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Effective visual scanning and perception are essential to perform teleoperation construction tasks. In long-distance teleoperation systems, such as between Earth and the Moon, communication delays of at least 3 seconds impact operator performance and situational awareness, influencing eye movement patterns that vary with time-delay conditions and task types. This study examines features of eye movement patterns during time-delayed teleoperation tasks in lunar construction environment. We analyzed and quantified eye movement trajectories using recurrence quantification analysis to identify spatiotemporal patterns and gain insights into how operators interact with visual scenes under time-delayed and task-specific conditions. By visualizing these patterns, we interpreted their temporal and spatial characteristics and compared them in different situations. Our findings, which address a gap in understanding operator eye movement patterns during teleoperation tasks, have potential practical implications for improving visual perception by identifying quantitative patterns along with qualitative interpretations. This study will contribute to visual interface design in teleoperation systems for lunar construction, providing actionable insights for the design and operation of such systems. 

Abstract Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent. 

Korea Polar Research Institute (KOPRI) installed an ionospheric sounding radar system called Vertical Incidence Pulsed Ionospheric Radar (VIPIR) at Jang Bogo Station (JBS) in 2015 in order to routinely monitor the state of the ionosphere in the auroral oval and polar cap regions. Since 2017, after two-year test operation, it has been continuously operated to produce various ionospheric parameters. In this article, we will introduce the characteristics of the JBS-VIPIR observations and possible applications of the data for the study on the polar ionosphere. The JBS-VIPIR utilizes a log periodic transmit antenna that transmits 0.5–25 MHz radio waves, and a receiving array of 8 dipole antennas. It is operated in the Dynasonde B-mode pulse scheme and utilizes the 3-D inversion program, called NeXtYZ, for the data acquisition and processing, instead of the conventional 1-D inversion procedure as used in the most of digisonde observations. The JBS-VIPIR outputs include the height profiles of the electron density, ionospheric tilts, and ion drifts with a 2-minute temporal resolution in the bottomside ionosphere. With these observations, possible research applications will be briefly described in combination with other observations for the aurora, the neutral atmosphere and the magnetosphere simultaneously conducted at JBS.