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This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The study evaluates the model's performance based on platform (ULS, MLS) and data density, testing five scenarios with varying input data, including sparse versions, to gauge adaptability and canopy layer efficacy. The model, based on PointGroup architecture, is a 3D CNN with separate heads for semantic and instance segmentation, validated on diverse point cloud datasets. Results show point cloud sparsification enhances performance, aiding sparse data handling and improving detection in dense forests. The model performs well with >50 points per sq. m densities but less so at 10 points per sq. m due to higher omission rates. It outperforms existing methods (e.g., Point2Tree, TLS2trees) in detection, omission, commission rates, and F1 score, setting new benchmarks on LAUTx, Wytham Woods, and TreeLearn datasets. In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests. This contributes to future ecological modeling and forest management advancements.

Pedestrian intention prediction is crucial for autonomous driving. In particular, knowing if pedestrians are going to cross in front of the ego-vehicle is core to performing safe and comfortable maneuvers. Creating accurate and fast models that predict such intentions from sequential images is challenging. A factor contributing to this is the lack of datasets with diverse crossing and non-crossing (C/NC) scenarios. We address this scarceness by introducing a framework, named ARCANE, which allows programmatically generating synthetic datasets consisting of C/NC video clip samples. As an example, we use ARCANE to generate a large and diverse dataset named PedSynth. We will show how PedSynth complements widely used real-world datasets such as JAAD and PIE, so enabling more accurate models for C/NC prediction. Considering the onboard deployment of C/NC prediction models, we also propose a deep model named PedGNN, which is fast and has a very low memory footprint. PedGNN is based on a GNN-GRU architecture that takes a sequence of pedestrian skeletons as input to predict crossing intentions.

This study examines the effectiveness of Spiking Neural Networks (SNNs) paired with Dynamic Vision Sensors (DVS) to improve pedestrian detection in adverse weather, a significant challenge for autonomous vehicles. Utilizing the high temporal resolution and low latency of DVS, which excels in dynamic, low-light, and high-contrast environments, we assess the efficiency of SNNs compared to traditional Convolutional Neural Networks (CNNs). Our experiments involved testing across diverse weather scenarios using a custom dataset from the CARLA simulator, mirroring real-world variability. SNN models, enhanced with Temporally Effective Batch Normalization, were trained and benchmarked against state-of-the-art CNNs to demonstrate superior accuracy and computational efficiency in complex conditions such as rain and fog. The results indicate that SNNs, integrated with DVS, significantly reduce computational overhead and improve detection accuracy in challenging conditions compared to CNNs. This highlights the potential of DVS combined with bio-inspired SNN processing to enhance autonomous vehicle perception and decision-making systems, advancing intelligent transportation systems' safety features in varying operational environments. Additionally, our research indicates that SNNs perform more efficiently in handling long perception windows and prediction tasks, rather than simple pedestrian detection., Comment: Submitted for peer review to IEEE Transactions on Intelligent Transportation Systems

Background Standardized results for laboratory tests are particularly important when their interpretation depends on fixed medical practice guidelines or common reference intervals. The medical laboratory community has developed a roadmap for an infrastructure to achieve standardized test results described in the International Organization for Standardization standard 17511:2020 In vitro diagnostic medical devices - Requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples. Among the challenges to implementing metrological traceability are the availability of fit-for-purpose matrix-based certified reference materials (CRMs) and requirements for regulatory review that differ among countries. A workshop in December 2021 focused on these two challenges and developed recommendations for improved practices. Discussion The participants agreed that prioritization of measurands for standardization should be based on their impact on medical decisions in a clinical pathway. Ensuring that matrix-based CRMs are globally available for more measurands will enable fit-for-purpose calibration hierarchies for more laboratory tests. Regulation of laboratory tests is important to ensure safety and effectiveness for the populations served. Because regulations are country or region specific, manufacturers must submit recalibration changes intended to standardize results for regulatory review to all areas in which a measuring system is marketed. Recommendations A standardization initiative requires collaboration and planning among all interested stakeholders. Global collaboration should be further developed for prioritization of measurands for standardization, and for coordinating the production and supply of CRMs worldwide. More uniform regulatory submission requirements are desirable when recalibration is implemented to achieve internationally standardized results.

Background Standardized results for laboratory tests are particularly important when their interpretation depends on fixed medical practice guidelines or common reference intervals. The medical laboratory community has developed a roadmap for an infrastructure to achieve standardized test results described in the International Organization for Standardization standard 17511:2020 In vitro diagnostic medical devices - Requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples. Among the challenges to implementing metrological traceability are the availability of fit-for-purpose matrix-based certified reference materials (CRMs) and requirements for regulatory review that differ among countries. A workshop in December 2021 focused on these two challenges and developed recommendations for improved practices. Discussion The participants agreed that prioritization of measurands for standardization should be based on their impact on medical decisions in a clinical pathway. Ensuring that matrix-based CRMs are globally available for more measurands will enable fit-for-purpose calibration hierarchies for more laboratory tests. Regulation of laboratory tests is important to ensure safety and effectiveness for the populations served. Because regulations are country or region specific, manufacturers must submit recalibration changes intended to standardize results for regulatory review to all areas in which a measuring system is marketed. Recommendations A standardization initiative requires collaboration and planning among all interested stakeholders. Global collaboration should be further developed for prioritization of measurands for standardization, and for coordinating the production and supply of CRMs worldwide. More uniform regulatory submission requirements are desirable when recalibration is implemented to achieve internationally standardized results.

Background Standardized results for laboratory tests are particularly important when their interpretation depends on fixed medical practice guidelines or common reference intervals. The medical laboratory community has developed a roadmap for an infrastructure to achieve standardized test results described in the International Organization for Standardization standard 17511:2020 In vitro diagnostic medical devices - Requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples. Among the challenges to implementing metrological traceability are the availability of fit-for-purpose matrix-based certified reference materials (CRMs) and requirements for regulatory review that differ among countries. A workshop in December 2021 focused on these two challenges and developed recommendations for improved practices. Discussion The participants agreed that prioritization of measurands for standardization should be based on their impact on medical decisions in a clinical pathway. Ensuring that matrix-based CRMs are globally available for more measurands will enable fit-for-purpose calibration hierarchies for more laboratory tests. Regulation of laboratory tests is important to ensure safety and effectiveness for the populations served. Because regulations are country or region specific, manufacturers must submit recalibration changes intended to standardize results for regulatory review to all areas in which a measuring system is marketed. Recommendations A standardization initiative requires collaboration and planning among all interested stakeholders. Global collaboration should be further developed for prioritization of measurands for standardization, and for coordinating the production and supply of CRMs worldwide. More uniform regulatory submission requirements are desirable when recalibration is implemented to achieve internationally standardized results.

Swarm electron density (Ne) observations from the Langmuir probe (LP) can detect ionospheric disturbances at the altitude of a satellite. Along-track satellite observations provide a large number of very short observations of different places in the ionosphere, where Ne is disturbed. Moreover, different perturbations occupy various Ne signal frequencies. Therefore, such short signals are more recognizable in two dimensions, where aside from their change in time, we can observe their diversity in the frequency domain. Spectral analysis is an essential tool applied here, as it enables signal decomposition and the recognition of composite patterns of Ne disturbances that occupy different frequencies. This study shows a high-resolution application of short-term Fourier transform (STFT) to Swarm Ne observations in the Papua New Guinea region in the vicinity of earthquakes, tsunamis, and related general seismic activity. The system of tectonic plate junctions, including the Pacific–Australian boundary, is located orthogonally to Swarm track footprints. The selected wavelengths of seismically induced ionospheric disturbances detected via Swarm are compared with the three sets of three-month records of seismic activity: in the winter solstice of 2016/2017, when seismic activity was highest, and in the summer solstice and vernal equinox of 2016, which were calmer. Moreover, more Swarm data records are analyzed at the same latitudes for validation purposes, in a place where there are no tectonic plate boundaries that are orthogonal to the Swarm orbital footprint. Additional validation is supplied through Swarm Ne observations from completely different latitudes, where the Swarm orbital footprint orthogonally crosses a different subducting plate boundary. Aside from the seismic energy, the solar radio flux (F10.7), equatorial plasma bubbles (EPBs), and geomagnetic ap and Dst indices are also reviewed here. Their influence on the ionospheric Ne is also found in Swarm observatio, This study was partially funded from the COSTO project, which has received funding from the ESA contract 4000126730/19/NL/IA in the framework of the “Swarm + Coupling: High-Low Atmosphere Interactions” Call, and partially from the Research Grant no. DEC-2021/41/B/ST10/03954, which has received funding from the National Science Centre (NCN) of Poland., Peer Reviewed, Postprint (published version)

This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly in the experiments, including damaged and unrecognized categories. The deep learning models employed in the system achieve a high score of average precision and recall metrics, i.e. 0.94 and 0.8 respectively, for the selected three types of tumors. This variety of label types allowed us to draw a meaningful conclusion that there are relatively few mistakes for tumor cell types. Additionally, the model learned tumor cell features well enough to avoid misclassification mistakes of one tumor type into another. The experiments also revealed that the quality of the results improves with the dataset size (excluding the damaged cells). It is worth noting that all the experiments were done using a custom dedicated dataset provided by the cooperating vet doctors.

This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular approach to processing laser points clouds in Forestry. We tested it on two independent datasets. The first area was located in an actively managed boreal coniferous dominated forest in V{\aa}ler, Norway, 16 circular plots of 400 square meters were selected to cover a range of forest conditions in terms of species composition and stand density. We trained a model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic segmentation. As a second step in our pipeline we used graph-based approach for instance segmentation which reached F1-score approx. 0.6. The optimization allowed to further boost the performance of the pipeline by approx. 4 \% points.

We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. For more information, visit https://project-arcane.eu/.

Background Standardized results for laboratory tests are particularly important when their interpretation depends on fixed medical practice guidelines or common reference intervals. The medical laboratory community has developed a roadmap for an infrastructure to achieve standardized test results described in the International Organization for Standardization standard 17511:2020 In vitro diagnostic medical devices - Requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples. Among the challenges to implementing metrological traceability are the availability of fit-for-purpose matrix-based certified reference materials (CRMs) and requirements for regulatory review that differ among countries. A workshop in December 2021 focused on these two challenges and developed recommendations for improved practices. Discussion The participants agreed that prioritization of measurands for standardization should be based on their impact on medical decisions in a clinical pathway. Ensuring that matrix-based CRMs are globally available for more measurands will enable fit-for-purpose calibration hierarchies for more laboratory tests. Regulation of laboratory tests is important to ensure safety and effectiveness for the populations served. Because regulations are country or region specific, manufacturers must submit recalibration changes intended to standardize results for regulatory review to all areas in which a measuring system is marketed. Recommendations A standardization initiative requires collaboration and planning among all interested stakeholders. Global collaboration should be further developed for prioritization of measurands for standardization, and for coordinating the production and supply of CRMs worldwide. More uniform regulatory submission requirements are desirable when recalibration is implemented to achieve internationally standardized results.

Background Standardized results for laboratory tests are particularly important when their interpretation depends on fixed medical practice guidelines or common reference intervals. The medical laboratory community has developed a roadmap for an infrastructure to achieve standardized test results described in the International Organization for Standardization standard 17511:2020 In vitro diagnostic medical devices - Requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples. Among the challenges to implementing metrological traceability are the availability of fit-for-purpose matrix-based certified reference materials (CRMs) and requirements for regulatory review that differ among countries. A workshop in December 2021 focused on these two challenges and developed recommendations for improved practices. Discussion The participants agreed that prioritization of measurands for standardization should be based on their impact on medical decisions in a clinical pathway. Ensuring that matrix-based CRMs are globally available for more measurands will enable fit-for-purpose calibration hierarchies for more laboratory tests. Regulation of laboratory tests is important to ensure safety and effectiveness for the populations served. Because regulations are country or region specific, manufacturers must submit recalibration changes intended to standardize results for regulatory review to all areas in which a measuring system is marketed. Recommendations A standardization initiative requires collaboration and planning among all interested stakeholders. Global collaboration should be further developed for prioritization of measurands for standardization, and for coordinating the production and supply of CRMs worldwide. More uniform regulatory submission requirements are desirable when recalibration is implemented to achieve internationally standardized results.

Swarm electron density (Ne) observations from the Langmuir probe (LP) can detect ionospheric disturbances at the altitude of a satellite. Along-track satellite observations provide a large number of very short observations of different places in the ionosphere, where Ne is disturbed. Moreover, different perturbations occupy various Ne signal frequencies. Therefore, such short signals are more recognizable in two dimensions, where aside from their change in time, we can observe their diversity in the frequency domain. Spectral analysis is an essential tool applied here, as it enables signal decomposition and the recognition of composite patterns of Ne disturbances that occupy different frequencies. This study shows a high-resolution application of short-term Fourier transform (STFT) to Swarm Ne observations in the Papua New Guinea region in the vicinity of earthquakes, tsunamis, and related general seismic activity. The system of tectonic plate junctions, including the Pacific–Australian boundary, is located orthogonally to Swarm track footprints. The selected wavelengths of seismically induced ionospheric disturbances detected via Swarm are compared with the three sets of three-month records of seismic activity: in the winter solstice of 2016/2017, when seismic activity was highest, and in the summer solstice and vernal equinox of 2016, which were calmer. Moreover, more Swarm data records are analyzed at the same latitudes for validation purposes, in a place where there are no tectonic plate boundaries that are orthogonal to the Swarm orbital footprint. Additional validation is supplied through Swarm Ne observations from completely different latitudes, where the Swarm orbital footprint orthogonally crosses a different subducting plate boundary. Aside from the seismic energy, the solar radio flux (F10.7), equatorial plasma bubbles (EPBs), and geomagnetic ap and Dst indices are also reviewed here. Their influence on the ionospheric Ne is also found in Swarm observatio

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.

The FOR-instance dataset (available at https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.

This paper presents a computer-aided cytology diagnosis system designed for animals, focusing on image quality assessment (IQA) using Convolutional Neural Networks (CNNs). The system's building blocks are tailored to seamlessly integrate IQA, ensuring reliable performance in disease classification. We extensively investigate the CNN's ability to handle various image variations and scenarios, analyzing the impact on detecting low-quality input data. Additionally, the network's capacity to differentiate valid cellular samples from those with artifacts is evaluated. Our study employs a ResNet18 network architecture and explores the effects of input sizes and cropping strategies on model performance. The research sheds light on the significance of CNN-based IQA in computer-aided cytology diagnosis for animals, enhancing the accuracy of disease classification.

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels, we illustrate the profound impact of dataset quality on model training and performance. We further introduce two additional datasets designed to represent specific data conditions - one maximizing entropy and the other demonstrating high redundancy. Our findings underscore the importance of appropriate feature selection, adequate data volume, and data quality in achieving high-performing machine learning models. To aid researchers and practitioners, we propose a comprehensive framework for dataset quality assessment, which can help evaluate if the dataset at hand is sufficient and of the required quality for specific tasks. This research offers valuable insights into data assessment practices, contributing to the development of more accurate and robust machine learning models.

The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) segmentation metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state of the art in cytology image analysis.

To ascertain the simultaneous thermal and electrical performance of a thermoelectric (TE) unicouple with interconnectors, a thermal-electric coupled iterative mathematical model is introduced. The non-linear constitutive equations describing TE phenomena within the unicouple are linked to a thermal resistance network describing the interconnectors’ behavior. Thereupon, the thermal resistance of the interconnectors, and Joule heat generated within, are considered. Temperature dependent material properties are handled by integral-averaging techniques and an iterative solution methodology. Model form uncertainty is quantified by evaluating four unique analytic models. The first, the Implicit Thomson Model (ITM), considers the Thomson effect via integral averaging of the Seebeck coefficient. The second, the Explicit Thomson Model (ETM), decouples the Thomson effect from the Peltier effect; Thomson heat is explicitly solved using the Thomson coefficient. The third, the zT model, uses the figure of merit to describe the optimum efficiency-maximizing load resistance, and quantify device efficiency under maximum power scenarios. The last, the Differential Equation Model, does not assume distributions of Joule and Thomson heats to the cold- and hot-side interfaces as do the ITM, ETM and zT model. The predictive ability of each analytic model used within the unicouple-level model is compared to high-fidelity numeric results obtained from a three-dimensional, thermal-electric coupled model implemented in ANSYS CFX. Considering a range of hot-side unicouple temperatures, each analytic model exhibits agreement with one another, and with the numeric model. With increasing load resistance values, model form uncertainty and disagreement between analytic and numeric predictions decreases to a couple of percent at optimum operating points.

Future National Aeronautics and Space Administration deep-space missions are seeking radioisotope propulsion systems (RPS) to have specific powers above 8 [We/kg], while having thermal conversion efficiencies greater than 12%. The design and optimization of segmented thermoelectric unicouples used within RPS requires a multi-faceted approach to maximize device performance. The design space of a unicouple can span multiple dimensions, requiring immense computational resources to conduct parametric studies. These dimensions include, but are not limited to, the independent cross-sectional areas of the $n$- and $p$-type legs, the total height of the unicouple, the length of the high-temperature n- and p-type segments, the cold-side junction temperature and the load resistance applied to the couple, considering a fixed hot-side junction temperature, fixed per-couple heat input, and desired output voltage. To this end, computationally-inexpensive methods that optimize segmented unicouples are presented and compared. These methods include physics-based algorithms that dynamically reduce the design space when nonviable configurations are found, implementation of Golden Section Search (GSS) algorithm when uni-modal behavior is observed for a specific degree of freedom, and successive design space refinement. When using both GSS and successive design space refinement algorithms, an optimum geometry was found with 5,755 times fewer solver calls in comparison to the conventional parametric study without any loss of fidelity. This comparison indicates the proposed optimization methods are robust and accurate, while also drastically reducing the computation time to find the optimum unicouple configuration that maximizes system-level power output. These methods allow for exhaustive trade studies to be conducted of newly proposed heat sources, converter materials and designs, and heat exchange systems.

Non-communicable diseases, particularly cardiovascular diseases, are the leading cause of decreased life expectancy and death in Latin America and the Caribbean. Although a lifestyle, which includes no tobacco use, good nutrition, and regular physical activity is touted as key to health, the environmental, racial, social and economic conditions, which underpin lifestyle are often ignored or considered only secondarily. Placing the main responsibility on a patient to change their lifestyle or to simply comply with pharmacological treatment ignores the specific conditions in which the individual lives. Furthermore, there are major disparities in access to both healthy living conditions as well as access to medical care. There is sufficient evidence to support advocating for policies that support healthy living, particularly healthy food choices. Progress is being made to improve the food environment with enactment of front of package nutritional labels. However, policies were enacted only after intense regional research and advocacy supporting their implementation. Government officials must rise above the pressures of commercial interests and support health-promoting policies or be exposed as self-interest groups themselves. Strong advocacy is required to persuade officials that all policies should take health into consideration both to improve lives and economies.

The effects of multiple wings interacting and the change in efficiency due to those effects as well as optimal sheeting angles are becoming an important area of study with the advent of wind-propelled ships for goods transport. This research presents a first analysis of wind tunnel tests carried out at the University of Southampton R.J. Mitchell wind tunnel where three wings are subject to turbulent flow with Reynolds number in excess of 1 million. A range of possible variations of ship heading and apparent wind angles are tested taking into consideration the blockage effects and the geometrical characteristics of the working section. The forces and moments are captured on each individual wing as well as in the overall wind tunnel balance with 6-components dynamometers. Furthermore, pressure sensors and PIV data are recorded during the tests to provide the experimental campaign with results that can validate both qualitatively and quantitatively the numerical tools developed to aid the design stage of wind propelled vessels., QC 20230403

Ionospheric irregularities impair Global Navigation Satellite System (GNSS) signals and, in turn, affect the performance of GNSS positioning. Such effects are especially evident at low and high latitudes, which are currently gaining the attention of research and industry sectors. This study evaluates the impact of ionospheric irregularities on GNSS positioning in Greenland. We assess the performance of positioning methods that meet the demands of a wide range of users. In particular, we address the needs of the users of mass-market single-frequency receivers and those who require a solution of high precision provided by geodetic dual-frequency receivers. We take advantage of the datasets collected during three ionospheric storms: the St. Patrick’s Day storm of March 17, 2015, the storm on June 22, 2015, and another on August 25–26, 2018. We discover a significant impact of the ionospheric disturbances on the ambiguity resolution performance and the accuracy of the float solution in Real Time Kinematics (RTK) positioning. Next, assessing the single-frequency ionosphere-free Precise Point Positioning (PPP), we demonstrate that the model is generally unaffected by ionospheric disturbances. Hence, the model is predestined for the application by the users of single-frequency receivers in the areas of frequent ionospheric disturbances. Finally, based on the observation analyses, we reveal that phase signals on the L2 frequency band are more prone to cycle slips induced by ionospheric irregularities than those transmitted on the L1. Such signal properties explain a noticeable decline in the dual-frequency RTK performance during the ionospherically disturbed period and merely no effect for the single-frequency ionosphere-free PPP model., Peer Reviewed, Postprint (published version)

This paper addresses the long-term climatology (over two solar cycles) of total electron content (TEC) irregularities from a polar cap station (Thule) using the rate of change of the TEC index (ROTI). The climatology reveals variabilities over different time scales, i.e., solar cycle, seasonal, and diurnal variations. These variations in different time scales can be explained by different drivers/contributors. The solar activity (represented by the solar radiation index F10.7P) dominates the longest time scale variations. The seasonal variations are controlled by the interplay of the energy input into the polar cap ionosphere and the solar illumination that damps the amplitude of ionospheric irregularities. The diurnal variations (with respect to local time) are controlled by the relative location of the station with respect to the auroral oval. We further decompose the climatology of ionospheric irregularities using the empirical orthogonal function (EOF) method. The first four EOFs could reflect the majority (99.49%) of the total data variability. A climatological model of ionospheric irregularities is developed by fitting the EOF coefficients using three geophysical proxies (namely, F10.7P, Bt, and Dst). The data-model comparison shows satisfactory results with a high Pearson correlation coefficient and adequate errors. Additionally, we modeled the historical ROTI during the modern grand maximum dating back to 1965 and made the prediction during solar cycle 25. In such a way, we can directly compare the climatic variations of the ROTI activity across six solar cycles., Peer Reviewed, Postprint (published version)

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., In this article, we show the capability of a global navigation satellite system (GNSS) precise orbit determination (POD) low Earth orbit (LEO) data to detect anomalous ionospheric disturbances in the spectral range of the signals associated with earthquakes and tsunamis, applied to two of these events in Papua New Guinea (PNG) and the Solomon Islands during 2016. This is achieved thanks to the new PIES approach (POD-GNSS LEO Detrended Ionospheric Electron Content Significant Deviations). The significance of such ionospheric signals above the swarm LEOs is confirmed with different types of independent data: in situ electron density measurements provided by the Langmuir Probe (LP) onboard swarm LEOs, DORIS, and ground-based GNSS colocated measurements, as it is described in this article. In this way, we conclude the possible detection of the tsunami-related ionospheric gravity wave in PNG 2016 event, consistent with the most-recent theory, which shows that a tsunami (which is localized in space and time) excites a spectrum of gravity waves, some of which have faster horizontal phase speeds than the tsunami. We believe that this work shows as well the feasibility of a future potential monitoring system of ionospheric disturbances, to be made possible by hundreds of CubeSats with POD GNSS receivers among other appropriate sensors, and supported for real-time or near real-time confirmation and characterization by thousands of worldwide existing ground GNSS receivers., This work has been partially supported by the COSTO ESA-funded project (ESA ITT AO/1-9514/18/NL/IA). Also it was partially supported by the 2017 SGR-0851 grant of the Generalitat de Catalunya and by the EU project 101007599 - PITHIA-NRF. The data for this paper are available and they can be requested from H. Yang (h.yang@upc.edu) and M. Hernandez-Pajares (manuel.hernandez@upc.edu)., Peer Reviewed, Postprint (author's final draft)

Future National Aeronautics and Space Administration deep-space missions are seeking radioisotope propulsion systems (RPS) to have specific powers above 8 [We/kg], while having thermal conversion efficiencies greater than 12%. The design and optimization of segmented thermoelectric unicouples used within RPS requires a multi-faceted approach to maximize device performance. The design space of a unicouple can span multiple dimensions, requiring immense computational resources to conduct parametric studies. These dimensions include, but are not limited to, the independent cross-sectional areas of the $n$- and $p$-type legs, the total height of the unicouple, the length of the high-temperature n- and p-type segments, the cold-side junction temperature and the load resistance applied to the couple, considering a fixed hot-side junction temperature, fixed per-couple heat input, and desired output voltage. To this end, computationally-inexpensive methods that optimize segmented unicouples are presented and compared. These methods include physics-based algorithms that dynamically reduce the design space when nonviable configurations are found, implementation of Golden Section Search (GSS) algorithm when uni-modal behavior is observed for a specific degree of freedom, and successive design space refinement. When using both GSS and successive design space refinement algorithms, an optimum geometry was found with 5,755 times fewer solver calls in comparison to the conventional parametric study without any loss of fidelity. This comparison indicates the proposed optimization methods are robust and accurate, while also drastically reducing the computation time to find the optimum unicouple configuration that maximizes system-level power output. These methods allow for exhaustive trade studies to be conducted of newly proposed heat sources, converter materials and designs, and heat exchange systems.

To ascertain the simultaneous thermal and electrical performance of a thermoelectric (TE) unicouple with interconnectors, a thermal-electric coupled iterative mathematical model is introduced. The non-linear constitutive equations describing TE phenomena within the unicouple are linked to a thermal resistance network describing the interconnectors’ behavior. Thereupon, the thermal resistance of the interconnectors, and Joule heat generated within, are considered. Temperature dependent material properties are handled by integral-averaging techniques and an iterative solution methodology. Model form uncertainty is quantified by evaluating four unique analytic models. The first, the Implicit Thomson Model (ITM), considers the Thomson effect via integral averaging of the Seebeck coefficient. The second, the Explicit Thomson Model (ETM), decouples the Thomson effect from the Peltier effect; Thomson heat is explicitly solved using the Thomson coefficient. The third, the zT model, uses the figure of merit to describe the optimum efficiency-maximizing load resistance, and quantify device efficiency under maximum power scenarios. The last, the Differential Equation Model, does not assume distributions of Joule and Thomson heats to the cold- and hot-side interfaces as do the ITM, ETM and zT model. The predictive ability of each analytic model used within the unicouple-level model is compared to high-fidelity numeric results obtained from a three-dimensional, thermal-electric coupled model implemented in ANSYS CFX. Considering a range of hot-side unicouple temperatures, each analytic model exhibits agreement with one another, and with the numeric model. With increasing load resistance values, model form uncertainty and disagreement between analytic and numeric predictions decreases to a couple of percent at optimum operating points.

In this article, we show the capability of a global navigation satellite system (GNSS) precise orbit determination (POD) low Earth orbit (LEO) data to detect anomalous ionospheric disturbances in the spectral range of the signals associated with earthquakes and tsunamis, applied to two of these events in Papua New Guinea (PNG) and the Solomon Islands during 2016. This is achieved thanks to the new PIES approach (POD-GNSS LEO Detrended Ionospheric Electron Content Significant Deviations). The significance of such ionospheric signals above the swarm LEOs is confirmed with different types of independent data: in situ electron density measurements provided by the Langmuir Probe (LP) onboard swarm LEOs, DORIS, and ground-based GNSS colocated measurements, as it is described in this article. In this way, we conclude the possible detection of the tsunami-related ionospheric gravity wave in PNG 2016 event, consistent with the most-recent theory, which shows that a tsunami (which is localized in space and time) excites a spectrum of gravity waves, some of which have faster horizontal phase speeds than the tsunami. We believe that this work shows as well the feasibility of a future potential monitoring system of ionospheric disturbances, to be made possible by hundreds of CubeSats with POD GNSS receivers among other appropriate sensors, and supported for real-time or near real-time confirmation and characterization by thousands of worldwide existing ground GNSS receivers.

We present the optical flow algorithm for forecasting the rate of total electron content index (OFROTI). It consists of a method for predicting maps of rapid fluctuations of ionospheric electron content in terms of global navigation satellite system (GNSS) dual-frequency phase measurements of the rate of change of total electron content index (ROTI). The forecast is made in space and time, at horizons up to more than 6 h. These forecast maps will consist of the ROTI spatial distribution in the northern hemisphere above 45° latitude. The prediction method models the ROTI spatial distribution as a pseudoconservative flux, i.e., exploiting the inertia of the flux of ROTI to determine the future position. This idea is implemented as a modification of the optical flow image processing technique. The algorithm has been modified to deal with the nonconservation of the ROTI quantity in time. We show that the method can predict both, the local value of ROTI and also the regions with ROTI above a given level, better than the prediction using the current map as forecast, i.e., predicting by a current map from horizons of 15 min up to 6 h. The method was tested on 11 representative active and calm days during 2015 and 2018 from the multi-GNSS (GPS, GLONASS, Galileo, and Beidou) multifrequency measurements of more than 250 multi-GNSS receivers above 45°N latitude, including the high rate (1 Hz) measurements of Greenland geodetic network (GNET) network among the International GNSS Service network., This work is funded by ESA ITT “Forecasting Space Weather Impacts on Navigation Systems in the Arctic (Green-land Area)” Expro+, Activity No. 1000026374. The GNET GNSS observations from Greenland was kindly provided by The Danish Agency for Data Supply and Efficiency, in the Danish Ministry of Energy, Utilities and Climate, Copenhagen, Denmark, Peer Reviewed, Postprint (author's final draft)

A well-posed thermal-electric coupled mathematical-numerical model to optimize the cross-sectional area per length of a thermoelectric (TE) leg is introduced to maximize thermal conversion efficiency (eta) or power output (Po). To employ such optimization, the p- or n-type leg was divided into uniform length segments, wherein the product of the electrical resistance (Rel) and thermal conductance (K) was minimized as to maximize the figure of merit (ZT) of each individual partition. The minimization of RelK was dependent upon the temperature difference established across each segment, which was resolved using a one-dimensional finite difference (FD) scheme of the TE general energy equation (GEQ). The TE GEQ included all pertinent phenomena - conduction, Joule, Peltier and Thomson effects - as well as temperature dependent properties. The boundary conditions of the FD scheme were provided via a one-dimensional thermal resistance network. The current output of the unicouple was determined by the temperature bounds across the junction and the internal resistance of the TE legs, and this was explicitly coupled to the TE GEQ to create a fully-coupled model. The proposed model was validated to a fully-coupled thermal-electric finite volume method model implemented in ANSYS CFX. The proposed optimization process yielded improvements in volumetric efficiency and volumetric power output of 4.60% and 3.75%, respectively, in comparison to conventional constant-area optimization processes.

This is a post-peer-review, pre-copyedit version of an article published in Journal of geodesy. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00190-021-01568-8, In this manuscript, we introduce the Ionospheric Tomographic Common Clock (ITCC) model of undifferenced uncombined GNSS measurements. It is intended for improving the Wide Area precise positioning in a consistent and simple way in the multi-GNSS context, and without the need of external precise real-time products. This is the case, in particular, of the satellite clocks, which are estimated at the Wide Area GNSS network Central Processing Facility (CPF) referred to the reference receiver one; and the precise realtime ionospheric corrections, simultaneously computed under a voxel-based tomographic model with satellite clocks and other geodetic unknowns, from the uncombined and undifferenced pseudoranges and carrier phase measurements at the CPF from the Wide Area GNSS network area. The model, without fixing the carrier phase ambiguities for the time being (just constraining them by the simultaneous solution of both ionospheric and geometric components of the uncombined GNSS model), has been successfully applied and assessed against previous precise positioning techniques. This has been done by emulating real-time conditions for Wide Area GPS users during 2018 in Poland., Peer Reviewed, Postprint (published version)

The recent growth of machine learning and deep learning applications in the industry these last years has encouraged me to carry out this project, in particular related to the Image Segmentation, whose methods can be applied to so many fields to automate and train different processes with a high rate of success. This thesis focuses on deep learning techniques applied to semantic segmentation of images and has the objective to clearly understand the performance of Neural Networks in different backgrounds,by training them how to learn from different images using a deep learning based approach. The proposed project is built around a VGG-16 convolutional neural network architecture model implemented, in order to detect the different parts and elements in an image by applying different layers, which will be used then to recognize and highlight the object in the final image. Finally, this work performs a comparative evaluation of the first pretrained network against the one which is going to be trained, throughseveral repetitions and tries with different changes in its structure. Experimental results indicate how the number of times an image has been seen while training varies between the different models, also known as epoch loss. These experimental results also demonstrate that segmentation methods can be very useful once trained the models., El reciente crecimiento de las aplicaciones de machine learning y deep learningen la industria estos últimos años me ha animado a llevar a cabo este proyecto, en particular relacionado con la Segmentación de Imágenes, cuyos métodos se pueden aplicar a numerosos campos para automatizar y entrenar diferentes procesos con un alta tasa de éxito. Esta tesis se centra en las técnicas de aprendizaje profundo aplicadas a la segmentación semántica de imágenes y tiene el objetivo de comprender claramente el desempeño de las redes neuronales en diferentes entornos, capacitándolos en cómo aprender de diferentes imágenes utilizando un enfoque basado en el aprendizaje profundo. El proyecto propuesto se construye en torno a un modelo implementado de arquitectura de red neuronal convolucional VGG-16, con el fin de detectar las diferentes partes y elementos de una imagen mediante la aplicación de diferentes capas, que luego serán utilizadas para reconocer y resaltar el objeto en la imagen final. Finalmente, este trabajo realiza una evaluación comparativa de la primera red pre-entrenada con la que se va a entrenar, mediante varias repeticiones e intentos con diferentes cambios en su estructura Los resultados experimentales indican cómo varía el número de veces que se ha visto una imagen durante el entrenamiento entre los diferentes modelos, también conocido como epoch loss. Estos resultados experimentales también demuestran que los métodos de segmentación pueden ser muy útiles una vez entrenados los modelos

A well-posed thermal-electric coupled mathematical-numerical model to optimize the cross-sectional area per length of a thermoelectric (TE) leg is introduced to maximize thermal conversion efficiency (eta) or power output (Po). To employ such optimization, the p- or n-type leg was divided into uniform length segments, wherein the product of the electrical resistance (Rel) and thermal conductance (K) was minimized as to maximize the figure of merit (ZT) of each individual partition. The minimization of RelK was dependent upon the temperature difference established across each segment, which was resolved using a one-dimensional finite difference (FD) scheme of the TE general energy equation (GEQ). The TE GEQ included all pertinent phenomena - conduction, Joule, Peltier and Thomson effects - as well as temperature dependent properties. The boundary conditions of the FD scheme were provided via a one-dimensional thermal resistance network. The current output of the unicouple was determined by the temperature bounds across the junction and the internal resistance of the TE legs, and this was explicitly coupled to the TE GEQ to create a fully-coupled model. The proposed model was validated to a fully-coupled thermal-electric finite volume method model implemented in ANSYS CFX. The proposed optimization process yielded improvements in volumetric efficiency and volumetric power output of 4.60% and 3.75%, respectively, in comparison to conventional constant-area optimization processes.

In this manuscript, we introduce the Ionospheric Tomographic Common Clock (ITCC) model of undifferenced uncombined GNSS measurements. It is intended for improving the Wide Area precise positioning in a consistent and simple way in the multi-GNSS context, and without the need of external precise real-time products. This is the case, in particular, of the satellite clocks, which are estimated at the Wide Area GNSS network Central Processing Facility (CPF) referred to the reference receiver one; and the precise realtime ionospheric corrections, simultaneously computed under a voxel-based tomographic model with satellite clocks and other geodetic unknowns, from the uncombined and undifferenced pseudoranges and carrier phase measurements at the CPF from the Wide Area GNSS network area. The model, without fixing the carrier phase ambiguities for the time being (just constraining them by the simultaneous solution of both ionospheric and geometric components of the uncombined GNSS model), has been successfully applied and assessed against previous precise positioning techniques. This has been done by emulating real-time conditions for Wide Area GPS users during 2018 in Poland.

The study investigates ionospheric electric field responses to the earthquake (EQ) of magnitude 8.3, and the related seismic activity and tsunami triggered by the mainshock in Chile-Illapel region, at 22:54 UTC, in the evening of September 16, 2015. The work is a wider review of available ground and satellite data and techniques available to detect seismically induced traveling ionospheric disturbances (TID) and irregularities of smaller scale. The data used in the experiment includes several types of ground and satellite observations from low-Earth orbit (LEO) satellites. The number of techniques applied here is also extended and includes spectral analysis of LEO along-track data and composed analysis of ground GNSS data. The timeframe of the analyses is focused on September 16 and 17, 2015 but also extended to several adjacent days, where an enhanced seismic activity has been recorded. Several examples of seismically triggered TIDs are shown, as detected by combined observations from more than one source and applying different methods, including spectral analysis. These disturbances occur before the mainshock, just after, or in time following this large EQ, and can be found in close neighborhood of Chile-Illapel or far away from the epicenter. The objective of the work was to demonstrate an increasing number of available data and techniques, which can be limited when applied alone, but their combination can provide many advantages in the analysis of seismically disturbed ionosphere. The combination of LEO satellite data reaching all regions of the globe with local but dense ground-based GNSS data and ionospheric HF sounders looks promising, especially in view of the nearby availability of CubeSat constellations equipped with instruments for ionosphere sounding. An important conclusion coming from the study is a need for spectral analysis techniques in the processing of LEO along-track data and the requirement of the validation of LEO observations with separate LEO

Cybersecurity is essential, and attacks are rapidly growing and getting more challenging to detect. The traditional Firewall and Intrusion Detection system, even though it is widely used and recommended but it fails to detect new attacks, zero-day attacks, and traffic patterns that do not match with any configured rules. Therefore, Machine Learning (ML) can be an efficient and cost-reduced solution in cybersecurity. We used Netflow datasets to extract features after applying data analysis. Then, a selection process has been applied to compare these features with one another. Our experiments focus on how efficient machine learning algorithms can detect Bot traffic, Malware traffic, and background traffic. We managed to get 0.903 precision value from a dataset that has 6.5% Bot flows, 1.57% Normal flows, 0.18% Command&Control (C&C) flows, and 91.7% background flows, from 2,753,884 total flows. The results show low false-negative with few false-positive detections., Comment: 17 pages, 8 figures

The study investigates ionospheric electric field responses to the earthquake (EQ) of magnitude 8.3, and the related seismic activity and tsunami triggered by the mainshock in Chile-Illapel region, at 22:54 UTC, in the evening of September 16, 2015. The work is a wider review of available ground and satellite data and techniques available to detect seismically induced traveling ionospheric disturbances (TID) and irregularities of smaller scale. The data used in the experiment includes several types of ground and satellite observations from low-Earth orbit (LEO) satellites. The number of techniques applied here is also extended and includes spectral analysis of LEO along-track data and composed analysis of ground GNSS data. The timeframe of the analyses is focused on September 16 and 17, 2015 but also extended to several adjacent days, where an enhanced seismic activity has been recorded. Several examples of seismically triggered TIDs are shown, as detected by combined observations from more than one source and applying different methods, including spectral analysis. These disturbances occur before the mainshock, just after, or in time following this large EQ, and can be found in close neighborhood of Chile-Illapel or far away from the epicenter. The objective of the work was to demonstrate an increasing number of available data and techniques, which can be limited when applied alone, but their combination can provide many advantages in the analysis of seismically disturbed ionosphere. The combination of LEO satellite data reaching all regions of the globe with local but dense ground-based GNSS data and ionospheric HF sounders looks promising, especially in view of the nearby availability of CubeSat constellations equipped with instruments for ionosphere sounding. An important conclusion coming from the study is a need for spectral analysis techniques in the processing of LEO along-track data and the requirement of the validation of LEO observations with separate LEO, Peer Reviewed, Postprint (published version)

This study explores the prospects of using scaled model experiments for capturing the influence of a novel spray deflection concept on planing craft performance in calm water and in waves. An experimental setup with a towed planing craft model is designed and systematic experiments are performed, first with a bare hull model and then with the same model equipped with spray deflectors. Measured trim, sinkage and resistance in calm water and accelerations and resistance in waves are presented. The results, as well as various aspects of the experimental setup and the spray phenomenon, are discussed. The experimental results indicate that the studied spray deflector concept can have positive effects by lowering the resistance as well as the acceleration in waves. The detailed tailoring of the deflectors is concluded to be crucial. The developed experimental setup is concluded to give consistent results that capture the influence of the spray deflection details on the craft resistance and responses and is concluded to be useful for further research as well as for design purposes., QC 20200421

The article briefly presents a series of problems discussed in the dissertation entitled Свето и пропадљиво. Тело у српској хагиографској књижевности (Holy and Worldly. Body in Serbian Hagiographic Literature) by Smilja Marjanović-Dušanić. Dedicated to a wide professional and non-professional audience, the book in question is a significant scientific work since it refers not only to the historical, but also to the anthropological and culturological description related to the activity of decoding of such essential socio-cultural category like body. Considered as a highly complex phenomenon, body is a sign generated by the realm of various traditions and discourses which render different cultural modes or primary and secondary contexts; it might be also conceived as a distinctive signature of given time-space, community and cultural system. It will not be an exaggeration to say that the dissertation is an extremely interesting and indeed outstanding study of the subject which impresses with its high cognitive value and research rank. What is more, it offers a serious and objective view of the convoluted and multidimensional cultural phenomenon and identity construct.

Convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation. Very often CNN networks have from several to hundred stacked layers with several megabytes of weights. One of the possible methods to reduce complexity and memory footprint is pruning. Pruning is a process of removing weights which connect neurons from two adjacent layers in the network. The process of finding near optimal solution with specified drop in accuracy can be more sophisticated when DL model has higher number of convolutional layers. In the paper few approaches based on retraining and no retraining are described and compared together.

Precise real-time kinematic (RTK) Global Navigation Satellite System (GNSS) positioning requires fixing integer ambiguities after a short initialization time. Originally, it was assumed that it was only possible at a relatively short distance from a reference station (<10 km), because otherwise the atmospheric effects prevent effective ambiguity fixing. Nowadays, through the use of VRS, MAC, or FKP corrections, the distances to the closest reference station have been increased to around 35 km. However, the baselines resolved in real time are not as far as in the case of static positioning. Further extension of the baseline requires the use of an ionosphere-weighted model with ionospheric delay corrections available in real time. This solution is now possible thanks to the Radio Technical Commission for Maritime (RTCM) stream of SSR corrections from, for example, Centre National d’Études Spatiales (CNES), the first analysis center to provide it in the context of the International GNSS Service. Then, ionospheric delays are treated as pseudo-observations that have a priori values from the CLK RTCM stream. Additionally, satellite orbit and clock errors are properly considered using space-state representation (SSR) real-time radial, along-track, and cross-track corrections. The following paper presents the initial results of such RTK positioning. Measurements were performed in various field conditions reflecting realistic scenarios that could have been experienced by actual RTK users. We have shown that the assumed methodology was suitable for single-epoch RTK positioning with up to 82 km baseline in solar minimum (30 March 2019) mid and high latitude (Olsztyn, Poland) conditions. We also confirmed that it is possible to obtain a rover position at the level of a few centimeters of precision. Finally, the possibility of using other newer experimental IGS RT Global Ionospheric Maps (GIMs), from Chinese Academy of Sciences (CAS) and Universitat Politècnica de Catalunya (UPC

In the article, a problem of translation of the old Serbian literary texts into Polish is discussed together with the basic mechanisms of the reception process of creativity revealed by the Orthodox Slavs from the Balkan region, that is, in other words, the works arisen in the circle of the Slaviae Orthodoxae and their presence in the Polish culture’s space which belonged to the circle of the Slaviae Latinae. The considerations regard the essence and function of translation, presence and absence of translated literature — its initial version in the target space, and their foundation is located in the collections of translations existing in the Polish reading circuit. The issue of the presence or position of the old South Slavonic literatures, namely, the old Serbian texts in their Polish translation, is featured in several possible perspectives. The problem is exposed in the aspect of these texts’ peripheral character and literary genetics.

Precise real-time kinematic (RTK) Global Navigation Satellite System (GNSS) positioning requires fixing integer ambiguities after a short initialization time. Originally, it was assumed that it was only possible at a relatively short distance from a reference station (<10 km), because otherwise the atmospheric effects prevent effective ambiguity fixing. Nowadays, through the use of VRS, MAC, or FKP corrections, the distances to the closest reference station have been increased to around 35 km. However, the baselines resolved in real time are not as far as in the case of static positioning. Further extension of the baseline requires the use of an ionosphere-weighted model with ionospheric delay corrections available in real time. This solution is now possible thanks to the Radio Technical Commission for Maritime (RTCM) stream of SSR corrections from, for example, Centre National d’Études Spatiales (CNES), the first analysis center to provide it in the context of the International GNSS Service. Then, ionospheric delays are treated as pseudo-observations that have a priori values from the CLK RTCM stream. Additionally, satellite orbit and clock errors are properly considered using space-state representation (SSR) real-time radial, along-track, and cross-track corrections. The following paper presents the initial results of such RTK positioning. Measurements were performed in various field conditions reflecting realistic scenarios that could have been experienced by actual RTK users. We have shown that the assumed methodology was suitable for single-epoch RTK positioning with up to 82 km baseline in solar minimum (30 March 2019) mid and high latitude (Olsztyn, Poland) conditions. We also confirmed that it is possible to obtain a rover position at the level of a few centimeters of precision. Finally, the possibility of using other newer experimental IGS RT Global Ionospheric Maps (GIMs), from Chinese Academy of Sciences (CAS) and Universitat Politècnica de Catalunya (UPC, Peer Reviewed, Postprint (published version)

Diatoms adapt to changing environmental conditions in very efficient ways. Among the mechanisms that can be activated, the reorientation of carbon metabolism is crucial because it allows the storage of energy into energy-dense molecules, typically lipids. Beside their roles in physiology, lipids are commercially interesting compounds. Therefore studies dealing with this topic are relevant for both basic and applied science. Although the molecular mechanisms involved in the reorientation of carbon metabolism as a response to a deficiency in nutrients such as nitrogen or phosphorus has been partially elucidated, the impacts of carbon availability on the implementation of the reorientation mechanisms remain unclear. Indeed, it has not been determined if the same types of mechanisms are activated under carbon and other nutrient deficiencies or limitations. The first aim of this work was to get insights into the physiological, biological and molecular processes triggered by progressive carbon starvation in the model diatom Phaeodactylum tricornutum. The second aim was to investigate the effects of the growth light intensity on these processes. For such a purpose three different photon flux densities 30, 300, and 1000 μmol photons m-2 s-1 were used. The results presented here demonstrate that under carbon limitation, diatom cells still reorient carbon metabolism toward either phosphoenolpyruvate or pyruvate, which serves as a hub for the production of more complex molecules. The distribution of carbon atoms between the different pathways was partially affected by the growth photon flux density because low light (LL) provides conditions for the accumulation of chrysolaminarin, while medium light mostly stimulated lipid synthesis. A significant increase in the amount of proteins was observed under high light (HL).

Nitrogen (N) and phosphorus (P) limitations induce triacylglycerol (TAG) accumulation and membrane lipid remodelling in the marine diatom Phaeodactylum tricornutum. However, a clear understanding of the metabolic reorientation is still lacking. Carbon partitioning is of great interest because this microalga produces various highly valuable molecules such as lipids and polyunsaturated fatty acids. This study compared growth, photosynthetic activity, biochemical and transcriptional responses of P. tricornutum throughout batch culture under N or P limitation. The integrated results show that the photosynthetic intensity was greatly reduced under N or P limitation. Under N limitation, the degradation and re-use of cellular N-containing compounds contributed to TAG accumulation, whilst P limitation favoured TAG accumulation due to the efficiency of carbon fixation, without massive degradation of essential compounds at cellular level. There was no difference in the partitioning of carbon to neutral lipids between N and P limitation. Substitution of phospholipids with betaine lipids appeared to be a P-specific acclimation strategy in P. tricornutum, which was largely regulated at the gene expression level. Betaine lipid synthesis was induced by P limitation. The lipid remodelling began once the medium became deficient in P. While the phospholipid biosynthesis pathway was not completely inhibited, a shift of lipid classes occurred immediately after their synthesis via phospholipid-recycling mechanisms.

Nitrogen (N) and phosphorus (P) limitations induce triacylglycerol (TAG) accumulation and membrane lipid remodelling in the marine diatom Phaeodactylum tricornutum. However, a clear understanding of the metabolic reorientation is still lacking. Carbon partitioning is of great interest because this microalga produces various highly valuable molecules such as lipids and polyunsaturated fatty acids. This study compared growth, photosynthetic activity, biochemical and transcriptional responses of P. tricornutum throughout batch culture under N or P limitation. The integrated results show that the photosynthetic intensity was greatly reduced under N or P limitation. Under N limitation, the degradation and re-use of cellular N-containing compounds contributed to TAG accumulation, whilst P limitation favoured TAG accumulation due to the efficiency of carbon fixation, without massive degradation of essential compounds at cellular level. There was no difference in the partitioning of carbon to neutral lipids between N and P limitation. Substitution of phospholipids with betaine lipids appeared to be a P-specific acclimation strategy in P. tricornutum, which was largely regulated at the gene expression level. Betaine lipid synthesis was induced by P limitation. The lipid remodelling began once the medium became deficient in P. While the phospholipid biosynthesis pathway was not completely inhibited, a shift of lipid classes occurred immediately after their synthesis via phospholipid-recycling mechanisms.

The selection of suitable and indigenous microalgae species is a fundamental requirement in developing added-value bioactive compounds recoverable in the food, health, and cosmetics markets. In this work, an integrated screening approach was developed to characterize the lipid rate of 33 diatom species (including 15 species studied for the first time) belonging to 16 genera from the Nantes Culture Collection, with the main objective of discovering bioactive lipid producers. For that purpose, a simple reliable method for establishing growth kinetics of strains and semi-quantitative analysis of lipid rates was developed. Growth kinetics measurements were achieved by daily minimal measurement fluorescence (F0) whereas lipid rate analyses were performed by high-throughput Fourier Transform Infrared spectroscopy on entire cells and lipid extracts. Results indicated that the method could be used directly on entire cells in spite of the presence of silica for the FTIR approach (due to frustule). The total lipid rate was species-dependant and ranged from 3.7% to 30.5% DW. Six strains out of 33 were found to present a higher total lipid rate superior to 15% DW, and 11 showed medium lipid rates ranging from 10% to 15% DW. The results revealed that five diatom species i.e. Amphora sp. NCC169, Nitzschia sp. NCC109, Nitzschia alexandrina NCC33, Opephora sp. NCC366 and Staurosira sp. NCC182 presented interesting growth capabilities and should be further investigated as potential sources for their original lipid rate.

Nitrogen (N) and phosphorus (P) limitations induce triacylglycerol (TAG) accumulation and membrane lipid remodelling in the marine diatom Phaeodactylum tricornutum. However, a clear understanding of the metabolic reorientation is still lacking. Carbon partitioning is of great interest because this microalga produces various highly valuable molecules such as lipids and polyunsaturated fatty acids. This study compared growth, photosynthetic activity, biochemical and transcriptional responses of P. tricornutum throughout batch culture under N or P limitation. The integrated results show that the photosynthetic intensity was greatly reduced under N or P limitation. Under N limitation, the degradation and re-use of cellular N-containing compounds contributed to TAG accumulation, whilst P limitation favoured TAG accumulation due to the efficiency of carbon fixation, without massive degradation of essential compounds at cellular level. There was no difference in the partitioning of carbon to neutral lipids between N and P limitation. Substitution of phospholipids with betaine lipids appeared to be a P-specific acclimation strategy in P. tricornutum, which was largely regulated at the gene expression level. Betaine lipid synthesis was induced by P limitation. The lipid remodelling began once the medium became deficient in P. While the phospholipid biosynthesis pathway was not completely inhibited, a shift of lipid classes occurred immediately after their synthesis via phospholipid-recycling mechanisms.