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   https://doi.org/10.1130/abs/2025CD-410480  

   Miller, Robert; Eddy, Michael P; Tepper, Jeffrey H; Gordon, Stacia (April 2025, Geological Society of America)
2. New Insight Into the Transition From a SAR Arc to STEVE

   https://doi.org/10.1029/2022GL101205  

   Gillies, D. M.; Liang, J.; Gallardo‐Lacourt, B.; Donovan, E. (March 2023, Geophysical Research Letters)

   Key Points Detailed analysis of spectral transition of a Stable Auroral Red (SAR) Arc into Strong Thermal Emission Velocity Enhancement (STEVE) emission Ionospheric threshold conditions may be a requirement for the evolution of STEVE Basic parameters of transition features from SAR Arc to STEVE presented 

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3. The Correlated Arc Orienteering Problem

   https://doi.org/10.1007/978-3-031-21090-7_24  

   Agarwal, Saurav; Akella, Srinivas (January 2023, Algorithmic Foundations of Robotics {XV}: Proceedings of the Fifteenth International Workshop on the Algorithmic Foundations of Robotics (WAFR 2022))

   LaValle, Steven M.; O'Kane, Jason M.; Otte, Michael; Sadigh, Dorsa; Tokekar, Pratap (Ed.)

   This paper introduces the correlated arc orienteering problem (CAOP), where the task is to find routes for a team of robots to maximize the collection of rewards associated with features in the environment. These features can be one-dimensional or points in the environment, and can have spatial correlation, i.e., visiting a feature in the environment may provide a portion of the reward associated with a correlated feature. A robot incurs costs as it traverses the environment, and the total cost for its route is limited by a resource constraint such as battery life or operation time. As environments are often large, we permit multiple depots where the robots must start and end their routes. The CAOP generalizes the correlated orienteering problem (COP), where the rewards are only associated with point features, and the arc orienteering problem (AOP), where the rewards are not spatially correlated. We formulate a mixed integer quadratic program (MIQP) that formalizes the problem and gives optimal solutions. However, the problem is NP-hard, and therefore we develop an efficient greedy constructive algorithm. We illustrate the problem with two different applications: informative path planning for methane gas leak detection and coverage of road networks. 

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4. Why Study The Cascade Arc?

   https://doi.org/10.2138/gselements.18.4.219  

   Humphreys, Eugene D.; Grunder, Anita L. (August 2022, Elements)
5. The Correlated Arc Orienteering Problem

   Agarwal, Saurav; Akella, Srinivas (June 2022, 15th International Workshop on the Algorithmic Foundations of Robotics (WAFR 2022))