We studied the response of the ionosphere (F region) in the Brazilian sector during extreme space weather event of 17 March 2015 using a large network of 102 GPS‐ total electron content (TEC) ...stations. It is observed that the vertical total electron content (VTEC) was severely disturbed during the storm main and recovery phases. A wavelike oscillation with three peaks was observed in the TEC diurnal variation from equator to low latitudes during the storm main phase on 17–18 March 2015. The latitudinal extent of the wavelike oscillation peaks decreased from the beginning of the main phase toward the recovery phase. The first peak extended from beyond 0°S to 30°S, the second occurred from 6°S to 25°S, whereas the third diurnal peaks was confined from 13°S to 25°S. In addition, a strong negative phase in VTEC variations was observed during the recovery phase on 18–19 March 2015. This ionospheric negative phase was stronger at low latitudes than in the equatorial region. Also, two latitudinal chains of GPS‐TEC stations from equatorial region to low latitudes in the east and west Brazilian sectors are used to investigate the storm time behavior of the equatorial ionization anomaly (EIA) in the east and west Brazilian sectors. We observed an anomalous behavior in EIA caused by the wavelike oscillations during the storm main phase on 17 March, and suppression of the EIA, resulting from the negative phase in VTEC, in the storm recovery phase.
Key Points
The 17 March 2015 St. Patrick´s day geomagnetic storm
Extreme ionospheric space weather event over the Brazilian sector
Anomalous behavior in EIA during main phase and recovery phases
In Switzerland, strict measures as a response to the Covid-19 pandemic were imposed on March 16, 2020, before being gradually relaxed from May 11 onwards. We report the impact of these measures on ...mobility behaviour based on a GPS tracking panel of 1439 Swiss residents. The participants were also exposed to online questionnaires. The impact of both the lockdown and the relaxation of the measures up until the middle of August 2020 are presented. Reductions of around 60% in the average daily distance were observed, with decreases of over 90% for public transport. Cycling increased in mode share drastically. Behavioural shifts can even be observed in response to the announcement of the measures and relaxation, a week before they came in to place. Long-term implications for policy are discussed, in particular the increased preference for cycling as a result of the pandemic.
•Understanding of the change in mobility patterns in Switzerland using a large GPS Panel during the Covid19 Pandemic.•Evidence for a large increase in cycling kilometers during the first lockdown which was sustained into the summer.•Changes in mobility behaviour varied across different socio-demographic groups.
In this paper we describe the statistical properties of the EUV solar flux sudden variation. The solar flux variation is modeled as a time series characterized by the subsolar Vertical Total Electron ...Content double difference in time, computed with dual‐frequency GNSS (Global Navigation Satellite Systems) measurements in the daylight hemisphere (GNSS solar flare indicator rate parameter). We propose a model that explains its characteristics and the forecasting limitations. The sudden overionization pattern is assumed to be of solar origin, and the data used in this study was collected during the last solar cycle. The two defining characteristics of this time series are an extreme variability (i.e., in a solar cycle one can find events at 400σ from the mean value) and a temporal correlation that is independent of the timescale. We give a characterization of a model that explains the empirical results and properties such as (a) the persistence and presence of bursts of solar flares and (b) their long tail peak values of the solar flux variation. We show that the solar flux variation time series can be characterized by a fractional Brownian model for the long‐term dependence, and a power law distribution for the extreme values that appear in the time series.
Key PointsStatistical properties of the EUV solar flux sudden variation as a time seriesSudden overionization studied during one solar cycle from GNSS signalsThe solar flux rate follows the Levy‐Mandelbrot and fractional Brownian models
Background: The impact of traumatic experiences or adverse life experiences has been shown to potentially affect a wide range of mental health outcomes. However, there was no brief instrument to ...screen for a range of psychological problems in different domains after a potentially traumatic event, and for risk factors and protective factors.
Objective: The aim of this study is to examine the internal consistency and concurrent validity of the Japanese version of the Global Psychotrauma Screen (GPS) in a traumatized sample in Japan.
Method: A total sample (n = 58) with varying levels of potential posttrauma symptoms due to domestic violence or other events were recruited into this study. Self-rating measures of posttraumatic stress disorder (PTSD), depression, anxiety, and alcohol problems were conducted to investigate the concurrent validity.
Results: The results show that a range of posttrauma symptoms assessed by the GPS were highly endorsed by this traumatized sample in all domains except for self-harm, derealization, and depersonalization. The GPS sum score was highly correlated (r > 0.79) with other measures of PTSD, depression, and anxiety symptoms. Also, the subdomain scores showed acceptable correlations with corresponding domain measures. Participants who had been sexually assaulted or had unwanted sexual experiences, and participants who had been physically assaulted during childhood, had higher scores on the total GPS and on subdomains of PTSD, as well as symptoms associated with Complex PTSD.
Conclusions: This study provides an initial indication that the GPS may be a useful screening tool for trauma survivors and elucidates that the consequences of trauma are not limited to PTSD.
Real‐time detection and precise estimation of strong ground motion are crucial for rapid assessment and early warning of geohazards such as earthquakes, landslides, and volcanic activity. This ...challenging task can be accomplished by combining GPS and accelerometer measurements because of their complementary capabilities to resolve broadband ground motion signals. However, for implementing an operational monitoring network of such joint measurement systems, cost‐effective techniques need to be developed and rigorously tested. We propose a new approach for joint processing of single‐frequency GPS and MEMS (microelectromechanical systems) accelerometer data in real time. To demonstrate the performance of our method, we describe results from outdoor experiments under controlled conditions. For validation, we analyzed dual‐frequency GPS data and images recorded by a video camera. The results of the different sensors agree very well, suggesting that real‐time broadband information of ground motion can be provided by using single‐frequency GPS and MEMS accelerometers.
Key Points
Real‐time joint processing of GPS and strong‐motion data
Combination of low‐cost GPS and accelerometer sensors
Broadband ground motion information associated with strong acceleration events
Abstract
The geometry of the GPS satellite recipient (s), which reflects the recipient (s) of the satellites, has a major influence on the total positioning precision. The more precise the position, ...the stronger the geometry of the satellite. This article provides the geometry of satellite clustering for the selection of suitable satellite navigation subsets. This technique is based on the GDOP (Geometric Precision Dilution) satellite factor cluster with the Ant Colony Optimization (ACO) algorithm that has been created by simulating real and artificial ways to locate the quickest route between nesting resources and food. Pheromones are utilised in the suggested technique to assess the iterative outcome of single colonies. The ACO method can measure all subsets of satellites while reducing computer load by eliminating the need for a matrix inversion. Based on the simulation results, the GPS GDOP clustering technique is more efficient at achieving its optimum value.
•A CNN architecture is proposed to infer transportation modes from GPS trajectories.•An adaptable and efficient layout for the input layer of the CNN is designed.•Key factors in the CNN: remove ...anomalies, data augmentation, use the bagging concept.•The proposed CNN achieves the accuracy of 84.8%, higher than other studies.
Identifying the distribution of users’ transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters’ mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human’s bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN’s input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.
The recent development of the International Global Navigation Satellite Systems Service Real‐Time Pilot Project and the enormous progress in precise point positioning (PPP) techniques provide a ...promising opportunity for real‐time determination of Integrated Water Vapor (IWV) using GPS ground networks for various geodetic and meteorological applications. In this study, we develop a new real‐time GPS water vapor processing system based on the PPP ambiguity fixing technique with real‐time satellite orbit, clock, and phase delay corrections. We demonstrate the performance of the new real‐time water vapor estimates using the currently operationally used near‐real‐time GPS atmospheric data and collocated microwave radiometer measurements as an independent reference. The results show that an accuracy of 1.0 ~ 2.0 mm is achievable for the new real‐time GPS based IWV value. Data of such accuracy might be highly valuable for time‐critical geodetic (positioning) and meteorological applications.
Key Points
We develop a new RT GPS water vapor processing system
PPP ambiguity fixing with RT satellite orbit, clock, and phase delays
Our results are very promising and demonstrate RT ZTD/IWV accuracy
The PRIDE Lab at GNSS Research Center of Wuhan University has developed an open-source software for GPS precise point positioning ambiguity resolution (PPP-AR) (i.e., PRIDE PPP-AR). Released under ...the terms of the GNU General Public License version 3 (GPLv3,
http://www.gnu.org/licenses/gpl.html
), PRIDE PPP-AR supports relevant research, application and development with GPS post-processing PPP-AR. PRIDE PPP-AR is mainly composed of two modules, undifferenced GPS processing and single-station ambiguity resolution. Undifferenced GPS processing provides float solutions with wide-lane and narrow-lane ambiguity estimates. Later, single-station ambiguity resolution makes use of the phase clock/bias products, which are released also by the PRIDE Lab at ftp://pridelab.whu.edu.cn/pub/whu/phasebias/, to recover the integer nature of single-station ambiguities and then carry out integer ambiguity resolution. PRIDE PPP-AR is based on a least-squares estimator to produce daily, sub-daily or kinematic solutions for various geophysical applications. To facilitate the usage of this software, a few user-friendly shell scripts for batch processing have also been provided along with PRIDE PPP-AR. In this article, we use 1 month of GPS data (days 001–031 in 2018) to demonstrate the performance of PRIDE PPP-AR software. The PRIDE Lab is committed to consistently improve the software package and keep users updated through our website.
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