esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 1 Introduction
According to the requirements of energy consumption and time delay, an online monitoring framework of transmission lines in mountainous areas was designed based on the hybrid network integrating LoRa and cellular mobile network, and a dynamic group network model of LoRa was also established. The multi-objective particle swarm optimization algorithm was used to optimize the energy consumption and time delay of the system, and then the suitable working mode for the three monitoring periods can be obtained. The structure of the paper is as follows. Section 2 introduces the scheme design of the online monitoring system for mountain transmission lines and the network architecture based on LPWAN in detail. In Section 3, the proposed dynamic network grouping method was obtained by establishing various models. The experimental results are discussed in Section 4 and three working modes suitable for mountainous transmission lines were obtained. Our conclusions are presented in Section 5.
## 3 DWS Measurement Error Suppression Strategy
### Design Results of the Imaging System  
The corrective efficacy of the imaging system for angular deviations was validated using a Gaussian beam with variable angles in Zemax. This process entailed measuring the centroid coordinates of the beam at the exit pupil under different angular shifts. As delineated in Table 1, the Gaussian beams were adjusted by 0.1 mrad, 0.2 mrad, 0.5 mrad, and 0.8 mrad, respectively. The resulting centroid coordinate offsets were all under 1 \(\upmu\)m, and there was no significant alteration in the spot radius. These observations affirm that the imaging system can proficiently rectify the beam walk without inducing spot distortion.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 4 Proposed Blind Watermarking
### Blind Watermarking Extraction  
The watermark extraction is done on a fully blind process. There is no need for the cover image; only the watermarked image is needed to generate an entirely successful extraction process by applying inverse operations of the embedding process. The extraction procedures are listed in the following steps in detail:  
Step 1: Split the input watermarked image \(f_{w}(m,n)\) into \(4\times 4\) non-interfering blocks, \(B_{k}^{\prime}\),  
\[B_{k}^{\prime}=\left\{B_{k}^{\prime}(m,n),0\leq m,n<3\right\}\,\ K=1,2, \ldots.\ N^{2}/16 \tag{27}\]  
Step 2: Compute MFrLFMs for each block by using the method in Section 3.  
Step 3: Select the MFrLFM coefficients' feature vector,  
\[M_{FrLFM}^{*}=\left\{m_{FrLFM}^{*}(i),0\leq i<p\times Q\right\}\]  
As shown in the embedding process.  
Step 4: Apply the fast Walsh-Hadamard transform (FWHT) on the selected MFrLFM magnitudes using Equation (1).  
Step 5: Extract the watermark binary data bit information from the MFrLFM magnitudes by applying the inverse operation of Arnold transformation using the secret key, K, where the 1D binary bit series is extracted.  
Step 6: Reconstruct the binary watermark image from the extracted one-dimensional binary bit sequence, \(W_{1}^{*}\), by rearranging it into a two-dimensional binary image as  
\[W^{\prime}=\left\{W^{*}(x,y),\in\{0,1\},0\leq x<p,0\leq y<Q\right\}\]  
Step 7: Finally, obtain the binary extracted watermark image, \(W^{\prime}\).
# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 3 Design of Imbalanced AD Medical Data Analysis Approach
### The Feature Extraction
#### 3.1.2 Texture Feature Extraction  
We extracted various texture features, based on the Gray-Level Co-occurrence Matrix and Gray-Gradient Co-occurrence Matrix techniques. The specific steps for texture feature extraction are as follows:  
Step 1: Gray-level co-occurrence matrix. The gray-level co-occurrence matrix reflects the comprehensive information on the gray level of the image concerning direction, adjacent spacing, and variation amplitude. It is the basis of analyzing the local patterns of images and their permutation rules. Therefore, we can extract a series of features to describe the image. The texture feature extraction method was proposed by [12; 56; 57], which provides us with a calculation and theoretical basis. In this paper, the selected spatial distance is from 1 to 6 pixels, and the directions are 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\). A total of 24 gray-level co-occurrence matrices were constructed, and 12 features were employed, including angular second moment, variance, inverse difference moment, sum averages,sum variances, contrast, entropy, correlation, difference averages, sum entropy, difference entropies, and difference variance;  
Step 2: Gray-gradient co-occurrence matrix. The gray-gradient co-occurrence matrix reflects the relationship between the gray level and the gradient (or edge) of the pixel in the image [58]. The gray level of each image point is the basis of the image, and the gradient is the element that constitutes the edge contour of the image. The main information of the image is provided by the edge contour. In this paper, 15 features were employed, including small gradient advantage, large gradient advantage, gray distribution inhomogeneity, gradient distribution inhomogeneity, energy, gray average, gradient average, gray mean square variance, gradient mean square variance, correlation, gray entropy, gradient entropy, mixed entropy, inertia, and inverse difference moment.
## 2 Results and Discussion
### Aroma-Active Compounds in Yongchuan Douchi
In addition, three phenols were found the aroma-active compounds and contributed a smoky note to the douchi's aroma. P-cresol (burnt, FD = 64) and 2-methoxyphenol (FD = 16) were perceived as the smoky, burnt aromas, and the eugenol (FD = 16) was perceived as the woody, spicy aroma. Heterocyclic compounds generally have low threshold values and are powerful aromatic compounds in most heat-treated foods. However, most of the heterocyclic compounds detected by the GC-O were not the aroma-active components in our sample. 2,3-Dihydro-3,5-dihydroxy-6-methyl-4(H)-pyran-4-one (FD = 256) gave the aroma of burnt sugar odor and was also discovered in Yangjiang douchi. Acetylhyrazine (FD = 16), which provided a roasted, popcorn-like note, was the only aroma-active pyrazine compound in Yongchuan douchi, and has never previously been detected in douchi or other soybean products. Maltol (FD = 16) is a common odorant in fermented soybean foods giving a pleasant, malty and caramel smell [13; 14; 17]. The odorants phenylacetic acid (FD = 1024), phenethyl alcohol (FD = 128), benzene-etaldehyde (FD = 16), citronello1 (FD = 16) and \(\beta\)-damascenone (FD = 16) showed floral, honey-like aromas, and all of them were detected in doubanjiang. In addition, 3-methyl-1,2-cyclopentanedione (FD = 16) and \(\gamma\)-nonanolactone (FD = 16) were characterized as aroma-active compounds contributing caramel and milky aromas, respectively, and the former was first detected in douchi product. The methyl palmit (FD = 128) existed in doubanjiang, contributing the aroma of wax, and decanal (FD = 16) was detected in Yangjiang douchi, producing a green, soap-like note.
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Conventional Algorithms (CA)  
There are several conventional optimization methods that have been applied for energy optimization in UAV-based cellular networks in the literature. Conventional methods are divided into three categories: exact methods, heuristic and meta-heuristic methods. Exact methods are not sufficient, especially in operational decision processes, due to the unacceptable solution times and their inability to reach solutions in large-size problems in a reasonable time [123]. Therefore, in this survey, we focus on the remaining two categories: heuristics and meta-heuristics. Heuristic algorithms are a set of procedures that are developed to specifically address an optimization problem [115; 124]. Meta-heuristic methods can be classified in terms of various features such as neighborhood structure and searching strategies. In this study, meta-heuristic methods are discussed in terms of three basic classes, namely: evolutionary-, swarm intelligence-, and trajectory-based. However, since there are many developed meta-heuristic methods in the literature in each class, the most-known methods are examined in a general framework. The general classification of conventional algorithms can be seen in Figure 6. In the following paragraphs, we briefly discuss the common conventional algorithms.
## 5 Outage Probability Estimation  
The OP for a P2P CDMA RoFSO link has been derived in [19] as:  
\[\begin{split} P_{out,m,k}&=\frac{\xi_{\text{mod,m} }^{2}A^{(\text{N})}}{2}\sum\limits_{j=1}^{\beta}\text{a}_{j}^{(\text{N})} \left(B^{(\text{N})}\right)^{-\frac{\alpha m+j}{2}}\times\\ &\times G_{2,4}^{3,1}\left(\frac{B^{(\text{N})}E[I_{m}]^{(\text{N })}}{A_{\text{mod,m}}}\sqrt{\frac{CNIR_{k,m,th}}{CNIR_{k,m,EX}}}\left|\begin{array} []{c}1,\ \xi_{\text{mod,m}}^{2}+1\\ \xi_{\text{mod,m}}^{2},\ \alpha_{m},\ \beta_{m},\ 0\end{array}\right. \right)\end{split} \tag{20}\]  
Thus, the OP for the spatially diverse link with \(M\) statistically independent links is given as [16] (Equation (10)):  
\[P_{out,k}=\prod_{m=1}^{M}P_{out,m,k}. \tag{21}\]
## 2 Test Samples and Methods
### Preparation of Test Materials  
The test samples were collected from the 1166 working face of coal seam 16# in Sijichun Coal Mine, Guizhou Province, with an average dip angle of 13\({}^{\circ}\) and an average thickness of 1.5 m. The original gas content in the working face area was 15.83 m\({}^{3}\)/t and the original gas pressure was 1.02 MPa. There was no large fault geological structure in the working face  
Figure 1: Experimental equipment (The red arrow indicates the direction of force).  
area of this coal seam, and the coal body strike structure was stable. The coal body of this working face was selected as the material for preparing \(\Phi\)50 mm \(\times\) 100 mm standard coal samples. Considering the stress balance between the upper and lower cylinders during sample loading, the upper and lower cylinders of the coal samples were polished to make their surfaces smooth. The coal samples are shown in Figure 2.  
The fabrication process of samples differing in moisture content was as follows [20; 21]:  
1. The processed samples in Figure 2 were dehydrated and dried in a drying box and weighed (\(m_{0}\)).
2. The dry coal samples were completely soaked in a closed container filled with distilled water for 3 h.
3. The coal samples were taken out and their soaking mass \(m_{s}\) was measured. The moisture content \(w_{s}\) of the coal samples is fixed as follows: \[w_{s}=\frac{m_{s}-m_{0}}{m_{0}}\times 100\%\] (1)
4. The soaked samples were placed in the drying box and weighed every 15 min. The weighing time could be adjusted according to the specific needs until their mass reached the required value \(m_{p}\). The coal samples were taken out, placed in a constant-temperature and -humidity box, and weighed at regular intervals until the mass \(m_{u}\) of the samples remained unchanged. The moisture content \(w_{u}\) of the coal samples is expressed as follows: \[w_{u}=\frac{m_{u}-m_{0}}{m_{0}}\times 100\%\] (2)
5. Steps (2) and (4) were repeated to acquire the moisture content of different coal samples.
6. The coal samples were divided into two groups in the experiment, with a moisture content of about 1.15% and 3.24%, respectively.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Metrics and Prediction Skills
#### 4.1.1 Classification Metrics  
The metrics used to track the progress of classification models during training are loss and accuracy. Cross-entropy loss is the particular formulation in Equation (1) where the index \(i\) is the \(i\)-th training example in a dataset, \(y_{i}\) is the ground-truth label for the \(i\)-th training example, and \(\hat{y_{i}}\) is the prediction for the \(i\)-th training example [33]. Cross-entropy loss is much larger for false predictions with a high level of confidence, resulting in those predictions being more denounced. Cross-entropy loss is used in many classifier models such as MobileNet and ResNet.  
\[\text{Cross-Entropy Loss}=-\frac{1}{N}\sum_{i=1}^{N}(y_{i}\log(\hat{y_{i}})+(1- y_{i})\log(1-\hat{y_{i}})). \tag{1}\]  
Accuracy is defined by Equation (2) for binary classification models in terms of the four possible predictions: true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), and false negative (\(FN\)). This metric simply measures the percentage of correct predictions made during the validation step of training when considering the total number of predictions.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
In addition to the training metrics, the metrics used for evaluating the classification models include precision, recall, and the F1 score. Precision is defined by Equation (3) and measures the percentage of correct positive predictions when considering the total number of positive predictions made.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{3}\]  
Recall is defined by Equation (4) and measures the percentage of positive predictions made when considering the total amount of positive samples.  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{4}\]  
The F1 score is an equally weighted combination of both precision and recall. Equation (5) describes the formulation of the F1 score which implies that both FP and FN predictions are considered in determining the value. This characteristic of the F1 score makes it a well-balanced measure of model performance.  
\[\text{F1 score}=\frac{2}{\frac{1}{\text{Precision}}+\frac{1}{\text{Recall}}} =\frac{2*(\text{Precision}^{*}\text{Recall})}{\text{Precision+Recall}}. \tag{5}\]
# Similarity Model Test on Rainfall Scouring Mechanism of High-Speed Railway Subgrade Slope
## 2 Materials and Methods
### The Method of the Model Test  
The slope-building process is made up of layer-by-layer slope building, slope cutting, compaction and laying a dyed layer, as shown in Figure 4. Please note that the prepared coarse-grained soil should be mixed according to the optimal moisture content needed to reach the optimal state, and then filled in layers of 10 cm in thickness into the model box. When filling in layers, we used a rammer to compact, with a compaction coefficient K of 0.95, to ensure that the compaction requirements for subgrade slope building were met. Finally, we compacted the dyeing layer to keep the thickness of 2 cm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Slope** & **Rainfall Intensity** & **Soil** & **Slope Height (m)** \\  & **Gradient** & **(mm/min)** & **Grading** & **Slope Height (m)** \\ \hline G1 & 1:1.5 & 0.5 & S1 & 0.6 \\ G2 & 1:1.5 & 1.0 & S1 & 0.6 \\ G3 & 1:1.5 & 1.5 & S1 & 0.6 \\ G4 & 1:1.5 & 1.0 & S2 & 0.6 \\ G5 & 1:1.25 & 1.0 & S1 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Groups for the model test.  
Figure 4: Slope building process: (**a**) Layer-by-layer building, (**b**) Slope cutting, (**c**) Compaction, (**d**) Dyed layer covered.  
The procedures of the model test were as follows:  
1. Debug the rainfall device to reach the target rainfall intensity;
2. Record the time of runoff generation with a stopwatch;
3. When observing the start-up of dyed soil particles on the slope, record the color of the start-up particle size and the flow velocity using the dye tracer method as the incipient flow velocity for this size of soil particle. The flow velocity was measured by the dye tracer method, and the water depth was measured by a water level probe;
4. Every 8 min, record the slope scouring erosion patterns at different stages with the digital camera. After 40 min, turn off the water supply and collect the washed soil. Then, dry the collected soil and measure its size. After finishing one test group, weigh the total collected soil as the erosion quantity.
5. Carry out the next group of tests.
## 3 Methodology to Develop and Execute a Redispatch Model in Real-Time Operation
### Mathematical Methodology to Implement the Modeling of a Redispatch
#### 3.2.7. Mathematical Formulation Defining Maximum Dispatch Power for System Security
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Plants** & **N\({}^{*}\)** & **P\({}_{\text{MIN}}\)** & **P\({}_{\text{MAX}}\)** & **T\({}_{\text{START}}\)** & **T\({}_{\text{ON}}\)** & **T\({}_{\text{DOWN}}\)** & **C\({}_{\text{START}}\)** & **C\({}_{\text{DOWN}}\)** & **CVNC** & **P\({}_{\text{FEEL}}\)** & **CEN** & **CVC** & **CVC** & **CV = CVC** \\  & & **[MW]** & **[MW]** & **[lh]** & **[lh]** & **[ls]** & **[s]** & **[s]** & **[s/MW]** & **[ls/m\({}^{2}\)]** & **[m\({}^{2}\)/MM]** & **[s/MW]** & **[s/MW]** & **[s/MW]** \\ \hline Solar & 12 & No & Yes & No & No & No & No & No & No & No & No \\ Wind & 17 & No & Yes & No & No & No & No & No & No & No & No \\ Geothermal & 1 & No & Yes & No & No & No & No & No & No & No \\ Cogeneration & 7 & No & Yes & No & No & No & No & No & No & No & No \\ Coal & 24 & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ Gas & 37 & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ Oil & 61 & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ Hydraulic-S & 29 & Yes & Yes & No & No & No & No & No & Yes & Yes & Yes \\ Hydraulic-R & 26 & No & Yes & No & No & No & No & No & No & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Central plant by technology and the technical-economic constraints of the redispatch model. Figure 7: Structure and design of an electrical power system segmented into electrical zones for a redispatch model. Thirdly, there is the modeling of demand, which is characterized by having regulated customers, which are divided into three segments: residential, commercial, and smaller-scale industrial demand. There is also the demand of free customers, which is characterized by covering large consumers, such as mining. It is important to note that the methodology for integrating demand in the redispatch model is based on the participation factors as a percentage of the total demand distributed in each bus of the system, as shown in Table 3.  
Execution of a Real-Time Redispatch through a Ranking of Technical-Economic Emergencies in Power System Generation  
To test the redispatch model, six critical emergency cases of real-time operation are used. These case studies are called real-time extreme scenarios (RTESs) and subject the power system to complex market operation deviating from the normal values of operating cost and marginal cost. The extreme scenarios for each case study of the redispatch model cause severe disruptions in the volume of gas storage, indiscriminate use of water reservoirs, and wind resource forecast errors, according to Table 4.
## Nomenclature  
**Indices**  
\(b\)  
Battery index  
Generator index  
Load index  
Main bus index  
Time index  
**Parameters**  
\begin{tabular}{l l} \(a_{0},a_{1},a_{2}\) & Coefficients of hydrogen consumption function \\ \(c^{h}\) & Hydrogen price (\$/g) \\ \(d^{f,\max}\) & Maximum fuel cell scale \\ \(d^{f,\min}\) & Minimum fuel cell scale \\ \(E_{b}\) & Capacity of battery \(b\) (kWh) \\ \(L^{h}\) & Lower heating value (J/g) \\ \(M\) & Scaling constant \\ \(m^{f,hu}\) & Total mass of baseline PEMFCS (kg) \\ \(m^{h}\) & On-board hydrogen mass (kg) \\ \(N_{T}\) & Number of time slots (s) \\ \(N_{q}\) & Number of main bus \\ \(N_{j}\) & Number of loads \\ \(p_{b}^{\mathbb{C}}\) & Maximum charging power (kW) \\ \(p_{b}^{\mathbb{D}}\) & Minimum discharging power (kW) \\ \(w_{ij}\) & Penalty of disconnection load \(j\) from main bus \(q\) \\ \(w^{\prime}_{gN}\) & Penalty of disconnection generator \(g\) from main bus \(q\) \\ \(\eta_{b}^{\mathbb{C}}\) & Charging efficiency of battery b \\ \(\eta_{b}^{\mathbb{D}}\) & Discharging efficiency of battery b \\ \(\beta\) & Weighting parameter \\
**Decision Variables** & \\ \(b_{b,d,t}^{C}\) & Charging status of battery \(b\) at main bus \(q\) at time \(t\), 1 if charging \\ \(b_{b,d,t}^{D}\) & Discharging status of battery \(b\) at main bus \(q\) at time \(t\), 1 if discharging \\ \(C^{h}\) & Hydrogen consumption cost (\$) \\ \(C^{Ls}\) & Load shedding cost \\ \(d^{f}\) & PEMFCS scaling factor \\ \(F\) & Main objective function \\ \(m^{f}\) & PEMFCS mass (kg) \\ \(p_{t}^{h}\) & Hydrogen power of scaled PEMFCS (kW) \\ \(p_{t}^{f}\) & PEMFCS net power (kW) \\ \(p_{b,d,t}^{\mathbb{C}}\) & Charging power of battery \(b\) at main bus \(q\) at time \(t\) (kW) \\ \(p_{b,d,t}^{D}\) & Discharging power of battery \(b\) at main bus \(q\) at time (kW) \\ \(p_{j,d,t}\) & Amount of load \(j\) at main bus \(q\) at time \(t\) (kW) \\ \(p_{g,d,t}\) & Power generated by generator g at main bus \(q\) at time \(t\) (kW) \\ \(p_{g,d}^{\prime\alpha}\) & Power requirement at main bus \(q\) at time \(t\) (kW) \\ \(p_{g,t}^{\prime\alpha}\) & Power generated at main bus \(q\) at time \(t\) (kW) \\ \(SoC_{b,d,t}\) & State of charge of battery \(b\) at main bus \(q\) at time \\ \(v_{t}^{f}\) & PEMFCS commitment, 1 if commit \\ \(y_{j,d,t}\) & Connection status of load \(j\) to main bus \(q\) at time \(t\) \\ \(\mu_{1,t}\), \(\mu_{2,t}\) & Lagrangian multipliers \\
**Abbreviation** & \\ ECS & Environmental control system \\ ESS & Energy storage system \\ ECMS & Equivalent consumption minimization strategy \\ HEV & Hybrid electric vehicle \\ MIQP & Mixed-integer quadratic programming \\ MEA & More-electric aircraft \\ PEMFCS & Proton exchange membrane fuel cell system \\ PI & Proportional integral controller \\ K.K.T & Karush-Kuhn-Tucker \\ \end{tabular}