o Computer science and engineering with respect to university faculties, o Computer science with respect to scientific publications, o ICT hardware and software with respect to R&d activity performed in R&d centres,
the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science
faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms
11 Scientific publications in Computer science Agrd 12 12 Internationalisation Outward ICT R&d internationalisation Intrd 1 13 Inward ICT R&d internationalisation Intrd
of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms FP7 funding
Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance
of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total
number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science
ICT firms in the EU to a region's share in the EU population Region's share in the total EU FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE Rev
of indicator FP7 participations FP7 funding to SMES FP7 participations by SMES Location of ICT R&d centres Ownership of ICT R&d centres Scientific publications in Computer science
in the Computer science area produced by organisations located in the observed region Unit of measurement Region's share in the total number of FP7 participations to a region's share in the EU population Region's share in the total EU FP7 funding
's share in the EU population Region's share in the total number of R&d centres owned by EU firms to a region's share in the EU population Region's share in the total number of publications in Computer science to a region's share in the
. 4) Computer science as defined by Web of Science classification of Research Areas Unit of observation NUTS 3 Source FP7 database by EC DG Connect (see Section 5. 2) ICT
The performance of universities and computer science faculties across the world, as reported by the QS University ranking.
measured in terms of the number of publications in the computer science research area, of the research institutions in Europe for the period 2000-2012 from the Web of Science by Thomson Reuters. For a detailed description of the data source, see Section 5. 3. 20 Company-level
and Computer science and Electronic Faculties originate from the QS WORLD UNIVERSITY RANKINGS. It was formed in 2008 to meet the increasing public interest in comparative data on universities and organisations,
of which is Computer science, additional faculty-level information is extracted for the purpose of the EIPE study.
including Computer science. This information allows us to observe the location of research and education in ICT activities at world-level.
For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered.
which permits the inclusion of EIPE-relevant fields such as Computer science. This information allows us to observe the location of ICT R&d activity.
An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality.
bad harvest Ag yield/shock predictions Campaign effectiveness Social network delineated market areas Predictive algorithms to anticipate prod. churn Social network targeted marketing Post-disaster refugee reunification
a correlation algorithm could be developed to reverse engineer approximate values for these indicators, in order to estimate user numbers in between surveys,
Dealing with large heterogeneous data sets calls for algorithms that can understand the data shape
or GIGO for short, is a computer science concept that refers to the fact that the veracity of the output of any logical process depends on the veracity of the input data.
using decision-tree algorithms or other techniques. However, data cleaning itself is a subjective process (for example,
For example, the famous Google pagerank algorithm has spawned an entire industry of organizations that claim to enhance website page rankings,
diminishing the robustness of the original algorithm. For example, the enthusiasm surrounding GFT may well have created rebound effects,
Similarly, Frias-Martinez and Virseda (2012) needed census data to build their algorithms and provide training data for their algorithms to reverse engineer approximate survey maps.
Official statistics will thus continue to be important to building the big data models and for periodic benchmarking
when it comes to working with large volumes of big data calling for computer science and decision-analysis skills that are emphasized not in traditional statistical courses (Mcafee and Brynjolfsson,
algorithms and software techniques that can be repurposed for business-use cases. Indeed, where the applications of data use for development are concerned,
A Faster Algorithm for Betweenness Centrality, Journal of Mathematical Sociology 25 (2): 163-177. Clauset, A. et al.
Innovative SMES often have dedicated high-tech solutions for specific functions (such as the 3d sound sensors or social media analysis algorithms in the Stratumseind example.
A two-stage clustering algorithm with robust recovery characteristics. Educational and Psychological Measurement 40,755-759.
Based on the analysis of the curriculum vitae of the top 1, 000 scientists in computer science, it shows that these conditions were met only in the US academic system.
particularly computer science, and industrial competitiveness. We will use original evidence, admittedly of preliminary type, to support this proposition.
In the fourth section we review descriptive evidence drawn from a large sample of CVS of the top 1, 010 scientists in computer science worldwide.
A few years ago we asked a small panel of scientific authorities in computer science, in both European and US universities,
while the seminal theoretical contributions to the entire field of computer science were conceived by European thinkers (Alan Turing
it nurtures the ecology of ideas and visions that feed innovation Table 1. Origins of most important ideas in computer science and technology Top ten ideas in computer science 1. Turing machine
pseudocausality 7. Relational database 8. Fourier fast transform (FFT)( Cooley and Tuckey) 9. Efficient algorithms; data structure (Knuth and Tarjan) 10.
Luckily, computer science and the computer industry have been the object of a massive historical literature, that has highlighted several key factors.
This opened the way to a large diffusion of courses in computer science across US universities. Meanwhile, US universities started to be involved in research on the component technologies underlying the computer.
In France the theoretical roots of computer science were laid down as early as the 1930s. The french mathematician Louis Couffignal demonstrated how a programmable binary calculator could be constructed using electromechanical technology as early as 1938,
and developed the notion of bracketed structures, a fundamental breakthrough in computer science, while Bauer was the first to propose the stack method of expression evaluation.
It is clear that the institutionalization of computer science as an academic discipline took place earlier in the USA, approximately in the 1950s,
characterizing the search regime of computer science In a stream of recent papers (Bonaccorsi, 2007; 2008;
It is therefore useful to try to characterize the history of computer science from the point of view of the underlying abstract dynamics of knowledge.
The National Research Council (NRC) of the US National Academies has edited a number of essays from leading scientists on the state of the art of computer science, with a collective introduction (NRC, 2004.
Computer science embraces questions ranging from the properties of electronic devices to the character of human understanding, from individual designer components to globally distributed systems,
Computer science encompasses basic research that seeks fundamental understanding of computational phenomena, as well as applied research. The two are coupled often;
in computer science there is a significant overlap. Great theorists also engage in developing (or have their students develop) software code
computer science research (NRC, 2004: 15: involves symbols and their manipulation and the creation and manipulation of abstractions. creates
and studies algorithms and artificial constructs, notably unlimited by physical laws. exploits and addresses exponential growth. seeks the fundamental limits on
On the basis of an extensive historical reconstruction and of informed reports from scientists, we can conclude that the search regime of computer science has been characterized by turbulent We conclude that the search regime of computer science has been characterized by a turbulent rate of growth, proliferation dynamics,
has attracted a large number of other disciplines into computer science, creating powerful forms of cognitive complementarity. Not only mathematics, logics,
and electric and electronic engineering have been involved into computer science since the beginning, but also biology and chemistry (bioinformatics), earth sciences (geographic information systems), psychology (artificial intelligence), visual art (computer graphics), operations management (enterprise resource planning),
All have been transformed deeply from the relationship with computer science. In all cases, there was not just‘application,
In computer science, this complementarity comes from the constitutive interplay between theoretical work and pragmatic goals (Bonaccorsi, 2010.
New evidence on scientific excellence in computer science An analysis of the CVS of top computer scientists An interesting perspective is to look at the large community of computer scientists and at their own self-validation processes.
Citations to papers in computer science are recorded automatically by Citeseer, 2 a highly structured indexing system established in 1997 and endorsed by most scientific societies and departments in computer science worldwide.
The Citeseer service ranks scientists by the total number of citations without checking for homonyms and controlling for the age of scientists.
These scientists have the largest cumulative number of citations in papers from a list of journals and conferences in computer science, irrespective of their age.
it is almost impossible to rank high in the computer science field without a Phd from either the USA or Europe,
In the period 1980 1989, a period of explosion of computer science and information technology, US Table 2. Distribution of degrees of top computer scientists by geographical area Area Phd degree Master degree
457 100.0 641 100.0 It is almost impossible to rank high in the computer science field without a Phd from either the USA or Europe, with the USA leading by a large margin European competitiveness:
and place of Phd degree of top scientists in computer science Year USA Europe Asia Other Not available Total<1950 4 4 0 0 0 8
Master and Bachelor degrees to top scientists in computer science Phd degree Master degree Bachelor degree Number%Number%Number%MIT 82 9. 6 47
when we move to the Bachelor degree, the entry point for students considering a career in computer science.
The talent pool for a career in computer science is worldwide. Entry points are good universities offering strong basic scientific knowledge
With few exceptions, European postgraduate education in computer science is not globally competitive. If it were competitive we would see more students migrating from Asia and the rest of the world into Europe, instead of the USA,
not computer science (see Table 5). The entry point of a scientific career is not in the specialised field,
Also interesting is the group of graduate students in physics who are recognized as key leaders in computer science.
computer science is number one at the level of Master degrees, a stage in which some focusing is required.
Finally, at the Phd stage the disciplinary affiliation of computer science dominates with 38.2%of cases.
because it is considered obvious that their Phd is in computer science?).At the same time an interesting tentative interpretation can be offered.
Computer science is a relatively young discipline. It has not the long scientific history of physics, mathematics, or chemistry.
a Table 5. Distribution of Phd, Master and Bachelor degrees by discipline Phd degree Master degree Bachelor degree Number%Number%Number%Computer science 327 38.2
Our data seem to suggest that computer science has been a gateway for cross-discipline mobility and cognitive recombination.
students with a background in human sciences (literature, linguistics, psychology) and social sciences (economics) may combine their domain expertise with advanced computer science.
This is roughly confirmed for computer science (79.4%on the diagonal cell) but not for mathematics and engineering.
We therefore conclude that computer science is characterized a field by a high degree of disciplinary mobility attracting competences from related fields.
which is Table 6. Transition matrix between disciplinary distribution of Bachelor and Phd degrees Bachelor degree Phd degree Mathematics Engineering Computer science Other disciplines No Phd Total
100.0 Engineering 4 41.8 69 34.5 57 17.6 29 17.6 6 3. 6 165 100.0 Computer science-2. 0 2 79.4
. 8 432 100.0 The search regime of computer science has been characterized by a turbulent rate of growth, proliferation dynamics,
Our data seem to suggest that in the computer sciences the pattern of geographic mobility has been an ingredient of long-term success. Scientific productivity We offer a very rough descriptive analysis of the scientific production of top scientists.
These cover only a subset of journals considered important in the computer science community, and do not include many top conferences,
This confirms the notion that institutional complementarity is an integral part of the search regime in computer science.
combine different disciplines around computer science, enjoy a rapid career, have extensive industry involvement, as witnessed by research collaborations,
Computer science has been based on a fierce competition for students and researchers worldwide. Knowing how severe these demands are,
However, there is also very recent evidence that the type of brain race that we have discovered in the computer science is becoming widespread (Wildavsky
The search regime in computer science is based on a massive and fast effort of exploration of many competing directions,
Computer science. Reflections on the Field, Reflections from the Field. WASHINGTON DC: National Academies Press. Nicoletti, G and S Scarpetta 2003.
an experiment that escaped from the lab. In Computer science. Reflections on the Field, Reflections from the Field, National Research Council (ed.),pp 129 133.
In Computer science. Reflections on the Field, Reflections from the Field, National Research Council (ed.),pp 151 158.
A Century of Electrical engineering and Computer science at MIT, 1882 1982. Cambridge, MA: MIT Press Williams, M R 2000 A preview of things to come:
In Foundations of Computer science. Potential-Theory-Cognition, C Freksa, M Jantzen and R Valk (eds..
including computer sciences. 3. 6. The role of foreign/international research infrastructures Not even the largest countries can afford to establish research infrastructures alone in certain research areas.
electronics, embedded system design, personal health system, ICT for energy efficiency and accessible and assistive ICT, Computer science and artificial intelligence.
owing to a tertiary education system that is relatively strong in natural sciences, mathematics, computer science, food science, agriculture,
but the region is particularly strong in natural sciences, mathematics, computer science, food engineering, agriculture, medical and veterinary sciences.
Clear strength in tertiary education programs for natural sciences, mathematics, computer science, food engineering, agriculture, as well as medical and veterinary sciences Important signs of entrepreneurial activity Existence of a relatively good network of industrial and technology parks High export performance Skills:
in order to capitalize on the local advantages in terms of skill availability, particularly mathematics and computer science which are areas of strength of the local universities,
The local universities are especially strong in natural sciences, mathematics, computer science, food engineering, agriculture, as well as medical and veterinary sciences.
the second type is built from a statistical algorithm for analysis of spatial agglomeration named LISA (i e.
Digital Cities, Lecture Notes in Computer science 2362, Springer-verlag Berlin Heidelberg, 101-109. Granovetter, M.,(1976.
Experiences, Technologies and Future Perspectives Lecture Notes in Computer science, 1765, Springer-verlag. Kavassalis, P.,Lelis, S.,Rafea, M. & Haridi, S. 2004.
Dr. Yu received his Phd in Computer science and Engineering from University of louisville. His research interests are in the fields of data/text mining, business process simulation, software agent applications,
Prashanth Kannan conducted research in the area of Social networking/Digital Cities and business innovations and received his MS degree in Computer sciences from University of missouri at Rolla.
and A m. S. in Computer science from Fairleigh Dickinson University. He has published various articles in academic book chapters, journals,
Tim Klaus is an Assistant professor of Management Information systems at Texas A&m University Corpus christi. He earned his Phd (Management Information systems) from University of South Florida and his MBA (Finance) and MS (Computer science) from Illinois State university.
For example, it shows that the Department of computer science at the University of Sheffield is to establish a Computer science Ambassador Scheme for 45 secondary school pupils
which will deliver short‘hands on'courses in core computer science for pupils aged 14-15,
and students to engage with computer science in a fun way. Schaart said that the Commission has promoted rightly digitalisation and its adoption by society as one of the most important sources for growth and employment.
No image problem Higgins said that employers face a big challenge in communicating what sort of exciting job opportunities the industry is providing, especially since fewer people study computer science.
Big data is a goldmine for companies Computer algorithms are better at diagnosing severe cancer than humans,
That's done by placing different algorithms onto these large amounts of data. Let me give you an example.
Google ran all the terms through an algorithm a way of making a calculation-that ranked the terms by how well they correlated with flu outbreaks.
It's a demonstrable fact that a computer algorithm is better at diagnosing severe cancer than a human.
Lecture Notes in Computer science 6656 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis,
235 A Use-Case on Testing Adaptive Admission Control and Resource Allocation Algorithms on the Federated Environment of Panlab...
These components could have direct interworking with control algorithms situated in the control plane of the Internet
the algorithms supporting dynamic mobility could also be distributed. Such integration is accomplished in 44 45 where authors introduce
These designs about architecture for the federated reference model by functional blocks addresses the specification of mechanisms including models, algorithms, processes, methodologies and architectures.
Algorithms and processes to allow federation in enterprise application systems to visualize software components, functionality and performance.
Algorithms and processes to allow federated application management systems reconfigure or redeploy software components realizing autonomic application functionality.
which forces to keep algorithms and procedures, laying at different layers, independent one another. In addition, even in the framework of a given layer, algorithms and procedures dealing with different tasks are designed often independently one another.
These issues greatly simplify the overall design of the telecommunication networks and greatly reduce processing capabilities,
Nevertheless, a major limitation of this approach derives from the fact that algorithms and procedures are poorly coordinated one another,
The issues above claim for a stronger coordination between algorithms and procedures dealing with different tasks.
most of the algorithms and procedures embedded in the telecommunication networks are open-loop, i e. they are based on off-line"reasonable"estimation of network variables (e g. offered traffic), rather than on real-time measurements of such variables.
This claims for an evolution towards closed-loop algorithms and procedures which are able to properly exploit appropriate real-time network measurements.
and hence embedding technology-dependent algorithms and procedures, as well as from the large variety of heterogeneous Actors who are playing in the ICT arena.
this framework, on the one hand, is expected to embed algorithms and procedures which, leaving out of consideration the specificity of the various networks,
which serve as Cognitive Enabler input, coupled with a proper design of Cognitive Enabler algorithms (e g.,
, multiobjective advanced control and optimization algorithms), lead to cross-layer and cross-network optimization. The Cognitive Framework can exploit one or more of the Cognitive Enablers in a dynamic fashion:
In particular, the concentration of control functionalities in a single framework allows the adoption of algorithms
The concentration of the control functionalities in such Cognitive Enablers allows the adoption of multi-object algorithms and procedures
for the algorithms and rules embedded in the Cognitive Enablers, which are expected to remarkably improve efficiency.
it has been implemented to perform technology independent resource management algorithms (e g.,, layer 2 path selection), in order to guarantee that flow's Qos requirements are satisfied during the transmission of its packets over the network.
In particular, a Connection Admission Control algorithm, a Path selection algorithm and a Load Balancing algorithm has been considered in our tests.
the algorithms the Cognitive Enabler will be based on, have all to be selected carefully case by case;
Stability of end-to-end algorithms for joint routing and rate control. Computer Communication Review 35,2 (2005) 20.
and a novel con 156 C. Kalogiros et al. gestion control algorithm that gives the right incentives to users of bandwidth intensive applications.
16th Annual Symposium on Theoretical Aspects of Computer science 1999, pp. 404 413 (1999) 13. MOBITHIN project:
routing algorithm, reachability, and Qos for the publication and may support transport abstraction specific policies such as replication and persistence for data-centric communication.
Lock-free wait-free algorithms for common software abstractions (queues, bags, etc. are one of the most effective approaches to exploit multi-core parallelism.
These algorithms are hard to design and prove correct, error-prone to program, and challenging to debug.
Proceedings of the 22nd Annual Symposium on Foundations of Computer science, WASHINGTON DC, USA, pp. 350 357.
Computer science Review 4 (2), 81 99 (2010) 19. Le Guernic, G.,Banerjee, A.,Jensen, T.,Schmidt, D. A.:
Proceedings of the 18th IEEE Symposium on Foundations of Computer science, pp. 46 57. IEEE Computer Society Press, Los Alamitos (1977) 26.
and Resource Allocation Algorithms on the Federated Environment of Panlab reports on experiments needing to directly interact with the environment during runtime,
The Author (s). This article is published with open access at Springerlink. com. A Use-Case on Testing Adaptive Admission Control and Resource Allocation Algorithms on the Federated Environment of Panlab Christos
This paper presents a use case where an adaptive resource allocation algorithm was tested utilizing Panlab's infrastructure.
i) to run the experiment by moving a designed algorithm from a simulating environment to near production besteffort environment
A Use-Case on Testing Adaptive Admission Control 239 2 Use Case Description In order for one to test an adaptive admission control and resource allocation algorithm,
Fig. 1. The setup for testing the algorithm The adaptive admission control and resource allocation algorithm is applied to succeed in specific target of network metrics,
like round trip time and throughput. This will be done by deploying a proxy-like control component for admission control
During this scenario the adaptive admission control and resource allocation algorithm is tested against network metrics, like round trip time and throughput.
During the setup, the researcher wants to test http proxy software written in C programming language that implements an admission algorithm.
The algorithm, which is located at the proxy unit, needs to monitor the CPU usage of the Web application and Database machines.
Then the algorithm should be able to set new CPU capacity limits on both resources.
Additionally the algorithm should be able to start and stop the work load generators on demand. 3 Technical Environment, Testbed Implementation and Deployment From the requirements of the use case,
and monitor resources within the C algorithm. So the resources need to provide monitoring and provisioning mechanisms.
-Linux machines for the RUBIS based work load generators-A Linux machine for the hosting the algorithm unit,
and database The final user needs to provide the algorithm under test. He will just login to the Proxy Unit,
For example the RUBIS clients need to know about the IP of the proxy which hosts the algorithm.
Fig. 5. Designing the algorithm to operate resources during execution In our testing scenario there is a need to configure resources
Figure 5 displays this condition where the System Under Test (SUT) is our algorithm. FCI automatically creates all the necessary code that the end user can then inject inside the algorithm's code.
The end-user needs just to ender his credentials in order 244 C. Tranoris, P Giacomin, and S. Denazis FCI to generate the necessary wrapper classes
()is able to give back the CPU usage of the database resource. 5 Conclusions The results of running an experiment in Panlab are encouraging in terms of moving the designed algorithms from simulating environments to near production environments.
What is really attractive is that such algorithms can be tested in a best-effort environment with real connectivity issues that cannot be performed easily in simulation environments.
although not comparable currently with similar approaches are really encouraging in terms of moving the designed algorithms from simulating environments to near production environments.
What is really attractive is that such algorithms can be tested in a best-effort environment with real connectivity issues that cannot be performed easily in simulation environments.
, Institute of Computer science, Wuerzburg, Germany, thomas. zinner christian. schwartz phuoc. trangia@informatik. uni-wuerzburg. de 3 University of Vienna,
Testing End-to-end Self-Management in a Wireless Future Internet Environment 265 Fig. 4. Decision-making algorithm for configuration action selection Simple Fig. 4 presents
Fig. 5. Decision making algorithm for configuration action selection Advanced 266 A. Kousaridas et al. The above figure (Fig. 5) illustrates the advanced version of the scheme presented above.
in order to evaluate the proposed algorithm and strengthen the proof of concept. Finally, the article concludes with key findings
The path computation is performed by dedicated PCES that implements enhanced computation algorithms able to combine both network
thereby addressing challenge#4. We evaluated an energy efficient routing algorithm (due to space limitations, the detailed algorithm is not 318 P. Vicat-Blanc et al.
Fig. 6. Number of activated fibers. Fig. 7. Number of activated data centers. shown here) from a networked IT use case:
Simulation results (see Fig. 4-6) indicate that our proposed algorithm can decrease the energy consumption by 10%compared to schemes where only IT infrastructure is considered and up to 50%when taking only the network into account
and on service discovery algorithms to provide a generic semantic service registry able to support advanced discovery over both Web APIS
Journal of Universal Computer science 16 (13), 1694 1719 (2010) 11. Maleshkova, M.,Pedrinaci, C.,Domingue, J.:
Special algorithms are needed to reduce the amount of processing of MANE in the data plane based on deep analysis of the first packets of a flow
The evaluation algorithm considers the user flow characteristics CAN policies and present network conditions. In order to attain the required flexibility,
and traffic filtering rules by executing security related algorithms over information gathered by the monitoring subsystem.
Engineering and Computer science, Queen Mary University of London, Mile end, London E1 4ns, United kingdom {Naeem.
and prioritized in our proposed system 4. 1 Piece Picking Policy The proposed solution is a variation of the"Give-To-Get"algorithm 8,
Engineering and Computer science Queen Mary University of London, UK {qianni. zhang, ebroul. izquierdo}@ elec. qmul. ac. uk Abstract.
While linking low-level features to mid-level concepts are relatively easy to solve using the well-defined algorithms in the state-of-the-art
and are extracted using algorithms with reasonable performance. The rest of this chapter is organised as follows: Section 2 gives a review on the state-of-the-art techniques on context reasoning for multimedia retrieval task;
or content descriptors that can be computed automatically by current machines and algorithms, and the richness,
Fig. 1. Semantic inference work flow One important feature in this module is that the Bayesian network model is constructed automatically using a learning approach based on K2 algorithm 8,
In this algorithm, a Bayesian network is created by starting with an empty network and iteratively adding a directed arc to a given node from each parent node.
Due to the scope of this paper, we give only a brief introduction to K2 algorithm here.
If the reader is interested in more details about this algorithm, please refer to 8. Then in the inference stage,
Modelling and inference in this case were carried out using the K2 algorithm. The proposed approach was tested on a large size video dataset.
Tech Rep. MIT Laboratory for Computer science (2003), http://www. isi. edu/newarch/15. Tselentis, G.,et al.
Based on this testbed network, experiments and research are performed targeting cloud management algorithms and optimization of the intermittently-available renewable energy sources.
simulation results can only give very limited information about the feasibility of an algorithm or a protocol in the field.
and Internet researchers to validate their cutting-edge technologies (protocols, algorithms, radio interfaces, etc.).Several use cases are currently under detailed analysis for their experimental deployment taking into account relevant criteria from local and regional authorities.
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