Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressive Sampling of Polarimetric Doppler Weather Radar Processing Via Inverse Fast Fourier Transform

Polarimetric Doppler frequency-modulated continuous-wave weather radar produces a tremendous amount of data during the observation of atmospheric conditions. Using conventional signal processing method, the beat signals are sampled at Nyquist rate, so that the range signal can be reconstructed perfectly. We propose compressive sampling (CS) technique to sample and reduce the data simultaneously by exploring the sparsity of the beat signal in transform domain. Reducing the number of the beat signal and the transform coefficients are natural choices, because sparse signals will be obtained by inverse Fourier transforming the beat signals. The proposed techniques are evaluated by constructing the plan position indicator of reflectivity and mean Doppler velocity measurements from real weather polarimetric data. Compared to the conventional method, the CS polarimetric Doppler processing significantly reduce the number of the data while pertaining important weather information. We show that the proposed method works properly to quantify and classify the precipitation, mainly when the number of the samples is above 25% of its original.
A Two-Stage Biomedical Event Trigger Detection Method Based on Hybrid Neural Network and Sentence Embeddings

Biomedical event extraction is a challenging task in biomedical text mining, which plays an important role in improving biomedical research and disease prevention. As the crucial and prerequisite step in event extraction, biomedical trigger detection has attracted much attention. Previous approaches usually depended on feature engineering with unbalanced data. In this paper, we propose a two-stage method based on hybrid neural network for trigger detection, which divides trigger detection into recognition stage and classification stage. In the first stage, we build a BiLSTM based recognition model integrating attention mechanism (Att-BiLSTM). In the second stage, the classification model based on Passive-Aggressive online algorithm is constructed. Furthermore, to enrich sentence-level features, we establish sentence embeddings and add reading gate. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 80.26%, which achieves the state-of-the-art systems.
Non-Invasive Flow Measurement Technology Based on Taylor's Frozen Turbulence Hypothesis

The article is devoted a new method of flow measurement using an original mathematical algorithm and piezo film sensor. We describe the first part of an investigative project to develop a new flow measurement system. The advantage of the presented method is that it supports noninvasive flow measurement through the pipe wall. We present different results of studying mathematical methods for signal processing and numerical simulations using LES turbulence models such as Stress-Blended Eddy Simulation (SBES). The physical effect based on Taylor's frozen turbulence hypothesis, which serves as the foundation of this measurement method, is described. The mathematical algorithm and signal processing method contain a two-dimensional Fourier transform to build the frequency-wavenumber spectrum and cross-correlation function. We provide a short description about the chosen sensor and justify its use for the proposed measured system. In conclusion, the findings of our investigation and a real prototype scheme are presented.
Design and Implementation of a Delta Robot Based on FPGA for the Automation of the Collection of Solid Products

Thus, a Delta robot maximizes the working speed and the payload that it supports. Thus, in current work, the development and implementation of inverse kinematics in a Delta robot can be observed from different approaches that help to optimize the processing with respect to the equations that govern its motion [4]–[5]. In this way, for the development of the project, as the main design requirements obtained from a task analysis, there is a need for a high speed in the transfer of solid products [6]. Additionally, to guarantee the precision of the mechanism and the parallel calculation of the movement, the embedded NI myRIO system is used, which is a hybrid device from National Instruments, which has a Xilinx Zynq-7010 FPGA and a dual ARM Cortex-A9 processor. Core, designed specifically for the development of complex engineering embedded systems [7].
Origami Store: UC-Secure Foldable Datachains for the Quantum Era

Traditional blockchains preserve everything, if verification is to avoid trusted third parties. The accumulation of outdated but undeletable data makes traditional blockchains non-scalable for practical applications. Stateless blockchains mitigate this problem via history deletion; however, existing stateless blockchains such as Mimblewimble are not quantum resistant as they rely on specific discrete-log-based cryptography to achieve history deletion. In this paper, we introduce the “Origami Store” (or O-store for short), as a general new approach to distributed data storage with a generic history deletion mechanism called “folding. ” O-store is built using a hash function and an arbitrary signature scheme. Since the signature scheme is decoupled from the framework, O-stores are compatible with any quantum-resistant signature scheme by design. More importantly, O-store only needs to preserve one signature and one verification key per account (or owner) for any number of transactions (or updates), thereby mitigating the inefficiency of large quantum-safe signatures and verification keys. We prove the security of the basic O-store using the framework of Universal Composability (UC); in particular, O-store’s universal security is shown to hold for a generic consensus mechanism, to facilitate implementation from a wide range of decentralized consensus protocols. The Origami store is thus the first UC-secure efficient distributed data storage framework that supports plug-and-play consensus mechanisms for fully decentralized trustless operation. We demonstrate working implementations of Origami stores with three post-quantum signature libraries—qTESLA, Dilithium, and Picnic—to illustrate its scalability and practicality for real-world applications.
ReliableBox: Secure and Verifiable Cloud Storage With Location-Aware Backup

While the prevalent cloud storage platforms are offering convenient services in support of diverse data-driven applications for clients, various security concerns raise in terms of data confidentiality, availability, and retrievability. Among them, servers’ dishonesty on the location-specific data backup becomes a serious concern when the data stands out clients’ control, considering the strict regulations imposed by many governments and organizations on data storage location. This article studies location-aware data backup verification for the data stored in clouds and aims to design a secure framework, named as ReliableBox, enabling the clients to verify if their data have been backed up on the remote servers with specific geolocation. In the design of ReliableBox, we leverage the prominent proof-of-storage techniques for data possession proof, and take advantage of multilateration geolocation and Intel SGX for the precise communication delay measurement and trust computing delay measurement, respectively. In ReliableBox, a client first computes integrity tags for the files and then outsources both the files and tags to the cloud storage server. In the later attestation, with the precise network delay and distance measurement from location-known verifiers, the client verifies that the outsourced files are intact and backed-up to hosts at the specific geolocation. With the customized design, ReliableBox can support the security needs in terms of both data integrity and backup location verification for clients, even when there exists potential dishonest cloud service providers who may manipulate the network delays or forge verification proofs. We provide security analysis to show the security property of ReliableBox in terms of data access, confidentiality, and verifications. In the end, we implement the system prototype and deploy it into several prevalent and commercial cloud platforms for performance evaluation. The experimental results demonstrate that ReliableBox is secure in support of data integrity checking and location-aware backup auditing, while it is robust to the data possession and location spoofing attacks.
Network Intrusion Detection Using Hybrid Machine Learning Model

In this era of network security, Intrusion Detection System plays an important role. It is used to predict network data traffic as normal or anomaly. Several machine learning models are used for building an accurate Intrusion Detection System. In this paper, a hybrid machine learning model with a new feature selection method is proposed for better performance of the Intrusion Detection System. In this proposed model, the Intrusion Detection System is built with a combination of supervised and unsupervised machine learning models. A brief comparison between the proposed model and the other machine learning models such as AdaBoost, XGBoost, Random Forest, Gaussian Naive Bayes, LGB is narrated in this paper. We applied the models on the NSL KDD dataset and experimentally proved that the accuracy level of our proposed model is approximately 11% higher than the others.
The Influence of O2 on Positive Streamer Initiation in Supercritical CO2 With Field Ionization Using a 3-D Particle Model

In this work, the influence of varying mixing ratios of O2 on positive streamer initiation in supercritical (SC) CO2 at different applied voltages was investigated using a 3-D particle model. Zener’s model was included in the 3-D particle model to calculate the field ionization rate during streamer formation and propagation. The evolution of positive streamer was classified into three main stages namely inception cloud, primary streamer channels, and successive streamer branching. The effect of O2 with different mixing ratios (2%, 10%, and 20%) on the morphology, average electron density, electric field distribution, streamer length, and electron energy distribution function (EEDF) for positive streamer in SC-CO2 at four different voltages (12, 12.5, 13, and 13.5 kV) is then investigated. The 2-D cross sections of the initial stage of the streamer and the electric field distribution are presented in detail. At the same applied voltage, the size of inception cloud, average electron density, electric field, and streamer length with the maximum electric field decreased with increasing oxygen concentration in SC-CO2. With increasing applied voltage, the apparent size of inception cloud, primary streamer channels, successive streamer branching, average electron density, electric field, and streamer length increased. The simulation results showed that adding O2 to SC-CO2 can significantly affect the streamer initiation and propagation.
Crystallization Morphology-Dependent Breakdown Strength of Polypropylene Films for Converter Valve Capacitor

In this paper, a novel modification method of polypropylene (PP) films is proposed in the field of metallized film capacitors for converter valves. An organic crystallization accelerator with good dispersion improves the micromorphology of PP films. The doping content of the organic crystallization accelerator is optimized based on breakdown experiments under DC voltage. The DC breakdown strength is 584.8 kV/mm, an increase of 19.6%, compared with that of the original film. Under DC, DC superimposed harmonics, and DC superimposed pulse voltages, the modified PP film has successfully withstood the tests, showing stable advantages in breakdown properties without increasing the conduction loss. Trap analysis and theoretical modeling were performed to clarify the mechanism of improving electrical performances based on crystallization control. The entanglement layer introduced by the organic crystallization accelerator helps to expand the crystal area, resulting in deep trap sites and limitation of carrier migration. This research can provide a reference for the PP performance optimization for metallized film capacitors of converter valves.
Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

The last decade has witnessed remarkable research advances at the intersection of machine learning (ML) and hardware security. The confluence of the two technologies has created many interesting and unique opportunities, but also left some issues in their wake. ML schemes have been extensively used to enhance the security and trust of embedded systems like hardware Trojans and malware detection. On the other hand, ML-based approaches have also been adopted by adversaries to assist side-channel attacks, reverse engineer integrated circuits and break hardware security primitives like Physically Unclonable Functions (PUFs). Deep learning is a subfield of ML. It can continuously learn from a large amount of labeled data with a layered structure. Despite the impressive outcomes demonstrated by deep learning in many application scenarios, the dark side of it has not been fully exposed yet. The inability to fully understand and explain what has been done within the super-intelligence can turn an inherently benevolent system into malevolent. Recent research has revealed that the outputs of Deep Neural Networks (DNNs) can be easily corrupted by imperceptibly small input perturbations. As computations are brought nearer to the source of data creation, the attack surface of DNN has also been extended from the input data to the edge devices. Accordingly, due to the opportunities of ML-assisted security and the vulnerabilities of ML implementation, in this paper, we will survey the applications, vulnerabilities and fortification of ML from the perspective of hardware security. We will discuss the possible future research directions, and thereby, sharing a roadmap for the hardware security community in general.
Computer Vision for Determining the Fraction of Proppant for Hydraulic Fracturing

In this paper is described experience of computer vision technologies to detect the size of the proppant fraction from a smartphone photo. Neuro net model was trained with PyTorch framework and TorchVision library and used pre-trained MobileNet V2. The paper solves the problem of classification. Using augmentation methods, the public dataset of 790 images was increased to 50,000. The results of research showed that the classification accuracy on the test sample according to the F1-score metric was equal to 0.95. The developed model can be recommended for use in the oil industry to detect the proppant fraction by a smartphone photo.
Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

Land-use mapping (LUM) using high-spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, and performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offer novel opportunities for the development of LUM for HSR-RSIs. This article aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single-object segmentation are presented first. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DL-based LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single-object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as fully convolutional network (FCN) and U-Net, following up with a comparison and discussion of the results.
Experiment and Simulation of Pressboard Barrier Effect on the Streamer Propagation in Natural Ester During Positive Pulsed Voltage

In this study, the effect of the pressboard barrier (PB) on streamer pattern in natural ester under the non-uniform electric field is investigated by experiment and simulation. The propagation patterns and characteristics of the streamer in the oil with/without a pressboard barrier are obtained by the shadowgraph technique. The mechanism of the pressboard barrier effect on the streamer pattern and propagation is revealed using a multi-streamer branch simulation method. Results show that the positional relationship between the needle electrode and the pressboard barrier significantly affects the streamer propagation and branching. The barrier effect of the pressboard, the distribution of space electric field affected by the pressboard barrier, and the image force of charge on the pressboard surface are all critical factors for affecting the propagation and branching of the streamer. The greatest threat to the pressboard failure is the z-axis streamer or those branches close to the z-axis that is severely inhibited by other streamer branches. It raises the risk of insulation failure caused by the breakdown of the insulating pressboard.
Sniffer: A Machine Learning Approach for DoS Attack Localization in NoC-Based SoCs

Flooding-based Denial-of-service (DoS) attacks have been prevalent in Network-on-Chip (NoC) architectures, due to its shared nature and open access to all the on-chip modules. A Malicious Intellectual Property (MIP) within a System-on-Chip (SoC) creates such an attack by flooding the NoC with useless packets resulting in significant bandwidth reduction. Finding the location of an MIP is crucial to restore regular network operations and curtail system performance degradation. In this work, we propose Sniffer, an efficient MIP localization framework which employs a low-overhead machine learning approach to accurately trace the attack path and take a collective decision to locate the MIPs. Experimental results show that Sniffer is able to provide high accuracy for MIP localization without incurring significant overheads.
Benign Effects of Battery Energy Storage System for Efficient Microgrid Operation along with its Management: A Review

The proliferation in the microgrid (MG) technology research undoubtedly shows its potential to overcome the disadvantages of the conventional grids employing conventional resources. The advantages include non-dependence on conventional resources, easy deployment to remote areas, efficient operation, less transmission losses, reducing carbon footprints and reduces utility grid dependence. There is ample amount of opportunities in this field that are yet to be discovered. This paper gives a review of the battery energy storage system (BESS) which is an important constituent of the MG and discusses about various battery energy management system (BEMS) techniques and throw a light on its benign behavior towards the operation of the MG.
A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments

The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols. The proposed IDS called MENSA (anoMaly dEtection aNd claSsificAtion) adopts a novel Autoencoder-Generative Adversarial Network (GAN) architecture for (a) detecting operational anomalies and (b) classifying Modbus/TCP and DNP3 cyberattacks. In particular, MENSA combines the aforementioned Deep Neural Networks (DNNs) in a common architecture, taking into account the adversarial loss and the reconstruction difference. The proposed IDS is validated in four real SG evaluation environments, namely (a) SG lab, (b) substation, (c) hydropower plant and (d) power plant, solving successfully an outlier detection (i.e., anomaly detection) problem as well as a challenging multiclass classification problem consisting of 14 classes (13 Modbus/TCP cyberattacks and normal instances). Furthermore, MENSA can discriminate five cyberattacks against DNP3. The evaluation results demonstrate the efficiency of MENSA compared to other Machine Learning (ML) and Deep Learning (DL) methods in terms of Accuracy, False Positive Rate (FPR), True Positive Rate (TPR) and the F1 score.
Fiber Core-Offset Humidity Sensor Based on Graphene Oxide Characteristics

On the basis of the photoelectric properties and hydrophilicity and multimode interference of Graphene Oxide (GO), this study proposes an all-fiber humidity sensor. Two core-offset regions are constructed with a fiber fusion splicer, and a GO film is coated on a single-mode fiber (SMF) between the core-offset regions to form a humidity-sensitive Mach–Zehnder interference structure. As the external humidity environment changes, the refractive index of the GO changes, and the light in the SMF is modulated by humidity. External humidity is measured by detecting the changes in the wavelength of the transmission spectrum of the sensing structure. A saturated salt solution is used to build a stable humidity environment. The sensitivity of the sensor is 0.05 nm/% relative humidity (RH) at 35.3%–95.8% RH, and the linear relationship between the characteristic wavelength and RH is 88%. In addition, the linear relationship between the light intensity and the RH of the sensor at a single wavelength at 1571 nm is analyzed, and the linearity is 97.45%. According to this finding, photoelectric conversion is used to convert the light intensity signal into a voltage signal. The data are processed by a computer to achieve the real-time and visual monitoring of the humidity environment. The fiber optic humidity sensor proposed in this work has a simple structure, simple production, low cost, and high sensitivity. This work provides a new method for humidity sensing.
Intrinsic Self-healing Characteristics of Electrical Tree in Silicone Rubber

The self-healing phenomenon of electrical tree in traditional silicone rubber (SiR) insulation material, which is called intrinsic self-healing, is demonstrated and investigated in this paper. For initial SiR samples with electrical trees, with the removal of applied voltage, it is found that some electrical tree branches naturally disappear in the visual field with time. A less branched morphology and decreasing fractal dimension of electrical tree are thus observed. The effects of temperature on the intrinsic self-healing characteristics are explored and the intrinsic self-healing process can be considered as taking place in three stages based on the temperature-dependent self-healing rate. It is proposed that the intrinsic self-healing behaviors are closely related to the gas motion in the tree channels and the retractive force of SiR, which is affected by the conformation of molecular chains. Re-growth performance of electrical tree is conducted on the self-healed samples and many tiny branches at the sidewall of main channels are found in the repeated electrical trees, while the tree length remains unchanged. Furthermore, some electrical trees vanish in self-healing regions after the re-growth process, which can be attributed to the shielding effect of dense branches and the reconstruction of some hydrogen bonds between nano-silica and molecular chains of SiR.