LOGISTICS IN CONTESTED ENVIRONMENTS

This report examines the transport and delivery of logistics in contested environments within the context of great-power competition (GPC). Across the Department of Defense (DOD), it is believed that GPC will strain our current supply lines beyond their capacity to maintain required warfighting capability. Current DOD efforts are underway to determine an appropriate range of platforms, platform quantities, and delivery tactics to meet the projected logistics demand in future conflicts. This report explores the effectiveness of various platforms and delivery methods through analysis in developed survivability, circulation, and network optimization models. Among other factors, platforms are discriminated by their radar cross-section (RCS), noise level, speed, cargo capacity, and self-defense capability. To maximize supply delivered and minimize the cost of losses, the results of this analysis indicate preference for utilization of well-defended convoys on supply routes where bulk supply is appropriate and smaller, and widely dispersed assets on shorter, more contested routes with less demand. Sensitivity analysis on these results indicates system survivability can be improved by applying RCS and noise-reduction measures to logistics assets.Director, Warfare Integration (OPNAV N9I)Major, Israel Defence ForcesCivilian, Singapore Technologies Engineering Ltd, SingaporeCommander, Republic of Singapore NavyCommander, United States NavyCaptain, Singapore ArmyLieutenant, United States NavyLieutenant, United States NavyMajor, Republic of Singapore Air ForceCaptain, United States Marine CorpsLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyCaptain, Singapore ArmyLieutenant Junior Grade, United States NavyCaptain, Singapore ArmyLieutenant Colonel, Republic of Singapore Air ForceApproved for public release. distribution is unlimite

THE APPLICATION AND EMPLOYMENT OF SPECIAL FORCES TO EFFECTIVELY OPERATE IN THE MULTI-DOMAIN OPERATIONS ENVIRONMENT OF LARGE-SCALE COMBAT OPERATIONS

With the U.S. military’s shift in focus to large-scale combat operations (LSCO) in a multi-domain operations (MDO) environment, special forces (SF) must adapt and develop new strategies to operate and succeed in a new technologically enabled combat environment. MDO warfare has evolved and now includes the 4th dimension of information, electronic, and cyber warfare. The prevalence of information through open-source internet, radio, and television as well as a high reliance on electronic communications equipment has enhanced the offense opportunities for both the United States and its adversaries alike. Designed and assembled in an era prior to the MDO environment, the SF force structure currently lacks some of the capabilities to effectively operate and thrive on modern battlefields. Capabilities such as electronic warfare or employment and exploitation of cyber space hold significant influence on the battlespace, which SF units are ill prepared to employ or exploit. As a result, SF units should reconsider their capabilities when it comes to penetrating and disabling the anti-access area denial (A2/AD) bubbles established by our enemies. SF should also consider the tools required to destroy or disable A2/AD. Ultimately, SF must reanalyze how they are positioned and employed within LSCO. A look at modern-era case studies show that SF must adapt its capabilities and force structure if it is to remain relevant and effective in future conflicts.Major, United States ArmyMajor, United States ArmyApproved for public release. Distribution is unlimited

Maritime Deception and Concealment: Concepts for Defeating Wide-Area Oceanic Surveillance-Reconnaissance-Strike Networks

Deception and concealment can help mitigate the risks that an adversary might cripple U.S. forward maritime forces in a massive, war-opening strike, achieve in the first days or weeks some fait accompli, or inflict severe losses on maritime forces as they maneuver within a contested zone to retake the initiative

A survey on vehicular communication for cooperative truck platooning application

Platooning is an application where a group of vehicles move one after each other in close proximity, acting jointly as a single physical system. The scope of platooning is to improve safety, reduce fuel consumption, and increase road use efficiency. Even if conceived several decades ago as a concept, based on the new progress in automation and vehicular networking platooning has attracted particular attention in the latest years and is expected to become of common implementation in the next future, at least for trucks.The platoon system is the result of a combination of multiple disciplines, from transportation, to automation, to electronics, to telecommunications. In this survey, we consider the platooning, and more specifically the platooning of trucks, from the point of view of wireless communications. Wireless communications are indeed a key element, since they allow the information to propagate within the convoy with an almost negligible delay and really making all vehicles acting as one. Scope of this paper is to present a comprehensive survey on connected vehicles for the platooning application, starting with an overview of the projects that are driving the development of this technology, followed by a brief overview of the current and upcoming vehicular networking architecture and standards, by a review of the main open issues related to wireless communications applied to platooning, and a discussion of security threats and privacy concerns. The survey will conclude with a discussion of the main areas that we consider still open and that can drive future research directions.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Predicting Marine Traffic in the Ice-Covered Baltic Sea

Icebreaking activity and seasonal ice propose challenges for marine traffic prediction in the Baltic Sea. Traffic prediction is a vital part in the planning of icebreaking activities, but it remains largely as a manual task. The aim of this thesis is to examine factors influencing marine traffic modelling in ice-covered waters and propose a novel A*-based method for modelling traffic in ice. The current state of the marine traffic modelling and factors affecting vessel movement are concluded by examining the literature and historical vessel tracks. The field of traffic modelling research is growing rapidly. Currently the biggest challenges are evaluation of results and the lack of publicly available datasets. Moreover, the current approaches to model vessel movement in ice are promising but fail to capture how icebreaking activity influences vessel routes. The proposed model consists of sea, maneuverability, route and speed modelling. The model uses historical AIS data, topography of the sea, vessel type and dirways as main data inputs. The model is trained with summer tracks and dirways are used for modelling the ice channels kept open by icebreakers. The accuracy of the model is evaluated by examining route, speed, traffic and ETA (estimated time of arrival) prediction results separately. Moreover, the area between the actual and predicted route is introduced as an accuracy measure for route prediction. The model shows that winter route prediction can be improved by incorporating dirways to the modelling. However, the use of dirways did not affect the speed, traffic or ETA prediction accuracy. Finally, the datasets and source code used in this thesis are published online

Predicting Marine Traffic in the Ice-Covered Baltic Sea

Icebreaking activity and seasonal ice propose challenges for marine traffic prediction in the Baltic Sea. Traffic prediction is a vital part in the planning of icebreaking activities, but it remains largely as a manual task. The aim of this thesis is to examine factors influencing marine traffic modelling in ice-covered waters and propose a novel A*-based method for modelling traffic in ice. The current state of the marine traffic modelling and factors affecting vessel movement are concluded by examining the literature and historical vessel tracks. The field of traffic modelling research is growing rapidly. Currently the biggest challenges are evaluation of results and the lack of publicly available datasets. Moreover, the current approaches to model vessel movement in ice are promising but fail to capture how icebreaking activity influences vessel routes. The proposed model consists of sea, maneuverability, route and speed modelling. The model uses historical AIS data, topography of the sea, vessel type and dirways as main data inputs. The model is trained with summer tracks and dirways are used for modelling the ice channels kept open by icebreakers. The accuracy of the model is evaluated by examining route, speed, traffic and ETA (estimated time of arrival) prediction results separately. Moreover, the area between the actual and predicted route is introduced as an accuracy measure for route prediction. The model shows that winter route prediction can be improved by incorporating dirways to the modelling. However, the use of dirways did not affect the speed, traffic or ETA prediction accuracy. Finally, the datasets and source code used in this thesis are published online

Modeling and characterization of traffic flow patterns and identification of airspace density for UTM application

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas represe ting safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management

Journal of Telecommunications and Information Technology, 2003, nr 4

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