Sana Benhamaid
email: sana.benhamaid@hds.utc.fr

Energy-Efficient and Context-aware Trajectory Planning for Mobile Data Collection in IoT using Deep Reinforcement Learning

Keywords: Internet-of-Things, Mobile data collection, energy efficiency, Adaptive trajectory planning, Deep Q-learning, Deep SARSA

IoT networks are often composed of spatially distributed nodes. This is why mobile data collection (MDC) emerged as an efficient solution to gather data from IoT networks that tolerate delay. In this paper, we study the use of reinforcement learning (RL) to plan the data collection trajectory of a mobile node (MN) in cluster-based IoT networks. Most of the existing solutions use static methods. However, in a context where the MN has little information (no previous data set) about the environment and where the environment is subject to changes (cluster mobility, etc.), we want the MN to learn an energyefficient trajectory and adapt the trajectory to the significant changes in the environment. For that purpose, we will train two reinforcement learning (RL) algorithms: Q-learning and state-action-reward-state-action (SARSA) combined with deep learning (DL). This solution will allow us to maximize the collected data while minimizing the energy consumption of the MN. These algorithms will also adapt the trajectory of the MN to the significant changes in the environment.

I. INTRODUCTION

In the era of pervasive IoT, millions of IoT devices are deployed in intelligent applications. These devices generate huge amounts of data and consume large amounts of energy to communicate their data to the base stations or their neighbors. Since IoT devices usually have limited batteries, once emptied or depleted, those batteries are often difficult to replace especially if the devices are placed in an isolated or a difficult-to-access position. In the literature, various authors studied how to reduce the energy consumption of spatially distributed IoT nodes [START_REF] Dehkordi | A survey on data aggregation techniques in IoT sensor networks[END_REF]. Researchers have studied cluster-based energy-efficient solutions which consist in regrouping IoT devices into clusters and electing a node depending on its energy capability. This node will serve as a relay node between the cluster devices and the gateways or receivers. The relay node will be responsible for sending data and will allow IoT devices to save their energy by only sending their data through the relay node [START_REF] Song | QoS-aware energy-efficient cooperative scheme for cluster-based IoT systems[END_REF]. To enhance the energy efficiency of cluster-based solutions, authors proposed energy-efficient routing protocols applied to these contexts.

For example, in [START_REF] Toor | Energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM) in wireless sensor networks[END_REF], the authors proposed an energy-efficient cluster-based routing protocol for wireless sensor networks that uses different parameters to balance the energy load and decrease the energy consumption of the network. However, using a routing protocol to reduce the energy consumption in an IoT network implies that these devices must be connected which is not always realistically possible if the nodes are placed in isolated or harsh conditions. One of the solutions proposed to tackle the limitation of routing-based energyefficient solutions are the MDC approaches [START_REF] Wala | Energy efficient data collection in smart cities using iot[END_REF]. However, the most challenging part in mobile sink-based solutions is to determine and plan the trajectory of the mobile sink in order to efficiently gather data from the IoT nodes. Most existing approaches to MDC are static and only find a solution for scenarios with fixed parameters. These solutions suppose that the MN has a great knowledge of its environment and do not consider a change of context in the system such as a change in the clusters' data generated or position. Recently, artificial intelligence techniques such as machine learning (ML) or DL are used in order to propose intelligent solutions that allow devices to learn and take intelligent decisions. In contrast to static methods, intelligent schemes will be able to discover new clusters at any point and adapt the trajectory of the MN accordingly without the need for expensive re-computation The paper is organized as follows: Section II discusses the works related to our problem, Section III presents background information on RL, Section IV presents the system model and Markov Decision Process. Sections V and VI describe our RL algorithms and present and discuss the simulation results. Finally, discussions and conclusions are presented in Section VII.

II. RELATED WORKS

In the literature, most of the existing data collection and trajectory planning solutions in the data collection context use non-learning based approaches. For example, in [START_REF] Mozaffari | Mérouane: Mobile unmanned aerial vehicles (UAVs) for energyefficient Internet of Things communications[END_REF], the authors proposed a framework that optimizes the deployment and mobility of multiple unmanned aerial vehicles (UAV) in order to collect data in the uplink from ground IoT devices and minimize the energy consumption of the MNs. In [START_REF] Lorincz | A Novel Real-Time Unmanned Aerial Vehicles-based Disaster Management Framework[END_REF], the authors proposed a computing and networking framework for energy-efficient disaster management using UAVs where UAVs use deep learning algorithms for real-time route planning, obstacle avoidance and object detection. In [START_REF] Ghorbel | Joint position and travel path optimization for energy efficient wireless data gathering using unmanned aerial vehicles[END_REF], the authors proposed an algorithm that optimizes the UAV stops for data collection from neighboring sensors and the itinerary followed by the UAV to ensure efficient collection of all data with minimum energy consumption. However, in IoT networks, devices do not constantly collect and transmit data, their activity depends on the environment around them or the period of time the node is visited in. In the previous solutions, a change in the activity of the IoT network is not considered. Consequently, having a mobile sink periodically collecting data, following a static trajectory, may cause energy waste and does not constitute an optimal solution to achieve energy efficiency. In recent years, researchers have considered learning based solutions and especially RL as a very promising trend in this field. In [START_REF] Hsu | Reinforcement Learning-based Collision Avoidance and Optimal Trajectory Planning in UAV Communication Networks[END_REF], the authors proposed a distributed RL approach for path planning and collision avoidance of UAVs. In this study, the MNs are given a visiting order and the algorithm designs an optimal path for data collection in IoT networks with devices having different communication radios. Compared to this study, our solution uses deep RL which is more suitable for complex environments and the MN can explore the environment and establish its own visiting order based on its experience. In [START_REF] Zhang | Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning[END_REF], the authors proposed a deep RL scheme that plans the trajectory of an MN which is used as a data collector and charger in wireless powered IoT networks. The solution minimizes the average data buffer length, the residual battery level of the system and avoid devices data overflow. In [START_REF] Bayerlein | Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning[END_REF], the authors proposed a multiagent RL approach that allows the control of the trajectory of a team of cooperative UAVs in order to maximize the collected data under flying time and collision constraints. In [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF], the authors proposed a double deep Q network to plan the trajectory of a UAV on an IoT data harvesting mission. The solution proposed aims to maximize the collected data under flying time and navigation constraints and allows the UAV to adapt to variations in the number of IoT devices. Contrary to our solution, the previously discussed solutions do not take into consideration the inconsistencies in the IoT devices activity (e.g., a device can collect more data on a certain period of the day more than another).

Our solution consists in planning an adaptive and energyefficient trajectory for the MN using a deep RL algorithm. The MN will visit clusters and collect data from an elected node in the cluster called the relay node. The relay node is responsible for collecting the data from the IoT nodes in the cluster and transmitting it to the MN. The objective of our solution is to maximize the collected data while minimizing the energy consumption of the MN and IoT devices. In our solution, the MN has information on the position of all the relay nodes. It will build an energy-efficient data collection trajectory depending on the context of the network (e.g., the clusters' activity). Fundamentally, the MN will adapt its trajectory and save energy by not visiting a cluster with no data to transmit. The learning algorithm used to plan the trajectory is also computed on the MN. Other solutions proposed RL solutions to plan the trajectory of MNs in order to either optimize data collection or minimize the energy consumption and need to perform expensive computations in order to adapt to a change in the context. This solution doesn't require a previously recorded data set. The MN will build its own knowledge by exploring the environment. In our paper :

• We formally describe our environment and propose a system model of the environment. • We define our problem as a Markov Decision Process.

• We propose a Deep SARSA (DS) and deep Q-learning (DQN) path planning algorithms.

• We carry out simulations on these algorithms and compare their performances in terms of data collection, energy consumption and adaptability.

III. BACKGROUND ON REINFORCEMENT LEARNING

Reinforcement learning is a machine learning technique that consists in learning by interacting with the environment [START_REF] Ray | A quick review of machine learning algorithms[END_REF] [START_REF] Wiering | Reinforcement learning[END_REF]. It is a computational and goal-oriented approach to learning by interaction as the agent learns how to map situations to actions to maximize a numerical reward. The agent is not given the right actions to take but instead explores the environment and tries which actions lead to the most reward. RL problems are often described with Markov decision processes [START_REF] Otterlo | Reinforcement learning and markov decision processes[END_REF]. Reinforcement learning is a semi-supervised machine learning algorithm. It is different from supervised learning which consists in learning from a set of labeled data provided by a knowledgeable external supervisor [START_REF] Sutton | Reinforcement learning: An introduction[END_REF]. The goal of supervised learning is to find the right answer while for RL it is to find how to carry out a certain task and when a data set is not available, the learner uses its previous experience. RL is also different from unsupervised learning. Unsupervised learning is a learning paradigm that aims to find correlations in sets of unlabeled data. This type of learning is not based on correct examples. Finding structure in an RL agent experience might be interesting. However, in our solution, the most important objective is to maximize the reward (e.g. maximize the collected data and minimize the energy consumption) in an environment the agent needs to explore and adapt the trajectory to the context's changes. covered by unsupervised learning. Consequently, RL is the most suitable technique to our problem.

IV. SYSTEM MODEL AND PROBLEM FORMULATION

In this section, we will formally describe the environment, present our system model and formulate our problem as a Markov decision process.

A. System Model

We consider an IoT network composed of a number of clusters randomly distributed in a square grid world of size M × M ∈ N 2 with an MN collecting data through a relay node from a number K of clusters of IoT devices. We suppose that the relay node is already chosen depending on its energy capability (i.e., the node with the biggest energy capacity). The k-th relay node is permanently located at [x k (t), y k (t)] in the grid world with k ∈ [1, K] and t = 1, 2, ..., T , represents the number of time slots. We consider the velocity v(t) of the MN constant and enough for the MN to move from one square of the grid to the one next to it. We consider the velocity as constant since we want our energy-efficient solution to be adapted to any type of MN. At time step t, the MN has an energy capacity u(t) and w c,i (t) the amount of energy spent to visit a cluster or a safe spot at time step t. We also consider w f (t) the minimum level of energy needed for the MN to move to a safe stop spot z with z in [1, Z]. A safe spot is an area in the square grid where the MN will go when its energy level is not sufficient to visit another cluster in order to recharge its energy. We calculate the minimum energy needed for the MN to reach a cluster or a safe spot by the following

w m,i (n) = |x m (t) -x i (t)|+|y m (t) -y i (t)| (1)
Where [x m (t), y m (t)] corresponds to the coordinates of the MN and [x i (t), y i (t)] are the coordinates of a cluster or a safe spot on the grid. We also suppose that the only information possessed by the MN about the clusters is their initial position.

The amount of collected data by the MN from a relay node is given by cd k . Figure 1 represents a system with an MN, four relay nodes and one safe spot.

B. Markov Decision Process

We will formulate our problem as a Markov decision process problem. In this section, we define the state space, action space and reward function. The RL algorithms will interact with the environment (the MDP) in order to find an optimal data collection policy that maximizes the data collection and minimizes the energy consumption of our MN guided by the rewards' feedback. The MDP is defined by the tuple (S, A, R, P ) with state-space S, action-space A and reward function R. The state in time t, is given by s t = p t , e t , C where:

• p t ∈ R 2 is the MN position on the grid; Negative if the MN energy reaches 0 without being in a safe spot

• e t ∈ N is the remaining energy of the MN in time t;

• C ∈ R 2×K represents the coordinates of the K cluster heads. The MN can move to one of the four adjacent grids from its current grid in each time slot. The action-space is defined as

A = north, east, south, west (2)
The movement of our MN from a position p t is expressed as

p t+1 =        p t + (-X, 0) if a t = west p t + (X, 0) if a t = east p t + (0, X) if a t = north p t + (0, -X) otherwise (3)
where X is length of a square in the grid. The reward function is a function that maps state-action pairs to a real-valued reward, i.e., R : S ×A → R. It consists of the positive rewards and negative rewards (penalties) summarized in Table I V. OUR SOLUTION The main aim of our solution is to plan an adaptive and energy-efficient trajectory that maximizes the quantity of collected data and minimizes the energy consumption of the MN. For that, we want our MN to explore the environment, find an optimal trajectory and modify it in case a cluster stops its activity or in case a new cluster appears. To achieve this goal, we chose to use two RL algorithms. In this section, we will present Q-learning and SARSA and discuss why allying DL with RL is more efficient in our case. Finally, we will present our DS algorithm specified in Algorithm 1 and our DQN algorithm specified in Algorithm 2.

A. Q-learning And SARSA

The standard architecture of an RL algorithm is given in figure 2. Formally, the MN observes a state s of its environment at time step t s t ∈ S where S is the set of states. It executes an action a t ∈ A, where A is the set of possible actions and interacts with its environment. This action changes the state of the environment to a new state s t+1 and the agent will receive a reward r t accordingly. The agent's goal is to find a policy π that maps a state s t to a probability of choosing action a t . This policy is represented as following

π : S → P (A) (4)
The reward is the sum of discounted future rewards. To calculate the reward we use γ, where γ ∈ [0, 1] is a discount factor that determines the effect of the future rewards to the current one and T is the time when the process terminates. The future reward is defined as follows

R t = T i=0 γ k r t+k+1 (5)
We also define the state-action value function which represents an agent at state s taking action a under policy π

Q π (s, a) = E π [R(t)|s t = s, a t = a] (6)
where E π [R(t)|s t = s, a t = a] is the expected return. The learning goal is to find the optimal state-action function which is based on the Bellman equation. Q-learning is the most famous RL algorithm. It is an offpolicy algorithm which means the agent will sometimes apply a policy even if it is sub-optimal in order to improve the policy. To update and estimate the current state-action value function, we use the next state-action value. It is important to note that even if the next state s t+1 is given, the next action a t+1 is still unknown. The update equation of the state-action value function in Q-learning is

Q(s, a) ← -Q(s, a) + α[r + γmaxQ(s ′ , a ′ ) -Q(s, a)] (7)
where α represents the learning rate which is a hyperparameter that controls the model change in response to the estimated error. As we can see, in Q-learning the next action is chosen greedily to maximize the next state-action value Q(s t+1 , a t+1 ). Contrary to Q-learning, SARSA is an on-policy RL algorithm. It means if the agent finds a good policy, it will be more likely to repeat it. In contrast to Qlearning, in SARSA, when updating the current state-action value, the action a t+1 will be taken instead of chosen greedily. Consequently, the update equation of the state-action value function in SARSA is

Q(s, a) ← -Q(s, a) + α[r + γQ(s ′ , a ′ ) -Q(s, a)] (8)
As its name suggests, in SARSA the training quintuple is (s, a, r, a ′ , s ′ ) which means in every update, the quintuple will be derived while a ′ in Q-learning is just for estimation. In a complex and sophisticated environment with large state and action spaces like our environment problem, computing and updating table values for each state-action pair is not efficient. In this case, it is more interesting to find an approximation of the Q-value rather than directly computing it. In order to do that, we will use DL combined with Q-Learning and SARSA to approximate the optimal Q-function Q π (s, a). DL is a machine learning technique based on neural networks (NNs) with a powerful generalizing ability. This approach

., F do

Initialize the environment and receive initial state st; Set t = 0; while u > wi,ss do if ϵ > ϵexpt then Choose at randomly from action space; else Select at = argmaxQ(st, at, θ); end Execute action at, compute rt and observe the next state st+1; Store experience (st, at, rt, st+1) in the replay memory with a random placement policy; Sample a random mini-batch of G experiences (si, ai, ri, si+1) from replay memory; Calculate the target value; Update the weights θ ′ = θ every J time step; Decrease u; Set t = t + 1; end Decrease ϵ; end Algorithm 2: DQN-based energy-efficient Mobile Data Collection can retrieve highly abstract structures or features from the real environment and then precisely depict the complicated dependencies between raw data. However, it can not directly select a policy for decision-making problems. Consequently, allying Q-learning and SARSA with DL constitutes the most appropriate solution for our problem.

B. The proposed algorithms

We propose a DS and DQN algorithm in order to plan the trajectory of our MN. The trajectory will maximize the quantity of collected data and minimize the energy consumed by the MN. The proposed algorithm will also adapt the MN trajectory depending on the clusters' activity. In addition to previously described specifications, DS and DQN uses experience replay which is a technique that allows us to store the agent's experiences at each time step in a data set called replay memory [START_REF] Fan | Zhuoran : A theoretical analysis of DQN[END_REF]. At time step t, the agent's experience is defined as following

e t = (s t , a t , r t+1 , s t+1 ) (9)
The main reason for using experience replay is to break the correlation of consecutive samples [START_REF] Bruin | The importance of experience replay database composition in deep reinforcement learning[END_REF]. In our solution, we use two NNs. The policy network θ approximates the optimal policy by finding the optimal Q-function. It accepts the current state s n and finds the evaluation of the value Q(s n , a n , θ).

We also use a second network called target network θ ′ to improve the stability of learning. The target network weights are frozen with the original policy network and are updated periodically. It accepts the next state s n+1 and outputs the Q-value Q(s n+1 , a n+1 , θ ′ ). These values are optimized to minimize the loss function defined by

L(θ) = E[(T t -Q(s t , a t )) 2 ] ( 10
)
where T n is the target value in the DQN algorithm. It is defined as following

T t = r t + γ t-1 maxQ(s t+1 , a t+1 , θ ′ ) (11)
For DS T t is described as

T t = r t + γ t-1 Q(s t+1 , a t+1 , θ ′ ) (12)
where the Q-value for the next state s t+1 is passed to the target NN θ ′ for more stability in learning. ϵ is the exploration rate which represents the probability that our MN will explore the environment and ϵ expt is the threshold from which our MN will stop exploring the environment and will exploit the experience acquired through the policy network.

VI. SIMULATION AND PERFORMANCE ANALYSIS

In this section, we will present the simulations carried out in order to assess the learning performance of using DQN and SARSA. Both of our algorithms are trained for a total of 8000 episodes. We assume that four clusters are randomly distributed in a 10 x 10 units area. The battery capacity of the MN is set to 100 units. The MN consumes 1 unit of energy to move from one area to another. The MN consumes 1 unit of energy to collect data when the relay node is in its range. The cluster head is considered in the range of the MN if they are both on the same position [x t , y t ] at time step t. An episode ends if the MN collects all the cluster data and is in a safe spot or if its energy is equal to 0. The maximum of possible data to collect for the MN for each episode is between 600 and 800 units. The MN will start by exploring the environment since ϵ is initialized to 1 and will exploit the data when ϵ is inferior to 0.001 (i.e., corresponding to ϵ expt in algorithms 1 and 2). The neural network is composed of an input layer of 256 neurons (i.e., the grid is fed to the NN as an image and is divided into 256 pixels). The output layer is a dense layer with the size of the action space. The configuration of the algorithms is given in Table II. We carried out this simulation with the goal of maximizing the amount of collected data while minimizing the energy consumption as well as adapting the MN's trajectory to the clusters' activity. We want to determine which approach is more suitable for our problem. It is important to note, that using learning approaches to plan a trajectory is more appropriate for networks with no critical data that need to be collected during a single training episode (e.g., all the clusters data should be collected during the maximum time where the MN can move). In order to observe the behavior of our algorithms in an environment where the context could change, we programmed a decrease in the activity for two clusters between episode 5000 and episode 6000 (e.g., no data to transmit). The clusters resume their activity after episode 6000. The rewards for this policy r ed , r sd , r ss , r ee , r nc , r ne , r f inish , r move are set to 500, 50, 1000, 10, -100, -300, -500, -1 respectively.

Before episode 5000, all the clusters are active. As shown in Figure 4, for the DQN algorithm, the amount of collected data is very low at the beginning of the training and then increases gradually. This means that our MN explores the environment, visits the cluster positions and builds a trajectory to efficiently collect the cluster data. While for DS, the agent quickly learns a trajectory and visits multiple clusters. We can notice that before episode 5000, the MN under the DS algorithm collects more data while consuming less energy than under the DQN algorithm. However, the MN is not visiting all the clusters as the maximum of possible data to collect for each episode is between 600 and 799 units.

Between episode 5000 and episode 6000, we programmed a decrease in activity of two clusters. When the clusters' activity drops, we notice that the amount of collected data drops for the two algorithms. We also remark that the energy consumption of the MN drops drastically for the DQN algorithm while it remains in the same range for the MN training under the DS algorithm (as shown in Figure 3. For the DQN algorithms, these results mean that the MN does not visit the clusters which stopped their activity and stops the data collection to save energy. The clusters resume their activity after episode 6000. We can see through Figure 4 that the amount of collected data increases lightly for the DS algorithm and drastically for the DQN algorithm. These results show that the MN training under the DQN algorithm finds a trajectory that not only collects all the clusters' data during one episode while consuming less energy compared to before episode 5000. While under the DS algorithm, the MN certainly consumes less energy but does not visit all the clusters. We can conclude through the results we obtained that in order to plan an energy-efficient and adaptive trajectory, DQN is more appropriate than DS. The DQN tries to learn the best trajectory to collect the data from all the clusters while consuming the minimum energy. The algorithm also showed great adaptability between episodes 5000 and 6000 and quickly re-learned the trajectory after episode 6000 without doing expensive re-computation and learning while enhancing the performances. We can also conclude that the DS algorithm took safer decisions (e.g., choosing a path is a more appropriate solution for problems that require less risk-taking). These results also show one of the drawbacks of SARSA as the algorithm is easily stuck at a local optimum.

VII. CONCLUSION

In this paper, we have introduced two energy-efficient deep RL algorithms with experience replay for trajectory planning of an MN in a MDC and cluster-based IoT networks scenario. The results we obtained in our paper show that the MN, knowing only the cluster positions, uses information about the environment to learn and find an energy-efficient data collection trajectory with both of the algorithms. However, under the same parameters, the DQN algorithm shows more adaptability to the significant changes in the context scenario (e.g., the amount of collectible data) without the need for expensive retraining or recollection of data while DS shows a less risky and safer trajectory.

Fig. 1 .

 1 Fig. 1. System representation

Fig. 2 .

 2 Fig. 2. Reinforcement learning standard architecture

  initialize action-value with random weights θ and θ ′ , replay memory size H, number of episodes F initialize ϵ for exploration and ϵexpt for episode :1, ..., F do Initialize the environment and receive initial state st; Choose at randomly from action space; Set t = 0; while u > wi,ss do Execute action at, compute rt and observe the next state st+1; Choose action at+1 from the st+1 ; Store experience (st, at, rt, st+1) in the replay memory with a random placement policy; Sample a random mini-batch of G experiences (si, ai, ri, si+1) from replay memory; Calculate the target value; Update the weights θ ′ = θ every J time step; Decrease u; Set t = t + 1; end Decrease ϵ; end Algorithm 1: DS energy-efficient Mobile Data Collection initialize action-value with random weights θ and θ ′ , replay memory size H, number of episodes F initialize ϵ for exploration and ϵexpt for episode :1, ..

Fig. 3 .

 3 Fig. 3. The training curves of the MN's energy consumption for DS and DQN

Fig. 4 .

 4 Fig. 4. The training curves of the MN's collected data for DS and DQN

  Positive if cd k > 0 and w c,i = w m,i r sd Positive if cd k > 0 and w c,i > w m,i ree Positive if cd k = 0 and w c,i = w m,i rne Negative if cd k = 0 and w c,i > w m,i rmove Negative if the MN moves without collecting data rss Positive if the MN collects data from the clusters and ends the episode on a SS r f inish

			TABLE I
			REWARDS DEFINITION
	Reward	Type	Environment Conditions
	r ed


Mahmood Arai
email: mahmood.arai@ne.su.se

Damien Besancenot
email: besancenot.damien@univ-paris13.fr

Kim Van Huynh
email: khuynh@u-paris2.fr

Ali Skalli
email: skalli@u-paris2.fr

Kim Van

ISABELLE, STEPHANIE, CELINE Catherine Nathalie

Nicolas

CHRISTOPHE Sebastien

PHILIPPE Laurent Natives

Europe Sarah

MARION Alexandra Lea

ALEXANDRE, MICKAEL, ADRIEN Aurelie Thomas

Sarah Valerie

PHILIPPE, THOMAS Catherine Nicolas

Aurelie Nathalie

Helene

Sandra Catherine

CHRISTOPHE Michel

MYRIAM, SONIA Fatima

Karima Nadia

Mohamed

RACHID Karim

Children's first names and immigration background in France

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

Introduction

There is evidence that names reflect cultural background and socio-economic belongings (see [START_REF] Lieberson | A Matter of Taste[END_REF] and [START_REF] Fryer | The causes and consequences of distinctively black names[END_REF]. In this paper we study for the first time the choice of first names given to children by immigrants in France and examine how these choices reflect cultural background. Immigrants and their children are influenced by the cultural features of the country of origin. At the same time the experience of interacting with the French society shapes their preferences and feelings of belongings. We examine the persistence of the impact of immigration background and the role of the French experience for naming patterns.

Parents choice of first names can be interpreted as a measure of their group belonging and might have consequences for the economic integration of their children. Based on a field experiment in the US, [START_REF] Bertrand | Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination[END_REF] report that job applicants carrying names such as Emily and Greg have higher chances of call back than those with names such as Lakisha and Jamal. [START_REF] Arai | Renouncing personal names: An empirical examination of surname change and earnings[END_REF] report a positive earnings effect associated with name change for immigrants who renounce their foreign names.

A large number of European immigrants in France originate from Italy, Portugal and Spain. Many first names in these countries have linguistic (Roman) and religious (Biblical) origins in common with French names. First names in the North African and Middle-Eastern countries are less similar to common French names due to differences in the linguistic and religious roots of these names. We should thus expect first names given by immigrants with Arabic/Muslim background to be less close to French names than those given by European immigrants with a Roman/Christian background. We study how these similarities/differences in the pool of available names are reflected in the naming practices of immigrants as well as those of natives with an immigrant background in Europe or the Maghreb/Middle-East.

The French survey Histoire de vie, conducted by the French National Statistics, INSEE in 2003, has recorded the first name of the children of the respondents. This feature enables us to construct an index of frenchness of a name based on probabilities of observing a name among various groups. Our French Name Index (FNI) ranges between zero and one, where values closer to one reflect higher frenchness. A name like AUDE has an index of one and a name like ABDELLAH has an index of zero.

Our results indicate that immigrants from Europe give first names to their children that are different from those given by native French with native parents. These differences are more pronounced for immigrants with foreign citizenship. As expected, the differences are much larger for immigrants from the Maghreb/Middle-East. However, these differences cannot be attributed to religious roots of common names in this group as we find no correlation between FNI and the religious practice of these immigrants. When it comes to natives with an immigrant background, we find no differences except a slightly lower FNI (0.09 in a scale from 0 to 1) for natives who have a European immigrant background. Though cultural background has strong impacts on what type of first name immigrants choose for their children, these differences seem to be rather weak among those who are born in France.

The remainder of the paper is as follows. In the next section, we present data and our empirical set-up. Regression results are presented in Section 3 and the paper is concluded in Section 4.

Data and Empirical Setup

The data used in this paper are from a household survey conducted in 2003 by the French National Statistics Institute (INSEE). The survey covers individuals 18 years and older living in the Metropolitan France. The original sample includes 8403 respondents. We remove all immigrants who came to France in 2003 and those who do not have any children. After also removing observations with missing values, we obtain a sample of 5682 individuals.

In our data we find the following top 10 names (5 girls and 5 boys) given by different groups as listed in Table 1. As expected, the first names chosen by immigrants from Maghreb/Middle-East are different from first names chosen by Europeans. However, the natives with a background in Maghreb/Middle-East choose names that are similar to other natives. Many parents, natives or not, choose names that are not among the top 10 names. To compare the naming patterns and not only popular names among various groups we need to classify names and use this classification to measure similarities and differences.

A name has three basic characteristics, root, spelling and pronounciation. There are several ways to classify names according to these characteristics and different classifications reflect more or less the proximity of the pool of names of the background culture to the pool of names in France. For our purpose, we need a measure that emphasizes the parental choice when facing a new cultural environment. We consider two names as identical if they are spelled in a similar way and pronounced in the same way by French speakers. Examples are Mohamed/Mohammed, Arnault/Arnaud or Isabel/Isabelle. Names are considered as different if they are pronounced in different ways by French speakers. Examples are Maria/Marie and Yusuf/Joseph. Finally we have all other different names with different roots, spelling and pronounciation.

We use a measure indicating the relative probability of observing a first name of children among native French population and other groups. This is similar to the index used by [START_REF] Fryer | The causes and consequences of distinctively black names[END_REF] defined as follows:

F N I(name) = P r(name|F )/(P r(name|F ) + P r(name|G)) (1)
The French Name Index F N I as defined above measures the relative probability associated with a name in two disjoint groups: F and G. We define F as the group of interviewed native parents not having a foreign citizen partner. This index is equal to one when only native French give this name to their children. The name AUDE is such a name in our sample. The other extreme is for example the case of ABDELLAH which is only given to children with two immigrant parents.

When cultural experience influences naming practices, we expect that natives with immigrant background choose names with higher FNI than immigrants do.

As shown in Table 2, we find that there is no variation in FNI with respect to the immigrant/native background of the individuals. The average FNI is lower for immigrants and foreign citizens.

Since many names have religious origins, we examine whether differences in naming patterns stem from parents' religiosity or simply reflect general differences in cultural background. We find more religious practice for foreign citizens than for French citizens and in general more religiosity among immigrants from Maghreb/Middle-East than among European immigrants. Among the natives, the pattern of religiosity is very similar for those who have immigrant parents and those who have native parents.1 For further sample statistics see Table 2.

Regression results

Results of our estimations are presented in Table 3. We find that immigrants from Europe give names with higher French Name Index (FNI) than immigrants from the Maghreb/Middle-East. This reflects the large overlap in the common French names and other European names and the small overlap for French names and the pool of names from the Maghreb/Middle-East. Furthermore, we find that while natives with immigration background in Maghreb/Middle-East choose the same type of name as other natives, natives with European immigration background choose names that are slightly less French as measured by the FNI.

Controlling for a set of demographic variables and educational attainment does not change the overall picture (see Table 3). Higher age at immigration, shorter duration of residence, foreign citizenship, lower education and not having French language as one of the main languages used during childhood are associated with lower FNI. Names have linguistic/cultural roots as well as religious loadings. As indicated by our results in Table 2, there is a variation in degree of religious practice between various groups of immigrants and natives.

A question to answer is whether the observed differences in naming practices are due to differences in the degree of religiosity, or if they simply reflect different cultural belongings in general.

Results in column 4, Table 3 indicate that only for immigrants from Europe we find that those who practice their religion choose significantly different names than those who do not practice a religion. This means that the lower FNI of immigrants from Maghreb/Middle-East does not seem to stem from the higher religiosity of this group but rather reflects the cultural roots of the pool of names. 2

Concluding Remarks

Measuring the relative probability of occurrence of a name among the majority population and those with immigrant background by a French Name Index (FNI) we reported results indicating that immigrants choose first names for their children that differ from names chosen by natives. The largest differences are observed for Maghreb/Middle-East immigrants. However, the native-born with immigrant background have similar naming practices for their children as the French majority population. The overall naming pattern is robust for controls of demographic and educational variables as well as a measure of religious practice.

We find that the FNI is higher for French citizens, those who are immigrated at younger age, those who have a longer residence in France and have higher education. Being born and raised up in France as well as being exposed to the French society and French culture through residence, citizenship and the educational system draws individuals with or without immigrant background into similar ways of expressing belongings when choosing first names for their children, indicating strong assimilating forces in the French society. Our results are based on data for parents born before the 1990s and children born before 2002. How these patterns will change in the future is a matter of future research. The naming patterns documented in this paper are most likely accompanied by other adjustments leading to assimilation in other dimensions of the various parts of the French population with and without recent immigration background.

Table 2

 2

	: Weighted Sample Means
		N A T I V E S		I M M I G R A N T S
		Native Immigrant	Europe	Maghreb/Mid.-East
		Parents	Parents	French Foreign French	Foregin
				citizen citizen citizen	citizen
	French Name Index
	-average for all Children	0.67	0.64	0.48	0.34	0.21	0.04
	Religious vocation
	-practice once a month	0.13	0.09	0.13	0.25	0.33	0.58
	-occasional practice	0.23	0.26	0.28	0.39	0.13	0.17
	-no practice/belonging	0.38	0.44	0.37	0.27	0.47	0.23
	-no practice/no belonging	0.21	0.19	0.19	0.08	0.08	0.02
	-rejection of religion	0.05	0.03	0.04	0.01	0.00	0.00
	Sex
	-female	0.54	0.55	0.61	0.68	0.42	0.50
	Age
	-mean age	51.96	49.49	59.71	51.56	46.77	44.66
	Years since immigration
	-average years	0.00	0.00	43.01	28.98	28.23	19.13
	Age at immigration
	-mean age	0.00	0.00	14.25	23.14	18.47	25.90
	Education
	-primary	0.23	0.21	0.41	0.45	0.29	0.66
	-secondary	0.11	0.13	0.08	0.19	0.19	0.07
	-tertiary	0.10	0.08	0.14	0.10	0.07	0.03
	-higher education	0.56	0.57	0.36	0.27	0.45	0.24
	Speaks with parents in:
	-French	0.84	0.55	0.07	0.03	0.09	0.01
	-another language	0.07	0.15	0.76	0.87	0.57	0.71
	-french and another language	0.08	0.28	0.14	0.07	0.31	0.20
	-two other languages	0.00	0.02	0.03	0.02	0.03	0.09
	Citizenship
	-foreign	0.00	0.02	0.00	1.00	0.00	1.00
	N	3921.00	440.00	218.00	179.00	95.00	152.00

NOTE: Huber/White standard errors in parentheses. * and ** indicate p < 0.05 and p < 0.01.

We measure religiosity on the basis of the following question: respondents are asked to report their relation to religion choosing among five alternatives: 1) religious practice at least once a month,

2) occasionally (except participations in marriages, baptism and funerals), 3) no practice but a feeling of belonging to a religion 4) no religious practice or feeling of belonging, or 5) rejection of religion.2 Separate regressions by groups yield similar results. We also experimented with including a variable whether the partner of the immigrant was native or not. Immigrants with native partners choose names with higher FNI compared to those who have immigrant partner. These estimations essentially confirms the picture presented by the models in Table3.


domain: spi phys

text:
Dr Philippe Varnoteaux
email: varnoteaux@orange.fr

Nicolas Bérend
email: nicolas.berend@onera.fr

Marcel Barrère

)

Center), Onera is in charge of conducting scientific and technical research for

A difficult start

Following World War II, the French aeronautical civil and military landscape is in a terrible situation (devastated plants, dispersed workforce, lack of supplies, etc.). The French Minister of Air then creates the Interim Scientific Air Superior Council (Conseil supérieur provisoire scientifique de l'Air, CSPSA).1 Made of specialists in aeronautical science and technology, this council is in charge of assessing the situation and make propositions to support the restart of aeronautics activities. 2 Under the impulse of general engineer Suffrin-Héberthead of Aeronautics Technical and Industrial Directorate (Direction technique et industrielle de l'aéronautique, DTIA)and communist minister Charles Tillon,3 the decision is made in summer 1945 to set up a company aimed at coordinating and supporting the efforts for the reconstruction of French aeronautics.

Thus, law bill n°46-895 of May 3 rd 1946 voted by the national constituent Assembly establishes National Office for Aeronautics Research and Studies (Office National d'Etudes et de Recherches Aéronautiques, Onera). Three main objectives are set : « to develop, orientate and coordinate scientific and technical aeronautical research » civil and military ; « design, manufacture and use the necessary means for research activities » and « ensure dissemination of research results at national and international level ». While it is not a joint military organization, Onera, through the July 11 th law bill, is allowed to resort to industry, to private 4 or public 5 establishments, institutes or laboratories and to military establishments. 6 The head, made of a president and an executive officer, is appointed by the minister in charge of aeronautics industry, in agreement with the ministry of defense, armament or army (the minister's designation varies with governments). In October 1946, general engineer Dumanois is appointed first president of the executive board, while René Jugeau, 7 former chief of staff of Charles Tillon, is executive director.

Starting from its early years, Onera has to deal (as other institutes) with the problem of re-starting the activities, completely disbanded by war, and the lack of qualified workforce. Furthermore, the presence of a communist at the head of the institute generate contestations, with some people accusing him of wanting to "colonize" the institute with communists and unionists, or even to fund the Communist Party using funds from the army. 8 The situation is so tense that Onera's executive and scientific boards are dismissed on July 1948.

Onera gets headless during several month. The situation only improves in 1949 when Maurice Roy is appointed as chief executive officer. 9 A Polytechnician with a strong character, he restores tranquility and discipline at the institute.

René Jugeau

Maurice Roy © Onera

In order to carry out its researches, Onera is organized into five directorates: « Aerodynamics » Laboratoire d'études de carburants de remplacement d'Arles (Arles laboratory of fuel substitutes studies), etc. 6 Service des recherches aéronautiques de l'Air (ministry of Air's Aeronautical Research Service, merged into Onera from September 1st 1946), Ateliers de Puteaux (Puteaux Factories), Arsenal de l'Aéronautique (Aeronautical Arsenal), Service des Poudres (Powders Service), DEFA, DTIA, etc. 7 Jugeau René, chemical engineer graduated in 1927 from ESPCI (Paris Ecole supérieure de physique et de Chimie industrielles), is in charge of a research laboratory before WWII. In 1945, he is in charge of Onera's inception. 8 Pinault Michel, Frédéric Joliot-Curie, Odile Jacob, p.324. 9 Roy Maurice (1899-1985), a polytechnician, (Ecole polytechnique alumni) general mining engineer, forecast as soon as 1929 the future of reaction propulsion. He teaches flight mechanics at Supaero (1930-45) then at Ecole Polytechnique (1947-1969), head of Onera from 1949 to 1962. Member (1949) and president (1966) of French Academy of sciences.

(OA), « Energy and Propulsion » (OE), « Structures design » (OR), « Materials » (OM) and « General Physics » (OP). The first studies are carried out on airframes or aircraft structure (long duration creep behavior of aluminum alloys, protection coating for turbine blades, refractory superalloys, etc.), but also on specific devices (sensors, measuring instruments, etc.). Due to recent progress in aviation, the question of reaction propulsion naturally retains the attention of Onera's engineers.

Propulsion studies.

All types of propulsion are tackled (solid, liquid, hybrid). Thus, since the beginning, an important workforce of 150 to 200 engineers and technicians (in a total around 900) is allocated to rocket motor studies. Among them, couples Simone and Marcel Barrère10 and Hélène and André Moutet11 and also Pierre Larue. Many of these specialists are chemists from the Toulouse area. Onera integrates in that way several specialized institutes such as Toulouse's Aeronautics Institute (Etablissement aéronautique de Toulouse), from which originate figures like Marcel Barrère, one of the greatest french rocket propulsion scientists, who was the first to publish a reference handbook in French dealing with this subject (along with three Belgian co-authors), 12 and to produce a second manual which was translated to german, english 13 and even russian.

Since 1947, Marcel Barrère and Hélène Moutet work on a german engine from a V1 vehicle. Then, Barrère studies the behavior of nitrogenous oxidizers, the problems of liquid propellant rocket ignition, combustion instability (with Hélène Moutet and others), and also high altitude propulsion.

Barrère M., Jaumotte A., Beubeke B., Vandenkerckhove J., La propulsion par fusées, Dunod, 1957. 13 Barrère M., Rocket Propulsion, Elsevier, 1960. When Onera develops its first rocket test-beds vehicles, the choice is made of solid propulsion. The rationale is twofold: simplicity and immediate availability. Indeed, in a solid rocket, the whole propulsion charge is stored in the combustion chamber under the form of grains, so engines can be easily stored and handled, without the risks of toxicity, corrosion and volatility that occur with liquid propellants.

Ramjet engines

The first test-bed rockets designed by Onera have ramjet engines, due, incidentally, to their "simplicity" and lower cost, and the fact that they are considered promising. The first vehicles are developed between 1948 and 1951 par the team of engineer Louis Viaud, from OE14 directorate. In the first experiments, ramjets, which are air launched from an airplane or ground launched using a ramp (at Mailly-le-camp) are inactive (VD 2010, 2020, 2040, 2070 and 2080). They provide some insight on aerodynamics. The first active ramjet is VD 2050, tested between 1951 and 1956. VD 2120 n°25, Levant island, November 4th, 1958 © Onera

As resources were limited at that time, the first studies are theoretical. Pushing research further required flight tests to validate hypotheses, as no ground facility could do it. Hence, the main purpose of the first VD vehicles is to help develop an experimental flight-test procedure. A second generation is developed in 1956-59 with VD 2140 and VD 2120. The latter vehicle allows the test of two side engines15 , instead of one single ramjet integrated into the missile body. The 2140 and 2120 vehicles can accelerate higher than Mach 2, with a range between 10 and 15. With vehicles getting more powerful, the flight experiments move to Levant island in 1956, where Onera can rely on the logistic support of the Navy and its Special vehicle research centre and test range (Centre d'essais et de recherches des engins spéciaux, CERES).

While Onera works for the development highspeed, low altitude missiles (in order to reduce vulnerability), it expands its research scope to ramjet engines capable of flying at higher altitude.

From self-propelled test-beds to technology demonstration rockets

In order to test correctly the ramjet engines, the OE directorate needs electromechanics and electric hardware. For their validation and operations, the OP directorate proposes a cheap but efficient solution, as recalls its initiator engineer Paul Lygrisse: « I had the idea of designing small rocket-propelled test-beds, later called OPd, that would allow testing at little cost a wide of range of devices in ambient conditions prescribed by their launch and their flight. 18 Back then, the initial design process of these vehicles is closer to that of scale models than rockets: the test-bed model integrates in its back a solid rocket grain that could be supplied by various institutes (Service des Poudres, SEPR, etc.).

OPd 220-Lp attached under an airplane, Brétigny, 1952 © Onera Designated OPd 19 , after the directorate which was set up to take care of the launches and the monitoring of the flight experiments, the first 1952 vehicles are rather simple: with a length from 0,80 to 1,8 m and a weight hardly exceeding the 10 to 15 kg range, the small OPd 80 rocket allows not only to perform telemetry tests, to qualify recording devices, to perform tracking, but also to understand and master the dynamics of a self-propelled vehicle at the time when its exists the launch ramp. In order 18 Correspondance from Paul Lygrisse to Philippe Varnoteaux, Mars 2 nd 2000. 19 For instance, OPd 220-Lp is the designation for a vehicle developed by the OPd division, with a 220 mm diameter, Largué (meaning "Jettisoned", from an airplane) and (self) Propelled.

Consequences of the first Sputniks

During the 1950s, the International Geophysical Year (Année Géophysique Internationale, AGI) takes place. This scientific event, organized between July 1 st and December 31 st 1957, aims at studying Earth as a whole, including the upper atmosphere. Several nations intend to use modern tools like sounding rockets, which has just emerged as a means to explore the Earth neighbourhood. The US and USSR even announce that they will take the opportunity of this event to launch the first artificial satellites into orbit. On October 4 th and November 3 rd 1957, the Soviets springs a surprise by launching into orbit the history's first and second artificial satellites Sputnik 1 and 2. The age of space has just begun.

In France, a sounding rocket program for the exploration of the higher atmosphere (Véronique AGI) existed, but its future was uncertain. 23 At that time, exploration of the higher atmosphere is not Onera's realm, nor, furthermore, the subject of artificial satellites. Thus, the occurrence of the first Sputniks did not triggered immediate reaction, as recalls former engineer André Rémondière 24 : « Sputnik? Yes, it was discussed, but this had no impact on our activities, at least at the beginning. We were not really concerned ». 25 Indeed, Onera was not a straightforward choice for this type of activity. Nevertheless, Onera's head is not insensitive to the subject of the rocket that would be used for orbital launch, as states Paul Lygrisse: « At the end of 1957, when the subject of ballistic missiles and outer space was in the air, chief executive Maurice Roy got interested in our modest OPd rockets. He probably realized that he could take advantage of them, especially when he learned that these rockets could be upgraded to four-stage vehicles. Ever since, these rockets became very 23 On October 29, 1954, since Hammaguir, physicist Etienne Vassy made a VLF (Very Low Frequency) transmission experiment (104 km) using a Véronique NA. As it was not equipped with telemetry system, it was not recovered before several months! This event has induced doubts and critics, leading to a fund cut during two years. Since 1956, thanks to the intervention of CASDN's military (Comité d'action scientifique de la défense nationale), the funding are restored, but a twoyear additional delay occur for two reasons: the time to set up the workforce back again for the manufacturing of Véronique AGI and also the need of identifying relevant ideas of experiments (later brought by physicist Jacques Blamont during summer 1958). 24 Rémondière André (1930-2019), Onera engineer from 1954 to 1969, is the leader of research group studying automatic control and stabilization of rockets. In 1969, he joins CNES, where he is in charge of several departments (sounding rockets, aerospace technology, satellites). From 1986 to 1991, he heads the Guyana Space Centre. 25 Correspondence from André Rémondière to Philippe Varnoteaux, Mars 25th 2009. useful to develop specific strategic missile technologies and become familiarized with them ». 26 Indeed, one important question for France at that time was the one of the ballistic missile it could eventually develop for the nuclear deterrence, currently in the making. 27 Until the return to power of general de Gaulle, this question was not settled at the political level. Meanwhile, Onera deploys intense lobbying for rockets. A special operation at Levant island is undertaken on November 28th 1957, i.e. twenty-five days after Sputnik 2: an OPd 220-ADX rocket is launched, reaching around 70 km. It is a three-stage rocket based on the booster for ADX, on which the ADX ramjet is replaced by a small OPd 220 rocket. Carrying an atmospheric probe, this vehicle flirts with the boundary of space. Shortly later, at a meeting of Onera's executive committee, Maurice Roy underscores that the November 28 launch make possible to « carry out an experiment in the higher atmosphere with a faultless professional discipline »… means to interest and convince the political authorities to support the development of rockets, including through exhibitions. In this way, at the Paris Air Show of May 24-June 2 1957, Onera displays a 3-stage rocket (Rocket 2012), that could admittedly carry out experiments in the upper atmosphere. One year later, on May 2-3 1958, on the bank of river Seine below Eiffel tower, a scale-1 OPd 56-39-22/D rocket mock-up is shown during the « Earth and Cosmos » exhibit. It was the future third generation OPd rocket with four stages. While the details about this rocket highlight that "In the descent phase, it allows to carry out atmospheric reentry experiments that would normally require much more powerful and excessively costly", they also mention that this rocket will be " capable de sending to a 350 km altitude equipment for observation and scientific measurements" … 28 , in other words, it could be used as a sounding rocket for space exploration. Since June 1958, when de Gaulle returns to power, a wave of reforms blows of France, at the institutional as well as economical and financial levels, with, at the heart of gaullian way of mind, an emphasize on science and technology. Military forces are not forgotten by this endeavour. In that way, at the level of the nuclear deterrence force, the subject of ballistic missile is back again, but with two alternatives: buying it through an american license or develop it alone … Considerable that the current context is now favorable, Maurice Roy foster preliminary studies related to ballistic missile, starting with the field of aerothermodynamics. In order to get real-flight data, the OPd 56-39-22/D demonstrator is then used: during the test, the first three stage ensure the 28 Anonymous, « La fusée française à quatre étages…exposée pour la première fois à ʺTerre et Cosmosʺ », in Le Figaro, Mai 1, 1958. ascent phase (up around 150 km) 29 , and the fourth stage accelerated the head during descent, in which the experiment itself occurs. While the first test at Levant island on May 2 , 1959 demonstrates the quality of instruments and transmission means, the second test on October 22 in the same year flies an instrumented head which collects data about the thermal and aerodynamical phenomenon during reentry.

However, to the great disappointment of Maurice Roy, the french government decides the attribution of ballistic research to a dedicated institute: Société d'étude et de réalisation d'engins balistiques (Society for the study and design of ballistic vehicles, SEREB). Founded on September 17 1959, SEREB is at a central place within the most experienced companies and institutes of that time, who are shareholders: Nord-Aviation, Sud-Aviation, SEPR, SNECMA, Dassault, Matra, CEA, military technical directorates (DEFA, DTIA, DCCAN), the Power directorate… but also Onera. Thus, Onera's works are recognized and supported but, as for May 1960, they are carried out under contract from Sub-Aviation contracts (which is the provider of reentry heads), SEPR (which provides solid grains) and SEREB (which manages the whole activities). A total of twelve OPd 56-39-22/D rocketsrenamed Antarès on October 1960perform tests until March 1961 of different heads with velocities up to Mach 8. Out of these twelve, seven rockets deliver promising but insufficient results. For this reason, Bérénice, a new research missile with higher performance is developed to increase knowledge in flight mechanics, guidance and control, and aerothermodynamics up to velocities around Mach 12.

Having lost the high ground on the subject of ballistic missile, Onera hopes to have a lead role in the one of space exploration, especially in sounding rockets, for which a potential already exists. Furthermore, the creation of NASA in the United States, between July and October 1958, apparently strikes Maurice Roy, as Paul Lygrisse remembers: « Ever since we heard about NASA, but also when the Americans launched their first satellites, space became a serious affair ». 30 « Maurice Roy was looking with great interest what NASA was doing», recalls physicist Jacques Blamont. He, at that time one of the first french scientists taking part in the emerging space activities, contributes in October 1958, to the establishment of Aeronomy Department (Service d'aéronomie, SA) within CNRS. Together with the Laboratory of atmosphere physics of Faculté de Paris (Etienne Vassy), Blamont's SA is about to explore the upper atmosphere using the first Véronique AGI sounding rockets (ready at last) ; a test campaign is being prepared for March 1959 since the Hammaguir range in the Algerian desert. In order not to be outdone, Onera also prepare an operation with the scientists of Commissariat à l'énergie atomique (Atomic Energy Institute, CEA).

During fall of 1958, Onera announces that it now have the ability to explore the higher atmosphere using a sounding rocket based on OPd 220 ADX: with a 8.48 m length and weighing 812.6 kg, this sounding rocketa first for Onerais made of three solid-propulsion stages, the first and second stages being an upgrade of the ADX booster and an OPd 220 rocket as a third stage. 31 The vehicle can carry a payload between 12 and 15 kg up to an altitude of 110 to 130 km. On December 11 1958, this rocket is given the more appealing name of « Daniel », as Paul Lygrisse recounts: « Maurice Roy, smiling, then asked me: ʺThan what would it be? ʺ I had my calendar in sight, it was Saint Daniel day, so I answered: ʺIt will be Danielʺ… As you see, you not always need to search solutions very far! ». 32 As for the mission itself, it is proposed by CEA, which identifies two major problems: the lack of knowledge about highenergy fluxes in the Earth near neighborhood, and concerns about the whereabouts of radioactive dusts from the atmospheric nuclear tests carried out by several countries, particularly numerous in year 1958. 33 In order to know more, Henri Le Boiteux, head of science at Onera and Jacques Labeyrie, head of the Electronics for physics unit (Service électronique physique, SEP) at CEA, get in touch to identify possible cooperation between the two institutes. Negotiations are completed in fall 1958: CEA brings the scientific hardware including three Geiger-Muller detectors, which are sensible to ionizing radiations (with sensors located on the rocket body), while Onera provides the sounding rocket and the interfaces that are necessary for the operation (telemetry, power supply, instruments connections, etc.).

Onera's sounding rockets explore the higher atmosphere

31 While the first stage remains the same, with the SPRAN 50 grain, le second one has Jericho II, an upgraded version of Jericho 1. As for the third one, it is equipped with the Mélanie grain from Onera. 32 Testimony of Paul Lygrisse to Philippe Varnoteaux, correspondence of January 30, 2001. 33 Year 1958 is a record in terms of nuclear bomb tests. In the atmosphere, the US carry out 60 tests (including 6 at high altitude), the Soviets 34 and the British 5. See the list of nuclear tests for the different nations : https://atlasocio.com/classements/defense/nucleaire/listeessais-nucleaires-etats-unis.php On January 27 1959, Daniel 01 lifts off successfully at Levant island, the third stage and the head reach 137 km. However due to a technical glitch, a part of the data from beyond 60 km is partly lost. While sensors can detect a certain radiation intensity around 18-20 km, they cannot identify precisely the nature of the detected particle.

Nevertheless, what Onera and CEA had just carried out was the second french space experiment, following the one of October 1954 (see footnote 23).

On May 13th 1961, a new joint Onera-CEA operation is organized using an Antarès rocket adapted as a sounding rocket, at Hammaguir test range in Algeria. At this occasion, Antarès 13 carries more precise instruments in order to measure gamma et Lyman radiation… five month after the third French nuclear test carried out at the Reggane centre (more than 650 km away south of Hammaguir). 34 Finally, on October 5 1961, at Levant island, Daniel 03 is launched and successfully measures the ionization rate of the boundary layer. Onera's endeavor in sounding rockets is apparently on good tracks. However, Daniel and Antarès are not enough performant and « competitive ». Scientists have a preference for Véronique and especially the new vehicles from Sud-Aviation (Bélier, Centaure, etc.), which are cheaper, easier to handle et manufactured industrially. 35 At that time, the political leaders decide to establish a new national space agency.

« CNES » vs. « French NASA »

At the time when Onera carries out the Daniel 01 experiment, the french government, under pressure from scientists; establish on January 7, 1959 the Committee of Space Research (Comité des recherches spatiales, CRS)under the supervision of physicist Pierre Auger. The scientist' goal was to have a federating institute, that could, on the one hand inform politicians about what could be done in France at the level of space studies, and, on the other hand, to represent themselves at international conferences like COSPAR. 36 Members of the CRS executive committee include the CASDN chief executive officer (general Guérin, also head of the Hammaguir military test ranges), the CNRS chief executive officer (Jean Coulomb) , the Observatoire de Paris chief executive officer (André Danjon), the general director of Higher education (Maurice Bayen), the general director of Cultural and technical affairs of the Ministry of Foreign Affairs (Roger Seydoux), one representative of the Ministry of finances, and Onera's chief executive officer.

The first months of CRS are sometime difficult, namely when budget questions are discussed. In addition, Onera's chief executive officer is sometime critical about some decisions of the Committee. In that way, when the first national space program is proposed, Maurice Roy reacts by noting his institute could be in charge of some parts of the activities like sounding rockets, provided it is appropriately funded.

Within CRS, the scientists find less and less the room they need. The idea is raised to make it evolve towards an institute that could play a role 35 Following four technological tests in Mai 1961 (all successful), the first Centaure rockets are exploited by scientists starting December 1961. 36 At an ICSU meeting (International Council for Scientific Unions) in Washington from October 2 to 4, 1958 the decision is made to create COSPAR (Committee on Space Research). COSPAR's mission is to « provide the international scientific community with the means to exploit for scientific goals the capabilities of all kinds of satellites space probes and to exchange the resulting data on a cooperative basis ». The first COSPAR meeting is organized in London on November 14-15, 1958. akin to the one of a space agency. Supported by Pierre Auger, the idea of a National Centre of Space Studies (Centre national d'études spatiales, CNES) gets more precise. For their side, the SEREB engineers take a stand by noting that, based on the Test vehicles (Véhicules d'essais) of the Ballistic basic studies program (Etudes balistiques de bases, EBB) or Gem stones (Pierres précieuses), they could design a rocket (Diamant) to loft into orbit the first French satellites. This is the moment when Maurice Roy tries again to convince the political leaders to transform his institute into a sort of « French NASA ». The rockets are used for this lobbying: in October 1960, OPd 59-36-22/D is given the name of giant star Antares. A sign of ambition… Several witnesses from that time confirm the tenacity of Onera's head: « Mister Roy had an excessive ambition towards space. At the beginning of the sixties, he wanted to make Onera his own ʺCNESʺ; he could not stop claiming to whoever was hearing: « The french NASA, it's me! », reminds Jacques Blamont. According to former Onera engineer Roger Marguet, « Mister Roy did all he could to be the head of space », but « he made a lot of enemies, including at the Ministry! ». Note that at that time Onera is under the direct supervision of the Prime minister and the Minister of Defence. Roger Marguet goes even further by noting that « Maurice Roy did not have many friends among political leaders and, he adds, I am convinced that had it been someone else than Maurice Roy, Onera certainly would have been in charge of space affairs eventually »37 and would have been an institute similar to the American. Nevertheless, unfortunately, his strong character eventually played against him, as other former Onera members acknowledged several years later: « Mister Roy a member of the Academy of sciences, he was a great man, but with a certain temper! Anyone who met him reminds this! At that time, there was a particular atmosphere at Onera; there was a true leader cult», notes, among others, Jean-Claude Théodore.38 However, the most nocuous was his hostility towards University scientists, as notes Paul Lygrisse: « When CNES was established, Maurice Roy was in a uncomfortable positon. The University scientists suddenly awoke and literally besieged Onera ». 39 Let alone Maurice Roy's personality, two other reasons explain the failure of his project, as believed Jacques Blamont : « The failure of Onera, in addition to the certainly decisive attitude of its head, shall be also understood by the fact that Onera did no knew how to get out of its "bunker" and work with French and foreign scientists, while as soon as 1960 the Auger committee [CRS] succeeded in [contributing to the] creation of COPERS and allowed me to negotiate the FR-1 satellite project with NASA. Finally, Onera kept the reputation of being a communist lair ». 40 Then, with Onera kept apart, for the first time, young scientists take over responsibilities that used to be attributed to general engineers. 41 Also, the rejection of Maurice Roy's project is certainly a that the government wants to avoid any monopoly situation, let alone the fact that Onera's status hardly offers the flexibility required in case it should engage itself in space affairs (workforce and contracts management, etc.).

The law bill for the creation of CNES, as an agency in charge of the management of french space programs, is approved by the Ministerial council on August 2 nd , 1961, then sent to the Parliament where it is voted on December 7th. As for the decision to develop the Diamant launcher, it is taken at the Inter-ministerial council on December 18. This launcher is manufactured by SEREB under supervision of the military of DMA (Délégation ministérielle l'armement, Armament Ministerial Delegation). Hence, what margin of maneuver remained for Onera?

Bérénice, the last card to play

While during spring 1962, CNES gets its bearing, Onera is working on its later research missile Bérénice. 42 More powerful than Antarès, Bérénice has a 3 340 kg total weight (vs. 1 785 for Antarès), and a 13.25 m length (vs. 12.3 m). Like Antarès, it relies entirely on solid propulsion (SEPR 739/Stromboli for the first stage, SEPR 740 for the second, SEPR 200/Tramontane for the third, Onera Mélanie grain for the fourth). Bérénice include several innovations, starting with its first stage (BER) equipped with an automated attitude control system using thrust vectoring of four small auxiliary thrusters. In the atmospheric re-entry version, the first two stages brings the missile up to 270 km, and the two others accelerates downward during descent around 55 km ; in the sounding rocket version, the four stages are used for ascent, allowing to reach roughly 650 km with 90 kg scientific payload. We eventually stopped rocket activities in order not to bother CNES people » who, as it should be underlined, had henceforth the high ground on the matter of sounding rockets. Another reason for this decline is the surge of artificial satellites during the second half of the 60s, which had the scientists' preference for carrying their instruments rather than sounding rockets, due to the limited testing time of the latter.

The Bérénice-Satmos project

Against all odds, while SEREB has engaged the development of the Diamant launcher, Onera starts in 1962 a feasibility study for the launch of a small artificial satellite (Satmos) by a rocket derived from the experimental missile Bérénice, an initiative from chief engineer Pierre Contensou. 46 Comparing a sketch of the SATMOS project (from Onera's archives) with that of the regular Bérénice missile, we observe from the dimensions that only the first stage appears identical, while the others are slightly different: a 3 586 mm length for the second stage (vs. 3 276 mm for the experimental missile), 1845 mm for the third stage (vs. 2340 mm) and 1585 mm for the upper stage (vs. 2947 mm). Unfortunately, this document does not bring information about the solid propellant grains that were considered… Rocket, launch trajectory and target orbit of the Satmos project (Onera)

Bérénice (n°09) (© Onera)

Roger Marguet, who participated to the Bérénice/Satmos study group, reminds: « Pierre Contensou was the one who asked us to study the 46 Contensou Pierre (1914-1987), a polytechnician, joins Onera in 1958 where he holds different director positions (Synthesis studies department 1964; central scientific directorate 1971), then general executive officer (1973-1979).

possibility of designing a small satellite, in order to launch it by 1964 using a Bérénice rocket ». 47 Weighing 3 to 3.5 kg, this satellite, named Satmos (Satellite pour l'étude de l'ATMOSphère, meaning "Satellite for the study of the ATMOSphere"), would carry instruments for measurements in the But what was Onera's reason for pushing such a project? According to Roger Marguet, « it was an interim solution for Diamant ». However, wasn't the actual reason to show that Onera could be faster than SEREB? Indeed, Roger Marguet acknowledges: « Satmos met a strong hostility, including from SEREB which considered this as a competitor ». This was obvious. There was probably a second reason explaining the demise of Satmos: « One should not forget how important the initial shot is. Yet, Onera's proposal was a satellite of a few kilos, this was ridiculous for a first shot48 », eagerly admits Roger Marguet. Satmos would be sent onto an orbit with a 250 km perigee and a 1800 km apogee altitude. The project looked even more ridiculous compared to SEREB's, which targets a 30 to 50 kg satellite inserted onto a 500  2200 km orbit. Eventually, Onera's project was considered so embarrassing that State Minister Palewski order its stop on October 7, 1963: « We have been requested to stop our project, while we were about to concretize it, maybe we could have orbited before Diamant! », tells Roger Marguet with some regret. However, later on, during an April 2000 interview, he adds: « I tell you frankly what I think about it now: it is better Satmos was not launch, because it would have been a very risky operation. Our rockets did not have the reliability needed for launching a payload of a few kilograms into orbit. Failure was not very far. Yet, a failure would be poorly looked upon », and would have probably lead to mockery on the international scene, hence the intervention of the State ministers.

The Héliotrope satellite project

Satmos is not Onera's only satellite project in the early sixties period. Indeed, Onera's rocket propulsion studies deals not only with launcher propulsion by also with on-orbit propulsion. One option for the latter is solar thermal propulsion. It is a combustion-less rocket propulsion mode that consists in heating a fluid using concentrated solar radiation, then expanding it through a nozzle. Copyright 2022 by Dr. Philippe Varnoteaux. Published by the IAF, with permission and released to the IAF to publish in all forms. IAC-22-E4.3.4 Page 11 of 12

In the beginning of the sixties, Onera studies the Héliotrope satellite demonstrator, aimed at testing in orbit this innovative propulsion mode (which was also studied by US companies like Sundstrand). The name of the satellite means "heliotropic", i.e. "moving with the sun", because it was equipped with a deployable parabola reflector that should be oriented towards the sun, in order to focus the concentrated solar radiation through a transparent window into the heating chamber. The fluid considered for this demonstrator was ammonia (NH3).

Unlike Satmos, which weighs a few kilograms, Héliotrope is an ambitious 100 kg satellite. This project is presented at the 1963 IAC conference in Paris49 and a mock-up is presented at the Paris Air show in Le Bourget on the same year. Héliotrope is an Onera-funded project, and it did not go beyond the stage of early design studies presumably due to the lack of funding.

Mock-up of the Héliotrope satellite (Onera)

Conclusion

While Onera did not evolved into a french space agency, it was nevertheless recognized as one on the foremost actors in the field. Consequently, the "Office National d'études et de recherches aéronautiques" (National institute of aeronautical studies and research) becomes on Avril 10, 1963 "Office national d'études et de recherches aérospatiales" (National institute of aerospace studies and research). Onera found its place as a major research center in aeronautics and space and major partner for institutes like CNES and ESA. Onera brings its multidisciplinary expertise to complex projects.

  to move to higher and better experiments, which are rapidly upgraded, a second generation appears in 1954, with OPd 100, 220, 250 and 320. While the concept and visual design do not change, these vehicles turn into full-fledged modular systems with one, two or even three stages, with lengths between 2 and 11 m, and weights ranging from 80 and 850 kg.20 On their launch ramp, the heavier OPd rockets are now akin to genuine technology demonstration rockets rather than mere selfpropelled test-beds. OPd vehicles is such as OP put them at the disposal of other Onera scientific directorates as well as studies managed by other institutes (SNCAN/Nord Aviation, Dassault, etc.). 22 This is the reason why a new directorate, the System and Test hardware directorate (Division des systèmes et moyens d'essais, DSME) is formed in 1958. This new structure regroups concerned Onera workforce in order to perform flight experiments more efficiently.Thus, until around 1958, Onera's technology development rockets make possible many research spanning five fields: the design of flight test hardware and methodologies; flight mechanics and aerodynamics; guidance and control; flutter; propulsion. In 1958-59, two other fields appear: aerothermodynamics and atmosphere measurements later followed in the 1960s by space experiments (geophysics, astronomy).

"

  Earth and Cosmos" exhibit. In the foreground: OPd 56-39-22/D; in the background: Véronique © All right reserved 6. The turning point of 1958-59

DEFA, Direction des études et fabrication d'armement) of the Land forces discards its ramjet research. Onera gets back some experts in the field, including Roger Marguet 16 who was then working on NA 250. From 1958, Marguet designs two other ramjet engines: Statex and

  To this end, a new missile is designed, named ADX or Ardaltex (acronym for "Allumage d'un Réacteur Déterminé à haute ALTitude EXpérimental", or "High altitude ignition of an experimental specific engine"). The operations of this vehicle require a two-stage rocket booster, enabling an ignition around 20 km in altitude. The first ADX is tested on January 27 th 1958. Stataltex. The experiments continued till 1964, when the ramjet program is stopped, because the government favors the investment on ballistic missiles, let alone the fact that « The Americans went to great length to deter us from following this path, arguing that ramjet missiles have no future », as Roger Marguet eagerly points. 17

	Jointly organized by OA, OE and OP directorates, this program nevertheless meets many technical problems… Ardaltex 04, June 24 th 1958, Levant island © Onera In 1957, the Armament design and manufacturing directorate (Roger Marguet © All rights reserved

  « After Antarès, we decided to choose again the name of a star or a constellation starting with a B[Bérénice's hair] hence the name Bérénice », according to André Rémondière's testimony.The first Bérénice launch occurs on June 27, 1962, but is fails due to the break of a telemetry antenna cable. The second test, occurring shortly later on July 6 is a success 43 While Bérénice would be used only once as a sounding rocket (1966), 44 some of its parts will be the basis of two new sounding rockets: Tacite and Titus. The payload capacity of this vehicle is 285 kg at a 160 km altitude. As for Titus (for Tentative upgrade of Titus for Scientific Use, Tentative d'Itération de Tacite à Usage Scientifique), it is a 11.5 m high vehicle, weighing 3 tons, using the first two stages of Bérénice, with a slightly longer second stage and allowing a high precision trajectory control. It can carry 400 kg of instruments up to a 250 km altitude. Tacite is used four times June 1965 and October 1968 for measurements and observations of the Earth infrared radiation, while Titus is launched twice, on November 1966, at the occasion of a solar eclipse observation from northern Argentine.45 Nevertheless, one can only observe that sounding rockets applications do not expands as Maurice Roy hoped, probably due to the lack of sufficient funding, but not only. It is interesting to note Paul Lygrisse's view about this question. According to him, « the decline [of Onera sounding rockets] became obvious with the creation of CNES.

	Tacite (for Tentative Analysis of Earth-Space
	Infrared Contrast, Tentative d'Analyse du Contraste
	Infrarouge Terre-Espace) is a single stage vehicle
	with a 7 m length, weighing 2 tons, equipped with
	solid propellant grain nearly similar (SEPR 739-2)
	to the one of Bérénice's first stage, but the
	stabilization technique is different, as it uses a
	cruciform empennage.
	40 Correspondence from Jacques Blamont to Philippe
	Varnoteaux, Octobre 28th 2013.
	41 Blamont Jacques, « La politique spatiale française et
	son avenir », in Cahiers de la Fondation Charles-de-
	Gaulle, n°12, December 2003.

42

In 1946, CSPSA becomes Onera's scientific and technical Committee.

Among CSPSA members, scientists such as Louis de Broglie, Frédéric Joliot-Curie (chemistry Nobel prize), René Jugeau, Joseph Pérès, Maurice Roy ; general engineers such as Maurice Suffrin-Hébert, Louis Bonte (head of CEV at Brétigny in 1948), Paul Dumanois, etc.

Charles Tillon is Minister of Air from September 1944 to November 1945, then minister of Armament from November 1945 to December 1946.

Dassault, SEPR, SNCAN, SNCASO, etc.

Institut de mécanique des fluides de Lille et de Toulouse (Institutes of Fluid Mechanics at Lille and Toulouse), the Etablissement de recherches aéronautiques de Toulouse (Toulouse Aeronautical Research Institute), the

Barrère Marcel (1920-1996), engineer (1950), joins Onera in 1946, where he spends his whole carrier. He becomes one of the rocket propulsion experts, for both solid and liquid.

Moutet André was a pioneer in new propellant synthesis; he has worked in particular on solid propellant ramjet.

Viaud Louis begins his research at Onera on scramjet engines.

Onera provides support to other institutes for some vehicle design studies, such as the CT-20 target-vehicle from Arsenal de l'Aéronautique (tested in S1MA wind tunnel at Modane-Avrieux, which starts industrial activities in 1954), the CT 41 supersonic target-vehicle from Nord-Aviation, etc. Like VD 2120, CT 41 is equipped with two side ramjet engines.

Marguet Roger (1924-2019), begins his engineer career at LRBA (Laboratoire de recherches balistiques et aérodynamiques) in Vernon, then at Satory within Direction technique de l'armement terrestre (DTAT). He joins Onera in 1957, where he works on ramjet propulsion and become one of the best world-class experts.

Interview of Roger Marguet with Philippe Varnoteaux, April 3, 2000.

Note that this approach is not new; other institutes had it like SNCASE. It was also used in the USA to test several technologies. See Thibodaux J., A Brief summary of experience in boosting aerodynamic research models, Washington, NACA, July 1956.

More details about the development of OPd rockets in « A History of Onera's OPd Rockets: Experimental Aircraft toSpace-Probe Rockets, 1952-1966 », 35th IAA History of Astronautics Symposium, Toulouse, France, 1-5 October 2001.

 22 Several OPd rockets have been used to flight-test the airfoils of Gerfaut, Mirage IV aircrafts, as well as the tail fins of CT41 target vehicle (Nord Aviation), etc.

Sketch of OPD 220 ADX rocket © Onera 2012 rocket, Le Bourget, May 1957 © All right reserved Even though this operation has no immediate follow-on, Onera nevertheless use all possible 26 Correspondence from Paul Lygrisse to Philippe Varnoteaux, March 2 nd 2000. 27 Remind that the nuclear deterrence force was decided between 1954 and 1956, a decision driven by Presidents of the Council Pierre Mendès-France and Guy Mollet.Regarding the launch vector, the initial choice was strategic bomber Mirage IV, a technology that more easily accessible than ballistic missile. In 1958, when he returns to power, de Gaulle confirms the order for the experimentation and series constructions of Mirage IV.

The engines of the first two stages were the ones of Navy's Masurca Mk1 missile.

Correspondence from Paul Lygrisse, op. cit., March 2 2000.

Scientific instruments carried by Daniel 01© Onera/CEA

Particularly polluting, the first three nuclear tests have been carried out on February 13 (Gerboise bleue operation), April 1 st (Gerboise blanche) and December 27, 1960 (Gerboise rouge).

Roger Marguet interview by Philippe Varnoteaux, Avril 3rd 2000.

Jean-Claude Théodore interview Philippe Varnoteaux, Janvier 24th 2000.

Indeed, at the beginning of the 1960s, less and less Polytechnicians (Ecole Polytechnique alumni) and more and more scientists join Onera. In 1962, when Maurice Roy resigns, he is replaced by Professor Lucien Malavard, a university scientist…

On June 27th 1963, Bérénice 05 soars up to an altitude around 270 km, then, during descent, reaches Mach 12, a velocity record for that time in Europe.

Bérénice 12 allows on May 4th 1966 to prepare the Titus astronomy mission, consisting in observing the solar corona.

For more information, see Varnoteaux Philippe, « En 1966, la campagne d'observation astronomique Titus », in l'Astronomie, vol. 118, March 2004.

Conversation between Roger Marguet and Philippe Varnoteaux, 3 avril 2000.

Did not the American ridiculed themselves on December 6, 1957 with their first launch that failed on live TV? This was the launch of Vanguard TV-3 satellite, weighing only 1.5 kg… while the Soviets had just put into orbit Sputnik 2, a 508 kg payload.

Surugue J. Le Grives E., Le propulseur héliothermique-performances théoriques et projet de réalisation ONERA, XIV th International Astronautical Congress, Paris, September 25-October 1, 1963.In the field of space applications, two examples highlight Onera's expertise today: the design of the ultra-precise accelerometers that equipped the ESA's GOCE satellite and Cnes's Microscope satellite,

 50 and the design of the space surveillance system GRAVES, operated by the Army of Air and Space, that allows maintaining a catalogue of space objects in low earth

orbit. 51

Launched on March 2009, GOCE measured until November 2013 the Earth gravitational field with very high precision.

Developed for the account of DMA, the Graves system (Grand Réseau Adapté à la Veille Spatiale) starts operations in 2005. It is capable of detecting and cataloguing space objects passing with a range up to 1000 km.

Created on as Office National d'Etudes et Recherches Aéronautique (French National Aeronautics Research aeronautics, for both civil and military purpose. contribute to scientific and technical research for space applications. Notable contributions are the development of a wide range of sounding rockets (totalling 400 launches until 1976) and scientific payloads for space experiments (satellite or suborbital rocket payloads). Some little known projects (eventually unrealized) illustrate the diversity of Onera's activity and its ambitions, like the Satmos project, for the launching of a small satellite using an enhanced 4-stage Bérénice rocket (which could have occurred before Diamant), and a satellite for the on-orbit demonstration of solar thermal propulsion name Héliotrope. As an acknowledgement of its important commitment to space activities, Onera's designation is changed to Office National d'Etudes et Recherches Aérospatiales (French National Aerospace Research Center) in 1963.