esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 3 Numerical Simulation
### Analysis of the Influence of Interface Element Cohesion on Stress Distribution Patterns  
Figure 11 reveals the regulation law of the cohesive force of the contact surface on the stress distribution of the stop filling body. The research results show that as the cohesion force of the contact surface increases from 0 to 200 kPa, both the vertical stress and the horizontal stress at the vertical centerline of the filling body present a significant decreasing trend. It is notable that this change process shows obvious phased characteristics: when the cohesion is less than 30 kPa, the stress state of the filling body shows strong sensitivity, and both the vertical stress and the horizontal stress fluctuate significantly. When the cohesive force exceeds 30 kPa, although the stress still increases with the increase of depth, its variation range tends to stabilize and no longer shows sharp fluctuations. This phenomenon reveals an important mechanical mechanism: there is a critical threshold effect on the influence of the bonding force of the contact surface on the stress distribution of the filling body. When the bonding force reaches or exceeds the critical value of 30 kPa, its regulatory effect on the stress distribution tends to be saturated. This discovery provides an important theoretical basis for the optimal design of the contact surface between the filling body and the surrounding rock.
## 4 Discussion
### Influences on Hearing Preservation
#### 4.4.2 The Electrode Depth Insertion and Hearing Preservation  
There was no statistically significant association between electrode insertion depth and hearing preservation rate in our research. Some studies support this discovery, and some have shown the opposite results, therefore, the dilemma exists of whether to implant a shorter electrode and increase the likelihood of hearing preservation or to choose a longer electrode and maximize cochlear coverage [24]. [PERSON] et al. [37] discovered no relationship between the angular insertion depth and loss of low-frequency pure-tone average, however, frequency-specific analysis revealed a weak but significant relationship between the angular insertion depth and loss of residual hearing at 250 Hz. [PERSON] et al. [38] and [PERSON] et al. [39] also discovered no significant correlation between the depth of implantation and hearing preservation, which is consistent with our findings. In contrast, [PERSON] et al. [24] found that deeper insertions were associated with greater loss in low-frequency hearing.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 2 Related Work
### Maize Leaf Severity Analysis
#### 2.2.2 Segmentation  
Several studies have used a threshold [40; 41; 42; 43; 44; 45; 46; 47] for leaf disease severity analysis. However, such techniques generally suffer from low classification rates due to poor sensitivity. [PERSON] addresses this problem by using an adaptive threshold for automatic segmentation. In their work, they first convert the RGB color images to Lab color space, and then the adaptive threshold value is obtained from the maximum intensity values of the blue color channel. Finally, disease quantification is performed by taking the ratio of the diseased parts to the ratio of the entire leaf image. Their method is computationally efficient, and, above all, delivers superior results even on poor-quality images. In particular, they were able to achieve a 99% classification accuracy on cherry crop images.
## 6 Simulation and Analysis
### Simulation Results
The larger the relay, the more energy the relay can receive, thereby increasing the transmitting power at the relay node to enhance the signal to interference and noise ratio at the destination node. This leads to higher system energy efficiency. Figure 6 shows the variation of the system energy efficiency with the energy conversion efficiency when the transmission power is 20 dB, 15 dB, and 10 dB, respectively. The energy efficiency increases as the energy conversion efficiency increases. From the Figure 6, we can also observe that under the three schemes, the speed of energy efficiency increase is rapid and then levels off. Figure 7 shows the system energy efficiency performance of FD dual-antenna energy harvesting, FD single-antenna energy harvesting, and HD when the \(P_{S}/N_{0}\) is different. The energy conversion efficiencies are 0.7 and 0.5, respectively. At this point, we set the time conversion factor to 0.4. According to Figure 5, the system energy efficiency of the FD system is always greater than that of the HD system. Figure 5: The system energy efficiency versus \(P_{S}/N_{0}\) with optimized \(\alpha^{*}\). Figure 6: The system energy efficiency versus \(\eta\) when \(P_{S}=[20 dB,15 dB,10 dB]\).
## 2 HFR Processing and Challenges
Also, HFR reproduction, decoding and editing require additional resource utilization compared with SFR due to the high number of frames per second, where possible effects of available acceleration approaches need to be researched further. Figure 2: Frame size trace sequence for (**a**) high-definition (HD) low frame rate (LFR) content in advanced video coding (AVC) format and (**b**) 4k or ultra-high-definition (UHD) HFR content in high-efficiency video coding (HEVC) format, as well as (**c**) their comparison after frame frequency alignment presented in a twenty seconds interval.
# Model-Based Adaptive Collaboration of Multi-Terminal Internal Force Tracking
## 1 Introduction
In the face of more complex models, complex dynamic forces, and unknown environments, the robustness of the algorithm still needs to be further strengthened to cope with uncertainty and unknowns. This paper aims to solve the time-varying complex internal force-tracking and control problem for strong, stable clamping and handling of unknown objects through a multi-terminal system. Specifically, in the proposed solution, the latter system is based on multi-terminal internal force workspace transformation, an adaptive control algorithm for time-varying force tracking from multiples slaves to a single master. Moreover, we study the estimation of uncertain and unknown motion and the adaptive reference trajectory generation method using absolute relative motion strategies. The proposed clamping and handling model-based multi-terminal internal force transformation and the adaptive collaboration scheme are simple and robust for operating unknown arbitrary objects. The overall control process simulates human gripping and handling of unknown objects. Compared to the existing literature, the main contributions of this paper can be summarized as follows:  
1. Based on the absolute relative motion strategy, we propose the time-varying force-tracking control mode and command internal force concept of a contact single-master-multi-slave terminal. Furthermore, the time-varying complex internal force working space is simplified and transformed according to the concept of command internal forces. 2. A method for estimating the unknown stiffness and motion of the direction of multi-internal force commands is proposed. The suggested method is simple and robust to object stiffness, shape changes, and the uncertain motion of contact points. 3. Based on the estimation of stiffness and motion in the command internal force direction, we use the uncertain motion and force of impedance model in the command internal force direction to track the formation method of the adaptive reference trajectory. This means that the force of the contact point measured by the force sensor and kinematics can stabilize the collaborative task without building a dynamic object model. The remainder of this paper is organized as follows: Section 2 describes a complex time-varying internal force workspace comprising multi-terminals and absolute and relative motion control. Section 3 introduces the methods of estimating the unknown dynamic parameters in the command internal force direction and the adaptive reference trajectory formation. Section 4 evaluates the proposed algorithms on a self-made multi-terminal collaborative robot platform. Finally, Section 5 concludes this paper and discusses future research directions.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 3 System Model and Problem Formulation  
In this section, we introduce the notation used in the paper and provide a detailed description of the system model. We also present the problem formulation for finding a network topology that minimizes the number of links and the network's diameter while maximizing the network's entropy and robustness.
# FedDB: A Federated Learning Approach Using DBSCAN for DDoS Attack Detection
## 5 Discussion  
We can explore how improving model accuracy and stability can contribute to the broader field of network security, particularly in the context of increasing DDoS threats. Enhancing these aspects not only improves the effectiveness of DDoS attack detection but also provides valuable insights for addressing other types of network attacks and different environments, such as IoT networks.  
Expanding our DDoS detection method to cover other types of network attacks or testing it in different application environments can help further validate its general applicability and flexibility. In IoT networks, where there is a large number of diverse devices, the network attack patterns may be more complex. Addressing these challenges with our approach could offer significant insights and aid in designing more comprehensive security solutions. Although our research focuses on using federated learning to detect DDoS attacks, we have not tested the model in real-time or near-real-time scenarios. To enhance the model's practicality, future research will focus on establishing an environment that is capable of capturing and processing real-time traffic to evaluate the model's responsiveness under dynamic conditions.  
In the future, the dataset will be expanded to include adversarial attack categories. By incorporating adversarial samples into the dataset, the LSTM can be trained to handle more complex and diverse attack scenarios. This inclusion of adversarial samples will enable the model to learn to recognize and defend against carefully crafted attacks, thereby enhancing its resilience to malicious behaviors.  
Moreover, data selection and processing are crucial in AI model development because data from different sources may contain biases or inaccuracies that affect training and results. Bias or imbalance in training data may cause AI models in network security to treat certain user groups unfairly. To avoid the ethical issues associated with data bias, our method incorporates a mutual learning mechanism, using Kullback-Leibler (KL) divergence to assess the similarity between different models. This process requires alignment in the predictive probability distributions among models to reduce model bias, thereby addressing bias problems that may arise due to uneven data distribution. Ensuring data diversity and representativeness helps mitigate these biases. Additionally, deploying AI in network security must consider its ethical implications, including preventing AI systems from being used for malicious purposes, such as unwarranted surveillance. Ethical guidelines should be established to manage the use of AI technology in security environments to prevent misuse. To address this issue, our approach employs federated learning, leveraging its feature of uploading only model parameters, rather than raw data, to the server to effectively safeguard data security.
# A Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive Panels
## 2 Narrow Slots in FDTD
### Conformal Approximation (CA) Model  
The CA method uses conformal techniques to manage the geometry of the aperture [9]. In this method, the aperture is placed within an FDTD cell. This approach simply assumes that the aperture size is smaller than the cell size, i.e., \(w\lesssim\Delta\). Figure 2 (left) displays a narrow aperture embedded in a staircase surface mesh aligned with the tangential electric FDTD discretization lines, and Figure 2 (right) depicted a conformal mesh generated according to the aperture geometry and the orientation of the cells that contain the slot. The staircase cells, which initially contain the slot, are substituted with conformal cells. The geometry of these cells is adjusted to account for half of the slot surface lying on each conformal patch. The magnetic field \(H_{t}\) in each zone is updated by computing the closed line integral of the tangential electric fields (\(E_{t}\), \(E_{w}\)) along its contour (see Figure 3), following [PERSON]'s law,  
\[H_{t,i+\frac{1}{2},j+\frac{1}{2},k}=-E_{i,i+\frac{1}{2},jk}\Delta+\frac{w}{2} \left(E_{w,i+1,j+\frac{1}{2},k}-E_{w,i,j+\frac{1}{2},k}\right) \tag{2}\]  
It shall be noted that the components \(E_{t}\) are not meaningful with respect to \(E_{w}\), and can be removed in the last equation.  
Figure 1: FDTD cells with a narrow slot centered on the dual grid (magnetic cell).
## 5 Experimental Results and Analysis
### Experimental Results and Discussion on Algorithms Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(1.27\times 10^{2}\) & \(4.49\times 10^{2}\) & \(2.23\times 10^{2}\) & \(2.36\times 10^{2}\) & \(9.86\times 10^{1}\) \\ MOPSO & \(1.25\times 10^{3}\) & \(6.06\times 10^{3}\) & \(2.05\times 10^{3}\) & \(2.34\times 10^{3}\) & \(1.25\times 10^{3}\) \\ MOAO & \(9.35\times 10^{2}\) & \(4.32\times 10^{3}\) & \(2.34\times 10^{3}\) & \(2.51\times 10^{3}\) & \(1.18\times 10^{3}\) \\ MOHBA & \(5.46\times 10^{2}\) & \(3.75\times 10^{3}\) & \(1.34\times 10^{3}\) & \(1.57\times 10^{3}\) & \(7.52\times 10^{2}\) \\ MOWOA & \(8.50\times 10^{2}\) & \(4.12\times 10^{3}\) & \(1.32\times 10^{3}\) & \(1.64\times 10^{3}\) & \(8.57\times 10^{2}\) \\ MOGWO & \(7.96\times 10^{2}\) & \(4.87\times 10^{3}\) & \(2.39\times 10^{3}\) & \(2.60\times 10^{3}\) & \(1.22\times 10^{3}\) \\ MOFECO & \(2.48\times 10^{2}\) & \(8.54\times 10^{2}\) & \(4.97\times 10^{2}\) & \(5.21\times 10^{2}\) & \(1.68\times 10^{2}\) \\ LSMOFECO & \(1.10\times 10^{2}\) & \(1.19\times 10^{3}\) & \(1.93\times 10^{2}\) & \(2.95\times 10^{2}\) & \(2.75\times 10^{2}\) \\ MOFECO-MS & \(8.84\times 10^{1}\) & \(3.99\times 10^{2}\) & \(1.54\times 10^{2}\) & \(1.83\times 10^{2}\) & \(8.29\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 36: Statistical results of SP indicator of the eight comparison algorithms for \(a280\_n1395\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(8.54\times 10^{6}\) & \(2.13\times 10^{6}\) & \(4.98\times 10^{6}\) & \(5.21\times 10^{6}\) & \(1.58\times 10^{6}\) \\ MOPSO & \(2.45\times 10^{7}\) & \(6.70\times 10^{6}\) & \(1.37\times 10^{7}\) & \(1.42\times 10^{7}\) & \(4.66\times 10^{6}\) \\ MOAO & \(2.02\times 10^{7}\) & \(7.40\times 10^{6}\) & \(1.47\times 10^{7}\) & \(1.48\times 10^{7}\) & \(3.34\times 10^{6}\) \\ MOHBA & \(2.44\times 10^{7}\) & \(1.13\times 10^{7}\) & \(1.92\times 10^{7}\) & \(1.84\times 10^{7}\) & \(4.24\times 10^{6}\) \\ MOWOA & \(1.96\times 10^{7}\) & \(2.89\times 10^{6}\) & \(7.13\times 10^{6}\) & \(8.01\times 10^{6}\) & \(4.37\times 10^{6}\) \\ MOGWO & \(1.78\times 10^{7}\) & \(3.03\times 10^{6}\) & \(9.74\times 10^{6}\) & \(9.95\times 10^{6}\) & \(4.02\times 10^{6}\) \\ MOFECO & \(9.10\times 10^{7}\) & \(4.20\times 10^{7}\) & \(5.98\times 10^{7}\) & \(6.26\times 10^{7}\) & \(1.25\times 10^{7}\) \\ LSMOFECO & \(5.83\times 10^{6}\) & \(2.11\times 10^{6}\) & \(3.47\times 10^{6}\) & \(3.62\times 10^{6}\) & \(1.14\times 10^{6}\) \\ MOFECO-MS & \(9.33\times 10^{6}\) & \(4.17\times 10^{6}\) & \(5.45\times 10^{6}\) & \(5.63\times 10^{6}\) & \(1.48\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 37: Statistical results of PD indicator of the eight comparison algorithms for \(a280\_n1395\).
Finally, the silhouette index (SI) was used to compare the traditional k-means algorithm, the particle swarm optimization-based k-means algorithm (PSO-K), and the improved k-means proposed in this paper. The silhouette index is defined as  
\[SI=\frac{d_{out}-d_{int}}{\max(d_{out},d_{in})} \tag{19}\]  
where \(d_{in}\) represents the average distance between the sample point and all other points in the same cluster; \(d_{out}\) represents the average distance between the sample point and all points in the next closest cluster. SI is a key indicator used to describe the difference between the inside and outside of the cluster. Its value range is \((-1,1)\). The closer to 1, the better the clustering effect. The comparison results are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation** & **K-Means** & **PSO-K** & **PSO-KD** \\ \hline Silhouette Index (SI) & 0.5644 & 0.6434 & 0.6345 \\ Time (T/s) & 17,263.3 & 39,736.62 & 10,274.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of clustering performance. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **A** & **SS** & **ST1** & **ST2** & **ST3** & **DPN** & **MSE** & **RMP** & **MINFP** \\ \hline
1 & 160,102.23 & 688.09 & 894.54 & 3094.59 & 2035.40 & 501.63 & 152.03 & 1,732,439.39 & 0.68 \\
2 & 2.95 & 0.00 & 0.03 & 0.05 & 0.03 & 0.02 & 0.00 & 0.00 & 0.82 \\
3 & 160.85 & 0.24 & 1.02 & 3.15 & 1.70 & 1.13 & 0.29 & 24.17 & 0.68 \\
4 & 1541.06 & 10.28 & 8.52 & 28.26 & 20.88 & 6.99 & 1.94 & 640.59 & 1.00 \\
5 & 23,964.09 & 111.05 & 174.16 & 462.87 & 238.34 & 83.37 & 24.49 & 217,997.52 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Electricity characteristics in August. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **A** & **SS** & **ST1** & **ST2** & **ST3** & **DPN** & **MSE** & **RMP** & **MINFP** \\ \hline
1 & 147,504.50 & 725.86 & 784.20 & 2874.68 & 1910.23 & 472.74 & 143.19 & 1,440,424.00 & 0.62 \\
2 & 113,077.80 & 436.06 & 842.00 & 2149.86 & 1139.75 & 362.00 & 107.54 & 1,330,185.00 & 0.67 \\
3 & 140.18 & 0.32 & 0.94 & 2.59 & 1.61 & 0.92 & 0.24 & 7.79 & 0.70 \\
4 & 1582.28 & 10.55 & 8.82 & 28.95 & 21.43 & 7.01 & 1.96 & 539.64 & 0.62 \\
5 & 1516.08 & 10.02 & 20.24 & 22.75 & 10.56 & 17.08 & 4.03 & 9265.71 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Electricity characteristics in November. It can be seen that although the SI of PSO-K is high, the efficiency is low. The SI of the algorithm proposed in this paper was high, the calculation time was short, and the efficiency was improved, which shows the superiority of the improved algorithm.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 3 Methodology
### Distortion Correction
#### 3.1.2 Radial Distortion Correction  
Radial distortion is a distortion distributed along the radius due to the non-ideal properties of the lens itself, which can be corrected by the radial correction-distortion model.  
It is assumed that the center of distortion is the origin of the coordinates, the coordinates of the corrected image is \((x_{u},y_{u})\), the coordinates corresponding to the distorted  
Figure 6: Schematic diagram of the perspective transformation.  
image is \((x_{d},y_{d})\), and the coordinates of the distorted center is \((x_{0},y_{0})\). The radial correction-distortion model is shown as follows:  
\[\left\{\begin{array}{l}x_{u}=x_{d}(1+k_{1}r_{d}^{2}+k_{2}r_{d}^{4})\\ y_{u}=y_{d}(1+k_{1}r_{d}^{2}+k_{2}r_{d}^{4})\end{array}\right. \tag{4}\]  
where \(r_{d}=\sqrt{x_{d}^{2}+y_{d}^{2}}\). In this paper, the radial distortion coefficient \(k_{1},k_{2}\) was calibrated by Zhang Zhengyou checkerboard grid method, and once calibrated, \(k_{1},k_{2}\) were also constant, and the real-time correction of radial distortion of cross laser-pattern image can be realized by using this formula.  
Projected error [21] is often used to evaluate distortion correction accuracy. In this paper, after image distortion corrections, the projected error was 0.35 pixel, indicating it had good distortion correction effect.
# Generation of Monoclonal Antibodies for Sensitive Detection of Pro-Inflammatory Protein S100A9
## 2 Materials and Methods
### Production of Monoclonal Anti-Human S100A9 Antibodies  
Monoclonal antibody was produced at A-Frontier (Seoul, Korea). Female BALB/c mice, 6 weeks old, were immunized by IP injection of 100 \(\upmu\)g of purified rhS100A9 in PBS with complete Freund's adjuvant. After 2 weeks, the mice were tail-bled and the titers of the serially diluted antisera were measured by indirect ELISA, resulting in titers of over 1.5 at the 100- or 1000-fold diluted antisera, which indicated that no additional immunizations would be necessary prior to cell fusion. Three days after the injection, 1-1.5 \(\times\) 10\({}^{8}\) spleen cells separated from the immunized mice were washed with culture medium and then fused with SP2/0 murine myeloma cells using polyethylene glycol as a fusing agent for hybridoma production. The fused cells were cultured using HAT medium and culture supernatants were screened by indirect ELISA with the use of 250 ng of rhS100A9 as a seeding antigen, TBST containing 2% skim milk as a blocking buffer, 50 \(\upmu\)L of hybridoma supernatant, and 10,000-fold diluted HRP-conjugated mouse anti-IgG antibody as a secondary antibody. Following the above fusion ELISA test, the cells of the 24 positive wells were moved to a 24-well plate and were cultured, and the activity was confirmed by indirect ELISA as above. The cells were pipetted into a 96-well plate using an HT culture medium (HAT without aminopterin) and then cultured for 2 weeks in a CO2 incubator at 37 \({}^{\circ}\)C. The single clones with the highest anti-S100A9 antibody titers using indirect ELISA were subcloned and the screening processes were repeated. The dose-response curves were fitted to a four-parameter equation, \(\text{y}=\text{a}+(\text{b}-\text{a})/(1+(\text{c}/\text{x})^{\text{c}})\), using GraphPad Prism software. In the equation, \(\text{a}\), \(\text{b}\), \(\text{c}\), and \(\text{d}\) indicate ABS in the absence of antigen, ABS in the presence of 300 ng antigen, EC50, and hill slope, respectively. The EC50 values were estimated based on the parameter \(\text{c}\) in the formula. The LOD was obtained as the estimated antigen concentration that shows the mean blank value +3 standard deviation (SD).
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 2 Results
### Evolution of Vegetation Restoration Potential in Northwest China
The results of the remote sensing data indicated that the vegetation recovery potential index of temperate steppe and desert was higher, which was manifested by the fact that more than 70% of these areas had an IVCP > 0.4, and the area with an IVCP < 0.05 had a lower index (Figure 6). The results of the LPJ model indicated that the vegetation recovery potential index of temperate steppe was higher, which was evident through the proportion of the area with an IVCP > 0.4 being greater than 20% and the proportion of the area with an IVCP < 0.05 being lower (Figure 6). We analyzed the differences between the remote sensing data and the vegetation natural recovery potential simulated by the LPJ model using the residual method based on ArcGIS 10.8 software, and the results are shown in Figure 7. The difference between the two was small, mostly between \(-0.2\) and \(0.2\); the areas greater than 0.6 were mainly the southern and northern parts of the edge of the Taklamakan Desert; and the areas less than \(-0.6\) were mainly the central-southern part of Gansu and the central part of Ningxia. Figure 5: Atlas of grassland resources in northwest China. Figure 6: Vegetation restoration potential indices under different grassland types simulated by satellite observation (**left**) and the LPJ model (**right**).