Miracle-dz/UniFAR-FTUs huggingface dataset:
A Human-in-Loop UAV Assisted Piloting Scheme Design with Constraint Flight Space

In this paper, we focus on the flight safety of human-operated UAVs in restricted airspace and design a human-in-loop assisted piloting scheme, which contains a new assisted piloting algorithm and the corresponding hardware and software. In the design process of the assisted piloting algorithm, the degree of intervention of the assisted piloting on the manual operation instructions is considered to be reduced under the premise of ensuring safety, and the scheme does not interrupt the mission process of the UAV and does not generate additional safety risks compared with traditional schemes such as forced landing. The compatibility with various flight controllers has been considered in the hardware design process to make the assisted pilot circuit more versatile. Finally, based on the Gazebo simulation platform, semi-physical simulation experiments are designed to verify the effectiveness.
An MWD Channel Estimation Method Based on Deep Learning

In the technology of MWD(Measurement While Drilling), due to the particularity of the transmission medium, the transmission signal method, and the complexity of the on-site working environment, the MWD telemetry system not only has the frequency selectivity caused by multipath propagation, high-intensity Gaussian noise, but also the channel nonlinearity and strong periodic noise. The performance of traditional channel estimation methods is severely limited under these circumstances, and may even fail to work under extreme conditions. Therefore, this paper proposes a new frequency domain channel estimation method based on deep learning, named DnCENet. Considering the serious impact of the noise in the system, the dataset will be input to a small denoising sub-network first, and then through a channel estimation sub-network, and finish the entire channel estimation process finally. To make the deep network converge quickly and achieve better performance, this article uses pre-training and model fine-tuning techniques in deep network optimization. The experiment results show that: compared with traditional methods, DnCENet has significantly lower MSE(Mean Square Error) than LS(Least Square) and MMSE(Minimum Mean Square Error) methods, and even compared with existing advanced deep learning methods, DnCENet still has obvious advantages. In terms of robustness, DnCENet has performed simulations on the measured noise of the MWD system channel without additional training and the simulation results show the superiority of the deep network proposed in this paper. This method can guide the correct selection of frequency points and code rates in the MWD telemetry system, and improve the communication quality and rate. In the future, this method could be used in the frequency domain equalization of the system to enchance the demodulation performance of the system.
Productivity and Human Factors Improvement in Manufacturing Systems. A Systems Modeling and Simulation Approach

Systems modeling, and simulation have been widely used to better understand the behavior of complex systems. Some of the main benefits of these tools are the cheaper and faster way of testing compared to real-life testing, and the flexibility that simulation offers via the tweaking of parameters, variables, and functions. It has drawbacks too, such as precision and accuracy, difficulty of modeling and simulating productivity. In fact, there are multiple ways of defining productivity, either through activities or output or time. This article offers a new definition of productivity and in contrast, non-productivity, and the different levels between them, and we study the possibility to model complex manufacturing systems and simulate them. The main contribution of this research consists in developing a flexible model and tool able to simulate complex manufacturing systems. The goal of this tool is to model productivity and be flexible enough to allow for easier and cheaper alternatives to testing, and complex enough to encompass a large part of possibilities.
An Improved Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on the Pontryagin’s Minimum Principle

In order to find a balance between fuel economy and durability of the hybrid power system, this paper presents an improved energy management method based on Pontryagin's minimum principle (IM-PMP). This strategy introduces a continuous cost function to limit the output power change rate of fuel cell, and introduces a satisfaction optimization function to limit the state of charge (SOC) of the lithium battery, then compares with the strategy based on the finite state machine (FSM). The simulation results verify that the presented method significantly improves the fuel economy, reduces the performance degradation of the fuel cell, and effectively maintains SOC of the lithium battery.
Probing the role of haem domain mobility in flavocytochrome b2

Flavocytochrome b2 (L-lactate: cytochrome c oxidoreductase (E.C. 1.1.2.3)) is a homotetramer from the yeast Saccharomyces cerevisiae. Each subunit is comprised of two domains. The first 100 residues being a cytochrome b5-like domain, with the remaining 411 residues forming a domain which binds a flavin mononucleotide (FMN). Two electrons from the oxidation of the substrate are deposited on the flavin and then passed individually to the haem. The physiological electron acceptor is cyto chrome c. The haem domain of flavocytochrome b2 has been identified as being mobile with respect to the flavin domain. Evidence for this has been derived from the crystal structure of the Saccharomyces cerevisiae enzyme, and from nuclear magnetic resonance (NMR) spectroscopy studies on the related flavocytochrome b2 from Hansenula anomala. The 2.4 A resolution crystal structure of the enzyme indicates an edge to edge, FMN to haem, distance of just under 10 A. The rate of electron transfer over this distance is —1500 s'. Redox potentials are known and the intervening medium has been examined. Considering these factors, the rate has been suggested to be lower than would be expected for an electron transfer over this distance. It has been suggested that domain mobility may account for the lower than expected rate of interdomain electron transfer. In order to test this idea, and develop the flavocytochrome b2 system as a model for biological electron transfer, the following approach has been taken: Site directed mutagenesis has been used to change an alanine residue to a cysteine residue at position 67 in the haem domain (Ala67Cys). The Ellman assay has been used to confirm the existence of a disulphide bond between position 67 and the naturally occurring cysteine at position 233 on the FMN domain. Changes in the electron transfer properties for this mutant form have been examined using steadystate and pre-steady-state kinetics. Whilst L-lactate dehydrogenase ability remains relatively unaltered, interdomain electron transfer has fallen by 40-fold, to 37 s ' .
Manifold-Derived Gradients Link the Cortical Morphology to Gene Expression in Patients with Epilepsy

Diffusion embedding techniques offer the means to map brain organizational principles into a single manifold, called gradients, which help us understand how multi-scale brain features are related to each other. These links can help us understand the neural mechanism, and further broaden our insight into the relation between structure and function. In this study, manifold gradient was leveraged to study the link of macroscopic morphology to cortical gene expression in 92 genetic generalized epilepsy with generalized tonic-clonic seizures (GTCS), and 116 healthy controls. Diffusion map embedding was used to estimate the gradients of morphometric similarity (MS) connectome, which described the interregional similarity through five cortical features. The results revealed significant between-group differences in the MS gradient in GTCS, mainly involved in high-level networks. And the changes in MS gradient showed a positively correlation between expression profile of epileptic risk genes.
Pulmonary Nodules Detection via 3D Multi-scale Dual Path Network

It has become an epidemic trend to use computer-aided detection (CADe) systems based on deep learning to help physicians improve the efficiency and diagnostic rate of pulmonary nodules. Considering the problems of low sensitivity, high false positives, and difficulty in detecting small nodules in the existing CADe system, this paper proposes a pulmonary nodule detection model based on a 3D multi-scale dual-path network. The proposed model uses a 3D convolutional neural network (CNN) for spatial information extraction to generate more representative features and extracts multi-scale feature information by constructing an Unet-like network structure and adding pre-processing blocks. The model uses multi-scale architecture and dual-path network (DPN) to extract deep features, combines the characteristics of ResNet and DenseNet to improve feature extraction capabilities. In addition, the focal loss is introduced as the classification loss function of the algorithm to alleviate the imbalance problem of positive and negative samples. The results based on the LUNA16 dataset show that this model can improve the diagnosis rate of pulmonary nodules with a low false-positive rate.
The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study

Wearable speech enhancement can improve the recognition accuracy of the speech signals in stationary noise environments at 0dB to 60dB signal to noise ratio. Beamforming, adaptive noise reduction, and voice activity detection algorithms are used in wearable speech enhancement systems to enhance speech signals. In recent works, a word rate recognition accuracy of 63% for a 0db signal-to-noise ratio is not satisfactory for a robust speech recognition system. This paper discusses the experimental study using fixed beamforming, adaptive noise reduction, and voice activity detection algorithms with the inclusion of −10dB to 20dB signal to noise ratio for different types of noises to test the wearable speech enhancement system’s performance in noisy environments. It also compares deep learning-based noise reduction methods as a benchmark for speech enhancement and word recognition for different noise levels. We have obtained an average word rate recognition accuracy of 5.74% at −10dB and 93.79% at 20dB for non-stationary noisy environments. The outcome of the experiments shows that the selected methods perform significantly better in the environment with high noise dB for both stationary and non-stationary noise. We found that there is no significant statistical difference between the stationary and non-stationary noise word recognition and SNRs level. However, the deep learning-based method performs significantly better than the fixed beamforming, adaptive noise reduction, and voice activity detection algorithms in all noisy levels.
A Compact and Broadband Chip-Resistor Loaded Circularly Polarized Microstrip Antenna

Abstract A novel single-feed circularly polarized (CP) microstrip antenna (MPA) is presented. Capacitive disk coupling feed is used in the proposed antenna. With a resistor load connecting with another capacitive disk at the opposite position, CP radiation over a wide frequency band is realized. Experimental results show that the fabricated antenna has a –10 dB impedance bandwidth of 36.5%, and a 3-dB axial ratio (AR) bandwidth of 15.5% at the center frequency 2 GHz, and owns feature of low profile (0.67λ × 0.47λ × 0.0402λ). This antenna can be used in occasions when wide CP performance and compactness is desired.
A Flow Scheduling Model for SDN Honeypot Using Multi-Layer Attack Graphs and Signaling Game

When analyzing the allocation of deception resources, there is often a general lack of support from geographic and high-dimensional information of attack graphs, seriously, the hierarchical approach to threat response is often neglected. In this paper, we propose an SDN-based deception resources allocation model using a multi-layer attack graph and signaling game. Depending on the threat level, our model enables the defender to dynamically allocate deception resources by regulating the SDN flow. We firstly design a multi-layer attack graph with geographical information and a Top-N module to quickly filter out interesting attack paths, which are classified according to the threat level. Secondly, we devise a signaling game model for flow scheduling quantified by the attack graph, to generate optimal allocation strategies. Two experiments are shown to illustrate the validity of our findings, and finally, we compare our model with other studies.
The educated citizen: a case study and guide for teaching public health to undergraduates in Australian universities

It has been recognised that a lack of understanding of the importance of public health within the community is a significant impediment to improvement in population health. This has prompted organisations in Australia and the United States to implement programs designed to improve the profile of public health. The American Institute of Medicine (IOM), for example, published a report in 2003 entitled 'Who will keep the public healthy? ' (Gebbie, Rosenstock, and Herandez, 2003) that challenged tertiary institutions to incorporate public health into non-specialist undergraduate curricula. In response, many tertiary education institutions in the US have begun incorporating public health components into non-specialist undergraduate curricula with the aim of producing graduates who are able to engage with and lead communities in building healthier and more sustainable societies. The University of Newcastle (Australia) was one of the first Australian universities to respond to the IOM report. Our efforts to increase undergraduate public health literacy via the provision of extra-curricular student forums, and the use of internet and Smartphone-based learning alongside more traditional teaching methods, has led to positive feedback from the students enrolled in our courses. We present our experience as a case study detailing the outcomes, potential challenges and recommendations for other institutions who wish to begin equipping students with the knowledge and skills required to lead communities towards a healthier future.
Vulmg: A Static Detection Solution For Source Code Vulnerabilities Based On Code Property Graph and Graph Attention Network

As the number of vulnerabilities continues to rise, security incidents triggered by vulnerabilities emerge endlessly. Current vulnerability detection methods still have some problems, such as detecting only a single function, relying heavily on expert knowledge, and being unable to achieve automation. According to the observation of the Juliet dataset, we find vulnerability exists not only within the single function but also between the called function and the calling function. Meanwhile, there are some differences between vulnerable functions and non-vulnerable functions in the code property graph. Therefore, this article proposes a vulnerability detection solution named VULMG, which converts vulnerability detection into the graph classification problem. VULMG includes a vectorization component named VecG and a deep learning classification model named MGGAT. Based on the code property graph, VecG extracts the lexical, grammatical, and semantic information of the source code as a feature matrix and extracts information such as structure, control, and dependence as three adjacency matrices. MGGAT is a deep learning model based on the graph attention network, which is used for graph classification. Besides, VULMG uses the FCG to associate the calling function with the called function so that it can detect the cross-function vulnerabilities. We selected CWE369 and CWE476 from the Juliet dataset for testing, and the F1 scores were 94.43% and 96.3%. The evaluation results indicate that VULMG outperforms Flawfinder, RATS, BiLSTM, SVM, and GCN, which verifies the effectiveness of the proposed solution.
Sustainability of Coexistence of Humans and Machines: An Evolution of Industry 5.0 from Industry 4.0

The fourth industrial revolution carries with it various expectations for the future of sustainable and effective development. Industry 4.0 takes a significant part of everyday life. There is no way. The application of digital life on smart phones, touch screens and smart classrooms may be significant indicators of this. Not just computer information and life but also human-like robotics would take a long time. In the immediate future, you will also see the picture of a modern 5.0 model for manufacturing. Since of emerging technology and increasingly evolving and increasing AI-based approaches, remaining on the top is getting more and more challenging. Robots are much more relevant as now the brain-machine’s technology can be merged with the human mind, as artificial intelligence advances. There are coercive problems in the world economy because efficiency is to be improved without the human workforce being robbed of the manufacturing sector. This paper presents the idea of sustainable business 5.0, where machines are intertwined with the human brain and function as partners rather than rivals, against these obstacles. In addition, the effect of Industry 5.0, which is more likely to create more jobs than to reduce, will be discussed on manufacturing and the economy as a whole. The paper describes modern technology, from IoT to news. Thanks to our view, the integration of these innovations would turn industry 4.0 into industry 5.0.
Constructing a Composite Indicator for Manufacturing Companies Using Lean Metrics and Analytic Hierarchy Process

The aim of this study is to construct a composite indicator which can measure the degree of leanness of manufacturing firms using the analytic hierarchy process. The composite lean indicator is based on several lean performance categories which in turn is composed of a number of indicators. Analytic hierarchy process is applied in order to determine the relative importance or weight of each lean performance category and indicator by getting the opinion of lean experts. Results show that Process & Equipment is deemed as most important compared to Supplier & Customer Relationships, Human Resources, and Manufacturing Planning & Control. Furthermore, Error-Proof Equipment, Customer Orders Delivered JIT, Worker Involvement in Continuous Improvement Programs, and Visual Control of the Shop Floor are all considered to be comparatively more important with respect to other lean indicators.
Three-phase series-connected photovoltaic generator for harmonic and reactive power compensation with battery energy storage device

This paper presents a photovoltaic (PV) generator along with a battery energy storage system connected in series with a three-phase grid. The objective of the proposed system is to provide uninterruptable compensation to the series-connected grid and non-linear load during strong sunlight as well as at night or in cloudy conditions. The interface between the grid and the PV is carried out through a voltage source converter (VSC), eliminating both the current and voltage harmonics and compensating the reactive power. The DC voltage control of the DC bus capacitor is employed in order to maintain unity power factor operation of the system, irrespective of changes in solar radiation level or due to change in load. Another control scheme is implemented to charge and discharge the connected battery whenever the sun goes out, to meet the DC bus voltage requirement of the VSC through a bidirectional DC-DC converter.
Application of MCST method to design the blade leading edge in axial compressors

A blade leading edge spike flow can usually be found on the condition that the discontinuous curvature curves between blade leading edge and blade surface, which is a common result of design the blade with separate curves of blade leading edge and blade surface in traditional blade design procedure. However, in recent, many researchers found that strong leading edge spike might cause significant detrimental effect on blade performance. In order to weaken or even eliminate the leading edge spike flow, blade leading edge should be designed carefully to connect with the blade surface with a smooth curvature change. In the present paper, a novel blade leading edge curve design method, named as CST (“class function/ shape function” transformation technique) method, was improved for compressor blade design. Shape function in the hyperbolic form was put forward to ensure radius and thickness of the leading edge can meet the requirements of the engineering manufacturing under the premise of continuous curvature. By using the aforementioned blade leading edge redesign method, a series of compressor blade profiles were designed with curvature-continuous blade leading edge. The designed operating Mach number ranged from 0.5 to 1.1. And then, the aerodynamic characteristics of each blade profile were analyzed by numerical method. The simulation results showed that after the redesign of blade leading edges, when the inlet Mach number less than 0.85, the useful blade incidence range can increase about 2 degree, and minimum blade profile loss can decrease about 8%. Finally, the profiles of an inlet stage in a transonic axial-compressor were regenerated by using the modified CST method. The numerical simulation results showed that, compared to the prototype, the compressor peak efficiency increased nearly 1.5 points, and the pressure ratio increased about 2%.
A Unified Programming Model over Heterogeneous Data Planes

Software Defined Networking (SDN) separates the data plane and the control plane in the traditional network, and the data plane communicates with the control plane through standard protocols. With the development of SDN, there are many southbound communication protocols in academia, such as OpenFlow, Protocol Oblivious Forwarding (POF), Programming protocol-independent packet processors (P4). The design ideas and implementation of these protocols are very different. Therefore, many controllers provide some high-level Application Programming Interfaces (API) supporting different protocols to shield the complexity of the protocol and facilitate the writing of network strategies, but none of them can support different southbound protocols at the runtime. That is, the controller can’t support a variety of different protocols while the data plane has kinds of switches. For example, Open Network Operating System (ONOS) supports P4 and OpenFlow, but it can only support a specific data plane at the runtime, so it cannot combine the strengths and advantages of different types of programmable switches. This paper proposes a unified programming model that can support both the P4 and POF protocols at the runtime so that network users can combine the high performance of the P4 and the flexibility of the POF. We also provide kinds of API to facilitate the writing of network strategies. In certain application scenarios, the losing of data packets caused by updating the data plane pipeline can be effectively eliminated with the programming model.