esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine
###### Abstract  
Identifying organic matter in laminae is fundamental to petroleum geology; however, many factors restrict manual quantification. Therefore, computer recognition is an appropriate method for accurately identifying microscopic components. In this study, we used support vector machine (SVM) to classify the preprocessed photomicrographs into seven categories: pyrite, amorphous organic matter, mineral matter, alginite, sporinite, vitrinite, and intertinite. Then, we performed a statistical analysis of the classification results and highlighted spatial aggregation of some categories using the kernel density estimation method. The results showed that the SVM can satisfactorily identify the macerals and minerals of the laminae, and its overall accuracy, kappa, precision, recall, and F1 are 82.86%, 0.80, 85.15%, 82.86%, and 82.75%, respectively. Statistical analyses revealed that pyrite was abundantly distributed in bright laminae; vitrinite and sporinite were abundantly distributed in dark laminae; and alginite and intertinite were equally distributed. Finally, the kernel density maps showed that all classification results, except intertinite, were characterized by aggregated distributions: pyrite with the distribution of multi-core centers, alginite, and sporinite with dotted distribution, and vitrinite with stripe distribution, respectively. This study may provide a new method to quantify the organic matter in laminae.  
macerals; laminae; organic petrology; SVM Article
## 4 Application Examples for Turbomachinery
### Hybrid RANS/LES Model Menter SST-SAS  
RANS-LES coupled simulations have recently been a hot topic in research. This is because this method not only saves a significant number of computational resources compared to LES and DNS, but also exhibits good analytical capabilities for large-scale structures in the mainstream flow [32]. Therefore, in 2018, [PERSON] proposed a new coupling method known as the Scale-Adaptive Simulation (SAS) model [33]. This method constructs a source term in the \(\omega\)-transport equation of the SST model, which is defined by a series of variables. Later, [PERSON] [34] made revisions to the Karman length. The formulation of the SST-SAS model and the improved von Karman scale \(L_{\text{vK}}\) are as follows:  
\[Q_{\text{SAS}}=\max\left[\rho\zeta_{2}xS^{2}\left(\frac{L}{L_{\text{vK}}} \right)^{2}-C_{\text{SAS}}\frac{2\rho k}{\sigma_{\Phi}}\max\left(\frac{|\
abla \omega|^{2}}{\omega^{2}},\frac{|\
abla k|^{2}}{k^{2}}\right),0\right], \tag{11}\]  
\[L_{\text{vK}}=\max\left(\kappa\left|\frac{U^{\prime}}{U^{\prime}}\right|^{ \prime},C_{S}\sqrt{\frac{\kappa S_{2}}{\left(\beta/C_{\mu}\right)-\gamma}} \cdot\Delta\right),\]  
\[\Delta=V_{\text{vC}}^{1/3},\]  
\[U^{\prime}=C_{1}S+C_{2}\Omega,\ S=\sqrt{2S_{ij}S_{ij}}, \tag{12}\]  
\[\Omega=\sqrt{2\Omega_{ij}\Omega_{ij}},S_{ij}=\frac{\left(\partial U_{i}/ \partial x_{j}\right)+\left(\partial U_{j}/\partial x_{i}\right)}{2},\]  
Figure 8: Pressure distribution of the guide vane: (**a**) \(11\%\) span; (**b**) \(50\%\) span; (**c**) \(89\%\) span.  
Figure 7: Mass-averaged efficiency vs. pressure ratio of PW E3.  
In Equation (11), \(\xi_{2}=3.51,\kappa=0.41,C_{SAS}=2.0,\sigma_{\Phi}=2/3\), \(L_{\text{vK}}\) represents the Karman scale, and \(L\) represents the turbulence model scale. The Karman scale is defined by [PERSON] using a combination of the first and second derivatives of velocity. In the original Karman scale, the first derivative of velocity is defined solely by the mean strain rate \(S\). [PERSON], based on [PERSON]'s velocity decomposition theorem, improved the first derivative of velocity to be defined by both the mean strain rate \(S\) and the mean vorticity. The tensor forms of strain rate and vorticity are provided in Equation (12).  
SST-SAS turbulence model demonstrates excellent responsiveness to unsteady flows, effectively triggering LES mode in unsteady regions. The high-accuracy format determines the resolution of the flow field under LES mode given the presence of unsteady flow structures such as wakes and secondary flows in high-pressure turbine flow fields. In this summary, we focus on the wake, which is small and difficult to capture in the high-pressure turbine, and the results of WENO-ZQ5 are compared with those in the literature.
## 6 Experimental Results and Evaluation
### N-Fold Cross-Validation  
Firstly, we have investigated the number 'N' in N-fold cross-validation. We have evaluated different N values to have optimal results. In this measure, the F1 mean value is crucial for us. The F1-max and F1-min are subject to more random results. Therefore, we have focused on the change of the F1-mean in the experiments. The change of the F1 measure concerning different 'N' values in N-fold cross-validation is plotted in Table 9.  
From Table 9, although we can observe that the F1 mean values are close for the selected N values, the best results are obtained using 10-fold cross-validation for the SVM linear kernel case.  
The most feasible bug classifier can be selected and applied after training the ML models using the preprocessing and tuning hyperparameters. Moreover, we have obtained the best F1 measure using SVM with a linear kernel, as stated above. Hence, this bug classifier can thus be used for classifying the newly detected bugs following the steps of the prediction process as depicted in the lower part of Figure 4.
# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 2 Related Works and Background
### Parallel Corpus Quality Assessment  
Accompanied by the increase of publicly released parallel corpus such as FLORES-101 [36] and AI hub, the importance of evaluating and improving the quality of the parallel corpus becomes higher. Especially for the data construction process, assessing the quality of the corpus is regarded as an essential process. For example, in the case of AI hub, all publicly available corpora were constructed through a multi-phase process that proceeded with machine translation followed by the human examination. For the corresponding examination process, semantic coherence and sentence alignment are mainly inspected. This can be viewed as checking its suitability to the intended purpose of the data construction.  
However, as data acquisition becomes more accessible and the amount of data used for training increases, exploring each data with human labor leads to considerable cost. For instance, as the total amount of parallel corpus released by the AI hub is approximately 7M, it can be expected that tremendous time and cost are required to examine the whole corpus.  
For the alleviation of such limitation, corpus evaluation studies have been made by mainly focusing on the minimization of direct human examination. Representative methods include the use of several translation rules established in advance [37], the Gale-Church algorithm [38] that evaluates the overall align of the sentence, and the Bilingual Sentence Aligner [39]. The validity of these corpus evaluation methodologies is generally evaluated based on the performance of the MT system generated through the corresponding corpus. In particular, evaluation criteria, such as sentence alignment, were confirmed to be effective as corpus evaluation metric through the performance verification of the SMT model generated through the corpus [40]. Furthermore, with the development of deep neural modeling, these methodologies are evaluated by the performance of NMT model [37].  
However, most of these studies aim to improve the performance of the MT system itself trained by the corresponding parallel corpus. These often led to inconsistent results where several data that was not considered to be noisy in training SMT system considerably deteriorated the performance of the NMT system when utilized in training process [17]. Thus, it shows that these corpus evaluation criteria may not be consistent enough to be directly related to actual quality assessment. In this study, we analyze the corpus using a sentence analysis tool called LIWC, which has not been utilized as a parallel corpus inspection and as an objective evaluation index for the corpus quality. In addition, following previous studies, we check the performance of the NMT system trained through the parallel corpus and analyze the characteristics and quality of the corresponding corpus that can be obtained through the result.
## 6 Simulation and Analysis
### Cross-RIES Optimization Results and Analysis
#### 6.4.2 Carbon-Trading Mechanism  
Carbon trading involves the market-based trading of carbon emission rights as commodities. Under this mechanism, when the actual carbon emissions of an RIES system are lower than the allocated carbon emission rights, the system can profit by selling the surplus carbon emission rights to other RIES systems. Conversely, if the system exceeds its carbon emission rights, it needs to purchase additional carbon emission rights to meet its carbon emission requirements. When the total carbon emissions of the entire cross-RIES system exceed the specified carbon emission rights, the entire system will face economic penalties.  
Figure 9: Marginal abatement costs of HP-RIES and CP-RIES in the scenarios of rapid, steady, and green developments.  
In this study, the carbon-trading price is based on the closing price of the Carbon Emission Allowance (CEA) listing agreement in the Chinese carbon market in 2023, set at 58 CNY per ton, and the carbon penalty price is set at 10,000 CNY per ton.  
Based on the optimization results of the green development scenario in Section 6.3, the total carbon emission rights for the cross-RIES system are determined based on the sum of the carbon emissions of the three RIES system configurations. The initial carbon emission right quotas for each RIES system are set based on their respective proportions of carbon emissions within the total carbon emission rights. It is important to note that the carbon emission right allocation in this case adopts the grandfathering method, which uses historical emissions as a reference and has the advantages for being convenient and practical. The specific data can be found in Table 10.
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 2 Phase Filtering of Shearlet Domain Based on Interferometric Phase Statistics
### Interferometric Phase Shearlet Transform  
For interferogram with a given size, the InSAR interferometric phase can be regarded as a complex matrix. We first get the real and imaginary parts of the phase, and then process them by the shearlet transform respectively. The continuous shearlet transform is defined as below [30].  
\[SH_{f}(a,s,t)=\langle f,\psi_{a,s,t}\rangle \tag{6}\]  
where \(f\) is the real parts \(\cos(\phi)\) and imaginary parts \(\sin(\phi)\). In addition, functions \(\psi_{a,s,t}(x)\) can be defined as  
\[\psi_{a,s,t}(x)=a^{-3/4}\psi(A^{-1}B^{-1}(x-t)) \tag{7}\]  
where \(a\in\mathbf{R}^{+}\) is the scale parameter, \(s\in\mathbf{R}\) is the shear parameter, \(t\in\mathbf{R}^{2}\) is the translation parameter \(\mathrm{A}=(a,0;0,a^{1/2})\) is an anisotropic dilation matrix associated with scale transformations, through which the shearlet transform can be associated with a multiresolution analysis, like wavelets. \(\mathrm{B}=(1,s;0,1)\) is the shear matrix, relating to area-preserving geometrical transformations, such as rotations and shear. We can get the frequency supports for \((a=1,s=0)\), \((a=1/4,s=0)\), \((a=1,s=-3)\), as shown in Figure 2.  
Shearlets have the following mathematical properties [24]. They:  
* are well localized;
* have parabolic scaling;
* are highly directionally sensitive;
* are spatially localized;
* and are optimally sparse.  
Shearlets form a tight frame of well-localized waveforms at various scales and directions, and are optimally sparse in representing images with edges [31]. The interferogram contains a large number of edges and details, so the shearlet transform used in interferogram filter is greatly superior.
# A Data Processing and Distribution System Based on Apache NiFi
## 5 Research and Results
### One Node
Simultaneously, the memory usage was kept at a similar level. This should be explained by the fact that the Java Virtual Machines (JVMs) had the same amount of RAM available. The increased CPU consumption in the case of the default configuration was related to the numerous I/O operations triggered by overloaded queues. More precisely, Apache NiFi saved operating memory by writing the FlowFiles to the disk. Thus, numerous high-density optical devices running in 5G/6G networks can easily over-utilize an Apache NiFi deployment in the default configuration. A regular-sized deployment may consist of a few hundred thousand components to be monitored. Due to the proposed improvements, it was possible to eliminate the queue overflow issue and enable the use of Apache NiFi in complex optical networks that support 5G infrastructures. In the future, this can be also examined with respect to the requirements of 6G networks. \begin{table}
\begin{tabular}{c c c c} \hline
**Node Number** & **CPU—User [\%]** & **CPU—System [\%]** & **RAM Used [MB]** \\ \hline \multicolumn{4}{c}{One Node—Optimized Configuration} \\ \hline
1 & \(81.36\pm 11.65\) & \(13.01\pm 1.54\) & \(3005.48\pm 125.98\) \\ \hline \multicolumn{4}{c}{One Node—Default Configuration} \\ \hline
1 & \(49.25\pm 6.37\) & \(30.1\pm 6.14\) & \(3006.04\pm 105.79\) \\ \hline \multicolumn{4}{c}{Two Nodes—Optimized Configuration} \\ \hline
1 & \(74.13\pm 14.62\) & \(14.64\pm 2.79\) & \(3015.77\pm 118.18\) \\
2 & \(73.86\pm 15.86\) & \(13.91\pm 2.89\) & \(2813.26\pm 155.01\) \\ average & \(73.99\pm 15.24\) & \(14.27\pm 2.84\) & \(2914.51\pm 136.6\) \\ \hline \multicolumn{4}{c}{Two Nodes—Default Configuration} \\ \hline
1 & \(40.45\pm 11.16\) & \(22.01\pm 6.85\) & \(2910.07\pm 134.71\) \\
2 & \(37.06\pm 13.32\) & \(21.47\pm 8.26\) & \(2624.18\pm 179.29\) \\ average & \(38.76\pm 12.24\) & \(21.74\pm 7.55\) & \(2767.13\pm 157.0\) \\ \hline \multicolumn{4}{c}{Three Nodes—Optimized Configuration} \\ \hline
1 & \(65.3\pm 18.17\) & \(14.6\pm 4.11\) & \(2966.05\pm 145.41\) \\
2 & \(67.09\pm 19.59\) & \(15.2\pm 3.71\) & \(2773.77\pm 195.91\) \\
3 & \(69.23\pm 17.27\) & \(14.05\pm 3.26\) & \(2781.91\pm 198.71\) \\ average & \(67.21\pm 18.34\) & \(14.62\pm 3.7\) & \(2840.58\pm 180.01\) \\ \hline \multicolumn{4}{c}{Three Nodes—Default Configuration} \\ \hline
1 & \(52.23\pm 11.89\) & \(29.92\pm 9.58\) & \(2846.2\pm 157.52\) \\
2 & \(46.07\pm 12.96\) & \(26.53\pm 9.63\) & \(2532.66\pm 254.31\) \\
3 & \(48.87\pm 11.35\) & \(27.19\pm 8.75\) & \(2588.47\pm 213.04\) \\ average & \(49.06\pm 12.07\) & \(27.88\pm 9.32\) & \(2655.78\pm 208.29\) \\ \hline \multicolumn{4}{c}{Four Nodes—Optimized Configuration} \\ \hline
1 & \(62.1\pm 18.63\) & \(15.23\pm 4.36\) & \(2919.01\pm 203.6\) \\
2 & \(63.47\pm 18.62\) & \(14.29\pm 3.89\) & \(2733.87\pm 240.76\) \\
3 & \(62.87\pm 18.25\) & \(13.54\pm 3.86\) & \(2728.67\pm 248.5\) \\
4 & \(63.76\pm 17.7\) & \(14.26\pm 4.6\) & \(2748.53\pm 243.51\) \\ average & \(63.05\pm 18.3\) & \(14.33\pm 4.18\) & \(2782.52\pm 234.1\) \\ \hline \end{tabular}
\end{table} 
Table 3: Numerical results.
# Longitudinal Wind Tunnel Tests of the PROSIB 19-Pax Airplane
## 3 Results and Discussion
### Effects of the Battery Pod  
The battery pod was installed on the belly of the aircraft model, as shown in Figure 13. With its length and cross-section, it affected the aerodynamic drag and the longitudinal stability. The pod was investigated on the body configuration only. Since there was no appreciable lift generated, only the drag polars and the pitching moment curves are presented in Figure 14. The increment in zero-lift drag coefficient \(C_{D_{0}}\) was about 0.0050. The fuselage alone provided 76 drag counts. The significant addition of 50 drag counts due to the pod has been attributed to:  
* Surface roughness of the pod material (ABS plastics), providing a larger skin friction drag than polished aluminum;
* Aerodynamic interference of the support strut crossing the pod;
* Pressure drag due to the pod blunt shape.  
Both body and body-pod combinations exhibited a linear variation of drag coefficient with lift coefficient. However, the presence of the pod also changed the slope of the drag curve. The slopes \(\mathrm{d}C_{D}/\mathrm{d}C_{L}\) were 0.2297 and 0.5362, respectively, for the body and the body-pod configurations.  
The pod did not alter the fuselage planform view, but only the cabin cross-section and side view. The pitching moment curves were parallel on most of the data points, as expected. Nevertheless, an 18% reduction in \(C_{M_{a}}\) for the body-pod combination has been measured in the angle of attack range \(-2^{\circ}\) to \(5^{\circ}\), with a 0.002 decrement in \(C_{M_{0}}\) magnitude. The reduction in the body unstable contribution to longitudinal stability was attributed to the blunt shape of the pod, which may have generated a pitch-down contribution at low angles of attack.  
Since the pod and the wing were installed in opposite locations, no additional aerodynamic interference was expected with other component combinations.  
Figure 13: Body–pod configuration: (**a**) front view; (**b**) side view.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \(C_{M_{a,B}}\) & \(C_{M_{a,WB}}\) & \(C_{M_{a,BH}}\) & \(C_{M_{a,WBH}}\) & \(\left(\mathbf{1-\frac{d\mathbf{r}}{d\mathbf{a}}}\right)\) & \(\frac{d\mathbf{c}}{d\mathbf{a}}\) \\ \hline Hb, \(\delta_{\mathrm{f}}=0^{\circ}\) & 0.0056 & 0.0059 & \(-0.0267\) & \(-0.0201\) & 0.805 & 0.195 \\ Ht, \(\delta_{\mathrm{f}}=0^{\circ}\) & 0.0056 & 0.0059 & \(-0.0249\) & \(-0.0167\) & 0.741 & 0.259 \\ Hb, \(\delta_{\mathrm{f}}=15^{\circ}\) & 0.0056 & \(-0.0003\) & \(-0.0267\) & \(-0.0211\) & 0.644 & 0.356 \\ Ht, \(\delta_{\mathrm{f}}=15^{\circ}\) & 0.0056 & \(-0.0003\) & \(-0.0249\) & \(-0.0208\) & 0.672 & 0.328 \\ Hb, \(\delta_{\mathrm{f}}=30^{\circ}\) & 0.0056 & \(-0.0012\) & \(-0.0267\) & \(-0.0241\) & 0.709 & 0.291 \\ Ht, \(\delta_{\mathrm{f}}=30^{\circ}\) & 0.0056 & \(-0.0012\) & \(-0.0249\) & \(-0.0218\) & 0.675 & 0.325 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation of the downwash gradient for the two empennage configurations. Aerodynamic derivatives in \(\mathrm{deg}^{-1}\). The downwash gradient is a dimensionless quantity.
## 1 Introduction
[21] and [PERSON] and [PERSON] [22] adopted an internal consistency check, which compared the baseline solutions resulting from the differential carrier phase processing against the relative positions derived from differential POD (dPOD) solutions. In 2012, an inter-agency comparison of the TanDEM-X mission baseline solution was performed by three institutions; the bias in each direction was better than 1 mm excluding the maneuver periods [23]. Today, many agencies in China are able to determine the precise orbit of LEOs,such as the National University of Defense Technology (NDT) [24], Wuhan University [25], and the Xi'an Research Institute of Surveying and Mapping (CHS) [26]. In this study, we used internal consistency checks and inter-agency (NDT and CHS) comparisons to access the accuracy of the GPS-based orbit. We briefly introduce the quality of onboard GPS and BDS2 data. Then, the methods for PROD at NDT and CHS are introduced. Subsequently, the GPS-based, BDS2-based and combined PROD of TH-2 is provided. Finally, conclusions are drawn.
## 5 Conclusions  
This paper established a sample database of bridge maintenance costs over the period 2010-2019, based on the actual engineering data of 268 bridges in coastal and inland areas of China. Two bridge maintenance cost prediction models were developed based on fully-connected ANN and CNN approaches, respectively, and their prediction results were compared. The conclusions of this study are as follows:  
(1) The eight main factors of bridge maintenance costs were evaluated based on the random forest method. The VIM of four indicators (technical condition, maintenance time, GDP growth rate, and highway grade) were greater than 0.2. The VIM values of the bridge age, superstructure, bridge grade, and bridge location were all less than 0.1.  
(2) An outlier detection method based on the isolation forest method was adopted to identify maintenance cost samples that were significantly different from the bridge maintenance costs. As a result, 27 maintenance samples, accounting for 6.8% of the total, were identified as outliers and replaced by the average values of similar data.  
(3) Prediction models were established based on fully-connected ANN and CNN, respectively. The mean absolute error, root mean square error, average relative error, and mean absolute percent error of the fully-connected ANN model were 17.4 Yuan/m\({}^{2}\), 21.2 Yuan/m\({}^{2}\), 13.02%, and 12.59% respectively. The mean absolute error, root mean square error, average relative error, and mean absolute percent error of the CNN model were 12.5 Yuan/m\({}^{2}\), 16.7 Yuan/m\({}^{2}\), 9.45%, and 9.35%, respectively. It can be concluded from the comparative analysis that the prediction accuracy of the CNN model is higher than that of the fully-connected ANN model.
# Challenges Associated with Implementing 5G in Manufacturing
## 3 Use Cases of 5G in Manufacturing
### Supply Chain Unification  
Digitalizing the modern manufacturing unit, introducing a higher degree of automation control and the advent of smart Internet of Things devices is a focal point of the fourth industrial revolution. The introduction of 5G into the manufacturing industry enables supply chains to pivot from a dispersed series of independently managed location networks to an increasingly connected ecosystem of devices that share knowledge across the network in pseudo real-time. As manufacturing units become more capable with the integration of 5G into smart devices, the manufacturing industry can leverage the advanced communication capabilities between devices, unifying the production process. 5G is rapidly becoming the connection of choice, serving as the intermediary medium of data transfer between every component of the manufacturing ecosystem [17].
## 3 Results and Discussion
### Image Segmentation Results
#### 3.3.3 Comparative Analysis  
This study compared seven popular image segmentation methods, including three fully supervised and four semi-supervised methods, using the datasets Synthetic20000,  
Figure 8: Comparison of segmentation model test results trained with consistency thresholds set at 28 (high consistency) and 18 (low consistency).The red box shows the detailed differences between different segmented images.  
train420, valid90, and test90. The fully supervised methods were trained on train420, while the semi-supervised methods, using a U-Net as the backbone network, were trained on Synthetic20000, train420, and valid90, with all the methods subsequently tested on test90. The evaluation metrics included IoU, PA, and porosity difference. As shown in Table 4, among the fully supervised methods, SegNet performed relatively poorly, with both IoU and PA around 0.95 and a porosity difference of 0.0968, indicating a deficiency in pore detection. In contrast, U-Net++ performed the best, achieving an IoU of 0.9806 and a PA of 0.9699, though there was still room for improvement in the porosity difference. Among the semi-supervised methods, including the one proposed in this study, all the metrics surpassed those of U-Net++, demonstrating that semi-supervised models trained on extended datasets could significantly enhance performance. Notably, the Mean Teacher (MN) method excelled, with IoU and PA both exceeding 0.98 and a porosity difference reduced to 0.0079, indicating high accuracy. However, the method proposed in this study, incorporating adaptive weight adjustment, ensemble strategies, and the optimization of the semi-supervised process, ultimately surpassed MN, achieving superior performance.