esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Theoretical Method
### Soil Pressure Method Establishment
#### 3.3.3 Mechanical Analysis and Equations for Part III  
The value range of z at the slip surface FG is \(h_{j}+h_{0}+h_{\rm s}\ -\ b\)tan\(\theta\) to \(h_{j}+h_{0}+h_{\rm s}\). A slice element is selected in part III, and its stress is similar to that of part I shown in Figure 3. The general solution is in the same form as the part I solution.  
\[\sigma_{z}\ =\ m_{3}(H+D-z)^{a_{2}}+\frac{\gamma(H+D-z)}{a_{2}-1} \tag{13a}\] \[\sigma_{x1}\ =\ k_{a}\sigma_{x} \tag{13b}\]  
where \(m_{3}\) is a coefficient. When \(z=D+H-b\)tan\(\theta\), substituting Equation (12) into Equation (13) results in  
\[m_{3}\ =\ \frac{\left[m_{2}e^{-a_{3}(D+H-b\tan\theta)}+\frac{\gamma}{a_{3}}- \frac{\gamma b\tan\theta}{a_{2}-1}\right]}{(b\tan\theta)^{a_{2}}} \tag{14}\]  
The enclosure structure is subject to the lateral active soil pressure calculation method, as shown in Equation (15):  
\[\left\{\begin{array}{l}\mathrm{CD}:\sigma_{x1}\ =\ k_{a}\Big{(}m_{1}(b\tan \theta+h_{j}-z)^{a_{2}}+\frac{\gamma(b\tan\theta+h_{j}-z)}{a_{2}-1}\Big{)}\\ \mathrm{GC}:\sigma_{x1}\ =\ k_{a}\Big{(}m_{2}e^{-a_{3}z}+\frac{\gamma}{a_{3}} \Big{)}\\ \mathrm{FG}:\sigma_{x1}\ =\ k_{a}\Big{(}m_{3}(H+D-z)^{a_{2}}+\frac{\gamma(H+D-z)}{a_{2}-1} \Big{)}\\ m_{1}\ =\ \Big{[}q-\frac{\gamma(H+D)}{a_{2}-1}\Big{]}(H+D)^{-a_{2}}\\ m_{2}\ =\ \Big{\{}\Big{[}m_{1}(h_{0}+h_{\rm s})^{a_{2}}+\frac{\gamma(h_{0}+h_{ \rm s})}{a_{2}-1}\Big{]}-\frac{\gamma}{a_{3}}\Big{\}}e^{a_{3}h_{j}}\\ m_{4}\ =\ \Big{[}q-\frac{\gamma(H+D)}{a_{2}-1}\Big{]}(H+D)^{-a_{2}}\end{array}\right. \tag{15}\]  
The magnitude of the resultant force on the enclosure structure with finite soil is \[E_{a}\ =\ \int_{0}^{H+D}\sigma_{x1}dz \tag{16}\]  
Integrating the soil pressure intensity of each slip surface of CD, GC, and FG in Equation (15) within the depth range provides the resultant force.  
\[\begin{array}{l}E_{a}\ =\ k_{a}(\frac{r^{[(h_{j}+b\tan\theta)^{2}-(b\tan\theta)^{2}] }}{2(a_{2}-1)}+\frac{m_{1}[(h_{j}+b\tan\theta)^{a_{2}+1}-(b\tan\theta)^{a_{2}+ 1}]}{a_{2}+1}+\\ \frac{(r(H+D-b\tan\theta)-m_{2}e^{-a_{3}(H+D-b\tan\theta)}-(rh_{j}-m_{2}e^{-a_{ 3}h_{j}})}{a_{3}}+\frac{r(b\tan\theta)^{2}}{2(a_{2}-1)}+\frac{m_{3}(b\tan \theta)^{a_{2}+1}}{a_{2}+1})\end{array} \tag{17}\]
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 4 Results and Discussion
### Random Forest  
A supervised machine learning technique called random forest is used for both classification and regression tasks. It is a member of the family of ensemble learning algorithms that combine a number of ineffective learners to produce a more robust model. A random forest is a group of decision trees, where each tree is trained on a random subset of the training data and a random subset of features is considered for splitting at each node of the tree. This lessens overfitting and improves the generalization capabilities of the model.  
The confusion matrix is given below for TON-IOT and BOT-IOT, representing binary and multiple-class classification for each (Case 1 to Case 4) for 15 features (Table 5). It is observed that in all cases the FP and FN values are zero, therefore, maximum accuracies will be achieved and this determines that the model is robust. The subsequent ROC curves are given for BOT-IOT and TON-IOT with 15 features for multiple-class and binary classifications. When the steepness of the ROC curve reaches the top-left corner then it is considered an excellent model, hence in this case all of them are very good models (Figure 6). The AUC scores provide a summary measure of the model's discriminatory power, so a higher AUC indicates a more effective DDoS attack detection model.  
Hence, all the models are proven effective in this case with the random forest algorithm. With a random forest model, a prediction is made by passing input data through each decision tree in the forest, and the forecast that receives the most votes across all the trees is chosen as the result. Random forest's key benefits include its high accuracy, resistance to noise and outliers, and capacity for handling big datasets with plenty of characteristics.  
In order to choose features or comprehend the data, it can also offer estimates of feature relevance. The number of trees and the maximum depth of each tree are two hyperparameters that may need to be adjusted because random forest can be computationally expensive. Additionally, it could struggle with unbalanced datasets and with data that have a lot of dimensions.
## 4 Main Issues and Challenges
### Open Issues
#### 4.2.1 High Dependency on Deterministic Plans for Non-Deterministic Environments  
Probabilistic planning is hard. Instead of generating a sequence of actions, tasks, or even goals, a probabilistic plan has to provide a policy, that is, a function that given a state returns the next action with higher probability and reward to reach another state. However, an agent using a deterministic plan in an uncertain environment cannot guarantee to satisfy its goals [18]. As the underwater environment is not only uncertain, but also unreliable and prone to unpredictable events, the typical approach has been to use a deterministic plan to set the sequence of actions to achieve the desired goals, and also use a subsumption [88] architecture where the agents react to the unexpected events and try to adapt themselves to continue with the plan. The problem is when the plan cannot be fulfilled, and a new mission plan has to be created, either for repairing the current plan, or to just re-plan a whole new mission.  
The use of other approaches for planning under uncertainty can provide an extra mechanism to improve the response to unexpected events that can be characterized with a stochastic model.
## 4 Numerical Results and Discussion
### Parametric Analysis
#### 4.3.2 Influence of Blade off Location
Figure 21: Vibration modes with large unbalance due to blade off at the first stage axial compressor. Figure 20: Impact factors for the blade off occurring at two different locations. In a word, the above analysis shows that the parameter of the blade off location will not only have a significant effect on the impact factor, but also on the frequency spectrum characteristics at each node of the rotor. The above-mentioned frequency spectrum difference and impact factor distribution characteristics can provide a better theoretical basis for the fault recognition and safety design for this type of rotor suffering from blade off. What needs to be pointed out is that some noise signals are generally mixed in the vibration signals obtained in the actual test. In order to isolate these low-frequency noises, a method using non-orthogonal wavelet packet denoising and harmonic detection proposed in [21; 22; 23; 24] is recommended to eliminate these disturbances, as it can isolate interference or non-coherent parts well.
###### Abstract  
Thin films of strontium titanate, which reveal high structure quality and tunable properties prospective for microwave applications at room temperature, were grown on a semi-insulating silicon carbide substrate using magnetron sputtering for the first time. The films' growth mechanisms were studied using medium-energy ion scattering, and the films' structures were investigated using X-ray diffraction. The electrical characteristics of planar capacitors based on strontium titanate films were measured at a frequency of 2 GHz using a high-precision resonance technique. It is shown that the tendency to improve the crystalline structure of strontium titanate film with an increase in the substrate temperature is most pronounced for films deposited at elevated working gas pressure under low supersaturation conditions. Planar capacitors formed on the basis of oriented SrTiO\({}_{3}\) films on silicon carbide showed tunability n = 36%, with a loss tangent of 0.008-0.009 at a level of slow relaxation of capacitance, which is significantly lower than the data published currently regarding planar tunable ferroelectric elements. This is the first successful attempt to realize a planar SrTiO\({}_{3}\) capacitor on a silicon carbide substrate, which exhibits a commutation quality factor more than 2500 at microwaves.  
strontium titanate; silicon carbide; thin films; nonlinear dielectric properties; microwave applications
# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 3 Method
### Training Process
SSIM and LPIPS metrics are used to achieve this, as they provide a more perceptually accurate assessment of differences as perceived by the human eye compared to simple pixel-space error measures;
* _Loss 4_ serves a similar purpose to Loss 3, but it is calculated for the final high-resolution image (after upsampling). Due to the larger matrix size and higher computational demands, MSE and PSNR metrics are used instead of SSIM and LPIPS. These metrics balance quality assessment accuracy and computational efficiency, which is particularly important for large images. The following equation describes the final loss function: \[Loss_{total}=Loss_{1}+Loss_{2}+Loss_{3}+Loss_{4}\]  
The following loss functions are summed and then subjected to the backpropagation process. In the implemented approach, an additional dynamic weighting mechanism adjusts the contribution of each loss component depending on the training stage. At the beginning of training, all components are calculated without modification. However, during the final summation, the loss terms responsible for maintaining high transparency (Loss 3 and Loss 4) are initially set to zero. This means that, at the start, the network does not receive a gradient related to minimizing image artifacts, and the entire focus is placed on ensuring the correct operation of the decoder, specifically on detecting manipulations. In practice, this approach rapidly improves the detection accuracy, as feedback from the decoder entirely drives the learning process. However, as training progresses, the weight of the transparency-related loss component (for the encoder) gradually increases. In this implementation, its value increases by 0.1 per epoch until reaching 0.4, after which it remains constant. Meanwhile, the decoder's weight remains fixed at 1.0 throughout the training process. This strategy, known as a warm-up, allows training an effective decoder first, then improving the embedding quality (transparency) without significantly compromising the detection performance. Observations indicate that training would take longer without this approach, and the manipulation detector would achieve inferior results. The encoder network plays a relatively more straightforward role, which means it tends to dominate the learning process early on. The remaining hyperparameters used during training include the Adam optimizer with a learning rate 0.0001 and betas set to (0.5, 0.999). The batch size is set to 32.
## 2 Related Work  
### Viewpoint and Shape Estimation  
In earlier days of computer vision, single objects, as well as entire scenes, were represented by simple primitives, such as polyhedra [2] and generalized cylinders [3]. These approaches provided rich descriptions of objects and could estimate the viewpoint and the shape parameters, but robustly matching them to cluttered real-world images proved to be difficult at the time. With the advent of computers and advances in machine learning, it has become feasible to detect objects and their parts robustly. Currently, vision-based methods can be broadly classified into 2D image-based and 3D model-based techniques [4].  
Image recognition techniques are employed by 2D image-based methods to attempt to directly restore pose information from the single image [5; 6]. These methods usually work by a set of trained model-views taken in a range around the known model with different locations and viewpoints, which always suffer from intra-category variations. Pose estimation using 3D model-based methods usually require a priori 3D model of the object, and the holistic cost function is defined when the 3D deformable model is fitted to the image features. [PERSON] [7] extended the deformable part model (DPM) to 3D, and [PERSON] [8] introduced a separate DPM component corresponding to each viewpoint.  
These methods can estimate the viewpoint and the shape parameters simultaneously with different representation including wireframe and 3D mesh. Lately, estimation accuracy and utility have been greatly improved in the deep learning era. The single image 3D interpreter network (3D-INN) [9] presented a sophisticated convolutional neural network (CNN) architecture to estimate a 3D skeleton containing viewpoint and shape information. [PERSON] obtained the 3D object structure by a deep CNN architecture with domain knowledge in hidden layers [10]. However, these methods rely on category-specific keypoint annotation and are not generalizable. When dealing with multi-class objects, different networks need to be trained separately, which ignores inter-category structure similarities and raises training costs significantly. This paper promotes a method to attain a generalization ability.
## 3 Results and Discussion
### Pollutant Contents in Soils
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline  & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** & **PCB** \\  & **105** & **114** & **118** & **123** & **126** & **156** & **157** & **167** & **169** & **170** & **180** & **189** & **77** & **81** \\ \hline Minimum & d & d & d & d & d & d & d & d & d & d & d & d & d & d & d \\ Maximum & 34.0 & 0.690 & 600 & 8.30 & 1.10 & 400 & 16.0 & 120 & 0.013 & 2600 & 3300 & 82.0 & 2.10 & 0.013 \\ Average & 6.33 & 0.193 & 58.5 & 1.47 & 0.118 & 28.5 & 1.60 & 8.16 & d & 169 & 220 & 5.83 & 0.448 & d \\ Standard & & 12.4 & 0.287 & 171 & 2.76 & 0.285 & 99.3 & 4.56 & 28.9 & d & 628 & 795 & 20.4 & 0.601 & d \\ Median & 0.97 & 0.033 & 4.2 & 0.13 & 0.027 & 0.8 & 0.16 & 0.250 & d & 3 & 5 & 0.13 & 0.220 & d \\ MAD a & 0.42 & 0.009 & 3.0 & 0.10 & 0.018 & 0.6 & 0.13 & 0.184 & d & 2 & 3 & 0.11 & 0.130 & d \\ LOD b & 0.23 & 0.01 & 0.62 & 0.02 & 0.01 & 0.17 & 0.02 & 0.10 & 0.01 & 0.88 & 1.74 & 0.04 & 0.11 & 0.01 \\ LOQ c & 0.78 & 0.03 & 2.07 & 0.05 & 0.02 & 0.56 & 0.06 & 0.32 & 0.02 & 2.92 & 5.79 & 0.14 & 0.38 & 0.02 \\ \hline \hline \end{tabular}  
* median of absolute deviations; b limit of detection; c limit of quantification; d not calculated because more than 80% of the data was below the detection limits.  
\end{table} 
Table 3: Basic statistics of the PCB contents in the soils (\(\mu\)g/kg). of the sum of seven PCBs in soils was set at 0.02 mg/kg (the congeners 156 and 170 are not included). These values were exceeded only in the sampling areas Mine Pavel and Viewpoint (Figure 2B). At the Mine Pavel location, the sum of PCB congeners at one of the sub-samples reached up to 7.1 mg/kg, substantially exceeding the indicative value that adversely affects human and animal health (1.5 mg/kg). Therefore, although the enhanced PCB and PCDD/F values did not occur in all the sampling areas and did not indicate diffuse pollution as in the case of PAHs, the identified hotspots indicate the necessity of a more detailed investigation of the urban soils to identify the potential environmental hazard of the pollution. The use of PCB-based materials, such as hydraulic oils, in underground and opencast mines is considered a potential source of PCB pollution. The potential exposure of mine workers to PCBs has already been investigated, and the elevated PCP contents in various matrices have been documented [51; 52; 53; 54]. Therefore, enhanced PCB contents in soil close to the abandoned mine can be related to the possible use of PCB-based materials in this case.
# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 2 Materials and Methods
### Evaluation Metrics  
In order to assess the neural network models and select our base model, we concentrated on their performance. This is performed to assess how well the models detect the input images of sunflowers according to the drought stress that the plants have experienced.  
We used several metrics relevant to our classification task: accuracy, precision, recall, and F1 score.  
Accuracy is a metric that quantifies the overall performance of a classifier (1).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{1}\]  
Precision measures the proportion of true positive predictions out of all positive predictions made by the classifier (2).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
The percentage of true positive outcomes among all actual positives is measured by recall (3).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
F1 score provides a single metric that balances precision and recall by capturing the overall effectiveness of the model in terms of positive class predictions (4).  
\[\text{F1 Score}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{4}\]  
where  
* TPs (true positives) are the number of instances correctly predicted as positive;
* FPs (false positives) are the number of instances incorrectly predicted as positive when they are actually negative;
* TNs (true negatives) are the number of instances correctly predicted as negative;
* FNs (false negatives) are the number of instances incorrectly predicted as negative when they are actually positive.
# Text Line Extraction in Historical Documents Using Mask R-CNN
## 6 Evaluation
### Diva Evaluation Metrics  
The DIVA Line Segmentation Evaluator ([[https://github.com/DIVA-DIA/DIVA_Line_Segmentation_Evaluator](https://github.com/DIVA-DIA/DIVA_Line_Segmentation_Evaluator)]([https://github.com/DIVA-DIA/DIVA_Line_Segmentation_Evaluator](https://github.com/DIVA-DIA/DIVA_Line_Segmentation_Evaluator))) (accessed on 1 April 2020) is based on the Intersection over Union (IU). We compute the IU score for each possible pair of Ground Truth (GT) and Prediction (P) polygons according to the Equation (1), where IP denotes the number of intersecting foreground pixels among the pair of polygons and UP denotes number of foreground pixels in the union of foreground pixels of the pair of polygons. The pairs with maximum IU score are selected as the matching pairs among the ground truth (GT) and predicted (P) polygons. Then, pixel IU and text line IU are calculated among these matching pairs.  
\[\text{IU}=\frac{\text{IP}}{\text{UP}} \tag{1}\]  
Pixel IU is measured at pixel level. For each matching pair, line TP, line FP, and line FN are computed. Line TP is the number of correctly classified text line pixels, line FP is the number of pixels falsely classified as text line pixels, and line FN is the number of pixels falsely classified as background. Then, pixel IU is calculated according to Equation (2), where \(\Sigma_{\text{TP}}\) is the global sum of line TPs, \(\Sigma_{\text{FP}}\) is the global sum of line FPs, and \(\Sigma_{\text{FN}}\) is the global sum of line FNs.  
\[\text{Pixel IU}=\frac{\Sigma_{\text{TP}}}{\Sigma_{\text{TP}}+\Sigma_{\text{ FP}}+\Sigma_{\text{FN}}} \tag{2}\]  
Line IU is measured at line level. For each matching pair, line precision and line recall are computed according to Equations (3) and (4). Line IU is calculated according to  
Figure 9: Example pages from the ICDAR2015-HTR dataset.  
Equation (5), where CL is the number of correctly predicted lines, ML is the number of missed lines, and EL is the number of falsely predicted lines.  
\[\text{Line precision}=\frac{\text{line TP}}{\text{line TP}+\text{line FP}} \tag{3}\]  
\[\text{Line recall}=\frac{\text{line TP}}{\text{line TP}+\text{line FN}} \tag{4}\]  
\[\text{Line IU}=\frac{\text{CL}}{\text{CL+ML+EL}} \tag{5}\]  
For each matching pair:  
* A line is correctly predicted (CL) if both, the line precision and the line recall, are above a threshold value \(T\);
* A line is missed (ML) if the line recall is below the threshold value \(T\);
* A line is falsely predicted (EL) if the line precision is below the threshold value \(T\).  
In order to be able to compare our results to the ICDAR2017 competition's results [27], we used the same threshold they did, \(T=75\%\).
# Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
## 4 Case Study  
As mentioned in the operating principle of the proposed optical encoding model, each data symbol is mapped to a corresponding \(u(r,\theta)\) profile according to the selected modal indices \(\ell\) and \(p\) in the selector beam. Since the reference beam is restricted to the \(LG(p^{\prime},\ell^{\prime}=1)\) mode, the alphabet for possible data symbols within a discrete time window can be calculated as \(log_{2}N\) with \(N=n_{\ell}n_{p}\), where \(n_{\ell,p}\) represents the number of \(\ell\) and \(p\) indices used in the selector arm, and \(N\) represents the different data symbols that can be encoded as \(N\) -ary numbers: \(0,1,\ldots(N-1)\)[14]. For validation purposes, a data symbol code based on a 4-bit data symbol \((N=16)\) is designed, which is associated with the resulting intensity profile according to the selection of the \((p,\ell)\) combination, as shown in Figure 2. In the simulations presented in this work, \(LG\) beams with wavelength \(\lambda=1550\) nm, fundamental beam width \(w_{0}=100\lambda\) and a propagation distance \(z=200\lambda\) have been considered. This table shows all possible combinations of data symbols and the normalized intensity profiles of the reference, selector and the transmitted beam for data mapping.  
Since the proposed optical encoding model operates based on the Mach-Zehnder [39; 40] interferometric method on the transmitter side, and an image-based detection system on the receiver side, the following OAM generation methods must be considered for an experimental implementation: For the experimental generation of OAM modes, it is common to use mode converters composed of several cylindrical lenses, which can convert high-order Hermite-Gaussian beams into high-order Laguerre-Gaussian beams. However, mode converters are limited to a specific order Hermite-Gaussian beam, which needs to be generated by certain technical means as presented in [2]. The size of the mode converter is large, which presents strict requirements for the relative position and angle of the cylindrical lens. Typical mode converter configurations can be founded in [41]. Another alternative of mechanism for the OAM generation mode is the employment of a spatial light modulator (SLM) [11] that uses configurations based on changing modulation patterns loaded into the spatial light modulator. This can be achieved with a laser that can achieve various OAM beams with different output degrees. However, it is important to consider that under current technical conditions, the reflectivity of liquid crystal spatial light modulators is from 60 % to 90% [11].
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 4 Experimental Results
### Results Analysis  
To analyze the performance of the proposed method, it was tested and compared with the other algorithms regarding throughput, fairness, and preemptibility.