esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 3 The Algorithms to Realize ASPF
### BPNN-Based Solar Power Compensation  
The BPNN, one of the most popular techniques in the filed of neural network, is a kind of supervised learning NN. It uses the steepest gradient descent method to reach very small approximation. Theoretically, BPNN can approximate any nonlinear functions [25], in fact, for the PV power prediction, the relationship between its input data and forecasting value is very complicated [8]. So it is suitable to adopt the BPNN in our ASPF to learn this feature. Therefore, a three-layered feed-forward neural network and the BP learning algorithm are adopted together to compensate the predictive error of solar power in this paper.  
The input nodes \(\{P_{b}^{k}(t)\}_{b=1}^{B}\) of the BPNN to the hidden layer is decided by the result from Section 3.2, \(x\) consists of the \(\{P_{b}^{k}(t)\}_{b=1}^{B}\) from \(t=1\) to \(T\). \(B\) denotes the number of the input nodes, and \(P_{b}^{k}(t)\) is the \(k\)th group of inputing predictive message at time slot \(t\). For the number of hidden neurons (HN) in the hidden layer, the method in [33] is used to get the upper bound on the number of hidden neurons, HN\({}_{max}\). Then the hidden neurons \(h\) is ranged from 1 to HN\({}_{max}\), and the model is trained for several times according the method in [34], and the forecasting mean square error from the testing data is calculated and recorded for comparisons. The optimal \(h^{*}\) is acquired in the end, which is the number occuring mostly in the iteration with the less mean square error.  
Initializing the weight is another important step in BPNN, and initialization of a network involves assigning initial values for the weights of all connecting links, here, initial values for the weights areset in the small random values between \(-0.3\) and \(+0.3\)[33]. Momentum coefficient and learning rate in BPNN are chosen from \([0.021,0.029]\) and \([0.345,0.539]\), respectively. Besides, in order to overcome the possible overfitting problem in forecasting, regularization is employed of the parameter ranged in \([0.87,0.9]\) for \(k^{*}\) groups of models.  
Finally, Maximum Absolute Error (MxAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) between the revised solar power and the actual value are used to quantify the performance of each group. MxAE, RMSE and MAPE are widely used statistical measurements of the accuracy of the models [22]. In addition, in each group, MxAE, RMSE and MAPE are defined as follows:  
\[\text{MxAE}=\text{max}\ \left|y_{n}-\hat{y}_{n}\right|,\ n=1,2,\cdots,N_{k} \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N_{k}}\sum_{n=1}^{N_{k}}(y_{n}-\hat{y}_{n})^{2}} \tag{10}\]  
\[\text{MAPE}=\frac{100}{N_{k}}\sum_{n=1}^{N_{k}}\left|\frac{y_{n}-\hat{y}_{n}} {y_{n}}\right| \tag{11}\]  
where \(N_{k}\) is the number of predicted points in type \(k\), \(y_{n}\) is actual solar power for the \(n\)th point, and \(\hat{y}_{n}\) represents the revised solar power.
## 2 Materials and Methods
### Model Evaluation  
In this study, the model's performance was assessed using accuracy, precision, sensitivity, and specificity obtained from the confusion matrix of the models [33].  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{3}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
where TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively. True positive is a consequence in which the model predicts the positive class accurately and true negative indicates accurate prediction of the negative class. False positive indicates the incorrect prediction of the positive class by the model and false negative indicates the incorrect prediction of the negative class.  
In addition to the previously mentioned metrics used to evaluate classifier performance, the models were also assessed based on the area under the receiver operating characteristic (ROC) curve, known as AUC. AUC is a significant scalar value that provides an overall assessment of a classifier's performance. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR), with TPR on the \(y\)-axis and FPR on the \(x\)-axis. AUC measures the classifier's ability to distinguish between classes and represents the degree of separability. It ranges from 0.5 to 1.0, where the minimum value corresponds to a random classifier and the maximum value indicates a perfect classifier. AUC has the advantageous properties of threshold and scale invariance, making it a robust metric. This implies that AUC is not influenced by the chosen threshold or the scale of probabilities. Computationally, AUC is determined by aggregating trapezoid areas beneath the ROC curve. AUC values between 0.7 and 0.8 are considered acceptable, 0.8 to 0.9 as excellent, and greater than 0.9 as outstanding [33].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Max Epochs & 5 \\ Mini Batch Size & 32 \\ Optimiser & Stochastic Gradient Descent (SGD) \\ Initial Learning Rate & 0.005 \\ Momentum & 0.9 \\ Loss Function & Categorical Cross Entropy \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of hyperparameters used in machine-learning models.  
The classification time measure would utilise the model's average time to predict an image class. This was accomplished by employing a timer at the beginning and end of the evaluation procedure, and the classification time was calculated using the following formula [34]:  
\[\text{Classification time}=\frac{\textit{Test evaluation time}}{\textit{Number of steps}\times\textit{Batch size}} \tag{5}\]  
The Python batch dataset format was applied to the test dataset. The batch size hyperparameter was used to group the images in this format. A batch in this experiment consisted of 32 images.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 3 Proposed En-LDM Scheme
### Channel Capacity Analysis
#### 3.2.3 BICM Capacity Analysis  
Equation (9) gives the Shannon limit, but it does not directly specify the type of signal that is close to the limit. BICM can be used to evaluate the system performance in a binary communication system, which is an effective solution approaching Shannon's limit with affordable complexity. The BICM capacity can be given by [13]:  
\[C_{B,X}=m-\sum_{i=1}^{m}E_{b,y}\Bigg{[}\log_{2}\frac{\sum_{x\in\chi_{\chi}}p(y| x)}{\sum_{x\in\chi_{\chi_{\chi_{\chi}}}^{i}}p(y|x)}\Bigg{]} \tag{15}\]  
where \(m\) is the order of the constellation, \(\chi\) represents the alphabet of size \(M=2^{m}\chi_{\chi}^{i}\) represents a subset of all signals \(x\in\chi_{\chi}\) and its tag has a value \(b\in\{0,1\}\) at the \(i\)-th bit. \(p(y|x)\) is the transition probability density function (p.d.f.) of the transmitted signal \(x\) and the received signal \(y\). In the AWGN channel, \(p(y|x)\) is given by:  
\[p(y|x)=\frac{1}{\sqrt{2\pi\sigma}}e^{-\frac{(y-x)^{2}}{2x^{2}}} \tag{16}\]  
The transmitted symbols need to be normalized, and the following constraints should be met:  
\[P_{tx}=\frac{1}{M}{\sum_{i=1}^{M}}{\left|x_{i}\right|^{2}}=1 \tag{17}\]  
On the premise of satisfying Equations (16) and (17), the BICM capacity obtained by Equation (15) is only determined by the symbol alphabet and the SNR of the signal. The signal power of each service in the En-LDM system obtained by Equation (10) can calculate the SNR of the signal, so the BICM capacity of each service of En-LDM can be calculated.
## 4 Methodology and Procedure
### Earthquake Loading  
The seismic records used in this investigation were obtained from the data set of the National Strong Motion Program of the United States Geological Survey (USGS). Fifteen records were selected in such a way that their spectral shape, tectonic regime, magnitude, and fault distance are similar to those controlling the seismic hazard of the area where the buildings are located. It is worth mentioning that the ASCE 7-16 [49] recommends a minimum of eleven pairs of ground motions to take into account the inherent record-to-record variability in the structural response. The main characteristics of such records can be seen in Table 3. It can also be observed from the table that the predominant periods of the strong motions records go from 0.13 to 0.72 s. The structural models are subjected to the simultaneous action of the horizontal seismic component (one at a time) of the records, the vertical component, and the gravity loads.  
To obtain different levels of deformation, the ground motions are scaled. This is made according to the geometric mean of spectral acceleration (\(Sa_{avg}\)) which is calculated as the \"average\" of the pseudo-accelerations (\(S_{\text{a}}\)) over a range of periods. The range of periods to calculate (\(Sa_{avg}\)) goes from 0.2 T\({}_{1}\) to 1.6 T\({}_{1}\), with constant increments of 0.01 s, where \(T_{1}\) is the fundamental period of the model. The values of \(Sa_{avg}\) range from 0.2 g up to 1.4 g with uniform increments of 0.2 g for Model 1, while for Model 2 such a range goes from 0.1 g up to 0.8 g with constant increments of 0.1 g. For the case of Model 3, the range of variation of \(Sa_{avg}\) goes from 0.05 g to 0.35 g with constant increments of 0.05 g. It is important to clarify that the maximum values of \(Sa_{avg}\) were chosen in such a way that a similar magnitude of the maximum inelastic deformation, were developed in the three models (drifts of about 3.5%).
## 3 Analytical Modeling of the 2-DOF CPM
### Decoupling Character  
The symmetric structure of the 2-DOF CPM has already ensured it with a total output decoupling performance. Therefore, only the analytical model for the input decoupling characteristic is investigated and evaluated using the ICD. Without loss of generality, the ICD of the 2-DOF CPM can  
Figure 11: Input stiffness model of CPM considering the stiffness of the actuator.  
be defined using the ratio of the bending deformation at the actuation point \(A_{2}\) along the \(x\)-axis to the axial deformation at the actuation point \(A_{1}\) along the \(x\)-axis when only Actuator I is actuated.  
The relationship between the displacement output of the terminal platform and the deformation at the actuation point \(A_{1}\) has already been obtained in Equation (23). Then the deformation at the actuation point \(A_{2}\) caused by the motion of the terminal platform must be calculated to obtain the ICD of the CPM. The stiffness model of the CPM along the \(y\)-axis is given in Figure 12, where \({}^{O}K_{T2}\) is the total stiffness of the first, third and fourth chains.  
Similarly, the relationship between the deformation at the actuation point \(A_{2}\) and the displacement of the terminal platform can be obtained by resorting to a force equilibrium condition of point \(A_{2}\) as  
\[\delta_{a2}=\left[\left({{}^{A_{2}}\mathbf{K}_{2-1a}+{}^{A_{2}}\mathbf{K}_{2-1b}+{}^{ A_{2}}\mathbf{K}_{2-2}}\right)^{-1}{}^{A_{2}}\mathbf{K}_{2-2}\right]\delta_{o} \tag{28}\]  
Combining Equations (23) and (27), the relationship between the deformations at two actuation points when only Actuator I is driven can be obtained as  
\[\delta_{a2}=\left({{}^{A_{2}}\mathbf{K}_{2-1a}+{}^{A_{2}}\mathbf{K}_{2-1b}+{}^{A_{2}} \mathbf{K}_{2-2}}\right)^{-1}{}^{A_{2}}\mathbf{K}_{2-2}\left({}^{O}\mathbf{K}_{1-2}+{}^{O }\mathbf{K}_{T1}\right)^{-1}{}^{O}\mathbf{K}_{1-2}\delta_{a1} \tag{29}\]  
Therefore, the analytical model of the ICD of the 2-DOF CPM can be defined as  
\[ICD=\left[\left({{}^{A_{2}}\mathbf{K}_{2-1a}+{}^{A_{2}}\mathbf{K}_{2-1b}+{}^{A_{2}}\mathbf{ K}_{2-2}}\right)^{-1}{}^{A_{2}}\mathbf{K}_{2-2}\left({}^{O}\mathbf{K}_{1-2}+{}^{O} \mathbf{K}_{T1}\right)^{-1}{}^{O}\mathbf{K}_{1-2}\mathbf{e}_{1}\right]^{\mathsf{T}}\!\! \mathbf{e}_{1} \tag{30}\]
# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 1 Introduction
By using basic arithmetic operations on adjacent pixels, these algorithms have been implemented on an FPGA platform. Additionally, [PERSON] et al. [13] proposed a scene-based bad pixel dynamic correction method. According to the self-adaptive median filter and the keyframe technique, this method can search for blind pixels with high detection accuracy. [PERSON] et al. [14] also gave an adaptive median filtering method to detect real-time blind and flickering pixels, in which a human-vision-based algorithm is applied to avoid misjudging blind pixels. However, these methods also have limitations. For instance, they detect blind pixels using high spatial frequency, which can easily cause the IRPT to be falsely identified as blind pixels. Therefore, these methods need dozens or hundreds of frames to ensure the accuracy of blind pixel detection, which may cause miss detection of the flickering pixel with low flickering frequency. The blind pixel compensation method is applied to overcome the bad effect of the blind pixel which is detected by the previous step. At present, the existing blind pixel compensation methods [15; 16; 17; 18] have achieved great compensation effect. These methods use adversarial networks, dual-band information, or regression model to compensate the blind pixel, and the rich scene details are preserved. Despite their success in estimated accuracy, aforementioned methods also have a limitation, since they generally treat each blind pixel as the constant blind pixel. However, the flickering pixels still retain a part of the information acquisition ability [19]. Therefore, how to choose the flickering pixel that should be compensated is worth studying. In this paper, in order to address the problems mentioned above, we proposed an on-board flickering pixel dynamic suppression method based on multi-feature fusion. The visual and motion features of flickering pixels were extracted from the result of IRPT detection and tracking, which economizes computing resources. Meanwhile, the above features are fused by the confidence of flickering pixel (CFP) evaluation strategy and selection mechanism to guarantee the accuracy of flickering pixel detection with a dozen frames. Finally, CFP and visual features of flickering pixels are combined to compensate flickering pixels dynamically and accurately. This method overcomes the drawbacks associated with the conventional scene-based method that depends on a single feature consuming large amounts of frames to ensure detection accuracy.
# The Concept of Segmented Wind Turbine Blades: A Review
## 5 Blade Segmentation Strategy
### Segmenting to Obtain a Reduced Component Length  
Large blades cannot get past narrow corners. This issue can be alleviated by splitting the blades into in-board and out-board segments. However, such a division requires the use of highly loaded structural joints to transfer loads between the segments. Introducing such additional joints goes against the historical trend in aerospace and wind energy of reducing the number of components [18]. Furthermore, fatigue design is better off without joints [64]. Additionally, there is a trend to produce more slender blades with higher tip speed ratios (TSR) and reduced chord lengths resulting in less space for a segmentation joint [65; 66]. While the split location may be determined as to minimize transportation costs, it may also be influenced by structural consequences. The blade loads increase non-linearly towards the root. Meanwhile, modern blade designs use very thick  
\begin{table}
\begin{tabular}{l l l l} \hline
**Segmentation Strategy** & **Type of Division** & **Advantages** & **Drawbacks** \\ \hline \multirow{3}{*}{Reducing length} & \multirow{3}{*}{Span-wise joint} & \multirow{3}{*}{Potential cost reductions} & Goes against historical trend \\  & & & Slender blades reduce available space \\  & & & Optimal split transport/structure differs \\  & & & Division of structural spar \\ \hline Reducing width/height & Chord-wise joint & Potential cost reductions & Transfer of edge-wise loads \\ \hline \multirow{3}{*}{Reducing rotor loading} & \multirow{3}{*}{Span-wise: telescopic blades} & Variable swept area & Division of structural spar \\  & & Reduced extreme loads. & \\ \cline{2-3}  & Chord-wise: trailing edge flaps & Variable blade shape & No need to divide structural spar \\ \cline{1-1}  & & Reduced extreme and fatigue loads & Increased complexity \\ \hline \end{tabular}
\end{table}
Table 2: Overview of segmentation strategies.  
Figure 4: Different segmentation strategies. (**a**) Blade with a separate TE-segment to reduce the blades width [61]. (**b**) Blade with separate LEand TE panel segments to reduce the blade width [62]. (**c**) Blade divided to reduce the length of the components [46]. (**d**) Telescopic wind turbine blade [63]. (Reprint with permission [63]; 2012, John Wiley and Sons.)airfoils near the root, where structural requirements dominate the design, and very thin ones toward the tip, where aerodynamic performance dominates. As a consequence, the ratio of section forces to the available cross-section is the highest around the center of the blade [60]. At this location, a very heavy joint would be required. The ratio of section forces to the available cross-section is lower towards the tip portion and towards the root portion, with the tip region experiencing the lowest section forces. However, while this was also true for the 61.5-m blade considered by [PERSON] [49], a mid-span location was still selected since this was optimal for transport.
## 10 Conclusions
The findings of a comprehensive review of intelligent-computing-based methodologies demonstrate a clear shift towards enhancing the training capability of neural networks, with the objective of achieving more promising results of the STLF model than those obtained through conventional techniques. In addition, a range of hybrid forecasting techniques have been employed with the objective of enhancing forecast accuracy. It has been demonstrated in the literature that the combination of two or more predictive models can create hybrid models with the required accuracy. In several hybrid methodologies, the widely used BPNN training algorithm is utilized to train the NN, yet these approaches do not fully resolve the aforementioned issues. In order to achieve an efficient power system utility, whereby the demand load can be forecast with a minimum error percentage, it has been demonstrated that SVM, ANN, LSTM, and their pertinent models have excellent prospects. Recent research has demonstrated that the heuristic search and population-based optimization learning algorithms of ANN for STLF produce superior outcomes compared to conventional neural networks. Nevertheless, the efficacy of an ANN-based forecast model can be enhanced by addressing shortcomings such as the reliance on initial weight values, local minima problems, inadequate network generalization, and slow convergence. The findings of the research have been included to provide readers with a comprehensive understanding of modern predictive models, which can be utilized in the selection of a specific model for the development of new hybrid forecasters in accordance with the characteristics of the individual approaches. Future research will focus on the design and comparison of hybrid models with three or more approaches to address the shortcomings of the current forecasters. Previous studies have demonstrated that hybrid models with more than two methods have a significant advantage over single or double models. It is thus concluded that several factors, including network complexity, an improved training algorithm, convergence rate, and the selection of highly correlated inputs for the forecast model, need to be addressed in addition to enhancing the prediction accuracy of forecasting models. Literature review, conceptualization: S.A., S.B. and A.T.; Implementation and investigation of the research: S.A., S.B., M.N.R. and A.T.; Writing--original draft preparation: S.A.; Writing--review and editing: S.A., S.B., M.N.R., P.P.P., D.F. and A.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflicts of interest.
## 3 Results and Discussion
### Morphological Characteristics  
The factors that affect the stiffening potential of particulate in the matrix phase are mainly dispersion, mixing, agglomeration, particle size and shape, gradation, and interface properties. Figure 4 indicates the morphology of limestone filler measured by performing SEM. We have assessed the basic shape in comparison to particle shapes such as rod, tile, disk, cubes, grains, and spheres, etc. [41].  
Figure 4: SEM image of limestone filler.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Filler**} & \multirow{2}{*}{**Mass of Filler**} & **Specific Gravity** & **Mass of Bitumen** & **Compositional Volume** & **Rigden Voids** & **Effective Volume** & **Free Bitumen Volume Content** \\ \cline{2-8}  & & **M\({}_{\text{f}}\)** & **S\({}_{\text{f}}\)** & **M\({}_{\text{b}}\)** & **V\({}_{\text{f}}\)** & \(\varepsilon\)** & **V\({}_{\text{e}}\)** & **FBV** \\ \hline Limestone (Ls) & 209.84 & 2.623 & 200 & 29 & 33 & 44 & 56 \\ \hline OPC & 241.6 & 3.02 & 200 & 29 & 45 & 53 & 47 \\ \hline Gritstone (Gs) & 224.64 & 2.808 & 200 & 29 & 40 & 49 & 51 \\ \hline Granite (Gr) & 216.48 & 2.706 & 200 & 29 & 38 & 47 & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compositional and effective volume calculation.  
[PERSON] et al. evaluated the effect of geometrical and physical properties in mastics. In view of the discussion of this research, the filler particles in our work can be classified as angular and granulous with smooth to rough texture [42]. Moreover, as the objective of this study is to evaluate different micromechanics models than to compare fillers, we have assumed the particle shape to be roughly spherical.  
For the non-spherical particles, the aspect ratio (longest and shortest dimension ratio) affects the rheology of the suspension. An increase in particle aspect ratio lowers maximum packing fraction (\(\varphi_{\text{m}}\)) and increases generalised Einstein's coefficient \(\text{K}_{\text{E}}\)[8]. The elliptical particles will have more \(\text{K}_{\text{E}}\) and \(\varphi_{\text{m}}\) as compared to spheres due to an increase in aspect ratio. This will eventually increase the stiffening effect. The irregular shape and rough texture will also increase the effective volume and result in an increase in viscosity.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 3 Results and Discussion
### Comparison  
The consumed and saved energies along with the energy losses of all considered cases are compared in this section; the results are summarized in Table 12.  
By comparing the saved, consumed and lost energies of all eight cases one can see that Case VII (24-h technique) yields the best results with the lowest network losses and the highest saved energy. One can examine all the DSM cases and realize that the DLC technique lowered the losses in the network as well as saved energy when compared to Case I or II. DLC can save much more energy than CVR, since CVR is constrained by voltage standards. DLC reduces energy losses since it decreases the power of the loads, which will lower the current drawn from the source, thus reducing losses. When deploying DLC and CVR together, CVR can control any voltage change caused by DLC when the load powers are varied. The effect of the DG on bus 21 is minimal, but it is important to notice that it affects CVR losses more severely than DLC losses. Thus, in summary, CVR can decrease consumed energy and increase the system's voltage control capabilities. DLC lowered circuit losses and consumed energy by a larger margin than CVR. The combined algorithm reduced system losses and consumed energy and gave the system increased voltage control capabilities.