Sabri Khamari
email: sabri.khamari@u-bordeaux.fr

Toufik Ahmed

Mohamed Mosbah
email: mohamed.mosbah@u-bordeaux.fr

Adaptive Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

Keywords: Multi-access edge computing, vehicular network, service migration, deep reinforcement learning. I

Multiaccess edge computing (MEC) has emerged as a promising technology for time-sensitive and computationintensive tasks. However, user mobility, particularly in vehicular networks, and limited coverage of Edge Server result in service interruptions and a decrease in Quality of Service (QoS). Service migration has the potential to effectively resolve this issue. In this paper, we investigate the problem of service migration in a MECenabled vehicular network to minimize the total service latency and migration cost. To this end, we formulate the service migration problem as a Markov decision process (MDP). We present novel contributions by providing optimal adaptive migration strategies which consider vehicle mobility, server load, and different service profiles. We solve the problem using the Double Deep Q-network algorithm (DDQN). Simulation results show that the proposed DDQN scheme achieves a better tradeoff between latency and migration cost compared with other approaches.

INTRODUCTION

The integration of connected vehicles and autonomous driving technology is poised to fundamentally transform transportation systems, yielding incalculable advantages for our society [START_REF] Lu | The Cognitive Internet of Vehicles for Autonomous Driving[END_REF] [START_REF] Coutinho | Guidelines for the Design of Vehicular Cloud Infrastructures for Connected Autonomous Vehicles[END_REF]. Intelligent Transportation Systems (ITS) have the potential to enhance transportation safety through effective vehicle coordination and resource management [START_REF] Lin | Intelligent transportation system (ITS): Concept, challenge and opportunity[END_REF]. Along with safety, ITS will offer entertainment services such as video streaming and gaming and may even extend to in-vehicle augmented reality [START_REF] Wang | An Investigation Into the Use of Virtual Reality Technology for Passenger Infotainment in a Vehicular Environment[END_REF] [START_REF] Khan | Enhancing Video Streaming in Vehicular Networks via Resource Slicing[END_REF]. For these capabilities to be realized, vehicles need to have the ability to communicate and access services with minimal latency. Thus, the ITS environment must meet these requirements. Therefore, novel strategies that are sensitive to mobility and resource constraints are required to diminish latency for applications and optimize the utilization of network and computing resources [START_REF] Aljeri | Fog-enabled vehicular networks: A new challenge for mobility management[END_REF].

To satisfy such rigorous quality of service (QoS) requirements, the Multi-access Edge Computing (MEC) paradigm has been proposed by deploying servers at the edge of the network. Efficient deployment of edge servers provides robust computational capabilities to vehicles while ensuring a low latency [START_REF] Zhang | Deep learning empowered task offloading for mobile edge computing in urban informatics[END_REF] [START_REF] Khamari | Efficient Edge Server Placement under Latency and Load Balancing Constraints for Vehicular Networks[END_REF]. As a result, applications that demand high computational power such as object detection, video stream analytics, and path navigation can be executed with edge servers in vehicular networks [START_REF] Zhang | Mobile edge intelligence and computing for the internet of vehicles[END_REF]. Although MEC-enabled vehicular networks offer numerous advantages, they present new challenges in task offloading and computing. A crucial challenge is the high mobility of vehicles which leads to a highly dynamic communication topology in vehicular networks resulting in unreliable communication links [START_REF] Lopes | Network mobility for vehicular networks[END_REF]. Vehicles may move beyond the coverage area of an edge server resulting in increased latency and the interruption of a service session between the vehicle and the edge server hosting this service.

To address these challenges, we investigate the service migration problem in MEC-enabled vehicular networks. To maintain the advantages of MEC, the user's service may require migration as they move between various geographical locations. The question is when and where to migrate the service. However, frequent service migrations can result in high migration costs, including increased backhaul load [START_REF] Peng | Deep reinforcement learning-based dynamic service migration in vehicular networks[END_REF]. Therefore, it is crucial to explore solutions to the service migration problem that strikes a tradeoff between maintaining QoS and minimizing migration costs.

In this paper, we formulate the service migration problem as a Markov decision process (MDP). We present novel contributions by providing optimal migration strategies for different service profiles. Based on the service's performance requirements, its required computing capacity, and its size, an optimal tradeoff between the migration cost and the latency is determined. In order to solve this problem efficiently, we use deep reinforcement learning (DRL) techniques, specifically, deep Q networks (DQN). The proposed DRL-based migration scheme ensures service continuity under high mobility constraints and offers optimal latency for each service profile and a reduced migration cost. The proposed scheme executes proactive service migration while considering the mobility of vehicles, the necessary amounts of computational, and the service profile.

To summarize, the main contributions of this paper are synthesized as follows:

• We approach the problem of service migration by formulating it as a Markov decision process (MDP),

where we meticulously define the states, actions, and reward function. By doing so, we can effectively determine the optimal time and location for migrating the service while the vehicles are in motion. Different from the previous approaches to service migration, our model considers the type of service. A customized migration strategy is adopted for each service profile.

• We employ DRL techniques to provide an efficient MDP model solution. Specifically, we propose a deep Q learning (DQL)-based solution that utilizes a double Q network and a replay buffer to enhance the learning outcomes.

• We evaluate the performance of the proposed DRLbased scheme and compare it to other service migration approaches.

The remainder of this paper is organized as follows. In Section II, recent research works on service migration are reviewed. The system model is presented in Section III. Then, the problem formulation is elaborated in Section IV. After that, Section V presents the simulation and evaluation of the solution. Finally, the paper is concluded in Section VI.

II. RELATED WORK

In recent years, researchers have proposed many strategies for service migration. Authors in [START_REF] Bao | Follow Me Fog: Toward Seamless Handover Timing Schemes in a Fog Computing Environment[END_REF] propose the "always migration" scheme so that the service always migrates to the nearest MEC server. However, the migration costs are neglected. Work in [START_REF] Ksentini | A markov decision processbased service migration procedure for follow me cloud[END_REF] proposes an MDP-based model that considers the distances between mobile users and edge servers as the states, associates each state with an action (migrate or not), and defines the transition probabilities between two states with a specific action and the rewards. Thus, a model based on MDP is proposed to address the service migration issue. However, this work does not consider other parameters besides the distance between the user and the service to decide the optimal migration. Peng et al. [START_REF] Peng | Deep reinforcement learning-based dynamic service migration in vehicular networks[END_REF] studied the dynamic migration decision in the vehicular network, which jointly considers the QoS and migration cost. However, this migration strategy determines whether to migrate a service or not without considering where to migrate the service, so the service is migrated to the nearest edge server. Abouaomar et al. [START_REF] Abouaomar | A deep reinforcement learning approach for service migration in mec-enabled vehicular networks[END_REF] studied the problem of service migration in a MEC-enabled vehicular network to minimize the total service latency and migration cost. They modeled the problem as a multi-agent Markov decision process and solved it by leveraging deep Q learning (DQL). Nevertheless, this paper considers some context conditions as vehicle positions, and their velocities; but it didn't consider the load of edge servers.

In this paper, we present novel contributions by providing optimal migration strategies while considering the available capacity on each edge server. Our model provides a personalized migration strategy for each service profile. To the best of our knowledge, no work considered the difference between the service's profile in the definition of the service migration strategy.

III. SYSTEM MODEL

We consider a vehicular MEC-enabled architecture covered with a set of Roadside units (RSU), each equipped with an Edge server n ∈ N:={ 1,2, … , N} as illustrated in Figure 1. There are K mobile users (or vehicles, interchangeably) requesting services from the Edge servers. Each vehicle requests some service to fulfill its requirements. Services are identified with a service profile that describes the service class, the required capacity (computing and memory), and the service image size.

Figure 1. Illustration of the system architecture [START_REF] Khamari | Efficient Edge Server Placement under Latency and Load Balancing Constraints for Vehicular Networks[END_REF] We adopted the service classification presented in [START_REF] Maaloul | Classification of C-ITS services in vehicular environments[END_REF] to identify three classes of services:

• Class 0 (used in Service Profile SP0): regroup highest priority services that need stringent requirements of low latency.

• Class 1 (used in Service Profile SP1): regroup second priority services with less stringent requirements than the first class.

• Class 2 (used in Service Profile SP2): regroup the lower priority services with nonspecific stringent requirements of latency.

IV. PROBLEM FORMULATION

In this section, we formulate the service migration problem as a Markov Decision Process (MDP). Then we present Double Deep Q Network (DDQN) method as an effective solution to the problem.

A. The MDP formulation

In reinforcement learning, the environment is formulated as an MDP which is represented as (S, A, P, R, γ), where S and A are state space and system actions, respectively. P represents the probability of each state transition when choosing an action a ∈ A as P (s'| s, a) : S x A x S → [0,1], where s ∈ S and s' ∈ S is the current state and the next state respectively. An immediate reward R is obtained from this state transition as R(s, a, s'): S x A x S → ℝ. In addition, γ∈[0,1] is the discount factor used to balance immediate and long-term rewards. At each time slot, the service provider is responsible for obtaining the state from vehicles and the MEC servers. Then, it transmits this accumulated state to the DQL and receives feedback regarding the optimal action for determining whether to migrate at this time and to which edge server.

The action is then carried out, and the system shifts to the next state. The key elements of the MDP are defined as follows:

1. State space:

We define the state space S = {>? | t = 1, 2, …, ?@AB} where a state at time slot t >? is a 4-tuple given by: >? = {C?[K], D?, E?[K], SF?}

Where:

C?[K]: The distance between the vehicle and the K edge Servers at time slot t where K is the number of the edge servers. D?: The Edge server hosting the vehicle's service at time slot t. E?[K]: Available capacity on the K edge server (CPU, memory, disk). GF?: Service Profile:

• Service class (class 0, class 1, or class 2).

• Required capacity by the service.

• Service Image Size. This definition of state space enables our model to function as a multi-criteria migration strategy. It takes into account the distance between the vehicle and each edge server in each time interval, and thus the vehicle's velocity implicitly. Also, it considers the load of edge servers and the required capacity of migrated service. And more importantly, our model adapts its migration strategy according to the service class.

Action space:

An action represents the decision to migrate a service to an Edge server ei at time slot t. therefore, an action at = {H1, H2, …,HI}, where Ai is the action of migrating the service to Edge server ei at time slot t, Hi ϵ {0,1} and ∑HJ=1. No migration will occur during time slot t if Hj is the hosting edge server and Aj=1. This action space representation allows our model to decide where and when a service should be migrated.

Reward function:

A MEC agent chooses an action at at time slot t and receives a reward Rt. Since we seek to reach optimal latency for each service profile and a reduced migration cost, we define the reward function as a combination of latency and migration cost as follows:

K L M N M = OP(1 O ѡ)R(?) S ѡET>?(?)U (2)
Where D(t) is the sum of communication delay, backhaul delay, and computing delay. And Cost(t) is the migration cost E V of service v at time slot t. ѡ is the weight factor, and ѡ W The training process of the DNN uses the experience replay memory mechanism by periodically storing MEC agent experience in a replay buffer. This experience consists primarily of the current state, the next state, the selected action, and the resulting reward. The experience replay memory mechanism provides uncorrelated data as inputs, thereby eliminating undesirable temporal correlations. The DDQN is used to make the training process faster and more reliable by using two DNNs [START_REF] Van Hasselt | Deep Reinforcement Learning with Double Q-Learning[END_REF]. The first DNN is called the main Qnetwork which is used to calculate the Q-values. And the second DNN is called the target Q-network which is used to provide the target Q-values Q(s, a, X -) to train the parameter X of the main Q-network. The training phase of our proposed DDQN is presented in Algorithm 1.

In each episode, the training continues for several time slots (or steps). Each MEC agent observes the current state of its environment and selects an action at from its action space at each step. The MEC agent utilizes the W-greedy policy to select an action. This policy selects an action randomly with probability W. The MEC agent receives its reward and moves to the next state. The obtained experience is stored in the replay buffer which is used to create a training dataset. The latter is used to perform the training process in order to minimize the loss function given by:

YT>>(X) = (1/[) \ [] ^O _(> ^, A ^, X)] ` (3)
Where _(> ^, A ^, X) is the Q-value of action A ^ given in the state > ^ which is calculated using the main Q-network with parameter X. And ] ^ is the target Q-value which is calculated using the target Q-network with parameter X -.

To update the parameter X of the main Q-network, the agent performs a gradient descent step. Finally, the parameter Xis updated after each K steps by copying the parameter X of the main Q-network.

The second phase is the inference phase of the DQL. Once the trained DDQN is obtained, the agent uses its optimal parameters to find the optimal migration strategy. The MEC agent observes the current state at each step and chooses the action that maximizes the Q-value based on the trained DDQN. Thus, we find the solution to the migration problem, and we can determine when and where to migrate a service to achieve an optimal balance between latency and cost.

V. SIMULATION AND EVALUATION

A. Simulation Envirenment

The simulation experiments are based on OMNeT++ [START_REF] Varga | OMNeT++ Discrete Event Simulation System[END_REF] and the Artery framework [START_REF] Riebl | Artery: Large Scale Simulation Environment for ITS Applications[END_REF] to simulate the simulate movement of the vehicles and the communication between the vehicles and the RSUs, on the one hand, and the communication between the RSUs and the edge servers, on the other. The implementation of the DDQN agent is performed using Python and Keras.

We consider a MEC-enabled vehicular network where five Edge servers are deployed over a highway as depicted in Figure 2. The distance between each two neighbor servers is assumed the same. The RSUs are deployed in every 800m and each RSU is attached to the nearest Edge server. The length of the highway is 30 km. The vehicles move with a randomly-chosen fixed speed from the range of [60, 110] km/h.

Figure 2. Simulation scenario

The DDQN and vehicular network parameters are presented in Table 1.

B. Simulation Results and Analysis

Figure 3 illustrates the average reward per episode. It is clear that the reward improves with the training episodes. This shows the effectiveness of our proposed DQL algorithm. We notice that the DDQN algorithm converges at approximately 190 episodes. In other words, the corresponding MEC agent converges to a good learning outcome, which implies that it will explore better actions. proposed in [START_REF] Bao | Follow Me Fog: Toward Seamless Handover Timing Schemes in a Fog Computing Environment[END_REF], considers the migration of the service to the nearest edge server. We considered also the load of the edge server, if the nearest server is at 100% of capacity the migration doesn't occur.

•

Peng et al. approach [START_REF] Peng | Deep reinforcement learning-based dynamic service migration in vehicular networks[END_REF]: a state-of-the-art DRL-based service migration strategy. In this work, the different service profiles are not considered.

The simulations were carried out under different configurations of service profiles (class of service). As explained earlier, we consider three service profiles: SP0, SP1, and SP2 where SP0 represents the highest priority services that need stringent requirements of low latency.

Figure 4 represents the migration cost results for a service size of 5. (unit). The NM method is excluded from this comparison (Migration cost equal to zero). The results indicate that our DDQN method has the lowest migration cost compared to other methods. In SP0, the service's latency requirements are stringent, so our model migrates the service more frequently to a more effective edge server near the vehicle. The approach of Peng et al. provides marginally lower costs than our model in SP0. In SP1 and SP2, however, our model's migration costs outperform those of other approaches. The cost of migration is reduced in SP1 and SP2 because services in these service profiles are less latency-sensitive. So we can reduce migration while maintaining the QoS of these services.

Figure 5 illustrates the average service latency under the same configuration. The results show that the latency of our model increases in SP1 and SP2 compared to SP0. This is justified by the fact that SP1 and SP2 services are tolerant of higher latencies compared with SP0 which has a stringent requirement of low latency. However, as depicted in Figure 4, this increased latency allowed us to minimize the migration cost. In other words, our model can be adapted to the requirement of each service in order to reach an optimal balance

VI. CONCLUSION

In this paper, we proposed a DRL-based scheme to solve the problem of vehicular service migration. The problem was initially formulated as a Markov Decision Process (MDP). This system takes into account various constraints, including vehicles' mobility, servers' capacity, and most importantly, the service profile. Considering this service profile enables the model to provide an adaptable migration strategy according to the service's requirements. We propose a deep Q learning (DQL)-based solution that utilizes a double Q network and a replay buffer to enhance the learning outcomes. Finally, we have demonstrated through extensive simulations that the proposed DQL algorithm, depending on the requirements of the service profile, achieves the best tradeoff between latency and migration cost compared to other strategies. In future work, we will expand our solution to a more realistic 2D scenario and transform the model into a multi-agent MDP to improve model training.

  proposed DDQN DQN is a sample and efficient Deep Reinforcement learning (DRL) algorithm. DQN approximates the Q-values Q(s, a, X) of each state-action pair (s, a) using a Deep Neural Network (DNN), where X represents the parameters of the Q-network.

Figure 3 .

 3 Figure 3. The training rewards for MEC agentOur proposed service migration strategy is compared with the composite of the following approaches:• Random migration (RM): the service is migrated randomly at each time slot t. • Never migrate (NM): the service is hosted in one of the edge servers and does not migrate during the simulation. • Always migration (AM): the migration schema proposed in[START_REF] Bao | Follow Me Fog: Toward Seamless Handover Timing Schemes in a Fog Computing Environment[END_REF], considers the migration of the service to the nearest edge server. We considered also the load of the edge server, if the nearest server is at 100% of capacity the migration doesn't occur.•Peng et al. approach[START_REF] Peng | Deep reinforcement learning-based dynamic service migration in vehicular networks[END_REF]: a state-of-the-art DRL-based service migration strategy. In this work, the different service profiles are not considered.

Figure 4 .Figure 5 .

 45 Figure 4. Migration cost results

Table 1 .

 1 Simulation parameters

	Parameter	Value
	Number of Edge servers	5
	RSU Transmission power	47,9 mW
	Vehicles transmission power	200 mW
	Access Technology	IEEE 802.11p
	Learning rate	5e -4
	Learning rate	0,99
	Replay memory size	1000000
	Mini-batch size	64
	Target update interval	100
	Optimizer	Adam
	RNN hidden layers	Two hidden layers of 256 neurons each.
	Activation function	ReLU

  and latency. Compared to the other approaches, our DDQN model outperforms RM, NM, and Peng et al. when the service is under SP0. AM offers the lowest latency because the service is always migrated to the nearest edge server, but the migration cost is significantly higher.

	between migration cost													Episodes
		0
		0	9	18	27	36	45	54	63	72	81	90	99	108	117	126	135	144	153	162	171	180	189	198	207	216	225	234	243	252	261	270
		-50
		-100
		-150
	Score	-200
		-250
		-300
		-350


Julie Puyo-Fourtine

Marie Juillé

Jérôme Hénin

Carine Clavaguéra

Iñaki Tuñon

Elise Duboué-Dijon

• Restrained MD Simulations • Convergence to the MFEP • Umbrella Sampling with Hamiltonian Replica Exchange → sample the Configurational Space

RNA enzymes, called ribozymes, are found to be involved in an increasing number of biological processes. An intriguing question pertains to the role played by divalent cations in the ribozyme activity; for the hammerhead ribozyme, the cleavage reaction is 500 times faster in the presence of Mg 2+ than in the presence of Ca 2+ and 10 000 times faster than in the presence of Na + .

We face several challenges: first, the interaction of divalent ions with negatively charged nucleic acids is notoriously poorly described in standard classical simulations due to an oversimplified description of electrostatic interactions, making it difficult to properly capture their slow reorganization along the reaction; here, we thus examine how different strategies to include electronic polarization effects in force fields -implicitly through the use of scaled charges or pair-specific Lennard-Jones parameters, or explicitly with polarizable force fields, the Drude polarizable force field and AMOEBA -capture the interaction of a model phosphate compound, dimethylphosphate. We show that the computational efficiency of implicit models makes them ideally suited for large-scale simulations of biological assemblies, with improved accuracy compared to state-of-the-art fixed-charge force fields. Since RNA is very flexible, it is necessary to properly sample their conformations with the use of specific tools (REST2) to characterize the conformational fluctuations of the active site in the presence of different cations.From the main identified conformations, the Adaptive String Method with a QM(DFTB3)/MM MD description is used in order to study the reaction mechanism.

How to describe cations-RNA interactions?

Comparison of the different approaches


K-L Jessup
email: jessup@boulder.swri.edu

E Marcq

J-L Bertaux

S Limaye

A Roman

IMPACT OF TOPOGRAPHY ON VENUS' CLOUD TOP PROPERTIES AS OBSERVED BY HST

Introduction:

Venus is permanently covered with ubiquitous H2SO4•H2O cloud and haze layers of temporally and vertically variant abundance and opacity [1][2][3][4]. Data obtained during the Akatsuki and Venus Express (VEx) missions are now revealing the interdependence of the cloud top properties on both Venus local solar time and topography [6][START_REF] Kouyama | Geophys[END_REF][8][9]. In 2010/2011 Hubble Space Telescope Imaging Spectrograph (HST/STIS) was used to record the cloud top properties over Aphrodite Terra and a low elevation region downwind of Aphrodite. The Aphrodite data were obtained on two dates in January 2011 separated by 5 days at 200-600 nm, while the low-elevation plains regions data were obtained in December 2010 at 200-300 nm (Table 1); on each date two 0.1ʺ wide maps of the cloud top radiance were obtained as function of latitude over local solar times extending from 7 to 11 hr. (Fig. 1) Aphrodite Terra Mountains ⱡPhase angle is the sun-target-observer angle; VMC observations show that the cloud top albedo brightens as the phase angle increases [13] Observed Trends and Lessons learned: Our analysis of these data shows distinct trends in Venus' cloud top properties (such as the albedo levels at 245 nm, 365 nm, and the overall cloud top SO2 gas abundance) as function of terrain type (mountain/plains), latitude and LST as summarized in Figures 2345and Table 2.

Table 2

No.

Observed Trend Implied Physics

1
The 245 nm albedo spatial variations replicate those observed at 365 nm (Fig. 3)

The species controlling the albedo at these wavelengths are linked [12,14] 2

Smooth increase in albedo with increasing latitude within longitude regions directly intersecting both the equatorial plains and Aphrodite Terra mountain range (Fig. 4)

Hadley cell Transport of absorbing materials [15] 3

Smooth & rapidly increasing albedo darkening (~20%) above the plains at LST between 10 and 11 hr (Fig. 5)

Manifestation of Shallow Cloud top

Convective Cells (±2 hr of noon) [16,17] 4 Limited (~ 0-10%) darkening of the albedo at LST between 10 and 11 hr at longitudes intersecting Aphrodite Terra mountain range (Fig. 5) Convective Cells formed near noon suppressed below cloud top altitudes; occurs when mixing rate or T in the stability layer is too high [START_REF] Baker | pdf 50th Lunar and Planetary Science Conference 2019[END_REF] 5

When visible, the degree of pre-noon darkening manifest at cloud tops above Aphrodite is smaller than the plains (Fig. 5) amount of darkening material and/or cloud top haze perturbation is lower over Aphrodite than over the plains 6

Smooth decrease in SO2 abundance with increasing latitude away from equator over the plains (Fig. 2)

Hadley cell transport of SO2 7 SO2 gas latitude gradient above Aphrodite observed to reverse from increasing in latitude away from equator to decreasing with increasing latitude in a 5 day period-such that the SO2 abundance at the equator was a minimum rather than a maximum (Fig. 2) Low vertical mixing rate within Hadley cell sufficient to allow photochemical destruction to minimize the equatorial SO2 over 5-day period [14,15] 8 Although a reversal in the SO2 gas latitude gradient was observed at longitude intersecting Aphrodite, no corresponding reversal in the cloud top albedo latitude gradient was observed [12] Timescales of vertical and meridional transport and/or chemical loss of the species controlling the albedo are longer than the SO2 photochemical loss timescale [12] 9 Equatorial SO2 gas abundances of 200 ppb, 20 ppb and 10 ppb were retrieved from the HST data; the lowest equatorial abundances were observed over Aphrodite Terra (Fig. 2)

Equatorial SO2 abundance depends on the vertical mixing in the upward branch of the Hadley cell -mixing rates over Aphrodite were suppressed relative to the plains; this impacts all species contributing to the cloud top albedo at Aphrodite [START_REF] Jessup | [END_REF]12] Fig. Conclusions: We find that both the observed SO2 behavior and the observed albedo behaviors implies that large and small scale equatorial vertical motions (and winds) are compressed (suppressed) over Aphrodite, and that this compression (suppression) impacts the abundance of SO2 gas at the cloud tops the, the cloud top albedo, and the manifestation of sub-solar convective cell activity at the cloud tops.

( 7 :

 7 20 to10:52)

Fig. 2 Fig. 3

 23 Fig. 2 SO2 gas abundance retrieved from the 2010/2011 HST data; two paths or maps were observed on each date as indicated by the color legend

Fig. 4

 4 Fig.4 Smooth increases in the cloud top 245 nm albedo with increasing latitude were observed over the plains and over Aphrodite Terra. The 20˚ higher phase angle value associated with the December 2010 observations produces a ~ 20% brightening in the cloud top albedo in December 2010 relative to the January 2011 dates. Therefore, to make a legitimate comparison of the albedo levels between the two terrain types we apply this brightening scale factor to the January albedo data.

Fig. 5

 5 Fig.5 Changes in the cloud top albedo as a function of LST between 10 and 11 hr is shown for the plains and Aphrodite; rapid and smooth darkening is more prominent over the plains Conclusions: We find that both the observed SO2 behavior and the observed albedo behaviors implies that large and small scale equatorial vertical motions (and winds) are compressed (suppressed) over Aphrodite, and that this compression (suppression) impacts the abundance of SO2 gas at the cloud tops the, the cloud top albedo, and the manifestation of sub-solar convective cell activity at the cloud tops.References:[1] Hansen and Hovenier, (1974) J AtmosScie, 31, 1137. [2] McGouldrick, K. and Tsang, C. C. C. (2017) Icarus, 286, 118-133. [3] Gao et al. 2014, Icarus, 231, 83-98. [4] Wilquet. et al. (2012) Icarus, 217(2), 875-881 [5] Lungin.et al. (2018) . Icarus, 311, 87-104. [6] Peralta et al. (2017) Nature Astronomy, 1, id. 0187. [7] Kouyama et al. (2017) Geophys. Res. Letters, 44, 12,098-12,105. [8] Bertuax et al. (2016) J. Geophys. Res. 121(6), 1087-1101 [9] Horinouchi. et al. (2018) Earth, Planets and Space, 70 (1), #10. [10] Jessup et al. (2012) VEXAG. [11] Jessup et al. (2015) Icarus, 258, 309-336. [12] Jessup et al. (2019) Icarus in the press [13] Lee et al. (2015) Icarus, 253, 1-15. [14] Krasnopolsky (2012), Icarus, 218, 230-246. [15] Marcq et al. (2013), Geoscience 6, 25-28 [16] Rossow et al. (1980), JGR, 85, 8107-8128 [17] Marckiewicz Nature, 450(7170), 633-636 [18] Baker (1999) J. Geophys. Res. 104, 3815-3832

Table I

 I

	Date	Number of maps	Phase Angle ⱡ	Geo. (LST, HH:MM) Longitude	Terrain
	December 28, 2010	2	97°	-5 to 45E; (7:20 to 10:41)	Plains
	January 22, 2011	2	82°	70 to 125E (7:20 to 10:54)	Aphrodite Terra Mountains
	January, 27, 2011	2	79°	85 to 140E


C D Parkinson

L Jessup

A C Vandaele

F P Mills

E Marcq

Y Yung

K L Jessup

Fenner

ATMOSPHERIC CHEMISTRY ON VENUS: AN OVERVIEW OF UNRESOLVED ISSUES

  L'archive ouverte pluridisciplinaire

Introduction: Venus' atmosphere is 96.5% CO2 and 3.5% N2 with trace abundances of SO2, OCS, H2O, HCl, HF, and HBr, as well as their photochemical and lightning-induced products. The global clouds are composed at least partly of concentrated sulfuric acid. The surface pressure is 90 atm and surface temperatures exceed 700 K. Atmospheric chemistry transitions from ion chemistry through photochemistry to thermal equilibrium chemistry with heterogeneous chemistry likely throughout the atmosphere.

Current understanding: Three major chemical cycles have been identified: the carbon dioxide, sulfur oxidation, and polysulfur cycles [e.g., 1], Fig. 1. The carbon dioxide cycle includes CO2 photolysis, transport of a significant fraction of CO and O to the night side, production of O2, and conversion of CO and O2 to CO2, mostly via chlorine catalyzed pathways. The sulfur oxidation cycle comprises transport upward of OCS, SO2, and H2O, oxidation to H2SO4, condensation to form the global 30-km thick cloud layers, and sulfuric acid rain. The polysulfur cycle involves the upward transport of OCS and SO2, disproportionation and production of Sx (x=2-8), and downward transport of Sx to react with CO and SO3. There is solid evidence for the carbon dioxide and sulfur oxidation cycles; the polysulfur cycle is more speculative but plausible. Alternatively, sulfur chemistry on Venus has been conceptually divided into fast and slow atmospheric cycles and a geological cycle [e.g., 2,3,4]. Recent work suggests the ternary SO2-H2O-H2SO4 system may bifurcate depending on the relative abundances of H2O and SO2 [5].

Key outstanding issues: Despite this general understanding, five decades of spacecraft observations, and 200 years of Earth-based observations, numerous significant unresolved issues remain. One is the means by which CO2 is stabilized over geologic time -models predict abundances a factor of 10 larger than the observational upper limit [e.g., 6,7]. Another is the lack of consistency among models of the chemistry and microphysics in different regions, especially in the cloud layers, where the mixing ratios of many important trace species change by orders of magnitude within several vertical scale heights, and at the surface [2]. A third is the mechanism(s) creating an inversion layer in SO2 abundances in the mesosphere, Fig. 2. Planet. Space Sci., 113-114, 226-236. [6] Mills and Allen (2007), Planet. Space Sci.,55, 1729-1740. [7] Krasnopolsky V. A., Planet. Space Sci.,54, 1352-1359.

  References: [1] Mills F. P. et al. (2007) Exploring Venus as a Terrestrial Planet, 73-100. [2] Marcq E. et al. (2018) Space Sci, Rev. 214, 10. [3] Krasnopolsky V. A. (2013) Icarus, 225, 570-580. [4] Krasnopolsky V. A. (2012) Icarus, 218, 230-246. [5] Parkinson C. D. (2015)

Fig. 1 .

 1 Fig. 1. Schematic diagram for the atmospheric chemistry on Venus. Catalytic schemes are indicated by the Greek letters in circles. The degree of laboratory confirmation is indicated by the lightness of the shade of red. The darkest red have received no confirmation. (After [1].)


J Helbert

D Dyar

I Walter

D Wendler

T Widemann

E Marcq

G Guignan

A Maturilli

I Varatharajan

S Ferrari

N Mueller

D Kappel

M. D 'amore

A Boerner

C Tsang

G E Arnold

S Smrekar

R Ghail

The Venus Emissivity Mapper -Obtaining Global Mineralogy of Venus from Orbit on the ESA EnVision and NASA VERITAS missions to Venus

Introduction:

The Venus Emissivity Mapper is the first flight instrument designed to focus on mapping the surface of Venus using several atmospheric windows around 1 µm. After years of development, VEM now has a mature design. An existing laboratory prototype has verified an achievable instrument SNR of well above 1000, as well as predicted error in retrieval of relative emissivity of better than 1%, assuming the availability of improved Venus topography.

VEM science goals: The instrument will provide a global map of rock type from orbit, assessing iron contents and the redox state of the surface by observing the surface with six narrow band filters, ranging from 0.86 to 1.18 µm. Three additional windows allow corrections for cloud composition and variability, two measure water abundance, and three compensate for stray light. Continuous observation of Venus' thermal emission will also place tighter constraints on current volcanic activity. Eight channels provide measurements of atmospheric water vapor abundance as well as cloud microphysics and dynamics, permitting accurate correction of atmospheric interference on the surface data.

The instrument is currently part of the payload suite of the ESA EnVision proposal as the VenSpec-M channel in the VenSpec spectrometer suite. It is also part of the NASA VERITAS mission proposal for the NASA Discovery call.

Combining VEM with a high-resolution radar mapper will provide key insights into the divergent evolution of Venus and Earth. Flying VEM on more than one mission will enable a long timeline of monitoring for volcanic activity on Venus. Combined with the existing VenusExpress data [1][2][3], VEM enables detection and mapping of surface changes over decades.

VEM Design: The VEM system design, discussed in details in [START_REF] Helbert | Proc. SPIE[END_REF][START_REF] Helbert | Proc. SPIE[END_REF][START_REF] Helbert | Proc SPIE[END_REF], is a pushbroom multispectral imaging system. It leverages a proven measurement technique pioneered by VIRTIS on Venus Express (VEX) [1][2][3][START_REF] Mueller | [END_REF][8][9][10][11]. It also incorporates lessons learned from VIRTIS to achieve greatly improved sensitivity and spectral and spatial coverage:

• A filter array (rather than a grating) provides wavelength stability (band-center and width-scatter) ~5× more stable and maximizes signal to the focal-plane array (FPA).

• Spectral windows below 1µm are covered for the first time. Following creation of the first breadboard model in 2015 during Phase A for the NASA Discovery proposal VERITAS, a laboratory prototype (LP) of the VEM instrument has been developed [START_REF] Helbert | Proc SPIE[END_REF]. This prototype (Figure 1) includes the development version of the VEM optics with a filter array with two active filter strips. The optics underwent a set of calibration measurements on sub-unit level at LATMOS prior to delivery to DLR. All key optical design parameters including transmission and wavelength coverage have been verified using the VEMO prototype.

For the laboratory prototype filter array (Figure 2), the substrate is fused silica and the deposition materials are Ta 2 O 5 and SiO 2 . The DMC (dark mirror coating / mask) is Cr + SiO 2 . The same materials are used for the FM units with the possible addition of Si for a few of the bands. All are materials that Materion routinely uses for space based optics. To reduce manufacturing time due to the time constraint in the Discovery Phase A study, the laboratory prototype has only 2 of the 14 filter strips in the flight array, as well as a clear area in the dimensions of a filter strip. However, the two filter strips have the same dimensions and spacing as in the flight array. This allows verification of the manufacturing process as well as testing of cross talk between adjacent filter strips. The clear area allows for a rapid verification of the performance of the whole optical system.

A first performance evaluation of the VEM prototype used two Venus analog samples heated to Venus surface temperatures. This was performed using the Venus simulations setup at the Planetary Spectroscopy Laboratory with the prototype mounted on the chamber [START_REF] Helbert | Proc SPIE[END_REF][START_REF] Maturilli | Proc. SPIE[END_REF]. The retrieved emissivities match laboratory values, and uncertainty for a single unbinned exposure is <0.35%. VEM uses onboard software developed for MERTIS to bin, co-add, and losslessly compress data upon uplink command. During science orbits, VEM oversamples at 10 km spatial resolution (33×33 pixel binning). To further enhance SNR, VEM uses digitial TDI to provide 189× gain over single-pixel SNR. Based upon current performance of the laboratory prototype for a single unbinned exposure and SNR enhancement due to onboard processing, we expect a system SNR of well beyond 1000.

VEM atmospheric correction: Methodology for retrieving surface emissivity is complex but well understood and demonstrated. To distinguish between surface and atmospheric contributions, VEM uses an updated version of the extensively tested pipeline developed to process VIRTIS data [2], combined with a radiative transfer model (RTM) [START_REF] Haus | [END_REF][14][15][16]. Surface emis-sivity retrieval techniques were developed based on Galileo NIMS observations at 1700, 1800 and 2300 nm [17]. VEM cloud bands occur at 1195, 1310, and 1510 nm [18], the first on the flank of the 1180-nm surface windows [19]. VEM's cloud bands are close to surface bands, providing near-optimal correction. Only relative emissivity measurements are needed to calculate the spectral slope to meet our surface emissivity requirements [START_REF] Helbert | Proc. SPIE[END_REF]. We do not have a requirement on the accuracy of the retrieved emissivity. However, we can now tie an emissivity retrieval to in situ measurements to assess accuracy. For this comparison, the Venera 9 and 10 landing sites [20] (not observed by VIRTIS) will be observed by VEM both on EnVision and on VERITAS.

VEM observes each spot on the surface multiple times. Therefore both atmospheric and instrument noise are reduced by averaging image swaths acquired at different times. Applying the updated analysis of atmospheric error for VEM parameters [15] and taking multiple-look averaging into account, our capability for emissivity precision is better than 1.5% for all bands and better than 1% in most bands.

Conclusion: VEM builds on recent advances in the laboratory analog spectroscopy at PSL at DLR [START_REF] Helbert | Proc. SPIE[END_REF]21]. It is the first flight instrument designed to focus on mapping the surface of Venus using atmospheric windows around 1 µm. VEM has a mature design with an existing laboratory prototype that verifies an achievable instrument SNR of >1000, as well as a predicted error in the retrieval of relative emissivity of better than 1%.

  • A two-stage baffle decreases scattered light and improves sensitivity. • Use of an InGaAs detector with an integrated thermal electric cooler (TEC) eliminates the need for cryogenic cooling. VEM's design draws heavily on DLR's BepiColombo MERTIS instrument (launched and successfully commissioned in 2018). This design maturity, combined with a standard camera optical design, leads to low development risk. VEM prototyping: VEM has been under development for several years with significantly financial investment from DLR. Part of this development was the Phase A study for the NASA Discovery proposal VERITAS in 2016 and the currently ongoing Phase A study for the ESA M5 proposal EnVision.

Figure 1 .

 1 Figure 1. VEM laboratory prototype incl. COTS version of flight detector, optics with radiation hard lenses and simplified filter array (Figure 2) (top cover removed for display)