Fordatis Sammlung:https://fordatis.fraunhofer.de/handle/fordatis/662026-05-07T07:12:27Z2026-05-07T07:12:27ZPublication Data for Patente Analysis in Technology ForesightMartini, Melaniehttps://fordatis.fraunhofer.de/handle/fordatis/4622026-05-05T08:10:07Z2026-01-01T00:00:00ZTitel: Publication Data for Patente Analysis in Technology Foresight
Datenautorinnen und Datenautoren: Martini, Melanie
Zusammenfassung: Document Overview:
This Excel document "Publications_Data_Patents_In_Technology_Foresight.xlsx" contains a comprehensive collection of 887 publications related to patent analysis for technology foresight. The data is organized into multiple columns that cover different aspects of patent analysis. For further computation, you can use the CSV document "Publications_Data_Patents_In_Technology_Foresight_macine.csv".
For more information about the data, please refer to the thesis "The Use of Patents in Technology Foresight" by Melanie Martini.
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Sheet Structure:
The data is organized in a single worksheet with the following main categories:
General Information: Contains basic information about the patents, such as title, authors, year, and DOI.
Use Cases: Applications in the context of technology foresight.
Data field: Different kinds of metadata associated with patents.
Methods: (Mostly data science) methods to analyze patents.
Requirements: An assessment of the methodological requirements of the methods.
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Application Examples:
Research: Use the data for academic papers or market analyses.
Technology Foresight: Find methods to analyze patents for specific use cases.2026-01-01T00:00:00ZEvaluating vector-based search and question answering approaches for an information system (QA Dataset)Dembach, MichaelDecher, Sophiehttps://fordatis.fraunhofer.de/handle/fordatis/4842026-03-03T02:30:20Z2026-01-01T00:00:00ZTitel: Evaluating vector-based search and question answering approaches for an information system (QA Dataset)
Datenautorinnen und Datenautoren: Dembach, Michael; Decher, Sophie
Zusammenfassung: This dataset contains 15 German question-answer sets from the domain of energy research. Each question has four possible answers: three generated by LLMs, and one written by a human domain expert.2026-01-01T00:00:00ZRF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part II)Henneke, Lukashttps://fordatis.fraunhofer.de/handle/fordatis/4612025-10-28T02:30:39Z2025-10-01T00:00:00ZTitel: RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part II)
Datenautorinnen und Datenautoren: Henneke, Lukas
Zusammenfassung: This dataset supplements the dataset "RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part I)" (https://fordatis.fraunhofer.de/handle/fordatis/460) and is part 3/3 of the evaluation data. The file "iqengine.zip" contains
(1) signal bursts extracted from "bluetooth.sigmf-data" (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/bluetooth; Author: Jacob Gilbert; License: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/),
(2) signal bursts extracted from "dect6.sigmf-data" (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/dect6; Author: Jacob Gilbert; License: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/), and
(3) signal bursts extracted from "rfd900p.sigmf-data" (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/rfd900p; Author: Unknown; License: Unknown).; Dieser Datensatz ergänzt den Datensatz „RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part I)“ (https://fordatis.fraunhofer.de/handle/fordatis/460) und ist Teil 3/3 der Evaluierungsdaten. Die Datei „iqengine.zip“ enthält
(1) Signalbursts, extrahiert aus „bluetooth.sigmf-data“ (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/bluetooth; Autor: Jacob Gilbert; Lizenz: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/),
(2) Signalbursts, die aus „dect6.sigmf-data“ extrahiert wurden (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/dect6; Autor: Jacob Gilbert; Lizenz: CC BY-SA 4.0 - https://creativecommons.org/licenses/by-sa/4.0/), und
(3) Signalbursts extrahiert aus „rfd900p.sigmf-data“ (https://iqengine.org/view/api/us-east-1/iqengine-gnuradio/rfd900p; Autor: Unbekannt; Lizenz: Unbekannt).2025-10-01T00:00:00ZRF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part I)Henneke, Lukashttps://fordatis.fraunhofer.de/handle/fordatis/4602025-10-28T02:30:40Z2025-10-01T00:00:00ZTitel: RF Communication Signal Dataset for Wireless Protocol Recognition based on Deep Embeddings (Part I)
Datenautorinnen und Datenautoren: Henneke, Lukas
Zusammenfassung: This dataset supplements the paper "Approaching Domain Generalisation with Embeddings for Robust Discrimination and Recognition of RF Communication Signals" and is composed as follows:
(1) The file "synthetic.zip" contains a total of 4,000 synthetically generated signals, created from 1,000 distinct synthetic wireless protocols. For each protocol, 4 signal examples were generated, each consisting of 16,384 samples. These signals represent the training data used for the RF signal embedding models.
(2) The file "vuorenmaa.zip" contains signal bursts of various types extracted from the dataset "Radio-Frequency Control and Video Signal Recordings of Drones" (https://doi.org/10.5281/zenodo.4264467; Authors: Miika Vuorenmaa, Jaakko Marin, Mikko Heino, Matias Turunen, & Taneli Riihonen; License: CC BY 4.0 - https://creativecommons.org/licenses/by/4.0/) and is part 1/3 of the evaluation data.
(3) The file "basak.zip" contains signal bursts of various types extracted from the dataset "Drone RF Dataset" (https://doi.org/10.48804/HZRVNZ; Authors: Sanjoy Basak, Sofie Pollin & Bart Scheers; License: CC BY-NC 4.0 - https://creativecommons.org/licenses/by-nc/4.0/) and is part 2/3 of the evaluation data.
(4) The files "files_train.json" and "files_eval.json" contain the splitting of the evaluation dataset used in section "4.2. Downstream classification task" of the paper.
(5) The file "plot_spectrograms.py" shows how to access the signal data using Python and visualizes the training and evaluation data. For the script to work, the contents of all *.zip files must be unzipped and located in the same folder as the Python script and the *.json files.
NOTE: Part 3/3 of the evaluation data has been moved to a separate dataset (https://fordatis.fraunhofer.de/handle/fordatis/461) due to incompatible licences.; Dieser Datensatz ergänzt das Paper „Approaching Domain Generalisation with Embeddings for Robust Discrimination and Recognition of RF Communication Signals“ und setzt sich wie folgt zusammen:
(1) Die Datei „synthetic.zip“ enthält insgesamt 4.000 synthetisch erzeugte Signale, die aus 1.000 verschiedenen synthetischen Funkprotokollen erstellt wurden. Für jedes Protokoll wurden 4 Signalbeispiele erzeugt, die jeweils aus 16.384 Samples bestehen. Diese Signale repräsentieren die Trainingsdaten, die für die RF-Signaleinbettungsmodelle verwendet werden.
(2) Die Datei „vuorenmaa.zip“ enthält Signalbursts verschiedener Typen, die aus dem Datensatz „Radio-Frequency Control and Video Signal Recordings of Drones“ (https://doi.org/10.5281/zenodo.4264467; Autoren: Miika Vuorenmaa, Jaakko Marin, Mikko Heino, Matias Turunen, & Taneli Riihonen; Lizenz: CC BY 4.0 - https://creativecommons.org/licenses/by/4.0/) und ist Teil 1/3 der Evaluierungsdaten.
(3) Die Datei „basak.zip“ enthält Signalbursts verschiedener Typen, die aus dem Datensatz „Drone RF Dataset“ (https://doi.org/10.48804/HZRVNZ; Autoren: Sanjoy Basak, Sofie Pollin & Bart Scheers; Lizenz: CC BY-NC 4.0 - https://creativecommons.org/licenses/by-nc/4.0/) und ist Teil 2/3 der Evaluierungsdaten.
(4) Die Dateien „files_train.json“ und „files_eval.json“ enthalten die Aufteilung des Evaluierungsdatensatzes, der in Abschnitt "4.2. Downstream classification task" des Papers verwendet wird.
(5) Die Datei „plot_spectrograms.py“ zeigt, wie man mit Python auf die Signaldaten zugreift und visualisiert die Trainings- und Evaluierungsdaten. Damit das Skript funktioniert, muss der Inhalt aller *.zip Dateien extrahiert und in demselben Ordner platziert werden, in dem sich das Python-Skript sowie die *.json Dateien befinden.
ANMERKUNG: Teil 3/3 der Evaluierungsdaten wurde in einen separaten Datensatz (https://fordatis.fraunhofer.de/handle/fordatis/461) wegen Lizenz-Inkompabilitäten ausgelagert.2025-10-01T00:00:00Z