In order to verify the robustness of the proposed algorithm, two cases of experiments with respect to different environment parameters are simulated as follows: Case 1: detection probability P D =0.9, measurement noise deviation σ r =10 m and (sigma _{dot r}=0.1 m/s), clutter measurement density ρ=0.001 (n u m b e r/m 2.s −1), Gaussian disturbance deviation σ I =10 m.
This number differs from the total number of data points possible in our other analyses, where we calculated a weighted average of the SMDs and 95% CIs in the cases of experiments that had more than one treatment arm.
Moreover, in the case of Experiment C, it does not make sense to present the estimates without occupational fixed effects.
When the vocabulary is kept the same as in the case of Experiment 1, Experiment 3, and Experiment 6 the proposed methods always outperform the baseline perplexity results.
However, in the case of Experiment A, the two estimates are very similar, which indicates that there is no perceived group difference in the variance of unobservables.
In the case of Experiment 3, the whole image was rotated 180°, but this was done before the addition of the black cue bracket.
In the case of experiment using satellite, a conditional setup is carried out prior to the experiment as follows: Master LMS, placed in ITS, having done setup for a particular course to be available and sharable using Master synchronizer console.
In case of experiment design to identify nonlinear Takagi-Sugeno (TS) models, non-model-based approaches or OED restricted to the local model parameters (assuming the partitioning to be given) have been proposed.
On the phone, participants were given information about the timing but, importantly, in case of Experiment 2, not about the specific content of the study and underwent a structured psychological interview (screening questions for axis-I disorders and psychotic disorders of the Structured Clinical Interview for DSM Disorders [SCID; german version: 57]).
