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"Machen Sie doch mal mehr PR und Werbung für Ihre Schule": Kommunikationscontrolling in Schulen
(2015)
Henry Fords Bonmot zur Werbeerfolgskontrolle ist sicherlich der bekannteste Satz im Sektor des Kommunikationscontrollings: „Die Hälfte unserer Werbegelder werfen wir zum Fenster raus. Ich weiß nur nicht, welche Hälfte das ist.“ Diese kritische Würdigung von Kommunikationsleistungen ist auch heute noch immer wieder Thema und gerade im Umfeld von Schule, wo diese Prozesse noch keine sehr lange Tradition haben, Teil der internen und externen Diskussion. Die Steuerung von Kommunikationsprozessen erfordert jedoch nicht nur die Quantifizierung von Kommunikationsleistungen, sondern eine Einbettung in die gesamte Marketingstrategie und in die Bewertung einzelner Marketingbereiche und der dort entwickelten Marketingziele.
Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver.
This article shows a different approach by defining a type-directed translation from FGG to an untyped lambda-calculus. The translation of an FGG program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary.
Every program in the image of the translation has the same dynamic semantics as its source FGG program. The proof of this result is based on a syntactic, step-indexed logical relation. The step-index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods.
The Advanced Innovation Design Approach is a holistic methodology for enhancing innovative and competitive capability of industrial companies. AIDA can be considered as an open mindset, an individually adaptable range of strongest innovation techniques such as comprehensive front-end innovation process, advanced innovation methods, best tools and methods of the TRIZ methodology, organizational measures for accelerating innovation, IT-solutions for Computer-Aided Innovation, and other innovation methods, elaborated in the recent decade in the industry and academia
Recent advances in spiked shoe design, characterized by increased longitudinal stiffness, thicker midsole foams, and reconfigured geometry are considered to improve sprint performance. However, so far there is no empirical data on the effects of advanced spikes technology on maximal sprinting speed (MSS) published yet. Consequently, we assessed MSS via ‘flying 30m’ sprints of 44 trained male (PR: 10.32 s - 12.08 s) and female (PR: 11.56 s - 14.18 s) athletes, wearing both traditional and advanced spikes in a randomized, repeated measures design. The results revealed a statistically significant increase in MSS by 1.21% on average when using advanced spikes technology. Notably, 87% of participants showed improved MSS with the use of advanced spikes. A cluster analysis unveiled that athletes with higher MSS may benefit to a greater extent. However, individual responses varied widely, suggesting the influence of multiple factors that need detailed exploration. Therefore, coaches and athletes are advised to interpret the promising performance enhancements cautiously and evaluate the appropriateness of the advanced spike technology for their athletes critically.
Financing trade and development sustainably will be crucial for Africa. Enhanced collaboration between multilateral development banks, development finance institutions and ECAs could greatly enhance intra-regional trade. Furthermore, setting up a ‘level playing field’ on the continent will allow governments to make strategic interventions for successful export credits and trade finance solutions, fostering growth through trade. African trade is already showing signs of rebounding from the coronavirus- induced recession. Through concerted, co-operative and continent-wide efforts, drawing on the knowledge and resources of all types of institutions and policy experts, Africa will continue to grow confidently and quickly into its increasingly important role as an engine of economic growth and global trade.
Objective: This paper deals with the design and the optimization of mechatronic devices.
Introduction: Comparing with existing works, the design approach presented in this paper aims to integrate optimization in the design phase of complex mechatronic systems in order to increase the efficiency of this method.
Methods: To solve this problem, a novel mechatronic system design approach has been developed in order to take the multidisciplinary aspect and to consider optimization as a tool that can be used within the embodiment design process to build mechatronic solutions from a set of solution concepts designed with innovative or routine design methods.
Conclusions: This approach has then been applied to the design and optimization of a wind turbine system that can be implemented to autonomously supply a mountain cottage.
The communication technologies for automatic me-ter reading (smart metering) and for energy production and distribution networks (smart grid) have the potential to be one of the first really highly scaled machine-to-machine-(M2M)-applications. During the last years two very promising devel-opments around the wireless part of smart grid communication were initialized, which possibly have an impact on the markets far beyond Europe and far beyond energy automation. Besides the specifications of the Open Metering System (OMS) Group, the German Federal Office for Information Security (Bundesamt für Sicherheit in der Informationstechnik, BSI) has designed a protection profile (PP) and a technical directive (TR) for the communication unit of an intelligent measurement sys-tem (smart meter gateway), which were released in March 2013. This design uses state-of-the-art technologies and prescribes their implementation in real-life systems. At first sight the expenditures for the prescribed solutions seem to be significant. But in the long run, this path is inevitable and comes with strategic advantages.
Motivated by the recent trend towards the usage of larger receptive fields for more context-aware neural networks in vision applications, we aim to investigate how large these receptive fields really need to be. To facilitate such study, several challenges need to be addressed, most importantly: (i) We need to provide an effective way for models to learn large filters (potentially as large as the input data) without increasing their memory consumption during training or inference, (ii) the study of filter sizes has to be decoupled from other effects such as the network width or number of learnable parameters, and (iii) the employed convolution operation should be a plug-and-play module that can replace any conventional convolution in a Convolutional Neural Network (CNN) and allow for an efficient implementation in current frameworks. To facilitate such models, we propose to learn not spatial but frequency representations of filter weights as neural implicit functions, such that even infinitely large filters can be parameterized by only a few learnable weights. The resulting neural implicit frequency CNNs are the first models to achieve results on par with the state-of-the-art on large image classification benchmarks while executing convolutions solely in the frequency domain and can be employed within any CNN architecture. They allow us to provide an extensive analysis of the learned receptive fields. Interestingly, our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters practically translate into well-localized and relatively small convolution kernels in the spatial domain.
Die Energiewende ist ein elementares Thema, für Deutschland wie auch für viele andere Regionen weltweit. Bei der Bereitstellung effizienter und stabiler Verteilnetze stellen Kommunikationslösungen einen zentralen Baustein dar, um auf der Grundlage eines zeitnahen Monitorings koordinierte Regelalgorithmen zu realisieren. Dies gilt für alle Ebenen der Versorgung, wobei aus Sicht der Kommunikationstechnik die unterste Ebene der Verteilnetze am interessantesten ist: Hier sind die anspruchsvollsten Anforderungen im Hinblick auf die Kosten- und die Energieoptimierung der Kommunikationsknoten sowie die Administrierbarkeit, die Stabilität und die Skalierbarkeit der Gesamtlösung zu berücksichtigen. Das Steinbeis-Transferzentrum Embedded Design und Networking an der Hochschule Offenburg unter der Leitung von Prof. Dr.-Ing. Axel Sikora hat in verschiedenen Projekten mit renommierten Partnern umfangreiche Lösungen für diese sogenannte Primärkommunikation entwickelt.
Auswirkung eines Importstopps russischer Energieträger auf die Klimaschutzziele in Deutschland
(2022)
Ein Importstopp russischer Energieträger nach Deutschland wird derzeit vermehrt diskutiert. Wir wollen die Diskussion unterstützen, indem wir einen Weg zeigen, wie das Elektrizitätssystem in Deutschland kurzfristig mit geringen Energieimporten auskommt und welche Maßnahmen notwendig sind, um die Klimaschutzziele trotzdem einzuhalten. Die Ergebnisse eines solchen Energiewendeszenarios mit reduzierter Importabhängigkeit werden mit dem Energiesystemmodell MyPyPSA-Ger berechnet. Die wichtigsten Erkenntnisse sind, dass ein zügiger Ausbau Erneuerbarer Energien und von Speichertechnologien • die Abhängigkeit des deutschen Elektrizitätssystems von Energieimporten deutlich reduziert. • auch langfristig keine wesentlichen Importe der Energieträger Erdgas, Steinkohle und Mineralöl nach sich zieht. • über die Klimaziele der Bundesregierung hinaus das 1,5-Grad-Ziel im Elektrizitätssystem erreicht wird.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
In the modern knowledge-based and digital economy, the value of knowledge is growing relative to other assets and new intellectual property is being created at an ever-increasing rate. Therefore, the ability to find non-trivial solutions, systematically generate new concepts, and create intellectual property rapidly become crucial to achieving competitive advantage and leveraging the intellectual potential of organizations.
Durch den Einsatz von Torschleieranlagen zwischen zwei Zonen mit unterschiedlichen Temperaturen kann der Luftaustausch aufgrund freier Konvektion verhindert werden. Der Einfluß unterschiedlicher Betriebsfälle auf den Energieverbrauch und die thermische Behaglichkeit wurde im Labor untersucht. Die Effizienz von Torschleieranlagen wurde zusätzlich im Praxisbetrieb beim Einsatz an einer Kühlzelle überprüft. Bei diesem Anwendungsfall steht nicht die thermische Behaglichkeit sondern der Energieverbrauch, die Gefahr der Kühlguterwärmung und die der Eisbildung vor dem Kühlzelleneingang im Vordergrund.
Beuys-Gespräch
(2022)
In sicherheitskritschen Systemen darf kein Stück Code im Produktionsbetrieb ablaufen, ohne vorher intensive Tests durchlaufen zu haben. Aber auch zur Qualitätssicherung muss Software getestet werden. Um die Codeüberdeckung zu prüfen, sind zusätzliche Prüf-Instruktionen im Quellcode erforderlich. Auf kleinen Systemen mit wenig RAM kann sich der Entwickler dann etwas einfallen lassen, damit das funktioniert.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the G are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
The energy supply of Offenburg University of Applied Sciences (HS OG) was changed from separate generation to trigeneration in 2007/2008. Trigeneration was installed for supplying heat, cooling and electrical power at HS OG. In this paper, trigeneration process and its modes of operation along with the layout of the energy facility at HS OG were described. Special emphasis was given to the operation schemes and control strategies of the operation modes: winter mode, transition mode and summer mode. The components used in the energy facility were also outlined. Monitoring and data analysis of the energy system was carried out after the commissioning of trigeneration in the period from 2008 to 2011. Thus, valuable performance data was obtained.
CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision.
Das Virtuelle Informatiklabor soll Schülern und Studierenden den übergroßen Respekt vor dem Fach Informatik nehmen und sie beim Lernen der Inhalte unterstützen. Zu diesem Zweck werden grundlegende Algorithmen der Informatik anhand konkreter Aufgabenstellungen in interaktiven Anwendungen behandelt, um den Lernenden das explorative Erkunden zu ermöglichen. Animationen sollen das Verstehen fördern, Experimente das eigenständige, durch vielfältige Hilfen unterstützte Anwenden und Umsetzen des Gelernten. Der erste Themenbereich im Virtuellen Informatiklabor umfasst die Rekursion, die in mehreren Anwendungen präsentiert wird.
Wer sich mit Digitalisierungsbestrebungen an Schulen befasst, stellt fest, dass die Tragweite der intendierten Transformation von Bildungseinrichtungen zu automatisierten Lernfabriken durch Digitaltechnik nur von Wenigen realisiert wird. Viele Beteiligte (wollen) glauben, es ginge nur um eine bessere technische Ausstattung der Lehreinrichtungen zur Unterstützung der Lehrkräfte – und übersehen, dass mit Kybernetik und Behaviorismus zwei den Menschen determinierende Theorien eine Renaissance erleben. Vertreter dieser Disziplinen glauben daran, dass sowohl der einzelne Mensch wie ganze Gesellschaften oder Sozialgemeinschaften wie ein Maschinenpark programmiert und gesteuert werden könne. Dabei werden Lernprozesse zu Akten der systematischen Selbstentmündigung umdefiniert: die Zurichtung der Lernenden auf abfragbare Kompetenzen mit Hilfe von Algorithmen und Software.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research has shown, how such detection can be achieved by deep learning methods. However, that particular approach is limited to the identification of only 4 types of vulnerabilities. Subsequently, we analyze to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardized LLVM Intermediate Representation. The vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, the methodology presented allows an accurate detection of 23 (compared to 4) vulnerabilities.
Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions and preferences regarding the suitable visual qualities of SARs in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Our results indicate that Israeli and German designers share similar perceptions of visual qualities and most of the robotics roles. However, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.
(1) Background: Little is known about the baroque composer Domenico Scarlatti (1685-1757), whose life was centred behind closed doors at the royal court in Spain. There are no reports about his illnesses. From his compositions, mainly for harpsichord, an outstanding virtuosity can be read. (2) Case Presentation: In this case report, the only known oil painting of Domenico Scarlatti is presented, on which he is about 50 years old. In it one recognizes conspicuous hands with hints of watch glass nails and drumstick fingers. (3) Discussion: Whether Scarlatti had chronic hypoxia of peripheral body regions as a sign of, e.g., bronchial cancer or a severe heart disease, is not known. (4) Conclusions: The above-mentioned signs recorded in the oil painting, even if they were not interpretable at that time, are clearly represented and recorded for us and are open to diagnostic discussion from today's point of view.
Minderjährige genießen in diversen Rechtsgebieten zu Recht besonderen Schutz. Dazu gehören das allgemeine Vertragsrecht des Bürgerlichen Gesetzbuchs (BGB), das Lauterkeitsrecht des UWG1 und auch das Datenschutzrecht, wo dies in der Datenschutz-Grundverordnung (DS-GVO) ausdrücklich festgeschrieben wird. Der Beitrag diskutiert einige der relevanten Fragen.
In an extensive research project, we have assessed the application of different service models by export credit agencies (ECAs) and export-import banks (EXIMs). We conducted interviews with 35 representatives of ECAs and EXIMs from 27 countries. The question guiding this study is: How do ECAs and EXIMs adopt public service models for supporting exporters? We conducted a holistic multiple case study, investigating if and how these organisations apply public service models developed by Schedler and Guenduez, and which roles of the state are relevant. We find that there is a variety of different service models used by ECAs and EXIMs, and that the service model approaches have great potential to learn from each other and innovate existing services.
High-tech running shoes and spikes ("super-footwear") are currently being debated in sports. There is direct evidence that distance running super shoes improve running economy; however, it is not well established to which extent world-class performances are affected over the range of track and road running events.
This study examined publicly available performance datasets of annual best track and road performances for evidence of potential systematic performance effects following the introduction of super footwear. The analysis was based on the 100 best performances per year for men and women in outdoor events from 2010 to 2022, provided by the world governing body of athletics (World Athletics).
We found evidence of progressing improvements in track and road running performances after the introduction of super distance running shoes in 2016 and super spike technology in 2019. This evidence is more pronounced for distances longer than 1500 m in women and longer than 5000 m in men. Women seem to benefit more from super footwear in distance running events than men.
While the observational study design limits causal inference, this study provides a database on potential systematic performance effects following the introduction of super shoes/spikes in track and road running events in world-class athletes. Further research is needed to examine the underlying mechanisms and, in particular, potential sex differences in the performance effects of super footwear.
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, especially in safety-critical domains like autonomous driving, but also in safety systems where malicious actors can digitally alter inputs to circumvent safety guards. However, designing effective out-of-distribution tests that encompass all possible scenarios while preserving accurate label information is a challenging task. Existing methodologies often entail a compromise between variety and constraint levels for attacks and sometimes even both. In a first step towards a more holistic robustness evaluation of image classification models, we introduce an attack method based on image solarization that is conceptually straightforward yet avoids jeopardizing the global structure of natural images independent of the intensity. Through comprehensive evaluations of multiple ImageNet models, we demonstrate the attack's capacity to degrade accuracy significantly, provided it is not integrated into the training augmentations. Interestingly, even then, no full immunity to accuracy deterioration is achieved. In other settings, the attack can often be simplified into a black-box attack with model-independent parameters. Defenses against other corruptions do not consistently extend to be effective against our specific attack.
Project website: https://github.com/paulgavrikov/adversarial_solarization
Die Weltwirtschaftskrise 2008 hat mit ihrer zeitweisen Verknappung von Acetonitril eindringlich gezeigt, dass man nicht nur auf eine einzige chromatographische Methode setzten sollte. Genau dies wird aber im Augenblick getan, denn Industrie und Forschung setzen mehrheitlich auf die High Performance Liquid Chromatography (HPLC) als die Trennmethode ihrer Wahl. Für viele Anwendungen in der Pharmazie, in der Umweltanalytik, der Lebensmittelanalytik, aber auch in der Inprozesskontrolle gibt es mit der Dünnschichtchromatografie eine Alternative.
Wissenschaftler des Institute for Trade and Innovation (IfTI) an der Hochschule Offenburg haben kürzlich Benchmarking-Analysen staatlicher Exportfinanzierungsinstrumente insbesondere in OECD-Ländern durchgeführt. In zwei Forschungsprojekten mit Fokus auf Dänemark und Norwegen wurde hierfür ein wertschöpfungsorientiertes Bewertungsmodell erarbeitet. Damit kann nun auf Basis von wissenschaftlich anerkannten Analyseverfahren gemessen werden, wie erfolgreich die staatliche Exportfinanzierung im Vergleich mit anderen Ländern ist.
Bluetooth Low Energy extends the Bluetooth standard in version 4.0 for ultra-low energy applications through the extensive usage of low-power sleeping periods, which inherently difficult in frequency hopping technologies. This paper gives an introduction into the specifics of the Bluetooth Low Energy protocol, shows a sample implementation, where an embedded device is controlled by an Android smart phone, and shows the results of timing and current consumption measurements.
Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
(2023)
Convolutional neural networks encode images through a sequence of convolutions, normalizations and non-linearities as well as downsampling operations into potentially strong semantic embeddings. Yet, previous work showed that even slight mistakes during sampling, leading to aliasing, can be directly attributed to the networks' lack in robustness. To address such issues and facilitate simpler and faster adversarial training, [12] recently proposed FLC pooling, a method for provably alias-free downsampling - in theory. In this work, we conduct a further analysis through the lens of signal processing and find that such current pooling methods, which address aliasing in the frequency domain, are still prone to spectral leakage artifacts. Hence, we propose aliasing and spectral artifact-free pooling, short ASAP. While only introducing a few modifications to FLC pooling, networks using ASAP as downsampling method exhibit higher native robustness against common corruptions, a property that FLC pooling was missing. ASAP also increases native robustness against adversarial attacks on high and low resolution data while maintaining similar clean accuracy or even outperforming the baseline.
Formal Description of Inductive Air Interfaces Using Thévenin's Theorem and Numerical Analysis
(2014)
With the development of new integrated circuits to interface radio frequency identification protocols, inductive air interfaces have become more and more important. Near field communication is not only able to communicate, but also possible to transfer power wirelessly and to build up passive devices for logistical and medical applications. In this way, the power management on the transponder becomes more and more relevant. A designer has to optimize power consumption as well as energy harvesting from the magnetic field. This paper discusses a model with simple equations to improve transponder antenna matching. Furthermore, a new numerical analysis technique is presented to calculate the coupling factors, inductions, and magnetic fields of multiantenna systems.
State-of-the-art models for pixel-wise prediction tasks such as image restoration, image segmentation, or disparity estimation, involve several stages of data resampling, in which the resolution of feature maps is first reduced to aggregate information and then sequentially increased to generate a high-resolution output. Several previous works have investigated the effect of artifacts that are invoked during downsampling and diverse cures have been proposed that facilitate to improve prediction stability and even robustness for image classification. However, equally relevant, artifacts that arise during upsampling have been less discussed. This is significantly relevant as upsampling and downsampling approaches face fundamentally different challenges. While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling. Blurring is therefore not an option and dedicated operations need to be considered. In this work, we are the first to explore the relevance of context during upsampling by employing convolutional upsampling operations with increasing kernel size while keeping the encoder unchanged. We find that increased kernel sizes can in general improve the prediction stability in tasks such as image restoration or image segmentation, while a block that allows for a combination of small-size kernels for fine details and large-size kernels for artifact removal and increased context yields the best results.
Die von der Bundesregierung beschlossene Energiewende stellt Politik und Gesellschaft, Wirtschaft und Wissenschaft vor große Herausforderungen. Entscheidend für den Erfolg der Energiewende wird es sein, die Wettbewerbsfähigkeit des Industriestandortes Deutschland zu erhalten. Dafür muss weiterhin eine hohe Stromversorgungsqualität bei zugleich international wettbewerbsfähigen Strompreisen sichergestellt sein. Der BDI stellt fünf Prinzipien auf dem Weg zu einem neuen Strommarktdesign auf und zeigt, dass eine informations- und kommunikationstechnische Vernetzung relevanter Komponenten des Energiesystems für das künftige System essenziell ist.