esa-sceva/satcom-chunk-collection huggingface dataset:
# The Electronic Switch of Windings of a Standard BLDC Motor
## 4 Conclusions  
The paper deals with extending the capability of the BLDC motor in terms of its wider operating range with usage of the electronic stator winding switch. Switching the winding schemes dynamically on demand under the operation increases the motor efficiency, makes it more reliable due to increased speed stability/control, and decreases thermal losses. For the demonstration purposes there was a standard 12 pole BLDC motor modified in the way that all windings wiring was made available for the switching purposes. Even though the motor used for the demonstration was a standard one not designed for its application with an electronic winding switch, it is obvious that when using all together, the dynamic range of the motor was significantly expanded. Even switching only four circuits (1-4-5-6) it covers wider range of speeds with lower operating currents than without the use of a switch. This aspect allows saving power during the operation which is important nowadays and also important when the battery is used as a main power source, for instance in the case of UAV applications.  
To confirm the approach, experiments with the modified BLDC motor accompanied with the electronic winding scheme switch were carried out. The results confirmed its better performance with increased efficiency within wider range of speed conditions when compared to a standard BLDC motor. As second aspect, it was confirmed that switching among optimal winding schemes reaches lower inner heating, which is a critical parameter affecting the motor life cycle and thus further improving the overall performance.  
**Author Contributions:** Conceptualization, V.C. and S.T.; methodology, V.C.; validation, V.C. and S.T.; formal analysis, V.C.; investigation, V.C.; resources, K.A.; data curation, V.C.; writing--original draft preparation, V.C. and S.T.; writing--review and editing, J.R. and K.A.; supervision, V.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the ESIF, EU Operational Programme Research, Development and Education and by the Center of Advanced Aerospace Technology (CZ.02.1.01/0.0/0.0/16_019/0000826) carried out at the Faculty of Mechanical Engineering, Czech Technical University in Prague.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud
## 4 Results and Discussion
### Communication
The computing load was balanced well enough in both cases (Figure 5(a),b). Evaluating the wait time, the percentage imbalance was 2.5% of the mean computing time. In contrast, the wait time due to communication imbalance can be unexpectedly long when the communication time is long (Figure 5(a)). The mean wait time due to communication imbalance was 188% of the mean communication time. The default mapping of subdomains to processes led to large data transfer and imbalanced communication when processes on one node needed to send and receive approximately twice the amount of data sent and received by processes running on other nodes. Even processes with the highest mean communication load sometimes needed to wait for others due to perturbations on the network switch, which further increased the wait time. The improved mapping took into account the spatial location and communication pattern of neighboring processes by distributing them among nodes. Thus, the data transferred between nodes reduced because the local communications between the processes within nodes increased. Figure 5(b) shows that improved mapping significantly reduced the communication time, which also decreased the mean wait time by up to 108% of the mean communication time. Thus, improved mapping reduced the communication-to-computation ratio from 0.26 to 0.08.
## 1 Introduction
It can be seen from these literatures that although there exist many studies on the application of TMDs for chatter in various machining processes, few studies have been conducted on applying the TMDs to a thin-walled workpiece. Few studies have been conducted to investigate the influence of attaching the TMDs to the rotating workpiece to suppress the chatter produced during the thin-walled cylindrical turning process. [PERSON] et al. [23] proposed the passive milling chatter control method using a damper composed of multiple masses equally arranged to the thin-walled cylindrical case through a viscoelastic layer. However, they did not focus on the effect of the natural frequencies of the dampers and the mounting arrangement on chatter and the damper had a slightly high mass ratio of 5% for each mass. In our previous study [27], we confirmed that the chatter suppression effect of the TMDs was higher compared with that of the additional masses with much higher natural frequencies than the chatter frequency. However, if the mass of the TMD is large, the centrifugal force of rotation can deform the workpiece owing to the low rigidity. Therefore, the mass of TMDs should be as small as possible. Moreover, when multiple chatters with different vibration modes occur, it is necessary to find the appropriate mounting arrangement of TMDs tuned to each mode. However, the lower limit of the TMD mass for achieving chatter suppression and the procedure for determining the suitable TMD parameters, including the mounting arrangements, are still unclarified. Thus, the present study aims to investigate the influence of the mounting arrangement and the tuning parameters of the TMDs on chatter during the turning process of a thin-walled cylinder to achieve effective chatter suppression using the TMDs with a small mass. The chatter vibration modes occurring in a cylindrical workpiece vary with the wall thickness of the workpiece, and multiple chatter vibration modes can occur at a particular wall thickness. The chatter suppression effect was examined on various circumferential mounting arrangements with varying equivalent mass ratios of small TMDs. Finally, it was shown that an appropriate mounting arrangement of lightweight TMDs could achieve a high chatter suppression effect even during the generation of multiple chatter vibration modes.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Bending (N/mm\({}^{2}\))**} \\ \cline{2-4}  & **MOE** & **MOR** & **Hardness (N)** \\ \hline
0 & 6280 (256.98) & 107.11 (24.21) & 5934 (583.12) \\
150 & 5946 (517.33) & 91.26 (26.63) & 5320 (827.11) \\
180 & 3820 (383.54) & 74.33 (21.55) & 3513 (889.13) \\
220 & 2646 (854.24) & 28.93 (11.42) & 2506 (388.42) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Swelling values of the heat treated samples. (The numbers in parentheses are the standard deviation values). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Color Parameters**} \\ \cline{2-4}  & **L \({}^{\ast}\)** & **a \({}^{\ast}\)** & **b \({}^{\ast}\)** \\ \hline
0 & 72.07 (1.28) & 5.65 (0.70) & 20.57 (1.10) \\
150 & 75.29 (0.83) & 5.56 (1.01) & 20.53 (0.73) \\
180 & 54.41 (1.23) & 8.32 (0.39) & 20.26 (0.40) \\
220 & 48.88 (1.31) & 9.69 (0.51) & 20.80 (0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Discoloration values of treated samples. (The numbers in parentheses are the standard deviation values. ). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Oven-Dry Density (kg/m\({}^{3}\))**} & \multirow{2}{*}{**Weight Loss (\%)**} & \multicolumn{2}{c}{**Water Absorption (\%)**} \\ \cline{3-5}  & & & **2-h** & **24-h** \\ \hline
0 & 648 (43) & - & 9.31 (1.22) & 26.92 (2.63) \\
150 & 613 (83) & 5.61 (3.42) & 8.89 (1.94) & 25.22 (6.32) \\
180 & 611 (31) & 8.32 (2.78) & 3.96 (1.89) & 20.00 (3.74) \\
220 & 556 (49) & 12.17 (4.91) & 3.73 (1.31) & 19.39 (3.25) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Density, weight loss and water absorption of the samples. (The numbers in parentheses are the standard deviation values. Figure 2: SEM micrographs of control sample (**a**) and the one exposed to 220 \({}^{\circ}\)C (**b**).
## 3 Results
### Screening for Energy Consumption
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Factors** & **FC1** & **FC2** & **F3** & **FC4** & **FC5** & **F6** & **F7** & **FC8** & **F9** & **F10** & **FC11** & **F12** \\ \hline
**level +** & 173.00 & 170.49 & 158.46 & 175.94 & 158.14 & 159.23 & 162.21 & 154.67 & 173.21 & 158.90 & 168.00 & 138.61 \\
**level +** & 138.14 & 140.66 & 152.69 & 135.20 & 153.00 & 151.91 & 148.93 & 156.47 & 137.93 & 152.24 & 143.14 & 172.53 \\ \hline
**Different** & 34.86 & 29.83 & 5.72 & 40.74 & 5.14 & 7.31 & 13.29 & 1.80 & 35.29 & 6.66 & 24.86 & 33.91 \\ \hline
**Rank** & **3** & **5** & **18** & **1** & **19** & **16** & **11** & **23** & **2** & **17** & **6** & **4** \\ \hline
**Factors** & **F13** & **F14** & **FC15** & **F16** & **F17** & **FC18** & **F19** & **F20** & **FC21** & **FC22** & **F23** & **F24** \\ \hline
**level -** & 164.07 & 150.53 & 164.63 & 164.07 & 156.26 & 153.27 & 157.51 & 160.51 & 144.20 & 150.39 & 151.66 & 158.03 \\
**level +** & 147.07 & 160.61 & 146.51 & 147.07 & 154.89 & 157.87 & 153.63 & 150.63 & 166.94 & 160.76 & 159.49 & 153.11 \\ \hline Difference & 17.00 & 10.09 & 18.11 & 17.00 & 1.37 & 4.60 & 3.89 & 9.89 & 22.74 & 10.37 & 7.83 & 4.91 \\
**Rank** & **9-10** & **13** & **8** & **9-10** & **24** & **21** & **22** & **14** & **7** & **12** & **15** & **20** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The energy consumption (EC) response table. Figure 7: Main effects plot of the annual energy consumption (EC) for the 24 controlling factors.
## 3. Results
### Comparison of Compression Rate  
Once the four cases were solved, a comparison of compression rates was carried out. A graph of the pressure in the median nerve as a function of the phalangeal angle of rotation is illustrated in Figure 4. Case three is hidden, since by definition it is a continuation of case two. It is worth noting that in each case, the maximum angle of the phalanges was different: 34, 30, and 17.5 degrees, respectively, in cases one, two, and four. However, compression rates can be compared. Wrist flexion showed the greatest rate of median nerve compression than the other movements studied. Wrist flexion was typical for the Phalen test (the patient flexes both hands 90 degrees) and caused the greatest rate of compression. The lowest rate of compression was noticed during finger flexion. Finger flexion did not cause flexion of the tendons in the carpal tunnel, and therefore there was less movement of the tendons near the median nerve than during flexion/extension of the wrist.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### GAN and LLM-Based Solutions  
Generative adversarial networks (GANs) play a key role in enhancing smart grid security by enabling the encryption and concealment of confidential energy data usage within cover images to safeguard consumer privacy and data integrity. As a result, several solutions were presented (see Table 31). In [294], [PERSON] et al. presented a novel data-driven approach to synthetic dataset generation using deep GAN. This method generates synthetic samples that closely resemble real data by learning the conditional probability distribution of essential features from real datasets. The e valuation results demonstrated a convergence between real and synthetic datasets, showing that it is indistinguishable from real data in terms of task outputs. These solutions can be used to increase the size of the dataset (in the case of a small dataset) and retrain the model to enhance its performance. On the other hand, attackers can also use these solutions to construct adversarial samples that can be used to compromise ML cybersecurity solutions. In [295], [PERSON] et al. presented a privacy-preserving architecture for fine-grained power data in smart grids using GANs and an obfuscator to generate synthetic time series with minimal energy difference. Therefore, it effectively addresses consumer privacy concerns in the context of smart meter data. In [296], [PERSON] et al. presented a method where data encryption is followed by embedding encrypted data into cover images using a GAN to enhance data security by concealing confidential information. In [297], [PERSON] presented a study to investigate the transformative potential of generative artificial intelligence, particularly GANs, in addressing challenges related to load forecasting, power outage prediction, and preventive maintenance within electricity distribution companies. In [298], [PERSON] et al. presented a novel zero-trust framework for a Power Grid Supply Chain (PGSC), which facilitates early detection of potential GenAI-driven attack vectors. Tail-based risk metrics were also introduced to quantify the risks associated with extreme attacks. The efficacy of this zero trust framework achieves 95.7% accuracy in attack generation and 99% confidence in defense against GenAI-driven attacks.
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 4 Experiments and Result Analysis
### Performance of 3D-SE-Net versus Other Network  
To demonstrate the capability of our proposed model in driving fatigue detection, we conducted a large number of comparative experiments on four datasets. The recognition results of the current mainstream networks including ResNet-50 [30], MobileNetV3 [31] and EfficientNet [32] were compared. Moreover, we simplified the proposed network with the main network containing three input branches including the right eye, the left eye and the mouth (Three-branch 3D-SE-Net). We modified the network to input only a single human eye and mouth (Double-branch 3D-SE-Net) as a set of comparison experiments. The specific experimental results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Training Object** & **Test Data** & **Method** & **Accuracy (\%)** \\ \hline \hline \multirow{2}{*}{_ZJU_} & Eyes & 975 & ResNet-50 & 95.64 \\  & & & MobileNetV3 & 95.73 \\  & & & EfficientNet & 96.22 \\  & & & 3D-SE-Net(T) & **98.35** \\  & & & 3D-SE-Net(D) & **97.72** \\  & & & ResNet-50 & 95.65 \\  & & & MobileNetV3 & 96.17 \\  & & & EfficientNet & 96.41 \\  & & & 3D-SE-Net(T) & **97.17** \\  & & & 3D-SE-Net(D) & **96.84** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of the proposed method and different classical CNN structures on four datasets (where T stands for Three-branch 3D-SE-Net and D stands for Double-branch 3D-SE-Net).  
Figure 7: Some samples of the fatigue driving dataset.  
As can be seen from Table 1, the proposed 3D-SE-Net performs significantly better than other networks on the ZJU dataset, due to the proposed model extracting features in both temporal and spatial dimensions through 3D convolution, capturing motion information from multiple consecutive frames and further improving performance compared with ordinary 2D convolution. Compared with other datasets, the ZJU dataset contains images with lower resolution and the final experimental results demonstrate that the proposed model still has better robustness in processing low-resolution images, with 3D convolution outperforming 2D convolution for the same dataset. On both the YawDD dataset and the NTHU dataset, the proposed model achieves high recognition accuracy compared with other models because the images contained in these two datasets are mostly frontal images of drivers, which are easy to recognize. On the FDF dataset, the accuracy of each model is improved to some extent as the dataset contains sufficient images and the models are well fitted under this dataset. We can also find that the recognition of Three-branch 3D-SE-Net is better than that of Double-branch 3D-SE-Net under the same dataset, which indicates that the proposed model strategy is successful.
# Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maximly Sinus Surgery
## 5 Conclusions  
This paper explored a highly accurate, reliable, and robust fault estimation, detection, isolation, and tolerant control for the sinus surgical robot. The proposed method had three main steps: (a) modeling of the surgical manipulator, (b) advanced observer for fault detection, estimation, and isolation, and (c) an advance fault-tolerant controller. In the first step, the surgical manipulator was modeled based on the robust and accurate fuzzy ARX-Laguerre technique. After modeling the maxillary sinus surgical robot, the advanced observer was designed for fault diagnosis. To increase the reliability, robustness, and fault estimation accuracy for fault diagnosis, the fuzzy ARX-Laguerre T-S fuzzy robust feedback linearization observer was designed. After modeling and fault diagnosis for the maxillary sinus surgical robot, the proposed fault-tolerant controller was proposed to reduce the effect of a fault. To increase the robustness and accuracy, an adaptive fuzzy observation-based feedback linearization technique was used in the last step. Regarding the results, the proposed algorithm reduced the average position error from 35 mm to 2.45 mm in the faulty condition. In the future, a hybrid higher-order observation technique will be designed to enhance the performance of fault diagnosis and fault-tolerant control for a surgical robot.  
All the authors contributed equally to the conception of the study, the design of the experiments, the analysis and interpretation of results, and the writing of the manuscript.  
This work was supported by the Korean Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea (No. 20172510102130).  
The authors declare no conflict of interest.  
Figure 24: Position error for normal condition, faulty condition with the proposed fault-tolerant controller, faulty condition with the feedback linearization fault-tolerant controller, and faulty condition with the observer feedback linearization fault-tolerant controller.
## 5 Practical Experience with L2S-M
### Functional Validation and Results
#### 5.3.3 Cluster Addition and Wi-Fi Download Test  
The last test of this validation section will provide some insights about the capabilities of L2S-M to incorporate a new infrastructure into a complex scenario such as the Smart Campus. It is common that universities hold events with many participants that require access to the Internet or/and retrieve content from the university to perform some activity. Some examples might include congresses, practical/lab sessions, etc. In all these cases, it is important to be able to set up an adequate infrastructure able to effectively host the network services required for each activity. Nevertheless, this set-up must be quick and dynamic since these events frequently change, so the requirements and equipment needed will vary depending on the type of activity being performed.  
In this regard, the set-up of a K8s cluster is well known to be simple and fast to deploy (a cluster can be set up in 20 min using administration tools such as Kubeadm [52]). In a similar fashion, L2S-M (and its inter-cluster functions) can be easily deployed within a K8s cluster as well since its configuration and set-up can be performed in a short period of time (approximately 10 min).  
For this test, the Campus 1 edge was deployed using two RPis (which can act as AP) and a Mini-ITx compute node to provide the connectivity with the main university cloud. This last battery of tests uses the RPis Wi-Fi module to provide the channel for the downloads, emulating a real scenario, where other clients would connect into an AP and download the content from there (rather than downloading it in the equipment, unlike in previous tests).  
In this case, the audiovisual content was downloaded from an external PC connected into the virtualised AP of an RPI, enabling and disabling the cache to test the CDN function  
Figure 11: Traffic capture in every CDN element of the cluster.  
ality. This process was repeated 30 times for every mode, obtaining the download values that can be seen in Table 2.  
As it can be seen in the table, the download speeds are lower than the ones in previous tests. These were due to the use of an unstable medium, such as the Wi-Fi connectivity of the RPis. Nevertheless, both download speeds and average throughput were improved when the cache was enabled in the site, proving again the effectiveness of the CDN.  
Beyond the particular results (throughput, etc.) obtained for the different featured scenarios, the main objective accomplished was to show that L2S-M can be used to provide complex network services that might involve different kind of network interfaces like wireless interfaces. This is also a starting point for the exploration of this concept since L2S-M could be used to enable the connectivity with private networks over an infrastructure (e.g., the same way in which VIMs like OpenStack connect to provider networks).