esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Hierarchical Two-Stage Robust DES Planning Model
### The Two-Stage Robust Planning Model at the Second Level  
#### 3.1.1 Objective Function  
The two-stage robust model consists of the strategic decision stage and the operational decision stage. The objective function of the strategic decision stage consists of the investment cost and the maintenance cost of the demand-side energy storage. Stage II is to obtain the optimal operational decision in the worst case of the uncertainty set. The objective function of Stage II consists of the power generation cost, demand-side electricity purchasing cost, and carbon incentive revenue as (5).  
\[\underset{E_{N}^{ES}}{\text{Min}}\left(\sum_{i\in\Omega^{L}}\left(c^{ES}+c^{MT} \right)E_{N,i}^{ES}+\underset{P_{j}^{L},\bar{P}_{j}^{L},\bar{P}_{j}^{R},\bar{P}_ {j}^{R},\bar{P}_{j}^{R}}{\text{min}}\left(\sum_{i=1}^{T}\left(\sum_{i\in \Omega^{G}}c^{G}P_{g,i}^{G}+\sum_{j\in\Omega^{L}}\left(c_{i}^{TOU}\left(\bar{ P}_{j,i}^{L}+d_{i,t}^{DR}\right)-C_{i,t}^{C\,t}\right)\right)\right)\right) \tag{5}\]  
where \(c^{ES}\) and \(c^{MT}\) denote the DES investment cost coefficient and maintenance cost factors as (6) and (7); \(c^{G}\) and \(c_{i}^{TOU}\) represent the power generation cost coefficient and time-of-use tariff, respectively; \(E_{N,i}^{ES}\) and \(P_{g,t}^{G}\) represent the rated capacity of the energy storage and the power generator output.  
\[c^{ES}=\frac{c_{E}^{ES}+c_{P}^{ES}/\Gamma_{N}}{365\times 24N_{Y}^{ES}}T(1- \gamma_{rec}) \tag{6}\]  
\[c^{MT}=\frac{\sum_{n=1}^{N_{Y}^{ES}}c_{Y}^{MT}\left(\frac{1+i_{j}}{1+d_{r}} \right)^{n}}{365\times 24N_{Y}^{ES}}T \tag{7}\]  
where \(c_{E}^{ES}\) and \(c_{P}^{ES}\) denote the investment cost factors of DES rated capacity and power; \(\Gamma_{N}\), \(\gamma_{rec}\) and \(N_{Y}^{ES}\) represent the energy storage duration, the recycling rate, and DES lifespan; \(c_{Y}^{MT}\) represents the annual maintenance cost factor; \(i_{r}\) and \(d_{r}\) denote the inflation rate and discount rate.
# Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation
## 3 Methodology
### Optimization  
Intuitively, our method can be formally defined as a weighted sum of two objectives, so we analyze the two parts separately.  
3.4.1. Gradient of \(\frac{\partial L_{FGL}}{\partial\hat{x}}\)  
Thinking about the combination of two domains' features, \(\hat{x}=[\hat{x}_{s},\hat{x}_{t}]\) the objective can be reformulated as:  
\[L_{FGL} =\frac{1}{2(n_{s}+n_{t})}\sum_{i=1}^{n_{s}+n_{t}}\sum_{j=1}^{n_{s}+n _{t}}(\hat{x}_{i}-\hat{x}_{j})^{2}W_{ij} \tag{13}\] \[=\frac{1}{2(n_{s}+n_{t})}(\sum_{i=1}^{n_{s}+n_{t}}\hat{x}_{i}^{T} D_{ii}\hat{x}_{i}-\sum_{i=1}^{n_{s}+n_{t}}\sum_{j=1}^{n_{s}+n_{t}}\hat{x}_{i}^{T}W _{ij}\hat{x}_{j})\] \[=\frac{1}{2(n_{s}+n_{t})}trace(\hat{x}^{T}L\hat{x})\]  
where \(Dii=\sum_{j=1}^{n_{s}+n_{t}}W_{ij}\) denotes the degree of \(\hat{x}_{i}\), and \(L=D-W\) is called Laplacian graph.  
Naturally, we can compute the gradient by:  
\[\frac{\partial L_{FGL}}{\partial\hat{x}}=\frac{1}{n_{s}+n_{t}}L\hat{x}=[\frac {\partial L_{FGL}}{\partial\hat{x}_{s}}\frac{\partial L_{FGL}}{\partial\hat{x }_{t}}] \tag{14}\]  
3.4.2. Gradient of \(L_{SF}\)  
Noted that we have \(\hat{x}\geq 0\) because of the activation. So the gradient of sparse filtering is simply a matrix full of 1.  
\[\frac{\partial L_{SF}}{\partial\hat{x}}=\mathbf{1}=[\frac{\partial L_{SF}}{ \partial\hat{x}_{s}}\frac{\partial L_{SF}}{\partial\hat{x}_{t}}] \tag{15}\]  
Here we use \(\mathbf{1}\) to represent the 1 matrix with certain dimension, e.g., \(\frac{\partial L_{SF}}{\partial\hat{x}_{s}}=\mathbf{1}\in R^{n_{s}\times k}\) and \(\frac{\partial L_{SF}}{\partial\hat{x}_{t}}=\mathbf{1}\in R^{n_{t}\times k}\).  
3.4.3. Gradient of \(k(\cdot)\)  
To propagate the gradient back to the input layer, we need \(\frac{\partial\hat{x}}{\partial x}\) to apply the chain rule. There are some differences for two domains since we adopt within-domain normalization. To obviate misunderstanding, we give the specific form for two domains.  
\[\frac{\partial\hat{x}_{s}}{\partial W} =x_{s}^{T}(M_{group}\circ M_{scol}\circ sign(x_{s}W)) \tag{16}\] \[\frac{\partial\hat{x}_{t}}{\partial W} =x_{t}^{T}(M_{trow}\circ M_{tcol}\circ sign(x_{t}W))\] \[\frac{\partial\hat{x}}{\partial W} =[\frac{\partial\hat{x}_{s}}{\partial W}\frac{\partial\hat{x}_{t }}{\partial W}]\]  
3.4.4. Unified Optimization Based on Gradient Descent  
Given above gradients, we are able to update \(W\) by gradient descent from random initialization. The training process is similar to train a neural network, some tricks that help converge can also be applied here, such as initializing with normal distribution and utilizing a decreasing step. The specific training scheme is shown in Algorithm 2.  
```
1 Random initialization.
2\(W=\mathcal{N}(0,1)\in R^{m\times k}\)
3 Gradient descent.
4 whilenot convergedo
5\(\Delta=(\frac{\partial L_{SF}}{\partial\hat{x}}+\lambda\frac{\partial L_{ FGL}}{\partial\hat{x}})\frac{\partial\hat{x}}{\partial W}\)
6\(W=W-\alpha\Delta\)
7 end while return\(W\)
```  
**Algorithm 2**Optimization of the proposed method.
###### Abstract  
The implementation of stranded conductors in flexible gas-insulated transmission lines (FGILs) requires field intensity minimization as well as field irregularity suppression in order to avoid dielectric breakdown. Moreover, the interdependence of enclosure and conductor sizes of FGILs regarding electrostatic aspects necessitate critical consideration of their dimensional specifications. In this research, geometric and electrostatic field optimization for FGILs regarding stranded conductors is performed. In addition, the effect of conductor irregularity on field dispersion is analyzed, and a semiconducting film (SCF)-coated stranded conductor is proposed as a potential candidate for FGILs. Considering the performed optimized design, an 11 kV scaled-down model of a 132-kV FGIL was also fabricated in order to practically analyze its electrostatic and dielectric performances regarding simple and SCF-coated stranded conductors. Simulation and experimental investigations revealed that the SCF-coated stranded conductor significantly minimized the field irregularity of the FGIL along with improving in its dielectric breakdown characteristics.  
dielectric strength; field grading; field utilization factor (FUF); gas-insulated transmission line; metropolitan; stranded conductor +
Footnote †: journal: Applied Sci. 2019, 9, 2988; doi:10.3390/app9152988
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 3 The Proposed Method
### Optimization of SVR Parameters
#### 3.2.3 Selection  
As shown in Figure 1, the selection process of the genetic algorithm in this study involves a tournament method. In order to create a new generation of populations, the tournament selection strategy first compares \(n(n<m)\) individuals from population \(S\) and chooses the individuals with the lowest fitness to form part of the offspring population. This process is repeated until the new population size equals the original population size.
## 6 Discussion  
The achieved accuracy of 97% on the test set of the whole data reflects the effectiveness of the proposed CNN model in accurately classifying various types of structural damage. The performance of the CNN model in damage detection surpassed traditional methods, particularly visual inspection and non-destructive testing. The speed and accuracy of the proposed approach highlight the potential for transitioning from labor-intensive manual inspections to automated, data-driven methods.  
The CNN's ability to accurately classify and detect structural damage, even with noisy signals, underscores its efficacy in non-destructive testing. This is particularly valuable in scenarios where destructive testing is impractical or poses risks to structural integrity. The high accuracy of the model suggests its potential for real-time monitoring of structural health. Rapid and precise detection of damage types can facilitate timely interventions, preventing the progression of structural issues and enhancing overall safety.  
An intriguing aspect of this study is the satisfactory performance achieved by the CNN models trained on individual accelerometer data from different floors. Despite the reduction in available data, the models demonstrated noteworthy accuracy levels: 89%, 90%, and 83% for acc1, acc2, and acc3, respectively. The ability to achieve meaningful results with models trained on data from a single accelerometer is promising for practical applications. This implies that in scenarios where sensor deployment is constrained or costly, a simplified monitoring set-up with fewer sensors may still provide valuable insights into structural health.  
The reduced dataset for single accelerometers implies a more data-efficient training process. This efficiency is particularly beneficial in situations where data collection is challenging or expensive, showcasing the adaptability of the model to resource constraints. The training and testing of these single-accelerometer models were notably faster compared with the model trained on the entire dataset. This computational advantage is crucial for real-world applications, enabling quicker assessments and interventions in time-sensitive situations.
###### Abstract  
Detecting subsurface delamination is a difficult and vital task to maintain the durability and serviceability of concrete structure for its whole life cycle. The aim of this work was to obtain better knowledge of the effect of depth, heating time, and rebar on the detectability capacity of delamination. Experimental tests were carried out on a concrete specimen in the laboratory using Long Pulsed Thermography (LPT). Six halogen lamps and a long wavelength infrared camera with a focal plane array of 640 \(\times\) 480 pixels were used as the heat source and infrared detector, respectively. The study focused on the embedded imitation delaminations with the size of 10 cm \(\times\) 10 cm \(\times\) 1 cm, located at depths varying from 1 to 8 cm. The signal-to-noise ratio (SNR) was applied as a criterion to assess the detectability of delamination. The results of this study indicate that as the provided heating time climbed, the SNR increased, and the defect could be identified more clearly. On the other hand, when using the same heating regime, a shallow delamination displayed a higher SNR than a deeper one. The moderate fall of the SNR in the case of imitating defect located below reinforced steel was also observed. The absolute contrast was monitored to determine the observation time, and the nondimensional prefactor \(k\) was empirically proposed to predict the depth of delamination. The mean absolute percentage error (MAPE) was used to quantitatively evaluate the difference between forecasted and real depth, which evaluation confirmed the high reliability of the estimated value of the prefactor \(k\).  
delamination; infrared thermography; signal-to-noise ratio (SNR); nondestructive technique (NDT); reinforced concrete; long pulsed thermography +
Footnote †: journal: Applied Sciences
# Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
## 3 Short-Term Emotion Recognition with Single-Pulse PPG Signal
### Feature Extractor Using Single-Pulse PPG Signal  
#### 3.2.1 Dividing PPG Signals as Single Pulse  
Measured signals can include patterns or trends that are not intrinsic to the data, which can hinder data analysis, and hence must be removed. PPGs are sensitive to disturbances such as the movement of the position where the sensor is attached. It is not easy for people to fix their fingers throughout the complete recording time, hence movements noise were generally included in the signal. PPG signals in the DEAP dataset included significant finger movement, as shown in Figure 5. Therefore, we preprocessed the PPG signals to eliminate movement trend before dividing the signals into single pulses.  
We fitted a high-order polynomial to the PPG signal, and subtracted the fitted curve from the original PPG signal to remove the movement trend. We used an order of 50 polynomials to fit the original signal (for 60 s video clip). The preprocessed data was significantly more robust to finger movement than the original data, as shown in Figure 6.  
After eliminating the trends due to the finger movement for PPG signal, we split the PPG signal into a single pulse. The PPG pulse always has a maximum value and we positioned the PPG signal maximum for each pulse at the center of the segmented signal. We used 1.1 s pulse length for optimal performance, corresponding to 140 samples for the DEAP dataset. The cutting window moved every  
Figure 4: Dividing PPG signals into a single pulse.  
maximum peak and there were, on average, 20 overlapping samples between adjacent segments. There are approximately 120 samples between maximum peaks which means the signal's average period is 120 samples, and it is necessary to use overlapping samples for using all the raw data.
## 4 Discussion
### Influence of Seasonal Change  
For multi-source satellite remote sensing images obtained in different seasons, the phenological differences cause false changes, which make the synergetic classification of wetlands very challenging [38]. Images taken at different times show different types of land types and different vegetation growth states. Therefore, when multi-source data are fused, images observed in the same season should be used; otherwise, the reliability of synergetic classification may be affected [38; 66].  
The YRD is located in the warm temperate zone with four distinct seasons and large seasonal variations in land cover [67]. The GF-3 and OHS remote sensing images in this study were collected on 13 March 2019, and 23 March 2020, respectively. As shown in Figure 14, the highest temperature in March in this region was 17 \({}^{\circ}\)C, the lowest temperature was 2 \({}^{\circ}\)C, and the highest precipitation was 4 mm. Most plants were not in the vigorous growth stage, and the change rate of land cover was relatively low. In addition, the study area is located in the YRD National Nature Reserve with relatively little human disturbance. In recent years, the impact of sediment transport on the Yellow River estuary has gradually decreased [39]. Therefore, the images collected in the same month in this study can ensure the maximum reduction in the impact of wetland phenological changes.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
### Relevant 4G LTE Performance Metrics
#### 3.2.2 Reference Signal Received Quality  
As stated earlier, the reference signal received quality (RSRQ) is a useful MBB metric used to examine cell reselection and handover. The relationship between the RSRQ and the RSRP can be seen in Equations (2) and (3). In terms of its application, in situations where the RSRP is not sufficient to determine the cell reselection and handover, the RSRQ must be used alongside the RSRP.  
\[\text{RSRQ}=10\log_{10}(\text{N})+\text{RSRP}(\text{dBm})-\text{RSSI}(\text{dBm}) \tag{2}\]  
\[\text{RSRQ }=(\text{N }\times\text{RSRP})/\text{RSSI} \tag{3}\]  
where \(\text{N}=\text{ number of resource blocks}\).
# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 4 Proposed Methodology
### Training
#### 4.1.2 Backpropagation  
If the error determined in the feed-forward stage is greater than 0.20, then the calculated error will be backpropagated to update the connected weights of each neuron in the hidden and input layers and the bias value associated with hidden-layer neurons until it satisfies the given error rate. Equation (6) is used in back-propagation to apply the difference in error generated by feed-forward to determine the gradient values for each node in the output layer.  
\[OGrad_{i}=AO_{i}(1-AO_{i})*Error \tag{6}\]  
Finding the rate of change in the hidden-to-output-layer-connection weights and the bias value associated with each output layer node is the next step in the back-propagation process. These rates of change can be calculated as follows:  
\[\Delta w_{ij}=LR*OGrad_{j}*Hlo_{i} \tag{7}\]  
\[\Delta b_{j}=LR*OGrad_{j} \tag{8}\]  
The learning rate (\(LR\)), which controls how quickly the back-propagation algorithm learns, is used in Equations (7) and (8). Greater values of '\(LR\)' will cause \(\Delta w\) to fluctuate more, increasing the possibility of overshooting a correct response. Thus, \"0.02\" is the value assigned to \"\(LR\)\" in our suggested process. Trial and error are used to determine this value of \"\(LR\)\". Next, utilizing the obtained \"\(\Delta w_{ij}\)\" and \"\(\Delta b\)\", the weights and biases related to the hidden to output layer are improved. After that, we used Equations (9)-(11) to calculate the gradient value, the rate of change in the input-weights-to-hidden-layer connections, and bias values for each node in the hidden layer, as follows:  
\[HGrad_{i}=Hlo_{i}(1-Hlo_{i})*\left(\sum_{j=1}^{3}OutGrad_{j}*W_{j}\right) \tag{9}\]  
\[\Delta w_{ki}=LR*HGrad_{i}*X_{k} \tag{10}\]  
\[\Delta b_{i}=LR*HGrad_{i} \tag{11}\]  
The feed-forward will now begin anew. For the given vector, [5; 5; 3; 6], feed-forward and back-propagation procedures will be performed until the generated gradient descent error reaches the desired error.  
Figure 4: First forward pass of the first input vector.  
After the updated weights and biases are generated, the training of the proposed system will be completed, shown in Figure 5. Following that, testing will be conducted using the remaining datasets of 3000 input vectors.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 4 Generalized Design Approach
### Systems Engineering and Practical Implementation
#### 4.3.2 Verification and Traceability  
Verification and traceability are critical in system-level RF design. In our framework, each domain--time, frequency, and space--is linked to measurable system-level goals, for example. A communication system may require high efficiency at high PAPR, which maps to optimize back-off efficiency via load/supply modulation (space domain) [44; 45].  
A wideband radar requires consistent performance across frequencies, directly influencing harmonic termination strategies and continuous-mode waveform design (frequency domain) [28; 29]. A scientific instrument that relies on ultra-linear signal reproduction may prioritize precise waveform shaping (time domain) [15; 21]. These mappings establish traceable design flows from high-level system specifications to circuit-level implementation, promoting design clarity, modularity, and verifiability within a system engineering framework.
# Application of Wavelet Transform and Fractal Analysis for
### Reflux Diseases of the Upper Gastrointestinal Tract  
Regurgitation of gastric contents happens to everyone--it is a physiological process [2; 11]. Problems arise, however, when the stomach contents stay in the esophagus too often and for too long. A healthy human esophagus is equipped with a physiological mechanism that protects the tissues of the esophagus against gastric contents--the so-called anti-reflux barrier. There are four components to it: the gastro-esophageal junction (otherwise known as the lower esophageal sphincter--LES); the mechanism of cleaning the esophagus of hydrochloric acid through the movement of the esophageal musculature; the upper esophageal sphincter protecting the upper sections of the esophagus (the pharynx and larynx) against exposure to gastric contents; and the esophageal mucosa with a natural pH neutralizing the acid reaction of gastric juice [12]. The histological structure of the esophagus does not adapt it to excessive exposure to acid content (the stomach is also protected)--it is only the anti-reflux barrier that protects the esophageal tissues (mucosa, submuc, and muscle) from destruction. If the anti-reflux barrier fails, the mucosal, submucosal, or muscular cells may be destroyed, resulting in ulceration, necrosis or dysplasia, and ultimately metaplasia. The above processes are often the cause of esophageal cancer or pathological changes in its structure (e.g., [PERSON]'s esophagus or [PERSON]'s diverticulum). A number of lifestyle changes or new eating trends lead to the diagnosis of pathological changes in the esophagus in younger and younger people. The increase in the trend is particularly visible over the last few years [13]. Concerning infants, it was discovered that GERD has an effect on ALTEs (apparent life-threatening events in infancy), while anti-reflux therapy considerably decreases the number of ALTE episodes [14].  
A very important group of pathological diseases, directly resulting from the insufficiency of the anti-reflux barrier and the increased exposure of the esophagus to hydrochloric acid and pepsin, are gastroesophageal, laryngeal, and pharyngeal reflux diseases. The most important diseases in this group include gastroesophageal reflux disease--GERD--and laryngopharyngeal reflux disease--LPR. These changes are most often manifested by very burdensome sensations for the patient--retrosteral pain, painful heartburn, painful animents that persist at night, dysphagia, odynophagia, and others. If left untreated, gastroesophageal reflux diseases may eventually lead to pathologies within the esophageal tissues and, consequently, to the above-mentioned pathologies, including esophageal cancer [2; 15].