Miracle-dz/UniFAR-FTUs huggingface dataset:
Seismic studies of continental lithosphere beneath SE Brazil

The Brazilian Lithosphere Seismic Project is part of a multinational geological and geophysical program to investigate the structure, tectonics, and geologic evolution of the continental lithosphere beneath SE Brazil. The project, which will run for a period of at least 18 months, is a collaborative effort by the Carnegie Institution of Washington and the Institute of Astronomy and Geophysics of the University of Sao Paulo. The broad band seismic studies are aimed at mapping the fine structure of the continental crust and lithospheric mantle beneath Brazil and interpreting it in terms of the origin, evolution, and composition of the Brazilian shield. The first nine stations of a planned 14 station portable array of digital REFTEK dataloggers and the broad band threecomponent Streckeisen STS-2 seismometers have been installed, or are in the process of being installed, in southeast Brazil (see Figure 1). The seismic array traverses the four major tectonic provinces of the region --- the Archean Sao Francisco craton, the surrounding late Proterozoic BrasilianolPan-African mobile belts, the intracratonic Parana Basin, and the coastal Ribeira belt, a complex transcurrent shear system. Stations are configured about 50-100 km apart in an approximately east-west saw-tooth array along 200 S latitude, with an E-W aperture of approximately 800 km. The station configuration is designed to capitalize on the abundance of high-quality seismic sources, particularly Andean, that surround and illuminate the Brazilian shield. Digital data are recorded continuously at 10 slsec and in triggered mode at 50 slsec. Timing is via GPS clocks and is accurate to better than 1 msec absolute. The matched STS-2 three component recording covers the frequency band 0.0083 to 20 Hz. Preliminary results from receiver function analysis indicate that crustal thicknesses beneath the Sao Francisco craton and surrounding Archean crust are about 40 to 42 km. Crustal velocities in these regions increase approximately linearly with depth and there is no evidence of major midcrustal discontinuities. Results from receiver functions and from two station surface wave phase velocity inversion indicate that mantle shear wave velocities are high, about 4.8 to 4.9 krn/sec as in other cratonic areas, in the uppermost mantle beneath the Sao Francisco craton and surrounding area and to at least 130 km depth beneath the Parana Basin.
Security Issues on Smart Grid and Blockchain-Based Secure Smart Energy Management Systems

The next generation of electricity grid infrastructure, known as the “smart grid”, integrates smart ICT (information and communication technology) into existing grids in order to alleviate the drawbacks of existing one-way grid systems. The security of the smart grid cyber infrastructure is a growing concern, though, as a result of the interconnection of significant power plants through communication networks. Since cyber-attacks can destroy energy data, beginning with personal information leaking from grid members, they can result in serious incidents like huge outages and the destruction of power network infrastructure. We shall thus propose a secure smart energy management system based on the inclusion of optical fiber sensors and blockchain technology in smart grids. While optical fiber sensors allow real-time monitoring and management of electrical energy flow, Blockchain offers a secure platform to safeguard the smart grid against cyberattacks and unauthorized access. Additionally, this integration has advantages in terms of improved security, efficiency, dependability, and flexibility in energy management. An in-depth analysis of the advantages and drawbacks of combining blockchain technology with optical fiber is provided in this paper.
Introduction to Physical Layer Security and Hardware Supply Chain Security: EM Tricks to Keep Your Information and Devices Safe

This paper focuses on the importance of ensuring hardware security to protect personal privacy, e-commerce, and data integrity in the current information society. In particular, we will focus on electromagnetic information security threats that leave few traces of attacks among hardware security threats, as well as security threats and countermeasures related to the hardware supply chain, which has been covered as a new threat in recent years. The need for offensive security measures to anticipate threats, as well as resilience functions to acquire resistance to threats after an incident, is also discussed. The paper suggests that training engineers and researchers with knowledge of both EMC and security is essential for future hardware designs.
Design of Circular Dual-Band Six-Elements Dipole Array for Omnidirectional Radiation Pattern

In this paper, a circular dual-band omnidirectional six dipole array is reported. The proposed antenna consists of six printed dipoles separated by an angle of 60 degrees. According to the simulated results the proposed structure operates at two resonance frequencies F1 = 2.29 GHz (WLAN band) and F2 = 3.45 GHz (5G band) with a corresponding overall bandwidth (S11 < - 10 dB) of 220 MHz and 310 MHz respectively. The maximum obtained peak gain are 4.5 dBi (F1) and 3.2 dBi (F2). The proposed antenna is fabricated and measured to verify the simulation results. This low-cost, efficient, and easy-to-design antenna array can be very useful for omnidirectional radiation pattern with horizontal polarization applications.
Intercontinental Transport and Chemical Transformation 2002 (ITCT 2K2) and Pacific Exploration of Asian Continental Emission (PEACE) experiments: An overview of the 2002 winter and spring intensives

[1] In the winter and spring of 2002, airborne and ground-based measurements of O3, aerosols, and their precursors were made in the eastern and western North Pacific regions. Three field studies were conducted by an international team of scientists collaborating as part of the Intercontinental Transport and Chemical Transformation (ITCT) program, an activity of the International Global Atmospheric Chemistry (IGAC) project of the International Geosphere-Biosphere Program (IGBP). Previous measurements have indicated that the transport of Asian emissions across the North Pacific Ocean influences the concentrations of trace tropospheric species over the Pacific and even the west coast of North America. In this special section, the recently acquired data are used to better characterize the contribution of continental sources to the aerosol, ozone, and related trace species concentrations over the North Pacific. This overview is aimed at providing the operational and logistical context of the study and introducing the principal findings and conclusions that have been drawn from the results.
Evaluation of Hotspot Performance in IoT Enabled Wireless Sensor Network using Special Intermediate Nodes in the Network

Wireless Sensor Network (WSN) equipped with Internet of Things (IoT) technology has been used in many aspects ranging from long-distance data transmission to healthcare, smart city and home automation applications. As part of WSN, a Hotspot node provides vital link between IoTs in heterogeneous networks and other remote networks. Hotspot nodes are used to bridge the gap between communication networks, providing an IPv4 address and establishing point-to-point (or multipoint-to-point) connectivity between the various IoTs and/or remote networks. In this paper, we discuss the performance of Hotspot nodes failure, recovery and its impact on the system performance. The impact of Hotspot redundancy and load balancing on system performance is also discussed. Finally, based on the results, the authors suggests various techniques and methods for improving the performance of Hotspot nodes.
Detection of Lithium Plating in Li-ion Batteries for Electric Vehicle Applications

This paper investigates the lithium plating problem in Li-ion batteries and proposes a diagnostic method of the lithium plating which is properly adapted to be used in electric automotive applications. The suggested lithium plating diagnostic method is based on the analysis of the cell impedance that is estimated by utilizing the electrochemical impedance spectroscopy (EIS) method. The EIS technique is realized by using a sinusoidal excitation current that is superimposed on the battery cell current, while the impedance is determined by adopting the single frequency method where the excitation signals is composed by sinusoidal currents of individual sequential frequencies. When the lithium plating is developed in a battery cell, its impedance is decreased and therefore, by monitoring it, the lithium plating can be identified in real-time. The functionality and practicality of the proposed lithium plating detection technique for using it in real-time in electric vehicle applications is experimentally verified and selective experimental results for several battery charging and temperature operating conditions are presented.
Mainlobe DRFM Jamming Suppression via Blind Source Separation Using Tensor Decomposition

This paper deals with the suppression problem of mainlobe digital radio frequency memory (DRFM) jamming especially for intra-pulse slice forwarding jamming for phased array radar. A novel mainlobe DRFM jamming suppression method resorting to blind source separation based on tensor decomposition (TD-BSS) is proposed to separate the target echo signal and jamming signal. Specifically, the received signals are reconstructed into higher-order tensor form. Further, the separating matrix optimization problem relying on tensor Canonical Polyadic (CP) decomposition principle is formulated and then solved by an enhanced line search alternating least square (ELS-ALS) algorithm. Finally, the performance of the proposed method is demonstrated through numerical simulations, showing its superior to suppress mainlobe DRFM jamming over the exiting method in terms of interrupted sampling and cyclic repeater jamming (ISCRJ) and chopping and interval (C&I) jamming.
Action Analyzer for Differently Abled People

This paper presents a simulation system for differently abled people that utilizes the Long Short-Term Memory (LSTM) algorithm and the Mediapipe Holistic Model (MHM). The system aims to enhance the daily lives of differently abled individuals by analyzing their body movements and translating them into meaningful actions. The Mediapipe Holistic model extracts key points of the movements from input video frames, and the LSTM algorithm processes the resulting data to recognize various actions such as scared, understand, Wednesday. The system is trained on a diverse set of images to ensure accuracy and robustness in recognizing a wide range of actions. Results show that the combination of MHM and LSTM provides a high accuracy model even with less dataset in comparison to many models proposed over the years for action analyzer system. The model has potential for use in assistive technologies for differently abled individuals. The model shows 92% accuracy on the training dataset and 90% accuracy on the testing dataset. Results show that the proposed system outperforms existing approaches and has potential for use in assistive technologies for differently abled individuals.
SeReIn: Smart Home Sensor Relationship Inference

Determining sensor relationships in smart environments is complex due to the variety and volume of time series information they provide. Moreover, identifying sensor relationships to connect them with actuators is difficult for smart home users who may not have technical experience. Therefore, in this paper, we propose a novel unsupervised learning approach, named SeReIn, to automatically group sensors by their inherent relationships solely using time series data. SeReIn extracts three features from smart home time series data -Frequent Next Event (FNE), Time Delta (TD), and Frequency (FQ), and applies Spectral Clustering to group the related sensors. The application of unsupervised learning enables our approach to operate anywhere in the smart home domain regardless of the sensor types and deployment scenarios. SeReIn functions on both large deployments consisting of around 70 sensors and small deployments of only 10 sensors. Evaluation of SeReIn on real-world smart home datasets has shown that it can recognize inherent spatial relationships in smart homes with a Calinski-Harabasz score of up to 90.
Domain Adaptive Recognition of Defects for Ground Penetrating Radar Images

GPR technology is of significant importance for road maintenance authorities to detect road defect under the road surface, for it helps to monitor the construction process and detect defects early, thus reducing maintenance costs. The high cost of data collection and core verification for underground defects makes the dataset very valuable. In practical applications, different radar types and imaging methods often lead to heterogeneous generated radar images. In addition, there are significant differences between training data and real-world test data, which may lead to ambiguous predictions. This is because as the discrepance in different data domains increase, the transferability of the features extracted by neural networks significantly decreases at higher levels. Therefore, it is important to reduce dataset bias and enhance transferability in specific task layers. This article specifically designs a feature transfer enhancement algorithm for heterogeneous ground penetrating radar data based on deep domain adaptation. Compared with other deep domain adaptation algorithms such as DAN, DANN, and DeepCoral, the proposed method has the best recognition performance on underground defect data of ground penetrating radar.
Synthesis of the Step Detection and Step Length Estimation Algorithms Based on IMU Measurements

This paper is devoted to the synthesis of algorithm for pedestrian dead reckoning (PDR) using an inertial measurement unit (IMU). In the introduction, a brief review of current solutions in the field of PDR is presented, and the relevance and feasibility of the research discussed in this paper is shown. The second section (Algorithm Synthesis) gives a detailed description of the algorithm under development, which consists of two stages: step detection and calculation of the walked distance based on biomechanical models (Weinberg, Mikov, Kim, and Scarlett) with adaptive estimation of the scaling factor. In the same section, a metric characterizing the accuracy of the proposed algorithm, calculated as the norm of relative errors of estimated distance based on a set of experiments, is introduced. Then, a detailed description of the proposed algorithm is given. The third section (Experiment) describes an experiment in which users walked along a given trajectories of different lengths. The fourth section of this paper presents the results of the experiment and their brief analysis. In this paper, we proposed an algorithm for step detection, and we combined direct and indirect methods for step length estimation, which helped to improve the accuracy of the PDR algorithm.
Leaf Recognition Using K-Nearest Neighbors Algorithm with Zernike Moments

Leaf recognition is a vital component of plant classification, which is crucial in agricultural automation. Previous studies have employed various machine learning algorithms, ranging from deep learning methods such as Convolutional Neural Network (CNN) to traditional methods like Support Vector Machine (SVM), and demonstrated success in leaf recognition. This study introduces a method for leaf recognition that employs the k-Nearest Neighbors (k-NN) algorithm as the classifier and utilizes Zernike moments as the image features. The proposed method is evaluated on the Flavia leaf dataset, and the results affirm the effectiveness of the approach. Furthermore, while Zernike moments have been extensively studied by researchers in the image recognition domain, they have predominantly been limited to a maximum order of 10. This study explores the use of Zernike moments with different maximum orders, including higher orders, to evaluate their classification abilities for leaf recognition.
Automatic Detection of Lumbar Spine from Ultrasound Images Using Fuzzy Clustering Techniques.

Spine is an important part of skeletal system which is responsible for the movements of the body. It supports the weight of the body and protects the spinal cord. Any change in the structure of the spine or a poor spinal health may affect the nervous system which in turn reduces the capability to lead a normal, active life. So it is important to monitor the condition of the spine. This paper deals with the automatic detection of lumbar spine from the ultrasound images. There are many techniques available for imaging the lumbar spine. Ultrasound technique is becoming the most preferred imaging technique, since it is safer than the other imaging techniques like, X-ray, MRI and CT. So far ultrasound images of spine have not been used for detection and diagnosis because of the noise and poor quality. The ultrasound image of lumbar spine is enhanced by image preprocessing. Noise removal has been carried out by filtering and the quality of the image is enhanced by contrast adjustment. The preprocessed image is segmented using fuzzy clustering techniques viz. Fuzzy C Means and Reformed Fuzzy C Means. The spectral signature of lumbar spine is extracted from the segmented image. The lumbar spine alone is separated from the extracted image. By comparing the two fuzzy clustering techniques, it is found that the detection of lumbar spine by Reformed Fuzzy C Means is more complete than the Fuzzy C Means technique.
A Self-Sustained Phononic Comb MEMS Oscillator with Loop Phase Tuning

SummaryThis work investigates a self-sustained phononic comb Micro-Electro-Mechanical Systems (MEMS) oscillator with comb control by using loop phase tuning. The formation of the phononic combs in this work is carried out through a self-oscillation (closed-loop) at the nonlinear state of a piezoelectric flexure mode resonator without any external parametric pumping. We experimentally confirm the tunability of the phononic combs by adjusting the loop phase (θT) of the Phase Lock Loop (PLL) in the lock-in amplifier at various oscillation points. The first two eigenmodes of the fully clamped thin-film resonator are adopted to demonstrate the proposed mechanism. Each of the eigenmodes is successfully sustained for oscillation by the use of the lock-in amplifier, thus featuring different phononic comb states. The achievement in this work could be further extended to frequency comb-based sensor technologies.
Development of a Quartz-MEMS Resonator

SummaryThis paper presents a miniaturized high Q-factor Thickness Shear Mode Resonator (TSMR), manufacture collectively thanks to the Deep Reactive Ion Etching (DRIE) of quartz crystal wafers. The intrinsic qualities of quartz (i.e. high Q factor, low temperature sensitivity and piezoelectricity) and the advantages of DRIE allowed to fabricate resonators of small dimensions and collectively manufactured. Samples vibrating at frequency f of 88 MHz have shown promising results which lead to quality factor and resonance frequency products (Q.f) near the theoretical limit for quartz of 3.2×1013 Hz [1].
Machine Learning-Based Anomaly Detection in the Internet of Things

One of the primary problems facing the Internet of Things(IoT) that demands immediate attention is the issue of anomaly and identifying attacks in IoT environments. The IoT resources might fail as a result of abnormalities and attacks in the environment of the IoTs, such as probing, Denial of Service(DOS), Root to Local(R2L) and User to Root(U2R) attacks. To ensure the dependable performance of IoT systems, anomaly identification in the realm of IoT is a crucial responsibility. Manually detecting anomalous occurrences has grown more challenging with the abundance of data collected by IoT devices. As a result, machine learning(ML) methods have been suggested for IoT systems' anomaly recognition. A publicly available dataset for anomaly detection was used to assess the model. For data preparation, selecting features, and choosing models, the approach employs a number of ML techniques, including Decision Tree(DT), Logistic Regression(LR), Support Vector Machine(SVM) and Random Forest(RF). Criteria like recall, accuracy, precision, and F1-score have been used to measure these models' performance. The outcomes show that the employed strategy is effective in detecting network anomalies and improving the security of IoT systems. The system discovered that RF had the highest accuracy, with a rate of 98.47%, compared to LR, DT, and SVM, which had the accuracy of 97.83%, 98.22%, and 92.80%, respectively.
MRTOM: Mostly Reliable Totally Ordered Multicast, a Network Primitive to Offload Distributed Systems

As datacenters become the new computing platform, integrating server-centric distributed systems into modern network hardware is gaining interest under the diminishing Moore's law. Researchers want to build reusable primitives that can take advantage of modern network hardware and offload common system components of a broad range of applications. In this paper, we present Mostly Reliable Totally Ordered Multicast, a reusable network primitive that can embed reliable group communication into the network. MRTOM is a network-centric approach that handles message replication, ordering, and reliable delivery using a network fast path, freeing server CPUs for application logic. MRTOM can be implemented in the programmable switches and edge interfaces (e.g., Smart-NICs), significantly reducing network traffic compared to the existing approaches and improving job finish time amid packet loss. With MRTOM, we were able to accelerate multiple high-performance applications whose fast path can be totally offloaded into the network. For example, a Paxos application, MRTOM-Paxos, achieves > 1,100,000 transactions/secs and $23\mu \mathrm{s}$ minimum latency. A replicated key-value store, MRTOM-KV, also shows significant latency reduction with eBPF/XDP in the Linux Kernel, which is further improved by offloading into SmartNICs.
Introduction to the WEC88 cruise: An investigation into why the equator is not greener

This issue of the Journal of Geophysical Research contains seven papers that describe results from a cruise of R/V Wecoma, which worked in the equatorial Pacific in February and March 1988. The name of the cruise was WEC88. The research plan for this cruise was based on the evolving understanding of the regulation of biological productivity in equatorial waters. When the eastern and central equatorial Pacific was originally found to be richer than waters to the north and south, the first question oceanographers asked was, “Why is the tropical region so rich? ” Early work established that a zonal band straddling the equator and extending across the entire Pacific basin has enhanced nutrient concentrations, increased phytoplankton productivity, and increased abundance of Zooplankton and fish [Sette, 1955].