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Breaking data streams into chunks, re-arranging the order in some predetermined pattern, and reforming the chunks into a new stream. This technique minimizes the problem caused by one long gap in the data stream. When the data is de-interleaved, the gap in the original data stream becomes many smaller gaps in the original data stream. This type of error is less perceptible, and easier to correct.

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