esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 4 Results and Discussion
### NIST STS Analysis  
The stochastic nature of the proposed methodology is substantiated through rigorous validation using the NIST STS, applied to the Lena image dataset. The outcomes of these assessments demonstrate a high degree of success and are systematically elucidated in Table 3.  
Figure 3: The dataset comprises three distinct categories of images: the original images, the encrypted (cipher) images, and the decrypted images.  
The comprehensive evaluation of the proposed methodology's stochastic characteristics, validated through the NIST statistical test suite applied to the Lena image dataset, reveals a consistently high level of success across a spectrum of statistical tests. The results, as summarized in Table 3, demonstrate that the methodology satisfies the stringent criteria set forth by each test within the suite. Specifically, each test, ranging from frequency and block frequency tests to discrete Fourier transform and cumulative sums analyses, yields \(p\)-values that surpass the commonly accepted significance level of 0.05. This signifies a robust adherence to randomness in the generated data, as indicated by the \"True\" conclusions associated with each test. Notably, the Random Excursion and Random Excursion Variant Tests, which assess the excursion behavior of the generated sequences, also exhibit conformity with expected randomness patterns. The \(p\)-values associated with various excursions, both positive and negative, consistently exceed the significance threshold, affirming the statistical integrity of the proposed methodology.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Test Name** & \(p\)**-Value** & **Conclusion** \\ \hline
2.01. Frequency Test: & 0.011603 & True \\
2.02. Block Frequency Test: & 0.898022 & True \\
2.03. Run Test: & 0.194845 & True \\
2.04. Run Test (Longest Run of Ones): & 0.366963 & True \\
2.05. Binary Matrix Rank Test: & 0.062628 & True \\
2.06. Discrete Fourier Transform (Spectral) Test: & 0.575633 & True \\
2.07. Non-overlapping Template Matching Test: & 0.985412 & True \\
2.08. Overlapping Template Matching Test: & 0.28564 & True \\
2.09. Universal Statistical Test: & 0.874014 & True \\
2.10. Linear Complexity Test: & 0.809612 & True \\
2.11. Serial Test: & 0.159966 & True \\  & 0.422105 & True \\  & 0.22. Approximate Entropy Test: & 0.224807 & True \\  & 2.13. Cumulative Sums (Forward): & 0.021299 & True \\  & 2.13. Cumulative Sums (Backward): & 0.021299 & True \\  & 2.14. Random Excursion Test: & 0.021299 & True \\  & 3.200982 & 0.669032 & True \\  & 6.112628 & 0.295414 & True \\  & 3.762932 & 0.584027 & True \\  & 10.89873 & 0.053425 & True \\  & 7.531646 & 0.184007 & True \\  & 5.497578 & 0.358212 & True \\  & 5.469185 & 0.361337 & True \\  & 4.987242 & 0.417439 & True \\  & 2.15. Random Excursion Variant Test: & & \\  & 123 & 0.39589 & True \\  & 119 & 0.411277 & True \\  & 101 & 0.627375 & True \\  & 66 & 0.755169 & True \\  & 50 & 0.44187 & True \\  & 54 & 0.452213 & True \\  & 57 & 0.433789 & True \\  & 64 & 0.49084 & True \\  & 85 & 0.63312
# Investigation of Inner Lining Loss and Correlation with Steel Structure Quality in Rotary Kilns
###### Abstract  
The article delves into the application of laser scanning within the realm of engineering, concentrating its examination on rotary kilns. Through an in-depth case study, this article meticulously assesses the advantages inherent in utilising laser scanning technology and draws comparisons with conventional measurement methods. The overarching objective of the investigation is to unravel the intricate relationship between the deterioration of the rotary kiln liners and the ovality of the underlying steel structure. By meticulously analysing these aspects, this article seeks to contribute valuable insights into the understanding of this complex interplay in the context of engineering practices. As a measurement apparatus, a terrestrial laser scanner was used. The interior and exterior of the rotary kiln were measured. The primary focus object was inner-lining loss and the geometric characteristics of the cylindrical shells. The research uncovered significant disparities in inner lining loss between the sections. A correlation was found between the ovality and elimination of the inner lining. Due to the hypothesis of constant inner lining loss in the middle of the rotary kiln, the investigation found that the loss of brick lining was less than the value reported from the wells. This study offers significant information on the maintenance and repair strategies for rotary kilns, which have the potential to increase their efficiency and useful life.  
laser scanning; rotary kiln; geometry analysis Article
## 2 Design Parameters of the Metamaterial and Simulation Setup
### Parametric Studies
#### 2.2.2 Effect of Substrate Materials  
Proper dielectric selection is an important task for any metamaterial design. An investigation is conducted to observe the response of different substrate materials. Commercially available flame-retardant FR-4 material, along with two Rogers dielectrics, RT 5880 and RT 6002, are taken into consideration. Three individual substrates are simulated by keeping the resonator structure unchanged. First, dielectric FR-4 shows resonance at 2.04 GHz, 6.66 GHz, 7.97 GHz and 9.42 GHz, with very low magnitudes. Secondly, Rogers RT 5880 yields triple-band resonance peaks at 3.7 GHz, 8.67 GHz and 11.33 GHz, whereas RT6002 shows quad-band resonance peaks at 2.35 GHz, 7.72 GHz, 9.23 GHz and 10.68 GHz, with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multicolumn{2}{c}{**Resonance Frequency**} & \multirow{2}{*}{**Structural Composition**} & \multirow{2}{*}{**Magnitude (dB)**} & \multirow{2}{*}{**Band**} \\  & **(GHz)** & & & \\ \hline Design-1 & 2.44, 8.67, 10.84 & SRR with single octagon & \(-\)40.41, \(-\)44.48, \(-\)31.43 & S-, X- \\ Design-2 & 2.51, 8.55, 9.50, 11.02 & SRR with double octagons & \(-\)38.91, \(-\)39.09, \(-\)37.82, \(-\)19.29 & S-, X- \\ Design-3 & 2.50, 8.57, 9.49, 11.03 & SRR with three octagons & \(-\)39.83, \(-\)39.84, \(-\)37.88, \(-\)19.16 & S-, X- \\ Design-4 & 2.51, 8.58, 11.05 & SRR with three attached octagons & \(-\)42.69, \(-\)37.28, \(-\)15.73 & S-, X- \\ Final Design & 2.35, 7.72, 9.23, 10.68 & SRR with three attached OSRRs & \(-\)43.23, \(-\)31.05, \(-\)44.58, \(-\)31.71 & S-, C-, X- \\ \hline \hline \end{tabular}
\end{table}
Table 2: Response of transmission coefficient (S\({}_{21}\)) for designs steps.  
Figure 3: CST result of transmission coefficient (S\({}_{21}\)) for various design steps.  
Figure 2: Design optimization views: (**a**) Design-1; (**b**) Design-2; (**c**) Design-3; (**d**) Design-4; (**e**) final design.  
satisfactory magnitudes and moderate bandwidths. The simulated results cover the S, C and X bands. The transmission coefficients (\(S_{21}\)) for the three substrate materials are shown in Figure 4.
# Evaluating the Effect of Road Surface Potholes Using a Microscopic Traffic Model
## 2 Model Formulation
Thus, the acceleration varies according to the environment, unlike the ID model which produces similar behavior for all road surface conditions. Traffic flow is the ratio of velocity and distance headway at equilibrium and so can be expressed as [23; 40]  
\[f=\frac{v}{h_{e}} \tag{7}\]  
At equilibrium, the velocity difference is equal to zero and thus the equilibrium distance headway for the proposed model is  
\[h_{e}=(J_{\text{s}}+Tv)\Bigg{(}1-\bigg{(}\frac{v}{v_{D}}\bigg{)}^{-\frac{1}{2 }\pi W(\frac{\tau}{\tau_{N}})(1-\frac{h}{h_{s}})\sqrt{\frac{W^{2}}{4}+D^{2}}} \Bigg{)}^{-1/2} \tag{8}\]  
Substituting (8) in (7) gives the flow for the proposed model as  
\[f=\frac{v}{(J_{\text{s}}+Tv)\Bigg{(}1-\Big{(}\frac{v}{v_{D}}\bigg{)}^{-\frac{ 1}{2}\pi W(\frac{\tau}{\tau_{N}})(1-\frac{h}{h_{s}})\sqrt{\frac{W^{2}}{4}+D^{ 2}}}\Bigg{)}^{-1/2}} \tag{9}\]  
Thus, traffic flow varies based on the road surface conditions and driver sensitivity. With a high sensitivity, driver reaction is fast and hence the flow is large as traffic conditions are predictable. Conversely, less sensitive drivers react more slowly to changes in the environment so the flow is large and congestion may occur. Furthermore, large-size potholes result in larger changes in traffic flow.
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies  
The first step in the development of hydrate-based technologies for hydrogen storage involves the formation of hydrate structure as the host for capturing hydrogen molecules. The main difficulties during large-scale hydrate formation of hydrate is (1) well mixing of the supplied guest gas with water to overcome mass-transfer challenges, and (2) an optimum heat-transfer mechanism to dissipate the latent heat released due to hydrate formation must be present. It should be noted that the water solubility of hydrogen is very low compared to methane.  
According to the literature, there are two main approaches in gas hydrates formation:Gas dispersion introduced in water continuous phase, or;  
Liquid dispersion, which involves introducing water droplets in a high pressure gas chamber.  
However, developing each type of above-mentioned technologies will have its own challenges. Gas dispersion is limited by large energy consumption when scaling up, whilst heat transfer (primarily latent-heat dissipation) will be the main drawback for scaling up liquid-dispersion techniques. Figure 2 presents the design approaches and philosophies discussed in the literature, which we shall examine in some detail in the following section.
## 4 Experimental Results and Discussion
### Results and Discussion  
The classification performance was evaluated between target and clutter data. Figure 14 shows the accuracy for training and testing of MFCC and PNCC for 50 epochs. In Figure 14, compared to MFCC, PNCC converges faster during training and testing.  
The results of classifying targets and clutters using CNN are shown in Table 3. It can be observed that the classification rate is higher when the feature is extracted using PNCC rather than MFCC, and it is classified using CNN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Number of Data** \\ \hline Target (data augmentation output) & 3610 \\ Clutter & 3351 \\ Training & 5568 \\ Testing & 1393 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input data numbers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Content** \\ \hline Epochs & 50 \\ Training rate & 0.001 \\ Batch size & 32 \\ Weight initialization & He \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameters.  
Figure 14: Accuracy depending on learning epochs: (**a**) MFCC accuracy; (**b**) PNCC accuracy.  
In the case of target classification, PNCC has a higher classification rate of 1.383% than MFCC, and for clutter classification, PNCC has a 0.597% higher classification rate than MFCC. From the classification results, Table 4 summarizes the analysis results of precision, recall, and F-measure using these two criteria (precision and recall). For all three analysis results in Table 4, PNCC shows better performance than MFCC.  
The result of converting the sea experiment data into a spectrogram and classifying the target using CNN showed that the classification rate of the test was 94% [26]. The sea experiment data used in [26] is the same as in this paper. Compared with the results of [26], the result using PNCC showed about 4.6% higher classification rate than the result using the spectrogram.  
In the proposed algorithm result, target echoes are well classified. Furthermore, this result shows that the PNCC has a better performance compared with the MFCC in terms of the classification rate. It can also greatly help sonar operators when detecting a target manually.  
The results of computational demands MFCC and PNCC are shown in Table 5[18]. Table 5 shows that PNCC has about 34.6% more computation than MFCC.  
In the case of feature extraction using PNCC, a better classification result can be obtained through an increase of only 34.6% of the computational amount compared to MFCC, and the addition of this amount of computation due to the development of computing power will not cause problems in real-time processing.
## 6 Results and Discussion
### Adaptive Threshold Under the Probability of Target Acquisition Success Probability  
As can be seen from Table 2, when the SNR before despreading is 3 dB, and \(L=128\), \(P_{acq}\geq 0.9999\). In Figure 15, \(\lambda_{min}\) and \(\lambda_{max}\) are obtained at \(P_{acq}=0.9999\). In this case, 100,000 Monte Carlo simulations are performed, and the simulation results are shown in Figure 16. Due to the fluctuation of noise variance, the acquisition thresholds also change. Similar to Figure 15, Table 3 can be obtained through simulation.  
According to Section 5.1, because \(V_{th}=\lambda P_{n}\), the acquisition threshold estimation error is \(\Delta V=\Delta\hat{P}_{n}\times\lambda_{opt}\). Taking the median noise \(P_{n}=0.2535\) of 100,000 simulations as an example, Table 4 can be calculated based on the data in Table 3. The noise power estimation error requirements \(\Delta\hat{P}_{n}\) under different acquisition success probabilities can be obtained by referring to Tables 4 and 5. For example, if we want \(\gamma=0.9999\), then the noise power estimation errors need to be \(0.01\leq\Delta\hat{P}_{n}\leq 0.005\). This method can effectively offset the negative effect of noise estimation error on \(V_{th}\).  
Figure 14: Comparison of theoretical curve and Monte Carlo simulation curve.  
Figure 16: Acquisition simulation diagram when Eb/N0 = 3 dB, L = 128.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(P_{n}\) & \(V_{opt}\) & \(\gamma\) & \(V_{\text{min}}\) & \(V_{max}\) \\ \hline  & & 0.9999 & 51.9928 & 55.9982 \\
0.2535 & 53.5899 & 0.999 & 50.4465 & 58.6092 \\  & & 0.99 & 48.8241 & 62.5892 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The acquisition threshold range under different acquisition success probability requirements \(\gamma\).  
Figure 15: The maximum and minimum normalized thresholds when the target acquisition success probability \(\gamma\) is 0.9999.  
Tables 6 and 7 show the simulation results of noise power under different observation times and numbers of auxiliary channels. Based on the experimental data in Tables 6 and 7, the noise observation error meets the requirement of \(P_{\alpha q}\geq 0.9999\). With the increase in observation time and the number of auxiliary channels, the estimated noise error decreases gradually.  
Taking the Monte Carlo simulation of a successful acquisition as an example, as shown in Figure 17, it can be observed that successful acquisition can be achieved when the acquisition threshold \(V_{th}\) is between \(V_{\min}\) and \(V_{max}\).
###### Abstract  
Artificial intelligence (AI) has become the mainstream of analysis tools in remote sensing. Various semantic segmentation models have been introduced to segment land cover from aerial or satellite images, and remarkable results have been achieved. However, they often lack universal performance on unseen images, making them challenging to provide as a service. One of the primary reasons for the lack of robustness is overfitting, resulting from errors and inconsistencies in the ground truth (GT). In this study, we propose a method to mitigate these inconsistencies by utilizing redundant models and verify the improvement using a public dataset based on Google Earth images. Redundant models share the same network architecture and hyperparameters but are trained with different combinations of training and validation data on the same dataset. Because of the variations in sample exposure during training, these models yield slightly different inference results. This variability allows for the estimation of pixel-level confidence levels for the GT. The confidence level is incorporated into the GT to influence the loss calculation during the training of the enhanced model. Furthermore, we implemented a consensus model that employs modified masks, where classes with low confidence are substituted by the dominant classes identified through a majority vote from the redundant models. To further improve robustness, we extended the same approach to fuse the dataset with different class compositions based on imagery from the Korea Multipurpose Satellite 3A (KOMPSAT-3A). Performance evaluations were conducted on three network architectures: a simple network, U-Net, and DeepLabV3. In the single-dataset case, the performance of the enhanced and consensus models improved by an average of 2.49% and 2.59% across the network architectures. In the multi-dataset scenario, the enhanced models and consensus models showed an average performance improvement of 3.37% and 3.02% across the network architectures, respectively, compared to an average increase of 1.55% without the proposed method.  
satellite imagery; KOMPSAT; deep-learning; land cover segmentation; overfitting; semantic segmentation
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 3 Methods
### Terrain Feature Extraction
#### 3.2.2 Terrain Feature Extraction Network  
To facilitate the processing of grid data by convolutional neural networks (CNNs), which are typically designed for RGB images, the channel dimension of the grid data must be expanded to simulate a three-channel image. This expansion is denoted by the operation expand_channels, which replicates the single-channel grid pseudo-image \(\mathbf{G}\) across three channels, yielding a tensor \(\mathbf{Grid}_{\text{RGB}}\) with dimensions \([3,H_{g},W_{g}]\), where \(H_{g}\) and \(W_{g}\) represent the height and width of the grid, respectively:  
\[\mathbf{Grid}_{\text{RGB}}=\text{expand\_channels}(\mathbf{G})\in\mathbb{R}^{ 3\times H_{g}\times W_{g}} \tag{15}\]  
For batch processing, the tensor is further extended to accommodate a batch size of \(bs\), resulting in a four-dimensional tensor \(\mathbf{Grid}_{\text{batch}}\):  
\[\mathbf{Grid}_{\text{batch}}\in\mathbb{R}^{bs\times 3\times H_{g}\times W_{g}} \tag{16}\]  
The environmental state vector \(\mathbf{State}\) is defined to encapsulate additional necessary inputs from the environment, excluding the target point cloud. It is represented as \(\mathbf{State}\in\mathbb{R}^{bs\times S_{d}}\), where \(S_{d}\) is the dimensionality of the state information.  
The feature extraction network, denoted as GridNet, is composed of a CNN model \(\text{CNN}_{\text{GridNet}}\) for processing grid data and a fully connected network \(\text{FCN}_{\text{GridNet}}\) for processing state data. The CNN model, incorporating depthwise separable convolutions and an inverted residual structure, transforms the input dimensions as follows:  
\[\text{CNN}_{\text{GridNet}}:\mathbb{R}^{3\times H_{g}\times W_{g}}\to \mathbb{R}^{C_{g}} \tag{17}\]  
where \(C_{g}\) represents the number of feature channels produced by the CNN. The fully connected network processes the state vector:  
\[\text{FCN}_{\text{GridNet}}:\mathbb{R}^{S_{d}}\to\mathbb{R}^{C_{s}} \tag{18}\]  
where \(C_{s}\) is the size of the feature vector for the state data.  
The environmental feature vector \(\mathbf{Feat}\) is obtained by concatenating the output of the CNN and FCN models:  
\[\mathbf{Feat}=\text{concat}(\text{CNN}_{\text{GridNet}}(\mathbf{Grid}_{ \text{RGB}}),\text{FCN}_{\text{GridNet}}(\mathbf{State}))\in\mathbb{R}^{C_{g} +C_{s}} \tag{19}\]MobileNetV2 [15] is selected for its efficiency and is specifically adapted for this application. This structure initially expands the channel count of the input feature map through a lightweight expansion layer. It elevates the dimensions before applying depthwise separable convolutions for feature extraction. Finally, a linear projection layer reduces the dimensions back to the original size.
## 4 Lessons Learned
### Understand Users' Data Requirements  
We have observed that providing satellite products that are fit for purpose, i.e., datasets that are appropriate for the operational applications on which the course participants are working, increases the likelihood that operational scientists will use satellite data in their studies. For example, fisheries management applications, such as determining ecological tends or characterizing fish habitats, require science quality datasets that go back decades. Typically, daily satellite observations are too frequent for these long-term studies, requiring weekly or monthly versions of the dataset, and often an anomaly product is required to evaluate changes in environmental conditions. In addition, most end-users are interested in a satellite product type, such as chlorophyll or sea surface temperature, and are less concerned with which sensor or satellite collected the data. Therefore, products such as the Multi-Scale Ultra-High Resolution sea surface temperature dataset [39] and the ESA's Climate Change Initiative Ocean Color products [25] that merge together data from multiple sensors and platforms to create long timeseries are extremely valuable for fisheries applications. Near real-time data are also needed for optimizing ship surveys in real-time and developing models in the emerging field of dynamic ocean management [9; 10; 11].
## 4 Discussion  
The NDVI is widely used in various applications and it is also a commonly used index to monitor drought severity because there is a close relationship between vegetation vigor and available soil moisture, especially in arid and semiarid areas [3]. The NDVI indicates the severity of vegetation stress resulting from water stress. One of the main advantages of the NDVI-derived VCI is that, because it is a satellite-based drought product, it can provide near real-time data over the globe at a relatively high spatial resolution. In addition, the NDVI-derived VCI uses a completely independent methodology for monitoring drought, while many of the other meteorological indices rely on station-based meteorological and agricultural data. However, studies showed that, in some cases, the sensitivity of the NDVI to the water stress of vegetation is limited. The VIUPD-derived VCI is also a satellite-based drought product and can use a completely independent methodology for monitoring without station-based data being inputted.