Global Positioning System Research Articles

Environmental Radioactivity Monitoring and Radiological Impact Assessment of Agbara Industrial Area, Ogun State, Nigeria

This study assessed the naturally occurring radioactivity of 40K, 238U, and 232Th, which pose a significant threat to human health, particularly when their concentrations exceed the threshold. Background radiation levels were measured at two specific locations, Access Bank and Market areas, across a total of forty (40) sample points. The measurements were taken using a calibrated RS125 Gamma Spectrometer (a portable NaI [Tl] detector) designed in Canada, in conjunction with a global positioning system (GPS) to accurately record the research coordinates within the Agbara industrial area, Ogun State, Nigeria. The mean activity concentrations of the primordial radionuclides were 177.87 Bqkg-1, 20.01 Bqkg-1, and 52.90 Bqkg-1 for 40K, 238U, and 232Th, respectively. More so, the in-situ measured dose rate (DR) ranges between 12.18 nGyh-1 (Access Bank area) and 97.95 nGyh-1 (Market area), with an average value of 47.22 nGyh-1. The average measured and estimated absorbed dose rates were within the safe limit of 57 nGyh-1 provided by UNSCEAR. However, the measured dose rates exceeded the recommended limit in ten locations, while measured activity for thorium exceeded the world average value for over half of the study locations. Although all estimated radiological parameters were within recommended threshold values, suggesting the low risk of exposure to higher levels of ionising radiation in most locations in the Agbara industrial area, there is a potential cancer risk for individuals who have resided in the area for 70 years or more due to long-term exposure to ionising radiation.

The Determinants of Maximal Speed Adaptation During Preseason in Subelite Female Australian Rules Footballers.

Maximal speed is an important physical-fitness attribute for female Australian footballers. The effects of sprint training, maximal strength, and technical training have been reported in laboratory studies. However, no study has determined the combined effect and relative contribution of these training modalities on maximal speed adaptation in situ. Therefore, the aim of this study was to determine the training factors affecting maximal speed adaptation during a preseason in subelite female Australian Rules footballers. Maximal speed during field training, predicted 1-repetition maximum (1RM) for box squat and hip thrust, and sprint biomechanics were assessed during early and late preseason (∼9wk apart) in 15 female subelite Australian Rules Football players (age 20 [3]y). On-field training volume and intensity (total distance, high-speed running, very-high-speed running, and maximal speed) were determined using a Global Positioning System. A multivariate regression model was used to determine the factors associated with changes in maximal speed across the preseason. The preseason training program had a small effect on maximal speed and large to very large effects on strength and sprint biomechanics. The multivariate regression with the greatest fit (P < .001, R2 = .939) included change in estimated 1RM box squat (β = -0.63), total distance per week (β = -0.55), and change in hip projection (β = 0.16) as factors. Multivariate regression of biomechanical factors (P = .044, R2 = .717) and maximal strength factors (P = .003, R2 = .676) were also significant. The development of maximal speed across a preseason is dependent on (1)total distance per week, (2)maximal strength adaptation, and (3)sprint technique adaptation in female subelite Australian rules football players.

Trade-Offs Between Forage Availability, Accessibility, and Predation Risk on Winter Foraging Strategies of Wood Bison (Bison bison athabascae).

Optimal foraging theory (OFT) and the energy maximization hypothesis (EMH) have long been essential when examining wildlife habitat selection. At high latitudes and altitudes, animals in winter face greater limitations in availability and accessibility of forage. Here we explore the foraging behavior of wood bison (Bison bison athabascae) during winter within the Ronald Lake bison herd in northeastern Alberta, Canada, and examine the trade-offs they face due to limitations in forage abundance and availability (snow conditions), as well as the need to minimize predation risk. We used Global Positioning System (GPS) location data collected from 70 female wood bison to identify winter foraging sites and craters selected by bison to access forage beneath the snow. Within wetlands used by bison we selected 190 pairs of used (foraged) and random (available) sites to test eight a priori hypotheses explaining how bison traded-off between forage availability, accessibility, and minimizing predation risk. We found with matched-paired logistic regression that Carex atherodes was 1.21-times more likely to be selected per unit increase in ground cover, compared to 1.17-times per unit ground cover for C. aquatilis and C. utriculata. However, all Carex species showed an increase in selection when cover was > 50% cover within individual craters. While the importance of Carex was clear, forage site selection was still inversely related to snow depth. There is also a neutralizing combined effect of snow depth and Carex species ground cover which suggests that bison maximized their energy return by avoiding areas with deep snow (> 30 cm) that demanded intensive cratering, even when highly selected forage was accessible beneath. Avoidance of forage areas with deep snow demonstrates that wood bison employed a foraging strategy that considers both forage availability and environmental conditions, with snow depth being a limiting factor. We highlight the relationship between optimal foraging based on food availability and the trade-offs within an energy restrictive winter season, furthering the understanding of how large herbivores forage strategically to maximize energy intake in northern environments.

Vertical Displacements and Sea‐Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: An Ensemble Modeling Approach

AbstractGlacial isostatic adjustment (GIA) describes the response of the solid Earth, oceans, and gravitational field to the spatio‐temporal evolution of ice sheets during a glacial cycle. Present‐day vertical displacements and sea‐level changes vary throughout eastern North America in response to the melting of the Laurentide Ice Sheet following the Last Glacial Maximum. We use the open‐source software SELEN4.0 (a SealEveL EquatioN solver) to investigate the influence of GIA on vertical land motions and sea‐level changes in eastern North America. Further, we evaluate the uncertainties associated with the lithospheric thickness and viscosity structure using an ensemble modeling approach (129,956 total simulations). We identify the best‐fitting rheological profiles by comparing modeled vertical displacements to vertical velocity rates derived from Global Positioning System (GPS). We find a general pattern of subsidence (causing accelerated relative sea‐level rise) in the eastern United States region and uplift (causing relative sea‐level fall) in the eastern Canada region consistent with previous studies for two tested ice sheet models (ICE‐6G(VM5a) and ICE‐7G(VM7)). Overall, we find lower rates of modeled vertical displacement using ICE‐6G(VM5a) compared with ICE‐7G(VM7), which produces lower residuals when compared with the GPS‐derived vertical velocity rates. Our ensemble analysis identifies adjustments to the nominal VM5a and VM7 viscosity models that improve fits to the GPS‐imaged vertical velocity rates throughout eastern North America and on the North American Atlantic Coast. The differences in our best‐fitting models for inland versus coastal regions highlight the importance of exploring lateral viscosity variations for GIA modeling throughout North America and elsewhere.

Improved GPS tropospheric path delay estimation using variable random walk process noise

Accurate positioning using the Global Positioning System relies on accurate modeling of tropospheric delay. Estimated tropospheric delay must vary sufficiently to capture true variations; otherwise, systematic errors propagate into estimated positions, particularly the vertical. However, if the allowed delay variation is too large, the propagation of data noise into all parameters is amplified, reducing precision. Here we investigate the optimal choice of tropospheric constraints applied in the GipsyX software, which are specified by values of random walk process noise. We use the variability of 5-min estimated positions as a proxy for tropospheric error. Given that weighted mean 5-min positions closely replicate 24-h solutions, our ultimate goal is to improve 24-h positions and other daily products, such as precise orbit parameters. The commonly adopted default constraint for the zenith wet delay (ZWD) is 3 mm/√(hr) for 5-min data intervals. Using this constraint, we observe spurious wave-like patterns of 5-min vertical displacement estimates with amplitudes ~ 100 mm coincident with Winter Storm Ezekiel of November 27, 2019, across the central/eastern USA. Loosening the constraint suppresses the spurious waves and reduces 5-min vertical displacement variability while improving water vapor estimates. Further improvement can be achieved when optimizing constraints regionally, or for each station. Globally, results are typically optimized in the range of 6–12 mm/√(hr). Generally, we at least recommend loosening the constraint from the current default of 3 mm/√(hr) to 6 mm/√(hr) for ZWD every 300 s. Constraint values must be scaled by √(x/300) for alternative data intervals of x seconds.

Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments.

Comparison of Weekly Training Load and Acute: Chronic Workload Ratio Methods to Estimate Change in Training Load in Running.

Before examining the impact of training load on injury risk in runners, it is important to gain insight into the differences between methods that are used to measure change in training load. To investigate differences between 4 methods when calculating change in training load: (1) weekly training load; (2) acute : chronic workload ratio (ACWR), coupled rolling average (RA); (3) ACWR, uncoupled RA; (4) ACWR, exponentially weighted moving average (EWMA). Descriptive epidemiology study. This study is part of a randomized controlled trial on running injury prevention among recreational runners. Runners received a baseline questionnaire and a request to share global positioning system training data. Runners who registered for running events (distances 10-42.195 km) in the Netherlands. The primary outcome measure was the predefined significant increase in training load (weekly training loads ≥ 30% progression and ACWRs ≥ 1.5), based on training distance. Proportional Venn diagrams visualized the differences between the methods. A total of 430 participants (73.3% men; mean age = 44.3 ± 12.2 years) shared their global positioning system training data for a total of 22 839 training sessions. For the weekly training load, coupled RA, uncoupled RA, and EWMA method, respectively, 33.4% (95% CI = 32.8, 34.0), 16.2% (95% CI = 15.7, 16.6), 25.8% (95% CI = 25.3, 26.4), and 18.9% (95% CI = 18.4, 19.4) of the training sessions were classified as significant increases in training load. Of the training sessions with significant increases in training load, 43.0% from the weekly training load method were different than the coupled RA and EWMA methods. Training sessions with significant increases in training load based on the coupled RA method showed 100% overlap with the uncoupled RA and EWMA methods. The difference in the change in training load measured by weekly training load and ACWR methods was high. To validate an appropriate measure of change in training load in runners, future research on the association between training loads and running-related injury risk is needed.

Microwave absorption properties of ytterbium substituted X-type ferrite in P-, L-, S-, and C-band

Ba1.5Sr0.5Zn2Fe28-xYbxO46 (x = 0.00, 0.07, 0.14, 0.21, and 0.28) hexaferrite were synthesized via sol-gel auto-combustion route to investigate the microwave absorption performance in different frequency bands. The structure has a single phase, and the variation in lattice parameter was observed due to Yb-content as host iron Fe3+ and substituted Yb3+ having different ionic radii. Microwave absorbing materials (MAMs) need to be lightweight, and the maximum crystallite size of prepared material is 41 nm. The physical properties vary with substitution, and the X-ray density value is higher than the bulk density, and the porosity of the prepared sample increases when the bulk density decreases. Micro-strain values are inverse to the crystallite size because Yb3+ has larger ionic radii than Fe3+. Rietveld refinement of the XRD patterns was conducted with a lower significance value. FTIR bands observed between 414 and 500 cm−1 are present due to iron-oxygen bond stretching and bending vibrations at octahedral and tetrahedral lattice sites. The cation distribution is highly responsible for the peak position of octahedral and tetrahedral sites—the dielectric constant increases with frequency because of dipole, interfacial, and electronic/atomic polarization. AC conductivity is very low, reflecting that the material can be used as a dielectric medium in the microwave frequency range's L, S, and C bands. The best MAM among all prepared materials is Ba1.5Sr0.5Zn2Fe27.72Yb0.28O46 which can be used for global positioning systems, weather radar, and satellite feeds as it has an excellent dielectric loss, remarkable tangential loss, and the reflection loss in P-, L-, S-, and C-bands. Other real-life application of all prepared materials are multi-layer chip inductor and longitudinal media recording.

Variable spatiotemporal ungulate behavioral response to predation risk

AbstractPrey must balance resource acquisition with predator avoidance for survival and reproduction. To reduce risk of predation, prey may avoid areas with high predator use, but if they are unable to due to resource acquisition requirements, they may instead change their habitat use or movement speed to mitigate predation risk. Prey risk response may depend on spatially or temporally varying forage availability as well as seasonal variation in prey vulnerability and availability of alternate foods for predators. To quantify how prey respond to spatial and temporal variation in risk of brown bear predation, we examined Roosevelt elk (Cervus canadensis roosevelti) spatiotemporal behavior responses to brown bear (Ursus arctos) habitat use on Afognak and Raspberry islands, Alaska, using Global Positioning System location data during elk parturition (20 May–15 June), summer (16 June–20 September), and autumn (21 September–10 November). During parturition and summer, elk used forest and shrub landcover in areas of higher brown bear probability of use. During parturition, elk used areas with lower forage productivity in areas of higher bear probability of use, and movement speed decreased with higher bear probability of use, especially in shrub landcover. During summer, elk used areas with higher forage productivity in areas of higher brown bear probability of use. During autumn, elk were less likely to use areas with higher bear habitat probability of use across landcover categories and forage productivity. During summer and autumn, elk movement speed increased with higher brown bear probability of use. Elk behavioral response to risk of brown bear predation could increase energy expenditure and decrease their ability to acquire forage, therefore negatively impacting survival and reproduction with spatiotemporal variation in risk response potentially amplifying these impacts.

Identifying Truck Activity from Global Positioning System Data Based on Satellite Imagery

Road freight transportation plays a crucial role in fostering economic growth and enhancing logistics efficiency. Existing research primarily focuses on analyzing freight truck activity characteristics based on global positioning system (GPS) data of freight truck trajectories. Those studies proposed a variety of identification methods from different perspectives, but they mainly rely on rule-based methods and subjectively defined key threshold parameters, ignoring the complexity of truck freight activities and relying on long sequences of trajectory data. To bridge this gap, we propose the satellite image augmented truck activity recognition method, a structured framework that utilizes satellite imagery to identify truck activities from GPS data. Firstly, we categorize truck activities into three types: loading/unloading, resting, and driving. We extract geographic entities from the area of interest data to create the satellite image dataset of truck activity. Secondly, we evaluate the recognition accuracy of benchmark image classification models on our dataset, and ResNet50 achieves 98.04% accuracy on the test set. Thirdly, we apply the image classification model to identify truck activity from GPS trajectory data and use the recognition results of neighboring points to improve the identification results. The framework delineated in this paper entails the extraction of truck activities from GPS data at the satellite image level. Our approach holds promise in facilitating logistics operation fleets and traffic management authorities to maintain near real-time surveillance of the truck status, thereby fostering enhancements in both the efficiency and safety of freight transport.

Aquifer delineation in parts of Akwa-Ibom State, Southern Nigeria using Resistivity Survey

In line with Sustainable Development Goals (SDG 6.6), aquifer delineation in parts of Akwa-Ibom State, Southern Nigeria was carried out using resistivity survey. The areas covered in the study included Uyo, Itu and Ibiono Ibom. Electrical resistivity technique was applied to obtain data from the subsurface layers. The study utilized a Signal Averaging System (SAS) 1000 Terrameter, four stainless steel electrodes, four reels of cable, geologic hammer, a measuring tape and a Global Positioning System (GPS). A SAS 1000 Terrameter was used in measuring the resistivity of the materials within the subsurface. Electric current was transmitted into the ground from the Terrameter using current electrodes while the potential difference between the current electrodes was measured. A total of four (4) stainless steel electrodes were used: two (2) current electrodes which transmitted electric current into the ground and two (2) potential electrodes from which the potential difference was measured between the current electrodes. Four reels of cable were used to link the electrodes and the Terrameter. The cable served as conductor of electric current into the ground. A total of four geologic hammers were used in sending the electrodes into the ground and in making them to be firmly fixed. A 100-m measuring tape was used to determine the intervals between the current electrode and potential electrode. A total of nine (9) ERT data points were obtained, processed and interpreted. The data obtained from the field were processed and also interpreted from curve matching techniques together with computer-aided software called Interplex 1-D. Deeper aquifers free from surface contamination and suitable for groundwater development are encountered at depth ranges of 90 m to 120 m, 70 m to 130 m, 80m to 160m and 70 m at Uyo, Itu, Ibiono Ibom and Ibesikpo respectively. The depths to aquifer, thicknesses and resistivity values were 1.086-445m, 1.086-248m and 0.114-8,132 Ωm, and 1.45-378m, 1.45-348m and 203-5,229Ωm, and 1.345-863.8m, 1.345-422.5m and 202.5-445.3Ωm, and 1.464-66.61m, 1.464-41.99m and 90.85-695.4Ωm in Uyo, Itu, Ibiono Ibom and Ibesikpo Asutan respectively. Borehole drilling within the locations where survey was carried out should target the depths of the delineated aquifers is recommended.

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

Impact of EMIC Waves on Electron Flux Dropouts Measured by GPS Spacecraft: Insights From ELFIN

AbstractAlthough the effects of electromagnetic ion cyclotron (EMIC) waves on the dynamics of the Earth's outer radiation belt have been a topic of intense research for more than 20 years, their influence on rapid dropouts of electron flux has not yet been fully assessed. Here, we make use of contemporaneous measurements on the same ‐shell of trapped electron fluxes at 20,000 km altitude by Global Positioning System (GPS) spacecraft and of trapped and precipitating electron fluxes at 450 km altitude by Electron Losses and Fields Investigation (ELFIN) CubeSats in 2020–2022, to investigate the impact of EMIC wave‐driven electron precipitation on the dynamics of the outer radiation belt below the last closed drift shell of trapped electrons. During six of the seven selected events, the strong 1–2 MeV electron precipitation measured at ELFIN, likely driven by EMIC waves, occurs within 1–2 hr from a dropout of relativistic electron flux at GPS spacecraft. Using quasi‐linear diffusion theory, EMIC wave‐driven pitch angle diffusion rates are inferred from ELFIN measurements, allowing us to quantitatively estimate the corresponding flux drop based on typical spatial and temporal extents of EMIC waves. We find that EMIC wave‐driven electron precipitation alone can account for the observed dropout magnitude at 1.5–3 MeV during all events and that, when dropouts extend down to 0.5 MeV, a fraction of electron loss may sometimes be due to EMIC waves. This suggests that EMIC wave‐driven electron precipitation could modulate dropout magnitude above 1 MeV in the heart of the outer radiation belt.

Evaluación de los efectos secundarios tras el sismo del 18 de enero de 2021 (Mw 6,5) en San Juan, Argentina: determinación de la intensidad sísmica a través de la escala ESI-2007

This study focuses on evaluating the numerous secondary effects, such as mass wasting and soil liquefaction, caused by the Mw 6.5 earthquake that occurred on January 18, 2021, in the province of San Juan, Argentina. The epicenter was located on the eastern flank of the Las Osamentas range. Although it caused significant damage to buildings and cultivated areas, there were no fatalities. The aim is to identify and describe these secondary effects and determine the seismic intensity of the event using the INQUA’s (International Union for Quaternary Research) 2007 ESI (Environmental Seismic Intensity) scale. Field assessments were conducted shortly after the earthquake, documenting liquefaction and mass wasting structures through direct observations, digital photographs, GPS (Global Positioning System) localization, and data inventory, among other methods. This procedure validates a protocol for future applications in estimating seismic intensities for sparsely populated areas. Additionally, the study seeks to confirm the scientific and cultural importance of these ephemeral structures as assets that could be protected, therefore contributing to regional development.

Evidence for a survival-driven traveling wave in a keystone boreal predator population

Cyclical population dynamics are a common phenomenon in populations worldwide, yet the spatial organization of these cycles remains poorly understood. In this study, we investigated the spatial form and timing of a population collapse from 2018 to 2022 in Canada lynx (Lynx canadensis) across the northwest boreal forest. We analyzed survival, reproduction, and dispersal data from 143 individual global positioning system (GPS) collared lynx from populations across five study sites spanning interior Alaska to determine whether lynx displayed characteristics of a population wave following a concurrent wave in snowshoe hare (Lepus americanus) abundance. Reproductive rates declined across the study sites; however, site-level reproduction declined first in our easternmost study sites, supporting the idea of a population wave. Despite a clear increase in percent of dispersing lynx, there was no evidence of directional bias in dispersal following a hare population wave. Analysis did show increasingly poor survival for lynx dispersing to the east compared to combined resident and westward dispersal. This pattern is consistent with a survival-mediated population wave in lynx as the driver of the theorized population wave. The combination of these factors supports the idea of a hierarchical response to snowshoe hare population declines with a drop in lynx reproduction followed by increased dispersal, and finally reduced survival. All of this evidence is consistent with the expected characteristics of a population undergoing a traveling wave and supports the hypothesis that lynx presence may facilitate and mirror the underlying wave patterns in snowshoe hare.

Assessments of GPS satellite orbiting period effects on diurnal and semi-diurnal luni-solar declinations utilizing Galileo satellites

Global Navigation Satellite Systems (GNSS) can observe a variety of surface deformations on Earth, including periodic oscillations at different frequencies. An example of such phenomena is ocean tide loadings (OTL), which result from the redistribution of water mass. The Global Positioning System (GPS) exhibits orbital geometry that causes its revisit and orbital periods to coincide with the diurnal and semi-diurnal luni-solar declination constituents, known as K1 and K2, respectively. Consequently, the system faces challenges in accurately estimating these periodic oscillations due to its orbital artifacts. This study aims to quantify the extent to which GPS orbital artifacts introduce periodic signals into the K1 and K2 constituents by utilizing the Galileo system and determining the most suitable positioning approach. A dataset from the International GNSS Service (IGS), spanning 40 days in 2024 and covering six stations, was analyzed. Coordinates were estimated using both kinematic positioning every 5 minutes and a 6-hour static precise point positioning (PPP) mode with a 3-hour shift. The power spectra for the east, north, and up components indicated that, on average, the GPS system contributes 52.8% to the K1 constituents and 66.3% to the K2 constituents. Despite expectations that the diurnal K1 and semi-diurnal K2 tidal constituents would be more prominent in the power spectra of the GPS comparing to that of natural signature or of other navigation system (Galileo for this study), the diurnal K1 tidal constituent appeared weak in the kinematic mode power spectra for the GPS system. These findings validate that the overlapped-static PPP mode is a more appropriate approach for estimating these periodic deformations.

Dead-reckoning facilitates determination of activity and habitat use: a case study with European badgers (Meles meles)

BackgroundStudies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited.MethodsCollars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type.ResultsNightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively).Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances.ConclusionsThis study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Improving Indoor WiFi Localization by Using Machine Learning Techniques

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

Employing a two stage process with resampling, decision rules, and fine acquisition to optimize the acquisition stage of GNSS receiver performance

The optimal design of Global Navigation Satellite System (GNSS) software receivers should enable the accurate estimation of the receiver's position, velocity, and time under various environmental conditions. The software receivers consist of three sections: acquisition, tracking, and navigation. This paper specifically centers on the acquisition sections of the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou, and Galileo satellite navigation systems. The acquisition stage necessitates the prompt and precise transmission of the satellite list within the receiver's view, along with Doppler frequency estimation and phase code offset, to facilitate a seamless transition to the tracking stage with the utmost speed and accuracy. Conventional acquisition demonstrates commendable speed, but lacks accuracy, especially in environments with a low Carrier-to-Noise Ratio (CNR). Contrastingly, precise acquisition methods will impede the speed of the acquisition stage. In this paper, a novel acquisition method is proposed. By integrating concepts like resampling, two-stage acquisition, and fine-acquisition, this method enables the attainment of higher accuracy without compromising speed. The results demonstrate that the proposed method enhances accuracy by 7.275% compared to conventional methods and boosts speed by 76.97% compared to methods focused on accuracy improvement.

Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model.

Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques.