9.1 Dataset 15
This study delves into the impactful realm of lightning strikes, a meteorological phenomenon that poses substantial risks to the process industry by potentially triggering technological disruptions. Despite constituting merely 13% of NaTech (Natural-Technological) events, lightning events hold significant consequences within the process industry. To comprehend this, a meticulous analysis of past lightning-triggered incidents was conducted, utilizing open-source industrial-accident databases t...
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
This repository contains the software and datasets needed to reproduce the results presented in the article "A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors", published in Annals of Nuclear Energy.
This repository contains the dataset, post-processing script and models needed to reproduce the results presented in the article "Nuclear Data Uncertainty Propagation for the Molten Salt Fast Reactor Design", published in the special issue of Nuclear Science and Engineering dedicated to the 1st Young Molten Salt Reactor Conference (held in Lecco in June 6th and 9th 2022). The dataset includes: the perturbed nuclear data files (in ACE and ENDF-6 formats) generated with the open-source, pyt...
This repository contains the MPOSE2021 Dataset for short-time pose-based Human Action Recognition (HAR). MPOSE2021 is specifically designed to perform short-time Human Action Recognition. MPOSE2021 is developed as an evolution of the MPOSE Dataset [1-3]. It is made by human pose data detected by OpenPose  and Posenet  on popular datasets for HAR, i.e. Weizmann , i3DPost , IXMAS , KTH , UTKinetic-Action3D (RGB only)  and UTD-MHAD (RGB only) , alongside original video ...
Appendix for "Identifying Imbalance Thresholds in Input Data to Achieve Desired Levels of Algorithmic Fairness"
In this document we provide three appendixes for the journal article “Identifying Imbalance Thresholds in Input Data to Achieve Desired Levels of Algorithmic Fairness”. In Appendix A we show predictors and targets that we took into account for each dataset employed in our study. In Appendix B we describe the configurations of the thresholds that we defined during the procedure of Identification of Risk Thresholds. In Appendix C, for each combination of balance-unfairness-algorithm we report t...
- 9. FAIR Data Collection20
Data di pubblicazione
- 2D cylindrical model1
- Algorithmic fairness1
- Automated decision-making1
- Change Management1
- Computer Vision1
- Control rod models1
- Data bias1
- Data imbalance1
Accesso al fulltext
- no fulltext16