A smartphone plummeted from a stratospheric height of 36 km, providing a near-real-time record of its rapid descent and ground impact. An app recorded and streamed useful internal multi-sensor data at high sample rates. Signal fusion with external and internal sensor systems permitted a more detailed reconstruction of the Skyfall chronology, including its descent speed, rotation rate, and impact deceleration. Our results reinforce the potential of smartphones as an agile and versatile geophysical data collection system for environmental and disaster monitoring IoT applications. We discuss mobile environmental sensing capabilities and present a flexible data model to record and stream signals of interest. The Skyfall case study can be used as a guide to smartphone signal processing methods that are transportable to other hardware platforms and operating systems.
Complex systems performing spiking dynamics are widespread in Nature. They cover from earthquakes, to neurons, variable stars, social networks, or stock markets. Understanding and characterizing their dynamics is relevant in order to detect transitions, or to predict unwanted extreme events. Here we study, under an ordinal patterns analysis, the output intensity of a semiconductor laser with feedback in a regime where it develops a complex spiking behavior. We unveil that, in the transitions towards and from the spiking regime, the complex dynamics presents two competing behaviors that can be distinguished with a thresholding method. Then we use time and intensity correlations to forecast different types of events, and transitions in the dynamics of the system.
<p>The explosive eruption of the Hunga Tonga-Hunga Ha&#8217;apai volcano on 15<sup>th</sup> of January 2022 impacted the Earth, its oceans and atmosphere on a global scale. Witnesses report an audible &#8220;bang&#8221; as a result of the event in distances of up to several thousand kilometers. With infrasound sensors this sound wave can be detected where the frequency content or the amplitude of the signal renders the event inaudible to the human ear. Infrasound sensors are distributed globally, a selection of these stations upload their data in real time to publicly available servers. In combination with Open Source libraries such as obspy or scipy it is possible to use these data sources to observe the atmospheric disturbances caused by the eruption on a global scale in near real time. With a minimum of data processing not only the first arrival peak of the atmospheric lamb wave can be identified at most stations but also further passes of the wave as it propagates around the planet several times. Having large amounts of publicly available data is crucial in that process. New data chunks can be analyzed and displayed immediately while the signal is still ongoing because data access requests are not required. Additionally, having immediate access to a large dataset allows for big data analysis and reduces the necessity to consider outliers at individual stations and increases the chance to identify the signal after multiple days when overall signal to noise ratios have decreased.</p>
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