Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation Analysis of GPS/GLONASS Absolute Positioning Performance in an Urban Canyon Environment

Abstract—This paper analyzes the positioning performance of Global Navigation Satellite Systems (GNSSs) in an urban canyon environment. The recently developed telematics technology requires highly accurate and robust positioning performance in urban areas. However, GPS positioning alone fails to provide adequate performance in this environment. In this paper, we developed a simulator and simulated the positioning process in an urban environment in order to analyze the performance of GPS and GLONASS combined positioning. In the results, the positioning possibility was improved but the accuracy was not enhanced when GPS/GLONASS combined positioning was performed. The accuracy was not improved because of geometrical instability. Therefore, for accurate and robust positioning in an urban canyon environment, GNSS should be integrated with other type of sensors such as an Inertial Navigation System or a Vision System.
Software Error Classification and Dependency Assessment for NASA Missions

Software anomalies are typically captured in databases, classified, and traced until they are resolved. These databases then remain part of the history for the space missions to refer to as necessary. The classifications used for the software anomalies are not always orthogonal and it is not trivial for developers and software assurance personnel to make the mapping between the anomaly in question and its related classification. Perhaps more significantly, the classification scheme and the attributes included in the standardized forms do not clarify whether or not the error in question, and its corresponding fix, affect other areas of the system. Our research indicates that there are a few keywords that can help the users of these systems identify each of these factors with a reasonable level of confidence. Of course, since the information is captured using natural language, and is highly specialized, these keywords may be different from one mission to another and from one center to another. However, tools that enable the team to develop their own keywords or modify keywords that are seeded into the system could be beneficial in helping to correctly classify the anomalies and better understand the dependencies between them. The first step, is, of course, a clear, concise and descriptive classification scheme. In this paper, we explain our efforts towards developing a standardized classification scheme, based on our examination of these schemes across several NASA centers, as well as the literature, and we also define the concept for a module for user-assisted analysis based on keywords that we have extracted from studying a subset of the problem failure reports for the Mars Perseverance Rover mission.
Novel Cosine Distance Based Semi-Supervised Learning Using Discriminant Graph Convolutional Network for Industrial Fault Diagnosis

Semi-supervised learning can effectively utilize limited labeled data and large amounts of unlabeled data to achieve fault diagnosis on process industries. This paper proposes a novel cosine distance based semi-supervised learning using discriminant graph convolutional networks (CD-GCN) at node-level Firstly, the CD-GCN method uses the discriminant information to pull the training sample features of different classes farther away from each other. Secondly, CD-GCN replaces Euclidean distance with Cosine distance as the distance metric in the original samples space and feature space. With Cosine distance and discriminant information, CD-GCN better considers spatial structure information and improve the whole graph by the dual effects of graph convolution and nearest neighboring with these moving training sample features. Finally, a real industrial simulation case is carried out to verify the performance of the proposed method. Compared with other related and classic methods, the simulation results show that the CD-GCN method achieves the best performance in diagnostic accuracy.
The NLM Indexing Initiative's Medical Text Indexer

The Medical Text Indexer (MTI) is a program for producing MeSH indexing recommendations. It is the major product of NLM's Indexing Initiative and has been used in both semi-automated and fully automated indexing environments at the Library since mid 2002. We report here on an experiment conducted with MEDLINE indexers to evaluate MTI's performance and to generate ideas for its improvement as a tool for user-assisted indexing. We also discuss some filtering techniques developed to improve MTI's accuracy for use primarily in automatically producing the indexing for several abstracts collections.
Circularly Polarized Pattern Reconfigurable Antenna using Coding Metasurface

In this paper, a circularly polarized pattern reconfigurable antenna operating at 3.5GHz is presented. The proposed antenna consists of a dual-feed antenna as a source and $7 \times 7$ coding metasurface as a superstrate. The feeding position of the patch antenna is orthogonal to each other and optimized to generate good circular polarization. The unit cell utilized in coding metasurface exhibits transmissive characteristics for dual-polarized incident waves, with its structure consisting of an even-loop and a diagonal strip. Three PIN diodes are placed on the unit cell to control overall emission. The proposed antenna performance is evaluated in nine distinct modes of operation. The antenna achieves circularly polarized beam steering from -18° to +22° in the $y z$-plane. In each beam steered direction axial ratio is less than 3-dB. The antennas overall bandwidth is from 3.48 to 3.56GHz with peak gain of 3.49dBic.
CAResNet Based Aero-engine Remaining Useful Life Prediction

The aero-engine is one of the core components of an aircraft. Failures in aero-engine are possible to lead to devastating consequences. Therefore, accurately predicting the remaining useful life (RUL) of the engine is crucial for ensuring the stable operation of the aircraft. In order to accurately predict the RUL value of aero-engines, we propose a model called CAResNet that combines improved channel attention and residual blocks to achieve accurate RUL prediction. Firstly, a channel attention module based on convolution operations in two dimensions is constructed. Attention weights for different information are obtained by summing the results of the two pooling methods. Secondly, a feature extraction module is built based on stacked residual blocks, which composed entirely of linear layers, achieves effective feature extraction while maintaining a simple network structure. Finally, an RUL prediction module consisting of dilated convolution blocks is utilized to predict the RUL value of the engine. To validate the effectiveness and superiority of this method, we conduct simulation experiments using the C-MAPSS turbofan engine dataset and compare the results with other relevant studies. The experimental results demonstrate that CAResNet performs well in RUL prediction tasks.
A Novel Real-Time Framework for Embedded Systems Health Monitoring

This paper proposes a real-time framework, used as a multi-step ahead forecasting system that can be used for prognostics and health management (PHM), aiming to address a number of industrial-scaled embedded systems where failures can lead to catastrophic events. A novel framework is presented, which combines strong offline training using machine learning techniques with online multi-step forecasting for realtime condition monitoring and prognostics. The offline training is performed with the objective to derive a model capable of predicting the system of interest within fixed performance criteria, which are the metrics that provided the best accuracy and the estimated prediction time to anticipate the performance in real-time. The online inference, on the other hand, uses techniques of multi-step forecasting to provide an advisory window for the operator in case one or multiple faults lead the system for a non-operational state or a hazardous situation. This paper propositions are tested and validated on a real system dataset, where frequent faults were detected during a certain period of time. Dimensionality reduction techniques were initially used on the dataset, ensuring that the overall model accuracy was not impaired, then an optimized bidirectional long short-term memory (LSTM) model was chosen due to its inherent capacity to solve the issue of vanishing gradients for time-dependent and sequential data series. The optimized model is then flashed on two target microcontrollers, which continuously refresh data received from the sensors and actuators in the field. The proposed framework was compared with other strategies, tested and validated on a real application case.
Consensus- Based Flexible Group Formation Control for Multi-UAVs Systems

This paper introduces a comprehensive approach to group formation control by introducing the concept of flexible formation while analyzing maneuverability. A consensus-based formation control algorithm is integrated into the flexible group formation control, with the formation point of the flexible formation acting as the group leader. The effectiveness of the suggested algorithm is substantiated through simulations executed on a laboratory-developed platform with a realistic fixed-wing UAV dynamic model.
Migration and Scheduling of Virtual Machines (VM) using Priority Based Load Balancing in Cloud Environment

The cloud technology is rapidly gaining prominence in both the IT industry and research domains. Virtualization is a crucial technology in the realm of cloud computing that enables the isolation of applications and provides service providers with the capability to transfer virtual machines (VMs) from one physical device to another via a process commonly referred to as VM migration. The process of migration involves several sequential stages, which comprise the selection of the source physical machine, selection of the virtual machine, selection of the target virtual machine, and the ultimate step of transferring the virtual machine data. Based on previous studies, the process of migrating virtual machines (VMs) presents notable difficulties, mainly attributable to the dynamic nature of VM demands, particularly in terms of their selection and placement. The given paper proposes a load balancing tactic that accords priority to the migration and scheduling of virtual machines contingent upon their designated profit-based priority. The findings shows the performance of the proposed approach via calculation of round trip time and throughput metrics.
The neural origin of muscle synergies

Muscle synergies are neural coordinative structures that function to alleviate the computational burden associated with the control of movement and posture. In this commentary, we address two critical questions: the explicit encoding of muscle synergies in the nervous system, and how muscle synergies simplify movement production. We argue that shared and task-specific muscle synergies are neurophysiological entities whose combination, orchestrated by the motor cortical areas and the afferent systems, facilitates motor control and motor learning.
ACR-ILPU: Internal Learning 3D Point Cloud Upsampling Network Using Attention Coordinate Refinement

The performance of conventional externally trained networks for point cloud upsampling is often influenced by the semantic range size of the training dataset, leading to limited capability in learning shapes at all levels. Consequently, the upsampling of complex shapes fails to preserve their original characteristics and generates an excessive number of dense point cloud outliers. To address these issues, we propose an internally learned method for point cloud upsampling, termed Internal learning 3D Point Cloud Upsampling Network using Attention Coordinate Refinement (ACR-ILPU). ACR-ILPU comprises three modules: an edge-aware generator, a discriminator, and attention-based coordinate refiners, which collectively facilitate the training and testing processes of the network. Notably, our approach eliminates the need for an external training dataset and solely relies on an input sparse point cloud for internal learning. To evaluate the effectiveness of ACR-ILPU, we conduct comprehensive qualitative and quantitative experiments using the PU1K dataset. The results demonstrate that ACR-ILPU significantly reduces training time while surpassing the performance of both externally trained and internally learned methods. Furthermore, ACR-ILPU exhibits promising results when applied to point clouds from the extensive KITTI autonomous driving dataset.
Performance Analysis of UAV Assisted Dual-Hop Multi-user Uplink NOMA System

In this paper we have presented an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) based communication model where UAV is working as a data collection unit (CU) for different IoT sensor nodes (ISN). In the proposed uplink model IoT-CU link is characterized by Rayleigh channel and mm-wave link between CU to BS is characterized by κ−µ channel. Multiple ISNs are being serviced by using NOMA scheme. The closed form expression for outage probability of each user has been presented. Further we have verified the mathematical frame-work with Monte-Carlo simulations. Simulation results shows the close agreement with analytical results.
LW-PointNeRF: A Point-Based Neural Radiance Field with a Lightweight Multi-View Stereo Network

In recent years, there have been significant advancements in novel view synthesis using Neural Radiance Fields ($N$ eRF). NeRF's application is limited because of a considerable amount of training time. In this paper, we present a method called LW-PointNeRF. This method can rapidly reconstruct radiance fields while reducing the number of model parameters and computational requirements. Specifically, LW-PointNeRF comprises two main modules: the LW-MVS module and the Radiance Field module. The LW-MVS module employs a lightweight convolutional neural network to extract image features. It then uses plane sweeping to construct a cost volume for depth estimation, generating an initial point cloud. The Radiance Field module aligns the image features with the initial point cloud to get a neural point cloud. Finally, it aggregates the features of neighboring points to construct the radiance field. Experimental evaluations of LW-PointNeRF are conducted on the DTU, NeRF synthetic, and ScanNet datasets. LW-PointNeRF performs similarly to PointNeRF, but uses 25% fewer parameters and 14% less computation.
Enhancing Satellite System Reliability through Bistable RF MEMS Switch: An Optimization Approach using XGBoost model

Radio Frequency Micro Electro Mechanical System (RF MEMS) switches offer unparalleled reliability and swift response times with compact design, making them an ideal choice for ensuring faultless redundancy in critical satellite systems. This paper proposes a Crab leg Bistable RF MEMS switch to perform redundancy functions in satellite applications. To optimize the proposed switch, eXtreme Gradient Boosting (XGBoost) model is presented, which is proven to be an efficient Machine Learning (ML) model. The proposed XGBoost model was developed based on both direct and indirect approaches to determine the RF and physical characteristics in both stable states of the switch. The XGBoost model overcomes the time-consuming EM optimization problem. In this work, the proposed model aims to significantly reduce the duration required for designing the switch. In comparison to the Electro-Magnetic (EM) simulator, which demands nearly 50 minutes for each iteration, the proposed model reduces the design time by 90%. The effectiveness of the developed ML model is validated by comparing predicted results with those obtained from the HFSS model, resulting in the achievement of favorable convergence.
Dual Linearly Polarized Common Aperture Microstrip Antenna for S- and C-Band Communication

This manuscript presents the design and development of an S-/C-band common aperture microstrip antenna. It is dual linearly polarized at both bands. The S-band radiator is proximity coupled to two orthogonal microstrip feedlines. The C-band array is an aperture coupled to other two mutually orthogonal microstrip feed network that generate vertical polarization (VP) and horizontal polarization (HP). The dependencies of isolation on relative slot positions and interlayer spacing have been investigated. The antenna yields an impedance bandwidth of wider than 3.65% at the S-band with isolation better than 26 dB. At C-band ports, it exhibits wider than 21.9% impedance bandwidth and isolation higher than 45 dB over the realized band. Design details, findings of the parametric investigation, and experimental results are presented here. Measured results are improved compared to the earlier reported works.
Electricity Market Design: An Integrated Approach to Reliability Assurance

System reliability is an integral part of a properly designed deregulated electricity market, even though wholesale energy prices are its most visible piece. This paper discusses the resources, collectively known as the ancillary services, in a power system for maintaining reliability. It then discusses an integrated approach in systematically determining and securing the needed ancillary services and system configuration across multiple time spans, culminating in the cooptimization of energy and ancillary services for security-constrained generator unit commitment in the day-ahead market and security-constrained economic dispatch of the generators in real-time operation. The paper also provides a summary of the ancillary services market in several U.S. power markets and proposes several technical issues for further discussion and research.
Advances in Feature Extraction for Brain Cancer Analysis: A Review of Traditional, Machine Learning, and Deep Learning Approaches

Brain cancer remains a formidable health challenge, necessitating continuous advancement in diagnostic and therapeutic strategies. Feature extraction techniques, encompassing traditional, machine learning, and deep learning-based methods, play a pivotal role in the early detection, prognosis, and treatment planning of brain tumors. This paper comprehensively analyses the state-of-the-art feature extraction methods employed in brain cancer research. The paper critically evaluates the strengths and weaknesses of various feature extraction approaches, including traditional handcrafted features, machine learning-based techniques, and advanced deep learning methods. Furthermore, we discuss the emerging trends and potential future directions in this dynamic field. By synthesizing the current literature, this review aims to provide researchers and clinicians with a valuable resource for understanding the role of feature extraction, spanning traditional, machine learning, and deep learning-based methods, in brain cancer analysis and promoting further advancements in this critical area of study.
1 introduction.

No. GA IV/J1/4279/10 Dated, Calicut University PO, 26.07. 2010 Read: 1. U.O. No. GAIV/J1/1373/08 dated, 23.07. 2010. 2. Minutes of the meeting of the Board of Studies in Zoology (PG) of 09.06. 2010, Item No.1 3. Orders of the Vice-Chancellor, in the file of even number dated 29.06. 2010. 4. Item No.III a.30 of the minutes of the meeting of the Academic Council, held on 03.07. 2010. O R D E R As per reference cited (1) above, Credit Semester System at Post Graduate level in affiliated colleges(CUCSS PG-2010) has been implemented from the academic year 2010, onwards. The Board of Studies at its meeting, vide reference cited (2) above, discussed the scheme and syllabus of PG (Zoology) of affiliated colleges and has forwarded the scheme and syllabus of Ist Semester to the University. The Vice-Chancellor, in view of exigency, has approved the minutes of the meeting of the Board, subject to ratification by the Academic Council. The Academic Council, vide paper read as 4th above, ratified the action of the Vice Chancellor in having approved the minutes of the meeting of the Board of Studies in Zoology. Sanction has therefore been accorded to implement the scheme and syllabus of MSc Zoology of affiliated colleges under Credit Semester System with effect from 2010 admission. Orders are issued accordingly. Scheme and syllabus appended. ` Sd/REGISTRAR To 1. The Principals of all affiliated Colleges offering M.Sc Zoology 2. Self financing centres of the University of Calicut offering Zoology(PG) Copy to: PS to VC/PA to Registrar/CE/ Digital wing (with a request to upload in the University website)/Enquiry/Information Centres/DR III Exams/EG-I/DR PG/Tabulation/GAI’F’ ‘A’ Sections/GAII/GAIII/DDLFA/SF/FC Forwarded/By Order Sd/SECTION OFFICER.
Comprehensive Study on ChatGPT Sentiment Analysis and Visualization

The rapid growth of artificial intelligence (AI) is poised to reshape industries and societal aspects, driven by its ability to simulate human intelligence and perform tasks requiring humanlike thinking and learning. ChatGpt has been leading this growth since its inception, enhancing user experience and garnering over 100 million users by February 2023. Social Networking sites have been a source for sharing the sentiments. This research explores tweets on ChatGpt in the field of sentiment analysis and its integration with data visualization techniques to enhance the comprehension of textual data. The research proposed a holistic approach by exploring the methodologies such as data collection, data preprocessing, tokenization, sentiment analysis using Natural Language Processing, accuracy assessment and visualization. For training and testing a dataset has been gathered from Kaggle which daily gets updated. The proposed study gives comprehensive results about sentiments on ChatGPT and explores different visualization techniques.
PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks

UNLABELLED The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. AVAILABILITY PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape. org/plugins/index.php or http://biotech.bmi. ac.cn/PerturbationAnalyzer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.