esa-sceva/satcom-chunk-collection huggingface dataset:
# Singing Transcription from Polyphonic Music Using Melody Contour Filtering
## 2 Melody Extraction
### Post-Processing  
In the previous section, we determined the contours of each region and ensured the uniqueness of the contours through non-overlapping filtering. However, the search for the region can only determine the local optimal solution; regarding the whole, a jump in the abnormal contour cannot be avoided. Based on the human auditory effect, the notes are distributed in a fluctuating way, so we maintain relative stability at the same level. For a few anomalous contours, we introduce the concept of an abnormal contour, assuming that this leads to an anomaly and deviates from the correct path (shown in Figure 6). On this basis, we propose a post-processing method to correct abnormal contours:  
(1) Calculate the average pitch \(\overline{P}\) of all current contours;  
(2) For the mean pitch \(\overline{P}_{l}\) of each contour, see Equation (11);  
Figure 4: Salience graphs after average energy contour filtering. Red lines are the melody contours: (**a**) ADC04.daisy01.wav, (**b**) ADC04.daisy02.wav.  
\[P_{l}=\left\{\begin{array}{ccc}P_{l}-19,&if\ \overline{P}_{l}-\overline{P}> \alpha 1\\ P_{l}-12,&elif\ \overline{P}_{l}-\overline{P}>\alpha 2\\ P_{l}+12,&elif\ \overline{P}_{l}-\overline{P}<\alpha 3\\ P_{l},&else\end{array}\right. \tag{11}\]  
According to the conversion characteristics of pitch, the double octaves and pitches are \(\pm 12\) and the triple octaves and pitches are \(\pm 19\). At the same time, this corresponds to the previous classification of the pitch as 60 semitones. Only the frequency doubling and the frequency tripling misjudgment interference are considered here, where \((\alpha 1,\alpha 2,\alpha 3)\) are (17,10,13), respectively.  
Correcting an abnormal contour is an important step that avoids the contour jump and greatly enhances the robustness and accuracy of the program.  
For polyphonic music with strong background sound, a Fourier transform will cause extremely high octave interference. The proposed post-processing method has a great effect on continuous abnormal contours, and it improved the overall result for about 5% of them.  
Therefore, due to the existence of edge branches, the edge searching algorithm can only classify branches in the same contour, and the energy mean calculation includes the peak points in each branch. A fundamental pitch \(p_{0}\) is generated by applying the strongest energy principle from each frame if additional branches exist, and then reprocessing \(p_{0}\) is undertaken by the application of median filtering. The use of median filtering has two main advantages: the first is the reduction in the problem of pitch fluctuation caused by the selection of peak points in the branches, and the second is the filling of the breakpoints caused by the sliding window algorithm.
## 3 Rationale of Deep Learning Implementation
### RNN with Long Short-Term Memory (LSTM)  
Long-term training of RNNs using backpropagation algorithms often encounters difficulties due to the issue of vanishing gradient descent. Apart from traditional RNN cells, the LSTM architecture consists of a special sharing parameter vector \(h^{t}\) that is deployed to retain memorized information using a backpropagation through time algorithm. Thus, it can addresses the challenge of vanishing gradient descent by incorporating internal self-loops, enabling the network to maintain memory over a longer term and effectively store information (Figure 2).  
Let \(X_{t}=[x_{t}^{1},x_{t}^{2},\ldots,x_{t}^{N}]\) be the input data, \(h_{t}=[h_{t}^{1},h_{t}^{2},\ldots,h_{t}^{K}]\) be the hidden units, and \(C_{t}=[c_{t}^{1},c_{t}^{2},\ldots,c_{t}^{K}]\) be the memory state of the LSTM network at time \(t\) (Figure 2). For each timestamp \(t\), a forget gate \(f_{t}\), input gate \(i_{t}\), and output gate \(O_{t}\) are involved for the following operations [49]:  
1. The forget gate \(f_{t}=\sigma(W_{f}X_{t}+U_{f}h_{t-1}+b_{f}\) is controlled based on the input \(x_{t}\) and the previous hidden state \(h_{t-1}\) that decides which of the previous information is to be discarded.
2. The input gate \(i_{t}=\sigma(W_{i}X_{t}+U_{i}h_{t-1}+b_{i})\) is the degree to which the new content added to the memory cell is modulated, i.e., selectively read into the information that is controlled based on the input. The weight of the input gate is independent from that of the forget gate.  
The output \(O_{t}=\sigma(W_{o}X_{t}+U_{o}h_{t-1}+b_{o})\) modulates the amount of memory content.  
Finally, the information of the current LSTM cell \(h_{t}\) is calculated as \(h_{t}=O_{t}\times tanh(C_{t})\) to pass to the next LSTM cell, where \(C_{t}=f_{t}\times C_{t-1}+i_{t}\times\hat{C}_{t}\) and \(\hat{C}_{t}=tanh(W_{c}X_{t}+U_{c}h_{t-1}+b_{c})\), by updating the previous information \(h_{t-1}\), where \(W_{f},W_{i},W_{C},W_{o}\) are the weight matrices corresponding to the input \(X_{t}\), \(U_{f},U_{i},U_{C},U_{o}\) are the recurrent weight matrices associated with the previous hidden state \(h_{t-1}\), and \(b_{f},b_{i},b_{C}\), and \(b_{o}\) are the bias vectors for the forget gate, input gate, candidate solution, and output gate, respectively.  
The RNN with LSTM architecture excels in capturing temporal features, making them well-suited for time series forecasting tasks. However, tuning the hyperparameters of LSTM networks can be a challenging task, involving considerations such as the number of hidden layers, nodes per layer, batch size, number of epochs, learning rate, and optimization of connection weights and biases [49]. Interestingly, during our experiments with the Thai dataset we found that training LSTM models was not overly complex. As a result, we have proposed an RNN cell that effectively manages the computation of input information and memory, leading to favorable convergence during the training process and producing excellent results.
# Development of Energy Storage Systems for High Penetration of Renewable Energy Grids
## 2 Battery Energy Storage System (BESS) Frequency Regulation Technology
### Static Regulation  
Static regulation control is a unidirectional frequency compensation strategy. If the battery energy storage system detects a grid frequency of less than 59.88 Hz, it should respond to the frequency drop within a few seconds. It actively adjusts the output power of the battery energy storage system to 100% within 10 s to instantly compensate for active power and maintain grid frequency stability [21]. When the grid frequency, then, increases to 59.98 Hz, the output power of the battery energy storage system should be reduced to 0%. As shown in Figure 1, the output power of the frequency operating curve for static regulation control is shown. The curve from point C to point A is the open curve, and the curve from point B to point D is the closed curve.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 4 Experimental Results  
### Model and Implementation Details  
This section presents an evaluation of the proposed MLKD-Net, a lightweight U-Net-based architecture developed for single-image dehazing. Model efficiency was assessed through comparative analysis of its performance metrics and parameter count. The Large Kernel (LK) block was constructed by combining a \(5\times 5\) depth-wise convolution and a \(7\times 7\) dilated depth-wise convolution with a dilation rate of 3, as recommended in the optimal configuration presented in [12]. This design approximates the effective receptive field of a \(21\times 21\) kernel. A dilation rate of 3 was selected based on the receptive field approximation shown in Equations (5) and (6), which achieves the desired kernel size efficiently. Although a larger dilation rate such as 7 could also approximate the same receptive field, it would require excessively large convolution kernels, which are inefficient and unsuitable for lightweight network design. Therefore, a dilation rate of 3 was adopted as a practical and effective choice.  
Two model variants were evaluated to examine scalability: MLKD-Net-Tiny and MLKD-Net-Small. As summarized in Table 1, the Tiny variant adopts a block configuration of [1, 1, 2, 1, 1] across five levels (\(L_{1}\) to \(L_{5}\)), totaling six MLKD blocks. These levels correspond to the sequential stages in the encoder-decoder structure, with \(L_{1}\) and \(L_{2}\) in the encoder, \(L_{3}\) at the bottleneck, and \(L_{4}\) and \(L_{5}\) in the decoder. The Small variant uses a deeper configuration of [2, 2, 4, 2, 2], totaling twelve blocks. Both models share identical embedding dimensions: [24, 48, 96, 48, 24]. Both variants use two attention heads per MLKD block, which was empirically determined to be the optimal configuration based on an ablation study with one, two, and four heads. Among the tested configurations, using two heads yielded the best dehazing performance. A detailed analysis is provided in Section 5.1.  
All models were optimized using the AdamW optimizer [29], with exponential decay rates of \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). An initial learning rate of 0.0002 was used and subsequently adjusted via a cosine annealing schedule. Training was conducted with a batch size of 16, and \(256\times 256\) input patches were generated through random cropping. Prior to training, all images were linearly scaled from the \([0,1]\) range to \([-1,1]\). During training, random horizontal flipping was applied as an additional augmentation technique. The model was supervised using a pixel-wise L1 loss, which encourages accurate restoration by minimizing the absolute difference between predicted and ground truth images. Following the evaluation protocol proposed in [7, 16], MLKD-Net was trained separately on the ITS and OTS subsets for 300 and 30 epochs, respectively. The final evaluation was conducted on the SOTS test set. All experiments were performed using PyTorch 1.10.1 on NVIDIA A100 GPUs (Algorithm 1).
## 3 Results
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Image** & **Design 1** & **Design 2** & **Design 3** \\ \hline \multirow{6}{*}{\(e\)} & Image 1 & 0.88 & 2.13 & **2.03** \\  & Image 2 & 1.45 & 2.15 & **3.47** \\  & Image 3 & 0.82 & 1.54 & **1.31** \\  & Image 4 & 0.72 & 2.09 & **1.82** \\  & Image 5 & 0.44 & 0.91 & **0.64** \\  & Image 6 & 0.63 & 0.45 & **0.52** \\  & & 0.82 & 1.55 & **1.63** \\ \hline \multirow{6}{*}{\(\overline{r}\)} & Image 1 & 2.91 & 4.08 & **4.45** \\  & Image 2 & 3.38 & 4.40 & **5.34** \\  & Image 3 & 2.56 & 3.63 & **3.82** \\  & Image 4 & 2.60 & 3.88 & **4.03** \\  & Image 5 & 2.02 & 2.40 & **2.28** \\  & Image 6 & 1.83 & 1.85 & **1.79** \\ \cline{1-1}  & Average & & 2.55 & 3.37 & **3.62** \\ \hline \multirow{6}{*}{\(NIQE\)} & Image 1 & 2.95 & 2.81 & **3.17** \\  & Image 2 & 3.09 & 2.71 & **2.86** \\ \cline{1-1}  & Image 3 & 3.00 & 2.72 & **3.03** \\ \cline{1-1}  & Image 4 & 2.86 & 2.61 & **2.95** \\ \cline{1-1}  & Image 5 & 2.80 & 2.49 & **2.54** \\ \cline{1-1}  & Image 6 & 3.33 & 2.94 & **3.11** \\ \cline{1-1}  & Average & & 3.01 & 2.71 & **2.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results for modules. Figure 8: Comparison chart of ablation experiment effect. Design 1 means that only sub-pixel convolution is added, Design 2 means that only instance normalization is added, and Design 3 is our proposed model, which adds sub-pixel convolution and instance normalization. (**(1–6)** in the picture represent Image 1 to Image 6). still smaller than those of design 3. Introducing the model with sub-pixel convolution and instance normalization achieves more thorough fog removal, and effectively improves the visible edge count ratio \(e\) and the mean visible edge gradient \(\overline{r}\), except that the dehazed result of Image 4 has little color distortion.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Performance Metrics
#### 5.3.2 Ssim  
Structural Similarity Index Measure (SSIM) is a perceptual metric that aims to approximate human visual perception when comparing the quality of digital images. SSIM is widely used in the field of image processing and computer vision [42; 43; 44]. It is a more comprehensive and perceptually consistent image quality assessment method. The SSIM value ranges between 0 and 1, where a value closer to 1 indicates greater similarity between the two images being compared. The calculation formula is  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_{ y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})}, \tag{16}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the average values of \(x\) and \(y\), \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of \(x\) and \(y\), and \(\sigma_{xy}\) is the covariance of \(x\) and \(y\). \(c_{1}\) and \(c_{2}\) are constants used to maintain stability, usually set to \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\), where \(L\) is the dynamic range of pixel values, and \(k_{1}\) and \(k_{2}\) are default values that usually take \(k_{1}=0.01\) and \(k_{2}=0.03\).
## 2 Dispersion Curve Calculation
### Velocity Dispersion Curves
We find that the ground work for the \(y-\)component has already been completed. The first zero is appended to the array \(\mathbf{k}_{x}\) to compute \(k_{y}\) corresponding to \(90^{\circ}\). It is important to understand that \(k_{x}\) is zero at this angle. Then, a for loop is executed, which iterates through each element of \(\mathbf{k}_{x}\). This time, the reduction is performed such that \(\mathbf{q}_{red}=\{\mathbf{q}_{\mathbf{u}}^{\mathrm{T}}\,\mathbf{q}_{\mathbf{u }}^{\mathrm{T}}\,\mathbf{q}_{\mathbf{v}}^{\mathrm{T}}\}^{\mathrm{T}}\). The entries of the transformation matrix are modified based on the new \(\mathbf{q}_{red}\) and now depend on the propagation constant \(\lambda_{x}\), as all the degrees of freedom are being projected on the nodes lying on the \(y-\)axis of the waveguide. The wave mode shape \(\boldsymbol{\phi}_{y}\) is the main unknown quantity that needs to be computed. After that, the calculation of \(c_{g,y}(\theta)\) follows straightforwardly from Equations (6)-(9). Thereafter, the group and phase velocity profiles in the first quadrant are given by:  
\[c_{g}(\theta)=\sqrt{(c_{g,x}(\theta))^{2}+(c_{g,y}(\theta))^{2}},\quad c_{p}( \theta)=\frac{\omega}{k(\theta)}\quad\text{where }k(\theta)=\sqrt{(k_{x}(\theta))^{2}+(k_{y}( \theta))^{2}}. \tag{10}\]  
The next step is to obtain the velocity profile in the quadrant of \(270^{\circ}\) to \(360^{\circ}\). The same algorithm is used to calculate the group velocity profile using the prior knowledge that \(k_{y}\leq 0\) in this quadrant. Hence, the step size \(\Delta k_{y}\) becomes negative. The rest of the steps for calculating the velocity profile remain the same as for the first quadrant. This essentially leads to the complete \(360^{\circ}\) profile by realising that the remaining two quadrants are mirror images (i.e., the profile in quadrant \(180^{\circ}\) to \(270^{\circ}\) is a mirror image of the profile in quadrant \(0^{\circ}\) to \(90^{\circ}\) and the profile in quadrant \(90^{\circ}\) to \(180^{\circ}\) is a mirror image of the profile in quadrant \(270^{\circ}\) to \(360^{\circ}\)).
## 4 Simulation Results
### BER Comparison with Other Detectors  
The numerical results of the following existing detectors are also given for comparison: (1) minimum mean squared error (MMSE) detection [13], (2) sorted QR decomposition (SQRD) detection [15], (3) basic GA (BGA) based detection [19], (4) basic GA (BGA) with SQRD detection [20], and (5) layered tabu search (LTS) algorithm [27]. The ML detection results were only performed in 64-QAM, and are not given for 256-QAM and 1024-QAM because of the prohibitively high complexity of carrying out a simulation in such large dimensions. All parameters for the proposed HGA and the basic GA are summarized in Table 1 and Table 2, respectively.  
Figure 7: Performance comparison between the proposed HGA without and with layering, and the typical GA without layering for a \(4\times 4\) MIMO system with 64-QAM, 256-QAM, and 1024-QAM. (**a**) BER performance. (**b**) Computational complexity.  
Figure 8 shows the BER performance of the various detectors. As mentioned before, it can be seen that the linear receiver, SQRD, BGA and the simple combination of the SQRD and BGA have poor BER performance in the higher order QAM systems. The proposed layered-HGA-based MIMO detection approach shows the best BER performance. The BGA-based MIMO detectors show poor BER performance due to their characteristics of searching large areas and difficulty in carrying out intensive searches of specific areas. The LTS shows significantly improved BER performance compared to GA-based detectors through its application of intensive searches in specific regions; however, it can be confirmed that the BER performance improvement gradually decreases with increasing SNR. In the high SNR region, the LTS focuses on a more intensive search of a specific region, making it difficult to consider the search of different regions. The LHGA shows improved BER performance compared to LTS by effectively utilizing the characteristics of the broad search in the GA and the local search in the TS. In particular, it can be seen that the performance improvement of LHGA increases compared to LTS as the modulation order increases.  
Figure 8: _Cont_.
## 6 Results and Discussion
### RNN-LSTM Performance: Scenario1  
For _Scenario1_, the \(RNN\)-\(LSTM\) model outperformed the \(RNN\)-\(GRU\) model while underperforming than \(FNN\) model. Through our \(RNN\)-\(LSTM\) experimental setup we achieved a minimum validation loss of 195.31 MWatt, which is a lower MAE than against 200.75 MWatt and higher than 170.41 MWatt. This outcome was obtained by selecting a single-layered network with 32 nodes, a lookback period of 5 days, and training the model for 69 epochs.  
By utilizing the optimized parameters mentioned earlier with a dataset size of 44,256 and a test size of 11,520, we conducted predictions for the electricity demand in 2013. The resulting Mean Absolute Percentage Error (MAPE) was found to be 2.58% for _scenario1_. It should be noted that this data setup specifically excluded holidays and weekends, focusing solely on weekdays. The visualization of the prediction results is presented in the accompanying Figure 16, where the actual demand is denoted as _load_ and the forecasting results are denoted as _load_pred_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**(a)**} \\ \hline
**Parameters** & \multicolumn{2}{c}{**Value**} \\ \hline Number of nodes & & [2, 4, 8, 16, 32, 64, 128] \\ Number of hidden layers & & [1 to 5] \\ Look back period & & [5 days to 10 days] \\ Dropout & & [0, 0.05, 0.1, 0.15] \\ Epochs & & [up to 1 million] \\ \hline \multicolumn{4}{c}{**(b)**} \\ \hline
**Parameters** & **FNN** & **LSTM** & **GRU** \\ \hline Time period & 48 & 48 & 48 \\ Delay & 20 & 20 & 20 \\ Pred batch size & 48 & 48 & 48 \\ Number of hidden layers & 3 & 2 & 2 \\ Dropout & 0 & 0 & 0 \\ Number of nodes & 64 & 64 & 64 \\ Epochs & 180 & 56 & 99 \\ Look back period & 8 days & 8 days & 8 days \\ Train\_fraction & 1 & 1 & 1 \\ Validation loss (MAE) & 237.82 & 242.12 & 234.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental hyperparameter testing setup for _Scenario2_: **(a)** hyperparameter variations and **(b)** tuned parameters for _Scenario2_.  
Figure 16: Weekday forecast using RNN-LSTM.  
Figure 15: Weekday forecast using FNN.
# Seismic Analysis of Steel Solid Web Girder-RC Tubular Column Hybrid Structure
## 3 Numerical Approaches and Validation
### Validation of the Modelling Approaches  
In the past, the authors conducted pseudo-dynamic tests (PDTs) on a 1/8 scaled substructure to investigate the seismic behavior of steel braced truss-RC tubular column hybrid structure [14]. The scaled El Centro (NS) records with PGA of 0.05, 0.10, 0.20, 0.30, 0.40, 0.60, and 0.80 g were used to simulate different hazard levels. The case of the test is very suitable to the objectives of this study, since the test specimen was also a steel-concrete hybrid supporting structure. Accordingly, the experimental results were adopted to validate the accuracy of the numerical simulation approaches as introduced above. Figure 4a shows the test specimen. The FE model of the specimen was established based on the proposed simulation approaches, as shown in Figure 4b. Figure 5 shows the comparison of time-history curves for lateral displacements and maximum lateral displacements versus height curves between the FE simulation and test results. It can be observed that the numerical and test results exhibited a good agreement, indicating that the proposed FE modelling approaches could reasonably simulate the seismic behavior of this kind of steel-concrete hybrid structure.  
Figure 3: The damage evolution rules of stress-strain laws in the CDP (concrete damaged plasticity) model.  
Figure 4: Test specimen and numerical model: (**a**) Test specimen; (**b**) numerical model.  
Figure 5: Comparison of numerical and test lateral displacements: (**a**) Top of A-shaped frame under 0.20 g; (**b**) top of A-shaped frame under 0.40 g; (**c**) top of A-shaped frame under 0.60 g; (**d**) maximum lateral displacements.
## 5 Mechanical Properties
### Texture Strengthening  
As a preferred grain orientation of most grains, crystallographic texture can significantly affect the mechanical properties of Mg alloys. The texture features can be influenced by alloying elements, initial grain orientation and deformation process. A (0001) basal fiber texture can be formed through hot rolling, extrusion or compression due to the imposed plane strain, and a shear texture can be observed after ECAP and HPT due to the deformation shear stress. Moreover, the addition of RE elements can change the texture components and further form the RE texture. [PERSON] et al. [98] investigated that a large twist angle between (110) <001> Goss-oriented grain and adjacent grains with other orientations exists and makes fatigue crack deflect during the propagation process. [PERSON] et al. [99] reported that the mechanical properties of Mg-4.7 Gd-3.4Y-1.2 Zn were related to the initial textures. Thus, adjusting the texture is an effective strategy to prepare Mg alloys with high comprehensive mechanical properties.  
Figure 9: TEM images of precipitates of aged Mg-1.4 Gd-1.2Y-0.4 Zn alloys shown in (**a**–**c**), and mechanical properties under different states shown in (**d**) [94].  
The effect of texture on the YS of HCP alloys can be obtained from the changing Hall-Petch constants. The contribution of texture on strength can be expressed as the following equation [100].  
\[\epsilon_{0}=m\tau_{0} \tag{5}\]  
where \(m\) is the value of the Taylor orientation factor, which is related to the basal texture. When the grain orientations are unfavorable for the activation of basal slip systems, the \(m\) will increase with material strength. The \(m\) can be taken as 6.5 when the materials have a random grain orientation. However, if the specific basal texture is formed by extrusion, compression or rolling, the value of \(m\) will higher than 6.5. The \(\tau_{0}\) can be obtained by the texture intensity.  
[PERSON] et al. [100] studied the strengthening mechanisms of extruded Mg-Sn-based alloys, and the contribution of grain refinement strengthening, precipitation strengthening, solid solution strengthening and texture strengthening were discussed. For Mg-8 Sn and Mg-8 Sn-1 Al-1 Zn alloys, the contribution values of texture strengthening using AZ31 alloys as the benchmark were calculated to be 2.77 MPa and 3.61 MPa, respectively. Also, [PERSON] et al. [101] prepared a high-strength and ductile Mg-Gd-Y-Zn-Zr alloy using an extrusion ratio of 3.6 by controlling the texture strengthening and dynamic precipitation. Similar research and conclusions were carried out by [PERSON] et al. [102], and the strong texture formed during extrusion and internal dislocation strengthened the coarse and deformed grain areas, resulting in significantly increasing the strength.