bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hidden layer target spatio-temporal correlation feature regression based soft sensing for industrial process
Authors: Guo Xiaoping , Lu Xiaoyu , Li Yuan 
Published: September 2024

Abstract
The soft sensor modeling method using deep neural networks has been widely adopted. However, issues arise from the layer-by-layer accumulation of target-irrelevant information during feature extraction and the under-exploration of temporal and spatial relationships between hidden layer features, which significantly affects model prediction accuracy. In this paper, we propose a feature regression method called the Hidden Layer Target Spatio-Temporal Correlation Feature Regression method, which simultaneously considers the correlation of hidden layer features with the target and their spatio-temporal correlations. First, we propose a Hidden Layer Target Correlation Auto-Encoder (HTAE) and then construct a deep network by stacking these encoders. In HTAE, the orthogonal typical correlation information between each hidden layer feature and the target variable is calculated. A gated value is utilized for the dynamic adjustment of this information, weighting and replacing the original hidden layer features to obtain deep layer features that are sufficiently correlated with the target. Subsequently, a time-weighted spatially dilated network is built. By incorporating the attention mechanism and dilated convolution to obtain target spatio-temporal correlation features, a fully connected layer is used to construct a feature regression soft sensor model. Simulation experiments are conducted using the debutanizer column and industrial sulfur recovery process, and the proposed method is compared with stacked autoencoder (SAE), variable-wise weighted SAE, spatio-temporal attentional neural network, information complementary fusion SAEs, and stacked spatial–temporal autoencoder (S2TAE) methods to validate its effectiveness.
GPAC-YOLOv8: lightweight target detection for fire scenarios
Authors: Yunyan Wang , Zhangyi Kou 
Published: September 2024

Abstract
Due to the large number of parameters in the deep network model, it is difficult for existing fire detection methods to adapt to limited hardware configurations. In addition, detecting targets in the early stages of a fire is challenging owing to their small size. Therefore, this study presents a novel fire and smoke detection framework called GPAC-YOLOv8, which is based on the YOLOv8 architecture. Firstly, the integration of the ghost module and the Polarized Self-Attention attention mechanism into the backbone culminates in the CGP module, which is designed to improve computational efficiency while maintaining accuracy. Next, an innovative feature fusion module, AC-Neck, is developed through the application of the adaptive spatial feature fusion strategy and the lightweight content-aware reassembly of features upsampling mechanism, aiming to optimize feature map fusion and increase small target detection efficiency. Finally, a Focal-WIoU loss function, augmented with a dual weighting mechanism, is formulated to precisely delineate the aspect ratios of the predicted bounding boxes, thereby strengthening the generalization capacity of the model. Experimental results, derived from the application of the proposed GEAC-YOLOv8 method to a specially constructed dataset, show significant improvements in detection speed while maintaining detection accuracy compared to conventional methods. Thus, the GPAC-YOLOv8 framework demonstrably improves the effectiveness of object detection in fire and smoke scenarios.
Reinforcement learning intermittent optimal formation control for multi-agent systems with disturbances
Authors: Liu Erliang , Miao Guoying , Hu Jingyu 
Published: September 2024

Abstract
This paper investigates disturbance-resistant intermittent event-triggered optimal formation control problems of second-order multi-agent systems by using the reinforcement learning method, which takes into account the influence of network damage including denial-of-service (DoS) and deception attacks, stochastic noises, and unknown external disturbances. Firstly, we propose a novel disturbance observer based on adaptive control to estimate unknown external disturbances under an event-triggered mechanism. Secondly, by use of estimation of disturbances, an innovative intermittent event-triggered optimal formation algorithm is given. By applying theories such as Lyapunov stability and stochastic stability, sufficient conditions are derived to guarantee that all agents achieve the desired formation in mean square sense. Additionally, in the model-free case, the optimal controller is solved using the least squares method, which is computationally less complex than some existing approaches. Finally, the theoretical results are effectively validated through simulation examples.
Robust knowledge transfer for bearing diagnosis in neural network models using multilayer maximum mean discrepancy loss function
Authors: Thuan Nguyen Duc 
Published: September 2024

Abstract
This paper introduces a robust transfer learning method to enhance bearing diagnosis, particularly in cross-machine scenarios. The method trains a shallow neural network using labeled data from a different machine and unlabeled data from the target monitoring machine. To facilitate effective knowledge transfer, a multilayer maximum mean discrepancy loss function is employed, enabling the model to adapt learned features from the source machine to the target machine’s unlabeled data. This approach addresses the challenges of low accuracy and robustness often seen in transfer learning, especially when dealing with different machines. Experiments conducted on the Hanoi University of Science and Technology bearing dataset validate the effectiveness of the proposed method. The results show significant improvements in prediction accuracy and robustness, making this method superior to existing transfer learning models for cross-machine bearing diagnosis.
Combined BDS/5G seamless positioning scheme based on joint switching strategy of satellite elevation angle and CNR in complex urban environments
Authors: Li Fangxin , Tu Rui , Geng Fanyue , Zhang Rui , Zhang Pengfei , Lu Xiaochun 
Published: September 2024

Abstract
Satellite-based positioning has gained popularity, but in bustling urban areas, signals face challenges like serious attenuation, affecting the reliability of positioning. To overcome this, we propose a seamless positioning approach that combines the BeiDou navigation satellite system (BDS) and 5G, specifically designed for complex urban settings. Initially, we establish static and dynamic positioning solutions using real 5G data measured through the time difference of arrival method, resulting in accurate 5G indoor positioning. Following this, we develop a combined BDS/5G positioning model incorporating joint switching based on satellite elevation angle and carrier-to-noise ratio (CNR). We conduct positioning experiments in intricate environments to validate the effectiveness of our model. The experimental outcomes demonstrate that our combined BDS/5G seamless positioning model, employing the joint switching strategy of satellite elevation angle and CNR, achieves impressive positioning accuracies ranging from decimeters to meters. This approach markedly enhances accuracy and reliability of seamless positioning in challenging urban scenarios.
Non-degenerate localised waves beyond Manakov system and their new perspectives
Authors: Pan Liuyi , Wang Lei , Liu Lei , Sun Wenrong , Ren Xiaoxia 
Published: September 2024

Abstract
We study the non-degenerate dynamics of localised waves beyond Manakov system and offer their new perspectives based on the wave component analysis. Our investigation is in the framework of the coupled Hirota (CH) equations. An exact multi-parameter family of solutions for the localised waves is derived within a new Lax pair which is necessary for producing the new types of solutions describing the non-degenerate localised waves, such as the non-degenerate general breathers, non-degenerate Akhmediev breathers, non-degenerate Kuznetsov-Ma solitons and non-degenerate rogue waves. Especially, the degenerate and non-degenerate solutions for rogue waves are different from previous ones, even within the context of the Manakov system. A new technique of wave mode analysis (or the characteristic line analysis) is provided to classify degenerate and non-degenerate solutions beyond the eigenvalue perspectives, namely the critical relative wave number. Such technique is suitable for both the CH equations as well as Manakov system. Hereby, we redefine the non-degenerate localised waves from a fully different view. We further prove that a transition between the non-degenerate localised waves to various types of solitons appears in the CH equations due to the higher-order effects and there is no analogue in Manakov system. In order to further understand such transition dynamics and physical properties of the non-degenerate solutions, the physical spectra are presented analytically. The higher-order terms take impacts on the spectra, for which the state transition solutions as well as a new type of breathers are found. Furthermore, we investigate the relation between non-degenerate modulation instability and higher-order effects. We also offer an exact initial condition to excite the degenerate and non-degenerate localised waves using the numerical simulation and test the stability for the excitation of such solutions by adding a weak perturbation. Since the CH equations can model a large number of physical phenomena in the deep ocean, in the birefringent fibre as well as in the nonlinear channel, our results may provide insights for the related experimental studies.
Electrical and optical characterisation of InGaAsSb-based photodetectors for SWIR applications
Authors: Mamić K , Hanks L A , Fletcher J E , Craig A P , Marshall A R J 
Published: September 2024

Abstract
The

In

x

Ga

1
−

x

A
s

y

Sb

1
−

y

alloy was studied in epilayers and photodiodes grown lattice matched to GaSb across a comprehensive composition range,

0
⩽

x

⩽
0.3

, as a promising technology to support the extended short-wave infrared region

∼
1.7
−
3

µm. Low background carrier concentrations between

6
×

10

14

and

1
×

10

15

cm

−
3

were achieved in all samples, reducing with In fraction. Both the absorption coefficient and external quantum efficiency were found to increase with indium fraction, up to

∼

10

4

cm

−
1

and 70% without an AR coating, respectively. Counter to the fundamental bandgap dependence, leakage current density initially reduced with the addition of low In and As fractions, before rising as the fractions increased and the bandgap reduced further. These properties resulted in specific detectivity reaching a maximum in the sample with x = 0.043, before decreasing towards higher alloy fractions. It is concluded that for moderate In fractions, the InGaAsSb alloy offers clear potential to improve on the disappointing material and photodiode properties of GaSb and support emerging SWIR sensing applications. However, development of fabrication and passivation technology is required to fully exploit this potential.
Detecting and eliminating quantum noise of quantum measurements
Authors: Tang Shuanghong , Zheng Congcong , Wang Kun 
Published: September 2024

Abstract
Quantum measurements are crucial for extracting information from quantum systems, but they are error-prone due to hardware imperfections in near-term devices. Measurement errors can be mitigated through classical post-processing, based on the assumption of a classical noise model. However, the coherence of quantum measurements leads to unavoidable quantum noise that defies this assumption. In this work, we introduce a two-stage procedure to systematically tackle such quantum noise in measurements. The idea is intuitive: we first detect and then eliminate quantum noise. In the first stage, inspired by coherence witness in the resource theory of quantum coherence, we design an efficient method to detect quantum noise. It works by fitting the difference between two measurement statistics to the Fourier series, where the statistics are obtained using maximally coherent states with relative phase and maximally mixed states as inputs. The fitting coefficients quantitatively benchmark quantum noise. In the second stage, we design various methods to eliminate quantum noise, inspired by the Pauli twirling technique. They work by executing randomly sampled Pauli gates before the measurement device and conditionally flipping the measurement outcomes in such a way that the effective measurement device contains only classical noise. We numerically demonstrate the two-stage procedure’s feasibility on the Baidu Quantum Platform. Notably, the results reveal significant suppression of quantum noise in measurement devices and substantial enhancement in quantum computation accuracy. We highlight that the two-stage procedure complements existing measurement error mitigation techniques, and they together form a standard toolbox for manipulating measurement errors in near-term quantum devices.
Dynamical generation of macroscale magnetic fields and fast flows in a four-component astrophysical plasma
Authors: Shazad Usman , Iqbal M 
Published: September 2024

Abstract
We explore the possibility of the generation or amplification of macroscale magnetic fields and flows in a four-component astrophysical dusty plasma composed of mobile massless electrons and positrons, inertial positive ions and negatively charged static dust particles. The investigation demonstrates that when microscopic turbulent ambient plasma energy is predominantly kinetic, a straight dynamo (DY) mechanism is feasible. Conversely, a unified reverse-dynamo/dynamo (RDY/DY) mechanism is possible when the microscopic turbulent ambient plasma energy is primarily magnetic. Additionally, the evolution of Alfvén Mach numbers at the macro- and microscale are significantly affected by plasma species densities and invariant helicities. The potential implications of the present study for astrophysical settings are also highlighted.
Distinguishing laser-induced dissociation pathways of O
2 molecule based on alignment-dependent dissociation spectra in intense laser fields
Authors: Sun Renping , Xin Peipei 
Published: September 2024

Abstract
We experimentally investigate the dissociative single ionization process, O
2 → O
+ + O, of aligned O
2 molecule in intense laser fields. The yield of a vibrational structure in the kinetic energy release spectra is measured as a function of alignment angle. By quantitative comparison of the measured angle-dependent dissociation probability with the simulation of a classical model that considers ionization and thus additional interaction within the laser pulse, we are able to distinguish the dissociation pathway that contribute to the vibrational structure. It is found that for a relative low laser intensity, the vibrational structure are produced from the dissociation pathway of a
4Π
u
→ f
4Π
g
− 1ω. As the laser intensity increases, the increasing of the population probability of higher vibrational states and the absorbtion probability of more photons makes another two dissociation pathways open and become dominant.
A pattern reconfigurable hybrid graphene-metal terahertz MIMO antenna with improved isolation by metasurface absorber for 6G communication
Authors: Karimi Pardis , Moradi Khatereh 
Published: September 2024

Abstract
This paper introduces an innovative 2 × 2 MIMO, Multi-Input Multi-Output, antenna working in the sub-Terahertz (THz) band for 6G, 6-Generation, communications (0.1–1.2 THz). This antenna was designed with main metal octagonal patches and parasitic graphene elements, providing pattern reconfigurable antenna for Single-Input Single-Output (SISO) and MIMO applications. In the YZ-plane, aforementioned antenna illustrates a tunable pattern (0°, +70°, −70°) for SISO in three distinct states. In the XZ-plane, it obtains a versatile MIMO function with a controllable pattern (0°, ±20°, ±45°, ±75°) across four states, simultaneously. The adaptability of the radiation pattern is originated from changing graphene conductivity through biasing. In addition, a graphene metasurface was applied as an absorber, moderately improving isolation between 2 antenna elements, minimizing interference, and enhancing the overall system capability. The proposed schematization achieves a significant isolation up to −35 dB within the MIMO antenna’s operational frequency band. Moreover, the MIMO antenna touches a maximum gain of

9.5
dBi
,

maximum impedance bandwidth of approximately 91% during different states,

10
dBi

Diversity Gain, and

2.6
2
×

10

−
7

ECC in the resonance frequency, which demonstrates novelty designing, remarkable pros, and broad applications in the landscape evolution of 6G communication networks.
Exploring the influence of pH on structural and magnetic properties of lead-free double perovskite, La2CoFeO6 nanoparticles
Authors: Jharwal Swati , Gupta Anurag , Kar Manoranjan , Verma Vivek Kumar , Kumar Arvind 
Published: September 2024

Abstract
Lead free perovskite is at the center of the present study due to its multifunctional properties. In the present work, pH (0–5) of the solution during the synthesis of La2CoFeO6 (LCFO) nanoparticles has been varied to study its impact on structural and magnetic properties. X-ray diffraction(XRD) pattern confirms the formation of LCFO nanoparticle with pH variation. Rietveld refinement of the XRD patterns indicates that the LCFO is formed in mixed phase of orthorhombic and rhombohedral structure. The percentage contribution of orthorhombic phase decreases with an increase in the pH value. Morphological studies show the increment in the particle size with pH value (from ∼16 nm to ∼32 nm). Structural and morphological investigations indicate that LCFO prepared at pH 0 are highly agglomerated and may have some amorphous phases. Magnetization curves indicate the ferromagnetic response for the LCFO nanoparticles at pH-0. The maximum magnetization (M

S
) is found to decrease whereas coercivity (H

C
) increases with the increase in pH value. This could be attributed to the structural changes incorporated and presence of anti-site disorder due to the variation of pH. A large exchange bias effect has also been noticed at 60 K for all the LCFO samples.
Comprehensive evaluation of recombination confined performance of CuGaO2 for solar cell application
Authors: Vijayakumar S , Kumar Atul , Radhika R , Thandaiah Prabu R 
Published: September 2024

Abstract
In the quest to find an outstanding solar energy capturing system that meets requirements like affordability, widespread availability, eco-friendliness, remarkable efficiency, and enduring stability, thorough investigations have been carried out to explore the possibilities presented by ‘Delafossite’ copper gallium oxide (CuGaO2). β-CuGaO2 has an ideal bandgap of 1.5 eV, along with a high absorption coefficient and excellent carrier mobility, making it well-suited for high-efficiency solar cell applications. Theoretical modelling, utilizing the optical and electrical attributes of the CuGaO2 (CGO) material, is employed to analyze its photovoltaic performance when used as an absorber. The detailed balance analysis showed 56.9% of the incident power is wasted in spectrum loss (as thermalisation and non-absorption loss), 10.1% is wasted in intrinsic losses (such as radiative recombination, radiation dilution, entropy generation etc,), extrinsic recombination (originating from electrical losses, parasitic resistance, finite mobility, surface recombination velocity (SRV), non-ohmic contacts etc), eats up another 9.5% and the resultant 23.6% is available as net useful efficiency. Through the careful selection of a suitable buffer counterpart and optimization of material parameters, absorber thickness, defect density, contacts, and SRV, the CGO device dem onstrates an efficiency of 23.6%.
Search for MeV gamma-ray emission from TeV bright red dwarfs with COMPTEL
Authors: Shrivastava Niharika , Manna Siddhant , Desai Shantanu 
Published: September 2024

Abstract
The SHALON atmospheric Cherenkov telescope has detected very high energy gamma-ray
emission at TeV energies from eight red dwarfs, namely, V388 Cas, V547 Cas, V780 Tau, V962 Tau,
V1589 Cyg, GJ 1078, GJ 3684 and GL 851.1. Consequently, these red dwarfs have been suggested as
sources of ultra-high energy cosmic rays. In this work, we search for soft gamma-ray emission
from these TeV bright red dwarfs between 0.75–30 MeV using archival data from the COMPTEL
gamma-ray imaging telescope, as a follow-up to a similar search for GeV gamma-ray emission using
the Fermi-LAT telescope. Although, prima-facie, we detect non-zero photon flux from three red
dwarfs with high significance, these signals can attributed to contamination from nearby sources
such as Crab and Cygnus, which are within the angular resolution of COMPTEL, and have been
previously detected as very bright point sources at MeV energies. Therefore, we could not detect
any statistically significant signal (> 3σ) from any of these eight red dwarfs from 0.75–30 MeV. We then report the 95% confidence level upper limits on the differential photon flux (at 30 MeV), integral photon flux and integral energy flux for all of the eight red dwarfs. The integral
energy flux limits range between 10-11 - 10-10-ergs/cm2/s.
STORM image denoising and information extraction
Authors: Lu Yuer , Ying Yongfa , Huang Chengliang , Li Xiang , Cheng Jinyan , Yu Rongwen , Ma Lixiang , Shuai Jianwei , Zhou Xuejin , Zhong Jinjin 
Published: September 2024

Abstract
Stochastic optical reconstruction microscopy (STORM) is extensively utilized in the fields of cell and molecular biology as a super-resolution imaging technique for visualizing cells and molecules. Nonetheless, the imaging process of STORM is frequently susceptible to noise, which can significantly impact the subsequent image analysis. Moreover, there is currently a lack of a comprehensive automated processing approach for analyzing protein aggregation states from a large number of STORM images. This paper initially applies our previously proposed denoising algorithm, UNet-Att, in STORM image denoising. This algorithm was constructed based on attention mechanism and multi-scale features, showcasing a remarkably efficient performance in denoising. Subsequently, we propose a collection of automated image processing algorithms for the ultimate feature extractions and data analyses of the STORM images. The information extraction workflow effectively integrates automated methods of image denoising, objective image segmentation and binarization, and object information extraction, and a novel image information clustering algorithm specifically developed for the morphological analysis of the objects in the STORM images. This automated workflow significantly improves the efficiency of the effective data analysis for large-scale original STORM images.