Migration to Greece is a structural feature of the country's social and political life, yet inflows fluctuate sharply, repeatedly straining reception, asylum and welfare systems. This article develops a pilot prediction model using a Hidden Markov Model (HMM) to identify latent 'regimes' of migratory pressure and derive evidence-based policy implications. Using harmonized quantitative data on arrivals, the model distinguishes three flow scenarios, soft, medium and hard, each reflecting distinct ranges and patterns of annual inflows. The regimes are discussed in relation to key geopolitical and socio-economic developments and used to derive scenario-based policy implications for reception capacity, asylum processing, and local welfare and integration. The engagement with securitization and human security is used as a contextual lens to interpret policy trade-offs. The article then sets out targeted implications for reception capacity, asylum processing, local welfare and integration, calibrated to each regime and to the institutional constraints of Greek migration governance.

The goal of the work presented here is to study a novel approach for inverting acoustic signals recorded in the marine environment for the estimation of environmental parameters of the water column and/or the seabed. The proposed approach is based on signal feature extraction using a discrete wavelet packet transform, applied to the measured signal, and hidden Markov models that exploit the sequential patterns of the signals. The signal feature is thereafter used in the framework of a mixture density network, which, after training with sets of simulated signals calculated within a predefined search space, provides conditional posterior distributions of the recoverable parameters. The technique is tested with two test cases corresponding to different types of inverse problems. The first case corresponds to a simple problem of geoacoustic inversion, while the second is referred to a, rather unusual, still interesting problem of recovering the shape of a seamount using long-range acoustic data. Both test cases are based on simulated experiments. The inversion results obtained using the proposed scheme are compared with inversion results using statistical features of the acoustic signal, which is another inversion approach well documented in the literature and is also based on the wavelet packet transform of the measured signal.

The Greek world has a long tradition in shipping and maritime trade, dating back to the first appearances of the Minoan civilization in Bronze Age. This tradition secured for the ancient Greek world dominance over a large area around the Mediterranean Sea throughout the Bronze Age. The evidence of this supremacy, based on the findings of archaeologists and historians, supports the statement that the term "thalassocracy," a Greek term meaning sea power, was coined by the Greeks to describe this regime. In time, other powers also demonstrated their ability to control the sea lanes, until today. The focus of this paper is a brief review of the evolution of Bronze Age thalassocracy and the important relationship between thalassocracy and sustainability during that period for the Mediterranean world. Keywords: Bronze Age, Prehistoric Times, History, Sustainability, Water Purification, Desalination, Navigation, Sea Power. Impact of Employee Compensation and Benefits on Operating Performance This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Copyright © Author(s) retain the copyright of this article.

Greece has a long tradition in acoustics.Already in the ancient era, the necessity for social gathering and cultural performance in the open air with optimum hearing and appreciation conditions, led the engineers of that time, to study and design theaters, conservatories and other public gathering places with excellent acoustic characteristics.In the modern era, the acoustic community in Greece is also flourishing, assisted by the close collaboration between industry, universities, and research centers.The Hellenic Institute of Acoustics (HELINA) is the scientific society of Greek acousticians.HELINA is a very active community, continuously growing in parallel with the continuous evolution of the Greek acousticians and of the acoustic education in Greece.HELINA and Greek acousticians play an important role in international acoustic affairs in both research and administrative issues.

This work presents a preliminary study on the effect that a blurring mechanism has on the quality of acoustic signals. The mechanism is mathematically modeled by means of a matrix that distorts the clear (blur and noise-free) digitized signal. Then, a combined deblurring-denoising scheme, adopted from image processing, is introduced and is shown to be very efficient for the simultaneous deblurring and denoising of the acoustic signal in cases where the blurring mechanism expressed through the corresponding matrix is known. Approximate matrices close to the actual one may be proven adequate for specific applications but it is important that the type of the matrix is approximated as much as possible. Comparison of raw and clear signals is made using a statistical characterization scheme, which is also appropriate for cases where the signal features have to be exploited for applications involving machine-learning. The deblurring of acoustic signals improve their quality and enhance their perceptibility, in favor of their exploitation for a wide range of applications, including human communication.

This paper presents a study on the applicability of a method for the statistical characterization of seismic signals which is based on the statistics of their wavelet sub-band coefficients using [Formula: see text] stable distributions. The method was originally applied to underwater acoustic signals of the type used in ocean acoustic tomography and seabed classification applications. The same protocol was applied to seismic signals representing the vertical displacement, measured on a traditional seismograph. The study showed that this process can indeed provide a means of characterizing a seismic signal and be used to estimate similarities between seismic signals, which is an important factor in studying seismic activity in an area.

The International Commission for Acoustics considered that the proclamation of an International Year of Sound (IYS) would create a worldwide awareness of the importance of sound in our world. Sound is an integral part of culture and society from the basic requirements for communication, awareness of our environment, and expression of our culture through to sophisticated scientific and technological research and applications. After a decade of preparation, the ICA decided to organize the International Year of Sound 2020. The moto of the IYS 2020 was “Importance of Sound for Society and the World” and the IYS was associated with the UNESCO Charter of Sound and resolution 39C/59 on the ‘’Importance of sound in today’s world—Promoting best practices.” The French Organization “La Semaine du Son” became partner in this initiative. The ICA planned a number of central activities and the majority of the ICA member organisations embraced the opportunity to organize local and regional events during 2020. Of course the arrival of the Covid 19 pandemic curtailed the majority of the planned events but the enthusiasm remained to adapt to virtual activities and develop more resources. In response to the continued enthusiasm the ICA Board agreed the IYS should become a two years celebration. This resulted to the new reference as International Year of Sound 2020–2021 (IYS 2020–21)

The International Year of Sound 2020–2021 includes activities organized by the IYS Steering Committee, ICA member societies, international affiliates and individual organizations. The web page www.sound2020.org, is the primary contact for the IYS and receives more than 3500 visitors per month. The opening was held in Paris at the Grand Amphitheatre of the Sorbonne on 31 January 2020. A film “Sounds of our Life” was launched and is also available on Youtube. An international student competition involved over 15 000 students and attracted excellent contributions. The international scientific conferences included outreach and promotions for the IYS. Despite the restrictions due to COVID-19, ICA members maintained their enthusiasm and contributed an amazing range of innovative virtual activities and resources. Additional outreach included various media items and podcasts. The IYS has encouraged further collaboration with institutions including the WHO, the CHC and groups dealing with sound. This will eventually will strengthen the acoustics community into the future. Information on the activities and all the resources are freely available from the website and will remain as a legacy of the IYS. Despite the challenges of 2020 and 2021, the efforts of so many have shown the resilience and innovation of the acoustic community to enhance the understanding worldwide of the importance of sound.

A probabilistic characterization scheme for acoustic signals with applications in acoustical oceanography is presented. This scheme aims at the definition of a set of stochastic observables that could characterize the signal. To this end, the signal is decomposed into several levels using the stationary wavelet packet transform. The extracted wavelet coefficients are then modeled by a hidden Markov model (HMM) with Gaussian emission distributions. The association of a signal with a representative HMM is performed utilizing the expectation-maximization algorithm. Eventually, the signal is characterized by the set of parameters that describe the HMM. The Kullback-Leibler divergence is employed as the similarity measure of two signals, comparing their corresponding HMMs. To validate the performance of the proposed characterization scheme, which is denoted as the probabilistic signal characterization scheme (PSCS), a simulated and a real experiment have been considered. The measured signal is characterized by the proposed PSCS method, and the model parameters of the seabed are estimated by means of an inversion procedure employing a genetic algorithm. The inversion results confirmed the reliability and efficiency of the proposed method when applied with typical signals used in applications of acoustical oceanography.

The analysis of long time series of measured acoustic or seismic signals may lead to the extraction/determination of specific features that characterise the signals and the information they carry. Two scientific fields that could make extensive use of signal characterisation are acoustical oceanography, where the acoustic signal can be used as a monitoring tool of the marine environment and seismology in which the seismic signals are rich in information about the geological structure of the earth. We are developing alternative tools for signal characterisation based on a time-frequency analysis of the corresponding recordings followed by a probabilistic feature extraction driven by the hidden Markov theory, a well-known machine learning approach for describing sequential data.

his paper presents an application of an acoustic signal characterization scheme for ocean acoustic tomography and geoacoustic inversions proposed by Taroudakis et al., using real data. The work is the first attempt to validate the proposed scheme with data taken from sea experiments. The data have been collected during the SW06 experiment held in the New Jersey Continental Shelf and the inversion results (sea-bed geoacoustic parameters and source range) are compared with those reported by Bonnel and Chapman. The comparison and the signal reconstruction using estimated values of the model parameters is satisfactory being an indication that the new signal characterization method can be used in practical applications of acoustical oceanography.

Statistical characterization of acoustic signals is a pre-processing technique aiming at the definition of signal observables, which can be used as input data in the formulation of inverse problems of acoustical oceanography aiming at the estimation of critical parameters of the marine environment. Statistical signal characterization is also a means to classify the signals and compare their properties without reference to any physical model that determines the signal observables. Thus, it can in principle be used for the comparison of the signals of any type, including seismic recordings, thus opening the area to many applications of geophysical monitoring, using signals of any type. The paper summarizes the first attempts to study the efficiency of a signal characterization method based on wavelet transform of the signal at various levels, followed by the statistical description of their sub-band coefficients. It is shown that A-stable symmetric distributions are capable of defining the statistics of these coefficients for signals used in applications of ocean acoustic tomography and geoacoustic inversions. It is further investigated if these distributions are capable of defining the statistics of seismic signals and it is shown by preliminary results, that they can indeed be considered as possible candidates in this respect.

The paper summarizes the research carried out at the University of Crete and the Foundation for Research and Technology-HELLAS aiming at the statistical characterization of underwater acoustic signals and their subsequent use for geoacoustic inversions and applications in ocean acoustic tomography. In these applications, an acoustic signal recorded in the marine environment due to some source is used as the carrier of information on the physical parameters of the environment. Statistical characterization of acoustic signals is a pre-processing technique aiming at the definition of signal observables, to be used as input data of appropriately defined inverse problems aiming at the estimation of critical parameters of the marine environment. The statistical characterization scheme was introduced as a way to define signal observables especially in cases that typical observables such as ray arrivals or modal arrivals cannot be identified in the recorded signals. Moreover, the setting of the associated inverse problem requires just a single recording device, which renders its application practically and relatively cheap in comparison with signal inversion methods requiring reception at an array of hydrophones. The characterization scheme is based on a wavelet transform of the signal at various levels, followed by the statistical description of the wavelet sub-band coefficients. It is shown that A-stable symmetric distributions are capable of defining the statistics of these coefficients, the characteristic parameters of which are the observables of the signal to be exploited for the inversions. As the inverse problems associated with the sought applications are formulated as optimization processes, an objective function to be used as a similarity measure is defined, which in the case of the statistical characterization method is the Kullback–Leibrer Divergence (KLD), capable of comparing probability density functions. The inversion processes are performed by means of neural networks or genetic algorithms, and the performance of the combined signal characterization and inversion method has been tested with simulated and real data. It is shown, that the method works well especially with noise-free or denoised signals.

Acoustical signals in applications of acoustical oceanography, such as ocean acoustic tomography and sea-bed classification using acoustic signals emitted from known sources, are optimally exploited if they are noise free. The effect of blur in acoustic signals has not been well studied, although the blurring mechanism might introduce severe problems in the use of the acoustic signals for specific applications, especially those using the full signal as the carrier of the relevant information. Deblurring of the signals in addition to denoising is therefore essential for the effective use of the signals. In our work, we apply a Statistical Optimal Filtering method that uses the singular value decomposition of a first estimate of the blurring matrix and statistics to deblur the signal in an efficient and effective way and to quantify uncertainty for the recovered signal. In this talk, we will present the method and discuss its effectiveness using as test case, an application of sea-bed classification based on a statistical characterization of an acoustic signal. The statistical characterization is particularly sensitive to noise and blur contamination of the exploitable signal and any attempt for getting a signal clear from noise and blur is absolutely necessary, for obtaining reliable results.

The work presents a method for characterizing underwater acoustic signals using a Markov chain, with hidden variables, based on their wavelet transform. Initially, we assign to the signal a Hidden Markov Model (HMM) for which the conditional posterior probability density function seems to be the most representative using an Expectation-Maximization algorithm. Special techniques are applied to avoid over-fitting which in principle is not desirable for the sought applications. The features used for the assignment consist of two dimensional time series obtained by preprocessing of signal’s wavelet packet coefficients. Subsequently, we use an approximation of the Kullback Leibler (KL) divergence as a similarity measure among the HMMs. The approximation is obtained by employing Monte-Carlo (MC) techniques simulating the significant sampling from the HMMs posterior distributions. This technique is used in cases where the similarity of two or more signals is to be exploited. These cases include a variety of problems associated with the monitoring of the marine environment using acoustic or seismic signals. The applications to be presented here are referred to problems of geoacoustic inversions (seabed mapping) using simulated acoustic data and seismic monitoring using real data from a terrestrial seismograph to illustrate the various possible applications of the suggested method.

EAA AWARD for contributions to the promotion of Acoustics in Europe

A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.

The signal characterization method suggested by Taroudakis et al. (J.Acoust. Soc. Am. 119, 1396-1405 (2006)) based on the statistics of its 1-D wavelet transform coefficients and successfully applied for inverting acoustic signals in applications of acoustical oceanography has been proven to be sensitive to noise contamination of the signal, but still, it provides good inversion results if an appropriate denoising strategy is applied. In this work, the statistical signal characterization is applied to signals which are both blurred and noise contaminated. Deblurring of the signal is achieved by means of a technique introduced by Taroudaki and O’ Leary (SIAM J. Sci. Comput. 37(6), A2947-A2968 (2015)) for image deblurring, and it is based on a statistical near optimal spectral filtering technique that takes advantage of the singular values of the approximated blurring matrix and the Picard Parameter of the signal that allows for estimation of the additive noise properties and estimation of the error. The study is extended to cases when no accurate knowledge of the blurring mechanism is available. It is shown by typical simulated experimental data that the combination of deblurring and simple denoising strategies provide good results with respect to both signal characterization and subsequent inversions.