Miracle-dz/UniFAR-FTUs huggingface dataset:
Cluster Analysis of User Preferences related to MaaS Aspects

Rapid changes are happening in urban transportation, where new mobility services based on mobile applications are becoming popular. Mobility as a Service (MaaS) integrates different transport modes through a mobile application, which includes many aspects of traveling (e.g., routing, booking, ticketing, payment). Previous studies primarily focus on the potential adoption of MaaS, while users’ preferences for MaaS aspects are not well explored. Thus, the paper aims to examine the preferences toward various aspects of MaaS. This study applies a Latent Class Cluster Analysis (LCCA) to investigate the clusters of preferences for MaaS aspects. A binary logistic regression is performed to examine the relationship between the obtained classes, socio-demographic, and travel characteristics. The LCCA results in two clusters of the aspects, i.e., advanced, and traditional clusters. The developed model indicates that females, individuals with high income, and shared-mobility users are more likely to use the advanced aspects. Meanwhile, public transport usage and employment status factors do not have significant relationships with the clustering. This study could be beneficial for transport planners and MaaS operators in designing a suitable MaaS application based on the user preferences.
A Scenario Selection for Tourism Promotions Serious Game Based on Player Expectations

A serious game serves as a marketing tool for tourist spots. Within this tourism-focused serious game, there’s a necessity for a system to choose scenarios of tourist destinations, aiding prospective tourists in finding options that align with their anticipations. In our research, we suggest employing TOPSIS (Technique for Other Reference by Similarity to Ideal Solution) for scenario choice, which takes into account the user’s input on criteria weight evaluation. Decision-making using the dynamic weighted TOPSIS technique considers alternatives, criteria, and criteria weights. Based on input responses to questions concerning criteria in the form of weather, visitor volume, tourist locations, entrance fees, and public amenities, fourteen alternative tourist attractions are processed. Based on what users anticipate from the game, weights are assigned to their criteria, leading to the generation of a game scenario option. The evaluation reveals that the scenario selection mechanism in the tourism promotions serious game achieves a precision rate of 77%.
Research on Intelligent Diagnosis Algorithm for Electrical Faults of Induction Motor Stator and Rotor Based on Coordinate Transformation and Support Vector Machine

As the key power source of train traction system, the safety and reliability of traction asynchronous motor operation is crucial. To improve the efficiency of vehicle maintenance, online monitoring of the working state of traction asynchronous motors has received increasing attention. This paper proposes a diagnosis algorithm based on the combination of coordinate transformation and support vector machine for traction asynchronous motors. Firstly, coordinate transformation can convert power frequency components into direct flow, facilitating the extraction of fault features. Then, from the perspective of data driven, this paper collects historical fault data under varying operating conditions and loads, and designs an intelligent fault diagnosis framework. Finally, a fault recognition classifier is trained based on support vector machines to identify stator inter turn short circuit and rotor bar breakage faults of traction asynchronous motors, and to guide accurate vehicle maintenance.
Integrated Intersection Control for Lane-Free Connected and Automated Driving

Connected and automated driving will not only impact traffic efficiency and safety in existing traffic environments but will also allow for a reorganization of traffic operation in the long-term. Lane-free automated driving allows for more flexible use of the road width, thus opening up new possibilities for intersection control as well. This paper presents an alternative approach to existing control methods by creating a dynamic environment that can later be expanded with selected optimization modules – with the aim of reduced computation times compared to a fully optimized approach. The presented methodology integrates the intersection control directly into a developed lane-free driving behavior based on artificial potential fields, similar to established social force models. Although this first development focuses on high capacity for vehicular traffic, lane-free force based environments have the potential to not only utilize existing urban space more efficiently but may also enable a seamless integration of vulnerable road users or prioritization of certain vehicle types (e.g., public transport). After an introduction to the developed methods, a proof of concept is performed in a customized simulation environment. Identified challenges are discussed and further research is identified.
Mushroom Unit Cell Loaded Triple Band Microstrip Antenna for Sub-6 GHz MIMO and Phased Array Application

In this paper, a mushroom unit-cell loaded mult-iband microstrip antenna sub-array is proposed. Here, the mushroom unit-cell surrounded with a square slot is etched out at the diagonal corner of the square patch antenna to achieve multi-band response (3.21 GHz, 3.42 GHz, 3.68 GHz) at sub-6 GHz frequency band. The proposed technique achieves antenna miniaturization yielding a footprint of 0. 19λ0 × 0. 19λ0, where λ0 is the free space wavelength at 3. 21 GHz. Next, the proposed antenna is used to con figure 2 × 2 sub-array for MIMO / phased array applications. The proposed sub-array achieves satisfactory impedance matching of ≤ −15 dB, interport isolation of ≤ 25 dB, better SLL of ≤ −20 dB, maximum sub-array gain of 7.6 dBi, and 3 dB beam width of 590. Further, the proposed 2 × 2 sub-array demonstrates beam steering up to 800. The proposed sub-array is fabricated and validated by s-parameter measurement.
Comparative Study of Semantic Segmentation Methods in Harbour Infrastructures

Nowadays, the semantic segmentation of the images of the underwater world is crucial, as these results can be used in various applications such as manipulation or one of the most important in the semantic mapping of the environment. In this way, the structure of the scene observed by the robot can be recovered, and at the same time, the robot can identify the class of objects seen and choose the next action during the mission. However, semantic segmentation using cameras in underwater environments is a non-trivial task, as it depends on the quality of the acquired images (which change over time due to various factors), the diversification of objects and structures that can be inspected during the mission, and the quality of the training performed prior to the evaluation, as poor training means an incorrect estimation of the object class or a poor delineation of the object. Therefore, in this paper, a comparative study of suitable modern semantic segmentation algorithms is conducted to determine whether they can be used in underwater scenarios. Nowadays, it is very important to equip the robot with the ability to inspect port facilities, as this scenario is of particular interest due to the large variety of objects and artificial structures, and to know and recognise most of them. For this purpose, the most suitable dataset available online was selected, which is the closest to the intended context. Therefore, several parameters and different conditions were considered to perform a complete evaluation, and some limitations and improvements are described. The SegNet model shows the best overall accuracy, reaching more than 80%, but some classes such as robots and plants degrade the quality of the performance (considering the mean accuracy and the mean IoU metric).
A Markov Chain Monte Carlo (MCMC) Gibbs Sampler Augmented with Zero Forcing Detection for OTFS Reception

The Orthogonal Time Frequency Space (OTFS) is a two-dimensional modulation scheme that operates in the Delay-Doppler domain and is robust in the presence of wireless channels experiencing a high Doppler spread. The OTFS converts a fast-varying doubly-dispersive wireless channel in the time-frequecy domain to a near-static channel in the Delay-Doppler domain. The informative symbols are transmitted in this Delay-Doppler domain with the effect that the transmitted symbols experience a channel fade that changes comparatively slowly with time. In this paper, we propose an enhanced Markov Chain Monte Carlo Markov Chain (MCMC) based Gibbs sampling scheme for the detection of transmitted symbols at the OTFS receiver. A novelty of our proposal is to combine the Zero Forcing Detection (ZFD) with the MCMC-Gibbs algorithm. We show that the proposed joint Gibbs-ZFD method avoids a performance loss that the Gibbs method exhibits at a high signal to noise ratios (SNRs). We conduct a simulation-based evaluation of our proposed approach over a range of the UE (User Equipment) speeds and for Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation schemes. The numerical results show that our proposal offers an improved probability of symbol detection error compared to the two alternative OTFS receivers, namely the Gibbs method and the ZFD.
Dual-Facet Loaded Dual-Polarized Quad-band Dielectric Resonator-Rectenna for RF Energy Harvesting in Smart City Applications

The most widely used approach for replacing traditional batteries and extending battery life is radio frequency energy harvesting (RFEH). However, the power density in the immediate environment is low thus the antenna with multiband features is essential to gather a huge amount of RF power from the low-density atmosphere. This paper presented a multiband dual-polarized dielectric resonator antenna for smart city applications using RFEH technology. The proposed DR antenna covers the frequency bands of WLAN 5.2 GHz, Wi-Fi 5.8 GHz, and future 6 GHz Wi-Fi bands of 6.2 GHz and 6.6 GHz. A rectangular dielectric resonator (DR) is fabricated using a K10 material and it is designed on a low-cost epoxy FR4 substrate (having εr=4.4). A coaxial probe feeding approach is used for excitation purposes. The substrate and DR material dimensions are 66 mm×68.2 mm×1.6 mm and 13.6 mm×11.4 mm×6.35 mm respectively. A spiral metallic coil is stuck on two faces (front and bottom) of a DR material to operate the antenna in the desired band of frequencies. The proposed technique is used to bring down the axial ratio (AR) value below 3dB level in two resonant bands. The maximum gain of 6.72 dB is observed experimentally at 5.2 GHz frequency.
A multi-scale and multi-domain digital twin modeling method for workshop equipment

In order to satisfy the requirements of smart manufacturing, realizing the convergence of the workshop cyber-physical spaces, so as to further achieve the intelligent control of the workshop operation, is a vital and challenging task. Digital twin (DT) is recognized as the core technology for achieving Cyber-Physical Systems (CPS). However, because of the complex coupling characteristics of the workshop equipment, the construction of its DT model is still a significant challenge, which hinders the development of CPS in the workshop. This paper proposed a multi-scale and multi-domain DT model construction method for the workshop equipment. Firstly, a multi-domain unified modeling language Modelica is proposed to realize the construction of workshop equipment DT model. Then, the specific process of digital twin model construction for the workshop equipment is explored. Furthermore, we proposed the digital twin workshop implementation framework. Finally, a case study of digital twin workshop system is represented to validate the feasibility of our proposed approach.
Investigation of a Transformer Connected Power Amplifier for Grid Emulation

Power grid emulators are used extensively for the testing and validation of grid-tied applications. Currently, power amplifiers are extensively used as grid emulators because of their higher bandwidth, compact size, and ease of control. Usually, amplifiers are designed for specific output voltage ratings. For an application requiring a higher voltage level, the output voltage of the grid emulator system can be increased by adding a step-up transformer at the amplifier output terminals. However, the conjunction of the transformer at the output of the amplifier will affect the amplifier and the transformer characteristics. Thus, it is important to investigate and analyze the effect of the amplifier transformer interconnection. This paper studies the transformer connected four quadrant power amplifier for grid emulation. The proposed work characterizes the amplifier and transformer in different operating conditions considering the influence of DC offset, THD level, transformer thermal behavior, amplifier control bandwidth, and the system startup performance. The proposed analyses have been tested and validated experimentally through a 5 kW grid emulator, while it is connected to a step-up transformer.
Trainable dynamical estimation of above-surface wind speed using underwater passive acoustics

Covering more than 70% of Earth surface, oceans play a key role in climate regulation, are the main medium of world commercial trade and are a source of renewable energy, to cite few aspects. Despite its importance, ocean surface state reconstruction poses some challenges, due to its non-linear behavior and the heterogeneity of the spatio-temporal scales involved. State-of-the-art techniques for forecast and prediction involve numerical weather models, such as data assimilation approaches. Besides, remote sensing techniques deliver finer-grained information about the surface state. Among others, underwater passive acoustics uses the underwater soundscape to infer the above-surface atmospheric state. In this work, with a particular focus on the surface wind speed reconstruction, we propose a framework that bridges data assimilation and machine learning schemes, to exploit both the prior physical knowledge and the capability of machine learning modelling to take advantage of large data bases. Extensive numerical experiments show that this hybrid framework can outperform the state-of-the-art data-driven models with a relative gain up to 16% in terms of root mean squared error. Experiments also involve tests on multi-modal data, namely underwater passive acoustics and wind speed reanalyses, giving promising results.
Changes in teachers’ world of work in a developing context: The case of Solomon Islands

This study explores the perceived magnitude of changes that have occurred in teachers’ world of work in the Solomon Islands. The survey-based inquiry consisting of both structured and open-ended questions was administered to a cohort of 38 teachers undertaking a flexi-school run by the University of the South Pacific at the Honiara Campus in Solomon Islands. The findings of the study indicate that most of the changes were of considerable magnitude. In terms of changes that teachers would like to see in future, most of them indicate the need for more professional development opportunities to enable them to manage and cope better with changes in their work. The other change they would like to see is the use of ICT in all schools. These changes have implications for the continuous professional development of teachers to meet the new demands of work so that they are better able to promote and maximise children’s learning experiences and outcomes.
Climate vulnerability : understanding and addressing threats to essential resources

Climate change has been the subject of thousands of books and magazines, scientific journals, and newspaper articles daily. It's a subject that can be very political and emotional, often blurring the lines between fact and fiction. The vast majority of research, studies, projections and recommendations tend to focus on the human influence on climate change and global warming as the result of CO2 emissions, often to the exclusion of other threats that include population growth and the stress placed on energy sources due to emerging global affluence. "Climate Vulnerability" seeks to strip away the politics and emotion that surround climate change and will assess the broad range of threats using the bottom up approach - including CO2 emissions, population growth, emerging affluence, and many others - to our five most critical resources: water, food, ecosystems, energy, and human health. Inclusively determining what these threats are while seeking preventive measures and adaptations is at the heart of this unique reference work. Takes a Bottom-Up approach, addressing climate change and the threat to our key resources at the local level first and globally second, providing a more accurate and inclusive approach. It includes extensive cross-referencing, which is key to readers as new connections between factors can be discovered. Cuts across a number of disciplines and will appeal to Biological Science, Earth & Environmental Science, Ecology, and Social Science, comprehensively addressing climate change and other threats to our key resources from multiple perspectives.
Using Deep Neural Networks to Detect and Track Fish in Underwater Video Sequences*

A crucial task in marine research is that of monitoring fish in order to quantify different species and analyze their behavior. Computer Vision clearly helps in solving this problem, especially when combined with Deep Learning. This paper focuses on the use of Deep Learning and Computer Vision to perform fish detection and tracking. More specifically, in this study we survey different state-of-the-art object detectors, we adapt and train them to perform fish detection, and we evaluate and compare them by means of a large underwater video sequence involving fish in their natural habitat. The experimental results, provided in terms of detection quality and time consuption, will help researchers to choose the appropriate object detection model depending on the pursued goals.
MULTI-AGENT REINFORCEMENT LEARNING

Agents cannot make sense of many-agent societies through direct consideration of small-scale, low-level agent identities, but instead must recognize emergent collective identities. Here, we take a first step towards a framework for recognizing this structure in large groups of low-level agents so that they can be modeled as a much smaller number of high-level agents—a process that we call agent abstraction. We illustrate this process by extending bisimulation metrics for state abstraction in reinforcement learning to the setting of multi-agent reinforcement learning and analyze a straightforward, if crude, abstraction based on experienced joint actions. It addresses non-stationarity due to other learning agents by improving minimax regret by a intuitive factor. To test if this compression factor provides signal for higher-level agency, we applied it to a large dataset of human play of the popular social dilemma game Diplomacy. We find that it correlates strongly with the degree of ground-truth abstraction of low-level units into the human players.
Laser Beam Control Method Based on Model Prediction and Sliding-Mode Dual Closed-Loop Controller

To address the problem that the electrostatic Micro-Electro-Mechanical-System (MEMS) micromirror cannot control the accurate beam deflection due to the torsion beam elastic deformation, mirror surface unevenness and residual vibration in the process of achieving laser beam deflection, a laser beam control method based on model prediction and sliding mode dual closed-loop controller is proposed to achieve fast, accurate and stable laser beam deflection by electrostatic MEMS micromirror. By analyzing the working principle of electrostatic MEMS micromirror, constructing the system model of electrostatic MEMS micromirror, designing the model prediction and sliding-mode dual closed-loop controller, which uses the sliding-mode control law to improve the model prediction controller, using the sliding-mode controller to compensate the controller amount of the model prediction controller, improving the control accuracy and response time of the system, enhancing the robustness of the system, and improving the effectiveness of this control algorithm is verified by step response simulation, tracking performance simulation and robustness simulation. The results show that the dual closed-loop controller has high control accuracy, fast response, good tracking performance and robustness.
Design and Analysis of Microstrip Dipole Antenna Stacked on a PV Panel for Dual-Energy Harvesting

The proposed work demonstrates a simulation-based study on implementation of dual-energy harvesting system using solar cells and antenna. To study and analyze the feasibility initially the design of a simple planar dipole antenna on a FR4 substrate of 80 x 40 x 1.6 mm3 was done. Later the single element antenna was intended use in conjunction with a solar cell. The resultant structure will be the solar cell will be on the top side in order to capture the solar energy and the antenna will be backside of the solar cell. The sandwich structural elements during integration of solar cell with antenna are also considered during simulation in order to analyze the effects on return loss and resonant frequencies of the antenna due to the stacked layers involved (Epoxy/Kevlar, Silicon and FR4 Substrates). The array implementations were done in 1x2, 1x4 and 2x2 configuration in order to increase the gain of the antenna so as to capture the EM energy in the respective bands across 4.1 GHz, 6.1 GHz and 7.6 GHz respectively. The simulation results proves that the antenna has good gain of 5.74 dB at 7.6 GHz (in 1x2 array configuration), 5.63 dB at 7.63 GHz (in 2x2 array configuration) and 4.67 dB at 7.42 GHz band (in 1x4 array configuration) makes the antenna suitable for operating in C-band and thus able to harvest the EM energy along with the solar energy harvesting system in the same structure.
Corner detection and curve representation by circular histograms of contour chain code

A new algorithm is proposed for representing and characterising the contours of 2D objects, as well as detecting their corners. The novelty lies in the curvature estimation technique which is based on circular histograms of the contour chain code. Two histograms are calculated at the neighbourhood of every point, and instead of considering them of linear type and being compared by correlation, they are considered to be circular and compared by a new method which defines a circular mean of variable origin. The main advantages of this method are its low computational overhead and the precision of the obtained curvature functions.
Optimization of Road Detection Using Pruning Method for Deep Neural Network

Road detection is essential for autonomous driving and road maintenance. However, the complexity of Deep Neural Networks (DNN) used for this task poses challenges for resource-constrained platforms like edge TPUs. To address this, we propose a pruning-based optimization technique in TensorFlow. Pruning selectively removes unnecessary parameters from the network, reducing its size without compromising accuracy. In our experiment, We asses the effect of pruning on road detection performance by utilizing the Fast R-CNN MobileNet V2 model. The results show that pruning significantly reduces parameters without sacrificing accuracy. We observed that the pruning-based optimization technique not only reduces the parameters of the network but also minimizes loss values. This indicates that the pruned model is able to maintain a high level of accuracy while producing more efficient representations of the road features. By reducing loss values, the pruned model demonstrates improved robustness and precision in detecting road conditions.