Vulnerability of Wireless Smart Meter to Electromagnetic Interference Sweep Frequency Jamming Signals

The installation and use of smart home technology that uses wireless communication channels, according to the 802.11 standard series, is rapidly increasing. This article discusses the effect of Electromagnetic Interference Sweep Frequency Jamming Signal applied to a wireless smart meter installed in a three-phase domestic and light commercial electricity distribution board. More specifically, a method of frequency jamming signal generation technique, jamming signal radiation and its interference measurements method are explained in this paper. Then, the impact of disturbances are discussed and mitigation mechanisms such as construction material shielding, digital filtering and a systematic approach of electromagnetic risk assessment are given.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Susceptibility of Power Line Communication (PLC) Channel to DS, AM and Jamming Intentional Electromagnetic Interferences

The use of power lines as a communication channel for transferring data between communication devices for power systems in smart grid communication systems is growing rapidly. This paper describes three different types of methods for radiating and conducting Intentional Electromagnetic Interference, IEMI, signals: Amplitude Modulated, Damped Sinusoidal and Sweep Frequency Jamming Signals. The severity of all three types of IEMI signals on a power line communication channel using a single phase of a three-phase, low-voltage power distribution board is compared. The method for measuring interference is then explained and the influence of radiated and conducted interferences on data transmission is assessed. After discussing the IEEE 1901 power line communication channel's vulnerability to IEMI, this article explains the need for a systematic risk-based approach, in coalition with the rules-based perspective, to mitigate its impact.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

IEMI Vulnerability Analysis for Different Smart Grid-enabled Devices

The smart grid concept aims to improve power systems’ robustness, efficiency, and reliability. The transition from conventional power grids to smart grids has been achieved mainly by integrating Smart Electronic Devices (SEDs) and advanced automatic control and communication systems. On the one hand, electronic devices have been integrated to make the system more decentralised from the national electrical grid. On the other hand, from the point of view of protection and control equipment, there is a growing tendency to replace arrays of analog devices with single digital units that perform multiple functions in a more integrated and efficient way. Despite the perceived benefits of such modernisation, security issues have arisen with substantial concern as electronic devices can be susceptible to Intentional Electromagnetic Interference (IEMI) [2]. The number of IEMI sources has grown significantly in recent decades. In 2014, 76 different types were reported, in which 21 sources were conducted, and 55 were irradiated. From a technical perspective, they can present different features, including band type, average / centre frequency, peak voltage (for conducted sources), or peak field (for irradiated sources) [4]. These sources also differ in technology level, associated cost, and mobility in approaching the target system. Therefore, they can be characterized by the easiness of occurrence in a given scenario and the increased probability of successful attacks on a target system. Under this perspective, a self-built jammer built with off-the-shelf components is more likely to be employed by an offender than a High-Power Electromagnetic (HPEM) source. On the other hand, despite being less probable on account of higher technological level, cost and mobility, a HPEM source may have a higher success rate to affect the target system than the self-built jammer. Coupled with this, based on the different characteristics of the IEMI sources, the electronic devices may present distinct effects, which may trigger severe impacts on a smart grid at a higher level [8]. Therefore, this study compares the IEMI vulnerability of three devices used in smart grid applications. The first device is a Wi-Fi-based smart home meter. It can read voltage and current signals of consumer units and remotely display real power, reactive power, and power factor. These measurements can be used in-house or transmitted to a Supervisory Control and Data Acquisition (SCADA) system from Distribution System Operators (DSOs). The second device is a Power Line Communication (PLC) unit, which enables data to be carried over conductors intended primarily for electrical power transmission. This technology is used in buildings to reduce the communication network’s material and installation costs and provide flexibility and faster data communication. The final device considered is a digital protection relay designed to trip circuit breakers when faults are detected. The latest digital relay units feature many protection functionalities, including overload and under-voltage/over-voltage protection, temperature monitoring, fault location, self-reclosure, among others. The three devices are subjected to self-built low-power jamming signals. As an extension, the protection relay is also subjected to a narrowband High Power Electromagnetic (HPEM) source

Vulnerability of Smart Grid-enabled Protection Relays to IEMI

The electricity sector has been undergoing transformations towards the smart grid concept, which aims to improve the robustness, efficiency, and flexibility of the power system. This transition has been achieved by the introduction of smart electronic devices (SEDs) and advanced automatic control and communication systems. Despite the benefits of such modernization, safety issues have emerged with significant concern by experts and entities worldwide. One of these issues is known as Intentional Electromagnetic Interference (IEMI), where offenders employ high-power electromagnetic sources to maliciously disrupt or damage electronic devices. One of the possible gateways for IEMI attacks targeting the smart grids is the microprocessor-based protection relays. On the one hand, the malfunctioning of these devices can lead to equipment damage, including high-voltage equipment (e.g., power transformers), which represent one of the most high-cost items of energy infrastructure. On the other hand, a possible misleading triggering of these devices could cause cascading effects along the various nodes of the power system, resulting in widespread blackouts. Thus, this study presents the possible recurring effects of IEMI exposure of a typical protection relay used in smart grid substations as part of the SCADA (Supervisory Control and Data Acquisition) system. For this purpose, a test setup containing a smart grid protective unit, a monitoring box, and the device's wiring harness is exposed to radiated IEMI threats with high-power narrowband signals using a TEM waveguide and horn antennas. The effects during the test campaigns are observed by means of an IEMI-hardened camera system and a software developed to real-time monitor the device's fibre optic communication link, which is established according to the IEC 60870-5-105 protocol. The results revealed failures ranging from display deviation to various types of protection relay shutdown. Moreover, the consequences of the identified failures in a power substation are discussed to feed into a risk analysis regarding the threat of IEMI to power infrastructures

Effect of Electromagnetic Interference on Integrated Circuits

Critical infrastructure may be disturbed by high power electromagnetic (HPEM) weapons. Both, short impulses and modulated/unmodulated radio frequency (RF) carrier signals may be used. The interfering electromagnetic waves may be coupled by lines between different electronic devices to the inputs or outputs of integrated circuits (ICs) [1]. By shielding the lines or the use of twisted symmetric transmission lines, this effect may be significantly reduced. On the other hand, ICs themselves are influenced by HPEM pulses. A quantitative estimate of the coupling of HPEM waves to lines on an IC itself is required to investigate the effectiveness of applicable protective measures against them

Evaluation of incidence rates in pre-clinical studies using a williams-type procedure

The analysis of dose-response relationships is a common problem in pre-clinical studies. For example, proportions such as mortality rates and histopathological findings are of particular interest in repeated toxicity studies. Commonly applied designs consist of an untreated control group and several, possibly unequally spaced, dosage groups. The Williams test can be formulated as a multiple contrast test and is a powerful option to evaluate such data. In this paper, we consider simultaneous inference for Williams-type multiple contrasts when the response variable is binomial and sample sizes are only moderate. Approximate simultaneous confidence limits can be constructed using the quantiles of a multivariate normal distribution taking the correlation into account. Alternatively, multiplicity-adjusted p-values can be calculated as well. A simulation study shows that a simple correction based on adding pseudo observations leads to acceptable performance for moderate sample sizes, such as 40 per group. In addition, the calculation of adjusted p-values and approximate power is presented. Finally, the proposed methods are applied to example data from two toxicological studiesthe methods are available in an R-package. © 2010 The Berkeley Electronic Press. All rights reserved

Taming ultrafast laser filaments for optimized semiconductor–metal welding

Ultrafast laser welding is a fast, clean, and contactless technique for joining a broad range of materials. Nevertheless, this technique cannot be applied for bonding semiconductors and metals. By investigating the nonlinear propagation of picosecond laser pulses in silicon, it is elucidated how the evolution of filaments during propagation prevents the energy deposition at the semiconductor–metal interface. While the restrictions imposed by nonlinear propagation effects in semiconductors usually inhibit countless applications, the possibility to perform semiconductor–metal ultrafast laser welding is demonstrated. This technique relies on the determination and the precompensation of the nonlinear focal shift for relocating filaments and thus optimizing the energy deposition at the interface between the materials. The resulting welds show remarkable shear joining strengths (up to 2.2 MPa) compatible with applications in microelectronics. Material analyses shed light on the physical mechanisms involved during the interaction

Methodology for Creating a FairShares Lab (Full Report)

Welcome to the full version of the first intellectual output (IO1) of the Erasmus+ project FairShares Labs for Social and Blue Innovation Project (Project 2016-1-DE02-KA204-003397). IO1 has been prepared by project partners to describe their methodology for creating FairShares Labs. Work started in Erfurt, Germany (7-9 December 2016) and has been discussed in three further transnational meetings in Sheffield (26-28 June 2017), Berlin (27-28 August 2017) and Osijek (20-22 Feb 2018). In this document, we set out the purpose of IO1. This document provides any person involved in the creation and development of a FairShares Lab (partners, coordinators, trainers and advisers) with an overview of the methodology for creating their lab. This includes an account of the FairShares Model itself as well as processes for setting up, recruiting people to and marketing a FairShares Lab, and supporting lab participants as they incubate new FairShares enterprises and contribute to building an ecosystem for FairShares. Section 1 provides background information and an overview of the methodology. Section 2 provides an overview of five elements of a FairShares Lab. Three elements come from the FairShares Model of social enterprise development (created by FairShares Association Ltd) - values and principles; key questions and; legal choices. The other two elements are social and technical support systems selected by the partners for this project. Social support is provided through learning and development methods (elaborated further in Section 3). These generate ideas, improve the effectiveness of team work and enable stakeholders to make decisions together. In Section 4, we examine the process of establishing a lab, inviting people to it, running activities, selecting projects, producing prototypes of goods and services, planning and incorporating (social) enterprises. In Section 5, we consider the marketing of FairShares Labs, who they are for, what needs they serve, what messages should be communicated to target groups (and future lab organisers)

Methodology for Creating a FairShares Lab

Welcome to the first intellectual output (IO1) of the Erasmus+ project FairShares Labs for Social and Blue Innovation Project (Project 2016-1-DE02-KA204-003397). IO1 has been prepared by project partners to describe their methodology for creating FairShares Labs. Work started in Erfurt, Germany (7-9 December 2016) and has been discussed in three further transnational meetings in Sheffield (26-28 June 2017), Berlin (27-28 August 2017) and Osijek (20-22 Feb 2018). In this document, we set out the purpose of IO1. This document provides any person involved in the creation and development of a FairShares Lab (partners, coordinators, trainers and advisers) with an overview of the methodology for creating their lab. This includes an account of the FairShares Model itself as well as processes for setting up, recruiting people to and marketing a FairShares Lab, and supporting lab participants as they incubate new FairShares enterprises and contribute to building an ecosystem for FairShares. Section 1 provides background information and an overview of the methodology. Section 2 provides an overview of five elements of a FairShares Lab. Three elements come from the FairShares Model of social enterprise development (created by FairShares Association Ltd) - values and principles; key questions and; legal choices. The other two elements are social and technical support systems selected by the partners for this project. Social support is provided through learning and development methods (elaborated further in Section 3). These generate ideas, improve the effectiveness of team work and enable stakeholders to make decisions together. In Section 4, we examine the process of establishing a lab, inviting people to it, running activities, selecting projects, producing prototypes of goods and services, planning and incorporating (social) enterprises. In Section 5, we consider the marketing of FairShares Labs, who they are for, what needs they serve, what messages should be communicated to target groups (and future lab organisers)

Seroepidemiological study on the spread of SARS-CoV-2 in Germany:

The SARS-CoV-2 coronavirus has spread rapidly across Germany. Infections are likely to be under-recorded in the notification data from local health authorities on laboratory-confirmed cases since SARS-CoV-2 infections can proceed with few symptoms and then often remain undetected. Seroepidemiological studies allow the estimation of the proportion in the population that has been infected with SARS-CoV-2 (seroprevalence) as well as the extent of undetected infections. The ‘CORONA-MONITORING bundesweit’ study (RKI-SOEP study) collects biospecimens and interview data in a nationwide population sample drawn from the German Socio-Economic Panel (SOEP). Participants are sent materials to self-collect a dry blood sample of capillary blood from their finger and a swab sample from their mouth and nose, as well as a questionnaire. The samples returned are tested for SARS-CoV-2 IgG antibodies and SARS-CoV-2 RNA to identify past or present infections. The methods applied enable the identification of SARS-CoV-2 infections, including those that previously went undetected. In addition, by linking the data collected with available SOEP data, the study has the potential to investigate social and health-related differences in infection status. Thus, the study contributes to an improved understanding of the extent of the epidemic in Germany, as well as identification of target groups for infection protection