esa-sceva/satcom-chunk-collection huggingface dataset:
# TEDL: A Text Encryption Method Based on Deep Learning
## 10 Experiments and Performance Analysis
### Correlation
#### 10.4.1 Directly Using Word Vector Table  
In case 1, the correlation between the encrypted data and the original data is measured by the cosine similarity defined by Equation (9), rewritten as follows:  
\[sim_{xx}(w)=\cos\Big{(}\left.\mathbf{v}_{\alpha}\right|_{w}\cdot\mathbf{v}_{ \gamma}^{T}\right|_{w}\Big{)} \tag{43}\]  
We explore the effects of different training conditions, including the number of iterations (_epoch_), the vector dimension (\(D\)), the ratio of \(C_{\beta}\) to \(C_{\alpha}\) (\(C\)_ratio_) and the size of window (_window_), which indicates the maximum distance between the current and predicted word within a sentence in the word embedding model.  
Changing **epoch** and **D**  
For other parameters, we set \(C\)_ratio_ = 1:10,000, \(seed=1\), \(window=5\). We sort the \(sim\) of each word from small to large and draw as Figure 12. Obviously, as \(D\) and _epoch_ increase, the correlation between the original data and the encrypted data decreases. Note that in the case where \(epoch=1\), regardless of the word vector dimension, there is a phenomenon that the correlation is 100%. On the other hand, as long as _epoch_ is greater than 1, this phenomenon no longer exists. In addition, considering that as _epoch_ increases, the training process converges, which means word vectors update slightly. Therefore, the setting of epoch is not included in the key but should be agreed at the algorithm level.  
Changing **C ratio**  
For other parameters, \(epoch=2\), \(D=200\), \(seed=1\), \(window=5\) and we test the correlation at a ratio of 1:10,000, 1:1000, 1:100. The results are shown in Figure 12(a). As \(C\)_ratio_ increases, the \(sim\) decreases, which is in line with expectations. However, the result  
Figure 12: Correlation (measured by \(sim\)) as \(D\) and _epoch_ change.  
relies on language consistency in \(C_{\beta}\) and \(C_{\alpha}\). If not, will the encryption effect drop? We answer this question in the next experiment. If not specified, subsequent experiments also proceed under the conditions mentioned above.  
Language Inconsistency Exists  
Unlike previous experiments, pure Chinese corpus serves as \(C_{\beta}\). Furthermore, we change the \(C\)_ratio_ to observe trends in correlation, pictured as Figure 12(b). Obviously, the encryption effect is not much different from the last result, which indicates the impact of language inconsistency is negligible.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 1 Introduction
### Contributions  
In this paper, we propose for the uplink a hybrid analog-digital MU equalizer for wideband mmWave mMIMO systems using GFDM as an access technique. To the best of our knowledge, hybrid MU equalizer schemes specifically designed for mmWave mMIMO GFDM systems have not been addressed in the literature yet. Given the hardware limitations of these systems, our design options are as follows:  
* The use of GFDM, which deals with multi-path effects as the OFDM scheme, but the OOB emissions and PAPR issues are decreased.
* The use of low-complexity UTs, employing a phase shifter network to perform the analog precoding. Two types of analog precoders are used based on the levels of CSI knowledge at the UTs: random and AoD-based precoders.
* The use of hybrid analog-digital receiver structure, because to have one dedicated RF chain per antenna would be impractical owing to hardware costs and power consumption.
* It is assumed that the analog coefficients are constant over the subcarriers owing to hardware constraints, because, if the analog equalizer is designed as a frequency selective filter, additional hardware would be needed. This assumption is followed by most of the previous works on hybrid beamforming [23; 24; 25]. The main contributions of this paper are as follows:
* The analog coefficients are derived by minimizing the MSE between the hybrid approach and the full digital counterpart. They are computed by selecting a set of vectors from a dictionary based on array response vectors of channel.
* The ZF criterion is considered at the digital part on a per subcarrier basis to remove the interference not mitigated on the analog part.
* A semi-analytical, yet accurate approach for obtaining the performance of the proposed hybrid GFDM system is also proposed.  
The remainder of this paper is organized as follows. Section 2 describes the system model considered in this work. Sections 3 and 4 present the design of the transmitter and receiver, respectively. Finally, the main performance results are shown in Section 5 and the conclusions are drawn in Section 6.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Performance Metrics  
#### 2.4.1 PDR  
PDR compares the number of packets successfully received by the destination node (\(Rn_{i}\)) with the total number of packages sent by the source node (\(Sn_{i}\)). PDR serves to measure the success of the delivery ratio. The higher the PDR value, the better the network performance [28; 29]. PDR is one of the QoS parameters. It indicates the success rate of a routing protocol and can be calculated using Equation (1).  
\[\text{PDR}=\frac{\left(\sum_{i=0}^{\text{N}}\text{Rn}_{i}\right)}{\left(\sum_ {i=0}^{\text{N}}\text{Sn}_{i}\right)}. \tag{1}\]
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 4 Methodology
### Evaluation Metrics  
In order to evaluate the performance of the approaches used, four statistical indicators were calculated: MAE (mean absolute error), RMSE (root mean square error), R\({}^{2}\) Square (coefficient of determination), and ESV (explained variance score):  
* Mean absolute error (MAE) is the mean of the absolute value of the errors; this indicator represents the average of the absolute difference between the actual and predicted values in the database. It measures the average of the residuals in the data set (29). \[MAE=\frac{1}{N}\sum_{i=1}^{N}{|y_{i}-\hat{y}|}\] (29) \[\frac{\hat{y}-\ \text{predicted value}}{\overline{y}-\ \text{ mean value}}\]
* Root mean squared error (RMSE); this measure represents the root mean square error of the squared difference between the original and predicted values of the model (30). \[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{(y_{i}-\hat{y})^{2}}}\] (30)
* R-Squared expresses the ratio of the variance of the variable explained by the model with the original value (31).  
\[R^{2}=1-\frac{\sum\left(y_{i}-\tilde{y}\right)^{2}}{\sum\left(y_{i}-\overline{y} \right)^{2}} \tag{31}\]  
Explained variance score (EVS); this metric expresses the rate representing the variance of the current value (true value) and the estimated value (32).  
\[\text{Explained variance}(y,\hat{y})=1-\frac{\text{Var}(y\ -\ \hat{y})}{\text{Var}(y)} \tag{32}\]
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 1  
#### 5.2.1 Residual Energy  
Figures 14-16 present comparisons of residual energy across different protocols at various network densities. As seen in Figure 12, with 100 nodes, the CHEF and EEUC protocols experienced near energy depletion around the 780 th round. In contrast, FQ-UCR retained 40.96% of its energy and continued operating until the 2477 th round. Figure 13 shows the residual energy performance with 200 nodes, where EEUC and CHEF depleted their energy around round 950. However, FQ-UCR still had 50.43% of the remaining energy. Figure 14 displays results for the 400-node experiment, with EEUC and CHEF running out of energy around the 1110 th round, while FQ-UCR operated beyond the 2499 th round. This advantage became more pronounced as the node count increased. Overall, FQ-UCR outperformed other protocols in energy efficiency, leading to a longer network lifespan and improved overall performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Scenario 1** & **Scenario 2** \\ \hline Network size & 200 m \(\times\) 200 m & 200 m \(\times\) 200 m \\ Number of sensor nodes & \(100,200,400\) & \(100,200,400\) \\ Base station location & \((100,100)\) for Scenario 1 & \((100,250)\) for Scenario 2 \\ Initial energy & 0.5 J & 0.5 J \\ Data packet size & 4000 bits & 4000 bits \\ Control packet size & 100 bits & 100 bits \\ \(E_{elec}\) & 50 nJ/bit & 50 nJ/bit \\ \(e_{fs}\) & 10 nJ/bit/m\({}^{2}\) & 10 nJ/bit/m\({}^{2}\) \\ \(\epsilon_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation-related parameters.  
Figure 13: (**a**) Node distribution for Scenario 1. (**b**) Node distribution for Scenario 2.  
Figure 16: The figure compares the residual energy (in Joules) of 400 nodes, with the BS located at (100, 100), across different protocols.  
Figure 14: The figure compares the residual energy (in Joules) of 100 nodes, with the BS located at (100, 100), across different protocols.  
Figure 15: The figure compares the residual energy (in Joules) of 200 nodes, with the BS located at (100, 100), across different protocols.
## 1 Introduction
### Literature Review  
#### 1.2.1 Conventional Contact-Type Methods  
Conventional contact-type methods, such as the centrifugal tachometer [11; 12] and magneto-sensitive sensors [13], rely on direct mechanical or electromagnetic coupling with rotating components to acquire rotational speed data. The centrifugal tachometer, historically employed in early engine testing, utilizes weighted flyweights driven by a rotational motion to generate centrifugal force, which is mechanically translated into a displacement signal proportional to rotational speed. While this method achieved accuracies within \(\pm\)0.2% in controlled laboratory environments [14], its application necessitates physical integration with the crankshaft or camshaft, requiring partial engine disassembly for installation. For instance, in diesel engine performance studies, centrifugal tachometers were mounted on exposed crankshaft ends to confirm their combustion efficiency under variable loads [15]. However, such invasive configurations risk inducing mechanical stress concentrations and oil seal degradation, limiting their utility to prototype validation rather than in-service diagnostics [16].
## 1 Introduction
However, the GH-based defense method may cause undesirable intersample behavior, since the signal generated by GH is typically uneven. The strategy employing GS is a more recently introduced method that overcomes the shortcomings of intersample behavior of the GH-based defense method. The GS constructs a new output by taking a weighted average of multiple output measurements from one sampling interval [20; 24], replacing the simple sampler. It is shown that the zeros can be assigned arbitrarily by using GS, hence, it can be used as a countermeasure against ZDA. Since the operation of GS does not affect the plant input, undesirable intersample behavior no longer occurs. The output of GS, however, may differ from that of a simple sampler and can be sensitive to the sensor noise. Most recently, cyberattacks including ZDA and their countermeasures are developed for more general systems having multiple agents. In [26], the concept of ZDA is generalized to a cooperative attack on multiagent systems considering network switching and a defense strategy involving the design of a series of network switching and the Luenberger observer has been proposed. In addition, the detection of cyberattacks including the false-data injection attack and replay attack is studied and applied to DC microgrids in [27], where the Luenberger observer and the unknown input observer are used to estimate the states of an agent and its neighbors. In this paper, we apply two security strategies based on GH and GS to wind turbine systems. Firstly, a GH is designed so that all the zeros of the sampled-data model of the wind turbine system are located inside the unit circle. It is shown through numerical simulations that the presence of the ZDA that has been designed using the unstable sampling zero is revealed shortly after the attack is injected. The effect of GH on the intersample behavior of the generator angular velocity is also discussed. Secondly, we shift the unstable sampling zero into the stable region by employing the GS, which reveals the presence of ZDA clearly. It is emphasized that the undesirable intersample behavior does not appear anymore. In addition to this advantage, the numerical simulations demonstrate that sensitivity to noise can be reduced substantially by properly choosing the zeros. The rest of this paper is organized as follows. In Section 2, we briefly describe the wind turbine's modeling. Section 3 addresses that the digitally controlled wind turbine system is vulnerable to ZDA. Section 4 briefly explains how to change the zeros of a system by using GH and GS. In Section 5, we apply the design of GH and GS against ZDA and demonstrate that ZDA can be neutralized via numerical simulations. Finally, Section 6 concludes the paper.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
## 3 Results
### Modulation Performance under Feedback
#### 3.2.3 Power Penalty  
The power penalty at the FEC limit of \(1\times 10^{-3}\) was summarized in Table 1. For the BtB configuration, the dependence of power penalty on the feedback levels is positively correlated. For the 20 km transmission, a positive correlation still exists. At the early stage of \(-30\) dB and \(-20\) dB, the penalty is slightly less than that in BtB due to the influence of the eye diagram distortion and the fiber dispersion. The feedback-induced power penalty remains below 1 dB until \(-12\) dB in BtB and \(-15\) dB, respectively, for the case of BtB and 20 km transmission, which satisfied the requirements of ONU transmitter in XG(S)-PON.
## 3 Model and Methods
### Implementation Details  
Our network was written in Python 3.7. The module and training environment in the network architecture was PyTorch 1.8.0. The main hardware was an NVIDIA GeForce RTX 3090 with 24 GB of memory. The backbone network used ViT-B16 [5], which was pre-trained on ImageNet to improve the performance and accuracy of the network. In the preprocessing of the dataset, we divided the video into eight segments, randomly took one frame from each segment, and used these eight frames to form a new video sequence to represent the video. The image size in the video sequence was uniformly adjusted to 256 \(\times\) 128 pixels, and random erasure, random cropping, and horizontal flipping were used for data enhancement. The batch size was set to 64 and contained eight video sequences. The overall structure of APMG network is shown in Figure 2. In the CAP module, the sliding window for preprocessing each frame of the video image was 4 \(\times\) 4 (\(a\) = 4) and the number of generated patches \(v_{an}\in\mathbb{R}^{h\times xv\times c}\) without overlapping pixels was 64 \(\times\) 32 \(\times\) 768. The sliding window for splitting patches was 4 \(\times\) 4 (\(b\) = 4) and the quantity of feature embeddings partitioned in the window was \(p\) = 2. After the CAP module had completed its task, the number of feature embeddings generated for each frame image was \(N\) = 256. The number of network layers was \(l\) = 12. For network training, the learning rate was initially set to 0.09, and the cosine algorithm was used to adjust the learning rate. The optimiser used stochastic gradient descent.
## 3 Methodology
### Dialogue Interaction Module
The hidden state \(h_{i}^{l}\) of the node at layer \(l\) is determined by \(h_{i}^{l-1}\) and its connected edges and the weight of the edge are related to the relationship type of the edge. \[h_{i}^{0}=x_{i}^{f} \tag{6}\]  
\[h_{i}^{l}=\text{sigmoid}\left(\sum_{j=1}^{i}\frac{h_{j}^{l-1}}{|j|}+b^{l}h_{i} ^{l-1}W_{ij}^{l}\right) \tag{7}\]  
\[v_{i}=h_{i}^{l} \tag{8}\]  
where \(h_{i}^{0}\in\mathbb{R}^{d}\) denotes the initial input state of GCN, and \(x_{i}^{f}\in\mathbb{R}^{d}\) is the sentence feature vector without context information. GCN first averages the embeddings of the previous layer of neighbor nodes and then fuses its information for information transmission. We use the sigmoid function to accomplish this operation. Both \(W_{ij}^{l}\) and \(b^{l}\) are parameters to be trained, which determine the importance of neighbor information and the importance of their characteristics, respectively. \(W_{ij}^{l}\in\left\{W_{0}^{l},W_{1}^{l}\right\}\) reflects different relationship types of edges. The output node embedding \(v_{i}\) will have both its feature information and the sentiment information of the interlocutor associated with it. Finally, the information updated by the GCN is used as the final representation of the sentence and it is passed through the feed-forward neural network to obtain the sentiment of the current discourse. \[d_{i}=ReLU\left(W^{d}v_{i}+b^{d}\right) \tag{9}\]  
\[D_{i}=tanh\left(W^{D}v_{i}+b^{D}\right) \tag{10}\]\[\hat{y_{i}}=argmax_{r\in R}(D_{i}[r]) \tag{11}\]  
where \(argmax\) represents the selection of the most likely sentiment label as the output \(\hat{y_{i}}\). \(W^{d}\in\mathbb{R}^{d\times d}\), and \(b^{d}\in\mathbb{R}^{d}\), and \(W^{D}\in\mathbb{R}^{d\times d}\), and \(b^{D}\in\mathbb{R}^{d}\) are all learnable parameters. In order to solve the slow training speed caused by too many GCN layers, as well as the possible gradient disappearance or gradient explosion [40], we use the Adam [41] optimizer to optimize the parameters that appear in the model.
# Self-Learning Power Control in Wireless Sensor Networks
## 5 Simulations
### Results
#### 5.2.1 Single Agent WSN
When the distance \(d\) doubles, the performance in HG worsens at the lowest transmission power levels. The attenuation is higher and more bit errors occur. Indeed, PRR varies from \(84\%\) to \(100\%\), increasing the transmission power from \(-35\) dBm to \(-27\) dBm, respectively. In the same range of power, the average latency is higher and reaches the plateau at \(-15\) dBm for the minimum values, instead of \(-23\) dBm when \(d=2\) m. The energy per bit is lower for values of transmission power between \(-35\) dBm and \(-17\) dBm. In such a situation (\(d=4\) m), the conditions are changed and we test the protocol as before. The node learns to use an average transmission power of around \(-31\) dBm with a standard deviation between \(0.04\) and \(0.2\) among different values of \(\mu\). The environment is found in states \(0\) and \(5\). Both PRR and latency are improved with respect to the homogeneous network at the same power, as depicted in Figure 9a,b. The PRR for all the inter-arrival time values in QL-TPC is around \(99.9\%\) compared to values around \(99.6\%\) in HG. The latency is decreased by \(1\) ms to \(5.5\pm 0.2\) ms, when \(\mu\) is equal to \(25\) ms, and by \(0.7\) ms to \(5.2\pm 0.3\) ms, when \(\mu\) is equal to \(100\) ms. The energy per bit values intercept the curve of the homogeneous case. The values are \(8.65\), \(6.44\), \(4.23\) and \(2.03\)\(\mu\)/bit, using the inter-arrival time from \(100\) ms to \(25\) ms, respectively, where the standard deviation is in the order of \(10^{-5}\).
## 5 Conclusions  
We established a thermal-hydraulic-mechanical coupling model of binary gas seepage and diffusion and analyzed the factors influencing conventional production and CO\({}_{2}\)-ECBM. The results are summarized as follows:  
1. Higher CO\({}_{2}\) injection pressure is associated with higher gas seepage velocities. CO\({}_{2}\) reserves and CH\({}_{4}\) production increase with increasing CO\({}_{2}\) injection pressure. When the injected CO\({}_{2}\) pressure is 8 MPa, the storage capacity of CO\({}_{2}\) is the highest, the radius of effected by CO\({}_{2}\) injection of 5, 10 and 30 years are 31, 44, and 58 m, respectively. After 30 years with injected CO\({}_{2}\) pressure of 4, 6, and 8 MPa, the productivity of CH\({}_{4}\) in the coal seam is 28%, 43%, and 92%, respectively. Therefore, storage of CO\({}_{2}\) and production of CH\({}_{4}\) can be significantly increased by increasing pressure.
2. Coal seam temperature has a significant impact on CO\({}_{2}\)-ECBM. Under the same CO\({}_{2}\) injection pressure, CO\({}_{2}\) reserves, CH\({}_{4}\) production, and coal seam permeability all decrease with increasing coal seam temperature. The coal seam matrix shrinks at the beginning due to the desorption of CH\({}_{4}\), and then it expands due to the adsorption of CO\({}_{2}\) and high temperature. This hinders the seepage and displacement of CO\({}_{2}\). Therefore, reserve of CO\({}_{2}\), production of CH\({}_{4}\), and permeability of coal seam all decrease with increasing coal seam temperature. When the injected CO\({}_{2}\) temperature is 363 K, the storage capacity of CO\({}_{2}\) is the lowest, the radius of effected by CO\({}_{2}\) injection of 10, 20, and 30 years are 22, 30, and 36 m, respectively. After 30 years with injected CO\({}_{2}\) temperature of 303, 333, and 363 K, the productivity of CH\({}_{4}\) in the coal seam is 25%, 22%, and 20%, respectively. The radius of effected by CO\({}_{2}\) injection reduces 10 m when the temperature of CO\({}_{2}\) injection increases from 303 to 363 K. Therefore, high temperatures are not conducive for CO\({}_{2}\) displacement of CH\({}_{4}\), and the injection temperature should be reduced.  
Methodology, H.L., F.C., H.J., and H.Y.; software, X.L., H.S., and H.L.; validation, F.C., H.Y., H.S., and L.X.; writing--original draft preparation, H.L. and H.Y.; writing--review and editing, H.S., H.L., H.J., H.Y., and F.C.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Science Foundation of China (51774092), the China Postdoctoral Science Foundation (2016M601399) and Guiding Projects of Daqing (ZD-2019-05). The authors thank [PERSON] (Massachusetts Institute of Technology, USA) for his help on COMSOL application. The authors declare no conflict of interest.  
Figure 10: Permeability ratio at the monitoring points at 6 MPa and different injection temperatures.
# An Accurate Probabilistic Model for TVWS Identification
## 4 Proposed Probabilistic Model
### Obtaining pdf and Performance Metrics  
Once the spectrum of interest is sensed, each RFE collects 112 samples with information given in dBm, the mean of the composite signal \(\overline{\mathrm{X}}(p)\), mean of the noise \(\overline{\mathrm{W}}(p)\), standard deviation of the composite signal \(\sigma_{\mathrm{X}}\), and standard deviation of the noise \(\sigma_{\mathrm{W}}\) are calculated and used to plot the pdf.  
Figure 8: Normal distribution pdf for \(W(p)\) in blue and \(X(p)\) in red, which are assumed to calculate the \(P_{fa}\) and the \(P_{d}\) according to the proposed model.  
Repeating the same procedure used for RFE device, and now using the SA, 1005 samples are collected, and then the mean and the standard deviation are calculated and used.  
In order to visualize the performance metrics of spectrum sensing, i.e., \(P_{fa}\), \(P_{d}\), and \(P_{m}\), we need to consider the pdf curves and \(T\) together as shown in Figure 9, and they are formally described next.  
**False Alarm Probability:**\(P_{fa}\) refers to the probability that a peak or peaks of the noise signal is detected above \(T\), and it is considered as a real signal of the PU when in reality no PU signal is present. In Figure 9, \(P_{fa}\) corresponds to the area bounded by \(T\) and \(W(p)\) colored with orange.  
**Detection Probability:**\(P_{d}\) is the probability that a real signal of the PU is detected above \(T\), and it is considered as a signal from PU. In Figure 9 it refers to the area bounded by \(T\) and \(X(p)\) colored with yellow.  
**Missed-Detection Probability:**\(P_{m}\) is the probability that a real signal of the PU is not detected above \(T\), and is considered as noise due to its low amplitude. In Figure 9 it is colored with light-blue.