esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mutation-Based Multivariate Time-Series Anomaly Generation in ICS
### Attention-Based Variational Recurrent Autoencoder
#### 4.1.2 Inference  
AVRAE follows an encoder-decoder architecture composed of RNNs. At each timestep, the RNN processes the input data \(x_{t}\) and the hidden state \(\mathbf{h}_{t-1}\) from the previous timestep to produce a new hidden state \(\mathbf{h}_{t}\). This hidden state \(\mathbf{h}_{t}\) is propagated to the next timestep, allowing the RNN to reflect the sequential nature of the data. However, basic RNNs perform all inference operations deterministically. Therefore, AVRAE introduces stochasticity into the inference process to enable variational inference.  
\[\mathbf{h}_{t}\sim q_{\theta}(\mathbf{h}_{t}|\mathbf{h}_{t-1},\mathbf{x}_{t}) \tag{3}\]  
Equation (3) represents the stochastic process of the RNN-based encoder at timestep \(t\), where \(q_{\theta}\) is the variational distribution parameterized by \(\theta\). The hidden state \(\mathbf{h}_{t}\) is sampled from the variational distribution \(q_{\theta}\), which is conditioned on the hidden state \(\mathbf{h}_{t-1}\) from the previous timestep and the input data \(\mathbf{x}_{t}\) at the current timestep.  
\[\mathbf{h}_{t}\sim\mathcal{N}(\mathbf{\mu}_{\mathbf{h}_{t}},\text{diag}(\mathbf{ \sigma}_{\mathbf{h}_{t}}^{2})),\quad\text{where}\;[\mathbf{\mu}_{\mathbf{h}_{t}}, \mathbf{\sigma}_{\mathbf{h}_{t}}]=\phi(\mathbf{h}_{t-1},\mathbf{x}_{t}) \tag{4}\]  
Equation (4) describes the sampling process assuming \(q_{\theta}\) is a Gaussian distribution. The function \(\phi(.)\) uses \(\mathbf{h}_{t-1}\) and \(\mathbf{x}_{t}\) to produce the mean \(\mathbf{\mu}_{\mathbf{h}_{t}}\) and variance \(\sigma_{\mathbf{h}_{t}}\) of the Gaussian distribution. Then, \(\mathbf{h}_{t}\) is sampled from the Gaussian distribution parameterized by \(\mathbf{\mu}_{\mathbf{h}_{t}}\) and \(\sigmaThese attention layers include cross-attention and self-attention. Cross-attention combines the encoder's hidden states \(\mathbf{h}^{e}_{1:T}\) and the decoder's hidden states \(\mathbf{h}^{d}_{1:T}\) to calculate attention weights, which are then used to produce the output \(\mathbf{o}^{c}_{1:T}\). Additionally, cross-attention employs a look-ahead mask to prevent future information from being referenced during sequence generation. Next, self-attention takes \(\mathbf{o}^{c}_{1:T}\) as input, calculates attention weights, and produces the output \(\mathbf{o}^{s}_{1:T}\). Finally, AVRAE uses a dense layer to reconstruct \(\mathbf{x}_{1:T}\) from \(\mathbf{o}^{s}_{1:T}\), resulting in \(\mathbf{\hat{x}}_{1:T}\).
## 4 Theoretical Investigation of Bond Behaviour
### Bond Strength Prediction Using an Artificial Neural Network (ANN)
#### 4.1.2 ANN Model Training and Analysis  
Once the input parameters and target outputs were transformed to the form of numeric arrays called datasets, the training of the model was complete. In the current model, a supervised learning approach was carried out for the development of the model. The training of the data was conducted using the Levenberg-Marquardt optimization method, consisting of logistic sigmoid activation function in the hidden layer, as shown in Figure 24 below:  
Figure 23: Artificial neural network architecture framework.  
Figure 24: Neural network training.  
Using the neural network fitting tool (nftool) in MATLAB, the input dataset consisting of six parameters--including compressive strength, bar diameter, cover/bar diameter ratio, embedment length/bar diameter, embedment length, and concrete--was fed to the model. This model uses 70% of the sample size for training of the model, and the remaining 30% for the validation and testing of the model, as shown in Figure 25 below:  
The model was trained to obtain better predictability with correlation coefficients of 0.99, 0.96, and 0.97 for the training, validation, and test data, respectively, as shown in Figure 26 below:  
Figure 26: Regression plots for training, validation, and testing of the ANN model.  
Figure 25: Neural fitting tool showing sample size.  
Figure 26 depicts the regression plots for the training, validation, and test datasets used by the ANN model, along with the validated result dataset. It is a screenshot of the plot drawn in the ANN model toolbox. The \"Target\" values in the X-Axis of the above plot refer to the experimental input values, and the Y-Axis output values correspond to the ANN-predicted values. Moreover, the vertical axes describe how output is calculated for each of the datasets used for the training, validation, and testing of the ANN model. From the regression plot, the accuracy of the model can be confirmed. This was obtained by training the network continually using the (nntraintool), which updates the weights until the mean squared error is small. However, repetitive training may cause the model to memorize the target values and result in overfitting of the model, and hence it is necessary to carry out regularization and identify the early fit during the training process [61; 68; 74]. Figure 27 below shows the best validation performance of the ANN model at epoch 4.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Frequency Bands in 5G
#### 2.1.4 5G New Radio Unlicensed Band  
The 5G standard also defines a non-licensed mode named 5G New Radio Unlicensed (NR-U). This mode of operation has been studied in [38] and introduced as a part of the 3 GPP Release 16 specifications [39]. It is an evolution of the 4G LTE License Assisted Access (LAA) standards. NR-U provides the necessary technology for cellular operations to integrate an unlicensed spectrum into 5G networks. The RAN provides support for a sidelink in the unlicensed spectrum, specifically aimed at FR1 unlicensed bands (n46 and n96/n102), which are the 5 GHz and 6 GHz [40] unlicensed bands, in line with the most recent regulations [36]. The NR-U is also considered to utilize mmWave frequency ranging from 57 GHz to 71 GHz [41].  
There are two operation modes defined for 5G NR-U:  
* Anchored NR-U: This requires an anchor in the licensed or shared spectrum. It combines the unlicensed spectrum with the licensed spectrum or shared spectrum such as Citizens Broadband Radio Service (CBRS) to boost deployments for a better user experience with higher 5G speeds.
* Standalone NR-U: This utilizes only the unlicensed spectrum, i.e., it does not require any licensed spectrum. It allows the deployment of 5G private networks entirely with the unlicensed spectrum.
# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Improving Network Performance and Survivability through Multi-Pathing  
Frequent link disconnections and network partitions occur due to high dynamicity (X.1) of network topology, and so, route discovery and maintenance must be performed frequently to reestablish routes, resulting in higher cost (X.2). Multi-pathing establishes more than a single route so that (a) multiple routes can be used at the same time to maximize resource utilization [143; 144] and to reduce network congestion [145; 146], which is important under ultra-densification, and (b) a backup route is available when the current route is broken to improve network survivability and fault tolerance [147], contributing to reduced routing overhead [148]. Further investigation could be pursued to investigate multi-pathing to improve network performance and survivability. Context-aware approaches, such as artificial intelligence approaches and bio-inspired algorithms [140], can be adopted to cater to the dynamic network condition that affects multi-pathing.
###### Abstract
These advantages significantly reduce the challenges associated with applying general data-driven methods, making it more convenient for damage detection in actual engineering scenarios. Additionally, response reconstruction methods do not require explicit mode shape functions, thereby avoiding the issue of insufficient sensor coverage. However, most existing research has focused solely on the response reconstruction of intact bridges. To the best of our knowledge, the effectiveness of the reconstruction method for damage localization has not been comprehensively validated. Therefore, further research is needed to explore the potential of this method in accurately identifying and localizing bridge damage under various conditions. To address the aforementioned challenges and to validate the potential effectiveness of the response reconstruction method for damage localization, this study introduces a novel data-driven approach tailored for beam bridges. Initially, we provide a concise exposition of the explicit mapping relationship by analyzing the dynamics within a simple VBI system [28]. Subsequently, we outline the mechanism by which damage localization is achieved through response reconstruction. Building on this theoretical groundwork, we propose a damage localization technique employing multiple BP-ANNs. For an intact bridge, the method reconstructs the inclinations at various cross-sections from data at two \"base\" cross-sections using the corresponding BP-ANNs. These same BP-ANNs are then applied to reconstruct responses at multiple cross-sections under damaged conditions. We conducted finite element analyses and laboratory experiments to validate our approach. The results demonstrated that by comparing the reconstructed and measured responses, the proposed method can accurately discriminate between damaged and undamaged areas of the bridge. This validation underscores the method's potential for practical application in the field of structural health monitoring for beam bridges. In Section 2, we provided a theoretical explanation of the mapping relationship and detailed the damage localization algorithm. In Section 3, a series of finite element models were constructed to verify the physical mechanism underlying the proposed damage localization method. In Section 4, a large-scale laboratory experiment was conducted to validate the effectiveness of the proposed method.
## 4 Experiment and Result Analysis
### Working Condition Migration Analysis
In addition to showing excellent performance in terms of average error, the RMSE of RT-TrAdaBoost is 0.81%, 1.32%, 0.83% and 0.46%, respectively. This means that there are almost no large outliers in the prediction results and reflects the robustness. From the color of the squares in Figure 8, the MAX and MAPE of the CNN-TL approach are much larger than the two ensemble learning methods. This shows that although the CNN-TL approach has a certain migration ability, it is still limited by the migration between different working conditions of the LFP battery. In summary, the RT-TrAdaBoost approach can efficiently predict the performance of the two methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Algorithms** & **MAE** & **MAX** & **RMSE** & **MAPE** \\ \hline A1 & CNN-TL & 0.0486 & 0.1590 & 0.0595 & 0.6924 \\  & AdaBoost & 0.0091 & 0.0679 & 0.0136 & 0.1299 \\  & RT-TradaBoost & 0.0058 & 0.0384 & 0.0081 & 0.1174 \\ \hline A2 & CNN-TL & 0.0825 & 0.3625 & 0.1086 & 1.0410 \\  & AdaBoost & 0.0099 & 0.0454 & 0.0133 & 0.0924 \\  & RT-TradaBoost & 0.0104 & 0.0378 & 0.0132 & 0.0983 \\ \hline A3 & CNN-TL & 0.0857 & 0.3577 & 0.1106 & 1.0534 \\  & AdaBoost & 0.0094 & 0.0536 & 0.0131 & 0.0987 \\  & RT-TradaBoost & 0.0063 & 0.0360 & 0.0083 & 0.0979 \\ \hline A4 & CNN-TL & 0.0394 & 0.3178 & 0.0589 & 0.2829 \\  & AdaBoost & 0.0058 & 0.0517 & 0.0083 & 0.0427 \\  & RT-TradaBoost & 0.0036 & 0.0134 & 0.0046 & 0.0390 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The evaluation criteria of the working condition migration. Figure 8: The evaluation criteria of lithium-ion battery RUL prediction results. (**a**) Transfer g5c31 to g1c38. (**b**) Transfer g3c2 to g2c48. (**c**) Transfer g5c31 to g2c48. (**d**) Transfer g2c32 to g7c47. transfer aging knowledge from the source domain composed of historical data to the target domain on the target domain based on small samples. Although the charging conditions of these batteries are complex and varied, the method achieved satisfactory RUL prediction results in this experiment.
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 5 Artificial Intelligence Applied to Building Height Estimation
### Model Training Datasets  
In semantic segmentation, most datasets with street-view imagery are created for tasks related to self-driving cars. These datasets usually contain a building class, a sky class and a road class, which are all useful to us. Unfortunately, other useful classes, such as windows and doors of buildings, building materials, etc., are not available in most datasets, as these classes are not relevant in the case of self-driving vehicles. To perform an initial, broad, low precision training, we used the Vistas [27], the GTA5 [46] and the Cityscapes coarse [26] datasets. The Vistas and Cityscapes coarse sets are coarsely annotated, which means that labels are only assigned to a small percentage of the pixels of an image. Vistas has around 25,000 images from countries all over the world, which makes it the only dataset with images from Central America. GTA5 is a dataset with around 25,000 images taken from the GTA5 videogame. The labels were automatically generated from game images from the city of Los Angeles, which appear in this videogame. The images, therefore, have different lighting conditions and even night scenes. Finally, Cityscapes is a dataset with images from German cities which has around 18,000 coarsely labelled and 5000 fine-labelled images. The coarse dataset was used for a broad, initial training, and the fine set was used as a last refinement. Through this training scheme, the model was trained with more diverse data, and to a certain degree, we were able to overcome the problems associated with real-world data.  
The model was initially trained using the 18,000 images from the Cityscapes coarse dataset followed by 25,000 images from Vistas (adapting the labels of the latter to the Cityscapes labelling scheme), 25,000 images from GTA5 and 5000 images from the Cityscapes fine dataset. Data augmentation was applied through 10-degree rotations and horizontal flipping.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Formation Moving Using Virtual Linkage
#### 4.1.2 Simulation Results  
The trajectories of the individual agents and snapshots during the simulation for 10 s are plotted in Figures 12 and 13, respectively.  
Figure 11: Predefined virtual linkage used in these two simulations.  
Notice how the agents fit themselves into the defined virtual linkage at the beginning when the agents are randomly initialized. This can be explained as follows. During the first few seconds,  
Figure 12: Snapshots of the formation moving.  
Figure 13: Trajectory of individual agents.  
the agents 1, 2, 3, 4, 5 fit themselves into virtual link1, shown by the blue line, and the agents 5, 6, 7, 8, 9 fit themselves into virtual link2, shown by the red line (See Figure 12). Then, the two virtual links fit themselves into the whole virtual linkage shown by the green line and determine the 'best' position and orientation of it. Once the states of the virtual linkage are determined, the agents quickly move to their corresponding placeholder in the virtual linkage to reach formation. Meanwhile, the whole virtual linkage moves along the circle; each agent then adjusts its velocity according to its position and the placeholder in the fitting virtual linkage.  
Figure 13 shows that the trajectory of each individual agent and indicates the effectiveness of moving in formation using the virtual linkage approach.
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 3 Multipath/NLoS Bias Estimation Using a Clustering Algorithm  
This section will elaborate on the method proposed by this research to estimate the pseudorange biases caused by multipath/NLoS effects. First, two probability distributions of leftover terms are generated, distinguishing between conditions with and without multipath/NLoS effects. These distinct distributions motivate the use of clustering algorithms to classify the leftover terms affected by multipath/NLoS effects from those that are not. Subsequently, we provide a detailed introduction to a density-based clustering algorithm, namely DBSCAN, and explain its suitability for this task of identifying multipath/NLoS events. Finally, this section illustrates the proposed procedure of utilizing DBSCAN for multipath/NLoS estimation.
## 4 Beam Capacity Estimates
### Shear Capacities  
The nominal shear strength of the FRP-strengthened MRF beam specimens, \(\mathrm{V_{n}}\), was evaluated at the end flexural-yielding region using Equation (1), as described in ACI 440.2R-08. \(\mathrm{V_{c}}\), \(\mathrm{V_{s}}\), and \(\mathrm{V_{f}}\) are the shear-strength contributions of concrete, reinforcing steel, and external FRP reinforcement, respectively. \(\mathrm{V_{n}}\) was calculated using expected material properties and a value of 1.0 for the shear-strength reduction factor (\(\mathrm{\phi_{V}}\)), per ASCE 41-13 [36]. The additional strength reduction factor for the FRP, \(\mathrm{\psi_{f}}\), was taken as 0.85 for both U-wrap schemes applied to S1A and S1B, regardless of the anchorage, and as 0.95 for the full-wrap scheme on S5.  
\[\mathrm{V_{n}}=\mathrm{\phi_{v}(V_{c}+V_{s}+\mathrm{\psi_{f}}V_{f})} \tag{1}\]  
\(\mathrm{V_{c}}\) and \(\mathrm{V_{s}}\) were computed per Section 22.5 in ACI 318-14 [39]. However, following the seismic provisions in Section 18.6.5 of the code for MRF beams, a reduced cyclic shear capacity, \(\mathrm{V_{n,cyclic}}\), was derived for the expected flexural-hinging region by eliminating the contribution of concrete (\(\mathrm{V_{c}}=0\)). For reference, ACI 318 and ACI 440.2R shear provisions are summarized in Appendix A.  
Table 6 provides the computed \(\mathrm{V_{c}}\), \(\mathrm{V_{s}}\), \(\mathrm{V_{f}}\), \(\mathrm{V_{n}}\), and \(\mathrm{V_{n,cyclic}}\) for the MRF beam specimens before and after the FRP-strengthening. In Table 6, n is the number of layers of FRP, and \(\mathrm{\epsilon_{fe}}\) is the effective strain used to calculate \(\mathrm{V_{f}}\).
# Design of a Low-Resolution Gamma-ray Spectrometer for Monitoring Radioactive Levels of Wastewater
## 2 Materials and Methods  
To the best of our knowledge, no study has continuously measured the radioactivity of radioactive wastewater decay tanks, and the continuous detection system proposed in this article is similar to underwater radiation monitoring systems that measure radioactivity in seawater or groundwater using NaI(TI), LaBr\({}_{3}\):Ce, Ce:GAGG, or CeBr\({}_{3}\) detectors [8; 9; 10; 11; 12; 13; 14] and in lakes using NaI(TI) detectors [15; 16; 17; 18]. A scintillation detector capable of examining radionuclides by analyzing the gamma radiation energy was used in all these studies. Considering the field characteristics--a semiconductor detector that requires cooling cannot be used--a scintillation detector is the best choice in situations that require direct installation in the field. High-purity germanium (HPGe) radiation detectors are not suitable for in situ monitoring because of their high cost and high consumption requirements [19].  
In addition, because water is a good shielding material, the efficiency decreases as the distance increases. If the distance exceeds a certain limit, measurement is not possible. In other words, a virtual sphere around the detector in water affects the detector. Owing to this shielding effect, the detection efficiency of the detector in water is extremely low. Therefore, the measurement error value increases, and the reliability of the measurement data decreases. Thus, it is necessary to increase the measurement time [16]. Fortunately, because radioactive wastewater in a decay tank is stored for weeks or months to reduce radioactivity owing to the decay of radionuclides, there is no practical limitation on the measurement time when a detector is installed on top of the tank.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 3 Model Introduction
### Cnn
To deeply explore the time series' inherent temporal dynamics and long-term dependence, we innovatively incorporated a temporal distribution layer, which implemented a hierarchical transformation of the continuous time slices to refine the historical timing information of the input series effectively. Therefore, the temporal distribution layer was selected to encapsulate data from the past t hours. Consequently, the input of the CNN constituted a 4-D array with dimensions of \(\mathrm{m}\times\mathrm{t}\times\mathrm{c}\times\mathrm{n}\) (\(6\times 6\times 3\times 4\); Figure 4). After iterative refinement by convolutional and pooling layers, the extracted spatiotemporal features were integrated and transformed into a highly condensed 1-D feature vector, which removed redundant information in the spatial and temporal dimensions. Through this series of operations,  
Figure 3: Structure of CNN. the model systematically extrapolated the predicted values of the air quality index for the next \(t^{\prime}\) hours at n different monitoring stations.
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 4 Swarming Approach Description
### Minimum Average One-Hop Delay
#### 4.1.1 Initialization Procedure  
As described at the beginning of Section 4.1, the optimization objective \(D_{avg}\) is set to be 0. In the initialization procedure, \(m\) combinations of minimum contention window sizes are initialized. Each combination includes the minimum contention window sizes of \(n\) backbone vehicles. Each minimum contention window size is an integer randomly selected from [1; 64]. Here, \(m\) is an integer that cannot be too large or too small. If it is too large, the complexity of the swarming approach would be increased. If it is too small, the minimum average one-hop delay may not be found.