Operational Satellite Research Articles

Adaptive variable neighborhood search algorithm with Metropolis rule and tabu list for satellite range scheduling problem

Efficient scheduling of measurement and control resources plays a critical role in successful satellite operations. The tremendous growth in the number of satellites in orbit exacerbates resource scarcity, further enhancing the significance of the satellite range scheduling problem (SRSP). In order to maximize the benefits of measurement and control tasks, we propose an innovative algorithm to solve the SRSP with numerous constraints. Firstly, the SRSP operation flow is analyzed to establish a constraint satisfaction model. A task-resource matching algorithm based on fitness is then designed to generate an initial feasible solution with superior quality by heuristic criteria. Subsequently, an adaptive variable neighborhood search algorithm with Metropolis rule and tabu list (AVNS-MT) is proposed to solve the mathematical model. Three swap neighborhood structures are constructed to explore the solution space efficiently during the variable neighborhood descent search process. Meanwhile, three migration neighborhood structures are devised to enable the shaking of solutions and evasion from the local optimum. Moreover, the incorporation of the Metropolis rule and tabu search strategy will contribute to enhancing the algorithm convergence. Finally, we have conducted extensive simulations to verify the effectiveness and efficiency of the proposed AVNS-MT. Comparison experiments with several state-of-the-art methods validate the superiority of the proposed algorithm.

SPRISS: Scalable and precise resistive impact sensor for smallsats, architecture description and tests

Space debris are a tangible risk for satellites in Earth orbits. Indeed, in the last decades fragmentation events have generated a large number of uncontrolled objects that have made the debris population grow. Modelling the space debris environment is becoming a fundamental task to evaluate the vulnerability of operational satellites, the probability of accidental collisions with uncontrolled objects, and the evolution of the debris population. With this aim, remote and in-situ measurements provide valuable data to tune the space debris population models and improve their reliability. While large satellites can be observed and tracked from ground, the sub-millimeter debris population requires in-situ measurements. In this context, in-orbit impact sensors are a key technology to obtain information about the sub-mm space debris environment. In this framework, a small-scale impact sensor sized to be integrated in a 2U CubeSat is being developed at the University of Padova. The sensor consists of a multitude of thin, conductive stripes arranged on a thin film of non-conductive material. When a debris hits the sensor, one or more stripes are severed, and the impact is detected. Moreover, the sensor design ensures low power consumption, making it feasible for CubeSat space missions. This work presents the latest outcomes obtained from the development of the sensor. Specifically, structural analyses are performed to assess that the sensor can withstand the launch loads, as well as thermal analyses to confirm its endurance capability with in-orbit temperatures. The number of expected impacts during the mission is predicted through orbital propagation, using state-of-the-art debris environment modelling tools. Finally, the paper presents a functional shooting test and vibration tests, executed onto a development model of the sensor, which verify its functionality and validate a Technology Readiness Level (TRL) of 4.

Maneuver strategies of Starlink satellite based on SpaceX-released ephemeris

In the face of huge collision risks posed by the Starlink mega-constellation, the detection of unpredictable orbital maneuvers of Starlink satellites emerges as an important topic of Space Situational Awareness (SSA). The ephemerides released by the SpaceX company on the Space-track website provide a timely and valuable opportunity for addressing this problem. Based on the mean orbital elements extracted from Starlink ephemerides released at the end of 2023, the maneuver strategies, including maneuver frequency and characteristics of Starlink satellites in different orbital phases, are discussed, and the start time and end time with error ranges of confirmed maneuvers are acquired. It is found that operational satellites in Shell 1 perform an orbit maintenance maneuver approximately every 2 days, while those in Shells 2–5 execute a maneuver roughly once a day. Moreover, the parking satellites maneuver approximately every 0.25 days on average. As for satellites in the climbing orbit and the deorbiting orbit, they maneuver around every 0.1 days. The maneuver strategies of Starlink summarized in this paper can contribute to optimizing the observation plan of the Starlink constellation.

SP-SAT: DEVELOPMENT OF AN EDUCATIONAL SATELLITE

n Bolivia, as in several other Latin American nations, there is a growing trend of burgeoning space projects and a rising interest among young students in the aerospace domain. Consequently, there arises an imperative to cultivate a new generation of space professionals. However, the exorbitant costs associated with the equipment utilized in these projects render them inaccessible to a vast majority of inter- ested students, including CubeSat kits, educational satellites, satellite kits, among others. Consequently, alternatives like educational sat- ellites have emerged to simulate the satellite design, construction, testing, launch, and operation processes on a scale conducive to student involvement. The primary challenge faced by students lies in integrating the essential subsystems of a satellite, encompassing power supply, sensors, and communication systems, within the confines of limited space. Considering this, the design and construction of an educational satellite with its respective signal reception equipment was developed. The equipment designed and manufactured is a low-cost one so that universities and educational centers could acquire it. This equipment educational kit is called SP-SAT (Space Program — educational satellite kit). This real satellite simulator offers a wide variety of educational activities. The kit’s equipment is distributed in two systems: 1) Space Segment, which consists of an Onboard Computer (OBC), Electrical Power System (EPS), Communications System (COMM), Experiment Plate (PYL), Complete PLA plastic structure (3D printed), rods, bolts, and nuts. 2) Ground segment: This segment includes all the equipment that is part of the ground station, which will receive data from the satellite.

Cold atom microwave clock based on intracavity cooling in China space station

Atomic clocks with higher frequency stability and accuracy than traditional space-borne atomic clocks are the cornerstone of long-term autonomous operation of space-time-frequency systems. We proposed a space cold atoms clock based on an intracavity cooling scheme, which captures cold atoms at the center of a microwave cavity and then executes in situ interactions between the cold atoms and microwaves. As a result of the microgravity environment in space, the cold atoms can interact with the microwaves for a longer time, which aids in realizing a high-precision atomic clock in space. This paper presents the overall design, operational characteristics, and reliability test results of the space atomic clock based on the intracavity cooling scheme designed for the operation onboard the China space station. In addition, the engineering prototype performance of the space cold atoms microwave clock is also presented. The ground test results for the clock show a fractional frequency stability of 1.1 × 10−12 τ−1/2 reaching 2.5 × 10−15 at 200,000 s, providing solid technical and data support for its future operation in orbit.

Enabling efficient satellite mission design with rule-based collision avoidance

The growing number of operational spacecraft in Earth's orbit entails an increasing operational effort for collision avoidance (COLA), particularly regarding the coordination of evasive measures. To reduce the associated workload, COLA operations should already be considered in the early planning phases of space missions. The Mission Analysis Software (MAS) is a web-based application developed within the ESA-funded CASCADE (Collision avoidance, satellite coordination assessment demonstration environment) project by OKAPI:Orbits and TU Darmstadt for this purpose. The software follows a data-driven approach to offer satellite operators, mission designers, service providers, agencies, and authorities two services: conjunction assessment and rule analysis. The conjunction assessment provides an estimation of the number and type of conjunctions to be expected on the targeted orbit and identifies frequently conjuncting parties for the current population of active satellites as well as for conceivable future scenarios. The rule analysis follows a rule-based approach to coordinate COLA between individual operators, allowing users to build custom hierarchical rule sets from pre-defined rule building blocks to achieve a desired split of effort between the conjunction parties. The MAS enables the assessment of the operational consequences for a chosen rule set, empowering users to reach bilateral agreements with identified frequently conjuncting parties to determine the obligation to take evasive measures for future conjunctions. The approach of the MAS allows for the pre-emptive reduction of the expected number of conjunctions enabling operators to optimize orbit parameters within their mission constraints as well as the automatization of COLA coordination during operations. Through this, the MAS optimizes propellant needs, mission time, and required workforce associated with COLA for space missions.This paper presents the MAS, showcasing the key features and use cases that have been developed closely with stakeholders and the European Space Agency. Furthermore, the data-based simulation approach of the MAS will be explained, covering the data sources and design choices for the conjunction detection and propagation module. The paper also presents the results of a preliminary rule analysis conducted for the population of active satellites in low Earth orbit as of April 2023. As an intermediate result of an on-going research activity involving the authoring entities, a major goal of this paper is to engage with satellite operators, mission designers, service providers, agencies, and authorities in order to tailor the results of the activity to their actual needs.

Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images

In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized.

Spacecraft attitude testbed

Spacecraft attitude determination and control systems (ADCS) stand as a pivotal factor for ensuring the success of missions. As satellite sizes continue to decrease, the need for more intelligent and efficient control algorithms becomes increasingly pronounced. In response to this imperative, we propose the development of a scaled-down testbed specifically designed for evaluating satellites (ACDS). This testbed will demonstrate effective control of a CubeSat through the use of Helmholtz coils, magnetic torquer solenoids, reaction wheels and inexpensive student-made components and available components off the shelf (COTS) in the San Diego State University SPACE Lab. Referred to as the Spacecraft Attitude Testbed (SAT), this custom-designed 3-DoF experimental attitude platform replicates the conditions experienced by a weightless satellite in space. The objective of this research paper is to improve the understanding of how to manufacture a testbed and components like those within this paper, expose some of the issues encountered and improve the outcomes for future academic testbeds. Within this study, the testbed employs torque application to magnetic torquers and reaction wheels to regulate attitude. In summary, this research endeavors to establish a comprehensive and cost-effective testbed platform dedicated to the assessment of spacecraft attitude control and determination systems. It not only seeks to enhance our understanding of advanced control algorithms but also aims to contribute valuable insights into fault detection and adaptive control strategies, ultimately bolstering the efficiency and robustness of satellite operations.

Synthetic orbit uncertainty generation through regression analysis of historical Conjunction Data Messages

In the last decades, the Earth-orbiting population of both active and non-active objects has grown significantly, leading to a substantial increase in number of possible in-orbit collisions. It is therefore crucial to monitor the orbit of space resident objects to assess in advance the threat of risky conjunctions. Within this framework, the 18th Space Defense Squadron (SDS) is consistently updating the orbit of thousands of tracked objects by processing observations of the U.S. Space Surveillance Network (SSN). The determined orbital data is continuously maintained in the Special Perturbation (SP) catalogue and used by the 19th SDS to issue close approach warnings to satellite operators around the globe in the form of Conjunction Data Messages (CDM). The Flight Dynamics (FD) group of the German Space Operation Centre (GSOC) receives on regular basis a subset of the SP catalogue data along with CDMs associated to the fleet of its controlled satellites. The SP ephemerides are in fact provided without any covariance information preventing any computation of the Probability of Collision (Pc). In GSOC FD we are implementing a service to link a series of synthetic orbital error covariance matrices to a given SP ephemeris by statistically analyzing historical CDMs of past events. More than 30 GB of past conjunction data are processed to extract state vector, covariance matrix and object size parameter of already encountered secondary objects. The orbital errors of these last are subsequently categorized and divided into orbital classes to decouple the high correlation the covariance has with respect to solar flux, object dimension, altitude of perigee, eccentricity and orbit inclination. The classification aims at collecting similar CDMs regarding the aforementioned dependencies, and approximates the predicted 1-sigma position errors in the orbital frame by optimal curve-fitting techniques. By evaluation of the curve fitting coefficients of a requested orbit class a covariance matrix can be generated for any prediction time in upcoming CDM refinements and other analyses. The work discusses the limiting cases of the classification approach, bringing possible solutions to the scenario of empty classes. An in-depth characterization of the parameters that affect the orbital errors is in fact performed to individualize the neighboring class that provides the closest and most meaningful covariance timeline. Successively, the effect of using synthetic covariance in a conjunction risk assessment is also explored, adapting the problem on real operations. Lastly, the entire data processing pipeline and how the described service fits into the GSOC Flight Dynamics System (FDS) framework is described.

Numerical simulation of gas-liquid interface in a blade type propellant tank

Abstract Currently, most of the satellite propulsion systems are mainly on chemical propulsion, and liquid propellant is the necessary power fuel for the propulsion system. The propellant tank of the satellite is a critical unit used to store and manage propellant. The hard core of the blade type tank is the Propellant Management Device (PMD), which serves to provide non entrained propellant for the propulser under specified flow and acceleration conditions during all phases of satellite operation. In this paper, the management performance of propellant under microgravity environment in the tank of a blade type is studied. Using the VOF model, the liquid management performance under microgravity environment in the tank is numerically simulated. The repositioning process is numerical simulated. The good management performance of the tank is verified, and the fluid distribution law is obtained, which can provide guidance for the design of the blade type tank.

Method of sequential intention inference for a space target based on meta-fuzzy decision tree

For an operational satellite in orbit, potential risks may arise when an unknown target approaches. Therefore, it is essential for the satellite to infer the intentions of the target in space. To achieve this objective, this paper proposes a method of sequential intention inference based on meta-fuzzy decision tree. The proposed method initially defines three situational functions: flyaround, collision, and observation. These functions help organize the inherent characteristics found in the trace and relative attitude of the target. Subsequently, by combining fuzzy decision tree (FDT), spatio-temporal modeling based on Long Short-Term Memory (LSTM), and task migration using meta-learning techniques, we can realize sequential intention inference for the target while ensuring quick adaptation to new tasks. Additionally, our proposed method takes into account the impact of fuzzy elements within FDT. Through quantum genetic algorithm (QGA) optimization, these fuzzy elements can be fine-tuned to enhance intention inference accuracy.

The study of thermal-structural coupling deformation analysis for flexible space manipulator in orbit

Abstract The space manipulator can assist astronauts to accomplish space activities, including docking, fixing, and grasping. It is subjected to thermal radiation and produces thermal deformation during orbit operation, which makes the operation of the space manipulator deviate from the predetermined trajectory and further affects its positioning accuracy. Therefore, to solve the problem of bidirectional coupling thermal-structure deformation analysis and positioning accuracy for space manipulator, based on the thermal-structural bidirectional coupling deformation analysis, a method of its thermal deformation on the output positioning accuracy of space flexible manipulator is proposed. It analyzes the bidirectional coupling relationship between the temperature and its thermal deformation for the manipulators. Then, the influence of thermal deformation on the output joint error and end positioning accuracy of the space manipulator are analyzed. Finally, the validity of this method is verified by numerical analysis. Compared with the unidirectional coupling model, the bidirectional coupling model comprehensively considers the structure, deformation and temperature of manipulators. It is closer to the real system. Thermal deformation will reduce the reliable runtime of the space manipulator in orbit. The study provides a theoretical basis for its thermal design and control.

A new spectrally-invariant approach to the remote sensing of inhomogeneous clouds

Current operational satellite retrievals of cloud optical and microphysical properties go back to the Nakajima-King technique developed at the end of the 1980 s. This technique is based on library calculations for plane-parallel homogeneous clouds. It often works well for overcast skies but leads to substantial errors for inhomogeneous and broken cloud fields where the plane-parallel-geometry assumption is no longer valid. The basic concept of a new technique was first introduced in nuclear physics to quantify the critical conditions of reactors. Based on the eigenvalues of the radiative transfer equation, their approach provides a powerful means to parameterize the structure of 3D media. This parameterization was later successfully applied to relate surface reflectance spectra to 3D canopy structure and is known as the spectrally-invariant approximation. The proposed approach adapts this technique to the remote sensing of cloud properties such as droplet single scattering albedo and the average number of scatterings (which are the fundamental parameters in radiative transfer theory), with an emphasis on quantifying the associated errors and uncertainties. This retrieval is free from the plane-parallel homogeneous cloud assumption.

Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests “ensemble of ensembles” technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.

BRDF-Based Photometric Modeling of LEO Constellation Satellite from Massive Observations

BRDF-Based Photometric Modeling of LEO Constellation Satellite from Massive Observations

Comprehensive Vibration Analysis and Modeling for ZY-3 Satellite's Lifecycle

Abstract. A stable platform environment is essential for achieving high-quality imaging and accurate attitude measurement of the ZY-3's stereo camera. It serves as the fundamental guarantee for the satellite's high geometric precision. Throughout its orbital operation, the platform encounters periodic vibrational occurrences resulting from internal load mechanics and significant external environmental variations, thereby impeding the high-fidelity collection of information by the satellite payload. The detection and modeling of vibrations are crucial tasks for the satellite ground system, as they provide reliable parameter inputs for optimizing attitude accuracy and image quality. This paper presents a method for developing a comprehensive frequency variation vibrational model of the ZY-3 satellite platform throughout its lifecycle, utilizing the inertial gyroscopes (gyros) data of the ZY-3 satellite. The instrument has identified vibrations at three specific frequencies: 0.21 Hz, 0.56 Hz, and 1.12 Hz, each with amplitudes of 0.07″, 0.04″, and 0.04″, respectively. Additionally, it has detected two fluctuating frequencies around 0.3 Hz and 0.6Hz. The period of frequency variation is approximately one year, with vibration ranges of (0.267, 0.299) and (0.603, 0.695). Following a malfunction of the solar panels on the satellite, there has been a shift in the vibration range. Moreover, as the satellite's on-orbit duration increases, this range is also undergoing gradual changes.

Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development

Abstract. Cloud feedbacks associated with deep convective anvils remain highly uncertain. In part, this uncertainty arises from a lack of understanding of how microphysical processes influence the cloud radiative effect. In particular, climate models have a poor representation of microphysics processes, thereby encouraging the collection and study of observation data to enable better representation of these processes in models. As such, the Deep Convective Microphysics Experiment (DCMEX) undertook an in situ aircraft and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar, thermodynamics, dynamics, electric fields, and weather. This paper introduces the potential data user to DCMEX observational campaign characteristics, relevant instrument details, and references to more detailed instrument descriptions. Also included is information on the structure and important files in the dataset in order to aid the accessibility of the dataset to new users. Our overview of the campaign cases illustrates the complementary operational observations available and demonstrates the breadth of the campaign cases observed. During the campaign, a wide selection of environmental conditions occurred, ranging from dry, northerly air masses with low wind shear to moist, southerly air masses with high wind shear. This provided a wide range of different convective growth situations. Of 19 flight days, only 2 d lacked the formation of convective cloud. The dataset presented (https://doi.org/10.5285/B1211AD185E24B488D41DD98F957506C; Facility for Airborne Atmospheric Measurements et al., 2024) will help establish a new understanding of processes on the smallest cloud- and aerosol-particle scales and, once combined with operational satellite observations and modelling, can support efforts to reduce the uncertainty of anvil cloud radiative impacts on climate scales.

Conserved spin operator of Dirac’s theory in spatially flat FLRW space-times

New conserved spin and orbital angular momentum operators of Dirac’s theory on spatially flat FLRW space-times are proposed generalizing thus the recent results concerning the role of Pryce’s spin operator in the flat case (Cotăescu in Eur Phys J C, 82, 1073, 2022). These operators split the conserved total angular momentum generating the new spin and orbital symmetries that form the rotations of the isometry groups. The new spin operator is defined and studied in active mode with the help of a suitable spectral representation giving its Fourier transform. Moreover, in the same manner is defined the operator of the fermion polarization. The orbital angular momentum is derived in passive mode using a new method, inspired by Wigner’s theory of induced representations, but working properly only for global rotations. In this approach the quantization is performed finding that the one-particle spin and orbital angular momentum operators have the same form in any FLRW spacetime regardless their concrete geometries given by various scale factors.

An economic indicator of the orbital debris environment

The continued growth of orbital debris increases potential losses faced by commercial operators in Earth's orbits. Yet, there is no commonly accepted measure that describes the orbital debris environment from an economic perspective. This study begins to fill that gap by developing an Orbital Debris Economic Loss Index (ODELI) that measures and tracks the changes in the expected negative economic impact of orbital debris on satellite operators, both in aggregate and in specific orbits. Such information is valuable to the stakeholders, such as policymakers, commercial operators, and the public, in communicating valuable information about the economic state of the orbital debris environment.We illustrate the calculation of the index utilizing the data from 2012 to 2022. The analysis is based on the publicly available data and the Meteoroid and Space Debris Terrestrial Environment Reference (MASTER) orbital debris environment model. Our analysis suggests that the aggregate expected economic damage to Earth's orbits is increasing at a slower rate than the growth rate of the number of satellites or trackable pieces of debris objects. The slower rate of growth in ODELI indices from 2012 to 2022 is explained by a decrease in the average mass of satellites, a reduction in the real cost of placing satellites in orbit, and a commercial preference to launch satellites into orbits with lower debris density.The estimates of annual expected economic losses from debris collisions increased from $86 million to $107 million from 2012 to 2022, and the losses are concentrated in the low-Earth orbit (LEO). However, LEO had the smallest rate of increase in ODELI compared to other orbits. Medium-Earth orbit (MEO), which has the smallest contribution to the combined expected economic losses from debris on the Earth's orbits, experienced the fastest rate of increase in ODELI during the same period.