Miracle-dz/UniFAR-FTUs huggingface dataset:
A CLCC Impedance Matching Method Under Maximum Efficiency Tracking in Wireless Power Transfer System

The key problem of the wireless power transfer (WPT) system is transfer efficiency, transfer efficiency will be affected by the variation of transfer distance or load resistance. The CLCC matching network is designed to eliminates the influence of nonlinear characteristics of the rectifier circuit, the programmable capacitor array (PCA) is introduced to realize the wide capacitance adjustable range, which ensures the system can always track the optimal resistance. The experimental results agree well with the theoretical analysis, the suggested impedance matching network can achieve load variation up to $600\Omega$ under maximum efficiency tracking control (METC).
Fracture healing is delayed in the absence of gasdermin signaling

Amino-terminal fragments from proteolytically cleaved gasdermins (GSDMs) form plasma membrane pores that enable the secretion of interleukin-1β (IL-1β) and IL-18. Excessive GSDM- mediated pore formation can compromise the integrity of the plasma membrane thereby causing the lytic inflammatory cell death, pyroptosis. We found that GSDMD and GSDME were the only GSDMs that were readily expressed in bone microenvironment. Therefore, we tested the hypothesis that GSDMD and GSDME are implicated in fracture healing owing to their role in the obligatory inflammatory response following injury. We found that bone callus volume and biomechanical properties of injured bones were significantly reduced in mice lacking either GSDM compared with wild-type (WT) mice, indicating that fracture healing was compromised in mutant mice. However, compound loss of GSDMD and GSDME did not exacerbate the outcomes, suggesting shared actions of both GSDMs in fracture healing. Mechanistically, bone injury induced IL-1β and IL-18 secretion in vivo, a response that was mimicked in vitro by bone debris and ATP, which function as inflammatory danger signals. Importantly, the secretion of these cytokines was attenuated in conditions of GSDMD deficiency. Finally, deletion of IL-1 receptor reproduced the phenotype of Gsdmd or Gsdme deficient mice, implying that inflammatory responses induced by the GSDM-IL-1 axis promote bone healing after fracture.
Fault Diagnosis of Electric Actuator Based on Spiking Neural Network

Actuator is an important part of the control system, and its fault diagnosis is essential to guide the safe and stable operation of the control process. This paper designed a fault diagnosis method for electric actuators based on the spiking neural network(SNN) of the adaptive threshold neuron model. First, the required training Dataset and test Dataset were obtained from the established experimental platform. Through the local mean decomposition(LMD) of the Dataset, the signal was decomposed into product function components with real physical meaning, so as to obtain the main characteristics of the Dataset; then the adaptive threshold was introduced to optimize the neuron structure, and through the relative ordering learning algorithm(ROL), the accuracy of fault recognition was improved. Finally, the test Dataset was used for verification. The experimental results showed that the method made full use of the effective information of the data and could be effectively applied to the fault diagnosis of the electric control valve.
REACT: Enabling Real-Time Container Orchestration

Fog and edge computing offer the flexibility and decentralized architecture benefits of cloud computing without suffering from the latency issues inherent in the cloud. This makes fog computing very attractive in real-time and safety-critical applications, especially if combined with container-based technologies. Whereas different orchestration systems are available to manage the container placement based on their resource demand, no orchestration system is considering real-time requirements for containerized applications. In this paper, we present the architecture and design of a real-time container orchestrator based on Kubernetes. Moreover, this paper defines metrics for the performance evaluation of real-time containers, and describes an initial model for allocating a mixture of real-time and non-real-time containers. We present an initial implementation of our real-time container extension and evaluate its feasibility on Linux-based systems.
A Preamble Based MAC Mechanism in Ad-Hoc Network

In this paper, we propose a preamble based medium access control (P-MAC) mechanism in Ad-Hoc network. Different from traditional carrier sense multiple access (CSMA) in Ad-Hoc network, P-MAC uses much shorter preamble to establish the network. First, we propose the P-MAC mechanism to shorten the time of establishing the Ad-Hoc network. Next, focusing on the Ad-Hoc power line communication (PLC) network, we propose a frequency division power line communication (FD-PLC) network architecture to obtain the best communication frequency. Due to the large-scale networks in industry, P-MAC can be exploited to speed up the establishment of the Ad-Hoc PLC network. Finally, we compare our mechanism with CSMA. Numerical results indicate that our strategy greatly shortens the time of establishing the Ad-Hoc network.
A Novel Sparse Reconstruction Based DOA Estimation Method Under Grid Mismatch

Under the condition of gain-phase errors, the existing methods only consider that the arrival angle strictly falls on the divided grid, which will pose limitation on the accuracy of the direction of arrival (DOA) estimation. In this paper, a new DOA estimation method based on sparse reconstruction is proposed, which applies the first-order Taylor expansion to modify the array output model under the condition of grid mismatch, and then uses the alternate optimization method to solve the non-convex optimization problem, obtaining the grid angle and bias angle. Compared with the existing methods, the proposed method improves the accuracy of the DOA estimation without requiring change of the grid spacing. This method also performs independent of array phase errors. Simulation results verify the effectiveness of the proposed method.
A Dual-Linear Polarized Wideband Waveguide-Based Antenna Block

This paper presents a novel dual-linear polarized wideband waveguide-based antenna block for Ku-band application. The designed antenna is composed of a square-radiator and a two-port feed network. The radiating element can be seen as a wide-slot-fed cavity-backed radiator; which is split into four smaller apertures by a set of cross thin-wall. Moreover, the feed network is divided into horizontal-polarized feeding part and vertical-polarized feeding part. Simulated results show that the dual-linear polarized antenna achieves an operating bandwidth from 12.36 GHz to 14.70 GHz in the horizontal polarization, and from 12.30 GHz to 14.1 GHz in the vertical polarization with a high isolation better than 44 dB. Besides, the simulated gain of the antenna is about 14 dBi and cross polarizations are better than -25 dB.
Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes

A variety of islet autoantibodies (AAbs) can predict and possibly dictate eventual type 1 diabetes (T1D) diagnosis. Upwards of 75% of those with T1D are positive for AAbs against glutamic acid decarboxylase (GAD65 or GAD), a producer of gamma-aminobutyric acid (GABA) in human pancreatic beta cells. Interestingly, bacterial populations within the human gut also express GAD and produce GABA. Evidence suggests that dysbiosis of the microbiome may correlate with T1D pathogenesis and physiology. Therefore, autoimmune linkages between the gut microbiome and islets susceptible to autoimmune attack need to be further elucidated. Utilizing in silico analyses, we show that 25 GAD sequences from human gut bacterial sources show sequence and motif similarities to human beta cell GAD65. Our motif analyses determined that most gut GAD sequences contain the pyroxical dependent decarboxylase (PDD) domain of human GAD65, which is important for its enzymatic activity. Additionally, we showed overlap with known human GAD65 T cell receptor epitopes, which may implicate the immune destruction of beta cells. Thus, we propose a physiological hypothesis in which changes in the gut microbiome in those with T1D result in a release of bacterial GAD, thus causing miseducation of the host immune system. Due to the notable similarities we found between human and bacterial GAD, these deputized immune cells may then target human beta cells leading to the development of T1D.
Realization of 3D Reconstruction Algorithm Based on 2D Video

At present, how to realize 3D reconstruction from 2D video is a research hotspot. However but the reconstruction accuracy for areas with less or no texture is low. Thus, for the scene reconstruction with less texture, this paper proposes a 3D reconstruction method based on the depth map. The first is to extract the relevant feature points from the two-dimensional image frame. During this process, Harris-Sift algorithm is used to extract features of the image frame, and Kanade-Lucas-Tomasi (KLT) tracking algorithm is used to tracke and matche feature points. In this way, all adjacent image frames are reconstructed by 3D point cloud, and the recondtruction result is optimized through the bundle adjustment algorithm. Furthermore, the depth map is calculated, and the depth map of each frame in the video is obtained by using the planar scanning algorithm. Finally, the three-dimensional reconstruction of the object is realized by fusing the depth map, and point cloud conversion. As shown by the experimental results, the real scene is accurately restored by the relevant algorithm provided in this paper, and the problem of reconstruction of areas with less texture is solved.
Mixed Logic Dynamic modeling used for Cloud Services Optimization in Smart City

The Internet of Things (IoT) technology for cloud services has made some progress. However, there are still many limitations in largescale information flow perception modeling and performance optimization. In this paper, the Mixed Logic Dynamic modeling method of IoT information flow perception is studied. The IoT sensor nodes, controlled nodes and coordination nodes are used to describe the system application scenarios, whereas the automaton is used to carry out internal information transmission, This lays an important foundation for the coordination and optimization of the system. Then, an open queuing method based on large-scale information flow perception modeling and network delay analysis method is proposed. By analyzing the end-to-end delay of the node path and the average delay analysis of the whole queuing network, the open queuing is obtained, So as to maximize the performance of the perceived network in the IoT. Aiming at the characteristics of distributed data of information flow perception in the IoT, a priority-based queuing network is proposed to model and analyze the aggregation nodes based on embedded multi-core System on Chip (SoC), which greatly improves the performance of embedded multicore SoC.
iMooX - a MOOC platform for all (universities)

iMooX.at is the Austrian MOOC platform founded in 2014. This platform offers free, openly licensed online courses for all, so called Massive Open Online Course (MOOCs). It aims to offer university education in an innovative and digital way.In this article, we will briefly look at the history of the platform and its main milestones till now. Finally, a few possible development steps will be pointed out and discussed.
A Pedestrian Detection Algorithm Based on Channel Attention Mechanism

The main contribution of this paper is to introduce the channel attention mechanism into the feature extraction network, and propose the channel attention mechanism module CA, which realizes the efficient fusion of multi-scale features. The deformable convolution is used to replace the traditional convolution operation, and a new detection head is designed, which can predict the position of pedestrians more accurately than the original detection head. CSP is a pedestrian detection algorithm with high accuracy and fast speed, and its structure is very simple. However, there is still great potential for improvement in multi-scale feature fusion and detection head design. This paper proposes a pedestrian detection algorithm based on channel attention mechanism, which is called CA-CSP. On the basis of the original CSP algorithm, the channel attention mechanism module CA is added, and the original detection head is replaced with a detection head based on deformable convolution. The new annotation is used to evaluate the proposed pedestrian detection algorithm CA-CSP on Caltech pedestrian dataset. On the reasonable setting, using a single Nvidia 1660 GPU, CA-CSP has obtained 3.97% of MR−2, and the original algorithm CSP has reached 4.59% of MR−2. Compared with CSP, CA-CSP has lower MR−2. Therefore, CA-CSP has better performance than the original CSP algorithm.
Flexural and compressive behavior of composite sandwich panel with novel M-Typed core

Present paper has experimentally and numerically investigation on the mechanical behavior of composite sandwich panel with novel M-Type lattice core subjected to three-point bending and compressive loads. For this purpose, a composite sandwich panel with M-typed core made of glass fiber has been fabricated in this experiment. In order to fabricate the sandwich panels, the vacuum assisted resin transfer molding (VARTM) has been used to achieve a laminate without any fault. Afterward, polyurethane foam has been injected into the core of the sandwich panel. The force-displacement curves obtained from sandwich panel compression and three-point bending tests, showed that an optimum mechanical strength with a considerable lightweight. Moreover, polyurethane foam has improved the flexural and compressive strength of sandwich panels by 15%. For the foam-filled sandwich panels, the experimental data was compared to numerical simulation and good agreement has been observed.
Pareto optimal design of multilayer microwave absorbers for wide-angle incidence using genetic algorithms

An application of the non-dominated sorting genetic algorithm II (NSGA-II) in designing high performance multilayer electromagnetic absorbers is discussed. Compared with traditional Pareto genetic algorithms, the NSGA-II can reduce the complex of calculation efficiently, as well as provide better optimisation results. Based on existing lossy materials, the Pareto fronts and corresponding optimisation schemes for different frequency bands and incident angles are presented. For instance, in the frequency band of 8–12 GHz with arbitrary incident angles and polarisations, a highest reflection of −17.02 dB can be obtained for the four-layer absorber with total thickness of 4.1933 mm.
Efficient Loop Closure Detection Method for Lidar SLAM in Challenging Environment

With the development of automatic driving cars, simultaneous localization and mapping (SLAM) becomes a widely discussed topic. Loop closure detection, as an important module in lidar slam, can provide strong constraint for localization and mapping. In classical lidar SLAM algorithm, traditional loop closure detection method often fails to find out the matching nodes, or even has false positive results, especially in challenging environment like the crossroads or sharp turns. In this paper, a lidar SLAM loop closure detection method using adjacent nodes to check the reliability is introduced. A series of experiments is implemented to verify the effectiveness of the method.
Modular Production Control with Multi-Agent Deep Q-Learning

The automotive industry is increasingly focusing on product customization. The concept of Modular Production addresses this issue by providing more flexibility in production with Automated Guided Vehicles transporting products between modular workstations. The added complexity of Modular Production Control calls for approaches that can handle the scheduling complexity while also minimizing production costs. As a result, literature has focused on two promising approaches: Deep Reinforcement Learning and Multi-Agent Systems. Both approaches have their advantages. Especially in complex, large-scale production environments with random breakdowns, those two fields have been seldomly combined, though. As a result, this article aims to fill that research gap by introducing a Deep Reinforcement Learning Multi-Agent System approach for Modular Production Control. We introduce a reward design incentivizing agents to achieve maximal throughput. In addition, we show that the method learns optimal behavior even in a large-scale production environment with random machine breakdowns. Lastly, we compare the Multi-Agent System to a single-agent implementation of the Deep Reinforcement Learning approach and conclude that the Multi-Agent Deep Reinforcement Learning method learns and solves the Modular Production Control problem with the same solution quality as the single agent. Hence, the approach allows to foster MAS benefits such as robustness without losses in the solution quality.
Reliability Research and Practice of Kilowatt P-band GaN Power Transistor

Staring from the practical applications, this paper studies the methods to improve P-band kilowatt-level GaN power transistor reliability. Through application circuit design, we discussed the use of grid circuit design, anti-self-excitation circuit design, and anti-voltage design to improve the reliability of the GaN power transistor profoundly, and through the integrated heat pipe type equilibrium temperature design and printed board heat dissipation design, we improved the heat dissipation capacity significantly, and effectively improved the reliability of GaN power transistor applications. The reliability growth test reduced the failure rate of the GaN power transistor to less than 0.5%, proved that the measures are reasonable and adequate, and it will be a good reference for GaN power transistor reliability research.
Anomaly root cause diagnosis from active and passive measurement analysis

Diagnosis demands a deep analysis of data to identify the root cause of an anomaly and still mostly relies on human experts. The increase of Internet traffic combined with the arrival of the encrypted protocol QUIC which invalidates many troubleshooting methods, urges to automate this process. To this effect, both domain familiarity and analysis skills are required. In this work we present our methods and strategies to detect the root cause of anomalies from active and passive network measurement and we share our plan towards an automatic root cause diagnosis. We focus on four root causes : transmission, congestion, application limited and packet delay variation, and present the building blocks of classification methods.
A Q-learning based Dynamic Power Control Algorithm for D2D Communication Underlaying Cellular Networks

This paper considers the dynamic power control problem in Device-to-Device (D2D) communication underlaying a cellular network. A resource allocation algorithm is proposed to dynamically perform spectrum and power allocation for cellular users (CUs) and D2D user (DU) pairs in the network. In particular, a Q-learning based dynamic power control (Q-DPC) algorithm is introduced to perform power allocation for DU pairs, which takes into account the minimum throughput requirement of CUs with an objective to maximize the overall system throughput of the network. The Q-DPC algorithm introduces Q-learning in power allocation, and aims to optimize the power allocation for all DU pairs sharing the same spectrum resources in the network at the arrival of a new DU pair, including the new arriving DU pair and all existing ones. Simulation results show that the proposed Q-DPC algorithm outperforms a random power allocation (RPA) algorithm in terms of the overall throughput of the network.
New concept of space radiator with variable emittance

A new concept of space radiator of variable emittance for satellite thermal control is presented. The radiator is composed of two stages which exchange heat through radiation between finned surfaces covered with variable emittance coatings, whose emissivity is increased with temperature. Under cold conditions the radiative heat coupling between the stages is minimal, preventing the equipment subcooling, while in hot conditions the heat exchange is increased. A steady-state mathematical model was developed and numerically coupled to an optimization algorithm, and a design optimization procedure was performed. Two optimization criteria were employed: minimization of the radiator mass and the power consumption of heater under cold conditions. The Generalized Extremal Optimization algorithm was used as the optimization tool. The design was then modeled in detail using the SINDA/Fluint package considering orbital conditions of the EQUARS satellite. The performance of the radiator concept proposed here was also compared to a conventional design, for the same operational conditions. It is envisioned that the utilization of such radiators in micro-satellites will lead to considerable electric power savings for safe heaters and may contribute to a longer satellite life. In the design trade-off, the cost for this saving is additional radiator mass and volume.
How does the representation rate of features in a landscape affect visual preferences? A case study from a post-mining landscape

Previous studies of visual preferences for landscapes have been dominated by papers monitoring the presence of individual landscape features or their physical attributes. The objective of our study has been to evaluate how the representation rate of these features affects visual preferences for a landscape. We have studied a post-mining landscape that provides component physical features of a diverse and contrasting character. On the basis of an analysis of surface images, in combination with a questionnaire survey, our study has determined that the representation rate of most evaluated features affects the aesthetic value of the entire landscape scene. It has been established that certain groups of features in a post-mining landscape evoke various preferential tendencies among the public. The aesthetic value of a landscape decreased with increasing representation of surface quarries and urban structures, while increased representation of water and of rural structures resulted in an increasing aesthetic value. Woody vegetation and arable land were most preferred when they were represented non-dominantly, while mining infrastructure features were evaluated most negatively when they were non-dominant features of the landscape. Our findings thus point to the importance of the representation rate of features in the landscape as one of the key factors influencing the visual perception of the entire landscape scene.
Sparse vs. Dense Signatures for Grant-Free NOMA with Deep Learning Based Activity Detection

Joint signature design and activity detection with deep learning has been extensively investigated for grant-free Non-Orthogonal Multiple Access (NOMA) in massive machine-type communications. In this paper, we propose to use sparse-pattern masked signatures for the purpose of joint signature design and activity detection. With a sparse mask matrix derived from a regular Low-Density Parity-Check (LDPC) code, we show that the use of regular sparse learnable signatures performs comparable to that of dense learnable signatures. Furthermore, there is no any observable performance advantage for the use of complex signatures compared to that of simple real signatures.