We present a comparison of void properties between the standard model of cosmology, Λ Cold Dark Matter (ΛCDM), and two alternative cosmological models with evolving and interacting dark sectors: a quintessence model (φCDM) and a Coupled Dark Matter-Dark Energy (CDE) model. Using N-body simulations of these models, we derive several measures of void statistics and properties, including distributions of void volume, ellipticity, prolateness, and average density. We find that the volume distribution derived from the CDE simulation deviates from the volume distribution derived from the ΛCDM simulation in the present-day universe, suggesting that the presence of a coupled dark sector could be observable through this statistic. We also find that the distributions of void ellipticity and prolateness are practically indistinguishable among the three models over the redshift range z = 0.0 − 1.0, indicating that simple void shape statistics are insensitive to small changes in dark sector physics. Interestingly, we find that the distributions of average void density measured in each of the three simulations are distinct from each other. In particular, voids on average tend to be emptiest under a quintessence model, and densest under the ΛCDM model. Our results suggest that it is the scalar field present in both alternative models that causes emptier voids to form, while the coupling of the dark sector mitigates this effect by slowing down the evacuation of matter from voids.

We compare the evolution of voids formed under the standard cosmological model and two alternative cosmological models. The two models are a quintessence model (φCDM) and a Coupled Dark Matter-Dark Energy (CDE) model, both of which have evolving and interacting dark sectors. From N-body adiabatic hydrodynamical simulations of these models, we measure the statistics and quantify the properties of voids over the redshift range z = 1.5 − 12: these include their population size, volumes, shapes and average densities. We find that the latter property has potential as a probe of cosmology, particularly dark energy, as significant differences in average void densities exist between the alternative models and the standard model. We postulate that this signature arises from an increased evacuation rate of particles out of voids, or an earlier start to void evacuation, in the alternative models as a direct consequence of the dynamical scalar field, which also leads to greater void merger rates. Additionally, differences between the two alternative models are likely due to the drag force arising from dark sector coupling, acting on dark matter particles in our coupled model.

Recent years have seen an increase in the use of remote-sensing based methods to assess soil erosion, mainly due to the availability of freely accessible satellite data, with successful results on a consistent basis. There would be valuable benefits from applying these techniques to the Arctic areas, where ground local studies are typically difficult to perform due to hardly accessible roads and lands. At the same time, however, the application of remote-sensing methods comes with its own set of challenges when it comes to the peculiar features of the Arctic: short growing periods, winter storms, wind, and frequent cloud and snow cover. In this study we perform a comparative analysis of three commonly used classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Multilayer Perceptron (MLP), in combination with ground truth samples from regions all over Iceland, provided by Iceland’s Soil Conservation Service department. The process can be automated to predict soil erosion risk for larger, less accessible areas from Sentinel-2 images. The analysis performed on validation data sets supports the effectiveness of both approaches for modeling soil erosion, albeit differences are highlighted.