Defence Technology Academic Journal

Benchmarking the Combat and Industrial Capabilities: A Case Study of Indigenous Rocket Development Projects in the Southeast Asian Region

Bodin Suntud — 2026-02-27

The objective of this research is to examine the combat capabilities and industrial potentials of rocket development projects in Southeast Asian countries, to assess their strategic impacts on regional security and to provide recommendations for coping with the situation and developing our capability. Using the "X and Y Axes" analytical framework, the study evaluates combat
capabilities across five factors: range, warhead, accuracy, mobility, and salvo capability, alongside
industrial capabilities through the "Ladder of Production" concept, which reflects the progression of defense industries. The analysis categorizes rocket development projects into four scenarios: projects with the highest strategic threats, underdeveloped projects, projects dependent on foreign technology, and low-risk projects, while proposing appropriate response strategies for each case. The findings indicate that
some rocket development projects, such as the Indonesia’s missile project called R-HAN 122 and the Singapore’s missile project called Blue Spear, significantly impact regional security and industrial
development. These projects demonstrate advanced domestic development capabilities that could pose threats if international relations deteriorate. Meanwhile, projects reliant on foreign technology or those that remain underdeveloped still exhibit strategic impacts, albeit with limited self-reliance. The study highlights that Thailand should assess these projects beyond mere military threat responses, emphasizing broader dimensions such as strategic alliances, joint development with Indonesia, and technology transfers from Singapore and Israel to enhance economic and industrial capacities. Advanced air defense systems and proactive measures, including cyber operations, may also serve
to balance regional power effectively. Thus, this research reframes rocket development projects
not only as military threats but also as opportunities to enhance Thailand’s economy, industry, and regional influence. It underscores the importance of building alliances, reducing international tensions, and implementing policies that address challenges comprehensively and sustainably.

Remote Sensing of Urban Forest Estimation via Space Technology: Biomass and Carbon Storage

Thanakrizt Peebkhunthod — 2026-02-28

Climate change, largely driven by rising atmospheric carbon dioxide, presents urgent
global challenges. This study estimated aboveground biomass (AGB) and carbon stock of urban
trees using space technology Sentinel-2 satellite imagery from combined with field surveys.
Thirty 20 × 20 m plots were established for data collection, and vegetation indices with fractional
cover were derived from satellite data. Allometric equations were applied to estimate AGB and
carbon stock, while exponential regression examined the relationship between biomass and
vegetation indices. Results showed GNDVI as the most effective index. Field surveys indicated
4,650.75 tons of AGB and 2,185.85 tons of carbon, whereas satellite-based estimates yielded
2,798.23 tons and 1,315.17 tons, respectively. These findings demonstrate the benefits of integrating
space technology with field measurements for reliable assessment of urban biomass and
carbon storage. The approach provides useful insights for urban green space planning, natural
resource management, and strategies to mitigate climate change.

Strength Analysis of a Metal Bridge for the Modular Fast Bridge Launching Vehicle Using the Finite Element Method

Achirakris Julniphitwong — 2026-02-28

The Modular Fast Bridge (MFB) is military equipment used for tactical support missions,
enabling the transport of vehicles, equipment, or personnel across canals or disrupted routes. Additionally, it is utilized in disaster relief operations to assist civilians when transportation routes are cut off. This study presents a strength analysis of a steel bridge capable using the Finite Element Method (FEM). The analysis focuses on evaluating the stress and strain of the bridge structure. Furthermore, the strength of steel bridge was tested by placing 60-ton load on the middle of the bridge and the tested results were used to compare against the results obtained from FEM. The results from the calculations and experiments showed that both were in good agreement, and the maximum stress occurred at the midpoint of the bridge, where the two bridge sections are connected.

A Study of the Effect of Liner Aging on Adhesion with Propellant Materials

Paisan Apinhapat — 2026-06-27

Solid rocket motors are widely used in military and defense applications due to their simplicity and high reliability. These motors typically consist of a motor case, thermal insulation, liner, and solid propellant grain. The strength of adhesion for each material in the solid rocket motor depends on processing, storage duration, and environmental changes. The quality of adhesion, especially between the liner and the propellant, play a role in ensuring proper motor performance. Poor bonding may lead to structural failure from design and performance. In a production line after curing liner already, there are a long period of times for waiting of propellant casting. The objective of this paper is to investigate the effect of liner aging on adhesion with propellant casting on the interfacial adhesion between the liner and the propellant in composite solid rocket motors.

The research considers various functional group ratios (Rt) in polyurethane formulations, Physical properties including density, tensile strength, and elongation rate are analyzed. Liner samples with Rt values ranging from 1.10 to 1.50 are produced and tested. Adhesion is evaluated using bonded rectangular specimens. The liner samples are stored for 3, 7, 10, and 15 days before propellant casting. The liner-propellant rectangular in each specimen is accelerated aging equivalent to 3 years of storage life. Experimental results show that liners with Rt at least 1.20 exhibited optimal mechanical properties. The bond strength of liner-propellant ranged from 0.76 to 1.06 MPa, which significantly exceed the design requirement of 0.50 MPa. This result indicates that storing the liner for up to 15 days prior to casting does not degrade adhesion quality. This research supports more flexible production timelines and enhances quality assurance in manufacturing of solid rocket motors.

Comparison of Interior Ballistic in 5.56 mm Rifle Using Single Perforated Grain and Spherical Grain Gunpowder

Surasith Palasarn — 2026-06-27

The 5.56-mm firearm is commonly used by infantry and border police units. Most firearms in service are gas-operated, which results from the internal combustion of gunpowder. Analysis of breech pressure and muzzle velocity is important for design and operation. This research comparatively examined two types of gunpowder single-perforated grains and spherical grains and two types of cartridges, M193 and M855. The interior-ballistic comparison showed that, for each cartridge type, single-perforated grains produced higher breech pressures and shorter burn times than spherical grains. The muzzle velocity and ballistic efficiency of single-perforated grains were also higher than those of spherical grains. A comparison between the M193 and M855 cartridges indicated that M855 exhibited better ballistic efficiency than M193 for both gunpowder types.

Preliminary Study on Neural Network-Based Insulator Detection for an Insulator-Cleaning Robot via MQTT with CPU-Based Processing

Pakamaj Wongsai — 2026-06-27

Object detection is a process that utilizes techniques from neural networks, which have gained significant popularity in robot vision research. This study applies such techniques to an insulator cleaning robot, where images from a camera are processed to determine the target’s bounding box coordinates. The coordinates are then transmitted to the robot’s control unit via long-range wireless communication using MQTT.The experiments were conducted on a 6-Core Intel Xeon E5 CPU (3.5 GHz) with 16 GB of RAM, without using a GPU for model inference. Among several tested architectures, YOLOv11n delivered the best performance, achieving an mAP@0.5 of 97.6% and a precision of 98.66%. The overall system speed (including both inference and data transmission) averaged 1.61 FPS. While this is slower than previous research reporting 24–51 FPS, the developed system offers a unique advantage: it can transmit target coordinates 0.62 seconds in advance, allowing the control unit to navigate toward the target without relying on real-time image processing at every step. Furthermore, the combined processing and transmission time accounted for only 52% of the total per-frame time, demonstrating the system’s suitability for practical applications—even when running solely on a CPU.

Development of an Intelligent Web Application Firewall (WAF) Using Machine Learning for Attack Detection and Prevention

Padet Sawipan — 2026-06-27

This research Development of an Intelligent Web Application Firewall (WAF) Using Machine
Learning for Attack Detection and Prevention. The models used in this research consist of Machine
Learning techniques, including Random Forest and Support Vector Machine (SVM), and a Deep
Learning technique, namely Deep Neural Network (DNN). The system was trained and evaluated
using data from the CIC-IDS2017 Dataset and the OWASP Benchmark Dataset. The experimental
results indicate that the Deep Neural Network (DNN) model, which belongs to the Deep Learning
group, achieved the highest performance, with an average accuracy of 97.60% and an F1-Score of
96.65%, outperforming the Machine Learning models, namely Random Forest and SVM. The
developed WAF system is integrated with the trained model through a RESTful API and is
capable of detecting web attacks in real time, with an average response time of 120 milliseconds.
The system evaluation results show that the developed system achieved an average detection
accuracy of 96.10%. In addition, user satisfaction with the system was at the highest level
(mean score of 4.53 out of 5), particularly in terms of detection accuracy and response speed.
The findings demonstrate that the application of Deep Learning techniques, especially the Deep
Neural Network (DNN) model, can significantly enhance the effectiveness of web attack detection
compared to traditional Machine Learning techniques. Furthermore, the proposed system shows
strong potential as a cybersecurity tool capable of accurately detecting both known and previously
unseen web attacks.

AI Regulatory Framework in Telecommunications Under the NBTC’s supervision

Thipnattha Kangwarmgraipaisarn — 2026-06-25

Artificial Intelligence (AI) is increasingly becoming a tool that plays a significant role across multiple sectors worldwide, including mass media, telecommunications, and national security. In particular, the integration of AI into military and defense systems is rapidly reshaping the landscape of modern warfare, raising concerns regarding appropriate use, governance, and potential risks. Despite these concerns, AI also presents substantial opportunities to drive economic growth and enhance quality of life, especially in developing economies such as Thailand. As AI has the potential to enhance human capabilities and resolve complex social challenges, Thailand has articulated a national vision to become an AI-driven economy by 2027. However, the rapid development and deployment of AI technologies also introduce critical challenges related to ethics, transparency, accountability, data privacy, and cybersecurity. These risks cover a wide range of issues, including violations of data privacy, algorithmic bias, misuse of AI systems, and adverse impacts on human rights. Thus, it is a challenge for industry stakeholders to design effective governance and regulatory frameworks that balance innovation with responsibility as delayed regulatory responses may amplify these risks and undermine the positive potential of AI.

Development of Prediction Model for Hovering Endurance of Heavy-lifting, Multirotor Drone using General Specifications of Propulsion and Battery Systems

Pimchanok Pakdeekong — 2026-06-26

Hovering endurance is one of the critical performance parameters for multirotor unmanned aerial vehicles (UAVs) in remote sensing and delivering applications. This study aims to obtain a generic model for hovering endurance prediction by using general specifications of commercial propeller, brushless direct current (BLDC) motor, and battery for heavy lifting drones (weighing over 25 kg.). The characteristics of propeller, motor, and battery sub-systems that govern the hovering endurance are analyzed using experimental data published in commercial websites and simplified for the prediction model. The proposed model predicts the hovering endurance by estimating the required electric current of the propulsion system and estimating the time duration that the battery can provide that required current. The prediction of electric current required from propulsion systems achieved the root mean square error of 8.59% and showed a prediction error of less than 5% on 43.5% of the available data points, and less than 10% on 81.9% of the data. A framework for hovering endurance prediction of heavy lifting drone is presented and applicable for platform selection, configuration optimization, and mission planning.