Military Domain Research Articles

Military Equipment Entity Extraction Based on Large Language Model

The technology of military equipment entity extraction, a crucial component in constructing military knowledge bases, holds significant research value and theoretical importance for guiding the development and improvement of equipment support forces. In the military domain, equipment entities exhibit a phenomenon of nesting, where one entity is contained within another, and abbreviations or codes are frequently used to represent these entities. To address this complexity, this paper proposes a method named CoTNER for extracting entities. Initially, a large-scale language model is used to perform data augmentation with chain-of-thought on the original dataset, providing additional semantic and contextual information. Subsequently, the augmented dataset is fine-tuned on a small-scale language model to adapt it to the task of military equipment entity extraction and to enhance its ability to learn complex rules specific to the domain of military equipment. Additionally, a high-quality data filtering strategy based on instruction-following difficulty scoring is proposed to address the catastrophic forgetting issue that may occur during the fine-tuning of large language models. The experimental results demonstrate that the proposed military equipment entity extraction method outperforms mainstream traditional deep learning methods, validating the effectiveness of CoTNER.

SDCINet: A novel cross-task integration network for segmentation and detection of damaged/changed building targets with optical remote sensing imagery

Buildings are primary locations for human activities and key focuses in the military domain. Rapidly detecting damaged/changed buildings (DCB) and conducting detailed assessments can effectively aid urbanization monitoring, disaster response, and humanitarian assistance. Currently, the tasks of object detection (OD) and change detection (CD) for DCB are almost independent of each other, making it difficult to simultaneously determine the location and details of changes. Based on this, we have designed a cross-task network called SDCINet, which integrates OD and CD, and have created four dual-task datasets focused on disasters and urbanization. SDCINet is a novel deep learning dual-task framework composed of a consistency encoder, differentiation decoder, and cross-task global attention collaboration module (CGAC). It is capable of modeling differential feature relationships based on bi-temporal images, performing end-to-end pixel-level prediction, and object bounding box regression. The bi-direction traction function of CGAC is used to deeply couple OD and CD tasks. Additionally, we collected bi-temporal images from 10 locations worldwide that experienced earthquakes, explosions, wars, and conflicts to construct two datasets specifically for damaged building OD and CD. We also constructed two datasets for changed building OD and CD based on two publicly available CD datasets. These four datasets can serve as data benchmarks for dual-task research on DCB. Using these datasets, we conducted extensive performance evaluations of 18 state-of-the-art models from the perspectives of OD, CD, and instance segmentation. Benchmark experimental results demonstrated the superior performance of SDCINet. Ablation experiments and evaluative analyses confirmed the effectiveness and unique value of CGAC.

Ensuring High-Quality Professional Training for Future Tactical Air Force Pilots in Combat Flight through the Pedagogical System's Effectiveness Justification

This article presents an in-depth analysis of the efficacy of the pedagogical system in providing high-quality professional training for prospective tactical air force pilots. The study encompasses the evaluation of cognitive, operational, motivational and conative, and physical components of professional training for combat flights through an extensive pedagogical experiment. Two distinct groups, namely the experimental group (EG) and the control group (CG), comprising a total of 190 participants, were established for the experiment. The assessment of professional readiness among KNAFU student pilots for combat flights involved the examination of criteria, indicators, and levels associated with each component. The experimental process entailed both quantitative measurement and qualitative interpretation of the outcomes. The study uncovered noteworthy enhancements in professional training for future tactical air force pilots, encompassing intellectual domain, simulator training quality, operational proficiency, motivational and conative aspects, physical capabilities, and psychophysiological elements. These findings contribute to the progression of military pilot education and carry positive implications for public health. By refining the training programs and pedagogical approaches for military pilots, the implemented measures can indirectly impact public health by ensuring the deployment of highly skilled and mentally resilient pilots to safeguard national security. This study underscores the interdependence between professional training for tactical air force pilots and broader public health objectives, underscoring the significance of holistic education and training approaches in the military domain.

Holographic visual cues for mission task elements

AbstractMission task elements (MTEs) are commonly used to evaluate the handling qualities (HQs) of rotorcraft. However, the traditional approach of physical ground courses faces challenges due to evolving rotorcraft designs and flight profiles. Infrastructure limitations and lack of flexibility for research pose significant obstacles, particularly for high-speed tasks. The emergence of new air vehicles, such as eVTOLs in the civil sector and future vertical lift configurations in the military domain, requires an adapted visual cueing process for previously unaddressed mission profiles. This paper proposes an innovative solution: an augmented reality (AR) system utilizing a head-mounted display to create virtual MTE courses called holographic visual cues (HVCs). A piloted simulation campaign performed at DLR’s AVES simulator compares the effectiveness of HVCs with dome projection-based visual cues, evaluating the performance of the AR system. The study investigated the impact of holographic visual cues on pilot handling qualities, workload ratings, and task performance, finding that although these cues typically do not significantly alter pilot ratings, individual responses varied, highlighting both the technology’s potential and the need for further refinement.

A giant leap for humankind: bridging space law and international humanitarian law*

As more parties have started to recognize the military potential of outer space, the idea of an armed conflict in outer space has become more than a theoretical possibility. Several states have conducted tests with anti-satellite weapons, NATO has officially recognized space as a military domain, and several states have started to develop specific military space capabilities. Such developments have also highlighted the consideration for a relevant legal framework for the conduct of hostilities in outer space. As a starting point, the conduct of hostilities would be governed by international humanitarian law concerning the substantive actions of parties when engaged in armed conflict. However, the specific location of these hostilities also leads to the relevance of space law, consisting of the particular obligations states have agreed upon for all conduct in outer space. Relying upon these two bodies of law, this article explores how international law might govern many of these activities. In doing so, this article aims to challenge the traditionally advanced lex specialis argument. It does so by arguing that, unlike what is typically assumed, space law does not necessarily present a conflict with international humanitarian law. Progressing from this central thesis, it advances the notion that this relationship might be best considered through systematic integration. Ultimately, it is argued that only in this way can the specific nature of the space domain be offered the required legal protection.

The role of science in resilience planning for military-civilian domains in the U.S. and NATO

ABSTRACT In recent years, the NATO member nations have committed to a coordinated approach to strengthening resilience among the Allies, including the development of National Resilience Plans (NRPs). The Allies outlined the extent to which the robustness of their respective military capacities requires the designed resilience of systems that bridge civilian and military domains. This article outlines the role that resilience plays in supporting tactical and strategic measures of national security and defense within military and civilian domains. This exploration provides an outline of how resilience is currently applied in practice by the U.S. Department of Defense (DOD) and NATO. Building on this diversity of applications, various categorical forms of resilience drawn from the empirical science of resilience are positioned within NATO’s emerging frame for ‘layered’ resilience. This article reinforces the scientific debate that an optimal orientation to resilience leaves open the door for the transformative adaptation of function and identity when the single-equilibrium processes of resilience reach their limits. This article concludes with a normative perspective on how military and civilian resilience planning could support the development of NRPs that would amplify the Allies’ collective capacity to face shared security threats.

"What about Military Decision-Making?": A Bibliometric Review of Published Articles.

Decision-making processes in the military domain constitute a strategic field of research in cognitive psychology, although there are currently few scientific publications addressing the topic. Professionals in the field and interested parties need access to data on military decision-making processes to understand where and how the scientific community is directing its investigations on the issue. Military decision-making is a strategic field of study because the military is crucial to the security and defense of a country or community. This work aims to be a point of reference for those involved in various capacities in military decision-making, providing key data regarding research trends over the years, the geographical distribution of scientific productivity, methodologies employed, annual statistics, and the prevalence of the most-investigated terms and topics. Therefore, this study serves as a bibliometric analysis of the literature on military decision-making publihed from 1992 to 2023 on the Scopus and Web of Science databases.

Australia’s Approach to Chinese Power and Managing Relations with Beijing: The Enduring Paradigm of Liberalism

ABSTRACT As China’s economy has grown, its military capabilities have expanded commensurately, and Beijing has adopted a more assertive foreign policy stance. Perceiving its primacy to be under threat, the response in Washington has been a hard turn towards a Realist paradigm evident across both the military and economic domains. The first contribution of this article is to document that, despite Australia being a staunch US security ally and having its own anxieties about Chinese power, Canberra has undertaken a more modest Realist tilt. This tilt has focused heavily on the military domain, whereas more broadly, an approach informed by the paradigm of Liberalism endures. The second contribution is to theorise this attachment to Liberalism by drawing on Australia’s recent experience of being targeted by Chinese power in the form of geoeconomic coercion. Australia’s interests were not protected by the power of Canberra’s geopolitical friends. Instead, economic interdependencies constrained Beijing’s options, and risks were mitigated by Australian exporters having access to a global trading system underpinned by rules and institutions. Rather than being rooted in ideological and normative appeal, Canberra’s ongoing attachment to Liberalism mostly reflects utilitarian considerations. Australia’s experience likely offers lessons for other lesser powers.

An automated model‐based testing approach for the self‐adaptive behavior of the unmanned aircraft system application software

SummaryThe unmanned aircraft system (UAS) is rapidly gaining popularity in civil and military domains. A UAS consists of an application software that is responsible for defining a UAS mission and its expected behavior. A UAS during its mission experiences changes (or interruptions) that require the unmanned aerial vehicle (UAV) in a UAS to self‐adapt, that is, to adjust both its behavior and position in real‐time, particularly for maintaining formation in the case of a UAS swarm. This adaptation is critical as the UAS operates in an open environment, interacting with humans, buildings, and neighboring UAVs. To verify if a UAS correctly makes an adaptation, it is important to test it. The current industrial practice for testing the self‐adaptive behaviors in UAS is to carry out testing activities manually. This is particularly true for existing UAS rather than newly developed ones. Manual testing is time‐consuming and allows the execution of a limited set of test cases. To address this problem, we propose an automated model‐based approach to test the self‐adaptive behavior of UAS application software. The work is conducted in collaboration with an industrial partner and demonstrated through a case study of UAS swarm formation flight application software. Further, the approach is verified on various self‐adaptive behaviors for three open‐source autopilots (i.e., Ardu‐Copter, Ardu‐Plane, and Quad‐Plane). Using the proposed model‐based testing approach we are able to test sixty unique self‐adaptive behaviors. The testing results show that around 80% of the behavior adaptations are correctly executed by UAS application software.

Specific Approaches to Augmenting Logistics Planning in Joint Multinational Operations

Abstract Challenges and actions by state adversaries in the economic-social and military domains at the regional and global levels necessitate a more detailed approach to combat power at the operational level of NATO. This concept refers to the availability of the necessary logistical capabilities, as well as the leadership’s skills and willingness to use them effectively and efficiently on operational components and functions in the area/theater of joint multinational operations. In this paper, I highlight the appropriate elements for augmenting the logistics planning of joint multinational operations, the primary role of tailored support following the new requirements revealed by the lessons identified in the armed conflict in Ukraine. The sequences and subsections developed in the material present an approach and correlation necessary for increasing the role of logistical planning in joint multinational operations to provide timely support with sufficient resources to the organized, engaged, and led multinational joint forces in achieving operational objectives and the end state.

Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs

UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies.

From finance to legacy: Exploring Bank of China’s modern buildings as serial cultural heritage

In the early 20th century, China underwent profound societal transformations across cultural, political, economic, and military domains, culminating in the birth of its first national bank, the Bank of China. Enduring for over a century, the Bank of China stands as a testament to the emergence and evolution of a modern nation-state. The edifices housing this paramount institution, as well as their geographical locations and architectural designs, serve as prime examples for studying modern Chinese architecture and social history. This article, rooted in field surveys, traces the development history and current conservation status of the 26 existing Bank of China buildings. It analyzes their locations, construction backgrounds, architectural features, and functional uses. Through an assessment of the historical, artistic, scientific, and socio-cultural values of these bank buildings, this study employs the perspective of “serial cultural heritage” to propose a value-based thematic interpretation aimed at revitalization. In particular, it accentuates that analyses and interpretation of values are fundamental prerequisites for rebuilding and displaying the cultural identity of these buildings.

Direction-finding for unmanned aerial vehicles using radio frequency methods

Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach.

Analyzing the Application of the Internet in Education of Ideology and Politics at the Grassroots Military Level

With the rapid development of information technology, the internet has become an indispensable part of modern society, finding widespread application in various fields. In the military domain, grassroots units play a crucial role in maintaining national security and stability. The effectiveness of their education of ideology and politics directly impacts the troops' combat capability and cohesion. Traditional methods of education of ideology and politics face challenges such as outdated content, monotonous educational forms, and low participation among officers and soldiers. The introduction of the internet offers new opportunities and challenges for education of ideology and politics at the grassroots military level. The richness and shareability of online resources provide a vast array of educational materials, broadening and deepening the content of education. The interactivity of online platforms enhances communication between officers, soldiers, and educators, improving the education's relevance and effectiveness. Furthermore, innovative internet technologies revolutionize educational methods, making them more diverse and personalized. To fully leverage the internet in grassroots military education of ideology and politics, it is necessary to build online education platforms, utilize big data and artificial intelligence, and conduct activities that integrate online and offline education. Implementing these innovative strategies can effectively address challenges and promote the innovative development of education of ideology and politics at the grassroots military level.

Explainable AI in the military domain

Artificial intelligence (AI) has become nearly ubiquitous in modern society, from components of mobile applications to medical support systems, and everything in between. In societally impactful systems imbued with AI, there has been increasing concern related to opaque AI, that is, artificial intelligence where it is unclear how or why certain decisions are reached. This has led to a recent boom in research on “explainable AI” (XAI), or approaches to making AI more explainable and understandable to human users. In the military domain, numerous bodies have argued that autonomous and AI-enabled weapon systems ought not incorporate unexplainable AI, with the International Committee of the Red Cross and the United States Department of Defense both explicitly including explainability as a relevant factor in the development and use of such systems. In this article, I present a cautiously critical assessment of this view, arguing that explainability will be irrelevant for many current and near-future autonomous systems in the military (which do not incorporate any AI), that it will be trivially incorporated into most military systems which do possess AI (as these generally possess simpler AI systems), and that for those systems with genuinely opaque AI, explainability will prove to be of more limited value than one might imagine. In particular, I argue that explainability, while indeed a virtue in design, is a virtue aimed primarily at designers and troubleshooters of AI-enabled systems, but is far less relevant for users and handlers actually deploying these systems. I further argue that human–machine teaming is a far more important element of responsibly using AI for military purposes, adding that explainability may undermine efforts to improve human–machine teamings by creating a prima facie sense that the AI, due to its explainability, may be utilized with little (or less) potential for mistakes. I conclude by clarifying that the arguments are not against XAI in the military, but are instead intended as a caution against over-inflating the value of XAI in this domain, or ignoring the limitations and potential pitfalls of this approach.

Machine Learning-based Approaches in Error Detection and Score Prediction for Small Arm Firing Systems in the Military Domain

Error pattern recognition is a routine job in the military to provide corrective guidelines to the shooter. Errors can be recognized with a visual approach based on the spreading pattern of bullets on the target board, which are categorized into four categories: long horizontal error, long vertical error, bi-focal error, and scattered error. Currently, this process is performed manually and requires active human involvement. Similarly, an automated system to predict the future performance of a shooter is not available in the military domain. Moreover, the performance of a shooter depends on several factors, including age, weather, ammunition type, availability of light, previous scores, shooting range, classification of firing, and other factors. The military domain has not addressed the automatic prediction of such performance. While error correction and performance analysis have been extensively explored in the field of sports, their application within the military domain remains an untapped area of research and investigation. Numerous recent endeavors have suggested the utilization of deep learning to tackle this challenge. However, the absence of real-time data poses a significant obstacle, rendering these solutions seemingly impractical. In this paper, we have applied machine- learning approaches and adopted the best algorithm to automate the error pattern recognition system within a military domain. Our proposed methodology has two modules. The first module uses various algorithms and finds a random forest classifier that can do better to recognize the pattern of error and in the second phase, we used the AdaBoost classifier to predict the score and performance of a firer. Several experiments have been conducted, and the results show an average accuracy of 0.968 using Random Forest to recognize the pattern of error and an accuracy of 0.69 using AdaBoost to predict score performance. The data has been collected from the real-time environment of the military domain and experiments have been carried out using real-time scenarios with the military in mind.

On the importance of continuous development in the military domain

A significant percentage of modern products and systems purchased from the defense industry are difficult for the buyer to adapt or develop for new needs after lessons learned on the battlefield. This results in the procured systems having unnecessarily short lifespans. This paper contains a literature survey of the implementation and adaptation of military systems, combined with the authors’ own observations from real cases of procurement in the military domain. Furthermore,this paper presents concepts for continuous development in the military domain, addressing several of the issues observed with modern systems. The term “continuous development” is here defined as the continuous modification, improvement and enhancement of military equipment or a military technical system over the course of its life cycle. The paper outlines how compatibility, flexibility and interoperability relate to unmanned aerial vehicles, counter unmanned aerial systems operations and electronic warfare operations, their role in continuous development and why these concepts are important on the modern battlefield. We contend that the ability to modify equipment increases adaptability, and therefore survivability, in conflict scenarios where new threats continuously appear. The paper concludes with recommendations of practical value for the defense industry on how equipment can be adapted to facilitate continuous development.

Theoretical and Methodological Approaches to Studying Artificial Intelligence in the Context of International Relations and International Law

This article addresses one of the pressing issues regarding the role of artificial intelligence (AI) in international relations and international law. The research question revolves around defining the theoretical and methodological approaches applicable to the strategic analysis of AI utilization in these fields. In the contemporary world, there is a demand at both interstate and societal levels to define the role of AI in the political and legal spheres. This is because AI development affects crucial areas of state relations such as security, international law, ethical norms, and dependencies. The prospective use of AI technologies without corresponding legal regulation may disrupt the already fragile balance of the world order, which could be exacerbated by state competition in AI technologies and AI applications in the military domain, a grey area in international law. Analyzing this issue from the perspective of international relations and international law theory allows for examining AI's impact on state interactions and developing new application strategies. Similarly, it helps understand how international law regulates state relations, including aspects related to AI applications. By examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, this article contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors.

Immersive Innovations: Exploring the Diverse Applications of Virtual Reality (VR) in Healthcare.

Virtual reality (VR) has experienced a remarkable evolution over recent decades, evolving from its initial applications in specific military domains to becoming a ubiquitous and easily accessible technology. This thorough review delves into the intricate domain of VR within healthcare, seeking to offer a comprehensive understanding of its historical evolution, theoretical foundations, and current adoption status. The examination explores the advantages of VR in enhancing the educational experience for medical students, with a particular focus on skill acquisition and retention. Within this exploration, the review dissects the applications of VR across diverse medical disciplines, highlighting its role in surgical training and anatomy/physiology education. While navigating the expansive landscape of VR, the review addresses challenges related to technology and pedagogy, providing insights into overcoming technical hurdles and seamlessly integrating VR into healthcare practices. Additionally, the review looks ahead to future directions and emerging trends, examining the potential impact of technological advancements and innovative applications in healthcare. This review illuminates the transformative potential of VR as a tool poised to revolutionize healthcare practices.

Elevating humanism in high-stakes automation: experts-in-the-loop and resort-to-force decision making

ABSTRACT Artificial intelligence (AI) technologies pervade myriad decision systems, mobilising data at a scale, speed, and scope that far exceed human capacities. Although it may be tempting to displace humans with these automated decision systems, doing so in high-stakes settings would be a mistake. Anchored by the example of states’ resort to force, I argue that human expertise should be elevated—not relegated—within high-stakes decision contexts that incorporate AI tools. This argument builds from an empirical reality in which defence institutions increasingly rely on and invest in AI capabilities, an active debate about how (and if) humans should figure into automated decision loops, and a socio-technical landscape marked by both promise and peril. The argument proceeds through a primary claim about the amplified relevance of expert humans in light of AI, underpinned by the assumed risks of omitting human experts, together motivating a tripartite call to action. The position presented herein speaks directly to the military domain, but also generalises to a broader worldbuilding project that preserves humanism amidst suffusive AI.