Data Retrieval Research Articles

A practical vehicular‐fog‐based privacy‐preserving attribute‐based keyword search system using potential games

AbstractThe growth of fog computing drives Internet of Things users to outsource data for services like storage and sharing. To ensure secure and efficient data search, extensive research has been conducted on searchable encryption, specifically attribute‐based keyword search (ABKS). ABKS facilitates precise access control over encrypted data while maintaining search functionality. However, delegating complex search operations to fog nodes introduces challenges like latency limitations, compromised search results, and unauthorized occupation of server resources. In this manuscript, we propose a Time‐Efficient Data Retrieval (TEDR) scheme to address these challenges. Our scheme incorporates two access policies, allowing data owners (DOs) and data users (DUs) to control data access and search queries. TEDR enables fog nodes to verify search queries before decryption, avoiding unnecessary decryption for invalid queries. We introduce a decentralized query submitting framework to allocate search queries among fog nodes, addressing redundancy concerns and latency issues. We analyze the decentralized query submitting game and classify it as a potential game, making it suitable for decentralized decision‐making. We evaluate TEDR performance on real‐world datasets, demonstrating practicality and efficiency. Our scheme reduces computational and communication costs by approximately 30% compared to existing methods.

Secured Sharing of Personal Health Records in Cloud with Optimised Signcryption: Improved Shark Smell Optimisation for Key Generation

Recently, personal health records (PHR) have been found to come into view as a patient- centric approach for exchanging health data. With the use of a PHR service, a physician may create, access and maintain all of their personal health information online in one convenient location. Additionally, it has greatly improved the efficient exchange, retrieval and storage of patient data. Each patient is also promised total ownership over their medical records and the option to share their data with a wider range of clients together with friends, family members or healthcare providers. Owing to the higher price of maintaining and building specific data centres, numerous PHR services were contracted out to third-party service providers. Therefore, this work is concerned with carrying out the secured sharing of PHR by inducing the signcryption model with an optimisation concept. In the signcryption model, the digital signature encryption is done using an improved AES approach. Further, the key generation is carried out using a self-customised shark smell algorithm (SC-SSA) algorithm. Finally, the evaluation is done to verify the improvement of the system that was used.

Unlocking the Potential of Secondary Data for Public Health Research: Retrospective Study With a Novel Clinical Platform.

Clinical routine data derived from university hospitals hold immense value for health-related research on large cohorts. However, using secondary data for hypothesis testing necessitates adherence to scientific, legal (such as the General Data Protection Regulation, federal and state protection legislations), technical, and administrative requirements. This process is intricate, time-consuming, and susceptible to errors. This study aims to develop a platform that enables clinicians to use current real-world data for testing research and evaluate advantages and limitations at a large university medical center (542,944 patients in 2022). We identified requirements from clinical practitioners, conceptualized and implemented a platform based on the existing components, and assessed its applicability in clinical reality quantitatively and qualitatively. The proposed platform was established at the University Medical Center Hamburg-Eppendorf and made 639 forms encompassing 10,629 data elements accessible to all resident scientists and clinicians. Every day, the number of patients rises, and parts of their electronic health records are made accessible through the platform. Qualitatively, we were able to conduct a retrospective analysis of Parkinson disease over 777 patients, where we provide additional evidence for a significantly higher proportion of action tremors in patients with rest tremors (340/777, 43.8%) compared with those without rest tremors (255/777, 32.8%), as determined by a chi-square test (P<.001). Quantitatively, our findings demonstrate increased user engagement within the last 90 days, underscoring clinicians' increasing adoption of the platform in their regular research activities. Notably, the platform facilitated the retrieval of clinical data from 600,000 patients, emphasizing its substantial added value. This study demonstrates the feasibility of simplifying the use of clinical data to enhance exploration and sustainability in scientific research. The proposed platform emerges as a potential technological and legal framework for other medical centers, providing them with the means to unlock untapped potential within their routine data.

Verifiable privacy-preserving semantic retrieval scheme in the edge computing

Edge computing, with its characteristics of low latency and low transmission costs, addresses the storage and computation challenges arising from the surge in network edge traffic. It enables users to leverage nearby edge servers for data outsourcing and retrieval. However, data outsourcing poses risks to data privacy. Although searchable encryption is proposed to secure search of outsourced data, existing schemes generally cannot meet the requirements of semantic search, and they also exhibit security risks and incur high search costs. In addition, edge servers may engage in malicious activities such as data tampering or forgery. Therefore, we propose a verifiable privacy-preserving semantic retrieval scheme named VPSR suitable for edge computing environments. We utilize the Doc2Vec method to extract text feature vectors and then convert them into matrix form to reduce storage space requirements for indexes, queries, and keys. We encrypt matrices using an improved secure k-nearest neighbor (kNN) algorithm based on learning with errors (LWE) and calculate text similarity by solving the Hadamard product between matrices. Additionally, we design an aggregable signature scheme and offload part of the result verification tasks to edge servers. Security and performance analysis results demonstrate that the VPSR scheme is suitable for edge computing environments with high encryption and search efficiency and low storage cost while ensuring security.

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

RadLab: An open science resource for radiation studies relevant to human spaceflight

In response to the growing need of the space life sciences community for a publicly available single access point for radiation physics data relevant to human space exploration, an open data repository and analysis platform, RadLab, has been developed. RadLab consists of a database and a user-friendly data retrieval, visualization, and analysis toolkit, including a graphical user interface (GUI) and an application programming interface (API). RadLab complements the space biology data in the NASA Open Science Data Repository (OSDR) and aims to provide open, centralized access to radiation physics data relevant to spaceflight. Analysis tools contained in RadLab will facilitate intercomparison among different detectors and radiation data sets to better understand the radiation environment in spacecraft and planetary habitats, and facilitate the use of space radiation data by space biology investigators and radiation instrument developers.

ROAD value-based detection for GAE enhanced interference mitigation in LDACS.

The increasing volume of air traffic has placed significant stress on the current Air Traffic Management (ATM) systems, especially concerning the use of Very High Frequency (VHF) communication bands. As air traffic continues to grow, the limitations of the existing spectrum and infrastructure necessitate significant upgrades to ensure safety, efficiency, and capacity. The modernization of air traffic management systems has led to the development and introduction of the L-band Digital Aeronautical Communication System (LDACS), a new communication protocol. LDACS is designed to operate alongside existing L-band systems, ensuring compatibility with legacy users. The coexistence of LDACS with legacy systems poses significant interference challenges, as any disruption in data retrieval can critically impact flight safety. This paper proposes four potential interference mitigation techniques that LDACS can employ to detect and reduce the primary source of interference: Distance Measuring Equipment (DME). The authors introduce a prototype LDACS receiver that uses Rank-Ordered Absolute Differences (ROAD) statistics for effective interference sensing and employs GAE-enhanced pulse peak processors to mitigate Distance Measuring Equipment (DME) interference. Unlike the current GAE-enhanced pulse peak processors, the proposed methods use ROAD value-based detection for identifying DME interference. The performance of the proposed methods - ROAD GAE enhanced Pulse Peak Attenuator (RGPPA), ROAD GAE enhanced Pulse Peak Limiter (RGPPL), ROAD joint GAE enhanced Pulse Peak Attenuator (RJGPPA), and ROAD joint GAE enhanced Pulse Peak Limiter (RJGPPL) is analyzed across different threshold ROAD values to determine their efficacy in various signal conditions. Moreover, the performance of the proposed methods is compared to existing methods such as conventional pulse blanking and GAE-enhanced nonlinear devices, which use the amplitude of the received signal for the detection of interference. Furthermore, the proposed method's performance is compared to another method, ROAD PB, which uses ROAD statistics to detect DME interference and pulse blanking for DME mitigation. The comparative results show that the proposed methods outperformed conventional pulse blanking and ROADPB. Besides, these methods outperformed existing GAE-enhanced methods for their optimum threshold ROAD value.

Vector Databases

Vector databases represent a significant advancement in data management, tailored to efficiently handle high-dimensional vector embeddings. Unlike traditional databases, which face challenges with the complexity and scale of high-dimensional data, vector databases are engineered for optimal storage, indexing, and retrieval of vector data. This work explores the challenges posed by high-dimensional data, including the curse of dimensionality, and examines how vector databases address these issues through advanced indexing techniques such as Inverted File (IVF), Product Quantization (PQ), and Locality Sensitive Hashing (LSH). It highlights the importance of vector databases in facilitating rapid similarity searches, which are crucial for applications such as recommendation systems and search engines. The discussion also covers the evolution of vector databases and their impact on AI and machine learning.

A Comprehensive Methodology for Analysing Options Trading: Utilizing Historical Data and the Black-Scholes Model

This paper presents a systematic methodology for analysing options trading, integrating user inputs with historical stock data to facilitate informed decision-making. The process begins with the collection of user-specified stock tickers and expiration dates, followed by the retrieval of historical price data to assess past stock performance. Key components of the analysis include calculating historical volatility, fetching the option chain, and filtering options into put and call categories. The methodology employs the Black-Scholes model to estimate fair option prices, which are further evaluated by calculating expected returns. The analysis culminates in the ranking of the top three put and call options based on their expected returns, providing users with actionable insights. Additionally, the methodology incorporates visual representations of stock prices and option data, alongside calculations of option Greeks to assess risk sensitivity. This comprehensive approach empowers traders to navigate the complexities of options trading with a clear understanding of potential outcomes and associated risks.

An Intelligent Mobile Search System

This article is devoted to the development of an intelligent mobile system used for humanitarian demining. At the same time, the problems of detection, localization and storage of the obtained data are solved. The system operation is based on the use of a synthetic aperture ground penetrating radar, which makes it possible to detect mines both on the earth's surface and underground. A quadcopter is used as a carrier. A set of technical means has been developed. The central and graphic processors are used as a processing unit. Intelligent elements for processing the obtained data are convolutional neural networks, for machine learning of which a synthetic dataset was used. The data is organized into S3 segments based on various parameters, such as date, location and sensor type. This organization facilitates data retrieval and management. Data is encrypted both during transmission and at rest using AWS Key Management Service to ensure confidentiality.

Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments

The Environmental Trace Gases Monitoring Instrument (EMI) series are second-generation Chinese spectrometers on board the GaoFen-5 (GF-5) and DaQi-1 (DQ-1) satellites. In this study, a comparative analysis of EMI series data was conducted to determine the daily trend of ozone concentration changes owing to different transit times and to improve the overall quality and reliability of EMI series datasets. The daily EMI total ozone column (TOC) obtained using the Differential Optical Absorption Spectroscopy (DOAS) method were compared to vertical column density (VCD) gathered by the TROPOspheric Monitoring Instrument (TROPOMI). The results from October to November 2023 indicated a fine correlation (R = 0.98) between the daily EMI series data and a fine correlation (R ≥ 0.95) and spatial distribution closely resembling that of the TROPOMI TOCs. Furthermore, the EMI series data fusion results were highly correlated with TROPOMI TOCs (R = 0.99). Since the EMI series instruments had two different overpass times and the volume of available data at same pixel was increased by approximately three-fold, the temporal and spatial resolution was improved a lot. The results indicated that, compared to a single sensor, the EMI series DOAS TOCs generated more accurate and stable global TOC results and also enabled looking at the changes in the intraday TOCs. These outcomes highlight the potential of the EMI instruments for reliably monitoring the ozone variations in polar regions.

Optimization model for vehicular network data queries in edge environments

As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency.

Comparison of cation exchange capacity extraction methods for soil data harmonization and soil classification in Central and East Europe

Cation exchange capacity and base saturation are of crucial importance for soil characterisation and classification; however, the interchangeability of soil data for international assessment of soil productivity, soil classification, mapping, and modelling is limited due to the lacking comparisons between results obtained using different analytical methods in particular regions of the world. The aim of the present study was to analyse the relationships between cation exchange capacity and base saturation measured using the methods adopted in Central and East Europe, and ‘standard’ techniques required for soil classification suggested by World Reference Base (WRB). This study was carried out using 183 soil samples collected from Luvisols, Retisols, Planosols, Chernozems, Phaeozems, Cambisols, and Arenosols in Poland, representing a wide range of texture, pH, and organic carbon content. A close comparability was found in noncalcareous soils between the ‘total sorption capacity’ (T) measured in Central and East Europe as the sum of base cations and ‘total’ (‘hydrolytic’) acidity, with the ‘standard’ cation exchange capacity (CEC) measured using 1 M ammonium acetate buffered at pH 7. The close correlation between T and CEC values (in noncalcareous soils) facilitates reliable data recalculation and its application in global modelling, soil classification and mapping. For soils containing carbonates, CEC must be measured using reference methods, because no relationship exists between T and CEC. The ‘potential’ base saturation (V) derived on the basis of T may be reliably recalculated to ‘standard’ base saturation (BS), allowing a retrieval of archival data from Central and East European regional databases and published reports. Similarities between the values of cation exchange capacity and base saturation, whether measured or calculated using the local and standard methods, allow a positive verification of previously proposed correlations between the local soil taxa and the reference soil groups of the WRB classification and soil orders defined by USDA Soil Taxonomy. The pH values corresponding to 50 % of ‘standard’, ‘potential’, and ‘effective’ base saturation were estimated at 5.5, 5.2, and 4.8, respectively. Irrespective of the differences between current estimates and previously reported pH threshold values corresponding to 50 % base saturation, the obtained results confirm that field measurements of soil pH may be considered a substitute for laboratory-measured base saturation for some purposes, such as soil classification.

A policy analysis of sleep-related legislation for Canadian licensed childcare facilities

BackgroundNational legislative guidelines for sleep and rest are lacking in the Canadian licensed childcare sector. No review of Canadian legislation for licensed childcare facilities has focused on sleep. This paper provides a review of the Canadian provincial and territorial legislative landscape, regarding sleep, rest, and naps in licensed childcare centers.MethodsChildcare statutes and regulations for each province and territory were identified and downloaded on a particular date. Statutes and regulations were reviewed focusing on sections articulating licensed childcare facility mandates governing sleep, rest, naps, and sleep equipment. An excel file was used to facilitate systematic data retrieval and comparisons across provinces and territories. Two authors developed and discussed themes that summarized data from the documents.ResultsNo statutes indicated recommendations for sleep, rest, or naps. Only one regulation defined rest (Alberta). Our analysis of regulations identified four themes representing sleep, rest, and naps: programming (general programming, daily programming); space (dedicated space, amount of space, age-specific space); equipment (developmental appropriateness, acceptable sleep equipment, age-specific equipment); and safety (staffing during sleep/rest, sleep position, sleep monitoring, sleep equipment safety, prohibited practices). In Canada, minimal regulatory consistency is evident in required sleep programming, space, acceptability of sleep equipment, and sleep safety considerations. Most jurisdictions’ regulations indicated necessity for developmentally appropriate rest or sleep areas and equipment, in particular for infants, but there was minimal consistency in defining infant age groups.ConclusionsAlthough we identified themes related to sleep across regulations, childcare regulations differ in their definitions of infants and specifications for children’s sleep and rest in licensed Canadian childcare facilities. Without adequate definitions in legislative components of appropriate sleep duration linked to children’s developmental stages, childcare facilities lack guidance to support healthy sleep for children in their care. Future research can examine translation of healthy sleep guidelines into government legislation and mandates for sleep, rest, and naps among young children in licensed childcare.

Flow Cytometry Sorting for Random Access in DNA Data Storage: Encapsulation for Enhanced Stability and Sequence Integrity of DNA.

As digital data undergo explosive growth, deoxyribonucleic acid (DNA) has emerged as a promising storage medium due to its high density, longevity, and ease of replication, offering vast potential in data storage solutions. This study focuses on the protection and retrieval of data during the DNA storage process, developing a technique that employs flow cytometry sorting (FCS) to segregate multicolored fluorescent DNA microparticles encoded with data and facilitating efficient random access. Moreover, the encapsulated fluorescent DNA microparticles, formed through layer-by-layer self-assembly, preserve structural and sequence integrity even under harsh conditions while also supporting a high-density DNA payload. Experimental results have shown that the encoded data can still be successfully recovered from encapsulated DNA microparticles following de-encapsulation. We also successfully demonstrated the automated encapsulation process of fluorescent DNA microparticles using a microfluidic chip. This research provides an innovative approach to the long-term stability and random readability of DNA data storage.

Maximum Error Insertion (MEI): A Novel Benchmarking Method for Data Hiding Algorithms

Data hiding is becoming increasingly important due to the growing threat to the privacy and security of data from intruders and hackers. This situation is accompanied by the advancement in artificial intelligence applications designed to reveal hidden data, making it difficult to choose the most appropriate hiding approach from those presented in literature. The benchmarking method serves as an important roadmap for making decisions. We propose a distinct plain benchmarking method called maximum error insertion (MEI) benchmarking. This approach intends to hide data using maximum error insertion. The MEI refers to the maximum amount of distortion that can be added to host data (such as image or audio) while still ensuring the successful retrieval of hidden data. The maximum error that can be generated by each hiding algorithm is intentionally inserted to the media file, thus giving us maximum error, maximum capacity, and maximum sensitivity to signal processing attacks. Investigation of the two hiding algorithms demonstrates their applicability and precision, and their implementation significantly enhances the reliability of results during the benchmarking stage.

Mapping the landscape of green banking strategies: a bibliometric approach

IntroductionGreen banking (GB) strategies are a set of financial practices and activities implemented by banks to encourage sustainability, environmental responsibility, and low-carbon impact. This bibliometric study examines the present state of research in this field by utilizing the Scopus database for data retrieval and VosViewer for network analysis.MethodA total of 149 researches were retrieved from the Scopus and analyzed using the VosViewer software following the PRISMA guidelines. The study examines year-by-year publication patterns, top-cited articles, contributing countries, journals, co-citation analysis of authors, and co-occurrence analysis of keywords.ResultStrategies such as online banking, solar ATMs, construction of green buildings, and making provisions for green loans are identified as the key GB strategies that can be adopted by the banks. Furthermore, the fundamental challenges banking organizations encounter while implementing GB strategies are underlined.DiscussionThe study suggests that “green brand image,” “green audit,” “environmental economics,” and “carbon footprint” can be considered as an area of interest in future. The study provides researchers, policymakers, regulators, and financial institutions with valuable insights by presenting the current state of green banking research and identifying emerging areas for further exploration. These findings will help others to discover the areas of interest in GB and advancing sustainable finance practices that can foster environmental responsibility in the financial sector.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Systematic analysis of SARS-CoV-2 Omicron subvariants' impact on B and T cell epitopes.

Epitopes are specific structures in antigens that are recognized by the immune system. They are widely used in the context of immunology-related applications, such as vaccine development, drug design, and diagnosis / treatment / prevention of disease. The SARS-CoV-2 virus has represented the main point of interest within the viral and genomic surveillance community in the last four years. Its ability to mutate and acquire new characteristics while it reorganizes into new variants has been analyzed from many perspectives. Understanding how epitopes are impacted by mutations that accumulate on the protein level cannot be underrated. With a focus on Omicron-named SARS-CoV-2 lineages, including the last WHO-designated Variants of Interest, we propose a workflow for data retrieval, integration, and analysis pipeline for conducting a database-wide study on the impact of lineages' characterizing mutations on all T cell and B cell linear epitopes collected in the Immune Epitope Database (IEDB) for SARS-CoV-2. Our workflow allows us to showcase novel qualitative and quantitative results on 1) coverage of viral proteins by deposited epitopes; 2) distribution of epitopes that are mutated across Omicron variants; 3) distribution of Omicron characterizing mutations across epitopes. Results are discussed based on the type of epitope, the response frequency of the assays, and the sample size. Our proposed workflow can be reproduced at any point in time, given updated variant characterizations and epitopes from IEDB, thereby guaranteeing to observe a quantitative landscape of mutations' impact on demand. A big data-driven analysis such as the one provided here can inform the next genomic surveillance policies in combatting SARS-CoV-2 and future epidemic viruses.

An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.