NKRAFA JOURNAL OF SCIENCE AND TECHNOLOGY

Enhancing Data and Communication Security with Key Recovery Technology

Jumroon Chankulchorn — 2025-10-27

In the digital era, network-based communication has become a cornerstone of diverse human and societal activities. This evolution has brought to the forefront the critical importance of information security and communication reliability. To protect sensitive data from threats that could compromise its integrity and availability, Key Recovery Technology (KRT) plays a pivotal role in enhancing trust and resilience in digital communication systems. It is particularly essential in cases where secret keys used for data encryption are lost or become unusable, which can result in a complete loss of access to encrypted information. Such incidents may result in significant damage at individual, organizational, and infrastructural levels, including the loss of business-critical evidence, breaches of personal data, or the disruption of essential public services. KRT enables the recovery of secret keys in a secure and privacy-preserving manner, ensuring that data remains accessible without violating user confidentiality. Moreover, this technology supports compliance with legal and regulatory frameworks by allowing authorized access to data under investigation, ensuring transparency and accountability while preventing unauthorized access. Therefore, KRT serves as a key enabler of robust information security, supporting business continuity and fostering sustainable trust in the digital ecosystem by upholding the principles of confidentiality, integrity, and user privacy.

The Application of Generative Artificial Intelligence Technology in Voice Conversion

Anon Bangsan — 2025-05-20

This research aims to 1) explore the appropriate application of artificial intelligence (AI) technology for voice spoofing, 2) develop a generative AI-based voice spoofing model and investigate optimization strategies to enhance its suitability for cyber domain applications, 3) evaluate the performance and deception potential of synthetic voices generated by the model, and 4) propose practical applications of generative AI technology in offensive cyber operations.
The findings indicated that MaskCycleGAN-VC was a highly effective generative artificial intelligence model suitable for voice spoofing in the Thai language. This model could generate synthetic voices that closely resembled the original in terms of naturalness, including rhythm, intonation, and emotional expression. A key feature of the model was its ability to be developed and trained within just one day, using only moderate computational resources. The synthetic voices generated by the model could deceive listeners into believing they were genuine voices with an accuracy of up to 56%, while genuine voices were misclassified as synthetic in up to 59% of cases. This highlighted the challenges of distinguishing between genuine and synthetic voices in noisy environments. Performance metrics included a Mean Opinion Score (MOS) score for naturalness of up to 3.9 and similarity of up to 4.2, with a minimum Mel Cepstral Distortion (MCD) of 5 dB and Kernel Deep Speech Distance (KDSD) of 15.9 mKDSD. This model demonstrated significant potential for applications in security and offensive cyber operations, including support for intelligence activities, confusion in emergency scenarios, and simulated training exercises. However, its usage should be approached with caution to prevent misuse in unethical contexts.

A Comparative Study of Thailand’s Eastern Aerotropolis and the Aerotropolis Model

Pensiri Laosu-angkoon — 2025-05-20

This research aims to 1) conduct a comparative study of Thailand’s Eastern Aerotropolis and The Aerotropolis Model and 2) provide development guidelines for the Eastern Aerotropolis. The study emphasizes structural analysis in terms of infrastructure, transportation systems, business zones, industrial areas, and logistics hubs to identify key success factors and challenges for aerotropolis development in the Thai context. The research examined and compared the Zhengzhou Aerotropolis in China with the Eastern Aerotropolis in Thailand across nine key dimensions: 1) airport connectivity with surrounding areas, 2) aircraft maintenance zones, 3) industrial zones, 4) logistics and cargo business hubs, 5) commercial business zones, 6) residential areas, 7) technology innovation zones, 8) educational zones, and 9) healthcare zones. This study employed a qualitative research approach using documentary research methods. Data were gathered from primary and secondary sources, and a comparative data synthesis was conducted based on internationally recognized aerotropolis city development frameworks.
The findings revealed that the Eastern Aerotropolis in Thailand made progress in transportation infrastructure and logistics systems, particularly in multimodal transportation connectivity and designated zones for business and specialized industries. However, limitations remained in several areas, including the capacity of the aircraft maintenance industry, the shortage of highly skilled labor, and the need for greater investment in advanced technology and innovation. These findings can serve as a foundation for policy recommendations to enhance the competitiveness of the Eastern Aerotropolis at an international level. Key policy directions should include national-level strategic planning, government support, private sector investment incentives, modern infrastructure development, and strengthened international collaborations. Future research should explore urban management strategies and the economic and social impacts of aerotropolis development to ensure balanced and sustainable growth.

Improving the Production Process of Curry Puff Products to Enhance the Sustainable Potential of Mae Baan Tapon Noi Community Enterprise, Khlung District, Chanthaburi Province

Pratchaporn Setsathien — 2025-06-13

The analysis of the curry puff production process of Mae Baan Tapon Noi Community Enterprise, Khlung District, Chanthaburi Province, using a fishbone diagram found that the main causes were that when the curry puffs were sold, the products were not crispy and there was an excessive waiting time. Therefore, the objective of this research was to study and improve the curry puff production process. From the study and analysis of the production process using a process flow diagram, it was found that it took a total of 14 hours and 15 minutes to produce 30 pieces. Improvements were made based on the ECRS principle, focusing particularly on the filling preparation stage. As a result, the time required for this stage was reduced by 72.30% of the original duration. The production process was revised and divided into two steps: 1) the twice-frying process and 2) the baking process. These two methods were evaluated through sensory quality evaluation method. The results showed that the twice-frying process received the highest overall preference, with an average score of 7.15. The moisture values of the traditional, twice-frying, and baked processes were 13.97, 13.10, and 12.70, respectively. The water activity values of the traditional, twice-frying, and baked processes were 0.787, 0.811, and 0.775, respectively. Therefore, the twice-frying process was selected for the curry puff product manufacturing process.

Assessing Vegetation Change from 2005 to 2024 Using Remote Sensing and Geographic Information Systems: A Case Study of the Phung River Basin, Sakon Nakhon Province

Narathip Ruksajai — 2025-06-19

This study enhances the detection of vegetation changes in Thailand’s Phung River Basin, Sakon Nakhon province, by applying the Normalized Difference Vegetation Index (NDVI) within a GIS and remote sensing framework from 2005 to 2024. NDVI, a globally recognized indicator of vegetation health and ecological conditions, was calculated using multi-temporal Landsat imagery. The analysis classified vegetation into five density categories across five time points—2005, 2010, 2015, 2020, and 2024—revealing significant ecological shifts. Dense vegetation increased notably from 11.39% to 20.58%, while areas with moderate and sparse vegetation declined. These changes aligned with demographic trends, including population growth from 2005 to 2020 and a sharp 24% decrease from 2020 to 2024, as well as the implementation of stricter land-use policies. By integrating NDVI-derived analysis, GIS-based spatial modeling, and NDVI change detection, the study substantially improved the monitoring of vegetation dynamics. This approach enabled precise identification of degradation and regeneration zones, offering a practical model for sustainable land management. The methodology presents a scalable tool for other tropical watersheds, supporting regional sustainability strategies across Southeast Asia. The results indicate that vegetation dynamics within the Phung River Basin are closely associated with human-driven activities, notably agricultural expansion and the implementation of land-use regulations. These findings provide essential baseline information to support the development of effective and sustainable forest management policies moving forward.

Reducing Life Cycle Costs of Bucket Elevator in Fertilizer Production Plant

Meen Angsuchoti — 2025-07-08

The objective of this research was to reduce the life cycle cost (LCC) of the bucket elevator in a chemical fertilizer plant. The study utilized a Pareto Chart and Failure Mode and Effect Analysis (FMEA) to identify and prioritize problems. The root causes of the issues were determined through a Why-Why Analysis, which showed that the conveyor parts had a short service life due to corrosion from the conveyed materials. To solve this, a new bucket elevator was designed, and the materials of the conveyor parts were changed. After the improvements, the Risk Priority Number (RPN) decreased, with the number of issues in the high, medium, and low-risk categories being reduced from three, four, and nine issues to zero, six, and seven issues, respectively. Additionally, the life cycle cost of the system was reduced from 9,336,313 THB to 4,961,279 THB, representing a 53.14% decrease. Furthermore, when calculating the Sensitivity Analysis of the life cycle costs with varying expenses, it was found that the life cycle cost of the new design bucket elevator was lower than that of the old bucket elevator.

Statistical Model for Air Quality Forecasting: A Case Study of Dust Particles No Larger Than 2.5 Microns (PM2.5) in Chiang Mai Province

Kittichai Rochana — 2025-10-08

This research aims to develop a statistical model for forecasting PM2.5 levels in Chiang Mai Province. Three forecasting techniques were compared: the Box-Jenkins model, Winter’s exponential smoothing model, and the linear regression model. The analysis utilized monthly average PM2.5 data from the Pollution Control Department covering 144 observations from 2013 to 2024. The dataset was divided into two subsets: a training set of 120 months (2013–2022) for model development and a testing set of 24 months (2023–2024) for model performance evaluation. Results showed that Winter’s exponential smoothing model yielded the most accurate predictions, with the smallest mean absolute percentage error (MAPE) among the three methods. The best-performing model was then used to forecast monthly PM2.5 levels for the year 2025. The forecast suggested that PM2.5 levels from February to April are likely to exceed the standard threshold of 37.5 µg/m3 set by the Pollution Control Department, underscoring the importance of proactive air quality management during this period.

Experimental Study of Hovering Rotor Performance with Ground Effect

Aekkapol Baipho — 2025-10-08

The hovering performance of two T-Motor propellers, sized 40” × 13.1” and 36” × 11.5”, under the influence of ground effect was experimentally investigated. This study is critical for understanding the operational capabilities of Vertical Takeoff and Landing (VTOL) aircraft in rescue missions. Experiments were conducted across a Reynolds number range of 6.49 × 10⁵ to 1.64 × 10⁶, with rotor speeds varying from 1,182 to 3,607 rpm, and at height-to-radius ratios (z/R) of 1, 1.5, 2, 2.5, and 3 to assess ground effect. The results revealed that the 40-inch propeller outperformed the 36-inch variant in hovering, achieving the maximum Figure of Merit of 0.42 at 2,365 RPM and a z/R of 3. Notably, the experimentally observed trends in ground effect deviated from theoretical predictions. Comparisons with both the manufacturer's (T-Motor) performance data and Blade Element Momentum Theory (BEMT) analysis indicated a tendency for the experimental values, suggesting potential impacts from air turbulence during testing and power transmission inefficiencies to the propellers.

Fire Incident Detection System for Housing Estates using IoT and Cloud Computing Technology

Yuttachak Inkhong — 2025-10-08

This research aims to develop a fire incident detection system for housing estates using IoT and cloud computing technologies. The developed system consists of sensors for detecting smoke, temperature, humidity, and motion, which are connected to a microcontroller to collect data and transmit it to cloud computing for analysis and processing. The results are displayed through a real-time web application. The results show that the system can accurately detect fire incidents and issue alerts, thus enhancing the safety of life and property. In addition, the system supports continuous device status monitoring and operates at low cost. A user satisfaction evaluation showed an average score of 4.70, indicating a very high level of satisfaction. Moreover, the system can be further enhanced for better efficiency, including integration with SMS alerts or mobile applications, as well as extension to other monitoring purposes in the future.

Development of Mathematical Models to Analyze the Impact of Geometric Small-Scale Building Designs on Energy Efficiency: A Case Study at Navaminda Kasatriyadhiraj Royal Air Force Academy, Muak Lek

Sirunya Thanompolkrang — 2025-10-20

This research presents a mathematical model for evaluating the internal temperature of buildings with symmetrical geometric cross-sections, including rectangles, hexagons, and octagons. It examines scenarios where the cross-sectional areas or perimeters are equal. The study focuses on the transfer of heat from the outside to the inside, with walls and indoor air as the mediums. Results are presented as contour plots that depict heat diffusion levels within the building, along with temperature distribution graphs values based on reference points. The results suggest that the internal temperature is more significantly affected by the direction of sunlight than by the type of building.

LSTM-Based Approach for Predictive Link Monitoring and RRU Anomaly Detection in 4G/5G Networks

Pannatorn Kijphitayarit — 2025-10-20

The increasing complexity of 4G and 5G networks has intensified the need for reliable and efficient telecommunication infrastructures. Remote Radio Units (RRUs) are crucial components responsible for ensuring seamless signal transmission between user devices and the core network. However, anomalies in RRU voltage and temperature can cause signal degradation, network inefficiencies, and potential failures. Traditional rule-based fault detection systems often struggle to adapt to dynamic network conditions, necessitating the integration of advanced deep learning models for proactive anomaly detection. Long Short-Term Memory (LSTM) networks have demonstrated superior capabilities in analyzing time-series data, making them well-suited for detecting performance anomalies in RRUs. However, a significant challenge in deploying these models is the class imbalance problem, where normal operational conditions vastly outnumber rare fault instances, leading to biased predictions and poor recall for minority-class anomalies. This research aims to determine the effectiveness of RandomOverSampler in improving the performance of LSTM-based anomaly detection models when applied to imbalanced RRU datasets in the context of 4G/5G network monitoring. To address this, a resampling strategy utilizing RandomOverSampler is implemented to balance the dataset, which consists of 5,000 time-series samples with two key features: voltage and temperature, ensuring improved detection of rare failures without introducing synthetic noise. The proposed framework processes sequential RRU voltage and temperature data, capturing temporal dependencies to improve failure predictions. Performance evaluations show that the minority-class recall improved from 33% to 99%, and the F1-score increased from 32% to 99% after resampling, effectively addressing a major limitation of conventional machine learning-based anomaly detection systems. The model also achieves an overall accuracy of 99%, demonstrating its robustness and suitability for real-world deployment in mobile network monitoring. Future work will focus on extending this framework to predict Radio Link Failure (RLF) based on RAW RRU data performance patterns.

Assessment of the Potential of Forage Maize Residues as an Alternative Biomass Fuel for Mitigating Haze Pollution in Chiang Mai

Chindamanee Pokson — 2025-10-21

This study aims to assess the potential of maize residues in Chiang Mai as an alternative biomass fuel for small-scale combustion systems (< 50 kW), as an alternative to conventional open-field burning practices. The results indicate that the total amount of agricultural residues generated in Chiang Mai during the 2023–2024 cropping season was approximately 1,159,316.84 tons, of which maize residues accounted for 731,910.66 tonnes. Analysis of their thermal properties shows an average lower heating value for maize residues of approximately 17.15 MJ/kg. Based on this value, the estimated thermal energy potential from maize residues is approximately 12,552,267.82 GJ. In terms of air pollution, utilizing maize residues as biomass fuel could reduce air pollutant emissions by approximately 31,387.55 tons, representing a reduction of about 63.08% compared to open-field burning. Furthermore, the environmental impact assessment using the ReCiPe 2016 methodology indicates that using maize residues as biomass fuel could mitigate human health impacts by 2.43x10-6 DALY/kg and ecosystem impacts by 6.63x10-10 species·y/kg, relative to open-field burning. The findings reveal that maize residues possess significant potential for energy production and environmental impact mitigation, representing a sustainable alternative to open-field burning.

Design and Simulation of a Guidance Control System for a 2.75-inch Rocket Using Pole Placement and PI Control

Nathawat Chaikam — 2025-10-27

This research focuses on the design and simulation of an autopilot system for a 2.75-inch Wrap-Around Fin Aerial Rocket (WAFAR), incorporating a guidance system into the traditional unguided airframe. The control system was designed using a combination of pole placement techniques and proportional-integral (PI) control to enable precise directional control and maintain stability during flight. The design process takes into account the dynamics and aerodynamic characteristics of the rocket. The pole placement technique was employed to define the pole locations in the system's transfer function, achieving the desired stability and response characteristics. Concurrently, the PI control enhances the system's responsiveness and tracking accuracy. The guidance of the rocket is achieved through acceleration control, enabling it to follow the desired trajectory. A nonlinear simulation of the rocket under different launch-to-target distances and LOS errors demonstrates the effectiveness and accuracy of the proposed control system in target tracking. Although the control system was designed based on a linearized model of the rocket dynamics, it was evaluated on a nonlinear plant model to verify its practical performance.