# Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic

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## Abstract

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## 1. Introduction

#### 1.1. Status Quo of Technology-Aided Contact Tracing

#### 1.2. Cabin Operations

#### 1.3. Focus and Structure of the Document

## 2. RSS Distance Estimation

## 3. Radiowave Simulation

- Empirical approaches rely on underlying measurements and describe the conditions in which the data was aggregated.
- Semi-empirical approaches enhance empirical models by additionally considered general factors, like underlying physics, however, these are difficult to utilize in demanding propagation scenarios [32].
- Numerical approaches try to directly solve Maxwell’s equation [33]. These methods require a lot of computation power and time and it is generally not possible to analytically solve the electromagnetic field in complex real-world propagation scenarios.
- Ray tracing approaches describe possible propagation paths of an emitted signal, obtained from a Maxwell high-frequency approximation [34], where rays are also used to describe several propagation phenomena.

## 4. Application of Radiowave Simulation on Aircraft Cabin

## 5. Conclusions and Outlook

- Real-time proximity warning: As soon as two passengers are detected below a given proximity threshold, an audio or vibration alarm is triggered in order to warn the respective persons.
- Post-processing contact tracing: Radio beacons or smartphone apps are used in order to store proximity information. If a person was tested positive afterward, all former contacts could be informed about this critical encounter. This scenario resembles the scheme of existing contact tracing apps.
- Boarding/deboarding scheduling: In order to ensure distancing while boarding/deboarding, passengers may be allocated to distinct groups, which are processed after each other. Group affiliation, boarding path, and time may be communicated via the beacon’s visual interface.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Three-dimensional model of an Airbus A321 cabin, including possible signal reception paths (blue rays) from a transmitter (blue ball, left side) to a possible receiver location (right side) obtained from a radio propagation simulation. Additionally signal reception power for various receiver areas is shown.

**Figure 3.**Optimized individual boarding sequence considering 31 passenger groups and a distance of 1.6 m between passenger groups using a single-aisle aircraft as reference [25].

**Figure 7.**Depiction of resulting errors in distance estimation as a function of RSSI measurement errors: Reference distance of $1.5\mathrm{m}$ (left), $2\mathrm{m}$ (middle) and $5\mathrm{m}$ (right) with respect to common path-loss exponents $\eta =1.7,\phantom{\rule{4.pt}{0ex}}2,\phantom{\rule{4.pt}{0ex}}4$.

**Figure 9.**Propagation scenario within the aircraft cabin: (

**a**) Birdview of propagation paths from transmitter antenna (black cross, left side) to possible receiver location (last seat row, right side) and (

**b**) CIR for the propagation scenario including time delays and reception powers of the individual paths.

**Figure 10.**Three-dimensional model of cabin passenger as an addition for the proposed propagation simulation: (

**a**) shows the model of a single sitting passenger and (

**b**) depicts a fully occupied aircraft cabin model.

**Figure 11.**Future localization framework for the connected cabin visualizing a Bayesian Monte Carlo sample (purple points) positioning. The necessary inputs are the coordinates of the stationary nodes (green and red squares) and the measured distances from these (black circles).

Environmental Model | Radio Model | ||||||
---|---|---|---|---|---|---|---|

Component | Material | ${\mathit{\u03f5}}_{\mathit{r}}$ | ${\mathit{\mu}}_{\mathit{r}}$ | $\mathit{\sigma}$ [S/m] | ${\mathit{L}}_{\mathbf{trans}}$ [dB] | ${\mathit{L}}_{\mathbf{refl}}$ [dB] | ${\mathit{L}}_{\mathbf{scat}}$ [dB] |

skin | metal | 1 | 20 | 11,111 | 119.39 | 0.05 | 20 |

frames | metal | 1 | 20 | 11,111 | 444.97 | 0.05 | 20 |

stringers | polystyrene | 2.55 | 1 | 0.166 | 3.76 | 11.76 | 20 |

windows | glass | 6 | 1 | 0.006 | 1.69 | 7.53 | 20 |

furniture | teflon | 2.1 | 1 | 0.00005 | 0.3 | 14.73 | 20 |

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**MDPI and ACS Style**

Schwarzbach, P.; Engelbrecht, J.; Michler, A.; Schultz, M.; Michler, O. Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic. *Sustainability* **2020**, *12*, 8724.
https://doi.org/10.3390/su12208724

**AMA Style**

Schwarzbach P, Engelbrecht J, Michler A, Schultz M, Michler O. Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic. *Sustainability*. 2020; 12(20):8724.
https://doi.org/10.3390/su12208724

**Chicago/Turabian Style**

Schwarzbach, Paul, Julia Engelbrecht, Albrecht Michler, Michael Schultz, and Oliver Michler. 2020. "Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic" *Sustainability* 12, no. 20: 8724.
https://doi.org/10.3390/su12208724