esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
It was shown that the random forest algorithm outperformed the decision tree and artificial neural network algorithms. In this study, the performances of the equations from the literature developed for aramid fiber-reinforced polymer (AFRP)-wrapped circular columns were compared to the predictions of the machine learning (ML) algorithms. A dataset of 225 samples with circular cross-sections was collected from the literature [3; 21; 22; 24; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49] which can be found in Supplementary Materials (under the GitHub link [[https://github.com/ccakiroglu/AFRP](https://github.com/ccakiroglu/AFRP)]([https://github.com/ccakiroglu/AFRP](https://github.com/ccakiroglu/AFRP)) (accessed on 27 October 2023)). A 159-sample subset of this dataset was used for the training of ML models for the prediction of ultimate strain. The experimental datasets were further enhanced using the tabular generative adversarial network (TGAN) methodology. It was shown that the currently available predictive equations in the literature can be significantly inaccurate when applied to new datasets. As an alternative approach to predictive equations, ML models were presented as a means of making accurate predictions about structural behavior. The impacts of different input features on the model predictions were quantified using the SHapley Additive exPlanations (SHAP) method, and the input features were ranked according to their impacts on the model predictions. Furthermore, the changes in the model predictions, with respect to the most impactful input features, were visualized using individual conditional expectation (ICE) plots. Due to the favorable mechanical properties of aramid fiber-reinforced polymers (AFRP), these materials are finding applications in various areas of structural engineering. Therefore, the availability of reliable and accurate prediction methods for the mechanical properties of AFRP-confined concrete structures is crucial. The current study presents a novel approach to predicting structural behavior, with the help of explainable ensemble ML models. The effect of different parameters on the mechanical behavior of AFRP-confined concrete is visualized using the SHAP algorithm. Furthermore, an alternative approach is proposed for generating large synthetic datasets, which could be utilized to train predictive models. The study is distinctive in that it aims to prepare big datasets, with the help of tabular generative adversarial networks, for applications in the prediction of the mechanical properties of composite materials. In the case of FRP wraps, the identification of mechanical properties necessitates costly experimental procedures or finite element modelling. Therefore, generative machine learning techniques present a valuable alternative to these traditional techniques for generating large datasets. The availability of large datasets is vital for the application of ML techniques. Therefore, the study makes a significant contribution towards the adoption of ML techniques in modelling engineering systems.
## 2 Study Area and Data
### Preprocessing of Time-Series InSAR Data  
Generally, regions with larger surface deformations are more susceptible to geological hazards. However, since InSAR technology only monitors deformation along the Line of Sight (LOS), and geological disasters typically involve multi-directional and multi-dimensional deformation processes, monitoring with a single orbit's deformation data has certain limitations in geological hazard assessment. Therefore, this study incorporates surface deformation data from both ascending and descending orbit time-series InSAR as two independent evaluation factors and integrates them into the geological hazard risk assessment model.  
This study employs the SBAS-InSAR technique. By restricting the temporal and spatial baselines between images, SAR images covering the same area are divided into several subsets, thereby effectively enhancing the quality and correlation of the interferograms [23]. The phase information after interferometry is processed to remove the flat ground phase and topographic phase using orbital information and SRTM DEM, and the differential interferometric phase information is obtained. High coherence points are extracted for phase unwrapping. To address the rank deficiency issue during pixel unwrapping, this paper converts the phase solution into the calculation of the phase change rate and uses the singular value decomposition method for solving. After obtaining the phase change in each time series, the phase time series is calculated and restored. Based on the polynomial deformation model, the linear deformation signal is solved, and the elevation error caused by inaccurate DEM is modeled and solved. The differential interferometric phase is then unwrapped after subtracting the linear deformation signal and elevation error. Due to the presence of noise and atmospheric phase in the phase after the above processing, to effectively separate the nonlinear deformation phase, this paper, based on the differences in spatiotemporal characteristics between the two, filters out the noise phase through spatial low-pass filtering and the atmospheric phase through temporal high-pass filtering, thereby obtaining the nonlinear deformation information within the study area. Adding the nonlinear deformation to the linear deformation yields the complete deformation time series [24; 25].  
Based on the 23 radar interferograms with a 20 m resolution obtained from the Sentinel-1A satellite system between January and December 2021, the SBAS-InSAR technique was used to derive surface deformation monitoring data for Xiaojin County. The average deformation rates calculated by SBAS-InSAR are shown in Figure 2 (positive values indicate displacement of the observed target toward the radar line of sight, while negative values indicate displacement away from the radar line of sight). As shown in Figure 2, the deformation rate range for the ascending orbit is [\(-\)159.1 mm/a, 69.9 mm/a], and for the descending orbit, the deformation rate range is [\(-\)198.7 mm/a, 49.9 mm/a].
# HVdc Circuit Breakers: Prospects and Challenges
## 4 HVdc Circuit Breaker Topologies
### Solid State HVdc Breakers
#### 4.2.2 Solid State HVdc Breakers Employing Free-Wheeling Diode  
The second variant of SSCB employs a free-wheeling diode in parallel with the IGBT to bypass any reverse voltage impulse and protect the IGBT against the voltage spikes, as shown in Figure 6. A surge arrester is also used to absorb the dissipated heat during the fault. This quick action of dissipation and absorption of generated heat improves CB's reliability.  
During normal operating conditions, IGBT (\(T\)) is on, and load current \(\mathrm{i_{L}}\) passes through it. However, on fault detection at t = 0, the fault current is commutated to D as T turns-off. So, the inductance \(\mathrm{L_{dc}}\) is gradually demagnetized by the \(\mathrm{R_{v}}\), thus the fault current starts reducing. If \(\mathrm{V_{v}}\) expresses the clamping voltage of \(\mathrm{R_{v}}\), the surge voltage across T will be \(\mathrm{V_{dc}}+\mathrm{V_{v}}\). As no power flow through \(T\) after turning off, thus energy absorbed in this breaker during turn-off operation is equal to \(\frac{1}{2}\mathrm{L_{dc}}\mathrm{L_{0}^{2}}\). Thus, the energy \(\mathrm{W_{R}}\) is:  
\[\mathrm{W_{R}}=\frac{1}{2}\mathrm{L_{dc}}\mathrm{L_{0}^{2}} \tag{6}\]  
Small values of \(\mathrm{W_{R}}\) in (6) than (5) suggest the small rating and volume of surge arrestor in the second topology with the reduced suppressed voltage across IGBT [94; 95].  
Several benefits are evolved for solid-state breakers compared to other circuit breaker technologies. The absence of mechanical structures improves the SSCB's response time, resulting in lower turn-off time. Additionally, the higher voltage across the inductor allows the demagnetization process to be performed faster [98]. The fast turn-off method of SSCB reduces the peak current to a limited level compared to other breaker technologies. Thus, the solid-state breaker has a lower power loss for the same grid voltage. Therefore, it may be assumed that an SSCB is an optimal choice due to lower turn-off time, reduced component cost, and lower peak current irrespective of cable length and grid voltage.  
However, this is not the case because of one substantial shortcoming; the high on-state losses. It can be concluded from comparison graphs (grid voltage vs. time and grid voltage vs. maximum current) provided in [98; 99] that for low to medium grid voltages, low turn-off time and low peak voltage are not quite enough to counter the power losses during the on-state. However, in high voltage grids, solid-state topology shows an advantage since the power saved per circuit breaker is sufficient to make them more economical than the other circuit breakers, even when the on-state losses are considered.
## 3 Results
### Implementation of Additional Shape Information  
To accurately segment ambiguous prostate regions, additional shape information can be incorporated into DL architectures. [PERSON] et al. (2020) [27] explored the use of boundary landmarks to improve segmentation in 2D TRUS images. They employed automatically extracted boundary landmarks to form a mask reconstruction fused with the decoder's output, aiming to make the final segmentation less prone to low contrast and artifacts across the prostate's full boundary. The encoder part of a modified U-Net extracted anatomical landmarks from the U-Net's bottleneck when the prostate was present in the image. This approach decreased HD by an average of 2.04 mm.  
[PERSON] et al. (2022) [23] aimed to improve the training phase of a traditional U-Net by combining boundary information using an active shape model. A boundary map was generated by processing the ground truth segmentation with morphological operations and key point initialization to eliminate boundary irregularities. This boundary map was used to evaluate the intermediate feature maps/segmentations as a deep supervision strategy. The DL architecture was trained with a loss function considering the discrepancy between the boundary map and the intermediate segmentation outcomes, increasing the DSC from 0.90 to 0.93.  
[PERSON] et al. (2019) [16] explored using an MRI-based statistical shape model (SSM) to improve the segmentation results of a U-Net ensemble trained on TRUS data. By matching an SSM containing the MRI information of the expected prostate shape, the HD was decreased by approximately 1 mm.
## 3 Analysis of Spatial Transformation and Proposal of Critical Distance
The average value of the characteristic parameters of the regression time is \(-4.2\). Therefore, the time window range of instruments could be obtained, as shown in Figure 7. When the offset distance was less than 10 m, the electromagnetic induction signal received by the vehicle-mounted detection system needed to be corrected in the time window of the vehicle-mounted instruments. When the offset distance was between 10 m to 300 m, the received electromagnetic induction signal needed to be corrected in part of the time window, that is, it did not need to be corrected in the early stage and needed to be corrected in the middle stage. When the offset distance was greater than 300 m, the received electromagnetic induction signal did not need to be corrected in the whole of the time window of the vehicle-mounted instruments. Therefore, the offset distance that the transient electromagnetic spatial transformation did not affect was defined as the critical distance without considering the topographic characteristics of the tunnel mountain and the influence of the operational tunnel section. Obviously, it could be judged that the critical distance was L = 300 m, through the above analysis. When the offset distance exceeded 300 m, the vehicle-mounted transient electromagnetic spatial transformation process could be ignored. To sum up, there is a transient electromagnetic spatial transformation in the running state of the vehicle-mounted detection system in operational tunnels, that is, from the 3/4 space state to the full space state, and, then, from the full space state to the 3/4 space state. By analyzing the offset distance, the closer the vehicle-mounted detection system is to the entrance and exit of the tunnel, the closer the electromagnetic induction signal received is to the 3/4 space state. The farther the vehicle-mounted detection system is to the entrance and exit of the tunnel, the closer the electromagnetic induction signal received is to the full space state. Based on the proposed parameters of vehicle-mounted transient electromagnetic instruments, the critical distance that the space does not affect in the transient electromagnetic spatial transformation process is determined to be 300 m.
## 2 Corpora
### mPower
We decided to limit the number of recordings used in this experiment to ten per participant in order to eliminate possible imbalance in demographic representation and to simplify our experiments. The exact subsets of the mPower corpora we created are published alongside this article to allow reproducibility ([[https://github.com/aozbolt/thingsToConsider](https://github.com/aozbolt/thingsToConsider)]([https://github.com/aozbolt/thingsToConsider](https://github.com/aozbolt/thingsToConsider)), last accessed on 9 January 2022). Lastly, we created four additional subsets \(S_{A4}\)-\(S_{A16}\), as described in Section 3.4.3, in which the age difference between classes ranged between 4 and 16 years to evaluate the influence of age difference in automatic detection accuracy. Figure 4 contains a flowchart representing the subset selection process and Table 7 summarizes all of the employed subsets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Total**} \\ \hline  & **PD** & **HC** & **PD** & **HC** & **PD** & **HC** \\ \hline \# of subjects & 78 & 78 & 257 & 257 & 335 & 335 \\ \hline Age, average (std) & 54.7 (10.3) & 54.2 (10.2) & 56.6 (10.2) & 56.3 (10.6) & 56.1 (10.3) & 55.8 (10.5) \\ \hline Age range & 34–86 & 30–86 & 32–83 & 30–81 & 32–86 & 30–86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Age demographics of subset \(S_{R0}\). Users were randomly selected from twelve age bins to create matching distributions of age, gender, and PD diagnosis. Five similar subsets (\(S_{R1\text{-}5}\)) were also created. \begin{table}
\begin{tabular}{l l} \hline \hline
**Subset Name** & **Description** \\ \hline  & PwPD have professional diagnosis, participated in the voice recording portion of the survey, never had brain surgery/deep brain stimulation, and have not smoked within the past ten years or for a total of over twenty years. \\  & MPD = 864, FPD = 484. MC = 2740, FC = 603 \\ \hline \multirow{2}{*}{\(S_{R}\)} & Same as \(S\), except users under 30 are discarded. MPD = 454, FPD = 260. \\  & MC = 805, and FC = 132. \\ \hline \multirow{2}{*}{\(S_{R0-5}\)} & Users selected from \(S_{R}\) to create six balanced distributions in age, sex, and condition, as seen in Figure 3. In each of the 6, MPD = MC = 257, and FPD = FC = 78. \\ \hline \multirow{2}{*}{\(S_{A4-16}\)} & Users from \(S_{R0}\), with some replaced by members of \(S_{R}\) to create four imbalanced distributions in age as described in Section 3.4.3. There still exists a balance in sex and condition.  
\end{table} 
Table 7: List of subsets created from mPower corpus as described in Figure 4. MPD and FPD indicate male and female participants with PD, respectively. MC and FC indicate male and female controls, respectively.
## 4 Time-Frequency Analysis for Feature Extraction
### Fractional Wavelet Transform (FRWT)  
The FRWT is an evolution of the standard WT, merging WT's multiresolution analysis advantages with the fractional domain representation similar to the FRFT. Essential aspects of the FRWT include multiresolution analysis akin to the classical WT, permitting the examination of both high and low frequencies. Additionally, the Fractional Domain Representation allows the FRWT to assess non-stationary signals with spectral characteristics that change over time. By integrating the FRFT with the conventional WT, the FRWT concurrently portrays signals in both the time and fractional Fourier domains.
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 6 Evaluation
The intersection of both masks is highlighted green, while the false positive predictions of the network are highlighted in yellow and the false negatives in red. The system correctly found all 50 defects. No defects were missed. Only two false positives were recorded, where the system predicted a defect where none was present on the manually created mask. Both false positives were close to each other. Figure 9 shows that they are a result of the same underlying image texture. The texture features small defects that are below the tolerance threshold. As such, they should not be detected by the system. The complete evaluation process is summarized in Figure 10. Further testing on a larger test set is required to confirm the above-mentioned measurements. Given the risk associated with false negatives, trading off a lower false positive rate with a higher false negative rate is inadvisable. Due to the limited test-set size, the given results serve only as preliminary insights. It is possible that the limited test-set does not capture the full variance of defects encountered in production. The measured accuracy during later deployment could therefore be lower. Averaged over 25 runs, the system takes 153 ms to process one 1395 \(\times\) 3335 px surface scan. This allows later scalability of the system to more or bigger scans during one scan period. Figure 11 shows an exemplary output of the network for an input image with a defect. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**TP** & **FP** & **FN** & **\(F_{1}\)** \\ \hline
50 & 2 & 0 & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: True positive, false positive and false negative rate of the classification system on the test set and a combined F\({}_{1}\) score for the three rates. Figure 10: A flowchart depicting the evaluation procedure. The test data flows through a pipeline, similar to the inference pipeline, but without the post-processing steps. The predicted segmentations are then compared with manually created label masks, according to the two mentioned metrics. Figure 9: The two false negatives (yellow) that were predicted by the system. The underlying scan exhibits small defects, but below the tolerance threshold.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### BOM and Work Hours of a Satellite Ground Station System  
From the design and development of the satellite ground station, this section details the specifications and prices of the equipment used, as shown in Table 4, to provide an understanding of the budget required to build one satellite ground station. The total budget amount is less than USD 4000, which is significantly less than purchasing commercial equipment.  
To estimate the effort required for developing the satellite ground station, a total of 280 work hours were spent, divided into key phases: design, calibration, hardware assembly and installation, software development, and system validation. The design phase, which included conceptual design and detailed design, required approximately 40 h, focusing on designing the system to meet the predefined requirements. Calibration, involving fine-tuning and testing each subsystem, such as the antenna rotator and signal reception system, took 80 h to ensure that all components operated as designed.  
The hardware assembly and installation phase, which included assembling the antenna, rotator, and feed system, as well as installing the antenna on the building, consumed the most significant effort, requiring 100 h. This task was particularly time-intensive due to the complexity of precisely aligning components and required the expertise of skilled technicians. Software development and customization, tailored to operational needs, accounted for another 40 h, while the testing and validation phase took 20 h to ensure the ground station's signal reception quality and operational stability.  
Overall, the hardware assembly and installation phase demanded the highest effort, requiring iterative testing and adjustments to achieve optimal performance.
# Peak Energy Reduction in Flow Shop including Switch-Off Policies and Battery Storage
## 5 Numerical Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Battery vs. No Battery**} \\ \hline
**Power 10\%** & & & \\  & & & **Battery capacity [\(kW^{*}PTi\)]**\(\rightarrow\) & \\
**Charging speed K** & **10** & **15** & **20** \\
**[\(kW^{*}PTi\)/minute]**\(\downarrow\) & & & \\
**10** & \(-1.59\%\) & \(-1.68\%\) & \(-1.70\%\) \\
**5** & \(-1.99\%\) & \(-1.99\%\) & \(-2.01\%\) \\
**2.5** & \(-2.01\%\) & \(-2.02\%\) & \(-2.03\%\) \\ \hline
**Power 20\%** & & & \\  & & & **Battery capacity [\(kW^{*}PTi\)]**\(\rightarrow\) & \\  & & & \\ Charging speed K & **10** & **15** & **20** \\
**[\(kW^{*}PTi\)/minute]**\(\downarrow\) & & & \\
**10** & \(-1.74\%\) & \(-1.82\%\) & \(-1.84\%\) \\
**5** & \(-2.71\%\) & \(-2.88\%\) & \(-2.97\%\) \\
**2.5** & \(-3.35\%\) & \(-3.55\%\) & \(-3.62\%\) \\ \hline
**Power 30\%** & & & \\  & & & **Battery capacity [\(kW^{*}PTi\)]**\(\rightarrow\) & \\  & & & \\ Charging speed K & **10** & **15** & **20** \\
**[\(kW^{*}PTi\)/minute]**\(\downarrow\) & & & \\
**10** & \(-1.79\%\) & \(-1.86\%\) & \(-1.90\%\) \\
**5** & \(-2.92\%\) & \(-3.08\%\) & \(-3.19\%\) \\
**2.5** & \(-3.99\%\) & \(-4.40\%\) & \(-4.62\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average use of electricity grid. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Exp. No.** & **\% Battery** & **Exp. No.** & **\% Battery** & **Exp. No.** & **\% Battery** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 5: Average utilization of the battery capacity. under 20%. These results are important for the choice of charge rate because it determines the price of the battery and deterioration. From the above results, the following issues can be drawn:  
* The introduction of the battery reduces drastically the energy used over the peak power; moreover, the introduction of the battery leads to a more uniform load of the electricity grid. * The switch-off policy allows to further reduce this part of energy. The introduction of the switch allows to improve this performance with lower battery performance and therefore with lower costs. The UP&D policy performs better when the power fluctuation is higher. The switch-off policy improves the performance when the charging speed is slow. * The charge rate and power fluctuation are the more important factors; moreover, these factors are relevant for the average utilization of the battery. Therefore, the definition of these parameters can affect the deterioration of the battery over the medium horizon.