Operational Network Research Articles

Segmentation of polarimetric radar imagery using statistical texture

Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification.

Satellite imagery in evaluating oil spill modelling scenarios for the Syrian oil spill crisis, summer 2021

The second-largest oil pollution incident in the Eastern Mediterranean Levantine basin, following the oil pollution crisis in Lebanon in 2006, is considered to be the oil leakage from the Syrian Baniyas power plant (summer 2021), during which 12,000 tons of oil were released. At the operational phase, the everyday predictions of oil drift were provided using the MEDSLIK and MEDSLIK-II models in the framework of an agreement between the Mediterranean Operational Network for Global Ocean Observing System (MONGOOS) and the Regional Marine Pollution Emergency Response Centre for the Mediterranean (REMPEC). In this work, we present a novel post-operational comprehensive model-based analysis, conducting a SAR validation in two model outputs: the MEDSLIK and the OpenDrift models. Each simulation is initiated with the operationally acquired EMSA-CSN and ESA SAR images. Moreover, the high-resolution met-ocean fields (CYCOFOS, SKIRON) are used to force the oil drift and transformation in both models. The spill was developed under the calm-wind conditions that prevailed during the incident. We found that the boundary sea currents developed on the periphery of the Lattakia eddies (anticyclonic and cyclonic) were responsible for the fast westward spreading of the oil spill offshore in the NE Levantine, the north-south pathway bifurcation, and re-landing of oil in the extended coastal area of Lattakia. Model outputs were validated against Synthetic-aperture radar (SAR) images with appropriate performance metrics, used for the first time, to assess the capacity of a reliable representation of oil spill drift. The intercomparison between the two oil spill models indicated that both models produce almost similar results, while their validation against the satellite SAR observations illustrates moderate accuracy.

FESSTVaL: The Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg

Abstract Numerical weather prediction models operate on grid spacings of a few kilometers, where deep convection begins to become resolvable. Around this scale, the emergence of coherent structures in the planetary boundary layer, often hypothesized to be caused by cold pools, forces the transition from shallow to deep convection. Yet, the kilometer-scale range is typically not resolved by standard surface operational measurement networks. The measurement campaign Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) aimed at addressing this gap by observing atmospheric variability at the hectometer-to-kilometer scale, with a particular emphasis on cold pools, wind gusts, and coherent patterns in the planetary boundary layer during summer. A unique feature was the distribution of 150 self-developed and low-cost instruments. More specifically, FESSTVaL included dense networks of 80 autonomous cold pool loggers, 19 weather stations, and 83 soil sensor systems, all installed in a rural region of 15-km radius in eastern Germany, as well as self-developed weather stations handed out to citizens. Boundary layer and upper-air observations were provided by eight Doppler lidars and four microwave radiometers distributed at three supersites; water vapor and temperature were also measured by advanced lidar systems and an infrared spectrometer; and rain was observed by a X-band radar. An uncrewed aircraft, multicopters, and a small radiometer network carried out additional measurements during a 4-week period. In this paper, we present FESSTVaL’s measurement strategy and show first observational results including unprecedented highly resolved spatiotemporal cold-pool structures, both in the horizontal as well as in the vertical dimension, associated with overpassing convective systems.

Discriminant Analysis for Severe Storm Environments in South-Central Brazil

Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

Blending Hydrogen With Natural Gas/Biomethane and Transportation in Existing Gas Networks

Abstract The existing European Union (EU) natural gas network provides large capacity to integrate renewable (RGs) and low-carbon gases. Today, hydrogen contributes only a few percent to Europe’s energy consumption and is almost exclusively produced from fossil fuels and used in the industry. Nevertheless, hydrogen has a significant role to play in emission reduction in hard-to-decarbonize sectors, in particular, as a fuel in transport applications and as a fuel or feedstock in certain industrial processes (steel, refining or chemical industries, the production of “green fertilizers”). Carbon dioxide (CO2) in reaction with hydrogen can also be further processed into synthetic fuels, such as synthetic kerosene in aviation. In addition, hydrogen brings other environmental co-benefits when used as fuel, such as the lack of air pollutant emissions. However, in transitional phase from fossil to RG, namely, renewable or green hydrogen, natural gas/biomethane and hydrogen blends, are needed to gradually replace natural in existing gas transmission and distribution networks. The gas networks are believed to be able to use natural gas/biomethane and hydrogen blends with 5–20 % of hydrogen by volume. Most systems and applications are able to handle it without a need for major infrastructure upgrades or end-use appliance retrofits or replacements. The promotion of hydrogen network such as European Hydrogen backbone (EHB) is gaining momentum in Europe. To decarbonize the natural gas grids, the threshold of hydrogen in the existing grid systems must be increased, which can be done by means of wider natural gas/biomethane and hydrogen blending and simultaneous transportation in currently operational gas networks.

LEAF: Navigating Concept Drift in Cellular Networks

Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, model accuracy can degrade due to concept drift, whereby the relationship between the features and the target to be predicted changes. Mitigating concept drift is an essential part of operationalizing machine learning models in general, but is of particular importance in networking's highly dynamic deployment environments. In this paper, we first characterize concept drift in a large cellular network for a major metropolitan area in the United States. We find that concept drift occurs across many important key performance indicators (KPIs), independently of the model, training set size, and time interval---thus necessitating practical approaches to detect, explain, and mitigate it. We then show that frequent model retraining with newly available data is not sufficient to mitigate concept drift, and can even degrade model accuracy further. Finally, we develop a new methodology for concept drift mitigation, Local Error Approximation of Features (LEAF). LEAF works by detecting drift; explaining the features and time intervals that contribute the most to drift; and mitigates it using forgetting and over-sampling. We evaluate LEAF against industry-standard mitigation approaches (notably, periodic retraining) with more than four years of cellular KPI data. Our initial tests with a major cellular provider in the US show that LEAF consistently outperforms periodic and triggered retraining on complex, real-world data while reducing costly retraining operations.

IWIN: the Isfjorden Weather Information Network

Abstract. In an effort led by the University Centre in Svalbard (UNIS), with support from the Norwegian Meteorological Institute (MET Norway), the Isfjorden Weather Information Network (IWIN) is under development in the Isfjorden region in central Svalbard. The network substantially expands upon the relatively sparse existing operational network of weather stations and consists of compact and cost-efficient all-in-one weather stations permanently installed at lighthouses around Isfjorden and on board small tourist cruise ships trafficking the fjord from spring to autumn. All data from the network until June 2023, as presented in this paper, can be found at https://doi.org/10.5281/zenodo.8137588 (Frank et al., 2023b). New data become freely available in near-real time via MET Norway's data portals (https://doi.org/10.21343/ebrw-w846, Frank et al., 2023a). The IWIN data are highly valuable for scientific purposes such as atmospheric boundary layer research, the validation and development of numerical weather prediction models, and assimilation in these, as well as the planning and safe conduction of outdoor activities in the region.

Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop

In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated “operation–machine” combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories.

Machine learning-based optimisation in a two-echelon logistics network for the dry port operation in China

ABSTRACT Efforts of creating economic recovery after the COVID-19 pandemic stipulate international logistics demands in the countries and regions affected by China's Belt and Road initiative. Considering the increasing number of small and middle-sized enterprises, there is a great challenge to make transportation plans for the hub port, dry ports, and related enterprises. We investigate a two-echelon vehicle routing problem with simultaneous pickups and deliveries. On one hand, freights are transported from a central depot to multiple satellites, then distributed from the satellites to customers. On the other hand, freights collected from the customers will be loaded at the satellites, then transported back to the depot. We model the problem as mixed integer programming and propose a machine learning-based hybrid algorithm to solve the problem. Our hybrid algorithm comprises a K-Nearest Neighbour algorithm and an Adaptive Large Neighbourhood Search heuristic. We apply our modelling and solution approach to a real case based on the online digital platform ‘Inland Port Cloud Wharf’ in China, which matches international demands of commodities with domestic supplies. Our computational experiments based on a practical case study show the efficiency of our KNN_ALNS algorithm in optimising networks with multimodal coordination and addressing real-world logistics complexities.

DISPLACEMENT BASED COMPARISON OF ACCELEROMETER AND LOW-COST GNSS WAVE BUOYS

Wave observations are critical in both coastal and deep water and feed into applications including improving ocean forecasting, industry activities, and marine safety. Wave buoys are typically the most robust method to collect in situ wave observations, particularly when real-time data is required, however they have historically been expensive to purchase and operate. With the advent of low-cost and small-format Global Navigation Satellite System (GNSS) based wave buoys (e.g. Sofar Spotter), the ability to collect wave observations across a wider range of locations is feasible at a much lower cost. However, understanding the performance and behaviors of small-format GNSS buoys (e.g. spectral response and mooring effects), against more traditional accelerometer based platforms, is critical to inform their use across research and industry applications. Here, we focus on comparing the Sofar Spotter to Datawell Waverider (Mark 3 and 4) buoys which are commonly deployed in operational networks globally. However, instead of comparing processed products (e.g. spectral statistics) produced by the buoy’s onboard software, we begin our analysis with the buoy displacement time series directly to ensure consistency. We also assess the detailed spectral properties/moments, the GNSS buoy performance in different conditions (locations), and with different mooring configurations.

SHATTER: Searching Heterogeneous Combat Network Attack Sequences Through Network Embedding and Reinforcement Learning

A distributed combat system consists of various types of combat units interconnected through the network, which can be abstracted as a complex heterogeneous combat network (HCN). It is of great military value to study the disintegration of the operational capability of HCNs for operational mission planning and improving the survivability of operational networks. Heterogeneous networks can effectively fuse more information than homogeneous networks can. The function implementation of heterogeneous networks is also more complex. Finding the key nodes and obtaining an efficient attack sequence is our focus. Most current research on network disintegration has difficulty achieving efficiency and timeliness. In this article, we specifically propose searching heterogeneous combat network attack sequences through network embedding and reinforcement learning (SHATTER) model, which uses the induction algorithm combined with the graph neural network and reinforcement learning to solve the network disintegration sequence problem. Through experiments with synthetic heterogeneous network datasets and real heterogeneous networks, we determined that SHATTER significantly outperformed existing technologies.

Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is largely due to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. These distinctive and unintentional features are attributed to slight manufacturing and assembly variations within and between the components, sub-systems, and systems comprising an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors, primarily focusing on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism; thus, we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure.

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning.

In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

Five Decades of the Nuclear Non-Proliferation Treaty: Time to Pass the Baton to the UN Security Council?

The Treaty on the Non-Proliferation of Nuclear Weapons (NPT) came into force in 1970. After renouncing its nuclear weapons programme, South Africa became a party to the NPT in 1991. The NPT is the principal international instrument aimed at preventing the spread of nuclear weapons. The NPT has generated various other treaties with similar aims, which, together with the NPT, constitute the ‘nuclear non-proliferation regime’. The NPT has, for various reasons, not been a success. Whether the NPT has achieved nuclear non-proliferation is a moot point–a concept which implies nuclear disarmament. For example, there is evidence of a network of covert operations marketing nuclear technology; some non-NPT states have clandestinely embarked on nuclear weapons programmes, and some NPT states have threatened to adopt a policy of actual or pre-emptive use of nuclear weapons even against non-nuclear weapons states for its deterrent value. Alarm bells are ringing regarding the interpretation of ‘peaceful’ and ‘non-peaceful’ uses of nuclear energy. Despite the NPT, nothing prohibits the testing and refinement of existing nuclear weapons technology. Certain questions need to be answered. Has the NPT not become an instrument of rhetorical posturing by the five major nuclear weapon states–the ‘Club of Five’? Why has none of the Club of Five given their nuclear weapons up? Where do the Club of Five get the moral authority to declare that introducing new members on the Club is unacceptable? Instead of concentrating on the non-proliferation of nuclear weapons as the NPT does, should there not rather be an emphasis on the total prohibition of all nuclear weapons? It is submitted that a more active role for the UN in nuclear non-proliferation issues be considered. All UN Secretaries-General have made nuclear non-proliferation one of their top priorities. Kofi Annan concluded that the NPT was the victim of insufficient progress in not only nuclear non-proliferation but also nuclear disarmament and that the world was sleepwalking towards a nuclear disaster. The basis for greater involvement of the UN Security Council in nuclear non-proliferation and nuclear disarmament is to be found in the Statute of the International Atomic Energy Agency, the Statute of the NPT, the UN Charter and in Security Council Relations 1540 of 2004 and 1929 of 2010. The argument is put forward that as the Security Council has the legal authority to determine threats to the peace or acts of aggression and make recommendations or decide what measures shall be taken regarding it, utilising that body may be a more meaningful route to follow than the NPT which appears to have reached its sell-by date.

First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding

Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.

Cost-effective network capacity upgrade by heterogeneous wavelength division multiplexing density with bandwidth-variable virtual direct links

A cost-effective and fast network capacity upgrade with what we believe to be novel bandwidth-variable virtual direct links (BV-VDLs) is proposed. BV-VDLs are designed to pass through the physical links expected to be heavily congested and realize very dense path accommodation. We adopt cut-set analysis to identify the links likely to be heavily congested; however, high computation loads will be incurred in finding all the cut-sets, and justifying these loads is essential. Thus a simple-path-search based graph splitting method is proposed that substantially shortens the calculation time. This strategy allows us to efficiently upgrade network capacity while keeping existing facilities unchanged and can be installed in networks in operation. The proposal is also valid for enhancing the capacity of new networks. The capacity enhancement can reach 13%–33% even in dynamic path operation scenarios on the real topologies of pan-European, North American, and Japanese networks.

Beam Selection for Cell-Free Millimeter-Wave Massive MIMO Systems: A Matching-Theoretic Approach

In this letter, we consider beam selection in cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To address the beam selection of all access points in the network for cell-free operation, a beam selection problem that maximizes the minimum of spectral efficiencies of all user equipments is formulated. We propose an extended deferred acceptance algorithm for beam selection based on the matching theory. Additionally, we propose two intuitive beam selection algorithms. System-level simulation results demonstrate the advantage of the proposed matching theory-based beam selection method in terms of spectral efficiency for cell-free mmWave massive MIMO systems.

Evaluation of additional electricity losses in electric networks using a meter

In this article, additional electricity losses are calculated using the voltage imbalance factor. There is also a method for predicting extra losses in the case of longitudinal and transverse asymmetry, as well as information on how to measure power quality indicators. It is suggested that home appliances be used to monitor a separate power system's power quality indicators. The findings of investigations on the condition of asymmetry conducted in operational low voltage electrical networks are presented, along with an illustration of how to calculate additional electricity losses in low-voltage networks. In this article for this purpose, the author developed a Malika device, which is fully capable to measure, store, analyze, and draw conclusions from the results of all electrical quality indicators.

Global Networks of Ground-Based Magnetometers Enable Cutting-Edge Heliophysics Research, Education, and Space Weather Operations

Global Networks of Ground-Based Magnetometers Enable Cutting-Edge Heliophysics Research, Education, and Space Weather Operations

Study of Echocardiogram Parameters from PPG Signal Using Self-Organized Operational Map-based Network.

Two crucial echocardiography parameters -left ventricular ejection fraction (LVEF) and myocardial performance index (MPI) are referred by clinicians to diagnose heart health. Here, an attempt was made to study the possibility of predicting values of LVEF and MPI from Photoplethysmography (PPG) signals. The classification of patients based on the LVEF and MPI values was also evaluated. After PPG signal feature extraction, the Dual Attention-Self Organised Operational Map-LSTM-Conv Network (DASLCN) was used to find the necessary results. Self-organized operational maps (SOOM) helped map the features before sending them to BiLSTM and 1D CNN layers. The results obtained were regression=0.86 with error% of 5.32±8.9 for MPI and accuracy=0.90 & sensitivity=0.89 for LVEF. This technique might help diagnose heart conditions from PPG signals without routine echocardiography.Clinical relevance- PPG is an easy cost-effective portable technique. Whereas, clinical echocardiography is possible only in specialized hospitals. Thus, exploring PPG signals to predict LVEF and MPI values were tried here. This study has been made on whether the grouping of patients based on the range of LVEF and MPI values was possible or not. Newly designed DASLCN helped to perform regression and classification in the same network.