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Im Rahmen des Projekts "Sicherheitsvorstellungen in der Antike" wurde unter Leitung von Daniel Pauling eine umfangreiche Datenbank erstellt, die sämtliche Quellenstellen aus der griechischen Archaik und Klassik enthällt, in denen das Schlagwort ἀσφάλεια (asphaleia) verwendet wurde, welches als "Sicherheit" ins Deutsche übertragen wird. Auf dieser Forschungsdatenbasis wurde die Dissertation von Pauling "Ἀσφάλεια. Die Entwicklung der Sicherheitsvorstellungen und der Diskurs über Sicherheit im archaischen und klassischen Griechenland" erstellt. Sie ist frei verfügbar über den OpenAccess-Server Qucosa sowie im Hochschulschriftenmagazin der SLUB Dresden. (Link folgt) Die Quellenstellen wurden gesammelt, archiviert und auf mehreren Ebenen intensiv verschlagwortet. Dies geschah ursprünglich im Dateiformat MS Access 2016 (Quellensammlung_Asphaleia_v1.0.1.accdb). Diese Datenbank wird hier der Forschungsgemeinschaft frei zugänglich gemacht. Um eine nachhaltige und dauerhafte Nutzbarkeit zu gewährleisten wurde die Access-Datenbank auch als valides TEI XML 1.0 Dokument veröffentlicht (Quellensammlung_Asphaleia_v1.0.1.xml). Die Erschließung der Datenbank in beiden Dateiformaten ermöglicht die Dokumentation im PDF-Format (Quellensammlung_Asphaleia_BeschreibungUndSchlagwortverzeichnis.pdf - Acrobat Reader o.ä. wird benötigt). In ihr werden sämtliche Felder bzw. XML-Elemente der Datenbank sowie die möglichen darin enthaltenen Werte der Datensätze beschrieben und erläutert.
Supplementary data to publication GPS Solutions 24:64 (2020) Wanninger, L., Heßelbarth, A. (2020): GNSS code and carrier phase observations of a Huawei P30 smartphone: quality assessment and centimeter-accurate positioning; GPS Solutions 24:64, https://doi.org/10.1007/s10291-020-00978-z (open access). The data set consists of GNSS observations of a smartphone Huawei P30. They were collected in 8 static observation sessions with an overall duration of 77 h. Each session data set includes raw data gathered by GNSSLogger and RINEX files for Huawei P30 and a local reference station. The data sets are stored in two separate zip-archives. This archive contains the RINEX observations of Huawei P30, RINEX observations of the local reference station "BZW1" equipped with SEPT POLARX5 / JAVRINGANT_DM JVDM, broadcast ephemerides in RINEX format, and antenna corrections in ANTEX format. All RINEX files containing Huawei P30 observations use station name "HUAW". The five rooftop sessions with different Huawei P30 orientations were used to calibrate the phase center of the smartphone. This was repeated with the DRB2 rotational device, which enables observations in four azimuthal orientations per minute. Our results of the HUAWEI P30 phase center calibration, valid for the setup as shown in the paper (Fig. 1), are found in the ANTEX directory. The two field sessions are complemented with additional observations by LEICA GRX1200+ GNSS / NAX3G+C NONE at the same stations as used by the Huawei P30. In field session 1 the station is named "1000". In the second session it is called "2000".
Supplementary data to publication GPS Solutions 24:64 (2020) Wanninger, L., Heßelbarth, A. (2020): GNSS code and carrier phase observations of a Huawei P30 smartphone: quality assessment and centimeter-accurate positioning; GPS Solutions 24:64, https://doi.org/10.1007/s10291-020-00978-z (open access). The data set consists of GNSS observations of a smartphone Huawei P30. They were collected in 8 static observation sessions with an overall duration of 77 h. Each session data set includes raw data gathered by GNSSLogger and RINEX files for Huawei P30 and a local reference station. The data sets are stored in two separate zip-archives. This archive contains the GNSSLogger raw data of the Huawei P30 GNSS measurements for all 8 sessions.
Reuse of articles in the Dissertation with permission from Elsevier (via RightsLink, 2017).
Supplementary image dataset of the ISPRS International Journal of Geoinformation publication "Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods". Historical image benchmark dataset including feature matches and four scene reconstructions. The dataset can be used for testing and evaluating feature matching methods on exclusively historical images. Additionally, the dataset can easily be extended as all tie point information is provided.
A workflow is introduced to automatically measure water stages based on image measurements using deep learning. So far, most camera gauges do not provide the needed robustness to achieve accurate water stage measurements because of changing environmental conditions. The novel, suggested approach is based on two CNNs (i.e. FCN and SegNet) to identify water in imagery. The image information is transformed into metric water level values intersecting the extracted water contour with a 3D model. The workflow allows for the densification of river monitoring networks based on low-cost camera gauges in various scenarios.
In this paper, we tested whether impulsive decision-making (1) differs between individuals with substance use disorders (SUD) or non-substance-related addictive disorders (ND) and healthy controls and (2) predicts the course of SUD and ND severity after one year.
Supplementary material to the publication: "Fast fourier transform and multi-Gaussian fitting of XRR data to determine the thickness of ALD grown thin films within the initial growth regime"
Medienbeilage zu ARRAY2019 Magazin der International Computer Music Association ICMA