Earth Orbit Satellite Research Articles

Gravity-based network traffic abstraction and laser ON/OFF control in optical satellite networks

As the cost of launching low Earth orbit satellites continuously decreases, satellite-based communications networks are emerging as a new area for both academia and industry. Lasers are already employed for building inter-satellite links, forming optical satellite networks. The orbiting nature of satellites determines that the optical satellite networks are usually uniformly distributed around the Earth, to provide seamless coverage to any place at all times. However, the end users on the Earth are non-uniformly distributed. As a result, many satellites with laser links might not be utilized efficiently. From the energy perspective, this work studies the energy-efficiency issues of the inter-satellite laser links. We first model the optical satellite networks by presenting the satellite constellation with inter-satellite laser design principles and introduce the laser ON/OFF control problem accordingly. To explore the possibility of saving energy in the massive satellite deployment, we further introduce the gravity-based network traffic model and propose a gravity-based network traffic abstraction (GNTA) model to evaluate the importance of each laser link. Accordingly, we further propose a GNTA-based ON/OFF control (GOOC) algorithm to improve the energy efficiency of inter-satellite laser links by switching OFF parts of the laser links that are less utilized. We evaluate the GOOC’s performance using simulation, and results show that switching OFF 20% of the laser terminals in the full-gridmesh topology can improve the energy efficiency by about 10%, with an acceptable cost of network performance degradation.

A Rapid-Convergence Precise Point Positioning Approach Using Double Augmentations of Low Earth Orbit Satellite and Atmospheric Information

The precise point positioning (PPP) technique generally takes tens of minutes to converge, severely limiting its use. This longer convergence time is mainly due to the slower variation of satellite geometry in space and the stronger correlation of unknown parameters to be estimated. Fortunately, the lower orbit altitude of Low Earth Orbit (LEO) satellites contributes to the fast variation of the satellites’ spatial geometry. In addition, high-precision atmospheric delay information has become readily available, which can help decrease unknown parameters’ correlation. This study proposes a double-augmentation PPP approach with accelerated convergence by tightly integrating the LEO/atmosphere-augmented information. The GNSS observations in both mid-latitude and low-latitude areas, and simulated LEO observations under a Walker/polar mixed constellation, are used to validate the double-augmentation PPP approach. Test results in both areas indicate that the double-augmentation PPP can converge within 0.8 min, improving the convergence time by over 73%, and over 83% compared to the LEO-only augmented PPP and atmosphere-only augmented PPP.

Precise orbit determination of Haiyang-2D using onboard BDS-3 B1C/B2a observations with ambiguity resolution

The Haiyang-2D altimetry mission of China is one of the first Low Earth Orbit (LEO) satellites that can receive new B1C/B2a signals from the BeiDou-3 Navigation Satellite System (BDS-3) for Precise Orbit Determination (POD). In this work, the achievable accuracy of the single-receiver ambiguity resolution for onboard LEO satellites is studied based on the real measurements of new BDS-3 frequencies. Under normal conditions, six BDS-3 satellites on average are visible. However, the multipath of the B1C/B2a code observations presents some patchy patterns that cause near-field variations with an amplitude of approximately 40 cm and deteriorate the ambiguity-fixed rate. By modeling those errors, for the B2a code, a remarkable reduction of 53% in the Root Mean Square (RMS) is achieved at high elevations, along with an increase of 8% in the ambiguity-fixed rates. Additionally, an analysis of the onboard antenna's phase center offsets reveals that when compared to the solutions with float ambiguities, the estimated values in the antenna’s Z direction in the solutions with fixed ambiguities are notably smaller. The independent validation of the resulting POD using satellite laser ranging at 16 selected high-performance stations shows that the residuals are reduced by a minimum of 15.4% for ambiguity-fixed solutions with an RMS consistency of approximately 2.2 cm. Furthermore, when compared to the DORIS-derived orbits, a 4.3 cm 3D RMS consistency is achieved for the BDS-3-derived orbits, and the along-track bias is reduced from 2.9 to 0.4 cm using ambiguity fixing.

OFDM-Based Massive Connectivity for LEO Satellite Internet of Things

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bay esian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.

Load-Balanced Collaborative Offloading for LEO Satellite Networks

Low earth orbit (LEO) satellite networks have become one of the hot research areas as an essential part of satellite communication networks. The dynamic topology and unbalanced traffic demand may lead to inter-satellite link congestion, thus, improving network load balancing performance is one of the key issues to be addressed in LEO satellite networks. We propose a load-balanced collaborative offloading (LBCO) strategy to achieve a balanced traffic distribution in LEO satellite networks. LBCO strategy consists of two algorithms, namely, channel-aware gradient fair association (CAGFA) algorithm and inter-satellite links collaborative offloading (ISLCO) algorithm. The CAGFA algorithm aims to maximize the aggregate weighted utility, and the ISLCO algorithm aims to achieve the traffic offloading and download observation data from the LEO satellite network. Specifically, we first determine the actual downloading satellite set and neighboring satellite set by constructing an earth station (ES) time-share graph and a space-time topology graph. Then, the LBCO strategy uses the CAGFA algorithm to obtain the optimal satellite terminal association indicator and the load of downloading satellites. Finally, the ISLCO algorithm is proposed to achieve proportional offloading of traffic among the neighboring satellites and download massive observation data. Simulations show that the proposed CAGFA algorithm improves the weighted utility by 3.3% and the convergence by 47.6% compared with the benchmark stochastic gradient descent-based association (SGDA) algorithm. We also validate the performance of the LBCO strategy by data download throughput, which performs better than other benchmark algorithms under three different load scenarios.

Design of Light, Small, and High-precision Digital Servo Clock for LEO Constellation

Modern low earth orbit (LEO) satellite systems have placed increasingly stringent requirements on spaceborne time-frequency systems in the fields of constellation autonomous management, distributed synthetic aperture radar (SAR) imaging, and navigation time service. Admittedly, the original high-stability constant-temperature crystal oscillator and high-stability temperature-compensated crystal oscillator can no longer comply with the indicator requirements of clock source frequency characteristics and clock synchronization accuracy. Likewise, traditional satellites use atomic clocks (rubidium atomic clocks and cesium atomic clocks). On account of their inherent characteristics, the ground receiving system needs to observe and count the clock parameter characteristics for a long time after the satellite enters orbit and frequently modify the future working state parameters. The features of being expensive and bulky increase the operation and maintenance costs of the ground system and the complexity of engineering implementation. In this paper, a kind of light and small high-precision digital servo clock based on the deep coupling of the global navigation satellite system (GNSS) is designed for the LEO satellite constellation. The tame clock is deeply coupled with the GNSS navigation receiver, and the high precision time-frequency difference between the local clock frequency source and GNSS is obtained by using the carrier and pseudo-range measurement information of visible satellite and carrier phase measurement information of the receiver. It not only utilizes the short-term high stability of OCXO but also employs the long-term time-frequency maintenance ability of GNSS so that the tamed OCXO is locked in GNSS for a long time, which can achieve frequency calibration of local clock sources and long-term output to various space-borne systems. The system is small in size and low in power consumption, with output frequency stability better than 3E-13 (10000 s), accuracy better than ±2E-13, and timing accuracy better than ±10 ns, which is comparable to the key technical indexes of spaceborne miniaturized rubidium clocks, in line with the current demand of miniaturization, low power consumption, and low cost of LEO constellations.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Modeling and analysis for reaction control thruster used for station-keeping maneuvers of a GEO satellite

This paper introduces a theoretical description, force, and torque analysis of a Reaction Control Thruster (RCT) used as an actuator in Attitude Determination and Control Subsystem (ADCS). Mathematical formalization, realization, and implementation are offered to implement the RCT model using Matlab-Simulink. Furthermore, this paper proposes an accurate RCT Simulink model to control the orientation and stabilizes the Geostationary Earth Orbit (GEO) satellite within its orbit. Finally, a comparative analysis is carried out with an existing satellite platform (Eurostar-3000) based on the Root Mean Square Error (RMSE) for the delta velocity (delta-𝒱) parameter. The outcomes of the model simulation validated the effectiveness of the proposed approach.

Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval

Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

MODEL FOR CALCULATION OF THE ENERGY POTENTIAL OF A SATELLITE RADIO LINK WITH ACCOUNT OF THE TRAJECTORY

A model of satellite communication channel taking into account the trajectory, specifically developed for low Earth orbit satellite constellations, is presented. Low and high orbits have different advantages and limitations. To efficiently utilize resources in low Earth orbit satellites, it is necessary to forecast changes in the energy potential and adapt the parameters of satellite modems. For this purpose, there are such programs as Computer-Aided Design (CAD) "Albatross" for evaluating the dynamics of resource changes. However, a realtime model has been developed for the operational management of modems, allowing the construction of temporal diagrams depicting changes in the energy potential of communication channels. The development of such models can be performed in the LabVIEW environment, a software developed by National Instruments for creating virtual instruments (VIs). The development process includes considering the motion of the satellite in a user-defined orbit, taking into account various parameters. SubVIs are used to simplify program creation, improve code readability and enable element reuse. To construct one orbit the number of iterations in the loop is calculated, which is the rounded value of the orbital rotation period divided by the time difference between the calculated sub-satellite points. The smaller the value of the time difference is, the more accurate the trajectory will be. Constructing a trajectory with a larger number of revolutions requires more time. For performing the energy calculation, the antenna pattern (AP) is calculated, and a satellite trajectory is constructed with the cross-section of the antenna pattern on Earth. The developed model allows evaluation of the satellite trajectory for a given number of revolutions around the Earth and conducting energy potential calculations for a specified location of the Earth station. The results can be used for building algorithms for adaptive control of parameters in satellite radio links.

A fast satellite selection algorithm for positioning in LEO constellation

In the near future, a much larger scale of low earth orbit (LEO) satellites will be deployed, and satellite selection is an effective way to overcome challenges to processing capability and channel limitation of receivers caused by superabundant satellites in view. However, existing algorithms do not take into account the fact that a large-scale LEO constellation satellite selection subset can be closer to the theoretical optimal configuration than the traditional brute force search and recursive algorithm computational overhead is too large for end devices. In this study, the solution of the minimum optimization problem of the GDOP (Geometric Dilution of Precision) under the condition of limited elevation angle is first analyzed to determine the optimal ratio configuration scheme of top and bottom satellites under the optimal geometric satellite positioning problem. Subsequently, a fast satellite selection algorithm is proposed, which selects a subset of satellites that meet the different numbers of observation satellites and is close to the optimal geometric configuration by geometric method. The comparative simulation shows that, in the 1586 LEO constellation system, the proposed algorithm has an average GDOP of 1.52 when the satellite subset size is 10 and 0.95 when the subset size is 30. Compared with the recursive, the Rotating Partition algorithm and the Quasi-optimal algorithm, the proposed algorithm achieves better accuracy when its size is not close to the total number of observable satellites.

An optimization framework with improved auction-based initialization for highly constrained on-orbit servicing mission planning

The on-orbit servicing (OOS) mission planning problem for multiple geosynchronous earth orbit (GSO) satellites is a critical issue due to its huge economic value. However, given the complexity of the space scenario and the subjective nature of the formulation process, the problem is highly constrained in actual use, and the performance of conventional optimization algorithms decreases due to fuel or mission deadline constraints. With the goal of enhancing solution quality and speeding up the solution process, this paper proposes a novel optimization framework. The problem is decomposed into three sub-problems and then solved using the proposed three-level algorithm to lessen its complexity. In addition, a new initialization method is embedded to improve global search capabilities. Notably, considering the high computation efficiency of the auction algorithm (AA), an improved version is designed to generate primary solutions, which introduces the grouping and reallocation mechanism. Then, several variations of the primary solutions are used as the initial variables in the optimization. Numerical simulations demonstrate that the proposed methodology has higher computational efficiency and convergence performance than conventional strategies.

GPS-SBAS-Based Orbit Determination for Low Earth Orbiting Satellites

A space-based augmentation system (SBAS) provides real-time GNSS correction signals via geostationary satellites for near-ground GNSS users. To use the SBAS correction for low Earth orbit (LEO) satellites, the correction, especially the ionosphere correction, must be adjusted for the LEO altitude. We apply modified SBAS data to LEO satellite onboard navigator to improve the positioning accuracy of a LEO satellite for possible real-time use. The onboard navigator requires high positioning reliability, and code pseudoranges, rather than phase pseudoranges, are used for the primary measurements. The Galileo NeQuick G model is used to determine the real-time conversion factor of the SBAS ionosphere correction for a LEO satellite. The GPS L1 data from GRACE satellite are combined with the SBAS data from the ground receiver. The onboard navigator combines the precise satellite dynamic model with an extended Kalman filter to improve positioning accuracy and stability. The kinematic positioning method, which uses the weighted least square method without the dynamic model, is also performed for comparison. The SBAS correction reduces the positioning error in both the kinematic positioning and the dynamic positioning. The positioning error reduction of the GPS and WAAS case over the GPS-only case is 25.2% for the kinematic method and 30.6% for the dynamic method. In the case of the dynamic method with the SBAS corrections, the positioning error remains smaller than that of the GPS-only dynamic method even after the satellite has left the SBAS service area.

Link duration-aware shared backup path protection (LD-SBPP) against SRLG failure in LEO-satellite-terrestrial integrated optical networks

Large-scale disasters are very destructive to terrestrial optical networks (TONs), and most existing survivability technologies that rely solely on TONs themselves can hardly combat disasters. Satellite networks, as a promising complement to terrestrial systems, can help to improve the survivability of TONs against disasters. However, the time-varying nature of satellite networks poses new challenges to traditional survivability technologies. This paper considers the low earth orbit (LEO) satellite network-assisted shared backup path protection. Since the link-switching consumes a certain amount of time and would cause the delay jitter and the quality of transmission (QoT) fluctuation of the space lightpath, a link duration-aware shared backup path protection (LD-SBPP) scheme is proposed to search space backup lightpaths with the minimum number of link-switching. A pre-calculated snapshot continuous routing and wavelength assignment (P-SCRWA) algorithm is proposed to pre-calculate the routing and reserve spectrum resources at each continuous snapshot. Simulations show that user requests cannot be effectively protected by relying on the TON solely, while the protection can be achieved with the help of LEO satellites. The LD-SBPP scheme has high wavelength utilization efficiency, resulting in lower blocking probability. Besides, the link duration-aware strategy can greatly reduce the number of link-switching along space backup lightpaths.

Enhancing accuracy of information processing in onboard subsystems of UAVs

The object of research is the onboard subsystems of Unmanned Aerial Vehicles (UAVs). The research is aimed at analyzing UAVs, specifically the integration and enhancement of satellite-based positioning systems, including Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). The problem concerns traditional satellite-based positioning services, especially those relying solely on medium earth orbit (MEO) satellites, which are insufficient for specific requirements. The study aims to address the limitations of these systems on onboard subsystems of UAVs, especially in challenging environments laden with jammers and interference, and to provide a more accurate, robust, and continuous positioning solution. The research proposes a «multilayer system of systems» approach that integrates signals from various sources, including low Earth orbit (LEO) satellites, ground-based positioning, navigation, and timing (PNT) systems, and user-centric sensors. The combined approach, termed LeGNSS/INS, leverages the strengths of each component, providing redundancy and enhanced accuracy. The system's performance was evaluated using pseudo-real output data, demonstrating its ability to generate quasi-real dynamic trajectories for UAV flight. The error analysis showed that the proposed method consistently outperforms traditional GNSS systems, especially in challenging environments. The enhanced performance of the LeGNSS/INS system can be attributed to integrating multiple satellite systems with INS and applying optimal filtering techniques. The research also employed mathematical modeling to represent the dependencies and interactions when combining data from different sources, such as GPS, LEO, and INS. The Kalman filter is a mechanism to fuse data from multiple sources optimally. The insights from this study apply to various sectors, including aviation, maritime navigation, autonomous drones, and defense. The enhanced positioning accuracy can significantly improve safety, navigation precision, and operational efficiency. However, the study assumes idealized conditions for satellite signal reception, which might not always be accurate in real-world scenarios. Challenges, such as the martial law conditions in Ukraine affecting data collection and potential satellite signal restrictions, were also highlighted. Further research can delve into the impact of more complex environmental factors and the integration of additional satellite systems or sensors to enhance accuracy further.

Metals from spacecraft reentry in stratospheric aerosol particles

Large increases in the number of low earth orbit satellites are projected in the coming decades [L. Schulz, K.-H. Glassmeier, Adv. Space Res. 67, 1002-1025 (2021)] with perhaps 50,000 additional satellites in orbit by 2030 [GAO, Large constellations of satellites: Mitigating environmental and other effects (2022)]. When spent rocket bodies and defunct satellites reenter the atmosphere, they produce metal vapors that condense into aerosol particles that descend into the stratosphere. So far, models of spacecraft reentry have focused on understanding the hazard presented by objects that survive to the surface rather than on the fate of the metals that vaporize. Here, we show that metals that vaporized during spacecraft reentries can be clearly measured in stratospheric sulfuric acid particles. Over 20 elements from reentry were detected and were present in ratios consistent with alloys used in spacecraft. The mass of lithium, aluminum, copper, and lead from the reentry of spacecraft was found to exceed the cosmic dust influx of those metals. About 10% of stratospheric sulfuric acid particles larger than 120 nm in diameter contain aluminum and other elements from spacecraft reentry. Planned increases in the number of low earth orbit satellites within the next few decades could cause up to half of stratospheric sulfuric acid particles to contain metals from reentry. The influence of this level of metallic content on the properties of stratospheric aerosol is unknown.

Deep-Reinforcement-Learning-Based NOMA-Aided Slotted ALOHA for LEO Satellite IoT Networks

The low earth orbit (LEO) satellites have received extensive attention as an essential supplement to the terrestrial network for supporting global Internet of Things (IoT) services. Considering the rapid growth of IoT devices and the significant satellite-to-ground latency, proposing low-latency, low-overhead access protocols for LEO satellite IoT systems is challenging. In this paper, we propose a multi-beam random access (RA) framework and deploy the deep reinforcement learning (DRL) algorithm to control the non-orthogonal multiple access (NOMA) aided random access strategy. First, we divide the satellite coverage region into multiple beams and assume that the adjacent beams share parts of regions. Hence, the devices in the sharing region are allowed to transmit packets in two periods allocated for the two beams. Then, packets in multiple beams can be decoded jointly by an inter-slot successive interference cancellation (SIC) decoder. In addition, we consider the heterogeneity among devices and assign different power levels for heterogeneous types of devices, which enables power-domain NOMA and the intra-slot SIC decoder in this system to mitigate the collision resolution. To maximize the average throughput, the deep deterministic policy gradient (DDPG) algorithm is adopted to achieve an online decision to optimize the random access protocol where the packet repetition strategies of devices are adjusted dynamically. The simulation results show that the proposed scheme outperforms the traditional benchmark schemes with significant throughput gain.

Analysis of the thermal design of a COTS-based modular battery system for satellites by thermal vacuum testing

The qualification of components for satellite applications is a costly process due to the extreme conditions that must be endured in space. Therefore, space market access of battery technology innovations is often inhibited. However, modern battery technologies offer great advantages for satellite applications. In this work, a commercial off-the-shelf (COTS) based and modular lithium-ion battery system for satellites in Low Earth Orbit (LEO) is presented. A comparative analysis to evaluate system parameters and functionality of the proposed battery system and literature data is performed. A thermal vacuum test campaign is carried out to investigate the behaviour under LEO relevant conditions and to achieve qualification of the system performance according to the ECSS (European Cooperation for Space Standardization) standard. The tested system consists of two modules with 28 V nominal voltage and eight battery cells each. Experiments were conducted inside a vacuum chamber. The battery system was charged and discharged in temperatures from 0 °C to 45 °C in a high-vacuum for three weeks. The influence of the battery management electronics, the behaviour of the cells and the heating were analyzed. The cell temperatures stayed in the operating limit during 3.5 A and 10 A cycling. The battery system, however, exceeded the cell’s upper operating limit with a 40 °C baseplate and 3.5 A charging. Despite the dense system architecture with electronics between the cells the battery system can safely deliver power in a broad temperature range. Further investigations regarding safety and failure modes are necessary, along with advancements on software and state estimation algorithms.

Data formats and standards for opportunistic rainfall sensors

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel.

This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT's location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.