Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors.

Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.

Far-field analytical solutions of the non-homogeneous Helmholtz and wave equations for spatially non-localized sources.

Non-localized impulsive sources are ubiquitous in underwater acoustic applications. However, analytical expressions of their acoustic field are usually not available. In this work, far-field analytical solutions of the non-homogeneous scalar Helmholtz and wave equations are developed for a class of spatially extended impulsive sources. The derived expressions can serve as benchmarks to verify the accuracy of numerical solvers.

Wearable Cardiorespiratory Sensors for Aerospace Applications.

Emerging Air Traffic Management (ATM) and avionics human-machine system concepts require the real-time monitoring of the human operator to support novel task assessment and system adaptation features. To realise these advanced concepts, it is essential to resort to a suite of sensors recording neurophysiological data reliably and accurately. This article presents the experimental verification and performance characterisation of a cardiorespiratory sensor for ATM and avionics applications. In particular, the processed physiological measurements from the designated commercial device are verified against clinical-grade equipment. Compared to other studies which only addressed physical workload, this characterisation was performed also looking at cognitive workload, which poses certain additional challenges to cardiorespiratory monitors. The article also addresses the quantification of uncertainty in the cognitive state estimation process as a function of the uncertainty in the input cardiorespiratory measurements. The results of the sensor verification and of the uncertainty propagation corroborate the basic suitability of the commercial cardiorespiratory sensor for the intended aerospace application but highlight the relatively poor performance in respiratory measurements during a purely mental activity.

Impact of COVID-19 on the parent-nurse relationship in NICU: a phenomenological study of nurses lived experience.

BACKGROUND: During the Covid-19 pandemic, the family presence in hospital was reduced, or excluded, to limit the contagiousness, but that could impact negatively on the quality of communication, family education, and the best development outcome for the newborn. AIM: This study aims to explore and describe if and how the pandemic influenced the relationship and care provided to the family in NICU (Neonatal Intensive Care Unit). METHODS: This is a descriptive qualitative study. The data were collected through a questionnaire and semi-structured interviews with 7 NICU nurses from different Italian Regions in the period June-August 2021, by snowball sampling. Thematic analysis by Braun and Clarke method was conducted. RESULTS: The data show an overview of the hospitals' choices about parents' access to NICUs, from total to partial closure. The thematic analysis revealed how some nurses have boycotted corporate choices in favor of child's and family's well-being, while others have consolidated the idea that NICUs must remain closed. Moreover, nurses report that they standardized their practice rather than individualize care. CONCLUSIONS: The Covid-19 limitations created many inconveniences and negatively impacted the quality of the care provided, as the safety of the discharge of these fragile patients. NURSING IMPLICATIONS: It is important to evaluate how this situation impacts long-term patient outcomes, but also how some organizational decisions influence the well-being of professionals, who facing difficult choices, during a health emergency. This study aims to give leadership data to understand the value that the family has in the care process in NICU and that parents cannot be considered "visitors".

Network Optimisation and Performance Analysis of a Multistatic Acoustic Navigation Sensor.

This paper addresses some of the existing research gaps in the practical use of acoustic waves for navigation of autonomous air and surface vehicles. After providing a characterisation of ultrasonic transducers, a multistatic sensor arrangement is discussed, with multiple transmitters broadcasting their respective signals in a round-robin fashion, following a time division multiple access (TDMA) scheme. In particular, an optimisation methodology for the placement of transmitters in a given test volume is presented with the objective of minimizing the position dilution of precision (PDOP) and maximizing the sensor availability. Additionally, the contribution of platform dynamics to positioning error is also analysed in order to support future ground and flight vehicle test activities. Results are presented of both theoretical and experimental data analysis performed to determine the positioning accuracy attainable from the proposed multistatic acoustic navigation sensor. In particular, the ranging errors due to signal delays and attenuation of sound waves in air are analytically derived, and static indoor positioning tests are performed to determine the positioning accuracy attainable with different transmitter-receiver-relative geometries. Additionally, it is shown that the proposed transmitter placement optimisation methodology leads to increased accuracy and better coverage in an indoor environment, where the required position, velocity, and time (PVT) data cannot be delivered by satellite-based navigation systems.

Vehicular Sensor Network and Data Analytics for a Health and Usage Management System.

Automated collection of on-vehicle sensor data allows the development of artificial intelligence (AI) techniques for vehicular systems' diagnostic and prognostic processes to better assess the state-of-health, predict faults and evaluate residual life of ground vehicle systems. One of the vital subsystems, in terms of safety and mission criticality, is the power train, (comprising the engine, transmission, and final drives), which provides the driving torque required for vehicle acceleration. In this paper, a novel health and usage monitoring system (HUMS) architecture is presented, together with dedicated diagnosis/prognosis algorithms that utilize data gathered from a sensor network embedded in an armoured personnel carrier (APC) vehicle. To model the drivetrain, a virtual dynamometer is introduced, which estimates the engine torque output for successive comparison with the measured torque values taken from the engine control unit. This virtual dynamometer is also used in conjunction with other sensed variables to determine the maximum torque output of the engine, which is considered to be the primary indicator of engine health. Regression analysis is performed to capture the effect of certain variables such as engine hours, oil temperature, and coolant temperature on the degradation of maximum engine torque. Degradations in the final drives system were identified using a comparison of the temperature trends between the left-hand and right-hand final drives. This research lays foundations for the development of real-time diagnosis and prognosis functions for an integrated vehicle health management (IVHM) system suitable for safety critical manned and unmanned vehicle applications.

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis.

The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human-Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators' cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators' MWL, whilst also allowing for increased integrity and reliability of the system.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops.

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

GNSS Performance Modelling and Augmentation for Urban Air Mobility.

One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.

Uncertainty Quantification for Space Situational Awareness and Traffic Management.

This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). In line with the envisioned Space Situational Awareness (SSA) evolutions, the method aims to represent the navigation and tracking errors in the form of an uncertainty volume that accurately depicts the size, shape, and orientation. Simulation case studies are then conducted to verify under which sensors performance the method meets Gaussian assumptions, with a greater view to the implications that uncertainty has on the cyber-physical architecture evolutions and Cognitive Human-Machine Systems required for Space Situational Awareness and the development of a comprehensive Space Traffic Management framework.

Sensor Networks for Aerospace Human-Machine Systems.

Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator's cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator's states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator's cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.

An immersed interface method for the solution of the standard parabolic equation in range-dependent ocean environments.

A numerical approach for the treatment of irregular ocean bottoms within the framework of the standard parabolic equation is proposed. The present technique is based on the immersed interface method originally developed by LeVeque and Li [(1994). SIAM J. Numer. Anal. 31(4), 1019-1044]. The method conserves energy to high order accuracy and naturally handles generic range-dependent bathymetries, without requiring any additional specific numerical procedure. An illustration of its capabilities is provided by solving the well-known wedge problem.

Acoustic Sensors for Air and Surface Navigation Applications.

This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for Intelligent Transport Systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems.

A Novel 3D Multilateration Sensor Using Distributed Ultrasonic Beacons for Indoor Navigation.

Navigation and guidance systems are a critical part of any autonomous vehicle. In this paper, a novel sensor grid using 40 KHz ultrasonic transmitters is presented for adoption in indoor 3D positioning applications. In the proposed technique, a vehicle measures the arrival time of incoming ultrasonic signals and calculates the position without broadcasting to the grid. This system allows for conducting silent or covert operations and can also be used for the simultaneous navigation of a large number of vehicles. The transmitters and receivers employed are first described. Transmission lobe patterns and receiver directionality determine the geometry of transmitter clusters. Range and accuracy of measurements dictate the number of sensors required to navigate in a given volume. Laboratory experiments were performed in which a small array of transmitters was set up and the sensor system was tested for position accuracy. The prototype system is shown to have a 1-sigma position error of about 16 cm, with errors between 7 and 11 cm in the local horizontal coordinates. This research work provides foundations for the future development of ultrasonic navigation sensors for a variety of autonomous vehicle applications.