Error probabilities for partially coherent diversity reception.
Error probabilities for partially coherent diversity reception, noting linearized receiver performance during random noise output
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Error probabilities for partially coherent diversity reception, noting linearized receiver performance during random noise output
The problem of recovering from processor transient faults in shared memory multiprocesses systems is examined. A user-transparent checkpointing and recovery scheme using private caches is presented. Processes can recover from errors due to faulty processors by restarting from the checkpointed computation state. Implementation techniques using checkpoint identifiers and recovery stacks are examined as a means of reducing performance degradation in processor utilization during normal execution. This cache-based checkpointing technique prevents rollback propagation, provides rapid recovery, and can be integrated into standard cache coherence protocols. An analytical model is used to estimate the relative performance of the scheme during normal execution. Extensions to take error latency into account are presented.
This paper presents an analysis of a satellite communication system using a Viterbi receiver. Here we have a bandlimited nonlinear channel where both uplink and downlink are taken into account as well as the effect of Intersymbol Interference, phase and time synchronization errors. In order that ISI can be combatted effectively, we use a Viterbi demodulator which is designed for the satellite channel when there is no uplink noise. The Viterbi demodulator for the channels with large memory is too complex to be implemented. To reduce the complexity, a Viterbi demodulator with memory shorter than the true channel memory is used. The objective of this paper is to analyze the performance degradation of this 'Mismatched Viterbi Receiver' due to the uplink noise and memory truncation, and to understand how the time and phase synchronization errors influence the performance.
The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.
A study comparing the performance of three approaches to estimating the spectral albedo of a typical land surface is presented. The most accurate albedo estimates under all atmospheric situations are those for which the scattering properties of the atmosphere can be used. Simply utilizing the direct-to-total ratio as a weight between direct and Lambertian albedos reduced the errors in broadband albedo to less than one percent for almost all simulated atmospheric conditions.
Relating the synthesis conditions of materials to their functional performance has long been an experience-based trial-and-error process. However, this methodology is not always efficient in identifying an appropriate protocol and can lead to overlooked opportunities for the performance optimization of materials through simple modifications of the synthesis process. In this work, we systematically track the structural evolution in the synthesis of a representative disordered rocksalt (a promising next-generation Li-ion cathode material) at the scale of both the long-range crystal structure and the short-range atomic structure using various in situ and ex situ techniques, including transmission electron microscopy, X-ray diffraction, and pair distribution function analysis. Further, an optimization strategy is proposed for the synthesis protocol, leading to a remarkably enhanced capacity (specific energy) of 313 mAh g –1 (987 Wh kg –1 ) at a low rate (20 mA g –1 ), with a capacity of more than 140 mAh g –1 retained even at a very high cycling rate of 2,000 mA g –1 . This strategy is further rationalized using ab initio calculations, and important opportunities for synthetic optimization demonstrated in this study are highlighted.
NASA WFIRST mission has planned to include a coronagraph instrument to find and characterize exoplanets. Masks are needed to suppress the host star light to better than 10-8 – 10-9 level contrast over a broad bandwidth to enable the coronagraph mission objectives. Such masks for high contrast coronagraphic imaging require various fabrication technologies to meet a wide range of specifications, including precise shapes, micron scale island features, ultra-low reflectivity regions, uniformity, wave front quality, etc. We present the technologies employed at JPL to produce these pupil plane and image plane coronagraph masks, and lab-scale external occulter masks, highlighting accomplishments from the high contrast imaging testbed (HCIT) at JPL and from the high contrast imaging lab (HCIL) at Princeton University. Inherent systematic and random errors in fabrication and their impact on coronagraph performance are discussed with model predictions and measurements.
A new finite settling time (FST) control law is presented. The performance and the flexibility of this dual-rate, sampled-data law as applied to the class of error driven, feedback control systems are demonstrated. A hybrid analog/digital simulation is performed in order to illustrate the practical aspects of this type of finite settling time design.
The objective of this study is to perform an independent analysis of the residual high resolution mirror assembly (HRMA) mirror distortions caused by force and moment errors in the mirror alignment system (MAS) to statistically predict the HRMA performance. These performance predictions are then compared with those performed by Kodak to verify their analysis results.
The feasibility is analyzed of upgrading the Intel 386 microprocessor, which has been proposed as the baseline processor for the Space Station Freedom (SSF) Data Management System (DMS), to the more advanced i486 microprocessors. The items compared between the two processors include the instruction set architecture, power consumption, the MIL-STD-883C Class S (Space) qualification schedule, and performance. The advantages of the i486 over the 386 are (1) lower power consumption; and (2) higher floating point performance. The i486 on-chip cache does not have parity check or error detection and correction circuitry. The i486 with on-chip cache disabled, however, has lower integer performance than the 386 without cache, which is the current DMS design choice. Adding cache to the 386/386 DX memory hierachy appears to be the most beneficial change to the current DMS design at this time.
The feasibility is analyzed of upgrading the Intel 386 microprocessor, which has been proposed as the baseline processor for the Space Station Freedom (SSF) Data Management System (DMS), to the more advanced i486 microprocessors. The items compared between the two processors include the instruction set architecture, power consumption, the MIL-STD-883C Class S (Space) qualification schedule, and performance. The advantages of the i486 over the 386 are (1) lower power consumption; and (2) higher floating point performance. The i486 on-chip cache does not have parity check or error detection and correction circuitry. The i486 with on-chip cache disabled, however, has lower integer performance than the 386 without cache, which is the current DMS design choice. Adding cache to the 386/387 DX memory hierarchy appears to be the most beneficial change to the current DMS design at this time.
The critical problems associated with the control of highly damped flexible structures are outlined. The practical problems include: high performance; assembly in space, configuration changes; on-line controller software design; and lack of test data. Underlying all of these problems is the central problem of modeling errors. To justify the expense of a space structure, the performance requirements will necessarily be very severe. On the other hand, the absence of economical tests precludes the availability of reliable data before flight. A design algorithm is offered which: (1) provides damping for a larger number of modes than the optimal attitude controller controls; (2) coordinates the rate of feedback design with the attitude control design by use of a similar cost function; and (3) provides model reduction and controller reduction decisions which are systematically connected to the mathematical statement of the control objectives and the disturbance models.
A current literature review of the analog and digital process of vestibular and optical kinetic nystagmus reveals little agreement in the methods used by various labs. The strategies for detection of saccade (fast phase velocity component of nystagmus) vary between labs, and most of the process have not been evaluated and validated with a standard database. A survey was made of major vestibular labs in the U.S. that perform computer analyses of vestibular and optokinetic reflexes to stimuli, and a baseline was established from which to standardize data acquisition and analysis programs. The concept of an Error Index was employed as the criterium for evaluating the performance of the vestibular analysis software programs. The performance criterium is based on the detection of saccades and is the average of the percentages of missed detections and false detections. Evaluation of the programs produced results for lateral gaze with saccadic amplitude of one, two, three, five, and ten degrees with various signal-to-noise ratios. In addition, results were obtained for sinusoidal pursuit of 0.05, 0.10, and 0.50 Hz with saccades from one to ten degrees at various signal-to-noise ratios. Selection of the best program was made from the performance in the lateral gaze with three degrees of saccadic amplitude and in the 0.10 Hz sinusoid with three degrees of saccadic amplitude.
Long-term time-trend analysis was performed on astronomical atmospheric extinction data in wideband UBV and various narrow-band systems recorded at Cerro Tololo, Kitt Peak, Lick, and McDonald observatories. All of the data had to be transformed into uniform monochromatic extinction data before trend analysis could be performed. The paper describes the various reduction techniques employed. The time-trend analysis was then carried out by the method of least squares. A special technique, called 'histogram shaping', was employed to adjust for the fact that the errors of the reduced monochromatic extinction data were not essentially Gaussian. On the assumption that there are no compensatory background and local extinction changes, the best values obtained for extinction trends due to background aerosol changes during the years 1960 to 1972 are 0.006 + or - 0.013 (rms) and 0.009 + or - 0.009 (rms) stellar magnitudes per air mass per decade in the blue and yellow wavelength regions, respectively.
A compensator was synthesized to minimize the mean square surface error of a wrap rib antenna. Two separate cases were considered; one in which the flexible ribs and mesh were given weight, thereby penalizing the actual error of the reflecting area; and the other in which the antenna is considered rigid, thus penalizing only the rigid body rotation. The numerical results show that the flexible modes of the antenna have to be included in the performance criterion to obtain a better performance.
Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the trainability of VQAs, e.g., by exponentially flattening the cost landscape and suppressing the magnitudes of cost gradients. Error Mitigation (EM) shows promise in reducing the impact of noise on near-term devices. Thus, it is natural to ask whether EM can improve the trainability of VQAs. In this work, we first show that, for a broad class of EM strategies, exponential cost concentration cannot be resolved without committing exponential resources elsewhere. This class of strategies includes as special cases Zero Noise Extrapolation, Virtual Distillation, Probabilistic Error Cancellation, and Clifford Data Regression. Second, we perform analytical and numerical analysis of these EM protocols, and we find that some of them (e.g., Virtual Distillation) can make it harder to resolve cost function values compared to running no EM at all. As a positive result, we do find numerical evidence that Clifford Data Regression (CDR) can aid the training process in certain settings where cost concentration is not too severe. Our results show that care should be taken in applying EM protocols as they can either worsen or not improve trainability. On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability.
A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.
Methane (CH 4 ) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH 4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH 4 emissions at the facility-level. Near-field quantification of methane (CH 4 ) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH 4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH 4 h −1 . Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.