Synthesis of KOH-based geopolymers for innovative applications

This study presents an evaluation on compressive strength of metakaolin-based geopolymers synthetized by using different activators, KOH and NaOH. The influence of NaOH/KOH concentration ratio together with curing temperature and time were investigated to find the best results from the compressive strength tests of metakaolin-based geopolymers, synthesized with a commercial metakaolin. Aggregates of small grain size referred as fillers, were added to reduce brittleness, and minimize the pore size and shrinkage of the final mixture creating a stronger network. In this work, silt recovered from industrial processes of wash water used for aggregates production was used as a filler in the production of KOH-based geopolymers, examining the possible influence on the mechanical strength of the final product. The curing temperatures chosen for the synthesis were 85°C, 60°C and 40°C. The samples were tested after 7 days and 28 days, according to the UNI EN 1015-11:2019 applied on Ca-based cements, analyzing the differences in mechanical strength comparing samples with similar and different compositions. The study presented in total 72 synthetized geopolymer specimens that were analyzed with unconfined compression test (UCT). The characterization of the starting materials metakaolin and silt was carried out using X- ray diffraction analysis (XRD). Whereas, the formed geopolymers were analyzed using X- ray diffraction (XRD), and scanning electron microscopy (SEM) with energy dispersive X- ray spectroscopy (EDS).

Exploiting the clustering of cosmic voids as a novel cosmological probe

The investigations of the large-scale structure of our Universe provide us with extremely powerful tools to shed light on some of the open issues of the currently accepted Standard Cosmological Model. Until recently, constraining the cosmological parameters from cosmic voids was almost infeasible, because the amount of data in void catalogues was not enough to ensure statistically relevant samples. The increasingly wide and deep fields in present and upcoming surveys have made the cosmic voids become promising probes, despite the fact that we are not yet provided with a unique and generally accepted definition for them. In this Thesis we address the two-point statistics of cosmic voids, in the very first attempt to model its features with cosmological purposes. To this end, we implement an improved version of the void power spectrum presented by Chan et al. (2014). We have been able to build up an exceptionally robust method to tackle with the void clustering statistics, by proposing a functional form that is entirely based on first principles. We extract our data from a suite of high-resolution N-body simulations both in the LCDM and alternative modified gravity scenarios. To accurately compare the data to the theory, we calibrate the model by accounting for a free parameter in the void radius that enters the theory of void exclusion. We then constrain the cosmological parameters by means of a Bayesian analysis. As far as the modified gravity effects are limited, our model is a reliable method to constrain the main LCDM parameters. By contrast, it cannot be used to model the void clustering in the presence of stronger modification of gravity. In future works, we will further develop our analysis on the void clustering statistics, by testing our model on large and high-resolution simulations and on real data, also addressing the void clustering in the halo distribution. Finally, we also plan to combine these constraints with those of other cosmological probes.

Performance Analysis of 5G based Positioning in Railway Applications

Rail transportation has significant importance in the future world. This importance is tightly bounded to accessible, sustainable, efficient and safe railway systems. Precise positioning in railway applications is essential for increasing railway traffic, train-track control, collision avoidance, train management and autonomous train driving. Hence, precise train positioning is a safety-critical application. Nowadays, positioning in railway applications highly depends on a cellular-based system called GSM-R, a railway-specific version of Global System for Mobile Communications (GSM). However, GSM-R is a relatively outdated technology and does not provide enough capacity and precision demanded by future railway networks. One option for positioning is mounting Global Navigation Satellite System (GNSS) receivers on trains as a low-cost solution. Nevertheless, GNSS can not provide continuous service due to signal interruption by harsh environments, tunnels etc. Another option is exploiting cellular-based positioning methods. The most recent cellular technology, 5G, provides high network capacity, low latency, high accuracy and high availability suitable for train positioning. In this thesis, an approach to 5G-based positioning for railway systems is discussed and simulated. Observed Time Difference of Arrival (OTDOA) method and 5G Positioning Reference Signal (PRS) are used. Simulations run using MATLAB, based on existing code developed for 5G positioning by extending it for Non Line of Sight (NLOS) link detection and base station exclusion algorithms. Performance analysis for different configurations is completed. Results show that efficient NLOS detection improves positioning accuracy and implementing a base station exclusion algorithm helps for further increase.