In the rapidly growing streaming service market, including satellite options, Internet Service Providers (ISPs) face the challenge of continually optimizing network performance to deliver superior video streaming quality, which is vital to optimize customer satisfaction. This pressing need has sparked a drive towards developing advanced Quality of Experience (QoE) prediction models, which are essential in enhancing streaming protocols and guaranteeing smooth viewing experiences for users. However, the efficacy of these models hinges on the availability of extensive, diverse datasets. To fill this critical data void, our study introduces the publicly available LIVE-Viasat Real-World Satellite QoE Database, with 179 videos from real-world streaming, encompassing a range of distortions. Enhanced by a study with 54 participants providing detailed QoE feedback, our work not only provides a rich analysis of the determinants of subjective QoE but also delves into how various streaming impairments influence user behavior, thereby offering a more holistic understanding of user satisfaction.

Geosynchronous satellite (GEO) networks are a crucial option for users beyond terrestrial connectivity. However, unlike terrestrial networks, GEO networks exhibit high latency and deploy TCP proxies and traffic shapers. The deployment of proxies mitigates the impact of high network latency, while traffic shapers help realize customer-controlled data-saver options that optimize data usage. It is unclear how the interplay between GEO networks' high latency, TCP proxies, and traffic-shaping policies affects the quality of experience (QoE) for commonly used video applications. In our study, we examine this relationship through a series of video streaming experiments at a shaped rate of 900kbps. Our preliminary analysis reveals that 28% of TCP sessions (with TCP proxies) and 18% of gQUIC sessions (without TCP proxies) experience rebuffering events, while the median average resolution is only 380p for TCP and 299p for gQUIC. Additionally, we identify two key factors contributing to sub-optimal performance: (i) unlike TCP, gQUIC only utilizes 63% of network capacity; and (ii) YouTube's chunk request pipelining is imperfect. To avoid potential degradation in video quality, the satellite provider subsequently discontinued providing data saver options that shape video traffic to US residential customers.

Summary form only given. Automated seizure detection with high specificity and sensitivity is a highly desirable but elusive goal. The failure to develop a reliable system despite decades of effort is due in part to the non-stationary and noise in the EEG/ECoG signals, as well as to the rudimentary mathematical treatment it has received. We have developed a method of automated seizure detection based on a combination of linear and nonlinear filtering techniques, including the discrete wavelet transform. To minimize noise, this method was first developed for intracranial signals, then later adapted to scalp recordings. Preliminary results indicate that this new method may be the fastest and most reliable to date. The generic algorithm has been tested on 5 patients and a total of 20 seizure segments and 7 interictal segments recorded form intracranial electrodes. We have also compared the method to expert visual analysis, performed through a review of polygraph tracings of the signals, and have found our method to be both fast and highly accurate. We are generally able to detect the electrographic seizure within a second of the time marked by the EEGer. The method is also highly adaptable-it automatically accounts for signal changes over time, and has a number of parameters which may be tuned to further improve accuracy for a given individual patient or for a particular signal or group of signals being monitored. The algorithm has been implemented and now allows real-time monitoring and detection on a 486/DX 33 MHz PC.>