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This paper presents novel methods for tuning inverter controller gains using deep reinforcement learning (DRL). A Simulink-developed inverter model is converted into a dynamic link library (DLL) and integrated with a Python-based RL environment, leveraging the multi-core deployment and accelerated computing to significantly reduce RL training time. A neural network-based mechanism is developed to transform the cascaded PI controller into an actor network, allowing optimized gain tuning by an RL agent to mitigate scenarios such as subsynchronous oscillations (SSO) and initial transients. Two distinct tuning approaches are demonstrated: a fixed gain strategy, where controller gains are represented as RL policy (actor network) weights, and an adaptive gain strategy, where gains are dynamically generated as RL policy (actor network) outputs. A comparative analysis of these methods is provided, showcasing their effectiveness in stabilizing the transient performance of grid-forming and grid-following converters and deployment challenges in hardware. Experimental results are presented, demonstrating the enhanced robustness and practical applicability of the RL-tuned controller gains in real-world systems.

6G is envisaged to provide multimodal sensing, pervasive intelligence, global coverage, global coverage, etc., which poses extreme intricacy and new challenges to the network design and optimization. As the core part of 6G, wireless channel is the carrier and enabler for the flourishing technologies and novel services, which intrinsically determines the ultimate system performance. However, how to describe and utilize the complicated and high-dynamic characteristics of wireless channel accurately and effectively still remains great hallenges. To tackle this, digital twin is envisioned as a powerful technology to migrate the physical entities to virtual and computational world. In this article, we propose a large model driven digital twin channel generator (ChannelGPT) embedded with environment intelligence (EI) to enable pervasive intelligence paradigm for 6G network. EI is an iterative and interactive procedure to boost the system performance with online environment adaptivity. Firstly, ChannelGPT is capable of utilization the multimodal data from wireless channel and corresponding physical environment with the equipped sensing ability. Then, based on the fine-tuned large model, ChannelGPT can generate multi-scenario channel parameters, associated map information and wireless knowledge simultaneously, in terms of each task requirement. Furthermore, with the support of online multidimensional channel and environment information, the network entity will make accurate and immediate decisions for each 6G system layer. In practice, we also establish a ChannelGPT prototype to generate high-fidelity channel data for varied scenarios to validate the accuracy and generalization ability based on environment intelligence.

In this document, we revise the results of [1] based on more reasonable assumptions regarding data shuffling and parameter setup of deep neural networks (DNNs). Thus, the simulation results can now more reasonably demonstrate the performance of both the proposed and compared beam alignment methods. We revise the simulation steps and make moderate modifications to the design of the vehicle distribution feature (VDF) for the proposed vision based beam alignment when the MS location is available (VBALA). Specifically, we replace the 2D grids of the VDF with 3D grids and utilize the vehicle locations to expand the dimensions of the VDF. Then, we revise the simulation results of Fig. 11, Fig. 12, Fig. 13, Fig. 14, and Fig. 15 in [1] to reaffirm the validity of the conclusions., Comment: 3 pages, 6 figures

In September 2024, Beijing University of Posts and Telecommunications and China Mobile Communications Group jointly releases a channel dataset for the sixth generation (6G) mobile communications, named BUPTCMCC-6G-DataAI+. BUPTCMCC-6G-DataAI+ is the update version of BUPTCMCC-6G-DataAI, which is already published in June 2023, aiming at extending 6G new technologies, frequency bands, and applications. BUPTCMCC-6G-DataAI+ provides deterministic data covering new mid-bands, millimeter wave (mmWave), and terahertz (THz), supports the features of XL-MIMO near-field, high mobility and provides multiple 6G scenarios such as reconfigurable intelligent surface (RIS) and industrial Internet. Configured with customized features according to different user needs, BUPTCMCC-6G-DataAI+ can adaptively generate scalable large-scale or small-scale parameters, providing data support for 6G research and development, and standardization., Comment: 2 pages, 1 figure

The air interface technology plays a crucial role in optimizing the communication quality for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environment information-aided 6G AI-enabled air interface (WEI-6G AI$^{2}$), which actively acquires real-time environment details to facilitate channel fading prediction and communication technology optimization. Specifically, we first outline the role of WEI in supporting the 6G AI$^{2}$ in scenario adaptability, real-time inference, and proactive action. Then, WEI is delineated into four progressive steps: raw sensing data, features obtained by data dimensionality reduction, semantics tailored to tasks, and knowledge that quantifies the environmental impact on the channel. To validate the availability and compare the effect of different types of WEI, a path loss prediction use case is designed. The results demonstrate that leveraging environment knowledge requires only 2.2 ms of model inference time, which can effectively support real-time design for future 6G AI$^{2}$. Additionally, WEI can reduce the pilot overhead by 25\%. Finally, several open issues are pointed out, including multi-modal sensing data synchronization and information extraction method construction.

Integrated Sensing and Communication (ISAC) is one of the key technologies in 6G, and related research and standardization efforts are progressing vigorously. Wireless channel simulation is the cornerstone for the evaluation and optimization of wireless communication technologies. This paper proposes a design and implementation method for an ISAC channel simulation based on a Geometry-Based Stochastic Model (GBSM) simulation framework. First, we introduce the progress of 3GPP ISAC channel standardization and the key topics of discussion. Second, addressing the current lack of a standardized ISAC channel simulation framework, we propose a cascaded ISAC channel simulation framework based on GBSM, leveraging our team's related measurements, analyses, and proposal results. Based on this framework, we develop and design the ISAC channel simulator BUPTCMCC-6G-CMG+. Finally, we analyze and validate the simulation platform results, and provide some prospects for future ISAC testing research combined with channel simulators., Comment: 12 pages,5 fiures,2 tables

The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods., Comment: 10 pages

Significant challenges remain for realizing precise positioning and velocity estimation in perceptive vehicular networks (PVN) enabled by the emerging integrated sensing and communication technology. First, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Second, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Third, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity. To overcome these challenges, 1) we introduce a moving target indication based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramer-Rao lower bound of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design algorithms for associating individual direction-of-arrival estimates with the paired range-velocity estimates based on "domain transformation"; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific "fingerprint spectrum", which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error performance of the proposed time-frequency synchronization algorithm. Simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis., Comment: 18 pages, 13 figures, 3 tables, accepted to publish on IEEE Journal on Selected Areas in Communications, vol. 42, no. 10, Oct. 2024

Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods.

Forensic pathology is critical in determining the cause and manner of death through post-mortem examinations, both macroscopic and microscopic. The field, however, grapples with issues such as outcome variability, laborious processes, and a scarcity of trained professionals. This paper presents SongCi, an innovative visual-language model (VLM) designed specifically for forensic pathology. SongCi utilizes advanced prototypical cross-modal self-supervised contrastive learning to enhance the accuracy, efficiency, and generalizability of forensic analyses. It was pre-trained and evaluated on a comprehensive multi-center dataset, which includes over 16 million high-resolution image patches, 2,228 vision-language pairs of post-mortem whole slide images (WSIs), and corresponding gross key findings, along with 471 distinct diagnostic outcomes. Our findings indicate that SongCi surpasses existing multi-modal AI models in many forensic pathology tasks, performs comparably to experienced forensic pathologists and significantly better than less experienced ones, and provides detailed multi-modal explainability, offering critical assistance in forensic investigations. To the best of our knowledge, SongCi is the first VLM specifically developed for forensic pathological analysis and the first large-vocabulary computational pathology (CPath) model that directly processes gigapixel WSIs in forensic science., Comment: 28 pages, 6 figures, under review

As Extremely Large-Scale Multiple-Input-Multiple-Output (XL-MIMO) technology advances and carrier frequency rises, the near-field effects in communication are intensifying. A concise and accurate near-field XL-MIMO channel model serves as the cornerstone for investigating the near-field effects. However, existing wave-number domain XL-MIMO channel models under near-field conditions require non-closed-form oscillatory integrals for computation, making it difficult to analyze the channel characteristics in closed-form. To obtain a more succinct channel model, this paper introduces a closed-form approximate expression based on the principle of stationary phase. It was subsequently shown that when the scatterer distance is larger than the array aperture, the closed-form model can be further simplified as a trapezoidal spectrum. We validate the accuracy of the proposed approximation through simulations of power angular spectrum similarity. The results indicate that the proposed approximation can accurately approximate the near-field wave-number domain channel within the effective Rayleigh distance.

Foundation models have become prominent in computer vision, achieving notable success in various tasks. However, their effectiveness largely depends on pre-training with extensive datasets. Applying foundation models directly to small datasets of capsule endoscopy images from scratch is challenging. Pre-training on broad, general vision datasets is crucial for successfully fine-tuning our model for specific tasks. In this work, we introduce a simplified approach called Adapt foundation models with a low-rank adaptation (LoRA) technique for easier customization. Our method, inspired by the DINOv2 foundation model, applies low-rank adaptation learning to tailor foundation models for capsule endoscopy diagnosis effectively. Unlike traditional fine-tuning methods, our strategy includes LoRA layers designed to absorb specific surgical domain knowledge. During the training process, we keep the main model (the backbone encoder) fixed and focus on optimizing the LoRA layers and the disease classification component. We tested our method on two publicly available datasets for capsule endoscopy disease classification. The results were impressive, with our model achieving 97.75% accuracy on the Kvasir-Capsule dataset and 98.81% on the Kvasirv2 dataset. Our solution demonstrates that foundation models can be adeptly adapted for capsule endoscopy diagnosis, highlighting that mere reliance on straightforward fine-tuning or pre-trained models from general computer vision tasks is inadequate for such specific applications., Comment: To appear in ICBIR 2024

As the underlying foundation of a digital twin network (DTN), a digital twin channel (DTC) can accurately depict the process of radio propagation in the air interface to support the DTN-based 6G wireless network. Since radio propagation is affected by the environment, constructing the relationship between the environment and radio wave propagation is the key to improving the accuracy of DTC, and the construction method based on artificial intelligence (AI) is the most concentrated. However, in the existing methods, the environment information input into the neural network (NN) has many dimensions, and the correlation between the environment and the channel relationship is unclear, resulting in a highly complex relationship construction process. To solve this issue, in this paper, we propose a construction method of radio environment knowledge (REK) inspired by the electromagnetic wave property to quantify the contribution of radio propagation. Specifically, a range selection scheme for effective environment information based on random geometry is proposed to reduce the redundancy of environment information. We quantify the contribution of radio propagation reflection, diffraction and scatterer blockage using environment information and propose a flow chart of REK construction to replace the feature extraction process partially based on NN. To validate REK's effectiveness, we conduct a path loss prediction task based on a lightweight convolutional neural network (CNN) employing a simple two-layer convolutional structure. The results show that the accuracy of the range selection method reaches 90\%; the constructed REK maintains the prediction error of 0.3 and only needs 0.04 seconds of testing time, effectively reducing the network complexity.

Technologies like ultra-massive multiple-input-multiple-output (UM-MIMO) and reconfigurable intelligent surfaces (RISs) are of special interest to meet the key performance indicators of future wireless systems including ubiquitous connectivity and lightning-fast data rates. One of their common features, the extremely large-scale antenna array (ELAA) systems with hundreds or thousands of antennas, give rise to near-field (NF) propagation and bring new challenges to channel modeling and characterization. In this paper, a cross-field channel model for ELAA systems is proposed, which improves the statistical model in 3GPP TR 38.901 by refining the propagation path with its first and last bounces and differentiating the characterization of parameters like path loss, delay, and angles in near- and far-fields. A comprehensive analysis of cross-field boundaries and closed-form expressions of corresponding NF or FF parameters are provided. Furthermore, cross-field experiments carried out in a typical indoor scenario at 300 GHz verify the variation of MPC parameters across the antenna array, and demonstrate the distinction of channels between different antenna elements. Finally, detailed generation procedures of the cross-field channel model are provided, based on which simulations and analysis on NF probabilities and channel coefficients are conducted for $4\times4$, $8\times8$, $16\times16$, and $9\times21$ uniform planar arrays at different frequency bands., Comment: 14 pages, 33 figures

6G communication will greatly benefit from using extremely large-scale antenna arrays (ELAAs) and new mid-band spectrums (7-24 GHz). These techniques require a thorough exploration of the challenges and potentials of the associated near-field (NF) phenomena. It is crucial to develop accurate NF channel models that include spherical wave propagation and spatial non-stationarity (SnS). However, channel measurement campaigns for mid-band ELAA systems have rarely been reported in the state-of-the-art. To this end, this work develops a channel sounder dedicated to mid-band ELAA systems based on a distributed modular vector network analyzer incorporating radio-over-fiber (RoF), phase compensation, and virtual antenna array schemes. This novel channel-sounding testbed based on off-the-shelf VNA has the potential to enable large-scale experimentation due to its generic and easy-accessible nature. The main challenges and solutions for developing NF channel models for mid-band ELAA systems are discussed, including channel sounders, multipath parameter estimation algorithms, and channel modeling frameworks. Besides, the study reports a measurement campaign in an indoor scenario using a 720-element virtual uniform circular array ELAA operating at {16-20} GHz, highlighting the presence of spherical wavefronts and spatial non-stationary effects. The effectiveness of the proposed near-field channel parameter estimator and channel modeling framework is also demonstrated using the measurement data., Comment: Submitted to IEEE Communication Magazine

With the development of sixth generation (6G) networks toward digitalization and intelligentization of communications, rapid and precise channel prediction is crucial for the network potential release. Interestingly, a dynamic ray tracing (DRT) approach for channel prediction has recently been proposed, which utilizes the results of traditional RT to extrapolate the multipath geometry evolution. However, both the priori environmental data and the regularity in multipath evolution can be further utilized. In this work, an enhanced-dynamic ray tracing (E-DRT) algorithm architecture based on multipath bidirectional extrapolation has been proposed. In terms of accuracy, all available environment information is utilized to predict the birth and death processes of multipath components (MPCs) through bidirectional geometry extrapolation. In terms of efficiency, bidirectional electric field extrapolation is employed based on the evolution regularity of the MPCs' electric field. The results in a Vehicle-to-Vehicle (V2V) scenario show that E-DRT improves the accuracy of the channel prediction from 68.3% to 94.8% while reducing the runtime by 7.2% compared to DRT.

Time series classification is one of the most critical and challenging problems in data mining, existing widely in various fields and holding significant research importance. Despite extensive research and notable achievements with successful real-world applications, addressing the challenge of capturing the appropriate receptive field (RF) size from one-dimensional or multi-dimensional time series of varying lengths remains a persistent issue, which greatly impacts performance and varies considerably across different datasets. In this paper, we propose an Adaptive and Effective Full-Scope Convolutional Neural Network (AdaFSNet) to enhance the accuracy of time series classification. This network includes two Dense Blocks. Particularly, it can dynamically choose a range of kernel sizes that effectively encompass the optimal RF size for various datasets by incorporating multiple prime numbers corresponding to the time series length. We also design a TargetDrop block, which can reduce redundancy while extracting a more effective RF. To assess the effectiveness of the AdaFSNet network, comprehensive experiments were conducted using the UCR and UEA datasets, which include one-dimensional and multi-dimensional time series data, respectively. Our model surpassed baseline models in terms of classification accuracy, underscoring the AdaFSNet network's efficiency and effectiveness in handling time series classification tasks., Comment: Accepted by IJCNN 2024

Order and disorder constitute two fundamental and opposite themes in condensed matter physics and materials science. Crystals are considered the epitome of order, characterised by long-range translational order. The discovery of quasicrystals, which exhibit rotational symmetries forbidden in crystals and lack periodicity, led to a paradigm shift in solid-state physics. Moving one step forward, it is intriguing to ask whether ordered matter can exist without apparent symmetry breaking. The same question arises considering how ordered amorphous (noncrystalline) solids can be structured. Here, we present the discovery of ideal noncrystals in two dimensions, which are disordered in the conventional sense, lacking Bragg peaks, but exhibit high orderliness based on the steric order, i.e., they are ideally packed. A path-integral-like scheme reveals the underlying long-range structural correlation. We find that these ideal noncrystals are characterised by phononic vibrational modes following the Debye law, fully affine elastic responses, and suppressed density fluctuations at longer wavelengths, reminiscent of hyperuniformity -- all characteristics typically associated with crystals. Therefore, ideal noncrystals represent a peculiar form of matter with a mixed nature -- noncrystalline yet possessing crystal-like properties. Notably, these states are found to be thermodynamically favourable, indicating them as a possible new class of ordered matter without apparent symmetry breaking. Our findings significantly broaden the conceptualization of ordered states of matter and may contribute to a deeper understanding of entropy-driven ordering, particularly in generic amorphous materials.

In the sixth-generation (6G), the extremely large-scale multiple-input-multiple-output (XL-MIMO) is considered a promising enabling technology. With the further expansion of array element number and frequency bands, near-field effects will be more likely to occur in 6G communication systems. The near-field radio communications (NFRC) will become crucial in 6G communication systems. It is known that the channel research is very important for the development and performance evaluation of the communication systems. In this paper, we will systematically investigate the channel measurements and modeling for the emerging NFRC. First, the principle design of massive MIMO channel measurement platform are solved. Second, an indoor XL-MIMO channel measurement campaign with 1600 array elements is conducted, and the channel characteristics are extracted and validated in the near-field region. Then, the outdoor XL-MIMO channel measurement campaign with 320 array elements is conducted, and the channel characteristics are extracted and modeled from near-field to far-field (NF-FF) region. The spatial non-stationary characteristics of angular spread at the transmitting end are more important in modeling. We hope that this work will give some reference to the near-field and far-field research for 6G.

In the sixth-generation (6G), with the further expansion of array element number and frequency bands, the wireless communications are expected to operate in the near-field region. The near-field radio communications (NFRC) will become crucial in 6G communication systems. The new mid-band (6-24 GHz) is the 6G potential candidate spectrum. In this paper, we will investigate the channel measurements and characteristics for the emerging NFRC. First, the near-field spherical-wave signal model is derived in detail, and the stationary interval (SI) division method is discussed based on the channel statistical properties. Then, the influence of line-of-sight (LOS) and obstructed-LOS (OLOS) environments on the near-field effects and spatial non-stationary (SnS) characteristic are explored based on the near-field channel measurements at 11-15 GHz band. We hope that this work will give some reference to the NFRC research., Comment: arXiv admin note: substantial text overlap with arXiv:2404.17270

Digital twin channel (DTC) is the real-time mapping of a wireless channel from the physical world to the digital world, which is expected to provide significant performance enhancements for the sixth-generation (6G) air-interface design. In this work, we first define five evolution levels of channel twins with the progression of wireless communication. The fifth level, autonomous DTC, is elaborated with multi-dimensional factors such as methodology, characterization precision, and data category. Then, we provide detailed insights into the requirements and architecture of a complete DTC for 6G. Subsequently, a sensing-enhanced real-time channel prediction platform and experimental validations are exhibited. Finally, drawing from the vision of the 6G network, we explore the potential applications and the open issues in future DTC research., Comment: 7 pages, 5 figures, 15 references. It is submitted to IEEE journal

We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead., Comment: 6 pages, 6 figures, accepted to appear on IEEE Transactions on Vehicular Technology, Mar. 2024

To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics. However, traditional vSLAM systems are limited by the camera's narrow field-of-view, resulting in challenges such as sparse feature distribution and lack of dense depth information. To overcome these limitations, this paper proposes a 360ORB-SLAM system for panoramic images that combines with a depth completion network. The system extracts feature points from the panoramic image, utilizes a panoramic triangulation module to generate sparse depth information, and employs a depth completion network to obtain a dense panoramic depth map. Experimental results on our novel panoramic dataset constructed based on Carla demonstrate that the proposed method achieves superior scale accuracy compared to existing monocular SLAM methods and effectively addresses the challenges of feature association and scale ambiguity. The integration of the depth completion network enhances system stability and mitigates the impact of dynamic elements on SLAM performance., Comment: 6 pages, 9 figures

State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods., Comment: 11 pages, 17 figures, journal accepted

The high-order Target ENO (TENO) scheme, known for its innovative weighting strategy, has demonstrated strong potential for complex flow predictions. This study extends the TENO weighting approach to develop non-oscillatory central TENO (CTENO and CTENOZ) family schemes for unstructured meshes. The CTENO schemes employ compact directional stencils, which increase the likelihood of finding stencils within smooth regions. The design is intentionally compact to simplify the implementation of directional stencils. An effective scale separation strategy is adopted using an ENO-like stencil selection method, which employs large central stencils in smooth regions to achieve high-order accuracy, and smaller directional stencils near discontinuities to improve shock-capturing capabilities. Extensive tests involving CWENO, TENO, CTENO, and CTENOZ schemes were conducted to assess their performance in terms of accuracy, robustness, parallel scalability, and computational efficiency. The findings indicate that the proposed CTENO and CTENOZ schemes deliver high-order precision, lower numerical dissipation, and excellent shock-capturing performance.

The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well known that the physical environment influences channels. A key task for accurately twinning channels in complex 6G scenarios is establishing precise relationships between the environment and the channels. In this article, the radio environment knowledge pool (REKP) is proposed, with its core function being to construct and store as much knowledge between the environment and channels as possible. Firstly, the research progress related to DTC is summarized, and a comparative analysis of these achievements on key indicators in digital twin is conducted, proposing the challenges faced in knowledge construction. Secondly, instructions on how to construct and update REKP are given. Then, a typical case is presented to demonstrate the great potential of REKP in enabling DTC. Finally, how to utilize REKP to address open issues in the 6G wireless communication system is discussed, including enhancing performance, reducing costs, and keeping a trustworthy DTC.

In this paper, we mainly study the turbulence of Lagrangian flow advected by stochastic 2D Navier-Stokes equations. It is proved that this system has observable full-horseshoes. The observable full-horseshoe means that it is a kind of chaotic structure and occurs on any two disjoint non-empty closed balls., Comment: 18 pages

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), as standardized for 5G systems, addresses solely communication channels and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular focus on capturing the sharing feature of both channels, including shared scatterers, clusters, paths, and similar propagation param-eters, which have been experimentally verified in the literature. The proposed approach can be summarized as follows: Firstly, an ISAC channel model is proposed, where shared and non-shared components are superimposed for both communication and sensing. Secondly, sensing channel is characterized as a cascade of TX-target, radar cross section, and target-RX, with the introduction of a novel parameter S for shared target extraction. Finally, an ISAC channel implementation framework is proposed, allowing flexible configuration of sharing feature and the joint generation of communication and sensing channels. The proposed ISAC channel model can be compatible with the 3GPP standards and offers promising support for ISAC technology evaluation.

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet., Comment: 12 pages

We report three-dimensional (3D) structure determination of a twisted hexagonal boron nitride (h-BN) heterointerface from a single-view data set using multislice ptychography. We identify the buried heterointerface between two twisted h-BN flakes with a lateral resolution of 0.57 {\AA} and a depth resolution of 2.5 nm. The latter is a significant improvement (~2.7 times) over the aperture-limited depth resolution of incoherent imaging modes such as annular-dark-field scanning transmission electron microscopy. This is attributed to the diffraction signal extending beyond the aperture edge with the depth resolution set by the curvature of the Ewald sphere. Future advances to this approach could improve the depth resolution to the sub-nanometer level and enable the identification of individual dopants, defects and color centers in twisted heterointerfaces and other materials.