Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of a SWIPT enabled Cognitive Radio Sensor Network using TS protocol

In this work, we analyze the performance of a simultaneous wireless information and power transfer (SWIPT) enabled Cognitive radio sensor network (CRSN) using time switching (TS) technique at the sensor node. We consider a secondary sensor node that conducts information transmission based on decode and forward approach. A CRSN is a new approach and is capable of solving the issues of spectrum crunch and power limited wireless sensor networks (WSNs). Energy harvesting is incorporated to ensure longer lifetime of the cognitive sensor nodes. The system performance is investigated with respect to the outage probability (OP) and overall network throughput of the CRSN. We extract the exact mathematical expression for the outage probability of both the primary as well as secondary networks. The effect of distinct system and channel parameters on the OP and the overall network throughput is evaluated. Monte Carlo simulations are performed and it verifies the accuracy of the mathematical analysis presented.
Detection of Back-Side Cracks in Steel Structure Using A Differential Eddy Current Testing Probe

Eddy current testing (ECT) is a technique that is used to identify deficiencies and faulty in metallic components including aircraft, piping, bridges, and other civil engineering structures. Early detection of cracks is essential for ensuring structural integrity, safety, and reliability before it reaches the point of fracture. ECT method becomes common in NDT because it can detect a defect in conductive materials. Crack can exist in any form of size and shape either on surface or subsurface components where it can contribute to the complete fracture. The development of an ECT probe for the evaluation of backside defects in the galvanized steel plate is discussed in this paper. The ECT probe is developed based on fluxgate sensors for identifications of defects. The ECT probe that was created is assessed by executing a phase-sensitive detection technique at the excitation field of 70 Hz to 310 Hz. The efficiency of the magnetic probe is estimated by identifying the existence of slits based on the changes of the magnetic response where the induced eddy current is caused. Using the developed magnetic probe, the signal intensity is evaluated on the backside of the sample plate. The results show a signal change in the crack area. The integrated ECT probe is expected to be applied for the evaluation of backside inaccuracies.
Mechanically Driven Solidly Mounted Resonator‐Based Nanoelectromechanical Systems Magnetoelectric Antennas

The miniaturization of antennas has been a significant challenge in the field of electronics and telecommunications. In recent years, mechanically driven thin‐film bulk acoustic resonator (FBAR) magnetoelectric (ME) antennas have emerged as a promising solution, demonstrating superior miniaturization capabilities compared to conventional state‐of‐the‐art compact antennas. While nanoelectromechanical systems (NEMS) FBAR ME antennas exhibit high miniaturization potential, their suspended thin‐film heterostructures render them fragile and exhibit low power handling capabilities. The findings demonstrate that solidly mounted resonator (SMR) NEMS ME antennas on a Bragg acoustic resonant reflector offer a compelling solution. With a circular resonating disk of 200 μm diameter operating at 1.75 GHz, these SMR‐based antennas display a high antenna gain of −18.8 dBi and a 1 dB compression point (P1dB) of 30.4 dBm. Compared to same‐size FBAR ME antennas with a free‐standing membrane, SMR‐based antennas exhibit significantly higher structural stability and 23.3 dB stronger power handling capability, in addition to easier fabrication processes. The compatibility of the simple fabrication processes with complementary metal–oxide–semiconductor technology, along with the dramatic miniaturization, high power handling, robust mechanical properties, and much higher antenna radiation gain, make these SMR‐based ME antennas a promising candidate for future antenna systems.
Event and Activity Recognition in Aerial Videos Using Deep Neural Networks and a New Dataset

Unmanned aerial vehicles (UAVs) are now widespread available. Yet the more UAVs there are in the skies, the more video data they create. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on UAV video content understanding is of great importance. In this paper, we introduce a novel task of event recognition in unconstrained aerial videos in the remote sensing community and present a dataset for this task. Organized in a rich semantic taxonomy, the proposed dataset covers a wide range of events involving diverse environments and scales. We report results of plenty of deep networks in two ways: single-frame classification and video classification. The dataset and trained models can be downloaded from https://1cmou.github. io/ERA_Dataset/.
Wideband High-Isolation Antenna Array for in-Band Full-Duplex Radios

In this article a wide-band high-isolation antenna with operating frequency of ${1.85 -2.55\ GHz}$ is presented. This design consist of co-located TX and RX antenna arrays at different angles with absorbing material between them to get the high isolation of −75dB over the fractional bandwidth of 31.8%. This antenna array can be useful for GSM-1900, Wi-Fi, LTE bands and simultaneous transmit and receive applications in several wireless communication bands.
Machine Learning-Based Resource Optimization for D2D Communication Underlaying Networks

Deploying device-to-device (D2D) communication over underlaying cellular network can significantly enhance the spectrum utilization. However, co-channel interference will occur when D2D pairs share the same radio resource with cellular users. To mitigate the interference within a reasonable range, a machine learning based resource reuse scheme for D2D and cellular users is proposed in this paper. Specifically, we formulate an optimization problem to maximize the total throughput of D2D pairs and cellular users by optimally allocating subcarrier and power within the limits of the interference threshold. Since the formulated problem is a mixed integer non-linear programming problem, we solve it in two steps. First, we assign the orthogonal subcarriers to different cellular users to maximize the total throughput of all cellular users. Then, D2D pairs are allowed to reuse different subcarriers to further enhance the throughput without affecting the performance of cellular users. The second step is still NP-hard and therefore we propose a low-complexity algorithm based on the pointer network, a specific neural network structure proposed recently. Results reveal that, with remarkably reduced complexity, the proposed scheme outperforms the conventional resource allocation algorithms.
A Scalable High-interaction Physical Honeypot Framework for Programmable Logic Controller

Programmable logic controller (PLC) is an industrial digital computer that has been ruggedized and adapted for the control of manufacturing processes, such as automobile manufacture, or gas pipelines, or power generation. Due to closed source and vendor-specific proprietary firmware, it is difficult to develop a scalable high-interaction honeypot for PLCs. In this paper, we present and discuss a new scalable high-interaction PLC honeypot framework based on physical devices. This framework aims to solve the problems of existing physical honeypots while providing the advantages of virtual honeypots. Specially, we first introduce the main gap existing in virtual PLC honeypots. Then, we present a cheap, flexible, and large-scale-deployment solution for physical PLC honeypots according to the concrete problems. Finally, we evaluated our framework based on Siemens S7-300 PLCs. Our experiment shows that physical PLC honeypots have the absolute advantage in interaction capability and it is entirely feasible to extend the deployment scope with low response delay.
Cloud-Edge Collaborative Optimization for Information Layer of Energy Internet

Accompanied with the energy crisis and global warming, the concept of Energy Internet (EI) emerges, which aims to make full use of renewable energy. Among many possible structures, a particularly interesting EI structure has multiple microgrids interconnected by energy routers. In order to ensure that energy routers can control and transmit energy efficiently and reliably among microgrids, information technology plays an important role. Focusing on the information layer of the considered EI structure, a novel edge-cloud coordination architecture is proposed in this paper. A task allocation strategy is developed to improve the reliability and efficiency of the information layer. In order to reduce the information delay as well as ensuring the reliability of information transmission, a joint optimization problem is formulated and solved by particle swarm optimization algorithm. The effectiveness of the proposed method are demonstrated by simulations.
PERONA-MALIK DIFFUSION DRIVEN CNN FOR SUPERVISED CLASSIFICATION OF HYPERSPECTRAL IMAGES

We present a novel auto-machine learning partial differential equations (PDE) driven deep learning framework for the classification of hyperspectral images (HSIs). The work is inspired by the famous PDE in image processing, namely, the Perona-Malik (PM) equation, which can form a scale-space and is capable of edge-preserving denoising using anisotropic diffusion (PM diffusion). In this framework, we firstly propose auto-machine learning-based trainable PM diffusion blocks (TPM-blocks) and then cascade them into a deep convolutional neural networks (CNN). Specifically, the $1 \times 1$ convolution layer and the trainable PM diffusion unit (TPMDU) are integrated as the TPM-block, and then multiple TPM-blocks are stacked to form a novel end-to-end deep learning architecture. We show that our deep learning method has the capacity of learning discriminative spectral and spatial features of HSIs. Experimental results on several popular datasets demonstrate that the proposed method achieves state-of-the-art performance compared with the several existing deep learning-based methods.
Analytical RF Pulse Heating Analysis for High Gradient Accelerating Structures

The main aim of this work is to present a simple method, based on analytical expressions, for obtaining the temperature increase due to the Joule effect inside the metallic walls of an RF accelerating component. This technique relies on solving the 1-D heat-transfer equation for a thick wall, considering that the heat sources inside the wall are the ohmic losses produced by the RF electromagnetic fields penetrating the metal with finite electrical conductivity. Furthermore, it is discussed how the theoretical expressions of this method can be applied to obtain an approximation to the temperature increase in realistic 3-D RF accelerating structures, taking as an example the cavity of an RF electron photoinjector and a traveling wave linac cavity. These theoretical results have been benchmarked with numerical simulations carried out with commercial finite-element method (FEM) software, finding good agreement among them. Besides, the advantage of the analytical method with respect to the numerical simulations is evidenced. In particular, the model could be very useful during the design and optimization phase of RF accelerating structures, where many different combinations of parameters must be analyzed in order to obtain the proper working point of the device, allowing to save time and speed up the process. However, it must be mentioned that the method described in this article is intended to provide a quick approximation to the temperature increase in the device, which of course is not as accurate as the proper 3-D numerical simulations of the component.
Effect of addition of ameliorative materials on the distribution of As, Cd, Pb, and Zn in extractable soil fractions

The effects of lime, limestone, and zeolite addition on the availability of As, Cd, Pb, and Zn in three contaminated soils were investigated in a pot experiment after four vegetation periods of spring wheat, spring barley, and oat. The results showed different responses of extractable element portions to soil amendment when 0.01 mol dm−3 aqueous CaCl2 was applied as a soil extraction agent. Substantial differences were evident among the investigated elements as well as among the individual soil treatments. Except natural zeolite, the ability of ameliorative materials to redistribute cadmium and zinc from a soil solution into less mobile but labile soil fractions was observed. The lead availability was less affected and the extractability of arsenic even increased in some of the treated pots. Moreover, the availability of arsenic was more affected by different characteristics of experimental soils than by individual soil treatments. It was found that these treatments can be applied neither for multicontaminated nor for all the soil types. The soil treatments had a lower effect on the less mobile soil fractions.
Small Object Change Detection Based on Multitask Siamese Network

This paper presents a small object, represented by approximately ten pixels in an image, change detection method based on multitask Siamese network for multitemporal SAR images. In our proposed method, not only change detection task but also object classification task is introduced to the network. The classification task is expected to enhance the performance of change detection by providing semantic information of changes and to focus attention of the network towards the target small object class. We tested the proposed method for a real-world application of car parking lot monitoring with 1-meter resolution TerraSAR-X images. Experimental results show that the f-measure of change class is improved by more than 7% over conventional methods based on post-classification, PCA+K-means and Siamese network. Furthermore, car-to-car type change is detected by the proposed method with 25% higher accuracy over the method without the classification task.
Supramolecular architecture in some 4-halophenylboronic acids

Crystal structures of 4-chloro- and 4-bromophenylboronic acids (1 and 2) and hydrates of 2 and 4-iodophenylboronic acid in two different forms (2a, 3a, and 3b), which were characterized by single-crystal X-ray diffraction methods, are reported. In structures 1 and 2, −B(OH)2 forms a syn−anti conformation, but it exists in syn−syn as well as anti−anti conformations in the hydrated structures 2a, 3a, and 3b. In all the structures, the molecules are held together by O−H···O interactions formed by −B(OH)2 groups. The C−H···X (X = Cl, Br, and I) interactions play an important role in crystal packing. In 2, Br···Br interactions are also observed. Interestingly, all the three hydrates form similar types of three-dimensional structures with the formation of channels, which are occupied by water molecules. The two hydrates of 4-iodophenylboronic acid (3a and 3b) are distinguishable on the basis of O···O short contacts, with an identical host lattice of the boronic acid.
Robust Paths in Random Geometric Graphs with Applications to Mobile Networks

Combating connectivity loss due to node movement is of fundamental importance in mobile networks and many routing protocols have been described in the literature to ensure that the delay due to such link breaks is as low as possible. In this paper, we propose a pre-emptive approach towards countering the adverse effects of mobility: Modeling the network as a random geometric graph G, we obtain paths in G from the source to destination that are guaranteed to survive for a target time, in the event of node mobility. We call such paths as robust paths and obtain bounds on the minimum lengths of paths with a given robustness parameter. We also show how such robust paths can be used to create routes with target survival time in mobile wireless networks.
Compact Dual Circularly Polarized Patch Antenna with High Ports Isolation for MIMO WLAN Application

This paper proposes a design of two-ports circularly polarized stacked patch antenna with square ring slot feeding technique for multiple-input-multiple-output (MIMO) application. The antenna with a quarter-wavelength slot and chamfering patch. , which radiates at 2.4 GHz. , occupies an overall area of $\mathbf{96}\times\mathbf{96}\times\mathbf{42\ mm}^{\mathbf{3}}$. The 7.6-dBic gain antenna prototype presents an axial ratio lower than 0.4 dB in the operating frequency band (2.4 to 2.485 GHz) as well as a port isolation higher than 27 dB. Therefore. , this antenna demonstrates good port-to-port isolation. , axial ratio (AR) as well as a compact dimension and is suitable to be used in systems that operate in a wireless local area network (WLAN) frequency band.
Spectral-Spatial Joint Target Detection of Hyperspectral Image Based on Transfer Learning

Hyperspectral image (HSI) target detection has become an increasingly important research topic while still facing various aspects of difficulties, such as the influence of spectral variability, the deficiency of available samples and the limited capability of utilizing spectral-spatial information. In this paper, a novel HSI target detection method combining spectral and spatial information is proposed. Siamese convolutional neural network (S-CNN) is applied as spectral feature extractor according to the spectral similarity, in which pixel pairs generated from the source domain HSI are transferred to target domain so as to solve the problem of insufficient samples. Then spatial post-processing guided by the initial result is used to combining spatial context information to further improve the detection performance. Experimental results show that the proposed method performs well in hyperspectral target detection.
A Beam Squinted Linearly Polarised Radial Line Slot Array Antenna with Improved Return Loss Bandwidth

This paper presents an investigation to improve the poor return loss performance of linearly polarised radial line slot array (RLSA) antenna. The problem was tackled by applying the beam squinting technique. The antenna consists of radiating surface with a circular cavity and supports outward-travelling electromagnetic waves. The antenna is excited via a rear mounted modified disk-ended dielectric coated SMA connector. The simulation results show that the return loss performance has improved significantly over the entire frequency band with a return loss bandwidth of 52.5%. The far-field result indicates that the antenna has achieved a peak directivity of 24.7 dBi at 17 GHz with a beam squinted angle of 18° and a side lobe level of −10 dB.
SURF-LSTM: A Descriptor Enhanced Recurrent Neural Network For Indoor Localization

We propose SURF-LSTM, a low complexity deep architecture to learn image absolute pose (position and orientation) in indoor environments using SURF descriptors and recurrent neural networks. Given the strongest SURF features descriptors of an input image, we use 2 layers of bidirectional long short term memory (LSTM) to model the sequential relation between them to learn the 6 degrees of freedom absolute pose in an arbitrary reference frame. In addition to achieving competitive performance compared to existing image localization methods, our system can be trained in less than 10 minutes instead of hours by the state of the art. It requires as small as 0.0128 MB to save the image frame rather than 0.08 MB compared to other methods that use the cropped images and the weights file needs 1.5 MB of storage compared to 100 MB of other methods which leads to significant time and space efficiency.
Are High Severity Fires Increasing in Southern Australia?

Wildfires worldwide are becoming more frequent but are they also becoming more severe? Here we used remotely sensed burn-severity data from wildfires in Victoria, southeastern Australia to address that question. We selected 162 wildfires of more than 1000 ha that occurred over the past 30 years across a wide range of forest types. Spectral indices derived from Landsat pre- and post- fire imagery were used to map fire severity. Our results show a significant increase in the absolute and proportional area burnt by high-severity fire over the last three decades. This study demonstrates that wildfires in the temperate forests of southern Australia are becoming more severe. Such change in fire regimes may have critical consequences for the sustainability and resilience of the studied forests.
Circularly Polarized Meandered-Loop Antenna for Ambient RF Energy Harvesting System

In this paper, the circularly polarized (CP) antenna is proposed based on the loop antenna for ambient RF energy harvesting (RFEH) applications. This loop antenna is meandered to achieve a compact size of 30 mm × 50 mm. The proposed CP meandered-loop antenna has a measured bandwidth of 150 MHz at – 10 dB with a center frequency of 2.14 GHz for 3G and 4G technologies. A maximum of 62 % RF-DC conversion efficiency is obtained using the proposed CP antenna and a load of 1.2 KΩ when the power of the incident wave is 0 dBm.
Piggyback-Based Distributed MAC Optimization for V2X Sidelink Communications

Vehicular communications, considered as one of the key technologies for autonomous driving, are still faced with significant challenges, e.g., distributed resource allocation in the direct communication mode. In Cellular Vehicle-to-Everything (C-V2X), a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol is adopted by 3rd Generation Partnership Project (3GPP) for its sidelink communications. However, the performance of either the SPS scheme in standard or the state-of-the-art enhancement works is not satisfactory. In this paper, we propose a piggyback-based collaboration method and firstly introduce Age of Information (AoI) to evaluate the status update performance of C-V2X. In addition, the closed-form AoI performance is obtained. Through extensive simulations, the proposed scheme exhibits better performance than the current schemes, both in terms of AoI and transmission reliability.
An Adaptive Vision-based Outdoor Car Parking Lot Monitoring System

Monitoring system installed at a parking field is to update real time data of vacant parking lots to central management and to provide search and book services to car drivers. Camera attached with image processing unit running Deep Learning based algorithms to detect vacant/occupied parking lots is promising with rather high accuracy. In this work, we propose a novel solution using mAlexNet, a CNN-based model, to classify vacant or occupied state of each parking lot snapshot, with a pre-processing stage, camera adjustment method, enabling the solution to be automatically adaptive with the condition setting variations. The solution is capable to run on resource constrained processors like Rasberry Pi 4 and has been tested on parking fields at our university campus, showing the accuracy of over 97% and rather fast processing pace of 0.743 seconds in average for each frame capturing 24 parking lots.
Milk Characterization Using Time Domain Wide Band Dielectric Spectroscopy

In this paper, a wide band time domain dielectric spectroscopy system for milk characterization is presented. The system is a kind of quasi mono static free space dielectric spectroscopy in which all measurements have been done with Xethru X4 sensor. The permittivity of low-fat milk and a mixture of 50% whole milk and 50% water have been calculated by mapping phase and amplitude differences of calibration materials. The calculated permittivity for low-fat milk and milk-water mixture show good agreement with their Debye model between 6.4 GHz-7.4 GHz.