Satellite-5G integration: a network perspective

Future 5G mobile communication systems are expected to integrate different radio access technologies, including the satellite component. Within the 5G framework, the terrestrial services can be augmented with the development of HTS systems and new mega-constellations meeting 5G requirements, such as high bandwidth, low latency, and increased coverage including rural areas, air, and seas. This article provides an overview of the current 5G initiatives and projects followed by a proposed architecture for 5G satellite networks where the SDN/NFV approach facilitates the integration with the 5G terrestrial system. In addition, a novel technique based on network coding is analyzed for the joint exploitation of multiple paths in such an integrated satellite-terrestrial system. For TCP-based applications, an analytical model is presented to achieve an optimal traffic split between terrestrial and satellite paths and optimal redundancy levels

Satellite - 5G Integration: a network perspective

Future 5G mobile communication systems are expected to integrate different radio access technologies, including the satellite component. Within the 5G framework, the terrestrial services can be augmented with the development of HTS systems and new mega-constellations meeting 5G requirements, such as high bandwidth, low latency, and increased coverage including rural areas, air, and seas. This article provides an overview of the current 5G initiatives and projects followed by a proposed architecture for 5G satellite networks where the SDN/NFV approach facilitates the integration with the 5G terrestrial system. In addition, a novel technique based on network coding is analyzed for the joint exploitation of multiple paths in such an integrated satellite-terrestrial system. For TCP-based applications, an analytical model is presented to achieve an optimal traffic split between terrestrial and satellite paths and optimal redundancy levels

A Raman study of the interaction of electron-donor and -acceptor molecules with chemically doped graphene

Effect of interaction of tetracyanoethylene (TCNE) and tetrathia fulvalene (TTF) with boron- and nitrogen-doped graphene has been investigated by Raman spectroscopy. The G- and 2D bands of boron- and nitrogen-doped graphenes in the Raman spectra show significantly different changes on interaction with electron-donor and -acceptor molecules. Thus, tetracyanoethylene (TCNE) and tetrathiafulvalene (TTF) have different effects on the Raman spectra of boron- and nitrogen-doped graphenes. The changes in the Raman spectra brought about by electron-donor and -acceptor molecules can be understood in general terms on the basis of molecular charge transfer. (c) 2012 Elsevier B.V. All rights reserved

Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures

Cracks are one of the forms of damage to concrete structures that debase the strength and durability of the building material and may pose a danger to the living being associated with it. Proper and regular diagnosis of concrete cracks is therefore necessary. Nowadays, for the more accurate identification and classification of cracks, various automated crack detection techniques are employed over a manual human inspection. Convolution Neural Network (CNN) has shown excellent performance in image processing. Thus, it is becoming the mainstream choice to replace the manual crack classification techniques, but this technique requires huge labeled data for training. Transfer learning is a strategy that tackles this issue by using pre-trained models. This work first time strives to classify concrete surface cracks by re-training of six pre-trained deep CNN models such as VGG-16, DenseNet-121, Inception-v3, ResNet-50, Xception, and InceptionResNet-v2 using transfer learning and comparing them with different metrics, such as Accuracy, Precision, Recall, F1-Score, Cohen Kappa, ROC AUC, and Error Rate in order to find the model with the best suitability. A dataset from two separate sources is considered for the retraining of pre-trained models, for the classification of cracks on concrete surfaces. Initially, the selective crack and non-crack images of the Mendeley dataset are considered, and later, a new dataset is used. As a result, the re-trained classifier of CNN models provides a consistent performance with an accuracy range of 0.95 to 0.99 on the first dataset and 0.85 to 0.98 on the new dataset. The results show that these CNN variants can produce the best outcome when finding cracks in the real situation and have strong generalization capabilities

Air pollution and plant health response-current status and future directions

Air pollutants influence the morphological, physiological, and biochemical status of plants, and their impacts vary substantially among different species and cultivars. Current review synthesises published literature on the assessment of air pollution impacts on vegetation, with a specific focus on chronicling and summarizing scientific methods that quantify those impacts. Investigations carried out globally on pollutant-plant exposure-response, and articles that describe impact of air pollutants on plants and pollutant abatement using green infrastructure (GI) were systematically reviewed. 273 articles reviewed indicated that a substantial number of past explorations were on a small spectrum of certain species, mainly wheat, rice, soybean and maize; and fewer on non-crop plant species, which cover most of the urban areas and are part of GI. Furthermore, in lower middle-income countries which face significant pollution loads, even studies on crop species are limited. Most studies either use Air Pollution Tolerance Index, which is not pollutant dependent or concentrate on either Ozone or Particulate Matter (PM) and rarely investigate the impact of multiple pollutants in the atmosphere. Also, very few studies differentiate the effect of PM on plants based on its composition. Subsequently, the best possible experimental set ups and wide array of plant health parameters for determining and understanding the effects of different air pollutants on a variety of plant species has been emphasized. While this review compiled literature-based commendations for academic federations wanting to study and quantify air pollutant impacts on vegetation, numerous pertinent vital topics for future research were identified