Abstract
In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

Abstract
The development of technologies to enable robots to operate autonomously in challenging forest environments is crucial for promoting effective natural resource management and preventing forest fires, standing out as a priority on environmental conservation and public safety agendas. This article presents a detailed discussion on the development of an innovative sensory architecture, specifically designed to integrate a wide range of advanced sensors. The main objective of this architecture is to provide highly accurate inputs to a system, thereby empowering a forest robot to make autonomous and adaptive decisions in real-time. To achieve this ambitious goal, the proposed sensory architecture defines a comprehensive set of crucial variables, which are carefully selected and strategically integrated. This design results in a distributed system capable of processing multiple subsystems in parallel and efficiently. This innovative approach enables the conversion of a conventional forest mulcher machine into a fully autonomous and highly intelligent forest robot. Furthermore, the article details the procedures and methodologies used to experimentally validate the robustness and effectiveness of the developed system. Through rigorous testing and comprehensive analyses, the system's ability to handle a variety of adverse environmental conditions and typical operational challenges in forest environments is demonstrated. These experimental validations are essential to ensure the reliability and accuracy of the system in real-world situations. © 2024, International Frequency Sensor Association (IFSA). All rights reserved.

Abstract
This study focuses on the role of autonomous control systems in robotics, focusing on how robot controls the actuator movements after meticulous information processing and decision-making within the robotic framework ROS. To go on this experimental challenge, a diesel tractor was modified into a versatile experimental platform capable of autonomous navigation and control. At the center of this tractor is the sensory module term1ed "Sentry," which consists of a network of interconnected sensors that have been methodically integrated to enable comprehensive ambient perception. The sensors use advanced technologies like 3D 360º LiDAR for spatial mapping, thermal cameras for object detection, RGBD cameras for visual perception, a microcontroller for control, GPS+RTK for precise positioning and a Jetson Xavier for high-performance computing. The experimental assessments done in this work covered a wide range of scenarios, from simulated environments with controlled variables to real-world terrains rife with uncertainty and variability. Valuable insights were gained by analyzing the resulting data, revealing light on the system's operation, performance, and efficacy under various scenarios. © 2024, International Frequency Sensor Association (IFSA). All rights reserved.

Abstract
The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Abstract

Abstract