esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 1 Introduction
Isolation forest has some advantages over other approaches as it does not require a normal operation dataset, and it requires less training data than other deep learning strategies. In this work, an anomaly detection model at wind park level is proposed based on the isolation forest methodology. Considering all the aforementioned references, in this work, a CBM strategy based on SCADA data is stated with a five-fold contribution: (i) It is an unsupervised approach, thus there is no need that the specific studied fault happened in the past to train the proposed models; (ii) It is an ensemble that combines the benefits of a WT normal behavior model with a wind park anomaly detection model; (iii) It combines different training and prediction window sizes; (iv) Fault prediction is accomplished months in advance prior to the fault, giving enough time to operators to plan ahead and schedule repairs; and (v) The validity and performance of the proposed methodology is demonstrated (tested) on a dataset covering two years and a half of operation from a real underproduction wind park composed by 18 WTs. The rest of the article is organized as follows. Section 2 presents the available SCADA data and work order logs used in this work. Next, Section 3 states the proposed ensemble methodology for WT main bearing fault diagnosis, including a comprehensive description of the single models and their indicators. Section 4 presents the results and their discussion to interpret and describe the significance of the ensemble method in comparison to the single models by themselves. Finally, in Section 5, conclusions are drawn, and future work is proposed.
## 3 Experimental Study
### Experimental Results and Discussion
#### 3.2.4 Comparison with Related Work  
The C-MAPSS dataset is a popular public dataset that has produced numerous state-of-the-art results in prognostic research. This paper proposes a new RUL prediction approach using a change-point-detection-based target labeling and feature engineering. We compared the prognostic performance of the proposed method with the studies that used the same dataset and similar prognostic concepts. The studies shown in Table 9 used engine-specific RUL labels determined with several methodologies, such as LSTM and a cumulative sum control chart. Also, most of the studies using engine-specific RUL labels made predictions on some proportion of the training dataset instead of using the test data set, as mentioned earlier. The \"Test Data\" column in the comparison below refers to the type of dataset used in the testing phase. \"Test\" indicates that the test dataset was used for testing, while \"Training\" indicates that a specific portion of the training dataset was used for testing.  
The test set of FD001 was terminated at an arbitrary operation cycle, and therefore, a few studies, which were based on the targets specific to the engine, used only the test set in the RUL prediction. The RMSE in [39] was the lowest because a part of the training data set, which had all the sensor values from the start to the failure time, was used as the test dataset. Despite [43]'s method yielding a lower score, our proposed method demonstrated the best outcome, as evidenced by the lowest RMSE value among the studies that used only the test dataset for RUL prediction.  
Figure 18: RUL predictions for the 100 testing engines (sorted with increasing RUL): (**a**) without difference feature; (**b**) with difference feature.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Authors** & **Approach** & **RMSE** & **Score** & **Test Data** \\ \hline [PERSON] et al. [71] & Bayesian neural network (BNN) & 17.92 & N/A & Test \\ [PERSON] and [PERSON] [43] & LSTM & 17.15 & 392 & Test \\ [PERSON] et al. [39] & Dual-task LSTM & 12.29 & N/A & Training \\ Proposed approach & 1D-CNN-LSTM with feat. eng. & 16.1 & 437.2 & Test \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison of the proposed method and the related papers on the C-MAPSS dataset.
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 2 State of the Art: AI Algorithms Applied or Applicable in the Global Agrifood Industry
### K-Means Clustering: An Unsupervised Learning Algorithm for Data Partitioning  
Within the agricultural sector, k-means clustering is extensively used to delineate homogeneous zones across expansive fields, which may exhibit disparities in soil characteristics, moisture levels, or nutrient requirements. This analytical method facilitates the adoption of targeted management practices, such as precision irrigation and tailored fertilization strategies. By grouping regions with similar attributes, farmers can optimize resource allocation, reduce waste, and subsequently enhance crop yields.  
Recent advancements have broadened the scope of k-means clustering applications within agriculture. For example, [PERSON] et al. [38] used k-means clustering to monitor canopy height uniformity, a critical indicator of crop health that significantly improves growth prediction and management accuracy, especially in large-scale field settings. Thisapplication underscores the algorithm's versatility and its capacity to contribute to comprehensive crop health assessments. Thereby, it supports more informed and effective agricultural decision-making processes. In addition, the k-means algorithm can categorize soil samples into distinct fertility classes, as mentioned by [PERSON] et al. [39], and in the refinement of agricultural maps using UAV and machine learning methods, as explored by [PERSON] et al. [40]. Their study, conducted in Italy, applied k-means clustering to enhance the classification of crop types from moderate resolution satellite imagery. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes, a setting with climatic and agricultural characteristics similar to viticultural regions in Spain.  
These developments highlight the enduring relevance and adaptability of k-means clustering in addressing the multifaceted challenges of modern agriculture. By enabling precise segmentation and insightful data analysis, k-means clustering continues to support the evolution of sustainable and efficient agricultural practices, thereby playing a crucial role in the advancement of the agrifood industry.
## 3 Results
### Macro Analysis  
Below, both teams' performance is presented in Table 3. The analysis is made under the scope of the mathematical models used in the macro analysis presented in Tables A11 and A12.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Mathematical Model** & **Team Performance** \\ \hline Team A’s Network Density (ND): & The team’s highest ND value was achieved in \(\Sigma_{\text{R}}\). \\ \hline Team B’s Network Density (ND): & Team B achieved a higher ND value in the \(\Sigma\)R than team A did in both \(\Sigma\)R and \(\Sigma\)G. The critical event analysis was very close to 0. \\ \hline Team A’s Network Heterogeneity: & In critical event B2, team A obtained the highest NHI value. This team displayed a lesser degree of cooperation between players in the critical event analysis. \\ \hline Team B’s Network Heterogeneity: & This team presented a great degree of deviance in the number of interactions each player was involved in when in critical moments, more specifically in the L1 moment, where they obtained their highest value of NH. \\ \hline Team A’s Reciprocity: & Regarding critical event analysis, it was observed that, for moments B2, B3, B1, B6, and \(\Sigma_{\text{G}}\) there was no calculation result, which may indicate a limitation in this model for this specific metric. \\ \hline Team A’s in—Assortativity Coefficient Matrix of Values: & The in-AC model values calculated for team A were, for the most part, negative. However, this team also reached the most positive value (in the first half) and also the most negative (\(\Sigma_{\text{G}}\)). \\ \hline Team B’s in—Assortativity Coefficient Matrix of Values & The value calculated for critical event L1 was the lowest in this regard, which means that the players who received the ball the most, did not pass it to the players which passed it the most. The values obtained in the analysis of both halves and of the full game presented the values closest to 0, this team in particular kept their values very close to 0. \\ \hline Team A’s out—Assortativity Coefficient Matrix of values & This team obtained the closest value to the very worst value possible in the \(\Sigma_{\text{R}}\), whereas team B achieved their best value for this model in the second half. Because team A’s value was so close to 0, it ended up as not being relevant to the graphical representation of the mathematical model. \\ \hline Team B’s out—Assortativity Coefficient Matrix of values: & For the out-AC model, team B reached their highest value in the 2 nd half of the game, that is, the players responsible for the most passes were interconnected between each other. However, the same did not occur during event L1. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Team performance according to each mathematical model.  
The majority of the group metrics presented in Table 3 present results very close to 0, which indicate that little connections were found during these events.
# Technology Behavior Model--Impact of Extended Reality on Patient Surgery
## 2 Literature Review
### Virtual Surgery  
Virtual surgery is achieved with virtual reality and mixed reality technologies [12]. Virtual reality is the use of the internet to collect medically relevant image signals and use computers to convert digital information into recognizable commands that are transmittedto display devices to complete the operation of human action commands [13]. Combined with mixed reality technology to establish a 3D image, the original 2D virtual image multilevel superposition, in the space x, y, z plane, marks each coordinate to achieve unique identification [14]. Avoiding the data errors in the model, especially during the second review of missing data, ensures the real data and virtual information have no difference and match [15]. For example, the preoperative virtual surgery for cardiac interventional procedures was obtained from the QpenEar dataset to obtain the original data, combined with the patient CT and NMR scans, to take the actual data to construct a 3D stereo heart model [16]. The model can identify arteries, veins, atria, ventricles, endothelial thickness, nerve distribution, and other aspects [17; 18]. The internal motion and orientation of the organ can be marked by a self-contained optical marker method [19]. The available kinetic and sensor data signals are combined with different colors to differentiate the space of interest.
## 3 EMI NO\({}_{2}\) Retrieval Algorithm  
### Spectral Fitting  
Based on the Beer-Lambert law, the DOAS method is widely used to retrieve trace gases such as O\({}_{3}\), NO\({}_{2}\), SO\({}_{2}\), and HCHO in the UV-VIS spectral range. The spectral fitting in this study is based on optical density fitting [38]:  
\[\tau(\lambda)=-ln[\frac{I(\lambda-\Delta(\lambda))-offset(\lambda)}{I_{0}( \lambda)}]=\sum_{i}\sigma^{\prime}_{i}(\lambda)SCD_{i}+\sum_{p}a_{p}\lambda^{p} \tag{1}\]  
where, \(\tau(\lambda)\) represents the optical density, \(I\) the measured radiance, \(I_{0}\) the measured irradiance, \(\Delta(\lambda)\) the wavelength shift of radiance, \(offset(\lambda)\) the intensity offset, \(\sigma^{\prime}_{i}(\lambda)\) the absorption cross-section (ACS), \(SCD_{i}\) the fitted SCD, \(a_{p}\) the polynomial coefficient and \(p\) the order of polynomial.  
Table 1 lists the fitting parameters used in the EMI NO\({}_{2}\) spectral fitting, and for comparison, the corresponding settings of OMI-NASA (SPv3) and TROPOMI NO\({}_{2}\). The single irradiance data measured on June 12, 2018, was used as the reference spectrum during the spectral fitting. The NO\({}_{2}\) SCDs represent fitting in the wavelength interval of 405-465 nm using a fifth polynomial. The ACS of NO\({}_{2}\)[39][220] K], O\({}_{3}\)[40][223 K], H\({}_{2}\)O vapor [41; 42], H\({}_{2}\)O liquid [43], and O\({}_{4}\)[44] are all degraded to the EMI spectral resolution, and then used in the fitting process. These were made by convolving the cross sections with the slit functions for different cross-track positions. Besides, the Ring effect was also taken into account by including an additional pseudo ACS, which was calculated by convolving the solar spectrum with the Raman cross sections. Intensity offset correction was also applied to the radiance spectrum. In this study, the spectral fitting step was implemented by using QDOAS software developed at the Belgian Institute for Space Aeronomy (BIRA-IASB) [45]. It should be noted that only pixels with small solar zenith angles (<80) and small cloud fractions (<0.2) were taken into consideration.
## 3 Calibration and Verification
### Calibration of the Drag Coefficient  
In this study, the expression of the drag coefficient refers to the empirical formula proposed by [PERSON] [14]:  
\[f_{w}=a\Bigg{(}\frac{v_{w}}{\sqrt{g\hbar_{a}}}\Bigg{)}^{2}, \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Air Intake Well**} & \multicolumn{2}{c}{**Inlet**} & \multicolumn{2}{c}{**Outlet**} \\ \cline{2-4}  & **Section Shape** & \
\begin{tabular}{c} **Section Size** \\ **(Radius/(Height \(\times\)Width))** \\ \end{tabular} & **Section Shape** &
\begin{tabular}{c} **Section Size** \\ **(Radius/(Height \(\times\)Width))** \\ \end{tabular} \\ \hline No. 1 & Circular & 2.6 m & Circular & 2.6 m \\ No. 2 & Half-moon-shaped & 6 m \(\times\) 6 m & Rectangular & 9 m \(\times\) 4 m \\ No. 3 & Half-moon-shaped & 6 m \(\times\) 6 m & Rectangular & 9 m \(\times\) 4 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameters of each air intake well.  
Figure 5: Schematic diagram of the free flow tunnel of the Jinping-I project. Note: AIW is the abbreviation of air intake well.  
where \(g\) is the gravitational acceleration; \(h_{a}\) is the height of the water-free space in the spillway tunnel; \(v_{w}\) is the water velocity; and \(\omega\) is a constant that depends on the data sample. According to [PERSON]'s analysis [PERSON] (1999), \(\omega=0.002\).  
The root mean square (RMS) of the air velocity's relative error inside the air intake wells between the theoretical value and prototype value is regarded as the criterion. When the RMS reaches its minimum, the theoretical results are the closest to the prototype data.  
The RMS of the air velocity's relative error is defined as  
\[RMS=\sqrt{\frac{\sum_{i=1}^{n}\left(v_{actual}-v_{theoretical}\right)^{2}}{n}}, \tag{16}\]  
where \(n\) is the number of samples and \(v_{actual}\) and \(v_{theoretical}\) are the actual and the theoretical air velocities in the air intake well, respectively.  
Based on the prototype data of the Jinping-I project, the trial of constant term \(\omega\) in Equation (15) is carried out. The trial results are shown in Figure 6. When \(\omega=0.0977\), the RMS of the air velocity's relative error obtains the minimum value. Clearly, if the constant term is in accordance with Luo (\(\omega=0.002\)), the relative error between the calculation results and the prototype data is large.  
Equation (15) is obtained by fitting the prototype data. The water head of the early hydraulic structure is relatively small. However, with the increase in the dam height, the water head becomes larger, even reaching up to 220 m in the Jinping-I project. The higher water velocity in the spillway tunnel leads to larger water surface fluctuations, which makes the drag force of the water flow to the air flow increase, so the previous drag coefficient is no longer applicable. The drag coefficient based on the prototype data of the Jinping-I project is expressed as  
\[f_{w}=0.097\Bigg{(}\frac{v_{w}}{\sqrt{gh_{a}}}\Bigg{)}^{2}. \tag{17}\]
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 2 Materials and Methods
### Randla-Spall
#### 2.3.3 CBAM Attention Module  
The Convolutional Block Attention Module is a lightweight attention module that reweights features by capturing interdependencies between channels and spatial dimensions. The introduction of the CBAM module in RandLA-Spall enhances the network's representation and feature extraction capabilities, as illustrated in Figure 10. In the point cloud segmentation task, the introduction of CBAM can improve the model's ability to capture both local and global features, thereby assisting RandLA-Spall in learning the local structure and global contextual information within the point cloud, thus enhancing task effectiveness. Given that this study focuses on segmentation for wall spall, this selective attention mechanism enables the network to learn features that contribute more effectively to the task, thereby enhancing the network's overall segmentation capability.  
Figure 11 shows the components of the CBAM and its overall architecture. The channel attention vector is first applied, followed by reweighting based on the spatial attention map, initiating the reweighting of the initial features [37].  
The formula for the channel attention branch is shown in Equation (13):  
\[M_{c}(F)=\sigma(FC(AugPool(F))+FC(MacPool(F))) \tag{13}\]  
where \(F\in R^{C\times H\times W}\) denotes the input feature map, C denotes the number of channels, H and W denote the spatial dimensions, AvgPool and MaxPool denote the two types of pooling operations, FC is the fully connected layer, and \(\sigma\) is the sigmoid activation function.  
Figure 11: CBAM module structure: (**a**) CAM, (**b**) SAM, and (**c**) CBAM.  
Figure 10: RandLA-Spall residual module.  
The spatial attention branching formula is shown in Equation (14):  
\[M_{s}\big{(}F^{\prime}\big{)}\sigma(Conv\big{[}AugPool\big{(}F^{\prime}\big{)}; \ MaxPool\big{(}F^{\prime}\big{)}\big{]}) \tag{14}\]  
where Conv denotes the convolutional layer, and the series pooling output is used as the input to the layer.  
Finally, the output feature map formula is obtained as in (15):  
\[\left\{\begin{aligned} & F^{\prime}=M_{c}(F)\otimes F,\\ & F^{\prime\prime}=M_{s}(F^{\prime})\otimes F^{\prime}\end{aligned}\right. \tag{15}\]  
In this study, the environmental site, as a civil building, features a complex and diverse background. The introduction of CBAM enhances the network's ability to capture key features at various scales and spatial locations, thereby improving the model's robustness in complex point cloud scenarios. Particularly, when handling data with complex backgrounds and noise, CBAM helps the model better recognize the target object.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Interferometric RO Processing
#### 4.3.5 Effect of the Local Oscillator Frequency Offset  
From Figure 14 (top), it can be noticed that although the Doppler estimates follow the predicted Doppler, they present small frequency jumps every 60 s. These frequency jumps can be observed in all the acquired data, and this particular data segment has been selected to zoom in, as it is representative of the acquired data. The reason for such jumps is that the receiver adjusts the local oscillator (LO) periodically to steer it towards its nominal frequency. These frequency jumps do not have any effect in the iRO technique computed at 1 ms delay, but in the Zenith iRO case, where the reference signal was acquired at a different time and with a slight different local oscillator frequency, this reflects into the phase measurement. As shown in Appendix A the Doppler frequency offset is given by  
\[\Delta f_{\text{d}}=-f_{\text{off}} \tag{26}\]  
for the Zenith iRO case. The SPIR receiver, that we used to record the data, also measures and records the actual reference clock from which the local oscillator is coherently derived. So we have a precise knowledge of the local oscillator frequency at any instant of time. Figure 15 shows corrected Doppler measurements for the same data segment as in the top plot in Figure 14. It can be appreciated that the jumps on the Doppler estimates have been corrected using Equation (26), and that the Doppler frequency evolves more smoothly.  
Figure 14: Doppler observation obtained with the clean-replica open loop and Zenith iRO approaches in two different periods. Both methods have a comparable performance. Top: representative period with high SNR. Jumps in the Doppler estimates every 60 s due to the adjustment of the local oscillator frequency can be observed. Bottom: End period of the experiment with occulting transmitter.
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
## 2 Shaking Table Tests
### RHV Inputs  
The shaking table was accelerated incrementally and the earthquake motion in the horizontal and vertical directions were applied synchronously. The acceleration amplitude of all the horizontal input motions was adjusted to 0.1 g and corresponding vertical acceleration amplitudes were adjusted to 0 g, 0.05 g, 0.1 g, and 0.15 g, respectively. Hence, different RVH that equals to 0, 0.5, 1, and 1.5 are obtained to investigate their influence on the seismic behavior of the soil and buried metro station. For convenience, a strong-motion record of the Loma Prieta, California, earthquake of 18 October 1989 from the PEER  
Figure 4: Photos of the experimental procedure.  
Strong Motion Database [33] was selected as the input motion for both types of the loading mentioned above, whose acceleration time history and corresponding Fourier amplitude spectra, together with target design spectrum of prototype ground concerning the experiment, are shown in Figure 5. It is seen that the acceleration response spectra of the selected 1989 Loma Prieta earthquake matched the target design spectrum well. Table 3 shows the test cases in this study.  
The targeted base motions were converted from prototype to model scale units. The base accelerations recorded on the shaking table were refered to as achieved motions. Figure 6 shows the comparison between targeted base motions and achieved base motions both in horizontal and vertical components, including all test cases in Table 3. For any test case, the whole acceleration time history was consistent and minor differences in amplitude were observed between achieved and targeted base motions. These minor differences were acceptable, in view of shaking table performance and such large model specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Cases** & **Waveform** & **Peak Acceleration (g)** \\ \hline  & & & **Horizontal** & **Vertical** \\ \hline
1 & LP-x0.1 & Loma Prieta & 0.1 & 0 \\
2 & LP-x0.1-z0.05 & Loma Prieta & 0.1 & 0.05 \\
3 & LP-x0.1-z0.1 & Loma Prieta & 0.1 & 0.1 \\
4 & LP-x0.1-z0.15 & Loma Prieta & 0.1 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 3: Test cases for the shaking table tests.  
Figure 5: The details of Loma Prieta ground motion: (**a**) accelerogram; (**b**) spectral acceleration compared with target design spectrum [34].  
Figure 6: _Cont._  
It is worth mentioning that some details of the experimental program, verification of the free-field test, and interpretation of some preliminary results can be found in prior papers [24; 25].