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High-intensity activities are related to success in football. We looked at whether high-intensity activity differed between home and away matches and its impact on the final score. Thirty football players (20.3 ± 0.8 years) were recruited from a Spanish semi-professional team. Footballers wore a GPS device to monitor high-intensity parameters from competition matches. The final score of every match was also recorded. Playing at home showed greater total distance (TD) > 24 km/h, >27 km/h, >85% Vmax, and the number of sprints > 24 km/h (all p < 0.05) than playing away. Positive correlations were also found between the match score and high-speed running (HSR) distance covered by the team per minute (r = 0.401), TD > 21 km/h (r = 0.417), TD > 24 km/h (r = 0.343), number of sprints > 24 km/h (r = 0.337), and HSR per minute (r = 0.459) (all p < 0.05). The results suggest that playing at home is associated with greater high-intensity effort than playing away. Moreover, the volume of high-intensity effort influences the final score. [ABSTRACT FROM AUTHOR]

In a trajectory management system, moving objects are typically equipped with GPS devices to report their locations to a data store. Given the potentially high frequency of location updates by multiple moving objects, these data stores often operate under write-intensive conditions. Existing trajectory indexing methods, such as XZ-ordering, which are geared towards static trajectory data, may experience considerable latency under such demanding workloads. In response, this paper introduces spatio-temporal index structures that can be constructed with low latency. Trajectories are categorized into two types for storage: 'live' and 'static'. Live trajectories are indexed by the Dual-Key Encoding (DKE) scheme, where each data point is represented by two key-value entries, facilitating both ID-temporal and spatial queries. Static trajectories, on the other hand, offer a more compact storage solution, reducing the overhead associated with live trajectories. Upon the completion of a trip, live trajectory data is transformed into a static trajectory entry through a compaction process. To augment the efficiency of spatial index construction for static trajectories, we introduce a new encoding scheme, XS2, coupled with an adaptive segmentation policy, AdaptSeg, to optimize trajectory segmentation, thereby enhancing index building and query processing efficiency. The indexing methods are demonstrated atop of LevelDB, an LSM-based key-value storage library, resulting the proposed LevelDBST system. Performance evaluations conducted using both synthetic and real-world datasets reveal that LevelDBST is capable of constructing spatial indexes for continuously updating trajectories with reduced latency, in comparison to traditional XZ-ordering methods. This efficiency is achieved while maintaining an acceptable balance in data accessing time and storage costs. [ABSTRACT FROM AUTHOR]

Some of the extreme space weather events like the geomagnetic storm of 17–18 March 2015 have produced dramatic effects on low-latitude ionosphere as reported in various studies. Similarly, some of the extreme storms are studied to emphasize the anomalous deviations in ionospheric delay; however, the effects of excess ionospheric delay on GPS-based positioning are rarely reported from India. This paper presents a robust analysis including estimation of GPS receiver position during 6 major geomagnetic storms using dual frequency GPS signals in L1 and L2 bands. Epoch-wise solution estimates are computed using precise orbit SP3 files for satellite orbit and clock corrections and receiver specific differential code biases are obtained from IGS-CODE center. A range domain Kalman filter has also been developed to smooth the 30 s sampled code pseudorange using carrier phase data and resolve the initial ambiguity. Then, the precise positioning residuals are estimated in two different cases of ionospheric corrected and ionospheric uncorrected error models. We then compare the results with ionospheric corrected position estimates to finally obtain the effect of excess daytime ionospheric variations during main phase of some extreme geomagnetic storms. Results from 5 GPS receivers located within 77°–80° E longitude sector are obtained for two 2-hour windows covering local noon during 6 storms including the St. Patrick's Day storm. The effect of severity of the storms and their impact on static precise positioning are brought out by comparing the results with performance on a quiet day. It is found that the magnitude of error in estimated altitude exhibits maximum deviations due to ionospheric variations during storms, and the dip latitude of station is important in terms of magnitude of ionospheric error in positioning over the equatorial ionization anomaly region. [ABSTRACT FROM AUTHOR]

An assistive intelligence sensor device is a comprehensive solution aimed at improving the mobility and independence of people with visual or hearing impairments. It integrates an array of advanced hardware components including Node MCU and Arduino board for data processing, GPS module with antenna for accurate location tracking, emergency key switch for instant warning activation, and time of flight sensor for accurate obstacle detection. A vibrator for haptic feedback, a Wi-Fi module for seamless connectivity and a display for visual feedback. Additionally, the device includes a laptop with a Python-coded program for real-time object detection using a camera, which helps identify and classify objects in the user's environment. Leveraging the Blynk IoT app, the device facilitates instant communication with caregivers or emergency responders, ensuring peace of mind and timely assistance when needed. Also, apart from detecting nearby obstacles and providing haptic feedback, the device uses a GPS module to live track the user's location, ensuring safety and security during outdoor navigation. The integration of intelligent software algorithms further enhances the functionality of the device, enabling it to adapt to various environments and user needs. This manuscript provides a comprehensive overview of the device's design, functionality, and potential applications to improve the quality of life of individuals with disabilities. By integrating state-of-the-art hardware components with intelligent software solutions, the Assistive Intelligence sensor device represents a significant advance in assistive technology, empowering users to navigate their surroundings with confidence and independence. [ABSTRACT FROM AUTHOR]

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns. [ABSTRACT FROM AUTHOR]

The scale factor serves as a ruler for converting raw phase measurements into physical displacements and significantly impacts the preprocessing of data from the Laser Ranging Interferometer (LRI) in laser ranging systems. In the current GRACE Follow-On (GRACE-FO) mission for low–low tracking gravity satellites, most of the existing LRI scale factor estimation algorithms heavily rely on cross-calibration between instantaneous/biased ranges from the Ka-Band Ranging Interferometer (KBR) and the LRI. However, due to the nonlinearity of the objective function (which includes terms involving the product of scale and time shifts), the scale factor estimation may absorb errors from timing noise. Moreover, future gravity missions or gravity detection tasks may no longer incorporate KBR ranging instruments. To address these challenges, this paper proposes an energy-based method for scale factor estimation using inter-satellite baseline solutions. Comparative analysis indicates that the proposed method effectively disentangles two parameters in the objective function and can be applied in scenarios where KBR data are unavailable, demonstrating promising prospects for practical application. The experimental results show that when the KBR validation residuals are lower than 0.8 mm, the SYSU LRI1B V01 products exhibit residuals below the payload design accuracy thresholds in the frequency band of 2 mHz to 0.1 Hz. Additionally, the stability of the scale factors obtained from the baseline can reach 10−7. Although this is still below the required precision of better than 10−8 for the laser frequency stability in next-generation gravity satellites, advancements in orbit determination technology and the enhanced stability of GPS receivers offer potential for future precision improvements. Currently, this method appears suitable for roughly extracting the scale factor as a stochastic mean over several months or serving as a backup validation strategy for future missions, but it is not well suited to measure day-to-day variations. [ABSTRACT FROM AUTHOR]

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi‐host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement‐based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS‐collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free‐range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi‐host systems in Mediterranean environments. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of this research was to develop a questionnaire for the subjective evaluation of the quantity and magnitude of impacts experienced by rugby players. Methods: Thirty-six male rugby union players (mean ± SD, age; 23.5 ± 3.6 years, height; 179 ± 10.0 cm, body mass; 89.58 ± 13.6 kg) participated in this study, comprising eighteen forwards and fourteen backs. Participants were tasked with completing self-reported questionnaires assessing their perception of the quantity and magnitude of impacts after thirteen consecutive matches. Objective data were concurrently collected using Micro-Electrical Mechanical Systems (MEMS) integrated with a GPS device (WIMU, Realtrack Systems, Almeria, Spain). Results: The statistical analysis revealed that 49.7% of players overestimated and 39.8% underestimated the number of impacts above 8G, with a mean team error of 0.017 impacts. Bayesian methods indicated a 95% high-density interval for the mean error between −0.46 and 0.45, with 95.8% within the region of practical equivalence, signifying negligible bias at the team level. Positional variations were notable, with Halfbacks overestimating and Fullbacks and Second Row underestimating impacts. The errors and number of impacts displayed a non-linear relationship, better described by an exponential model. Additionally, the study identified significant correlations between the number of impacts players estimated and the actual impacts received, further underlined by position-specific trends, suggesting that players' estimation abilities are influenced by the actual exposure to impacts and their playing positions. Conclusions: The utilization of a subjective impact questionnaire is a valid tool to assess rugby players level of impacts during a match. This approach proves particularly valuable in situations where technology is unavailable, showcasing its adaptability as a practical tool in diverse sporting contexts. [ABSTRACT FROM AUTHOR]

This study investigated the reliability and validity of linear sprint tests (LS) and curvilinear sprint tests (CS) using a GPS device (GPEXE lt). Twenty-one (21) student athletes completed a 40 m LS as well as a left and right CS. Maximum sprint speed (MSS) and split times at short (0–10 m), intermediate (0–20 m), and long (0–30 m) distances were determined using the GPEXE lt and single-beam timing gates (TG). Intrasession reliability and concurrent validity of GPEXE lt were assessed. The GPEXE lt consistently showed high reliability for MSS (ICC ≥ 0.95; CV ≤ 1.8%), despite underestimation compared to TG (bias = −2.57 to −0.95%; ICC ≥ 0.87). Acceptable reliability was observed for CS and LS intermediate and long distance split times (ICC ≥ 0.91; CV ≤ 2.1%), with lower reliability for short distances (ICC ≥ 0.70; CV ≤ 3.6%). The GPEXE lt split times for CS and LS showed good agreement with TG (ICC ≥ 0.66), but were overestimated at long distances (5.5–9.7%) and short distances (11.1–14.6%). Although the MSS was found to be the most reliable and valid variable to assess LS and CS speed performance with GPEXE lt, caution is needed due to limited validity. [ABSTRACT FROM AUTHOR]

The widespread use of GPS tracking devices has made them an indispensable solution in various sectors such as transportation, logistics, and security. However, the complexity of cyber attacks such as denial of service (DoS) attacks have made these devices vulnerable, thereby compromising the security of the data and devices. This has a significant impact on business and applicable legislation related to essential services. In this paper, we propose the integration of security mechanisms and algorithms into the Teltonika IoT GPS tracking device's firmware, including DoS protection to detect, prevent, and secure these devices against DoS cyber attacks. [ABSTRACT FROM AUTHOR]

Accounting for uncertainty is essential for precautionary approaches to managing seabird bycatch in commercial fisheries. However, there is no existing mechanism to explicitly quantify the uncertainty of seabird‐vessel interactions (i.e. co‐occurrence in space and time). Here we develop a time geographic method to measure the probability of individual birds encountering (co‐occurring within 30 km) and attending (within 5 km) individual fishing vessels. The approach involves creating voxel‐based probabilistic space–time prisms (PSTPs) to model the movements of individual birds and vessels, with trajectory data from bird‐borne GPS devices and vessel Automatic Identification Systems (AIS). We intersected these PSTPs to quantify the probability of interaction between bird‐vessel pairs over time and space. We demonstrate the approach with a case study of interactions of Endangered Toroa (Antipodean Albatross; Diomedea antipodensis antipodensis) with pelagic longline vessels in part of the South Pacific high seas. We found 15 vessels within 150 km and 3 h of two birds, yet interaction occurred with only two of those vessels. We visualised the probability of encounter and attendance over time and space and determined that interactions lasted several hours each (up to 6.2–14.1 h attendance, 20.8–26.1 h encounter for one bird‐vessel pair). Our time geographic approach adds to existing tools to quantify seabird bycatch risk by providing an explicit measure of uncertainty of seabird‐vessel interactions. We provide a flexible methodological pathway and R scripts, the application of which would allow managers to estimate interaction probability for multiple marine species and fisheries, including those with lower‐resolution positional datasets. [ABSTRACT FROM AUTHOR]

The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. "GPS cluster studies" are aimed at pinpointing and investigating identified clusters in the field. Activity clusters can be based on various parameters (e.g., distance between GPS locations and the number of locations needed to establish a cluster), which are closely related to the set research questions. Variation in methods across years within the same study may result in data collection biases. Therefore, a streamlined method to parametrize, generate interactive maps, and extract activity cluster data using a predefined approach will limit biases, and make field work and data management straightforward for field technicians. We developed the "ClusterApp" Shiny application in the R software to facilitate a step‐by‐step guide to execute cluster analyses and data management of cluster studies on any species using GPS data. We illustrate the use of the "ClusterApp" with two location datasets constructed by data collected on brown bears (Ursus arctos) and gray wolves (Canis lupus). [ABSTRACT FROM AUTHOR]

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html. [ABSTRACT FROM AUTHOR]

Background Neighborhood walkability may encourage greater out-of-home travel (ie, community mobility) to support independent functioning in later life. We examined associations between a novel walkability audit index and Global Positioning System (GPS)-derived community mobility in community-dwelling older adults. We compared associations with the validated Environmental Protection Agency (EPA) National Walkability Index and further examined moderation by clinical walking speed. Methods Participants were 146 older adults (Mean  = 77.0 ± 6.5 years, 68% women) at baseline of a randomized trial to improve walking speed. A walkability index (range: 0–5; eg, land-use mix, crosswalks, and so on) was created using Google Street View audits within 1/8-mile of the home. Participants carried a GPS device for 5–7 days to derive objective measures of community mobility (eg, time spent out of home, accumulated distance from home). Results Each 1 SD (~1.3-point) greater walkability audit score was associated with a median 2.16% more time spent out of home (95% confidence interval [95% CI]: 0.30–4.03, p  = .023), adjusting for individual demographics/health and neighborhood socioeconomic status. For slower walkers (4-m walking speed <1 m/s), each 1 SD greater audit score was also associated with a median 4.54 km greater accumulated distance from home (95% CI: 0.01–9.07, p (interaction) = .034). No significant associations were found for the EPA walkability index. Conclusions Walkability immediately outside the home was related to greater community mobility, especially for older adults with slower walking speeds. Results emphasize the need to consider the joint influence of local environment and individual functioning when addressing community mobility in older populations. [ABSTRACT FROM AUTHOR]

One of the ways for measuring environmental parameters is using GNSS (Global Navigation Satellite System) reflected signals from the Earth surface that are received by GNSS antennas. Environmental parameters include soil moisture, seasonal snow accumulation, ice thickness, vegetation cover and water level changes in dams, lakes and seas (tide). The focus of this research is on soil moisture. Reflection of GNSS signals from a surface is called multipath. When the goal is positioning, multipath is one of the most significant sources of error in GNSS observations. But, by analyzing those reflected signals, we will get useful information about the reflection surface. This technique is called GNSS interferometric reflectometry (GNSS-IR). By this definition, GNSS-IR can be considered as a remote sensing technique for continuous and local monitoring of environmental parameters which can be performed in various weather conditions such as rainy and cloudy conditions, as well as different lighting conditions such as day and night. Signal-to-Noise Ratio (SNR) is a measure of the strength of a signal relative to the background noise level. In GNSS, SNR is used to evaluate the quality of the received signal. It is calculated as the ratio of the power of the received signal to the power of the noise in the receiver's bandwidth. Some of the receivers can also record SNR data which includes SNR component of reflected signals. In GNSS-IR, changes in soil moisture result in changes in the SNR component of the reflected signals. Specifically, as the soil moisture content increases, the dielectric constant of the soil increases, which causes the reflected signals to have higher amplitudes and higher SNR. Conversely, as the soil moisture content decreases, the reflected signals have lower amplitudes and lower SNR. Therefore, analyzing the SNR of the reflected signals can provide useful information about the soil moisture content. In addition to soil moisture, SNR can also be affected by other factors such as atmospheric conditions and receiver noise. Therefore, it is important to carefully analyze and process the SNR data to accurately estimate the soil moisture content. The soil moisture algorithm to be used in this study, is currently implemented at stations in the EarthScope PBO H2O network with the greatest variations in vegetation. Among the sites in the PBO H2O network, data from P038 in the New Mexico region is used. This site is located at in a flat area in a ecosystem characterized as grass land. SNR data from the new L2C signals are used by this site because the quality of the data are higher than those either the legacy L1 or L2P signals. The frequency of the L2C signal corresponds to a maximum penetration depth of 5 cm. The multipath signals are used to estimate soil moisture changes over a four-years period from 2017 to 2020. The calculations were done in four main steps. In the first step, appropriate satellite tracks with elevation angle between 5 to 30 degrees were selected and SNR data were extracted from RINEX files. In the second step the initial reflector height is estimated for each track and then the phase is obtained for each satellite track on each day. In the third step, SNR metrics are calculated, and finally, vegetation cover effects are removed and the result is converted to volumetric water content. According to the estimations, the volumetric water content in 2017 was 8.88%, which increased to 11.74% in 2018, then slightly decreased to 10.88% in 2019 and finally increased to 12.49% in 2020. In the fourth step, the effectiveness of the LSTM neural network model in predicting the time series of volumetric soil moisture obtained from GNSS-IR signals is investigated. The LSTM neural network can maintain its content over a long period of time and essentially remember previous information. This prediction will help farmers to prepare their irrigation schedules more efficiently. For this purpose, it is suggested to use cheap GPS receivers in agricultural lands in rural areas. The model is trained using 80% of station observations. By updating the network status with observed values instead of predicted values, the root mean square error decreased from 0.09 to 0.04, and the predictions became more accurate. Handling the location and type of receivers located in the Iranian Permanent GPS Network for Geodynamics (IPGN) and making the necessary settings in order to determine environmental parameters are suggested as byproducts for IPGN. Investigations have shown that performing GNSS observations produces more homogeneous reflective effects. Therefore, in order to increase the accuracy and quality of the results, it is suggested to use GNSS-IR instead of just GPS-IR. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the influence of environmental factors (i.e., terrain and surface coverage) around the GPS receivers, the snow depth retrieval results obtained by the existing global positioning system interferometric reflection (GPS-IR) method show significant variability. The resulting loss of reliability and accuracy limits the broad application of this technology. Therefore, this paper proposes a dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR to fully leverage multi-source satellite observation data for automatic and high-precision snow depth retrieval. The model employs Dynamic Time Warping distance measurement combined with the K-Medoids clustering algorithm to categorize frequency sequences obtained from various satellite trajectories, facilitating effective integration of multi-constellation data and acquisition of optimal datasets. Additionally, Long Short-Term Memory networks are integrated to capture and process the long-term dependencies in snow depth data, enhancing the model's adaptability in handling time-series data. Validated against SNOTEL measured data and standard machine learning algorithms (such as BP Neural Networks, RBF, and SVM), the model's retrieval capability is confirmed. For P351 and AB39 sites, the correlation coefficients for L1 band data retrieval were both 0.996, with RMSEs of 0.051 and 0.018 m, respectively. The experiment results show that the proposed model demonstrates superior precision and robustness in snow depth retrieval compared to the previous method. Then, we analyze the accuracy loss caused by sudden snowfall events. The proposed model and methodology offer new insights into the in-depth study of snow depth monitoring. [ABSTRACT FROM AUTHOR]

To investigate solar activity dependence of the coupling between medium-scale traveling ionosphere disturbance (MSTID) and sporadic E (Es) layer, we analyzed the total electron content (TEC) obtained from a Japanese global positioning system (GPS) receivers and ionosonde at Kokubunji (35.7° N, 139.5° E) in Japan during the summer period of May–August from 1998 to 2019. To obtain perturbation TEC caused by MSTIDs, the detrended TEC is calculated by subtracting 1-h moving averages from the measured TEC for each pair of GPS satellite and receiver. The detrended TEC data are mapped on to the geographical coordinates to make detrended 2-D maps with spatial resolution of 0.15° × 0.15° in longitude and latitude. The MSTID activity is defined as a ratio of the standard deviation to the background TEC over Kokubunji in Japan. Day-to-day variations of the MSTID activity during summer nights was compared to Es layer parameters [critical frequency ( f o E s ) and Δ f o - b ≡ f o E s - f b E , where f b E s is blanketing frequency] derived from ionosonde station at Kokubunji. We have found that the correlation coefficient between the MSTID activity and f o E s ( Δ f o - b ) between 1998 and 2019 is 0.5 3 (0.46) on average, suggesting that there is an electrodynamical coupling between the Es layer and F region could generate nighttime MSTIDs. We also have found that the correlation coefficient positively correlates with solar activity. This finding indicates that in the high solar activity conditions, when the growth rate of Perkins instability is relatively low, generation of the polarization electric fields in the Es layer could play a more important role to grow MSTIDs than in the low solar activity conditions. [ABSTRACT FROM AUTHOR]

The study of large carnivores in semi-arid ecosystems presents inherent challenges due to their low densities, extensive home ranges, and elusive nature. We explore the potential for the synthesis of traditional knowledge (i.e. art of tracking) and modern technology to address challenges in conservation and wildlife research in these challenging environments. Our research focuses on the African lion (Panthera leo) in the Central Kalahari region of Botswana as a model system to demonstrate the potential of this integrative approach. Combining GPS tracking and traditional San trackers’ expertise, we present two case studies: (1) the individual identification of lions via a combination of tracking and footprint analysis and (2) the monitoring of territorial behavior through a combination of GPS technology (i.e. GPS collars and handheld GPS devices) and non-invasive tracking. These approaches enhance our understanding of carnivore ecology as well as support conservation efforts by offering a non-invasive, cost-effective, and highly accurate means of monitoring populations. Our findings underscore the value of merging traditional tracking skills with contemporary analytical and technological developments to offer new insights into the ecology of carnivores in challenging environments. This approach not only improves data collection accuracy and efficiency but also fosters a deeper understanding of wildlife, ensuring the conservation and sustainable management of these species. Our work advocates for the inclusion of indigenous knowledge in conservation science, highlighting its relevance and applicability across various disciplines, thereby broadening the methodologies used to study wildlife, monitor populations, and inform conservation strategies. [ABSTRACT FROM AUTHOR]

Understanding how animals move and use space within an environment is vital for the development and implementation of effective management actions. Within airfield environments, animal movement can present a substantial risk to aircraft, resulting in wildlife‐aircraft collisions (strikes) if animals enter into the manoeuvring areas of the airfield, namely the runways, taxiways and areas that connect the two (hereafter collectively referred to as 'tarmacked areas'). However, reliable ecological data to inform management decisions can be difficult to obtain in such environments, due to access restrictions. Here, we present the first GPS data describing the movement ecology and spatial use of mammals on an airfield – Irish hare (Lepus timidus hibernicus), at Dublin International Airport – through the deployment of five GPS collars. A total of 4571 tarmacked area interactions were recorded between December 2021 and August 2022, with all five hares engaging with tarmacked areas. Between December and August, the highest number of interactions were recorded for the month of April (n = 1073), followed by March (n = 703). There was a mean of 4.3 (range: 0–65) interactions with tarmacked areas, per hare, per day throughout the study period. Hares most frequently engaged with tarmacked areas between 05:00 and 07:59, with some seasonal variation. The greatest cumulative distance moved per month was observed in May (505 km) and April (503 km). We identified that the average home range size of collared hares was 2.8 km2 (±SD 0.1 km2), based on 95% Kernel Utilisation Distribution. Furthermore, we demonstrate that the hares incorporate tarmacked area habitat types into their home ranges with up to 13% of one individual's movements incorporating these areas. Our study demonstrates the suitability of GPS tracking devices for studying the movement ecology of high‐risk mammal species at airfields in order to inform airside management practices. [ABSTRACT FROM AUTHOR]

Commonly, Global Positioning System (GPS)-based precise orbit determination (POD) of a low Earth orbiting (LEO) satellite is conducted by introducing fixed GPS orbits and clock corrections that were derived in a previous independent network solution using ground-based GPS receivers only. There have been a number of studies showing that the integration of LEO GPS observations can be advantageous in global network solutions, particularly when it comes to estimating geodetic parameters, such as the Earth's center-of-mass. There has been an increase in GPS data available from LEO CubeSats over the past few years, such as from the Spire satellites, which are equipped with dual-frequency GPS receivers. The goal of the present study is to determine how GPS observations collected by specific Spire satellites affect global network solutions when combined with GPS observations from ground stations of the International GNSS Service. To obtain a combined GPS-LEO solution, GPS observations collected from receivers of selected Spire satellites and additional scientific LEOs are used to obtain combined GPS-LEO solutions by performing one joint least-squares adjustment process. Using this method, LEO satellite orbit parameters are determined along with GPS orbit parameters and geodetic parameters, namely GNSS station coordinates, Earth rotation parameters (ERPs), and the Earth's center-of-mass. The influence of the Spire LEOs is assessed by analyzing different solutions that use GPS observations from different Spire and LEO satellites. Besides comparing the different global network solutions with one another, a comparison to a solution using only terrestrial GPS data is performed as well. As part of the quality assessment of the GPS-LEO solution, quality characteristics are analyzed, which are the geodetic parameters, orbit overlaps at arc boundaries, and formal errors determined for the estimated parameters. The results show that the estimation of the Earth's center-of-mass coordinates benefits from including GPS-LEO observations. The solution is more improved by integrating GPS-LEO observations from scientific LEOs than from GPS-Spire-included solutions. The analysis reveals that GPS orbits are improved using the LEO-integrated approach, while orbit misclosures of LEO and Spire trajectories currently indicate a decrease in LEO orbit precision due to LEO orbit modeling deficiencies. [ABSTRACT FROM AUTHOR]

Topographic measurements made with the drone is an alternative way for creating 3D models, point clouds, etc., but they should be considered correct placement of ground control points to complete the required works precisely. To meet these needs to have a sub millimeter precision in this publication we will treat such as positioning, verification and comparison precisions achieved by attaching ground control points to the point cloud. These ground control points are measured with two different devices, namely the robotic total station and two dual-frequency GPS receivers used in base & rover mode. Errors made on ground control points can influence the whole cloud of points and we will demonstrate the extent to which this occurs. [ABSTRACT FROM AUTHOR]

The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data. [ABSTRACT FROM AUTHOR]

The southern region of Kuhdasht is situated in the western part of Luristan Province, on the periphery of the Central Zagros in Iran. It is the second largest plain in the province, following the Sialakhor Plain. This area is bordered by Khuzestan to the south, parts of Ilam to the west, and Kermanshah Province to the north. Through archaeological investigations and the analysis of cultural artifacts and settlement patterns, we can expand our understanding of the internal and regional connections within the area, as well as the development of local cultures during the Bronze Age. The methods utilized for this research involved a combination of library research, field investigations, and the interpretation of surface findings. ArcGIS was used to analyze the settlement patterns of the plain during the Bronze Age. Locational information of settlements was recorded using GPS devices, maps were geo-referenced, and necessary outputs were obtained to estimate population distribution and biological density. The archaeological investigations revealed human presence from prehistoric to historical and Islamic eras. During the Old Bronze Age, three sites were documented in the plain, while the Middle Bronze Age consisted of three sites and one cemetery. Furthermore, the Late Bronze Age included eleven sites and one cemetery. Factors such as distance to water sources, appropriate sediments, proximity to communication routes, slope, and elevation of the plain, and the presence of fertile pastures have played a crucial role in the establishment of settlements in the South Kuhdasht Plain. The small size of the sites suggests that the inhabitants of the plain were likely cattle herders and seasonal farmers during the period under study. [ABSTRACT FROM AUTHOR]

• The effect of winter anomaly caused hemispherical asymmetry IEC and PEC. • The accuracy of IRI-2016 model was higher in the high- and mid-latitude. • IRI-2016 model performed better during solar minimum than during solar maximum. • IRI-2016 model performed better at night than during the day. This study assesses the accuracy of the International Reference Ionosphere IRI-2016 model prediction at the equatorial-, low-, mid-, and high-latitude ionosphere along 30–45oE meridian during minimum (2009) and maximum (2013) phases of solar cycle 24. We used raw TEC derived from GPS observation data from GPS receivers within ∼±70o latitudinal band, along the African/European longitudinal sector. We compared the GNSS-TEC data after the Plasmaspheric Electron Content (PEC) has been excluded with those derived from the two components of the IRI-2016 model. The percentage contributions of PEC to GNSS-TEC was minimum in June solstice, and maximum in December solstice with the highest values at the high-latitude regions, but with a clear hemispherical asymmetry; manifesting the winter anomaly effect. Higher TEC values were recorded in the equatorial/low-latitude regions than in the high- and mid-latitude regions. Overall, in both hemispheres, the accuracy of prediction of the IRI-2016 model was higher in the high- and mid-latitude regions than in the equatorial/low-latitude regions. The accuracy of the model was higher during solar minimum than during solar maximum. Both options of IRI-2016 performed better at nighttime than during daytime. IRI-NeQuick option generally performed better than the IRI-Corr option. TEC values were higher in the southern hemisphere than in the northern hemisphere. [ABSTRACT FROM AUTHOR]

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices. [ABSTRACT FROM AUTHOR]

Location and navigation information has become an important part of our lives. GPS data is vital in many areas from transportation to economy, from energy to security. The alteration of this information can have deleterious downstream effects that cannot be reversed. Today, it is possible to attack GPS data with accessible hardware and open source software. In this paper, the signal obtained with the help of a transceiver that samples the high frequency RF signals down to the intermediate frequency was compared with the RMS value by windowing over PSD. Then this data was statistically analyzed with the NIST runs test. In order to realize GPS signal spoofing in a short time that after GPS receiver lock the spoofing signal, field programmable gates connected to the transceiver were used. The results obtained varied according to the dimensions of the windowing process, and it was understood that the window size was in a correlation with the detection of deception in a short time. The suggested method's key benefit is its simplicity, which makes it adaptable to various GNSS systems. [ABSTRACT FROM AUTHOR]

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO's Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone's propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem. [ABSTRACT FROM AUTHOR]

This study evaluated the applicability of IRI-2016 model in predicting GPS TEC using the monthly means of the five (5) quiet days for equinoxes and solstices months. GPS-derived TEC data were obtained from the IGS network of ground based dual frequency GPS receivers from three stations [(KYN3 0.53° S, 38.53° E; Geom. Lat. 3.91.63° S), (MBAR 0.60° S, 30.74° E; Geom. Lat. 2.76° S) and HOID 1.45° S, 31.34° E; Geom. Lat. 3.71° S]. All the three options for topside Ne of IRI-2016 model and ABT-2009 for bottomside thickness have been used to compute the IRI TEC. The results were compared with the GPS TEC measurements. Correlation Coefficients between the two sets of data, the Root-Mean Square Errors of the IRI-TEC from the GPS-TEC, and the percentage RMSE of the IRI-TEC from the GPS-TEC have been computed. In general, the IRI-2016 model underestimated GPS-TEC during the nighttime, whereas the model overestimated GPS-TEC values during the daytime. At most of the stations and during all seasons where data were available, correlation coefficient was above 0.9, which is quite strong. The variation of O/N2 ratio may potentially be the cause of the IRI TEC deviation from the GPS TEC. This variation arises from lower thermosphere plasma drift that moves upward. [ABSTRACT FROM AUTHOR]

Ionospheric density structures at low latitudes range in size from thousands of kilometers down to a few meters. Radio frequency (RF) signals, such as those from global navigation satellite systems, that propagate through irregularities suffer from rapid fluctuations in phase and intensity, known as scintillations. In this study, we use the high‐sample‐rate measurements of the Swarm Echo (CASSIOPE/e‐POP) satellite's GPS Occultation (GAP‐O) receiver taken after its antenna was re‐oriented to vertical‐pointing, simultaneously with e‐POP Ion Mass Spectrometer surface current observations as a proxy for plasma density, to obtain the spectral characteristics of GPS signal intensity and in‐situ irregularities at altitudes from 350 to 1,280 km. We show that the power spectra of both measurements can generally be characterized by a power law. In the case of density irregularities, the spectral index with the highest occurrence rate is around 1.7, which is consistent with previous studies. Also, all the power spectra of GPS signal intensity in this study show a single spectral index near 2. Moreover, roll‐off frequencies estimated in this work range from 0.4 to 2.5 Hz, which is significantly higher than Fresnel frequencies calculated from ground GPS receivers at low latitudes (between 0.2 and 0.45 Hz). Part of this increase is due to the 8 km/s orbital velocity of Swarm Echo near perigee. Another key difference is that variations in the GPS signals in this study are dominated by the topside ionosphere, whereas GPS signals received from ground are affected mostly by the relatively dense F‐region plasma in the 250–350 km altitudinal range. Plain Language Summary: Variations in ionospheric electron density, called irregularities, cause fluctuations in radio frequency signals traversing the ionosphere. The Swarm Echo satellite carries a high‐sample‐rate GPS receiver with an antenna normally pointing in the horizontal direction. In this study, the satellite was reoriented while it was passing the low latitude region so that the receiver antenna would point vertically in order to probe irregularities above the satellite at high resolution. Our spectral analysis of GPS signal intensity and plasma density variations measured in situ (on board the spacecraft) revealed power law behavior in both measurements, a well‐established feature of ionospheric turbulence. More specifically, our results show that the spectral index (which is the negative of the power law slope) of in‐situ density irregularities and GPS signal intensities are 1.7 and 2, respectively. The roll‐off frequencies in the GPS signal intensity results are considerably higher than ground‐based estimates. This difference is attributable to the satellite's high velocity (8 km/s) and the fact that, comparing to the ground estimates which are dominated by the dense plasma density between 250 and 350 km altitude, Swarm Echo observations are mostly affected by the upper reaches of the ionosphere. Key Points: The most frequently occurring spectral index of the in‐situ irregularity power spectra is 1.7The power spectra of GPS signal intensity show a single spectral index near 2, which is smaller than ground‐based weak scintillation valuesThe roll‐off frequencies in the signal intensity power spectra are 0.4–2.5 Hz, significantly exceeding ground‐based Fresnel frequency values [ABSTRACT FROM AUTHOR]

Haiyang-2A (HY-2A) is the first marine dynamic environment satellite established by China, which has made significant contributions to the marine scientific research field. It carries the satellite-based GPS receiver named HY2, which was independently developed by China. It is an experimental satellite-borne GPS receiver for low earth orbit satellites, and during its operational period in orbit, the satellite-borne GPS data are not made accessible to the public. Therefore, this paper assesses the quality of HY-2A satellite-borne GPS data based on indicators such as satellite visibility, multipath effect, and ionospheric delay. The results indicate that the data acquired by the HY2 receiver are of high quality. The precise orbit determination (POD) uses the reduced-dynamic (RD) method. The adjustment effects of the pseudo-stochastic pulse time interval and a priori sigma on POD are analyzed, and the antenna phase center variation (PCV) is estimated using the direct method and residual method. Furthermore, this paper investigates the impact of PCV models with different resolutions (10° × 10° and 5° × 5°) on satellite orbit determination. To evaluate the orbit precision, three methods are used for validation, including carrier phase residual analysis, external precise science orbit (PSO) validation, and SLR three-dimensional (3D) validation, respectively. The results indicate that the highest orbit precision is achieved when the pseudo-stochastic pulse time interval is configured to 15 min, with the a priori sigma of 1 × 10−8 m/s2. The orbit carrier phase residuals reach the millimeter level. The 10° × 10°PCV model was estimated using the direct method and residual method, respectively; the root mean square of the external orbit validation for both methods show a millimeter-level improvement. The results obtained from the direct method and residual method are comparable. The resolution is increased from 10° to 5°, and the improvement in orbital precision is relatively small. The results obtained from the SLR 3D validation are consistent with those from the external PSO validation. The experimental results contribute valuable information for the POD of the HY2 series satellites. [ABSTRACT FROM AUTHOR]

Understanding the amount of space required by animals to fulfill their biological needs is essential for comprehending their behavior, their ecological role within their community, and for effective conservation planning and resource management. The space-use patterns of habituated primates often are studied by using handheld GPS devices, which provide detailed movement information that can link patterns of ranging and space-use to the behavioral decisions that generate these patterns. However, these data may not accurately represent an animal's total movements, posing challenges when the desired inference is at the home range scale. To address this problem, we used a 13-year dataset from 11 groups of white-faced capuchins (Cebus capucinus imitator) to examine the impact of sampling elements, such as sample size, regularity, and temporal coverage, on home range estimation accuracy. We found that accurate home range estimation is feasible with relatively small absolute sample sizes and irregular sampling, as long as the data are collected over extended time periods. Also, concentrated sampling can lead to bias and overconfidence due to uncaptured variations in space use and underlying movement behaviors. Sampling protocols relying on handheld GPS for home range estimation are improved by maximizing independent location data distributed across time periods much longer than the target species' home range crossing timescale. [ABSTRACT FROM AUTHOR]

The GPS Aided Geo Augmented Navigation (GAGAN) system provides the navigational services for single-frequency GNSS user via broadcasting the differential corrections with GEO stationary satellites. The significant differential correction contribution comes from ionospheric time delays and is necessary to be determined precisely. Dual-frequency GPS receivers measure the ionospheric time delays using GPS code and carrier phase measurements. The determination of absolute ionospheric Total Electron Content (TEC) requires the calibration of GPS satellites and receiver hardware biases due to different frequency-dependent signals (L1 and L2) due to environmental changes (Temperature and Humidity). In this paper, A receiver-based Differential Code Biases (DCB) algorithm is implemented to derive a joint estimation of TEC and RDCB parameters using the weighted Least Square (WLS) method. The daily averaged DCBs data for 26 GPS receivers are obtained for 3 years (2014–2016) from 26 GPS reeivers over Indian region. The receiver DCB algorithmis validated with the Fitted Receiver Biases (FRB) method. The correlation (R) between VTEC and RDCB is conducted to investigate the RDCB stability. The results would be useful for the accurate determination of ionospheric differential corrections to GAGAN users. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites. [ABSTRACT FROM AUTHOR]

Aerobic capacity plays a crucial role in football performance, making it a focal point in training processes. Small-sided games (SSGs) are widely used in football training, but the relationship between aerobic capacity and running performance during SSGs remains unclear. The aim of this study was to investigate possible correlations between maximum oxygen uptake (VO2max) and running performance in youth football players in SSGs (4:4, 3:3, 2:2, 1:1) with three different pitch sizes per player (150, 100, 75 m2/player). Sixteen male U15 football players participated in the study. Players underwent the Yo-Yo intermittent recovery test level 1, and their VO2max was estimated based on their performance. Subsequently, players participated in SSGs wearing GPS devices to measure internal and external load. Pearson or Spearman correlation was applied for statistical analysis depending on the normal distribution of the data. The results reveal that, for 4:4 and 3:3 relationships, larger pitches led to a greater impact of aerobic capacity (total distance (TD): 4:4, 150 m2/pl, r = 0.715, p = 0.002; 100 m2/pl, r = 0.656, p = 0.006; 75 m2/pl, r = 0.586, p = 0.017). In the 2:2 relationship, the opposite was observed, with more correlations appearing on smaller pitches (TD: 2:2, 100 m2/pl, r = 0.581, p = 0.018; 75 m2/pl, r = 0.747, p < 0.001). In the 1:1 relationship, correlations with VO2max, total distance, and speed were observed only on the larger pitch. In conclusion, the aerobic capacity of young football players can influence running performance indicators in SSGs. Therefore, aerobic capacity could serve as a criterion for team composition, making SSGs more competitive. Additionally, the variation in correlations in the 2:2 relationship and their limited presence in the 1:1 relationship may be attributed to technical–tactical factors, such as increased ball contacts and one-on-one situations typically occurring in smaller setups. [ABSTRACT FROM AUTHOR]

Autonomous agricultural vehicles rely on accurate real-time positioning information to perform precise farming operations, including seeding, fertilizing and harvesting. Although many positioning solutions exist on the market, their cost is out of many farmers' budgets. Relative positioning can be used for accurate positioning purposes in agriculture applications. Despite the fact that several research attempts have addressed the issue of relative positioning, no agriculture-specific study appears to exist in the literature at the time of writing. In this paper, the opportunity of using real-time GPS-based relative positioning for agriculture applications instead of the typical absolute positioning solutions has been investigated. To this end, we propose to implement and evaluate different relative positioning approaches, namely absolute position differencing, pseudorange single differencing as well as pseudorange double differencing (PDD). Real-world experiments, including static and dynamic scenarios, have been conducted using low-cost single-frequency GPS receivers in an open sky environment and an agricultural field. The obtained results show the superiority of the PDD approach with about 76% of estimation errors less than 1 m in the open sky environment and more than 97% of estimation errors less than 3 m in the agricultural field, representing a considerable accuracy enhancement compared to GPS standalone positioning. [ABSTRACT FROM AUTHOR]

The carrier phase received at the receivers of the Global Positioning System (GPS) links is used to detect navigation data and to precisely determine the position, speed and time corresponding to the user's equipment. Therefore, subsystems for carrier phase tracking are crucial parts in all GPS receivers. When the propagation conditions are favorable, the method frequently used for phase tracking is based on Digital Phase-Locked Loop (DPLL)) and implemented through the discrete Costas loop operating under the modulated L1 carrier, in the case of a GPS receiver. This technique is quite simple, well known and very suitable for implementation in low-cost receivers. In this article, we revisit the traditional Costas loop design and point out some issues that affect the phase tracking performance of this loop. In order to overcome these problems, we propose some modifications to the traditional Costas loop. The resulting architecture presents better performance and complexity equivalent to the original loop. Another contribution of this work is the mathematical analysis to evaluate the performance of the new architecture when operating on an Additive White Gaussian Noise (AWGN) channel. Various results from computational simulations carried out with the two architectures, in different operating scenarios, including AWGN, dynamic stress and ionospheric scintillation are presented and discussed. We conclude that the new architecture outperforms the traditional Costas loop in terms of the variance of the estimated phase error, root mean squared error of the estimated phase and robustness to cycle-slip and loss of lock. [ABSTRACT FROM AUTHOR]

Semi-captive elephants have been extensively used in the tourism sector in Myanmar. However, these elephants have not been provided with the range of habitats they require, resulting in increased Human-Elephant Conflict (HEC) within the local community. In addition, there are very limited studies on the assessment of their home ranges, habitat preferences, and preferred food plants. We aimed to address three research questions: (1) What is the size of semi-captive elephants' daily home range? (2) In which habitat types do they prefer to stay? (3) What are their preferred food plants? We fitted GPS devices on the six semi-captive elephants 3–4 times a month in three study sites over seven months. The sample collection of food plants was carried out at each site. The results indicated that the mean average size of the daily home range for semi-captive elephants was 0.90 km2 (95% KDE), with a range of 0.38 to 2.38 km2. We found that the habitat provided for semi-captive elephants was not large enough to be inhabited. The analysis of habitat selection showed that elephants prefer to use secondary forests. The Poaceae family was found to be the main food taxon. We suggest that more areas be provided around elephant camps and that the currently permitted habitats be protected from land use that overlaps with other forestry operations. [ABSTRACT FROM AUTHOR]

Kalman filtering (KF)-based tracking has been commonly employed in global navigation satellite system (GNSS) receivers to achieve robust tracking. However, under more serious conditions, such as severe strength attenuation and abrupt dynamic coexisting environments, it is difficult for KF-based tracking to keep tracking well due to the fixed noise statistics. To further enhance the carrier tracking performance, this paper proposes an adaptive KF carrier tracking method for resisting signal strength fading and high dynamic environments. The proposed method introduces the adaptive factor to adjust the process noise covariance to accommodate the noise statistics in actual variable situations. Moreover, we apply the chi-square hypothesis test to detect system stability. The adaptive factor is only applied when the system is not stable, which can enhance computational efficiency. The proposed method is conducted in the GPS L1 software receivers. According to the results, the proposed algorithm can improve the robustness in tracking performance compared with other tracking methods under signal serious fading and high dynamic conditions. Using the proposed method, GNSS receivers' navigation performance can be improved under complex conditions. [ABSTRACT FROM AUTHOR]

• Performance evaluation of ionospheric models over equatorial Indian region done. • An extensive year- long evaluation of TEC is performed. • TEC obtained from TIE-GCM simulations and IRI-2020 is compared with GPS TEC. An extensive year-long evaluation of Total Electron Content (TEC) obtained from NCAR Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM) simulations along with TEC obtained from International Reference Ionosphere (IRI-2020) is carried out with respect to observations made by a GPS TEC receiver over the dip equatorial location of Thiruvananthapuram (8.5° N, 77° E, 0.5° magnetic), in the Indian region during solar minimum period of 2005–2006. The TIE-GCM model and IRI model were run for the period of 2005 (November December) and till October 2006 for each day, and the hourly values of TEC were compared to GPS TEC observations. Overall the temporal and seasonal characteristics of TEC over Thiruvananthapuram were simulated reasonably well by the TIE-GCM model and IRI model. The skill scores of normalized Root Mean Square Error (nRMSE), Prediction Efficiency (P.E), and Ratio between maximum and minimum values were estimated for both models to evaluate their performance. The mean percentage difference between the measured and the simulated TEC was found to be around −25% for IRI, indicating underestimation of the observations, and between −40% and −50% for TIE-GCM again indicating underestimation. A comprehensive analysis revealed that there is good conformity between the modeled and measured values for the whole observation period, with a correlation coefficient R-value of 0.8 and 0.9 for the TIE-GCM and IRI models respectively. This study brings out the competence of the two models in simulating the temporal and seasonal pattern of variability of ionospheric TEC over the equatorial Indian region. [ABSTRACT FROM AUTHOR]

Accurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data for a single‐station study over Ethiopia. The models employed include gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial 6 months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the ML models used in this study demonstrated promising potential for accurate single‐station VTEC prediction over Ethiopia. Plain Language Summary: Studying the ionosphere is crucial as it significantly impacts satellite navigation and communication systems. However, a major challenge in ionospheric studies is the unavailability of observational total electron content data in some regions. To tackle this problem, researchers have employed machine learning (ML) modeling as a solution. In this study, we used ML algorithms such as gradient boosting machine, XGBoost, and LightGBM, as well as their stacked integration along with the linear regression algorithm, to model the ionospheric vertical total electron content over a single GPS receiver station in the low‐latitude ionospheric region. The methods employed are highly efficient in terms of computational resources. Key Points: Three gradient‐boosting decision tree‐based algorithms and their stacked combination with the linear regression algorithm are appliedThe predictions of the machine learning (ML) models are strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96The ML models utilized in this work significantly outperformed the International Reference Ionosphere (IRI 2020) global model [ABSTRACT FROM AUTHOR]

Beach handball is a fully developed sporting discipline on all five continents which has attracted the attention of researchers in the last decade, resulting in a proliferation of different studies focusing on players but not on referees. The main objective of this cross-sectional research was to determine the physical demands on elite male beach handball referees in four different competitions: U18 male; U18 female; senior male; and senior female. Twelve elite federated male referees (age: 30.86 ± 8 years; body height: 175.72 ± 4.51 cm; body weight: 80.18 ± 17.99 kg; fat percentage: 20.1 ± 4.41%; national or international experience) belonging to the Technical Committee of the Royal Spanish Handball Federation were recruited for this the study. The physical demands required of referees in official matches were measured by installing a GPS device. The sampling frequency used to record their speed and distance was 15 Hz. A triaxial accelerometer (100 Hz) was used to determine their acceleration. An analysis of variance (ANOVA) between competitions with post hoc comparisons using the Bonferroni adjustment was used to compare among categories. A higher distance covered in zone 1 and speeds of 0 to 6 km-h−1 were recorded. Most accelerations and decelerations occurred in zones 0 and 1 (zone 0: 0 to 1 m·s−2; zone 1: 1 to 2 m·s−2). The lack of differences (p > 0.05) between most analysed variables suggest quite similar physical demands of the four analysed competitions. These results provide relevant information to design optimal training plans oriented to the real physical demands on referees in an official competition. [ABSTRACT FROM AUTHOR]