The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal simulations are performed regarding the designed system and a physical prototype is manufactured, assembled and programmed. Functional and field test results resorting to unmanned aerial vehicles, as well as the system’s integration within VIRIATO project’s mock-up vehicle, are presented. These tests demonstrate the viability of this system as an independent data acquisition system, and simulation results show that commercial off-the-shelf components have the capability of surviving expected launch environments.

Group VI elemental 2D materials have been predicted to possess promising properties in electronics, optoelectronics, thermoelectricity, sensors, and detectors[1–5]. In this thesis, we present the electronic and optical properties of square selenene (s-Se), a 2D allotrope of selenium. We start by calculating the electronic properties using Density Functional Theory (DFT). These ab initio calculations show the formation of an anisotropic cone with semi-Dirac dispersion in a non-symmetric point, denoted P1, in the Brillouin zone, which forms a narrow gap (∼0.1 eV) due to spin-orbit coupling (SOC). Then, we uncover an intricate reciprocal space spin texture along the Brillouin zone, with spin-momentum locking around P1. To conclude the DFT calculations, we show the existence of edge states in two types of s-Se nanoribbons, with one of them presenting topologically non-trivial features, which are confirmed in other works[1,6]. We then replicate a k⋅p Hamiltonian, for the band structure around P1, from the literature[1] and add various SOC terms to obtain an effective model that describes the band structure data from fully relativistic DFT calculations and qualitatively reproduces the spin texture in reciprocal space. Using this effective model, we describe the exciton formation in the Dirac-like cone gap using the variational method while considering an anisotropic version of the Rytova-Keldysh potential[7]. This analysis provides a first characterisation of the exciton ground-state in square selenene, describing its binding energy (Eb=-24 meV) and wavefunction, which, to the best of our knowledge, have not been previously reported.

We present a novel artificial intelligence approach to explore beyond Standard Model parameter spaces by leveraging a multi-objective optimisation algorithm. We apply this methodology to a non-minimal scotogenic model which is constrained by Higgs mass, anomalous magnetic moment of the muon, dark matter relic density, dark matter direct detection, neutrino masses and mixing, and lepton flavour violating processes. Our results successfully expand on the phenomenological realisations presented in previous work. We compare between multi- and single-objective algorithms and we observe more phenomenologically diverse solutions and an improved search capacity coming from the former. We use novelty detection to further explore sparsely populated regions of phenomenological interest. These results suggest a powerful search strategy that combines the global exploration of multi-objective optimisation with the exploitation of single-objective optimisation.

Density functional calculation of electronic structures of materials is one of the most used techniques in theoretical solid state physics. These calculations retrieve single electron wavefunctions and their eigenenergies. The berry suite of programs amplifies the usefulness of DFT by ordering the eigenstates in analytic bands, allowing the differentiation of the wavefunctions in reciprocal space. It can then calculate Berry connections and curvatures and the second harmonic generation conductivity. The berry software is implemented for two dimensional materials and was tested in hBN and InSe. In the near future, more properties and functionalities are expected to be added.

This thesis reports on the development of an automated anomaly detection (AD) systemfor industrial quality control (QC), addressing inefficiencies of manual inspections and current practices. Leveraging machine learning and computer vision techniques, this work optimizes QC using AD algorithms. The research includes a review of AD techniques, algorithm development, hyperparameter optimization, performance evaluation, real-time implementation, and API backend creation. Central to this work is a framework built on an autoencoder (AE) architecture, optimized with a custom objective function that leverages data augmentation techniques to be trained using only defect-free images. Designed as a plug-and-play solution, it can be deployed across various production lines via API calls. This study assessed fourmethodologies, each building upon the one before, except for the variational approach, which showed no improvements. The best strategy involved a refined hyperparameter optimization coupled with advanced data augmentation techniques. The overall results were primarily attributed to the developed post-processing techniques and the use of the BCE metric for evaluation. In academic evaluations on theMetal Nut subset of the MVTec AD dataset, thiswork surpassedSOTA in the MSE AE regime, achieving an AUC of 0.87 and an IoU of 0.37, outperforming handcrafted methods in the literature by ≈ 1.2% and ≈ 42.3%, respectively. Although only slightly outperformed in AUC by the top SOTA method, which achieved 0.89, the methodology still delivered highly competitive results. In collaboration with industrial partners, the system successfully detected anomalous parts using a real-world dataset. The results validate the novel methodology and showcases the potential of data-augmented NOKs as an alternative to real-world defects. Aligned with Industry 4.0 objectives, this work contributes to the advancement of smarter, data-driven manufacturing processes, facilitating the development of more automated quality control systems, reducing industrywaste, and improving market competitiveness.