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PROCEDURE FOR DETERMINING LUMINAIRE MAINTENANCE CATEGORIES To assist in determining luminaire dirt depreciation (LDD) factors, luminaires are separated into six maintenance categories (I through VI). To arrive at categories, luminaires are arbitrarily divided into sections a top enclosure and a bottom enclosure by drawing a horizontal line through the light center of the lamp or lamps. The characteristics listed for the enclosures are then selected as best describing the luminaire. Only one characteristic for the top enclosure and one for the bottom enclosure are used in determining the category of a luminaire. Percentage of uplight is based on 100% light output for the luminaire. The maintenance category is determined by the characteristics in both enclosure columns. If a luminaire falls into more than one category, the lower-numbered category is used. Maintenance Category I II 1. None 1. None 2. Transparent with 15% or more uplight through apertures 3. Translucent with 15% or more uplight through apertures 4. Opaque with 15% or more uplight through apertures III 1. Transparent with less than 15% upward light through apertures 2. Translucent with less than 15% upward light through apertures 3. Opaque with less than 15% upward light through apertures IV 1. Transparent unapertured 2. Translucent unapertured 3. Opaque unapertured V 1. Transparent unapertured 2. Translucent unapertured 3. Opaque unapertured VI 1. None 2. Transparent unapertured 3. Translucent unapertured 4. Opaque unapertured Reprinted from the IES Lighting Handbook, 9th Ed. Used with permission from the Illuminating Engineering Society of North America. 264 1. Transparent unapertured 2. Translucent unapertured 3. Opaque unapertured 1. Transparent unapertured 2. Translucent unapertured 1. None 2. Louvers or baffles 1. None 2. Louvers or baffles Top Enclosure Bottom Enclosure 1. None 1. None 2. Louvers or baffles

In a Web-based system, the Web developer is responsible for the user experience. The Web developer creates the code, templates, style sheets, and forms that will be used to collect and render information provided by the end-users. The Web developer creates the user interface that will be rendered by the user s Web browser.

FIGURE AP8.2-3

Car = new Array("Ford", "Toyota", "Volkswagen", "Mercedes", "Honda");

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We mentioned earlier in the chapter that the JRE is a delivery platform for platform-independent Java applications. The Java 2 Enterprise Edition, or J2EE, is the Java enterprise platform. J2EE is designed as a platform that can be used to integrate the various systems within an enterprise. In many ways, Java is uniquely suited to this task. The Java language was designed to be independent of the hardware and operating that it executes on. This makes it possible to use one language to write components for each of the various other platforms that may exist within the enterprise. Java has some features for distributed processing built into the language. Java also has a very strong set of libraries that can be used for database access and network communication. Still, an enterprise platform needs more than these basic libraries, and J2EE provides much of its required functionality. The primary components that make up a J2EE platform are: Java servlets JavaServer Pages Enterprise Java Beans Dynamic Web content generation Dynamic Web pages Distributed component technology

Summary

The FSS earth station of the ITALSAT system utilizes 3.5 m circular antennas or 3.5 x 7 m elliptical antennas, depending on climatic zones. The selected antenna configuration is a multi-reflector offset type with shaped sub-reflectors.

While the mean absolute deviation is a functional description of the variability in a series of returns, it is not commonly used in performance analysis. This is because there are measures of variability with better statistical properties than the mean absolute deviation that also convey the same information. We use the deviations from the mean in the calculation of the most commonly used statistic representing return dispersion, the standard deviation of returns. Standard deviation is a measure of how widely the actual returns were dispersed from the average return. When calculating standard deviation, instead of taking the absolute

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