# Skyfall: Signal Fusion from a Smartphone Falling from the Stratosphere
## 2 Summary of Systems, Sensors, and Signals
Extension of calibration methods to the infrasound range were performed by [PERSON] [35; 36] on an ensemble of Samsung S8 smartphones. Model-by-model calibration of smartphone sensor systems is a daunting task. By the time a study is completed and published (e.g., [35; 36]), new models, apps, and operating system updates can render the results obsolete. It is a Sisyphean task to defend a given model's sensor stack; we instead focus on the signals that are produced by the sensor systems and develop robust data models and transportable signal processing methods that can be implemented across diverse platforms. Even without precise calibrations, smartphones can produce useful data. The embedded accelerometers and Global Navigation Satellite System (GNSS) receivers of smartphones have proven useful in earthquake monitoring studies. [PERSON] et al. [16] confirmed the capability of smartphones in detecting displacements from moderate and larger earthquakes, and proposed the use of crowdsourcing with smartphone data to achieve Earthquake Early Warning Systems. The MyShake app [17; 18] developed for the collection and analysis of earthquake data has matured and integrated into operational early warning systems [19]. Shortly after the infrasound app concept was proposed and developed [20] to extend the smartphone microphones to the infrasound range, it was applied to volcano monitoring [37], near-shore underwater and surf observations [37; 38], and nuclear reactor monitoring [39; 25; 40]. Extension to the audio range and the inclusion of additional sensor channels allowed improved surface explosion characterization [21; 41; 42], as well as the collection of moving aircraft [43], rocket [44; 45], and space craft [46] signatures in microphone, barometer, accelerometer, and gyroscope sensors. The 2022 Tonga eruption pressure signal was clearly observed by smartphone barometers in Hawaii and elsewhere at distances of thousands of kilometers [22], opening new possibilities for global monitoring applications. Digital data collections in high-altitude balloons are relatively new and are considered viable platforms for the exploration of Venus (e.g., [24; 47]), where the ground is too hot for surface deployments. High-altitude balloons present an extreme environment outside the design parameters of most consumer electronics, and rapid technical improvements in recent years [23; 24; 47; 48; 49] have made it possible to observe a wider range of signals. In the next section we present the geophysical and environmental data recorded by the RedVox Android application on a S10 smartphone during Skyfall. As mentioned in the Introduction, one of our educational goals is to demonstrate how to access and process the heterogeneous multimodal signals typically available in a smartphone. To this end, raw data and Python open-source software are provided in [50; 51; 52; 53; 54; 55; 56; 57]. The raw data are provided as an event report in the RedVox Cloud interface at red-vox.io [50]. The methods and Python code used to produce the figures are provided as a GitHub repository [51]. The RedVox app's Application Programming Interface (API) is available online as an open repository [52]. The Python Software Development Kit (SDK) for the RedVox app is also openly available [53] to facilitate the loading and inspection of the raw data. The open-source Redpandas framework [54] builds on the Pandas [55] serialized data structures and is used to process the Skyfall smartphone data. Standardized time-frequency representations use a quantized Gabor atom framework [56] and incorporate a multiresolution weighted wavelet z-transform [57]. Various digital signal processing algorithms and geophysical methods are documented in these open-source repositories, and the interested reader is invited to look into the functions imported to process the Skyfall data. With the raw data [50] and open-source software [51; 52; 53; 54; 55; 56; 57], a reader with minimal Python experience will be able to reproduce the results and use the paper as a guide.