esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The results indicated that the obtained materials used on glass surface presented superhydrophobicity and self-cleaning properties. [PERSON] et al. [28] created nanohybrid silica coating materials by polymerization and the sol-gel process. The performed analysis showed that the nanohybrid film exhibited hydrophobic properties and high transmittance (89-97% in the visible light area). [PERSON] et al. [29] evaluated the effect of silica nanoparticles coated with the per-fluorinated group on the wettability of the surface. It was revealed that the surfaces presented superhydrophobic properties, which was determined using different liquids (water, ethylene glycol, and seed oil). [PERSON] et al. [30] showed that the mesoporous silica can be modified using fluorinated organosilane. They indicated that the glass surface covered with the prepared materials presented superhydrophobic qualities and excellent thermal stability. [PERSON] et al. obtained fluorinated silica nanoparticles by the sol-gel process. These were deposited on the steel substrates, thus achieving films with superhydrophobic wetting behavior [31]. [PERSON] et al. [32] synthesized fluorinated nanoparticles through the sol-gel method in order to create surfaces with ultrahydrophobic and ultraoleophobic properties (carbonate and sandstone rock surfaces). Results showedthat the surfaces were stable and presented different levels of wettability as a function of the liquid deposited on the surface. In the present work, the effect of the fluorinated solution on the structural, morphological, and optical properties of coatings deposited on glass substrates was examined. This fluorinated solution was realized through the dissolution of perfluorooctanoic acid (PFOA) in ethanol, and sol-gel synthesis of hybrid silica materials was utilized to obtain fluorinated coated materials. Silane precursors with different alkyl chain lengths (tetraethyl orthosilicate (TEOS), triethoxymethylsilane (MTES), and trimethoxyhexadecylsilane (HDTMES)) were utilized to synthesize hybrid silica materials. It was demonstrated that the process of obtaining sol-gel fluorinated silica materials does not require the existence of stabilizing agents that reduce the storage period, are easy to implement, and provide smooth layers with uniform thicknesses. The chemical structure, thermal behavior, morphology, wettability, surface roughness, and optical properties of final materials were assessed.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 5 Results
### Signal #1
#### 5.1.1 Sensitivity Analysis for the GWT Parameters
The corresponding TF representations are reported in Figure 8.  
On the other hand, \(\beta\geq 4\div 6\) might return acceptable results, depending on the paired \(\gamma\) values. Generally speaking, \(\gamma\leq 2.0\div 2.5\) returned distorted results, at least for the application investigated here. This is even more noticeable for \(\gamma\leq 1.5\), which were found  
Figure 8: Time–frequency distributions corresponding to the results shown in Figure 7 (\(\beta=2\) and \(\gamma\) equal to (**a**) 1, (**b**) 2, (**c**) 3, and (**d**) 4). to be unreliable for any value of \(\beta\). The results were instead less influenced by \(\beta\) when the symmetry parameter was set as equal or larger than \(2.0\div 3.0\). By way of example, for \(\beta=20\), any gamma larger than 2 returned almost the same ISE(\(t\)) time history (see Figure 9; the corresponding TF transforms are reported in Figure 10). In conclusion, the following points can be highlighted from this sensitivity analysis:  
1. Values of \(\beta\geq 20\) are all deemed suitable. Increasing \(\beta\) reduces the variability of the results, at least on the specific case study investigated here. Therefore, large values of \(\beta\) are recommended, independently from the selected \(\gamma\). Figure 10: Time–frequency distributions corresponding to the results shown in Figure 9 (\(\beta=20\) and \(\gamma\) equal to (**a**) 1, (**b**) 2, (**c**) 3, and (**d**) 4). Figure 9: Resulting ISE (signal #1). Values for. \(\beta=20\) and \(\gamma\) equal to (**a**) 1, (**b**) 2, (**c**) 3, and (**d**) 4. 2. \(\gamma\geq 3\) is suggested to avoid potential issues, even if \(2.5\leq\gamma\leq 3\) were found to be suitable as well for the range of \(\beta\) selected above, at least for this case study. The suggested range is depicted in Figure 11, considering the mean and standard deviation of the ISE(\(t\)) computed over the NOCs. As mentioned previously, it is important to have stationary and low variance over the training dataset. This condition was confirmed for \(\beta\geq 20\) and \(\gamma\geq 3\). This selected area is also intended to avoid the undesired time-domain sidelobes and frequency-domain asymmetries that arise from very small time-bandwidth and large symmetry values.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 2 Materials and Methods
### Acoustic Parameters of Water, Fat, and Glandular Tissue of the Breast
For soft tissues not containing fat, the average values of acoustic parameters are respectively: \(c\) = 1575 m/s, \(\alpha\) = 0.3 (dB/cm)/MHz \(\approx\) 3.5 (Np/m)/MHz, while for tissues containing fat: \(c\) = 1465 m/s, \(\alpha\) = 0.6 (dB/cm)/MHz \(\approx\) 6.9 (Np/m)/MHz [45]. The ultrasound velocity in water is, respectively: 1496.7 m/s for 25 \({}^{\circ}\)C, 1509.2 m/s for 30 \({}^{\circ}\)C, and 1522.9 m/s for 36.6 \({}^{\circ}\)C [40]. Based on data from the literature, ultrasound velocity in breast adipose tissue (fat layer under the skin) is 1470 m/s in vivo (at body temperature \(T\) = 36.6 \({}^{\circ}\)C). The temperature coefficient of breast fat in the temperature range 22-37 \({}^{\circ}\)C is \(dc/dT\) = \(-\)3.1 (m/s)/\({}^{\circ}\)C. The temperature dependence of the ultrasound velocity in water and fat is illustrated as a graph in Figure 6.  
According to data from the literature [40,41], the average ultrasound velocity in glandular breast tissue of a premenopausal woman in vivo is in the range 1450-1570 m/s, with 1510 m/s being the average value. The thickness of the breast skin is in the range of approximately 0.5-2.5 mm [46]. The average velocity of ultrasound in the skin is 1642 m/s [47]. The ultrasound attenuation coefficient \(\alpha\) in water is small, so it is defined in (dB/m), unlike in tissues where it is in dB/cm [40]. At 2 MHz, the attenuation coefficient of ultrasound in water is, respectively: 0.87 dB/m for 20 \({}^{\circ}\)C, 0.76 dB/m for 25 \({}^{\circ}\)C, 0.66 dB/m  
Figure 5: Three different scenarios for the passage of ultrasound beam rays in divergent geometry from the transmitter to four different receivers of a multi-element ultrasound ring array through a breast with a subcutaneous fat layer, immersed in water. for 30 \({}^{\circ}\)C, and 0.57 dB/m for 36.6 \({}^{\circ}\)C. Thus, it can be concluded that the attenuation of ultrasound in water is negligibly small compared to that in tissue. Attenuation in fat at body temperature is about 1.2 dB/cm for 2 MHz (increases approximately linearly with frequency). The attenuation coefficient of the ultrasonic wave in breast tissue for frequencies of 0.5-6 MHz in the temperature range of 25-37 \({}^{\circ}\)C is, on average, \(\alpha\) = (0.75 \(\pm\) 0.3).\(f^{1.5}\) dB/cm, i.e., 2.12 \(\pm\) 0.85 dB/cm for 2 MHz [40]. Decreasing the temperature increases the attenuation, but the changes are not large. The attenuation of ultrasound in the skin of the breast is, on average, \(\alpha\) = 2.4\(f^{0.6}\) dB/cm [40].
## 3 Results and Discussion
### Determination of Dissipated Power  
The representative acquired voltage charge plots (Q-V plots) with a quartz substrate placed 5 mm away are shown in Figure 5. For the grounded ring electrode around the capillary, the voltage-charge curve does not resemble a parallelogram but is similar to the shape of another system [57]. The shape remains similar with increased applied voltage, but the charge amplitude increases for argon and helium operation. The quartz surface is placed on top of a grounded copper plate and the measured voltage-charge curve is mostly rounded with one more or less pronounced jump on the rising slope.  
From the enclosed loop of the Q-V plot, one can determine the energy of a cycle and by multiplying with the frequency the determination of power is possible [71]. The respective energy values are 56 \(\upmu\)J (G1), 63 \(\upmu\)J (G2) for argon and 76 \(\upmu\)J (G1), 54 \(\upmu\)J (G2) for helium operation, representing a power dissipation of 1.00 W (G1), 1.15 W (G2) for argon and 1.38 W (G1), 0.97 W (G2) for helium. Previous investigation already states that plasma jet systems are not closed and energy is consumed by further reactions of the plasma inside as well as outside the capillary like light emission, heat production, and reactive species generation [56; 72].  
In Figure 6a-c, the determined power is presented at given applied voltage over different distances as well as for given distance over increased applied voltage. When changing the distance at constant voltage, a constant power is observed for helium and argon while an increase in power is found when the voltage amplitude is increased.  
Between G1 and G2 in each case, a tendency is observed for an increase in power dissipation for G1 in exchange for a decrease in power dissipation for G2. Increasing the applied voltage at a fixed distance leads to a continuous increase of dissipated power in G1 and G2. For the highest investigated voltage settings, G2 surpasses G1 in dissipated power. The power range overall between helium and argon shows comparable values for G2, while G1 in helium surpasses argon by a total amount of 0.7 W with a final value around 2.1 W. The total power dissipation achieved within this measurements is 3.9 W for argon and 4.7 W for helium operation. For further investigations, the applied voltage was set to either 8 kV\({}_{pp}\) with identical power dissipation values or 14 kV\({}_{pp}\) with comparable G2 values yet already increased G1 value for helium.  
Figure 5: Charge-voltage plot for device operation in argon (**left**) and helium (**right**), each for an applied voltage of 14 kV\({}_{pp}\) and a quartz plate at 5 mm distance.
# Research on Grouting Dynamic Monitoring Based on Borehole-Tunnel Joint Resistivity Method
## 3 Resistivity Monitoring  
During the stratum grouting process, the variation in resistivity exhibits distinct stage-specific patterns: in the initial stage, when grout rich in free water is injected into fractures, it introduces high ion-concentration conductive pathways into the rock strata, thereby causing a significant decrease in resistivity and forming low-resistivity conductive channels. As the grout diffuses, resistivity gradually decreases along the fracture strike, revealing anisotropic characteristics. During the solidification phase, water expulsion and gel structure densification drive a slow resistivity rebound. Upon complete consolidation, resistivity stabilizes at values 3-5 times higher than the original rock, forming high-resistivity anomalies. Under special conditions, such as fractured strata or high-pressure grouting, secondary resistivity fluctuations may occur, necessitating dynamic monitoring to identify abnormal responses. These patterns provide critical electrical criteria for evaluating grouting efficacy and issuing early warnings for potential water-conducting channels.
# Drill Hole Orientation: Its Role and Importance on the Compression Response of Pure Magnesium
## 1 Introduction
From identifying the time to failure in engineered components, such as pipelines [32; 33] and aerospace equipment [34], to medical devices, such as smart bandages [35], the information obtained from real-time monitoring forms an integral part of the design process and helps optimize the design to meet various requirements while avoiding predictable disasters. In all these cases, access to the region of interest is crucial. The easiest way to get access to these monitoring locations is by drilling a hole through the object of interest and placing necessary sensors in them to obtain real-time information. However, common sense dictates that this ease of access should not come at the expense of the strength of the component. Research into the impact of this drill/access hole on the strength of a component is surprisingly minimal in this regard, especially when it comes to light-weight materials and in the aerospace sector. Our earlier work [36] evaluated the effect of drill hole diameter in a vertical orientation on the strength to weight ratio of magnesium. It also detailed how the drill hole deforms under a compression load to give information on the size of sensors that can be placed inside. The results from that study indicated that the component need not necessarily experience a weakening in strength but can instead experience an improved mechanical response to compressive loads (i.e., when the drill hole diameter is 1.5 mm in an 8 mm cylindrical sample with d/D = 0.1875). That study was performed in a single orientation and suggested a hypothesis correlating light-weighting and strength enhancement. It is common sense that the orientation of the drill hole as well as its location can significantly affect the mechanical strength of the component. However, the lack of research in this regard implies that the influence of the orientation of a drill hole on the strength retention of a component warrants study. Accordingly, this study explores the effect of drill hole orientation on the compressive response of a cylindrical component while maintaining the drill hole diameter at 1.5 mm (the optimum drill hole diameter in the previous study). While several materials are employed in compressive applications, this study chose pure magnesium as the material of interest due to its versatile application in light-weighting. The effects of vertical, angular, and horizontal drill holes on the compressive strength of pure magnesium have been compared with monolithic or undrilled magnesium. The question of whether this compressive behaviour depends on the material itself warrants a separate study for the future.
## 3 System Model  
An overlay diagram of the HSTSNs with multiple potential secondary relays is considered as illustrated in Figure 1. This scenario comprises a primary satellite transmitter \(S\), a sensor node \(D\), i.e., the PU, potential CR secondary relays sets \(\{R_{1},R_{2},\cdots,R_{N}\}\) and the secondary users (SUs) sets \(\{C_{1},C_{2},\cdots,C_{N}\}\), respectively. Due to the heavy shadowing and obstacles, it is difficult to maintain a reliable direct communication (DC) link between \(S\) and \(D\). Therefore, the primary satellite has to recruit a secondary transmitter as a relay. In return, the primary networks authorize secondary transmitter spectrum access opportunities. All this potential secondary relay would have the willingness to be recruited by the primary satellite network. The chosen secondary relay does its best to transmit the data as much as possible. Without loss of generality, we assume one PU and one SU to simplify the analysis, which can be easy to extend to multi-users scenarios by using other subchannels.  
In the proposed model, all users access the channel based on time division multiple access (TDMA) to transmit their data, which means that the time slot is divided into three sub-time slots. The first fraction of a time slot is used for primary satellite transmitter broadcast its data to the selected secondary relay, and then, the selected relay retransmits the signal to the PU in the secondary sub-time slot by AF or DF relay protocols. At the last sub-time slot, the selected relay transmits its signal to the PU as payoffs. Therefore, if the multi sensor nodes are exist, the same progress can implement in different subchannels.
## 4 Results
### Performance of the Models
#### 4.1.1 Error Rates for Geography Categories  
Table 1 shows the error rates for new cancer cases and cancer incidence rates in geographical regions. The error rates have been interpreted to indicate the best models, which have been highlighted in bold and italic. The error rates show that the hybrid model performs the best in forecasting new cancer cases and cancer incidence rates for geographic regions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Geography** & **Category** & **Model** & **MSE** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline \multirow{8}{*}{Alberta} & \multirow{4}{*}{New Cancer Cases} & ARIMA & 0.0028 & 0.0377 & 0.0528 & 0.0444 & 0.6795 \\  & & LSTM & 0.0028 & 0.0379 & 0.0527 & 0.0452 & 0.6905 \\  & & _Hybrid_ & _0.0015_ & _0.0344_ & _0.0385_ & _0.0402_ & _0.8296_ \\  & & Prophet & 0.0032 & 0.0509 & 0.0564 & 0.0596 & 0.3438 \\ \cline{2-8}  & \multirow{4}{*}{Cancer Incidence Rate} & ARIMA & 0.0102 & 0.0629 & 0.1012 & 0.0786 & 0.0832 \\  & & LSTM & 0.0081 & 0.0671 & 0.0907 & 0.0820 & 0.0898 \\  & & _Hybrid_ & _0.0013_ & _0.0293_ & _0.0357_ & _0.0315_ & _0.8651_ \\  & & Prophet & 0.0051 & 0.0568 & 0.0714 & 0.0656 & 0.3887 \\ \hline \multirow{8}{*}{British Columbia} & \multirow{4}{*}{New Cancer Cases} & ARIMA & 0.0032 & 0.0383 & 0.0567 & 0.0468 & 0.6612 \\  & & LSTM & 0.0028 & 0.0416 & 0.0525 & 0.0491 & 0.7100 \\  & & _Hybrid_ & _0.0018_ & _0.0317_ & _0.0419_ & _0.0382_ & _0.8151_ \\  & & Prophet & 0.0038 & 0.0544 & 0.0614 & 0.0665 & 0.3208 \\ \cline{2-8}  & \multirow{4}{*}{Cancer Incidence Rate} & ARIMA & 0.0112 & 0.0816 & 0.0961 & 0.0935 & 0.0659 \\  & & LSTM & 0.0091 & 0.0769 & 0.0954 & 0.0934 & 0.1547 \\  & & _Hybrid_ & _0.0064_ & _0.0628_ & _0.0799_ & _0.0766_ & _0.3516_ \\  & & Prophet & 0.0071 & 0.0595 & 0.0845 & 0.0765 & 0.0301 \\ \hline \multirow{8}{*}{Manitoba} & \multirow{4}{*}{New Cancer Cases} & ARIMA & 0.0092 & 0.0762 & 0.0960 & 0.0844 & 0.4569 \\  & & LSTM & 0.0047 & 0.0547 & 0.0687 & 0.0677 & 0.6866 \\ \cline{1-1}  & & _Hybrid_ & _0.0004_ & _0.0169_ & _0.0205_ & _0.0199_ & _0.9752_ \\ \cline{1-1}  & & Prophet & 0.0061 & 0.0583 & 0.0779 & 0.0661 & 0.5133 \\ \cline{1-1} \cline{2-8}  & \multirow{4}{*}{Cancer Incidence Rate} & ARIMA & 0.0148 & 0.0910 & 0.1218 & 0.1202 & 0.1289 \\ \cline{1-1}  & & LSTM & 0.0054 & 0.0564 & 0.0732 & 0.0598 & 0.2090 \\ \cline{1-1}  & & _Hybrid_ & _0.0015_ & _0.0240_ & _0.0390_ & _0.0279_ & _0.8844_ \\ \cline{1-1}  & & Prophet & 0.0089 & 0.0877 & 0.1174 & 0.0922 & \(-1.7234\) \\ \hline \end{tabular}
\end{table} 
Table 1: Error rates of geography regions for new cancer cases and cancer incidence rate.
# Image Analysis Applications for Building Inter-Story Drift Monitoring
## 4 Results
While the point tracking technique is now typically capable of positioning a target in image with an accuracy as small as 0.02 to 0.03 pixels in crystal  
Figure 7: Inter-story responses for the 1-Hz test. clear images (references), image blur would significantly reduce the accuracy. Reducing the exposure time of the camera could reduce image blur to improve the point tracking accuracy. Using strong environmental lights or adopting a camera with high low-light sensitivity can help reduce the exposure time of the camera, and thus further reduce the blur due to fast movement. The rolling shutter effect is one of the potential sources of the measurement error. Typically, an image taken by a camera with a rolling shutter is generated by scanning a scene line by line from the top to the bottom of the image, rather than being taken at a single instant in time. A camera with a global shutter, which conceptually generates an image at an instant in time, is typically expensive. Most of the inexpensive cameras in the consumer market are rolling shutter based. In our cases, the reference points at the upper part of the image are scanned earlier than the tracking points at the lower part. The time differences between these points generate errors in the inter-story response measurement. Unfortunately, reducing exposure time by using a stronger environmental lighting does  
Figure 8: Inter-story responses for the 3-Hz test. Figure 9: Blurring of images due to movement. (**a**) Tracking point. (**b**) Tracking point images blurring due to movement (\(t\) = 1.514 to 1.616 s). (**c**) Tracking point images blurring due to movement (\(t\) = 1.717 to 1.818 s). not improve the rolling shutter effect. The image blurring effect and many other site dependent effects could be significantly higher than those in the laboratory. It also implies that higher camera resolution does not guarantee similar accuracy.
## 9 Conclusions  
This systematic literature review has provided a structured evaluation of ML and DL techniques in UWA communication, focusing on four key research questions. For **RQ1**, ML and DL models enhance _channel estimation_ by improving signal prediction and reducing BER. Techniques such as CNNs, LSTMs, and GANs adapt well to underwater variations, achieving MSE values as low as \(10^{-4}\). Regarding **RQ2**, _adaptive modulation_ powered by ML and DL dynamically optimizes transmission parameters. RL and meta-learning approaches yield BER reductions of up to 14.8% and throughput gains of 25%. For **RQ3**, ML/DL-driven _modulation recognition_ achieves classification accuracies exceeding 95%. CNN-RNN hybrids, transformers, and contrastive learning models improve signal detection, though precision variability (40-100%) under low-SNR and high-Doppler conditions necessitates refinements. **RQ4** highlights future research directions, including the development of lightweight and real-time ML/DL models, the integration of Physics-Informed Neural Networks, and reinforcement learning-based adaptive modulation strategies. Additionally, hybrid deep learning architectures using multi-scale feature fusion, data augmentation, and domain adaptation are promising for enhancing modulation recognition accuracy in noisy and dynamic underwater environments. This review provides a foundation for future AI-driven UWA communication advancements, unlocking new possibilities in deep-sea exploration, naval security, and maritime connectivity.  
This research was funded by Umm Al-Qura University, Saudi Arabia under grant number: 25 UQU4320199 GSSR01.  
**Acknowledgments: The authors extend their appreciation to Umm Al-Qura University, Saudi Arabia for funding this research work through grant number: 25 UQU4320199 GSSR01.**  
**Conflicts of Interest: The authors declare no conflicts of interest.**