esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Application and Analysis
### Optimization in Engineering Design

Table 6: Objective function values across various working condition combinations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{Parameters}\) & \(\mathbf{\mathit{E_{c}}}\) & \(\mathbf{\mathit{f_{c}}}\) & \(\mathbf{\mathit{\varphi}}\) & \(\mathbf{\mathit{c}}\) & \(\mathbf{\mathit{u_{wmax}}}\) \\ \hline Value & 31.5 GPa & 16.7 MPa & 0.66 & 0.624 MPa & 26.9 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter values of the objective function. Optimization reduced the lining thickness by approximately 36.5% compared to the original 74 cm, leading to a decrease in structural weight and material costs. Additionally, the optimal support installation time, corresponding to a tunnel wall displacement of 9 mm (approximately 33.5% of the maximum wall displacement), offers more precise control over construction timing. The refined tunnel lining design not only alleviates structural weight and reduces material and construction expenses but also enhances construction efficiency and overall tunnel safety. The concrete lining's strength grade substantially affects the lining's equivalent resistance coefficient. Given identical surrounding rock conditions, varying concrete strength grades result in alterations to the optimal combination of lining thickness and reserved deformation, as detailed in Table 7.  
A decrease in the concrete lining's strength grade necessitates an increase in the optimal lining thickness to maintain the stability of the rock-lining system. For instance, while the optimal combination for C40 concrete requires only a 17.5 cm lining thickness, the optimal combination for C20 concrete increases this to 56.2 cm. This indicates that the reduction in the equivalent resistance coefficient due to a lower strength grade can be partially offset by increasing the lining thickness. Furthermore, for the C30 and C20 combinations, the lining thickness does not increase markedly, but the reserved deformation rises from 9 mm to 12 mm, demonstrating that an insufficient equivalent resistance coefficient can also be compensated for by adjusting the reserved deformation.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 2 Sparse Aperture Segmented Mirror Aberration Evaluation and Active Optical Control  
In this section, we derive the sparse aperture orthogonal polynomial based on the single-aperture Zernike polynomial. The derivation is stemmed from the hard spot localization theory to establish the traditional co-phase maintenance active optical model of the sparse aperture segmented mirror based on the change in the edge sensor position and aberration. Then, In the end, a whiffletree structured triangular plate is taken as an example to derive the correction principle of WH technology.
## 1 Introduction
For some antenna designs, such as the patch antenna with a W-shaped ground plane [19], the impedance bandwidth increases with the flaring angle as a result of improved impedance matching. The objective of this paper is to improve the gain and impedance bandwidth of the SBF antenna design by integrating two design principles: introducing a flare at the rim and adding an inductive iris at the waveguide feed aperture. To describe the performance, a simulation study was conducted, and the principles were verified by fabricating a prototype using a 3D printer. The methods examined in this article have the potential to be applied to similar types of antennas. Section 2 describes the geometry of the SBF antenna, and the study on the effect of the flaring angle and the rim height on the SBF antenna is presented. The design of the flared SBF antenna with an inductive iris and the parametric studies are presented in Section 3. In Section 4, we provide the measurement results for the fabricated SBF antenna, while the conclusion and future work are presented in Section 5.
## 2 Materials and Methods
### Classical Feature Selection Algorithms  
The developed spectral band selection algorithm was compared with existing classical techniques like Random Forest (RF) [39], IG [37], mRMR [40], Adaboost [41], Chi-Square (Chi2) [42], Analysis of Variance (ANOVA) [43], and ReliefF [44] to detect aflatoxin B1 in almonds for industrial in-line and quality control applications. RF is an ensemble machine learning algorithm that uses bagging techniques. It selects the optimal feature from a randomly chosen subset. RF finds the best splitting tree based on the subset data and features based on majority voting. It enhances overall classification performance. The model randomness can be increased by imposing a threshold for each feature, rather than pursuing the optimal threshold [39]. IG is also used to select relevant features in many applications. Decision tree classifiers use IG to split datasets into different groups for efficient classification [37]. Adaptive Boosting (AdaBoost) is a powerful binary ensemble learning technique that combines the prediction of several weak learners and makes a single strong learner achieve higher classification performance [41]. In AdaBoost feature selection, the importance of each feature is inferred from its frequency of use across multiple weak learners trained during the boosting process. Features that were consistently selected across different weak learners were considered more important, while those rarely chosen were deemed less relevant. The mRMR method prioritizes features that exhibit a high relevance to the target variable while simultaneously minimizing redundancy among the selected features [40].  
The Chi2 assesses the statistical significance of the relationship between each feature and the target variable, and it identifies the most discriminative features for the training model [42]. Due to simplicity, computational efficiency, and robustness, Chi2 feature selection has been widely applied in diverse fields such as text classification, image recognition, and bioinformatics. The ANOVA compares means among multiple groups [43]. By partitioning the total variance into between-group and within-group components, ANOVA determines if the differences between group means are statistically significant. Renowned for its versatility and robustness, ANOVA is employed across diverse disciplines, including psychology, biology, economics, and engineering, to analyze experimental data and draw meaningful conclusions. The Relieff assesses the relevance of features based on their ability to distinguish between instances [44]. It efficiently identifies the most informative features while minimizing the impact of noise.
## 6 Conclusions  
Based on the identification of types and features of mineralization and alteration, with a detailed study on the element migration of mineralization-alteration zones, allowing us to establish the mineralization and alteration zoning model at Beiya, we draw the following conclusions.  
1. Three types of porphyry alteration, contact alteration, and wall-rock alteration are developed; and the porphyry alteration comprises four alteration zones of potassic, phyllic, propylitic, and argillic.
2. Five types of mineralization with various occurrence characteristics and distinctive metallogenic element associations are dominantly controlled by structures and lithologies.
3. Concentric-banded mineralization-alteration zones occur centrally from alkaline porphyry outward or upward, presenting common alteration zones of potassic \(\rightarrow\) phyllic \(\rightarrow\) propylitic \(\rightarrow\) argillic \(\rightarrow\) skarn alteration \(\rightarrow\) wall-rock alteration, accompanying with individual elements gains and losses in different zones.
4. An inferred mineralization and alteration zoning model indicates the Beiya deposit is similar to the typical porphyry copper systems, and it also deepens the understanding of metallogenic regularity and provides scientific information for prospecting at Beiya, as well as at the same type of deposits in the Sanjiang metallogenic belt and elsewhere worldwide.  
In addition, we suggest that in conjunction with the study of the properties and evolution of ore-forming fluids, isotopes and alteration minerals' microanalysis, among  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
others, studies will obtain a more reliable research results with respect to the element migration of mineralization-alteration zones.  
Conceptualization, R.H.; methodology, F.L.; software, Y.G.; validation, R.H., F.L., and Y.G.; formal analysis, F.L.; investigation, R.H., F.L., Y.G., M.W. and W.T.; resources, R.H. and F.L.; data curation, F.L. and Y.G.; writing--original draft preparation, F.L.; writing--review and editing, F.L.; visualization, F.L., Y.G., M.W. and W.T.; supervision, R.H.; project administration, R.H.; funding acquisition, R.H. and F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project funded by China Postdoctoral Science Foundation (2022M711437), the Program of China Geological Survey (12120113095900), the Program of \"Yunling Scholar\" of Yunnan Province (2014), the Projects of Yunnan Engineering Laboratory of Mineral Resources Prediction and Evaluation (YM Lab) (2010), and the Innovation Team of Yunnan province and KMUST (2008, 2012). And the APC was funded by the Projects of Yunnan Engineering Laboratory of Mineral Resources Prediction.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Sino-Zijin Resources Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Mathematical Model
### Nanofluid Heat Transfer Fluid  
The basic heat transfer fluid in the PV-thermal loop, as shown in Figure 1, is water-based nanofluid. Nanofluids were added to the water-based flow to enhance their thermal properties. \"References [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] presented equations to calculate the thermophysical and thermodynamic properties of nanofluids such as specific heat, thermal conductivity, viscosity, and density by employing the law of mixtures, as a function of the volumetric concentration of nanoparticles\":  
\[\alpha_{total}=\alpha_{particles}+\alpha_{base\ fluid} \tag{13}\]  
where \(\alpha\) represents a particular thermophysical property of the nanofluid under investigation.  
The nanofluid thermal and thermophysical properties, \(\alpha_{total}\), can be calculated as follows:  
\[\alpha_{total}=\alpha_{base\ fluid}+\alpha_{particles}(\Phi) \tag{14}\]  
where \(\Phi\) represents the nanoparticles' volumetric concentration.  
The thermal conductivity is related to thermal diffusivity and density of the nanofluids as follows:  
\[\lambda=\alpha\delta C_{p} \tag{15}\]  
where \(C_{p}\) is the specific heat, \(\alpha\) is the thermal diffusivity, \(\lambda\) and \(\rho\) represent the thermal conductivity and density, respectively.  
The specific heat is calculated for nanofluids as follows [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]:  
\[C_{pnf}=\frac{O\big{(}\rho C_{p}\big{)}_{bf}+O\big{(}\rho C_{p}\big{)}_{p}}{(1 -O)\rho_{bf}+O\rho_{p}} \tag{16}\]  
where \"_nf_\" and \"_bf_\" refer to nanofluid and basic fluid, respectively. \(\mathcal{O}\) is the nanofluid particle concentration. \(\rho\) represents the density.  
The density of nanofluids can be written as follows [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]:  
\[\rho_{nf}=O_{p}\rho_{p}+(1-O)\rho_{bf} \tag{17}\]  
\(\rho_{p}\) represents the density of the nanoparticles.
###### Abstract  
One recent trend in optical remote sensing is to increase observation frequencies. However, there are still challenges on the night side when sunlight is not available. Due to their powerful capabilities in low-light sensing, nightlight satellite sensors have been deployed to capture nightscapes of Earth from space, observing anthropomorphic and natural activities at night. To date, the mainstream of nightlight remote sensing applications has mainly focused on artificial lights, especially within cities or self-luminous bodies, such as fisheries, oil, offshore rigs, etc. Observations taken under moonlight are often discarded or corrected to reduce lunar effects. Some researchers have discussed the possibility of using moonlight as a useful illuminating source at night for the detection of nocturnal features on Earth, but no quantitative analysis has been reported so far. This study aims to systematically evaluate the potential of moonlight remote sensing with mono-spectral Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB) imagery and multi-spectral photos taken by astronauts from the International Space Station (ISS), as well as unmanned aerial vehicle (UAV) night-time imagery. Using the VIIRS/DNB, ISS and UAV moonlight images, the possibilities of the moonlight remote sensing were first discussed. Then, the VIIRS/DNB, ISS, UAV images were classified over different non-self-lighting land surfaces to explore the potential of moonlight remote sensing. The overall accuracies (OA) and kappa coefficients are 79.80% and 0.45, 87.16% and 0.77, 91.49% and 0.85, respectively, indicating a capability to characterize land surface that is very similar to daytime remote sensing. Finally, the characteristics of current moonlight remote sensing are discussed in terms of bands, spatial resolutions, and sensors. The results confirm that moonlight remote sensing has huge potential for Earth observation, which will be of great importance to significantly increase the temporal coverage of optical remote sensing during the whole diurnal cycle. Based on these discussions, we further examined requirements for next-generation nightlight remote sensing satellite sensors.  
moonlight remote sensing; VIIRS/DNB; ISS; UAV; land surface; next-generation moonlight sensors 2021202021334639[[https://doi.org/10.3390/rs13224639](https://doi.org/10.3390/rs13224639)]([https://doi.org/10.3390/rs13224639](https://doi.org/10.3390/rs13224639))
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.19 Reliable Intervehicular Routing (RIVER) Protocol  
RIVER [11] is a position-based and greedy V2V routing protocol for vehicular ad hoc networks. This proposal choose the forwarding path by using traffic monitoring in real-time. RIVER is a geographic protocol that identifies the neighbor location using signaling messages. It also uses traffic monitoring to avoid routes with a sparse density which do not guarantee information transmission. So, traffic monitoring by RIVER can calculate reliable paths to forward data through the network. This proposal is not a shortest-path routing algorithm in a general sense; its edges are weighted with their reliability rating.  
In the RIVER model, each node assigns a weight to every edge in its street graph. These weights are used to determine reliable routes based on first-hand observation (including information that each node sends or receives as messages to and from another node) and knowledge (including passive monitoring of known edge lists stored in beacons, probes, and routing packets). A small weight (the minimum weight is zero) indicates greater reliability; a large weight indicates an unreliable edge, and the maximum weight indicates an edge that is known to not be traversable.  
When using reliability as a metric path, distance is still taken into consideration. [PERSON]'s least-weighted-path algorithm finds the least-weighted path based on the sum of the path weights. If two paths \(P_{x}\) and \(P_{y}\) have equal weights on each edge, but \(P_{x}\) has more edges (is a longer path) than \(P_{y}\), then \(P_{y}\) is chosen because its total weight is less. The shortest path between these two is chosen.  
An example of this is demonstrated in Figure 11. In this figure, all edge weights (shown as \(w\)) except for \(V_{s}\to V_{d}\) are of equal weight. Shortest path \(V_{s}\to V_{d}\) represents an unreliable path where packets would be dropped if transmission were attempted along this path. The other two paths from \(V_{s}\) to \(V_{d}\) have equal edge weights along each edge, but the paths are different lengths. For path (\(V_{s}\to V_{1}\to V_{3}\to V_{d}\)), the total weight is 3. Each edge of the other remaining path (\(V_{s}\to V_{1}\to V_{2}\to V_{3}\to V_{4}\to V_{d}\)) is equally reliable, but the total weight is 5, so RIVER chooses the shorter path.
## 6 Experimental Evaluation
### Experiment II: Service Allocation
#### 6.2.3 Metrics  
The mean user delay metric measures the average wait time for a service in a communication system, which is essential for assessing user experience in high-demand networks with potential latencies [38]. The total waiting time is divided by the total number of users:  
\[\text{Mean User Delay}=\frac{\sum_{i=1}^{n}T_{i}}{n},\]  
where \(T_{i}\) represents the waiting time of user \(i\), and \(n\) is the total number of users in the system.  
The total user delay metric refers to the sum of the delays experienced by all users in a communication system. It is a relevant metric for understanding the collective impact of delays on the network and optimizing resource allocation, especially in large-scale environments [39]. It can be expressed as follows:  
\[\text{Total User Delay}=\sum_{i=1}^{n}T_{i},\]  
where \(T_{i}\) is the delay for each user \(i\), and \(n\) is the total number of users.  
The mean bandwidth metric represents the average bandwidth available on network links, as determined by the allocation strategy of each compared algorithm. The CPU and memory consumption metrics compare how each algorithm manages such consumptions as it performs the service allocations. The user request success rate reflects the ability of each algorithm to serve the requests received during the simulation window.  
To statistically validate the data obtained from the EdgesimPy simulator, we used the Student's \(t\)-test between the samples generated by Ubunye and the others across all scenarios. Employing the Student's \(t\)-test is a fundamental statistical tool for determining if a significant difference exists between two independent samples.
## 3 Test Results and Analysis  
### Axial Load-Displacement Curves Analysis  
Figure 4 displays the axial load-displacement curves of the specimens during the four-point bending test. From the axial load-displacement curves, we can see that the fiber shotcrete beams with different thickness ratios bend less than 0.7 mm before getting damaged. In Figure 4b,c, we can see that the fiber shotcrete beams with the thickness ratio \(\lambda_{h}=0\) first reach a maximum load of 20.85 kN after a flexible phase; then, the bending increases quickly, and the samples break in a brittle way. The fiber-reinforced shotcrete beams with thickness ratios \(\lambda_{h}=1\), 5 showed a slight increase in the deflection rate after the elastic stage. They then entered the crack stable propagation stage, where the load reached its maximum value. The specimen failed, with a rapid increase in deflection, which belongs to ductile fracture.  
Figure 3: Experimental process.  
The crack formation threshold \(\sigma_{cr}\) is the bending strength of the specimen at the point of initial crack generation. It reflects the elastic limit of the specimen and its ability to resist bending at the point of first crack formation, which is calculated by Equation (1).  
\[\sigma_{cr}=\frac{F_{cr}L}{bh^{2}} \tag{1}\]  
In the formula, \(\sigma_{cr}\) is the crack formation threshold of the specimen, MPa; \(F_{cr}\) is the load of the specimen to produce the initial crack, N; and L, b, and h are the span, width, and height of the specimen, respectively, mm.  
Peak flexural tensile strength \(\sigma_{\text{max}}\) is the flexural tensile strength corresponding to the peak load that a specimen can withstand before a fracture occurs. \(\sigma_{\text{max}}\) reflects the maximum stress that the specimen can withstand during the bending test. It is an important parameter for assessing the performance and reliability of the specimen, as calculated by Equation (2).  
\[\sigma_{\text{max}}=\frac{F_{\text{max}}L}{bh^{2}} \tag{2}\]  
In the formula, \(\sigma_{\text{max}}\) is the peak flexural tensile strength of the specimen, MPa, and \(F_{\text{max}}\) is the peak load applied to the specimen during loading, N.  
The test results are shown in Table 5.  
Figure 4: Axial load–displacement curves. (**a**) Axial load–displacement curves. (**b**) Axial load–time curves. (**c**) Axial displacement-time curves.
# Par3 DNet: Using 3 DCNNs for Object Recognition on Tridimensional Partial Views
## 4 Experimentation
### Results on the ObjectNet Dataset  
In order to test the generalization capabilities of our approach, we tested it with the ObjectNet [36] dataset. This is a dataset for 3D object recognition and is composed of 100 categories, 90,127 images, 201,888 instances of objects in these images and 44,147 3D shapes. Objects in the images in this database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Nonetheless, we only are interested in the 3D objects. The authors provided not only full models of the objects but the parts of it too. To serve our purpose, we took the full models of the following categories: bathtub, bed, chair, sofa, table and toilet. The rest  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline Noise Level & 0.01 & 0.05 & 0.15 & 0.20 & 0.25 & 0.30 & 0.40 & 0.50 & 0.75 & 1.00 \\ \hline Par3 DNet Accuracy (\%) & 78.41 & 78.14 & 76.27 & 73.76 & 70.9 & 56.79 & 54.81 & 52.35 & 45.63 & 43.61 \\ \hline PointNet Accuracy (\%) & 56.27 & 56.36 & 56.10 & 56.15 & 56.20 & 56.13 & 55.86 & 55.97 & 55.19 & 54.50 \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy obtained for the ModelNet10 test split after introducing different levels of noise for Par3 DNet and PointNet.  
Figure 8: The leftmost image is a random sample with no added noise, as captured by the virtual 3D camera. The image in the center corresponds to a noise level of 0.25. Finally, the leftmost image is disturbed by a factor of 1.  
of the categories are omitted as they are not considered by our approach. Finally, only 65 models were used for this experiment.  
We preprocessed the CAD models as explained in Section 3.2 and fed the resultant point clouds to our architecture. The accuracy was 64.4% and the results are as shown in Table 4.  
The accuracy of 32% for the bathtub category is due to the inclusion of different objects that are actually not bathtubs. For instance, jacuzzi tubs and swimming pools were labeled as bathtubs in this dataset. In addition to this, this is the category with the lowest performance also in the results for the ModelNet10 experiment. The sofa category also achieved a low accuracy for the same reason. There are objects like armchairs and other objects labeled as sofa that should not be included in that category. Some of these mislabeled objects are shown in Figure 9. Overall, we also found that the CAD models of this dataset lack of detail and are of poor quality compared with ModelNet. The accuracy of the rest of the categories is high.