esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This article takes the fault diagnosis of box-type substations as an example to study and improve the fault diagnosis model under the conditions of scarce samples and unbalanced classes, aiming to enhance its prediction accuracy. An improved CTGAN data derivation method based on a self-attention mechanism is proposed, which can take into account the strong correlation between the monitoring data features of box-type substations while deriving and enhancing the samples. It solves the problem of CTGAN being unable to model the dependency relationship between various features. The established SA-CTGAN data derivation model can generate enough samples similar to the original data based on a small amount of data to support the training of the fault diagnosis model. Furthermore, a box-type substation fault diagnosis model based on AlexNet is established to verify the proposed SA-CTGAN data derivation model. Experimental results show that compared with SMOTE and CTGAN data derivation models, the model trained with the dataset generated by the SA-CTGAN model has the best performance. The proposed method can effectively improve the fault diagnosis accuracy, and the diagnosis accuracy reaches 94.81%. Compared with the model trained with the original data, the accuracy is improved by about 11%, effectively solving the problem that the high-performance diagnosis model cannot be trained due to the scarcity of box-type substation fault data.  
Methodology, Y.L., M.Y. and X.G.; Formal analysis, D.Z. and S.W.; Writing--original draft, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Key Research and Development Program of Shaanxi (Program No. 2021 SF-421, 2021 SF-422), the Key Scientific Research Program of Shaanxi Provincial Education Department (Program No. 20 JY047), and the Collaborative Innovation Center of Modern Equipment Green Manufacturing in Shaanxi Province, China.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **Original Dataset** & **SMOTE** & **CTGAN** & **SA-CTGAN** \\ \hline Training set & 89.32\% & 91.47\% & 94.46\% & 94.91\% \\ Validation set & 84.15\% & 90.78\% & 92.55\% & 94.34\% \\ Testing set & 82.94\% & 89.94\% & 92.61\% & 94.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of different input datasets.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is the private engineering data collected from the practical application system.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Signals Intelligence System with Software-Defined Radio
## 4 Discussion  
In this paper, we have shown that signal processing can be performed using cloud computing. It can be used for artificial intelligence network definition, training, and deployment. This method enhances the traditional method of AI development for signal processing. Additionally, compared to the traditional way of using dedicated hardware, this method ensures similar results but in a faster and cheaper way.  
We have shown that by having a good database with training samples and minimal knowledge of signal processing and neural networks, neural network models can be trained and deployed.  
Compared to the standard approach of using neural networks on a local computer, cloud-based neural networks have both advantages and disadvantages. These are presented in Table 6, and they were extracted by comparing the results obtained in this paper with the ones obtained in paper [12].  
In Table 7, a comparison between the results obtained in this paper and the results obtained in [12] using conventional neural networks is listed. This table shows that for BPSK, FM, OOK, and GMSK, the detection rate is similar. For the rest of the modulations, the difference is higher in favor of the conventional neural network developed on a local computer.  
Figure 6: Received BPSK signal test.  
The main further research direction consists of improving the neural network model by using a database with more modulation types that have a wider range of noise and interference. Another future research direction consists of the integration of the trained model into a system that will detect modulation type in real-time and will be able to demodulate it directly without human intervention.
# Remanufacturing of Industrial Electronics: A Case Study from the GCC Region
## 4 GCC Region a Case Study
### Industrial Remanufacturing for Sustainable e-Waste Practices in GCC Region
The percentage of savings ranges from 50% for AC controllers and electronic obsolete boards to as high as 70% for shaft encoders and smoke detectors. With even the lowest percentage of savings being 50%, it is evident that opting for remanufactured equipment can be a cost-effective choice for businesses and organizations. Generally, the cost of remanufacturing falls between 30 and 50% of the price of new EEE products. In conclusion, Table 3 highlights the potential financial advantages of choosing remanufactured equipment over new products. Remanufacturing can offer considerable savings, often resulting in a 50% or more cost reduction. As a result, stakeholders should consider integrating remanufactured equipment into their operations to maximize cost-efficiency and optimize their financial resources. Figure 14: Number of successfully remanufactured products over few years. \begin{table}
\begin{tabular}{c c c c} \hline
**Equipment Name** & **New Unit Price (5)** & **Cost of Remanufacturing (\$)** & **Percentage of Savings (\%)** \\ \hline AC controller & 3000 & 1500 & 50 \\ Electronic obsolete board & 300 & 150 & 50 \\ Controller & 1120 & 538 & 52 \\ Electrical pump & 675 & 270 & 60 \\ Insulation tester & 6000 & 2100 & 65 \\ Flow management unit & 590 & 189 & 68 \\ Power module & 2320 & 720 & 69 \\ Shaft encoder & 1380 & 415 & 70 \\ Smoke detector & 260 & 78 & 70 \\ \hline \end{tabular}
\end{table} 
Table 3: Cost comparison between remanufacturing EoL EEE and purchasing new units, highlighting potential savings.
## 2 Integration of Artificial Intelligence (AI) in Robotic Surgery
### AI for Postoperative Predictions  
One of the most promising applications of AI in robotic surgery is in the prediction of postoperative outcomes. By analyzing data from various sources, including surgical performance metrics and patient histories, AI algorithms can forecast complications such as infection risk, recovery timelines, and potential for recurrence. This capability allows surgeons to customize post-operative care plans based on predictive models, leading to better patient outcomes. A Finnish multicenter study developed a machine learning model to estimate 90-day mortality after radical cystectomy using only preoperative data [6]. The model achieved good accuracy (AUROC 0.73) and identified key risk factors such as ASA class, congestive heart failure, and chronic pulmonary disease. A practical risk table was also created to support clinical decision-making. In the context of robot-assisted radical prostatectomy (RARP), [PERSON] et al. applied machine learning to intraoperative automated performance metrics (APMs) to predict key outcomes, such as hospital length of stay, surgery time, and Foley catheter duration, achieving 87.2% accuracy for the length of stay (LOS) [7]. In a follow-up study, the same group used a deep learning model (DeepSurv) combining APMs and clinicopathological data to predict urinary commence recovery after RARP [8]. The model identified APMs as stronger predictors than clinical features, and surgeons with more efficient APMs had significantly better commence outcomes at 3 and 6 months [8]. Collectively, these studies highlight how AI-driven tools can enhance perioperative decision-making, functional outcome prediction, and individualized patient care in robot-assisted urologic oncology.
## 4 Experiments and Results
### Experimental Results
#### 4.4.2 Comparative Evaluation  
We compared the subjective experience of using HybridFilm to traditional desktop history tool PyMOL Scenes using a 7-point Likert scale, with the results presented in Figure 15. In conjunction with the retrospective interviews, the following provides a summary of the participants' subjective evaluations for each item in the questionnaire.  
Figure 14: The word cloud of participant evaluations, with orange representing positive feedback and blue representing neutral feedback.  
Figure 15: Box plot of 7-point Likert scale scores comparing HybridFilm and PyMOL Scenes. Red asterisks indicate significant differences, and red dots represent outliers.  
To verify whether the differences between HybridFilm and PyMOL were statistically significant, we used the Wilcoxon signed-rank test. Since the comparison was based on evaluation data from the same users, the data are related. Therefore, we chose the Wilcoxon test to compare the differences between the two tools.  
Wilcoxon Signed-Rank Test Calculation Method: We computed the test statistics by ranking the absolute differences between the pairs of scores and then determining the sum of the ranks for the positive and negative differences. The null hypothesis for this test assumes that there is no significant difference between the two tools, while the alternative hypothesis suggests a significant difference.  
\(p\)-value Threshold: We set the significance level at \(\alpha=0.05\). Therefore, if the \(p\)-value is less than 0.05, we reject the null hypothesis, indicating that the two groups exhibit a significant difference.  
In the results shown in Figure 15, we marked the statistical significance, with an asterisk indicating that the \(p\)-value is less than 0.05, signifying that the difference between HybridFilm and PyMOL is statistically significant for that criterion. For example, for Intuitiveness, the Wilcoxon test resulted in a p-value of 0.026, which means HybridFilm performs significantly better than PyMOL in this subject. However, for eye comfort, the \(p\)-value was 0.143, indicating that the difference is not statistically significant. Through Wilcoxon test and \(p\)-value analysis, we can clearly observe that HybridFilm demonstrates significant advantages over PyMOL in the criteria of intuitiveness, immersion, and satisfaction. However, in the other three subjects, the differences between the two tools are minimal.  
Overall, HybridFilm demonstrates comparable performance to traditional desktop history recording tools in the subjects of eye comfort, hand comfort, and fluency while offering superior intuitiveness, immersion, and user satisfaction.
# A Novel MEMS Capacitor with a Side Wall for High Tuning Ranges
###### Abstract  
These days, the market of wireless communications has developed dramatically. Thus, demand increased with respect to wireless communication terminals that consume less power. Consequently, integration technology for passive elements and circuit module development with respect to 'Radio frequency systems' using 'Micro electro mechanical system' is becoming a focused topic. Many researchers focused various perspectives in this field. The development of ultra-low operating voltage elements is required in voltage-tunable MEMS-type capacitors, but development for satisfying demands has not occurred. Among many studies, a capacitor with a high tuning range using carrier beams has been proposed by [PERSON] and others in 2006. In the proposed study, a high tuning range was generated using a carrier beam when the voltage was applied, passing through the pull-in point and exceeding the controllable range. However, these carrier beams are relatively inefficient because these are applied only in one location and not in several air gaps. Therefore, this study dealt with the optimization of a new MEMS capacitor concept, such as using a total of four beams and by applying a side wall instead of a single carrier beam compared to the previous study. Therefore, in this study, further steps were applied to existing research findings to optimize a new concept MEMS capacitor by applying the side wall instead of a carrier beam. The side wall is the same as the one used in the four-beam example.  
capacitor; micro electro mechanical systems (MEMS); tuning range +
Footnote †: journal: Applied Sciences
## 4 Used Platform, Tools, and Research Methodology  
### Gns3  
For the purpose of this work, the GNS3 IP network modeling platform [31] will be used. Why this platform? There are several specific reasons for its use, which are related to specific capabilities that are unique to this platform:  
* Working with disk images of real operating systems of real network devices, such as routers, switches, and more. IP network models built in this way are as close as possible to real IP networks that are built from such network devices.
* Ability to connect the developed models to real IP networks or to the Internet.
* Completely free.  
These capabilities make GNS3 ideal for studying IP networks by creating models of IP networks made from disk images of real network devices. The platform enables the construction of all kinds of IP network models, from very simple to very complex. The platform can also be used in the study of PEDs, and for the study of Cyber-Physical Power Systems (CPPSs).
## 2 Materials and Methods
### Virtual Surgery Scenario for Nuss Procedure  
#### 2.3.1 Definition of Haller Index  
Two conditions must be considered when performing the Nuss procedure. The first condition is that the Haller index (HI) must be an HI value of a normal chest wall. The second condition is that the damage of the Nuss bar and its adjacent tissues must be minimized. In order to satisfy these two conditions, the insertion point and shape of the Nuss bar must be determined before performing the Nuss procedure. In this study, the optimal operation position and shape were found by quantitatively deriving the Haller index values before and after operation for each Nuss bar insertion point based on the actual CT images of the patients.  
Figure 3 shows the pectorus indices. The Haller index is a simple mathematical method for measuring and representing PE with a known pectorus index. It is calculated by the ratio of the maximum transverse diameter (the maximum length inside the thoracic cage, A) measured on the axial CT section of the chest with the largest deformation and the minimum anterior-posterior (AP) distance (minimum distance between the spine and the sternum, B) [10; 11; 12].  
Equation (1) is the Haller index equation before the Nuss operation:  
\[\text{Pre-operation: HI = A/B.} \tag{1}\]  
The Haller index expected after the PE operation is called the ideal chest index (ICI), which is determined by dividing the corrected maximum transverse diameter (**A\({}^{\prime}\)**) by the corrected minimum AP distance (C) [13].  
Figure 3: Illustration of pectorus indices calculated from the computed tomography (CT) axial image with greatest sternum depression: (**a**) description of the Haller index; (**b**) measurement of the Haller index before and after the Nuss operation.  
Equation (2) is the ideal chest index (ICI) equation, which is used instead of the Haller index after the Nuss operation:  
\[\text{Post-operation: ICI}=\textbf{A}^{\prime}/\text{C}. \tag{2}\]  
The severity degree of PE is classified by the HI value. Dr. [PERSON] classified the degree of PE by HI value as is shown in Table 1[14].  
According to the results of many studies that investigated the correlations between HI and PE, the PE operation is required if the HI is equal to or greater than 3.2[11; 15].
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 3 Methodology
### Feature Selection  
We have aggregated features over different length time windows, which contain moments, area, and count information of coke and limestone for each frame. In addition, we have also used feature selection techniques to improve our algorithm's performance and reduce the computation time of machine learning algorithms. There are multiple ways for feature selection, and some of the most common ones for regression model feature selection are forward, backward, and stepwise.  
Forward feature selection [53] begins with no features and then adds the most significant variable. At each subsequent step, it adds features one-by-one into the model until no features are left. In contrast, backward selection [54] begins with all the features and removes the least significant one at each step until none meets the criterion. Finally, the stepwise selection is a mixture of the forward and backward selection techniques. Features  
Figure 15: \(K\)-means segmentation results with different values of \(k\).  
are added as described in the forward feature selection, but backward elimination is used for feature elimination after each step. The assumption is that new variables that are better at explaining the dependent variable and variables that are already included may become redundant.  
The recursive feature elimination technique is an extension of backward elimination [55]. Recursive feature elimination works on a feature ranking system and is a wrapper-type feature selection technique. In recursive feature elimination, a model is trained on the entire set of features, and an importance score is computed for each predictor variable. Then, the least important predictors are removed from the subset, the model is re-trained, and importance scores are computed again. This process is iterated until the desired number remains. These methods are usually computationally very expensive in case of a large number of features. This is in contrast to filter-based feature selections that score each feature by finding its correlation with the dependent variable and selecting those features with the highest or lowest score.  
A significant challenge in feature selection is to decide the number of features to select as it is not known in advance how many features are valid. To find the optimal number of features, cross-validation is used with recursive feature elimination to score different subsets of features and select the best scoring collection of features. We utilized the Decision Tree Regressor as the base estimator for the Recursive Feature Elimination with Cross-Validation technique. The plot in Figure 16 shows the feature selection approach to training data for one of the cross-validation sets. It can be seen that the number of features is added or removed from it iteratively, and a score is calculated at each step. The best negative root mean squared error is achieved with 160 features for that particular training set.
## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### Setting up the Case Study Scenario
#### 5.1.1 Traffic Profile Definition  
High mobility scenarios in the O-RAN represent some of the most challenging environments for resource allocation, where efficient management can significantly affect network performance. Such scenarios inherently introduce dynamic and heterogeneous traffic conditions, making an approach of establishing specific traffic profiles for each environment practical and enlightening.  
Each defined profile represents a realistic high mobility scenario, highlighting the diverse and dynamic nature of traffic patterns observed in modern cellular networks. These profiles allow for a comprehensive evaluation of the adaptability, scalability, and performance of the FDRL system under different conditions:  
1. Dense urban mobility Urban areas often experience the following: * High user density, resulting in frequent reservation of resources. * Dynamic traffic patterns due to fluctuating user activity (e.g., commuting, leisure, emergencies, etc.).
2. Moderate suburban mobility Suburban regions, with lower user density and more stable traffic, offer the opposite scenario to urban areas. They test the system's ability to effectively manage less dynamic but resource-intensive traffic.
* Mobility on highways Highways pose unique challenges:
* Users (e.g., vehicles) move quickly through the coverage areas.
* Requests are less frequent but may require greater resources for real-time applications such as autonomous driving, streaming, and security systems. This scenario evaluates system performance under low but critical demand.
* Event-based mobility Temporary events such as concerts or sports matches introduce the following:
* Extremely high and localized traffic surges.
* Brief but intense demands for radio and MEC resources. This scenario tests the system's ability to allocate resources efficiently during peak loads.
## 5 Discussion  
During the elaboration of this work and especially the experimental part, in order to validate the sensor fusion algorithms along with the upper limb modeling for the successful 3D motion reconstruction, several issues were encountered. Some of them need more attention and further investigation, aiming at the improvement of the upper limb motion tracking.  
The experimental results showed that the proposed custom-made motion capturing system had an acceptable performance, as shown by the estimated error indicators in Tables 4 and 5. Using optical motion capturing systems instead, like Vicon, the position error would be much smaller [52]. Nevertheless, in order to avoid the occlusion problem, which is the main drawback of this kind of systems, multiple cameras should be installed in the rehabilitation center. However, the overall cost may be prohibited for an average healthcare facility.  
The choice of Euler angles representation found to be more understandable to handle and to identify failing results in the implementation that then could be solved. At the current status of research, the range of motions, which were performed during the experimental tests, is not affected by the gimbal lock problem. Thus, the adoption of Euler configuration was finally chosen. A quaternion-based option that is currently examined is the Geometrical Constraints Modeling and Fusion methodology presented in [53, 54], where each IMU placement defines the corresponding segment's reference frame. When the quaternion representation of the sensors' measurements flow is provided, a fusion algorithm accounts for the conversion from quaternion sequences into the axis-angular sequences. In this case, an upper limb model is also needed, in order to express the biomechanical characteristics and contribute to the 3D motion reconstruction.  
Regarding the current state of the presented custom-made sensor system, upper limb movements, such as horizontal abduction that has a range of 130\({}^{\circ}\) towards the human chest or scapula protraction-retraction, has not been examined yet. In this case, actually, an extra DoF has to be included in the upper limb model. The complete model for both upper limbs can be defined, as well, as an extension to this work.