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    <id>51399</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber/>
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    <type>lecture</type>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mapping the Distribution of Air Pollutants with Mobile Robots and Stationary Sensors</title>
    <abstract language="eng">Precise knowledge about the distribution of air pollutants is necessary to develop plausible occupational health measures. Combinatory systems, consisting of mobile robots and stationary sensors, can be effective solutions for the coverage of large measurement areas. However, further research is needed to fully understand their performance in comparison to traditional sensing strategies. Therefore, multiple sensor networks layouts will be set up in a simulation environment as well as in real industrial environments. Models for distribution mapping will be developed and evaluated to investigate the performance and opportunities of hybrid-mobility sensor networks for the task of distribution mapping.</abstract>
    <enrichment key="eventName">AASS Seminar</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">24.09.2020</enrichment>
    <enrichment key="InvitedTalks">0</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile Robot Olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aerial Robot Olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless Sensor Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Occupational Health</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
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    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>51400</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <type>lecture</type>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Breaking the wall of air pollution monitoring</title>
    <abstract language="eng">Air pollution in industrial environments is a major risk. Precise knowledge about the distribution of air pollutants is necessary to develop plausible occupational health measures. Combinatory systems, consisting of mobile robots and stationary sensors, can be effective solutions for the coverage of large measurement areas.</abstract>
    <enrichment key="eventName">Falling Walls Adlershof</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">02.10.2020</enrichment>
    <enrichment key="InvitedTalks">0</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile Robot Olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aerial Robot Olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Occupational Health</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless Sensor Network</value>
    </subject>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
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    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>53940</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Using Redundancy in a Sensor Network to Compensate Sensor Failures</title>
    <abstract language="deu">Sensors can fail. Redundancy should therefore be a design driver of wireless sensor networks. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the network can compensate for the outage of sensors.</abstract>
    <enrichment key="eventName">IEEE SENSORS 2021</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">31.10.2021</enrichment>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <author>Nicolas P. Winkler</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Environmental monitoring</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Wireless sensor network</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Sensor placement</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>52609</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring</title>
    <abstract language="eng">A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.</abstract>
    <enrichment key="eventName">SMSI 2021 Conference: Sensor and Measurement Science International</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">03.05.2021</enrichment>
    <enrichment key="eventEnd">06.05.2021</enrichment>
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    <enrichment key="InvitedTalks">0</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless sensing node</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Air pollution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental monitoring</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
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    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
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    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>54251</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Robot-Assisted Air Quality Monitoring</title>
    <abstract language="eng">This presentation gives an introduction to robot-assisted air quality monitoring based and shows results of the research project RASEM.</abstract>
    <enrichment key="eventName">The 6th Meeting of the Investigation Committee on Measurement of Odors and Applications of Their Psychological/Physiological Effects</enrichment>
    <enrichment key="eventPlace">Online meeting</enrichment>
    <enrichment key="eventStart">21.01.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="InvitedTalks">1</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental Monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Air Pollution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Air Quality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless Sensor Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile Robot Olfaction</value>
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    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
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    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
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    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
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  </doc>
  <doc>
    <id>54953</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">RASEM - Robot-Assisted Environmental Monitoring for Air Quality Assessment</title>
    <abstract language="eng">This presentation was held at the SAF€RA symposium and gives an overview to the research project RASEM and its results.</abstract>
    <enrichment key="eventName">2022 SAF€RA Symposium</enrichment>
    <enrichment key="eventPlace">Rome, Italy</enrichment>
    <enrichment key="eventStart">19.05.2022</enrichment>
    <enrichment key="eventEnd">20.05.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="InvitedTalks">1</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Air Pollution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Air Quality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental Monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile Robot Olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless Sensor Network</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="628">Sanitär- und Kommunaltechnik; Umwelttechnik</collection>
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    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
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    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
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  </doc>
  <doc>
    <id>54954</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Super-Resolution for Gas Distribution Mapping: Convolutional Encoder Decoder Network</title>
    <abstract language="eng">Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.</abstract>
    <enrichment key="eventName">19th International Symposium on Olfaction and Electronic Nose</enrichment>
    <enrichment key="eventPlace">Aveiro, Portugal</enrichment>
    <enrichment key="eventStart">29.05.2022</enrichment>
    <enrichment key="eventEnd">01.06.2022</enrichment>
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    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas Distribution Mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatial Interpolation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Super-Resolution</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor Network</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
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    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>57455</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Revisiting Environmental Sensing Nodes: Lessons Learned and Way Forward</title>
    <abstract language="eng">Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like.</abstract>
    <enrichment key="eventName">SMSI 2023 Conference Sensor and Measurement Science International</enrichment>
    <enrichment key="eventPlace">Nuremberg, Germany</enrichment>
    <enrichment key="eventStart">08.05.2023</enrichment>
    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <enrichment key="InvitedTalks">0</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensing node</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Low-cost</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LoRaWAN</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="ddc" number="624">Ingenieurbau</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
    <collection role="themenfelder" number="">Infrastruktur</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>55908</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory</title>
    <abstract language="eng">The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation.</abstract>
    <enrichment key="eventName">38th Danubia-Adria Symposium on Advances in Experimental Mechanics</enrichment>
    <enrichment key="eventPlace">Poros, Greece</enrichment>
    <enrichment key="eventStart">20.09.2022</enrichment>
    <enrichment key="eventEnd">23.09.2022</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Low-cost</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dust sensor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor drift</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
  <doc>
    <id>57635</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DeepGDM: Deep Learning for Gas Distribution Mapping</title>
    <abstract language="eng">Gas distribution mapping (GDM) is a valuable tool for monitoring the distribution of gases in various applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This presentation gives an overview of the different approaches to GDM and motivate the use of a deep-neural network-based approach. As access to ground truth representations of gas distributions remains a challenge in GDM research, an approach for the simulation of realistic-shaped synthetic gas plumes is described, which was used for training Gas Distribution Decoder, a deep neural network for spatial interpolation of spatially sparse gas measurements.</abstract>
    <enrichment key="eventName">AASS Seminar</enrichment>
    <enrichment key="eventPlace">Örebro, Sweden</enrichment>
    <enrichment key="eventStart">08.06.2023</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <enrichment key="InvitedTalks">0</enrichment>
    <author>Nicolas P. Winkler</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mobile robot olfaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental monitoring</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="institutes" number="">8 Zerstörungsfreie Prüfung</collection>
    <collection role="institutes" number="">8.1 Sensorik, mess- und prüftechnische Verfahren</collection>
    <collection role="themenfelder" number="">Umwelt</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="themenfelder" number="">Sensorik</collection>
  </doc>
</export-example>
