esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Design  
### Data Enhancement  
The quality of the dataset in deep learning has a great impact on the performance of the model, and an extremely unbalanced dataset will cause distress to the training of the model and may lead to overfitting. As shown in Figure 1, there is a data imbalance in the text data of college textbooks, and the quantity of each category varies greatly [29]. Therefore, this paper needs to use data enhancement techniques to obtain a more balanced dataset.  
Data augmentation is an effective method to expand the sample size of data, and commonly used methods include oversampling, undersampling, back-translation, and disrupting the order of words within sentences and sentence order. Among these, oversampling is used to balance the dataset by duplication, autonomous sampling, or the synthetic  
Figure 1: Distribution of each category before data enhancement.  
minority class oversampling technique (SMOTE) [30] on the categories with insufficient data, and SMOTE is also used to create new instances of minority classes by constructing convex combinations of neighboring instances without using duplicate samples and without excessive overfitting. Meanwhile, the authors of [31] proposed a simple and general NLP data enhancement technique called EDA, which consists of four simple operations to perform data enhancement, including Synonym Replacement (SR), Randomly Insert (RI), and Randomly Swap (RS). EDA uses the NLTK for English synonym queries to generate the same number of new texts for instances in the dataset, and achieves outstanding performance when training on smaller datasets, significantly improving the performance of the model.  
Therefore, to address the problem of the unbalanced text datasets of college textbooks, this paper combines the ideas of SMOTE and EDA to propose a new data enhancement method, i.e., using self-built deactivated word lists and synonym word forests to conduct synonym queries, achieving oversampling on categories with fewer indicators, and randomly disrupting the order of words within sentences as well as the order of samples. As shown in Figure 2, the dataset after data augmentation essentially meets the demand, effectively preventing the occurrence of overfitting and improving the generalization ability of the model.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 4 Comparison of Coarseness Index and Maximum Diameter by Cutting Conditions  
### Effect of Penetration Depth  
Penetration depth (\(p\)) is a major cutting parameter that affects the cutter head design of the excavation machine. In general, deeper penetration increases the size of the rock fragments, resulting in higher cutting efficiency, and vice versa. Because the excavation efficiency and particle distribution have a significant correlation, the penetration depth affects the particle size parameters as well as the _CI_.  
Figure 7a shows the relationship between penetration depth and _CI_ obtained in this study. As the penetration depth increased, _CI_ increased, with a power relationship. This result is consistent with that reported by [PERSON], et al. [19]. The relationship between \(d_{\text{max}}\) and penetration depth is shown in Figure 7b. In general, particle size parameters have an exponential relationship with penetration depth [18,19]. However, \(d_{\text{max}}\) was found to have a linear relationship with penetration depth in this study. In addition, regardless of rock strength, the correlation of penetration depth with \(d_{\text{max}}\) (R\({}^{2}>0.74\)) was stronger than that with _CI_ (R\({}^{2}<0.39\)).
# Optimization of AZ91D Process and Corrosion Resistance Using Wire Arc Additive Manufacturing
###### Abstract  
Progress on Additive Manufacturing (AM) techniques focusing on ceramics and polymers evolves, as metals continue to be a challenging material to manipulate when fabricating products. Current methods, such as Selective Laser Sintering (SLS) and Electron Beam Melting (EBM), face many intrinsic limitations due to the nature of their processes. Material selection, elevated cost, and low deposition rates are some of the barriers to consider when one of these methods is to be used for the fabrication of engineering products. The research presented demonstrates the use of a Wire and Arc Additive Manufacturing (WAAM) system for the creation of metallic specimens. This project explored the feasibility of fabricating elements made from magnesium alloys with the potential to be used in biomedical applications. It is known that the elastic modulus of magnesium closely approximates that of natural bone than other metals. Thus, stress shielding phenomena can be reduced. Furthermore, the decomposition of magnesium shows no harm inside the human body since it is an essential element in the body and its decomposition products can be easily excreted through the urine. By alloying magnesium with aluminum and zinc, or rare earths such as yttrium, neodymium, cerium, and dysprosium, the structural integrity of specimens inside the human body can be assured. However, the in vivo corrosion rates of these products can be accelerated by the presence of impurities, voids, or segregation created during the manufacturing process. Fast corrosion rates would produce improper healing, which, in turn, involve subsequent surgical intervention. However, in this study, it has been proven that magnesium alloy AZ91D produced by WAAM has higher corrosion resistance than the cast AZ91D. Due to its structure, which has porosity or cracking only at the surface of the individual printed lines, the central sections present a void-less structure composed by an HCP magnesium matrix and a high density of well dispersed aluminum-zinc rich precipitates. Also, specimens created under different conditions have been analyzed in the macroscale and microscale to determine the parameters that yield the best visual and microstructural results.  
additive manufacturing; WAAM; corrosion; AZ91D +
Footnote †: journal: Applied Physics Letters
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Color Difference Metrics  
In this section, we present the color difference metrics associated to each HDR color space. Because the color spaces are uniform, it is possible to calculate the perceptual difference between two colors.  
* For \(HDR\)-\(Lab\) color space, the Euclidean distance \(\Delta E_{HDR\text{-}Lab}\) is used: \[\Delta\text{E}_{HDR\text{-}Lab}=\sqrt{(\Delta L)^{2}+(\Delta a)^{2}+(\Delta b) ^{2}}\] (14)
* For the \(J_{z}a_{z}b_{z}\), [PERSON] et al. [22] proposed the following formula: \[\Delta\text{E}_{J_{z}a_{z}b_{z}}=\sqrt{(\Delta J_{z})^{2}+(\Delta C_{z})^{2} +(\Delta H_{z})^{2}}\] (15) where \(C_{z}\) corresponds to the color saturation and \(h_{z}\) to the hue: \[C_{z}=\sqrt{(a_{z})^{2}+(b_{z})^{2}}\] (16) \[h_{z}=arctan(\frac{b_{z}}{a_{z}})\] (17) \[\Delta H_{z}=2\sqrt{C_{z1}C_{z2}}\times sin(\frac{\Delta h_{z}}{2})\] (18) where \(C_{z1}\) and \(C_{z2}\) correspond to the saturation of the two compared colors.
* For \(ICtCp\), a weighted Euclidean distance formula was proposed in [25]: \[\Delta\text{E}_{ICtCp}=720\sqrt{(\Delta I)^{2}+0.25(\Delta Ct)^{2}+(\Delta Cp )^{2}}\] (19) Then, to have a \(ICtCp\) color space truly perceptually linear, the coefficient 0.25 is applied to the \(Ct\) component before using any SDR metric.  
These color difference metrics work well for measuring perceptual differences of uniform patches. Although that we do not perceive color differences in the same way in textured images or in uniform and large patches, they are often used to compare the distortion between two images. The mean of the difference between the distorted and the reference images can be used as an indicator of image quality:  
\[\overline{\Delta\text{E}}_{colorspace}=\frac{1}{IJ}\sum_{i=1}^{I}\sum_{j=1}^{J} \Delta\text{E}_{colorspace}(i,j) \tag{20}\]  
where \(I\) and \(J\) correspond to the dimensions of the image and \((i,j)\) corresponds to the spatial coordinates of the pixel.
## 3 Performance Analysis of the Original Subframe  
_Strength Performance Analysis_  
Based on the hard point loads obtained from the above decomposition, the finite element model was loaded in the Hypermesh software. The steel grade used in the original subframe was SAPH440. For the above ten typical extreme working conditions, the stress distribution cloud diagrams obtained from the strength analysis are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating Condition** & **F\({}_{X}\)/N** & **F\({}_{Y}\)/N** & **F\({}_{Z}\)/N** \\ \hline
1 & \(-120\) & \(-2479\) & \(-1498\) \\
2 & \(-179\) & \(-8550\) & \(-1331\) \\
3 & \(-249\) & \(-1248\) & \(-1430\) \\
4 & \(-197\) & \(-8233\) & \(-1596\) \\
5 & \(-120\) & \(-3060\) & \(-1496\) \\
6 & \(-189\) & \(-85\) & \(-1368\) \\
7 & \(-98\) & \(-1538\) & \(-1347\) \\
8 & \(-211\) & \(-11,501\) & \(-2766\) \\
9 & \(19\) & \(937\) & \(587\) \\
10 & \(-97\) & \(-3395\) & \(-2210\) \\ \hline \hline \end{tabular} Note: The above working conditions, in sequence, are as follows: static load working condition, double wheel runout working condition, longitudinal force working condition, lateral force working condition, forward acceleration working condition, forward braking working condition, backward braking working condition, right turn working condition, left turn working condition, braking + right turn working condition.  
\end{table}
Table 2: Static load of the lower swing arm connected on the left side.  
Figure 5: _Cont._  
Figure 5: Stress cloud maps of ten working conditions.  
According to the simulation analysis results, the maximum stresses of the primary and rear subframes under ten working conditions are 115 MPa, 229 MPa, 245 MPa, 204 MPa, 126 MPa, 113 MPa, 99.6 MPa, 211 MPa, 144 MPa, and 131 MPa, respectively. None of them exceed the yield ultimate stress of 306 MPa of the materials used, which is in line with the enterprise standards. However, there is also an uneven stress distribution. Stress concentration occurs at the connection points with parts such as the lower control arm and the front beam tie rod, while the stress at the front crossbeam position is relatively small. It can be seen that there is still room for optimization in the original subframe structure.
## 6 Results and Discussion  
This research proposes predictive models based on machine learning to detect and classify network activity. Ten models were trained, tested, and validated, five for detection and the remaining for classification purposes. For the detection model, network activities were classified into two groups (normal and anomaly). Meanwhile, for the classification model, network activities were classified into five groups (Normal, Mirai attack, DoS attack, Scan attack, and MITM attack)
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 4 System Model
### Different Trajectories for UABs  
As mentioned, we analyze multiple possible trajectories for one UAB flying over clusters of IoT nodes. These trajectories follow a predefined path, since IoT nodes are placed in fixed positions and usually have a traffic demand that is easily predicted or periodical. In this way, we can avoid static positioning of multiple drones, that would require increasing capital expenses. Moreover, a static deployment of hovering UABs has further energy consumption issues from the UAB side, that may be less under control. We consider the following possible trajectory design:  
* Circular path;
* Paparazzi-like trajectory;
* Flight following the solution of a Traveling Salesman Problem (TSP) over clusters' parent points.  
Each of these trajectories has its pros and cons. Thanks to the wide coverage which can be achieved by the three coverage classes of NB-IoT, the circular path might be an option for its short path length. On the other side, if IoT nodes are not adequately covered, the paparazzi-like trajectory is able to scan the entire area. However, since the UAB has to serve clusters of fixed nodes, there is a third option. We can consider the locations of the parent points as reference coordinates to model the trajectory as a TSP [35], as also proposed in [1]. In this way, we can observe the effectiveness of these choice compared to other known alternatives. The TSP determines, for a finite set of points whose pairwise distances are known, the shortest route connecting all points. The circular path has a radius length equal to half the length of the circumscribed circle of the service area. To better adapt the circular trajectory to the nodes deployment, we consider as the perimeter of the service area the maximum extension of nodes' location in all directions, centering it and the circular path consequently. A similar implementation is repeated for the paparazzi trajectory, which considers also a sensing radius for the UAV to define the width of the serpentine, fixed to 500 m. Examples of trajectories and cluster positions for a scenario snapshot are represented in Figures 2-4.  
Figure 2: Example of circular path (blue line) and clusters with nodes (red dots) location; the dark blue circle indicates the start of the trajectory.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(\mathbf{I_{\text{tx}}}\) **[mA]** & \(\mathbf{I_{\text{rx}}}\) **[mA]** & \(\mathbf{I_{\text{idle}}}\) **[mA]** & \(\mathbf{I_{\text{sleep}}}\) **[mA]** & \(\mathbf{V}\) **[V]** \\ \hline
**Value** & 220 & 46 & 6 & 0.003 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy Consumption Parameters.  
As one can easily see, the performance of the considered network depends both on the UAB mobility pattern and the UAB NB-IoT cell configuration. In the following section, we consider how the respective parameters (e.g., UAB height and speed, NB-IoT coverage class configuration and cluster dimension) affect the performance.
## 3 Maximum Power Point Tracking Algorithms
### The Cuckoo Search Optimizer (CSO)
\begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline
1 & Initialize the population of host nests (solutions) within the search space (n, \(\beta\), Pa, k, iter\({}_{\text{max}}\), D\({}_{\text{min}}\), D\({}_{\text{max}}\)). \\ \hline
2 & Assess the fitness of each nest using the objective function, which in this case is the PV energy system’s power output under varying environmental circumstances. \\ \hline
3 & Update the best solution: Identify the best solution (nest) based on the fitness evaluation. \\ \hline  & Generate new solutions: Employ the cuckoo’s random walk behavior to generate new candidate solutions using the following equation: \\
4 & \(\text{D}_{\text{i}}^{\text{k}+1}=\text{D}_{\text{i}}^{\text{k}}+\text{L}_{ \text{m}}\left(\frac{\text{u}}{|\text{v}|^{\frac{1}{\beta}}}\right)\left( \text{D}_{\text{best}}^{\text{k}}-\text{D}_{\text{i}}^{\text{k}}\right)\) \\  & where \(\beta=1.5\), \(\text{L}_{\text{m}}\) is the Lévy multiplication coefficient, and \(\text{u}\) and \(\text{v}\) are randomly estimated from the normal distribution function. \\ \hline
5 & Evaluate new solutions: Evaluate the fitness of the new candidate solutions. \\ \hline
6 & Replace solutions: Replace the host nests with the new solutions based on a predefined selection criterion. If rand \(>\) pa, apply step 7; otherwise, apply step 4. \\ \hline
7 & Destroy nests: With a probability of Pa, certain nests are destroyed, and new random nests are generated to replace them. Evaluate the fitness of all new nests. \\ \hline
8 & Update the best solution: Identify the best solution (nest) based on the fitness evaluation. \\ \hline  & Check for the termination criteria: \\
9 & If the stopping condition are met, stop the algorithm and output the GPP. \\  & Otherwise, go to step 4. \\  & Initialize the population of host nests within the search space (CSO parameters: n, \(\beta\), Pa, k, iter\({}_{\text{max}}\), D\({}_{\text{min}}\), D\({}_{\text{max}}\)). \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pseudo code of CSO for following the global peak in PV energy systems.
## 4 Flying Base Station Channel Capacity and Positioning
### Flying Base Station Channel Capacity
#### 4.2.2 Half-Duplex Case  
If the flying base station does not have full-duplex capability and can only either transmit or receive (half-duplex), e.g., such as when there are not enough antennas on-board, it must alternate between communicating with the user and the stationary base station. Therefore, the transmission data bandwidth is halved from the channel bandwidth, i.e., \(W^{\prime}=0.5\ W\).  
Theorem 4.3: _If the flying base station has half-duplex capability, then the flying base station channel capacity is as follows:_  
\[C_{FU}=0.5\cdot W\cdot\log_{2}(1+\eta\ 2^{\gamma}\ \mathrm{SNR}_{SU}) \tag{7}\]  
Proof: Use Equation (5) with \(W^{\prime}=0.5\ W\), \(P_{\mathrm{F}}=\eta\cdot P_{\mathrm{S}}\), and \(d_{FU}=0.5\cdot d_{SU}\) from Theorem 4.3. The algebraic steps and derivations are omitted in this paper.
## 3 Results and Discussion
### Cr-Mn-Si (\(\sigma\) Phase)  
From now on, we will focus on the \(\sigma\) phase. This phase has five inequivalent sites of multiplicity 2, 4, 8, 8, and 8 and CN 12, 15, 14, 12, and 14, respectively (for a detailed review on the crystal chemistry of this phase, see [20]). In binary systems, it tends to order at the composition \(A_{2}B\). To start with, we can present an example taken from the literature: the Cr-Mn-Si system [21]. The phase diagram has been plotted in Figure 9. This system is interesting for different reasons. The \(\sigma\) phase is stable in the Cr-Mn system but very far from the ordered stoichiometric composition corresponding to the occupancy of the high CN by the larger atom (Cr) and the low CN by the smaller atoms (Mn): Cr2 Mn. Secondly, from the study of the phase diagram, it appears that the \(\sigma\) is very close to stable in the binary Mn-Si system since its homogeneity domain stops only \(\sim\)1 at. % from the binary border. Finally, the \(\sigma\) phase is not only extending between the Cr-Mn and Mn-Si systems, suggesting a possible replacement of Cr by Si atoms, but also points towards the third binary border of Cr-Si, parallel to the Cr-Mn binary with the same orientation as the homogeneity domain of \(A15\) Cr\({}_{3}\)Si, suggesting a replacement of Mn by Cr atoms. In the absence of ternary site occupancy data, it is difficult to conclude about the site reversal and the stability of the phase in the ternary field. Given the atomic sizes and electronic properties, it is more probable that the intermediate atom is, in this case, Mn and that the reversal line would be going from the Cr-Mn to the Mn-Si system. As it corresponds to the stable homogeneity range of the phase, this would deserve an additional experimental study of site occupancies. As we will see, this type of data has been obtained for the \(\sigma\) phase in a number of other systems.  
Figure 8: Calculated site occupancies at 1500 \({}^{\circ}\)C for the \(\chi\) phase in the Zr–W–Re system along the composition line joining Zr\({}_{5}\)W\({}_{24}\) and W\({}_{5}\)Re\({}_{24}\) (dashed line in Figure 7).
## 2 Materials and Methods
### Track Simulator
#### 2.3.4 Automated Scenario Generation  
Text files (input files) are used as an interface in gprMax simulation software, containing all information for conducting a single simulation. Various commands are available to define objects (geometry, position, etc.), material properties (permittivity, conductivity, etc.), or general properties (simulation time, resolution, etc.). These commands are each marked by a hash symbol (#) in the text file.  
The task of the generation script is the automated creation of the input files, based on the defined track in the configuration file (see Section 2.3.3). This process involves extracting specific information for each segment based on its position from the configuration file and translating this information into gprMax commands. By creating separate input files for each segment, the generation script enables the modular and efficient execution of simulations. Each segment can be simulated independently, improving the parallelization and scalability of the simulations. Additionally, managing separate text files for track segments facilitates the post-processing and analysis of simulation results for individual sections of the track.  
Beyond the standard track and sleepers in each section, the substructure can incorporate extra objects with customizable material properties. Not being limited to basic shapes like cuboids, cylinders, and spheres, it also supports the integration of digital design files, regardless of size and complexity. The antennas were based on the digital twin of a ground-penetrating radar system, as described in Section 2.2. They were inserted in triplicate in the middle in the direction of travel and a defined transverse offset, so as depicted in Figure 7, a total number of 11 channels were available for evaluation by default.  
The output of a simulation run is structured as a folder structure. In addition to the measurement results, which are available channel-wise for each track segment as.out files, additional data from the simulation environment (resolution, objects, substructure course, etc.) are stored as.json files. To enable a meaningful further processing of the simulation data, each channel is accompanied by a Label.txt file, which defines the positions of the objects visible in the respective channel.
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 4 Balancing Strategy  
Based on the analysis of Section 3, to take full advantage of this balancing circuit, an \"adaptive selection mode based on the state of high energy battery\" balancing strategy is proposed. In this strategy, suitable balancing modes of the balancing system are selected after matching the number of high-energy cell clusters to the number of low-energy cell clusters. \(\mathrm{U_{ref1}}\) and \(\mathrm{U_{ref2}}\) are two key parameters in the process and are set in the balancing strategy according to the circuit operation requirements. The execution flow of the balancing strategy is shown in Figure 11. The balancing strategy is divided into five main steps:  
1. The voltage of each cell in the battery pack is measured. \(\mathrm{U_{max}}\) and \(\mathrm{U_{min}}\) are found by the measured voltages. Then the numbers of cells of \(\mathrm{U_{max}}\) and \(\mathrm{U_{min}}\) are recorded.
2. \(\mathrm{(U_{max}-U_{min})}\) is calculated and compared with \(\mathrm{U_{ref1}}\). If \(\mathrm{(U_{max}-U_{min})>U_{ref1}}\), the balancing circuit is activated. If \(\mathrm{(U_{max}-U_{min})\leq U_{ref1}}\), the system is stopped to enter the balancing operation.
3. When the balancing system is controlled to enter the balancing state, \(\mathrm{U_{avg}}\) is calculated, and \(\mathrm{(U_{i}-U_{avg})}\) is calculated with \(\mathrm{U_{i}}\). Then \(\mathrm{(U_{i}-U_{avg})}\) is compared with \(\mathrm{U_{ref2}}\). If \(\mathrm{(U_{i}-U_{avg})\geq U_{ref2}}\), the cell is identified as a high-energy cell. If \(\mathrm{(U_{i}-U_{avg})\leq-U_{ref2}}\), the cell is identified as a low-energy cell.
4. The numbers of high-energy cells and low-energy cells involved in balancing are counted, and their numbers are noted as a and b. The balancing mode is selected according to the result of the comparison of a and b.
5. After completing the above operations, the balancing system enters a waiting state, and the next cycle is entered by the balancing system.  
Figure 10: Balancing performances of different balancing modes.  
In this paper, U\({}_{\text{ref1}}\) and U\({}_{\text{ref2}}\) are set to 0.05 V and 0.01 V, respectively.