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Dissociation of passenger travel from baggage delivery has been proposed as one of the radical innovations in future air travel. This concept is still relatively new and largely unexplored, so there are many issues that need to be resolved. For instance, a complete end-to-end baggage dissociation will require the ground distribution networks to deliver passenger luggage to and from the departing and arriving airports. This paper proposes to design such networks as the existing parcel delivery networks. In particular, baggage sorting centers (BSCs) can serve as local hubs for creasing a scalable, multi-level topology of the delivery network in order to manage baggage flows in a given geographical area around the selected airports. Assuming the population density as a proxy for estimating the baggage delivery service demands, the optimum locations of BSCs are determined by formulating and solving the standard p-median and the maximal covering location problems. The numerical results were obtained for Greater London, and also for the whole UK assuming all its major civilian airports. The Greater London area could be served by 36 BSCs to achieve a full service coverage. The 90% service coverage of the whole UK can be achieved by about the same number of BSCs, provided that the coverage distance is increased. In practice, the actual number of required BSCs crucially depends on the operational and capital costs, and the maximum processing capacity of each BSC. These findings have direct implications on the long-term planning and innovations in future air transport.

Future distribution networks (DN) are subject to rapid load changes and high penetration of variable distributed energy resources (DER). Due to this, the DN operators face several operational challenges, especially voltage violations. Optimal power flow (OPF)-based reactive power control (RPC) from the smart converter (SC) is one of the viable solutions to address such violations. However, sufficient communication and monitoring infrastructures are not available for OPF-based RPC. With the development of the latest information communication technology in SC, cyber-physical co-simulation (CPCS) has been extensively used for real-time monitoring and control. Moreover, deploying OPF-based RPC using CPCS considering the controller design of SC for a realistic DN is still a big challenge. Hence, this paper aims to mitigate voltage violations by using OPF-based RPC in a real-time CPCS framework with multiple SCs in a realistic DN. The OPF-based RPC is achieved by performing the CPCS framework developed in this study. The CIGRE medium-voltage DN is considered as a test system. Real-time optimization and signal processing are achieved by Python-based programs using a model-based toolchain of a real-time DN solver and simulator. Real-time simulation studies showed that the proposed method is capable of handling uncertain voltage violations in real time.

Effective bioremediation of hydrocarbons requires innovative approaches to minimize phosphate precipitation in soils of different buffering capacities. Understanding the mechanisms underlying sustained stimulation of bacterial activity remains a key challenge for optimizing bioremediation—particularly in northern regions. Positron emission tomography (PET) can trace microbial activity within the naturally occurring soil structure of intact soils. Here, we use PET to test two hypotheses: (1) optimizing phosphate bioavailability in soil will outperform a generic biostimulatory solution in promoting hydrocarbon remediation and (2) oligotrophic biostimulation will be more effective than eutrophic approaches. In so doing, we highlight the key bacterial taxa that underlie aerobic and anaerobic hydrocarbon degradation in subarctic soils. In particular, we showed that (i) optimized phosphate bioavailability outperformed generic biostimulatory solutions in promoting hydrocarbon degradation, (ii) oligotrophic biostimulation is more effective than eutrophic approaches, and (iii) optimized biostimulatory solutions stimulated specific soil regions and bacterial consortia. The knowledge gleaned from this study will be crucial in developing field-scale biodegradation treatments for sustained stimulation of bacterial activity in northern regions.

The inability to effectively and systematically identify and measure the damage in bridges will lead to an acceleration and dangerous deterioration of the health state of these structures. To repair and replace the aging and damaged bridge infrastructures, and prevent catastrophic bridge collapse, there is an urgent need to develop reliable, innovative, and efficient approaches to the performance assessment and inspection of bridges. Unmanned Aerial Vehicles (UAVs), also knowns as drone, technology has found its way into a number of civilian applications including inspection in the last 20 years, predominantly due to lower cost and tangible scientific improvements. The intent of this paper is to map the current state-of-the-art drone-enabled bridge inspection practices and investigated their associated hazards and risks. This paper will integrate scenario prediction and, assess hazards as well as the social and environmental loss in the case of drone-enabled bridge inspection. Further, this paper will follow rather closely a three-phase process: hazard identification, hazard analysis, and hazard evaluation, all executed with qualitative data and methods by experts of a variety of fields, methodologies for recognition of the impact of cold operating environment on the performance of drone and drone -pilots, creative interpretation of the hazard factors of identifiable problems. The proposed Preliminary Hazard Analysis (PHA) is exemplified via drone-enabled inspection of Håkenby bridge, which is located in the Viken county, in the eastern part of Norway.

Mapping Arctic renewable energy resources, particularly wind, is important to ensure the transition into renewable energy in this environmentally vulnerable region. The statistical characterisation of wind is critical for effectively assessing energy potential and planning wind park sites and is, therefore, an important input for wind power policymaking. In this article, different probability density functions are used to model wind speed for five wind parks in the Norwegian Arctic region. A comparison between wind speed data from numerical weather prediction models and measurements is made, and a probability analysis for the wind speed interval corresponding to the rated power, which is largely absent in the existing literature, is presented. The results of the present study suggest that no single probability function outperforms across all scenarios. However, some differences emerged from the models when applied to different wind parks. The Nakagami and Generalised extreme value distributions were chosen for the numerical weather predicted prediction and the observed wind speed modelling, respectively, due to their superiority and stability compared with other methods. This paper, therefore, provides a novel direction for understanding the numerical weather prediction wind model and shows that its speed statistical features are better captured than those of real wind.

In the present study, a hypoelastic–plastic formulation of porous crystal plasticity with a regularized version of Schmid’s law is proposed. The equation describing the effect of the voids on plasticity is modified to allow for an explicit analytical solution for the effective resolved shear stress. The regularized porous crystal plasticity model is implemented as a material model in a finite element code using the cutting plane algorithm. Fracture is described by element erosion at a critical porosity. The proposed model is used for two test cases of two- and three-dimensional polycrystals deformed in tension until full fracture is achieved. The simulations demonstrate the capability of the proposed model to account for the interaction between different modes of strain localization, such as shear bands and necking, and the initiation and propagation of ductile fracture in large scale polycrystal models with detailed grain description and realistic boundary conditions.

In the late 2000s, a process started that was designed to approximate the national disaster risk assessments in the European Union. Member states are currently obliged to prepare their assessments every three years. The European Commission will summarize the results, which should not only lead to a better overview of common risks but also direct future joint activities and investments. To date, two rounds of this new practice have been implemented and summarised. The present study investigates how and why this largely informal integrative practice was born, how it is facilitated, and how successful it has been vis-à-vis the expectations, especially achieving a relative comparability of the national risk assessments.

In this study, a numerical weak coupling strategy for the modeling of a conjugate heat transfer phenomenon is considered. Where the incompressible Navier-Stokes equations are solved using the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) as a first step, and then the heat conduction equation for solid is solved in a second step considering the convective velocity field resulting from the first step. A finite-difference approach is used for both discretized time and spatial operators. In this paper, a two-dimensional simulation case study of a steady uniform streamwise flow around heated rectangular and triangle solids is presented. The simulation is forward in time until the steady-state regime is reached as the residuals converge and tend to zero. The spatial analysis of the temperature is obtained through the numerical resolution of the incompressible Navier-Stokes energy equation and the heat diffusion equation for the fluid and solid media, respectively. The results show the temperature, velocity, and pressure fields in the space domain. The code is written in MATLAB®, and the flow chart of the method is provided. It was noted that the convection was more dominant than the diffusion.

This article addresses methodologies for remote sensing of ocean Chlorophyll-a (Chl-a), with emphasis on the Barents Sea. We aim at improving the monitoring capacity by integrating in situ Chl-a observations and optical remote sensing to locally train machine learning (ML) models. For this purpose, in situ measurements of Chl-a ranging from 0.014–10.81 mg/m$^{3}$, collected for the years 2016–2018, were used to train and validate models. To accurately estimate Chl-a, we propose to use additional information on pigment content within the productive column by matching the depth-integrated Chl-a concentrations with the satellite data. Using the optical images captured by the multispectral imager instrument on Sentinel-2 and the in situ measurements, a new spatial window-based match-up dataset creation method is proposed to increase the number of match-ups and hence improve the training of the ML models. The match-ups are then filtered to eliminate erroneous samples based on the spectral distribution of the remotely sensed reflectance. In addition, we design and implement a neural network model dubbed as the ocean color net (OCN), that has performed better than existing ML-based techniques, including the Gaussian process Regression (GPR), regionally tuned empirical techniques, including the ocean color (OC3) algorithm and the spectral band ratios, as well as the globally trained Case-2 regional/coast colour (C2RCC) processing chain model C2RCC-networks. The proposed OCN model achieved reduced mean absolute error compared to the GPR by 5.2%, C2RCC by 51.7%, OC3 by 22.6%, and spectral band ratios by 29%. Moreover, the proposed spatial window and depth-integrated match-up creation techniques improved the performance of the proposed OCN by 57%, GPR by 41.9%, OC3 by 5.3%, and spectral band ratio method by 24% in terms of RMSE compared to the conventional match-up selection approach.

Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier.

Snow avalanches are a significant natural hazard in Norway. One method to manage the backcountry avalanche hazard is through detailed mapping of avalanche terrain. Avalanche terrain can be mapped using a variety of methods, including using the Avalanche Terrain Exposure Scale (ATES); however, manual classification of terrain using ATES is time consuming. This study has developed and compared a fully automated algorithm to provide ATES mapping for all of Norway. Our new algorithm is based on the technical model for ATES mapping. This model has specific terrain-based thresholds that can be applied for automated terrain-based modeling. Our algorithm expands on prior work by including the potential release area (PRA) model to identify and calculate the likelihood of an avalanche releasing from a start zone. We also use the raster-based TauDEM-model to determine the avalanche runout length. The final product is a 10-m resolution ATES map. We compared this nationwide ATES map with areas that have been manually mapped by avalanche experts, and find that the automated approach yields similar and reliable results. In addition to comparing mapped areas, we also examine manually mapped linear routes and compare these with the automated mapped ATES areas. Our results suggest that for open terrain, the vast majority of the manually classified tracks are predominantly in the same ATES class as our algorithm. For forested areas, we get mixed results, which can be attributed to a lack of suitable vegetation data at an appropriate scale. Despite this limitation, the current ATES algorithm and resulting spatial data are already valuable as a large portion (~ 70%) of the Norwegian backcountry terrain is above tree line. The automated algorithm is also useful to ensure consistent manual classification across different regions in Norway, or globally, and will permit greater reproducibility and easier updating of mapping for the future.

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead.

Nowadays, the market competition becomes increasingly fierce due to diversified customer needs, stringent environmental requirements, and global competitors. One of the most important factors for companies to not only survive but also thrive in today’s competitive market is their logistics performance. This paper aims, through a systematic literature analysis of 115 papers from 2012 to 2020, at presenting quantitative insights and comprehensive overviews of the current and future research landscapes of sustainable logistics in the Industry 4.0 era. The results show that Industry 4.0 technologies provide opportunities for improving the economic efficiency, environmental performance, and social impact of logistics sectors. However, several challenges arise with this technological transformation, i.e., trade-offs among different sustainability indicators, unclear benefits, lifecycle environmental impact, inequity issues, and technology maturity. Thus, to better tackle the current research gaps, future suggestions are given to focus on the balance among different sustainability indicators through the entire lifecycle, human-centric technological transformation, system integration and digital twin, semi-autonomous transportation solutions, smart reverse logistics, and so forth.

Adequate thermal protection for passengers travelling on-board vessels in cold climate regions, such as that provided by thermal protective immersion suits (TPIS), enhances passenger survivability in emergency situations, in particular those requiring the abandonment of the vessel. As emergency abandonment is a time critical process, it is essential to consider the time required to correctly don the TPIS. Testing standards, such as the International Maritime Organization guidelines, require that TPIS must be able to be donned within 2 minutes. Unfortunately, current practices quantifying donning times are questionable and so there is a limited evidence base that reliably quantifies donning times required by typical passengers. This paper presents a test procedure designed to reliably quantify the time required by test subjects to don the TPIS. Furthermore, the procedure assesses the donning correctness – a TPIS that is incorrectly donned is unlikely to offer appropriate thermal protection. The paper will also discuss the deficiencies in current practices to assess required donning time.

The COVID-19 pandemic has suddenly switched most education processes from face-to-face to remote mode, obliging millions of students to utilize their residences as study spaces. However, the characteristics of their residential built environments differ in terms of regional, social, cultural, and technological aspects. These differences should impact the students' performance and satisfaction which needs to be measured and studied. The present study aims to identify the effect of the residential built environment on students' satisfaction and academic performance during the COVID-19 pandemic. It was conducted in two countries, Kazakhstan (KZ) and Norway (NO), using a comprehensive online survey to gather data. An empirical assessment based on the structural equation model was employed to identify links between health, safety, and comfort of students' facilities and academic performance and satisfaction. We conclude that the built environment affects both satisfaction for remote education and their learning performance. Significant differences in readiness for remote education have been observed between urban and non-urban living areas: (1) The role of health-and-safety convenience seems to increase with the urbanization level of the respondents’ living spaces; (2) in contrast, for non-urban residents, the provision of comfort facilities is dominant. In the meantime, an analysis “by regions” revealed that health-and-safety-related facilities in residences are more critical for remote education in Central Asia (KZ). In contrast, the comfort features of residences being more important for the students studying remotely in Northern Europe (NO). These results provide an understanding that would assist in improving remote education and preparing pandemic-ready living areas.

This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

The topic of transition curves is well known in track and road engineering. Over the years the need for easement between straight and curved sections in railway and road design has grown, with an important reason being increase in vehicle speed. Testing of transitions is often done with graph analysis where the property of lateral change of acceleration is compared between varieties of curves. However, graph analysis does not give a clear understanding of the behaviour of a vehicle in a transition phase the way, for instance, field or laboratory experiments might do. In this paper we present an initial study on the behaviour of a down-scaled vehicle model driving through three distinct transitions from straight to curved sections. In addition we show a comparison of the physical model against a virtual model.

Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable, the accuracy of wind forecasts is a key element for the electric system operators, as it strongly affects the decision-making processes. The planning horizon can be short term (1 -3 months) and long-term (6–12 months) depending on the process.The objective of this paper is to conduct a performance comparison of five deep learning models each combined with three types of data pre-processing and used for short term and long-term multi-variate predictions. The input data are time series of the wind power capacity factor and the temperature. In addition, this paper sets out to demonstrate and review the state-of-the-art deep learning models for prediction with a secondary objective to present the reader a reference point to better understand which model to choose and what factors are significant. The first contribution of this paper is to apply, assess and compare a selection of the novel and cutting-edge deep learning models for multi-variate prediction. Multi-variate predication is achieved through a proposed multiple input and multiple output (MIMO) architecture. Compared to traditional prediction models, machine learning techniques have the advantage of generalization. Among various techniques deep learning is particularly getting more attention due to the applicability to various dataset such as numerical and character. This investigation focuses on five models — Deep Feed Forward (DFF), Deep Convolutional Network (DCN), Recurrent Neural Network (RNN), Attention mechanism (Attention) and Long Short-Term Memory Networks (LSTM). The second contribution is to propose a novel approach to transform the time series dataset to signal for input and reconstruct the model predictions through inverse transformation, by means of the so-called discrete wavelet transformation and fast Fourier transformation. The different models are assessed also by comparing their performance with and without the input dataset manipulation through wavelet and FFT transformation. Beyond that, the model performances are outlined in detail, to give the reader an overview of the models to choose from for short-term or long-term prediction. The results demonstrate that the Attention and DCN perform best with Wavelet or FFT signal, whereas some other models perform better with no data preprocessing. Keywords: Unsupervised machine learning, Multi-variate prediction, Short and long term prediction, Wind power forecasting, Time series to frequency transformation, Performance evaluation and comparison

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Extracting snowpack parameters from snow cover on sea ice or land is a time-consuming and potentially high-risk task. Moreover, deriving such parameters by manually digging a snow pit evidently yields low area coverage. We, therefore, propose a practical solution to this problem by mounting an ultrawideband radar system onto an unmanned aerial vehicle (UAV) to obtain information such as snowpack depth, density, and stratigraphy in order to increase personnel safety and extend coverage area. In this paper, we describe the development of radar system hardware and its mounting onto a UAV, as well as initial tests with this radar as a snow measuring device. Preliminary results from both ground and airborne testing show that the radar system is capable of obtaining snow depth information that corresponds well to in situ validation data with a correlation of 0.87. The radar system also works well while mounted on a UAV platform with little additional signal noise from vibrational and translatory movements.

Due to the unprecedentedly increasing demands of waterway transportation on the Yangtze River during the latest years, traffic congestion at the Three Gorges Dam (TGD) has becoming a serious problem. In busiest seasons, a vessel may need to wait up to 3 days for passing the TGD via the ship lift or the five-stage ship lock. In order to improve the navigation efficiency and the utilization of passing facilities, a co-scheduling problem of ship lift and ship lock at the TGD is modeled in this paper. Besides, the improvement on effectiveness, safety and fairness of the navigation scheduling by introducing the ship lift is taken into account. The mathematical model proposed is, by nature, a two-stage problem, where the first-stage variables determine the assignments of passing facilities and the second-stage variables schedule the vessels. In order to solve the proposed mathematical model, a hybrid metaheuristic algorithm is used at the first stage to determine the allocation of vessels to different passing facilities and to different lockage through the maximization of facility utilization rate. The second-stage problem is then solved by CPLEX. The proposed mathematical model and algorithm are validated through a set of numerical experiments and a case study. The results show both the navigation efficiency and the utilization of passing facilities can be improved by using the proposed methods.

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments.

The performance of piezoelectric sensors deteriorated due to the presence of defect, delamination, and corrosion that needed to be diagnosed for the effective implementation of the structural health monitoring (SHM) framework. A novel experimental approach based on Coulomb coupling is devised to visualise the interaction of ultrasonic waves with microscale defects in the Lead Zirconate Titanate (PZT). Multiresolution dynamic mode decomposition (mrDMD) technique in conjunction with image registration, and Kullback Leibler (KL) divergence is utilised to diagnose and localise the surface defect in the PZT. The mrDMD technique extracts the spatiotemporal coherent mode and provides an equation-free architecture to reconstruct underlying system dynamics. Additionally, due to the strong connection between mrDMD and Koopman operator theory, the proposed technique is well suited to resolve the nonlinear and dispersive interaction of elastic waves with boundaries and defects. The mrDMD sequentially decomposes the three-dimensional spatiotemporal data into low and high frequency modes. The spectral modes are sensitive to defects based on the scaling of wavelength with the size of the defect. The error due to offset and distortion was minimised with ad hoc image registration technique. Further, localisation and quantification of defect are performed by evaluating the distance metric of the probability distribution of coherent data of mrDMD acquired from healthy and defected samples. In the arena of big-data that is ubiquitous in SHM, the paper demonstrates an efficient damage localisation algorithm that explores the nonlinear system dynamics using spectral multi-mode resolution techniques by sensitising the damage features.

Background Without confirmation of the ventilation design conditions (typology and airflow rate), the common practice of identifying unidirectional airflow (UDAF) systems as equivalent to ultra-clean air ventilation systems may be misleading, but also any claims about the ineffectiveness of UDAF systems should be doubted. The aim of this review was to assess and compare ventilation system design conditions for which ultra-clean air (mean

This paper discusses the Inspiria charging station facility in Norway, which enables various charging point operators to offer different charging systems for different purposes and needs. The charging station can be considered a specific case of a shared economy, as users share the same infrastructure and much of the same space. By utilizing new technology and the differences in charging needs in an innovative way, the power requirements for charging can be controlled and the severity of high-load periods can be reduced—both within the charging station’s system and outside it. Using historical traffic data from the Inspiria charging station’s area and Monte Carlo simulations, this study investigated the impact of charging on the grid—both in the current period and in the future. Attention was paid to the impact associated with the usage of superfast chargers. The possibility of containing grid disturbances through utilization of local flexibility was investigated. Finally, we investigated the benefits that the charging station model brings to charging point operators and car owners. The research reported provides support for ambitions for accelerated roll-out and increased density of cost-effective charging points, the wider implication of which concerns the transition to fossil-free transport and the utilization of locally generated, renewable energy.

This paper presents a modeling approach for efficient simulation of slender structures, such as wires, cables and ropes. Lumped structural elements are connected using constraints. These are solved explicitly, using an elastic version of Baumgarte stabilization. This avoids singularities in the matrix inversions. The resulting explicit state-space formulation filters the higher order dynamics and can be solved using simple numerical integration methods. Constraints are demonstrated for modeling different aspects: Internal cable forces, one cable sliding along another cable and contact between cable and seabed. Also, a cable initialization routine is presented for rapid building of different interconnected cable geometries, ranging from cases in offshore crane operations to in-sea equipment such as seismic cables. Two case studies are presented to illustrate the effectiveness and the robustness of the proposed modeling approach; the first one being a test of two connected, sinking cables, and the last one being a larger case demonstrating the use of the cable library in an offshore seismic survey case.

This paper presents the benefits of PVA fibers in improving the mechanical properties of class F fly-ash-based geopolymer concrete. The activator used in the geopolymer was 8M sodium hydroxide (NaOH) and sodium silicate (Na2SiO3), with a mass ratio of Na2SiO3 to NaOH varied from 1.5 to 2.5. Cylindrical specimens with a diameter of 100 mm and a height of 200 mm were prepared for mechanical strength tests. The PVA fibers in the geopolymers were fixed at 0.4%, 0.6%, and 0.8% by total volume. Some mechanical tests were carried out, including compression, splitting, direct tensile and elastic modulus tests. It was found that the mixture with a ratio of alkalis of 1.5 and the PVA fiber content of 0.4% had the best workability. The highest compressive strength was obtained in a mixture with alkali activator ratios of 1.5 and 2.0, and with 0.6% fiber addition. The ratio of the tensile (and splitting-tensile) strength to compressive strength was found to increase with the certain amount of PVA fibers and the ratio of the alkali activators. The workability issue and fiber direction in the concrete were the dominant factors influencing the properties of geopolymer concrete.

Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

The objective of this conceptual study is to reveal the substantial potential and synergy of solar energy and electric vehicles (EVs) working together. This potential is demonstrated by studying the feasibility of a nationwide energy system solely reliant on solar energy and EVs. Photovoltaic (PV) solar energy is already an important energy source globally, but due to its intermittency it requires energy storage to balance between times of high and low production. At the same time, a global drive is underway in the transport sector: the change from internal combustion engines to EVs. Cars are in fact stationary 95% of the time, and when the vehicle is connected to the grid, the EV battery can regulate the intermittent PV source using vehicle-to-grid (V2G) technology. This paper presents a conceptual study of a pure PV-EV based energy system, with Spain as a case study. Provided that Spain’s entire fleet of 29.4 million road going vehicles is switched to EVs, the study shows that 3.45 billion m2 of PV (73 m2 per capita) could give Spain a completely self-reliant energy system. The theoretical study is based on a combination of measured values, simulations, and assumptions. The conclusion of the analysis is undoubtedly extraordinary, namely that an entire country like Spain can power its complete energy system solely on PV, using EVs as the only energy storage resource.

Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at https://github.com/NaveenPaluru/Segmentation-COVID-19 .

Shridhar D. Jawak, Bo N. Andersen, Veijo Pohjola, Øystein Godøy, Kim Holmén, Agnar Sivertsen, Richard Hann, Hans Tømmervik, Andreas Kääb, Małgorzata Błaszczyk, Roberto Salzano, Bartłomiej Luks, Kjell Arild Høgda, Rune Storvold, Lennart Nilsen, Rosamaria Salvatori, Kottekkatu Padinchati Krishnan, Sourav Chatterjee, Dag A. Lorentzen, Rasmus Erlandsson, Tom Rune Lauknes, Eirik Malnes, Stein Rune Karlsen, Hiroyuki Enomoto, Ann Mari Fjæraa, Jie Zhang, Sabine Marty, Knut Ove Nygård, Heikki Lihavainen, Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard. SIOS is developing an efficient observing system, where researchers share technology, experience, and data, work together to close knowledge gaps, and decrease the environmental footprint of science. SIOS maintains and facilitates various scientific activities such as the State of the Environmental Science in Svalbard (SESS) report, international access to research infrastructure in Svalbard, Earth observation and remote sensing services, training courses for the Arctic science community, and open access to data. This perspective paper highlights the activities of SIOS Knowledge Centre, the central hub of SIOS, and the SIOS Remote Sensing Working Group (RSWG) in response to the unprecedented situation imposed by the global pandemic coronavirus (SARS-CoV-2) disease 2019 (COVID-19). The pandemic has affected Svalbard research in several ways. When Norway declared a nationwide lockdown to decrease the rate of spread of the COVID-19 in the community, even more strict measures were taken to protect the Svalbard community from the potential spread of the disease. Due to the lockdown, travel restrictions, and quarantine regulations declared by many nations, most physical meetings, training courses, conferences, and workshops worldwide were cancelled by the first week of March 2020. The resumption of physical scientific meetings is still uncertain in the foreseeable future. Additionally, field campaigns to polar regions, including Svalbard, were and remain severely affected. In response to this changing situation, SIOS initiated several operational activities suitable to mitigate the new challenges resulting from the pandemic. This article provides an extensive overview of SIOS’s Earth observation (EO), remote sensing (RS) and other operational activities strengthened and developed in response to COVID-19 to support the Svalbard scientific community in times of cancelled/postponed field campaigns in Svalbard. These include (1) an initiative to patch up field data (in situ) with RS observations, (2) a logistics sharing notice board for effective coordinating field activities in the pandemic times, (3) a monthly webinar series and panel discussion on EO talks, (4) an online conference on EO and RS, (5) the SIOS’s special issue in the Remote Sensing (MDPI) journal, (6) the conversion of a terrestrial remote sensing training course into an online edition, and (7) the announcement of opportunity (AO) in airborne remote sensing for filling the data gaps using aerial imagery and hyperspectral data. As SIOS is a consortium of 24 research institutions from 9 nations, this paper also presents an extensive overview of the activities from a few research institutes in pandemic times and highlights our upcoming activities for the next year 2021. Finally, we provide a critical perspective on our overall response, possible broader impacts, relevance to other observing systems, and future directions. We hope that our practical services, experiences, and activities implemented in these difficult times will motivate other similar monitoring programs and observing systems when responding to future challenging situations. With a broad scientific audience in mind, we present our perspective paper on activities in Svalbard as a case study.

As the scaling of silicon PV cells and module manufacturing has driven solar energy penetration up and costs down, concentrator photovoltaic technologies, originally conceived as a cost-saving measure, have largely been left behind. The loss of market share by CPV is being locked in even as solar energy development encounters significant obstacles related to space constraints in many parts of the world. The inherently higher collection efficiency enabled by the use of concentrators could substantially alleviate these challenges, but the revival of CPV for this purpose requires substantial reinvention of the technology to actually capture the theoretically possible efficiency gains, and to do so at market-friendly costs. This article will discuss recent progress in key areas central to this reinvention, including miniaturization of cells and optics to produce compact, lightweight “micro-CPV” systems; hybridization of CPV with thermal, illumination and other applications to make use of unused energy streams such as diffuse light and waste heat; and the integration of sun-tracking into the CPV module architecture to enable greater light collection and more flexible deployment, including integration into built structures. Applications showing particular promise include thermal applications such as water heating, industrial processes and desalination; agricultural photovoltaics; building-integrated photovoltaics with dynamic daylighting capabilities; and chemical processes including photocatalysis and hydrogen production. By appropriately tailoring systems to the available solar resource and local energy demand, we demonstrate how CPV can finally achieve real-world efficiencies, or solar resource utilization factors, far higher than those of standard silicon-based PV systems. This makes the argument for sustained development of novel CPV designs that can be applied to the real-world settings where this efficiency boost will be most beneficial.

An important property associated with turbulence in plasmas and fluids is anomalous transport. Plasma, being a good conductor, can in addition be affected by turbulence causing an anomalous resistivity that can significantly exceed its classical counterpart. While turbulent transport may be adequately described in configuration space, some aspects of the anomalous resistivity are best accounted for in phase space. Kinetic phenomena like electron and ion phase space vortices can thus act as obstacles for the free flow of slow charged particles. Plasma instabilities and large amplitude plasma waves are candidates for contributions to the anomalous resistivity by generating such structures. Langmuir waves can be relevant, but also others, such as upper- as well as lower-hybrid waves in magnetized plasmas. Often these anomalous resistivity effects can be small, but due to the large spatial and temporal scales involved in space plasmas, planetary ionosphere and magnetosphere in particular, even such moderate effects can be important. This mini-review is discussing elements of the description of plasma turbulence with particular attention to wave phenomena that contribute to anomalous resistivity and diffusion. Turbulence effects can have relevance for space weather phenomena as well, where ground based and airborne activities relying on for instance Global Positioning and Global Navigation Satellite Systems are influenced by plasma conditions in geospace.

On-chip super-resolution optical microscopy is an emerging field relying on waveguide excitation with visible light. Here, we investigate two commonly used high-refractive index waveguide platforms, tantalum pentoxide (Ta2O5) and silicon nitride (Si3N4), with respect to their background with excitation in the range 488–640 nm. The background strength from these waveguides were estimated by imaging fluorescent beads. The spectral dependence of the background from these waveguide platforms was also measured. For 640 nm wavelength excitation both the materials had a weak background, but the background increases progressively for shorter wavelengths for Si3N4. We further explored the effect of the waveguide background on localization precision of single molecule localization for direct stochastic optical reconstruction microscopy (dSTORM). An increase in background for Si3N4 at 488 nm is shown to reduce the localization precision and thus the resolution of the reconstructed images. The localization precision at 640nm was very similar for both the materials. Thus, for shorter wavelength applications Ta2O5 is preferable. Reducing the background from Si3N4 at shorter wavelengths via improved fabrication will be worth pursuing.

Polar lows are intense mesoscale cyclones that develop in polar marine air masses. Motivated by the large variety of their proposed intensification mechanisms, cloud structure, and ambient sub-synoptic environment, we use self-organising maps to classify polar lows. The method is applied to 370 polar lows in the north-eastern Atlantic, which were obtained by matching mesoscale cyclones from the ERA-5 reanalysis to polar lows registered in the STARS dataset by the Norwegian Meteorological Institute. ERA-5 reproduces most of the STARS polar lows. We identify five different polar-low configurations which are characterised by the vertical wind shear vector, the change in the horizontal-wind vector with height, relative to the propagation direction. Four categories feature a strong shear with different orientations of the shear vector, whereas the fifth category contains conditions with weak shear. This confirms the relevance of a previously identified categorisation into forward- and reverse-shear polar lows. We expand the categorisation with right- and left-shear polar lows that propagate towards colder and warmer environments, respectively. For the strong-shear categories, the shear vector organises the moist-baroclinic dynamics of the systems. This is apparent in the low-pressure anomaly tilting with height against the shear vector and the main updrafts occurring along the warm front located in the forward-left direction relative to the shear vector. These main updrafts contribute to the intensification through latent heat release and are typically associated with comma-shaped clouds. Polar-low situations with a weak shear, which often feature spirali-form clouds, occur mainly at decaying stages of the development. We thus find no evidence for hurricane-like intensification of polar lows and propose instead that spiraliform clouds are associated with a warm seclusion process.

Abstract In this paper, solar photovoltaic hosting capacity within the electrical distribution network is estimated for different buses, and the impacts of high PV penetration are evaluated using power hardware-in-loop testing methods. It is observed that the considered operational constraints (i.e. voltage and loadings) and their operational limits have a significant impact on the hosting capacity results. However, with increasing photovoltaic penetration, some of the network buses reach maximum hosting capacity, which affects the network operation (e.g. bus voltages, line loading). The results show that even distributing the maximum hosting capacity among different buses can increase the bus voltage rise to 9%. To maintain the network bus voltages within acceptable limits, reactive power voltage-based droop control is implemented in the photovoltaic conditioning devices to test the dynamics of the network operation. The results show that implementation of the droop control technique can reduce the maximum voltage rise from 9% to 4% in the considered case. This paper also presents the impact of forming a mesh type network (i.e. from radial network) on the voltage profile during PV penetration, and a comparative analysis of the operational performance of a mesh type and radial type electrical network is performed. It is observed that the cumulative effect of forming a mesh type network along with a droop control strategy can further improve the voltage profile and contribute to increase photovoltaic penetration. The results are verified using an experimental setup of digital real-time simulator and power hardware-in-loop test methods. The results from this work will be useful for estimating the appropriate photovoltaic hosting capacity within a distribution network and implementation of a droop control strategy in power conditioning devices to maintain the network operational parameters within the specified limits. Highlights Voltage and line loading constraints’ combination can reduce PV hosting capacity by 50% as compared to only voltage as a constraint. Implementation of reactive power versus voltage droop control in PV power conditioning device can reduce voltage variation from 9% to 4%. In a PV integrated electrical energy network, line loading can be reduced by 20% if the network is configured from radial to mesh type.

Additive manufacturing is a rapidly evolving manufacturing technology bringing numerous and wide opportunities for the design team involved in the process by creating intricate and customized products with saving labor, time, and other expenses. Innovative AM methods and numerous practical applications in aerospace, automotive, medical, energy, and other industries have been developed and commercialized through extensive research over the last two decades. One embraced industry among others that benefited from the advances of AM is the healthcare industry. This paper focuses on addressing the challenges and opportunities in Additive manufacturing for healthcare. Although there are advanced possibilities in AM, there are also numerous issues needed to be overcome. The paper is based upon the current state-of-the-art review and study visits. The purpose of this work has been to identify the opportunities and limitations associated with additive manufacturing in healthcare applications and to highlight the identified research needs.

International audience; Crystallographic aspects of heterogeneous plastic flow during ductile tearing are assessed experimentally by combining in situ synchrotron laminography imaging of notched flat samples with a full-field measurement technique and numerical simulations with the crystal plasticity finite element method (CP-FEM). Advantage is taken of the plane strain condition in the region ahead of the notch root (i.e., in the plane normal to the crack propagation direction) to perform two-dimensional image correlation in the material bulk. Intermetallic particles provide image contrast that is registered during the correlation analyses. They reveal the early onset of slant strained bands along which the final slant crack propagated. Two-dimensional plane strain CP-FEM simulations using the grain shape and crystallographic orientations of the studied material show the same trends as those observed experimentally, namely, the appearance of early slant strained bands that cross tens of grains and that remain stable over the loading history. The ratio of the strains within and outside the bands is also similar to that observed experimentally.

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.

The emergence of autonomous ship technologies has attracted a growing body of academic studies, regulatory discussions and exploration endeavours in recent years. With the introduction of new technology comes the need for the seafarers to be trained in its use. The purpose of this paper is 1) to examine the suitability of the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) Table A-II/1 competence framework for navigators under Maritime Autonomous Surface Ship (MASS) operations, and 2) to propose future technical and non-technical competencies that will be needed in autonomous shipping era. A mixed method approach was adopted with collection of both quantitative and qualitative data through a survey instrument developed on the basis of the literature and current STCW Table A-II/1, in which the 66 Knowledge, Understanding & Proficiency (KUP) requirements for navigators were converted into measurement items. Statistical analysis of the data has aided in identifying a list of key technical and non-technical competence requirements for the navigators under MASS operations. The results can be used as an input for revision of the STCW competence requirements and to facilitate the preparation and implementation of novel training frameworks for autonomous shipping.

Training of Artificial Neural Networks (ANNs) have been improved over the years using meta heuristic algorithms that introduce randomness into the training method but they might be prone to falling into a local minima in a high-dimensional space and have low convergence rate with the iterative process. To cater for the inefficiencies of training such an ANN, a novel neural network is presented in this paper using the bio-inspired algorithm of the movement and mating of the mayflies. The proposed Mayfly algorithm is explored as a means to update weights and biases of the neural network. As compared to previous meta heuristic algorithms, the proposed approach finds the global minima cost at far less number of iterations and with higher accuracy. The network proposed, which is named Mayfly Algorithm based Neural Network (MFANN) consists of an input layer, a single hidden layer of 10 neurons and an output layer. Two University of California Irvine (UCI) database sample datasets have been used as benchmark for this study, namely the Banknote Authentication (BA) and the Cryotherapy, for which the training accuracy achieved is 99.2350% and 96.6102%, whereas the Testing accuracy is 99.1247% and 90% respectively. Comparative study with grey wolf optimization neural network (GWONN) and particle swarm optimization neural network (PSONN) reveal that the proposed MFANN achieves 1–2% better accuracy with training dataset and 2% better accuracy with testing dataset.

The need for better natural resource use is currently increasingly recognised, and high emphasis is given to the circularity of building materials and the reduction of activities with negative environmental impact. Legislation, guidelines, and other documentation play an important role in improving demolition activities and construction and demolition waste (CDW) management. Good practices in CDW handling is not achievable without knowledge about CDW recovery techniques described in guidelines and other documents. Demolition activities in arctic regions could be more challenging due to harsh climate conditions, and therefore the cooperation between Russia, Norway and Finland was established to boost the uptake of good practices in demolition activities and CDW management. The main subject of this article is an overview of presently used demolition practices, CDW management, and verification of areas where practices with lower environmental impact and increase of material circularity could be utilised. Two fundamental documents, namely “EU Construction & Demolition Waste Management Protocol” and “Guidelines for the waste audits before demolition and renovation works of buildings” [1, 2], were published by the European Union (EU) in 2019 and serve as a foundation for changes in demolition activities and CDW management in EU and adventitiously also in the Russian Federation and Norway.

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm3. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique.

Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles.

Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

Nanophotonic waveguides are at the core of a great variety of optical sensors. These structures confine light along defined paths on photonic chips and provide light–matter interaction via an evanescent field. However, waveguides still lag behind free-space optics for sensitivity-critical applications such as trace gas detection. Short optical pathlengths, low interaction strengths, and spurious etalon fringes in spectral transmission are among the main reasons why on-chip gas sensing is still in its infancy. In this work, we report on a mid-infrared integrated waveguide sensor that successfully addresses these drawbacks. This sensor operates with a 107% evanescent field confinement factor in air, which not only matches but also outperforms free-space beams in terms of the per-length optical interaction. Furthermore, negligible facet reflections result in a flat spectral background and record-low absorbance noise that can finally compete with free-space spectroscopy. The sensor performance was validated at 2.566 μm, which showed a 7 ppm detection limit for acetylene with only a 2 cm long waveguide.

Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power.

Composite materials are becoming more popular in technological applications due to the significant weight savings and strength these materials offer compared to metallic materials. In many of these practical situations, the structures suffer from drop impact loads. Materials and structures significantly change their behavior when submitted to impact loading conditions as compared to quasi-static loading. The present work is devoted to investigating the elastic strain wave in Carbon-Fiber-Reinforced Polymers (CFRP) when subjected to a drop test. A novel drop weight impact test experimental method evaluates parameters specific to 3D composite materials during the study. A strain gauge rosette is employed to record the kinematic on the composites' surface. Experimental results were validated through numerical analysis by FDM Numerical Simulations in Matlab® and Ansys Explicit Dynamic Module. A MATLAB® code was developed to solve wave equation in a 2-D polar coordinate system by discretizing through a Forward-Time Central-Space (FTCS) Finite Difference Method (FDM). Another FEA analysis was performed in ANSYS® Workbench Explicit Dynamics module to simulate the elastic waves produced during the DWIT. The study demonstrates that the elastic waves generated upon impact with a 33 g steel ball from a height of 1 m in a quasi-isotropic CFRP sheet give a strain wave frequency of 205 Hz and finish in almost 0.015 s due to a significant damping effect. Numerical simulations were in good agreement with the experimental findings. The publication charges for this article were funded by a grant from the publication fund of UiT-The Arctic University of Norway.