Unmanned aerial vehicles (UAV) are spreading their usage in many areas, including last-mile distribution. In this research, a UAV is used for performing light parts delivery to workstation operators within a manufacturing plant, where GPS is no valid solution for indoor positioning. A generic localization solution is designed to provide navigation using RFID received signal strength measures and sonar values. A system on chip computer is onboarded with two missions: first, compute positioning and provide communication with backend software; second, provide an artificial vision system that cooperates with UAV’s navigation to perform landing procedures. An Industrial Internet of Things solution is defined for workstations to allow wireless mesh communication between the logistics vehicle and the backend software. Design is corroborated through experiments that validate planned solutions.

This research investigates the optimization of airfoil design for fixed-wing drones, aiming to enhance aerodynamic efficiency and reduce drag. The research employs Kulfan CST and Bézier surface parameterization methods combined with global sensitivity analysis (GSA) and machine learning techniques to improve airfoil performance under various operational conditions. Particle swarm optimization (PSO) is utilized to optimize the airfoil design, minimizing drag in cruise and ascent conditions while ensuring lift at takeoff. Computational fluid dynamics (CFD) simulations, primarily using XFOIL, validate the aerodynamic performance of the optimized airfoils. This study also explores the generative design approach using a neural network trained on 10 million airfoil simulations to predict airfoil geometry based on desired performance criteria. The results show important improvements in drag reduction, especially during low-speed cruise and ascent phases, contributing to extended flight endurance and efficiency. These results can be used for small unmanned aerial vehicles (UAVs) in real-world applications to develop better-performance UAVs under mission-specific constraints.

Communications, navigation, and other related systems need to have a well-defined antenna radiation pattern. In onboard vessel systems, the radiation pattern can be much different than the one obtained for an isolated antenna (because of the vessel’s structure and other nearby radiating systems interference). Finding out the onboard antenna’s radiation pattern is a well-known problem for any shipbuilder/owner. The conventional method consists of measuring radiation patterns from a fixed antenna on the coast while the ship is navigating in circles. Recent electronic systems in the market now allow for an alternative method: keeping the ship static while an unmanned aerial vehicle (UAV) circles it, measuring the antenna’s transmitted power. This research paper examines the airspace volume and optimal flight path of an off-the-shelf UAV system for measuring the onboard antenna’s radiation pattern in the presence of physical constraints such as the vessel’s dimensions, safety zone, distance to base, Fresnel’s and multipath distances, and considering the loss due to polarization decoupling between the antenna under test and UAV’s antenna.

Airports, broadly spread world-wide, present continuously increasing energy demands for heating and cooling purposes. Relocatable facilities within them could be built on recycling shipping containers provided with the right insulation layer, to reduce the outstanding consumption of the heating, ventilation and air conditioning systems (HVAC). This research focuses on studying the effect of added insulation on the thermal performance of a construction in the scope of an airport facility, based on a recycled shipping container. Passive heating and cooling insulation strategies have shown good results in terms of energy savings. A series of simulations were performed along six different Spanish airports locations, selected to represent several climate conditions. Temperature evolution inside the container, and energy demands of the HVAC system were obtained to show that the insulation provided by phase change materials (PCM) is performing better than traditional insulation, or a raw container. Although there are slight behavior differences according to the climate, PCM can increase inside temperature even with no HVAC under certain circumstances.

Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms.