esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## 4 Results
### Numerical Experiment Results  
As an initial outcome, we will present a comparison between the accuracy of the strain field computed using our method and the median filter method. To the best of our knowledge, the utilization of median filters is a widely employed technique for noiseremoval in strain fields involving cracks, both in commercial and open-source software. This method is preferred due to its ability to preserve edges, unlike many other approaches. Additionally, since we aimed to replicate the crack configuration achieved by [10] and they specifically utilized the median filter technique, outlining its limitations, we sought to conduct a direct comparison between the two methods.  
In order to examine the impact of the median filter window size, three distinct window sizes were investigated (w = 7, 15, 31). The computed strain fields were compared with the simulated ground truth through both qualitative assessment using images and profile inspection (Figure 5), as well as quantitative evaluation using the mean absolute strain error (MAE) calculation. It has been found that the error obtained by applying TV-reg is much lower than the error obtained by using any other analyzed method. Based on the MAE values, the strain field that exhibits the highest deviation from the simulated one is the one obtaining after filtering the displacement field using a median filter with a window size of 31. The improvement in MAE obtained with TV-reg is 75.5% compared to the DVC without regularization and 51.2%, 55.45%, and 83.10% compared respectively to smooth-reg with window size equal to 7, 15, and 31.  
When evaluating crack detection, the presence of noise poses a challenge in determining an optimal threshold that can accurately identify cracks while excluding noise. This difficulty becomes evident when working with the raw strain field without any regularization or filtering. In Figure 5b, the effect of three different threshold levels is reported. \(T1_{\epsilon}=10^{-3}\) is evidently under the level of the noise; \(T2_{\epsilon}=2.2\times 10^{-3}\), instead, highlights clearly the cracks location, but residual noise, that might lead to a misinterpreted crack detection, is still visible; \(T3_{\epsilon}=4\times 10^{-3}\), instead, is likely to be too high to properly detect the crack location.  
If we examine the strain field obtained after applying the median filter, we can observe that imposing a threshold value of \(T1\) for window sizes of 7 and 15 leads to the detection of noise as an additional branch of the crack (see Figure 5c). On the other hand, when using a window size of 31, the two branches of the crack are not detected as separate branches. Applying a threshold of \(T3\), only one of the two branches is detected in all cases (w = 7, 15, 31). The only solution to accurately detect the cracks is by setting the threshold value to \(T2\).  
In the case of the strain field computed using TV regularization, the accuracy of crack detection is independent of the chosen threshold.  
Figure 5: _Cont._
## 3 Analysis of Calculation Results
### Wind Turbine Structural Characterization
#### 3.1.2 Blade Surface Equivalent Forces  
From Figure 5, the stress curve along the spreading direction of the blade with different numbers of layers can be seen. The five kinds of layers show the maximum stress at the root of the leaf, the minimum stress at the tip of the leaf, and from the root of the leaf to the tip of the leaf, the stress size and the distribution of the area of the stress gradually decrease the distribution of the stress situation. With the increase in the number of layers, the maximum stress of the blade gradually decreases, but the stress concentration area at the leaf root position gradually expands, and the unevenness of the stress distribution on the surface of the blade gradually slows down. The five kinds of layers correspond to the maximum stress at the leaf root of 0.1-0.2\(R\), which are 7.066 Mpa, 6.569 Mpa, 5.908 Mpa, 5.2564 Mpa, and 2.787 Mpa, respectively. The lowest stresses are applied at the tip position, which are 0.051 Mpa, 0.042 Mpa, 0.053 Mpa, 0.051 Mpa, and 0.089 Mpa, respectively. The equivalent stress of the root part of the leaf with 16 layers is 2.54 times that of the leaf with 44 layers, while that of the tip part of the leaf with 44 layers is 1.45 times that of the leaf with 16 layers.  
In summary, it can be concluded that changing the number of layers will significantly affect the stress distribution on the blade. The root position of the blade is easy to break in actual operation, and the stress in this area is the largest. With the increase in the number of layers, the stiffness increases. Moreover, the deformation decreases at the same time as the resistance to deformation generated by the stress is also reduced. The tip part experiences minimum stress. The larger the number of layers, the greater the stress.  
Figure 4: Deformations along the blade spread.  
Therefore, in the root part of the leaf, having more layers will improve its stiffness and strength. This will avoid fatigue fracture caused by the accumulation of excessive stress on the blade and increase the service life of the blade. Selecting fewer layers in the root part of the blade reduces the quality of the blade and improves its wind energy utilization.
## 2 Materials and Methods
### In Vivo T\({}_{1}\) and T\({}_{2}\) MR Image Measurements  
In vivo animal imaging was conducted based on the rules and regulations and permission of the animal research committee of the Korea Institute of Radiological and Medical Sciences (KIRAMS). The same 3.0 T MRI scanner used for relaxivity measurements was used to obtain T\({}_{1}\) and T\({}_{2}\) MR images. Two Institute of Cancer Research mice weighing \(\sim\)30 g (N\({}_{\rm mice}\) = 2) were used for each modal imaging. The mice were anesthetized using 1.5% isoflurane in oxygen. Measurements were made before and after injecting an aqueous nanoparticle solution sample into mice tail veins. The injection doses were approximately 0.013 and 0.1 mmol Gd/kg for T\({}_{1}\) and T\({}_{2}\) MR image measurements, respectively. During measurements, the body temperature of the mice was maintained at 37 \({}^{\circ}\)C using a warm water blanket. After measurements, the mice were revived from anesthesia and placed in a cage with free access to food and water. The spin-echo sequence was used for MR image measurements at 3.0 T. The typical measurement parameters were as follows: echo time = 10 (or 37) ms, repetition time = 385 (or 1620) ms, pixel bandwidth = 299 (or 197) Hz, number of acquisitions = 8 (or 4), echo train length = 3 (or 13), flip angle = 120\({}^{\rm o}\) (or 120\({}^{\rm o}\)), slice thickness = 1.0 (or 1.0) mm, and slice gap = 1.1 (or 1.1) mm for T\({}_{1}\) (or T\({}_{2}\)) MR image measurements.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Large Intelligent Surfaces
#### 2.2.1 Power Consumption LIS-Aided Communications  
Because users and the LIS are close to each other, there is less route loss, and the antenna gain is high, so it is expected that the transmit power on both sides of the communication will be low. This makes it possible for inexpensive and energy-efficient analog elements to be widely used.  
In comparison to massive MIMO, users are in the near field region, which is adjacent to the LIS in reference to its size. Being located in the near field necessitates using spherical waveform channel models instead of the planar wave approximation, which is often used in massive MIMO and further cellular systems [28].  
The LIS is composed of a variety of tiny panels, each of which produces multiple baseband outputs and may be set on and off. Terminals in panel-based LIS must be allocated to panels in such a way that all terminals obtain an acceptable quality of service (QoS). In order to achieve the highest possible signal-to-interference plus noise ratio (SINR) across all terminals, scientists want to choose a certain number of panels to activate and allocate them to a predetermined number of terminals. Therefore, it is advantageous to turn off the components with poor channels to conserve energy (there is a hardware-induced power cost for having antennas on). We must make sure that the baseband outputs at the back are connected to the activated parts of the LIS because a significant portion of the LIS is currently deactivated [7].
# Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
## 3 Preliminaries
### The Shuffle Model of Differential Privacy
A security-focused alternative in [25] employs lightweight secret-sharing techniques across three parties, integrating real-time malicious behavior detection and abort protocols to enforce non-collusion guarantees. Each approach strategically balances performance, scalability, and verifiable trust assumptions inherent to shuffle model deployments. Building on these advancements, this work assumes access to a semi-honest shuffle service. **Key advantages over secure aggregation protocols. ** The shuffle model in differential privacy provides key advantages over secure aggregation. First, it ensures information-theoretic privacy--resilient to quantum attacks--unlike secure aggregation's computational guarantees. Second, it avoids heavy cryptographic protocols (e.g., MPC or HE), reducing client computation and communication overhead. Third, it requires only single-round client-server interaction, eliminating multi-round coordination and lowering latency. These features make the shuffle model scalable and practical for privacy-critical applications, such as federated learning, IoT applications, or large-scale data analytics. **Key advantages over alternative DP models. ** Classical differential privacy (DP) frameworks face a fundamental trust-efficiency trade-off. The centralized model assumes a trusted curator that collects raw user data and applies calibrated noise to final outputs--an idealized but often impractical requirement. Local DP (LDP) eliminates this trust assumption by having users sanitize their data before submission, yet incurs substantial utility loss due to extreme noise amplification. The shuffle model reconciles this tension through a novel trust architecture: by introducing a cryptographically anonymized shuffler between users and analysts, it enables (1) formal privacy amplification via distributed noise injection, (2) removal of the omnipotent trusted curator, and (3) near-central-DP utility guarantees. This paradigm shift establishes the shuffler as a minimal trust anchor--sufficient to break the direct linkage between individual inputs and outputs, yet insufficient to reconstruct user-specific behavior--thereby achieving an operational sweet spot between LDP's distrust and central DP's oversimplified trust model.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 5 Evaluation Results  
For the performance evaluation, we introduce the following comparison schemes: (1) MAX where IoT devices always transmit data with the maximum power; (2) MIN where IoT devices always conduct the transmission with the minimum power; (3) RAND where IoT devices randomly select their actions; (4) PROB where IoT devices select their actions according to the predefined probability. (In PROB, \(A=0\), \(A=0\), \(A=0\), \(A=0\), and \(A=0\) are selected by 0.1, 0.3, 0.25, 0.25, and 0.1. Even though we have conducted extensive simulations with various probabilities, we cannot obtain different results. Therefore, in this paper, only results with the mentioned probabilities are included.) The performance metrics are the aggregated data rate of IoT devices \(\zeta_{R}\) and the energy outage probability \(\zeta_{E}\).  
We have conducted the simulation with the following default parameters. The total number of IoT devices is 5. The maximum energy level of IoT devices, \(E_{\text{max}}\), is 9. In addition, the maximum transmission power \(A_{\text{max}}\) is 4. The energy harvesting probability \(P^{H}\) of IoT devices is set to \([0.3\,0.7]\), where \([a\ b]\) denotes a random value between \(a\) and \(b\). In addition, we consider that each device has i.i.d. Rayleigh fading channel and its mean is set to \([0\,2]\)[30]. \(\sigma^{2}\) is set to 0.5 [30]. The target energy outage probability \(\theta_{E}\) is set to 0.05.
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 2 Description of Roll Eccentricity Problems
Therefore, the eccentricity signal model can be expressed as:  
\[y(n)=\sum_{i=1}^{k}A_{i}cos(\omega_{i}n\Delta t+\varphi_{i})+v(n) \tag{7}\]  
where \(y(n)\) is the sampling value of the eccentricity signal, \(k\) is the number of harmonic components, i.e., the sparsity of the eccentricity signal, \(A_{i}\) is the amplitude of the \(i\)th eccentricity signal, \(\omega_{i}\) is the angular frequency of the \(i\)th eccentricity signal, \(n\) is the number of samples, \(\Delta t\) is the sampling period, and \(\varphi_{i}\) is the initial phase angle of the \(i\)th eccentricity signal. \(v(n)\) is a random noise, whose amplitude distribution follows a Gaussian distribution and whose power spectral density follows a uniform distribution, i.e., white Gaussian noise. Moreover, it should be pointed out that the method proposed in the following sections needs to use the least squares estimation, whose premise is that the noise satisfies the Gaussian distribution. An accurate eccentricity model can be established based on the frequency \(\omega_{i}\), amplitude \(A_{i}\), initial phase angle \(\varphi_{i}\), and other parameters of each harmonic of the above eccentricity components, thus achieving the compensation and control of eccentricity.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 3 Proposed Model  
Because the pixel information is not fully considered in the traditional ATV model, only the information of the first-order gradient is considered. To enhance the deblurring effect, the traditional ATV model is extended to the high-order TV model, which considers the first-order gradient information, and adds the high-order gradient information to the prior term. Figure 1 depicts the contour maps that highlight the advantage of Lp-pseudonorms. The contours of the L1-norm, L2-norm and Lp-pseudonorm are depicted in Figure 1a-c. The L1-norm and L2-norm can be expressed as \(\|\mathbf{X}\|_{1}^{1}=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\mathbf{X}_{ij}\|\) and \(\|\mathbf{X}\|_{2}^{2}=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\mathbf{X}_{ij}^{2}\), respectively; whereas, the Lp-pseudonorm is defined in the following paper. In Figure 1, the dotted lines represent the fidelity term, while the solid blue lines represent the contours of the prior item. The red dots represent the intersection of the dotted line and the solid line, while the intersections with the axes represent the better sparseness of the image gradients. It is also observed that the Lp-pseudonorm provides a greater degree of freedom compared with L1-norm and L2-norm, which can better depict the sparsity of the image gradients. Therefore, the Lp-pseudonorm was added to the prior terms to improve the robustness of the image gradients.  
The high-order gradient information and Lp-pseudonorm were introduced to increase the accuracy of the prior knowledge, thereby preserving the edge of the image while suppressing the influence of the small edge on the estimation of the blurring core. Therefore, the proposed high-order total variation with Lp-pseudonorm shrinkage (HTV-Lp) model is defined as follows:  
\[\begin{array}{l}\vspace{-0.2 cm}F=\underset{F}{\arg\min}\frac{1}{4}\|\mathbf{H*F-G} \|_{2}^{2}+u_{1}\|\mathbf{K}_{h}*\mathbf{F}\|_{p_{1}}^{p_{1}}+u_{2}\|\mathbf{K}_{v}*\mathbf{F} \|_{p_{2}}^{p_{2}}\\ +u_{3}\|\mathbf{K}_{h}*\mathbf{K}_{h}*\mathbf{F}\|_{p_{3}}^{p_{3}}+u_{4}\|\mathbf{K}_{v}*\mathbf{K} _{v}*\mathbf{F}\|_{p_{4}}^{p_{4}}+u_{5}\|\mathbf{K}_{h}*\mathbf{K}_{v}*\mathbf{F}\|_{p_{5}}^{p_ {5}},\end{array} \tag{3}\]  
where \(\mathbf{K}_{h}=[-1,1],\mathbf{K}_{v}=\left[\begin{array}{c}-1\\ 1\end{array}\right]\), the Lp-norm is defined as \(\|\mathbf{X}\|_{p}=\left(\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left|\mathbf{X}_ {ij}\right|^{p}\right)^{1/p}(0<p<1)\), while the Lp-pseudonorm is defined as \(\|\mathbf{X}\|_{p}^{p}=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left|X_{ij} \right|^{p}(0\leq p\leq 1)\), and \(u_{i}(i=1,2,\cdots,5)\) are the balance parameters.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 3 The Proposed Secret Message Multiplexing Scheme
### Secret Extraction Procedure  
At the receiving side, the secret message can be easily extracted in a manner similar to the secret embedding procedure. The flowchart of secret extraction procedure is shown in Figure 3. By raster scanning the marked image \(I^{\prime}\), we get the one-dimensional pixel sequence \(\{x^{\prime}_{n}\}\). For each pixel, perform the modulo operation according to Equation (3). The results correspond to the \((2N+1)\)-ary CDMA codeword. After being decreased by \(N\), the codeword is divided into \(L\) groups with each group composed of \(N\) digits. Thereafter, the CDMA demultiplexing process is performed on every group. The process mainly involves the inner product operation of a codeword group and a secret key. Based on a threshold value of zero, the value of the inner product is converted to a binary form of secret message. It is noteworthy that this CDMA demultiplexing is performed by each participant separately. Since the cross-correlation between any two secret keys equals zero, only the authorized user can extract the dedicated secret message with a correct secret key. Thus, unauthorized data access is prevented, and data confidentiality is ensured.  
The secret extraction procedure is summarized in the following steps.  
1. Rearrange the marked image \(I^{\prime}\) into a one-dimensional sequence \(\{x^{\prime}_{n}\}\) of length \(H\times W\).
2. Map each pixel value \(x^{\prime}_{i}\) to a \((2N+1)\)-ary number \(f^{\prime}_{i}\) accoridng to Equation (3) and decrease \(f^{\prime}_{i}\) by \(N\). The results represent the original CDMA codeword.
3. Divide the CDMA codeword into \(L\) groups with each group composed of \(N\) digits. Perform CDMA demultiplexing group-wise with a specific secret key to retrieve the individual \(L\)-bit secret message \(S_{i}\)\((1\leq i\leq N)\).  
Take the same example described in the secret embedding procedure subsection to illustrate the process of secret message extraction.  
For the pixel vector \(X^{\prime}=[166\,157\,161\,157]\) of the marked image, the modulo result \([4\,4\,8\,4]\) implies that \([0\,0\,4\,0]\) is the original CDMA codeword vector. Recall that the four secret keys are \([1\,1\,1\,1]\), \([1\,-1\,1\,-1]\), \([1\,1\,-1\,-1]\), and \([1\,-1\,-1\,1]\). They are used to calculate the inner product with \([0\,0\,4\,0]\) individually. The results are \(4,\,4,\,-4\), and \(-4\). We observe that the converted binary forms \((1,\,1,\,0,\,0)\) coincide with the original four secret message bits. In other words, the extraction result is correct.
## 3 Methodology and the Model
### Simulation Setup  
The factors affecting the perforation tunnel pressurization and the fracture initiation were analyzed. Simulations were conducted based upon the values presented in Table 2 to discuss the influence of various factors. The basic case was defined as follows.  
For the SC-CO\({}_{2}\) jet model, the jet inlet pressure was 20 MPa, and the ambient pressure was 10 MPa. The nozzle outlet length was 2 mm. In addition, the nozzle outlet radius was 1 mm, and the nozzle-to-casing inlet radius ratio (aperture ratio) was 2/3. The fluid used was SC-CO\({}_{2}\), and the PR equation of state was used for calculations.  
For the shale rock model, the perforation pressure was 20 MPa, while the in situ stress values were 8 and 10 MPa, respectively. Additionally, \(\alpha\) was 3, and \(\beta\) had the value of 1.6. The initial tangent modulus was 14 GPa, while the original elastic modulus (\(E_{T}\)) was 30 GPa. Furthermore, the average elastic modulus was 6 GPa, and the Poisson's ratio was 0.22.
## 4 Results and Discussion
### Data Augmentation Experiments  
To demonstrate the effectiveness of our proposed data augmentation methods, we used the YOLOv5s model as a baseline model for training. For the negative sample data augmentation method, we used the training set of the negative sample dataset (A2 and B2) to train and the test set of the unprocessed dataset (A1 and B1) to test. For the fogging data augmentation method, we used the training set of the fogging dataset (A3 and B3) to train and the test set of the unprocessed dataset (A1 and B1) to test. For the mixed enhancement method of fogging and negative samples, we used the training set of the fogging and negative sample mixed dataset (A4 and B4) to train and the test set of the unprocessed dataset (A1 and B1) to test. Similarly, we performed the experiments on the self-made dataset and the pothole600 dataset. The experimental results are shown below.  
As shown in Table 5, in the self-made dataset, the experimental F1 value is increased by 17.72% with the negative sample data augmentation method. After the fogging data augmentation method, the effect of improving the detection accuracy is more obvious; the F1 value is increased by 25.01%. When both the negative samples and fogging data augmentation methods are used at the same time, the effect of improving the detection accuracy is not only better with one of them, but the F1 value is also improved by 16.84%. The actual results (Figure 14) are visualized.  
As shown in Table 6, in the pothole600 dataset, the F1 values are increased by 2.29%, 2.29%, and 0.25%, respectively, after the negative samples, fogging, and mixed data augmentation methods. The actual effects (Figure 15) are visualized.  
Figure 14: Visualization of images detected on the self-made dataset using different data augmentation methods.  
Figure 15: Visualization of images detected on the pathole600 dataset using different data augmentation methods.