Situational Awareness Capabilities Research Articles

Multi-radio fast routing recovery protocol based on spectrum situation awareness

UAV networks often encounter jamming attacks, under which multi-radio protocols have to switch radios to accelerate communication recovery. However, the existing protocols rely on exchange of hello messages to detect jamming, leading to long sensing time and thus slow routing recovery. To address the issues raised by jamming attacks, we propose a new routing protocol, Electromagnetic Spectrum situation awareness Optimized Link State Routing (ESOLSR) protocol, to improve the existing OLSRv2 protocol. ESOLSR utilizes the spectrum situation awareness capability from the physical layer, and adopts joint-updating of link status, updating of interface functions, and adaptive adjustment of parameters. Our simulation results show that the improved protocol, ESOLSR, can recover routing and resume normal communication 26.6% faster compared to the existing protocols.

Synchro-waveform data compression using multi-stage hybrid coding algorithm

To augment situational awareness capabilities for high-frequency disturbances, the modern power system necessitates the implementation of high-sampling rates for real-time Synchro-Waveform (SW) measurements. Nevertheless, the nonlinear nature of data collected from SW measurement devices poses significant challenges to the communication and storage capacities of data centers. To effectively tackle this issue, this paper proposes a Multi-stage Hybrid Coding (MHC) method for the lossless compression of SW data. The MHC method utilizes a well-structured two-stage approach to achieve efficient compression. In the first stage, a variable frame-based method is designed to reduce redundant information and achieve initial compression using multiple periodic recursive compression. Subsequently, the dictionary-based method, specifically the Lempel–Ziv–Markov chain algorithm, is seamlessly integrated to further compress the SW data. Experimental and numerical analyses are conducted using both simulated and real-world source data, with real-time performance validation in SW measurement units. The obtained results convincingly demonstrate that the proposed method enables online compression of both event and non-event SW data, achieving an impressive reduction of over 73.4% in data space, while maintaining a data loss ratio lower than 0.4% for high-density SW data.

Effects of video display terminal fatigue on situational awareness ability of operators and modeling study

In order to study the changing pattern of the posture perception ability of video display terminal operators in the main control room of energy power plants such as nuclear power plants in a state of fatigue, subjective and objective data of 19 energy system operators were collected through experiments. The effect of video display terminal fatigue on the operator's level of situational awareness was analyzed, and the subjective and objective data were selected to construct an XGboost model to predict the identified operator's situational awareness capability. The study results showed that the subjective and objective data showed significant differences, and it could be inferred that the subjects had developed video display terminal fatigue. The combination of data showed that fatigue affected the manipulator's ability to perceive and understand, decreasing the manipulator's situational awareness. The XGboost model, based on subjective and objective data, achieved a validation rate of 88.90%, indicating that the model can identify and predict the situational awareness of video display terminal manipulators. The study's results can be used to assess the situational awareness of video display terminal operators in nuclear power plants and provide a scientific basis for safety training in nuclear power plants and other energy power plants.

VisionICE: Air–Ground Integrated Intelligent Cognition Visual Enhancement System Based on a UAV

Post-disaster search and rescue is critical to disaster response and recovery efforts and is often conducted in hazardous and challenging environments. However, the existing post-disaster search and rescue operations have problems such as low efficiency, limited search range, difficulty in identifying the nature of the target, and wrong target location. Therefore, this study develops an air–ground integrated intelligent cognition visual enhancement system based on a UAV (VisionICE). The technique combines a portable AR display device, a camera-equipped helmet, and a quadcopter UAV for efficient patrols over a wide area. First, the system utilizes wireless image sensors on the UAV and helmet to capture images from the air and ground views. Using the YOLOv7 algorithm, the cloud server calculates and analyzes these visual data to accurately identify and detect targets. Lastly, the AR display device obtains real-time intelligent cognitive results. The system allows personnel to simultaneously acquire air and ground dual views and achieve brilliant cognitive results and immersive visual experiences in real time. The findings indicate that the system demonstrates significant recognition accuracy and mobility. In contrast to conventional post-disaster search and rescue operations, the system can autonomously identify and track targets of interest, addressing the difficulty of a person needing help to conduct field inspections in particular environments. At the same time, the system can issue potential threat or anomaly alerts to searchers, significantly enhancing their situational awareness capabilities.

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. Case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

Tackling Verification and Validation Techniques to Evaluate Cyber Situational Awareness Capabilities

Since cyberspace was identified as a domain of operations, defence practitioners started a race with academy, researchers, and industry and military organizations working together towards defining related lines of capability development (e.g., DOTMLPFI) and exploring the needs and opportunities they entail. An essential cornerstone of adapting to the convergence of the cyber domain with conventional theaters of operation is the need for producing tools for easing to acquire cyber situational awareness (CSA), from which human operators shall be able to perceive, reason and project situations and events observed in cyberspace that may vertically/horizontally propagate from technological to tactical, operational and strategic planes. Benefiting from the higher maturity level of civilian capabilities for cybersecurity, the military sector has embraced the challenge of creating related beyond state-of-the-art CSA enablers that comprise the existing technological background while adopting concepts such as operations, missions or courses of action (CoAs), properly aligning them with military doctrine. Beyond ongoing development efforts, there is a wide methodological gap in the lack of suitable CSA verification and validation (V&V) frameworks, which are expected to analyze if related capabilities meet the requirements to operate in the military context; at the same time supporting the thorough development life-cycle of brand new cyber defence technologies. With the motivation of closing the identified gap, this research introduces a novel V&V framework able to guide the evaluation of CSA-related tools, which makes converge purely military aspects with dual-use state-of-the-art V&V approaches. Three core CSA evaluation concepts are discussed in-depth: software, operational and application tests. They range from the daily application of new capabilities to their ability to enable the acquisition of a joint operational picture understandable by human decision makers.

Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness

Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.

Mission ORCA: Orbit Refinement for Collision Avoidance

Forecasting of collisions between resident space objects (RSOs) is becoming critical for the future exploitation of near-Earth space. A constellation of 28 spacecrafts (plus in-orbit spares) in sun synchronous orbits is proposed as a solution for improving the current space situational awareness capabilities. Each satellite uses an optical payload to track target RSOs, with the satellite's position precisely determined. Multiple pictures of the RSO are taken, and the spacecraft attitude used to calculate the target's position relative to the spacecraft. The target's orbit is then determined from the movement of the target through the field of view over time. The system outputs orbit state vectors of the tracked object, allowing precise orbit characterisation and collision forecasting to be delivered. The constellation's design allows high temporal resolution, so reliable information can be supplied to end-users. The paper shows the results of the system design of a demonstration mission meant to verify the feasibility of the concept, performed by a team of students of Cranfield University. The exercise addresses all the aspects of the preliminary design, including the definition of the mission and system requirements, the selection of the overall mission architecture, operations, and mission phases. A cap on the overall cost allows for the realisation of the platform within a university budget. The outline of the design includes not only the selection and sizing of all the subsystems and payload but also suggests a new strategy for deploying the constellation if the demonstration mission is successful. The utilisation of high TRL and COTS components, as well as mass, power, and link budgets, demonstrate the feasibility of the overall mission concept.

Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

A growing interest in ocean exploration for scientific and commercial research has been shown, mainly due to the technological developments for maritime and offshore industries. The use of Autonomous surface vehicles (ASV) have a promising role to revolutionize the transportation, monitorization, operation and maintenance areas, allowing to perform distinct task from offshore assets inspection to harbor patrolling. This work presents SENSE, an autonomouS vEssel for multi-domaiN inSpection and maintEnance. It provides an open-source hardware and software architecture that is easy to replicate for both research institutes and industry. This is a multi-purpose vehicle capable of acquiring multi-domain data for inspecting and reconstructing maritime infrastructures. SENSE provides a research platform which can increase the situational awareness capabilities for ASVs. SENSE full configuration provides multimodal sensory data acquired from both domains using a Light Detection And Ranging (LiDAR), a stereoscopic camera, and a multibeam echosounder. In addition, it supplies navigation information obtained from a real-time kinematic satellite navigation system and inertial measurement units. Moreover, the tests performed at the harbor of Marina de Leça, at Porto, Portugal, resulted in a dataset which captures a fully operational harbor. It illustrates several conditions on maritime scenarios, such as undocking and docking examples, crossings with other vehicles and distinct types of moored vessels. The data available represents both domains of the maritime scenario, being the first public dataset acquired for multi-domain observation using a single vehicle. This paper also provides examples of applications for navigation and inspection on multi-domain scenarios, such as odometry estimation, bathymetric surveying and multi-domain mapping.

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large-scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra-high density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication.

Hyper-Enabled Operator: Situational Awareness as Armor

The Hyper-Enabled Operator (HEO) project seeks to increase the survivability of special forces operators through increased situational awareness (SA) capabilities. The precursor to HEO was the Tactical Assault Light Operator Suit (TALOS) which sought to protect operators during room clearing operations by increasing their armor coverage. This study used combat modeling to compare the benefits of HEO to TALOS. This project developed four base models, each based on a relevant mission set. Each base model was compared against two variant models. The first variant provided the operators with enhanced SA. The second variant increased operator armor coverage without a performance degradation from increased weight. For some scenarios the incorporation of SA increased survivability more than a large increase in armor. Further, the study found that enhancing SA increases survivability by 8.3% on average for the four mission sets; this increase is similar to increasing armor coverage by a factor of four.

The Effect of Studying Flight Training Materials utilizing Encoding Techniques on Situational Awareness Capabilities of Students in PPL Training

The pilot’s aeronautical decision-making during the flying greatly affects flight safety, and the importance of situational awareness has been greatly emphasized as a prerequisite for making the right decision. This is the reason why more research and interests are needed to help students entering the pilot training program develop excellent situational awareness from the initial stage of training. Situational awareness is closely related to long-term memory activities in human information processing, and pedagogy and cognitive psychology have emphasized the encoding techniques as an effective long-term memory method. This study was conducted to confirm whether pilot students’ using the encoding techniques to learn flight education materials in the early stage of their training at domestic universities has a positive effect on improving their situational awareness.

Online sequential attack detection for ADS-B data based on hierarchical temporal memory

In the next generation air traffic surveillance, ADS-B is the primary surveillance method to improve situation awareness capabilities. But ADS-B protocol is absent of sufficient security considerations, especially for data integrity and authentication. As a result, attack patterns on ADS-B data are emerging and efficient attack detection strategies are in great demand to enhance data security. To decrease the time delay of detection, enhance accuracy and mitigate the concept drift impacts, the online sequential attack detection strategy based on hierarchical temporal memory are proposed. By applying binary encoding, ADS-B data is transformed into sparse distribution representation with temporal and spatial correlations. The encoded data is push into hierarchical temporal memory and online learning schemes are established for the ADS-B stream data. With the sequential ADS-B data, hierarchical temporal memory is updated and used to generate the deviations between predictions and original values for the corresponding ADS-B data. Designing and applying deviation analysis, sequential analysis and adaptive threshold check, the differences between normal and novelty are magnified and easy to be distinguished. According to experimental analysis, the attack detection strategy is efficient on processing time and accuracy.

Low-altitude contour mapping of radiation fields using UAS swarm

This paper addresses the design of lightweight radiation sensors for the small-scale unmanned aerial system (UAS) and its implementation for low-altitude radiation source localization and contour mapping. The compact high-resolution gamma-ray CZT sensors were integrated into UAS platforms as plug-and-play components using robot operating system. The swarm of UAS has advantages over a single agent-based approach in detecting radiative sources and effectively mapping the area. The proposed swarm consists of three UAS platforms in a circular formation. The proposed approach can potentially be used for low-altitude clustered environments where a conventional helicopter-based platform cannot be utilized. It can provide a relatively precise boundary of the safe area for potential human exploration as well as enhancing situation awareness capabilities for first responders. The source seeking and contour mapping algorithms are developed based on a simple 1/R2 radiation field, but they are validated in more realistic radiation field having multiple sources and physical structures with scattering and attenuation effects simulated by MCNP code. Also, gradient estimation and contour mapping algorithms are validated experimentally with small-scale multicopter platforms in the indoor flight testbed.

Subjective Evaluation of Seafarers� Situational Awareness on Collision Avoidance

Objectives: This study aims to investigate the situational awareness capabilities of seafarers which is the weakness of the safety management system studies conducted under the human element researches in maritime literature. Methods/ Statistical Analysis: The SART multi-dimensional, self-rating, post-trial situational awareness measurement technique was used to assess the situational awareness on collision avoidance of the seafarers who have different ages and experience levels. The results were analyzed usingdescriptive statistics, one-way ANOVA and post-hoc tests (Tukey, LSD and Bonferroni). Findings: Results indicate that a gradual decline in mean SART scores of the seafarer from oceangoing watchkeeping officers to oceangoing masters is clearly detected. Although this decline is observed in the all SART main dimensions (Understanding + Attentional Demand + Attentional Supply), there is no statistically significant difference among the situational awareness levels of seafarers in SART - generic analysis. Application/Improvements: The findings of this study suggest that, recruiting seafarers by examining the requirements ofthe situational awareness will be a critical stepto minimizeaccidents on board. Keywords: Seafarers, Situational Awareness, SART

Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

A Markov Multi-Phase Transferable Belief Model for Cyber Situational Awareness

eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with critical information exfiltration from High Valued Targets (HVTs). Existing Cyber Defence frameworks and data fusion models cannot cope with XAPTs due to a lack of provision for multi-phase attacks characterized by uncertainty and conflicting information. The Markov Multi-phase Transferable Belief Model (MM-TBM) extends the Transferable Belief Model to address the multi-phase nature of cyber-attacks and to obtain previously indeterminable Cyber SA. As a data fusion technique, MM-TBM constitutes a novel approach for performing hypothesis assessment and evidence combination across phases, by means of a new combination rule, called the Multi-phase Combination Rule with conflict Reset (MCR 2 ). The impact of MM-TBM as a Cyber Situational Awareness capability and its implications as a multi-phase data fusion theory have been empirically validated through a series of scenario-based Cyber SA experiments for detecting, tracking, and predicting XAPTs.

Post-fault prediction of transient instabilities using stacked sparse autoencoder

Post-fault prediction of transient stability of power systems has a great impact on the performance of wide area monitoring, protection and control systems. Situational awareness capabilities of a power system are improved by fast detection of instabilities after severe fault occurrences. This allows sufficient time to take necessary corrective control actions. In this paper, a novel method based on stacked sparse autoencoder is proposed to predict the post-fault transient stability status of the power system directly after clearing the fault. A dataset is generated off-line to train a stacked sparse autoencoder, and then the trained stacked sparse autoencoder is used in an online application of predicting any transient instability. The stacked sparse autoencoder is fed by the inputs, which are specific points extracted from the fault-on voltage magnitude measurements collected from the phasor measurement units. The effectiveness of the proposed method is demonstrated and compared with the conventional approaches that adopt multilayer perceptrons or post-fault measurements as it is applied to the 127-bus WSCC test system and to the Turkish power system.

Drone-Aided Border Surveillance with an Electrification Line Battery Charging System

This paper proposes to develop a drone-aided border surveillance system with electrification line battery charging systems (DABS-E). Currently, mobile and fixed border surveillance systems such as truck-mounted video recording units, agent portable surveillance units, aerostats, and fixed towers are often used to enhance the comprehensive situational awareness along the U.S. border lines. However, a few drawbacks of the existing systems include limited operating capability, blind spots, physical fatigue of field agents, and lack of fast-responding situational awareness capability. The use of drones and mobile technologies are an ideal way to overcome these issues in border patrol activities. Even though drones bring numerous technical advantages (i.e., short response time, being able to access dangerous areas, and no on-board pilot required) for the border patrol mission, a relatively short flight duration is the main concern for the full implementation for patrol at this time. Therefore, this paper proposes a new concept that is built on electrification line (E-line) systems to wirelessly charge drones during the flight to extend flight duration. As a result, extra power can be provided for drones without the need of landing, stopping or returning back to ground control centers. To accomplish our goal, this paper proposes an optimization model and algorithm to schedule drone flights for a DABS-E. Through a numerical example, this paper shows the feasibility of our proposed method and corresponding economic benefits.

Recovering area-to-mass ratio of resident space objects through data mining

The area-to-mass ratio (AMR) of a resident space object (RSO) is an important parameter for improved space situation awareness capability due to its effect on the non-conservative forces including the atmosphere drag force and the solar radiation pressure force. However, information about AMR is often not provided in most space catalogs. The present paper investigates recovering the AMR information from the consistency error, which refers to the difference between the orbit predicted from an earlier estimate and the orbit estimated at the current epoch. A data mining technique, particularly the random forest (RF) method, is used to discover the relationship between the consistency error and the AMR. Using a simulation-based space catalog environment as the testbed, this paper demonstrates that the classification RF model can determine the RSO's category AMR and the regression RF model can generate continuous AMR values, both with good accuracies. Furthermore, the paper reveals that by recording additional information besides the consistency error, the RF model can estimate the AMR with even higher accuracy.