esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performances of Supervised Machine Learning Approach  
Across subjects, 849 samples were available for the regression analysis. A significant correlation between SI and predicted SI (SIcross) was obtained (\(r\) = 0.61, \(p\) = \(\sim\)0) (Figure 6a), demonstrating a good performance of the multivariate analysis [50]. The weights associated to each z-scored regressor for each ROI are shown in Figure 6b. Considering that both the regressors and SI were normalized, the values of the weights were indicative of the contribution of each model input in the estimation of the SI.  
Since the two classes had a different number of samples (125 samples for STRESS versus 696 samples for NO STRESS conditions), a bootstrap procedure was implemented to provide a classification estimates using balanced classes [47].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject ID** & **Success (\%)** & **Confidence** \\ \hline Subject 01 & 100.00 & 0.93 \\ Subject 02 & 99.87 & 0.98 \\ Subject 03 & 77.90 & 0.76 \\ Subject 04 & 99.97 & 0.98 \\ Subject 05 & 70.54 & 0.66 \\ Subject 06 & 98.94 & 0.96 \\ Subject 07 & 99.87 & 0.91 \\ Subject 08 & 99.80 & 0.93 \\ Subject 09 & 99.86 & 0.97 \\ Subject 10 & 99.81 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Indices of performance of landmark identification and face classification on visible imaging.  
Figure 7a reports the among iterations average ROC curve (bootstrap performed for \(n\) = 10,000 iterations). The average area under curve (AUC) was 0.80 and with standard deviation of 0.01. The distribution of the AUC obtained after the bootstrap is reported in Figure 7b.  
By choosing a specific threshold for SICross, a sensitivity of 77% and a specificity of 78% were obtained as reported in the confusion matrix (Table 5).  
Figure 6: (**a**) Correlation plot between SI and SICross. The equation of the interpolating line is reported in the top left section of the graph. A good performance of the multivariate analysis is revealed by the correlation score (\(r\) = 0.61, \(p\) = -0); (**b**) weights associated to each z-scored regressor for each ROI. The weights are indicative of the contribution of each model input in the estimation of the SI.  
Figure 7: Results after bootstrap procedure (n_iterations = 10,000). (**a**) Among iteration average ROC curve and (**b**) distribution of the Area Under Curve (AUC) obtained after the bootstrap procedure. The average AUC was 0.80 with a standard deviation of 0.01.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 4 Experiments and Discussion
### Performance of Perceptual Robustness  
Perceptual robustness is the most significant difference between perceptual hash and cryptography hash. An ideal perceptual hash algorithm should be resistant to commonly-used remote sensing image content-preserving manipulations, which means that the normalized Hamming distance between the two hash values of the original image and the processed one by the content-preserving manipulation should be under the pre-determined threshold \(T\). In this paper, the threshold \(T\) is set as 0.05.  
In order to evaluate the performance of perceptual robustness for the algorithm, we utilized data compaction and digital watermark embedding as examples of content-preserving manipulations for testing, in which data compaction involves lossy compression (90% JPEG compression) and lossless compression, and digital watermark embedding adopts least significant bit (LSB) embedding. In this paper, to describe the perceptual robustness, we adopt the percentage of the grid's hash values in which no changes occurred that exceed the threshold \(T\); the results are shown in Table 3. It can be seen from Table 3 that this algorithm can maintain its robustness with respect to the lossless compression of multi-spectral images and LSB watermark embedding, and can maintain near-robustness to lossy compression.  
The robustness of the algorithm can be adjusted by the pre-determined threshold \(T\), i.e., the greater the threshold \(T\) is, the stronger the robustness of the algorithm. However, the relationship between robustness and sensitivity to tampering is contradictory, and may directly affect sensitivity to tampering if the robustness is overemphasized.  
In contrast, cryptographic authentication methods cannot achieve better certification, since they treat the above manipulation as illegal operations and their hash value would be changed dramatically after content-preserving manipulations. We utilized SHA-256 (Secure Hash Algorithm 256) as an example of cryptographic algorithms to compare with our proposed algorithm. Figure 8 shows examples of content-preserving manipulations; Figure 8a shows the original grid and Figure 8b-c show the results of content-preserving manipulations. Obviously, there are almost no differences between each of them, while the hash values of SHA-256 are very different, as shown in Table 4. By contrast, our proposal keeps the content-preserving manipulations robust, and the perceptual hash values remain unchanged. On the other hand, as the conventional perceptual hash algorithms for images do not take  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Band &  
the multiband characteristic of MS images into account and cannot be applied directly to MS images, we do not compare them with our proposed algorithm.
## 4 Spatial Cooperative Game Models and Their Applications in Terms of Locales
### Return/Benefit Rules for Coalition Strategies  
Coalition benefits emerge when the barriers between countries are removed and connections (physical and institutional) are established for allowing the free flow of goods and services among the countries. The sources of trade benefits can be attributed to such things as scale economies, positive external economies, agglomeration economies, trade through comparative advantages, intra-industry trades, and efficient factor markets. This study focuses on the generation of coalition benefits requiring spatial connectedness and multinational border connections, similar to the cases in urban regeneration projects that take advantage of FAR bonuses. As a case study area, the EU countries' common intra-union physical and institutional projects and policies are the sources of the cooperating union members' long-term economic returns. In order to pursue a multinational spatial cooperative game analysis, having a return function to compute the returns of country coalition strategies is necessary. Unfortunately, the literature has no return function that can be effectively used. Because the goal of the study is to present a developed spatial cooperative game model for analyzing and identifying the spatial aspects of country locations at the international level, a hypothetical return function (Equation (11)) has been designed for illustrative purposes. In a spatial setting, the countries with critical geographic locations, large numbers of neighborhood relations, and larger economic sizes are expected to be more likely to have relatively higher returns from any form of coalition, with higher returns being expected from larger coalition sizes. Otherwise, adding new members to a union is meaningless. All coalitions are rewarded by incrementally increasing returns until the highest reward is achieved from the grand coalition. The highest return is generated from the largest coalition of all EU members, which is the grand coalition. In order to measure the spatially and economically critical players in the EU, the return function (Equation (11)) and its parameters have been specifically designed to endorse multinational coalitions. Equation (11) is a modified version of the reward function presented in Equation (10). Letting \(A_{i}\) be the country GDP size (Table 8), \(\sum_{i\in S}A_{i}\) be the sub-coalition GDP size, and \(k\) and \(\alpha\) be the constant and the power parameters, respectively, allows \(b_{S}\) to be computed as per Equation (11). All returns are computed as a function of the accumulated base GDP sizes (\(\sum_{i\in S}A_{i}\)) from Table 8.  
\[b_{S}\ =\ k\Big{(}\sum_{i\in S}A_{i}\Big{)}^{\alpha}\ \ \rightarrow\ \ b_{S}\ =\ \begin{cases}A<215,\ 1.0(\sum_{i\in S}A_{i})^{1.0}\\ A\geq 215,\ 0.2(\sum_{i\in S}A_{i})^{1.3}\end{cases}  \tag{11}\]  
Figure 6: European Union countries and their neighborhood relations. \[\sum_{i\in N}A_{i}=\ 6398\implies b_{N}=\ 17,739=\ 0.2(6398)^{1.3} \tag{12}\]  
The reward function (Equation (11)) has two segments.
# Mechanical Performance of Concrete Made with the Addition of Recycled Macro Plastic Fibres
## 4 Results and Discussion
### Compressive Strength  
As can be seen from the graphs in Figure 2, the addition of RPF to the mixture reduced the compressive strength at 7 days and 28 days in the cubic specimens, as well as in the cylindrical specimens at 28 days. This reduction increased with the increase in the amount of fibre added. The percentage variations in the compressive strength, as a function of the type and quantity of fibre, compared to the control specimen (C-REF), exhibited, for the cubic specimens at 7 days of curing, decreases of 16.66% for PFRC-REF, 31.72% for PFRC-2, 32.68% for PFRC-4, and 31.62% for PFRC-6. For the cubic specimens, at 28 days of curing, the corresponding decreases were 15.95% for PFRC-REF, 20.73% for PFRC-2, 23.98% for PFRC-4, and 26.28% for PFRC-6. From these data, it can be seen that the compressive strength, in the cubic specimens, underwent a minor reduction compared to C-REF, with an increase in curing time. For the cylindrical specimens, at 28 days of curing, the corresponding decreases were 18.39% for PFRC-2, 36.78% for PFRC-2, 47.89% for PFRC-4, and 54.38% for PFRC-6.  
For the cylindrical specimens, there was a greater reduction in compressive strength than recorded for the cubic specimens. This behaviour could be attributed to the different manufacturing methods of the two types of specimens, i.e., the vibrating method for cubic specimens and tamping with a steel rod for cylindrical specimens. Furthermore, the similar compressive strength performance presented by cubic specimens with RPF and CPF can be highlighted.  
In general, a reduction in compressive strength with an increase in RPF content occurred, consistent with the investigations of [PERSON] et al. [42] and [PERSON] and [PERSON] [29]. This could, according to some authors, have been due to the poor homogeneity and compactness of the mixture [43; 44], or to other factors such as (1) the formation of zones inside the mixture where stress concentrations are created that favour the propagation of damage, (2) a weak connection at the concrete-plastic fibre interface, (3) an increase in air content [45], and (4) the presence of further voids due to the fibres [4; 46].  
However, other researchers--such as [PERSON] and [PERSON] [36], [PERSON] et al. [37], [PERSON] et al. [47], [PERSON] and [PERSON] [18], and [PERSON] et al. [48]--reported the oppo  
Figure 2: Compressive strength values for each mixture, along with the standard deviation bands: at 7 and 28 days (\(t_{\text{ck,cube}}\)) (**a**); at 28 days (\(t_{\text{ck,cyl}}\)) (**b**).  
site, i.e., that the compressive strength of the concrete improved upon the addition of plastic fibre.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 2 Methods
### Virtual Experiment of Flood Monitoring  
#### 2.3.1 Time Series Data of Flood Extent  
We investigated how a time series fluctuation of flood extent could be observed by the virtual SAR small-satellite constellation. However, as mentioned above, observation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Swath** & **30 km2** \\ \hline Look direction & both LEFT and RIGHT \\ Incidence angle range & 15.0\({}^{\circ}\)–30.0\({}^{\circ}\) \\ Nominal altitude & 570 km \\ Inclination & 97.69\({}^{\circ}\) \\ Ground track repeat & 18 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of the reference SAR small-satellite.  
Figure 2: (**a**) The locations of 5 GCPs; (**b**) the ground tracks of a satellite (purple line), observable range L (yellow belt zone), and swath width (orange belt zone) in an ascending pass and a descending pass.  
data with a high temporal resolution are currently unavailable. We therefore prepared the numerical simulation data of hourly flood depth from the catchment-based macro-scale-floodplain (CaMa-Flood) model, and assumed that these simulation outputs can be used as virtual truth of flood fluctuation in this analysis. The CaMa-Flood model is designed to simulate hydrodynamics in continental-scale rivers [19]. CaMa-Flood routes input runoff generated by a hydrological or land surface model to oceans or inland seas along a prescribed river network map. It calculates river and floodplain water storage, river discharge, water depth, and inundated area at each grid point. The flow characteristics are calculated with the local inertial equations along the river network map. The detailed model structures and parameters can refer to the original paper of [PERSON] et al. [19]. The hourly runoff data were input from Today's Earth Japan, with a land surface model MATSIRO and forcing inputs from MSM-GPV [20; 21]. This simulation does not take into account anthropogenic water management, such as dam operation and reservoir effect, but this can be considered irrelevant to this virtual experiment; the simulation outputs still provide sufficient reference for the likely behavior of flooding observed with satellites.  
In the flood simulation, the spatial resolution was downscaled to 30 m. As noted in the previous section, the designed SAR small-satellite constellation is assumed to use Stripmap mode with a resolution of 3 m (and a corresponding observation width of 30 km). Although this resolution does not coincide with those of simulation outputs, this may not be a problem because the imagery resolution represents a size per pixel rather than the discriminable size. For a specific object to be recognized, the object must typically be 10-20 times larger than the resolution. Taking this point into account, even if SAR data were actually acquired with 3 m resolution, the flood extent that emerges after image processing would cover an area of at least 30 \(\times\) 30 m\({}^{2}\). Therefore, the resolution of simulation outputs may be considered reasonable for this analysis.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 3 Background  
In this section, we discuss the system, energy and propagation models along with the type of packets used to configure the network. First of all, the system model is presented along with the assumptions made in the proposed work. Then, the energy and propagation models are briefly discussed followed by packet types.  
Figure 1: Illustration of the void hole problem in WDFAD-DBR.
# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 2 Literature Review
The focus of [26] proposed methods lies in the precise detection and classification of skin cancer, employing a specialized implementation of deep learning techniques. The approaches detailed within this paper involve the comprehensive analysis of datasets, notably MNIST: HAM10000, featuring an assortment of seven distinct skin lesion types comprising a sample set of 10,015, and the PH2 dataset, encompassing 200 skin lesion images. These methods encompass vital elements such as data augmentation while training the model utilizing prominent deep learning architectures like mobilenet and VGG-16 [27]. The ensuing accuracy attained stands at 81.52% through the application of mobilenet and 80.07% through the utilization of VGG-16. By reviewing several papers within the literature, it becomes evident that previous research has made significant progress in understanding skin lesion classification. However, there remain certain limitations, particularly in achieving consistently high levels of accuracy in predicting whether lesions are benign or malignant. To address this challenge, our research employs a transfer learning approach by fine-tuning pretrained convolutional neural network models. Transfer learning has emerged as a promising technique in various domains, leveraging the knowledge learned from one task to improve performance on another. By fine-tuning pretrained models on skin lesion images, we aim to capitalize on the wealth of information already captured by these networks, thereby enhancing the accuracy of lesion classification. This approach offers several advantages, including reduced training time and the ability to work effectively with smaller datasets. Moreover, by leveraging the rich feature representations learned from large-scale image datasets, we anticipate that our model will exhibit improved generalization and robustness, leading to more accurate predictions of skin lesion malignancy. Overall, through the application of transfer learning via fine-tuning, our research holds the promise of significantly advancing the field of skin lesion classification, ultimately contributing to improved diagnostic accuracy and patient care.
## 2 Numerical Models
### Numerical Method
Numerical simulations are carried out by the STAR-CCM+ 13.06, which is based on the finite-volume method (FVM). For the temporal discretization, a second-order implicit scheme is used. Pressure-velocity coupling is based on the density implicit with the SIMPLE method. A second-order upwind scheme is employed for spatial discretization. In this study, 128 CPUs with 24 h for each case are used for simulation.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 2 Characteristics of NUC  
NUC can be classified into 1D-NUC and 2D-NUC, and its design process has been described in [14; 15; 16; 17; 18]. Symmetry is the first characteristic of NUC. To reduce the complexity of design and demapping, NUC only optimized the constellation points in the first quadrant, constellation points in other quadrants are derived from the first quadrant. Moreover, only the I component needs to be considered during 1D-NUC optimization, and the Q component is obtained according to symmetry. Thus, the I and Q components of 1D-NUC are independent and can be decomposed into two NU-PAMs. LLRs of 1D-NUC can be calculated using the one-dimensional demapper. The I and Q components of 2D-NUC constellation points are jointly optimized, so they are dependent, and a two-dimensional demapper is required to calculate LLRs.  
Condensation is another characteristic of NUC. Since NUC is designed according to its working SNR, and its working SNR is determined based on the waterfall area of FEC code, the shape of NUC is different for different CRs. In low and medium CRs, some constellation points are very close and form a constellation cluster. Especially, when CR is very low, the shape of a high-order NUC is closer to a low-order NUC. As CR increases, the constellation points are gradually dispersed. When CR is very high, the shape of NUC is closer to a uniform constellation.  
The symmetry and condensation characteristics of NUC are shown in Figure 1. These two characteristics will be used in the design of the SCSR algorithm in Section 4.  
Figure 1: 2D-256 NUC and 1D-1024 NUC designed for different CRs in ATSC 3.0. **(a)** 2D-256 NUC designed for CR = 3/15; **(b)** 2D-256 NUC designed for CR = 13/15; **(c)** 1D-1024 NUC designed for CR = 3/15; **(d)** 1D-1024 NUC designed for CR = 13/15.
## 5 Experiments Results
### Effect of the Traing Sample Size  
Deep-learning-based model is a data-driven algorithm that depends on large amounts of labelled samples. In this section, experiments are conducted to explore the performance of the proposed hybrid DenseNet with limited training samples. For the active HSI, we use 1%, 5%, 10%, 40%, 70% and 100% samples from the spectral library to form the training sets, respectively, and examine how the accuracy of each class, AA, OA and Kappa may be impacted.  
The results are shown in Figure 12. As expected, the accuracy increases rapidly with the increase of training samples at first. However, when the proportion of training samples reaches 10%, the accuracy of most classes plateaued. For Class 4 and Class 6, their accuracies reach the highest level when the proportion is 100%. The results reveal that 10% of the training samples are enough for the proposed model to learn classification features, but for some classes, more training samples are needed, which may be caused by the spectral variability within the classes shown in Section 5.6.  
Figure 11: Effect of different model architecture.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 4 Numerical Model for Validation
### Solution Technique  
The CFD calculations are conducted with Ansys CFX software. The CFD analysis assumes the fluid to be ideal gas and the entire flow to be turbulent. The \(k\)-\(\varepsilon\) turbulence model is used because it can meet the requirements of excellent stability and accuracy [25; 26; 27]. The scalable wall function method is applied to describe the near wall flow conditions. These wall functions produce consistent results for grids of arbitrary refinement. The rotor and stator walls are defined to be adiabatic, smooth, and nonslip.  
For the transient CFD method, the rotor surface, in addition to its rotational speed, is given a periodic elliptical orbit motion, specified by Equation (1). Five whirl frequencies ranging from 30 Hz to 150 Hz are considered in this study. For each frequency, 720 time steps are specified during each period. The transient computation is run for several whirl cycles until the periodicity of fluid-induced forces is reached. The steady solution is prepared for the initial condition of the transient computation. The steady solution is obtained in the case that the rotor and stator are concentric. The rotational speed, inlet total pressure, inlet temperature, and outlet static pressure are specified in the computation.  
For the orbit decomposition method, two steady state simulations based on the circular orbit model are calculated. The amplitudes of the forward and backward circular orbit whirls are calculated according to Equation (8). Detailed calculating conditions for the two methods are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Transient Method** & \multicolumn{2}{c}{**Orbit Decomposition Method**} \\ \cline{2-4}
**Method** & **Transient Simulation** & **Steady State** & **Steady State** \\  & **(Forward Whirl)** & **Simulation** & **Simulation** \\  & & **(Forward Whirl)** & **(Backward Whirl)** \\ \hline Fluid & Ideal gas & Ideal gas & Ideal gas \\ Turbulence model & \(K\)-\(\varepsilon\) & \(k\)-\(\varepsilon\) & \(k\)-\(\varepsilon\) \\ Wall properties & Adiabatic, & Adiabatic, & Adiabatic, \\  & smooth surface & smooth surface & smooth surface \\ Whirl orbit & Elliptical orbit & Circular orbit & Circular orbit \\ Major semiaxis, \(a/\)m & 0.1 \(C_{r}\), 0.2 \(C_{r}\), 0.3 \(C_{r}\), 0.4 \(C_{r}\), 0.5 \(C_{r}\) & / & / \\ Minor semiaxis, \(b/\)m & 0, 0.5 \(a\), 0.9 \(a\) & / & / \\ Ellipse attitude angle, \(\alpha\)/rad & 0 & / & / \\ Whirl radius, \(r/\)m & / & \((a+b)/2\) & \((a-b)/2\) \\ Rotational speed, \(\omega/\)rad/s & 1162 & 1162 & 1162 \\ Whirl frequency, \(f/\)Hz & 30, 60, 90, 120, 150 & 30, 60, 90, 120, 150 & 30, 60, 90, 120, 150 \\ Time step, \(\Delta t/\)s & 1/720 \(\times f\) & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculating conditions.  
Figure 11: Mesh independence study of leakage and fluid-induced forces.