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Here, quantum key distribution (QKD) promises unconditional security in data communication and is currently being deployed in commercial applications. Nonetheless, before QKD can be widely adopted, it faces a number of important challenges such as secret key rate, distance, size, cost and practical security. Here, we survey those key challenges and the approaches that are currently being taken to address them.
This paper presents the modeling approach, methodologies, and initial results of setting up a multi-layer, hierarchical information management system (IMS) for the smart grid. The IMS allows its users to analyze the data collected by multiple control and communication networks to characterize the states of the smart grid. Abnormal, corrupted, or erroneous measurement data and outliers are detected and analyzed to identify whether they are caused by random equipment failures, unintentional human errors, or deliberate tempering attempts. Data collected from different information networks are crosschecked for data integrity based on redundancy, dependency, correlation, or cross-correlations, which reveal the interdependency between data sets. A hierarchically structured reasoning mechanism is used to rank possible causes of an event to aid the system operators to proactively respond or provide mitigation recommendations to remove or neutralize the threats. The model provides satisfactory performance on identifying the cause of an event and significantly reduces the need of processing myriads of data collected.
WRS is the digital backbone of the Weapons Program—delivering trusted data assets, cyber-assured software and systems, and AI-enabling software—that transform insights into decisive action. We empower physicists, engineers, researchers, and scientists to think faster, act strategically, and stay ahead in an ever-evolving threat landscape. Our efforts ensure critical nuclear weapons data remains secure, accessible, and usable—supporting mission-critical work, informed decision making, and scientific advancement at LANL and across the Nuclear Security Enterprise (NSE).
Automakers, commercial fleet operators, component manufacturers, and government agencies all turn to the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) to help put more green vehicles on the road. The lab’s independent analysis and evaluation pinpoint fuel-efficient and low-emission strategies to support economic and operational goals, while breaking down barriers to widespread adoption. Customized assessment of existing equipment and practices, energy-saving alternatives, operational considerations, and marketplace realities factor in the multitude of variables needed to ensure meaningful performance, financial, and environmental benefits. NREL provides integrated, unbiased, 360-degree sustainable transportation deployment expertise encompassing alternative fuels, advanced vehicles, and related infrastructure. Hands-on support comes from technical experts experienced in advanced vehicle technologies, fleet operations, and field data collection coupled with extensive modeling and analysis capabilities. The lab’s research team works closely with automakers and vehicle equipment manufacturers to test, analyze, develop, and evaluate high-performance fuel-efficient technologies that meet marketplace needs.
High resolution optical wavelength spectroscopic data were secured in the optical wavelengths, 3700A - 10,050A, for the planetary nebula IC 5217 with the Hamilton Echelle Spectrograph at Lick Observatory. These optical spectra have been analyzed along with the near-UV and UV archive data. Diagnostic analyses indicate a nebular physical condition with electron temperature of about 10,700 K (from the [O III] lines) and the density of N(sub epsilon) = 5000/cm. Ionic concentrations have been derived with the representative diagnostics, and with the aid of a photoionization model construction, we derived the elemental abundances. Contrary to the previous studies found in the literature, He and C appear to be depleted compared to the average planetary nebula and to the Sun (and S marginally so), while the remaining elements appear to be close to the average value. IC 5217 may have evolved from an O-rich progenitor and the central star temperature of IC 5217 is likely to be 92,000 K.
From our Secret Lives of Cepheids program, the prototype Classical Cepheid, δ Cep, is found to be an X-ray source with periodic pulsation-modulated X-ray variations. This finding complements our earlier reported phase-dependent FUV–UV emissions of the star that increase ∼10–20 times with highest fluxes at ∼0.90--0.95ϕ, just prior to maximum brightness. Previously δ Cep was found as potentially X-ray variable, using XMM-Newton observations. Additional phase-constrained data were secured with Chandra near X-ray emission peak, to determine if the emission and variability were pulsation-phase-specific to δ Cep and not transient or due to a possible coronally active, cool companion. The Chandra data were combined with prior XMM-Newton observations, and were found to very closely match the previously observed X-ray behavior. From the combined data set, a ∼4 increase in X-ray flux is measured, reaching a peak L{sub X} = 1.7 × 10{sup 29} erg s{sup −1} near 0.45ϕ. The precise X-ray flux phasing with the star’s pulsation indicates that the emissions arise from the Cepheid and not from a companion. However, it is puzzling that the maximum X-ray flux occurs ∼0.5ϕ (∼3 days) later than the FUV–UV maximum. There are several other potential Cepheid X-ray detections with properties similar to δ Cep, and comparable X-ray variability is indicated for two other Cepheids: β Dor and V473 Lyr. X-ray generating mechanisms in δ Cep and other Cepheids are discussed. If additional Cepheids are confirmed to show phased X-ray variations, then δ Cep will be the prototype of a new class of pulsation-induced X-ray variables.
High performance computing environments are often used for a wide variety of workloads ranging from simulation, data transformation and analysis, and complex workflows to name just a few. These systems may process data at various security levels but in so doing are often enclaved at the highest security posture. This approach places significant restrictions on the users of the system even when processing data at a lower security level and exposes data at higher levels of confidentiality to a much broader population than otherwise necessary. The traditional approach of isolation, while effective in establishing security enclaves poses significant challenges for the use of shared infrastructure in HPC environments. This report details current state-of-the-art in reconfigurable network enclaving through Software Defined Networking (SDN) and Network Function Virtualization (NFV) and their applicability to secure enclaves in HPC environments. SDN and NFV methods are based on a solid foundation of system wide virtualization. The purpose of which is very straight forward, the system administrator can deploy networks that are more amenable to customer needs, and at the same time achieve increased scalability making it easier to increase overall capacity as needed without negatively affecting functionality. The network administration of both the server system and the virtual sub-systems is simplified allowing control of the infrastructure through well-defined APIs (Application Programming Interface). While SDN and NFV technologies offer significant promise in meeting these goals, they also provide the ability to address a significant component of the multi-tenant challenge in HPC environments, namely resource isolation. Traditional HPC systems are built upon scalable high-performance networking technologies designed to meet specific application requirements. Dynamic isolation of resources within these environments has remained difficult to achieve. SDN and NFV methodology provide us with relevant concepts and available open standards based APIs that isolate compute and storage resources within an otherwise common networking infrastructure. Additionally, the integration of the networking APIs within larger system frameworks such as OpenStack provide the tools necessary to establish isolated enclaves dynamically allowing the benefits of HPC while providing a controlled security structure surrounding these systems.
Advanced Air Mobility is a new aviation vision, where unmanned aerial systems will trans- port passengers and cargo across urban and rural areas. Critical to the realization of this vision is the development of a digital marketplace, which allows service providers and consumers operating in the airspace ecosystem to securely exchange data and reasoning insights. In this paper, we present the architecture of a decentralized data marketplace that connects data and reasoning service providers to vehicles and other service consumers along the cloud-to-edge continuum. We also present two example use cases to demonstrate the value of our approach.
Results of measurements on the burning of free n-heptane droplets (that is, droplets without fiber supports) performed in Spacelab during the flights of the first Microgravity Science Laboratory (MSL-1) are presented. The droplet combustion occurred in oxidizing atmospheres which were at an ambient temperature within a few degrees of 300 K. A total of 34 droplets were burned in helium-oxygen atmospheres having oxygen mole fractions ranging from 20 to 50 percent, at pressures from 0.25 to 1.00 bar. In addition, four droplets were burned in air at 1.00 bar, bringing the total number of droplets for which combustion data were secured to 38; two of these four air tests were fiber-supported to facilitate comparisons with other fiber-support experiments, results of which also are given here. Initial diameters of free droplets ranged from about 1 to 4 mm. The primary data obtained were histories of droplet diameters, recorded in backlight on 35 mm film at 80 frames per second, and histories of flame diameters, inferred from emissions through a narrow-band interference filter centered at the 310 micron OH chemiluminescent ultraviolet band, recorded at 30 frames per second by a intensified-array camera. These data are reported here both in raw form and in a smoothed form with estimated error bars. In addition, summaries are presented of measured burning-rate constants, final droplet diameters, and final flame diameters. Both diffusive and radiative extinctions were exhibited under different conditions. Although some interpretations are reported and conclusions drawn concerning the combustion mechanisms, the principal intent of this report is to provide a complete, documented data set for future analysis.
We present a study of stellar populations in a sample of spectroscopically confirmed Lyman-break galaxies (LBGs) and Lyα emitters (LAEs) at 5.7<z<7. These galaxies have deep images from Subaru, the Hubble Space Telescope, and Spitzer/IRAC. We focus on 27 galaxies with IRAC detections, and characterize their stellar populations based on the multi-band data and secure redshifts. By estimating nebular emission from the observed Lyα flux, we break the strong model degeneracy between young galaxies with prominent nebular emission and older galaxies with strong Balmer breaks. The results show that our galaxies cover a wide range of ages from several to a few hundred million years (Myr), and stellar masses from ∼10{sup 8} to ∼10{sup 11} M{sub ⊙}. These galaxies can be roughly divided into two subsamples: an “old” subsample consisting of galaxies older than 100 Myr, with stellar masses higher than 10{sup 9} M{sub ⊙}, and a “young” subsample consisting of galaxies younger than ∼30 Myr, with masses ranging between ∼10{sup 8} and ∼ 3×10{sup 9} M{sub ⊙}. Both subsamples display a correlation between stellar mass and star formation rate (SFR), but with very different normalizations. The average specific SFR (sSFR) of the “old” subsample is 3–4 Gyr{sup −1}, consistent with previous studies of “normal” star-forming galaxies at z⩾6. The average sSFR of the “young” subsample is an order of magnitude higher, likely due to starburst activity. Our results also indicate little dust extinction in the majority of the galaxies, as already suggested by their steep rest-frame UV slopes. Finally, LAEs and LBGs with strong Lyα emission are indistinguishable in terms of age, stellar mass, and SFR.
This code implements a Fully Homomorphic Encryption (FHE) system, enabling secure computation on encrypted data without requiring decryption. It supports encryption, decryption, and homomorphic operations like matrix multiplication and addition. This code is adaptable for integrating FHE into linear-time invariant (LTI) systems, including digital control and filtering. With proper configuration from subject matter expertise, encrypted system parameters and signals can be manipulated to perform tasks like state updates, output calculations, and convolution in the encrypted domain. By preserving the structure of LTI systems while ensuring privacy, the framework facilitates secure applications in areas such as autonomous systems, signal processing, and industrial automation. The code initializes the encryption system using parameters provided in the env dictionary. These parameters include the ciphertext modulus, key dimension, plaintext fixed-point scaling factor, and noise bound. During initialization, a secret key is generated, which is essential for encrypting and decrypting data securely. The modular design allows users to tailor these parameters to specific use cases or security requirements. The code implements multiple cryptographic schemes. The learning with errors (LWE) encryption method encodes cleartext message to their plaintext fixed-point representation then encrypted into ciphertext space with additive noise. This noise ensures the security of the scheme, relying on the computational hardness of the LWE problem. The code also includes the Gentry-Sahai-Waters (GSW) scheme based off the LWE problem. Homomorphic matrix multiplication is performed between the LWE and GSW to encrypted data. This is achieved using a decomposition function on the LWE ciphertext during the multiplication operation. For higher-dimensional data, the code includes a method to encrypt entire matrices (GSWMat) using GSW encryption. These encrypted matrices can then be used for homomorphic matrix multiplications (MatMult). The decryption function uses the secret key to recover the original plaintext, removing the added noise and scaling that was originally applied during encryption.
Presented at the Sustainable Transportation Energy Pathways Spring 2015 Symposium on May 13, 2015, this presentation by Jeff Gonder of the National Renewable Energy Laboratory (NREL) provides information about NREL's transportation energy impact analysis of connected and automated vehicles.
Abstract not provided.
External interaction:The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will include a hyperspectral imaging radiometer to advance ecosystem monitoring beyond heritage retrievals of the concentration of surface chlorophyll and other traditional ocean color variables, offering potential for novel science and applications. PACE is the first NASA ocean color mission to occur under the agency's new and evolving effort to directly engage practical end users prior to satellite launch to increase adoption of this freely available data toward societal challenges. Here we describe early efforts to engage a community of practice around marine food-related resource management, business decisions, and policy analysis. Obviously one satellite cannot meet diverse end user needs at all scales and locations, but understanding downstream needs helps in the assessment of information gaps and planning how to optimize the unique strengths of PACE data in combination with the strengths of other satellite retrievals, in situ measurements, and models. Higher spectral resolution data from PACE can be fused with information from satellites with higher spatial or temporal resolution, plus other information, to enable identification and tracking of new marine biological indicators to guide sustainable management. Accounting for the needs of applied researchers as well as non-traditional users of satellite data early in the PACE mission process will ultimately serve to broaden the base of informed users and facilitate faster adoption of the most advanced science and technology toward the challenge of mitigating food insecurity.
Network protocols implemented in optical domain. Fiber-optic data-communication networks utilize fully available bandwidth of single-mode optical fibers. Two key features of method: use of subpicosecond laser pulses as carrier signals and spectral phase modulation of pulses for optical implementation of code-division multiple access as multiplexing network protocol. Local-area network designed according to concept offers full crossbar functionality, security of data in transit through network, and capacity about 100 times that of typical fiber-optic local-area network in current use.
The availability of fine grained time series data is a pre-requisite for research in smart-grids. While data for transmission systems is relatively easily obtainable, issues related to data collection, security and privacy hinder the widespread public availability/accessibility of such datasets at the distribution system level. This has prevented the larger research community from effectively applying sophisticated machine learning algorithms to significantly improve the distribution-level accuracy of predictions and increase the efficiency of grid operations. Synthetic dataset generation has proven to be a promising solution for addressing data availability issues in various domains such as computer vision, natural language processing and medicine. However, its exploration in the smart grid context remains unsatisfactory. Previous works have tried to generate synthetic datasets by modeling the underlying system dynamics: an approach which is difficult, time consuming, error prone and often times infeasible in many problems. In this work, we propose a novel data-driven approach to synthetic dataset generation by utilizing deep generative adversarial networks (GAN) to learn the conditional probability distribution of essential features in the real dataset and generate samples based on the learned distribution. To evaluate our synthetically generated dataset, we measure the maximum mean discrepancy (MMD) between real and synthetic datasets as probability distributions, and show that their sampling distance converges. To further validate our synthetic dataset, we perform common smart grid tasks such as k-means clustering and short-term prediction on both datasets. Experimental results show the efficacy of our synthetic dataset approach: the real and synthetic datasets are indistinguishable by solely examining the output of these tasks.
Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year–1 from the atmosphere, deliver up to 16 Tg CE year–1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.
Early research points to large potential impacts of connected and automated vehicles (CAVs) on transportation energy use - dramatic savings, increased use, or anything in between. Due to a lack of suitable data and integrated modeling tools to explore these complex future systems, analyses to date have relied on simple combinations of isolated effects. This poster proposes a framework for modeling the potential energy implications from increasing penetration of CAV technologies and for assessing technology and policy options to steer them toward favorable energy outcomes. Current CAV modeling challenges include estimating behavior change, understanding potential vehicle-to-vehicle interactions, and assessing traffic flow and vehicle use under different automation scenarios. To bridge these gaps and develop a picture of potential future automated systems, NREL is integrating existing modeling capabilities with additional tools and data inputs to create a more fully integrated CAV assessment toolkit.