Naval Postgraduate School

Abstract This study evaluates the logistics efficiency of the China–Pakistan Economic Corridor (CPEC) by conducting a route-based simulation of transportation cost, travel time, and distance between China and major ports of the Gulf Cooperation Council (GCC). Using geographic data, descriptive statistics, and scenario-based comparisons, the analysis contrasts an existing logistics configuration combining overland transport from Kashgar to Shanghai with maritime shipping to GCC ports, with an alternative CPEC-enabled route linking Kashgar to Gwadar by land and Gwadar to GCC ports by sea. Transportation costs and transit times are estimated for a standard 40-foot container under transparent and replicable assumptions. The results indicate substantial reductions in logistics costs and travel time under the CPEC scenario, driven primarily by shorter overland distances and significantly reduced maritime sailing distances from the GCC ports. On average, the simulated CPEC route reduces total transportation costs by approximately 48% and transit time by about 58% compared with the baseline route.

Abstract The phenomenon of ducting, influenced by variations in air moisture and temperature above the sea surface, introduces non‐standard propagation characteristics that can significantly affect marine communication and radar systems. Therefore, it is necessary to understand these duct formations in order to accurately predict system performance. Atmospheric ducts are characterized by variations in the vertical refractivity profile. The Refractivity‐From‐Clutter (RFC) technique, known for real‐time low tropospheric refractivity profiling, is the focus of this study. This paper explores the concept of employing Multiple Launch Angles (MLA) to enhance RFC inversion precision. The optimal number and distribution of launch angles, multiple‐angle cost functions, and corresponding MLA‐RFC performance computations are demonstrated via simulations. An MLA‐RFC‐capable radar is built and deployed during the Coastal Land‐Air‐Sea Interaction (CLASI) Study in Moss Landing, CA in 2021 and 2022. The Lower ATmospheric PROPagation (LATPROP) radar system is a modified, low‐cost marine radar. The capabilities of MLA‐RFC are demonstrated using multiple launch angle LATPROP radar measurements collected during the CLASI campaign. The results show that the MLA‐RFC method outperforms the single‐angle RFC approach by generating reliable launch angle weights and applying them to the inversion algorithm, which leads to reduced bias and RMS error in evaporation duct height estimates. These weights are derived from the averaged simulated energy trapped in a duct for a given evaporation duct height. Both simulation and experimental results indicate that MLA improves estimation performance by up to 77% and 67%, respectively, compared to the classical single‐launch angle RFC.

Red teaming is a common cybersecurity practice that simulates real-world adversarial cyber operations on defended systems to identify vulnerabilities. Current red team tools often have limited logging capabilities, resulting in insufficient analysis that prevents red teams from receiving real-time feedback and insights after operations. The application of machine learning for automating the analysis of red team operations is severely constrained by the scarcity of labeled, real-world log data. This research addresses this challenge by exploring the potential of using synthetic data to train attack-detection models for Cobalt Strike logs. We systematically evaluate three different training approaches for analyzing Cobalt Strike operational logs: synthetic-only, real-world-only, and a hybrid approach that combines both data types. Our methodology employs a comprehensive feature engineering pipeline that includes both programmatic log generation for creating large-scale structured data and large language model techniques for introducing variety and edge cases. We transform each log file into a high-dimensional vector that includes event types, command verbs, temporal activity patterns, and mappings to the MITRE ATT&CK knowledge base. Random Forest classification models are trained using this feature set to distinguish between successful and failed attack scenarios. By rigorously testing each training approach against a manually labeled ground-truth set of 112 authentic Cobalt Strike logs, we quantify the performance and limitations of each strategy. Our main contribution is demonstrating that a hybrid training strategy achieves 94% accuracy, greatly surpassing synthetic-only models (56%) and real-world-only models (79%). This combined approach effectively addresses both the domain gap in synthetic data and the data scarcity in small, real-world datasets. The hybrid model learns attack diversity from over 30,000 synthetic scenarios while grounding understanding in the authentic structural patterns of real logs, providing a 15 percentage-point improvement over real-data-only approaches. This research offers a practical framework for enhancing limited real-world cybersecurity datasets by strategically integrating synthetic data, enabling immediate use in Department of Defense red team operations and wider cybersecurity machine learning applications.

Previous work on fuel-optimal trajectory planning for long endurance unmanned aircraft has focused on a point-to-point optimization task. The underlying optimization problem accounts for practical complexities, including the uncertainty in energy consumption arising from propulsion system dynamics and the influence of three-dimensional, time-varying weather conditions. Such factors often lead to optimized trajectories that deviate significantly from great-circle paths. When missions require the UAV to traverse a sequence of intermediate way points to satisfy operational objectives, a common approach is to solve a series of independent point-to-point trajectory optimization problems. However, this method may not yield the best solution with respect to energy consumption. This is because in a point-to-point routing each flight segment is optimized independently of the others. In this paper, we construct and solve a multi-point trajectory optimization problem for minimum-fuel UAV routing. We demonstrate that by optimizing over a set of multiple way points, better solutions can be obtained as compared to the sequential point-to-point approach. The key insight is that the multi-point problem formulation allows the performance over the entire mission time horizon to be considered rather than over the discrete time segments used to solve the point-to-point problems. This allows fuel consumption to be minimized by considering all previous and future actions leading to a more efficient solution.

Helicopters, quadcopters, and multi-rotor-copters play a crucial role in transporting supplies to remote areas worldwide, emphasizing the necessity for the development of rotorcraft systems capable of efficiently carrying heavy external loads. This paper proposes a novel approach involving a cable system that connects the rotorcraft to the payload, ensuring safe and straightforward loading and unloading operations. However, the challenge lies in piloting rotorcraft systems with underslung payloads, which are prone to payload oscillations, necessitating a careful balance between safety and efficiency. This paper focuses on devising optimal trajectory planning strategies applicable to rotorcraft systems of varying sizes with underslung payloads. A dynamic model will be developed and validated for rotorcrafts equipped with underslung payload systems. An optimal trajectory problem specific to this configuration will be addressed through analysis based on Pontryagin’s Principle, utilizing the DIDO optimal control toolbox to solve two distinct cases of optimal trajectories. The solutions will be verified and validated by optimality and feasibility checks. The findings will highlight the operational characteristics and advantages of each case, demonstrating the trade-offs between operational speed, oscillation magnitude, and vibration frequency. By adjusting the weighting coefficient in the cost function, a hybrid approach combining the strengths of both cases can be achieved.