esa-sceva/satcom-chunk-collection huggingface dataset:
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 3 Controller Design
### A Safe Adaptive Controller Design
With the help of Theorem 1, a safe controller is obtained by solving the following CBF-based quadratic programming problem:  
\[\min_{\mathbf{u}_{i}^{*}} \|\mathbf{u}_{i}-\hat{\mathbf{u}}_{i}\|^{2}\] s.t. \[\hat{\theta}_{i}=\mathrm{Proj}\left(\hat{\theta}_{i},-\frac{1}{2K_{i}}\left( \frac{\partial h_{i}}{\partial\mathbf{p}_{i}}\right)^{\top}\mathbf{u}_{i}-\frac{\mu} {2}\hat{\theta}_{i},l(\hat{\theta}_{i})\right), \tag{13}\]0  
where \(\hat{\theta}_{i}\) is updated according to (13), \(h_{i}\) is given in (11), and \(\hat{\mathbf{u}}_{i}\) represents a nominal control law that may be unsafe in the intersection. The proposed adaptive CBF (aCBF) controller for the AIM is shown in \[\hat{\theta}_{i}=\mathrm{Proj}\left(\hat{\theta}_{i},-\frac{1}{2K_{i}}\left( \frac{\partial h_{i}}{\partial\mathbf{p}_{i}}\right)^{\top}\mathbf{u}_{i}-\frac{\mu} {2}\hat{\theta}_{i},l(\hat{\theta}_{i})\right), \tag{13}\]1 The entire working process of the proposed algorithm is presented in Algorithm 1. \[\hat{\theta}_{i}=\mathrm{Proj}\left(\hat{\theta}_{i},-\frac{1}{2K_{i}}\left( \frac{\partial h_{i}}{\partial\mathbf{p}_{i}}\right)^{\top}\mathbf{u}_{i}-\frac{\mu} {2}\hat{\theta}_{i},l(\hat{\theta}_{i})\right), \tag{13}\]2 The proposed aCBF-based safe controller for the AIM ensures safety even in the presence of potential power transmission loss. ```
Input: Initial state \(p_{i}(0)\), initial power transmission efficiency \(\theta_{i}\), safe set \(\mathcal{C}_{i}\), nominal control input \(\hat{u}_{i}\), obstacle velocity bound \(V_{z}\). Output: Safe control input \(u_{i}\) ensuring collision-free navigation. 1 Initialization: Set parameters \(\alpha_{i},\mu,K_{i}\); Compute initial safe set boundary \(h_{i}(p_{i})\). 2 whilethe system is operatingdo
3 Measure current state \(p_{i}\) and compute \(h_{i}(p_{i})\);
4 Detect nearest obstacle \(z_{i}\) and obtain its velocity \(\dot{z}_{i}\);
5 Compute nominal control input \(\hat{u}_{i}\);
6 if\(\hat{u}_{i}\) ensures collision-free motionthen
7\(u_{i}\leftarrow\hat{u}_{i}\);
8
9 else
10 Apply aCBF correction:
11 Compute parameter update law \(\hat{\theta}_{i}\) using Equation (13);
12 Compute CBF constraint based on \(h_{i}\), \(\hat{\theta}_{i}\), and \(V_{z}\);
13 Solve the Quadratic Programming (QP) problem:
14
15 \[\hat{\theta}_{i}=\mathrm{Proj}\left(\hat{\theta}_{i},-\frac{1}{2K_{i}}\left( \frac{\partial h_{i}}{\partial\mathbf{p}_{i}}\right)^{\top}\mathbf{u}_{i}-\frac{\mu} {2}\hat{\theta}_{i},l(\hat{\theta}_{i})\right), \tag{13}\]3 (20)
16 Subject to the CBF safety constraint:
17
18 \[\hat{\theta}_{i}=\mathrm{Proj}\left(\hat{\theta}_{i},-\frac{1}{2K_{i}}\left( \frac{\partial h_{i}}{\partial\mathbf{p}_{i}}\right)^{\top}\mathbf{u}_{i}-\frac{\mu} {2}\hat{\theta}_{i},l(\hat{\theta}_{i})\right), \tag{13}\]4 (21)
19 Apply optimized control input \(u_{i}\) to the agent;
```  
**Algorithm 1**Adaptive CBF-based Controller for AIM
## 5 Joint Optimization of Resource Allocation and Computation-Offloading Decisions
### Computational Resource Allocation  
For a given task-offloading decision, \(\mathbf{Y}=\mathbf{Y_{0}}\), \(\mathbf{Z}=\mathbf{Z_{0}}\), Equation (16) for MDs offloading to sMEC \(n\) can rewritten as follows:  
\[\underset{\mathbf{F}}{\text{minimize}} \sum_{n\in\mathcal{N}}\sum_{m\in\mathcal{M}_{n}}\frac{\lambda_{m} ^{t}C_{m}}{f_{mn}}\] (17) subject to C5 and C6.  
**Theorem 1**: _The optimization problem in Equation (17) is a convex optimization problem._  
Proof of Theorem 1.: We define \(G(\mathbf{F})\) as the objective function in Equation (17) and obtain the Hessian matrix \(G(\mathbf{F})\) with respect to \(\mathbf{F}\), as follows:  
\[\frac{\partial^{2}G}{\partial f_{mn}^{2}}=\frac{2\lambda_{m}^{t}C _{m}}{f_{mn}^{2}},\forall m\in\mathcal{M}_{\text{off}},\forall n\in\mathcal{N}. \tag{18}\] \[\frac{\partial^{2}h}{\partial f_{mn}\partial f_{ij}}=0,\forall(m, n)\
eq(i,j). \tag{19}\]  
We can verify that \(\frac{\partial^{2}G}{\partial f_{ii}}\succeq 0\); therefore, the Hessian matrix is a positive semi-definite matrix. According to previously described theorems [43], we conclude that the objective function \(G(\mathbf{F})\) is convex. Note that the solution space of Equation (17) is convex, which suggests that Equation (17) is convex.  
Because Equation (17) is convex, it can be solved using KKT optimality conditions. The optimal resource allocation \(\mathbf{F}\) is computed as follows:  
\[f_{mn}^{*}=\frac{f_{m}^{\max}\sqrt{\lambda_{m}^{t}C_{m}}}{\sum_{m\in\mathcal{M }_{n}}\sqrt{\lambda_{m}^{t}C_{m}}}. \tag{20}\]
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Forensic Features Based
#### 4.1.3 Resampling  
To detect resampling detection, [PERSON] and [PERSON] [58] used expectation-maximization (EM) algorithm to evaluate probability maps and spot the image's explicit correlations. Each sample's image is interpreted with its probability of being connected to its neighbors. This technique was verified on basic resampling methods only and cannot be used on compressed images. [PERSON] and [PERSON] [59] proposed an approach to detecting content-aware scaling of images using seam carving algorithms. The study was made from seam behaviors--like the distance between seams and energy along the path. The features of a seam are likely to be affected by the seam-carving approach. These seam features were used in the SVM classifier, which delivered an accuracy of 91%. [PERSON] et al. [60] used an interpolation-related spectral signature method that spots simple image alterations like resampling. It also detects histogram equalization and contrast enhancements. The fingerprint detection method is used for histogram equalization and contrast enhancement. However, this resampling detection algorithm fails when JPEG compression is performed. [PERSON] et al. [61] recommended using complex-valued invariant features to enhance earlier keypoint-based methods. Multiple clone concerns and geometric transformation problems with earlier keypoint-based approaches were overcome by [PERSON] et al.  
\begin{table}
\begin{tabular}{c c c} \hline
**Methods** & **Feature Length** & **Feature** \\ \hline [PERSON] et al. [43] & 100 & GLCM + BFS \\ \hline [PERSON] et al. [44] & 60 & RLRN \\ \hline [PERSON] et al. [47] & 100 & SVM + RFE \\ \hline [PERSON] et al. [49] & 50 & SVD + DCT \\ \hline [PERSON] et al. [50] & 972 & DOCT + Markov \\ \hline [PERSON] and [PERSON] [54] & 768 & Illumination–Reflectance and LBP \\ \hline \end{tabular}
\end{table}
Table 3: Computational efficiency comparison—image splicing.
# Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound
## 2 Materials and Methods
### Modified APES Beamformer Weighted by gPCF  
In the present study, two strategies were examined to implement the gPCF into the mAPES beamformer. In both strategies, the outputs of the mAPES beamforer were weighted by the gPCF, but the gPCFs was estimated differently. The schematic diagrams are shown in Figure 1. In one of the examined strategies, as illustrated in Figure 1a, the gPCF was estimated from the outputs of sub-aperture beamformers before application of the mAPES beamformer weights, i.e., the gPCF is estimated using \(y_{k}\)\((k=0,1,\cdots,K-1)\) in Equation (8). In another strategy, the output signals from the sub-aperture beamformer weighted by the mAPES beamfomer weights were used to estimate the gPCF, i.e., the gPCF is estimated using \(y_{k}\cdot w_{m\mathrm{APES},k}\)\((k=0,1,\cdots,K-1)\), where \(w_{m\mathrm{APES},k}\) is the \(k\)-th element of \(\mathbf{w}_{m\mathrm{APES}}\) defined in Equation (11). To make a B-mode image, either Figure 1a or Figure 1b is adopted to estimate the gPCF applied to the output of the adaptive beamformer. Performances of such two procedures were examined in the subsequent section.
## 2 General Design and Features of the Instrument v2021
### Battery Autonomy and Power-Saving Strategies  
A reliable power supply is a critical concern for waves in ice instruments due to the cold temperature conditions and the inability for solar power usage during polar night. In the present design, we decided to avoid the use of solar panels altogether (though solar panels could be easily added for building an open ocean buoy with unlimited autonomyoutside of the polar regions). This allows us to simplify the design, makes it less expensive and less labor-intensive to produce, as well as avoids compromising the water-tightening of the instrument. In our case, the combination of a power-efficient microcontroller and 9-dof, as well as power-optimized firmware, led to a drastic reduction of the power consumption compared with previous generations of the instrument. Table 1 summarizes the power consumption of the instrument in different modes. We use as power sources Lithium D-cells (SAFT LSH20) that have a nominal capacity of around 13 Ah at 3.6 V. In addition, a 3.3 V step-up/step-down buck converter [59] is used to provide a stable 3.3 V power source, even when the iridium moden briefly needs to draw much more power (bursts up to 250 mA). This results in a typical operational time--with 2 Lithium D-cells mounted in parallel and no solar power--of around 4.5 months with our standard GNSS and waves measurement rates (GNSS position every 30 min and wave spectrum every 2 h). If needed, increasing the number of batteries used in parallel will allow us to increase the autonomy, proportionally to the number of cells used. In order to simplify the handling and deployment of the instrument, a magnetic switch was added to the design [60]. When the magnet is attached, the instrument is powered down. The instrument starts operating as soon as the magnet is removed, and can be switched on and off as many often as necessary. Some LEDs are used to provide a visual indication of instrument activity, while their blinking frequency is made low enough to preserve battery.  
The high-level logics of the instrument (wake-up patterns, measurements patterns, iridium burst mode transmission) is implemented as a few lines of code that leverage an object-oriented implementation of the processing components described above. To increase robustness of the firmware after instrument malfunction, we resort to a double strategy. First, the firmware is coded following defensive programming patterns, with extensive quality checks of all inputs and outputs for the different modules used. Second, the integrated hardware watchdog present on the microcontroller is enabled at all times in order to force a complete reboot of the microcontroller in case of a hardware or software malfunction. The test deployments have all been highly successful, as described in Section 3, which is a testimony to the robustness of the design.
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 3 Results and Discussion
### Analysis of Greenhouse Data
#### 3.1.1 Cooling Season  
Figure 1 shows the air temperatures inside and outside the greenhouse and soil temperatures at depths of 0 m, 0.1 m, 0.2 m, and 0.3 m during the cooling season. The cooling set point was 35 \({}^{\circ}\)C during the study period, except for 6-10 September, when the cooling system was deactivated.  
On clear-sky days, the greenhouse air temperature increased with the thermal load from solar radiation until it reached the cooling temperature set point. Due to on-off cooling control actions, the air temperature oscillated slightly around the cooling temperature set point, 35 \({}^{\circ}\)C. In contrast, when the cooling control was deactivated from 6 September to 10 September, the air temperature reached as high as over 42 \({}^{\circ}\)C. In general, the air temperature profile showed a sinusoidal shape. The temperature profile looked differently on a rainy day (14 September) when the solar radiation was low. On that day, the greenhouse air temperature varied with the outside air temperature and formed two peaks.  
Figure 1b shows that the soil temperature profiles had similar sinusoidal shapes as the greenhouse air profile but with smaller amplitudes and larger hysteretic phase shifts as the depth increased. When the cooling system was deactivated from 6 September to 10 September, the soil temperature increased with increasing greenhouse air temperature.
# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 5 Results
### Model Verification and Feature Stability Analysis  
To assess the transferability of our findings, we conducted a feature stability analysis using a separate verification dataset. This approach helped to identify which relationships remained consistent across different data contexts.  
When analyzing individual feature relationships in the verification dataset, we discovered a remarkably strong correlation between peak_speed_time and coverage (r = 0.995, \(p<~{}0.001\)). This nearly perfect correlation (Figure 33) provided compelling evidence that temporal dynamics represented the fundamental driving force behind information diffusion.  
Figure 33: Peak speed time relationship with coverage.  
The feature importance rankings (Figure 34) showed notable consistency with our synthetic data findings. Peak_speed_time maintained its dominant position (importance = 0.809), followed by avg_degree (importance = 0.118). This stability in feature rankings across different datasets strongly validated our identification of the key drivers of information diffusion.  
While the overall model fit in the verification context presented challenges, this actually strengthened our core finding: general network properties must be supplemented with temporal dynamic measures for the effective prediction of information spread. The verification analysis showed that relying solely on static network features would be insufficient without accounting for the temporal component.  
Other structural features showed more context-dependent relationships: avg_degree (r = 0.220, \(p\) = 0.125), avg_closeness_centrality (r = 0.106, \(p\) = 0.464), and density (r = \(-\)0.061, \(p\) = 0.674). This pattern suggests that network structural effects may be more variable across contexts, while temporal dynamics maintain consistent influence.  
Our verification analysis included both basic bootstrapping (1000 resamples) to establish confidence intervals for feature importance and targeted comparisons between synthetic and real-world performance. The performance gap between synthetic (R\({}^{2}\) = 0.847) and real data (R\({}^{2}\) = 0.512) highlighted how authentic social networks contain additional complexities not fully captured in controlled simulations, including external influences and complex temporal dynamics like activity resurgence patterns.  
The verification phase provided valuable practical insights: when predicting information diffusion in new contexts, peak_speed_time serves as a reliable indicator of potential coverage. This finding has important implications for real-time monitoring and the prediction of information spread, as early temporal patterns can provide strong signals about eventual reach.  
Figure 34: Feature importance in verification analysis.
# Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning
## 3 Methodology
### Clothing Type Category Classification Methods
#### 3.1.3 Evaluation Metrics  
As already explained in Section 2, most authors evaluate their models with top 3 and top 5 scores, meaning that the correct label is among the top \(k\) predicted labels. Nevertheless, the top 1 score is required in this work due to the practical application in sight, which translates to the fact that the predicted class with the highest probability is the same as the target value.  
As a result, it is necessary to use the following metrics.  
* Accuracy (acc), i.e., the relation of correctly predicted observations to the total observations: \[\text{acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{TN }},\] (1)
* Precision (P), i.e., the relation of correctly predicted positive observations to the total predicted positive observations: \[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (2)
* Recall (R), i.e., the relation of correctly predicted positive observations to the observations in the actual class: \[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (3)
* F1 score, i.e., the weighted average of precision and recall: \[\text{FIScore}=2*\frac{\text{P}*\text{R}}{\text{P}+\text{R}}\] (4)  
where TP, FP and FN represent, respectively, the number of true positives (TP), false positives (FP) and false negatives (FN).
## 3 Results and Discussion
### Normalization of Experimental Results from the Literature and Model Analysis
#### 3.1.1 Low-Plasticity Soils  
To assess the PWP excitation mode of low-plasticity sandy soils, the experimental results reported by [PERSON] et al. [7] were first inspected. Clean sands obtained through a sieve analysis of sandy Liugui soil from Kaohsiung were used in the experiment. Fines were added to the clean sand to obtain specimens with FCs of 0-60%, and the void ratio, relative density, and peak deviator stress were controlled at 0.589, 30%, and 270 kPa, respectively, to facilitate comparison. The effects of plastic fines on WP excitation were assessed according to the PWP data. The experimental results are given in Figure 6. The focus of the present study was the increase in the PWP of plastic sandy soils. As indicated in Figure 6a-c, the PWP of the low-plasticity sandy soils increased in a fixed pattern under different conditions, and the excitation occurred in three stages. Therefore, in the present study, 15 sets of data were compiled, the upper and lower limits were identified, and the PWP excitation range of low-plasticity sandy soils in cyclic triaxial tests was assessed, as shown in Figure 6d. Because numerous sets of data were included, the ranges of the upper limit and the lower limit were large.  
After the PWP excitation range of the low-plasticity sandy soils in the cyclic triaxial tests was mapped, the WP curves of the non-plastic soils were illustrated. Subsequently, the experimental curves of the low-plasticity sandy soils and non-plastic soils were compared to the curves produced using the model developed by [PERSON] et al. [42] to identify which \(\beta\) value corresponded most closely to the excitation range in the experiment. The calculation revealed that when \(\beta\) was 2.0, the curve produced by the model was closest to the experimental curve at the upper limit, indicating that the model results were consistent with the experimental results. When \(\beta\) was 0.45, the curve produced by the model was closest to the experimental curve at the lower limit, which also indicated that the model results were consistent with the experimental results. Accordingly, when low-plasticity sandy soils are tested, the \(\beta\) value in the model developed by [PERSON] et al. [42] should be between 0.45 and 2.0 (Figure 7).  
Figure 6: PWP ratios with their respective to \(\mathrm{N/N_{L}}\) contents based on the test results of [PERSON] et al. [5] for low-plasticity soils. (**a**) Same void ratio 0.582, (**b**) same deviator stress 270 kPa, (**c**) same relative density 30%. (**d**) Ru and normalized \(\mathrm{N/N_{L}}\) values are mapped between two color lines for low plastic soils.
## 4 Experiment and Security Analysis
### Image Quality Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Target**} & \multicolumn{2}{c}{\(I^{\prime\prime}_{ouct}\)} \\ \cline{2-7}  & **ENIQA** & **BRISQUE** & **ENIQA** & **BRISQUE** & **ENIQA** & **BRISQUE** \\ \hline
1 & 0.2157 & 16.1316 & 0.0814 & 35.7050 & 0.2125 & 17.2771 \\
2 & 0.0952 & 2.4450 & 0.1726 & 12.4404 & 0.0755 & 3.1437 \\
3 & 0.1233 & 19.1092 & 0.3492 & 42.8666 & 0.1312 & 21.3583 \\
4 & 0.1028 & 16.9239 & 0.0219 & 13.6321 & 0.0961 & 22.7102 \\
5 & 0.0102 & 1.8924 & 0.0099 & 11.3557 & 0.0285 & 7.6039 \\
6 & 0.0120 & 12.4011 & 0.0222 & 7.9306 & 0.0414 & 11.4272 \\ Avg. & 0.0932 & 11.4839 & 0.1095 & 20.6551 & 0.0975 & 13.9201 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: No-reference image quality assessment for 6 test cases. The values for MSE or IW-MSE are small, less than 0.61 and no more than 0.15, respectively (the smaller, the better). In contrast, the PSNR values are high, over 50.34 dB (the higher, the better). Finally, scores for SSIM, Q-index, VSI, and IW-SSIM are close to 1.0. All the statistics indicate that there is no notable difference between the reference and the test images. These results confirm that the distortion in the dual stego images is insignificant, even though they have concealed a large number of secret messages.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Performance Evaluation Criteria  
Four quantitative criteria are considered to evaluate the proposed approach's performance. These evaluation criteria are explained as follows:Network Lifetime [15] is the time elapsed from the start of the network operation till the first node in the network fails due to battery depletion;
2. Packet delivery ratio (PDR) [15] concerns the number of successful messages received by the sink node to that sent by the source nodes;
3. Deadline miss ratio [15] is the percentage of packets that could not reach the sink node during their deadline;
4. Average end-to-end delay [15] is the average time taken by the data packet to travel from source node to the sink.