Abstract
The presence or absence of long-range correlations in the environmental radioactivity fluctuations has recently attracted considerable interest. Among a multiplicity of practically relevant applications, identifying and disentangling the environmental factors controlling the variable concentrations of the radioactive noble gas radon is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we present a critical re-assessment of a multiplicity of complementary methods that have been previously applied for evaluating the presence of long-range correlations and fractal scaling in environmental radon variations with a particular focus on the specific properties of the underlying time series. As an illustrative case study, we subsequently re-analyze two high-frequency records of indoor radon concentrations from Coimbra, Portugal, each of which spans several weeks of continuous measurements at a high temporal resolution of five minutes.Our results reveal that at the study site, radon concentrations exhibit complex multi-scale dynamics with qualitatively different properties at different time-scales: (i) essentially white noise in the high-frequency part (up to time-scales of about one hour), (ii) spurious indications of a non-stationary, apparently long-range correlated process (at time scales between some hours and one day) arising from marked periodic components, and (iii) low-frequency variability indicating a true long-range dependent process. In the presence of such multi-scale variability, common estimators of long-range memory in time series are prone to fail if applied to the raw data without previous separation of time-scales with qualitatively different dynamics.

Abstract
Radon monitoring at different levels of the cover of the Urgeirica tailings shows that the sealing is effective and performing as desired in terms of containing the strongly radioactive waste resulting from uranium ore processing. However, the analysis of the time series of radon concentration shows a very complex temporal structure, particularly at depth, including very large and fast variations from a few tens of kBq m(-3) to more than a million kBq m(-3) in less than one day. The diurnal variability is strongly asymmetric, peaking at 18 h/19 h and decreasing very fast around 21 h/22 h. The analysis is performed for summer and for a period with no rain in order to avoid the potential influence of precipitation and related environmental conditions on the radon variability. Analysis of ancillary measurements of temperature, relative humidity, wind speed and wind direction, as well as atmospheric pressure reanalysis data shows that the daily averaged radon concentration in the taillings material is anti-correlated with the atmospheric pressure and that the diurnal amplitude is associated with the magnitude of atmospheric pressure daily oscillations.

Abstract
During the last decades, the radioactive noble gas radon has found a variety of geoscientific applications, ranging from its utilization as a potential earthquake precursor and proxy of tectonic stress over its specific role in volcanic environments to a wide range of applications as a tracer in marine and hydrological settings. This topical issue summarizes the current state of research as exemplified by some original research articles covering the aforementioned as well as other closely related aspects and points to some important future directions of radon application in geosciences. This editorial provides a more detailed overview of the contents of this volume, a brief summary of the rationale underlying the diverse applications, and outlines some important perspectives.

Abstract
Crop monitoring and harvesting by ground robots on mountain vineyards is an intrinsically complex challenge, due to two main reasons: harsh conditions of the terrain and reduced time availability and unstable localization accuracy of the GPS system. In this paper is presented a cost effective robot that can be used on these mountain vineyards for crop monitoring tasks. Also it is explored a natural vineyard feature as the input of a standard 2D simultaneous localization and mapping approach (SLAM) for feature-based map extraction. In order to be possible to evaluate these natural features for mapping and localization purposes, a virtual scenario under ROS/Gazebo has been built and described. A low cost artificial landmark and an hybrid SLAM is proposed to increase the localization accuracy, robustness and redundancy on these mountain vineyards. The obtained results, on the simulation framework, validates the use of a localization system based on natural mountain vineyard features.

Abstract
In this article, the problem of deriving a physical model of a mechanical structure from an arbitrary state-space realization is addressed. As an alternative to finite element formulations, the physical parameters of a model may be directly obtained from identified parametric models. However, these methods are limited by the number of available sensors and often lead to poor predictive models. Additionally, the most efficient identification algorithms retrieve models where the physical parameters are hidden. This last difficulty is known in the literature as the inverse vibration problem. In this work, an approach to the inverse vibration problem is proposed. It is based on a similarity transformation and the requirement that every degree of freedom should contain a sensor and an actuator (full instrumented system) is relaxed to a sensor or an actuator per degree of freedom, with at least one co-located pair (partially instrumented system). The physical parameters are extracted from a state-space realization of the former system. It is shown that this system has a symmetric transfer function and this symmetry is exploited to derive a state-space realization from an identified model of the partially instrumented system. A subspace continuous-time system identification algorithm previously proposed by the authors in [1] is used to estimate this model from the IO data.

Abstract
In this paper, the gas dynamics within the pipelines is modelled as a repetitive process with smoothing. Controllability and observability criteria when the system is steered through initial and boundary data, which is achieved by an adequate choice of the homogeneity, are obtained. From the point of view of the technical applications, it seems to make more sense to consider boundary data controls as for instance in the management of high pressure gas networks. Stability criteria suitable computer simulations are also included.