New Mass and Radius Constraints on the LHS 1140 Planets -- LHS 1140 b is Either a Temperate Mini-Neptune or a Water World

The two-planet transiting system LHS 1140 has been extensively observed since its discovery in 2017, notably with $Spitzer$, HST, TESS, and ESPRESSO, placing strong constraints on the parameters of the M4.5 host star and its small temperate exoplanets, LHS 1140 b and c. Here, we reanalyse the ESPRESSO observations of LHS 1140 with the novel line-by-line framework designed to fully exploit the radial velocity content of a stellar spectrum while being resilient to outlier measurements. The improved radial velocities, combined with updated stellar parameters, consolidate our knowledge on the mass of LHS 1140 b (5.60$\pm$0.19 M$_{\oplus}$) and LHS 1140 c (1.91$\pm$0.06 M$_{\oplus}$) with unprecedented precision of 3%. Transits from $Spitzer$, HST, and TESS are jointly analysed for the first time, allowing us to refine the planetary radii of b (1.730$\pm$0.025 R$_{\oplus}$) and c (1.272$\pm$0.026 R$_{\oplus}$). Stellar abundance measurements of refractory elements (Fe, Mg and Si) obtained with NIRPS are used to constrain the internal structure of LHS 1140 b. This planet is unlikely to be a rocky super-Earth as previously reported, but rather a mini-Neptune with a $\sim$0.1% H/He envelope by mass or a water world with a water-mass fraction between 9 and 19% depending on the atmospheric composition and relative abundance of Fe and Mg. While the mini-Neptune case would not be habitable, a water-abundant LHS 1140 b potentially has habitable surface conditions according to 3D global climate models, suggesting liquid water at the substellar point for atmospheres with relatively low CO$_2$ concentration, from Earth-like to a few bars.Comment: 31 pages, 18 figures, accepted for publication in ApJ

Generalized delay-secrecy-throughput trade-offs in mobile ad-hoc networks

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Impact Evaluation of Control Signalling onto Distributed Learning-based Packet Routing

International audienceIn recent years, several works have studied Multi-Agent Deep Reinforcement Learning for the Distributed Packet Routing problem, with promising results in various scenarios where network status changes dynamically, is uncertain, or is partially hidden (e.g., wireless ad hoc networks or wired multidomain networks). Unfortunately, these previous works focus on an ideal scenario where the impact of control signalling is neglected, and network simulation is tailored to simplistic assumptions. In this article, we present the first experimental investigation of control signalling mechanisms for distributed learning-based packet routing. We rely on PRISMA, our opensource simulation ns-3-based module. We formulate two signalling mechanisms between agents (value sharing and model sharing). We investigate the net gains considering in-band signalling and show that routing policies close to those provided by an oracle with full knowledge of traffic and network topology can be discovered with a control overhead of 150 % with respect to injected data packets, if neighboring agents share their Deep Neural Network models. We discuss the generality of our results to underline the importance of assessing net gains of Multi-Agent Deep Reinforcement Learning (MA-DRL)-based routing

PRISMA: A Packet Routing Simulator for Multi-Agent Reinforcement Learning

International audienceIn this paper, we present PRISMA: Packet Routing Simulator for Multi-Agent Reinforcement Learning. To the best of our knowledge, this is the first tool specifically conceived to develop and test Reinforcement Learning (RL) algorithms for the Distributed Packet Routing (DPR) problem. In this problem, where a communication node selects the outgoing port to forward a packet using local information, distance-vector routing protocol (e.g., RIP) are traditionally applied. However, when network status changes very dynamically, is uncertain, or is partially hidden (e.g., wireless ad hoc networks or wired multi-domain networks), RL is an alternate solution to discover routing policies better fitted to these cases. Unfortunately, no RL tools have been developed to tackle the DPR problem, forcing the researchers to implement their own simplified RL simulation environments, complicating reproducibility and reducing realism. To overcome these issues, we present PRISMA, which offers to the community a standardized framework where: (i) communication process is realistically modelled (thanks to ns3); (ii) distributed nature is explicitly considered (nodes are implemented as separated threads); (iii) and, RL proposals can be easily developed (thanks to a modular code design and real-time training visualization interfaces) and fairly compared them

SUPERLUBRICITY OF STEEL SURFACES IN PRESENCE OF POLYHYDRIC ALCOHOLS

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Impact Evaluation of Control Signalling onto Distributed Learning-based Packet Routing

International audienceIn recent years, several works have studied Multi-Agent Deep Reinforcement Learning for the Distributed Packet Routing problem, with promising results in various scenarios where network status changes dynamically, is uncertain, or is partially hidden (e.g., wireless ad hoc networks or wired multidomain networks). Unfortunately, these previous works focus on an ideal scenario where the impact of control signalling is neglected, and network simulation is tailored to simplistic assumptions. In this article, we present the first experimental investigation of control signalling mechanisms for distributed learning-based packet routing. We rely on PRISMA, our opensource simulation ns-3-based module. We formulate two signalling mechanisms between agents (value sharing and model sharing). We investigate the net gains considering in-band signalling and show that routing policies close to those provided by an oracle with full knowledge of traffic and network topology can be discovered with a control overhead of 150 % with respect to injected data packets, if neighboring agents share their Deep Neural Network models. We discuss the generality of our results to underline the importance of assessing net gains of Multi-Agent Deep Reinforcement Learning (MA-DRL)-based routing

prisma-v2: Extension to Cloud Overlay Networks

International audienceIn this paper, we present prisma-v2, a new release of prisma, a Packet Routing Simulator for Multi-Agent Reinforcement Learning. prisma-v2 brings a new set of features. First, it allows simulating overlay network topologies, by integrating virtual links. Second, this release offers the possibility to simulate control packets, which allows to better evaluate the overhead of the network protocol. Last, we integrate the modules along with the core (ns-3) to a docker container, so that it can be run in any machine or platform. prisma-v2 is, to the best of our knowledge, the first realistic overlay network simulation playground that offers to the community the possibility to test and evaluate new network protocols

Packet Routing Simulator for Multi-Agent Reinforcement Learning (PRISMA) (Version v0.1)

PRISMA (Packet Routing Simulator for Multi-Agent Reinforcement Learning) is a network simulation playground for developing and testing Multi-Agent Reinforcement Learning (MARL) solutions for dynamic packet routing (DPR). This framework is based on the OpenAI Gym toolkit and the ns-3 simulator.The OpenAI Gym is a toolkit for RL widely used in research. The network simulator ns–3 is a standard library, which may provide useful simulation tools. It generates discrete events and provides several protocol implementations.Moreover, the NetSim implementation is based on ns3-gym, which integrates OpenAI Gym and ns-3.The main contributions of this framework: 1) A RL framework designed for specifically the DPR problem, serving as a playground where the community can easily validate their own RL approaches and compare them. 2) A more realistic modelling based on: (i) the well-known ns-3 network simulator, and (ii) a multi-threaded implementation for each agent. 3) A modular code design, which allows a researcher to test their own RL algorithm for the DPR problem, without needing to work on the implementation of the environment

Anomalous low friction under boundary lubrication of steel surfaces by polyols

International audienceWe present here anomalous low friction obtained with highly polished steel on steel hard contact lubricated by glycerol under severe mixed and boundary regimes (λ ratio below 1). We investigated the effects of contact pressure, sliding speed, and temperature on friction coefficient and electrical contact resistance. The mechanism of low friction (typically below 0.02) is thought to have two origins: first a contribution of an ultrathin EHL film of glycerol providing easy shear under pressure, second the chemical degradation of glycerol inside the contact when more severe conditions are attained, generating a nanometer-thick film containing shear-induced water molecules. This new mechanism, called “H-bond Network model”, is completely different from the well-accepted “Monolayer” model working with polar molecules containing long aliphatic chains. Moreover, we show outstanding superlubricity (friction coefficient below 0.01) of steel surfaces directly lubricated by a solution of myo-inositol (also called vitamin Bh) in glycerol at ambient temperature (25 °C) and high contact pressure (0.8 GPa) in the absence of any long chain polar molecules. Mechanism is still unknown but could be associated with friction-induced dissociation of inositol and H-bond interactions network of water-like species with steel surface

Superlow friction of ta-C lubricated by glycerol: an electron energy loss spectroscopy study

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