bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quad-Port Circular Polarized MIMO Dielectric Resonator Antenna Featuring Beam Tilting for Vehicular Communication
Authors: Sahu Jayant Kumar , Gupta Anshul 
Published: December 2024

Abstract
This paper presents the design and implementation of a quad-port circular polarized Dielectric Resonator Antenna (DRA) with Multiple-Input Multiple-Output (MIMO) capabilities and beam tilting features. The beam tilting feature allows for dynamic adjustment of the radiation pattern, improving signal coverage and reducing interference in complex environments. Beam tilting is achieved, and correlation is minimized in the far-field patterns by positioning a conducting plate between the antenna elements. Measurements indicate that the antenna achieves an Axial-Ratio Bandwidth (ARBW) of 4.69 GHz to 4.84 GHz with an AR of less than 3 dB and an impedance bandwidth from 4.6 GHz to 5.2 GHz. The DRA's resonating frequencies are produced by electromagnetically exciting it through optimized unequal-length loaded cross slots with a microstrip line. All ports are arranged in parallel configuration. The adjacent ports are 180 degrees out of phase each other. The antenna achieves maximum beam tilting of ± 30 degrees at 4.82 GHz. Oppositely oriented ports show polarization diversity with Left Hand Circular Polarization (LHCP) and Right Hand Circular Polarization (RHCP). The MIMO DRA's optimal diversity parameters make it ideal for high-speed vehicular Communication and WLAN application.&#xD;
A novel optical multi-image data protection schemebased on phase-truncated asymmetric ciphers and2D-CSCCM
Authors: Li Jinqing , Tian Keyong , Liu Zhenxun , Di Xiaoqiang , Ibrahim Makram 
Published: December 2024

Abstract
Existing remote sensing platforms often overlook the security of the&#xD;remote sensing image acquisition process, creating opportunities for potential attackers&#xD;to exploit. To address this issue, this paper proposes a novel optical encryption&#xD;framework (ECC DPM) based on elliptic curves and phase truncation to ensure the&#xD;secure transmission of optical keys. Furthermore, an optical encryption scheme is&#xD;designed based on ECC DPM to enable synchronous secure encryption during the&#xD;remote sensing data acquisition stage, ensuring that remote sensing data remains in&#xD;a ciphertext state from acquisition to storage and distribution. Finally, the proposed&#xD;framework and encryption scheme are evaluated for security using a threat model and&#xD;simulation experiments. The experimental results demonstrate that the encryption&#xD;scheme effectively resists various statistical analysis attacks.
Insight into the optoelectronic and thermoelectric properties of novel LaSeM (M = Cl, F) chalcohalide semiconductors: ab-initio study
Authors: Khan Muhammad Salman , Gul Banat , Mohamed Abdelhay Salah , Aziz Siti Maisarah , Abbas Faheem 
Published: December 2024

Abstract
Chalcohalide materials demonstrate extraordinary thermal capacity and adjustable optoelectronic properties. Here we used the density functional theory and studied the structural, electronic, optical, and thermoelectric properties of novel LaSeM (M = Cl, F) ternary chalcohalides. The formation energies of LaSeCl and LaSeF were determined to be −2.21 (eV/f. u) and −2.32 (eV/f. u), respectively. The cohesive and formation energies well impacted the stable nature and phase transition features of these materials. The studeid materials were predicted to have a direct band gap nature as observed from the band structure investigation. For LaSeCl material, the projected band gaps as determined by the PBE-GGA and TB-mBJ potentials are 1.41 eV and 1.95 eV, whereas for the LaSeF the values are 1.72 eV and 2.36 eV, respectively. The complex dielectric function along with the other noteworthy optical parameters are computed for their potential applications in optoelectronics devices. The spectral region with a negative value of ε
1(ω), suggests these materials to have a metallic behavior. The predicted negative Seebeck coefficient values throughout the temperature range for LaSeCl and LaSeF materials display n-type conduction behavior. LaSeF exhibits more noticeable temperature-dependent improvements in electrical conductivity compared to LaSeCl.
The Large Magellanic Cloud: expanding the low-mass parameter space of dark matter direct detection
Authors: Reynoso-Cordova Javier , Bozorgnia Nassim , Piro Marie-Cécile 
Published: December 2024

Abstract
We investigate how the Large Magellanic Cloud (LMC) impacts the predicted signals in near-future direct detection experiments for non-standard dark matter (DM) interactions, using the Auriga cosmological simulations. We extract the local DM distribution of a simulated Milky Way-like halo that has an LMC analogue and study the expected signals in DarkSide-20k, SBC, DARWIN/XLZD, SuperCDMS, NEWS-G, and DarkSPHERE considering DM-nucleon effective interactions, as well as inelastic DM scattering. We find that the LMC causes substantial shifts in direct detection exclusion limits towards smaller cross sections and DM masses for all non-relativistic effective field theory (NREFT) operators, with the impact being highly pronounced for velocity-dependent operators at low DM masses. For inelastic DM, where the DM particle up-scatters to a heavier state, the LMC shifts the direct detection exclusion limits towards larger DM mass splitting and smaller cross sections. Thus, we show that the LMC significantly expands the parameter space that can be probed by direct detection experiments towards smaller DM-nucleon cross sections for all NREFT operators and larger values of mass splitting for inelastic DM.
Projected gravitational wave constraints on primordial black hole abundance for extended mass distributions
Authors: Dizon G.L.A. , Reyes R.C. 
Published: December 2024

Abstract
We investigate the projected minimum constraints set by next-generation gravitational wave detectors Einstein Telescope and LISA on the abundance of primordial black holes relative to dark matter from both resolvable mergers and the stochastic gravitational wave background (SGWB) for extended primordial black hole mass distributions. We consider broad power law distributions for a range of negative and positive exponents γ and top-hat distributions (with γ = 0) and use the IMRPhenomXAS waveforms to simulate binary sources up to mass ratios q
max = 1000 and redshifts z = 300. Our results suggest that accounting for extended mass distributions have the most apparent impact when considering mergers at high redshifts z > 30, for which the constraint curves have broader mass windows and shift to higher abundances compared to when a monochromatic distribution is assumed; on the other hand, constraints from low-redshift mergers and the SGWB do not change much with the assumed mass distribution. At high redshifts, astrophysical black holes are not expected to contribute significantly, providing possible smoking-gun evidence for PBHs. Constraints derived from LISA and ET observations would complement each other by probing different PBH mass windows and this holds for the extended mass distributions studied.
Influence of Deposition Temperature ZrO2 Thin Films Deposited on Transparent Conductive ITO Substrates
Authors: Xiao Ruiheng , Sun Wenrui , Gan Neng , Liu Zhifeng , Tong Zhengfu 
Published: December 2024

Abstract
In this paper, ZrO
2 thin films were deposited by magnetron sputtering DC method on transparent electrode ITO, The effects of different deposition temperatures on the physical phase, surface morphology and electrical properties of ZrO
2 thin films are investigated. The films were amorphous when the deposition temperature was increased from room temperature(RT) to 260°C, and turn to the orthorhombic crystal when the temperature was increased to 350°C. At the deposition temperature of 260 °C, the ZrO2 film had a dense surface with a roughness of 2.94 nm.At 260 °C, the film exhibit the best electric performance with the lowest leakage current density 7.580×10−4
A/cm
2, and is not breakdown when electric field is up to 200K(V/cm).
Enzymatic Hydrolysis and Composition Analysis of Polypeptide from Eggshell Membrane
Authors: Jian Weijie , Yan Zheng , Zhao Xinqiang , Wang Zhangang , Wang Haiyan 
Published: December 2024

Abstract
Eggshell membrane, also called “Feng Huang Yi” in traditional Chinese medicine, is a protein-based fibrous tissue and has various biological functions. This study aims to obtain an optimal condition for preparing eggshell membrane peptides (ESMP) using alkaline protease. Then the free and protein amino acid compositions, and the molecular weight distribution of the ESMP were analyzed in order to investigate the effect of enzymatic hydrolysis. Through single factor experiments and orthogonal optimization, the optimal condition for preparing ESMP was obtained. Under the conditions of enzyme dosage 13,000 U/g, hydrolysis time 6.5 h, and solid-liquid ratio 1:35, the hydrolysis degree was 62.10%. The top five predominant protein amino acids in ESMP were Glu, Asp, Pro, Cys and Arg, and the predominant free amino acids in ESMP were Met, Val, His and Cys. The content of peptides below 10,000 Da in ESMP was 98.07%.
Influence of mesoscale sea surface temperature anomaly on polar lows
Authors: Lin Ting , Rutgersson Anna , Wu Lichuan 
Published: December 2024

Abstract
Sea surface temperature anomaly (SSTA) associated with mesoscale oceanic processes, which are prevalent throughout the ocean, can significantly influence the atmospheric boundary layer and consequently atmospheric systems. While its influences on tropical and extratropical cyclones have been well-documented, the influence of mesoscale SSTA on polar lows (PLs) remains unexplored. To bridge this knowledge gap, we conducted a series of sensitivity numerical experiments with different SST configurations. The simulation results indicate that, over the lifespan of a PL, SSTA does not significantly influence PL intensity but does enhance latent heat release. On a longer time scale, based on simulations of five winter seasons over the Nordic Sea, we found that the accumulated impact of mesoscale SSTA creates favorable environments for PL intensification, characterized by higher moisture levels and lower static stability. These results highlight the importance of considering high-resolution SST boundary conditions, i.e. resolving mesoscale SST, in climate simulations of PLs.
Predictions of H-mode access and edge pedestal instability in the EHL-2 spherical torus
Authors: WANG 王 Yumin 嵎民 , LI 李 Kai 凯 , HUANG 黄 Zhuo 卓 , LIU 刘 Yiliang 一良 , DAI 戴 Shuyu 舒宇 , ZHANG 张 Jie 洁 , HUANG 黄 Yanqing 艳清 , GU 顾 Xiang 翔 , ZHAO 赵 Yihang 毅航 , XU 徐 Shuai 帅 , WANG 王 Erhui 二辉 , GUO 郭 Dong 栋 , SHI 石 Yuejiang 跃江 , XIE 谢 Huasheng 华生 , LIANG 梁 Yunfeng 云峰 , LIU 刘 Minsheng 敏胜 , the EHL-2 Team  
Published: December 2024

Abstract
The EHL-2 spherical torus is designed to demonstrate proton-boron (p-11B) fusion within a compact spherical tokamak. Its planned heating system includes a negative ion-based neutral beam injection (N-NBI), two positive ion-based NBI systems (P-NBI), electron cyclotron resonance heating (ECRH), ion cyclotron resonance heating (ICRH), and high harmonic fast wave (HHFW), with a total power output of 31 MW. According to scaling law estimates, the device is capable of achieving H-mode operation. The plasma density,

, at the minimum L-H power threshold,

, is estimated to be

. The pedestal parameters were calculated using the REPED model. Assuming B as the primary impurity ion, the predicted pedestal width and height are lower compared to the typical case with carbon impurities. The pedestal collisionality for EHL-2 is estimated to range between 0.06 and 0.17, indicating the potential for significant energy loss due to edge localized modes (ELMs). The heat flux on the divertor plate has been calculated using the JOREK code. The peak heat fluxes during ELM bursts are approximately 31.0 MW/m2 at the lower inboard target and 39.5 MW/m2 at the lower outboard target. A preliminary design of the resonant magnetic perturbation (RMP) coils has been completed to both control type-I ELMs and correct error fields. The system comprises 16 coils arranged into 2

4 pairs. In ELM control mode, a 14/2 component is generated at 1.7 G/kAt, with a current of 4.9 kA required to achieve

at the resonant surface, where the normalized poloidal magnetic flux is 0.85. In error field (EF) modulation mode, 2/1 and 3/1 components are generated at 3.5 G/kAt and 2.8 G/kAt, respectively.
Hamiltonian and Liouvillian learning in weakly-dissipative quantum many-body systems
Authors: Olsacher Tobias , Kraft Tristan , Kokail Christian , Kraus Barbara , Zoller Peter 
Published: December 2024

Abstract
We discuss Hamiltonian and Liouvillian learning for analog quantum simulation from non-equilibrium quench dynamics in the limit of weakly dissipative many-body systems. We present and compare various methods and strategies to learn the operator content of the Hamiltonian and the Lindblad operators of the Liouvillian. We compare different ansätze based on an experimentally accessible ‘learning error’ which we consider as a function of the number of runs of the experiment. Initially, the learning error decreases with the inverse square root of the number of runs, as the error in the reconstructed parameters is dominated by shot noise. Eventually the learning error remains constant, allowing us to recognize missing ansatz terms. A central aspect of our approaches is to (re-)parametrize ansätze by introducing and varying the dependencies between parameters. This allows us to identify the relevant parameters of the system, thereby reducing the complexity of the learning task. Importantly, this (re-)parametrization relies solely on classical post-processing, which is compelling given the finite amount of data available from experiments. We illustrate and compare our methods with two experimentally relevant spin models.
Financing structural and non-structural extreme heat adaptation measures in Southeast Asian cities: statuses and prospects
Authors: Lam Rainbow Yi Hung , Delina Laurence L 
Published: December 2024

Abstract
Extreme heat, often called a ‘silent killer,’ poses significant threats, particularly in Southeast Asia, where rapid urbanisation and increasing population density exacerbate vulnerability. Populations in this region face heightened risks of heat-related mortality and illness and diminished efficiency of the energy systems essential for cooling. Additionally, limited financial resources impede cities’ adaptation efforts, establishing finance as a critical factor influencing vulnerability to extreme heat. Despite growing recognition of the necessity for climate adaptation and mitigation financing, as highlighted by the Sendai Framework for Disaster Risk Reduction and the outcomes of the Conferences of the Parties to the United Nations Framework Convention on Climate Change, the adaptation finance gap continues to widen. This review article examines the literature on potential structural and non-structural adaptation strategies for extreme heat and identifies financing opportunities in Southeast Asia, thereby contributing to the Priority 3 agenda of the Sendai Framework. The review concludes with three recommendations: promoting assessment tools to increase adaptation finance and facilitate adoption, addressing issues of maladaptation related to adaptation finance, and aligning adaptation finance with long-term investment benefits.
Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery
Authors: Jaffray Wallace , Guo Ziheng , Di Falco Andrea , Ferrera Marcello 
Published: December 2024

Abstract
Ultrafast pulse characterisation is crucial for studying processes that occur at femtosecond timescales and below. Because of this, various methods have been developed to recover a pulse’s electric field profile at these durations, with the frequency-resolved optical gating (FROG) technique being the most common. However, this approach is computationally expensive and suffers from limitations in terms of robustness and reliability. In this regard, recent publications have demonstrated that applying machine learning towards ultrafast pulse recovery can alleviate these issues, providing more accurate retrievals. Inspired by these works, we propose an encoder–decoder scheme for a FROG system which exploits dual harmonic generation in low-index thin films. Specifically, we demonstrate enhanced reliability and accuracy of ultrafast pulse recovery when compared to machine learning approaches using second or third harmonic signals independently. As the amount of information used to train each neural network is kept constant, this study demonstrates and benchmarks the technological advantages of contextual information analysis involving multiple nonlinear processes.
Many-body expansion based machine learning models for octahedral transition metal complexes
Authors: Meyer Ralf , Chu Daniel B K , Kulik Heather J 
Published: December 2024

Abstract
Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, a molecular graph featurization method, for predicting spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used ML methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary TMCs, the best MBE models achieve mean absolute errors (MAEs) of 2.75 kcal mol−1 on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30%–40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit MAEs of 4.00 kcal mol−1 (i.e. a 0.73 kcal mol−1 reduction) on the spin-splitting energies and 0.53 eV (i.e. a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces.
Few-shot classification of Cryo-ET subvolumes with deep Brownian distance covariance
Authors: Yu Xueshi , Han Renmin , Jiao Haitao , Meng Wenjia 
Published: December 2024

Abstract
Few-shot learning is a crucial approach for macromolecule classification of the cryo-electron tomography (Cryo-ET) subvolumes, enabling rapid adaptation to novel tasks with a small support set of labeled data. However, existing few-shot classification methods for macromolecules in Cryo-ET consider only marginal distributions and overlook joint distributions, failing to capture feature dependencies fully. To address this issue, we propose a method for macromolecular few-shot classification using deep Brownian Distance Covariance (BDC). Our method models the joint distribution within a transfer learning framework, enhancing the modeling capabilities. We insert the BDC module after the feature extractor and only train the feature extractor during the training phase. Then, we enhance the model’s generalization capability with self-distillation techniques. In the adaptation phase, we fine-tune the classifier with minimal labeled data. We conduct experiments on publicly available SHREC datasets and a small-scale synthetic dataset to evaluate our method. Results show that our method improves the classification capabilities by introducing the joint distribution.
TG-CDDPM: text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model
Authors: Cao Junhang , Zhang Jun , Yu Qiyuan , Ji Junkai , Li Jianqiang , He Shan , Zhu Zexuan 
Published: December 2024

Abstract
Antimicrobial peptides (AMPs) have emerged as a promising substitution to antibiotics thanks to their boarder range of activities, less likelihood of drug resistance, and low toxicity. Traditional biochemical methods for AMP discovery are costly and inefficient. Deep generative models, including the long-short term memory model, variational autoencoder model, and generative adversarial model, have been widely introduced to expedite AMP discovery. However, these models tend to suffer from the lack of diversity in generating AMPs. The denoising diffusion probabilistic model serves as a good candidate for solving this issue. We proposed a three-stage Text-Guided Conditional Denoising Diffusion Probabilistic Model (TG-CDDPM) to generate novel and homologous AMPs. In the first two stages, contrastive learning and inferring models are crafted to create better conditions for guiding AMP generation, respectively. In the last stage, a pre-trained conditional denoising diffusion probabilistic model is leveraged to enrich the peptide knowledge and fine-tuned to learn feature representation in downstream. TG-CDDPM was compared to the state-of-the-art generative models for AMP generation, and it demonstrated competitive or better performance with the assistance of text description as supervised information. The membrane penetration capabilities of the identified candidate AMPs by TG-CDDPM were also validated through molecular weight dynamics experiments.
Optimal adaptive SMART designs with binary outcomes
Authors: Ghosh Rik , Chakraborty Bibhas , Nahum-Shani Inbal , Patrick Megan E , Ghosh Palash 
Published: December 2024

ABSTRACT
In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though SMART designs are getting popular among clinical researchers, the methodologies for adaptive randomization at different stages of a SMART are few and not sophisticated enough to handle the complexity of optimal allocation of treatments at every stage of a trial. Lack of optimal allocation methodologies can raise critical concerns about SMART designs from an ethical point of view. In this work, we develop an optimal adaptive allocation procedure using a constrained optimization that minimizes the total expected number of treatment failures for a SMART with a binary primary outcome, subject to a fixed asymptotic variance of a predefined objective function. Issues related to optimal adaptive allocations are explored theoretically with supporting simulations. The applicability of the proposed methodology is demonstrated using a recently conducted SMART study named M-bridge for developing universal and resource-efficient dynamic treatment regimes for incoming first-year college students as a bridge to desirable treatments to address alcohol-related risks.