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Silicon Photomultipliers (SiPMs) are single photon detectors that gained increasing interest in many applications as an alternative to photomultiplier tubes. In the field of space experiments, where volume, weight and power consumption are a major constraint, their advantages like compactness, ruggedness, and their potential to achieve high quantum efficiency from UV to NIR makes them ideal candidates for spaceborne, low photon flux detectors. During space missions however, SiPMs are usually exposed to high levels of radiation, both ionizing and non-ionizing, which can deteriorate the performance of these detectors over time. The goal of this work is to compare process and layout variation of SiPMs in terms of their radiation damage effects to identify the features that helps reduce the deterioration of the performance and develop the next generation of more radiation-tolerant detectors. To do this, we used protons and X-rays to irradiate several Near Ultraviolet High-Density (NUV-HD) SiPMs with small areas (single microcell, 0.2 × 0.2 mm 2 and 1 × 1 mm 2 ) produced at Fondazione Bruno Kessler (FBK), Italy. We performed online current-voltage measurements right after each irradiation step, and a complete functional characterization before and after irradiation. We observed that the main contribution to performance degradation in space applications comes from proton damage in the form of an increase in primary dark count rate (DCR) proportional to the proton fluence and a reduction in activation energy. In this context, small active area devices show a lower DCR before and after irradiation, and we propose light or charge-focusing mechanisms as future developments for high-sensitivity radiation-tolerant detectors.

This paper presents a mesoscale damage model for composite materials and its validation at the coupon level by predicting scaling effects in un-notched carbon-fiber reinforced polymer (CFRP) laminates. The proposed material model presents a revised longitudinal damage law that accounts for the effect of complex 3D stress states in the prediction of onset and broadening of longitudinal compressive failure mechanisms. To predict transverse failure mechanisms of unidirectional CFRPs, this model was then combined with a 3D frictional smeared crack model. The complete mesoscale damage model was implemented in ABAQUS ® /Explicit. Intralaminar damage onset and propagation were predicted using solid elements, and in-situ properties were included using different material cards according to the position and effective thickness of the plies. Delamination was captured using cohesive elements. To validate the implemented damage model, the analysis of size effects in quasi-isotropic un-notched coupons under tensile and compressive loading was compared with the test data available in the literature. Two types of scaling were addressed: sublaminate-level scaling, obtained by the repetition of the sublaminate stacking sequence, and ply-level scaling, realized by changing the effective thickness of each ply block. Validation was successfully completed as the obtained results were in agreement with the experimental findings, having an acceptable deviation from the mean experimental values.

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover.

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs.

For the last two decades, the CNES optoelectronics detection department and partners have evaluated space environment effects on a large panel of CMOS image sensors (CIS) from a wide range of commercial foundries and device providers. Many environmental tests have been realized in order to provide insights into detection chain degradation in modern CIS for space applications. CIS technology has drastically improved in the last decade, reaching very high performances in terms of quantum efficiency (QE) and spectral selectivity. These improvements are obtained thanks to the introduction of various components in the pixel optical stack, such as microlenses, color filters, and polarizing filters. However, since these parts have been developed only for commercial applications suitable for on-ground environment, it is crucial to evaluate if these technologies can handle space environments for future space imaging missions. There are few results on that robustness in the literature. The objective of this article is to give an overview of CNES and partner experiments from numerous works, showing that the performance gain from the optical stack is greater than the degradation induced by the space environment. Consequently, optical stacks can be used for space missions because they are not the main contributor to the degradation in the detection chain.

Epoxy resins are highly valued for their remarkable mechanical and chemical properties and are extensively used in various applications such as coatings, adhesives, and fiber-reinforced composites in lightweight construction. Composites are especially important for the development and implementation of sustainable technologies such as wind power, energy-efficient aircrafts, and electric cars. Despite their advantages, their non-biodegradability raises challenges for the recycling of polymer and composites in particular. Conventional methods employed for epoxy recycling are characterized by their high energy consumption and the utilization of toxic chemicals, rendering them rather unsustainable. Recent progress has been made in the field of plastic biodegradation, which is considered more sustainable than energy-intensive mechanical or thermal recycling methods. However, the current successful approaches in plastic biodegradation are predominantly focused on polyester-based polymers, leaving more recalcitrant plastics underrepresented in this area of research. Epoxy polymers, characterized by their strong cross-linking and predominantly ether-based backbone, exhibit a highly rigid and durable structure, placing them within this category. Therefore, the objective of this review paper is to examine the various approaches that have been employed for the biodegradation of epoxy so far. Additionally, the paper sheds light on the analytical techniques utilized in the development of these recycling methods. Moreover, the review addresses the challenges and opportunities entailed in epoxy recycling through bio-based approaches.

This paper provides a study of the different alternatives that are being considered in the 5G-ROUTES project to establish seamless 5G connectivity in a maritime environment both from an architectural point of view and also from the definition of field trials to evaluate the performance and dependability of the proposed solution. As expected, the main challenge in providing 5G connectivity on the sea is to provide coverage over large areas of open water. Thus, as a starting point, this paper presents a measurement campaign that was conducted to assess the current coverage in the Baltic Sea, which concluded that the current terrestrial networks cannot guarantee sufficient coverage. Next, the solution architecture and trials proposed by 5G-ROUTES are described, which are based on the integration of satellite and leading-edge multi-hop connectivity in 5G networks. Utilizing satellite backhaul can potentially overcome the connectivity challenge from the terrestrial domain to the maritime domain, while multi-hop connectivity ensures that coverage is extended among the different ships that are navigating the sea. Furthermore, this paper describes how the project will evaluate, in field trials tailored to this maritime environment, common connectivity key performance indicators (KPIs) such as latency, throughput, availability and reliability. This paper concludes by providing a vision for applying the obtained results and insights to maritime transportation and other remote areas where the deployment of a suitable 5G infrastructure may be challenging or costly. The findings will be used to guide the design of future 5G networks for marine applications and to identify the most effective methods for providing secure and dependable communication in a maritime setting.

Electronic manufacturing and design companies maintain test sites for a range of products. These products are designed according to the end-user requirements. The end user requirement, then, determines which of the proof of design and manufacturing tests are needed. Test sites are designed to carry out two things, i.e., proof of design and manufacturing tests. The team responsible for designing test sites considers several parameters like deployment cost, test time, test coverage, etc. In this study, an automated test site using a supervised machine learning algorithm for testing an ultra-high frequency (UHF) transceiver is presented. The test site is designed in three steps. Firstly, an initial manual test site is designed. Secondly, the manual design is upgraded into a fully automated test site. And finally supervised machine learning is applied to the automated design to further enhance the capability. The manual test site setup is required to streamline the test sequence and validate the control and measurements taken from the test equipment and unit under test (UUT) performance. The manual test results showed a high test time, and some inconsistencies were observed when the test operator was required to change component values to tune the UUT. There was also a sudden increase in the UUT quantities and so, to cater for this, the test site is upgraded to an automated test site while the issue of inconsistencies is resolved through the application of machine learning. The automated test site significantly reduced test time per UUT. To support the test operator in selecting the correct component value the first time, a supervised machine learning algorithm is applied. The results show an overall improvement in terms of reduced test time, increased consistency, and improved quality through automation and machine learning.

Accurate and reliable positioning solution is an important requirement for many applications, for instance, emergency services and vehicular-related use cases. Positioning using cellular signals has emerged as a promising solution in Global Navigation Satellite System (GNSS) challenging environments, such as deep urban canyons. However, harsh working conditions of urban scenarios, such as with dense multipath and Non-Line of Sight (NLoS), remain as one of the key factors causing the detriment of the positioning estimation accuracy. This paper demonstrates that the use of joint Uplink Time Difference of Arrival (UTDoA) and Angle of Arrival (AoA) gives a significant improvement in the position accuracy thanks to the use of antenna arrays. The new advances of this technology enable more accurate user locations by exploiting angular domains of propagation channel in combination with time measurements. Moreover, it is shown that a better localization is achieved by combining the joined UTDoA and AoA with a base-station selective exclusion method that is able to detect and eliminate measurements affected by NLoS. The proposed approach has been tested through simulations based on a deep urban deployment map, which comes with an experimental data file of the user's position. A sounding reference signal of 5G new radio operating in the centimeter-wave band is used. The obtained results add value to the use of advance antennas in 5G positioning. In addition, they contribute towards the fulfillment of high-accuracy positioning requirements in challenging environments when using cellular networks.

Single-Photon Avalanche Diodes (SPAD) in Complementary Metal-Oxide Semiconductor (CMOS) technology are potential candidates for future "Light Detection and Ranging" (Lidar) space systems. Among the SPAD performance parameters, the Photon Detection Probability (PDP) is one of the principal parameters. Indeed, this parameter is used to evaluate the SPAD sensitivity, which directly affects the laser power or the telescope diameter of space-borne Lidars. In this work, we developed a model and a simulation method to predict accurately the PDP of CMOS SPAD, based on a combination of measurements to acquire the CMOS process doping profile, Technology Computer-Aided Design (TCAD) simulations, and a Matlab routine. We compare our simulation results with a SPAD designed and processed in CMOS 180 nm technology. Our results show good agreement between PDP predictions and measurements, with a mean error around 18.5%, for wavelength between 450 and 950 nm and for a typical range of excess voltages between 15 and 30% of the breakdown voltage. Due to our SPAD architecture, the high field region is not entirely insulated from the substrate, a comparison between simulations performed with and without the substrate contribution indicates that PDP can be simulated without this latter with a moderate loss of precision, around 4.5 percentage points.

Though current remote sensing technologies, especially synthetic aperture radars (SARs), exhibit huge potential for soil moisture content (SMC) retrievals, such technologies also present several performance disadvantages. This study explored the merits of proposing a novel data fusion methodology (partly decision level and partly feature level) for SMC estimation. Initially, individual estimations were derived from three distinct methods: the inversion of an Empirically Adapted Integral Equation Model (EA-IEM) applied to SAR data, the Perpendicular Drought Index (PDI), and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat-8 data. Subsequently, three feature level fusions were performed to produce three different novel salient feature combinations where said features were extracted from each of the previously mentioned methods to be the input of an artificial neural network (ANN). The latter underwent a modification of its performance function, more specifically from absolute error to root mean square error (RMSE). Eventually, all SMC estimations, including the feature level fusion estimation, were fused at the decision level through a novel weight-based estimation. The performance of the proposed system was analysed and validated by measurements collected from three study areas, an agricultural field in Blackwell farms, Guildford, United Kingdom, and two different agricultural fields in Sidi Rached, Tipasa, Algeria. Those measurements contained SMC levels and surface roughness profiles. The proposed SMC estimation system yielded stronger correlations and lower RMSE values than any of the considered SMC estimation methods in the order of 0.38%, 1.4%, and 1.09% for the Blackwell farms, Sidi Rached 1, and Sidi Rached 2 datasets, respectively.

The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.

With the increasing number of spaceflights, it is crucial to understand the changes occurring in human cells exposed to real microgravity (r-µ g ) conditions. We tested the effect of r-µ g on MCF-7 breast cancer cells with the objective to investigate cytoskeletal alterations and early changes in the gene expression of factors belonging to the cytoskeleton, extracellular matrix, focal adhesion, and cytokines. In the Technische Experimente unter Schwerelosigkeit (TEXUS) 54 rocket mission, we had the opportunity to conduct our experiment during 6 min of r-µ g and focused on cytoskeletal alterations of MCF-7 breast cancer cells expressing the Lifeact-GFP marker protein for the visualization of F-actin as well as the mCherry-tubulin fusion protein using the Fluorescence Microscopy Analysis System (FLUMIAS) for fast live-cell imaging under r-µ g . Moreover, in a second mission we investigated changes in RNA transcription and morphology in breast cancer cells exposed to parabolic flight (PF) maneuvers (31st Deutsches Zentrum für Luft- und Raumfahrt (DLR) PF campaign). The MCF-7 cells showed a rearrangement of the F-actin and tubulin with holes, accumulations in the tubulin network, and the appearance of filopodia- and lamellipodia-like structures in the F-actin cytoskeleton shortly after the beginning of the r-µ g period. PF maneuvers induced an early up-regulation of KRT8, RDX, TIMP1, CXCL8 mRNAs, and a down-regulation of VCL after the first parabola. E-cadherin protein was significantly reduced and is involved in cell adhesion processes, and plays a significant role in tumorigenesis. Changes in the E-cadherin protein synthesis can lead to tumor progression. Pathway analyses indicate that VCL protein has an activating effect on CDH1 . In conclusion, live-cell imaging visualized similar changes as those occurring in thyroid cancer cells in r-µ g . This result indicates the presence of a common mechanism of gravity perception and sensation.

The Aerosol Deposition (AD, also known as gas kinetic spraying or vacuum deposition) method is a rather novel coating process to produce dense thick films directly from dry ceramic (or metal) powders on a variety of substrates without any heat treatment. Because of the similarity of the up to now used powders and lunar regolith, it is imaginable to use AD systems for future in situ resource utilization missions on the Moon planned by several space agencies. To test the feasibility of such an endeavor, the processability of lunar mare simulant EAC-1 by the AD method has been examined in this study. Three regolith films with an area of 25 × 10 mm², and thicknesses between 2.50 µm and 5.36 µm have been deposited on steel substrates using a standard AD setup. Deposited films have been investigated by Laser Scanning Microscopy (LSM) and Scanning Electron Microscopy (SEM). Moreover, the roughness and Vickers hardness of the deposited films and the underlying substrates have been measured. It has been shown that dense consolidated films of regolith simulant can be produced within minutes by AD. The deposited films show a higher roughness and, on average, a higher hardness than the steel substrates. Since on the Moon, naturally available regolith powders are abundant and very dry, and since the required process vacuum is available, AD appears to be a very promising method for producing dense coatings in future Moon exploration and utilization missions.

Thyroid cancer is the most abundant tumor of the endocrine organs. Poorly differentiated thyroid cancer is still difficult to treat. Human cells exposed to long-term real (r-) and simulated (s-) microgravity (µ g ) revealed morphological alterations and changes in the expression profile of genes involved in several biological processes. The objective of this study was to examine the effects of short-term µ g on poorly differentiated follicular thyroid cancer cells (FTC-133 cell line) resulting from 6 min of exposure to µ g on a sounding rocket flight. As sounding rocket flights consist of several flight phases with different acceleration forces, rigorous control experiments are mandatory. Hypergravity (hyper- g ) experiments were performed at 18 g on a centrifuge in simulation of the rocket launch and s-µ g was simulated by a random positioning machine (RPM). qPCR analyses of selected genes revealed no remarkable expression changes in controls as well as in hyper- g samples taken at the end of the first minute of launch. Using a centrifuge initiating 18 g for 1 min, however, presented moderate gene expression changes, which were significant for COL1A1 , VCL , CFL1 , PTK2 , IL6, and CXCL8 . We also identified a network of mutual interactions of the investigated genes and proteins by employing MMP14 . We also identified a network of mutual interactions of the investigated genes and proteins by employing in-silico analyses. Lastly, µ g -samples indicated that microgravity is a stronger regulator of gene expression than hyper- g .

This article demonstrated an accurate analysis technique for dual-reflectarray antennas that take into account the angle of incidence of the impinging electric field on the main reflectarray cells. The reflected field on the sub and the main reflectarray surfaces is computed using Method of Moments in the spectral domain and assuming local periodicity. The sub-reflectarray is divided into groups of elements and the field radiated by each group is used to compute the incident and reflected field on the main reflectarray cells. A 50-cm demonstrator in Ku-band that provides European coverage has been designed, manufactured and tested to validate the analysis technique. The measured radiation patterns match the simulations and they fulfill the coverage requirements, achieving a cross-polar discrimination better than 25 dB in the frequency range: 12.975-14.25 GHz., Competing Interests: The authors declare no conflict of interest.

In real aircraft structures the comfort and the occupational performance of crewmembers and passengers are affected by the presence of noise. In this sense, special attention is focused on mechanical and material design for isolation and vibration control. Experimental characterization and, in particular, experimental modal analysis, provides information for adequate cabin noise control. Traditional sensors employed in the aircraft industry for this purpose are invasive and provide a low spatial resolution. This paper presents a methodology for experimental modal characterization of a front fuselage full-scale demonstrator using high-speed 3D digital image correlation, which is non-invasive, ensuring that the structural response is unperturbed by the instrumentation mass. Specifically, full-field measurements on the passenger window area were conducted when the structure was excited using an electrodynamic shaker. The spectral analysis of the measured time-domain displacements made it possible to identify natural frequencies and full-field operational deflection shapes. Changes in the modal parameters due to cabin pressurization and the behavior of different local structural modifications were assessed using this methodology. The proposed full-field methodology allowed the characterization of relevant dynamic response patterns, complementing the capabilities provided by accelerometers., Competing Interests: The authors declare no conflict of interest.