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Software Engineering for Systems-of-Systems and Software Ecosystems
No full textSoftware Engineering has faced several challenges in the last decade, especially those related to aspects beyond the technical side. As such, technological, organizational and social aspects should be considered altogether in research and practice in the field so that complexity could be handled in order to provide solution to the existing problems from the software industry demands. In this context, systems-of-systems (SoS) and software ecosystems (SECO) emerged as topics that joined researchers and practitioners interested in understanding how to manage and engineer software-intensive systems within modern, complex, distributed, dynamic, and open environments. An SoS comprises independent constituent systems which work together to fulfill missions driven by architectural concerns. In turn, a SECO consists of a set of actors and artifacts, as well as their relationships, to produce value over a common technology platform driven by external contributions. Both classes of systems have a distributed nature, focus on optimizing the cost-benefit trade-off, and aim to reach global markets. In this special section, we introduce extended versions of two papers selected from the 10th IEEE/ACM International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS 2022). These articles provide researchers and practitioners with advances on the development and evolution of complex software-intensive systems
Introduction and fundamentals
No full textThis chapter introduces the book and presents the main principles and fundamentals for positioning and location-based analytics, thus effectively providing the basis for the following chapters. After a brief introduction and motivation for the book, we present the main use cases, verticals, and applications for positioning and location-based analytics. Then, we provide the technical fundamentals for understanding positioning and navigation algorithms, as well as location-based analytics. An introduction to the architectural principles is presented. Finally, an outline of the book chapters is provided
Deep Nearest Neighbors for\ua0Anomaly Detection in\ua0Chest X-Rays
No full textIdentifying medically abnormal images is crucial to the diagnosis procedure in medical imaging. Due to the scarcity of annotated abnormal images, most reconstruction-based approaches for anomaly detection are trained only with normal images. At test time, images with large reconstruction errors are declared abnormal. In this work, we propose a novel feature-based method for anomaly detection in chest x-rays in a setting where only normal images are provided during training. The model consists of lightweight adaptor and predictor networks on top of a pre-trained feature extractor. The parameters of the pre-trained feature extractor are frozen, and training only involves fine-tuning the proposed adaptor and predictor layers using Siamese representation learning. During inference, multiple augmentations are applied to the test image, and our proposed anomaly score is simply the geometric mean of the k-nearest neighbor distances between the augmented test image features and the training image features. Our method achieves state-of-the-art results on two challenging benchmark datasets, the RSNA Pneumonia Detection Challenge dataset, and the VinBigData Chest X-ray Abnormalities Detection dataset. Furthermore, we empirically show that our method is robust to different amounts of anomalies among the normal images in the training dataset. The code is available at: https://github.com/XixiLiu95/deep-kNN-anomaly-detection
Backscatter of scalar variance in turbulent premixed flames
Full text linkTo explore direction of inter-scale transfer of scalar variance between subgrid scale (SGS) and resolved scalar fields,\ua0direct numerical simulation data obtained earlier from two complex-chemistry lean hydrogen-air flames are analyzed by applying Helmholtz-Hodge decomposition (HHD) to the simulated velocity fields.\ua0Computed results show backscatter of scalar (combustion progress variable c) variance, i.e., its transfer from SGS to resolved scales, even in a highly turbulent flame characterized by a unity-order Damk\uf6hler number and a ratio of Kolmogorov length scale to thermal laminar flame thickness as low as 0.05. Analysis of scalar fluxes associated with the solenoidal and potential velocity fields yielded by HHD shows that the documented backscatter stems primarily from the potential velocity perturbations generated due to dilatation in instantaneous local flames, with the backscatter being substantially promoted by a close alignment of the spatial gradient of mean scalar progress variable and the potential-velocity contribution to the local SGS scalar flux. The alignment is associated with the fact that combustion-induced thermal expansion increases local velocity in the direction of . These results call for development of SGS models capable of predicting backscatter of scalar variance in turbulent flames in large eddy simulations
Experimental review of the performances of protective coatings for interconnects in solid oxide fuel cells
Full text linkFerritic stainless steel interconnects are used in solid oxide fuel cells; however, coatings are required to improve their performance. Although several types of coatings have been proposed, they have been scarcely investigated under similar conditions. This study compares the characteristics of uncoated Crofer 22 APU and eight different coatings on Crofer 22 APU for up to 3000\ua0h at 800\ua0\ub0C. The coatings were deposited at various research laboratories around the world, and the experiments were performed at Chalmers University of Technology, Sweden. Cross-sections of the samples were analysed using scanning electron microscopy and energy-dispersive x-ray spectroscopy. The (Co,Mn)-based coated steels showed more than 50-fold lower chromium evaporation and at least 3 times thinner Cr2O3 scale thickness compared to uncoated steel. The coated steel samples showed lower area-specific resistance (ASR) values than the uncoated steel after 3000\ua0h of exposure, irrespective of the coating thickness, composition and deposition method
Design and Service Provisioning Methods for Optical Networks in 5G and Beyond Scenarios
Full text linkNetwork operators are deploying 5G while also considering the evolution towards 6G. They consider different enablers and address various challenges. One trend in the 5G deployment is network densification, i.e., deploying many small cell sites close to the users, which need a well-designed transport network (TN). The choice of the TN technology and the location for processing the 5G protocol stack functions are critical to contain capital and operational expenditures. Furthermore, it is crucial to ensure the resiliency of the TN infrastructure in case of a failure in nodes and/or links while the resource efficiency is maximized.Operators are also interested in 5G networks with flexibility and scalability features. In this context, one main question is where to deploy network functions so that the connectivity and compute resources are utilized efficiently while meeting strict service latency and availability requirements. Off-loading compute resources to large and central data centers (DCs) has some advantages, i.e., better utilization of compute resources at a lower cost. A backup path can be added to address service availability requirements when using compute off-loading strategies. This might impact the service blocking ratio and limit operators’ profit. The importance of this trade-off becomes more critical with the emergence of new 6G verticals.This thesis proposes novel methods to address the issues outlined above. To address the challenge of cost-efficient TN deployment, the thesis introduces a framework to study the total cost of ownership (TCO), latency, and reliability performance of a set of TN architectures for high-layer and low-layer functional split options. The architectural options are fiber- or microwave-based. To address the strict availability requirement, the thesis proposes a resource-efficient protection strategy against single node/link failure of the midhaul segment. The method selects primary and backup DCs for each aggregation node (i.e., nodes to which cell sites are connected) while maximizing the sharing of backup resources. Finally, to address the challenge of resource efficiency while provisioning services, the thesis proposes a backup-enhanced compute off-loading strategy (i.e., resource-efficient provisioning (REP)). REP selects a DC, a connectivity path, and (optionally) a backup path for each service request with the aim of minimizing resource usage while the service latency and availability requirements are met.Our results of the techno-economic assessment of the TN options reveal that, in some cases, microwave can be a good substitute for fiber technology. Several factors, including the geo-type, functional split option, and the cost of fiber trenching and microwave equipment, influence the effectiveness of the microwave. The considered architectures show similar latency and reliability performance and meet the 5G service requirements. The thesis also shows that a protection strategy based on shared connectivity and compute resources can lead to significant cost savings compared to benchmarks based on dedicated backup resources. Finally, the thesis shows that the proposed backup-enhanced compute off-loading strategy offers advantages in service blocking ratio and profit gain compared to a conventional off-loading approach that does not add a backup path. Benefits are even more evident considering next-generation services, e.g., expected on the market in 3 to 5 years, as the demand for services with stringent latency and availability will increase
A unified approach coupling linearized Navier-Stokes equations and Helmholtz equations to predict sound propagation with viscothermal losses in acoustic liners
No full textThis paper introduces a unified approach for coupling the classical linearized Navier-Stokes equations (LNSE), the original Helmholtz equation and a modified form of the Helmholtz equation for porous material modelling in frequency domain. This approach is termed the unified LNSE (ULNSE). It accounts for viscothermal losses of sound propagation in acoustic liners with small-scale perforation. This method also provides a possibility to switch from LNSE to the classical and modified Helmholtz equations depending on local acoustic properties. The ULNSE is validated and applied to simulation of conventional and hybrid liners in three dimensions (3D), and also compared to a semi-empirical model and experiments. The hybrid acoustic liner consists of a perforated plate, a porous foam layer, and a rectangular cavity. Unlike the hybrid liner, the porous foam is not mounted in the conventional liners. In the perforated plate where the viscothermal effect plays a role, sound waves are solved using the LNSE. The modified Helmholtz equation is formulated with a complex wavenumber to model the porous foam as an equivalent fluid model. In the liner cavity and external duct, the original Helmholtz equation is utilized. The ULNSE is cheaper than the classical LNSE since the Helmholtz equations, which consume less computational resources, are used locally where the viscothermal losses are negligible. Meanwhile, the ULNSE can maintain high numerical accuracy. The liners are analyzed in terms of critical parameters such as the porous foam material, and perforated plate thickness and porosity. The porous foam proves to be effective in sound absorption
The radio detection and accretion properties of the peculiar nuclear transient AT 2019avd
No full textAT 2019avd is a nuclear transient detected from infrared to soft X-rays, though its nature is yet unclear. The source has shown two consecutive flaring episodes in the optical and the infrared bands, and its second flare was covered by X-ray monitoring programs. During this flare, the UVOT/Swift photometries revealed two plateaus: one observed after the peak and the other one appeared similar to 240 d later. Meanwhile, our NICER and XRT/Swift campaigns show two declines in the X-ray emission, one during the first optical plateau and one 70-90 d after the optical/UV decline. The evidence suggests that the optical/UV could not have been primarily originated from X-ray reprocessing. Furthermore, we detected a timelag of similar to 16-34 d between the optical and UV emission, which indicates the optical likely comes from UV reprocessing by a gas at a distance of 0.01-0.03 pc. We also report the first VLA and VLBA detection of this source at different frequencies and different stages of the second flare. The information obtained in the radio band - namely a steep and a late-time inverted radio spectrum, a high brightness temperature and a radio-loud state at late times - together with the multiwavelength properties of AT 2019avd suggests the launching and evolution of outflows such as disc winds or jets. In conclusion, we propose that after the ignition of black hole activity in the first flare, a super-Eddington flaring accretion disc formed and settled to a sub-Eddington state by the end of the second flare, associated with a compact radio outflow
An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions
No full textIllegal running into the opposite lane (IROL) on curve sections of two-lane rural roads is a frequently hazardous behavior and highly prone to fatal crashes. Although driving behaviors are always determined by the information from drivers’ visual perceptions, current studies do not consider visual perceptions in predicting the occurrence of IROL. In addition, most machine learning methods belong to black-box algorithms and lack the interpretation of prediction results. Therefore, this study aims to propose an interpretable prediction model of IROL on curve sections of two-lane rural roads from drivers’ visual perceptions. A new visual road environment model, consisting of five different visual layers, was established to better quantify drivers’ visual perceptions by using deep neural networks. In this study, naturalistic driving data was collected on curve sections of typical two-lane rural roads in Tibet, China. There were 25 input variables extracted from the visual road environment, vehicle kinematics, and driver characteristics. Then, XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive exPlanation) methods were combined to build a prediction model. The results showed that our prediction model performed well, with an accuracy of 86.2% and an AUC value of 0.921. The average lead time of this prediction model was 4.4 s, sufficient for drivers to respond. Due to the advantages of SHAP, this study interpreted the impacting factors on this illegal behavior from three aspects, including relative importance, specific impacts, and variable dependency. After offering more quantitative information on the visual road environment, the findings of this study could improve the current prediction model and optimize road environment design, thereby reducing IROL on curve sections of two-lane rural roads
Understanding preferences for night trains and their potential to replace flights in Europe. The case of Sweden
Full text linkPossible strategies to mitigate the climate impact of tourism transport include encouraging tourism to closer destinations and supporting more sustainable modes of transport, including trains. Today international trips by railways only have a small market share but night trains are considered an important part of a future green Europe. However, little is known about travelers\u27 preferences for night trains for long-distance travel in Europe. The results of an integrated choice and latent variable model (ICLV) applied to stated preference (SP) data collected from 1691 residents of Sweden show that, depending on place of origin in Sweden, in response to a set of innovations, including reduced travel time emanating from ongoing infrastructure investments, and the introduction of new, more comfortable trains, the share of plane users willing to switch to night trains to Central Europe could reach 20–30% and to Southern Europe, 6–10%