Real-time Event Research Articles

Tracking aftershock sequences using empirical matched field processing

SUMMARY Extensive aftershock sequences present a significant problem to seismological data centres attempting to produce near real-time comprehensive seismic event bulletins. An elevated number of events to process and poorer performance of automatic phase association algorithms can lead to large delays in processing and a greatly increased human workload. Global monitoring is often performed using seismic array stations at considerable distances from the events involved. Empirical matched field processing (EMFP) is a narrow-frequency band array signal processing technique that recognizes the inter-sensor phase and amplitude relations associated with wavefronts approaching a sensor array from a given direction. We demonstrate that EMFP, using a template obtained from the first P arrival from the main shock alone, can efficiently detect and identify P arrivals on that array from subsequent events in the aftershock zone with exceptionally few false alarms (signals from other sources). The empirical wavefield template encodes all the narrow-band phase and amplitude relations observed for the main shock signal. These relations are also often robust and repeatable characteristics of signals from nearby events. The EMFP detection statistic compares the phase and amplitude relations at a given time in the incoming data stream with those for the template and is sensitive to very short-duration signals with the required characteristics. Significant deviations from the plane wavefront model that typically degrade the performance of standard beamforming techniques can enhance signal characterization using EMFP. Waveform correlation techniques typically perform poorly for aftershocks from large earthquakes due to the distances between hypocentres and the wide range of event magnitudes and source mechanisms. EMFP on remote seismic arrays mitigates these difficulties; the narrow-band nature of the procedure makes arrival identification less sensitive to the signals’ temporal form and spectral content. The empirical steering vectors derived for the main shock P arrival can reduce the frequency dependency of the slowness vector estimates. This property helps us to automatically screen out arrivals from outside of the aftershock zone. Standard array processing pipelines could be enhanced by including both plane-wave and empirical matched field steering vectors. This would maintain present capability for the plane-wave steering vectors and provide increased sensitivity and resolution for those sources for which we have empirical calibrations.

A wearable group-synchronized EEG system for multi-subject brain-computer interfaces.

The multi-subject brain-computer interface (mBCI) is becoming a key tool for the analysis of group behaviors. It is necessary to adopt a neural recording system for collaborative brain signal acquisition, which is usually in the form of a fixed wire. In this study, we designed a wireless group-synchronized neural recording system that supports real-time mBCI and event-related potential (ERP) analysis. This system uses a wireless synchronizer to broadcast events to multiple wearable EEG amplifiers. The simultaneously received broadcast signals are marked in data packets to achieve real-time event correlation analysis of multiple targets in a group. To evaluate the performance of the proposed real-time group-synchronized neural recording system, we conducted collaborative signal sampling on 10 wireless mBCI devices. The average signal correlation reached 99.8%, the amplitude of average noise was 0.87 μV, and the average common mode rejection ratio (CMRR) reached 109.02 dB. The minimum synchronization error is 237 μs. We also tested the system in real-time processing of the steady-state visual-evoked potential (SSVEP) ranging from 8 to 15.8 Hz. Under 40 target stimulators, with 2 s data length, the average information transfer rate (ITR) reached 150 ± 20 bits/min, and the highest reached 260 bits/min, which was comparable to the marketing leading EEG system (the average: 150 ± 15 bits/min; the highest: 280 bits/min). The accuracy of target recognition in 2 s was 98%, similar to that of the Synamps2 (99%), but a higher signal-to-noise ratio (SNR) of 5.08 dB was achieved. We designed a group EEG cognitive experiment; to verify, this system can be used in noisy settings. The evaluation results revealed that the proposed real-time group-synchronized neural recording system is a high-performance tool for real-time mBCI research. It is an enabler for a wide range of future applications in collaborative intelligence, cognitive neurology, and rehabilitation.

The Aerosol Optical Characteristics in Different Dust Events Based on a 532 nm and 355 nm Polarization Lidar in Beijing

Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, surface particle concentration, meteorological parameters, and MODIS–derived aerosol optical depth, as well as backward trajectory analysis, were employed to analyze two dust events from April to May in Beijing. The dust plumes were mainly concentrated below 0.8 km, with peak PM10 values of 1000 μg·m−3 and 300 μg·m−3 in the two cases. The aerosols showed different vertical distribution characteristics. The pure dust in case 1 from 4 to 5 May 2017 had a longer duration (2 days) and presented a larger aerosol extinction coefficient (2.27 km−1 at 355 nm and 1.25 km−1 at 532 nm) than that of the mixed dust in case 2 on 17 April 2017 (2.01 km−1 at 355 nm and 1.33 km−1 at 532 nm). The particle depolarization ratio (PDR) remained constant (0.24 ± 0.03 in case 1) from the surface to 0.8 km in height. In contrast, the PDR profile in the mixed dust (case 2) layer was split into two regions—large values exceeding 0.15 above 0.6 km and small values of 0.11 ± 0.03 below 0.6 km. The influence of meteorological information on aerosol distribution was also investigated, and wind was predominant through the observing period. The pure dust in case 1 was mainly from Mongolia, with strong northwest winds, while the near-surface mixed pollution was caused by the combination of long-transported sand and local emission. Furthermore, lidar-derived profiles of dust mass concentrations in the two cases were presented. This study reveals the vertical characteristics of dust aerosols in the production and dissipation of localized dust events and confirms the efficacy of thorough observations with multiple approaches from the ground to space to monitor dust events in real time.

Performance of the MALTA telescope

MALTA is part of the Depleted Monolithic Active Pixel sensors designed in Tower 180 nm CMOS imaging technology. A custom telescope with six MALTA planes has been developed for test beam campaigns at SPS, CERN, with the ability to host several devices under test. The telescope system has a dedicated custom readout, online monitoring integrated into DAQ with realtime hit map, time distribution and event hit multiplicity. It hosts a dedicated fully configurable trigger system enabling to trigger on coincidence between telescope planes and timing reference from a scintillator. The excellent time resolution performance allows for fast track reconstruction, due to the possibility to retain a low hit multiplicity per event which reduces the combinatorics. This paper reviews the architecture of the system and its performance during the 2021 and 2022 test beam campaign at the SPS North Area.

Tsunami waveform forecasting at cooling water intakes of nuclear reactors with deep learning model

The Fukushima nuclear disaster highlights the importance of accurate and fast predictions of tsunami hazard to critical coastal infrastructure to devise mitigation strategies in both long-term and real-time events. Recently, deep learning models allowed us to make accurate and rapid forecasts on high dimensional, non-linear, and non-stationary time series data such as that associated with tsunami waveforms. Thus, this study uses a one-dimensional convolutional neural network (CNN) model to predict waveforms at cooling water intakes of nuclear power plant at Uljin in South Korea. The site is particularly vulnerable to tsunamis originating from the west coast of Japan. Data for the CNN model are generated by numerical simulation of 1107 cases of tsunami propagation initiating from fault locations. The time series data for waveforms were predicted at 13 virtual gauges located in the nearshore region of the study area, 10 of which were classified as observation points and 3 gauges situated at the cooling water intakes were categorized as target locations. The performance assessment of the model's forecasts showed excellent results with rapid predictions. The study highlights two main points: (i) deep learning models can be based on sparse waveform in situ data (such as that recorded by deep-ocean assessment and reporting of tsunamis or any locally operating monitoring stations for ocean waves) or numerically simulated data at only a few points along the dominant wave propagation direction, and (ii) deep learning models are fully capable of accurate and fast predictions of complex geo-hazards that prompt rapid emergency response to coordinate mitigation efforts.

Generative and non-parametric model for real-time event detection in social networks based on textual analysis

One of the things that is followed in monitoring systems is the detection of rare events in real time among the multitude of common events in social networks. Considering the lack of recognition and unavailability of rare events, their detection is considered a challenge. In this research, a new architecture and approach based on generative adversarial network infrastructure was presented to detect common and rare events in real time. In this research, the attempt is to provide a new approach to the performance of architectures based on deep generative adversarial networks, a way to solve various problems without supervision with a semi-supervisory approach and adversarial generative infrastructure. This architecture is based on the automatic extraction and use of video input data features. The results of the equal error rate in the UCSDped1 and UCSDped2 datasets were 2.0 and 17.0, respectively, in the performance characteristic curve.

Monitoring System for Operating Variables in Incubators in the Neonatology Service of a Highly Complex Hospital through the Internet of Things (IoT)

Background: Around 15 million premature babies are born annually, requiring specialized care. Incubators are vital for maintaining their body temperature, which is crucial for their well-being. Ensuring optimal conditions in incubators, including constant temperature, oxygen control, and comfort, is essential for improving the care and survival rates of these infants. Methods: To address this, an IoT-based monitoring system was developed in a hospital setting. The system comprised hardware components such as sensors and a microcontroller, along with software components including a database and a web application. The microcontroller collected data from the sensors, which was then transmitted to a broker via WiFi using the MQTT protocol. The broker validated and stored the data in the database, while the web application provided real-time access, alerts, and event recording. Results: Two certified devices were created, employing high quality components. The system was successfully implemented and tested in both the biomedical engineering laboratory and the neonatology service of the hospital. The results of the pilot test supported the concept of IoT-based technology, demonstrating satisfactory responses in temperature, humidity, and sound variables within the incubators. Conclusions: The monitoring system facilitated efficient record traceability, allowing access to data over various timeframes. It also captured event records (alerts) related to variable problems, providing information on duration, date, hour, and minutes. Overall, the system offered valuable insights and enhanced monitoring capabilities for neonatal care.

Real-Time Gait Event Detection with Adaptive Frequency Oscillators From a Single Head-Mounted IMU

Accurate real-time gait event detection is the basis for the development of new gait rehabilitation techniques, especially when utilizing robotics or virtual reality (VR). The recent emergence of affordable wearable technologies, especially inertial measurement units (IMUs), has brought forth various new methods and algorithms for gait analysis. In this paper, we highlight some advantages of using adaptive frequency oscillators (AFOs) over traditional gait event detection algorithms, implemented a real-time AFO-based algorithm that estimates the gait phase from a single head-mounted IMU, and validated our method on a group of healthy subjects. Gait event detection was accurate at two different walking speeds. The method was reliable for symmetric, but not asymmetric gait patterns. Our method could prove especially useful in VR applications since a head-mounted IMU is already an integral part of commercial VR products.

Driving Aggressively or Conservatively? Investigating the Effects of Automated Vehicle Interaction Type and Road Event on Drivers' Trust and Preferred Driving Style.

This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events. The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation. Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors. Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts. Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles. Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.

A Neural-Based Approach for Detecting the Situational Information from Twitter During Disaster

Each day, vast quantities of social media data is being produced and consumed in a constantly increasing price. A user’s digital footprint coming from internet sites or mobile products, such as comments, check-ins and Location traces contains valuable details about her behavior under normal and also emergency conditions. The collection and analysis of cellular and social media data before, during and after a tragedy opens new perspectives in areas such as for example real-time event detection, Emergency administration and personalization and valuable insights about the degree of the disaster, it is effect on the affected population and the rate of disaster recovery. Traditional storage space and processing systems cannot cope with how big is the collected data and the complexity of the applied analysis, thus distributed approaches are generally employed. In this function, we propose an open-source distributed platform that may serve while a backend for applications and solutions related to Emergency detection and administration by combining spatio-textual consumer generated data. The machine targets scalability and uses combination of state-of-the artwork Big Data frameworks. It presently supports the most popular internet sites, being easily extensible to any social system. The experimental evaluation of our prototype attests its overall performance and scalability even under large load, using different query types more than various cluster sizes.

0949 Real-time acoustic apnea event detector by training a deep learning model with home noise added data

Abstract Introduction For diagnosis and management of Obstructive Sleep Apnea (OSA), long-term multi-night monitoring is crucial. Convenient detection of OSA at home is required for this purpose. Using sound recorded by smartphone can provide a convenient way to detect OSA. In this study, we present a sound-based OSA detection deep learning model that can detect OSA in real-time even in a home environment where various noises exist. The model is trained with home noise simulated sound to be robust for detecting home noises. Methods Two types of data were used for training and testing. The first type was sleep breathing sound data collected at the hospital while patients underwent a PSG. It included 1,154 and 297 nights recorded by a PSG microphone and a smartphone, respectively. We split them into 150 nights of smartphone data for testing and the rest for training. The second type was home noise data, which included 22,500 noises that might occur in a residential environment. The proposed acoustic apnea event detector inputs Mel spectrograms of sleep breathing sounds and outputs OSA event classes for each epoch (APNEA, HYPOPNEA, or NO-EVENT). The home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed by epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). Results Our model achieved 86 % epoch-by-epoch agreement (0.75 in macro F1) for 3-class event detection task. The model had an accuracy of 92% for NO-EVENT, 84% for APNEA, and 51% for HYPOPNEA. Most misclassifications were made for HYPOPNEA, with 15% and 34% of HYPOPNEA being wrongly predicted as APNEA and NO-EVENT, respectively. The sensitivity and specificity of OSA severity classification (AHI ≥ 15) were 0.85 and 0.84, respectively. Conclusion Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multi-night monitoring and real-time diagnostic technologies in the home environment. Support (if any)

Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μPMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μPMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN.

Analysis of the Current Chinese Consumer for Luxury Goods - Generation Z

As a mainstay of the luxury consumer group, Gen Z has become a global phenomenon, especially in the United States and China. Marketing strategies for attracting this emerging consumer segment can be learned from the Chinese luxury market. This study used the Chinese luxury market and its Gen Z consumers as a case study to offer scientific recommendations to luxury marketers based on various statistics and related literature, including demographic descriptions, market reports, and consumer behaviors. It also discusses how Gen Z differs from its elder sibling, millennials, who are currently the focus of luxury brands' marketing efforts. Consequently, three factors contributing to Gen Zs dominance of the Chinese luxury market have been found. Firstly, the optimism and impulsivity of Gen Z make them spend more than they can afford. In addition, Gen Zs are exposed to a constant stream of uncensored, real-time events, information, and entertainment, which makes it easier for them to access information related to luxury goods. Lastly, digitalization facilitates online shopping for luxury goods. Accordingly, as Chinese Gen Z crave unique and personalized experiences, customized services can be a winning point for luxury brands.

Lessons Learned from an OB / Newborn / Neonatal Intensive Care Full-Scale Exercise

Introduction:Children are frequently victims of disasters. However, gaps remain in disaster planning for pediatric patients. The New York City Pediatric Disaster Coalition (NYCPDC) is funded by the NYC Department of Health and Mental Hygiene (DOHMH) to prepare NYC for mass casualty incidents that involve large numbers of children.On April 26, 2018, the NYC PDC conducted a first in NY, full-scale exercise with the NYC Fire Department (FDNY) testing evacuation, patient tracking, communications, and emergency response of the Obstetrics, Newborn and Neonatal units at a NYC based hospital. The goal of the exercise was to evaluate current Ob/Newborn/Neonatal plans and assess the hospital’s ability to evacuate patients.Method:The exercise planning process included a review of existing OB / Newborn / Neonatal plans, four group planning meetings, as well as, targeted specific area meetings and plan revisions. The exercise incorporated scenario-driven, operations-based activities, which challenged participants to employ the facility's existing evacuation plans during an emergency.Results:The Exercise assessed the following: Communication, Emergency Operation Plans, Evacuation, Patient Tracking, Supplies and Staffing. Internal and external evaluators rated exercise performance on a scale from 1-4. Evaluators completed an exercise evaluation guide based on the Master Scenario Event List.An After Action Report was written based on the information from the exercise evaluation guides, participant feedback forms, hot-wash session, and after action review meeting. Strengths included the meaningful improvement of plans before the exercise (including the fire department) and the overall meeting of exercise objectives.Conclusion:Lessons learned included: addressing gaps in effective internal and external communications, adequate supplies of space, staff, equipment needed for vertical evacuations; providing staging and alternate care sites with sufficient patient care and electrical-power resources. The lessons learned are being utilized to improve existing hospital plans to prepare for future full-scale exercise and or real-time events.

Lessons Learned from 28 Hospitals and City Agencies: Pediatric Disaster Exercise

Introduction:Children are frequently victims of disasters. However, gaps remain in pediatric disaster preparedness. The New York City Pediatric Disaster Coalition (NYCPDC) is funded by the NYC Department of Health and Mental Hygiene (DOHMH) to prepare NYC for mass casualty events that involve large numbers of children. The NYC PDC conducted a functional exercise testing surge, communications, and secondary transport. Participants included 28 NYC hospitals, the NYC Fire Department-Emergency Medical Services (FDNY-EMS), NYC Emergency Management (NYCEM), NYC DOHMH and the NYC Medical Reserve Corps (MRC).Method:The hospitals and agencies participated in group and individual planning meetings. Scenario-driven, operations-based activities challenged participants to employ their facility's existing pediatric surge and secondary transport plans during an event. The exercise assessed: Communications, Emergency Operation Plans, Surge, Patient Tracking, Patient Transfer, Supplies, and Staffing. Internal and external evaluators assessed the exercise performance.Results:An After-Action Report was written based on information from evaluation data, site-specific and group hot-washes, and an after-action conference. Strengths included meaningful improvement of plans before/after the exercise and doubling pediatric critical care capacity through the implementation of the exercise objectives. Challenges included: gaps in communication/patient tracking, lack of sufficient sub-specialty support, the need for "babysitters” and inadequate supplies of blood products and ventilators.Conclusion:Conducting a multi-hospital and agency pediatric specific exercise demonstrated current planning and produced lessons learned to address planning and training gaps that can improve citywide planning and capabilities during future full-scale exercise and real-time events.

A Comprehensive Coalition-Based Regional Approach to Pediatric Disaster Planning

Introduction:Children, who comprise 25% of the US population, are frequently victims of disasters and have special needs during these events. To prepare NYC for a large-scale Pediatric Disaster, NYCPDC has worked with an increasing number of providers that initially included only a small number of hospitals and agencies. Through a cooperative team approach, stakeholders now include local public health, emergency management and emergency medical services, 28 hospitals, community-based providers, and the Medical Reserve Corps.Method:The NYCPDC utilized an inclusive iterative process model whereby a desired plan was achieved by stakeholders reviewing the literature and current practice through repeated discussion and consensus building. NYCPDC used this model in developing a comprehensive regional pediatric disaster plan.Results:The plan included disaster scene triage (adapted for pediatric use) to transport (with prioritization) to surge and evacuation. Additionally, site-specific plans utilizing guidelines and templates now include Pediatric Long-Term Care Facilities, Hospital Pediatric Departments including Pediatric and Neonatal Intensive Care Services and Outpatient/Urgent Care Centers. A force multiplier course in critical care for non-intensivists has been provided. An extensive Pediatric Exercise program has been used to develop, operationalize and revise plans based on lessons learned. This initially included pediatric tabletop, functional and full-scale exercises at individual hospitals leading to citywide exercises at 13 and subsequently all 28 hospitals caring for children.Conclusion:The NYCPDC has comprehensively planned for the special needs of children during disasters utilizing a pediatric coalition based regional approach that matches pediatric resources to needs to provide best outcomes.The NYCPDC has responded to real time events (H1N1, Haiti Earthquake, Superstorm Sandy, Ebola), and participated in local (NYC boroughs and executive leadership) and nationwide coalitions (including the National Pediatric Disaster Coalition). The NYCPDC has had the opportunity to present their Pediatric Disaster Planning and Response efforts at local, national and International conferences.

2D Numerical Simulation of Floods in Ebro River and Analysis of Boundary Conditions to Model the Mequinenza Reservoir Dam

The computational simulation of rivers is a useful tool that can be applied in a wide range of situations from providing real time alerts to the design of future mitigation plans. However, for all the applications, there are two important requirements when modeling river behavior: accuracy and reasonable computational times. This target has led to recent developments in numerical models based on the full two-dimensional (2D) shallow water equations (SWE). This work presents a GPU accelerated 2D SW model for the simulation of flood events in real time. It is based on a well-balanced explicit first-order finite volume scheme able to run over dry beds without the numerical instabilities that are likely to occur when used in complex topography. The model is applied to reproduce a real event in the reach of the Ebro River (Spain) with a downstream reservoir, in which a study of the most appropriate boundary condition (BC) for modeling of the dam is assessed (time-dependent level condition and weir condition). The whole creation of the model is detailed in terms of mesh optimization and validation. The simulation results are compared with field data over the flood duration (up to 20 days), allowing an analysis of the performance and time saved by different GPU devices and with the different BCs. The high values of fit between observed and simulated results, as well as the computational times achieved, are encouraging to propose the use of the model as a forecasting system.

Data-Driven, Short-Term Prediction of Charging Station Occupation

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events.

Evaluation of a Resilience-Driven Operational Concept to Manage Drone Intrusions in Airports

The drone market’s growth poses a serious threat to the negligent, illicit, or non-cooperative use of drones, especially in airports and their surroundings. Effective protection of an airport against drone intrusions should guarantee mandatory safety levels but should also rely on a resilience-driven operational concept aimed at managing the intrusions without necessarily implying the closure of the airport. The concept faces both safety-related and security-related threats and is based on the definitions of: (i) new roles and responsibilities; (ii) a set of operational phases, accomplished by means of specific technological building blocks; (iii) a new operational procedure blending smoothly with existing aerodrome procedures in place. The paper investigates the evaluation of such a resilience-driven operational concept tailored to drone-intrusion features, airport features, and current operations. The proposed concept was evaluated by applying it to a concrete case study related to Milan Malpensa Airport. The evaluation was carried out by real-time simulations and event tree analysis, exploiting the implementation of specific simulation tools and the assessment of resilience-oriented metrics. The achieved results show the effectiveness of the proposed operational concept and elicit further requirements for future counter-drone systems in airports.

Correction to: FPGA implementation of hardware accelerated RTOS based on real-time event handling

Correction to: FPGA implementation of hardware accelerated RTOS based on real-time event handling