esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 2 Preparatory Knowledge
### Feature Parameter Selection Based on the Improved Relief Algorithm  
#### 2.3.1 ReliefF Feature Selection Theory  
The ReliefF algorithm is a widely used feature selection technique aimed at selecting features with high correlation and significance from a given feature set. Its core concept involves assessing feature importance by computing distances between features and samples. In essence, it assigns weights based on the capacity of feature variables to distinguish between various samples: boosting the weight for features with strong discriminatory power and reducing it for those with weaker discriminatory capability. The process entails randomly selecting a sample A from the training set S, computing the nearest neighbors of different classes (NearMiss) and the same class (NearHit) for A, and then iteratively updating the weight of each feature according to predefined rules [26].  
\[W(X)=w(X)-\frac{\sum_{j=1}^{k}diff(X,A_{i},H_{j})}{nk}+\sum\
olimits_{C^{1} Class(A)}\frac{P(C)}{1-P(Class(A_{i}))}*\frac{\sum_{j=1}^{k}diff(X,A_{i},S_{j})}{nk} \tag{1}\]  
In the formula, \(X\) represents a certain feature parameter; \(\omega(X)\) represents the weight of feature \(X\), typically initialized to 0; \(n\) represents the number of iterations; \(A_{i}\) represents the randomly selected sample in the \(i\)th iteration; \(k\) represents the number of nearest neighbors selected; \(H_{j}\) represents the nearest neighbor sample in the same class as sample \(A_{i}\); \(S_{j}\) represents the nearest neighbor sample in a different class from sample \(A_{i}\); \(Class(A_{i})\) represents the class of sample \(A_{i}\); \(P(C)\) represents the proportion of samples in class \(C\); \(diff(X,A_{i},H_{j})\) represents the difference in feature parameter \(X\) for faulty samples in the same class.
# Wall Structure Geometry Verification Using TLS Data and BIM Model
## 1 Introduction
For this issue, a deviation histogram for the individual segments can be generated to find the magnitude of the deviations between the two models. The histogram's advantage against the color map is that it is easier to locate the magnitude of the deviations from the histogram [26; 27; 28; 29]. The scan-versus-BIM process can be also performed with point clouds, which are generated from photogrammetric surveys. The advantage of cameras over laser scanners is, that they are more flexible to use, and they are financially more available. However, the final cost of using photogrammetry depends on the number of required images and the processing time. A disadvantage of using photogrammetry for data acquisition is that it is not as robust as TLS and the point cloud is the result of image processing (not raw result of measurement), which in some cases may lead to a lower geometric accuracy compared to TLS point clouds [30; 31]. The paper describes an approach for verifying the geometry of wall structures using TLS data and BIM models. The advantages of the proposed approach over existing approaches are that it combines all the information that can be derived from a point cloud (3D geometry, RGB color, intensity of the reflected laser beam). An extended approach of evolving closed plane curve had been developed, which uses a fast and stable Lagrangean approach. The as-built models of structures are segmented from the measured point clouds. The designed and the as-built models are compared consequently. In addition, the flatness of the chosen structures is inspected and visualized.
## 3 Methodology
### The Loss Functions
#### 3.2.1 L2 Loss  
The regression theorem and least-squares approach are the origins of this function. It is the sum of the squared distances between the target value \(y_{t}\) and the predicted value \(y_{p}\). The L2 loss formulation is presented as:  
\[L2=\big{(}y_{t}-y_{p}\big{)}^{2} \tag{3}\]
## 2 Overall System Design  
Figure 1 shows the proposed dynamic metaheuristic FLC implemented with closed-loop control strategies and the master-slave [103] control mechanism. Controller A represents the proposed dynamic metaheuristic FLC. The proposed dynamic metaheuristic FLC is a combination of four identical controllers (the Takagi-Sugeno-Kang particle swarm optimization fuzzy logic controller (TSK-PSO-FLC)), which are separately dedicated to each wheel while synchronizing together in order to regulate the wheel slip. A previous study [104] showed that the designed and developed differential fuzzy logic controller (D-FLC (_controller C_)) was needed to generate the four excitation signals (desired wheel speeds) for each TSK-PSO-FLC, as per the desired throttle amount and the steering angle. Moreover, when the rover travels under high-speed conditions on slippery roads, the change and rate of change of the desired orientation are rectified via the steering fuzzy logic controller (S-FLC (_controller B_)) by continuously monitoring the actual orientation of the rover through the feedback path. Therefore, Figure 1 shows that with respect to the desired steering angle and the actual yaw angle (orientation) of the rover, the S-FLC generates the corrected steering angle and feeds this into the D-FLC. The S-FLC (_controller B_) and the D-FLC (_controller C_) had to be developed due to the nature of the application (electric rover). The modelling of the system dynamics of the rover has been discussed in [104]. However, this paper focuses on the design and development of a dynamic metaheuristic FLC for other research scientists to absorb into their developments.
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 5 Study of Sand Emergence Phenomenon Based on In Situ Reservoir Samples
### Experimental Methodology  
Since the reservoir after hydrate decomposition is usually an unconsolidated formation, this experiment first does not use a cementing agent to consolidate the in-situ mud sand or formation sand. The specific experimental steps are as follows:  
1. Open the top cover and the window of the core thin-section model, fill the core cementing space with stratum sand and other mixtures of a certain grain size according to the requirements of the experiment, and fill the experimental thin-section model according to the density of the in-situ stratum to ensure the consistent effect of each filling;
2. Install and seal the upper cover and the sight window, and connect the flake to the experimental device after the core is filled;
3. Open the visual microscope and aim the lens at the visualisation window of the radial thin section model;
4. Open the inflow and outflow valves of the core thin section model, and open the outflow valve of the liquid piston container at the same time;
5. Open the data acquisition software and input the basic conditions and parameters of the experiment;
6. Through the software control, inject the liquid of certain viscosity into the thin section model according to the set flow rate to carry out the experiment;
7. Collect, dry and weigh the stratum sand brought out by the fluid after the injection process is completed, and use it for later experimental analysis;
8. According to the automatically collected flow information can be calculated to inject the fluid volume;
9. Clean up the residual sand and liquid in the thin-section model and end the experiment.
## 4 Analysis and Discussion
### Capture the Traffic Flow Change Under the Critical-Link Attacks
This study identified that these jumps in certain critical segments are evident in all the topological parameters considered. For instance, critical-link attacks on SC resulted in significant traffic flow changes after disruptions to links 1, 4, and 16. This occurred because the network lost its highly connected road segments, leading to a significant impact on traffic volume. As a result, the network became scattered, and trips shifted towards locally connected segments for trip generation. Subsequently, critical road segments with high BC identify the key flow-concentrated links, which contain greater traffic flow. A critical-link attack on these segments drastically reduces traffic volume by disrupting the shortest path that carries the most trips. This reduction is evident in the traffic volume changes observed in the critical-link attacks on segments 1, 10, and 20. Similarly, initial disruptions to the highly globally connected EC segment 1 cause a significant decline in traffic volume, as these segments influence a larger number of trip movements by capturing both direct and indirect influences across the entire topological network. However, as the network begins to fracture, trips are redistributed to alternative segments, leading to a relatively lower impact on traffic volume, as the network's functionality becomes more localized. Meanwhile, disruptions in critical road segments with high CC have a significant impact on the decline in traffic volume. This occurs in several stages, starting with the loss of the shortest-path road segment that is closely connected to other segments, causing all connected clusters to lose their network accessibility. Shifts in network accessibility to alternative segments occur after each loss, further reducing traffic volume. Neighboring segments are also affected as they lose accessibility due to the loss of the shortest-path road segment. This process is evident from the larger jumps observed during critical-link attacks on segments 9, 12, 23, and 25. This process continues until the network becomes fragmented, with each segment isolated and no trips generated.
## 5 BER and Data Rate Calculation of the Proposed Network
### The DSCA-MC-CR Asynchronous Model  
The SINR term of asynchronous model 1 and model 2 at any receiver antenna is estimated as  
\[SINR_{u}=\frac{P_{s}}{\sigma_{MAI}{}^{2}+\sigma_{n}{}^{2}}, \tag{11}\]  
Equations (A3), (A6) and (A8) from Appendix A are applied, also assuming the signal power \(P_{s}=\frac{E_{\tilde{n}}T_{\tilde{n}}}{2}\) and assuming the channel gain of the desired signal and interferers. Then,  
\[SINR_{u}=\frac{\alpha_{u}{}^{2}\frac{E_{\tilde{n}}T_{\tilde{n}}}{2}}{\sum_{ \tilde{n}=1,\tilde{n}\
eq u}^{U}\frac{T_{e}E_{\tilde{n}}}{6}\alpha_{\tilde{u}} ^{2}+\frac{\eta_{o}T_{\tilde{n}}}{4}}. \tag{12}\]  
The BER calculations assume a white Gaussian channel. At the \(PU_{i}\) receiver, the BER, without incentivizing the PU system by using the QPSK-OFDM scheme, can be defined as \(P_{e}^{p}=Q\left(\sqrt{\frac{P_{p}}{\eta_{o}}}\right)\), where \(Q(\cdot)\) is the Q-function. The probability of error in incentivizing the PU system using the QPSK-OFDM modulation scheme within the OC-DSA system in [33] is defined as  
\[P_{\textit{{\tiny{OC-DSA}}}}^{p}=Q\left(\sqrt{\frac{\left(\sqrt{P_{p}+a\sqrt{ \gamma P_{s}}}\right)^{2}}{\eta_{o}}}\right), \tag{13}\]  
The probability of error in incentivizing the PU system and using the QPSK-OFDM modulation scheme within the proposed DSCA-MC-CR system can be defined as  
\[P_{\textit{{\tiny{EDSCA-MC}}}}^{p}=Q\left(\sqrt{\frac{\alpha_{u}^{2}\frac{E_ {\tilde{n}}T_{\tilde{n}}}{2}(1+K)}{\sum_{\tilde{n}=1,\tilde{n}\
eq u}^{U}\frac {T_{e}E_{\tilde{n}}}{6}\alpha_{\tilde{u}}^{2}+\frac{\eta_{o}T_{\tilde{n}}}{4} }}\right). \tag{14}\]  
The BER at the \(SU_{j}\) receiver without incentivizing the PU system and using the QPSK-OFDM modulation scheme can be defined as \(P_{e}^{s}=Q\left(\sqrt{\frac{P_{s}}{\eta_{o}}}\right)\). The BER calculations assume an additive white Gaussian noise channel. The probability of error in incentivizing the PU system and using the QPSK-OFDM modulation scheme within the OC-DSA system given in [33] can be defined as  
\[P_{\textit{{\tiny{OC-DSA}}}}^{s}=Q\left(\sqrt{\frac{(1-\gamma)P_{\tilde{s}}}{ \eta_{o}}}\right), \tag{15}\]The probability of error in incentivizing the PU system and using the QPSK-OFDM modulation scheme within the proposed DSCA-MC-CR system can be defined as  
\[P^{s}_{e_{\textit{DSCA-MC}}} = \mathcal{Q}\Bigg{(}\sqrt{\frac{\alpha_{u}^{2}\frac{E_{u}T_{h}}{2}(1 -\gamma)}{\sum_{\tilde{u}=1,\tilde{u}\
eq u}^{U}\frac{T_{e}E_{h}}{6}\alpha_{ \tilde{u}}^{2}+\frac{\eta_{u}T_{h}}{4}}}\Bigg{)}\,. \tag{16}\]  
The maximum data rate of the PU system and the SU system within the proposed DSCA-MC-CR technique can be formulated using the Shannon rule as  
\[R^{p}_{DSCA-MC} = \frac{1}{2}\Bigg{(}1+\frac{\alpha_{u}^{2}\frac{E_{u}T_{h}}{2}(1+ K)}{\sum_{\tilde{u}=1,\tilde{u}\
eq u}^{U}\frac{T_{e}E_{h}}{6}\alpha_{\tilde{u}}^{2 }+\frac{\eta_{u}T_{h}}{4}}\Bigg{)}\,, \tag{17}\] \[R^{s}_{DSCA-MC} = \frac{1}{2}\Bigg{(}1+\frac{\alpha_{u}^{2}\frac{E_{u}T_{h}}{2}(1- \gamma)}{\sum_{\tilde{u}=1,\tilde{u}\
eq u}^{U}\frac{T_{e}E_{h}}{6}\alpha_{ \tilde{u}}^{2}+\frac{\eta_{u}T_{h}}{4}}\Bigg{)}\,. \tag{18}\]
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 1 Introduction
### General Theory
#### 1.3.2 Secondary Atomization  
The droplets generated after primary atomization will be further atomized into fine drops and this is achieved by secondary atomization. When there is an increase in the relative motion between liquid droplet and the encircling gas, the droplets will be further broken down and this is explained by three breakup systems: bag breakup, stretching-thinning breakup and catastrophic breakup (briefed in Table 1) [16]. Weber number (We) is considered as the commanding factor for the liquids that have a low viscosity [11].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Breakup Systems** & **Weber Number Range** & **Description** & **Ref** \\ \hline Bag breakup & 12–80 & The region at which there is no movement in a droplet will be puffed out into a narrow void (bag) which is anchored at the middle; holes will be developed in this void that shrink and split into small drops & [17; 18] \\ \hline Stretching-thinning breakup & 80–300 & The droplets are flattened and stretched and the middle of these stretched droplets are strained into thin stream by the force exerted on liquid by the air; this stream split into thin ligaments which breaks into small drops & [19; 20] \\ \hline Catastrophic breakup & \textgreater{}300 & Same as stretching-thinning system; the edges of the droplets are strained into thin liquid stream by the force of high velocity air; also causes surface instabilities and then breaks up into fine jet of drops & [21; 22] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different types of breakup systems experiencing in secondary atomization.  
Figure 2: Schematic representation of the wave and its wavelength, d—Initial diameter of the jet; \(\lambda_{\text{opt}}\)—Optimum wavelength; reproduced from [15].
## 4 Results and Discussion  
In recent years, MP-AES has been slowly emerging as one of the most powerful and popular analytical tools for the analysis of various materials. The mechanism of MP-AES is very similar to that of ICP-OES. The only difference between these two techniques is the ionization/atomization source. MP-AES uses nitrogen plasma instead of argon plasma, which is used in ICP-OES. Nitrogen plasma is sustained by a lower pressure microwave source that operates at atmospheric pressure. Nitrogen is non-flammable and cost-efficient, and can be easily sourced from the atmospheric air using a compressor. Moreover, nitrogen plasma temperature is lower (5000 K) compared to argon plasma (8000-10,000 K) [22]. As a result of this temperature difference, spectral interferences are less  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**S. No.**} & \multirow{2}{*}{**Elements**} & \multirow{2}{*}{**Linearity**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**Y-Intercept**} & \multicolumn{2}{c}{**Correlation**} \\  & & & & & & **Coefficient (\(\boldsymbol{r^{2}}\))** \\ \cline{5-8}  & & & & **MP-AES** & **ICP-OES** & **MP-AES** & **ICP-OES** & **MP-AES** & **ICP-OES** \\ \hline
1 & B & 0.0–2.0 & 6962 & 9642 & 10.63 & 16.28 & 0.999 & 0.999 \\
2 & P & 0.0–200.0 & 116 & 208.6 & 84.06 & 95.8 & 0.998 & 0.999 \\
3 & Mo & 0.0–1.0 & 6075 & 7297 & 17.70 & 18.7 & 0.999 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linearity data for boron, phosphorous and molybdenum using MP-AES and ICP-OES.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & \multicolumn{2}{c}{**Power (W)**} & \multicolumn{2}{c}{**Ramp (min)**} & \multicolumn{2}{c}{**Temp. (\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**Holding Time (min)**} \\ \hline Step & Max (w) & \% & & & \\ \hline
1 & 800 & 70 & 15 & 180 & 10 \\
2 & 800 & 80 & 15 & 190 & 10 \\
3 & 800 & 100 & 10 & 220 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operating conditions for microwave assisted acid digestion system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elements** & **MP-AES** & **ICP-OES** & **FAAS** \\ \hline B & 249.677 & 249.677 & 249.8 \\ P & 213.618 & 213.618 & 213.6 \\ Mo & 379.825 & 379.825 & 313.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic wavelength (nm) for MP-AES, ICP-OES, and FAAS.  
significant and the majority of elements remain in the atomic state, thereby resulting in simpler atomic emission spectral lines than those obtained with argon plasma [19].
# Use of Total Least Squares Adjustment in Geodetic Applications
###### Abstract  
This article discusses the method of computing the values of the unknowns under the condition of the minimum sum of the squares of the residuals of the observations, also known as the least squares method, with the additional condition of taking into account the errors in the unknowns. The problem has already been treated by many authors, especially in the field of regression analysis and the computation of transformation parameters. We give an overview of the theoretical foundations of the least squares method and extensions of this method by considering the errors in the unknowns in the model matrix. So, the total least squares method is presented in this paper, fitting the regression line to a set of points and computing transformation parameters for the transition between the old and the new Slovenian national coordinate systems. Furthermore, for the first time, the method is also presented and tested in the S-transformation between different geodetic datum-dependent solutions. Also, for the first time, we systematically compare the results of the approach with conventional approaches in all three considered tasks. With the results based on relevant statistics, we confirm the suitability of the described method for dealing with the considered computational tasks.  
total least squares; errors; coordinate transformation; S-transformation +
Footnote †: journal: Applied Sciences
## 3 Examples and Discussion
### Band-Gap Property and Vibration Attenuation
When applying the damping loss factors to the resonator springs, it has little effect on the RMSV in a lower frequency range under 150 Hz in this case. With \(\eta_{R2}\) kept as zero and \(\eta_{R1}\) varied by 0, 0.05, and 0.1, the attenuation band covering the 1 st band-gap frequency range becomes wider, though the RMSV in the original 1 st band-gap frequency range is increased. Meanwhile, the RMSV within the 2 nd band-gap frequency range changes little, as shown in Figure 9a. Figure 9b shows an analogous effect in the case where \(\eta_{R1}\) is zero and \(\eta_{R2}\) is varied by 0, 0.05, and 0.1. With the damping loss factor increasing simultaneously, as shown in Figure 9c, the RMSV is decreased between the two band-gap frequency ranges, and a much wider attenuation band appears covering them. Moreover, the RMSV curves become smoother in the higher frequency range with the application of the resonator damping. It is revealed that a better attenuation performance can be achieved in the LR plate with multiple resonators when further considering their damping characteristic. Figure 8: Displacement profiles of the finite locally resonant plate with fully free boundary conditions at (**a**) 202 Hz within the 1 st band gap and (**b**) 310 Hz within the 2 nd band gap. Figure 7: **(a) Band structures of the infinite mass-loaded (ML) plate and locally resonant (LR) plate, and (b) the vibration transmittances of their finite counterparts with fully free boundary conditions. **
## 8 Practical Deployment Considerations
### Power Consumption and Energy Profile  
The Raspberry Pi 4B consumes approximately 3-6 watts under continuous operation. Our sensing setup used a fixed packet transmission rate of 1300 Hz to maintain high resolution. However, this rate can be adjusted to reduce power consumption. Although we did not explicitly optimise energy usage in this work, several future directions can enhance deployment efficiency:  
1. Reducing the transmission frequency during idle periods.
2. Dynamically adjusting sensing parameters based on context.
3. Offloading computation to energy-efficient edge processors.
## 3 Results and Discussion
### FSCs with Hybrid Carbon Nanofluids  
As shown in Section 3.2, the simple carbon nanofluids evaluated in this study perform similarly to the simple metal oxide nanofluids of aluminum (Al\({}_{2}\)O\({}_{3}\)) and magnesium (MgO). Consequently, to visualize the improvement in the performance of FSCs using combinations of metal oxide nanoparticles with the carbon nanoparticles evaluated here, this section presents results from the evaluation of hybrid nanofluids based on graphite and fullerene combined with Al\({}_{2}\)O\({}_{3}\) and MgO nanoparticles.  
Figures 11 and 12 show the useful heat and the efficiency, respectively, of the FSCs using carbon hybrid nanofluids with respect to volume fraction. From these figures, it can be seen that both parameters increase when the volume fraction increases in all cases. However, when metal oxide nanoparticles are added to carbon nanoparticles, at the volume fraction of 0.1, both the useful heat and the efficiency decrease to 0.85% for G-Al\({}_{2}\)O\({}_{3}\)-water and G-Al\({}_{3}\)O\({}_{2}\)-water hybrid nanofluids. While the C\({}_{60}\)-Al\({}_{3}\)O\({}_{2}\)-water and C\({}_{60}\)-MgO-water  
Figure 9: Useful heat with respect to the volume fraction for metal oxide and carbon nanofluids.  
hybrid nanofluids decrease to 0.48% and 0.56%, respectively. This behavior has also been observed by authors such as [PERSON] et al. [13], where the addition of magnesium and copper nanoparticles reduced the efficiency of the collector with carbon nanotubes. This may be due to the pressure drop and irreversibilities, as the authors have noted.  
From the evaluation of FSCs using hybrid carbon nanofluids presented here, it can be concluded that while the performance of the solar collector is not significantly improved, the density of the nanofluid decreases, which reduces the pressure drop in fluid flow. As the decrease in efficiency using fullerene with magnesium oxide is less than 0.85%, fullerene hybrid nanofluids could still be used in FSCs.  
Figure 11: Useful heat of hybrid carbon nanofluids with respect to volume fraction.  
Figure 10: Efficiency with respect to the volume fraction for metal oxide and carbon nanofluids.