esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Algorithm: Space of Convergence Vectors (SCV)  
The conceptual design of a technical system is an iterative process. At each increment, a solution is formulated and afterward analyzed against a set of specifications (performance indicators and related target values). If the solution does not satisfy the intended targets, a derived solution is further formulated, and iterations continue until an acceptable solution is derived. The SCV algorithm works similarly. Here, a particle swarm intelligence-type formalism defines the \"position\" of each iteration. Position in the SCV algorithm means a well-defined set of GIVs. Each GIV of the set is related to a certain performance indicator in the list of design specifications. Using the indications given by the GIVs, a solution to the design problem is formulated.  
Results are verified against a utility function, defined through the performance indicators and their related targets. Accordingly, a new \"position\" is formulated, or the process is stopped if an acceptable solution is obtained. An acceptable solution means a \"close to ideality\" solution (if ideality cannot be reached). Ideality is when all targets of all performance indicators for the design problem under consideration are reached. It is important to highlight here that, for some complex design problems, consideration of a unique, best solution is not realistic, if it may be possible that several concepts to reach the target. A rule for verifying convergence after each iteration is also considered within the SCV algorithm.  
A detailed step-by-step description of the SCV algorithm is the subject of the next paragraphs of this section. The SCV algorithm includes two major phases. The first phase is about preparation for algorithm application, and the second phase is the effective application of the algorithm.
## 2 Method
### Reconstruction of borehole Models  
The raw point clouds acquired via laser scanning are typically of inferior quality and contain noise and outliers resulting from the subsurface environment, equipment errors,  
Figure 3: Schematic diagram of the laser scanning system.  
Figure 2: Photographs of the LiDAR system.  
and other factors. To reconstruct a superior mesh model, these data must undergo filtering to remove these artifacts. As outliers are characterized by a sparse distribution, and are situated at a considerable distance from the main body of the point cloud, they can be effectively removed through the utilization of statistical filtering. Statistical filtering is a method of removing outliers on the basis of the density of the point cloud. The fundamental premise is to initially traverse and calculate the distance between each point and its K nearest neighbors, which satisfies a Gaussian distribution. The distances of each point are subsequently compared with the population mean and variance, with points outside the n-fold variance considered outliers. Additionally, noise is typically Gaussian distributed near the hole wall and is eliminated through bilateral filtering. Compared with the commonly utilized Gaussian filtering, nonlinear bilateral filtering is capable of more effectively preserving the edge features of the point cloud while simultaneously facilitating superior handling of Gaussian noise. This prevents the loss of the roughness of the whole wall due to excessive smoothing.  
Given the discontinuous nature of the 3D point cloud, it is not possible to extract and numerically simulate the roughness of the continuous hole wall. Accordingly, this paper presents a methodology for extracting a continuous surface model of a borehole from an unorganized point cloud. There are numerous surface reconstruction algorithms that are based on point clouds. In this paper, we utilize the Delaunay triangulation algorithm, which preserves the original position of the points. Unlike other algorithms, this approach does not alter the position of the point cloud, ensuring that the dissection results are unique and accurately reflect the true geometric features of the borehole. Additionally, it avoids smoothing the surface of the model, thus preventing any adverse effects on the roughness of the borehole surface.
## 5 Conclusions  
The research results and conclusions are summarized as follows:  
1. Based on the meshing relationship between the drum-shaped grinding tool and the internal helical gear, the transverse profile curves of the drum-shaped grinding tool are calculated, providing a computational method for the profile of the drum-shaped grinding tool suitable for mass finishing of the hardened internal helical gear.
2. The distribution of the internal helical gear tooth surface deviation is analyzed in the presence of four kinds of drum-shaped grinding tool installation errors. The results indicate that varying degrees of distortion along both the tooth height and width directions on the tooth surface of the internal helical gear occur when there are installation errors in the drum-shaped grinding tool. The research provided a theoretical basis for compensating for the tooth surface of the internal helical gear.
3. The average normal deviation of the tooth surface shows a periodic variation when the eccentric angle changes from \(-180^{\circ}\) to \(180^{\circ}\). The sensitive directions of eccentric error occur when the eccentric angle approaches \(-60^{\circ}\) and \(120^{\circ}\), the average normal deviation of the tooth surface reaches its maximum, while the insensitive directions of eccentric error occur when the eccentric angle approaches \(-150^{\circ}\) and \(30^{\circ}\), the average normal deviation of the tooth surface reaches its minimum. The average normal deviation of the tooth surface increases linearly with the increase in the eccentricity.
4. Leaning around the \(y_{w}\) axis is the sensitive direction of perpendicularity error. The average normal deviation of the tooth surface is linearly related to the perpendicularity error.  
Conceptualization, J.Y. and Z.Z.; methodology, J.Y. and Z.Z.; validation, J.Y., Z.Z. and S.W.; formal analysis, J.W.; investigation, J.Y. and Z.Z.; resources, J.Y.; writing--original draft preparation, Z.Z.; writing--review and editing, J.Y.; supervision, S.W.; project administration, J.W.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 52275054), Key Research and Development Projects in Henan Province (No. 241111221200) and Frontier Exploration Project of Longmen Laboratory (No. LMQYTSKT023).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Data Description and Preparation  
In order to develop a statistical association between traffic factors and road accident severity, the data was collected from National Motorways and Highway Police (NHM&P) and National Highway Authority (NHA) for the Motorway (Lahore-Islamabad section: 200-300 km) for six years (2009-2014) as shown in Table 1. Number of accidents (NOA) and number of affected persons i.e., injured or killed (NOAP) were safety outcome or outputs. Data output variables were tested for Isotonic behavior having a positive correlation between them.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Stage** & **Variable** & **Description** & **N** & **Mean** & **SD** & **Min.** & **Q1** & **Med.** & **Q3** & **Max.** \\ \hline \multirow{2}{*}{Output} & NOA & No. of accidents & 99 & - & - & 1 & 1 & 1 & 2 & 14 \\  & NOAP & No. of affected persons: & 99 & - & - & 1 & 2 & 4 & 7 & 162 \\  & killed or injured & & & & & & & & & & \\ \hline \multirow{3}{*}{Input} & V/C & Volume/capacity & 99 & 0.050 & 0.006 & 0.042 & 0.045 & 0.047 & 0.056 & 0.060 \\  & VKT & Vehicle kilometers & 99 & 8562 & 1077 & 7301 & 7838 & 8092 & 9690 & 10375 \\ \cline{1-1}  & VHT & Vehicle hours traveled & 99 & 7486 & 4324 & 3877 & 4855 & 5509 & 7781 & 19421 \\ \hline \multicolumn{10}{l}{Note: N = number of road segments (Size-1 km each), SD = standard deviation, Q1 and Q3 = quartiles of data} \\ \end{tabular}
\end{table}
Table 1: Description for road accident data analysis.  
Figure 4: Methodological framework. DMUs: decision-making units; GIS: geographical information system; V/C: volume/capacity; VKT: vehicle kilometers travelled; VHT: vehicle hours traveled.
## 3 Methodology
### Decoupled Head  
The main function of the detection head is to convert the extracted feature map from the network into the target category, location, confidence, and other information. In the YOLOv7-tiny model, the classification and location regression tasks share a fully connected layer. But classification pays more attention to calculating the most probable category, whereas regression focuses on calculating positions and boundary boxes of targets. The two tasks are different, and may lead to mutual interference if there is only a single head. To solve the contradiction between classification tasks and regression tasks with a single head (which leads to problems of low detection accuracy and slow convergence speed), a decoupled head (including a classification part and a regression part) was added to replace the detection head in YOLOv7-tiny. A decoupled head separates the two tasks of classification and location regression, since two different head structures can optimize their own tasks without affecting each other. Furthermore, the weight of each task can also be adjusted finely. The decoupled head structure, depicted in Figure 3, utilizes an initial 1 \(\times\) 1 convolution for dimensionality reduction, followed by two 3 \(\times\) 3 convolutions for each of the two parallel classification and regression branches. In the classification branch, a 1 \(\times\) 1 convolution was used for the classification operation. In the regression branch, a 1 \(\times\) 1 convolution was used for the positioning and confidence operations in the two parallel positioning and confidence branches, respectively. Finally, the anchor-based [42] method was used to extract the object box, compare it with the marked ground truth to determine the difference between the two, and finally produce a detection result. As shown in the decoupled head section of Figure 2, the output of the CBL module in the head is divided into CLs, Reg, and IoU. Our method separates classification and regression tasks with a decoupled head, thereby improving the detection accuracy, accelerating network convergence, and improving the detection results.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 3 Simulation Scenarios
### SVS Indices Implementation  
In this section, indices that are presented in Section 2 are used to SVS assessment for the defined scenarios in the previous section. In the online voltage stability monitoring, the time series of voltage magnitude at system buses are built using PMU measurement. The number of installed PMUs in power system are limited in practice due to some factors such as existing communication infrastructure big data and economic issues. If power system is observable, with an \(n_{pmu}\) PMU device, voltage variation in all system buses can be obtained by state estimation techniques [24]. However, in unobservable systems, voltage time series can be obtained only in the buses with PMU, so SVS monitoring can be highly affected. It has been assumed that just voltage of buses 5, 6, 7, 8, 12, 14, 15, 16, 17, 21, 24, and 27 are available as time series. These time series include buses voltage magnitude after fault clearing time (\(t_{d}\)).  
In WAMS, PMUs send data with sampling rates in the range of 0.5-4 per cycle to the monitoring center. In the simulation, PMU data with sampling rate 2 per cycle is used to mimic the practical  
Figure 2: Time domain voltage simulation at various buses for defined scenarios: (**a**) Case a, (**b**) Case b, (**c**) Case c, (**d**) Case d, (**e**) Case e, (**f**) Case f.  
cases. The considered time for short-term voltage stability analysis is 1-2 s. Therefore, the time of 2 s is considered for our calculations. Simulations are performed using MATLAB codes and indices are calculated for scenarios. Then, by analyzing results, an instruction for short-term voltage instability assessment by each index is suggested.
## 2 Materials and Methods
### Second-Order, Time-Difference Carrier Phase Combination
\begin{array}{ll}\Delta\Delta(\overset{\rightarrow}{e}\cdot d\overset{ \rightarrow}{r})&=\overset{\rightarrow}{e}(t-\Delta t)\cdot d\overset{ \rightarrow}{r}(t-\Delta t)-2\overset{\rightarrow}{e}(t)\cdot d\overset{ \rightarrow}{r}(t)+\overset{\rightarrow}{e}(t+\Delta t)\cdot d\overset{ \rightarrow}{r}(t+\Delta t)\\ &=\overset{\rightarrow}{e}(t)\cdot\Delta\Delta d\overset{\rightarrow}{r}\\ &+d\overset{\rightarrow}{r}(t+\Delta t)\cdot\left[\overset{\rightarrow}{e}(t +\Delta t)-\overset{\rightarrow}{e}(t)\right]-d\overset{\rightarrow}{r}(t- \Delta t)\cdot\left[\overset{\rightarrow}{e}(t)-\overset{\rightarrow}{e}(t- \Delta t)\right]\end{array} , \tag{5}\]  
since  
\[\overset{\rightarrow}{e}(t+\Delta t)-\overset{\rightarrow}{e}(t)\approx \overset{\rightarrow}{e}(t)-\overset{\rightarrow}{e}(t-\Delta t)\approx \overset{\rightarrow}{e}(t)\cdot\Delta t\approx\frac{\overset{\cdot}{r}_{r}^{G} }{\rho_{0}}\cdot\Delta t. \tag{6}\]  
Considering the altitude of the GPS satellite, \(\rho_{0}\) is usually larger than 20,000 km, even for LEO satellites. Additionally, the GPS and LEO satellite velocities are approximately 4 km/s and 8 km/s, respectively. Then, we have  
\[\overset{\cdot}{|\overset{\rightarrow}{e}(t)|}\approx\frac{\overset{\cdot}{r} _{r}^{G}}{\rho_{0}}<6\times 10^{-4}\textit{rads}^{-1}. \tag{7}\]  
Thus, the maximum amplitude of \(\overset{\rightarrow}{e}(t+\Delta t)-\overset{\rightarrow}{e}(t)\) is less than \(2\times 10^{-2}\), even with 30-s sampling intervals. Therefore, the last two terms \(d\overset{\rightarrow}{r}(t+\Delta t)\cdot\left(\overset{\rightarrow}{e}(t+ \Delta t)-\overset{\rightarrow}{e}(t)\right)\) and \(d\overset{\rightarrow}{r}(t-\Delta t)\cdot\left(\overset{\rightarrow}{e}(t)- \overset{\rightarrow}{e}(t-\Delta t)\right)\) in Equation (5) can be ignored. The STP values \(\Delta\Delta\overset{\rightarrow}{r}^{G}\) and \(\Delta\Delta\overset{\rightarrow}{r}_{r}\) can be derived with the satellite orbit dynamic models (presented in Section 2.3. Thus, \(\Delta\Delta(\overset{\rightarrow}{e}\cdot d\overset{\rightarrow}{r})\) in Equation (4) can be treated as known and used to remove the geometric variations between the receiver and the GPS satellites. The second-order, time-difference GPS clock errors \(\Delta\Delta dt^{G}\) are very small, due to the high-stability atomic clocks equipped on the GPS satellites, which can be practically ignored for cycle slip detection and repair (presented in Section 3). Moreover, for LEO satellite GPS data, STPIR is significantly smaller than the first-order, time-difference phase ionospheric residual (PIR), which is similar to the ground-based GPS data [11]. What is more, as the distance variations are removed, several kinds of STG combinations can be formed to reduce the ionospheric residuals \(\Delta\Delta I\) further, as shown in the next section. The numerical results and analysis of the second-order time-difference values mentioned above, \(\Delta\Delta\overset{\rightarrow}{r}^{G}\), \(\Delta\Delta\overset{\rightarrow}{r}_{r}\), \(\Delta\Delta t^{G}\), and \(\Delta\Delta I\), are presented in Section 3.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## 4 Case Studies
### Numerical Examples
\begin{table}
\begin{tabular}{c c c} \hline \hline _ndv_ & **Model** & **Details 1** \\ \hline \multirow{3}{*}{2} & KRG & Constant regression, Gaussian correlation, \(\theta_{0}=ndv^{(1/2)},0.01<\theta_{i}<20\) \\  & RBF & Gaussian basis functions, kernel parameter \(\gamma=4\), no polynomial term \\  & SVR & Gaussian kernel \(\gamma=5\), regularization parameter \(C=\infty\), quadratic loss \(\varepsilon=0.01\) \\ \hline \multirow{3}{*}{3} & KRG & Constant regression, Gaussian correlation, \(\theta_{0}=ndv^{(1/3)},0.01<\theta_{i}<20\) \\  & RBF & Gaussian basis functions, kernel parameter \(\gamma=0.5\), No polynomial term \\  & SVR & Gaussian kernel \(\gamma=0.5\), regularization parameter \(C=100\), quadratic loss \(\varepsilon=0.01\) \\ \hline \multirow{3}{*}{6} & KRG & Linear regression, Gaussian correlation, \(\theta_{0}=ndv^{(1/6)},0.01<\theta_{i}<20\) \\  & RBF & Gaussian basis functions, kernel parameter \(\gamma=0.5\), no polynomial term \\  & SVR & Gaussian kernel \(\gamma=0.5\), regularization parameter \(C=\infty\), quadratic loss \(\varepsilon=0.001\) \\ \hline \multirow{3}{*}{9} & KRG & Linear regression, Gaussian correlation, \(\theta_{0}=ndv^{(1/9)},0.01<\theta_{i}<20\) \\  & RBF & Gaussian basis functions, kernel parameter \(\gamma=1\), polynomial term \(=1\) \\  & SVR & Gaussian kernel \(\gamma=0.5\), regularization parameter \(C=100\), quadratic loss \(\varepsilon=0.001\) \\ \hline \multirow{3}{*}{12} & KRG & Quadratic regression, Gaussian correlation, \(\theta_{0}=ndv^{(1/2)},0.01<\theta_{i}<20\) \\  & RBF & Gaussian basis functions, kernel parameter \(\gamma=2\), polynomial term \(=1\) \\  & SVR & Gaussian kernel \(\gamma=0.5\), regularization parameter \(C=100\), quadratic loss \(\varepsilon=0.0001\) \\ \hline \hline \end{tabular}  
* The above parameters were set according to experience and can be fine-tuned by many optimization algorithms, which is not covered in this study.  
\end{table} 
Table 2: Surrogate models setup details. KRG: Kriging, RBF: radial basis function, SVR: support vector regression. Figure 1: Surface plots of the Branin-Hoo function. From Table 3, and Figures 3-5, we can see that no individual surrogate model is always accurate for all test cases, KRG fits the Branin-Hoo function well, while RBF shows a better fitting precision than KRG for the Camelback function. The superiority of ensemble models is not evident for low-dimensional variables functions; however, as the variable dimension and the degree of nonlinearity increase, the ensemble models perform better than most of the individual surrogate models. RLS-EM outperforms all the models in most of the error metrics for the six numerical problems. It shows good fitting performance and lower RMSE values on the Camelback, Hartmann-3, Hartmann-6, and Dixon-Price functions. The RMSE and AAE values in Table 3 and their boxplots in Figures 3-5 also show that the RLS-EM is robust. Figure 2: Surface plots of the Camelback function.
## 4 Comparison of Pseudo-Dynamic Test Results and Non-Linear Dynamic Analysis Results  
Based on the pseudo-dynamic test results obtained from PD-CFT-MF, the test frame retrofitted with the CFT-MF system, described in Chapter 3, the restoring force characteristics of beams, columns, and reinforcing members were proposed to implement non-linear dynamic analysis of the full-size two-story test frame retrofitted with the CFT-MF seismic retrofitting method. The proposed restoring force characteristics were used to conduct non-linear dynamic analysis of the two-story pseudo-dynamic test frame, and the results were then compared with the pseudo-dynamic test results.
## 4 Materials and Methods
### Assessment of Cell Viability  
Propidium iodide (1 \(\upmu\)M) was used to evaluate the number of dead cells in the cell cultures before and after OGD. The cells were stained for 5 min with the probes diluted in HBSS and then rinsed with HBSS. Fluorescence of the probes was detected with an inverted fluorescent microscope Zeiss Axio Observer Z1 (Carl Zeiss, Oberkochen, Germany) using Filter Set 20. Cell death induced by OGD was assessed by propidium iodide staining (PI, 1 \(\upmu\)M) before and after the exposures in the same microscopic field. Furthermore, we used the Ca\({}^{2+}\) signals (presence or absence of a global increase in [Ca\({}^{2+}\)]\({}_{i}\) during OGD) as an additional indicator of cell viability [88; 89].  
Hoechst 33342 (2 \(\upmu\)M) and propidium iodide (1 \(\upmu\)M) were used to evaluate the number of dead cells in the cell cultures before and after 2 h OGD and 24-h reoxygenation (OGD/R conditions). The cells were stained for 5 min with the probes diluted in HBSS and then rinsed with HBSS. Fluorescence of the probes was detected with an inverted fluorescent microscope Zeiss Axio Observer Z1 using Filter Set 01 and Filter Set 20. Discrimination of early and late apoptotic cells was performed according to the previously described method [89; 90]. Five different areas of each cell culture were analyzed. Each experimental group consisted of three cell cultures from different passages.  
To simultaneously monitor apoptotic and healthy cells after SeNrs treatment and OGD/R with fluorescence microscope, an Apoptosis/Necrosis Detection Kit (ab176750, Abcam) was used [28]. Cells were washed 1-2 times and resuspended with Assay Buffer. To detect apoptotic cells, Appoxin Green Indicator was used. Apoptotic cells were visualized using the FITC channel (Ex/Em = 490/525 nm). For staining necrotic cells, we used 7-aminoactinomycin D (Ex/Em = 550/650 nm). To detect healthy cells, CytoCalcein 450 was used and cells were visualized using the violet channel (Ex/Em = 405/450 nm).
## Materials and Methods
### Laboratory Bioassays  
The allelopathic effects of juglone were investigated on the growth of two harmful algal species, _S. hantzschii_ and _M. aeruginosa_, and on several selected non-target algae, _A. formosa_, _F. crotonensis_,S. acus, S. quadricauda, and C. vulgaris, at three concentrations: 0.01, 0.1, and 1 mg L\({}^{-1}\). The strains were cultured using pre-filtered (Whatman GF/F filter, pore size 0.7 \(\upmu\)m; Whatman plc, Maidstone, UK), sterile (autoclaved for 20 min at 121 \({}^{\circ}\)C), eutrophic lake water (Lake Ilgam, Seoul, South Korea; 37\({}^{\circ}\)32\({}^{\prime}\)23\" N, 127\({}^{\circ}\)04\({}^{\prime}\)36\" E) in which in situ enclosure bioassays were conducted. To conduct the test, S. hantzschii, M. aeruginosa, A. formosa, F. crotonensis, S. acus, S. quadricauda, and C. vulgaris, were inoculated at 16,800 cells mL\({}^{-1}\), 810,000 cells mL\({}^{-1}\), 1460 cells mL\({}^{-1}\), 1830 cells mL\({}^{-1}\), 1860 cells mL\({}^{-1}\), 132,000 cells mL\({}^{-1}\), and 875,000 cells mL\({}^{-1}\) of the initial cell density, respectively. Diatom strains were grown at 10 \({}^{\circ}\)C, while M. aeruginosa and green algae were grown at 25 \({}^{\circ}\)C, respectively. These strains were grown under a 14:10 h light-dark cycle of 100 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) provided by cool-white fluorescent lamps in a shaking incubator for 7 days. During all experiments, 10 mL of algal samples were added to 250 mL test flasks containing 90 mL filtered sterile lake water and then juglone was added in concentrations of 0.01, 0.1, and 1 mg L\({}^{-1}\). All experiments were conducted in triplicate. Regarding cell counting, 1 mL aliquots out of 100 mL of test cultures were taken from thoroughly mixed test flasks and fixed with Lugol's solution (2% final concentration, v/v).