Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Identification Algorithm of Ballistic Missile based on Affiliation Degree and GA

The identification of warhead of the ballistic missile is one of the difficult problems in anti-missile warning. Due to the adoption of various defense measures of ballistic missile, such as the light or heavy decoys, and the active decoys etc. It is necessary to use multiple features to accurately identify the real warhead from the targets group. In this paper, for the problems of conflicting conclusion of multi-features recognition for the real warhead, it is proposed the genetic algorithm to optimize the conflictions in comprehensive identification algorithm, which include the time sequential fusion algorithm and the multi-features fusion algorithm, to enhance the correct recognition rate of the real warhead target.
Influence of Time-frequency Diagram Aggregation and Fault Mechanism on CNN-based Bearing Fault Diagnosis Accuracy

As a representative deep learning network, convolutional neural network (CNN) has been widely used in bearing fault diagnosis and achieved many good results. The input images of CNN are commonly generated via time-frequency analysis (TFA) methods that could characterize the rich information of bearing fault vibration signals. However, the usage of TFA methods and selection of input sampling points directly affect the final fault diagnosis accuracy of CNN, which has yet to be thoroughly investigated. In this study, three TFA algorithms, continuous wavelet transform (CWT) together with its two advanced variants, wavelet synchro-squeezed transform (WSST) and wavelet-based synchro-extracting transform (WSET) are firstly used to explore the influence of TF aggregation on performance of CNN in which the SqueezeNet and AlexNet pre-trained networks are applied respectively. Meanwhile, to further make the selection of input sampling points interpretable, this study also explores the correlation between the number of fault shocks contained in input sampling points and the final CNN accuracy. Both experiments are validated with three popular public datasets (MFPT, CWRU, and PU). Results confirm the importance and necessity of the appropriate selection of TFA methods together with sampling points of input vibration signals on final accuracy of CNN.
Handover Forecasting in Cellular Networks: A Spatio-temporal Graph Neural Network Approach

The proliferation of connected devices, as well as the continuous integration of data demanding applications, have brought significant challenges to mobile network operators. Indeed, mobile networks need to accommodate the resulting exponential growth of traffic, while still ensuring a decent Quality of Service (QoS) and Quality of Experience (QoE) to their users. To do so, adequate management solutions are needed. So far, a variety of problems have been tackled, including resource allocation and network planning. Yet, handovers (HO) management has not received much attention, despite its importance for users QoS and QoE. In this work, we study the problem of mobile devices HO forecasting, over time, among base stations. To solve the problem, we introduce a Handover Graph Convolutional Long Short-Term Memory (HGC-LSTM) neural network forecasting approach. Our approach allows capturing spatio-temporal correlations among neighboring pairs of base stations in the forecasting process. The evaluation on a real-world dataset shows that the proposed HGC-LSTM approach outperforms other methods in the state-of-art in terms of accuracy and execution time.
Characterization of Optical Bi-Stability in Active Silicon Microring Resonators for All-Optical Switching

In this work, we experimentally study the optical bistability (BI) behavior in active silicon microring resonators (MRR). A stable hysteresis loop response at the output port is demonstrated at a power level higher than the BI threshold, taking into account the different detuning and free carrier life-times. That hysteresis is observed at low and high BI thresholds, which can be used for all-optical switching and optical routers. The MRR is placed inside a reverse-biased PIN junction and fabricated by CMOS technology. It records a loaded Q-factor, linewidth (3 dB width of the spectrum), and extinction ratio (ER) around $10^{5},15.3$ pm, and 20 dB, respectively.
Online Fatigue Life Prediction and Crack Length Estimation Based on LSTM

In this paper, a prediction method based on LSTM model is proposed for the online prediction problem of fatigue life and crack length in crack propagation and fracture stage. The proposed method uses the online collected data to predict the health indicator (HI) of the specimen based on a long short-term memory (LSTM) model; Then a particle filtering algorithm and polynomial fitting are used to convert the health indicator into the remaining fatigue life and crack length. Tests were performed with several experimental data under the same load, and the results showed that the deviation between the predicted life and the real life of the model is small, with an error value of less than 5% relative to the total fatigue life in the middle and later stages of the prediction.
Performance Evaluation of Urban Traffic Using Simulation: A Case Study in Brazil

As a result of a fast-growing population, an increasing number of vehicles on the road, and inadequate public policies, metropolitan areas in Latin America are dealing with significant traffic congestion problems. Most cities do not have real-time urban traffic control systems. Therefore, the use of simulation software is a cost-effective solution to evaluate and reduce congestion in metropolitan areas. This paper seeks to assess urban traffic performance using the Urban Mobility Simulator (SUMO) on Fernandes Lima Avenue, the most important thoroughfare in Maceio, Alagoas, Brazil, which features distinctive characteristics such as a dedicated lane for public transportation and three segments with pedestrian traffic lights. Comparing the real observations with the simulation results, it was confirmed that the model provided accurate estimates, with errors of less than 5% for vehicle traffic volume and 10% for total travel time. After conducting experimental studies on four different scenarios, including the current state (1), no blue lane (2), no pedestrian traffic lights (3), and no blue lane and no pedestrian traffic lights (4), it was found that significant improvements in efficiency indicators, such as travel time, waiting time, fuel consumption, and carbon dioxide emissions, could be achieved. Scenarios 2, 3, and 4 were particularly effective, resulting in volumetric increases of 9.95%, 7.88%, and 10.77%, respectively, in vehicle traffic.
Static Test Case Prioritization Strategies for Grammar-Based Testing

Grammar-based test case generators can quickly produce large test suites that structurally and systematically cover the input space of a system under test. However, running these large test suites requires large computation resources. In this paper, we investigate whether simple test case prioritization strategies based on statically determined grammar-related properties (e.g., token-length of input, novel rule coverage, and relative frequency of rule coverage), can detect faults in the system under test faster. Our preliminary results indicate that different test execution orderings from these strategies have an effect on the fault detection rate. Their performance varies across the different test suites, but they generally perform better than a simple random ordering of test executions.
Can Code Representation Boost IR-Based Test Case Prioritization?

Test case prioritization (TCP) aims to schedule the execution order of test cases for earlier fault detection. A recent study has demonstrated that the information-retrieval-based (IR-based) TCP approaches achieve the state-of-the-art effectiveness. The current IR-based TCP approaches leverage lexical similarity between test cases and code changes to guide TCP while ignoring rich code semantics, which may limit their effectiveness to some degree. In this paper, we conduct the first study to explore whether code semantic information can further boost IR-based TCP. Here, we studied two types of code representation methods (i.e., general-purpose and task-associated models) and explored two modes of utilizing the code representation embeddings (i.e., unsupervised and supervised modes) for IR-based TCP. Our results demonstrate that incorporating code semantics through the supervised mode of code representation can achieve a 16.96% improvement in the efficiency of fault detection over the state-of-the-art IR-based TCP approach (which is based on lexical similarity).
In vitro conservation of plant genetic resources

The conservation of biodiversity is widely recognized as a high priority area for attention in the ongoing debate linking environment and development [1,2]. In particular, the conservation of plant genetic resources for food and agriculture, one sector of biodiversity, is considered to be a major element of any strategy to achieve sustainable agricultural development, along with the conservation of other natural resources. Diverse conservation methods are pursued according to the situation at hand. These methods can be divided broadly into ex situ and in situ. The latter cover conservation in wilderness areas, reserves, protected areas, and within traditional farming systems (so-called, on-farm conservation). Ex situ conservation involves removing the plant genetic resources from their natural habitat and placing them under artificial storage conditions. The following sections examine the different ex situ options available.
COVID-19 Fake News Detector

The increasing number of Novel Coronavirus (COVID-19) cases has given rise to a proliferation of misinformation related to COVID-19. This misinformation makes it difﬁcult for individuals to ﬁnd reliable news sources, resulting in protest against government measures to control the virus, social turmoil, and even death. To help alleviate the spread of misinformation, we built and evaluated a variety of machine learning models to help predict the reliability of COVID-19 related news. We combined data from two sources, which include news articles and website posts from ofﬁcial institutions. As our ﬁnal method, we presented an ensemble of meth-ods that achieves an Area Under Curve (AUC) of 0.97 and an F1-score of 0.92. Additionally, we created a website for readers to interact with the model at: www.modellingcomp. com .
Enhancing Vehicular Systems through the Synergy between Visible Light Communication (VLC) and Radio Frequency (RF)

Vehicular communication is a critical technology in Intelligent Transportation Systems (ITS) that aims to improve transportation safety and efficiency. However, traditional radio-based systems, such as Cellular V2X (C-V2X) and Dedicated Short Range Communication (DSRC), may suffer from performance degradation in dense traffic scenarios. To address this issue, Line of Sight (LoS) technologies such as Visible Light Communication (VLC) are being explored as complementary technologies to RF.VLC utilizes LEDs on vehicles to exchange information with preceding and subsequent vehicles, allowing ITS to create a safer and less congested transportation system. Recent studies have shown that combining DSRC and VLC can minimize the performance degradation experienced by RF communication technologies.This paper highlights the need to combine RF and LoS technologies to improve the stability and reliability of V2V communication. It discusses various LoS and RF technologies and presents combinations that can be used for communication. Finally, a hybrid strategy that combines the best properties of individual technologies is proposed, demonstrating the feasibility of such a solution.
Free-Space Terahertz Spectrum Analysis With an Optoelectronic Hybrid System

We present an optoelectronic hybrid system to perform high-resolution spectrum analysis in the terahertz regime. The concept is based on phase-locking of the difference frequencies of at least three continuous-wave (CW) distributed-feedback diode lasers by mixing the laser light with the radiation from a high-power electronic narrow-band source. Unlike for state-of-the-art spectrum analysis, no additional frequency extenders are required, keeping the system less complex and more cost-effective. Following our concept, we can generate laser difference frequencies from 0 to more than 1 THz with several hertz precision, which can be easily expanded to even higher frequencies, e.g., by cascading more lasers within our synchronization scheme. We demonstrate the systems frequency resolution and stability by measuring the first five harmonics of an electronic W-band source, and the first three of an active multiplier chain millimeter-wave source, using a CW gallium arsenide (GaAs) photoconductive antenna as a receiver.
Multimode Bipolar Hybrid Asymmetrical Dual Active Bridge Converter for Bipolar DC System

A bipolar hybrid asymmetrical dual active bridge (BiHADAB) converter is proposed in this paper. The BiHADAB converter can reconfigure and operate in the quasi-parallel or series modes by rearranging the switching pattern. Power transfer and current stress of the converter with the two modes are analyzed. It is revealed that the converter can achieve higher power transfer capability with the quasi-parallel mode. In light-load cases, the current stress can be lower if the converter switch to the series mode. Theoretical analysis is verified with simulation and experiment.
FSSD: FPGA-Based Emulator for SSDs

Solid State Drives (SSDs) have become increasingly popular due to their superior access latency and bandwidth compared to Hard Disk Drives (HDDs). However, to fully understand the impact of SSD design and microarchitecture on end-to-end application performance, researchers need to move beyond treating SSDs as black-box components. Unfortunately, purchasing multiple SSDs for research is expensive and ineffective since the underlying microarchitecture is still unknown to the system designer. While simulators have become the most popular method for studying SSDs, existing software-based simulators lack real data transfers and cannot simulate the latency from NVMe and PCIe interfaces. Additionally, simulating the entire SSD using software codes is time-consuming and limits the number of experiments that can be run in a reasonable amount of time. To address these issues, we present FSSD, an FPGA-based emulation system that models the latency and access patterns of an actual NVMe SSD. FSSD takes advantage of the flexibility of an FPGA, enabling users to customize SSD microarchitecture features and explore the design space for data-intensive applications. FSSD can be interacting with real operating systems, instead of relying on Virtual Machines like most other software simulators do. Evaluations show that FSSD provides over 1000x speedup compared to software-based simulation using the SimpleSSD simulator. The ability to customize SSD parameters and emulate NAND latency with high precision makes FSSD a valuable platform for SSD research and development. FSSD is also open-sourced to benefit the research community.
Application of the Method of Multivariate Multi-stage Forecasting Based on the LSTM Deep Learning Model for Bitcoin Price Time Series

Forecasting data and research on cryptocurrency price forecasting methods are increasing in importance. So far, methods based on LSTM deep learning architecture have shown the best results in forecasting cryptocurrency prices. In order to improve the accuracy of forecasting data, this paper investigates the application of a multivariate multistep forecasting method based on the LSTM deep learning model for the bitcoin price time series and evaluates its effectiveness. The variants of multivariate multistep forecasting implementation based on deep learning LSTM are analyzed, and a direct approach for building multistep forecasts is chosen. Time series of bitcoin price and cumulative stability and drawdowns are used as input data. Based on our research, we found that short-term predictions were most accurate using models trained on trading data. However, for long-term forecasts, incorporating stability features slightly improved accuracy.
Extending ROS Transform Library for Massive Autonomous Robots

TF is a core library in ROS that manages positional relationships between objects. TF has fundamental problems in that accesses are sequentially executed due to the giant locking and its interfaces' limited functionality. TF is designed for managing only a few objects, and it has two problems supporting such use cases as pedestrian prediction or traffic congestion avoidance. The first problem is poor operators, and the second problem is the generation of phantom anomalies. We address the first problem by designing a SQL-based interface with various powerful operators and the second problem by designing a phantom anomaly protocol based on ghost record protocols.
Characterizing Runtime Performance Variation in Error Detection by Duplicating Instructions

Soft error rate has been increasing due to the shrinking size of transistors, leading to an elevated risk of catastrophic failures in modern computer systems. Error detection by duplicating instructions (EDDI) is a software-based technique to mitigate soft errors with a low runtime performance overhead and has been widely adopted in many safety- and mission-critical real-time systems such as space applications. However, these systems are commonly sensitive to runtime performance overheads the protection techniques incur. Few studies have investigated the performance of EDDI across various system designs and operational parameters, hence lacking a complete understanding in the literature. In this paper, we conduct comprehensive experiments to study the variation of EDDI runtime performance overhead and characterize the root causes. We find that there exist significant variations in performance overheads of EDDI, due to a few architectural and program-level factors. Based on the findings, we propose two practical techniques FuzzyB and Celer: FuzzyB uses an input searching technique to bound EDDI runtime performance overhead across different inputs for a given program; while Celer reduces EDDI run-time performance overheads using compiler transformation (by 25.08% reduction).
Mobility Extraction Methods in AlGaN/GaN HEMTs

This work deals with the comparison of the results of different methods for the extraction of charge mobility on AlGaN/GaN HEMT devices. They are, field effect mobility (μ<inf>FE</inf>), effective mobility (μ<inf>eff</inf>) and low field mobility (μ<inf>0</inf>), varying the gate length (L<inf>g</inf>) and channel width (W), temperature (T) and gate voltage (V<inf>g</inf>). In addition, the available data allowed the examination of figures of merit for the drain current (I<inf>d</inf>), output conductance (g<inf>d</inf>) and transconductance (g<inf>m</inf>), as well as the analysis of the behavior of the series resistance (R<inf>SD</inf>), threshold voltage (V<inf>T</inf>), and subthreshold slope (S). In this study, it was found that HEMTs have higher mobility for higher W, W/L<inf>g</inf> and V<inf>g</inf>, and for lower T in a range between 298K to 373K.
PPTD: A Privacy-Preserving Truth Discovery Scheme in Mobile Crowdsensing

Truth discovery (TD) can discover ground truths of sensing tasks from the submitted reliable or unreliable sensing data. However, the existing TD algorithms mainly focus on the privacy preservation of mobile users, and few studies consider the reliability of mobile users when they participate in calculating the ground truths of sensing tasks. To simultaneously ensure the reliability and privacy preservation of mobile users, this paper proposes a privacy-preserving truth discovery scheme in mobile crowdsensing. Firstly, we select reputation value to evaluate the reliability of mobile users. Meanwhile, to preserve the privacy of reputation value, we define two novel concepts, namely reliability level and contribution degree, to represent the reliability and contribution of each mobile user in calculating the ground truths of sensing tasks. Secondly, according to the contribution degrees, we redesign the process of truth discovery which can publicly verify the contribution degrees of mobile users and improve the accuracy of ground truths of sensing tasks. Finally, theoretical analysis shows that our scheme is private and secure. The comparisons with the existing schemes show that the error rate, mean absolute error, and root mean squared error in the proposed scheme are reduced by about 54%-63%, 13%-34%, and 15%-46%, respectively.
Comparing Signature Detection of Convolutional Neural Network in Low-Level and Large Language Model in High-Level Programming Language

Can GPT models, specifically those designed for natural language processing, outperform convolutional neural networks (CNN) when it comes to signature detection? Signature detection is one of the most important tasks involved in deep learning. It is used to identify objects and patterns in data. On the other hand, the extremely rapid development of chat bots like chat GPT creates the potential for their use in malware detection with preprocessed code. In this study, the authors compare the performance of signature detection using low-level on the ResNet50 network and high-level programming languages on the GPT 2 model. The results show that signature detection in high-level languages perform better than ResNet50 in low-level languages. This suggests that GPT 2 and general chat bot related models are more suited for signature detection tasks.
Low-Carbon Economic Dispatch of Park-Level Integrated Energy System Considering Carbon Trading and Flexible Response of Supply Side

To reduce the carbon emissions of park-level integrated energy system (PIES) and achieve the low-carbon economic operation of PIES, a low-carbon economic dispatch strategy for PIES that considers ladder-type carbon trading mechanism and flexible response of supply side is proposed. Firstly, carbon trading model is introduced in PIES to preliminarily constrain carbon emissions. Secondly, Kalina cycle that can use waste heat to generate electricity is introduced, and combine it with gas turbine (GT) and waste heat boiler (WHB) to construct the combined heat and power (CHP) unit to achieve flexible response on the supply side. Then, carbon trading and flexible response on supply side are jointly optimized to establish a low-carbon economic dispatch model of PIES. Finally, the superiority of proposed model in improving system economy, low carbon, and exergy efficiency is verified by multiple scenarios.
Inter-turn Fault Diagnosis for Permanent Magnet Synchronous Motor Based on High Frequency Rotating Voltage Injection

The degradation of insulation between adjacent coils of the stator winding of a permanent magnet synchronous motor (PMSM) can lead to inter-turn short-circuit faults, which will cause more serious faults if they are not detected. Therefore, in order to improve the reliability of PMSM, this paper proposes an inter-turn fault (ITF) online diagnosis method, which evaluates the ITF of a motor by injecting a high-frequency rotating voltage of fixed frequency, analyzing the current response generated by this voltage and the high-frequency voltage in the control loop, and assessing the ITF of the motor by the asymmetry of the high-frequency component of the modulated wave. Experimental results show that since the injection of high frequency rotating voltage is not affected by the current fundamental asymmetry, our proposed method is capable of diagnosing mild ITF, and the fault indicator is insensitive to sudden changes in speed and current, thus improving the robustness of fault diagnosis.