On-board Data Research Articles

Directional wave spectrum estimation through onboard measurement data utilizing B-spline basis functions

The utilization of digital twin technology, which integrates real-world measurement data with a digital representation of the vessel, is garnering increasing attention within the realm of structural integrity management. Due to limitations in sensor deployment on ships, the integration of the digital twin framework, which merges measurement data with the ship's digital design model, becomes crucial for ensuring the effectiveness of structural integrity management. This investigation introduces a methodological approach for estimating localized responses at unmeasured locations, leveraging both measurement and design datasets. To approximate the response at unobserved sites, the directional wave spectrum is initially determined utilizing the least squares method, thereby, minimizing the disparity between the estimated and measured response spectra. The methodology notably incorporates cubic B-spline interpolation specifically to smooth the directional wave spectrum deployed on the heading-frequency domain. This deliberate choice of employing cubic B-spline interpolation underscores the emphasis on achieving a refined and continuous representation of the directional wave spectrum, thus enhancing the accuracy and reliability of the estimation process. To validate the proposed methodology, synthetic pseudo-measurement data derived from the wave spectrum and RAO of a 13,000 TEU container ship are employed. Subsequently, the directional wave spectrum is estimated utilizing the pseudo-measurement data, and the estimated directional wave spectrum, in conjunction with the Response Amplitude Operator (RAO), is harnessed to approximate the response spectrum at the location where sensors are not installed.

A Neural-Metaheuristic Kalman Filter for Moving Microburst Wind Shear Identification

Microburst wind shears (MB) are powerful localized three dimensional (3D) columns of wind that occur when cooled air drops from the base of thunderstorms at high speeds. They eventually hit the ground and spread out in all directions. As such MBs are considered a major atmospheric hazard for aircrafts (ACs), especially in terminal flight phases. Though pilots train regularly to survive it, yet the most recommended practice within the aviation community is to avoid its encounter upon detection. Some modern aircrafts have predictive wind shear warning systems, but they have limited short range capability and do not provide quantitative data for engineering application. In this sense, accurate onboard detection and identification of MBs and its characteristics is of importance for flight safety, whose success can lead to design of automatic flight control systems (FCSs) that reduce the risk of aircraft crash landings and accidents. Even though there are some studies on FCS designs for MB encounter, research on MB identification is rare but ongoing. To this aim, the present study is among the firsts that focuses on online identification of multiple and moving MB parameters using onboard aircraft air data and inertial sensors. The identification task is initially performed using the aircraft six degrees of freedom (6DoF) equations of motion (EOM) integrated with a 3D MB model via the utility of nonlinear Kalman filtering (KF) algorithms. Subsequently, an enhanced neural metaheuristic Kalman filter (NMKF) is proposed that improves the estimation accuracy of the MB model parameters. The results demonstrate the efficacy of the proposed NMKF in comparison with previous studies.

Advancements in smart agriculture through innovative weed management using wavelet-based convolution neural network

Smart agriculture has shifted the paradigm by integrating advanced technologies, particularly weed management. This paper introduces an innovative approach to weed control by applying a Wavelet-based Convolution Neural Network (WCNN). In the era of precision agriculture, our study explores the integration of WCNN into real-world scenarios, emphasizing its adaptability to diverse environmental conditions. Utilizing the spatial-frequency analysis features of wavelets and convolutional neural networks, the WCNN model is the most effective at finding weeds, classifying them, and managing them specifically in agricultural fields in real-time. This research contributes to the scientific discourse on smart agriculture and addresses the challenges of invasive weeds, presenting a sustainable solution for optimizing resource utilization. Our investigation includes a detailed exploration of WCNN’s adaptive learning mechanisms and dynamic adjustment to changing agricultural landscapes. The model seamlessly integrates with existing smart farming infrastructure, showcasing a substantial reduction in manual intervention and a simultaneous increase in agricultural productivity. We incorporate fog computing and resource optimization into our framework, enhancing the efficiency of onboard data processing. To evaluate the real-world efficacy of WCNN, we conducted comprehensive experiments in texture classification and image labelling using two distinct datasets: the plant seedling and soybean weed datasets. Results demonstrate the superior performance of WCNN, achieving higher accuracy in training and test scenarios with significantly fewer parameters than traditional CNNs. For the soybean weed dataset, WCNN achieved remarkable accuracy in the training (0.9970) and testing (0.9987) phases, with correspondingly low losses of 0.0109 and 0.0048. The WCNN model demonstrated high accuracy during training (0.9739) and testing (0.9902), with minimal losses of 0.0898 and 0.0239 in the plant seedling dataset.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Assessing the reliability of a ship energy performance simulation tool through on-board data

To mitigate the environmental impact of shipping on climate change, it is nowadays urgent to explore energy efficiency and cost-effective measures by means of reliable digital tools. These are becoming essential for assisting the design of new ships and the refurbishment of the existing ones, as well as for assessing and optimizing the energy performance of a ship during its entire life-cycle. This paper focuses on the verification of the reliability of a novel ship energy dynamic simulation tool, developed for the calculation of the energy, economic, and environmental performance of large ships, the design and optimization of energy system layouts and operational logics, as well as the definition of design and management guidelines for ships. The reliability of the developed model was verified through the use of data measured onboard an existing sample cruise ship. The verification procedure is conducted for assessing the reliability and for exploiting the potentiality of dynamic analysis for a more precise evaluation of ships energy loads, temperature levels, and necessary sizes of possible saving technologies. The validated tool will enable reaching two different goals: i) to evaluate different energy system layouts and to define the optimal one for achieving the present imposed targets and constraints, ii) to obtain a large amount of data for allowing the implementation of the digital twin approach in the maritime sector. The model validation was successfully achieved and very low or negligible deviations, lower than 4%, of numerical data vs. measurements were observed. Through the validated simulation tool approximately 23.4 GWh of primary energy from polluting fuels over two weeks is computed. In addition, the model provided insights into the flow rate, temperature levels, and energy flows of the ship energy system, necessary for identifying the amount of thermal energy to be recovered as well as potential solutions to enhance the energy efficiency of the entire system.

Maritime Cyber Security: Adopting a Checklist Based on IACS UR E26 Standard

The efficient operation of ship systems that control navigation, communications, sensors, and power and machinery is dependent on the increasing digitization of the maritime sector and the intense use of information and operational technologies. The goal of issuing and enforcing global regulations and standards is to lessen the impact of potential dangers that could jeopardize on-board systems, network and data integrity, and operation, functionality and safety. At this point, "Cyber Resilience of Ships" (UR E26) is recently released by the International Association of Classification Societies (IACS) to address the need to improve ships' cyber resilience. This regulation will be applicable to new ships built on and after 1 July 2024. This study aims to create a check list for ship cyber security based on IACS UR E26 standard. A ship cyber security checklist was developed by first analyzing ship operational technologies, identifying potential cyber risks and vulnerabilities, and then creating a checklist in accordance with the IACS UR E26 standard to ensure cyber security on board. With a focus on clean seas and safe ships, the IACS provides technical assistance, verifies compliance, and conducts research and development to enhance maritime safety, security and regulation. This study provides practical tool to ships for ship cyber security management under the safety management system besides IACS standard benefits. Creating a checklist in accordance with the IACS UR E26 standard also allows ship owners and operators to comply with the standards and facilitate inspection processes. This reduces the effort spent to comply with international regulations. It helps to proactively manage cyber risks by providing a systematic approach to ship cyber security management.

Occurrence characteristics of authigenic pyrite in the deep-sea environment and its paleoceanographic implications based on core sediments from IODP Expedition 342 Site U1406

Although authigenic pyrite (FeS2) in marine sediment is an important proxy for oxygen conditions and microbial activities at its formation site, very little is known about its morphological characteristics in the deep-sea (> 3500 m) environment and their paleoceanographic implications. Here, we report the occurrence characteristics of authigenic pyrite including texture, size distribution, and relative content in North Atlantic deep-sea sediments from Integrated Ocean Drilling Program (IODP) Expedition 342 Site U1406. The results of electron microscopy show octahedral pyrite microcrystals of 1.5–4.0 μm, the spherical texture of greigite (Fe3S4), and large pyrite framboids (average, 25 μm) relative to those of the continental margin. These features may demonstrate an unchanged supersaturation level of the pore water for octahedral pyrite after its initial formation, replacement of the precursor greigite by pyrite, and an expanded pyrite framboid formation zone with a low sulfate reduction rate in deep-sea sediments, respectively. Although the size distribution of pyrite framboids measured in this study falls into the range of pyrites formed in the sulfate–methane transition zone, based on onboard geochemical data and previous research, these pyrites most likely formed through organoclastic sulfate reduction during diagenesis. We assessed the implications of these findings for paleoceanographic changes, as the formation of diagenetic pyrite is controlled by the supply rate and reactivity of organic matter, which are closely related to ocean productivity and circulation. A comparison between the profiles of relative pyrite content and benthic foraminiferal oxygen isotope (δ18Obf) values indicated that these factors were anti-phased during the late Oligocene. Based on our results, we conclude that the formation of diagenetic pyrite was favored during interglacial periods due to increases in the amount and reactivity of organic matter driven by more energetic formation of proto-North Atlantic Deep Water. Our findings provide new insights into the formation of authigenic pyrite in deep-sea environments and its potential as a paleoceanographic proxy.

Towards Simpler Approaches for Assessing Fuel Efficiency and CO2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data

Towards Simpler Approaches for Assessing Fuel Efficiency and CO2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data

Real-time assessment of ship collision risk using image processing techniques

The poor quality or the miss of Automatic Identification System (AIS) data may cause erroneous judgement of the potential navigational risk. Therefore, this study proposes a real-time framework for assessing ship collision risk using onboard video data in order to improve the risk perception ability of navigators. Firstly, the Squeeze-and-Excitation (SE) attention mechanism and the K-means algorithm are simultaneously utilized for the framework to enhance the multi-scale ship detection capability. The Deep-SORT is employed to complete multi-ship feature matching. Secondly, the distances between two ships and their speeds are measured using the pinhole imaging principle based on the ship visual feature extraction results. Moreover, the ship distance-speed correction method is designed to improve the reliability of estimated results. Finally, the effectiveness of the framework is validated using naturalistic driving data from the “He Hua Hai” ship. The results show that the proposed framework could demonstrate an excellent performance in assessing ship collision risk using the onboard video data. The proposed framework could help precisely detect and promptly provide warnings about potential ship collision risks. This could help prevent catastrophic accidents that pose a threat to oceans and coasts, particularly in situations when AIS data proves to be unreliable or ineffective.

Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics

Abstract Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.

Enhanced Particle Classification in Water Cherenkov Detectors Using Machine Learning: Modeling and Validation with Monte Carlo Simulation Datasets

The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis.

Research on truck mass estimation based on long short-term memory network

To address the issues of convenience, scalability, and accuracy in existing truck mass estimation technologies, a truck mass estimation model based on Long Short-Term Memory Network (LSTM) using onboard OBD data is proposed. Seven-dimensional feature parameters, including vehicle driving speed, engine speed, and net output torque of the engine, are selected as input features to achieve accurate estimation of truck mass. This study collaborated with transportation enterprises from Yulin, China to collect real-time data using onboard OBD devices. The actual vehicle mass was obtained by using the data from the weighing scales installed at the departure and unloading points. These data were divided into training and validation sets for the model, and improvements were made step by step based on the prediction results. It's showed that when estimating vehicle mass based on trips, the average relative error between the estimated validation set and other vehicle masses is around 2.3 %; Compared with existing estimation models (estimation model based on extended Kalman filter and detection device based on EPB system), LSTM model has advantages such as high estimation accuracy and strong generalization ability, and has broad application space in overload transportation monitoring, safe transportation, energy conservation and emission reduction, etc.

Method of Forming and Algorithm of Saving a Photo Map of the Seafloor during its Photography by an Unmanned Underwater Vehicle

The paper proposes a new method and algorithm for creating a photo map of the seabed from photographs obtained from on-board photo and video systems of autonomous uninhabited underwater vehicles during fulfillment of their various missions. This photo map is formed from photographs of the seabed by sequentially overlapping them. At the same time, data on the position of the underwater vehicle received from the on-board navigation system at the time of receipt of each photograph is used to position each photo image on the generated photo map. And to rotate and scale these photographic images, data on the orientation of the underwater vehicle in the global coordinate system and its height above the bottom, also obtained from the on-board navigation system, are used. To provide quick access to the photo map, it is proposed to store it on an on-board data storage device in the form of a tile map, widely used when creating interactive geographical maps, for example, Google Maps. However, the formation of a tile map is usually carried out on the basis of all the already received photographic images, as is done with satellite images, which cannot be applied on board underwater vehicles when data from the photo-video system is received sequentially. Therefore, a special algorithm has been developed and implemented that generates a tile map during the movement of the underwater vehicle and updates only the area of the tile map in which the photo is currently being taken. This minimizes the computational load and ensures the formation of a photo map of the bottom using standard on-board computing devices. The conducted semi-natural experiments with data obtained as a result of performing real missions using the MMT-3000 underwater vehicle confirmed the high efficiency of using the developed method and algorithm.

Progressive updates of convolutional neural networks for enhanced reliability in small satellite applications

Small satellites enable many important applications for both economic and scientific purposes. Many of these applications are inherently data-centric and rely on large amounts of high-resolution satellite imagery to be delivered in a timely manner. However, communicating this data to Earth is challenging due to intermittent connectivity, high packet losses, low data rates, and similar issues. Therefore, efficient onboard prioritization and data processing are essential for future satellite missions. Machine learning methods, such as deep neural networks, are very suitable for such prioritization, as they are already used extensively for satellite imagery processing and they can be deployed onboard of satellites. However, updating them to support new classification requirements when the satellite is already in orbit is difficult, as often multiple passes are required to complete model transmission due to the communication challenges. To cope with this issue, we propose a progressive transmission mechanism for model updates, which leverages vector quantization and arithmetic coding. Our mechanism allows to achieve high accuracies even with partially updated models. Evaluation results show that our mechanism significantly outperforms other less optimized transmission schemes.

Unlocking the geochemical features of the Paleocene southern Pacific Ocean using carbon isotopes and biolipids

Little is known about the Paleocene in the southern Pacific Ocean due to scarce marine records. Here, we present a systematic geochemical investigation using biomarkers, carbonate content, and carbon isotopes of a set of early Paleocene deep-sea cores from International Ocean Discovery Program Expedition 378 Site U1553. The results provide a new Paleocene biomarker profile for the Campbell Plateau, New Zealand. The occurrence and distribution of a series of hopenes, ββ hopanes, sterenes and biomarker-based maturity parameters indicate that these organically-lean sediments are in an early diagenetic stage, with an equivalent vitrinite reflectance of approximately 0.4%. Biomarker data (e.g., gammacerane index and homohopane index), carbonate carbon isotopes (−27.9 ‰ to +2.0 ‰) combined with onboard data (sulphur and methane contents) indicate that the redox properties of the Paleocene southern Pacific Ocean changed from an oxidising-anoxic transition zone to a more reducing marine environment (water depth > 1000 m). The sources of the organic matter are diverse, including algae, bacteria, diatoms, dinoflagellates, and higher plants, but are dominated by aquatic organisms. Our new biomarker record bridges a Paleocene biosphere knowledge gap in the high latitude South Pacific.

Precise Orbit Determination for Maneuvering HY2D Using Onboard GNSS Data

The Haiyang-2D (HY2D) satellite is the fourth ocean dynamics environment monitoring satellite launched by China. The satellite operates on a re-entry frozen orbit, which necessitates orbital maneuvers to maintain its designated path once the satellite’s sub-satellite point deviates beyond a certain threshold. However, the execution of orbit maneuvers presents a significant challenge to the field of Precise Orbit Determination (POD). The thesis selects the on-board GPS data of HY2D satellite in December 2023 and five maneuvering days of that year. Employing a multifaceted approach that includes the assessment of observational data quality, orbit overlap, external orbit validation, and SLR (Satellite Laser Ranging) verification, the research delves into precise orbit determination during both maneuver and non-maneuver periods. The results indicate that: (1) The average number of satellites tracked by the receiver is 6.4; (2) During the non-maneuver periods, the average RMS (Root Mean Square) value of the radial difference in the 6-h overlapping arc segment is 0.66 cm, and the three-dimensional position difference is about 1.16 cm; (3) When compared with the precision science orbits (PSO) provided by CNES (Centre National d’Études Spatiales), the average values of the RMS values of the differences in the radial (R), transverse (T), and normal (N) directions during the non-maneuver and maneuver periods are respectively 1.32 cm, 2.31 cm, 1.92 cm and 3.04 cm, 8.78 cm, 2.72 cm. (4) The SLR verification of the orbit revealed a residual RMS of 2.24 cm. This suggests that by incorporating the modeling of maneuver forces during the maneuver periods, the impact of orbital maneuvers on orbit determination can be mitigated.

Extension of the BDS IGSO and MEO satellite antenna patterns with FengYun-3C and LuTan-1 onboard data

The International Global Navigation Satellite System (GNSS) Service (IGS) has recommended calibrating the extended satellite antenna Phase Center Variation (PCV) model for all navigation satellites to support low-Earth-Orbit (LEO) satellite applications. Owing to the absence of ground-calibrated PCVs for the BeiDou Navigation Satellite System (BDS), tracking data from global ground stations were used for in-orbit estimations. However, limited by the observed maximum nadir angle, PCVs based on ground data cannot cover the entire range of nadir angles observed using by LEO satellites. In this study, the FengYun-3C and LuTan-1 missions were used to extend the PCVs of the BDS Inclined GeoSynchronous orbit (IGSO) and medium-Earth-orbit (MEO) satellites. The PCVs corresponding to the ionosphere-free linear combinations of B1I/B2I, B1I/B3I, and B1C/B2a were calibrated in-orbit by extending the maximum nadir angle from 9° to 10° for the IGSO and from 13° to 15° for the MEO. There was good consistency among the estimated PCVs of satellites of the same type. In addition, these estimated PCVs were close to the corresponding satellite block-specific values estimated based on ground tracking data. The impact of the extended PCVs of the BDS satellite antenna was investigated by comparing them with the unextended PCVs model in the receiver antenna PCVs pattern estimation and precise orbit determination of the FengYun-3C and LuTan-1 satellites. The results showed that the extended BDS satellite PCVs effectively reduced the unmodeled error of the unextended BDS satellite PCVs in the low-elevation region of the LEO receiver antenna PCV patterns. In addition, compared with the introduction of phase center offset alone, the introduction of ground-based PCVs can improve the overlapping orbit differences (OODs) of LEO satellites, and the extended BDS satellite PCVs can further improve the OODs. Taking B1C/B2a as an example, the improvement ratio of OODs in the 3D direction of Lutan-1A increased from 5.2 % to 8.0 %, and that of Lutan-1B can rise from 1.7 % to 4.2 %.

Aircraft Takeoff and Landing Weight Estimation from Surveillance Data

Aircraft weight estimation is a common problem facing researchers working with aircraft surveillance data. Although knowledge of an aircraft’s weight and thrust is required for many types of analyses, such as those evaluating aircraft acoustic noise, fuel burn, and emissions, these parameters are typically not available from surveillance sources. Instead, researchers generally only have access to basic aircraft states: lateral position, groundspeed, and altitude. Therefore, methods for estimating the weight of aircraft from these basic states become necessary in cases where aircraft performance is a key component of the analysis. This paper introduces two weight estimation models: one for the estimation of aircraft takeoff weight from departure data, and another for the estimation of aircraft landing weight from arrival data. The models are mathematically simple but grounded in knowledge of aircraft certification, airline operations, and aircraft flight management system logic. The landing weight estimation model proposed is shown to have a mean absolute error equivalent to 2.66% of maximum takeoff weight and a standard deviation of 3.35% of maximum takeoff weight when validated using onboard data recordings from 240 Airbus A320 flights. Similarly, the proposed takeoff weight estimation model is shown to have a mean absolute error of 2.83% of the maximum takeoff weight and a standard deviation of 3.55% of the maximum takeoff weight when applied to the same validation dataset.

Implementation of an ISO 15189 accredited next generation sequencing service for cell-free total nucleic acid (cfTNA) analysis to facilitate driver mutation reporting in blood: the experience of a clinical diagnostic laboratory

AimsNext generation sequencing (NGS) on tumour tissue is integral to the delivery of personalised medicine and targeted therapy. NGS on liquid biopsy, a much less invasive technology, is an emerging...

Prediction of brake pad wear and remaining useful life considering varying vehicle mass and an experimental holistic approach

Estimation of passenger car brake pad wear is considered, including real-time mass estimation to account for variations in vehicle mass. A longitudinal dynamics model is used to estimate mass, the application of the brakes, and a longitudinal braking force. Wear is modelled from brake work based on the product of the braking force and distance travelled during braking. Data is provided by standard vehicle on-board diagnostics data and a sensor fusion based on an inertial measurement unit and satellite position data, sampled from a single car with 10,200 km of on-road driving, including 31 brake pad wear measurement samples, and driving with up to 23% increased vehicle mass. The mass is accurately estimated within ±5% of the actual. Braking states determined from comparisons of measured and modelled deceleration provide accuracies of ≈91%, precisions of ≈96%, and recall rates of ≈86%, with inclusion of varying mass only resulting in minor differences. Brake work is estimated with (a) constant mass, (b) estimated mass, (c) actual mass, and (d) actual mass and braking states. Compared to (a), (b) and (c) provide 9% and 11% increased work, while (d) provides an increase of about 5% compared to (c). Wear coefficients are fitted on the initial half of the wear data considering (a), (b), and (d). Here (a) and (b) provide similar wear rates of k≈−1.22 mm/GJ, while (d) is about 5% lower. Validating on the second half of the data, (b) and (d) provide very similar wear estimates, with overall lower errors than (a). At the final sample, however, (a) provides a slightly lower absolute error, overestimating the wear by about 2%, while (b) and (d) underestimate it by about 3%. RUL, in terms of distance, is estimated across about 9,500 km of data by estimating a running ratio between cumulated work and distance. Here only (a) and (b) are considered, with (a) overestimating the RUL by 600 km at the actual wear limit, while (b) underestimates it by 190 km. A distance-based model is also considered, resulting in a constant RUL error of about 2,000 km.