https://hdl.handle.net/10259/5684 Tue, 05 May 2026 11:57:55 GMT 2026-05-05T11:57:55Z https://hdl.handle.net/10259/11498 Images_dataset_NF-UNSW-NB15-v3_1d_to_2d Villar Val, Álvaro; Martínez Fuentes, Virginia; Granados López, Diego; Arroyo Puente, Ángel; Herrero Cosío, Álvaro This dataset represents a higher-dimensional extension of the NF-UNSW-NB15 v3 dataset [Luay et al., 2025; Luay et al., 2025], in which the correlations among variables are explicitly considered and used to organize them spatially as pixels. Sun, 22 Mar 2026 00:00:00 GMT https://hdl.handle.net/10259/11498 2026-03-22T00:00:00Z https://hdl.handle.net/10259/11497 Labeled IoT Window-Based Random Network Pattern Dataset for Reinforcement Learning Rodríguez Villagrá, César; Martin Reizabal, Sergio; Ruiz González, Rubén; Basurto Hornillos, Nuño; Herrero Cosío, Álvaro This dataset is designed to support the training and evaluation of reinforcement learning models in the context of network traffic analysis. It is derived from an existing IoT network traffic dataset, from which packet capture (pcap) files were selected and processed following a custom methodology explained in [Methodological Information](methodological-information). The resulting data representation is based on a windowing approach, where network traffic is segmented into fixed-size temporal windows. Each window aggregates traffic instances and is labeled according to its composition as benign, attack, or mixed (containing both benign and malicious activity). The final datasets are generated through random combinations of these windows, enabling the creation of diverse traffic patterns that better reflect dynamic and random network conditions. This structure facilitates the use of the dataset in reinforcement learning scenarios, where agents must learn to identify, classify, or respond to varying traffic behaviors over time. Additionally, the evaluation datasets are generated following the same methodology as the training datasets, but are kept separate and are not used during the training process, allowing for an independent evaluation of model performance. Sun, 12 Apr 2026 00:00:00 GMT https://hdl.handle.net/10259/11497 2026-04-12T00:00:00Z https://hdl.handle.net/10259/11496 Iterative Stepped Search algorithm for the clique partitioning problem Solana Ezquerra, Mario; Pacheco Bonrostro, Joaquín; Casado Yusta, Silvia The dataset is a set of txt files with the fictitious instaces used in the paper "Iterative Stepped Search algorithm for the clique partitioning problem" The dataset is a text file containing the solutions obtained in the article “Iterative Stepped Search Algorithm for the Clique Partitioning Problem” for a set of instances from the DIMACS library. Sat, 21 Mar 2026 00:00:00 GMT https://hdl.handle.net/10259/11496 2026-03-21T00:00:00Z https://hdl.handle.net/10259/11486 Dataset of the work "Naked-eye detection of Listeria monocytogenes using smart chromogenic polymers with tuneable surface morphologies" Arnáiz Alonso, Ana; Melero Gil, Beatriz; Trigo López, Miriam; Mendía Jalón, Aránzazu; Fuente Vivas, Dalia de la; Iñigo Martínez, María Emilia; Gómez Cuadrado, Laura; Ibeas Cortes, Saturnino; Vallejos Calzada, Saúl The dataset contains all raw data of the work "Naked-eye detection of Listeria monocytogenes using smart chromogenic polymers with tuneable surface morphologies" Tue, 13 Jan 2026 00:00:00 GMT https://hdl.handle.net/10259/11486 2026-01-13T00:00:00Z