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The article examines the social and political importance of current conflicts surrounding public memory. It suggests that these manifestations are often characterized by assigning alternative values and meanings to collective memories, rather than enforcing historical censorship or perpetuating destructive forgetting. In a second passage, the article advances a more general idea and explores its implications: we are witnessing a global movement of political reactivation of the past and democratization of history, which consists of the recent and sudden emergence of the memories of a galaxy of groups and actors ‘for whom rehabilitating their past is part and parcel of reaffirming their identity’ (Nora 2002). What explains these fluctuations in the relationship between memory and social change? What new entrepreneurs break into the structure of participation in the definition of institutional memory? To what extent does this dynamic stand in discontinuity or continuity with the past?

The issue of the US military presence in Okinawa has marked the political life of the Japanese prefecture since the post-war period. Over time, a composite “community of protest” has taken shape - developing through different organisational models and symbolic references. It is a movement that brings together heterogeneous groups in terms of thematic interests, but which converge on a common goal, namely the removal of US military facilities from Okinawan soil, as well as on an elastic and polysemic repertoire of symbols and memories, which is conveyed through texts, images and songs. This article focuses on the Henoko protest scenario and, through the analysis and interpretation of texts collected through fieldwork, investigates the protest imaginary, questioning its composition and the possible emotional and organisational functions it performs.

The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in particle accelerators. In this paper, we search for the existence of previously unrecognized features related to the incidence of rf breakdowns by applying a machine learning strategy to high-gradient cavity data from CERN’s test stand for the Compact Linear Collider (CLIC). By interpreting the parameters of the learned models with explainable artificial intelligence (AI), we reverse-engineer physical properties for deriving fast, reliable, and simple rule–based models. Based on 6 months of historical data and dedicated experiments, our models show fractions of data with a high influence on the occurrence of breakdowns. Specifically, it is shown that the field emitted current following an initial breakdown is closely related to the probability of another breakdown occurring shortly thereafter. Results also indicate that the cavity pressure should be monitored with increased temporal resolution in future experiments, to further explore the vacuum activity associated with breakdowns.

Okinawa è un contesto privilegiato per lo studio del conflitto sociale e politico. Una composita \"comunità di protesta\" agisce per la rimozione delle basi militari statunitensi, presenti nella prefettura meridionale giapponese fin dal secondo dopoguerra. La lotta antimilitarista si esprime da un lato attraverso alcune strategie classiche di protesta, come sit-in e petizioni, e dall'altro articolando a livello simbolico una contro-narrazione che fornisce una peculiare interpretazione dei fatti sociali e storici. Questo repertorio simbolico, che riunisce immagini, idee, canzoni e storie, produce unità in un insieme di gruppi che, di fatto, sono frammentati e differenziati. I simboli stessi, grazie all'ambiguità semantica che li sottende, stimolano il processo di costruzione di un'identità collettiva, che altrimenti non potrebbe superare la radicale varietà di interessi, motivazioni e significati tra gruppi e individui, spingendoli ad agire insieme.

The article examines the social and political importance of current conflicts surrounding public memory. It suggests that these manifestations are often characterized by assigning alternative values and meanings to collective memories, rather than enforcing historical censorship or perpetuating destructive forgetting. In a second passage, the article advances a more general idea and explores its implications: we are witnessing a global movement of political reactivation of the past and democratization of history, which consists of the recent and sudden emergence of the memories of a galaxy of groups and actors for whom rehabilitating their past is part and parcel of reaffirming their identity (Nora 2002). What explains these fluctuations in the relationship between memory and social change? What new entrepreneurs break into the structure of participation in the definition of institutional memory? To what extent does this dynamic stand in discontinuity or continuity with the past?

Reaching the challenging integrated luminosity production goals of a future circular hadron collider (FCC-hh) and high luminosity LHC (HL-LHC) requires a thorough understanding of today’s most powerful high energy physics research infrastructure, the LHC accelerator complex at CERN. FCC-hh, a 4 times larger collider ring aims at delivering 10–20ab−1 of integrated luminosity at 7 times higher collision energy. Since the identification of the key factors that impact availability and cost is far from obvious, a dedicated activity has been launched in the frame of the future circular collider study to develop models to study possible ways to optimize accelerator availability. This paper introduces the FCC reliability and availability study, which takes a fresh new look at assessing and modeling reliability and availability of particle accelerator infrastructures. The paper presents a probabilistic approach for Monte Carlo simulation of the machine operational cycle, schedule and availability for physics. The approach is based on best-practice, industrially applied reliability analysis methods. It relies on failure rate and repair time distributions to calculate impacts on availability. The main source of information for the study is coming from CERN accelerator operation and maintenance data. Recent improvements in LHC failure tracking help improving the accuracy of modeling of LHC performance. The model accuracy and prediction capabilities are discussed by comparing obtained results with past LHC operational data.

Responding to the European Strategy for Particle Physics update 2013, the Future Circular Collider study explores scenarios of circular frontier colliders for the post-LHC era. One branch of the study assesses industrial approaches to model and simulate the reliability and availability of the entire particle collider complex based on the continuous monitoring of CERN's accelerator complex operation. The modelling is based on an in-depth study of the CERN injector chain and LHC, and is carried out as a cooperative effort with the HL-LHC project. The work so far has revealed that a major challenge is obtaining accelerator monitoring and operational data with sufficient quality, to automate the data quality annotation and calculation of reliability distribution functions for systems, subsystems and components where needed. A flexible data management and analytics environment that permits integrating the heterogeneous data sources, the domain-specific data quality management algorithms and the reliability modelling and simulation suite is a key enabler to complete this accelerator operation study. This paper describes the Big Data infrastructure and analytics ecosystem that has been put in operation at CERN, serving as the foundation on which reliability and availability analysis and simulations can be built. This contribution focuses on data infrastructure and data management aspects and presents case studies chosen for its validation.

The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in particle accelerators. In this paper, we search for the existence of previously unrecognized features related to the incidence of rf breakdowns by applying a machine learning strategy to high-gradient cavity data from CERN's test stand for the Compact Linear Collider (CLIC). By interpreting the parameters of the learned models with explainable artificial intelligence (AI), we reverse-engineer physical properties for deriving fast, reliable, and simple rule-based models. Based on 6 months of historical data and dedicated experiments, our models show fractions of data with a high influence on the occurrence of breakdowns. Specifically, it is shown that the field emitted current following an initial breakdown is closely related to the probability of another breakdown occurring shortly thereafter. Results also indicate that the cavity pressure should be monitored with increased temporal resolution in future experiments, to further explore the vacuum activity associated with breakdowns.

Automated image-based 3D reconstruction methods are more and more flooding our 3D modeling applications. Fully automated solutions give the impression that from a sample of randomly acquired images we can derive quite impressive visual 3D models. Although the level of automation is reaching very high standards, image quality is a fundamental pre-requisite to produce successful and photo-realistic 3D products, in particular when dealing with large datasets of images. This article presents an efficient pipeline based on color enhancement, image denoising, color-to-gray conversion and image content enrichment. The pipeline stems from an analysis of various state-of-the-art algorithms and aims to adjust the most promising methods, giving solutions to typical failure causes. The assessment evaluation proves how an effective image pre-processing, which considers the entire image dataset, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in the case of poor texture scenarios.

Ensuring color fidelity in image-based 3D modeling of heritage scenarios is nowadays still an open research matter. Image colors are important during the data processing as they affect algorithm outcomes, therefore their correct treatment, reduction and enhancement is fundamental. In this contribution, we present an automated solution developed to improve the radiometric quality of an image datasets and the performances of two main steps of the photogrammetric pipeline (camera orientation and dense image matching). The suggested solution aims to achieve a robust automatic color balance and exposure equalization, stability of the RGB-to-gray image conversion and faithful color appearance of a digitized artifact. The innovative aspects of the article are: complete automation, better color target detection, a MATLAB implementation of the ACR scripts created by Fraser and the use of a specific weighted polynomial regression. A series of tests are presented to demonstrate the efficiency of the developed methodology and to evaluate color accuracy (‘color characterization’).