esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 5 Wilcoxon Rank and Tests  
In order to verify whether there is a significant difference between CWTSSA and the other five algorithms in solving complex optimization problems, the Wilcoxon rank and test [42] is used to compare the characteristics of the performance test between the two algorithms. Wilcoxon rank and test is a non-parametric null hypothesis test statistical method, which is used to evaluate the fairness and robustness of the algorithm. Here, the null hypothesis H0 is selected. Equivalent in performance, the alternative hypothesis is H1. The performance of the two algorithms is significantly different. The test result \(p\)-value is used to compare the differences between the two algorithms. When the \(p\)-value is 0.05, H0 is accepted. That is, the two algorithms have equivalent performance in global optimization. The rank sum test results for 15 test functions independently run 30 times under the dimension dim = 30 are shown in Table 5, where \"S\" means discriminatory discrimination, and \"+/=/-\" means that the CWTSSA is \"better than/\" in performance. Equivalent/inferior to other algorithms, N/A means that the performance of the two algorithms is equivalent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Functions**} & **vsDE** & **vsGWO** & **vsPSO** & **vsSSA** & **vsCASSA** \\ \cline{2-6}  & \(p\)**-Value S** & \(p\)**-Value S** & \(p\)**-Value S** & \(p\)**-Value S** & \(p\)**-Value S** \\ \hline \(f_{1}\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & N/A & N/A \\ \(f_{2}\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & N/A & 1.12E\(-\)36+ \\ \(f_{3}\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & N/A & 4.76E\(-\)25+ \\ \(f_{4}\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & N/A & 4.76E\(-\)25+ \\ \(f_{5}\) & 3.34E\(-\)01\(-\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 1.15E\(-\)01\(-\) & 4.76E\(-\)25+ \\ \(f_{6}\) & 1.07E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ \\ \(f_{7}\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ \\ \(f_{8}\) & 1.21E\(-\)01\(-\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & N/A & N/A \\ \(f_{9}\) & N/A & 4.76E\(-\)25+ & 4.76E\(-\)25+ & N/A & N/A \\ \(f_{10}\) & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ & 4.76E\(-\)25+ \\ \(f_{11}\) & 4.76 It can be seen from Table 5 that most of the \(p\)-values of the CWTSSA are less than 0.05 and are \"+\", indicating that the CWTSSA has better performance than the other five algorithms. The performance is equivalent in the global optimization, and the optimal value can be obtained. Overall, the CWTSSA has significant advantages over the other compared algorithms.
## 3 Results and Discussion
### Analysis of Volatile Compounds
#### 3.7.1 Volatile Compounds Content
However, it should be noted that in Jinhua hams, 3-hydroxy-2-butanone was shown not to be key in differentiating between normal and spoiled ham [16]. The total ketone content in the BF of normal ham was higher than that in spoiled ham, with most of the ketones originating from microbial metabolism, specifically carbohydrate fermentation, lipid and ethyl ester oxidation, and amino acid catabolism [86]. Additionally, it was found that the ketone content was higher in BF than in SM in hams of the same grade quality (\(p<0.05\)). Similar results were reported in Slovenian dry-cured hams, which were the result of fatty acid oxidation [5]. Alkanes, aromatics, and terpen are classified as volatile compounds with higher odour thresholds that do not significantly contribute to flavour [31]. Our findings indicated that alkanes, pyrazines, and terpen were present in higher content in normal ham compared to spoiled ham (\(p<0.05\)). Conversely, aromatic compounds were found at higher levels in spoiled ham (\(p<0.05\)). The main aromatic compounds detected in spoiled ham were p-xylene and o-xylene, which could potentially originate from the pig feed in Iberian dry-cured hams [87]. It is worth mentioning that an increase in aromatic compounds during maturation has been observed in dry-cured meat products and media inoculated with microorganisms [85]. Moreover, the levels of these compounds varied among samples of dry-cured ham inoculated with different microorganisms obtained from different locations [88]. It was worth mentioning that trimethylamine occurred among other volatile compounds and was detected only in spoiled ham. Trimethylamine belonged to the group of biogenic amines, which were degradation products produced by bacteria in meat products [89]. Trimethylamine has a rancid, fishy, fishy odour that is unacceptable and easily detectable with a low odour threshold [90].
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 3 Results and Discussion
### Quantitative Assessment
#### 3.1.1 General Settings  
Following the discussion of Section 2.2.2, the chosen performance metrics for validation include frame-wise and unit-wise measures, with detail, are presented below:  
* Frame-wise accuracy (not necessarily informative, as detailed in Section 2.2.2).
* Frame-wise F1-score.
* Unit-wise margin-based F1-score F1\({}_{pr}^{\star}(t_{w})\), with margin \(t_{w}=12\) frames (half a second).
* Unit-wise normalized intersection-based F1-score F1\({}_{pr}^{\star}(\bar{t}_{p},\bar{t}_{r})\), with \(\bar{t}_{p}=0,\bar{t}_{r}=0\) (counting positive recognition for units with at least one intersecting frame), as well as the associated integral value \(I_{pr}\). All training sessions, unless otherwise specified, are conducted with the following common settings:
* Network parameters: One BLSTM layer; 50 units in each LSTM cell; 200 convolutional filters as a first neural layer, with a kernel width of size 3.
* Training hyperparameters: A batch size of 200 sequences; A dropout rate of 0.5; No weight penalty in the learning loss; Samples arranged with a sequence length of 100 frames.
* The training loss is the weighted binary/categorical cross-entropy.
* The gradient descent optimizer is RMSProp [56].
* A cross-validation split of the data is realized in a signer-independent fashion, with 12 signers in the training set, 2 in the validation set and 2 in the test set.
* Each run consists of 150 epochs. Only the best model was retained, in terms of performance on the validation set. During training, only the frame-wise F1-score was used to make this decision.  
Furthermore, we defined 16 combinations of the feature vectors presented in Section 2.3.1. The detail of these configurations is given in Table 6, in which we also indicate the final representation vector size (for each frame), ranging from 218 for combination 5 to 494 for combination 9. Body and face data were either 2D or 3D, raw or made of preprocessed features, while hand data were made of OpenPose estimates, Deep Hand predictions or both.  
In the following, we analyze the results for the signer representation #16, which usually gives best or close to best results. Then, we analyze the impact of varying the signer representation on the recognition results.
# A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors
## 6 The Experimental Results and Analysis
### Feature Selection Algorithm Experiment
#### 6.5.4 Feature Selection Comparison  
[PERSON] et al. [18] proposed random forest-forward selection sorting (RF-FSR) and random forest-backward sorting (RF-BER). The experimental results show that the selected features on the KDD-Cup 99 dataset effectively improve their detection rate and reduce the false positive rate, reaching 99.8% and 0.001%, respectively. [PERSON] et al. [15] used information gain and chi-square methods to rank the importance of the 41 attributes extracted from network traffic. The Bayesian network and C4.5 algorithm are then used to detect the attack and determine the size of the attribute suitable for fast detection. The empirical results show that using only the most important nine attributes, the detection accuracy remains the same or even achieves some improvement compared to all 41 attributes based on the Bayesian network and the C4.5 method. [PERSON] et al. [19] proposed combining the consistent subset evaluation (CSE) and DDoS characteristic features (DCF) techniques in feature selection algorithms to identify and select the most important and relevant features associated with DDoS attacks. This paper compares the RF-FSR algorithm [18], RF-BER algorithm [18], GI (information gain) [15], CS (chi-square test) [15], and CSE [19]. In this paper, different methods are used to select features using the 20k TCP flow dataset and 20k UDP flow dataset. The selected feature subset results are shown in Tables 6 and 7. RF-FSR, RF-BER, GI, and CS are feature selection algorithms, and the  
Figure 11: TCP flow datasets feature selection and dimensionality reduction.  
Figure 12: UDP flow datasets feature selection and dimensionality reduction.  
selected feature subsets are large in size. The accuracy of the features selected by different algorithms is shown in Figure 13.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 7 Conclusions  
Many authentication schemes overlook the potential vulnerability of vehicles to physical attacks, which could compromise the confidentiality of the secret key. Indeed, side-channel attacks such as power consumption, electromagnetic radiation, and timing analysis remain viable avenues for accessing the secret key stored in TPDs. To mitigate the risk of physical theft of the secret key, we propose a physically preserving authentication approach  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Area & 3000 m \(\times\) 2500 m \(\times\) 50 m \\ MAC Layer & 802.11p \\ Data Rate & 5 Mb/s \\ Broadcast Interval & 1000 m \\ Number of RSUs & 1 \\ Number of Vehicles & 20–100 \\ Vehicle Speed & 5–30 m/s \\ Simulation Time & 450 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Environment configuration for simulation.  
Figure 9: Simulation result of packet loss rate. Chatterjee et al. proposed the scheme [19], Aman et al. proposed the scheme [20], Cui et al. proposed the scheme [21], and Kumar et al. proposed the scheme [22].  
based on PUFs for VANETs. In our proposed scheme, PUFs are utilized to generate the secret key only when necessary for V2I authentication, eliminating the need for storing the secret key in the permanent storage of TPDs. This ensures the physical security of the system. Moreover, we integrate route planning into the protocol to enhance the efficiency of V2I authentication. Comparative analysis with recent schemes reveals that our proposed approach exhibits lower computational and communication overhead, while still satisfying fundamental security requirements such as authentication, integrity, anonymity, and traceability.
## 5 Energy Result Analysis and Reliability Evaluation
### Energy Result Analysis
#### 5.1.2 Test of Normality  
The derivation given in this article is based on the premise that the performance parameters of the products obey the normal distribution. Therefore, if the test results need to be used for reliability evaluation, it is necessary to verify whether the data obtained from the tests obey the normal distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Size** & **Sample Mean ()** & **Sample Standard Deviation ()** & **Sample Coefficient of Variation** \\ \hline
18 & 197.917 & 19.493 & 0.098 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sample statistical data of transferred energy measured by the pin-rod device.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Velocity (m-s-1)** & **No.** & **Velocity (m-s-1)** & **No.** & **Velocity (m-s-1)** \\ \hline
1 & 49.06 & 7 & 47.62 & 13 & 52.60 \\
2 & 48.77 & 8 & 44.54 & 14 & 49.50 \\
3 & 50.19 & 9 & 45.27 & 15 & 49.78 \\
4 & 44.73 & 10 & 45.38 & 16 & 50.38 \\
5 & 47.16 & 11 & 51.12 & 17 & 51.65 \\
6 & 49.85 & 12 & 48.48 & 18 & 47.41 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Velocity of the pin-rod.  
The Shapiro-Wilk test method [19] is used to test the normality of the data, and the significance level is \(\alpha=0.05\). The Shapiro-Wilk test is based on the regression of order statistics on their expected values. The test statistic is the ratio of the square of the linear combination of the sample order statistics to the variance estimator. Denoting \(n\) independent observations in a non-descending order as x\({}_{(1)}\), x\({}_{(2)}\),..., x\({}_{(n)}\), then the test statistic can be calculated as  
\[W=\frac{\left[\sum\limits_{k}a_{k}(x_{n+1-k}-x_{k})\right]^{2}}{\frac{n}{ \sum\limits_{i}(x_{i}-\overline{x})^{2}}},\]  
where, n is the sample size, the value of subscript \(k\) is taken as 1, 2,..., \(n/2\) (when n is an even number) or 1, 2,..., \((n-1)/2\) (when n is an odd number), \(a_{k}\) has a specific value under the sample size n, which can be obtained in the reference material. At the significance level \(\alpha=p=0.05\), if the value of the test statistic \(W\) is less than its \(\alpha\)-quantile, then reject the null hypothesis. Otherwise, it is shown to obey the normal distribution.  
After careful calculation, we can obtain the four test statistics of the four energy values measured by the four devices, which are \(W_{1}=0.963\), \(W_{2}=0.854\), \(W_{3}=0.948\), \(W_{4}=0.959\), respectively, while the \(\alpha\)-quantiles of each test are \(w_{\alpha 1}=0.881\), \(w_{\alpha 2}=0.850\), \(w_{\alpha 3}=0.887\), \(w_{\alpha 4}=0.897\), respectively. It can be seen that all test statistics are greater than their respective \(\alpha\)-quantiles.  
Therefore, the test results show that the sample values of the transferred energy measured in all four experiments do not refuse to admit that they obey the normal distribution.
## 3 Results
### Prediction Accuracy
The predictions of the methane emissions for the different scenarios that were obtained with the multilinear regression in that reduced model setup can be seen in Figure 4. For other methods, such as GradBoost, including all features usually produced slighly better results. In summary, the worst results were obtained with fixAnn followed by GausProc. LinReg and ANN performed better for scenarios with six measurement periods, but variability for scenarios with fewer measurement periods was also high, if all features were taken into account for modeling. Including only the sine and cosine transformed day-time feature, the prediction accuracy of LinReg considerably increased for scenarios with fewer measurement periods. The results from Ridge and SVM were comparable to LinReg and ANN for six measurement periods and all features included. The variability for scenarios with fewer measurement periods was lower in this setup. The variability of the Ridge results could be further decreased by neglecting less linearly correlated features. GradBoost and RandFor achieved the least variable results in the setup with all features combined with on average small deviations from the actual value. The results of the ensemble methods were comparable to those of LinReg and Ridge with reduced number of input features, but more robust against the selection of features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Features** & **Emiss.** & **MAE** & **TAE** & **Test Err.** \\ \hline \multirow{4}{*}{GradBoost} & all but \(T\), \(T^{2}\) & 11.583 & 1.372 & 0.118 & 1.460 \\  & all but \(T\), \(T^{2}\) & 11.604 & 1.466 & 0.099 & 1.514 \\  & day-time (sin, cos) & 11.612 & 1.434 & 0.083 & 1.466 \\  & day-time (sin) & 11.532 & 1.888 & 0.091 & 1.945 \\ \hline \multirow{4}{*}{LinReg} & all but \(T\), \(T^{2}\) & 11.627 & 1.666 & 0.188 & 7.567 \\  & all but \(T\), \(T^{2}\) & 11.645 & 1.829 & 0.257 & 7.244 \\  & day-time (sin, cos) & 11.613 & 1.406 & 0.074 & 1.426 \\  & day-time (sin) & 11.691 & 1.882 & 0.116 & 1.939 \\ \hline \multirow{4}{*}{Ridge} & all but \(T\), \(T^{2}\) & 11.627 & 1.691 & 0.159 & 1.974 \\  & all but \(T\), \(T^{2}\) & 11.645 & 1.717 & 0.154 & 1.904 \\ \cline{1-1}  & day-time (sin, cos) & 11.613 & 1.544 & 0.132 & 1.588 \\ \cline{1-1}  & day-time (sin) & 11.691 & 1.940 & 0.134 & 1.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Averaged emission (Emiss. ), mean absolute extrapolation error (MAE), total absolute error (TAE), and mean absolute test error (test err.) averaged over all scenarios. Emission, MAE, TAE and test error are provided in g h\({}^{-1}\) LU\({}^{-1}\). Note: The averaged experimental emission was 11.596 g h\({}^{-1}\) LU\({}^{-1}\). The smallest errors are highlighted in red.
## 5 Conclusions  
This paper describes the design of a vector modulator circuit for use in receive phased arrays of major LEO constellations operating in the 17.8 to 20.2 GHz frequency range. The modulator uses a compact architecture composed of four VGAs that are active at any given time and are switched to generate the four quadrants. The circuit was fabricated using a 130 nm SiGe BiCMOS process and occupies an area of 1309.4 \(\upmu\)m \(\times\) 1783.8 \(\upmu\)m (with pads). The simulation and measurement results demonstrate that the vector modulator is capable of generating every phase from 0\({}^{\circ}\) to 360\({}^{\circ}\) with a 6-bit phase resolution over the 2.4 GHz band.  
Methodology, J.d.P. and S.L.K.; Investigation, J.d.P., S.L.K., M.S.-M.-M., S.M.-A., D.M.-D., J.L.S.-P. and D.G.-S.; Supervision, J.d.P. and S.L.K.; Writing--original draft, J.d.P. and S.L.K.; Writing--review and editing, S.L.K. and J.d.P.; Visualization, J.d.P. and S.L.K.; Project administration, J.d.P. and S.L.K.; Funding acquisition, J.d.P. and S.L.K. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by Grant PID2021-127712 OB-C21 funded by MCIN/AEI/10.13039/501100011033 and by \"ERDF a way of making Europe\"; Grant DIN2020-011622 funded by MCIN/AEI/10.13039/501100011033 and by the \"European union NextGenerationEU/PRTR\"; and Grant TESIS2019010100 funded by the Canary Agency for Research, Innovation, and Information Society (ACIISI).  
The authors declare no conflict of interest.
# Mesh Twisting Technique for Swirl Induced Laminar Flow Used to Determine a Desired Blade Shape
## 2 Materials and Methods
### Mesh Morphing  
The interior of the pipe needs to be covered with a spatial three-dimensional mesh. The mesh is constructed of block structured hexahedrals. One can describe the mesh as a rectangular inner core of cells at the center of the cylinder, with radial cells spreading from the rectangular inner core to the outer diameter. A cross section of the mesh without guidance blades can be seen in Figure 4.  
It is inevitable that some of the cells in the mesh are skewed, which is most notable at four locations in the mesh (see Figure 4). Skewness is a measure on how equilateral and equiangular a cell is and its value gives a good indication of the quality of the mesh (zero being perfect and 1 being degenerated). The skewness value of the mesh (without blades) is shown in Figure 5.  
Figure 4: Internal mesh arrangement with no guidance blades. (**a**) coarse mesh (left); (**b**) intermediate mesh (center) and (**c**) fine mesh (right) are shown.  
Figure 3: A single streamline flowing from right to left through a swirler (**upper**) for a typical case; as well as the effect of twist angle \(\theta\) on the mesh (**lower**). The twist angle moves all mesh points on the surface from the upper horizontal red line to the lower one.  
Most of the mesh has a skewness value below 0.25 which is considered excellent. The maximum skewness value in the mesh is 0.49 which is between 0.25-0.50 and is considered good. Increasing the number of cells does not affect the maximum skewness value. In addition to skewness, the loss of orthogonality of the cell walls can cause numerical errors. The mesh met the constraint required by OpenFOAM and, as shown in Section 3, the mesh did not affect the solution greatly and will, therefore, not be explored further.
# Effects of Heavy Rainfall on Shallow Foundations in Bukit Timah Granite in Singapore
###### Abstract
However, the nature of rainfall is such that it is a transient and time-dependent process. Thus, in this study, a coupled flow-deformation analysis was used to simulate both rainfall and shallow foundation response simultaneously [20], with the aim of simulating the deformation response due to changes in hydraulic conditions and transient pore-water pressure of residual soil in the area of Bukit Timah Granit. Conventional design for shallow foundations has always assumed that the soil is fully saturated. However, shallow foundations typically sit on unsaturated soils with the groundwater table far below surface level. This leads to an overestimation of settlement, and discrepancies between actual and estimated settlement. This problem is exacerbated when rainfall is introduced and seeps into the soil, causing a change in matric suction of the soil. This change in matric suction will alter many soil properties, e.g., elastic modulus and shear strength to the hydraulic conductivity of the soil. This study attempts to evaluate the impact of climate change-induced heavy rainfall events on the stability of shallow foundations in the Bukit Timah region in Singapore using finite element methods (FEM). The results obtained from this study will serve as a reference for engineers to provide sufficient considerations for unsaturated soil transient rainfall infiltration conditions, specifically, for shallow foundation purposes. Two-dimensional (2D) coupled flow-deformation analyses were carried out using commercial software, SIGMA/W [21]. Bukit Timah Granite (BTG) residual soil was used as the main soil type in this analysis. Boundary conditions of rainfall intensity, duration, and the groundwater table position varied through parametric studies to explore the influence of these controlling parameters on the load-settlement response of the shallow foundation in BTG.