This repository is used to support the research article "Machine learning-driven reconstruction of the climatological geomagnetic diurnal variations at middle to low latitudes: Leveraging Swarm-to-ground observations mapping to bridge unmonitored regions". It contains the code and data for mapping Swarm satellite magnetic observations to ground-based climatological geomagnetic diurnal variations using a CNN-based supervised and semi-supervised learning framework.
This ZIP archive contains the infrared (IR) spectral dataset of polycyclic aromatic hydrocarbons (PAHs) presented in the companion paper. The dataset comprises 144,111 IR spectra across 48,037 closed-shell, even-carbon benzenoid PAH structures in neutral, cationic, and anionic ch…
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…
Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…
Modern machine learning systems are increasingly deployed in settings that require persistent interaction, adaptation, memory, and decision-making over time. Yet, most learning paradigms remove the temporal pressures faced by physically embedded agents: the world waits for comput…
Machine Learning (ML) predictions of cement and concrete quality and subsequent data-driven mixture optimization has been advertised for almost three decades. However, supervised ML leverages correlations, not causal relationships. Aiming for hybrid models, we derive the first ca…