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Table D-1. Severity Classification scheme in ATM

in WEB Site: www.eurocontrol.int
by Richard J. Kennedy (boeing, Keith Slater (nats, Andrea Pechhacker, Centre De Bois Des Bordes 2005

Table 1: Several classes of location-aware tasks.

in Location-Aware Scheduling With Minimal Infrastructure
by John Heidemann, Dhaval Shah 2000
"... In PAGE 2: ... We believe that many of the tasks which would benefit from location awareness require a much more refined definition of location than simple physical location as provided by GPS. Table1 breaks these examples into several classes. Although some tasks depend on physical location, many tasks depend on hardware or some external computing capability.... ..."
Cited by 1

Table 1: Several classes of location-aware tasks.

in Location-Aware Scheduling With Minimal Infrastructure
by John Heidemann, Dhaval Shah 2000
"... In PAGE 2: ... We believe that many of the tasks which would benefit from location awareness require a much more refined definition of location than simple physical location as provided by GPS. Table1 breaks these examples into several classes. Although some tasks depend on physical location, many tasks depend on hardware or some external computing capability.... ..."
Cited by 1

Table 1 gives the stacknumber and queuenumber for several classes of planar graphs;; the results

in Stack and Queue Layouts of Halin Graphs
by Joseph L. Ganley 1995
"... In PAGE 2: ... Table1 : Stack- and queuenumbers for some classes of planar graphs.... ..."
Cited by 5

Table 1: Openness of several classes of plane straight line graphs.

in Maximizing Maximal Angles for Plane Straight Line Graphs
by Oswin Aichholzer, Thomas Hackl, Michael Hoffmann, Clemens Huemer, Francisco Santos, Bettina Speckmann , Birgit Vogtenhuber
"... In PAGE 2: ...Table1... ..."

Table 2 Detection conditions for several fault classes.

in Comparison of Fault Classes in Specification-Based Testing
by Vadim Okun , et al.
"... In PAGE 11: ...The notation SF is used to represent the detection condition for an arbitrary fault belonging to fault class F . The detection conditions for fault classes CRF, CNF, ENF, CCF, and CDF are summarized in Table2 . There, x is a clause in S, y is another valid clause 3 , and X is an expression in S.... In PAGE 11: ... 4 Proof. By Table2 , we must establish that, for a predicate P and a clause x occurring in P , dP x y ! dP x x holds, where y 6 = x is another valid clause. Rewriting with (9), we have ((x apos; y) ^ dP dx ) ! ((x apos; x) ^ dP dx ) Since x apos; x = 1, and in view of (7), the Theorem holds.... In PAGE 12: ... Proof. By Table2 , we must establish that, for a predicate P , with a clause x occurring in a subpredicate E of P , dP xx ! dP E E holds. Rewriting with (9), we have ((x apos; x) ^ dP dx ) ! ((E apos; E) ^ dP dE ) Since the exclusive-or of a predicate with its negation is trivially true, this can be rewritten as dP dx ! dP dE Since clause x is a subpredicate of E, the theorem follows from Lemma 1.... In PAGE 13: ... Proof. By Table2 , we must establish that, for a predicate P and a clause x occurring in P , ((dP x x^y _ dP x x_y) $ dP x y) ^ (dP x x^y _ dP x x_y) where y 6 = x is another valid clause. By (9), the detection conditions for CRF, CCF, and CDF are dP x y = (x apos; y) ^ dP dx dP x x^y = (x apos; (x ^ y)) ^ dP dx = x y ^ dP dx by (2) dP x x_y = (x apos; (x _ y)) ^ dP dx = xy ^ dP dx by (4) The disjunction of the detection conditions for CCF and CDF is dP x x^y _ dP x x_y = (x y ^ dP dx _ xy ^ dP dx ) = (x apos; y) ^ dP dx (10) Additionally, x y ^ dP dx _ xy ^ dP dx = x _ y _ x _ y _ dP dx = 1 (11) In view of (10) and (11), the Theorem holds.... ..."

Table 1. Means and standard deviations (in parenthesis) of the percent cover of organic and inorganic ground cover components by burn severity class. N is the number of plots in each burn severity class.

in ABSTRACT POST-WILDFIRE GROUND COVER MAPPING BY SPECTRAL UNMIXING OF HYPERSPECTRAL DATA
by Sarah A. Lewis, Civil Engineer, Andrew T. Hudak, Peter R. Robichaud, Leigh B. Lentile, Fire Ecologist, Penelope Morgan, Fire Ecologist, Michael J. Bobbitt, Gis Analyst
"... In PAGE 4: ...g., green vegetation and NPV), charred organics (burned shrub stems and needles), uncharred inorganics (rocks and soil), and charred inorganics (rock, soil, and ash) ( Table1 ). These classes broadly relate to burn severity and erosion potential as post- fire organic ground cover decreases erosion potential by protecting the soil from wind or water.... ..."

Table 3 Recognition rates on the Test Set, in absence of rejection, for several class numbers

in
by Francesco Camastra A, Ro Vinciarelli B 2001
"... In PAGE 9: ... The number of LVQ codevectors, assigned to each class, was proportional to the a-priori class probability. In Table3 , for diVTerent class numbers, the performances on the test set, measured in terms of recognition rate in absence of rejection, are reported. The performance is shown to be improved by decreasing the number of classes when this is higher than an optimal value (in this case 39).... ..."

Table 2: Results of simulating several classes of agents searching for a service on a MANET.

in Network Awareness and the Philadelphia Area Urban Wireless Network Testbed
by Joseph B. Kopena, Kris Malfettone, Evan Sultanik
"... In PAGE 4: ...igration failure, e.g. disconnection in transit. This simulation also makes three large assumptions: correct network topology is instantly, globally available; correct service knowledge is in- stantly, globally available; service matching is instantaneous. Table2 presents results for 36 trials, each consisting of 1,000 agents per class over 15,000 iterations.... ..."

Table 2. We have computed the Cholesky factorization of several classes of tridiagonal matrices, including (a) the matrix

in A Fast Parallel Cholesky Decomposition Algorithm for Tridiagonal Symmetric Matrices
by Ilan Bar-on, Bruno Codenotti, Mauro Leoncini
"... In PAGE 14: ... Section 6). Table2 gives the running times for each of the following stages of the algorithm. 1.... In PAGE 15: ...850 speed-up 1322 1302 1240 Table 2 Computational examples on the CM5, q = 216. The results shown in Table2 are those obtained for the matrix T above. Very similar results have been obtained with the other test cases.... ..."
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