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Precise orbits of altimetry satellites are a prerequisite for the investigation of global, regional, and coastal sea levels together with their changes, since accurate satellite positions in the radial direction are required for the reliable determination of the water surface height (distance between the altimeter position in space and the water surface). Significant progress in the improvement of altimetry satellite orbit quality has been achieved in the last 30 years increasing the orbit accuracy in the radial direction from decimeter to centimeter and even sub-centimeter level. That was possible due to the improvements in the modeling of Earth’s time variable gravity field, ocean tides, terrestrial and celestial reference frames, but also due to the accomplishments reached in the observation methods used for altimetry satellites, namely Satellite Laser Ranging (SLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), and Global Positioning System (GPS—used for some satellites). In this paper, we review the main improvements in the models used for the determination of orbits of altimetry satellites, namely, in so called Geophysical Data Records (GDR) orbit standards from GDR-C to Precise Orbit Ephemeris-F (POE-F), illustrate the impact of the improvements in precise orbit determination of these satellites on the orbit accuracy in the radial direction. Additionally we investigate orbit differences in the radial direction, single-satellite crossover differences, radial, and geographically correlated orbit errors of contemporary orbits of various altimetry satellites namely Cryosat-2, Envisat, ERS-1, ERS-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A, Sentinel-3B, and TOPEX/Poseidon derived by different institutions.

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Sleep is critical to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night's rest is going to be. For an activity that humans spend around a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is important to create a personalized sleep model. This research proposes a sleep model that can identify causal relationships between daily activities and sleep quality and present the user with specific feedback about how their lifestyle affects their sleep. Our method uses N-of-1 experiments on longitudinal user data and event mining to generate understanding between lifestyle choices (exercise, eating, circadian rhythm) and their impact on sleep quality. Our experimental results identified and quantified relationships while extracting confounding variables through a causal framework. These insights can be used by the user or a personal health navigator to provide guidance in improving sleep.