Altitude Information Research Articles

A bio-inspired looming detection for stable landing in unmanned aerial vehicles**This research was funded by the National Natural Science Foundation ofchina, grant number 62206065, and the Bagui Scholar Program of Guangxi.

Flying insects, such as flies and bees, have evolved the capability to rely solely on visual cues for smooth and secure landings on various surfaces. In the process of carrying out tasks, micro unmanned aerial vehicles (UAVs) may encounter various emergencies, and it is necessary to land safely in complex and unpredictable ground environments, especially when altitude information is not accurately obtained, which undoubtedly poses a significant challenge. Our study draws on the remarkable response mechanism of the Lobula Giant Movement Detector to looming scenarios to develop a novel UAV landing strategy. The proposed strategy does not require distance estimation, making it particularly suitable for payload-constrained micro aerial vehicles. Through a series of experiments, this strategy has proven to effectively achieve stable and high-performance landings in unknown and complex environments using only a monocular camera. Furthermore, a novel mechanism to trigger the final landing phase has been introduced, further ensuring the safe and stable touchdown of the drone.

Contrail altitude estimation using GOES-16 ABI data and deep learning

Abstract. The climate impact of persistent aircraft contrails is currently estimated to be comparable to that due to aviation-emitted CO2. A potential near-term and low-cost mitigation option is contrail avoidance, which involves rerouting aircraft around ice-supersaturated regions, preventing the formation of persistent contrails. Current forecasting methods for these regions of ice supersaturation have been found to be inaccurate when compared to in situ measurements. Further assessment and improvements of the quality of these predictions can be realized by comparison with observations of persistent contrails, such as those found in satellite imagery. In order to further enable comparison between these observations and contrail predictions, we develop a deep learning algorithm to estimate contrail altitudes based on GOES-16 Advanced Baseline Imager (ABI) infrared imagery. This algorithm is trained using a dataset of 3267 contrails found within Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data and achieves a root mean square error (RMSE) of 570 m. The altitude estimation algorithm outputs probability distributions for the contrail top altitude in order to represent predictive uncertainty. The 95 % confidence intervals constructed using these distributions, which are shown to contain approximately 95 % of the contrail data points, are found to be 2.2 km thick on average. These intervals are found to be 34.1 % smaller than the 95 % confidence intervals constructed using flight altitude information alone, which are 3.3 km thick on average. Furthermore, we show that the contrail altitude estimates are consistent in time and, in combination with contrail detections, can be used to observe the persistence and three-dimensional (3D) evolution of contrail-forming regions from satellite images alone.

Altitude and Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) in China: A Population-Based Study.

Yufei Wang, Qiong Meng, Jin Zhang, Bing Guo, Nanyan Li, Qian Deng, Julinling Hu, Deji Quzong, Han Guan, Danzhen Wangjiu, Hui Yu, Zhifeng Li, and Junmin Zhou. Altitude and metabolic dysfunction-associated fatty liver disease (MAFLD) in China: a population-based study. High Alt Med Biol. 00:00-00, 2024. Objectives: The epidemiological evidence for the relationship between altitude and metabolic dysfunction-associated fatty liver disease (MAFLD) is scarce. This study aims to examine the altitude-MAFLD relationship and explore the potential mediators explaining the relationship. Methods: Data were derived from the China Multi-Ethnic Cohort. The participants' altitude information was extracted from their residential addresses. MAFLD was diagnosed based on radiographically confirmed hepatic steatosis and any one of the following three items: overweight/obese status, diabetes mellitus, or metabolic dysregulation. We performed multivariable logistic regression and mediation analyses to assess the altitude-MAFLD associations and potential mediators, respectively. In the mediation analysis, mediation proportion is an estimate of the extent to which the total effect (altitude-MAFLD association) is accounted for by the pathway through the mediators. Results: In total, 87,679 participants (female: 60.7%, mean age: 51.36 years) were included. The odds ratio of MAFLD was 1.61 (95% confidence interval [CI]: 1.52-1.71) between high and low altitudes, 1.52 (95% CI: 1.43-1.62) between high and middle altitudes, and 1.06 (95% CI: 1.01-1.10) between middle and low altitudes. Of the total estimated effect between high and low altitude, physical activity and vegetable intake accounted for 15.7% (95% CI: 12.8-19.1) and 3.8% (95% CI: 1.2-6.6), respectively. Of the total estimated effect between high and middle altitude, physical activity and vegetable intake accounted for 31.4% (95% CI: 26.2-34.8) and 2.3% (95% CI: 0.6-3.8), respectively. Of the total estimated effect between middle and low altitude, vegetable intake accounted for 11.8% (95% CI: 3.2-61.5). Conclusion: Higher altitude was associated with increased odds of MAFLD, and physical activity and vegetable intake mediated such association. Multifaceted efforts should be taken in public health to promote healthy lifestyles among higher altitude residents.

An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective

PurposeUnlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.Design/methodology/approachThe paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.FindingsSimulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.Originality/valueConventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

Indoor altitude estimation assisted by inertial compensation and online floor modeling

Abstract Indoor pedestrian altitude information plays a key role in such applications as emergency relief and military reconnaissance. However, height errors of inertial positioning grow without boundary because of the altitude channel divergence of the Strap-down Inertial Navigation System (SINS). This article proposes a new vertical location method based on the foot-mounted Inertial Measurement Unit (IMU) and online floor modeling. First, the number of stairs at each step is calculated after the height of each step is corrected by the error compensation model. Second, the prior height is estimated by utilizing the number of stairs and the stair height. Then, the key point library related to the floor is built online. Finally, the error correction of the vertical displacement is carried out by matching the prior height with the key point library. The gait classification shows that the accuracy based on the error compensation model can reach up to 99%. Moreover, the maximum attitude error is less than 1 m and the accumulated vertical positioning errors are eliminated completely when a pedestrian walks up and down inside a multi-story building, and all these have verified accuracy and robustness of vertical indoor positioning.

Aerial Map-Based Navigation by Ground Object Pattern Matching

This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS.

Classification of Slope for Coffee Plantation in Ngajum District, Indonesia

Slope classification activity aims to provide information to coffee farmers about the slope, especially in Ngajum District, Malang Regency. Ngajum is one of the sub-districts located on the slopes of Mount Kawi. The people of Ngajum generally work as cattle and goat breeders as well as coffee plantations. Information on the slope (altitude) affects the classification of the quality of the coffee produced. In addition, the varieties of coffee planted also depend on the slope of the mountain slopes. Making a slope map utilizes satellite imagery which has altitude information, namely in the form of Digital Elevation Model (DEM) satellite imagery. Administrative boundary data are used according to the sub-district so that the slope classification can be focused. The method used in this activity is the analysis of spatial data from the results of slope classification. Slope class is divided into 7 (seven) slope classes, i.e. flat, wavy, wavy-bumpy, bumpy-hilly, hilly-mountainous, steep mountain, and mountainous. The results of slope classification show that 2377.171 Ha or 35.971% of the Ngajum area is undulating class. The slope of the wavy slope is a suitable class for coffee cultivation but must be accompanied by the suitability of other parameters so that the productivity of coffee plants increases. The drawback of the results of this activity is that it has not been able to determine which varieties of coffee plants are suitable for planting with the slope of the area, so further research is needed.

#2489 The prevalence of chronic kidney disease in different altitude areas: results from the China multi-ethnic cohort study

Abstract Background and Aims Chronic Kidney Disease (CKD) poses a significant public health challenge with substantial implications for human well-being. In China, there are significant geographical differences in the prevalence of CKD, with the highest prevalence in the southwest region, which may be related to differences in geographical environment and lifestyle. This study aimed to investigate variances in the prevalence of CKD across different altitude areas in southwest China. Method In this cross-sectional study, we used data from the China Multi-Ethnic Cohort (CMEC). CMEC was established in five provinces of Southwest China and recruited a total of 99,556 participants between 2018 and 2019. Altitude information for each participant was extracted from the Shuttle Radar Topography Mission (SRTM) (version 4) data based on their residential address. To facilitate analysis, we categorized them into groups of low altitude (0-1499 m) and high altitude (≥1500 m). CKD was defined as an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2, using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Crude and age-standardized prevalence of CKD was calculated by using R. Results A total of 92,163 participants were included in the study, and 39.7% were men. Among the current cohort, 30,904 were high-altitude residents, and 61,259 were from low altitude. Among the participants, 1636 cases of CKD were identified, corresponding to a prevalence of 1.78%. We found, the prevalence of CKD in high altitude group was higher than that in low altitude group, and the former is 2.53%, the latter is 1.40%. The age-standardized prevalence were 1.35% and 2.73% for the low and high altitude groups, respectively, and 1.38% and 2.57% for the sex-standardized groups. Conclusion In southwestern China, the prevalence of CKD was higher in the high altitude group than the low altitude group. And after adjusting for age and gender, the prevalence of CKD remained lower in the low-altitude group, and the gap slightly increased.

Self-adaptive and soft-contact ellipsoidal pendulum-structured triboelectric nanogenerator for harvesting water wave energy

The global increasing energy shortage has prompted extensive research into exploiting environmentally friendly clean energy. As one of the most promising new energy candidates, water wave energy has been regarded as a prominent clean energy. Therefore, it is imperative to develop highly efficient techniques for harvesting water wave energy. The advent of triboelectric nanogenerators (TENGs), aiming to capture low-frequency, large-scale, and random energy, aroused significant attention. However, traditional TENGs for wave energy harvesting are always limited by the small contact area and incomplete separation. In this paper, a soft-contact and self-adaptive ellipsoidal-pendular-structured TENG (SSEP-TENG) is designed for harvesting water wave energy. Compared to traditional spherical TENGs, the unique spring-actuated soft contact not only effectively increases the contact area and reduces the wear to the TENG, but creates a quick and complete contact-separation process, which effectively improves the SSEP-TENG’s output performance. Moreover, the synergy of the two solid–solid contact-separations enhances the SSEP-TENG’s output performance. Finally, the maritime IoT application, a self-powered Global Positioning System (GPS) wireless positioning and tracking, was successfully realized by the SSEP-TENG, including the tracked target's longitude, latitude, altitude, and other location information. This work demonstrates the advantage of the SSEP-TENG in harvesting wave energy.

Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

2‐D Total Electron Content and 3‐D Ionospheric Electron Density Variations During the 14 October 2023 Annular Solar Eclipse

AbstractThis study investigates the ionospheric total electron content (TEC) responses in the 2‐D spatial domain and electron density variations in the 3‐D spatial domain during the annular solar eclipse on 14 October 2023, using ground‐based Global Navigation Satellite System (GNSS) observations, a novel TEC‐based ionospheric data assimilation system (TIDAS), ionosonde measurements, and satellite in situ data. The main results are summarized as follows: (a) The 2‐D TEC responses exhibited distinct latitudinal differences. The mid‐latitude ionosphere exhibited a more substantial TEC decrease of 25%–40% along with an extended recovery time of 3–4 hr. In contrast, the equatorial and low‐latitude ionosphere experienced a smaller TEC reduction of 10%–25% and a faster recovery time of 20–50 min. The minimal eclipse effect was observed near the northern equatorial ionization anomaly crest region. (b) The ionospheric electron density variations during the eclipse were effectively reconstructed by TIDAS data assimilation in the 3‐D domain, providing important altitude information with validity. (c) The ionospheric electron density variations showed a notable altitude‐dependent feature. The eclipse led to a substantial electron density reduction of 30%–50%, with the maximum depletion occurring around the ionospheric F2‐layer peak height (hmF2) of 250–350 km. The post‐eclipse recovery of electron density exhibited a relatively slower pace near the F2‐layer peak height than that at lower and higher altitudes.

Barometric geolocators can reveal unprecedented details about the migratory ecology of small birds

Abstract Knowing the location of migratory birds throughout their annual cycle is fundamental for many questions in ornithology. Technological advances have provided several approaches, with increasing miniaturization allowing deployment on smaller and smaller birds. Here, we examine the strengths and limitations of barometric pressure geolocators (pressure tags), which have recently been shown to be extremely effective in tracking small birds (<25 g). The newly developed geolocation by pressure workflow (GeoPressureR) uses a global weather analysis dataset and hidden Markov movement model to reveal locations more accurately or continuously than can be achieved by other available light-weight devices (e.g., light-level geolocators, automated radio telemetry, and archival GPS tags). Additionally, pressure data can provide altitude information and document generally intractable flight behavior, as well as exact migratory phenologies. This new technology can be applied to important unresolved problems such as altitudinal migration, stopover site use, molt migration, vertical airspace use, and drivers of migratory flight initiation, duration, and direction. We provide an example of a pressure tag on a Swainson’s Warbler (Limnothlypis swainsonii) deployed on its breeding territory in Virginia, USA, revealing the complete story of its migration to and from Cuba, including its refined wintering site, stopover sites, and migration routes, all with precise timing. Studies relying on pressure tags are still subject to biases related to recovery of the devices for data extraction, and application to birds that exhibit extensive aerial movements (e.g., swallows and swifts) remains challenging. Widespread deployment of pressure tags could show unprecedented details of bird migration and other aspects of aerial behavior, which could benefit conservation and greatly enrich our understanding of avian movement ecology.

Fast surface signal extraction method for photon point clouds with strong background noise without prior altitude information.

It is extremely challenging to rapidly and accurately extract target echo photon signals from massive photon point clouds with strong background noise without any prior geographic information. Herein, we propose a fast surface detection method realized by combining the improved density-dimension algorithm (DDA) and Kalman filtering (KF), termed the DDA-KF algorithm, for photon signals with a high background noise rate (BNR) to improve the extraction of surface photon signals from spacecraft platforms. The results showed that the algorithm exhibited good adaptability to strong background noise and terrain slope variations, and had real-time processing capabilities for massive photon point clouds in large-scale detection range without prior altitude information of target. Our research provides a practical technical solution for single-photon lidar applications in deep space navigation and can help improve the performance in environments characterized by strong background noise.

Responses of small mountain glaciers in the Maritime Alps (south-western European Alps) to climatic changes during the Last Glacial Maximum

We present new chronological data and Equilibrium Line Altitude (ELA) information for palaeoglaciers in the Maritime Alps during the Last Glacial Maximum (LGM) and the early deglaciation. Three relatively small catchments were investigated to test if the response of small (1–10 km2) glaciers to LGM climatic forcing was distinguishable from that of larger glacial systems. Palaeoglacier geometries and ELAs were reconstructed using geomorphological evidence and toolboxes in a geographic information system. Chronological control was provided through 10Be surface exposure dating of erratic boulders located on frontal and lateral moraine ridges. Our data indicate two phases of glacier advance or stabilisation in the Maritime Alps, the first occurring at ca. 25 to 24 ka and the second at ca. 20.5 to 19 ka. This is consistent with ages that have been reported from larger outlet lobes along the south-western Alpine fringe, where the second LGM advance is usually preserved in the form of a recessional moraine. Within the uncertainty of the dating technique, there are no distinguishable differences between the responses of small and large Alpine glaciers to the LGM climate. The reconstructed ELAs for palaeoglaciers in the Maritime Alps indicate a considerable variability, which appears to be linked to differences in received solar radiation, from ca. 1600 m a.s.l. for north-facing cirque glaciers to almost 2000 m a.s.l. for glaciers with a southerly aspect. We argue that such influence of solar radiation needs to be considered when using ELAs for palaeoclimatic inferences at regional scales. The calculated ELAs are up to 450 m higher than those in the northern Apennines or the Corsican mountains, indicating a relatively dry LGM climate on the Italian side of the Maritime Alps.

Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking

Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife. Outlier measurements can severely corrupt estimated locations. This article describes a new suite of location estimation algorithms for such systems. The new algorithms detect and discard outlier time-of-arrival observations, which can be caused by non-line-of-sight propagation, radio interference, clock glitches, or an overestimation of the signal-to-noise ratio. The new algorithms also detect cases in which two locations are equally consistent with measurements and can usually select the correct one. The new algorithms can also infer approximate altitude information from a digital elevation map to improve location estimates close to one of the sensors. Finally, the new algorithms approximate the covariance matrix of location estimates in a simpler and more reliable way than the baseline algorithm. Extensive testing on real-world data involving mobile transmitters attached to wild animals demonstrates the efficacy of the new algorithms. Performance testing also shows that the new algorithms are fast and that they can easily cope with high-throughput real-time loads.

3-D Ionospheric Electron Density Variations during the 2017 Great American Solar Eclipse: A Revisit

This paper studies the three-dimensional (3-D) ionospheric electron density variation over the continental US and adjacent regions during the August 2017 Great American Solar Eclipse event, using Millstone Hill incoherent scatter radar observations, ionosonde data, the Swarm satellite measurements, and a new TEC-based ionospheric data assimilation system (TIDAS). The TIDAS data assimilation system can reconstruct a 3-D electron density distribution over continental US and adjacent regions, with a spatial–temporal resolution of 1∘× 1∘ in latitude and longitude, 20 km in altitude, and 5 min in universal time. The combination of multi-instrumental observations and the high-resolution TIDAS data assimilation products can well represent the dynamic 3-D ionospheric electron density response to the solar eclipse, providing important altitude information and fine-scale details. Results show that the eclipse-induced ionospheric electron density depletion can exceed 50% around the F2-layer peak height between 200 and 300 km. The recovery of electron density following the maximum depletion exhibits an altitude-dependent feature, with lower altitudes exhibiting a faster recovery than the F2 peak region and above. The recovery feature was also characterized by a post-eclipse electron density enhancement of 15–30%, which is particularly prominent in the topside ionosphere at altitudes above 300 km.

Modeling of annual maximum flows with geographic data components and artificial neural networks

The flow rate at which the instantaneous maximum flow is recorded throughout the year is called the Annual Maximum Flow (AMF). These flow rates often cause disasters such as floods. Snow melts and extreme precipitation associated with temperature fluctuations are the two most important factors that occurred flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of a flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. This study was used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012. Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. In these packages, the AMF data measured for the 5 stations mentioned above between 1965 and 2012 were divided into 4 and used by creating 25% (test) and 75% (training) packages. Root Means Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.

Altitude and metabolic syndrome in China: Beneficial effects of healthy diet and physical activity.

The correlation between altitude and metabolic syndrome has not been extensively studied, and the mediation effects of diet and physical activity remain unclear. We evaluated the cross-sectional correlations between altitude and metabolic syndrome and the possible mediation effects of diet and physical activity in China. We included 89 485 participants from the China Multi-Ethnic Cohort. We extracted their altitude information from their residential addresses and determined if they had metabolic syndrome by the presence of three or more of the following components: abdominal obesity, reduced high-density lipoprotein cholesterol (HDL-C), elevated triglycerides, elevated glucose, and high blood pressure at recruitment. We conducted multivariable logistic regression and mediation analyses for all and separately for Han ethnic participants. The participants had a mean age of 51.67 years and 60.56% were female. The risk difference of metabolic syndrome was -3.54% (95% confidence interval (CI) = -4.24, -2.86) between middle and low altitudes, -1.53% (95%CI = -2.53, -0.46) between high and low altitudes, and 2.01% (95% CI = 0.92, 3.09) between high and middle altitudes. Of the total estimated effect between middle and low altitude, the effect mediated by increased physical activity was -0.94% (95% CI = -1.04, -0.86). Compared to low altitude, the effects mediated by a healthier diet were -0.40% (95% CI = -0.47, -0.32) for middle altitude and -0.72% (95% CI = -0.87, -0.58) for high altitude. Estimates were similar in the Han ethnic group. Living at middle and high altitudes was significantly associated with lower risk of metabolic syndrome compared to low altitude, with middle altitude having the lowest risk. We found mediation effects of diet and physical activity.

Three Case Studies on Small Uncrewed Aerial Systems Near Midair Collisions with Aircraft: An Evidence-Based Approach for Using Objective Uncrewed Aerial Systems Detection Technology

<div>Small uncrewed aircraft systems (sUAS) growth continues for recreational and commercial applications. By 2025, the Federal Aviation Administration (FAA) predicts the sUAS fleet to number nearly 2.4 million units. As sUAS operations expand within the National Airspace System (NAS), so too does the probability of near midair collisions (NMACs) between sUAS and aircraft. Currently, the primary means of recognizing sUAS NMACs rely on pilots to visually spot and evade conflicting sUAS. Pilots may report such encounters to the FAA as UAS Sighting Reports. Sighting reports are of limited value as they are highly subjective and dependent on the pilot to accurately estimate range and altitude information. Moreover, they do not account for NMACs that an aircrew member does not spot. The purpose of this study was to examine objective sUAS and aircraft telemetry data collected using a DJI Aeroscope sensor and Automatic Dependent Surveillance-Broadcast (ADS-B)/Mode S messages throughout 36 months near a major United States (U.S.) airport. This data offers objective insights into the interaction of sUAS and aircraft in the airspace surrounding this airport. Using the data, three NMAC case studies are presented based on three varying mission profiles: (a) commercial air carriers, (b) general aviation (GA) aircraft, and (c) helicopters. The findings inform on sUAS-aircraft encounter evolution and trends, including areas of encounter risk, lateral and vertical encounter separation distances, sUAS operator compliance with operational and altitude restrictions, and comparisons of objective detection data against sUAS sighting reports. Recommendations are provided to mitigate risks associated with encounter trends to further enhance safety within the NAS.</div>

Driving altitude generation method with pseudo‐3D building model for unmanned aerial vehicles

AbstractSpatial information is geometrical information combined with the properties of an object. In city areas where unmanned aerial vehicle (UAV) usage demand is high, it is necessary to determine the appropriate driving altitude considering the height of buildings for safe driving. In this study, we propose a data‐provision method that generates the driving altitude of UAVs with a pseudo‐3D building model. The pseudo‐3D building model is developed using high‐precision spatial information provided by the National Geographic Information Institute. The proposed method generates the driving altitude of the UAV in terms of tile information, including the UAV's starting and arrival points and a straight line between the two points, and provides the data to users. To evaluate the efficacy of the proposed method, UAV driving altitude information was generated using data of 763 551 pseudo‐3D buildings in Seoul. Subsequently, the generated driving altitude data of the UAV was verified in AirSim. In addition, the execution time of the proposed method and the calculated driving altitude were analyzed.