# Acoustic Rocket Signatures Collected by Smartphones
## 2 Materials and Methods  
### Data Collection  
The data used in this study were collected by a network of smartphones located 10-70 km from the launch sites. Smartphones are used in this study for several reasons, first of which is their low cost compared to traditional infrasound sensors, which allowed for a much denser network. In addition, smartphones are attributable, user-friendly, and able to run machine learning models in the field, which may prove valuable for near-real-time detection and identification studies in the future [19; 20]. Smartphones are also commercially off-the-shelf (COTS) devices, thus stations can be replaced quickly and easily by simply buying a new smartphone on location, which could be valuable for monitoring applications. There are important limitations to keep in mind when working with smartphones, however. Firstly, ground-truth information about a smartphone's on-board microphone is usually unavailable, as the information is considered proprietary. In addition, we know through observation that the model of microphone used can differ between smartphone models and even individual devices of the same make and model [21]. The frequency response of smartphones can and has been studied [22; 23; 24]; however, it is impossible to know the exact frequency response of a specific smartphone without calibration due to the microphone model being unknown. Requiring each smartphone to be individually calibrated would negate many of the previously discussed strengths of using a smartphone network. Thus, we instead leave all the phones uncalibrated, display normalized amplitudes to avoid assigning meaning to uncalibrated values, and focus on the signal-to-noise ratio and information in the time-frequency domain, in which important characteristics of acoustic signals (such as shape) have been shown to remain stable between traditional infrasound microphones and smartphones [23; 25] over the relatively unstable shape and amplitude of the signal in the time domain. However, possible inconsistencies due to the lack of calibration must still be kept in mind during analysis. For those interested, examples of how signals collected by smartphone microphones compare to those collected by traditional sensors can be found in previous studies [21; 22; 23; 24; 25], and the raw amplitudes of the waveforms as well as the makes, models, and unique identification numbers of the smartphones are included in the dataset. Each of the phones collected data from multiple internal sensors through the RedVox Android application [26], the specifications of which are detailed in [27]. While data from multiple sensors can be collected using the RedVox application, the only sensor relevant to this work is the microphone. The smartphone microphones collected acoustic data at a sampling rate of 800 Hz and uploaded the data in near-real time, after which they were downloaded from the cloud using the Redvox SDK [26] and processed in Python [28] using open-source packages [26; 29; 30; 31; 32; 33]. The makes and models of the smartphones represented in ASTRA are shown in Figure 1 along with the distributions of rocket types and propagation ranges. The data were largely recorded by Samsung Galaxy models (see Figure 1a), as the network was originally deployed with Galaxy S8 phones, which were replaced in time with S10s, and finally with S20s. The other models represented in ASTRA originate from instances where a single station was replaced or when other smartphones were temporarily added to the network. As the network's day-to-day operation was performed by citizen scientists, not every phone in the network was turned on and recording for every launch, resulting in some individual launches being recorded by only a subset of the phones. The vast majority of the signals (949 out of 1089) are of SpaceX Falcon 9 launches (see Figure 1b), as Falcon 9s were the most commonly launched rocket during the collection period.