In this paper, we introduce the notion of snapstabilization. A snap-stabilizing algorithm protocol guarantees that, starting from an arbitrary system configuration, the protocol always behaves according to its specification. So, a snap-stabilizing protocol is a self-stabilizing protocol which stabilizes in 0 steps.We propose a snap-stabilizing Propagation of Information with Feedback (PIF) scheme on a rooted tree network. We call this scheme Propagation of information with Feedback and Cleaning (P F C ). We present two algorithms. The first one is a basic P F C scheme which is inherently snapstabilizing. However, it can be delayed Oh 2 steps (where h is the height of the tree) due to some undesirable local states. The second algorithm improves the worst delay of the basic P F C algorithm from Oh 2 to 1 step. The P F C scheme can be used to implement the distributed reset, the distributed infimum computation, and the global synchronizer in O1 waves (or PIF cycles). Moreover, assuming that a (local) checking mechanism exists to detect transient failures or topological changes, the P F C scheme allows processors to (locally) "detect" if the system is stabilized, in O1 waves without using any global metric (such as the diameter or size of the network).Finally, we show that the state requirement for both P F C algorithms matches the exact lower bound of the PIF algorithms on tree networks-3 states per processor, except for the root and leaf processors which use only 2 states. Thus, the proposed algorithms are optimal PIF schemes in terms of the number of states.

The contribution of this paper is threefold. First, we present the paradigm of snap-stabilization. A snapstabilizing protocol guarantees that, starting from an arbitrary system configuration, the protocol always behaves according to its specification. So, a snap-stabilizing protocol is a time optimal self-stabilizing protocol (because it stabilizes in 0 rounds). Second, we propose a new Propagation of Information with Feedback (PIF) cycle, called Propagation of Information with Feedback and Cleaning (PFC). We show three different implementations of this new PIF. The first one is a basic PFC cycle which is inherently snap-stabilizing. However, the first PIF cycle can be delayed O(h 2 ) rounds (where h is the height of the tree) due to some undesirable local states. WARNING:The concept of snap-stabilization was first introduced in [12]. The concept evolved over the last eight years. We take this evolution in consideration in this paper, which includes the early results published in [10] and [12]. In particular, infinite repetition of computation cycles is a requirement of self -stabilizing systems. This is not required in snap-stabilization because snap-stabilization ensures that the first completed computation cycle is executed according to the specification of the problem. The correctness proofs conform to this basic property.The second algorithm improves the worst delay of the basic PFC algorithm from O(h 2 ) to 1 round. The state requirement for the above two algorithms is 3 states per processor, except for the root and leaf processors that use only 2 states. Also, they work on oriented trees. We then propose a third snap-stabilizing PIF algorithm on un-oriented tree networks. The state requirement of the third algorithm depends on the degree of the processors, and the delay is at most h rounds. Next, we analyze the maximum waiting time before a PIF cycle can be initiated whether the PIF cycle is infinitely and sequentially repeated or launch as an isolated PIF cycle. The analysis is made for both oriented and un-oriented trees. We show or conjecture that the two best of the above algorithms produce optimal waiting time. Finally, we compute the minimal number of states the processors require to implement a single PIF cycle, and show that both algorithms for oriented trees are also (in addition to being time optimal) optimal in terms of the number of states.

The UltraViolet and infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) mission aims to demonstrate pioneering technologies for broadband measurement of the Earth’s radiation budget (ERB) and solar spectral irradiance (SSI) in the Herzberg continuum (200–242 nm) using high quantum efficiency ultraviolet and infrared sensors. This research and innovation mission has been initiated by the University of Versailles Saint-Quentin-en-Yvelines (UVSQ) with the support of the International Satellite Program in Research and Education (INSPIRE). The motivation of the UVSQ-SAT mission is to experiment miniaturized remote sensing sensors that could be used in the multi-point observation of Essential Climate Variables (ECV) by a small satellite constellation. UVSQ-SAT represents the first step in this ambitious satellite constellation project which is currently under development under the responsibility of the Laboratory Atmospheres, Environments, Space Observations (LATMOS), with the UVSQ-SAT CubeSat launch planned for 2020/2021. The UVSQ-SAT scientific payload consists of twelve miniaturized thermopile-based radiation sensors for monitoring incoming solar radiation and outgoing terrestrial radiation, four photodiodes that benefit from the intrinsic advantages of Ga 2 O 3 alloy-based sensors made by pulsed laser deposition for measuring solar UV spectral irradiance, and a new three-axis accelerometer/gyroscope/compass for satellite attitude estimation. We present here the scientific objectives of the UVSQ-SAT mission along the concepts and properties of the CubeSat platform and its payload. We also present the results of a numerical simulation study on the spatial reconstruction of the Earth’s radiation budget, on a geographical grid of 1 ° × 1 ° degree latitude-longitude, that could be achieved with UVSQ-SAT for different observation periods.

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With urging problem of energy and pollution, smart grid is becoming ever important. By gradually changing the actual power grid system, smart grid may evolve into different systems by means of size, elements and strategies, but its fundamental requirements and objectives will not change such as optimizing production, transmission and consumption. Studying the smart grid through modeling and simulation provides us with valuable results which can not be obtained in real world due to time and cost related constraints. However, due to the complexity of the smart grid, achieving optimization is not an easy task, even using computer models. In this paper, we propose a complex system based approach to the smart grid modeling, accentuating on the optimization by combining game theoretical and classical methods in different levels. Thanks to this combination, the optimization can be achieved with flexibility and scalability, while keeping its generality.

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