Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Optimization of Multihop Broadcast Communication in the Internet of Vehicles Based on C-V2X Mode 4

Cellular Vehicle-to-Everything (C-V2X) Mode 4, developed by 3GPP, supports vehicle-to-vehicle communication without a cellular base station via LTE sidelink. The messages transmitted in the Internet of Vehicles (IoV) include periodic single-hop broadcast Beacon messages and multi-hop broadcast Emergency messages triggered by events. In this paper, we analyze the impact of multi-hop broadcast of Emergency messages in C-V2X Mode 4 on the performance of Beacon messages and propose collision probability models for Beacon messages and Emergency messages. The non-periodicity nature and lower broadcast latency requirement of the multi-hop Emergency messages may cause resource allocation conflicts in the resource selection process of C-V2X Mode 4, thus result in forwarding collision of Emergency messages and collision of subsequent Beacon messages. Therefore, we propose several multi-hop broadcast schemes for Emergency messages to reduce the collision probability by assigning independent resource grants for Emergency messages and adjusting the number of forwarding nodes. The performance of Beacon messages and Emergency messages of several different forwarding schemes proposed are simulated. The simulation results show that the proposed broadcasting schemes significantly improve the performance of Beacon messages and Emergency messages.
Quantitative Risk Assessment Method Development in the Context of the SDLC-model

The quantitative software development risk assessment method, which was developed to solve the problem of contradictions arising in the software development, if software developers neglect the factors of software security vulnerabilities, is examined in this work. A distinctive feature of the examined method is the integrated use of the “Fault tree analysis” technique and the method of estimating the net present value of the software development project with taking into account the negative factors of the possibility of not revealing software security vulnerabilities. The use of the improved “Fault tree analysis” technique will allow to increase the accuracy of quantitative risk assessment of software development by up to 22%. At the same time, the use of the method of estimating the net present value of the software development project allows the project to be considered in a comprehensive manner, taking into account the need for security accounting and software vulnerability testing using tools.
High Possibility Classrooms: A New Model for Technology Integration

This chapter reports on a case study of a high school teacher from a larger study of ‘exemplary’ teachers and how they conceptualized their knowledge of technology integration in education contexts (Hunter, 2013). The research was a series of purposeful case studies of teachers in classrooms in Australia. The study found that theory, creativity, public learning, life preparation and contextual accommodations are crucial. Each conception of the teachers’ knowledge is underpinned by particular pedagogical themes that together form a fresh vision for technology integration known as High Possibility Classrooms or HPC. Kitty, the teacher featured in this chapter, conceptualized her knowledge of technology integration based on flexibility, experiential learning and creativity, preparation of learning, and whole school culture. This case study builds on the TPACK framework (Mishra & Koehler, 2006) and provides an important theoretical and practical exemplar of technology integration in practice for teacher education in a digital age.
Resilience Analysis of Distributed Wireless Spiking Neural Networks

Spiking neural networks (SNN) are expected to enable several use-cases in future communication networks (beyond 5G and 6G), as edge AI and battery-constrained systems can leverage the fast computation and high-power efficiency offered by SNNs. In this work we consider a Distributed Wireless SNN (DW-SNN) system and we analyze its performance in terms of inference accuracy and total neural activity when radio losses are applied to spikes transferred during the inference phase. Our aim is to understand how radio losses impact performance when considering different SNN spike communication types, i.e., input, excitatory, and inhibitory spikes. Then we evaluate the impact of different traffic prioritization approaches among SNN spikes when considering a shared channel capacity being available for SNN activity. From these analyses, we derive some key insights and features that can be considered when applying a DW-SNN and handling its traffic over wireless communication systems. Finally, we report a prototype implementation of DW-SNN using custom-built IoT components, which we use to further investigate different coverage scenarios.
Experimental Studies to Test a Predictive Indoor Radon Model

The accumulation of the radioactive gas radon in closed environments, such as dwellings, is the result of a quite complex set of processes related to the contribution of different sources. As it undergoes different physical mechanisms, all occurring at the same time, models describing the general dynamic turns out to be difficult to apply because of the dependence on many parameters not easy to measure or calculate. In this context, the authors developed, in a previous work, a simplified approach based on the combination of a physics-mathematical model and on-site experimental measurements. Three experimental studies were performed in order to preliminarily test the goodness of the model to simulate indoor radon concentrations in closed environments. In this paper, an application on a new experimental site was realized in order to evaluate the adaptability of the model to different house typologies and environmental contexts. Radon activity measurements were performed using a portable radon detector and results, showing again good performance of the model. Results are discussed and future efforts are outlined for the refining and implementation of the model into software.
Friendly Machines: interaction-oriented robots today and tomorrow

Robot engineers have recently built machines whose functions are based on acting jointly with human beings. By participating freely in social life, interaction-oriented robots attract us to establish relationships with them. They may use facial expressions to reproduce aspects of human behaviour (Breazea1 & Scassellati, 1999), or visual and auditory data that mimic aspects of personality (Okuno et al. 2002) They may carry out tasks like guiding people in a museum (Burgard et al. 1998), or use gaze to identify 'intentions' implicit in behaviour (Scassellati, 2000). Below, we report on how an interaction-oriented robot influences schoolchildren who 'get to know' the machine. In descriptionbing Robovie, we spell out why it could be of use in, for example, peer tutoring in a foreign language (Kanda et al. 2004a). In this paper, our main goal is to show how the friendly machine builds relationships and, thus, shows potential for making a positive contribution to society.
Experimental investigation of an active twist model rotor blade with a low voltage actuation system

Abstract Smart materials that are directly embedded in the rotor blade structure are an attractive concept for active blade control. A promising approach is the use of anisotropic piezoelectric strain actuators embedded in the rotor blade skin. Especially in Europe and the US this concept has been intensively investigated over the past years. A major drawback of all configurations studied so far is the high operation voltage of up to 2,000V of state of the art piezoelectric actuators. Within the Green Rotorcraft Project of the European Joint Technology Initiative Clean Sky, a new approach with a low voltage piezoelectric actuation system is investigated to demonstrate the feasibility of this technology. A first major step in this direction was completed by conducting a centrifugal test with a model rotor blade. The objective of the centrifugal test was to demonstrate the performance of the actuation system and the structural concept under centrifugal loads by showing that the expected twist deformation can be achieved at the nominal rotation speed and different actuation frequencies. It was demonstrated that the new actuation system is capable of operating under representative centrifugal loads. In comparison to state-of-the-art actuators (operation voltage 500V to +1500V) the new actuation system (operation voltage -20V to 120V) exhibits higher active twist performance per active area.
Analysis on the Efficiency of Engineering Based on Engineering Structure

Structural simulation analysis has become an effective and general research means in engineering field. Through the finite element modeling and calculation, we can obtain the engineering performance, stress mechanism, collapse state, etc so as to guide practical engineering application. This paper attempted to respectively elaborate the characteristics of conceptual model, mathematical model, calculation model and construction idea and method by putting forward a kind of integrate system. Whether they had reached to the effectiveness index was examined through comparing the results of effectiveness experiment and simulation analysis. The accuracy of this simulation analysis was obtained through defining the effective index and comparing the results between effectiveness experiment and simulation analysis. It has provided reliable system evaluation for whether the simulation analysis results of effectiveness experiment can be applied or not.
Reconfigurable Modular Robot Adaptively Transforming a Mechanical Structure

This paper describes self-reconfigurable robot CHOBIE that adaptively construct a mechanical structure. CHOBIE modules have slide motion mechanisms and transform the structure by synchronous movements. First of all, mechanical features of the robots are described. Second, as an effective control method for the synchronous robotic system, a new scheme “temporary leader” is introduced. Some experimental results show the validity of the scheme. Third, scalability of the system is discussed. In particular, we focus on problems of synchronous of the modules and amount of data processing in the system composed of large number of modules.
Design and Rationale of the Sevoflurane for Sedation in Acute Respiratory Distress Syndrome (SESAR) Randomized Controlled Trial

Preclinical studies have shown that volatile anesthetics may have beneficial effects on injured lungs, and pilot clinical data support improved arterial oxygenation, attenuated inflammation, and decreased lung epithelial injury in patients with acute respiratory distress syndrome (ARDS) receiving inhaled sevoflurane compared to intravenous midazolam. Whether sevoflurane is effective in improving clinical outcomes among patients with ARDS is unknown, and the benefits and risks of inhaled sedation in ARDS require further evaluation. Here, we describe the SESAR (Sevoflurane for Sedation in ARDS) trial designed to address this question. SESAR is a two-arm, investigator-initiated, multicenter, prospective, randomized, stratified, parallel-group clinical trial with blinded outcome assessment designed to test the efficacy of sedation with sevoflurane compared to intravenous propofol in patients with moderate to severe ARDS. The primary outcome is the number of days alive and off the ventilator at 28 days, considering death as a competing event, and the key secondary outcome is 90 day survival. The planned enrollment is 700 adult participants at 37 French academic and non-academic centers. Safety and long-term outcomes will be evaluated, and biomarker measurements will help better understand mechanisms of action. The trial is funded by the French Ministry of Health, the European Society of Anaesthesiology, and Sedana Medical.
Probabilistic Shaping for Multidimensional Signals with Autoencoder-based End-to-end Learning

This work proposes a system that optimises multidimensional signal transmission, utilising signals with probabilistic shaping designed with the aid of end-to-end learning of an autoencoder-based architecture. For the first time, this work reports bit mapping optimisation for multidimensional signals and applied the newly derived optimised signals to the probabilistic shaping system. The autoencoder employs two neural networks for the transceiver, separated by the embedded channel. The optimisation of the autoencoder configuration is implemented for probabilistic shaping for n-dimensional signals. Specifically, We investigate a 4-dimensional (4D) signal employing 2 successive time slots that has better noise immunity relative to regular 2-dimensional quadrature amplitude modulation (QAM) signals. We propose a new application of autoencoders in communication systems based on 4D signals and apply machine learning to optimise the 4D probabilistic shaping on the basis of receiver signal-to-noise-ratio (SNR). The performance of the optimised probabilistically shaped 4D signals is evaluated in terms of the bit error rate (BER) and mutual information. Simulation results show that the proposed probabilistically shaped 4D signal achieves better BER performance relative to the unshaped 4D and regular 2D QAM. We demonstrate the mutual information of the proposed signal with varying SNR, showing its improved capacity in comparison with other constellations.
Experimental Evaluation of Wake-up Radio Ranges for UAV-assisted Mobile Data Collection

This paper investigates the physical performance of mobile data collection systems comprising Unmanned Aerial Vehicles (UAVs) in conjunction with Wake-up Radio (WuR) technology to minimize the energy consumption of data exchange with Wireless Sensor Network (WSN) nodes. We setup data collection experiments using a quad-rotor drone as the UAV and WuR-enabled motes as the communication nodes. Our experiments are calibrated using tests that measure flight time, communication range and the performance of data collection using WuR compared with that of data collection when the mote duty cycles. We confirm that collection using duty cycling consumes far more power and achieves lower reliability than collection using WuR technology. In our ranging experiments we observe that while the Mobile Data Collector (MDC) is flying at an altitude of approximately 5 m, reliability decreases monotonically with horizontal distance, averaging at 75.4% of all data packets being successfully collected, while latency averages at 27 ms. At an altitude of 10 m, reliability drops considerably to an average of 14.33%, while latency increases with horizontal distance, averaging at 71.16 ms.
Provision of facilities management services in Sri Lankan commercial organisations: Is in-house involvement necessary?

Purpose The purpose of this paper is to explore sourcing strategies for facilities management services using core-competency theory of outsourcing. The aim is to develop a screening framework for deciding the suitability of outsourcing versus in-house delivery for these services based on three levels of managerial functions prevalent in a typical commercial organization. Design/methodology/approach A questionnaire survey was administered for facilities managers in Sri Lanka to investigate the relative importance of these managerial functions for facilities management services and obtain their opinions on the best delivery mode. The managerial functions were derived from a literature review and verified using three semi-structured interviews prior to the questionnaire survey design. Findings The findings showed that facilities management services that are aligned to strategic functions are suitable for in-house delivery, while those that are aligned to tactical and operational functions for outsourcing. Research limitations/implications Further studies should be conducted and extended to other types of organizations beyond commercial ones. Secondly, the quantitative study employed a smaller sample (n = 40), and the survey items were based on the review of literature which was verified using a very small number of interviews (n = 3). Practical implications The proposed framework can be utilized when choosing the best facilities management approach for commercial organizations in developing countries such as Sri Lanka. Originality/value This study contributes to the body of knowledge on the subject of facilities management by exploring the context in Sri Lankan which has not previously been done.
A Grid Connected Three Phase PV Quasi-Z-Source Inverter with Double Frequency Ripple Suppression Control

In a Grid connected three-phase photovoltaic (PV) system, there is double-frequency power mismatch existed between the dc input and ac output. The double-frequency ripple (DFR) energy needs to be buffered by passive network. Otherwise, the ripple energy will flow into the input side and adversely affect the PV energy harvest. In a conventional PV system, electrolytic capacitors are usually used for this purpose due to their high capacitance. However, electrolytic capacitors are considered to be one of the most failure prone components in a PV inverter. In this project, a capacitance reduction control strategy is proposed to buffer the DFR energy in three-phase Z-source/quasi-Z-source inverter applications. Without using any extra hardware components, the proposed control strategy can significantly reduce the capacitance requirement and achieve low input voltage DFR. Consequently, highly reliable film capacitors can be used. The increased switching device voltage stress and power loss due to the proposed control strategy will also be discussed. The proposed system is simulated in MATLAB/Simulink platform.
Attention-Based Cross-Modality Feature Complementation for Multispectral Pedestrian Detection

Multispectral pedestrian detection based on deep learning can provide a robust and accurate detection under different illumination conditions, which has important research significance in safety. In order to reduce the log-average miss rate of the object under different illumination conditions, a new one-stage detector suitable for multispectral pedestrian detection is proposed. First, in order to realize the complementarity between the information flows of the two modalities in the feature extraction stage to reduce the object loss, a low-cost cross-modality feature complementary module (CFCM) is proposed. Second, in order to suppress the background noise in different environments and enhance the semantic information and location information of the object, so as to reduce the error detection of the object, an attention-based feature enhancement fusion module (AFEFM) is proposed. Thirdly, through the feature complementarity of color-thermal image pair and the multi-scale fusion of depth feature layer, the horizontal and vertical multi-dimensional data mining of parallel deep neural network is realized, which provides effective data support for object detection algorithm. Finally, through the reasonable arrangement of proposed modules, a robust multispectral detection framework is proposed. The experimental results on the Korea Advanced Institute of Science and Technology (KAIST) pedestrian benchmark show that the proposed method has the lowest log-average miss rate compared with other state-of-the-art multispectral pedestrian detectors, and has a good balance in speed and accuracy.
A novel analysis method for fault diagnosis of hydro-turbine governing system

The fault diagnosis of hydro-turbine governing system is important to the operation of the hydropower station and the stability of the power grid. In order to improve the diagnostic accuracy and efficiency, a novel fault diagnosis method based on nonlinear output frequency response functions and a novel identification method of nonlinear output frequency response functions have been proposed and applied to the problem of hydro-turbine governing system fault diagnostics. First, the nonlinear model of hydro-turbine governing system is built. And the fault diagnosis principles based on nonlinear output frequency response functions are also introduced. Then, the disadvantages of the traditional identification method are discussed, and a novel identification method is proposed for nonlinear output frequency response functions according to the operation characteristic of hydro-turbine governing system. Finally, simulation verification and experimental studies have been presented to demonstrate the accuracy and efficiency of the proposed fault diagnosis method. The results indicate that the proposed method is simple and practical for fault diagnosis of hydro-turbine governing system.
Performance evaluation of two-stage production systems with time-lag effects: An application in the horticulture industry

In standard data envelopment analysis (DEA), it is assumed that inputs of a specific production period are used to generate outputs of the same period. However, in some practical examples, time-lag effects exist between inputs and outputs. The inputs of one period are used to generate outputs for several periods, or inputs of several periods are used to create outputs for one period. In this paper, we present some new DEA models for performance assessment of network production systems with time-lag effects. An empirical application in the horticulture sector in Iran shows the usefulness and capabilities of our proposed approach.
Re-ECN: A Framework for adding Congestion Accountability to TCP/IP

This document describes the framework to support a new protocol for explicit congestion notification (ECN), termed re-ECN, which can be deployed incrementally around unmodified routers. Re-ECN allows accurate congestion monitoring throughout the network thus enabling the upstream party at any trust boundary in the internetwork to be held responsible for the congestion they cause, or allow to be caused. So, networks can introduce straightforward accountability for congestion and policing mechanisms for incoming traffic from end- customers or from neighbouring network domains. As well as giving the motivation for re-ECN this document also gives examples of mechanisms that can use the protocol to ensure data sources respond correctly to congestion. And it describes example mechanisms that ensure the dominant selfish strategy of both network domains and end- points will be to use the protocol honestly.
Predictive hierarchical beam training with noisy ranging measurements for mmWave vehicular communications

Beam alignment is not only a challenging but also an expensive task for massive multiple-input multiple-output (MIMO) enabled millimeter wave (mmWave) vehicular communications. In this paper, We propose a predictive hierarchical beam training strategy that only uses noisy-ranging measurements. The position and also angular deviation are first predicted based on the noisy ranging measurements. Then the initial searching layer and also the corresponding codewords are derived from the predicted position, the angular deviation of vehicular user equipment (VUE), as well as the ranging error. Simulation results show that even with dynamic scatters and imperfect knowledge of the VUE locations, the proposed strategy can reliably find the optimal beam with greatly reduced training time.
Circulation Of Activity In A Loop Of Model Myocardial Cells

Loops of myocardial cells were modelled as closed continuous cables, using an Hodgkin-Huxley representation of the electrical properties of the membrane. The bifurcation structure of the unidirectional propagation was studied as a function of the radius of the loop. With parameters of the ionic model at their nominal values, period doubling was detected prior to reaching the critical radius where circulation cannot be sustained. Injection of a bias current may modify the bifurcation structure. AS the loop was shortenned transition to an aperiodic regime was detected in a range of bias current intensities. This is related to the presence of an hyperpolarized phase near the end of the action potential.