On-board Data Research Articles

Cosimulation of a direct-acting riser-tensioner system – validation with field measurements and sample simulations

In this paper, the cosimulation methodology, presented in earlier works by the authors, is extended to a full scale riser-tensioner system, with the semi-submersible platform and the riser stack being modelled in the ocean engineering software, OrcaFlex, and the hydro-pneumatic tensioner being modelled in the multiphysics software, SimulationX. An interface file coded in Python is used to couple the two models to run the cosimulation. Field measurements from an on-board data acquisition system, mounted on an operational semi-submersible drilling platform, is used to benchmark model performance. The motion time–history of the platform, available in the field data, is used to specify heave motions of the model platform. A planned disconnect sequence is simulated, and the results are compared with field measurements. Subsequently, sample simulations are presented to demonstrate the possibilities of the proposed co-simulation methodology, including a simulation of the whole disconnect procedure, covering the connected, pull-off, disconnected, and soft hang-off phases of the system.

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader-follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios.

Wavelength Calibration for the LIBS Spectra of the Zhurong Mars Rover

China’s first Mars rover, Zhurong, landed on the southern region of Utopia Planitia, Mars, on 14 May 2021 (UTC). Zhurong is equipped with the Mars Surface Composition Detection Package (MarSCoDe), which analyzes the Martian surface’s material composition. Composed of laser-induced breakdown spectroscopy (LIBS), short-wave infrared spectroscopy (SWIR), and a microimaging camera, MarsCoDe can work at a distance of 1.6–7 m to analyze element abundance and the mineralogy of targets on the Martian surface. Analysis shows that the wavelengths of MarSCoDe onboard LIBS spectra acquired within the same probe period will have different degrees of drift, leading to deviation in qualitative and quantitative elemental analysis. This paper finds that the spectrum drift follows a quadratic function relationship with the CCD temperature of the MarSCoDe spectrometer, based on which a wavelength calibration method is established. According to the function, the drift of a certain channel is calculated by the corresponding CCD temperature, and then the wavelength of the spectrum is calibrated by the drift. The accuracy of this calibration method for the position of peak wavelength in the LIBS spectrum can reach about 1/5 of the apparatus spectral width, and the cross-validation analysis using a norite standard sample shows that it is comparable to the wavelength calibration accuracy of the ChemCam onboard data product.

Battery-powered FPGA-based embedded system for ultrasonic pipe inspection and gauging systems.

A water-immersible battery-powered field programmable gate array (FPGA)-based embedded system is a most suitable tool required to check the health of the pipe operating under harsh conditions. A novel, water-immersible, battery-powered, compact, stand-alone and FPGA-based embedded system has been designed and developed, which is suitable for ultrasonic pipe inspection and gauging systems useful for major applications in the petrochemical and nuclear industries. The developed FPGA-based embedded system is operated with lithium-ion batteries for more than five hours, and the IP67-grade system modules are capable of floating inside the pipe along with the flow of oil or water. Such applications require a system that is capable of acquiring large data under water-immersed conditions of battery-operated instrumentation. The onboard Double Data Rate (DDR) RAM of the FPGA module has been utilized for the storage of the 256 MBytes streams of A-scan data during evaluation for more than five hours. The experimentation of the battery-powered embedded system was carried out inside two samples of SS and MS pipes with the help of the in-house developed nylon inspection-head installed with two sets of spring-loaded eight Teflon balls and two 5MHz focused immersion transducers placed along the circumference at 180° apart. This paper provides brief details of the design, development, and evaluation phases of the battery-powered water-immersible embedded system suitable for ultrasonic pipe inspection and gauging, which can be expanded to 256 channels for demanding applications.

Cellular V2X Communications in the Presence of Big Vehicle Shadowing: Performance Analysis and Mitigation

In Intelligent Transportation Systems (ITS), vehicular networks are enabling technologies for onboard data services such as traffic safety, user infotainment, etc. Vehicular networks face many challenges when it comes to providing satisfactory quality of service, mainly because of issues that arise from unreliable communication in unfavorable propagation conditions. One prime example is Vehicle-to-Vehicle (V2V) communication in the presence of big vehicles that present obstacles in the communication path of smaller vehicles, where the signal strength decays drastically due to the big vehicle shadowing. As a result, the communication range is shortened, and the safety message dissemination capability is reduced. In this paper, we analyze the impact of big vehicle shadowing on V2V communications, taking into account Cellular Vehicle-to-Everything (C-V2X) networks. A geometric as well as a stochastic approach is employed to analyze the length of shadow regions for conventional cars and big vehicles on the road in different scenarios. This paper analyses the effect of large vehicles shadowing on a V2V communication link as a function of the shadow region length. A beamforming-based signal reception technique is proposed in order to mitigate packet collisions caused by hidden nodes. Considering relaying operation by big vehicles, three relaying schemes are proposed to improve the V2V message dissemination performance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed schemes.

A turbulence data reduction scheme for autonomous and expendable profiling floats

Abstract. Autonomous and expendable profiling-float arrays such as those deployed in the Argo Program require the transmission of reliable data from remote sites. However, existing satellite data transfer rates preclude complete transmission of rapidly sampled turbulence measurements. It is therefore necessary to reduce turbulence data on board. Here we propose a scheme for onboard data reduction and test it with existing turbulence data obtained with a modified SOLO-II profiling float. First, voltage spectra are derived from shear probe and fast-thermistor signals. Then, we focus on a fixed-frequency band that we know to be unaffected by vibrations and that approximately corresponds to a wavenumber band of 5–25 cpm. Over the fixed-frequency band, we make simple power law fits that – after calibration and correction in post-processing – yield values for the turbulent kinetic energy dissipation rate ϵ and thermal-variance dissipation rate χ. With roughly 1 m vertical segments, this scheme reduces the necessary data transfer volume 300-fold to approximately 2.5 kB for every 100 m of a profile (when profiling at 0.2 m s−1). As a test, we apply our scheme to a dataset comprising 650 profiles and compare its output to that from our standard turbulence-processing algorithm. For ϵ, values from the two approaches agree within a factor of 2 87 % of the time; for χ, they agree 78 % of the time. These levels of agreement are greater than or comparable to that between the ϵ and χ values derived from two shear probes and two fast thermistors, respectively, on the same profiler.

An integrated data collection system for line transect surveys

A computer based system for the collection of line transect survey data is described. The primary goals of the system were to measure (rather than estimate) distances and angles wherever possible, to provide accurate time-stamps for surfacing events as an aid to duplicate identification and to facilitate accurate data collection by using computers to automate data collection wherever possible. Distance and angle measurements were made using established photogrammetric techniques. Collection of photogrammetric data from video was automated and included a system of data buffering so that several seconds of data prior to each observer sighting could be captured. An additional goal of the system was to eliminate the need for post-cruise data entry and validation through the use of on-board data validation software. The system was successfully used during the 2005 SCANS-II and the 2007 CODA surveys.

Aircraft piston engine load distribution in steady state operating conditions

The article presents the results of analysis of operational parameters of piston engine CA 912 ULT which is a propulsion system of ultralight gyroplane Tercel produced by Aviation Artur Trendak. Research was conducted under normal operating conditions of the autogyro and data was collected from 15 independent tests including a total of 14 flight hours conducted during training flights. Engine and flight parameters were recorded at 9 Hz during each flight using on-board Fligh Data Recorded system. Collective results were presented to show the statistical analyses of the individual parameters by determining the mean values, standard deviations and histograms of the distribution of these parameters. The analyses included engine speed, charge pressure, air temperature, oil temperature and exhaust temperature. Histograms of operating points defined by both engine speed and manifold air pressure were also determined. Analyses were carried out to determine the contribution of steady-state and transient engine operation. It was also shown that the dynamics of engine work in real operating conditions is small. Detailed analyses were conducted for steady-state conditions. The value of the engine load index was determined and its statistical analyses were carried out. It was shown that the engine operating points are concentrated mainly in the range of idle and power above 50% of nominal power. The most frequent range is 50% to 80% of nominal power.

On rudder-roll stabilization autopilot based on response models

This study proposes a rudder-roll stabilization (RRS) autopilot by using novel response models developed by Yasukawa and Yoshimura (YY models) since they include the roll-coupling effect. The statistical optimal control theory which used Akaike information criterion (AIC) was used. To verify the proposed RRS autopilot, numerical simulations based were also performed on the onboard monitoring data of a fishery research vessel; the results confirmed its effectiveness for course keeping and roll reduction.

What factors contribute to e-scooter crashes: A first look using a naturalistic riding approach

Introduction: Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors. Method: A fleet of 200 e-scooters was deployed on Virginia Tech’s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios. Results: Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech’s pedestrian-dense campus environment. Conclusions: To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders. Practical Applications: The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.

Method for estimating aircraft noise corresponding to changes in thrust and speed during start of takeoff roll

In aircraft noise calculation by international guidelines such as ICAO Doc.9911, NPD (Noise Power Distance) data that relates aircraft thrust and noise are used. In these guidelines net-thrust use as power in NPD data. However, net-thrust of an aircraft engine cannot be measured directly. Therefore, net-thrust has to be estimated using the conversion equation with engine parameters such as N1% RPM and EPR, as well as speed. Meanwhile, for recent aircraft types, the coefficients for the N1% and speed terms of the conversion equation are not available. For this reason, Japanese aircraft noise models use N1% and EPR directly in place of net-thrust to create NPD. These parameters change the most at the start of the takeoff roll, and it is necessary to confirm the validity of the noise calculation in this area. In this study, noise measurements at the side of the runway and aircraft onboard data were analyzed to investigate an alternative method of estimating noise changes to net-thrust. The result shows that it is reasonable to correct the noise according to the speed change in addition to the noise calculation in the section of lower speed state of takeoff roll.

Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire

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.

A Simple Lossless Algorithm for On-Board Satellite Hyperspectral Data Compression

As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression methods to meet timing, bandwidth and resource requirements with error resilience. This letter proposes a new lossless, prediction based algorithm for on-board satellite hyperspectral data compression that utilizes spectral as well as spatial correlation and at the same time, is computationally less complex. Concept of non-binary tree traversal is used with nearest neighbor method and implemented using neighbor driven decision making in pre-processing stage. Previously processed pixels are used to minimize the prediction residual, which makes more than 80% calculations causal in nature and thereby reducing the computational complexity of the algorithm. The prediction residual is then encoded using sample adaptive Golomb coding in band-sequential order. CCSDS corpus of data for hyperspectral images is used for evaluating the performance of the algorithm. The proposed method shows reduced computational complexity and lesser data dependencies compared to the CCSDS 123.0-B-1 standard when similar spectral vicinity is considered, and comparable compression performance compared to other state-of-the-art on-board lossless compression methods.

A Novel Adaptive Spectral Drift Correction Method for Recalibrating the MarSCoDe LIBS Data in China's Tianwen-1 Mars Mission

The Tianwen-1 mission, China's first interplanetary endeavor and Mars Mission, touched the surface of the Red Planet on May 15, 2021. With the successful landing of the Zhurong rover on the southern Utopian Plain of the Mars, the Mars Surface Composition Detector (MarSCoDe) on board of the rover has started to analyze the material composition of the Martian surface by using the Laser Induced Breakdown Spectroscopy (LIBS). However, changes in instrument temperature and external environment during operation will cause spectral drift in LIBS data, leading to inaccurate material composition inversion results due to shifts in elemental characteristic peak positions. To address this problem, an adaptive spectral drift correction (ASDC) approach is proposed. By considering the distribution of characteristic peaks and drift differences between different elements, the proposed ASDC method corrects the spectral drift in LIBS science data according to an adaptive spectral segmentation strategy. Experimental results obtained on 36 real LIBS science data that acquired over 19 exploration activities confirmed the effectiveness of the proposed approach in terms of lower average drift values, by comparing with two reference methods. Taking the on-board calibration data as a reference, the average spectral drift values of the LIBS science data before and after applying ASDC decreased from 0.1012 nm to 0.0179 nm, 0.0926 nm to 0.0158 nm, and 0.1697 nm to 0.0103 nm in three LIBS channels, respectively. Furthermore, the resulting decrease in the standard deviation and root mean square error of the average drift values also proved the robustness of proposed method.

A Study on Risk Assessment for Anchor Dragging Using On-board Measurement Data of a Single Anchored Vessel

A Study on Risk Assessment for Anchor Dragging Using On-board Measurement Data of a Single Anchored Vessel

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

Integrated Navigation and Communication Service for LEO Satellites Based on BDS-3 Global Short Message Communication

Massive global Low Earth Orbit (LEO) constellations have been developed rapidly with the characteristics of larger number and shorter orbital periods of LEO satellites in recent years. The increasing requirements of real-time orbit determination and emergency data transfer in full arc can hardly meet the traditional strategies based on regional ground stations. An integrated communication and navigation service architecture based on the third phase of the BeiDou system (BDS-3) Global Short Message Communication (GSMC) is proposed, which enables global coverage and random access. The GSMC downlink single-frequency signal is used for real-time orbit determination, and is further combined with B1C navigation signal for real-time dual-frequency high-precision orbit determination. Considering fast moving of LEO satellites and short burst of uplink messages, the uplink BDS-3 satellite selection strategy based on carrier-to-noise ratio, and uplink high-precision compensation of frequency shift are adopted to increase the communication success rate. The service architecture proposed can improve the precision of the autonomous orbit determination and enhance the capability of real-time command transmission of LEO satellites even without the traditional ground stations involved. Integrated communication and navigation service has been validated by simulation and on-board BDS-3 GSMC test data. The precision of single-frequency real-time orbit determination can reach 2.86m, and the precision of dual-frequency can reach 0.26m. The average time delay of communication is about 11.45s, and the success rate is about 99.8%. The method may provide alternative means of real-time high-precision orbit determination and emergency data transfer of LEO satellites.

Towards improved understanding of naval ship structural performance via virtual hull monitoring

Weight-optimised ships, such as High Speed Light Craft (HSLC), are operated by navies around the world. Naval ships must perform in harsh and contested ocean environments. Operations in high sea states result in large linear and nonlinear ship motions which in turn, induce significant loads on ship structures and accelerate structural fatigue. Monitoring and assessment of fatigue on ship structures is important for navies, to understand the performance, limitations, and life-cycle costs of their ships. An established method for monitoring structural responses is via long-term measurements, using Instrumented Hull Monitoring (IHM). However, this approach is generally too resource-intensive to be implemented on a broad scale. Virtual Hull Monitoring (VHM) is a technique to couple on-board ship data, such as Global Positioning System (GPS) data, with hindcast wave data. The resulting enriched dataset enables robust numerical fatigue analysis, because the structural responses are related to the encountered wave environment, rather than based on global wave statistics. Thus, the concept of VHM is receiving increased attention in both commercial and military sectors. This is due to its low cost and the relative ease of implementation compared to IHM. Using a Royal Australian Navy HSLC as the test bed, this study presents an investigation into the feasibility of VHM by comparing results with available IHM data. An efficient framework was developed in PythonTM to extract and couple hindcast wave data to ship speed and position, calculating the resultant stresses on the ship structure, and comparing with the measured stresses from IHM. The novel aspects of this work include the use of a semi-displacement hullform, and the utilisation of both sea-trials and long-term measurements. The study shows promising results for VHM. Finally, recommendations for further work are provided.

Dynamic Predictive Quantization for Staggered SAR: Experiments With Real Data

Present and future spaceborne synthetic aperture radar (SAR) missions are designed to acquire an increasingly large amount of onboard data. This is a consequence of the use of large bandwidths, multiple polarizations, and the acquisition of large swath widths at fine spatial resolutions, which result in challenging requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect, as it affects the volume of data to be stored and transmitted to the ground as well as the quality of the resulting SAR products. Dynamic predictive block-adaptive quantization (DP-BAQ) is a novel technique, recently proposed by the authors, consisting of a low-complexity data compression method, and its application is particularly suitable for staggered SAR systems. DP-BAQ exploits the existing correlation among the azimuth raw data samples by applying linear predictive coding (LPC). This results in a data rate reduction of up to 25% with respect to state-of-the-art SAR quantization methods. In this letter, we test and validate the potential of DP-BAQ on airborne SAR data which emulates the system scenario of Tandem-L, a German Aerospace Center (DLR) mission proposal for a bistatic L-band system. For this purpose, an experimental SAR image has been acquired at the L-band by the airborne DLR flugzeug-SAR (F-SAR) sensor over the Kaufbeuren area, in Southern Germany. In order to simulate the staggered SAR acquisition mode, we implemented a dedicated resampling and filtering of the data. Our analyses confirm the effectiveness of DP-BAQ for efficient data volume reduction, exhibiting a consistent and promising performance when tested on areas characterized by different land cover types and backscatter statistics.

Onboard Data Management Approach Based on a Discrete Grid System for Multi-UAV Cooperative Image Localization

Onboard Data Management Approach Based on a Discrete Grid System for Multi-UAV Cooperative Image Localization