This paper introduces a hands-on, low-cost device (German industrial property right no. 20 2014 106 048.0) that uses common adapters to mount p-ED-XRF devices so that these can provide bulk-sedimentary chemistry data from non-destructive measurements at the surface of a split sediment core or from other solid samples. The strength of combining p-ED-XRF analyses with this new sample chamber is demonstrated by exemplary sediment cores from an archaeological research project.

A numerical inversion approach to detect and localize inclusions in thick walls under quasi-periodic natural solicitations is presented. It is based on a preliminary analysis of surface temperature field evolution with time. This analysis is improved by taking advantage of a priori information provided by ground-penetrating radar reconstructions. In this way, it is possible to improve the accuracy of the images achievable with the stand-alone thermal reconstruction method.

Ground-penetrating radar (GPR) is a powerful tool that uses electromagnetic waves to obtain 3-dimensional images of natural or man-made structures and subsoil. It is employed in a large variety of applications in which non-destructive investigations are required. This paper deals with the use of GPR for the evaluation of moisture content in wood. The results of our work are of great interest for the preservation of timber structures, as moisture content is highly relevant to wood deterioration.

Application of elementary particle physics to the measurements of rock overburden density structures that might be directly applicable to natural resources and undiscovered cave explorations, and even to searching for hidden chambers in historic architectural structures.

Physically based models that predict the properties of snow on the ground are used in many applications, but meteorological input data required by these models are hard to obtain in cold regions. Monitoring at the Sodankyla research station allows construction of model input and evaluation datasets covering several years for the first time in the Arctic. The data are used to show that a sophisticated snow model developed for warmer and wetter sites can perform well in very different conditions.