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The large and rich quantity of data now becoming available through the Southern California Integrated GPS Network (SCIGN) makes possible a wide variety of research involving analysis and modeling.
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).
Networked microgrids are clusters of geographically-close, islanded microgrids that can function as a single, aggregate island. This flexibility enables customer-level resilience and reliability improvements during extreme event outages and also reduces utility costs during normal grid operations. To achieve this cohesive operation, microgrid controllers and external connections (including advanced communication protocols, protocol translators, and/or internet connection) are needed. However, these advancements also increase the vulnerability landscape of networked microgrids, and significant consequences could arise during networked operation, increasing cascading impact. To address these issues, this report seeks to understand the unique components, functions, and communications within networked microgrids and what cybersecurity solutions can be implemented and what solutions need to be developed. A literature review of microgrid cybersecurity research is provided and a gap analysis of what is additionally needed for securing networked microgrids is performed. Relevant cyber hygiene and best practices to implement are provided, as well as ideas on how cybersecurity can be integrated into networked microgrid design. Lastly, future directions of networked microgrid cybersecurity R&D are provided to inform next steps.
Using small-angle neutron scattering, we studied the nanostructure of a prototypical D(H)-bonded network electrolyte, alkaline sodium aluminate bisalt, at concentrations up to solute volume fraction of ~0.5. Analysis of the structure factor prepeak at 0.1 < Q < 1.0 Å-1 showed that its evolution is associated with nanoscopic species distribution in water-anion network., with differences in characteristic distance d between OD- and Al(OD)4- anions related to their distinct sizes and interactions. When the solute volume fraction approached 0.5, a common/maximum value of ~4.5 Å was found for the correlation length ? that characterizes the electrostatic force in concentrated electrolyte solution before precipitation occurred. This implies that the solutions’ morphology and behavior at high concentrations may be governed by geometric factors, rather than the chemistry of the specific anion. Furthermore, the scaling of ? with concentration yielded an exponent of 1.25(1), suggesting that ? is limited to a few Å. By combining the current observations of structural heterogeneity at the nanoscale, with dynamic heterogeneity at the microscopic scale from our previous quasi-elastic neutron scattering study, we have established a structural origin of local “caging” and restricted structural relaxation processes. These local solvent-solute interactions not only control dynamics heterogeneity in concentrated electrolytes but also are responsible for crystallization processes in industrial setting, such as aluminum production and radioactive waste treatment.
The NASA Meteoroid Environment Office (MEO) uses two main meteor camera networks to characterize the meteoroid environment: an all sky system and a wide field system to study cm and mm size meteors respectively. The NASA All Sky Fireball Network consists of fifteen meteor video cameras in the United States, with plans to expand to eighteen cameras by the end of 2015. The camera design and All-Sky Guided and Real-time Detection (ASGARD) meteor detection software [1, 2] were adopted from the University of Western Ontario's Southern Ontario Meteor Network (SOMN). After seven years of operation, the network has detected over 12,000 multi-station meteors, including meteors from at least 53 different meteor showers. The network is used for speed distribution determination, characterization of meteor showers and sporadic sources, and for informing the public on bright meteor events. The NASA Wide Field Meteor Network was established in December of 2012 with two cameras and expanded to eight cameras in December of 2014. The two camera configuration saw 5470 meteors over two years of operation with two cameras, and has detected 3423 meteors in the first five months of operation (Dec 12, 2014 - May 12, 2015) with eight cameras. We expect to see over 10,000 meteors per year with the expanded system. The cameras have a 20 degree field of view and an approximate limiting meteor magnitude of +5. The network's primary goal is determining the nightly shower and sporadic meteor fluxes. Both camera networks function almost fully autonomously with little human interaction required for upkeep and analysis. The cameras send their data to a central server for storage and automatic analysis. Every morning the servers automatically generates an e-mail and web page containing an analysis of the previous night's events. The current status of the networks will be described, alongside with preliminary results. In addition, future projects, CCD photometry and broadband meteor color camera system, will be discussed.
A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measurement Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/MSFC are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.
A description of a recommended air quality monitoring network to characterize the impact on ambient air quality in the Kennedy Space Center (KSC) (area) of space shuttle launch operations is given. Analysis of ground cloud processes and prevalent meteorological conditions indicates that transient HCl depositions can be a cause for concern. The system designed to monitor HCl employs an extensive network of inexpensive detectors combined with a central analysis device. An acid rain network is also recommended. A quantitative measure of projected minimal long-term impact involves the limited monitoring of NOx and particulates. All recommended monitoring is confined ti KSC property.
X-ray spectroscopic data from high-energy-density laser-produced plasmas has long required thorough, time-consuming analysis to extract meaningful source conditions. There are often confounding factors due to rapidly evolving states and finite spatial gradients (e.g., the existence of multi-temperature, multi-density, multi-ionization states, etc.) that make spectral measurements and analysis difficult. Here, in this paper, we demonstrate how deep learning can be applied to enhance x-ray spectral data analysis in both speed and intricacy. Neural networks (NNs) are trained on ensemble atomic physics simulations so that they can subsequently construct a model capable of extracting plasma parameters directly from experimental spectra. Through deep learning, the models can extract temperature distributions as opposed to single or dual temperature/density fits from standard trial-and-error atomic modeling at a significantly reduced computational cost compared to traditional trial-and-error methods. These NNs are envisioned to be deployed with high repetition rate x-ray spectrometers in order to provide detailed real-time analysis of experimental spectra.
The onshore Gulf of Mexico region presents significant opportunities for CO2 capture, transport, and storage due to its numerous CO2 sources, such as power plants, refineries, and its substantial CO2 storage potential. However, operators face critical decisions in designing an efficient and cost-effective CO2 pipeline network. This study examines the economic implications of two primary strategies: constructing a trunkline with excess initial capacity versus developing dedicated pipelines incrementally as new CO2 sources come online. Building a trunkline first offers the advantage of future-proofing the network, allowing for the accommodation of increased CO2 volumes from various sources over time. However, this approach incurs higher upfront costs and risks underutilizing the transport capacity in the initial stages, potentially resulting in economic inefficiencies. Conversely, constructing dedicated pipelines for each new CO2 source as it becomes operational may avoid the initial overcapacity issue but fails to capitalize on the economies of scale. This could lead to higher overall costs due to the duplication of infrastructure and increased complexity in network management. This research employs a comprehensive cost-benefit analysis, integrating factors such as capital expenditure, operational costs, projected CO2 volumes, and potential economies of scale. Through this analysis, we aim to provide operators with insights into the most economically viable strategy for CO2 pipeline network design in the region. The findings underscore the importance of strategic planning and highlight the trade-offs between immediate capacity utilization and long-term cost savings, ultimately guiding stakeholders towards informed decision-making in the development of CO2 transport infrastructure. Presented at the 41st USAEE/IAEE North American Conference, 3-6 November 2024, Baton Rouge, LA, United States.
This report describes the development of a three-dimensional database of aircraft fuel burn and emissions (fuel burned, NOx, CO, and hydrocarbons) from projected fleets of high speed civil transports (HSCT's) on a universal airline network.Inventories for 500 and 1000 HSCT fleets, as well as the concurrent subsonic fleets, were calculated. The objective of this work was to evaluate the changes in geographical distribution of the HSCT emissions as the fleet size grew from 500 to 1000 HSCT's. For this work, a new expanded HSCT network was used and flights projected using a market penetration analysis rather than assuming equal penetration as was done in the earlier studies. Emission inventories on this network were calculated for both Mach 2.0 and Mach 2.4 HSCT fleets with NOx cruise emission indices of approximately 5 and 15 grams NOx/kg fuel. These emissions inventories are available for use by atmospheric scientists conducting the Atmospheric Effects of Stratospheric Aircraft (AESA) modeling studies. Fuel burned and emissions of nitrogen oxides (NOx as NO2), carbon monoxide, and hydrocarbons have been calculated on a 1 degree latitude x 1 degree longitude x 1 kilometer attitude grid and delivered to NASA as electronic files.
The disturbance response of the angle dynamics for a droop-controlled islanded microgrid is characterized. Specifically, a notion of propagation stability is defined, which is concerned with spatial attenuation vs amplification of input- output responses in the network in a H8 or H2 sense. Criteria for propagation stability are developed, phrased in terms of the microgrid’s inverter control parameters. The input frequency range over which the network is susceptible to amplification is also characterized, in the case that the criteria are not met. Based on the formal analysis, a the design of resilient controls that trade off coherence and disturbance propagation goals is briefly conceptualized. Finally, the propagation stability analysis is illustrate using a 15-bus example microgrid network.
Computer system VISTA, automatically plots selective PERT networks in order to develop accurate, standardized scheduling documentation as an essential element to project planning. This automation of plotting networks generates standardized networks due to a priority scheme adopted for calculating paths between events.
Orbiting lunar relay satellite networks for manned landing missions on lunar far side
The spherical harmonic analysis of cosmic ray neutron data from the worldwide network neutron monitor stations during the years, 1966 to 1969 was carried out. The second zonal harmonic component obtained from the analysis corresponds to the Pole-Equator anisotropy of the cosmic ray neutron intensity. Such an anisotropy makes a semiannual variation. In addition to this, it is shown that the Pole-Equator anisotropy makes a variation depending on the interplanetary magnetic field (IMF) sector polarities around the passages of the IMF sector boundary. A mechanism to interpret these results is also discussed.
NASA operates a network of ground-based electric field mills at Kennedy Space Center (KSC) to identify clouds which might be an electrical hazard to space vehicle prior to and during launch or to the various ground operations performed at the center. Artificial intelligence has been used to develop an expert system for analyzing electric field mill data. The application of the system to expert system to small thunderstorms at KSC is shown.
This paper reviews Space Network (SN) demand forecasting experience over the past 16 years and describes methods used in the forecasts. The paper focuses on the Single Access (SA) service, the most sought-after resource in the Space Network. Of the ten years of actual demand data available, only the last five years (1989 to 1993) were considered predictive due to the extensive impact of the Challenger accident of 1986. NASA's Space Network provides tracking and communications services to user spacecraft such as the Shuttle and the Hubble Space Telescope. Forecasting the customer requirements is essential to planning network resources and to establishing service commitments to future customers. The lead time to procure Tracking and Data Relay Satellites (TDRS's) requires demand forecasts ten years in the future a planning horizon beyond the funding commitments for missions to be supported. The long range forecasts are shown to have had a bias toward underestimation in the 1991 -1992 period. The trend of underestimation can be expected to be replaced by overestimation for a number of years starting with 1998. At that time demand from new missions slated for launch will be larger than the demand from ongoing missions, making the potential for delay the dominant factor. If the new missions appear as scheduled, the forecasts are likely to be moderately underestimated. The SN commitment to meet the negotiated customer's requirements calls for conservatism in the forecasting. Modification of the forecasting procedure to account for a delay bias is, therefore, not advised. Fine tuning the mission model to more accurately reflect the current actual demand is recommended as it may marginally improve the first year forecasting.
A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/ Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/Marshall Space Flight Center (MSFC) are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.
An autonomous, low-power atmospheric lidar instrument is being developed at NASA Goddard Space Flight Center. This compact, portable lidar will operate continuously in a temperature controlled enclosure, charge its own batteries through a combination of a small rugged wind generator and solar panels, and transmit its data from remote locations to ground stations via satellite. A network of these instruments will be established by co-locating them at remote Automatic Weather Station (AWS) sites in Antarctica under the auspices of the National Science Foundation (NSF). The NSF Office of Polar Programs provides support to place the weather stations in remote areas of Antarctica in support of meteorological research and operations. The AWS meteorological data will directly benefit the analysis of the lidar data while a network of ground based atmospheric lidar will provide knowledge regarding the temporal evolution and spatial extent of Type la polar stratospheric clouds (PSC). These clouds play a crucial role in the annual austral springtime destruction of stratospheric ozone over Antarctica, i.e. the ozone hole. In addition, the lidar will monitor and record the general atmospheric conditions (transmission and backscatter) of the overlying atmosphere which will benefit the Geoscience Laser Altimeter System (GLAS). Prototype lidar instruments have been deployed to the Amundsen-Scott South Pole Station (1995-96, 2000) and to an Automated Geophysical Observatory site (AGO 1) in January 1999. We report on data acquired with these instruments, instrument performance, and anticipated performance of the AWS Lidar.