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A coverage map may be produced to indicate the area in which a certain signal strength is delivered. [1] Even if it is 100% accurate (which it never is), a major factor on whether a signal is receivable depends very much on whether the receiving apparatus is sensitive enough to use a signal of that level.
With baserunners on first and second bases, sometimes the shortstop will cover third base on a bunt, a coverage plan known as the "rotation play" or "wheelplay." This occurs if the batter bunts the ball toward third base so that the third baseman cannot cover third base because he is moving forward to field the ball.
Coverage depends on several factors, such as orography (i.e. mountains) and buildings, technology, radio frequency and perhaps most importantly for two-way telecommunications the sensitivity and transmit efficiency of the consumer equipment. Some frequencies provide better regional coverage, while other frequencies penetrate better through ...
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Unlike the OGC Web Map Service (WMS), which portrays spatial data as static, server-rendered images (maps), the Web Coverage Service delivers underlying data values along with their detailed descriptions. This enables a rich syntax for queries against the data and returns information with its original semantics, rather than just pictures ...
In US broadcasting, service contour (or protected contour) refers to the area in which the Federal Communications Commission (FCC) predicts coverage.. The FCC calculates FM and TV contours based on effective radiated power (ERP) in a given direction, the radial height above average terrain (HAAT) in a given direction, the FCC's propagation curves, and the station's class.
The Transmission Gallery: photographs, coverage maps and information; Info and pictures of Sutton Coldfield TV transmitter including historical power/frequency changes and present co-receivable transmitters; Sutton Coldfield Transmitter at thebigtower.com; Tales from a cold field -Reminiscences of a former BBC engineer
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.