Power Domain Research Articles

Power Domain Superimposed Modulation Method Based on Dimensional Level Transformation and 5D Constellation Shaping

Power Domain Superimposed Modulation Method Based on Dimensional Level Transformation and 5D Constellation Shaping

An Identical Code–Data Based Nonorthogonal Multiple Access

ABSTRACTNonorthogonal multiple access (NOMA) is a promising technique operating with limited resources. NOMA can be executed in both power and code domains. Cyclic interleaved NOMA strategies are debated in recent literature, combining power and code domains. Identical code cyclic shift (ICCS) transmission is viewed to be a bridge between code division access and NOMA. We propose identical code–data cyclic shift NOMA (ICDCS‐NOMA) in which the code and data are cyclically shifted in distinction to simple cyclic interleaved transmission introduced in our earlier work and the ICCS transmission. The cell‐edge user in a NOMA pair engages its signal transmission using ICDCS; in contrast, the cell‐center user prefers simple cyclic interleaved transmission. The spread data stream and its cyclic shifted version are blended in ICDCS, followed by cyclic interleaving. A distinct and extreme fading channel pair is assumed with the same number of paths for the edge and center users. Diversity combining multipath signals is embraced at the edge user terminal, and frequency domain equalization (FDE)/matched filter (MF) receiver is employed at the center user. The simulation indicates the dominance of the proposed ICDCS‐NOMA transmission in terms of the attained diversity gain over the conventional, ICCS, and simple cyclic interleaved transmissions.

A Multi-Decadal Hourly Coincident Wind and Solar Power Production Dataset for the Contiguous United States

As renewable energy continues its rapid expansion in the Unites States, multi-decadal hourly datasets of electricity production are needed to asses reliability and resource adequacy of power grids. Recent years have seen the release of grid-cell-level simulated meteorological variables, however these are not extended to the power domain, are not developed from a dynamically consistent numerical weather model, and only cover a historical baseline of less than a decade. To fill this gap, this work provides a dataset of 43 years of coincident plant-level wind and solar power production data. The dataset is designed to be aggregated to appropriate scales of interest for bulk system studies such as Balancing Authorities (BAs), states, and nodes of a production cost model. The dataset covers every plant in the contiguous U.S. that is reported in the U.S. Energy Information Administration (EIA) Form 860 as of 2020. When compared with the EIA-923 monthly generation, we find minimal bias (less than 5%). When compared with BA-reported hourly generation, we find low bias in solar (less than 7%), and slight underdispersion in wind. This coincident multi-decadal historical dataset provides a documented and evaluated multi-resource baseline for studies on reliability, resource adequacy, climate change impacts, and characterization of emergent climate threats on renewable resources.

A novel superposition coding scheme based on polar code for multi‐user detection in underwater acoustic OFDM communication system

SummarySuperposition coding (SC) is a non‐orthogonal multiple access (NOMA) scheme for the downlink communication system, which allows signals of different users to be stacked and forwarded simultaneously in the same frequency band. The codeword for each user is assigned a different weight at the transmitter to avoid signal conflicts and interference after stacking, and successive interference cancellation (SIC) is adopted to decode. A novel SC scheme based on polar code, suitable for the downlink multi‐user underwater acoustic (UWA) communication system using orthogonal frequency division multiplexing (OFDM), is proposed in this paper. Polar code is utilized to create a nested code structure that is divided into several subsets. We exploit this feature of the polar codes and assign each subset to the corresponding user in the channel coding process. The information bits are placed on the independent subset for each user, and the random bits are placed on the subset of other users while all the users share the same set of frozen bits. Then, the codewords are stacked with different weights. The selection range of power factors can be expanded through the double superposition of the coding and the power domains, and higher system throughput and fairness can be achieved. The simulation and tank experiment results significantly verify the effectiveness of the proposed scheme, making it most suitable for the downlink UWA multi‐user communication systems.

Explicit Gender Stereotyping in Bilingualism: The Impact of the Foreign Language Effect

This study investigates how the language used to present lexical stimuli influences the expression of gender biases, with a particular focus on the “Foreign Language Effect” (FLE) – the phenomenon where using a foreign language affects judgment and reasoning. Italian native speakers (N=213), English native speakers (N=105), and Italian-English bilinguals (N=192) evaluated 58 words categorized into Power, Weakness, Warmth and Coldness domains for gender connotation. Results indicated consistent stereotypical associations across languages, albeit with variable strength. Critically, participants exhibited attenuated stereotyping when assessing words in a non-native language, providing further empirical validation for the FLE. Foreign language use appears to elicit less automatic social biases, potentially due to decreased emotional activation and increased cognitive distance that occur when processing information in a less proficient language. Additionally, this reduction in stereotyping may be attributed to less exposure to and internalization of sexist language in a second language. By demonstrating the role of language in modulating bias expression, these insights have implications for understanding sociocultural perception formation and developing equitable language policies.

Enhancing energy storage performance of dielectric capacitors: Tailoring CaO-SrO-Na2O-Nb2O5-SiO2 glass-ceramics through controlled crystallization for pulse power applications

As potential dielectric materials for capacitors, glass-ceramics exhibit significant promise in the realm of pulse power supply. Extensive research has been undertaken to explore the commendable voltage resistance and favorable dielectric properties of glass-ceramics. They exhibit a rapid charge and discharge rate. However, the limited energy storage density of glass-ceramics constrains their practical application. In this study, we focused on the preparation of CaO-SrO-Na2O-Nb2O5-SiO2(CSNNS) glass-ceramics through conventional melting and high-temperature crystallization processes. Our investigation delved into the impact of crystallization temperature on the phase composition, dielectric properties, and energy storage characteristics of the CSNNS glass-ceramics. The results indicate a direct correlation between the dielectric constant and dielectric loss, both exhibiting an upward trend with increasing crystallization temperature. Simultaneously, the microstructure of the glass-ceramics manifests signs of deterioration, characterized by larger grain size and heightened porosity, leading to a reduction in breakdown strength. At a crystallization temperature of 1100 °C, the CSNNS glass-ceramics demonstrated a remarkable combination of a high dielectric constant (∼280) and superior breakdown strength (481 kV/cm). The achieved maximum theoretical energy storage density reached 2.87 J/cm3. At an electric field of 100 kV/cm, the effective energy storage density is 0.23 J/cm3, and the energy storage efficiency is 72 %. These findings demonstrate the broad application potential of the CSNNS glass-ceramics in the domain of pulse power, highlighting their relevance for future developments in this field.

Blockchain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output nonorthogonal multiple access

One of the greatest ways to guarantee that networks designed for fifth generation (5G) and beyond reach the required levels of spectrum efficiency (SE) is through nonorthogonal multiple access (NOMA). This work presents two new blockchain-based techniques that take advantage of massive multiple input multiple output in a single-cell network to improve performance. NOMA power domain is used in the 5G cooperative cognitive radio network (CCRN) to improve the SE of the downlink. This study investigates a novel cooperative NOMA-based CCRN for underlay spectrum sharing. A cooperative NOMA technique is proposed by considering the access modes of relays and secondary users (SUs) on the secondary network. The proposed system's performance is assessed with respect to random channel characteristics, frequency-selective Rayleigh fading and perfect successive interference cancellation (SIC). The first signal decoded by SU identifies the relay states with the best channel quality between users and the destination users to offset the bit error rate. The throughput dropped during the period because of the perfect SIC. The precise closed-form expressions for the system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the efficacy of the suggested cooperative strategy. The suggested approaches are assessed under various conditions using the MATLAB application by considering varying transmit power levels, power location coefficients and lengths. In every case, four users are assumed to be using a 90 MHz bandwidth and M-ary Quadrature Amplitude Modulation technology.

Development of adeno-associated viral vectors targeting cardiac fibroblasts for efficient in vivo cardiac reprogramming

Overexpression of cardiac reprogramming factors, including GATA4, HAND2, TBX5, and MEF2C (GHT/M), can directly reprogram cardiac fibroblasts (CFs) into induced cardiomyocytes (iCMs). Adeno-associated virus (AAV) vectors are widely used clinically, and vectors targeting cardiomyocytes (CMs) have been extensively studied. However, safe and efficient AAV vectors targeting CFs for invivo cardiac reprogramming remain elusive. Therefore, we screened multiple AAV capsids and promoters to develop efficient and safe CF-targeting AAV vectors for invivo cardiac reprogramming. AAV-DJ capsids containing periostin promoter (AAV-DJ-Postn) strongly and specifically expressed transgenes in resident CFs in mice after myocardial infarction (MI). Lineage tracing revealed that AAV-DJ-Postn vectors expressing GHT/M reprogrammed CFs into iCMs, which was further increased 2-fold using activated MEF2C via the fusion of the powerful MYOD transactivation domain (M-TAD) with GHT (AAV-DJ-Postn-GHT/M-TAD). AAV-DJ-Postn-GHT/M-TAD injection improved cardiac function and reduced fibrosis after MI. Overall, we developed new AAV vectors that target CFs for cardiac reprogramming.

MDD-FedGNN: A vertical federated graph learning framework for malicious domain detection

The domain name system (DNS) serves as a fundamental component of the Internet infrastructure, but it is also exploited by attackers in various cyber-crimes, underscoring the significance of malicious domain detection (MDD). Recent advances show that graph-based models exhibit potential for inferring malicious domains and demonstrate superior performance. However, acquiring large-scale and high-quality graph datasets for MDD proves challenging for individual security institutes. Hence, a promising research direction involves employing vertical federated graph learning scheme to unite diverse security institutes and enhance local datasets resulting in more robust and powerful detection models. Nonetheless, directly applying vertical federated graph neural networks for MDD confronts challenges posed by noisy labels and noisy edges among security institutes, which ultimately diminish detection performance. This paper introduces a novel vertical federated learning framework, called MDD-FedGNN, that applies contrastive learning with two different encoders to deal with noisy labels and employs a new loss function based on the information bottleneck theory to handle noisy edges. Comparative experiments are conducted on a publicly available DNS dataset to evaluate the effectiveness of MDD-FedGNN in addressing the challenges of noisy labels and edges in vertical federated graph learning. The results demonstrate that MDD-FedGNN outperforms baseline methods, confirming the feasibility of training more powerful malicious domain detection models through data sharing and vertical federated learning among different security agencies.

Waveform optimization for future wireless communication: numerical simulation under mobile and vehicular scenarios in 5G and beyond

Code Division Multiple Access (CDMA) and Orthogonal Frequency Division Multiplexing (OFDM) are multicarrier regulating systems developed for quick communication. OFDM and its improved multi-carrier regulation technique employ equally distributed orthogonal sub-carriers for the transmission of data. These sub-carriers are modified using a conventional tweak plot, maintaining full information rates comparable to single carrier regulation schemes. The research also compares the two 5G modulation techniques, FBMC and UFMC, to OFDM, the 4G communication modulation technology. To evaluate the benefits of each contender, we compare important characteristics of different modulation approaches. The research compares the 5G modulation techniques FBMC and UFMC to OFDM, focusing on factors like power spectrum density, spectral efficiency, Bit Error rate, and Peak to Average Power Radio. To boost channel capacity and decrease interferences, the approach merges three alternative 5G waveforms under the LTE-V paradigm which is a specialized mode or configuration of Long Term Evolution (LTE) technology designed specifically for vehicular communication. MATLAB simulations show that FBMC performs better in terms of BER and capacity. In scenarios characterized by high interference due to power domain multiplexing and vehicular environment, FBMC demonstrates a maximum capacity that is 3.5 times greater than OFDM. However, in low-interference conditions, FBMC’s capacity exceeds UFMC’s by only a marginal 5%.

The application of vibrational spectroscopy in forensic analysis of biological evidence.

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

Enhanced Non-Orthogonal Multiple Access Performance Using Channel Coding for High-Altitude Platform System (HAPS)

The high-altitude platform system (HAPS) is a promising technology for providing high-speed data transmission and coverage for remote areas. Still, it suffers from unpredictable channel transmission, especially in the Rician channel. To overcome the issue in transmission, we used one channel coding strategy, namely the quasi-cyclic low-density parity-check (QC-LDPC). This study aims to evaluate the performance of power domain non-orthogonal multiple access (PD-NOMA), which allows multiple users to share the same spectrum in a downlink communication system from HAPS to three ground stations. We simulated the system using MATLAB simulation with different power allocation coefficients for every ground station. The simulation results showed that QC-LDPC codes can significantly lower the signal-to-noise ratio (SNR) required to achieve the same bit error rate (BER) as uncoded NOMA, especially in scenarios with uneven power allocation. The average improvement of QC-LDPC over uncoded NOMA was 8.4% in SNR. However, we also observed that NOMA is prone to domino errors, where decoding errors in one ground station can propagate and degrade the performance of the subsequent ground station. Furthermore, we found that power allocation significantly impacts the system performance, while the location and power allocation of the second ground station have a minor effect. Based on these findings, we conclude that QC-LDPC can enhance system performance in scenarios with low received power and that power allocation is a crucial factor for NOMA systems with multiple ground stations.

Research on Abnormity Detection based on Big Data Analysis of Smart Meter

There are over five hundred million smart meters in China. The current standard for the use of smart meters is physical inspection of meter dismantling within 8 years. The method leads to many issues including high cost of testing, low sampling rate, unknown meter status huge waste of resources etc. Searching for non- dismantling meter detection solution is necessary. Although the smart grid can be managed much better with the increasing use of smart meters, the current standard brings many issues. To solve the problems like a huge waste of resources, detecting inaccurate smart meters and targeting them for replacement must be done. Based on the big data analysis of smart meters, abnormity can be predicted and diagnosed. For this purpose, the method is based on Long Short-Term Memory (LSTM) and a modified Convolutional Neural Network (CNN) to predict electricity usage patterns based on historical data. In this process, LSTM is used to fit the trend prediction of smart meters, and recurrence plot is used to detect the abnormality of smart meter. Both LSTM and recurrence plot method is the first time to be used in smart meter detection. In actual research, many methods including Elastic Net, GBR, LSTM and etc. are used to predict the trend of smart meters. Through the best method LSTM, the accurate rate of the trend prediction of smart meters can arrive at about 96%. Similarly many methods are used to detect the abnormality of smart meters. In single-input modeling, there are sequence-input and matrix-input methods. In dual-input modeling, there are TS-RP CNN, VGG+BiLSTM, ResNet50+1D-CNN and ResNet50+BiLSTM etc. Eventually based on the most successful method recurrence plot, the abnormity testing and failure recognition can be got at 82% roughly. This is the breakthrough in the electricity power domain. With the success of the solution, the service time of a normal meter can be prolonged by abnormity detection. This will lead to saving a lot of resources on smart meter applications.

Multi-zero pole dynamically compensated LDO with low quiescent current

This paper presents an LDO design tailored to the various power domains of CPUs. In contemporary CPU designs, different functional modules typically necessitate distinct voltage levels, thus requiring a flexible and adjustable power management approach. An LDO with an internally compensated super source follower (SSF) and a multi-zero-pole compensated loop stability is proposed. The SSF is used to reduce the output impedance of the LDO, enhancing its carry-under-load capability. The loop is then transformed into a multi-zero-pole system through Miller compensation, feed-forward compensation, and zero-pole tracking compensation techniques, enabling the LDO to achieve stability and reliability under different loads. The LDO proposed in this study was designed using a 110 nm CMOS process. The LDO can operate within a temperature range of −40–150 °C, with a low quiescent current of 57 μA. The power supply rejection ratio of the circuit is −22 dB at 1 MHz. When the load current jumps from 5 mA to 300 mA within 40 μs, the output voltage experiences an undershoot of 271 mV, an overshoot of 174 mV, and a maximum adjustment time of 100 μs. The load regulation is 0.00267 mV/mA, and the line regulation is 1 mV/V.

Full-Swing Nanosecond Delay Hybrid Level Shifter for Time-Critical Applications

A crucial component of digital integrated circuits with numerous power domains is the level shifter (LS). The traditional topologies are currently the mirror LS and cross-coupled LS. For full-swing level conversions from extremely low voltage to the nominal voltage of the supply, ahybrid LS is proposed in this paper, which is a combination of the current mirror LS and cross-coupled LS with a swing-aware output inverter. The proposed LS circuit is designed to ensure full swing, static current-free, and limited current-contention level conversions by preserving the benefits of the current mirror and cross-coupled LS, and using them to eliminate each other’s shortcomings. The proposed hybrid LS is designed and implemented by using 45-nm technology in Cadence Virtuoso tool. Pass transistors and current limiters with multiple thresholds are included for the suggested LS. The proposed LS provides voltage conversion from 0.10 V to 1.20 V. At alevel-shifting voltage of 0.20 V, the proposed hybrid LS at 1 MHz input frequency demonstrates a delay of 8.38 ns, an average power consumption of 3.81 µW, and an energy per transition of 26.64 fJ. Additionally, the suggested LS has low delay, good supply voltage scaling, and delay scalability.

Application of Intelligent Cloud Computing Technology in Optical Communication Network Security of Smart Grid

In order to improve the security of smart grid optical communication network, this paper combines intelligent cloud computing technology to build a security system of optical communication network of power grid. In the aspect of improving communication security performance, multi-user access is realized by non-orthogonal power domain segmentation, and different users use different powers to add and superimpose the same spectrum resources, so as to increase spectrum utilization. At the sending end, this paper improves the safe channel capacity of users by means of pre-coding and artificial noise, and realizes the safe transmission of information. In terms of transmission stability, the cloud computing platform is used as a data processing platform, and multiple nodes are processed synchronously through optical communication state identification, which can more effectively improve the speed of optical communication state identification data. In order to test the performance of the power grid information dispatching model designed in this paper in optimizing power grid configuration and improving power grid load, simulation experiments are carried out. Through the experimental analysis, we can see that the communication method proposed in this paper can accurately identify the intrusion factors, and can effectively improve the security of smart grid optical communication network.

User Clustering with Spatial Concept using Supervised Learning for NOMA Downlink

This research aims to optimize the performance of Successive Interference Cancellation (SIC) in Power Domain Non-Orthogonal Multiple Access (PD-NOMA) technology by applying spatial concepts through the use of beamforming techniques. User clustering is a key element in achieving this goal, and this research applies various supervised machine learning classification algorithms including Decision Tree, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes. The experimental results show that Random Forest achieves the highest accuracy in classifying users, followed by Decision Tree. In addition, in measuring performance using ROC (Receiver Operating Characteristic) and AUC (Area Under the Curve) curves, Decision Tree and Random Forest achieved the best results as well. While in terms of experimentation process time, decision tree has a faster time than random forest. Overall, Random Forest and Decision Tree algorithms are suitable for the task of user clustering in the context of PD-NOMA which utilizes the spatial concept of user to Base Station (BS).

A Survey on Throughput Maximization in IoT Networks

This paper presents a comprehensive survey on Non-Orthogonal Multiple access for multiple access in wireless networks. As networks are now migrating towards the wireless domain as opposed to the wired domain, the need for more efficient bandwidth allocation mechanism is necessary. With the advent of big data applications, increasing number of users, increased bandwidth and data requirement yet limited bandwidth availability have become serious challenges. Hence future generation wireless networks would need improved multiple access techniques than their present day counterparts. Non-Orthogonal Multiple access is a technique in which multiple users data is separated in the power domain. The problems addressed by NOMA are low overall bandwidth for multiple users. This paper presents a comprehensive review and taxonomy of the NOMA based multiple access framework. Index Terms:- Non-Orthogonal Multiple Access, Wireless Networks, Quality of Service (QoS), Bit Error Rate, Throughput.

Guest Editorial: Identification, stability analysis, control, and situation awareness of power systems with high penetrations of renewable energy resources

AbstractIt is with great pleasure that the authors introduce this special issue, commemorating the 8th Asia Conference on Power and Electrical Engineering held in Tianjin in 2023. This conference served as a nexus for researchers, practitioners, and industry experts from around the globe to convene and exchange cutting‐edge insights, innovative ideas, and transformative advancements in the field of power and electrical engineering. The contributions featured in this special issue represent a diverse array of research endeavours, spanning from fundamental theories to practical applications, all aimed at addressing the myriad challenges and opportunities facing the power and electrical engineering domain. From novel methodologies in renewable energy integration to advancements in smart grid technologies, each article encapsulates the spirit of innovation and collaboration that characterised the conference. This special issue includes scientific investigations on topology modelling and virtual stability analysis methods for distribution networks with high penetration of renewable energy resources, monitoring and situation awareness on grid inertia and power‐frequency evolution, novel voltage source converter control schemes, and reviews of low‐carbon planning and operation of electricity, hydrogen fuel, and transportation networks.

Teaching Race in Business Schools: The Challenges and Possibilities of Anti-Racist Education

This article explores anti-racist education in business schools amidst the backlash against critical race theory in an anti-Black world. I conduct an autoethnography of my experiences as a woman of colour and management educator who has attempted to bring critical discussions of race and racism into my classrooms. The article examines the barriers to anti-racist teaching in business schools and shows how they interweave individual/interpersonal, institutional, and ideological domains of power. Through my stories, I offer an account of the ways anti-racist education may be limited when it relies on the efforts of individual academics and reveal the tolls that anti-racist education can take on the educator, especially when they are navigating wider systems that are hostile to racial justice. By interrogating the challenges of anti-racist education, I also reflect on the practices and conditions that make meaningful anti-racist education possible.