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Influence of input data on airflow network accuracy in residential buildings with natural wind - and stack - driven ventilation.
PublicationW artykule omówiono wpływ danych wejściowych na dokładność modelu przepływu sieciowego powietrza w budynkach mieszkalnych z naturalną i kominową wentylacją. Zastosowano połączony model AFN-BES. Wyniki numeryczne omówiono dla 8 różnych przypadków z różnymi danymi ciśnienia wiatru. Wyniki pokazały, że ogromny wpływ danych wejściowych dotyczących ciśnienia wiatru na wyniki numeryczne.
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Resilience of 5G Mobile Communication Systems to Massive Disruptions
PublicationThis chapter discusses vital techniques to enhance the resilience of 5G systems. It starts with dependability assessment of 5G networks. Next, it describes (a) the frequency fallback technique to improve availability and survivability of 5G services, (b) segment interleaving scheme to enhance communications resilience between base stations and the remaining part of the network and (c) multi-operator protection to address the resilience...
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The influence of screw configuration and screw speed of co-rotating twin screw extruder on the properties of products obtained by thermomechanical reclaiming of ground tire rubber
PublicationThe results of our investigations on the process of continuous thermomechanical reclaiming of ground tire rubber (GTR) carried out using a twin screw extruder are presented.We used a co-rotating twin screw extruder with a special configuration of plasticizing unit, enabling generation of considerable shear forces. The influence of screw configuration and screw speed on breaking of chemical crosslink bonds contained in ground tire...
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Management information base module for electrical power system configuration and use = Baza informacji zarządzania opisująca konfigurację i wykorzystanie systemu elektroenergetycznego
PublicationStandardy dotyczące internetu opisują wirtualną bazę danych do zarządzania (MIB), dostępną za pośrednictwem protokołu SNMP. Nie istnieje aktualnie standardowy moduł MIB do monitorowania za pośrednictwem protokołu IP linii zasilających w budynkach, czy poza nimi. Niniejszy artykuł definiuje zawartość MIB do monitorowania systemu energetycznego.
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QUASI-DISTRIBUTED NETWORK OF LOW-COHERENCE FIBER-OPTIC FABRY-PÉROT SENSORS WITH CAVITY LENGTH-BASED ADDRESSING
PublicationDistributed measurement often relies on sensor networks. In this paper, we present the construction of low coherent fiber-optic Fabry-Pérot sensors connected into a quasi-distributed network. We discuss the mechanism of spectrum modulation in this type of sensor and the constraints of assembly of such sensors in the network. Particular attention was paid to separate the signals from individual sensors, which can be achieved by...
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An Adaptive Network Model Simulating the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning
PublicationThis paper investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organizational culture results in better mistake management and thus better organizational learning, (2) Effective organizational learning requires a transformational leader to have both high social and formal status and consistency, and (3) Company culture and leader's behavior must align for the best learning effects....
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An Adaptive Network Model Simulating the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning
PublicationThis paper investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organizational culture results in better mistake management and thus better organizational learning, (2) Effective organizational learning requires a transformational leader to have both high social and formal status and consistency, and (3) Company culture and leader’s behavior must align for the best learning effects....
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Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
PublicationSegmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on...
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<title>Selection of GRNN network parameters for the needs of state vector estimation of maneuvering target in ARPA devices</title>
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Optimal Selection of Input Features and an Acompanying Neural Network Structure for the Classification Purposes - Skin Lesions Case Study
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Artificial neural network prophecy of ion exchange process for Cu (II) eradication from acid mine drainage
PublicationThe removal of heavy metal ions from wastewater was found to be significant when the cation exchange procedure was used effectively. The model of the cation exchange process was built using an artificial neural network (ANN). The acid mine drainage waste’s Cu(II) ion was removed using Indion 730 cation exchange resin. Experimental data from 252 cycles were recorded. In a column study, 252 experimental observations validated the...
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Performance analysis of an rfid-based 3d indoor positioning system combining scene analysis and neural network methods
PublicationThe main purpose of this research is to improve localization accuracy of an active Radio Frequency Identification, RFID tag, in 3D indoor space. The paper presents a new RFID based 3D Indoor Positioning System which shows performance improvement. The proposed positioning system combines two methods: the Scene Analysis technique and Artificial Neural Network. The results of both simulation using Log-Distance Path Loss Model and...
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The possibility of estimating the height of the ionospheric inhomogeneities based on TEC variations maps obtained from dense GPS network
PublicationA state of the ionosphere can be effectively studied using electromagnetic signals received from global navigation satellite systems (GNSS). Utilization of the dual frequency observations allows estimating values of the total electron content (TEC). They can be used for a number of scientific studies such as detection and monitoring of traveling ionospheric disturbances or plasma bubbles. Moreover, maps of TEC variations allow...
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Optimal selection of input features and an acompanying neural network structure for the classification purposes - skin lesions case study
PublicationMalignant melanomas are the most deadly type of skin cancers however detected early enough give a high chances for successful treatment. The last years saw the dynamic growth of interest of automatic computer-aided skin cancer diagnosis. Every month brings new research results on new approaches to this problem, new methods of preprocessing, new classifiers, new ideas to follow etc. In particular, the rapid development of dermatoscopy,...
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DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublicationMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
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Adaptive Algorithm of a Tap-Changer Controller of the Power Transformer Supplying the Radial Network Reducing the Risk of Voltage Collapse
Publicationhe development of renewable energy, including wind farms, photovoltaic farms as well as prosumer installations, and the development of electromobility pose new challenges for network operators. The results of these changes are, among others, the change of network load profiles and load flows determining greater volatility of voltages. Most of the proposed solutions do not assume a change of the transformer regulator algorithm....
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Modeling of TEC Variations Based on Signals from Near Zenith GNSS Satellite Observed by Dense Regional Network
PublicationCurrently the substantial successes in high-resolution ionospheric mapping is declared in many publications. Nevertheless, up to now there are no examples of dynamic visualization of TEC disturbances on regional scale with as high resolution as tropospheric models. Over the years, ionosphere has been modeling basing on the simple assumption, that it is a thin layer, which surrounds the Earth at some arbitrary height. However, the...
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Novel Adaptive Method for Data Streams Allocation Based on the Estimate of Radio Channel Parameters in Heterogeneous WBAN Network
PublicationThe new adaptive method for data streams allocation in heterogeneous Wireless Body Area Networks and meas-urement equipment is presented. The results obtained using the developed method compared with the selected algorithms likely to be used in those networks. The pro-posed adaptive data streams allocation method based on radio channel parameters makes it even twice as efficient to use in terms of resources usage in a WBAN heterogeneous...
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Mathematical modeling and prediction of pit to crack transition under cyclic thermal load using artificial neural network
PublicationThe formation of pitting is a major problem in most metals, which is caused by extremely localized corrosion that creates small holes in metal and subsequently, it changes into cracks under mechanical load, thermo-mechanical stress, and corrosion process factors. This research aims to study pit to crack transition phenomenon of steel boiler heat tubes under cyclic thermal load, and mathematical modeling...
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Radioimmunotherapy Confers Long-Term Survival to Lymphoma Patients with Acceptable Toxicity: Registry Analysis by the International Radioimmunotherapy Network
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Using Statistical Methods to Estimate The Worst Case Response Time of Network Software Running on Indeterministic Hardware Platforms
PublicationIn this paper we investigate whether the statistical Worst Case Execution Time (WCET) estimation methods devised for embedded platforms can be successfully applied to find the Worst Case Response Time (WCRT) of a network application running on a complex hardware platform such as a contemporary commercial off-the-shelf (COTS) system. Establishing easy-to-use timing validation techniques is crucial for real-time applications and...
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Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
PublicationAbstract Background In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Contact force network evolution in active earth pressure state of granular materials: photo‑elastic tests and DEM
PublicationArtykuł omawia ewolucję sieci sił kontaktowych w materiałach granulowanych podczas quasi-statycznego stanu aktywnego. Doświadczenia foto sprężyste zostały wykonane dla kulek szklanych. Doświadczenia zostały symulowane stosując metodę elementów dyskretnych (DEM). Model DEM prawidłowo przewidział strukturę sił kontaktowych i ich wielkość, lokalizację odkształceń oraz obszary zmian fazowych.
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublicationThe presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods....
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ON DYNAMICS OF ELASTIC NETWORKS WITH RIGID JUNCTIONS WITHIN NONLINEAR MICRO-POLAR ELASTICITY
PublicationWithin the nonlinear micropolar elasticity we discuss effective dynamic (kinetic) properties of elastic networks with rigid joints. The model of a hyperelastic micropolar continuum is based on two constitutive relations, i.e., static and kinetic ones. They introduce a strain energy density and a kinetic energy density, respectively. Here we consider a three-dimensional elastic network made of three families of elastic fibers connected...
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Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective
PublicationCurrently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublicationFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis
PublicationNumerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending...
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Service-based Resilience via Shared Protection in Mission-critical Embedded Networks
PublicationMission-critical networks, which for example can be found in autonomous cars and avionics, are complex systems with a multitude of interconnected embedded nodes and various service demands. Their resilience against failures and attacks is a crucial property and has to be already considered in their design phase. In this paper, we introduce a novel approach for optimal joint service allocation and routing, leveraging virtualized...
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Towards bees detection on images: study of different color models for neural networks
PublicationThis paper presents an approach to bee detection in videostreams using a neural network classifier. We describe the motivationfor our research and the methodology of data acquisition. The maincontribution to this work is a comparison of different color models usedas an input format for a feedforward convolutional architecture appliedto bee detection. The detection process has is based on a neural...
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Entangled rendezvous: a possible application of Bell non-locality for mobile agents on networks
PublicationRendezvous is an old problem of assuring that two or more parties, initially separated, not knowing the position of each other, and not allowed to communicate, are striving to meet without pre-agreement on the meeting point. This problem has been extensively studied in classical computer science and has vivid importance to modern and future applications. Quantum non-locality, like Bell inequality violation, has shown that in many...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublicationIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Fast Fading Characterization for Body Area Networks in Circular Metallic Indoor Environments
PublicationWith the increasing development of 5G and Body Area Network based systems being implemented in unusual environments, propagation inside metallic structures is a key aspect to characterize propagation effects inside ships and other similar environments, mostly composed of metallic walls. In this paper, indoor propagation inside circular metallic structures is addressed and fast fading statistical distributions parameters are obtained...
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Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks
PublicationTraffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building...
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Closed-loop stimulation of temporal cortex rescues functional networks and improves memory
PublicationMemory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation of brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation and often target the hippocampus and medial temporal lobes. Here we use a closed-loop system to monitor and decode neural activity from direct...
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Heavy duty vehicle fuel consumption modelling using artificial neural networks
PublicationIn this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly,...
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Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
PublicationBeta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural...
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Improving voltage levels in low-voltage networks with distributed generation – case study
PublicationThe use of distributed generation in low-voltage networks may cause the voltage variation in them, within the wide range. In unfavourable circumstances, the voltage may reach unacceptable values. The paper presents the effect of distributed generation on voltage levels in a selected low-voltage rural distribution network in Poland. An analysis of possible methods for improving voltage levels in this network is conducted. The most...
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A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks
PublicationThe visual data acquisition from small unmanned aerial vehicles (UAVs) may encounter a situation in which blur appears on the images. Image blurring caused by camera motion during exposure significantly impacts the images interpretation quality and consequently the quality of photogrammetric products. On blurred images, it is difficult to visually locate ground control points, and the number of identified feature points decreases...
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System for monitoring road slippery based on CCTV cameras and convolutional neural networks
PublicationThe slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transferlearning method employed to the processing of images acquired with video...
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A Wideband Channel Model for Body Area Networks in Circular Metallic Indoor Environments
PublicationIn this paper, the wideband characterization of the propagation channel in circular metallic indoor environments is addressed, regarding Body Area Networks and 5G small cells, an analytical model for the dependence of the mean delay and the average delay spread on the circle radius, the working frequency and the distance between the transmitter and the receiver being proposed. The derivation of the model is initially done analytically,...
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The Analysis of Cross-Polarisation Discrimination for Body Area Networks in Cylindrical Metallic Environment
PublicationThe analysis of cross-polarisation discrimination for Body Area Networks in an untypical environment of cylindrical metallic room has been performed in the paper. This analysis was done based on the measurements carried out for dynamic narrowband off-body channels operating at the frequency of 2.45 GHz. The results have shown that there is a strong dependence of the depolarisation effect on the existence of direct component in...
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A bound on the number of middle-stage crossbars in f-cast rearrangeable Clos networks
PublicationIn 2006 Chen and Hwang gave a necessary and sufficient condition under which a three-stage Clos network is rearrangeable for broadcast connections. Assuming that only crossbars of the first stage have no fan-out property, we give similar conditions for f-cast Clos networks, where f is an arbitrary but fixed invariant of the network. Such assumptions are valid for some practical switching systems, e.g. high-speed crossconnects....
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Towards the boundary between easy and hard control problems in multicast Clos networks
PublicationIn this article we study 3-stage Clos networks with multicast calls in general and 2-cast calls, in particular. We investigate various sizes of input and output switches and discuss some routing problems involved in blocking states. To express our results in a formal way we introduce a model of hypergraph edge-coloring. A new class of bipartite hypergraphs corresponding to Clos networks is studied. We identify some polynomially...
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Detection of roles of actors in social networks using the properties of actors' neighborhood structure.
PublicationArtykuł opisuje metodę identyfikacji ról aktorów sieci społecznej. Metoda ta może być szczególnie przydatna w sieciach społecznych, o których posiadamy ograniczoną wiedzę, głównie zawężoną do lokalnych powiązań pomiędzy aktorami. Przedstawiona w artykule metoda korzysta z grafu relacji społecznych, algorytmu identyfikacji ról oraz zbioru grafów wzorców relacji. Rozwiązanie zostało przetestowane w społeczności użytkowników serwisu...
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The application of neural networks in forecasting the influence of traffic-induced vibrations on residential buildings
PublicationTraffic-induced vibrations may cause the cracking of plaster, damage to structural elements and, in extreme cases, may even lead to the structural collapse of residential buildings. The aim of this article is to analyse the effectiveness of a method of forecasting the impact of vibrations on residential buildings using the concept of artificial intelligence. The article presents several alternative forecasting systems for which...
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New approach for determining the QoS of MP3-coded voice signals in IP networks
PublicationPresent-day IP transport platforms being what they are, it will never be possible to rule out conflicts between the available services. The logical consequence of this assertion is the inevitable conclusion that the quality of service (QoS) must always be quantifiable no matter what. This paper focuses on one method to determine QoS. It defines an innovative, simple model that can evaluate the QoS of MP3-coded voice data transported...
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Spatio-temporal filtering for determination of common mode error in regional GNSS networks
PublicationThe spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually...
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A survey of strategies for communication networks to protect against large-scale natural disasters
PublicationRecent natural disasters have revealed that emergency networks presently cannot disseminate the necessary disaster information, making it difficult to deploy and coordinate relief operations. These disasters have reinforced the knowledge that telecommunication networks constitute a critical infrastructure of our society, and the urgency in establishing protection mechanisms against disaster-based disruptions. Hence, it is important...