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This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage.

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based obse

Over the past few decades, Industrial Control Systems (ICS) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper introduces the 'ICS-Flow' dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, where the anomalies were applied to the system through various cyberattacks. We also proposed an open-source tool, "ICSFlowGenerator," for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.

The aim of the Nor-FORCeS project “Integrating Nordic air quality and climate policies using FORCEeS” was to investigate to what extent aerosol particles and greenhouse gases contribute to climate warming in the context of the Nordic emission picture. This knowledge is necessary to meet the Paris Agreement while simultaneously addressing harmful air pollution. The project has been executed by a Nordic research team from @Stockholm University, @Finnish MeteorologicaI Institute, @CICERO and @Aarhus University and supported by grants from the Nordic Council of Ministers’ working group for Climate and Air (NKL).

We are currently witnessing the second wave of cloud services that go beyond web storefronts and IT systems, aiming for digitalization of industrial systems. Automation and time-sensitive systems are now taking their first steps toward the cloud. The AORTA project aims to facilitate this transition by providing key technology components needed for real-time services running in the cloud. The ambition is to support a future robotics ecosystem that enables a new level of flexible productivity in industrial production. AORTA will develop technologies that allow offloading of real-time services/functions to the edge and cloud. We will build upon recent advances in 5G, cloud, and networking technologies. The AORTA framework will support a fluid compute model where functionality will be dynamically deployed locally, in the edge, or in the cloud and support integration and real-time performance irrespective of where it executes. Results of the project will be demonstrated in a real-world robotics manufacturing and construction scenarios operating via a 5G network with real-time edge and large-scale cloud service. The AORTA technologies will provide opportunities for automation enterprises and system integrators by adding real-time capabilities needed to evolve beyond the currently closed ecosystem. They will also add value to telecom providers and operators that may host these new automation services in addition to their current portfolio.

Practitioners report improved productivity as one of the main benefits of using autonomous dump trucks in underground mining. However, manned vehicles are still needed to transport materials and personnel in the tunnels, which requires practices that may diminish autonomy benefits. Thus, both fleets shall be efficiently mixed to maximize the autonomy potential. In addition, sufficient safety shall be demonstrated for operations approval. This paper proposes a strategy to populate a GSN (Goal Structuring Notation) structure to argue for the sufficient safety of mixed traffic operations in underground mining. Our strategy considers SoS (System of Systems) concepts to describe the operations baseline and the initial argumentation line, i.e., risk reduction mitigation strategies for existing SoS components. Such a strategy is further detailed with risk reduction mitigation arguments for control systems. Mitigation strategies at both levels are derived from safety analysis supported by STPA (System-Theoretic Process Analysis), a safety analysis technique that aligns well with the SoS perspective. We also incorporate regulatory frameworks addressing machinery to align the arguments with mandatory statements of the machinery directive. Our strategy combines SoS concepts with analysis techniques and regulatory frameworks to facilitate safety case argumentation for operations approval in the European mining context.

Industrial control systems are undergoing a trans-formation driven by business requirements as well as technical advances, aiming towards increased connectivity, flexibility and high level of modularity, that implies a need to revise existing cybersecurity measures. Access control, being one of the major security mechanisms in any system, is largely affected by these advances.In this article we investigate access control enforcement architectures, aiming at the principle of least privilege1 in dynamically changing access control scenarios of dynamic manufacturing systems. Several approaches for permission delegation of dynamic access control policy decisions are described. We present an implementation using the most promising combination of architecture and delegation mechanism for which available industrial standards are applicable.

With the advent of the smart industry, Industrial Control Systems (ICS) moved from isolated environments to connected platforms to meet Industry 4.0 targets. The inherent connectivity in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems (IDS) empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research IDSs due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their IDSs in a controlled environment. Although various ICS testbeds have been developed, researchers' access to a low-cost, extendable, and customizable testbed that can accurately simulate ICSs and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for building customized virtual ICS security testbeds in which various cyber threats and network attacks can be effectively and efficiently investigated. This framework contains base classes to simulate control system components and communications. Simulated components are deployable on actual hardware such as Raspberry Pis, containerized environments like Docker, and simulation environments such as GNS-3. ICSSIM also offers physical process modeling using software and hardware in the loop simulation. This framework reduces the time for developing ICS components and aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high fidelity. We demonstrate ICSSIM by creating a testbed and validating its functionality by showing how different cyberattacks can be applied.

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based obse