esa-sceva/satcom-chunk-collection huggingface dataset:
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## 2 Tolerance Analysis Considering Form Errors
### Tolerance Sampling Method Based on Variance Separation
Then for each specific flatness \(p_{k}\), the realistic performance \(y\) of the antenna is randomly distributed, supposing the conditional PDF is \(\psi(y|p_{k})\), and the corresponding variance is \(\alpha_{k}^{2}\). According to Appendix C, the sample size needed to simulate the realistic performance \(y\) with flatness \(p_{k}\) is:  
\[N_{k}\geq\frac{\sigma_{k}^{2}c^{2}}{\varepsilon^{2}} \tag{22}\]  
The total sample size \(D\) needed for the double loop process is:  
\[D=\sum_{k=1}^{K}N_{k}\geq\sum_{k=1}^{K}\frac{\sigma_{k}^{2}c^{2}}{\varepsilon^{ 2}} \tag{23}\]  
**(2) Single loop process:** Supposing the PDF of the realistic performance \(y\) of the antenna is \(\psi(y)\) in the whole design interval of flatness, \(p\in[a,b]\), and the corresponding variance is \(\sigma_{r}^{2}\). According to Appendix C, the sample size needed to simulate the realistic performance \(y\) is:  
\[S\geq\frac{\sigma_{r}^{2}c^{2}}{\varepsilon^{2}} \tag{24}\]  
For the same simulation precision, the sample size needed in single loop process is smaller than the sample size needed in double loop process, as shown in Equation (25). \[S=\frac{\sigma_{r}^{2}}{\sum_{k=1}^{K}\sigma_{k}^{2}}D \tag{25}\]  
As the flatness increases, the variance of the antenna performance increases, that is:  
\[\sigma_{r}^{2}\leq\max\{\sigma_{k}^{2}\}\Rightarrow S<D \tag{26}\]  
According to Equations (25) and (26), as the sample size \(K\) in double loop process increases, the efficiency of the single cycle process improves significantly.
## 4 Performance Assessment
### Latency  
Latency decreases concerning user and scheduling intervals, which deploys the service distribution. Here, scheduling and queuing are derived for the control data plane in the network, indicating power management. Based on the scheduling process, queuing is performed for the resources and valid access is forwarded on time. The power allocation is performed for probabilistic balancing, and it is represented as \(X\times\left(q_{0}+\frac{r_{0}(D)}{V}\right)\). Concurrent service sharing and resource access determine the neuron state and deploy the hidden layers. The analysis is estimated for service distribution for the numbers of users and deploys the sharing efficiently. The training devices are used to deploy the scheduling for power management and determine the current state. The predictions are made for resource access, estimated power allocation, and derived scheduling. Joint scheduling is performed for service distribution to determine the SDN-based IoT application. Here, service sharing is conducted by determining the neuron's current state and evaluating the probabilistic balancing. Integration is achieved for joint scheduling and power allocation and determines better processing. The hidden layer derives reliable service sharing and addresses cumulative congestion and power management (Figure 7). Figure 7 shows latency over different scheduling intervals, and a smaller curve for the CS3 indicates consistently lower latency across all intervals tested.  
Figure 6: Service dissemination comparisons.  
Figure 7: Latency comparisons.
## 4 Materials and Methods
### RNA Isolation, cDNA Synthesis and Quantitative Real-Time PCR (qRT-PCR)  
Total RNA was extracted from frozen crushed crystals of Stevia leaves, flowers, stems, and roots using the Spectrum(tm) Plant Total RNA Kit (Sigma-Aldrich, St. Louis, MO, USA) according to manufacturer instructions. On column RNase-free DNase I (Qiagen, Hilden, Germany), treatment was carried out to remove the residual genomic DNA. Total RNA was quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and stored at -80 \({}^{\circ}\)C until use.  
Complementary DNA (cDNA) was synthesized from 1 ug of the total RNA using M-MLV reverse transcriptase (Promega, Madison, WI, USA), dNTP, and oligo (dT) primer according to manufacturer instructions. cDNA was diluted with RNase free water to a final concentration of 10 ng/uL and stored at -20 \({}^{\circ}\)C until use.  
qRT-PCR was performed using an Applied Biosystems 7900 HT fast real-time PCR system and TAKARA SYBR Premix Ex Taq II (TaKaRa, Kusatsu, Japan). Primers for qRT-PCR were designed using a Primer3 program ([[http://bioinfo.ut.ee/primer3-0.4.0/](http://bioinfo.ut.ee/primer3-0.4.0/)]([http://bioinfo.ut.ee/primer3-0.4.0/](http://bioinfo.ut.ee/primer3-0.4.0/))) and are listed in Supplementary Table S1. PCR primer efficiency was determined using four serial cDNA dilutions (1:1, 1:5, 1:25, and 1:125) and the equation (%) = (10\({}^{(-1/\text{slope})}\)-1) \(\times\) 100) [51]. Primer pairs with efficiency values between 90 and 110% were chosen. Stevia _actin_ gene was used as an internal control for data normalization. Relative quantitation by RT-PCR was performed in a 10 uL volume containing 1 uL of cDNA, 5 uL of 2 x TAKARA SYBR Premix Ex Taq II, 0.5 uL of 10 uM forward primer, 0.5 uL of 10 uM reverse primer, and 3 uL of water. The plate was covered using a microscal 'B' seal (Bio-Rad, Hercules, CA, USA) for optical transparency and centrifuged briefly using a PCR plate spinner (VWR, Radnor, PA, USA). The plate was subjected to the following cycling program: 3 min at 95 \({}^{\circ}\)C, followed by 40 cycles of 10 s at 95 \({}^{\circ}\)C and 30 s at 60 \({}^{\circ}\)C; then final ramping to 95 \({}^{\circ}\)C at the rate of 0.5 \({}^{\circ}\)C/5 s for melting curve analysis. For verification of a single product amplification, both melting curve analysis and gel electrophoresis were used. Non-template control and non-RTase treated templates were included to detect and eradicate primer-dimer formation and genomic DNA contamination. All qRT-PCR experiments were carried out in three technical replicates of two biological replicates. SDS 2.4 (Applied Biosystems, Waltham, MA, USA) was used to analyze the results. For the relative expression of _SrTPSs_ among different tissues, comparative dCt values of target genes to _actin_ were calculated by 2\({}^{-(\)dct\()}\) where dCt = Ct,_target_ - Ct,_actin_. For stress assays, data were analyzed using 2\({}^{-(\)ddCt\()}\) where ddCt = (Ct,_target_ - Ct,_actin_)Time x - (Ct,_target_ - Ct,_actin_)Time 0. Time x represents the treatment duration and Time 0 represents the untreated control [52].
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 2 Materials and Methods
### Methods  
Multiple Linear Regression (MLR) analysis refers to a statistical method where one variable is considered the dependent variable, while one or more other variables are treated as independent variables within a set of correlated variables. It involves establishing a linear mathematical model that quantifies the relationships among these variables and uses sample data for analysis. On the other hand, stepwise multiple regression, after introducing new variables, systematically examines existing variables and eliminates any that prove to be statistically insignificant. This approach can be employed to enhance the precision of the wheat yield estimation model. The main concept behind Principal Component Analysis (PCA) is dimensionality reduction. It achieves this by orthogonal transformation, converting correlated components of the original random vectors into new, uncorrelated random vectors. Algebraically, this transformation results in the covariance matrix becoming diagonal, while geometrically, it represents the conversion of the original coordinate system into a new orthogonal coordinate system, aligning with the p orthogonal directions that capture the maximum spread of the data points. Subsequently, PCA reduces the dimensionality of the multivariate data system while preserving a high degree of precision. It further simplifies the system into a one-dimensional representation through the construction of a value function. The central challenge in PCA is to obtain the projection matrix. Similar to other machine learning algorithms, PCA generalizes from vector space to one-dimensional space and then extends to the general case. One of the primary advantages of Principal Component Regression lies in the fact that the principal components are uncorrelated, thus mitigating issues of information overlap. In the research process, selecting an appropriate number of principal components for model fitting can enhance the precision of the wheat yield estimation model. Neural networks have rapidly advanced in the field of big data and intelligent computing in recent years. They are distinguished by their capabilities for autonomous learning, adaptation and distributed storage of information. Consequently, they have found extensive applications in areas such as intelligent control, identification and monitoring. The primary approach utilized for building the wheat yield estimation model is Artificial Neural Networks (ANN), specifically the Multi-Layer Perceptron (MLP). The MLP is designed to process data by receiving inputs in the input layer and executing the wheat yield estimation task through the output layer. Neurons within the MLP can employ various activation functions and undergo training and learning algorithms via backpropagation. In an MLP, each linear combination is propagated to the next layer, with each layer providing its computed results for the subsequent layer. The backpropagation process enables the MLP
## 3 Experimental Testing
### Metrological Characterization of the Contact Measurement Chain
Reference displacement values and the corresponding LVDT sensor readings in the data acquisition system were obtained. Tests were performed with and without the complete set of assembly accessories (including two springs). Although a similar calibration standard uncertainty was obtained, significant changes were observed in (i) repeatability, which increased up to 47 \(\upmu\)m (with accessories) and up to 43 \(\upmu\)m (without accessories), and (ii) linearity, which increased up to 115 \(\upmu\)m.  
These differences (relative to the horizontal position) are justified by (i) the vertical misalignment between the LVDT core stem and the measurement standard vertical axis, (ii) the type of stem (non-telescopic, with free movement), and (iii) the transverse movement of the core stem inside the coil housing, which does not occur in the horizontal position, where the core stem follows the inner surface of the coil housing by the gravity effect. These results illustrate the measurement accuracy vulnerability in the use of LVDT sensors for displacement measurements. The proposed optical approach, as described in Section 2.2, is a non-contact measurement that can not only can be used in destructive testing but, in addition, it is less vulnerable to measurement uncertainty components related to instrumentation assembly onto the masonry specimen.
# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation
### Evaluation of the Forwarding Phase in GHR
\(T=1\) means that our Tabu routing is used. The four _FT_ are: best node selection (\(\alpha\rightarrow\infty\)), simulated annealing (\(\alpha=3\)), random forwarding (\(\alpha=0\)) and first legal node (_First_). (**a**) Percentage of packet losses; (**b**) average end-to-end packet delay; (**c**) average number of hops; (**d**) percentage of idle time. see Table 4. In the high density scenario, the \(First\) strategy has the same high level of idle time as the best node selection (\(\alpha\rightarrow\infty\)), while random (\(\alpha=0\)) and SA (\(\alpha=3\)) have the lowest level of idle time because they perform more hops than the classical selection of the best node. However, it is worth noting that simulated annealing forwarding obtains the lowest percentage of packet losses with Tabu for this high vehicle density.
## 4 Results and Discussion
### Algorithm Validation  
The computational algorithm was validated by comparing simulation results with stress distributions for viscoelastic stress effects published by [PERSON] et al. [13]. In their work, the rotor is constructed from two CFRP rims press-fitted together. The CFRP is an IM7/8552 transversely isotropic composite with no viscoelastic behavior in the fiber direction. Material properties are given in [13]. The simulation results are plotted alongside the published data in Figure 2. The close congruence that is observed between the published results and the current model provides validation that the present modeling approach is capable of accurately predicting the stress response in the flywheel rotor. Hence, model stress responses will herein be used in conjunction with the Tsai-Wu criterion to predict failure location and behavior.  
Recalling the plane strain assumption made for the present analysis, modeling results validated the chosen approach, which simplified solving the radial inhomogeneous equilibrium equation. Contrasting present work with published literature, see e.g., [14; 29; 37; 38; 39], comparable results were achieved. It should be noted that some of these studies assume generalized plane strain. In addition, analyses that quantified axial stress [40] showed it to be an order of magnitude less than radial stress, and two orders less than the circumferential stress. Given the body of published works that impose and validate the plane strain assumption, and the comparatively small magnitude of axial stress, applying a plane strain assumption for the present analysis was seen as appropriate.
## 4 Effects of Weather Conditions in the Gulf of Guinea on the IoT Quality of Service
### Storms (Heavy Rain and Wind) at HFSWR Sites Which Partially Cover the Observation Area  
In Figure 12 a satellite image of the bad weather conditions over the HFSWR sites which partially extended to the surveillance area is shown.  
From Figure 12 it can be seen that the storm has hit the coast where HFSWRs were installed, but the majority of the HFSWR surveillance area has not been affected. Even in these weather conditions, the range of HFSWRs will decrease due to harsh surface wave propagation conditions. Yet, this decrease is not as drastic as that in the previous example, since the furthest target is detected at the distance of about 180 km (Figure 13).  
It should be emphasized here that detection files from HFSWRs are not received at regular intervals, as the satellite communication is very difficult, due to heavy rain. In the event of severe weather conditions over the HFSWR sites, there are occasional interruptions in the satellite communication.  
Therefore, data delays occur and algorithms for multi-sensor data fusion must perform HFSWR tracks predictions and maintain tracks based on them. Despite all this, in Figure 13 it can be seen that the algorithms manage to keep the traces stable, thus providing the full information to the end user. However, this will not be possible without thorough statistical  
Figure 12: Bad weather conditions over the HFSWR sites.  
Figure 13: HFSWR detection capabilities during bad weather conditions over part of the HFSWR surveillance area and HFSWR sites.  
analyses of the IoT OTH maritime surveillance service, which is presented in the following section.  
Figure 14 shows the satellite link operation in one day by hour during a period in a day in the conditions of the storms (at the top of the Figure, we have shown the weather conditions for that day in parallel).  
Drop in the transfer rate is obvious in comparison to Figure 9 during favorable weather conditions of about 40-50%.  
The analysis given in this section shows that weather conditions over the surveilled maritime area and over the HFSWR sites have impact on the IoT OTH maritime surveillance service, but the impact type differs. Weather conditions over the surveilled maritime area impact on the range that HFSWRs cover. Weather conditions also impact the quality of the content in the detection files which then impacts the fusion algorithms effort to provide the high-quality results to the end users. On the other hand, weather conditions over the HFSWR sites impact on the transmission of the detection files to C2 center. In case of severe weather conditions, even temporary outages of the satellite link may occur, which can lead to problems in providing the relevant information about the surveilled maritime area to the end users.
## 3 Results and Discussion
### Pore Structure Analysis of CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)  
Figure 4 shows the N\({}_{2}\) adsorption and desorption isotherms of the catalysts. The adsorption and desorption isotherms of the four catalysts were all type III [25]. When the relative pressure (_P_/_P\({}_{0}\)) was low, the adsorption and desorption curves of the catalysts overlapped. When the relative pressure (_P_/_P\({}_{0}\)) was high, the adsorption-desorption curves had a hysteresis loop. This indicates that the pore structure in each catalyst results from the slit-like pores formed by the accumulation of loose flaky particles. In addition, when the relative pressure (_P_/_P\({}_{0}\)) was close to 1, the adsorption isotherm did not reach equilibrium, confirming the existence of a slit-like pore structure in the catalyst.  
Table 2 shows the specific surface area, pore diameter, and pore volume of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\). The specific surface area and pore volume of the catalysts decreased with increasing Ce loading. In addition, the mean pore size of CuO-CeO\({}_{2}\)/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) was similar to that of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\). The experimental results show that the CeO\({}_{2}\) loading does not have a significant effect on the pore size of the catalyst.
## 3 Results
### Impact of the Stents on the WSS-Related Indices: Osi  
Contrarily to the TAWSS distribution, the OSI spatial distribution depicted in Figure 7 looks non-uniform and reveals a clear increase from left to right. The STENT 1, STENT 2 and STENT 3 promote a visible increase of the OSI that is progressively larger in the order STENT 3 \(\gg\) STENT 2 \(\gg\) STENT 1. The increasing OSI may be related to a large recirculation within the dome of the aneurysm. However, regions with local high OSI values have been also related to the rupture of the aneurysm [44]. In this sense, it is possible to see that all the implants promote local regions with high OSI values near the aneurys neck. Qualitatively, from Figure 7 this result seems to be independent of the type of implant and the configuration.  
In particular, it seems that the worst situation occurred in the in the S-bent vessel. The extent of the region with high OSI values seems to be reduced for the V-bent and C-bent configurations. The OSI generally increased for the configurations with the stents, and this could benefit the development of the desired thrombosis. In addition, it should be observed that the non-stented vessel does not result in high values for this variable. A local increase of the WSS-derived indices that could lead to an adverse modification of the flow activity inside the aneurys sac has been found by other authors [32]. These regions can be observed also in Figure 8a,c,e within the aneurysm and Figure 8b,d,f within the artery. OSI = 0.1 is present in around the 50% of the artery (see Figure 8b,d,f). In the aneurysm, the effect of the stent is clearly visible as the OSI tends to increase when the stents are introduced. Increased maximal values of the OSI can be seen for each stented configuration. In addition, it is clearly visible that the  
Figure 6: Histograms of the normalized area associated to the TAWSS of the aneurysm and of the artery (and stent) for V-bent, C-bent and S-bent vessel configurations. The depicted areas are normalized with respect to the area of whole geometry model.  
stent that promotes the highest increase in the OSI values inside the aneurys sac is STENT 3 (OSI = 0.4-0.5 in 10 to around 20% of the model depending on the configuration). As mentioned before, from a qualitative point of view, the increase of the region with \(\text{OSI}=0.5\) is considered extremely important for aneurysm rupture. However, also STENT 2 also promotes an increase of OSI that ranges from 0.3 to 0.4 in around 20% of the whole models. Within the artery, all the stents promote moderate increases of the OSI, especially STENT 2 in the V-bent configuration (OSI = 0.5 on about the 20% of the model; see Figure 8b) and STENT 3 (OSI = 0.3-0.5 on about 10-15% of the areas of the models depending on the configuration, as shown in Figure 8d,f).  
Figure 8: _Cont._  
Figure 7: Spatial distribution of the shear index (OSI) for the three vessel configurations (top row, V-bent vessel; middle row, C-bent vessel; bottom row, S-bent vessel) and the three stents: (**a**) no stent, (**b**) STENT 1, (**c**) STENT 2 and (**d**) STENT 3.
## 3 Results and Discussion
### Real-World Implementation of the Baffle
Experienced surfers who had previously felt uncomfortable in higher inflows and beginners had no problems exiting the flow area behind the wave with ease. This feedback was pivotal in confirming that the baffle made the wave system safer and more accessible across all skill levels. Figure 11: The figure shows the velocity flow field comparison for (**A1–A3**) for the default configuration (\(S_{D}\)); (**B1–B3**) for the configuration with baffles positioned at 2 m (\(S_{\rm C}^{3m}\)); and (**C1–C3**) for the configuration with baffles at 3 m (\(S_{MF}\)). Sections (1, 2, 3) represent land, middle, and island, respectively, as shown in Figure 7.  
**Tailwater Dynamics and Areas for Optimization**  
Here, we refer to the conditions that arise immediately downstream of the wave (the tailwater region) and the ways in which these conditions may be refined to enhance performance and safety. Overall feedback was positive, yet some surfers have mentioned that the tailwater region behind the wave can feel somewhat chaotic due to higher turbulence and increased air entrainment. Under certain conditions, parts of the surface flow may become stagnant, requiring surfers to actively swim out of this zone. Although this situation is considered safe--i.e., there is no risk of being pulled back into the wave--it still represents an opportunity for further optimization. From an ergonomic standpoint, the baffle introduces a slight elevation on the riverbed. However, strong currents near the bottom effectively lift surfers over this obstruction, minimizing the potential for contact. As a result, any risk posed by the baffle is negligible. A secondary issue arises when a surfer reaches the bottom area between the wave and the baffle, where hold-down times can increase slightly. Although there is no significant hold-down or pull-back, and surfers are ultimately carried downstream, this slower flow contrasts with the previous setup, which provided a constant, robust outflow. Adapting to this new dynamic may require some adjustments to be made by surfers and highlights another avenue for refinement. Despite these minor concerns, installing the baffle has markedly improved wave performance and safety, allowing for higher inflows without risking recirculation. This improvement makes the system accessible to surfers of varied skill levels, with overall feedback from those familiar with both configurations being overwhelmingly positive.