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Real-time high-precision autonomous orbit determination of low Earth orbit (LEO) satellites is crucial for achieving high-precision LEO satellite navigation augmentation. This study presents a real-time high-precision undifferenced kinematic POD algorithm. In contrast to traditional orbit determination algorithms, a modular strategy is employed to decompose the orbit determination algorithm. Additionally, considering the error characteristics of LEO satellite observations, the data processing flow of on-board GNSS receivers is simplified. This approach not only ensures the accuracy of orbit determination but also reduces the computational burden, enhances the real-time performance of the algorithm, and facilitates its hardware implementation. The test results indicate that the orbit determination error of the proposed algorithm exhibits no significant systematic differences, with the RMS values of the three-axis pointing all less than 3 cm. The experimental findings validate the effectiveness and correctness of the proposed method.

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.

Context: Scientific data reduction on-board deep space missions is a powerful approach to maximise science return, in the absence of wide telemetry bandwidths. The Polarimetric and Helioseismic Imager (PHI) on-board the Solar Orbiter (SO) is the first solar spectropolarimeter that opted for this solution, and provides the scientific community with science-ready data directly from orbit. This is the first instance of full solar spectropolarimetric data reduction on a spacecraft. Methods: In this paper, we analyse the accuracy achieved by the on-board data reduction, which is determined by the trade-offs taken to reduce computational demands and ensure autonomous operation of the instrument during the data reduction process. We look at the magnitude and nature of errors introduced in the different pipeline steps of the processing. We use an MHD sunspot simulation to isolate the data processing from other sources of inaccuracy. We process the data set with calibration data obtained from SO/PHI in orbit, and compare results calculated on a representative SO/PHI model on ground with a reference implementation of the same pipeline, without the on-board processing trade-offs. Results: Our investigation shows that the accuracy in the determination of the Stokes vectors, achieved by the data processing, is at least two orders of magnitude better than what the instrument was designed to achieve as final accuracy. Therefore, the data accuracy and the polarimetric sensitivity are not compromised by the on-board data processing. Furthermore, we also found that the errors in the physical parameters are within the numerical accuracy of typical RTE inversions with a Milne-Eddington approximation of the atmosphere. Conclusion: This paper demonstrates that the on-board data reduction of the data from SO/PHI does not compromise the accuracy of the processing. This places on-board data processing as a viable alternative for future scientific instruments that would need more telemetry than many missions are able to provide, in particular those in deep space.

Wearable electromyography devices can detect muscular activity for health monitoring and body motion tracking, but this approach is limited by weak and stochastic signals with a low spatial resolution. Alternatively, echomyography can detect muscle movement using ultrasound waves, but typically relies on complex transducer arrays, which are bulky, have high power consumption and can limit user mobility. Here we report a fully integrated wearable echomyography system that consists of a customized single transducer, a wireless circuit for data processing and an on-board battery for power. The system can be attached to the skin and provides accurate long-term wireless monitoring of muscles. To illustrate its capabilities, we use this system to detect the activity of the diaphragm, which allows the recognition of different breathing modes. We also develop a deep learning algorithm to correlate the single-transducer radio-frequency data from forearm muscles with hand gestures to accurately and continuously track 13 hand joints with a mean error of only 7.9°. An echomyography system based on a single transducer can be integrated into a wearable patch and used to monitor breathing patterns and hand gestures.

The maritime world experienced a very significant development such as the process of cargo planning is the existence of a system called loading instrument which is regulated by several regulations from both Classification and IACS. Damage stability calculation is also very important for loading instruments. Currently in Indonesia, there is no known algorithm to calculate direct damage stability from 3D models. And also there is still no loading instrument in Indonesia that applies the algorithm. Therefore, this research aims to develop a direct damage stability calculation algorithm for the development of loading instruments in Indonesia. Direct damage stability uses the lost buoyancy method which is calculated from 3D geometry model directly. The calculation process starts by dividing the ship geometry and compartment with the desired station. In general, the numerical integration used in the calculation process is the Simpson method. Then Newton’s iteration is used to find an equilibrium condition by predetermined conditions. Finally, the calculation results will be compared with the cases of LR Rules and IACS Rules UR L5 as a form of validation. The calculation algorithm can later be applied to the onboard loading software computer needed by the ship for the loading process with damage applications.

The increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety of applications for Earth observation and monitoring. At the same time, it requires new techniques that are able to manage the amount of data stored and transmitted to the ground. Advanced techniques for on-board data processing answer this problem, offering the possibility to select only the data of interest for a specific application or to extract specific information from data. However, the computational resources that exist on-board are limited compared to the ground segment availability. Alternatively, in applications such as change detection, only images containing changes are useful and worth being stored and sent to the ground. In this paper, we propose a change detection scheme that could be run on-board. It relies on a feature-based representation of the acquired images which is obtained by means of an auto-associative neural network (AANN). Once the AANN is trained, the dissimilarity between two images is evaluated in terms of the extracted features. This information can be subsequently turned into a change detection result. This study, which presents one of the first techniques for on-board change detection, yielded encouraging results on a set of Sentinel-2 images, even in light of comparison with a benchmark technique.

Remotely sensed hyperspectral imaging is a very active research area, with numerous contributions in the recent scientific literature. The analysis of these images represents an extremely complex procedure from a computational point of view, mainly due to the high dimensionality of the data and the inherent complexity of the state-of-the-art algorithms for processing hyperspectral images. This computational cost represents a significant disadvantage in applications that require real-time response, such as fire tracing, prevention and monitoring of natural disasters, chemical spills, and other environmental pollution. Many of these algorithms consider, as one of their fundamental stages to fully process a hyperspectral image, a dimensionality reduction in order to remove noise and redundant information in the hyperspectral images under analysis. Therefore, it is possible to significantly reduce the size of the images, and hence, alleviate data storage requirements. However, this step is not exempt of computationally complex matrix operations, such as the computation of the eigenvalues and the eigenvectors of large and dense matrices. Hence, for the aforementioned applications in which prompt replies are mandatory, this dimensionality reduction must be considerably accelerated, typically through the utilization of high-performance computing platforms. For this purpose, reconfigurable hardware solutions such as field-programmable gate arrays have been consolidated during the last years as one of the standard choices for the fast processing of hyperspectral remotely sensed images due to their smaller size, weight and power consumption when compared with other high-performance computing systems. In this paper, we propose the implementation in reconfigurable hardware of the principal component analysis (PCA) algorithm to carry out the dimensionality reduction in hyperspectral images. Experimental results demonstrate that our hardware version of the PCA algorithm significantly outperforms a commercial software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing. Furthermore, our implementation exhibits real-time performance with regard to the time that the targeted hyperspectral instrument takes to collect the image data.

The explosive growth of lean satellites (small/micro/nano/pico) has positioned this class of satellites as a unique tool for space science missions. Langmuir probe is one of the most frequently used payloads to achieve space plasma measurement missions because of their low requirement in terms of system development, size, and relatively low power requirements to drive the mission. Double Langmuir probe can overcome the limitations of a single Langmuir probe measurement as reference ground in the satellite is not required. The limitation of data communication speed is one of the problems that always occur in the development of a multi-mission lean satellite. Data communication speed can be improved by using another system such as S-band or X-band, but those will be required for high power consumption. One of the solutions is the reduced size of the data by using on-board data processing on satellites. On-board data processing aims to improve data communication speed when sending DLP data to ground stations and how to determine plasma parameters. On-board data processing for a double Langmuir probe will be calculated electron temperature and electron density by using Raspberry PI zero. On-board data processing aims to reduce the size of data so that the time of transmission between satellite and ground stations will be shorter. On-board data processing can be a pragmatic solution because processed data only contains useful information according to user requirements.

The on-board real-time tree crown detection from high-resolution remote sensing images is beneficial for avoiding the delay between data acquisition and processing, reducing the quantity of data transmission from the satellite to the ground, monitoring the growing condition of individual trees, and discovering the damage of trees as early as possible, etc. Existing high performance platform based tree crown detection studies either focus on processing images in a small size or suffer from high power consumption or slow processing speed. In this paper, we propose the first FPGA-based real-time tree crown detection approach for large-scale satellite images. A pipelined-friendly and resource-economic tree crown detection algorithm (PF-TCD) is designed through reconstructing and modifying the workflow of the original algorithm into three computational kernels on FPGAs. Compared with the well-optimized software implementation of the original algorithm on an Intel 12-core CPU, our proposed PF-TCD obtains the speedup of 18.75 times for a satellite image with a size of 12,188 × 12,576 pixels without reducing the detection accuracy. The image processing time for the large-scale remote sensing image is only 0.33 s, which satisfies the requirements of the on-board real-time data processing on satellites.

The Fengyun-4A (FY-4A) Lightning Mapping Imager (LMI) is the first satellite-borne lightning imager developed in China, which can detect lightning over China and its neighboring regions based on a geostationary satellite platform. In this study, the spatial distribution and temporal variation characteristics of lightning activity over China and its neighboring regions were analyzed in detail based on 2018 LMI observations. The observation characteristics of the LMI were revealed through a comparison with the Tropical Rainfall Measuring Mission (TRMM)-Lightning Imaging Sensor (LIS) and World Wide Lightning Location Network (WWLLN) observations. Moreover, the optical radiation characteristics of lightning signals detected by the LMI were examined. Factors that may affect LMI detection were discussed by analyzing the differences in optical radiation characteristics between LMI and LIS flashes. The results are as follows. Spatially, the flash density distribution pattern detected by the LMI was similar to those detected by the LIS and WWLLN. High-flash density regions were mainly concentrated over Southeastern China and Northeastern India. Temporally, LMI flashes exhibited notable seasonal and diurnal variation characteristics. The LMI detected a concentrated lightning outbreak over Northeastern India in the premonsoon season and over Southeastern China in the monsoon season, which was consistent with LIS and WWLLN observations. LMI-observed diurnal peak flash rates occurred in the afternoon over most of the regions. There was a “stepwise” decrease in the LMI-observed optical radiance, footprint size, duration, and number of groups per flash, from the ocean to the coastal regions to the inland regions. LMI flashes exhibited higher optical radiance but lasted for shorter durations than LIS flashes. LMI observations are not only related to instrument performance but are also closely linked to onboard and ground data processing. In future, targeted improvements can be made to the data processing algorithm for the LMI to further enhance its detection capability.