esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores
## 5 Results
After completing the tests, we performed an inversion of the complex resistivity data of the two samples using the Cole-Cole model. The model equation is as follows:  
\[\rho(i\omega)\ =\ \rho_{0}\bigg{\{}1-m\bigg{[}1-\frac{1}{1+\left(i\omega\tau \right)^{c}}\bigg{]}\bigg{\}} \tag{25}\]  
In Equation (25):  
\(\omega\) represents the angular frequency, measured in rad/s. \(\rho\) is the complex resistivity, expressed in \(\Omega\cdot\)m.  
\(\rho_{0}\) denotes the zero-frequency resistivity, which characterizes the electrical conductivity of the rock, measured in \(\Omega\cdot\)m.  
\(m\) is the chargeability, describing the intensity of the induced polarization effect in rocks and ores. \(\tau\) is the time constant, representing the speed of the induced polarization process in rocks and ores, measured in seconds (s). \(c\) is the frequency dependence coefficient, ranging from [0,1], which reflects the gradual or abrupt nature of the dispersion process as indicated by the shape of the dispersion curve. By comparing the test results of the two rock samples, it is evident that the chalcopyrite sample exhibits lower resistivity, and its phase curve shows a significant deviation at low frequencies, indicating a stronger polarization effect. In contrast, the diorite sample displays high-resistance characteristics, and its phase curve approaches zero at low frequencies, revealing a clear physical property difference between the two samples. Based on the inversion results of both rock samples, the time constant of the chalcopyrite sample is significantly larger than that of the diorite sample. This parameter is often used to describe the mineralization characteristics of rock samples. Therefore, this calibration method proves to be effective in distinguishing and identifying mineralized rock samples. Figure 16: Inversion results of two rock samples: (**a**) chalcopyrite sample; and (**b**) diorite sample.
# Focal Dice Loss-Based V-Net for Liver Segments Classification
###### Abstract  
Liver segmentation is a crucial step in surgical planning from computed tomography scans. The possibility to obtain a precise delineation of the liver boundaries with the exploitation of automatic techniques can help the radiologists, reducing the annotation time and providing more objective and repeatable results. Subsequent phases typically involve liver vessels' segmentation and liver segments' classification. It is especially important to recognize different segments, since each has its own vascularization, and so, hepatic segmentectomies can be performed during surgery, avoiding the unnecessary removal of healthy liver parenchyma. In this work, we focused on the liver segments' classification task. We exploited a 2.5D Convolutional Neural Network (CNN), namely V-Net, trained with the multi-class focal Dice loss. The idea of focal loss was originally thought as the cross-entropy loss function, aiming at focusing on \"hard\" samples, avoiding the gradient being overwhelmed by a large number of false negatives. In this paper, we introduce two novel focal Dice formulations, one based on the concept of individual voxel's probability and another related to the Dice formulation for sets. By applying multi-class focal Dice loss to the aforementioned task, we were able to obtain respectable results, with an average Dice coefficient among classes of 82.91%. Moreover, the knowledge of anatomic segments' configurations allowed the application of a set of rules during the post-processing phase, slightly improving the final segmentation results, obtaining an average Dice coefficient of 83.38%. The average accuracy was close to 99%. The best model turned out to be the one with the focal Dice formulation based on sets. We conducted the Wilcoxon signed-rank test to check if these results were statistically significant, confirming their relevance.  
liver segmentation; focal Dice loss; liver segments' classification; convolutional neural network; V-Net +
Footnote †: journal: Applied Sciences  
Acticle  
Focal Dice Loss-Based V-Net for Liver Segments Classification
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 4 Development of a Pantograph Slide Wear Model
### Pantograph Slide Wear Prediction Model  
The least squares method is a flexible and widely used mathematical approach, characterized by its simplicity, fast convergence, and ease of implementation. Its fundamental principle is to minimize the sum of the squared differences between the model's predicted values and the actual data, making it suitable for data with random errors that exhibit a linear relationship [27].  
The contact currents were set to 100 A, 150 A, 200 A, and 250 A, with twenty data points collected under different pressure and speed conditions. The least squares method was used to establish a linear model between pantograph-catenary current-carrying frictional energy dissipation and pantograph slide wear. The fitting relationship between pantograph-catenary current-carrying frictional energy dissipation and pantograph slide wear is shown in Figure 3. At a confidence level of 95%, the coefficient of determination (R\({}^{2}\)) is 0.982. Meanwhile, the \(p\)-value of the correlation coefficient, calculated via the \(t\)-test, yields \(p<\) 0.05, indicating that this correlation is highly statistically significant.  
The relationship between pantograph-catenary current-carrying frictional energy dissipation \(E\) and pantograph slide wear \(m\) is expressed as  
\[m=1.870\times 10^{-6}E+8.121\times 10^{-4} \tag{12}\]  
According to the above expression, a significant correlation between the wear volume and energy dissipation rate is observed (r = 0.989396, \(p<0.05\)). However, it is critical to emphasize that this correlation only reflects a covariant relationship between the variables, as causal inference methods have not yet been used to prove that energy dissipation directly induces wear. In fact, external variables such as the electric current, the evolution of the material surface topography, the contact stress distribution, and ambient temperature fluctuations may independently affect both the wear process and energy dissipation mechanism, forming mediating effects.  
In actual train operations, the contact current, contact pressure, sliding speed, and operating time are readily measurable. Based on the experimental conditions, parameters \(A\), \(H\), \(\rho\), and \(l\) are predetermined. Therefore, by obtaining the value of the friction coefficient \(\mu\), the pantograph-catenary current-carrying frictional energy dissipation \(E\) can be calculated, enabling the prediction of the pantograph slide wear \(m\).  
Therefore, by substituting the friction coefficient from Equation (11) into Equation (6) and combining it with Equation (12), the mathematical model to predict the wear of the pantograph slide can be obtained, as shown in Equation (13):  
\[\begin{array}{c}m=\frac{1.870\times 10^{-6}}{A}[\frac{\rho(lH^{2})}{F^{2}}-(1.0 02\times 10^{-4}I-0.072)v^{2}\\ -(1.002\times 10^{-4}I-0.101)Fv]t+8.121\times 10^{-4}\end{array} \tag{13}\]
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI  
The proposed reversible data hiding in encrypted images with extended parametric binary tree labeling (EPBTL-RDHEI) consists of three phases, namely (1) the image encryption and pixel grouping, (2) the pixel labeling and data hiding, and (3) the data extraction and image recovery. As shown in Figure 4, in the first phase, the content owner uses a predictor to calculate the prediction errors of the cover image. Then, the image is encrypted with the encryption key \(k_{e}\) and the pixels are grouped according to the prediction errors. In the second phase, the data hider embeds the secret information, which is encrypted by the data-hiding key \(k_{d}\), into the encrypted image by bit substitution. In the third phase, the receiver can separately use the data-hiding key \(k_{d}\) to correctly extract the secret information from the encrypted image, or use the encryption key \(k_{e}\) to recover the original image based on the prediction errors. The receiver can do both when the two keys are in hand.  
Figure 4: Procedure of the proposed method.
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 3 Optically Interconnected Multi-Chip Machine-Learning System
### Network of Multi-Chip Machine-Learning Architecture  
We show how to interconnect the multi-chip packages described above for building a cluster. We continue to use the 2D-mesh topology originally adopted in DaDianNao. To exploit the _broadcasting_ characteristic of optical interconnects (e.g., single-write-multi-read), using a _serpentine_ waveguide is an option [18]. However, due to its limited laser source power and large fan-out, it cannot reach beyond the chip boundary.  
Figure 3: Modified DaDianNao chip and its optically connected multi-chip package.  
Figure 4 shows the hardware implementation of the entire multi-chip machine-learning architecture. Six multi-chip packages, for example, can be bonded (or plugged) on the mainboard of a half-height _blade server_. This is possible because DaDianNao can work without main memory and other storage devices (although the host server requires them). In a blade server, packages are interconnected through fiber bunches (each with eight or four fibers, or fewer fibers with multiple wavelengths while providing 200 or 800 Gbps _bidirectionally_). Another option is using waveguides implanted on PCB or a flexible ribbon [14]. However, all optical ports of the multi-chip package should be identical (symmetric), and some ports will be connected to another external blade server with fibers and not waveguides. To build a cluster with more than 24 accelerators (chips), multiple blade servers can also be connected through optical fibers. Consequently, fibers are extended out of the server through optic ferrules [14] and up to 64 blade servers (1536 accelerators) can be installed in a standard rack (42U in size).
# Design Options to Improve the Dynamic Behavior and the Control of Small H-Darrieus VAWTs
## 2 Background
### Acceleration/Deceleration Capability  
The above-described difference in inertial properties between HAWT and VAWT, combined with the different adopted tip speed ratios, allow to draw some consideration about the different acceleration/deceleration capacity of the VAWTs, compared to HAWTs.  
The ratio between the angular accelerations of the two architectures, considering the relative rotor inertias (assuming the same number of blades), and the ranges of the design tip speed ratios (\(\lambda_{HAWT}=6\div 9\), \(\lambda_{H-VAWT}=2\div 3\)), is therefore equal to the following for slow running, small size turbines (\(\dfrac{I_{H-VAWT}^{\prime}}{I_{HAWT}^{\prime}}=300\)):  
\[\dfrac{\Omega_{H-VAWT}}{\Omega_{HAWT}}=\dfrac{\lambda_{HAWT}}{\lambda_{H-VAWT }}\dfrac{I_{HAWT}^{\prime}}{I_{H-VAWT}^{\prime}}\approx\dfrac{1}{100}\]  
Figure 3: Cumulative gust probability of urban sites (**a**) and \(WTI\) as a function of the rotor radius, and three typical wind acceleration rates for some commercial HAWTs and H-type Darrieus VAWTs (**b**) \(\bar{V}_{\infty}=0.6\) [m/s\({}^{2}\)], (**c**) \(\bar{V}_{\infty}=0.9\) [m/s\({}^{2}\)], (**d**) \(\bar{V}_{\infty}=1.20\) [m/s\({}^{2}\)]), for different initial wind speeds \(V_{0}=5\) [m/s], \(V_{0}=8\) [m/s], \(V_{0}=9\) [m/s].  
and equal to the following for fast running, medium size turbines (\(\dfrac{I_{H-VAWT}^{\prime}}{I_{HAWT}^{\prime}}=30\)):  
\[\dfrac{\Omega_{H-VAWT}}{\Omega_{HAWT}}=\dfrac{\lambda_{HAWT}}{\lambda_{H-VAWT}} \dfrac{I_{HAWT}^{\prime}}{I_{H-VAWT}^{\prime}}\approx\dfrac{1}{10}\]  
clearly showing an intrinsic poor accelerating/decelerating capability of the H-type vertical-axis architecture, compared to the case of HAWTs.
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 4 Results
### Force-Contraction Response  
The isobaric force-contraction response of the tested PAM is shown in Figure 12a. These results closely replicate those of other PAMs of the same size and construction. The characteristic hysteretic response of PAMs, as seen in Figure 12a, results in the PAM expressing higher forces in eccentric motion than in concentric motion. Despite this fact, subsequent analysis of the shape profile of the PAM has found no dependency of the shape profile on the direction of motion. When comparing the shape profile of the PAM for the full contraction range at 689 kPa, there was found to be an average differential of only 0.013 mm between the profiles for concentric and eccentric motion.
## 3 Results and Analysis
The result of VIFF yet demonstrates that our method is great, consistent with the human visual system. Although the VIFF of NST is close to our method, other metrics imply a certain gap between NST and the proposed method. The result of SD shows that our fused image expressed high contrast but not clear enough to be considered as the best. According to Table 5, FusionGAN shows much better fusion performance than the other method in the \(10^{th}\) image, and results in low reliability according to the \(t\)-test shown in Table 6. Further research implies that SD of our method is highly dependent on acoustic image. The low SD of the fused image is the result of low contrast of the \(10^{th}\) acoustic image. The fusion metrics above indicate that our method is good at preserving acoustic detail inside the cell, which is beneficial to analyzing the nanoscale and subcellular structure of the cell. Figure 6: Fusion results on three typical morphology and acoustic image. (From **left** to **right**: morphology image, acoustic image, Laplacian pyramid (LP), nonsubsampled contourlet transform (NSCT), fusion method based on Laplacian pyramid and sparse representation (LP-SR), fusion GAN, proposed). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **LP** & **NACT** & **LP-SR** & **FusionGAN** & **Proposed** \\ \hline
1 & 3.4138 & 3.5749 & 3.7945 & 4.4764 & **7.7349** \\ \hline
2 & 3.8354 & 4.2508 & 4.1686 & 4.8191 & **8.0714** \\ \hline
3 & 3.7562 & 4.0227 & 3.7419 & 4.7124 & **7.9157** \\ \hline
4 & 4.3860 & 4.9246 & 4.6630 & 5.3442 & **8.8741** \\ \hline
5 & 3.4103 & 3.8163 & 3.8791 & 4.3792 & **8.7135** \\ \hline
6 & 4.0918 & 4.5772 & 4.3210 & 4.5940 & **7.8550** \\ \hline
7 & 3.7621 & 4.2687 & 4.0084 & 4.7183 & **7.8393** \\ \hline
8 & 3.3896 & 4.3825 & 4.3964 & 4.7479 & **8.1814** \\ \hline
9 & 2.2528 & 2.3980 & 2.6728 & 3.6253 & **7.0375** \\ \hline
10 & 3.1164 & 4.0883 & 3.1396 & 5.3285 & **6.3951** \\ \hline
11 & 3.9045 & 4.0335 & 4.2035 & 4.4849 & **7.7655** \\ \hline
12 & 3.4848 & 3.8969 & 3.7320 & 4.1230 & **8.0500** \\ \hline
13 & 4.2784 & 4.5256 & 4.1659 & 4.5776 & **8.0357** \\ \hline
14 & 4.8693 & 5.3385 & 4.7021 & 5.1016 & **8.8752** \\ \hline
15 & 3.5955 & 3.8034 & 3.7454 & 3.9664 & **7.9100** \\ \hline
16 & 3.4814 & 3.7239 & 3.9381 & 3.9120 & **8.3066** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mutual information (MI) between proposed and compared methods. The bold represents the best value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **LP** & **NACT** & **LP-SR** & **FusionGAN** & **Proposed** \\ \hline
1 & 0.3402 & 0.4143 & 0.3211 & 0.1969 & **0.6621** \\ \hline
2 & 0.3506 & 0.4801 & 0.3490 & 0.2113 & **0.7311** \\ \hline
3 & 0.3383 & 0.4259 & 0.3068 & 0.2457 & 0.6585 \\ \hline
4 & 0.3116 & 0.4388 & 0.2884 & 0.2585 & **0.6331** \\ \hline
5 & 0.3978 & 0.5288 & 0.3846 & 0.2410 & **0.6962** \\ \hline
6 & 0.3695 & 0.5073 & 0.3417 & 0.2488 & **0.7218** \\ \hline
7 & 0.3437 & 0.4264 & 0.3379 & 0.2317 & **0.7033** \\ \hline
8 & 0.3885 & 0.4846 & 0.3971 & 0.2116 & **0.7361** \\ \hline
9 & 0.3552 & 0.3298
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 3 Estimation of the Reliability Index of Driven Piles  
### General Reliability Evaluation Method of Driven Piles  
In engineering practice, there are many factors that affect the bearing capacity of driven piles, including pile geometry size, soil type, spatial randomness, variability, etc. At present, the measurement error of soil physical properties and the influence of the pile forming process on soil properties cannot be accurately analyzed, and the main factors affecting pile bearing capacity can only be reflected in the uncertainty of parameters for calculating bearing capacity [28]. The following limit state equation is established to analyze the reliability of driven piles:  
\[g=R-Q=0 \tag{3}\]  
The load effect \(Q\), herein, only includes the combination of dead load \(Q_{\text{D}}\) and live load \(Q_{\text{L}}\); therefore, the reliability index \(\beta\) can be estimated using the following reliability method [29]:  
\[\beta=\frac{\ln\left[\frac{\lambda_{\text{R}}R_{\text{n}}}{\lambda_{\text{QD }}Q_{\text{D}}+\lambda_{\text{QL}}\sqrt{\frac{1+\text{COV}_{\text{QD}}^{2}+ \text{COV}_{\text{QL}}^{2}}{1+\text{COV}_{\text{R}}^{2}}}}\right]}{\sqrt{\ln \left[\left(1+\text{COV}_{\text{R}}^{2}\right)\left(1+\text{COV}_{\text{QD}}^{ 2}+\text{COV}_{\text{QL}}^{2}\right)\right]}} \tag{4}\]  
where \(\lambda_{\text{R}}\), \(\lambda_{\text{QD}}\), and \(\lambda_{\text{QL}}\) are the bias factors for resistance, dead load, and live load, respectively; \(\text{COV}_{\text{R}}\), \(\text{COV}_{\text{QD}}\), and \(\text{COV}_{\text{QL}}\) are the coefficients of variation (COVs) for resistance, dead load, and live load, respectively.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### Dataset Generation and Pre-Processing
#### 4.2.4 Quantum-Compatible Data Scaling  
To ensure compatibility between classical data and quantum state initialization via rotation gates, data scaling to angles suitable for quantum gate encoding is performed. Each principal component feature is linearly mapped to the range \([0,\frac{\pi}{2}]\) radians, aligning with quantum state preparation protocols utilized in quantum computing:  
\[\textbf{X}_{scaled}=\frac{\pi}{2}\cdot\frac{X_{PCA}-\min(X_{PCA})}{\max(X_{PCA} )-\min(X_{PCA})} \tag{10}\]  
Figure 6: Explained variance distribution and cumulative explained variance for up to 11 qubits.  
Figure 7: Original input digits and after PCA extraction.  
This scaling procedure ensures efficient and accurate representations of classical data within quantum state vectors.
### Contribution of This Research Work  
According to the previous work of the authors, new, fast and powerful CNN architectures were proposed to classify bone scintigraphy images for prostate and breast cancer patients in nuclear medicine [33, 56, 57, 59]. More specifically, an RGB-based CNN model has been proposed to automatically identify whether a patient has bone metastasis or not by viewing whole-body scans. The results showed the superior performance of the RGB-CNN model against other state-of-the-art CNN models in this field [56, 57]. Based on the advantageous features of the model, such as robustness, efficacy, low time cost, simple architecture, and training with a relatively small dataset [57], along with the promising results, authors were driven to study further and test the generalization capabilities of this methodology. This research work investigates the performance of the new models, applying them in nuclear cardiology, making the necessary parameterization and regularization to address the recognition of myocardial perfusion imaging from SPECT scans of patients with ischemia or infarction. Hence, the objectives are: (i) to diagnose cardiovascular disease by exploring efficient and robust CNN-based methods and (ii) to evaluate their performance, in line with SPECT MPI scans. A straightforward comparative analysis between the proposed RGB-based CNN methods with state-of-the-art deep learning methods, such as VGG16, Densenet, Mobilenet, etc., found in the literature was also conducted to show their classification performance. The produced results reveal that the proposed deep learning models achieve high classification accuracy with small datasets in SPECT MPI analysis and could show the path to future research directions with a view to a further investigation of the classification method application to other malignant medical states.  
Overall, the innovation of this paper which highlights its contribution is two-fold: (a) the application of a predefined CNN-based structure, namely RGB-CNN [56, 57], which was recently proposed in bone scintigraphy after a meticulous exploration analysis on its architecture and (b) the implementation of a deep learning classification model utilizing transfer learning for improving CAD diagnosis. The produced fast, robust and highly efficient model, in terms of accuracy and AUC score, can be applied to automatically identify patients with known or suspected coronary artery disease by looking at SPECT MPI scans.  
This work is structured as follows: Section 2 includes the methods and materials used in this study. Section 3 provides the proposed deep learning architectures for SPECT MPI classification. In Section 4, a rigorous analysis is conducted, including the exploration of different configurations to determine the most accurate of the proposed classification models. Finally, the discussion of results and the conclusions follow in Section 5.