Closeout Document: USDOE grant DE-FG02-94ER40870
Final Technical Report
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Final Technical Report
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These are the slides I will be presenting to Sandia's 2025 MLDL workshop
This is an example of how to file for alternative compliance waiver application under the US DOE's Energy Policy Act (EPAct).
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This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.
Coastal regions are highly used by humans. The growing marine renewable energy (MRE) industry will add to existing anthropogenic pressures in these regions. Regulatory bodies require animal risk assessment before new industrial activities can progress, and MRE is no exception. Preliminary data of marine mammal use of an MRE device deployment location could be informative to permitting. A combination of downlooking hydroacoustics using an echosounder and acoustic camera (imaging sonar) was used to provide a number of large targets (proxy for large fish and marine mammals) in an area of interest for MRE tidal turbine deployment in Western Passage, Maine, USA. Data were collected in May, June, August, and September of 2010 and 2011. Of the nine large targets confirmed to be animals, eight were porpoises and one was a shark. Few large targets were observed in May and June, with the majority (90%) being present in August and September of both years. The most large targets were observed when tidal current speed was less than 1 m·s−1. These data provide a preliminary assessment of large targets in a single location over sixteen 24-h surveys. The aforementioned methodology could be used for future pre- and post-installation assessments at MRE device deployment locations. Their use in concert with visual and passive acoustic monitoring can provide water depth usage by marine mammals, which is a metric that is difficult to assess with passive acoustic and visual techniques.
Chlorophyll has long been used as a natural indicator of plant health and photosynthetic efficiency. Laser-induced fluorescence (LIF) is an emerging technique for understanding broad spectrum organic processes and has more recently been used to monitor chlorophyll response in plants. Previous work has focused on developing a LIF technique for imaging moss mats to identify metal contamination with the current focus shifting toward application to moss fronds and aiding sample collection for chemical analysis. Two laser systems (CoCoBi a Nd:YGa pulsed laser system and Chl-SL with two blue continuous semiconductor diodes) were used to collect images of moss fronds exposed to increasing levels of Cu (1, 10, and 100 nmol/cm 2 ) using a CMOS camera. The best methods for the preprocessing of images were conducted before the analysis of fluorescence signatures were compared to a control. The Chl-SL system performed better than the CoCoBi, with dynamic time warping (DTW) proving the most effective for image analysis. Manual thresholding to remove lower decimal code values improved the data distributions and proved whether using one or two fronds in an image was more advantageous. A higher DTW difference from the control correlated to lower chlorophyll a/b ratios and a higher metal content, indicating that LIF, with the aid of image processing, can be an effective technique for identifying Cu contamination shortly after an event.
Using the Hubble Space Telescope (HST) and the Large Binocular Telescope, we followed the evolution of the Type Ia supernova (SN Ia) 2011fe for an unprecedented 1840 days past B-band maximum light and over a factor of 7 million in flux. At 1840 days, the 4000–17000 Å quasi-bolometric luminosity is just (420±20) L{sub ⊙}. By measuring the late-time quasi-bolometric light curve, we present the first confident detection of {sup 57}Co decay in a SN Ia light curve and estimate a mass ratio of log({sup 57}Co/{sup 56}Co)=−1.59{sub −0.07}{sup +0.06}. We do not have a clean detection of {sup 55}Fe, but find a limit of {sup 55}Fe/{sup 57}Co<0.22 with 99% confidence. These abundance ratios provide unique constraints on the progenitor system because the central density of the exploding white dwarf(s) dictates these nucleosynthetic yields. The observed ratios strongly prefer the lower central densities of double-degenerate models ({sup 55}Fe/{sup 57}Co=0.27) over the higher central densities of near-Chandrasekhar-mass single-degenerate models ({sup 55}Fe/{sup 57}Co=0.68). However, additional theoretical studies predicting isotopic yields from a broader range of progenitor systems are motivated by these unique observations. We will continue to observe SN 2011fe for another ∼600 days with HST and possibly beyond.
This dataset was derived from UAS aerial photos and dGPS data collected at the Teller mile marker 47 site in 2018 and 2019 and used to support research quantifying the timing and rate of surface movements and analyze soil transport process in Arctic landscapes. In July 2018, a Phantom uncrewed aerial system (UAS) collected >6800 high resolution aerial photos of the lower portion of the Teller 47 watershed (see for raw photos; pending archive NGA281). During the UAS survey, 25 x 25 cm tile ground control points (GCPs) were laid out and secured with one rebar rod in the center. The four corners and rebar tops were surveyed with differential GPS, and these coordinates were used to construct and validate an orthomosaic of the site. These points were resurveyed in August 2019 and used to track annual movement. Changes in position between the two surveys are reported in (Lathrop et al. 2022; NGA254). Agisoft Metashape photogrammetry software was used to construct a georeferenced orthomosaic image (*.tif file) of a portion (0.68 km2) of the watershed with a final resolution of ~1 cm. Manual delineation of the perimeters of failures visible in the UAS imagery (*.tif files) was conducted to create shapefile polygons (two *.zip files). The failures were identified by the exposure of bare mineral soils, which were made visible by disruption of the overlying tundra vegetation. The shapefiles were then used to analyse the topographic distribution and sizes of the failures. In August 2019 an additional ~300 georeferenced UAS images of slope instability features were collected (data pending submission) and compared with the 2018 orthomosaic. Overview maps of the failure locations included as a *.pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
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The Greenhouse gas Life cycle Emissions Assessment Model (GLEAM) estimates life cycle greenhouse gas emissions from future scenarios of electricity generation considering a wide range of generation technologies. Building on the National Laboratory of the Rockies longstanding effort to quantify life cycle emissions by electricity generation technology under the LCA Harmonization Project, GLEAM streamlines the process of estimating cumulative greenhouse gas emissions on a life cycle basis. Given a set of inputs regarding annual installed and decommissioned generation capacity, as well as generation, GLEAM estimates the carbon dioxide equivalent emissions by year. The model also offers optional modules to decompose carbon dioxide equivalent emissions into constituent greenhouse gases (e.g., carbon dioxide, methane, and nitrous oxide) as well as estimate hydrogen leakage from relevant technologies. The results from GLEAM can be used to inform future electricity planning scenarios as well as investment or regulatory decisions.
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Abstract not provided.
Abstract not provided