Analyse temps-frequence et traitement des signaux RSO à haute résolution spatiale pour la surveillance des grands ouvrages d'art

The thesis is composed of two research axis. The first one consists in proposing time-frequency signal processing tools for frequency modulated continuous wave (FMCW) radars used for displacements measurements, while the second one consists in designing a spaceborne synthetic aperture radar (SAR) signal processing methodology for infrastructure monitoring when an external point cloud of the envisaged structure is available. In the first part of the thesis, we propose our solutions to the nonlinearity problem of an X-band FMCW radar designed for millimetric displacement measurements of short-range targets. The nonlinear tuning curve of the voltage controlled oscillator from the transceiver can cause a dramatic resolution degradation for wideband sweeps. To mitigate this shortcoming, we have developed two time warping-based methods adapted to wideband nonlinearities: one estimates the nonlinear terms using the high order ambiguity function, while the other is an autofocus approach which exploits the spectral concentration of the beat signal. Onwards, as the core of the thesis, we propose a novel method for scattering centers detection and tracking in spaceborne SAR images adapted to infrastructure monitoring applications. The method is based on refocusing each SAR image on a provided 3D point cloud of the envisaged infrastructure and identifying the reliable scatterers to be monitored by means of four dimensional (4D) tomography. The refocusing algorithm is compatible with stripmap, spotlight and sliding spotlight SAR images and consists of an azimuth defocusing followed by a modified back-projection algorithm on the given set of points which exploits the time-frequency structure of the defocused azimuth signal. The scattering centers of the refocused image are detected in the 4D tomography framework by testing if the main response is at zero elevation in the local elevation-velocity spectral distribution. The mean displacement velocity is estimated from the peak response on the zero elevation axis, while the displacements time series for detected single scatterers is computed as double phase difference of complex amplitudes.Finally, we present the measurement campaigns carried out on the Puylaurent water-dam and the Chastel landslide using GPS measurements, topographic surveys and laser scans to generate the point clouds of the two structures. The comparison between in-situ data and the results obtained by combining TerraSAR-X data with the generated point clouds validate the developed SAR signal processing chain.Cette thèse s'articule autour de deux axes de recherche. Le premier axe aborde les aspects méthodologiques liés au traitement temps-fréquence des signaux issus d'un radar FMCW (à onde continue modulée en fréquence) dans le contexte de la mesure des déplacements fins. Le second axe est dédié à la conception et à la validation d'une chaîne de traitement des images RSO (radar à synthèse d'ouverture) satellitaire. Lorsqu'un maillage 3D de la structure envisagée est disponible, les traitements proposés sont validés par l'intercomparaison avec les techniques conventionnelles d'auscultation des grands ouvrages d'art.D'une part, nous étudions la correction de la non-linéarité d'un radar FMCW en bande X, à courte portée, conçu pour la mesure des déplacements millimétriques. La caractéristique de commande non linéaire de l'oscillateur à large bande, entraine une perte de résolution à la réception. Afin de pallier cet inconvénient, nous avons développé deux méthodes basées sur le ré-échantillonnage temporel (time warping) dans le cas des signaux à large bande non-stationnaires. La première approche estime la loi de fréquence instantanée non linéaire à l'aide de la fonction d'ambiguïté d'ordre supérieur, tandis que la deuxième approche exploite la mesure de concentration spectrale du signal de battement dans un algorithme d'autofocus radial.D'autre part, nous proposons un cadre méthodologique général pour la détection et le pistage des centres de diffusion dans les images RSO pour la surveillance des grands ouvrages d'art. La méthode est basée sur la ré-focalisation de chaque image radar sur le maillage 3D de l'infrastructure étudiée afin d'identifier les diffuseurs pertinents par tomographie 4D (distance – azimut – élévation – vitesse de déformation). L'algorithme de ré-focalisation est parfaitement compatible avec les images RSO acquises dans les différents modes (« stripmap », « spotlight » et « sliding spotlight ») : dé-focalisation en azimut suivie par rétroprojection modifiée (conditionnée par la structure temps-fréquence du signal) sur l'ensemble donné des points. Dans la pile d'images ré-focalisées, les centres de diffusion sont détectés par tomographie 4D : test de conformité à l'hypothèse d'élévation zéro dans le plan élévation – vitesse de déformation. La vitesse moyenne correspond au maximum à l'élévation zéro, tandis que la série temporelle des déplacements est obtenue par double différence de phase des amplitudes complexes pour chaque diffuseur pertinent.Nous présentons également les campagnes in situ effectuées au barrage de Puylaurent (et glissement de Chastel) : les relevés GPS, topographiques et LIDAR sol employées au calcul des maillages 3D. La comparaison entre les déplacements mesurés in situ et les résultats obtenus par l'exploitation conjointe de la télédétection RSO satellitaires et les maillages 3D valident la chaîne de traitement proposée.Teza cuprinde două axe principale de cercetare. Prima axă abordează aspecte metodologice de prelucraretimp-frecvenţă a semnalelor furnizate de radare cu emisie continuă şi modulaţie de frecvenţă (FMCW)în contextul măsurării deplasărilor milimetrice. În cadrul celei de-a doua axe, este proiectată şi validatăo metodă de prelucrare a imaginilor satelitare SAR (radar cu apertură sintetică) ce este destinatămonitorizării infrastructurii critice şi care se bazează pe existenţa unui model 3D al structurii respective.În prima parte a tezei, sunt investigate soluţii de corecţie a neliniarităţii unui radar FMCW în bandaX destinat măsurării deplasărilor milimetrice. Caracteristica de comandă neliniară a oscilatorului debandă largă determină o degradare a rezoluţiei în distanţă. Pentru a rezolva acest inconvenient, au fostelaborate două metode de corecţie a neliniarităţii, adaptate pentru semnale de bandă largă, ce se bazeazăpe conceptul de reeşantionare neuniformă sau deformare a axei temporare. Prima abordare estimeazăparametrii neliniarităţii utilizând funcţii de ambiguitate de ordin superior, iar cea de-a doua exploateazăo măsură de concentraţie spectrală a semnalului de bătăi într-un algoritm de autofocalizare în distanţă.În a doua parte a lucrării, este propusă o metodologie generală de detecţie şi monitorizare a centrilorde împrăştiere în imagini SAR în scopul monitorizării elementelor de infrastructură critică. Metoda sebazează pe refocalizarea fiecărei imagini radar pe un model 3D al structurii investigate în scopul identificăriicentrilor de împrăştiere pertinenţi (ţinte fiabile ce pot fi monitorizate în timp) cu ajutorul tomografiei SAR4D (distanţă-azimut-elevaţie-viteză de deplasare). Algoritmul de refocalizare este compatibil cu imaginiSAR achiziţionate în moduri diferite (« stripmap », « spotlight » şi « sliding spotlight ») şi constă într-odefocalizare în azimut urmată de o retroproiecţie modificată (condiţionată de structura timp-frecvenţă asemnalului) pe modelul 3D al structurii. Ţintele sunt identificate în stiva de imagini refocalizate cu ajutorultomografiei 4D prin efectuarea unui test de conformitate cu ipoteza că centrii de împrăştiere pertinenţivor avea elevaţie zero în planul local elevaţie-viteză. Viteza medie de deformare corespunde maximuluide pe axa de elevaţie nulă, iar seria temporară a deplasărilor se obţine printr-o dublă diferenţă de fază aamplitudinilor complexe corespunzătoare ţintelor identificate.În final sunt prezentate campaniile de măsurători pe teren efectuate la un baraj şi o alunecare de terendin regiunea Puylaurent (Franţa) destinate obţinerii modelului 3D al celor două elemente de infrastructurăprin măsurători GPS, topografice şi LIDAR. Comparaţia între deformările măsurate pe teren şi rezultateleobţinute prin combinarea imaginilor SAR cu modelele 3D au permis validarea metodologiei propuse

THE ARES PENDULUM: AN ETHICAL PERSPECTIVE

The expansion observed, in the last half-century, in the theorization of intelligence activity has magnetically attracted the need for an ethical topography of intelligence services’ behaviour and states. Drawing on the fundamental concepts and theories of this discipline, the article applies a complex evaluation grid to a recent case, subsumed within a less popularized operation in the history of the intelligence service of communist Romania. By accumulating evidence from open sources, books, studies and corroborating all available records in declassified archives, the paper presumes that the investigations and measures undertaken by the State Security Department (Securitate) in the sphere of the cinematographic environment are suitable for an analysis from an ethical angle. Attempting to answer some fundamental ethical questions, the article includes a brief presentation of the main theories in the field of intelligence ethics, followed by a historical illustration of the main milestones in the issues addressed, during Ceauşescu’s rule (1965-1989). Then, the combination of the two results in the ethical judgement, which is the fundamental subject of the article. The ethical perspective is enriched by the author’s proposal of a theoretical model for evaluating and deciphering the case under inspection. The model has an adjuvant role and does not imply its dissociation from the pre-existing theoretical foundation. Its purpose is to contextualise the ethical interpretation and create a scale applicable to the subjects and facts examined

The impact of building location on green certification price premiums:Evidence from three European countries

Green building certification has gained global prominence in the wake of the recent calls for ensuring the sustainable development of expanding urban areas. This trend rooted in the fact that buildings are among the main sources of energy consumption and CO2 emissions. Green certification therefore emerged in response to sustainability concerns throughout the building sector. Nonetheless, the significant costs required by green investments have elicited scholars' attention, in an attempt to determine if the benefits of green certification outweigh its costs. This study uses a proprietary data-set of office building transactions from three major European countries - Finland, France, and Germany - in order to analyze the price premium of green certification over the 2010-2015 period. Considering the increasing demand for certification in the European Union (EU) after 2010, it is expected that green office buildings would sell at higher prices relative to non-green buildings. Empirical tests suggest that office buildings with green certification have a 19 percent higher price relative to non-certified buildings. Further, the study aims to assess whether the premium varies with the location of the green buildings within the urban area. Given the price premium brought by a central location - irrespective of green certification - it is expected that the price premium of green investments would incrementally increase in non-central locations. The distance variable is hand-constructed based on geocoding all properties in the dataset - empirical results indicate that the green certification price premium incrementally increases by 10.5 percent for 1-km distance from the city center. Further tests show that the distance effect becomes insignificant in both (i) large cities and (ii) cities of under 200,000 inhabitants. In these two contingencies, the price premium associated with central locations is reduced - which also diminishes the relevance of the green buildings' location. The empirical results are robust to eliminating 2010 and 2011 from the sample and to employing a propensity score matching approach, aimed at increasing the similarity of the treatment and control groups. This paper adds to the rising literature on the topic of green buildings, as it is the first international study to assess the price impact of green certification as a function of office building location

Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks

Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions. Accurate and timely measurement of sea ice parameters is important for understanding and predicting the impacts of climate change. Nevertheless, the amount of satellite data acquired over ice areas is huge, making the subjective measurements ineffective. Therefore, automated algorithms must be used in order to fully exploit the continuous data feeds coming from satellites. In this paper, we present a novel approach for sea ice segmentation based on SAR satellite imagery using hybrid convolutional transformer (ConvTr) networks. We show that our approach outperforms classical convolutional networks, while being considerably more efficient than pure transformer models. ConvTr obtained a mean intersection over union (mIoU) of 63.68% on the AI4Arctic data set, assuming an inference time of 120ms for a 400 x 400 squared km product

The Impact of the First Covid-19 Wave on Migrant Workers: The Case of Romanians in Italy

The Covid-19 pandemic is having an unprecedented impact on health systems, on many economic sectors and on the labour market. This critical situation is also accompanied by social destabilisation, which has exacerbated inequalities and severely affected the most disadvantaged population groups, such as migrant workers. This study provides insights into the consequences of the first wave and the lockdown period in Spring 2020 of the Covid-19 pandemic on Romanians living in Italy, using data collected by the International Association Italy-Romania ‘Cuore Romeno’, within a project financed by the Romanian Department for Di-aspora and developed to support actions while strengthening the link with Romanian institutions during the pandemic. Findings show that, during the lockdown, two opposite situations occurred among Romanians. Workers in the ‘key sector’ become indispensable and experienced only small changes, while others lost their job or experienced a worsening of working conditions, with lower wages or an increase in working hours. Most workers chose to stay in Italy, relying on their savings or the support of the Italian government. Job losses, not having new employment, and having limited savings all influenced the decision of a smaller group to return to Romania. In conclusion, the analysis suggests that measures adopted should take into consider-ation that the Covid-19 pandemic might disproportionally hit population groups such as migrants, women, young people and temporary and unprotected workers, particularly those employed in trade, hospitality and agriculture

The Dirac spectrum on manifolds with gradient conformal vector fields

We show that the Dirac operator on a spin manifold does not admit $L^2$ eigenspinors provided the metric has a certain asymptotic behaviour and is a warped product near infinity. These conditions on the metric are fulfilled in particular if the manifold is complete and carries a non-complete vector field which outside a compact set is gradient conformal and non-vanishing.Comment: 12 page

A note on different types of ransomware attacks

Ransomware are malware whose purpose is to generate income for the attacker. The first of these malware made intense use of cryptography, specifically for file encryption. They encrypt some or most files on the computer before asking a ransom for the decryption. Since they appeared, however, ransomware have evolved into different types which fulfill their task in different ways. Some encrypt files and data from the hard drive, others block access to the OS or use private user data to blackmail the user, some aren’t even a real threat, but they scare the user into paying for some fake service or software. The software security industry is well aware of these threats and is constantly analyzing the new versions and types to determine how dangerous they are and to provide an updated protection solution. This article tries to investigate and compare the way these malware work and how they affect the victims computer. Our analysis will provide interesting insight into how they work, it will highlight the particularities of ransomware and will give some information about why some of these malware are more dangerous than others

Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require great amount of labeled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improve the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms

Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery

Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave mode vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition as a preprocessing stage for SAR deep learning models. Our data centring approach surpassed the baseline by 0.7, obtaining state of the art on the TenGeoPSARwv data set. In addition, we empirically showed that subaperture decomposition could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models