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The physics of communication give an advantage to multi-hop systems, because the ratio of power received to power transmitted on a telecommunications link over a fixed distance S increases by a factor of n(sup 2) if the link is divided in to n equal hops of length S/n.
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In this paper, we describe a new discrete recurrent model with discrete external stacks for learning context-free grammars (or pushdown automata).
In support of the Adaptive Compliant Trailing Edge [ACTE] project at the NASA Armstrong Flight Research Center, displacement transfer functions were applied to the swept wing of a Gulfstream G-III airplane (Gulfstream Aerospace Corporation, Savannah, Georgia) to obtain deformed shape predictions. Four strainsensing lines (two on the lower surface, two on the upper surface) were used to calculate the deformed shape of the G III wing under bending and torsion. There being an insufficient number of surface strain sensors, the existing G III wing box finite element model was used to generate simulated surface strains for input to the displacement transfer functions. The resulting predicted deflections have good correlation with the finite-element generated deflections as well as the measured deflections from the ground load calibration test. The convergence study showed that the displacement prediction error at the G III wing tip can be reduced by increasing the number of strain stations (for each strain-sensing line) down to a minimum error of l.6 percent at 17 strain stations; using more than 17 strain stations yielded no benefit because the error slightly increased to 1.9% when 32 strain stations were used.
The displacement transfer functions (DTFs) were applied to the GIII swept wing for the deformed shape prediction. The calculated deformed shapes are very close to the correlated finite element results as well as the measured data. The convergence study showed that using 17 strain stations, the wing-tip displacement prediction error was 1.6 percent, and that there is no need to use a large number of strain stations for G-III wing shape predictions.