esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development of Full-Process Numerical Analyzing Method
### Development of the Progressive Low-Velocity Impact Damage Prediction Method
#### 2.1.3 Degradation Method of Material Performance of Impact Damage  
After an impact failure occurs in a localized area of the laminate, the bearing capacity of the localized area will be decreased. As an important part of progressive damage analysis, the specific of partial degradation is to update the material stiffness matrices of the elements according to the types of failure. According to the References [21; 25; 27], the material property degradation rules with regard to different failure modes during the impact numerical analysis process are assumed, as follows:  
(1) Fiber tension failure: all the material parameters are reduced to 0.07 of the original;  
(2) Fiber compression failure: all the material parameters are reduced to 0.14 of the original;  
(3) Matrix tension failure: \(E_{2}\), \(G_{12}\), \(G_{13}\) are reduced to 0.2 of the original;  
(4) Matrix compression failure: \(E_{2}\), \(G_{12}\), \(G_{13}\) are reduced to 0.4 of the original;  
(5) Delamination failure: \(E_{3}\), \(G_{12}\), \(G_{13}\), \(\
u_{12}\), \(\
u_{13}\) are reduced to 0.01 of the original; and,  
(6) Suture breakage failure: The stiffness of the stitches degenerates to 100 Pa.  
Where \(E_{2}\) and \(E_{3}\) are respectively transverse and normal tensile modulus; \(G_{12}\) and \(G_{13}\) are in-plane and out-plane shear modulus respectively; and, \(\
u_{12}\) and \(\
u_{13}\) are in-plane and out-plane Poisson's ratio, respectively.  
Different damage types are allowed to be coupled in the numerical calculation due to the coupling of it in the actual situation. If there are many kinds of damage in the numerical calculation, the material coefficient will be degraded correspondingly many times.
## 2 Radio Planning Aspects
### Radio Planning Tool  
To carry out the radioelectric coverage analysis, the application RADIOGIS [6], which is a tool developed by the authors for the management and calculation of the radioelectric coverage of radio communication systems such as GSM, UMTS, LTE, TETRA, TDT, WiFi, etc., was used. RADIOGIS works on Windows PCs and is integrated with the GIS software ArcGIS 9.1 from ESRI. RADIOGIS has, among others, the following functionalities:  
Figure 3: Digital terrain model (raster), sites (vector points) and municipal areas (vector polygons).  
Figure 2: (**a**) Four-wheel drive vehicle with the TETRA antenna and (**b**) TETRA Mobile station equipment.  
Calculations of power, electric field, or power density radioelectric coverage while being able to select the propagation model to be applied: ITUR-526 [8] and ITUR-1546 [9] for rural environments; Okumura-Hata [10], COST-231 [11] and Walfisch-Bertoni [12] for urban areas.
2. Percentage threshold calculations using a vector layer containing municipal areas, roads, etc.
3. Management of databases of sites, power density radioelectric coverage, power systems, measurement campaigns, etc.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Pillar Stability Calculation  
Stability calculations are performed using the K. Kegel strength calculation formula [32].  
\[\sigma_{p}=\sigma_{\text{C}}\times B\times\sqrt{\frac{w}{H}} \tag{1}\]  
Here, \(\sigma_{\text{p}}\)--pillar strength, MPa; \(\sigma_{\text{c}}\)--rock's uniaxial compressive strength, MPa; w--pillar width, m; H--pillar height, m; and B--fissure factor, whose value ranges from 0.4 to 1.0, typically taken as 0.7. When using the K. Kegel strength design formula, the safety factor of the pillar is  
\[F=\frac{\sigma_{\text{C}}\times B\times\sqrt{\frac{w_{0}}{H}}}{\gamma\times h \times\left(1+\frac{w_{0}}{w_{p}}\right)} \tag{2}\]  
Here, \(\sigma_{\text{c}}\)--rock's uniaxial compressive strength; B--fissure factor; \(w_{0}\)--average width of the stope; H--pillar height; \(\gamma\)--rock mass density; h--mining depth; and \(w_{p}\)--pillar width.  
Substituting the parameters into the above formula yields the safety factors corresponding to different pillar widths, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Stope Span** & **Pillar Width** & **10 m** & **11 m** & **12 m** & **13 m** & **14 m** \\ \hline
15 m & 1.276 & 1.416 & 1.553 & 1.689 & 1.823 \\
16 m & 1.235 & 1.372 & 1.507 & 1.641 & 1.772 \\
17 m & 1.196 & 1.330 & 1.464 & 1.595 & 1.725 \\
18 m & 1.16 & 1.292 & 1.423 & 1.553 & 1.681 \\
19 m & 1.127 & 1.256 & 1.385 & 1.513 & 1.640 \\
20 m & 1.096 & 1.223 & 1.35 & 1.476 & 1.601 \\ \hline \end{tabular}
\end{table}
Table 2: Safety factors for different pillar widths.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Rock Type** & **Tensile** & **Compressive** & **Elastic** & **Cohesion** & **Friction** & **Density** & **Poisson’s** \\
**Strength** & **Strength** & **Strength** & **Modulus** & **Cm/KPa** & **Cm/KPa** & **Angle** & **\(\varphi\) (c)** & \(\gamma\)**/g/cm3** & **Ratio** \\  & **\(\sigma_{\text{c}}\)/MPa** & **\(\sigma_{\text{c}}\)/MPa** & **Em/GPa** & **Em/GPa** & **\(\varphi\)** (c)** & **\(\gamma\)**/g/cm3** & **Ratio** \\ \hline Ore Body & 0.341 & 5.973 & 8.867 & 1086 & 48.90 & 2.65 & 0.23 \\ Limestone & 3.063 & 37.342 & 24.75 & 6122 & 52.80 & 2.70 & 0.21 \\ \hline \end{tabular}
\end{table}
Table 1: Rock mass mechanical parameters.  
Since the pillar must support a long-term load to maintain the stability of the stope, the allowable safety factor should be greater than 1. Therefore, the average stope span is 15-20 m, and the pillar width is 10-14 m.
## 1 Introduction
Therefore, a fault-tolerant routing algorithm with good effects can be achieved by improving the traditional routing algorithm. Fault-tolerant routing algorithms for different networks have been studied. [PERSON] et al. [21] propose an algorithm for routing the packets in the case of a link fault in Mesh-of-Tree (MoT) topology. The algorithm ensures that the packets will reach their destination via the shortest possible path. [PERSON] et al. [22] introduce a new fault-tolerant routing algorithm, to tolerate permanent and transient faults in network-on-chips. [PERSON] et al. [23] propose a novel fault-tolerant routing algorithm for 3-ary n-cube networks based on the disjoint path with structure faults. [PERSON] et al. [24] propose a stochastic link-fault-tolerant routing algorithm in a folded hypercube by introducing a type of limited global information called routing probabilities. [PERSON] et al. [25] propose a highly adaptive fault-tolerant routing algorithm for two-dimensional network-on-chips. In this paper, we study the vertex disjoint path construction algorithm and fault-tolerant routing algorithm under restricted connectivity in the BCCC. The major contributions are as follows:  
1. We first propose a vertex disjoint path construction algorithm in BCCC. Time complexity is o(\(nk\)). With this algorithm, \(n\) vertex disjoint paths can be constructed for any two vertices in the BCCC. 2. We prove that under the condition that each vertex has at least one fault-free neighbor in BCCC, its restricted connectivity is \(2\kappa(G)-k-1\) and \(2\kappa(G)-n-2\), when \(n<k+1\) and \(n\geq k+1\), respectively, where \(\kappa(G)\) is the connectivity. 3. We give an \(O(kn\kappa^{1}(G))\) fault-free algorithm, where \(\kappa^{1}(G)\) denotes the restricted connectivity of BCCC. This algorithm can obtain a fault-free path between any two distinct fault-free vertices under the condition that each vertex has at least one fault-free neighbor. The rest of this paper is organized as follows: Section 2 provides the preliminaries. Section 3 proposes a vertex disjoint path construction algorithm. Section 4 discusses the restricted connectivity of BCCC. Section 5 gives a fault-free algorithm to obtain a fault-free path and an analysis of the time complexity of the algorithm. Section 6 provides the discussion and conclusions.
# Decoupled MPC Power Balancing Strategy for Coupled Inductor Flying Capacitor DC-DC Converter
## 4 Experimental Verification  
In order to verify the effectiveness of the proposed MPC power balancing strategy, an experimental platform is built in the laboratory, which is shown in Figure 6. A dual-core control architecture based on TMS320F28335 + XC3S500E is adopted, TMS320F28335 is responsible for sampling and calculation, while XC3S500E is responsible for generating PWM signals and achieving system protection. The experimental parameters are shown in Table 2. To simulate the imbalanced operation conditions, an inductor with 1 mH is connected in series in phase b.
## 2 Materials and Methods
### Methodology  
#### 2.3.1 M5P Model Tree (M5P)  
The M5P model tree (M5P) is a regression tree algorithm that develops conventional decision trees with the addition of linear regression functions to the nodes [51]. This technique has been successful in predicting continuous values, which can be achieved by employing the conversion of the classification problem into a functional optimization problem [52]. The M5 Tree denotes a segmented linear function, testing the value of a specific property at each internal node and predicting the class value at each leaf node. To predict the class value of a new sample, the tree is interpreted starting from the root node. At each internal node, the left or the right branch is selected based on the value of a particular attribute of the sample. The advantage of model trees over regression trees is that regression trees' computational load increases rapidly as the dimensionality increases, while model trees are significantly less than conventional models. Therefore, model trees are more efficient when handling with high-dimensional data as leaf nodes use linear functions rather than constants, leading to more accurate predictions. All enumerated attributes in the M5P algorithm are converted into binary variables before the tree is constructed. This algorithm can efficiently handle missing values. There are three main steps in the M5P tree algorithm, which are tree construction, tree pruning, and tree smoothing [53]. The basic tree is constructed with the splitting criterion, which takes the standard deviation of the class values reaching a node as the error of that node. It computes the expected error reduction resulting from testing each property at that node. The property that maximizes the expected error reduction is then selected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Models** & **Input Combinations** \\ \hline
**M5P** & **RF** & **XGBoost** & \\ \hline M5P1 & RF1 & XGBoost1 & T\({}_{\text{mean}}\), RH, R\({}_{\text{s}}\), U\({}_{2}\) \\ M5P2 & RF2 & XGBoost2 & T\({}_{\text{mean}}\) \\ M5P3 & RF3 & XGBoost3 & T\({}_{\text{mean}}\), RH \\ M5P4 & RF4 & XGBoost4 & T\({}_{\text{mean}}\), R\({}_{\text{s}}\) \\ M5P5 & RF5 & XGBoost5 & T\({}_{\text{max}}\), T\({}_{\text{min}}\) \\ M5P6 & RF6 & XGBoost6 & T\({}_{\text{mean}}\), U\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The input combinations of meteorological variables for various machine learning models.
## 3 Bit Error Rate Analysis of RSST-SR-DCSK System
In other words, the value \(\psi\) is a constant, dependent upon only the type of the chaotic sequence but not the average power of the sequence. Now, we rewrite (19) as  
\[BER^{(l)}_{RSST-SR-DCSK}=\frac{1}{2}erfc\Bigg{[}\left(\frac{2\psi}{R}+\frac{(R+ \beta)(P+1)N_{0}}{PRE_{b}}+\frac{(R+\beta)^{2}N_{0}^{2}}{2 PRE_{b}^{2}}\right)^ {-\frac{1}{2}}\Bigg{]} \tag{20}\]  
Example--consider the case where the Second-order Chebyshev map is used for chaos generation. Formula (20) becomes  
\[BER^{(l)}_{RSST-SR-DCSK}=\frac{1}{2}erfc\Bigg{[}\left(\frac{(R+\beta)(P+1)N_{0 }}{PRE_{b}}+\frac{(R+\beta)^{2}N_{0}^{2}}{2 PRE_{b}^{2}}\right)^{-\frac{1}{2}} \Bigg{]} \tag{21}\]
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
###### Abstract
Moreover, spurred to [5], the spatial diversity isolation is adopted to arrange the positions of the antennas carefully. By mixing the two isolation methods, the number of ground slots can be reduced to four. In the latest reported works [42; 43; 44], MIMO antennas are all designed with microstrip patches. The back cover of the design [42] has clearance areas which is not all metal. In the design of [43; 44], the size of the antenna is large to satisfy the wide bandwidth. The planar structure is adopted in [45; 46] to integrate the antenna with the back cover of the mobile phone, but the bandwidth of the designs is also affected by the metal back cover. In [45], the antenna obtains a narrow bandwidth since the footprint of the metal back cover is larger than the antenna itself. The methodology in [46] does not use a large metal ground, so it is not conducive to integration in the metal back cover. The designs in [42; 43; 44; 45] are not suitable for 5G metal bodies but for non-metal bodies. This paper focuses on MIMO antenna systems for 5G metal frame smartphone applications and addresses the common problems of MIMO antenna systems discussed above, such as large size, complex structure, and low isolation. We etched eight inverted-F-shaped grooves as an 8 \(\times\) 8 MIMO antenna system for the metal bezel of a 5G smartphone. The proposed MIMO antenna system can cover the entire N79 5G frequency band (4.4-5.0 GHz). Facing the restricted radiation environment brought by the metal frame, we have explored some radiation methods for MIMO antennas in such case. The small slot is etched as an antenna on the metal frame. What is more, the position of each antenna is carefully arranged so that it can be beautiful and leave redundant space for the design of 2G/3G/4G antennas. More importantly, the small-size combined slot with the C-shape and vertical I-shape connected to each other is proposed to improve isolation. The isolation in the target band can be achieved at 22 dB, and ECC is less than 0.049. Ideally, the total efficiency is 55% to 78%. Simulation and measurement results of the 8 \(\times\) 8 MIMO antenna show satisfactory performance. In addition, the effect of the human hand model and the impact of the antenna on the user have also been researched. It shows great potential in the 5G mobile metal frame terminal application.
# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
## 5 Conclusions  
This paper focuses on the active global localization of mobile robots in outdoor environments. In terms of current problems such as low calculation efficiency, poor robustness capability, and lack of active localization path planning standards of existing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AMCL 3D** & \multicolumn{2}{c}{**Straight Line**} & \multicolumn{2}{c}{**Entropy Increment**} & \multicolumn{2}{c}{**Curve Localizability**} \\ \hline
**Start Point** & **Time(s)** & **Relocation** & **Time(s)** & **Relocation** & **Time(s)** & **Relocation** & **Time(s)** \\ \hline
1 & Failed & 7 & 52.2 & 4 & 41.8 & 4 & 37.1 \\
2 & 7.3 & 0 & 5.1 & 0 & 5.5 & 0 & 5.3 \\
3 & 52.4 & 4 & 43.5 & 5 & 37.1 & 2 & 20.2 \\
4 & Failed & 5 & 50.8 & 5 & 44.4 & 4 & 35.7 \\
5 & Failed & Failed & Failed & Failed & Failed & 7 & 51.9 \\
6 & 8.4 & 0 & 5.0 & 1 & 16.4 & 1 & 14.3 \\
7 & 8.7 & 0 & 5.3 & 0 & 5.6 & 0 & 6.0 \\
8 & Failed & 3 & 26.1 & 2 & 19.8 & 2 & 18.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global localization results of different algorithms.  
Figure 11: Trajectories of active localization algorithms.  
algorithms, this paper proposes a curve localizability index, based on OctoMap and 3D Lidar model, to estimate the relationship between lidar observation status and the robot working pose, so that we can generate an active path which is more conducive to robot localization and improve the efficiency of active localization method. We conducted comparison experiments for the proposed algorithm by actual robot platforms, and verified that our algorithm has higher computational and localization efficiency and stability under different environmental conditions through qualitative and quantitative analysis. In the future, we can consider expanding the curve localizability index to dynamic environments, or combine with other path planning and global localization strategies to satisfy specific demands of different application environments.  
Conceptualization, L.G. and X.Y.; methodology, L.G.; software, X.Y. and J.W.; validation, L.G., X.Y., and J.W.; formal analysis, X.Y. and J.W.; investigation, J.W.; resources, J.W.; writing--review and editing, L.G., X.Y., and J.W.;supervision, J.W. All authors have read and agreed to the published version of the manuscript.  
This work is partly supported by the National Natural Science Foundation of China under Grant No. 61773261, and by the Science and Technology Commission of Shanghai Municipality under Grant No. 20 DZ220400.  
Not Applicable.  
The authors declare no conflict of interest.
# A Brazilian Space Launch System for the Small Satellite Market
## 3 Results and Discussion

Table 4: Launch vehicle (LV) related costs in WYr for the three concepts, C-1, C-2, and C-3. As it can be seen, the proposed configurations (concepts 1, 2 and 3) present larger average payload capacity along with lower specific transportation costs compared to most of the small launchers currently under development (black crosses) and in commercial operation. Also, the price per kilogram of the payload of concept 3 is at the same level of those charged by a launch service provider selling rideshare opportunities [19]. Concept 3, however, would offer the service, for a 500 kg class satellite, in a dedicated flight for an equivalent price per kilogram thus opening the opportunity for satellite providers to establish their planned constellations [20,21].
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Evaluation Metrics  
The metrics used for evaluation purposes are accuracy (\(ACC\)), precision (\(PR\)), recall (\(REC\)), and F1-score (\(F_{1}\)), as defined in Equations (11)-(14) below:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
\[PR=\frac{TP}{TP+FP} \tag{12}\]  
\[REC=\frac{TP}{TP+FN} \tag{13}\]  
\[F_{1}=\frac{2\cdot PR\cdot REC}{PR+REC} \tag{14}\]  
\(TP\), \(FP\), \(TN\), and \(FN\) stand for true positive, false positive, true negative, and false negative, respectively. Additionally, we have calculated the average value of the F1-score (\(\overline{F_{1}}\)) over the whole dataset, which is given by averaging the corresponding F1-score of each image of the dataset.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work  
### Fall Detection Technologies: A Classification Based on AI Paradigms  
Traditionally, fall detection systems have been classified according to the types of sensors they employ--wearables, ambient, or vision-based devices [9]. While this classification highlights the hardware involved, it does not adequately reflect the rapid evolution of computational techniques driving these systems. A more insightful fall detection technology is achieved by the data processing paradigms they utilize, ranging from simple rule-based methods to advanced multimodal AI models.
## Materials and Methods
### Analysis of Organic Acids  
After subjecting 5 mL of millet Huangjiu to centrifugation at 10,000\(\times\)_g_ for 10 min, the resulting mixture was filtered through a 0.45 \(\upmu\)m microporous membrane. An HPLC analysis was performed to determine organic acids. The separation was conducted on an Agilent 1260 Infinity II system with a 250 \(\times\) 4.6 mm and 5 \(\upmu\)Ultisil XB-C18 column, following the chromatographic conditions described by [PERSON] et al. [10]. The column temperature was maintained at 30 \({}^{\circ}\)C, and a mobile phase consisting of pH 2.3 phosphate buffer (0.01 mol/L KH\({}_{2}\)PO\({}_{4}\)) adjusted with a 5% (\(w/v\)) acetic acid solution was used. The flow rate was set to 0.8 mL/min, and detection was carried out at a wavelength of 210 nm. Organic acid quantification of the samples was performed using an external standard method. The calibration curve equations for each organic acid were as follows: oxalic acid: y = 0.0026x \(-\) 0.0034 (R\({}^{2}\) = 0.9993); tartaric acid: y = 0.0034x + 0.042 (R\({}^{2}\) = 0.9998); pyruvic acid: y = 0.0018x + 0.0076 (R\({}^{2}\) = 0.9987); malic acid: y = 0.0032x + 0.0017 (R\({}^{2}\) = 0.9991); lactic acid: y = 0.0507x + 0.0233 (R\({}^{2}\) = 0.9986); citric acid: y = 0.0198x \(-\) 0.0411 (R\({}^{2}\) = 0.9995); succin acid: y = 0.0746x + 0.0347 (R\({}^{2}\) = 0.9994); acetic acid: y = 0.0269x \(-\) 0.0163 (R\({}^{2}\) = 0.9993);and fumaric acid: y = 0.0027x + 0.0015 (R2 = 0.9971). The linear ranges of oxalic acid, tartaric acid, pyrruvic acid, malic acid, and fumaric acid were 5.0-500 mg/L. For citric acid and acetic acid, the linear range was 0.05-5 g/L, and for lactic acid and succinic acid it was 0.10-10 g/L.
# Improved Calculation of Load and Resistance Factors Based on Third-Moment Method
## 5 Influence of Distribution Type on the Result of the Proposed Method  
In this example, several distribution types are assigned to observe their influence on the precision of the proposed method. Nine cases with different distribution types are applied, as presented in Table 6, along with different presumed resistances and loads listed in Table 7. Considering \(\beta_{T}\) = 1.0, 2.0, 3.0, 4.0, the reliability indices calculated using the proposed method are illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **R** & **S1** & **S2** & **S3** \\ \hline Case 1 & Lognormal & Lognormal & Lognormal & Lognormal \\ \hline Case 2 & Lognormal & Normal & Lognormal & Lognormal \\ \hline Case 3 & Lognormal & Gamma & Lognormal & Lognormal \\ \hline Case 4 & Lognormal & Normal & Normal & Normal \\ \hline Case 5 & Lognormal & Gamma & Gamma & Gamma \\ \hline Case 6 & Lognormal & Gumbel & Lognormal & Lognormal \\ \hline Case 7 & Lognormal & Gumbel & Gumbel & Gumbel \\ \hline Case 8 & Lognormal & Weibull & Lognormal & Lognormal \\ \hline Case 9 & Lognormal & Weibull & Weibull & Weibull \\ \hline \end{tabular}
\end{table}
Table 6: Distribution types of the random variables.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(\mathbf{\mu_{R}}\) & \(\mathbf{\mu_{S1}}\) & \(\mathbf{\mu_{S2}}\) & \(\mathbf{\mu_{S3}}\) & \(\mathbf{V_{R}}\) & \(\mathbf{V_{S1}}\) & \(\mathbf{V_{S2}}\) & \(\mathbf{V_{S3}}\) \\ \hline A & - & 10 & 10 & 10 & 0.15 & 0.35 & 0.4 & 0.1 \\ \hline B & - & 10 & 6 & 4 & 0.15 & 0.35 & 0.4 & 0.1 \\ \hline C & - & 10 & 9 & 1 & 0.15 & 0.35 & 0.4 & 0.1 \\ \hline D & - & 10 & 4.6 & 0.8 & 0.15 & 0.35 & 0.4 & 0.1 \\ \hline E & - & 10 & 3.6 & 0.7 & 0.15 & 0.35 & 0.4 & 0.1 \\ \hline \end{tabular}
\end{table}
Table 7: Basic random variable.  
Figure 6: Reliability calculated by different methods for \(L_{n}/D_{n}\) = 0.5–2.5 (\(\beta_{T}\) = 2).  
Figure 7: Reliability index (\(\beta\)) computed using the proposed method.  
According to Figure 7a-i, it can be clearly seen that the reliability index \(\beta\) increases with the increase in \(\beta_{T}\). For \(\beta_{T}\) =1.0-2.0, the results are close to the target reliability in case 1 to 9. For \(\beta_{T}\) =3.0-4.0, the results become smaller than \(\beta_{T}\) except case 7. It shows that a change in distribution type has a moderate effect on the accuracy of the proposed method when \(\beta_{T}\) is great, which needs to be further refined and perfected in future studies.