esa-sceva/satcom-chunk-collection huggingface dataset:
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 2 Methodology
### Methodological Process Design  
SVM [32] is a kind of supervised machine learning method widely used for classification and classification tasks [33]. It is particularly effective for binary classification problems. The goal is to identify support vectors, i.e., subsets of samples that are farthest from the hyperplane and represent different classes.  
One of the strengths of SVM [34] is its stability, even with small sample sizes. It can handle different classification problems and optimize the classification results by choosing appropriate kernel functions. For this experiment, \"poly\", \"rbf\", \"linear\" and \"sigmoid\" were chosen to train and classify the experimental dataset to obtain the best classification model.  
Logistic regression [35] serves as a widely employed classification algorithm that is particularly suitable for binary classification tasks. Hence, it is embraced as a classifier model. Additionally, naive Bayes classifiers employ Bayes' theorem for classification. Thus, the Gaussian naive Bayes classifier was selected [36].  
The entire experiment was conducted using Python, utilizing machine learning techniques. The training process consists of using the extracted features to train the melanoma classification model. The workflow of the machine learning process is shown in Figure 4.  
The entire experimental procedure was executed using Python, employing various machine learning techniques. The training phase involved utilizing the extracted features to train the melanoma classification model. The workflow of this machine learning process is visually depicted in Figure 4. The dataset was divided into two subsets: an 80% portion earmarked as the training set, and a remaining 20% portion designated as the test set:  
1. The initial step encompassed the utilization of all feature sets (TCS) as input features for the machine learning process. The recursive feature elimination (RFE) algorithm was employed for feature ranking and selection. To facilitate an internal visualization of these features, a 100-fold randomization method was employed for carrying out the statistical analysis. The outcomes of the feature selection process were then subjected to comprehensive statistical analysis, culminating in the derivation of a ranking indicating the importance of each feature. Based on this ranking, features with high importance were selectively chosen to create a novel feature set termed \"RFE-TCS\";
2. The repertoire of machine learning methods encompassed SVM (using the polynomial kernel, radial basis function kernel, linear kernel, and sigmoid kernel), logistic regression, and a Gaussian Bayesian classifier;
3. This divisional process was repeated 100 times, with each instance involving the random shuffling of the dataset. The models were all cross-validated using tenfold cross-validation. As part of the evaluation protocol, performance metrics such as average classification accuracy and the area under the curve (AUC) were meticulously computed.
## 3 Methods
### Accuracy Assessment  
The accuracy evaluation metrics employed in this study included the overall accuracy (OA), F-score, kappa coefficient, recall, and precision. The overall accuracy represents the proportion of correctly classified pixels in the classification results. Kappa measures the agreement between the classification image and the reference image. Recall is the proportion of rice pixels in the reference image that have been correctly classified. Precision is the proportion of rice pixels in the classification image that are correctly classified. F-score is the harmonic mean of precision and recall. It takes into account how the data are distributed.  
Additionally, Sankey maps were used to show the flow direction of the classification results and visually analyze the misclassification situation. The width of the arrows in the Sankey maps was proportional to the flow rate of each class.  
The formulas for the accuracy evaluation metrics were as follows.  
\[Recall=\frac{Num\big{(}P_{rice}^{c}\cap P_{rice}^{r}\big{)}}{Num\big{(}P_{rice }^{r}\big{)}}, \tag{10}\]  
\[Precision=\frac{Num\big{(}P_{rice}^{c}\cap P_{rice}^{r}\big{)}}{Num\big{(}P_{rice }^{c}\big{)}}, \tag{11}\]  
\[F_{score}=\frac{2\times Recall\times Precision}{Recall+Precision}, \tag{12}\]  
\[OA=\frac{Num\big{[}\big{(}P_{rice}^{c}\cap P_{rice}^{r}\big{)}\cup\big{(}P_{ others}^{c}\cap P_{others}^{r}\big{)}\big{]}}{Num\big{(}P_{all}\big{)}} \tag{13}\]  
\[P_{e}=\frac{Num\big{(}P_{rice}^{c}\big{)}\times Num\big{(}P_{rice}^{r}\big{)}+ Num\big{(}P_{others}^{c}\big{)}\times Num\big{(}P_{others}^{r}\big{)}}{ \big{[}Num\big{(}P_{all}\big{)}\big{]}^{2}} \tag{14}\]  
\[Kappa=\frac{OA-P_{e}}{1-P_{e}} \tag{15}\]  
where \(Num\big{(}\ast\big{)}\) denotes a function that calculates the number of pixels; \(P_{rice}^{c}\) and \(P_{rice}^{r}\) refer to the rice pixels in the classification image and corresponding reference image, respectively; \(P_{others}^{c}\) and \(P_{others}^{r}\) refer to all non-rice pixels in the classification and reference images, respectively; and \(P_{all}\) refers to all the pixels in the classification image.
###### Abstract  
Various construction activities (such as piling) often generate high-intensity ground vibrations that adversely affect the surrounding environment. A common way of assessing vibration impact is to conduct on-site ground vibration monitoring at several selected locations. However, as vibration sources are often not pinpointed in the construction process, this approach cannot predict the vibration intensities at locations other than those monitored points. Therefore, the localization of vibration sources (e.g., vibratory sheet pile driving location) is crucial to quantify the corresponding vibration intensities in a broad area. This paper investigates a time-based source localization method based on wave propagation characteristics derived via three-dimensional finite element modeling of vibratory sheet pile driving in an infinite half-space soil domain. Satisfactory accuracy in the localization of the vibratory driving sources was achieved in all investigated numerical examples. Field validation tests were also conducted on a construction site with ongoing vibratory sheet pile driving work. A site-specific empirical formula was adopted to model the attenuation of measured vibration intensities with the increasing distance from the localized vibration source. As such, the combined utilization of the estimated vibration source location and the adopted empirical formula can achieve vibration intensity assessment in a broad surrounding area rather than being confined to a few monitored points.  
Keywords:construction-induced vibrations; spatial wave propagation; three-dimensional finite element modeling; time-based source localization method; global vibration intensity estimation +
Footnote †: journal: Applied Sciences
## 4 Experiments and Result Analysis
### Result Analysis  
Figure 6 illustrates the changes in key metrics during the training process before and after the improvement of the YOLOv5 model. Detailed values are provided in Table 1. As shown in Figure 6a, the improved model converges faster and achieves a lower optimal loss value during stabilization, decreasing from 0.014 to 0.011. This indicates that the WIoU loss function better optimizes boundary box matching and localization during training, effectively handling complex defect shapes while improving model performance and significantly accelerating the convergence speed. Upon examining Figure 6b,c, it is observed that the improved YOLOv5 achieves a precision of 0.941, which is 11.5%  
higher than the value of 0.826 before the improvement. Additionally, the recall rate of the improved YOLOv5 is 0.892, showing a 5.4% increase compared to the value of 0.838 before the improvement. Figure 6d shows that a higher [EMAIL_ADDRESS] value indicates better detection accuracy and overall model performance. The improved YOLOv5 achieves an [EMAIL_ADDRESS] value of 0.946, which is 6.9% higher than the value of 0.877 before the improvement. Notably, as shown in Table 1, the improved model introduces only a slight increase in the number of parameters compared to the original model. Although its inference speed (FPS) decreases by 18.94, the [EMAIL_ADDRESS] significantly improves by 6.9%. These results suggest that the improved model achieves a more favorable balance between accuracy and speed, making it better suited for real-time industrial X-ray weld defect detection.  
Figure 7 compares the optimal [EMAIL_ADDRESS] values for circular defects, linear defects, non-fusion defects, non-penetration defects, and crack defects before and after the YOLOv5 model improvement. It is evident that the improved model shows enhanced detection accuracy for all types of defects, with a significant increase in accuracy for non-penetration  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Loss** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]/\%** & **Params** & **FPS** \\ \hline YOLOv5 & 0.014 & 82.6 \(\pm\) 0.2 & 83.8 \(\pm\) 0.1 & 87.7 \(\pm\) 0.2 & 7,023,610 & 227.27 \\ Improved YOLOv5 & 0.011 & 94.1 \(\pm\) 0.3 & 89.2 \(\pm\) 0.1 & 94.6 \(\pm\) 0.2 & 7,023,789 & 208.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of evaluation metrics between the original YOLOv5 model and its improved version.  
Figure 6: Comparison of the performance between the original YOLOv5 model and its improved version: (**a**) Total Loss; (**b**) Precision; (**c**) Recall; (**d**) [EMAIL_ADDRESS].  
and crack, which improved by 14.3% and 15.3%, respectively. This indicates that the increase model effectively addresses the original model's poor detection accuracy for non-penetration and crack defects, thereby enhancing the overall defect detection precision.
## 2 Experimental Setup and Methods  
### Actuator Structure and Vertical Tail Model Design  
The internal structure of the self-excited oscillating jet actuator has been relatively mature. The actuator used in this paper has been modified based on the conceptual design drawing. The structure and size parameters are shown in Figure 1. The width of the actuator feedback channel is 3 mm, the width of the mixing chamber inlet is 6 mm, the width and height of the jet outlet are 14.5 mm \(\times\) 2 mm, and the outlet sweep angle is 80\({}^{\circ}\).  
The vertical tail scale model is made of 3D printing resin material. The airfoil is NACA0012, with a span of 530 mm, a root tip ratio of 3.77, an average aerodynamic chord length of 281 mm, a leading-edge sweep angle of 45\({}^{\circ}\), and a reference area of 0.134 m\({}^{2}\). An equal percentage of rudder chord length is taken, accounting for 35% of the vertical tail chord length. The blockage percentage of the model in the wind tunnel is less than 3%. Nine oscillating jet actuators are arranged at the rear edge of the stabilizer in front of the hinge at an equal distance of 65 mm along the span direction. The air-blowing section of the actuator is parallel to the symmetrical plane of the vertical tail model, as shown in Figure 2. Nine actuators are numbered 1-9 from wing root to wing tip and divided into three groups, three for each group. Three for wing root are defined as group I, and three for the wing tip as group III. The air compressor's three groups of actuators are supplied with air from the wing root separately to avoid insufficient air supply to the actuator at the wing tip due to the weakening of the air source along the span. The electric turntable controls the sideslip angle of the vertical tail and the rudder deflection angle. The turntable resolution and absolute positioning accuracy are 0.01\({}^{\circ}\).  
The definitions of the model sideslip angle and rudder deflection angle under typical working conditions are shown in Figure 3. This paper investigates typical problems with the takeoff or landing of civilian aircraft. When the aircraft encounters a left gust or a high-speed crosswind, the incoming flow will generate a force on the vertical tail to yaw  
Figure 1: Structure and size of oscillating jet actuator.  
the aircraft, resulting in an accident. In this case, \(+\delta\) is the rudder deflection angle, and the actuator on the windward side of the vertical tail is turned on, making the vertical tail generate aerodynamic force in the positive Y direction. In this way, the vertical tail can provide positive lateral force at the negative sideslip angle, give the right yaw moment for the whole aircraft, and enable the aircraft to take off and land safely in significant crosswind conditions.
## 2 Materials and Methods
### Data Pre-Processing  
The spectral data mostly contained a considerable level of noise, possibly generated from the camera and environmental effect. The acquired spectral data were subjected to several pre-processing methods, including mean normalization, multiplication scatters correction (MSC), standard normal variate (SNV), and Savitzky-Golay first derivative (Figure 2). Normalization was used to fit spectral data within a comparable range (0-1) to compensate for inconsistencies due to the thickness of the sample and optical source length. Standard normal variate (SNV) pre-processing was used to correct spectral information changes caused by scattering and particle size variability [31]. MSC was used to correct the spectra scattering intensity [31], while Savitzky-Golay's first derivatives were applied to correct the baseline effect and eliminate overlapping peaks [32].
## 3 Results and Discussions
### Analysis of Prediction Results  
#### 3.2.1 Microstructure  
Figure 4a,d show the EBSD observations of the two grain morphologies, indicating that they show significant changes in grain size and preferred crystal orientation. The microstructure of the AB sample consists mainly of columnar grains with a strong \(<\)100\(>\) texture and a maximum multiples of uniform density (MUD) value of 14.06, as shown in Figure 4b. Figure 4e displays that complete recrystallisation occurs in the microstructure of the HT-1200 sample, which consists mainly of equiaxed grains with relatively random grain orientation and a weak texture (max MUD = 4.40). The percentage of low angle grain boundaries (LAGBs) in AB specimens was 17.07%, as shown in Figure 4c. After heat treatment, the percentage of LAGBs in the HT-1200 sample decreased due to the occurrence of recrystallization, and a large number of TGBs appeared at the 60\({}^{\circ}\) position, as shown in Figure 4f. In addition, compared with the AB sample, the proportion of recrystallized grains in the microstructure of the HT-1200 specimen increased dramatically, whereas the deformed grains almost completely disappeared, as shown in Figure 4g.  
The mechanical properties of the AB and HT-1200 samples are shown in Figure 5a. The yield strength (YS) of the specimens decreased after heat treatment at 1200 \({}^{\circ}\)C, but the elongation increased significantly. The decrease in YS may be attributed to the dissolution of Laves and carbides. The inset shows the fracture surfaces of both samples. A high number of dimples and tear ridges are observed on the fracture of both samples, indicating that both samples are ductile fractures. The HT-1200 sample has a larger dimple size and a deeper tear ridge, which has a better plastic deformation capacity than the AB sample. Figure 5b presents the statistical histogram of tensile test results.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 2 Background
### UAV Modulation and Protocols  
RC, telemetry and video links use RF channels such as Wi-Fi and LTE [12]. Video transmission employs Orthogonal Frequency-Division Multiplexing (OFDM) [5], while many systems operate in the 2.4 GHz band, using spread spectrum technologies for interference resilience [13; 14].  
Spread spectrum signals extend the bandwidth over the original channel, reducing interference and enabling secure communications [15]. Common digital modulation techniques used in drone technologies include frequency hopping spread spectrum (FHSS), direct sequence spread spectrum (DSSS) and OFDM [14; 16].  
* **OFDM**: Enhances data rates and spectral efficiency by employing multiple subcarriers, a key feature in systems like 802.11 WLAN, 802.16 WiMAX, and 3 GPP LTE [17; 18; 19].
* **DSSS**: Uses a Pseudo Noise code to spread the signal, employing Binary Phase Shift Keying (BPSK) or Quadrature Phase Shift Keying (QPSK) for data encoding. It operates below the noise floor but is vulnerable to strong jamming signals [20; 21].
* **FHSS**: Regularly hops carrier frequency between channels based on a pre-agreed sequence, enhancing resistance to interference. The 2.4 GHz and 5.8 GHz bands typically contain 30 to 60 channels, spaced by 1.5 to 2 MHz [5; 15].  
Control signals use DSSS+FHSS, DSSS, or OFDM. Video transmission in both bands predominantly relies on OFDM, while telemetry in the 2.4 GHz band uses both OFDM and DSSS, with the 5 GHz band typically using OFDM [16].
## Appendix A LSTM Model for MAP Prediction  
The LSTM model operates on the training dataset of the inputs (CeProp and CeRemi) \(\{x_{t}\}\) and the output (MAP) \(\{y_{t}\}\), with \(t\) denoting time steps. The LSTM model consists of the following layers:  
1. _Sequence input layer_: This layer accepts the input data and initializes the sequence for LSTM processing. Each time step \(t\) passes the feature vector \(x_{t}\) to the subsequent layers.  
2. _LSTM layer_: the model's core, the LSTM layer, operates with mathematical equations. It maintains internal variables, including the following:  
Figure 8: Boxplot representing the distribution of average correlation between MAP and CeRemi and CeProp across different BMI categories.  
* Input gate (\(i_{t}\)): The input gate controls the flow of new information into the cell state. It is computed using the sigmoid activation function \(\sigma\) and is defined as follows: \[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}).\] (1)
* Forget gate (\(f_{t}\)): The forget gate controls the retention of past information in the cell state and acts as a weighting factor of past-to-new data. It is computed similarly to the input gate and is defined as follows: \[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}).\] (2)
* Candidate cell state (\(\tilde{C_{t}}\)): The candidate cell state represents the new information that could be stored in the cell state. It is computed using the hyperbolic tangent activation function, defined as follows: \[\tilde{C_{t}}=\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}).\] (3)
* Cell state update (\(C_{t}\)): The cell state \(C_{t}\) is updated by combining the previous cell state \(C_{t-1}\) with the new information from the input gate and candidate cell state: \[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\tilde{C_{t}}.\] (4)
* Output gate (\(o_{t}\)): The output gate controls what information from the cell state should be used to compute the output. It is defined as follows: \[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o}).\] (5)  
Here, \(\sigma\) represents the sigmoid activation function, \(\tanh\) is the hyperbolic tangent activation function, \(W\) are weight matrices, \(b\) are bias vectors, and \(h_{t-1}\) is the previous hidden state.  
3. _Fully Connected Layer_: Following the LSTM layer, the Fully Connected Layer prepares the data for classification. It applies learned weights and biases to the output of LSTM.  
4. _Regression Layer_: The final Regression Layer assigns a continuous MAP value to each time step, ensuring accurate prediction.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 3 Proposed Resistorless Mixed-Mode Universal Filter
### TIM Filter  
With \(v_{in}=0\), the following LP, BP, and HP filter response in TIM can be obtained at the output terminals \(v_{01}\), \(v_{02}\), and \(v_{03}\), respectively:  
\[T_{ZLP}(s)=\frac{v_{01}}{i_{in}}=-\left(\frac{1}{g_{mA1}}\right)T_{LP}(s), \tag{14}\]\[T_{ZBP}(s)=\frac{v_{o2}}{i_{in}}=\bigg{(}\frac{1}{g_{mB2}\eta_{1}}\bigg{)}T_{BP}(s), \tag{15}\]  
and  
\[T_{ZHP}(s)=\frac{v_{o3}}{i_{in}}=-\bigg{(}\frac{1}{g_{mA2}}\bigg{)}T_{HP}(s). \tag{16}\]  
The realized filtering functions demonstrate that the circuit can generate all three standard biquadratic filter functions in all four operational modes utilizing the same circuit design. Thus, the circuit depicted in Figure 2 operates as a mixed-mode universal biquad filter, featuring the following key parameters:  
\[\omega_{p}=2\pi f_{p}=\sqrt{\frac{g_{mA1 SmB1}}{C_{1}C_{2}}}, \tag{17}\]  
and  
\[Q=\bigg{(}\frac{1}{g_{mB2}\eta_{1}}\bigg{)}\sqrt{\frac{g_{mA1 SmB1}C_{2}}{C_{1 }}}=\bigg{(}\frac{g_{mC1}}{g_{mB2}}\bigg{)}\sqrt{\frac{g_{mA1}C_{2}}{g_{mB1}C _{1}}}. \tag{18}\]  
where \(\eta_{1}=g_{mB1}/g_{mC1}\), \(\omega_{p}\) is the pole frequency in radians/second and \(Q\) is the quality factor of the filter. Equation (17) indicates that the circuit possesses electronic adjustability of the frequency \(\omega_{p}\) through \(g_{mA1}\) and/or \(g_{mB1}\). On the other hand, the parameter \(Q\) in Equation (18) can also be electronically and independently tuned without affecting \(\omega_{p}\) by \(g_{mB2}\) and/or \(g_{mC1}\). Consequently, the parameters \(\omega_{p}\) and \(Q\) are orthogonally tunable by controlling (\(g_{mA1}\) and/or \(g_{mB1}\)) and (\(g_{mB2}\) and/or \(g_{mC1}\)) in that order. This feature is desired in universal biquadratic filters to provide flexibility of designing and tuning. Table 2 provides the passband gains for the proposed filter functioning in all four modes.
## 4 Conclusions  
In this paper, we propose a modified RP model by adding trainable parameters to the standard RP model branches, using the SSFM to generate labels, and optimizing parameters through traditional backpropagation. While maintaining the same complexity as the RP model, the accuracy of the MRP model is greatly improved. When the input signal is a dual-polarization signal with a launch power of 13 dBm, the fitting error of the RP model is reduced by more than 75% after transmission through a 120 km standard single-mode fiber. We have also noted that under all launch power conditions in our paper, the accuracy of the MRP model is significantly higher than that of the RP model. In general, the paper validates the MRP model's performance under different launch powers, numbers of branches, fiber lengths, and pulse shaping filters, all of which show significant performance improvements. Furthermore, we point out that, compared to SSFM, the MRP model significantly reduces complexity while achieving the same degree of nonlinear fitting.  
Conceptualization, S.H.; methodology, S.H.; validation, Z.L. ([PERSON]); visualization, A.Y.; formal analysis, S.H.; investigation, S.H., Z.L. ([PERSON]), S.X. and A.Y.; resources, J.Z. and N.C.; data curation, S.H., Z.L. ([PERSON]) and S.X.; writing--original draft preparation, S.H.; writing--review and editing, J.Z., N.C., Y.Z., J.S., C.S., Z.L. ([PERSON]), Z.H. and W.C.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. and N.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China (2023 YFB2905700), in part by the National Natural Science Foundation of China under Grants (62171137, 62235005 and 61925104), in part by Key Research and Development Program of Jiangsu Province, China (No. SBE202202059), Key Project of Jiangsu Province Industrial Prospect and Key Core Technology (BE2022055-4).  
Figure 11: **(a)** RMSE and **(b)** EVM of the MRP and RP models under different roll-off factors (fiber length = 120 km, launch power = 13 dBm, branches number \(N_{step}\) = 20).  
Figure 12: Computational complexity and \(\Delta\)EVM of the SSFM and MRP or RP models (fiber length = 120 km, launch power = 13 dBm, sequence length \(S\) = 8192).