Situational Awareness Research Articles

Atmosphere, imminence and the Manchester Arena Inquiry: on the affective modalities of becoming situationally aware to urban terrorism

ABSTRACT Building situational awareness has become a central logic among contemporary security apparatuses for abating no-warning urban terrorism. Situational awareness, we contend, casts the sensory, perceptual and affective capacities of human bodies to decipher their immediate surroundings and attune to the non-representational and more-than-known of an enfolding situation, as essential to its execution. What modalities of sense-making and attunement, then, does this burgeoning security rationality demand? And what sensibilities, affectivities and subjectivities are (re)produced and legitimated across urban life? In this paper, we unpack these questions by examining the ways affective and atmospheric attunement(s) are infused within orthodox and emergent security approaches for developing and honing situational awareness. We argue that affective atmospheres in particular furnish the theoretical architecture for attending to the spatio-affective-material registers through which imminence becomes palpable. Empirically, by analysing the Public Inquiry following the 2017 Manchester Arena attack, we elucidate where the Inquiry advanced discussions on (the limits of) situational awareness and its sensorial, affective and atmospheric dimensions, thereby extending situational awareness into an atmospheric agenda within urban geopolitics. Finally, we reflect on how the impending UK Protect Duty might reconfigure the legal landscape of situational awareness, city resilience, and the affective economies of (in)attention.

Influential factors of adherence to inhalation drug therapy in patients with stable chronic obstructive pulmonary disease.

This study aimed to investigate the influential factors of adherence to inhalation drug therapy (IDT) in patients with stable chronic obstructive pulmonary disease (COPD). A total of 243 patients with stable COPD who visited the chronic disease clinic of the respiratory department of our hospital between April 2022 and October 2022 were selected as participants using the convenience sampling method. Relevant information about all participants was collected by questionnaire for investigation, including basic information, clinical characteristics, inhaled drug names, situational awareness, dose and frequency. Univariate analysis revealed positive correlations between the following factors: (1) the total score of drug adherence and the total scores of the COPD knowledge questionnaire (COPD-Q), social support, subjective support, objective support and support utilisation, (2) the total score of dosage adherence and the total scores of COPD-Q, objective support and support utilisation and (3) the total score of technical standardisation and the total scores of social support, subjective support and objective support (p < 0.05). Multifactorial analysis showed that COPD health literacy, number of acute exacerbations in the past year and social support factors collectively accounted for 37.4% of the variable of patient adherence to IDT, as did COPD health literacy, modified Medical Research Council (mMRC) grading, duration of COPD, utilisation of support and marital status collectively account for 47.4% of the variable of patient dosage adherence. The goodness-of-fit of age, mMRC grading, social support, mode of residence, number of acute exacerbations in the past year and literacy to the patients' inhalation technical standardisation in the model was 47.4%. Dose adherence was predominantly influenced by COPD health literacy, mMRC grading, duration of COPD, utilisation of support and marital status. Inhalation technical standardisation was substantially limited by age, mMRC grading, social support, mode of residence, number of acute exacerbations in the past year and literacy.

Driving assistant using generative AI pre-generated messages in simulator-based driving assessment: A step towards low-cost simulator-based driving assessment

This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.

Evaluation Resources for Asthma Programs and Beyond.

Evaluation can ensure the quality of public health programs. Systematic efforts to identify and fully engage everyone involved with or affected by a program can provide critical information about asthma programs and the broader environment in which they operate. To assist evaluators working at programs funded by the Centers for Disease Control and Prevention (CDC's) National Asthma Control Program (NACP), we developed a package of tools that build on the CDC's 1999 Framework for Program Evaluation in Public Health. The resulting suite of evaluation tools guides evaluators through a structured but flexible process, engaging a diverse array of interest holders and actively involving them in evaluation planning and implementation, all while strengthening their capacity to meaningfully contribute to the evaluation process. For our newest tool, our team reviewed the recent evaluation literature to create an enhanced version of the 1999 framework that describes important elements of professional evaluation practice. Although the original framework describes the steps to take in conducting an evaluation and the standards for a high-quality evaluation, our enhanced framework includes an explanation of how evaluators should approach their work: by incorporating critical reflection, interpersonal competence, situational awareness, and cultural responsiveness. In this article, we highlight many of the evaluation resources our team has created since the NACP's inception, culminating in a free e-text called Planting the Seeds of High-Quality Program Evaluation in Public Health. Public health professionals working in many types of programs - not just asthma - may find these resources useful.

Text Entry Performance and Situation Awareness of a Joint Optical See-Through Head-Mounted Display and Smartphone System.

Optical see-through head-mounted displays (OST HMDs) are a popular output medium for mobile Augmented Reality (AR) applications. To date, they lack efficient text entry techniques. Smartphones are a major text entry medium in mobile contexts but attentional demands can contribute to accidents while typing on the go. Mobile multi-display ecologies, such as combined OST HMD-smartphone systems, promise performance and situation awareness benefits over single-device use. We study the joint performance of text entry on mobile phones with text output on optical see-through head-mounted displays. A series of five experiments with a total of 86 participants indicate that, as of today, the challenges in such a joint interactive system outweigh the potential benefits.

Hazard prediction and risky decision-making by drivers affected by non-hemineglect strokes

The stroke survivor’s independence is severely limited if they are prevented from driving, and regaining this ability could be vital to their recovery. Our pioneering study analysed hazard prediction and risky decision-making among drivers who had suffered non-hemineglect strokes. Two groups of experienced drivers were recruited with one group comprising 37 healthy drivers (M = 54.5 years old, SD = 6.59) and the other consisting of 37 drivers who had had non-hemineglect strokes (M = 55.1 years old, SD = 13.7). In terms of lesion location, 59 % (n = 22) had suffered a left-hemisphere injury, 35 % (n = 13) a right-hemisphere injury, and 5 % (n = 2) a bilateral injury. Hemineglect patients were excluded. Participants were shown naturalistic driving video clips which had been recorded from the driver’s point of view. These were filmed both in the participants’ familiar home country (Spain) and also in an unfamiliar country (England). The hazard prediction test showed a number of hazardous situations which were caused by another road user (e.g. an oncoming car invading the lane). Each video clip ended as the hazard began to unfold, and participants had to choose the correct response to identify the hazard, where it occurred, and the likely outcome in that particular traffic situation (situation awareness). In the risky decision-making test, participants were shown video clips whereby the hazards were possibly caused by risky driving behaviour (e.g. driving through a junction with an amber traffic light). The participants were also shown video clips which set up the potential for risky behaviour and they were asked how likely they were to proceed with that risky manoeuvre (e.g. to overtake a cyclist or drive through a junction with an amber traffic light) on a 6-point Likert scale. In the hazard prediction test, a statistically significant group effect was found, with healthy drivers attaining greater accuracy compared to drivers who had suffered a stroke. More specifically, drivers with left-sided strokes had lower hazard prediction accuracy (M = 59.1 %, SD = 16.7) than drivers with right-sided strokes (M = 64.6 %, SD = 11.8) and healthy drivers, who had the highest accuracy percentage (M = 68.6 %, SD = 13.1). Moreover, a significant effect was found regarding familiarity, and driver accuracy in the hazard perception test was found to be greater in the video clips filmed in Spain than those filmed in England. However, the interaction between these factors was not significant. Regarding the risky decision-making test, the only statistically significant effect was with the familiarity of the video clips, whereby drivers displayed a greater disposition to assume risks in the English video clips (unfamiliar driving environment) than in the Spanish clips. Hazard prediction training would be particularly helpful for drivers who had suffered left-hemisphere strokes since it is more difficult for such drivers to process details.

Understanding the Use of Mobility Data in Disasters: Exploratory Qualitative Study of COVID-19 User Feedback.

Human mobility data have been used as a potential novel data source to guide policies and response planning during the COVID-19 global pandemic. The COVID-19 Mobility Data Network (CMDN) facilitated the use of human mobility data around the world. Both researchers and policy makers assumed that mobility data would provide insights to help policy makers and response planners. However, evidence that human mobility data were operationally useful and provided added value for public health response planners remains largely unknown. This exploratory study focuses on advancing the understanding of the use of human mobility data during the early phase of the COVID-19 pandemic. The study explored how researchers and practitioners around the world used these data in response planning and policy making, focusing on processing data and human factors enabling or hindering use of the data. Our project was based on phenomenology and used an inductive approach to thematic analysis. Transcripts were open-coded to create the codebook that was then applied by 2 team members who blind-coded all transcripts. Consensus coding was used for coding discrepancies. Interviews were conducted with 45 individuals during the early period of the COVID-19 pandemic. Although some teams used mobility data for response planning, few were able to describe their uses in policy making, and there were no standardized ways that teams used mobility data. Mobility data played a larger role in providing situational awareness for government partners, helping to understand where people were moving in relation to the spread of COVID-19 variants and reactions to stay-at-home orders. Interviewees who felt they were more successful using mobility data often cited an individual who was able to answer general questions about mobility data; provide interactive feedback on results; and enable a 2-way communication exchange about data, meaning, value, and potential use. Human mobility data were used as a novel data source in the COVID-19 pandemic by a network of academic researchers and practitioners using privacy-preserving and anonymized mobility data. This study reflects the processes in analyzing and communicating human mobility data, as well as how these data were used in response planning and how the data were intended for use in policy making. The study reveals several valuable use cases. Ultimately, the role of a data translator was crucial in understanding the complexities of this novel data source. With this role, teams were able to adapt workflows, visualizations, and reports to align with end users and decision makers while communicating this information meaningfully to address the goals of responders and policy makers.

Toward the Development of an Impact-Based Decision Support Tool for Surface-Transportation Hazards. Part II: An Hourly Winter Storm Severity Index

Abstract In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety. Significance Statement A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.

Extracting geo-references from social media text using bi-long short term memory networks

&lt;p&gt;The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches.&lt;/p&gt;

Information retrieval and classification of real-time multi-source hurricane evacuation notices

In the United States, tracking time-sensitive critical information such as evacuation notices for approaching natural hazards like hurricanes poses a significant challenge. This difficulty arises from the rapid issuing and distribution of these notices by numerous local authorities that may span multiple states. Additionally, evacuation notices often undergo frequent updates, and they are distributed through various online portals lacking standard formats. Unlike weather warnings, there is no centralized database for live (or past) evacuation notices. To meet the demand of data for crisis-related government notices, we developed an approach to detect and retrieve locally issued hurricane evacuation notices in real time. Evacuation-related text data were collected mainly through spatially targeted information retrieval. These data were then manually labeled and used to train natural language processing (NLP) models. The NLP models in turn classified all text data into one of the three notice categories, i.e., mandatory evacuation notices, voluntary evacuation notices, and not an evacuation notice. The classification of mandatory evacuation notices achieved a very high accuracy (recall = 96 %). These NLP models, when applied to future hurricanes, will provide real-time evacuation notices for situation awareness to higher-level government agencies and news outlets. The archived evacuation notices serve as a valuable resource for scholars to study government responses to weather warnings and individual behaviors influenced by evacuation history. Our research framework may extend to other disaster types, allowing for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.

Determination of the spacecraft’s spin axis orientation. Photometric patterns method

Near-Earth space is becoming increasingly congested; the number of satellites in low Earth orbit (LEO), where there is already the greatest spatial density of objects (including man-made debris), is increasing rapidly. Knowing the status of non-cooperative space objects is one of the requirements of space situational awareness (SSA). Assessing the dynamic properties of large inactive satellites and rocket bodies, such as their rotation period and the spatial location of the rotation axis, is necessary to predict their orientation. This information is critical to both the success of active debris removal (ADR) missions and improved orbital propagation of objects in LEO. Monitoring the state of RSO is carried out by various means, including using ground-based optical sensors by collecting photometric data, processing it and analyzing light curves. This paper presents a new method for estimating the orientation of the RSO rotation axis in space. This method relies on structural analysis of RSO light curves and the search for similar fragments, called ”photometric patterns,” in observations obtained from one or several sites simultaneously or over a short period of time. The method does not require knowledge of the RSO shape and does not impose strict requirements on the quality of photometric observations.

Design and Research of an Intelligent Learning System for University Physics

In order to break through the limitations of traditional teaching, realize the integration of online and offline teaching, and optimize the intelligent learning experience of university physics, this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms, and applies this system to teaching environment of university physics. It successfully integrates emerging technologies such as cloud computing, machine learning, and situational awareness, integrates learning context awareness, intelligent recording and broadcasting, resource sharing, learning performance prediction, and content planning and recommendation, and comprehensively improves the quality of university physics teaching. It can optimize the teaching process and deepen intelligent teaching reform, aiming at providing references for the teaching practice of university physics.

Enhancing UAV Crew Performance and Safety: A Technology and Innovation Management Perspective

The integration of Unmanned aerial vehicles (UAVs) into various sectors underscores the importance of optimizing human factors to ensure operational efficiency, safety, and mission success. This study presents a comprehensive bibliometric analysis of the literature on human factors in UAV operations, focusing on cognitive workload, situational awareness, decision-making, ergonomic design, and human-machine interaction. The analysis was conducted using the WoS, covering publications from 2000 to 2023. Key findings include a significant increase in research output over the last decade, highlighting the growing interest and investment in UAV technology and human factors. Influential authors such as Rosenstein (2006), Patterson (2010), Reason (1990), Wiegmann (2001), and Shappell (2007), along with institutions like Beijing University of Posts and Telecommunications, Southeast University China, Xidian University, and Nanjing University of Aeronautics and Astronautics, have emerged as leaders in this field, contributing to advancements in ergonomic design and decision-making processes. Notably, there is a lack of comprehensive studies addressing the long-term cognitive workload effects on UAV operators and the development of standardized ergonomic guidelines tailored specifically for UAV operation environments. The integration of advanced human-machine interaction technologies remains underexplored, indicating a need for further research in this area. By highlighting these gaps, the analysis provides a nuanced understanding of current research dynamics, offering valuable implications for UAV operators, regulators, and policymakers. These findings are pivotal for advancing the field and guiding future research initiatives aimed at enhancing crew performance and safety in UAV operations.

Harvesting energy from a soldier's gait using the piezoelectric effect

Abstract There is an increasing dependence on electronic devices in the battlefield, including equipment for communications, positioning and navigation, and combat situation awareness. These devices are usually powered by batteries because they are reliable and have good power density (energy per kilogram). Nevertheless, batteries are heavy and bulky, which reduces agility and increases fatigue of the ground troops. An alternative is to extract energy from renewable sources (like the sun and the wind) or harvest energy from the human body itself (like temperature gradients and walking). This article studies the feasibility of a piezoelectric generator, stimulated by human gait, to power electronic devices on the battlefield. Tests were carried out to measure the energy harvested from several piezoelectric ceramics placed under the sole of a soldier’s boot. Different materials and arrangements were tried to maximize the power, leading to the development of a prototype. The prototype was able to harvest 875 μ J {\rm{\mu }}{\rm{J}} on each step, on average. Walking 1 h, at a pace of 40 steps/min, leads to 2.1 J of energy, enough to supply a 3.3 V device consuming 10 mA for around a minute. These results are interesting for any situation that requires emergency power on the battlefield (e.g., trigger a device, call for rescue, acquire localization).

Internet of robotic things with a local LoRa network for teleoperation of an agricultural mobile robot using a digital shadow

In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments.

Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges

Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges

Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.

Situational awareness across different temporalities: scenario exercises for health, security and energy futures

ABSTRACT This article looks at the concept of situational awareness in the context of scenario planning. It draws on three cases in which scenarios are used – scenario-based Turning Point exercises in Israel, which focus on security and emergency situations; health scenarios and simulations conducted by the World Health Organization’s Regional Office for Europe; and energy scenarios in the World Energy Council – and examines the different ways in which situational awareness is manifested in these scenarios. In WHO scenarios, situational awareness is initially intricately connected to specific details of the situation described in the scenario, triggering a predefined model for reporting and responding to health crises. In Turning Point scenarios, however, the use of situational awareness not only activates a predetermined response but also encourages critical thinking about the broader context of the event and the important questions and challenges that arise from it. In the WEC scenarios, situational awareness is directed towards potential future changes and developments by creating rich narratives of plausible futures that enable engagement with the future. The analysis shows that the particular temporality of the context in which the scenario technology is used affects the way in which situational awareness is manifested in each case.

Exploring the role of judgement and shared situation awareness when working with AI recommender systems

AbstractAI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This work seeks to examine the importance of judgement and shared situation awareness between humans and automated agents when interacting together in the form of a recommender systems. We propose manipulating both human judgement and shared situation awareness by providing the human decision maker with relevant information that the automated agent (AI), in the form of a recommender system, uses to generate possible courses of action. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of relevant information the participant had, the assessment technique of the proposed solution, and the reliability of the recommender system. Findings indicate that this technique of supporting the human’s judgement and establishing a shared situation awareness is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to pinpoint the limitations and boundaries of the AI partner’s capabilities. They were able to discern situations where the AI’s recommendations could be trusted versus instances when they should not rely on the AI’s advice. This work proposes and validates a way to provide model-agnostic transparency into recommender systems that can support the human decision maker and lead to improved team performance.

The Evolution of Dismounted Soldier Systems: A Comprehensive Review of GOSSRA’s Innovations

The Generic Open Soldier System Reference Architecture (GOSSRA) project represents a significant step forward in the evolution of Dismounted Soldier Systems (DSS). This article provides an in-depth analysis of the project, drawing on direct author participation, extensive project documentation, and a wide range of academic literature. It synthesizes key insights into the technological advancements, operational challenges, ethical considerations, and strategic implications of DSS. The research highlights the potential of advanced technologies to substantially enhance soldier capabilities but also underscores the complexities of integrating these technologies into cohesive and functional systems. The analysis delves into the operational effectiveness and flexibility of DSS, illustrating their ability to improve situational awareness, decision-making, and mobility in diverse operational contexts. However, it also points out the need for systems to adapt to varying scenarios and environments. The ethical and strategic dimensions of DSS deployment are explored, emphasizing the need for robust ethical frameworks and strategic planning to ensure that advancements in military capabilities align with ethical norms and contribute positively to global security. Lessons learned from past deployments and case studies provide invaluable insights into the real-world performance, user acceptance, and integration challenges of DSS. The article concludes with a forward-looking perspective, anticipating future developments in DSS and outlining strategies for their successful implementation. The conclusions and recommendations presented provide a roadmap for the development and integration of DSS, ensuring they evolve in alignment with the imperatives of modern and future warfare.