esa-sceva/satcom-chunk-collection huggingface dataset:
# Haze-Aware Attention Network for Single-Image Dehazing
## 2 Related Work
### Attention-Based Image Dehazing  
Attention mechanisms have been playing a crucial role in the field of dehazing. A lot of effective attention mechanisms have been proposed to enhance hazy images. FFA-Net [2] introduced attention mechanisms and achieved impressive results in metrics like the PSNR and SSIM. MSAFF-Net [33] used a channel attention module and a multiscale spatial attention module to focus on areas with features related to fog. [PERSON] et al. [34] proposed the Detail-Enhanced Attention Block (DEAB), which enhances feature learning by combining Detail-Enhanced Convolution and Content-Guided Attention, thereby further improving dehazing performance. [PERSON] et al. [35] proposed a Residual Nonlocal Attention Network that takes into account the uneven distribution of information in corrupted images. They designed both local and nonlocal attention blocks to extract features for high-quality image restoration. [PERSON] et al. [36] introduced the COLA-Net for image restoration, which combines local and nonlocal attention mechanisms to restore areas with complex textures and highly repetitive details. However, they had high complexity and slow processing during the hazing process. These methods overlook physical characteristics. To tackle this, we propose the Haze-Aware Attention Module, thereby considering the physical model in the feature space of low-resolution images. By incorporating physical priors, we obtained effective features with fewer parameters, thus leading to higher PSNR and SSIM values.
### Technical Motivation Based on MAE  
1.1.1. Fundamental Reason: True Time-Domain Compression and Self-Supervised Reconstruction  
* Physical Signal Shortening vs. Traditional Equalization
* Traditional deep learning methods for optical wireless links (e.g., CNN- or DNN-based equalizers) assume full waveform transmission and focus on compensating channel impairments.
* Our goal, however, is to physically \"shorten\" the transmitted DMT waveform (by deleting large fractions of samples), thereby freeing up more channel capacity.
* Because the MAE forces the model to learn how to \"fill in\" missing segments, we can remove a substantial portion (up to 75-85%) of the waveform and still reconstruct it accurately at the receiver.
* This self-supervised \"inpainting\" approach--where the network learns from unmasked patches to predict masked ones--differentiates the MAE from other autoencoders or supervised methods.
* Self-Supervised Learning with Minimal Overhead
* A masked autoencoder does not require specialized pilot sequences or external labels; the only references for missing parts are the original waveforms themselves.
* It is thus naturally suited to handling missing data in a self-supervised manner, minimizing extra signaling overhead often necessary in end-to-end or classical channel-estimation approaches.
* Better Generalization and Reduced Overfitting
* Through random masking, an MAE forces the model to learn global structure rather than memorizing local correlations.
* Our experiments show that increasing the masking ratio can paradoxically decrease errors (EVM) because the model becomes more robust to variations in the DMT waveform.
* Standard autoencoders (without masking) risk local overfitting, as they see the entire waveform in each training pass.  
1.1.2. Technical Justification: Treating DMT as Multivariate Time-Series  
* DMT Waveforms as Patches
* We divide the original DMT waveform into patches, then randomly delete (mask) a fraction of them. The MAE reconstructs these missing patches by contextualizing the unmasked patches.
* This works well because DMT signals are effectively multivariate time-series data, with multiple subcarriers modulated in parallel.
* The MAE's self-attention mechanism can exploit both time-domain continuity and cross-subcarrier correlations more effectively than purely local methods.  
Positional Encoding for Time-Series ReconstructionWe embed each patch with a positional encoding, so the MAE knows where each patch belongs.
* This is crucial for a time-series context: unlike images or simple feed-forward networks, we need to preserve temporal and subcarrier ordering.
* By leveraging the transformer-style attention, the MAE can identify subtle relationships across patches, enabling accurate reconstructions even with high masking ratios.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 3 Parallel Implicit Assembly Method
### Automatic Determination of Overlapping Relation between Component Grids
#### 3.2.1 Overlapping Relation Determination Based on the OBB Technique  
Inspired by the oriented boundary box (OBB) technique proposed by [PERSON] [22], we construct a covariance matrix for the coordinates of grid nodes on solid wall boundaries. Combined with a spectral analysis, a minimal internal box \(B_{N}\) and a minimal external box \(B_{X}\) for the component grids are created to assist the rapid identification of the overlapping relation.  
Figure 3: Comparison of two partition strategies.  
Let the number of nodes \(N_{w}^{i}\) on the solid wall of component \(\gamma^{i}\in\Gamma\) be \(\kappa\). All the node coordinates are stored in the following three \(\kappa\)-dimensional vectors:  
\[\begin{cases}X=(x_{0},x_{1},\cdots,x_{\kappa-1})\\ Y=(y_{0},y_{1},\cdots,y_{\kappa-1})\\ Z=(z_{0},z_{1},\cdots,z_{\kappa-1})\end{cases} \tag{3}\]  
where \((x_{l},y_{l},z_{l})\) are the coordinates of the \(l\)th node. The covariance matrix is  
\[D=\begin{pmatrix}Cov(X,X)&Cov(X,Y)&Cov(X,Z)\\ Cov(Y,X)&Cov(Y,Y)&Cov(Y,Z)\\ Cov(Z,X)&Cov(Z,Y)&Cov(Z,Z)\end{pmatrix} \tag{4}\]  
The normalized eigenvectors of \(D\) can be represented by  
\[n_{s}=(a_{s},b_{s},c_{s})^{T}\quad(s=0,1,2) \tag{5}\]  
Given the direction index \(s\), the parametric equations of spatial planes can be established with \(n_{s}\) as the normal vector  
\[a_{s}x+b_{s}y+c_{s}z+\lambda_{s}=0 \tag{6}\]  
All the nodes in \(N_{w}^{i}\) are traversed to obtain the minimal \(\lambda_{s}^{0}\) and maximal \(\lambda_{s}^{1}\). Two instances of Equation (6) with \(\lambda_{s}^{0}\) and \(\lambda_{s}^{1}\) substituting \(\lambda_{s}\) can determine the borders of the spatial plane specified by the eigenvector \(n_{s}\). Similarly, the three-dimensional equations corresponding to the three normal vectors \((n_{0},n_{1},n_{2})\) describe a minimal interior box \(B_{N}^{i}\) surrounding the component wall surface, as depicted by red lines in Figure 4. Analogously, a minimal external box \(B_{X}^{i}\) as depicted by blue lines in Figure 4 can be constructed by applying the aforementioned procedures to all the nodes \(N^{i}\) instead of solid-wall nodes \(N_{w}^{i}\).  
Furthermore, the overlapping grids are restricted by two additional requirements in the current study: (1) the region covered by the minimal interior box \(B_{N}^{i}\) is included in the entire grid space; (2) a certain distance between the external boundaries and the solid wall boundaries is necessary to ensure the accuracy of overlapping interpolation.  
Figure 4: Overlapping relationship of the propeller blade mesh.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 5 Performance and Evaluation
### CoFi in a Dynamic Topology Setting  
To further validate the design of CoFi, we tested it in a simulated dynamic topology with one AP and five mobile clients. Each client moved according to the random waypoint model with speed 2 m/s and sojourn time uniformly distributed within 10 and 30 s. The terrain was a 50 m-radius circular region, and the average reception probability at the edge of the region was 0.5. The maximum data rate of each link was set to 2 Mb/s, i.e., the basic data rate of 802.11.  
Figures 14 and 15 plot the total network throughput and per-packet delay performance, respectively. Owing to its batch transmission, CoFi with or without MAC caching mechanisms improved the throughput of 802.11 by a significant margin, and the amplitude elevated was slightly superior to the value in the elementary topology. Furthermore, with the use of MAC caching scheme, the throughput was opportunistically improved, according to whether a relay was available nearby. It can be expected that in a dense network with many clients, CoFi will demonstrate even higher throughput gains due to more available relays. Another observation was that the per-packet delay of CoFi could be lower than the 802.11 protocol (Figure 15). This is because when more mobile clients are subscribed to the same AP, the probability that one of them experiences low channel quality becomes higher. Clients with low channel quality could encumber the ones with high channel quality. This will lead to transmission time increasment in the 802.11 protocol. In addition, comparing with the elementary topology, we also observed that the time of per-packet delay increased and the volatility of value is quite large. This indicated that the dynamic topology needs higher level protocol and it is much more complex to be implemented. The results show that the volatility in running CoFi was much smaller than that in the 802.11 protocol, which means the present method is more suitable for application in a practical scenario. Therefore, the case where the low-quality link brings down the performance of the entire network becomes the common case.  
Figure 14: The total throughput of the network running 802.11 protocol and CoFi protocol with/without MAC caching in a dynamic topology.
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 4 Simulations and Remarks
### Reliability of OFDM/FHMA System  
Based on Equations (7)-(10), derived in Section 3.1, the reliability in terms of the BER of the OFDM/FHMA system can be plotted as in Figure 5, where the number of FH frequency slots \(N_{h}=30\), the number of nodes \(N_{u}=4\), and \(SJ=0\) dB. As shown in this figure, for the traditional OFDM/FHMA system, the nodes operating on the bands with good qualityattain the best case for the BER (the lowest BER), while the nodes operating on the contaminated/jammed channels have the worst-case BER. Thus, the traditional OFDM/FDMA system provides an unbalanced reliability for various nodes, which implies that such a system is not suitable to deploy in power systems. For the proposed OFDM/FHMA, it is found in the figure that all nodes have a similar BER behavior, for which the curve lies between the worst- and best-case curves. The reason for this phenomenon is that FH technique can randomize the effect of deeply contaminated channels on the system reliability, so that all nodes can approach the balanced BER performance.  
The effects of system capacity \((N_{u})\) and the number of FH frequency slots \((N_{h})\) on the reliability of the proposed OFDM/FHMA are presented in Figure 6. Other parameters were set the same as those in Figure 5. From these two figures, we find that, given a value of \(N_{h}\), the BER of OFDM/FHMA gets worse as \(N_{u}\) increases; in addition, for a given \(N_{u}\), the system with a larger \(N_{h}\) attains a lower BER. This phenomenon can be explained by the collision properties of the random hopping strategy, as shown in Equations (8)-(10), which determines the amount of multi-node interference. From Figure 6, it also can be seen that, when the SNR obtains a large value (i.e., \(SNR>16\) dB), the number of nodes is small (i.e., \(N_{u}<10\)), and with more frequency slots (i.e., \(N_{h}>20\)), the proposed OFDM/FHMA can offer enough reliable transmission for most electric power services; however, for other cases, the OFDM/FHMA with a single transmission mechanism cannot meet the reliability target.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Services** & **Reliability** & **Latency** \\ \hline Distributed generation & \textgreater{}99.9\% & \textless{}2 s \\ \hline Charging pile & \textgreater{}99.9\% & \textless{}5 s \\ \hline Load control service & \textgreater{}99.99\% & \textless{}20 ms \\ \hline Fault diagnostics and removal & \textgreater{}99.999\% & \textless{}20 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: The reliability and latency requirements of typical electric power services [19].  
Figure 5: The reliability comparisons between the proposed system and the traditional OFDM/FDMA.
## 3 Iterative Data Collection through Phonetic Decoding and Alignment
Recurrence (2) resembles that of the well-known _quicksort_ algorithm, which, despite  
Figure 1: An audio chunk \(U\) with 8 slices: the optimal segment \(s^{*}\) is chosen (the longest one among those with the highest PRR); the procedure continues recursively on the left and right chunks, \(U_{I}\) and \(U_{r}\), until the number of slices is \(n\leq 1\). being \(O(n^{2})\) in the worst case, has an average cost of \(O(n\log n)\). Finally, if \(K\) audio files are to be processed, the time complexity of the segment extraction procedure will be in \(O(\sum_{k=1}^{K}n_{k}\log n_{k})\), \(n_{k}\) being the number of slices in the \(k\)-th audio file. Once the list of segments \(S\) is obtained for the whole set of BP sessions in the training set, a new set of acoustic models can be trained by using only those segments for which the provided transcription best matches the speech contents, either by requiring PRR to be higher than a given threshold or by using the top ranking segments amounting to a given number of hours (e.g., 998 h). The resulting models can be then applied again to perform phone recognition, obtain new alignments and hopefully a better set of segments for training. Remind that we aim to collect those segments that best match acoustically the provided transcripts. However, after each iteration the models will better adjust to the provided (possibly wrong) transcripts, so that after many iterations we may eventually achieve a PRR of 100% for all segments, with no way to distinguish _truly good_ transcripts from _bad_ transcripts to which our models have adapted to. This will prevent us from running too many iterations and will force us to carefully set the threshold that separates _good_ from _bad_ segments after each iteration. We will come back to this issue in Section 5.
## 3 Results
### Ablation Experiments of BMP_Net
#### 3.4.3 Ablation Experiments on the Number of Point Cloud Sampling Points  
To determine the optimal number of sampling points for the proposed BMP_Net model, ablation experiments on the number of point cloud sampling points were conducted. The results of these ablation experiments are presented in Table 6. In the original PCD files, the number of points in the point clouds varies significantly and is often excessively large, making it impractical to directly input all data points into a point cloud network for training. Therefore, the original point cloud data must undergo a sampling process, with the sampled point cloud data serving as the input to the point cloud network. This process is analogous to resizing image data in convolutional neural networks. The FPS (Farthest Point Sampling) algorithm was employed as the sampling method, with the number of sampling points set to powers of 2. According to the experimental results, the performance of the three evaluation metrics reaches its optimum when the number of sampling points is 4096. However, when the number of sampling points is either too small (e.g., num_points = 1024) or too large (e.g., num_points = 16,384), the experimental results exhibit relatively mediocre performance. An insufficient number of sampling points leads to information loss, which prevents the full representation of critical features in the point cloud--such as wheat fruit density information highly correlated with biomass--thereby limiting the model's ability to learn the relationship between point cloud data and biomass from limited information. Conversely, an excessively large number of sampling points increases the computational burden, resulting in prolonged training times and reduced model inference efficiency. Additionally, excessive point cloud data introduces more redundant information and noise, which may cause the model to overfit the training data, consequently degrading its performance. These ablation studies demonstrate the process of parameter selection and provide insights into model sensitivity, thereby contributing to the justification of our final model configuration.
# Experimental Investigation of Anisotropic Diffusion Applied in Ghost Imaging Reconstruction
## 4 Conclusions  
In this paper, we propose a method combining anisotropic diffusion with IPGI, which can smooth the noise in the images used to construct noise interference terms during iterations. To verify the effectiveness of our method, the imaging objects \"Zhong\" and \"Slits\" were selected to make comparative experiments between AD-IPGI and IPGI. Experiments showed that our method was superior to IPGI both in visual effect and PSNR. In the third group of experiments, we utilized the median filter, mean filter and Gaussian filter combined with IPGI, respectively, to smooth the images. The experimental results show that the denoising effect and edge detail protection capability of AD-IPGI are better than those of the Median-IPGI, Mean-IPGI and Gaussian-IPGI. Based on the above advantages, we believe that this method has a promising future to be helpful in the application of ghost imaging.  
Methodology, X.G.; project administration, M.T.; validation, G.S.; formal analysis, B.L.; investigation, J.S.; resources, W.G.; data curation, S.Y.; writing--original draft preparation, X.G.; writing--review and editing, J.G.; supervision, F.G.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China, grant number 2016 YFE0200700, the National Natural Science Foundation of China (NSFC), grant numbers 61627820 and 61934003, the Jilin University Science and Technology Cooperation Special Project of Science and Technology Innovation Development Plan of Jilin City, grant number 201830797 and the Youth Innovation Promotion Association of the Chinese Academy of sciences.  
We are grateful to the anonymous reviewers for constructive comments that improved the quality of the work.  
The authors declare no conflict of interest.
## 3 Results
### Effect of EC on In Vivo Bone Formation  
The bone density of the OVX (0.143 \(\pm\) 0.006 g/cm\({}^{3}\)), OR (0.147 \(\pm\) 0.005 g/cm\({}^{3}\)), and OR + fluoxetine (0.141 \(\pm\) 0.003 g/cm\({}^{3}\)) groups was decreased compared with the vehicle group (0.239 \(\pm\) 0.013 g/cm\({}^{3}\)), and a significant increase was noted in the OR + E2 (0.177 \(\pm\) 0.014 g/cm\({}^{3}\)), OR + EC150 (0.157 \(\pm\) 0.012 g/cm\({}^{3}\)), and OR + EC300 (0.185 \(\pm\) 0.020 g/cm\({}^{3}\)) groups. A notable difference was observed in the high-concentration treatment group compared with the low-concentration EC-extract treatment group. This confirmed that the bone density improved depending on the concentration. BV/TV represents the proportion of trabecular bone in whole femur tissue; its observed trend was similar to that of BMD. The tendencies of Tb.Th and Tb.N were also observed as being similar to bone density; blood concentrations of CTX and osteecalcin, which are related to bone resorption, were significantly increased by ovariectomy. However, a decrease in the expression of CTX and osteecalcin was found in the E2-treated group, and a concentration-dependent significant reduction effect was established in the EC extract treatment group (Figure 3).  
Figure 2: The chemical structures and effects of EC extract and its components ((**A**) phloroglucinol, (**B**) eckol, (**C**) dieckol, (**D**) 6,6′-biekcol, and (**E**) 8,8′-biekcol) on the ALP activity in MC3T3-E1 cells. EC extract, phloroglucinol, eckol, dieckol, 6,6′-biekcol, and 8,8′-biekcol were measured using a microplate reader at a wavelength of 405 nm. \({}^{\text{a}}\)\(p\)\(<\) 0.05 vs. the control group.
## 4 Harmonic Suppression Method
### Hscm  
The HSCM takes harmonics as the control object. The harmonic amplitude of the AC bus current is controlled to be 0, so as to eliminate the influence of harmonics.  
The method for extracting harmonic components from input current is to use a 50 Hz notch filter to eliminate the fundamental component, and the rest is the harmonic component. The expression of the notch filter is shown in (17).  
Figure 11: The Bode diagrams of the QPR controller for three different values.  
\[G_{\rm n}(s)=\frac{s^{2}+\left(\omega_{\rm 0n}\right)^{2}}{s^{2}+\left(\frac{ \omega_{\rm 0n}}{\zeta_{\rm n}}\right)s+\left(\omega_{\rm 0n}\right)^{2}} \tag{17}\]  
where \(Q_{\rm n}\) is the quality factor.  
When \(\omega_{\rm 0n}=100\pi\), the fundamental component of the current will be filtered out and the harmonic component will be retained. The harmonic suppression control loop is shown in Figure 12.  
Where \(i_{\rm sh}\) is the AC bus current harmonics. \(i_{\rm shr}\) is the reference value of \(i_{\rm sh}\), \(u_{\rm ph}\) is the voltage harmonic of the inductor, and \(G_{\rm h}\)(s) is the controller of the harmonic suppression. When \(i_{\rm shr}=0\), the controller will reduce the harmonic amplitude to 0 by adjusting the duty cycle.  
The harmonic suppression loop is added to the inner current loop [29; 30]. The proposed harmonic suppression method is presented in Figure 13.  
In Figure 13, \(i_{\rm sr}\) is the reference value of \(i_{\rm s}\). The current loop should have a high gain for 50 Hz current. The output of \(G_{\rm f}\)(s) is  
\[u_{\rm p}=u_{\rm pb}-u_{\rm ph} \tag{18}\]  
where \(u_{\rm p}\) is the voltage of the inductor, and \(u_{\rm pb}\) is the fundamental component of \(u_{\rm p}\). When the output of \(G_{\rm h}\)(s) is -\(u_{\rm ph}\), the harmonic component of \(u_{\rm p}\) will be offset. Thus, HSCM is more suitable for application in HPR connected to the RSHPES with a rich frequency spectrum. It can effectively suppress the impact of integer multiple harmonics, interharmonics, and ultraharmonics. \(G_{\rm h}\)(s) and \(G_{\rm f}\)(s) can use exactly the same controller, and a PI controller will suffice.  
To highlight the superiority and reliability of the HSCM, the methods mentioned above are listed in Table 1 for comparison, with performance indicators including (a) low-frequency filtering performance, (b) high-frequency filtering performance, (c) stability, and (d) whether they can be applied to the HPR in the RSHPES.  
Figure 12: The harmonic suppression control loop.  
Figure 13: The control block diagram of the harmonic suppression method.
## 4 Analysis  
In this section, the CRLB for position and velocity estimation will be provided, and the performance of the proposed MLE will be investigated. The covariance analysis will be limited to the first order, disregarding second- and higher-order noise terms in the estimate, which is valid only within a small error region [8; 18]. The bias evaluation will consider up to second-order noise terms [6; 52].