"... In PAGE 9: ...raffic.............................................................................................................................2-15 Table3 -1.... In PAGE 9: ...able 3-1. Percentiles of Average Daily GA Operations at TAF Airports............................3-1 Table3 -2.... In PAGE 9: ...able 3-2. Samples of ETMS Data in the Distance Distribution Estimation.........................3-4 Table3 -3.... In PAGE 9: ...able 3-3. Distance (nmi) Statistics for Combined Data Sets................................................3-6 Table3 -4.... In PAGE 9: ...able 3-4. Model Parameters for Weibull Distribution .........................................................3-6 Table3 -5.... In PAGE 9: ...able 3-5. Estimated Distance (nmi) Statistics Using a Weibull Distribution......................3-6 Table3... In PAGE 30: ... However, typical airports have an average of only 13 operations a day, or about one an hour. Table3 -1 shows the average total itinerant GA operations for all TAF airports in 1998. Table 3-1.... In PAGE 30: ... Table 3-1 shows the average total itinerant GA operations for all TAF airports in 1998. Table3 -1. Percentiles of Average Daily GA Operations at TAF Airports Cumulative distribution (%) 1 5 255075909599 Percentile 0.... In PAGE 32: ... GA FLIGHT PROFILE Distance Distributions We need to construct the probability distribution function, based on ETMS, for single engine, multi-engine, and jet equipment categories. We selected 12 ETMS samples, shown in Table3 -2, to include different seasons, days of the week, and... In PAGE 33: ... Table3 -2. Samples of ETMS Data in the Distance Distribution Estimation Date Time Day 6/19/00 0900 MON 6/10/00 1500 SAT 6/10/00 0900 SAT 5/23/00 0900 TUES 5/23/00 1800 TUES 3/29/00 1200 WED 10/1/99 2000 FRI 9/30/99 1200 THURS 9/29/99 1200 WED 9/28/99 1600 TUES 4/16/99 0800 FRI 4/16/99 1200 FRI Figures 3-1 through 3-3 show histograms of the flight distance for the combined data.... In PAGE 35: ... The parameters for the combined sample are shown in Table 3-3. Table3 -3. Distance (nmi) Statistics for Combined Data Sets Single engine Multi-engine Jet engine Mean 227.... In PAGE 35: ...18 3.99 Table3 -4. Model Parameters for Weibull Distribution Single engine Multi-engine Jet engine Scale 237 289 826 Shape 1.... In PAGE 35: ...16 1.14 Table3 -5. Estimated Distance (nmi) Statistics Using a Weibull Distribution Single engine Multi-engine Jet engine Mean 227.... ..."

"... In PAGE 9: ...raffic.............................................................................................................................2-15 Table3 -1.... In PAGE 9: ...able 3-1. Percentiles of Average Daily GA Operations at TAF Airports............................3-1 Table3 -2.... In PAGE 9: ...able 3-2. Samples of ETMS Data in the Distance Distribution Estimation.........................3-4 Table3 -3.... In PAGE 9: ...able 3-3. Distance (nmi) Statistics for Combined Data Sets................................................3-6 Table3 -4.... In PAGE 9: ...able 3-4. Model Parameters for Weibull Distribution .........................................................3-6 Table3 -5.... In PAGE 9: ...able 3-5. Estimated Distance (nmi) Statistics Using a Weibull Distribution......................3-6 Table3... In PAGE 30: ... However, typical airports have an average of only 13 operations a day, or about one an hour. Table3 -1 shows the average total itinerant GA operations for all TAF airports in 1998. Table 3-1.... In PAGE 30: ... Table 3-1 shows the average total itinerant GA operations for all TAF airports in 1998. Table3 -1. Percentiles of Average Daily GA Operations at TAF Airports Cumulative distribution (%) 1 5 255075909599 Percentile 0.... In PAGE 32: ... GA FLIGHT PROFILE Distance Distributions We need to construct the probability distribution function, based on ETMS, for single engine, multi-engine, and jet equipment categories. We selected 12 ETMS samples, shown in Table3 -2, to include different seasons, days of the week, and... In PAGE 33: ... Table3 -2. Samples of ETMS Data in the Distance Distribution Estimation Date Time Day 6/19/00 0900 MON 6/10/00 1500 SAT 6/10/00 0900 SAT 5/23/00 0900 TUES 5/23/00 1800 TUES 3/29/00 1200 WED 10/1/99 2000 FRI 9/30/99 1200 THURS 9/29/99 1200 WED 9/28/99 1600 TUES 4/16/99 0800 FRI 4/16/99 1200 FRI Figures 3-1 through 3-3 show histograms of the flight distance for the combined data.... In PAGE 35: ... The parameters for the combined sample are shown in Table 3-3. Table3 -3. Distance (nmi) Statistics for Combined Data Sets Single engine Multi-engine Jet engine Mean 227.... In PAGE 35: ...18 3.99 Table3 -4. Model Parameters for Weibull Distribution Single engine Multi-engine Jet engine Scale 237 289 826 Shape 1.... In PAGE 35: ...16 1.14 Table3 -5. Estimated Distance (nmi) Statistics Using a Weibull Distribution Single engine Multi-engine Jet engine Mean 227.... ..."

"... In PAGE 16: ... This was not the case for all stocks and the worst results were obtained for the mid-quote model, which only had three of the LM2 statistics insignificant in Table 7. In Table10 the estimates of the mid-quote models with dummies are reported and these are very similar to those of Table 6, except for the coefficient on D0D2B4 CQ AW CXA0BD B5 which has seven positive and significant compared to four in Table 6. This just reinforces the conclusion of the previous section.... In PAGE 29: ... Table10 gives the estimates of the quote equation including time-of-the-day dummies defined as above, using the thinned quote series containing only quotes with mid-quote changes.... ..."

"... In PAGE 9: ...e will show evidence for this in Section 6.2. But in gen- eral, it seems that accuracy is independent of time-of-day. Likewise, Table3 shows a high accuracy overall in spite of seasonal di erences. 0 5 10 15 20 25 30 0 0.... ..."

"... In PAGE 4: ... For international measurement, because of time zone di erence, the ef- fect of time-of-day should be even less. The results for US regional measurements are shown in Table2 and Fig.3.... ..."

"... In PAGE 23: ... On the contrary, information events lead to movements of the bid and ask quotes in the same direction and thus move signi cantly the mid-point. In Table6 , we give characteristics of price durations X p computed using dif- ferent thresholds c p . These price durations feature a strong time-of-day e ect (see... In PAGE 24: ... Cubic splines such as used in Section 3 are then used on the thirtyminutes intervals to smooth the time-of-day function. As indicated in Table6 , increasing the minimum amount of price change needed to retain a duration decreases the number of observations and also the autocor- relation and overdispersion exhibited by the ltered durations. As could be ex- pected, with c p being increased, the characteristics of the ltered durations get closer to those featured by a IID Poisson distribution, i.... In PAGE 32: ... The average un- derdispersions are given in Table 10 and indicate that the underdispersion of the simulated data is close to the one featured by the data used to estimate the model (see Table 8). Thus, according to the evidence given in Table6 -9, the Log-ACD model can successfully deal with durations that are correlated and that feature either under or overdispersion.... ..."

"... In PAGE 10: ...6 kg/MBtu) or kg/kWh (in year t in factor emissions carbon The $/MBtu) or $/kWh (in year t in price energy The MBtu) or kWh (in year t in savings energy annual aggregate The lifetime product MBtu) or kWh (in n year in sold units of savings energy unit The n year in sold units of number The ? ? ? ? ? ? t t t n n C P AES L UES X Peak power reductions are estimated from aggregate energy savings using a conservation load factor (CLF) that relates average load savings to peak load savings for a conservation measure. CLFs for each ENERGY STAR product are shown in Table5 . Conservation load factors were obtained from previous research (when available), developed from time-of-day metered data or based on assumed time-of-day and seasonal operating patterns where no metered data were available.... ..."

"... In PAGE 6: ... The first step (Table 3) calculates the peak throughput for extreme scenarios in which only one user application is carried at a time. The second step ( Table4 ) estimates the monthly billable bits of an all broadcast video extreme scenario while considering the traffic load variations over one month. The combination of application models, traffic scenarios, time-of-day, day-of week, and beam loading models are defined to be the Baseline Traffic Model.... In PAGE 6: ...124 10,838.0 Table4 compares the best case of the Bent Pipe and IF Circuit Switch architectures against the worst case of the ATM Cell Switch architecture over a month using Time-of-Day, Day-of-Week and Beam Loading Traffic Models (Appendix A). Even under these unfavorable assumptions the ATM Cell Switch delivers the most billable-bits per month.... ..."

"... In PAGE 8: ... Figure 8 is a summary of the time-of-day tra c volume patterns we obtained from [9, 24]. From the data in [9], wehave derived statistics (see Table3 ) relating to mode of transportation, travel distance, and travel time statistics for various movementtypes and their percentages of occurrence. In our movement model, we then represent each trip purpose in Table 3 as a movement class with their appropriate mean movevelocity and distance.... In PAGE 8: ... From the data in [9], wehave derived statistics (see Table 3) relating to mode of transportation, travel distance, and travel time statistics for various movementtypes and their percentages of occurrence. In our movement model, we then represent each trip purpose in Table3 as a movement class with their appropriate mean movevelocity and distance. Wehave applied METMOD to a geographical model of the San Francisco Bay Area.... ..."

"... In PAGE 45: ... The ITS should not be used for the measurement of frequency, since the large amount of phase noise introduced by variations in network path delay make it nearly impossible to measure frequency precisely, even if very long averaging times are used. A sample traceability chain for time-of-day information is listed in Table4 . This example lists typical uncertainties.... ..."