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This thesis contributes to the global efforts of space debris mitigation and monitoring by offering a photometric model of space debris in Earth’s orbit with the aim to integrate such a model into arcsec’s star simulator Intergalactic, aiding arcsec’s DeDUST project in the elaboration of a space situational awareness strategy based on star trackers. The model has been elaborated following a thorough review of the current literature body surrounding the photometric representation of spacecraft and their debris. After a mathematical basis has been established, an algorithm was implemented in MATLAB, with the aid of such open-source tools and NAIF’s Spice and NASA’ GMAT. The produced algorithm has been tested against both simulated cases and real photometric measures in order to both validate the model and find an optimal arrangement of its numerical parameters. The produced optimal model has been used to analyze the possible applications for optics-based space debris detection. It was found that a brightness model with a 26.5% specular component allows to most closely estimate the visual magnitude of several bodies of varying nature and size. The model’s limitation has been identified with respect to an object’s size-to-distance from the sensor (d to R) ratio. Indeed, when (d/R)^2 ≤ 1.5 × 10^−13, the model provides a visual magnitude estimation with a maximum of 10% error, and a maximum of 20% error for 1.5 × 10^−13 ≤ (d/R)^2 ≤ 1.5 × 10^−12. An inspection of the model’s response has confirmed the DeDUST project’s aim to detect debris down to 3 centimeters in size to be a perfectly realizable task. In fact, it was found that a debris detecting system based on arcsec’s Sagitta star tracker is bound to have an operating range of at least 10 kilometers for small untracked debris of 3 to 10 centimeters in size. It was also confirmed that the debris is most likely to appear as streaks to a star tracker rather than a singular and well-defined object. Ultimately, this thesis proves as an enlightening tool in the elaboration of space-debris detection strategies as well as completes the existing body of literature by focusing on the intricacies of space debris photometry.
photometry --- space debris --- star trackers --- modelling --- Space situational awareness --- optic sensors --- Ingénierie, informatique & technologie > Ingénierie aérospatiale
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Introduction : La qualité des soins et la sécurité du patient ont été décrites comme des enjeux majeurs dans le monde de la santé. Les compétences non techniques sont régulièrement identifiées comme faisant défaut lors de la retranscription des faits qui ont mené à des évènements indésirables liés aux soins. Dans cet ensemble, c’est la conscience situationnelle qui a été sélectionnée comme sujet principal de l’étude. Après une revue de la littérature, l’échelle SAGAT est admise comme le gold standard pour l’évaluation de cette capacité. Parmi les différentes solutions mises en évidence, le TeamSTEPPS est un programme développé dans le but d’optimiser les facteurs non techniques. L’objectif de l’étude est donc d’améliorer la conscience situationnelle, sous le postulat que cela améliorera la performance des soignants lorsqu’ils font face à une situation aigue. Matériel et méthodes : 30 individus travaillant dans le service des soins intensifs sur le site de Mont-Godinne du CHU UCL Namur sont sélectionnés. Ils constituent 10 groupes, composés chacun d’un médecin et de 2 infirmiers. La moitié des équipes subissent l’intervention, une mini-session de cours issu du programme TeamSTEPPS, sur le module spécifique de monitorage de la situation. Les 5 autres groupes ne reçoivent quant à eux pas d’intervention. La conscience situationnelle de chaque individu est évaluée grâce à des échelles SAGAT. Celles-ci sont administrées lors de séances de simulation avant et après l’intervention. Les scores sont ensuite analysés et comparés sur le plan statistique. Résultats : Les équipes qui ont suivi la mini session de cours augmentent leurs niveaux de conscience situationnelle en moyenne de 5%, en comparaison aux groupes n’ayant pas subi l’intervention. Les valeurs ne sont toutefois pas statistiquement significatives (P = 0,2). Les modèles de régression linéaires dévoilent des résultats similaires (simple, beta = 6,3%, P = 0,2 ; multiple, beta = 8,4%, P = 0,3). Conclusion : Le TeamSTEPPS semble améliorer le niveau de conscience situationnelle des individus ayant suivi le programme. Toutefois, les valeurs décelées ne permettent pas d’affirmer de certitudes sur le plan statistique, et donc scientifique. Des études supplémentaires, multicentriques et avec des échantillons plus importants sont nécessaires. Introduction : Quality of care and patient safety have been described as major issues in the healthcare world. Non-technical skills are regularly identified as lacking in the transcription of events leading to adverse events in healthcare. In this context, situational awareness was selected as the main focus of the study. After a review of the literature, the SAGAT scale was accepted as the gold standard for assessing this ability. Among the various solutions identified, TeamSTEPPS is a program developed to optimize non-technical factors. The aim of the study is therefore to improve situational awareness, on the assumption that this will improve caregivers' performance when faced with an acute situation. Materials and methods : 30 individuals working in the intensive care unit at the Mont-Godinne site of CHU UCL Namur were selected. They form 10 groups, each comprising a doctor and 2 nurses. Half of the teams undergo the intervention, a mini-course from the TeamSTEPPS program, on the specific situation monitoring module. The other 5 groups receive no intervention. Each individual's situational awareness is assessed using SAGAT scales. These are administered during simulation sessions before and after the intervention. Scores are then analyzed and statistically compared. Results : Teams who attended the mini-course increased their levels of situational awareness by an average of 5%, compared with groups who did not undergo the intervention. However, the values were not statistically significant (P = 0.2). Linear regression models revealed similar results (simple, beta = 6.3%, P = 0.2; multiple, beta = 8.4%, P = 0.3). Conclusion : TeamSTEPPS appears to improve the level of situational awareness of individuals who have completed the program. However, the values detected do not allow us to assert statistical, and therefore scientific, certainties. Further multi-center studies with larger sample sizes are required.
conscience situationnelle --- TeamSTEPPS --- médecins --- infirmiers --- soins de réanimation --- situational awareness --- physicians --- nurses --- critical care --- Sciences de la santé humaine > Santé publique, services médicaux & soins de santé
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