Network Coding-based Routing and Spectrum Allocation in Elastic Optical Networks for Enhanced Physical Layer Security

In this work, an eavesdropping-aware routing and spectrum allocation approach is proposed utilizing network coding (NC) in elastic optical networks (EONs). To provide physical layer security in EONs and secure the confidential connections against eavesdropping attacks using NC, the signals of the confidential connections are combined (XOR-ed) with other signals at different nodes in their path, while transmitted through the network. The combination of signals through NC significantly increases the security of confidential connections, since an eavesdropper must access all individual signals, traversing different links, in order to decrypt the combined signal. A novel heuristic approach is proposed, that solves the combined network coding and routing and spectrum allocation (NC-RSA) problem, that also takes into account additional NC constraints that are required in order to consider a confidential connection as secure. Different routing and spectrum allocation strategies are proposed, aiming to maximize the level of security provided for the confidential demands, followed by an extensive performance evaluation of each approach in terms of the level of security provided, as well as the spectrum utilization and blocking probability, under different network parameters. Performance results demonstrate that the proposed approaches can provide efficient solutions in terms of network performance, while providing the level of security required for each demand

Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks

Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work we examine the ML-based Quality-of-Transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. We examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their accuracy and training time. We show that the distributed QoT models outperform the centralized QoT model, especially as the number of diverse QoT requirements increases.Comment: accepted for presentation at the IEEE GLOBECOM 201

Large-scale benchmarks of the Time-Warp/Graph-Theoretical Kinetic Monte Carlo approach for distributed on-lattice simulations of catalytic kinetics

We extend the work of Ravipati et al.[Comput. Phys. Commun., 2022, 270, 108148] in benchmarking the performance of large-scale, distributed, on-lattice kinetic Monte Carlo (KMC) simulations. Our software package, Zacros, employs a graph-theoretical approach to KMC, coupled with the Time-Warp algorithm for parallel discrete event simulations. The lattice is divided into equal subdomains, each assigned to a single processor; the cornerstone of the Time-Warp algorithm is the state queue, to which snapshots of the KMC (lattice) state are saved regularly, enabling historical KMC information to be corrected when conflicts occur at the subdomain boundaries. Focusing on three model systems, we highlight the key Time-Warp parameters that can be tuned to optimise KMC performance. The frequency of state saving, controlled by the state saving interval, δsnap, is shown to have the largest effect on performance, which favours balancing the overhead of re-simulating KMC history with that of writing state snapshots to memory. Also important is the global virtual time (GVT) computation interval, ΔτGVT, which has little direct effect on the progress of the simulation but controls how often the state queue memory can be freed up. We find that a vector data structure is, in general, more favourable than a linked list for storing the state queue, due to the reduced time required for allocating and de-allocating memory. These findings will guide users in maximising the efficiency of Zacros or other distributed KMC software, which is a vital step towards realising accurate, meso-scale simulations of heterogeneous catalysis

High Performance Inverted Organic Photovoltaics Without Hole Selective Contact

A detailed investigation of the functionality of inverted organic photovoltaics (OPVs) using bare Ag contacts as top electrode is presented. The inverted OPVs without hole transporting layer (HTL) exhibit a significant gain in hole carrier selectivity and power conversion efficiency (PCE) after exposure in ambient conditions. Inverted OPVs comprised of ITO/ZnO/poly(3-hexylthiophene-2,5-diyl):phenyl-C61-butyric acid methyl ester (P3HT:PCBM)/Ag demonstrate over 3.5% power conversion efficiency only if the devices are exposed in air for over 4 days. As concluded through a series of measurements, the oxygen presence is essential to obtain fully operational solar cell devices without HTL. Moreover, accelerated stability tests under damp heat conditions (RH=85% and T=65oC) performed to non-encapsulated OPVs demonstrate that HTL-free inverted OPVs exhibit comparable stability to the reference inverted OPVs. Importantly, it is shown that bare Ag top electrodes can be efficiently used in inverted OPVs using various high performance polymer:fullerene bulk heterojunction material systems demonstrating 6.5% power conversion efficiencies

Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service

Development, implementation and efficiency optimization of novel methods to accelerate kinetic Monte Carlo simulations of reactive systems

On-lattice Kinetic Monte Carlo (KMC) is a powerful computational method that is widely used to study chemical reaction on catalytic surfaces. It is an exact method able to capture surface inhomogeneities, e.g. due to interactions among the participating species, and handle systems with complex chemistries. KMC is exact in the sense that the method itself does not introduce approximations of any kind. Therefore, the results produced from a KMC simulation depend exclusively on the input, i.e. the lattice, the chemical reaction model, and the kinetic and energetic parameters thereof. However, KMC simulations of realistic systems tend to be computationally demanding, mainly due to the inherently serial nature of KMC since the reaction events are scheduled and executed one at a time. This thesis focuses on methods and approaches to accelerate KMC simulations of reactive systems. First, the focus is on the scheduling of KMC events undertaken by suitable queueing systems. Different data structures are developed, implemented and benchmarked to identify those that deliver the best computational performance. Next, detailed performance evaluation and optimisation studies are performed for a newly implemented algorithm that enables distributed, on-lattice, KMC simulations. Lastly, the focus turns towards well-mixed chemical reaction systems exhibiting timescale disparity, i.e. system in which some reactions occur much more frequently than others. To tackle timescale disparity, a novel method is developed that reduces (downscales) the appropriate rate constants on the fly in an optimal and data-driven way. The developed method also provides estimates for the error introduced by the downscaling procedure. The approaches developed and benchmarked enable KMC simulations to reach temporal and spatial scales that were previously unattainable. Thus, these methodological advancements are expected to have significant positive impact in future studies of complex systems

Setting up the photoluminescence laboratory at ISOLDE & Perturbed Angular Correlation spectroscopy for BIO physics experiments using radioactive ions

The proposed project I was assigned was to set up the photoluminescence (PL) laboratory at ISOLDE, under the supervision of Karl Johnston. My first week at CERN coincided with the run of a BIO physics experiment using radioactive Hg(II) ions in which I also participated under the supervision of Stavroula Pallada. This gave me the opportunity to work in two projects during my stay at CERN and in the present report I describe briefly my contribution to them

Computational design and analysis of a roller pump flexible tube for delivering constant flow rate

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Υπολογιστική Μηχανική