### Table 1. Problem 1: Constant scalar coe cients

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### Table 1: Rij is a Ricci tensor of manifold M corresponds with metric Gij. R is a scalar curvature. There gij is a metric of the Euclidean space.

### Table 19: Example Scalars from Calibrator Demo

in DISCLAIMER

"... In PAGE 5: ...Technical Notes on the EEC-IV MCU Knock Sensor - - - - - - - - - - - - - - - - - - - - 25 LIMITED OUTPUT STRATEGY (LOS) OPERATION - - - - - - - - - 25 FUNCTIONS / SCALARS / TABLES - - - - - - - - - - - - - 25 Table 17: EEC Functions - - - - - - - - - - - - - - - - - - 26 Table 18: EEC Scalars - - - - - - - - - - - - - - - - - - - 26 Table19 : Example Scalars from Calibrator Demo - - - - - - - - - - 27 A9L Constants amp; Locations - - - - - - - - - - - - - - - - 28 Table 20: EEC Tables - - - - - - - - - - - - - - - - - - - 28 Table 21: Accel Enrichment Fuel [lb/min] - - - - - - - - - - - - 28 Table 22: Startup Fuel Ratio - - - - - - - - - - - - - - - - 28 Base Fuel A:F Ratio: - - - - - - - - - - - - - - - - 29 Table 23: Base Fuel A:F Ratio - - - - - - - - - - - - - - - - 29 Table 24: Injector Timing - - - - - - - - - - - - - - - - - 29 Table 25: Injector Firing Order - - - - - - - - - - - - - - - 29 Table 26: Base Spark [Deg BTDC] - - - - - - - - - - - - - - - 30 Table 27: Altitude Base Spark [Deg BTDC] - - - - - - - - - - - - 30 Table 28: Limp Mode Spark Table [Deg BTDC] - - - - - - - - - - - - 31 Table 29: Injector Output Port Table - - - - - - - - - - - - - - 31 Table 30: Load Scaling - - - - - - - - - - - - - - - - - - 31 Table 31: MAF Transfer Function - - - - - - - - - - - - - - - 32 Table 32: WOT Spark Advance vs RPM - - - - - - - - - - - - - - 32 Table 33: WOT Spark Advance vs. ECT - - - - - - - - - - - - - - 33 Table 34: WOT Spark Advance vs.... ..."

### Table 2. Values of volume V , surface area A, average curvature jK

"... In PAGE 6: ... 23 Equation ( 13) is a discrete approximation of the total curvature integral (Gauss-Bonnet theorem 27 ) and provides information about model complexity y . Table2 lists values of the total volume, surface area, norm of the Gaussian curvature and polygon count for the models extracted from scalar volume datasets (V 1 and V 2 ), before and after the level set algorithm is applied to the volumes. We note that the polygon count drops, because of the simpli ed form of the nal extracted triangular mesh.... ..."

### Table 1. Optical parameters obtained after the optimiza- tion process. z is the vertex coordinate, R is the radius of curvature and K is the conic constant. Surface z (mm) R (mm) K

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"... In PAGE 2: ... 3. OPTO/MECHANICAL ANALYSIS OF THE OPTICAL CONFIGURATION The nominal optical con guration represented by the parameters of Table1 is only ideal because the optical surfaces can change in shape and position for di erent causes. So, small perturbations have been applied to optical surfaces to analyse the monochromatic on- axis PSF behaviour.... ..."

### Table 2. Summary of the numerical results. All models are at (curvature free) universes with a Hubble constant of H0 = 66km sec?1Mpc?1 and with adiabatic initial perturbations.

1998

### Table 3: Curvature measurements.

"... In PAGE 5: ... The tangents apos; rate of change provided an estimate of local curvature at the contact. Table3 shows the mean and standard... ..."

### Table 1 Curvature classes

in Corner

1998

"... In PAGE 5: ... As a result, H gxxgyy g2 xy 10 and K gxx gyy 2 : 11 The signs and zeros of the mean and Gaussian curvatures can be used to categorise the local surface geometry into a number of distinct topo- graphic classes. These classes are summarised in Table1 . In this paper, we are interested in the saddle-structures which are labelled as hyperbolic features in the table.... ..."

### Table 1: Curvature classes

"... In PAGE 5: ... According to the definitions given above K = 1 2( + ) (8) H = ? 2 (9) The signs and zeros of these two quantities can be used to label the surface according to curvature class. The different classes are defined in Table1 . In Figure Figure 2 we show the relationship between the classes.... In PAGE 10: ... Specifically, P(n)(Si = !) is the weight of evidence assigned to the label assignment ! at site Si for the iterative epoch n of the algorithm. These weights are initialised with the probability that label ! takes on one of the eight possibilities from Table1 . Initially, these probabilities are calculated using the computed values of H and K, together with their known covariance structure.... ..."