314 research outputs found
Paolo e Francesca. romanzo
No full textPaolo e Francesca : romanzo / Michele Saponaro. - [Milano] : A. Mondadori, [1930]
Dedica manoscritta dell\u27autore: a Emilio Bodrero / amico primo e lontano / M. Saponaro / nov. 1930.
https://galileodiscovery.unipd.it/discovery/fulldisplay?context=L&vid=39UPD_INST:VU1&search_scope=MyInst_and_CI&tab=Everything&docid=alma99000125392020604
Effect of CO2 concentration in air on electrolytic conductivity of aqueous solutions of KCl
No full textElectrolytic conductivity measurements of potassium chloride (KCl) solutions are carried out in the “Electrochemical measurements” laboratory of Istituto Elettrotecnico Nazionale Galileo Ferraris (IEN). Such measurements are in the range between 1400 S/cm and 1.3 S/m and are traceable to the International System units. Since carbon dioxide (CO2) concentration in air is one of the influence quantities of this measurement, it is detected by means of a non-dispersive infrared (NDIR) analyser of the Istituto di Metrologia “G. Colonnetti” (IMGC). Aim of this paper is to present the uncertainty budget for a typical electrolytic conductivity measurement and to evaluate the contribution due to the level of CO2 in air.All’Istituto Elettrotecnico Nazionale Galileo Ferraris (IEN), laboratorio “Misure elettrochimiche” si eseguono misure di conducibilità elettrolitica riferibili alle unità del Sistema Internazionale (SI), nell’intervallo compreso tra 1400 S/cm e 1.3 S/m, di soluzioni acquose di cloruro di potassio (KCl). Una grandezza che influenza tale misura è costituita dalla concentrazione del biossido di carbonio (CO2) in aria che viene rilevata con un analizzatore infrarosso non dispersivo (NDIR) dell’Istituto di Metrologia “G. Colonnetti” (IMGC). Lo scopo di questo lavoro, è quello di presentare il bilancio dell’incertezza per una misura tipica di conducibilità elettrolitica e di valutare il contributo dovuto al livello di CO2 nell’aria
2013 Sardinia floods. Exploring conversations on Twitter among citizens, institutions and Twitstars
No full textOur research focuses on the Twitter activity related to the heavy floods that occurred in Sardinia in November 2013, with special regard to the hashtag #allertameteoSAR. As institutional social media communication was generally lacking, the hashtag witnessed a user-driven shift: at the beginning it was used as a general-purpose hashtag; in the following days, some active Twitter users succeeded in transforming it into the “(un)official” hashtag for disaster recovery-related conversations.
We analyze the whole dataset of the tweets with hashtag #allertameteoSAR that have been produced during the first week of the Sardinian floods (around 70.000 tweets have been extracted through GNIP “Historical Power Track”)
#allertameteoSAR: analisi di un hashtag di servizio tra dinamiche di influenza e nuove forme di engagement
No full textObiettivo del contributo è analizzare le forme di attivazione che si sono aggregate intorno all'hashtag allertameteoSAR, osservando le pratiche attraverso le quali gli utenti hanno creato uno spazio nel quale condividere informazioni utili, verificandole e valicandole attraverso una serie di azioni di "volontariato digitale" che si sono dispiegate, a partire da Twitter, su una pluralità di piattaforme digitali.Our research focuses on the social media activity related to the heavy floods that occurred in Sardinia in November 2013. We adopt an ecological approach, considering a variety of (social media) platforms that have been used during the floods. As institutional social media communication was generally lacking, we have witnessed what we could define as “crowdsourced social media emergency management”. Some digital volunteers created a specific Twitter hashtag (#allertameteoSAR), and a related Facebook Page, and succeeded in transforming it into the “(un)official” hashtag for disaster recovery-related conversations
Unsupervised semantic segmentation of radar sounder data using contrastive learning
No full textRadar Sounders (RSs) are active sensors widely used for planetary exploration and Earth observation that probe the subsurface in a non-intrusive way by acquiring vertical profiles, called radargrams. Radargrams contain information on subsurface geology and are analyzed with neural networks for segmentation and target detection. However, most of these methods rely on supervised training, which requires a large amount of labeled data that is hard to retrieve. Hence, a need emerges for a novel method for unsupervised radargram segmentation. This paper proposes a novel method for unsupervised radargram segmentation by analyzing semantically meaningful features extracted from a deep network trained with a contrastive logic. First, the network (encoder) is trained using a pretext task to extract meaningful features (query). Considering a dictionary of possible features (keys), the encoder training loss can be defined as a dictionary look-up problem. Each query is matched to a key in a large and consistent dictionary. Although such a dictionary is not available for RS data, it is dynamically computed by extracting meaningful features with another deep network called the momentum encoder. Secondly, deep feature vectors are extracted from the encoder for all radargram pixels. After the feature selection, the feature vectors are binarized. Since pixels of the same class are expected to have similar feature vectors, we compute the similarity between the feature vectors to generate a cluster of pixels for each class. We applied the proposed method to segment radargrams acquired in Greenland by the MCoRDS-3 sensor, achieving good overall accuracy
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