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Based on satellite measurements and oceanic reanalysis data, it has been possible to investigate the spatiotemporal variability of the mesoscale phenomena in the northern part of the East Sea (NES) where direct observations of currents and hydrographical conditions are scarce. For the first time, this study identifies the detailed spatiotemporal structure of the mesoscale features in the NES and the mechanism of its occurrence and evolution, which have important consequences on the distribution of the intermediate water masses in the East Sea. Here, we show that mesoscale thermodynamic phenomena in the northwestern region of the East Sea are characterized by a dipole structure associated with positive and negative sea surface height anomalies. These result in a strong thermal gradient between the seasonally non-persistent anomalies, which emerge and strengthen during late fall and early winter. In contrast to the previous finding of the relationship between winter monsoon winds and mesoscale features in the NES, we found that this relationship is crucial only to the emergence of the mesoscale phenomena. Consequently, we present a new perspective on the evolution mechanism of the mesoscale features in the NES. Of direct significance to the present study, thermohaline transport into the northwestern region of the East Sea regulates the strengthening and weakening of mesoscale features in the NES. Wind forcing may contribute to the emergence of the mesoscale features in the NES and then the intensification of the mesoscale activities is attributed to the intrusion of warm and fresh surface water advected from the southern part of the East Sea.

LiDAR place recognition is a crucial component of autonomous navigation, essential for loop closure in simultaneous localization and mapping (SLAM) systems. Notably, while camera-based methods struggle in fluctuating environments, such as weather or light, LiDAR demonstrates robustness against such challenges. This study introduces the intensity and spatial cross-attention transformer, which is a novel approach that utilizes LiDAR to generate global descriptors by fusing spatial and intensity data for enhanced place recognition. The proposed model leveraged a cross attention to a concatenation mechanism to process and integrate multi-layered LiDAR projections. Consequently, the previously unexplored synergy between spatial and intensity data was addressed. We demonstrated the performance of IS-CAT through extensive validation on the NCLT dataset. Additionally, we performed indoor evaluations on our Sejong indoor-5F dataset and demonstrated successful application to a 3D LiDAR SLAM system. Our findings highlight descriptors that demonstrate superior performance in various environments. This performance enhancement is evident in both indoor and outdoor settings, underscoring the practical effectiveness and advancements of our approach.