esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 An Architecture for IoT and Multimedia Data Handling
### Downstream Data Flow Management
#### 4.1.2 Responsibility of the Clustered Node and Cluster Head Node  
The cluster nodes are the edge device of the access network in some cases. Therefore, they are receiving the content request message directly from the access network devices. They are located in one-hop or two-hop distance of content requester and the other access network devices are also available in the one-hop distance of content requester. When a cluster node receives any content request message from the content requester, the node itself tries to serve the request if the requested content is available in the CS of the node. If the content is not available in the CS, then the content request message is forwarded to the cluster head and the cluster head tries to serve the request based on the availability of the content. If the content is not available in the cluster head, the cluster head checks its repository to find the availability of the content within the cluster because the cluster head knows the available content information within the cluster. We apply algorithm 3 to make a cluster head from a cluster.  
**Input**: _C_List_  
**Output**: Cluster_head_list, \(k\)th list  
1 Definition of used variables:  
2 MAX\({}_{\text{mem}}\) = Maximum memory of a node;  
3 Cluster_head_list = List of the heads of all clusters;  
4 C_List = List of all clusters in the network;  
5
6 Initialization:  
7 For each Cluster k = 1 to Cluster_head_list \(\
eq\)\(\O\);  
8
9 Begin;  
10 For each node j = 1 to n, in cluster k;  
11 Inform self_memory to all cluster members;  
12 Self_memory = node[j] memory;  
13 For each node Do  
14 if Self_memory > Neighbor(j) memory  
15 MAX\({}_{\text{mem}}\) = Self_memory;  
16 else if Self_memory == MAX\({}_{\text{mem}}\)  
17 Cluster_head = node j in Cluster k;  
18 Cluster_head_list = Cluster_head_list \(\cup\);  
19 \(k\)th list = \(j\)th node is the head of the \(k\)th cluster;  
20 k++;  
21 Continue until C_List \(\
eq\)\(\O\);  
22 End;  
23 return Cluster_head_list and \(k\)th list;The content request message is forwarded to the neighbor cluster head if the content is not available within the cluster. Similarly, the neighboring cluster heads do the same procedure to serve the request. Following these procedures, a content request message is served by the core network. The cluster head handles the caching mechanism within the cluster by keeping the information of all the cluster nodes. The most popular content is cached in the cluster head within the core network. Each node knows the available free cache memory of self-node and they share this information with the cluster head. As a result, the cluster head can share the more popular content with them, and the popular or the less popular contents are also cached based on the availability of the free memory in the cluster node. The cluster head also shares the information with the neighboring cluster head and maintains the same caching mechanism.
## 5 Hypothesis Testing and Discussion
### Testing Hypothesis H1  
**Proof of Hypothesis 1 (H1).** In hours with high generation from solar and wind resources, there is a systematic decrease in the market price of electricity and an increase in price volatility. \(\square\)  
The results of the analyses show that increased generation from renewable sources--mainly wind and solar--is associated with a decrease in average electricity prices and a concomitant increase in their short-term volatility. This effect has been particularly observed in countries with high RES penetration (e.g., Germany, Estonia), where significant price fluctuations, including negative prices, have been observed during 2022 and 2024. At the same time, during periods of high demand and low RES generation (e.g., winter 2021), average prices were found to increase significantly, indicating a strong link between variable supply and market equilibrium.  
This behaviour of markets is consistent with Hypothesis H1, which is supported by available data across multiple countries and time periods.
## 2 Materials and Methods
### Features  
The dataset used in the context of the current study is a minimalistic one. Being based on five features and one reference feature, this dataset exclusively employs clinical data, without any image data whatsoever. This adds to the versatility and applicability of the prediction model, as no special information is needed for a possible application of the classification mechanism to most relative datasets.  
On the matter of age, we opted for a form of normalization. Given that the rest of the fields had integer values, we split up the age information into four different fields. More specifically, the 4 different fields used for age are <40, 40-50, 50-60, and >60. This way, it is also easier to highlight common patterns between specific age ranges and each class of this scenario (adenoma/MG).  
The most important information for this dataset is included in the three medical indexes used: the max diameter index, the number of deficiencies, and the Wisconsin index. All the above indices have been proposed in ref. ([PERSON] et al., 2017) as part of a scoring system for the prediction of multigland disease in primary hyperparathyroidism. According to their study, the bigger the score (corresponding to the summation of all the above indices), the higher the probability of multigland disease.  
In particular, as reported in the literature, the imaging method of four-dimensional computed tomography (4D-CT) was used to build a scoring model based on the features  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Feature Name** & **Description** & **Feature Class/Type** \\ \hline
1 & Sex & Male/female & Demographics \\
2 & \textless{}40 & Aged under 40 & Demographics \\
3 & 40–50 & Aged between 40 and 50 & Demographics \\
4 & 51–60 & Aged between 51 and 60 & Demographics \\
5 & \textgreater{}60 & Aged over 60 & Demographics \\
6 & Max diameter index & Maximum diameter of findings & Medical index \\
7 & Num of defs & Number of findings & Medical index \\
8 & Wisconsin index & Wisconsin index & Medical index \\
9 & Parathyroid gland & Adenoma or multiglandular (MG) & Reference variable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features used as input by the prediction model, after binary normalization.  
of 4D-CT, including largest lesion size and the number of suspicious lesions. The 4D-CT MGD score achieved a good specificity of 81-96% and variable sensitivity of 39-64% for predicting MGD. Moreover, the previous study reported that patients with multigland disease had significantly lower Wisconsin index scores, smaller lesion size, and a higher likelihood of having either multiple or zero lesions identified on 4D-CT.
## 5 Parameter Analysis
### Parameter Analysis for the Highest Any Number's Generation \(k_{min}\)  
The highest any number's generation \(k_{min}\) is used to set the generation of the initial any number \(N(0)\) needed to remain. The value of the \(k_{min}\) determines from which generation the any number begins to decline adaptively. If \(k_{min}\) is too low, the algorithm is easy to fall into a locally optimal solution. If \(k_{min}\) is too high, the algorithm will need to spend much more time. Therefore, the selection of an appropriate highest any number's generation is very important.  
In this section, experiments are carried out on AD-ADENSGA with different values of \(k_{min}\).  
In this experiment, set \(N(0)=50\), \(k_{max}=200\), set 7 different values for \(k_{min}\in[0\ 5\ 10\ 15\ 20\ 25\ 30]\). According to Section 4.3, set \(Score_{limit}=0.035\), \(k_{limit}=40\). Other parameters used in the experiment are shown in Table 1.  
The influences of the highest any number's generation \(k_{min}\) for a random node placement are shown in Figure 7. Each figure is composed of (a), (b), (c) and (d), respectively corresponding to four evaluation indexes, average Coverage, average Rest Energy, average \(Score\) and max \(Score\).  
In order to observe the influence of \(k_{min}\) on the optimization results more intuitively, the average any number of five runs changing with the generation is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(N\) & **2** & **30** & **50** & **70** & **90** \\ \hline Average running time/s & 1245.1 & 1248.1 & 1246.4 & 1251.2 & 1250.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running time for different values of \(N(0)\).  
Figure 6: The average any number changing with the generation for different values of \(N(0)\).  
Figure 7a shows the influence of \(k_{min}\) on the objective of coverage. When \(k_{min}\) is too low, the optimization speed and final results of coverage are poor, the algorithm may fall into local optimum. Figure 7b shows \(k_{min}\) has little influence on the objective of rest energy. Figure 7c,d show that considering the two optimization objectives comprehensively, \(k_{min}\) should be chosen over 10.  
In this paper, \(k_{min}=15\) is chosen.
## 3 Results and Discussion  
### Selection of the Monitored Emission LInes  
Two different emission lines were recorded for each analyte. These emission lines were 338.289 nm and 328.068 nm (Ag), 237.313 nm and 308.215 nm (Al), 208.957 nm and 249.772 nm (B), 233.527 nm and 230.425 nm (Ba), 190.171 nm and 223.061 nm (Bi), 396.847 nm and 317.933 nm (Ca), 214.440 nm and 226.502 nm (Cd), 228.616 nm and 238.892 nm (Co), 283.563 nm and 357.869 nm (Cr), 224.700 nm and 324.752 nm (Cu), 239.562 nm and 238.204 nm (Fe), 279.077 nm and 280.271 nm (Mg), 259.372 nm and 257.610 nm (Mn), 221.648 and 232.003 nm (Ni), 220.353 nm and 217.000 nm (Pb), 190.801 nm and 276.787 nm (TI), 202.548 nm and 213.857 nm (Zn). The selection of the monitored emission lines was based on their sensitivity (in terms of the slope of the respective calibration curve for each analyte), their linearity and the absence of potential interferences. As such, the following emission lines were finally chosen: 328.068 nm (Ag), 308.215 nm (Al), 249.772 nm (B), 230.425 nm (Ba), 223.061 nm (Bi), 317.933 nm (Ca), 226.502 nm (Cd), 238.892 nm (Co), 357.869 nm (Cr), 324.752 nm (Cu), 238.204 nm (Fe), 280.271 nm (Mg), 257.610 nm (Mn), 232.003 nm (Ni), 217.000 nm (Pb), 276.787 nm (TI), and 213.857 nm (Zn).
## 2 Background
### Hybrid Wireless Mesh Protocol
#### 2.2.2 Operation Mode [23]
The root MP forwards the data frame to the destination together with an indication that both the source and destination are in the same mesh. This data frame activates the destination MP to initiate a path discovery for the destination. This procedure will establish the optimal path between the source and destination MPs, and the subsequent data frames will be forwarded on this path.
# Study of the Dynamic Response of a Rigid Runway with Different Void States during Aircraft Taxing
## 4 Analysis of Results and Discussion
### Analysis of Dynamic Displacement Calculation Results with Void  
When the main landing gear was located directly above the acquisition path of the VDD, the VDD results for the middle of the surface layer were extracted from several groups of models with different degrees of the void.  
For the slight void state, the change in the dynamic displacement of the surface layer with the decrease in \(\xi\) is shown in Figure 11a. With the decrease in \(\xi\), the instantaneous VDD of the pavement increased on the whole, and the variation in the VDD on the VDD acquisition path gradually became larger from both sides to the runway center. \(\xi\) varied from 1 to 0.1, and the variation in the VDD of the road surface was not obvious, while the five curves almost overlapped, and the VDD in the runway center was about 0.138 mm. When \(\xi\) was 0.01, 0.001, or 0.0001, the VDD in the runway center was 0.151 mm, 0.164 mm, or 0.166 mm, respectively. The difference between the curves was minimal when \(\xi\) was 0.001 or 0.0001, indicating that the change in the VDD of the pavement was not noticeable when the modulus of the void area was less than 2.692 MPa. Figure 11b shows the results of the regression analysis of the VDD in the middle of the surface layer with \(\xi\), where \(U_{Y,0}\), \(U_{Y,L}\), and \(U_{Y,R}\) represent the VDDs in the middle of the surface layer in the runway center and under the left and right main landing gears. \(U_{Y,0}\) showed a negative exponential variation law with \(\xi\), with a coefficient of determination of 0.99932. \(U_{Y,L}\), and \(U_{Y,R}\) showed a logarithmic variation relationship with \(\xi\), with coefficients of determination of 0.9853 and 0.9855, respectively.  
When the pavement slab had a serious void, with the increase in \(S_{V\!A}\), the VDD of the surface layer changed as shown in Figure 12a. With the increase in \(S_{V\!A}\), the VDD under the two main landing gears had a relatively obvious increase. The VDD in the runway center was the most obvious distinction. The VDD of the surface layer was 0.139 mm when it had no void; compared with that when \(S_{V\!A}\) assumed values of 0 m\({}^{2}\), 0.75 m\({}^{2}\), 1.50 m\({}^{2}\), 2.25 m\({}^{2}\), 3.00 m\({}^{2}\), 3.75 m\({}^{2}\), and 4.5 m\({}^{2}\), the increases were 10.1%, 16.5%, 25.9%, 37.4%, 50.4%, and 64.7%, respectively. Figure 12b shows the regression analysis results of the VDD in the middle of the surface layer with \(S_{V\!A}\), where \(U_{Y,0}\), \(U_{Y,L}\), and \(U_{Y,R}\) represent the VDD in the middle of the surface layer in the runway center and under the left and right main landing gears. It was obtained that \(U_{Y,0}\), and \(S_{V\!A}\) showed an exponential variation law with a coefficient of determination of 0.99857. \(U_{Y,L}\), and \(U_{Y,R}\) showed a linear variation relationship with \(\xi\) with the coefficients of determination of 0.99907 and 0.99885, respectively.  
Figure 10: VDDs of different structural layers.
## 4 Prediction Performance Analysis  
We use the temperature readings from the four crucial thermal points at a spindle speed of V4 = 5500 r/min and the corresponding thermal error data to serve as the training dataset and the temperature measurements from the four identified temperature-sensitive points of the remaining six sets of experiments and the recorded data of the associated thermal error measurements as the test set for the analysis and establishment of the model. Before feeding the input data into the model, data preprocessing was performed to normalize the input data to the range of (0, 1), ensuring uniformity in the magnitude of the input variables and eliminating potential adverse effects on the model caused by outlier data. Here, the min-max normalization method was used for data preprocessing. The formula is as follows:  
\[w_{nor}=\frac{w-w_{min}}{w_{max}-w_{min}} \tag{25}\]  
In the formula, \(w\) represents the original data value: \(w_{max}\) and \(w_{min}\) represent the maximum and minimum values in the original data samples, respectively: and \(w_{nor}\) represents the normalized value after preprocessing. After data normalization, the speed of gradient descent in finding the optimal solution can be accelerated, leading to improved model efficiency. To demonstrate the effectiveness of the proposed POA-CNN-LSTMNN model, its prediction performance was analyzed in comparison with three additional distinct neural network architectures: the CSOA-CNN-LSTMNN model, the CNN-LSTMNN model, and the LSTMNN model. Throughout the training process, the input and output data remained consistent across all models. Finally, each model s performance was assessed by utilizing the test set data in diverse operational scenarios. Both the POA and the CSOA are promising optimization techniques that have been recently developed for enhancing the optimization of neural network parameters. The convergence curve of their fitness functions is shown in Figure 12. As is evident from Figure 12, the number of iterations needed for POA to converge was lower than that of CSOA across various speeds, indicating that POA exhibited superior optimization performance compared to CSOA. \begin{table}
\begin{tabular}{c c c} \hline
**Temperature Measuring** & **Pearson Correlation Coefficient** & **Clustering** \\
**Point** & **Value** & **Category** \\ \hline T1 & 0.805973 & 1 \\ T2 & 0.864496 & 1 \\ T3 & 0.845729 & 1 \\ T4 & 0.854433 & 1 \\ T5 & 0.838828 & 1 \\ T6 & 0.842743 & 1 \\ T7 & 0.881779 & 1 \\ T8 & 0.829695 & 1 \\ T9 & 0.952268 & 3 \\ T10 & 0.996416 & 2 \\ T11 & 0.801127 &Let us take a look at the dissimilarity between the predicted and the true thermal error curve of each model on different test sets. As can be seen in Figures 13-18, the POA-CNN-LSTMNN model s thermal error prediction curves most closely matched the actual curves for each speed, followed by the CSOA-CNN-LSTMNN model and the CNN-LSTMNN model, and the LSTMNN model s predictive curves exhibited the greatest deviation from the actual values.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 3 Methodology
### Convolutional Neural Network Model  
The CNN structure consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer, as illustrated in Figure 1.  
At the input layer, n nodes correspond to n real-time measurements. In the first convolutional layer, filters are applied to the input windows, creating features via convolution, normalization, and ReLU for nonlinearity. The feature map c1j of the initial convolutional layer generated from the input data z can be expressed as:  
\[\mathrm{c_{1j}}=\mathrm{ReLU}(\mathrm{z}*\mathrm{h_{1j}}+\mathrm{b_{1j}}) \tag{4}\]  
where h1j denotes the j-th convolution kernel, and b1j denotes the corresponding biases. The features generated by the filters in the (q-1) convolution layer are used as inputs to the q convolution layer and are processed similarly. The output can be expressed as:  
\[\mathrm{c_{qj}}=\mathrm{ReLU}(\mathrm{c_{q-1j}}*\mathrm{h_{qj}}+\mathrm{b_{qj}}) \tag{5}\]  
where cqj denotes the j-th feature map of the q-th convolution layer. The extracted features learned by the last convolution layer are combined into a single vector in the flattened layer and fed into the fully connected layer via the ReLU activation function, as follows:  
\[\mathrm{c_{Fj}}=\mathrm{ReLU}\big{(}\mathrm{w_{F}}\times\mathrm{c_{q^{max}}} +\mathrm{b_{F}}\big{)} \tag{6}\]  
Figure 1: CNN model structure.  
where \(\rm c_{Fj}\), \(\rm w_{F}\), and \(\rm b_{F}\) denote the feature map, the weight, and the deviation of the flattened layer, respectively. In the fully connected layer, every node is connected to n nodes in the output layer. Outputs are normalized to a 0-1 range by the sigmoid function, enabling the categorization of each measured value's nature. The ultimate multi-label classification result \(\rm\hat{y}_{j}^{t}\) is formulated as:  
\[\rm\hat{y}_{j}^{t}=\rm sigmoid(w_{D}\times c_{F}+b_{D}) \tag{7}\]  
where \(\rm w_{D}\) and \(\rm b_{D}\) denote the weight and the bias of the fully connected layer, respectively.
# Set-Point Control of a Spatially Distributed Buck Converter
## 5 Closed-Loop Control of the Distributed Buck Converter
### Discontinuous Control Mode  
As has been described before, the actuation for the buck converter is achieved via a switching element. The described continuous operation modes can be seen as a certain approximation assuming high switching frequencies. However, for a practical application it is preferable to actuate the switch directly. A simple switching control law based on the sign of the mean current deviation along the transmission line can be derived from the time derivative (9) of the control-Lyapunov functional (8):  
\[d(t)=\begin{cases}1&\text{if }\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z>0,\\ 0&\text{if }\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\leq 0.\end{cases} \tag{13}\]  
In Equation (13), \(d\in\{0,1\}\) is the switch signal depicted in Figure 2, i.e., \(d=0\) switch open and \(d=1\) closed switch. The according voltage at the receiving end of the transmission line hence becomes  
\[v(0,t)=\begin{cases}E&\text{if }\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z>0,\\ 0&\text{if }\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\leq 0,\end{cases} \tag{14}\]  
where the reference current \(i_{d}\) is assumed to be constant as in Section 5.1.  
To prove the closed-loop stability applying the control law (14), the time derivative (9) of the control-Lyapunov functional (8) is investigated for two cases: positive and negative including zero mean current deviation. In case of a positive mean current deviation, i.e., \(\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z>0\), the switch is closed and the input voltage \(E\) is applied to the sending end of the transmission line \(v(0,t)=E\). Assuming that the input voltage is sufficiently high to compensate for the transmission losses, which is an obvious design requirement, the following inequality holds:  
\[v(l,t)+L^{\prime}\int_{0}^{l}\frac{\partial i_{d}}{\partial t}\,\mathrm{d}\,z +R^{\prime}\int_{0}^{l}i\,\mathrm{d}\,z\leq E \tag{15}\]  
Due to this inequality (15) the time derivative (9) of the control-Lyapunov functional (8) is in case of a positive mean current deviation less or equal than zero:  
\[\dot{V}=\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\,\frac{1}{L^{\prime}}\left[v(l,t )+R^{\prime}\int_{0}^{l}i\,\mathrm{d}\,z-E\right]\leq 0. \tag{16}\]  
For a negative or vanishing mean current deviation \(\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\leq 0\), the switch is open and no input voltage is applied \(v(0,t)=0\). As both current and voltage are assumed to be non-negative the following inequality results:  
\[v(l,t)+R^{\prime}\int_{0}^{l}i\,\mathrm{d}\,z\geq 0. \tag{17}\]  
Therefore, the time derivative (9) of the control-Lyapunov functional (8) is again less or equal than zero:  
\[\dot{V}=\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\,\frac{1}{L^{\prime}}\left[v(l,t )+R^{\prime}\int_{0}^{l}i\,\mathrm{d}\,z\right]\leq 0, \tag{18}\]  
which proves stability in the sense of Lyapunov with respect to the discrepancy \(\rho\).