Real-World Challenges and Lessons-Learned from Cyber-Physical Security Technology Testing at a Utility Solar Photovoltaic Field Site
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Chavez, Adrian R. [Sandia National Laboratories (S↗
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History of the Chemical Stockpile Emergency Preparedness Program – Volume 2: Lessons Learned (Final Report 2026)
The CSEP Program involved multiple federal agencies, multiple states, and numerous local units of government. The involvement of diverse agencies at different levels of government led to conflict and difficulty in resolving issues. That, in turn, led to slow progress in achieving program goals. Program management was criticized repeatedly by the U.S. General Accounting Office (GAO). After other mechanisms for interagency coordination had failed to give the desired results, FEMA and the Army agreed to form site-specific and national-level IPTs. The IPTs worked well to break the logjam and accelerate implementation of the program. Key elements of the IPTs’ success included constant interagency communication, solving problems at the lowest level, and obtaining buy-in from higher authority. The IPT technique could be applied to other emergency management and homeland security programs that require interagency coordination.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Lessons Learned 2022: Cyber Halo Innovation Research Program First Year
This report contains information about the first year development of the Cyber Halo Innovation Research Program (CHIRP) for Space Systems Command Leadership. It details success of the program and next steps.
99 GENERAL AND MISCELLANEOUS↗
Shaping the FutureWorkforce: Challenges and Lessons Learned in HPC Education from National Labs and Computing Centers
Workforce training at national laboratories and computing centers is essential and typically falls into two categories: foundational training for newcomers and advanced training for experienced users. Foundational topics—such as version control, build systems, and basic HPC usage—are largely transferable across institutions, while cluster-specific training varies due to differences in hardware, job schedulers, and local workflows. Training on emerging technologies is split between hardware-specific content and broadly applicable programming paradigms. Here, to reduce redundancy and increase impact, national labs, computing centers, and vendors are collaborating through initiatives like the HPC Training Working Group to share best practices, co-develop materials, and broaden outreach. These coordinated efforts aim to make HPC training more accessible, scalable, and consistent across the community.
HPC↗
Challenges and Lessons Learned in Applying Sensitivity Analysis to Building Stock Energy Models
Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. However, despite a well established body of work applying UA and SA to models of individual buildings, a review of the literature relating to energy models for larger groups of buildings undertaken by Fennell et al. (2019) highlighted very limited application at larger scales. This contribution describes the efforts undertaken by a group of research teams in the context of IEA-EBC Annex 70 working with a diverse set of Building Stock Models (BSMs) to apply global sensitivity analysis methods and compare their results. Since BSMs are a class of model defined by their output and coverage rather than their structure and inputs, they represent a diverse set of modelling approaches. Key challenges for the application of SA are identified and explored, including the influence of model form, input data types and model outputs. This study combines results from 7 different modelling teams, each using different models across a range of urban areas to explore these challenges and begin the process of developing standardised workflows for SA of BSMs.
ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE
Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.
54 ENVIRONMENTAL SCIENCES↗
Adaptable Monitoring Package Development and Deployment: Lessons Learned for Integrated Instrumentation at Marine Energy Sites
Integrated instrumentation packages are an attractive option for environmental and ecological monitoring at marine energy sites, as they can support a range of sensors in a form factor compact enough for the operational constraints posed by energetic waves and currents. Here we present details of the architecture and performance for one such system—the Adaptable Monitoring Package—which supports active acoustic, passive acoustic, and optical sensing to quantify the physical environment and animal presence at marine energy sites. we describe cabled and autonomous deployments and contrast the relatively limited system capabilities in an autonomous operating mode with more expansive capabilities, including real-time data processing, afforded by shore power or in situ power harvesting from waves. Across these deployments, we describe sensor performance, outcomes for biological target classification algorithms using data from multibeam sonars and optical cameras, and the effectiveness of measures to limit biofouling and corrosion. On the basis of these experiences, we discuss the demonstrated requirements for integrated instrumentation, possible operational concepts for monitoring the environmental and ecological effects of marine energy converters using such systems, and the engineering trade-offs inherent in their development. Overall, we find that integrated instrumentation can provide powerful capabilities for observing rare events, managing the volume of data collected, and mitigating potential bias to marine animal behavior. These capabilities may be as relevant to the broader oceanographic community as they are to the emerging marine energy sector.
16 TIDAL AND WAVE POWER↗
Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling
AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.
54 ENVIRONMENTAL SCIENCES↗
Observations and Lessons Learned in Residential Roofing Integrated Photovoltaics
The data file includes field observations of residential roofing integrated photovoltaics installation that happened between July 2021 and June 2022 in California. There are 21 observations - 2 in the re-roofing category and 19 in the new construction category. The file includes two time and motion forms (Re-Roofing & New Construction) we used to capture the time it takes for each activity in the rooftop solar and electrical installation process. You will see the duration for detailed steps along with activities that are included for total installation time for the paper. There are three parts to the time and motion form - Part 1 has project and crew characteristics, product information, and inspection and permitting data and can be filled before the installation. Part 2 has the actual installation time stamps and related notes. Part 3 has the crew information and can be filled on-site or off-site, but this section is optional. The re-roofing form has pre-solar activities and gutters and vents section in Part 2 of the form, whereas new construction form includes a section for capturing the time it took for rough wiring. The re-roofing form does not include rough wiring but instead includes the final electrical wiring process. These are the key differences in both the forms. Each day/step/installation activity needs to include the crew break time as they happen and there is space available to capture the crew breaks duration. In Table 1 below, the different terms used in the time and motion form are defined and their corresponding unit of measurement stated. We also highlight the optional sections. The data is captured in total mins, total hours, person mins, person hours. You can use this form or make edits to the form to recreate the study or make your own observations. The data herein was reviewed but may not be comprehensive. NREL invites questions and inputs to improve the data, including to: Correct erroneous information Fill in missing/updated information Clarifications on data and variables Updated information may be submitted to Sushmita Jena at sushmita.jena@nlr.gov.
14 SOLAR ENERGY↗
Designing remote operations strategies to optimize science mission goals: Lessons learned from the Moon Mars Analog Mission Activities Mauna Kea 2012 field test
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R.A. Yingst↗
REVIEW OF LESSONS LEARNED IN THE MERCURY PROGRAM RELATIVE TO SPACECRAFT DESIGN AND OPERATIONS
Spacecraft design and operations as learned from mercury project
MERCURY PROJECT↗
Some lessons learned in clean room design
Clean room design problems concerning dampers, high pressure blowers, humidifiers, filters, etc
Weber, G. F.↗
Management information systems - Lessons learned in the Apollo program
Management information systems based on Apollo program experience, considering improvements in data accuracy, display, feedback, etc
Andressen, C. E., Jr.↗
Lessons learned from a decade of international space cooperation.
A case history of the Alouette/ISIS series is presented, giving attention to aspects of U.S.-Canadian cooperation in this program. The project is only one example of a broad network of international cooperative relationships established by NASA over the past decade. Participation by other countries in important space science and applications projects is not limited to those providing flight hardware or conducting flight observations. Much valuable work is done on the ground, ranging from analysis in the laboratories of samples returned from the moon to correlated ground-based reception and analysis of radio beacon signals from satellites. It was found that cooperation is more likely to proceed smoothly and produce beneficial results if it is focused on projects that are clearly defined and agreed on in advance.
Barnes, R. J. H.↗
Imaging asteroids: Some lessons learned from the Viking investigation of Phobos and Deimos
Specific experiences from the study of Phobos and Deimos during the Viking mission are used to formulate three basic goals for imaging studies of asteroids. These are: (1) the highest possible resolution; (2) complete coverage of the surface; and (3) data over a wide range of phase angles.
Veverka, J.↗
Mission management - Lessons learned from early Spacelab missions
The concept and the responsibilities of a mission manager approach are reviewed, and some of the associated problems in implementing Spacelab mission are discussed. Consideration is given to program control, science management, integrated payload mission planning, and integration requirements. Payload specialist training, payload and launch site integration, payload flight/mission operations, and postmission activities are outlined.
Craft, H. G., Jr.↗
The OSS-1 mission - Lessons learned in developing and integrating experiments
Experience gained in the development and integration of experiments for the OSS-1 payload to be carried on board the Space Shuttle for the characterization of the effects of the STS environment on future scientific missions is related. Following a review of mission objectives, including the demonstration of Shuttle research capabilities and the evaluation of new technology, the experiments and mission operations to be performed are outlined, and mission support requirements are indicated. The conduct of mission management by the Goddard Space Flight Center, which required interfacing with a large number of other agencies, centers, and investigators and contractors and an enormous amount of formal documentation, is then examined, with consideration given to the management of mechanical, electrical, and thermal systems development mock-up construction, EMI/EMC and acoustic testing, the formation of an integration and test team, and trade-offs between cost, schedule and performance. It is concluded that although work still remains to be done, significant progress has been made in ensuring that the STS is a viable system for space exploration.
Kissin, K.↗
Lessons learned from the CSIS
A discussion is presented of the Centralized Storm Information System (CSIS), a joint effort by several organizations including NASA and the National Weather Service, which has been developed to aid the forecaster in evaluating weather data. The CSIS was developed to provide a handling, analyzing, intercomparison, and display system for real-time data from all available sources. The CSIS hardware and software, the impact of the CSIS, and the initial experience gained following the implementation of the CSIS are examined. Several general insights learned from this interactive processing system are examined in detail, including the benefits of an evolutionary bottom up rather than top down design approach, the crucial nature of scheduler function to any real time system, the recognition that meteorological interactive terminals requirements are different from image processing terminals requirements, and the knowledge that forecasters generate a large peak load on computer resources.
Mosher, F. R.↗