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.

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.

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.

The use of inclusive language, among many other gender equality initiatives in society, has garnered great attention in recent years. Gender equality offices in universities and public administration cannot cope with the task of manually checking the use of non-inclusive language in the documentation that those institutions generate. In this research, an automated solution for the detection of non-inclusive uses of the Spanish language in doctoral theses generated in Spanish universities is introduced using machine learning techniques. A large dataset has been used to train, validate, and analyze the use of inclusive language; the result is an algorithm that detects, within any Spanish text document, non-inclusive uses of the language with error, false positive, and false negative ratios slightly over 10%, and precision, recall, and F-measure percentages over 86%. Results also show the evolution with time of the ratio of non-inclusive usages per document, having a pronounced reduction in the last years under study.

The design of a vehicle launch comprises many factors, including the optimization of the climb path and the distribution of the mass in stages. The optimization process has been addressed historically from different points of view, using proprietary software solutions to obtain an ideal mass distribution among stages. In this research, we propose software for the separate optimization of the trajectory of a launch rocket, maximizing the payload weight and the global design, while varying the power plant selection. The launch is mathematically modeled considering its propulsive, gravitational, and aerodynamical aspects. The ascent trajectory is optimized by discretizing the trajectory using structural and physical constraints, and the design accounts for the mass and power plant of each stage. The optimization algorithm is checked against various real rockets and other modeling algorithms, obtaining differences of up to 9%.