esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 4 Result and Discussion
### Multi-Satellite-Key-Node Attack  
In order to verify the relationship between the number of key nodes and the attack effect, according to the ranking results of key nodes' importance, the network security performance of several satellite key nodes under the OSFPv2 protocol after being attacked is analyzed.  
As shown in Figures 7 and 8, when DDoS attacks are applied to four key satellite nodes No. 121, No. 120, No. 119, and No. 67, the LSCN can still communicate by changing its satellite links. Although its communication links have changed, the number of satellite nodes that the path passes through remains the same, and so its communication propagation delay has no significant change.  
As shown in Figures 9-11, when the key nodes of satellites No.121, No.120, No.119, No.194, and No.67 were attacked, the number of nodes increased after the link path was changed. Figure 11 clearly shows that with the increase in the number of key nodes attacked, the propagation delay also increases gradually, from the initial 0.049 s to 0.087 s. In addition, the number of data packets decreases to 3519, and the attack effect becomes more and more obvious.  
Figure 6: Average end-to-end delay under two-satellite-key-node attack scenario.  
Figure 7: Four-satellite-key-node attack scenario.  
Figure 8: Average end-to-end delay under four-satellite-key-node attack scenario.  
Figure 10: Average end-to-end delay under five-satellite-key-node attack scenario.  
Figure 9: Five-satellite-key-node attack scenario.  
We continued to verify the relationship between the number of key nodes and the attack effect and continued to carry out DDoS attacks on 10 key nodes and 12 key nodes of the network. The findings demonstrate that when the number of key nodes of attacked satellites increases, the transmission delay between satellites increases, which is proportional to the number of attacked satellites. When the number of attacked satellites reaches 12, the latency rises to 0.1003 s. However, the packet loss rate hardly changes, which may be the reason why the satellite quickly switches the backup link after being attacked.
## 3 Results and Discussions
The \(\kappa_{\rm lat}\) value of the pristine Cu\({}_{0.01}\)Bi\({}_{2}\)Te\({}_{2.7}\)Se\({}_{0.3}\) (\(\sim\)0.785 W m\({}^{-1}\) K\({}^{-1}\)) at 300 K is gradually reduced with the Hf-doping content up to \(x=0.1\), and reaches the minimum value of \(\sim\)0.612 W m\({}^{-1}\) K\({}^{-1}\) due to the intensified alloy scattering of the phonons. Phonon scattering by Hf\({}_{\rm Bi}\) is determined by (1) the fraction of the point defect (Hf\({}_{\rm Bi}\)); (2) the mass difference between the doping element (Hf) and host atom (Bi); and (3) the lattice mismatch between the disordered and pure alloys according to the Callaway model and following the phonon scattering parameter (\(\Gamma\)) in Equation (3), where \(x\) is the fraction of the doping element, \(\Delta M/M\) is the rate of change of the atomic mass, \(\varepsilon\) is the elastic property, and \(a_{\rm disorder}\) and \(a_{\rm pure}\) are the lattice parameters of the disordered and pure alloys, respectively [18; 19]. \[\Gamma=x(1-x)\left[\left(\frac{\Delta M}{M}\right)^{2}+\varepsilon\left(\frac{ a_{disorder}-a_{pure}}{a_{pure}}\right)^{2}\right] \tag{3}\]  
Thus, the \(\kappa_{\rm lat}\) reduction by Hf-doping might be related both with mass fluctuation (by the atomic mass difference between Bi (\(M_{\rm Bi}=208.98\)) and Hf (\(M_{\rm Hf}=178.49\))) and strain field fluctuation (by  
Figure 4: Temperature dependences of (**a**) total thermal conductivity (\(\kappa\)) and lattice thermal conductivity (\(\kappa_{\rm lat}\)); (**b**) electronic thermal conductivity (\(\kappa_{\rm ele}\)); and (**c**) a dimensionless figure of merit (\(zT\)) for the SPSed Cu\({}_{0.01}\)Bi\({}_{2\cdot x}\)Hf\({}_{x}\)Te\({}_{2.7}\)Se\({}_{0.3}\) (\(x=0\), 0.05, 0.075, 0.1, 0.125) bulks. The inset of (**b**) shows the temperature-dependent Lorenz number (\(L\)). the atomic radius difference between Bi (230 pm) and Hf (225 pm). Despite the small mass and size difference between Bi and Hf, \(\kappa_{\rm lat}\) is reduced \(\sim\)25%, since high-frequency phonons are dominant in Bi-Te-based alloys [6]. A slightly increased \(\kappa_{\rm lat}\) value in Cu\({}_{0.01}\)Bi\({}_{1.875}\)Hf\({}_{0.125}\)Te\({}_{2.7}\)Se\({}_{0.3}\) is owing to the formation of the secondary phase (Hf\({}_{3}\)Te\({}_{2}\)). Figure 4c shows the temperature dependences of the \(zT\) values for the Cu\({}_{0.01}\)Bi\({}_{2.x}\)Hf\({}_{x}\)Te\({}_{2.7}\)Se\({}_{0.3}\) bulks. An enhanced \(zT\) of 0.83 is obtained at 320 K in Cu\({}_{0.01}\)Bi\({}_{1.925}\)Hf\({}_{0.075}\)Te\({}_{2.7}\)Se\({}_{0.3}\) due to the simultaneous improvement of the electronic and thermal transport properties by Hf-doping.
# Behavior of Fiber-Reinforced and Lime-Stabilized Clayey Soil in Triaxial Tests
## 1 Introduction
### Sample Preparation  
Four kinds of sample, differentiated according to the material added to soil, were considered in this study: (1) an untreated sample; (2) a lime-stabilized sample; (3) a fiber-reinforced sample; and (4) a lime-stabilized sample reinforced with fibers. In the samples containing lime, lime was added in three different content levels of 2%, 4%, and 6% as a percentage of the dry weight of the soil. For the samples into which wheat straw fibers were mixed, fiber contents of 0.2%, 0.4%, and 0.6% by dry weight of soil were chosen.  
Cylindrical samples with a diameter of 39.1 mm and a height of 80 mm were prepared at the optimum moisture content and maximum dry density determined for untreated soil, no matter what material was added to the specimen. This operation was reasonable because the effect of the addition of these materials on the compaction characteristics of Hefei clayey soil was negligible according to the compaction testing results from this study. To achieve the desired maximum dry density, untreated soil and a mixture of soil and added material were poured progressively into a steel tube of inner diameter 39.1 mm and height 100 mm and then subjected to static compaction by a hydraulic jack.  
The mixing of soil and fibers involved two steps. First, fibers were manually mixed into soil with approximately 10% water until a random distribution of fibers by visual examination was achieved. In this step, 10% moisture content was considered for the prevention of segregation of fibers from the soil. Second, a certain amount of water was then added to the mixture of fibers and soil prepared in the first step to achieve the optimum moisture content. On the other hand, three steps were involved in the mixing of mixture of soil, fiber, and lime. The first step was thoroughly mixing lime with soil with approximately 10% water. The following two steps were as those for the preparation of the mixture of soil and fiber. After the completion of the static compaction, all the samples were transferred into a curing room of 25 \(\pm\) 1 \({}^{\circ}\)C and 95% humidity in which they were cured for 21 days.
# Building Wireless Control Applications with XBee and LabVIEW
## 5 Case Study  
This section analyzed the performance of the XBee communications in a particular control application. Namely, a high precision positioning application, based on a piezoelectric actuator. Piezoelectric actuators (PEAs) are widely used in a variety of applications where ultrahigh precision is required [31]. They are frequently introduced at micro-positioning applications, due to their high resolution (in the range of nanometers), large blocking force (hundreds of N), high stiffness, large bandwidth, and fast response. Unfortunately, the biggest problem of piezo-driven stages comes from the nonlinearities in the PEA, attributed to hysteresis, creep, and drift. These problems could be addressed by selecting appropriate controllers aimed at compensating these effects [32]. In this work, the authors focused on evaluating the performance of wireless communications (based on XBee technology) for avoiding the wiring between the controller, executed in a NI myRIO computing platform, and a commercial PEA. Since the purpose of this work was to test wireless communications in control applications, a well-established control algorithm was implemented at the NI myRIO, namely a proportional-integral (PI) controller. However, the authors were aware that the performance of this controller could be improved [32]. The presented tests are aimed at evaluating the performance of XBee communications in wireless control networked applications.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 5 Results of Attack-Specific Two-Class Models
### Exposure to Unseen Data
#### 5.2.1 FTP/SSH Brute Force  
Days 0 of both CSE-CIC-IDS2018 and CIC-IDS2017 contain brute force attacks targeted at an FTP or an SSH server. These samples serve as a proxy for brute force traffic in general, because many more service endpoints, both public and non-public exist on the internet that are susceptible to brute force attempts (e.g., API servers, VPN access points, databases, RDP servers, etc.).  
The very weak performance for most tree-based models is observed with class separability (balanced accuracy) often at 50%, indicating that the models are no better than chance. The results are worse when employing normalization, because the values are squeezed into a range that is too narrow. Even the models trained on very low training volumes (high vertical data reduction) still overfit on the data. There are some exceptions (subfigures of Figure 3), most notably (and expectedly) when using very low training volumes (0.1%).  
Recall is typically very low, but the malicious samples in that recalled percentage are classified with high precision. Models that were trained with the most discriminative features removed fail immediately.  
Figure 3: Performance metrics of two tree-algorithms trained on FTP-SSH brute force (CIC-IDS2017), evaluating FTP-SSH brute force from CSE-CIC-IDS2018.  
A very similar conclusion is reached for the RBF-SVC. It manages to reach moderate recall (71%) with a high precision of 93.8%, but only with normalized features, and 0.1% training volume. Increases in training volume lead to very low recall with high precision. It is more resistant to feature removal than the tree-based methods. The logistic regression and linear SVM do not have noteworthy results.  
Nearest neighbors is useless, because it has low precision and recall, regardless of the scaling and invariant to training volume. Nearest centroids has stable sections with precision-recall pairs at approximately 60% and 75%, respectively. These are maintained fairly well when reducing features, but only if the features were normalized or min-max-scaled. Without feature scaling, lots of performance is lost quickly after removing top-features. The classifier seems brittle overall. Curiously, the best recall results, up to perfect recall, happen when using only 0.1% of data for training.
## 3 Results and Discussion
### Method Validation
#### 3.2.5 Method Robustness  
The changes introduced to the chromatographic conditions (flow rate, pH and mobile phase ratio) did not reveal any significant changes to peak retention times, sharpness of the peaks, peak symmetry (tailing factor is still <2) or the resolution between the analyte peaks of the ternary mixture, indicating robustness of the HPLC method in analysis and separation of metformin, glipizide and gliclazide. The % RSD in all cases never exceeded 0.009% (acceptance limit % RSD < 2). The results of the robustness studies are presented in Table 5. \begin{table}
\begin{tabular}{c|c c c|c c c c|c c} \hline \hline  & \multicolumn{3}{c}{**Day 1**} & \multicolumn{3}{c}{**Day 2**} & \multicolumn{3}{c}{**Day 3**} \\ \hline  & **Mit** & **Gp** & **Gc** & **Mit** & **Gp** & **Gc** & **Mit** & **Gp** & **Gc** \\ \hline
1 & 2,524,316 & 1,498,721 & 1,224,836 & 2,538,229 & 1,516,208 & 1,242,575 & 2,537,781 & 1,503,571 & 1,210,330 \\ \hline
2 & 2,522,351 & 1,492,431 & 1,229,109 & 2,533,455 & 1,512,581 & 1,233,285 & 2,536,367 & 1,500,636 & 1,209,836 \\ \hline
3 & 2,528,793 & 1,501,196 & 1,226,464 & 2,539,516 & 1,513,522 & 1,224,957 & 2,534,351 & 1,504,932 & 1,211,956 \\ \hline
4 & 2,526,275 & 1,497,532 & 1,222,251 & 2,532,284 & 1,510,925 & 1,223,146 & 2,530,360 & 1,500,344 & 1,202,551 \\ \hline
5 & 2,525,035 & 1,500,292 & 1,226,690 & 2,536,583 & 1,513,716 & 1,222,057 & 2,534,389 & 1,497,937 & 1,206,360 \\ \hline
6 & 2,522,528 & 1,496,113 & 1,225,867 & 2,536,419 & 1,513,320 & 1,224,946 & 2,530,408 & 1494224 & 1,202,892 \\ \hline Mean & 2,524,883,000 & 1,497,714.167 & 1,225,869.500 & 2,536,081.000 & 1,513,378.667 & 1,228,494.333 & 2,533,942.667 & 1,500,274.000 & 1,207,320.883 \\ \hline SD & 2430.120 & 3171.462 & 2266.393 & 2760.162 & 1720.445 & 7954.407 & 3043.206 & 3866.967 & 4003.873 \\ \hline \% RSD & 0.001 & 0.002 & 0.002 & 0.001 & 0.001 & 0.006 & 0.001 & 0.003 & 0.003 \\ \hline \hline \end{tabular}  
* Key: Mt: metformin, Op: glipizide, Gc: gliclazide, SD: standard deviation, RSD: relative standard deviation. Metformin results appear in yellow, glipizide results appear in purple and gliclazide results appear in blue. Other colours were added to allow for fast readability of the table.  
\end{table} 
Table 4: Repeatability results and inter-day precision results of the produced peak areas of independent replicates of metformin (Mit), glipizide (GP) and gliclazide (Gc) over 3 consecutive days.
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 4 Results
### Robustness Analysis  
K-fold cross-validation has been implemented using the Soyuz_easy dataset, with 1000 randomly selected samples, to assess the robustness of PE-PSO in comparison to the conventional method. The selected dataset is split into 10 groups, with each group containing 100 samples. Ten groups of datasets are deployed with cross-validation techniques. If K is defined as the index of the testing dataset, with the remaining groups serving as the training dataset, the minimum value of K is 1, and the maximum value is 10. The COCO pretrained weight has been implemented to conduct a robustness analysis with the weight \(\left(\omega\right)\) of 0.3 and a weight damping ratio \(\left(\omega_{damp}\right)\) of 0.5, which is shown as the best performance in the previous section. The robustness analysis is performed over 30 epochs, with the results presented in Tables 7-9. Although the results for orientation error and total error, presented in Tables 8 and 9, do not show a significant improvement, the PE-PSO method demonstrates a reduction in position error. The mean position error across all 10 test cases is reduced by 1.5%, with the highest reduction observed at K = 8, where it is mitigated by 2.8%. Moreover, the overall trend of the robustness analysis favors the PE-PSO method, particularly with further iterations, as highlighted in the previous section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Test Case no.**} & **Mean** & **Mean** \\  & **Position Estimation Error** & **Orientation Estimation Error** \\ \hline
1 & 0.6950 & 4.7611 \\
2 & 0.6036 & 4.8252 \\
3 & **0.6410** & **4.7488** \\
4 & 0.8222 & 5.1743 \\
5 & 0.8534 & 4.7671 \\ \hline \hline \end{tabular} The bold in table show the best value.  
\end{table}
Table 6: The mean estimation of position and orientation errors for the testing dataset using the Soyuz_hard pretrained weights.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Test Case no.**} & **Mean** & **Mean** \\  & **Position Estimation Error** & **Orientation Estimation Error** \\ \hline
1 & 1.3053 & 29.7798 \\
2 & 1.2267 & 25.5245 \\
3 & **1.1346** & **21.1206** \\
4 & 1.2475 & 21.4514 \\
5 & 1.2375 & 27.2040 \\ \hline \hline \end{tabular} The bold in table show the best value.  
\end{table}
Table 5: The mean estimation of position and orientation errors for the testing dataset using the COCO pretrained weights.
## 2 Materials and Methods
### Performance Measures  
The performance of the classification algorithms was evaluated using metrics of accuracy (Equation (6)), the proportion of correct predictions; precision (Equation (7)), the proportion of true positive predictions out of all positive predictions; recall (Equation (8)), the proportion of true positive predictions out of all actual positive instances; specificity measures the proportion  
Figure 4: Process of the development of apple varieties classification.  
of true negative predictions out of all actual negative instances (Equation (10)); and F1-score (Equation (9)), the weighted harmonic mean of precision and recall.  
To examine the trade-off between true positive rate (TPR) and false positive rate (FPR), AUC-ROC (area under the receiver operating characteristic curve), and the trade-off between precision and recall for different classification thresholds, AUC-PR (area under the precision-recall curve) were used. In addition, we considered [PERSON]'s kappa (Equation (11) where Po: relative observed agreement among raters, and Pe: relative observed agreement among raters), a measure of inter-rater agreement expected by chance. Additionally, we used MCC (Matthews correlation coefficient) (Equation (12)), which represents the correlation between the predicted and actual classifications to assess the overall performance of the models [38].  
As provided in the equations below, the performance metrics were computed using the following: the true positives (TP) objects, where their actual label is positive and whose class is correctly predicted as positive; the true negatives (TN) objects, where the actual label is negative and whose class is correctly predicted to be negative; the false positives (FP) objects, where the actual label is negative and whose class label is incorrectly predicted as positive; and the false negatives (FN) objects, where the true label is positive and whose class is incorrectly predicted as negative as obtained from the confusion matrix of the classification results of every model [38].  
\[\text{Accuracy}=\frac{\text{TP }+\text{ TN}}{\text{TP }+\text{ TN}+\text{FP}+\text{FN}} \tag{6}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP }+\text{ FP}} \tag{7}\]  
\[\text{Recall }=\frac{\text{TP}}{\text{TP }+\text{ FN}} \tag{8}\]  
\[\text{F1}-\text{Score}=2*\frac{\text{Precision }*\text{Recall}}{\text{Precision }+\text{Recall}} \tag{9}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{10}\]  
\[\text{K}=\frac{\text{P0}-\text{Pe}}{1-\text{Pe}} \tag{11}\]  
\[\text{MCC}=\frac{\left[\left(\text{TP}*\text{TN}\right)-\left(\text{FP}* \text{FN}\right)\right]}{\sqrt{\left[\left(\text{TP}+\text{FP}\right)\left( \text{TP}+\text{FN}\right)\left(\text{TN}+\text{FP}\right)\left(\text{TN}+ \text{FN}\right)\right]}} \tag{12}\]
## 3 Design and Geometry of the Proposed Antenna
### Manufacturing and Assembly  
Two prototypes have been developed based on the same electromagnetic design, with the objective of qualifying them with space levels prior to integration within the Leader-S structure. It is noteworthy that both prototypes are functionally identical: the Engineering Qualification Model (QM), which will be qualified with values and duration of qualification, and the flight model (FM), which will be qualified with duration and values of acceptance. It is anticipated that the FM will be the antenna that will fly placed on Leader-S. The design, integration and measurement of these prototypes have been conducted at INTA facilities, yielding identical results. With regard to the assembly, the layers are manufactured separately: a first plate containing the ground plane, to which the connector flanges are soldered (see Figure 5c); a second plate containing the lower patch, to which the connector pins are soldered (see Figure 5b); and a third plate containing the parasitic patch (see Figure 5a). These plates are separated by two Rohacell spacers, which provide structural rigidity. In order to adjust the antenna to the available slot in the CubeSat, the corners of each layer are chamfered and a central pin has been included to avoid unwanted discharges. Finally, only 12 nylon screws are required for fastening the multilayer, and two miniaturized SMP connectors for right and left polarizations feed the antenna.  
Figure 4: General overview of S—Band antenna design.  
Figure 3: S—Band antenna stack-up.  
Once the antenna has been assembled with materials that have undergone a bakeout process, it is bordered with an adhesive (SCOTCH WELD 2216 A + B. Batch EGE 208 [30]), which is applied to both antennas to prevent the intermediate layers forming the antenna from releasing unwanted particles in space. Following the application of the adhesive, the ANSER QM S Band antenna undergoes a complete qualification test campaign.  
Due to space specifications, the manufacturing process, material handling, assembly, and antenna measurements are carried out in the clean rooms of the INTA facilities, following a written, predefined procedure.
## 1 Introduction
For example, the classification or localization stage can be replaced by neural network models if it turns out to be appropriate for a particular task. This hybrid approach allows the algorithm to be adapted to different scenarios, combining the advantages of traditional mathematical methods and machine learning. Some algorithm parameters, such as thresholds and other coefficients, depend on the subject area and the specifics of the problem to be solved. Their selection is based on the characteristics of the data and may require additional calibration for optimal model performance. In general, the proposed method combines the advantages of wavelet transform for local data analysis, numerical optimization methods for efficient classification, and the flexibility of a modular approach, allowing for the integration of neural network components as needed. This makes it a competitive alternative to purely neural network models, especially in cases where the availability of large training samples is limited and adaptation to new data must be performed with minimal computational cost.
## 3 Results and Discussion
### Effects of Drying Process on Volatile Arroma Components  
In F vs. D groups, 33 of these volatiles were significantly upregulated, including \(\beta\)-cyclodextrin, (E)-\(\beta\)-ionone, and (E)-geranylacetone, etc. \(\beta\)-Cyclocitral had lemon and fruity aromas [31], while (E)-geranylacetone had fresh, rose, and floral aromas [35]. (E)-\(\beta\)-Ionone exhibited floral, woody, and berry aromas and had a low odor threshold (0.007 ug/L) [31], which played an important role in the presentation of black tea flavors and was driven by enzymatic oxidation during (E)-\(\beta\)-carotene fermentation or thermal degradation during drying [24; 51]. In this study, the enrichment of (E)-\(\beta\)-ionone occurred mainly during the drying process and reached a maximum value (14.52 \(\mu\)g/L) 3.34 times that of the fermented leaves. This indicated that the pyrolysis of carotenoids to form (E)-\(\beta\)-ionone was more severe compared with the enzymatic degradation.  
In addition, 1-octen-3-ol (cucumber, mushroom), pentanal (almond, malt), (E)-2-pentenal (strawberry, fruit), octanal (fatty, lemon, fragrance), decanal (orange peel odor), and 1-penten-3-one (onion odor) were all derived from unsaturated fatty acid metabolic pathways, because unsaturated fatty acids were oxidized and degraded during the drying process [33; 34]. Moreover, 2-pentylfuran had fruity, green, earthy, bean, and vegetable flavors and was a substance that had an important contribution to fruity aroma [36]. The formation of 2-pentylfuran is associated with the Maillard reaction and Strecker degradation of sugars and amino acids [33]. These aroma substances had certain promoting effects on the appearance of sweet and fruity aroma of finished tea.