Large Parameter Research Articles

MCTrans++: a 0-D model for centrifugal mirrors

The centrifugal mirror confinement scheme incorporates supersonic rotation of a plasma into a magnetic mirror device. This concept has been shown experimentally to drastically decrease parallel losses and increase plasma stability as compared with prior axisymmetric mirrors. MCTrans++ is a dimensionless (0-D) scoping tool which rapidly models experimental operating points in the Centrifugal Mirror Fusion Experiment (CMFX) at the University of Maryland. In the low-collisionality regime, parallel losses can be modelled analytically. A confining potential is set up that is partially ambipolar and partially centrifugal. Due to the stabilizing effects of flow shear, the perpendicular losses can be modelled as classical. Radiation losses such as bremsstrahlung and cyclotron emission are taken into account. A neutrals model is included, and, in some circumstances, charge-exchange losses are found to exceed all other loss mechanisms. We use the SUNDIALS ARKODE library to solve the underlying equations of this model; the resulting software is suitable for scanning large parameter spaces, and can also be used to model time-dependent phenomena such as a capacitive discharge. MCTrans++ has been used to verify results from prior centrifugal mirrors, create an experimental plan for CMFX and find configurations for future reactor-scale fusion devices.

Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretization of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures

Energy-dependent self-adaptive mesh refinement algorithms are developed for a symmetric interior-penalty scheme for a discontinuous Galerkin spatial discretization of the multi-group neutron diffusion equation using NURBS-based isogeometric analysis (IGA). The spatially self-adaptive algorithms employ both mesh (h) and polynomial degree (p) refinement. The discretized system becomes increasingly ill-conditioned for increasingly large penalty parameters; and there is no gain in accuracy for over penalization. Therefore, optimized penalty parameters are rigorously calculated, for general element types, from a coercivity analysis of the bilinear form. Local mesh refinement allows for a better allocation of computational resources; and thus, more accuracy per degree of freedom. Two a posteriori interpolation-based error measures are proposed. The first heuristically minimizes local contributions to the discretization error, which becomes competitive for global quantities of interest (QoIs). However, for localized QoIs, over energy-dependent meshes, certain multi-group components may become under-resolved. The second employs duality arguments to minimize important error contributions, which consistently and reliably reduces the error in the QoI.

Improved Lightweight Head Detection Based on GhostNet-SSD

This abstract proposes an algorithm for human head detection in elevator cabins that addresses the challenges of improving detection accuracy, reducing detection speed, and decreasing the number of parameters. The algorithm is based on GhostNet-SSD and includes several improvements, such as an efficient coordinate attention mechanism to replace the Squeeze-and-Excitation attention mechanism, optimization of auxiliary convolutional layer with large parameter weight, and adjustment of anchor ratio based on the statistical results of human head labeling frame. In addition, data normalization and convolutional fusion methods are used for inference acceleration. The algorithm was tested on JETSON XAVIER NX development board and achieved a new state-of-the-art 97.91% AP at 61FPS, outperforming other detectors with similar inference speed. The effectiveness of each component was validated through careful experimentation.

AC-CfC: An attention-based convolutional closed-form continuous-time neural network for raw multi-channel EEG-based emotion recognition

Emotion recognition based on electroencephalogram (EEG) is a critical task in the field of affective brain-computer interfaces. However, due to the non-stationarity and individual variability of EEG, hand-designed features cannot adequately capture the nonlinear and high-dimensional properties of raw EEG. Currently, spatial–temporal models have been verified to effectively capture the spatial–temporal information for EEG. However, these models are characterized by complex structures, large parameter counts, and the need for extensive training data. To overcome these disadvantages, this paper propose an end-to-end spatial–temporal model for emotion recognition based on raw EEG, called attention-based convolutional closed-form continuous-time neural network (AC-CfC). This model employs a channel attention mechanism to weight and encode EEG, capturing channel dependencies. Subsequently, one-dimensional convolutional neural networks and closed-form continuous-time neural networks are used to extract deep spatial–temporal features. Additionally, an adaptive loss-controlling mechanism is designed to enhance the model's decision-making ability between classes that are easily confused. To verify the effectiveness of the proposed model, experiments are conducted on the DEAP and DREAMER datasets. The average accuracies of the proposed model reach to 94.76% and 93.01% for valence and arousal in subject-independent experiments on the DEAP dataset, with an improvement of 11.8% and 8.73% respectively compared with the state-of-art model ACRNN. On the DREAMER dataset, the average accuracies reach to 81.83%, 81.22%, and 80.63% for valence, arousal and dominance, with an improvement of 2.55%, 6.64%, and 6.98% respectively over ACRNN. These results show that our proposed model exhibits better performance than some state-of-art models in the same category.

Adhesive contact between a rigid toroidal indenter and an elastic half-space

The adhesive contact between a rigid toroidal indenter and an elastic half space is investigated numerically. The traction is modeled using the Lennard-Jones potential with Derjaguin’s approximation. It is found that toroidal Johnson–Kendall–Roberts (JKR) model can predict the adhesive contact for large toroidal Tabor parameters and larger radius of the cross section of the torus, while toroidal Bradley model can predict the adhesive contact for small toroidal Tabor parameters. Toroidal indenter can generate larger adhesion than spherical indenter. The contour for the ratio of the pull-off forces between toroidal indenter and spherical indenter is plotted. The pull-off force is larger for larger radius of the cross section of the torus. Thus, the pull-off force of volcano-shaped indenter is larger than that of donut-shaped indenter.

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection.

Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

Combined vibration analysis of a top tensioned riser with the geometrical nonlinearity

A coupling model representing the interactions between the riser and vortices is proposed, of which the time-varying top tension and the internal nonlinear axial force due to the bending of the riser are considered. In addition, the van der Pol oscillator is introduced to simulate the time-varying characteristic of the vortices. The motion equations are derived and the first-order mode approximation is obtained with the Galerkin approach. The multiple scale method is applied to study the steady-state solutions of the system. Effects of system parameters (the structural damping, nonlinearity, the amplitude ratio of the varying-tension, the shedding frequency) on the responses are investigated in detail. The synchronous and non-synchronous motions between the structure and wake oscillator are studied. The bifurcation diagrams for the responses in terms of the amplitude ratio and the nonlinear parameter are obtained in the large parameter ranges. The results show that responses with different topological properties including quasi-periodic, periodic and chaotic solutions can occur under different parameter conditions. A pair of chaotic attractors can be found for the large parameter range of the amplitude ratio. The joint effects of the amplitude ratio and the nonlinear parameter on the responses in the large parameter range are studied as well, which are further verified by the phase projections and Poincaré sections. The results show that the topological properties of responses can be controlled by the amplitude ratio. These results can be helpful to the dynamic design of the riser in practice.

The central black hole in the dwarf spheroidal galaxy Leo I: Not supermassive, at most an intermediate-mass candidate

It has recently been claimed that a surprisingly massive black hole (BH) is present in the core of the dwarf spheroidal galaxy (dSph) Leo I. This finding, based on integral field spectroscopy, challenges the typical expectation that dSphs host intermediate-mass BHs since such a BH would be classified as supermassive. Indeed, the analysis points toward Leo I harboring a BH with a lower mass limit exceeding a few $10^6 at $1 and the no-BH case excluded at 95<!PCT!> significance. Such a mass, which is comparable to the entire stellar mass of the galaxy, makes Leo I a unique system that warrants further investigation. Using equilibrium models based on distribution functions that depend on actions fJ coupled with the same integral field spectroscopy data and an extensive exploration of a very large parameter space, we demonstrate, within a comprehensive Bayesian model--data comparison framework, that the posterior on the BH mass is flat toward the low-mass end and, thus, that the kinematics of the central galaxy region only imposes an upper limit on the BH mass of few $10^5 (at $3 Such an upper limit indicates that the putative BH of Leo I is indeed an intermediate-mass BH, and it is also in line with formation scenarios and expectations from scaling relations at the mass regime of dwarf galaxies.

NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks

Network Digital Twin (NDT) and Network Simulation (NS) are two paradigms leveraging virtual representations of networks to help decision-making. These tools may seem similar or interchangeable and are often confused or opposed. However, they have their respective purposes, use cases and underlying concepts, which differ and are complementary. The goal of this article is to explore and clarify the specificity, the benefits and the limits of these two decision support tools, analyze how they complement each other and can be nicely combined. We argue that a smart integration of NS in NDT, named NS+NDT, can ease, accelerate and strength decisions for network design, deployment, operations, management and evolution. To study and demonstrate this claim, we focus on the domain of Internet of Things (IoT) solutions, where wireless networks are critical for connecting the physical assets to the Internet, but are complicated to configure for meeting the requirements of a specific application. We examine how NS, coupled with NDT, can contribute to support IoT architects and operators decisions throughout the life cycle of an IoT network. We analyze the different steps required to use NS in the context of NDT and examine how this helps remove NS barriers such as credibility and reliability. In particular, we show how NDT data enable to fine tune and customize the energy consumption models, making the simulation results more context-aware and insightful. Then, addressing the often-prohibitive simulation cost for exploring a large parameter space, we propose to associate surrogate modeling to NS+NDT. As surrogate models, we first introduce a simple ML (Machine Learning)-based surrogate model and illustrate this method with two IoT network configuration optimization use cases. Secondly, we propose a Bayesian optimization approach based on Gaussian Processes as surrogate model to further accelerate the (re)configuration decisions. We show how this method enables to select simulation scenarios that converge rapidly to the optimal solution, and allows the NDT to timely perform the adaptation. The contribution of this article is threefold. It provides (i) the first systematic analysis of the differences and potential synergies between NDT and NS; (ii) a synthetic presentation of the integration of NS and associated decision algorithms within a NDT to unlock NS accessibility and credibility throughout the life cycle of an IoT solution; (iii) a proposal for a smart and cost-efficient integration of NS in NDT via surrogate modeling, for reducing evaluation and optimization cost, paving the way to NS-augmented NDT-based dynamic adaptation and real-time optimization of IoT networks.

Rashba spin-splitting and spin Hall effect in Janus monolayers Sb2XSX’ (X, X’= S, Se, or Te; X ≠ X’)

The combined influence of spin–orbit coupling and spatial inversion asymmetry leads to an enhancement of electronic properties, including Rashba spin-splittings as well as spin Hall effect. Recent research has shown the possibility to create two-dimensional Janus materials with inherent structural asymmetry. In this work, the structural stability, piezoelectricity, electronic properties, and intrinsic spin Hall conductivity of quintuple-layer atomic Janus Sb2XSX’ (X, X’ = S, Se, Te; X ≠ X’) monolayers are investigated using first-principles calculations within the framework of density functional theory. They demonstrate relatively high in-plane piezoelectric coefficients (d22) and also possess out-of-plane piezoelectric coefficients (d31), which is due to the breaking of inversion symmetry in the crystal structure with the space group P3m1. Large Rashba parameters are obtained in Janus Sb2XSX’ monolayers, especially high for Sb2S2Te (1.62 eV Å) and Sb2SeSTe (1.33 eV Å) due to strong spin–orbit coupling. Moreover, Rashba-like spin-splitting is also observed in the edge-states as well, which is highest for Sb2SeSTe with 2.17 eV Å. Furthermore, Sb2S2Te and Sb2SeSTe monolayers reveal a significantly high Berry curvature (65.59 and 61.05 Bohr2), spin Berry curvature (−118.4 and −120.6 Bohr2), and spin Hall conductivity (1.8 and 1.6 e2/h). Our results suggest that Janus Sb2S2Te and Sb2SeSTe monolayers could be an excellent platform for multifunctional electronic applications.

Signatures of nearly compensated magnetism and spin glass behavior in highly frustrated β-Mn-type Mn50Fe25+xAl25-x Heusler alloys.

The present study examines the effect of Fe/Al concentration on the structural and magnetic properties of Mn-rich Mn50Fe25+xAl25-x (x = 5, 10, 15) Heusler alloys through x-ray diffraction, temperature- and field-dependent DC magnetization, thermoremanent magnetization, magnetic memory effect, AC susceptibility measurements, and DFT calculations. The samples crystallize in a cubic β-Mn structure. The trend shows a reduction in lattice parameters (unit cell volume) with the increasing Fe proportion. These alloys exhibit strong antiferromagnetic interactions with large frustration parameters, indicating the presence of competing magnetic interactions. The DC magnetization data reveal spin glass-like features with a peak at spin glass freezing temperature (Tf). The observation of bifurcation in temperature-dependent zero-field-cooled and field-cooled magnetization curves, exponential dependence of the temperature variation of remanence and coercivity, magnetic relaxation, and magnetic memory effect below Tf support the spin-glass character of these alloys. The frequency dependence of Tf is also examined in the context of dynamic scaling laws, such as the Vogel-Fulcher law and critical slowing down model, which further supports the presence of spin glass behavior. In the theoretical DFT calculations, the electronic structure is found to be metallic and similar for both spin projections. Moreover, the antiferromagnetic arrangement of the magnetic moments, in line with the experimental observations, is stabilized by exchange interactions, resulting in an almost compensated total magnetic moment of 0.02-0.38µB/f.u. This is probably caused by the frustrated structure and non-stoichiometric compositions of Mn50Fe25+xAl25-x.

A nondominated sorting genetic model for co-clustering

Co-clustering aims to cluster the rows and columns of data simultaneously and can be often formulated as a two-objective optimization problem (one objective for rows and the other for columns) and the solution is a Pareto-optimal solution set in principle. Existing methods usually convert the co-clustering problem into a single-objective optimization problem by setting a hyper-parameter λ between the two objectives. However, finding a good value of λ is not easy because of its large parameter space; also, there may exist many equally good λs. In this paper, a nondominated sorting genetic model (NSGC) is proposed to tackle the co-clustering problem, totally bypassing the trade-off parameter λ and returning to the original two-objective problem. The core of our model is to group a row objective function and a column objective function and integrate them into a genetic algorithm as the fitness functions. After this reformulation, we follow a standard genetic algorithm procedure to iteratively find the Pareto-optimal solutions. Finally, to fish out a single best solution we further design a sorting criterion according to which the Pareto-optimal solution set can be totally ordered. Extensive experiments with 16 public datasets are conducted, and the results demonstrate the superiority of our approach.

Monitoring of operational conditions of fuel cells by using machine learning

The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.

Importing Experimental Results via S2D Model in ADS Tool for Power Amplifier Design

In this paper, behavioral modeling of a driver amplifier is implemented by using S2P and S2D model setup. By using the standard silicon-based driver amplifier for obtaining the measured results of Scattering (S)- parameters and large signal parameters for temperatures 25°C and 85°C. These parameters are imported into the S2P and S2D models to perform the small signal and large signal analysis at 3 GHz frequency for temperatures 25°C and 85°C. Then, the simulated results are compared with the measured results to verify the efficient utilization of behavioral models. The novelty of this work deals with the simulation study of the characteristics of a silicon-based driver amplifier, which are obtained directly from the measurements. This proposed method is essential for performing hetero-integration of transceiver design, which can be further utilized to improve the efficiency of transceiver for high-frequency band. At last, the study of relative error performance analysis between measured and simulated results of different parameters is carried out and calculated value of NMSE (in %) for S11, S12, S21, S22, gain, pout, 1 dB compression point, ACP, 3rd harmonics, 4th harmonics, and 5th harmonics are 0.0083, 0.0055, 0.0086, 0.011, 0.0844, 0.814, 0.926, 0.71, 0.22, 0.012 and 0.070 respectively.

The first demonstration of entirely roll-to-roll fabricated perovskite solar cell modules under ambient room conditions

The rapid development of organic-inorganic hybrid perovskite solar cells has resulted in laboratory-scale devices having power conversion efficiencies that are competitive with commercialised technologies. However, hybrid perovskite solar cells are yet to make an impact beyond the research community, with translation to large-area devices fabricated by industry-relevant manufacturing methods remaining a critical challenge. Here we report the first demonstration of hybrid perovskite solar cell modules, comprising serially-interconnected cells, produced entirely using industrial roll-to-roll printing tools under ambient room conditions. As part of this development, costly vacuum-deposited metal electrodes are replaced with printed carbon electrodes. A high-throughput experiment involving the analysis of batches of 1600 cells produced using 20 parameter combinations enabled rapid optimisation over a large parameter space. The optimised roll-to-roll fabricated hybrid perovskite solar cells show power conversion efficiencies of up to 15.5% for individual small-area cells and 11.0% for serially-interconnected cells in large-area modules. Based on the devices produced in this work, a cost of ~0.7 USD W−1 is predicted for a production rate of 1,000,000 m² per year in Australia, with potential for further significant cost reductions.

Asymptotic analysis of three-parameter Mittag-Leffler function with large parameters, and application to sub-diffusion equation involving Bessel operator

Asymptotic analysis of three-parameter Mittag-Leffler function with large parameters, and application to sub-diffusion equation involving Bessel operator

Lower Bounds on the Noiseless Worst-Case Complexity of Efficient Global Optimization

Efficient global optimization is a widely used method for optimizing expensive black-box functions. In this paper, we study the worst-case oracle complexity of the efficient global optimization problem. In contrast to existing kernel-specific results, we derive a unified lower bound for the oracle complexity of efficient global optimization in terms of the metric entropy of a ball in its corresponding reproducing kernel Hilbert space. Moreover, we show that this lower bound nearly matches the upper bound attained by non-adaptive search algorithms, for the commonly used squared exponential kernel and the Matérn kernel with a large smoothness parameter ν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ u $$\\end{document}. This matching is up to a replacement of d/2 by d and a logarithmic term logRϵ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log \\frac{R}{\\epsilon }$$\\end{document}, where d is the dimension of input space, R is the upper bound for the norm of the unknown black-box function, and ϵ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\epsilon $$\\end{document} is the desired accuracy. That is to say, our lower bound is nearly optimal for these kernels.

Identification of leaf diseases in field crops based on improved ShuffleNetV2.

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Implementation of the Projective Quantum Eigensolver on a Quantum Computer.

We study the performance of our previously proposed projective quantum eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H2, we find that we are able to obtain energies within 4 millihartree (2.5 kcal/mol) of the exact energy along the entire potential energy curve, with the accuracy limited by both the stochastic error and the inconsistent performance of the IBM devices. We find that an optimization algorithm using direct inversion of the iterative subspace can converge swiftly, even to excited states, but stochastic noise can prompt large parameter updates. For the 4-site transverse-field Ising model at its critical point, PQE with an appropriate application of qubit tapering can recover 99% of the correlation energy, even after discarding several parameters. The large number of CNOT gates needed for the additional parameters introduces a concomitant error that, on the IBM devices, results in a loss of accuracy despite the increased expressivity of the trial state. Error extrapolation techniques and tapering or postselection are recommended to mitigate errors in PQE hardware experiments.

Non-Abelian chiral soliton lattice in rotating QCD matter: Nambu-Goldstone and excited modes

The ground state of QCD with two flavors at a finite baryon chemical potential under rapid rotation is a chiral soliton lattice (CSL) of the η meson, consisting of a stack of sine-Gordon solitons carrying a baryon number, due to the anomalous coupling of the η meson to the rotation. In a large parameter region, the ground state becomes a non-Abelian CSL, in which due to the neutral pion condensation each η soliton decays into a pair of non-Abelian sine-Gordon solitons carrying S2 moduli originated from Nambu-Goldstone (NG) modes localized around it, corresponding to the spontaneously broken vector symmetry SU(2)V. There, the S2 modes of neighboring solitons are anti-aligned, and these modes should propagate in the transverse direction of the lattice due to the interaction between the S2 modes of neighboring solitons. In this paper, we calculate excitations including gapless NG modes and excited modes around non-Abelian and Abelian (η) CSLs, and find three gapless NG modes with linear dispersion relations (type-A NG modes): two isospinons (S2 modes) and a phonon corresponding to the spontaneously broken vector SU(2)V and translational symmetries around the non-Abelian CSL, respectively, and only a phonon for the Abelian CSL because of the recovering SU(2)V. We also find in the deconfined phase that the dispersion relation of the isospinons becomes of the Dirac type, i.e. linear even at large momentum.