esa-sceva/satcom-chunk-collection huggingface dataset:
### Implementation of Uniformly Distributed Chaotic Signals  
Uniform distribution chaotic signals have broad applications in many research fields, such as sampling survey research, simulation modeling, information encryption, image processing, and so on.  
This section takes the implementation of a uniform distribution chaotic signal as an example to verify the effectiveness of the proposed strategy. Given the boundedness of the chaotic signal \(x\), this article sets the domain of the target chaotic signal \(y\) to a symmetric interval \([c,d]=[-10,10]\). The probability density function of an ideal uniform distribution is represented as  
\[f_{y}(y)=\frac{1}{20},y\in[-10,10] \tag{7}\]  
The probability density function and distribution function of the ideal uniform distribution are shown in Figure 6.  
The transformation relationship from the intrinsic signal \(x\) to the uniform distribution chaotic signal \(y\) is shown in Figure 7.  
The waveform, probability density function, and distribution function of the chaotic signal \(y\) are shown in Figure 8.  
By comparing Figure 4a with Figure 8a, it can be seen that the distribution of \(y\) is more uniform than the distribution of \(x\). By comparing Figure 6 with Figure 8b, it can be seen that the probability density function of chaotic signal \(y\) follows a uniform distribution.  
Therefore, the proposed strategy can transform the intrinsic signal \(x\) into a uniform distribution chaotic signal \(y\).  
The autocorrelation function and power spectral density of chaotic signals \(x\) and \(y\) are shown in Figure 9a,b,c,d, respectively.  
Figure 8: Waveform, probability density function, and distribution function of intrinsic signal \(y\): (**a**) waveform; (**b**) probability density function and distribution function.  
Figure 6: Probability density function and distribution function of the ideal uniform distribution.  
Figure 7: Mapping from \(x\) to uniform distribution \(y\).  
Figure 9a,c show that the autocorrelation functions of the chaotic signals before and after the distribution transformation are very small, and the difference is not significant. Figure 9b,d show that the power spectral density before and after the distribution change is a continuous distribution, with no obvious discrete spectral lines. Therefore, the proposed strategy does not change the \"quasi-random\" characteristic of the chaotic signal, and the resulting chaotic signal y remains a chaotic signal.  
In conclusion, the proposed strategy can flexibly adjust the probability distribution of chaotic signals based on the characteristics of the target distribution of chaotic signals.
# Clinical Applications of Nano-Hydroxapatite in Dentistry
## 2 Materials and Methods  
### Protocol Development and Eligibility Criteria  
The review protocol was prepared according to the Preferred Reporting Items Systematic review and Meta-Analyses (PRISMA) [11; 12; 13], and registered in PROSPERO (Prospective Register of Systematic Reviews). The following focused question was phrased: \"What are the clinical applications of nano-HAp in dentistry?\"  
The inclusion criteria were:  
* Human studies
* Observational, case-control, randomized control ed trial and interventional study design
* A minimum of 10 participants in the study
* Articles written in English
* The exclusion criteria were:
* Animal studies
* In vitro studies
* Systematic reviews and review
* Case reports and case series with less than 10 participants
* Articles not written in English.  
No time limitations were set.
## 4 Discussion
### Influence of the Background on LAI Estimation  
Image segmentation can enhance the accuracy of LAI estimation (Figure 6; Table 4). Paddy field image segmentation refers to the process of categorizing the pixels in an image into rice and background. CIs are calculated by the average of the entire set of pixels in images. The background can contribute to the overall gap fraction when mixed with the rice. This mixing effect can lead to underestimation or overestimation of LAI if not properly accounted for [50]. After segmenting the image into rice and background, only the rice pixels were considered for the calculation of CIs. The background elements of paddy field images include soil, water, reflections of rice plants, and weeds. The CIs of the background exhibited significant differences from the CIs of crops [29].  
The correlations between segmented CIs and LAI at the tillering and jointing stages increased less compared to the booting, heading, and grouting stages. Due to the uneven growth of our experimental rice plants, soil, weeds, and shade are still observable in the images taken after the booting stage. The background elements in paddy field images of reproductive stages largely consist of soil and weeds, making the background relatively simple. This results in a higher accuracy of image segmentation. In contrast, the background elements at the tillering and elongation stages are more complex, including water, water hyacinth, and shadows [35]. This complexity leads to lower segmentation accuracy in the images of these two growth stages [37], ultimately affecting the correlations between CIs and LAI as well as the accuracy of the models. From the holistic perspective of the entire growth period, image segmentation can effectively enhance the accuracy of LAI estimation.  
After correction for illumination and image segmentation, the correlation of CIs at various growth stages from tillering to grouting stages was significantly improved. The multiple linear models built for the elongation and heading stages showed increases in R\({}^{2}\) of up by 0.27 (Table 3). This demonstrates the significant impact of illumination variations and image backgrounds on the estimation of rice LAI.
# Optimization Algorithm for 3D Smooth Path of Robotic Arm in Dynamic Obstacle Environments
###### Abstract  
With the rapid development of industrial automation, robotic arms are increasingly required to operate in dynamic and unstructured environments. Path planning for such systems has become a critical challenge, particularly in scenarios involving dynamic obstacles and complex environments. This paper addresses the challenge of obstacle avoidance path planning for robotic arms in dynamic and complex environments. It introduces a hybrid trajectory planning method that combines the strengths of the rapidly exploring random tree (RRT) and artificial potential field (APF) algorithms. The method divides the process into two phases: initial static path planning and dynamic path replanning. An adaptive angle guidance strategy is proposed to enhance the efficiency of RRT in static environments by improving target directionality and reducing redundant nodes, followed by Catmull-Rom spline interpolation for path smoothing. For dynamic environments, the algorithm dynamically adjusts the weight factors of RRT and APF, enabling rapid local path replanning to avoid both static and dynamic obstacles. Experimental results demonstrate that the proposed method achieves significant improvements in trajectory smoothness, path length, and computational efficiency compared to traditional approaches. This work offers a robust solution for real-time motion planning, contributing to the advancement of robotic arm control in industrial and unstructured settings.  
path planning; rapidly exploring random tree (RRT); artificial potential field (APF); dynamic obstacle avoidance
## 3 Results and Discussion
Thus, bacterial cells were subjected to high stress by the  
Figure 1: Bacterial inhibition on polyester surfaces with the presence of bacterial colonies of _S. aureus_ in TSA agar incubated for 24 h at 37 \({}^{\circ}\)C: (**a**) bacterial colonies growing on a surface not treated with triclosan tested in dry test conditions; (**b**) bacterial inhibition on a surface treated with 400 ppm of triclosan in humid conditions. Epifluorescence microscopy images of _S. aureus_ cells stained with the LIVE/DEAD® kit (Molecular Probes, Eugene, OR, USA) after 24 h incubation: (**c**) presence of _S. aureus_ live cells (green) and dead or injured (red) cells on a surface not treated with triclosan; (**d**) inhibition of _S. aureus_ growth on a surface treated with 400 ppm of triclosan. presence of triclosan. It was evidenced by the low presence of dead/injured cells for the concentrations at 650 ppm and 850 ppm in dryness (\(1.76\times 10^{2}\) and \(8.42\times 10^{1}\), respectively) (Table 2). The presence of dead or injured cells of _S. aureus_ was greater in the control than in the active concentrations (\(p<0.05\)). Also, as show in Table 2, the highest bacterial inhibition results were found at the highest concentration (850 ppm). These values were 0.88 cm for the dry condition and 0.94 cm for the humid condition. Although no significant differences were found (\(p>0.05\)) between the concentrations of 500 ppm (Id3) and 650 ppm (Id4) in either test conditions, an increase in the size of the inhibition zones was observed. This fact was observed when the relationship between the bacterial inhibition and the concentrations (400, 500, 650 and 850 ppm) was studied for both humid and dry conditions (R\({}^{2}=0.986\) and R\({}^{2}=0.968\), respectively) (Figure 2). The microscopy and the bacterial inhibition tests also showed the bacteriostatic activity of triclosan. This activity is related to the concentrations in the study. If the concentrations of triclosan had been higher, the biocidal efficacy would also likely have been considerable. However, concentration is equivalent to durability. Greater bacterial inhibition means a greater biocide reserve, causing biological activity to be maintained for a longer period of time. Consequently, the choice of concentration will depend on the desired durability. It has also been indicated that humid conditions are favorable for the colonization and subsequent adherence of bacteria to surfaces. When a biocide is added with bacteriostatic properties, favorable conditions for the formation of biofilms are reduced [49]. In the case of the present study, with concentrations of triclosan between 400 ppm (Id2) and 850 ppm (Id5),
## 4 Results
### Analysis of Landscape Pattern Changes in Xiong'an  
Seven landscape-level pattern indices, including the AREA_MN, SHDI, DIVISION, ED, CONTAG, NP, and AI, were systematically analyzed (Figure 6), yielding the following key findings:  
1. The NP in Xiong'an showed a marked increase, while the AREA_MN significantly decreased, indicating enhanced landscape fragmentation and finer patch compositions. Combined with SLUDD analysis, these patterns reveal frequent land use conversions, fragmented originally contiguous patches into smaller, more heterogeneous spatial units. Quantitatively, the AREA_MN declined from 1.44 ha to 0.85 ha, while the NP increased from 69.26 pcs/ha to 118.24 pcs/ha, demonstrating growing spatial heterogeneity per unit area.
2. The ED, reflecting patch shape complexity, increased by 66.23% (from 127.42 to 211.81 m/ha) during 2014-2022. This pronounced growth indicated that intensified anthropogenic activities altered patch geometries substantially, strengthening ecological interactions between adjacent patch types through more convoluted boundaries.
3. The AI showed an overall decreasing trend, while the DIVISION showed a slightly increasing trend, signaling progressive dispersal of landscape patches. These complementary metrics indicate deteriorating spatial cohesion, where formerly aggregated patches became increasingly fragmented, compromising landscape structural integrity.
4. The SHDI is sensitive to the non-equilibrium distribution of landscape patches. Between 2014 and 2022, the SHDI increased by 45%, signifying a more balanced proportion of different landscape types. This shift was accompanied by a weakening of the dominant role of cultivated land, with portions of it being converted into other patch types. The CONTAG further supported this conclusion. With a 33.79% decrease, the CONTAG indicated a decline in the connectivity of dominant patches, consistent with the ongoing subdivision of large patches into small ones.
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
## 2 Principle of Enhanced Oil Recovery by Nanomaterials
### Lower Oil Viscosity
The high-viscosity characteristics of nano-oil displacement agents help to maintain a low mobility ratio during the oil displacement process, enhance the flow capacity of the displacement phase, and expand the degree of oil washing, thereby improving oil recovery [31]. In summary, nanomaterials can be uniformly dispersed in heavy oil, which greatly inhibits the formation of large-area viscoelastic networks. Thus, the viscosity of heavy oil is reduced. These nanoparticles include silica, iron oxide, alumina, nano-ceramics, etc. Nanoparticles affect the viscosity of crude oil by affecting the chemical bonds between substances or changing the structure of substances. The degree of adsorption of nanoparticles on asphaltene is an important manifestation of their ability to reduce the viscosity of crude oil. Nanomaterials reduce the viscosity of heavy oil and thus reduce the difficulty of oil start and improve the efficiency of oil production.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 3 Simulation Test
### Case 2: Maolin District
#### 3.2.1 Image Shooting Distance  
The results of the grid-type showed a maximum distance of 234 m, a minimum distance of 113 m, and an average distance of 186 m, with 43% of the images in the 200 \(\pm\) 10 m range. In addition, the contour-type results showed a maximum value of 219 m, a minimum value of 182 m, an average distance of 200 m, and images of 87% in the 200 \(\pm\) 10 m range. This shows that the contour-type enables most of the images to be taken at a distance more in line with the 200 m set by the user.
## 3 Calculation Results and Influence Factors Analysis  
### Transverse Ground Settlement with Different Tunnel Spaces  
To analyze the influence of different tunnel spacings on the lateral settlement of the ground, the lateral ground settlement data of single-line and double-line tunnels under different tunnel spacings are sorted out, as shown in Figure 2. The transverse ground settlement of the double-line tunnel is obtained based on continuous excavation of two tunnels. Figure 2 shows the lateral settlement curves of single-line and double-line tunnels with 1.0D, 1.5D, and 2.0D tunnel spacings, respectively. The parameter D is the tunnel diameter.  
Figure 2: Effect of different tunnel spacings on ground settlement during continuous excavation of double-line tunnels. (**a**) 1.0D tunnel space; (**b**) 1.5D tunnel space; (**c**) 2.0D tunnel space.  
As illustrated in Figure 2a-c, the settlement distribution curves of single-line and double-line tunnels exhibit a high degree of similarity under the three tunnel space conditions. The ground horizontal settlement is observed to be substantial in the middle and minimal at both ends. Furthermore, as shown in Table 2, the maximum ground subsidence after excavation of double-line tunnels is 29.6%, 18.9%, and 2.7% higher than that of single-line tunnels, respectively, under the spatial conditions of 1.0D, 1.5D, and 2.0D tunnels. With the increase in tunnel spacing, the maximum settlement point moves to the left side.  
In summary, an increase in the lateral spacing of the double-line tunnel results in an increase in the width of the settlement trough, a weakening of the superposition effect, a gradual decrease in the maximum value of the ground settlement, and a gradual movement of the position to the left-line tunnel. This indicates that the influence of the right-line tunnel excavation on the left-line tunnel decreases with an increase in the spacing between the two tunnels. This finding is consistent with the model test results of [PERSON] et al. [26; 28].
## 2 Materials and Methods
#### 2.1.3 Statistical Analyses and Model Validation  
The effects of the four factors and their interactions on the strand width were assessed using analysis of variance (ANOVA) (\(\alpha\) = 0.05). A predictive equation was developed to estimate the strand width based on statistically significant factors by fitting the best least square regression model. All statistical analyses were performed in JMP Pro (SAS, Cary, NC, USA). To validate the predictive model, strands (n = 3) were biopltted using a different set of process parameters within the design space (extrusion pressure of 0.35 N/mm\({}^{2}\) and nozzle speed of 0.9 mm/s), and the experimental strand width was compared to its model estimate.
## 4 Discussion
Additionally, the SCAT-A [54], which assesses anxiety through 15 statements with responses categorized as rarely, sometimes, and often, was employed in the same study [45] to evaluate competitive anxiety, an especially pertinent measure for athletes in aesthetic sports like rhythmic gymnastics, who are particularly susceptible to anxiety [79]. Thus, monitoring competitive anxiety is crucial during pre-competitive and competitive periods, as high levels of competitive trait anxiety can detrimentally affect performance, and coping strategies should be incorporated by professional sports psychologists. These questionnaires were combined with external training data, such as the number of training sessions and hours per week, and hours per day, to elucidate performance scores. While nutrient deficiencies and body composition were also examined, their results were not included in this review, as the study [45] did not establish a direct relationship between training load and these parameters, with only relevant findings extracted. The alternative measurements of training loads could be potentially adopted in rhythmic gymnastics. For example, the countermovement-jump test was recognized as a practical tool suitable to detect neuromuscular fatigue [80] and was widely used in monitoring athletes representing both individual and team sports [81]. Future research should consider the possibility of including this tool in monitoring loads in this sport discipline.
# Numerical Simulation on Dynamic Behavior of Slab-Column Connections Subjected to Blast Loads
## 4 Analysis Results
### Steel Stresses in Slab  
The peak stresses in the reinforcement of the slab are shown in Table 5. In every specimen with TNT 4 kg and 12 kg, the peak rebar stress occurred near the explosive load. Although a large fracture occurred in the slab, the peak stresses of all reinforcing bars did not reach the maximum strength to which dynamic increased factor was applied. This phenomenon is believed to occur because the concrete is destroyed by the explosive energy at a moment and the energy is not sufficiently transmitted to the reinforcing bar. However, in the previous study, it has been confirmed that the reinforcing bars affect the blast-resistance capacities in the beam-column connections [65].  
For most specimens, the stresses of bottom reinforcements of the slab were larger than those of top slab reinforcements. This phenomenon is due to the failure mode in which  
Figure 10: Slab deflection at the critical section with TNT 12 kg.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & \multicolumn{3}{c}{**Peak Stress (MPa)**} \\  & **Top Reinforcing Bar** & **Bottom Reinforcing Bar** \\ \hline IC4 & 32.40 & 157.04 \\ IC-D4 & 33.93 & 164.08 \\ EC4 & 29.52 & 162.08 \\ CC4 & 31.17 & 137.43 \\ IC12 & 122.62 & 298.52 \\ IC-D12 & 128.38 & 279.72 \\ EC12 & 176.12 & 312.99 \\ CC12 & 440.92 & 440.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Peak stresses in reinforcements of the slab.  
the rear fracture was more severe than the fracture of the front face to which the explosive load was applied. The effect of the drop panel on the reinforcement stress was not clearly observed, considering that the slab reinforcement stresses of IC and IC-D were similar.
## 4 Methodology
### Response and Track Prioritization  
_Response Prioritization:_ The vehicle ride behaviour is objectively evaluated by means of pitch and roll rates (\(\dot{\theta},\dot{\phi}\)) and body and axle acceleration responses \(\ddot{z}_{i},\ddot{y}_{i}\forall i\in 1-4\). These vehicle responses (physics) on a particular test track are a function of the vehicle parameters. To understand the effects of parameters on the responses of interest, each parameter is varied one at a time. Each of these vehicle parameters is increased by 25% of its baseline value for the vehicle simulation performed at low and high speeds on the low-severity herringbone track. The effects of the parametric sweep on the vehicle responses (\(\dot{\phi},\dot{\theta},\ddot{z},A_{b}(\dot{z}_{i}),A_{w}(\dot{y}_{i})\)) are quantitatively estimated in terms of percentage variation of the peak value of the responses obtained using the baseline and the new sets of parameters. As an example, the results of the simulation responses of a vehicle traversing on a low-severity herringbone track are tabulated in Table 4 for low- and high-speed tests (\(V\in\mathrm{L}\) and \(\mathrm{H}\), respectively). The responses that are unaffected by variation of a certain parameter are denoted with \(\varnothing\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Track** & \(S\) & \begin{tabular}{c} **Mag** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Pitch** \\ **(mm)** \\ \end{tabular} & \(V\) &
\begin{tabular}{c} **Value** \\ **(kmph)** \\ \end{tabular} \\ \hline \multirow{4}{*}{1. Chassis-twist} & L & 150 & 2000 & L & 7.5 \\  & L & 25 & 600 & H & 15.5 \\  & H & 200 & 4000 & L & 5.5 \\  & H & 25 & 600 & H & 14.5 \\ \hline \multirow{4}{*}{2. Herringbone} & L & 10 & 500 & L & 9 \\  & L & 25 & 600 & H & 14 \\  & H & 20 & 800 & L & 14 \\  & L & 25 & 600 & H & 19 \\ \hline \multirow{4}{*}{3. Washboard} & L & 15 & 400 & L & 7.5 \\  & H & 25 & 600 & H & 9.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the vehicle running on continuous displacement tracks with low (L) and high (H) severity levels \(S\) at low (L) and high (H) speeds \(V\). For developing this parameter-to-response mapping, the dependence of a parameter to a particular response as presented in Table 4 is translated into a _dependency-star rating_ as shown in Table 5. The \(\star\) rating indicates the level of the dependence, i.e., whether the particular response is loosely or strongly affected by the underlying vehicle parameter of