Simulation Campaign Research Articles

Improving the Effectiveness of Eigentrust in Computing the Reputation of Social Agents in Presence of Collusion.

The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.

Transport and evaporation of exhaled respiratory droplets: An analytical model

An important vector for host-to-host infectious disease transmission is given by the transport of tiny pathogen-laden droplets. These are commonly exhaled by individuals while breathing, speaking, coughing, or sneezing. Depending on their size and ambient conditions, they may follow different paths, either settling on surfaces where the pathogen can be further transmitted by contact, or remaining airborne after evaporation where the pathogen can be inhaled. Our understanding of pathogen transmission from the fluid mechanics perspective is still somewhat limited, especially in quantitative terms. In the current work, starting from the fundamental laws of fluid mechanics and diffusion, a detailed analytical model of droplet transport and evaporation in humid air streams is presented and successfully validated against available data in the literature finding remarkable agreement. The model implements closed-form analytical solutions of the equations of transport, evaporation, and energy balance, and an algebraic model to account for the droplet chemical composition. It also features an analytical model of droplet transport within the buoyant exhaled breath cloud based on momentum conservation addressing both jet and puff phases and is able to handle periodic respiratory events. Turbulent dispersion is modeled with a discrete random walk approach. A simple inhalation model is also proposed. Such a model may help in better understanding droplets' fluid dynamic behavior and may be used to assess the risks associated with pathogen transmission under different scenarios for any type of respiratory event. Overall, the computational cost is relatively low, allowing extensive simulation campaigns to be performed easily.

Towards optimal and robust f_nl constraints with multi-tracer analyses

We discuss the potential of the multi-tracer technique to improve observational constraints of the local primordial non-Gaussianity (PNG) parameter f nl from the galaxy power spectrum. For two galaxy samples A and B, the constraining power is ∝ |bB 1 bA ϕ - bA 1 bB ϕ |, where b 1 and bϕ are the linear and PNG galaxy bias parameters. We show this allows for significantly improved constraints compared to the traditional expectation ∝ |bA - bB | based on naive universality-like relations where bϕ ∝ b 1. Using IllustrisTNG galaxy simulation data, we find that different equal galaxy number splits of the full sample lead to different |bB 1 bA ϕ - bA 1 bB ϕ |, and thus have different constraining power. Of all of the strategies explored, splitting by g-r color is the most promising, more than doubling the significance of detecting f nl bϕ ≠ 0. Importantly, since these are constraints on f nl bϕ and not f nl , they do not require priors on the bϕ (b 1) relation. For direct constraints on f nl , we show that multi-tracer constraints can be significantly more robust than single-tracer to bϕ misspecifications and uncertainties; this relaxes the precision and accuracy requirements for bϕ priors. Our results present new opportunities to improve our chances to detect and robustly constrain f nl , and strongly motivate galaxy formation simulation campaigns to calibrate the bϕ (b 1) relation.

Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision

This paper presents a task allocation strategy for a multi-robot system with a human supervisor. The multi-robot system consists of a team of heterogeneous robots with different capabilities that operate in a dynamic scenario that can change in the robots’ capabilities or in the operational requirements. The human supervisor can intervene in the operation scenario by approving the final plan before its execution or forcing a robot to execute a specific task. The proposed task allocation strategy leverages an auction-based method in combination with a sampling-based multi-goal motion planning. The latter is used to evaluate the costs of execution of tasks based on realistic features of paths. The proposed architecture enables the allocation of tasks accounting for priorities and precedence constraints, as well as the quick re-allocation of tasks after a dynamic perturbation occurs –a crucial feature when the human supervisor preempts the outcome of the algorithm and makes manual adjustments. An extensive simulation campaign in a rescue scenario validates our approach in dynamic scenarios comprising a sensor failure of a robot, a total failure of a robot, and a human-driven re-allocation. We highlight the benefits of the proposed multi-goal strategy by comparing it with single-goal motion planning strategies at the state of the art. Finally, we provide evidence for the system efficiency by demonstrating the powerful synergistic combination of the auction-based allocation and the multi-goal motion planning approach.

Overarching Data Management Ecosystem at HZDR

When dealing with research data management, researchers at Helmholtz- Zentrum Dresden – Rossendorf (HZDR) face a variety of systems and tools. These range from the project planning phase (proposal management, data management plans and policies), over documentation during the experiment or simulation campaign, to the publication (collaborative authoring tools, metadata catalogs, publication systems, data repositories). In addition, modern research projects usually are required to interact with a variety of software stacks and workflow management systems to allow comprehensi- ble and FAIR science on the underlying IT infrastructure (HPC, data storage, network file systems, archival). This article first demonstrates the data management systems and services provided at HZDR, followed by an overview of a self-developed guidance system. It is concluded by a real-world example.

5G NR Sidelink Multi-Hop Transmission in Public Safety and Factory Automation Scenarios

The deployment of D2D communications (also known as ProSe or sidelink transmissions) in cellular networks benefits from proximity, multi-hop, and spatial reuse gains. In this article, we first describe the main advancements of NR sidelink compared to LTE-A sidelink. Then, we run a simulation campaign to test D2D-based ProSe for public safety and factory automation scenarios with their mission-critical requirements and ultra-reliable low-latency communications, respectively. A preliminary study on NR sidelink usage for both considered use cases is performed, aiming to identify the main advantages and disadvantages thereof. Finally, important future directions for the NR sidelink development from a standardization perspective are highlighted.

Negativizability: A useful property for distributed state estimation and control in Cyber–Physical Systems

In this paper we focus our attention on an interesting property of linear and time-invariant systems, namely negativizability: a pair (A,C) is negativizable if a gain matrix K exists such that A−KC is negative definite. Notably, in this paper we show that negativizability can be a useful feature for solving distributed estimation and control problems in Cyber–Physical Systems (CPS), since such a property allows a network of agents to bring the estimation error to zero or to control the overall system by designing gains that only require information locally available to each agent. In detail, we first characterize the negativizability problem, developing a necessary and sufficient condition for the problem to admit a solution. Then, we show how distributed estimation and control schemes for linear and time-invariant CPSs can greatly benefit from this property. A simulation campaign aiming at showing the potential of negativizability in the context of distributed state estimation and control of CPSs concludes the paper.

A Social Edge-Based IoT Framework Using Reputation-Based Clustering for Enhancing Competitiveness

The introduction of the IoT technology and its pervasive penetration in our daily life implies that IoT smart objects can often participate in social events, and in this case their paradigm of interaction is commonly denoted as social IoT (SIoT). To make reliable transactions into a SIoT scenario, in this article, we introduce a multi-agent SIoT architecture, which integrates a reputation system based on a clustering of the SOs. In our framework, when a SO looks for a resource and detects a reliable partner having that resource, then the two SOs can interact to make a transaction, and at the end of the transaction each of them provides a feedback about the partner to the local reputation system of the edge server, to update the SOs’ reputation scores. Moreover, each edge server sends these reputation scores to the cloud, which updates the associated reputation values of the SOs, deriving from experiences coming from all the edge domains. This architecture provides a given object, moving from an edge domain to another one, with the possibility to have an updated value of its reputation, represented by the value stored in the cloud. We have validated our approach by a campaign of simulations, whose results seem particularly promising.

Improving the accuracy of a weighing scale by rigorously combining measurements

In this paper, we investigate the question of how much measurement accuracy of additive quantities can be increased by the method of combined measurements. Therefore, we consider measurements of all possible combinations of n labeled masses and then estimate the individual weights of the n masses by a linear regression approach. We present experimental results which motivate comprehensive simulation campaigns. These simulations provide valid statistical statements and reliable forecasts of the experimental results. A profound analytical treatment, in turn, supports these simulation outcomes with excellent consistency. One important achievement therein is a general analytical expression for the accuracy that can be achieved by combined measurements. It turns out this method allows to estimate the weight of mass elements with an accuracy that potentially under-runs by orders of magnitude the resolution of the scale used. In a broader sense, our work wants to promote the method and give inspirations to applications in various metrological fields beyond high-precision mass determination.

Distributed Ledger as a Service: A Web 3.0-Oriented Architecture

This paper proposes a general and interoperable Web of Things (WoT)-oriented architecture to support a distributed storage application. In particular, the focus is on a distributed ledger service dedicated to machine-to-machine (M2M) transactions occurring in an intelligent ecosystem. For this purpose, the basic functional modules have been characterized and integrated into a comprehensive framework relying on an IOTA approach. Furthermore, a general protocol that is built upon an underlying publish-and-subscribe framework is proposed to support all the application phases. The proposed approach has been validated by a simulation campaign targeting the achievable latency and throughput and, further, by a qualitative analysis of high-level metrics, both pointing out several advantages in terms of interoperability, scalability, and mobility support, together with addressing some constraints affecting service availability and security.

Simulation-based optimization for the TRIUMF FEBIAD ion source

The TRIUMF-FEBIAD ion source is typically used to generate radioactive ion beams of molecules, halogens, and noble gases. The experimental campaign undertaken in the past couple of years has shown room for operational improvement, for example, by increasing the thermionic emission from the hot cathode. However, this work has also identify reliability issues in the thermo-mechanical aspect of the source. To mitigate failures observed due to unwanted excessive heating, a simulation campaign has been performed to increase the reliability and performance of the FEBIAD ion source. This paper presents the results derived from a simulation-based optimization campaign that can increase the overall ionization efficiency and robustness of the TRIUMF-FEBIAD ion source.

Galinstan liquid metal as the heat transfer fluid in magnetic refrigeration

Room-temperature magnetic refrigeration is a promising technology in which cooling is achieved by exploiting a special property of some materials, the “magnetocaloric effect” (MCE), i.e., the ability of the material to change its temperature when it undergoes changes in the applied external magnetic field. For magnetic field variations on the order of 1–1.5 T, the MCE results in small adiabatic temperature changes of a few degrees so, in most cases, the designs and prototypes presented by various research groups employ active magnetic regeneration to increase the temperature span required for practical cooling devices. The effectiveness of the regenerator, and thus of the refrigeration device, depends heavily on the properties of the heat transfer fluid, which is usually a water-based mixture. High values of the fluid thermal conductivity greatly affect the heat transfer between the MCE and the fluid allowing faster operation and higher cooling capacity. In this study, the use of a gallium based liquid metal, i.e., Galinstan, is investigated to quantify this improvement. Galinstan was found not to be aggressive to gadolinium at room temperature and it resulted not affected by magnetic induction. The authors report the results of an extensive simulation campaign to characterize the performance of an active magnetic regenerator made of 0.5 mm thick gadolinium sheets using Galinstan as a heat transfer fluid. The results are compared to those of the same device using water; friction is also accounted for since the high density of this liquid metal leads to a high demand for pumping power. On average, a more than tenfold increase in cooling capacity was observed when using Galinstan, but the COP was severely penalized by the higher frictional losses. To avoid this effect, the cycle time and the utilization factor must be adjusted, or the geometry changed by using thicker sheets and ducts for the same Gd weight. In this way a cooling increase of 400% is achieved with the same COP.

Single-Pilot Incapacitation in commercial aviation - Evaluation of an Operational Concept

To implement Single Pilot Operations (SPO) in commercial aviation, safety levels must be maintained compared to current operations. In this paper we present a concept of operations aiming at supporting flight and landing of a single-piloted aircraft in case the onboard single pilot becomes incapacitated. The concept relies on Ground Station positions being manned by ground station operators who monitor the aircraft, support the single pilot as needed, and take over control in case of incapacitation. The concept was evaluated through a number of evaluation activities, including two simulation campaigns. The results suggest that the concept could be a robust proposal to address single pilot incapacitation, and no major showstoppers have been identified from an operational, legal, and regulatory point of view. In this paper, the key technological, infrastructural, and procedural enablers that shall contribute to maintain SPO safety levels as in current dual-pilot operations are also discussed.

Application and validation of a method to assess the energy reduction and environmental impact of renovation alternatives

The renovation of residential stock is one of the most promising areas, in terms of energy reduction, because these buildings are highly inefficient and represent the largest part of the building stock. However, the environmental impact assessment over the life cycle of building renovation is rare. It is more common to develop an assessment for new buildings. This study presents a method that combines the evaluation of the benefits of renovating residential buildings, considering cost, energy and environmental benefits using Life Cycle Assessment (LCA). The method is based on 3 stages of development. First, the database of energy certificates, costs and LCA was analysed. The second step is to develop a workflow in Rhino/Grasshopper/E-Plus to automatically model a residential building and feed the simulation model with the data obtained from the databases. Finally, a simulation campaign was carried out to obtain an optimal renovation package, minimising energy consumption and environmental impact. The research was carried out in a case study in Uddevalla, Sweden. The residential building has different measurements including energy consumption data before and after renovation. This was used to validate the proposed methodology. The validation shows that accurate results are achievable with potential for mass application.

Enhancing 5G QoS Management for XR Traffic Through XR Loopback Mechanism

5G networks are designed to support a variety of services with highly demanding Quality-of-Service (QoS) requirements. This opened the door for novel extended reality (XR) media applications to emerge with 5G. However, recent 5G field tests and system-level simulation studies show that further XR enhancements are required to support a massive adoption of XR services in 5G networks. Such enhancements are expected to come into play with 5G-Advanced. In this line, we propose and study an XR loopback mechanism that adapts the XR traffic to the instantaneous 5G network conditions by exploiting an XR application feedback. We propose various XR loopback algorithms, strategies, and parameters’ configurations and study their impact on the 5G end-to-end performance. We conduct extensive simulation campaigns by building realistic end-to-end 5G network scenarios with 3GPP mixed XR traffic setups. Results show that the proposed XR loopback mechanism can boost XR performance in 5G networks by adapting to 5G network conditions, while keeping the XR QoS requirements under control. We provide various insights and practical directions on XR loopback design that allow us to take full advantage of the 5G network capabilities and progress toward 5G-Advanced network design.

GPU accelerated digital twins of the human heart open new routes for cardiovascular research

The recruitment of patients for rare or complex cardiovascular diseases is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative. In this paper we present an unprecedented cardiovascular computer model which, relying on the latest GPU-acceleration technologies, replicates the full multi-physics dynamics of the human heart within a few hours per heartbeat. This opens the way to extensive simulation campaigns to study the response of synthetic cohorts of patients to cardiovascular disorders, novel prosthetic devices or surgical procedures. As a proof-of-concept we show the results obtained for left bundle branch block disorder and the subsequent cardiac resynchronization obtained by pacemaker implantation. The in-silico results closely match those obtained in clinical practice, confirming the reliability of the method. This innovative approach makes possible a systematic use of digital twins in cardiovascular research, thus reducing the need of real patients with their economical and ethical implications. This study is a major step towards in-silico clinical trials in the era of digital medicine.

Swarm UAV networking with collaborative beamforming and automated ESN learning in the presence of unknown blockages

This paper aims at designing high-data-rate swarm UAV networks with distributed beamforming capabilities. The primary challenge is that the beamforming gain in swarm UAV networks is highly affected by the UAVs’ flight altitude, their movements and the resulting intermittent link blockages, as well as the availability of channel state information (CSI) at individual UAVs. To address this challenge, we propose FlyBeam, a learning-based framework for joint flight and beamforming control in swarm UAV networks. We first present a mathematical formulation of the control problem with the objective of maximizing the throughput of swarm UAV networks by jointly controlling the flight and distributed beamforming of UAVs. Then, a distributed solution algorithm is designed based on a combination of Echo State Network (ESN) learning and online reinforcement learning. The former is adopted to approximate the utility function for individual UAVs based on online measurements, by jointly considering the unknown blockage dynamics and other factors that affect the beamforming gain. The latter is used to guide the exploitation and exploration in FlyBeam. We further design a scheme referred to as AutoESN to automate the training of the ESN model. AutoESN can update the configurable ESN parameters automatically using a combination of loss function and step size. The effectiveness of FlyBeam is evaluated through an extensive simulation campaign on UBSim, a Python-based Universal Broadband Simulator for integrated aerial and ground wireless networking. The performance of FlyBeam is compared with two benchmark schemes, one designed based on traditional optimization techniques and the other based on learning with utility function approximation through Long Short-Term Memory (LSTM). In the performance evaluation, we consider both Zero-Forcing (ZF) and Maximum Ratio Transfer (MRT) for beamforming with sequential and simultaneous channel state estimation. It is shown that significant (up to 450%) beamforming gain can be achieved by FlyBeam. We also investigate the effects of blockages and UAV flight altitude on the beamforming gain. It is found that, somewhat surprisingly, higher (rather than lower) beamforming gain can be achieved by FlyBeam with denser blockages in swarm UAV networks.

Approaching 6G Use Case Requirements with Multicasting

The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.

A direct numerical simulation study for confined non-isothermal jet impingement at moderate nozzle-to-plate distances: Capturing jet-to-ambient density effects

A direct numerical simulation (DNS) campaign is deployed for a series of confined downward oriented, non-isothermal turbulent impinging jet configurations. A baseline Reynolds number of 9960 is obtained through a precursor DNS pipe flow simulation (Reτ=505). Three jet temperature configurations (confinement height to nozzle diameter of three) enter a cylindrical domain that share ambient and impingement plate temperatures (298.15K). The range of jet temperatures are crafted such that the ratio of inlet to ambient density varies from unity to 0.52, showcasing the effect of density disparity on flow characteristics such as core collapse, radial mixing of momentum and energy, near-wall stagnation behavior, wall-jet profiles, and large-scale vortical structures. Surface quantities provided include mean radial heat flux and wall-shear stress profiles, and heat flux histograms at select radial stations. Results showcase increased radial normal stresses for higher temperature jets that support increased mixing, resulting in large-scale recirculation structures that are smaller, while retaining similar normalized radial wall profiles for shear stress, heat flux and pressure. Radial plots for wall shear stress and Nusselt number showcase strong radial decay as compared to previous configurations that share similar jet and ambient temperatures. For the 373.15 K case, a Gaussian-like histogram for heat fluxes at the impingement plate transitions to a log-normal profile as radial distances increase. In contrast, the 573.15 K configuration displays a bi-modal heat flux characteristic at the impingement plate, and in similar manner to the moderate temperature counterpart, transitions to a log-normal profile at larger radial distances.

Cross-verification and benchmarking analysis of electrodynamic tether simulators

Five electrodynamic tether simulators (BETsMA v2.0, DYNATETHER, EDTSim, FLEX, and TeMPEST) have been cross-verified by running and analysing simulations of increasing complexity. A set of ten simulations without any tether was run to test the orbital propagators and the implementation of the perturbation force due to the non-sphericity of the Earth and its non-homogeneous mass distribution. The environmental models of the five codes and their implementation were then cross-verified by analysing the evolution of the magnetic field and the plasma and atmospheric densities. The electric modules of the simulators for electrodynamic tethers working in the passive mode, i.e., the routines in charge of computing the current and the voltage profiles along the tethers as well as the Lorentz force, were compared by running simulations with bare tethers. Configurations with an ideal electron emitter (zero potential drop), a real emitter, and a resistor and an ideal emitter were considered, as well as round and tape tethers. The electric models of BETsMA, EDTSim, FLEX, and TeMPEST, which assumed a straight tether, were also compared with the result of DYNATETHER for curved tethers. The consistency of the five simulators as a whole was tested by preparing performance maps with the deorbit time versus orbit inclination for a reference scenario and considering tape and round electrodynamic tethers. Although they implement different models and make different assumptions, the results of the five codes are consistent for the full simulation campaign. The simulation data and the software to visualize them are available in a public repository.