esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 2 Methodology
### Turbulence Models Equations
#### 2.3.5 Reynolds Stress Model  
The transport equations for the transport of the Reynolds stresses, \(\rho\overline{u_{i}^{\prime}u_{j}}\), can be written as follows (e.g., [PERSON] et al. [32]):  
\[\begin{split}&\frac{\partial}{\partial t}\left(\rho\overline{u_{ i}^{\prime}u_{j}}\right)+\underbrace{\frac{\partial}{\partial x_{k}}\left(\rho u_{k}\overline{u_{ i}^{\prime}u_{j}}\right)}_{C_{ij}\equiv\text{Convection}}=-\frac{\partial}{\partial x_{k} }\left[\rho\overline{u_{i}^{\prime}u_{j}u_{k}}^{\prime}+\overline{p\left( \delta_{ij}u_{i}^{\prime}+\delta_{ik}u_{j}^{\prime}\right)}\right]}_{D_{T,ij} \equiv\text{Turbulent Diffusion}}\\ &+\underbrace{\frac{\partial}{\partial x_{k}}\left[\mu\frac{ \partial}{\partial x_{k}}\left(\overline{u_{i}^{\prime}u_{j}}\right)\right]}_{D_ {L,ij}\equiv\text{Molecular Diffusion}}-\underbrace{p\left(\overline{u_{i}^{\prime}u _{k}}\frac{\partial u_{i}}{\partial x_{k}}+\overline{u_{j}^{\prime}u_{k}^{ \prime}}\frac{\partial u_{i}}{\partial x_{k}}\right)}_{P_{ij}\equiv\text{ Stress Production}}\underbrace{-\rho\beta\left(\overline{g\left(\overline{u_{i}^{\prime}\theta}+g\overline{u_{i}^{ \prime}\theta}\right)}\right)}_{C_{ij}}\\ &+\underbrace{p\left(\frac{\partial u_{i}^{\prime}}{\partial x_{j }}+\frac{\partial u_{j}^{\prime}}{\partial x_{j}}\right)}_{\Phi_{ij}}- \underbrace{2\mu\frac{\partial u_{i}^{\prime}}{\partial x_{k}}\frac{\partial u _{j}^{\prime}}{\partial x_{k}}}_{\text{PressureStrain }F_{ij}\equiv\text{Production by System Rotation}}+\underbrace{S_{\text{user}}}_{\text{ User-DefinedSourceTerm}}.\end{split} \tag{18}\]  
The provided set of equations describes the conservation equations for turbulent flow. Several terms in these equations, namely, \(C_{ij}\), \(D_{L,ij}\), \(P_{ij}\), and \(F_{ij}\), do not necessitate any additional modeling. However, \(D_{T,ij}\), \(G_{ij}\), \(\Phi_{ij}\), and \(cij\) require modeling to complete the equations. The following sections elaborate on the modeling assumptions necessary to solve this set of equations.
# Characterization of Isoclinic, Transversally Geodesic and Grassmannian Webs
## 2 Almost-Grassmann Structures
It is easy to see that Chern connection is given by:  
\[\
abla_{X}Y=h\{j[hX,jhY]+[vX,hY]\}+v\{j[vX,jvY]+[hX,vY]\}.\]  
Definition 3: Let \(M^{rp}\) be an \(rp\)-dimensional manifold. Define the almost-Grassmann structure of type \((p,r)\) on \(M\) to be a field of isomorphismes  
\[\Phi_{x}:\ T_{x}M\to V^{p}\otimes V^{r}\]  
where \(V^{p}\) and \(V^{r}\) are vector spaces of dimension \(p\) and \(r\) respectively. An almost-Grassmann structure is integrable if and only if for all \(x\in M\) there exists a neighborhood \(U\) of \(x\) such that the locally induced almost-Grassmann structure in this neighborhood is diffeomorphic to the Grassmann structure on \(R^{r+p}\) defined by the constant mapping \(R^{r+p}\to V^{p}\otimes V^{r}\), \(V^{p}\), and \(V^{r}\) being two sub-vector spaces of \(R^{r+p}\) of dimension \(p\) and \(r\), respectively. In terms of \(G\)-structures, an almost-Grassmann structure is a \(SL(p,\mathbb{R})\otimes GL(r,\mathbb{R})\)-structure. The following theorem is due to [PERSON] [21]. Theorem 3.2 ([PERSON]): _Let \(M\) be an almost-Grassmann manifold, the almost-Grassmann structure is integrable if and only if \(M\) is locally a Grassmann manifold._  
Remark 1: _We can define a web structure on \(G(r+1,1)\) as follows:_  
_Let \(x\in\mathbb{P}^{r+1}\) and \(\hat{x}\) the Schubert manifold of all straight lines intersecting at the point \(x\). It's an \(r-\) dimensional submanifold of \(G(r+1,1)\). A hypersurface \(V\) of \(\mathbb{P}^{r+1}\) defines \(r\)-dimensional foliation on an open set \(\mathcal{U}\subset G(r+1,1)\): the leaves are \(\hat{x}\) with \(x\in V\). If we consider \(3\) hypersurfaces \(V_{a}\)\((\alpha=1,2,3)\), in a general position in \(\mathbb{P}^{r+1}\), they define a \(3\)-web of dimension \(r\) on an open set \(\mathcal{U}\) of \(G(r+1,1)\)._  
Definition 4: A Grassmann web is the \(3-\) web on \(G(r+1,1)\) defined as in the previous remark. A \(3-\) web is Grassmannizable if it is equivalent to a Grassmann web. In dimension \(1\), [PERSON] showed that the Grassmannian webs generalize the linearizable webs:  
Theorem 3.3 ([PERSON]): _A \(3-\) web of dimension \(1\) on a \(2-\) dimensional manifold is Grassmannian if and only if it is linearizable._  
_Indeed, if \(r=1\), the manifold \(G(2,1)\) is the dual projective space \((\mathbb{P}^{2})^{*}\); if \(x\in\mathbb{P}^{2}\), \(\hat{x}\) is a straight line in \((\mathbb{P}^{2})^{*}\) formed by straight lines of \(\mathbb{P}^{2}\) with the vertex at \(x\). Let \(k:(\mathbb{P}^{2})^{*}\to\mathbb{P}^{2}\) be the one-to-one mapping defined by \(k(ax+by+cz)=[a:b:c]\). This mapping is well-defined since \(k(\lambda(ax+by+cz))=[a:b:c]\). The image by \(k\) of \(\hat{x}\) is then a straight line in \(\mathbb{P}^{2}\). Thus, a Grassmannian web is linearizable._  
_Conversely, if a web is linearizable, the image by \(k^{-1}\) of its equivalent linear web is a Grassmann web._
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 1 Introduction
### Related Work
In [19], a smart e-health IoT service is introduced, which is based on SDN-powered MEC within a vehicular ad-hoc network architecture to detect heart attacks in a real-time manner. In [20], the authors focused on virtual reality and vehicular IoT applications and they propose an SDN-based MEC framework to provide the necessary data-plane flexibility, programmability, and reduced latency. Furthermore, in [21], the adoption of SDN and MEC is presented to overcome the barriers of network densification of IoT cloud integration within a smart home environment. Following the above discussion, the interest of the research community in the SDN-based MEC framework is growing and calls for advanced flexible, dynamic and programmable data offloading mechanisms. In addition, the problem of monetary-based pricing of the MEC servers computing services has not been addressed in the existing literature.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 2 Definition of Concepts and Terms Regarding Critical Latency
### Colregs  
The concept of critical latency regarding ship collisions in remote maneuvers of autonomous ships was established based on COLREGs specified in both head-on and crossing situations. In COLREGs, encountering situations requiring collision-avoidance actions are divided into head-on and crossing situations. These two rules are as follows.  
* COLREGs Rule 14: Head-on situation
* When two power-driven vessels are meeting on reciprocal or nearly reciprocal courses so as to involve risk of collision each shall alter her course to starboard so that each shall pass on the port side of the other.
* Such a situation shall be deemed to exist when a vessel sees the other ahead or nearly ahead and by night she could see the masthead lights of the other in a line or nearly in a line and/or both sidelights and by day she observes the corresponding aspect of the other vessel.
* When a vessel is in any doubt as to whether such a situation exists, she shall assume that it does exist and act accordingly.
* COLREGs Rule 15: Crossing situation When two power-driven vessels are crossing so as to involve risk of collision, the vessel which has the other on her own starboard side shall keep out of the way and shall, if the circumstances of the case admit, avoid crossing ahead of the other vessel.  
The proposed critical latency estimating technique is based on the two rules of COLREGs that are described above.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 2 Materials and Methods
### Satellite Data  
The publicly accessible Sentinel-2 MSI (Multispectral Imager) Surface Reflectance (SR), previously denoted as Bottom of Atmosphere (BOA) reflectance, datasets were selected for this investigation owing to their superior spatial resolution, regular temporal coverage, and convenient accessibility. The atmospheric bands, which encompass Coastal Aerosol, Water Vapor, and SWIR-Cirrus, were omitted from the analysis and are consequently not represented in Table 3. All datasets were procured at no financial cost from the Copernicus Database (formerly known as the Copernicus Open Access Hub) [45].  
The acquisition dates of the images used in this study are presented in Table 4, along with the corresponding phenological stages. The phenological stages are defined based on the BBCH (Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) scale. This scale categorizes the plant's developmental cycle into 10 distinct principal stages [46].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} Well-Drained \\ Soils \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil characteristics for each field under investigation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sentinel-2 Bands** & **Spectral Region** &
\begin{tabular}{c} **Central** \\ **Wavelength (\(\mu\)m)** \\ \end{tabular} & **Resolution (m)** \\ \hline Band 2 & Blue & 0.490 & 10 \\ Band 3 & Green & 0.560 & 10 \\ Band 4 & Red & 0.665 & 10 \\ Band 5 & Red Edge 1 & 0.705 & 20 \\ Band 6 & Red Edge 2 & 0.740 & 20 \\ Band 7 & Red Edge 3 & 0.783 & 20 \\ Band 8 & NIR & 0.842 & 10 \\ Band 8A & Vegetation Red & 0.865 & 20 \\ Band 11 & SWIR 1 & 1.610 & 20 \\ Band 12 & SWIR 2 & 2.190 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentinel-2 MSI bands.
## 3 Results and Discussions
### SEM-EDS Analysis
#### 3.4.5 Temperature and Humidity Cycle Test  
The temperature and humidity changes in the environment can have an important impact on the preservation of cultural relics. Therefore, an accelerated aging test temperature and humidity cycle can effectively evaluate HL and NHL durability. Figure 14 shows the changes in strengths between before and after tests for SK, SL2 and SL5. Results reveal that the compressive strengths of SK, SL2, and SL5 decreased by 3.60, 25.21, and 3.27% after the test, respectively, meanwhile, flexural strengths decrease respectively by 13.54, 43.55, and 30.43%. SK and SL5 had smaller strength loss than SL2. Furthermore, the surfaces of the three specimens did not have any cracks. It should be mentioned that despite the strength reduction, the loss rate of compressive strength is relatively small, which can still guarantee reinforcement performance. In all, SK and SL5 have good resistance to temperature and humidity cycles.  
Figure 14: Strength comparison in temperature and humidity cycle.  
Figure 13: Strength comparison in frost–thaw cycle.
## 3 Results
### Statistical Tests
#### 3.4.6 Corrected Random Resampled Cross-Validation Paired \(t\)-Test  
The corrected random resampled CV paired \(t\)-test, proposed by [PERSON] and [PERSON] [20], was introduced to mitigate the Type I error inherent in the classical resampled CV paired \(t\)-test, which has been shown to exhibit increasing bias as the number of repetitions grows. Similar to repeated 10-fold cross-validation, this corrected test generated 100 estimates, aligning with the recommendations of statistical power analysis to ensure sufficient power and minimize the risk of Type II error.  
The summarized pairwise results are presented in Table 11, while a detailed breakdown of the statistical test, including t-scores and p-values, is provided in Appendix A (Table A5). The results reveal a structure consistent with previous analyses, particularly the corrected 10-times repeated 10-fold CV paired \(t\)-test (Table 10), with most statistically significant differences observed among LR, SVM, and RF model pairs.  
Test again affirmed that LR performed significantly worse than all other models across all metrics (e.g., accuracy vs. RF: t = \(-\)4.769, \(p\)! 0.001; precision vs. CB: t = \(-\)3.591, \(p\)! 0.001; recall vs. XGB: t = \(-\)3.384, \(p\) = 0.001), consistently positioning it at the lower tier.  
Regarding accuracy, all classifiers except LR displayed mostly comparable results, with no statistically significant differences observed among RF, ANN, SVM, LGBM, CB, and XGB (e.g., ANN vs. CB: \(p\) = 0.444; CB vs. XGB: \(p\) = 0.888). In contrast, for precision, CB and XGB exhibited significant advantages over RF (\(p\) = 0.020 and \(p\) = 0.027, respectively) as well as over SVM and LGBM. The recall results underlined the dominance of SVM, which outperformed ANN, LGBM, CB, and XGB (\(p\)! 0.05 in each pairwise test) and also significantly outperformed LR. Meanwhile, in the F1-score, all models except LR formed a statistically indistinguishable result. Lastly, ROC-AUC analyses confirmed the overall strength of CB and XGB, as each surpassed RF and SVM (e.g., CB vs. RF: t = \(-\)3.915, \(p\)! 0.001; XGB vs. SVM: t = \(-\)4.064, \(p\)! 0.001) and did not significantly differ from each other (\(p\) = 0.1789). ANN also showed good results under the ROC-AUC metric, outperforming RF (t = 2.129, \(p\) = 0.036) and SVM (t = 4.065, \(p\)! 0.001). In sum, CB, XGB, and ANN formed a top-performing cluster, RF, SVM, and LGBM resided in a lower and mid-range position, and LR consistently placed at the lower bound of the comparative evaluation.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 3 Results and Discussion
### The Results of PCA  
PCA was performed on all samples in the 420-1000 nm spectrum range. The results are shown in Figure 4. Since the first two principal components of cumulative credibility reached 92.97% (above 90%), the main information of the original spectra could be represented by the first two principal components.  
Figure 3: Averaged spectra between 420 and 1000 nm of bruised and normal pears of all samples. Two absorption peaks in the black frame represent the absorption of chlorophyll (680 nm) and water (960 nm), respectively.  
Figure 4 is a clustering plot of the PC1 and PC2 scores for modeling samples, in which the horizontal coordinate represents the first principal component and the vertical coordinate represents the second principal component of each sample. It can be seen from the graph that the bruised samples clustered mainly in the lower-right corner while the normal samples clustered mainly in the upper-left corner. Though these two kinds of samples overlapped in some places, they could be well distinguished in general. Generally speaking, PC1 and PC2 had good effects on clustering.  
Figure 5 shows the loading plots of PC1 and PC2 in the spectral region of 420-1000 nm. The peaks and valleys of the loading plots indicated the importance of the wavelengths. Wavelengths 472, 544, 655, 688 and 967 nm were selected as the characteristic wavelengths. The first three wavelengths (472, 544 and 655 nm) were related to the color information of blue (around 450 nm), green (around 550 nm) and red (around 650 nm); the wavelength of 688 nm represented the absorption of chlorophylls; the wavelength of 967 nm represented the absorption of water. As seen in Figure 1, the differences between water and color could be obviously visualized in the bruise region, indicating the efficiency of the characteristic wavelengths' selection.  
Figure 4: Principal component analysis (PCA) scores plots (PC1 vs. PC2) for modeling samples (45 normal samples in black and 45 bruised samples in red.  
Figure 5: The loading plot of PC1 and PC2 in the spectral region of 420 to 1000 nm for modeling samples (45 normal samples and 45 bruised samples).
## 2 Mathematical Model
### Calculation of Nonlinear Optimization Parameters  
In order to obtain parameters by the proposed method, estimated and measured values of diaphragm displacement were used to define the target function \(J\).  
\[J(\mathbf{w})=J_{1}(\mathbf{w})+J_{2}(\mathbf{w})=\int_{0}^{t_{f}}\big{[} \mathbf{x}(t;\mathbf{w})-\mathbf{x}_{mea}(t)\big{]}^{2}dt+\mu\int_{0}^{t_{f}} \big{[}\mathbf{i}(t;\mathbf{w})-\mathbf{i}_{mea}(t)\big{]}^{2}dt \tag{8}\]  
where the unknown vector \(\mathbf{w}\) = [\(M_{m}\), \(R_{m}(\mathbf{x})\), \(Bl(\mathbf{x})\), \(K_{m}(\mathbf{x})\), \(L_{e}(\mathbf{x})\)] is the unknown nonlinear parameter that needs to be determined; \(\mathbf{x}(t;\mathbf{w})\) is the estimated displacement; \(\mathbf{x}_{mea}(t)\) is the measured displacement using a precise displacement sensor; \(\mathbf{i}(t;\mathbf{w})\) is the estimated current; \(\mathbf{i}_{mea}(t)\) is the measured current; and, \(\mu\) is the weight coefficient of the difference between \(J_{1}(\mathbf{w})\) and \(J_{2}(\mathbf{w})\). The equation shows that when the target function \(J(\mathbf{w})\) reaches the minimum value, the estimated value \(\mathbf{x}(t)\) approaches the measured value \(\mathbf{x}_{mea}(t)\). Moreover, when the unknown coefficients of the vector \(\mathbf{w}\) approach its extreme value, a positive solution can be reached for \(\mathbf{w}\). The parameter \(\mathbf{w}\) can be adjusted, as follows:  
\[\mathbf{w}_{(n+1)}=\mathbf{w}_{(n)}-\beta_{(n)}\mathbf{P}_{(n)} \tag{9}\]  
where n represents the \(n\)th iteration; \(\beta_{(n)}\) represents the \(n\)th step; and \(\mathbf{P}_{(n)}\) is the \(n\)th direction with decreasing values of unknown parameter vector.  
Figure 2: Comparison of the power series curve in exponential and polynomial function.
# Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting
## 3 Design of Fault Diagnosis Algorithm for Key Parts of Wind Turbine  
In order to reduce and avoid the losses caused by the wind turbine shutdown, it is necessary to timely and accurately detect whether there are faults and specific fault types in the gearbox and generator of the wind turbine. Therefore, the fault diagnosis algorithm proposed in this paper is as follows.  
Step 1: Data acquisition and data preprocessing. First of all, we need to obtain the operation monitoring records and fault records of the SCADA system. Select relevant faults of gearbox and generator from the fault records of the SCADA system, and sort out the types of fault records. We need to find the normal data set and each type of fault data set according to the fault record and SCADA system monitoring record, and combine the normal data set and fault data set as the experimental data set. Before selecting the features of the SCADA system data, the experimental data set needs to be pre-processed, including deleting the empty data record. After data preprocessing, data categories need to be labeled. Common sequence encoding and one hot encoding can be adopted.  
Step 2: Use the ReliefF algorithm to find the weight of each SCADA system observation parameter in the fault classification. Due to the imbalance of data proportion between normal data and fault data in the experimental data set, this paper adopts a stratified sampling strategy for the experimental data set in the calculation process of the ReliefF algorithm, which not only covers all the classified sample features, but also avoids the result deviation caused by the different sample data amount. Then we arrange the feature parameters calculated by the ReliefF algorithm in the order of weight from large to small, select the feature parameters that have a greater impact on the related faults of the gearbox and generator, and remove the observation features with a smaller correlation. Next, we reorganize the experimental data set according to the selected characteristic parameters.  
Step 3: We divide the rearranged experimental data set into a training set, validation set and a test set. Then we use the training set to train the XGBoost multi classification model, and use the validation set to adjust the model parameters.  
Step 4: Use the optimized XGBoost model to perform a fault multi-classification test on the test set data. The algorithm flow chart is shown in Figure 3.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods
### Temperature and Humidity Sensor  
The DHT11 sensor is a temperature and humidity sensor that consists of the following three distinct parts: a humidity detection component, an NTC temperature sensor, and an integrated circuit on the back of the sensor.  
To perform humidity detection, this sensor consists of two electrodes with a moisture absorption plate between them. As the humidity changes, the conductivity of the substrate also changes, which causes the resistance between these two electrodes to change as well.  
To measure the temperature, the DHT11 sensor makes use of an NTC (negative temperature coefficient) temperature sensor, or, in other words, a thermistor. Thermistors are, by definition, semiconductors that change their resistance with temperature changes and are made mostly of ceramics or polymers in order to provide larger changes in resistance with small temperature changes. Since we are talking about an NTC whose coefficient of resistance variation with temperature is negative, the resistance decreases with an increase in the external temperature.  
The data obtained from both the temperature and humidity are analyzed and converted from analog to digital via the 8-bit integrated circuit, in order to be read in a simplified way by any microcontroller.
## 3 Results  
### Thin Film Characteristics  
Figure 1a shows the XRD data of the reference LSCO film and LNO-decorated LSCO films. The films clearly show the presence of the (00)\({}_{tetra.}\) peaks of LSCO and (00)\({}_{cubic}\) peaks of GDC and YSZ, indicating that the LSCO films grew epitaxially. With the LNO coverage of 3 nm thickness, the (00)\({}_{pc}\) peaks of LNO become visible. The subscripts \(tetra.\) and \(pc\) denote the tetragonal [20,21] and pseudo-cubic [36] notation, respectively. The \(\phi\) scans of the LNO-decorated LSCO films showed that LNO (202)\({}_{pc}\), LSCO (103)\({}_{tetra.}\), GDC (202)\({}_{cubic}\), and YSZ (202)\({}_{cubic}\) have strong peaks with four-fold cubic symmetry (Figure 1b), which reveals the in-plane crystallographic relationships between GDC and YSZ (cube-on-cube alignment), LSCO and GDC (in-plane 45\({}^{\circ}\) rotation), and LSCO and LNO (no rotation). The measured in-plane lattice parameters of the LSCO films were found to be \(a\) = 3.801 A for the undecorated LSCO film, \(a\) = 3.792 A for \(\sim\)3 nm LNO-decorated LSCO film, and \(a\) = 3.804 for \(\sim\)15 nm LNO-decorated LSCO film, which are slightly larger compared with the literature data (\(a\) = 3.777 A, \(c\) = 13.226 A for bulk LSCO [26]). Considering the lattice mismatch between LSCO and GDC, the LSCO films were tensile-strained, which was further confirmed by XRD reciprocal space maps (RSMs) shown in Figure 1c-e. Therefore, the effect of strain on the oxygen surface exchange kinetics was ruled out in this study.