Miracle-dz/UniFAR-FTUs huggingface dataset:
FORMATION OF EXTRAORDINARILY HIGH WAVES IN SPACE AND TIME

The existence of freak waves is indisputable due to observations, registrations, and severe accidents. The occurrence of extreme waves, their characteristics and their impact on offshore structures is one of the main topics of the ocean engineering research community. Real sea measurements play a major role for the complete understanding of this phenomenon. In the majority of cases only single point registrations of real sea measurements are available which hinders to draw conclusions on the formation process and spatial development in front of and behind the respective registration points. One famous freak wave is the “New Year Wave”, recorded in the North Sea at the Draupner jacket platform on January 1st, 1995. This wave has been reproduced in a large wave tank and measured at different locations, in a range from 2163 m (full scale) ahead of to 1470 m behind the target position — 520 registrations altogether. Former investigations of the test results reveal freak waves occurring at three different positions in the wave tank and these extreme waves are developing mainly from a wave group. The possible physical mechanisms of the sudden occurrence of exceptionally high waves have already been identified — superposition of (nonlinear) component waves and/or modulation instability (wave-current interaction can be excluded in the wave tank). This paper presents experimental and numerical investigations on the formation process of extraordinarily high waves. The objective is to gain a deeper understanding on the formation process of freak waves in intermediate water depth such as at the location of the Draupner jacket platform where the “New Year Wave” occurred. The paper deals with the propagation of large amplitude breathers. It is shown that the mechanism of modulation instability also leads to extraordinarily high waves in limited water depth. Thereby different carrier wave length and steepnesses are systematically investigated to obtain conclusions on the influence of the water depth on the modulation instability and are accompanied by numerical simulations using a nonlinear potential solver.Copyright © 2011 by ASME
Investigation of Unsteady Vehicle Aerodynamics under Time-Dependent Flow Conditions

ow conditions aecting driving stability. Based on experimental and numerical simulations it is shown that the wake ow of a vehicle being subjected to constant crosswind is characterized by a region of high velocity at the leeward side and two counter-rotating vortices establishing above and below that region. This structure reacts with a time delay to a change of the oncoming ow. The delayed response of the wake ow causes phase shifted pressure uctuations at the rear side of the vehicle and thus causes an unsteady increase of the yaw moment and decrease of side force and roll moment. Numerical and experimental results both capture the unsteady phenomena occurring at transient oncoming ow conditions. However, the quantitative estimation of the pressure amplitude at the rear side diers, which alters the ratio of unsteady to quasi-steady loads as well as their phase lag.
Can Justice be a measurable value for AI? Proposed evaluation of the relationship between NLP models and principles of Justice

NLP models, such as chat-based generative ones, have gained widespread use and influence in various fields. Considering AI as a valuable resource, it seems critical to determine the ethical principles to which these models should adhere to be considered usable. Applying the philosophical concept of justice to the evaluation of NLP models can help improve their functioning, performance, and overall ethical implications. Although strict adherence to traditional codes of justice may not be sufficient, it is proposed that the concept be adapted to evaluate the performance of NLP systems. Creating a rating scale based on these values may help estimate the "amount of Justice" or fairness demonstrated by different NLP systems. By emphasizing the importance of certain values central to assessing the fairness of NLP models, it becomes possible to develop parameters for evaluation. As these models become increasingly pervasive, this research can help improve this models’ performance and their socio-cultural significance. The awareness of building more equitable technologies raises consciousness about the potential consequences of irresponsible use of these models, highlighting the significance of ethical considerations. This approach can lead to a more comprehensive understanding of these systems, promoting their improvement and fostering greater knowledge of the ethical implications associated with their functioning.
Journal Pre-proof An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

: The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO 2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO 2 emissions in the UK’s transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK’s transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO 2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment.
An Optimized Fuzzy Adaptive Distributed Secondary Controller for Micro Grids

In this research paper, an optimized fuzzy adaptive distributed secondary controller is proposed to address frequency deviations in Micro Grids (MG) caused by the primary controller. The performance of the secondary controller has been improved by optimizing the parameters of the fuzzy logic controller (FLC) using a combined genetic algorithm (GA) and particle swarm optimization (PSO). The proposed FLC uses the frequency variation and its derivative as inputs and the parameter of the secondary controller as the output. In this paper, the range of changes in fuzzy rules and fuzzy membership is defined as an optimization problem and optimized by the proposed algorithm. The effectiveness of the suggested controller is evaluated by conducting simulations with Simulink. Multiple scenarios are simulated, and the results indicate that the optimized secondary controller successfully reduces both the frequency overshoot and response time.
Distributed Energy Resources and Fast Charging Stations Allocation for Enhanced System Operation and Charging Infrastructure Coverage

The growing insertion of Electric Vehicles (EVs) in the distribution system is a promising solution to mitigate the greenhouse emission issue. However, as the number of EVs significantly increases, the excessive charging demand of Fast Charging Stations (FCSs) may lead to instability problems in the distribution network. Therefore, this paper proposes a multi-objective optimization approach for the allocation and sizing of Fast Charging Stations (FCSs) with the objective of minimizing their installation and operation costs, while maximizing the coverage for EVs. It is well-known that the integration of EVs results in increased power losses. As a consequence, in this study, an optimized planning of Distributed Energy Resources (DERs) was also performed with the purpose of mitigating this issue. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the 33-bus test feeder and 25-node transportation network, considering commercial and residential areas. Based on the solution selected by the Fuzzy Decision-Making method, it was possible to minimize the voltage deviation even with the insertion of the FCSs in the system.
Comparative Analysis of Wireless Transmission Methods for Firefighting Communication in Challenging Indoor Environments

The demand for firefighting has significantly risen in recent decades, accompanied by increased risks faced by firefighters. Tragic incidents, such as the Shanghai factory fires, have resulted in the loss of over thirty firefighter lives. One of the primary contributing factors is the abrupt breakdown in communication between firefighters inside a building and the commanding officer stationed outside, attributable to the harsh and complex indoor environment. This study aims to conduct a comparative analysis of different widely used wireless transmission methods, including Wi-Fi, Bluetooth Low Energy (BLE), and Long Range (LoRa). The experiments are conducted in a two-room setup, with two brick walls acting as a barrier. A wireless data transmitter is placed in one room, while smoke is generated. A receiver placed at varying distances collects the signal strengths. The findings indicate that LoRa exhibits the least drop in signal strength compared to the other methods. In contrast, BLE shows high signal strength variation for the same distances and is not recommended for firefighting communication purposes. This study provides valuable insights for selecting suitable wireless communication modules, particularly in the design of wearable devices for assessing safety risks faced by firefighters.
An Edge Intelligent Device for Advanced Monitoring and Control of DER Inverters in a High Penetration Distribution System

As the electric power grid is brought up to date with state-of-the-art devices connected to the internet, pertinent information needs to reliably make its way from the edge to the cloud for system operators to visualize grid health. Conversely, control commands also need to be sent from the cloud to individual devices on the grid in order to maintain grid stability and safety, especially during unbalanced cases. This paper presents an affordable, brand-agnostic solution to grid data aggregation and communication using off-the-shelf products pieced together with IEC 61131–3 open source language for Programmable Logic Controllers (PLCs). A Phoenix Contact PLC along with some ancillary devices used to provide wireless communication capabilities were used to build an Edge Intelligent Device (EID) which interfaces with edge devices like solar inverters using Modbus protocol and the cloud using MQTT with an LTE backend for internet connection. The advantage of the EID over other similar devices is affordability and it is highly customizable. The EID was tested in both simulated and real-world applications: over 750 solar inverters were simulated in OPAL-RT and their data were transmitted to the cloud and visualized with Grafana in intervals under 30 seconds; data from a field testbed generating real data from solar panels and inverters, as well as another inverter built in-house powered by a grid simulator, was also transmitted through the EID. The paper will describe how the EID was built and coded, how the test beds were created, and show the results in Grafana.
From 2D TCAD Model to a Physical 1D Equivalent Circuit Model for a GaN HEMT

This paper presents a physics-based one-dimensional model calibrated from two-dimensional technology computer-aided design (TCAD) simulations for AlGaN/GaN HEMT devices. The proposed model formulation is based on the integration of the Poisson equation along the transversal direction of the channel current, and assumes that the diffusion current in the continuity equation (drift-diffusion model) can be neglected. The model was implemented in a commercial circuit simulator by discretizing the Poisson and transport equations. The model is able to predict all important physical characteristics of the HEMT along its longitudinal direction, making it very useful for understanding the device operation and thus providing valuable information for the derivation of accurate equivalent circuit models. The model predictions are compared with similar TCAD simulations.
Improvement in QCN with BIER for Chassis Topology

In the present era, networking has become an essential requirement as individuals seek interconnectivity and efficient means to share data rapidly. When discussing the swift exchange of information, a network characterized by high speed and minimal congestion is favored by all. In networking, Switches play a very important role to transfer packets of information from source to destination, as routers do in any network topology. For switches to exchange data from the source port of one switch to the destination port of another switch, it requires a special arrangement where every switch is connected to another using chassis topology. When packets transfer, in certain cases, it leads to congestion in the output port due to heavy traffic. A method for resolving the congestion is by signaling to the source port(s), sending the traffic causing the congestion, to throttle the traffic towards the congested port. One such signaling may be based on Quantized Congestion Notification (QCN). As the packets might be received from different sources, the notification needs to be replicated to a subset of the source ports sending to the congested target. This paper proposes the analysis of QCN signaling within a chassis topology, based on different cases using an advanced replication architecture i.e., BIER (Bit Index Explicit Replication) to study its effect to achieve high throughput and low latency of the whole system and proposes a novel approach called periodic QCN for handling persistent congestion.
iHyptn: Predicting Hypertension using PPG signal for Cardiovascular Disease with Machine Learning Models

Cardiovascular diseases (CVDs) are a key global health concern, accounting for a significant proportion of deaths worldwide. Early detection and precise diagnosis are required for effective CVD treatment and control. One non-invasive method that has shown promise for detecting CVDs through hypertension is the analysis of photoplethysmography(PPG) signals. It measures the difference in amount of blood and oxygen saturation in the underlying venule. PPG signals are non-invasive, cost-effective and easy to acquire, making them an ideal candidate for hypertension screening. This study proposes intelligent machine learning models to detect hypertension with the PPG signal's frequency-domain and time-domain features. The performance of the classification models are evaluated using various performance metrics such as accuracy, specificity, recall and F1-score. The results show that PPG signals can be used to accurately detect hypertension and Possibly facilitate in the early diagnosis of cardiovascular diseases. We saw that random forest classification had the highest accuracy of 93 % for detecting hypertension.
Image-Guided Teleoperation for Soft Bronchoscopy Robot

Currently, the majority of traditional bronchoscopes require skilled doctors to manually insert them through the patient's nose or mouth into the airway, where a slight mistake can lead to severe consequences like tracheal perforation or pneumothorax. In order to address this issue, This paper introduces a soft bronchoscopy robot designed to align with human intuition during operation. By employing U-Net to process the image of the airway, a method based on artificial potential fields is proposed, which introduces a force feedback mechanism to intervene in the operator's actions, thereby enhancing the safety and reliability of bronchoscopy procedures performed remotely. The presented robotic system and approach are validated for safety and reliability on a human lung bronchial model.
Breast Density Classification to Aid Clinical Workflow in Breast Cancer Detection Using Deep Learning Network

Breast density is an important bio-marker for predicting the risk of breast dancer. Studies have showed that women with dense breast have higher probability of breast cancer. The density, assessed using mammogram images, is a measure of amount of fibro-glandular tissues in a breast and the appearance of it can mask the lesions leading to lesser sensitivity in breast cancer detection. Hence the current clinical workflow for breast detection incorporates a density classification stage which suggest detailed analysis of dense breast using additional imaging modalities like Digital Breast Tomosynthesis (DBT). In this work a breast density classifier using deep neural network architecture employing transfer learning is proposed for the binary classification of breast density to assist in the clinical workflows. The results are evaluated on publicly available databases namely DDSM, MIAS and InBreast. The proposed model outperforms the existing works in the literature giving on an average 96 % and 94 % accuracy respectively, when tested on DDSM and MIAS database.
Integration of Facial and Speech Expressions for Multimodal Emotional Recognition

The detection and interpretation of human emotions using a variety of sensory cues, including visual, auditory, body language, and physiological indicators, is commonly referred to as emotional recognition. This technology has a broad range of applications, including medical diagnosis, customer service satisfaction, and intelligent call center routing. While facial expressions represent one of the most conspicuous emotional cues utilized in emotional recognition, the integration of additional expressions may enhance its efficacy. To this end, this study proposes an integration of facial and speech expressions as emotional features for the prediction of emotions in machine learning models. The emotional predictions from facial and speech recognition models are subsequently combined to generate the final emotional prediction. Several facial and speech benchmark datasets are employed to evaluate the proposed methodology. The experimental results demonstrate significant promise and the continued development of emotional recognition as a supplementary system for assisting doctors in clinical and research studies appears feasible.
Experiment Design for EEG-based Continuous Action Sense of Agency

Previous research on EEG-based Sense of Agency (SoA) has identified frequency bands and locations associated with SoA. However, the time appearance of SoA on EEG is rarely discussed. Other studies have linked the relationship between SoA and error-related negativity (ERN), which gives hope for studying SoA in the time domain. As a result, EEG data with time markers that can be analyzed in the time domain is necessary. This research aims to create an experiment game that can provide SoA stimulus while also recording all of the subject's response activities. The initial experiment discovered that calculating the subject's movements can estimate the feeling of being in control. Furthermore, as a result of losing agency, the subject's response time is longer in bad moves than in good moves.
Few-shot HPC application runtime prediction

Precise runtime prediction for HPC jobs is essential for streamlined hardware/software co-design, resource allocation, and assessing the impact of hardware alterations. Existing runtime prediction methods, however, are generally application and architecture-specific, hindering their broad applicability. In response, we propose a novel meta-learning and simulation-based model that accommodates a wide range of applications and architectures. This method efficiently addresses new runtime challenges using only a limited number of samples. As demonstrated by our experiments, with just ten training samples, this model attains an average MAPE of 19% on the SPEC CPU 2006 benchmarks.
Utilisation de la télédétection en régions sahariennes, pour l’analyse et l’extrapolation spatiale des pédopaysages

Les etats de surface (ES) du sol sont lies assez fortement a la dynamique des sols des regions arides. L’objectif est d’etudier l’apport de la teledetection dans ces regions (cas de la cuvette de Ouargla au Sahara d’Algerie) pour l’extrapolation spatiale des resultats obtenus a partir d’un secteur de reference (imagette : 1/16 de l’image Spot entiere) pour lequel on dispose de donnees de terrain, a l’ensemble de la region couverte par l’image entiere. La classification ascendante hierarchique (CAH) de l’imagette en fonction de donnees de terrain a permis de distinguer 13 groupes d’ES (4 salins, 2 gypseux, 3 a reg, 3 sableux et 1 a affleurement de roches) et un groupe d’ES de vegetation et autres. L’extrapolation de ces resultats a l’ensemble de l’image par une classification bayesienne par maximum de vraisemblance (CBMV) a montre que les treize ES definis dans l’imagette sont representes dans l’image. La validation de l’extrapolation est faite par comparaison de l’imagette des 13 ES obtenus par CAH avec la meme zone decoupee dans l’image extrapolee par MV. Dans 82,6 % des cas, les pixels sont classes dans le meme groupe d’ES avant et apres l’extrapolation. A partir de ces resultats d’extrapolation satisfaisants nous avons etabli une carte de reconnaissance regionale de pedopaysage en 6 unites.
PR-PUF: A Reconfigurable Strong RRAM PUF

Physical Unclonable Functions (PUFs) offer the natural advantage of built-in key generation, thus eliminating the costly process of embedding unique key after manufacturing millions of integrated circuits. When PUFs are deployed for the application, all the Challenge-Response Pairs (CRP) are collected and stored in a trusted server, and the responses are compared with the one from the device during the run-time - forming the crux of various security protocols. Two issues are commonly faced during PUF designs. First, to enhance the applicability of PUF, larger set of CRP is desirable, which is referred to as a strong PUF. Second, due to the emergence of machine learning-based PUF modelling attacks, it is now imperative to have a PUF demonstrating resistance against such attacks. In this paper, we propose a novel Parity Resistive RAM PUF (PR-PUF) implemented using RRAM crossbar architecture. PR-PUF supports low-overhead reconfiguration, where both the original and reconfigured CRP space enhances the CRP size, with average uniqueness between reconfiguration of 49.98%. The construction also demonstrates excellent robustness against various modeling attacks. We present detailed design analysis and circuit-level simulation studies.