Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Cybersecurity for LDACS: a Secure and Lightweight Mutual Authentication and Key Agreement Protocol

The aviation industry faces significant challenges due to rising global air travel demand. Frequency saturation in Air Traffic Management (ATM) leads to communication problems, necessitating the enhancement of traditional systems. The Single European Sky ATM Research (SESAR) initiative, backed by the European Commission, aims to digitize ATM, with the L-band Digital Aeronautical Communications System (LDACS) as a key component. LDACS aims to improve communication, enhance surveillance, and optimize airspace usage for safer, more efficient ATM. Although LDACS is protected against most cyberattacks, a critical security objective, anonymity, is currently overlooked. To strengthen LDACS’s security, robust authentication mechanisms, Post-Quantum security, and measures to ensure aircraft anonymity are crucial. Therefore, we propose a comprehensive security framework to enhance LDACS’s cybersecurity, focusing on mutual authentication and key agreement. The protocol uses Physical Unclonable Function (PUF) for robust mutual authentication and Bit-flipping Key Encapsulation (BIKE) for secure session key establishment utilizing Post-Quantum Cryptography (PQC). This framework ensures anonymity and secure communication between aircraft and ground stations while minimizing message exchange, latency, and data overhead. An informal security analysis confirms our proposed framework’s potential to augment the efficiency and security of ATM operations.
An Intelligent Fuzzy Inference System for High Priority Vehicles in Vehicular Named Data Networks

With the tremendous increase in the number of on-road vehicles, travel time for High Priority vehicles is touching alarming levels. In case of an emergency, this can lead to loss of life. To cater or propose a solution to this problem an efficient model can be developed which focuses on selecting vehicles obstructing the path of high-priority vehicles (HPV) and making communication with selected vehicles. This paper proposes a solution based on a hybrid approach using communication handled by Vehicular Named Data Networks (V-NDN) and decision support through a fuzzy inference system. Considering the basic V-NDN capabilities of transmitting packets and developing communication with vehicles in the periphery, a model is developed which will send Interest Packets to all vehicles in range and prioritize vehicles using directional values to find out which vehicle is obstructing the path of HPV. The decision support is provided through the fuzzy inference system which utilizes “If-Then” rules to implement directional identification of vehicles. Simulations have been run on the ndnSim simulator to verify the suggested scheme's performance. The results have been analyzed using the ndnSim simulator on different scenarios and their efficiency has been observed in terms of average delay, data retrieval performance and data delivery efficiency.
The Evolving Design of a Socially Therapeutic Robotic Dog*

This paper presents data from an exploratory user design evaluation of Therabot™, a robotic support companion in the form of a stuffed animal dog and discusses how these findings fit into the larger design process of a therapeutic platform with a focus on implications that may extend to similar platforms. While prior investigations have guided the robot’s technical development, in this work participants wore physiological sensors while engaging in an interview similar to cognitive behavioral therapy followed by an interactive design evaluation of the robot. Physiological measures of heart and respiration rate data were collected during the interactions with the robot. Participants holding the robot during their interview experienced a significant reduction in heart rate compared to those not holding the robot. The evaluations from the 32 participants in the study highlighted platform-specific refinements, characterized essential attributes of aliveness in animal-like companion robots, and emphasized the importance of privacy in the design of personal robots, especially for companionship and therapeutic applications.
Fast Planning and Tracking of Complex Autonomous Parking Maneuvers With Optimal Control and Pseudo-Neural Networks

This paper presents a framework to plan and execute autonomous parking maneuvers in complex parking scenarios. We formulate a minimum-time optimal control problem for trajectory planning, using an indirect optimal control approach. A novel smooth penalty function is devised for collision avoidance with optimal control, and an effective technique is adopted to compute an initial solution guess. The trajectory planning tasks are solved with low computational times, and a dense mesh is used to discretize the domain of the optimal control problems, resulting in accurate collision-free solutions. The planned parking maneuvers are tracked with an original pseudo-neural feedforward-feedback steering controller, which outperforms other techniques from the literature, and a feedback longitudinal controller, to drive a realistic 14-degree-of-freedom vehicle simulator. We validate the planning and tracking algorithms in challenging narrow parking scenarios, including reverse, parallel and angle parking, and unstructured environments. The framework robustness is assessed by changing the vehicle mass, the road adherence conditions, and by introducing measurement noise with realistic sensor models. A video of the trajectory planning and tracking results is available as supplementary material.
On the Implementation of a Multi-level Three-phase Inverter with NPC Topology and PD-PWM Modulation

In this paper, the design and implementation of a three-phase multilevel inverter NPC topology with phase-disposition PWM modulation are presented. This work describes step-by-step the process that was carried out to build a multilevel converter, taking into account all the considerations and solutions to the different inconveniences that appeared in the process. The results show that the system, which can operate at different operating voltage and frequency points, works correctly. This technology will enable the development of advanced control strategies in a variety of future applications, including machine control, industrial applications, the integration of renewable energy sources into the electrical grid, or traction applications.
Interleukin-l Increases 15-Hydroxyeicosatetraenoic Acid Production in Human Dermal Fibroblasts

Inhibition of the formation of pro-inflammatory eicosanoids such as leukotrienes and 12-hydroxyeicosatetraenoic acid by 15-hydroxyeicosatetraenoic acid (15-HETE) has been re ported. Psoriatic dermis synthesizes reduced levels of 15-HETE and it has been postulated to playa role in the patho physiology of this disease. Interleukin-1 stimulates the production of prostaglandin E2 in fibroblasts, but its effect on the synthesis of 15-HETE is at present unknown. The aim of this study was to investigate the modulation of 15-HETE formation by interleukin-l in dermal fibroblasts. Cells were treated with recombinant interleukin-1a or P prior to incu bation with exogenous 14C-arachidonic acid, and eicosanoids were analyzed by HPLC. Interleukin-1 significantly in creased the production of 15-HETE, but also 12-hydroxy heptadecatrienoic acid, 11-hydroxyeicosatetraenoic acid, and prostaglandins, in a concentration-and time-dependent fash ion. No significant differences between the two types of L
Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field.
Interfacing Synchronous Modules to Multi-Clock Synchronous Digital Systems Implemented in FPGA

Nowadays, digital circuits lead to low-power consumption, high performance, reuse, etc. These circuits are typically synthesized in the synchronous paradigm, where the global clock signal is responsible for state transitions. They can be implemented in Deep-Sub-Micron (MOS-DSM) technology, in the case Very Large-Scale Integration (VLSI) or Field Programmable Gate Array (FPGA). The synchronous digital design implemented in VLSI_DSM or FPGA is hampered by the global clock signal that makes it challenging to meet these requirements. One solution is to treat the system as a set of modules operating with multiple clocks. In this paper, we propose three new asynchronous interfaces of wrapper type, which perform data communication between such modules efficiently and satisfying the elasticity property of the module. The wrapper application is intended for multi-point systems; in this case, modules with up to two inputs or up to two outputs, but not both. A comparison is realized with communication ports used in literature GALS systems, which shows an average reduction of 41.0% and 48.5%, respectively, in the number of LUTS (Look-Up-Tables) and latency.
A Comparative Study of Evolutionary Algorithms for Multi-Objective Recommender Systems

Recommender systems face the challenge of optimizing diverse objectives in personalized item recommendations. This study presents a comparative study of evolutionary algorithms for multi-objective optimization in recommender systems, evaluated using RMSE, precision, novelty, diversity, and coverage metrics on Movielens-10OK and Book-Crossing datasets. The support vector decision (SVD) algorithm emerges as superior with RMSE 0.89 and precision 0.42, balancing accuracy and diversity. We identify optimal combinations for each dataset: SVD with NSGA-II for Movielens-10OK and SVD with e-MOEA for Book-Crossing, achieving low RMSE, high precision, and diverse recommendations. The comparative results will offer valuable insights and suggestions to researchers seeking to choose appropriate algorithms that align with specific problem requirements.
Intertidal Bathymetry Estimation Using Video Images on a Dissipative Beach

ABSTRACT Sobral, F.N. C. , Pereira, P.S. , Cavalcanti, P.G. , Guedes, R.M. C. , Calliari, L.J., 2013. Intertidal Bathymetry Estimation Using Video Images on a Dissipative Beach. Located at southern of Brazil, Cassino beach is a multiple sandbar beach. A video technique to map intertidal bathymetry changes was applied using CMYK color model to find the waterline position, hydrodynamic data and runup 2% (R2) parameterizations. Linear regression between waterline video detected and elevation estimated with in situ measured maximum swash, showed a correlation coefficient of r2=0.82 and r2=0.91, respectively. Rmse values for those comparison was 10.14 m (horizontal detection) and 0.12 m (elevation). The empirical swash coefficient (Kosc) was 1.08 with a total error δz of rms=0.25 m and represents a waterline underestimated to compensate the overestimated horizontal determination. The principal error was found to be at horizontal position, which means that the R2 parameterization was efficient. The estimated bathymetry temporal evolution changes showed a similar morphologic evolution when compared with beach profile with a total rmse of 0.13 m.
Burden of disease from environmental noise: Quantification of healthy life years lost in Europe

The health impacts of environmental noise are a growing concern. At least one million healthy life years are lost every year from traffic-related noise in the western part of Europe. This publication summarises the evidence on the relationship between environmental noise and health effects, including cardiovascular disease, cognitive impairment, sleep disturbance, tinnitus, and annoyance. For each one, the environmental burden of disease methodology, based on exposure-response relationship, exposure distribution, background prevalence of disease and disability weights of the outcome, is applied to calculate the burden of disease in terms of disability-adjusted life-years. Data are still lacking for the rest of the WHO European Region. This publication provides policy-makers and their advisers with technical support in their quantitative risk assessment of environmental noise. International, national and local authorities can use the procedure for estimating burdens presented here to prioritize and plan environmental and public health policies.
Slab low‐velocity layer in the eastern Aleutian subduction zone

SUMMARY Local earthquakes in the vicinity of the Alaskan Peninsula's Shumagin Islands often produce arrivals between the main P and S arrivals not predicted by standard traveltime tables. Based on traveltime and polarization, these anomalous arrivals appear to be from P-to-S conversions at the surface of the subducted Pacific Plate beneath the recording stations. The P-to-S conversion occurs at the top of a low-velocity layer which extends to at least 150 km depth and is 8 f 2 per cent slower than the overlying mantle. The slab is -7 per cent faster than the mantle. The low-velocity layer contains the foci of the earthquakes in the upper plane of the double seismic zone and confines PS ray paths to lie within it. These observations indicate that layered structures persist to positions well past the surface location of the volcanic front. Reactions forming high-pressure minerals do not yield slab-like velocities until beyond the point that subduction zone magma genesis occurs. If the subducted oceanic crust forms the layer, it is subducted essentially intact.
Assessment Fargas and BLM Models for Identification of Erosion Degree and Critical Sediment Sources (Case Study: Aghbolagh Drainage Basin, Hashtrood City)

The identification of the different areas in drainage basin (as a natural planning unit) for occurring the sedimentation and its severity in different phases of basic studies has been always one of the most important purposes of the natural resources experts. For achieving to this purpose some experimental models has been presented that Some of them have high efficiency and others have weaknesses. Fargas and BLM models are used in this research and they are run in Aghbolagh drainage basin in Hashtrood town, East Azarbayjan province with 7.8 km 2 area. Fargas model includes only two factors, the rock type erosivity and drainage density in the every rock unit, whereas BLM model includes seven factors; the surface erosion, the litter cover, the rock cover on the surface, the affection of destruction on the surface, the surface rill erosion, the affection of the sedimentation due to the water flow and the amplification of gully erosion. The objective of this research is the handling of these two models for different phases of basic studies in the study area. The results of two models showed, in Fargas model 3.67% area of the basin has high erosion, 14.26% area of the basin has severe erosion and 81.04% has very severe erosion and in BLM model 42.97% area of the basin has moderate erosion and 24.93% has high erosion, therefore 52.85% of the study area in Fargas and BLM models has concurrence in the erosion severity.
A Calculus for Mobile Mixins *

MoMi (Mobile Mixins) is a coordination language for mobile processes that communicate and exchange object-oriented code in a distributed context. MoMi's key idea is structuring mobile object-oriented code by using mixin-based inheritance. Mobile code is compiled and typed locally, and can successfully interact with code present on foreign sites only if its type is subtyping-compliant with the type of what is expected by the receiving site. The key feature of the paper is the definition of this subtyping relation on classes and mixins that enables a significantly flexible, yet still simple, communication pattern. We show that communication by subtyping is type safe in that exchanged code is merged into local code without requiring further type analysis and recompilation.
Novel Convolutional Transformer for Autonomous Driving

A crucial component of an autonomous driving vehicle (ADV) is the machine learning, which is able to drive towards a desired destination. Nowadays, there are different paradigms addressing the development of ADV. On the one hand, the modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, the end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are simple and save computational cost. In this project, we focus on the end-to-end autonomous driving. How should representations from complementary modality be integrated for end-to-end autonomous driving? Recent multi-modal methods have shown that complementing RGB images with depth and semantics has the potential to improve driving performance. However, those methods can’t fuse the multi-modal input well and may negatively affect driving performance. Therefore, in this project, we propose the multi-modal transformer to integrate the sensor information. We also apply the auto-regressive method for the temporal reasoning to predict the waypoint. Finally, in order to address the semantic gap between the waypoint values and senor features, we will tokenize the waypoint values and equip the model with the ability to learn the representation of them. We experimentally validate the efficacy of our approach in urban settings involving complex scenarios using the CARLA urban driving simulator. Our code is available in the attached files.
Effect of Wellness Engagement on Identity Formation and Academic Performance of College Students

: This study determined the effect of wellness engagement on identity formation and academic performance of college students. The components of wellness engagement involved, physical activities, diet, and night activities. It also determined the implications of wellness engagement on the students’ identity formation in terms of self-efficacy, critical thinking skills and social competence. The profile of the respondents was determined in terms of gender, living condition, family monthly income, attitude towards studies, and stress coping skills. Descriptive-correlation method of research was used which described the respondents’ wellness engagement, identity formation, and academic performance Likewise, descriptive statistics was used to determine the significant difference of the dependent variables when grouped according to the independent variables. The effect of students’ wellness engagement on their identity formation and academic performance, linear regression was used. The finding of the study revealed that students’ wellness engagement is highly significant on their identity formation and academic performance. This further denotes that wellness engagement has a significant contribution to the development of the respondents’ identity formation particularly on critical thinking skills, social competence and self-efficacy which are important factors for the improvement of students’ academic performance. Further, statistical data shows that self-efficacy mediated the relationship between wellness engagement of the respondents and identity formation particularly on self-efficacy with “high” coping skills.
Evaluation of factors affecting the behaviour of Bengal tiger ( Panthera tigris ) in captivity

The National Zoological Park (NZP), New Delhi is one of the large category zoos of the country housing a number of endangered species of animals. The zoo is visited by a large number of visitors every year. The zoo provides a naturalistic environment and other facilities like feed, healthcare, etc. to the captive animals. However, in captivity the housing conditions, visitation and other factors may jeopardize the natural behaviour, health as well as welfare of animals due to which the animal shows abnormal behaviour i.e. stereotypic behaviour. The study aims to investigate the effect of zoo visitors (visitor noise and density) and different types of enrichment on the behaviour of Bengal tigers ( Panthera tigris ) housed in the NZP. During the study, 10 individual Bengal tigers were studied during two phases: pre-enrichment and post-enrichment. The effect of visitor noise and density were also taken into account during both phases. The results of the study revealed a positive correlation between visitor density and noise with stereotypic behaviours in Bengal tigers ( Panthera tigris ) housed in the NZP. Therefore, there is an urgent need to review the existing visitation pattern at the zoo, and the carrying capacity of the visitor display area for the enclosures housing tigers so that the abnormal or stereotypic behaviour performed by the animals may be reduced and the welfare status of the captive animals can be improved which in turn helps in conserving the animals in captivity. Ultimately, the outcomes of this study can contribute to the development of better management strategies and enriching experiences for both the animals and visitors in zoological parks.