36 research outputs found
Assessing thermal imagery integration into object detection methods on ground-based and air-based collection platforms
Object detection models commonly deployed on uncrewed aerial systems (UAS)
focus on identifying objects in the visible spectrum using Red-Green-Blue (RGB)
imagery. However, there is growing interest in fusing RGB with thermal long
wave infrared (LWIR) images to increase the performance of object detection
machine learning (ML) models. Currently LWIR ML models have received less
research attention, especially for both ground- and air-based platforms,
leading to a lack of baseline performance metrics evaluating LWIR, RGB and
LWIR-RGB fused object detection models. Therefore, this research contributes
such quantitative metrics to the literature. The results found that the
ground-based blended RGB-LWIR model exhibited superior performance compared to
the RGB or LWIR approaches, achieving a mAP of 98.4%. Additionally, the blended
RGB-LWIR model was also the only object detection model to work in both day and
night conditions, providing superior operational capabilities. This research
additionally contributes a novel labelled training dataset of 12,600 images for
RGB, LWIR, and RGB-LWIR fused imagery, collected from ground-based and
air-based platforms, enabling further multispectral machine-driven object
detection research.Comment: 18 pages, 12 figures, 2 table
The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks
Cyber-Physical Systems (CPS) are increasingly complex and frequently
integrated into modern societies via critical infrastructure systems, products,
and services. Consequently, there is a need for reliable functionality of these
complex systems under various scenarios, from physical failures due to aging,
through to cyber attacks. Indeed, the development of effective strategies to
restore disrupted infrastructure systems continues to be a major challenge.
Hitherto, there have been an increasing number of papers evaluating
cyber-physical infrastructures, yet a comprehensive review focusing on
mathematical modeling and different optimization methods is still lacking.
Thus, this review paper appraises the literature on optimization techniques for
CPS facing disruption, to synthesize key findings on the current methods in
this domain. A total of 108 relevant research papers are reviewed following an
extensive assessment of all major scientific databases. The main mathematical
modeling practices and optimization methods are identified for both
deterministic and stochastic formulations, categorizing them based on the
solution approach (exact, heuristic, meta-heuristic), objective function, and
network size. We also perform keyword clustering and bibliographic coupling
analyses to summarize the current research trends. Future research needs in
terms of the scalability of optimization algorithms are discussed. Overall,
there is a need to shift towards more scalable optimization solution
algorithms, empowered by data-driven methods and machine learning, to provide
reliable decision-support systems for decision-makers and practitioners
Quantifying polarization across political groups on key policy issues using sentiment analysis
There is growing concern that over the past decade, industrialized democratic
nations are becoming increasingly politically polarized. Indeed, elections in
the US, UK, France, and Germany have all seen tightly won races, with notable
examples including the 2016 Trump vs. Clinton presidential election and the
UK's Brexit referendum. However, while there has been much qualitative
discussion of polarization on key issues, there are few examples of formal
quantitative assessments examining this topic. Therefore, in this paper, we
undertake a statistical evaluation of political polarization for
representatives elected to the US congress on key policy issues between
2021-2022. The method is based on applying sentiment analysis to Twitter data
and developing quantitative analysis for six political groupings defined based
on voting records. Two sets of policy groups are explored, including
geopolitical policies (e.g., Ukraine-Russia, China, Taiwan, etc.) and domestic
policies (e.g., abortion, climate change, LGBTQ, immigration, etc.). We find
that out of the twelve policies explored here, gun control was the most
politically polarizing, with significant polarization results found for all
groups (four of which were P < 0.001). The next most polarizing issues include
immigration and border control, fossil fuels, and Ukraine-Russia.
Interestingly, the least polarized policy topics were Taiwan, LGBTQ, and the
Chinese Communist Party, potentially demonstrating the highest degree of
bipartisanship on these issues. The results can be used to guide future policy
making, by helping to identify areas of common ground across political groups.Comment: 31 pages, 7 figure
Sustainability assessment of Low Earth Orbit (LEO) satellite broadband mega-constellations
The growth of mega-constellations is rapidly increasing the number of rocket
launches required to place new satellites in space. While Low Earth Orbit (LEO)
broadband satellites help to connect unconnected communities and achieve the
Sustainable Development Goals, there are also a range of negative environmental
externalities, from the burning of rocket fuels and resulting environmental
emissions. We present sustainability analytics for phase 1 of the three main
LEO constellations including Amazon Kuiper (3,236 satellites), OneWeb (648
satellites), and SpaceX Starlink (4,425 satellites). In baseline scenarios over
five years, we find a per subscriber carbon dioxide equivalent (COeq) of
0.700.34 tonnes for Kuiper, 1.410.71 tonnes for OneWeb and
0.470.15 tonnes COeq/subscriber for Starlink. However, in the
worst-case emissions scenario these values increase to 3.021.48 tonnes for
Kuiper, 1.70.71 tonnes for OneWeb and 1.040.33 tonnes
COeq/subscriber for Starlink, more than 31-91 times higher than equivalent
terrestrial mobile broadband. Importantly, phase 2 constellations propose to
increase the number of satellites by an order-of-magnitude higher, highlighting
the pressing need to mitigate negative environmental impacts. Strategic choices
in rocket design and fuel options can help to substantially mitigate negative
sustainability impacts
Assessing the Socio-economic Impacts of Secure Texting and Anti-Jamming Technologies in Non-Cooperative Networks
Operating securely over 5G (and legacy) infrastructure is a challenge. In
non-cooperative networks, malicious actors may try to decipher, block encrypted
messages, or specifically jam wireless radio systems. Such activities can
disrupt operations, from causing minor inconvenience, through to fully
paralyzing the functionality of critical infrastructure. While technological
mitigation measures do exist, there are very few methods capable of assessing
the socio-economic impacts from different mitigation strategies. This leads to
a lack of robust evidence to inform cost-benefit analysis, and thus support
decision makers in industry and government. Consequently, this paper presents
two open-source simulation models for assessing the socio-economic impacts of
operating in untrusted non-cooperative networks. The first focuses on using
multiple non-cooperative networks to transmit a message. The second model
simulates a case where a message is converted into alternative plain language
to avoid detection, separated into different portions and then transmitted over
multiple non-cooperative networks. A probabilistic simulation of the two models
is performed for a 15 km by 15 km spatial grid with 5 untrusted non-cooperative
networks and intercepting agents. The results are used to estimate economic
losses for private, commercial, government and military sectors. The highest
probabilistic total losses for military applications include US150,
and US$75, incurred for a 1, 3 and 5 site multi-transmission approach,
respectively, for non-cooperative networks when considering 1,000 texts being
sent. These results form a framework for deterministic socio-economic impact
analysis of using non-cooperative networks and secure texting as protection
against radio network attacks. The simulation data and the open-source codebase
is provided for reproducibility
Techno-economic assessment of 5G infrastructure sharing business models in rural areas
How cost-efficient are potential infrastructure sharing business models for the 5G era (and beyond)? This significant question needs to be addressed if we are to deliver universal affordable broadband in line with Target 9.1 of the UN Sustainable Development Goals. Although almost two-thirds of the global population is now connected, many users still lack access to high-speed and reliable broadband connectivity. Indeed, some of the largest connectivity issues are associated with those living in areas of low economic viability. Consequently, this assessment evaluates the cost implications of different infrastructure sharing business models using a techno-economic assessment framework. The results indicate that a rural 5G neutral host network (NHN) strategy helps to reduce total cost between 10 and 50% compared with other sharing strategies. We also find that, compared to a baseline strategy with No Sharing, the net present value of rural 5G sharing strategies can earn between 30 and 90% more profit. The network upgrades to 5G using various sharing strategies are most sensitive to changes in the average revenue per user, the adoption rate, and the amount of existing site infrastructure. For example, the results from this study show that a 20% variation in demand revenue is estimated to increase the net present value of the sharing strategies by 2–5 times compared to the No Sharing strategy. Similarly, a 10% increase in existing infrastructure lowers the net present value by 8–30%. The infrastructure sharing strategies outlined in this study have the potential to enhance network viability while bridging the digital divide in remote and rural locations
Quantifying the daily economic impact of extreme space weather due to failure in electricity transmission infrastructure
Extreme space weather due to coronal mass ejections has the potential to cause considerable disruption to the global economy by damaging the transformers required to operate electricity transmission infrastructure. However, expert opinion is split between the potential outcome being one of a temporary regional blackout and of a more prolonged event. The temporary blackout scenario proposed by some is expected to last the length of the disturbance, with normal operations resuming after a couple of days. On the other hand, others have predicted widespread equipment damage with blackout scenarios lasting months. In this paper we explore the potential costs associated with failure in the electricity transmission infrastructure in the U.S. due to extreme space weather, focusing on daily economic loss. This provides insight into the direct and indirect economic consequences of how an extreme space weather event may affect domestic production, as well as other nations, via supply chain linkages. By exploring the sensitivity of the blackout zone, we show that on average the direct economic cost incurred from disruption to electricity represents only 49% of the total potential macroeconomic cost. Therefore, if indirect supply chain costs are not considered when undertaking cost-benefit analysis of space weather forecasting and mitigation investment, the total potential macroeconomic cost is not correctly represented. The paper contributes to our understanding of the economic impact of space weather, as well as making a number of key methodological contributions relevant for future work. Further economic impact assessment of this threat must consider multiday, multiregional event