esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Finite Element Model of the Tire Established in ABAQUS
### Global Sensitivity Analysis of Tire Forces
In the global sensitivity analysis of tire forces with the slip ratio (SR) as the input variable, 14 sensitive response variables across different areas exhibit global sensitivity main index values above 0.6, including radial acceleration (\(A_{1}\)) and circumferential displacement (\(U_{2}\)), both of which are associated with longitudinal force-sensitive response variables, as shown in Figure 7. Among these, the radial acceleration (\(A_{1}\)) in the sidewall and rim areas is most significantly affected by the slip ratio, showing high sensitivity. The global sensitivity main index value of the radial acceleration (\(A_{1}\)) in the sidewall area can reach up to 0.9, indicating higher sensitivity compared to that in the rim area. When the tire enters contact patch area A and exits contact patch area C, the circumferential displacement (\(U_{2}\)) in the crown area exhibits high sensitivity, with the sensitivity during entry into contact patch area A being greater than during exit from contact patch area C.  
In the global sensitivity analysis of tire forces with the slip angle (SA) as the input variable, the number of sensitive response variables in the areas affected by lateral forces is significantly reduced. Only six sensitive response variables exhibit global sensitivity main index values above 0.6, including lateral acceleration (\(A_{3}\)) and lateral displacement (\(U_{3}\)), as shown in Figure 8. In contact patch area B, the lateral acceleration (\(A_{3}\)) and lateral displacement (\(U_{3}\)) in the sidewall area exhibit high sensitivity. When the tire enters contact patch area A and exits contact patch area C, the lateral acceleration (\(A_{3}\)) and lateral displacement (\(U_{3}\)) in the sidewall area show moderate sensitivity. In addition, the lateral \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scheme Number**} & \multicolumn{2}{c}{**Input Variables**} \\ \cline{2-3}  & **Slip Ratio (SR)** & **Slip Angle (SA)** \\ \hline
1 & 0.47\% & 4.0\({}^{\circ}\) \\
2 & 2.31\% & 4.0\({}^{\circ}\) \\
3 & 5.0\% & 4.0\({}^{\circ}\) \\
4 & 7.69\% & 4.0\({}^{\circ}\) \\
5 & 9.53\% & 4.0\({}^{\circ}\) \\
6 & 5.0\% & 1.8\({}^{\circ}\) \\
7 & 5.0\% & 4.0\({}^{\circ}\) \\
8 & 5.0\% & 6.2\({}^{\circ}\) \\
9 & 5.0\% & 7.6\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Global sensitivity analysis scheme for tire forces. force-sensitive response variables exhibit noticeable asymmetry between the left and right sidewall areas. Specifically, the lateral displacement (\(U_{3}\)) in the left sidewall area shows high sensitivity, while the lateral acceleration (\(A_{3}\)) in the right sidewall area exhibits greater sensitivity. (When the slip angle is positive, the tire experiences a lateral force directed to the left). Therefore, the direction of the lateral force can lead to differences in the contribution levels of acceleration and displacement response variables between the left and right sidewall areas.
## 3 Results and Discussion
### Characterization of Triple-Cation Perovskite Thin Films
#### 3.1.3 Performance Evaluation of Sensors with Triple-Cation Perovskite Structure  
To evaluate the performance of the light sensor incorporating a triple mixed-cation (CsFAMAPbI\({}_{3}\)) perovskite, the I-V characteristics were measured. As shown in Figure 5, the bare (FAMAPbI\({}_{3}\)) sample showed a current of 81.4 uA. At a Cs concentration of 0.03, the current of the film increased to 82.3 uA, which further increased to 87.3 uA at Cs = 0.05. At Cs = 0.10, the current decreased to 80.3 uA. This indicates that Cs incorporation significantly affected the electrical properties of the FAMAPbI\({}_{3}\) film and that the optimum Cs concentration enhanced the performance of the sensor.  
The changes in the electrical characteristics with increasing Cs concentration may be attributed to the influence of the grain size. As the Cs content increased, the grain size increased, resulting in a reduced number of grain boundaries and enhanced charge mobility. In particular, up to Cs = 0.05, the increased grain size reduced the charge scattering, leading to an increase in the current. However, at Cs = 0.10, supersaturation hindered crystal growth, which decreased the grain size and increased the number of grain boundaries. This, in turn, promoted charge recombination and caused a decrease in the current.  
These findings confirm that precise control of the Cs concentration is a critical factor in optimizing the electrical properties of light sensors. Notably, the Cs0.05 sample exhibited the highest current value, demonstrating the optimal electrical performance within the triple mixed-cation perovskite structure.  
To evaluate the optoelectronic performance of the CsFAMAPbI\({}_{3}\) perovskite-based PDs, their responsivity (R) and detectivity (D\({}^{\bullet}\)) were analyzed, as shown in Figure 6. The results indicated that both R and D\({}^{\bullet}\) increased with increasing Cs incorporation up to Cs = 0.05, reaching maximum values of approximately 58.2 mA/W and 3.52 \(\times\) 10\({}^{14}\) Jones,  
Figure 5: Detection characteristics of the UV sensor with varying Cs concentrations: (**a**) bare (FAMAPbI\({}_{3}\)), (**b**) Cs0.03, (**c**) Cs0.05, and (**d**) Cs0.10.  
respectively. This enhancement can be attributed to the improved charge transport and reduced recombination owing to the optimized grain size and crystallinity. However, at Cs = 0.10, a decline in device performance was observed, likely because the excessive Cs content led to crystallographic defects and an increased number of grain boundaries.
## 4 Complexity and Performance
### Computational Complexity  
The computational complexity of the proposed S-EFB-LNMS LDPC decoding algorithm mainly involves the calculation of \(\mathbf{f}_{\text{efv}}\) and \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\), and the determination of RLPU and URLPU. Assume that the number of rows in the check matrix is \(m\) and the number of columns is \(n\). The calculation of \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) involves the dot product of each vector and its length. Calculating the length of a vector and performing dot product operations both have a time complexity of O(\(n\)). Therefore, for each row in the check matrix, \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) is calculated, and the total time complexity is O(\(mn\)). In addition, the time complexity of updating \(\mathbf{f}_{\text{efv}}\) is O(\(n\)). Based on the result of \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\), the complexity  
Figure 8: BER/FER results for LSB pages using LNMS, S-EFB-LNMS, and P-EFB-LNMS LDPC decoding algorithms: \(T=5000\), with PE cycles ranging from 22,000 to 26,000. (**a**) FER. (**b**) BER/RBER.  
Figure 7: Average number of LPUs processed during decoding (ANLPU) for LSB pages using LNMS, S-EFB-LNMS, and P-EFB-LNMS LDPC decoding algorithms: \(T=5000\), with PE cycles ranging from 22,000 to 26,000. Lower ANLPU values indicate reduced decoding latency.  
of determining whether the LPU is RLPU or URLPU is O(\(m\)). It can be seen that the running time of the algorithm increases linearly with the increase of the number of rows and columns of the check matrix, which shows that the S-EFB-LNMS LDPC decoding algorithm is efficient and practical when processing large-scale data sets. For the proposed P-EFB-LNMS algorithm, although additional bit width is required to calculate \(\mathbf{f}_{\text{efv}}\) and \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) during the calculation process, it is notable to point out that the calculation of \(\mathbf{f}_{\text{efv}}\) and \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) merely impacts on the computational complexity in the design process but has no impact in the decoding process.
## 1 Introduction  
Oral rehabilitation with osseointegrated implants has a high survival rate, which is well supported by the literature [1, 2]. However, there are challenges inherent to oral rehabilitation for completely dedentulous patients, such as clinical situations of low bone availability, resulting in the need for inclined positioning of the implants, selection of components, adaptation of infrastructure, and greater difficulty impressing the alveolar mucosa. These aspects increase the level of difficulty during the prosthesis fabrication stages; errors at any stage can negatively affect the clinical performance of the prosthesis, potentially leading to a higher complication rate and reduced survival [3; 4; 5].  
A current clinical execution protocol should be based on scientific evidence and consider the professional's experience and the patient's expectations for the treatment [5]. The conventional method for fabricating full-arch fixed prostheses involves a carefully organized sequence of steps [3; 4; 6; 7]. Errors at any stage of this process can result in poor prosthesis adaptation, increased stress concentration on both prosthetic components and the surrounding bone, screw loosening, and various failures in oral rehabilitation [3; 4; 8].  
In contrast, the use of a fully digital workflow has intensified based on the significant evolution of intraoral and facial scanners and resources, such as cone beam computed tomography (CBCT). Additionally, new occlusal materials have become available for dental use [3; 9]. The literature has highlighted that intraoral scanning has the advantages of digital storage, reduced infrastructure mismatch, more accessible communication between the technician and the dentist, and reduction of chair-side time and laboratory steps, in addition to increasing patient comfort when compared to the conventional technique [6; 9; 10; 11]. However, in addition to the need for a defined protocol for use, there is disagreement in the literature regarding its total effectiveness. Some studies do not identify advantages in comparing the intraoral scanning and conventional techniques [12]. Other studies demonstrate a success rate similar to the conventional technique [6]. There are even studies that conclude there is a disadvantage in using the digital technique for intraoral scanning in partially edentulous patients [10; 13].  
Given the diversity of results in the literature, a systematic review incorporating the best scientific evidence on this subject is necessary. Therefore, the present study aims to conduct a comprehensive analysis of published clinical studies (study type) involving completely edentulous patients (population) who received implants and oral rehabilitation using the intraoral scanning method (intervention) compared to the conventional impression technique (comparison). This analysis seeks to identify potential differences regarding the survival rates and complications of implant-supported prostheses, marginal bone loss, scanning and impression time, and number of retakes required for scanning and impressions (outcomes).
## 2 Materials and Methods
### Investment in the PV System  
For investment in a PV system, both the expected yearly production and module degradation must be considered. The annual electricity production depends on the capacity of a PV system and its characteristics. Production is calculated based on the methodology provided in Section 2.1.2. When the PV system's production exceeds the aluminium smelter's consumption at any given time, the considered production is limited to the smelter's peak consumption. Yearly production (\(EO\)) is calculated as follows:  
\[EO(y)=EO(y)(1.0-deg_{p}*y)\ [\text{MWh}], \tag{9}\]  
where \(deg_{p}\ [\%]\) is the yearly degradation factor of a PV system. For this type of investment, (6) is replaced with (10) as follows:  
\[AL(y)=\frac{EO(y)}{E_{AL}}\ [t] \tag{10}\]  
When calculating the ROI for a given year, it is not necessary to take market electricity surplus prices into account, as this electricity is generated by the PV system. As a result,we calculate the ROI for each year based solely on the volume of aluminium and its price. Equation (7) from the base methodology is then modified as follows:  
\[ROI(y)=AL(y)*p_{AL}-AL(y)*c_{AL}\text{ [EUR]}, \tag{11}\]  
The estimation of investment in a PV system considers the surface area of all the modules, as well as the nominal power generated depending on the investment price and other costs. It is calculated using the following equation, which replaces (8):  
\[INV=A*(g*o+O)\text{ [EUR]}, \tag{12}\]  
where \(A\) [m\({}^{2}\)] is the area of solar modules; \(g\) [Wp/m\({}^{2}\)] is the nominal generated electrical power; \(o\) [EUR/Wp] is the price of investment per Wp; and \(O\) [EUR/m\({}^{2}\)] represents other investment costs, including the costs of a solar inverter, system balancing, engineering procurement and construction, permitting, and insurance [37].  
The IRR is calculated by solving the following equation:  
\[0=\sum_{y=1}^{N}\frac{ROI(y)}{(1+IRR)^{y}}-INV. \tag{13}\]  
The PP is determined by finding the year when the cumulative cash flow equals or exceeds the initial investment. The cumulative cash flow up to year \(y\) is expressed as follows:  
\[CCF(y)=\sum_{k=1}^{y}ROI(k). \tag{14}\]  
Finally, the PP is calculated as follows:  
\[PP=y-1+\frac{INV-CCF(y-1)}{ROI(y)}. \tag{15}\]
# Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model
## 3 Network Model Based on Faster R-CNN-LSTM  
### Structure Feature Extraction Strategy  
With the proliferation of camera installations across construction sites, advancements in data collection speed, information processing capabilities, and swift transmission have been realized. Such progress furnishes an expansive data trove, brimming with images of construction personnel donned in protective gear. Capitalizing on this, our study harnesses site surveillance footage, further augmented with web crawler technology, focusing on a quintessential safety accessory: the safety helmet.  
Utilizing the TensorFlow framework, this paper endeavors to discern the adherence to safety helmet usage among construction workers. Our methodology commences with a meticulous collection of images, capturing workers both with and without safety helmets across a plethora of intricate operational backdrops. Once acquired, these images undergo pre-annotation and are segmented to align with the training prerequisites of the Faster R-CNN-LSTM network framework. In light of the initial training and test results, tweaks are made to the Faster R-CNN-LSTM framework, fostering iterative refinements. This iterative process culminates in target detection that satisfactorily meets the accuracy benchmarks. A schematic representation of the safety helmet identification procedure can be glimpsed in Figure 5.
## 3 Theoretical Framework
### Socio-Economic Impact of PM\({}_{2.5}\)  
[PERSON] et al. [41] discovered that in urban agglomerations along China's Yangtze River, rising PM\({}_{2.5}\) levels were driven by factors like population growth, urbanization, and higher per capita GDP. They also found that a larger share of the population being non-agricultural was associated with increased PM\({}_{2.5}\), while foreign direct investment (FDI) was linked to lower PM\({}_{2.5}\) levels--possibly due to the adoption of cleaner technologies or the enforcement of stricter environmental policies. [PERSON] et al. [42] investigated how income levels relate to PM\({}_{2.5}\) emissions and found that higher income levels are associated with lower PM\({}_{2.5}\) concentrations. This implies that PM\({}_{2.5}\) pollution tends to be more severe in lower-income countries than in wealthier ones. One study found that industrial emissions significantly impact PM\({}_{2.5}\) concentrations [43]. It also identified population density as an important factor contributing to PM\({}_{2.5}\) levels. Similarly, [PERSON] et al. [44] identified industrial output and climate as the two most influential factors affecting PM\({}_{2.5}\) emissions. They also found that construction activity, road density, and fossil fuel use significantly contribute to PM\({}_{2.5}\) levels, along with environmental conditions such as the state of the ecological environment. At the national level, its findings revealed the following relationships: [PERSON] et al. [45] reported that PM\({}_{2.5}\) emissions were negatively associated with the urban population ratio (URB), per capita GDP (GDP), and energy intensity (ENI). In contrast, PM\({}_{2.5}\) emissions showed a positive association with the share of the secondary industry in the GDP (SEC), the length of highways (WAY), the total volume of urban central heating (HEA), and the extent of built-up areas (BUI). References [46; 47] identified several key factors influencing the Air Quality Index (AQI) in Jinan City and Hohhot, including energy consumption, emissions of SO\({}_{2}\), NOx, particulate matter, PM\({}_{2.5}\), and PM10. They also found that meteorological conditions--such as average wind speed and atmospheric pressure--and emissions of sulfur dioxide, nitrogen oxides, and particulates significantly affect air quality, contributing positively to changes in the AQI.  
Based on our literature reviews and Table 3, we can state that population density, GPP per capita, industrial density, energy intensity, and other variables affect PM\({}_{2.5}\) pollution both directly and indirectly. Most air pollution studies in our literature review focus on the Environmental Kuznets Curve (EKC) pattern in developing countries with economic structures similar to that of Thailand. Therefore, the researchers use the above case studies as benchmarks against which to compare the results of this Thailand case study. Hence, this study sets hypotheses based on these literature reviews.
# Research on Secure Community Opportunity Network Based on Trust Model
## 3 Trust Community Routing Algorithm  
In this section, we present a detailed exposition of the Secure Community Opportunistic Network algorithm based on the trust model. This algorithm primarily encompasses three pivotal steps: (1) calculation of node trust degree; (2) construction of the security community; (3) selection of forwarding nodes. Initially, nodes involved in this study locally store historical interaction records, enabling prediction of subsequent node forwarding behavior and computation of the node's direct trust value. The node's indirect trust value is then determined by assessing the identical nodes within the node community, culminating in the derivation of the node's comprehensive trust value. Subsequently, a trusted community is established based on the cumulative trust value, with the maintenance of trust communities facilitated through periodic updates employing trust value decay. Finally, leveraging the concepts of local and global centrality in conjunction with the node's residual energy and cache space, relay nodes are meticulously selected from neighboring nodes, facilitating sequential data transmission in accordance with message priority.  
The calculation of node trust value forms the foundal underpinning for constructing a secure community, ensuring message transmission predominantly occurs within safe communities. Moreover, the selection of relay nodes within the security community based on high centrality, residual energy, and cache space expedites data transmission while mitigating network congestion. The iterative coordination of the three algorithms encompassing node trust value calculation, trust community division, and relay node selection collectively contribute to achieving efficient data transmission.
# Comparison of Various Models for Optimal Water Distribution in Canal Systems
## 3 Results  
### Study Area  
The Bojili Irrigation District is located in the western part of Binzhou City, Shandong Province, and encompasses Huimin County, Yangxin County, and Wudi County. The geographical coordinates of the irrigation district are 117\({}^{\circ}\)14\({}^{\prime}\)37\({}^{\prime\prime}\)-117\({}^{\circ}\)58\({}^{\prime}\)44\({}^{\prime\prime}\) E and 37\({}^{\circ}\)07\({}^{\prime}\)41\({}^{\prime\prime}\)-38\({}^{\circ}\)14\({}^{\prime}\)57\({}^{\prime\prime}\) N. The irrigation area of the district covers 60,000 hectares, making it one of the 434 large irrigation districts in China. The Fengqigou branch canal and its tertiary canals were selected as the research focus for this study. Basic information about the canal is provided in Table 1, and a schematic diagram of the canal location is shown in Figure 3. This study selects the first irrigation during the wheat spring irrigation period in the irrigation district as the object of water distribution timing research. The gross irrigation quota (crop water requirement) for wheat in this irrigation event is 540 cubic meters per hectare, which is based on the calculated results from many years of experience in the irrigation district. The irrigation quota is calculated using the field water balance method, where the irrigation quota equals the crop water requirement minus effective rainfall, soil water storage, and groundwater replenishment [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Canal** & **Design Flow Rate (m\({}^{3}\)s\({}^{-1}\))** & **Crop Area (ha)** & **Water Requirement (m\({}^{3}\))** \\ \hline Fengqigou branch canal & 1.8 & 1339.3 & 723,222 \\ Tertiary canal 1 & 0.5 & 63.3 & 34,182 \\ Tertiary canal 2 & 0.6 & 71.5 & 38,610 \\ Tertiary canal 3 & 0.5 & 58.4 & 31,536 \\ Tertiary canal 4 & 0.5 & 59.6 & 32,184 \\ Tertiary canal 5 & 0.8 & 67.9 & 36,666 \\ Tertiary canal 6 & 0.5 & 74.6 & 40,284 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information about the Fengqigou branch canal.
## 4 Discussion
### Recommendations for Further Research  
The analyzed SRs used different methodologies for making the PRF. The SRs included the following values for rotation per minute and time of centrifugation: 3000 rpm for 10 min [1; 14; 15; 16; 17; 18; 20], 2030 rpm for 10 min [14; 15; 16; 17; 20], 2700 rpm for 12 min [1; 15; 16; 17; 18], 360-400 rpm for 20 min [1; 9], 400 g for 10 min [31], and not reported [19; 24; 26]. It is important to note the wide variety of protocols used in these studies; however, all methods produced PRF, a clot composed of a dense fibrin mesh with a large presence of blood cells for the production of growth factors [1; 14; 15; 16; 17; 18; 20; 25]. The current changes in the standards of measurement of the force exerted for the production of PRF should be taken into account. The authors of future works should refer to speed and time and the relative centrifugal force (RCF) applied during the centrifugation process. In addition, it should be reported at which point the blood collection tube will be calculated in the upper side (minimal RCF), the buffy coat/clot region (middle RCF), and at the end of the tube (maximum RCF). Moreover, size of the rotor (radius at the clot and end of the tube); rotor angulation for the tube holder; revolutions per minute and time; structure and size of tubes used to produce PRF; and centrifugation model utilized must also be considered. This standardization was recently proposed and is a way to reduce bias in studies with PRF [32; 33].  
Authors of future SRs are encouraged to follow the guidelines of the PRISMA checklist in order to improve the quality of the work.