Fifth-generation (5G) mobile communication systems are currently implemented worldwide. Moreover, the diversity between different deployments is growing, from operator-deployed macro networks to local private networks. The 5G system is very flexible with a large number of parameters and configurations impacting platform performance. With the availability of private spectrum and open-source software, there is an increasing possibility to get insight into what performance can be achieved in different dimensions, and how it depends on the configuration of various parameters, through experiments and testing. This paper presents a detailed account of a 5G Standalone (SA) platform based on the open5GS and srsRAN software suites installed on a general-purpose workstation. The performance of the platform is measured and compared to theoretical results and information from the 3GPP standardisation documentation. While some aspects of the performance are close to the theoretical expectations, others are further away. The software-defined radio (SDR) platform and the lack of hardware acceleration are probably the main limiting factors on coverage, uplink throughput and latency. The paper goes through several radio parameters and their impact on performance and discusses the performance implications and limitations of the experimental platform to share the lessons learned.

Emerging precision agriculture techniques rely on the frequent collection of high-quality data which can be acquired efficiently by unmanned aerial systems (UAS). The main obstacle for wider adoption of this technology is related to UAS operational costs. The path forward requires a high degree of autonomy and integration of the UAS and other cyber physical systems on the farm into a common Farm Management System (FMS) to facilitate the use of big data and artificial intelligence (AI) techniques for decision support. Such a solution has been implemented in the EU project AFarCloud (Aggregated Farming in the Cloud). The regulation of UAS operations is another important factor that impacts the adoption rate of agricultural UAS. An analysis of the new European UAS regulations relevant for autonomous operation is included. Autonomous UAS operation through the AFarCloud FMS solution has been demonstrated at several test farms in multiple European countries. Novel applications have been developed, such as the retrieval of data from remote field sensors using UAS and in situ measurements using dedicated UAS payloads designed for physical contact with the environment. The main findings include that (1) autonomous UAS operation in the agricultural sector is feasible once the regulations allow this; (2) the UAS should be integrated with the FMS and include autonomous data processing and charging functionality to offer a practical solution; and (3) several applications beyond just asset monitoring are relevant for the UAS and will help to justify the cost of this equipment.

As conventional power generation units are being replaced with distributed energy resources, operational practices such as voltage regulation and congestion management are expected to be challenging. To address these challenges, regular and automated inter-control centre operational coordination will be needed between Transmissions and Distributions System Operators (TSO and DSO). In this study, the data exchange required for the near-real-time operational coordination is investigated for a reactive power management use case. A realistic ICCP/TASE 2 protocol is implemented in laboratory environment where the physical network is simulated in a real-time simulator while optimal set-points are communicated from the control centers to simulated assets being communicated through the IEC 60870-5-104. In addition, the sufficiency of the Common Information Model (CIM) and Common Grid Model Exchange Standard (CGMES) is evaluated for TSO-DSO network data exchange. The results from the cyber-physical test setup show that the detailed DSO grid model knowledge by the TSO results in lower system losses. Such data can be difficult to prepare, exchange and compute. However, results show that simplified equivalent models can be acceptable if they are properly tailored to the specific use case. The experiences from the CIM implementation with the CGMES profile are found out to be sufficient for such operational data exchange.

High-quality real-time data from the grid is essential for data-driven real-time operations. With a geographically widespread grid, there is a lot of interest in using mobile networks for communications to avoid the need to invest in and operate an additional communication network, but the quality and reliability of the mobile network need to be sufficient. In this paper, we investigate configurations of a 5G testbed for transmission of IEC 61850 sample values, in particular with respect to achieving low latency. Considering that different grid use cases have different traffic patterns we show how the performance can vary and the impact of adjusting multiple network parameters. The results demonstrate that the interaction between different mechanisms is complex, but there are important gains to be made from good UE specific parameter configurations.

Regular inspection and maintenance (I&M) of road tunnels is critical for ensuring safe operation and maximising the infrastructure's longevity. Today's I&M operations are time-consuming and disruptive to normal operations, but advances within robotics, automation, and digitalisation promise significant productivity gains. Accurate and reliable localisation is key to achieving this, but poses significant challenges in tunnels due to the absence of GNSS signals and the self-similar nature of the environment. This paper presents a novel approach for achieving real-time high-accuracy localisation in tunnels such that it can be used for autonomous navigation. The proposed system implements a simultaneous localisation and mapping (SLAM) solution that integrates data from scanning LiDAR, camera and inertial measurement unit (IMU). We have developed a novel approach that fuses the information from these sensors at the feature level and jointly optimises over all constraints. This enables our system to overcome the degeneracy of typical SLAM solutions in self-similar environments such as tunnels. To evaluate the performance of the proposed system, experiments and autonomous missions were conducted in real tunnels, and comparisons were made against existing localisation methods. The results demonstrate that the proposed system achieves high accuracy and exhibits good robustness in challenging tunnel conditions.