esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison of Model Geometry Obtained by Photogrammetry and Scanning in CloudCompare Software
#### 4.2.4 Comparison of the Model with Digital Error Obtained by Scanning and by Photogrammetry  
The comparative analysis of the model with a digital error obtained by scanning and the model with a digital error obtained by photogrammetry determined the values of deviations between these two models. In this case, the reference model is the model obtained by scanning. Figure 13 shows the obtained results. By analyzing these two models, it was estimated that the absolute deviation has a value of 2 mm. One point (on the chin) was singled out from the area with the largest deviation, and the deviation for that point is 0.631 mm. The points on the parts of the face on the left side (ear, cheekbone, hair) have deviations of 0.101 mm, 0.163 mm, and 0.431 mm, respectively.  
Figure 12: Results of the comparison of the original models obtained by scanning and photogrammetry in CloudCompare software.  
The histogram shows that the largest number of vertices between the two analyzed models have a deviation in the interval 0.0004-0.4 mm. The value of the average deviation is 0.015 mm, while the standard deviation of the model with digital error is 0.180 mm.  
Based on the presented results, it can be concluded that both techniques, i.e., photogrammetry and scanning, recognize the parts of the sculpture where clay masses have been added. It can be noticed that the 3D models obtained by photogrammetry are visually rougher compared to the 3D models obtained by scanning. For the photogrammetric survey, the dark and uniform texture without many features is not suitable for data processing in the software we used for photo processing. For that reason, a photogrammetric survey has resulted in the 3D models of sculptures with a noticeable surface roughness of the model, which is the consequence of the inability of the software to find a larger number of reference points in the photographs. On the other hand, shooting with structured light, in addition to being a cheaper time procedure, in this case also led us to a 3D surface that is smoother compared to the photogrammetric model. All four models have about 300,000 points and about 550,000 polygons. Due to the difference in surface fineness, for further work on the project and a survey of a group of 195 subjects, we used models obtained using structured light.  
In any case, the obtained results are satisfactory in the sense that the errors occur only in the parts where we assumed that they would occur. The error/difference between the obtained models of these dimensions is not visible to the average observer. Based on these results of comparing the original with the model with a digital error, we can conclude that 3D scanning is a more favorable technique in the case of a smaller object such as a sculpture [78; 79].  
Figure 13: Results of the comparison of model with digital error obtained by scanning and by photogrammetry in CloudCompare software.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
###### Abstract  
The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method--either k-means or k-medoids--to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.  
machine learning; UAV placement; artificial neural network (ANN); deep neural network (DNN); NOMA; cooperative communications; D2D
## 3 Design Patterns of Vectorized Programming for FIR Image Filtering  
### Data Loading and Storing in Vectorized Programming  
The SIMD operations calculate multiple data at once; hence, the SIMD operations are high performance. Such operations constrain all vector elements to follow the same control flow. That is, only one element in a vector cannot be processed with a different operation as a conditional branch. Therefore, we require data structures, wherein processing data are continuously in memory, for the load and store instructions. Such instructions move continuous data from the memory/register to the register/memory. For this case, spatial locality in memory is high. On the other hand, we can relieve the restriction by performing discontinuous loading and storing. The operations are realized with set instruction or scalar operations. These methods use scalar registers; thus, these methods are slower than the load and store instructions. Recent SIMD instruction sets have the gather and scatter instructions, which load and store for discontinuous positions in memory. However, such instructions also have higher latency than the sequential load and store instructions. Discontinuous load and store operations also decrease spatial locality in memory access; hence, cache-missing occurs. Cache-missing decreases performance. Moreover, memory alignment is important for the load and store instructions. Most CPUs are word-oriented. Data are aligned in word-length chunks, with 32 bits and 64 bits. In aligned data loading and storing, CPUs access memory only once. In non-aligned data loading and storing, CPUs access memory twice. Therefore, the performance of the load and store instructions decreases if non-aligned data loading or storing occurs. Furthermore, vectorized programming requires padding if the data size is smaller than the SIMD register size. This is because SIMD operations require the number of register-size elements to be at least even if the data length is shorter than the SIMD register size. In this case, the data are padded with a value such as zero, which reduces the valid vectorization ratio.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 6 Conclusions  
This study has explored the application of heuristic global optimization methods for thermal model reduction and correlation in aerospace applications. The key contributions of this work are summarized below.  
* Comprehensive Literature Review: A systematic review of 24 publications from 2007-2024 identified 26 heuristic optimization methods, highlighting a significant gap in the application of many techniques.
* High-Accuracy Correlation: The application of heuristic methods resulted in temperature discrepancies below 3\({}^{\circ}\)C under transient conditions, demonstrating their effectiveness in improving model accuracy.
* Comparative Evaluation of Methods: While Genetic Algorithms (GAs) have been the dominant approach since [PERSON]'s pioneering work in 2007, this study highlights that other methods--such as Cultural Algorithms (CAs) and Artificial Immune Systems (AISs)--can perform competitively.  
In conclusion, this study confirms that heuristic global optimization methods are effective for thermal model reduction and correlation in aerospace engineering. These findings contribute to both theoretical understanding and practical applications, providing a solid foundation for improving thermal model accuracy and optimization processes in complex engineering systems.  
Conceptualization, J.P.C. and B.N.A.; methodology, J.P.C. and B.N.A.; software, J.P.C.; validation, R.M. and P.G.; investigation, J.P.C.; resources, R.M.; data curation, J.P.C.; writing--original draft preparation, J.P.C.; writing--review and editing, R.M. and P.G.; visualization, J.P.C. and B.N.A.; supervision, R.M., P.G., and A.R.R.S.; project administration, R.M. and P.G.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the support provided by the Research project called Mobilising Agenda: New Space Portugal (Ref. C644936537-00000046, Notice ACC02/COS-i01/2022), funded by the \"Mobilising Agendas for Business Innovation\" through the \"Recovery and Resilience Programme (PRR)\". The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT) for its financial support via the projects LAETA Base Funding (DOI: 10.54499/UIDB/50022/2020), LAETA Programmatic Funding (DOI: 10.54499/UIDP/50022/2020). The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT) for its financial support via the projects: LIP Base Funding (UID/50007/2023).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Theoretical Background
### Objective Functions
#### 2.1.1 Natural Frequency  
The frequency difference between the experimental and analytical frequencies is given as:  
\[F_{1}=\sum_{i=1}^{n}\ [\left(\omega_{a,i}-\omega_{e,i}\right)/\omega_{e,i}]^{2} \tag{1}\]  
where \(n\) is total number of modes, subscript \(a\) and \(e\) represent analytical and experimental counterparts, respectively, \(n\) is the number of measured modes and \(\omega\) is the natural frequency of the mode in question.
## 3 Methods and Results
### Robot Deployment  
The robot, largely constructed from thin plastic films, can be compactly folded before pressurization, making it suitable for natural orifice transluminal surgery applications. As shown in Figure 12a,b, the robot can be folded into a cylinder measuring 110 mm in length and 14 mm in diameter, which satisfies the lower limit of the colon's size for comfortable introduction [27]. Upon inflation, the volume of the scaffold significantly increases, eventually forming a hollow hexagonal prismatic structure with edge lengths of 34 mm and a length of 100 mm (Supplementary Materials). This test and all subsequent experiments are conducted at a scaffold pressure of 100 kPa. Figure 12c,d illustrates the robot's successful deployment within a colon phantom, which has an inner perimeter of 204 mm, equivalent to the perimeter of the deployed scaffold. Within the phantom, the robot assumes the form of a regular hexagonal prism, showcasing its ability to conform to luminal structures effectively. An envisioned operational sequence for the robot involves its deflated state being mounted alongside and enveloped around a carrier endoscope, facilitating delivery through a natural orifice and into the lumen (e.g., the colon). After reaching the designated operative site, the robot stabilizes itself against the lumen wall upon inflating the scaffold, thereby enabling the performance of a surgical procedure. Following the surgical procedure, the robot undergoes deflation, collapse, packing, and extraction from the colon.  
Figure 11: Cable tensile test. (**a**) A platform diagram of the cable tensile test. (**b**) The test results.
# Optimization Design of RV Reducer Crankshaft Bearing
## 5 Conclusions  
This study proposes a novel optimization method for RV reducer crankshaft bearings considering the bearing geometry and crowned roller profile. The working load of the bearing and the structure of the reducer are considered. Constraints derived from the geometry, lubrication, strength of crankshaft bearings, and structure of the RV reducer are established. The fatigue life of the bearing is optimized by the proposed optimization method. The research summary of conclusions is as follows:  
1. A comprehensive optimization design model of an RV reducer crankshaft bearing is constructed. Based on the CSA algorithm, a nonlinear constraint optimization program is developed to solve the optimization problem. The convergence process shows that the optimization program has high efficiency and ability to solve the optimization problem of RV reducer crankshaft bearings.
2. The roller profile and macro-geometric parameters of the bearing are both considered in this study. Through the optimization design, the optimum crowned roller profile suitable for the working load of the bearing is obtained, and the stress concentration between the roller and the raceway is eliminated. Compared with the situation before optimization, the limited geometric space of the cycloidal gear is used more effectively.
3. Taking the RV-20E and RV-110E reducer crankshaft bearings as examples, the two bearings are optimized by the proposed method. After optimization, all the optimization variables satisfy the constraint conditions. The RV-20E bearing life is increased by 196%, whereas that of the RV110 is increased by 168%.  
The optimization design method proposed in this study has an important guiding significance for improving the service life of RV reducers. It can thus provide a theoretical basis for the design of RV reducer crankshaft bearings. The optimization method developed in this study can also be used in the design of other nonstandard roller bearings  
The work described in this article is the collaborative development of all authors. [PERSON] and [PERSON] contributed to the idea of the optimization model and participated in the writing of the paper. [PERSON] contributed to the validation of the method and funding acquisition. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Key Research & Development Program of China (Grant No. 2017 YFB1300700); National Natural Science Foundation of China (Grant No. 51775059). The authors declare no conflict of interest.  
Figure 14: Contact stress between raceway and roller of crankshaft bearings of RV-110E: (**a**) Contact stress before optimization; (**b**) Contact stress after optimization.  
* [PERSON] (2016) [PERSON] A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. _Comput. Struct._**2016**, _169_, 1-12. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A Novel Approach Based on Crow Search Algorithm for Solving Reactive Power Dispatch Problem. _Energies_**2018**, _11_, 3321. [CrossRef]
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods
### Model Evaluation and Performance Metrics  
To ensure the accuracy and reliability of our results, we conducted a total of 100 training and testing epochs. This extensive process allowed us to minimize any potential misalignment or inconsistencies arising from dataset segmentation. To begin, we divided our dataset into two subsets: a training set and a test set. The training set accounted for 70% of the data and, therefore, provided a substantial amount of information for the model to learn from. The remaining 30% was allocated to the test set, which served as an independent evaluation for assessing model performance. Three key metrics were used to evaluate the effectiveness of the model: coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), and mean absolute error (MAE). RMSE is defined as the square root of the mean of the summed squared regression residuals [48]. MAE is defined as the average of the absolute errors [49]. These metrics are commonly used in regression analysis to measure the accuracy and precision of predictions and are calculated as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}( \hat{y}_{i}-\bar{y}_{i})^{2}} \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{n}} \tag{6}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert(\hat{y}_{i}-y_{i})\rvert \tag{7}\]  
where \(n\) is the number of samples, \(\hat{y}_{i}\) is the model prediction, \(y_{i}\) is the field observation, and \(\bar{y}\) is the arithmetic mean of all observations.
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 2 Materials and Methods
### The Process of Monte Carlo Numerical Simulation
#### 2.3.1 The Initial Parameters of Photon Packets
\begin{cases}\alpha_{0}=2\pi R\\ \psi_{0}=2\pi R\end{cases}  \tag{7}\]  
The energy distribution of the laser beam in the simulation follows Gaussian distribution characteristics. This distribution is crucial for determining the radius \(r_{0}\) on the Transmission Plane, which represents the distance between a given photon and the center of the light source. The calculation of \(r_{0}\) is based on the Gaussian distribution, as outlined in Equation (8). In this equation, \(w_{0}\) represents the waist radius of the laser beam, and \(R\) is a random value drawn from the normalized interval \([0,1]\). \[r_{0}=w_{0}\sqrt{-ln(1-R)} \tag{8}\]  
The initial elevation angle \(\varphi_{0}\) of each photon is derived based on the initial divergence half-angle (\(\theta_{div}\)) of the beam, the beam waist radius (\(w_{0}\)), and the radius (\(r_{0}\)) on the Transmission Plane. The specific relationship among these parameters is articulated in Equation (9) [15]. This equation ensures that the elevation angle of each photon is accurately aligned with the beam's divergence characteristics. \[\varphi_{0}=\frac{r_{0}\theta_{div}}{w_{0}} \tag{9}\]  
The Equations (5)-(9) discussed previously demonstrate that the initial elevation angle, initial azimuth angle, and initial position of each photon in the Monte Carlo simulation are determined through random calculations. This randomization process is crucial for accurately simulating the inherently stochastic nature of photon behavior in harbor water.
###### Abstract  
Past literature has advocated the integration of channels through the offline-to-online and online-to-offline models. However, little research has investigated the interrelationships effects between the two channels. Drawing on the literature from self-service technology (SST) and expectation-confirmation theory, this study aims to investigate key attributes of SST and assess their impact on consumer evaluations across offline-to-online and online-to-offline channels. A questionnaire survey was administered at convenience stores and 360 user responses were collected through a physical self-service kiosk. Two-stage structural equation modeling with linear structural relations (LISREL version 8.54) software was used for data analysis. The empirical results verified the attributes of physical SST (physical system's service quality and perceived convenience) and online SST (virtual system's service quality and perceived ubiquity) as critical antecedents of satisfaction and attitude and the subsequent behavioral intentions toward each channel. However, some transitional effects from offline (physical kiosk use) to online (kiosk app adoption) intention were not as significant as hypothesized. The offline attributes of perceived convenience and satisfaction had no significant impact on online SST significantly (kiosk app), except for the physical system's service quality. Discussions and implications are provided, including strategies for concise functional design and essential SST services.  
self-service technology; offline-to-online; online-to-offline; kiosk app; system service quality; perceived convenience; perceived ubiquity +
Footnote †: This article is a revised and expanded version of a paper entitled [Does offline drive online?: A study of interconnected effects of technology attributes in self-service systems], which was presented at [2017 Portland International Conference on Management of Engineering and Technology, Portland, OR, USA, 9-13 July 2017].
## 2 Advanced Oxidation Processes
### Fenton's Process  
There are not many studies regarding the paraben's abatement using [PERSON]'s process. The most conventional Fenton's oxidation uses Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\) for the elimination of organic molecules through the generation of oxidative radicals. The presence of iron allows the continuous generation of hydroxyl radicals that are the most wanted radicals for pollutants removal.  
[PERSON] et al. (2018) used Fenton for the degradation of a mixture of parabens (10 mg/L). In this experiment, the initial pH was 6, and after the addition of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\), the pH decreased to 3, which is generally the optimal condition for this reaction, due to a better generation of oxidizing radicals. This reaction led to the complete removal of parabens. [PERSON] et al. (2018) used [PERSON]'s reagent to remove 3-benzophenone and methylparaben. Several conditions were tested, and the MP removal was between 45.39% and 89.96% after 30 min. Using [PERSON]'s reagent and light, the removal was 98% for the best operational conditions, while the use of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\) only removed 35%, which shows a clear improvement when UV radiation was applied to the Fenton's process.  
[PERSON] et al. (2018) treated 150 mL mixture with 5 mg/L of each paraben (MP, EP, PP, and BuP). An experimental design was applied in which the initial concentration of the reagents (Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\)) were variable. Best performance achieved 95.8%, 97.7%, 98.0%, and 98.6% of MP, EP, PP, and BuP removal for concentrations of 2.70 \(\times\) 10\({}^{-4}\) mol/L and 2.70 \(\times\) 10\({}^{-5}\) mol/L for H\({}_{2}\)O\({}_{2}\) and Fe\({}^{2+}\), respectively.  
In general, the studies suggest a good performance of Fenton's over parabens abatement, since high removal rates were generally reported. Table 6 presents an overview of the use of Fenton's technology regarding parabens removal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pollutant** & **AOP** & **Iron Source** & **Test Conditions** & \multicolumn{2}{c}{**Results and**} \\  & & & & **Conclusions** & **References** \\ \hline MP, EP, PP, BuP, and BeP & Fenton & Iron (II) sulfate & \(\bullet\) & Parabens mixture in ultrapure water at 10 mg/L each. & \(\bullet\) & Fenton’s process led to total & \\  & and BeP & Fenton & heptalydrated & \(\bullet\) & 120 min reaction and initial pH = 6. & \(\bullet\) & \\ \hline
3-Benzophenone & Fenton & Iron (II) chloride & \(\bullet\) & 300–800 nm radiation. & \(\bullet\) & MP removal in 120 min & \\  & and MP & Fenton & tetrahydrate & \(\bullet\) & 1 mg/L of pollutants and pH = 3. & \(\bullet\) & \\