bluuebunny/crossref_metadata_2025_split huggingface dataset:
Boosting efficiency in dual-absorber RbPbBr3 perovskite solar cell: the role of two-dimensional GeS and SnS2 as electron transport layers
Authors: Rafiee Rafat Shahram , Ahangari Zahra , Ahadian Mohammad Mahdi , Hosseini Seied Ali 
Published: September 2024

Abstract
This paper presents a comprehensive investigation into the electrical characteristics of a perovskite solar cell. The n-i-p cell is based on a low band gap rubidium–lead-bromide (RbPbBr3) perovskite with an energy level of 1.31 eV. The study also evaluates the impact of high mobility two-dimensional GeS and SnS2 as electron transport layers (ETLs) on the cell’s performance. These ETLs have a wide band gap and provide a hole blocking layer due to their high valence band-offset. Additionally, a thin film MoTe2 with a band gap of 1 eV is considered as a complementary absorber for capturing near-infrared solar spectrum. The investigation focuses on the influence of critical physical and structural design parameters on the electrical parameters of the cell. The optimized device with SnS2 as the ETL exhibits a power conversion efficiency (PCE) of 25.03%, an open circuit voltage of 0.95 V, a short circuit current density of 33 mA cm−2, and a fill factor of 80.31%. Similarly, the device with GeS as the ETL achieves a PCE of 25.14%, an open circuit voltage of 0.96 V, a short circuit current density of 33.01 mA cm−2, and a fill factor of 80.66%. Furthermore, a statistical analysis is conducted by calculating the coefficient of variation to assess the sensitivity of the cell’s electrical measures to the variation of design parameters and operating temperature. The results highlight that defects in the absorber layer, work function of the back contact, and ambient temperature are critical design parameters that can significantly impact the device performance. Overall, the utilization of high mobility wide band gap ETLs, in combination with the low band gap perovskite, offers a promising approach for the design of high-performance solar cells.
Leading two-point resistances from transfer matrices in cylindrical, spider web, axial and grid resistor networks
Authors: Perrier Frédéric , Girault Frédéric 
Published: September 2024

Abstract
Resistor networks are increasingly being considered in heuristic research as models for natural or artificial matter. The equivalent resistance between two nodes, the Two-Point Resistance (TPR), can be calculated using a variety of methods. The transfer matrix (TM) method was originally considered as a numerical tool for estimating percolation thresholds in random networks with a repeating pattern. The TM method is revisited here as an efficient tool to obtain, in a fast and elegant way, iteration relations and exact explicit expressions for leading TPRs that include a node in the last repeated pattern. Several rotationally invariant networks are studied, such as simple cylindrical networks, spider web networks and cylindrical networks with a central resistive axis, in which case the TM matrices are circulant matrices. Examples of explicit expressions are given for orders of rotation ≤4 or 5, depending on the case. The method can be applied in a similar way to networks with less symmetry, such as grids. The general expressions of TPRs obtained using the TM method can provide quantitative guidelines for resistor networks developed in materials science, environmental issues or industrial applications.
High performance NiCoSe2@NiFe-MOF@AC Nanocomposite electrode for energy storage and precise detection of mono-sodium glutamate
Authors: Safdar Samia , Mumtaz Muhammad Azhar , Afzal Amir Muhammad , Muzaffar Nimra , Waris Muhammad Hamza , Iqbal Muhammad Waqas , Mumtaz Sohail , Iqbal Muhammad Zahir , Zafar Muhammad , Alaraidh Ibrahim A , Okla Mohammad K 
Published: September 2024

Abstract
Hybrid supercapacitors, a fascinating appliances that combines the best of both batteries and supercapacitors, showcase remarkable improvements in power and energy densities. Here, a two-step technique was used to synthesis NiCoSe2@NiFe-MOF. In first step NiCoS was synthesis using electrodeposition approach and in second step the NiCoSe2@NiFe-MOF was synthesis using vacuum-assisted filtering. The specific capacitance of the NiCoSe2@NiFe-MOF composite used as the supercapacitor electrode in a three-electrode system was (2882.5 F g−1) and specific capacity is 1729.8 C g−1, much greater than that of the NiCoSe2 electrode material which was 967.4 C g−1 at current density of 1.5 Ag−1, In addition, a hybrid supercapacitor device (NiCoSe2@NiFe-MOF//AC) has been developed and successfully demonstrates a specific capacity of 205.45 C g−1 at 0.5 A g−1. The energy density is measured in units of WhKg−1 with a numeric values of 78.3, while at 2.9 KWKg−1 the power density is recorded. This device has been tested for up to five thousands cycles of discharging (87.8%) and charging (94.2%), achieving an impressive capacity retention rate of 96.8%.Additionally, an amperometric immunosensor was fabricated by employing the NiCoSe2@NiFe-MOF nanocomposite to detect Mono-Sodium Glutamate (MSG). A constant linear association was observed between the concentration of MSG and the variation in current, encompassing the entire detection range of 0.05–200 μM. The findings of our study offer an exciting starting point for the development of energy storage systems with greater capacity.
Static reconfiguration method of photovoltaic array based on improved competence square
Authors: Yi Lingzhi , Tan Jingxuan , Wang Yahui , Luo Bote , Fan Lü , Cheng Siyue 
Published: September 2024

Abstract
Partial shading can reduce the power output of a photovoltaic (PV) array due to mismatch losses. Therefore, various static and dynamic reconfiguration techniques have been proposed to address this problem. Although dynamic reconfiguration methods are fast and flexible, they face challenges such as complex hardware circuits and high costs. In contrast, static reconfiguration methods simplify control complexity, improve system stability, and significantly reduce costs. Among the existing static reconfiguration techniques, the competence square (CS) method, which changes the physical positions of PV panels without modifying the total cross tied (TCT) based electrical connections, has been introduced to enhance power generation. However, this arrangement faces drawbacks due to the lack of effective chromatic dispersion and insufficient power enhancement under shading conditions. This paper proposes an improved competence square (ICS) reconfiguration method to further improve the power output. The ICS method first reconfigures the PV array using the CS method, then applies the lo shu (LS) method for secondary reconfiguration to determine the optimal connections. This method is compared with TCT, CS and improved northern goshawk optimization (INGO) methods. Additionally, the performance of the proposed method is evaluated against various existing photovoltaic array configurations by comparing the global maximum power point (GMPP), fill factor (FF), mismatch loss (ML), and power enhancement (PE). The experiment shows that under shading conditions of square shading, rectangle shading, trapezoid shading, triangle shading, L-shaped shading, cross shading, discontinuous shading, and irregular shading, reconfiguring PV arrays using ICS consistently achieves the highest power improvement. Compared to TCT, the power output increases significantly by 16.6%, 2.1%, 18.0%, 16.0%, 19.1%, 5.0%, 4.0%, and 3.0%, respectively. Comparison results validate the proposed ICS method in enhancing the global maximum power under shaded conditions.
CMB-PAInT: An inpainting tool for the cosmic microwave background
Authors: Gimeno-Amo C. , Martínez-González E. , Barreiro R.B. 
Published: September 2024

Abstract
The presence of astrophysical emissions in microwave observations forces us to perform
component separation to extract the Cosmic Microwave Background (CMB) signal. However, even in the
most optimistic cases, there are still strongly contaminated regions, such as the Galactic plane
or those with emission from extragalactic point sources, which require the use of a mask. Since
many CMB analyses, especially the ones working in harmonic space, need the whole sky map, it is
crucial to develop a reliable inpainting algorithm that replaces the values of the excluded pixels
by others statistically compatible with the rest of the sky. This is especially important when
working with Q and U sky maps in order to obtain E- and B-mode maps which are free from
E-to-B leakage. In this work we study a method based on Gaussian Constrained Realizations
(GCR), that can deal with both intensity and polarization. Several tests have been performed to
asses the validation of the method, including the study of the one-dimensional probability
distribution function (1-PDF), E- and B-mode map reconstruction, and power spectra estimation. We
have considered two scenarios for the input simulation: one case with only CMB signal and a second
one including also Planck PR4 semi-realistic noise. Even if we are limited to low resolution maps,
Nside = 64 if T, Q and U are considered, we believe that this is a useful
approach to be applied to future missions such as LiteBIRD, where the target are the
largest scales.
Dark-bright solitons and the role of their interaction in breather generation
Authors: Li Le , Che Wen-Juan , Liu Xi-Zhe , Liu Chong , Akhmediev Nail 
Published: September 2024

Abstract
We study fundamental dark-bright solitons and the interaction of vector nonlinear Schrödinger equations in both focusing and defocusing regimes. Classification of possible types of soliton solutions is given. There are two types of solitons in the defocusing case and four types of solitons in the focusing case. The number of possible variations of two-soliton solutions depends on this classification. We demonstrate that only special types of two-soliton solutions in the focusing regime can generate breathers of the scalar nonlinear Schrödinger equation. The cases of solitons with equal and unequal velocities in the superposition are considered. Numerical simulations confirm the validity of our exact solutions.
Vectorial manipulation of twisted vector vortex optical fields in strongly nonlocal nonlinear media
Authors: Liu Cai-xia , Hu Xiao-bo , Wu Feng-min , Chen Rui-Pin 
Published: September 2024

Abstract
We theoretically investigate the propagation properties and vectorial manipulation of twisted vector vortex beams (TVVB) with a cross-phase in a strongly nonlocal nonlinear medium (SNNM). The root mean square beam-width (RMS-BW) and the critical power required to retain the invariant RMS-BM of the TVVB in an SNNM are derived using the coupled nonlocal nonlinear Schrödinger equation. Numerical calculations reveal novel characteristics of the evolution of the state of polarization (SoP) and the optical intensity distributions during the TVVB propagating in an SNNM. It is found that mode conversions between a Laguerre Gaussian and a Hermite Gaussian mode take place during propagation in an SNNM, and the topological charge of the TVVB can be accurately measured by observing the interference intensity structure in the cross-section. Manipulation of the beam shape, SoP, and rotation of the TVVB is achieved by controlling factors such as the initial power, twisting coefficient, initial beam-width, and topological charge. These findings hold promise for applications in optical micro-manipulation, optical communication, and material processing.
Carbon-based Wearable Pressure Sensor System for Real-time Step Frequency Monitoring
Authors: Zhao Weikai , Liu Haibo , He Pei , Yang Junliang 
Published: September 2024

Abstract
Wearable pressure sensors have attracted great attention in human motion and health monitoring due to their sensitivity, flexibility and portability that can provide continuous physiological information recording. However, current pressure sensors used for wearable motion monitoring suffer from low sensitivity, poor resistance to interference, and the inability to be mass-produced. In this study, we proposed the utilization of multi-walled carbon nanotubes (MWNTs) as sensing materials, fabric as substrates, and screen printing process for fabricating flexible pressure sensors. The fabricated sensors exhibited a high sensitivity at low pressure, and good stability of more than 3000 cycles. Moreover, we designed a wearable step frequency monitoring system for real-time recording of footstep frequency during human walking activities. The results demonstrated that the system accurately monitors frequency under different walking speeds and footstep activities during various movements, providing a promising strategy to develop wearable and wireless sensing system for real-time motion monitoring.
Rapid Label-free and Amplification-free Detection of SARS-CoV-2 Using Solution-Gated Graphene Transistor-Based Nucleic Acid Biosensors in a Portable Biosensing Microsystem
Authors: Yu Haiyang , Li Jun , Liu Zhe , Feng Linrun , Li Jinhua , Tang Wei 
Published: September 2024

Abstract
The COVID-19 pandemic has highlighted the significance of swift and efficient large-scale screening to prevent epidemic outbreaks. Solution-gated graphene transistors (SGGTs) have emerged as a valuable asset in creating point-of-care (POC) sensing platforms for detecting SARS-CoV-2, owing to their notable stability and sensitivity in solution environments. However, the poor interface engineering of sensing electrodes is of great concern for reliable functionalization, leading to poor sensitivity. In this study, we present an interface engineering strategy for realizing the functionalization of SGGT sensing electrodes with high stability and sensitivity to construct a SARS-CoV-2 nucleic acid biosensor. The developed biosensor exhibits the capability to detect ORF1ab gene fragments at a concentration as low as 10−16 M without requiring additional nucleic acid amplification. Furthermore, the entire detection process can be accomplished using a portable biosensing microsystem within 30 minutes. This research holds promise for advancing the study of FET-based biosensors and facilitating their practical application in clinical settings.
Design and Investigation of a 10 Gb/s Hybrid Spatial-time Diversity FSO-CDMA Communication System
Authors: Ji Jianhua , Zheng Zihua , Zhang Wanying , Cao Wenming , Wang Ke , Xu Ming 
Published: September 2024

Abstract
This paper presents a novel strategy for free space optical (FSO) communication system that combines hybrid spatial-time diversity and optical code division multiple access (OCDMA). The authors demonstrate a 10 Gb/s hybrid spatial-time diversity FSO-CDMA experimental system for the first time. They investigate the bit error rate (BER) and physical-layer security under weak and strong turbulence conditions. The experimental results demonstrate that the proposed scheme can improve the performance of the FSO-CDMA system. In the hybrid spatial-time diversity FSO-CDMA system, the BER of the eavesdropper (Eve) exceeds the hard-decision forward-error-correction threshold of 3.8E-3. It is also worth noting that Eve can intercept and recover data without a matched decoder in a spatial diversity FSO-CDMA system. As a result, the proposed scheme can simultaneously enhance reliability and physical-layer security compared to time or spatial diversity systems.
Physical Layer Security of Fiber-optic Wiretap Channel with Forward Error Correction Coding
Authors: Xu Rongwo , Ji Jianhua , Wang Zhenhong , Song Yufeng , Wang Ke , Xu Ming 
Published: September 2024

Abstract
In this paper, by using effective leakage value, we quantitatively assess the security performance of the fiber-optic wiretap channel with forward error correction coding. As the eavesdropper is far from the transmitter, effective leakage value initially decreases slowly and then will vary rapidly. As the eavesdropper is close to the transmitter, effective leakage value of (7,4) block code is larger than that of the (7,3) block code. However, when the eavesdropping distance is far, the system security performances of both are almost the same. Furthermore, effective leakage value using OTDR is analyzed and discussed.
Base Station Energy Saving based on Imitation Learning in 5G Network
Authors: Li Ziyi , Wang Yu , Fan Yanlin , Wang Zhaobo , Lin Shangjing , Sun Lei 
Published: September 2024

Abstract
With the rapid development of communication technology, the large-scale deployment of base stations (BSs) has led to an increase in power consumption. To reduce power consumption, energy saving technologies for BSs have emerged, which are in line with the concept of green communications and can save operators’ costs. In this paper, our goal is to minimize the total power consumption of the base station by dynamically controlling the switching status of the base station. This article first proposes a dynamic base station switching framework based on deep reinforcement learning (DRL), which optimizes the power consumption of switching BSs. Then, considering the dynamic nature of the environment, this article proposes a dynamic BS switching algorithm that introduces the idea of imitation learning (IL) to update the policy by learning the expert’s action. The simulation results show that this scheme can effectively reduce power consumption.
SSFPN: Selective Spatial Feature Pyramid Network for Remote Sensing Object Detection
Authors: Xu Ziming , Ren Bin , Zhang Kunhua 
Published: September 2024

Abstract
Object detection, particularly for oriented small objects, benefits greatly from the generation of multi-scale feature maps by the feature fusion module. Despite the success of series of Feature Pyramid Network models in general object detection, their application in remote sensing object detection has received limited attention. Small oriented objects require contextual information for detection, while various types of objects need different long-range context. In this paper, we propose an intuitive and simple fusion module to be added in Feature Pyramid Network called Selective Spatial Feature Pyramid Network (SSFPN) to address these challenges. SSFPN dynamically adjusts the spatial receptive field across multi-scale feature maps, enhancing the modeling of contextual variations among different objects in remote sensing scenarios. In extensive experiments, SSFPN has achieved competitive results, i.e., an improvement of 0.34% after adding SSFPN to the Oriented RCNN model. The codes are available at https://github.com/Atlantisming/SSFPN.
Self-supervised Entity Alignment Model Based on Joint Embedding
Authors: Mao Zhifang , Fan Chunxiao , Wu Yuexin 
Published: September 2024

Abstract
Entity alignment is an important problem for constructing Web-scale KGs. Self-supervised entity alignment models represented by SelfKG, proposed in 2022, have eliminated the essential requirement of accurate alignment: human labeling, which significantly saves resources. However, when SelfKG scales up to improve entity alignment accuracy, it introduces neighbor noise, leading to a decrease in alignment performance. This indicates that the ability of self-supervised models to aggregate and utilize knowledge graph information needs further exploration. To address this issue, we proposed the following approach: using a Graph Convolutional Network(GCN) to collect global entity and structural information, and a Graph Attention Network(GAT) to collect local neighborhood information from subgraphs. Subsequently, we introduced a novel fusion method to merge the two parts through weighted sum and residual connection, and completed self-supervised entity alignment using the Relative Similarity Metric(RSM) mechanism proposed by SelfKG. This model has both the ability to scale up and the flexibility to avoid neighbor noise, resulting in a significant improvement in performance.
Small Object Detection with lightweight PointNet Based on Attention Mechanisms
Authors: Peng Hao , Huang Duan 
Published: September 2024

Abstract
In response to the issue of subpar small object detection outcomes, the improved 3D object detection method for Point Net is proposed. First, multi-scale fusion method is added to the channel attention mechanism into the feature extraction stage of Point Net; then, the loss function is adjusted to optimize the backbone network and reduce the number of model parameters while improving the accuracy. The experimental results show that in the dataset, the proposed method has higher detection accuracy, the improved algorithm average accuracy improves 3.9%, the model size decreases 20%, effectively improves the detection effect of small objects, and realizes the lightweight of the network.
Focus-SLAM: A visual Monocular SLAM
Authors: Yin Haohui 
Published: September 2024

Abstract
This research presents a novel simultaneous localization and mapping algorithm, called Focus-SLAM, which simulates human navigation strategies by synthesizing a optical saliency model, SalNavNet, in a traditional single-shot SLAM paradigm. SalNavNet introduces an innovative design that incorporates a correlation module and an adaptive Exponential Moving Average (EMA) component, effectively addressing the prevalent center bias issue found in contemporary saliency models. As a result, the system enhances target fixation by accentuating salient features more effectively. Extensive experimental evaluations, conducted across a spectrum of indoor environments exhibiting diverse luminosity conditions. These findings attest to the efficacy of Focus-SLAM in enhancing both the accuracy and efficiency of SLAM operations under challenging real-world conditions.
Research on signal denoising algorithm based on ICEEMDAN eddy current detection
Authors: Liu Qi , Zhao Zhifan , Hou Huaishu , Li Jinhao , Xia Shuaijun 
Published: September 2024

Abstract
This study addresses the challenges of non-stationarity and
significant background noise interference in eddy current detection
signals by proposing a noise reduction method based on Improved
Complete Ensemble Empirical Mode Decomposition with Adapted Noise
(ICEEMDAN). The process commences with the signal being decomposed
using Improved Complete Ensemble Empirical Mode Decomposition with
Adapted Noise into a finite number of Intrinsic Mode Functions
(IMFs). Each Intrinsic Mode Function is then evaluated for the
presence of high-frequency noise using a Power Spectral Density
(PSD) analysis. The high-frequency noise present in the Intrinsic
Mode Functions is then reduced using Normalized Least Mean Squares
(NLMS) before being reconstructed with the remaining Intrinsic Mode
Functions. Subsequently, the reconstructed signals are subjected to
another round of decomposition using Improved Complete Ensemble
Empirical Mode Decomposition with Adapted Noise. The Pearson Product
Moment Correlation Coefficient (PPMCC) is utilised to calculate the
correlation between the Intrinsic Mode Functions within each layer,
retaining those with a strong correlation to further attenuate
noise. Ultimately, the local maxima judgement method selectively
amplifies defect signals by assessing changes in peak and valley
degrees, thereby improving the signal-to-noise ratio of the eddy
current detection signal. The experimental results demonstrate that,
in comparison to the use of only the conventional Improved Complete
Ensemble Empirical Mode Decomposition with Adapted Noise and
Normalized Least Mean Squares denoising methods, the proposed method
increases the Signal-to-Noise Ratio (SNR) by 1.08 dB and 2.31 dB,
respectively, and decreases the Mean Square Error (MSE) by 106.9 and
223.9, respectively. The false alarm rate for stainless steel welded
tubes with defects is 1.4%, while the false alarm rate for
stainless steel welded tubes without defects is 0.4%.