Electromagnetic Wave Simulation in Insects:  A CT Data Approach
Creators: name: Felipe, Oliveira Ribas
Publisher: name: Zenodo
Year: 2024

description: Introduction



Insects are crucial for the sustainability of ecosystems.

Telecommunication antennas emit Radio-Frequency Electromagnetic Fields (RF-EMFs) that could affect insects. As telecommunications evolve (from 4G to 5G), the exposure of insects to RF-EMFs is likely to vary [1].

Near powerful antennas, pollinators like bees face relatively high levels of RF-EMF exposure. This exposure can cause biological reactions in insects [2], yet the consequences of RF-EMF exposure on insects remain uncertain [2].


Goals





Assess the absorption of RF-EMFs in insects by utilizing detailed anatomical 3D models derived from CT scans.




Develop adaptable models compatible with EMF simulation software for various simulation scenarios.

In this poster, we focus on the process of extracting CT scan data to 3D models.



Methodology

3D Models were created using CT scans following these steps:



Sample Collection



Samples of different insects (Honeybees, Solitary Bees, and Bumblebees) were scanned in TIF format, 16-bit depth, 1920x1920 pixels.



CT Scan Processing



CT scans in raw setup, without filtering. In some cases, Python scripts were required (see Figure 1).



3D Model Creation



Processed in Blender 3D software using automatic and manual tools for cleaning up (see Figure 2).



Model Refinement



Model after cleaning up and with internal armature for rigging (see Figure 3).



Final Positioning



Model in natural position, with wings fixed, ready to be exported.



Rendering



Rendered model made in Blender Cycles for scientific illustration.



Voxelization



Voxelized model in Sim4Life ready for simulations (3.5 GHz, 20M cells).




Scripts Implementation:



Scripts have been implemented to streamline data aggregation into a unified table, capturing species name, collection site, and date.

Additionally, it compiles morphological metrics calculated in Blender alongside Sim4Life simulation parameters and outcomes, providing a consolidated view of each sample's characteristics and simulation data.


Results



25 CT scans were processed into 3D models in STL format, ready to be used in RF-EMF simulations.

Compatibility Testing:



Models were tested for compatibility with Sim4Life software.

For some models, reconstruction of missing parts such as wings and legs was necessary using references from literature.



Automation:



For manual tasks, Python scripts using Blender API workflows were created where possible to automate the work.

Scripts can generate simple structures like antennae and leg segments.



Accessibility:



Selected models have been made accessible on Sketchfab, showcasing the project's progress and outcomes.

View Models on Sketchfab


Next Steps



Conduct Simulations:



Perform simulations across each model at frequencies (1.8, 3.5, and 26 GHz) to verify whole-body averaged absorbed power, specific absorption rates, and absorbed power densities.

This will facilitate the analysis of how the distinct shapes of insects influence the results of these simulations.



Implement Simulations on Heterogeneous Models:



Assign varied dielectric properties to different parts of the insects.

This approach will enable a more detailed and accurate representation of real-world conditions.



Focus on Internal Structures:



Undertake simulations that focus on the internal structures of insects, providing deeper insights into the internal effects of RF-EMF exposure.




By executing these steps, the project aims to shed light on the complex interactions between electromagnetic fields and biological entities, offering valuable data for both scientific understanding and practical applications in the field of bioelectromagnetics.

References



Thielens, et al. Scientific Reports, 8(1), 3924, 2018.

Thielens, PE. PE 690.021, 2021. doi: 10.2861/318352


Acknowledgements

ETAIN has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No. 10105721.