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Geophysical Data (GPR, ERT, Seismic) Duvensee 2019
No full textWe present an integrated approach of multi-method geophysical sounding and local soil sampling for differentiate, and map organic sediments. Our study is based on ground-penetrating radar (GPR), electrical resistivity tomography (ERT) and shear-wave seismic (SH seismic) profiling applied to sediments of the former Lake Duvensee (northern Germany), nowadays a bog. The bog is embedded in low conductive glacial sand and is characterized by layers of different gyttja sediments (detritus and calcareous). The present study was conducted in order to identify the bog morphology and the thickness of the peat body and lake sediments, in order to understand the basin evolution. To validate the geophysical results, derived from surface measurements, drilling, soil analyses as well as borehole guided wave analysis of electromagnetic waves and Direct-Push (DP-EC) have been carried out and used for comparison. It turned out that each method can distinguish between sediments that differ in grain size, particularly between peat, lake sediments (gyttjas and mud) and basal glacial sand deposits. GPR is even able to separate between strongly and weakly decomposed peat layers, which is also clear considering resistivity variations in the ERT computation. From the association between geophysical properties and sediment analysis (e.g., water content and organic matter) different gyttjas were distinguished (coarse and fine) and seismic velocity was correlated to bulk density. Moreover, GPR and SH-wave seismics present different resolutions, confirming that the latter allows measurements, which are more focused on determining the extension of basal sand deposits, the depth of which is diffcult to reach with GPR. Representative values of electrical resistivity, dielectric permittivity, and shear wave velocity have been determined for each sediment type and are therefore available to complete the investigation of wetland environments. Fine grained lake sediments were diffcult to differentiate by the applied methods
Anhang Dissertation Horst Schilling
No full textIn diesem Ordner finden sich die erhobenen Daten und Auswertungen zu der Dissertation "Fragen. Fragen, die nach einer Antwort verlangen. Eine qualitative Studie zur Entwicklung einer Kompetenzerfassung zur historischen Fragekompetenz." von Horst Schilling
XANES / EXAFS data on in situ thermal annealing of metallic glasses FeGaB and FeCoSiB
No full textXANES / EXAFS data and analysis results relating to the publication "In-situ X-Ray Absorption Studies on Local Structure of Annealed Metallic Glasses FeGaB and FeCoSiB" (DOI: 10.1002/pssa.202400607). Metallic glasses (Fe80Ga20)88B12 and (Fe90Co10)78Si12B10 were investigated at the synchrotron DELTA during thermal annealing. This data is made accessible under DAPHNE4NFDI
Code for paper "Variationally consistent magnetodynamic computational homogenization of particulate composites using an incremental potential"
No full textFEAP-subroutines and related code associated with the paper "Variationally consistent magnetodynamic computational homogenization of particulate composites using an incremental potential
Additional material to 'Bio-inspired augmented reality: an interactive, digital twin of C. elegans'
No full textThe presented data is additional material to the publication 'Bio-inspired augmented reality: an interactive, digital twin of C. elegans'. (DOI: 10.1101/2024.05.29.596399) published as a preprint in bioRxiv. It contains the source code of the developed software, an .apk for android and a build for windows
Geophysical data: GPR Data Interpretation (3D model, pattern recognition) Duvensee 2023
No full textUnderstanding the landscape evolution and human-environmental interaction within it is one of the key tasks of early Holocene research. As mobile hunter–gatherers leave few traces of structural organization, understanding their habitats is relevant for comprehending these people. Rarely does the spatial distribution of artifacts correspond to the real pattern of past human activity, but rather shows the pattern of identified artifacts. Geophysical investigations try to fill this gap and have been applied increasingly in archaeological prospection delivering landscape reconstruction, which are verified and fine-tuned using corings and excavations. Despite promising 3D models, a tool to predict the location of undiscovered former human presence and the conditions which influenced people to move across the landscape is not well developed. The primary goal of this paper is to present a methodology for connecting spatial patterns of past human activity based on archaeological and geophysical data. We discuss different GPR (ground-penetrating radar) facies classified at the shoreline of the former Lake Duvensee and geomorphological variables, which leads to the possibility of understanding where and why people chose preferred areas to settle on former islands. We also demonstrate that Mesolithic hunter–gatherer groups preferred dry areas with access to open water for short-term campsites and flatter and more protected areas for specialized and repeatedly occupied campsites. The cardinal orientation of a campsite seems to be secondary to the local peat over-growing process and access to water
Geophysical data: GPR Data Dümmer 2020
No full textGround penetrating radar (GPR) investigation at Hunte 4 in the Dümmer region with the focus on a landscape reconstruction where Neolithic settlements have been found. This region is characterized by extensive wetlands on the southern border of the Northern Lowland and has been the subject of several research projects on Mesolithic and Neolithic sites since the last century. In 2020, new research was carried out at this site, which, through the integration of archaeology, geophysics, and palynology, reconstructed the surrounding landscape.Lake Dümmer probably formed as a thermokarst lake at the end of the last ice age. Several sites are known along the lakeshore, ranging from the late Palaeolithic to the Bronze Age and possibly into the Iron Age and early Middle Ages. The prehistoric sites are found at different distances from the present day lakeshore
Cucuteni-Trypillia time series data on settlement size, location, house use-life, and ratio between houses and institutions
No full textDynamic impedance and compliance surfaces for twin adjacent surface foundations
No full textThe data contains the dynamic impedance and compliance surfaces for twin adjacent surface foundations under synchronous and asynchronous external harmonic loads. The impedance and compliance surfaces are MATLAB interactive figures (*.fig). The impedances are also provided as text files
UPDATE | HERMiNe: A neural network for kinetic Mie polarimetry - particle size diagnostics in nanodusty plasmas
No full textHERMiNe is a deep neural network for solving the kinetic Mie problem of light, scattered by nanoparticles. The polarization state (depicted as the ellipsometric angles Ψ, Δ) of the scatterd light is used as in-situ diagnostics for the size and refractive index of the nanoparticles. For the kinetic Mie polarimetry to work, it is necessary for the particles to change in size (eg. via growth or etching process), while the refractive index is assumed to be constant in time. This way, each refractive index gives rise to an unique Δ(Ψ) curve. The present network is intended to be used with a-C:H particles grown from a reactive Argon-Acytelene plasma as discussed in the article cited below. Given a time series input of the ellipsometric angles Ψ and Δ, the network predicts the best fitting refractive index n, from which the particle radii can be calculated via Mie theory. The network accepts a (2 × k) matrix as input, where k is the number of data points of the time series. The first row contains the Ψ values, whereas the second row are the Δ values. The network output is a complex scalar, which is the prediction of the refractive index. Notice: When using the network without the recommended wrapper script (see below), be aware that the imaginary part in the raw output is raised by a factor of 10 for numerical reasons. A detailed treatise on the theory, network properties and uncertainties can be found in: A neural network approach to kinetic Mie polarimetry for particle size diagnostics in nanodusty plasmas by Schmitz et al. (DOI 10.1088/1361-6463/aceb71). Repository data: I) The network itself is provided as: MATALB DAGNetwork format. Requires the MATLAB Deep Learning Toolbox (version 2022a or newer). ONNX format for the use with other, open frameworks, such as Pytorch etc. II) Wrapper script for MATLAB For the MATLAB framework, the wrapper script 'predictN.m' is provided to faciliate the operation of the network. It takes two vectors of polarization data as input and returns the refractive index as vector elements. The vectors must be of equal length. Example: n = predictN(Psi,Delta); % n is the complex refractive index a+i*b By default, the wrapper script loads the HERMiNe network from the same folder as the script. When making a large number of function calls, it is advantageous to instead pass the net as a function argument. In this case, the network is taken from the workspace and the file loading process is omitet. Example: % 'net' is the network's variable name in the workspace n = predictN(Psi,Delta,net); Detailed documentation is also available via the standard Matlab command 'doc predictN' III) Particle size estimater The MATLAB script 'getParticleSize.m' gives the particle radii of a Psi-Delta curve, given the corresponding complex refractive index and the light's wavelength. The calculation is done as pointwise a best-fit of the input data to the respective reverence curve defined by N. Usage: A = getParticleSize(Psi,Delta,N,lambda