Miracle-dz/UniFAR-FTUs huggingface dataset:
FPGA Implementation of CCCDS Standards Based on Satellite Telecommand System

The Onboard Data Handling (OBDH) is a functional chain in a satellite. The embedded calculator insures the data management between the satellite subsystems and the ground station. During the AIT (Assembly, Integration & Test) phase of a satellite development project, we test this communication in a simulated environment using the on-board computer as the satellite segment, a test bench as a ground station and throughout a physical link (cable) as the space channel. In order to satisfy the AIT requirements, the need to develop other alternatives for establishing this communication has become necessary. For that, the goal of our project is the design and the implementation of a Telecommand and Telemetry (TC/TM) card according to the CCSDS standard, which will be compatible with our AIT test bench in the Center of development of satellite (CDS). This system will be useful for prototyping and early phase test projects. This paper is concerned only for the implementation of CCSDS Telecommand process with their errors control techniques on a Field Programmable Gate Array (FPGA).
Twitter Interactions as a Data Source for Transportation Incidents

Twitter is a microblogging platform that contains a large amount of publically accessible user generated content. This content consists of short social interactions between users. These interactions often describe day-to-day events, and can include location information, making them potentially suitable for use in transportation-related analysis. This paper evaluates the use of data from public social interactions on Twitter as a potential complement to traffic incident data. We compare incident records from the California Highway Patrol with Twitter messages related to roadway events over the same time period. Relationships between the two datasets are evaluated by visualizing the density of incidents and tweets that coincide near the same location. Additionally, the content of Twitter messages is weighted by its relevance to traffic incidents. This weighting is then compared to the time and space proximity of the message to an incident record to determine if more vivid Twitter messages may correspond to the presence of incidents. Twitter information is interesting because it is inexpensive, readily accessible, has broad geographic coverage, and provides a uniquely passenger-centric perspective. It is expected that this research will lead to a better understanding of the potential for information from Twitter to add context to other traffic measurements as a supplemental data source.
Implementing Three Factors Authentication System (3FA) to Maintain Employee Attendance Record to Enhance Security

Today as we all know that maintaining of employee credential have been some major issues around the world. Finding it more difficult to keep track of employees. With this, the company, institution, or government entity does not know who has entered their work-spaces to steal important information from their systems or databases. This serves as a deep wound for any company that does not keep track of its employees. This has been a major issue within so many Africans country, most especially Liberia. where so many companies, institutions, and governmental entities losing huge amounts of data. The means by which they do not track employee records have been with the traditional sign-in method, which has to do with the pen and paper or book sign in and sign out method, which can easily be stolen or damaged. This is one of the methods that employees use to not sign in when they get to work in order to steal data without anyone knowing that they were at work that day. Coming up with a method that will enable strong security and secure maintenance of employees records with less consuming and a strong level of three-factor authentication (3FA) will enable all employees to sign in with their three-level credentials, which have to do with something you know (knowledge), something you have (possession), or something you are (Inherence). The trustworthiness of identification is increased because this data cannot be copied or hacked. Furthermore, Three (3) Factor Authentication attempts to demonstrate, from a broader perspective, why it is more important to review your security procedures in order to prevent unauthorized persons from gaining access to your systems and obtaining data and information that you have not provided them with. Our system is going to allow user signing in with their user credential on your system after entering your username and password which authenticate you into the system, you as an employee to be verified and attendance to be mark you will use your Face, recognizing you and comparing it to the data stored with in the database for verification, after than been verified and mark your attendance for the day. This method going to be used is less time consuming and low cost attached.
An EEG-Based Brain-Computer Interface for Guiding Mobile Robots

The term "robot" refers to an electromechanical device that, as a result of its incorporation of electronic and computer programming, may carry out tasks either independently or in conjunction with a human operator [1]. Robots can be designed to perform functions in any order that the programmer specifies. Robots have found uses in a broad variety of disciplines, including those connected to the military, healthcare, and industry, among a number of other fields. Robots can be programmed to perform in either a mobile or stationary manner, and the choice of which mode to use is often dictated by the tasks that are intended to be carried out by the robots. It is extremely essential for a mobile robot to be able to traverse its environment in order for the robot to be capable of efficiently completing tasks, avoiding obstacles, and participating in other activities. This capability for navigation, which is dependent on sensors to supply environmental data as feedback signals, can be operator-independent or autonomous if "intelligence" is built into the computer code. Sensors are required to provide environmental data as feedback signals. Sensors are required to provide environmental data as return signals. This learning opportunity can be further used in affordable energy, agriculture, environmentally sound technologies, etc.
Estudio de la influencia de los aerosoles sobre la reflectancia de los canales 1 y 2 del sensor AVHRR-NOAA y el NDVI.

En este trabajo se presenta un estudio sobre el efec. to de la atmosfera en la radiancia de los canales 1 y 2 del sensor AVHRR-NOAA, asi como sobre el indice de vegetacion NDVI derivado de ellas. Para ello se ha puesto a punto un modelo de transferencia radiativa que permite evaluar la radiancia o la reflectancia en el limite superior de la atmosfera a partir de las medidas experimentales del albedo de la superficie y de los parametros atmosfericos necesarios. Se han establecido las diferencias entre el NDVI calculado en el nivel de observacion del sensor del satelite con el medido en el nivel de suelo, asi como las dependencias del mismo y de las senales de los canales 1 y 2 del sensor referido con los parametros que definen las caracte. risticas de los aerosoles atmosfericos. Estos se destacan como el factor mas importante debido a su varia. bilidad tanto temporal como espacial. Se concluye la importancia de la correccion atmosferica en el estudio y comparacion de las diversas cubiertas vegetales en las imagenes de este sensor, asi como la necesidad de medir, a tal efecto, el espesor optico de aerosoles en la atmosfera
Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma

The untargeted approach to mass spectrometry-based metabolomics has a wide potential to investigate health and disease states, identify new biomarkers for diseases, and elucidate metabolic pathways. All this holds great promise for many applications in biological and chemical research. However, the complexity of instrumental parameters on advanced hybrid mass spectrometers can make the optimization of the analytical method immensely challenging. Here, we report a strategy to optimize the selected settings of a hydrophilic interaction liquid chromatography-tandem mass spectrometry method for untargeted metabolomics studies of human plasma, as a sample matrix. Specifically, we evaluated the effects of the reconstitution solvent in the sample preparation procedure, the injection volume employed, and different mass spectrometry-related operating parameters including mass range, the number of data-dependent fragmentation scans, collision energy mode, duration of dynamic exclusion time, and mass resolution settings on the metabolomics data quality and output. This study highlights key instrumental variables influencing the detection of metabolites along with suggested settings for the IQ-X tribrid system and proposes a new methodological framework to ensure increased metabolome coverage.
Physics-Informed Data-Based LPV Modeling and Validations of Lateral Vehicle Dynamics

This article introduces an integrated physics-data-based approach to derive a linear parameter varying (LPV) model for nonlinear lateral vehicle dynamics. The proposed method can accurately capture the nonlinearity of the lateral vehicle dynamics using only a few data points without model training or calibrations. Specifically, a novel nonlinear tire force representation based on time-varying stiffness is first proposed. Then, from the physical principles of vehicle motions, the nonlinear lateral dynamics are expressed in the LPV formulation, where the scheduling parameters are the longitudinal speed and slip ratio. After that, a data-based LPV representation is formulated based on a small set of data points satisfying the condition of persistent excitation. The system evolution of nonlinear lateral dynamics can be precisely recovered by parametric matrices formed by these data snapshots. Simulation data from CarSim and experimental data from multiple commercial vehicles are utilized to validate the proposed physics-informed data-based LPV model. The results demonstrate exceptional accuracy and noise-handling ability of the proposed modeling approach across multiple scenarios and vehicles.
Linking Entrepreneurs’ Felt Responsibility to Ventures’ Innovation Performance: The Roles of External Learning and Shared Vision

Introduction In the aftermath of the COVID-19 pandemic, innovation has become a crucial factor for the success of entrepreneurial ventures in China. However, the mechanisms underlying the promotion of innovation by entrepreneurs are not yet fully understood. Drawing on self-determination theory, this study develops a comprehensive “motivation-behaviors-performance” model to examine the relationship between entrepreneurs’ felt responsibility for constructive change and ventures’ innovation performance. Methods Two waves of questionnaire surveys with a sample of 376 entrepreneurs in East China were conducted. Hierarchical regression analysis was performed to test the hypotheses. Results The empirical findings demonstrate that entrepreneurs’ felt responsibility for constructive change is a key driver of innovation performance in ventures. Furthermore, external learning, encompassing technological and market learning, serves as a mediator for the positive link between entrepreneurs’ felt responsibility and innovation performance. The study also reveals that entrepreneurs’ perception of shared vision moderates the relationships between entrepreneurs’ felt responsibility and both technological and market learning, such that the relationships are stronger when the perception of shared vision is higher. Discussion These results yield important contributions to the entrepreneurial psychology research and self-determination theory. The study also has managerial implications for entrepreneurs aiming to enhance ventures’ innovation performance.
Virtualization Development Trends: Scoping Review

Introduction: The development of virtualization tools, which rapidly unfolded in the 1990s, continues to demonstrate a dynamic trajectory. Modern solutions in this area have significantly evolved and diversified. The relevance of the stated issues in the era of Covid-19 has received an even deeper interpretation.Purpose: This review analyzes articles on virtualization published during the pandemic years with the aim of identifying virtualization development trends.Materials and Methods: We conducted a keyword search in the Scopus database. The focus was on articles published in English from December 2019 to December 2022. The authors followed an expanded PRISMA-ScR protocol. The search identified 164 articles, of which 131, based on their title and abstract, were marked as relevant.Results: The selected articles were classified by year of publication, type of publication, country of publication, area of virtualization, mention of Covid-19, and type of virtualization. The articles included in our review indicate that virtualization had already been integrated into all spheres of life before Covid-19. However, the pandemic caused a surge, which accelerated its implementation in various areas, ranging from education and medicine to smart cities and computer technologies.Conclusion: The presented review of the subject area allowed us to identify sectors of public life that significantly influenced virtualization during the Covid19 years, as well as the most actively developing forms of virtualization, in particular, those that allow segregating computational processes and resources. It was recorded that modern solutions in the field of virtualization have significantly diversified compared to previous years and can be classified into separate categories.
Baseline Profiles of Drug Prescriptions Prior to Diagnosis of Mild Cognitive Impairment (MCI) Obtained by Latent Class Analysis (LCA), and Assessment of Their Association with Conversion to Dementia

Polypharmacy has been linked to cognitive decline. However, interventions targeting modifiable risk factors, some of which are targets of the most commonly used drugs, could reduce the prevalence of dementia. Our aim was to determine the drug prescription regimen at baseline, prior to the diagnosis of mild cognitive impairment (MCI), and its possible association with progression to dementia. Data were collected from the electronic medical records of 342 MCI outpatients diagnosed during 2006–2017 at their first neurology consultation. We followed the classical three-step method of statistical analysis, starting with a Latent Class Analysis (LCA) to discover subgroups of drug prescription probability. Half of the patients were under polypharmacy (≥5 drugs), 17.5% had no recorded medication, 33.3% progressed to dementia (94.7% in ≤5 years), and 84.1% of them to Alzheimer’s disease (AD). According to the LCA and based on 20 therapeutic indicators obtained from 240 substances and regrouped according the Anatomical Therapeutic Chemical Classification, we identified a four-profile model: (1) low (35.7% of patients); (2) mixed (28.7%); (3) cardio-metabolic (19.3%); and (4) psychotropic (16.4%). The binomial regression logistic model showed that profiles 2 and 3 (and 4 for AD), with a higher drug prescription conditioned probability against classic risk factors, were protective than profile 1 (OR = 0.421, p = 0.004; OR = 0.278, p = 0.000; OR = 0.457, p = 0.040, respectively), despite polypharmacy being significant in profiles 2 and 3 (mean > 7 drugs) vs. profile 1 (1.4 ± 1.6) (p = 0.000). Patients in the latter group were not significantly older, although being aged 65–79 years old quadrupled (OR = 4.217, p = 000) and being >79 tripled (OR = 2.945, p = 0.010) the conversion risk compared to patients <65 years old. According to the proposed analytical model, profiling the heterogeneous association of risk factors, which were taken prior to diagnosis, could be explored as an indicator of prior care and a predictor of conversion to dementia.
Multi-dimensional task recognition for human-robot teaming: literature review

Human-robot teams collaborating to achieve tasks under various conditions, especially in unstructured, dynamic environments will require robots to adapt autonomously to a human teammate’s state. An important element of such adaptation is the robot’s ability to infer the human teammate’s tasks. Environmentally embedded sensors (e.g., motion capture and cameras) are infeasible in such environments for task recognition, but wearable sensors are a viable task recognition alternative. Human-robot teams will perform a wide variety of composite and atomic tasks, involving multiple activity components (i.e., gross motor, fine-grained motor, tactile, visual, cognitive, speech and auditory) that may occur concurrently. A robot’s ability to recognize the human’s composite, concurrent tasks is a key requirement for realizing successful teaming. Over a hundred task recognition algorithms across multiple activity components are evaluated based on six criteria: sensitivity, suitability, generalizability, composite factor, concurrency and anomaly awareness. The majority of the reviewed task recognition algorithms are not viable for human-robot teams in unstructured, dynamic environments, as they only detect tasks from a subset of activity components, incorporate non-wearable sensors, and rarely detect composite, concurrent tasks across multiple activity components.
INDOOR 3D SCANNING AND NAVIGATION SYSTEM FOR AN AUTOMATED GUIDED VEHICLE

The paper presents the development of a 3D laser scanning system that can produce an indoor navigation map for an automated guided vehicle. The system uses only a 1D Light Detection and Ranging (LiDAR) as a measuring device and two stepper motors for positioning. Several tests are performed to determine the best trade-off between the time needed for the scan and the required resolution to produce an indoor navigation map, and the relationship between these variables is presented.
Landing with Confidence: The Role of Digital Twin in UAV Precision Landing

UAVs have ignited our curiosity to understand the mechanism of flight and improve the way these vehicles fly to enable them to be of great benefit in various applications such as exploration, package delivery, warehouse inventory scanning, military use, and many other applications. Nevertheless, these applications of drones face limitations due to their precision landing accuracy. The problem occurs due to sensing errors, control algorithms, external factors, and operator errors. Also, the current system needs to make it possible to fix real-time errors caused by the environment. Our research aims to help UAVs solve the problem by developing a digital twin that can land precisely using sensor data to optimize motion planning. A promising way to design, test, and improve UAV precision landing systems is to use digital twin technology. It can be used to design, test, and improve the way a drone lands precisely. This can help make the landing process more accurate and safe. This paper gives an overview of the current state of digital twin technology for UAV precision landing, including different ways to sense, control, and optimize. Along with the Digital Twin, we also propose an error calculation and optimization algorithm that helps the drone land exactly where it needs to. The results strongly substantiate that both the algorithm and the digital twin performed well.
Prevention of Aviation Accidents with Prediction of Cognitive States

Aviation has long been a high-level industry along with its advanced technical and technological processes and operations. Latest advances made it possible to possess larger and faster aircraft than before, resulting in a huge increase in the demand for air transportation. Aviation accidents have also increased largely due to the growth in traffic volume. In particular, human-induced errors, alone or together with other factors, are determined as the main factor in the vast majority of accidents. In this study, some theories and methods that include human errors are examined. Among human-induced errors, the conditions that lead to unsafe actions in which situational awareness decreases and cognitive state changes occur are discussed. The study proposes a cognitive information system framework to identify cognitive states. Our preliminary experiment revealed that it is possible to design a real-time cognitive state identification scheme without the need for extensive signal processing.
Robust Type-2 Fuzzy Controller for DC-DC Power Converter Feeding Constant Power Load

As renewable energy sources gain popularity, the need for efficient DC microgrid systems powered by power converters becomes increasingly important but the main concern is the stability of the converter when feeding Constant power loads. This paper presents a comprehensive discussion of various feedback control techniques used in a boost converter fed with CPLs. Due to the negative impedance behaviour of CPLs, an intelligent control approach is essential to eliminate instabilities. In this work conventional proportional-integral (PI) control, sliding mode control, and both Type 1 and Type 2 fuzzy logic controls (FLCs) are analysed. The simulations are performed in MATLAB/SIMULINK by subjecting the system to a sudden load change and analysing the output voltage behavior. Type 2 FLC is an advanced version of Type 1 FLC, offering more modeling flexibility and better control of complex systems. Type 2 FLC allows for the representation of uncertainties and ambiguities in the membership functions and rules, which leads to more robust and nonlinear system handling. This results in superior controller performance in terms of stability, tracking, and disturbance rejection compared to state of the art approach.
Personal respirable dust sampler

Equipment for evaluating the respirable dust exposure of individual underground coal miners was designed, constructed, and tested by the U.S. Bureau of Mines. As a result, the guidelines for sampling and evaluating the respirable dust concentrations in the breathing zone of individual miners were finalized, and the equipment was made commercially available. The equipment and techniques, which are described in this report, are consistent with the provisions of pending coal mine health legislation governing respirable gravimetric dust concentrations.
Unsupervised Domain Factorization Network for Thick Cloud Removal of Multitemporal Remotely Sensed Images

Cloud removal is an important task in remotely sensed images (RSIs) processing, which is beneficial for downstream applications, such as unmixing, fusion, and target detection. Multitemporal RSIs (MRSIs), which contains abundant spatial–spectral–temporal (SST) information, potentially bring new opportunities for cloud removal. However, how to effectively and efficiently explore the rich information of MRSIs remains a challenge. Inspired by the low-rankness of MRSIs, we propose an Unsupervised Domain Factorization Network (UnDFN) for thick cloud removal, which allows us to effectively and efficiently exploit the rich SST information of MRSIs. In the UnDFN framework, we first factorize RSI for each time node of MRSIs into its corresponding spatial factor and spectral factor. Due to powerful expressive ability, untrained neural networks (NNs) are leveraged to faithfully capture the spatial and spectral factors. Especially, motivated by the low-rankness of the concatenated spatial factors of all time nodes, a low-rank spatial factor module is elaborately designed to effectively and efficiently capture the spatial factors of all time nodes as compared with separately using networks to capture spatial factors for each time node. Extensive experiments on simulated and real MRSIs of different satellites (including Sentinel-2 and Landsat-8) substantiate that the proposed UnDFN achieves state-of-the-art performance in thick cloud removal compared to other methods.