Abstract

Abstract
Wildfire disturbances can profoundly impact many aspects of both ecosystem functioning and resilience. This study proposes a satellite-based approach to assess ecosystem resilience to wildfires based on post-fire trajec-tories of four key functional dimensions of ecosystems related to carbon, water, and energy exchanges: (i) vegetation primary production; (ii) vegetation and soil water content; (iii) land surface albedo; and (iv) land surface sensible heat. For each dimension, several metrics extracted from satellite image time-series, at the short, medium and long-term, describe both resistance (the ability to withstand environmental disturbances) and re-covery (the ability to pull back towards equilibrium). We used MODIS data for 2000-2018 to analyze trajectories after the 2005 wildfires in NW Iberian Peninsula. Primary production exhibited low resistance, with abrupt breaks immediately after the fire, but rapid recoveries, starting within six months after the fire and reaching stable pre-fire levels two years after. Loss of water content after the fire showed slightly higher resistance but slower and more gradual recoveries than primary production. On the other hand, albedo exhibited varying levels of resistance and recovery, with post-fire breaks often followed by increases to levels above pre-fire within the first two years, but sometimes with effects that persisted for many years. Finally, wildfire effects on sensible heat were generally more transient, with effects starting to dissipate after one year and overall rapid recoveries. Our approach was able to successfully depict key features of post-fire processes of ecosystem functioning at different timeframes. The added value of our multi-indicator approach for analyzing ecosystem resilience to wildfires was highlighted by the independence and complementarity among the proposed indicators targeting four dimensions of ecosystem functioning. We argue that such approaches can provide an enhanced characterization of ecosystem resilience to disturbances, ultimately upholding promising implications for post-fire ecosystem management and targeting different dimensions of ecosystem functioning.

Abstract
The aspect of energy constraint and simulation of battery behavior in robotic simulators has been partially neglected by most of the available simulation software and is offered unlimited energy instead. This lack does not reflect the importance of batteries in robots, as the battery is one of the most crucial elements. With the implementation of an adequate battery simulation, it is possible to perform a study on the energy requirements of the robot through these simulators. Thus, this paper describes a Lithium-ion battery model implemented on SimTwo robotic simulator software, in which various physical parameters such as internal resistance and capacity are modeled to mimic real-world battery behavior. The experiments and comparisons with a real robot have assessed the viability of this model. This battery simulation is intended as an additional tool for the roboticists, scientific community, researchers, and engineers to implement energy constraints in the early stages of robot design, architecture, or control.

Abstract
Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.

Abstract

Abstract
The widespread of distributed renewable energy is leading to an increased need for advanced energy management solutions in buildings. The variability of generation needs to be balanced by consumer flexibility, which needs to be accomplished by keeping the consumption cost as low as possible, while guaranteeing consumer comfort. This paper proposes a rule-based system with the aim of generating recommendations for actions regarding the energy management of different energy consumption devices, namely lights and air conditioning. The proposed set of rules considers the forecasted values of building generation, consumption, user presence in different rooms and energy prices. In this way, building energy management systems are endowed with increased adaptability and reliability considering the lowering of energy costs and maintenance of user comfort. Results, using real data from an office building, demonstrate the appropriateness of the proposed model in generating recommendations that are in line with current context.