esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Hardened Tests
#### 6.2.3 Compressive Strength
Also, SNF improves the contact between cement particles and water, which provides the basis for more complete hydration and results in a strong structure. On the other hand, the SNF used can reduce segregation (where coarse aggregates are separated from the cement paste) by creating more uniform areas in the concrete [94; 110; 111; 112; 113]. In contrast, increasing SF from 15% to higher levels generally led to reduced compressive strength at both 7 and 28 days, particularly beyond the optimal dosage. While small additions (up to 20%) may improve strength slightly in some series, excessive SF (25%) consistently results in strength losses. The optimal SF content for maximum strength was found to be 15% for series 1 and 3 and 20% for series 2. Raising the w/b ratio from 0.18 to 0.22 caused a notable decrease in compressive strength across all series. Furthermore, increasing SNF from 0.7% to 0.9% resulted in modest strength gains (1.5-3.5%). Figure 16: Results of compressive strength (7 and 28 days): (**a**) effect of SF; (**b**) effect of w/b ratio and SNF. Figure 17 illustrates the impact of the w/b ratio and SNF on compressive strength. As shown in Figure 17a, as the w/b ratio rises from 0.18 to 0.22, compressive strength decreases. Series 3 mixtures exhibit the highest bending strength relative to the w/b ratio. According to Figure 17b, as the SNF ratio increases from 0.7 to 0.9, compressive strength rises. Series 3 mixtures show the highest compressive strength concerning the SNF ratio.
## 3 Physical Dimensions and Fabrication  
Through the above analysis, the physical dimensions of the proposed multilayer antenna array are shown in Figure 9 and Table 3.  
Figure 8: (**a**) S parameter of the CSC feeding network; (**b**) surface current distribution diagram at 30 GHz of the feeding network with or without vias.  
In this paper, the multilayer antenna array consists of three layers of metal plates and two layers of Rogers 5880 plates, and multiple plates are connected by bonding layers (FR28-0040-50, \(\varepsilon_{\mathrm{r}}\) = 2.74, \(h\) = 0.1 mm). From bottom to top, the first metal plate layer is  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Parasitic antenna array layer**} & & & & & & & \\ \hline
**Symbol** & W\({}_{\mathrm{fs}}\) & L\({}_{\mathrm{fs}}\) & W\({}_{\mathrm{pn}}\) & gy & gx & L\({}_{\mathrm{pn}}\) & & \\
**Value** & 8.2 & 8.2 & 3.1 & 2 & 1.4 & 2.6 & & \\ \hline \multicolumn{1}{c}{**Driven slot patch antenna layer**} & & & & & & & \\ \hline
**Symbol** & W\({}_{1}\) & L\({}_{1}\) & L\({}_{2}\) & W\({}_{\mathrm{d}}\) & L\({}_{\mathrm{d}}\) & W\({}_{\mathrm{Pa}}\) & L\({}_{\mathrm{Pa}}\) & g \\
**Value** & 7.48 & 14.68 & 14.96 & 7 & 3.2 & 3.12 & 2.32 & 0.29 \\
**Symbol** & W\({}_{\mathrm{c}}\) & L\({}_{\mathrm{c}}\) & S\({}_{\mathrm{W}}\) & S\({}_{\mathrm{L}}\) & & & & \\
**Value** & 1.4 & 2.16 & 1.03 & 0.36 & & & & \\ \hline \multicolumn{1}{c}{**CSC feeding network layer**} & & & & & & & \\ \hline
**Symbol** & W\({}_{z1}\) & G\({}_{z1}\) & L\({}_{z1}\) & W\({}_{z2}\) & L\({}_{z2}\) & W\({}_{z3}\) & G\({}_{z3}\) & L\({}_{z3}\) \\
**Value** & 1.578 & 2.578 & 7.2 & 0.484 & 3.74 & 0.484 & 1.484 & 14.96 \\
**Symbol** & W\({}_{z\!f}\) & G\({}_{z\!f}\) & L\({}_{z\!f1}\) & L\({}_{z\!f2}\) & Slot\({}_{\mathrm{\_}{x}}\) & Slot\({}_{\mathrm{\_}{y}}\) & P & \\
**Value** & 1.578 & 2.578 & 5.61 & 7.48 & 1.77 & 1.01 & 1.98 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical dimensions of the antenna (unit: mm).  
Figure 9: Schematic diagram of antenna layers: (**a**) parasitic antenna array layer; (**b**) driven slot patch antenna layer; (**c**) CSC feeding network layer.  
the 1 to4 CSC feeding network, which also serves as the ground for the second layer of the 2 \(\times\) 2 driven slot patch antenna array; the third metal plate layer is the 4 \(\times\) 4 parasitic antenna array, which is couple-fed by a driven antenna element; each layer of the antenna array is connected by metal vias. The fabricated antenna array is shown in Figure 10.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### Data Generation  
Synthetic datasets were generated by systematically varying the key input parameters. The spot price (S) ranged from 10 to 200, while the strike price (K) varied between 10 and 200, ensuring meaningful interaction between these variables. The risk-free interest rate (r) was sampled between 0% and 5%, consistent with historical interest rates in developed markets. Volatility (\(\sigma\)) was varied between 0.1 and 0.5 to capture a broad spectrum of market conditions. Finally, the time to maturity (T) spanned 0.1 to 2 years, representing short- to medium-term options.  
Each combination of these parameters was used to calculate the theoretical option price via the Black-Scholes formula, producing noise-free labels for supervised learning. This systematic approach allowed the model to learn pricing patterns across a wide range of scenarios.  
Synthetic data were selected for this study due to their ability to provide precise labels based on a well-defined analytical model, ensuring accurate validation of the neural network's predictions. By relying on the Black-Scholes framework, the dataset maintains theoretical consistency, allowing the model to learn fundamental pricing patterns without the influence of market distortions. Additionally, the structured generation process enables the dataset to systematically cover a broad range of parameter values, ensuring that the model encounters diverse market conditions during training. This comprehensive coverage helps improve generalization, reducing the likelihood of overfitting to specific input distributions.  
Another advantage of using synthetic data is their ability to eliminate the noise and anomalies often present in real-world financial data. Market datasets frequently contain irregularities caused by liquidity fluctuations, sudden economic events, or microstructural inefficiencies, which can obscure meaningful patterns. By training on a clean dataset, the neural network can focus on learning the true functional relationships governing option pricing, rather than adapting to unpredictable variations.  
However, despite these benefits, synthetic data introduce certain limitations. They lacks the stochastic volatility, market frictions, and unpredictable shocks that characterize real financial markets. As a result, while the model demonstrates strong performance in a controlled environment, its applicability to real-world scenarios remains uncertain. Addressing this limitation will require the incorporation of real-market datasets in future research. By training on historical options data, the model can be exposed to genuine market dynamics, enabling it to refine its predictions and improve its robustness in practical applications.
## 3 Feature Extraction
### Weighted-Averaging  
When calculating features over multiple onsets or over multiple time windows, a single value is usually obtained by taking the mean value. However, it may be more perceptually relevant to take a weighted-average, which can be calculated with the equation  
\[\text{weighted-average}=\frac{\sum WV}{\sum W}, \tag{1}\]  
where \(W\) is the weight of the values, \(V\). For example, the weighting values could be the root mean square (RMS) of the signal amplitude. In this way, the weighted-average will be influenced most heavily by features calculated from the parts of the signal with the greatest amplitudes.
## 4 Engineering Application
### Model Performance Analysis  
To validate the performance of the proposed anomaly detection method in this paper, we compared the effectiveness of comparative models and the method we propose in terms of three aspects and conducted three comparative experiments. The settings for the comparative models are shown in Table 2. Firstly, to compare the performance of different anomaly detection algorithms, we compared the anomaly detection results of DT_VAE-LSTM with those of VAE and AE-LSTM. Furthermore, to validate the effectiveness of the dynamic threshold proposed in Section 3.2, we constructed the VAE-LSTM with a fixed threshold. The fixed threshold was set using the maximum reconstruction error of the training dataset obtained during the initial model training. We compared its detection performance with that of the DT_VAE-LSTM. Secondly, to investigate the impact of selecting different monitoring parameters on detection performance, we replaced the monitoring parameter of the anomaly detection model with the cutterhead torque and total thrust. We compared the detection performance of the DT_VAE-LSTM (torque) and DT_VAE-LSTM (thrust) with the DT_VAE-LSTM (energy) model using the same DT_VAE-LSTM algorithm. Lastly, the proposed AD_SI model combined the results of the anomaly detection model and scenario identification model to make the final decision. Therefore, we also  
Figure 6: The mudcake formation inside the shield machine’s earth chamber.  
verified whether the fusion of the two-stage results contributes to improving anomaly detection performance.  
We used precision, recall, and \(F1\) as evaluation metrics for the model's performance, as shown in Formulas (8)-(10). \(TP\) represents the number of data points correctly labeled as anomalies, \(FP\) represents the number of data points incorrectly labeled as anomalies, \(TN\) represents the number of data points correctly labeled as normal, and \(FN\) represents the number of data points incorrectly labeled as normal.  
\[\text{precision}=\frac{TP}{TP+FP} \tag{8}\]  
\[\text{recall}=\frac{TP}{TP+FN} \tag{9}\]  
\[F1=2\times\frac{\text{precision}\times\text{recall}}{\text{precision}+\text{ recall}} \tag{10}\]
## 2 Materials and Methods
### Wireless Electroencephalograph
#### 2.1.1 Animal Module  
This module could be attached to the animal's neck, and it performed three tasks: (1) signal amplification and conditioning, (2) analog-to-digital conversion, and (3) wireless transmission of the digital data. A microcontroller sampled the EEG analog signal, converted the analog signal to a digital signal, and transmitted the recorded data [54]. Each of these parts is detailed below.  
The electronic amplification and conditioning circuit consisted of (i) an amplifier that pre-amplified the EEG signal and (ii) three analog active filters: a 0.05 Hz high-pass filter, a 1.5 kHz low-pass filter for antialasing, and a band-pass filter to remove 60 Hz electromagnetic interference. The input impedance was on the order of Megaohms. The total gain of the circuit was 10 V/mV.  
The A/D converter stage of the circuit used a 10-bit analog-to-digital converter of a Microchip(tm) PIC16F877A microprocessor [55]. Through its USART interface, the microprocessor also controlled telemetric transmission. The circuit used the BIM2-433-160 transceiver [56], which transmitted and received the data at 433 MHz with low energy consumption in a range of up to 200 m. This setup allowed for the real-time transmission of up to 120 data points per second. Therefore, a safe sampling frequency of 100 Hz was chosen.
## 7 Simulation and Results
### Varying Density of the Network
#### 7.2.2 Average End-to-End Delay  
Figure 21 shows average end-to-end delay _versus_ the network density. It is clear even from this figure that hotline assisted routing achieves higher performance as compared to the all wireless routing. Average end-to-end delay stays much lower and almost unchanged with the increasing number of nodes as compared to all wireless routing. The average end-to-end delay increases with the increase in number of nodes in the case of all wireless routing. The probable cause of this raise is the increase in the density of the network. Due to this increase the communication takes longer as compared to a less dense network [32].  
Figure 20: Average Packet Success ratio _versus_ number of Nodes
## 3 Proposed Methodology
### Datasets
#### 3.1.2 Image Cropping and Resizing  
The selected MRI datasets predominantly exhibit undesirable gaps and areas, hence diminishing classification performance. Consequently, cropping images to eliminate extraneous regions is crucial in the image collection. The cropping technique outlined in [27], which relies on extreme points computation, is utilized. Figure 4 illustrates the approach of cropping through extreme point computation. Initially, we provide the original MR images for pre-processing. Secondly, we apply thresholding to the MR images to generate binary images.  
We performed dilatation and erosion to eliminate noise from the images. Subsequently, we discerned the most prominent contour from the threshold images and continued to pinpoint the four extreme points (most superior, most inferior, rightmost, and leftmost) of the photographs. Finally, we crop the image utilizing contour data and extreme points. The tumor photos are scaled using bicubic interpolation. The primary rationale for selecting bicubic interpolation is its ability to generate a smoother curve compared to alternative approaches like bilinear interpolation, making it more suitable for MR images, which often exhibit significant edge noise. Due to the varying width, height, and sizes of the MR images  
Figure 4: Image cropping using extreme point calculation in two steps.  
in the selected dataset, it is advisable to scale them to uniform dimensions for optimal results. In this study, the MR images are downsized to \(250\times 250\times 3\) pixels, as the suggested model requires this input dimension. Both datasets are shrunk to the input dimensions of the employed pre-trained CNN models during implementation.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 3 Performance Requirements in Digital Watermarking
### Capacity  
The capacity of a watermarking method represents the highest amount of information that can be hidden in the cover document while maintaining the imperceptibility property. This quantity of information depends essentially on the type of intended application [55]. The information is commonly represented as bits per pixel (bpp). The capacity value can be defined as follows:  
\[Maximum\,\textit{embedding capacity}=\frac{Number\,\textit{of pixels with peak point}}{image\,\textit{length}\times image\,\textit{width}} \tag{8}\]  
For example, high watermark robustness is achieved at the expense of robustness, imperceptibility, or both. Similarly, the more information that is inserted, the more distortion appears on the host image. As a result, a good trade-off has to be maintained between these types of properties. Figure 7 illustrates the trade-off among these three requirements.  
There are other properties related to watermarking algorithms that must be taken into account, such as complexity, security, and false positive rate.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Analysis of Mine Development Systems in the Transition from Open-Pit to Underground Mining
#### 2.1.3 Fully Integrated Development System  
The fully integrated development system focuses on operational integration, where open-pit and underground operations share transportation, ventilation, and drainage facilities. This system is widely used in mines with moderately shallow ore bodies, providing benefits such as reduced infrastructure investment and enhanced operational efficiency. The Hasselmi mine in Finland is a typical example, where efficient integration of open-pit and underground operations is achieved by sharing the lower slope tunnel. However, this model imposes stricter requirements on tunnel layout precision, ventilation safety, and traffic management, particularly during high-intensity integrated operations, where stringent control of operational interference and risk propagation is crucial.  
The three mining system types each offer distinct advantages in terms of engineering adaptability, construction cost, operational safety, and efficiency. The selection of an appropriate mining system requires a well-planned approach that accounts for factors such as the depth of the ore body, geological structure, existing infrastructure, and ecological constraints. In practical applications, independent systems are most suitable for mines in the early stages of transformation, where minimizing operational disruptions is critical. Combined systems are better suited for mines characterized by strong continuity and high synergy between surface and underground resources, while certain hybrid systems provide a flexible transitional model, particularly for complex mines undergoing extended transition periods. Xinqiao Iron Mine, a representative example of China's transition from open-pit to underground mining, has innovatively adopted a combined mining system utilizing both the main and auxiliary shafts on the upper bench side along with inclined ramps, tailored to the ore body's burial characteristics, resource distribution, and ecological protection requirements. The mining system layout at Xinqiao Mine is illustrated in Figure 2. This approach has not only increased mining efficiency by 25% but also reduced surface disturbance by 40%, thus complying with green mining construction standards. The successful implementation of the Xinqiao Mine model demonstrates that, through modular design and thoughtful system selection, mining efficiency can be enhanced while significantly minimizing environmental impact, offering valuable insights for the transformation of other mining operations.
## 4 Evaluation  
In this section, the IO performance of F3 BFS at runtime and its functional utilities are evaluated. The comparison of the performance, including the latency and throughput, is carried out with Ceph (version 15.2) and XFS (version 5.18), two general-purpose file systems. Ceph is a widely used distributed file system, while XFS is a local file system. Since local file systems do not need to communicate with the network, unlike distributed file systems, they generally have better performance when the data stored is not too large, so we use XFS as the baseline standard in performance evaluation.
## 5 Implementation and Testing Environment  
In this section, we present the implementation environment in detail. This section includes experimental setup details, explanation of data collection process and data description, and the model structure. First, we describe the dataset used for experiments and the parameter selection. We compared the GCN model with our optimized GCN. We also compared the results with other baseline model and SR-GCN model.
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 2 YOLOv4 Algorithm  
The YOLOv4 algorithm is an enhanced version of the YOLOv3 algorithm. As illustrated in Figure 1, YOLOv4 comprises three main components: the backbone feature extraction network CSPDarknet53, the feature fusion network PANet, and the prediction head. Specifically, CSPDarknet53 integrates Darknet-53 with CSPNet, incorporating partial dense blocks and transition layers. The backbone feature extraction process involves three steps: (1) performing convolution, normalization, and activation function operations on the input image; (2) conducting preliminary feature extraction through five Res_body blocks; and (3) extracting feature maps with higher semantic information from the last three Res_body blocks. These feature maps are then convolved and processed through Spatial Pyramid Pooling (SPP) before being fed into the PANet for further feature refinement. Finally, the prediction results are generated via three YOLO Head layers. Mish, used as the activation function, is defined by Equation (1) and visualized in Figure 2.  
\[\textit{Mish}=x\times tanh(ln(1+\textit{exp}(x))) \tag{1}\]  
Figure 1: Mish activation function.
# Adaptive Model-Free Coupling Controller Design for Multi-Axis Motion Systems
## 3 Adaptive Model-Free Coupling Controller Design Using RFNN
### Discussion on Adaptation of Controller  
In this section, we would like to illustrate the necessity of being adaptive through the discussion of system with nonlinear phenomenon. Herein, we consider that the nonlinear phenomenon includes force ripple and frictional force. The used frictional force (\(F_{f}\)) is the identification results of [29]  
\[F_{f}=(f_{c}+(f_{s}+f_{c})e^{-(\dot{x}/\dot{x}_{s})^{2}})sign(\dot{x}) \tag{29}\]  
where \(f_{s}=20\)N, \(f_{c}=10\)N, and \(\dot{x}_{s}=0.1\) m/s, and the used force ripple is provided by the identification result of [28]. The actual mass and viscous friction coefficient are 6.2152 kg and 0.000000001349 Ns/m, respectively. The desired trajectory in X-Y-Z axis is  
\[x_{d}(t)=\left[\begin{array}{c}r\cos(wt)\cdot\cos(30)\\ r\sin(wt)\\ r\cos(wt)\cdot\sin(30)\end{array}\right] \tag{30}\]  
where \(r=0.5\) m, \(v=0.05\) m/s, and sampling time is 0.0001 s. Figure 7 shows the simulation results, (a) system trajectories; (b) contour errors; (c) shows the control performance of each axis; and, (d) shows the control effort of each axis, in which three cases are considered; green-line (case 1): system without \(F_{f}\), \(F_{r}\), and controller parameters without update; red-line (case 2): system with \(F_{f}\), \(F_{r}\), and controller parameters without update; black-line (case 3): system with \(F_{f}\), \(F_{r}\), and controller parameters with update). In addition, Table 1 introduces the performance of different case in maximum contour error, total contour error, and RMSE. From Table 1, we can clearly observe that the nonlinear phenomenon affects the performance of controller, but through the adaptive approach we can reduce the effect or nonlinear phenomenon in max error and total error. From Figure 7 and Table 1, we can find that the proposed approach performs well with small contour errors. The performances of Cases 2 and 3 can conclude that the adaption of controller is necessary for nonlinear uncertain systems.  
Figure 6: Comparison of actual and calculation contour errors, (**a**) trajectories; (**b**) contour errors.
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 6 Results
### Output SIR vs. Number of Interferences  
The impact of the number of interferences on the output SIR can be seen in Figure 7.  
For reference, Figure 6 is included in Figure 7 in the case of 0 interferences, and it can be observed that the overall enhancement performance is reduced considerably when interferences are present. However, both strategies were able to improve upon the average performance of the original Demucs, Demucs-PhaseBeamform providing the best performance in all cases in which there are interferences present.