfordy | Mechanical and thermal properties of lime and hemp concrete ("hempcrete") manufactured by a projection process[END_REF][START_REF] Walker | Mechanical properties and durability of hemp-lime concretes[END_REF].

However, HLC shows interesting hygrothermal properties for its use in buildings: a low thermal conductivity about 0.1 W.m -1 .K -1 and a high vapor permeability about 10 -11 -10 -10 kg.m -1 .s -1 .Pa -1 [START_REF] Collet | Caractérisation hydrique et thermique de matériaux de génie civil à faibles impacts environnementaux[END_REF][START_REF] Cerezo | Propriétés mécaniques, thermiques et acoustiques d'un matériau à base de particules végétales : approche expérimentale et modélisation théorique[END_REF][START_REF] Samri | Analyse physique et caractérisation hygrothermique des matériaux de construction[END_REF][START_REF] Chamoin | Optimisation des propriétés (physiques, mécaniques et hydriques) de bétons de chanvre par la maîtrise de la formulation[END_REF][START_REF] Walker | Mechanical properties and durability of hemp-lime concretes[END_REF][START_REF] Collet | Thermal conductivity of hemp concretes: Variation with formulation, density and water content[END_REF][START_REF] De Bruijn | Moisture fixation and thermal properties of lime-hemp concrete[END_REF][START_REF] Strandberg-De Bruijn | Moisture transport properties of lime-hemp concrete determined over the complete moisture range[END_REF]. A moisture buffer value (MBV) above 2 g.m -2 /%RH determined

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in accordance with the Nordtest protocol [START_REF] Rode | Moisture buffering of building materials[END_REF] qualifies HLC as an excellent moisture regulator [START_REF] Costantine | Experimental and numerical investigations of thermal performance of a Hemp Lime external building insulation[END_REF][START_REF] Collet | Comparison of the hygric behaviour of three hemp concretes[END_REF][START_REF] Collet | Experimental investigation of moisture buffering capacity of sprayed hemp concrete[END_REF].

Moreover, HLC presents a significant hysteretic behavior that influences the evolution of the moisture content inside the material. Hysteresis is often not considered in sorption process modeling. The relation between moisture content and relative humidity is modelled by a single curve (usually the main adsorption curve). Due to the hysteretic effect, the equilibrium moisture content depends not only on relative humidity but also on moisture history.

Adsorption and desorption cycles measured in [START_REF] Aït Oumeziane | Hysteretic Behaviour and Moisture Buffering of Hemp Concrete[END_REF] for HLC show the influence of hysteresis on the moisture content and hygric capacity evolutions. Temperature influences also the equilibrium moisture content: the warmer the temperature, the lower will be the equilibrium moisture content at the same relative humidity. Some measurements at 10°C and 23°C are provided for HLC in [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF]. Hysteresis phenomenon and temperature effects on sorption process are most often neglected for modeling the moisture content evolution in heat and moisture transfer models. This can cause significant discrepancies to predict the hygrothermal response of a material subjected to climatic variations as shown by Zhang et al. in the case of wood materials [START_REF] Zhang | Combined effects of sorption hysteresis and its temperature dependency on wood materials and building enclosurespart II: Hygrothermal modeling[END_REF].

The last researches on the transient hygrothermal response of HLC have been essentially led on the wall scale in laboratory conditions [START_REF] Tran Le | Transient hygrothermal behaviour of a hemp concrete building envelope[END_REF][START_REF] Colinart | Experimental Study on the Hygrothermal Behavior of a Coated Sprayed Hemp Concrete Wall[END_REF][START_REF] Lelievre | Hygrothermal behavior of bio-based building materials including hysteresis effects: Experimental and numerical analyses[END_REF][START_REF] Aït Oumeziane | Influence of hysteresis on the transient hygrothermal response of a hemp concrete wall[END_REF]. In particular, these studies show the relevance to consider hysteresis and hygric history to improve numerical results [START_REF] Lelievre | Hygrothermal behavior of bio-based building materials including hysteresis effects: Experimental and numerical analyses[END_REF][START_REF] Aït Oumeziane | Influence of hysteresis on the transient hygrothermal response of a hemp concrete wall[END_REF][START_REF] Colinart | Experimental and numerical analysis of the transient hygrothermal behavior of multilayered hemp concrete wall[END_REF].

Nevertheless, there are few works about its hygrothermal performance in real climatic conditions. Bejat et al. in France [START_REF] Bejat | Study of Two Hemp Concrete Walls in Real Weather Conditions[END_REF] and Shea et al. in Great Britain [START_REF] Shea | Hygrothermal performance of an experimental hemp-lime building[END_REF] have led some experimental campaigns on hemp concrete wall subjected to real climatic conditions. The comparison between numerical and experimental results show the inability of commercial softwares to reproduce the effective hygrothermal response of the wall. More recently, Costantine et al. [START_REF] Costantine | Experimental and numerical investigations of thermal performance of a Hemp Lime external building insulation[END_REF] have focused their study on the scale of a hemp-concrete building for a 10-month period under real weather conditions, but they are not interested in the effective hygrothermal behavior of HLC and temperature and relative humidity inside the wall were not monitored.

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In order to predict the real hygrothermal behavior of this material, this paper presents a field study of the hygrothermal response of a HLC building subjected to climatic variations, located in South West of France.

The main objective is to look for more realistic thermal and hygric behavior of hemp concrete under real climatic conditions. The originality of this paper lies in studying the hygrothermal response of a HLC individual dwelling-house in real climatic conditions, at both wall and building scales, for a long monitoring period covering four whole years. First, a global experimental analysis is performed to evaluate on the full scale the performance of the building. External weather conditions, indoor air temperature and relative humidity of several rooms are monitored to discuss the indoor comfort. Temperature and relative humidity of north-faced and west-faced walls are monitored at two different depths and at surfaces to analyze the hygrothermal behavior of walls. Then, the comparison between experimental and numerical results of instrumented walls are discussed considering the effect of temperature dependence and hysteresis on HLC sorption process.

Experimental setup

The monitored building is a 2-floor single-detached house of 250 m 2 with a semi-basement. It is located close to Perigueux in South West of France at 180 m above sea level. The weather is mild and humid, typical of temperate and oceanic climates. The building is surrounded by vegetation that increases the humidity of the local environment.

The house was built in 2011 and is occupied by four inhabitants (2 adults and 2 children) since December 2011. The building design is based on bioclimatic architecture with large glazing area of 18 m² on the south façade and prow roof overhang (Fig. 1) in order to maximize the solar gain during winter while avoiding summer overheating. Its envelope is made of 30 cm thick HLC sprayed into walls of a timber frame structure. HLC is also used in roof and intermediate floor in 10 cm and 15 cm thickness respectively. The walls are internally and externally protected with lime-sand plasters, except the internal face of the walls in the office room. The HLC was sprayed during April-May 2011, and the plasters were

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applied immediately after finishing the walls construction. A wood pellet boiler coupled with 12.6 m² of solar collectors provides energy for heating and domestic hot water. Heat is distributed in the house through radiant floor at the ground level and radiant walls at the top level. A balanced ventilation system with heat recovery is used for ventilation.

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Thermographic inspection and airtightness test were carried out in order to evaluate the building envelope performance. The thermographic inspection helps to detect insulation defects, especially thermal bridges that may occur due to the timber frame structure. The blower door test helps to evaluate the level of air permeability according to the standard NF EN 13829 [START_REF]Thermal performance of buildings -Determination of air permeability of buildings -Fan pressurization method[END_REF] and to detect the air leakages through the envelope. The thermographic diagnostic is performed using the FLIR® Thermacam E4 infrared camera, and the airtightness testing with the blower door Minneapolis® system.

The hygrothermal performance of the indoor environment was monitored using 8 Hobo data loggers (HOBO U12-002) that measured the indoor air temperature and relative humidity in different rooms of the house. Special care was taken to prevent the probe from being subjected to radiation from neighboring heat sources. Fig. 2 shows the locations of the sensors in the different rooms.

In order to evaluate the hygrothermal performance of the envelope, temperature and relative humidity monitoring was done for the west and north-facing walls of the office room. The north-facing wall is sunless; it was monitored for comparison purposes with the west-facing wall. Both walls are made of 30 cm of HLC, and 3 cm of exterior lime-sand plaster. They were initially uncoated at the interior surface. A textile inner lining was added few months after the beginning the monitoring for aesthetic reason, but its hygrothermal resistance is neglected in this study. Each wall is monitored as shown in Fig. 3.

Two sensors are placed within the wall and measure temperature and relative humidity at two different depths (15 and 25 cm from the interior surface). The sensors are 12 mm diameter. They were inserted into the wall through drilled holes at the specified depths. Once the sensors placed, holes were sealed with acrylic sealant. This measurement technique is validated in [START_REF] Samri | Analyse physique et caractérisation hygrothermique des matériaux de construction[END_REF]. Two thermocouples (STCS from Prosensor), fixed with adhesive tape, measure the interior and exterior wall surface temperatures. The indoor air temperature and relative humidity of the studied room are measured with two Hobo data loggers located near the walls and at the center of the room. Outdoor air temperature and relative humidity are All data were monitored continuously from February 2012 till January 2016 during 4 years with a time step of 15 minutes. Table 1 presents the measurement parameters, the devices and their specifications. These devices are all new devices whose calibration has been certified by the manufacturer.

Model

Heat and moisture transfer model

The hygrothermal performance of the monitored north HLC wall located in the office room is simulated with two different simulation tools.

The first simulation tool used in this paper is WUFI Pro 5.1® software [START_REF] Künzel | Simultaneous heat and moisture transport in building components. Oneand two-dimensional calculation using simple parameters[END_REF]. This version of WUFI is able to simulate one-dimensional heat and moisture transfer in porous materials.

The second simulation tool uses the Matlab software. The developed model is fully described in [START_REF] Oumeziane | Evaluation des performances hygrothermiques d'une paroi par simulation numérique : application aux parois en béton de chanvre[END_REF] and main points are reported in [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF]. Only a short summary is presented in this paper.

According to Künzel formalism [START_REF] Künzel | Simultaneous heat and moisture transport in building components. Oneand two-dimensional calculation using simple parameters[END_REF], the driving potentials of the 1D hygrothermal model can be expressed in terms of two potentials: temperature T and relative humidity φ. The governing system of equations of moisture and heat transfer is given in Eq. ( 1) and ( 2):

+ = ∇ ∇ + ∇ ∇φ (1)
+ + -!"# $ $ + % -!"# $ & = ∇ '∇ + %( ) + ) -!"# $& ∇ + ∇ -!"# $ ∇φ (2)
δp [kg.m -1 . s -1 .Pa -1 ] is the vapor permeability, Dl [kg.m -1 .s -1 ] the liquid conduction coefficient, psat [Pa] the saturated vapor pressure and Tref [K] the reference temperature (equal to 296.15K). cv [J.kg -1 .K -1 ] and cl [J.kg -1 .K -1 ] are the specific heat capacity of, respectively, vapor and liquid water. c0 [J.kg -1 .K -1 ] is the specific heat capacity of the dry material. λ [W.m -1 .K -1 ] is the thermal conductivity of the moist material and lv the latent heat of evaporation [2.45×10 6 J.kg -1 at the temperature of 296.15K].

In these equations, ξ [kg.m -3 ] is the hygric capacity with u [kg.kg -1 ] the moisture content and ρ0 [kg.m -3 ] the density of the dry material.

= ;< ; = (3)
= ;< ; = (4)

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Neumann boundary conditions are chosen to model the exchange between the HLC wall and the indoor and outdoor environments.

Moisture exchange coefficient is deduced from the heat transfer coefficient with the Lewis relation [START_REF] Lewis | The Evaporation of Liquid into a Gas[END_REF]. The governing system of strongly coupled unsteady differential equations is implemented in Matlab with a finite differences method. The model was benchmarked against the international benchmark HAMSTAD WP2 [START_REF] Hagentoft | MANUSCRIPT and Moisture Transfer in Building Components: Benchmarks for Onedimensional Cases[END_REF]. The model was validated in [START_REF] Oumeziane | Evaluation des performances hygrothermiques d'une paroi par simulation numérique : application aux parois en béton de chanvre[END_REF] against all the test cases of the benchmark.

Material properties modeling

The properties of the HLC wall provided by the manufacturer gives a dry bulk density ρ0 = 450 kg.m -3 and a dry heat conductivity λ0 = 0.11 W.m -1 .K -1 . The hemp concrete hygrothermal properties used in this study are those experimentally determined in [START_REF] Chamoin | Optimisation des propriétés (physiques, mécaniques et hydriques) de bétons de chanvre par la maîtrise de la formulation[END_REF][START_REF] Collet | Comparison of the hygric behaviour of three hemp concretes[END_REF], which gives similar values.

In Wufi, logarithmic interpolations between the experimental data allow to determine the value of a parameter.

In the Matlab program, the hygrothermal properties of HLC are modeled in [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF] from the experimental results obtained in [START_REF] Chamoin | Optimisation des propriétés (physiques, mécaniques et hydriques) de bétons de chanvre par la maîtrise de la formulation[END_REF][START_REF] Collet | Comparison of the hygric behaviour of three hemp concretes[END_REF]. These properties are listed in Table 2.

The main adsorption and desorption curves are built using the Van Genuchten model [START_REF] Van Genuchten | closed-form equation for predicting the hydraulic conductivity of unsatured soils[END_REF].

> ? φ = > @%1 + =B ? C D E F (G φ = H I & JKLJ M N I O P , R = STU VW TXU (5)
where uads and udes respectively represent the main adsorption and desorption functions. usat

[kg.kg -1 ] is the saturated moisture content. R [8.314 J.mol -1 .K -1 ]
is the ideal gas constant, Ml [18 g.mol -1 ] the molar water mass, g [9.81 m 2 .s -1 ] the gravity acceleration and T [296.15K] the reference temperature.

The values of the parameters are given in Table 3.

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Property Expressions

Dry density ρ 0 450 kg.m -3

Open porosity n 0 68%

Vapor permeability δ p 5.10 -11 kg.m -1 .s -1 .Pa -1

Vapor diffusion resistance factor

µ 0 ] = , / Liquid diffusion coefficient D l = 1/] * > -1/] ,
Fictitious vapor diffusion resistance factor µ* ] * > = , 23°c /4.0664. 10 JLL Xg 20.814. >

Critical moisture content u cr ] * > i! = ] Equivalent thermal conductivity λ ' = ' + ' -' > -> /> Dry thermal conductivity λ 0 λ 0 = 0.117 W.m -1 .K -1 Saturated thermal conductivity λ s λ s = 0.6 W.m -1 .K -1 Equivalent specific heat capacity c* * = + > Dry specific heat capacity c 0 c 0 = 1250 J.kg -1 .K -1
Vapor permeability of air δ p,a , = 2.31. 10 Jj k /l /273.15 L,oL

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The properties of the render provided by the manufacturer gives a dry bulk density ρ0 = 935 kg.m -3 and a dry heat conductivity λ0 = 0.19 W.m -1 .K -1 .

The hygrothermal properties of the render selected for this study are given in WUFI database and reported in [START_REF] Evrard | Transient hygrothermal behaviour of Lime-Hemp Materials[END_REF]. The main properties are summarized in Table 4.

Property Expressions

Dry density ρ 0 988 kg.m -3

Total porosity n 0 58%

Vapor diffusion resistance factor µ 0 ] = 24

Dry thermal conductivity λ 0 λ 0 = 0.165 W.m -1 .K -1
Dry specific heat capacity c 0 c 0 = 850 J.kg -1 .K -1 Table 4. Main hygrothermal properties of the render.

Hysteresis model

In comparison with Wufi, Matlab gives more computational scopes to the users. In particular, advanced hysteresis models can be implemented. In this paper, Huang's hysteresis model is chosen [START_REF] Huang | A novel hysteresis model in unsaturated soil[END_REF]. This model gives the more consistent results in comparison with other hysteresis models [START_REF] Aït Oumeziane | Hysteretic Behaviour and Moisture Buffering of Hemp Concrete[END_REF] for the studied hemp concrete.

Eq. ( 6) and ( 7) describe respectively the adsorption and desorption scanning curves after a series of alternating processes of desorption and adsorption: The consideration of the effect of the temperature on moisture content relies on the thermodynamic evolution of the sorption mechanism [START_REF] Poyet | Experimental investigation of the effect of temperature on the first desorption isotherm of concrete[END_REF]. This approach is based on the exothermic process of adsorption [START_REF] Brunauer | The Adsorption of Gases and Vapors[END_REF] and on the principle of Le Chatelier and the rule of Van't Hoff. As presented in [START_REF] Poyet | Experimental investigation of the effect of temperature on the first desorption isotherm of concrete[END_REF], the Clausius-Clapeyron relation is used to express the heat involved in the sorption process [START_REF] Brunauer | The Adsorption of Gases and Vapors[END_REF] called isosteric heat qst [J.kg -1 ]: The isosteric heat qst is determined from the experimental data of sorption isotherms obtained in [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF] for hemp concrete at two temperatures: 10 and 23°C. In this paper, the relation proposed by Powers and Brownyard [START_REF] Powers | Studies of the physical properties of hardened portland cement paste[END_REF] is implemented:

> , p = > ! p + > p -> ! p $ < qrs < sqt (6)
> , p = > ! p + > p -> ! p $ < rus < sqt (7)
x > = - C D E y z { % M | & }~< (8
x > = ( ) + •€< • (9)
The parameters a, b and c are derived to fit the experimental data: a = 0.697 J.kg -1 , b = 0.0886 kg.kg -1 and c = 6.443. Expressed under this form, the isosteric heat decreases with moisture content and asymptotically tends to the latent heat of vaporization.

The sorption isotherm at any temperature T is deduced from the reference sorption isotherm φ Tref,u as follows:

φ , > = φ !"# , >$ sqt ƒu" $ sqt | X … st < † E %| ‡| ƒu" & ˆ|| ƒu" (10)

Results and discussion

Experimental results

Performance of the envelope and hygrothermal comfort analysis

The thermographic inspection was performed in March 2013 with covered sky, and adequate temperature differential of 15°C between the indoor and the outdoor.

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Finally, the indoor air temperature and relative humidity of different rooms were analyzed and compared to the comfort zones of the EN 15251 standard [START_REF]Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics[END_REF] as shown in Fig. 7 for each of the different winter and summer periods. We only considered the lower limit of comfort zone for winter, and the upper limit of comfort zone for summer. The measurements of different rooms showed a good level of thermal comfort with values in category IV less than 5% of occupied hours, except the bedroom1 during winter as the parents set lower temperature during night. The higher values of relative humidity during the first year could be explained by the fact that the HLC wall was still drying even though the measurements were undertaken one year after the walls construction. In fact, the lime-sand plaster was applied shortly after the walls construction. Besides the outdoor RH showed high values over the period of the measurements (daily average varies between 60 and 95%). This could induce a significantly longer drying time. Yearly values of wall temperature range from 10°C to 25°C while the outdoor temperature values range from less -5°C to more than 35°C. Similarly, the monthly variations of wall temperature were much lower regarding the variations of outdoor temperature (5°C against 20°C).

M A N U S C R I P T A C C E P T E D

The measured temperature and relative humidity at 25 cm depth of the North-facing wall (near the outdoor surface) show the same annual trend with higher values and higher variations than in the middle of the wall. Relative humidity of the wall at 25 cm depth seems to be more influenced by outdoor air conditions than at 15 cm depth.

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Moreover, Fig. 11 shows that the wall is able to dampen the outside daily variations by 90% and to delay the effects of peak values about a dozen of hours. Similar trends are also observed in winter. This result confirms the good thermal inertia of 30 cm thick HLC walls.

Experimental versus numerical results

Simulations are set up during a period of one year from 1 st January 2015 to 31 st December 2015. Measured indoor (in the office room) and outdoor temperatures and relative humidities are presented respectively in Fig. 12a and Fig. 12b. The wind velocity and the global and diffuse solar radiations measured on site are respectively represented in Fig. 12c and Fig. 12d. Meteonorm weather files for the city of Bergerac (located at 40 km from the site) are selected to consider short wave solar radiation. The short wave solar absorption coefficient is set to 0.6.

Standard interior surface exchange coefficients are quite representative of the effective values considering natural convection on a vertical wall located in a room at ambient temperature around 20°C and, at a lesser degree, radiative contribution of the house lighting.

The exterior surface of the wall is subjected to natural convection due to the thermal gradient between exterior air and the wall surface temperature, to forced convection due to wind, to solar radiation and to wind-driven rain. All these phenomena are significantly weatherdependent and may highly fluctuate during all the year. In order to evaluate the impact of these exterior phenomena on the relative humidity and temperature evolutions, a set of correlations detailed in [START_REF] Oumeziane | Evaluation des performances hygrothermiques d'une paroi par simulation numérique : application aux parois en béton de chanvre[END_REF] have been implemented to consider natural and forced (considering wind effects) convections and long wave solar radiative exchange. The results, not presented in this paper, show that under the studied climatic conditions, the standard heat and moisture exchange coefficients are quite able to represent the surface exchange.

The standard interior and exterior heat exchange coefficients considered in the simulations are thus chosen respectively at 7.7 and 25 W.m -2 .K -1 . The interior and exterior surface moisture exchange coefficients are respectively 2.5 10 -8 and 1.3 10 -7 kg.Pa -1 .m -2 .s -1 .

M A N U S C R I P T A C C E P T E D

Comparison between Wufi and Matlab numerical results

In this section, the hygrothermal response of the north wall is simulated with Wufi and Matlab and the numerical results are compared with the experimental ones.

The initial temperature of the wall is 20°C and the initial relative humidity 65%RH. These values are the average ones measured in the wall the 1 st January 2015 at midnight.

For a reason of readability, Fig. 13a and Fig. 13b show the evolution of temperature respectively at 25 cm and 15 cm depth during August, and Fig. 13c and Fig. 13d during March. Fig. 14a and Fig. 14b show the evolution of the relative humidity and Fig. 14c and Fig. 14d the evolution of temperature throughout the year respectively at 25 cm and 15 cm depths.

Concerning temperature evolutions, the results show a quite good agreement between Wufi and Matlab simulations. However, during winter the simulated temperatures are lower by 2-3°C than the experimental ones and their amplitude of variation is higher. February to July, experimental and numerical relative humidities slightly decrease then increase during the stage of adsorption from July to January. However, at 15 cm depth, some differences can be observed: a delay occurs between the stages of adsorption and desorption when the numerical results are compared to the experimental ones. Several reasons can explain this difference. First, the models do not consider the hysteresis phenomenon which occurs during the sorption process. Kwiatkowski [START_REF] Kwiatkowski | Modelling of hysteresis influence on mass transfer in building materials[END_REF] confirms through a sensitivity analysis that, in realistic conditions, neglecting hysteresis leads to an overestimation of the moisture buffering properties of the material. The temperature variations have also an impact on the sorption process [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF][START_REF] Colinart | Experimental and numerical analysis of the transient hygrothermal behavior of multilayered hemp concrete wall[END_REF][START_REF] Colinart | Temperature dependence of sorption isotherm of hygroscopic building materials. Part 2: Influence on hygrothermal behavior of hemp concrete[END_REF] and may significantly influence the evolution of relative humidity through the wall.

These elements are investigated and discussed in the following sections with the Matlab model.

Influence of hysteresis and hygric history

In this paragraph, hysteresis phenomenon is considered. The influence of this phenomenon is discussed from Matlab simulations. The Huang's hysteresis model associated with the Van Genuchten model, selected to represent HLC hygric sorption process, is implemented in the heat and moisture transfer model.

First, the simulation with hysteresis is performed from 1 st January 2015 to 31 st December 2015 from the initial conditions described in the previous paragraph. Temperature evolutions are not presented but hysteresis does not create significant modifications in this case. This explain why the evolution of relative humidity is the same during this period. Then, during the desorption stage from 15 th April to 20 th July, with hysteresis, moisture content does not follow the main adsorption curve but a primary desorption curve. Consequently, the hygric capacity is reduced which explains the evolution of relative humidity. Finally, during the second stage of adsorption from 20 th July to 31 st December, moisture content follows a secondary adsorption curve. This curve has also a reduced hygric capacity in comparison with the main adsorption curve, case for which hysteresis is not considered. For both models with or without hysteresis, the consideration of the hygric history lived by the wall before the 1 st January 2015 allow finding a better agreement between experimental and

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numerical results especially at 25 cm depth. This is explained by a better estimation of the initial relative humidity and temperature through the wall at the beginning of the simulation.

Despite a more physical and realistic description of the sorption process, numerical results obtained with or without hysteresis are equivalent. Under these hygrothermal conditions, the relevance to consider hysteresis phenomenon is poor. Indeed, the variations of outside and inside hygrothermal conditions are not enough high to observe an effective influence of the hysteresis consideration on the relative humidity and temperature evolution through the wall.

Influence of temperature-dependent sorption process

The influence of the temperature effect on moisture content is analyzed in this section with Temperature evolutions are not represented but, as observed in [START_REF] Colinart | Temperature dependence of sorption isotherm of hygroscopic building materials. Part 2: Influence on hygrothermal behavior of hemp concrete[END_REF], few differences occur in the evolution of the temperature with or without consideration of the temperature effect on sorption process.

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These results are relevant with the temperature effect on sorption process highlighted in [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF][START_REF] Colinart | Temperature dependence of sorption isotherm of hygroscopic building materials. Part 2: Influence on hygrothermal behavior of hemp concrete[END_REF]. Moreover, the simulation with hysteresis gives better results than the simulation without.

Fig. 18a and Fig. 18b show the evolution of the relative humidity throughout the year respectively at 25 cm and 15 cm depth. Whatever the case, the simulated results presented Fig. 18 show that the evolution of the relative humidity is in a good agreement with the experimental results until the end of July.

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During this period, the temperature increases when moisture content highly decreases especially at 25 cm depth (Fig. 19). Consequently, a slight diminution of relative humidity is observed. In summer, when temperatures are the highest, the moisture content is minimum throughout the wall. Then, from the end of August to the end of the year, moisture content slightly increases with decreasing temperatures. During this period, some discrepancies are observed between numerical and experimental results up to 15%RH even though the global trend is respected. This can be explained by the low level of moisture content found with the developed modeling for the highest temperatures reached in the wall (Fig. 19). Indeed, as shown in previous studies [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF][START_REF] Poyet | Experimental investigation of the effect of temperature on the first desorption isotherm of concrete[END_REF], the consistency of the modeling of the temperaturedependence of moisture content strongly depends on the quality and the quantity of the experimental data collected. In the case of hemp concrete, additional measurements campaigns have to be launched especially at high relative humidities and temperatures [START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF].

Conclusion

In this paper, the hygrothermal performance of a hemp-lime building located in South-West of France is analyzed. The study presents the monitoring results considering the hygrothermal comfort and the hygrothermal behavior of a wall. The evolution of relative humidity and temperature experimentally collected through the wall are also compared with numerical simulations.

Building envelope inspections show a good level of insulation for the 30 cm thick HLC walls with homogeneous temperature at the exterior surfaces. However, the exposed timber frame breaks the continuity of the finishing plaster. The shrinkage of the timber frame creates gaps at the joints between the frame elements and interior finishing of the walls. Due to air leakages at these locations, the result of the envelope airtightness test was poor. This problem can be avoided by applying a continuous plaster over the internal face of the walls.

The in situ monitoring of the building shows that HLC helps to maintain hygrothermal conditions at winter and summer comfort levels while outside temperature and relative humidity daily variations are up to 15°C and 50%RH respectively. In fact, relative humidity of indoor air never falls below 30% during the heating period, and indoor air temperature never exceeds 27°C during summer (even when outdoor air temperature is higher than 35°C). The measurements show also the good thermal inertia of 30 cm thick HLC wall, which allowed them to dampen the daily temperature and relative humidity variations by 90% and to delay the effects of peak values up to about 12 hours. It confirms the global idea that bio-based materials are good hygrothermal regulators. However, relative humidity values within the wall are slightly high and need to be further examined in order to evaluate the risk of molds development.

The numerical evolutions of relative humidity and temperature inside the wall give promising results in comparison with the experimental measurements. Nevertheless, despite a more physical representation of the effective hygric behavior of HLC, numerical simulations with hysteresis do not significantly improve the results under the studied hygrothermal conditions.

However, the simulations performed show that the consideration of the temperaturedependence of the sorption isotherms gives promising results. The predicted local daily variations of relative humidity through the wall are in a good agreement with the experimental results which show that relative humidity evolutions follow the temperature evolutions.

However, some complementary experimental investigations have to be performed to better identify the influence of temperature on sorption process mechanism for HLC. An uncertainty remains also concerning the effective hygrothermal properties of the coating in lime-sand plaster. In this way, further investigations should be done in the future.

Fig. 2

 2 Fig. 2 presents the ground and floor plans of the building. The main living room and the

Fig. 1 .

 1 Fig. 1. View of the south facade of the building.

Fig. 2 .

 2 Fig. 2. Floor plans of the building with the location of sensors.

  Hobo datalogger that is placed on the north facade (HOBO U23-001 from Onset). Besides, a complete weather station (HOBO U30 from Onset) was added lately in December 2014 close to the house. It measures outdoor air temperature and relative humidity, horizontal solar radiation, wind speed and direction, and rainfall.

Fig. 3 .

 3 Fig. 3. Sensors location through the wall and on the internal and external surfaces.

  ur i and us i respectively represent the residual and the saturated moisture contents. The index i represents the number of switches between adsorption and desorption phases. The calculation of these parameters is based on the perfect closure of the scanning curve at reversal points. Scanning curve indexed i includes the last reversal point φi,ui and the penultimate reversal point φi-1,ui-1 .M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 3.4. Temperature-dependence of moisture content modeling

  ) pv [Pa] is the vapor pressure with psat [Pa] the saturated vapor pressure and T [K] the temperature.

Fig. 4

 4 shows the thermographic image of the exterior surfaces of the north/east corner of the house. TheM A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPTexterior surface temperature of the walls is clearly homogeneous. No structural thermal bridge due to the timber framework is observed. Also, no thermal losses are observed on the walls of the floor level due to radiant wall heating. Hence, the thermal insulation level of the HLC is good enough to prevent structural thermal bridges.The airtightness test was carried out on the same day. The air permeability was calculated according to the French indicator Q4Pa-Surf [m 3 .h -1 .m-2 ]. It represents the ratio of the air leakage rate at 4 Pa over the envelope area of the building except the ground floor area[START_REF] Richieri | Airtightness impact on energy needs and airflow pattern: a numerical evaluation for mechanically ventilated dwellings in France[END_REF]. The test result indicates an air permeability of 1.32 m 3 .h -1 .m -2 , more than twice as the French mandatory level (0.6 m 3 .h -1 .m -2 for a single-family house). This can be explained by the shrinkage of the timber frame structure which creates gaps at the joints between the frame elements and interior finishing of the walls. Thermogram of Fig.4shows an example of air leakage at the joints between the roof frame and the wall.

Fig. 4 .

 4 Fig. 4. Thermogram of the North and East facades of the building (on the left), and

Fig. 5

 5 Fig.5shows the statistical results of the measured indoor air temperature and relative

Fig. 5 .

 5 Fig. 5. Statistical monthly results of the measured indoor air temperature (a) and relative

Fig. 6 .

 6 Fig. 6. The measured psychrometric data of the living room for the winter periods (left) and

Fig. 7 . 3 .Fig. 8 and

 738 Fig. 7. Quality of thermal environment in percentage of occupancy time in the four categories

Fig. 8 .

 8 Fig. 8. Statistical monthly results of the measured relative humidity of the outdoor air (a), the

Fig. 9 .

 9 Fig. 9. Statistical monthly results of the measured temperature of the outdoor air (a), the

  relative humidity, wall temperature values at 15 cm depth show almost no variation from year to year with monthly median values around 13°C in winter and 25°C in summer.

Fig. 10 .

 10 Fig. 10. Statistical monthly results of relative humidity and temperature of the West wall at 25

Fig. 10 Fig. 11 .

 1011 Fig.10shows the statistical results of the measured relative humidity and temperature of the

Fig. 12 .

 12 Fig. 12. : Evolution of the exterior and interior temperatures a), relative humidity b), wind

Fig. 13 .Fig. 14 .

 1314 Fig. 13. : Evolution of the temperature at 25 cm depth in August a), at 15 cm depth in August

Fig. 15 .

 15 Fig. 15. : Evolution of the relative humidity at 25 cm depth a) and at 15 cm depth b) from 1 st January to 31 st December 2015. The black solid line stands for the experimental values, the

  Then, in order to evaluate the influence of the previous hygrothermal variations, the simulations are performed from the 1 st July 2012 (date of the beginning of the experimental campaign) to the 31 st December 2015. The results are still presented from 1 st January 2015 to 31 st December 2015 in Fig.16aand Fig.16bonly concerning the relative humidity evolution.

Fig. 16 .

 16 Fig. 16. Evolution of the relative humidity at 25 cm depth a) and at 15 cm depth b) from 1 st January to 31 st December 2015 considering the previous hygrothermal variations from the 1 st

  and without hysteresis. The model based on the Clausius-Clapeyron equation and the Powers and Brownyard relation used to express the evolution of the isosteric heat is implemented in the heat and moisture transfer model. Starting from the effective initial relative humidity and temperature determined in the previous paragraph with the hygric history consideration, two simulation runs are performed considering the temperature effect on sorption process, with and without hysteresis. The results are compared with the experimental data and the standard simulation results obtained when hysteresis and temperature effect on sorption are not considered.

Fig. 17a and

 17a Fig.17aand Fig.17bwhich compare the evolution of the relative humidity with the evolution of temperature for 3 days from the 1 st June to the 4 th June respectively at 25 cm and 15 cm depths show that the consideration of the temperature-dependence of the moisture content

Fig. 17 .

 17 Fig. 17. Evolution of relative humidity and temperature at 25 cm depth a) and at 15 cm depth b) from 1 st to 4 th June 2015 considering the temperature effect on sorption process, with and

Fig. 18 :

 18 Fig. 18: Evolution of the relative humidity at 25 cm depth a) and at 15 cm depth b) from 1 st January to 31 st December 2015 considering the temperature effect on sorption process, with

Fig. 19 .

 19 Fig. 19. Evolution of the moisture content at x = 25 cm depth and x = 15 cm depth with

Table 1 .

 1 Specifications of the measurement devices.

		T: ± 0.35°C	T: 0.1°C/yr
	RH: 5% to 95%	RH<90%: ± 2.5%	RH: < 1%/yr
		RH>90%: ± 5%

Table 2 .

 2 Hygrothermal properties of HLC[START_REF] Aït Oumeziane | Influence of temperature on sorption process in hemp concrete[END_REF].

	Parameter	Unity	Value
	u sat	kg/kg	1.500
	Adsorption
	α ads	-	0.0119
	η ads	-	2.0462
	Desorption
	α des	-	0.0185
	η des	-	1.647

Table 3 .

 3 Hygrothermal properties of HLC.

Office air relative humidity [%RH]

						ACCEPTED MANUSCRIPT
			2012				2013				2014				2015
		Winter	Spring	Summer	Autumn	Winter	Spring	Summer	Autumn	Winter	Spring	Summer	Autumn	Winter	Spring	Summer	Autumn
	Outdoor air temperature [°C]
	Mean	7.7 13.9 19.6 10.3	4.7 12.5 19.7 15.3	7.0 14.2 19.2 6.5	5.8 14.6 19.4 10.5
	S.D.	5.1 5.7 5.5 5.9	4.4 5.3 5.5 5.0	4.6 5.4 4.4 3.0	4.8 6.0 6.2 5.2
	Min	-5.7 -1.0 3.6 -7.0	-5.4 -1.5 7.2 0.6	-7.1 0.3 8.0 -1.6	-6.7 -1.0 5.7 -4.5
	Max	24.6 30.1 35.8 27.6	18.8 30.0 35.2 27.2	20.8 30.0 34.1 12.7	21.0 33.9 37.8 23.0
	Office air temperature [°C]
	Mean 20.9 21.1 22.9 21.1	20.2 20.8 23.5 21.9	20.2 21.1 23.2 20.9	20.3 21.2 23.1 20.4
	S.D.	0.5 1.5 1.2 0.9	0.6 1.0 1.2 1.2	0.7 1.3 1.0 1.0	0.7 1.3 1.3 0.6
	Min	19.7 16.7 19.9 18.5	17.6 18.5 20.7 19.4	17.4 18.8 20.6 19.3	17.6 19.0 20.5 18.6
	Max	22.6 24.9 27.3 24.0	21.7 24.9 26.6 24.3	23.1 25.4 25.5 24.5	22.1 25.0 26.9 22.6
	Living air temperature [°C]
	Mean 21.7 21.8 23.2 21.6	20.4 21.3 23.5 22.5	20.7 21.6 23.5 21.3	20.4 21.5 23.2 21.1
	S.D.	1.0 1.5 1.2 1.1	0.9 1.1 1.2 1.3	0.8 1.3 1.1 1.2	0.9 1.3 1.5 1.1
	Min	19.4 17.2 18.9 15.8	18.1 19.1 19.5 19.3	18.0 19.6 19.1 18.9	18.0 18.7 18.5 18.8
	Max	24.4 25.8 27.2 25.4	23.4 24.4 26.9 25.5	22.9 26.1 26.2 24.4	24.9 25.8 27.2 29.3
	Bedroom1 air temperature [°C]
	Mean 21.1 21.5 23.2 21.1	19.6 20.9 23.1 22.5	19.9 21.1 23.0 20.8	19.5 21.2 22.9 20.6
	S.D.	1.1 1.6 1.3 1.3	1.0 1.2 1.3 1.3	1.1 1.4 1.2 1.4	0.9 1.3 1.4 1.0
	Min	19.2 17.5 19.3 17.8	16.8 18.8 20.2 19.5	16.9 18.6 19.0 18.1	16.5 18.3 19.6 18.4
	Max	24.0 25.9 27.2 24.8	23.2 25.3 26.9 25.6	23.4 25.8 25.8 25.1	22.3 25.3 26.4 23.6
	Bedroom4 air temperature [°C]
	Mean 22.3 22.3 24.1 21.9	21.1 21.9 24.2 22.7	21.2 22.2 24.2 21.7	21.1 22.4 24.2 21.5
	S.D.	0.8 1.5 1.3 1.0	0.7 1.1 1.4 1.4	0.7 1.3 1.1 1.1	0.7 1.3 1.4 0.6
	Min	19.8 17.8 21.2 19.2	19.2 19.9 21.0 19.6	19.2 20.1 21.3 19.8	18.9 20.0 21.5 19.6
	Max	24.3 26.4 28.0 25.2	24.0 25.3 27.9 25.8	23.6 27.0 26.9 25.1	23.2 26.6 27.6 23.5
	Outdoor air relative humidity [%RH]
	Mean	79	76	70	90	88	79	74	89	84	75	79	93	88	76	75	88
	S.D.	21	20	19	11	15	18	19	11	18	19	17	8	14	20	19	12
	Min	20	20	21	41	33	30	23	44	26	20	21	21	22	17	23	36
	Max	100 100 100 100	100 100 100 100	100 100 100 100	100 100 100 100
	Mean	41	51	58	52	40	47	57	60	43	49	59	54	40	49	57	51
	S.D.	5	7	4	7	4	5	4	4	2	5	4	5	4	7	4	5
	Min	31	31	40	38	31	37	45	51	36	39	51	41	31	33	47	41
	Max	52	66	69	67	50	65	70	68	49	67	70	65	50	62	65	63
	Living air relative humidity [%RH]
	Mean	39	49	56	51	41	49	59	61	46	51	61	56	44	52	60	53
	S.D.	6	7	5	7	5	6	5	3	3	5	4	5	4	6	4	5
	Min	28	27	34	34	31	35	43	52	34	39	49	40	32	33	49	38
	Max	52	69	73	70	52	68	73	68	55	72	76	67	54	69	72	67
	Bedroom1 air relative humidity [%RH]
	Mean	43	52	58	56	47	52	60	62	49	53	63	58	46	53	60	54
	S.D.	7	8	5	6	5	6	5	4	3	5	4	5	4	7	4	5
	Min	29	28	40	41	35	38	44	48	38	41	51	46	31	26	50	45
	Max	61	67	73	73	61	68	73	70	63	74	75	71	59	74	71	66
	Bedroom4 air relative humidity [%RH]
	Mean	34	47	54	56	-	47	55	60	43	48	57	54	41	48	56	51
	S.D.	3	7	4	5	-	5	4	3	2	5	4	5	4	6	4	5
	Min	28	29	35	45	-	36	43	51	33	38	47	42	33	32	46	41
	Max	41	64	66	68	-	63	66	67	50	64	68	67	49	62	65	64

Table 5 .

 5 Statistical

summary of indoor and outdoor air temperature and relative humidity (S.D.: Standard-Deviation).

Acknowledgement

The authors would like to thank the French Ministry of Sustainable Development for its financial contributions. The authors thank also DB Chanvre Company that provided this construction, and building owners for collaboration.

 Hysteresis and temperature-dependence of moisture content effects are discussed.

 HLC helps to maintain a good hygrothermal comfort level in winter and summer.

 HLC is confirmed to be a good hygrothermal regulator.

 Effect of temperature on sorption enables more realistic results in simulation.