esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Nozzle on Arc Characteristic Parameters
1 discharge cone was used, the breakdown position of the air gap was basically unchanged, and the cathode discharge point always stayed at P during shooting, which limited the area covered by the arc bending deformation. When using the No. 2, No. 5, and No. 6 discharge cones, although the arc coverage area was improved, the cathode discharge point had not been closed into a circle. The closure of the cathode discharge points of the No. 3 and No. 4 discharge cones was better, but it can be seen from Figure 11 that the arc distribution in the annular area between the anode and the cathode was discrete and not uniformly dispersed, which indicates that the arc distribution in the swept area was very uneven. In addition, the arc deformation was very severe due to the lack of the extension tube and rectification effect. The arc bending deformation developed to the outer side of the cathode ring, and the deformation was most obvious when the No. 3 discharge cone was used. In contrast, the uniformity of the arc distribution could be significantly improved by adding the extension tube. On the one hand, the circumferential distribution of the cathode discharge points was closed, on the other hand, the arc distribution between the electrodes was more uniform. Among them, the No. 5 discharge cone was the most prominent. Although some areas of the arc diffused beyond the boundary of the cathode circle, the overall arc brightness was higher, which further shows that the discharge current increased with the reduction of the electrode gap. The arc rotation frequencies are compared in Figure 12. As the arc could not complete a full cycle rotation without the extension tube, only the rotation frequencies of the discharge with the extension tube were recorded. The results showed that the effect of the electrode gap on rotation frequency was very weak, but there was an optimal electrode. The rotational frequency of the No. 5 discharge cone was the highest, reaching 228 Hz/s. Combined with the analysis of arc length and regeneration frequency, the angle of the arc rotation was larger in a breakdown-stretching-quenching cycle when the No. 5 discharge cone was used. Therefore, the rotation frequency could be increased by sacrificing part of the regeneration frequency on the basis of ensuring a sufficient arc length.
## 4 Factors that Affect the Adsorption Process
### Effect of Other Co-Existing Ions  
The extraction of toxic heavy metal ions from aquatic systems is also affected by the presence of a mixture of the ions in the form of either cations or anions. These metal ions compete for their adsorption on vacant sites of the adsorbent [55; 71]. In a study, the impact of coexisting ions on the adsorption process was examined in a solution containing a combination of metal ions Ni\({}^{2+}\), Cu\({}^{2+}\), and Cr\({}^{6+}\). Adsorption by PAN/PPy nanofiber mats (100 mg) was done by dissolving the adsorbent in a 30 mL solution of a mixture of metal ions, with the initial concentration of every metal ion being 200 ppm at pH 2. It was found that uptake of Cr\({}^{6+}\) ions was decreased in the presence of Ni\({}^{2+}\) and Cu\({}^{2+}\) ions (Figure 10). However, PAN/PPy nanofiber was still more selective for Cr\({}^{6+}\) ions compared with Ni\({}^{2+}\) and Cu\({}^{2+}\) ions [77].  
Figure 10: Effect of coexisting ions on adsorption capacities of PAN/PPy nanofiber that for Cr\({}^{6+}\), Ni\({}^{2+}\), and Cu\({}^{2+}\). Adapted with permission from reference [77].  
Figure 9: Effect of contact time and initial concentration on the adsorption of Cr\({}^{6+}\) of the PAN/PPy nanofiber mat. Temperature = 25\({}^{\circ}\)C, pH = 2.0. Adapted with permission from reference [77].
## 5 Conclusions and Future Perspectives
(2022), where an SfM-based approach was proposed and tested, the use of thermal images for photogrammetric purposes presents a long series of challenges [41], mainly due to the following reasons:  
* The low spatial resolution of this type of imagery and, consequently, the low level of detail in the 3D reconstruction;
* The low radiometric contrast;
* The acquired elements characterized by similar emissivity, leading to similar temperature;
* The environmental conditions;
* The difficulties in the detection of the traditional low-emissivity photogrammetric markers--used for the absolute orientation of the photogrammetric block in thermal images;  
All these aspects could lead to significant difficulties in image orientation, especially when acquisition takes place in challenging contexts. In the case of the present research, for example, the presence of neighbouring buildings on three out of four sides of the studied building resulted in an extremely reduced acquisition distance (4-5 m), causing difficulties in capturing converging frames with an adequate percentage of overlap. For this reason, the texture-based methodology proposed in the current research was chosen in this case, despite the limitations outlined above. The development of new strategies for further enhancing this process, with the goal of providing more effective tools for researchers working in the field of cultural heritage documentation, is undoubtedly an intriguing challenge and a research topic that is likely to advance with the support of new technologies. Conceptualization, A.G.-G.; methodology, A.G.-G., G.P. and J.L.L. ; software, G.P. ; validation, A.G.-G. and J.L.L. ; investigation, A.G.-G., G.P. and J.L.L. ; resources, A.G.-G., G.P. and J.L.L. ; writing--original draft preparation, A.G.-G.; writing--review and editing, A.G.-G., G.P. and J.L.L. ; visualization, A.G.-G. and G.P. ; supervision, J.L.L. ; funding acquisition, A.G.-G., G.P. and J.L.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflict of interest.
# Dual Enhancement Network for Infrared Small Target Detection
## 4 Experiment  
We trained and tested our model on the NUAA-SIRST [20], NUDT-SIRST [24], and IRSTD-1K [22] datasets, and we followed references [24; 31] to set the same train-test ratio. We used three semantic segmentations (i.e., IoU, \(P_{d}\), \(F_{a}\)) as performance criteria. For IoU and Pd, larger values indicate higher performance. For Fa, smaller values indicate higher performance. To evaluate the segmentation performance, we compared our method with nine state-of-the-art (SOTA) algorithms, including three traditional methods (Top-Hat [18], IPI [14], PSTNN [19]) and six deep learning-based methods (MDvsFAcGAN [7], ACM [20], ALCNet [21], ISNet [22], UIU-Net [23], and DNA-Net [24]). We implemented our method with the PyTorch framework and used an NVIDIA GeForce RTX 3090 (24 GB) in our experiment. Before training, all input images were first resized to a resolution of 512 \(\times\) 512. During the network training, the AdamW optimizer was adopted for optimization with the CosineAnnealingLR scheduler, and the weight decay was adjusted to 1 \(\times 10^{-4}\). The batch size, learning rate, and total epoch number were set to be 6, 1 \(\times 10^{-4}\), and 100, respectively. Additionally, specific hyperparameters were configured as follows: the random seed was set to 3407 to ensure reproducibility; the patch size was defined as 512 to facilitate efficient processing of input data. Moreover, to prevent overfitting and to stabilize training, the weight decay rate was set to 0.5, with weight decay applied every 15 epochs.
## 3 Results and Discussion
### Determination of CMC of Shell Breaking
#### 3.1.2 Determination of CMC of Kernels  
To visualize the effect of MC on the fragmentation degree of kernels, Figure 10 shows snapshots of the crushing state of the kernels at different MCs. It can be seen that when the MC decreased from 32.69 to 11.27%, the number of large fragments increased and the overall size of the fragments increased (Figure 10A,B), which suggests that the degree of fragmentation decreased. Conversely, when the MC was further decreased from 18.91 to 2.31%, the number of small fragments increased and the overall size of the fragments decreased significantly (Figure 10B,C), which implies that the degree of fragmentation increased significantly. Note that although the MC of 32.69% is similar to the MC of 2.31% in terms of crushing state, the latter corresponds to a much more serious fragmentation of the kernel. Therefore, we guess that kernels with too high or too low MC are unsuitable for shell breaking.  
In order to quantitatively analyze the effect of MC on the fragmentation degree of the kernels, Figure 11 shows the variation of \(d_{\text{M}}\) with MC. As shown, the \(d_{\text{M}}\) increased rapidly when the MC decreased from 32.69 to 11.27% (i.e., stage I). This is mainly due to the fact that, as MC decreases, the hardness and mechanical strength of the kernel increases due to its progressively tighter internal tissue structure [17; 36]. At the same time, the shell, which is an important structure for moisture retention, creates a warm yet moist environment inside the walnut, thus improving the toughness of the kernels [37]. Hence, the dynamic interactions of self-hardening and external toughening endow kernels with mechanical strength and flexibility, thereby increasing the kernel's resistance to fragmentation. In contrast, \(d_{\text{M}}\) decreased rapidly when MC decreased from 11.27 to 2.31% (i.e., stage II). As MC continued to decrease, more cross-cracks were produced during multi-point extrusion due to the increased brittleness, resulting in a decrease in the size and an increase in the  
Figure 9: \(D_{\text{b}}\) of the shell fragments at different MCs.  
number of broken kernels [26]. Obviously, the change in \(d_{\rm M}\) with MC is consistent with the results of the qualitative analysis mentioned above. In addition, the results show that when MC was 11.27%, \(d_{\rm M}\) reached a maximum value of 15.39, corresponding to the smaller fragmentation degree of the shell, which conforms to the objective of walnut shell breaking. Therefore, an MC of 11.27% is treated as the CMC of kernels in this work.  
Figure 11: \(d_{\rm M}\) of the kernel fragments at different MCs.  
Figure 10: Crushing state of the kernels at different MCs (32.69% (**A**), 11.27% (**B**) and 2.31% (**C**)).  
According to the comprehensive analyses above, the CMCs of shells and kernels that correspond to the shell breaking are 9.41% and 11.27%, respectively. Nonetheless, in order to ensure that both shell and kernel have CMC, how to dry walnuts and what is the optimal dry condition still need to be studied and analyzed deeply.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 2 Background and Related Work
### Denial of Service Attack  
Denial of service (DoS) attacks disrupt target systems by exhausting computational resources through malicious traffic overload. The evolution to DDoS attacks enables attackers to coordinate geographically dispersed botnets--compromised devices spanning thousands of networks--to amplify attack magnitude exponentially. A seminal 1999 DDoS incident against the University of Minnesota incapacitated critical servers for 48 h, demonstrating early Internet vulnerabilities.  
Modern variants like [PERSON]'s link-flooding attack (LFA) exploit legitimate-appearing traffic between botnet nodes to congest critical network links rather than end-hosts, evading conventional intrusion detection systems [37]. Crossfire extends this paradigm by decoupling control-plane stability from data-plane attacks, enabling persistent regional service degradation through strategic target selection [38].  
In satellite network contexts, the ICARUS framework adapts these principles to LSN environments. Attackers leverage compromised ground terminals across multiple regions to generate coordinated \"normal\" traffic patterns, strategically selecting source-destination pairs to saturate uplinks, downlinks, or inter-satellite connections.  
As reported in [39], attackers are increasingly adopting hybrid DDoS strategies that combine carpet-bombing techniques with TCP reflection and amplification attacks, leading to highly disruptive traffic patterns across distributed ground terminals and satellite links, such as satellite ISPs or terminal services. These approaches indirectly launch amplified attacks against specific downstream targets.  
[PERSON] et al. proposed DoSat [17], a DDoS attack model targeting LEO satellite networks by combining link flooding with temporal lensing. By leveraging the time-varying topology of satellite constellations, DoSat concentrates attack traffic to overwhelm target links.  
Simulations show that it reduces attack cost by approximately 20% compared to prior methods such as ICARUS, while maintaining stealth.
# Federated Pseudo-Sample Clustering Algorithm:
## 3 Statement of Label Personalization Problem  
In this section, we focus on describing the personalized federated learning problem, which aims to facilitate collaboration among a group of clients to collectively train personalized models while ensuring privacy protection. Consider the existence of \(N\) clients \(C_{1},C_{2},\ldots,C_{n}\) in our proposed personalization scenario. Assume that different clients have different classification criteria for the same dataset. As a result, for the same type of image, different clients may assign different labels, that is:  
\[l_{D_{i,k}}\
eq l_{D_{j,k}}\Big{(}i\
eq j,D_{i,k}=D_{j,k},1\leq i,j\leq N\Big{)} \tag{1}\]  
The \(D_{i,k}\) represents the data of the \(k\)-th type from the \(i\)-th client and \(l_{D_{i,k}}\) represents the label of \(D_{i,k}\). Different model structures and different labeling of images of the same type will lead to different parameters being obtained from training. Therefore, the simple parameter aggregation methods in federated learning are not suitable for our work. Sharing samples generated by generative adversarial networks and then randomly distributing them to local clients will produce images belonging to the same category as the local images but with different labels, which will have a negative impact on the model update. For the classification tasks described in this article, different clients label each type of image according to their own task criteria. Then, the labeled images are used to train the model \(M_{i}\), and obtain the parameters \(w_{i}\). Here, \(M_{i}\) represents the local model of the \(i\)-th client, and \(w_{i}\) represents the parameters of the model \(M_{i}\). A function \(f(x,l_{x};M_{i},w_{i})\) is defined to evaluate the performance of the model \(M_{i}\), where \(x\) represents a sample from the local dataset \(D_{i}\) of the \(i\)-th client and \(l_{x}\) represents the label of sample \(x\). The goal of the client's classification task is to optimize the parameters \(w_{i}\) of the given model \(M_{i}\) by minimizing the expectation of function \(f(x,l_{x};M_{i},w_{i})\) on \(D_{i}\):  
\[w_{i}^{*}=\underset{w_{i}}{\text{argmin}}\operatorname*{E}_{x\sim D_{i}}\big{[} f\big{(}D_{i}l_{D_{i}};M_{i},w_{i}\big{)}\big{]} \tag{2}\]  
The \(\operatorname{E}[\cdot]\) is the expectation function. Suppose client \(C_{i}\) obtains the optimal model parameters \(w_{i,loc}^{*}\) through \(D_{i}\) training, that is:  
\[w_{i,loc}^{*}\stackrel{{ local}}{{=}}\underset{w_{i}}{\text{ argmin}}\operatorname*{E}_{x\sim D_{i}}\big{[}f\big{(}x,l_{x};M_{i},w_{i} \big{)}\big{]} \tag{3}\]
# Precision Analysis and Error Compensation of a Telescope Truss Structure Based on Robotics
## 3 Aberration-Compensation Method  
The Cassegrain telescope parameters considered in this study are listed in Table 4.  
The light path is shown in Figure 7. From Table 2 and Figure 3, it can be seen that although the offset produced by the truss is small, the system aberration is very sensitive to the misalignment of the secondary mirror, and the aberration changes caused by the pitching movement are obvious. In addition, every time the robotics change from the transport state to the working state, an assembly error will also occur. Therefore, this study introduces a second-order compensation term to the sensitivity matrix to adjust the SM in order to compensate for the offset due to different pitch angles and the positioning error during assembly. The repeatability of the robotic arm utilized in this study is 0.03 mm, which guarantees its adjustment accuracy. Considering the scope of the misalignment of the secondary mirror and environmental factors in actual work, such as wind load and temperature, will have a significant impact on the truss. In order to ensure the effectiveness of the compensation in the case of large offsets, the misalignment range of the SM is set in the analysis and modeling (see Table 5).  
The traditional aberration compensation method uses the difference quotient to analyze the linear equations of the Zernike coefficient and the misalignment to obtain the sensitivity matrix. If the Zernike coefficient is a continuous linear function in the misalignment interval, as shown in Figure 8a, the accurate adjustment can be calculated according to the sensitivity matrix. But, some Zernike coefficients show a significant non-linear relationship with the adjustment error, as shown in Figure 8b,c.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optical Surface** & **Radius of Curvature (mm)** & **Material** & **Aspheric Coefficient** & **Radius** \\ \hline Primary mirror & 5500 & SiC & K = \(-1\) & 1000 \\ Secondary mirror & 1051.5 & Zerodur & K = \(-1\) & 166 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optical system parameters.  
Figure 6: (**a**) The change of each joint in serial 1, (**b**) the change of each joint in serial 2, and (**c**) the change of each joint in serial 3.  
In this case, the traditional sensitivity matrix model causes the equation to converge slowly and cannot obtain an accurate solution because of the coupling between the Zernike coefficient and the offset. To solve this problem, this study reduced the nonlinearity of the Zernike coefficient and the offset function that characterizes wave aberration by adding a second-order correction term to the mathematical model of the sensitivity matrix method.
## 4 Results and Discussion
### Comparison of TextBlob Results with VADER and SentiWordNet  
To analyze the performance of TextBlob, VADER and SentiWordNet are also adopted in this study. VADER is used to find the polarity of social media posts to categorize them with respect to the sentiments such as positive, negative, and neutral [25]. It is a rule-based technique that shows the intensity of positive or negative emotion in text. Similarly, another lexicon-based technique, SentiWordNet is also used in comparison to Textblob and VADER. SentiWordNet finds the polarity score from the text to categorize the data into positive, negative, and neutral sentiment [51]. The ratio of sentiments such as positive, negative, and neutral with VADER, and SentiWordNet is shown in Table 17.  
Table 18 shows the results using VADER and SentiWordNet, which indicate that the performance of VADER is slightly better as compared to SentiWordNet. VADER is suitable especially for social media posts and shows better performance. ETC and SGC achieve the highest accuracy of 0.90 using TF-IDF features with VADER and the SMOTE technique while RF achieves 0.90 accuracy with VADER and BoW features. In the case of SentiWordNet, the highest accuracy is 0.88 by RF using TF-IDF features with the SMOTE technique. The comparison between Textblob, VADER, and SentiWordNet using BoW and TF-IDF features with and without SMOTE is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**SMOTE**} & \multicolumn{2}{c}{**Original**} \\ \cline{2-5}  & **BoW** & **TF-IDF** & **BoW** & **TF-IDF** \\ \hline LR & 0.93 (\(\pm\)0.03) & 0.90 (\(\pm\)0.03) & 0.91 (\(\pm\)0.06) & 0.91 (\(\pm\)0.04) \\ \hline RF & 0.93 (\(\pm\)0.03) & 0.93 (\(\pm\)0.03) & 0.83 (\(\pm\)0.08) & 0.93 (\(\pm\)0.04) \\ \hline DT & 0.93 (\(\pm\)0.04) & 0.92 (\(\pm\)0.04) & 0.80 (\(\pm\)0.08) & 0.92 (\(\pm\)0.05) \\ \hline KNN & 0.58 (\(\pm\)0.08) & 0.56 (\(\pm\)0.08) & 0.48 (\(\pm\)0.10) & 0.47 (\(\pm\)0.09) \\ \hline SVM & 0.94 (\(\pm\)0.03) & 0.94 (\(\pm\)0.02) & 0.92 (\(\pm\)0.06) & 0.93(\(\pm\)0.04) \\ \hline Adaboost & 0.77 (\(\pm\)0.04) & 0.77 (\(\pm\)0.04) & 0.69 (\(\pm\)0.04) & 0.74 (\(\pm\)0.04) \\ \hline GNB & 0.78 (\(\pm\)0.03) & 0.78 (\(\pm\)0.05) & 0.74 (\(\pm\)0.04) & 0.75 (\(\pm\)0.04) \\ \hline ETC & 0.86 (\(\pm\)0.05) & 0.86 (\(\pm\)0.04) & 0.77 (\(\pm\)0.06) & 0.85 (\(\pm\)0.05) \\ \hline SGD & 0.95 (\(\pm\)0.02) & 0.92(\(\pm\)0.02) & 0.93 (\(\pm\)0.04) & 0.92(\(\pm\)0.04) \\ \hline \hline \end{tabular}
\end{table}
Table 16: 10-fold cross-validation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Re-Sampling** & **Technique** & **Positive** & **Negative** & **Neutral** \\ \hline \multirow{2}{*}{Without SMOTE} & VADER & 8861 & 2373 & 5921 \\ \cline{2-5}  & SentiWordNet & 9606 & 2547 & 5002 \\ \hline \multirow{2}{*}{SMOTE} & VADER & 8861 & 8861 & 8861 \\ \cline{2-5}  & SentiWordNet & 9606 & 9606 & 9606 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Vader and SWN train and test count after data splitting.
# Contact Analysis and Friction Prediction of Non-Gaussian Random Surfaces
## 2 Methodology
### Methodology for Non-Gaussian Rough Surface Generation
The prescribed non-Gaussian sequence with desired skewness \(Sk_{z}\) and kurtosis \(K_{z}\) can be generated by feeding the Gaussian random sequence \(\eta\) into the [PERSON] translation system. The translation system involves three main types of curves, \(S_{U}\), \(S_{L}\), \(S_{B}\)  
1. The unbound system \(S_{U}:\eta=\gamma+\delta\sin\mathrm{h}^{-1}\left(\frac{\eta^{\prime}-\xi}{ \lambda}\right)\);
2. The logarithm normal system \(S_{L}:\eta=\gamma+\delta\log\left(\frac{\eta^{\prime}-\xi}{\lambda}\right)\), where \(\eta^{\prime}>\xi\);
3. The bounded system \(S_{B}:\ \eta=\gamma+\delta\log\left(\frac{\eta^{\prime}-\xi}{\xi+\lambda-\eta^{ \prime}}\right)\) where \(\xi<\eta^{\prime}<\xi+\lambda\). where \(\eta\) represents Gaussian random series, and \(\eta^{\prime}\) represents the non-Gaussian random series with desired skewness and kurtosis. The parameters \(\gamma,\ \delta,\ \ \zeta\), and \(\lambda\) are used to identify the statistical distribution of the non-Gaussian sequence. Those four parameters can be calculated through the expected mean value, standard deviation, skewness, and kurtosis. In addition, the linear transformation as a pure MA process would change the kurtosis and skewness of the input random series slightly. The relations for input skewness \(Sk_{\eta}\) and kurtosis \(K_{\eta}\) and output skewness \(Sk_{\eta^{\prime}}\) and kurtosis \(K_{\eta^{\prime}}\) can be given as:  
\[Sk_{\eta^{\prime}}=\frac{\sum_{i=1}^{n}a_{i}^{3}}{\left(\sum_{i=1}^{n}a_{i}^{ 2}\right)^{\frac{3}{2}}}Sk_{\eta} \tag{9}\]  
\[K_{\eta^{\prime}}=\frac{K_{\eta}\sum_{i=1}^{n}a_{i}^{2}+6\sum_{i=1}^{n}\sum_{i =1}^{n}a_{i}^{2}\alpha_{i}^{2}}{\left(\sum_{i=1}^{n}a_{i}^{2}\right)^{2}} \tag{10}\]  
Therefore, the skewness and kurtosis which are fed into the Johnson translator system should be modified according to Equations (9) and (10). In summary, the following general procedure should be carried out for generating the non-Gaussian surfaces:  
1. Determine the transform coefficients through solving the nonlinear equations described by Equation (4);
2. Modify the input kurtosis and skewness using Equations (9) and (10);
3. Generate the random sequence, and then substitute the sequence and the modified skewness and kurtosis into the [PERSON] translation system for obtaining the prescribed non-Gaussian sequence;
4. Generate the non-Gaussian surface with specified height distribution and ACF through linear transformation between the transform coefficients and the non-Gaussian sequence. The generation of non-Gaussian surface is presented schematically in Figure 1.