# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Dresden University of Applied Sciences.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Dresden University of Applied Sciences

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Hochschule für Technik und Wirtschaft Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Dresden University of Applied Sciences.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Dresden University of Applied Sciences

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Hochschule für Technik und Wirtschaft Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Freiberg University of Mining and Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Freiberg University of Mining and Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Dresden University of Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Dresden University of Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Leipzig University.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Leipzig University

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
A tsv file with associated metadata is stored for each of the individual combinations of displacement step (1-14 mm), gage pitch (0.65 mm, 1.30 mm, 2.60 mm, 5.20 mm) and DFOS type (ACR, ES, ORM, V9). This makes a total of 14 x 4 x 4 = 224 files. The files were processed using Python.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Based on the principle of coherent optical frequency domain reflectometery (c-OFDR), cracks in concrete structures can be precisely localized and their widths determined due to the high spatial resolution in the sub-millimeter range. However, with longer distributed fiber optic sensors (DFOS) lengths, as is common in structural health monitoring, the spatial resolution is reduced. Experimental studies were carried out to investigate the influence of the spatial resolution on the measurement quality in the area of large strain gradients. Four different DFOS, two robust and two filigree DFOS types, were subsequently installed on a 4 m long reinforced concrete beam. In a displacement controlled 4-point bending test, crack widths of up to 0.5 mm were measured with four different gage pitch (gp).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The goal of the IDA-KI research project is to develop methods for automated analysis and evaluation of monitoring data. The validation is carried out using real measurement data acquired on a 45 m long research bridge (openLAB). First, the reference condition of the bridge is measured with and without service loads over a period of one year. Subsequently, targeted load tests will be performed up to the severe damage state. At the same time, redundant sensors will be damaged or manipulated to create a real database with specific signal characteristics for metrological and structural anomalies. This will provide an important basis for the development of evaluation methods (also based on machine learning), so that measurement faults in monitoring data can be corrected in the future and differentiated from structural damage.

Further information can be found in: https://onlinelibrary.wiley.com/doi/10.1002/best.202300094

Project title: IDA-KI (Automated assessment of monitoring data for infrastructure constructions using AI and IoT)
Funding: Federal Ministry for Digital and Transport, Germany, within the innovation program mFUND
Funding reference: 19FS2013A

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
STABEEL - Messkampagne 2023/Q1 - Experimentelle Verifikation von Umrichterinteraktionen Q(U)-geregelter Erzeugungsanlagen am Dynamischen Netzmodell (physikalische 220-kV-Netznachbildung) des TUD - IEEH

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TechnicalInfo[].value
Bezüglich der Informationen zum Messaufbau siehe Messaufbau.pdf.
Bezüglich der Messgrößenbeschriebung und Details zu den einzelnen Experimenten siehe Experimente.pdf.

Die Rohdaten befinden im DewesoftX-Format .dxd im Verzeichnis Messdaten_dxd.zip und die daraus extrahierten, in HD5-codierten, Matlab-Datein im Verzeichnis Messdaten_mat.zip.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
In den durchgeführten Versuchen wurden zwei Q(U)-geregelte Erzeugeranlagen mit Hilfe der freiparametrierbaren Regelungsplattform des Lehrstuhls für Leistungselektronik an einem 220-kV-Modellnetz betrieben. Bei jedem Durchlauf wurden verschiedene repräsentative Tagesprofile von Wind- und Solareinspeisern zugrunde gelegt. Zwischen den einzelnen Durchläufen wurde die Parametrierung der Erzeugungsanalgen und die Topologie des zugrunde liegenden Netzes variiert. Weitere Informationen zum Messaufbau befinden sich in Messaufbau.pdf und Details zu den einzelnen Experimenten in Experimente.pdf. 

Die Topologie und physikalischen Eigenschaften des verwendeten Netzes befinden sich im Verzeichnis Netzdaten als PowerFactory-Projekt und im PSDM-Format (https://github.com/ieeh-tu-dresden/power-system-data-model). Die zugrundeliegenden Erzeugerzeitreihen für die spezifizierten Szenarien wurden dem SimBench-Projekt entnommen und befinden sich im Verzeichnis Erzeugerzeitreihen.

Im Experiment wurden Strom- und Spannungsverläufe hochaufgelöst mit Hilfe eines Transientenrekorders gemessen. Daraus wurden automatisiert Größen der Leistung, Frequenz sowie Grundschwingungen ermittelt und dem Datenset hinzugefügt.

Inhaltsverzeichnis:
- Beschreibung des Messaufbaus und der Messpunktzuordnung (Messaufbau.pdf)
- Beschreibung der durchgeführten Szenarien (Experimente.pdf)
- Erzeugerzeitreihen (Erzeugerzeitreihen.zip -> profiles_year_2016, Szenario_X.png)
- Topologiedaten und physikalische Daten des zugrunde liegenden Netzes (Netzdaten.zip -> DNM.pfd, psdm_2.2.0\xx.json)
- Messdaten (Messdaten_dxd.zip -> Experiment_00.dxd - Experiment_15.dxd; Messdaten_mat.zip -> Experiment_00.mat - Experiment_15.mat )

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Dieses Datenset enthält die erstellten Netz- und Anlagenmodelle, die Beschreibung der durchgeführten Experimente sowie die aufgezeichneten Strom- und Spannungsverläufe (Messdaten) zur Verifikation und Weiterentwicklung der im STABELL-Projekt aufgestellten Stabilitätskriterien. Die Versuche wurden am Dynamischen Netzmodell des Instituts für Elektroenergieversorgung und Hochspannungstechnik durchgeführt. Dieses Labornetz bildet ein 220-kV-Netz in einem Modellmaßstab von 440 V ab. In dem Versuch wurden die Interaktionen von Q(U)-geregelten Erzeugeranlagen untersucht, in dem diese mit Hilfe der freiparametrierbaren Regelungsplattform (https://tu-dresden.de/ing/elektrotechnik/eti/le/die-professur/Einrichtungen) des Lehrstuhls für Leistungselektronik nachgebildet wurden.

Die Arbeiten entstanden im Rahmen des Projektes STABEEL, gefördert durch die Deutsche Forschungsgemeinschaft (DFG, doi: 10.13039/501100001659) – Projektnummer 442893506.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
STABEEL - Messkampagne 2023/Q1 - Experimentelle Verifikation von Umrichterinteraktionen Q(U)-geregelter Erzeugungsanlagen am Dynamischen Netzmodell (physikalische 220-kV-Netznachbildung) des TUD - IEEH

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionObject.PhysicalObject[].value
Dynamisches Netzmodell des Instituts für Elektroenergieversorgung und Hochspannungstechnik der TU Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionObject.PhysicalObject[].value
Lehrstuhl-Wechselrichter des Lehrstuhls für Leistungselektronik der TU Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The aim of the project is to develop application-oriented guidelines for the evaluation of the robust stability of control systems for distributed generators in electrical power supply networks. 
It is a cooperation between the Institute of Control Theory and the Institute of Electrical Power Systems and High Voltage Engineering at TU Dresden. Projekt runtime is 3 years. 

The transition of the electrical energy system requires adaptable operational management concepts with regard to the large number of decentralized generators (DGs) in order to ensure the maintenance voltage. However, passive methods such as Q(U)-control continue to serve at least as a fallback level since they are very easy to use and can locally influence the system voltage by adaptively providing reactive power. The plant control should be able to safely handle voltage jumps (due to time-synchronous DG behavior) in order to exclude undesirable overshooting or even unstable behavior. However, the problem in this setup is that each plant controller works for itself, i.e., therefore only monitoring and processing the controlled quantity at its own measurement point in the grid. The plant controllers do not consider the overall impact of all control activities. This decentralized control strategy leads to highly interesting and challenging control theoretic problems. On the one hand, it is important to have the exact definition in terms of control-theory and, thus, ensure stability - even in the event of subsystem failures - on the other hand, practical applicability of the criteria in the form of guidelines and easy-to-use formulas is desired. In addition, questions on robustness against parameter uncertainties and grid-typical disturbances as well as the detection and identification of incorrect plant controls will be addressed. To this end, methods are going to be developed that allow i. a. the grid operator to carry out an evaluation of the current system stability margin considering the full available parameter space within a limited grid area and with minimal effort. Furthermore, methods for the detection and isolation of faulty plant controls or modified controller structures are to be developed and tested. With these multi-layered approaches, the problem will be approached in a sophisticated way from the theoretic side (by using residual generators and/or pattern recognition) as well as from the practitioners perspective (with a fallback solution working on a sensor basis). The findings can serve as a basis for future research projects in the field of automated grid operation management. The algorithms and concepts developed during the project will be tested on a powerful network model, which is physically available at the applicants of this proposal.

Das Projekt STABEEL wurde gefördert durch die Deutsche Forschungsgemeinschaft (DFG, doi: 10.13039/501100001659) – Projektnummer 442893506.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
STABEEL – Stabilität dezentraler Erzeuger im Elektroenergieversorgungsnetz bei der Erbringung von Systemdienstleistungen

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Guidelines for quantitative survey on perception of urban green spaces during COVID-19 pandemic

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
This is the survey guideline used for the quantitative survey.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Guidelines for quantitative survey on perception of urban green spaces during COVID-19 pandemic

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Crises such as the COVID-19 pandemic challenge some established human-landscape interactions notably. In this article, we analyse whether the pandemic had an impact on the perception of urban green spaces (UGS) and usage behaviours in Leipzig, Germany. We use a quantitative survey to understand people’s attitudes. Our study is novel in that it firstly explores the relationship between UGS and visitors during the final phase of the COVID-19 pandemic (winter 2022/2023), contrary to the vast majority of already existing studies that relied on digitally distributed surveys due to the lockdown protocols. Secondly our study does not apply exclusively online methods to reach out to the participants. The survey results show that about 40% (of the 115 participants) use parks more frequently during the final phase of the COVID-19 pandemic compared to before 2020. Characteristics such as proximity to home, naturalness and cleanliness have become the most relevant. We see a notable increase in the demand for secure public green spaces, particularly among female visitors. Every second respondent confirmed experiencing considerable difficulties when accessing UGS, revealing the existing (spatial) deficits in environmental justice. These results should be considered by urban planners to adapt UGS to the changing demands of the citizens.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
COVID-19 pandemic and urban green spaces

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Extended Phase-Field Method (XPFM) - Data corresponding to publication: "An enriched phase-field method for the efficient simulation of fracture processes" by Loehnert et al. (2023)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
This dataset contains the numerical values corresponding to the graphs within the scientific (open-access) contribution "An enriched phase-field method for the efficient simulation of fracture processes" by Loehnert et al. (2023) (https://doi.org/10.1007/s00466-023-02285-z).

Abstract of Paper:
The efficient simulation of complex fracture processes is still a challenging task. In this contribution, an enriched phase-field method for the simulation of 2D fracture processes is presented. It has the potential to drastically reduce computational cost compared to the classical phase-field method (PFM). The method is based on the combination of a phase-field approach with an ansatz transformation for the simulation of fracture processes and an enrichment technique for the displacement field as it is used in the extended finite element method (XFEM) or generalised finite element method (GFEM). This combination allows for the application of significantly coarser meshes than it is possible in PFM while still obtaining accurate solutions. In contrast to classical XFEM / GFEM, the presented method does not require level set techniques or explicit representations of crack geometries, considerably simplifying the simulation of crack initiation, propagation, and coalescence. The efficiency and accuracy of this new method is shown in 2D simulations.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Extended Phase-Field Method (XPFM) - Data corresponding to publication: "An enriched phase-field method for the efficient simulation of fracture processes" by Loehnert et al. (2023)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Extended Phase-Field Method (XPFM) - SFB TRR 280-TP-C05

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Supplementary data of publication: "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
This dataset contains the supplementary data of the open-access publication "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation". It provides all the necessary data and scripts to replicate the results of the study and to further use the synthetic data generation to train one's own deep learning image segmentation model. The original scripts have been slightly modified to make them more suitable for general use, e.g. parameters have been passed to an argument parser instead of hard-coding them whenever possible. 

Abstract of Paper:  Monitoring particle properties directly in the process using in-situ microscopy can provide valuable input to control loops, improve process understanding and facilitate process optimization. However, obtaining reliable information from these images remains a challenge, especially for higher solids concentrations or agglomerating systems. Recent studies have successfully applied deep learning models to extract particle characteristics from in-situ image data. Despite these advances, the problem of generating training data has not been properly addressed. Manual annotation is time-consuming and prone to bias due to high particle counts, particle overlap, and out-of-focus objects. This paper presents a new approach to generate training data for segmentation models by combining conventional segmentation methods with copy-paste augmentation. A case study was conducted in which depth filtration experiments were performed on irregularly shaped alumina particles. An instance segmentation model trained on data generated using the proposed approach successfully detected and characterized particles, even at high solids concentrations.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Supplementary data of publication: "In-line Image Analysis of Particulate Processes with Deep Learning: Optimizing Training Data Generation via Copy-Paste Augmentation"

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Traffic Light Data of Hamburg collected in September and October 2023 (Observations from SensorThings API)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.contributor.Producer[].value
Free and Hanseatic City of Hamburg (LSBG)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TechnicalInfo[].value
The data consists of SensorThings Observations for different Datastreams: primary signal, secondary signal, cycle second and program observations. Furthermore, detector observations for traffic participants such as cars, cyclists and pedestrians are included. The data was collected using MQTT clients. In addition, we used an HTTP polling script to supplement dropped Observations. Nonetheless, it should be considered during the processing of this dataset that the completeness of the Observations can not be fully guaranteed, due to instabilities in the Traffic Lights Data system.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Real-time traffic light data collected from the Traffic Lights Data system in Hamburg from September to October 2023. The collected data was recorded under application of the Data license Germany (Attribution: Version 2.0 (https://www.govdata.de/dl-de/by-2-0 (retrieved on January 24, 2024)). It is provided by the the Free and Hanseatic City of Hamburg (LSBG). The data source can be found under: https://metaver.de/trefferanzeige?docuuid=AB32CF78-389A-4579-9C5E-867EF31CA225 (retrieved on January 24, 2024).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Traffic Light Data of Hamburg collected in September and October 2023 (Observations from SensorThings API)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
In Germany, the bicycle is becoming increasingly important as an environmentally friendly means of transportation. At the same time, however, traffic lights are often geared towards motorized private transport. In the PrioBike research project, we are developing a green wave app for Hamburg to enable cyclists to travel faster, more comfortably and more safely through the city with fewer stops.

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PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
Pictures
ROOT files 
Scripts for analysis

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Proton therapy requires range verification in order to exploit its full potential. One of the most promising approaches is to monitor prompt gamma-rays produced by nuclear interactions of the therapeutic particles in the patient tissues. In our paper, we test PETsys electronics with a detector with a wide energy range from 100 keV to 15 MeV. We tested what time resolution we could achieve as a high time resolution is required to achieve millimetric precision in the proton range. PETsys should survive high count rates and the fraction of pile-up events should be low or separatable. We are investigating a full acceptance approach with increased granularity in order to reduce the size of the scintillators and consequently the count rate per channel. Ideally, we want to stack the scintillators in matrices that require suitable multi-channel photo-multipliers and a fitting acquisition system. Here, we present two geometries of CeBr3 crystals 5 × 5 × 20 mm3 and 10 × 10 × 30 mm3, together with modern silicon photo-multipliers (SiPM) adapted to work with the PETsys TOFPET2 ASIC. The TOFPET2 ASIC was developed for Time-of-Flight Positron Emission Tomography (TOF-PET) applications. Here are our data measured for the publication for time resolution and coincidence time resolution, energy resolution with AmBe source, and dead time studies.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
PETsys Electronics Evaluation Kit, PMT, CAEN ADC v1730

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Proton therapy requires range verification in order to exploit its full potential. One of the most promising approaches is to monitor prompt gamma-rays produced by nuclear interactions of the therapeutic particles in the patient tissues. In our paper, we test PETsys electronics with a detector with a wide energy range from 100 keV to 15 MeV. We tested what time resolution we could achieve as a high time resolution is required to achieve millimetric precision in the proton range. PETsys should survive high count rates and the fraction of pile-up events should be low or separatable. We are investigating a full acceptance approach with increased granularity in order to reduce the size of the scintillators and consequently the count rate per channel. Ideally, we want to stack the scintillators in matrices that require suitable multi-channel photo-multipliers and a fitting acquisition system. Here, we present two geometries of CeBr3 crystals 5 × 5 × 20 mm3 and 10 × 10 × 30 mm3, together with modern silicon photo-multipliers (SiPM) adapted to work with the PETsys TOFPET2 ASIC. The TOFPET2 ASIC was developed for Time-of-Flight Positron Emission Tomography (TOF-PET) applications. Here are our data measured for the publication for time resolution and coincidence time resolution, energy resolution with AmBe source, and dead time studies.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
Original particle 3D data comes in the form of STL files for three types of solids: aluminium oxide, soda-lime glass, dolomite, mica, limestone, and quartz. STL files should all represent closed meshes and have been generated from VTK data derived from reconstructed tomography data. Please refer to the publication of Ditscherlein et. al (2022) for the desription of the measurement and origin of the solids:
Ditscherlein R, Furat O, Löwer E, et al. PARROT: A Pilot Study on the Open Access Provision of Particle-Discrete Tomographic Datasets. Microscopy and Microanalysis. 2022;28(2):350-360. doi:10.1017/S143192762101391X

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
2D projection imaging techniques are simulated with 3D particle data from tomography measurements.
This submission contains three distinct parts: original particle data, Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. 
The particle data comes as STLs that have been converted from VTK particle data as provided by the PARROT database (https://parrot.tu-freiberg.de/). The STL files are *not* identical with the STLs provided by PARROT as of May 2024! The particle data folder is provided as separate archive because of its large size.
The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment.
The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames.
Please refer to the included Readme for a detailed description of the files contained in the archive.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The 3D size and shape of particles is usually estimated from 2D imaging techniques. The correlation of 2D and 3D shape factors has been the subject of several publications over the past decades. Because most research is necessarily limited to the simulation of 2D imaging of simple mathematical shapes, the results are limited with regards to the applicability to real-life measurement scenarios. Here, a particle dataset of 3D particle shapes of six solids, as measured in an X-ray microsope, are used to simulate 2D imaging techniques. Static image analysis is simulated by aligning the particles to their principal axes of inertia or by orienting them into stable positions and producing projection silhouettes afterwards. Dynamic image analysis is simulated by randomly orienting the particles in space and producing projection silhouettes from that. Equivalent diameters and shape factors are calculated for 3D and 2D geometric measures for the purpose of correlation.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Source code, scripts, and data used in the PhD thesis "Fine-grained OS Control over High-performance Networking"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
Source code, experimental and analysis scripts, and data used in the thesis

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The dataset comprises the data from three projects: MigrOS, CoRD, and Fastcalls. The MigrOS project is a system for live migration of high-performance networking applications. The CoRD project is a system for enabling fine-grained control over the RDMA network data plane. The Fastcalls project is a system for enabling fast system calls in the Linux kernel. This project includes both privileged and unprivileged fastcalls.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Source code, scripts, and data used in the PhD thesis "Fine-grained OS Control over High-performance Networking"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Source code, experimental and analysis scripts, and data from Maksym Planeta PhD thesis

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Datensatz zur Überarbeitung der SAP UCC Fallstudie "Materialwirtschaft"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Der Datensatz umfasst Daten, welche im Rahmen der Übung "Materialwirtschaft" zur Lehrveranstaltung Grundlagen betrieblicher Anwendungssysteme in den im WiSe 22/23, SoSe 23, WiSe 23/24 und SoSe 24 erhoben worden sind. Folgende Daten wurden hierfür erfasst:
- bildungsbiografische Daten
- Big Five Inventory (BFI-10)
- Skalen zur Erfassung der Lern- und Leistungsmotivation (SELLMO)
- Fragebogen zur Erfassung aktueller Motivation (FAM)
- Cognitive Load Scale
- Wissenstest im Pre-Posttest-Design
- SAP UCC GBI Fallstudienmonitor

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Datensatz zur Überarbeitung der SAP UCC Fallstudie "Materialwirtschaft"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Im Rahmen unserer Forschung haben wir als Untersuchungsgegenstand die Lehrveranstaltung Grundlagen Betrieblicher Anwendungssystem der Professur für Wirtschaftsinformatik, insb. Informationssysteme in Industrie und Handel ausgewählt. Wir begleiten die zugehörige PC-gestützte Übung in Form von Klickfallstudien, in welcher Studierende das ERP-System SAP S/4HANA kennenlernen und betriebswirtschaftliche Fallstudien bearbeiten.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Amplification of Negative Gas Adsorption in a multivariate framework

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The approach of multivariate MOFs was used to fine-tune the mechanical properties of the flexible framework DUT-49. In situ XRD, NMR and physisorption studies showed that the partial incorporation of a more rigid linker into DUT-49 framework enables a stabilization of the metastable open pore phase which led to a twofold amplification of the expelled gas amount upon “Negative Gas Adsorption” transition.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Amplification of Negative Gas Adsorption in a multivariate framework

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Negative gas adsorption (NGA) is a new and counterintuitive phenomenon, for the first time reported in 2016: Normal solid materials with significant outer or inner surface area always take up gas when the pressure in the surrounding reservoir is increased (adsorption). NGA networks instead react at a certain point in the opposite direction: They release gas upon external gas pressure increase, leading to an overall pressure amplification in a closed system.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Understanding negative gas adsorption in highly porous networks for the design of pressure amplifying materials

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Data set of the energy demand and associated movement of automated storage and retrieval machines

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The database consists of measurement data from multiple pallet racking storage and retrieval machines (SRMs) and the devices from automatic small parts storage systems (ASPS). The power consumption and the distance traveled by the systems were measured during several measurement operations. There are isolated measurements for the travel and hoist mechanisms of the systems. For power measurement, the Hioki 3193 Power HiTester was used. To capture the distance to the computer, a Dimetix laser distance sensor, model FLS-C 10, was employed. To account for all components and their losses, the power was measured at the aisle connection point. Accordingly, all components of the SRMs, including their losses, were recorded. The measurement setup and detailed methodology are described in the dissertation "Minimierung der Netzanschlussleistung und des Energiebedarfs von Regalbediengerät-Lagersystemen" / "Minimization of grid connection power and energy demand in automated storage and retrieval systems" by Armin Siegel.
In addition to the database, which is provided as a compressed SQL file, example queries in Matlab (R2017b) and Python 3 are included.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Data set of the energy demand and associated movement of automated storage and retrieval machines

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Bistable Organic Electrochemical Transistors: Enthalpy vs. Entropy

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Organic electrochemical transistors (OECTs) serve as the foundation for a wide range of emerging applications, from bioelectronic implants and smart sensor systems to neuromorphic computing. Their ascent originates from a distinctive switching mechanism based on the coupling of electronic and ionic charge carriers, which gives rise to a multitude of unique characteristics. Notably, various OECT systems have been reported with significant hysteresis in their transfer curve. While being a feature sought after as non-volatile memory in neuromorphic systems, no universal explanation has yet been given for its physical origin, impeding its advanced implementation. Herein, we present a thermodynamic framework that readily elucidates the emergence of bistable OECT operation through the interplay of enthalpy and entropy. We validate our model through three experimental approaches, covering temperature-resolved characterizations, targeted material manipulation, and thermal imaging. In this context, we demonstrate the exceptional scenario where the subthreshold swing deviates from Boltzmann statistics, and we provide an alternate view on existing data in literature, which further supports our model. Finally, we leverage the bistability in form of a single-OECT Schmitt trigger, thus compacting the complexity of a multi-component circuit into a single device. These insights offer a revised understanding of OECT physics and promote their application in non-conventional computing, where symmetry-breaking phenomena are pivotal to unlock novel paradigms.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Bistable Organic Electrochemical Transistors: Enthalpy vs. Entropy

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Colloidal probe atomic force microscopy measurements on pristine gas bubbles, agglomerates and particle-laden gas bubbles

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Experimental Atomic Force Microscopy data of agglomeration and bubble-particle hetero-coagulation

Detailed interpretation of the raw AFM data in this repository is found in these research articles: 

https://doi.org/10.1021/acs.langmuir.3c01781	Jan Nicklas, Lisa Ditscherlein and Urs A. Peuker. "Particle–Bubble Interactions: an Investigation of the Three-Phase Contact Line by Atomic Force Microscopy." Langmuir 39.38 (2023): 13630-13640.
https://doi.org/10.1016/j.colsurfa.2024.134660	Jan Nicklas and Urs A. Peuker. "Stability of Bubble-Particle Hetero-Coagulates Accessed by Atomic Force Microscopy to Understand the Froth Flotation of Agglomerating Hydrophobic Fines." Colloids and Surfaces A: Physicochemical and Engineering Aspects (2024)

If you are new to the interpretation of AFM data, the following resource is a good starting point: 
https://doi.org/10.1016/j.surfrep.2005.08.003	Butt, Hans-Jürgen, Brunero Cappella, and Michael Kappl. "Force measurements with the atomic force microscope: Technique, interpretation and applications." Surface science reports 59.1-6 (2005): 1-152.

All measurements were carried out with silanized alumina colloidal probes. The spherical alumina particles (DAW-07, Denka, Japan) were hydrophobized by silanization with the silane 1H,1H,2H,2H-Perfluoro
octyltriethoxysilane (AB104055, C14H19F13O3Si, abcr, Germany). 

The following data sets of base-line corrected raw AFM-data are included in the folder "...\AFM_data":

3 x particle - bubble interaction 
3 x particle-laden bubble - particle interaction
1 x agglomerate - particle interaction

The numbered files "measurement_*.txt" are measurements of the force F as a function of the piezo displacement dX
for the interaction between a colloidal probe particle and an agglomerate or a concentrically aligned bubble or particle-laden bubble.

1. column 	dX_t	µm	piezo displacement on approach (trace)
2. column 	F_t	nN	measured force on approach (trace)
3. column 	dX_r	µm	piezo displacement on retraction (retrace)
4. column 	F_r	nN	measured force on retraction (retrace)

Each dataset includes a metadata file "info_*.txt" with the following information:
- interaction type
- cantilever spring constant k_c
- colloidal probe radius R_CP
- bubble radius R_b (if applicable)
- colloidal probe material
- electroly solution the measurement was taken in
- piezo displacement velocity v
- comment (if applicable)

AFM:
Measurements were taken with an XE100 AFM (Park Systems, Korea) that was equipped with the long-range scan head (25 µm scan range) and a liquid probe hand. 
The measurements were taken in a cylindrical dish (~3 µL volume) filled with electrolyte solution.

Base-line correction:
The dX-F raw data was corrected in Matlab by a linear first order correction. 
The slope for this correction was determined by fitting straight line to the first 100-2000 data points of the measurements. The number of data points 
used in the correction is the same for each "measurement_*.txt" of a dataset.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
This dataset contains experimental Atomic Force Microscopy (AFM) force-displacement measurements of poorly wetted alumina particles (colloidal probes) < 15 µm interacting with pristine gas bubbles, agglomerates and particle-laden gas bubbles (hetero-coagulates) in an aqueous environment. The data was generated in the context of the Collaborative Research Center CRC 920 (Project ID 169148856 – subproject B04) that is funded by the German Research Foundation (DFG). The data set is of interest for researches that study the agglomeration or hetero-coagulation in liquid environment, and it is expected to be of particular interest to those that are concerned with the simulation and modeling of these processes (for example by Discrete Element Method). The AFM raw data was baseline corrected to increase the accessibility of the data sets to those less familiar with the AFM.  A detailed interpretation of the AFM data in this repository is found in https://doi.org/10.1021/acs.langmuir.3c01781	and https://doi.org/10.1016/j.colsurfa.2024.134660.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Colloidal probe atomic force microscopy measurements on pristine gas bubbles, agglomerates and particle-laden gas bubbles

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
Atomic Force Microscope XE100 (Park Systems, Korea) equipped with the long-range scan head (25 µm) and a liquid probe hand

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The project investigates the mechanisms of agglomeration and heterocoagulation of model inclusion particles in a water-based ambient temperature model. The focus is on the kinetics of continuous heterocoagulation and on the micro-processes during the contact between bubble and particle. Furthermore, for the first time flotation of inclusions is considered and quantified as a cleaning mechanism. The insight in the fundamental mechanical properties of the agglomerates will be generated with a molecular-dynamic multi-scale approach both in the model system and in the real system, allowing the validation of the results of the model system. 
Project description taken from GEPRIS (https://gepris.dfg.de/gepris/projekt/196865244?language=en).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
CRC920 (B04) - Tailored agglomeration to increase separation efficiency

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Supplementary materials for the publication "Assessing perceived landscape change from opportunistic spatio-temporal occurrence data"

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The repository contains:
- Input Data: Observed and Expected Frequencies abstracted using probabilistic HyperLogLog data
- Code: *.py
- Jupyter Notebooks: *.ipynb (Code)
- Jupyter Notebooks: *.md (code, comments)
- Jupyter Notebooks: *.html (code, comments, output, for archive purposes)
- Output: png (figures and graphics)
- Output: svg (selected figures and graphics)
- Chore: Other files necessary to reproduce figures and output

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Supporting Information for the publication "Assessing perceived landscape change from opportunistic spatio-temporal occurrence data" (Release v1.0.1).
Updates from the peer review process may not be reflected in this submission.
The original git repository with the latest versions can be found at https://gitlab.hrz.tu-chemnitz.de/ad/temporal_landscapes

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Supplementary materials for the publication "Assessing perceived landscape change from opportunistic spatio-temporal occurrence data"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
Python, API, JupyterLab, CartoLab Docker

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Assessing perceived landscape change from opportunistic spatio-temporal occurrence data