No abstract

Herein, the effect of Mn content on the characteristics and the formation of inclusions in light‐weight Fe–Mn–Al steels is investigated. Three laboratory‐produced steels, containing different manganese contents (2%, 5%, and 20%) are investigated. 2D and 3D inclusion characterization methods are used to establish inclusion classification rules for oxide, sulfide, and nitride inclusions using an automated scanning electron microscope (SEM) equipped with energy‐dispersive X‐ray spectroscopy (EDS) (ASPEX system). The observed inclusions are classified into Al2O3(pure), Al2O3–MnS, AlN(pure), AlN–MnS, AlON–MnS, AlON, and MnS. The results show that an increased Mn content of steel increases the number of inclusions, especially Al2O3–MnS and AlN–MnS inclusions. In the case of Al2O3–MnS inclusions, Al2O3 inclusions serve as the site for precipitation of MnS. Thermodynamic calculations suggest that the AlN‐containing inclusions formed during cooling and solidification of steels. Moreover, the formation of AlN–MnS inclusions can take place by the nucleation of MnS on AlN inclusions and vice versa.

In this paper, the BATS project is presented, which aims to track the behavior of bats via an ultra-low power wireless sensor network. An overview about the whole project and its parts like sensor node design, tracking grid and software infrastructure is given and the evaluation of the project is shown. The BATS project includes a lightweight sensor node that is attached to bats and combines multiple features. Communication among sensor nodes allows tracking of bat encounters. Flight trajectories of individual tagged bats can be recorded at high spatial and temporal resolution by a ground node grid. To increase the communication range, the BATS project implemented a long-range telemetry system to still receive sensor data outside the standard ground node network. The whole system is designed with the common goal of ultra-low energy consumption while still maintaining optimal measurement results. To this end, the system is designed in a flexible way and is able to adapt its functionality according to the current situation. In this way, it uses the energy available on the sensor node as efficient as possible.

No abstract