Ultra-fine dark matter structure in the solar neighbourhood

Dark matter plays a fundamental role in theories of the formation and evolution of galaxies. Thus every attempt to model galaxy formation and evolution has to take into consideration the presence of dark halos. Moreover, mergers and accretion appear to be fundamental driving mechanisms in determining the present day properties of galaxies. The aim of this thesis is to study the ultra-fine distribution of dark matter in the Solar neighbourhood, and to investigate the implications for the current and next generation of dark matter directional detectors. For this purpose we develop a model for halo mergers in a Milky Way-like galaxy. The signals expected in lab-based dark matter detection experiments depend on the phase-space distribution on sub-milliparsec scales. With our numerical technique it is possible to resolve structures produced by minor mergers of subhalos with a larger parent halo. This type of substructure is unaccessible to conventional N-body simulations. When applied in a cosmological context,this method becomes a powerful instrument to reproduce and analyse the complete multiple merger history of a Milky Way-like system. The results obtained simulating the Galactic halo suggest that the velocity distribution in the solar neighbourhood after an evolution time corresponding to the lifetime of our galaxy (≃ 14Gyr) is smooth. This result suggests the presence of a huge number of dark matter streams that overlap to form a smooth distribution. Nevertheless, the final velocity distribution has overdensities for all the cases that has been analysed. They are generated by a very large number of merger events, but the current generation of detectors have not the angular resolution required to observe these features. A future generation of detectors with a resolution of ~ 1◦ would start to resolve them, allowing the merger history of the Galaxy to begin to be unravelled using this diagnostic

Lidar-based Norwegian tree species detection using deep learning

Background: The mapping of tree species within Norwegian forests is a time-consuming process, involving forest associations relying on manual labeling by experts. The process can involve both aerial imagery, personal familiarity, or on-scene references, and remote sensing data. The state-of-the-art methods usually use high resolution aerial imagery with semantic segmentation methods. Methods: We present a deep learning based tree species classification model utilizing only lidar (Light Detection And Ranging) data. The lidar images are segmented into four classes (Norway Spruce, Scots Pine, Birch, background) with a U-Net based network. The model is trained with focal loss over partial weak labels. A major benefit of the approach is that both the lidar imagery and the base map for the labels have free and open access. Results: Our tree species classification model achieves a macro-averaged F1 score of 0.70 on an independent validation with National Forest Inventory (NFI) in-situ sample plots. That is close to, but below the performance of aerial, or aerial and lidar combined models

Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations

Estimating building footprint maps from geospatial data is of paramount importance in urban planning, development, disaster management, and various other applications. Deep learning methodologies have gained prominence in building segmentation maps, offering the promise of precise footprint extraction without extensive post-processing. However, these methods face challenges in generalization and label efficiency, particularly in remote sensing, where obtaining accurate labels can be both expensive and time-consuming. To address these challenges, we propose terrain-aware self-supervised learning, tailored to remote sensing, using digital elevation models from LiDAR data. We propose to learn a model to differentiate between bare Earth and superimposed structures enabling the network to implicitly learn domain-relevant features without the need for extensive pixel-level annotations. We test the effectiveness of our approach by evaluating building segmentation performance on test datasets with varying label fractions. Remarkably, with only 1% of the labels (equivalent to 25 labeled examples), our method improves over ImageNet pre-training, showing the advantage of leveraging unlabeled data for feature extraction in the domain of remote sensing. The performance improvement is more pronounced in few-shot scenarios and gradually closes the gap with ImageNet pre-training as the label fraction increases. We test on a dataset characterized by substantial distribution shifts and labeling errors to demonstrate the generalizability of our approach. When compared to other baselines, including ImageNet pretraining and more complex architectures, our approach consistently performs better, demonstrating the efficiency and effectiveness of self-supervised terrain-aware feature learning

Ultra-fine dark matter structure in the Solar neighbourhood

The direct detection of dark matter on Earth depends crucially on its density and its velocity distribution on a milliparsec scale. Conventional N-body simulations are unable to access this scale, making the development of other approaches necessary. In this paper, we apply the method developed in Fantin et al. 2008 to a cosmologically-based merger tree, transforming it into a useful instrument to reproduce and analyse the merger history of a Milky Way-like system. The aim of the model is to investigate the implications of any ultra-fine structure for the current and next generation of directional dark matter detectors. We find that the velocity distribution of a Milky Way-like Galaxy is almost smooth, due to the overlap of many streams of particles generated by multiple mergers. Only the merger of a 10^10 Msun analyse can generate significant features in the ultra-local velocity distribution, detectable at the resolution attainable by current experiments.Comment: 9 pages, 6 figures, accepted for publication in MNRA

Co-creation, innovation, decision-making, tech-transfer, and sustainability actions

Funding Information: Open access funding provided by FCT|FCCN (b-on). This work was funded by the European Union’s Horizon 2020 program [H2020-SC5-2019–2]—869520 NextLand, [H2020-SPACE-202]—101004362 NextOcean, Fundação para a Ciência e a Tecnologia (UIDB/00124/2020 and Social Sciences DataLab, PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Publisher Copyright: © 2023, The Author(s).European Community (EC) Horizon-funded projects and Earth Observation-based Consortia aim to create sustainable value for Space, Land, and Oceans. They typically focus on addressing Sustainable Development Goals (SDGs). Many of these projects (e.g. Commercialization and Innovation Actions) have an ambitious challenge to ensure that partners share core competencies to simultaneously achieve technological and commercial success and sustainability after the end of the EC funds. To achieve this ambitious challenge, Horizon projects must have a proper governance model and a systematized process that can manage the existing paradoxical tensions involving numerous European partners and their respective agendas and stakeholders. This article presents the VCW-Value Creation Wheel (Lages in J Bus Res 69: 4849–4855, 2016), as a framework that has its roots back in 1995 and has been used since 2015 in the context of numerous Space Business, Earth Observation, and European Community (EC) projects, to address complex problems and paradoxical tensions. In this article, we discuss six of these paradoxical tensions that large Horizon Consortia face in commercialization, namely when managing innovation ecosystems, co-creating, taking digitalization, decision-making, tech-transfer, and sustainability actions. We discuss and evaluate how alliance partners could find the optimal balance between (1) cooperation, competition, and coopetition perspectives; (2) financial, environmental, and social value creation; (3) tech-push and market-pull orientations; (4) global and local market solutions; (5) functionality driven and human-centered design (UX/UI); (6) centralized and decentralized online store approaches. We discuss these challenges within the case of the EC H2020 NextLand project answering the call for greening the economy in line with the Sustainable Development Goals (SDGs). We analyze NextLand Online Store, and its Business and Innovation Ecosystem while considering the input of its different stakeholders, such as NextLand’s commercial team, service providers, users, advisors, EC referees, and internal and external stakeholders. Preliminary insights from a twin project in the field of Blue Economy (EC H2020 NextOcean), are also used to support our arguments. Partners, referees, and EC officers should address the tensions mentioned in this article during the referee and approval processes in the pre-grant and post-grant agreement stages. Moreover, we propose using the Value Creation Wheel (VCW) method and the VCW meta-framework as a systematized process that allows us to co-create and manage the innovation ecosystem while engaging all the stakeholders and presenting solutions to address these tensions. The article concludes with theoretical implications and limitations, managerial and public policy implications, and lessons for Horizon Europe, earth observation, remote sensing, and space business projects.publishersversionpublishe

Educomunicação e suas áreas de intervenção: Novos paradigmas para o diálogo intercultural

oai:omp.abpeducom.org.br:publicationFormat/1O material aqui divulgado representa, em essência, a contribuição do VII Encontro Brasileiro de Educomunicação ao V Global MIL Week, da UNESCO, ocorrido na ECA/USP, entre 3 e 5 de novembro de 2016. Estamos diante de um conjunto de 104 papers executivos, com uma média de entre 7 e 10 páginas, cada um. Com este rico e abundante material, chegamos ao sétimo e-book publicado pela ABPEducom, em seus seis primeiros anos de existência. A especificidade desta obra é a de trazer as “Áreas de Intervenção” do campo da Educomunicação, colocando-as a serviço de uma meta essencial ao agir educomunicativo: o diálogo intercultural, trabalhado na linha do tema geral do evento internacional: Media and Information Literacy: New Paradigms for Intercultural Dialogue

Ultra-fine dark matter structure in the solar neighbourhood

Dark matter plays a fundamental role in theories of the formation and evolution of galaxies. Thus every attempt to model galaxy formation and evolution has to take into consideration the presence of dark halos. Moreover, mergers and accretion appear to be fundamental driving mechanisms in determining the present day properties of galaxies. The aim of this thesis is to study the ultra-fine distribution of dark matter in the Solar neighbourhood, and to investigate the implications for the current and next generation of dark matter directional detectors. For this purpose we develop a model for halo mergers in a Milky Way-like galaxy. The signals expected in lab-based dark matter detection experiments depend on the phase-space distribution on sub-milliparsec scales. With our numerical technique it is possible to resolve structures produced by minor mergers of subhalos with a larger parent halo. This type of substructure is unaccessible to conventional N-body simulations. When applied in a cosmological context,this method becomes a powerful instrument to reproduce and analyse the complete multiple merger history of a Milky Way-like system. The results obtained simulating the Galactic halo suggest that the velocity distribution in the solar neighbourhood after an evolution time corresponding to the lifetime of our galaxy (≃ 14Gyr) is smooth. This result suggests the presence of a huge number of dark matter streams that overlap to form a smooth distribution. Nevertheless, the final velocity distribution has overdensities for all the cases that has been analysed. They are generated by a very large number of merger events, but the current generation of detectors have not the angular resolution required to observe these features. A future generation of detectors with a resolution of ~ 1◦ would start to resolve them, allowing the merger history of the Galaxy to begin to be unravelled using this diagnostic.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

CryoSat-2 waveform classification for melt event monitoring

Measuring the mass balance of ice sheets is important with respect to understanding among others sea level rise, glacier dynamics, global ocean circulation and marine ecosystems. One important parameter of the mass balance is surface melt, which can be estimated from different satellite data sources. In this study we investigate the potential of utilizing machine learning techniques for CryoSat-2 (CS2) radar altimeter waveform classification in order to derive melt information. Training data is derived by spatio-temporally matching of CS2 measurements with MODIS land surface temperature measurements. We propose a time convolution network with a fully connected classifier tail for CS2 waveform classifcation. In addition a non-deep learning model is implemented, providing a baseline. One of the main challenges is the high class imbalance, as surface temperatures on the interior of Greenland rarely reach the freezing point. The model performance is measured by several metrics: F1 score, average recall and Matthews correlation coefficient. The results of this proof of concept study indicate feasibility