esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 4 Results and Discussion  
### Comparison of Different Models' Prediction Results  
To predict the magnitude of horizontal principal stress in the Luzhou area, this study selected three machine learning models: XGBoost, SVM, and RF. These models were trained using the nine key factors selected by the RF-RFE algorithm as inputs, with the maximum and minimum horizontal principal stresses as target values. During training, Bayesian optimization was used to determine the optimal hyperparameters. The three models were then compared based on simulation results and evaluation metrics to identify the best one.  
The nine selected factors were used to train and optimize the models. As shown in Figure 5, the XGBoost and SVM models predicted horizontal principal stress values closer to the actual values, while the RF model showed larger deviations in some data points. However, it was difficult to determine which of the XGBoost or SVM models had predictions closer to the true values based on the average.  
Figure 4: Changes in R\({}^{2}\) Test Scores with Feature Selection Using Recursive Feature Elimination and Cross-Validation.  
Figure 3: Distribution of Feature Importances After Screening.  
Figure 5: Comparison of Predicted and Observed Values for Different Models. (**a**,**b**) The predicted vs. observed values of SHmin and SHmax for the XGBoost model. (**c**,**d**) The values for the SVM model. (**e**,**f**) The results for the RF model.  
To better observe the actual prediction performance of the models, scatter plots of actual versus predicted values along with three reference lines, were created to compare and evaluate the performance of the XGBoost, SVM, and RF models in predicting the SHmax and SHmin, as shown in Figure 6. The red dashed line indicates where the predicted values match the actual values, while the green and blue dashed lines represent the \(\pm\)1% error bounds. The scatter points of the XGBoost model are tightly clustered around the red dashed line and within the \(\pm\)1% error bounds, indicating that its predictions are highly accurate and stable with minimal deviation from the actual values. The SVM model's scatter points also show a certain degree of regularity, with most points lying within the \(\pm\)1% error bounds despite some dispersion, suggesting strong prediction capability and stability. In contrast, the RF model's scatter points exhibit a higher degree of dispersion and are less constrained by the \(\pm\)1% error bounds, indicating relatively weaker prediction accuracy and stability with potential prediction biases.  
Figure 6: \(Cont.\)
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### SDN-Switch Discovery
The packet flows via an OpenFlow SDN-Switch in both directions (ingress and egress) are illustrated in Figure 4.  
In the case of the network traffic being huge and complex, many unknown stream packets will arrive at the forwarding node, thus, it will produce a large number of Packet-In messages. On the other hand, sending a flow request (Packet-In message) to the SDN-Controller for each unknown packet will confuse the SDN-Controller because the SDN-Controller has to compute the forwarding rules for each new packet and then install it to the flow tables in all the data forwarding nodes (SDN-Switches). This high volume of traffic and computational overhead will cause SDN-Controller overhead and increase the time it takes for flow rules to be placed, affecting network efficiency and scalability [1,2]. Moreover, [59] claimed that the data center with a 4 K server can handle up to 200,000 flows per second. Another study found that the average flow width was roughly 20 packets per flow, with latency between flows less than 30 milliseconds [60]. These costs are very high, but the memory available to hold sending entries is limited. However, the entries for typical OpenFlow SDN-Switch flow tables were stored in Ternary Content Addressable Memory (TCAM), a type of high-speed memory that allows looking up a  
Figure 3: Creating the SDN-Switch discovery process. continuous flow entry in a clock cycle (1). Although the TCAM search is fast, its capacity is limited to a few thousands of entries [61]. On another hand, Increasing the TCAM size raises additional issues such as cost, and will require high power consumption. Therefore, the researchers tried to optimize the flow schedule to take full advantage of it.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Comparative Experiments
#### 4.5.2 Varying-Condition Comparison Experiment  
In the varying-condition experiment, we used Scenario 1 from the previous sections, where the dataset with a speed of 1797 rpm was used as the training set, and datasets with other speeds (1772 rpm, 1750 rpm, and 1730 rpm) were used as the testing sets. On the basis of the single-condition comparison experiment, we added the DTL and DAN models to evaluate the classification accuracy, training time, and testing time of each model under  
Figure 11: Confusion matrix for classification results: (**a**) Scenario 1; (**b**) Scenario 2; (**c**) Scenario 3; and (**d**) Scenario 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Classification Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline SVM & 75.32 & 144 & 27 \\ CNN & 90.45 & 962 & 62 \\ LSTM & 92.87 & 1089 & 74 \\ Proposed Method & 99.28 & 182 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative test results between different models under single-condition conditions.  
varying working conditions. Each experiment was also repeated 20 times, and the average result was taken as the final result, as shown in Table 9.  
The experimental results show that under varying conditions, the classification performances of SVM, CNN, and LSTM drop significantly, indicating that these models struggle to effectively capture the signal feature changes caused by the varying conditions. In contrast, the DTL and DAN models, through transfer learning and domain adaptation techniques, improved the adaptability of the models to different working conditions, with classification accuracies of 91.56% and 93.43%, respectively, demonstrating good robustness. However, these methods performed suboptimally in small-sample environments, particularly when faced with the dual challenges of condition variation and insufficient sample size. In comparison, our proposed MTC-GAN model maintained excellent performance under both small-sample and varying-condition scenarios, achieving a classification accuracy of 98.50% while keeping training and testing times relatively short. These results fully demonstrate the high efficiency and broad applicability of the proposed method in real-world applications. This confirms that the method is not only capable of maintaining high accuracy but also offers exceptional computational efficiency, making it well-suited for practical fault diagnosis tasks.
## 7 Performance Analysis
### Energy Consumption  
Researchers need to consider the size and speed of the message being sent to the recipient. This is because data transmission occurs under Dedicated Short-Range Communication (DSRC) and, in the case of vehicle networks defined in IEEE 802.11p, it belongs to the physical protocol layer. This IEEE standard operates at 10 MHz channel bandwidth, 5.8 GHz frequency, 25 dBm transmit power, and 6 Mbps data rate [35]. The energy consumption for the verification scheme can be calculated as \(E_{et}\) (for the execution time of key generation and message confirmation) \(E_{co}\) (for the communication cost for message confirmation) and it is measured in millijoule (mJ). For the execution time, \(E_{et}=T_{c}*C\), where \(T_{c}\) = Total computation cost, \(C\) = cpu maximum power, which is 10.88 W  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Protocols** & **Communication Cost** & **Storage Cost** & **Total Memory** \\ \hline [PERSON] et al. [13] & 132 bytes & 528 bytes & 660 bytes \\ [PERSON] et al. [16] & 100 bytes & 136 bytes & 236 bytes \\ [PERSON] et al. [4] & 124 bytes & 32 bytes & 156 bytes \\ Ours & 100 bytes & 224 bytes & 324 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Communication cost and storage cost.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Protocols** & **Computational Complexity** & **Total Cost** \\ \hline [PERSON] et al. [13] & \(T_{bpsm}+3T_{bp}+T_{MPH}\) & 18.748 ms \\ [PERSON] et al. [16] & \(5T_{sem}\)+\(3T_{h}\)+\(Tea\) & 0.0711 ms \\ [PERSON] et al. [4] & \(4T_{h}\)+\(2T_{sem}\) & 0.0280 ms \\ Ours & \(22T_{h}\) & 0.0022 ms \\ \hline \hline \end{tabular} XOR operation is negligible as compared to other operations.  
\end{table}
Table 3: Computation cost of key generation and message confirmation phase.  
for wireless communication networks [36]. \(E_{et}=(D_{m}*C)/(D_{r})\), where \(D_{m}\) = the size of message, \(D_{r}\) = the data rate for vehicular communications (6000 Kbps). By referring to Table 5, we can say that the proposed protocol consumes the least energy.
# The FEM Model of the Pump Made of Dielectric Electroactive Polymer Membrane
## 5 Conclusions  
DEAPs are a subclass of electroactive polymers in which actuation is produced by an elastic deformation resulting from the compressive electrostatic forces. Their most important advantages like energy efficiency, lightweight and scalability are key features for actuators in applications such as pumps. Dielectric electroactive polymer technology is able to fulfill requirements as well as commonly used technology e.g., solenoids, but its limitations concern relatively low force [19; 20]. One way to increase force generated by these devices can be the application of neodymium permanent magnets. The concept for pump presented in this paper consists of a stack of DEAP membranes combined with a permanent magnets. These two components are combined in a novel pump construction, which allows a compact design by integrating the biasing mechanism with the DEAP membranes. Subsequently, the single components are manufactured, tested, and their force-displacement characteristic is documented. Identifying the parameters experimentally allowed to develop a faithful FEM model of the pump device and make its simulations for different voltage. FEM modeling of the DEAP membrane behavior is a useful tool to understand such systems better and helps to create the optimal design of prototypes. These modeling efforts must account for the electromechanical coupling in order to accurately predict their response to multiple loading conditions expected during real operating conditions. Because of the complexity of the nonlinear processes, the finite element analysis is reasonable to be used and study the behaviour of dielectric elastomer pump. The experiments under static and transient considerations have been performed to validate the design and implementation. It is shown, that the model is in close agreement with the measured responses of the real DEAP actuator. The collected data allow for the analysis of various other device designs using DEAP membrane in the future.  
This research was funded by NATIONAL SCIENCE CENTRE (Poland), project SONATA13 grant number No. 2017/26/D/ST7/00092. The APC was funded by NATIONAL SCIENCE CENTRE (Poland).  
**Conflicts of Interest:** The author declare no conflict of interest.  
Figure 12: Supply voltage waveform and volume change of one pump chamber.
# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
## 5 Counting the Number of Breath Cycles and Calculating the Breathing Rate  
The flex sensor data before and after filtering are shown in Figure 10a, and the spirometer signal, serving as the ground truth, is illustrated in Figure 10b. Before applying the developed approach, the flex sensor data had 31 peaks, indicating 31 breath cycles. However, after applying the developed fusion technique, the number of breath cycles extracted from the filtered flex sensor data is seven, which is consistent with the count obtained using the ground truth. This demonstrates that, in this test, body motion leads to a false alarm rate of more than 300% in counting the breath cycles when the developed approach is not applied. The results highlight the effectiveness of the developed approach and its ability to avoid counting cycles corresponding to body motion as breath cycles.
## 4 Discussion
### Shortage and Future Work  
This study is a preliminary attempt to explore the whether batch normalization based ResNet50 model can be applied in automatic classification of AM locusts, rice locusts, and cotton locusts, and to identify the growth status information of the AM locust (third-instar, fifth-instar, and adult) with high accuracy. In our future work, we aim to overcome several challenges for realizing the engineering application of locusts' instars, species, and distribution information acquisition in an automatic manner. First and foremost, YOLOv2 network based model will be developed and integrated into the locust species and instars identification workflow so that the manual labeling can be replaced by automated target detection in the sample blocks generation phase. Secondly, integrated application of multispectral, hyperspectral, and laser point cloud observation technology will be explored to ascertain their ability to enhance the accuracy of locust sample detection and identification in complex backgrounds, e.g., plants, soil, and rocks, different types of insects, and other important elements. Thirdly, this study only addresses the automatic identification of AM locusts (third-instar, fifth-instar, and adult), rice locusts (adult), and cotton locusts (adult) as examples. In possible follow-up studies, it would be necessary to enrich the locust species and instars sample datasets to improve the automatic collection ability of the model. Fourthly, the five CNN models used in the comparison experiment are a bit old, thus, we will explore the possibility of using newer models such as InceptionNet [46], DensNet [47], ResNeXt [48], and SE-Net [10] to perform comparative experiments. Further, our future work will focus on developing a device that can be deployed in the field to automatically collect and transmit information concerning the species, instars, and distribution density of locusts. On this basis, and driven by a large number of quadrat-scale observation data [49], the relation between locust distribution density and the community structure changes in different regions habitats (water and heat conditions and vegetative growth state) can be evaluated as well as how to realize an integrated application of locust quadrant observation data and multisource satellite remote-sensing data.
## 3 Results
### Quantitative Evaluation
Especially in the OceanDark dataset, which includes low-illumination and hazy scenes, statistical significance was achieved at the \(p<0.01\) level against methods such as WCD, UTV, ULAP, UDCP, RGHS, HP and FUnIEGAN in all metrics. In comparison with methods such as UT, TOPAL and [PERSON] et al. 's, significant differences were found in the IE, UIQM and IL-NIQE metrics, while only some AG and UICM comparisons had borderline significance. As a result of the numerical analyses performed, it is proven that the proposed method provides consistent superiority over existing methods in terms of perceptual quality, structural detail preservation and color accuracy in both Test-C60 and OceanDark datasets. Especially the best scores obtained in critical metrics such as IL-NIQE, UIQM and AG show that the proposed approach can increase contrast and detail without creating noise and produce results compatible with natural scene statistics by minimizing color distortions. In addition, with the Wilcoxon signed-rank tests, a significant superiority at \(p<0.01\) level was achieved over the existing methods compared in IE, IL-NIQE, AG, UIQM and UICM metrics, thus statistically strongly supporting that the proposed method offers a reliable and effective solution in the field of underwater image enhancement.
# Air-Based Contactless Wafer Precision Positioning System
## 2 State of the Art
### Variable Inlet Pressure  
In 2007, [PERSON] designed a contactless positioning system based on a fixed geometry and a variable inlet pressure to control the thin film airflow in his PhD thesis \"Contactless Positioning Using an Active Air-Film\" [3]. Figure 1a shows a cross-section of one of the actuator cells, where a substrate is floating on top of the pressurized air.  
The air will flow from the inlet \(p+\) with high pressure to the outlet \(p\)-, where the air is removed from the cell at a sub-ambient pressure. The viscous shear of the airflow creates a traction force on the substrate. The direction of the flow within a cell is controlled by changing the pressure at the different inlets. Four actuator cells have to work together to actuate the substrate in three degrees of freedom. The flow of the different cells is separated by dams. Four actuator cells are shown in Figure 1b.  
The final system was used to position a 4-inch silicon wafer, a demonstrator as shown in Figure 1c. A bandwidth of 50 Hz was realized using a PID controller. The system had a maximum acceleration of 600 mm/s\({}^{2}\), with an error of 6 nm (1\(\sigma\)), while using an external active vibration isolation system. The concept's performance was limited by the manufacturing tolerances of the system, since this placed a lower limit on the fly height of 15 um. A lower fly height would result in a higher actuation force. The other limiting factor was the time delay caused by the pressure lines between the control valve and the pressure inlet in the cell.
## 2 Thermal Energy Storage Systems in Buildings
### Passive Storage Systems Incorporating PCMs
#### 2.2.5 Incorporation of PCMs into Building Envelopes  
The integration of PCMs into building envelopes improves thermal comfort by reducing peak and fluctuating indoor temperatures due to a storage density 5-10 times higher than that of standard walls [17]. Moreover, thanks to this high thermal inertia, the temperature peak is delayed in time, resulting in a reduction in peak temperature, which allows taking advantage of lower electricity costs.  
PCMs can be incorporated into the entire building envelope: walls, roofs, windows, and floors. Four techniques for incorporating PCMs into building envelopes are presented in the literature: direct incorporation, impregnation, encapsulation, and shape-stabilized.  
The distinction between the last two methods is not clear in the literature. Some authors [9; 18; 45] distinguish between encapsulation and shape stabilization, while [PERSON] et al. [46] classify encapsulation in shape stabilization. In this section, we will distinguish between encapsulation and shape stabilization.
# Experimental Simulation of Lightning Current Discharge on Rocks
## 2 Materials and Methods  
### Target Rocks  
Experiments were performed using four basalt samples (labeled sample A1-A4) from Pennsylvania (USA) and two granite samples (labeled samples B1, B2) from Spain. The samples' dimensions were 15 \(\times\) 15 cm (thickness = 1.1 cm). A testing sample (a basalt rock from India labelled A0) was used to verify that the measurement equipment was properly configured. In order to elucidate possible differences in the arc duration and electrical properties needed to form fulgurite glass, dry and wet experimental conditions were considered. In wet experimental conditions, 1 mL of water was used. The experiment was repeated several times to verify the consistency of data on each sample type.
# MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
## 3 Sierpinski Resonators
### Results on the Switched Triangular 20 GHz Resonators  
The results of the 20 GHz resonators, combining the C0 and C1 geometries, are presented in this section. The switched configuration is shown in Figure 7, where the two possible states are given, considering an actuation on the filter's upper or lower branch, as shown in the figure. This configuration was simulated to predict its response in the two possible states, and the simulation is presented in Figure 8.  
The experimental result for the transmission \(\mathrm{S_{21}}\) parameter of this structure is shown in Figure 9.  
Despite the non-perfect matching causing significant losses along the line, the result agreed with the general expectations of this device, with a shift of the output resonance when the signal was switched from the C0 branch to the C1 one and a dynamic response for the ON/OFF states of the switch in the order of at least 15 dB. The predicted resonance frequency was around 20.16 GHz for the C0 response, while the measured one was 21.27 GHz; for the C1 output, the expected resonance was 20.93 GHz, while the measured one was 21.75 GHz. So, both frequencies were shifted by approx. 1 GHz, and the difference between the two frequencies of resonance was 500 MHz, and not 700 MHz as expected.  
From the analysis of the obtained results, we can conclude that the big resonators working around 20 GHz probably interacted between them in proximity to the internal corners. Better isolation could be provided by a post of absorbing material separating the two devices transversally or shifting the relative position to avoid interference due to the proximity between the corners of the triangles. The situation also needed to be improved at the I/O ports of the entire device to decrease the insertion losses significantly. So far, proof-of-concept was demonstrated at this stage, but the complete device needed to be correctly engineered for application purposes.  
Figure 7: Layout of the switched C0 and C1 resonators for the 20 GHz operation. The RF MEMS switches are evidenced on the upper and bottom sides of the figure to allow the signal to pass through the C0 or the C1 resonator, depending on the actuated branch.  
Figure 8: Simulation of the two-port filter implemented with the resonators C0 and C1 for the 20 GHz operation. In (**a**) the C0 configuration is activated, while in (**b**) the C1 resonator is used as the output of the SPDT-driven structure.
## Materials and Methodology
### Evaluation of Quantity Accuracy  
The water area extracted from OLI was regarded as the ground-truth water area [24] to examine the classification accuracy of S1A in three steps. First, the spatial resolution of S1A was resized to 30 m using the nearest-neighbor method to match the spatial resolution of OLI. Then, three periods of ground-truth water areas from OLI (two OLI scenes acquired on 9 September 2015 and 27 September 2016 in Poyang Lake region, and one OLI scene acquired on 2 February 2016 in the Taihu Lake region) were selected to assess the accuracy of the SWI category during different periods and in different regions using the confusion matrix method. Finally, quantity accuracy was computed with the following equation:  
\[\zeta=(1-|A_{extracted}-A_{truth}|/A_{truth})\times 100 \tag{7}\]  
where \(\zeta\) represents the quantity accuracy; \(A_{extracted}\) represents the water surface area extracted from SWI imagery; and \(A_{truth}\) represents the ground-truth water area extracted from the OLI bands.