bluuebunny/crossref_metadata_2025_split huggingface dataset:
An improved waveguide method for accurate complex permittivity measurement of medium/high-loss material
Authors: Wang Weijie , Jiang Wei , Yao Yelei , Wang Jianxun , Liu Guo 
Published: February 2024

Abstract
In this paper, we propose an improved method for accurately measuring medium/high-loss material permittivity to overcome the air gap problem in the conventional waveguide method. This method improves the sample fixture and has a high tolerance on the wide side air gap, which is a dominant factor affecting the accuracy of the conventional waveguide method. Meanwhile, the proposed method avoids the effects of tilting and displacement during sample assembly. In our study, a typical lossy material commonly used in vacuum tubes has been quantitatively analyzed in Ka-band. The simulation results show that the relative error of ϵr′ can be reduced from more than 10% to less than 1% compared with the conventional waveguide method with a 0.1 mm wide side tolerance. The relative error of tanδ can be reduced from over 20% to below 6%. In addition, different lossy materials are discussed. The accuracy improvement was verified by measuring different BeO-TiO2 samples using conventional and improved methods.
Analysis of the characteristics of silver nanowires (AgNW) random network for transparent heater applications
Authors: Duc Thinh-Vuong , Nguyen Van-Cuong , Kim Huyn-Chul 
Published: February 2024

Abstract
Transparent heaters (THs) find widespread application in various indoor and outdoor settings, such as LCD panels and motorcycle helmet visors. Among the materials used for efficient TH performance, the AgNW network stands out due to its high conductivity, substantial transmittance, and minimal solution requirement. Extensive research has been directed towards enhancing AgNW characteristics, focusing on smaller diameters and longer wires. In TH applications, the primary considerations include a rapid response and elevated temperature. Consequently, this research delves into investigating the impact of parameters like diameter, length, and density on random AgNW networks under varying applied voltages. The finite element method is employed for analyzing temperature changes in response to voltage application, particularly in scenarios involving small-scale setups with high-density and high-percolation AgNW networks. The results reveal a significant increase in the thermal transition rate, ranging from 28% to 36%, with varying densities in the random network. Within the same density, the AgNW network with larger diameters and lengths demonstrates the highest temperatures, aligning with previous calculations. Furthermore, a trade-off exists between optical properties in smaller diameters and electrical properties in larger diameters within a relatively narrow temperature range.
PVDF piezoelectric sensor based on solution blow spinning fibers for structural stress/strain health monitoring
Authors: Zou Xuexue , Zhu Ruijian , Chen Xing , Ran Qianping , Wang Zengmei 
Published: February 2024

Abstract
Strain monitoring is of great significance to the maintenance and safe operation of engineering structures. To address the shortcomings of piezoelectric ceramics such as high inflexibility and brittleness, in this study, polyvinylidene fluoride (PVDF) fibrous films were prepared by a safe and efficient solution blow spinning technique. In the case of similar β-phase content, the preparing efficiency of PVDF fibers by the solution blow spinning method increased by about 8–10 times compared to that of electrospinning method in our previous work. The fibers with average diameter of 0.79 μm had the highest β-phase content, around 83%. A flexible piezoelectric fiber sensor with simple design for structural strain monitoring was prepared based on the PVDF fibers. The sensor responded well to strains as low as 23 μϵ, better than commercial strain gauges, with a high response accuracy of 1.11 mV/μϵ. And the piezoelectric properties of the sensors could charge a 4.7 μF capacitor to 3 V in 40 s, offering the potential for low-cost, self-powered structural health monitoring.
The influence of microwave pulse conditions on enantiomer-specific state transfer
Authors: Lee JuHyeon , Bischoff Johannes , Hernandez-Castillo A O , Abdiha Elahe , Sartakov Boris G , Meijer Gerard , Eibenberger-Arias Sandra 
Published: February 2024

Abstract
We report a combined experimental and theoretical study on the influence of microwave pulse durations on enantiomer-specific state transfer. Two triads of rotational states within a chiral molecule (1-indanol) are selected to address the possible scenarios. In the triad connected to the absolute ground state, the simplest triad that exists for all chiral molecules, the enantiomer-specific state transfer process simplifies into a sequence of two-level transitions. The second triad, including higher rotational states, represents a more generic scenario that involves multiple Rabi frequencies for each transition. Our study reveals that the conventional

π
2

−
π
−

π
2

pulse sequence is not the optimal choice, except for the ideal case when in the simplest triad only the lowest state is initially populated. We find that employing a shorter duration for the first and last pulse of the sequence leads to significantly higher state-specific enantiomeric enrichment, albeit at the expense of overall population in the target state. Our experimental results are in very good agreement with theory, substantiating the quantitative understanding of enantiomer-specific state transfer.
More-than-moore steep slope devices for higher frequency switching applications: a designer’s perspective
Authors: Chowdhury Joy , Sarkar Angsuman , Mahapatra Kamalakanta , Das J K 
Published: February 2024

Abstract
The progress in IC miniaturization dictated by Moore’s Law has taken a leap from mere circuit integration to IoT enabled System-on-Chip (SoC) deployments. Such systems are connoted by contemporary advancements in the semiconductor industry roadmaps namely, ‘More-Moore’ and ‘More-than-Moore’ (MtM). For meaningful integration of digital and non-digital blocks, a power performance tradeoff is essential for maximum and fruitful utilization of the silicon area. Using the techniques under the MtM nomenclature allows the use of unconventional steep slope devices like Tunneling FETs, Negative Capacitance (NC) FETs, Gate-all-around FETs (GAA) and FinFETs etc, which can exhibit reasonable performance with lower supply voltages. Following the Device Technology Co-optimization (DTCO) and System Technology Co-optimization (STCO) the advanced 3D heterogenous integration technologies allow sensors, analog/mixed signal and passive components to be assimilated within the same package as the CMOS blocks. Appropriate device engineering techniques like multi-gate architectures, vertical stacking transistors, compound semiconductors and alternate carrier transport phenomena are required to improve the current drive and scaling performance of advanced CMOS devices. CMOS based codesign is essential to realize new topologies for energy economical computation, sensing and information processing as the beyond CMOS steep slope devices are independently incapable of replacing conventional bulk CMOS devices. This article presents a detailed qualitative review of the various aspects of MtM beyond CMOS steep slope switches and their prospective integration technologies. For system level integration, various aspects of device performance and optimizations, related device-circuit interactions, dielectric technologies at the advance nanometer nodes have been probed into. Additionally, novel circuit topologies, synthesis algorithms and processor level performance evaluation using steep slope switches have been investigated. An exclusive compact overview for contemporary insights into integrated device-system development methodology and its performance evaluation is presented.
Two-component dimers of ultracold atoms with center-of-mass-momentum dependent interactions
Authors: Liu Yaru , Yang Shu , Zhang Peng 
Published: February 2024

Abstract
In a previous paper [Phys. Rev. A
95, 060 701(R) (2017)], we demonstrated that a new type of two-body interaction, which depends on the center of mass (CoM) momentum, can be realized for ultracold atoms via laser-modulated magnetic Feshbach resonance (MFR). Further studies (e.g. L He et al, Phys. Rev. Lett.
120, 045 302 (2018)) show that various interesting phenomena, such as Fulde–Ferrell superfluids, can be induced by scattering between ultracold atoms with this interaction. In this work we investigate the shallow bound states of two ultracold atoms with this type of interaction. We show that when the magnetic field B is below the MFR point B
0, two shallow bound states can appear in this system. Namely, a ‘two-component dimer’ or a dimer with pseudo-spin 1/2 can be formed by two atoms. Furthermore, the dispersion curve of the dimer may have either single or double minimums in the CoM momentum space. The latter case can be explained as a result from significant pseudo-spin-orbital coupling (SOC) effects. Our results show that the ultracold gases with CoM momentum dependent interaction may be a candidate for quantum simulations with ultracold two-component molecules, especially the molecule gases with SOC.
On the Performances of Estimating Stellar Atmospheric Parameters from CSST Broad-band Photometry
Authors: Shi Rui-Feng , Huang Yang , Li Xin-Yi , Zhang Hua-Wei 
Published: February 2024

Abstract
Deriving atmospheric parameters of a large sample of stars is of vital importance to understand the formation and evolution of the Milky Way. Photometric surveys, especially those with near-ultraviolet filters, can offer accurate measurements of stellar parameters, with the precision comparable to that from low/medium resolution spectroscopy. In this study, we explore the capability of measuring stellar atmospheric parameters from Chinese Space Station Telescope (CSST) broad-band photometry (particularly in the near-ultraviolet bands), based on synthetic colors derived from model spectra. We find that colors from the optical and near-ultraviolet filter systems adopted by CSST show significant sensitivities to the stellar atmospheric parameters, especially the metallicity. According to our mock data tests, the precision of the photometric metallicity is quite high, with typical values of 0.17 and 0.20 dex for dwarf and giant stars, respectively. The precision of the effective temperature estimated from broad-band colors are within 50 K.
Attention-based deep learning approach for CSI feedback under 5G TDL channel
Authors: Peng Hanli 
Published: February 2024

Abstract
In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems.
Improving small sample medical image segmentation using CBAM: Insights from two datasets
Authors: Tan Yusheng 
Published: February 2024

Abstract
Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical images. In the VOC dataset experiments, we used the code from the BAM project on GitHub as a benchmark and compared it. Additionally, we improved the benchmark code by adding the Attention Mechanism (CBAM), placing its position before the support set undergoes global average pooling. By comparing the experimental results, we found that the model with the added CBAM achieved better segmentation results under small sample conditions. In the esophageal medical image experiments, the code from the BAM project was also used as a benchmark and a similar experimental design was performed. We further added CBAM by placing it before the global average pooling of the support set. The experimental results show that the model with the addition of CBAM achieves a significant improvement in the small sample segmentation task of esophageal medical images. To summarize, this study validates the effectiveness of using the BAM model in the small-sample medical image segmentation task on two different datasets. By adding CBAM, our model exhibits better segmentation performance under small sample conditions. These findings provide useful insights for small-sample medical image segmentation and are important for future research and practice.
Explainer on GNN-based segmentation networks
Authors: Wu Shuaimin 
Published: February 2024

Abstract
Graph Neural Networks (GNN) are powerful tools for deep learning. Similar to other neural networks, GNNs are complex models, in which humans can’t understand the decision-making procedures of the models. Therefore, it brings the need to explainability of GNNs. Explainability is critical for deep learning to support its predictions. In this paper, we will investigate the Grad-Cam and Integrated-Gradients explaining methods. The Grad-Cam applies a global average pooling over the feature activation mapping, and then which was followed by a ReLU activation to obtain an attribution. The Integrated-Gradients explains models by taking a line integral between the baseline image (a black image) and the source image. We demonstrate how Grad-Cam and the Integrated-Gradients methods explain the graph-deep model in semantic segmentation tasks over the Cityscapes dataset. FCN and LRASSP-MobileNet are used as a comparison to the DualGCN in the experiment to show the explaining effect.
The parasitic capacitance considerations of metal interconnects in sub 10 nm era
Authors: Wu Qizhe 
Published: February 2024

Abstract
With the development of the integrated circuit industry and semiconductor technology, we have entered the sub-10 nanometers era, which means the distance between adjacent components in a device is less than 10 nm. This is a situation where the parasitic capacitance of metal interconnects must be considered. Parasitic capacitance can act as a significant influence in different ways on disparate devices. Fin field-effect transistors and microelectromechanical systems are typical devices and deserve further detailed explanation on their parasitic capacitance. There are many ways to reduce parasitic capacitance and its attendant effects, including using materials with low dielectric constants and optimizing the structure of the layout. Some others are newly brought out in recent years due to advanced industrial technology and new materials, including carbon nanotubes, oxide-free spacer layers, and tunneling hybrid technology. All these methods are of great significance and practicability to producing and applying semiconductor devices and structures. Knowing more about this expertise about parasitic capacitance can help us better understand its nature and specialties.
Leaf disease detection using deep Convolutional Neural Networks
Authors: Hu Mingyu , Long Shanru , Wang Chenle , Wang Ziqi 
Published: February 2024

Abstract
The automatic recognition of plant diseases is of crucial importance for the current development of agriculture. Fast and efficient identification can greatly reduce the natural, economic, and human resource loss caused to agricultural practitioners. Deep neural networks allow computers to learn plant disease detection in an end-to-end manner, thereby obtaining better results and higher efficiency. While Convolutional Neural Network (CNN) models have become a well-established tool for detecting plant diseases, the lack of robustness of the models due to environmental variations remains to be a critical concern. Recent research into overcoming this challenge includes domain adaptation (DA) algorithms like classic Domain-Adversarial Neural Network (DANN) or the innovative Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). However, the topic remains under-explored as the newly developed methods were not tested on many crop species and diseases. This research focuses on four deep CNN models (MobileNet, VGG, GoogLenet, and ResNet). The models are developed and tested using the New Plant Diseases dataset on Kaggle, which comprises 70,000+ training images (offline-augmented) and 17,000+ validation images encompassing 38 different classes of healthy and diseased plant leaves. The models would be cross-evaluated upon their accuracy and training speed, as well as their change in performance after optimization and applying DA methods. With an uppermost accuracy of 86.4% in test dataset from the wild, results show that Transfer Learning, Model Ensemble as well as Domain Adaptation works effectively to increase the robustness of models which will ultimately benefit farmers in detecting plant diseases and deciding on the best treatment in real-time.
High precision map crowdsource update technology and SLAM technology - Application in autonomous driving
Authors: Liu Dapeng 
Published: February 2024

Abstract
In response to the challenges faced by real-time dynamic updates of high-precision maps for autonomous driving in terms of high precision, high reliability, and high safety, this paper summarizes the difficulties and challenges currently faced by high-precision map updates. Accelerate the large-scale commercialization of autonomous driving high-precision maps. Thus, improving the safety and stability of intelligent vehicles and providing critical support for high-level autonomous driving. Firstly, the definition and connotation of autonomous driving maps were described, and the data characteristics and functional applications of autonomous driving maps were pointed out. Secondly, the development status and trends of high-precision map updates were reviewed, and the advantages and disadvantages of centralized map updates and crowdsourced map updates were summarized. Pointing out that crowdsource updates have become a new trend in the development of map updates. Once again, the basic architecture and key core modules of current crowdsource updates were summarized, and an analysis was conducted on key core technologies, summarizing the current status and trends of key technologies involved in crowdsource updates. The results indicate that the current technology still faces 7 main challenges, including map modeling, high-precision positioning, 3D reconstruction, fusion updates, data security, rapid review, and standard laws and regulations to address these challenges.
A readout system based on SiPM for the dRICH detector at the EIC
Authors: Rignanese Luigi Pio , Agrawaal Neelima , Alexeev Maxim , Antonioli Pietro , Baldanza Casimiro , Barion Luca , Bufalino Stefania , Capua Marcella , Contalbrigo Marco , Cossio Fabio , Chiosso Michela , Da Rocha Rolo Manuel , De Caro Annalisa , De Gruttola Daniele , Dellacasa Giulio , Dello Stritto Luigi , Falchieri Davide , Fazio Salvatore , Funicello Nicola , Giacalone Marco , Garbini Marco , Malaguti Roberto , Mignone Marco , Paladino Antonio , Preghenella Roberto , Panzieri Daniele , Ripoli Cristina , Rubini Nicola , Ruspa Marta , Tassi Enrico , Tuvè Cristina , Vallarino Simone 
Published: February 2024

Abstract
The ePIC experiment at the future Electron-Ion Collider (EIC) aims to use silicon photomultipliers (SiPMs) as the photodetector technology for the dual-radiator ring-imaging Cherenkov detector (dRICH). Despite their advantages for this low light application and insensitivity to high magnetic fields, SiPMs are sensitive to radiation and require rigorous testing to ensure that their single-photon counting capabilities and dark count rate are kept under control over the years of operation. The presented results show the successful use of a complete prototype readout chain based on the ALCOR chip for SiPM characterization measurements and assembled in an optical plane for test-beam measurements using the dRICH prototype.
Studies of C2H2F4 and SF6 alternative gas mixtures for HPL RPC detectors for High Energy Physics applications
Authors: Verzeroli M. , Guida R. , Juks S.A. , Mandelli B. , Rigoletti G. 
Published: February 2024

Abstract
This research investigates the viability of replacing greenhouse gases C2H2F4 and SF6 with eco-friendly gas mixtures in Resistive Plate Chamber (RPC) detectors used at CERN LHC experiments. The study focuses on the addition of CO2 as a substitute of C2H2F4 to minimize greenhouse gas emissions and reduce operational costs. Initial performance studies with cosmic muons evaluate key parameters such as efficiency, current, streamer probability, prompt charge, cluster size, and time resolution of the detectors. Subsequent assessments take place at the CERN Gamma Irradiation Facility, emulating the LHC experiment background radiation. Furthermore, the study explores alternatives to SF6, including 3M NOVECTM 4710 and AMOLEATM 1224yd, presenting perforances results about these gas alternatives in High Pressure Laminate RPCs.
SoLAr: a novel LAr TPC to detect MeV-scale neutrinos
Authors: Kunzmann J. ,   the SoLAr collaboration
Published: February 2024

Abstract
SoLAr is a proposed liquid argon time projection chamber experiment that will be able to measure the “hep branch” of solar neutrinos. The scintillation light and the deposited charge signals are collected on the same readout plane that contains a combined grid of silicon photon multipliers and charge collection pixels. The combined light and charge readout allows for millimeter-scale resolution and light and charge matching. First prototypes were successfully operated in Bern, CH in cosmic-rays. The conceptual design of SoLAr, the advantages, and the first results of the SoLAr prototypes will be discussed in details in this proceeding.