esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids
#### 2.2.2 Modeling and Analysis  
Increasing attention is being directed to integration of VRE inverters and their controllers into positive-sequence, fundamental-frequency simulation tools. However, the development and analysis of fast inner-loop controllers are challenging, both to simulate and explore experimentally. Simulation requires faster time steps and more complex functionalities to represent the non-linear dynamics of VRE sources and the interfacing inverters [2; 4; 42]. For example, the phase-locked loop (PLL) controllers of VRE inverters must be accurately and completely modelled to foresee whether a grid interfacing with the controllers, containing few synchronous generators and thus with low system strength, will survive large disturbances and, if not, when the grid becomes unstable [42]. However, in existing simulation tools, the PLL controller models are greatly simplified. Such tools  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Challenges** & **Research Questions and Requirements** \\ \hline Interactions across regions and sectors & Energy storage systems and controllable loads in low-voltage networks are cost-effective resources for providing essential reliability services to bulk transmission systems. This requires _a detailed representation of the complex constraints pertaining to aggregation of distributed energy storage and loads_. _Coupling of energy sectors must be modeled in sufficient detail (i.e., high resolution) in terms of both flexibility and process-specific constraints_, making it possible to include many flexible resources in decarbonized power grids and greatly improve grid operations and economics. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
cannot model future power grids effectively. New modeling and analysis issues are also apparent, caused by behaviors at non-fundamental frequencies and control interference attributable to sub-synchronous resonance [43]. This further weakens the utilities of existing positive-sequence fundamental-frequency simulations tools. Recently, electro-magnetic transient (EMT) tools with fast time steps and more comprehensive models have received extensive attention [44]. However, EMT tools cannot be used for real-time modeling or analysis of an electrical network with more than hundreds of components because the computation time becomes excessive. To overcome this, co-simulation using positive-sequence and EMT tools is increasingly considered to be promising to balance modeling accuracy with computational efficiency.
## 2 Background
### Healthcare
Information consuming: In the information consumption phase, any decisions that medical teams must make on behalf of patients can be made utilizing the outputs and analytics from the data processing phase. This analytical information can even be used to activate the actuators. The application and business layers are used for this phase. For greater understanding, the aforementioned processes are depicted in Figure 1 in a layered IoT architecture of a healthcare ecosystem. Wearable sensors are used to gather the user's vital signs, as seen in this picture. An infrared link is then used to transmit the data to the smartphone. Finally, the data are communicated from the mobile device to the server across a network, such as an advanced fifth-generation network. By pressing a button, the database server receives information from sensors or the patient's details in an instantaneous way [47]. Other information is gathered from healthcare facilities and stored in the database, such as physician opinions and medical analytics. The intuitive engine of the system then studies the data that have been stored in the database to determine whether any aberrant data have occurred. An alarm is transmitted to the patient or doctor when the system recognizes an abnormal situation, and the patient is admitted to the hospital if the alarm is accepted. As a consequence of recent improvements in healthcare systems and digital communications, it is now feasible to connect to remote healthcare services from any location, providing patients with the greatest options for personalized healthcare services.
# An Enhanced Adaptive Block Truncation Coding with Edge Quantization Scheme +
## 4 Experiment and Comparison  
### Experimental Results  
Five test images, Lena, Butterfly, Cameraman, Lake, and Peppers are used for evaluating all BTC-like schemes: AMBTC, MBTC, ABTC-EQ, and the proposed schemes (Scheme A, Scheme B and Scheme C). To properly deal with all (\(k\times k\)) blocks, where \(k=4\), 6 and 8, we use all test images of the size \(504\times 504\) pixels. The evaluation metrics, PSNR, CR, structural similarity (SSIM) index, and feature similarity (FSIM) index, are used to compare the performance of all these schemes. Table 1 illustrates the comparison of all BTC-like Schemes. For the test image Lena, consider dealing with (\(4\times 4\)) blocks by all schemes. Scheme C adopts quad-clustering, and also uses 24 bits to represent four quantization values by the approach of using difference. Therefore, Scheme C has the best visual quality (PSNR = 39.62 dB) and meanwhile has the same CR = 3.09 as ABTC-EQ. While AMBTC and MBTC have high CR, they have poor PSNR because they only use the bi-clustering approach. The PSNR = 33.87 dB of MBTC is slightly greater than the PSNR = 33.42 dB of AMBTC. This slight enhancement comes from using the more precise threshold value for MBTC. Scheme A uses tri-clustering and same quantization ranges like ABTC-EQ, and thus Scheme A and ABTC-EQ have the same PSNR = 37.49 dB. Because of using a variable-length code to record the index of the cluster, Scheme A has a higher CR = 3.24 than the CR = 3.09 of ABTC-EQ. On the other hand, Scheme B may trade-off PSNR for CR by using different quantization ranges. Scheme B-I has PSNR = 37.47 dB almost the same to PSNR = 37.49 dB of ABTC-EQ, and has the higher CR than Scheme A. If we want to achieve a high CR and meanwhile retain a moderate PSNR, we may choose Scheme B-IV, which has the CR = 3.62 PSNR = 35.96 dB. Moreover, all the values of SSIM and FSIM demonstrate consistency with the performance of PSNR. For simplicity, we only show experimental results for Lena.
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.2 \"Akiyo\" Video Sequence
The graph in Figure 3 illustrates the RD performance of the codecs on the \"Akiyo\" test video sequence at all quantization points. Both DIVCOM and PDIVCOM perform better than DIS by having a better coding efficiency and PSNR at all quantization points. From the RD graph, it is noticed that for lower quantization points, the DIVCOM achieves a coding efficiency of up to 1.31 kbps and PSNR gain of up to 0.017 dB. However, the DIVCOM coding efficiency slightly reduces at higher quantization points but still manages to achieve a PSNR gain of 0.021 dB for these points. PDIVCOM achieves a higher coding efficiency than both DIS and DIVCOM. At low quantization points, the coding efficiency of PDIVCOM can go up to 3.71 kbps, and up to 11 kbps at higher quantization points. The PSNR gain achieved by PDIVCOM is up to 0.05 dB at the lower quantization points up to 0.12 dB at higher points. Compared to Intra H.264, the DIVCOM achieves a higher coding efficiency ranging between 1 kbps and 40 kbps and PSNR gain ranging from 0.06 dB to 0.29 dB for low and intermediate quantization points. However, at high quantization points, the coding efficiency of DIVCOM degrades by 2.8 kbps to 14.4 kbps. The PSNR of DIVCOM is also worse than Intra H.264 by 0.10 dB. The comparison between PDIVCOM and Intra H.264 shows that PDIVCOM achieves a better coding efficiency ranging from 4 kbps to 48 kbps for all quantization points and a PSNR gain ranging from 0.02 dB to 0.32 dB.
## 5 Research Challenges and Future Directions
### Mobility Management  
A critical component of mobility is a reliable and constant connection, which may be achieved using a variety of wireless communication methods [159]. Based on user-centric or network operator needs, utility functions and efficient algorithms should be designed with reduced complexity while mobile call patterns became closely correlated with connectivity patterns in the service area of the same BS at the same time, HetNet optimization should integrate user mobility data to improve performance in the context of large-scale user mobility [160]. An interesting area for further study is the development of mobility-aware adaptive approaches for the efficient optimization of HetNet system parameters, as well as the investigation of acceptable mobility models for various traffics (human, machine). In order to make HetNets more compatible with 6G NR networks, future research may combine NR and HetNet technologies [161]. HetNet might benefit from the introduction of new dual-connectivity processes and innovative vertical handover methods to enhance global network coverage, seamless mobility, and service continuity. To further facilitate changeover and paging operations, solutions for user geolocation are needed to identify the belonging beam, the next-to-switch beam, and the beam belonging time.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion
### Impact of Number of Comments  
Figure 10 illustrates the trend of the ReCOVery model's accuracy and training time as the number of annotated comments increases. It can be observed that the accuracy steadily improves from 0.918 with 10 comments to 0.928 with 100 comments, indicating that additional human feedback contributes positively to model performance. However, the training time also increases significantly, especially beyond 50 comments, where the duration grows from 3.2 to 4.8 h. This suggests a nonlinear relationship between annotation volume and computational cost.  
Figure 11 provides a more comprehensive comparison, showing the performance of both the ReCOVery and MMCoVaR models. MMCoVaR consistently achieves higher accuracy across all annotation levels, with the performance gap widening as more comments are incorporated, demonstrating MMCoVaR's superior ability to leverage annotated information. In terms of resource consumption, both memory usage and training time  
Figure 8: Performance comparison of ReCOVery and MMCoVaR with varying \(\lambda\).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Selection Strategy** & **ReCOVery Accuracy** & **MMCoVaR Accuracy** & **Processing Time** \\ \hline Random Selection & 0.902 & 0.915 & 156 ms \\ Time Order & 0.908 & 0.921 & 143 ms \\ By Length & 0.911 & 0.923 & 167 ms \\ By Engagement & 0.918 & 0.926 & 189 ms \\ importance Score & 0.927 & 0.931 & 203 ms \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comment selection strategy analysis.  
Inference Performance Metrics: Our full UC-CMAF model with 50 comments requires an average inference time of 203 ms per sample. The memory footprint during inference reaches 2.9 GB, primarily allocated for storing comment embeddings and attention matrices across multiple fusion components.  
Baseline Computational Comparison: While UC-CMAF introduces computational overhead compared to simpler baselines (BERT-only: 45 ms; Simple Concatenation: 78 ms), this additional cost is justified by substantial performance improvements. The 2.6\(\times\) increase in inference time corresponds to a 9.1% improvement in F1-score on ReCOVery dataset, demonstrating favorable cost-performance trade-offs.  
Scalability Bottleneck Analysis: Profiling analysis reveals that cross-modal attention computation constitutes the primary computational bottleneck, accounting for approximately 60% of total inference time. The comment-image fusion (UCI) and comment-text fusion (UCT) modules contribute 35% and 25% respectively to the overall computational load. Memory consumption scales linearly with comment volume, with each additional comment requiring approximately 58 MB.  
Deployment Optimization Strategies: For resource-constrained environments, we recommend adaptive comment sampling (reducing to 20 comments) which maintains 99.7% of full model accuracy while achieving 30% speed improvement. Additionally, batch processing techniques can improve throughput by 40% for large-scale deployment scenarios.
## 3 The Proposed Method
### Data Extraction and Image Recovery
#### 3.4.1 Secret Data Extraction  
In the role of the recipient, the initial step involves employing the key \(K_{sl}\) to perform inverse scrambling and XOR decryption on the marked encrypted image \(\mathbf{IM}_{k}\), resulting in the recovery of the image \(\mathbf{IA}_{k}\). Subsequently, the receiver implements analogous procedures to those used during the secret data embedding phase to extract both the edge information \(\mathbf{O}\) and the encrypted data \(\mathbf{m}_{i}^{\prime}\). Finally, decryption of \(\mathbf{m}_{i}^{\prime}\) is carried out with the key \(K_{si}\) to unveil the secret data \(\mathbf{m}_{i}\).
# Damage Imaging in Lamb Wave-Based Inspection of Adhesive Joints
## 3 Results and Discussion
### WRMS Imaging
#### Influence of Excitation Frequency  
An additional aspect of the analysis was the influence of the excitation frequency on the obtained results. Figures 12 and 13 show the WRMS maps for specimens #6 and #7 for the linear and square weighting factors, respectively. The measurement of the joints was repeated three times, with different excitation frequencies, namely 200 kHz, 100 kHz, and 50 kHz. It was observed that the excitation frequency was an important factor that significantly affected the readability of the maps. The efficiency of the WRMS damage imaging increased with the increase in the excitation frequency value.
# Modulated Noncollinear Optical Parametric Amplifier Output Induced by Stimulated Raman Scattering
## 4 Results and Discussion
### Stimulated Raman Processes in Optical Parametric Amplification
#### 4.1.1.1 Structure in the Amplified Signals  
Rapidly varying peaks and valleys appear in the signal spectra (see Figure 2) for certain configurations of precompression and stretching when the conditions of the chirped-mirror system in Figure 1 are varied. The wavenumber differences between adjacent peaks and adjacent valleys together with the peak to adjacent valley values are plotted in Figure 4. They exhibit a nearly random distribution, making it difficult to relate these peaks and/or  
Figure 3: (**a**) Two-dimensional plot of the pump–seed delay time (from \(-300\) fs to \(+200\) fs) dependence of the amplified signal spectral shape measured by a commercial spectrometer (Ocean Optics model USB400). (**b**) The cross-sectional intensities of amplified signal at delays of \(-15\) fs, \(-60\) fs, and \(-80\) fs for blue solid, red dashed, and blue dashed lines, respectively.  
valleys to the difference frequencies of the Raman shift signals in the BBO crystal [36] listed in Table 1.  
There are two possible explanations for such an apparently random distribution. One is that the peak-and-valley structure is introduced by the stimulated Raman scattering with many Raman shift signals for the BBO crystal listed in Table 1 so that all the Raman signals become congested and interference between the Raman signal produces the complex structure of the spectrum. The second possible explanation is the combined contribution of the Raman shift and four-wave mixing, which requires a phase-matching condition to produce Raman signal frequencies, produces substantial offsets from the pure Raman shift frequency.
## 5 Site Experiment Results
### Training and Testing Process  
The input and output data are divided into 80% training data and 20% testing data. At the same time, the training part is randomly split into a training set and validation set with a ratio of 9:1. The loss function adopts the mean square error (MSE) function, and the teacher-forcing method is used to speed up the training of the transformer model. The Adam optimizer is adopted in the training process. The initial learning rate is \(1\times 10^{-4}\), and the learning rate decays strategy adopted reduces to a plateau (patience = 10). The three deep learning models are trained on the same desktop with an Intel Core i7-9700k CPU and an Nvidia RTX3050 GPU with 4 GB of memory. The training software environment is configured with PyTorch framework on the Ubuntu 22.04 system LTS.  
Three deep learning models were adopted in this site experiment to compare the performance of different models for the prediction of tunnel face deformation. Because of the optimum model structures, a grid search method was applied to determine the optimal transformer parameters and model architecture with the MSE on the validation set as the criterion. All three models had the same input dimension of two (input_size) and output dimension of one (output_size). The model structure parameters that were strongly related to the performance of the model were the number of layers (num_layers), the hidden layer feature dimension (hidden_size) of the feed-forward network (FFN), the time steps (max_len) of the input time series, and the data feature dimension (model_dim) after embedding in the transformer.  
Nine different model structures of the transformer model and the corresponding RMSE values of the testing data are shown in Table 2. Based on this grid search approach, the best model structure among the nine cases was determined based on the RMSE value for the testing data. It can be seen that the increased layers, model dimension, hidden size, and length of the input time steps can enhance the model performance. However, as shown in Case 9, too complex a model structure can cause overfitting and less optimal prediction performance. Therefore, the model structure in Case 8 was selected.  
Figure 10: Monitored deformation data of the transverse section (Section 1) in front of the tunnel face: (a) crown settlement; (b) horizontal convergence; (c) tunnel face deformation.  
Using this adapted model structure, the loss curves of the transformer model training and validation are shown in Figure 11. After 20,000 epochs, the transformer model tends to converge, and the loss of the validation set drops to 0.0008. Based on the optimized structure of the transformer model, both LSTM models adopted similar model structures for performance comparison (Table 3).
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 1 Introduction
To fully utilize beam resources, a cooperative MARL-VDN algorithm is applied for joint beam hopping pattern and power allocation decisions, achieving multi-objective optimization of the system and enabling limited on-board resources to dynamically match nonuniform and time-varying traffic demands. * The optimization algorithm for the joint beam hopping pattern and power allocation needs to make decisions sequentially in multiple time slots. However, traditional optimization methods exhibit high computational complexity, resulting in a long convergence time and failing to meet the real-time requirements. Therefore, this paper employs a cooperative MARL-VDN algorithm framework using the centralized training and decentralized execution (CTDE) paradigm to address sequential decision-making problems. After offline training, the model can be deployed on LEO satellites to achieve real-time beam hopping scheduling. * In the applied cooperative MARL-VDN algorithm framework, each agent only manages the hopping pattern and power allocation decision for a single beam, thereby reducing both the individual agent's action space and the overall joint action space across all agents. In addition, agents can continuously optimize their policies through ongoing interaction with the environment to better adapt to changing and complex communication environments. The rest of this paper is structured as follows. Section 2 first introduces the system model then explores interference suppression strategies and dynamic beam optimization problems in LEO beam hopping satellite communication systems. A cooperative MARL-VDN algorithm framework is applied to solve this problem in Section 3. Section 4 providesthe complexity analysis and simulation results, which demonstrate the excellent performance of the MARL-VDN algorithm when it is applied to the LEO beam hopping satellite system. Finally, Section 5 concludes the paper. Notation: The notations and definitions used in this paper are summarized in Table 1.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 2 Theoretical Background
The effectiveness of the GoF test to detect continuous wave (CW) interferers and spoofing attacks was proved in [44; 45; 46], while its validity with jamming has not been fully investigated yet. In this paper the selected algorithms, included the GoF test, has been analyzed to deal with jamming signals and properly modified to target a real-time Android based implementation, which is the main focus of this work. Although calibrated on jamming interferers, it is worth noticing that the proposed detection methods are not limited to that specific interfering signal. Being able to detect any abnormal received power and correlation distortions, their effectiveness with other kinds of interference cannot be excluded. The goal is to quickly and successfully detect the presence of a disturbing signal, while the classification of such disturbance is left for future work.
## 3 Simulation Results
### BLER in Mixture Scenarios of Single-Carrier and Multi-Carrier  
In the mixture scenario of the single-carrier and the multi-carrier modulation, as in Figure 7b, subband 3 and subband 6 are the single-carrier subbands; the remaining subbands are multi-carrier subbands with subcarrier spacings of 480 kHz which employ an oversampling IFFT operation with 16,384 points. CP-OFDM requires alternative processing for the single-carrier and the multi-carrier subbands, followed by time-domain superposition, with each single-carrier subband being sent individually. In contrast, the proposed GRB-OFDM scheme allows any subband to be used as either a single-carrier or a multi-carrier subband without increasing any complexity, enabling all single-carrier and multi-carrier subbands to be processed simultaneously. The specific process is illustrated in Figure 10.  
Figure 11 presents a comparison of the BLER performance for demodulating the data transmitted on subband 3 at the receiver. Due to interference from other subbands, subband 3 experiences some degradation in terms of BLER performance. From the comparison results shown in the figure, it can be observed that GRB-OFDM outperforms CP-OFDM to some extent. This is mainly because GRB-OFDM has a polyphase filter effect at the transmitter, resulting in low out-of-band leakage for each subband and thus smaller inter-subband interference compared to CP-OFDM, leading to a better BLER performance. Therefore, in scenarios involving a mixture of single-carrier and multi-carrier transmissions, GRB-OFDM exhibits slightly better performance than CP-OFDM while maintaining lower processing complexity and greater flexibility.  
Figure 9: The BLER of CP-OFDM and GRB-OFDM is evaluated in different subcarrier spacing scenarios. The rate-\(\frac{3}{4}\) LDPC channel coding, the 64 QAM modulation, and the AWGN channel are employed. Specifically, subbands with subcarrier spacings of 480 kHz and 960 kHz coexist. The result demonstrates that the BLER performance of GRB-OFDM is significantly superior to that of CP-OFDM.