esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Optimization Results for In-Orbit SAR Operational Paramters  
Tables 4 and 5 show the SAR operational parameters that have been optimized considering the altitude variation when a satellite is in orbit. The designed ground swath width is 5.3 km. PRF has been optimized using proposed method while other parameters have been designed using the look angle as the reference angle. Due to the optimization procedure, the parameters related to the SAR instrument operation are different from those in Table 3.  
Figure 17 shows the SAR performances: NESZ, RAR, and AAR for the altitude variation. In Figure 17, the blue and green lines represent the SAR performances at the minimum and maximum altitudes, respectively. Figure 17a-c show NESZ, RAR, and AAR, respectively, for PRF that has been optimized using second method (i.e., calculated using the ratio between the nominal and predicted altitudes) while using the incidence angle as the reference angle. Each of NESZ and RAR significantly changes for the altitude variation. RAR at maximum altitude is out of the requirement. Figure 17d-f show NESZ, RAR, and AAR, respectively, for PRF that has been optimized using the proposed method while  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Parameter** & **SW81** & **SW82** & **SW83** & **SW84** & **SW85** & **SW86** & **SW87** & **SW88** \\ \hline Incidence angle at center (\({}^{\circ}\)) & 19.99 & 25.10 & 29.86 & 35.10 & 39.82 & 45.05 & 49.89 & 54.80 \\ PRF (Hz) & 12.092.43 & 8733.80 & 12,014.83 & 12,137.03 & 9155.67 & 7002.14 & 5873.40 & 4810.17 \\ Tx pulse length (us) & 24.81 & 34.35 & 24.97 & 24.72 & 32.76 & 42.84 & 51.08 & 62.36 \\ Maximum azimuth steering angle (\({}^{\circ}\)) & 1.68 & 1.65 & 1.62 & 1.58 & 1.53 & 1.48 & 1.43 & 1.45 \\ Image acquisition time (s) & 5.90 & 6.00 & 6.12 & 6.29 & 6.48 & 6.75 & 7.06 & 7.67 \\ \hline \end{tabular}
\end{table}
Table 4: Optimized SAR operational parameters at minimum altitude.  
Figure 16: PRF and slant range margins according to altitude variation for: (**a**) PRF margin at the minimum altitude; (**b**) PRF margin at max. altitude; (**c**) slant range margin at the minimum altitude; (**d**) slant range margin at the maximum altitude.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Parameter** & **SW81** & **SW82** & **SW83** & **SW84** & **SW85** & **SW86** & **SW87** & **SW88** \\ \hline Incidence angle at center (\({}^{\circ}\)) & 20.09 & 25.22 & 30.01 & 35.28 & 40.03 & 45.31 & 50.20 & 55.17 \\ PRF (Hz) & 11,395.36 & 8228.86 & 11,317.62 & 11,428.90 & 8618.00 & 6586.90 & 5520.74 & 4516.21 \\ Tx pulse length (us) & 26.32 & 36.46 & 26.51 & 26.25 & 34.81 & 45.54 & 54.33 & 66.42 \\ Maximum azimuth steering angle (\({}^{\circ}\)) & 1.62 & 1.59 & 1.56 & 1.52 & 1.48 & 1.43 & 1.39 & 1.40 \\ Image acquisition time (s) & 6.08 & 6.19 & 6.32 & 6.51 & 6.71 & 7.00 & 7.34 & 7.98 \\ \hline \end{tabular}
\end{table}
Table 5: Optimized SAR operational parameters at maximum altitude.  
using the look angle as the reference angle. All the performances satisfy the requirements even for the altitude variation.
## 3 Performance Analysis
### Efficiency Discussions  
Table 1 displays the comparisons of EE and the data rate, which are discussed in this section. For visual representation, the comparisons of EE and the data rate are also given in Figures 5 and 6, respectively. \(N\) is the number of parallel groups. Here, we set M = 4, i.e., \(m_{ci}=m_{cm}=2\). The better performance in EE and the data rate are demonstrated by the proposed system.  
To research the EE, we calculate the transmitted data-energy-to-bit-energy ratio (DBR) [10]:  
\[DBR=\frac{E_{data}}{E_{b}} \tag{45}\]  
where \(E_{data}\) is the energy to transmit the data, and \(E_{b}\) is the transmitted bit energy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **MCIM-SR-DCSK-CC** & **CIM-SR-DCSK-CC** \\ \hline EE & \(\frac{N^{2}(1+m_{ci}+m_{cm})}{N+1}\) & \(\frac{N^{2}(1+m_{ci})}{N+1}\) \\ Data rate & \(N(1+m_{ci}+m_{cm})\) & \(1+m_{ci}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons between systems.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### SIMD Scalability Characterization  
To evaluate scalability, we measured performance on images of increasing resolution up to the 4096-pixel limit of our FHE parameters. This analysis demonstrates how client- and server-side processing times behave as the amount of data within a single ciphertext grows. The results are presented in Table 3 and Figures 1 and 2.  
The results show that SIMD optimization works extremely effectively. As visualized in the figures, the processing times are almost constant across the tested range, demonstrating that computational cost scales per ciphertext rather than per pixel. For example, the client encryption time (Figure 1) remains stable at \(8\) ms, and the server-side brightness adjustment (Figure 2) holds at \(1.6\) ms for images up to 3136 pixels. The moderate latency increase at the maximum size (4096 pixels) is expected as the system approaches its hardware or cache limits. This feature is especially useful for high-resolution applications, where previous techniques would incur a linear scaling penalty.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cryptographic Operation** & **Execution Time (ms)** \\ \hline Key Pair Generation & 0.48 \\ Encapsulation & 0.11 \\ Decapsulation & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CRYSTALS-Kyber (kyber512) performance characteristics (averaged).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Client Context Initialization Time & 285.12 ms \\ Public Material Size (Amortized) & 33.83 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: FHE setup costs (128-bit security).
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 4 The Comparisons of Simulated Data and Measured Results of The Prototype
### Reflection Coefficients  
Figure 13 compares the simulated and measured reflection coefficients of the proposed antenna. The simulated curve shows that the proposed antenna can meet \(|S_{11}|\leq-10\) dB from 2.11 to 2.58 GHz and 5.06 to 7.5 GHz, where the fractional bandwidths are 20.04%  
Figure 11: The prototype of the proposed antenna with an SMA connector soldered to it.  
Figure 12: The measurement setup for radiation patterns. (**a**) x–y plane; (**b**) x–y plane; (**c**) x–y plane.  
and 38.05%, respectively. The measured result agrees with the simulated one and can meet Wi-Fi 2.4 and 5.8 GHz requirements for small UAV applications.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Deep Learning Models
#### 3.5.3 Long Short-Term Memory (LSTM)  
LSTM addresses RNN's limitations by maintaining long-term dependencies in text data. LSTM models use memory cells and gating mechanisms to control information retention, making them suitable for context-dependent detection of hate speech [45].
## 4 Evaluation Metrics
### Non-Detectability  
Hopping patterns and hopping rates are key features of the end hopping system. The former is generated with pseudo-random sequences and the latter is dynamically controlled according to the current network environment [18]. To mimic the normal end hopping (i.e., the overt channel) as much as possible, the CCES scheme has to behave similarly to the above key features of end hopping. Since the CCES scheme is insensitive to the timing, it could stay completely consistent with the hopping rate during the normal end hopping communication, thereby evading the detection or elimination methods used for the timing covert channels with great ease. To mimic the hopping pattern, the CCES scheme utilizes the original message to modulate the pseudo-random sequences, hoping to present the approximate pseudo-randomness of the hopping pattern. We introduce the entropy to measure the pseudo-randomness.  
Originating from thermodynamics and proposed by [PERSON] in 1948 [35], entropy is a quantitative measure of the randomness for random variables [36]. The formula of entropy is as follows:  
\[EN(X)=-\sum_{i=1}^{n}p_{i}\log_{2}p_{i}. \tag{12}\]  
where \(p_{i}\) represents the probability of each event occurring in the system. By calculating and comparing the corresponding end information sequence entropy in both normal end hopping and the CCES scheme, we could judge whether the CCES scheme's pseudo-randomness is significantly different from that of normal end hopping, thus evaluating its non-detectability.
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 6 Discussions of Parameter Setting in Implementation Aspect  
Parameter setting in practical use should be determined according to a CDN's actual operational purpose and its historical traffic patterns. In practical implementation, the adopter of our design could set the parameters according to our analysis. For example, if the CDN is over-congested, then \(\tau\) should be set lower than 0.2. Figure 13 shows that \(\tau\geq 0.2\) will lead to a significant increase in bandwidth cost in Traffic Pattern 4. In addition, if congestion continuously exists for a period of time, the CDN may raise the max price but keep the max price lower than 0.5. Figure 18 shows that \(P_{max}\geq 0.5\) will lead to a significant increase in user payment that may lead to critical user complaint.
## 5 Conclusions  
The industrial application of the NIR-HSI system combined with multivariate analysis demonstrated the classification of almonds adulterated with apricot kernels. The experimental result of this study shows that PLS-DA combined with raw preprocessing can discriminate the almonds from apricot kernels with an accuracy of more than 97% in the validation set. Additionally, a visualized chemical content map was developed using the resultant beta coefficient, clearly discriminating based on the mapped colors. Furthermore, the resultant model beta coefficient was applied in a prototype NIR-HSI system with a GUI controller, displaying initial steps towards industrial application of the system for the high-scale, non-destructive, and rapid measurement of adulterated almonds in a high throughput process. The tested result based on this prototype system is promising, exhibiting a 90% accuracy for classifying almonds and 80% for apricot seeds. The prototype NIR-HSI online application showed a high potential for the classification of adulterated seeds, with a total accuracy of 85% in both groups. The obtained accuracy is promising for the classification of adulterated seeds in large-scale industrial applications.  
Conceptualization, S.L., H.L., G.K., B.-K.C.; Handling experimental for data collection M.A.F., M.P., R.J.; Data analysis and model development M.A.F., H.L., C.W., S.L. Manuscript writing and editing M.A.F., B.-K.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Proposed Protocol Design Scheme
### Cooperation and Relay Node Selection  
All receiver sensor nodes received the data broadcast by the source node Source. The source node follows the cooperation strategies. A simple cooperation model shows in Figure 3, where the Source, the source node, transmits the signals to relay nodes Relay1, Relay2, and the Destination. The signal received by Relay1, Relay2, and Destination is developed as [14].  
\[Y_{sd}=X_{s}h_{sd}+n_{sd} \tag{12}\]  
Here, \(X_{s}\) represents the transmitted signal in its original state. The symbols \(n_{sd}\) and \(h_{sd}\) represent the noise, and channel gain, respectively, from Source to Destination. The symbol represents the output signal at Destination \(Y_{sd}\). The signals sent from Source to Relay1 and Relay2 are described as follows:  
\[Y_{sr1}=X_{s}h_{sr1}+n_{sr1} \tag{13}\]  
\[Y_{sr2}=X_{s}h_{sr2}+n_{sr2} \tag{14}\]  
Figure 3: A simple model of cooperation.  
Here, \(Y_{sr2}\) and \(Y_{sr1}\) are the Relay2 and Relay1 signals output, respectively. The \(h_{sr1}\) and the \(n_{sr1}\) are the channel noise and channel gain from Source to Relay1 over the link. The \(h_{sr2}\) and the \(n_{sr2}\) are the channel noise and channel gain from Source to Relay2, respectively. The signal from Relay1 and Relay2 at Destination is modeled as:  
\[Y_{r1d}=\beta Y_{sr1}\;h_{r1d}+n_{r1d} \tag{15}\]  
\[Y_{r2d}=\beta Y_{sr2}\;h_{r2d}+n_{r2d} \tag{16}\]  
Here, the received signal from Relay1 to Destination is shown by \(Y_{r1d}\) and \(Y_{r2d}\) respectively. \(h_{r1d}\) and \(n_{r1d}\) are the channel gain and channel noise from Relay1 to Destination, respectively. \(h_{r2d}\) and \(n_{r2d}\) are the channel noise and channel gain from Relay2 to Destination. Algorithm 1 explains the proposed CEER routing protocol.  
```
\(S_{i}\) = Sender Nodes \(r_{min}\) = minimum radius \(S_{i}\) = Sink node \(S_{j}\) = Cosign B/W Source and Destination \(r_{j}\) = Advertisement packet \(S_{u}\) = All Candidates \(p\) = routing path While (TTL > 0) and (\(S_{i}\
eq S_{i}\)) do \(S_{i}.S_{n}\leftarrow\partial_{i}\) \(S_{i}\) Transform an advertisement packet with a radius \(r_{min}\) for all \(S_{j}\) with \(\delta(i,\;j)<r_{min}\) do if cos (TTL) < 0 then \(S_{i}.\)sleep (); else\(S_{i}.S_{n}\)add (\(S_{j}\)) \(S_{j}\) transmits an advertisement packet with a radius \(r_{j}\) Based on Equation (11); for all \(S_{k}\) with \(\delta(j\;,\;k)<r_{j}\)do if\((\;\epsilon_{i}^{\text{res}}<\epsilon_{j}^{\text{res}})\) then \(S_{i}.\)sleep(); else\(S_{i}.S_{n}\)add (\(S_{k}\)) endif endfor endif endwhile
```  
**Algorithm 1**Shows the proposed CEER routing protocol
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible MIMO Antennas for Sub-6 GHz 5G Band
#### 4.3.2 DGS-Based Flexible Antenna  
Defected ground plane structures are found to enhance the gain and provide better isolation among the elements. In [76] a wideband MIMO is fabricated on a flexible paper substrate, as shown in Figure 19a. The inverted L-shaped stubs that connect the ground plane enhance the gain and decrease the coupling effect of the antenna elements. The antenna has a CPW feed with a bandwidth that of 2.22 to 3.85 GHz. A measured gain of 0.4, 0.56, and 0.92 dBi at 2.4, 3.5, and 2.6 GHz, respectively, is achieved suitable for 5G mid-band applications. as shown in Figure 19b. There is a negligible effect on the resonant frequency with the bending analysis, but the S-parameter is more deviated in the horizontal bending than in the vertical bending due to the deformation of the ground extension stub. The antenna shows a directional radiation pattern, as shown in Figure 19c.  
Figure 19: _Cont._  
In another design in [77], a flexible four-port and four-element flexible MIMO antenna with the truncated ground are proposed for sub-6 GHz and X-band applications. The antenna consists of an inverted E and inverted L-shaped conductive material on either side of the central feed line, as shown in Figure 20a. The bottom part consists of a ground plane which is truncated, and a vertical stub placed on one side for achieving the wideband performance for the sub-6 GHz band. The flexible four-port resonator has a connected ground with an I-shaped strip with a slotted rectangular stub for better isolation of more than 20 dB. The antenna achieves a 70% efficiency and 4 dBi gain covering 3.34-5.0 GHz and 8.9-9.2 GHz for the sub-6 GHz and X-band applications. Figure 20b shows the S-parameter of the proposed antenna. By bending the antenna around a cylinder with a 20 cm radius along the x- and y-axes, the antenna's bending analysis is carried out. The antenna demonstrates a favorable gain and ECC under the bending scenarios, as well as good transmission and scattering curves, making it a suitable choice for the aforementioned applications. The antenna gives a directional and omnidirectional pattern in the E-plane and H-plane, respectively, as shown in Figure 20c.  
Figure 19: Wideband MIMO on a paper substrate: (**a**) Antenna prototype; (**b**) Gain analysis; (**c**) Radiation pattern at 3.5 GHz. Reprinted from ref. [76].
## 2 Error Analysis of Grating Ruling Machine
### Effect of Screw Error on Grating Spectral Performance
#### 2.3.3 The Random Grooving Error on Spectral Performance  
Assuming that the distribution of the grating groove is: \(d_{1},d_{2},\ldots\ldots,d_{k}\), based on Equation (10), can be obtained from the complex amplitude distribution function is  
\[U(u)=c\sum\limits_{i=1}^{m}\left(d_{i}e^{-i2\pi\xi\sum\limits_{j=1}^{i-1}{(d_{ j})}}e^{-i2\pi(\sum\limits_{j=1}^{i-1}{(d_{j})}+\frac{d_{j}}{2})(u-u_{0}-\xi)} \sin c(d_{i}(u-u_{0}-\xi))\right) \tag{20}\]  
Compared with Equation (18), it can be observed that there is no interference factor in the complex amplitude distribution of random groove error, and the position of each maximum is determined by the phase expansion factor \(\left(d_{i}e^{-i2\pi\xi\sum\limits_{j=1}^{i-1}{(d_{j})}}e^{-i2\pi(\sum\limits_ {j=1}^{i-1}{(d_{j})}+\frac{d_{j}}{2})(u-u_{0}-\xi)}\sin c(d_{i}(u-u_{0}-\xi)) \right)\) and diffraction factor \(\sin c(d_{i}(u-u_{0}-\xi))\). According to Equation (20), the light intensity distribution function is  
\[I(u)=\left|c\sum\limits_{i=1}^{m}\left(d_{i}e^{-i2\pi\xi\sum\limits_{j=1}^{i-1 }{(d_{j})}}e^{-i2\pi(\sum\limits_{j=1}^{i-1}{(d_{j})}+\frac{d_{j}}{2})(u-u_{0} -\xi)}\sin c(d_{i}(u-u_{0}-\xi))\right)\right|^{2} \tag{21}\]  
The diffraction spectrum distribution of grating with random error is solved by Equation (21). The grating parameters are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Groove Density** & **Wave Length** & **Blaze Angle** & **Blaze Level** & **Error Distribution** \\ \hline
600 g/mm & 632.8 nm & 22\({}^{\circ}\) & \(-2\) & White noise, extreme values \(\pm\) 10 nm \\ \hline \end{tabular}
\end{table}
Table 3: Diffraction grating parameters.  
Figure 9: The relative intensity distribution of diffracted light.  
According to Figure 10, it can be observed that the diffraction grating still has five levels of \(-3\), \(-2\), \(-1\), \(0\), and \(1\), but the stray light background is significantly enhanced between the levels. Therefore, the random groove error has a large impact on the stray light of the ruled grating.
# Last-Mile Optimization Using Neural Networks
## 4 Results and Discussion
The outcomes indicate that the model attained the highest level of accuracy for the Leisure, tourism, and food class; the Services and others class; and the Books, press, and multimedia class with 100%, followed by the Fashion, beauty, and decoration class with 50%. The confusion matrix shows that category 1 (fashion, beauty, and decoration) was incorrectly classified as category 2 (leisure, tourism, and food), resulting 25% of accuracy. Category 1 being incorrectly classified as category 3 (services and others) formed 25% of accuracy. The MLP may predict incorrectly and confuse category 1 with category 2 and category 3. The similarity between categories can stem from various sources. Some possible reasons for the observed similarity between categories 1, 2, and 3 include shared characteristics, making it challenging for the model to differentiate between them as it struggles to identify distinct patterns. Additionally, limited data size during training can also be a factor, as a dataset with restricted size may not fully represent the variability of the data, hindering the model's ability to generalize effectively. This study employs various machine learning classifiers distinct from MLP neural networks. The experimentation involves training the dataset on diverse classifiers, followed by validation using test data, and the subsequent analysis of results. The outcomes are assessed based on the following metrics: accuracy, error rate, precision, recall, and F1 score. Figure 4: The learning curves: evolution of the loss function, and classification accuracy and testing samples, on training and testing samples. Table 5 provides a thorough examination of the outcomes obtained from the products dataset using various machine learning classifiers. The results focus on accuracy, where KNN, configured with k = 4, exhibits the highest classification accuracy at 95.12% followed by RF with n estimators = 30 at 93.9%, LR at 92.68%, and SVM with a linear kernel at 93.9%. The analysis extends to error rates, precision, recall, and F1 score, outlined comprehensively in Table 5. Notably, KNN stands out in its accurate categorization of products, even though its accuracy slightly lags behind that of the MLP neural network.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 4 Dynamic Precision Reliability Modeling of Multilink Mechanisms with Clearances
### Mechanism Dynamically Precise Reliability Model  
Assuming that operation time of mechanism is denoted as \((t_{0},t_{1})\), the function of error is defined as:  
\[g(x,t)=P-P^{\prime} \tag{54}\]  
where \(P\) represents the slider's actual position and \(P^{\prime}\) denotes the slider's ideal position. The error limit is defined as \(\varepsilon\), when \(|g(x,t)|\leq|\varepsilon|\) the mechanism is reliable and vice versa mechanism fails.  
Based on theory of stress-strength interference, reliability of mechanism under various clearance conditions is addressed by assuming that both the actual errors and permissible error of the slider follow normal distributions. Real error of the slider serves as the stress, while the permissible error of the slider represents the strength.  
Using the theory of stress-strength interference as a foundation, and taking calculation of displacement reliability as an example, within a time range \((t_{0},t_{1})\) for the mechanism, the reliability index for displacement can be obtained.  
\[\beta_{S}=\frac{\mu_{z}}{\sigma_{z}}=\frac{\mu_{S}-\mu_{s}}{\sqrt{\sigma_{S}^{ 2}+\sigma_{s}^{2}}} \tag{55}\]  
where \(\mu_{S}\) and \(\sigma_{S}\) represent mean and RMS of allowable displacement error, respectively, and \(\mu_{s}\) and \(\sigma_{s}\) denote mean and mean square deviation of displacement errors, respectively.  
Reliability of mechanism's displacement is denoted as:  
\[R=\Phi(\beta_{S}) \tag{56}\]  
A rigid body dynamics model of a multilink mechanism can be established to solve for \(\mu_{S}\) and \(\sigma_{S}\). Instead, the mean and RMS deviation of the permissible displacement tolerances is calculated by the following formulae:  
\[\mu_{S}=\frac{\Delta\mu+\Delta v}{2},\sigma_{S}=\frac{\Delta\mu-\Delta v}{6} \tag{57}\]  
where \(\Delta\mu\) and \(\Delta v\) denote the maximum and minimum bounds of allowable tolerance, respectively.