The acorn is the fruit of the oak and is an important crop in the Spanish dehesa extreme\~na, especially for the value it provides in the Iberian pig food to obtain the "acorn" certification. For this reason, we want to maximise the production of Iberian pigs with the appropriate weight. Hence the need to know the area covered by the crowns of the acorn trees, to determine the covered wooded area (CWA, from the Spanish Superficie Arbolada Cubierta SAC) and thereby estimate the number of Iberian pigs that can be released per hectare, as indicated by the royal decree 4/2014. In this work, we propose the automatic estimation of the CWA, through aerial digital images (orthophotos) of the pastureland of Extremadura, and with this, to offer the possibility of determining the number of Iberian pigs to be released in a specific plot of land. Among the main issues for automatic detection are, first, the correct identification of acorn trees, secondly, correctly discriminating the shades of the acorn trees and, finally, detect the arbuscles (young acorn trees not yet productive, or shrubs that are not oaks). These difficulties represent a real challenge, both for the automatic segmentation process and for manual segmentation. In this work, the proposed method for automatic segmentation is based on the clustering algorithm proposed by Gustafson-Kessel (GK) but the modified version of Babuska (GK-B) and on the use of real orthophotos. The obtained results are promising both in their comparison with the real images and when compared with the images segmented by hand. The whole set of orthophotos used in this work correspond to an approximate area of 142 hectares, and the results are of great interest to producers of certified "acorn" pork.

This volume comprises the Late-Breaking Abstracts accepted for the Evo* 2023 Conference, hosted in Brno (Czech Republic), from April 12th to 14th. These abstracts were featured in both short talks and the conference's poster session, offering insights into ongoing research and preliminary findings exploring the application of various Evolutionary Computation approaches and other Nature-Inspired techniques to real-world problems. These contributions represent promising developments, highlighting forthcoming advances and applications in the field of nature-inspired methods, particularly Evolutionary Algorithms.

People with diabetes have to manage their blood glucose level to keep it within an appropriate range. Predicting whether future glucose values will be outside the healthy threshold is of vital importance in order to take corrective actions to avoid potential health damage. In this paper we describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated. The approach of this work is the application of transformation functions on glucose time series, and their use in convolutional neural networks. We have tested our proposed method using real data from 4 different diabetes patients with promising results.

Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers face to a wide and time-consuming exploration space. In this paper we propose an optimization framework based on Grammatical Evolution (GE) which is able to efficiently find the best cache configurations for a given set of benchmark applications. This metaheuristic allows an important reduction of the optimization runtime obtaining good results in a low number of generations. Besides, this reduction is also increased due to the efficient storage of evaluated caches. Moreover, we selected GE because the plasticity of the grammar eases the creation of phenotypes that form the call to the cache simulator required for the evaluation of the different configurations. Experimental results for the Mediabench suite show that our proposal is able to find cache configurations that obtain an average improvement of $62\%$ versus a real world baseline configuration.

Current embedded systems are specifically designed to run multimedia applications. These applications have a big impact on both performance and energy consumption. Both metrics can be optimized selecting the best cache configuration for a target set of applications. Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. In this work, we propose an optimization method that based on Multi-Objective Evolutionary Algorithms, is able to find the best cache configuration for a given set of applications. To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools. Results show that our optimization framework is able to obtain an optimized cache for Mediabench applications. Compared to a baseline cache memory, our design method reaches an average improvement of 64.43\% and 91.69\% in execution time and energy consumption, respectively.

The developments of satellite communication in network systems require strong and effective security plans. Attacks such as denial of service (DoS) can be detected through the use of machine learning techniques, especially under normal operational conditions. This work aims to provide an interruption detection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks using deep learning algorithms. Both the training, and the testing of the proposed models are carried out with our own communication datasets, created by utilizing a satellite traffic (benign and malicious) that was generated using satellite networks simulation platforms, Omnet++ and Inet. We test different deep learning algorithms including Multi Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), and Long Short-term Memory (LSTM). Followed by a full analysis and investigation of detection rate in both binary classification, and multi-classes classification that includes different interruption categories such as Distributed DoS (DDoS), Network Jamming, and meteorological disturbances. Simulation results for both classification types surpassed 99.33% in terms of detection rate in scenarios of full network surveillance. However, in more realistic scenarios, the best-recorded performance was 96.12% for the detection of binary traffic and 94.35% for the detection of multi-class traffic with a false positive rate of 3.72%, using a hybrid model that combines MLP and GRU. This Deep Learning approach efficiency calls for the necessity of using machine learning methods to improve security and to give more awareness to search for solutions that facilitate data collection in LEO satellite networks.

A collection of 5,000-year-old cave paintings depicting various figures and symbols has been discovered in Spain. The drawings were discovered in the rocky area of San Juan, near the town of Albuquerque in the province of Badajoz in western Spain. They are around 4 inches in length and include some anthropomorphic figures, as well as an arrow and other symbols, according to Spanish daily newspaper La Vanguardia. The doodlings were discovered by Agustín Palomo, an historic researcher who lives locally to the caves, while he was looking for a type of tomb known as a Dolmen. Mr Palomo immediately recognised their significance, given their location not far from two other well-known sets of cave drawings - 'Risco de San Blas', of the Sierra de la Carava and those of Azagala - the latter of which were only discovered around 20 years ago.