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No robust evidence is provided by literature regarding the management of intracranial hypertension following severe traumatic brain injury (TBI). This is mostly due to the lack of prospective randomized controlled trials (RCTs), the presence of studies containing extreme heterogeneously collected populations and controversial considerations about chosen outcome. A scientific society should provide guidelines for care management and scientific support for those areas for which evidence-based medicine has not been identified. However, RCTs in severe TBI have failed to establish intervention effectiveness, arising the need to make greater use of tools such as Consensus Conferences between experts, which have the advantage of providing recommendations based on experience, on the analysis of updated literature data and on the direct comparison of different logistic realities. The Italian scientific societies should provide guidelines following the national laws ruling the best medical practice. However, many limitations do not allow the collection of data supporting high levels of evidence for intracranial pressure (ICP) monitoring and decompressive craniectomy (DC) in patients with severe TBI. This intersociety document proposes best practice guidelines for this subsetting of patients to be adopted on a national Italian level, along with joint statements from "TBI Section" of the Italian Society of Neurosurgery (SINch) endorsed by the Neuroanesthesia and Neurocritical Care Study Group of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI). Presented here is a recap of recommendations on management of ICP and DC supported a high level of available evidence and rate of agreement expressed by the assemblies during the more recent consensus conferences, where members of both groups have had a role of active participants and supporters. The listed recommendations have been sent to a panel of experts consisting of the 107 members of the "TBI Section" of the SINch and the 111 members of the Neuroanesthesia and Neurocritical Care Study Group of the SIAARTI. The aim of the survey was to test a preliminary evaluation of the grade of predictable future adherence of the recommendations following this intersociety proposal. The following recommendations are suggested as representing best clinical practice, nevertheless, adoption of local multidisciplinary protocols regarding thresholds of ICP values, drug therapies, hemostasis management and perioperative care of decompressed patients is strongly recommended to improve treatment efficiency, to increase the quality of data collection and to provide more powerful evidence with future studies. Thus, for this future perspective a rapid overview of the role of the multimodal neuromonitoring in the optimal severe TBI management is also provided in this document. It is reasonable to assume that the recommendations reported in this paper will in future be updated by new observations arising from future trials. They are not binding, and this document should be offered as a guidance for clinical practice through an intersociety agreement, taking in consideration the low level of evidence.

In the era of the internet of things (IoT), software-enabled inter-connected devices are of paramount importance. The embedded systems are very frequently used in both security and privacy-sensitive applications. However, the underlying software (a.k.a. firmware) very often suffers from a wide range of security vulnerabilities, mainly due to their outdated systems or reusing existing vulnerable libraries; which is evident by the surprising rise in the number of attacks against embedded systems. Therefore, to protect those embedded systems, detecting the presence of vulnerabilities in the large pool of embedded devices and their firmware plays a vital role. To this end, there exist several approaches to identify and trigger potential vulnerabilities within deployed embedded systems firmware. In this survey, we provide a comprehensive review of the state-of-the-art proposals, which detect vulnerabilities in embedded systems and firmware images by employing various analysis techniques, including static analysis, dynamic analysis, symbolic execution, and hybrid approaches. Furthermore, we perform both quantitative and qualitative comparisons among the surveyed approaches. Moreover, we devise taxonomies based on the applications of those approaches, the features used in the literature, and the type of the analysis. Finally, we identify the unresolved challenges and discuss possible future directions in this field of research.

Industrial Control Systems (ICSs) are cyber-physical systems that offer attractive targets to threat actors due to the scale of damages, both physical and cyber, that successful exploitation can cause. As such, ICSs often find themselves victims to reconnaissance campaigns - coordinated scanning activity that targets a wide subset of the Internet - that aim to discover vulnerable systems. As these campaigns likely scan broad netblocks of the Internet, some traffic is directed to network telescopes, which are routable, allocated, and unused IP space. In this paper, we explore the threat landscape of ICS devices by analyzing and investigating network telescope traffic. Our network traffic analysis tool takes darknet traffic and generates threat intelligence on scanning campaigns targeting ICSs in the form of campaign fragments, which we leverage in new ways to get more in-depth knowledge of the cybersecurity threats. We investigate the payloads of the identified campaigns using a custom Deep Packet Inspection (DPI) technique to dissect and analyze the packets. We found 13 distinct payload templates and deduced their purpose, and by extension the campaign goals. We use machine learning to classify the sources behind the campaigns and identify threat actors such as botnets, malicious attackers, or researchers, and establish a methodology to rank our campaigns to prioritize our analysis. To conduct our analysis of the threats targeting ICSs, we have leveraged 12.85 TB (330 days) of network traffic received by our observed darknet IP space. Combining these investigative threads, we provide a thorough overview of the threat landscape targeting ICS systems.

The rise of the Internet of Things (IoT) presents important challenges for future radio networks. Non-orthogonal multiple access (NOMA), which allows the network to support more than one user per orthogonal resource element, was recently proposed as a promising solution that can ultimately support the daunting requirements of such networks including massive connectivity, high spectral efficiency, and low latency. Nevertheless, numerous ultra-high-reliability applications of IoT present environments that are hampered by impulsive electromagnetic interference, referred to as impulsive noise. Such noise is known to cause degradation to the overall system performance. Moreover, given the non-orthogonal multiplexing in NOMA, such noise is expected to have a relatively more pronounced impact on the system performance. Therefore, this article sheds light on the performance degradation and mitigation of impulsive noise in the context of NOMA-based IoT networks. It proposes a multistage nonlinear processing approach specifically designed for OFDM-based PDM-NOMA systems. To obtain the optimum threshold of the corresponding users, we propose a deep learning approach to estimate the impulsive noise parameters from the received OFDM symbol. This information can consequently be used to evaluate the corresponding optimal threshold using Siegert's ideal observer criterion. Finally, this work sheds light on potential opportunities and challenges that are expected to arise during the implementation of NOMA in impulsive environments.

Impulsive noise, a common impediment preventing the system from achieving error-free transmission, is significant in many wireless and power line communication environments. Although the performance of several mitigation techniques for orthogonal frequency division multiplexing (OFDM)-based multicarrier communication systems impaired by memoryless impulsive noise are widely acknowledged, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which is likely to exist in contemporary communication systems including smart grid communications. On the other hand, many communication technologies used in the smart grid do not employ OFDM and likewise, the assumption of memoryless noise is not valid for such communication scenarios. Memoryless noise models cannot take into account one of the main features of the actual noise, i.e., the time-correlation among the noise samples. The aim of this article is to compare and analyze several mitigation techniques such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for low-density parity-check coded single-carrier communication systems. Moreover, we propose a log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the clipping/blanking schemes.

Security metric deployment is of immense importance for power utilities especially in future smart grids, which are being actively pursued in smart cities initiatives around the world. Through these metrics, utilities attain real-time awareness of the grid’s security posture. In this paper, we propose a contingency analysis based security evaluation framework for smart grid systems - CASeS. Based on the power flow equations of the grid, communication network characteristics, and concurrent power contingencies, CASeS leverages Markovian Decison Processes (MDP) to quantify the smart grid security. The security index delivered through CASeS emerges as a novel solution for concurrent power contingency consideration, and allows for the preparation of corrective actions to address the quantified grid criticality level. We evaluate CASeS on the standard IEEE 14-Bus and 39-Bus systems. The collected results demonstrate the usefulness of CASeS in quantifying the security of those systems.

Influenza B viruses circulate in two lineages (B/Victoria and B/Yamagata). Although classically affecting children, recently it has shown a high rate of infection and increased hospitalization in the elderly. To describe and analyze the clinical and epidemiological characteristics of severe hospitalized laboratory-confirmed influenza B virus (SHLCI-B) cases in Catalonia associated with mismatch from Influenza B virus strain included in the trivalent influenza vaccine (TIV). SHLCI-B was registered by the influenza sentinel surveillance system of Catalonia (PIDIRAC) during ten surveillance seasons from 2010 to 2020. Variables age, comorbidities, and vaccination status were recorded. Vaccine effectiveness was estimated as (1-OR) for intensive care unit (ICU) admission. Statistical significance was established at p < 0.05. A total of 1159 SHLCI-B were registered, of these 68.2% (791) corresponded to the 2017-2018 season; 21.8% (253) were admitted to ICU and 13.8% (160) were exitus; 62.5% (725) cases occurred in those aged >64 years; most frequent risk factor was cardiovascular disease (35.1%, 407) followed by chronic pulmonary obstructive disease-COPD (24.6%, 285) and diabetes (24.1%, 279). In four seasons, the predominant circulating lineage was B/Victoria, in two seasons the B/Yamagata lineage and four seasons had no IBV activity. Four seasons presented discordance with the strain included within the TIV. Vaccine effectiveness (VE) to prevent ICU admission was 31% (95% confidence interval [CI]: 4%-51%; p = 0.03); being 29% (95% CI: -3% to 51%) in discordant and 43% (95% CI:-43% to 77%) in concordant seasons. Significant differences were observed in the number of affected aged > 64 years (odds ratio [OR] = 2.5; 95% CI: 1.9-3.4; p < 0.001) and in patients with heart disease (OR = 2.40 95% CI: 1.7-3.4; p < 0.001), COPD (OR = 1.6 95% CI: 1.1-2.3; p = 0.01), and diabetes (OR = 1.5 95% CI: 1.1-2.1; p = 0.04) between discordant and concordant seasons. The increase in hospitalization rate in people> 64 years of age and those presenting comorbidities in seasons with circulating influenza B virus belonging to a lineage discordant with the strain included in the TIV and the decrease of VE to prevent ICU admissions evidence the vital need to administer the quadrivalent influenza vaccine regardless of the findings of predominant circulation in the previous season.

Best relay selection (BRS) is crucial in enhancing the performance of cooperative networks. In contrast to most previous works, where the guidelines for BRS are limited to Gaussian noise, in this paper, we propose a novel relay selection protocol for a decode-and-forward cooperative network taking into account the bursty impulsive noise (IN). The proposed protocol chooses the $N$ th best relay considering both the channel gains and the states of the IN of the source-relay and relay-destination links. For this scheme, to obtain the state of IN, we propose a state detection algorithm using maximum a posteriori (MAP) detection. To analyze the performance of the proposed protocol, we first derive closed-form expressions for the probability density function (PDF) of the received signal-to-noise ratio assuming all the relays know the state of IN perfectly (genie-condition). Then, these PDFs are used to derive closed-form expressions for the bit error rate (BER) and the outage probability. Finally, we also derive the asymptotic BER and outage expressions to quantify the diversity benefits. We show that the proposed MAP-based $N$ th BRS protocol attains the derived genie-aided analytical results and outperforms the conventional relay selection protocol, optimized for the Gaussian case, and which does not take into account the IN memory.

The use of rapid antigenic tests (Ag-RDTs) to diagnose a SARS-CoV-2 infection has become a common practice recently. This study aimed to evaluate performance of Abbott Panbio(TM) Ag-RDTs with regard to nucleic acid amplification testing (NAAT) in the early stages of the disease. A cohort of 149,026 infected symptomatic patients, reported in Catalonia from November 2020 to January 2021, was selected. The positivity rates of the two tests were compared with respect to the dates of symptom onset. Ag-RDTs presented positivity rates of 84% in the transmission phases of the disease and 31% in the pre-symptomatic period, compared to 93% and 91%, respectively, for NAAT. The detection of many false negatives with Ag-RDTs during the pre-symptomatic period demonstrates the risk of virus dissemination with this diagnostic technique if used outside the symptomatic period.

Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. To overcome the detrimental effects of such impulsive interference, knowledge of impulsive noise parameters is generally required by the available mitigation techniques. This work considers a machine learning perspective for the estimation of the impulsive noise parameters in communication systems under the influence of Middleton class-A noise. Precisely, we consider a deep learning approach and design a deep neural network (DNN) that classifies a set of received symbols according to the parameters of the impulsive noise affecting them. It is sown that the classification accuracy greatly depends on the number of symbols fed into the neural network as well as the number of considered states in the classification, where the proposed approach can reach a testing accuracy of more than 99%.

Non-orthogonal multiple access (NOMA) was recently proposed as a viable technology that can potentially provide the spectral efficiency, low latency, and massive connectivity requirements of future radio networks. In this context, numerous ultra-high reliability technologies such as the industrial Internet of things, smart grids, and smart homes present environments which are characterized by the presence of impulsive electromagnetic interference, known as impulsive noise. Under such conditions, the power domain multiplexing in NOMA is expected to render the system particularly sensitive to this additional impulsive noise. Therefore, in this article, we quantify the effects of impulsive noise on the outage performance of uplink NOMA systems. Extensive Monte-Carlo simulations as well as offered analytical results demonstrate the vulnerability of the involved NOMA users to this type of noise. This highlights the need for effective modeling of the impulsive noise as well as the design of mitigation techniques that are suitable for the particular demands and challenges of NOMA.

In this letter, we derive the minimum mean square error (MMSE) optimal Bayesian estimation (OBE) for a Gaussian source, in the presence of bursty impulsive noise, as essentially encountered within power substations. Clearly, it is observed that the presence of bursty impulsive noise makes the input–output characteristics of MMSE OBE non-linear. To handle the non-linearity, we propose a novel MMSE estimator, based on the detection of the unobservable states of the noise process, using the maximum a posteriori (MAP) detector. Resultantly, the proposed MAP-based MMSE estimator is shown to achieve the lower bound derived for the proposed scenario and outperform the various MMSE estimators that neglect the noise memory.

—We address the distributed estimation of a scalar Gaussian source in wireless sensor networks (WSNs). The sensor nodes transmit their noisy observations, using the amplifyand- forward relaying strategy through coherent multiple access channel to the fusion center (FC) that reconstructs the source parameter. In this letter, we assume that the received signal at the FC is corrupted by impulsive noise and channel fading, as encountered for instance within power substations. Over Rayleigh fading channel and in presence of Middleton class-A impulsive noise, we derive the minimum mean square error (MMSE) optimal Bayesian estimator along with its mean square error (MSE) performance bounds. From the obtained results, we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian noise scenario. Index Terms—Distributed WSN, MMSE optimal Bayesian estimation, Middleton class-A impulsive noise, Rayleigh fading.

A smarter power grid can improve the maintenance system by providing a real-time measurement of equipment operating conditions. Such a monitoring system requires the deployment of an increased number of sensors. However, the wiring sensors in a high voltage environment such as power substation is a very expensive procedure. An autonomous wireless sensor network can reduce the installation cost and make sensors more viable throughout the network. In this paper, we study the possibility of deploying an autonomous wireless sensor network in a substation environment. To this end, we merge energy harvesting and wireless transfer of energy to propose a hierarchical energy harvesting model. In this paper, we show that despite of the wasteful nature of the wireless transfer of energy and with an efficiency not more than what existing technologies can provide, a self-sustainable wireless sensor network in a substation area can be accomplished.

Background Monitoring seasonal influenza epidemics is the corner stone to epidemiological surveillance of acute respiratory virus infections worldwide. This work aims to compare two sentinel surveillance systems within the Daily Acute Respiratory Infection Information System of Catalonia (PIDIRAC), the primary care ILI and Influenza confirmed samples from primary care (PIDIRAC-ILI and PIDIRAC-FLU) and the severe hospitalized laboratory confirmed influenza system (SHLCI), in regard to how they behave in the forecasting of epidemic onset and severity allowing for healthcare preparedness. Methods Epidemiological study carried out during seven influenza seasons (2010-2017) in Catalonia, with data from influenza sentinel surveillance of primary care physicians reporting ILI along with laboratory confirmation of influenza from systematic sampling of ILI cases and 12 hospitals that provided data on severe hospitalized cases with laboratory-confirmed influenza (SHLCI-FLU). Epidemic thresholds for ILI and SHLCI-FLU (overall) as well as influenza A (SHLCI-FLUA) and influenza B (SHLCI-FLUB) incidence rates were assessed by the Moving Epidemics Method. Results Epidemic thresholds for primary care sentinel surveillance influenza-like illness (PIDIRAC-ILI) incidence rates ranged from 83.65 to 503.92 per 100.000 h. Paired incidence rate curves for SHLCI -FLU / PIDIRAC-ILI and SHLCI-FLUA/ PIDIRAC-FLUA showed best correlation index' (0.805 and 0.724 respectively). Assessing delay in reaching epidemic level, PIDIRAC-ILI source forecasts an average of 1.6 weeks before the rest of sources paired. Differences are higher when SHLCI cases are paired to PIDIRAC-ILI and PIDIRAC-FLUB although statistical significance was observed only for SHLCI-FLU/PIDIRAC-ILI (p-value Wilcoxon test = 0.039). Conclusions The combined ILI and confirmed influenza from primary care along with the severe hospitalized laboratory confirmed influenza data from PIDIRAC sentinel surveillance system provides timely and accurate syndromic and virological surveillance of influenza from the community level to hospitalization of severe cases.

Background: Chagas disease is endemic in Latin America and affects 8 million people worldwide. In 2010, Catalonia introduced systematic public health surveillance to detect and treat congenital Chagas disease. Aim: The objective was to evaluate the health outcomes of the congenital Chagas disease screening programme during the first 6 years (2010-2015) after its introduction in Catalonia. Methods: In a surveillance system, we screened pregnant women and newborns and other children of positive mothers, and treated Chagas-positive newborns and children. Diagnosis was confirmed for pregnant women and children with two positive serological tests and for newborns with microhaematocrit and/or PCR at birth or serology at age 9 months. Results: From 2010 to 2015, the estimated screening coverage rate increased from 68.4% to 88.6%. In this period, 33,469 pregnant women were tested for Trypanosoma cruzi and 937 positive cases were diagnosed. The overall prevalence was 2.8 cases per loo pregnancies per year (15.8 in Bolivian women). We followed 82.8% of newborns until serological testing at age 9-12 months and 28 were diagnosed with Chagas disease (congenital transmission rate: 4.17%). Of 518 siblings, 178 (34.3%) were tested and 14 (7.8%) were positive for T. cruzi. Having other children with Chagas disease and the heart clinical form of Chagas disease were maternal risk factors associated with congenital T. cruzi infection (p < 0.05). Conclusion: The increased screening coverage rate indicates consolidation of the programme in Catalonia. The rate of Chagas disease congenital transmission in Catalonia is in accordance with the range in non-endemic countries.

Industrial Control Systems (ICS) are attractive targets to attackers because of the significant cyber-physical damage they can inflict. As such, they are often subjected to reconnaissance campaigns aiming at discovering vulnerabilities that can be exploited online. As these campaigns scan large netblocks of the Internet, some of the IP packets are directed to the darknet, routable, allocated and unused IP space. In this paper, we propose a new technique to detect, fingerprint, and track probing campaigns targeting ICS systems by leveraging a /13 darknet traffic. Our proposed technique detects, automatically, and in near-real time such ICS probing campaigns and generates relevant and timely cyber threat intelligence using graph-theoretic methods to compare and aggregate packets into campaigns. Besides, it ascribes to each observed campaign a fingerprint that uniquely characterizes it and allows its tracking over time. Our technique has been tested over 12.85 TB of data, which represents 330 days of darknet network traffic received. The result of our analysis allows for the discovery of not only known legitimate recurrent probing campaigns such as those performed by Shodan and Censys but also uncovers coordinated campaigns launched by other organizations. Furthermore, we give details on a campaign linked to botnet activity targeting the EtherNet/IP protocol.

Aim: To analyze the impact of implementing a program integrating cardiology and primary care in clinical practice. Methods: In the integrated care model, every cardiologist was assigned to each primary care center. Results & conclusion: The implementation of the new care model was associated with a significant reduction of 31.2% in requests of first visits. In addition, the delay to the cardiologist consultation significantly decreased by 54.5% for the first visits, and by 57.1% for the follow-up visits. The proportion of patients that achieved recommended low density lipoprotein-cholesterol goals significantly increased from 20.8 to 29.6%. The proportion of patients submitted to anticoagulant therapy significantly increased from 69.3 to 74.2%, as well as the proportion of patients taking direct oral anticoagulants (from 7.9 to 28.4%).

The rapid evolution of the power grid into a smart one calls for innovative and compelling means to experiment with the upcoming expansions, and analyze their behavioral response under normal circumstances and when targeted by attacks. Such analysis is fundamental to setting up solid foundations for the smart grid. Smart grid Hardware-In-the-Loop (HIL) co-simulation environments serve as a key approach to answer questions on the systems components, functionality, security concerns along with analysis of the system outcome and expected behavior. In this paper, we introduce a HIL co-simulation framework capable of simulating the smart grid actions and responses to attacks targeting its power and communication components. Our testbed is equipped with a real-time power grid simulator, and an associated OpenStack-based communication network. Through the utilized communication network, we can emulate a multitude of attacks targeting the power system, and evaluating the grid response to those attacks. Moreover, we present different illustrative cyber attacks use cases, and analyze the smart grid behavior in the presence of those attacks.

There exist other possibilities of introducing correlation as compared to impulse response models of Chap. 5. We propose in this chapter an impulsive noise model using a Partitioned Markov Chain (PMC) in order to generate samples with a correlation that produces impulses with a damped oscillating waveform.

In several measurement campaigns in substations reported in Chap. 3, wideband impulsive noise waveforms emitted by partial discharges are transient, their samples are correlated and their power spectral densities have approximately a form of ∼ (f − f0)−γ, where f0 is the resonant frequency of the RF measurement setup and γ > 0 an arbitrary exponent.

In last chapters, an overview of electromagnetic interference induced by partial discharges and their impact on wireless communication systems has been discussed. Measurement campaigns in substations have been conducted. These sources can take place in high-voltage equipment like transformers, powerlines, bushing bars, etc.

This chapter gives a general overview of EMI and describes the measurement and characterization methods associated with them, in keeping with the latest research. Impulsive noise phenomenon in substations is particularly focused and existing impulsive noise models are discussed.

In Chaps. 4– 6 accurate impulsive noise models have been proposed. These can be used for performance analyses of robust wireless communication systems as well as the development of diagnostic tools in high voltage equipments. The development of rapid and online methods of detection, identification and/or localization of partial discharge (PD) sources using wireless intelligent electronic devices is an area of growing interest.

The phenomenon of impulsive noise is generated by Partial discharge (PD) sources in high-voltage substations. A PD generates a current impulse, acoustic noise, visible and ultraviolet (UV) light and electromagnetic radiation , and accordingly its presence can be detected via several measurement methods.

There is a widespread adoption of intelligent electronic devices (IEDs) in modern-day smart grid deployments. Consequently, any vulnerabilities in IED firmware might greatly affect the security and functionality of the smart grid. Although general-purpose techniques exist for vulnerability detection in firmware, they usually cannot meet the specific needs, e.g., they lack the domain knowledge specific to IED vulnerabilities, and they are often not efficient enough for handling larger firmware of IEDs. In this paper, we present BinArm, a scalable approach to detecting vulnerable functions in smart grid IED firmware mainly based on the ARM architecture. To this end, we build comprehensive databases of vulnerabilities and firmware that are both specific to smart grid IEDs. Then, we propose a multi-stage detection engine to minimize the computational cost of function matching and to address the scalability issue in handling large IED firmware. Specifically, the proposed engine takes a coarse-to-fine grained multi-stage function matching approach by (i) first filtering out dissimilar functions based on a group of heterogeneous features; (ii) further filtering out dissimilar functions based on their execution paths; and (iii) finally identifying candidate functions based on fuzzy graph matching. Our experiments show that BinArm accurately identifies vulnerable functions with an average accuracy of 0.92. The experimental results also show that our detection engine can speed up the existing fuzzy matching approach by three orders of magnitude. Finally, as a practical framework, BinArm successfully detects 93 real-world CVE vulnerability entries, the majority of which have been confirmed, and the detection takes as little as 0.09 s per function on average.

The design of robust receivers in environments with strong impulsive noise may be challenging due to their high-amplitude events and their rare but nonnegligible occurrence. In substation environments, these impulsive noises are predominant. They are induced by corona discharges such as electrical arcs or partial discharges in the air. The emitted radiations can occupy a wide range of frequencies typically reaching a few gigahertz with very high amplitude. In this paper, we develop a generalized model of impulsive electromagnetic interference induced by corona discharges for wireless channels. Radiations emitted by these sources are generally transient impulses, their occurrences follow cyclostationary process, due to their generation by an alternative current. In this model, we both take into account the physical mechanism of these discharges and the induced electromagnetic radiations. Experimental results validate the approach in terms of first- and second-order statistics such as amplitudes, interarrival time, occurrences, and power spectrum densities of impulsive transient noise.

A technique for the fast identification of partial discharge (PD) sources is proposed for the detection of mechanical failure or damage to insulation materials by using wireless remote control and monitoring systems in substations. An estimation of the number of PD sources can help to evaluate the insulation performance and lifetime of power equipment. Multiple PD sources can be generated during the operating voltage where their electromagnetic radiations are highly impulsive, non-Gaussian noise and the resulting probability distribution function is heavy-tailed. Multiple PD sources can be estimated by their electromagnetic radiations via blind source separation (BSS) and measuring the excess kurtosis using low-cost wireless intelligent electronic devices. The efficiency and performance of the proposed method is demonstrated by simulating PD sources based on the spatial Poisson point process where the number of sources is a random variable not known by the receiver. Assuming non-white and decorrelated or non-Gaussian and independent sources, results show that the number of significant PD sources can be estimated with low error rate. Underdetermined problems in BSS can affect performances.

The installation of wireless technologies in power substations requires characterizing the impulsive noise produced by the high-voltage equipment. Substation impulsive noise might interfere with classic wireless communications and none of the existing models can reliably represent this noise in wide band. Previous studies have shown that impulsive noise is characterized by series of damped oscillations with the amplitude, the duration and the occurrence times of the impulses that are random. All these characteristics make this noise time-correlated and the partitioned Markov chain remains an efficient model that can ensure the correlation between the samples. In this study, we propose to design a partitioned Markov chain to generate an impulsive noise that is similar to the noise measured in existing substations, in time and frequency domains. We configure our Markov chain to produce the impulses with the damped oscillation effect, then, we determine the probability transition matrix and the distribution of each state of the Markov chain. Finally, we generate noise samples and we study the distribution of the impulsive noise characteristics. Our Markov chain model can replicate the correlation between the measured noise samples; also the distributions of the noise characteristics are similar in the simulations and the measurements.

Installing wireless Intelligent Electronic Devices (IED) for Substation Automation (SA) requires a thorough study of the electromagnetic radiations coming from the power equipment. In our previous work, we have performed a measurement campaign within several substations working under different voltages and we have recorded around 120 sequences of impulsive noise samples in the 700 MHz–2.5 GHz band. In this paper, we present a method to classify substation impulsive noise in order to characterize a representative Radio Frequency (RF) environment of substations for specific substation voltages. The main contribution of this work is to provide representative impulsive noise characteristics in order to calculate parameters for impulsive noise models and to improve the characterization of substation RF noise. To reach this objective, we classify impulsive noise characteristics, such as the impulse amplitude, the impulse duration and the repetition rate for substations under 25 kv, 230 kV, 315 kV and 735 kV. By using the impulsive noise characteristics, we estimate representative parameters for two impulsive noise models: the Middleton class-A (MCA) and the Bernoulli-Gaussian with memory (BGM).

Power substations generate a significant “bursty impulse noise” that might interfere with wireless technologies working in the vicinity of power equipment. Existing wireless systems are not designed for such an environment; we propose a Maximum a Posteriori (MAP) receiver designed with Markov-Gaussian models in order to mitigate the impact of impulsive noise in substations. We study and compare different noise models implemented in the receiver and we discuss the performance of the receiver based on the characteristics of the impulsive noise. Our proposed model can be used by a MAP receiver to offer optimum performances from low signal to noise ratio (SNR). When the communication is disturbed by impulsive noise measured in the field, the MAP receiver still offers better performance than when using other models, but mainly at higher SNR.

Power utilities around the world are modernizing their grid by adding layers of communication capabilities to allow for more advanced control, monitoring and preventive maintenance. Wireless Sensor Networks (WSNs), due to their ease of deployment, low cost and flexibility, are considered as a solution to provide diagnostics information about the health of the connected devices and equipment in the electrical grid. However, in specific environments such as high voltage substations, the equipment in the grid produces a strong and specific radio noise, which is impulsive in nature. The robustness of off-the-shelf equipment to this type of noise is not guaranteed; it is therefore important to analyze the characteristics of devices, algorithms and protocols to understand whether they are suited to such harsh environments. In this paper, we review several WSN standards: 6LoWPAN, Zigbee, WirelessHART, ISA100.11a and OCARI. Physical layer specifications (IEEE 802.15.4) are similar for all standards, with considerable architectural differences present in the higher layers. The purpose of this paper is to determine the appropriate WSN standard that could support reliable communication in the impulsive noise environment, in electrical substations. Our review concludes that the WirelessHART sensor network is one of the most suitable to be implemented in a harsh impulsive noise environment.

Power substations constitute a unique environment where the wireless medium is corrupted by impulsive electromagnetic interference. Conventional receivers are designed to operate in typical environments where the noise is assumed to be a white Gaussian noise. Hence, using conventional receivers in power substation environments may result in poor performance. To design reliable communication system, we must consider a receiver that is more adapted to these particular environments. In this paper, we first start by extending a single antenna impulsive interference model to a multi-antenna scenario. Then, we introduce a new receiver that is more suitable to the studied environment. This receiver takes advantage of multi-antenna at the receiver side to mitigate the interference. Simulations show that using the proposed approach gives significant performance upgrade compared to the conventional Gaussian receiver.

It is well known that the propagation of electromagnetic waves is affected by the refractivity of the atmosphere. This refractivity is characterized by the value of the radio refractive index, which depends on the temperature, pressure, and humidity in the atmosphere. In this paper, we estimate the radio refractive index (N), the point refractivity gradient (dN 1 ) as well as, the geoclimatic factor (K) at altitude 65 m above the ground based on the radiosonde meteorological data in the coastal north-east station, Spet-iles (Quebec), at latitude 50.22°N and longitude 66.27° W, with altitude of 52 m above mean sea level from 2009 to 2013. These data have been obtained from NOAA/ESRL 1 Earth System Research Laboratory database. The average values of daily, monthly and seasonal variations of the dN 1 and K are analysed for a given year. Thereafter, the obtained average annual values of the dN 1 and K for several years are compared with the values obtained from the ITU-R world map.

The partitioned Markov chain is a sample noise model that can represent impulsive noise in power substation including the time-correlation between the samples. In order to use this model, algorithms are needed to detect and to estimate the impulses characteristics, such as the duration, the samples values and the occurrence times of the impulses. Unsupervised learning of these characteristics is very complex, we propose then to use the fuzzy C-means algorithm to analyze impulses from substation measurements and to configure the partitioned Markov chain model by instantiating the transition matrix and by estimating the parameters of the Gaussian distributions associated with the Markov states. After simulating sequences of samples with our model, we noticed that the distribution of the impulsive noise characteristics and the power spectrum of the impulses are satisfyingly close to the measurements. The fuzzy C-means algorithm is appropriate to estimate the parameters required by the partitioned Markov chain model and to reduce the complexity of the parameter estimation.

Impulsive noise in air-insulated substations is created by partial discharges, Corona and gap noises. In order to install a wireless sensors network in such an environment, the impulsive noise must be characterized and studied to predict its evolution. The first observations made during a measurement campaign in power substation indicate impulsive noise RF characteristics depends on the feeding voltage of the equipment. In laboratory, a test bench with impulsive noise sources has been designed and used in order to verify the assumptions made from measurement campaigns and partial discharge theory. Using two impulsive noise specimens, the generator bar and the Tesla-coil, we study the impact of the source voltage and the inter-electrode distance (gap) on the RF characteristics of the emitted impulsive noise. The experiences confirm that, when the feeding voltage increases, the noise becomes more powerful both in time and frequency domains. The same is observed when the gap becomes shorter. Furthermore, the ISM frequency content of impulsive noise also increases, which could severely affect wireless communications.

Microwave telecommunications links are severely influenced by the frequency and climatic parameters as refractivity gradient (dN1), geoclimatic factor (K), rain, temperature, pressure, and humidity. This influence increases with the frequency of the wave. In microwave band, recent research has shown that the geoclimatic factor (K), rain and temperature are the most important parameters affecting the performance of terrestrial links. K is an important parameter as directly proportional to the worst month outage probability P w . K depends on the refractivity gradient dN1. The dN1 depends itself on the pressure, the temperature and the humidity. In this paper, we demonstrated from the data of the terrestrial links located in Quebec, Canada, that there is a seasonal variation of the geoclimatic factor, K mes . This variation of K mes is mainly due to the variation of the humidity. The variation of the geoclimatic factor is not limited to the seasonal variation. But it is true from one year to another. IUT recommendations do not embed climate change, but assume a stationary climate. We recommend to improve data analysis and predictive models by taking into account the temporal changes.

The use of wireless technologies in high voltage environments is increasing with the growing need for monitoring and preventive maintenance in electricity utilities. The feasibility of deploying wireless technologies in high voltage environments such as substations is addressed in this paper. High voltage substations are different from common area due to the presence of many metallic structures causing multiple reflexions, diffractions and scattering. The simulations are performed using realistic substations representation and up-to-date radio equipments specifications. The propagation coverage and the maximum throughput achievable are presented for three wireless technologies, WiFi, WiMAX and 900 MHz systems. The simulation results are discussed and some recommendations are given. Note that the impact of impulsive noise such as partial discharges is not addressed in this paper which only focuses on the evaluation of wireless systems under specific multipath scenario of substations.

Toumpis-Goldsmith introduced a strong theoretical formulation of capacity regions by computing achievable rate combinations between all pairs of communication within arbitrary topology and number of nodes. Our paper proposes a new formulation of their capacity regions with the same strong mathematical definition and results, but with less complexity computation. We use interference model of conflict graph with different levels of SINRth to reduce the complexity of computation and reach the same previous result. Our results show that for the same conditions of simulation present in previous works, we keep the same capacity regions with 75% of complexity reduction. The range of 0 to 0.61 dB represents the SINRth levels that keep more in touch with optimal results in terms of complexity and time computation. More higher values move away from optimal results

The channel capacity and the channel statistics are both important performance metrics to consider for appropriate design of wireless systems. In tactical mobile ad hoc networks environment, the capacity and the second order statistics are investigated based on an optimized physical layer simulation tool which includes tactical scenarios generator and propagation modeler. The possible link data rate is stemmed from the average channel capacity analysis between all pair of nodes. And based on the minimum data rate requirement for typical services (web, audio and video streaming, VoIP, videoconference), connectivity graphs are constructed. Moreover, level cross rate (ICR) and average duration of fade (ADF) are simulated as function of double mobility degree and the results show how the double mobility affects the second order channel statistics and the error probability.

The bit error rate is an important metric to consider when analysing performance of wireless system. The mobile-to-mobile channels are quite different from fixed-to-mobile channels especially the power spectral density of the Doppler spread. This paper proposes the performances simulation of M2M channel in ad hoc networks context. The simulations are carried out using a realistic channel model and a DQPSK modulation with differential detection. The results show the effect of double mobility degree on bit error rate.