### Table 3.1: Cumulative Solution Times for Long Trajectories Trajectory Design Without Initial Guess With Initial Guess

2002

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### Table 1: Accuracy of position model Ball trajectory: The test is conducted on 15 long shot sequences (176 seconds in total). These sequences are representative in that they have different length, view type and ball appearance. Table 2 shows the overall tracking accuracy and more detailed results can be found in [6].

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### Table 2 Comparison between the trajectories by linkages and humans Lateral chewing Vertical chewing

in 1 2

2007

"... In PAGE 4: ... 100 To make the actual linkage chewing device to be as compact as possible, a smallest feasible physical crank 101 chosen is 10 mm long when its pivotal and joint bearings are taken into account. 102 The chewing trajectories by the linkages are compared with those from real measurements of human chew- 103 ing in Table2 . It can be found that in terms of the occlusal angles, the linkage can achieve a close match with 104 the lateral chewing trajectory while still having reasonable trajectories for the vertical chewing; however, the 105 linkage has larger vertical opening and lateral displacements.... ..."

### Table 8 Two versions of a 15-State Usage Model: trajectory entropy

"... In PAGE 9: ... For both versions of the 15-state usage model in Fig. 2, Table8 gives the matrix of trajectory entropies as defined by Ekroot and Cover [2]. For the model with uniform transition probabilities, H(T{Inv, Term}) 19.... In PAGE 9: ...erm}) 11.144. The second version has fewer typical paths from invocation to termination than the first. The entries in Table8 provide a measure of the uncertainty in selection of a path between two states where that path occurs within a long-run chain of states generated by the usage model. Table 9 gives the matrix of test trajectory entropies where C S: Blank cells in the matrices indicate that there are no paths in the corresponding trajectory.... In PAGE 9: ... The entries in this table provide a measure of the uncertainty in selection of a path between two states where that path occurs within a single test case. Note that, except for the last two rows, the right hand column of each matrix corresponds to those of Table8 . The value for each element of a given row of Table 9 is less than or equal to the value of the right-hand column element of the correspond- ing row.... ..."

### Table 1: Trajectories measured.

2000

"... In PAGE 19: ... Ip = mpr2 p 2 = 7:168 10?6kg m2. Table 2 shows the measured pre-impact and post-impact velocities for nine sample trajectories from Table1 . By substituting the pre-impact velocities into the no sliding condition (48), we predict that sliding will not terminate in trajectories 4,5, and 9 and will terminate in the other trajectories.... ..."

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### Table 1: Trajectories measured.

2000

"... In PAGE 19: ... I p = m p r 2 p 2 =7:168 10 ;6 kg m 2 . Table 2 shows the measured pre-impact and post-impact velocities for nine sample trajectories from Table1 . By substituting the pre-impact velocities into the no sliding condition (48), we predict that sliding will not terminate in trajectories 4,5, and 9 and will terminate in the other trajectories.... ..."

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### Table 14: Results for the tremor and ionosphere data for longer simulation times. The fourth column (`ARD long apos;) shows the estimated generalisation error (in terms of the negative log likelihood) obtained with ARD after a substantial increase of the equilibration and sampling periods. For the tremor data, the simulation time was increased by a factor of 25 (that is, 25 times 400 hybrid Monte-Carlo trajectories over 1000 leapfrog steps each were computed), for the ionosphere data the simulation time was increased by a factor of 50 (that is, 50 times 600 hybrid Monte-Carlo trajectories over 1000 leapfrog steps each were computed). The rst half of the con gurations were discarded (equilibration period). The columns entitled `ARD normal apos; and `no ARD normal apos; repeat the results of the earlier simulations with `normal apos; simulation times.

1999

"... In PAGE 36: ...urations thus sampled are shown in Table14 . It is seen that increasing the equilibration and sampling times consistently improves the generalisation performance.... ..."

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### Table 1: Matching trajectories

2005

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### Table 1: Pooled statistics of qualitative measures for each configuration.

"... In PAGE 3: ... Qualitative Measures In addition to geographical data analysis, we developed qualitative measures to address our hypothesis that there might be some structure to the movements we observed. Qualitative measures in this study comprise 38 characteristic variables in representing specific patterns of trajectories ( Table1 ). Each variable was deduced from the trajectories drawn by participants.... In PAGE 3: ...86 indicating strong agreement among researchers and an acceptable qualitative coding approach. Pooled statistics ( Table1 ) of each qualitative measure reveal that in the line configurations with and without obstacles as well as the circular configuration, participants tended to initially move away in reference to target locations. Corresponding patterns of trajectories to each column in Table 1 are shown in Figure 2.... In PAGE 3: ... Pooled statistics (Table 1) of each qualitative measure reveal that in the line configurations with and without obstacles as well as the circular configuration, participants tended to initially move away in reference to target locations. Corresponding patterns of trajectories to each column in Table1 are shown in Figure 2. Additionally, the top two variables that were highly consistent in each configuration are summarized in Table 2.... In PAGE 5: ... As a result, a total of 34 qualitative variables were analyzed using the SOM technique. Table1 details a full description of each variable. Subplots in Figure 7 illustrate characteristics of the 34 variables, relative to all other variables.... ..."

### Table 3. JUPITER ORBITER TRAJECTORIES

"... In PAGE 13: ... The closest approach altitudes for these gravity assists were constrained to 300 km unless the optimization criteria aUowed more distant flybys. Table3 presents a tabulation of the results of this comparison. As expected the indirect transfer trajectory yields the lowest performance with the trajectory with a single Venus gravity assist slightly better.... In PAGE 13: ...ssist. The best performance is realized for the Venus-Earth gravity assist trajectory. Note that these conclusions only apply to the particular examples presented here. Other launch opportunities may make those trajectories involving a Venus gravity assist either better or worse than those shown in Table3 . In general the performance for both the indirect trajectory and single Earth gravity assist trajectory vary only slightly from one Earth-Jupiter launch opportunity to the next.... In PAGE 14: ...A plot of the trajectory for the Earth Gravity Assist mission in Table3 is shown in Figure 9 below. This gravity assist trajectory trajectory differs from the more usual two year SEP Earth gravity assist trajectory shown later in this paper for a Uranus orbiter mission in using a shorter one and a quarter year Earth return.... ..."