https://scholarworks.unist.ac.kr/handle/201301/23 2026-04-19T14:10:09Z 2026-04-19T14:10:09Z Jeon, Ha Jeong https://scholarworks.unist.ac.kr/handle/201301/90937 2026-03-26T13:13:44Z 2026-01-31T15:00:00Z

Title: Assessing the Effect of Bias Correction on GEMS Level-3 Ozone, NO₂, and AOD Products Using Polar-Orbiting Satellite References over East Asia Author(s): Jeon, Ha Jeong Abstract: As global climate change intensifies, the demand for satellite-based Level 3 data for long-term climate monitoring and analysis has been growing. However, the limited observation lifespan of a single satellite makes it difficult to ensure continuous data records suitable for climate trend analysis. Therefore, it is necessary to reconstruct climate data with multi-decadal consistency by integrating multiple generations of satellite observations. In this study, a Level 3 algorithm was developed using data from the Geostationary Environmental Monitoring Spectrometer (GEMS), which was launched in 2020. To this end, NO₂ and O₃ data retrieved from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, and AOD data from the MODerate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites were used to correct spatiotemporal biases. The Level 3 data agreed well with both reference satellites and ground observations, and the hourly ozone data generated using the same algorithm showed strong consistency with Pandora measurements. This result confirms the stability of the bias correction approach and demonstrates its capability for analyzing diurnal variations using satellite data. Furthermore, this approach is expected to establish a foundation for producing high-resolution climate data using geostationary satellite observations and contribute to securing temporal consistency through the integration of multiple satellite datasets. This will enable continuous monitoring and reliable long-term trend analysis of air quality and climate change over East Asia. Major: Department of Civil, Urban, Earth, and Environmental Engineering

2026-01-31T15:00:00Z Lee, Seungwon https://scholarworks.unist.ac.kr/handle/201301/90936 2026-03-26T13:13:43Z 2026-01-31T15:00:00Z

Title: Impacts of Contact-to-Stabilization Time Ratio and Organic Loading on Feast-Famine Regime and Microbial Kinetics in High-rate Contact Stabilization Process Author(s): Lee, Seungwon Abstract: High-rate contact stabilization (HRCS) process has emerged as a promising wastewater treatment process for carbon recovery from wastewater. However, the relationship between process performance and the actual induction of the feast-famine (FF) regime under varying contact-to-stabilization time ratios (tc/ts) remains poorly understood. This study investigated the effect of tc/ts ratios (0.2, 0.33, 0.5) on the metabolic characteristics of the HRCS process under varying organic loading rates. The results demonstrated that a low tc/ts ratio (0.2), characterized by a prolonged stabilization phase, exhibited superior organic removal efficiency. In contrast, the tc/ts condition failed to maintain performance during high loading, attributed to insufficient stabilization time, which induced substrate stress rather than metabolic activation. Crucially, these distinct performance outcomes are closely linked to the actual induction of the FF regime, which was successfully established in the low tc/ts. Furthermore, distinct metabolic profiles of polyhydroxybutyrate (PHB) and extracellular polymeric substances were confirmed depending on the tc/ts ratio and OLR, with the most active PHB metabolism observed in the tc/ts = 0.2 condition. Growth kinetic batch tests using flow cytometry revealed that specific growth rates (µ) increased as the tc/ts ratio decreased. Furthermore, biomass subjected to starvation exhibited accelerated µ compared to non-starved biomass. Taxonomic analysis revealed distinct community shift driven by operational conditions: Burkholderiales were enriched in the low tc/ts (0.2) condition, whereas the relative abundance of Rhodocyclaceae, including genus Zoogloea, increased as the tc/ts ratio increased. Consequently, these results suggest that the tc/ts ratio is a critical design parameter that determines the intensity of the FF regime, ensuring both process efficiency and operational stability. Major: Department of Civil, Urban, Earth, and Environmental Engineering

2026-01-31T15:00:00Z Yang, Ho-Suk https://scholarworks.unist.ac.kr/handle/201301/90935 2026-03-26T13:13:43Z 2026-01-31T15:00:00Z

Title: Risk Perception and Acceptance of Autonomous Weapon Systems: A Comparison Between Public and Military Personnel Author(s): Yang, Ho-Suk Abstract: This study aims to examine the acceptance of autonomous weapon systems (AWS) among public and military personnel, distinguishing between acceptance of development and adoption and acceptance of deployment and use, and further disaggregating use acceptance by mission context. Based on just war theory, particularly jus in bello principles, this study conceptualizes AWS acceptance not as a technological preference but as a normative evaluation of whether AWS are perceived to satisfy core requirements in the conduct of war, such as distinction and proportionality. For this purpose, a survey was conducted with a nationally stratified sample of public (N = 1,222) and a sample of active-duty military personnel (N = 155). Regression analyses were conducted to compare the effects of demographic characteristics, value orientations, perceptions related to just war principles and risk, and attitudes toward artificial intelligence on AWS acceptance across stages and contexts. The results show that patterns of acceptance differ systematically between public and military personnel. Military respondents exhibit significantly higher acceptance of AWS development and adoption than public, whereas they show lower acceptance of AWS deployment and use. The results also show that perceptions related to just war principles and risk are the strongest predictors of acceptance in both groups, and their effects are particularly pronounced for use-related judgments. In contrast, demographic factors and general AI attitudes play relatively secondary roles. Notably, higher objective AI knowledge is associated with lower acceptance of AWS use. These findings suggest that AWS acceptance is structured by roles, normative perceptions, and risk-related evaluations rather than by technological optimism alone. Furthermore, the lower use acceptance among military personnel may reflect organizational and contextual factors that are not fully captured in survey responses, indicating the need for follow-up qualitative analysis. We conclude governance debates on autonomous weapon systems should move beyond treating social acceptance of AWS as a uniform stance, and instead adopt a differentiated approach that recognizes how judgment logics vary according to the object of acceptance and the contextual conditions under which evaluations are formed. Major: Department of Civil, Urban, Earth, and Environmental Engineering

2026-01-31T15:00:00Z Rana, S. M. Sohel https://scholarworks.unist.ac.kr/handle/201301/88287 2025-11-06T00:58:19Z 2025-07-31T15:00:00Z

Title: Exploring the impacts of dual-polarized vegetation indices and U-shaped deep learning architectures on SAR-based burned area mapping Author(s): Rana, S. M. Sohel Abstract: Frequent and severe wildfires driven by climate change intensify the need for accurate burned area (BA) mapping, which can be effectively addressed using synthetic aperture radar (SAR) due to its cloud- penetration capability and sensitivity to vegetation and moisture changes. However, BA mapping based on SAR-only approaches relies on U-Net with ResNet50 backbone or fully convolutional neural network, while the potential of advanced architectural components remains underexplored. Moreover, prior research primarily emphasizes log-ratio features, with limited focus on standalone capacity of dual polarized vegetation indices (VIs). This study addresses these gaps by evaluating the performance of five U-Net variants (U-Net, Attention U-Net, Residual Attention U-Net, U-Net++, and U-Net 3+) using four input schemes: log-ratio, log-ratio without cross-ratio, VIs, and a combined feature set of all. Three combinations of loss function - binary cross entropy (BCE), dice, and focal - were also applied to the best model of all schemes. Experimental results showed that U-Net++ with log-ratio inputs under BCE loss function achieves the highest performance, yielding an F1 score of 0.8218 and an Intersection of Union (IoU) of 0.6795. Further analysis revealed that VIs alone can effectively delineate burned areas (F1: 0.8244; IoU: 0.7013) with focal loss and combining them with log-ratio features delivered the best performance (F1: 0.8364; IoU: 0.7188), when dice and focal loss functions were applied. Overall, this study provided a quantitative evaluation of how dual-polarized VIs and deep learning architectures affect SAR-based BA mapping performance and suggested promising directions for future enhancement through advanced feature extraction techniques. Major: Department of Civil, Urban, Earth, and Environmental Engineering

2025-07-31T15:00:00Z