Diego Granados-López

This article presents a benchmarking of indicators for automated defect detection in photovoltaic modules based on I‐V curves. The methodology is structured in three stages. First, an ideal defect‐free I‐V curve is generated through simulation to provide a reference for comparison with experimentally measured I‐V data under standard test conditions. Second, several electrical and curve‐shape indicators are computed to quantify the deviation between simulated and measured curves. Electrical parameters are expressed as ratios between experimental and simulated values, whereas curve‐shape descriptors integrate the comparison directly into their formulation. Finally, a comprehensive feature selection strategy is applied; filter (Pearson correlation and Mutual Information) and wrapper methods (Recursive Feature Elimination, Permutation Importance, and Boruta) are tested across several machine learning classifiers, including Decision Trees, Support Vector Classifiers, K‐nearest neighbors, Random Forests, Gradient Boosting, XGBoost, and LightGBM. The approach is evaluated using an experimental I‐V curve database generated by the GdS‐Optronlab research group. Results consistently discard indicators such as the open‐circuit voltage ratio and short‐circuit current ratio, while highlighting the current at the maximum power point ratio, the fill factor, and the mutual information of the maximum power point.

The stompx library is a Python framework designed to support the simulation, analysis, and visualization of stochastic spreading processes on networks. It provides implementations of continuous-time and discrete-time stochastic simulation schemes, together with tools to compute outbreak statistics and to visualize the temporal evolution of network states. As a case study, it is applied to cibersecurity in general and malware propagation in IoT networks, more specifically.

The rapid expansion of Internet of Things (IoT) systems, found in environments such as smart homes, poses growing cybersecurity challenges. In response, research has examined the role of artificial intelligence, particularly machine learning, in enhancing IoT security. To support this effort, machine learning models have been developed and evaluated on benchmark datasets. However, preparing datasets for machine learning requires preprocessing techniques that are tailored to the specific characteristics of the data. In this context, exploratory data analysis provides insights into dataset structure and distribution, thereby supporting informed preprocessing decisions prior to modeling. Accordingly, this study introduces a reproducible five‑step preprocessing framework for IoT cybersecurity datasets and demonstrates its application to the NF‑ToN‑IoT V1 dataset. The proposed framework is organized into two phases: an exploratory data analysis phase consisting of (1) dataset overview and identification of categorical and numerical features, (2) analysis of missing and zero values, (3) assessment of categorical feature distributions, and (4) assessment of numerical feature distributions; and a preprocessing phase consisting of (5) proportional stratified random downsampling to produce a reduced dataset that preserves the original class distribution. By establishing a systematic, data-driven framework, this study contributes to the preparation of structured datasets for attack detection in IoT environments, with potential applications in smart homes.