esa-sceva/satcom-chunk-collection huggingface dataset:
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 3 Deep Variational Semantic Coding
### Parameterized Mutual Information
#### 3.1.2 Estimation of Mutual Information  
Given two random variables \(X\) and \(Y\), mutual information quantifies statistical dependency between them, defined as  
\[I(X;Y)=\sum_{x,y}P_{X,Y}(x,y)\log\frac{P_{X,Y}(x,y)}{P_{X}(x)P_{Y}(y)}. \tag{16}\]  
Alternatively, mutual information can be expressed in terms of KL divergence, given by  
\[I(X;Y)=D_{\text{KL}}(P_{X,Y}\|P_{X}P_{Y}). \tag{17}\]  
This formulation allows for a similar deep variational approach for mutual information estimation. Specifically, we introduce a discriminator network \(T_{\phi}(x,y)\), and the training objective is given by  
\[T_{\phi^{*}}=\arg\max_{T_{\phi}}\big{\{}\mathbb{E}_{P_{X,Y}}\big{[}\log\sigma( T_{\phi}(X,Y))\big{]}+\mathbb{E}_{P_{X}P_{Y}}\big{[}\log(1-\sigma(T_{\phi}(X,Y))) \big{]}\big{\}}. \tag{18}\]  
Once the neural network \(T_{\phi}(x,y)\) is trained, the mutual information can be estimated as  
\[I_{\phi^{*}}(X;Y)=\mathbb{E}_{P_{X,Y}}[T_{\phi^{*}}(X,Y)]. \tag{19}\]  
We note that the mutual information term in Equation (19) is estimated via a variational upper bound, which provides a tractable surrogate for optimization. While this introduces a relaxation compared to the exact mutual information, our experiments indicate that the training process converges stably, and the resulting approximation does not negativelyaffect the optimization of the overall objective in Equation (11). This suggests that the bound is sufficiently tight for the purposes of our model.
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 3 Test Results
### Training Data Selection  
For the future day loads to be predicted, the training samples are chosen from historical days with a similar load pattern. The input-target pairs are the historical temperature (predicted and actual) and load data recorded during the corresponding days in the previous 28 days (4 weeks), together with the 6 weeks around the same period of the previous year and the predicted temperatures of the future days from TCWB. To select a subset of model bases as predictors for estimating the future day loads, a training set that has 70 daily loads and temperature data corresponding to the time period shown in Figure 4 is used. The forecasting process begins every morning at 9:00 a.m. to forecast demand up to next 7 days with 15 min resolution. The test results obtained when applying the method to forecast the load in year 2018-2019 are presented. STLF performance indices, such as the mean absolute mean error (MAE), root mean square error (RMSE), absolute performance error (APE) and mean absolute percentage error (MAPE), are used to evaluate the forecasting accuracy of the model used [25].
## 3 Results and Discussion
### Radiation Shielding Properties
Among the investigated systems, those doped with RE ions, especially the PbO\({}_{2}\)-SiO\({}_{2}\)-Yb\({}_{2}\)O\({}_{3}\) system, exhibit the most promising characteristics for gamma radiation attenuation applications. Incorporating RE into the lead-dioxide-based vitreous matrix not only reduced the PbO\({}_{2}\) content but also increased material absorption.
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 5 Experiments for Clustering in WSNs Problem  
In designing and deploying sensor networks, a core demand is prolonging the lifetime of the network. A crucial factor for extending WSN lifetime is reduction of the energy consumption of the entire network. The power consumption of WSNs is affected directly by the clustering criterion problem. Furthermore, the cause of the hot spot problem in a WSN with multi-hop communications is unbalanced energy consumption. The higher energy consumption nodes are closer to the base station (BS) because of the heavy traffic flows. In this section, cBA is applied to optimize the design of the WSN by balancing the load between the CHs. We utilize adjustable parameter of loudness of BA for supporting and preventing unbalanced energy consumption. The loudness \(Ai\) of the BA can vary and alter responds to distances in clustering criterion for unequal clustering in WSN. The optimized total communication distances from the cluster members to the CHs and the CHs to the BS in the WSNs can provide the energy savings [42]. The sequence of experimental steps consists of modeling objective function, setting up a mapping solution model, describing proper agent representation, optimizing CHs, and comparing results.
###### Abstract  
The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.  
spread spectrum; adaptive; frequency hopping; clustering; network scalability; energy efficiency; network reliability; network coding; network lifetime +
Footnote †: journal:  
Article
# Interferometric Wavefront Sensing System Based on Deep Learning
## 2 Method
### Wavefront Analysis Neural Network
#### 2.2.2 Training Data Generated
Then, we use the least square method to find the value of the coefficients \(\rm C_{\rm j}\) when the variance \(\sigma^{2}\) is minimum; the functions can be expressed as:  
\[\sigma^{2}=\frac{1}{\rm N}\sum_{i=1}^{\rm N}\Biggl{(}U_{\rm mi}-\sum_{j=1}^{n} C_{\rm j}P_{\rm ji}\Biggr{)}^{2} \tag{20}\]  
\[\frac{\partial\sigma^{2}}{\partial C_{\rm j}}=-\frac{2}{\rm N}\sum_{i=1}^{\rm N }P_{\rm ji}\Biggl{(}U_{\rm mi}-\sum_{j=1}^{n}C_{\rm j}P_{\rm ji}\Biggr{)} \tag{21}\]  
\[C_{\rm j}=\frac{\sum_{i=1}^{\rm N}U_{\rm mi}P_{\rm ji}}{\sum_{i=1}^{\rm N}P_{ \rm ji}P_{\rm ji}}. \tag{22}\]  
Combining Equations (13) and (15), we can get the relationship between \(\rm C_{\rm j}\) and \(\rm a_{\rm j}\), which can be expressed as:  
\[(C_{\rm 1}C_{\rm 2}\cdots C_{\rm n})=(a_{\rm 1}a_{\rm 2}\cdots a_{\rm n})\left( \begin{array}{cccc}1&0&\cdots&0\\ a_{21}&1&\cdots&0\\ \cdots&\cdots&\cdots&0\\ a_{n1}&a_{n2}&\cdots&1\end{array} \right) \tag{23}\]  
\[a_{\rm j}=C_{\rm j}-\sum_{i=j+1}^{n}\alpha_{\rm ij}a_{\rm i},\qquad(\rm\,j=1,\,2,\,\cdots,\,n-1). \tag{24}\]  
When the index \(\rm\,j=n\), \(a_{\rm n}\) is equal to \(C_{\rm n}\). According to Equation (12), \(a_{0}\) is equal to \(\overline{W}-\sum_{j=1}^{n}a_{\rm j}\overline{Z}_{\rm j}\). Thus, we can get the value of the first 36 Zernike coefficients of the wavefront  
Since the number of the detected points is limited, the Zernike coefficients fitted here are not accurate. So, we reconstructed the wavefront based on the obtained Zernike coefficients and generated a new wavefront phase map. In this way, we not only get the phase map of the desired type but also ensure that the corresponding Zernike coefficients are accurate. We also added 20-50% salt and pepper noise with a random value within [-2, 2] to a little part of the training dataset of net1 to improve its anti-noise ability. A training dataset consisting of 10,000 pairs was generated, and 100 pairs were generated for testing. The image size is 201 \(\times\) 201 pixels. Figure 4 shows the data used in the training process.
# Estimation of Tendon Force Distribution in Prestressed Concrete Girders Using Smart Strand
## 3 Numerical Verification  
### Without Variation  
Figure 4 presents the example adopted to verify numerically the formulation process described in Section 2. The 20-m-long PSC girder (width of 0.6 m, height of 2.0 m) is equipped with three 12-strand tendons. The prestress force of each tendon is 2476 kN, and prestressing is performed sequentially downward from the tendon at the top (tendon #1). The smart strand is installed in tendon #1 and the strain is measured at seven points.  
The proposed optimization method is performed considering the analysis cases listed in Table 1 for the girder shown in Figure 4. Figure 5 compares the tendon prestress forces for each of these analysis cases. The dots for tendon #1 indicate the measured values used as input during the optimization in Equation (10). The dots of tendons #2 and #3 represent the values obtained by finite element analysis for comparison and are not used in the optimization. The lines of each tendon plot the prestress forces obtained through optimization and are seen to be in good agreement with the measured or analytical
## 3 Methodology
### Monitoring Method of Glacier Flow Velocity in Wide-Area POT
#### 3.1.2 The POT Method  
The intensity-based pixel offset tracking (POT) technique estimates pixel offsets in both the azimuth and range directions of the satellite by calculating the displacement of each pixel between two finely registered SAR images. At its core, this method relies on normalized cross-correlation calculations. Firstly, template windows and search windows of the same size were set in the primary and secondary images, respectively. Then, the cross-correlation matrix was obtained by traversing all pixels in the secondary image search window. Then, according to the position of the peak pixel of the cross-correlation matrix, the displacements of the template window and the search window of the primary and secondary image in the range direction and azimuth direction were calculated. The normalized cross correlation coefficient can be calculated as follows [13]:  
\[\gamma(u,v)=\frac{\sum_{i}^{m}\sum_{j}^{n}\left(M(i,j)-\overline{M}\right) \left(S(i+u,j+v)-\overline{S}\right)}{\sqrt{\sum_{i}^{m}\sum_{j}^{n}\left(M(i, j)-\overline{M}\right)^{2}}\sqrt{\sum_{i}^{m}\sum_{j}^{n}\left(S(i+u,j+v)- \overline{S}\right)^{2}}} \tag{2}\]  
where \(M\), \(S\) is the intensity value of template window and the search window. \((i,j)\) is the center coordinate of template window. \((u,v)\) is the center coordinate of the search window. \(\overline{M}\), \(\overline{S}\) is the average intensity value of the template window and the search window. \(m\) and \(n\) are the sizes of the matching window. The \(\gamma(u,v)\) closer to 1, the higher the match. When \((u,v)=0\), it means that the primary and secondary image windows are completely uncorrelated, resulting in a data hole.  
The ratio of the peak value of the correlation coefficient matrix to the standard deviation of the correlation coefficient matrix was used as the signal-to-noise ratio (SNR) to evaluate the confidence of the offset estimation [30]:  
\[SNR=\sqrt{\frac{\gamma^{2}(n-1)}{\sum_{i}^{m}\sum_{j}^{n}\left(\gamma_{ij}- \overline{\gamma}\right)^{2}}} \tag{3}\]where \(\gamma\) is the peak value of the cross-correlation coefficient, \(\gamma_{ij}\) is the cross-correlation value of each primary and secondary image matching window, and \(\overline{\gamma}\) is the mean value of the correlation coefficient matrix.  
The successful estimation of the offset depends on the existence of high consistency distribution characteristics of the two SAR images at the adopted window scale. When the correlation coefficient is generally at a high level, the backscatter of the primary and secondary images also remains consistent. In this case, a small matching window can achieve high accuracy. In the case of poor correlation, a large matching window is used to improve the estimation accuracy. In this case, the original image can be oversampled, and a quadratic regression function around the peak can be fitted by four-point interpolation. At the same time, oversampling the original image by a factor of 2 or higher is conducted to reduce the spectral aliasing in the image detection process.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 3 Method: LEACH-MTC Based on Moving Target State Prediction
### Classical LEACH Algorithm  
The LEACH algorithm is the earliest clustering routing algorithm, which is mainly based on the process wheel and node clustering [24; 25]. This algorithm regards a work cycle as a round and divides the work process into several rounds; this algorithm periodically executes the work process. Each round contains two instances of processing: the node cluster and the data transfer. In the processing of node clustering, cluster heads of network nodes should be selected first, and a cluster head set should be constructed. Then, cluster heads with hierarchical structured should be formed by each cluster head node and non-cluster head nodes.  
Whether some nodes become cluster heads can be judged by the threshold \(T(s_{i})\), which is as the Equation (14). Assume that the value generated by node \(s_{i}\) is \(T_{rand}(s_{i})\), if \(T_{rand}(s_{i})\leq T(s_{i})\), \(s_{i}\) is selected as cluster head node; otherwise, \(s_{i}\) is non-cluster head node.  
\[T(s_{i})=\left\{\begin{array}{cc}\frac{p}{1-p|r\bmod(1/p)|}&s_{i}\in G\\ 0&others\end{array}\right. \tag{14}\]  
where \(p\) is the ratio of the number of expected cluster head nodes selected to the total number of nodes in the monitoring area. \(r\) is the current network running time (i.e., the number of running rounds). \(G\) is the node set that has not been selected as cluster head in the latest round \(1/p\).  
In the cluster stage, each cluster head node sends signals to the network in the form of broadcast. According to the received signal strength, each non-cluster head node chooses the cluster head to join. In order to reduce network energy consumption, non-cluster head nodes are usually added to the nearest cluster head. When all the non-cluster head nodes are added to the corresponding cluster head, all the nodes complete the cluster forming.  
In the data transmission stage, cluster member nodes send some monitoring data to the cluster head according to the timing schedule. Then, the cluster head fuses the received data and transmits the fusion results to the base station.
# Blow-Up Dynamics and Synchronization in Tri-Trophic Food Chain Models
## 2 Generalized Synchronization Using the OPCL Coupling Method  
**Definition 1**.: _Let \(\mathbf{w}\in\mathbb{R}^{n}\). Then, we define \(||\mathbf{w}||_{\infty}=\text{sup}\{|w_{i}|:i=1,2,\ldots,n\}\)._  
**Definition 2**.: _Given a differentiable functional relation \(\phi:\mathbb{R}^{n}\to\mathbb{R}^{n}\) between the state variables of the driver and response system, if there exists a controller \(D(y(t),g(t))\) such that \(||y(t)-\phi(g(t))||_{\infty}\to 0\) as \(t\to\infty\), then the coupled system exhibits the property of generalized synchronization [27].__We define the OPCL coupling term (also the controller) as_  
\[D(y(t),g(t))=g(t)-G(g(t))+(H-J(g(t)))(y(t)-g(t)), \tag{1}\]  
_where \(g(t)=\alpha x(t)\) is the goal state and \(\alpha\) is a multiplicative factor [28]. Here, \(\alpha\) can be referred to as an \(n\times n\) transformation matrix whose elements may be constants, state variables of the driver system, or a combination of both. In general, we define the goal state of a three-dimensional system as_  
\[\begin{pmatrix}g1\\ \delta z\\ g3\end{pmatrix}=\begin{pmatrix}\alpha_{11}&\alpha_{12}&\alpha_{13}\\ \alpha_{21}&\alpha_{22}&\alpha_{23}\\ \alpha_{31}&\alpha_{32}&\alpha_{33}\end{pmatrix}\begin{pmatrix}x_{1}\\ x_{2}\\ x_{3}\end{pmatrix}. \tag{2}\]  
_In addition, \(H\) is an \(N\times N\) constant Hurwitz matrix with all its eigenvalues having negative real parts._  
**Remark 1**.: \(J(g(t))\) _is the Jacobian of \(G(g(t))\)._  
The OPCL coupling term is added to the RHS of the response system to obtain the desired response dynamics. We write the GS scheme as  
\[\begin{cases}x(t)=F(x(t)),\\ y(t)=G(y(t))+D(y(t),g(t)).\end{cases} \tag{3}\]  
We define the error function of the coupled system as \(e(t)=y(t)-g(t)\) and write \(G(y)\) using Taylor series expansion as  
\[G(y)=G(g)+\frac{\partial G(g)}{\partial g}(y-g)+\ldots \tag{4}\]  
We keep the first-order terms of the series expansion and substitute them into Equation (3) to obtain the error dynamics \(\dot{e}=He\). Since \(H\) is a Hurwitz matrix, then \(e\to 0\) as \(t\to\infty\) and thus obtain an asymptotically stable GS. The reader is referred to [15] for how to construct the \(H\) matrix.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 3 Results
### Validation of River Monitoring Station Data
#### 3.1.2 Comparison of Daily Data  
Figure 14 shows the comparison of daily average discharge values between HPP and the river monitoring station for the periods of 13-19 March 2023, 15-21 May 2023,  
Figure 13: Comparison of Hourly Discharge Variations.  
and 19-25 June 2023. In the comparison for the period 13-19 March 2023, the weekly average discharge for HPP was 90.70 m\({}^{3}\)/s, while it was 73.65 m\({}^{3}\)/s for the observation station. The correlation coefficient between the two datasets was found to be \(r\) = 0.99, with a determination coefficient of 97.9%. This high level of agreement indicates the consistency and reliability of the measurements.  
In the comparison for the period 15-21 May 2023, a strong correlation is observed between the HPP data and the river monitoring station. The weekly average discharge for the HPP was recorded as 58.70 m\({}^{3}\)/s, while for the river monitoring station it was 56.96 m\({}^{3}\)/s. The maximum discharge values were 75.91 m\({}^{3}\)/s for the HPP and 70.92 m\({}^{3}\)/s for the river monitoring station. These results support the accuracy of the river monitoring station data and demonstrate the consistency of the measurements when compared to the HPP data. In the comparison for the period 19-25 June 2023, small differences are observed between the HPP and the river monitoring station. The weekly average discharge for the HPP was 91.61 m\({}^{3}\)/s, while for the river monitoring station it was 81.99 m\({}^{3}\)/s. The maximum discharge was 114.41 m\({}^{3}\)/s for the HPP and 108.89 m\({}^{3}\)/s for the river monitoring station.  
The positioning of the hydroelectric power plant (HPP) approximately 450 m upstream from the river monitoring station, along with dam operations, leads to differences in the discharge values measured at the HPP and the river monitoring station. The processes of retaining water in the reservoir and controlled release back into the river channel by the HPP have a reducing effect on the total measured discharge. These operational differences are fundamental factors in explaining the variations observed between the discharge values measured at the HPP and the river monitoring station, while simultaneously reinforcing the accuracy and reliability of the data provided by the river monitoring station.
## 6 Deep Learning Fault Classification
### Convolutional Neural Network  
The architecture of the CNN [35] proposed in this work for multi-fault classification is depicted in Figure 8. The input image is the sub-array of size \(96\times M\) extracted from the features array. This input is considered a gray-level image represented in double precision, and for illustrating the architecture, we are considering images of size \(96\times 96\). Each of the filters in the convolutional layers has a size of \(4\times 4\). The first convolutional layer considers eight filters with stride one and a padding of \(1\). The next layer is a batch normalization layer, aiming to speed the training and reduce network initialization sensitivity. The batch normalization layer is followed by a Rectified Linear Unit (ReLU) layer. The combination of these three layers is denoted CBR block (CBR-B), i.e., CRB-B contains a convolutional layer, a batch normalization layer, and a ReLU layer. A max-pooling layer (MP) with size \(2\times 2\) and stride two is used for down-sampling the output from the previous ReLU layer. The two following stages include a CBR-B and MP layer. The third stage includes a CBR-B that provides 64 images of size \(12\times 12\), connected to a DropOut (DO) layer. The DO layer  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Equipment** & **Model** & **M** & **Number of Patches \(N_{B}\)** & **Patches in Dataset** \\ \hline Centrifugal & CNN & 128 & 30 & 117,000 \\ Pump & LSTM & 148 & 26 & 101,400 \\ Multi-fault & BiLSTM & 148 & 26 & 101,400 \\ \hline Reciprocating & CNN & 128 & 30 & 38,250 \\ Compressor & LSTM & 136 & 26 & 33,150 \\ Multi-valves & BiLSTM & 136 & 26 & 33,150 \\ \hline \end{tabular}
\end{table}
Table 5: Data augmentation using patches of the MFCC feature array. The parameter \(N_{B}\) represents the number of patches of size \(96\times M\) in a signal fragment. The centrifugal pump multi-fault dataset has 3900 signal fragments, and the reciprocating compressor multi-valves dataset has 1275 signal fragments.  
is finally used to feed the classification stage, composed of a fully connected layer (FC) for 13 classes and a softmax layer (SOFT).
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 4 Architecture Improvements for Multi-Target Detection  
This section introduces architectural enhancements to improve multi-target detection in infrared-visible fused images. The proposed improvements address challenges such as small-object visibility, occlusion, and feature redundancy. A dedicated detection layer is added to improve sensitivity to small targets, while a Global Attention Mechanism (GAM) helps the network focus on key spatial and channel features. In addition, a refined regression loss function, WIoU, is used to handle noisy labels and imprecise boundaries. These components work together to build a more accurate and robust detection framework for complex fusion scenarios.