esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Verification
### Results and Discussion  
The cubic prism mounting angle was measured after the experimental environment was set up as shown in Table 5. The theodolite and laser tracker data after datum transfer are compared with the photogrammetric data. After datum alignment, the measurement data are calculated to obtain the rotation angle of the \(XYZ\) coordinate axis as shown in Figure 16a-i. The horizontal coordinate is the number of experiments and the vertical coordinate is the angle of rotation of the coordinate axis.  
According to the Grubbs criterion, there is no gross error in the experimental data. Based on the analysis of the experimental data, the transfer errors (\(3\sigma\)) of the \(XYZ\) axis rotation angles for (1) are 13.9\", 13.1\", and 18.3\"; (2), 8.2\", 14.3\", and 20.6\"; (3), 18.1\", 19.8\", and 13.7\". For the analysis of the experimental data, the rotation angles of the \(XYZ\) axes obtained from the photogrammetric data are used as a reference. The maximum error of \(XYZ\) axis rotation angle calculated from theodolite and laser tracker data relative to  
Figure 16: The rotation angles of the truss structure in XYZ directions are measured by two kinds of instruments: (**a–c**) corresponds to (1); (**d–f**) corresponds to (2); (**g–i**) corresponds to (3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angular Relationships** & **Symbols** & **Theoretical Value** \\ \hline cubic prism 1 & \([\alpha_{1},\beta_{1},\gamma_{1}]\) & \([0.0198^{{}^{\circ}},-0.0903^{{}^{\circ}},0.7164^{{}^{\circ}}]\) \\ cubic prism 2 & \([\alpha_{2},\beta_{2},\gamma_{2}]\) & \([0.0220^{{}^{\circ}},0.2857^{{}^{\circ}},0.0536^{{}^{\circ}}]\) \\ cubic prism 3 & \([\alpha_{3},\beta_{3},\gamma_{3}]\) & \([39.6879^{{}^{\circ}},32.3428^{{}^{\circ}},83.0320^{{}^{\circ}}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cubic prism mounting angle.  
photogrammetric data is less than 7.6\" (Figure 17a-c, horizontal coordinates are the number of experiments and vertical coordinates are the absolute deviations). The experimental results deviate from the simulated calculated values due to errors in machining, installation, personnel, and stress release during measurement of the truss structure. The error is within acceptable limits against the engineering requirements. The datum transfer experiment of the coordinate system of the truss structure verifies the effectiveness of the datum alignment method in the stability measurement experiment of the space telescope. On the other hand, the angle of rotation of each axis is solved by datum transfer and, combined with photogrammetry data, provides a good description of the deformation of the structural features of the truss structure.
# Random Fiber Array Generation Considering Actual Noncircular Fibers with a Particle-Shape Library
## 2 Random RVE Generation Using Actual Noncircular Fibers from the Particle-Shape Library
### RVE Generation with Diverse \(V_{f}\)
#### 2.2.2 RVE Generation Using RFR Algorithm  
To apply the RFR algorithm with various \(V_{f}\), a master RVE with the highest \(V_{f}\) is required. For this purpose, we used the RSE algorithm to generate the master RVE with \(V_{f}\) = 65.57% containing 102 fibers. At this time, the parameters for the minimum distance between the fibers were set to \(l_{\min}=0.07r_{f}\) and \(l_{\max}=0.08r_{f}\). The 3D FE model of the master RVE finally was created similarly as mentioned in Section 2.2.1. The total number of nodes and elements were 46,047 and 60,668, respectively. The procedure of applying the RFR algorithm to this master RVE is summarized as follows.  
1. First, the value of \(V_{f}^{\text{req}}\) is set.
2. One of the fibers is randomly selected to be removed, and its fiber volume fraction is calculated.
3. The elements of the fiber selected in step 2 are replaced by those of the matrix.
4. Steps 2 and 3 are repeated until \(V_{f}^{\text{car}}\) is close to \(V_{f}^{\text{req}}\).  
In this work, with the RFR algorithm, 100 RVEs were generated for each \(V_{f}\) for 60%, 55%, 45%, 35%, 25%, 15%, and 5%.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 3 Effect of Sub-Mirror Position Error on WFE
### Tip & Tilt Around the X and Y Axis  
In order to visually compare and analyze the influence of different positions of sub-mirror Tip & Tilt on the WFE, a local coordinate system was established with the center of each sub-mirror as the origin, with the Z'-axis as the normal direction of the center of the sub-mirror, and the X' and Y'-axis located in the tangent plane of the center of the sub-mirror and perpendicular to each other, respectively.  
Taking the center point of the sub-mirror as the origin of the local coordinate system tip & tilt around the X' and Y' axis. The sub-mirror tipping(tilting) ranges are all within 1 \(\upmu\)rad, and three different positions of the sub-mirror tip (tilt) are taken respectively to get the variation of the RMS of the system WFE as shown in Figure 6. It can be seen that within 1 \(\upmu\)rad, the RMS of the WFE is approximately linearly related to the tip (tilt) angle of the sub-mirror. When the sub-mirror tilt is 1 \(\upmu\)rad, the RMS is 0.157\(\uplambda\). The tilt of the sub-mirror causes misalignment between the sub-mirrors, directly disrupting the co-phasing and co-focusing conditions of the primary mirror. This error needs to be strictly controlled to ensure good imaging quality.
## 4 Salinity Origin and Its Impacts on Plants
### Primary Metabolism
#### Protein Synthesis  
Many plants respond to salinity through modifications in their protein status. Salinity may interfere with the regulation of protein activity, protein synthesis, and even with the availability of the substrates needed for protein synthesis (namely N). Cellular suspensions of _Nicotiana tabucum_ and _Citrus_ sp. plants sensitive to salinity have distinct proteomic profiles under saline and non-saline conditions [94]. The disturbance of protein synthesis tends to be a major cause of Na\({}^{+}\) damage [82]. Similarly, high Na\({}^{+}\) concentration or Na\({}^{+}\):K\({}^{+}\) ratio may disturb several enzymatic mechanisms. K\({}^{+}\) stimulates and/or regulates the activity of more than 50 enzymes and is a critical component of the protein synthesis as it attaches tRNA to ribosomes [95].  
Salinity inhibits root nitrate uptake [96] and thus the availability of N needed for protein synthesis [97; 98], as it was evidenced by the decrease of free amino acid concentration in _Glycine max_ roots under salinity [99]. However when grown under saline conditions tolerant plants may increase the concentration and/or the activity of key proteins that contribute to salt resistance reactions, such as heat shock proteins (HSPs), ribulose-1,5-bisphosphate carboxylase (RuBisCo), antioxidative enzymes, late embryogenesis abundant (LEA) proteins, glycerate dehydrogenase, glucose-6-phosphate 1-dehydrogenase, glutamate synthase, NADPH-producing dehydrogenase, and glutamine synthetase [100].
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 4 Experiments
### Performance Comparison  
#### 4.4.1 Quantitative Comparisons  
We compare the proposed CISNet regarding S-measure, mean E-measure, weighted F-measure, and mean absolute error scores with 19 state-of-the-art COD methods, including SINet [9], SINet-V2 [66], PFNet [17], C2 FNet [15], LSR [10], OSFormer [67], TPRNet [68], SegMaR [28], BSANet [18], DGNet [69], AGNet [70], BGNet [71], ZoomNet [26], CINet [72], EAMNet [73], MFFN [74], FDNet [75], FEDER [76], and HitNet [52]. For accurate comparison, all results were either provided by the published papers or reproduced by the released source codes. And the quantitative comparison results are summarized in Table 1.  
As can be seen from Table 1, the proposed CISNet has achieved the best performance on all indicators for these datasets compared to all other methods. For instance, compared with the second-best method, HitNet, CISNet takes more account of the extraction of local information and utilizes difference information between cross-level features to iteratively refine these features. These approaches effectively exploit the characteristics of features across different scales, resulting in CISNet being more adept at capturing refined edge details. As Table 1 shows, on the most challenging COD10K test dataset, CISNet increases \(S_{\alpha}\) and \(F_{\beta}^{\omega}\) by 1.2% and 1.7%, respectively, and decreases MAE by 8% compared with  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multicolumn{4}{c}{**CHAMELEON**} & \multicolumn{4}{c}{**CAMO-Test**} & \multicolumn{4}{c}{**COD10K-Test**} & \multicolumn{4}{c}{**NC4K**} \\ \cline{3-14}  & & \(S_{\alpha}\uparrow\) & \(E_{\phi}\uparrow\) & \(F_{\beta}^{\omega}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(E_{\phi}\uparrow\) & \(F_{\beta}^{\omega}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(E_{\phi}\uparrow\) & \(F_{\beta}^{\omega}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(E_{\phi}\uparrow\) & \(F_{\beta}^{\omega}\uparrow\) & \(M\downarrow\) \\ \hline SINet [9] & 2020 & 0.869 & 0.891 & 0.740 & 0.044 & 0.751 & 0.771 & 0.606 & 0.100 & 0.771 & 0.806 & 0.551 & 0.051 & 0.810 & 0.873 & 0.7  
HitNet. On the NC4K dataset, CISNet increases \(S_{n}\) and \(F_{\beta}^{\omega}\) by 1.1% and 1.4%, respectively, and decreases MAE by 12% compared with HitNet. And we also achieved improvements on the other two datasets compared with HitNet. This demonstrates that our method has stronger generalization ability.
## 4 Experiments
### Global Feature Explainability  
To complement insights from RFE, LIME, and SHAP, we performed a global feature importance analysis using RF and GBM, as per Figures 3-8. These techniques offer a comprehensive view of which features most significantly impact model performance across the entire dataset.  
* RF evaluates feature importance using the Mean Decrease in Gini (MDG), which quantifies how much a feature reduces uncertainty or impurity in the model's predictions.
* GBM captures complex interactions between features and assesses feature importance based on their cumulative contribution across all boosting rounds, evaluated through relative importance (RI).  
Our analysis revealed that appendix diameter is the most influential feature across all target variables in both models. It showed the highest Mean Decrease in Gini (147.71) and a substantial relative importance (69.73) for the Diagnosis target. Length of stay is also a significant predictor for Management (MDG = 104.79, RI = 55.44) and Severity (MDG = 74.34, RI = 57.83). In contrast, features like WBC count and neutrophil percentage are prominent in RF across all targets but exhibit lower relative importance in GBM, especially for Diagnosis (RI = 0.46 and 0.48, respectively). This suggests that GBM may underemphasize these features compared with RF. Additionally, peritonitis is moderately important in RF for Management (MDG = 38.31) but less significant in GBM (RI = 8.64).  
Figure 4: Management—RF.  
Figure 3: Diagnosis—RF.  
Overall, RF highlights a broader range of important features, while gradient boosting machines focus on fewer features with higher relative importance. This contrast underscores the complementary strengths of these models: RF provides a wider array of significant predictors, whereas GBM emphasizes a few key variables with greater impact.  
Figure 5: Severity—RF.  
Figure 6: Diagnosis—GBM.
###### Abstract  
Turbulence and absorption of seawater are two important factors affecting the signal transmission quality of underwater optical communication link. Here, we study the channel capacity and bit error rate of an underwater extinction communication link with a coaxial double-position power Gaussian vortex carrier based on Rytov approximation theory. The study finds that channel capacity and bit error rate are the nonlinear functions of the dimensionless structural parameter and reach maximum and minimum values at \(|a|=1\), respectively. The seawater absorption has a great influence on the channel capacity but not bit error rate. The communication link with large receiving aperture, small transmitting beam diameter, long wavelength of light source in a seawater window, and more OAM channels has high channel capacity.  
oceanic turbulence; seawater absorption; position power Gaussian vortex; channel capacity +
Footnote †: journal: Marine Science and Engineering  
0
Footnote 0: _J. Mar. Sci. Eng._**2021**, \(9\), 1117. [[https://doi.org/10.3390/jmse9101117](https://doi.org/10.3390/jmse9101117)]([https://doi.org/10.3390/jmse9101117](https://doi.org/10.3390/jmse9101117))
## 3 System Model  
We investigate the modulation recognition of the OTFS transmission system depicted in Figure 2. The information symbols \(\{x_{\rm dd}[k,l],k=0,\ldots,N-1,l=0,\ldots,M-1\}\) are in a two-dimensional DD grid of size \(N\times M\), where \(N\) and \(M\) are the numbers of indices along the Doppler and delay axes, respectively. The symbol \(x_{\rm dd}[k,l]\) is transformed to the TF-domain symbol \(X_{\rm tf}[n,m]\), \(n=0,\ldots,N-1\), \(m=0,\ldots,M-1\) through the inverse symplectic finite Fourier transform (ISFFT) [43]:  
\[X_{\rm tf}[n,m]=\frac{1}{\sqrt{NM}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}x_{\rm dd}[k,l]\,e^{i2\pi\left(\frac{\pi}{N}-\frac{\pi}{M}\right)}. \tag{1}\]  
The signal is transformed from the TF domain to the time domain via the Heisenberg transform [34], and the transmitted time-domain signal is  
\[s(t)=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}X_{\rm tf}[n,m]\,g_{\rm tx}(t-nT)\,e^{i2 \pi m\Delta f(t-nT)}, \tag{2}\]  
where \(\Delta f\) is the subcarrier spacing, \(T\) is the symbol duration, and \(g_{\rm tx}(t)\) denotes the transmit pulse shaping filter.  
The signal transmitted through the channel is affected by the channel impulse response \(h(\tau,\
u)\) in the DD domain:  
\[h(\tau,\
u)=\sum_{i=1}^{P}h_{i}\,\delta(\tau-\tau_{i})\,\delta(\
u-\
u_{i}), \tag{3}\]  
Figure 2: System model and acquisition of the time and delay-Doppler domain data.  
where \(P\) is the number of propagation paths, and \(h_{i}\) is the gain of the \(i\)-th path. \(\tau_{i}\) and \(v_{i}\) respectively denote the delay and Doppler shift of the \(i\)-th path,  
\[\tau_{i}=\frac{l_{i}}{M\Delta f},\quad v_{i}=\frac{k_{i}}{NT}, \tag{4}\]  
where \(l_{i}\) is the delay index, and \(k_{i}\) is the Doppler index.  
Then, the received signal \(r(t)\) is  
\[r(t)=\iint h(\tau,\
u)s(t-\tau)e^{j2\pi\
u(t-\tau)}\,d\tau dv+\omega(t), \tag{5}\]  
where \(\omega(t)\) represents the additive white Gaussian noise.  
The received symbols in the TF domain \(Y_{\text{tf}}[n,m]\) can be obtained via Wigner Transform:  
\[Y_{\text{tf}}[n,m]=\int r(t)\,g_{\text{rx}}^{*}(t-nT)\,e^{-j2\pi m\Delta f(t- nT)}\,dt. \tag{6}\]  
Here, \(g_{\text{rx}}(t)\) denotes the pulse shaping filter at the receiver.  
Finally, applying the symplectic finite Fourier transform (SFFT) to the samples yields the symbols in the DD domain [43]:  
\[y_{\text{dd}}[k,l]=\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}Y_{\text {tf}}[n,m]e^{-j2\pi\left(\frac{\#}{\Delta}-\frac{\#}{\Delta}\right)}. \tag{7}\]  
In this paper, we propose an automatic modulation classification framework for OTFS systems as described above, which involves a differentiated embedded pilot insertion based DD plane structure, dataset construction scheme, and a dual-stream CNN architecture-based multi-domain fusion approach.
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 7 Discussion
### Why We Chose UniXcoder for Code Representation  
One significant advantage of using UniXcoder [21] for code representation lies in its exceptional cross-language generalization capability. UniXcoder is a pre-trained code representation model that has been trained on vast amounts of code data, allowing it to learn the syntactic structures and semantic information of multiple programming languages. Since UniXcoder has already encompassed diverse programming languages during its training, it can automatically adapt to different languages when performing code representation, eliminating the need for prior uniform and rigorous data preprocessing. UniXcoder effectively captures the distinctive features and structures of code, enabling consistent representation and transformation across various programming languages.  
We employed CodeBERT for code representation and fine-tuned it using the same distance-weighted sampling-triplet-learning technique. Subsequently, we performed an effectiveness comparison with TCCCD based on the results. Regarding the dataset, we trained CodeBERT on the dataset described in Section 4.1, which was the same dataset used in TCCCD.  
In our experiments, as presented in Table 7, we conducted comparative trials between CodeBERT and UniXcoder, employing triplet learning for optimization. The results highlighted UniXcoder's superiority in terms of the precision and F1-score. UniXcoder demonstrated impressive effectiveness in multiple downstream tasks related to code processing. As a result, we chose UniXcoder as our pre-trained model for code representation.  
The emergence of TCCCD has a multifaceted impact on both researchers and industry practitioners. Here are some of the primary impacts:  
1. Innovative approach: TCCCD introduces a novel and highly efficient method for addressing cross-language code clones, providing researchers with a platform to explore new technologies and algorithms.
2. Enhanced effectiveness: For industry professionals, TCCCD significantly improves the accuracy of code clone detection, thereby reducing false positives and false negatives. This has profound implications for software development and maintenance.
3. Multilingual support: TCCCD's cross-language capabilities open up new possibilities for research in multilingual software projects, encouraging researchers to investigate clone relationships across different programming languages. For multilingual software development teams, TCCCD offers an effective solution for addressing clone-related challenges among diverse languages, ultimately reducing the complexity of software maintenance.
## 3 Proposed System Model
### Caching and IPFS  
In the proposed VN, smart vehicles communicate with the edge nodes and request for a service. The edge nodes either fulfill the service request by themselves or communicate with RSUs for the fulfillment of user requests. This whole process requires a large execution time, involving various delays like propagation delay, transmission delay, etc. However, the real-time VN is a time-sensitive system. To optimize the gas consumption and reduce the execution time, cache memory is used at the edge nodes, where frequently used sensory information is stored. Instead of fetching the required information from RSU, cache memory provides the vehicles with the requested information, which leads to reduction in the execution time. IPFS is used for distributed data storage while considering the issues of centralized systems, such as single point of failure, data leakage, etc. [27]. In IPFS, data integrity is also achieved using the unique hashes assigned to data blocks. In case of data tampering, the complete hash of data block changes. Algorithm 2 gives the flow of IPFS.
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 2 Preliminary Knowledge  
### Chimp Optimization Algorithm  
ChOA is a heuristic optimization algorithm based on the chimp population's hunting behaviour. Within the chimp population, the groups of chimps are classified as \"Attacker\", \"Barrier\", \"Chaser\", and \"Driver\", according to the diversity of intelligence and abilities that individuals display during hunting. Each chimp species has the ability to think independently and use its search strategy to explore and predict the location of prey. Whilethey have their tasks, they are also socially motivated to acquire sex and benefits in the final stages of the hunts. During this process, the chaotic individual hunting behaviour occurs.  
The standard ChOA is as follows: Suppose there are \(N\) chimps and the position of the \(i\)-th chimp is \(X_{i}\). The optimal solution is \"Attacker\", the second optimal solution is \"Barrier\", the third optimal solution is \"Chaser,\" and the fourth optimal solution is \"Driver\". The behaviour of chimps approaching and surrounding the prey and their position update equations are as follows:  
\[D=|C\cdot X_{prey}(t)-m\cdot X_{chimp}(t)| \tag{1}\]  
\[X_{chimp}(t+1)=X_{chimp}(t)-A\cdot D \tag{2}\]  
\[A=f\cdot(2\cdot r_{1}-1),C=2\cdot r_{2} \tag{3}\]  
\[m=chaotic\_value \tag{4}\]  
where \(r_{1}\) and \(r_{2}\) are random vectors with values in the range of \([0,1]\). \(f\) is the non-linear decay factor, and its value decreases linearly from 2.5 to 0 with the increase in the number of iterations. \(t\) represents the current number of iterations. \(A\) is a random vector, and its value is a random number between \([-f,f]\). \(m\) is a chaotic factor representing the influence of sexual behaviour incentives on the individual position of chimps. \(C\) is a random variable that influences the position of prey within \([0,2]\) on the individual position of chimps (when \(C<1\), the degree of influence weakens; when \(C>1\), the degree of influence strengthens).  
The positions of other chimps in the population are determined by the positions of Attacker, Barrier, Chaser, and Driver, and the position update equations are as follows:  
\[\left\{\begin{array}{l}D_{Attacker}=|C_{1}*X_{Attacker}-m_{1}*X|\\ \\ D_{Barrier}=|C_{2}*X_{Barrier}-m_{2}*X|\\ \\ D_{Chaser}=|C_{3}*X_{Chaser}-m_{3}*X|\\ \\ D_{Driver}=|C_{4}*X_{Driver}-m_{4}*X|\\ \\ \end{array}\right. \tag{5}\]  
\[\left\{\begin{array}{l}X_{1}=X_{Attacker}-A_{1}*D_{Attacker}\\ \\ X_{2}=X_{Barrier}-A_{2}*D_{Barrier}\\ \\ X_{3}=X_{Chaser}-A_{3}*D_{Chaser}\\ \\ X_{4}=X_{Driver}-A_{4}*D_{Driver}\\ \\ X(t+1)=(X_{1}+X_{2}+X_{3}+X_{4})/4\end{array}\right. \tag{6}\]  
where \(C_{1}\), \(C_{2}\), \(C_{3}\) and \(C_{4}\) are similar to \(C\); \(A_{1}\), \(A_{2}\), \(A_{3}\) and \(A_{4}\) are similar to \(A\); And \(m_{1}\), \(m_{2}\), \(m_{3}\) and \(m_{4}\) are similar to \(m\).  
The pseudo-code of the ChOA is listed in Algorithm 1.