Evidence for moving breathers in a layered crystal insulator at 300K

We report the ejection of atoms at a crystal surface caused by energetic breathers which have travelled more than 10^7 unit cells in atomic chain directions. The breathers were created by bombardment of a crystal face with heavy ions. This effect was observed at 300K in the layered crystal muscovite, which has linear chains of atoms for which the surrounding lattice has C_2 symmetry. The experimental techniques described could be used to study breathers in other materials and configurations.Comment: 7 pages, 3 figure

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

AIDA - Antennas diagnostics enhancement by combined use of AI and experts' knowledge

The increasing demand arisen in the last decades for high-quality performance of Radio-Frequency (RF) systems to be exploited in space applications, brought up the need for accurate measurements. Nowadays, several methods can be used to measure antennas far field properties, including Near-Field Test Ranges (NFTR) carried out in anechoic chambers. From the measured properties, gain or phase patterns are reconstructed and compared with theoretical patterns. The theoretical antenna patterns are produced by electromagnetic (EM) computational methods and are used in combination with the results of the measurement process in order to obtain the best test prediction of the configuration realized in the NFTR. The comparison between reference theoretical patterns and in-field measurements could highlight discrepancies which may be caused by misalignments between the antenna under test (AUT) and the measurement system or by the presence of an anomaly introduced by the manufacturing process. These discrepancies require an accurate post-test analysis to understand the anomaly typology and the associated root cause. The activity of smoothing the theoretical model to the best representation of the measured case is time and cost demanding, because it is based on the iteration up to convergence of the model-to-measure comparison process, and deeply depends on the expertise of antenna engineers. However, anomaly data are normally not recorded, nor is the experts' knowledge on how to quickly converge to the right diagnostic result. The huge amount of antenna test data and the experts' knowledge could be exploited to develop models that can detect the presence and the type of an anomaly based on the analysis of the antenna radiation patterns, thereby supporting young engineers addressing similar tasks or expert engineers speeding up the diagnostic process. AIDA is the result of a project carried out for the European Space Agency by S.A.T.E., Thales Alenia Space Italy and Ca' Foscari University of Venice, aiming at the development of a methodology and a software prototype intended to improve the iterative process of telecommunication antenna performance measurement, by supporting the anomaly detection due to different error sources, implementing an AI-based solution. This has been developed using state-of-the-art AI techniques, in particular implementing a fully supervised approach, exploiting a set of labelled observations (i.e. patterns of antennas with known anomaly class and anomaly entity) generated by simulations and real data. This contribution will describe the main results obtained with reflector and phased array antennas use cases

Fast-neutron-induced fission cross section of 242Pu^{242}Pu measured at the neutron time-of-flight facility nELBEnELBE

The fast-neutron-induced fission cross section of Pu242 was measured at the neutron time-of-flight facility nELBE. A parallel-plate fission ionization chamber with novel, homogeneous, large-area Pu242 deposits on Si-wafer backings was used to determine this quantity relative to the IAEA neutron cross-section standard U235(n,f) in the energy range of 0.5 to 10 MeV. The number of target nuclei was determined from the measured spontaneous fission rate of Pu242. This helps to reduce the influence of the fission fragment detection efficiency on the cross section. Neutron transport simulations performed with geant4, mcnp6, and fluka2011 are used to correct the cross-section data for neutron scattering. In the reported energy range the systematic uncertainty is below 2.7% and on average the statistical uncertainty is 4.9%. The determined results show an agreement within 0.67(16)% to recently published data and a good accordance to current evaluated data sets