Resultados de investigación

Dentro de los objetivos de la Cátedra “Smart Rural IoT and Secure Environments" nos planteamos la necesidad de prevenir los principales ataques en entornos IoT. Para ello debemos poder evaluar y auditar los mecanismos de seguridad en entornos IoT. Es por tanto necesario comprender cómo los cibercriminales están atacando actualmente los dichos sistemas. La mayoría de los atacantes se apoyan en explotar las vulnerabilidades existentes en los sistemas IoT para tener éxito en sus propósitos. CVE (Common Vulnerabilities and Exposures, sitio web: https://www.cve.org/) es un sistema público de catalogación que identifica y enumera vulnerabilidades de seguridad conocidas en productos de software y hardware. Los registros CVE nos ofrecen una fuente de datos no sesgada dónde localizar las principales vulnerabilidades explotables de los diferentes componentes de los sistemas IoT. El siguiente conjunto de datos se ha construido extrayendo los principales registros CVE asociados a las cadenas de búsqueda: “IoT”,“LoRA”,”MQTT”,”Node-RED”,”MongoDB”,”Grafana”,”ZigBee” y “Dragino”. Dichos registros se obtienen genalmente en ficheros JSON con distintas estructuras. En este conjunto de datos se ha intentado organizar y estructurar dichos ficheros para ofrecer un formato regular y sencillo de manejar para su investigación. Adicionalmente se adjunta el código utilizado para la generación de dicho conjunto de datos.

La vida de antes era dura, aunque también había ocasiones para divertirse; contraste con la etapa actual, muy permisiva, que no valora el esfuerzo. La vida doméstica (especialmente la colada). A los 11 años, la informante ya hacía tareas agrícolas, como la siega de los cereales o los trabajos de la aceituna. Denominaciones de algunos oficios y trabajos del campo. Había mucha penuria y los trabajadores agrícolas vivían en muy malas condiciones y estaban muy mal pagados, A vida no passado era dura, embora também houvesse ocasiões para se divertir; em contraste com a situação actual, muito permissiva, que não valoriza o esforço. Vida doméstica (especialmente lavagem de roupa). Aos 11 anos de idade, a informante já fazia trabalhos agrícolas, tais como a colheita de cereais ou o trabalho com azeitonas. Nomes de alguns ofícios e empregos no campo. Havia muitas dificuldades e os trabalhadores agrícolas viviam em muito más condições e eram muito mal pagos., Life in the past was hard, although there were also occasions to have fun; contrast with the current stage, very permissive, which does not value effort. Domestic life (especially laundry). At the age of 11, the informant was already doing agricultural work, such as harvesting cereals or working with olives. Names of some trades and jobs in the countryside. There was a lot of hardship and agricultural workers lived in very bad conditions and were very poorly paid.

Entrevista a Informante 1 y 2 en La Alamedilla. Las telas y el telar. Vino y aguardiente. Harina y pan, As teias e o tear. Vinho e aguardente. Farinha e pão, Cloth and loom. Wine and spirits. Flour and bread

Coal prices in Uruguay and New Zealand (1890-1911) and coal price composition in Uruguay (1887-1913) reconstructed from primary sources.

Entrevista a informante 1 en Laranjeiras. Murciélago. Perro y lobo. Jabalí. Caza. Depredadores. Peces. Antes el agua del río era dulce, pero desde que hicieron el embalse y llueve menos, hay momentos del año en que está bastante salada., Morcego. Cão e lobo. Javali. Caça. Predadores. Peixes. Antes a água do rio era doce, mas desde que fizeram o reservatório e chove menos, há épocas do ano em que é bastante salgada., Bat. Dog and wolf. Wild pig. Hunting. Predators. Fishes. Before the river water was sweet, but since they made the reservoir and it rains less, there are times of the year when it is quite salty.

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: French Equatorial Africa (Congo and Chad)

This dataset contains files that comprise a numerical model for simulating cerebrospinal fluid (CSF) movement and solute transport in an idealized spinal canal. Among other things, the files allow the calculation of solute transport with densities different from that of CSF, for a relevant Richardson number of order one. This model replicates the numerical simulations performed in the project using canonical configurations and also forms the basis for simulations in more realistic anatomical and physiological configurations. The model presents a constant eccentricity between the dura and pia membranes equal to beta=0.5, computed as the distance between axes divided by the mean characteristic radial gap between the membranes. The simulation is implemented for a Womersley number equal to 3, computed as the square root of the mean characteristic radial gap power to two times the angular frequency divided by the kinematic viscosity, and a solute of molecular such that the Schmidt number is equal to 1000.

The database comprises the examination of the censors' reports on Graham Greene's early novels written before "Brighton Rock" (1938) and submitted to the Spanish censorship board in the 1940s i.e. The Man Within (1929), Rumour at Nightfall (1931), Stamboul Train (1932), It’s a Battlefield (1934), England Made Me (1935) and A Gun for Sale (1936).

The Project "The Reception of English Fiction in Twentieth-century Spain: Editions, Criticism and Censorship" was funded by the Spanish Ministry of Education under the 2007 programme of grants for research projects (Reference HUM2007-63296/FILO).The research team carried out a study on the reception of English and Irish narrative in Spain during the 20th century, paying special attention to the problems of censorship that arose during the Franco regime.

This database includes the information of the samples used in the study, as well as the results obtained for the chiral separation of (+)- and (-)-hyoscyamine using different chiral stationary phases and elution modes, followed by the purification step by solid-phase extraction, and the properties of the prepared functionalised mesostructured silicas. Then, all the results obtained from the validation of the analytical methodology and the racemisation study of atropine after the solid phase extraction procedure. Finally, the concentrations of atropine enantiomers found in the samples of herbal infusions for babies using the proposed analytical methodology.

This dataset is the result of the work done in the project GRESEL-UAM: About GRESEL: AI Generation Results Enriched with Simplified Explanations Based on Linguistic Features (Resultados de Generación de IA Enriquecidos con Explicaciones Simplificadas Basadas en Características Lingüísticas).

This dataset is part of the publication titled "Assessing a Literary RAG System with a Human-Evaluated Synthetic QA Dataset Generated by an LLM: Experiments with Knowledge Graphs," which will be presented in September 2025 in Zaragoza, within the framework of the conference of the Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN). The work has already been accepted for publication in SEPLN’s official journal, Procesamiento del Lenguaje Natural.

This dataset consists of three synthetically generated datasets, a process known as Synthetic Data Generation (SDG). We used three different LLMs: deepseek-r1:14b, llama3.1:8b-instruct-q8_0, and mistral:7b-instruct. Each was given a prompt instructing them to generate a question answering (QA) dataset based on context fragments from the novel Trafalgar by Benito Pérez Galdós.

These datasets were later used to evaluate a Retrieval-Augmented Generation (RAG) system.

Three CSV files are provided, each corresponding to the synthetic dataset generated by one of the models. In total, the dataset contains 359 items. The header includes the following fields: id, context, question, answer, and success. Fields are separated by tabs.

The id column is simply an identifier number. The context column contains the text fragment from which the model generated the questions and answers. The question and answer fields contain the generated questions and answers, respectively. The success column indicates whether the model successfully generated the question and answer in the corresponding fields ("yes" or "no").