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1. “Assessment of Public Space Visitors of Unmanned Aerial Vehicles (UAVs) in Outdoor Public Space and the development of Countermeasures to Control Aerial Visual Access”

   Nassar, Hala ; Hewitt, Robert ; Cummings, Mary ( January 2020 , Landscape research record)

   Use of unmanned aerial vehicle (UAV) technology is predicted to increase dramatically from more than 600,000 drones registered just with the US Federal Aviation Administration (FAA) to nearly 7,000,000 over the next 12 years ( FAA 1,2 ) This popularity is evident in their increasing use in and around public outdoor spaces, including parks, stadiums, outdoor amphitheaters, festival grounds, or outdoor markets. While there is considerable research on unmanned aerial vehicle (UAV) applications and navigation (Koh 2012, Nemeth 2010) and an emerging body of work in landscape architecture (Kullmann 2017, Park 2016), there is no research addressing increasing conflicts between public space visitors, drone navigation in public space, and its effect on the planning and design of public space. The paper presents initial findings from funded research to develop landscape architectural design and planning responses supporting low cost detection technology to deter the illegal use of drones in public spaces. Methods of data collection employed surveys of botanical garden visitors concerning their preferences for site landscape features and experiences, their awareness, attitudes, and preferences about the presence of drones in public space, and potential aerial visual access to a range of forested and open landscapes frequented by visitors in the garden. Findings suggest that given public concern about the presence of drones, landscape planning and design of such public spaces should provide continuous landscape features with restricted aerial visual access surrounding and connecting public areas with open aerial visual access. 

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2. Using unmanned aerial vehicles to sample aquatic ecosystems: Unmanned aerial vehicles in limnology

   https://doi.org/10.1002/lom3.10222  

   Song, Keunyea ; Brewer, Austin ; Ahmadian, Shawyan ; Shankar, Ajay ; Detweiler, Carrick ; Burgin, Amy J. ( December 2017 , Limnology and Oceanography: Methods)
3. Aerial dispersal ability does not drive spider success in a crop landscape: Aerial dispersal and spider success

   https://doi.org/10.1111/een.12641  

   Hogg, Brian N. ; Daane, Kent M. ( October 2018 , Ecological Entomology)
4. Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach

   https://doi.org/10.1109/WCNC51071.2022.9771893  

   Abdalla, Aly Sabri ; Behfarnia, Ali ; Marojevic, Vuk ( April 2022 , 2022 IEEE Wireless Communications and Networking Conference (WCNC))

   The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAVs can also enhance the security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy capacity without the system knowing the location of the eavesdropper. Simulation results show fast convergence and the highest secrecy capacity of the proposed DQN compared to Q-learning and two baseline approaches. 

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5. Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles

   https://doi.org/10.1109/eIT57321.2023.10187274  

   Khoei, Tala Talaei ; Aissou, Ghilas ; Al Shamaileh, Khair ; Devabhaktuni, Vijaya Kumar ; Kaabouch, Naima ( October 2023 , IEEE International Conference on Electro Information Technology (eIT))

   Unmanned Aerial Networks (UAVs) are prone to several cyber-attacks, including Global Positioning Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence techniques; however, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with an accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short-Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs. 

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