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Location modeling, both inductive and deductive, is widely used in archaeology to predict or investigate the spatial distribution of sites. The commonality among these approaches is their consideration of only spatial effects of the first order (i.e., the interaction of the locations with the site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely considered. We introduce a deductive approach to investigating such second-order effects using linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to simulate a point distribution using expert knowledge of two distinct hunter–gatherer groups, namely, reindeer hunters and elk hunters. The modeled points and point densities were compared with the actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects of varying intensity for different periods, were applied. The results reveal differences between the two investigated groups, with the reindeer hunters showing location-related interaction patterns, indicating a spatial memory of the preferred locations over an extended period of time. Overall, this paper shows that second-order effects occur in the geographical modeling of archaeological finds and should be taken into account by using approaches such as the one presented in this paper. [ABSTRACT FROM AUTHOR]

Like many other countries, Germany has defined goals to reduce its CO2-emissions following the Paris Agreement of the 21st Conference of the Parties (COP). The first successes in decarbonizing the electricity sector were already achieved under the German Energiewende. However, further steps in this direction, also concerning the heat and transport sectors, have stalled. This paper describes three possible pathways for the transformation of the German energy system until 2050. The scenarios take into account current climate politics on a global, European, and German level and also include different demand projections, technological trends and resource prices. The model includes the sectors power, heat, and transportation and works on a Federal State level. For the analysis, the linear cost-optimizing Global Energy System Model (GENeSYS-MOD) is used to calculate the cost-efficient paths and technology mixes. We find that a reduction of CO2 of more than 80% in the less ambitious scenario can be welfare enhancing compared to a scenario without any climate mitigating policies. Even higher decarbonization rates of 95% are feasible and needed to comply with international climate targets, yet related to high effort in transforming the subsector of process heat. The different pathways depicted in this paper render chances and risks of transforming the German energy system under various external influences. [ABSTRACT FROM AUTHOR]

In August 2020, Germany and Ukraine launched an energy partnership that includes the development of a hydrogen economy. Ukraine has vast renewable energy resources for "green" hydrogen production and a gas transmission system for transportation instead of Russian natural gas. Based on estimates by Hydrogen Europe, Ukraine can install 8000 MW of total electrolyser capacity by 2030. For these reasons, Ukraine is among the EU's priority partners concerning clean hydrogen, according to the EU Hydrogen strategy. Germany plans to reach climate neutrality by 2045, and "green" hydrogen plays an important role in achieving this target. However, according to the National Hydrogen Strategy of Germany, local production of "green" hydrogen will not cover all internal demand in Germany. For this reason, Germany considers importing hydrogen from Ukraine. To govern the production and import of "green" hydrogen, Germany and Ukraine shall introduce legal regulations, the initial analysis of which is covered in this study. Based on observation and comparison, this paper presents and compares approaches while exploring the current stage and further perspectives for legal regulation of hydrogen in Germany and Ukraine. This research identifies opportunities in hydrogen production to improve the flexibility of the Ukrainian power system. This is an important issue for Ukrainian energy security. In the meantime, hydrogen can be a driver for decarbonisation according to the initial plans of Germany, and it may also have positive impact on the operation of Germany's energy system with a high share of renewables. [ABSTRACT FROM AUTHOR]

Hydrogen is fast becoming a new international "super fuel" to accelerate global climate change ambitions. This paper has two inter-weaving themes. Contextually, it focuses on the potential impact of the EU's new Carbon Border Adjustment Mechanism (CBAM) on fossil fuel-generated as opposed to green hydrogen imports. The CBAM, as a transnational carbon adjustment mechanism, has the potential to impact international trade in energy. It seeks both a level playing field between imports and EU internal markets (subject to ambitious EU climate change policies), and to encourage emissions reduction laggards through its "carbon diplomacy". Countries without a price on carbon will be charged for embodied carbon in their supply chains when they export to the EU. Empirically, we focus on two hydrogen export/import case studies: Australia as a non-EU state with ambitions to export hydrogen, and Germany as an EU Member State reliant on energy imports. Energy security is central to energy trade debates but needs to be conceptualized beyond supply and demand economics to include geopolitics, just transitions and the impacts of border carbon taxes and EU carbon diplomacy. Accordingly, we apply and further develop a seven-dimension energy security-justice framework to the examples of brown, blue and green hydrogen export/import hydrogen operations, with varying carbon-intensity supply chains, in Australia and Germany. Applying the framework, we identify potential impact—risks and opportunities—associated with identified brown, blue and green hydrogen export/import projects in the two countries. This research contributes to the emerging fields of international hydrogen trade, supply chains, and international carbon diplomacy and develops a potentially useful seven-dimension energy security-justice framework for energy researchers and policy analysts. [ABSTRACT FROM AUTHOR]

As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks' layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision. [ABSTRACT FROM AUTHOR]

With the increasing use of sensitive loads in frequency converters and in relays in distribution networks, voltage sag has become a major power quality issue that urgently needs to be solved. For the purpose of improving the understanding of voltage sag severity in distribution networks, a comprehensive weight-based severity evaluation method of voltage sag is presented in this paper. First, a multi-side index system that takes into account the combined influence of the source, network, and the load is established. A comprehensive weight method, which combines the improved analytic hierarchy process (IAHP) and the entropy method, is then adopted to determine the index weight. The weight of each index and the degree of superiority are linearly weighted to obtain the severity of voltage sag at different observation points. Finally, the effectiveness of the proposed method is verified using a distribution network model established in DIgSILENT PowerFactory (15.1.7, Gomaringen, Germany). [ABSTRACT FROM AUTHOR]

Due to higher penetration of renewable energy sources, grid reinforcements, and the utilization of local voltage control strategies, a significant change in the reactive power behavior as well as an increased demand for additional reactive power flexibility in the German power system can be predicted. In this paper, an application-oriented reactive power management concept is proposed, which allows distribution system operators (DSO) to enable a certain amount of reactive power flexibility at the grid interfaces while supporting voltage imitations in the grid. To evaluate its feasibility, the proposed concept is applied for real medium voltage grids in the south of Germany and is investigated comprehensively in different case studies. The results prove the feasibility and reliability of the proposed concept, which allows the DSO to control the reactive power exchange at grid interfaces without causing undesired local voltage problems. In addition, it can be simply adjusted and widely applied in real distribution grids without requiring high investment costs for complex information and communication infrastructures. As a significant contribution, this study provides an ideal bridging solution for DSOs who are facing reactive power issues but have no detailed and advanced monitoring system for their grid. Moreover, the comprehensive investigations in this study are performed in close cooperation with a German DSO, based on a detailed grid model and real measurement data. [ABSTRACT FROM AUTHOR]

Social acceptance is increasingly becoming a limiting factor in implementing the energy transition in Germany. From today's perspective, the expansion of wind energy and future transmission grids is only somewhat a technical or economic challenge rather than a social one. Since political decisions on the energy system transformation are often derived from findings of energy system modeling, it seems necessary to increasingly integrate the effects of socio-ecological aspects, such as acceptance issues in energy models. In this paper, an approach is introduced to address effects of social acceptance in energy system models by comparing the influence of different distribution scenarios of wind energy in Germany on the expansion need for future transmission lines. The results show that a socio-ecologic distribution of onshore wind installations according to a balanced burden of the German society does not reduce the grid expansion need significantly compared to an economic siting. An actual reduction of planned transmission grids could just be achieved by a more decentral scenario, including decentral market design. The sensitivity of regionalization is an opportunity to consider local acceptance issues within energy system models and should move more into focus inside the procedure of the current grid development process in Germany. [ABSTRACT FROM AUTHOR]

Long-term transition pathways to a low-carbon energy system are analysed by applying the energy system model REMod. All in all, the paper contributes to the current research through an innovative scenario approach, using assumptions for societal trends and quantitative results for scenarios, analysing the paths towards climate neutrality and defossilization in 2050. In the case study of Germany, these trends and drivers influence the results and the technology composition in each consumption sector (buildings, transport, and industry). Across all scenarios, it can be observed that the electrification of all sectors is important for the defossilization of the energy system, as the direct use of electricity from renewable energy is more efficient than the consumption of carbon-neutral synthetic energy carriers. However, different consumer behavior (e.g., non-acceptance or resistance against specific technologies) influences not only the efficient use of (green) electricity, it also changes the optimal pathways of the transition to paths with greater efforts. One potential societal trend—sufficiency—could be an important cornerstone for reaching the targets, as the required expansion and exchange of technologies are lower and thus facilitate the transition. [ABSTRACT FROM AUTHOR]

Organisations attempt to contribute their share towards fighting the climate crisis by trying to reduce their emission of greenhouse gases effectively towards net zero. An instrument to guide their reduction efforts is internal carbon pricing. Next to choosing the right pricing tool, defining the exact value of an internal carbon price, especially against the background of potential regulatory external carbon prices, and assessing its impact on business units' energy systems poses a challenge for organisations. The academic literature has so far not examined the impact differences of an internal carbon price across different countries, which this paper addresses by using an optimisation model. First, it analyses the energy system cost increase of a real-world facility based on an internal carbon price compared to a potential regulatory carbon price within a country. Second, we evaluate the energy system cost increase based on an internal carbon price across different countries. The results show that with regard to internal carbon prices the additional total system cost compared to potential external carbon prices stays within 9%, 15%, and 59% for Germany, Japan, and the United Kingdom, respectively. The increase in the energy system cost in each country varies between 3% and 93%. For all countries, the cost differences can be reduced by allowing the installation of renewables. The integration of renewables via energy storage and power-to-heat technologies depends on the renewable potentials and the availability of carbon capture and storage. If organisations do not account for these differences, it might raise the disapproval of internal carbon prices within the organisation. [ABSTRACT FROM AUTHOR]

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany. [ABSTRACT FROM AUTHOR]

Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes or redefinition of the production concepts is meaningful in light of the current industrial and research agendas. Both the mentioned optimization and redefinition are considered in numerous subtopics and technologies. One of the most significant topics in these areas is the newest findings and applications of artificial intelligence (AI)—machine learning (ML) and deep convolutional neural networks (DCNNs). The authors invented a method and device that supports the wiring assembly in the control cabinet production process, namely, the Wire Label Reader (WLR) industrial system. The implementation of this device was a big technical challenge. It required very advanced IT technologies, ML, image recognition, and DCNN as well. This paper focuses on an in-depth description of the underlying methodology of this device, its construction, and foremostly, the assembly industrial processes, through which this device is implemented. It was significant for the authors to validate the usability of the device within mentioned production processes and to express both advantages and challenges connected to such assembly process development. The authors noted that in-depth studies connected to the effects of AI applications in the presented area are sparse. Further, the idea of the WLR device is presented while also including results of DCNN training (with recognition results of 99.7% although challenging conditions), the device implementation in the wire assembly production process, and its users' opinions. The authors have analyzed how the WLR affects assembly process time and energy consumption, and accordingly, the advantages and challenges of the device. Among the most impressive results of the WLR implementation in the assembly process one can be mentioned—the device ensures significant process time reduction regardless of the number of characters printed on a wire. [ABSTRACT FROM AUTHOR]

There are 1454 district heating systems in Germany. Most of them are fossil based and with high temperature levels, which is neither efficient nor sustainable and needs to be changed for reaching the 2050 climate goals. In this paper, we present a case study for transforming a high to low temperature district heating system which is more suitable for renewable energy supply. With the Carnot Toolbox, a dynamic model of a potential district heating system is simulated and then transformed to a low temperature supply. A sensitivity analysis is carried out to see the system performance in case space constrains restrict the transformation. Finally, an economic comparison is performed. Results show that it is technically possible to perform the transformation until a very low temperature system. The use of decentralized renewable sources, decentralized heat storage tanks and the placement of a heat pump on each building are the key points to achieve the transformation. Regarding the sensitivity analysis, the transformation is worth doing until the seasonal storage and solar collector field sizes are reduced to 60% and 80% of their values in the reference case, respectively. The economic analysis shows, however, that it is hard for highly efficient low temperature renewable based heat networks to compete with district heating systems based on a centralized fossile CHP solution. Thus, though the presented transformation is technically possible, there is a strong need to change existing economic schemes and policies for fostering a stronger promotion of renewable energy policies in the heat sector. [ABSTRACT FROM AUTHOR]

Increasing offshore wind farms are rapidly installed and planned. However, this will pose a bottle neck challenge for long-distance transmission as well as inherent variation of their generating power outputs to the existing AC grid. VSC-HVDC links could be an effective and flexible method for this issue. With the growing use of voltage source converter high-voltage direct current (VSC-HVDC) technology, the hybrid VSC-HVDC and AC system will be a next-generation transmission network. This paper analyzes the contribution of the multi VSC-HVDC system on the AC voltage stability of the hybrid system. A key contribution of this research is proposing a novel adaptive control approach of the VSC-HVDC as a so-called dynamic reactive power booster to enhance the voltage stability of the AC system. The core idea is that the novel control system is automatically providing a reactive current based on dynamic frequency of the AC system to maximal AC voltage support. Based on the analysis, an adaptive control method applied to the multi VSC-HVDC system is proposed to realize maximum capacity of VSC for reactive power according to the change of the system frequency during severe faults of the AC grid. A representative hybrid AC-DC network based on Germany is developed. Detailed modeling of the hybrid AC-DC network and its proposed control is derived in PSCAD software. PSCAD simulation results and analysis verify the effective performance of this novel adaptive control of VSC-HVDC for voltage support. Thanks to this control scheme, the hybrid AC-DC network can avoid circumstances that lead to voltage instability. [ABSTRACT FROM AUTHOR]

Energy generation and consumption in the power grid must be balanced at every single moment. Within the synchronous area of continental Europe, flexible generators and loads can provide Frequency Containment Reserve and Frequency Restoration Reserve marketed through the balancing markets. The Transmission System Operators use these flexibilities to maintain or restore the grid frequency when there are deviations. This paper shows the future flexibility potential of Germany's household sector, in particular for single-family and twin homes in 2025 and 2030 with the assumption that households primarily optimize their self-consumption. The primary focus is directed to the flexibility potential of Electric Vehicles, Heat Pumps, Photovoltaics and Battery Storage Systems. A total of 10 different household system configurations were considered and combined in a weighted average based on the scenario framework of the German Grid Development Plan. The household generation, consumption and storage units were simulated in a mixed-integer linear programming model to create the time series for the self-consumption optimized households. This solved the unit commitment problem for each of the decentralized households in their individual configurations. Finally, the individual household flexibilities were evaluated and then aggregated to a Germany-wide flexibility profile for single-family and twin homes. The results indicate that the household sector can contribute significantly to system stabilization with an average potential of 30 GW negative and 3 GW positive flexibility in 2025. In 2030, the corresponding flexibilities potentially increase to 90 GW and 30 GW, respectively. This underlines that considerable flexibility reserves could be provided by single-family and twin homes in the future. [ABSTRACT FROM AUTHOR]

The analysis of soil and land cover scattering properties and their connection with the parameters of microwave scattering is a longstanding research topic. Recently, the advent of modern space-borne microwave radiometers like SMAP in addition to the trend towards open data for scientific use fostered the development of enhanced models based on data fusion from different platforms permitting more accurate assessments. SMAP was designed to operate on an integrated combination of a radiometer and a radar, both operating in L-band. Unexpected failure of the radar component encouraged scientists to experiment various combination of data from the surviving radiometer with other sources of radar data, notably C-band Sentinel-1 data. In this work, we present a case study on a possible combination of SMAP radiometer data with X-band radar data from TerraSAR-X and COSMO-SkyMed, comparing results with those provided by NASA from their standard production procedures. The study was performed on two test sites, one at an agricultural site in Germany and one in the Brazilian Amazon, to explore very different vegetation conditions. This work is a part of a broader research effort addressing the combination of multiple sources of passive and active microwave sensing data. The research question defining this research effort is whether the use of data from multiple active sources affords either obtaining more accurate estimates of active–passive co-variation parameters for a given observation period, or shortening the minimum observation period by increasing the temporal density of active samples. In this framework, this paper addresses a preliminary comparison of fresh and past results obtained from C-, X-, and L-band active sensing data. The observed relations offer interesting clues on the impact of band selection on soil vegetation analysis. [ABSTRACT FROM AUTHOR]

Performance of continuous emission noise radar systems are affected by the sidelobes of the output of the matched filter, with significant effects on detection and dynamic range. Hence, the sidelobe level has to be controlled by a careful design of the transmitted waveform and of the transmit/receive parts of the radar. In this context, the average transmitted power has to be optimized by choosing waveforms with a peak-to-average power ratio as close to the unity as possible. However, after coherent demodulation and acquisition of the received signal and of the reference signal at the transmitting antenna port, the goodness (low sidelobes) of the output from the matched filter can be considerably reduced by the deleterious effects due to the radar hardware, including the analog-to-digital converter (ADC). This paper aims to solve the above problems from both the theoretical and the practical viewpoint and recommends the use of tailored waveforms for mitigating the dynamic range issues. The new findings are corroborated by the results from two noise radar demonstrators operating in Germany (rural environment) and in Turkey (coast and sea environment) and the related lessons learnt. [ABSTRACT FROM AUTHOR]

Electrification offers an opportunity to decarbonize the transport sector, but it might also increase the need for flexibility options in the energy system, as the uncoordinated charging process of battery electric vehicles (BEV) can lead to a demand with high simultaneity. However, coordinating BEV charging by means of smart charging control can also offer substantial flexibility potential. This potential is limited by restrictions resulting from individual mobility behavior and preferences. It cannot be assumed that storage capacity will be available at times when the impact of additional flexibility potential is highest from a systemic point of view. Hence, it is important to determine the flexibility available per vehicle in high temporal (and spatial) resolution. Therefore, in this paper a Markov-Chain Monte Carlo simulation is carried out based on a vast empirical data set to quantify mobility profiles as accurately as possible and to subsequently derive charging load profiles. An hourly flexibility potential is derived and integrated as load shift potential into a linear optimization model for the simultaneous cost-optimal calculation of the dispatch of technology options and long-term capacity planning to meet a given electricity demand. It is shown that the costs induced by BEV charging are largely determined by the profile costs from the combination of the profiles of charging load and renewable generation, and not only by the additional energy and capacity demand. If the charging process can be flexibly controlled, the storage requirement can be reduced and generation from renewable energies can be better integrated. [ABSTRACT FROM AUTHOR]

The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making. [ABSTRACT FROM AUTHOR]

Energy communities (EC) are among the new actors in the energy market, playing an important role in the uptake of photovoltaics (PV) in European markets. This paper estimates their aggregate contribution to the low-carbon energy transition in terms of installed capacities for PV and evaluates their economic performance comparing with market prices. We compiled a database of PV facilities with 3672 entries for Germany and 64 entries for Italy. Our statistical analysis does not support an economic under-performance of EC. The aggregate contribution of EC currently amounts to 600–838 MWp installed capacity in Germany and 10.6 MWp installed capacity in Italy, which makes 1.2–1.7% and 0.07% of all PV installations in Germany and Italy, respectively. [ABSTRACT FROM AUTHOR]

Among numerous publications about the SARS-CoV-2, many articles present research from the geographic point of view. The cartographic research method used in this area of science can be successfully applied to analyze the spatiotemporal characteristics of the pandemic using limited data and can be useful for a quick and preliminary assessment of the spread of infections. In this paper, research on the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was undertaken. The phenomena were investigated in a period of two infection waves in Germany: in spring and autumn 2020. We applied the potential model, entropy, centrographic method, and Lorenz curve in spatial analysis. The potentials model made it possible to distinguish core regions with a high level of the growth of new infections, along with areas of their impact, and regions with a low level of generation of new infections. The entropy showed the spatial distribution of differentiation of the studied system and the change of these characteristics between spring and autumn. The concentration method allowed for spatial and numerical demonstration of the concentration of infected population in a given area. We wanted to show that it is possible to draw meaningful conclusions about the pandemic characteristics using only basic data about infections, along with proper cartographic methods. The results can be used to designate the zones of the greatest threats, and thus, the areas where the most intense actions should be taken. [ABSTRACT FROM AUTHOR]

Climate changes necessitate measures to reduce greenhouse gas emissions (GHG). The European Union's climate and energy policy places particular emphasis on the development of renewable energy, considered to be the primary mean in achieving the climate neutrality goal by 2050. Having in mind the aforementioned, the study was conducted to determine time delay in the development of renewable energy sources between the Visegrad Group (V4) countries and Germany, considered the most advanced country in developing renewable energy and having the greatest impact on shaping European Union climate and energy policy. The time delay econometric method was used in four of its variants. The research results show that, despite the existence of support mechanisms, the development of renewable energy sources (RES) in the V4 countries has a high mutual correlation. There is a hight correlation between RES penetration in energy mix and UE goals of the climate and energy policy until 2020. At the same time, the available data from the analysed period 2004–2019 indicate growing time delay between the reference facility—Germany and the surveyed group. The paper identifies the primary factors behind the identified time delay and proposes research areas that could direct the future research of those aspects. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a "learning model" which outranks countries according to their confrontation of historical macroeconomic indicators for a given period of time with the spreads at the end of that time and to formulate a forward-looking investment strategy regarding government bonds for the following time period. The mechanism of identifying investment opportunities among government bonds is based on the multiple criteria decision making technique, and we look to the Promethee II method as a symmetry approach to country ordering. The spread is defined as the difference between the yield to maturity of the 10-year government bond of a country and the Germany government bond with the same maturity. In this paper, an optimization approach based on three models is developed to find the weights of importance for macroeconomic characteristics, together with a sensitivity analysis on changes in these characteristics. The method was applied to 17 European countries characterized by 16 macroeconomic characteristics. The originality of this paper lies in the two-stage approach to the investment strategy construction based on criteria weights optimization with stability intervals for their values. [ABSTRACT FROM AUTHOR]

This paper presents new insights into the relationship between inequality in access to higher education and social stratification through the analytical lens of refugees' access to high participation systems of higher education (HPS). Taking stock of the growing numbers of refugees and their increasing—yet still marginal—demand for accessing higher education, the paper analyses the specific statuses and rights they are granted, and how they combine in two European Higher Education Area HPS, England and Germany. The comparative analysis draws on the desk-based study of immigration and access to higher education policies and mechanisms for refugees in the two countries. The concept of assemblage is called upon to highlight how complex combinations of asylum, welfare and access to higher education policies lead to differential rights which create different spaces of opportunity for refugees with higher education aspirations. More generally, analysing how these rights intersect allows for a better understanding of inequalities in access to higher education. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a laboratory study of the shear strength and yielding behavior of two glacial till soil deposits from the area of Heiligenhafen, northern Germany. The tests were conducted on reconstituted forms of the soils using a triaxial cell capable of controlling the temperature of the specimens. The experimental program included a series of multi-stage consolidated drained (CD) compression triaxial tests at temperature ranges between 20 and 60 °C. For the temperature range considered in this study, a mild reduction in the effective friction angle of the two till soils of less than 1° was observed due to an increase in temperature from 20 to 60 °C. All the results were carefully assessed in view of the intrinsic soil behavior and fabric, and existing trends are highlighted. The findings of this study provide valuable insights into the shearing properties of till deposits, and can contribute to the enhancement of existing soil constitutive models as well as the development of new models that are particularly suited to the behavior of glacial tills under elevated temperatures. [ABSTRACT FROM AUTHOR]

Extensive surfaces of land are currently under-utilized, marginal and/or contaminated (MUC) in many EU and neighbouring countries. In the past few years, scientific research has demonstrated that bioenergy crops can potentially render this land profitable, generating income for the local populations and, at the same time, reaching the goals of the new Renewable Energy Directive (REDII) without interfering with food production. The main purpose of this paper is to measure net economic returns by computing benefits and costs of low indirect Land Use Change (iLUC) biofuel production on MUC land from the perspective of both the private investors and social welfare. A standard cost-benefit technique was applied to analyse and compare net returns of different advanced bioenergy value-chains in monetary terms. Productivity, economic feasibility and green-house gas (GHG) emissions impact were assessed and considered for the economic analysis. The considered pathways were cellulosic or second generation (2G) ethanol from Giant reed (Arundo donax) in Italy, electricity from miscanthus, biochemicals from spontaneous grass and cultivated Lucerne (Alpha-alfae) with sorghum for biomethane in Germany, and 2G ethanol from Willow (Salix viminalis) in Ukraine. For the risk assessment, Monte Carlo simulation was applied. The results indicated that in Italy and Ukraine, although the production of 2G ethanol would allow positive net yearly margins, the investments will not be profitable compared to the baseline scenarios. In Germany, the work showed good profitability for combined heat and power (CHP) and biochemicals. On the other hand, investments in biomethane showed negative results compared with the baseline scenarios. Finally, the Monte Carlo simulation enabled us to identify the range of possible economic results that could be attained once volatility is factored in. While for Italy the likelihood of yielding positive results remains lower than 20 percent, case studies in Ukraine and Germany showed higher certainty levels, ranging from 49 to 91 percent. [ABSTRACT FROM AUTHOR]

Within the Clim2Power project, two case studies focus on seasonal variations of the hydropower production in the river basins of the Danube (Germany/Austria) and the Douro (Portugal). To deliver spatially highly resolved climate data as an input for the hydrological models, the forecasts of the German Climate Forecast System (GCFS2.0) need to be downscaled. The statistical-empirical method EPISODES is used in this approach. It is adapted to the seasonal data, which consists of ensemble hindcasts and forecasts. Beside this, the two case study regions need specific configurations of the statistical model, providing appropriate predictors for the meteorological variables. This paper describes the technical details of the adaptation of the EPISODES method for the needs of Clim2Power. We analyse the hindcast skill of the downscaled hindcasts of all four seasons for the two variables near-surface (2 m) temperature and precipitation, and conclude that on the average the skill is conserved compared to the global model. This means that the seasonal information is available at a higher spatial resolution without losing skill. Furthermore, the output of the statistical downscaling is nearly bias-free, which is, beside the higher spatial resolution, an added value for the climate service. [ABSTRACT FROM AUTHOR]

In this paper, we present the estimation of surface NO 2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO 2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO 2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO 2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO 2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO 2 concentrations over Germany from 2018 to 2020. Estimated surface NO 2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO 2 in Germany. The estimated surface NO 2 concentrations provide comprehensive information of NO 2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO 2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µ g/m 3 , respectively. While the annual average NO 2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µ g/m 3 . In addition, we used the surface NO 2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO 2 levels in Germany. In general, 10–30% lower surface NO 2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus. [ABSTRACT FROM AUTHOR]

In wind energy site assessment, one major challenge is to represent both the local characteristics as well as general representation of the wind climate on site. Micro-scale models (e.g., Reynolds-Averaged-Navier-Stokes (RANS)) excel in the former, while meso-scale models (e.g., Weather Research and Forecasting (WRF)) in the latter. This paper presents a fast approach for meso–micro downscaling to an industry-applicable computational fluid dynamics (CFD) modeling framework. The model independent postprocessing tool chain is applied using the New European Wind Atlas (NEWA) on the meso-scale and THETA on the micro-scale side. We adapt on a previously developed methodology and extend it using a micro-scale model including stratification. We compare a single- and multi-point downscaling in critical flow situations and proof the concept on long-term mast data at Rödeser Berg in central Germany. In the longterm analysis, in respect to the pure meso-scale results, the statistical bias can be reduced up to 45% with a single-point downscaling and up to 107% (overcorrection of 7%) with a multi-point downscaling. We conclude that single-point downscaling is vital to combine meso-scale wind climate and micro-scale accuracy. The multi-point downscaling is further capable to include wind shear or veer from the meso-scale model into the downscaled velocity field. This adds both, accuracy and robustness, by minimal computational cost. The new introduction of stratification in the micro-scale model provides a marginal difference for the selected stability conditions, but gives a prospect on handling stratification in wind energy site assessment for future applications. [ABSTRACT FROM AUTHOR]

Fast-growing energy demand of the world makes the researchers focus on finding new energy sources or optimizing already-developed approaches. For an efficient use of solar and wind energy in an energy system, correct design and sizing of a power system is of high importance and improving or optimizing the process of data obtaining for this purpose leads to higher performance and lower cost per unit of energy. It is essential to have the most precise possible estimation of solar and wind energy potential and other local weather parameters in order to fully feed the demand and avoid extra costs. There are various methods for obtaining local data, such as local measurements, official organizational data, satellite obtained, and reanalysis data. In this paper, the Modern-Era Retrospective analysis for Research and Applications dataset version 2 (MERRA-2) dataset provided by NASA is introduced and its performance is evaluated by comparison to various locally measured datasets offered by meteorological institutions such as Meteonorm and Deutscher Wetterdienst (DWD, or Germany's National Meteorological Service) around the world. After comparison, correlation coefficients from 0.95 to 0.99 are observed for monthly global horizontal irradiance values. In the case of air temperature, correlation coefficients of 0.99 and for wind speed from 0.81 to 0.99 are observed. High correlation with ground measurements and relatively low errors are confirmed, especially for irradiance and temperature values, that makes MERRA-2 a valuable dataset, considering its world coverage and availability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground. [ABSTRACT FROM AUTHOR]

The energy transition in Germany takes part in decentral structures. With the ongoing integration of Renewable Energy Sources (RES) into the electricity supply system, supply-side is therefore becoming increasingly decentral and volatile due to the specific generation characteristics. A rather inflexible demand-side, on the other hand, increases the effort to gain the necessary equilibrium between generation and consumption. This paper discusses how consumer behaviour can be influenced by real-time pricing to align demand with generation. Therefore, a combination of two different approaches is used, (I) The Cellular Approach (CA) and (II) Agent Based Modelling (ABM). A model is set up considering a regional energy market, where regional electricity products can be traded peer-to-peer regarding each consumer's preferences. The observation is made for a whole distribution grid including all types of consumers. The investigations show that energy purchases can be stimulated individually by a flexible pricing mechanism and met preferences. Moreover, benefits occur for the whole region and potentials arise to smooth the exchange balance to the superordinate grid level. Running the model for one entire year in a conservative generation scenario, hours of oversupply could be reduced by 18 % and the consumption of green electricity generated regionally could be increased by over 125   MWh within the region itself, in comparison to a base scenario. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable resources into the low-voltage distribution grid, the demand for alternatives to grid reinforcement measures has risen. One possible solution is the use of battery systems to balance the power flow at crucial locations in the grid. Hereby, the optimal location and size of the system have to be determined in regard to investment and its effect on grid stability. In this paper, the optimal placement and sizing of battery storage systems for grid stabilization in a small low-voltage distribution grid in southern Germany with high PV penetration are investigated and compared to a grid heuristic reinforcement strategy. [ABSTRACT FROM AUTHOR]

Simple Summary: Pathogenic nematode Ostertagia leptospicularis, as the sole member of the subfamily Ostertagiinae, occurs in both cervid and bovid host species. The broad host specificity of this parasite draws special attention and requires a more in-depth investigation. This study was carried out to find out whether the differences in the nematode morphology were only due to the host influence, or whether genetic differences should be taken into account. To resolve this issue, the classification of O. leptospicularis was raised and discussed based on its host specificity, as well as morphological and genetic characteristics. A combined morphological–molecular approach was used to compare specimens resembling O. leptospicularis collected from naturally infected hosts of various ruminant species (roe deer, red deer, fallow deer, and cattle). Both morphological and molecular analyses highlighted the distinctiveness of O. leptospicularis collected from cattle in Germany, and therefore should now be considered to be a different strain that those collected form cervids in central Europe. Ostertagia leptospicularis Assadov, 1953 was formally described in roe deer Capreolus capreolus and has been reported in a wide range of ruminants, including other Cervidae, as well as Bovidae. Nematode specimens derived from various host species exhibit morphological similarity; however, some differences can be observed. It is unclear if this is due to the differential reaction of one nematode species in different host species (i.e., host-induced changes) or because of distinct nematode species in these hosts (i.e., species complex). This paper focuses on specimens resembling O. leptospicularis f. leptospicularis and its closely related species (Ostertagia ostertagi f. ostertagi) collected from various hosts. Morphometric and molecular techniques were applied to assess host-induced changes in nematode morphology and to clarify its systematic classification. There was an overall effect of host species on measurements of nematodes resembling O. leptospicularis (both males and females), but the distinctiveness of the specimens from cattle Bos taurus were highlighted. The results obtained may suggest that the specimens of O. leptospicularis from cattle in Germany and cervids in central Europe belong to different strains. Furthermore, nematodes from the cervid strain appear to circulate within particular host species, which can be seen in the stated morphological variations. [ABSTRACT FROM AUTHOR]

Stenting is a widely used treatment procedure for coronary artery disease around the world. Stents have a complex geometry, which makes the characterization of their corrosion difficult due to the absence of a mathematical model to calculate the entire stent surface area (ESSA). Therefore, corrosion experiments with stents are mostly based on qualitative analysis. Additionally, the quantitative analysis of corrosion is conducted with simpler samples made of stent material instead of stents, in most cases. At present, several methods are available to calculate the stent outer surface area (SOSA), whereas no model exists for the calculation of the ESSA. This paper presents a novel mathematical model for the calculation of the ESSA using the SOSA as one of the main parameters. The ESSA of seven magnesium alloy stents (MeKo Laser Material Processing GmbH, Sarstedt, Germany) were calculated using the developed model. The calculated SOSA and ESSA for all stents are 33.34%(±0.26%) and 111.86 mm (±0.85 mm), respectively. The model is validated by micro-computed tomography (micro-CT), with a difference of 12.34% (±0.46%). The value of corrosion rates calculated using the ESSA computed with the developed model will be 12.34% (±0.46%) less than that of using ESSA obtained by micro-CT. [ABSTRACT FROM AUTHOR]

The paper pursues the question of the relationship between secularization, religious denominations, and regional characteristics. A literature review leads to the formation of six hypotheses. The analysis of regional statistics for Germany shows clear regional differences in the distribution of the denominational affiliation as well as the secularization of the population. Traditional differences between the federal states and their sub-regions are still recognizable. In particular, there is an obvious difference in secularization between the East German and West German states in relation to the significance of the two large Christian denominations. These differences between the paths of regional development and degree of secularization are so large that most of the hypothesis testing on the association between religious affiliation and demographic and socio-economic variables resulted in different findings for the two areas. Further research is required to pursue more regional differentiation and to include non-Christian faiths in the investigations. [ABSTRACT FROM AUTHOR]

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations. [ABSTRACT FROM AUTHOR]

This paper discusses the issues of strength and creep of polymeric materials used in orthodontic appliances. Orthodontic biomechanics is focused on the movement of individual teeth or dental groups as a result of the force applied by orthodontic appliances. Stresses in the construction of functional and biomechanical appliances is generated when using the apparatus in the oral cavity. The orthodontic appliance must maintain its shape and not be damaged during treatment so strength and creep resistance are fundamental properties. It was assumed that the clinical success of orthodontic appliances can be determined by these performance properties. The aim of the work was the experimental assessment of comparative bending strength and creep resistance of selected popular polymer materials used in the production of biomechanical orthodontic appliances. Four commercial materials manufactured by the world class producers were tested: NextDent Ortho Rigid (Vertex-Dental B.V., Soesterberg, The Netherlands) marked as "1A"; Erkocryl (ERKODENT Erich Kopp GmbH, Pfalzgrafenweiler, Germany)-"2A"; Vertex Orthoplast (Vertex Dental B.V.), blue, marked as "3A" and material with the same name as "3A" but orange, marked in the article as "4A". All the tests were carried out after aging in artificial saliva for 48 h at a temperature of 37 °C. Flexular strength and flexular modulus were made using the three point bending method according to the ISO 178 technical standard. Creep tests were carried out according to the method contained in ISO 899-2. The creep test was carried out in an artificial saliva bath at 37 °C. The creep tests showed significant differences in the strength, modulus and deformability of the tested materials. The strength reliability of the tested materials also varied. The research shows that the 2A material can be used for orthodontic applications in which long-term stresses should be lower than 20 MPa. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs. [ABSTRACT FROM AUTHOR]

Humans' activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to prove the feasibility of the presented approach: on average, per capita water demand and aggregated water demand deviates by less than 7% from real demand data. Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%. [ABSTRACT FROM AUTHOR]

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements. [ABSTRACT FROM AUTHOR]

In decentralized energy systems, electricity generated and flexibility offered by households can be organized in the form of community energy systems. Business models, which enable this aggregation at the community level, will impact on the involved actors and the electricity market. For the case of Germany, in this paper different aggregation scenarios are analyzed from the perspective of actors and the market. The main components in these scenarios are the Community Energy Storage (CES) technology, the electricity tariff structure, and the aggregation goal. For this evaluation, a bottom-up community energy system model is presented, in which the households and retailer are the key actors. In our model, we distinguish between the households with inflexible electricity load and the flexible households that own a heat pump or Photovoltaic (PV) storage systems. By using a game-theoretic approach and modeling the interaction between the retailer and households as a Stackelberg game, a community real-time pricing structure is derived. To find the solution of the modeled Stackelberg game, a genetic algorithm is implemented. To analyze the impact of the aggregation scenarios on the electricity market, a "Market Alignment Indicator" is proposed. The results show that under the considered regulatory framework, the deployment of a CES can increase the retailer's operational profits while improving the alignment of the community energy system with the signals from the electricity market. Depending on the aggregation goal of the retailer, the implementation of community real-time pricing could lead to a similar impact. Moreover, such a tariff structure can lead to financial benefits for flexible households. [ABSTRACT FROM AUTHOR]

The retrieval of soil surface parameters, in particular soil moisture and roughness, based on Synthetic Aperture Radar (SAR) data, has been the subject of a large number of studies, of which results are available in the scientific literature. However, although refined methods based on theoretical/analytical scattering models have been proposed and successfully applied in experimental studies, at the operative level very simple, empirical models with a number of adjustable parameters are usually employed. One of the reasons for this situation is that retrieval methods based on analytical scattering models are not easy to implement and to be employed by non-expert users. Related to this, commercially and freely available software tools for the processing of SAR data, although including routines for basic manipulation of polarimetric SAR data (e.g., coherency and covariance matrix calculation, Pauli decomposition, etc.), do not implement easy-to-use methods for surface parameter retrieval. In order to try to fill this gap, in this paper we present a user-friendly computer program for the retrieval of soil surface parameters from Polarimetric Synthetic Aperture Radar (PolSAR) imageries. The program evaluates soil permittivity, soil moisture and soil roughness based on the theoretical predictions of the electromagnetic scattering provided by the Polarimetric Two-Scale Model (PTSM) and the Polarimetric Two-Scale Two-Component Model (PTSTCM). In particular, nine different retrieval methodologies, whose applicability depends on both the used polarimetric data (dual- or full-pol) and the characteristics of the observed scene (e.g., on its topography and on its vegetation cover), as well as their implementation in the Interactive Data Language (IDL) platform, are discussed. One specific example from Germany's Demmin test-site is presented in detail, in order to provide a first guide to the use of the tool. Obtained retrieval results are in agreement with what was expected according to the available literature. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of regional renewable electricity self-sufficiency targets on the power system in Germany. For this purpose, an interdisciplinary approach from social sciences and energy system modelling was chosen, which allows considering qualitative factors such as public acceptance or political stability. Following the concept of context scenarios, consistent raw scenarios are generated by a cross-impact balance analysis (CIB), and the scenarios are quantified by the unit commitment and expansion cost minimisation model ENTIGRIS considering power plants, storages, and the electricity grid. This approach enables an understanding of the system framework conditions and their relationships and allows the combination of qualitative and quantitative scenario descriptors. The most important factors for setting regional self-sufficiency targets were identified through interviews. The main system effects identified are: The regional distribution of generation capacities is strongly influenced by a more demand-oriented installation of generation capacities. This leads to less grid reinforcement, but higher rates of curtailment. In all scenarios, higher utilization of the PV roof potential instead of ground mounted could be observed. The total system costs are increasing only slightly with regional self-supply targets. In general, it was found that the influence of regional self-sufficiency targets is less pronounced in scenarios that already achieve high national RES shares than in scenarios that achieve lower shares, since technology, storage and grid expansion measures are necessary anyway to achieve high RES shares. Overall, the effects here are rather small and the regional objective is not associated with major disadvantages for the system. In a future characterised by stagnation, the system can benefit from regional targeting, as higher renewable shares and lower costs can result. The main conclusion therefore is that regional target setting seem to be beneficial for the overall power system, in terms of system cost, national RE share, acceptance and CO2-emissions. [ABSTRACT FROM AUTHOR]

Digitalization of production environment, also called Industry 4.0 (the term invented by Wahlster Wolfgang in Germany) is now one of the hottest topics in the computer science departments at universities and companies. One of the most significant topics in this area is augmented reality (AR). The interest in AR has grown especially after the introduction of the Microsoft HoloLens in 2016, which made this technology available for researchers and developers all around the world. It is divided into numerous subtopics and technologies. These wireless, see-through glasses give a very natural human-machine interface, with the possibility to present certain necessary information right in front of the user's eyes as 3D virtual objects, in parallel with the observation of the real world, and the possibility to communicate with the system by simple gestures and speech. Scientists noted that in-depth studies connected to the effects of AR applications are presently sparse. In the first part of this paper, the authors recall the research from 2019 about the new method of manual wiring support with the AR glasses. In the second part, the study (tests) for this method carried out by the research team is described. The method was applied in the actual production environment with consideration of the actual production process, which is manual wiring of the industrial enclosures (control cabinets). Finally, authors deliberate on conclusions, technology's imperfections, limitations, and future possible development of the presented solution. [ABSTRACT FROM AUTHOR]

Integrated care is regarded as a key for care delivery to persons with chronic long-term conditions such as Parkinson's disease. For persons with Parkinson's disease, obtaining self-management support is a top priority in the context of integrated care. Self-management is regarded as a crucial competence in chronic diseases since the affected persons and their caregivers inevitably take up the main responsibility when it comes to day-to-day management. Formal self-management education programs with the focus on behavioral skills relevant to the induction and maintenance of behavioral change have been implemented as a standard in many chronic long-term conditions. However, besides the example of the Swedish National Parkinson School, the offers for persons with Parkinson's disease remain fragmented and limited in availability. Today, no such program is implemented as a nationwide standard in Germany. This paper provides (1) a systematic review on structured self-management education programs specifically designed or adopted for persons with Parkinson's disease, (2) presents the Swedish National Parkinson School as an example for a successfully implemented nationwide program and (3) presents a concept for the design, evaluation and long-term implementation of a future-orientated self-management education program for persons with Parkinson's disease in Germany. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a complex neurodegenerative disease that can be best treated with a multi-disciplinary care approach. Building care networks has been shown as a useful tool to facilitate the integration of care services and improve outcomes for patients and care providers. However, experiences and practices relating to building a network are very limited in the field of Parkinson's disease. This paper portrays existing Parkinson networks in Germany. With the help of a standardized template, description of networks and their building-blocks, so-called modules, were collected from all over Germany. Modules were rated in terms of their expected benefit and the required effort when implementing them, with the help of an expert survey. The rating showed that some modules were perceived as more important than others, but all modules were recognized as beneficial for patients and care providers. Overall, the German experience shows that building a Parkinson network facilitates the integration of care and provides a benefit to all stakeholders involved. [ABSTRACT FROM AUTHOR]

Coherent wind doppler lidar (CWDL) is a cost-effective way to estimate wind power potential at hub height without the need to build a meteorological tower. However, fog and low stratus (FLS) can have a negative impact on the availability of lidar measurements. Information about such reductions in wind data availability for a prospective lidar deployment site in advance is beneficial in the planning process for a measurement strategy. In this paper, we show that availability reductions by FLS can be estimated by comparing time series of lidar measurements, conducted with WindCubes v1 and v2, with time series of cloud base altitude (CBA) derived from satellite data. This enables us to compute average maps (2006–2017) of estimated availability, including FLS-induced data losses for Germany which can be used for planning purposes. These maps show that the lower mountain ranges and the Alpine regions in Germany often reach the critical data availability threshold of 80% or below. Especially during the winter time special care must be taken when using lidar in southern and central regions of Germany. If only shorter lidar campaigns are planned (3–6 months) the representativeness of weather types should be considered as well, because in individual years and under persistent weather types, lowland areas might also be temporally affected by higher rates of data losses. This is shown by different examples, e.g., during radiation fog under anticyclonic weather types. [ABSTRACT FROM AUTHOR]

Despite the difficulties in obtaining the ultimate capacity of the large diameter bored piles (LDBP) using the in situ loading test, this method is the most recommended by several codes and design standards. However, several settlement-based approaches, alongside the conventional capacity-based design approach for LDBP, are proposed in the event of the impossibility of performing a pile-loading test during the design phase. With that in mind, natural clays usually involve some degree of over consolidation; there is considerable debate among the various approaches on how to represent the behavior of the overconsolidated (OC)stiff clay and its design parameters, whether drained or undrained, in the pile-load test problems. In this paper, field measurements of axial loaded to failure LDBP load test installed in OC stiff clay (Alzey Bridge Case Study, Germany) have been used to assess the quality of two numerical models established to simulate the pile behavior in both drained and undrained conditions. After calibration, the load transfer mechanism of the LDBP in both drained and undrained conditions has been explored. Results of the numerical analyses showed the main differences between the soil pile interaction in both drained and undrained conditions. Also, field measurements have been used to assess the ultimate pile capacity estimated using different methods. [ABSTRACT FROM AUTHOR]