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Publications (10 of 372) Show all publications
Kousias, K., Rajiullah, M., Caso, G., Ali, U., Alay, Ö., Brunstrom, A., . . . Di Benedetto, M.-G. (2024). A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements. IEEE Communications Magazine, 62(5), 44-49
Open this publication in new window or tab >>A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
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2024 (English)In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 62, no 5, p. 44-49Article in journal (Refereed) Published
Abstract [en]

Mobile networks are highly complex systems. Therefore, it is crucial to examine them from an empirical perspective to better understand how network features affect performance, so to suggest additional improvements. To this aim, this paper presents a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT), and 5G New Radio (NR) connectivity. We collected our dataset during seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization.

Place, publisher, year, edition, pages
IEEE, 2024
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-97321 (URN)10.1109/mcom.011.2200707 (DOI)001216638200001 ()2-s2.0-85171556164 (Scopus ID)
Available from: 2023-11-07 Created: 2023-11-07 Last updated: 2024-07-08Bibliographically approved
Kousias, K., Rajiullah, M., Caso, G., Alay, Ö., Brunstrom, A., Ali, U., . . . Di Benedetto, M.-G. (2024). Empirical performance analysis and ML-based modeling of 5G non-standalone networks. Computer Networks, 241, Article ID 110207.
Open this publication in new window or tab >>Empirical performance analysis and ML-based modeling of 5G non-standalone networks
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2024 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 241, article id 110207Article in journal (Refereed) Published
Abstract [en]

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models. 

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
5G mobile communication systems, Machine learning, Telecommunication industry, 5g non standalone, Empirical performance analysis, Fourth-generation (4G) networks, Global telecommunication, Learning Based Models, Machine-learning, Mobile network operators, Performance, Radio coverage, Telecommunications industry, Wireless networks
National Category
Telecommunications Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98647 (URN)10.1016/j.comnet.2024.110207 (DOI)001176361800001 ()2-s2.0-85184023794 (Scopus ID)
Funder
Knowledge FoundationEuropean Commission
Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-04-04Bibliographically approved
Garcia, J., Sundberg, S. & Brunstrom, A. (2024). Fine-Grained Starlink Throughput Variation Examined With State-Transition Modeling. In: 2024 19th Wireless On-Demand Network Systems and Services Conference (WONS): . Paper presented at 19th Wireless On-demand Network systems and Services Conference (WONS), Chamonix, France, January 29-31, 2024. (pp. 69-76). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Fine-Grained Starlink Throughput Variation Examined With State-Transition Modeling
2024 (English)In: 2024 19th Wireless On-Demand Network Systems and Services Conference (WONS), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 69-76Conference paper, Published paper (Refereed)
Abstract [en]

Leveraging a data set of almost half a billion packets with high-precision packet times and sizes, we process it to extract characteristics of the bursts emitted over Starlink's Ethernet interface. The structure of these bursts directly reflect the physical layer receipt of OFDMA frames on the satellite link. We study these bursts by analyzing their rates, and by proxy the transition between different physical layer rates. The results highlight that  there is definitive structure in the transition behavior, and we note specific behaviors such as  particular transitionsteps associated with rate switching, and that rate switching occurs mainly to neighboring rates. We also study the joint burst rate and burst duration transitions, noting that transitions occur mainly within the same rate, and that changes in burst duration are often performed with an intermediate short burst in-between.Finally, we examine the configurations of the three factors burst rate, burst duration, and inter-burst silent time, which together determine the effective throughput of a Starlink connection.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Series
Annual Conference on Wireless On Demand Network Systems and Services (WONS), ISSN 2688-4917, E-ISSN 2688-4909
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98081 (URN)10.23919/WONS60642.2024.10449629 (DOI)978-3-903176-61-4 (ISBN)979-8-3503-6062-2 (ISBN)
Conference
19th Wireless On-demand Network systems and Services Conference (WONS), Chamonix, France, January 29-31, 2024.
Projects
DRIVE
Funder
Knowledge Foundation
Available from: 2024-01-19 Created: 2024-01-19 Last updated: 2024-04-05Bibliographically approved
Garcia, J., Sundberg, S. & Brunstrom, A. (2024). Inferring Starlink Physical Layer Transmission Rates Through Receiver Packet Timestamps. In: : . Paper presented at IEEE Wireless Communications and Networking Conference, Dubai,United Arab Emirates, April 21-24, 2024.. IEEE
Open this publication in new window or tab >>Inferring Starlink Physical Layer Transmission Rates Through Receiver Packet Timestamps
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Although Starlink has been deployed for several years, a detailed understanding of system internals is still lacking. In this work we employ precise per-packet timestamps obtained from a hardware-timestamp capable NIC connected to a Starlink terminal.We find that Starlink frame timing details are readily observable at the network layer by analyzing the packet timing patterns.Based on a one-week measurement campaign we collect around half a billion of packet size and timing observations. Processing these observations yields 2.3 million transmission bursts. To learn details on the radio resource management we develop a methodology to infer the effective physical layer sending rate. Our findings show that although Starlink throughput can vary widely over multiple time-scales, there are a small number of fundamental physical layer transmission rates. We employ Gaussian Mixture Modeling to determine 14 such fundamental transmission rates, and relate the obtained rates to previous knowledge of the Starlink OFDMA frame structure. Our empirical observations provide an excellent match for a radio resource configuration where a Starlink frame employs 1000 subcarriers and 287 symbols per frame for user traffic transmission, which for uniform 4-QAM modulation yields a base rate of 430.5 Mbps. This physical layer base rate appears to mostly be varied by multiples of 27 Mbps, in several instances likely by modifying the modulation of a subset of the symbols in multiples of 18 symbols. 

Place, publisher, year, edition, pages
IEEE, 2024
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98080 (URN)
Conference
IEEE Wireless Communications and Networking Conference, Dubai,United Arab Emirates, April 21-24, 2024.
Projects
DRIVE
Funder
Knowledge Foundation
Available from: 2024-01-19 Created: 2024-01-19 Last updated: 2024-01-22
Ramaswamy, A., Ma, Y., Alfredsson, S., Collyer, F. & Brunstrom, A. (2024). Information Theoretic Deductions Using Machine Learning with an Application in Sociology. In: Modesto Castrillon-Santana; Maria De Marsico; Ana Fred (Ed.), Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM: . Paper presented at 13th International Conference on Pattern Recognition Applications and Methods ICPRAM, Rome, Italy, February 24-26, 2024. (pp. 320-328). SciTePress, 1
Open this publication in new window or tab >>Information Theoretic Deductions Using Machine Learning with an Application in Sociology
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2024 (English)In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM / [ed] Modesto Castrillon-Santana; Maria De Marsico; Ana Fred, SciTePress, 2024, Vol. 1, p. 320-328Conference paper, Published paper (Refereed)
Abstract [en]

Conditional entropy is an important concept that naturally arises in fields such as finance, sociology, and intelligent decision making when solving problems involving statistical inferences. Formally speaking, given two random variables X and Y, one is interested in the amount and direction of information flow between X and Y . It helps to draw conclusions about Y while only observing X. Conditional entropy H(Y |X) quantifies the amount of information flow from X to Y . In practice, calculating H(Y |X) exactly is infeasible. Current estimation methods are complex and suffer from estimation bias issues. In this paper, we present a simple Machine Learning based estimation method. Our method can be used to estimate H(Y |X) for discrete X and bi-valued Y. Given X and Y observations, we first construct a natural binary classification training dataset. We then train a supervised learning algorithm on this dataset, and use its prediction accuracy to estimate H(Y |X). We also present a simple condition on the prediction accuracy to determine if there is information flow from X to Y. We support our ideas using formal arguments and through an experiment involving a gender-bias study using a part of the employee database of Karlstad University, Sweden. 

Place, publisher, year, edition, pages
SciTePress, 2024
Keywords
Conditional Entropy, Binary Classification, Information Theory, Supervised Machine Learning, Automated Data Mining, Machine Learning in Sociology
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-99723 (URN)10.5220/0012371600003654 (DOI)2-s2.0-85190701985 (Scopus ID)978-989-758-684-2 (ISBN)
Conference
13th International Conference on Pattern Recognition Applications and Methods ICPRAM, Rome, Italy, February 24-26, 2024.
Available from: 2024-05-22 Created: 2024-05-22 Last updated: 2024-05-22Bibliographically approved
Pieskä, M., Rabitsch, A., Brunstrom, A., Kassler, A., Amend, M. & Bogenfeld, E. (2024). Low-delay cost-aware multipath scheduling over dynamic links for access traffic steering, switching, and splitting. Computer Networks, 241, Article ID 110186.
Open this publication in new window or tab >>Low-delay cost-aware multipath scheduling over dynamic links for access traffic steering, switching, and splitting
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2024 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 241, article id 110186Article in journal (Refereed) Published
Abstract [en]

Bundling of multiple access technologies is currently being standardized by 3GPP in the 5G access traffic steering, switching and splitting (ATSSS) framework, with the goal to increase robustness, resiliency and capacity of wireless access. A key part of an ATSSS framework is the packet scheduler, which decides the access network over which each packet is to be transmitted. As wireless channels are highly dynamic, a challenge for any scheduler is to correctly estimate the capacity of each path, and thereby avoid congesting the paths. In this paper, we further develop a recent packet scheduler that exploits cross-layer information from the congestion control state of individual transport layer tunnels when making scheduling decisions. Our aim is to achieve good path utilization while keeping the congestion delay low. Extensive emulations show that our approach reduces the excess delay at the bottleneck to as little as 34%. We furthermore show that our approach improves the performance of end-to-end applications including WebRTC and YouTube compared to state-of-the art. 

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Scheduling algorithms, Traffic congestion, 5g, Access traffic steering, switching and splitting, Heterogeneous wireless access, MP-DCCP, Multi-path transport layer tunneling, Multipath, Packet scheduling, Splittings, Transport layers, Unreliable traffic, Wireless access, 5G mobile communication systems
National Category
Communication Systems Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98641 (URN)10.1016/j.comnet.2024.110186 (DOI)001173486200001 ()2-s2.0-85183909966 (Scopus ID)
Funder
Knowledge Foundation, Dnr 20220072
Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-05-10Bibliographically approved
Alfredsson, F., Hurtig, P., Brunstrom, A., Hoiland-Jorgensen, T. & Brouer, J. D. (2024). XDQ: Enhancing XDP with Queuing and Packet Scheduling. In: Chemouil P., Martini B., Machuca C.M., Papadimitriou P., Borsatti D., Rovedakis S. (Ed.), Proceedings of the 27th Conference on Innovation in Clouds, Internet and Networks, ICIN 2024: . Paper presented at The 27th Conference on Innovation in Clouds, Internet and Networks, ICIN, Paris, France, March 11-14, 2024. (pp. 52-56). IEEE
Open this publication in new window or tab >>XDQ: Enhancing XDP with Queuing and Packet Scheduling
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2024 (English)In: Proceedings of the 27th Conference on Innovation in Clouds, Internet and Networks, ICIN 2024 / [ed] Chemouil P., Martini B., Machuca C.M., Papadimitriou P., Borsatti D., Rovedakis S., IEEE, 2024, p. 52-56Conference paper, Published paper (Refereed)
Abstract [en]

Modern internet users expect seamless, uninterrupted communication when using real-time applications, even when sharing the network connection with devices that produce heavy traffic. This trend has pushed the demand for more sophisticated packet schedulers on routers. However, given the limited resources on these routers, it is increasingly important to reduce the overhead associated with these schedulers. This paper presents XDP Queuing (XDQ), our ongoing work on achieving faster packet scheduling on the Linux operating system, which is popular on networking equipment. Linux provides eXpress Data Path (XDP), a high-performance programmable network data path using the eBPF framework, which allows code to process packets early from the driver. While XDP has found numerous uses in the industry, such as Denial-of-Service attack mitigation, load-balancers, and intrusion prevention systems, it currently has no mechanism for queueing or reordering packets and cannot implement traffic scheduling policies. Our contribution, XDQ, is a programmable packet scheduling extension for the XDP framework. XDQ uses recently proposed schemes for programmable queues. It allows writing packet schedulers using eBPF while benefiting from the XDP fast data path. 

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
BPF, eBPF, Eiffel, Linux, PIFO, Programmable scheduling, Queueing, Scheduling, SPRIO, WFQ, XDP
National Category
Computer Sciences Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-99732 (URN)10.1109/ICIN60470.2024.10494444 (DOI)2-s2.0-85191255891 (Scopus ID)979-8-3503-9377-4 (ISBN)979-8-3503-9376-7 (ISBN)
Conference
The 27th Conference on Innovation in Clouds, Internet and Networks, ICIN, Paris, France, March 11-14, 2024.
Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-03Bibliographically approved
Paiva, T. W., Ferlin, S., Brunstrom, A., Alay, O. & Kimura, B. Y. (2023). A First Look at Adaptive Video Streaming over Multipath QUIC with Shared Bottleneck Detection. In: Shervin Shirmohammadi, Mohamed Hefeeda , Roger Zimmermann, Carsten Griwodz, Mea Wang (Ed.), Proceedings of the 14th ACM Multimedia Systems Conference: . Paper presented at 14th ACM Multimedia Systems Conference, MMSys 2023, Vancouver, Canada, June 7-10, 2023. (pp. 161-172). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>A First Look at Adaptive Video Streaming over Multipath QUIC with Shared Bottleneck Detection
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2023 (English)In: Proceedings of the 14th ACM Multimedia Systems Conference / [ed] Shervin Shirmohammadi, Mohamed Hefeeda , Roger Zimmermann, Carsten Griwodz, Mea Wang, Association for Computing Machinery (ACM), 2023, p. 161-172Conference paper, Published paper (Refereed)
Abstract [en]

The promises of multipath transport is to aggregate bandwidth and improve resource utilisation and reliability. We demonstrate in this paper that the way multipath coupled congestion control is defined today RFC6359 leads to a sub-optimal resource utilisation when network paths are mainly disjoint, i.e., they do not share a bottleneck. With growing interest to standardise Multipath QUIC (MPQUIC), we implement the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC, namely MPQUIC-SBD. We evaluate MPQUIC-SBD through extensive emulation experiments in the context of video streaming. We show that MPQUIC-SBD is able to correctly detect shared bottlenecks over 90% of the time as the video segments’ size increase depending on the Adaptive Bitrate (ABR) algorithm. In non-shared bottleneck scenarios, MPQUIC-SBD results in video throughput gains of more than 13% compared to MPQUIC, which directly translates into better video quality metrics.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
Traffic congestion; Bottleneck detection; Congestion control; DASH; First look; Multipath; Multipath QUIC; Multipath transport; QUIC; Resources utilizations; Shared bottleneck detection; Video streaming
National Category
Computer Sciences Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-96260 (URN)10.1145/3587819.3590982 (DOI)2-s2.0-85163575009 (Scopus ID)
Conference
14th ACM Multimedia Systems Conference, MMSys 2023, Vancouver, Canada, June 7-10, 2023.
Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2023-08-10Bibliographically approved
Magnusson, J., Müller, M., Brunstrom, A. & Pulls, T. (2023). A Second Look at DNS QNAME Minimization. In: Anna Brunström; Marcel Flores; Marco Fiore (Ed.), Passive and Active Measurement: 24th International Conference, PAM 2023, Virtual Event, March 21–23, 2023, Proceedings. Paper presented at 24th International Conference, PAM 2023, Virtual Event, March 21–23, 2023 (pp. 496-521). Springer
Open this publication in new window or tab >>A Second Look at DNS QNAME Minimization
2023 (English)In: Passive and Active Measurement: 24th International Conference, PAM 2023, Virtual Event, March 21–23, 2023, Proceedings / [ed] Anna Brunström; Marcel Flores; Marco Fiore, Springer, 2023, p. 496-521Conference paper, Published paper (Refereed)
Abstract [en]

The Domain Name System (DNS) is a critical Internet infrastructure that translates human-readable domain names to IP addresses. It was originally designed over 35 years ago and multiple enhancements have since then been made, in particular to make DNS lookups more secure and privacy preserving. Query name minimization (qmin) was initially introduced in 2016 to limit the exposure of queries sent across DNS and thereby enhance privacy. In this paper, we take a look at the adoption of qmin, building upon and extending measurements made by De Vries et al. in 2018. We analyze qmin adoption on the Internet using active measurements both on resolvers used by RIPE Atlas probes and on open resolvers. Aside from adding more vantage points when measuring qmin adoption on open resolvers, we also increase the number of repetitions, which reveals conflicting resolvers – resolvers that support qmin for some queries but not for others. For the passive measurements at root and Top-Level Domain (TLD) name servers, we extend the analysis over a longer period of time, introduce additional sources, and filter out non-valid queries. Furthermore, our controlled experiments measure performance and result quality of newer versions of the qmin -enabled open source resolvers used in the previous study, with the addition of PowerDNS. Our results, using extended methods from previous work, show that the adoption of qmin has significantly increased since 2018. New controlled experiments also show a trend of higher number of packets used by resolvers and lower error rates in the DNS queries. Since qmin is a balance between performance and privacy, we further discuss the depth limit of minimizing labels and propose the use of a public suffix list for setting this limit.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13882
Keywords
Internet protocols; Privacy-preserving techniques, Controlled experiment; Domain name system; Domain names; Human-readable; Internet infrastructure; Lookups; Minimisation; Performance; Privacy; QNAME minimization, Quality control
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-94279 (URN)10.1007/978-3-031-28486-1_21 (DOI)2-s2.0-85151060508 (Scopus ID)
Conference
24th International Conference, PAM 2023, Virtual Event, March 21–23, 2023
Available from: 2023-04-19 Created: 2023-04-19 Last updated: 2023-04-20Bibliographically approved
Chahed, H., Usman, M., Chatterjee, A., Bayram, F., Chaudhary, R., Brunstrom, A., . . . Kassler, A. (2023). AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications. Internet of Things: Engineering Cyber Physical Human Systems, 22, Article ID 100805.
Open this publication in new window or tab >>AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications
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2023 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 22, article id 100805Article in journal (Refereed) Published
Abstract [en]

Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Edge/cloud computing, Internet of Things (IoT), Machine Learning, Time-Sensitive Networks (TSN)
National Category
Computer Engineering Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-94900 (URN)10.1016/j.iot.2023.100805 (DOI)001053228900001 ()2-s2.0-85159450974 (Scopus ID)
Funder
Knowledge Foundation, 20200067
Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2024-02-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7311-9334

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