esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dry (DBD) and Wet (WBD) Bulk Density  
The ANOVA (95% significance) for DBD and WBD in water are shown in Tables 4 and 5, respectively. FA and CDWG factors had significant influences on DBD and WBD. There is also an interaction between factors; thus, one factor can influence the other for both densities.  
The behavior of DBD and WBD as a function of FA and CDWG can be seen in Figure 5a,b. Figure 5a shows that increasing FA in the mortar causes a nonlinear DBD decrease. The WBD was shown to be almost constant for FA variations from 3.8% to 5.5% and from 6.7% to 8.1%. However, there was a decrease in WBD with an FA increase from 5.3% to 6.7%. Figure 5b shows that both densities (dry and saturated) decrease with an increase in CDWG. With a smaller addition of foam, the bubbles of air incorporated gather by dehydration, creating larger bubbles. These density variations occur given an increase in water content (FA increase) that makes bubbles collapse by a reduction of the hydro-lipophilic film, which makes them less resistant to the strength of the medium [52; 53]. It is also due to the union of air bubbles incorporated into the mortar by dehydration; that is, it is a consequence of having CDW granulometry variation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Degrees of Freedom** & **Sum of Squares** & **Mean of Squares** & \multirow{2}{*}{_F-Value_} & \multirow{2}{*}{_p-Value_} & **Percent** \\  & & & & & & **Contribution** \\ \hline CDW granulometry (CDWG) & 2 & 51,367 & 25,683 & 85.55 & 0.0000 & 10 \\ Foam amount (FA) & 3 & 440,010 & 146,670 & 488.57 & 0.0000 & 81 \\ Interaction between CDWG and FA & 6 & 41,163 & 6860 & 22.85 & 0.0000 & 8 \\ Error & 24 & 7205 & 300 & & 1 \\ Total & 35 & 539,744 & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dry bulk density (DBD) ANOVA.  
Figure 5: Densities (DBD and WBD) as a function of FA (**a**) and CDW granulometry (CDWG) (**b**).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Degrees of Freedom** & **Sum of Squares** & **Mean of Squares** & \multirow{2}{*}{_F-Value_} & \multirow{2}{*}{_p-Value_} & **Percent** \\  & **Percent** & & & & & **Contribution** \\ \hline CDW granulometry (CDWG) & 2 & 87,120 & 43,560 & 146.8 & 0.0000 & 45 \\ Foam amount (FA) & 3 & 67,239 & 22,413 & 75.5 & 0.0000 & 35 \\ Interaction between CDWG and FA & 6 & 31,670 & 5278 & 17.8 & 0.0000 & 16 \\ Error & 24 & 7122 & 297 & & & 4 \\ Total & 35 & 193,151 & & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wet bulk density (WBD) ANOVA.  
A significant difference between DBD and WBD values can also be observed. This difference is associated with elevated values of WA, which are discussed below.
# Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications
## 3 Performance Parameters
### Stretchability  
Traditional silicon electronics for sensing are rigid and brittle. Large deformations lead to structural damage and performance failure. However, the skin accommodates the movements of the body by bending and stretching itself. Wearable strain sensors must be stretchable to suit body activities. PDMS-based flexible resistive strain sensors have different stretchabilities that depend on their microstructure and sensing component materials. Graphene/PDMS composite strain sensors typically exhibit low stretchability; their measured structural strain is within a range of 0-10% due to the lack of robust percolating networks of the low aspect ratio nanomaterials [71; 73; 74]. The stretchability is considerably improved to 60-100% by using silver nanowire networks/PDMS [68], carbon black nanoparticles/PDMS [75], and EGaIn/PDMS [66] as the active sensing material of the flexible strain sensors. Some researchers have taken advantage of carbon nanotube (CNT) films with highly homogeneous microcrack propagation and lateral interconnection; highly stretchable PDMS-based strain sensors have been fabricated and are capable of measuring strains up to 280% [46]. PDMS-based flexible resistive strain sensors with different stretchabilities can detect different types of human motion, including breathing, speech, blinks, pulses, and bending/unbending of joints.
## 3 Exploration and Production
### Production Technique  
In situ and surface retort technologies are used to extract oil from oil shale. These methods vary in their processes and environmental impacts. Table 1 summarizes common in situ and surface retort technologies [36].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Description** & **Advantage** & **Disadvantage** \\ \hline \multirow{8}{*}{In situ} & This method & \multirow{2}{*}{Environnentally} & \multirow{2}{*}{Relies heavily on electricity} \\  & involves & & \\  & underground & friendly due to the & \\  & heating of oil shale, & freeze wall that & \\  & transforming & prevents & \\  & kerogen into shale & groundwater & \\  & oil & it employs a & contamination, \\  & [37; 38; 39] & freeze wall for & also does not & \\  & groundwater & require surface & \\  & preservation and & mining, reducing & \\  & utilizes electrical & environmental & \\  & power for shale & disruption. & \\  & heating & & \\ \hline \multirow{8}{*}{In situ} & This technique & \multirow{2}{*}{The fracturing of oil shale improves electric fractions. By fracturing the oil shale, it allows for more efficient heat distribution and oil extraction.} & Like ICP, the use of electric heating could be an \\  & & oil extraction. & \\  & oil extraction. & & \\ \hline \multirow{8}{*}{Chevron CRUSH} & This approach & \multirow{2}{*}{Staged process of heating and solvent injection for oil shale. For improved efficiency, it uses a staged procedure.} & \multirow{2}{*}{The process is complex and may require substantial capital investment.} \\  & & & \\ \end{tabular}
\end{table}
Table 1: Description of in situ and surface retort of oil shale resources.  
These technologies have been developed to enhance the extraction of oil from oil shale while simultaneously addressing issues related to environmental sustainability and energy efficiency. They continue to evolve, responding to new global challenges, such as climate change, in a quest to create more sustainable and economically viable solutions. While all these methods have their distinct advantages and disadvantages, the choice of method would depend on several factors, such as the specific geology of the oil shale deposit, local environmental regulations, availability of resources, and capital investment capabilities.
## 5 Verification on Robustness of the Used Methods against Noise
### Simulation Experiment Verification  
To verify the feasibility of the proposed method for noisy signal analysis, a simulation experiment is designed and carried out. First, 100 simulated signals with White Gaussian noise are generated to simulate the actual case without fault and a sample is shown in Figure 21. When a defect appears on one of the main parts of rotating machinery, such as bearing or gearbox, periodic impulse signals are generated in operation. Nevertheless, influenced by non-stationary operation, noise in sampling and so on, it is very difficult to extract features corresponding to the defect. To simulate the related actual case, 100 simulated signal of the length 2048 is composed for the periodic impulses with a period of 0.25 s, as illustrated in Figure 22a. The sampling frequency is 2048 Hz. White Gaussian noise is added with the signal-to-noise ratio per sample of 9 dB, illustrated in Figure 22b. Obviously, the impulse components are masked by the noise, and we can hardly find the features of periodic impulses of the noisy signal sample in Figure 22b.  
Figure 21: The simulated signal sample with White Gaussian noise.  
Figure 22: **(a)** The periodic impulse signal sample with a period of 0.25 s and **(b)** the noisy impulse signal sample.  
The proposed multiwavelet method is adopted for analyzing all the simulated signal samples. Since the multiwavelet transform is applied to process the signals of all the simulated condition to the extent of level 3, by research experience, there are eight frequency bands attained. The normalized multiwavelet transform information entropy of the simulated signal is computed according to Equation (19), which is shown in Figure 23. According to Figure 23, we can find that the multiwavelet entropy values of White Gaussian noise samples on normal condition are 0.67-0.83. Meanwhile, the multiwavelet entropy values of noisy simulated signals on fault condition are 0.52-0.59. The result shows that the proposed method can effectively distinguish the different conditions using the noisy signals.
# Imogolite Nanotubes: A Flexible Nanoplatform with Multipurpose Applications
## 2 Imogolite Synthesis
### Effects of Ge Substitution  
One of the most blatant examples of progress in INT synthesis arises from the isomorphic substitution of silicon by germanium [48; 49]. Beyond a twofold increase in external diameter, replacing TEOS with a Ge alkoxide (e.g., TEOG: tetraethoxys germane) enables producing concentrated (molar) suspensions of Ge-imogolite analogues [50], an important key step towards large scale applications. [PERSON] et al. observed that the initial concentration of aluminum salt (C\({}_{\rm Al}\)) controlled the resulting  
Figure 2: Flowchart for the two main synthesis methods of imogolite nanotubes: [PERSON] et al. [22]; [PERSON] et al. [38]. TEOS: tetraethylorthosilicate; ASB: Aluminum tri-sec-butoxide.  
nanotube shape, producing either double-walled (Ge-DWINT, C\({}_{\rm AI}\) \(<\) 0.4 mol\(\cdot\)L\({}^{-1}\)) or single-walled nanotubes (Ge-SWINT, C\({}_{\rm AI}\) \(>\) 0.75 mol\(\cdot\)L\({}^{-1}\)) [51]. The mechanism involved seems to be a subtle balance between attractive and curvature energies during the early stage of the synthesis [51; 52]. More interestingly, the energy minimum in the strain energy depends on the substitution ratio [Si]/([Si] + [Ge]) and allows us to predict the INT diameters with respect to the initial synthesis conditions [53; 54]. In contrast to Si-NTs, Ge-analogues form short nanotubes with a reduced length (\(<\)100 nm) [29; 49; 54]. On the other hand, [PERSON] et al. demonstrated that micron-long Ge-INTs can be synthesized by using the thermal decomposition of urea (CO(NH\({}_{2}\))\({}_{2}\)) to produce hydroxyl ions instead of the slow injection of NaOH [55]. Hence, the synthesis of (OH)\({}_{3}\)Al\({}_{2}\)O\({}_{3}\)Si\({}_{x}\)Ge\({}_{1-x}\)(OH) INT provides us with a unique system of 1D nanostructures with monodisperse sizes and changeable aspect ratio (Figure 3).
## 2 SE-TransUNet Water Seepage Detection Model
### SE Channel Attention Mechanism  
The SE-Block (Squeeze-and-Excitation block), proposed by [PERSON] et al. [18] in 2017, establishes dependencies among feature channels. Beyond conventional CNN properties like local connectivity and weight sharing, the SE-Block module learns inter-channel relationships to derive channel-wise weights, highlighting informative feature channels while suppressing less relevant ones for the target task. This mechanism enhances neural network performance [19] and enables effective extraction of critical features from leakage images. The SE-Block structure comprises four components, as illustrated in Figure 2.  
Figure 1: SE-TransUNet model architecture for water seepage detection.  
Figure 2: Squeeze-and-Excitation block structure.  
Transformation  
A standard convolutional operation generates uncalibrated feature maps \(\mathbf{U}\in\mathbf{R}^{H\times W\times C}\), where \(H\), \(W\), and \(C\) denote the height, width, and channel dimensions, respectively.  
Squeeze Operation  
Global Average Pooling (GAP) aggregates spatial information within each channel to produce channel-wise statistics \(\mathbf{z}\in\mathbf{R}^{C}\). For the \(c\)-th channel:  
\[z_{c}=\mathbf{F}_{\text{squeeze}}(\mathbf{u}_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j =1}^{W}u_{c}(i,j). \tag{1}\]  
This eliminates spatial interference while preserving inter-channel relationships, forming a compressed descriptor that can capture global contextual information for the identification of water leakage in tunnel secondary lining.  
Excitation Operation  
A gating mechanism with two fully connected (FC) layers learns nonlinear channel dependencies:  
\[\mathbf{s}=\mathbf{F}_{\text{excitation}}(\mathbf{z},\mathbf{W})=\sigma[g(\mathbf{z},\mathbf{W})]=\sigma [\mathbf{W}_{2}\delta(\mathbf{W}_{1}\mathbf{z})]. \tag{2}\]  
where \(\mathbf{W}_{1}\in\mathbf{R}^{(C/r)\times C}\) and \(\mathbf{W}_{2}\in\mathbf{R}^{C\times(C/r)}\) are learnable weights, \(\delta(\cdot)\) denotes ReLU activation, \(\sigma(\cdot)\) is the sigmoid function, and \(r\) is a reduction ratio controlling model complexity.  
Scale Operation  
The learned channel-wise weights \(s\) are applied to the original feature map \(U\) via element-wise multiplication across channels, generating the recalibrated output \(\widetilde{\mathbf{X}}\):  
\[\widetilde{\mathbf{x}}_{c}=\mathbf{F}_{\text{scale}}(\mathbf{u}_{c},s_{c})=s_{c}\mathbf{u}_{c}. \tag{3}\]  
\[\widetilde{\mathbf{X}}=[\widetilde{\mathbf{x}}_{1},\cdots,\widetilde{\mathbf{x}}_{c}, \cdots,\widetilde{\mathbf{x}}_{c}]. \tag{4}\]
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology
### AI Model Framework for Doppler Weather Profile Analysis
#### 2.4.2 The Proposed IIS-LSTM Modelling Approach
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyper-Parameters** & **Optimal Set Grid Search** \\ \hline \multirow{8}{*}{_IIS-LSTM_} & Epochs & [5; 10; 50; 100; 200] \\  & Activation function & [SoftMax, _Tanh, ReLU,_ \\  & Leaky _ReLU, sig_] \\  & Batch size & [100; 500, 700, 1000, 2000] \\  & Optimiser, Drop rate & [_Adam_], [0.1, 0.2] \\  & _IIS-LSTM_ filter & [50, 60, 100, 200, 500] \\  & Dropout & Yes \\  & SNR & [0–30] \\  & Platform & Python (version 3.9) \\ \hline \multirow{8}{*}{_CNN_} & Epochs & [5; 10; 50; 100, 200] \\  & Activation function & [SoftMax, _tanh, ReLU,_ \\  & Leaky\_ReLU, sig_] \\  & Batch Size & [100; 500, 700, 1000, 2000] \\  & Optmiser, Drop rate & [Adam ], [0.1, 0.2] \\  & _CNN_ filter & [50; 60, 100, 200, 500] \\  & Pooling, Padding & [same] \\  & Number of Dense & [3 for Input and 1 for Output] \\  & layers used & \\  & Dropout & Yes \\  & SNR & [0–30] \\  & Platform & Python (version 3.9) \\ \hline \multirow{8}{*}{_XGBoost_} & Gamma & [Tuned with loss function] \\  & Maximum Delta & [No] \\  & Step & [0.5–1] \\  & Lambda & [used for reducing overfitting] \\ \cline{1-1}  & Alpha & [used for high dimensionality] \\ \cline{1-1}  & Weight & [\textgreater{}0] \\ \cline{1-1}  & Seed & [0] \\ \cline{1-1}  & max depth & [3–10] \\ \cline{1-1}  & Platform & _MATLAB_ R2023b \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal architecture (in RED for _IIS-LSTM_). In the context of our work, \(DS_{g}\) represents the normalised ground truth Doppler shift, while \(-\bar{DS}_{g}\) denotes the estimated normalised Doppler shift for sample g within the total N training samples. In contrast, the LSTM algorithm serves as the learning model, demonstrating proficiency in comprehending and forecasting patterns. Figure 13 shows the effects of varying spreading factors (SFs) on LoRa performance. Simulation started on 1 January 2024 for 24 h, with simulation steps executed every second. The bandwidth is set at 500 MHz, the carrier frequency range is between 5091 to 5250 MHz, and the application payload is set to 55. Figure 5: Doppler shift of the Globalstar satellite with initial satellite data. Figure 6: Doppler rate of the Globalstar satellite with initial satellite data. Figure 8: Latency Rate of Globalstar satellite with initial satellite data. Figure 7: Latency of the Globalstar satellite with initial satellite data. Figure 9: Doppler shift of the ISS NAUKA satellite with initial satellite data. Figure 11: Latency of the ISS NAUKA satellite with initial satellite data. Figure 12: Latency rate of the ISS NAUKA satellite with initial satellite data. Figure 10: Doppler rate of the ISS NAUKA satellite with initial satellite data.
## 3 Results
### Factor Analysis on UD and USD Guidelines  
Table 4 shows the results of factor analysis using the principal component method on the subjective scores of the UD guidelines. In Table 4, the results of factor analysis were summarized as one principal component for UD. The highest explanatory guideline for UD was simplicity (U3), followed by appropriate proper size and space (U7), error-proof design (U5), equivalent use (U1), low physical effort (U6), and flexible design (U2).  
Table 5 shows the results of factor analysis on the subjective scores of the USD guidelines. In Table 5, the results were summarized as one principal component for USD. The highest explanatory guideline for USD was ensuring safety and health (S4), followed by diversity and inclusion (S5), accessibility (S3), flexibility (S2), sustainability (S6), and physical support (S1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**UD Scores**} & \multirow{2}{*}{**USD Scores (B)**} & \multirow{2}{*}{**Mean Test \(p\)-Value *} & \multirow{2}{*}{**(\%) = B/A**} \\
**USD Guideline** & & & & & \\ \cline{2-5}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline S1. Physical support & 5.256 & 0.737 & 5.412 & 0.781 & 0.063 & 103.0\% \\ S2. Flexibility & 5.062 & 0.752 & 5.160 & 0.715 & 0.226 & 101.9\% \\ S3. Accessibility & 5.152 & 0.735 & 5.506 & 0.669 & 0.001 * & 106.9\% \\ S4. Ensuring safety and health & 5.448 & 0.734 & 5.679 & 0.728 & 0.004 * & 104.2\% \\ S5. Diversity and inclusion & 5.157 & 0.791 & 5.456 & 0.786 & 0.001 * & 105.8\% \\ S6. Sustainability & 5.028 & 0.892 & 5.493 & 0.831 & 0.001 * & 109.2\% \\ Total mean & 5.184 & 0.629 & 5.443 & 0.622 & 0.001 * & 105.0\% \\ \hline \hline \end{tabular}  
* Significant at significance level 0.01, SD = Standard deviation.  
\end{table}
Table 3: Mean comparison tests between UD and USD scores according to USD guidelines.  
Figure 3: Mean scores of UD and USD according to USD guidelines.  
Table 5 also shows the results of factor analysis on the subjective scores of the detailed USD guidelines. In Table 5, the results of factor analysis show one principal component. The highest explanatory guideline for the detailed USD was simplicity (S3.1), followed by equitable use (S5.1), error-proof design (S4.1), work sustainability (S6.2), appropriate size and space (S1.2), and safety and health assurance policy (S4.2).
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 5 Genomics in Prognosis and Early Detection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Liquid Biopsy** & **Tissue Biopsy** \\ \hline Sample Type & Blood (ctDNA, cfDNA) & Solid tumor tissue \\ Invasiveness & Minimally invasive & Invasive (surgical) \\ Real-Time Monitoring & Yes & No \\ MRD Detection & Possible & Limited \\ Genomic Insights & Dynamic and longitudinal & Static \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between liquid biopsy and traditional tissue biopsy in cancer diagnostics. Liquid biopsy offers a minimally invasive alternative with dynamic monitoring capabilities. MRD = minimal residual disease. more integral part of cancer care, helping to detect cancer earlier, predict outcomes more accurately, and tailor treatments to each patient's unique genetic profile.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Preprocess**} & \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Dataset**} \\ \cline{3-8}  & & **F-MNIST** & **CIFAR10** & **SVHN** & **CAR196** & **CUB200** & **CUB200** & **EuroSAT** \\  & & **(28 \(\times\) 28)** & **(32 \(\times\) 32)** & **(32 \(\times\) 32)** & **(224 \(\times\) 224)** & **(64 \(\times\) 64)** & **(128 \(\times\) 128)** & **(64 \(\times\) 64)** \\ \hline \hline \multirow{8}{*}{SLIC-75} & \multirow{4}{*}{GCN-1 Layer} & \multirow{4}{*}{\(89.18\to\)} & \multirow{4}{*}{\(50.56\to\)} & \multirow{4}{*}{\(74.13\to\)} & \multirow{4}{*}{\(55.71\to\)} & \multirow{4}{*}{\(61.92\to\)} & \multirow{4}{*}{\(65.74\to\)} \\  & & & & & & & & \\ \cline{1-1} \cline{4-10}  & & & **59.32** & **55.85** & **75.38** & **55.79** & **63.01** & **65.93** & **66.04** \\ \cline{1-1} \cline{4-10}  & & & 60.46\(\to\) & 75.47\(\to\) & 57.61 \(\to\) & 65.85 \(\to\) & 67.21 \(\to\) & 66.39 \(\to\) \\ \cline{1-1} \cline{4-10}  & & **90.98** & **60.84** & **76.61** & **57.88** & **65.92** & **67.74** & **66.77** \\ \cline{1-1} \cline{4-10}  & & 90.62\(\to\) & 66.46 \(\to\) & 80.51\(\to\) & 61.44 \(\to\) & 68.22 \(\to\) & 69.90 \(\to\) & 74.76 \(\to\) \\ \cline{1-1} \cline{4-10}  & & **90.90** & **67.28** & **81.17** & **61.76** & **69.06** & **72.45** & **74.96** \\ \cline{1-1} \cline{4-10}  & & 92.01\(\to\) & 68.63 \(\to\) & 81.99 \(\to\) & 62.73 \(\to\) & 70.19 \(\to\) & 72.10 \(\to\) & 76.37 \(\to\) \\ \cline{1-1} \cline{4-10}  & & **92.25** & **69.71** & **82.52** & **63.46** & **70.62** & **73.85** & **76.96** \\ \cline{1-1} \cline{4-10}  & & 91.21 \(\to\) & 73.09 \(\to\) & 80.71 \(\to\) & 60.86 \(\to\) & 70.48 \(\to\) & 69.64 \(\to\) & 75.48 \(\to\) \\ \cline{1-1} \cline{4-10}  & & **91.63** & **73.22** & **82.02** & **61.20** & **72.99** & **71.26** & **76.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of ChebNet, GCN, and GAT after adding the ArcFace loss function with a weight of 0.2.  
can be found at [[http://www.vision.caltech.edu/datasets/cub_200_2011](http://www.vision.caltech.edu/datasets/cub_200_2011)]([http://www.vision.caltech.edu/datasets/cub_200_2011](http://www.vision.caltech.edu/datasets/cub_200_2011)), and the EuroSAT dataset can be found at [[https://github.com/phelber/EuroSAT](https://github.com/phelber/EuroSAT)]([https://github.com/phelber/EuroSAT](https://github.com/phelber/EuroSAT)). The authors declare no conflict of interest.
## 3 Underwater Routing Protocols
### Ducs  
The distributed underwater clustering scheme (DUCS) [67] divides an underwater network into clusters. Every cluster has one major node called a cluster head that receives information from the rest of the one-hop neighbors in the same cluster. It then processes the information to remove redundant data and efficiently transmit the desired data to the sink using other cluster heads. A cluster head transfers the information of a node in one cluster to a node in another cluster. It achieves energy efficiency by reducing the number of control messages among sensor nodes through the cluster head. It also leads to a high packet delivery ratio. However, the movement of nodes with water currents seriously degrades the performance of the system. In addition, the cluster head is overloaded, and its energy is depleted rapidly, which creates void holes in the network and degrades its performance.