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The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) scan line corrector (SLC) failed on 31 May 2003, causing the SLC to turn off. Many gap-filled products were developed and deployed to combat this situation. The majority of these products used a primary image taken by the SLC when functioning properly in an attempt to correct SLC-off images. However, temporal atmospheric elements could not be reliably reflected using a primary image, and therefore the corrected image was not viable for use by monitoring systems. To bypass this limitation, this study has developed the Gap Interpolation and Filtering (GIF) method that relies on one-dimensional interpolation filtering to conveniently recover pixels within a single image at a high level of accuracy without borrowing from images acquired at a different time or by another sensor. The GIF method was compared to two other methods-Global Linear Histogram Match (GLHM), and the Local Linear Histogram Match (LLHM)-both developed by National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) to determine its accuracy. The GIF method accuracy was found superior in land, sea, and cloud imaging. In particular, its sea and cloud images returned Root Mean Square Error (RMSE) values close to or less than 1. We expect the GIF method developed in this research to be of invaluable aid to monitoring systems that depend heavily on Landsat imagery.

The popularity of conversational agents (CAs) in the form of AI speakers that support ubiquitous smart homes has increased because of their seamless interaction. However, recent studies have revealed that the use of AI speakers decreases over time, which shows that current agents do not fully support smart homes. Because of this problem, the possibility of unobtrusive, invisible intelligence without a physical device has been suggested. To explore CA design direction that enhances the user experience in smart homes, we aimed to understand each feature by comparing an invisible agent with visible ones embedded in stand-alone AI speakers. We conducted a drawing study to examine users' mental models formed through communicating with two different physical entities (i.e., visible and invisible CAs). From the drawings, interviews, and surveys, we identified how users' mental models and interactions differed depending on the presence of a physical entity. We found that a physical entity affected users' perceptions, expectations, and interactions toward the agent.

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