Based on a cosmological N-body simulation, we analyze spatial and kinematic alignments of satellite halos within 6 times the virial radius of group-sized host halos (r vir ). We measure three different types of spatial alignment: halo alignment between the orientation of the group central substructure (GCS) and the distribution of its satellites, radial alignment between the orientation of a satellite and the direction toward its GCS, and direct alignment between the orientation of the GCS and that of its satellites. Analogously, we use the directions of satellite velocities and probe three further types of alignment: the radial velocity alignment between the satellite velocity and the connecting line between the satellite and GCS, the halo velocity alignment between the orientation of the GCS and satellite velocities, and the autovelocity alignment between the satellite orientations and their velocities. We find that satellites are preferentially located along the major axis of the GCS within at least 6r vir (the range probed here). Furthermore, satellites preferentially point toward the GCS. The most pronounced signal is detected on small scales, but a detectable signal extends out to $6r vir . The direct alignment signal is weaker; however, a systematic trend is visible at distances P2r vir . All velocity alignments are highly significant on small scales. The halo velocity alignment is constant within 2r vir and declines rapidly beyond. The halo and the autovelocity alignments are maximal at small scales and disappear beyond 1r vir and 1.5r vir , respectively. Our results suggest that the halo alignment reflects the filamentary large-scale structure that extends far beyond the virial radii of the groups. In contrast, the main contribution to the radial alignment arises from the adjustment of the satellite orientations in the group tidal field. The projected data reveal good agreement with recent results derived from large galaxy surveys.

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The design of heating, ventilation and air conditioning (HVAC) systems is crucial for reducing energy consumption in buildings. As complex cyber-physical systems, HVAC systems involve three closely-related subsystems -the control algorithm, the physical building and environment and the embedded implementation platform. In the traditional topdown approach, the control algorithm and the embedded platform are in general designed separately leading to suboptimal systems. We propose a co-design approach that analyzes the interaction between the control algorithm and the embedded platform through a set of interface variables (in this paper we address in particular sensing accuracy). We present six control algorithms that take into account the sensing error, and model the relation of control performance and cost versus sensing error. We also capture the relation of embedded platform cost versus sensing error by analysis of the collected data from a testbed. Based on these models, we explore the co-design of the control algorithm and the temperature sensing subsystem of the embedded platform to optimize with respect to energy cost and monetary cost while satisfying the constraints for user comfort level.

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