Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Alkyl-Terminated Silicon Nanoclusters by a Solution Route

We describe the synthesis and characterization of alkyl-capped nanocrystalline Si (R-n−Si) prepared by the reaction of SiCl4 with Mg2Si in ethylene glycol dimethyl ether (glyme) and surface-terminated with various alkyl groups, R-n−Si (R = methyl, ethyl, n-butyl, and n-octyl). This reaction produces crystalline nanoparticles with surfaces that can be chemically modified. The resultant crystalline nanoparticles can be suspended in organic solvents or isolated as a powder. The nanoclusters were characterized by transmission electron microscopy (TEM), high-resolution TEM, selected area electron diffraction (SAED), and Fourier transform infrared (FTIR) spectroscopy, electron paramagnetic resonance (EPR) spectroscopy, UV−vis absorption, and photoluminescence spectroscopy. The average cluster size depends on the reflux time of Mg2Si with SiCl4, which provided nanoclusters with an average size of 2−5 nm. HRTEM confirms the presence of crystalline nanoclusters, and SAED is consistent with diamond-structured sil.
Seven Reasons Why: An In-Depth Study of the Limitations of Random Test Input Generation for Android

Experience paper: Testing of mobile apps is time-consuming and requires a great deal of manual effort. For this reason, industry and academic researchers have proposed a number of test input generation techniques for automating app testing. Although useful, these techniques have weaknesses and limitations that often prevent them from achieving high coverage. We believe that one of the reasons for these limitations is that tool developers tend to focus mainly on improving the strategy the techniques employ to explore app behavior, whereas limited effort has been put into investigating other ways to improve the performance of these techniques. To address this problem, and get a better understanding of the limitations of input-generation techniques for mobile apps, we conducted an in-depth study of the limitations of MONKEy-arguably the most widely used tool for automated testing of Android apps. Specifically, in our study, we manually analyzed Monkey's performance on a benchmark of 64 apps to identify the common limitations that prevent the tool from achieving better coverage results. We then assessed the coverage improvement that Monkey could achieve if these limitations were eliminated. In our analysis of the results, we also discuss whether other existing test input generation tools suffer from these common limitations and provide insights on how they could address them.
Policy Controlled Multi-domain cloud-network Slice Orchestration Strategy based on Reinforcement Learning

The concept of network slicing plays a thriving role as 5G rolls out business models vouched by different stakeholders. The dynamic and variable characterization of end-to-end cloud-network slices encompasses the composition of different slice parts laying at different administrative domains. Following a profit-maximizing Slice-as-a-Service (SaaS) model, such a multi-domain facet offers promising business opportunities in support of diverse vertical industries, rendering to network slicing marketplace members the roles of Infrastructure Provider, Slice Provider, and Tenants. The effective realization of SaaS approaches introduces a dynamic resource allocation problem, manifested as challenging run-time decisions upon on-demand slice part requests. The Orchestrator is hence responsible to perform an optimized decision on-the-fly on which elasticity requests to address based on an orchestration policy defined within the context of Network Slice architecture for the followed revenue model. This paper presents a slice management strategy for such an orchestrator can follow, based on reinforcement learning, able to efficiently orchestrate slice elasticity requests to comprehend the maximum revenue for the stakeholders of end-to-end network slice lifecycle. The proposed strategy orients a Slice Orchestrator to learn which slice requests to address as per availability of the required resources at the different participating Infrastructure Providers. The experimental results show the Reinforcement Learning based Orchestrator outperforms several benchmark heuristics focused on revenue maximization.
Localization Methods based on Error Analysis and Modeling in One Dimension

Wireless sensor networks (WSNs) have shown promise in a broad range of applications. One of the primary challenges in leveraging them lies in gathering accurate position information for the deployed sensors while minimizing power cost. Through analyzing the error associated with acquiring such position information, we developed several novel localization methods based on modeling the analyzed error and applying rigorous mathematical and statistical principles in order to produce improved location estimates compared with existing methods. The methods presented herein have been utilized for a one-dimensional space for proof-of-concept, simplicity of presentation, and to illustrate how viable, single-dimensional applications can be approached. These methods utilize only two mobile beacons that can be mounted to a vehicle, rather than a costly, large array. The primary measurement taken to perform localizations is received signal strength (RSS). Unlike many previously existing methods, the techniques presented herein utilize practical, realistic assumptions and were progressively designed to mitigate incrementally discovered limitations. To exercise and analyze the developed methods, a multiple-layered simulation environment was developed in tandem. The approach, developed methodologies, and software infrastructure presented herein provide a framework for future endeavors within the field of wireless sensor networks.
Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks

Cascading delays that propagate from a primary source along a railway network are an immediate concern for British railway systems. Complex nonlinear interactions between various spatio-temporal variables govern the propagation of these delays which can quickly spread throughout railway networks, causing further severe disruptions. To better understand the effects of these nonlinear interactions, we present a novel, graph-based formulation of a subset of the British railway network. Using this graph-based formulation, we apply the Spatial-Temporal Graph Convolutional Network (STGCN) model to predict cascading delays throughout the railway network. We find that this model outperforms other statistical models which do not explicitly account for interactions on the rail network, thus showing the value of a Graph Neural Network (GNN) approach in predicting delays for the British railway system.
A Performance Comparative at Low Temperatures of Two FET Technologies: 65 nm and 14 nm

Since Moore's law has promoted a rapid evolution of FET technologies sometimes, key aspects stay unnoticed while novel nodes appear at the horizon. In this manuscript we present a study comparing the performance of the 65 nm MOSFET and the 14 nm SOI finFET technology, both at low temperatures, where main electrical parameters are analyzed. In the case of the MOSFET we cooled it down to 77 K, while for the finFET, we achieve to cooled it down to 340 mK. Thus, the intend of this work is to provide a vision of which technology is better at each application, such as quantum computing, astronomy, infrared and UV detectors, particle detectors, etc.
A Novel five phase current reconstruction technique fed by dual inverter

Techniques of phase current reconstruction through a single current sensor have been reported for motor drive systems to reduce cost and volume. In the existing studies, all the reconstruction methods are based on conventional motor. And the reconstruction precision is largely affected by the dead zones. For the purpose of improving the DC voltage utilization and fault tolerant capability, in this paper, a phase current reconstruction technique based on five phase open-end winding permanent magnet synchronous motor (OW-PMSM) is proposed by measuring the DC link current. Four nonzero and one zero voltage vector combinations are used to estimate the currents. A simple phase shifting method for the pulse width-modulated (PWM) signals is proposed so that the complex work of phase shifting in different sectors of five phase motor is avoided. The effectiveness of the proposed method is verified by simulation.
Classifying Early and Late Mild Cognitive Impairment Stages of Alzheimer’s Disease by Analyzing Different Brain Areas

Early detection of the stage of mild cognitive impairment (MCI) is very important for early diagnosis of dementia and slowing down the progression of Alzheimer’s disease. Atrophy values obtained by magnetic resonance imaging (MRI), one of the neuroimaging techniques, are considered to be a fairly powerful diagnostic biomarker used in the detection of Alzheimer. Since the transition from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) is irreversible and implies a significant change in a patient’s condition, we focus on to the classification of these two stages in this work. In this study, atrophy values of 13 brain areas of 90 early mild cognitive impairment, 38 late mild cognitive impairment, 14 mild cognitive impairment participants were used in the diagnosis of the disease. Diagnosis groups have been classified with an accuracy of 68.8% as a result of data estimations obtained using classification algorithms. When the classification has been made only by taking effective values, an accuracy rate of 75% has been achieved and this means a significative improvement. The deep analysis of the disease and the focusing on the brain regions where it has more impact in order to distinguish the stages early, show the potential of utilizing MRI features to improve cognitive assessment.
High Precision Laser Fault Injection using Low-cost Components.

This paper demonstrates that it is possible to execute sophisticated and powerful fault injection attacks on microcontrollers using low-cost equipment and readily available components. Earlier work had implied that powerful lasers and high grade optics frequently used to execute such attacks were being underutilized and that attacks were equally effective when using low-power settings and imprecise focus.This work has exploited these earlier findings to develop a lowcost laser workstation capable of generating multiple discrete faults with timing accuracy capable of targeting consecutive instruction cycles. We have shown that the capabilities of this new device exceed those of the expensive laboratory equipment typically used in related work.We describe a simplified fault model to categorize the effects of induced errors on running code and use it, along with the new device, to reevaluate the efficacy of different defensive coding techniques. This has enabled us to demonstrate an efficient hybrid defense that outperforms the individual defenses on our chosen target.This approach enables device programmers to select an appropriate compromise between the extremes of undefended code and unusable overdefended code, to do so specifically for their chosen device and without the need for prohibitively expensive equipment. This work has particular relevance in the burgeoning IoT world where many small companies with limited budgets are deploying low-cost microprocessors in ever more security sensitive roles.
Towards IoT Slicing for Centralized WLANs in Enterprise Networks

Emergence of Internet of Things (IoT) and its affiliated services brought new challenges, such as diversity of service requirements, massive connectivity, security and isolating concerns, and integration issues in networks and business. Network slicing is a powerful tool to address these challenges leveraging technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this article, we propose an architecture to address diverse requirements and resource provisioning challenges of IoT in WiFi networks. The difference between this work and the prior work is on the combination of IoT slicing with WiFi. We utilize the WiFi slicing paradigm to leverage SDN capabilities, such as service classification based on user demands and elastic resource allocation, to map different services into IoT slices. We translate Quality of Service (QoS) requirements into different IoT slices and formulate system requirements as an optimization problem with the objective of maximizing the total throughput of the WiFi system while meeting the IoT slices’ constraints. Based on the numerical results, our proposed architecture not only guarantees all slicing service demands but also improves the total throughput of a WiFi network by 8%.
Study and Research on the Identification of the Leaves of Indonesian Herbal Medicines Using Manhattan Distance and Neural Network Algorithms

Indonesia is one country that has enormous potential in the use of medicinal plants as herbal medicines. Utilization or use of plants, especially medicinal plants as a means of healing disease has long been used. However, people in general still have difficulty knowing the types of plants that can be used as herbal medicines. This is due to the limited information and knowledge possessed by the community to identify and identify the use of medicinal plants. This study describes the development of feature extraction in leaf images for the identification of medicinal plants, where the main difficulty in the leaf identification stage is that the morphological (physical leaf shape) and physiological (leaf shape characteristics) are different for each type of leaf. There are three methods proposed in this research, namely the first is the proposed leaf feature model in the form of 16 perimeter point distances to leaf centroid points and seven median line connectors. The second is to develop leaf feature extraction methods and algorithms so that 23 leaf shape features can be generated for each type of medicinal plant. Third, making a prototype identification system or the introduction of medicinal plants based on leaf morphological characteristics. The identification process is carried out using two approaches, namely the Manhattan Distance and Artificial Neural Networksimilar. In the testing phase of the resulting software prototype, 51 types of medicinal plant leaves were used where each type consisted of 10 different leaf images. Based on the trial results, the accuracy rate of identification or recognition of medicinal plants using Manhattan Distance is 99.0196%, and when using Neural Networks, the accuracy rate reaches 100% for training data and 84.31% for testing data.
A Decentralized Strategy for Cooperative Driving among Autonomous Cars at Lane Closures

This paper presents a cooperative driving strategy - Follow Along Strategy (FAS) - for autonomous cars, aimed at improving traffic conditions on a two-lane freeway with a lane closure. The strategy is designed to minimize communication requirements; cars only need an intimation of the lane closure. FAS is compared with two baseline cases based on current road scenarios - one where cars are informed of lane closures well ahead and another where they are not - via Agent-based Modeling (ABM) simulations. Results show that FAS increases traffic throughput while reducing the proximity between cars and keeping long halts at a minimum.
M-BEB: Enhanced and Fair Binary Exponential Backoff

Due to the development and success that MANETs have encountered, they have become a primary choice in many research and practical areas. The vast majority of wireless technology is based on the IEEE 802.11 standard. The MAC layer of the IEEE 802.11 Distributed Coordination Function (DCF) uses the Binary Exponential Backoff (BEB) algorithm to treat collisions in wireless networks, which is useful in reducing the probability of collision but at the cost of many network performance measures, such as throughput, energy consumption, equity. Several research studies have been proposed to improve the performance of the MAC layer of IEEE 802.11 in MANETs. This work discusses some of these approaches and proposes a new algorithm to treat throughput problems, the rate of packets delivered, and fairness in a mobile environment. The algorithm is simulated under NS2. The simulation results were used to compare the algorithm’s performance regarding the BEB of the IEEE 802.11 DCF, the Improved Exponential Backoff (IBEB), and the opposite Exponential Backoff (OBEB).
A Parallel-Connected Hybrid Inductive and Capacitive Coupler With Double-Sided LCL Compensation Topology

With a combination of the parallel compensated inductive and capacitive coupler, a hybrid coupler is proposed to take full advantage of the resonant components. The basic characteristics of the hybrid coupler are analyzed. To transfer the voltage excitation provided by the inverter into the current excitation, a double-sided LCL compensation topology is added and designed to work at the resonant frequency, so that it has no effect on the basic characteristics of the coupler. Based on the basic characteristics, a discussion of the voltage and current stress is conducted. The experiments are conducted with a 100 $W$ output hybrid power transfer (HPT) system, achieving a DC-DC efficiency of 77.8%. The experimental results show that the proposed hybrid coupler can realize a unity current gain, which is in coincidence with the basic characteristics. Moreover, the measured voltage and current stress show good agreement with the calculated values, verifying the accuracy of the stress analysis.
Research Spectrum Framework in the Field of Educational Technology

Interest in research in the field of educational technology has increased recently due to the rapid development of information and communication technology. The industrial revolution 4.0 is one of the causes of various disruptions in the education sector which require in-depth study in various contexts. Various studies at research institutions and universities are basically concerned with a number of aspects. This article shows the results of grouping the focus of research on various domains. This cluster mapping was developed after various theoretical analyzes that formed the educational technology ecosystem were carried out.
Electrochemical biosensors for pesticide determination in food samples

An overview of the different electrochemical bio-s e n s o rs for pesticide determination in fo o d matrices is presented. Cholinesterase-based bio-sensors, for the analysis of carbamate and orga-nophosphorus pesticides have been the most w i d e ly used in this specific area of research
Ultra-High Resolution KOMPSAT-5 SAR Sea-Ice Random Radiometric Anomaly Formulation Based On Hybrid Domain Texture Decomposition

Image texture in synthetic aperture radar (SAR) is a mixture of inherent texture of the surface backscattering and non-coherent backscattering interactions in the form of random radiometric anomalies that ends up in the loss of information and data inconsistency. According to the multilayer structure of polar region, which is a non-deterministic and constantly changing medium on the basis of time, these time- dependent anomalies can lead to misinterpretation. As a novel approach, a time-frequency Gamma probability distribution function (PDF) formulation of such multiplicative anomaly has been proposed for high-resolution SAR sea-ice dataset information analysis and the texture quality improvement. The proposed hybrid-domain approach is tested on a real KOMPSAT-5 SAR satellite sea-ice image and the results verify the formulation, modeling, and their suppression scenario.
Crossing-Road Pedestrian Trajectory Prediction Based on Intention and Behavior Identification

Pedestrian trajectory prediction plays an important role in both pedestrian collision avoidance systems and autonomous driving. However, most of the previous works have ignored the interaction between traffic participants or only take it into account implicitly based on neural networks, which need a large number of training data and hold poor scenario adaptability. Meanwhile, pedestrian changeable behaviors are also always overlooked in trajectory prediction. In this paper, we present a novel pedestrian trajectory prediction method that involves pedestrian intention and behavior information into prediction. Verification of this method has been conducted in our provided BPI dataset. Without previous training of pedestrian trajectories, the method shows good scenario adaptability and provides accurate path prediction results for eight defined typical pedestrian crossing-road scenarios in Is prediction horizon, especially for stopping scenarios.
Generation of Driving Scenario Trajectories with Generative Adversarial Networks

The future of transportation is tightly connected to Autonomous Driving (AD). While a lot of progress has been made in recent years, there are still obstacles to overcome. One of the most critical issues is the safety verification of AD. A scenario-based verification approach that shifts tests from the fields to a virtual environment seems like a sophisticated approach to tackle the safety verification as tests need to be revised whenever changes are made to the AD. However, collecting and labelling data that can be used to construct scenarios is expensive and time-consuming to compute. In this work, we propose a unified framework for trajectory generation and validation in a consistent and principled way. We first explore methods to generate artificial trajectories that resemble the previously captured ones. More specifically, we consider two architectures based on Generative Adversarial Networks (GANs): recurrent GANs and a recurrent Autoencoder in combination with GANs. Moreover, we investigate the use of different metrics to evaluate the quality of generated trajectories which is a nontrivial task.
Feature Extraction on Brain Wave Activities in Rapid Serial Visual Presentation Stimulus

The images stimulus that appear rapidly and contain different frequencies might have an effect on brain activities. This paper proposed how to determine a rapid visual stimulus and its effects to the brain activities. The stimulus that used in this paper is the Rapid Serial Visual Presentation (RSVP) in the shape of London’s aerial images. This stimulus are presented with three kinds of frequencies that are 5 Hz, 6Hz, and 10 Hz. The RSVP stimulus is often used to determine the changes of brain activities that connected to attention and concentration level. Our works were analyzing the changing of EEG waves (Alpha and Gamma) and the EEG spectral. The outcomes represented of brain activities in the occipital and parietal areas. The results shown the 5 Hz of RSVP stimulus had the most influences to the brain hemispheres.
Agile Edge Classification of Ocean Sounds

The maritime environment is characterized by a scarcity of resources of power, sensing, processing, and communications. The resource constraints impose limitations in information acquisition which involves data collection and data processing to yield meaningful statistics. The contribution of this work is on custom software and hardware methods for low power, low data-rate processing for the application of classification of ocean sounds. The combination of light processing software and custom hardware allow the development of efficient cyber-physical maritime IoT systems. A simulation-based study is provided to evaluate the ability of the software method for agile learning of features for ocean sounds classification. In addition, a practical implementation on a custom hardware emulator is provided to demonstrate the potential of the method to classify ocean sounds on low power, inexpensive seaborne IoT nodes.