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Advanced data-driven methods can facilitate the participation of renewable energy sources in competitive electricity markets by leveraging available contextual information, such as weather and market conditions. However, the underpinning assumption is that data will always be available in an operational setting, which is not always the case in industrial applications. In this work, we present a feature-driven method that both directly forecasts the trading decisions of a renewable producer participating in a day-ahead market, and is resilient to missing data in an operational setting. Specifically, we leverage robust optimization to formulate a feature-driven method that minimizes the worst-case trading cost when a subset of features used during model training is missing at test time. The proposed approach is validated in numerical experiments against impute-then-regress benchmarks, with the results showcasing that it leads to improved trading performance when data are missing.

One of the most challenging aspects in the study of the complex dynamical systems is the estimation of their underlying, interdependence structure. Being in the era of Big Data, this problem gets even more complicated since more observed variables are available. To estimate direct causality effects in this setting, dimension reduction has to be employed in the Granger causality measure. The measure should also be capable to detect non-linear effects, persistently present in real-world complex systems. The model-free information-based measure of partial mutual information from mixed embedding (PMIME) has been developed to address these issues and it was found to perform well on multivariate time series of moderately high dimension. Here, the problem of forming complex networks from direct, possibly non-linear, high-dimensional time series at the order of hundreds is investigated. The performance of the measure PMIME is tested on two coupled dynamical systems in discrete time (coupled Hénon maps) and continuous time (coupled Mackey–Glass delay differential equations). It is concluded that the correct detection of the underlying causality network depends mainly on the network density rather than on its size (number of nodes). Finally, the effect of network size is investigated in the study of the British stock market in the period around Brexit.

A theoretical study is made of the stability of propagating internal gravity wave modes along a horizontal stratified fluid layer bounded by rigid walls. The analysis is based on the Floquet eigenvalue problem for infinitesimal perturbations to a wave mode of small amplitude. The appropriate instability mechanism hinges on how the perturbation spatial scale relative to the basic-state wavelength, controlled by a parameter $\mu$ , compares to the basic-state amplitude parameter, $\epsilon \ll 1$ . For $\mu ={O}(1)$ , the onset of instability arises due to perturbations that form resonant triads with the underlying wave mode. For short-scale perturbations such that $\mu \ll 1$ but $\alpha =\mu /\epsilon \gg 1$ , this triad resonance instability reduces to the familiar parametric subharmonic instability (PSI), where triads comprise fine-scale perturbations with half the basic-wave frequency. However, as $\mu$ is further decreased holding $\epsilon$ fixed, higher-frequency perturbations than these two subharmonics come into play, and when $\alpha ={O}(1)$ Floquet modes feature broadband spectrum. This broadening phenomenon is a manifestation of the advection of small-scale perturbations by the basic-wave velocity field. By working with a set of ‘streamline coordinates’ in the frame of the basic wave, this advection can be ‘factored out’. Importantly, when $\alpha ={O}(1)$ PSI is replaced by a novel, multi-mode resonance mechanism which has a stabilising effect that provides an inviscid short-scale cut-off to PSI. The theoretical predictions are supported by numerical results from solving the Floquet eigenvalue problem for a mode-1 basic state.

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H0) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H0; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H0, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times.

Η γενετική μηχανική, δηλαδή η τροποποίηση των γονιδίων, που προκαλεί μεταβολή της γενετικής πληροφορίας και κατά συνέπεια των χαρακτηριστικών ενός οργανισμού έχει πολλές εφαρμογές. Το CRISPR είναι μία σύγχρονη τεχνική γενετικής μηχανικής που επιτρέπει την επιλεκτική τροποποίηση του γονιδιώματος, μέσω της αποκοπής συγκεκριμένου τμήματος του γονιδίου και εισαγωγής νέου τμήματος με υψηλή ακρίβεια. Στην παρούσα εργασία εξετάζεται η λειτουργία αυτού του «γενετικού ψαλιδιού» να θεραπεύει και να προλαμβάνει ασθένειες στον ανθρώπινο οργανισμό με παράλληλη αναφορά σε θέματα βιοηθικής που σχετίζονται με τη χρήση του και την ανθρώπινη επέμβαση στο γονιδίωμα. Παράλληλα παρατίθενται επιστημονικές απόψεις αλλά και οι απόψεις της μαθητικής κοινότητας για τη γενετική μηχανική και το CRISPR.

XMM-Newton, a European Space Agency observatory, has been observing the X-ray, ultra-violet and optical sky for 23 years. During this time, astronomy has evolved from mainly studying single sources to populations and from a single wavelength, to multi-wavelength or messenger data. We are also moving into an era of time domain astronomy. New software and methods are required to accompany evolving astronomy and prepare for the next generation X-ray observatory, Athena. Here we present XMM2ATHENA, a programme funded by the European Union's Horizon 2020 research and innovation programme. XMM2ATHENA builds on foundations laid by the XMM-Newton Survey Science Centre (XMM-SSC), including key members of this consortium and the Athena Science ground segment, along with members of the X-ray community. The project is developing and testing new methods and software to allow the community to follow the X-ray transient sky in quasi-real time, identify multi-wavelength or messenger counterparts of XMM-Newton sources and determine their nature using machine learning. We detail here the first milestone delivery of the project, a new online, sensitivity estimator. We also outline other products, including the forthcoming innovative stacking procedure and detection algorithms to detect the faintest sources. These tools will then be adapted for Athena and the newly detected or identified sources will enhance preparation for observing the Athena X-ray sky., Comment: 7 pages, 1 figure, accepted for publication in Astronomische Nachrichten

Στα πλαίσια του ερευνητικού προγράμματος Interreg ΘΑΛ-ΧΩΡ 2, με τη συνδυασμένη χρήση μετεωρολογικών μετρήσεων σε όλο το νησί, προγνώσεων του ανέμου από μετεωρολογικά μοντέλα υψηλής ανάλυσης, και στοιχείων από δύο ευρωπαϊκές βάσεις μετεωρολογικών δεδομένων, δημιουργήθηκε βάση δεδομένων για την αποτύπωση μιας όσο το δυνατόν πιο αξιόπιστης εικόνας της χωρικής και χρονικής κατανομής του θαλάσσιου αιολικού δυναμικού, σε ετήσια και σε εποχική βάση. Η αποτυπωμένη πληροφορία αναλύθηκε, συνδυάστηκε και έγινε επέκτασή της σε υψηλότερη ανάλυση 1x1 km, σε ωριαία βάση, για ορίζοντα δεκαετίας. Τα αποτελέσματα κωδικοποιήθηκαν σε εύχρηστη πλατφόρμα Excel, η οποία μπορεί να δοθεί σε οποιονδήποτε ενδιαφερόμενο χρήστη για να εκτιμήσει το αιολικό δυναμικό σε οποιοδήποτε σημείο της Κύπρου, καθώς και της παράκτιας περιοχής της, με αυτόματη παρεμβολή μεταξύ των εκτιμήσεων. Η πλατφόρμα επιτρέπει την εξαγωγή ολικής εικόνας για τον άνεμο στην περιοχή της Κύπρου και, εκτός από τη χωρική εστίαση, δίνει τη δυνατότητα χρονικής επιλογής οποιασδήποτε περιόδου συγκεκριμένων διαστημάτων ωρών, μηνών και ετών, για την περίοδο 2008-2019. Επίσης, αποδίδονται στατιστικές πληροφορίες για τη μέση, μέγιστη, ελάχιστη τιμή, τη διακύμανση της ταχύτητας, καθώς και για τους συντελεστές της κατανομής Weibull. Παράλληλα, παρατίθενται αυτόματα και πληροφορίες για την ημερήσια, μηνιαία και ετήσια κατανομή της ταχύτητας, όπως και οι κατανομές Weibull. Σε μελλοντικό χρόνο, οι παραπάνω δυνατότητες θα προσφέρονται και διαδικτυακά.

This paper presents the construction of the RapidXMM database that is available through the XMM-Newton Science Archive and offers access to upper limits and aperture photometry across the field of view of the XMM-Newton Pointed and Slew Survey observations. The feature of RapidXMM is speed. It enables the fast retrieval of X-ray upper limits and photometry products in three energy bands (0.2-2, 2-12, 0.2-12 keV) for large numbers of input sky positions. This is accomplished using the Hierarchical Equal Area Iso Latitude pixelation of the sphere (HEALPix). The pre-calculated upper-limits and associated X-ray photometry products are reprojected into the HEALPix grid of cells before being ingested into the RapidXMM database. This results in tables of upper limits and aperture photometry within HEALPix cells of size ~3 arcsec (Pointed Observations) and ~6 arcsec (Slew Survey). The database tables are indexed by the unique integer number of the HEALPix cells. This reduces spatial nearest-neighbor queries by sky position to an integer-matching exercise and significantly accelerates the retrieval of results. We describe in detail the processing steps that lead from the science products available in the XMM-Newton archive to a database optimised for sky queries. We also present two simple show-case applications of RapidXMM for scientific studies: searching for variable X-ray sources, and stacking analysis of X-ray faint populations, Comment: 20 pages, 20 figures. This article has been accepted for publication in MNRAS. Published by Oxford University Press on behalf of the Royal Astronomical Society

Seaborne movement underpins frontier research in prehistoric archaeology, including water-crossings in the context of human dispersals, and island colonisation. Yet, it also controls the degree of interaction between locations, which in turn is essential for investigating the properties of maritime networks. The onset of the Holocene (circa 12,000 years ago) is a critical period for understanding the origins of early visitors/inhabitants to the island of Cyprus in the Eastern Mediterranean in connection with the spread of Neolithic cultures in the region. The research undertaken in this work exemplifies the synergies between archaeology, physical sciences and geomatics, towards providing novel insights on the feasibility of drift-induced seaborne movement and the corresponding trip duration between Cyprus and coastal regions on the surrounding mainland. The overarching objective is to support archaeological inquiry regarding the possible origins of these visitors/inhabitants—Anatolia and/or the Levant being two suggested origins.

The implementation of Maritime Spatial Planning (MSP) in each European country aims to bring together different public authorities and stakeholders to sustainably organize and map marine and coastal resources. The first step for the development of MSP was achieved successfully for Cyprus, through the implementation of the first cross-border project, "THAL-CHOR." Following the "THAL-CHOR" project, its successor project entitled "THAL-CHOR 2", is expected to evaluate the increased needs for the management and monitoring of the maritime space, the design and implementation of relevant policies focusing on the European MSPs’ good practices and to set up the draft maritime spatial plan for the marine waters of Cyprus. The implementation was achieved by combining the principles of maritime spatial planning with issues of wider maritime governance, politics and economics while taking into account good practices by international organizations and individual states. For the study of MSP, all data from THAL-CHOR project were combined with the updated data resulting from the implementation of "THAL-CHOR 2". The project is based on the institutional framework to analyze and organize human activities at sea and coastal space to achieve the synthesis of social, economic, environmental, and cultural objectives of the Republic. The aim is to submit different MSP scenarios to be a policy proposal that will promote sustainable development in the marine area and the coastal zones, pursuing the harmonious coexistence of activities and uses, applying an approach based on principles of sustainability.

Spectroscopic redshift extraction is an expensive process. In this work, we have attempted to extract redshift information solely from the X-ray AGN spectra by employing an automated method. For this purpose, we have used highly quality NuSTAR hard X-ray spectra to obtain a set of models that best represent the X-ray spectral shape of AGN. We have used UXCLUMPY, a relatively new clumpy torus model that allows for self-consistent multi-wavelength analyses. The models were then used to simulate spectra with Athena WFI response matrices in the redshift range of 1 - 4, Luminosity LX (2−10keV) = 1 x 10^44, 5 × 10^44, 1 x 10^45erg/s and exposure time of 1 Ms. These simulated spectra were then passed on to a classical automated redshift estimating algorithm to recover the redshifts. Our preliminary results show that redshifts can be recovered within 5% for upto 50% of the sample simulations.

International audience; Short-term forecasting is key to the safe, reliable, and economic operation of modern power systems. As the majority of modern forecasting tools are purely data-driven, their performance relies heavily on the quality and availability of data. In this work, we examine forecasting when a subset of features used during model training becomes unavailable (deleted or missing) in an operational setting, a subject that has been largely overlooked by previous works. Several reasons could lead to feature deletion, including malicious data-integrity attacks, network latency, and equipment malfunctions, among others. We leverage tools from robust optimization and machine learning and formulate a linear regression model that is optimally resilient to the deletion of features at test time. The robust counterpart of the proposed model is a linear program whose size grows polynomially with the number of training observations and the number of features; we further provide a decomposition algorithm based on the alternating direction method of multipliers to deal with large problem instances that are typically found in energy forecasting applications. We further extend to the case of Probabilistic Forecasting by robustifying the standard linear quantile regression model. To validate empirically the proposed approach, we examine several prevalent forecasting practices in power systems, namely electricity prices, load, wind production, and solar production forecasting. We compare against regularized and randomization-based models and benchmark their performance for the case of feature deletion. The results show that the proposed solution successfully mitigates the adverse effects of missing features, leading to the lowest overall performance degradation. Further, it successfully hedges against the most adverse scenario of deleting the most important feature from the test set. The results persist both for point and probabilistic forecasts and across the different series. Overall, this work highlights the benefits of leveraging robust optimization and provides a new perspective on how to deal with feature uncertainty in energy forecasting applications.

International audience; Power systems feature an inherent hierarchical structure. Ensuring that forecasts across a hierarchy are coherent presents an important challenge in energy forecasting. In this context, proposed reconciliation or end-to-end learning approaches assume coherent historical observations by construction; this assumption, however, is often violated in practice due to equipment failures. This work proposes an end-to-end learning approach for hierarchical forecasting that directly handles missing values. First, we show that a class of off-the-shelf machine learning algorithms already leads to coherent hierarchical forecasts. Next, we describe a conditional stochastic optimization approach based on prescriptive trees for end-to-end learning with missing values, without imputation or disregarding of quality observations. We validate the proposed approach in two case studies comprising 60 wind turbines and 20 photovoltaic parks, respectively. The empirical results show that end-to-end learning outperforms twostep reconciliation approaches and that the proposed solution mitigates the adverse effect of missing values.

International audience; Energy forecasting models deployed in industrial applications face uncertainty w.r.t. data availability, due to network latency, equipment malfunctions or data-integrity attacks. In particular, the case when a subset of features that has been used for model training becomes unavailable when the model is used operationally, poses a major challenge to forecasting performance. In this work, we present a principled approach to introducing resilience against missing features in energy forecasting applications via robust optimization. Specifically, we formulate a robust regression model that is optimally resilient against missing features at test time, considering both point and probabilistic forecasting. We develop three solution methods for the proposed robust formulation, all leading to Linear Programming problems, with varying degrees of tractability and conservativeness. We provide an extensive empirical validation of the proposed methods in prevalent applications, namely, electricity price, load, wind production, and solar production, forecasting, and we further compare against well-established benchmark models and methods of dealing with missing features, i.e., imputation and retraining. Our results demonstrate that the proposed robust optimization approach outperforms imputationbased models and exhibits similar performance to retraining without the missing features, while also maintaining practicality. To the best of our knowledge, this is the first work that introduces resilience against missing features into energy forecasting.

In this presentation we detail highlight results obtained from the research work within the European Horizon 2020 project Smart4RES (http://www.smart4res.eu). The project, which started in 2019 and runs until 2023, aims at a better modelling and forecasting of weather variables necessary to optimise the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond, through thematic objectives including:Improvement of weather and RES forecasting, Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models; New data-driven optimization and decision-aid tools for market and grid management applications; Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). In this presentation we will focus on our results on models that permit to improve forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras). Also results will be presented on the development of seamless approach able to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions. Advances on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation will be presented with evaluation results on real case studies like the Rhodes island in Greece.When it comes to forecasting the power output of RES plants, mainly wind and solar, the focus is on improving predictability using multiple sources of data. The proposed modelling approaches aim to efficiently combine highly dimensionally input (various types of satellite images, numerical weather predictions, spatially distributed measurements etc.). A priority has been to propose models that permit to generate probabilistic forecasts for multiple time frames in a seamless way. Thus, the objective is not only to improve accuracy and uncertainty estimations, but also to simplify complex forecasting modelling chains for applications that use forecasts at different time frames (i.e. a virtual power plant - VPP- with or without storage that participates in multiple markets). Our results show that the proposed seamless models permit to reach these performance objectives. Results will be presented also on how these approaches can be extended to aggregations of RES plants which is relevant for forecasting VPP production.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

Offshore wind offers an excellent opportunity for domestic renewable energy production with a vast potential for future energy systems. Offshore wind resource assessment, however, can be challenging. Remote sensing data e.g., Synthetic Aperture Radar (SAR), provide high spatial resolution detailed information on the spatial variability of offshore wind and have been used for wind resource assessment, as well as for the long-term validation of wind speed estimates from other sources (e.g. Numerical Weather Prediction models). This paper focuses on the evaluation of a 26-month time-series of Sentinel-1 SAR Level 2 OCN products for wind resource assessment in the offshore areas around Cyprus. Sentinel data were evaluated against a 10-year regional reanalysis dataset (UERRA) time-series and wind measurements from 5 coastal meteorological stations in Cyprus. Comparison revealed an overall agreement between the fitted stations and Sentinel Weibull distributions while discrepancies exist between the two data sources and UERRA. Bias observed between Sentinel and UERRA Weibull-derived statistics appears to be spatially dependent. Preliminary wind power assessment results indicate a significant wind power potential for the southwestern offshore areas of Cyprus, surpassing 400 W / m 2 on average, offering thus economically viable solutions in terms of a future offshore wind power project development.

We study the effect of viscosity on the 2D reflection of internal gravity wave beams when the Reynolds number is large (Re>>1). Specifically we make an asymptotic analysis for Re>>1 to derive a reduced equation describing reflection of wave beam from a sloping boundary. The key is to assume that the beam inclination angle 0 and the slope angle a are close to each other so that a reflected beam propagates inside the boundary layer along the slope. Solution of the derived equation agrees well with the corresponding NS solution for Re>>1.

David J. Benney (1930–2015) was an applied mathematician and fluid dynamicist whose highly original work has shaped our understanding of nonlinear wave and instability processes in fluid flows. This article discusses the new paradigm he pioneered in the study of nonlinear phenomena, which transcends fluid mechanics, and it highlights the common threads of his research contributions, namely, resonant nonlinear wave interactions; the derivation of nonlinear evolution equations, including the celebrated nonlinear Schrödinger equation for modulated wave trains; and the significance of three-dimensional disturbances in shear flow instability and transition.

International audience; This work presents a fix-and-optimize matheuristic to solve timetabling problems under uncertainty. Specifically, a combined university timetabling and electricity storage scheduling problem is considered, subject to uncertainty stemming from renewable energy production and electricity demand. The problem is formulated as a large Mixed Integer Program (MIP) and the proposed solution combines Large Neighborhood Search coupled with scenario-based robust optimization for handling uncertainty in the objective function. First, an adequate feasible schedule is derived considering only hard problem constraints, in this case scheduling of recurring lecture activities. Next, the solution is improved with a fix-and-optimize heuristic search. In each iteration, the MIP solver explores a large neighborhood by fixing a subset of variables and optimizing over the remaining free variables. The process is repeated several times until a stopping criterion is met. To address uncertainty in the objective, probabilistic scenarios are derived from interval forecasts and the worst-case energy cost is minimized. The results derived from the participation in a technical challenge show that the proposed approach provides competitive solutions relatively fast, even for large problem instances, while also hedging against large forecast errors. Index Terms-fix-and-optimize, local neighborhood search, renewable energy forecasting, robust optimization, university timetabling.

Decision-making in the presence of contextual information is a ubiquitous problem in modern power systems. The typical data-decisions pipeline comprises several forecasting and optimization components deployed in sequence. However, the loss function employed during learning is only a proxy for task-specific costs (e.g. scheduling, trading). This work describes a data-driven alternative to improve prescriptive performance in conditional stochastic optimization problems based on nonparametric machine learning. Specifically, we propose prescriptive trees that minimize task-specific costs during learning, embedded with a scenario reduction procedure to reduce computations, and then derive a weighted Sample Average Approximation of the original problem. We present experimental results for two problems: storage scheduling for price arbitrage and networkconstrained stochastic market clearing, respectively associated with electricity price and load forecasting. The empirical results show significant improvements on deterministic and stochastic lookahead policies, with the relative difference being more pronounced when training data are limited.

Our objective is to critically assess the X-ray flux variability as a tool for measuring the black hole (BH) mass in active galactic nuclei (AGN). We aim to establish a prescription for estimating BH masses based on measurements of the normalised excess variance from X-ray data. We discuss the minimum requirements in terms of the light-curve duration and X-ray signal-to-noise ratio (S/N) to enable a reliable determination that is comparable to what can be derived from the continuum and emission line reverberation studies. We used the light curves of local Seyfert from the Nuclear Spectroscopic Telescope Array hard X-ray mission (NuSTAR), to compute the normalised excess variance (NXV) in the 3-10 and 10-20 keV bands, thus extending the analysis to an energy band higher than 10 keV. The excess variance measurements were then combined with independent BH mass estimates from the literature to establish the MBH versus NXV relation for different samples and weigh its accuracy in terms of the light-curve duration and X-ray S/N. We find that it is possible to accurately measure the BH mass in AGN using excess variance measurements in the 3-10 and the 10-20 keV bands, however, strong quality requirements should be applied. The minimum necessary S/N and duration of the light curves used to compute the excess variance ought to be 3 and approximately 100 ks, respectively. We provide a linear relationship between the normalised excess variance and the black hole mass that can be used to estimate the latter, with an average uncertainty of the order of 0.4 to 0.25 dex (depending on the adopted light-curve segment duration)., 20 pages, 8 figures

Motivated by the question of whether and how wave–wave interactions should be implemented into atmospheric gravity‐wave parametrizations, the modulation of triadic gravity‐wave interactions by a slowly varying and vertically sheared mean flow is considered for a non‐rotating Boussinesq fluid with constant stratification. An analysis using a multiple‐scale WKBJ (Wentzel–Kramers–Brillouin–Jeffreys) expansion identifies two distinct scaling regimes, a linear off‐resonance regime, and a nonlinear near‐resonance regime. Simplifying the near‐resonance interaction equations allows for the construction of a parametrization for the triadic energy exchange which has been implemented into a one‐dimensional WKBJ ray‐tracing code. Theory and numerical implementation are validated for test cases where two wave trains generate a third wave train while spectrally passing through resonance. In various settings, of interacting vertical wavenumbers, mean‐flow shear, and initial wave amplitudes, the WKBJ simulations are generally in good agreement with wave‐resolving simulations. Both stronger mean‐flow shear and smaller wave amplitudes suppress the energy exchange among a resonantly interacting triad. Experiments with mean‐flow shear as strong as in the vicinity of atmospheric jets suggest that internal gravity‐wave dynamics are dominated in such regions by wave modulation. However, triadic gravity‐wave interactions are likely to be relevant in weakly sheared regions of the atmosphere., This study explores wave–wave interactions of modulated internal gravity waves (GWs) in varying background flows using WKBJ techniques. The resulting ray‐tracing model (b) is compared to wave‐resolving LES (a). As a key result, we find that wave modulation partially suppresses the energy exchange in triadic GW interactions, and thus triadic GW interactions are likely to be relevant in weakly sheared regions of the atmosphere., German Research Foundation (DFG) US National Science Foundation

A simple method is presented for estimating the specific storage of a confined aquifer from an overdamped slug test in a fully penetrating well. This method is based on the quasi-steady flow model developed previously by the authors, simplified to decoupled algebraic equations upon using Hvorslev’s estimate of the hydraulic conductivity. The model equations are solved efficiently on a spreadsheet sequentially. The ability of the algebraic method to estimate the specific storage of a confined aquifer is assessed against slug test data from three field cases and against sets of synthetic data generated with the rigorous Cooper-Bredehoeft-Papadopulos method, which is used as reference. The algebraic method estimates the order of magnitude of the specific storage correctly in all three field cases, with two of them arithmetically very close to the Cooper-Bredehoeft-Papadopulos solution, while the hydraulic parameters computed with the numerical quasi-steady solution nearly match the reference values. In the cases of synthetic data, the algebraic method estimates the specific storage reliably when the storage parameter α equals 10−4 and within an order of magnitude when α equals 10−5.

Η παρούσα εργασία έχει ως στόχο την ανάπτυξη μιας βελτιωμένης μεθοδολογίας υπολογισμού διασποράς ατμοσφαιρικών ρύπων σε σύνθετες αστικές γεωμετρίες. Για τον σκοπό αυτό πραγματοποιήθηκε η μελέτη της μεταβολής των αριθμητικών αποτελεσμάτων στη διασπορά αέριων ρύπων σε αστικές περιοχές υπό οριακές συνθήκες που περιγράφουν το Οριακό Ατμοσφαιρικό Στρώμα (ΑΟΣ), που αποτελεί το κατώτερο στρώμα της Ατμόσφαιρας. Βασική γνώση για την ανάπτυξη μεθοδολογίας αποτελούν η κατακόρυφη κατανομή του ΑΟΣ, ο ορισμός του υπολογιστικού πεδίου ενός μοντέλου μικροκλίμακας καθώς και η αριθμητική προσέγγιση λαμβάνοντας υπόψη την ανάγκη για υψηλή διακριτοποίηση των αστικών δομών. Η προτεινόμενη μεθοδολογία, που βασίζεται σε αυτή την προσέγγιση προϋποθέτει ότι η κατακόρυφη κατανομή της μέσης ταχύτητας και των παραμέτρων τύρβης εντός του ΑΟΣ διατηρούν την οριζόντια ομοιογένεια στην περιοχή ανάντη και κατάντη των εμποδίων που επιλύονται ρητά. Οι καθιερωμένες και ευρέως διαδεδομένες οριακές συνθήκες για την περιγραφή του ΑΟΣ που το επιτυγχάνουν αποκλίνουν αισθητά από επιτόπιες μετρήσεις ενώ αντίστοιχα η χρήση μετρήσεων πεδίου στο μοντέλο μπορεί να είναι προβληματική καθώς δεν διατηρείται η ζητούμενη ομοιογένεια. Πάνω σε αυτή την ιδιότητα, στην παρούσα εργασία εξετάζεται η μεταβολή των αποτελεσμάτων του μοντέλου ανάλογα με τη χρησιμοποιούμενη οριακή συνθήκη και επιπλέον αναπτύσσεται, πιστοποιείται και εφαρμόζεται μια καινοτόμα προσέγγιση που βασίζεται στην εκθετική περιγραφή της μέσης ταχύτητας. Ο σκοπός είναι η δημιουργία της κατάλληλη κατακόρυφης κατανομής για την περιγραφή των τυρβωδών ιδιοτήτων του ΑΟΣ, που μπορεί να χρησιμοποιηθεί για συνθήκες όπου ο εκθετικός νόμος για τη μέση ταχύτητα έχει πιο ισχυρή θεώρηση. Για τη συγκριτική θεώρηση των οριακών συνθηκών συγκροτήθηκε ένα πλήρες και διεξοδικό υπολογιστικό πλαίσιο. Αρχικά μελετήθηκε η περίπτωση της δισδιάστατης προσομοίωσης της ατμοσφαιρικής ροής πάνω από τραχύ έδαφος και έπειτα η τρισδιάστατη επίλυση του ίδιου πεδίου με την παρουσία ενός μοναδικού εμποδίου που εδράζεται στο έδαφος. Εν συνεχεία εξετάστηκε η περίπτωση πολλαπλών εμποδίων και το συγκριτικό πλαίσιο ολοκληρώθηκε με μελέτη διασποράς ρύπου για την περίπτωση πολλών κτηρίων. Τα αποτελέσματα που προέκυψαν από τις παραπάνω αριθμητικές προσομοιώσεις αξιολογήθηκαν βάσει συγκρίσεων με μετρήσεις σε αεροσήραγγα και με δημοσιευμένα αποτελέσματα άλλων ερευνητών. Σε γενικές γραμμές, οι οριακές συνθήκες που προτάθηκαν παρήγαγαν καλύτερα αποτελέσματα, ιδίως στα αποτελέσματα της τυρβώδους κινητικής ενέργειας και της συγκέντρωσης του αδρανούς ρύπου. Η πρακτική σημασία της προτεινόμενης οριακής συνθήκης εξετάζεται σε δύο περιπτώσεις όπου μελετάται η μεταβολή των αποτελεσμάτων μεταξύ της αρχικής και της νέας επίλυσης. Η πρώτη περίπτωση αφορά αποτελέσματα που προκύπτουν από τη σύζευξη μοντέλου μεσοκλίμακας-μικροκλίμακας Η δεύτερη περίπτωση αφορά μελέτη διασποράς αδρανούς ρύπου εντός αστικής χαράδρας. Και στις δύο περιπτώσεις υπήρξε μεταβολή στα παραγόμενα αποτελέσματα που καταδεικνύει τη σημασία της επιλογής των κατάλληλων οριακών συνθηκών κατά περίπτωση.

In this work we apply superstatistics, in a rather novel way, to derive the blackbody radiation laws in the context of the kappa distribution. Superstatistics incorporates automatically an important aspect of the kappa distribution i.e. the correlations between non-interacting degrees of freedom; this is actually a feature of any stationary non-Boltzmannian distribution derived by superstatistics. The correlations between photons in the same state affect the modified Planck and Wien laws. The correlations between photons of different states affect the fluctuations of the radiated energy, inducing very large fluctuations unless the kappa index is fairly large (i.e. near to the Boltzmannian limit), thereby compromising the very notion of stationarity. It is also shown that the Einstein absorption/emission coefficients are left intact, under the plausible assumption that the system matter-radiation obeys the given non-Boltzmannian distribution. The first-order correction to the Planck’s law near the Boltzmannian limit is deduced, and compared with the residual spectrum of the cosmic microwave background radiation from the FIRAS-COBE project data; the modified law cannot account for the residuals. No thermodynamic/entropic arguments are employed in this work.

International audience; Short-term electricity load forecasting is key to the safe, reliable, and economical operation of power systems. An important challenge that arises with high-frequency load series, e.g., hourly load, is how to deal with the complex seasonal patterns that are present. Standard approaches suggest either removing seasonality prior to modeling or applying time series decomposition. This work proposes a hybrid approach that combines Singular Spectrum Analysis (SSA)-based decomposition and Artificial Neural Networks (ANNs) for day-ahead hourly load forecasting. First, the trajectory matrix of the time series is constructed and decomposed into trend, oscillating, and noise components. Next, the extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or a Long Short-Term Memory (LSTM) predictive layer. The model is further extended to include exogenous features, e.g., weather forecasts, transformed via parallel dense layers. The predictive performance is evaluated on two real-world datasets, controlling for the effect of exogenous features on predictive accuracy. The results showcase that the decomposition step improves the relative performance for ANN models, with the combination of LSTM and SAA providing the best overall performance.

International audience; The participation of renewable generators in electricity markets involves employing a number of forecasting and decision-making tools. The standard approach consists in forecasting power output and market quantities, and then inputting the results into an optimization problem to derive optimal decisions. Typically, forecasting models are trained to optimize accuracy without considering the subsequent decision-making process. In this paper, we consider training forecasting models with a value-oriented approach that aims to minimize the suboptimality of decisions induced by a set of predicted inputs. We consider a risk-aware renewable generator participating in a day-ahead market subject to imbalance costs, and train ensembles of decision trees to forecast the imbalance penalty by directly minimizing trading costs for the provided strategy. The results indicate that our innovative approach leads to improved trading performance, compared to the standard method in which forecasting models are trained to minimize prediction errors.

The diffusion of particles with kappa distributed velocities is strongly influenced by statistical correlations. We argue that the consistent way to deduce the diffusion laws of any one degree of freedom is to analyze the simultaneous diffusion of virtually infinite correlated degrees of freedom. This is done by deriving the diffusion laws (I) by utilizing the superstatistics interpretation of the kappa distribution and averaging the usual Brownian motions correlators over the super-ensemble of fluctuating temperatures, (II) through the one degree of freedom Langevin equation, (III) through the many degrees of freedom Langevin equation, calculating the diffusion of any one degree of freedom. It turns out that only the results (I) and (III) agree. The disagreement between (II) and (III) is a striking outcome of the strong correlations between kappa distributed degrees of freedom. The agreement between (I) and (III) shows that the superstatistics is a fundamental interpretation of the kappa distribution. The discrepancy of (II) shows that focusing on a single degree of freedom or particle is inconsistent with a superstatistics interpretation. Derivation (III) explicitly realizes the recent observation by the authors that the mean energy per degree of freedom is the superstatistical fluctuating temperature in a system with a large number of particles. We conclude that superstatistics is intimately related to a system of correlated degrees of freedom (in our case, kappa distributed); one cannot consistently reason with a single degree of freedom.

In this work, we analyze the capacity of the superstatistics construction to provide modeling of the velocity field probability density functions (PDFs) of isotropic turbulence. Generalizing along the lines of the kappa distribution, superstatistics is understood here as a PDF for the statistical temperature that depends on a single dimensionful parameter θ 2 and a dimensionless parameter κ 0 , which both depend on the size of the fluid eddies and the Reynolds number, and possibly on auxiliary dimensionless constants that depend only on the Reynolds number. We show that such superstatistics –in some sense, the simplest class of models– cannot provide PDFs for scales outside the dissipation subrange for the currently accessible Reynolds numbers in Direct Numerical Simulations (DNS). The obstruction results from realizability constraints and an associated bound, and is related to the flatness factor of the velocity derivative distribution. Greater values of the flatness extend the applicability of superstatistics to larger scales. We argue that phenomenologically effective superstatistics models will require a value of flatness F ∼ 25 or larger in order to cover the inertial subrange scales. The argument is assisted by constructing and analyzing a family of models which derive from modifying the gamma distribution in the regime of large statistical temperatures and nearly realize the realizability bound.

Weakly nonlinear analysis is utilized to discuss the mean flow induced by the reflection of an internal gravity wave beam at a uniform rigid slope, in the case where the beam planes of constant phase meet the slope at an arbitrary direction, not necessarily parallel to the isobaths, and the flow cannot be taken as two-dimensional. Along the vertical, the Eulerian mean flow due to such an oblique reflection is equal and opposite to the Stokes drift so the Lagrangian mean flow vanishes, similar to a two-dimensional reflection. The horizontal Eulerian mean flow, however, is controlled by the mean potential vorticity (PV) and the corresponding Lagrangian mean flow is generally nonzero, in contrast to two-dimensional flow where PV identically vamishes. For an oblique reflection, furthermore, viscous dissipation can trigger generation of horizontal mean flow via irreversible production of mean PV, a phenomenon akin to streaming. This report is based on Kataoka& Akylas (2019).

The parametric subharmonic instability (PSI) of finite-width internal gravity wave beams is revisited using a formal linear stability analysis based on Floquet theory. The Floquet stability eigenvalue problem is studied asymptotically in the limit where PSI arises, namely for a small-amplitude beam of frequency no longer dominate. By adopting a frame riding with the wave beam, this advection effect is factored out and it is shown that small-amplitude beams that are not generally susceptible to PSI may develop an essentially inviscid instability with broadband frequency spectrum.

The effect of a small background constant horizontal mean flow on the parametric subharmonic instability (PSI) of locally confined internal wave beams is discussed for the case where the beam frequency is close to twice the inertial frequency due to background rotation. Under this condition, PSI is particularly potent because of the vanishing of the group velocity at the inertial frequency, which prolongs contact of near-inertial subharmonic perturbations with the primary wave. The mean flow generally stabilizes the very short-scale limit of such perturbations. By contrast, the stability of longer-scale perturbations hinges on the strength and the direction of the mean flow; particularly, a negative mean flow (antiparallel to the horizontal projection of the beam group velocity) can extend the sub-inertial range of PSI. However, a large enough mean flow of either sign ultimately weakens PSI.

International audience; The mean flow induced by a three-dimensional propagating internal gravity wave beam in a uniformly stratified fluid, is studied experimentally and theoretically. Previous related work concentrated on the early stage of mean-flow generation, dominated by the phenomenon of streaming-a horizontal mean flow that grows linearly in time-due to resonant production of mean potential vorticity (PV) in the vicinity of the beam. The focus here, by contrast, is on the long-time mean-flow evolution. Experimental observations in a stratified fluid tank for times up to t = 120 T 0 , where T 0 is the beam period, reveal that the induced mean flow undergoes three distinct stages: (i) resonant growth of streaming in the beam vicinity; (ii) saturation of streaming and onset of horizontal advection; and (iii) establishment of a quasi-steady state where the mean flow is highly

This paper presents how the EXSELSIOR H2020 Teaming project will support the management and monitoring of the cultural heritage domain through its research and innovation agenda within the Eastern Mediterranean, Middle East, and North Africa (region known as EMMENA). The pressing need for protecting the cultural heritage assets is highlighted throughout the document by reviewing the conducted research work in key funded projects attained in the past by the Cyprus University of Technology team in collaboration with EXCELSIOR’s partners.

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more. © 2021 American Meteorological Society

The mean flow induced by a three-dimensional propagating internal gravity wave beam in a uniformly stratified fluid is studied experimentally and theoretically. Previous related work concentrated on the early stage of mean-flow generation, dominated by the phenomenon of streaming – a horizontal mean flow that grows linearly in time – due to resonant production of mean potential vorticity in the vicinity of the beam. The focus here, by contrast, is on the long-time mean-flow evolution. Experimental observations in a stratified fluid tank for times up to is the beam period, reveal that the induced mean flow undergoes three distinct stages: (i) resonant growth of streaming in the beam vicinity; (ii) saturation of streaming and onset of horizontal advection; and (iii) establishment of a quasi-steady state where the mean flow is highly elongated and stretches in the along-tank horizontal direction. To capture (i)–(iii), the theoretical model of Fan et al. (J. Fluid Mech., vol. 838, 2018, R1) is extended by accounting for the effects of horizontal advection and viscous diffusion of mean potential vorticity. The predictions of the proposed model, over the entire mean-flow evolution, are in excellent agreement with the experimental observations as well as numerical simulations based on the full Navier–Stokes equations.

In this paper, we propose an algebraic model for turbulent scalar-flux vector that stems from tensor representation theory. The resulting closure contains direct dependence on mean velocity gradients and quadratic products of the Reynolds stress tensor. Model coefficients are determined from Direct Numerical Simulations (DNS) data of homogeneous shear flows subjected to arbitrary mean scalar gradient orientations, while a correction function was applied at one model coefficient based on a turbulent channel flow case. Model performance is evaluated in Poiseuille and Couette flows at several Reynolds numbers for Pr=0.7, along with a case at a higher Prandtl number (Pr=7.0) that typically occurs in water&ndash, boundary interaction applications. Overall, the proposed model provides promising results for wide near-wall interaction applications. To put the performance of the proposed model into context, we compare with Younis algebraic model, which is known to provide reasonable predictions for several engineering flows.

A joint experimental and theoretical study is made of instability mechanisms of locally confined internal gravity wave beams in a stratified fluid. Using as forcing a horizontal cylinder that is oscillated harmonically in the direction of beam propagation makes it possible to generate coherent finite-amplitude internal wave beams whose spatial profile comprises no more than a single wavelength. For forcing amplitude above a certain threshold depending on the driving frequency, such thin wave beams are observed to undergo an instability that involves two subharmonic perturbations with wavepacket-like spatial structure. Although it bears resemblance to the triadic resonant instability (TRI) of small-amplitude sinusoidal waves, the present instability cannot be predicted by TRI theory as the primary wave is not nearly monochromatic, but instead contains broadband wavenumber spectrum. In contrast, the experimental observations are in good agreement with the predictions of a formal linear stability analysis based on Floquet theory. Finally, experimental evidence is presented that transverse beam variations induce a horizontal mean flow of the streaming type and greatly subdue the instability.

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

This paper presents the significance and importance of promoting the benefits of Earth Observation when implemented in ‘secondary’ and ‘higher education’ in Cyprus schools. Firstly, examples of how Earth observation is used in other countries for the secondary education such as Germany and China and how ESA through Earth Observation education materials are used to promote STEM education are presented. Secondly, ‘EXCELSIOR for Schools’ & ‘SOFIA ESA’ initiatives for promoting earth observation education in Cyprus are also presented. Indeed. examples of how earth observation is presented through seminars, workshops, science cafes, researcher's night, from earth observation experts into schools in Cyprus is also described and analysed. Examples of how earth observation is used in the existing curriculum for undergraduate, postgraduate courses for surveying and civil engineers is demonstrated for monitoring and providing solutions for civil and geomatics engineering aspects at the Cyprus University of Technology through the EXCELSIOR H2020 Teaming project.

Detailed resource or impact assessment studies often call for higher spatial resolution data products. Remote sensing data e.g., SAR (Synthetic Aperture Radar) provide high spatial resolution wind information but suffer from the satellite’s relatively poor revisit time. This paper focuses on the preliminary assessment of offshore wind speed derived from diverse data sources, characterized by different spatial and temporal resolution. A univariate statistical analysis and a match-up comparison between in-situ Cyprus meteorological stations wind speed measurements and Sentinel-1A and 1B VV-polarized SAR wind field estimates were conducted for a two-year period between 1st June, 2017-1st June, 2019. Results indicate that SAR data overestimate the in-situ measurements resulting in an average bias of 0.7 m/s, which was gradually increasing in parallel to the wind speed values. Errors also proved to vary spatially with Akrotiri demonstrating the highest average errors comparing to Pafos and Larnaca. Despite the discrepancies found, Sentinel-1 SAR Level-2 OCN can be considered as a reliable source for offshore wind field estimation. The initial assessment presented in this paper can be considered as the basis for a future in-depth analysis of Cyprus offshore wind resource assessment.