Instruments

Wavelength-shifting (WLS) materials are used in radiation detectors to convert ultraviolet photons into visible light, enabling improved photon detection in systems such as scintillators and optical diagnostics for nuclear fusion devices. However, the long-term performance of these materials under radiation is still a critical issue in high-dose environments. In this work, we investigated the radiation tolerance of three WLS compounds (TPB, NOL1, and SB2001), each deposited on reflective substrates (ESR and E-PTFE), resulting in six distinct WLS/substrate systems. The samples underwent gamma irradiation at absorbed doses of 100 kGy, 500 kGy, and 1000 kGy, as well as fast neutron (14.1 MeV) irradiation up to a fluence of 1.9 × 10¹³ n/cm². Qualitative photoluminescence and reflectance measurements were performed before and after irradiation to assess changes in optical performance. Gamma exposure caused spectral broadening in several samples, particularly those with TPB and SB2001, with variations of the two metrics used to compare the performance of the materials exceeding 10% at the highest doses. Neutron-induced effects were generally weaker and did not exhibit a clear fluence dependence. Reflectance degradation was also observed, with variations depending on both the WLS material and the deposition method. These findings contribute to the understanding of WLS material stability under radiation and support their qualification for use in optical components exposed to harsh nuclear environments.

EuPRAXIA@SPARC_LAB is an FEL (Free-Electron Laser) user facility currently under construction at INFN-LNF in the framework of the EuPRAXIA collaboration. The electron beam will be accelerated to 1 GeV by an X-band RF linac followed by a plasma wakefield acceleration stage. This high-brightness linac requires diagnostic devices able to measure the beam parameters with high accuracy and resolution. To monitor the beam energy and its spread, magnetic dipoles and quadrupoles will be installed along the linac, in combination with viewing screens and CMOS cameras. Macroparticle beam dynamics simulations have been performed to determine the optimal energy measurement setup in terms of accuracy and resolution. Similar diagnostics evaluations have been carried out for the spectrometer installed at the 100 MeV RF linac of the radioactive beam facility SSRIP (IFIN-HH, Romania), whose commissioning, foreseen for 2026, will be performed by INFN-LNF in collaboration with IFIN-HH. Optics measurements have been performed to characterize the resolution and magnification of the optical system that will be used at SSRIP, and probably also at EuPRAXIA@SPARC_LAB, for beam energy monitoring.

Predictive maintenance in industrial machinery relies on the timely detection of component faults to prevent costly downtime. Rolling bearings, being critical elements, are particularly prone to defects such as outer race faults and ball spin defects, which manifest as characteristic vibration patterns. In this study, we introduce a novel bearing vibration dataset collected on a testbench under both constant and variable rotational speeds (0–5000 rpm), encompassing healthy and faulty conditions. The dataset was used for failure classification and further enriched through feature engineering, resulting in input features that include raw acceleration, signal envelopes, and time- and frequency-domain statistical descriptors, which capture fault-specific signatures. To quantify prediction uncertainty, two different approaches are applied, providing confidence measures alongside model outputs. Our results demonstrate the progressive improvement of classification accuracy from 87.2% using only raw acceleration data to 99.3% with a CNN-BiLSTM (Convolutional Neural Network–Bidirectional Long Short-Term Memory) ensemble and advanced features. Shapley Additive Explanation (SHAP)-based explainability further validates the relevance of frequency-domain features for distinguishing fault types. The proposed methodology offers a robust and interpretable framework for industrial fault diagnosis, capable of handling both stationary and non-stationary operating conditions.