Mathematical Approach to Security Risk Assessment

The goal of this paper is to provide a mathematical threat modeling methodology and a threat risk assessment tool that may assist security consultants at assessing the security risks in their protected systems/plants, nuclear power plants and stores of hazardous substances: explosive atmospheres and flammable and combustible gases and liquids, and so forth, and at building an appropriate risk mitigation policy. The probability of a penetration into the protected objects is estimated by combining the probability of the penetration by overcoming the security barriers with a vulnerability model. On the basis of the topographical placement of the protected objects, their security features, and the probability of the penetration, we propose a model of risk mitigation and effective decision making

Active Control of Oscillation Patterns in the Presence of Multiarmed Pitchfork Structure of the Critical Manifold of Singularly Perturbed System

We analyze the possibility of control of oscillation patterns for nonlinear dynamical systems without the excitation of oscillatory inputs. We propose a general method for the partition of the space of initial states to the areas allowing active control of the stable steady-state oscillations. Furthermore, we show that the frequency of oscillations can be controlled by an appropriately positioned parameter in the mathematical model. This paper extends the knowledge of the nature of the oscillations with emphasis on its consequences for active control. The results of the analysis are numerically verified and provide the feedback for further design of oscillator circuits

An end-to-end in-flight calibration of Mini-EUSO detector

International audienceMini-EUSO is a wide Field-of-View (FoV, 44°) telescope currently in operation from a nadir-facing UV-transparent window in the Russian Zvezda module on the International Space Station (ISS). It is the first detector of the JEM-EUSO program deployed on the ISS, launched in August 2019. The main goal of Mini-EUSO is to measure the UV emissions from the ground and atmosphere, using an orbital platform. Mini-EUSO is mainly sensitive in the 290–430 nm bandwidth. Light is focused by a system of two Fresnel lenses of 25 cm diameter each on the Photo-Detector-Module (PDM), which consists of an array of 36 Multi-Anode Photomultiplier Tubes (MAPMTs), arranged in blocks of 2 × 2 called Elementary Cells (ECs), for a total of 2304 pixels working in photon counting mode, in three different time resolutions of 2.5 µs (defined as 1 Gate Time Unit, GTU), 320 µs and 40.96 ms operating in parallel. In the longest time scale, the data is continuously acquired to monitor the UV emission of the Earth. It is best suited for the observation of ground sources and therefore has been used for the observational campaigns of the ground-based UV flasher in order to perform an end-to-end calibration of Mini-EUSO. In this contribution, the assembled UV flasher, the operation of the field campaign and the analysis of the obtained data are presented. The result is compared with the overall effi ciency computed from the expectations which takes into account the atmospheric attenuation and the parametrisation of different effects such as the optics effi ciency, the MAPMT detection effi ciency, BG3 filter transmittance and the transparency of the ISS window

Implications of Mini-EUSO measurements for a space-based observation of UHECRs

International audienceMini-EUSO is the first mission of the JEM-EUSO program on board the International Space Station. It was launched in 2019 and it is currently located in the Russian section (Zvezda module) of the station and viewing our planet from a nadir-facing UV-transparent window. The instrument is based on the concept of the original JEM-EUSO mission and consists of an optical system employing two Fresnel lenses and a focal surface composed of 36 Multi-Anode Photomultiplier tubes, 64 channels each, for a total of 2304 channels with single photon counting sensitivity and an overall field of view of 44° × 44°. Mini-EUSO can map the night-time Earth in the near UV range (predominantly between 290 nm and 430 nm), with a spatial resolution of about 6.3 km and different temporal resolutions of 2.5 µ, 320 µs and 41 ms. Mini-EUSO observations are extremely important to better assess the potential of a space-based detector in studying Ultra-High Energy Cosmic Rays (UHECRs) such as K-EUSO and POEMMA. In this contribution we focus the attention on UV measurements, the observation of clouds and of certain categories of events that Mini-EUSO triggers with the shortest temporal resolution. We place them in the context of UHECR observations from space, namely the estimation of exposure and sensitivity to Extensive Air Showers

Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

International audienceIn this work we present cutting-edge machine learning based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station (ISS), and pointing towards the Earth. We base our approach on a recent technique, the Stack-CNN, originally developed as an online trigger in a orbiting remediation system to detect space debris. Our proposed method, the Refined Stack-CNN (R-Stack- CNN), makes the STACK-CNN more robust thanks to a Random Forest (RF) that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data