esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 5 Results
### Varying Cache Size
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & & & & & \\ \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Cache Size**} & \multirow{2}{*}{**Cache \%**} & \multirow{2}{*}{**R0**} & \multirow{2}{*}{**R1**} & \multirow{2}{*}{**R2**} & \multirow{2}{*}{**R3**} & \multirow{2}{*}{**R4**} \\ \cline{1-1}  & & & & & & & & \\ \hline PFP-DA & 100 & 1\% & 18.00\% & 8.00\% & 23.00\% & 16.00\% & 8.00\% \\ LFU-DA & 100 & 1\% & 17.00\% & 22.00\% & 17.00\% & 22.00\% & 14.00\% \\ LRU & 100 & 1\% & 18.00\% & 18.00\% & 17.00\% & 19.00\% & 17.00\% \\ PFP-DA & 200 & 2\% & 22.00\% & 7.00\% & 24.00\% & 23.00\% & 7.50\% \\ LFU-DA & 200 & 2\% & 21.50\% & 23.50\% & 21.50\% & 21.50\% & 19.50\% \\ LRU & 200 & 2\% & 17.00\% & 22.00\% & 19.00\% & 22.00\% & 16.50\% \\ PFP-DA & 300 & 3\% & 22.33\% & 7.00\% & 14.67\% & 21.67\% & 8.67\% \\ LFU-DA & 300 & 3\% & 22.33\% & 22.33\% & 21.67\% & 22.67\% & 22.00\% \\ LRU & 300 & 3\% & 17.33\% & 22.33\% & 19.67\% & 19.67\% & 17.67\% \\ PFP-DA & 500 & 5\% & 23.40\% & 6.60\% & 12.40\% & 19.20\% & 9.20\% \\ LFU-DA & 500 & 5\% & 23.00\% & 24.60\% & 22.80\% & 23.40\% & 23.20\% \\ LRU & 500 & 5\% & 19.80\% & 25.00\% & 22.80\% & 22.20\% & 20.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AT with varying cache sizes, single attacker, and 720 frequency—pollution %. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Cache Size** \\ **(\# of Packets)** \\ \end{tabular} & **Cache \%** & **Pollution \%** & **Hit \%** & **Top 100** & **Top 50** \\ \hline PFP-DA & 100 & 1\% & 15.00\% & 19.02\% & 62.80\% & 75.53\% \\ LFU-DA & 100 & 1\% & 36.00\% & 12.67\% & 41.17\% & 50.00\% \\ LRU & 100 & 1\% & 36.00\% & 8.82\% & 28.72\% & 34.64\% \\ PFP-DA & 200 & 2\% & 14.00\% & 25.57\% & 80.02\% & 91.53\% \\ LFU-DA & 200 & 2\% & 39.00\% & 17.08\% & 52.83\% & 62.73\% \\ LRU & 200 & 2\% & 37.00\% & 14.66\% & 46.04\% & 54.76\% \\ PFP-DA & 300 & 3\% & 12.67\% & 29.32\% & 87.76\% & 97.96\% \\ LFU-DA & 300 & 3\% & 42.00\% & 20.53\% & 62.67\% & 72.19\% \\ LRU & 300 & 3\% & 38.00\% & 18.58\% & 56.46\% & 66.13\% \\ PFP-DA & 500 & 5\% & 12.40\% & 34.89\% & 93.65\% & 98.96\% \\ LFU-DA & 500 & 5\% & 39.40\% & 25.84\% & 73.58\% & 81.03\% \\ LRU & 500 & 5\% & 39.00\% & 24.53\% & 70.51\% & 80.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ST with varying cache sizes and single attacker at frequency of 720.
# Extending the Coverage of IEEE 802.15.4z HRP UWB Ranging
## 3 Link Analysis of HRP UWB Communication
### Link Analysis of Preamble in TWR Procedure
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Preamble** & \
\begin{tabular}{c} **Data Portion** \\ **(850 kbps)** \\ \end{tabular} &
\begin{tabular}{c} **Data Portion** \\ **(6.8 Mbps)** \\ \end{tabular} \\ \hline Code length & 127 & - & - \\ No. of symbols & 256 & - & - \\ PAC size & 32 & - & - \\ Preamble length & 268.6 & - & - \\
[us] & 268.6 & - & - \\ PRF [MHz] & 64 & 62.4 & 62.4 \\ Coding gain [dB] & - & 5.6 & 5.6 \\ Coded \(E_{b}/N_{0}\) [dB] & - & 3.5 & 3.5 \\ Payload [bits] & - & 160 & 160 \\ Packet length & - & 306.4 & 100.9 \\ PSD [dBm/MHz] & \(-\)41.3 & \(-\)41.3 & \(-\)41.3 \\ Bandwidth [MHz] & 499.2 & 499.2 & 499.2 \\ Frequency [GHz] & 7.98 & 7.98 & 7.98 \\ NF [dB] & 6 & 6 & 6 \\ PG [dB] & 36.1 & - & - \\ GG [dB] & 0 & 0 & 0 \\ \(P_{TX}\) [dBm] & \(-\)14.3 & \(-\)14.3 & \(-\)14.3 \\ \(SNR_{min}\) [dB] & 10.0 & \(-\)21.6 & \(-\)12.5 \\ S [dB] & \(-\)106.7 & \(-\)105.5 & \(-\)96.5 \\ Path Loss [dB] & 92.6 & 91.1 & 82.1 \\ \(PL_{1m}\) [dB] & 50.5 & 50.5 & 50.5 \\ Link margin & 42.2 & 40.7 & 31.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Link budget calculation of IEEE 802.15.4z HRP UWB. Figure 8: Link budget of IEEE 802.15.4z HRP UWB (**a**) in the case of the preamble link and (**b**) in the case of a 6.8 Mbps data portion. since the message must be successfully received for two-way ranging, the characteristics of the preamble need to be appropriately adjusted to the link of the data portion. After the link balances of the DP section and the SYNC section are matched, it is possible to additionally increase the transmission power using the gating gain if the packet length is less than 1 ms. This has been confirmed to be consistent with the results of many experimental papers. The results of this study can help to adjust the physical layer parameters when installing actual HRP UWB products to increase the ranging coverage.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Theoretical Validation of HSALA
#### 5.3.3 Adherence to Security Best Practices  
Table 11 illustrates how HSALA embodies the defense-in-depth principle and integrates security throughout the development lifecycle.  
HSALA's integrated security approach aligns with the industry's best practices, enhancing the application's resilience against threats and ensuring the compliance with stringent data protection regulations.  
\begin{table}
\begin{tabular}{c c} \hline
**Aspect** & **HSALA Alignment** \\ \hline \multirow{2}{*}{WCAG Compliance} & The Accessibility Enhancement Layer ensures compliance with WCAG 2.1 guidelines, promoting inclusivity and accessibility for individuals with disabilities. \\ \hline \multirow{2}{*}{Legal Requirements} & Aligns with regulations like the Americans with Disabilities Act (ADA) and the European Accessibility Act (EAA), mitigating legal risks and ensuring equitable access. \\ \hline \end{tabular}
\end{table}
Table 10: HSALA’s compliance with WCAG and legal requirements.  
\begin{table}
\begin{tabular}{c c} \hline
**Principle** & **HSALA Alignment** \\ \hline Separation of Concerns & Divides the application into distinct layers, each with a specific responsibility, reducing complexity and enhancing clarity. \\ \hline Modularity & Enhances maintainability and scalability by allowing independent development and updates of each layer. \\ \hline Encapsulation & Each layer encapsulates its functionality and security measures, preventing unintended interactions and side effects between layers. \\ \hline \end{tabular}
\end{table}
Table 9: HSALA’s alignment with modularity, encapsulation, and separation of concerns principles.  
By addressing both accessibility and security comprehensively, HSALA fosters a trustworthy and satisfying user experience, which is critical for the success and reputation of web applications.
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 3 Lagrange Points and Basins of Convergence
### Validating the Algorithm: The Sun-Earth System
#### 3.2.1 The Effect of Radiation Forces and Mean Motion  
Firstly, the algorithm is tested against the effective potential given in (9). In this case, we try to reproduce the results obtained by [PERSON] et al. [20], computing our algorithm under the same conditions. So, the BCs without radiation forces, that is, taking\(q_{1}=q_{2}=1\left[-\right]\) (the symbol \(\left[-\right]\) is used to indicate that the quantity is dimensionless) but \(\mu=0.05\left[-\right]\) and \(n=0.5\left[-\right]\), are shown in Figure 1. In addition, the BCs obtained for \(\mu=0.5\left[-\right]\), \(q_{1}=0.15\left[-\right]\), \(q_{2}=0.25\left[-\right]\), and \(n=0.25\left[-\right]\) are shown in Figure 2, while Figure 3 shows the BCs with the same mass and radiation parameters but with the mean motion changed to \(n=0.95\left[-\right]\). These cases would be equivalent to studying a classic CR3 BP in which the masses orbit more slowly since \(n<1\). In Tables 1-3, the coordinates of the Lagrange points obtained in each of the cases analyzed are given.  
As can be observed, the Lagrange points change depending on the values of parameters \(\mu\), \(q_{1}\), \(q_{2}\), and \(n\). These results agree with those obtained by [PERSON] et al. [20] under the same conditions.
# Vector Optical Bullets in Dielectric Media: Polarization Structures and Group-Velocity Effects
## 3 Vector Optical Bullets in Dielectric Material
Using the definition of second-moment beam size, we have determined the following constant longitudinal beam lengths: 44.35 \(\upmu\)m for \(V/c=0.63685\) and \(\gamma=-0.628\)\(\upmu\)m\({}^{-1}\), and 47.24 \(\upmu\)m for \(V/c=-1.2\) and \(\gamma=6\pi\)\(\upmu\)m\({}^{-1}\). Another key point of interest in this investigation is the relative strengths of the intensity of individual electric field components for different frequencies. In Figure 17a, we give these dependencies for \(V/c=0.63685\) and \(\gamma=-0.628\)\(\upmu\)m\({}^{-1}\). The solid lines represent the linear TE polarization, and the dashed line represents the linear TM polarization. For linear polarization, with lower frequency values, we can see that the \(z\) component has an intensity of up to 10%. With increasing frequency, the \(z\) component of the electric field decreases and, around the value of \(\omega_{c}/\omega_{0}\approx 1\), the \(z\) component approaches 0. For the TE polarization, the \(y\) component is nonexistent; for the TM polarization, the \(x\) component is nonexistent. The azimuthal polarization has constant \(x\)- and \(y\)-components of equal proportions at all frequencies tested, without a \(z\) component of the electric field. Radial polarization has the \(z\) component for low frequencies, which decreases rapidly with increasing frequency, see Figure 17a. In the case where the vector FWM propagates backward, i.e., \(V/c=-1.2\) and \(\gamma=6\pi\)\(\upmu\)m\({}^{-1}\), the dependencies of the normalized intensities of the individual components are more pronounced. The linear TE optical bullet and radially polarized FWM have strong \(z\)-components of the electric fields that increase with increasing frequency. On the other hand, the linear TM polarization has a strong \(x\) component of the electric field, but with increasing frequency, the maximal intensities of the \(y\) and \(z\) electric field components start to dominate, see Figure 17b. From the frequencies of \(\omega_{c}/\omega_{0}\approx 1.8\), the intensity of the \(z\) component of the electric field slowly decreases, increasing the \(y\) component. The azimuthally polarized FWM shows steady and equally strong \(x\) and \(y\) components at all tested frequencies, as expected. The radially polarized FWM has the strongest \(z\) electric field component, which increases rapidly with increasing frequency, see Figure 17b.
# Analytical Modeling and Application for Semi-Circular Notch Flexure Hinges
## 5 Example of Application: Flexure-Based Artificial Finger  
### Model and Characterization of Artificial Finger  
The semi-circular notch flexure hinge can act as a joint that drives the finger to stretch. To illustrate the practicability of the semi-circular notch flexure hinge and general design equations, a finger of the artificial hand based on the flexure hinge is designed through the proposed optimization approach and equations. The PRB model of the finger is shown in Figure 12. The \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) in Figure 12 represent the rotation angles of metacarpophalangeal point (MCP) joint, proximal interphalangeal point (PIP) joint, and distal interphalangeal joint (DIP) joint, respectively.  
Figure 11: Errors between theoretical moment and FEA moment.  
Three semi-circular notch flexure hinges are utilized in the model acting as the three joints of the finger. Specifically, in this study, we determined the overall length and height of the artificial hand by considering the dimensions and grasping capabilities of the Chinese human hand. Additionally, we established the length of each hinge based on both its height and maximum rotation angel. The two-dimensional diagram is shown in Figure 13. The metacarpal is fixed, the geometric parameters of the finger and the semi-circular flexure hinge are determined by the physiological structure of the human finger. The rotation angle of the finger is given in advance. The parameters of the finger are shown in Table 7. The minimum thicknesses of the three flexure hinges are determined so that their rotation angles are consistent with those of the human fingers. In addition, the force is applied to the finger in order to bend the finger.
## 3 Methods
### Pre-Processing of Images  
Each Sentinel scene was pre-processed with the Sentinel-1 Toolbox using the following steps: (1) thermal noise removal, which mainly removes additive noise [25]; (2) radiometric calibration, which refers to computing backscatter intensity values using sensor calibration parameters; (3) terrain correction using ASTER GDEM V2, where the final terrain-corrected values are converted to decibels; and (4) mountain shadow mask, where because potential lake areas are generally on a flat surface, a slope larger than \(15^{\circ}\) was set to remove the mountain shadows [20].  
To assess the accuracy of the lake extraction results from the SAR data, the GF-2 imagery was geocoded on a Universal Transverse Mercator map projection. Because the spatial resolution of the multispectral image was not that high for use in the precise evaluation of lake maps, the reference lake outlines were manually delineated on the panchromatic image.
## 5 The Validation of Experiments
### The Sideband Acoustic Comparative Test  
To confirm that MRCFM can suppress the sideband acoustic response of PWM strategies, this paper conducts noise tests on the prototypes under different PWM strategies. In order to better reflect the real experience of the human ear in the measured noise, the acoustic data obtained from the experimental acquisition are subjected to A-weighting of the sound pressure level (SPL-A). Figure 21 presents the sideband acoustic response spectra of different PWM strategies. The primary characteristics of the DPWM acoustic response resemble the vibration signal. The sideband noise peaks caused by various PWM strategies are all over 61.0 dBA without optimization. After the optimization of MRCFM, the acoustic response spectra of various PWM strategies are uniformly distributed, and the peaks of the sideband noises are all less than 57.2 dBA. Figure 22 shows the comparison curves between simulated and measured sideband acoustic response peaks of various PWM strategies. The simulation results are fundamentally consistent with the measured results, which verifies the accuracy of the theoretical analysis and the multi-physics numerical prediction model. After optimization, each PWM's peak sideband acoustic response drops by 7-9 dBA. The results reveal that the MRCFM has a better suppression effect on the sideband acoustic response generated by the PWM strategy.  
Figure 21: Experimental comparison of sideband acoustic response before and after MRCFM optimization: (**a**) Fixed carrier frequency modulation; (**b**) Markov chain random carrier frequency modulation.  
Figure 20: Comparison of simulated and measured peak sideband vibration response with different modulation strategies.
## 4 Results
### Simulation Study
#### 4.2.2 Reliability Measures and Number of Items Used  
The mean and SD of the number of items used, reliability values, and correlation values were computed in the simulation study (Table 3). The mean and SD of the items used increased when the stopping rule decreased (i.e., measurement precision was made stricter). The average reliability values by Cronbach's alpha were computed using the used items used in the simulation study. The reliability values increased as the stopping rules decreased.  
Figure 4: Information and standard error of measurement curve of the item bank.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stopping Rule**} & \multicolumn{2}{c}{**Number of Items Used**} & \multirow{2}{*}{**Reliability**} & \multirow{2}{*}{**r**} \\ \cline{2-2} \cline{5-5}  & **Mean** & & & \\ \hline None & 40 & 0 & 0.98 & 1 \\ SE (theta) \textless{} 0.2 & 27.67 & 12.01 & 0.95 & 0.97 ** \\ SE (theta) \textless{} 0.3 & 15.80 & 8.93 & 0.92 & 0.94 ** \\ SE (theta) \textless{} 0.4 & 11.23 & 4.92 & 0.89 & 0.91 ** \\ \hline \hline \end{tabular} Note. ** is p-value \(<\) 0.01.  
\end{table}
Table 3: Reliability measures.  
That is because more items were used as the stopping rule decreased and the measurement precision increased. These results show that the reliability was already above 0.9 by using only an average of 15.8 items. This indicates that CAFT is efficient, which means that CAFT can estimate a learner's ability while using less items without loss of measurement precision. Correlations between the estimated learner's ability and the no stopping rule or the remaining stopping rules were computed. The values of [PERSON]'s correlation ranged from 0.91 to 1, indicating that CAFT functioned well under different stopping rules. In more detail, the correlation reached 0.97 between the estimated learner's abilities by CAFT using only 27.67 items from the item bank and the estimated learner's abilities when using all items in the item bank. Therefore, CAFT reached similar reliability while using 31% less items than a fixed test in this case.
## 4 Experiment Details and Results
### Baseline (Unconstrained) Solution  
After the preprocessing phase, the user-item ratings matrix was created, based on which the analysis and modeling was performed. Regarding the baseline solution and the coverage constraints, the methodology described in [7] has been followed.  
Specifically, after applying five-fold cross validation to the user item (80-20% training and test sets, accordingly), the Pearson similarity between items in the training set is computed, and after some experimentation only the 20 nearest neighbors are kept for each item. More specifically, 20 nearest neighbors led to the lowest Mean Squared Error in predicted ratings on the test set of the initial dataset. Afterwards, based on this similarity metric, ratings for items of the test set were estimated. As a next step, the performance of the item-based CollaborativeFiltering (CF) based on mean squared error, which measures the squared difference between predicted and actual ratings, was evaluated. The resulting mean squared error was 0.8605 for the test set. This means that the model had a relatively satisfactory performance, as the predicted ratings deviate 0.86 stars from actual ones in the test set.  
Therefore, by adopting this baseline recommendation system, the ratings that users would give to items, which they have not rated so far, are predicted (null values in the initial user-item ratings matrix). The final output is a list of 10 items for each user with the highest predicted ratings that the algorithm produces with no further constraint. The resulted average rating for recommended items for all users was 4.938.
## 2 Communication Network Infrastructure
### Area Border Router (ABR)  
This network node has the capability to establish and manage high-speed connections with the LSR satellites in the network, allowing effective interconnectivity of different subnetworks. Its high processing capacity with multiple cores is based on the RISC architecture, offering a better balance between performance and energy consumption. This enables data processing for navigation, orbit control, and protocol management with low latency.  
The ABR is equipped with solar power systems for its payload systems and energy storage via supercapacitors for actions that require a higher amount of power, such as antenna orientation corrections. In addition to these energy systems, for locations and potential uncontrollable events where sunlight is limited, these satellites are equipped with radioisotope thermoelectric generators (RTGs).  
Regarding navigability, to maintain a correct path, the ABR adjusts its course based on position feedback from the LSR satellites in orbit to ensure uninterrupted communication.
## 3 Optimized Joint Signal Processing Schemes
### Sparsity Reconstruction-Based Improved Parameter Estimation  
For target echo signals, compared with the MIMO-OFDM system, the subcarriers in the proposed integrated MIMO-OFDM-IM system are sparse, so the variance of the ambiguity function of the integrated signal will increase, which leads to the increase in the sidelobe level obtained by the traditional radar signal processing method. In the theoretical system of compressive sensing, in order to express a signal more accurately, the signal can usually be transformed into a new basis or framework. As long as we know in advance that the signal has sparse expression under this framework, we can reconstruct the transmitted signal. Therefore, we propose a target parameter estimation method based on information compensation and sparse signal reconstruction.
## 3 Results and Discussions
### Aggregate Quality Tests
#### 3.1.3 Dynamic Modulus Test  
As the results of the dynamic modulus tests conducted on the test specimens, Figures 11-13 show the comparison of the dynamic modulus master curve and phase angle of the test specimens. The dynamic modulus master curve is presented at two different scales, semi-logarithmic and log-log scales, to highlight the material's behavior in both low and high temperature, respectively. Meanwhile, the phase angle provides insights into the elastic behavior characteristics of these mixtures. A low phase angle indicates a high elastic response, while a high phase angle suggests lower elastic properties and more viscous behavior.  
The results demonstrate that the typical SMA exhibits a higher elastic modulus across various temperatures compared to both colored asphalt and colored asphalt with additional pigment. The elastic moduli of the colored asphalt and colored asphalt with additional pigment remain almost identical across all temperature ranges. Notably, the higher elastic modulus of the typical SMA at lower temperatures indicates increased stiffness and brittleness, which could potentially reduce its crack resistance. Conversely, at elevated temperatures, all three mixtures show similar elastic moduli, suggesting comparable resistance to plastic deformation across the mixtures.  
Figure 11: Dynamic modulus master curve in semi-logarithmic scale.  
Figure 10: IDT strength test results.  
Moreover, it can be observed that the three asphalt mixtures have the same trend of the phase angle in the span of the reduced frequency. This implies that all three asphalt mixtures exhibit elastic behavior at low temperatures, with the elastic modulus decreasing as the temperature rises. Beyond a certain temperature threshold, the aggregate begins to dominate the elastic response, shifting the behavior towards that governed by aggregate-aggregate interactions, and the phase angle subsequently decreases. This phenomenon indicates that at lower phase angles, the mixture demonstrates more pronounced elastic behavior, while higher phase angles indicate the simultaneous interaction of both viscous and elastic characteristics.