This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024 (Second Terrestrial Very-Long-Baseline Atom Interferometry Workshop, Imperial College, April 2024), building on the initial discussions during the inaugural workshop held at CERN in March 2023 (First Terrestrial Very-Long-Baseline Atom Interferometry Workshop, CERN, March 2023). Like the summary of the first workshop (Abend et al. in AVS Quantum Sci. 6:024701, 2024), this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions (Memorandum of Understanding for the Terrestrial Very Long Baseline Atom Interferometer Study).

In this paper, an innovative method for the determination of the dynamic coefficients of tilting pad journal bearings (TPJBs) is described, and some of its characteristics are analyzed. The calculation is based on a parabolic modeling of the dependence of the dynamic coefficients on the excitation frequency, on the estimation of the forces acting on the bearing as a function of the estimated displacements using a linear model and, finally, on the search for the best estimate of the parabola coefficients by minimizing the sum of the squares of the normalized residuals of displacements and forces on the bearings. The normalization is performed by dividing the deviations (between the measured values and those calculated by the model) by an estimate of the standard deviation of the force and displacement measurements. The results for a flooded tilting pad journal bearing, TPJB, are presented and compared with those obtained using traditional methods. The synchronous coefficients are also calculated and compared with those determined by linear interpolation. A preliminary statistical analysis of the sensitivity of the results to the variation in the standard deviation of the forces and displacements is presented. An extension of the model is proposed so that the coefficients of the optimal parabolas can be estimated as a function of the shaft rotation frequency.

The aim of this study is to model the impact of surface roughness and pores on the fatigue strength of plain and V-notched specimens made of Inconel 718 under as-built and machined conditions and produced by laser powder bed fusion (LPBF). Combining fractographic analyses with the Gumbel and the exponential distribution functions, the statistical analyses of the diameters of the pores and of their distances from the external surfaces were implemented. Surface roughness scans were performed with the optical profilometer. The finite element (FE) method was used to simulate a sample of pores generated by the identified probability distributions and the surface profiles obtained with the scans. The theory of critical distances (TCD) was implemented combining the blunt and sharp V-notched specimens in the machined condition, and it was combined with the Gumbel or the generalized extreme values distributions to calculate the fatigue strength concentration factors provided by the pores and the surface roughness at 99% of probability. Finally, the proposed model was used to predict the fatigue strength of the blunt V-notched specimens in the as-built conditions and of the plain specimens in the as-built and machined conditions resulting appreciably similar to the experimental data.

Precipitation is crucial for water resource renewal, but climate change alters their frequency and amounts, challenging societies for correct and effective water management. However, modifications of precipitation dynamics appear to be not uniformly distributed, both in space and time. Even in relatively small areas, precipitation shows the coexistence of positive and negative trends. Local topography seems to be a strong driver of precipitation changes. Understanding precipitation changes and their relationship with local topography is crucial for society’s resilience. Taking advantage of a dense and long-lasting (1920–2019) meteorological monitoring network, we analyzed the precipitation changes over the last century in a sensitive and strategic area in the Mediterranean hotspot. The study area corresponds to northern Tuscany (Italy), where its topography comprises mountain ridges and coastal and river plains. Forty-eight rain gauges were selected with continuous annual precipitation time series. These were analyzed for trends and differences in mean annual precipitation between the stable period of 1921–1970 and the last 30-year 1990–2019. The relationship between precipitation changes and local topography was also examined. The results show the following highlights: (i) A general decrease in precipitation was found through the century, even if variability is marked. (ii) The mountain ridges show the largest decrease in mean annual precipitation. (iii) The precipitation change entity over the last century was not homogenous and was dependent on topography and geographical setting. (iv) A decrease in annual precipitation of up to 400 mm was found for the mountainous sites.

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more km-scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions.

We present data from six years (January 2016–December 2021) of monitoring the isotope composition of precipitation at the Sv. Urban station in Eastern Slovenia. The 68 precipitation samples were collected as a monthly composite. The complete dataset (193 data pints) includes information on the stable isotope composition of hydrogen (δ2H) and oxygen (δ18O) and tritium activity concentration (A), obtained using isotope ratio mass spectrometry (IRMS) and liquid scintillation counting (LSC) following electrolytic enrichment (EE), respectively. The isotope data, together with meteorological data, are reported. Calculations of the deuterium excess (d-excess), monthly, seasonal, and annual unweighted and precipitation-weighted means and local meteoric water lines (LMWLs) were conducted. The mean values for δ2H, δ18O, d-excess, and A, weighted by precipitation, were −59.9‰, −8.81‰, 10.6‰, and 7.7 TU. The disparities between unweighted and precipitation-weighted δ2H, δ18O, d-excess, A, and LMWLs underscore the significance of non-uniformly distributed precipitation. Annual variations in slope and intercept of the LMWLs emphasize the importance of longer data records (48+ months) to capture consistent trends, while combining data over longer periods may distort accuracy due to distinct isotope differences between individual years related to the variability of climate conditions typical for Slovenia.

Fatigue assessment of components subjected to random loads is a challenging task both due to the variability in amplitude and frequency of the loads and for the computational times required to perform classical time domain fatigue analysis. The frequency domain approach to fatigue life assessment offers a solution by utilizing the power spectral density of the random load, requiring minimal computational effort. However, frequency domain methods are limited to stationary Gaussian signals, while real-world loads often exhibit non-Gaussian characteristics. Previous research proposed formulas to extend frequency domain methods to non-Gaussian cases, but they require knowledge of the parameters related to non-Gaussianity of the component's stress (skewness and kurtosis), which would require a time domain analysis of the stress history on the component and a strong reduction of the computational advantages. This paper aims to address this gap by conducting an extensive campaign of numerical simulations to evaluate the influence of various parameters on the degree of non-Gaussianity of the response of a system. A single-dof mass-spring-damper system was subjected to non-Gaussian random loads of different natures, and the response is analyzed to determine the values of skewness and kurtosis. The study investigated the influence on non-normality indexes of the system's output of several input parameters, which include both the characteristics of the input load and the properties of the dynamic system. The findings contribute to a better understanding of non-Gaussianity in dynamic systems and pave the way for conducting efficient fatigue analyses in the frequency domain. Future work will extend the study to non-stationary random loads, further advancing the understanding of non-Gaussianity and non-stationarity in dynamic systems.

In this work, a statistical method to determine the dynamic coefficients of Tilting Pad Journal Bearings (TPJBs) is described. The method is based on a priori modelling of the dependency of the dynamic coefficients on the excitation frequency, on the estimation of the forces acting on bearing as a function of the estimated displacements (using a linear model) and, finally, on a search of the optimum functions by minimization of the squares of the normalized residuals of displacements and forces on bearings. Normalization is done by dividing the residuals by estimating the standard deviation of the forces and displacements. The results for a flooded Rocker Back TPJB are presented.

The southern Apuan Alps (Italy) are prone to rainfall-induced landslides. A first attempt to calculate rainfall thresholds was made in 2006 using non-statistical and repeatable methods for the 1975–2002 period. This research aims to update, validate, and compare the results of that attempt through different statistical approaches. Furthermore, a new dataset of rainfall and landslides from 2008 to 2016 was collected and analyzed by reconstructing the rainfall events via an automatic procedure. To obtain the rainfall thresholds in terms of the duration–intensity relationship, we applied three different statistical methods for the first time in this area: logistic regression (LR), quantile regression (QR), and least-squares linear fit (LSQ). The updated rainfall thresholds, obtained through statistical methods and related to the 1975–2002 dataset, resulted in little difference from the ones obtained with non-statistical methods and have similar efficiency values among themselves. The best one is provided by the LR, with a landslide probability of 0.55 (efficiency of 89.8%). The new rainfall thresholds, calculated by applying the three statistical methods on the dataset from 2008–2016, are similar to the 1975–2002 ones, except for the LR threshold, which exhibits a higher slope. This result confirms the validity of the thresholds obtained with the old database.

In recent years, artificial intelligence in geosciences is spreading more and more, thanks to the availability of a large amount of data. In particular, the development of automatic raingauges networks allows to get rainfall data and makes these techniques effective, even if the performance of artificial intelligence models is a consequence of the coherency and quality of the input data. In this work, we intended to provide machine learning models capable of predicting rainfall data starting from the values of the nearest raingauges at one historic time point. Moreover, we investigated the influence of the anomalous input data on the prediction of rainfall data. We pursued these goals by applying machine learning models based on Linear Regression, LSTM and CNN architectures to several raingauges in Tuscany (central Italy). More than 75% of the cases show an R2 higher than 0.65 and a MAE lower than 4 mm. As expected, we emphasized a strong influence of the input data on the prediction capacity of the models. We quantified the model inaccuracy using the Pearson's correlation. Measurement anomalies in time series cause major errors in deep learning models. These anomalous data may be due to several factors such as temporary malfunctions of raingauges or weather conditions. We showed that, in both cases, the data-driven model features could highlight these situations, allowing a better management of the raingauges network and rainfall databases.

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.

Current global warming causes a change in atmospheric dynamics, with consequent variations in the rainfall regimes. Understanding the relationship between global climate patterns, global warming, and rainfall regimes is crucial for the creation of future scenarios and for the relative modification of water management. The aim of this study is to improve knowledge of the relationship between North Atlantic Oscillation (NAO), East Atlantic (EA), and Western Mediterranean Oscillation (WeMO) with the seasonal rainfalls in Tuscany, Italy. The study area occupies a strategic position since it lies in a transition zone between the wet area of northern Europe and the dry area of the northern coast of Africa. This research, based on a statistical correlation method and on linear models, is designed to understand the relationship between seasonal rainfalls and climate patterns. The results of this study demonstrate that the use of linear models can yield more information than traditional statistical corre-lations. The results show a decrease in rainfall in the warm period of the year, namely in the summer, when its expression is most visible. This phenomenon is ascribable to current global warming, which causes an increase in sea-surface temperatures. An increase in the Northern Atlantic Sea Surface Temperature and in the Mediterra-nean Sea Surface Temperature causes a reduction of the Iceland Low, with an extension of the Azores High. Moreover, an increase in the Genoa Gulf SST induces a weakening of the Genoa Gulf Low, one of the main cyclogenetic systems of the Mediterranean.

This study aims to explore the reliability of flood warning forecasts based on deep learning models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify the applicability of flood event predictions for a river with flood events lasting only a few hours, with the aid of hydrometric control stations. This methodology allows for the creation of a system able to identify flood events with acceptable errors within several hours' notice. In terms of errors, the results obtained in this study can be compared to those obtained by using physics-based models for the same study area. These kinds of models use few types of data, unlike physical models that require the estimation of several parameters. However, the deep learning models are data-driven and for this reason they can influence the results obtained. Therefore, we tested the stability of the models by simulating the missing or wrong input data of the model, and this allowed us to achieve excellent results. Indeed, the models were stable even if several data were missing. This method makes it possible to lay the foundations for the future application of these techniques when there is an absence of geological-hydrogeological information preventing physical modeling of the run-off process or in cases of relatively small basins, where the complex system and the unsatisfactory modeling of the phenomenon do not allow a correct application of physical-based models. The forecast of flood events is fundamental for correct and adequate territory management, in particular when significant climatic changes occur. The study area is that of the Arno River (in Tuscany, Italy), which crosses some of the most important cities of central Italy, in terms of population, cultural heritage, and socio-economic activities.

In this paper, we present the results of the activities carried out to estimate the axial fatigue strength of ASTM E566 Gr. 70 sheet metal. We describe the experimental setup, followed by the collected data. To highlight a possible inhomogeneity in some of the material properties, we estimate the endurance limit for the investigated material using various statistical approaches on the set of test articles. The data analysis was carried out both by considering as a whole the set of test articles and by dividing the set into two groups. As a new analysis technique, we propose a method introducing a bimodal distribution (Gaussian mixture).

In the present work a multiaxial model is proposed to describe the elastoplastic behaviour of a structural steel in the presence of loading-unloading cycles. The activity is a part of a more estensive research carried out in collaboration with Baker & Hughes aimed at developing an accurate model for accurately predicing residual stresses caused by autofrettage processes in thick cylinders and its experimental verification. It has been demostrated that in order to obtain estimates of the residual stress with errors within a few percent, the constitutive behavior of the material has to be reproduced very accurately. To this purpose, both the Bauschinger effect and the variation of the elastic modulus with the accumulated plastic deformation have to be accurately measured and properly included in the constitutive model.

In this work, we investigated the correlation between the North Atlantic Oscillation (NAO) index and the rainfall trend in Tuscany (Italy) by using a large number of rain gauges for a high-resolution spatial scale study in a region characterized by significant morphological and climatic variability and equipped with an efficient measurement network. The relationship between NAO and rainfall was calculated by the Spearman's correlation coefficient. Our study shows that the correlation between NAO and precipitation has two types of oscillations as functions of the time scale. During the year the correlation is negative in winter and positive in summer, and this is due to global atmospheric circulation, which causes the area to be affected by humid air masses from the Atlantic Ocean during the winter. It is difficult to understand these experimental observations for the summer period because the correlation is influenced by the low rain levels of this area and the results could be easily influenced by other global patterns. Investigating in particular the period from December to March, we observed a variation in the NAO precipitation correlation also over time, with periods characterized by an increase in anti-correlation. The results obtained for Tuscany were contextualized and compared with other areas of Europe and of the Mediterranean basin by applying the same methodology. Spatial analysis showed that the trend of the NAO and rainfall correlation depends on latitude. In northern Europe, the behaviour of the correlation between NAO and rainfall over time is very similar to the temporal trend of NAO itself; instead, in the southern Mediterranean area this correlation has a trend over time that is very similar to the one of the Atlantic Multidecadal Oscillation (AMO) Index. This allowed us to observe a different regulation in the circulation of the humid air masses coming from the Atlantic Ocean, which induced rainfall in the Mediterranean and in northern Europe. The circulation of humid air masses in Northern Europe is linked to the pressure difference between high and low latitudes (represented by the NAO index), whereas in the Mediterranean it is linked to the temperature of the Atlantic Ocean (represented by the AMO index). The areas between high and low latitudes, characterized by a mixed behaviour, are regulated by both pressure difference and ocean temperature. This study has made it possible to investigate a specific area of the Mediterranean and then to extend and contextualize to more geographical locations, highlighting the fact that simple linear regression models can help to investigate the role of global patterns on the local effects.

Tilting pad journal bearings (TPJBs) are crucial elements in turbomachinery applications providing stiffness and damping characteristics that determine rotor system dynamic behavior. Hence, a correct design and an accurate dynamic properties prediction is fundamental for the successful industrial operation of rotating machinery. Current design trends in turbomachinery aiming at higher efficiency and power through weight optimization and higher operating speeds determine the development of large flexible rotors that are particularly important from the rotordynamic standpoint. The dynamic feasibility of this type of machine relies on bearing stiffness and damping characteristics that must be predicted with a certain level of confidence in order to increase the accuracy of the expected rotordynamic behaviour and avoid unpredicted vibration issues when rotors are operated. Furthermore, large centrifugal compressors commonly used in Liquified Natural Gas (LNG) applications make the bearings operate at very high peripheral speed where the transition from laminar to turbulent regime occurs, increasing the necessity of predictions accuracy. In this paper a test campaign on different large TPJB solutions operating in turbulent lubrication regime has been performed on a dedicated test rig designed for investigations on large size high-performance oil bearings. In the present work both static performance and dynamic identification of the tested TPJB solutions are presented and compared to numerical model predictions. The results of an uncertainty quantification, performed to validate the experimental results, are also shown.

Data-driven methodologies are widely used for the reconstruction of shallow landslide susceptibility maps. The outputs of these models strictly depend on the type of landslide inventories used for the modelling. Thus, this work performed a deep analysis of the role of different inventories on the definition of shallow landslide susceptibility of a particular area, modeled through a data-driven technique (Generalized Additive Model). Distinct shallow landslide inventories referred to the same rainfall event or to different rainfall events can produce different maps of susceptibility distribution for the same area. Despite a similar predictive capability, the models built for the same area produced different susceptibility maps, as estimated by low values of the Cohen's Kappa coefficient (0.13-0.46). If inventories obtained using different landslide detection methods are available, the models of susceptibility will be affected by the position and shape of the phenomena that could be detected by each tool. An inventory created integrating the visual interpretation of high-resolution images with field surveys allows to obtain more accurate inventories, to derive a better representation of the proneness of the territory towards shallow landsliding. In the case of inventories related to the same rainfall event carried out with different criteria, the susceptibility distribution is influenced by the choices adopted as regards, in particular, the type and the mapping criteria of phenomena included in the inventory. For shallow landslide inventories triggered by different rainfall events in the same area, the classification of the territory in terms of susceptibility is strictly connected to each specific rainfall event and reflects the relative shallow slope stability of a region only under similar rainfall conditions. Shallow landslide susceptibility obtained with a multi-temporal inventory, grouping landslides occurred in few years during more events, provides a more accurate estimation of the predisposition to shallow landsliding in consequence of the typical rainfall triggering patterns of an area.

We discuss the fluid structure in the quantum phases of a 1D spinful Fermi gas of atoms interacting via an infinitely long-range coupling, as it may result from a photon-mediated two-body coupling in optical cavities. The system reveals a rich physics, where the spin/charge-density wave and superfluid-like order compete with each other. Following our previous work based on a combined mean-field, exact diagonalization and bosonization analysis, we provide the phase diagram of the system and discuss the structure of the fluid, addressing the main features in momentum space of the order parameters, momentum distribution and two-body correlations. We highlight that the nesting of the Fermi surface in 1D ultimately drives the formation of periodic structures commensurate with the cavity-induced mean-field potential.

In this work, a method for the determination of the systematic error affecting the computation of dynamic coefficients of Tilting Pad Journal Bearings (TPJB) is described. This statistical method is applied to the coefficients obtained for a 5-pad TPJB with 280 mm diameter. Experimental activity was carried out on an advanced experimental test rig specifically designed for investigations on large size high-performance bearings for turbomachinery. The adopted configuration has the test article (TA) floating at the center of a rotor supported by two rolling bearings. The TA is statically loaded by a hydraulic actuator and excited by two orthogonal hydraulic actuators with a single tone or multitone dynamic load. The linear coefficient computation method is based on the dynamic measurement of forces, accelerations and relative displacements of rotor and bearing. The analysis of the acquired data is carried out in the frequency domain, after signal Fast Fourier Transform (FFT) computation. Along with a description of the experimental setup and of the main characteristics of the sensors used for data acquisition, this paper presents a statistical technique for the estimation of the systematic error and a comparison with the random error.