Impact of Saturn's rings on mission analysis for MJS 77

Mariner Jupiter-Saturn '77 mission objectives for Saturn are considered which affect the trajectory design and in particular the aim point at Saturn. These objectives include the following: (1) earth, sun occulation of Saturn, rings, and satellites; (2) close as possible to surface; (3) close as possible to rings; (4) close encounter with Titan (approximately 20,000 km); (5) close encounter with lapetus; (6) multiple satellite encounters; (7) post-Saturn trajectory to Uranus; and (8) post-Saturn trajectory toward solar apex

Leveraging Recursive Neural Networks on Dependency Trees for Online-Toxicity Detection on Twitter

openCurrent social dynamics are strongly linked to what happens on Social Media. Opinions, emotions, and how people perceive the world around them are strongly influenced by what they see or read on Social Platforms. We can insert in this field Social Media phenomena like Fake News, Hate Speech, Propaganda, Race and Gender biases. All these events are considered to be among the most significant problems for social stability and one of the most effective means of influencing people. Much work has been done by researchers from different areas of Computer Science, in particular from Natural Language Processing and Network Analysis, focusing on textual information in the first case (articles, posts, comments, etc.) or graph structures and node activities in the second (detection of malicious spreaders, polarization, etc.). In this thesis, we will clarify what are the main problems in this area of research, known by most as Computational Social Science, providing the theoretical basis of the most used tools. Then, we will go into specifics dealing with the topic of the detection of toxic messages on Twitter at the level of the single tweet, comparing different Deep Learning models, among which some innovative solutions proposed by us, trying to answer the following question: can Natural Language syntax be useful in such task? Unlike, for instance, Sentiment Analysis, we have not yet achieved high performance, especially because the models typically used, given a sentence, turn out to focus a lot on the occurring words rather than on the meaning of the sentence itself. Our idea starts from the assumption that exploiting syntactic information can be effective to overcome this obstacle. In the end, we will provide the results of our experiments and possible related interpretations, proposing scientific and ethical reflections, and finally try to convince the reader on why research should invest efforts on this topic, and what future scenarios we should focus on.Current social dynamics are strongly linked to what happens on Social Media. Opinions, emotions, and how people perceive the world around them are strongly influenced by what they see or read on Social Platforms. We can insert in this field Social Media phenomena like Fake News, Hate Speech, Propaganda, Race and Gender biases. All these events are considered to be among the most significant problems for social stability and one of the most effective means of influencing people. Much work has been done by researchers from different areas of Computer Science, in particular from Natural Language Processing and Network Analysis, focusing on textual information in the first case (articles, posts, comments, etc.) or graph structures and node activities in the second (detection of malicious spreaders, polarization, etc.). In this thesis, we will clarify what are the main problems in this area of research, known by most as Computational Social Science, providing the theoretical basis of the most used tools. Then, we will go into specifics dealing with the topic of the detection of toxic messages on Twitter at the level of the single tweet, comparing different Deep Learning models, among which some innovative solutions proposed by us, trying to answer the following question: can Natural Language syntax be useful in such task? Unlike, for instance, Sentiment Analysis, we have not yet achieved high performance, especially because the models typically used, given a sentence, turn out to focus a lot on the occurring words rather than on the meaning of the sentence itself. Our idea starts from the assumption that exploiting syntactic information can be effective to overcome this obstacle. In the end, we will provide the results of our experiments and possible related interpretations, proposing scientific and ethical reflections, and finally try to convince the reader on why research should invest efforts on this topic, and what future scenarios we should focus on

Green radios systems

Essential overview of GRS implementation scenarios. The needs of GRS and some concrete cases exampleope

Dark MaGICC: the effect of Dark Energy on galaxy formation. Cosmology does matter

We present the Dark MaGICC project, which aims to investigate the effect of Dark Energy (DE) modeling on galaxy formation via hydrodynamical cosmological simulations. Dark MaGICC includes four dynamical Dark Energy scenarios with time varying equations of state, one with a self-interacting Ratra-Peebles model. In each scenario we simulate three galaxies with high resolution using smoothed particle hydrodynamics (SPH). The baryonic physics model is the same used in the Making Galaxies in a Cosmological Context (MaGICC) project, and we varied only the background cosmology. We find that the Dark Energy parameterization has a surprisingly important impact on galaxy evolution and on structural properties of galaxies at z=0, in striking contrast with predictions from pure Nbody simulations. The different background evolutions can (depending on the behavior of the DE equation of state) either enhance or quench star formation with respect to a LCDM model, at a level similar to the variation of the stellar feedback parameterization, with strong effects on the final galaxy rotation curves. While overall stellar feedback is still the driving force in shaping galaxies, we show that the effect of the Dark Energy parameterization plays a larger role than previously thought, especially at lower redshifts. For this reason, the influence of Dark Energy parametrization on galaxy formation must be taken into account, especially in the era of precision cosmology.Comment: 11 pages, 13 figure

Residence time in micro-tidal basins

To evaluate water quality, it is possible to use hydrodynamic indicators, instead of ecological models; the most widely used are some characteristic times-scale. Among them, in this work, residence time is considered. In this thesis residence time in Venice Lagoon is calculated starting from a punctual injection of a conservative tracer and then the results are compared with the outcomes of a previuos work in which residence time was computed starting from a diffused injection of the tracerope

Quantum spin Hall effect in bound states in continuum

Moving the polarization of the incident wave along a meridian of the Poincaré sphere, experimentally we show that the coupling with the fundamental Bloch's surface waves of the mode, provide a spatially coherent, macroscopic spinmomentum locked propagation along the symmetry axes of the PhCM. This novel mechanism of light-spin manipulation enables a versatile implementation of spin-optical structures that may pave the way to novel strategies for light spin technology and photonic multiplatform implementations