esa-sceva/satcom-chunk-collection huggingface dataset:
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 5 Network Performance Comparison
### Latency  
Latency is the time it takes for data to travel from the sender to the destination across a network, measured in seconds (s). The overall latency consists of three components: propagation, transmission, and queuing [44].  
\[Latency\ (s)=Propagation+Transmit+Queuing \tag{3}\]  
Propagation is the time it takes for a signal to travel from the sender to the receiver and is a function of the distance between the endpoints and the speed in the given communication medium.  
\[Propagation\ (s)=\frac{Distance\ (m)}{Speed\ (m/s)} \tag{4}\]  
Transmission delay considers the actual size of the data and the available bandwidth.  
\[Transmit\ (s)=\frac{Packet\ Size\ (bit)}{Bandwidth\ (bps)} \tag{5}\]  
Queuing delay considers the waiting times for packets before they can be transmitted.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 4 Experimental Set-Up
### Evaluation Metrics  
For a two-class problem, Table 3 shows a \(2\times 2\) confusion matrix where each entry contains the number of correct/incorrect classifications [63]. Thus, the true-positive (TP) and the true-negative (TN) values represent the amount of instances from each class that were correctly classified, whereas the false-positive (FP) and false-negative (FN) indicate the amount of positive and negative instances misclassified.  
From this confusion matrix, two plain performance evaluation indices can be easily calculated:  
* True-positive rate (or sensitivity). It measures the proportion of positive instances correctly classified, \(TPR=TP/(TP+FN)\).
* True-negative rate (or specificity). It measures the proportion of negative examples correctly classified, \(TNR=TN/(TN+FP)\).  
The assessment of classification performance is often carried out using more powerful measures that can be derived from straightforward indices such as the true-positive and true-negative rates. The most popular measure is the overall accuracy, but it is not appropriate when the prior class probabilities are very different because it does not consider misclassification costs and is strongly biased towards the majority class [64]. Thus, the performance of classifiers in the context of class imbalance has commonly been evaluated using other metrics, such as the geometric mean, the \(F\)-measure, and the area under the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Description** \\ \hline Random under-sampling (RUS) & It balances the data set through the random elimination of instances that belong to the over-sized class. \\ Evolutionary under-sampling (EUS) & It consists of removing instances in the majority class by using the guide of a genetic-based algorithm [58]. \\ Easy ensemble (EE) & The data set is divided into several subsets by random resampling with replacement, and each subset is then used to train a base classifier of the ensemble with AdaBoost [59]. \\ Balance cascade (BC) & It performs bagging and removes negative instances that can be classified correctly with high confidence from future selections [59]. \\ Random under-sampling boosting (RUSBOOST) & It combines RUS with the AdaBoost algorithm [60]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Other under-sampling methods included in the experiments.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Predicted as Positive** & **Predicted as Negative** \\ \hline Actually positive & TP & FN \\ Actually negative & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
ROC curve. For the present experimental study, the geometric mean was chosen since it represents a good trade-off between the accuracy rates measured on each class:  
\[Gmean=\sqrt{TPR\cdot TNR} \tag{1}\]
## 6 Simulation Study
### Diagnosis at Different Loads  
Figure 11 shows the harmonic component at the frequency of \(2f_{r}\) with a healthy state, 2%, 6% and 10% of purely DE under four different loads, respectively. The amplitudes of the FSF reduce dramatically due to the increasing of the rated load. Even for the healthy motor, the amplitude of FSF decreases from 0.23165% to 0.03709%. It is proved that the load is an inhibiting factor for the diagnosis of the fault signature. The decrease of the FSF has been demonstrated in Table 2. Table 2 shows the FSF variations of the healthy and faulty motors with four different rated loads.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Healthy Motor** & **Motor with 2\% DE** & **Motor with 6\% DE** & **Motor with 10\% DE** \\ \hline
**Percentage of Rated Load** & \(2f_{r}\) (\%) & \(2f_{r}\) (\%)** & \(2f_{r}\) (\%) & \(2f_{r}\) (\%) \\ \hline
0\% & 0.23165 & 0.23523 & 0.23548 & 0.23996 \\
33.3\% & 0.13274 & 0.13816 & 0.15022 & 0.16983 \\
66.7\% & 0.05007 & 0.05361 & 0.05642 & 0.06606 \\
100\% & 0.03709 & 0.03906 & 0.04598 & 0.04737 \\ \hline \end{tabular}
\end{table}
Table 2: The amplitudes of FSF at a frequency of \(2f_{r}\) for the healthy and faulty motors from no load to full load.  
Figure 10: The characteristic time-domain signals of apparent power at no load under different levels of DE.  
Figure 9: Variation of FSF for an induction motor at no load.
###### Abstract  
The continuous growth of global trade and maritime transport has significantly heightened the challenges of managing ship traffic in port waters, particularly within fairways. Effective traffic management in these channels is crucial not only for ensuring navigational safety but also for optimizing port efficiency. A deep understanding of ship behavior within fairways is essential for effective traffic management. This paper applies machine learning techniques, including Decision Tree, Random Forest, and Gradient Boosting Regression, to model and analyze the behavior of various types of ships at specific moments within fairways. The study focuses on predicting four key behavioral parameters: latitude, longitude, speed, and heading. The experimental results reveal that the Random Forest model achieves adjusted \(R^{2}\) scores of 0.9999 for both longitude and latitude, 0.9957 for speed, and 0.9727 for heading. All three models perform well in accurately predicting ship positions at different times, with the Random Forest model particularly excelling in speed and heading predictions. It effectively captures the behavior of ships within fairways and provides accurate predictions for different types and sizes of vessels, especially in terms of speed and heading variations as they approach or leave berths. This model offers valuable support for predicting ship behavior, enhancing ship traffic management, optimizing port scheduling, and detecting anomalies.  
AIS data; ship behavior; fairway; random forest; trajectory prediction 20242024148484[[https://doi.org/10.3390/app148848](https://doi.org/10.3390/app148848)]([https://doi.org/10.3390/app148848](https://doi.org/10.3390/app148848))
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 2 Temporal Convolutional Neural Networks
### Proposed Network Architecture  
In this section, we present the baseline architecture that will be discussed in this manuscript. Note that the goal of this paper is not to propose _the_ best architecture for our data, but rather to explore the behaviour of TempCNNs for SITS classification through exhaustive experiments.  
Figure 4 displays the base TempCNN architecture used in the experiments (Section 4). The architecture is composed of three convolutional layers (64 units), one dense layer (256 units) and one Softmax layer. The filter size of convolutions is set to 5. Section 4.4 will justify the width (i.e., number of units) and the depth (i.e., number of convolution layers) of this architecture. Moreover, Section 4.2 will study the influence of convolution filter size. Finally, the experimental section will also examine the use of pooling layers (Section 4.3).  
To control for overfitting, we use four regularization mechanisms: dropout with a dropout rate of 0.5 [56], a \(\mathcal{L}_{2}\)-regularization on the weights (also named weight-decay) applied for all the layers with a small rate of \(10^{-6}\), a validation set corresponding to 5% of the training set, and batch normalisation [24].  
Similarly to the split between the train and test set (see the details in Section 3.2), the validation set is built such that instances do not come from the same polygons as ones from the train set. Section 4.5 will detail the influence of these four regularization mechanisms.  
We train the network using Adam optimization (standard parameter values: \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\)) [57] with a batch size of 32, and a maximum number of epochs set to 20, with an early stopping mechanism with a patience of zero. The influence of the batch size on the accuracy and the training time are analysed in Section 4.6.  
All the studied CNN models have been implemented with the Keras library [58], with Tensorflow as the backend [59]. To facilitate others to build on this work, we have made our code available at [[https://github.com/charlotte-pel/temporalCNN](https://github.com/charlotte-pel/temporalCNN)]([https://github.com/charlotte-pel/temporalCNN](https://github.com/charlotte-pel/temporalCNN)).  
Figure 4: Proposed temporal Convolutional Neural Network (TempCNN). The network input is a multi-variate time series. Three convolutional filters are consecutively applied, then one dense layer, and finally the Softmax layer, that provides the predicting class distribution.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 2 Multistatic Architecture
### Imaging  
The imaging process involves creating an individual image for each transmitting antenna, combining contributions from all receivers to create the final image. Figure 1 provides a visual representation, simplifying the procedure within a basic system configuration of NTX = 3 and NRX = 2.  
In this concept, each transmitting antenna emits signals toward the OUT, creating distinct electromagnetic responses from the target. Receiving antennas systematically  
Figure 1: Graph of the imaging process in a simplified system with NTX = 3 and NRX = 2.  
capture these responses, reflecting the unique interaction of the transmitted signals with the object. Coherent summation of these responses, encompassing phase and amplitude information, is performed for each transmitting antenna.  
As a result, for a system with NTX transmitting antennas, NTX transmitter images are generated, each representing a unique perspective offered by a specific transmitting element.  
The final image is created by combining these transmitter images coherently. It provides a comprehensive depiction of the object under scrutiny, revealing intricate characteristics and spatial distribution. This fusion of information from multiple transmitting antennas enhances imaging resolution and fidelity, offering a more detailed and accurate representation of the target object.
## 5 Conclusions  
In summary, based on the conducted research and analysis of their results, it can be concluded that the number of features used in classification is a key factor that affects the quality and efficiency of the model. Classifiers based on a larger number of features (50 and 100 features) achieved better results in terms of accuracy (ACC), sensitivity (TPR), and specificity (TNR). Especially, KNN with 50 features and MLP with 100 features achieved very high accuracy at levels of 96.75% and 96.50%, which suggests that a larger number of features allows for better capturing important information in the data. These models were able to achieve high sensitivity (97.50% and 97.00%) and specificity (96.00%), which indicates their ability to effectively classify both positive and negative cases. Therefore, in applications requiring high precision, a larger number of features can improve the classification quality.  
In the case of models using only six features (as in [32]), the results were noticeably worse, with an accuracy of 90%, which is the lowest result in the studies. Although the 5-NN model achieved high specificity (95.36%), its sensitivity (85.71%) was much lower. The QDA model had higher sensitivity (87.50%), but lower specificity (92.50%). These differences indicate that a smaller number of features limits the model's ability to capture the full structure of the data, which affects its performance, especially in the case of detecting positive cases. The use of different feature selection methods, such as RELIEF, RFE, or ANOVA, in combination with different classifiers (SVM, NuSVM), leads to stable but slightly lower results than those obtained by models with more features. Although this approach provides a balance between detecting positive and negative cases, its results do not reach the level of those obtained by more advanced classification models with more features.  
Finally, the results suggest that selecting the appropriate number of features is crucial to achieve a high-quality classification model. A larger number of features allows us to better capture the complexity of the data, which translates into higher classification results, while limiting the number of features may lead to a decrease in performance, especially in cases where important information is contained in a larger number of features. Therefore, future studies should focus on further optimizing the number of features and feature selection methods to achieve the best results in the context of osteoporosis diagnosis based on CT scans of spongy tissue.  
This research was funded by the Ministry of Education and Science--Poland, grant number FD-20/EE-2/999.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the author.
## 3 Results and Discussion
### Fusion Model Building and Discriminant
#### 3.3.2 Discriminant Model Based on Data Feature Layer Fusion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Selection Method**} & \multirow{2}{*}{**Wavelength Range**} & \multicolumn{3}{c}{**Kernel Function**} & \multirow{2}{*}{**Operation Time/s**} & \multirow{2}{*}{**Identification Accuracy/\%**} \\  & & & **Number** & & & \\
**Method** & & & & & \\
**500–2500** & **RBF\_Kernel & 11 & \(\gamma=251.44~{}\sigma^{2}=46.01\) & 1.63 & 100\% \\  & before normalized & Lin\_Kernel & 11 & \(\gamma=0.09\) & 0.66 & 100\% \\  & 500–2500 after & RBF\_Kernel & 11 & \(\gamma=2047.44~{}\sigma^{2}=101.57\) & 1.63 & 100\% \\  & normalized & Lin\_Kernel & 11 & \(\gamma=8.81\) & 2.05 & 100\% \\  & 500–2500 & RBF\_Kernel & 55 & \(\gamma=0.93~{}\sigma^{2}=6.11\) & 1.42 & 100\% \\  & before normalized & Lin\_Kernel & 55 & \(\gamma=0.19\) & 1.01 & 100\% \\  & 500–2500 after & RBF\_Kernel & 60 & \(\gamma=13.17~{}\sigma^{2}=70.27\) & 1.89 & 96\% \\  & normalized & Lin\_Kernel & 60 & \(\gamma=29.12\) & 1.39 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The result of PLS-DA before and after normalization for second derivative spectrum in 500–2500. It is shown in Table 5 that the overall effect of the PCA compression model is better than that of SPA. The unnormalized feature level fusion model in the former method has the best recognition accuracy of 90%, while the unnormalized feature level fusion model in the latter method has the worst recognition accuracy of 65%, which may be caused by the small amount of effective information contained in the features selected by SPA.
## 3 Results and Discussion
### Hardness of the Coating-Substrate System  
The microhardness distribution presented in Figure 10 indicates significant differences in the hardness between Stellite 6 and other investigated high nickel alloys. The Vickers  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Coating Material** & **Ni** & **Cr** & **Mo** & **Co** & **C** & **Mn** & **Si** & **Fe** & **Ti** & **Al** & **W** & **Nb + Ta** \\ \hline \begin{tabular}{c} Inconel 625 \\ nominal \\ \end{tabular} & \begin{tabular}{c} base \\ (63.43) \\ \end{tabular} & \(20\div 23\) & \(8\div 10\) & \(0\div 1\) & \(0\div 0.1\) & \(0\div 0.5\) & \(0\div 0.5\) & \(0\div 1\) & \(0\div 0.4\) & \(0\div 0.4\) & - & \(3.15\div 4.15\) \\ \begin{tabular}{c} cladded coating \\ \end{tabular} & \(1.93\) & \(9.42\) & - & \(0.1\) & - & - & \(0.81\) & \(0.23\) & \(0.17\) & - & \(3.94\) \\ \begin{tabular}{c} Inconel 718 \\ nominal \\ \end{tabular} & \(50\div 55\) & \(17\div 21\) & \(2.8\)-\(3.3\) & \(0\div 1\) & \(0\div 0.08\) & \(0\div 0.35\) & \(0\div 0.35\) & \(\mathbf{18}\) & \(0.65\div 1.15\) & \(0.2\div 0.8\) & - & \(4.75\div 5.5\) \\ \begin{tabular}{c} cladded coating \\ \end{tabular} & \(17.49\) & \(3.22\) & - & \(0.08\) & - & \(0.02\) & \(\mathbf{21.45}\) & (0.92) & \(0.49\) & - & \(5.5\) \\ \begin{tabular}{c} Alloy 33 \\ \end{tabular} & \(30\div 33\) & \(31\div 35\) & - & - & \(0\div 015\) & \(0\div 2\) & \(0\div 0.5\) & Rest & - & - & - & - \\ \begin{tabular}{c} cladded coating \\ \end{tabular} & \(32.34\) & \(1.7\) & - & \(0.01\) & \(0.6\) & \(0.2\) & \(35.33\) & - & - & - & - \\ \
\begin{tabular}{c} Mulline 6 \\ \end{tabular} & \(2.5\) & \(28.92\) & \(0.013\) & rest & \(1.38\) & \(1.56\) & \(0.92\) & \(\mathbf{4.19}\) & - & - & \(3.9\) & - \\
\begin{tabular}{c} cladded coating \\ \end{tabular} & \(27.55\) & \(\begin{array}{c}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{} \text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{} \text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{} \text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{ }\text{}\text{}\text{}\text{}\text{}\textHV0.2 hardness measurements were taken in accordance with EN ISO 9015:2011. Hardness of the substrate material (16 Mo3 steel) is similar for all cases with a relatively small standard deviation, compared to harder HAZ (Heat Affected Zone) in substrate deposited with Stellite 6. In the deposited coatings, a nearly two times higher standard deviation from the mean values than in the base material has been recorded, which is the effect of the coarse-grained structure of the coatings crystallizing from the liquid.
## 2 A Quantitative Description of a Communication System
### Language Recognition  
Consider a description consisting of a message \(\mathbf{M}\) built up with a sequence of \(N\) characters or elementary symbols. The message \(\mathbf{M}\) can be treated as an ordered set of characters \(C_{i}\) as:  
\[\mathbf{M}=\left\{C_{1},C_{2},...,C_{N}\right\}. \tag{4}\]  
No restriction is imposed over the possibility of repeating characters. Consider also the language \(\mathbf{B}\), consisting of a set of \(D_{\mathbf{B}}\) different symbols \(Y_{i}\), each formed with a sequence of \(E_{Y_{i}}\) consecutive characters found with probability \(P(Y_{i})>0\) in message \(\mathbf{M}\). Thus:  
\[\mathbf{B}=\left\{Y_{1},Y_{2},...,Y_{D_{\mathbf{B}}},P(Y_{i})\right\}. \tag{5}\]  
\[Y_{i}=\ C_{j},C_{j+1},...,C_{j+E_{Y_{i}}-1}\,1\leq i\leq D_{\mathbf{B}}\,,1\leq j \leq N-E_{Y_{i}}+1\,. \tag{6}\]  
The symbol probability distribution \(P(Y_{i})\) can be obtained dividing the frequency distribution \(f_{i}\) by the total number of symbols \(N\) in the message:  
\[P(Y_{i})=\frac{f_{i}}{N} \tag{7}\]  
Language \(\mathbf{B}\), used to convey the message \(\mathbf{M}\), can now be specified as the set of \(D_{\mathbf{B}}\) different symbols and the probability density function \(P(Y_{i})\) which establishes the relative frequencies of appearance of the symbols \(Y_{i}\). Each symbol \(Y_{i}\) is constructed with a sequence of contiguous characters as indicated in Equation (6). The set of symbols that describes the message \(\mathbf{M}\) with the least entropy comes after the solution of the following optimization problem:  
\[\min_{\mathbf{B}}\ -\sum_{i=1}^{D_{\mathbf{B}}}\frac{F_{Y_{i}}\cdot E_{Y_{i}}}{N} \cdot log_{D_{\mathbf{B}}}\frac{F_{Y_{i}}\cdot E_{Y_{i}}}{N},\] (8) Subject to: \[\mathbf{B}=\left\{Y_{1},Y_{2},...\,Y_{i}\,...\,,Y_{D_{\mathbf{B}}},\mathbf{P }(Y_{i})\right\}\text{, for }i=1,2,...\,,D_{\mathbf{B}}\] \[Y_{i}=\left\{C_{j},C_{j+1},...\,,C_{j+E_{Y_{i}}-1}\right\}\text {, for }i=1,2,...\,,D_{\mathbf{B}}\text{ and }j=\ 1,2,...\,N-E_{Y_{i}}+1\,.\] \[\sum_{i=1}^{D_{\mathbf{B}}}\,F_{Y_{i}}\cdot E_{Y_{i}}=N\,,\] \[F_{Y_{i}}\geq 1\,\ E_{Y_{i}}\geq 1\,for\ i=1,2,3,...\,,D_{\mathbf{B}}.\]  
The resulting language will be the best in the sense that it is the set of symbols that offers a maximum organization of the message. The symbol lengths will range from a minimum to a maximum defining a distribution of symbol lengths characteristic of this scale of observation which is referred to as the _Fundamental Scale_.
# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### Preliminary Works  
(1) Experimental platform. For this study, a complex assembled product with 113 components was constructed, including 27 different types of equipment, 10 parts (e.g., craft cap), 14 plugs, and 62 screws. The appearance of this product is inconsistent from different viewpoints. Some equipment and parts were surface-treated to exhibit low texture and high color consistency.  
(2) Parameter configuration. Migration factor \([\lambda_{0},\lambda_{end}]=[0.5,0.05]\), scale disturbance factor \([s_{0},s_{end}]=[0.10,0.001]\), angle disturbance factor \([\tau_{0},\tau_{end}]=[20^{{}^{\circ}},0.0]\), relaxed factor \([\alpha_{0},\alpha_{end}]=[0.3,0.0]\), search factor \([\sigma_{0},\sigma_{end}]=[25.0,5.0]\), and termination threshold \(\epsilon=4.0\)\(\left\{vp^{0},vp^{41}\right\}=\left\{0.1,0.7\right\}\). The number of parent nodes is 10, and the number of child nodes is 5.  
(3) Dataset. As shown in Figure 7, the dataset consists of about 240 viewpoints, which were captured by a hand-held industrial camera and a mobile robot vision system, respectively. The working distance is about 1.8 m, the view width is about 1.5 m, and the resolution is 3264 \(\times\) 2448. The ground truth was calculated through artificial markers and manual refinement (the beige base is only used to calculate ground truth).  
(4) Evaluation Indicator. The pose of complex products in the manufacturing industry mainly serves subsequent inspection, AR-based projection, or robot operation. Therefore, the positioning accuracy of each component can better reflect the practical significance of pose accuracy. For this reason, in addition to the widely used pose deviation (mean translation error \(\overline{t}_{e}\) and mean angular error \(\overline{a}_{e}\)), we have also modified IoU as follows:  
\[l_{*}\left(\overline{IoU}_{e}\right)=\frac{1}{|l_{*}|}{\sum_{w\in l_{*}}{\rm IoU }\left(rect\left(\mathbf{p}^{w,\mathbf{\beta}}\right),rect\left(\mathbf{p}_{gt}^{w,\mathbf{ \beta}}\right)\right)},\mathbf{p}^{w,\mathbf{\beta}}=\pi\left(\mathbf{TP}^{w,\mathbf{\beta }}\right) \tag{23}\]  
to better distinguish performance, where \(\mathbf{P}^{w,\mathbf{\beta}}\) is the 3D 8-point box coordinate of component \(w\), \(l_{*}\) represents a set of elements (\(|l_{*}|\) is the number) belonging to Range * (Range I, II, III, plugs, and skews).