esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Thin films technology could solve the reusability problems. Thin films of the desired material are deposited in various ways: spin coating [18], chemical vapor deposition (_CVD_) [19], e-beam evaporation, magnetron sputtering [20], etc. The magnetron sputtering technique has advantages against chemical methods such as controllable structure, stoichiometry, and thickness, good adhesion, homogeneity, low concentration of impurities, etc. The properties of thin films can be controlled by changing sputtering power, gas pressure, the ratio between oxygen and argon, the distance between sample and target, or even the deposition angle [21, 22, 23, 24, 25]. The nanoparticles can also be formed using various techniques, but solid-state dewetting is the simplest and probably cheapest method [26]. Pre-existing defects, such as holes, grain boundaries, and thin-film edges, are common in thin films. When such metallic thin films are heated to high enough temperatures, they often dewet the substrate and form isolated islands. The flat films start to form a rim at the grain boundaries, the edges of the film, or other defects. The mass diffuses from the triple point of grain boundaries (or from the defect site) to the flat film resulting in a rim on the surface [27]. The holes expand and develop a thickened surface around them (edge retraction) due to the local curvature gradient at their edges. The net curvature decreases as the rim gets thicker, and edge retraction slows down. The result is a thick rim and valley behind it [28]. From this moment, two outcomes, pinch-off and fingering instability, are possible [29]. The rim cuts off the rest of the mass during the pinch-off process. On the other hand, the rim starts to form finger-like structures of the film during the fingering process if coalescence of the holes occurs. The last part of this evolution is Rayleigh instability, which causes the fingers or wire-like structures to decompose into nanoparticles [30]. The kinetics of the dewetting process and the properties of nanoparticles mainly depend on the temperature, annealing time, and thickness of thin films. The aim of this paper is to evaluate photocatalytic efficiency by investigating the photodegradation of Rhodamine B aqueous solution under UV light using prepared AgNP/TiO\({}_{2}\) structures as photocatalysts. For this reason, AgNPs were formed on the surface of TiO\({}_{2}\) thin films using the solid state dewetting technique. Later, the size, distribution, structure, and photocatalytic activity of AgNPs on TiO\({}_{2}\) thin films were analyzed.
# Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment
## 4 Case Study
### Simulation and Sensitivity Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **3 Months** & **6 Months** & **9 Months** & **Average** \\ \hline D07 & 73\% & 72\% & 76\% & 74\% \\ \hline D05 & 74\% & 71\% & **77\%** & 74\% \\ \hline D08 & 74\% & 73\% & 75\% & 74\% \\ \hline H25 & 73\% & 72\% & 75\% & 74\% \\ \hline
**S02** & **76\%** & **75\%** & **77\%** & 76\% \\ \hline H34 & 71\% & 73\% & 71\% & 71\% \\ \hline H43 & **76\%** & **75\%** & 73\% & 75\% \\ \hline H31 & 75\% & 73\% & 74\% & 74\% \\ \hline H04 & 74\% & 75\% & 73\% & 74\% \\ \hline Ave. & 74\% & 73\% & 74\% & 74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation experiment result: planned maintenance to each equipment (duration: 18 h). maintenance show little variability and are about 30% higher than the resilience index obtained from the historical series. Tables 3-5 highlight the results of the highest indices for each group of experiments. In addition, the averages of each of them are presented for each analyzed equipment. Based on these results, it can be seen that, in five of the nine experiments, the highest value of the resilience index at the system level was obtained when applying planned maintenance activities to the S02 equipment, the most critical piece of equipment (higher failure frequency among critical equipment). In two other opportunities, and when applying planned maintenance activities to this same equipment (S02), the system resilience index occupied the second position among the tested ones. This allows us to observe that the planned maintenance activities in the S02 equipment are those that have more influence in the resilience of the system. When observing the H43 equipment, a truck, it can be seen that, on four occasions, the maximum resilience value was achieved by applying preventive maintenance to the equipment. However, this truck has not shown significant values either in terms of reliability or maintainability, within the group of the nine most critical items of equipment. In regard to experiment (iii), it seeks to measure the impact on the resilience index from increases in redundancy in certain pieces of equipment. For this case, the increase, separately, in a drilling unit, to each of the existing sub-fleets was analyzed. Figure 12 shows the values of the resilience index at the system level in its original configuration, and by adding an identical unit (with the same reliability and maintainability parameters) as the original one(s). The resilience resulting from the incorporation of an additional piece of equipment to each of the drilling fleets is shown in Figure 13.  
Figure 13 shows that the largest relative increase is achieved by adding a drill to the ROC sub-fleet, where the rate of the ROC sub-fleet increased by more than four times. On the other hand, it can be seen that the smallest increase is achieved by adding a drill to the Drilltech sub-fleet, as the increase in the resilience of this sub-fleet is a little over 40%. Figure 12: Systemic resilience indexes with original topology and addition of drills.
## 3 Design Procedure
For instance, the efficiency of a microstrip antenna can be influenced by the permittivity, dielectric losses, and thickness of the dielectric substrate. In textile antenna designs, typical non-conductive dielectric cloths can be employed as substrates. Textile fabrics such as felt, fleece, denim, and cotton can be used as antenna substrates to ensure both satisfactory antenna performance and wearability. In terms of conductive materials, the textile antennas can utilize thin and uniform metallization (e.g., copper tape), woven or knitted conductive textiles, also known  
Figure 5: Diagram of the “Concept Selection” design step. as electrotextiles (e.g., Nora Dell), and conductive yarns (e.g., Amberstrand) that can be sewn or be embroidered to form the required antenna pattern. The selection of the conductive textiles for the conductive antenna elements (e.g., patch and ground plane) is significant for the antenna radiation response, as well. In addition to these materials, the CC textile antennas make use of clothing components which in most cases should be conductive (Figures 1-4). For example, a top-loaded monopole can be applied through a button design. This will require the use of a conductive button. The textile materials, prior to implementation, need to be defined and characterized in terms of their electrical properties. The determination and characterization of the materials can be specified as follows:  
_(b1)_ _Conductive cloth or yarn_  
The electrical properties of the conductive materials need to be specified. In general, conductive textiles should present low electrical surface resistance to reduce the electrical losses and, therefore, improve antenna efficiency. Regarding the antenna design, the critical parameter to determine is the conductivity of the materials [6]. In the case of thin uniform metallization (e.g., copper tape), the conductivity is directly provided by the suppliers and it is on the order of magnitude \(10^{7}\) S/m. When woven conductive cloth (e.g., Nora Dell) is used, surface resistivity is usually given by the manufacturers. The conductivity can be calculated from the resistance data and thickness of the fabric and it is on the order of magnitude \(10^{6}\) S/m. In the case of an embroidered conductive pattern, an equivalent conductivity should be derived, as analytically described in [54]. A descent achieved conductivity of an embroidered pattern is on the order of magnitude \(10^{4}\)-\(10^{6}\) S/m depending on stitch density. Figure 6 presents a microstrip patch antenna applying conductive materials of different equivalent conductivity [55].
## 7 Risk Analysis
### Privacy Measures
**Zero-knowledge proofs**: Allows it to be proved that a statement is true, without revealing any specific information about the statement itself. 5. **Trusted execution environments**: Secure hardware-based environments that ensure the confidentiality and integrity of computations and data even in the presence of potentially compromised software. 6. **Secure multiparty computation**: Enabling multiple parties to perform joint computations on their private data without revealing the actual data to each other. 7. **Private set intersection**: A cryptographic technique that allows the intersection of their datasets to be determined without sharing the actual data contained within them. 8. **Federated learning**: A machine learning approach where models are trained across decentralized devices or servers, allowing data to remain local and reducing the need for centralized data sharing. These privacy enhancing technologies contribute to preserving privacy while enabling data analysis and processing, promoting a balance between utility and individual data protection.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 4 Simulations
### BER Performance Analysis for Stationary Locations  
In this subsection, we simulate the BER performance of our proposed APC strategy and TDMA for given fixed PD's location.  
We first evaluate the BER performance of our proposed APC over TDMA for different \(K_{i}\). We consider that the coordinates of the PD receiver for two LEDs, three LEDs and four LEDs scenarios are \((1.5\;\text{m},2.0\;\text{m},3.0\;\text{m})\), \((1.5\;\text{m},2.2\;\text{m},3.0\;\text{m})\), and \((1.5\;\text{m},1.8\;\text{m},3.0\;\text{m})\), respectively. The corresponding simulation results are illustrated by Figures 2b and 3a,b. Then, in order to obtain the BER performance of our proposed APC over TDMA for different PD's locations, for two LED transmitters scenario with \(K_{1}=1,K_{2}=3\), we examine the BER performance of APC and TDMA for several fixed PD's locations and we show the simulation results in Figure 4a. From the above four figures, we can observe that the APC strategy we proposed significant outperforms TDMA. This is mainly because the error performance of ML detection in high SNR region is dominated by the minimum Euclidean distance (MED) of the received constellation. By computations, the attained gain in terms of the ratio of MED of APC over TDMA is determined by  
\[Gain=20\log_{10}(\frac{h_{1}\gamma_{1}}{\sum_{i=1}^{N}2^{K_{i}}-N})/(\min_{1 \leqslant i\leqslant N}\frac{h_{i}}{2^{NK_{i}}-1}) \tag{8}\]  
For three LED transmitters scenario, for example, \(K_{1}=1,K_{2}=1,K_{3}=2\) and the PD receiver is located at \((1.5\;\text{m},2.2\;\text{m},3.0\;\text{m})\). By computation based on (2), we can get that the value of the normalized and sorted channel coefficients are \(h_{1}=0.3942,h_{2}=0.7032,h_{3}=0.3942\) and then according to Algorithm 1, we have \(\hat{\gamma}_{1}=0.4713,\hat{\gamma}_{2}=0.5284,\;\hat{\gamma}_{3}=1\). Therefore, we can obtain that \(Gain=20\log_{10}(\frac{h_{1}\gamma_{1}}{\sum_{i=1}^{N}2^{K_{i}}-N})/(\min_{1 \leqslant i\leqslant N}\frac{h_{i}}{2^{NK_{i}}-1})=20\log_{10}(\frac{0.3942 \times 0.4713}{2+2+2^{2}-3})/(\frac{0.7431}{2^{6}-1})\approx 9.97\) dB, which confirms with the simulation results in Figure 3a. For four LED transmitters scenario, for instance, \(K_{1}=1,K_{2}=1,K_{3}=2,K_{4}=2\) and the PD receiver is located at \((1.5\;\text{m},1.8\;\text{m},0.85\;\text{m})\). According to (2) and Algorithm 1, we have \(h_{1}=0.3092,h_{2}=0.3786,h_{3}=0.5362,h_{4}=0.7032\) and \(\hat{\gamma}_{1}=0.2842,\;\hat{\gamma}_{2}=0.4643,\hat{\gamma}_{3}=6557,\hat{ \gamma}_{4}=1\). Then, we can get that \(Gain=20\log_{10}(\frac{h_{1}\hat{\gamma}_{1}}{\sum_{i=1}^{N}2^{K_{i}}-N})/( \min_{1\leqslant i\leqslant N}\frac{h_{i}}{2^{NK_{i}}-1})=20\log_{10}(\frac{0.309 2\times 0.2842}{2+2+2^{2}-4})/(\frac{0.7032}{2^{8}-1})\approx 14.5\) dB, which is consistent with the simulation results in Figure 3b.  
Figure 3: (a) Three users’ BER performance comparisons of our proposed APC and TDMA for different \((K_{1},K_{2},K_{3})\); (b) Four users’ BER performance comparisons of our proposed APC and TDMA for different \((K_{1},K_{2},K_{3},K_{4})\).
## 3 Testing Results Analysis
### Analysis of Experimental Results of Upper Step Specim
#### 3.1.2 Characteristics of Acoustic Emission Energy  
In acoustic emission nondestructive testing, the energy of acoustic emission signal is defined for the signal waveform, representing parameters related to the amplitude and amplitude distribution of acoustic emission signal, and corresponding to the released energy, which has important research significance for effectively measuring and accurately evaluating the degree of rock fracture and damage. Figure 9 is the acoustic emission absolute energy and accumulated energy diagram of the upper step specimen. Among them, T1, T2 and T3 were the key points at which the absolute energy of the three acoustic emissions changed abruptly. In general, the absolute energy of acoustic emission was released step by step in the whole process by using the step method, which led to the gradual growth of the accumulated energy curve of acoustic emission, and the effect ofenergy release step by step was achieved. In the initial geostress loading stage, the upper step specimen was affected by the crack propagation and grain slip in the rock, and the absolute energy and accumulated energy of acoustic emission were in a low and stable state. In the second half of the disturbance stage and the rockburst stage, the characteristics of steep increase and step-like rise were observed. During the disturbance process, an obvious acoustic emission energy release occurred at the T1 point of the upper step specimen, and the released energy was 2.24 \(\times\) 10\({}^{7}\) aJ. When the fifth-order disturbance was applied, there was also an obvious acoustic emission energy release, and the released energy was 5.06 \(\times\) 10\({}^{7}\) aJ. At this time, multiple transverse shear cracks occurred in the right arch roof and arch footing of the specimen, and then a fracture zone with a certain width was formed on the surface of the tunnel surrounding rock. Before the rockburst, a large number of acoustic emission energy releases of 1.15 \(\times\) 10\({}^{7}\) aJ-1.02 \(\times\) 10\({}^{8}\) aJ began to appear, which represented the process of further expansion and extension of cracks in the fracture zone of the specimen, and consumed a lot of energy in this process, and then the fracture zone of the surrounding rock showed the characteristics of banded and interval distribution, which played a role in reducing the risk and grade of rockburst. When the rockburst was destroyed, the absolute energy of the acoustic emission of the upper step specimen reached 4.25 \(\times\) 10\({}^{8}\) aJ, and the accumulated energy of the acoustic emission reached 8.18 \(\times\) 10\({}^{8}\) aJ.
# The Wettability and Numerical Model of Different Silicon Microstructural Surfaces
## 4 Analysis of the Numerical Results
### Static Contact Angle of the Microstructural Surface  
To examine the influence of the microstructures on the droplet change process, the change process of the droplet on the rectangular parameterized surface of w = h = 2 was observed. Figure 13 presents the droplet morphology of the typical times during the droplet change process on the microstructural surface. The droplet process exhibited stages in Table 5--initial state, rising, oscillating, and stable state.  
The change process of the droplet on the two different surfaces was compared. For the smooth surface, the droplets exhibited an initial state, rising, spreading, shrinking, and oscillation to stabilize, wherein the oscillating process encompassed 50% of the whole variation process. For the microstructural surface, the droplets exhibited an initial state, rising, spreading, and oscillation to stabilize, wherein the oscillating process covered 80% of the whole variation process. Due to the hindrance in the microstructure of droplets and the interactions among the main droplets and those among the spreading droplets, the droplets were not completely spread on the surface of the microstructures, which enhanced the oscillation of the liquid. Therefore, the equilibrium state of the droplets on the microstructural surface required more time than the smooth surface. As seen in References [31] and [32], the wetting anisotropy of the micro-groove surface, i.e., the contact angle  
Figure 12: Change process of the droplets on a smooth surface.  
Figure 13: Change process of the droplets on the microstructural surface.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(0\) ms & The initial state \\ \hline \(0\)–\(0.075\) ms & The droplet rose to its maximum height \\ \(0.075\)–\(0.5\) ms & The oscillating state \\ \(0.5\)–\(0.7\) ms & The stable state \\ \hline \hline \end{tabular}
\end{table}
Table 5: The state changes of the droplet.  
in the vertical direction was greater than that in the parallel direction because the droplet spreading in the parallel micro-groove was similar to the smooth surface, and the droplet spreading process in the vertical micro-groove was similar to the microstructural surface. The wettability was related to the characteristics of the microstructure surface, thereby resulting in a larger contact angle on the microstructure surface as compared to that of the smooth surface.
## 4 Sentiment Analysis Datasets
### SemEval-2017 Task 4  
The International Workshop on Semantic Evaluation (SemEval) has been a leading forum for evaluating the performance of sentiment analysis models on social media text data. One of the primary tasks hosted by SemEval is sentiment analysis on Twitter, which has been a shared task since 2013. The latest edition of this shared task is the SemEval-2017 Task 4 dataset, as referenced in [65].  
Unlike previous SemEval datasets, which only included English data, the SemEval-2017 Task 4 dataset is a multilingual dataset, containing both English and Arabic data. The authors of the paper focused on the English portion of the dataset.  
The SemEval-2017 Task 4 dataset is a comprehensive benchmark dataset for sentiment analysis models, with a total of five subtasks covering different aspects of sentiment analysis. These subtasks include message polarity classification, topic-specific message polarity classification, and Tweet quantification. Each of these subtasks has a different number of sentiment polarity classes, providing a comprehensive evaluation of sentiment analysis models in different scenarios.  
A summary of the sentiment analysis datasets is provided in Table 4. The table provides a concise overview of the characteristics of the datasets, making it easy for researchers and practitioners to compare the different datasets and choose the most appropriate one for their needs.
## 3 Materials and Methods
### Problem Formulation  
The problem of Robust Spatio-temporal Databases is addressed in an environment suitable for Big Spatial Data Management. The \(k\)-anonymization approach adopted in [30] for preventing mobile objects identity reveal, is related to \(k\)-NN algorithm as described in Section 3.2. Specifically, the \(k\)-anonymity set is formulated by the unique object identifier, denoted as \(id\), of the \(k\) nearest neighbors, exploiting as a result the spatio-temporal data of a set of mobile objects. Through the SMaRT system, a set of mobile users' trajectory data is recorded per timestamp, that is, the mobile user trajectory \(id\) and the values of longitude and latitude are recorded. From these location features, the four attributes \((x,y,\theta,u)\) (as presented in Table 1) along with the values of Hough-\(X\) and Hough-\(Y\) of \((x,y)\)[9], \((U_{x},a_{x},U_{y},a_{y},b_{x},w_{x},b_{y},w_{y})\) (as presented in Table 2), are computed. Figure 1: An Overview of Continuous Trajectory Point \(k\) Nearest Neighbor (\(CTPkNN\)) Query. So, by employing the \(k\)-NN method for different pairs of features, this fact enables us to form the \(k\)-anonymity set of each mobile object per timestamp, as depicted in Table 3.  
For each mobile user \(i\) and per timestamp \(l\), the \(k\) nearest neighbors \(id\) it is computed and in the following is kept in a vector form \(k_{\text{{nnts}}_{il}}=[id_{il1}id_{il2}\ldots id_{ilk}]\) for \(l=1,2,\ldots,L\) as presented in Table 3. For each user, the number \(k_{s}\) out of the \(k\) nearest neighbors, which remains the same from one timestamp to another, is computed in order to estimate the vulnerability ratio \(\frac{1}{k_{s}}\). Hence, higher \(k_{s}\) is associated with lower probability (i.e., lower vulnerability) a moving object's identity being unveiled. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **Timestamp** & **Time to next Point** & **x** & **y** & **Angle \(\theta\)** & **Velocity u** \\ \hline
1 & 9 March 2013 10:00:01 & 0 & 21,082 & 56436 & 1.23 & 0 \\
1 & 9 March 2013 10:00:04 & 3 & 21,099 & 56,432 & 1.16 & 4.5 \\
1 & 9 March 2013 10:00:11 & 7 & 21,221 & 56,484 & 1.51 & 14.6 \\
1 & 9 March 2013 10:00:19 & 8 & 21,331 & 56,524 & 1.95 & 11.3 \\
1 & 9 March 2013 10:00:21 & 2 & 21,402 & 56,495 & 0 & 29.5 \\
2 & 9 March 2013 10:00:03 & 0 & 35,587 & 59,829 & \(-\)2.76 & 0 \\
2 & 9 March 2013 10:00:08 & 5 & 35,568 & 59,782 & 2.94 & 7.8 \\
2 & 9 March 2013 10:00:16 & 8 & 35,580 & 59,723 & \(-\)2.07 & 5.8 \\
2 & 9 March 2013 10:00:25 & 9 & 35,530 & 59,668 & \(-\)1.52 & 6.4 \\
2 & 9 March 2013 10:00:34 & 9 & 35,476 & 59,671 & \(-\)2.85 & 4.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An Overview of an Original Spatio-Temporal Database.