## 2 Materials and Methods
### GNSS Quality Management
Multipath effects, represented by MP\({}_{1}\) and MP\({}_{2}\) values, arise when GNSS signals are reflected off surrounding surfaces, leading to interference with the direct signal path. Minimizing multipath effects requires careful selection of installation sites, the use of high-quality antennas, and appropriate receiver configurations that support multipath mitigation features [16; 17]. Cycle slips, which represent discontinuities in carrier-phase tracking, can significantly degrade positioning accuracy. IGS recommends limiting cycle slips to fewer than 1 per 1000 observations, a guideline supported by early foundational work [18]. As cycle slips are among the most critical factors affecting the integrity of GNSS measurements, effective detection and removal are essential in the preprocessing stage. To address this, various quality control tools and algorithms have been developed. For example, [PERSON] (1990) [19] introduced the TurboEdit algorithm, which automatically identifies and eliminates outliers and cycle slips from GNSS data. More recent advancements include a window-based QC tool that allows for the evaluation of multipath, signal delays, and data gaps [20]. In this study, we employed PyRINEX, an open-source Python (3.12) package designed to streamline the QC process by automating the detection and classification of low-quality GNSS data [21]. To automate the quality control (QC) process, this study employed the open-source PyRINEX package, applying standardized thresholds tailored to the characteristics of the observation network. These thresholds--acquisition rate \(\geq\) 70%, multipath noise \(\leq\) 0.5 m, and cycle slip \(\leq\) 400--were determined through empirical analysis of over 17,000 RINEX files. Unlike continuously operating IGS stations, the control points in this study are monitored intermittently (typically once every 2 to 3 years for 4 to 8 h per session), justifying the adoption of relaxed QC thresholds. While tools like TEQC are widely used for GNSS data validation, they do not define fixed cutoff values for quality metrics. Therefore, an internal calibration approach was necessary to establish meaningful thresholds that balance data quality and observational constraints [22; 23; 24]. Table 2 illustrates sample file classification based on acquisition rate and cycle slips, while Table 3 summarizes corresponding multipath statistics derived through the QC workflow. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Satellite** & **Total Observations** & **Acquisitions** & **Acquisition Rate** & **Threshold** & **Cycle Slip** & **Comparison** \\ \hline G02 & 141 & 19 & 13.5\% & 70\% & 0 & unpassed \\ G03 & 522 & 481 & 92.1\% & 70\% & 0 & passed \\ G04 & 374 & 274 & 73.3\% & 70\% & 0 & passed \\... &... &... &... &... &... &... \\ G06 & 448 & 405 & 90.4\% & 70\% & 1 & passed \\ G07 & 594 & 473 & 79.6\% & 70\% & 2 & unpassed \\ \hline \end{tabular}
\end{table} 
Table 2: RINEX file QC analysis (data acquisition rate and cycle slip). This degradation is primarily attributed to a shift in operational practice in South Korea, where GNSS observation sessions have increasingly been conducted for less than 4 h, rather than the previously common 8 h standard. The shortened observation duration likely contributed to a higher incidence of incomplete or low-quality data. Previous studies have emphasized that robust GNSS data quality assessment is essential for ensuring the reliability of positioning and ionospheric modeling [25]. In particular, systems with low-duty cycles or suboptimal installation environments tend to experience increased cycle slips and multipath errors, while core quality metrics such as signal acquisition rate and satellite geometry may progressively deteriorate over time [26; 27]. In summary, the proposed QC framework served as a rigorous data-screening mechanism, allowing only high-integrity GNSS observations to be included in the national geodetic adjustment.