esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This study highlights the critical role that existing technologies and emerging frameworks can play in achieving the 2030 and 2050 decarbonization and grid reliability goals of the US energy sector. Energy-efficient technologies, such as high-performance HVAC systems, smart lighting, building insulation, advanced sensors, and smart energy management systems (HEMS/BEMS), have already demonstrated measurable reductions in energy consumption and CO\({}_{2}\) emissions. For example, structural upgrades and sensor driven HVAC systems can reduce building energy use by up to 55%, while smart controls and energy management can achieve an additional 23% in savings. These technologies provide a robust foundation for immediate and scalable deployment. However, while these technologies contribute significantly toward CO2 reduction targets, their integration into large-scale distributed energy systems introduces operational challenges that current methods do not fully address. Existing centralized demand-response (DR) aggregator models often require extensive access to prosumer data, including personally identifiable information (PI), device-level demand profiles, and behavioral patterns. This access creates substantial privacy risks and scalability bottlenecks, particularly as the number of participating prosumers grows into the thousands or millions. Moreover, current DR frameworks frequently fall short in managing uncertainty, ensuring competitive fairness between prosumers and aggregators and enabling flexible market participation that aligns with individual prosumer constraints and preferences when evaluated through the proposed FCUPS criteria--flexibility, competition, uncertainty quantification, privacy preservation, and scalability. Centralized models also struggle to efficiently coordinate the diverse and dynamic load types that characterize modern prosumers: from residential solar and battery systems to smart appliances and electric vehicles. To address these gaps, this paper proposes future research directions centered on decentralized, privacy-preserving, and scalable solutions. Specifically, federated multi-agent learning, hierarchical distributionally robust optimization with privacy guarantees, and blockchain-enabled transactive energy markets are identified as promising avenues. These approaches can enable prosumers to retain local control over their data while contributing optimal demand bids to the market. Furthermore, integrating game-theoretic models that rigorously capture competition and cooperation dynamics, along with reinforcement learning for adaptive bidding under uncertainty, could greatly enhance market resilience and efficiency. By focusing on these advanced methods, future DR aggregator frameworks can overcome the limitations of current approaches, facilitating the large-scale adoption of distributed load participation and strengthening the grid's ability to meet 2030 and 2050 targets for decarbonization, reliability, and a net-zero economy.
## 4 Simulation Test
in Figure 2; at this time, the receiver is in an extremely harsh high dynamic weak signal environment. After 120 s, the number of signal channels is restored to 12, and the uniform linear motion is restored. Figure 3 shows the relationship between the RMS frequency error of channel 2 and the carrier-to-noise ratio (CNR). The frequency estimation value of channel 2 is obtained by multiplying the speed estimation value of the receiver by the scale factor \(\mu\)[17]. \[\mu=\frac{f_{B1}}{c}\approx 5.20366\ \ \mathrm{Hz} \tag{37}\]  
Figure 3 shows the relationship curve between the RMS frequency error and CNR. It can be seen from Figure 3 that, compared to SRCKF VLL and ASRCKF SLL, the ASRCKF VLL has a significant improvement in frequency tracking accuracy. When C/N\({}_{0}\) = 21.5, the tracking accuracy of the ASRCKF VLL is 6 dB higher than that of the SRCKF VLL and 10 dB higher than that of the ASRCKF SLL. When C/N\({}_{0}\) = 22.5, the mean square error of the frequency estimation of the ASRCKF VLL is 31 Hz, which is 5 dB higher than that of the SRCKF VLL and 9 dB higher than that of the ASRCKF SLL, but as the carrier-to-noise ratio C/N\({}_{0}\) increases, this improvement gradually decreases. In addition, Figure 3 shows that the tracking performance of the SRCKF VLL is better than that of the ASRCKF SLL. This is because when the carrier-to-noise ratio is less than the tracking threshold, the signal strength is weak. In a weak signal environment, the weak signal channel of the vector tracking loop can be assisted by the strong signal channel, and the loop can achieve rapid recovery when the signal is blocked. Therefore, even if the SRCKF VLL cannot adaptively adjust the observed noise covariance matrix as the environment changes, it shows better tracking performance than the ASRCKF SLL. From Figures 4 and 5, it can be seen that the ASRCKF scalar tracking loop has good tracking performance in the initial 25 s, but during 25-120 s, the position error and speed error increase rapidly, and the tracking performance gap with the other two vector loops also increases rapidly; its tracking performance deteriorates further with the high dynamic movement of the receiver during 60-85 s. After 120 s, the number of channels with signals is restored to 12. At this time, the position error and speed error of the ASRCKF scalar loop are greatly reduced, but the tracking state before 25 s cannot be restored. The maximum  
Figure 3: The relationship between RMS frequency estimation error and CNR. The ASRCKF VLL and the SRCKF VLL can maintain normal operation during the entire 180 s. Under the highly dynamic and weak signal environment during 60-85 s, the three-dimensional position error of the SRCKF VLL does not exceed 65 m, the three-dimensional velocity error does not exceed 4.9 m/s, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s. During the entire 180 s, among the three loops, the position error and velocity error of the ASRCKF vector tracking loop are the smallest, and the tracking performance is the best.
###### Abstract  
The safety of transmission lines is a crucial guarantee for the operation of the power grid. To address the issue of low detection accuracy for icing transmission line defects with existing models, this paper proposes a defect detection algorithm for icing transmission line defects under different weather conditions based on a Large Dynamic Kernel Aggregation Net (LDKA-NET). First, a wide field of view convolutional network (WFVC Net) is introduced to enhance the network's perception and generalization capabilities, enabling better adaptation to complex scene targets. Secondly, a full-dimensional dynamic convolutional feature fusion network is proposed, which strengthens the model's feature extraction ability by learning linear combinations of multiple convolution kernels and their weighted input-related attention. Finally, an Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism is introduced, which focuses on and utilizes important information in the input data to help the model better allocate attention, thereby improving its generalization and robustness. Experimental results show that on the dataset proposed in this paper, the average accuracy of the improved algorithm ([EMAIL_ADDRESS]) reaches 99.01%. The model parameters decreased by 47.7 M compared with the baseline model and 91.26 M compared with the Faster region with CNN feature (Faster R-CNN) model. The accuracy is 4.81% and 3.11% higher than the SSD model and YOLOv5-L model, respectively. Compared with the existing models, our model is smaller and has higher detection accuracy. It can accurately detect ice-covered wires and complete the task of the ice-covered detection of transmission lines.  
online monitoring; object detection; ice-covered transmission monitoring; expectation maximization dynamic convolutional attention +
Footnote †: journal: applied sciences
# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 4 Experiments
### Speech Experiment
#### 4.1.4 Experimental Results of the Objective Measures for Speech Enhancement
Figure 6 shows the speech recognition results for the unprocessed mixture signal and signals enhanced by the deep learning models. Figure 5(a)-c show the WER results with SNR of \(-\)10, 0, and 10 dB, respectively. In Figure 5(a), MMDenseLSTM+ shows the best performance. However, Figure 5(b),c shows how the performance of MMDenseLSTM+ deteriorated more and more in an environment with higher SNR. GRN showed the lower overall performance than others, and then MMDenseLSTM and DillDenseNet showed good performance in order. The proposed architecture showed the best performance after MMDenseLSTM+ at \(-\)10 dB SNR, but the difference of performance from MMDenseLSTM+ was not large, and it showed the best performance in 0 dB and 10 dB SNR environments, as can be seen in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Architecture** & **The Number of Parameters (\(\times 10^{6}\))** \\ \hline GRN & 3.11 \\ \hline MMDenseLSTM & 0.98 \\ \hline MMDenseLSTM+ & 1.51 \\ \hline DiDenseNet & 0.47 \\ \hline Proposed & 1.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of parameters for each architecture in this experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **SNR** & \(-\)**10** & **0** & **10** \\ \hline GRN & 87.26 & 44.04 & 24.89 \\ \hline MMDenseLSTM & 84.01 & 37.79 & 21.91 \\ \hline MMDenseLSTM+ & **79.24** & 41.51 & 23.01 \\ \hline DilDenseNet & 82.26 & 35.54 & 20.77 \\ \hline Proposed & 79.55 & **32.83** & **19.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average word error rate (WER) over the entire signal-to-music ratio (SMR) at each signal-to-noise ratio (SNR). Lower WER indicates better performance. Figure 5: Comparison of perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), short-time objective intelligibility (STOI), coherence-based speech intelligibility index (CSII), normalized-covariance measure (NCM), and word error rate (WER) results when the music and noise are mixed at 0 dB. PESQ, SDR, STOI, CSII, and NCM are calculated using average statistics. PESQ ranges from \(-0.5\) to \(4.5\), STOI, NCM, and CSII range from 0 to 1, and SDR has no fixed range. Larger values of the performance evaluation indicators represent better performance. Figure 6: Speech recognition results using unprocessed mixture signals mixture signals enhanced using GRN, etc. WER results according to signal-to-music ratio (SMR). (**a**) when noise is mixed at \(-10\) dB signal-to-noise ratio (SNR), (**b**) when noise is mixed at 0 dB SNR, (**c**) when noise is mixed at 10 dB SNR. Lower WER indicates better performance.
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 3 Results and Discussion
### Droplets Infection Risk Assessment  
#### 3.3.1. CMVS  
Figure 6 and Table 3 display the infection risks and intensity of CMVS in case of different infectious sources, respectively.  
Figure 5: Droplets transient transmission of CMVS: (**a**) case A—patient A in Row 4; (**b**) case B—patient B in Row 4; (**c**) case C—patient C in Row 4; (**d**) case D—patient D in Row 4; (**e**) case E—patient E in Row 4.  
Figure 6: Infection risks of CMVS in case of different infectious sources. (**a**) Case A. (**b**) Case B. (**c**) Case C. (**d**) Case D. (**e**) Case E.  
As shown in Figure 6, the infection risk of the next two rows of passengers is relatively high in case A. The infection risk of passengers on Aisile II in the fifth row is greater than 0.5, which is higher than that of the passengers directly behind the infectious source. In case B, the two rows behind the infectious source also have a relatively high risk of infection, which is the same in case A. However, the two passengers in the same row of the infectious source have the highest risk in the cabin. In particular, the passenger near the window has the highest infection risk of 0.9. In case C, the infection risk of passengers in the same row is higher than that in case B and the infection risk of passenger B even reached 1.0. In case D, the infection risk between the same row is reduced because the exhaled droplets moved in the leg areas of passengers B and C in the same row. In case E, the infection risk in the same row is further reduced. In addition, different from the previous four cases, the passengers on both sides of the cabin behind the infectious source have the risk of infection. As shown in Table 3, case C is the least desirable condition of all, which is prone to infection throughout the cabin. It is worth noting that the number of these bullets is deduced from Figure 6 in the following way: 0 bullet/person (\(PI\leq 0.3\)), 1 bullet/person (\(0.3<PI\leq 0.6\)), 2 bullets/person (\(0.6<PI\leq 0.8\)), 3 bullets/person (\(0.8<PI\leq 1\)), and then the sum of all the bullets for all people is the corresponding infection intensity.
## 3 Network Model Design  
### Methodology  
Through innovative deep learning architecture design (a dual-model framework integrating ResNet and Transformer) and dual-feature joint learning (combining TFR with HOS), this study systematically addresses the challenge of modulation recognition for underwater acoustic communication signals in complex channels. The proposed approach provides an efficient end-to-end solution for underwater non-cooperative communication scenarios. The referenced model architectures include ResNet [24], Transformer [16], CNNTransformer [17; 18; 19], and ResNet-Transformer [25]. The core structure of the DFRT model is illustrated in Figure 2.  
In the feature extraction module of the proposed model, we designed two parallel branches (corresponding to the oyster gray blocks in Figure 2) by considering three key factors: feature type discrepancy, network adaptability, and computational efficiency.  
1. Feature Type Discrepancy: Time-frequency maps are 2D images containing both local structures (e.g., frequency-hopping patterns and energy distributions) and global trends (e.g., periodicity of modulation modes). HOS are low-dimensional vector features derived from signal statistics (e.g., real/imaginary parts of second/fourth-order statistics), reflecting non-Gaussianity and phase information as global statistical properties without local spatial structures.
2. Network Adaptability: Statistical features, being mathematically transformed statistics without inherent spatial dependencies, are ill-suited for convolutional operations. Thus, ResNet is ineffective for their extraction.
3. Computational Efficiency: The time-frequency branch employs a dual-network architecture to enhance multi-scale feature extraction, prioritizing expressive power. The statistics branch leverages Transformer's global modeling capability to efficiently capture statistical correlations, avoiding overfitting from excessive layers.  
In the classification module of the model, we designed the classification layer (corresponding to the dark gray block in Figure 2) with a focus on three key aspects: effective feature fusion, nonlinear expressiveness, and model generalization.  
Figure 2: The model structure of the DFRT.  
Effective Feature Fusion:  
A multi-head attention mechanism is employed to fuse the TFR (256 dimensions) extracted by the ResNet branch and the HOS features (256 dimensions) from the Transformer branch. This approach addresses the limitations of single-modal features (e.g., the insensitivity of TFR to phase information and the lack of spatial details in statistics).  
Nonlinear Expressiveness:  
A fully connected layer maps the 256-dimensional fused features to 128 dimensions, reducing parameter redundancy and enhancing nonlinear separability. Meanwhile, nonlinear activation functions are applied to break linear classification constraints, enabling the model to capture complex nonlinear relationships among modulation types.  
Model Generalization:  
Dropout regularization is incorporated to mitigate overfitting and improve the model's generalization capability across diverse underwater scenarios.
# Underwater Wireless Communications for Cooperative Robotics with UWSim-NET
## 3 Results  
Several experiments were performed to demonstrate the capabilities of UWSim-NET to model the communications of a group of underwater autonomous vehicles. First, an experiment with two robots is presented to illustrate the communication mechanisms modeled in UWSim-NET. A second experiment simulating the teleoperation of two groups of robots is presented to demonstrate the capabilities of UWSim-NET in modeling acoustic and RF communications and several MAC protocols. A final teleoperation experiment combining a real robot and three modeled robots illustrates the possibility to conduct HIL experiments with UWSim-NET.
## 3 RLBM Parallel Strategy
### RLBM Parallel Algorithm  
RLBM parallel iterative calculation can be described as an Algorithm 2. We first read the surface mesh file, then the domain decomposition and the calculation of sub-domain range are carried out. After generating a Cartesian grid, the flow field information is initialized, and then the RLBM parallel iterative calculation starts. The regularization calculation is required before the collision. After the collision, the MPI process must exchange data with the neighboring processes based on DDM. After the data exchange, the propagation operation is carried out, and finally the boundary and macro-quantity calculations are carried out. Compared to the serial RLBM algorithm shown in Figure 3, the parallel RLBM Algorithm 2 includes the domain decomposition method, Cartesian grid generation and MPI communication.  
According to the computational features of the combination of RLBM and LES, since RLBM involves only the information of local neighboring nodes in the process of computation, it adopts the computational volume load priority (i.e., the computational volume corresponding to the mesh division is basically the same), and then designs the communication strategy based on it to realize the load balancing based on MPI. Parallel algorithms need to be designed with adaptability on high performance computers in mind. Since high performance computers have a departmental structure with distributed storage, MPI techniques must be used to implement the data exchange.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**DDM** & \(nDDX\) & \(nDDY\) & \(nDDZ\) & \(\frac{nLatX}{nDDX}\) & \(\frac{nLatY}{nDDY}\) & \(\frac{nLatZ}{nDDZ}\) & **Lattice Amount** \\ \hline
1D & 2048 & 1 & 1 & 1 & 512 & 512 & 786,432 \\ \hline \multirow{4}{*}{2D} & 256 & 8 & 1 & 8 & 64 & 512 & 337,920 \\  & 128 & 16 & 1 & 16 & 32 & 512 & 313,344 \\  & 64 & 32 & 1 & 32 & 16 & 512 & 313,344 \\ \hline \multirow{4}{*}{3D} & 256 & 4 & 2 & 8 & 128 & 256 & 335,400 \\  & 128 & 8 & 2 & 16 & 64 & 256 & 306,504 \\  & 128 & 4 & 4 & 16 & 128 & 128 & 304,200 \\  & 64 & 16 & 2 & 32 & 32 & 256 & 298,248 \\  & 64 & 8 & 4 & 32 & 64 & 128 & 291,720 \\  & 32 & 32 & 2 & 64 & 16 & 256 & 306,504 \\  & 32 & 16 & 4 & 64 & 32 & 128 & 291,720 \\  & 32 & 8 & 8 & 64 & 64 & 64 & 287,496 \\  & 16 & 16 & 8 & 128 & 32 & 64 & 291,720 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of lattice needed to be judged among three kinds of domain decomposition.
## 8 Discussion  
### Repeatability of Nonlinear Error  
With the limit of sensor manufacturing technology, the nonlinear error of FOGs and ACCs sometimes does not have good repeatability. When the working environment has been changed, the nonlinear error changes with it. So, some researchers held the opinion that not compensating the nonlinear error is better than wrongly compensating it. The sensors used in this paper have been selected carefully and the nonlinear error coefficients of the sensors all have good repeatability. In the authors' opinion, an adaptive calibration method for the nonlinear error should be studied in the future.
## 1 Introduction
To enhance the signal reception, a 25.4 mm biconvex lens was positioned in front of the photodetector, allowing for reliable operation under daylight conditions at distances of up to 20 m. Field evaluations demonstrated a stable SNR, low packet loss, and acceptable BER, supporting the practicality of VLC for short-range V2V communication. The work of [24] presented a rate-adaptive orthogonal frequency division multiplexing (OFDM) vehicular VLC prototype implemented on a software-defined radio (SDR) platform. The system employed direct current-biased optical OFDM at the physical layer and supported dynamic modulation adaptation among multiple schemes. A lightweight OOK feedback link was used to transmit real-time channel state information from the receiver to the transmitter, enabling the selection of the highest-order modulation that satisfied a predefined BER threshold. These initial results demonstrated the potential of adaptive OFDM-based VLC to achieve high-throughput, reliable V2V communication in future 6G vehicular networks. Despite numerous existing works on the implementation of vehicular VLC systems, including [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24], there remains a significant gap in studies addressing beam alignment issues, which are critical for ensuring seamless V2V link connections between vehicles via VLC. Furthermore, there is limited research providing a detailed explanation of how the developed VLC systems are integrated into LED lamps in actual vehicles and how they operate. Based on the above discussion, the main contributions of this work are summarized as follows:  
* We propose and implement a vehicular VLC system designed to establish an expandable VLC-based chain network for the real-time exchange of driving dynamics data (e.g., target speed and acceleration) among consecutive vehicles in tunnel environments. The system is tailored for GPS-denied scenarios, where limited visibility and poor situational awareness increase the risk of chain collisions. The system architecture is validated through experimental scenarios representative of real-world tunnel hazards, including sudden braking and unexpected congestion. * To ensure reliable optical links even during curved driving, the system incorporates an adaptive LED beam alignment mechanism that dynamically adjusts the transmitter's orientation based on yaw angle, derived from a nine-axis inertial measurement unit (IMU). This closed-loop control significantly enhances communication stability in conditions where traditional fixed-beam VLC systems are prone to misalignment. * The proposed system can be practically deployable in existing vehicles with minimal hardware modification, as it reuses built-in LED tail-lights as VLC transmitters and requires only low-cost embedded circuitry. This makes the approach both cost-effective and scalable, offering a feasible path toward the real-world adoption of VLC-based V2V communication.