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Electromagnetic induction (EMI) data at the Roman Burgus of Astheim
No full textEMI data measured with the GF Instruments CMD-Explorer. Positioning was done using a Stonex s9i RTk-GNSS GPS. Measured at a Roman river fortlet (burgus) site in Hesse (Germany). During the 1st century AD the Romans performed several river alterations in the vicinity of the River Rhine in Hesse, including channels, holding anchoring sites protected by these burgi. The regarded example profiles crosses such a channel. In the middle of the assumed channel, a direct push EC-log was performed along site a hydraulic profiling tool, using a Geoprobe 540 MO system mounted to a Nordmeyer drill rig in combination with a Geoprobe K6050 HPT probe
Code and data used for "Computing Barkhausen noise spectra for magnetostrictive thin film composites using efficient magnetization-magnitude preserving simulation techniques" [before review]
No full textCode used for "A continuum theory for stripe-shaped magnetic domains in thin films"
No full textElement subroutines and associated code for use in conjunction with FEAP (Finite Element Analysis Program, Robert Taylor, Berkeley, not included in this repository
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 of the scattered light is usable as in-situ size diagnostics for the size and the complex refractive index of particles. For the kinetic Mie polarimetry to work, it is necessary for the particles to undergo a change in size (eg. via growth processes or etching), while the refractive index is assumed to be sufficiently constant in time. Given a time series input of the ellipsometric angles Psi and Delta, the network predicts the best-fitting refractive index, from which the particle radii can be calculated via Mie theory. 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). The network itself is provided as: MATALB DAGNetwork format. Requires the MATLAB Deep Learning Toolbox (version 2022a or newer) [tested]. ONNX format for use with other, open frameworks, such as Pytorch etc [not tested]. For the MATLAB framework, a script is provided to facilitate operation. It takes the measurement data as input and returns the refractive index as vector elements. The documentation is accessible via the command help predictN When using another framework or custom readout functions, please note, that the network output for the imaginary part of the refractive index is increased by a factor of ten compared to the physical value, due to numerical reasons
Archaeo-magnetic plans of the Pre-cucuteni and Cucuteni A-B settlements Baia-În Muchie and Adâncata-Dealul Lipovanului, Suceava County, Bucovina, Eastern Romania
No full textGeomagnetic research and drillings provide new results regarding settlement organisation and population size of three small settlements from the Pre-Cucuteni and the Cucuteni A-B period of Suceava County in Romanian Bucovina. In these settlements from different stages of the Cucuteni-Tripolye complex, domestic dwellings can be distinguished from clearly oversized (special?) buildings, which are situated in central locations and sometimes contain special inventories. Different principles of settlement organisation are visible, which each show far-reaching references to the Central Balkans, on the one hand, and the Bug-Dnieper interfluve on the other hand. Based on analogies with other Cucuteni-Tripolye sites, consistent populations with less than 200 inhabitants are reconstructed
Electromagentic Induction profile on a burial mount at the Uzun-Rama plateau
No full textEMI conductivity data measured with the CMD Miniexplorer in the kurgan (burial mount) area on the Uzun-Rama plateau in central Azerbaijan. These mounts were constructed and used from the mid-4th to 1st millennium BC. For details of the site, see \cite{Laneri2019}. Laneri(2019). Jalilov B. Crescioli L. Guarducci G. Kneisel J. Poulmarc’H M. Ricci A. Valentini S. Laneri, N. GaRKAP 2018: The first season of the Azero-Italian Ganja Region Kurgan Archae- ological Project in Western Azerbaijan. Ancient Near Eastern Studies , December 2019. URL https://hal.science/hal-02409061
Friesack 4, Complex I, Animal Remains
No full textThe Mesolithic campsites at Friesack were located on an island amidst a swampy valley with slowly flowing water, forests dominated by birch and pine, sandy hills covered with pine, and open grasslands. Due to excellent preservation conditions, thousands of mammal remains could be excavated in the refuse areas of the site Friesack 4 by B. Gramsch and his team between 1978 and 1989. 826 identified mammal remains (NISP) derive from the oldest, mid-Preboreal layers of the station (complex I), 1200 bones from the following late Preboreal layers (complex II), and 3082 remains from the subsequent early Boreal horizons (complex III). This data collection includes the archaeozoological results from complex I
Friesack 4, Complex II, Animal Remains
No full textThe Mesolithic campsites at Friesack were located on an island amidst a swampy valley with slowly flowing water, forests dominated by birch and pine, sandy hills covered with pine, and open grasslands. Due to excellent preservation conditions, thousands of mammal remains could be excavated in the refuse areas of the site Friesack 4 by B. Gramsch and his team between 1978 and 1989. 826 identified mammal remains (NISP) derive from the oldest, mid-Preboreal layers of the station (complex I), 1200 bones from the following late Preboreal layers (complex II), and 3082 remains from the subsequent early Boreal horizons (complex III). This data collection includes the archaeozoological results from complex II
