esa-sceva/satcom-chunk-collection huggingface dataset:
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 2 Compensation Models and Methods
### RBF Artificial Neural Network
Initialize the center parameters of each neuron in the hidden layer: In order to reflect the characteristics of the input information to the greatest extent, the values of the centers of neurons in different hidden layers should be as different as possible and should correspond to the width vector. In order to show the characteristics of the input information more obviously, this paper changes the initial value of the central component of each neuron in the hidden layer from small to large equal spacing, so that the weaker input information produces a stronger response near the smaller center. The size of the spacing is determined by the number of neurons in the hidden layer. Finally, the initial value of the central parameter of each neuron in the hidden layer of the BRF-ANN can be expressed as:  
\[c_{ji}=i_{\text{min}}+\frac{i_{max}-i_{\text{min}}}{2p}+(j-1)\frac{i_{max}-i_{ \text{min}}}{p}, \tag{27}\]  
where \(i_{min}\) is the minimum value entered for the \(i\)-th feature value \(i_{max}\) is the maximum value entered in the \(i\)-th feature, and \(p\) is the number of neurons in the hidden layer. Figure 4: Gaussian radial basis function. Initialize the width vector: As can be seen from Figure 4, the smaller the width vector, the narrower the image of the activation function, and the smaller the response of other neuron centers in this neuron. Its calculation formula is:  
\[d_{ji}=d_{f}\sqrt{\frac{1}{N}\sum_{k=1}^{N}\left(x_{i}^{k}-c_{ji}\right)}, \tag{28}\]  
1. Input layer to hidden layer calculation: \[z_{i}=e^{-\left\|\frac{x-c_{ji}}{D_{ji}}\right\|^{2}},\] (29) where \(c_{j}\) is the center vector corresponding to the \(j\)-th hidden layer neuron and \(D_{j}\) is the width vector of the \(j\)-th hidden layer neuron. 2. Calculation of the output layer: \[y_{k}=\sum_{j=1}^{p}w_{kj}z_{j},\] (30)
3. Updated iteration of weight: \[w_{kj}(t)=w_{kj}(t-1)-\eta\frac{\partial E}{\partial w_{kj}(t-1)}+\alpha[w_{ kj}(t-1)-w_{kj}(t-2)]\] (31) \[f(x)=e^{-\frac{x^{2}}{2\pi^{2}}}, \tag{25}\]0 (32) \[f(x)=e^{-\frac{x^{2}}{2\pi^{2}}}, \tag{25}\]1 (33) where is \(\eta\) the learning rate and \(E\) represents the loss function. In the training process, this paper first initializes the weight from the hidden layer to the output layer, the central parameters of each neuron in the hidden layer, and the width vector, and then calculates the loss function, when the loss function is less than the given threshold or the number of iterations is greater than the set number of times, stop the iteration, otherwise the gradient descent method is used to recalculate each weight until the conditions are met.
## 2 Methods
### Test Procedures  
The test procedures for this study, which are modified from [PERSON] et al. [26], are as follows:  
1. Build the slope model, installing sensors and cameras.
2. Let the slope model sit for 5 h, allowing the water inside the slope model to naturally drain.
3. Once the slope model has settled, pump water from the reservoir into the flume's headwater until a sufficient volume is reached.
4. Begin recording data (water level in the flume, seismic signal, acoustic signal, pore pressure, and video).
5. Open the shuice to release water into the flume until the water level reaches 0.3 m.
6. Allow the slope model to sit until the water level in the slope model also reaches 0.3 m.
7. Turn on the wave maker motor and record the seismic and acoustic signals due to the motor for analyzing its noise.
8. Start the test. Turn on the wave maker to create waves that erode the slope model.
9. When the slope profile reaches a state of equilibrium, stop the wave maker, and the test ends.  
The Baseline Case consists of three stages. The first stage ends when there is minimal change in the slope's geometry, at which time the wave maker was turned off. When the amplitude of the pore pressure in the slope model decreased and stabilized, the wave maker was started again, marking the beginning of the next stage of the test.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Model Scale** & \multirow{2}{*}{**Prototype Scale**} & **Douglas Sea** \\  & **(Baseline Case)** & & **Scale/State of the Sea** \\ \hline Significant wave height: & & & \\ Stage 1 & 1.75 cm & 1.4 m & 4/Moderate \\ Stage 2 & 2.29 cm & 1.83 m & 4/Moderate \\ Stage 3 & 4.7 cm & 3.76 m & 5/Rough \\ \hline Wave frequency (Period): & & & \\ Stage 1 & 0.33 Hz (3.03 s) & 0.037 Hz (27.05 s) & 0.037 Hz (27.05 s) \\ Stage 2 & 0.416 Hz (2.4 s) & 0.047 Hz (21.43 s) & 0.047 Hz (21.43 s) \\ Stage 3 & 0.5 Hz (2 s) & 0.056 Hz (17.86 s) & 0.056 Hz (17.86 s) \\ \hline \hline \end{tabular} Note: The [PERSON] model law scaling ratio, \(\lambda=1/80\).  
\end{table}
Table 3: Significant wave height H\({}_{\rm s}\) and frequency/period of the model scale (Baseline Case) corresponding to prototype scales.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 2 Concepts
### Electronic Noise, Model Error and Estimation Errors
For instance, when the observation lag is 15 s, we get 5780 measurement epochs per day, and if we assume that on average we track 15 satellites per epoch on two frequencies, we obtain 174,800 phases per receiver. For a short base with only two stations and using a technique of double differencing, in the easiest case, one has to determine, beyond the three components of the base vector, some 15,000 other parameters. Under these conditions, a least-squares adjustment gives for the coordinates and estimated error at the sub-millimeter level. This is an overly optimistic estimate due to undervaluation of the model errors, which might be individually small but altogether affect the coordinates at the millimeter level. Since the mean-square-error estimate of the coordinates is a sensible issue when we have to design an alarm, we analyze the matter more closely in the next section. Here, we close by underlining that the estimated coordinates are time-averaged over the period of the data on which they are considered as constant in the least-squares model. This mean that, for instance, if we take daily solutions or hourly solutions, they are physically two different variables, since in the former, periodic motions with a period shorter than 1 day will be averaged out, while in the latter, averaging will be effective only for periods shorter than 1 h. It follows that hourly solutions describe the motion more tightly than daily solutions; on the other hand, we expect them to be noisier since they are derived from the compensation of a smaller number of observations. Thus, there is a typical trade-off between latency of the solution and its accuracy. The choice of the length of the period of compensation depends mainly on two factors: the kind of position variation we want to monitor and the presence of a warning that we are close to a critical variation, in which case, we might want to follow the motion more closely despite the lower accuracy of the estimate.
# A Survey on Parameters Affecting MANET Performance
## 3 Simulation in MANETs
### Simulation and Attack Parameters
* Mobility speed of malicious nodes: MANETs have a dynamic network structure, which means that at certain times the network consists of some nodes that could leave the network after a while. Malicious node mobility speed is a key factor in affecting network performance. The attacker could use its speed to target an innocent node and isolate it from the network by simply taking a position between this innocent node and the destination node while traveling. * Transmission power of malicious nodes: The power of transmission for a malicious node could be valuable when the attacker aims to introduce itself as the shortest path between source and destination nodes. This malicious node can then drop the network packets later. Figure 6 summarizes the different types of MANETs' parameters.
This work is the first step towards this final issue, and the purpose here is to prove the applicability and efficiency of this method to detect and localize a defect in a single immersed plate using experimental data, where the transducers are set in the fluid above one face of the plate only. The use of leaky Lamb waves associated with the topological energy method constitutes an innovative work in view of the existing literature. Firstly, the generation and propagation of leaky Lamb waves in parallel and immersed plates are pointed out. Dispersion equations that apply to the Lamb wave propagation are solved numerically and discussed. Then, the topological energy imaging method is applied in order to detect and to localize two kinds of defects: the edge of the plate (representative of a through-crack) and a notch close to the edge. The mode A1 is experimentally generated, and a phased array system acquires the ultrasonic signature of the defect. Afterwards, the two propagation problems are solved numerically. Results eventually show the reconstructed images based on the experimental datasets in two cases.
## 4 Results and Discussion
### Experiment with Random Test Cases
#### 4.1.3. SVR  
The SVR models were built with a Radial Basis Function (RBF) kernel. This kernel function was used to transform an n-dimensional input to an m-dimensional input, where m denotes much higher than n. The RBF kernel finds the dot product in higher dimensional more efficiently than any regular linear or polynomial kernels. The main idea to use kernel is to fit a linear classifier or regression curve in higher dimensions, which consequently produces a nonlinear classifier or regression curve in lower dimensions. In our study, the size of the kernel cache was set to 400 for training and the constant regularization parameter C was set to 1.0. The maximum penalty \(\varepsilon\) (epsilon) was tuned from 0 to 0.6 for each of the SVR model to reduce SEP. Figure 11 illustrates the estimated SEP of SVR models built with different features against epsilon tuned within a certain range. With the exception of the first derivative SVR model, a decreasing trend in SEP could be observed for the SVR models with an increase in epsilon from 0 to 0.5. The lowest SEP recorded for different models is shown in Table 4. The SVR model with second derivative characteristics features achieved the lowest SEP of 18.52 mg/dL even though other models also had comparable margins of error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **SEP (mg/dL)** & **Number of PCs** \\ \hline DelT & 23.09 & 2 \\
1 st Derivative & 22.09 & 2 \\
1 st Derivative Characteristics Points & 17.02 & 2 \\
2 nd Derivative & 21.37 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of performances of different PLS models.  
Figure 10: SEP against a different number of principal components (PCs) achieved for Partial Least Square Regression (PLS) model built with different preprocessing approaches.
## 2 Methodology: Computation of the Altimetry Performance
### Non-Instrumental Error Sources in GNSS-R Altimetry  
In addition to the geometric delay to be measured, there are several other contributions to the estimated average delay: the clock offsets of receiver and transmitter relative to GPS time scale, the electromagnetic (EM) bias, the tropospheric, and the ionospheric delays.  
The EM bias basic is defined as the ratio of the average of the radar cross-section density (\(\sigma^{0}\)) times the sea surface elevation (\(\xi\)), divided by the average \(\sigma^{0}\)[10]:  
\[\beta_{EM}=\frac{\langle\xi\sigma^{0}\rangle}{\langle\sigma^{0}\rangle} \tag{6}\]  
Equation (6) takes into account that wave valleys are flatter and appear brighter in the scattered signal, than the wave crests. Therefore, the mean sea level appears to be at a lower height than it actually is.  
The tropospheric delay has two contributions, the dry delay and the wet delay. The dry delay has an average value of 2.3 m, and a residual error of \(\sim\)0.7 cm, while the tropospheric wet delay is highly variable, typically from 5 to 30 cm, and can be computed using atmospheric models or microwave radiometers, with a precision of \(\sim\)1.1 cm.  
The ionospheric delay is also highly variable, typically from 1 to 20 m, and it is estimated using dual-frequency observations with a precision of \(\sim\)0.5 cm. However, the impact of ionospheric amplitude and phase scintillations in GNSS-R have not been assessed yet.
###### Abstract
Nodes learn efficient transmission scheduling while continuously interacting with the environment without enumerating the state space. Therefore, DRLSA-IA is suitable for the highly dynamic underwater environment and has no computational restriction. * We put forth a TDIA-based reward mechanism to increase the network throughput. With the reward mechanism, nodes can take full advantage of large propagation delays to achieve parallel transmissions. * Considering large propagation delays of UWANs, we use an Long Short-Term Memory (LSTM) layer in DRLSA-IA. LSTM maintains an internal state and aggregates observations over time. Thus, DRLSA-IA can estimate the true state using history information. * We conduct extensive simulation experiments to demonstrate the performance of the proposed algorithm. DRLSA-IA outperforms in terms of throughput, packet delivery ratio, and fairness compared to the other MAC protocols. The remaining sections of the paper are structured as follows. We review the underwater MAC protocols in Section 2. The system model and problem statement are described in Section 3. Section 4 presents the proposed DRLSA-IA in detail. Section 5 shows the simulation results. Finally, we conclude the paper in Section 6.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 4 Intelligent Channel Selection Scheme
### Discrete Soft Actor-Critic Algorithm
Under the framework of the Discrete Soft Actor-Critic algorithm, and in consideration of the actual conditions of the interference environment, the required basic elements are defined as follows:  
State Space: The state space \(S^{i}\) should include the physical quantities that the relay drone needs to compute, primarily characterized by the energy state of the channel. Its definition is as follows:  
\[S^{i}=\left\{S^{1}_{mix},...,S^{F}_{mix}\right\} \tag{44}\]  
In this context, \(S^{i}_{mix}\) represents the energy state of channel \(i\). When the channel is idle, only environmental noise \(n_{0}B^{Rustov}_{n}\) is present. During normal communication, the energy is characterized by \(P_{nc}\). Based on prior information, when the cluster drones communicate at full power, the maximum reception power of the relay drone is \(P_{\text{max}}=\lambda_{i}P^{F}H^{w}_{i}\). When there is interference, the channel energy is represented by \(P_{if}\). The current energy state of the channel can be used to determine the communication status of the channel. Action Space: The action space \(A^{i}\) represents the possible choices the relay drone can make regarding channel selection during time slot \(k\). Therefore, the action space is designed as follows:  
\[A^{i}=\left\{a|a\in\{0,1\},1\leq a\leq F\right\} \tag{45}\]  
Therefore, the size of the action space is \(2^{F}\), where \(a=0\) indicates that the channel is not selected, and \(a=1\) indicates that the channel is selected. During each interaction, the agent selects only one channel for communication at a time. Reward Function: The real-time reward function \(R^{i}\) measures the approval of action \(a\) and is designed with the following expression:  
\[R^{i}= \begin{cases}1&\left(SINR_{i,R}\geq D_{n}\right)\cap(f_{i}=f_{n}^{[PERSON]} )\\ 1-\Delta r&\left(SINR_{i,R}\geq D_{n}\right)\cap(f_{i}\
eq f_{n}^{[PERSON]})\\ 0&\left(SINR_{i,R}<D_{n}\right)\cap(f_{i}=f_{n}^{[PERSON]})\\ -\Delta r&\left(SINR_{i,R}<D_{n}\right)\cap(f_{i}\
eq f_{n}^{[PERSON]})\\ \end{cases}  \tag{46}\]  
The relay drone receives the maximum reward if it successfully avoids interference while maintaining the same working channel. A slightly lower reward is given if the drone successfully avoids interference by switching channels. If the drone fails to avoid interference and does not change channels, it receives zero reward. However, if it changeschannels and still fails to avoid interference, a penalty is applied. In this setup, \(\Delta r\) is set to 0.4 to measure the reward for channel switching decisions.
## 2 Materials and Methods
### Feature Extraction
#### 2.3.4 Deep (\(D\)) Feature  
A deep learning-based prediction model (DL model) was constructed for comparison with a hand-crafted (\(R\), \(L\), and \(T\)) feature-based radiomics model. The Inception-ResNet-v2 network [34], which has been pre-trained on more than a million natural images from ImageNet database [35] was employed. This network represented the best performance in the prediction of HNSCC regression for adaptive radiotherapy [36]. Therefore, for this study, \(D\) features were extracted using transfer learning to predict the treatment failure in HNSCC from the Inception-ResNet-v2 network. The transfer learning was performed using the training dataset, and the trained DL model was validated with the test dataset. Input images have three channels (1: CT image data, 2: DD data, and 3: Sobel-filtered CT image) and were resized to 299 \(\times\) 299. We then extracted 1532 \(D\) features from the last fully connected layer after the transfer learning of this network using input images.
# A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network
## 3 Proposed Approach  
This study introduces an efficient and accurate model. The proposed CNN model is shown in Figure 3. It deals with the image as a whole. The traditional approaches deal with the image as a block. Our approach consists of three stages.  
* The first stage is preprocessing. In this stage, the image is resized to a suitable size to be inserted into the next stage without cutting any part of the entered image.
* The second stage is feature extraction. At this stage, there are four convolutional layers. Each convolutional layer is followed by the following: a max-pooling layer, one global average pooling layer, and one fully connected layer. The first convolutional layer has 16 feature maps, a filter size of (3,3), an input shape of (224,224), and an activation function (RELU). The first max-pooling layer has a pool size of (2,2). The second convolutional layer has 32 feature maps, a filter size of (3,3), a shape of (111,111), and  
Figure 3: The structure of the proposed algorithm CNN layers.  
Figure 2: Shows the overall architectural of the convolutional neural network, which includes an input layer, multiple alternating convolution and max-pooling layers, one follow connected layer, and one classification layer.  
an activation function (RELU). The second max-pooling layer has a pool size of (2,2). The third convolutional layer has 64 feature maps, a filter size of (3,3), an input shape of (54,54), and an activation function (RELU). The third max-pooling layer has a pool size of (2,2). The fourth convolutional layer has 128 feature maps, a filter size of (3,3), an input shape of (26,26), and an activation function (RELU). The fourth max-pooling has a pool size of (2,2). These hyperparameters were tabulated in Table 1. The final stage is a dense layer called the classification stage, and it classifies data into two categories: authentic or forgery. The main role of the convolutional layer is to extract features. Each convolutional layer has its own feature maps based on its specified filter. In the first convolutional layer, feature map sizes were reduced, which is important for providing next-layer feature maps. This process is called max-pooling [36]. This map works as an input to the next convolutional layer.
* The third stage is the classification stage: the output of the last block of the convolutional part represents the input of the global average pooling layer of the classification part. The final pooled feature maps of the global average pooling layer are formulated as vectors and fed to the fully connected layer. Finally, we can detect whether the input image is a forgery or authentic.
# BGP Neighbor Trust Establishment Mechanism Based on the Bargaining Game
## 3 The BGP Neighbor Trust Mechanism Based on the Bargaining Game
### Bargaining Game Model  
This section draws on the bargaining game model. The bargaining model is made of seven tuples of the form <_seller_, _buyer_, _X\({}^{acc}\)_, _X\({}^{pro}\)_, \(U_{n}\)_, \(\delta_{n}\), \(T_{n}\)>. Here, _seller_ represents the owner of the resource; _buyer_ represents the requester of good QoS; _X\({}^{acc}\)_ represents the range of acceptable service quality attribute vector for the _buyer_; _X\({}^{pro}\)_ represents the range of service quality attribute vector that the _seller_ can provide; \(U_{n}\) represents _buyer_'s or _seller_'s payoffs; \(\delta_{n}\) represents _buyer_ or _seller_'s negotiation ability; and \(T_{n}\) represents _buyer_'sor \(seller\)'s number of quotations. A bargaining game consists of three steps--setup system parameter, quote, and dicker judgment--as follows:  
1. Setup System Parameter. \(buyer\) sets the service quality attribute vector range \(X^{acc}\). \(seller\) sets the service quality attribute vector range \(X^{pro}\). \(X^{acc}\) and \(X^{pro}\) are private information and will not be disclosed to the public.
2. Quote. Within the number of quotations \(T_{n}\), given \(X^{acc}/X^{pro}\), \(\delta_{n}\) and the current quotation number \(t_{n}(t_{n}\leq T_{n})\), \(buyer/seller\) generates the \(t_{n}\)th service quality attribute quotation vector \(X^{(t_{n})}\), \(n\in\{seller,\ buyer\}\).
3. Dicker Judgment. Within the number of quotations \(T_{n}\), given the service quality attribute quotation vector \(X^{(t_{n})}\), \(buyer/seller\) calculates the payoff \(U_{n}\). When \(U_{n}\) is greater than or equal to the expected payoff, it outputs \"True\". The negotiation is successful and the game ends. When \(U_{n}\) is less than the expected payoff, it outputs \"False\". The negotiation continues.
4. If the \(buyer\) and \(seller\) fail to reach an agreement within the deadline, the negotiation ends.