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Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms. [ABSTRACT FROM AUTHOR]

Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets. [ABSTRACT FROM AUTHOR]

Rainfall data is pivotal for hydrological studies, water resource management, and climate analysis. However, regions like Indonesia face challenges due to uneven rainfall station distribution. This study explores the potential of Tropical Rainfall Measuring Mission (TRMM) 3B42RT Daily V7 satellite data for rainfall estimation in such areas, focusing on the Brantas sub-watersheds: Kedak, Ngampel, and Kresek. Multiple TRMM scenarios are assessed for reliability and accuracy against ground-based rainfall station data. In monthly rainfall analysis, TRMM exhibits stronger correlations with average ground station data (coefficients of determination ranging from 0.62 to 0.74 for individual stations and 0.76 to 0.79 for averages) with an average PBIAS of 35.07%. The TRMM data exhibited weak performance on daily rainfall records, as evidenced by the low coefficient of determination (R²) values ranging from 0.04 to 0.21. TRMM proves sufficiently reliable for areas with limited rainfall records, particularly for cumulative monthly rainfall assessment. A comparative analysis of rainfall values for different return periods reveals that TRMM tends to provide lower values compared to single-station ground-based estimates. However, nearly similar values were obtained for area-averaged ground-based stations. Combining area-averaged TRMM data with cumulative ground-based rainfall data reduces variability in values for diverse return periods (3-8%), enhancing data accuracy. In conclusion, TRMM 3B42RT Daily V7 data integration enhances rainfall estimation accuracy in regions with limited data availability. [ABSTRACT FROM AUTHOR]

The downward surface longwave flux (DSLF) plays a relevant role in the Earth's surface radiative budget, which is crucial to monitor, understand and model the impact of changes at local and global scales on surface temperature and surface conditions. This study focuses on the evaluation and intercomparison of four DSLF products: (a) a recently developed all‐weather DSLF product based on the multivariate adaptive regression splines (MARS) algorithm driven by satellite cloud information from the Meteosat Second Generation (MSG) and ERA5 reanalysis screen variables; (b) the Satellite Application Facility on Land Surface Analysis (LSA SAF); (c) CERES Synoptic top‐of‐atmosphere and surface fluxes and clouds (CERES‐SYN1deg) and (d) ERA5 reanalysis. The study covers the period 2005–2021 and the MSG region focusing on monthly means. The evaluation performed against 48 ground stations from the Baseline Surface Radiation Network (BSRN) and FLUXNET2015 networks showed that the MARS product outperforms the remaining products, particularly the LSA SAF, while ERA5 and CERES show similar performance metrics. The fours products are intercompared in terms of their mean spatial variability and temporal mean annual cycles and inter‐annual variability in four selected regions, showing a high level of agreement, particularly between MARS, ERA5 and CERES. Our results highlight the clear added value of MARS with respect to LSA SAF, while providing higher spatial resolution (0.05°), constrained by satellite cloud information, when compared with ERA5 (0.25°) or CERES (1°). Plain Language Summary: Radiation that reaches the Earth's surface is fundamental for our understanding of the Earth's energy balance. In this work, we use a complex mathematical method together with data from weather satellites and ground‐based instruments to estimate the heat radiation component. To evaluate our results, we perform a comparison with those from various existing methods, including the one used by the Portuguese meteorological service. Our method generally outperforms the already existing techniques, with small errors as compared to surface measurements, demonstrating the added value of this new approach in improving our understanding of Earth's energy budget. Key Points: Evaluation of a new high spatial (0.05°) and temporal (30 min) resolution DSLF product constrained by MSG satellite cloud informationThe new DSLF MARS product outperforms the current LSA SAF when evaluated against 48 ground stationsHigh level of spatial and temporal variability consistency between four DSLF products MARS, LSA SAF, ERA5, CERES [ABSTRACT FROM AUTHOR]

This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following results: 96% accuracy for YOLOv8 classification, 89% accuracy for YOLOv8n object detection, 96% accuracy for CNN-RCNN classification, and 89% accuracy for YOLOv5n object detection. [ABSTRACT FROM AUTHOR]

Space Information Network (SIN) has significant benefits of providing communication anywhere at any time. This feature offers an innovative way for conventional wireless customers to access enhanced internet services by using SIN. However, SIN's characteristics, such as naked links and maximum signal latency, make it difficult to design efficient security and routing protocols, etc. Similarly, existing SIN authentication techniques can't satisfy all of the essentials for secure communication, such as privacy leaks or rising authentication latency. The article aims to develop a novel blockchain-based access authentication mechanism for SIN. The proposed scheme uses a blockchain application, which has offered anonymity to mobile users while considering the satellites' limited processing capacity. The proposed scheme uses a blockchain application, which offers anonymity to mobile users while considering the satellites' limited processing capacity. The SIN gains the likelihood of far greater computational capacity devices as technology evolves. Since authenticating in SIN, the technique comprises three entities: low Earth orbit, mobile user, and network control centre. The proposed mutual authentication mechanism avoids the requirement of a ground station, resulting in less latency and overhead during mobile user authentication. Finally, the new blockchain-based authentication approach is being evaluated with AVISPA, a formal security tool. The simulation and performance study results illustrate that the proposed technique delivers efficient security characteristics such as low authentication latency, minimal signal overhead and less computational cost with group authentication. [ABSTRACT FROM AUTHOR]

Space-based opportunistic positioning is a crucial component of resilient positioning, navigation, and timing (PNT) systems, and it requires the acquisition of orbit information for non-cooperative low Earth orbit (LEO) satellites. Traditional methods for orbit determination (OD) of non-cooperative LEO satellites have difficulty in achieving a balance between reliability, hardware costs, and availability duration. To address these challenges, this study proposes a framework for single-station orbit determination of non-cooperative LEO satellites. By utilizing signals of opportunity (SOPs) captured by a single ground station, the system performs initial orbit determination (IOD), precise orbit determination (POD), and orbit prediction (OP), enabling the long-term determination of satellite positions and velocities. Under the proposed framework, the reliability and real-time performance are dependent on the initial orbit determination and the orbit calculation based on the dynamical model. To achieve initial orbit determination, a three-step algorithm is designed. (1) An improved search method is employed to estimate a coarse orbit using single-pass Doppler measurements. (2) Data association is conducted to obtain multi-pass Doppler observations. (3) The least squares (LS) is implemented to determine the initial orbit using the associated multi-pass Doppler measurements and the coarse orbit. Additionally, to enhance computational efficiency, two fast orbit calculation algorithms are devised. These algorithms leverage the numerical stability of the Runge–Kutta integrator to reduce computations and exploit the strong correlation among nearby time intervals of orbits with small eccentricities to minimize redundant calculations, thereby achieving orbit calculation efficiently. Finally, through positioning experiments, the determined orbits are demonstrated to have accuracy comparable to that of two-line elements (TLE) updated by the North American Aerospace Defense Command (NORAD). [ABSTRACT FROM AUTHOR]

Low Earth orbit (LEO) satellite constellations have emerged as an effective alternative for the provision of high-accuracy positioning, navigation and timing (PNT) solutions which are based on high-precision orbit and clock information. Determining an orbit with high precision is dependent on the number and distribution of ground tracking stations. Therefore, it is important to investigate methodologies that can ensure the adequate observing coverage of LEO navigation constellations. In this study, an evolutionary algorithm is applied to optimize the number and deployment of ground stations for tracking LEO constellations. According to the distribution area, two schemes of study are analyzed: (a) global deployment—the ground stations are deployed throughout the globe; (b) regional deployment—a selected region is used for deployment. For global deployment, the optimization objectives are focused on the ground station and observing rate for k-heavy observing coverage (HC), while the sole objective for the regional deployment scheme is the satellite position dilution of precision (SPDOP). It is shown that a deployment of 95 ground stations is optimal for achieving 3-HC with an observing rate of 97.37% and 4-HC with an observing rate of 92.01%. For regional distribution, 15, 20 and 25 ground stations are used for three optimal configurations of SPDOP at 2.058, 1.399 and 1.330, respectively. The results are significantly enhanced using intersatellite links for SPDOP evaluation, from 2.058, 1.399 and 1.330 to 0.439, 0.422 and 0.409, with 15, 20 and 25 ground stations, respectively. [ABSTRACT FROM AUTHOR]

The northern part of Thailand always has PM 1 0exceeding the standard value which causes danger to public health. Major cause arises from open burning and forest fires, in addition, its meteorology and topography are likely to accumulate PM10. This study aims to determine the relationship between PM10 quantity from the ground station and Aerosol Optical Thickness (AOT) from the Terra Satellite-MODIS system in Chiang Mai province in northern Thailand. The operations are 1) analyze data of PM10 and AOT, 2) analyze the statistical relationship in five models, namely exponential models, linear models, logarithmic models, polynomials models, and power models, and 3) PM10 and spatial AQI. The result found that it has a consistency between PM 1 0and AOT and PM10 and spatial AQI. The statistical relationship in five models found that January has the most R² in polynomials models, February has the most R² in logarithmic models, March has the most R² in power models, and April has the most R² in linear models and polynomials models. [ABSTRACT FROM AUTHOR]

In this work, the advancing wireless revolution (AWR) modeling and Microwave Office tools are used to develop and execute a broadband up‐converter system. Before sending the input frequency to the antenna interface, a radio frequency block upconverts it once to the appropriate frequency level. Mixing blocks, oscillators, amplifiers, step attenuators, isolators, and filters are included. The input frequency (IF) band is 720±200 $720\pm 200$ MHz, while the output frequency (radio frequency [RF]) band is 8250±250 $8250\pm 250$ MHz. The usage of attenuators increases the dramatic range of the circuit. It is a one‐stage up‐conversion circuit that combines the IF band in one pass to create the appropriate RF spectrum. Here, a single phase‐locked oscillator is employed at the local oscillator port of the mixers, while the other is a programmable frequency generator that generates X‐band frequency channels with a 10 MHz spacing. [ABSTRACT FROM AUTHOR]

Low earth orbit (LEO) satellite constellations have emerged as a promising architecture integrated with ground networks, which can offer high-speed Internet services to global users. However, the security challenges faced by satellite networks are increasing, with the potential for a few satellite failures to trigger cascading failures and network outages. Therefore, enhancing the robustness of the network in the face of cascading failures is of utmost importance. This paper aims to explore the robustness of LEO satellite networks when encountering cascading failures and then proposes a modeling method based on virtual nodes and load capacity. In addition, considering that the ground station layout and the number of connected satellites together determine the structure of the final LEO satellite network, we here propose an improved ground station establishment method that is more suitable for the current network model. Finally, the robustness of the LEO satellite networks is deeply studied under two different attacks and cost constraints. Simulations of LEO satellite networks with different topologies show that the maximum load attacks have a destructive impact on the network, which can be mitigated by adjusting the topology and parameters to ensure normal network operation. The current model and related results provide practical insights into the protection of LEO satellite networks, which can mitigate cascading risks and enhance the robustness of LEO systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for identifying and tracking PocketQube satellites: the MRC-100 satellite is a model, and this method is based on a resonant radar reflector. The resonant reflector’s basic concept is that the resonant reflector uses a VHF/UHF communication subsystem antenna; there is no radiated RF signal, which means the power consumption is only some Milliampere (mA). The continuous wave (CW) illuminator RF source is on the ground, and the onboard antenna receives the CW RF signal from the Earth. The microcontroller (uC) periodically switches PIN diode forming BPSK modulated signal reflection so that another Earth station can receive the backscattered Binary Phase Shift Keying (BPSK) modulated signal. Also, it can detect the satellite if the ground station receiver can use a matched filter like a correlation receiver. If the ground station receiver knows the BPSK code of the satellite, it can detect it. If not, there is no way to detect the satellite. This method is similar to Radio Frequency Identification (RFID) applications, but the reader is the ground station, and the tag is the satellite. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are increasingly pivotal in operations such as flood rescue, wildfire surveillance, and covert military endeavors, with their integration into the Internet of Things (IoT) networks broadening the scope of services they provide. Amidst this expansion, security concerns for UAVs have come to the forefront, particularly in open communication environments where they face authentication challenges and risks of sensitive data, including location information, being exposed to unauthorized parties. To address these issues, we propose a secure and lightweight authentication scheme that combines the use of anonymity mechanisms and Physical Unclonable Functions (PUFs). Specifically, we employ pseudo- and temporary identities to maintain the anonymity of UAVs, while also utilizing PUF technology to strengthen the security of Ground Station Servers (GSSs) against physical threats. Rigorous validation through ProVerif and the Random Oracle (ROR) Model indicates our scheme's superior performance over existing protocols in terms of both efficiency and security. [ABSTRACT FROM AUTHOR]

This paper releases two regional precipitation products produced in the Himalayan Re-gridded and Observational Experiment (HiROX) conducted over the Central (Uttarakhand) Himalayas including the Upper Ganga Basin. The distributed precipitation products cover a period of about 70 yrs: HiROX-1 ranges from 1972 to 2018 (47 yrs) and is a regridded static daily precipitation product, and HiROX-2 ranges from 2000 to 2021 (22 yrs) and is a regridded dynamic daily precipitation product with continuous upgradation capacity. These datasets are capable of (a) precisely capturing and reproducing refined spatial distribution of precipitation and (b) preserving data accuracy along with other spatiotemporal characteristics over the study region, better than that observed in popular precipitation products covering the study region. The comparative investigation established HiROX-1 as better than some or all the tested datasets in terms of (a) spatial resolution, (b) accuracy, (c) temporal resolution, (d) temporal coverage, and (e) spatial distribution, and presents their application suitability. The second precipitation product, HiROX-2, closely followed HiROX-1, as reported in the ensuing investigation. We employed an open-source precipitation model, HiROX-M1, a flexible machine learning framework capable of processing precipitation information at fine spatial resolution. These regional precipitation products are provided at a spatial resolution of 5 × 5 km2 on daily scale over projected and geographic coordinate systems. HiROX-1 is a station-dependent product and HiROX-2 is a near real-time station-independent product with an updated frequency equal to that of Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Precipitation L3 1 day 0.1° × 0.1° V06 (GPM_3IMERGDF V06) product. The released products are platform-independent in choice of operating system and geographic information system (GIS) interface. The released data shall assist in closing the spatial discontinuity of ground station information over the Central Himalayan region over Uttarakhand, including the Upper Ganga Basin. [ABSTRACT FROM AUTHOR]

Summary: Recent space projects are designed by satellite constellations with a large number of spacecraft, a global character (i.e., from equatorial to high inclination orbits), and the possibility to transfer information to different satellites of the constellation (inter satellite link) in order to deliver the information to the ground as soon as possible. To cope with a large number of parameters, a fast tool for constellation design, performance evaluation and networking strategies is needed. The aim of this paper is to obtain the performance of any constellation in less than 1 s, even when the number of satellites in the constellation and the duration of the analysis is large (e.g., more than 200 satellites in a period of some days). The proposed algorithm is based on analytical formulae obtained by using the stereographic projection on the equatorial plane of the satellite orbits and the projection of the target and ground station orbits. It is believed that the two‐dimensional projection proposed here can offer some advantages with respect to the spatial analysis of satellite orbits and their ground tracks, such as the reduction in time required to calculate station/satellite and satellite/satellite encounter conditions, and the clear and simple representation of the motion of the satellite of the constellation and of the ground stations of interest. [ABSTRACT FROM AUTHOR]

In recent years, two of the largest open-source ground station (GS) networks capable of enabling Earth–satellite communication have emerged: TinyGS and SatNOGS. These open-source projects enable anyone to build their own GS inexpensively and easily, integrate into a GS network, and receive data from satellites listed in the database. Additionally, it enables satellite developers to add satellites to the databases of these projects and take advantage of this GS network to receive data from the satellites. This article introduces the TinyGS and SatNOGS projects and conducts a comparative analysis between them. Generally, the TinyGS project seems to have simpler implementation as well as lower associated costs. In a deeper analysis, it was observed that on the 29 July 2023, the TinyGS project had a higher number of online GSs and a more favorable geographic distribution. On the other hand, the SatNOGS project managed to communicate and decode a larger number of satellites up to 29 July 2023. Additionally, in both projects, it was noted that frequencies between 436 and 437 had the highest number of satellites with decoded data. Ultimately, the choice between these projects depends on critical parameters defined by the reader. [ABSTRACT FROM AUTHOR]

Within the framework of the European Union's Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of operations (ConOps) provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic Conflict Resolution, Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Moreover, a Web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their tasks. The development and testing of the environment provided lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles. [ABSTRACT FROM AUTHOR]

In the context of a main road area with significant traffic flow, posing challenges to constructing the freezing station on the ground, an innovative proposal suggests situating the freezing station at the station. This approach aims to facilitate construction at the same time for the connection aisle, tunneling, and track laying, thereby reducing the construction period; however, this will lead to a corresponding increase in the freezing pipeline distance. The theoretical analysis, numerical analysis, and integration with engineering practices were employed to examine the essential aspects and key technologies in the long-distance freezing design and construction, including the freezing hole construction, thermal insulation method of brine pipelines and tunnel segments, and technique program to retain the brine pressure and flow discharge, as well as the method to reduce the interplay of cross-construction. The validity of the construction program for the long-distance frozen excavation was finally evaluated based on onsite monitoring and theoretical analysis. The results show that the temperature of the brine in both the delivery and return pipelines first decreases linearly and then stabilizes gradually with freezing time, and the temperature difference is between 1 °C and 1.5 °C at the later freezing period. The temperature variation of the frozen wall is similar to that of brine in the delivery and return pipelines, and there is a good correlation between them. After the frozen wall encloses, the internal pressure of the frozen wall increases quickly, which can be effectively reduced to prevent wall cracking and breakage by regulating the pressure relief holes. The above theoretical analysis result shows that the average temperature of the frozen wall should be less than −9.7 °C when the designed thickness of the frozen wall is 2.2 m. The monitoring data indicates that the average temperature of the frozen wall reaches −13.9 °C, which satisfies the design requirement. The design and construction technology of long-distance freezing enhance the construction of the subway connection aisle. The novel method deviates from the conventional practice of establishing freezing stations within tunnels and offers valuable insight and guidance for comparable projects. [ABSTRACT FROM AUTHOR]

Conventional Mobile Communication Systems (MCS) reliably transfer critical messages from authorised remote information sources like military bases and ground control stations in the war field. As tactical advancement grows rapidly, the challenges of transmitting tactical messages via conventional satellite methods increase the processing overhead related to cost, Line of Sight (LOS) communication, packet loss, delay, and retransmission requests. Modern mobile communication systems use Airborne Node Networks (ANN) between satellites and Mobile Ground Nodes (MGNs) as they provide many advantages in mobile communication systems. Hence airborne network reduces the burden of satellites as they are only used as relay stations. The mobility problem caused by ANN and MGNs is solved by proposing a novel constructive airborne‐ground matrix algorithm known as the Who‐To‐Whom (WTW) matrix. With the help of this matrix acting as a reference index, it determines which nodes relate to whom at every time interval T in the tactical environment. This WTW matrix holds precise status/information about connectivity among all stakeholders in the operating environment by carefully and effectively accounting for frequent location changes. The methodology of the proposed matrix is that it contains the physical parameters of the nodes and their behaviour in the tactical environment. The WTW matrix construction algorithm discusses how the reference matrix is constructed in every aerial node to monitor the mobile ground nodes by leading them to safety and providing aerial guidance as they move along the tactical environment. The performance metrics are measured with other existing schemes, and the merits and demerits of the proposed WTW matrix are identified and discussed in detail. [ABSTRACT FROM AUTHOR]

In this work, design of a five-element compact dual-polarization X band monopulse feed antenna using a 7.3 m Cassegrain-type reflector for Low Earth Orbit (LEO) satellite ground station is presented. The designed antenna has reached 32 dB/K G/T value, the side lobe level of the designed antenna is at the level of −24 dB, the crosspolarization value is −35 dB, and the port isolation value is below −29 dB in the 8–8.5 GHz frequency range. The antenna efficiency of the designed antenna is measured as 79.93%. [ABSTRACT FROM AUTHOR]

Due to the advantage of high spatial coverage, using satellite-retrieved aerosol optical depth (AOD) data to estimate PM2.5 and PM10 mass concentrations is a current research priority. Statistical models are the common method of PM estimation currently, which do not require the knowledge of complex chemical and physical interactions. However, the statistical models rely on station data, which results in less accurate PM estimation concentrations in areas where station data are missing. Hence, a new hybrid model, with low dependency on on-site data, was proposed for PM2.5 and PM10 mass concentration estimation. The Gaofen-1 satellite and MODIS data were employed to estimate PM2.5 and PM10 concentrations with 100 m spatial resolution in Beijing, China. Then, the estimated PM2.5/10 mass concentration data in 2020 were employed to conduct a spatio-temporal analysis for the investigation of the particulate matter characteristic in Beijing. The estimation result of PM2.5 was validated by the ground stations with R2 ranging from 0.91 to 0.98 and the root mean square error (RMSE) ranging from 4.51 μg/m3 to 17.04 μg/m3, and that for PM10 was validated by the ground stations with R2 ranging from 0.85 to 0.98 and the RMSE ranging from 6.98 µg/m3 to 29.00 µg/m3. The results showed that the hybrid model has a good performance in PM2.5/10 estimation and can improve the coverage of the results without sacrificing the effectiveness of the model, providing more detailed spatial information for urban-scale studies. [ABSTRACT FROM AUTHOR]

Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity. [ABSTRACT FROM AUTHOR]

Measurements collected from ground monitoring stations have gained popularity as a valuable data source for calibrating numerical models and correcting model errors through data assimilation. Both model calibration and assimilation are driven by the penalty quantified by simulation-minus-observations. However, the penal forces are challenged by the existence of a spatial scale disparity between model simulations and observations. The Chemical Transport Models (CTMs) allow the division of the atmosphere into grid cells, yet their spatial resolution may not align with the limited range of in-situ measurements, particularly for short-lived air pollutants. Within a broad grid pattern, air pollutant concentrations can exhibit significant heterogeneity due to their rapid generation and dissipation. Ground observations with traditional methods (including nearest search and grid mean) are less representative when compared to model simulations. This study develops a new land-use-based representative (LUBR) observational operator to generate spatially representative gridded observation for model calibration and evaluation. It incorporates high-resolution urban-rural land use data to address intra-grid variability. The LUBR operator is validated to consistently provide insights that align with satellite OMI measurements. It is an effective solution to accurately quantify these spatial scale mismatches and further resolve them via assimilation. Model calibrations with 2015-2017 NO2 measurement in China demonstrates biases and errors differed substantially when the LUBR and other operator are used, respectively. The results highlight the importance of considering fine-scale urban-rural differences when comparing models and observations, especially for short-lived pollutants like NO2. [ABSTRACT FROM AUTHOR]

A custom lightweight, miniaturized, and trackable meteorological probe was launched by a model rocket into the inflow region of an EF4, long-tracked tornado south of Lawrence, Kansas, on 28 May 2019 and sampled tornado core flow. The rocket reached apogee at 439 m a.g.l., releasing the "pseudo-Lagrangian drifter" by parachute directly into the tornado vortex. The probe reached a three-dimensional (3D) speed of 85.1 m s -1 in the first revolution around the tornado, measured an altitude-corrected pressure deficit of - 113.5 hPa at 475 m a.s.l., and sampled a tornadic updraft speed of 65.0 m s -1. The probe then transitioned to an environment exhibiting a more tilted ascent above an altitude of 4300 m a.s.l. at speeds up to 84.0 m s -1 to a maximum altitude of 11 914 m a.s.l. 1 Hz pressure, temperature, relative humidity, GPS, acceleration, gyroscope, and magnetometer data for the flight were transmitted in real time to a ground station until 10 680 m a.s.l. and the probe landed 51 km northeast of the launch position. The probe was recovered without damage, which is attributed to the pseudo-Lagrangian drifter design, and then higher-resolution and complete 10 Hz data were downloaded for the flight. This novel deployment method and design facilitate data collection in real time from within tornadoes, the mesocyclone, and downdraft without requiring the probes to be recovered or for researchers to enter the circulation to deploy equipment. [ABSTRACT FROM AUTHOR]

The continuous technical advancement of scientific space missions has resulted in a surge in the amount of data that is transferred to ground stations within short satellite visibility windows, which has consequently led to higher throughput requirements for the hardware involved. To aid synchronization algorithms, the communication standards commonly used in such applications define a physical layer frame structure that is composed of a preamble, segments of modulation symbols, and segments of pilot symbols. Therefore, the detection of a frame start becomes an essential operation, whose accuracy is undermined by the large Doppler shift and quantization errors in hardware implementations. In this work, we present a design methodology for frame synchronization modules that are robust against large frequency offsets and rely on a parallel architecture to support high throughput requirements. Several algorithms are evaluated in terms of the trade-off between accuracy and resource utilization, and the best solution is exemplified through its application to the CCSDS 131.2-B-1 and CCSDS 131.21-O-1 standards. The implementation results are reported for a Xilinx KU115 FPGA, thereby showing the capability of supporting baud rates that are greater than 2 Gbaud, as well as a corresponding throughput of 15.80 Gbps. To the best of our knowledge, this paper is the first to propose a design methodology for parallel frame synchronization modules that has applicability to the CCSDS 131.2-B-1 and CCSDS 131.21-O-1 standards. [ABSTRACT FROM AUTHOR]

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Utilizing unmanned aerial vehicles (UAVs) to facilitate wireless communication has emerged as a viable and promising strategy to enhance current and prospective wireless systems. This approach offers many advantages by establishing line-of-sight connections, optimizing operational efficiency, and enabling flexible deployment capabilities in various terrains. Thus, in this paper, we investigate UAV communication in a relaying network in which a UAV helps communication between a source and two destination users while flying to a location. To have a complete view of our proposed system, we consider both orthogonal multiple access, such as OFDMs and non-orthogonal multiple access (NOMA) scenarios. Moreover, we apply successive convex optimization (SCO) and the block-coordinate gradient descent (BCGD) for the sum-rate optimization problems to improve the system performance under constraints of total bandwidth and total power at the ground base station and UAV. The experimental results validate that the achievable secrecy rates are notably enhanced using our proposed algorithms and show optimal trends for critical parameters, such as transmit powers, the flight trajectory and speed of the UAV, and resource allocation of OFDM and NOMA. [ABSTRACT FROM AUTHOR]

This study proposes a drone system with visual identification and tracking capabilities to address the issue of limited communication bandwidth for drones. This system can lock onto a target during flight and transmit its simple features to the ground station, thereby reducing communication bandwidth demands. RealFlight is used as the simulation environment to validate the proposed drone algorithm. The core components of the system include DeepSORT and MobileNet lightweight models for target tracking. The designed fuzzy controller enables the system to adjust the drone's motors, gradually moving the locked target to the center of the frame and maintaining continuous tracking. Additionally, this study introduces channel and spatial reliability tracking (CSRT) switching from multi-object to single-object tracking and multithreading technology to enhance the system's execution speed. The experimental results demonstrate that the system can accurately adjust the target to the frame's center within approximately 1.5 s, maintaining precision within ±0.5 degrees. On the Jetson Xavier NX embedded platform, the average frame rate (FPS) for the multi-object tracker was only 1.37, with a standard deviation of 1.05. In contrast, the single-object tracker CSRT exhibited a significant improvement, achieving an average FPS of 9.77 with a standard deviation of 1.86. This study provides an effective solution for visual tracking in drone systems that is efficient and conserves communication bandwidth. The validation of the embedded platform highlighted its practicality and performance. [ABSTRACT FROM AUTHOR]

Remote sensing has become an essential tool for geological exploration, disaster monitoring, emergency rescue, and environmental supervision, while the limited number of remote sensing satellites and ground stations restricts the timeliness of remote sensing services. Satellite Internet has features of large bandwidth, low latency, and wide coverage, which can provide ubiquitous high-speed access for time-sensitive remote sensing users. This study proposes a near real-time remote sensing (NRRS) architecture, which allows satellites to transmit remote sensing data via inter-satellite links and offload to the Earth Stations from the satellite that moves overhead. The NRRS architecture has the advantages of instant response, ubiquitous access, and intelligent integration. Based on a test communication constellation, a vehicle-mounted Satcom on-the-move experiment was conducted to validate the presented NRRS architecture. The results show that the whole process from demand collection to image acquisition takes no more than 25 min, which provides an engineering reference for the subsequent implementation of near real-time remote sensing. [ABSTRACT FROM AUTHOR]

With the advantages of flexible deployment and configuration, fast response, and strong environmental adaptability, UAV-aided communication has significant applications in temporary hotspot areas, edge areas, and emergency communication scenarios. In comparison to traditional wired backhaul links from ground base stations, this paper considers a UAV-aided communication system that utilizes in-band backhaul technology to maximize the minimum reachable rate of ground users by jointly optimizing the hovering position, bandwidth allocation, transmit power, and user association of UAVs. To tackle this problem, a multi-coding artificial bee colony algorithm is proposed in this study to address the joint optimization problem, thereby expanding the dimensionality of the search conducted in the solution space. Simulation results demonstrate that the proposed multivariate coded artificial bee colony algorithm achieves substantial rate gains and improves the minimum reachable rate of ground users in UAV-aided communication systems within a limited number of iterations, as compared to the BCD (BCD) method. [ABSTRACT FROM AUTHOR]

This paper analyzes low-Earth-orbit (LEO) satellite downlinks when an airborne interference source moves parallel to the satellite trajectory by considering the relative angle differences between the satellites and the interference sources. To make the experimental interference situations more like actual environments, the LEO trajectories are obtained from two-line element set (TLE) data. Airborne interference sources with various altitudes move parallel to the LEO trajectories, and a jamming to signal (J/S) ratio is calculated based on the relative angle differences between the ground station, the LEO satellite, and the interference source. To accurately calculate the J/S ratio, we should apply the sidelobe gain from which the interference signal enters to the ground station antenna. In order to calculate the relative angle difference ψ, the coordinates of the satellite and the interference source are converted from the World Geodetic System 1984 (WGS84) to the ground station-centered east–north-up (ENU) system. The resulting J/S ratio demonstrates that the distance and the relative angle difference ψ between the ground stations, LEO satellite, and airborne interference source appear to be important factors causing changes in the J/S ratio. Among them, the relative angle difference ψ, which determines the sidelobe gain of the ground station antenna, is the most significant factor affecting the J/S ratio variation. [ABSTRACT FROM AUTHOR]

Due to the unique soil, morphological, and subsurface topographical conditions, amplified and prolonged seismic demand traces were observed in historical strong ground motion records from Bayrakli-Izmir-Turkiye. A vivid example of this response was recorded during the Mw 7.0 Samos event on October 30, 2020. After the event, structural damage and loss of life were unexpectedly concentrated in Bayrakli-Izmir, even though the fault rupture was located 70 km away. The presence of strong ground motion stations (SGMS) located on rock (#3514) and soil (#3513) sites enabled a quantitative assessment of the amplified and prolonged seismic demand traces. The seismic response of SGMS #3513 site was assessed by using 1-D equivalent linear and analytical methods. The idealized 1-D soil profile and input parameters were calibrated and fine-tuned by using the 2020 Samos earthquake accelerograms. Then, the calibrated equivalent linear site response model was further validated by the recordings from historical events. Alternatively, an analytical wave propagation-based model was proposed, the input parameters of which were probabilistically estimated based on, again, historical recordings. Finally, the seismic responses of the site during future earthquakes were predicted based on the calibrated and validated site response models. The predicted intensity-dependent amplification spectral responses were compared with the provisions of the TEC (2018). Even though limited in number in all five future seismic scenario events, amplification ratios suggested by TEC were exceeded by a factor of 2–4 at periods falling in the range of 0.5 to 1.2 s. This clearly suggested the need to further quantify the Bayrakli seismic basin responses with basin-specific models, rather than code-based, intensity-dependent generalized amplification factors. [ABSTRACT FROM AUTHOR]

Snow modelling is often hampered by the availability of input and calibration data, which can affect the choice of models, their complexity, and transferability. To address the trade-off between model parsimony and transferability, we present the Generalizable Empirical Model of Snow Accumulation and Melt (GEMS), a machine-learning-based model, which requires only daily precipitation, temperature or its daily diurnal cycle, and basic topographic features to simulate snow water equivalent (SWE). The model embeds a support vector regression pretrained on a large dataset of daily observations from a diverse set of the SNOwpack TELemetry Network (SNOTEL) stations in the United States. GEMS does not require any user calibration, except for the option to adjust the temperature threshold for rain–snow partitioning, though the model achieves robust simulation results with the default value. We validated the model with long-term daily observations from numerous independent SNOTEL stations not included in the training and with data from reference stations of the Earth System Model–Snow Model Intercomparison Project. We demonstrate how the model advances large-scale SWE modelling in regions with complex terrain that lack in situ snow mass observations for calibration, such as the Pamir and Andes mountains, by assessing the model's ability to reproduce daily snow cover dynamics. Future model improvements should consider the effects of vegetation, improve simulation accuracy for shallow snow in warm locations at lower elevations, and possibly address wind-induced snow redistribution. Overall, GEMS provides a new approach for snow modelling that can be useful for hydroclimatic research and operational monitoring in regions where in situ snow observations are scarce. [ABSTRACT FROM AUTHOR]

This research investigates heatwaves spatiotemporal variations, their decadal trends, and aspects in different climate zones over Iran. The mean daily maximum temperature and the mean daily minimum temperature from 49 ground stations covering 40 years (1981–2020) were collected. Using excess heat factor, we computed five heatwave aspects, including heat wave number (HWN), heat wave frequency (HWF), heat wave duration (HWD), heat wave magnitude (HWM), and heat wave amplitude (HWA), over different climate zones of Iran. The result showed that the average HWN, HWF, and HWD had been about 2.5 events, 12 days, and 6 days/year, respectively. The findings highlight that distinct climate zones may have different heatwave responses within the country. The maximum HWN observes in the eastern and southwestern parts of the country due to the higher sensible heat flux over these regions. The maximum HWM is seen in the northwest, northeast, and mountainous regions. The high values of the HWA are seen in the northwest due to the more significant positive temperature anomaly in this region. This result emphasizes the latitudinal effect of local climatology in the HWA and HWM variations. The findings show that all heatwave aspects, especially HWF and HWD, have increased rapidly in the recent decade (the 2010s) compared to previous decades in different climate zones. The heatwave trend showed that HWN and HWF increased in 87.75% of the country and HWD and HWA increased in 73.46% and 71.42% of the country, respectively. [ABSTRACT FROM AUTHOR]

Estimates of PM2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM2.5 retrieval, which involves conversion of high-resolution satellite retrieval of Aerosol Optical Depth (AOD) into high-resolution PM2.5 retrieval. To improve the accuracy of the AOD to PM2.5 conversion, we employ the machine learning based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high resolution geographical indicators, meteorological data and ground station observations for learning a predictor for the approximation error in the AOD to PM2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. Our model produces PM2.5 estimates with a spatial resolution of 100 meters at satellite overpass times. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine learning related uncertainty in the PM2.5 estimates. [ABSTRACT FROM AUTHOR]

Recent startups and university space activities extend to cis -lunar and near-Earth interplanetary field. One of the biggest hurdles in those missions is in the orbit determination based on the range measurements, while the radio transmission and reception capability becomes within those small organizations' facilities. Also, about the navigation in cis -lunar space, conventional GNSS (Global Navigation Satellite System) real time positioning is hardly available and some alternative methods have been sought. The most straight forward approach is to be supported by the space agencies' facilities for the range measurement with the transponders onboard the probes. However, it depends on the facilities availability and also on the relatively expensive, old and analogue transponders. This paper presents a new method deriving from the GNSS based devices. It does not require the transponders but inexpensive radio circuits easily built. And both the probes and the ground stations have only to be equipped with the common and simple inexpensive devices. The method enables the small organizations to perform autonomous operation of the probes in a distance beyond the moon's orbit. The paper first presents the latest test data obtained during the development using the Software Defined Radio (SDR) boards. • Low-Cost Ranging Measurement for Deep Space beyond Cis-Lunar Region. • Real Test Results using Software Defined Radio Boards, Hardware. • Semi-Real-Time and Simultaneous Range Measurement and Clock Synchronization. • Absolute Clock Relay Capability via A Pair of Spacecraft. [ABSTRACT FROM AUTHOR]

In this study, the trajectory mapping domain-filling technology, which can provide more reliable statistical estimates of long-lived gas concentrations in a broader geographical area based on limited station data, is used to map the CO2 concentration data of six ground observation stations to the entire Shanxi Province. The technology combines a dynamical model of the atmosphere with trace gas observations, combining forward and backward trajectories to greatly expand the information on long-lived CO2 gas concentrations over a trajectory path. The mapped results show good agreement with the observation results, which reveals the generalizability of the trajectory mapping domain-filling technology. The results show that the spatio-temporal distribution characteristics of CO2 concentration in the entire Shanxi region is significant: during the five years, the provincial average CO2 concentration exhibits an overall increasing trend. The CO2 concentration increases from the north to the south across the province. Influenced by the economic growth rate and COVID-19, there are differences in the annual variation characteristics of the CO2 concentration across the entire province, with the highest year-on-year growth in 2019 and a year-on-year decrease in 2020. The increasing rate of the CO2 concentration in the northern low-value areas is faster than that in the southern high-value areas. Overall, there is a decreasing trend in the CO2 concentration growth from the north to the south in the entire province. There are seasonal differences in the CO2 concentration distribution across the entire province. The CO2 concentration and amplitude are higher in autumn and winter than they are in spring and summer. This study can provide scientific support and methodological reference for the spatio-temporal distribution characteristics analysis of GHGs at the provincial–regional scale, as well as at the national and global scales. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Assessment of climate change in the North-East region of Côte d'Ivoire: Future precipitation, temperature, and meteorological drought using CMIP6 models." The authors of the article have added appendix figures that were initially missing from the online version of the journal. The figures include a bias correction approach, a comparison of raw and bias-corrected precipitation and temperature data, temporal variability in annual mean temperature, and mean monthly changes in precipitation. The article is published under an Open Access license, allowing for unrestricted use, distribution, and reproduction with proper citation. [Extracted from the article]

Continuously operating reference stations (CORS) provide augmentation services for the highly accurate, cm-level GNSS positioning needs of land surveyors, agriculture, and even autonomous vehicles. These stations have accurate coordinates, thus they can be used to estimate the signal delay caused by the neutral atmosphere including the atmospheric water vapor. The estimated zenith wet delay (ZWD) is in a close correlation with the integrated water vapor in the atmospheric column. Since a ground station tracks several satellites at every epoch, one could also estimate the slant tropospheric delays, which can provide information on the spatial distribution of the atmospheric water vapor, too. This paper introduces a near real-time multi-GNSS processing approach to estimate slant wet tropospheric delays and a coupled tomographic reconstruction technique to estimate the 3D wet refractivity model that can be assimilated in numerical weather models. The estimated zenith tropospheric delays (ZTDs) and tropospheric gradients are used to restore the slant wet delays (SWD) affecting the observed satellite-receiver range. The SWDs are used as input for a tomographic reconstruction algorithm providing the wet refractivities in a pre-defined voxel model. The derived refractivity profiles have been validated with radiosonde observations. The results show that our GNSS tomography approach could reconstruct the refractivities with the uncertainty of 10 ppm below 3 km of altitude and of 0.3 ppm at the altitude of 10 km in terms of standard deviation. [ABSTRACT FROM AUTHOR]

In laser-based space technologies where laser beams are transmitted from a ground station, it is required for the ground station to avoid irradiating aircraft with high energy lasers. A simple, inexpensive acoustic positioning technique using four microphones is developed and its performance is evaluated. It is demonstrated to be sensitive to and effective in detecting aircraft flying at short ranges. Aircraft closer than 5 km can easily be detected and the maximum range is about 10 km. The time series of two-dimensional directional position estimates are harmonic with those of ADS-B (Automatic Dependent Surveillance-Broadcast), and the precision reaches 1 degree (standard deviation) in the best case. The processing time for data collection and computation is short enough to establish a realtime feedback mechanism to stop firing laser. With its advantages and disadvantages, effective combinations with existing methods are discussed to avoid injury to the eyes, in which the acoustic approach is found to be useful to enhance safety measures in particular of low-altitude aircraft with a large angular velocity. [ABSTRACT FROM AUTHOR]

Time-varying formation tracking problems for multi-agent systems (MASs) under distributed Denial-of-Service (DDoS) attacks and actuator faults are studied. To deal with the hybrid threats at the cyber and physical layers, an estimator-based fault-tolerant hierarchical control scheme is introduced, which is applicable to channel-wise asynchronous communication. Sufficient conditions for formation tracking of maneuvering leader with ultimately bounded error are obtained, and the particular case of periodic communication and attacks with constrained duration and frequency is further analyzed. Comparative physical simulations based on ROS and Gazebo are first conducted to show the resilience of our scheme against the threats. Finally, an experimental platform containing DJI Tello quadrotors and a self-developed ground control station is built, based on which practical experiments with four quadrotors are carried out to evaluate the effectiveness and engineering practicability of the proposed control framework. • A formation tracking controller against DDoS attacks and actuator faults is proposed. • Conditions for achieving formation under hybrid constraints are obtained. • Quadrotor-based experiments validate the practicability of the control strategy. [ABSTRACT FROM AUTHOR]

Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a non-stationary, sparse process such as precipitation at very high spatial resolution requires the interpolation of global datasets at the location where ground stations are available with statistical models able to capture complex non-Gaussian global space–time dependence structures. In this work, we propose a new approach based on capturing the spatially varying anisotropy of a latent Gaussian process via a locally deformed stochastic partial differential equation (SPDE) with a buffer allowing for a different spatial structure across land and sea. The finite volume approximation of the SPDE, coupled with integrated nested Laplace approximation ensures feasible Bayesian inference for tens of millions of observations. The simulation studies showcase the improved predictability of the proposed approach against stationary and no-buffer alternatives. The proposed approach is then used to yield high-resolution simulations of daily precipitation across the United States. [ABSTRACT FROM AUTHOR]

Clouds affect the Earth climate with an impact that depends on the cloud nature (solid and/or liquid water). Although the Antarctic climate is changing rapidly, cloud observations are sparse over Antarctica due to few ground stations and satellite observations. The Concordia station is located on the eastern Antarctic Plateau (75 ∘ S, 123 ∘ E; 3233 m above mean sea level), one of the driest and coldest places on Earth. We used observations of clouds, temperature, liquid water, and surface irradiance performed at Concordia during four austral summers (December 2018–2021) to analyse the link between liquid water and temperature and its impact on surface irradiance in the presence of supercooled liquid water (liquid water for temperature less than 0 ∘ C) clouds (SLWCs). Our analysis shows that, within SLWCs, temperature logarithmically increases from -36.0 to -16.0 ∘ C when liquid water path increases from 1.0 to 14.0 g m -2. The SLWC radiative forcing is positive and logarithmically increases from 0.0 to 70.0 W m -2 when liquid water path increases from 1.2 to 3.5 g m -2. This is mainly due to the downward longwave component that logarithmically increases from 0 to 90 W m -2 when liquid water path increases from 1.0 to 3.5 g m -2. The attenuation of shortwave incoming irradiance (that can reach more than 100 W m -2) is almost compensated for by the upward shortwave irradiance because of high values of surface albedo. Based on our study, we can extrapolate that, over the Antarctic continent, SLWCs have a maximum radiative forcing that is rather weak over the eastern Antarctic Plateau (0 to 7 W m -2) but 3 to 5 times larger over West Antarctica (0 to 40 W m -2), maximizing in summer and over the Antarctic Peninsula. [ABSTRACT FROM AUTHOR]

In recent years, the number of Earth Observation missions has been exponentially increasing. Satellites dedicated to these missions usually embark with payloads that produce large amount of data and that need to be transmitted towards ground stations, in time-limited windows. Moreover, the noisy nature of the link between satellites and ground stations makes it hard to achieve reliable communication. To address these problems, a standard for a flexible advanced coding and modulation scheme for high-rate telemetry applications has been defined by the Consultative Committee for Space Data Systems (CCSDS). The defined standard, referred to as CCSDS 131.2-B, makes use of Serially Concatenated Convolutional Codes (SCCC) based on 27 ModCods to optimize transmission quality. A limiting factor in the adoption of this standard is represented by the complexity and the cost of the hardware required for developing high-performance receivers. In the last decade, the performance of software has grown due to the advancement of general-purpose processing hardware, leading to the development of many high-performance software systems even in the telecommunication sector. These are commonly referred to as Software-Defined Radio (SDR), indicating a radio communication system in which components that are usually implemented in hardware, by means of FPGAs or ASICs, are instead implemented in software, offering many advantages such as flexibility, modularity, extensibility, cheaper maintenance, and cost saving. This paper proposes the development of an SDR based on NVIDIA Graphics Processing Units (GPU) for implementing the receiver end of the CCSDS 131.2-B standard. At first, a brief description of the CCSDS 131.2-B standard is given, focusing on the architecture of the transmitter and receiver sides. Then, the receiver architecture is shown, giving an overview of its functional blocks and of the implementation choices made to optimize the processing of the signal, especially for the SCCC Decoder. Finally, the performance of the system is analyzed in terms of data-rate and error correction and compared with other SW systems to highlight the achieved improvements. The presented system has been demonstrated to be a perfect solution for CCSDS 131.2-B-compliant device testing and for its use in science missions, providing a valid low-cost alternative with respect to the state-of-the-art HW receivers. [ABSTRACT FROM AUTHOR]

In recent years, the space industry has witnessed a resurgence, characterized by a notable proliferation of satellites operating at progressively lower altitudes, promising extensive global coverage and terrestrial-level data transfer speeds, while remaining cost-effective solutions. In particular, Wireless Sensor Networks (WSNs) can benefit from the wide coverage of space infrastructure due to their extensive deployment, disrupted communication nature, and the potential absence of terrestrial support. This study explored the utility of Low-Earth Orbit (LEO) satellite constellations as a communication infrastructure for interconnecting "smart" devices via ground stations in Internet of Things (IoT) scenarios. To this end, we designed and implemented a series of experiments conducted within the OMNeT++ simulator, utilizing an updated iteration of the original Open Source Satellite Simulator (OS3) framework. Our research encompassed an IoT Case Study, incorporating authentic sensor data sourced from the Smart Santander testbed. Throughout our experimentation, we investigated the impact of the constellation design parameters such as the number of satellites and orbital planes, as well as the inter-satellite link configuration on the obtained Round-Trip Time (RTT) and packet loss rates. [ABSTRACT FROM AUTHOR]

Copyright of Journal of European Integration History is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)