esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Model
### Regime of Boundary Lubrication  
When the nominal film thickness is less than or equal to the combined surface roughness:  
\[\frac{h}{\sigma}\leq 1, \tag{14}\]  
the contact forces are carried predominantly by the surface asperities. This is the case of the boundary lubrication regime. Several computational models have been developed to capture the effect of asperity contact [20; 33; 34]. However, the most popular theory of the rough surface contact remains to be that conceived by [PERSON] and [PERSON] [35] (or [PERSON] and [PERSON] [36]). According to their work, the formula for the asperity contact pressure \(p_{\rm c}\) takes the form:  
\[p_{\rm c}=\frac{8\pi}{5}\eta_{\rm r}\sigma\beta KF_{5/2}, \tag{15}\]  
where \(\eta_{\rm r}\) indicates the surface density of roughness peaks, \(\beta\) reflects the average radius of the curvature of the rough surface asperities, and \(F_{5/2}\) represents the function describing the increase in the contact pressure dependent on the thickness ratio [35]. Parameter \(K\) is:  
\[K=\frac{2\sqrt{2}}{3}\eta_{\rm r}\sigma\beta E^{\prime}\sqrt{\frac{\sigma}{ \beta}}, \tag{16}\]  
where \(E^{\prime}\) indicates the combined elastic modulus:  
\[\frac{1}{E^{\prime}}=\frac{1-\
u_{1}^{2}}{E_{1}}+\frac{1-\
u_{2}^{2}}{E_{2}}, \tag{17}\]  
where \(E_{1}\) and \(E_{2}\) represent the elastic modules of each of the contact surfaces, and \(\
u_{1}\) and \(\
u_{2}\) represent their [PERSON]'s ratios. [PERSON]'s law of friction is valid for the asperity contact:  
\[F_{\rm asp,f}=f_{\rm dry}\int\int_{A}p_{\rm c}\mathrm{d}x\mathrm{d}y, \tag{18}\]  
where \(F_{\rm asp,f}\) indicates the asperity contact frictional force, \(A\) represents the contact area, and \(f_{\rm dry}\) indicates the dry friction coefficient.  
From a vibration point of view, contact asperities can lead to a random excitation of resonances--a phenomenon called the Tribofilm-Asperity Interaction. The vibrations then affect the pressure distribution in the lubricant layer. It is a stochastic process and is not considered in the case of this manuscript. More details can be found in Ref. [37].
## 4 Experimentation and Results
### Sensitivity Analysis Results  
Table 16 summarizes the impact of varying these key parameters on the model's performance:  
This investigation shows that attaining optimal performance requires careful parameter adjustment. The parameters were adjusted explicitly for retail settings where high accuracy and low latency are critical.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Low Setting** & **Optimal Setting** & **High Setting** \\ \hline Confidence Threshold & 0.3 (High Recall, Low Precision) & 0.45 (Balanced) & 0.7 (High Precision, Low Recall) \\ \hline IoU Threshold & 0.5 (High Recall) & 0.55 (Balanced) & 0.7 (High Localization Accuracy) \\ \hline Max Cosine Distance (DeepSORT) & 0.2 (Low Identity Switch) & 0.3 (Balanced) & 0.5 (High Flexibility) \\ \hline Re-ID Confidence Threshold & 0.55 (High Recall) & 0.65 (Balanced) & 0.75 (High Precision) \\ \hline \end{tabular}
\end{table}
Table 16: Impact of key parameters on model performance.  
Figure 17: DeepSORT tracking performance.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 5 Simulation and Analysis  
In this section, the proposed anti-jamming method is simulated and validated. The existing deceptive interference false targets are named EFT1 and EFT2, respectively, and new deceptive interference false targets are named NFT1 and NFT2, respectively. Considering that the number of transmit array elements of the radar is \(M=10\), the number of receive array elements is \(N=10\), the information of the range parameter and angle parameter of the desired target is \((20\ \text{km},5\ ^{\circ})\), and the pulse repetition cycle is 0.2 ms, so the maximum blur-free distance is 30 km, and the range bin is set to 300. Table 1 shows the simulation parameter settings of the FDA-MIMO radar.
## Abstract  
Slope displacement is a crucial factor that affects slope stability in engineering construction. The monitoring and prediction of slope displacement are especially important to ensure slope stability. To achieve this goal, it is necessary to establish an effective prediction model and analyze the patterns and trends of slope displacement. In recent years, monitoring efforts for high slopes have increased. With the growing availability of means and data for slope monitoring, the accurate prediction of slope displacement accidents has become even more critical. However, the lack of an accurate and efficient algorithm has resulted in an underutilization of available data. In this paper, we propose a combined EEMD-IESSA-LSSVM algorithm. Firstly, we use EEMD to decompose the slope displacement data and then introduce a more efficient and improved version of the sparrow search algorithm, called the irrational escape sparrow search algorithm (IESSA), by optimizing it and incorporating adaptive weight factors. We compare the IESSA algorithm with SSA, CSSOA, PSO, and GWO algorithms through validation using three different sets of benchmark functions. This comparison demonstrates that the IESSA algorithm achieves higher accuracy and a faster solving speed in solving these functions. Finally, we optimize LSSVM to predict slope displacement by incorporating rainfall and water level data. To verify the reliability of the algorithm, we conduct simulation analysis using slope data from the xGTX1 monitoring point and the xGTX3 monitoring point in the Yangtze River Xin Tan landslide and compare the results with those obtained using EEMD-LSSVM, EEMD-SSA-LSSVM, and EEMD-GWO-LSSVM. After numerical simulation, the goodness-of-fit of the two monitoring points is 0.98998 and 0.97714, respectively, which is 42% and 34% better than before. Using Friedman and Nemenyi tests, the algorithms were ranked as follows: IESSA-LSSVM \(>\) GWO-LSSVM \(>\) SSA-LSSVM \(>\) LSSVM \(>\) LSSVM. The findings indicate that the combined EEMD-IESSA-LSSVM algorithm exhibits a superior prediction ability and provides more accurate predictions for slope landslides compared to other algorithms.  
S 2023 13 9582 1
## 1 Introduction
The development of this paper was based on two assumptions. First, to overcome the methodological limitations currently reported for eSSPM, field partitioning essays should consist of a two-steps process (i.e., nested field partitioning) where a rough distinction between pest presence and pest absence zones (i.e., binary field partitioning) precedes the subdivision of resulting sub-field units (i.e., complementary field partitioning). Second, external validation of eSSPM field partitioning essays based on biologically interpretable CVI should facilitate the selection of optimal MC algorithms to be used and appropriate numbers of MZ to be delineated. To prove these statements: (1) we used high-resolution environmental data from a Tahiti line orchard and realistic simulations of six common citrus pests to generate a series of virtual infestation scenarios suitable for methodological testing purposes; (2) we implemented a series of nested field partitioning essays to test their capability to minimize sub-optimal zoning results reported for current eSSPM methods, and (3) we used two biologically meaningful CVI to compare the performance of 10 MC algorithms and to determine appropriate numbers of MZ to be considered during field partitioning essays. The use of simulated pest data allowed the development of testing essays under controlled virtual scenarios, an advised condition to assess modeling method ologies in ecology since it grants researchers unrestricted access to statistical processes and evidence necessary for drawing robust conclusions about natural mechanisms [12; 13; 14]. Five main contributions are presented in this paper. First, a clear distinction between SSIPM and eSSPM as conceptually and methodologically independent implementations of SSPM. Second, robust empirical evidence regarding the utility of nested field partitioning essays on overcoming the limitations reported for current eSSPM zoning approaches. Third, solid empirical evidence regarding the utility of biologically meaningful CVI to test the performance of MC algorithms and select adequate numbers of MZ during eSSPM zoning essays. Fourth, the first precedent on the simultaneous use of multiple MC algorithms to delineate MZ within the context of PA. Finally, an up-to-date workflow that considerably improves the accuracy of zoning methods presently implemented in eSSPM (Figure 1).
###### Abstract  
In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss-Newton, Levenberg-Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (\(\sim\)49%) was more than two-fold higher than that of the computationally obtained one (\(\sim\)22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers.  
system identification; flapping-wing micro air vehicle; longitudinal mode; model refinement; gray box model; onboard sensors +
Footnote †: journal: Applied  
Acticle Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors
## 2 Proposed Enhanced PNCC Algorithm
### Noise Suppression  
Most noise suppression techniques that are used in speech recognition systems are based on using different filtering techniques on several frequency frames. Increasing the number of analysis frames provides high performance for modeling and eliminating noise effects [6; 20; 21; 22]. Many algorithms are based on high-pass or band filtering techniques along with frames such as the RASTA-PLP feature extraction technique. RASTA filtering is used in this technique. This filter is a band-pass filter designed to remove the slowly varying background noise along with frames [6]. The mathematical expression of PSD should be a positive value. One of the drawbacks of using high-pass filtering is that it can obtain a negative value [6]. For the noise suppression technique in the standard PNCC system, firstly the medium-time power is applied by calculating the running average of five following frequency channels. It is considered low-pass filtering along with frames. Secondly, another filtering technique is applied using asymmetric noise suppression filtering, which is combined with temporal masking. This technique is used to remove the impact of slowly-varying background noise and define excitation and non-excitation states. The excitation state represents the filtered speech activity state, while the non-excitation states represent the non-voice activity, which is determined from the estimated floor-level. Then, weight smoothing is applied by calculating the running average across channels. Finally, the original time-frequency domain is modulated by the constructed transfer function. Nevertheless, in the proposed PNCC method, the large-time power is used instead of medium-time power and then channel bias minimizing is applied as explicated in the following section.
## 3 Data Pre-Processing and Feature Analysis
### XGB Feature Importance  
Several features described in Section 2.1 are used as inputs for the XGB algorithm to compute the feature importance with a charging load for a fleet of EVs (Figure 5). The increase in the feature importance of the individual predictors in the tree is then visualized. A higher value of the metric \"Feature gain score\", compared to another feature, implies it is more important for generating a prediction. It can be seen from Figure 5 that the charge load is sensitive to temperature variables. In addition, the SD charging load, which has the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Units** & \
\begin{tabular}{c} **MAPE** \\ **(Testing)** \\ \end{tabular} &
\begin{tabular}{c} **MAPE (Training)** \\ \end{tabular} \\ \hline
1 & 5 & 3.24 & 3.04 \\
1 & 30 & 3.10 & 2.99 \\
1 & 50 & 3.15 & 2.98 \\
1 & 100 & 3.12 & 2.95 \\
2 & 5 & 2.95 & 2.96 \\
2 & 30 & 2.84 & 3.02 \\
2 & 50 & 2.79 & 3.03 \\
2 & 100 & 2.63 & 2.93 \\
3 & 5 & 2.99 & 3.11 \\
3 & 30 & 2.89 & 3.05 \\
3 & 50 & 2.87 & 3.02 \\
3 & 100 & 2.83 & 3.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MAPE (%) of the proposed model architecture.  
Figure 4: Number and distribution of EVs used in this project.  
highest feature gain score, is an important feature for load prediction. This assumption is consistent with the data analysis results.
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 2 Basic Knowledge
### Risk Measurement  
Risk measurement mainly comes from the financial sector, where the loss of an investment is the risk, and its size is proportional to the magnitude or amount of the asset reduction. Three common risk measures are volatility, value at risk (VaR), and conditional value at risk (CVaR) [25].  
Volatility generally refers to the change in a financial asset over a period of time, usually measured by the standard deviation of the return of the asset over a past period of time. The higher the volatility of an asset, the riskier it can be considered.  
At a certain confidence level, the expected losses that may occur in a certain period of time in the future do not exceed a certain value, which is called the value at risk. As shown in Figure 3, suppose the sum of the probability values of the shaded regions is 5%, so \(x\) is the value at risk under the 95% confidence level. The value at risk can be calculated by Equation (1):  
\[VaR=\mu+\sigma\times Z(\alpha) \tag{1}\]  
where \(\mu\) is the expected return, \(\sigma\) represents the volatility of events, and \(Z(\alpha)\) represents the quantile at the confidence level \(\alpha\).  
The method of calculating value at risk is to obtain its quantile at a specific sub-site that can be effectively combined with the value-distribution reinforcement learning algorithm to directly obtain quantile estimates of specific loci of the model's output. But it still has some  
Figure 3: The diagram of value at risk.  
Figure 2: The difference between probability distribution function and quantile function. (**a**) Probability distribution function; (**b**) quantile function.  
shortcomings: (1) It may underestimate the risk in many cases; (2) it only applies to the normal distribution, when the distribution of random variables presents the characteristics of a fat-tailed distribution, the calculation of value at risk is unlikely to accurately reflect the risk of events. This is because the fat-tailed distribution means that the probability of occurrence of some extreme events does not decrease with the increase in the extreme degree, but there is a considerable probability of occurrence. Therefore, the probability will not gradually decrease in the end segment, but there is a peak probability, as shown in Figure 4.  
The conditional value at risk can make up for the deficiency of value at risk; it is defined as the expected loss that a random event can produce at a given confidence level over a period of time. As shown in Figure 4, the conditional value at risk is to calculate the expected value of the shaded part with Equation (2).  
\[CVaR=\mu+\sigma\frac{e^{-\frac{Y^{2}}{2}}}{\sqrt{2\pi}(1-X)} \tag{2}\]  
where \(Y\) is the critical value at the \((1-X)\%\) confidence level under the standard normal distribution.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model
### Overlap Avoidance for Distributed Pheromone Repel Mobility Model
#### 3.5.2 Proposed Overlap Avoidance Scheme for Distributed Pheromone Repel  
We enhance DPR model by avoiding overlapped reconnaissance. We use the same overlap detection and guidance vector scheme as in Section 3.4.1. In order to assign the amount of increasing probability, we calculate the difference between the current direction and the guidance vector, which is denoted as \(\psi\). For example, Figure 4a shows \(\psi_{1}\) and \(\psi_{2}\) of two UAVs. Thus, the action table of Table 4 of the DPR model is changed as Table 5.  
Table 5 is the action table for avoiding overlap. In Figure 4a, \(u_{2}\)'s guidance vector heads the right direction so that we add the weighted value in _turn right_ probability.  
**Example 2**.: _Let us consider the same UAV of Example 1 and assume that its guidance vector is located in the left side with \(\psi\) of 90\({}^{\circ}\). Then, the probability of turning left is calculated as_  
\[\frac{T_{p}-L_{p}}{2\times T_{p}}\times(1+\frac{\psi}{180})\ =\ \frac{42-18}{2\times 42} \times(1+\frac{90}{180})\ =\ \frac{24}{84}\times\frac{3}{2}\ =\ \frac{36}{84}.\]  
Figure 6: An example of pheromone scanning pattern [12].  
Since \(\beta_{L}\) is \(\frac{42-18}{42+18}\cdot\frac{90}{180}\ =\ \frac{1}{5}\), the probability of straight ahead is given by  
\[\frac{T_{p}-C_{p}}{2\times T_{p}}\times(1-\beta_{L})\ =\ \frac{42-17}{2\times 42} \times(1-\frac{1}{5})\ =\ \frac{25}{84}\times\frac{4}{5}=\frac{20}{84}.\]  
Similarly, the probability of turning right is obtained by \(\frac{28}{84}\). Thus, the UAV turns to the left side with highest probability so that it can avoid from the overlapped area. (Let us note that the probability of turning right is highest in case of no overlapped area, as in Example 1.)
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 3 Image Processing
### Multi-Scale Recursive Interrogation Filter (MRIF)
#### 3.4.1 MRIF Core
### Luminance Map-Based False Positive Filtering

The MRIF procedure, in addition to its intrinsic false positive filtering capacity stemming from the recursive multi-scale analysis, is supplemented by a dedicated false positive identification algorithm that processes the segments output by MRIF. The procedure is outlined in Algorithm 6. ```
1:Input: \(\vec{r}_{0}^{\prime}=\vec{r}_{0}^{\prime}+\vec{\Lambda}(L,\vec{r}_{\rm{iw}})\), \(\vec{r}_{\rm{iw}}^{\prime}=\vec{r}_{0}^{\prime}+\vec{\Lambda}(L,  
uses a luminance compression factor \(c=0.5\) for SCTMM. We found that false positives are efficiently filtered (i.e., also minimizing true positive elimination) when \(\eta\in[0.1;0.125]\). ```
Input: \(\bullet\) Updated bubble shape masks for the FOV (Algorithm 4)  Global SCTMM filter output (Algorithm 3, Step 4)  Converged IWs (Algorithm 4)  Map the global SCTMM filter output onto converged IWs  Apply the SCTMM correction (7)  Multiply by the segment binary mask  Normalize the image  Compute \(\langle I\rangle\cdot\max(I)\) for all segment regions  Thresholding for all segments: if\(\langle I\rangle\cdot\max(I)<\eta\); \(\eta\in[0;1]\) (user-defined)then  Flag the segment as a false positive else  Nothing  end if Remove the identified false positives from the bubble shape mask Output: FOV bubble shape masks without detected false positives
```
**Algorithm 6**Luminance map-based false positive filtering for MRIF output  
After filtering the MRIF output using Algorithm 6, all properties of interest are measured for all remaining bubble segments and logical filters can be applied for further false positive elimination. In our case, logical filters check for implausible bubble coordinates, sizes and aspect ratios and remove the outliers from the dataset of measured bubble shapes. Finally, the resulting data can be post-processed and interpreted.
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 4 Experiment  
### Setup  
After comparing RCAN [15], SAN [20], EDSR [28], and NLSN [31], we used DIV2K [41] as the training dataset for the network, which contains 800 samples of RGB images with a resolution of 2K. For model testing, we used five SISR benchmark datasets: Set5 [42], Set14 [43], B100 [44], Urban100 [45], and Manga109 [46]. All samples were analyzed with a bicubic downsampling (BI) degenerate model and a blurred descent (BD) model. We evaluated the predicted SR results using PSNR and SSIM, and all tests were performed on the Y channel after the image was converted to YCbCr space. During the model training process, we augmented the dataset images by RFP data augmentation. We provided 16 LR image patches with a size of 48 \(\times\) 48 as the network input for each iteration batch. The LNLCA model was iteratively trained using the ADAM optimizer [47], where \(\beta_{1}=0.9\), \(\beta_{2}=0.99\), and \(\epsilon=10^{-8}\). During training, we initially set the learning rate to \(10^{-4}\) and then halved it every 200 batches. We first trained the model for \(8.4\times 10^{5}\) iterations, after which we used the ATG training strategy to fine-tune the entire network for \(5.6\times 10^{4}\) iterations with a learning rate of \(10^{-5}\). In our LNLCN model, we used the ReLU activation function in most layers to help the model better learn nonlinear relationships between input data. At the same time, we also used some other activation functions, such as Sigmoid and Softmax. Our model was implemented in the PyTorch [48] on an Nvidia 1080 Ti GPU.