esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
## 3 Results
### Analysis of Wake Vortex Parameters
#### 3.3.2 Analysis of Wake Vortex Parameters under Different Wind Speeds  
Figures 19 and 20 depict the horizontal and vertical displacement distributions of the vortex core under various positive wind speeds, respectively. Figure 18 shows that under wind speeds of 1 m/s and 2 m/s, the vortex core undergoes a brief contraction before stabilizing and oscillating. The vertical wind component restrains the reduction in vortex core spacing. Under wind speeds of 3 m/s and 4 m/s, the vortex core initially contracts rapidly (with contraction nodes at x/c = 4 and x/c = 8) and then steadily contracts at a slower rate. As indicated in Figure 11e, the sudden horizontal displacement of the vortex core results from the detachment of secondary vortices from within the primary vortex due to the instability of its rotation. After the secondary vortices detach, the primary vortex structure stabilizes, and the rate of horizontal displacement reduction becomes steady. Consequently, the changes in vortex horizontal displacement do not exhibit a linear relationship with vertical wind speed. Under wind speeds of 1 m/s and 2 m/s, the vortex core spacing reduction rate is less than that under still air conditions, resulting in a larger downwash danger zone, indicating an increased risk of encountering wake turbulence for following aircraft.  
Figure 18: Vertical velocity and axial velocity of the vortex core at different wind speeds. (**a**) Vertical velocity; (**b**) axial velocity.  
Figure 20 shows the interaction between wind and the vortex core, leading to the descent of the vortex core through the momentum transfer process. As wind speed increases, the descent displacement of the vortex core also increases. For instance, at the x/c = 10 cross-section, the vertical removal of the vortex core under vertical wind conditions of 1 m/s, 2 m/s, 3 m/s, and 4 m/s is approximately 0.08 B, 0.3 B, 0.4 B, and 0.5 B, respectively. Notably, the descent effect on the vortex core is most pronounced under wind speeds of 1 m/s and 2 m/s. Beyond a certain wind speed threshold, the descent effect on the vortex core remains relatively consistent. This may be influenced by factors such as the vortex core's saturation state and the airflow's inertia.
## 3 Results and Discussion
### Growth Endpoints  
The fish subjected to sub-lethal doses of diazinon showed a concentration-dependent and time-dependent reduction in physiological state and retarded growth rate as reflected by the K, HSI, and SGR, respectively (Figure 2a-c). Due to varied underlying physiological processes and synchrony with environmental changes, growth provides a robust indicator of ecological risks, particularly for chronic scale exposures [62; 63]. Condition factor is an individual-level biomarker of physiological condition and reflective of the health and fitness state of the fish as a product of interactions with environmental quality [64; 65]. As such, a significantly lower K at elevated diazinon exposures (Figure 2a) is strongly depictive of physiological stress and loss of fitness for survival [66].  
Figure 1: (**a–d)** Mean and SD values of hematological profiles and indices of _Clarias__butrachus_ exposed to different concentrations of diazinon (0.64 and 1.28 mg/L) for different exposure times (15, 30, and 45 days). *—denotes significant differences to control within the same exposure time (* \(p<0.05\), ** \(p<0.01\), and *** \(p<0.0001\)). (**a**) Hemoglobin; (**b**) total white blood cells; (**c**) total red blood cells; (**d**) mean corpuscular hemoglobin.  
Figure 2: (**a–c**) Mean and SD values of growth endpoints and indices of _Clarias batrachus_ exposed to different concentrations of diazinon (0.64 and 1.28 mg/L) for different exposure times (15, 30, and 45 days). *—denotes significant differences to control within the same exposure time (* \(p<0.05\), **\(p<0.01\), ***\(p<0.001\) and ***\(p<0.0001\)). (**a**) Condition factor (K); (**b**) hepatosomatic index (HSI); (**c**) specific growth rate (SGR).  
due to destructive changes [69; 70], the enhancement of HSI may also reflect reduced body weight due to low food conversion [71].  
Fish from other diazinon treatments (0.64 and 1.28 mg/L) displayed significantly lower SGR than in control groups (\(p\) < 0.05) (Figure 2c), indicating greater toxicity with increasing diazinon concentrations. This is consistent with previous research on various fish treated with multiple pesticides [72; 73]. In this study, the specific growth rate of fish exposed to diazinon remained lower than control fish throughout the exposure intervals from 15 to 45 days. This unchanged significant difference between the SGR fish in the exposure groups and control highlights a possible inability to undertake compensatory growth response [74]. Although lowered SGR has been attributed to lower food intake and conversion [75], stress-induced chemical exposures have been implicated in the reduction in SGR [76], with endocrine disturbance as a possible mechanism [77].
# Assessing the Effects of Gamification on Enhancing Information Security Awareness Knowledge
## 4 Results
### Effects on Information Security Awareness Knowledge  
Although the teachers differed across the classes, our pre-experiment assessment regarding ISA knowledge showed no significant differences (\(t=0.397\), \(p>0.05\)). It is fair to assume that the two groups started with a similar level of knowledge, suggesting a further analysis was acceptable. This study employed an independent samples \(t\) test to examine  
Figure 3: IS learning game interface and procedure. Source: PaGamO website. Note: Non-English words in the screen of the step 1 show the brief descriptions of game task and introduction to the skills and tools of game avatars in Chinese. The screen of the step 2 represents the ISA multiple question and answers concerning firewall issue when users selected a specific territory that they aimed to challenge. The two screens of the step 3 are the expanded territory when users submitted the correct answers and a learning mechanism of correct answer and explanations concerning virus issue when users did not challenge successfully. The screen of the step 4 relates to a user’s achievement information in Chinese that are leaderboard, points, weapons, and rewards gained when completed the game tasks.  
the effect of gamification on students' ISA knowledge enhancement in terms of overall and specific focus areas of ISA. Table 3 shows the descriptive statistics and results of samples \(t\) test, effect sizes, and observed power among game-based and lecture-based groups.  
The results indicated that an IS learning game had a significant effect on students' overall security learning performance. Students within a gamified class performed better than that of students within a lecture-based instructional group (\(t\) = 2.04, \(p\) < 0.05, \(\eta^{2}\) = 0.04, observed power = 0.53). In addition to overall posttest performance, a closer look at specific ISA focus areas revealed that the differences between two groups were found. The performance differences were observed in three areas of password management (\(t\) = 2.42, \(p\) < 0.05, \(\eta^{2}\) = 0.05, observed power = 0.68), Internet use (\(t\) = 2.94, \(p\) < 0.01, \(\eta^{2}\) = 0.08, observed power = 0.83), and information handling (\(t\) = 2.55, \(p\) < 0.01, \(\eta^{2}\) = 0.06, observed power = 0.72).
# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Emotion**} \\ \hline
**Classifier/Measure** & **Angry** & **Disgust** & **Fear** & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline \multicolumn{8}{c}{**RDF**} \\ \hline Precision & 71.0 & 85.0 & 69.0 & 68.0 & 95.0 & 59.0 & 100.0 \\ \hline Recall & 70.0 & 84.0 & 65.0 & 67.0 & 68.0 & 88.0 & 100.0 \\ \hline F1-score & 71.0 & 84.0 & 68.0 & 68.0 & 74.0 & 71.0 & 100.0 \\ \hline Accuracy & 77.0 & 73.0 & 63.0 & 70.0 & 69.0 & 82.0 & 100.0 \\ \hline \multicolumn{8}{c}{**GBM**} \\ \hline Precision & 72.0 & 65.0 & 83.0 & 47.0 & 93.0 & 69.0 & 100.0 \\ \hline Recall & 69.0 & 74.0 & 69.0 & 62.0 & 71.0 & 77.0 & 100.0 \\ \hline F1-score & 70.0 & 68.0 & 71.0 & 48.0 & 77.0 & 75.0 & 100.0 \\ \hline Accuracy & 74.0 & 71.0 & 59.0 & 73.0 & 69.0 & 86.0 & 92.0 \\ \hline \multicolumn{8}{c}{ABC} \\ \hline Precision & 74.0 & 71.0 & 70.0 & 58.0 & 69.0 & 73.0 & 99.0 \\ \hline Recall & 76.0 & 72.0 & 68.0 & 61.0 & 68.0 & 71.0 & 100.0 \\ \hline F1-score & 74.0 & 75.0 & 73.0 & 59.0 & 65.0 & 73.0 & 100.0 \\ \hline Accuracy & 71.0 & 67.0 & 56.0 & 69.0 & 69.0 & 71.0 & 99.0 \\ \hline \multicolumn{8}{c}{**BSVM**} \\ \hline Precision & 71.0 & 68.0 & 66.0 & 59.0 & 67.0 & 71.0 & 98.0 \\ \hline Recall & 72.0 & 72.0 & 64.0 & 61.0 & 64.0 & 75.0 & 100.0 \\ \hline F1-score & 70.0 & 70.0 & 66.0 & 60.0 & 65.0 & 73.0 & 100.0 \\ \hline Accuracy & 71.0 & 62.0 & 51.0 & 67.0 & 68.0 & 73.0 & 100.0 \\ \hline \multicolumn{8}{c}{**BMLP**} \\ \hline Precision & 70.0 & 66.0 & 64.0 & 57.0 & 66.0 & 67.0 & 93.0 \\ \hline Recall & 72.0 & 70.0 & 66.0 & 59.0 & 68.0 & 72.0 & 97.0 \\ \hline F1-score & 71.0 & 68.0 & 64.0 & 59.0 & 65.0 & 69.0 & 96.0 \\ \hline Accuracy & 56.0 & 52.0 & 49.0 & 65.0 & 54.0 & 64.0 & 85.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Percentage precision, recall and F1-score and accuracy on SAVEE MFCC2. Tables 4-10 show that HAF features had a higher discriminating power because higher performance results were achieved across all emotion classes in the SAVEE and RAVDESS corpora. The performance of each ensemble learning algorithm was generally low when MFCC1 and MFCC2 were applied because the sets of the features had a much lower discriminating power compared to HAF. The recognition results across the experimental databases were different because RAVDESS had more instances than SAVEE. Moreover, SAVEE constitutes male speakers only while RAVDESS is comprised of both male and female speakers. According to the literature, this diversity has an impact on the performance of emotion recognition [35]. The literature review revealed that most emotion recognition models have not been able to recognize the fear emotion with a higher classification accuracy of 98%, but the results in Table 4 show that HAF significantly improved the recognition performance of emotion beyond expectation.
# A Novel Hierarchical Multi-Stable Cylindrical Structure with Superior Energy Trapping
## 3 Numerical Simulation  
In this section, as shown in Figure 5, finite element analysis models were developed by ABAQUS to study the snap-through behavior of the structure using the (static, general) analysis step. The model was constructed using S4R shell elements, suitable for thin-walled structures. The mesh size was set to 0.5 mm as 1/20 of the cosine beam width to ensure computational accuracy and efficiency. The R3D TPU 95A (thermoplastic polyurethane, Wuhu R3D Technology Co., Ltd., Wuhan, China) material was used for both numerical simulation and experimental fabrication, as it is a commercially available filament commonly used in FDM 3D printing. The elastic modulus (\(E=36.991\) MPa) and Poisson's ratio (\(\
u=0.4\)) were obtained from the manufacturer's technical datasheet.  
Although additively manufactured polymers can exhibit anisotropic behavior due to the layer-by-layer deposition process, the TPU specimens in this work were printed using a consistent raster angle and uniform wall thickness, and the deformation under quasi-static compression was predominantly axisymmetric. Therefore, an isotropic material assumption was adopted in the finite element analysis [58].  
To simulate the snap-though behavior of the structure, the bottom surface of the finite element model was fully fixed, and the top surface was connected to a reference point using MPC constraints. A vertical displacement load was applied directly to this reference point. This reference point is restricted in all degrees of freedom except for the vertical downward direction. The loading displacement is set to twice the height of the cosine beam to ensure that the cosine beam completes the entire snap-though process. In the subsequent simulation analysis of the effect of structural parameters on the HMCS  
Figure 4: The geometry of the multilayer HMCS.  
Figure 5: Finite element model of HMCS.  
snap-through behaviors, the height \(h\) of the HMCS was varied from 6 mm to 10 mm in increments of 0.1 mm. The thickness t ranged from 1 mm to 2.5 mm, also with a step size of 0.1 mm.
## 3 System Model of DCK-LEACH
### Network Model  
Our algorithm is applicable to static networks where there are one base station and n static nodes. We assume that a wireless sensor network is a two-dimensional plane. There is a fixed base station for receiving data from nodes, and n randomly distributed sensor nodes for collecting information.  
We make the following assumptions for simulating the network.  
1. The sensor nodes are randomly distributed in a two-dimensional plane, and they are static, thus will not move once they are deployed.
2. In the monitoring range, all sensor nodes are homogeneous, which have the same initial energy, data processing, and communication capabilities.
3. Sensor nodes have the ability to access to their own location in the network, their own residual energy, and the ability to bring cluster-heads to fuse redundant data.
4. The base station has an unlimited power supply and computing power, and each node can communicate directly with this base station.  
Figure 1 shows the network topology of the proposed algorithm. There are three phases in the algorithm: the cluster establishment phase, cluster-head selection phase, and stable operation phase.  
In the cluster establishment phase, rough clustering is preformed in terms of the Canopy algorithm, and then precise clustering is preformed using the K-means algorithm.  
Figure 1: Network topology of the proposed algorithm.  
We finally obtained K clusters and the cluster IDs with the nodes they belonged to in a network. The cluster-head selection phase includes two elections: the primary cluster-head, and its vice cluster-head in the same cluster. The election of the primary cluster-head is decided by two factors: the residual energy of the node, and the distance from the node to the cluster center. The vice cluster-head is selected based on two factors: the residual energy of the node, and its distance from the base station.  
In the stable operation phase, data which is collected from the inter-cluster normal nodes are forwarded to the primary cluster-heads, and then to the vice cluster-heads, and finally are forwarded to the base station. The intra-cluster data are forwarded in a hierarchy (see Figure 1), where normal nodes collect data, and forward them to the primary cluster-head in the cluster where the node belongs. The primary cluster-head aggregates data and transmits it to the vice cluster-head, and the latter receives the data and sends it to the base station.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Confidentiality  
Confidentiality pertains to safeguarding privacy and preventing unauthorised access to sensitive data during transmission. It ensures that sensitive or private information remains protected and inaccessible to unauthorised entities. A secure and reliable routing approach for mobile networks was proposed by [PERSON] (2018), using cryptography and the Euclidean Distance formula for confidentiality, reliability, and integrity in data transmission.
## 4 Discussion
### ANOVA on Factors that Affect Dynamic Peak Strength of jointed Specim  
Statistical analysis of variance (ANOVA) was carried out to identify the factors that affect the dynamic behaviors of a specimen that contains various joint patterns and to facilitate subsequent investigation. The loading rate, \(\beta\), and \(\alpha\) were selected as the independent variables and the dynamic peak stress that was obtained in the SHPB test is the dependent variable. The independent variables were normalized using their respective average values and standard deviations since the magnitude  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equation** & **Coefficient of Determination, \(\mathbf{R^{2}}\)** & **Corresponding Figure** \\ \hline \(\sigma_{peak}^{\text{interf.}}=0.0320\times\dot{\alpha}+15.31\) & 0.630 & 8(a) \\ \(\sigma_{peak}^{\beta-39\cdot\mu-0^{\circ}-21^{\circ}}=0.0288\times\dot{\alpha}+15.36\) & 0.862 & 8(b) \\ \(\sigma_  
of the loading rate is much greater than those of \(\beta\) and \(\alpha\). ANOVA was separately performed on the results of slide failure, fracture failure, and crushing failure to eliminate any effect of failure type.  
A total of 36, 112, and 47 data associated with slide failure, fracture failure, and crush failure were obtained after the SHPB tests. Table 5 lists the results of the ANOVA for specimens with fracture failure. The statistical \(F\) value is very high, indicating that variations in repeated treatments (experimental groups with the same independent variables) are significantly smaller than those among various treatments with different independent variables: this fact permits an examination of the influence of each independent variable. For the jointed specimens that fail by fracture, the test statistics of the loading rate, \(\beta\), and \(\alpha\) all exceed the thresholds for rejection at a 90% level of significance, and the \(p\)-values are all less than 0.10 (Table 5), indicating that all of these factors significantly affect the dynamic peak stresses of jointed specimen in a SHPB test. Table 6 lists the significant factors affecting the dynamic peak strength for various failure types. For the jointed specimens with slide failure, the loading rate and \(\beta\) are significantly affected factors, and for the jointed specimens that are crushed to failure, the loading rate is the only significantly affected factor.
###### Abstract  
The current study aimed to assess the effects of a customized three-dimensional (3D) printed writing and typing assistive device in patients with cervical spinal cord injury who presented with severe hand dysfunction. Three patients with cervical spinal cord injury who presented with severe hand dysfunction were included in the analysis. The patients' writing and typing abilities were evaluated after using the silicon assistive device made from a 3D-printed frame for 4 weeks. Patient discomfort and issues were evaluated. Customized 3D-printed writing and typing assistive devices were developed. The Korean Western Aphasia Battery (K-WAB), particularly the writing part, and the word practice program of Hangeul were utilized to assess device effects. All patients with cervical spinal cord injuries (SCIs) performed writing or typing using a customized assistive device. Patients 2 and 3 had better typing and writing accuracies based on the word practice program of Hangeul and the K-WAB, respectively. However, patient 3 had increased time, which was associated with the process of adapting to the use of the customized device. Nevertheless, he was highly satisfied with the device. The patient's typing and writing speed and accuracy improve with the customized 3D-printed device, which can lead to a better performance in the activities of daily living.  
three-dimensional printing; assistive devices; spinal cord injury; hand function; tetraplegia
## 3 Results
### Information-Theoretic Domain Classification Results  
The classification results of the information-theoretic domain features with \(p\)-values < 0.05 are reported in Table 25. The random forest classifier reported the best accuracy of 58.05%.  
Extensive classification performance metrics for different BAC classes are summarized in Table 26 for the random forest classifier.  
Figure 8 visualized the confusion matrix performance of the random forest classifier on information-theoretic domain features with \(p\)-values < 0.05. The classifier struggled to classify BAC classes 0.12, 0.2 due to low amounts of data in those classes.  
Table 27 summarizes the model configuration for this random forest classifier for information-theoretic domain features.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline BAC = 0 & 0.654 & 0.195 & 0.415 & 0.654 & 0.507 & 0.820 \\ BAC = 0.05 & 0.650 & 0.097 & 0.510 & 0.650 & 0.571 & 0.776 \\ BAC = 0.12 & 0.298 & 0.100 & 0.415 & 0.298 & 0.347 & 0.768 \\ BAC = 0.2 & 0.237 & 0.031 & 0.529 & 0.237 & 0.327 & 0.709 \\ BAC = 0.3 & 0.784 & 0.107 & 0.813 & 0.784 & 0.798 & 0.901 \\ \hline Weighted Avg. & 0.581 & 0.110 & 0.590 & 0.581 & 0.571 & 0.820 \\ \hline \end{tabular}
\end{table}
Table 26: Performance metrics for different BAC classes.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Classifier & \multicolumn{1}{c}{weka.classifiers.trees.J48} \\ \hline Confidence Factor for Pruning (C) & 0.15 \\ \hline Min. No. of Instances per Leaf (M) & 3 \\ \hline Unpruned & False \\ \hline Reduced Error Pruning & False \\ \hline Binary Splits & False \\ \hline Collapse Tree & True \\ \hline Subtree Raising & True \\ \hline Laplace for Smoothing & False \\ \hline Seed & 1 \\ \hline \end{tabular}
\end{table}
Table 24: J48 configuration for statistical domain features.  
\begin{table}
\begin{tabular}{l l} \hline
**Classifier Type** & **Accuracy** \\ \hline
**Random Forest** & **58.05\%** \\ J48 & 57.05\% \\ Decision Table & 53.36\% \\ JRip & 43.29\% \\ Naive Bayes & 37.92\% \\ SMO (SVM in WEKA) & 37.25\% \\ \hline \end{tabular}
\end{table}
Table 25: Classifiers ranked by accuracy for information-theoretic features with \(p\)-values < 0.05.
## 2 Introduction to Theory
### Sparse Filtering  
As a simple and effective unsupervised feature learning algorithm, the sparse filtering algorithm only needs one parameter adjustment, namely the number of features to be learned [20]. Its working principle is mainly to bypass the estimation of data distribution and optimize only a simple cost function, the L2 norm, to directly analyze the distribution of optimized features.  
The structure of sparse filtering is a two-layer neural network including input layer, output layer, and weight matrix. The input is the collected signal and the output is the learned feature. The collected signals are divided into many identical samples to form a training set \(\left\{x^{i}\right\}_{i=1}^{M}\), where \(x^{i}=\Re^{N\times 1}\) is a sample containing \(N\) data points and \(M\) is the number of samples. The sample is mapped to the eigenvector \(\boldsymbol{f}\in\Re^{L\times 1}\) by the usual-value matrix \(\boldsymbol{W}\in R^{N\times L}\), and \(f_{j}^{i}\) was used to represent the jth eigenvalue of the ith sample of the eigenmatrix. The features learned are linear:  
\[f_{j}^{i}=W_{j}^{T}x^{i} \tag{21}\]  
First, all row vectors of the eigenmatrix are normalized, then all column vectors of the eigenmatrix are normalized, and finally the sum of the absolute values of all elements of the matrix is obtained. The details are as follows:  
First, each feature is normalized to an equal activation value, that is, the L2 norm of each feature divided by all the samples in which it is located:  
\[\widetilde{f_{j}}=f_{j}/\left\|f_{j}\right\|_{2} \tag{22}\]  
Then, the characteristics of each sample are normalized, so that the characteristics of all samples fall on the unit sphere of L2 norm:  
\[\widetilde{f^{i}}=\widetilde{f^{i}}/\left\|\widetilde{f^{i}}\right\|_{2} \tag{23}\]  
Finally, the L1 norm penalty is used to carry out the sparsity constraint to realize all the normalized features before optimization. Assuming that a dataset has M samples, the objective function of sparse filtering is:  
\[\underset{W}{\text{minimize}}\sum_{i=1}^{M}\left\|f^{i}\right\|_{1}=\sum_{i=1 }^{M}\left\|\frac{\widetilde{f^{i}}}{\left\|\widetilde{f^{i}}\right\|_{2}}\right\| \tag{24}\]