Data Processing for Energetic Particle Measurements from the Global Positioning System (GPS) constellation
Abstract Not Provided
Plasma Physics & Fusion Technology(70)↗
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Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction
Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.
33 ADVANCED PROPULSION SYSTEMS↗
Global Prompt Proton Sensor Network: Monitoring Solar Energetic Protons Based on GPS Satellite Constellation
Energetic particle instruments on board Global Positioning System (GPS) satellites form a powerful global prompt proton sensor network (GPPSn) that provides an unprecedented opportunity to monitor and characterize solar energetic protons targeting the Earth. The medium-Earth orbits of the GPS constellation have the unique advantage of allowing solar energetic protons to be simultaneously measured from multiple points in both open- and closed-field line regions. Examining two example intervals of solar proton events, we showcase in this study how GPS proton data are prepared, calibrated, and utilized to reveal important features of solar protons, including their source, acceleration/scattering by interplanetary shocks, the relative position of Earth when impinged by these shocks, the shape of solar particle fronts, the access of solar protons inside the dynamic geomagnetic field, and temporally varying proton distributions in both energy and space. By comparing to Van Allen Probes data, GPS proton observations are further demonstrated not only to be useful for qualitatively monitoring the dynamics of solar protons but also for quantitative scientific research including determining cutoff L-shells. Finally, our results establish that this GPPSn can join forces with other existing solar proton monitors and contribute to observing, warning, understanding, and ultimately forecasting the incoming solar energetic proton events.
47 OTHER INSTRUMENTATION↗
Uranium Attenuated by a Wetland 50 Years after Release into a Stream
: Wetlands have several important roles in the hydrological cycle, including maintaining water quality by removing surface and groundwater contaminants. Over time, the wetlands themselves can become contaminated, posing a secondary environmental threat. The objective of this study was to calculate the inventory of uranium (U) remaining in a wetland 50 years after the Fuel Fabrication Facility on the Savannah River Site located in South Carolina released 43.5 Mg of U into the nearby environment. Over 232 700 gamma spectra and their associated global positioning system (GPS) coordinates were collected and collated into a map of the contaminated land area. Five core samples were also collected that contained U concentrations as high as 14 099 mg/kg (background levels are about 2.7 mg/kg U). The contaminated area was 278 000 m2 , and it contained 36.2 Mg U, about 83% of the U released. About 80% of the U in the wetland was concentrated in a former beaver pond, a 73 000 m2 area (26% of the contaminated area). This contaminated wetland area was almost 2 km from the source, indicating that it comprised unique hydro-biogeochemical properties for immobilizing the released U. To the best of our knowledge, this is the first data-rich study to quantify the long-term effectiveness of a wetland to immobilize inorganic contaminants. Significant environmental changes to the system, such as those associated with hydrology, forest fires, or anthropogenic land use, may alter the complex hydro-biogeochemical interactions necessary for the long-term immobilization of the U.
Contaminant inventory↗
Convective-Permitting Hindcast Simulations during the North American Monsoon GPS Transect Experiment 2013: Establishing Baseline Model Performance without Data Assimilation
During the North American monsoon global positioning system (GPS) Transect Experiment 2013, daily convective-permitting WRF simulations are performed in northwestern Mexico and the southern Arizona border region using the operational Global Forecast System (GFS) and North American Mesoscale Forecast System (NAM) models as lateral boundary forcing and initial conditions. Compared to GPS precipitable water vapor (PWV), the WRF simulations display a consistent moist bias in the initial specification of PWV leading to convection beginning 3–6 h early. Given appreciable observed rainfall, days are classified as strongly and weakly forced based only on the presence of an inverted trough (IV); gulf surges did not noticeably impact the development of mesoscale convective systems (MCSs) and related convection in northwestern Mexico. Strongly forced days display higher modeled precipitation forecast skill than weakly forced days in the slopes of the northern Sierra Madre Occidental (SMO) away from the crest, especially toward the west where MCSs account for the greatest proportion of all monsoon-related precipitation. A case study spanning 8–10 July 2013 illustrates two consecutive days when nearly identical MCSs evolved over northern Sonora. Although a salient MCS is simulated on the strongly forced day (9–10 July 2013) when an IV is approaching the core monsoon region, a simulated MCS is basically nonexistent on the weakly forced day (8–9 July 2013) when the IV is farther away. Finally, the greater sensitivity to the initial specification of PWV in the weakly forced day suggests that assimilation of GPS-derived PWV for these types of days may be of greatest value in improving model precipitation forecasts.
54 ENVIRONMENTAL SCIENCES↗
Deformation analysis of Aceh April 11{sup th} 2012 earthquake using GPS observation data
This research tries to estimate the co-seismic deformation of intraplate earthquake occurred off northern Sumatra coast which is about 100-200 km southwest of Sumatrasubduction zone. The earthquake mechanism was strike-slip with magnitude 8.6 and triggering aftershock with magnitude 8.2 two hours later. We estimated the co-seismic deformation by using the GPS (Global Positioning System) continuous data along western Sumatra coast. The GPS observation derived from Sumatran GPS Array (SuGAr) and Geospatial Information Agency (BIG). For data processing we used GPS Analyze at Massachusetts Institute of Technology (GAMIT) software and Global Kalman Filter (GLOBK) to estimate the co-seismic deformation. From the GPS daily solution, the result shows that the earthquake caused displacement for the GPS stations in Sumatra. GPS stations in northern Sumatra showed the displacement to the northeast with the average displacement was 15 cm. The biggest displacement was found at station BSIM which is located at Simeuleu Island off north west Sumatra coast. GPS station in middle part of Sumatra, the displacement was northwest. The earthquake also caused subsidence for stations in northern Sumatra, but from the time series there was not sign of subsidence was found at middle part of Sumatra. In addition, the effect of the earthquake was worldwide and affected the other GPS Stations around Hindia oceanic.
58 GEOSCIENCES↗
Deriving spatially explicit direct and indirect interaction networks from animal movement data
Abstract Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs ( Sus scrofa ) that can host African Swine Fever and mule deer ( Odocoileus hemionus ) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.
59 BASIC BIOLOGICAL SCIENCES↗
Using Map Service API for Driving Cycle Detection for Wearable GPS Data: Preprint
Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method's ability to detect vehicle mode accurately.
33 ADVANCED PROPULSION SYSTEMS↗
Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems
Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Finally, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method.
97 MATHEMATICS AND COMPUTING↗
Washington Play Fairway Analysis - Analysis of High Precision GPS Time Series and Strain Rates for the Cascade Range
Global Positioning System (GPS) time series from the National Science Foundation (NSF) Earthscope's Plate Boundary Observatory (PBO) and Central Washington University's Pacific Northwest Geodetic Array (PANGA). GPS station velocities were used to infer strain rates using the "splines in tension" method. Strain rates were derived separately for subduction zone locking at depth and block rotation near the surface within crustal block boundaries.
15 GEOTHERMAL ENERGY↗
Appending High-Resolution Elevation Data to GPS Speed Traces for Vehicle Energy Modeling and Simulation
Accurate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerations. Model-based design, real-world fuel economy analysis, and the growing field of autonomous and connected technologies (including predictive powertrain control and self-driving cars) all have a vested interest in high-fidelity estimation of powertrain loads and vehicle usage profiles. Unfortunately, road grade can be a difficult property to extract from GPS data with consistency. In this report, we present a methodology for appending high-resolution elevation data to GPS speed traces via a static digital elevation model. Anomalous data points in the digital elevation model are addressed during a filtration/smoothing routine, resulting in an elevation profile that can be used to calculate road grade. This process is evaluated against a large, commercially available height/slope dataset from the Navteq/Nokia/HERE Advanced Driver Assistance Systems product. Results will show good agreement with the Advanced Driver Assistance Systems data in the ability to estimate road grade between any two consecutive points in the contiguous United States.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data
With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-to-Home and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers’ activity behavior features for an extended period. After that, the variability pattern of an individual’s travel behavior is used for estimating the individual’s social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment’s result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
GPS Spoofing Attack Characterization and Detection in Smart Grids
The problem of global positioning system (GPS) spoofing attacks on smart grids endowed with phasor measurement units (PMUs) is addressed, taking into account the dynamical behavior of the states of the system. First, it is shown how GPS spoofing introduces a timing synchronization error in the phasor readings recorded by the PMUs and alters the measurement matrix of the dynamical model. Then, a generalized likelihood ratio-based hypotheses testing procedure is devised to detect changes in the measurement matrix when the system is subjected to a spoofing attack. Monte Carlo simulations are performed on the 9-bus, 3-machine test grid to demonstrate the implication of the spoofing attack on dynamic state estimation and to analyze the performance of the proposed hypotheses test.
97 MATHEMATICS AND COMPUTING↗
Impact of GPS Signal Loss and Its Mitigation in Power System Synchronized Measurement Devices
With the aid of global positioning system (GPS), synchronized measurement devices (SMDs) are increasingly deployed across power systems to monitor the status of electric grids by providing accurate measurement data along with unified time stamps. Unfortunately, GPS receivers tend to lose signal lock when certain uncontrollable and unpredictable factors arise. In order to investigate the presence of GPS signal loss (GSL) issues on measurement devices, analysis is performed on historical data from both phasor data concentrators and FNET/GridEye servers. Meanwhile, the impact of GSL on field measurement accuracy has not been previously explored in depth. Through analysis and experimental tests, this paper discovers angle drift caused by GSL, which consequently leads to the total vector error exceeding the IEEE standard C37.118.1-2011. Furthermore, a compensation method is proposed to rectify the angle drift and laboratory experiments demonstrate that the proposed method does effectively reduce angle drift and mitigate the impact of GSL in SMDs.
47 OTHER INSTRUMENTATION↗
GPS signal loss in the wide area monitoring system: Prevalence, impact, and solution
The phasor measurement unit (PMUs), equipped with Global Positioning System (GPS) receivers for precise time synchronization, provides measurements of voltage and current phasors at different nodes of the wide area monitoring system. However, GPS receivers are likely to lose satellite signals due to various unpredictable factors. The prevalence of GPS signal loss (GSL) on PMUs is first investigated using real PMU data. The historical GSL events are extracted from a phasor data concentrator (PDC) and FNET/GridEye server. The correlation between GSL and time, spatial location, solar activity are explored via comprehensive statistical analysis. Furthermore, the impact of GSL on phasor measurement accuracy has been studied via experiments. Finally, several potential solutions to mitigate the impact of GSL on PMUs are discussed and compared.
47 OTHER INSTRUMENTATION↗
Mule Deer and Pronghorn Antelope on the Nevada National Security Site GPS Radio-Collar Study 2019 – 2022 Radiological Results
A research study involving the capture, global-positioning system (GPS) radio-collaring, and tracking of mule deer (Odocoileus hemionus) and pronghorn antelope (Antilocapra americana) on the NNSS was conducted November 2019 through October 2022. The purpose of this study is to better understand their habitat use, potential overlap with radiological areas on the NNSS, radionuclide concentrations in, and dose to, pronghorn and mule deer, and radiological dose to humans potentially consuming these animals.
dose↗
Use of a GPS-Derived Troposphere Model to Improve InSAR Deformation Estimates in the San Gabriel Valley, California
We evaluate the potential of troposphere models derived from ground meteorological data (pressure, temperature, and relative humidity) and Global Positioning System (GPS) data to improve InSAR measurements and models derived from them. We test this approach on an ERS-2/Envisat data set collected during a transient surface deformation episode that occurred from January to July 2005 in the San Gabriel Valley, southern California, USA. We find that the interferometric phase change observed over the corresponding period cannot be solely attributed to hydrological uplift associated with rising groundwater levels but also includes a significant contribution from differential tropospheric delay due to differing quantities of water vapor in the troposphere on the two SAR observation dates. We show that, if the tropospheric phase contribution is mistakenly interpreted as the range change associated with changes in groundwater storage, both the surface displacement and the groundwater storage coefficient may be overestimated by up to 30%. Finally, this method could be applied in real time where meteorological measurements are available near one or more GPS permanent site(s).
54 ENVIRONMENTAL SCIENCES↗
Closed-Form Solution for Synchrophasor Data Correction under GPS Spoofing Attack
Phasor Measurement Units (PMUs) are increasingly being deployed to augment monitoring, protection and control applications in the power grid. PMUs rely on the Global Positioning System (GPS) for generation of time-synchronized and accurate measurements. However, civilian GPS signals, being unencrypted, are susceptible to GPS spoofing attacks (GSA). Hence ensuring the integrity of GPS-timing dependent synchrophasor data has become critical. In this paper, a closed-form analytical solution for estimating the GSA phase shift has been proposed and integrated to the spoofing detection & correction framework. Extensive simulations have been performed in order to verify the accuracy of the solution in determining the location and the phase shift of the compromised PMU. Time domain dynamic simulations presented in this paper demonstrate the applicability of the proposed solution for near-real-time detection of GSA, and this will enable faster detection and correction of phase angles of compromised synchrophasor data.
Synchrophasor, PMUs, GPS, Spoofing, Data Correctio↗