28 Nov 2022

Cross Country Mobility (CCM) Modeling Using Triangulated Irregular Networks (TIN)

Abstract: Cross country mobility (CCM) models terrain that has insufficient or unavailable infrastructure for crossing. This historically has been done with either hand-drawn and estimated maps or with raster-based terrain analysis, both of which have their own strengths and weaknesses. In this report the authors explore the possibility of using triangulated irregular networks (TINs) as a means of representing terrain characteristics used in CCM and discuss the possibilities of using such networks for routing capabilities in lieu of a traditional road-based network. The factors used to calculate CCM are modified from previous methods to capture a more accurate measurement of terrain characteristics. Using a TIN to store and represent CCM information achieves comparable results to raster cost analysis with the additional benefits of an integrated network useful for visualization and routing and a reduction in the number of related files. Additionally, TINs can in some cases more accurately show the contours of the landscape and reveal feature details or impediments that may be lost within a raster, thus improving the quality of CCM overlays.

10 Nov 2022

Understanding Plant Volatiles for Environmental Awareness: Chemical Composition in Response to Natural Light Cycles and Wounding

Abstract: Plants emit a bouquet of volatile organic compounds (VOCs) in response to both biotic and abiotic stresses and, simultaneously, eavesdrop on emitted signals to activate direct and indirect defenses. By gaining even a slight insight into the semantics of interplant communications, a unique awareness of the operational environment may be obtainable (e.g., knowledge of a disturbance within). In this effort, we used five species of plants, Arabidopsis thaliana, Panicum virgatum, Festuca rubra, Tradescantia zebrina, and Achillea millefolium, to produce and query VOCs emitted in response to mechanical wounding and light cycles. These plants provide a basis for further investigation in this communication system as they span model organisms, common house plants, and Arctic plants. The VOC composition was complex; our parameter filtering often enabled us to reduce the noise to fewer than 50 compounds emitted over minutes to hours in a day. We were able to detect and measure the plant response through two analytical methods. This report documents the methods used, the data collected, and the analyses performed on the VOCs to determine if they can be used to increase environmental awareness of the battlespace.

9 Nov 2022

Meteorological Influences of a Major Dust Storm in Southwest Asia during July–August 2018

Abstract: Dust storms can be hazardous for aviation, military activities, and respiratory health and can occur on a wide variety of spatiotemporal scales with little to no warning. To properly forecast these storms, a comprehensive understanding of the meteorological dynamics that control their evolution is a prerequisite. To that end, we chose a major dust storm that occurred in Southwest Asia during July–August 2018 and conducted an observation-based analysis of the meteorological conditions that influenced the storm’s evolution. We found that the main impetus behind the dust storm was a large-scale meteorological system (i.e., a cyclone) that affected Southwest Asia. It seems that cascading effects from this system produced a smaller, near-surface warm anomaly in Mesopotamia that may have triggered the dust storm, guided its trajectory over the Arabian Peninsula, and potentially catalyzed the development of a small low-pressure system over the southeastern end of the peninsula. This low-pressure system may have contributed to some convective activity over the same region. This type of analysis may provide important information about large-scale meteorological forcings for not only this particular dust storm but also for future dust storms in Southwest Asia and other regions of the world.

8 Nov 2022

Network Development and Autonomous Vehicles: A Smart Transportation Testbed at Fort Carson

Abstract: In this work, a smart transportation testbed was utilized at Fort Carson to demonstrate three use cases for the primary purpose to plan, develop, demonstrate, and employ autonomous vehicle technologies at military installations and within the surrounding communities to evaluate commercially available Connected and Automated Vehicles and the potential to reduce base operating costs, improve safety and quality of life for military service members and their families, and deliver services more efficiently and effectively. To meet this purpose, an automated vehicle shuttle, an unmanned aerial system, and a wireless network were used and tested during the project. Results for the automated shuttle indicated that de-spite the quantity of data generated by operations, the contractors may not be ready to share information in a readily usable format. Additionally, successful use by the public is predicated on both knowing their mobility patterns and staff members promoting trust in the technology to prospective riders. Results for the unmanned aerial system showed successful identification of foreign object debris and runway cracks at the airfield. The wireless network is now operational and is used for additional work which utilizes the installed traffic cameras.

8 Nov 2022

A 𝘬-Means Analysis of the Voltage Response of a Soil-Based Microbial Fuel Cell to an Injected Military-Relevant Compound (Urea)

Abstract: Biotechnology offers new ways to use biological processes as environmental sensors. For example, in soil microbial fuel cells (MFCs), soil electro-genic microorganisms are recruited to electrodes embedded in soil and produce electricity (measured by voltage) through the breakdown of substrate. Because the voltage produced by the electrogenic microbes is a function of their environment, we hypothesize that the voltage may change in a characteristic manner given environmental disturbances, such as the contamination by exogenous material, in a way that can be modelled and serve as a diagnostic. In this study, we aimed to statistically analyze voltage from soil MFCs injected with urea as a proxy for gross contamination. Specifically, we used 𝘬-means clustering to discern between voltage output before and after the injection of urea. Our results showed that the 𝘬-means algorithm recognized 4–6 distinctive voltage regions, defining unique periods of the MFC voltage that clearly identify pre- and postinjection and other phases of the MFC lifecycle. This demonstrates that 𝘬-means can identify voltage patterns temporally, which could be further improve the sensing capabilities of MFCs by identifying specific regions of dissimilarity in voltage, indicating changes in the environment.

7 Nov 2022

Cold Regions Vehicle Start: Next-Generation Lithium-Ion Battery Technologies for Stryker Vehicles

Abstract: Operating vehicles in extremely cold environments is a significant problem for not only the public but also the military. The Department of Defense has encountered issues when trying to reliably cold start large, heavy-duty military vehicles, specifically the M1126 Stryker Combat Vehicle, in cold regions. As noted in previous work, the issue stems from the current battery technology’s limited temperature range. This current project utilized the protocol established in the previous phase to evaluate next-generation lithium-ion battery technologies for use in cold regions. Selected battery technologies met necessary military specifications for use in large military combat vehicles and were evaluated using a mechanical load system developed in previous work to simulate the starting of a Stryker engine. This work also evaluated the performance of the existing battery technology of a Stryker under Alaskan winter temperatures, which will verify the accuracy of the simulated cold room testing on the mechanical load system. The results of the tests showed that while the system was able to reliably operate down to −20°C, the battery management system encountered challenges at the lower end of the temperature range. This technology has a potential to reliably support cold regions operations but needs further evaluation.

7 Nov 2022

Dissolution of NTO, DNAN, and Insensitive Munitions Formulations and Their Fates in Soils: SERDP ER-2220

Abstract: The US military is interested in replacing TNT (2,4,6-trinitrotoluene) and RDX (1,3,5-hexahydro-1,3,5-trinitro-1,3,5-triazine) with DNAN (2,4-dinitroanisole) and NTO (3-nitro-1,2,4-triazol-5-one), which have similar explosive characteristics but are less likely to detonate unintentionally. Although these replacements are good explosives, basic information about their fate and transport was needed to evaluate their environmental impact and life-cycle management. This project measured their dissolution, photodegradation, and how aqueous solutions interact with soils, data critical to determining exposure potential and, consequently, risk.

4 Nov 2022

Environmentally Informed Buried Object Recognition

The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming short-wave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.

2 Nov 2022

Willis Coupling in One-dimensional Layered Bulk Media

Abstract: Willis coupling, which couples the constitutive equations of an acoustical material, has been applied to acoustic metasurfaces with promising results. However, less is understood about Willis coupling in bulk media. In this paper a multiple-scales homogenization method is used to analyze the source and interpretation of Willis coupling in one-dimensional bulk media without any hidden degrees of freedom, or one-dimensional layered media. As expected from previous work, Willis coupling is shown to arise from geometric asymmetries, but is further shown to depend greatly on the measurement position. In addition, a discussion of the predicted material properties, including Willis coupling, of macroscopically inhomogeneous media is presented.

1 Nov 2022

Snow-Covered Region Improvements to a Support Vector Machine-Based Semi-Automated Land Cover Mapping Decision Support Tool

Abstract: This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.