Resultados de investigación

The geometric data of files bevelgear1.txt, bevelgear2.txt, bevelgear3.txt and bevelgear4.txt were generated using IGD (Integrated Gear Design), an in-house software whose generation methods are based on the book "Litvin, F.L., Fuentes, A., Gear Geometry and Applied Theory, Cambridge University Press, 2004, ISBN 0521815177, New York" and on the methodology described in the related publication (http://hdl.handle.net/10317/24399). The function data of files fig13a.txt, fig13b.txt, fig13c.txt, fig13d.txt, fig14a.txt, fig14b.txt, fig14c.txt, fig14d.txt, fig15a.txt, fig15b.txt, fig15c.txt and fig15d.txt were generated using IGD based on the methodology described in the related publication (http://hdl.handle.net/10317/24399). The function data of files fig16.txt, fig17a.txt, fig17b.txt, fig19a.txt, fig19b.txt, fig21a.txt, fig21b.txt, fig22a.txt and fig22b.txt were generated using a specialized finite element analysis software (not relevant for visualization of the results) and postprocessing them in IGD. Files bevelgear*.txt can be imported and visualized using a user-created program considering the structure of the data described in the Summary section. Files fig*.txt can be visualized using the package TIKZ (\usepackage{tikz}) in the preamble of a Latex document., The dataset is comprised of text files. Four files contain geometric data and twenty one files contain function data. The geometric data include coordinates x, y and z, in mm, of surface points for both tooth sides of one gear tooth as follows: - 11 points along the longitudinal direction. - 15 points along the profile direction. - 8 points along the fillet direction. The function data include pairs of values corresponding to the graphics displayed in several figures of the related publication (http://hdl.handle.net/10317/24399). The graphics can illustrate tooth contact analysis results (kinematic transmission errors in arcsec) and stress analysis results (loaded transmission errors in arcsec, maximum contact pressures in MPa and maximum principal stresses in MPa)., Research project PID2023-152913NB-I00 funded by MICIU/AEI/10.13039/501100011033 and FEDER, UE.

OpenMC was evaluated over the open-source bLEO network emulator using controlled packet-level Multi-Connectivity and FEC experiments. The dataset contains 120 runs for the Fig. 2 delay-asymmetry campaign, 120 runs for the Fig. 3 scheduling campaign, 25 frozen Reed–Solomon rate-study runs, and 10 frozen RaptorQ rate-study runs. For the latency experiments, per-block measurements are first averaged within each independent run, and the resulting run-level observations are used to compute the reported means and two-sided 95% Student-t confidence intervals. The sustainable-rate study characterizes the complete experimental datapath rather than an intrinsic codec-only throughput limit. This institutional UPCT deposit preserves the experimental dataset and provides an expanded standalone README.txt with additional documentation of processed and raw CSV variables. Funding: project PID2023-148214OB-C21, funded by MICIU/AEI/10.13039/501100011033 and ERDF/EU., This dataset contains the raw and processed experimental data, configuration files, execution metadata, analysis and validation scripts, and final figures supporting the experimental evaluation of OpenMC, an open-source framework for packet-level experimentation with Multi-Connectivity (MC) and Forward Error Correction (FEC). The dataset supports four main evaluations: (i) the comparison of Reed–Solomon (RS) and systematic RaptorQ (RQ) under increasing delay asymmetry using Default scheduling; (ii) the comparison of RaptorQ Quality-based and Adaptive scheduling policies; (iii) the characterization of computational and packet-processing overhead; and (iv) the evaluation of the sustainable offered rate of the complete containerized experimental datapath. The archive includes per-block and run-level measurements, processed numerical results, experimental manifests, software and environment provenance, frozen configurations, reproduction and validation scripts, and the final publication figures. The accompanying README.txt provides a detailed description of the dataset structure, methodology, variables, units, and relationships between raw and processed data., This dataset was generated as part of project PID2023-148214OB-C21, funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.

Propagation through the laboratories The files named P1_medidas1_6_5_4096_2024-10-07-xxxxxx contains the channel frequency response, with dimensions of 1×5×5×4096×19 . The first dimension corresponds to the single transmitter antenna. The second and third dimensions (5×5) represent the receiver uniform rectangular array (URA). The fourth dimension contains the frequency samples measured by the VNA (4096). The fifth dimension (23) corresponds to the different positions at which the transmitting was placed in the laboratory (Lab_02_11). The correspondence between the measurement files and the transmitter positions inside the different laboratories is defined as follows: P1_medidas1_6_5_4096_2024-10-07-130934 Lab_02_11 P2_medidas1_6_5_4096_2024-10-07-134433 P3_medidas1_6_5_4096_2024-10-07-135429 Pos1_medidas1_6_5_4096_2024-10-10-160350%Lab_2_23 Pos2_medidas1_6_5_4096_2024-10-10-161621 Pos3_medidas1_6_5_4096_2024-10-10-162637 Pos4_medidas1_6_5_4096_2024-10-10-163537 Pos5_medidas1_6_5_4096_2024-10-10-164438 Pos6_medidas1_6_5_4096_2024-10-10-165238 Pos7_medidas1_6_5_4096_2024-10-10-170113 Pos8_medidas1_6_5_4096_2024-10-10-182938 Pos1_medidas1_6_5_4096_2024-10-10-171309 %lab_2_10 Pos2_medidas1_6_5_4096_2024-10-10-172127 Pos3_medidas1_6_5_4096_2024-10-10-172926 Pos4_medidas1_6_5_4096_2024-10-10-173812 Pos5_medidas1_6_5_4096_2024-10-10-174722 Pos6_medidas1_6_5_4096_2024-10-10-175615 Pos7_medidas1_6_5_4096_2024-10-10-180734 Pos8_medidas1_6_5_4096_2024-10-10-181756 The MATLAB script ELDI_partition_Loss_Full_BW computes the parameters of the FI with partition loss model across the entire FR3 bandwidth (7–24 GHz). The MATLAB script ELDI_partition_Loss_subbands computes the parameters of the FI with partition loss model for the FR3 sub-bands: (7.125–8.4 GHz, 10–10.5 GHz, 10.7–13.25 GHz, 14.8–15.35 GHz, and 21.2–23.6 GHz). Propagation along the corridor The files named P1_PC_medidas1_6_5_4096_2024-10-08-xxxxxx contains the channel frequency response, with dimensions of 1×5×5×4096×24 . The first dimension corresponds to the single transmitter antenna. The second and third dimensions (5×5) represent the receiver uniform rectangular array (URA). The fourth dimension contains the frequency samples measured by the VNA (4096). The fifth dimension (24) corresponds to the different positions at which the transmitting was placed along the corridor. The correspondence between the measurement files and the transmitter positions along the corridor is defined as follows: P1_PC_medidas1_6_5_4096_2024-10-08-103450 Tx pos. 1 P2_PC_medidas1_6_5_4096_2024-10-08-105003 Tx pos. 2 P3_PC_medidas1_6_5_4096_2024-10-08-110545 Tx pos. 3 P4_PC_medidas1_6_5_4096_2024-10-08-112110 Tx pos. 4 P5_PC_medidas1_6_5_4096_2024-10-08-113534 Tx pos. 5 P6_PC_medidas1_6_5_4096_2024-10-08-115011 Tx pos. 6 P7_PC_medidas1_6_5_4096_2024-10-08-120556 Tx pos. 7 P8_PC_medidas1_6_5_4096_2024-10-08-122717 Tx pos. 8 P9_PC_medidas1_6_5_4096_2024-10-08-124217 Tx pos. 9 P10_PC_medidas1_6_5_4096_2024-10-08-125957 Tx pos. 10 P11_PC_medidas1_6_5_4096_2024-10-08-153253 Tx pos. 11 P12_PC_medidas1_6_5_4096_2024-10-08-155009 Tx pos. 12 P13_PC_medidas1_6_5_4096_2024-10-08-160433 Tx pos. 13 P14_PC_medidas1_6_5_4096_2024-10-08-161839 Tx pos. 14 P15_PC_medidas1_6_5_4096_2024-10-08-163704 Tx pos. 15 P16_PC_medidas1_6_5_4096_2024-10-08-165323 Tx pos. 16 P17_PC_medidas1_6_5_4096_2024-10-08-170828 Tx pos. 17 P18_PC_medidas1_6_5_4096_2024-10-08-133236 Tx pos. 18 P19_PC_medidas1_6_5_4096_2024-10-08-172615 Tx pos. 19 P20_PC_medidas1_6_5_4096_2024-10-08-174148 Tx pos. 20 P21_PC_medidas1_6_5_4096_2024-10-08-175618 Tx pos. 21 P22_PC_medidas1_6_5_4096_2024-10-08-181148 Tx pos. 22 P23_PC_medidas1_6_5_4096_2024-10-08-182903 Tx pos. 23 P24_PC_medidas1_6_5_4096_2024-10-08-184431 Tx pos. 24 The MATLAB script ELDI_PC computes the parameters of the partition FI model across the entire FR3 bandwidth (7–24 GHz) for the corridor scenario. Functions The function calcula_L_Wall_FI computes the parameters of the partition FI loss model. The function calcula_L_Wall_CI computes the parameters of the partition CI loss model. The function coherence_bandwidth_frequency computes the coherence bandwidth., This dataset is composed of a set of measurements and the code to analyze the measurements. The files named P1_medidas1_6_5_4096_2024-10-07-xxxxxx contains the channel frequency response, with dimensions of 1×5×5×4096×19 . The first dimension corresponds to the single transmitter antenna. The second and third dimensions (5×5) represent the receiver uniform rectangular array (URA). The fourth dimension contains the frequency samples measured by the VNA (4096). The fifth dimension (23) corresponds to the different positions at which the transmitting was placed in the laboratory (Lab_02_11). The files named P1_PC_medidas1_6_5_4096_2024-10-08-xxxxxx contains the channel frequency response, with dimensions of 1×5×5×4096×24 . The first dimension corresponds to the single transmitter antenna. The second and third dimensions (5×5) represent the receiver uniform rectangular array (URA). The fourth dimension contains the frequency samples measured by the VNA (4096). The fifth dimension (24) corresponds to the different positions at which the transmitting was placed along the corridor.

Key Characteristics: Single-Class Session Policy: The dataset is built on a design where each capture session contains exclusively benign traffic or a single attack family. This prevents temporal overlap and ensures that every bi-flow has an unambiguous label. Modern Traffic and APIs: The benign traffic simulates enterprise-grade services (HTTP/S, DNS, SSH, MySQL) and includes modern API traffic (REST, GraphQL, SOAP), which is critical for representing contemporary edge environments. Diverse Threat Coverage: It incorporates 15 distinct attack families, including volumetric attacks (DDoS/DoS), reconnaissance (Portscan), web and API exploits, brute force, DNS abuse, MITM, NIDS evasion, and persistence vectors like C2/beaconing and exfiltration. CICFlowMeter Integration: The raw traffic was processed using CICFlowMeter (version 4.0) to generate bidirectional flows (bi-flows). It provides a standardized schema of 80 statistical features per flow, allowing for lightweight detection without payload inspection. Structure and Scale: The dataset comprises approximately 4.6 million bi-flows distributed across 16 independent single-class CSV files. This modular structure allows researchers to construct custom distributions and prevents temporal leakage during model training. Realistic Imbalance: The data reflects a realistic traffic distribution where benign activity dominates, with a ratio of approximately 1.3:1 between benign and malicious flows., Intrusion Detection Systems (IDS) for IoT and edge environments require datasets with unambiguous labels. Existing corpora often mix benign and malicious traffic within the same capture window, producing ambiguous flow labels that distort model evaluation. This work introduces the TRUST Lab Dataset, a flow-based traffic corpus designed under a single-class session policy: each capture contains exclusively benign traffic or a single attack family, preventing temporal overlap and ensuring label integrity at the bi-flow level. The dataset was generated in an operational testbed reproducing enterprise-grade services (HTTP/S, DNS, email, SSH, SNMP, NTP, MySQL) and modern APIs (REST, GraphQL, SOAP), combined with 15 attack families spanning DDoS/DoS, port scanning, brute force, web/API exploits, DNS abuses, MITM, NIDS evasion, tunneling/exfiltration, C2/beaconing, TLS/SSL anomalies, buffer overflow, and slowloris. Traffic was processed with CICFlowMeter into 16 single-class CSV files totaling ~4.6million bi-flows with 80 features per flow. Comprehensive statistical analyses (distributions, correlation, ANOVA, PCA) confirm discriminative signal without payload inspection. A binary meta-classifier achieves ROC-AUC 0.9676 and recall 0.95, validating TRUST Lab's utility for lightweight edge-oriented IDS evaluation., This research is part of the project TRUST Lab, R&D&I Laboratory in Cybersecurity, Privacy, and Secure Communications, financed by European Union NextGeneration-EU, the Recovery Plan, Transformation and Resilience, through INCIBE.

Supporting material associated with the article submitted to the Journal of Hydraulic Engineering (ASCE). This file includes Appendix S1–S3 removed from the manuscript., This record contains the supplementary material associated with the article “Free-surface maps and profiles of hydraulic jumps measured with a depth camera,” accepted for publication in the Journal of Hydraulic Engineering (ASCE). The file compiles three appendices removed from the manuscript for editorial length compliance. Appendix S1 presents air-concentration profiles and average air-concentration distributions used to support the characterization of partially developed inflow conditions. Appendix S2 documents the phase-space thresholding filter method applied to the depth-camera time series, including the governing equations and spike-replacement procedure. Appendix S3 provides additional free-surface topography, fluctuation maps, longitudinal and transverse profiles, and supplementary comparisons with previous studies for hydraulic jumps with Fr1 = 4.45, 5.97, 8.33, and 9.11. This material complements the main article and facilitates transparency, reproducibility, and reuse of the supporting analyses., Grant PID2022-142037OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, by the “European Union”.

This dataset was created based on the FLAME dataset, namely the ninth and tenth repositories which contain 2,003 fire frames with a resolution of 3480x2160 in JPEG format and their corresponding masks, respectively. The folder dataset contains a copy of the original dataset divided into training (first 80% of the samples) and validation (last 20%), where the labels have been converted from the masks in the original dataset to the YoLo bounding boxes format. The folder fire_tracking.zip contains the results after applying object tracking to the fires in the dataset, with a file for each fire detected wich contains the file name and bouding box of every time that fire is detected. This tracking was manually supervised, with spetial interest in the validation part of the dataset. The process is described in the Methods section of this dataset. The file fires_tracked.mp4 represents shows the dataset at a rate of 10 frames per second, showing the fire ids (i.e. the results of the fire tracking) of the validation part of the dataset., This work was supported by the grant PID2023-148214OB-C21 funded by MICIU/AEI/10.13039/501100011033 and by FEDER/EU. This work was also supported by the grants PCI2024-153485 and PDC2025-165710-C22 funded by MICIU/AEI/10.13039/501100011033 and by the European Union. This research was also funded by the PRIMA Programme under Grant Agreement No. 2431 (FUSION). The work of Francisco-Jose Alvarado-Alcon was supported by the Spain’s Ministry of Universities under Grant FPU22/00316.

This dataset contains the simulation results obtained from experiments E1, E2, and E3 conducted with MultiOperatorBus_ABM_V7, an agent-based model developed in NetLogo to investigate collaboration among multiple bus operators. The experiments examine the effects of different operational, institutional, and fare-related configurations on passenger, operator, and system-level performance. The archived results provide the empirical simulation basis for the analyses reported in the associated research article. The dataset is organised by experiment (E1–E3). The accompanying INFO file describes the purpose of each experiment, experimental design, parameter settings, number of replications, output variables, and the relationship between individual data files and the analyses reported in the article. The NetLogo model, model documentation, and BehaviorSpace experimental definitions are archived separately in CoMSES Computational Model Library., An agent-based modeling approach was developed to disentangle operational interoperability, fare integration, and revenue-sharing governance in multi-operator bus systems. The model, developed in NetLogo 7.0.3, represents passengers, buses, and stations within a stylized network comprising 14 stations, nine routes, and three operators. To verify model behavior, three levels of robustness testing were incorporated into the final program, totaling 43,200 simulation runs. In the experimental verification exercises, E1 varies seven behavioral assumptions, E2 evaluates the 243 combinations of five operational factors, and E3 combines eight adverse behavioral profiles with 12 selected stress-test operational configurations.

The combination of computer vision devices such as multispectral cameras coupled with Artificial Intelligence has provided a major leap forward in image-based analysis of biological processes. Supervised Artificial Intelligence algorithms require large ground truth image datasets for model training, which allows to validate or refute research hypotheses and to carry out comparisons between models. However, public datasets of images are scarce and ground truth images are surprisingly few considering the numbers required for training algorithms. We created a dataset of 1283 multidimensional arrays, using berries from five different grape varieties. Each array has 37 images of wavelengths between 488.38nm and 952.76nm obtained from single berries. Coupled to each multispectral image we added a dataset with measurements including, weight, anthocyanin content and Brix index for each independent grape. Thus, the images have paired measures creating a ground truth dataset. We tested the dataset with two neural network algorithms: multilayer perceptron (MLP), three-dimensional convolutional neural network (3D-CNN). A perfect (100% accuracy) classification model was fit with either the MLP or 3D-CNN algorithms. This is the first public dataset of grape ground truth multispectral images. Associated with each multispectral image there are measures of the weight, anthocyanins, and Brix index. The dataset should be useful to develop deep learning algorithms for classification, dimensionality reduction, regression, and prediction analysis.

The dataset comprises two files: File DOIS: A database extracted and refined from the results of the search strategy defined in the associated paper and conducted in the Web of Science. File KEYWORDS: Contains the keywords corresponding to the papers included in the file DOIS., The dataset includes the academic papers reviewed and the corresponding keywords extracted from each paper, which were used in the bibliometric analysis and literature review of a research study on pedestrians in urban design.

El depósito contiene 2 archivos de bases de datos y un archivo README. Cada archivo de las bases de datos incluye doce variables correspondientes a la estación de muestreo, fecha y otras diez variables correspondientes a las concentraciones de 10 tipos polínicos obtenidas mediante el método EN 16868:2019, con expresión de la concentración media móvil de 3 días como granos de polen/m3 para las ciudades de Cartagena y Toledo, obtenidas en el marcho del proyecto AVIONETA (PID2024-157581OB-I00 financiado por MICIU/AEI/10.13039/501100011033 y por FEDER, UE ). El equipo utilizado para la captación del polen en aire ambiente fue el modelo Lanzoni VPPS 2000. El procedimiento siguió las prescripciones de calidad de la Red Española de Aerobiología, sometiéndose a sus estándares y correspondientes pruebas de intercomparación entre laboratorios. Los valores de concentración se pasaron a una hoja de cálculo en formato .CSV derivando un archivo para cada ciudad., Datos de concentración de polen en aire ambiente obtenidos mediante el método EN 16868:2019 en las ciudades de Cartagena y Toledo en el marco del proyecto AVIONETA (Proyecto PID2024-157581OB-I00 financiado por MICIU/AEI /10.13039/501100011033 y por FEDER, UE) a partir del 01 de septiembre de 2025.