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Inertial measurement units (IMUs) are key components of various applications including navigation, robotics, aerospace, and automotive systems. IMU sensor characteristics have a significant impact on the accuracy and reliability of these applications. In particular, noise characteristics and bias stability are critical for proper filter settings to perform a combined GNSS/IMU solution. This paper presents an analysis based on the Allan deviation of different IMU sensors that correspond to different grades of micro-electromechanical systems (MEMS)-type IMUs in order to evaluate their accuracy and stability over time. The study covers three IMU sensors of different grades (ascending order): Rokubun Argonaut navigator sensor (InvenSense TDK MPU9250), Samsung Galaxy Note10 phone sensor (STMicroelectronics LSM6DSR), and NovAtel PwrPak7 sensor (Epson EG320N). The noise components of the sensors are computed using overlapped Allan deviation analysis on data collected over the course of a week in a static position. The focus of the analysis is to characterize the random walk noise and bias stability, which are the most critical for combined GNSS/IMU navigation and may differ or may not be listed in manufacturers’ specifications. Noise characteristics are calculated for the studied sensors and examples of their use in loosely coupled GNSS/IMU processing are assessed. This work proposes a structured and reproducible approach for working with sensors for their use in navigation tasks in combination with GNSS, and can be used for sensors of different levels to supplement missing or incorrect sensor manufacturers’ data., This research was funded by Rokubun S.L. and Universitat Politècnica de Catalunya with industrial PhD grant number 2020 DI 108 from the Generalitat de Catalunya; This research was partially funded by the Spanish Ministry of Science and Innovation and European Union FEDER through projects CNS2022-135383 and PID2022-138485OB-I00., Peer Reviewed, Postprint (published version)

This document is the partial result of an ongoing satellite research activity carried out by Universitat Politècnica de Catalunya NanoSat Lab and i2CAT Foundation, submitted to the Horizon Europe Program in 2022., Thousands of rockets, spaceships, and satellites have been launched, resulting in a steady increase in the number of space debris notably in Low Earth Orbit (LEO) and Geostationary Orbit (GEO). Among the objects comprising space debris, defunct satellites are commonly found, which may have been abandoned in space due to various anomalies. While these satellite failures may not be catastrophic, certain subsystems could still be repurposed for alternative uses beyond their initial design. This study aims to assess the feasibility of “recycling” partially failed satellites, also known as “Zombie Satellites”, by statistically evaluating the severity of their anomalies and quantifying their remaining capabilities and limitations. By conducting a comprehensive statistical analysis, the anomalies have been categorized based on subsystem, orbit type, occurrence date during the satellite’s lifespan, and severity. Additionally, an analysis is presented to identify failed satellites from some of the current large constellations., This work was sponsored in part by the I+D+i project of UPC GENESIS project PID 2021-1264360B-C21 financed by MCIN/AEI. Additionally, this work was supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union–NextGeneration EU , in the framework of the Recovery Plan, Transformation and Resilience (PRTR) (Call UNICO I+D 5G 2021, ref. number TSI-063000-2021-8-6GSatNet-SeS)., Peer Reviewed, Postprint (published version)

As new supercomputer architectures become more heavily focused on using hardware accelerators, in particular general-purpose graphical processors, it is therefore relevant that algorithms for computational fluid dynamics, especially those targeting scale-resolving simulations, be designed in such a way as to make efficient use of such hardware. In this paper, we propose one such hardware-accelerated Continuous Galerkin Finite Elements model, aimed at handling simulations of turbulent compressible flows over complex geometries. As this model is intended for use in Large-Eddy and Direct Numerical simulations, it is necessary that the resulting scheme introduces only small amounts of artificial (numerical) diffusion for stabilization purposes. We achieve this through a combination of Spectral Finite Elements, operator splittings on the convective term, and use of the Entropy Viscosity stabilization model adapted to spectral elements scheme. The paper will present the resulting algorithm, how it is made to work efficiently on the accelerators, and results obtained so far that demonstrate its high-accuracy capabilities.As new supercomputer architectures become more heavily focused on using hardware accelerators, in particular general-purpose graphical processors, it is therefore relevant that algorithms for computational fluid dynamics, especially those targeting scale-resolving simulations, be designed in such a way as to make efficient use of such hardware. In this paper, we propose one such hardware-accelerated Continuous Galerkin Finite Elements model, aimed at handling simulations of turbulent compressible flows over complex geometries. As this model is intended for use in Large-Eddy and Direct Numerical simulations, it is necessary that the resulting scheme introduces only small amounts of artificial (numerical) diffusion for stabilization purposes. We achieve this through a combination of Spectral Finite Elements, operator splittings on the convective term, and us, This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 956104 and co-founded by the Spanish Agencia Estatal de Investigación (AEI) under grant agreement PCI2021-121962. O. Lehmkuhl work is financed by a Ramón y Cajal postdoctoral contract by the Ministerio de Economía y Competitividad, Secretaría de Estado de Investigación, Desarrollo e Innovación, Spain (RYC2018-025949-I). Lucas Gasparino has received financial support from ’la Caixa’ Foundation (ID 100010434). The fellowship grant code is LCF/BQ/DI18/11660051., Peer Reviewed, Postprint (published version)

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals., This project is supported and funded by the IEEE Geoscience and Remote Sensing Society (GRSS), as one of the winners of the 2nd IEEE GRSS Student Grand Challenge, and supported grants for recruitment of early-stage research staff of Agencia Gestió d’Ajuts Universitaris i de Recerca (AGAUR) Generalitat de Catalunya, Spain (FI2019/1183, FISDUR2020/105, FI2022/809, and FI2023/239). This article was part of the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigacion (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (author's final draft)

Earth observation satellite missions are becoming increasingly important and have been also boosted by the appearance of small satellites, affordable to small companies and universities. Technology miniaturization and the reduction in power consumption have been a key point in the reduction of the dimensions of these spacecrafts, being able to integrate powerful payloads in a very limited space. In missions where the observations are made at lower frequency bands, the antenna dimensions limit the achievable angular resolution. For this reason, significant investments are being made to develop antenna deployment systems for small satellites, and the deployable antennas themselves.This work presents the design and prototyping of a deployable Fresnel Zone Plate antenna for CubeSats. The first model is a technical demonstrator, but it is also meant for a GNSS-Radio Occultations payload on a 6U CubeSat. Due to its characteristics and modularity it could be easily redesigned and modified to be used in communications, etc. at other frequency bands., This project was part of the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (author's final draft)

An OpenFOAM computational fluid dynamics model setup is proposed for simulating thermally driven winds in mountain–valley systems. As a first step, the choice of Reynolds Averaged Navier–Stokes k-e turbulence model is validated on a 3D geometry by comparing its results vs. large-eddy simulations reported in the literature. Then, a numerical model of an idealised 2D mountain–valley system with mountain slope angle of 20° is developed to simulate thermally driven winds. A couple of top surface boundary conditions (BC) and various combinations of temperature initial conditions (IC) are tested. A transient solver for buoyant, turbulent flow of incompressible fluids is used. Contrary to classical approaches where buoyancy is set as a variable of the problem, here temperature linearly dependent with altitude is imposed as BC on the slope and successfully leads to thermally driven wind generation. The minimum fluid domain height needed to properly simulate the thermally driven winds and the effects of the different setups on the results are discussed. Slip wall BC on the top surface of the fluid domain and uniform temperature IC are found to be the most adequate choices. Finally, valleys with different widths are simulated to see how the mountain–valley geometry affects the flow behaviour, both for anabatic (daytime, up-slope) and katabatic (nighttime, down-slope) winds. The simulations correctly reproduce the acceleration and deceleration of the flow along the slope. Increasing the valley width does not significantly affect the magnitude of the thermally driven wind but does produce a displacement of the generated convective cell., This research was funded by AGAUR/Generalitat de Catalunya, with grant number 2017 SGR 1278, and by the Spanish Science and Innovation Ministry (MCIN) within the project TABL4CW, with grant number PID2019-105162RB-I00, funded by MCIN/AEI/10.13039/501100011033., Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energia, Postprint (published version)

Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area., Peer Reviewed, Postprint (published version)

Three volcanic plumes were detected during the Tajogaite volcano eruptive activity (Canary Islands, Spain, September–December 2021) over the Iberian Peninsula. The spatiotemporal evolution of these events is characterised by combining passive satellite remote sensing and ground-based lidar and sun-photometer systems. The inversion algorithm GRASP is used with a suite of ground-based remote sensing instruments such as lidar/ceilometer and sun-photometer from eight sites at different locations throughout the Iberian Peninsula. Satellite observations showed that the volcanic ash plumes remained nearby the Canary Islands covering a mean area of 120 ± 202 km2 during the whole period of eruptive activity and that sulphur dioxide plumes reached the Iberian Peninsula. Remote sensing observations showed that the three events were mainly composed of sulphates, which were transported from the volcano into the free troposphere. The high backscatter-related Ångström exponents for wavelengths 532–1064 nm (1.17 ± 0.20 to 1.40 ± 0.24) and low particle depolarization ratios (0.08 ± 0.02 to 0.09 ± 0.02), measured by the multi-wavelength Raman lidar, hinted at the presence of spherical small particles. The layer aerosol optical depth at 532 nm (AODL532) obtained from lidar measurements contributed between 49% and 82% to the AERONET total column AOD at 532 nm in event II (11–13 October). According to the GRASP retrievals, the layer aerosol optical depth at 440 nm (AODL440) was higher in all sites during event II with values between 0.097 (Badajoz) and 0.233 (Guadiana-UGR) and lower in event III (19–21 October) varying between 0.003 (Granada) and 0.026 (Évora). Compared with the GRASP retrievals of total column AOD at 440 nm, the AODL440 had contributions between 21% and 52% during event II. In the event I (25–28 September), the mean volume concentrations (VC) varied between 5 ± 4 µm3cm-3 (El-Arenosillo/Huelva) and 17 ± 10 µm3cm-3 (Guadiana-UGR), while in event II this variation was fro, Évora team funded by national funds through FCT - Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project with the references UIDB/04683/2020 and UIDP/04683/2020 and by TOMAQAPA (PTDC/CTAMET/29678/2017). The authors acknowledge the GRASP-ACE (Grant agreement ID: 778349), ACTRIS-IMP (Grant agreement ID:871115), ATMO-ACCESS (Grant Agreement ID: 101008004), PROBE (COST Action number: CA18235), HARMONIA (COST Action number: CA21119), EUMETNET through the E-PROFILE program and REALISTIC (Grant agreement ID:101086690) projects. The authors acknowledge the support through ACTRIS-2 under grant agreement no.654109. This work was partially supported by the Spanish national projects PID2019-103886RB-I00/AEI/10.13039/501100011033, INTEGRATYON3 (PID2020-117825GB-C21 and PID2020-117825GB-C22), ELPIS (PID2020-120015RB-I00), CLARIN (CGL2016-81092-R), ePOLAAR (RTI2018-097864-B-I00), CAMELIA (PID2019-104205GB-C21/AEI/10.13039/501100011033) and ACTRIS-España (CGL2017-90884REDT), by University of Granada Plan Propio through Singular Laboratory (LS2022-1) program, by the Andalusia Autonomous Government projects AEROPRE (P18-RT-3820) and ADAPNE (P20_00136), by the UGR-FEDER projects DEM3TRIOS (A-RNM-430-UGR20) and MOGATRACO (A-RNM-524-UGR20) and partially by the Scientific Units of Excellence Program (grant no. UCE-PP2017-02), and by the R+D+i grant RTI 2018-097332-B-C22 funded by MCIN/AEI/ 10.13039/501100011033/ and “ERDF A Way of Doing Europe”. M.-Á. López-Cayuela and C.V. Carvajal-Pérez are supported by the INTA predoctoral contract program., Peer Reviewed, Postprint (published version)

An intense and long-lasting Saharan dust outbreak crossed the Iberian Peninsula (IP) from the southwest (SW) to the northeast (NE) from 25 March until 7 April 2021. This work aims to assess the optical and mass contribution of both fine and coarse dust particles along their transport. Five Iberian lidar stations were monitoring the transport and evolution of the Saharan dust particles, i.e. El Arenosillo/Huelva, Granada, Torrejón/Madrid and Barcelona in Spain, and Évora in Portugal. The particular meteorological conditions determined the aerosol scenario along the overall dust event, differing in the first part of the event (25–31 March), in which the strongest dust incidence occurred on 29–31 March at the south and central stations and 1 April at Barcelona, from the second one (1–7 April). The use of the two-step POLIPHON algorithm showed the relevance of using polarized lidar measurements for separating the aerosol properties of dust fine and coarse particles as an added value. Both the fine dust (Df) and coarse dust (Dc) components of the total particle backscatter coefficient (total dust, DD¿=¿Dc¿+¿Df) were separately derived. The dust plume was well-mixed with height and no significant differences were found in the vertical structure of both the Dc and Df particle backscatter coefficients. From the beginning of the dust outbreak until 1 April, the vertical Df¿¿DD mass ratio was nearly constant in time at each station and also in altitude with values of ~¿10¿%. Moreover, the mean dust optical depth at 532¿nm was decreasing along that dust pathway, reporting values from SW to NE stations of 0.34 at El Arenosillo/Huelva, 0.28 at Granada, 0.20 at Évora, 0.28 at Torrejón/Madrid, and 0.14 at Barcelona, although its Df¿¿DD ratio remained almost constant (28¿%–30¿%). A similar pattern was found for the total dust mass loading and its Df¿¿DD ratio, i.e. mostly decreasing mean mass values were reported, being constant in its Df¿¿DD ratio (~¿10¿%) along the SW–NE dust pat, This research has been supported by the Spanish Ministry of Science and Innovation (grant no. PID2019-104205GB-C21/AEI/10.13039/501100011033)., Peer Reviewed, Postprint (published version)

Estimating inter-system biases (ISBs) is important in multi-constellation Global Navigation Satellite System (GNSS) processing. The present study aims to evaluate and screen out an optimal estimation strategy of ISB for multi-GNSS kinematic precise point positioning (PPP). The candidate strategies considered for ISB estimation are white noise process (ISB-WN), random walk process (ISB-RW), constant (ISB-CT) and eliminated by between-satellite single-differenced observations (ISB-SD). We first present the mathematical model of ISB derived from the observation combination among different GNSSs, and we demonstrate the equivalence between ISB-WN and ISB-SD in the Kalman filter. In order to evaluate the performance of these four ISB solution strategies, we implement kinematic PPP with 1-month static data from 112 International GNSS service stations and two-hour dynamic vehicular data collected in an urban case. For comparison, precise orbit and clock products from the Center for Orbit Determination in Europe (CODE), GeoForschungsZentrum in Germany (GFZ) and Wuhan University (WHU) are employed in our experiments. The results of static tests show that the positioning accuracy is comparable among the four strategies, but ISB-CT performs slightly better in convergence time. In the kinematic test, there are more cycle slips than static test, and the ISB-CT improves the positioning accuracy by 15.7%, 38.9% and 63.2% in east, north and up components, and reduces the convergence time by 60.1% comparing with the other strategies. Moreover, both the static and kinematic tests prove the consistence among CODE, GFZ and WHU precise products and the equivalence between ISB-WN and ISB-SD strategies. Finally, more, i.e., the same amount of cycle slips as for the dynamic data, are artificially added to the static data to conduct the pseudo-kinematic test. The result shows that ISB-CT improves the positioning accuracy and convergence time by 19.2% and 24.4%, respectively., The study is funded by Laoshan Laboratory (LSKJ202205104, LSKJ202205104_01), National Key Research and Development Program of China (2020YFB0505800, 2020YFB0505804), National Natural Science Foundation of China (42004012), Natural Science Foundation of Shandong Province, China (ZR2020QD048) and by the project RTI2018-094295-B-I00 funded by the MCIN/AEI 1013039/501100011033 which is co-funded by the FEDER program., Peer Reviewed, Postprint (published version)

A compressible direct numerical simulation (DNS) has been conducted to analyze the behavior of low-speed flows submitted to a strong temperature gradient in a fully developed and asymmetrically heated turbulent channel flow. The focus is to understand the relevance of the variable thermophysical properties and their impact on the heat transfer mechanisms. The simulations have been performed at mean friction Reynolds number Ret m = 400 submitted to a high-temperature ratio between the two walls (Thot/Tcold = 2). The results show an increased flow mixing and recirculation due to the temperature difference between the walls., This work has been partially financially supported by the Ministerio de Economía y Competitividad, Secretaría de Estado de Investigación, Desarrollo e Innovación, Spain (ref. PID2020-116937RB-C21 and PID2020-116937RB-C22). We also acknowledge Red Española de Supercomputación (RES) for awarding access to the MareNostrum IV machine based in Barcelona, Spain (IM-2022-3-0010). The corresponding first author gratefully acknowledges the Universitat Politècnica de Catalunya and the Banco Santander for the financial support of her predoctoral grant FPI- UPC., Peer Reviewed, Postprint (published version)

Publicat en accés obert amb el permís explícit de l'editorial., High-fidelity large eddy simulations (LES) are conducted for lean natural gas flames with different levels of hydrogen enrichment in a technically premixed swirl-stabilized combustor (PRECCINSTA) operated at atmospheric pressure. The modelling approach relies on tabulation of premixed flamelets and presumed-shape probability density functions (PDF) to account for subgrid turbulence-chemistry interactions. Results are presented for non-reacting and reacting conditions with 0, 40 and 50% hydrogen content in the natural gas. The influence of hydrogen-enrichment is investigated here by combining LES with Raman measurements. The assessment of LES shows good predictions of the flame stabilization mechanism, flow field and flame dynamics as compared to experiments. The natural gas flame develops a self-excited flow oscillation characterized as a precessing vortex core, which is well reproduced by the LES. The lean operation of the burner with natural gas shows a stable M-shape flame that transitions to a V-shape fully attached flame as the main fuel is blended with hydrogen. Raman measurements are compared with LES data to examine the flame structure and burning characteristics. It is concluded that hydrogen addition makes the flame more compact, induces higher reactivity of the fuel-air mixture and leads to a stable V-shape flame fully attached to the burner’s nozzle-cone., This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 682383), the Center of Excellence in Combustion project (grant agreement No 952181), and the AHEAD PID2020-118387RB-C33 and ORION TRA2017-89139-C2-2-R projects from the Ministerio de Ciencia e Innovación. Leonardo Pachano acknowledges the Margarita Salas grant from Ministerio de Universidades (Spain) funded by the European Union-Next Generation EU. The authors thankfully acknowledge the computer resources from the RES (IM-2022-2-0003)., Peer Reviewed, Postprint (published version)

Due to the recent licensing of the 26 GHz 5G communications bands, the passive microeaves Remote Sensing community is concerned that spurious out-of-band emissions could interfere with the measurements acquired in the 23.8 GHz water vapor resonance band. For this reason, the Frequency Allocations in Remote Sensing (FARS) Technical Committee from the IEEE Geoscience and Remote Sensing Society (GRSS) has procured the development of a RFI monitoring payload to detect possible interferences in the 24 to 25 GHz band. This payload is compatible with the PocketQubes being developed in the framework of the "IEEE GRSS Open PocketQube Kit" educational initiative, and it will be used to measure and determine the occurrence of possible interferences close to the 23.8 GHz water vapor band. This work presents the design, implementation and testing of the 24 to 25 GHz RFI monitoring payload designed for a 1P PocketQube., This work was sponsored by the Frequency Allocations in Remote Sensing (FARS) Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS). This project was also sposored in part by the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (author's final draft)

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI)., This project is supported and funded by the IEEE Geoscience and Remote Sensing Society (GRSS), as one of the winners of the 2nd IEEE GRSS Student Grand Challenge. It is also part of the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN) / Agencia Estatal de Investigación (AEI) / 10.13039/501100011033 and EU FEDER “Una manera de hacer Europa., Peer Reviewed, Postprint (author's final draft)

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the ca, This research received no external funding. The author F.R. was supported by the grant PID2021-124272OB-C22 funded by MCIN/AEI/10.13039/501100011033/ of the Spanish government and by “ERDF A way of making Europe” of the European Union., Peer Reviewed, Postprint (published version)

For the first time in fifty years, the Cumbre Vieja volcanic area (La Palma, Canary Islands, Spain) erupted on September 19, 2021, giving birth to a new volcano. Fresh volcanic aerosols were continuously injected into the troposphere at different height levels, decreasing with time until the end of December 2021 (15 weeks duration). A wide set of different instrumentation was deployed all over the Island in order to evaluate the effects of the volcanic plumes on the atmosphere and the air quality. For the first time, a long-term study of the relative mass contribution and vertical impact of the volcanic components, ash and non-ash particles separately, during the eruptive activity was carried out in this work. In particular, a polarized Micro-Pulse Lidar (P-MPL) was deployed at Tazacorte (at around 8 km west from the volcano) in 24/7 operation from October 17, 2021 until the end of the volcano activity (11 weeks) for vertical monitoring of the volcanic particles. First, a statistical study of the mass conversion factors for mass concentration estimation of the volcanic (ash and non-ash) particles was performed by using the AERONET sun/sky-photometer dataset at Fuencaliente (at around 18 km south from the volcano). A representative mass conversion factor was obtained for ash and non-ash particles: 1.89 ± 0.53 and 0.31 ± 0.06 g m-2, respectively, with no dependence on time and optical depth. Second, these factors were used to calculate the ash and non-ash mass concentrations from P-MPL observations. Ash particles dominated 11% of the time and mostly until week 3 (i.e. week 7 from the volcanic eruption). Their mass concentration decreased by one order of magnitude: the relative ash mass contribution was 73 ± 18% with a total mass loading of 566 ± 281 mg m-2 at week 1, reducing gradually down to 38 ± 32% and 120 ± 49 mg m-2, respectively, at week 11. Layer-to-layer, it decreased with increasing layer-height; no ash was detected above 4 km at the end of the volcanic peri, This research was funded by the Spanish Ministry of Science and Innovation (PID2020- 118793GA-I00, PID2019-104205GB-C21/AEI/10.13039/501100011 033, PID2019-103886RB-I00, and EQC2018-004686-P) and the Unit of Excellence “María de Maeztu” (MDM-2017-0737) and ACTRIS-Spain (CGL2017-90884-REDT) by the Spanish State Research Agency (AEI). The authors wish to thank the support from the H2020 program of the European Union and ACTRIS (GA n. 19ENV04, 654109, 778349, 871115, 101008004, and 101086690), Peer Reviewed, Postprint (published version)

Solving chemistry is an integral part of reacting flow simulations, usually dominating the computational cost. Among the different strategies to accelerate the solution of chemistry and to achieve realizable simulations, the use of Dynamic Adaptive Chemistry (DAC) stands out among other methods. DAC methods are based on the use of reduced mechanisms generated from local conditions. The reduction process is computationally expensive and strategies for reducing the frequency of reduction and the re-utilization of the generated reduced mechanisms are key in making DAC methods computationally affordable. In this study, a new method hereby referred as Tabulated Reactions for Adaptive Chemistry (TRAC) is proposed to correlate chemical states with their reduced mechanisms in order to reduce both the frequency of reduction and to allow for re-utilization of reduced chemical schemes. TRAC introduces a mechanism tabulation strategy based on the use of a low-dimensional space that defines the thermo-chemical conditions for which specific reduced reaction mechanisms are stored. Chemistry reduction is achieved by the use of Path Flux Analysis (PFA) with a reaction rate-sensitivity method to achieve further reduction in the reaction mechanisms. The new TRAC proposal is applied to various canonical transient problems and the results are compared with reference solutions obtained from detailed chemistry calculations. A speedup of about 4x was achieved with TRAC while maintaining an error under 3% in the prediction of the major and minor species, flame structure, and flame propagation., This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation program (grant agreement No. 682383), the Center of Excellence in Combustion project (grant agreement No 952181), and the AHEAD PID2020-118387RB-C33 and ORION TRA2017-89139-C2-2-R projects from the Ministerio de Ciencia e Innovación. Anurag Surapaneni acknowledges the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)

Having real-time and accurate numerical predictions of urban wind flow can be extremely useful for developing tools intended to improve citizens’ life quality and health. However, traditional methods such as Computational Fluid Dynamics (CFD) are unsuitable for fast prediction. This work proposes using a Convolutional Neural Network (CNN) trained with a newly created vast dataset to enable fast and accurate flow predictions for any urban geometry. The dataset has been generated through high-fidelity CFD simulations of 30 different European Urban areas and 90 meteorological conditions. The geometries were selected to have a wide variety of urban flow patterns and geometrical features allowing the Neural Network (NN) to learn a representative range of urban flow conditions. Then, a CNN was trained to reproduce the urban wind flow for any urban geometry and meteorological condition. The strategy allows for predicting accurate mean wind flow in urban areas that have not been seen in training time, showing good generalization properties., Postprint (published version)

Genetic Algorithms (GA) are useful optimization methods for exploration of the search space, but they usually have slowness problems to exploit and converge to the minimum. On the other hand, gradient based methods converge faster to local minimums, although are not so robust (e.g., flat areas and discontinuities can cause problems) and they lack exploration capabilities. This article presents a hybrid optimization method trying to combine the virtues of genetic and gradient based algorithms, and to overcome their corresponding drawbacks. The performance of the Hybrid Method is compared against a gradient based method and a Genetic Algorithm, both used alone. The rate of convergence of the methods is used to compare their performance. To take into account the robustness of the methods, each one has been executed more than once, with different starting points for the gradient based method and different random seeds for the Genetic Algorithm and the Hybrid Method. The performance of the different methods is tested against an optimization Active Flow Control (AFC) problem over a 2D Selig–Donovan 7003 (SD7003) airfoil at Reynolds number 6×104 and a 14 degree angle of attack. Five design variables are considered: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. The objective function is defined as minus the lift coefficient (-Cl), so it is defined as a minimization problem. The proposed Hybrid Method enables working with N optimization algorithms, multiple objective functions and design variables per optimization algorithm., This research has been partially supported through the Severo Ochoa Centre of Excellence (2019-2023) under the grant CEX2018-000797-S funded by MCIN/AEI/10.13039/501100011033. The third author, Jordi Pons-Prats, is a Serra Hunter Fellow., Peer Reviewed, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Postprint (published version)

Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictions, This work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber PID2020-116377RB-C21. This project has also received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 783287., Peer Reviewed, Postprint (published version)

Airlines develop an operation plan, during the day prior to operations (D-1), to identify potential network issues and prepare potential pre-tactical preventing measures such as aircraft tail swapping or crew reassignment to be applied on D0. Flights might experience discrepancies between their plan and execution due to many different factors, and in particular demand-capacity imbalances in the network leading to Air Traffic Flow Management (ATFM) regulations. Dispatcher3, a Clean Sky 2 innovation action, focuses on the use of machine learning techniques to support the airlines processes prior departure: dispatching, understood as the broad flight planning from the day prior to operations to the flight plan definition and selection, and advisories to pilot. This paper focuses on the estimation of ATFM delay for individual flights during the pre-tactical phase (D-1), which could help airspace users apply mitigation actions. Four machine learning models are developed to produce individual independent estimations with different level of granularity. The first two are binary classifier models that provide information on the probability of a given flight being affected by ATFM delay, and the reason for this delay (airport or airspace congestion). These models reported an accuracy between 75% and 88%. The later two models estimate the impact of the delay (amount of delay assigned to the flight if regulated), with a Mean Absolute Error close to 9.35 minutes., This work has been performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein., Peer Reviewed, Postprint (published version)

Monitoring ground deformation at national and regional level with millimetre-scale precision, nowadays, is possible by using Advanced Differential Interferometric SAR (A-DInSAR) techniques. This study concerns the results of the European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service, which detects and measures land displacement at European scale. This Service provides reliable and consistent information regarding natural ground motion phenomena such as landslides and subsidence. The ground motion is derived from Synthetic Aperture Radar (SAR) time-series analysis of Sentinel-1A/B data. These data, which provide full coverage of Europe from two different observation geometries (ascending and descending) every six days, are processed at full resolution. The paper is focused on the exploitation of the basic product of EGMS for both regional and local purposes. Analysing the slope and aspect of the deformation field is the novelty of this investigation. In particular, the focus is put on the generation of wide-area differential deformation maps. Such maps indicate the gradient of the deformation field. The obtained information is not only beneficial for monitoring anthropogenic phenomena but also vital for urban management and planning. Most of the significant damages to manmade structures and infrastructures are associated with high deformation gradient values. Thus, monitoring the temporal and spatial variations of deformation gradient is essential for dynamic analysis, early-warning, and risk assessment in urban areas. Although EGMS productions are prepared for monitoring at regional level, their resolutions are high enough to investigate at local level. Therefore, this paper considers the local deformations that affect single structures or infrastructures. Local differences in such deformation can indicate damages in the corresponding structures and infrastructures. We illustrate these types of analysis to generate differential deforma, This work has been funded by the Agency for University and Research Grants Management, AGAUR, Generalitat de Catalunya, through a grant for the recruitment of early-stage research staff (Ref: 2021FI_B 00077). This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/AEI/10.13039/501100011033, Peer Reviewed, Postprint (published version)

Temporal analysis of deformations Time Series (TS) provides detailed information of various natural and humanmade displacements. Interferometric Synthetic Aperture Radar (InSAR) generates millimetre-scale products, indicating the chronicle behaviour of detected targets via TS products. Deep Learning (DL) can handle a massive load of InSAR TS to categorize significant movements from non-moving targets. To this end, we employed a supervised Convolutional Neural Network (CNN) model to distinguish five deformations trends, including Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error (PUE). Considering several arguments in a CNN model, we trained numerous combinations to explore the most accurate combination from 5000 samples extracted from a Persistent Scatterer Interferometry (PSI) technique and Sentinel-1 images over the Granada region, Spain. The model overall accuracy exceeds 92%. Deformations of three cases of landslides were also detected over the same area, including the Cortijo de Lorenzo, El Arrecife, and Rules Viaduct areas., Peer Reviewed, Postprint (published version)

Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DInSAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs., This research was partially funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya—through a grant for the recruitment of early-stage research staff (Ref: 2021FI_B2 00186)., Peer Reviewed, Postprint (published version)

The future success of rice farming will lie in developing productive, sustainable, and resilient farming systems in relation to coexistent ecosystems. Thus, accurate information on agricultural practices and grain yield at optimum temporal and spatial scales is crucial. This study evaluates the potential application of Sentinel-2 (S2) to monitor the dynamics of rice fields in two consecutive seasons (2018 and 2019) in the Ebro Delta growing area. For this purpose, time series of four different spectral indices (NDVI, NDWIMF, NDWIGAO, and BSI), derived from smoothed S2 data at 20 m spatial resolution, were generated. Then, a combination of the first and second derivative analysis on the temporal profiles of spectral indices was used to automatically identify key phenology and management features from regional to field scale; and for estimating crop yield at fields. Features extracted from NDVI and NDWIGAO were used for identifying significant phenological stage dates (i.e. Tillering, Heading Date, and Maturity), and field status (i.e. hydroperiod), although the performance of the proposed method at field-scale was limited by S2 data gaps. The absolute minimum of NDWIMF showed great potential for estimating rice yield, including different cultivars (r = - 0.8), and less sensibility to the number of valid images. Sentinel-2 alone cannot assure a consistent phenology monitoring at all fields but demonstrated strong capabilities for studying the performance of rice fields, thus must be considered in the development of new strategies for the management of rice-growing areas., Peer Reviewed, Award-winning, Postprint (published version)

The availability of high fidelity data due to the recent developments in high-performance computing(HPC), has opened new doors for wall modeling in Large Eddy Simulation(LES). By augmenting the physics-based model with information gained from data, new methodologies of model development are being developed. Towards this goal, a preliminary work on developing a non-equilibrium wall model is presented here briefly describing the method., Peer Reviewed, Postprint (published version)

We present a framework to generate, in a multi-aircraft environment, 4D optimized trajectories in a scenario with several weather constraints obtained with advanced weather prediction models. We focus on the trajectory optimization module of this framework, which is based on a point-mass representation of the aircraft. By tuning some of the parameters of this module, we compute several alternative trajectories avoiding these constraints both laterally and vertically. The experiment conducted involves flights crossing the North Atlantic region, while in the en-route phase. This preliminary framework is also used to run the experiments in multiple cycles or consecutive time periods, assuming different update times for the weather constraints, and choosing the best trajectory per flight and per cycle. The ultimate goal of the framework is to develop innovative procedures in the air traffic management system to reduce the climate and environmental impact of aviation, while increasing the resilience of air operations to weather phenomena., Peer Reviewed, Postprint (published version)

Commercial computational fluid dynamics (CFD) codes have often been used for simulation of atmospheric boundary layer (ABL) flows. The present work explores the potential of the open-source CFD software OpenFOAM for simulating thermally-driven winds, by comparing several turbulence models. Indeed, in ABL and other large-scale flows, turbulence is critical to the mixing process of momentum and buoyancy, and simulations with commercial CFD codes have usually been done with Reynolds-Averaged Navier-Stokes (RANS) turbulence modelling. In this work, the formation of thermally-driven winds is studied in an idealised mountain-valley system, with realistic values of parameters such as the slope angle, the diurnal temperature cycle, etc. Performances of various OpenFOAM RANS turbulence models (k–e, re-normalisation group (RNG) k–e, k–¿ shear stress transport (SST)) are compared. A preliminary study of LES using Smagorinsky closure is also contemplated. Velocity contours, velocity and temperature profiles, the shapes of vortexes/convective cells, and the computational times are presented for all the studied turbulence models, to help identify the most suitable one for simulation of thermally-driven winds., This work is supported by the project PID2019-105162RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by the project 2017 SGR 1278 from the AGAUR Generalitat de Catalunya., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.1 - Per a 2030, garantir l’accés universal a serveis d’energia assequibles, confiables i moderns, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energia, Postprint (published version)

Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring., Postprint (author's final draft)

Global Navigation Satellite System signals have been used for years to study high-frequency fluctuations (f>0.1Hz)intheionosphere. The customary procedure uses the geometry-free (GF) combination ofL1andL2carriers, for which it is necessaryto acquire theL2GPS signal. Initially,L2had to be acquired from a codeless signal, L2P(Y), using several techniques, someof them requiring the aid ofL1. New GPS satellites transmit the newC2civil code, which can be used to acquire directlyL2,i.e. L2C. Several publications have reported differences in the GF combination when it is computed from L2P(Y) or L2C.Using two ionospheric scintillation monitoring receivers (ISMRs), these differences were shown to be related to how theyacquireL2, i.e. if the receiver acquiresL2with theL1aid. However, ISMRs are scarce, so the extension of such a study is notstraightforward. The present work uses the geodetic detrending technique to identify whether a conventional geodetic-gradereceiver acquiresL2with the aid ofL1. The study employs six different receiver types with measurements stored in RINEXformats version 2 and 3. In both formats, we are able to identify ifL2signal is acquired withL1aid. In this way, we showthat some receiver types heavily underestimate high-frequency ionospheric fluctuations when using the GF combination.Our results show that the ionosphere-free combination of these carrier phases is not free from high-frequency ionosphericfluctuations, but in some receivers, almost 90% of the high-frequency effects inL1remain in such combination., Postprint (published version)

Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures on both airports and airlines operations. Thus, the central flow management unit continually seeks to improve traffic flow management to reduce delays and congestion. In this work, we investigated the use of reinforcement learning (RL) methods to compute policies to solve demand–capacity imbalances (a.k.a. congestion) during the pre-tactical phase. To address cases where the expected demands exceed the airspace sector capacity, we considered agents representing flights who have to decide on ground delays jointly. To overcome scalability issues, we propose using raw pixel images as input, which can represent an arbitrary number of agents without changing the system’s architecture. This article compares deep Q-learning and deep deterministic policy gradient algorithms with different configurations. Experimental results, using real-world data for training and validation, confirm the effectiveness of our approach to resolving demand–capacity balancing problems, showing the robustness of the RL approach presented in this article., This work was funded by EUROCONTROL under Ph.D. Research contract no. 18-220569- C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under grant number PID2020-116377RB-C21., Peer Reviewed, Postprint (published version)

This study presents a sensitivity analysis to model mismatch of an interacting multiple model (IMM) filter when used for aircraft guidance modes identification. The mismatch appears in the guidance command parameters (related to the elevator flight surface and engine throttle), which are assumed to be known filter inputs. First, we analyse the filtering sensitivity when the model mismatch is induced by additive Gaussian noise. Three model mismatch scenarios are considered: i) mismatch on the elevator parameter; ii) mismatch on the throttle parameter; and iii) mismatch on both guidance commands. Such sensitivity analysis is conducted for several realistic trajectories. Second, we consider the case where the guidance parameters are estimated. It is found that a guidance command parameters misspecification clearly affects the IMM performance, therefore such possible mismatch must be taken into account for real-life applicability., Peer Reviewed, Postprint (author's final draft)

A critical issue in long-term space exploration missions is the storage and management of cryogenic propellants. Storage tanks are exposed to extreme temperatures and radiation, and hot spots might appear on the tank walls even with an efficient temperature insulation. We propose a technique based on acoustic fields to manage the generation of vapour bubbles in microgravity. During an experimental campaign carried out at the ZARM drop tower in Germany, the acoustic approach was evaluated to manage heat transfer and vapour bubble dynamics in a tank filled with HFE-7100 during microgravity periods of 4.5 seconds. We present the acoustic technique and results obtained in the microgravity campaign at different scenarios, where the acoustic technique was found to be a feasible approach for bubble and heat management in cryogenic propellant tanks., This work was financially supported by the Agencia Estatal de Investigación (Spain) projects ESP2016- 79196-P (AEI/FEDER, UE) and PID2020- 116413GB-I00 (MCIN / AEI / 10.13039/501100011033). We acknowledge the European Space Agency (ESA) for providing access to the ZARM drop tower. We thank ZARM engineering team for valuable technical support., Peer Reviewed, Postprint (published version)

The continuous development of technical innovations provides the opportunity to create new economic markets and a wealth of new services. However, these innovations sometimes raise concerns, notably in terms of societal, safety, and environmental impacts. This is the case for services related to the operation of unmanned aerial vehicles (UAV), which are emerging rapidly. Unmanned aerial vehicles, also called drones, date back to the first third of the twentieth century in aviation industry, when they were mostly used for military purposes. Nowadays, drones of various types and sizes are used for many purposes, such as precision agriculture, search and rescue missions, aerial photography, shipping and delivery, etc. Starting to operate in areas with low population density, drones are now looking for business in urban and suburban areas, in what is called urban air mobility (UAM). However, this rapid growth of the drone industry creates psychological fear of the unknown in some parts of society. Reducing this fear will play an important role in public acceptance of drone operations in urban areas. This paper presents the main concerns of society with regard to drone operations, as already captured in some public surveys, and proposes a list of mitigation measures to reduce these concerns. The proposed list is then analyzed, and its applicability to individual, urban, very large demonstration flights is explained, using the feedback from the CORUS-XUAM project. CORUS-XUAM will organize a set of very large drone flight demonstrations across seven European countries to investigate how to safely integrate drone operations into airspace with the support of the U-space., This project has received funding from the Ministry of Science and Innovation of Spain under grant PID2020-116377RB-C21 and from the SESAR Joint Undertaking (JU) under grant agreement No 101017682. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union., Peer Reviewed, Postprint (published version)

This article presents a method for determining near-Pole ionization depletion regions and troughs from global navigation satellite system (GNSS) vertical total electron content (VTEC) maps. To define the regions, we use Image processing tools, considering the VTEC maps as gray-level images. In this work, watershed segmentation is applied to determine the edges of regions, based on the flow of areas between the lowest ionization zones (<1%) and the medium ionization regions (>10%). In order to enhance the contrast between low-ionization regions and those with a higher relative ionization, the histogram of the maps is equalized, which increases the contrast of the VTEC maps, which is the distance between low-ionization regions and those with a higher relative ionization are increased. The use of percentile thresholds makes this method independent of hypotheses about the generation of depletions, their morphology, or the number of depletion regions that can occur on a map. We test the performance by comparing the depletion regions identified in eight events reported in the literature under different conditions of the ionosphere. The events used as reference were measured by satellites, radar, and GNSS. The method that we propose allows for real-time processing of GNSS VTEC maps. The morphology of the detected regions matches the shape that is reported in the literature., This work was supported in part by Ministerio de Ciencia e Inovación (MICINN) under Project PID2019-107579RB-I00, and in part by the European Commission (EC)-Funded Activity PolarGIMs2., Peer Reviewed, Postprint (author's final draft)

Multispectral satellite remote sensing imagery, together with appropriate modeling, have been proven to provide chlorophyll-a maps that are useful to evaluate the suitability of coastal areas for carrying out shellfish aquaculture. However, current approaches used for chlorophyll-a estimation in very shallow coastal areas often fail in their accuracy. To overcome this limitation, an algorithm that provides an accurate estimation of chlorophyll-a concentration in the coastal areas of the Ebro delta (North Western Mediterranean) using atmospherically corrected Sentinel 2 (S2) remote sensing reflectances (Rrs) has been calibrated and validated. The derived chlorophyll-a maps created have been used in a dynamic carrying capacity model that covers areas from very rich waters inside the embayment to the more oligotrophic waters in the open sea. The use of carrying capacity models is recommended to evaluate the potential of marine coastal areas for bivalve mollusk aquaculture. In this context, the depletion of chlorophyll-a is an indicator of negative environmental impact and thus a continuous monitoring of chlorophyll-a is key. The proposed methodology allows estimation of chlorophyll-a concentration from Sentinel-2 with an accuracy higher than 70% in most cases. The carrying capacity and the suitability of the external areas of the Ebro delta have been determined. The results show that these areas can hold a significant mussel production. The methodology presented in this study aims to provide a tool to the shellfish aquaculture industry., Postprint (published version)

Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we developed a fast and cost-effective remote sensing methodology for characterizing burned areas for the entire country of Iran. We mapped the fire-affected areas using a post-classification supervised method and Landsat 8 time-series images. To this end, the Google Earth Engine (GEE) and Google Colab computing services were used to facilitate the downloading and processing of images as well as allowing for effective implementation of the algorithms. In total, 13 spectral indices were calculated using Landsat 8 images and were added to the nine original bands of Landsat 8. The training polygons of the burned and unburned areas were accurately distinguished based on the information acquired from the Iranian Space Agency (ISA), Sentinel-2 images, and Fire Information for Resource Management System (FIRMS) products. A combination of Genetic Algorithm (GA) and Neural Network (NN) approaches was then implemented to specify 19 optimal features out of the 22 bands. The 19 optimal bands were subsequently applied to two classifiers of NN and Random Forest (RF) in the timespans of 1 January 2019 to 30 December 2020 and of 1 January 2021 to 30 September 2021. The overall classification accuracies of 94% and 96% were obtained for these two classifiers, respectively. The omission and commission errors of both classifiers were also less than 10%, indicating the promising capability of the proposed methodology in detecting the burned areas. To detect the burned areas caused by the wildfire in 2021, the image differencing method was used as well. The resultant models were finally compared to the MODIS fire products over 10 sampled polygons of the burned areas. Overall, the models had a high accuracy in detecting the burned areas in terms o, Peer Reviewed, Postprint (published version)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed., Peer Reviewed, Postprint (published version)

MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types., Peer Reviewed, Postprint (published version)

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS., This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/ AEI/10.13039/501100011033. Additionally, it has been supported by the European Regional Devel- opment Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. Additionally, this work has been co-funded by the European Union Civil Protection through the H2020 project RASTOOL (UCPM-2021-PP-101048474)., Peer Reviewed, Postprint (published version)

The first phase of the RT-WMIS activity comprises two Work Packages (WPs). WP 10 is aimed at performing a revision of the state-of-the-art RT products and requirements for the RT-WMIS activity. On the other hand, WP 20 is devoted to carry out the software design for the subsequent implementation of the RT-WMIS tool. The present technical note (TN-1) describes the activities developed in the framework of WP 10., Preprint

Los tripulantes en misiones análogas participan en misiones espaciales simuladas que experimentan aislamiento y confinamiento con el fin de educar y realizar experimentos de ciencia, tecnología, ingeniería entre otros. Estos tripulantes análogos suelen ser estudiantes o personas dispuestos a desarrollar una carrera en la ciencia espacial y la industria. En este artículo se describe la primera simulación análoga lunar realizada por profesionales militares de la Fuerza Aérea Colombiana, misión que fue diseñada para proporcionar capacitación eficiente para futuras operaciones que van encaminadas en la línea del desarrollo del programa nacional de capacitación de astronautas en Colombia. La misión THOR (Team of Human Operation Research) se realizó en agosto del 2022, con el apoyo de un Centro de Control de Misión (MCC), misión análoga de aislamiento y confinamiento de siete días la cual tenía como objetivo promover el desarrollo cognitivo, físico, fisiológico, psicológico y tecnológico durante esta misión espacial simulada. La misión THOR fue la misión número 50 del Centro de Entrenamiento para Astronautas Análogos (AATC), se dividió en una tripulación dentro del hábitat compuesta por 5 tripulantes análogos con roles específicos basados en su experiencia, antecedentes y adecuados a los roles proporcionados por el AATC, y dos miembros de tripulación externos adicionales que brindaron apoyo remoto e investigación externa. Durante el periodo de misión se realizaron pruebas tales como aprendizaje espacial, memoria de trabajo, abstracción, velocidad sensorio-motora, orientación espacial, identificación de emociones, razonamiento abstracto, toma de decisiones de riesgo, dinámica e equipo, calidad y cantidad de sueño, puntuaciones de fatiga, intervalo R-R, mediante actigrafia de muñeca y antropometría, la vigilancia psicomotora, la percepción del tiempo y tareas críticas en el hábitat se midió simulando una misión corta en la superficie lunar mediante encuesta NASA-TLX, así co, Peer Reviewed, Postprint (published version)

Este artículo presenta el rediseño de un sistema de compras aeronáuticas para consolidar los canales de suministro en la Fuerza Aérea Colombiana, basado en una reingeniería de procesos que se enfoque en la simplificación de operaciones innecesarias mediante la aplicación de herramientas de ingeniería. El rediseño propuesto se desarrolla en dos fases: la caracterización de las operaciones logísticas del proceso de compras y el diseño de un sistema de inteligencia de negocios. Este planteamiento presenta mejoras considerables en cuando a la planeación y el seguimiento del proceso de compras, y en la reducción de tiempos en la entrega de material aeronáutico, entre otros., Peer Reviewed, Postprint (published version)

The aim of this study was to analyze the potential of the GRASP code to retrieve optical and microphysical properties vertically-resolved using a synergy of polarized Micro-Pulse Lidar and Sun/sky photometer observations. The focus was on the long-range transport of Canadian aged-smoke plumes observed at El Arenosillo/Huelva (Spain) from 7 to 8 September 2017. Both the columnar and height-resolved microphysical and optical properties were assessed in comparison with AERONET data and vertical lidar-retrieved profiles, respectively. In particular, the vertical properties were also derived using the POLIPHON approach, which serves as a comparison for GRASP retrievals. The retrieved columnar aerosol microphysical properties (volume concentration and effective radius) showed an excellent agreement, with negligible differences, and were within the uncertainties. Nevertheless, for the retrieved columnar optical properties, we could only perform an individual comparison, due to the strong AERONET limitations, and although the agreements were generally good, no conclusions were obtained, due to differences in the real refractive index and due to the large uncertainties obtained in the retrievals. For the vertical profiles, however, we present a large advance that permits obtaining aerosol backscatter and extinction coefficients, plus volume concentrations, without the need for internal assumptions (extinction-to-backscatter ratios and depolarization measurements), due to the very good agreement observed between GRASP and the lidar-derived methodologies. However, the separation of the properties into their fine and coarse modes was not feasible using the one-wavelength elastic lidar measurements with the GRASP retrieval configuration used in this work. Therefore, current studies are being addressed to assessing the introduction of lidar depolarization in the GRASP code as an encouraged added-value, for the improvement of the retrieval of vertical aerosol properties., This work was funded by the Spanish Ministry of Science and Innovation (PID2019- 104205GB-C21, PID2020-117825GB-C21), and partly by the Spanish Ministry of Science, Innovation, and Universities (CGL2017-90884-REDT), the Regional Government of Andalusia (P18-RT-3820, P20_00136, A-RNM-430-UGR20), and the Marie Sklodowska-Curie Research Innovation and Staff Exchange (RISE) GRASPACE (GA n. 778349). Authors acknowledge the support of the European Union’s H2020 research and innovation program (ACTRIS GA n. 654109, 871115). M.-Á.L.-C. and C.V.C.-P. are supported by the INTA predoctoral contract program., Peer Reviewed, Postprint (published version)

The paper summarizes the results of an ongoing research project carried out in cooperation between CTTC (Spain) and the University of Milan (Italy). The work aimed at investigating the role of quality indicators in the analysis of differential interferometric SAR time series products. Small baseline multi-temporal differential interferometric techniques have been used to derive TS products from six-year Sentinel-1 images covering railway networks in Barcelona, Spain. Redundancies of interferograms and post-phase unwrapping phase estimation residuals were pivotal parameters in determining the reliabilities of measurements. Preliminary results have supported the importance of quality indicators as well as the feasibility of multi-temporal differential interferometric techniques in monitoring subsidence along railway infrastructures. The time series evolutions of measurements from coherent scatterers have also shown that the target area is stable in the study period., AGAUR, Generalitat de Catalunya, has partially funded this work through a grant to recruit early-stage research staff (Ref: 2021FI_B2_00186)., Peer Reviewed, Postprint (published version)

In this paper, we focus on the propellant–free station-keeping design of a solar sail spacecraft around the unstable Sun–Earth collinear equilibrium point L2. The dynamical model considered is the Sun–Earth restricted three-body problem, including the solar radiation pressure on a sail that depends on three parameters: its reflectivity and its attitude with respect to the Sun, expressed by means of two angles. In the libration zone, the solar sail maneuvers performed by means of changing the values of the sail parameters, can be understood as ”jumps” in position instead of in velocity inside the phase space. The paper uses this fact to systematically analyze the impact of a maneuver (an instantaneous sail reorientation) on a spacecraft moving along a libration point Lissajous orbit. The station–keeping strategy periodically performs maneuvers to prevent the spacecraft to escape from a certain Lissajous orbit following its unstable manifold. Random errors in the execution of the maneuvers are also considered., D.X. special thanks to all the colleagues from the News and Publicity Center of CNSA and the support of the Chinese Scholarship Council. G. G. thanks the Ministerio de Ciencia e Innovación for the grant PID2019-104851GBI00. J.J.M. thanks the Ministerio de Ciencia e Innovación- FEDER for the grant PID2021-123968NB-I00 and the Catalan government for the grant 2017SGR-1049., Peer Reviewed, Postprint (published version)

The deliverable provides the results from the verification and validation activities within Dispatcher3 project. The document reviews the internal and external validation activities that were carried out during the course of Dispatcher3 project according to the plan defined in D5.1. Dispatcher3 is organised in three layers: data acquisition and preparation, predictive layer, with machine learning models, and prospective models, with the integration of the individual machine learning models in an interactive Advice Generator and an estimator of rotation/reactionary delay. This deliverable presents the verification and validation activities performed on these three components. For the data acquisition and preparation layer the data-pipelines, including the transformation verification and validation activities are described. In the predictive layer both the models developed for the first release and their evolution for the final prototype are described and presented. Finally, for the prospective layer, the interactive interface with its functional requirements is presented and verified, while the reactionary delay model is described, and different scenarios evaluated for its validation. The deliverable also describes the different internal and external activities and meetings, workshops and dedicated online site visits that have been performed during the duration of the project. Finally, the document assesses the verification of the high-level system-wide requirements identified at the beginning of the project in D1.1 – Technical resources and problem definition, and the research questions identified in the Verification and validation plan (D5.1)., This deliverable is part of a project that has received funding from the Clean Sky Joint Undertaking under grant agreement No 886461 under European Union’s Horizon 2020 research and innovation programme., Peer Reviewed, Postprint (published version)

Displacement time series (TS) provides temporal and spatial information related to ground defor- mation. This study aims to investigate temporal behavior of ground deformation TS, including classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow of Persistent Scatterers Interferometry (PSI) TS using new tests to classify ground deformations into seven main trends: Stable, Linear, Quadratic, Bilinear, Phase Unwrapping Errors (PUE), Discontinuous with constant and different velocities. We illustrate our approach over 1500 km2 of the Granada region and the metropolitan area of Barcelona, which were monitored using Sentinel-1 images and a PSI technique. This study provided the spatial distribution of different ground movement types and was useful to detect several TS anomalies due to PUE. The proposed approach also identified stable targets, which were wrongly classified as moving scatterers by the existing classification method. A periodicity analysis was finally performed using the Welch’s power spectral density estimator to investigate seasonal and yearly fluctuations. The method was vali- dated using simulated data, where the classified TSs characterized by probable phase unwrapping errors were verified by PSI experts. The overall classification accuracy was 77.8%, indicating that the proposed method has a considerable TS classification potential., The work of S. Mohammad Mirmazloumi has been funded by the Spanish State Research Agency, through a grant for a pre-doctorate contract (Ref: PRE2018-083394). The work of Yismaw Wassie has been funded by AGAUR, Generalitat de Catalunya, through a grant for the recruitment of early- stage research staff (Ref: 2019 FI_B 00050). The processing of Granada has been funded by the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme., Peer Reviewed, Postprint (published version)

Machine learning techniques to support decisionmaking processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general., This work is performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The Topic Manager is Thales AVS France SAS. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein., Postprint (published version)

Police tasks related with law enforcement and citizen protection have gained a very useful asset in drones. Crowded demonstrations, large sporting events, or summer festivals are typical situations when aerial surveillance is necessary. The eyes in the sky are moving from the use of manned helicopters to drones due to costs, environmental impact, and discretion, resulting in local, regional, and national police forces possessing specific units equipped with drones. In this paper, we describe an artificial intelligence solution developed for the Castelldefels local police (Barcelona, Spain) to enhance the capabilities of drones used for the surveillance of large events. In particular, we propose a novel methodology for the efficient integration of deep learning algorithms in drone avionics. This integration improves the capabilities of the drone for tasks related with capacity control. These tasks have been very relevant during the pandemic and beyond. Controlling the number of persons in an open area is crucial when the expected crowd might exceed the capacity of the area and put humans in danger. The new methodology proposes an efficient and accurate execution of deep learning algorithms, which are usually highly demanding for computation resources. Results show that the state-of-the-art artificial intelligence models are too slow when utilised in the drone standard equipment. These models lose accuracy when images are taken at altitudes above 30 metres. With our new methodology, these two drawbacks can be overcome and results with good accuracy (96% correct segmentation and between 20% and 35% mean average proportional error) can be obtained in less than 20 s., This research was partially funded by the AGAUR research agency of Catalonia under grant number 2020PANDE00141 and by the Ministry of Science and Education of Spain under grant number PID2020-116377RB-C21., Peer Reviewed, Postprint (published version)