esa-sceva/satcom-chunk-collection huggingface dataset:
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Quality of Services  
Generally, the quality of service (QoS) is a set of techniques that identifies the network behavior and intelligence of routing protocols under a limited network capacity. This paper uses four primary QoS techniques, namely, packet delivery ratio (%), delay(s), throughput(kbps), and normalized routing load(%).  
1. Packet delivery ratio (PDR %): PDR (%) is the best QoS technique for determining a routing protocol's operational capability. It shows the percentage (%) of packets those successfully arrive at the destination UAV node over packets originating from the source. Equation (20) shows the calculation of the PDR: \[PDR=\frac{\sum\textit{Total number of packets received}}{\sum\textit{Total number of packets originated}}\] (20)
2. Average Delay: Delay indicates the total taken time taken by received data packets take to reach the destination UAV. It is in seconds. Equation (21) shows the average delay. To determine the average delay (\(D_{T}\)), the total time of the received data packet (\(R_{T}\)) is subtracted from the total time of sent data packets (\(S_{T}\)). The remaining value is divided by the total number of packets received: \[D_{T}=\frac{\sum R_{T}-\sum S_{T}}{\sum R}\] (21)
3. Throughput: It is the amount of data delivered to the receiver in a unit average time over the number of nodes [51], usually calculated in bits per second (bps) or kilobits per second (kbps). Equation (22) shows the calculation of throughput:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline MAC Standard & 802.11 g \\ Frequency & 2.4 Ghz \\ Propagation Model & 3D Free Space Model \\ Mobility Model & Random waypoint \\ Antenna Model & Omnidirectional Antenna \\ Transmitted Signal Power & 0.2818 W \\ Carrier Sense Threshold & \(1.559\times 10^{-11}\) W \\ Receive Power Threshold & \(3.652\times 10^{-10}\) W \\ System Loss Factor & 1.0 \\ Network Coverage Area & \(1000\times 1000\times 500\) (m\({}^{3}\)) \\ Communication Range & 250 m \\ Max number of UAVs & 50 \\ The transport protocol and traffic & UDP and CBR traffic \\ Packet Rate/Data flow & 24 Packet/s \\ Packet size & 512 kb \\ Number Nodes in Movement & 10, 20, 30, 40, 50 \\ Pause time & Dynamic \\ Mobility speeds & 20, 40, 60 (m/s) \\ Connections & 20 \\ Simulation time & 60 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
\[Throughput(kbps)=\frac{\sum\ packets(kbps)\ successfully\ Delivered}{T} \tag{22}\]
4. Normalized Routing Load (NRL): NRL is defined as the number routing packets transmitted divided by data packets from the source. NRL is calculated from Equation (23): \[NRL\frac{\sum\ routing\ control\ packets}{\sum\ data\ packets\ reviewed}\] (23)
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 3 Proposed Method
### Component Extraction
#### 3.2.2 Component Matching  
The Hausdorff distance [37] reflects the gap of two sets by measuring the distance between two nonempty subsets. Using Hausdorff distance for model matching can simplify the calculation, but it is frequently interfered with by noise. In order to build thecorresponding relationship between the components of the two models, this paper adopts the improved Hausdorff distance to match the components.  
For two sets \(A=\{a_{1},a_{2},\cdots,a_{i}\}\) and \(B=\{b_{1},b_{2},\cdots,b_{j}\}\), the bidirectional Hausdorff distance between these two sets is defined as follows:  
\[H(A,B)=\max\{h(A,B),h(B,A)\} \tag{3}\]  
where \(h(A,B)\) denotes the unidirectional Hausdorff distance from set \(A\) to set \(B\), \(h(B,A)\) denotes the unidirectional Hausdorff distance from set \(B\) to set \(A\). The improved unidirectional Hausdorff distance is written as:  
\[h(A,B)=\frac{1}{N}\sum_{a\in A}\left\{\min_{b\in B}\|a-b\|\right\} \tag{4}\]  
where \(N\) represents the number of elements in set \(A\), \(\|\cdot\|\) represents the distance between two elements in different sets.  
We first select component \(A\) in the manifold \(M\) and calculate its Hausdorff distance to each component in the manifold \(N\) to obtain the distance set \(D=\{d_{1},d_{2},\cdots,d_{k}\}\). Then we choose the minimum distance \(d_{\text{min}}\) in the set to form a component pair and delete the two components from the original set. We repeat the steps for the remaining components in turn to construct a pair of components.
# Using an Opportunity Matrix to Select Centers for RBF Neural Networks
## 1 Introduction
### Research Goal  
The preceding discussion introduced RBF neural networks and their benefits, explained the role of RBF centers in the overall performance of an RBF neural network, and described the two most widely used methods of selecting the locations for an RBF network's centers. With this foundational information in mind, we are now well equipped to understand the primary goal of the current research project.  
In brief, the current paper seeks to develop a new algorithm--based on the concept of an \"opportunity matrix\"--whose purpose is to select locations for an RBF network's centers that will consistently yield superior overall function approximation performance for the RBF neural network. In pursuit of this goal, the following section first describes the proposed opportunity matrix algorithm for selecting RBF node centers, and then provides details about the series of experiments that were conducted in order to compare the performance of the proposed algorithm against that of the standard random sampling and \(k\)-means clustering techniques. The results of the experiments are subsequently presented and discussed in Section 3. The paper concludes in Section 4 with a summary, a description of the study's limitations, and suggestions for future research opportunities in this area. A graphical overview of the current study is provided in Figure 3 below.
## 3 Results
### Comparison of Prediction Accuracy for All Variable Selection Approaches
#### 3.3.2 Comparison of Prediction Accuracy of Classical and Penalized Regression Methods  
Figure 3 compares the prediction accuracy of BE, FS, NNG, ALASSO, RLASSO, and lasso in low- (upper panel) and high (lower panel)-correlation settings.  
Figure 3: Comparison of RTE between classical (BE and FS) and penalized (NNG, ALASSO, RLASSO, and lasso) methods for beta-type A. The **upper panel** shows results for low correlation (C2), while the **lower panel** shows results for high correlation (C3). The **left** and **right panels** show results for small (\(n=100\)) and large (\(n=400\)) sample sizes, respectively. Different \(y\)-axis scales are used to enhance the visibility of performance differences. Overlapping lines indicate methods with very similar results, such as BE and FS in the upper panel. One standard error bars were added but are not visible due to the small standard errors.  
Low-Correlation Settings  
BE and FS performed similarly and showed inferior prediction accuracy compared to penalized methods in small sample sizes (top-left panel) and in large sample sizes with low SNR (top-right panel, lower end). However, in large sample sizes with high SNR (top-right panel, upper end), BE and FS outperformed lasso and were comparable to the other penalized methods.  
Among the penalized methods, lasso performed best in small sample sizes with low SNR (top-left panel), but was outperformed by other methods in large sample sizes with high SNR (top-left panel). NNG, ALASSO, and RLASSO performed similarly, although RLASSO was slightly less accurate in scenarios with large sample sizes and low SNR (top-right panel, lower end).  
High-Correlation Settings  
BE and FS consistently showed inferior predictive performance to the penalized methods, regardless of sample size or SNR (lower panel). In scenarios with small sample sizes or low SNR, FS performed slightly better than BE--likely due to the fact that BE begins with a full model containing many parameters, which can increase variability in coefficient estimates and lead to suboptimal predictions under limited information. Lasso outperformed other approaches across different sample sizes and SNR levels (bottom-left panel). NNG, ALASSO, and RLASSO showed comparable performance across settings (lower panel).  
Overall, lasso performed best in scenarios with small sample sizes, low SNR, and high correlation, while BE and FS performed the worst under these settings. Conversely, in scenarios with large sample sizes, low correlation, and high SNR, lasso performed poorly, whereas other approaches, including BE and FS, produced better results. In terms of model complexity, lasso selected models with a larger number of variables, while BE and FS selected simpler models. The other penalized methods selected models of intermediate complexity, with only minor differences among them (Figure 6).
# Impact of Audio Delay and Quality in Network Music Performance +
## 4 Subjective Evaluation Design
### Questions Specific to Scenario A  
Evaluate the degree of delay you perceived. The Perception of Audio Delay (PoAD) variable asked participants to rate the perceived audio delay on a 5-point scale (1 = no delay, 5 = excessive delay). This helped us evaluate the alignment between actual delay settings and subjective perception.  
Evaluate the degree of synchronization. Synchronization is critical in NMP. The Perception of Synchronization Degree (PoSD) metric captured the musicians' subjective evaluation of their ability to play in sync, rated from 1 (not at all synchronized) to 5 (perfectly synchronized).  
To what degree did you follow your partner? The Trying to Follow (TTF) variable assessed whether musicians adjusted their tempo to align with their partner's playing. Prior studies suggest that higher delays lead musicians to adopt such compensatory strategies. This was rated on a scale from 1 (not at all) to 5 (completely followed).  
Did you focus on video or audio? As visual cues may support synchronization [12], we examined which modality participants relied on more. Given that video delay was constant and only audio delay varied, musicians indicated their primary focus on a scale from 1 (only video) to 5 (only audio).
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 2 TiCl\({}_{4}\) Hydrolysis Process and Mathematical Model
### Mathematical Model
#### 2.2.3 Growth Kinetics  
According to the research [25; 26], supersaturation propels the growth of the existing nucleation or seed after its formation. The crystal nucleus growth rate is calculated using the following formula:  
\[G=\eta k_{g}(a+\beta L)^{m}\frac{(c-c_{sat})^{n}}{c_{sat}^{n^{\prime}}}\tau^{o} \text{exp}\left(-\frac{E_{a}}{R}\left(\frac{1}{T}-\frac{1}{T_{ref}}\right)\right) \tag{17}\]  
In Formula (17), \(G\) signifies the nucleation growth rate (\(\mu\)m/s), \(k_{g}\) is the growth rate constant, while \(a\), \(\beta\), and \(m\) represent the growth factors related to crystal size. \(E_{a}\) is the activation energy, \(T\) is the reaction temperature, and \(T_{ref}\) is the reference temperature. Using the experimental parameters and Formula (3), we can derive the crystal growth rate equation as follows:  
\[G(t)=4.488d\eta\times 10^{-3}\text{exp}(1.995-\frac{596.87}{T})[\text{exp}(-0.098 t+2.708)-0.6856] \tag{18}\]  
Formula (18) shows that the crystal growth rate is influenced by the reaction temperature, reaction time, and crystal structure. Once the temperature and crystal structure are determined, the growth rate fluctuates over time. The crystal size can then be represented as a differential over a specific time period:  
\[D_{t}=\int_{0}^{t}G(x)x\ dx \tag{19}\]  
when \(t=0\), \(G(0)=59.76\times 10^{-3}\)\(\mu\)m/s. From the above analysis, we can establish the connection between the crystal growth rate and various factors, and further investigate the effects of the reaction time, reaction temperature, water content, pH value, and ethanol content on crystal growth.
# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 3 Proposed Methods
### Aluminum Profiles Surface-Defect Detection
#### 3.3.2 Definition of Judgment Thresholds  
The MSSIM comparison between the eliminated complex-texture image mentioned in Section 3.3.1 and its corresponding restored image is performed using the following principle:  
\[MSSIM=\frac{1}{N}\sum_{k=1}^{N}SSIM(x_{k},y_{k}) \tag{26}\]  
\(N\) in the formula is the number of image division blocks, \(SSIM\) is a number from 0 to 1, the larger indicates that the gap between the output image and the distortion-free image is smaller, i.e., the better the quality of the image, and the principle of \(SSIM\) is shown in Equations (27)-(30):  
\[SSIM(x,y)=[l(x,y)]^{\alpha}[c(x,y)]^{\beta}[s(x,y)]^{\gamma} \tag{27}\]  
\[l(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}^{2}\mu_{y}^{2}+c_{1}} \tag{28}\]  
\[c(x,y)=\frac{2\sigma_{x}\sigma_{y}+c_{2}}{\sigma_{x}^{2}\sigma_{y}^{2}+c_{2}} \tag{29}\]  
\[s(x,y)=\frac{\sigma_{x,y}+c_{3}}{\sigma_{x}\sigma_{y}+c_{3}} \tag{30}\]  
where \(l(x,y)\) is the luminance comparison, \(c(x,y)\) is the contrast comparison, \(s(x,y)\) is the structural comparison, \(\mu_{x}\) and \(\mu_{y}\) denote the mean of \(x\) and \(y\), respectively, \(\sigma_{x}\) and \(\sigma_{y}\) denote the standard deviation of \(x\) and \(y\), respectively, \(\sigma_{xy}\) denotes the covariance of \(x\) and \(y\), and \(c_{1},c_{2},c_{3}\) denotes the constants, \(\alpha>0\), \(\beta>0\), and \(\gamma>0\), respectively, and in this paper we take \(\alpha=\beta=\gamma=1\).  
Figure 11: Mask images.  
The \"3\(\sigma\) criterion\" [39] is used to select the judgment threshold, and the selection of the judgment threshold in this paper can be described as:  
\[T=\mu+\eta*\sigma \tag{31}\]  
In the formula, \(\mu\) denotes the mean of the image, \(\sigma\) denotes the standard deviation of the image, \(\eta\) is the coefficient of the standard deviation \(\sigma\), and \(T\) is the threshold. The mean and standard deviation used in this paper are defined as:  
\[\mu=\frac{\sum\Delta x_{(i,j,k)}}{n} \tag{32}\]  
\[\sigma=\sqrt{\frac{\sum\Delta x_{(i,j,k)}-\mu}{n}} \tag{33}\]  
where \(n\) denotes the number of all pixel points involved in the computation of the image. After experimentation \(\eta\) finally takes the value of 3.
# Infection Control with Antimicrobial Solid-State ZnO Nanoparticles on Silk Fiborin Gauze
###### Abstract  
Conventional antibiotic treatments for wound infections have the risk of developing microbial resistance, necessitating the search for innovative, alternative solutions like nanoparticles as advanced antibiotics. This work introduces a novel approach for managing acute and chronic wounds by creating an active wound dressing designed to both protect and eliminate bacteria from the injury site. We focused on the electroless deposition of large zinc oxide nanoparticles (ZnO NPs), aiming for a particle size of around 200 nm to reduce cytotoxicity, onto electrospinningned silk fibroin (SF) gauze. We assessed the biocompatibility and antimicrobial effectiveness of the ZnO NP-embedded silk dressing against gram-positive (_Staphylococcus aureus_) and gram-negative (_Pseudomonas aeruginosa_) bacteria. Our analysis indicates that incorporating ZnO nanoparticles into silk wound dressings maintains biocompatibility, achieving 70% cell viability while suppressing the growth of _S. aureus_ and _P. aeruginosa_, particularly during the initial 24 h after application. By employing 200 nm particle sizes, we facilitated a significant release of zinc ions without producing harmful reactive oxygen species (ROSs) that could damage both bacteria and host tissues. These findings emphasize the therapeutic potential of bioresorbable bandages enhanced with large ZnO nanoparticles, presenting an innovative approach to clinical wound treatment.  
electrospinning; electroless deposition; biodegradable; bactericide
## 3 Preliminaries
### Core Switching  
#### 3.4.1 Basic Idea  
When allocating spectrum resources to a connection request, the spectrum constraints of continuity and consistency need to be satisfied, which will generate many discontinuous spectrum fragments. Such a small fragment cannot be used to carry the request with a higher bit rate, thus increasing the network RBR. Core switching can partially utilize the spectrum fragments on different links, allowing connection requests that could not be transported without core switching to be served. In this paper, it is assumed that core switching can be realized at each node, which is illustrated in Figure 4. Request 1 occupies core 1 on link 1, and core 2 on link 2 after passing through the core switching at the node, which is more likely to utilize the spectrum resources on other cores that cannot be selected in the scenario of unswitched cores. Accordingly, the optical path from the source node to the destination node has a larger routing space, thus obtaining a higher utilization of resources.
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 4 Application to Case Study  
### Project Description  
To validate the proposed approach, a field pipe-jacking project of water-conveying tunnels was introduced. Figure 6 depicts the location of pipe-jacking construction site. The field site is located at the junction of Huangxiang River and Guan River, in the northern Jiangsu province. Four jacking pipes cross through 450 m under Guan River. The interval between the two adjacent jacking pipes was about 9 m. The diameter of the jacking machine is 4200 mm, while the external diameter of the pipe segment is 4160 mm. The synchronous grouting with pressure ranging from 0.10 to 0.15 MPa is conducted to fill the gap and reduce the friction between pipe segments and surrounding soils.  
The ground consisted of various soil layers, including backfill, clay, stiff clay, silty clay, cemented silty sand, and silty sand. Figure 7 shows the distribution of soil layers. The average depth of the pipe-jacking is about 18.9 m below the ground surface. The jacking region is mainly in silty clay, cemented silty sand, and silty sand. These layers contain irregular cementitious sand particles, which may cause the blocking in slurry pipelines and difficulties in excavation process. Figure 8 shows the soil profile and the soil properties in the construction site. The natural water content of these soils in the pipe-jacking zone ranges from 25% to 28% and the compression index varies between 0.04 and 0.16.  
During the pipe-jacking process, layered settlement gauges were employed to observe the ground deformation. Figure 9 illustrates the plane view of layered settlement gauges. The distance between two adjacent layered settlements is 4.5 m. The distance between the gauges and the south jacking shaft is 60 m. Figure 9 also depicts the sectional view of layered settlement gauges. The vertical depth of the gauge is 12.5 m under the ground. Three layered settlement gauges combine into a monitoring group. As Line 1 and Line 3 are constructed prior to Line 2 and Line 4, ground settlements caused by Line 2 and Line 4 have been disturbed by those caused by Line 1 and Line 3. Therefore, only two monitoring groups were employed to record the ground settlement of Line 1 and Line 3. The recording period starts at the initial penetration of pipe-jacking, ending when the jacking machine is 30 m away from the monitoring point.  
Figure 6: Location of pipe-jacking construction site.  
Figure 7: Distribution of soil layers in construction site.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 3 Dry Methods for Black Silicon Fabrication  
The BSi microfabrication process employs a toolbox of dry microfabrication techniques to precisely sculpt microscopic features. This typically involves RIE methods for controlled material removal, ion implantation for introducing specific dopants into the material, plasma etching for creating intricate patterns, and even femtosecond laser pulses for ultrafast and high-precision ablation. A review of the recent works in dry etching for BSi fabrication is presented in the following subsections.
## Abstract  
Cleft lip and palate is a congenital maxillofacial deformity. Unilateral complete cleft lip and palate is one of the most common clinical types. Nasal alveolar molding (PNAM) is a recognized strategy for the treatment of cleft lip and palate. However, the current design of PNAM devices mainly relies on the subjective experience of doctors. The purpose of this paper is to describe the design and manufacture of a new computer-aided design appliance, which can be applied to the presurgical nasoalveolar molding of unilateral complete cleft lip and palate, eliminate individual differences, and improve production efficiency. In this paper, seven feature points on the healthy side and the affected side are extracted by the method of Gaussian curvature and ridge line extraction, and the healthy side rotation and built-in model are designed by using these seven feature points, which can quickly generate eight treatment stages of PNAM. The correction effects of the PNAM appliance designed in this paper were compared with the original maxillary model and the clinical PNAM appliance (hand-made by subjective experience) from the aspects of alveolar fissure width and symmetry. The PNAM appliance designed in this paper can effectively improve the symmetry of patients with unilateral complete cleft lip and palate (morphological similarity: t = 3.250, \(p\leq 0.01\); length similarity: t = 1.559, \(p=0.150\)) and reduce the width of alveolar cleft ( t = 8.330, \(p<0.01\)). This can fully achieve the therapeutic effect of PNAM appliances prepared by experienced doctors and is more efficient. The method based on complex 3D surface feature point extraction can provide the basis for the design and evaluation of a unilateral complete PNAM correction model, improve the design and production efficiency of unilateral complete cleft lip and palate appliance, eliminate the design problems caused by individual differences, and reduce the burden of doctors.  
Keywords:unilateral complete cleft lip and palate; computer-aided design; PNAM correction; feature points extraction +
Footnote †: journal: Applied Sciences