ASTRO APS Autonomous Star Sensor First Year Flight Experience and Operations on AlphaSat (Invited)

Significant advances have been made in the field of vibration energy harvesting. The efficiency of microgenerators has reached a stage where they can be considered as reliable power sources for demanding sensing applications. When combined with low power electronic sensors, ultra low power signal processing and radio communications, the potential for an autonomous sensor operating without batteries or wires is now a reality. The railway industry faces a number of challenges which will require autonomous sensors and innovative power sources often in hostile environmental circumstances. The paper introduces the fundamental operating principles of vibration energy harvesting (VEH), and includes initial results from field trials on fixed frequency vibration sources. Further field tests have been undertaken on broadband vibration energy sources such as those found in transport applications. Initial results indicate that energy harvesting in the sub 20 Hz frequency band can deliver useful levels of power and can realistically support remote long-range autonomous sensing applications. The key benefit of the microgenerator is the ease of inservice installation and hence low cost and minimal downtime for the rolling stock. An outline architecture for an autonomous sensor condition monitoring application is presented.

Technical advances since Apollo make it possible to perform robotic reconnaissance to gain a better understanding of lunar sites prior to human exploration. NASA is conducting analog field tests to investigate these operations concepts with advanced robots and simulated flight operations. We have developed robot performance monitoring software for use during robotic reconnaissance operations. We measure robot performance by monitoring robot data in real-time and computing robot performance metrics from that data. Metrics are computed for two regimes of flight operations -- remote supervision of autonomous robot operations and debrief support after a flight operations shift. In this paper we describe our performance monitoring software, define the metrics we compute, discuss how these metrics are used in flight operations, and summarize results from recent field tests.

With the rapid development of the civil aviation industry, flight safety has become a core concern, and Quick Access Recorder (QAR) data, as a key carrier of aviation safety big data, plays a crucial role in safety management. However, current QAR data research in China's civil aviation Flight Operations Quality Assurance (FOQA) mainly focuses on exceedance event analysis and non-exceedance data statistics, lacking in-depth exploration of exceedance causes, over-relying on exceedance data, and failing to form a complete technical chain from data preprocessing to risk early warning—making it difficult to support the transformation of safety management from “post-event analysis” to “pre-event early warning”. To address these issues, this study conducts in-depth mining of QAR data and constructs a full-chain flight safety management system integrating data preprocessing, operational quantitative analysis, technical evaluation, and real-time early warning. First, QAR data is divided into six flight phases (taxiing, takeoff, climb, cruise, approach, landing) based on altitude; outliers are screened and removed using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{3} \sigma$</tex> criterion, and data gaps are filled with the average of adjacent data points to ensure data reliability. Second, through the Augmented Dickey-Fuller (ADF) test, key indicators such as control column input and landing G-force are confirmed as weakly stationary sequences, and sudden changes in these indicators within 7 seconds before and after landing are identified as core causes of hard landings. Third, four machine learning algorithms (Random Forest (RF), Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Decision Tree (DT)) are used to build pilot technical proficiency prediction models; among them, the LGBM model achieves a test set accuracy of 0.656, enabling quantitative evaluation of pilot operational stability. Finally, an ILA+GAN-based real-time early warning mechanism is proposed, which reaches an accuracy of 92.55% in time-series prediction and outperforms comparative algorithms such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{OB}+\text{RNN}$</tex> and TF+RNN. This study innovatively integrates Principal Component Analysis (PCA) with flight experience and applies the ADF test to operational behavior traceability, providing a quantitative tool for civil aviation flight risk management. Future research will expand extreme scenario data coverage and explore deep learning models like Transformer to enhance early warning real-time performance and robustness.

&lt;div class="htmlview paragraph"&gt;The Flash Evaporator Subsystem (FES) provides active cooling for the Shuttle Orbiter vehicle during the ascent and re-entry phases of the flight and provides supplemental cooling to the radiators while on-orbit. This paper describes the design and operation of the FES and summarizes the operational flight experience to date. As the fleet of orbiters grows older, contamination and corrosion are two issues on which attention has focused. A discussion of these conditions and the subsequent design changes and operational workarounds will be summarized.&lt;/div&gt;

Pratt & Whitney's RL10 engine line has a long and rich history, beginning in 1958 and continuing today. This paper provides a historical summary of launch vehicles using RL10 engine derivatives dating from 1962 - 2005. The historical launch data is used to derive baseline launch success rates and growth curves for vehicles configured with RL10 engines in the upper stage.Because it was the first liquid hydrogen fueled rocket engine, the RL10 engine launch history provides a unique opportunity to investigate the maturity trends for revolutionary new complex systems. All of the data used in this survey was acquired through publicly-available sources. In all, 190 vehicles configured with RL10 upper stage engines were launched between 1962 and 2005. There were 12 upper stage failures that either failed to reach orbit, or reached a lower, unintended orbit. The early failures were dominated by knowledge gaps in system interactions and operational flight conditions. There is a clear trend of early development growth with an eventual plateau as system knowledge improved as a result of flight experience and more thorough test programs. Failures due to process-based issues (fabrication techniques, quality control, etc.), however, do not appear to exhibit maturity growth. Eventually, as the knowledge-based failures are removed, these process-based failures become the dominant risk driver. Vehicles that use mature, highly-reliable components are still vulnerable to process or functional changes, and failures of this type occur fairly uniformly with flight experience. In order to improve future reliability estimates for such systems, it is important to understand the trends and relationship between the knowledge-based and process-based issues, and determine which class of issues currently dominates. It should be noted that of the 12 upper stage failures, only one was caused by a defective part.

Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Uncooperative pose estimation with a LIDAR-based system

This paper presents a study of recent development in the field of acoustic energy harvesters (AEHs) technique and the maximum power generated from each of these techniques. AEHs convert the acoustic energy into useful electrical energy for the operation of autonomous wireless sensors. Acoustic energy is an important form of efficiency energy which is created and unused all around us. Due to the majority of acoustic energy a main challenge of energy harvesting is the low power density in typical acoustic noise fields. With a superior emphasis on green energy, acoustic energy is one of the enormously existing energy sources and so harvesting this energy plays an significant role in research. Since acoustic energy is environmentally friendly and sufficient to replace as disadvantages of conventional battery which have limited lifespan and problematic in electronics could expire at any time and replacement of the battery causing some cost and time. The induced acoustic wave can be transformed into electricity by presenting novel environment friendly energy converters.

Before developing an integrated navigation system, it is an efficient method to build an integrated navigation simulator to reduce cost and shorten period of development. Building a distributed simulator with multiple computers will be more close to the actual system. A distributed integrated simulator for spacecraft autonomous navigation, with the characteristics of modularization and universalisation, was developed with computers and data bus. Based on the previously research, autonomous navigation sensors consists of Strap-down Inertial Navigation System (SINS), Global Positioning System (GPS) and Star Sensor (SS). To realize integrated navigation function, the configuration of the distributed simulator was designed to include Trajectory Generator subsystem, Inertial Measurement Unit subsystem, SINS subsystem, GPS subsystem, SS subsystem and Integrated Navigation subsystem. Hardware design and software development of the simulator are discussed. The federated/centralized Kalman filters are used as simulator information fusion algorithm. The distributed simulator has been applied successfully to provide useful advice for designers.

The felicitous medical and health care domain inherits the Internet of Things for the popularity of autonomous sensor operations with minimum expense. In the health care ecosystem, the Internet of Things develops the trust model wireless body area network to oversee the medical attributes such as temperature, oxygen level, heartbeat, and blood pressure. The WBAN is a standard communication, low-power optimized device deployed with the biosensor nodes. It needs long-run battery power for the conventional functioning of handling the medical care data. The major hindrance of WBAN is energy consumption in deploying the biosensor nodes, which leads to quitting the network early. Various discussions on efficient energy routing protocols are discussed to minimize energy consumption, thereby maximizing the network lifetime to address this limitation. The study of different energy-efficient routing protocols suitable for WBAN supports the researchers in stimulating the balance of the low energy consumption and enhancing the network’s efficiency.

SED16 Autonomous Star Sensor Product Line in Flight Results, New Developments and Improvements in Progress

HYDRA is EADS SODERN new product line of APS-based autonomous star trackers. The baseline is a multiple head sensor made of three separated optical heads and one electronic unit. Actually the concept which was chosen offers more than three single-head star trackers working independently. Since HYDRA merges all fields of view the result is a more accurate, more robust and completely autonomous multiple-head sensor, releasing the AOCS from the need to manage the outputs of independent single-head star trackers. Specific to the multiple head architecture and the underlying data fusion, is the calibration of the relative alignments between the sensor optical heads. The performance of the sensor is related to its estimation of such alignments. HYDRA design is first reminded in this paper along with simplification it can bring at system level (AOCS). Then self-calibration of optical heads alignment is highlighted through descriptions and simulation results, thus demonstrating the performances of a key part of HYDRA multiple-head concept.

Towards an autonomous integrated sensor system

In recent years, there has been significant research focus that revolves around harvesting and minimising energy consumption by wireless sensor network nodes. When a sensor node is depleted of energy, it becomes unresponsive and disconnected from the network that can significantly influence the performance of the whole network. The purpose of the present research is to create a power supply management module in order to provide stable operating voltage for autonomous operations of radio signal repeaters, sensors or gateways of WSN. The developed management module is composed of a solar panel, lithium battery and power supply management module. The novelty of the research is the management module, which ensures stable and uninterrupted operations of electronic equipment in various power supply modes in different situations, simultaneously ensuring energy protection and sustainability of the module components. The management module is able to provide power supply of 5 V for electronics scheme independently, without power interruption switching between power sources and power flows in different directions.

Our overall goal is to develop a cognitive architecture which will allow autonomous and robust operation of sensor–actuator networks. To achieve this, the perception, concept formation, action cycle will be informed by domain theories of signal analysis, physical phenomena, and behavior. Example scenarios include cognitive vehicles and buildings in which the system understands itself and the activities in and around it by means of distributed video and other sensors. This includes discovery of the cognitive system’s own sensing and actuation capabilities.