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Thin, flexible sheets can be patterned and bonded to form internal fluidic networks, which enable actuation, sensing, and control, but failure of these sheet-based systems—and how to take advantage of this failure—remains relatively unexplored. Here, we examine this concept using heat-sealable textiles as a material platform. We determine the effects of geometry and material processing on bond strength and burst pressure; these findings can ensure a sheet-based fluidic system is sufficiently robust for a given use case. Building on this framework, we introduce a fuse-like component into which failure is deliberately programmed. In addition to limiting damage in the case of overpressurization, we leverage this programmed failure to enable distinct capabilities including (1) the binary selection of operating modes and (2) the sequencing of a series of tasks with a single pressure input. These findings will facilitate the development of more intelligent sheet-based fluidic systems. 

The performance of Wi-Fi networks depends on the ability of devices to adapt their transmissions to dynamic channel/network conditions. Hence, “Rate Adaptation Algorithms (RAAs)” have been devised to allow nodes to select appropriate modulation and coding schemes (and other parameters) in response to varying channel/network conditions. These algorithms are neither standardized nor typically divulged by vendors, and devising a ‘performance-optimal’ RAA for specific scenario remains an active topic that necessitates a complex, multi-parameter cross-layer (PHY/MAC) approach. The ns-3 network simulator offers detailed models of the Wi-Fi medium access control (MAC) layer, including three reference RAA implementations; however testing and validation of these RAA models has been very limited to date. This paper reports on initial test and validation for ns-3 RAA models via 802.11n/ac/ax simulations. After describing the RAA scope and implementations, we explore and summarize insights from test results as to a) whether the ns-3 RAAs are able to achieve the correct rates as configuration is varied and b) how they respond to step changes in the received signal-to-noise ratio (SNR) as a means for exploring their convergence properties.