Due to the fact that the current variability of services is brought by the current networks and the new possibilities that will appear thanks to the near-future networks, Network Slicing has become one of the key elements to allow the co-existence of multiple computing and transportservices with different requirements (i.e., performance, security, isolation) over the same infrastructure in multi-tenant and multi-domain (i.e., edge, transport, core) scenarios. The use of this and other technologies allow to have only one generic infrastructure (e.g., an optical transport domain) despite the services differences, instead of needing specific resources (e.g., on single optical fiber) for each type of service. Multiple works have been published about Network Slicing, Network Function Virtualization and Software Defined Networks using multiple computing and transport domains but, based on our literature research, there is one important aspect with a low amount of attention: the security management around network slices and their enforcement. It is essential to ensure that the expected Quality of Security (QoSec) is accomplished based on the correct deployment and posterior monitoring of the security metrics defined in the agreed Security Service Level Agreement (SSLA) between the service requester and the provider. This article aims to present an architecture designed to manage and control the life-cycle of secured End-to-End (E2E) network slices involving multiple domains based on the SSLA requirements. The security management architecture is described with its components together with the deployment and monitoring processes and the data objects used. Finally, an experimental validation is described using the use case of a DoS attack scenario and its resolution.
This paper presents jamming and jamming mitigation techniques, which can be used in relation to emerging military systems based on fifth-generation (5G) technology. Nowadays, 5G technology provides incremental improvements over Long Term Evolution (LTE) networks resulting in the enhancement of civilian communications. Considering enormous possible applications of this new technology, it is feasible to use them in military utilities. The authors want to introduce the most important aspects related to the 5G system vulnerability in the context of its use in military scenarios. We also present a quality analysis of adequate solutions for 5G to mitigate the jamming and improve the system immunity. The description of use case scenarios depicts how 5G applications can fit in typical use cases.
A simple reactor design for the conversion of CO2 methanation into synthetic methane based on free convection is an interesting option for small-scale, decentralised locations. In this work, we present a heat-management design of a multi-tubular reactor assisted by CFD (Ansys Fluent®) as an interesting tool for scaling-up laboratory reactor designs. The simulation results pointed out that the scale-up of an individual reactive channel (d = 1/4′, H = 300 mm) through a hexagonal-shaped distribution of 23 reactive channels separated by 40 mm allows to obtain a suitable decreasing temperature profile (T = 487–230 °C) for the reaction using natural convection cooling. The resulting heat-management configuration was composed of three zones: (i) preheating of the reactants up to 230 °C, followed by (ii) a free-convection zone (1 m/s air flow) in the first reactor section (0–25 mm) to limit overheating and, thus, catalyst deactivation, followed by (iii) an isolation zone in the main reactor section (25–300 mm) to guarantee a proper reactor temperature and favourable kinetics. The evaluation of the geometry, reactive channel separation, and a simple heat-management strategy by CFD indicated that the implementation of an intensive reactor cooling system could be omitted with natural air circulation.
SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.
A C-band Low-cost Active Reflector (AR) has been tested in a real experimental campaign aimed at monitoring through multi-temporal InSAR an area threatened by a landslide that occurred in 2019. To monitor and characterize the movement, a network of eight Passive Corner Reflectors and one Active Reflector were installed along a forested slope. A set of 285 interferograms obtained combining 60 Sentinel-1 SAR images were processed to evaluate the stability of the area. The AR, installed in a stable location close to the landslide, was used to provide a reference point in this low coherence area. Despite the high sensitivity of the phase response of such devices to temperature changes, the device operates with a stability of ±2 mm in deformation retrieval, a value acceptable for monitoring purposes, with a moderate range of temperature values.
This interdisciplinary study brings together acousticians and anthropologists to examine the memory of the acoustics of Notre-Dame de Paris before the fire of April 2019, using a qualitative approach to collect the testimonies of 18 people involved in the sound usages of the cathedral. Testimonies were analyzed in light of research conducted in the anthropology of the senses, sensory perception, memory, and cultural heritage. Analysis highlights an apparent contradiction between the remarkable acoustics of the monument before the fire and the impression of musicians. These musicians reported a struggle to tame the cathedral’s sound space, to hear each other well enough to craft their performances and to reach an acceptable level of clarity in their musical practice. These phenomena are examined with acoustic measurements and numerical simulations using a calibrated geometrical acoustics model of the cathedral before the fire, which allows for an objective exploration of the acoustic characteristics of Notre-Dame. This analysis concludes that the well-known reverberation of Notre-Dame, as well as the acoustic barrier of the transept and the poor acoustic return on the podium (the usual place for concert performers) negatively impact singers’ comfort. This highlights the tension between the original architectural design of the cathedral and its modern religious and cultural usages. However, the regular occupants have developed a deep familiarity with these constraints during their ritual and musical practices, adjusting to the acoustics in a unique way. Such a tradition of adaptation must be considered as a part of cultural practice, not to be overlooked during the reconstruction.
The Lisbon metropolitan area (LMA, central-west of Portugal) has been severely affected by different geohazards (flooding episodes, landslides, subsidence, and earthquakes) that have generated considerable damage to properties and infrastructures, in the order of millions of euros per year. This study is focused on the analysis of subsidence, as related to urban and industrial activity. Utilizing the A-DInSAR dataset and applying active deformation areas (ADA) processing at the regional scale has allowed us to perform a detailed analysis of subsidence phenomena in the LMA. The dataset consisted of 48 ascending and 61 descending SAR IW-SLC images acquired by the Sentinel-1 A satellite between January 2018 and April 2020. The line-of-sight (LOS), mean deformation velocity (VLOS) maps (mm year−1), and deformation time series (mm) were obtained via the Geohazard Exploitation Platform service of the European Space Agency. The maximum VLOS detected, with ascending and descending datasets, were −38.0 and −32.2 mm year−1, respectively. ADA processing over the LMA allowed for 592 ascending and 560 descending ADAs to be extracted and delimited. From the VLOS measured in both trajectories, a vertical velocity with a maximum value of −32.4 mm year−1 was estimated. The analyzed subsidence was associated to four ascending and three descending ADAs and characterized by maximum VLOS of −25.5 and −25.2 mm year−1. The maximum vertical velocity associated with urban subsidence was −32.4 mm year−1. This subsidence is mainly linked to the compaction of the alluvial and anthropic deposits in the areas where urban and industrial sectors are located. The results of this work have allowed to: (1) detect and assess, from a quantitative point of view, the subsidence phenomena in populated and industrial areas of LMA; (2) establish the relationships between the subsidence phenomena and geological and hydrological characteristics.
In this work one examines the prospects of using the surface plasmon resonance effect in the Otto configuration for pressure and deformation sensing. An open Otto chip device was employed and the chip was characterized by use of an automated reflectometer operating at an wavelength λ = 975.1 nm. The active area of the device was characterized by reflectometry based profilometry. Significant modulation of the SPR effect due to a variable gas pressure was observed at a fixed point on the device's active area. A gap reduction to approximately 50% of its nominal value was observed when the gas pressure changed from 1.7 to 0.28 bar. In addition, profilometry characterization in conjunction with a rigorous regression analysis allowed determining, unambiguously, the gap profile of the device under a fixed stress. The results indicate that the technique is feasible for either pressure or tactile sensing applications.
The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.
Objective: Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization (ERS) are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject's condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close). Approach: An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution tomography (LORETA). Main results: Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis. Significance: Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography.
Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.
Real Time Mission Critical Communication (RTMCC) in emergency situations can include real‐time video and audio calls between peers and first responders all occurring simultaneously. RTMCC also requires secure end‐to‐end (E2E) group communication (GC) sessions against potential security threats during such incidents. In this paper, we explore all aspects of the possible methods that are suitable for a software implementation of for session key change during GC in E2E encryption of RTMCC. Later, we introduce our Entropy Service concept, which can be very effective in secure E2E RTMCC sessions. The proposed method ensures E2E security in real‐time communication systems while allowing very fast session key change for clients involved in an RTMCC session with a computational complexity of 𝒪(1). Our experimental results show that the proposed Entropy Service can reduce total time by 99.6% and 99.2%, the idle time by 99.4% and 98.99%, and the number of messages by 51.4% and 35.33% compared to the key refreshing and hash methods, respectively, when the number of users in the system increases to 45. These results show that both communication and computation complexity are significantly reduced with the proposed RTMCC session key change.
In this paper, two different types of graphene-based rectangular patch antennas are designed for broadband terahertz applications. First, a patch antenna is modeled using copper metal for terahertz applications. The simulation results show a degraded reflection coefficient due to the use of copper. Therefore, the reflection coefficient is above -10 dB at 4.6 THz, then a VSWR is less than 3 dB when we used copper for the top patch. The patch antenna results are improved when graphene material is used, which has good conductivity, as demonstrated through simulations. Furthermore, an increasing band-width of 1 THz instead to 0.8 THz when we used the graphene material, due to a better impedance match. On the other hand, we achieved a slight increase in gain and the VSWR is less than 2 dB inside the available bandwidth. The time-domain solver of CST MWS software is used to evaluate the performance of the SIW (Substrate Integrated Waveguide) patch antennas. The SIW, PBG technology and the graphene material makes the antenna very important due to performance, such as the gain increases to about 7 dB, the bandwidth is about 1.6 THz duo to increase the chemical potential of the graphene material. The results obtained with CST are compared with simulations using HFSS to validate the design further. In addition, the 10 g peak skin SAR values of the antenna are 1.726e7 W/Kg instead of 9.55e5 W/Kg. In these results we conclude the antenna can detect tumor presence.
Despite the key importance of public transportation for the accessibility, attractiveness, and sustainability of tourist areas, little is known about how the COVID-19 pandemic may have impacted its use among tourists. In response, we compared the likelihood of using transit among visitors in a Catalan coastal area based on surveys conducted in 2019 (n = 1493) and 2020 (n = 1465). The pandemic caused a significant decline in tourists' use of public transportation, from 54.5% in 2019 to 34.6% in 2020, and in mobility at the destination. Results from a set of bivariate probabilistic models revealed that though most of the traditional determinants of visitors' use of transit remained unaltered, pandemic-related factors were associated with its decline. For the tourism sector and for local authorities and transit agencies, those results characterize the crucial challenge of ensuring the use of public transit among visitors in consideration of its many environmental and social benefits.
The quality of the drinking water distributed through the networks has become the main concern of most operators. This work focuses on one of the most important variables of the drinking water distribution networks (WDN) that use disinfection, chlorine. This powerful disinfectant must be dosed carefully in order to reduce disinfection byproducts (DBPs). The literature demonstrates researchers’ interest in modelling chlorine decay and using several different approaches. Nevertheless, the full-scale application of these models is far from being a reality in the supervision of water distribution networks. This paper combines the use of validated chlorine prediction models with an intensive study of a large amount of data and its influence on the model’s parameters. These parameters are estimated and validated using data coming from the Supervisory Control and Data Acquisition (SCADA) software, a full-scale water distribution system, and using off-line analytics. The result is a powerful methodology for calibrating a chlorine decay model on-line which coherently evolves over time along with the significant variables that influence it.
Researchers have shown the limitations of using the single-modal data stream for emotion classification. Multi-modal data streams are therefore deemed necessary to improve the accuracy and performance of online emotion classifiers. An online decision ensemble is a widely used approach to classify emotions in real-time using multi-modal data streams. There is a plethora of online ensemble approaches; these approaches use a fixed parameter($\beta$) to adjust the weights of each classifier (called penalty) in case of wrong classification and no reward for a good performing classifier. Also, the performance of the ensemble depends on the $\beta$, which is set using trial and error. This paper presents a new Reward Penalty-based Weighted Ensemble (RPWE) for real-time multi-modal emotion classification using multi-modal physiological data streams. The proposed RPWE is thoroughly tested using two prevalent benchmark data sets, DEAP and AMIGOS. The first experiment confirms the impact of the base stream classifier with RPWE for emotion classification in real-time. The RPWE is compared with different popular and widely used online ensemble approaches using multi-modal data streams in the second experiment. The average balanced accuracy, F1-score results showed the usefulness and robustness of RPWE in emotion classification in real-time from the multi-modal data stream.
River eelgrass has a vital role in the coastal ecosystem, which can be threatened by human activities and climate change. Accurate mapping of eelgrass habitats is important for sustainable eelgrass conservation and management. A large portion of the Shediac river in the province of New Brunswick, Canada is covered by eelgrass, which can be threatened by different natural and anthropogenic activities, such as bridge construction. Thus, mapping and monitoring eelgrass in this river are of interest to the province. In this study, a very high-resolution Worldview-3 image along with an Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification algorithm was applied to map eelgrass over a portion of the Shediac river. The results indicated a reasonable classification accuracy, and it was observed that 90,416.43 m2 of the study area was covered by river eelgrass.
A current trend in the evolution of mobile communication networks consists in integrating Non-Terrestrial Networks (NTN) with the Terrestrial ones. One option to implement the NTN part of this hybrid architecture using Unmanned Aerial Vehicles (UAV) that relay the uplink radio signals through optical wireless backhaul links. A good choice for the radio uplink waveform is conventional SC-FDMA, which mitigates the PAPR and enables a longer battery lifetime at the transmitter side. For the optical backhaul link, which is based on low-cost Visible Light Communication (VLC) technology, a non-orthogonal implementation of SC-FDMA is proposed. By doing so, it is possible to improve the end-to-end throughput by reducing the communication bandwidth (to make it fit the LED frequency response), mitigate the effect of light reflections, and increase the energy efficiency in the backhaul link. Since VLC relies on non-coherent IM/DD, the non-orthogonal SC-FDMA waveform must rotate the phase of the IDFT subcarriers, in order to obtain real-valued signal samples at the output. Two strategies for relaying the data in the UAV node are evaluated, namely: Detect-and-Forward and Decode-and-Forward. The first one recovers the modulation part (i.e. partial regeneration), whereas the second one regenerates the transmitted message up to the bit level (i.e., total regeneration). This paper studies the combination of relaying strategy and NB-IoT Modulation and Coding Scheme (MCS) that maximizes the end-to-end throughput at different UAV altitudes.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.