esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Coatings and Treatments for Controlled Hydrate Formation: A Mini Review
## 3 Hydrate-Public Surface Treatments and Coatings
### Superhydrophobic and Omniphobic Coatings
Later, the water contact angles on the modified surface in air and cyclopentane were found to be 160\({}^{\circ}\) and 170.7\({}^{\circ}\) compared to 51\({}^{\circ}\) and 93\({}^{\circ}\) on bare X90 steel. The prepared surface turned the water droplets spherical when dropped on it. Just by tilting the surface at 5.7\({}^{\circ}\) and 2.4\({}^{\circ}\) in air and cyclopentane, respectively, the droplets can be rolled off from this surface immediately. Morphological analysis indicated that the hydrate particles formed spherical morphology, resulting in the reduction of the interfacial contact area and hydrate adhesion force to the solid surface. It was found during the hydrate detachment tests that a force of 0.0016 N was not enough to remove the hydrate particle from bare X90 steel, whereas from the superhydrophobic surface it can be removed by just applying a force of 0.00013 N.  
More recently, in a similar work, [PERSON] et al. [54] reported the preparation of a bio-inspired superhydrophobic coating using a static self-assembly method. Inorganic material of high hardness, flushing resistance, and controllable morphology, i.e., cerium oxide (CeO\({}_{2}\)) was used for polishing along with polydopamine (pDA) as a binder on X80 pipeline steel. Water contact angles of 154.7\({}^{\circ}\) and 155.5\({}^{\circ}\) were obtained on the CeO\({}_{2}\)/pDA@X80 coating in air and cyclopentane, respectively, compared to 82.6\({}^{\circ}\) and 84.7\({}^{\circ}\) on bare X80 substrate. The water droplet sticks to the metal substrate higher than a 90\({}^{\circ}\) tilt angle, while on the prepared surface just at an angle of 3\({}^{\circ}\) and 2\({}^{\circ}\) in air and CP, respectively, the drop can be rolled off. Mechanical stability of the coating was successfully ascertained through a 2-h-long sand abrasion test. Hydrate adhesion measurements on the coating were done with the help of a micromechanical force apparatus using cyclopentane hydrates before and after the sand abrasion tests. It was reported that the adhesion forces were reduced 98.9% on the superhydrophobic coatings compared to bare steel surface; this ultralow adhesion was maintained even after the sand abrasion tests. The adhesion force between the X80 steel surface and hydrate in the presence of a water droplet was too large to be measured using the micromechanical apparatus. However, the superhydrophobic coating did not allow this situation due to easy detachment of water droplets from it (Figure 3).
## 3 Controller Design and Stability Analysis
### Simulation Results for HSTs with Larger Lumped Uncertainty  
In order to prove the robustness of the system to the lumped uncertainty, two cases with lager lumped uncertainties are considered:  
Case1: \(o(\cdot)=2500\sin(0.02 vt)\), \(\theta=1.1*\pi/180\) and the parametric uncertainties are increased by 50%.  
Case2: \(o(\cdot)=5000\sin(0.02 vt)\), \(\theta=1.2*\pi/180\) and the parametric uncertainties are increased by 100%.  
Given that \(k_{c}=0.7\). Using the same design parameters and initial values, the position tracking error and velocity tracking error are shown in Figure 8, which illustrates the proposed controller has strong robustness to the lumped uncertainty.  
Figure 8: Control performance for HSTs with larger lumped uncertainty: (**a**) position tracking error, (**b**) velocity tracking error.  
Figure 7: Control performance for HSTs without anti-actuator saturation: (**a**) position tracking error, (**b**) velocity tracking error, (**c**) actual control force.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline  & & **T1** & \\
**Variable, n/nt (\%)** & **CEF** & **CEF + TASE** & _p_-**Value** \\ \hline Nasal secretions 1 & 8/8 (100) & 7/9 (78) & 0.471 \\ Ocular secretions 1 & 7/8 (88) & 7/9 (78) & 0.600 \\ Whiskers separation 1 & 2/8 (25) & 6/9 (67) & 0.153 \\ Lack of grooming 1 & 8/8 (100) & 6/9 (67) & 0.206 \\ Pilorectomy 1 & 8/8 (100) & 8/9 (89) & 0.999 \\ Hypoactivity 1 & 8/8 (100) & 9/9 (100) & - \\ Diarrhea 1 & 2/8 (25) & 0/9 (0) & 0.206 \\ \hline  & & **T2** & \\
**Variable, n/nt (\%)** & **CEF** & **CEF + TASE** & _p_-**Value** \\ \hline Nasal secretions 2 & 7/8 (88) & 6/9 (67) & 0.576 \\ Ocular secretions 2 & 6/8 (75) & 5/9 (56) & 0.620 \\ Whiskers separation 2 & 2/8 (25) & 7/9 (78) & 0.057 \\ Lack of grooming 2 & 8/8 (100) & 6/9 (67) & 0.206 \\ Piloerection 2 & 7/8 (88) & 5/9 (56) & 0.294 \\ Hypoactivity 2 & 6/8 (75) & 4/9 (44) & 0.335 \\ Diarrhea 2 & 2/8 (25) & 0/9 (0) & 0.206 \\ \hline  & & **T3** & \\
**Variable, n/nt (\%)** & **CEF** & **CEF + TASE** & _p_-**Value** \\ \hline Nasal secretions 3 & 6/8 (75) & 3/9 (33) & 0.153 \\ Ocular secretions 3 & 5/8 (63) & 1/9 (11) & 0.05 * \\ Whiskers separation 3 & 6/8 (75) & 2/9 (22) & 0.05 * \\ Lack of grooming 3 & 0/8 (0) & 0/9 (0) & - \\ Pilorection 3 & 5/8 (63) & 2/9 (22) & 0.153 \\ Hypoactivity 3 & 5/8 (63) & 1/9 (11) & 0.05 * \\ Diarrhea 3 & 1/8 (13) & 0/9 (0) & 0.471 \\ \hline \end{tabular}
\end{table} 
Table 2: Clinical parameters in relation to stress and suffering at T1, T2 and T3 (24, 48 and 72 h post-inoculum, respectively) for nasal secretions, eye secretions, position of whiskers, lack of grooming, hair erection, lethrapy, and diarrhea. * _p_\(\leq\) 0.05. Figure 3: Interleukin levels in T3 and T7 (treatment day 3 and 7, respectively). CEF = ceftaxone. CEF + TASE = ceftaxone + thiosulfinate-enriched _Allium sativum_ extract. Mean \(\pm\) SEM. * _p_\(<\) 0.05.  
showed _Enterococcus faecalis_ in peritoneal liquid. There was also one rat that was negative for both bacteria. Regarding the histopathological analysis and organ evaluation, the inflammatory cell count, the presence of bacteria in the liver and on the peritoneal surface, as well as the congestion and hepatic vacuolization between treatment groups (group II and III), no statistically significant differences were found (Table 4).
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 3 Results and Discussion
### Parameterization of the MNAG Model
#### 3.3.2 Nanocrystalline Ta  
To further test the robustness of the MNAG model, additional shock simulations were performed on modified microstructures in Ta--in this case, nanocrystalline Ta with varying grain sizes. The fitted void number density and damage based on the MNAG model, compared to the MD data, are shown in Figure 5 for nanocrystalline Ta with grain sizes of 20 nm, 10 nm, and 5 nm. At the smaller grain size, there was a higher number density of voids due to a greater fraction of grain boundaries that served as void nucleation sites. However, the overall damage evolution did not show any significant variation in grain size. In addition, from the right-hand panels in Figure 5, it could be observed that void coalescence set in earlier in nanocrystalline Ta compared to the single-crystal Ta. This could be explained by the fact that most of the voids nucleated at grain boundaries and, as a result, coalesced relatively easily along the grain boundaries due to higher local stresses as well as faster matter transport at the grain boundaries [67].  
Figure 4: Snapshots of single-crystal Ta along (**a**) (001), (**b**) (110), and (**c**) (111) orientation at 50 ps. The atoms are colored based on centrosymmetric parameters [60].  
Figure 5: _Cont._The fitted set of MNAG parameters for these simulations are listed in Table 3. With the decrease in grain size, there was a decrease in the void nucleation stress \(\sigma_{n}\), which suggested a higher void nucleation rate due to a higher number of void nucleation sites at a smaller grain size. The void growth threshold \(\sigma_{g0}\) was similar for a grain size of 20 nm and 10 nm but much higher for 5 nm suggesting arrested void growth at a small grain size of 5 nm. For all the grain sizes, the void coalescence coefficient, \(V_{c0}\), was similar suggesting that the void coalescence rate was insensitive to the grain size in the range of grain sizes considered here. Therefore, the spall failure was void-nucleation dominated at a smaller grain size of 5 nm, and void-growth dominated at a larger grain size of 10 nm and 20 nm, whereas the void coalescence rate was insensitive to the grain size. As a result, the final amount of damage was similar for all grain sizes, as shown in Figure 5.
## 1 Introduction
The task at hand encompasses not only the magnitude of data but also the speed and diversity of data that necessitates prompt processing and action-taking to guarantee uninterrupted functionality and safety within the realm of road transport [20; 22]. This paper aims to create a secure and efficient framework for VANETs by addressing trust, privacy, and scalability challenges. This study focuses on strategic aspects of VANETs, translating theoretical foundations into feasible solutions. The contributions of this paper can be summarized as follows:  
* We propose a new framework that prevents single-point failures in VANETs by improving security, privacy, availability, integrity, and network resilience through the use of DLT with multiparty threshold key management. The architecture features multiple layers, including the Application Layer for data security and authentication (e.g., Layer 2 solutions on Ethereum such as zkSync [23], Polygon zkEVM [24], Scroll [25]), a peer-to-peer network layer for data accessibility (e.g., Arweive [26], Siacoin [27], IPFS [28]), and the physical network layer for structural integrity and operational stability. * We also aim to incorporate PETs, such as threshold key management, homomorphic encryption, and secure multiparty computation into the new decentralized framework of VANET systems for further privacy-preserving data analysis. This keeps data confidential during transmission and processing. Multiparty threshold signatures use multiple signers to sign transactions, reducing fraud [29; 30]. The threshold homomorphic encryption system [31; 32; 33] requires a threshold number of participants for decryption, ensuring data privacy to analyze data without revealing the private data of participating parties. * We finally identify some potential future directions for VANETs to integrate with emerging technologies like AI and IoT, developing quantum-resistant security solutions (i.e., postquantum cryptographic algorithms), improving scalability and efficiency in high-density urban contexts, and conducting real-world implementation and testing to gain insights into the actual challenges and performance of the proposed architecture.
## 5 Results
### Influence of Uncertain Parameters
#### 5.1.1 Fragility Analysis  
The results from the 30 uncertain transmission tower-line system models (U1-U30) and the 1 certain model (C1) are substituted into Equation (5) for regression analysis, thereby determining the corresponding unknown parameters and establishing the calculation formula for \(EDP\). Subsequently, the fragility functions for the 31 models are obtained using Equations (6) and (7), and the fragility surfaces under the collapse state of the transmission tower are drawn. Figure 10 presents the fragility surfaces of some models under combined ice and wind loads. It can be observed that significant differences exist in the fragility of transmission towers when considering the uncertainties of material parameters and geometric dimensions.  
To more clearly compare the influence of uncertain parameters on the fragility of the tower-line system under combined ice and wind loads, Figure 11 presents the fragility curves under various ice thicknesses. It can be observed that the fragility curve of the C1 model is located in the middle of the fragility curves of U1-U30 models for all ice thicknesses. Table 5 compares the maximum and minimum exceedance probabilities of the transmission tower-line system under different ice thicknesses. When the ice thickness is 5 mm, the differences between the maximum and minimum exceedance probabilities at the three wind speeds are 0.07%, 1.65%, and 11.37%, respectively. It can be observed that the influence of uncertain parameters increases with the increasing wind speed with the same ice thickness when considering parameter uncertainties.  
Figure 8: Load-displacement curves with different ice thicknesses.  
Figure 9: Random load samples.  
Figure 11: _Cont._  
Figure 10: The fragility surfaces: (**a**) C1; (**b**) U1; (**c**) U6; (**d**) U21.
## 2 Materials and Methods
### Road DEM Construction Method  
As a typical artificial terrain, roads have not only their own special surface morphology, but also an obvious boundary with other terrain, that is, the road boundary line. Therefore, to accurately construct the road DEM, we must begin with two tasks. The first is to determine the skeleton line of the road, that is, to determine the basic undulation characteristics of the road in the extension direction. At the early stage of research, the road is regarded as undulating in its extension direction, while in the horizontal artificial terrain, the skeleton line of the road encompasses its two sidelines. However, in reality, the road is not horizontal in the horizontal direction, but slightly bulged in the middle then lowered at both sides, especially the road with middle green belt, where this is more obvious. Thus, the road centerline is also considered a part of the road skeleton line in this study. After  
Figure 1: Image map of the study area.  
determining the road skeleton line, the second part of the work was to ensure the local shape characteristics of the road surface. As mentioned above, the road surface undulated in both the road extension direction and the transverse direction. However, as the road was a type of artificial terrain with clear functions, the undulating slope in any direction was relatively small and the surface was very smooth. Therefore, the corresponding local shape control method should be designed considering these characteristics.  
Given the above analysis, the road DEM construction method designed in this study consisted of two parts. The first part entailed the elevation assignment of the road skeleton line. The nodes were inserted into the two road sidelines and the road centerline according to a certain step length, and the initial elevation value of the relevant nodes was assigned with the Lidar point cloud as the elevation information source. Then, the node elevation value was dynamically adjusted considering the longitudinal undulation characteristics of the road. The second part involved constructing a local surface morphological constraint equation for the road to ensure that the road surface exemplified basic characteristics, such as smoothness. In addition, considering the morphological difference between the road and its adjacent terrain, after constructing the road DEM, it was necessary to consider the integration of the road DEM and other terrain DEM. Therefore, the above three tasks constitute the key methodologies of this study, which will be detailed in this section.
## 1 Introduction
With the catheterization procedures, the ML model, predicting fractional flow reserve, achieved 84% sensitivity, 80% specificity, 82% accuracy, and 0.87 AUROC [14]. Similarly, another ML model showed 84% accuracy and 0.89 AUROC during external validation for the assessment of myocardial ischemia [15]. These catheterization-based methods ignore the patient's medical/physical features (such as laboratory results and physical examinations), which are not applicable to personalized medicine developments. Including patient medical history, vital measurements, and laboratory results before catheterization procedures can enhance the ML model's performance and practicality for diagnostic purposes [16]. To fill this gap, this study adapted ML-based approaches to effectively identify high-risk patients who lack sequential catheterization history. These findings would provide diagnostic assistance in cardiovascular treatment planning with patients' short-term diagnostic information, contributing to improved diagnosis and treatment outcomes in cardiovascular medicine. Further, the DukeCath database [17] contains 155,980 catheterization procedures (diagnostic and interventional), and to the best of our knowledge, this study is the first to apply ML methods to this dataset. This study aimed to explore the potential of ML applications in predicting CVD in suspected patients with no previously diagnosed CVD or any apparent severe symptoms of fatal CVD. The DukeCath database was utilized to provide insightsinto the effectiveness of ML algorithms for detecting CVD in this specific target population. Initially, ML methods were used to predict cardiovascular-related scenarios (i.e., treatments and events) occurring within 90 days of the first catheterization procedure to provide overall cardiovascular risk assessments and classify high-risk patients. The patients' features included medical history, vital measurements, laboratory results, and CR. The treatments and events include coronary artery bypass surgery (CABG), valve-related surgery (VS), PCI, stroke, and MI. For CVD, the PCI and CABG treatments are the main revascularization procedures [18; 19]. Therefore, the study generated treatment predictions (PCI and CABG) for suspected patients using ML models to facilitate treatment planning. Finally, the model's performance was evaluated by measuring accuracy, sensitivity, specificity, and AUROC.
## 3 Experiments and Analysis
### Evaluation Metrics  
To evaluate the detection performance of the proposed method, we utilized the Signal-to-Clutter Ratio Gain (SCRG), Background Suppression Factor (BSF), and Receiver Operating Characteristic (ROC) curve based on the True Positive Rate (TPR) and False Positive Rate (FPR). SCRG measures the target enhancement capability, the BSF assesses global background suppression, and the ROC curve depicts the relationship between the FPR and TPR across varying thresholds.  
The definition of SCRG is as follows:  
\[\text{SCR}=\frac{|\mu_{t}-\mu_{b}|}{\sigma_{b}+\varepsilon},\quad\text{SCRG}= \frac{\text{SCR}_{\text{out}}}{\text{SCR}_{\text{in}}}, \tag{11}\]  
where \(\mu_{t}\), \(\mu_{b}\), and \(\sigma_{b}\) denote the mean of the target region, the mean, and the standard deviation of the surrounding local neighborhood, respectively. \(\text{SCR}_{\text{in}}\) and \(\text{SCR}_{\text{out}}\) represent the SCR of the original and detection results, respectively.  
The BSF is defined as:  
\[\text{BSF}=\frac{\sigma_{\text{in}}}{\sigma_{\text{out}}+\varepsilon}, \tag{12}\]  
where \(\sigma_{\text{in}}\) and \(\sigma_{\text{out}}\) represent the standard deviations of the original and detection results, respectively, and \(\varepsilon\) prevents division by zero.  
TPR and FPR are defined as:  
\[\text{TPR}=\frac{\text{number of detected true targets}}{\text{number of actual targets}}, \tag{13}\]  
\[\text{FPR}=\frac{\text{number of detected false targets}}{\text{number of image pixels}}. \tag{14}\]  
The Area Under the Curve (AUC) from the ROC curve quantifies detection performance, with higher values indicating superior accuracy. A curve closer to the top-left corner reflects better target identification with fewer false alarms.  
This paper employs the Kolmogorov-Smirnov (KS) test to evaluate the distribution similarity between detection results and ground truth masks. For datasets without ground truth masks, we generate binary masks using target location annotations through element-wise multiplication with images and threshold processing. Let \(F_{\text{det}}(x)\) and \(F_{\text{gt}}(x)\) denote the empirical cumulative distribution functions (ECDFs) of pixel intensities from detection results and ground truth, respectively. The KS statistic is defined as:  
\[D_{statistic}=\max_{x}|F_{\text{det}}(x)-F_{\text{gt}}(x)|. \tag{15}\]Here, \(D\) quantifies the maximum divergence between distributions. Smaller \(D\) values indicate stronger alignment with ground truth. In infrared dim-target scenarios characterized by extremely sparse targets (typically \(<\)0.01% pixels) and dominant background regions, the D-statistic derived from the KS test between threshold-constrained detection results and ground truth specifically reflects the suppression capability against high-confidence false alarms.
## 2 Fatigue Life Prediction  
### Strain-Based Life Method  
This investigation intends to evaluate the fatigue strength of chromium-vanadium alloyed steel under cyclic straining conditions. Under stabilized conditions, steady-state hysteresis loops are taken into account for the use of fatigue strain-life methods, and the total strain range, \(\Delta\varepsilon\), is defined as  
\[\Delta\varepsilon=\Delta\varepsilon^{e}+\Delta\varepsilon^{p}=\frac{\Delta \sigma}{E}+\Delta\varepsilon^{p}, \tag{1}\]  
where \(\Delta\sigma\) is the stress range and \(\Delta\varepsilon\) is the strain range. The superscripts \(e\) and \(p\) denote the elastic and plastic parts of the strain.  
Figure 1: Illustration of a typical crossing nose geometry for a rail track.  
In the presence of plastic deformations, the strain-based model suggested by [PERSON] and [PERSON] is more suitable [15; 16; 17], as has been proven in many research and engineering projects. For cyclic fatigue testing, the CMB relationship relates the number of cycles to failure, \(N_{f}\), with the total strain amplitude \(\varepsilon_{a}^{t}\), indirectly using the number of reversals, \(2N_{f}\), such as  
\[\varepsilon_{a}^{t}=\frac{\Delta\varepsilon^{e}}{2}+\frac{\Delta\varepsilon^{ p}}{2}=\frac{\sigma_{f}^{\prime}}{E}\Big{(}2N_{f}\Big{)}^{b_{f}}+\varepsilon_{f}^{ \prime}\Big{(}2N_{f}\Big{)}^{c_{f}^{\prime}}. \tag{2}\]  
where \(E\) is the longitudinal elasticity modulus, \(\sigma_{f}^{\prime}\) is the fatigue strength coefficient, \(\varepsilon_{f}^{\prime}\) is the fatigue ductility coefficient, \(b_{f}^{\prime}\) is the fatigue strength exponent, and \(c_{f}^{\prime}\) is the fatigue ductility exponent. The transition point at which both elastic and plastic strain components have the same weight for fatigue is given by Equation (3):  
\[2N_{f_{t}}=\left(\frac{\varepsilon_{f}^{\prime}E}{\sigma_{f}^{\prime}}\right) ^{\frac{1}{b_{f}^{\prime}-c_{f}}}, \tag{3}\]  
Determination of the coefficients and exponents of the Equation (2) is performed for each stabilized loop of strain amplitude by first computing the elastic parameters and then the plastic ones.