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Over the past decades, Unmanned Aerial Vehicles (UAVs) have increasingly been used in a wide variety of missions that range from surveillance to delivery. Unlike aircraft that always carry goods and passengers from an airport to another, UAVs do not systematically implement the same type of mission. UAVs are indeed multi‐mission during their time in operation, and the systems engineering approaches developed for one mission aircraft must be adapted to the multi‐mission context. Therefore, UAV design requires application of mission engineering upstream systems engineering, either to assess there is a UAV system that may accomplish a new mission, or to specify a new UAV system according to a given mission. To achieve that goal, the authors of the paper support the use of Model‐Based Mission Engineering. A three‐layer architecture ‐ purpose, operation, functions or capabilities ‐ is proposed as a design framework for missions. The Goal‐Oriented Requirements Language (GRL) serves as mission description language. The paper extends GRL to better address mission‐based design of UAVs. It is proposed to distinguish between internal and external resources. A goal detailing mechanism is introduced. A degraded mode evaluation becomes possible. GRL tools make it possible to evaluate how much a UAV system ‐ at least, an operator, a ground station, and a UAV ‐ may satisfy every stakeholder in both nominal and degraded modes. The proposed approach is applied to a high voltage surveillance UAV. The case study enables the introduction of four actors—Authority, Client, UAV and MissionSupervisor—that turn out to be generic and can be reused for other missions and UAV designs. [ABSTRACT FROM AUTHOR]

OMOTENASHI is a 6U CubeSat launched by the first NASA SLS rocket. Its mission was to demonstrate that a CubeSat can make a semi-hard landing on the Moon. Tight resource constraints make it difficult to mount a heater and a radiator in a CubeSat, so passive thermal control is a prominent design feature of OMOTENASHI that resolves the technical difficulty. In November 2019, a thermal vacuum test was conducted. This paper discusses the thermal design and the test results. OMOTENASHI was launched by SLS rocket on November 16, 2022. After the SLS rocket separation, OMOTENASHI's attitude control malfunctioned so it could not generate power from its solar cells. Eventually, communications were lost and have not been restored. Brief telemetry data confirmed that the temperature monitor functioned correctly for 30 min. This paper reports the flight results of the thermal status during the first ground station pass. Also, a comparison between the analysis model and telemetry data is shown. Finally, based on its estimated trajectory, the future thermal profile of the spacecraft and the result of the search operation are discussed. • OMOTENASHI was developed as the world's smallest moon lander that was launched by a NASA SLS rocket. • Passive thermal control design without a thermal controller is adopted by using a white paint and a graphite sheet. • Passive thermal control design can be utilized for future CubeSat missions under tight resource constraints. • This paper introduced the experiment and analysis method that achieves the thermal design for lunar missions by CubeSat. • The flight data after the launch of the SLS rocket showed the feasibility of this passive thermal control design. [ABSTRACT FROM AUTHOR]

This article describes a new design for the sum-channel, circularly-polarized, radiating patch element of a multimode patch-monopole monopulse feed. Such feeds are suitable for the prime-focus illumination of a deep symmetric reflector. A relatively simple and compact feed design allows for a compact single-mirror antenna with monopulse tracking. The feed is about a wavelength in diameter, also making it suitable for illuminating smaller antennas, for example, in LEO-satellite ground (client) stations. The design applies a slotted circular patch as a circularly-polarized sum-channel element. The design variables are optimized mainly for the sum-channel aperture illumination efficiency in an S-band satellite ground station. After a few cut-and-try iterations, the final feed prototype was produced and measured. In addition to an additional degree of freedom in the geometry and potentially easier fabrication, the new sum-channel radiating-element shape allows for a slightly better monopulse channel isolation, and a higher radiation-pattern symmetry compared to the previous design. [ABSTRACT FROM AUTHOR]

High-precision Global Navigation Satellite System (GNSS) orbit and clock products are crucial for precise applications. An evenly distributed global network enables continuous tracking for GNSS satellites, while a regional network may result in tracking gaps in the areas where monitoring stations are not deployed. This also means that the orbit determination accuracy based on a regional network is not comparable to that with a global network. Integrating the measurements from regional ground stations and Low Earth Orbit (LEO) satellites onboard receivers is a potential approach for generating GNSS orbit and clock products with centimeter-level accuracy, which is particularly important for BDS and the local commercial providers relying on a regional network. In the integrated Precise Orbit Determination (POD), LEO satellites are used to compensate for the drawback of regional ground stations in the precise orbit and clock determination of GNSS satellites. To validate the role of LEO satellites in the orbit determination with a regional network, 6 International GNSS Service stations around China and 13 LEO satellites from January 20 to 26, 2019, including GRACE-C/D, SWARM-A/B/C, Jason-3, Sentinel-3A/B, and SAT-A/B/C/D/E are selected in this study to perform the integrated POD. The orbit and clock accuracies of GPS and LEO satellites are evaluated by comparison with precise products. The average Root Mean Square (RMS)of GPS orbit errors in the radial (R), along-track (T) and cross-track (N) directions are 2.27 cm, 3.45 cm, and 3.08 cm, respectively, and the clock accuracy is better than 0.15 ns based on a comparison with the final products provided by Center for Orbit Determination in Europe (CODE). The LEO orbit accuracy is better than 2 cm in the R direction, and the position errors are mostly within 4 cm. The results indicate that the integrated POD can generate high-precision orbit and clock products for GPS and LEO satellites based on regional network stations. Finally, the integrated POD products are assessed for Precise Point Positioning (PPP). Simulated kinematic PPP has a comparable performance in terms of the convergence time and positioning accuracy. With more LEO satellites available, the orbit and clock determination accuracy and PPP positioning accuracy can be improved. [ABSTRACT FROM AUTHOR]

Atmospheric pollution is a major concern due to its well-documented and detrimental impacts on human health, with millions of excess deaths attributed to it annually. Particulate matter (PM), comprising airborne pollutants in the form of solid and liquid particles suspended in the air, has been particularly concerning. Historically, research has focused on PM with an aerodynamic diameter less than 10 μ m (PM10) and 2.5 μ m (PM2.5), referred to as coarse and fine particulate matter, respectively. The long term exposure to both classes of PM have been shown to impact human health, being linked to a range of respiratory and cardiovascular complications. Recently, attention has been drawn to the lower end of the size distribution, specifically ultrafine particles (UFPs), with an aerodynamic diameter less than 100 nm (PM0.1). UFPs can deeply penetrate the respiratory system, reach the bloodstream, and have been increasingly associated with chronic health conditions, including cardiovascular disease. Accurate mapping of UFP concentrations at high spatial resolution is crucial considering strong gradients near the sources. However, due to the relatively recent focus on this class of PM, there is a scarcity of long-term measurements, particularly on the global scale. In this study, we employed a machine learning methodology to produce the first global maps of UFP concentrations at high spatial resolution (1 km) by leveraging limited ground station measurements worldwide. We trained an XGBoost model to predict annual UFP concentrations for a decade (2010–2019) and utilized the conformal prediction framework to provide reliable prediction intervals. This approach makes local-to-global UFP data available to support assessments of the health implications associated with long-term exposure. [ABSTRACT FROM AUTHOR]

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends. [ABSTRACT FROM AUTHOR]

The sustainability of irrigated agricultural systems depends on the water use efficiency based on management. The efficiency is reach first by an adequate estimation of crop water demand. However, the lack of weather data is a limitation to plan and estimation irrigation demand in Brazil. This way, the aim of this study was to investigate NASAPOWER gridded system as alternative source of weather data in Brazil. First, we tested how reliable are the meteorological variables between NASAPOWER and ground weather stations. Further, we calculated the maximum number of consecutive dry days within the crop cycle, and the irrigation demand through the soil-crop water balance for nine crops. The performance was investigated through coefficients of determination (r2), agreement (d) and normalized root mean square error (nRMSE). In general, air temperature and incoming solar radiation presented the best statistical metrics (r2 ~ 0.53–0.83; d ~ 0.84–0.94; and nRMSE ~ 8.61–23.5%), whereas the wind speed had the worst (r2 ~ 0.09; d ~ 0.53; and nRMSE ~ 93%). NASAPOWER figured as a valuable tool for determine the number of dry days and number of irrigation events during the crop cycle. Irrigation demand showed a good relation between NASAPOWER and ground weather station (r² = 0.79 and d = 0.94), but with nRMSE of 53%, due to a higher deviation when irrigation demand is above 200 mm cycle− 1. NASAPOWER showed potential as source of meteorological information for irrigation management for different cropping systems, where local adjustment could improve performances for crops with long cycle. [ABSTRACT FROM AUTHOR]

In most cases, the utilization of the costly payload onboard Low Earth Orbit (LEO) satellites is restricted by the limited throughput of the payload data downlink to the ground station during the visibility window. The usefulness of these data in critical applications reduces due to the large latency of the process. Different techniques involving efficient modulation schemes, increased power within the allowed level and frequency band, and capacity enhancement using close-by satellites have been studied and implemented with their relative merits and limitations in an attempt to reduce the latency of the data to the user. The present study proposes a constellation of low-cost data relay satellites placed in the same orbital plane along with the main satellite, increasing directly the effective visibility window. As a result, the utility of the main satellite is also increased by the same factor. A detailed analysis of the constellation and configuration of the relay satellites is presented in this paper. [ABSTRACT FROM AUTHOR]

Detached subauroral proton arcs are commonly observed during the recovery phase of geomagnetic storms, and have been extensively investigated. However, there is limited study on their occurrence during the main phase of storms. This study investigated nightside detached auroras (NDAs) observed by the far‐ultraviolet imager onboard the Defense Meteorological Satellite Program spacecraft. The NDAs occurred in the nightside sector, separated from the equatorward boundary of the auroral oval, and were observed during the main and recovery phases of the geomagnetic storm on 02 October 2013. The occurrence of the NDAs appears to correlate with the expanding auroral oval toward lower latitudes, and is independent of the polarity change in the interplanetary magnetic field Bz component. Particle measurements indicate that the NDAs were generated by energetic protons, primarily above 10 keV, originating from the ring current. These precipitating proton fluxes, predominantly anisotropic, were observed to be detached from the isotropic boundary within the auroral oval. Analysis of Pc1 data obtained by ground stations suggests that electromagnetic ion cyclotron waves account for the generation of the NDAs. The limited latitudinal distribution of the NDAs indicates the wave activity in the magnetospheric source region within a narrow L‐shell region. The observations presented in this study would contribute to our understanding of the coupling processes between the magnetosphere and ionosphere within the subauroral region. Key Points: The occurrence of nightside detached auroras (NDAs) correlates with an expanding auroral oval toward lower latitudesAnisotropic proton precipitation in the energy range above 10 keV is responsible for generating the NDAsEnhanced Pc1 pulsations indicate the presence of EMIC waves, which can scatter ring current protons and subsequently cause the NDAs [ABSTRACT FROM AUTHOR]

We document the arrival of seismic energy in the core shadow zone up to large distances beyond 150° more than 100 s prior to the core phases. Numerical simulations of the energy transport in an established heterogeneity model show that scattering throughout the entire mantle explains these observations. Diffraction at the core‐mantle boundary is unlikely in our 1–2 Hz frequency band and is not required indicating misleading terminology with reference to Pdiff for the scattered P∗P‐energy. Records of the largest deep earthquakes at low‐noise stations are key to the observation of the faint precursory signal which changes appearance with increasing distance from a coda‐like decay over a constant amplitude level around 130° to an emergent wave train. According to our simulations, different depth layers in the mantle dominate different time‐distance windows of the scattered wave train, providing the opportunity to improve the depth resolution of mantle heterogeneity models. Plain Language Summary: Earthquakes producing different types of waves that travel through the Earth help understand the structure of the Earth. We show that there is seismic energy arriving at stations in the shadow of the Earth's core more than 100 s before the waves usually considered the first arrivals. We used computer simulations to explain how this energy can travel so far in such a short time. Our results show that when there are heterogeneous structures distributed throughout the Earth's mantle the seismic energy can change direction at these structures due to a phenomenon called scattering. This allows the seismic energy to travel in the fast mantle material around the slow Earth's core. Previously, seismologists thought that part of this energy travels along the boundary between core and mantle by a process called diffraction. Our study provides a more elegant explanation for the observed energy and offers new possibilities for the investigation of Earth's structure. Key Points: We present observations of high‐frequency Pdiff coda more than 100 s before the core phases even at large distances beyond 150°Seismic energy simulations in published models of Earth's mantle heterogeneity show that scattered energy explains Pdiff and its codaDifferent‐depth mantle layers contribute to different time‐distance windows of the Pdiff coda benefiting imaging the mantle heterogeneity [ABSTRACT FROM AUTHOR]

This article studies the model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems in the presence of an active leader. The core role of human‐in‐the‐loop is to use the ground station to send control commands to the non‐zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model‐based human‐in‐the‐loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias‐based data‐driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above‐mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example. [ABSTRACT FROM AUTHOR]

Quantum key distribution is costly and, at the moment, offers low performance in space applications. Other more recent protocols could offer a potential practical solution to this problem. In this work, a preliminary optical payload design using commercial off-the-shelf elements for a quantum communication downlink in a 3U CubeSat is proposed. It is shown that this quantum state emitter allows the establishment of two types of quantum communication between the satellite and the ground station: quantum key distribution and quantum keyless private communication. Numerical simulations are provided that show the feasibility of the scheme for both protocols as well as their performance. For the simplified BB84, a maximum secret key rate of about 80 kHz and minimum QBER of slightly more than 0.07% is found, at the zenith, while for quantum private keyless communication, a 700 MHz private rate is achieved. This design serves as a platform for the implementation of novel quantum communication protocols that can improve the performance of quantum communications in space. [ABSTRACT FROM AUTHOR]

The Internet of things is increasingly focused on UAV-based long-range applications. This necessitates versatile, low-cost antenna designs for both ground stations and drones. This study proposes the design of a pattern reconfigurable parasitic element antenna system operating in the LoRa 868 MHz band and its on-field characterization. The electronic steerable parasitic array radiator (ESPAR) antenna consists of a single-fed shorted patch surrounded by four rectangular parasitic elements. The antenna system can develop four directive beams, each activated by one of four PIN diodes, and an omnidirectional pattern obtained when all diodes are turned off. The antenna has been characterized both in an anechoic controlled environment and in a practical on-field drone-to-ground packet transmission scenario. Results based on received signal strength indicator (RSSI) show that the pattern reconfigurability becomes increasingly relevant with the end-point distance and that pointing the beam at the end point always allows the highest RSSI level to be received. [ABSTRACT FROM AUTHOR]

Construction of wireless infrastructure using unmanned aerial vehicle (UAV) can effectively expand the coverage and support high-density traffic of next-generation communication systems. Designing wireless systems including UAVs as aerial base stations (ABSs) is a challenging task, due to the mobility of ABSs causing time-varying nature of environmental surroundings and relative propagation paths to user equipment (UE) devices. Therefore, it is essential to have an accurate estimate of the channel for varying positioning of the UAVs. In this paper, we propose to adopt a digital twin based performance evaluation procedure for wireless systems including ABSs, providing enhanced accuracy of channel modeling for specific target deployment areas. Using ray-tracing channel models reflecting detailed building and terrain information of the transmission environment, an UAV position optimization algorithm based on reinforcement learning is presented. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm calculates the overall throughput in the digital twin and determines the desired states of the UAV. Performance evaluation results demonstrate the trajectory training ability of the algorithm and the performance advantage of the system with a reduced amount of shadow area compared to those with ground base stations (GBSs). [ABSTRACT FROM AUTHOR]

UAVs in most real-world deployments communicate with a ground control station either in a one-hop manner, which has a limited working range, or relies on infrastructure support such as satellite communication, which is expensive. Although multi-hop transmission is more economic, it is lossy and generally incurs large delays. In this paper, we consider a mixed method, where a few UAVs are equipped with satellite modules, and aggregate and forward traffic of nearby UAVs. The surrounding UAVs and the forwarding UAV form a star topology. Based on this model, we design an UAV formation strategy to cover a given region to minimize both the total number of UAVs and the number of UAVs with satellite modules. Since these two goals may be competing, we propose seven strategies and analyze them theoretically. Simulations show that a good tradeoff between the two goals can be obtained when each star network consists of four UAVs. [ABSTRACT FROM AUTHOR]

Context. Jupiter and its moons form a complex dynamical system that includes several coupling dynamics at different frequencies. In particular the Laplace resonance is fundamental to maintaining the energy dissipation that sustain Io's volcanic activity and Europa's subsurface ocean; studying its stability is thus crucial for characterizing the potential habitability of these moons. The origin and evolution of the Laplace resonance is driven by the strong tidal interactions between Jupiter and its Galilean moons, and the future planetary exploration missions JUICE and Europa Clipper could bring new light to this unsolved mechanism. During the Jupiter tours of both missions and JUICE's Ganymede orbital phase, two-way radiometric range and Doppler data will be acquired between Earth ground stations and the spacecraft, which will be processed to recover the static and time-varying gravity field of the moons. Moreover, range and Doppler data will improve the orbit accuracy of the moons, providing precise measurements of Jupiter's tidal parameters. Aims. This work presents a covariance analysis of the joint orbit determination of JUICE and Europa Clipper, aimed at quantifying the expected uncertainties on the main parameters that characterize the dynamics of the Jupiter system. Methods. We simulated radio science data from JUICE and Clipper missions under conservative noise assumptions, using a multi-arc approach to estimate the ephemerides and dissipation in the system. Results. Even though JUICE and Europa Clipper will not perform flybys of Io, the strong coupling with Europa and Ganymede will allow an improvement of our knowledge of the Jupiter-Io dissipation parameters thanks to JUICE and Europa Clipper radiometric data. Moreover, the expected uncertainty in Jupiter's dissipation at the frequency of Callisto could unveil a potential resonance locking mechanism between Jupiter and Callisto. [ABSTRACT FROM AUTHOR]

Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization. [ABSTRACT FROM AUTHOR]

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users' understanding of error characteristics and sources in IMERG. [ABSTRACT FROM AUTHOR]

The multipath effect is a major Global Navigation Satellite System (GNSS) error source due to its environment-dependent characteristic, which complicates its mitigation process for the high-rate determination of displacements. For instance, Sidereal Filtering (SF) and Multipath Hemispherical Map (MHM) require the observations spanning at least one full cycle of satellite orbit repeat period (e.g., ten days for Galileo navigation satellite system (Galileo) to reproduce the satellite geometry against ground stations. As a consequence, the practicability of SF and MHM is limited due to potential station-surrounding changes over a long period. In this study, we used the overlap-frequency signals on Global Positioning System (GPS) L1/L5, Galileo E1/E5a, and BeiDou-3 Navigation Satellite System (BDS-3) B1C/B2a to construct an interoperable MHM (i.e., MHM_GEC) across constellations to mitigate multipath more efficiently. We thus used 31 days of 1-Hz GPS/Galileo/BDS-3 data at 21 stations in Europe to compare this overlap-frequency MHM with those GNSS-specific MHMs (i.e., MHM_G for GPS, MHM_E for Galileo, and MHM_C for BDS-3), as well as SF. It is confirmed that the multipath effects on overlap-frequency signals are of a high spatial consistency across all GNSS. The mean reduction rate of applying MHM_GEC to GPS, Galileo, and BDS-3 carrier-phase residuals is 25%, 31%, and 28.5%, respectively, which are up to 25 percentage points higher than those of MHM_G, MHM_E, and MHM _C. Furthermore, the MHM_GEC constructed using 5 to 6 days of data can improve the positioning precision by 40%, outperforming the MHM_E, MHM_C, and SF using 10 days of data. Therefore, the interoperable MHM_GEC is more efficient in mitigating multipath effects for high-precision GNSS positioning. [ABSTRACT FROM AUTHOR]

Net radiation (Rn) is a key component of the Earth's energy balance. With the rise of deep learning technology, remote sensing technology has made significant progress in the acquisition of large-scale surface parameters. However, the generally low spatial resolution of net radiation data and the relative scarcity of surface flux site data at home and abroad limit the potential of deep learning methods in constructing high spatial resolution net radiation models. To address this challenge, this study proposes an innovative approach of a multi-scale transfer learning framework, which assumes that composite models at different spatial scales are similar in structure and parameters, thus enabling the training of accurate high-resolution models using fewer samples. In this study, the Heihe River Basin was taken as the study area and the Rn products of the Global Land Surface Satellite (GLASS) were selected as the target for coarse model training. Based on the dense convolutional network (DenseNet) architecture, 25 deep learning models were constructed to learn the spatial and temporal distribution patterns of GLASS Rn products by combining multi-source data, and a 5 km coarse resolution net radiation model was trained. Subsequently, the parameters of the pre-trained coarse-resolution model were fine-tuned with a small amount of measured ground station data to achieve the transfer from the 5 km coarse-resolution model to the 1 km high-resolution model, and a daily high-resolution net radiation model with 1 km resolution for the Heihe River Basin was finally constructed. The results showed that the bias, R2, and RMSE of the high-resolution net radiation model obtained by transfer learning were 0.184 W/m2, 0.924, and 24.29 W/m2, respectively, which was better than those of the GLASS Rn products. The predicted values were highly correlated with the measured values at the stations and the fitted curves were closer to the measured values at the stations than those of the GLASS Rn products, which further demonstrated that the transfer learning method could capture the soil moisture and temporal variation of net radiation. Finally, the model was used to generate 1 km daily net radiation products for the Heihe River Basin in 2020. This study provides new perspectives and methods for future large-scale and long-time-series estimations of surface net radiation. [ABSTRACT FROM AUTHOR]

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas. [ABSTRACT FROM AUTHOR]

The impact of short-term exposure to air pollutants on the morbidity of schizophrenia, particularly in low- and middle-income countries, remains inadequately explored. The objective of this research was to investigate the relationship between short-term exposure to ambient air pollutants and the risk of schizophrenia hospitalization in Jingmen, China. We performed a time-stratified case-crossover study using daily records of hospital admissions due to schizophrenia in Jingmen Mental Health Center from 2015 to 2017. Environmental exposures to air pollutants and meteorological conditions on case and control days were estimated on the basis of measurements from ground monitoring stations. To investigate the relationship between short-term exposure to ambient air pollutants and the risk of hospitalization for schizophrenia, a conditional logistic regression model was employed. We performed subgroup analyses stratified according to sex, age groups, and season. In total, 4079 schizophrenia hospitalizations were recorded during the designated period. Increased risk of schizophrenia was merely associated with short-term exposure to SO2 and NO2. The estimated odds per interquartile range (IQR) increase in exposure was 1.112 (95% confidence interval (CI): 1.033, 1.196) for SO2 (IQR = 12 µg/m3) and 1.112 (95% CI: 1.033, 1.197) for NO2 (IQR = 18 µg/m3) on lag-0 day. Greater air pollution-schizophrenia associations were observed among middle-aged and older adults (over 45 years of age) and during the cold season. This study added case-crossover evidence indicating that short-term exposure to ambient air pollution, specifically SO2 and NO2, is linked to a higher risk of hospital admissions for schizophrenia. These findings contribute to a better understanding of the detrimental effects of air pollution on neuropsychiatric health conditions. [ABSTRACT FROM AUTHOR]

Optical satellite networks (OSNs) play an important role in space applications since it has some merits such as high data rate and low power. Increasing space applications generate a large amount of data on satellite so that satellites have difficulty in downloading these data to ground station timely under the limited contact window. Thus, we investigate the problem of collaborative data downloading in this paper. The problem lies on how to energy-efficiently offload data by optical inter-satellite links (ISLs) to balance data load and downlink capacity of each satellite so as to improve the performance of data downloading in OSN. Firstly, we develop the Cost Time-evolving Graph (CTEG) to describe optical transmission cost and the time-varying topology of OSN. Secondly, the problem of collaborative data downloading is formulated as Multi-objective Mixed-integer Linear Programming (MOMILP) which is proven to be NP-hard. For reducing computational complexity, we divide the scheduling time into multiple stages and propose the Multi-stage Collaborative Scheduling Algorithm (MCSA) which operates on a slot-by-slot basis. Simulations are conducted in the joint platform and results demonstrate that, compared with CoDld and No-ISLs, the MCSA provides a relatively high data throughput meanwhile drastically reducing energy consumption generated by data offloading. [ABSTRACT FROM AUTHOR]

The feasibility of calculating the Standardized Precipitation Index (SPI) at different meteorological ground stations (GS) using monthly precipitation data from Satellite-based Precipitation Products (SbPP) was investigated in this study. Iraq was divided into three Köppen climate zones for spatial comparisons. Monthly precipitation time series data from the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) 1983–2007 and Climate Hazards Group of Infra-Red Precipitation with Stations (CHIRPS) 1983–2017 were statistically compared against GS data from the three zones to estimate potential droughts at the timescales of SPI-3, SPI-6, and SPI-12. SbPPs not only had reasonable correlations with GS data, but also effectively represented the spatial distribution of rainfall. There was satisfactory consistency between APHRODITE and GS values in high-rainfall zones in the SPI estimation. This current work also found that APHRODITE was more dependable than CHIRPS when spatially and temporally determining drought over Iraq. These results are particularly significant as Iraq faces the twin difficulties of high drought risk and a lack of accurate meteorological data, making an improvement in monthly precipitation data of vital importance. [ABSTRACT FROM AUTHOR]

In response to the insufficient of the traditional "RTK+leveling"measurement method for retest of existing railway lines, such as high safety risks, slow measurement speed, and poor applicability of multisource data. This research proposed a non-contact measurement operation for existing railway lines based on "UAV+LiDAR". Under the control of flight speed, aerial photography altitude, ground base stations, and equipment pulse frequency, high-precision and high-density point cloud data could be obtained. At the same time, target points were set up along both sides of the existing line at a distance of 500 m. After point cloud aerial belt registration and target correction processing, the relative and absolute accuracy could be further improved. The research results indicate that the mean squared error of the existing line centerline plane and elevation as determined by the method, can reach 1.77 cm and 1.27 cm, respectively. The method enhances safety and flexibility by avoiding on-railway work, reduces field measurement labor, improves production efficiency, and offers extensive potential for point cloud data applications. It shows promising prospects for has good application prospects in railway capacity expansion and transformation. [ABSTRACT FROM AUTHOR]

This research presents the design and development of a near-space picosatellite platform intended to operate at high altitudes between 20–40 km above sea level. These picosatellites function similarly to orbital satellites but float and travel with the wind in the atmosphere instead of orbiting the Earth. The platform utilizes a super-pressure balloon to provide buoyancy, allowing the picosatellite to remain airborne and operational for several days to months, capable of flying around the world multiple times. This study focuses on the cost-effective design of high-altitude platforms, telecommunication systems, and energy consumption for the picosatellite, enabling it to communicate with ground stations from anywhere in the world while consuming low power. The use of common COTS (Commercial Off-The-Shelf) equipment aims to enhance accessibility for educational applications. This research gathered data from our previous studies for over 20 high-altitude platform flights, analyzed the necessary factors for whole system design, and developed a new prototype that has been successfully built and tested. [ABSTRACT FROM AUTHOR]

This article presents the results of bandwidth enhancement method analysis for a stacked microstrip antenna. Based on the analysis results, a new design of a wideband, compact, high-strength antenna is proposed. The antenna operates in a wide frequency band of 4660 to 6048 MHz (∼ 26%) with an impedance bandwidth matching of 15 dB; throughout its whole operating frequency range, the antenna gain is from 11 to 13.4 dBi. The antenna allows it to form a specific shape of radiation pattern with coverage predominantly in the upper (lower) hemisphere and a fixed main lobe deflection angle about 4 degrees in the elevation plane. The antenna consists of a wideband E-shaped active exciter and four passive rectangular exciters placed above the conductive plane (screen). All elements are made of sheet metal (e.g., stainless steel). The antenna size is 1.4λmax × 1.4λmax (1.6λ0 × 1.6λ0). The analysis of the characteristics of the designed antenna was performed using simulation in the ANSYS EM Suite. A prototype was made, and its properties were measured. The proposed antenna may be designed with a different frequency band with a matching band about 25% and can be used as a wireless communication system repeater or small cell antenna, as a ground station antenna in unmanned aircraft systems, or for other wideband applications with high gain. [ABSTRACT FROM AUTHOR]

We evaluate average wave intensities at frequencies up to 10 kHz measured by two ground stations in Canada and two others in Finland at auroral and subauroral latitudes over a full year, as well as by the low‐altitude DEMETER spacecraft during the years 2004–2010. Lightning location and energy data obtained by the World Wide Lightning Location Network, along with geomagnetic activity characterized by the Kp index, are further used. Latitudinal, diurnal, and annual variations are analyzed, and the global intensities measured on the ground and by the spacecraft are systematically compared for the first time. We show that lightning‐generated waves often dominate the measured wave intensities, particularly during the night, in summer, and at higher frequencies. DEMETER observations, supported by ray‐tracing analysis, reveal a significant role of nonducted lightning‐generated whistler propagation between the hemispheres. Finally, the wave intensity response to geomagnetic activity variations is quite different on the ground compared to in space. While spacecraft‐measured wave intensities are considerably larger during periods of enhanced geomagnetic activity, the ground‐based intensities are only sporadically enhanced during geomagnetically active periods. Key Points: A global comparison of wave intensities measured by the DEMETER satellite and the Kannuslehto/PWING ground stations is performedLightning‐generated waves often dominate both DEMETER and ground observations, especially at night, in summer, and at higher frequenciesDaytime intensities measured by DEMETER are larger during active periods, while on the ground they are only sporadically enhanced [ABSTRACT FROM AUTHOR]

Summary: The study of multi‐interferences plays a pivotal role in advancing the development of next‐generation, high‐performance communication devices. In this paper, we propose a design approach for auxiliary antennas aimed at mitigating multi‐interference challenges within satellite communication earth stations. We introduce an eight‐element auxiliary antenna with an elliptical beam comprising dual radiation patches and a T‐shaped power divider, to attenuate interferences originating from three distinct directions. To enhance the azimuthal beamwidth, we adjust the dimensions of the ground plane and extend the substrate. At a frequency of 12.5 GHz, the antenna exhibits a 3‐dB beam spacing of 38° and an azimuthal width of 162°, with a maximum gain of 7.9 dBi. We employ a sidelobe canceller to optimize the antenna's performance, and both simulations and measurements affirm that the auxiliary antenna achieves an extinction interference cancelation ratio exceeding 27 dB under all circumstances. [ABSTRACT FROM AUTHOR]

In disaster scenarios with cellular base station failures, the importance of wireless communication further rises, necessitating the deployment of Public Safety Communication (PSC) networks for critical missions. This manuscript delves into an innovative approach that harnesses the power of Unmanned Aerial Vehicles (UAVs) and Intelligent Reflecting Surfaces (IRS) to elevate the capabilities of a PSC network. Within this framework, an observation UAV (Ob-UAV) captures videos from ground users in affected areas (AGUs) and seamlessly relays them to nearby operational ground Base Stations (GBS) through a passive IRS-equipped UAV. The central objective is to maximize the average utility of video streams from AGUs at the GBS. The optimization involves positions of the observation UAV and IRS-UAV, IRS-UAV phase shift coefficients, and communication resource distribution. We formulate a mathematical solution addressing these optimizations with system-defined constraints. Due to the non-convex nature of the problem, finding a solution poses a significant challenge. To address this challenge, we suggest an efficient iterative algorithm that relies on block coordinate descent and successive convex approximation techniques. Thorough simulations confirm that our proposed optimization approach surpasses the outcomes of other compared schemes. [ABSTRACT FROM AUTHOR]

Low-Earth orbit (LEO) satellites have fast motion and thus provide rapid geometric change relation to the ground stations in a short period of time, which are expected to improve the positioning performance. In this study, a LEO constellation of 160 LEO satellites was simulated and simulated LEO observation data at global, European, and American stations were used to estimate the uncalibrated phase delays (UPDs) and achieve static and kinematic precise point positioning (PPP) ambiguity resolution (AR). The quality of the estimated UPD products were evaluated. More than 89 % and 96 % of the wide- and narrow-lane UPDs had a posteriori residual of less than 0.15 and 0.25 cycles, respectively. In static PPP, all ambiguity fixing rates were larger than 63 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 8 %, 6 % and 7 %, respectively; the average root-mean-square errors (RMSEs) in the east, north, and up components at the global, European and American stations were decreased by (22 %, 20 %, 15 %), (36 %, 34 %, 28 %) and (44 %, 31 %, 29 %), respectively. In kinematic PPP, all ambiguity fixing rates were larger than 40 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 4 %, 19 % and 14 %, respectively; the average RMSEs in the east, north, and up components at the global, European and American stations were decreased by (10 %, 10 %, 7 %), (27 %, 22 %, 11 %) and (32 %, 20 %, 10 %), respectively. In both static and kinematic PPP, AR accelerated the convergence time and improved the positioning accuracy. [ABSTRACT FROM AUTHOR]

The can-sized satellite, ScoreSAT satellite is a small communications satellite. ScoreSAT helps to develop a platform for finding directions and the exact spot where a lack of communications signal occurs, as well as a realtime visual feed for analysis of communication during and after landing. The project focuses on the design of ScoreSAT and provides a real-time system for capturing real-time data during descent. The objective of the realtime system is to improve the accuracy and location of ScoreSAT data collection, which can provide pressure, humidity, temperature, altitude, latitude, and longitude readings. The main components of this platform are the hardware design that comprises the flight controller, GPS module, and telemetry kit, the software design, which are Mission Planner, and the realtime system (RTS). Based on the entire research, the compact design of the ScoreSAT and ground station was developed to provide alternative meteorological parameter monitoring to complement primary meteorology ground observation such as weather station and radiosonde and to enhance the reliability of the remote sensing observation for environmental studies concerning the factors determining the environment and atmospheric. [ABSTRACT FROM AUTHOR]

Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region. Plain Language Summary: Timely and accurate forecasts of weather events before their occurrence is important for decision‐makers in effective planning and actions to cope with weather‐induced disasters. Such forecast plays a crucial role in disaster preparedness over complex terrain regions like Nepal. Since sub‐seasonal to seasonal (S2S) scale prediction can provide forecasts beyond 10 days up to 30 days, is getting popular nowadays. However, their skill needs to be evaluated before any operational use which requires reliable observation data. Since, Nepal lacks the proper ground observing stations: mainly over the high mountains, gridded precipitation datasets produced using multiple sources (like satellites, and numerical models) have been good alternatives. Thus, we evaluated 11 such gridded datasets against the existing ground observing stations. We found that these datasets struggle to estimate extreme precipitation. Additionally, they also fail to capture precipitation characteristics over the complex terrain of the high mountain region compared to flat regions. Overall, gauge interpolated and merged datasets demonstrated better performance in estimating precipitation characteristics over complex terrain; hence, can serve as a benchmark dataset for S2S forecast evaluation in this region. Key Points: Multi‐source precipitation estimates captured the annual to weekly precipitation cycle, but only roughly captured the spatial patternMulti‐source precipitation estimates performed poorly in estimating extreme precipitation and struggled over complex terrainAmong high‐resolution datasets, TPMFD has the potential to be used as a reference dataset for sub‐seasonal to seasonal model evaluation [ABSTRACT FROM AUTHOR]

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks. [ABSTRACT FROM AUTHOR]

Due to the openness of inter-satellite links (ISLs) in mega-constellations, the threat posed by jamming from non-cooperative constellations is becoming increasingly significant. Most of the existing approaches focus on the up/down link capacity between satellites and ground stations, which differs greatly from the situation whereby ISLs are subjected to non-cooperative jamming. Therefore, this work investigates the transmission rates of ISLs under jamming from non-cooperative mega-constellations. Based on this, a novel satellite network capacity calculation method is proposed to evaluate the mega-constellation network capacity when the transmission rates change dynamically. The simulation results show that the satellite number, jamming power and inclination of non-cooperative constellations have a significant influence on the network capacity. The optimal jamming efficiency occurs when the constellation inclinations are close. [ABSTRACT FROM AUTHOR]

We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Türkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal–Wallis and Mann–Whitney U methods, and long-term trends were analyzed using Modified Mann–Kendall (MMK), innovative trend analysis (ITA), and empirical mode decomposition (EMD). Our results show that at least one GPP product significantly differs from the others over the seven geographic regions of Türkiye (χ2 values of 50.8, 21.9, 76.9, 42.6, 149, 34.5, and 168; p < 0.05), and trend analyses reveal a significant increase in GPP from all satellite-based products over the latter half of the study period. Throughout the year, the average number of months in which each dataset showed significant increases across all study regions are 6.7, 8.1, 5.9, 9.6, and 8.7 for MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2, respectively. The ITA and EMD methods provided additional insight into the MMK test in both visualizing and detecting trends due to their graphical techniques. Overall, the GPP products investigated here suggest 'greening' for Türkiye, consistent with the findings from global studies, but the use of different statistical approaches and satellite-based GPP estimates creates different interpretations of how these trends have emerged. Ground stations, such as eddy covariance towers, can help further improve our understanding of the carbon cycle across the diverse ecosystem of Türkiye. [ABSTRACT FROM AUTHOR]

This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions. [ABSTRACT FROM AUTHOR]

By processing satellite data on the total electron content on a track between GLONASS, GPS, and ground receiving stations located in Primorsky Krai of Russia, disturbances of an electron layer with periods from 5 to 45 min, which are caused by natural atmospheric fluctuations, were identified. They affect the sea surface and are responsible for aquatic fluctuations with periods from 5 to 40 min. [ABSTRACT FROM AUTHOR]

A coupled numerical approach that combines the WRF model and the Mike 3 (DHI) hydrodynamic model was developed and applied in two semi-enclosed basins in the Ionian Sea (Italy) to assess the wind-driven current. To gain a better understanding of how the sea current field can vary depending on meteorological data forcing, three different scenario were set up. The sensitivity of the sea current pattern was investigated as a function of the type of meteorological forcing and appreciating the differences in the results. The aims of this study are threefold. Firstly, we wish to define an ad hoc procedure to join the model-computed meteorological parameters in the hydrodynamic model. Secondly, we will investigate the feedback from the Mar Piccolo and Mar Grande basins in the Ionian Sea using fully coupled simulations and an uncoupled system where the atmospheric parameters are derived from a ground station. Finally, we will evaluate the results achieved by applying two scenarios of typical meteorological conditions to the study site. The model results highlighted the variability of sea currents depending on meteorological forcing. [ABSTRACT FROM AUTHOR]

Space-air-ground integrated (SAGI) communication networks have been envisioned as one of the pillars of the next generation communications (6 G). This type of networks are expected to offer fairness and high quality of services in various scenarios where traditional terrestrial communication systems cannot efficiently support due to inherent limitations, e.g., limited coverage areas. In this paper, a distributed transmission scheme is proposed in a SAGI communication network that utilizes free-space-optical (FSO)/radio frequency (RF) links. More specifically, a set of N high altitude platform systems are cooperatively transmit the received signal to the ground station, using the decode-and-forward relaying protocol. For this system, generic analytical expressions for the outage probability (OP) have been derived that take into consideration the impact of turbulence, pointing errors, and atmospheric attenuation (for the FSO links) as well as the impact of composite fading (for the RF links). Moreover, asymptotic closed-form expressions for the OP are also presented, which are used to extract useful insights for the system under consideration. The numerical evaluated results that are illustrated reveal the impact of various system and channel models parameters on the system's performance. From these results, it is shown that the proposed scheme outperforms a pure FSO-based relaying scheme, which has been used as a benchmark, under different environmental conditions. [ABSTRACT FROM AUTHOR]

The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on "Convective phenomena at high resolution over Europe and the Mediterranean", a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas. [ABSTRACT FROM AUTHOR]

In the field of Cryptography, Quantum Key Distribution (QKD) is an application of Quantum Information theory that obtained a great deal of attention in recent years. It allows to establish secret keys between two or more parties, in a much safer way than that implemented by classic cryptography (based on discrete logarithms and factorization of prime numbers). The most promising way of realizing a QKD network (especially over great distances) in the near future is by a constellation of satellites. This paper considers the problem of optimizing the orbits of the satellites in order to maximize the minimum key length shared in a network of ground stations over a fixed amount of time. Different networks of stations are considered and the influence of their geographical disposition on the design and the performance index is highlighted. The networks considered are: a global constellation, a regional European constellation, and two in which there are groups of stations in two different narrow bands of latitude. The effect of Inter-satellite links is then taken into account and how, in some cases, they can improve the performances. Finally the daily performance of the considered constellations are analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available. [ABSTRACT FROM AUTHOR]

The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations. This operational mode relies heavily on the support of ground stations. To enhance the high-precision autonomous timing capability of next-generation navigation satellites, it is necessary to autonomously generate a comprehensive space-based time scale on orbit and make long-term, high-precision predictions for the clock error of this time scale. In order to solve these two problems, this paper proposed a two-level satellite timing system, and used multiple time-keeping node satellites to generate a more stable space-based time scale. Then this paper used the sparse sampling Long Short-Term Memory (LSTM) algorithm to improve the accuracy of clock error long-term prediction on space-based time scale. After simulation, at sampling times of 300 s, 8.64 × 104 s, and 1 × 106 s, the frequency stabilities of the spaceborne timescale reach 1.35 × 10–15, 3.37 × 10–16, and 2.81 × 10–16, respectively. When applying the improved clock error prediction algorithm, the ten-day prediction error is 3.16 × 10–10 s. Compared with those of the continuous sampling LSTM, Kalman filter, polynomial and quadratic polynomial models, the corresponding prediction accuracies are 1.72, 1.56, 1.83 and 1.36 times greater, respectively. [ABSTRACT FROM AUTHOR]

Most anthropogenic carbon dioxide (CO2) emissions originate from urban areas. To improve understandings of urban and regional emissions, we design and construct a low-cost UAV coordinated carbon observation network (LUCCN) which uses mid-accuracy (±1 ppm) CO2 sensors. In this paper, we introduce our multi-variable non-linear regression method for calibrating the non-dispersive infrared (NDIR) CO2 sensors for LUCCN's ground stations. We tested our calibration method with concentration data collected at the Xinglong Atmospheric Background Observatory. With comparison against data simultaneously collected by a high-accuracy cavity ring-down spectrometer, we found the maximum standard deviation of LUCCN's sensors to be 0.782 ppm in a controlled laboratory environment with a 1-second window size and 0.53 ppm in an outdoor environment with a 1-hour running average window size. As validation of LUCCN's ground measurements, we identify and present consistent trends between local CO2 concentration variations and aerosol pollution events captured by the space-based moderate resolution imaging spectrometer (MODIS). [ABSTRACT FROM AUTHOR]

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites' performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing. [ABSTRACT FROM AUTHOR]

Monitoring the Greenland mass balance (GMB) is crucial in the context of global sea level rise. Currently, three main methods are used to measure GMB, with the primary source of uncertainty arising from the glacial isostatic adjustment (GIA) contribution. Here, we propose a novel approach based on a simple methodology that uses the entire Greenland GNSS network (GNET) as an instrument to monitor the present‐day mass changes. Our method is validated against GRACE‐derived GMB, and we find a very good agreement. This leads to an independent methodology for estimating present‐day mass changes from GNSS, bridging the gap between GRACE and GRACE‐FO in GMB estimates. Through a combined analysis of GMB from GRACE and GNET, we identify a consistency relation between the gravity and uplift signature of GIA, providing a new robust constraint for GIA models. Plain Language Summary: The rate of Greenland ice mass change is constantly monitored by satellites measuring gravity and surface elevation variations, airborne surveys, and a growing network of GNSS stations on the ground. Each technique has its strengths and weaknesses and spatial/temporal resolution. We developed an approach that uses the whole GNSS network as a "virtual instrument", capable of measuring the total ice loss from Greenland. Comparing this with more standard mass balance estimate techniques, we validate the method, and, by combining these techniques, we find a novel constraint on both the mass variations and glacial isostatic adjustment models. Key Points: New methodology for monitoring daily estimates of the Greenland Mass Balance (GMB) derived from the GNSS networkNew glacial isostatic adjustment constraint from combined GRACE and GNSS observationsFilling the gap in the GMB between GRACE and GRACE‐FO using GNSS data [ABSTRACT FROM AUTHOR]

The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, which has far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the IMIS network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 K and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of -0.02 K yr-1 and 0.13 K yr-1, respectively. However, we find that Landsat-LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from the 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modelled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, remaining LST trend values average between 0.07 and 0.10 K yr-1. Further, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr-1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain, where monitoring is generally difficult. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains. [ABSTRACT FROM AUTHOR]

Summary: Flying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics. [ABSTRACT FROM AUTHOR]

There is a growing interest in the development of transportable optical ground station (TOGS) as an important component of laser communication network. The object of this paper is a TOGS designed for optical LEO satellite downlink as well as for long-distance aircraft downlink. A composite gravity compensation mount is proposed to simplify and efficiently suppress the low-order aberrations of an 800 mm aperture primary mirror with variable orientation of the optical antenna of a TOGS as it changes pointing. In this study, first we describe the composition of the TOGS and the utility of each part. Second, taking the minimization of the primary mirror deformation as the optimization objective, we employ the particle swarm optimization algorithm to find the optima of several key structural parameters by Isight software. Next, the annular Zernike polynomials are adopted to fit the wavefront error over the entire surface of the primary mirror for describing low-order aberrations, proving that the composite mount effectively suppresses the wavefront error to smaller than ⋋ /27, as the elevation angle changes from the horizontal to the vertical one. Finally, the ZYGO interferometer wavefront detections show that, after eliminating fabrication imperfections, the RMS of the wavefront error over the entire surface of the mirrors for horizontal and vertical axes, due to deformation, are 0.04⋋ and 0.035⋋, respectively. Compared to the results of the fitted wavefront error, the relative errors are 9.5% and 8.1%. The results show the validity and feasibility of the optimized composite mount and the wavefront fitting method, which can prove its reference significance for the budget and allocation of the systematic wavefront error in meter-scale optical antenna of the TOGS for satellite downlink. [ABSTRACT FROM AUTHOR]