Spain is the third-largest producer of pork meat in the world, and many farms in several regions depend on the evolution of this market. However, the current pricing system is unfair, as some actors have better market information than others. In this context, historical pricing is an easy-to-find and affordable data source that can help all agents to be better informed. However, the time lag in data acquisition can affect their pricing decisions. In this paper, we study the effect that data acquisition delay has on a price prediction system using multiple prediction algorithms. We describe the integration of the best proposal into a decision support system prototype and test it in a real-case scenario. Specifically, we use public data from the most important regional pork meat markets in Spain published by the Ministry of Agriculture with a two-week delay and subscription-based data of the same markets obtained on the same day. The results show that the error difference between the best public and data subscription models is 0.6 Euro cents in favour of the data without delay. The market dimension makes these differences significant in the supply chain, giving pricing agents a better tool to negotiate market prices.

Monitoring human activities in remote areas presents significant challenges due to lacking communication networks and infrastructure. In this context, using publicly available satellite imagery offers a cost-effective solution, as it enables the identification of changes in these areas. However, specific scenarios make detection more complicated. One such scenario is detecting indoor activity within buildings in remote areas. Walls and roofs create barriers for most sensors. Nevertheless, activities inside buildings can be associated with heat emissions, which specific remote sensors can detect. Unfortunately, publicly available satellite data does not include information from such sensors. In light of this limitation, this study investigates the opportunity of using machine learning models to interpret public-available data. Specifically, we trained four machine learning models (XGBoost, LGBM, DNN, and CNN) using images from Sentinel-2 Band 12 (the sensor with the frequency range closest to the heat emission peak) and meteorological data (temperature). Our results show that these models can identify farm-building activity, with the XGBoost model achieving the highest accuracy of 0.96 by integrating satellite data and temperature information; the findings suggest that leveraging public satellite sensors can effectively detect human heat emissions and improve surveillance in remote areas, overcoming some limitations of traditional methods.

SyWeDaG is an open-source desktop application for generating multivariable synthetic weather time series at hourly resolution from historical daily observations and monthly climate projections. The software addresses the mismatch between aggregated climate products and the fine-resolution inputs required by engineering and environmental simulations, a gap that constrains fields such as renewable-energy assessment, building-energy modelling, urban demand studies or maintenance planning. It integrates historical-data acquisition, local persistence, climate conditioning, multivariable correction, temporal disaggregation, validation, visualization, and export in a single workflow. Historical observations can currently be retrieved from AEMET and Open-Meteo through a common provider interface. Synthetic daily series are constructed by resampling complete historical years and, when projections are supplied, adjusting temperature and precipitation to monthly targets. Humidity, pressure, and wind are then corrected using either nearest-neighbour transfer or XGBoost regression. Variable-specific algorithms transform daily records into continuous hourly profiles while checking consistency across monthly, daily, and hourly scales. A Streamlit interface enables users without specialist programming knowledge to select locations, configure scenarios, inspect results, and export reproducible packages containing data and provenance metadata. The implementation is intended for scenario exploration rather than operational forecasting, and its modular architecture supports the addition of data providers, climatic variables, correction models, and alternative disaggregation procedures.