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Abstract IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems have been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements (i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.

Groundwater flow systems are influenced by the changes in surface waters as well as climatic factors. These teleconnections significantly increase in cases of extreme weather conditions. To prepare and mitigate the effect of such phenomena, the background factors that create and influence natural processes must be recognized. In the present study, 94 shallow groundwater (SGW) wells’ water level time series were analyzed in the inner delta of the River Danube (Europe) the Szigetköz region to explore which factors contribute to the development of diurnal periodicity of SGW and what its drivers are. The relationship between surface meteorological processes and SGW dynamics in the Szigetköz region was investigated using hourly data from monitoring wells. Hourly water temperature data exhibited weak correlations with meteorological parameters. However, daily averaged data revealed stronger correlations, particularly between SGW levels and air temperature and potential evapotranspiration. Diurnal periodicity in SGW fluctuations correlated strongly with potential evapotranspiration. The study also demonstrated the role of capillary fringe dynamics in linking surface evapotranspiration with SGW fluctuations. Changes in groundwater levels, even small, can significantly affect soil moisture, vegetation, and ecosystem functioning, highlighting the sensitivity of the unsaturated zone to SGW fluctuations driven by surface processes.

The use of mature, reliable, and validated solutions can save significant time and cost when introducing new technologies to companies. Reference Architectures represent such best-practice techniques and have the potential to increase the speed and reliability of the development process in many application domains. One area where Reference Architectures are increasingly utilized is cloud-based systems. Exploiting the high-performance computing capability offered by clouds, while keeping sovereignty and governance of proprietary information assets can be challenging. This paper explores how Reference Architectures can be applied to overcome this challenge when developing cloud-based applications. The presented approach was developed within the DIGITbrain European project, which aims at supporting small and medium-sized enterprises (SMEs) and mid-caps in realizing smart business models called Manufacturing as a Service, via the efficient utilization of Digital Twins. In this paper, an overview of Reference Architecture concepts, as well as their classification, specialization, and particular application possibilities are presented. Various data management and potentially spatially detached data processing configurations are discussed, with special attention to machine learning techniques, which are of high interest within various sectors, including manufacturing. A framework that enables the deployment and orchestration of such overall data analytics Reference Architectures in clouds resources is also presented, followed by a demonstrative application example where the applicability of the introduced techniques and solutions are showcased in practice.

Two PT-symmetric potentials are compared, which possess asymptotically finite imaginary components: the PT-symmetric Rosen-Morse II and the finite PT-symmetric square well potentials. Despite their different mathematical structure, their shape is rather similar, and this fact leads to similarities in their physical characteristics. Their bound-state energy spectrum was found to be purely real, an this finding was attributed to their asymptotically non-vanishing imaginary potential components. Here the V(x)= γδ(x)+ i2Λ sgn(x) potential is discussed, which can be obtained as the common limit of the two other potentials. The energy spectrum, the bound-state wave functions and the transmission and reflection coefficients are studied in the respective limits, and the results are compared.

The purpose of the paper is to analyze on micro level the stream of building materials needed for raising cattle. The scope of the produced material flow chart will cover the inflow and storage of the materials. The overall objective of material flow analysis is to contribute to the data inventory development process which is needed to use these construction materials as secondary raw materials in the future. The assessment is based on a concrete new development in a Hungarian region being carried out nowadays as a development and modernization of an existing cattle raising farm. Driven by the concern of resource depletion, further research on higher level, based on larger database will be needed to verify the estimated national level results for the magnitude of growth of anthropogenic stocks.

The modeling of karst water level fluctuations is a crucial task in the water resource management of vulnerable karstic areas. In the Transdanubian Range (East Central Europe, Hungary), from 1950 to 1990, coal and bauxite mining were carried out, with large amounts of karst water being extracted, thus lowering the water table by amounts ranging between 10 and 100 m. Since the cessation of mining activities in the early 1990s, the volume of natural recharge has exceeded the amount of dewatering, and the system has begun to return to its original undisturbed state. This apparently welcome development does, however, bring economic and technical engineering problems. The estimation and prediction of such water level changes is often tackled via the use of deterministic approaches, however, in the present case, it is also addressed with an alternative approach using trend estimation to monthly water level data from 107 karst water wells over the period 1990–2017. To approximate the change in karst water levels, (i) growth curve models were fitted to the monthly data, allowing the estimation of karst water levels, at least as far as 2030. Similarly, this was also done with (ii) deterministic modelling in order to describe the recovery process up to 2030. Specifically, measured and predicted values for karst water level were used to derive interpolated (kriged) maps to compare the forecasting power of the two approaches. Comparing the results of the trend analysis with those of the traditional deterministic modelling results, it is apparent that the two approaches predict similar spatial distribution of water levels, but slightly different future water level values.

Shallow groundwater is one of the primary sources of fresh water, providing river base-flow and root-zone soil water between precipitation events. However, with urbanization and the increase in demand for water for irrigation, shallow groundwater bodies are being endangered. In the present study, 101 hydrographs of shallow groundwater monitoring wells from the watershed of the westernmost brackish lake in Europe were examined for the years 1997–2012 using a combination of dynamic factor and cluster analyses. The aims were (i) the determination of the main driving factors of the water table, (ii) the determination of the spatial distribution and importance of these factors, and (iii) the estimation of shallow groundwater levels using the obtained model. Results indicate that the dynamic factor models were capable of accurately estimating the hydrographs (avg. mean squared error = 0.29 for standardized water levels), meaning that the two driving factors identified (evapotranspiration and precipitation) describe most of the variances of the fluctuations in water level. Both meteorological parameters correlated with an obtained dynamic factor (r = −0.41 for evapotranspiration & r = 0.76 for precipitation). The strength of these effects displayed a spatial pattern, as did the factor loadings. On this basis, the monitoring wells could be objectively distinguished into two groups using hierarchical cluster analysis and verified by linear discriminant analysis in 98% of the cases. This grouping in turn was determined to be primarily related to the elevation and the geology of the area. It can be concluded that the application of the data analysis toolset suggested herein permits a more efficient, objective, and reproducible delineation of the primary driving factors of the shallow groundwater table in the area. Additionally, it represents an effective toolset for the forecasting of water table variations, a quality which, in the view of the likelihood of further climate change to come, is a distinctive advantage. The knowledge of these factors is crucial to a better understanding of the hydrogeological processes that characterize the water table and, thus, to developing a proper water resource management strategy for the area.

Since groundwater is a major source of water for drinking and for industrial and irrigation uses, the identification of the environmental processes determining groundwater level fluctuation is potentially a matter of great consequence, especially in light of the fact that the frequency of extreme climate events may be expected to increase, causing changes in groundwater recharge systems. In the recent study, data measured at a frequency of one hour were collected from the Szigetköz, an inland delta of the Danube. These were then used to determine the presence, or not, and magnitude of any hidden environmental background factors that may be causing groundwater level fluctuations. Through the application of dynamic factor analysis, it was revealed that changes in groundwater level are mainly determined by (i) the water level of neighboring rivers and (ii) evapotranspiration. The intensity of these factors may also be estimated spatially. If the background factors determined by dynamic factor analysis do indeed figure in the linear model as variables, then the time series of groundwater levels can be said to have been accurately estimated with the use of linear regression. The accuracy of the estimate is indicated by the fact that adjusted coefficient of determination exceeds 0.9 in 80% of the wells. The results, via an enhanced understanding of the reasons for changes in the fluctuation of groundwater, could assist in the development of sustainable water management and irrigation strategies and the preparation for varying potential climate change scenarios.

Shallow groundwater acts as an important source of water for the ecosystem, agriculture, drinking water supply, etc.; it is, however, among those water resources most sensitive to climate change, and especially to aridification. In the present study, the delineation of regional recharge and discharge zones of the Danube−Tisza Interfluve (Hungary, 8000 km2) is presented via the combination of multivariate time series and geomathematical methods to explore the subregions most sensitive to dewatering. The shallow groundwater level time series of 190 wells, covering a semicentennial period (1961 to 2010), were grouped into three validated clusters representing characteristically different subregions. Then, the subregions’ means and individual shallow groundwater level time series were investigated for long-term trends and compared with local meteorological variability (precipitation, evapotranspiration, etc.) to determine their regime characteristics. As a result, shallow recharge and discharge zones, a gravity-driven flow system, and the discharge zone of a deeper, overpressured flow system could be discerned with distinctive long-term changes in water levels. The semicentennial trends in shallow groundwater levels were significant (p < 0.05) in the recharge (−0.042 m y−1) and in the overpressured discharge zone (0.009 m y−1), and insignificant in the rest of the area (−0.005 m yr−1). The present results concur with previous findings from the area but provide a statistically sound and reproducible delineation of the regime areas on a much finer scale than before. With the determination of the different climatic processes driving the semicentennial trends prevailing in the shallow groundwater, the high vulnerability of the recharge zone is underlined, while the outlined overpressured flow system seems to act independently from semicentennial precipitation trends. This study provides a more in-depth picture of the long-term changes in shallow groundwater and its drivers in of one of the most important agricultural areas in Hungary. It outlines, in a generally applicable way, the most vulnerable subareas for irrigation relaying on shallow groundwater extraction. In addition, the results can help adaptation-strategy decision makers to initiate a more effective and area-focused intervention in the case of the predicted negative trends for vulnerable recharge areas under various climate change scenarios.

One of the main driving forces in the era of cyber-physical systems (CPSs) is the introduction of massive sensor networks (or nowadays various Internet of things solutions as well) into manufacturing processes, connected cars, precision agriculture, and so on. Therefore, large amounts of sensor data have to be ingested at the server side in order to generate and make the “twin digital model” or virtual factory of the existing physical processes for (among others) predictive simulation and scheduling purposes usable. In this paper, we focus on our ultimate goal, a novel software container-based approach with cloud agnostic orchestration facilities that enable the system operators in the industry to create and manage scalable, virtual IT platforms on-demand for these two typical major pillars of CPS: (1) server-side (i.e., back-end) framework for sensor networks and (2) configurable simulation tool for predicting the behavior of manufacturing systems. The paper discusses the scalability of the applied discrete-event simulation tool and the layered back-end framework starting from simple virtual machine-level to sophisticated multilevel autoscaling use case scenario. The presented achievements and evaluations leverage on (among others) the synergy of the existing EasySim simulator, our new CQueue software container manager, the continuously developed Occopus cloud orchestrator tool, and the latest version of the evolving MiCADO framework for integrating such tools into a unified platform.

Our geological heritage is increasingly threatened by anthropogenic activity. This is especially true of the Pál-völgyi Cave System beneath Budapest. It is among the 150 longest and at the same time most endangered cave systems in the world. The aims of the study were (i) to set up a monitoring system in the cave, (ii) to track the daily changes in the quality and quantity of drip water, and (iii) to determine the exposure of the cave. Monitoring was conducted at two locations in a shallow area next to a fracture zone (site name: TG) and one lying in a tectonically less disturbed, geologically more homogeneous location 20 m deeper (site YC). The data obtained in respect of 13 variables were assessed using descriptive statistics, principal component- and periodicity analyses. At first glance, it was apparent that the eight water quality parameters differed in quantity between the two sites. Furthermore, using principal component analysis it was shown that in the fractured-shallow setting, anthropogenic activity (external urban pollution, e.g. de-icing, decrease of land cover etc.) is the driving process determining water quality. At the tectonically less fractured site (YC) external influences originating above ground may be added to the natural karst-forming processes. The assessment of drip intensity and electric conductivity again highlighted the differences between the sites in terms of their reaction to precipitation. With regard to diurnal periodicity, although pH and Eh indicated a mature periodic behavior at both sites (covering 56−65 % of the total observed time), at site TG electric conductivity displayed diurnal periodicity over only 21 % of the total time, compared to 56 % at YC. All results pointed towards a conclusion that at site YC daily periodicity and water quality are much more connected to natural processes, while at site TG anthropogenic external influences suppress these. Key words: cave drip water, hydrochemistry, karst, Pál-völgyi Cave System, time series analysis, urban pollution. Analiza prenikle vode v urbani kraški jami pod madžarsko prestolnico (Budimpešta) Naša geološka dediščina je vse bolj ogrožena zaradi človekovih dejavnosti. To še posebej velja za jamski sistem Pál-völgyi pod Budimpešto. Sodi med 150 najdaljših in hkrati najbolj ogroženih jamskih sistemov na svetu. Cilji raziskave so bili (i) vzpostaviti sistem spremljanja izbranih parametrov v jami, (ii) spremljati dnevne spremembe kakovosti in količine prenikle vode, in (iii) določiti izpostavljenost jame onesnaženju. Raziskava je bila izvedena na dveh lokacijah v plitvem območju blizu razpoklinske cone (ime vzorčnega mesta: TG) in na tektonsko manj pretrti in geološko bolj homogeni lokaciji 20 m globlje (ime vzorčnega mesta: YC). Pridobljeni podatki 13 spremenljivk so bili obdelani s pomočjo opisne statistike, analize glavnih komponent in periodičostno analizo. Na prvi pogled je bilo očitno, da se je osem parametrov kakovosti vode kvantitativno razlikovalo med obema mestoma. Poleg tega se je s pomočjo analize glavnih komponent pokazalo, da je mesto v plitvem pretrtem območju pod velikim vplivom antropogenih aktivnosti (urbano onesnaženje, npr. sredstva proti zmrzovanju, zmanjšanje pokrovnih plasti, itd.), ki vplivajo na kakovost vode. Na tektonsko manj preoblikovanem mestu (YC) se zunanji vplivis površja mešajo z naravnimi kraškimi procesi. Ocena intenzivnosti kapljanja in električne prevodnosti je ponovno izpostavila razlike med vzorčnimi mesti v smislu njunega odziva na padavine. V zvezi z dnevno periodičnostjo je vzorčno mesto TG kazalo dnevno periodičnost več kot 21 % celotnega časa, mesto YC 56 %, čeprav sta pH in Eh izkazali zrelo periodično obnašanje (56−65 % celotnega opazovanega časa) na obeh mestih. Vsi rezultati kažejo na sklep, da je na mestu YC dnevna periodičnost in kakovost vode veliko bolj povezana z naravnimi procesi, medtem ko jih na mestu TG antropogeni zunanji vplivi zadušijo. Ključne besede: jamska prenikla voda, hidrokemija, kras, jamski sistem Pál-völgyi, analiza časovne vrste, urbano onesnaževanje.