### Table 1. Some de nite values in the domains of k0; pF for zeros in the dispersion relation eq. (1), using the interaction in eq. (2) and F (k) = e?(k=6:2). The minimum value (!2)min which occurs at kc for the xed pF is also tabulated, with kc.

"... In PAGE 8: ... (3) makes possible an estimate of the condensate energy density [F 5] Ec=V = ?j(!2 min)j2=16 ?1:5MeV fm3 (6) In this estimate we have used !2 min apos; ?0:9 and 1:7 for kc 1:9, with m . As a check on the approximate stability of the results in Table1 , we have ex- amined the domains of k0; pF for zeros in the dispersion relation in eq. (1), using the standard analytical form for the Lindhard function as evaluated in a spherical system [14].... In PAGE 20: ... [F 5 ] The standing{wave form A sin kxx which could give a more negative conden- sate energy density may evolve later, in particular for a \ 0 quot; condensate. [F 6 ] For the examples listed in Table1 , the approximate value is 4D 2. [F 7 ] In [1] we have utilized these numerical estimates in a phenomenological repre- sentation of the localized structure in the di erential cross section for di rac- tive, elastic scattering.... ..."

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### Table 1. Some de nite values in the domains of k0; pF for zeros in the dispersion relation eq. (1), using the interaction in eq. (2) and F (k) = e?(k=6:2). The minimum value (!2)min which occurs at kc for the xed pF is also tabulated, with kc.

"... In PAGE 8: ... (3) makes possible an estimate of the condensate energy density [F 5] Ec=V = ?j(!2 min)j2=16 ?1:5MeV fm3 (6) In this estimate we have used !2 min apos; ?0:9 and 1:7 for kc 1:9, with m . As a check on the approximate stability of the results in Table1 , we have ex- amined the domains of k0; pF for zeros in the dispersion relation in eq. (1), using the standard analytical form for the Lindhard function as evaluated in a spherical system [14].... In PAGE 20: ... [F 5 ] The standing{wave form A sin kxx which could give a more negative conden- sate energy density may evolve later, in particular for a \ 0 quot; condensate. [F 6 ] For the examples listed in Table1 , the approximate value is 4D 2. [F 7 ] In [1] we have utilized these numerical estimates in a phenomenological repre- sentation of the localized structure in the di erential cross section for di rac- tive, elastic scattering.... ..."

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### Table 2: Model parameters of all inputs and outputs The simulation results shown in gure 5 and 6 have been obtained using the Pad e Approximation of the matrix exponential. In gure 7 and 8 the results are illustrated which have been obtained calculating the eigenvalues and eigenvectors with the orthonormaliza- tion procedure. As this method is working in the fre- quency domain, the comparison with FREACS{results was done by calculating ve periods of the signal to en- sure the steady state of the voltages.

1994

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### Table 1. FastSLAM 2.0 Simulation Parameters

"... In PAGE 2: ... We use a FastSLAM 2.0 simula- tor with the simulation parameters in Table1 . The sensor model is assumed to be known and both an accurate sensor model and two inaccurate sensor models are used to run a SLAM algorithm.... ..."

### Table 10 Web 2.0 Sites and the URL for their API

"... In PAGE 22: ... Fortunately, many of the Web 2.0 services have APIs to help automate interaction, as is shown in Table10 . Our group will be working with these APIs as we develop our digital library.... In PAGE 22: ... This will facilitate identification and in some cases collection of information, when access permissions allow. lt;Have Table10 around here. gt; ... ..."

### Table 9 Proportional hazard model of retention-NELS-88

### Table 1: HSC Domain Model Changes Detected by Sleuth Domain Changes HSC v2.0.0 vs v1.0.2 HSC v2.0.1 vs v2.0.0

1999

"... In PAGE 4: ... Regression testing techniques di er depending on focus and objectives. Table1 shows a sam- pling of techniques in various categories. These include white-box versus black-box testing and testing of procedural languages rather than object-oriented systems.... In PAGE 5: ... Most of the research has been in the area of white-box testing. This paper focuses on the automation of regression testing for black-box testing (shown as stars in Table1 ). Speci - cally, we base our regression testing approach on domain-based testing (von Mayrhauser et al.... In PAGE 5: ..., 1994c). Table1 : Regression Testing Regression Testing Concept Proc. Lang.... In PAGE 9: ...Table1 : Domain Analysis Steps for Domain Based Testing (von Mayrhauser et al., 1994c) Domain Analysis Step Domain Model Component 1.... In PAGE 9: ... Modi cations to the domain model create testing subdomains that describe parts of the software that should be tested, or focus on error recovery by providing a testing subdomain that describes erroneous behavior for the domain. Table1 lists the steps for the domain analysis and the domain model components generated at each step. The next subsections describe details of applying this analysis (von Mayrhauser et al.... In PAGE 16: ... If, on the other hand, a command is not in Cmd, none of its parameters is in Param, but at least one of its parameters is in Factor, then the command is indirectly a ected by changes. Table1 shows the de nitions of the three sets Cmd, Param, and Factor. Table 2 shows how to construct Cmd, Param and Factor.... In PAGE 17: ... None are considered indirectly a ected. Table1 : Sets used for determining regression test subdomain Cmd set of commands directly a ected by change Param set of parameters directly a ected by change Factor set of parameters indirectly a ected by change Based on these two regression testing approaches, small impact or large impact, two di erent regression subdomains are built, the basic and the extended regression subdomain. The basic regression subdomain consists of, C = Cmd [ f cjc has at least one parameter 2 P aramg That is, only commands that are directly impacted by domain model changes are included.... In PAGE 21: ... According to the algorithm we explained in the previous section, CDS is considered a directly a ected command. Thus CDS is shown as one of the changed commands in Table1 , and is included in the basic regression subdomain automatically gen- erated by Sleuth (refer to Table 2). Tables 1 and 2 show the domain changes detected and the regression subdomains generated by Sleuth.... ..."

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### Table 1. Patterns with the lowest values of the Lyapunov function and their corresponding ranges of . These values correspond to mixed capillary-gravity waves with ?0 = 1=3. range of favored pattern

"... In PAGE 20: ...alue of the self-interaction nonlinear coe cient g(0) becomes larger (see Fig. 5). In other words, the curve g(cjl) has a wider at center region and a sharper increase near cjl = 1, and therefore pattern structures with larger N are favored. Table1 shows the favored structures, and the corresponding ranges of . A couple of comments on the various patterns discussed above are in order.... ..."

### Table 1: The detection limits used for the 24 chemical species (SO4 = SO4; NO3 = NO3;

2001

"... In PAGE 7: ...latitude 82.3 N, longitude 62.5) by the Atmospheric Environment Service of Canada since July 1980. The data we use here are the same as those analyzed by Xie, Hopke, Paatero, Barrie, and Li (1999), and consist of the concentrations of 24 particulate constituents (listed in Table1 ) measured in samples obtained between September 1980 and August 1991, cov- ering a time period of 572 weeks. The units for the data are nanograms of the individual... In PAGE 8: ... In general, assessing detection limits/critical values is not an easy task (see, for example, Lambert, Peterson, and Terpenning (1991), Singh (1993), Currie (1995), Spiegelman and Tarlow (1997), and references therein). Here, we use the detection limits provided in Table1 , which are treated as known. The censored data are thus assumed to be censored at random (Heitjan and Rubin, 1991).... ..."

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### Table 24: Domain Reuse Example Num HSC 1.2 HSC 2.0

1999

"... In PAGE 43: ... We included this command in the SETUP script- ing class. Table24 compares HSC1.... ..."

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