This paper presents the analysis and simulation of the performance of fault location and isolation (FLI) in an automated power distribution feeder. This system is composed of two coupled networks: a power system consisting of a distribution feeder with multiple load buses, and an unreliable communication network of the distributed intelligent agents in the system, namely, the substation automation and fault protection units separating segments of the feeder. We provide a complete specification of a distributed algorithm for FLI and an exact characterization of the time from the occurrence of a fault to its location and isolation. Both apply to a distribution feeder with an arbitrary number of buses. These models are then refined into a hybrid simulation that combines three models executing in parallel: a power system model based on dc power flow, a distributed automation system model for the intelligent agents constructed using the IEC 61499 distributed automation standard, and an abstract communication network model that unreliably links the physically distributed agents. The results demonstrate the effect of communication network reliability at two levels of design abstraction, the correspondence of results at the two levels, and the use of a modern cosimulation framework to verify the performance of distributed smart grid automation algorithms.

Modern computer microarchitectures build on well-established foundations that have encouraged a pattern of computational homogeneity that many cyberattacks depend on. We suggest that balanced ternary logic can be valuable to Internet of Things (IoT) security, authentication of connected vehicles, as well as hardware and software assurance, and have developed a ternary encryption scheme between a computer and smartcard based on public key exchange through non-secure communication channels to demonstrate the value of balanced ternary systems. The concurrent generation of private keys by the computer and the smartcard uses ternary schemes and cryptographic primitives such as ternary physical unclonable functions. While general purpose ternary computers have not succeeded in general use, heterogeneous computing systems with small ternary computing units dedicated to cryptographic functions have the potential to improve information assurance, and may also be designed to execute binary legacy codes.

Uncertainties in ecophysiological responses to environment, such as the impact of atmospheric and soil moisture conditions on plant water regulation, limit our ability to estimate key inputs for ecosystem models. Advanced statistical frameworks provide coherent methodologies for relating observed data, such as stem sap flux density, to unobserved processes, such as canopy conductance and transpiration. To address this need, we developed a hierarchical Bayesian State-Space Canopy Conductance (StaCC) model linking canopy conductance and transpiration to tree sap flux density from a 4-year experiment in the North Carolina Piedmont, USA. Our model builds on existing ecophysiological knowledge, but explicitly incorporates uncertainty in canopy conductance, internal tree hydraulics and observation error to improve estimation of canopy conductance responses to atmospheric drought (i.e., vapor pressure deficit), soil drought (i.e., soil moisture) and above canopy light. Our statistical framework not only predicted sap flux observations well, but it also allowed us to simultaneously gap-fill missing data as we made inference on canopy processes, marking a substantial advance over traditional methods. The predicted and observed sap flux data were highly correlated (mean sensor-level Pearson correlation coefficient = 0.88). Variations in canopy conductance and transpiration associated with environmental variation across days to years were many times greater than the variation associated with model uncertainties. Because some variables, such as vapor pressure deficit and soil moisture, were correlated at the scale of days to weeks, canopy conductance responses to individual environmental variables were difficult to interpret in isolation. Still, our results highlight the importance of accounting for uncertainty in models of ecophysiological and ecosystem function where the process of interest, canopy conductance in this case, is not observed directly. The StaCC modeling framework provides a statistically coherent approach to estimating canopy conductance and transpiration and propagating estimation uncertainty into ecosystem models, paving the way for improved prediction of water and carbon uptake responses to environmental change.

The authors present a description and performance analysis of a new PN code acquisition approach for the tracking and data relay satellite system with multiple access (TDRSS MA) return subsystem. The MA subsystem uses a phased array on-board the TDRS with 30 elements whose received signals are each independently retransmitted to the ground for beam forming. In the current operational system, acquisition is performed by using a serial search of the possible PN epochs on the beam-formed 30-element signal. In contrast, the new approach performs acquisition on a single (arbitrary) element signal using a parallel (i.e., simultaneous) search of all possible PN epochs. Since only one element signal is used, no beam forming is required prior to PN acquisition. The approach is based on sampling and digital processing of the received signal. The new method also incorporates techniques which counteract the effects of Doppler and asynchronous data modulation. The probability of correct acquisition as a function of acquisition time, signal-to-noise ratio, data rate, and user satellite Doppler is analyzed. >