Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Sensitive Reflective-Mode Phase-Variation Permittivity Sensor Based on a Coplanar Waveguide Terminated With an Open Complementary Split Ring Resonator (OCSRR)

This paper presents a one-port reflective-mode phase-variation microwave sensor consisting of a coplanar waveguide (CPW) transmission line terminated with a grounded open complementary split ring resonator (OCSRR). The sensor is useful for measuring the dielectric constant of the so-called material under test (MUT), which should be placed in contact with the OCSRR, the sensitive element. The output variable is the phase of the reflection coefficient. Design guidelines for the implementation of highly sensitive sensors are derived in the paper, and validated through simulation and experiment. As compared to other reflective-mode phase-variation sensors based on open-ended sensing lines, the designed and fabricated devices exhibit a very small sensitive region by virtue of the use of an electrically small resonant element, the OCSRR. The relevant figure of merit, defined as the ratio between the maximum sensitivity and the size of the sensing area (expressed in terms of the squared wavelength), is as high as FoM $= 5643^{\circ }/\lambda ^{2}$ in one of the reported prototypes. Moreover, the paper analyzes the effects of losses. From this study, it is concluded that MUT losses do not significantly affect the output variable, provided losses are small. It is also demonstrated that the sensor is useful to estimate the loss tangent of the considered MUT samples.
Forecasting Renewable Energy Generation Scenarios Based on Multi-Agent Diverse GANs

In this paper, we propose an approach for renewable energy scenario forecasts based on an improved generative adversarial networks (GANs) with multiple generators. Basically, the proposed approach can capture almost all the renewable scenario patterns from historical measurements and generate more accurate future scenarios without the knowledge of any explicit model. More specifically, Multi-Agent Diverse GAN is proposed as a data-driven deep generative modeling framework to learn distinct patterns inherent with renewable power data, which lays the foundation of diverse scenario forecasts. Furthermore, an optimization problem is formulated and solved to adjust input noises after the GAN training process in order to generate future scenarios closer to the real situation. Experiment results show that the proposed method can capture renewable generation patterns in a more sophisticated way so as to gain diverse and more accurate scenario forecasts.
Study of the Method of Compact Testing of Technical Means of Data Transmission Networks

The article discusses the issues of assessing the reliability of control of compact testing of technical means of data transmission networks by the analytical method and simulation. We have completed the life cycle of the technical means of data transmission systems and the ways of their enhanced reliability. Methods of calculation and determination of signatures for signature analysis in the diagnosis of microprocessor devices are considered. The analysis of tasks and classification of systems of technical diagnostics of microprocessor devices of data transmission systems is carried out. The analysis of modern microprocessor devices of data transmission systems as an object of control and diagnostics necessary for the choice of characteristics of signature analysis methods is made, that the main documents of signature analysis are dictionaries of the reference signatures. It is shown that the main document of signature analysis is the dictionary of reference signatures. A comparative analysis of the methods of calculation and determination of reference signatures used in the diagnosis of microprocessor devices has been performed.
Connectivity Technology Selection and Deployment Strategies for IoT Service Provision Over LPWAN

The plethora of available IoT connectivity technologies makes selection of the most suitable and affordable offering challenging for IoT service providers. This is a typical problem when considering deployments over Low-Power Wide-Area Networks (LPWAN). Each technology has different implications as to how edge, fog, and cloud resources can be utilized to provide an end-to-end service. The connectivity decision directly affects the initial investment, operating expenditure, level of control desired over the underlying infrastructure, and the resulting managerial overhead. This article provides a generic cost model and framework to assist IoT service providers towards cost-aware LPWAN technology selection and dimensioning decisions. Our framework captures key cost factors and considers both network and compute infrastructure elements. Considering a monitoring IoT application as a reference, we analyze various deployment strategies enabled by two major IoT connectivity technologies, namely LoRaWAN and NB-IoT, quantifying their associated costs and distilling general deployment guidelines.
Dual-Frequency Microwave Resonant Sensor to Detect Noninvasive Glucose-Level Changes Through the Fingertip

In this article, which is a fundamental step in an underway research, a microwave resonant sensor at two resonance frequencies of 5.5 and 8.5 GHz (in order to increase the accuracy of the application) with a quality factor of 180 and 106, for blood glucose level monitoring is designed, fabricated, and tested by users’ fingertips. In this study, 11 volunteers were tested invasively (using a glucometer) and noninvasively (using the proposed glucose resonant sensor) with range of glucose-level changes from 89 to 262 mg/dL. For this range of glucose-level changes, the frequency detection resolution is 3.53 and 3.58 MHz/ (mg/dL), respectively. The results of the experiments are relatively well matched; so that compared to the glucometer device, the proposed sensor shows a maximum measurement error of about 3% to detect glucose levels.
Observability Analysis and Consistency Improvements for Visual-Inertial Odometry on the Matrix Lie Group of Extended Poses

In this paper, we present a novel extended Kalman filter (EKF)-based visual-inertial odometry for robotic platforms by modeling the state space as the recently proposed matrix Lie group of extended poses. Specifically, we found that the proposed estimator suffers from an inconsistency similar to that of the conventional $SO\left ({3}\right)\times \mathbb {R}^{6}$ uncertainty representation from the standpoint of an observability analysis. The inconsistency mainly is a result of spurious information along the unobservable directions. An inconsistent estimator would lead to overconfidently reducing the state uncertainty and larger estimation errors that would in turn cause system divergence. We applied the first-estimate Jacobian (FEJ) framework and observability constrained (OC) techniques to avoid spurious information and improve consistency. The performance of the proposed estimator is validated using both simulated and real-world datasets.
Multi-objective transmission network expansion planning based on Reinforcement Learning

With the rapid expansion of power system scale and the increase of complexity, the application effect of conventional power grid planning method is limited due to the use of artificial judgment and engineering experience. In this paper, the power grid planning algorithm based on reinforcement learning is used to plan the power grid, which has the characteristics of continuous interactive and autonomous learning with the environment, so as to realize large-scale and complex transmission network expansion planning. Firstly, the characteristics of reinforcement learning and Markov decision-making process are summarized; secondly, the digital model of power grid planning is established, and the multi-layer and multi-objective grid evaluation system is constructed through the lack of power, the number of electrical mediums and the cost of grid construction; finally, the accuracy and effectiveness of this method are verified by the planning calculation of IEEE garver-6 section.
Technology research of demand side resources participating in dispatching control in energy Internet

Energy Internet technology can realize the information fusion of demand side adjustable power load resources, transmission and distribution resources, power generation resources, and play the value benefits of demand side adjustable power load in peak load regulation and frequency modulation of power grid. In this paper, by integrating the advantages and disadvantages of different control modes for demand side resources, a framework and method of decentralized collaborative control within demand side resource cluster and regional grid coordinated control are proposed, which can effectively improve the flexibility and rapidity of adjustable resource response grid frequency modulation.
Energy Barrier Alignment for Efficient Perovskite Photodetectors Consisting of PEIE Combined With PCBM Electron Transport Layer

A polyelectrolyte material of ethoxylated polyethylenimine (PEIE) was combined with the conventional electron transport material of [6, 6]-phenyl-C61-butyricacidmethylester (PCBM) as an electron transport layer (ETL), and high-performance perovskite photodetectors (PePDs) consisting of the PEIE:PCBM combination system as the ETL were fabricated. The PEIE:PCBM combined ETL could tune the energy barrier for invert injected hole charges between the interface of ETL and electrode. As a result, the reduced dark current and improved photoresponse of PePDs were realized. The results showed that the obtained devices possessed a high detectivity (D*) of 4.77 × 1012 Jones, which was about three folds higher than that of controlled devices with pristine PCBM ETL. Meanwhile, the PePDs exhibited excellent recovery characteristics in light/dark conversion mode. Through a series of structure, morphology, and photoelectric characterization, the detailed mechanisms for the performance improvement of PePDs are disclosed.
Partitioning of metals to sediment organic carbon

The partition coefficients and sorption capacities for cadmium, copper, and lead in freshwater sediments are derived from fitting a Langmuir isotherm to data from 14 sediments. The metal activity in the solution phase was measured using specific ion electrodes. The metal sorption due to acid-volatile sulfide was subtracted from the total sorbed metal to produce the metal bound to other sediment phases. Organic carbon normalized isotherms provide a reasonable representation of the metal sorption. Partition coefficients and capacities are calculated for pH = 6, 7, and 8 (for Cd). The organic carbon normalized isotherm parameters for the sediments are compared to those reported for soils and sludges. The comparisons for cadmium exhibit differences for which no ready explanations are available. For copper and lead, however, the differences can be attributed to the use of specific ion electrodes for measuring metal activity in the sediment experiments. The cadmium sediment partition coefficients contribute a small increment to the sediment quality criteria. For copper and lead, however, the additional binding to sediment organic carbon is significant and can amount to 1 to 10 and 10 to 100 μmol/g dry weight for lead and copper, respectively, depending on the organic carbon concentration of the sediment.
Full D-Band Transmit–Receive Module for Phased Array Systems in 130-nm SiGe BiCMOS

This letter presents a D-band (110 to 170 GHz) transmit–receive module in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> silicon–germanium (SiGe) BiCMOS for phased-array applications. The module includes single-pole double throw (SPDT) switches, a low noise amplifier (LNA), a power amplifier (PA), and two variable gain amplifiers (VGAs). A broadband quarter-wave SPDT is designed with power handling capacity of 17 dBm and a state-of-the-art insertion loss of 2 dB at 140 GHz. The three-stage cascode LNA and PA and the two-stage phase-compensated VGA cover the entire D-band. In the receive mode, the module has a measured peak gain of 28.3 dB with a 3-dB bandwidth (BW) of 110–170 GHz, along with a minimum noise figure (NF) of 9 dB (at 120 GHz) and an IP<sub>1dB</sub> of −21 dBm. In the transmit mode, the peak gain is 22.4 dB within a 3-dB BW of 113–170 GHz, while the OP<sub>1dB</sub> is 7 dBm and the <inline-formula> <tex-math notation="LaTeX">$\text{P}_{\mathrm{ sat}}~9.5$ </tex-math></inline-formula> dBm at 140 GHz.
Stability Analysis of Two-Stage Single-Phase Converter System Adopting Electrolytic Capacitor-Less Second Harmonic Current Compensator

For the two-stage single-phase converter, including the power factor correction (PFC) ac-dc converter and dc-ac inverter, a second harmonic current compensator (SHCC) can be introduced to handle the second harmonic current (SHC) for removing the undesired electrolytic capacitor. Consequently, the two-stage single-phase converter becomes a multi-converter system. In this paper, the ac-dc stage or dc-ac stage in the system is categorized into two types in terms of the control objectives, i.e., the bus-voltage-controlled converter (BVCC) and the bus-current controlled converter (BCCC). The SHCC is a BCCC since its bus-port current is directly controlled, and the bus-port impedance of the SHCC is derived, which is affected by the ac-dc stage or dc-ac stage since the SHC reference is extracted from them. The system stability is investigated with the impedance-based stability criterion, and it is found that the system stability is different when the ac-dc stage or dc-ac stage is operated as a BVCC or a BCCC. Specifically, only when the ac-dc stage is operated as a BVCC, the system is unstable; while for other cases, the system is certainly stable. For the applications where the ac-dc stage is operated as a BVCC, a virtual resistor is introduced and realized by the control scheme of the SHCC to ensure the system stability. Finally, a two-stage single-phase PFC ac-dc converter is fabricated and tested to verify the theoretical analysis.
FedSCR: Structure-Based Communication Reduction for Federated Learning

Federated Learning allows edge devices to collaboratively train a shared model on their local data without leaking user privacy. The non-independent-and-identically-distributed (Non-IID) property of data distribution, which leads to severe accuracy degradation, and enormous communication overhead for aggregating parameters should be tackled in federated learning. In this article, we conduct a detailed analysis of parameter updates on the Non-IID datasets and compare the difference with the IID setting. Experimental results exhibit that parameter update matrices are structure-sparse and show that more gradients could be identified as negligible updates on the Non-IID data. As a result, we propose a structure-based communication reduction algorithm, called FedSCR, that reduces the number of parameters transported through the network while maintaining the model accuracy. FedSCR aggregates the parameter updates over channels and filters, identifies and removes the redundant updates by comparing the aggregated values with a threshold. Unlike the traditional structured pruning methods, FedSCR retains the complete model that does not require to be retrained and fine-tuned. The local loss and weight divergence on each device vary a lot because of the unbalanced data distribution. We further propose an adaptive FedSCR, that dynamically changes the bounded threshold, to enhance the model robustness on the Non-IID data. Evaluation results show that our proposed strategies achieve almost 50 percent upstream communication reduction without loss of accuracy. FedSCR can be integrated into state-of-the-art federated learning algorithms to dramatically reduce the number of parameters pushed to the global server with a tolerable accuracy reduction.
The Active Distribution Network and DG Planning considering DR Under Dynamic Electricity Price Strategy

Aiming at the problem that it is difficult to consider the interaction between distributed power sources and network structures of active power distribution systems in the process of collaborative planning, a mathematical model and solution algorithm considering the balance of multi-agent interests and alternate optimization of planning and operation are proposed. Firstly, a three-tier optimization framework and mathematical model are established that aim at the optimal cost-benefit of distributed power sources and distribution networks, and embedded demand response and operation strategies; secondly, a simplified network structure based on the principle of topology equivalence is proposed. Based on the method, combined with the in-depth analysis of the topological structure change characteristics during the grid generation process, a rapid generation method of single-contact network based on the breaking method is proposed, and the encoding/decoding scheme of the particle swarm optimization algorithm combined with it is formulated; again, Based on the characteristics of network load and user demand response willingness, the dynamic time-of-use electricity price optimization method and operation strategy of active distribution network are proposed, and the source network collaborative planning and solving process with built-in refined operation is established; finally, it is verified by an example The rationality and effectiveness of the method are described.
Technologies and Applications of Angle Sensors: A Review

This paper presents a critical review of advancements made in the area of non-contact angle sensing in the last two decades. Angular position sensing is an essential function in most of the engineering systems associated with industries such as automotive, robotics, process, and aircraft. The review shows significant progress that has been achieved in the performance aspects of angle sensors, overcoming the challenges introduced by new and specific applications. Progress is also visible in effectively utilizing the emerging allied technologies on the sensing and electronics field for the development of high-end angle sensors. As a part of the review, the important sensing techniques utilized for the angle sensors are identified and carefully looked at the advantages and limitations of those. Besides, the potential areas for improvements are also suggested. The important techniques employed to design and develop the angle sensors are based on capacitive, inductive, magneto-resistive, and optical schemes. Signal conditioning circuits, analog or digital, or a combination of it, are necessary to derive the best from the sensing elements. They play a vital role in the overall performance of the sensor. The review addresses the advancement in the sensing element, methodology, and signal conditioning aspect in detail. In this review, the important angle sensors reported are evaluated against the performance parameters such as range, resolution, linearity, accuracy, sensitivity, power consumption, complexity, cost, manufacturability, and reliability.
Trust Model to Minimize the Influence of Malicious Attacks in Sharding Based Blockchain Networks

A sharding mechanism could potentially be the solution to enhance the scalability of blockchain networks and makes the distributed ledger technology more feasible. Despite the scalability improvement, it increases the influence of malicious attacks on blockchain networks. We develop a comprehensive trust model by enhancing the trust score of nodes to minimize the adversary influences of malicious attacks in sharding based blockchain networks. Firstly, a penalty factor is incorporated into this trust model to decrease the probability of malicious nodes becoming leaders in the shards. Then, we examine the leader selection probability for varying penalty factors. We also observe the influence of the global reputation on the trust score for a varying number of nodes. Secondly, we increase the trustworthiness of nodes by including penalty factors and reputation scores to nodes that could then identify the malicious influence. The fair node distribution among shards is achieved by distributing the nodes with the same aggregated trustworthiness scores. Finally, we develop a probability distribution model to identify the probabilities of clustering corrupted nodes into single shards and the existence of such corrupted shards in the entire network. Uncorrupted or honest shard probability is shown to be higher in the RapidChain than the Elastico and OmniLedger sharding protocols. This could be as a result of the shard resiliency of the RapidChain (n/2) protocol being more significant than that of the Elastico (n/3) and in OmniLedger (n/3) protocols. Low message complexity of single intra-shard consensus of the RapidChain protocol $\mathcal{O}(n)$ may contribute to perform security algorithms more efficiently than that of the Elastico $\mathcal{O}({n^2})$ and OmniLedger $\mathcal{O}(n)$ sharding protocols. The probabilities of clustering corrupted nodes into single shards can be estimated, and the existence of such corrupted shards in entire networks can be identified using the proposed model.
Critical Analysis of Cross-Validation Methods and Their Impact on Neural Networks Performance Inflation in Electroencephalography Analysis

The performance of a brain–computer interface (BCI) system is usually measured by its classification accuracy. This creates motivation to increase system accuracies. This article investigates the variation of accuracy values in emotion recognition studies and their relation to cross-validation methods. The literature shows values of accuracies ranging from 60% to 99% while using similar classifiers when tested on the same data set. The study included a literature review of 65 articles testing their algorithms on the DEAP data set up until 2019. Moreover, the study involved the reimplementation of a neural network classifier in both nested and nonnested cross-validation methods. The results show accuracies up to 90% when using nonnested cross validation, while nested cross-validation achieves accuracies around 60%. This article aims to motivate researchers to clearly describe their cross-validation method to avoid confusing other researchers when benchmarking their algorithms.