esa-sceva/satcom-chunk-collection huggingface dataset:
# Dimensional Control of Aircraft Transmission Bodies Using CNC Machines and Neuro-Fuzzy Systems
## 4 Solution to the Problem
### Application of Neuro-Fuzzy Models to Determine Bearing Seat Position Deviation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of the** & & & & **Value** & & \\
**Tested Model** & **Variable** & & & **mm** & & \\ \hline \multirow{4}{*}{Model 1} & \(\text{HPD}_{\text{CMM}}\) & 0.0740 & 0.0750 & 0.0500 & 0.0990 & 0.1020 \\  & \(\text{HPD}_{\text{FUZZY}}\) & 0.0660 & 0.0724 & 0.0384 & 0.0877 & 0.1159 \\  & Model error \(\delta\) & 0.0080 & 0.0026 & 0.0116 & 0.0113 & 0.0139 \\  & Maximum error \(\delta_{\text{max}}\) & & & 0.0139 & & \\ \hline \multirow{4}{*}{Model 2} & \(\text{HPD}_{\text{CMM}}\) & 0.0770 & 0.0690 & 0.1030 & 0.1040 & 0.0770 \\  & \(\text{HPD}_{\text{FUZZY}}\) & 0.0557 & 0.0927 & 0.0936 & 0.0930 & 0.1017 \\  & Model error \(\delta\) & 0.0213 & 0.0237 & 0.0094 & 0.0110 & 0.0247 \\  & Maximum error \(\delta_{\text{max}}\) & & & 0.0247 & & \\ \hline \multirow{4}{*}{Model 3} & \(\text{HPD}_{\text{CMM}}\) & 0.0660 & 0.0340 & 0.0440 & 0.1120 & 0.0550 \\  & \(\text{HPD}_{\text{FUZZZY}}\) & 0.0699 & 0.0329 & 0.0625 & 0.1171 & 0.0532 \\  & Model error \(\delta\) & 0.0039 & 0.0011 & 0.0185 & 0.0051 & 0.0018 \\  & Maximum error \(\delta_{\text{max}}\) & & & 0.0185 & & \\ \hline \multirow{4}{*}{Model 4} & \(\text{HPD}_{\text{CMM}}\) & 0.0740 & 0.1090 & 0.0470 & 0.0340 & 0.0640 \\  & \(\text{HPD}_{\text{FUZZZY}}\) & 0.0934 & 0.0832 & 0.0407 & 0.0523 & 0.0545 \\  & Model error \(\delta\) & 0.0194 & 0.0258 & 0.0063 & 0.0183 & 0.0095 \\  & Maximum error \(\delta_{\text{max}}\) & & & 0.0258 & & \\ \hline \multirow{4}{*}{Model 5} & \(\text{HPD}_{\text{CMM}}\) & 0.0800 & 0.1140 & 0.0340 & 0.0390 & 0.0620 \\  & \(\text{HPD}_{\text{FUZZZY}}\) & 0.0727 & 0.0865 & 0.0142 & 0.0574 & 0.0723 \\ \cline{1-1}  & Model error \(\delta\) & 0.0073 & 0.0275 & 0.0198 & 0.0184 & 0.0103 \\ \cline{1-1}  & Maximum error \(\delta_{\text{max}}\) & & & 0.0275 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results of neuro-fuzzy models.
## 6 Discussion  
None of the anticipated problems during the demonstration mission turned out to be showstoppers. For instance, we opted to use the laser without the dedicated plate for passive cooling and did not implement any active temperature control or ventilator. The laser provider specifies an operational temperature range from 15 to 30 \({}^{\circ}\)C, which raised concerns about potential conflicts with local temperatures at the test site, where mornings can be rather chilly and afternoons very hot. However, we did not encounter any issues related to the laser being too cold or becoming too hot. In the ARCHES mission scenarios, the time needed to move between different measurement locations was sufficient for the laser to deliver several hundred laser pulses without additional cooling.  
The housing and envelope of the LIBS module were chosen as constraints from the rover and were kept to facilitate the handling of the module by the rover. However, during the field test on Mt. Etna, this design revealed drawbacks when strong winds imposed additional loads on the rover's arm, making it challenging to handle. Since the hardware of the LIBS instrument only occupies a fraction of the current modular box, future designs can be more compact, lightweight, and less susceptible to winds.  
Furthermore, since the visual context of the measured target is essential for the scientific analysis and interpretation of the spectral data, a camera is typically an important part of a LIBS payload. Unfortunately, the integrated camera in this first version of the ARCHES LIBS module was faulty and could not be used in the demonstration mission. A functional camera will be integrated in the next upgrade of the LIBS modular payload box.  
The spectrometer we utilized in this initial version of the LIBS module was originally configured for Raman spectroscopy with a 50 \(\upmu\)m slit to achieve higher throughput. An upgrade could involve using a spectrometer with a smaller slit to improve the spectral resolution for LIBS, which would also allow for the use of a larger 200 \(\upmu\)m fiber. Nevertheless, the selected spectrometer and its spectral range proved sufficient for demonstrating the functionality of the LIBS module for the DLR LRU2, enabling the collection of meaningful LIBS data from the field. However, to maximize the scientific value, an extended wavelength range that covers additional major elements such as magnesium, iron, and titanium, as well as more minor and trace elements, would be beneficial. In an upgraded version, we will explore options to increase the spectral range, either at the cost of resolution or by adding an additional spectrometer.
## 4 Engineering Application
### The Hybrid Model Verification  
#### 4.2.1 Factor Selection for the LSTM Model  
When using the LSTM as a dam displacement prediction method, the input factors consider three main factors and residual, as shown in (11). The output is the displacement measured with sensors.  
\[\delta=\delta_{H}(t)+\delta_{T}(t)+\delta_{\theta}(t)+\varepsilon \tag{11}\]  
where \(\delta\) represents the predicted displacement of the dam, \(\delta_{H}(t)\) represents the hydraulic component, \(\delta_{T}(t)\) represents the thermal component, \(\delta_{\theta}(t)\) means the aging component, and \(\varepsilon\) is the residual.  
Figure 6: Actual view of the dam.  
Figure 7: Layout of sensors.  
The hydraulic component of a gravity dam can be depicted using a cubic polynomial function of the reservoir water level as \(X_{1}=H-H_{0}\), \(X_{2}=H^{2}-H_{0}^{2}\), and \(X_{3}=H^{3}-H_{0}^{3}\), where \(H_{0}\) denotes the data on the initial monitoring day, and \(H\) denotes the data on the current monitoring day.  
When the temperature monitoring period is not continuous, or the measured value is not available, the thermal component can be expressed with a combination of harmonic functions. In this paper, multiperiodic harmonic factors are chosen to represent the thermal component:  
\[\begin{array}{l}X_{4}=sin\frac{\pi t}{365}-sin\frac{\pi t_{0}}{365},X_{5}= cos\frac{\pi t}{365}-cos\frac{\pi t_{0}}{365},\\ X_{6}=sin\frac{2\pi t}{365}-sin\frac{2\pi t_{0}}{365},X_{7}=cos\frac{2\pi t}{365 }-cos\frac{2\pi t_{0}}{365}\end{array} \tag{12}\]  
where \(t_{0}\) and \(t\) are the same as \(H_{0}\) and \(H\), respectively.  
The irreversible component reflects the plastic deformation of the dam in a certain direction with time. It can be approximated with a polynomial function containing four types of functions. The four factors chosen are as follows:  
\[X_{8}=\theta-\theta_{0},X_{9}=ln\theta-ln\theta_{0},X_{10}=\frac{\theta-\theta _{0}}{\theta-\theta_{0}+1},X_{11}=1-e^{-(\theta-\theta_{0})} \tag{13}\]  
where \(\theta_{0}\) is \(t_{0}/100\), and \(\theta\) is t/100.  
In summary, the statistical model for the horizontal displacement of the dam is shown in (14).  
\[\begin{array}{l}\delta=a_{0}+\sum\limits_{i=1}a_{i}\big{(}H^{i}-H_{0}^{i} \big{)}+\sum\limits_{i=1}^{2}\Big{[}b_{1i}\Big{(}\sin\frac{\pi it}{365}-\sin \frac{\pi it_{0}}{365}\Big{)}+b_{2i}\Big{(}\cos\frac{\pi it}{365}-\cos\frac{ \pi it_{0}}{365}\Big{)}\Big{]}\\ +c_{1}(\theta-\theta_{0})+c_{2}(\ln\theta-\ln\theta_{0})+c_{3}\frac{\theta- \theta_{0}}{\theta-\theta_{0}+1}+c_{4}\big{(}1-e^{-(\theta-\theta_{0})}\big{)} \end{array} \tag{14}\]
# Methane Production Potential from Apple Pomace, Cabbage Leaves, Pumpkin Residue and Walnut Husks
## 3 Results
### Production of Methane  
The tested substrates differed in terms of methane production (Table 2). During the 30 days of the experiment, the highest SMY (297.81 \(\pm\) 0.65 NL kgvs\({}^{-1}\)) was observed in the case of cabbage leaves, while the lowest SMY (131.07 \(\pm\) 1.30 NL kgvs\({}^{-1}\)) was obtained from walnut husks. The daily methane production followed similar patterns for all the substrates (Figure 2). The first peak in the daily production was observed on day 1 for all the studied wastes except the walnut husks. The cabbage leaves and apple pomace produced ca. 17 NL kgvs\({}^{-1}\) d\({}^{-1}\) while the pumpkin residue peak of daily production reached only 5 NL kgvs\({}^{-1}\) d\({}^{-1}\). The next significant peak in daily production was detected on day 10 when all the substrates reached the maximum daily production of methane. The highest methane production on day 10 was observed for pumpkin residue (30.18 NL kgvs\({}^{-1}\) d\({}^{-1}\)), followed by a rapid decline to 0.89 NL kgvs\({}^{-1}\) d\({}^{-1}\) on day 16, from which low stable methane production was noted. A similar pattern with a rapid decrease in daily methane production from 26.83 NL kgvs\({}^{-1}\) d\({}^{-1}\) on day 10 to 1.08 NL kgvs\({}^{-1}\) d\({}^{-1}\) on day 18 was found for apple pomace. In contrast, three peaks of daily methane production appeared during the AD of cabbage leaves. The first two peaks were detected on day 1 and day 10 with the third one observed on day 14, followed by a slow decline to 2.09 NL kgvs\({}^{-1}\) d\({}^{-1}\) on day 23 and stable negligible production until day 30. Even though the maximum daily production for cabbage leaves on day 10 was lower than production for pumpkin or apple residues, the third peak and slower decline resulted in the highest cumulative methane production from cabbage leaves. The daily methane production of walnut husks followed a different pattern with a very slow increase in the first 9 days followed by the smallest peak \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Residues**} & \multirow{2}{*}{**Methane Production**} & \multicolumn{2}{c}{**Maximum of Daily**} & \multirow{2}{*}{**Lag Phase**} \\  & & & **Methane Production** & \\ \hline Cabbage leaves & 297.81 \(\pm\) 0.65 & 22.05 \(\pm\) 2.56 & 4.31 \(\pm\) 0.68 \\ Apple pomace & 232.20 \(\pm\) 6.88 & 26.83 \(\pm\) 7.00 & 4.18 \(\pm\) 0.11 \\ Pumpkin residue & 199.18 \(\pm\) 12.45 & 30.18 \(\pm\) 3.04 & 4.01 \(\pm\) 0.13 \\ Walnut husks & 131.07 \(\pm\) 1.30 & 12.98 \(\pm\) 1.38 & 3.87 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methane production (NL kgvs\({}^{-1}\)) and lag phase (days) of studied residues during 30 days of the experiment.
## 6 AOG Downtime Reduction and Implementation Results
### Holt-Winters Model
#### Logistic Regression
Logistic Regression  
- Parameters Used: Aircraft type, part failure rate, and maintenance compliance. - Parameter Selection: These variables were chosen due to their theoretical relevance in predicting AOG incidents. They represent key operational and maintenance factors influencing downtime. - Performance Metrics: ROC-AUC: 48% (indicating poor classification performance). - Scientific Analysis: Logistic regression performed poorly due to class imbalance in the dataset, with a high number of non-AOG events dominating AOG incidents. The model primarily identified non-AOG incidents with high accuracy but failed to generalize for AOG classification. Its low ROC-AUC suggests that the model does not effectively discriminate between classes, necessitating rebalancing techniques like oversampling or advanced ensemble methods.
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 4 Comparative Experiment on Deep Learning Model Performance
### Experiment Results  
Consequently, the results of this investigation with AP values are compared for the test dataset independently across each of the four sections, as illustrated in Figure 16, and subsequently compare the absolute AP values. In Section 1, case ORG and case TRANS yield AP values of 0.985 and 0.956, respectively. While the AP value of case ORG is 0.029 higher, the absolute value itself underscores that both cases exhibit a high level of OD performance. For Section 2, case ORG and case TRANS register AP values of 0.951 and 0.917, respectively, signaling a comparable level of OD performance. Case ORG's AP value outperforms by 0.034. In this section, both cases can be attributed to having commendable OD performance. However, within Section 3, the AP values for case ORG and case TRANS stand at 0.61 and 0.795, respectively. In comparison to Section 2, the disparity in OD performance becomes notably pronounced, driven by the AP value of case TRANS' decreasing by 0.122, and that of case ORG decreasing by 0.341. Moving to Section 4, the contrast between case ORG and case TRANS becomes substantially more pronounced, reflecting AP values of 0.069 for case ORG and 0.952 for case TRANS. In comparison to Section 3, the AP value of case TRANS is actually enhanced by 0.157, whereas case ORG experiences a significant drop of 0.541. Given case ORG's AP value, it becomes evident that detecting vehicles within distant Section 4 is particularly challenging due to the vehicles' diminutive size.  
Figure 16: Evaluation of OD performances of trained deep learning models on test dataset using AP metric for both ORG and TRANS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Case** & **Dataset Type** & **Experiment Section** & **Number of Images** & **Number of Car Objects** \\ \hline \multirow{4}{*}{case ORG} & train & - & 421 & 1387 \\ \cline{2-5}  & & Section 1 & 58 & 111 \\  & test & Section 2 & 55 & 87 \\  & test & Section 3 & 63 & 83 \\  & & Section 4 & 70 & 92 \\ \cline{2-5}  & & total & 106 & 373 \\ \hline \multirow{4}{*}{case TRANS} & train & - & 421 & 1387 \\ \cline{2-5}  & & Section 1 & 59 & 104 \\ \cline{1-1}  & test & Section 2 & 63 & 91 \\ \cline{1-1}  & test & Section 3 & 62 & 83 \\ \cline{1-1}  & & Section 4 & 67 & 95 \\ \cline{1-1} \cline{2-5}  & & total & 106 & 373 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Allocation status of image and object data into distance sections.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm
### Minimum-Energy-Based Imaging Algorithm  
Based on Equation (8), this part proposes a minimum-energy-based algorithm that mainly includes five parts: range compression, observation vector extraction, sensing matrix construction, interpolation for coarse observation, and minimum-energy-based estimation. The procedure is shown in Figure 6, and each step is introduced as follows.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
###### Abstract  
In order to reveal the heat transfer performance of nanofluids in solar collectors, the thermal conductivity and dynamic viscosity of five kinds of graphene oxide nanofluids, with a mass fraction of 0.002% to 0.01%, were studied in the temperature range of 25-50 \({}^{\circ}\)C. To ensure the dispersion and stability of the prepared nanofluids, UV-Vis absorption spectrum, zeta potential and particle size distribution were employed for nanofluid characterization. Agglomeration and sedimentation of the prepared nanofluids after standing for 20 days were observed, showing the good stability of the prepared graphene oxide-water nanofluid. The dynamic viscosity and thermal conductivity were measured. They show that with the increase in temperature, the dynamic viscosity of nanofluids decreases and the thermal conductivity increases. With the increase in mass concentration, the viscosity and thermal conductivity are improved. The highest thermal conductivity increase is obtained when the nanofluid concentration is 0.01% and the temperature is 50 \({}^{\circ}\)C. Finally, and most importantly, considering the inaccuracy of the existing experimental correlations to the predicted values of thermal conductivity, we propose our new mathematical model of correlation and carry out a series of tests to verify its reliability. The experimental correlations with temperature and concentration as independent variables show good agreement and accuracy with the experimental data.  
graphene oxide; nanofluids; viscosity; thermal conductivity; experimental correlation +
Footnote †: journal: Applied Sciences
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 3 Methodology
### Proposed Degradation Indicator Set
Steps 1-6 include the process for removing the irrelevant and weakly correlated features using the symmetric uncertainty feature ordering method, to finally obtain a feature subset \(F^{\prime}\) with strong correlation with the bearing degradation state. \(F^{\prime}\) contains many redundant features that will be deleted in the approximate Markov blanket method in steps 7-14. The predominant feature \(f_{i}\) is selected from feature subset \(F^{\prime}\) and deleted; predominant feature \(f_{i}\) is added to the initial bearing degradation indicator subset. The above process is repeated until feature subset \(F^{\prime}\) is an empty subset. After the first stage of processing obtains a smaller subset of bearing degradation indicators, two subsets of bearing degradation indicator based on the \(MIC\) feature selection method are constructed in the second stage. Calculate the correlation \(MIC(f_{i},f_{j})\) between features, the correlation \(MIC(f_{i},R)\) between features and real RUL values. \(MIC-FF\) refers to the matrix that can measure the correlation between features, whereas \(MIC-FR\) refers to the matrix that can measure the correlation between features and real RUL values. We find the minimum values for each column in the \(MIC-FF\) matrix and combine these minimum values into a set \(min_{FF}=\{min_{FF0},min_{FF1},\cdots,min_{FF24}\}\), where each column corresponds to one feature, and there are 25 columns in this matrix. We find the maximum value as the \(FF-threshold\). Then, we count the number of elements in each column that are less than the threshold value, and combine the numbers into a set \(Num_{FF}=\{Num_{FF0},Num_{FF1},\cdots,Num_{FF24}\}\), and sort the numbers to find the median. If the number of values is greater than the median, the features corresponding to this column are weakly correlated, the more likely to be selected, they will be the elements of the bearings degradation indicator subset \(F_{F-F}\) as strong irrelevance features. Similarly, the \(MIC(f_{i},R)\) values are sorted in descending order to find the median of the \(FR-threshold\) value in the \(MIC-FR\) matrix. Then, the values greater than the threshold, will be the elements of the bearing degradation indicator subset \(F_{F-R}\) as strongly relevant features. In the process of feature extraction for the bearing training sets, we found that the median value of \(MIC(f_{i},R)\) was 0.5 and the maximum value of \(min_{FFi}\) in the set \(min_{FF}\) was 0.1. We set the features' maximum value \(FR-threshold\) to 0.5 and the minimum value \(FF-threshold\) to 0.1. The steps for obtaining the bearing degradation indicator subsets are shown in Algorithm 2. ```
0: Original data set \(D\), original feature set \(F=\{f_{1},f_{2},\cdots,f_{m},R\}\), real RUL value \(R\). 0: Initial bearing degradation indicator subset \(F_{F-F}\), subset \(F_{F-R}\).
## 3 Theoretical Performance of DPMM Using MLE under One LOS and One Reflected Path Environment  
In this section, we extend the ML estimator from single-polarization to dual-polarization and combine it with the Monte Carlo (MC) simulation to evaluate the theoretical performance of DPMM under the one LOS and one reflected path environment. We compare it with those ML estimators designed for the RHCP antenna, e.g., the one-path ML estimator and the two-path MMT algorithm [9] under the multipath environments defined by the received signal cases in Table 2, to find out: (1) in which condition the ML estimator based on a dual-polarization antenna can outperform that based on an RHCP antenna and (2) how much the performance improvement is under this condition.
## 3 Data Analysis and Model Training
### Data Processing  
The rock burst early warning model employs a supervised learning method to predict by constructing a mapping between features and label information. The data set comprises both sample features and sample labels. The Pearson and Spearman correlation coefficients indicate that the three indexes of daily total energy, daily frequency, and daily maximum energy are positively correlated with the risk of impact. Accordingly, the sample characteristics are selected as the model inputs: the maximum microseismic energy of n days before time t, the total daily energy of n days before time t, and the frequency of n days before time t, as illustrated in Figure 5a-c. The sample label is established through the expert evaluation system, based on the evaluation results of the microseismic monitoring data. The results are obtained by impact pressure researchers scoring the impact risk at time t in the historical data.  
(1) Handling Missing Values  
Due to network signal interference during the transmission of microseismic data, missing or abnormal data may occur, which may subsequently be filtered out. Therefore, it is necessary to handle missing values in the collected microseismic data. If the amount of missing data is small and the missing values are randomly distributed, the records with missing values can be deleted. However, in microseismic data, the missing parts often contain critical information, and deleting them may affect the completeness and accuracy of the model. As a result, linear interpolation and spline interpolation methods will be used to fill in the missing values.  
Linear interpolation: If the missing value is assumed to lie between two data points that exhibit linear variation, it can be filled using linear interpolation.  
Figure 4: Correlation charts: (**a**) Pearson correlation charts; (**b**) Spearman charts diagram.
## 1 Introduction
### Projection-Like Geometry of Radon
In the frequency domain, the diffraction transfer function of a perturbation \(f\) is defined as follows:  
\[\dot{\beta}(k,\ \mathbf{n}_{0},\mathbf{n})=\int_{\mathbb{R}^{2}}f(\mathbf{\alpha})e^{-ik.\mathbf{\alpha}.(\mathbf{n}-\mathbf{n}_{0})}d^{2}\mathbf{\alpha} \tag{9}\]  
The equivalent in the space domain is:  
\[g(\beta,\ \mathbf{n}_{0},\mathbf{n})=\int_{\mathbb{R}^{2}}f(\mathbf{\alpha})\delta(\beta- \mathbf{\alpha}.(\mathbf{n}-\mathbf{n}_{0}))d^{2}\mathbf{\alpha} \tag{10}\]  
This relationship is similar to the Radon transform as described in Equation (2). The spatial spectrum of the object \(f\) is projected onto spheres of center \(-k\mathbf{n}_{0}\) and radius \(k\), defined by the equation \(\mathbf{k}=k\ (\mathbf{n}-\mathbf{n}_{0})\). The spatial spectrum is generally divided into two parts. The first half-sphere, defined by \(\mathbf{n}.\mathbf{n}_{0}>0\), corresponds to transmission measurements and gives information on the lower frequencies of the spectrum. The second half-sphere, defined by \(\mathbf{n}.\mathbf{n}_{0}<0\), corresponds to backscattering measurements and gives information on the high frequencies of the spectrum. The geometric locations associated with the Radon kernels are planes of normal \(\theta\) and distant from the geometric center of the image by a distance \(\beta\). The inverse kernels are identified with the high-pass-filtered versions \(|k|\) of the forward kernels, depending on the variable \(\beta\), defined by Equation (4). Adjoint reconstruction occurs when the adjoint kernel \(K^{*}\) is used to reconstruct a pseudo-inverse image. The inversion algorithms are based on the discrete versions of the Radon transform [6; 31; 33]. The Euclidean space \(\mathbb{R}^{2}\) is discretized into pixels. The information following \(\theta\) corresponds to the projections (or signals in time). Discrete versions of the function \(\hat{\delta}\) are obtained by defining a discrete ramp filter. In practice, the reconstruction filter introduces oscillations that disturb the image. Several methods can be used to apodize these effects [31]. In the present work, an alternative filtering approach is studied, based on an wavelet processing method and an analogy between the Radon transform and the wavelet transform [34].