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Acoustic Cavity Spectra for Classifying Salt Concentrations
No full textWe present and validate an audible-band, resonant-cavity acoustic system that operates both as a sensor and as an automatic classifier for the nondestructive analysis of aqueous solutions, enabling the discrimination of solute concentrations using small liquid volumes.
Frequency spectra computed from impact recordings on a glass resonator were used to construct ten datasets spanning two salts (MgCl2 and KCl), four concentrations (0.001, 0.01, 0.1, and 1\,M), for a total of 1918 normalized spectra represented by 511 features.
We evaluate three supervised learning approaches coupled with feature selection to reduce dimensionality: Random Forest (RF), linear-kernel Support Vector Machine (SVM) and a hybrid Genetic Algorithm with Extreme Learning Machine (GA+ELM) as fitness function tailored to select frequency bands.
Across all datasets, every method exceeds 99\% accuracy on held-out validation sets; SVM attains the highest accuracy among the three strategies in the binary and 4-class settings, whereas GA+ELM yields the top scores in the 9-class setting, highlighting the value of band-oriented selection as spectral overlap increases.
RF consistently ranks last both in accuracy and in the compactness of the selected frequency regions across the audible spectrum.
For SVM and GA+ELM, the feature reduction surpasses 92\%, evidencing a high signal-information ratio.
Analysis of the selected bands reveals clear algorithmic profiles: RF tends to disperse selections; SVM concentrates on mid-to-high bands; and GA+ELM emphasizes a few contiguous intervals that are amenable to physical interpretation and to the design of bespoke sensing front-ends.
These results indicate a practical route to rapid, low-cost, and interpretable acoustic characterization of aqueous solutions.
A large number of frequency spectra were generated from each audio signal recorded during the impact of the pendulum on the surface of the resonant vessel for all experiments conducted on the nine aqueous solutions.
The frequency spectra were computed using the Fast Fourier Transform (FFT) implemented in Audacity 3.7.4.
A 1024-point Hann window was selected for the FFT computation (sampling rate of fs = 44.1 kHz used during acoustic acquisition).
All measurements were acquired in a single session under identical temperature and humidity conditions using the same acquisition procedure and geometry.
To compensate for small variations between individual measurements, the spectra were averaged over groups of 3 and 5 consecutive impacts.
This averaging procedure reduces noise and enhances the reliability of the spectral data.
For each aqueous solution, at least 80 spectra averaged over 3 impacts and 150 spectra averaged over 5 impacts were obtained.
Spectra exhibiting interference or excessive noise were discarded, resulting in a final total of 1918 valid spectra with broadband SNR>20 dB.
Following this processing stage, several datasets were constructed to perform the automatic classification of the aqueous solutions using Machine Learning algorithms.
Five classification tasks were defined according to the number of classes involved (2, 4, or 9) and the number of averaged impacts (3 or 5), resulting in ten different datasets, labeled Id = 1 to Id = 10.
Datasets with an odd identifier correspond to spectra averaged over 3 consecutive impacts, whereas those with an even identifier contain spectra averaged over 5 consecutive impacts.
Datasets Id = 1 and Id = 2 comprise all spectra from the nine aqueous solutions (complete set of 1918 spectra obtained in the experiments).
Datasets Id = 3 through Id = 6 correspond to binary classification tasks, containing spectra from two classes: pure water and the lowest concentration (0.001 M) of each salt.
Finally, datasets Id = 7 through Id = 10 contain spectra from four classes, corresponding to the four concentrations of a single salt.</p
Métricas de centralidad basadas en ACI IoT Network Traffic Dataset 2023
No full textLos datos recogidos en este dataset representan las métricas de centralidad asociadas al grafo que representa una red de comunicaciones de dispositivos IoT. La estructura de la red se ha inferido a parir de ACI IoT Network Traffic Dataset 2023, preparado por Internet of Things Research Lab (IoTRL). Los datos preparados tienen por objetivo facilitar los estudios en el ámbito de la Cibersegurida
Proteomics and RNA-seq datasets associated with: “Tamoxifen mechanically reprograms the tumor microenvironment via HIF-1A and reduces cancer cell survival”
No full textThe project investigates how tamoxifen modulates the tumor microenvironment through
mechanobiological changes involving HIF-1A, and how these changes influence cancer cell survival. The study integrates proteomics and RNA sequencing (RNA-seq) as described in the associated publication.Proteomics analysis data deposited in PeptideAtlas under reference PASS01070.
PSCs RNA sequencing (RNA-seq) data deposited in the European Nucleotide Archive (ENA) under accession ERP023834.
This e-cienciaDatos entry is intended to provide institutional Open Access traceability by referencing the original repositories
Dataset on the literature surrounding the use of AI-Chatbots in the educational field
No full textThis dataset is linked to a research project that conducts a systematic literature review on the use of AI chatbots in education, specifically analyzing their potential applications, benefits, challenges, and future areas of development.
Variables: Publication Type, Authors, Article Title, Source Title, Publication Year
Study of the effect of magnetic fields on static degradation of Fe and Fe-12Mn-1.2C in balanced salts modified Hanks’ solution
No full textWe present the study of the efect of magnetic fields, both direct and alternating, on ferrous materials, as a way to modulate the biodegradation of medical implants.Pure iron and a FeMnC alloy have been compared, the former is ferromagnetic and the latter paramagnetic
Future Work Self, Proactive Career, Resilience and Academic Passion Undergraduate Students
No full textThis dataset contains responses from 802 undergraduate students (ages 18–27; M = 23.2, SD = 2.7) who participated in a study examining the mediating role of academic passion and resilience in the relationship between future work self and proactive career behavior. The dataset includes sociodemographic information (age, gender, university), as well as scores for Future Work Self, Academic Passion (harmonious and obsessive), Resilience, and Proactive Career Behavior. Analyses performed in the study included descriptive statistics, correlations, reliability checks, and hierarchical regression with serial mediation using PROCESS
PERSEIDAS – Percepción de seguridad del alumnado de la URJC en sus desplazamientos de acceso a los campus y dentro de los mismos. Datos de réplica
No full textEl presente data set muestra los resultados acerca de la percepción de seguridad del alumnado de la URJC en sus desplazamientos de acceso a los campus de nuestra universidad y dentro de los mismos- tiene como objetivo determinar la percepción de seguridad de nuestro alumnado en el acceso a los campus y dentro de los mismos, analizando para ello dicha percepción en sus trayectos desde el transporte público a nuestra universidad (tanto ida como vuelta) y también dentro de los propios campus (zonas de paseo, aparcamiento, etc.). La obtención de dicho conocimiento es relevante para la mejora de su integración en la vida académica
Force vs. deflection in the flexural test
No full textEl objetivo principal de estos datos es comparar el rendimiento mecánico de muestras de mortero producidas mediante moldeo tradicional e impresión 3D, centrándose en el comportamiento a la flexión.
La investigación completa se puede encontrar aquí:
Gil-Lopez, Tomas; Amirfiroozkoohi, Alireza; Valiente-Lopez, Mercedes; Verdu-Vazquez, Amparo (2026). The Impact of 3D Printing on Mortar Strength and Flexibility: A Comparative Analysis of Conventional and Additive Manufacturing Techniques. Accesible online en: https://oa.upm.es/93015
Mobile robots control simulation results
No full textThis dataset contains the MATLAB code to perform simulation results of the position control of mobile robots developed in the collaboration UNED-PUCV-FAU for the article "Simulation and experimental results of a new control strategy for point stabilization of nonholonomic mobile robots"
2DoF Feedback + Feedforward simulations
No full textThis dataset contains the MATLAB code to perform simulation results of 2DoF and Feedforward controllers for the articles "Revisiting the simplified IMC tuning rules for low-order controllers: 2DoF feedback controller" and "Revisiting the simplified IMC tuning rules for low-order controllers: Feedforward controller"