Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Deformation of Earth's Crust Caused by Man-made Surface Unloading

An anomalous crustal uplift was observed at the west coast of Boso Peninsula. The crustal uplift began at the same time as the mining of mountain rock which began about ten years ago. It seems that the crustal uplift is the result of the elastic recovery deformation of the earth's crust caused by the man-made surface unloading. An elastic modulus of the uppermost earth's crust estimated by the total amounts of crustal uplift and mined rock is about 1-2X1011dyne/cm2.
Capacity Evaluation of Hybrid TDMA-NOMA for High Altitude Platform Systems

The integration of Non-Terrestrial Networks (NTN) with 5th generation (5G) wireless technology is a topic of exploration by the 3rd generation partnership project (3GPP). High Altitude Platform Station (HAPS) is part of NTN which works at an altitude of 20 km above the earth's surface. Technically, HAPS will have a similar role to Base Transceiver Station (BTS). To be able to integrate HAPS and 5G, it is necessary to support multiple access techniques that are in accordance with 5G specifications. Non-Orthogonal Multiple Access (NOMA) is one of the multiple access options that is considered to meet the specifications of 5G. NOMA uses the principle of superposition coding by dividing each user based on the available power allocation. To decipher the information at the receiver, the successive interference cancellation (SIC) technique is still the favorite. NOMA is not a perfect technique, there are parts that need to be further developed to improve its performance. One part that is still being discussed is SIC. The process of parsing information using SIC will take more time when experiencing an increase in users. In addition, the potential for misreading the information will also result in accumulative errors for other users. In order to improve the performance of traditional NOMA, this paper will use a combination of TDMA-NOMA techniques to limit users sharing power allocation. The TDMA timeslot will limit NOMA pairs so the SIC process will not be more complex. As an illustration, the simulation shown in the form of a summary graph of each technique NOMA, TDMA-NOMA, and OMA which leads to the conclusion that TDMA-NOMA shows a superior performance to other techniques.
Distributed access and offloading scheme for multiple UAVs assisted MEC networks

Unmanned aerial vehicles (UAVs) have improved the capacity and coverage of wireless networks. Mobile edge computing (MEC) has provided substantial computation capability to user equipment (UEs). The integration of UAV and MEC can take advantages of both to provide flexible computation service. In UAV assisted MEC networks, delay and energy consumption are two main concerns, which are conflicting to a certain extent. This paper investigates delay and energy consumption jointly in a multiple UAVs assisted MEC network. A cost function is defined to balance the delay and the energy consumption. The user access, task offloading, and computational resource allocation are jointly considered to minimize the long-term cost. To tackle this difficult problem, we formulate the long-term problem into sequential decision problem and treat all UEs as intelligent agents. Each UE decides its access UAV, task offloading proportion, and required edge computation resource to minimize the its own cost. Moreover, the optimal task offloading proportion and required computation resource can be obtained in closed-form given user access so that the action space can be significantly reduced. Then, an adversarial multi-armed bandit based algorithm is employed at each UE and a distributed scheme is proposed to solve the joint optimization problem. Simulation results validate the effectiveness and robustness of the distributed scheme and show its superiority to benchmarks.
Regional Bowen ratio controls on afternoon moist convection: A large eddy simulation study

This study examines the effect of the regional Bowen ratio β, the ratio of the domain‐averaged surface sensible heat flux (SHF) to latent heat flux (LHF), on afternoon moist convection. With a temporally evolving but spatially uniform surface available energy over a mesoscale domain under a weak capping inversion, we run large eddy simulation of the afternoon convective boundary layer (CBL). We first prescribe a small β of 0.56 (a wet surface) and then the reversed large β of 1.80 (a dry surface) by switching the SHF and LHF fields. The perturbation fields of the fluxes are prescribed with the Fourier spectra of κ− 3 (κ is horizontal wave number; strong mesoscale heterogeneity) and κ0 (homogeneity). The large β cases have strong vertical buoyancy fluxes and produce more vigorous updrafts. In the heterogeneous, large β surface case, with the removal of convective inhibition over a mesoscale subdomain of large SHF, deep convection develops. In the heterogeneous, small β surface case, convective clouds develop but do not progress into precipitating convection. In the homogeneous surface cases, randomly distributed shallow clouds develop with significantly more and thicker clouds in the large β case. (Co)spectral analyses confirm the more vigorous turbulent thermals in the large β cases and reveal that the moisture advection by the surface heterogeneity‐induced mesoscale flows makes the correlation between mesoscale temperature and moisture perturbations change from negative to positive, which facilitates the mesoscale pool of high relative humidity air just above the CBL top, a necessary condition for deep convection.
Are VANETs pseudonyms effective? An experimental evaluation of pseudonym tracking in adversarial scenario

With the increasing adoption of Vehicular Ad Hoc Networks (VANETs) for the development of Cooperative Intelligent Transportation Systems (C-ITS) many concerns regarding privacy and anonymity in VANETs have been raised by security researchers and practitioners, highlighting the need for effective mechanisms to protect sensitive information exchanged by connected vehicles. One of the first concerns is related to the vehicle’s identifier, a field contained in the messages sent from the vehicle and that can be used to track the vehicle across the infrastructure, with consequent severe implications on the privacy of the driver. Consequently, VANET communications leverage short-lived pseudonyms instead of persistent vehicle’s identifiers, aiming to enhance the privacy of the vehicle. Pseudonym change schemes proposed in the literature are effective in masking the real sender of a given message, but they do not guarantee privacy against attackers that can monitor and correlate multiple messages among themselves. This paper evaluates 5 different pseudonym change mechanisms against a realistic threat model. Our results demonstrate that it is possible for a realistic attacker to reliably track multiple vehicles, with minor differences across different pseudonym change schemes.
Improving Human-Robot Interaction Effectiveness in Human-Robot Collaborative Object Transportation Using Force Prediction

In this work, we analyse the use of a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We check that this force prediction can improve multiple parameters associated with effective Human-Robot Interaction (HRI) such as perception of the robot's contribution to the task, comfort or trust in the robot in a physical Human Robot Interaction (pHRI). We present a Deep Learning model that allows to predict the force that a human will exert in the next 1 $s$ using as inputs the force previously exerted by the human, the robot's velocity and environment information obtained from the robot's LiDAR. Its success rate is up to 92.3% in testset and up to 89.1 % in real experiments. We demonstrate that this force prediction, in addition to being able to be used directly to detect changes in the human's intention, can be processed to obtain an estimate of the human's desired trajectory. We have validated this approach with a user study involving 18 volunteers.
Model Sensemaking Strategies: Exploiting Meta-Model Patterns to Understand Large Models

The increasing popularity of model-based and low-code platforms has raised the need to understand large models – especially in industrial settings. However, current approaches mainly rely on graph-based visual metaphors, which do not scale well with large model sizes. To address this issue, we introduce model sensemaking strategies: purposeful model visualisations based on alternative visual metaphors. We define them as reusable patterns that yield tailored visualisations when applied to meta-models. This paper presents a catalogue of domain-specific and domain-agnostic sensemaking strategies, and a recommender that suggests suitable strategies for a given meta-model. To showcase the framework’s applicability, we have implemented some of these strategies in Dandelion, an industrial, low-code graphical language workbench for the cloud. Using this platform, we have evaluated the effectiveness of the strategies to visualise large industrial models by the UGROUND company.
A Highly Maneuverable Flying Squirrel Drone with Controllable Foldable Wings

Typical drones with multi rotors are generally less maneuverable due to unidirectional thrust, which may be unfavorable to agile flight in very narrow and confined spaces. This paper suggests a new bio-inspired drone that is empowered with high maneuverability in a lightweight and easy-to-carry way. The proposed flying squirrel inspired drone has controllable foldable wings to cover a wider range of flight attitudes and provide more maneuverable flight capability with stable tracking performance. The wings of a drone are fabricated with silicone membranes and sophisticatedly controlled by reinforcement learning based on human-demonstrated data. Specially, such learning based wing control serves to capture even the complex aerodynamics that are often impossible to model mathematically. It is shown through experiment that the proposed flying squirrel drone intentionally induces aerodynamic drag and hence provides the desired additional repulsive force even under saturated mechanical thrust. This work is very meaningful in demonstrating the potential of biomimicry and machine learning for realizing an animal-like agile drone.
Predictive Shared Control of Robotic Arms Using Simulated Brain-Computer Interface Inputs

Low decoding accuracy makes brain-computer interface (BCI) control of a robotic arm difficult. Shared control (SC) can overcome limitations of a BCI by leveraging external sensor data and generating commands to assist the user. Our study explored whether reaching targets with a robot end-effector was easier using SC rather than direct control (DC). We simulated a motor imagery BCI using a joystick with noise introduced to explicitly control interface accuracy to be 65% or 79%. Compared to DC, our prediction-based implementation of SC led to a significant reduction in the trajectory length of successful reaches for 4 (3) out of 5 targets using the 65% (79%) accurate interface, with failure rates being equivalent to DC for 2 (1) out of 5 targets. Therefore, this implementation of SC is likely to improve reaching efficiency but at the cost of more failures. Additionally, the NASA Task Load Index results suggest SC reduced user workload.Clinical relevance—Shared control can minimise the impact of BCI decoder errors on robot motion, making robotic arm control using noninvasive BCIs more viable
P4SQA: A P4 Switch-Based QoS Assurance Mechanism for SDN

Data center networks include a wide variety of service categories, which leads to a complex network topology. As a result, the QoS of different service flows cannot be guaranteed. In this paper, we propose a QoS assurance mechanism based on P4 switches (P4SQA) to make full use of network resources. P4SQA includes two functions: in-network traffic classification and in-network queue management. To reduce the communication overhead and computational pressure on the Software Defined Network (SDN) controller, we offload part of the neural computation from the control plane to the data plane. A fully connected neural network-based traffic classification method is deployed in the programmable data plane to classify different traffic types. In addition, the optimized active queue management algorithm CoDeL is integrated into the P4 switch (PCoDeL) to prevent network congestion while guaranteeing QoS more effectively. The evaluation results show that traffic classification accuracy has improved, especially in terms of the F1_score, which reached 0.97, significantly better than other state-of-the-art methods. Moreover, compared to traditional queue management algorithms, PCoDeL can improve network throughput and reduce transmission delay, which verifies the effectiveness and superiority of P4SQA in ensuring QoS.
Finite Element Model of Giant Magnetoresistance (GMR) Based Sensors for Microfluidic Applications

The design of novel Lab-on-Chip (LOC) devices for Point-of-Care Testing (POCT) demands the development of multiphysics-based models taking into account the different involved phenomena, in a global approach. We are here proposing a finite element method (FEM, based on COMSOL) model of a magnetic microparticle-tracking device. The considered device includes a giant magneto resistance (GMR) sensor composed of a linear array of 8xl elements, attached to a microfluidic channel. The model takes into account the magnetic field generated by the particles, their position in the channel, and the magneto resistance phenomena induced in the sensing elements. Preliminary results using a millimeter-scaled 3D printed fluidic channel and spherical magnets are presented, so demonstrating the proper performance of the model.
Word Embeddings for Model-Driven Engineering

Model-Driven Engineering practitioners have to deal with the construction of modelling evironments by devising meta-models, grammars, editors, etc. One of the goals of the application of Machine Learning to MDE is to use ML algorithms to assist the MDE expert in these tasks. These algorithms cannot directly receive raw models or meta-models as input, but they typically have to be transformed into a numeric representation, i.e., a vector. In this context, a common approach is to use pre-trained Word Embeddings, which define mapping functions that associate words to semantic vectors. However, current word embeddings are trained with general texts and lack the technical words which typically arise in the modelling domain. To tackle this issue, we have collected a corpus of modelling texts from well-known modelling venues, and we have trained two types of word embedding models. The resulting embeddings (named WordE4MDE) are specialised to address ML tasks in the MDE domain. We have performed an extensive evaluation using the Ecore models of the ModelSet dataset and two state-of-the-art word embeddings (GloVe and Word2Vec) as baselines. We show that WordE4MDE outperforms these two baselines in three meta-modelling tasks, namely meta-model classification, meta-model clustering, and meta-model concept recommendation. WordE4MDE embeddings are available at https://github.com/models-lab/worde4mde and can be loaded using standard Python libraries for their use in ML pipelines.
Smart Meter Fault Diagnosis based on Directional Gradient KNN

This paper presents a novel approach to enhance the effectiveness and precision of smart meter fault diagnosis by utilizing the K-nearest neighbor (KNN) algorithm. Initially, the standard fault monitoring data of smart meters, comprising forward active energy data and reverse active energy data, are collected. Then, the diagnostic data is segregated based on the proposed directional function. The gradient of the data change is computed and the sensitive features of the data are extracted. Subsequently, the sensitive feature parameters are employed to construct the forward K-nearest neighbor algorithm model and the reverse K-nearest neighbor algorithm model of the data, respectively, for the purpose of fault diagnosis of smart meters. By devising a directional function and extracting gradient features, a novel fault diagnosis model is established, which enhances the diagnostic efficiency and accuracy of the model.
Locomotion Planning of a Truss Robot on Irregular Terrain

This paper proposes a new locomotion algorithm for truss robots on irregular terrain, in particular, for the Variable Topology Truss (VTT) system. The previous Polygon-based Random Tree (PRT) search algorithm for support polygon generation is extended to irregular terrain while considering friction and internal force limitations. By characterizing terrain, unreachable areas are excluded from search to increase efficiency. A one-step rolling motion primitive is generated based on the kinematics, statics, and constraints of VTT. The locomotion planning is completed by transforming and connecting multiple motion primitives with respect to the desired support polygons. The algorithm's performance is verified by conducting simulations in multiple types of environments.
MmWave Multi-beam V2X with Fountain Code for Joint Ultra-Broadband, Reliable, and Low Latency Communication

We propose to combine multi-beam transmission with fountain code toward full automated driving. The proposed scheme consider a scenario where a vehicle with multiple antenna communicate with BS with multiple antenna using multi-beam. In our proposed scheme, data encoded by fountain encoder are transmitted over multi-beam. Multi-beam transmission can improve datarate due to spatial multiplexing. Fountain code is expected to reduce latency due to no need for retransmission. Moreover, the combination of fountain code and multi-beam transmission is expected to efficiently add redundancy efficiently in spatial domain, thus improve the system’s reliability in the presence of random blockage. In this paper, the reliability of our proposed scheme against different required latencies in comparison with other conventional schemes is analysed. Furthermore, we propose a beam-aware beam selection method taking into account the information about blockage probability. Numerical results show that the proposed scheme can achieve higher reliability and lower latency in blockage environment.
Predictive Modeling Of Alzheimer’s Disease Prognosis Using Anatomical & Diffusion MRI

Mild cognitive impairment (MCI) is an intermediate stage between healthy aging and Alzheimer’s disease (AD), and AD is a progressive neurodegenerative disorder that affects around 50 million people worldwide. As new AD treatments begin to be developed, one key goal of AD research is to predict which individuals with MCI are most likely to progress to AD over a given interval (such as 2 years); if successful, these individuals could be preferentially enrolled in drug trials that aim to slow AD progression. Here we benchmarked a range of MCI-to-AD predictive models including linear regressions, support vector machines, and random forests, using predictors from anatomical and diffusion-weighted brain MRI, age, sex, APOE genotype and standardized clinical scores. In evaluations on 2,448 subjects (1,132 MCI, 883 healthy controls, 433 with dementia) from the ADNI study, models including PCA-compacted features achieved a balanced accuracy of 75.3% (using cortical features) and 89.7% using diffusion MRI measures on test set, suggesting the added prognostic value of microstructural metrics obtainable with diffusion MRI.
GIGN: Learning Graph-in-graph Representations of EEG Signals for Continuous Emotion Recognition

Effectively learning the spatial topology information of EEG channels as well as the temporal contextual information underlying emotions is crucial for EEG emotion regression tasks. In this paper, we represent EEG signals as spatial graphs in a temporal graph (SGTG). A graph-in-graph neural network (GIGN) is proposed to learn the spatial-temporal information from the proposed SGTG for continuous EEG emotion recognition. A spatial graph neural network (GCN) with a learnable adjacency matrix is utilized to capture the dynamical relations among EEG channels. To learn the temporal contextual information, we propose to use GCN to combine the short-time emotional states of each spatial graph embeddings with the help of a learnable adjacency matrix. Experiments on a public dataset, MAHNOB-HCI, show the proposed GIGN achieves better regression results than recently published methods for the same task. The code of GIGN is available at: https://github.com/yi-ding-cs/GIGN
A Survey of Service Function Chain Orchestration Based on Neural Network

With the rapid development of Internet of Things, edge computing, SDN/NFV and other network communication technologies, the demand for dynamic service function chain orchestration is increasing. Because the machine learning method based on neural network can flexibly predict and select the next optimal action according to the existing data model, the algorithm based on neural network has been more and more applied in the dynamic orchestration of service function chain in recent years. This paper analyzes the current research situation of service function chain orchestration algorithm based on neural network from two aspects of algorithm types and experimental simulation results, and puts forward the existing problems of service function chain orchestration algorithm based on neural network and the direction of further research and expansion in the future.
Analyzing Tidal, Diurnal, Synoptic, and Seasonal Drivers of Acoustic Telemetry Efficiency on a Coastal Reef

Acoustic telemetry is a useful research technique, however analyzing the results requires accounting for variation in the detection efficiency. This efficiency and the estimated detection range are dynamic and change on different scales due to environmental variation such as nightly increases in noise, tidal and wind forcing on the environment, and seasonal differences. Instruments were moored on a shallow coastal reef for almost a full year, transmitting while also collecting noise and temperature data, allowing for the pairing of measured detection efficiency with environmental variables. Daily average detection efficiencies are minimal when parallel tidal currents are strongest and highest at high and low tide (lowest currents); sunset brings with it the highest noise and the least detection efficiency; on synoptic (7–10) timescales, high winds appear to be correlated with far more detections, and successful detections occurred far less often during hours with the weakest winds; seasonal differences can be seen as a combination of the other frequencies, warmer water columns with more noise interference leading to less detection efficiency. These measured differences in detection efficiency can be used to estimate detection probability in other situations.