esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Proposed Protocol
### The Overall Protocol  
The proposed LQFC protocol is summarized in Algorithm 1. In detail, the network nodes first exchange the Hello message with each other in a cooperative manner to discover available neighbor nodes. Then, the SN calculates the LQoS from the SN to the neighbor node and the NFC from the neighbor node to the DN. Afterward, the SN selects the next-hop RNs (the neighbor vehicles or UAVs) according to the LQoS and the NFC for the message forwarding. Finally, the RN adopts the utility function to calculate the message utility value and decides whether to cache the message. This process continues until the DN receives the message.  
```
1:Initialization
2:Input \(N_{a}\) and \(N_{b}\).
3: Set \(R\) and \(M_{th}\).
4:for\(j=1\) to \(N\)do
5: Calculate \(C_{a,j}\), \(T_{a,j}\), \(D_{j}\left(\tau_{m}\right)\), and \(F_{j,b}\left(\tau_{m}\right)\).
6:endfor
7:\(E_{j}\Leftarrow\left[I_{a,j}Q_{j,b}\left(\tau_{m}\right)\right]_{1\times r}\).
8: Find the feasible solutions \(x_{j}^{*}\Leftarrow\left[I_{a,j}Q_{j,b}\left(\tau_{m}\right)\right]_{1\times k}\).
9: Select the neighbor nodes in \(x_{j}^{*}\) as the RN.
10: Calculate \(\psi\left(x_{j}^{*},N_{b}\right)\) and \(E\left(\psi\right)\).
11:if\(\psi\left(x_{j}^{*},N_{b}\right)\geq D_{j}\left(\tau_{m}\right)E\left(\psi\right)\)then
12: Store the message.
13:else
14: Discard the message.
15:endif
```  
**Algorithm 1** The Proposed LQFC Relay Selection Protocol
## 4 Experiment and Results
### Model Performance
#### 4.4.3 Performance with Weighing Schemes  
Various weighting schemes were applied to the joint model to find the optimal balance between discrete and dimensional tasks. The results, summarized in Table 5, reveal that dynamic and joint weighting schemes yield better performance compared to other schemes. The HuBERT feature encoder with the joint weighting scheme achieved the highest overall performance, with a WA of 70.37% and a UA of 70.27%. For Wav2 Vec 2.0, dynamic weighting achieved the best performance, with a WA of 69.31%, a UA of 68.37%, and a mean CCC of 0.3736. These findings emphasize the importance of adaptive weighting schemes in improving the model's performance across both classification and regression tasks.
## 2 Tower Capacity
### Limit Capacity
#### 2.2.2 Example  
Here, we take a practical transmission line and the typical transmission tower in the line as the example, and generate the capacity surfaces for the typical transmission tower by numerical simulations. There are 109 towers in the example line with total length of 31.12 km. The typical transmission tower named tower ZY, as the suspension tower [19], accounts for more than 80% of the towers in the line. Table 1 shows the design information of tower ZY, transmission wires, and the line.  
Figure 2: Capacity surfaces of transmission towers.  
Figure 3a gives the configuration of the typical transmission tower ZY with four cross arms and the tower body is divided into seven sections. Figure 3b,c present a sketch of the wind loading on the towers of a transmission line.  
For the limit capacity surface \(\theta_{L}\) of tower ZY, the change range of horizontal span \(L_{h}\) is set to 100-400 m according to Table 1 and the wind angle of attack \(\varphi\) keeps changing in the range of 0\({}^{\circ}\)-90\({}^{\circ}\). For a certain \(L_{h}\) and \(\varphi\), the capacity curve of tower ZY can be achieved by the explicit algorithm-based non-linear static analysis [19] on the finite element model of the tower structure with the wind speed \(U\) increasing. The wind loads at each increment is calculated by Equations (4)-(6). The interaction parameters involved in wind loads are determined by the rules shown in Table 1 in Appendix A. Then, the transition point of the simulated capacity curve directs to one point of surface \(\theta_{L}\) as well as one point of surface \(\theta_{H}\). Simulating capacity curves in the ranges of \(L_{h}\) and \(\varphi\), we get a group of points for surface \(\theta_{L}\), shown in Figure 4a. Based on the simulation results of surface \(\theta_{L}\), the capacity surface \(\theta_{H}\) can be generated depending on the wind loading calculated from Equations (4)-(6) (see Figure 4b,c).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Tower ZY** \\ \hline
**Type** & **Double-Circuit Angle-Steel Lattice Suspension Tower** \\ \hline Total height (m) & 45.5 \\ Body height (m) & 30 \\ Steel type & Q345, Q235 [14] \\ Foot distance (m) & 6.4 \\ Natural frequency (Hz) & 2.037 (transverse), 2.047 (longitudinal) \\ Damping ratio & 0.01 \\ \hline \hline \multicolumn{3}{c}{**Transmission wires (conductors and ground wires)**} \\ \hline Type & LGJQ-300/40 (two-bundle conductors) \\ LGJQ-95/55 (ground wires) \\ Linear density (kg/m) & 2.2660 (conductors), 0.7077 (ground wires) \\ Effective diameters (mm) & 47.88 (conductors), 16 (ground wires) \\ \hline \hline \multicolumn{3}{c}{**Transmission line**} \\ \hline Horizontal span (m) & maximum 370/minimum 127/average 275 \\ Direction (azimuth, \({}^{\circ}\)) & maximum 265.82/minimum 113.49/average 181.08 \\ Terrain & Open (C exposure) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design specifications for the transmission line.  
Figure 3: Transmission line towers and the wind loading.
# Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models
## 2 Related Works  
[PERSON] et al. [4] proposed a differentially private stochastic gradient descent (DP-SGD) method, in which a controlled noise is added to the gradient of parameters and then clipped during the training of a deep model. [PERSON] et al. [5] proposed the training of a segmentation network for CXR images using a discriminative model trained with DP-SGD. [PERSON] et al. [6] proposed the generation of differentially private time-of-flight magnetic resonance angiography (TOF-MRA) images using generative adversarial networks (GANs) trained with DP-SGD. For DP-SGD, the theoretical guarantee that images generated using GANs trained with DP-SGD satisfy \(\epsilon\)-LDP is not apparent.  
[PERSON] [7] adopted LDP (although it is not explicitly stated in the paper) for pixelized images. Image pixelization has the effect of reducing the global sensitivity of the LDP algorithm. [PERSON] [8] proposed another LDP algorithm using pixelization and Gaussian blur. However, these methods can significantly degrade the quality of the original images. [PERSON] et al. [9], [PERSON] et al. [10], and [PERSON] and [PERSON] [11] almost simultaneously proposed another LDP algorithm for images. [PERSON] et al. [10] showed a concrete implementation using GANs, whereas [PERSON] et al. [9] showed an abstract formulation. The implementation of the LDP algorithm for images by [PERSON] and [PERSON] is similar to that by [PERSON] et al., but [PERSON] and [PERSON] adopt clipping so that the generated LDP images are within the probabilistic distribution of training images. Finally, [PERSON] et al. [12] experimented with an LDP algorithm for facial obfuscation. On the other hand, we propose an algorithm for local differential privacy using GLOW [13], one of the flow-based deep generative models. We adopt GLOW as it is a renowned and representative flow-based deep generative model. While it is challenging to map real images to latent space without degrading them in GANs, it is easily achievable with flow-based deep generative models.  
In previous studies [7; 8; 9; 10; 11; 12], LDP was not adopted for medical images. Therefore, previous studies did not evaluate the usefulness of LDP-processed images being able to contain valuable information for AI for medical imaging; in this study, we aim to evaluate the usefulness experimentally and quantitatively. As a metric of usefulness, we adopt the area under the curve (AUC) for pathology detection (we assume pneumonia detection in this study), which is essential in AI for medical imaging.
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## 2 Materials and Methods
### Bezier Search Differential Evolution Algorithm Description
The raw order of patterns of \(dw_{1}\), which are bijective patterns, is different from the raw order of M's patterns, assuring that the latter will always evolve towards a different pattern. The vectors \(v_{3}\) and \(v_{4}\) are used to compute the Bezier mutation vectors \(dw_{2}\), in which \(B_{0,3},B_{1,3},B_{2,3}\), and \(B_{3,3}\) are the real values given by the Bernstein polynomials. \(dw_{2}\) patterns define Bezier locations on the hyperfine segments, which vary according to the row order of the Y pattern matrix. As \(Permutation(1:N)\) replaces the row numbers of the Y patterns, \(dw_{2}-M\) change direction frequently and efficiently, enhancing the exploitation attributes of the algorithm in a search for the local optima. ```
Define the Objective Function: \(OF\), Search Space Limits: \((MinVar,MaxVar)\), Size of the Pattern Matrix \(M\): \(N\), Dimensions of the problem: \(D\), Maximum Number of Iterations: \(MaxIt\), and size control value of pre-pattern Matrix G: \(L\)  Initialize pre-pattern matrix \(G\), objective function \(OF\), and the \(best_{global}\) solution along with its value \(min_{global}\) using Equations (2)-(4), accordingly for\(It=1\)to\(MaxIt\)do  Generate vectors \(v_{0},v_{1},v_{2}\)  count = 0 whiletruedo \(v_{1}=\text{Permutation}(1:L\cdot N)\), \(v_{2}=\text{Permutation}(1:L\cdot N)\) \(v_{1}:=v_{1}(1:N)\), \(v_{2}:=v_{1}(1:N)\) \(\big{|}\) / : := / update operator if\(((v_{1}\
eq[1:N])\wedge(v_{2}\
eq[1:N])\wedge(v_{1}\
eq v_{2}))\vee( \text{count}>(N\cdot\text{D}))\)then  false endif  count = count + 1 endwhile \(v_{0}=v_{1}\)  Compute pattern matrix \(M\), and \(fitM\) for\(i=1\)to\(N\)do \([M_{i},\text{fit}(M_{i})]=[G_{v_{1}}(i),\text{fit}(G_{v_{1}}(i))]\) endfor  Generate Bijective vectors \(dw_{1}\) \(dw_{1}=G_{v_{2}}\)  Select top N best pattern vectors \(N_{best}\) for\(i=1\)to\(N\)do \(N_{best}=H_{[^{3}\cdot K\cdot N]}\)\(|\)\(H=G_{\beta}\) \(\beta\leftarrow\text{sort}(\text{fit}(G),\text{ascent})\), \(\beta\in\{1:L\cdot N\}\) endfor  Compute vectors \(v_{3}\) and \(v_{4}\)  count = 0 whiletruedo \(v_{3}=\text{Permutation}(1:N)\), \(v_{4}=\text{Permutation}(1:N)\) if\(((v_{3}\
eq[1:N])\wedge(v_{4}\
eq[1:N])\wedge(v_{3}\
eq v_{4}))\vee( count>N\cdot\text{D})\)then  false endif  count = count + 1 endwhile
```  
**Algorithm 1** Pseudo-code for the Bezier Search Differential Evolution Algorithm.
## 5 Experiment and Results
### Results and Analysis
#### 5.4.2 Hyperparameters Results
As the target update frequency increases, the agent's reward value decreases, which indicates a negative impact of rare frequencies on the performance of the DQN algorithm. This trend is consistent with the distribution in Figure 10b. * Figure 12c The replay buffer size spread shows larger clusters of high scores around 60000, which is consistent with the distribution in Figure 10c. However, it can be seen that the range of values used does not have such an impact on the obtained result. * Figure 12d The spread of the gamma hyperparameter values indicates that as this hyperparameter increases, the agent obtains higher scores. This confirms the distribution in Figure 10d and clearly indicates that for this particular problem, the agent takes into account long-term rewards. Figure 13 presents a graph showing the SHAP values, which show the impact of individual hyperparameters on the result. The blue points indicate low values, while the red points indicate high values of a given hyperparameter. It follows that higher values of gamma and repeat buffer size have a positive impact on the model. The hyperparameter learning rate and the agent network update rate, assuming low values, have a positive impact on the agent's performance. SHAP analysis indicates that higher values of gamma and replay buffer size positively impact model performance, supporting better long-term decision-making, while lower learning rates and update frequencies improve stability in agent performance. These results highlight the importance of balancing hyperparameter values, as both high and low settings contribute critically to optimisation. The insights from SHAP allow for targeted fine-tuning, guiding adjustments to enhance agent effectiveness. Figure 11: Correlation matrix of hyperparameters and agent’s score.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 4 Experiment and Results
### Training Data Sets  
The demodulation performance of the 1D-CNN demodulator is obtained by training, so appropriate training data sets must be generated first. To enhance the adaptability of the 1D-CNN demodulator for a real channel condition, five nonideal factors were taken into consideration during the generation of training data \(X_{i}\): signal-to-noise ratio (SNR), carrier frequency offset \(o_{c}\), symbol rate offset \(o_{b}\), sampling frequency offset \(o_{s}\), and initial phase \(\varphi\). Training data \(X_{i}\) can be regarded as the function of these five variables, which is  
\[X_{i}=F(SNR,o_{c},o_{b},o_{s},\varphi), \tag{7}\]  
Empirically, we set the range of each variable, as shown in Table 1.  
Figure 10: The block diagram of the experimental platform. \(\chi_{\text{i}}\): training data set; AWGN: additive white Gaussian noise.  
MATLAB was employed to generate training data sets. Firstly, these five variables were assigned randomly in their respective range, and a set of values of the five variables was obtained. According to the carrier frequency, the symbol rate, and the initial phase, a BPSK-modulated waveform was generated, with the length fixed to 100 random symbols. Next, the noise signal of certain power was created referring to the SNR value, and the noise signal was added to the modulated waveform. Then, we extracted samples from the modulated waveform according to the sampling frequency. Finally, phase shifts labels were added to the sample sequence. In this way, the first data set \(X_{1}\) was generated.  
By repeating the above steps, a total of 1000 sets of training data were yielded. Next, the structures of the 1D-CNNs were built in a PC using a Keras neural network toolkit, and the generated training data sets \(X_{1}\)-\(X_{1000}\) were provided to train the neural network. Data sets \(X_{1}\)-\(X_{1000}\) were iterated for 100 times. The training loss curve is shown in Figure 11. It can be seen that the training process converged well. The well-trained network parameters were then imported to the FPGA.
## 3 Experiments and Analyses
### Experiment Setup
#### 3.1.2 Mutual Information  
MI for images can be formalized as Equation (14) [35].  
\[MI=\sum_{i=1}^{L}\sum_{j=1}^{L}h_{A,F}(i,j)log_{2}\frac{h_{A,F}(i,j)}{h_{A}(i)h _{F(j)}}\enspace, \tag{14}\]  
where \(L\) is the number of gray-level, \(h_{R,F}(i,j)\) is the gray histogram of image A and F. The \(h_{A}(i)\) and \(h_{F}(j)\) are edge histogram of image A and F. For a fused image, the MI of the fused image can be calculated by Equation (15).  
\[MI(A,B,F)=MI(A,F)+MI(B,F)\enspace, \tag{15}\]  
where \(MI(A,F)\) represents the MI value between input image A and fused image F; \(MI(B,F)\) represents the MI value of input image B and fused image F.
# A New Compact Double-Negative Miniaturized Metamaterial for Wideband Operation
## 5 Results and Discussion
### Metamaterial Arrays Configurations
#### 5.2.1 \(1\times 1\) Array Analysis  
Figure 6(a) shows the \(1\times 1\) array geometry at 0-degree rotation. The magnitudes of the effective permeability and permittivity of the Z-shape unit cell are plotted in Figure 6(b,c), respectively. In Figure 6(b), negative permeability is exhibited from 7.928 to 14 GHz for 0-degree rotation and from 7.688 to 14 GHz for 90-degree rotation. Figure 6(c) depicts negative permittivity from 3.904 to 7.652 GHz, 8.396 to 8.888 GHz, and 9.036 to 10.34 GHz for 0-degree rotation and from 4.22 to 7.40 GHz, 7.796 to 9.512 GHz, 11.372 to 11.984 GHz, and 13.328 to 13.916 GHz for 90-degree rotation.  
Figure 6(d) expresses the magnitude of the refractive index (\(\eta\)) vs. frequency from 3.38 to 7.34 GHz (bandwidth of 3.96 GHz), 7.80 to 10.256 GHz (bandwidth of 2.456 GHz), 11.132 to 12.524 GHz (bandwidth of 1.392 GHz), and 13.268 to 14 GHz (bandwidth of 0.732 GHz) for 0-degree rotation and from 3.752 to 7.124 GHz (bandwidth of 3.372 GHz), 7.592 to 9.536 GHz (bandwidth of 1.944 GHz), and 9.944 to 14 GHz (bandwidth of 4.056 GHz) for 90-degree rotation. The 0-degree and 90-degree \(1\times 1\) arrays exhibit almost the same bandwidth from 3 to 10 GHz. In addition, between 7.592 GHz and 10.256 GHz, the negative regions of the refractive index, permittivity and permeability curves display negative peaks. As a result, the proposed array structure appears to be a double-negative metamaterial.  
Figure 6: Real magnitude of the refractive index (\(\eta\)) vs. the frequency of the 0-degree and 90-degree rotated unit cell.  
Figure 7: _Cont._
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 2 Materials and Methods
### Methods
#### 2.3.3 Regional-Scale CLQ Estimation from HJ-1A HSI Data  
The optimal CLQ estimation algorithm was applied to estimate the CLQ at the regional scale in the Conghua district using the HJ-1A HSI image. Due to limitations in the wavelength range (459-956 nm) of the HJ-1A imagery, only the optimal spectral variables in this wavelength range were selected to estimate CLQ. Moreover, due to the effects of soil moisture, the spectral variables obtained from the HJ-1A HSI image reflected soil information that differed from the measured dry soil spectral data obtained from the AvaField portable spectrometer [35]. Therefore, a linkage model was adopted to transform the spectral variables obtained from the HJ-1A HSI data into dry soil spectral variables. The linkage model is expressed as [36]:  
\[DSSR=OSSR\times a\times e^{bx} \tag{11}\]  
where \(DSSR\) is the dry soil spectral reflectance from HJ-1A HSI data, \(OSSR\) is the spectral reflectance obtained from the HJ-1A imagery, \(x\) is the soil moisture content obtained from the soil moisture active passive (SMAP) satellite mission data acquired on 30 October 2017, and a and b represent model coefficients [36].  
After the selection of the optimal variables, the model-based optimal algorithm was revised to estimate CLQ.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 4 Results
### Discussions
#### 4.4.2 Limitations  
Although the method we proposed has achieved outstanding results on the overall dataset, there are still some limitations. In the phase of anomaly detection, we used the multivariate Gaussian distribution to model the sample data, but in actual industrial production, there may be some samples that do not conform to such a data distribution, leading to difficulties in detection. Meanwhile, calculating the Mahalanobis distance at each location as an anomaly score places high demands on image alignment. We assumed that image acquisition was performed under the same conditions and satisfied the multivariate Gaussian distribution. If this assumption is deviated from, it may lead to poor detection results.  
For instance, our method performs poorly for the Pill, Capsule, and Screw categories in Table 2. Analysis reveals that for Pill, it is challenging to learn its normal Gaussian distribution due to the irregularly distributed small red dots in its normal image, as shown in Figure 8a. Additionally, for Capsule and Screw, their normal images exhibit significant variations in the normal distribution at each position due to different rotations and angles, as shown in Figure 8b,c, respectively, resulting in lower scores that require optimization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Rect** & **Scar** &
\begin{tabular}{c} **Rect \&** \\ **Scar** \\ \end{tabular} & **2-Way** & **w/o Mask** & **Mask** \\ \hline Texture & (97.8, 95.3) & (98.4, 95.5) & (98.3, 95.6) & (97.7, 94.9) & (98.7, 96.7) & (98.2, 95.5) \\ Object & (91.4, 95.7) & (91.5, 95.4) & (92.7, 95.6) & (92.9, 95.9) & (92.4, 96.3) & (93.9, 96.5) \\ Average & (93.5, 95.6) & (93.8, 95.5) & (94.6, 95.6) & (94.5, 95.6) & (94.5, 96.4) & (95.7, 96.2) \\ \hline \hline \end{tabular}
\end{table}
Table 8: The impact of different anomaly simulation strategies on anomaly detection performance on MVTec AD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **I-AUROC** & **P-AUROC** & **Memory Bank (GB)** & **Parameters (M)** \\ \hline PaDiM (R18-RD100) & 90.5 & 96.5 & 0.12 & — \\ PaDiM (R18-Full) & 93.9 & 97.1 & 2.3 & — \\ PaDiM (WR50-RD550) & 95.5 & 97.3 & 3.5 & — \\ CutPaste & 95.2 & 96.0 & — & 11.8 \\ Ours & 95.7 & 96.2 & 0.12 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of our method with state-of-the-art methods (PaDiM and CutPaste) on MVTec.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Next Generation Sequencing (NGS)  
Next-generation sequencing, including Whole-Genome sequencing, can also be used for the identification of _Candida_ species [112]. NGS can detect markers of antifungal drug resistance from pathogenic _Candida_ strains [113; 114], _ITS_ variabilities in prevalent pathogenic _Candida_ spp. [115] and provide insightful metagenomic studies [116]. NGS provided valuable input in the diagnosis of rare infections such as _Candida_ meningitis [117] and pseudomenbranous oral candidiasis [118].  
Although discontinued, pyrosequencing was the first of the NGS technologies to be commercially available and has provided large amounts of sequence data [119], becoming a technology of historical interest. DNA pyrosequencing, or sequencing by synthesis, became possible in the late 1990s as a rapid, cost-effective alternative to Sanger (di-deoxy) DNA sequencing [120]. Third-generation sequencing, also known as next next-generation sequencing, refers to those technologies that do not depend on the PCR amplification of DNA.  
The identification of yeasts, including _Candida_ spp., has been performed by pyrosequencing using different targets like 185 rRNA gene [52], _ITS1_, and _ITS2_[63; 121; 122], with results consistent with classic biochemical tests [121; 123; 124]. Pyrosequencing has also been used for the identification of _Candida_ pathogens in various clinical samples such as vaginal [125], blood [126], and oral samples [127]. Pyrosequencing is only able to read short-length sequences of nucleotides, providing a disadvantage for the technique when the target has a longer sequencer. Pyrosequencing data analysis can be complex and challenging. This approach has provided evidence about mutations, with no known previous association with phenotypic drug resistance of the _ERG_ and _FKS_ genes in _Candida_ spp. [128].  
The use of pyrosequencing has declined because of the rise of new methodologies of NGS such as Illumina [112], which are less expensive, provide longer sequences, higher sensitivity to detect low-frequency variants, have a faster turnaround time for high sample volumes, and a comprehensive genomic coverage [129].