esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Digital Model of the Area of Interest  
### Area of Interest-Estimation of the Number of Sensory Units  
Our project MOVIR aimed to test mobile sensory units and the network of sensory elements in a small monitored area. A small monitored space means a rectangular area with edges of a few dozen meters. The methodology for testing projects like MOVIR is to test the project in laboratory conditions, then \(1/2\) size and finally full size. Due to the area's size, the MOVIR project's testing is now \(1/2\) the size. However, the procedures that form the network of sensor units are independent of the size of the area of interest. In this chapter, we define the mathematical and data model of the area of interest.  
The basic principle of determining the area of interest is the principle used by Free Route Airspace (FRA) see [17] when defining the horizontal part of the airspace. It is needed to enter special significant points of space which belong to the given area. These points will be given by GPS coordinates. Significant points with coordinates \([u_{l},v_{l},h_{l}]\) will be divided into three sets. One of the sets will be the internal points (blue color in the Figure 1, obstacles in the terrain can be entered this way). In the second set are possible boundary points of the area (marked with red color), and in the third set are necessary points that must be included into the sensory network units (yellow color). We assume that some boundary points will form a simple closed polygon determined by the system user. It will be the polygon of the area of interest.  
\[P=\{[u_{i},v_{i},h_{i}];i=0,1,\cdots,n\} \tag{1}\]  
The number \(n+1\) is the number of significant points and \(i\) is the ordinal number of the given significant point (from 0 to \(n\)). Unlike FRA, this polygon has three dimensions (FRA points only have GPS coordinates, heights are solved otherwise). We consider the height because of the terrain profile. If we denote the length of the polygon as \(L(P)\), the communication range of the signaling sensor with \(s\) like \(range(s)\) and the sensitivity of the sensor element \(s\) with \(\tau\), then we can set the maximum distances between signaling sensors as \(\tau\times range(s)\). While parameter \(\tau\) can take on values from the interval \((1,2)\). The value of \(\tau=2\) means that neighboring sensory units work at a distance of \(2\times range(s)\). Using these parameters, we can estimate the number of sensory units needed to create the network of mobile sensory units.  
\[p(P)=L(P)/(\tau\times range(s)) \tag{2}\]This value is the minimum number of sensory network units. Here we assume that there are used the same length measurement units for the length of the polygon and the range of sensory units. We note that the length of the polygon is the circumference of the area of interest. The number of the nodes of the closed simple polygon \(P\) is marked with \(|P|\). Polygon which does not contain any nodes is marked \(P=\emptyset\).
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 3 The Proposed Method
### Attention Enhancement Feature Fusion Network
#### 3.3.1 Attention Mechanism  
Attention is the fundamental component in feature fusion networks. \(N\) represents the size of the input feature sequence. The attention mechanism can be viewed as a mapping function that takes \(Q,K,V\in R^{N\times d}\) as inputs, \(Q\) and \(K\) as two feature vector sets with dimensions \(d_{k}\), and \(V\) as a feature vector set with dimensions \(d_{p}\). The process can be interpreted as queries, keys, and values in a continuous dictionary. Thus, the attention scores between \(Q\) and \(K\) are obtained through scaled dot-product operations, while the attention map can be generated through softmax operations. The proposed GFETrack can reweight \(V\) based on the attention map. Thus, it can adaptively adjust the weights of features in \(V\) by leveraging the correlation between \(Q\) and \(K\), allowing the algorithm to better focus on the features at useful positions within \(V\). The traditional form of attention is as shown in Equation (1):  
\[Attn(Q,K,V)=softmax(A)V,A=exp(\frac{QK^{T}}{\sqrt{d_{k}}}), \tag{1}\]  
where \(A\in R^{N\times N}\) is the attention matrix. Because the attention matrix \(A\) must be saved explicitly, the time complexity and space complexity of Equation (1) are \(O(N^{2}d)\) and \(O(N^{2}+Nd)\), respectively. When extending to the multi-head attention, the formula is as shown in Equation (2):  
\[\begin{split} MultiHead(Q,K,V)=Concat(H_{1},...H_{nh})W^{o},\\ H_{i}=Attn(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}),\end{split} \tag{2}\]where \(W_{i}^{Q}\in R^{d_{m}\times d_{k}}\), \(W_{i}^{K}\in R^{d_{m}\times d_{k}}\), \(W_{i}^{V}\in R^{d_{m}\times d_{k}}\) and \(W^{0}\in R^{d_{m}\times d_{k}n_{h}}\) are parameter matrices. \(H_{i}\) represents the attention matrix for different heads. In this paper, \(n_{h}=8\), \(d_{m}=256\), \(d_{k}=d_{v}=\frac{d_{m}}{n_{h}}=32\).
## 6 Installation Error Compensation for the Shearer's SINS
### Simulation Analysis of Lever Arm Effect Error Compensation  
The influence of lever arm effect on the attitude angle error in initial alignment is analyzed using the software MATLAB. It is assumed that the error angle of initial attitude angle is \(\varphi=[0.1^{\circ},\,0.1^{\circ},\,0.5^{\circ}]\) (\(m\)), the bias drift and random drift of the gyroscope are \(0.01^{\circ}\)/h, and \(0.005^{\circ}\)/h, respectively, the bias drift and random drift of the accelerometer are \(10^{-4}\) g, and \(10^{-5}\) g, respectively, and the simulation time is \(400\) s.  
We assume that the shearer motion satisfies sinusoidal motion characteristics as follows:  
\[\left\{\begin{array}{l}\theta=\theta_{m}\sin\frac{2\pi}{T_{\varphi}}t\\ \gamma=\gamma_{m}\sin\frac{2\pi}{T_{\varphi}}t\\ \varphi=\varphi_{m}\sin\frac{2\pi}{T_{\varphi}}t\end{array}\right. \tag{54}\]  
It is set that \(\theta_{m}=10^{\circ}\), \(T_{\varphi}=6\) s, \(\gamma_{m}=10^{\circ}\), \(T_{\varphi}=8\) s, \(\varphi_{m}=10^{\circ}\), \(T_{\varphi}=10\) s, and lever arm length \(r_{p}=\) [1,1,1] (m). Based on the above parameter values, the accelerations on three axes by level arm effect obtained through simulation are shown in Figure 12.  
As can be seen from the figure, the lever arm effect leads to some acceleration errors in all of the east, north and sky directions. To improve the positioning accuracy of shearer SINS, the errors caused by lever arm effect must be compensated. The comparison of attitude angle error between two cases with and without lever arm effect error being compensated is shown in Figure 13 and the maximum attitude angle errors are shown in Table 2.  
Figure 12: Acceleration of rod arm effect.  
Figure 13: The comparison of attitude angle error.  
SINS monitoring accuracy, and it is necessary to compensate the vibration error. The errors caused by shearer vibration mainly include coning error and sculling error. The following section mainly focuses on the compensation methods for the above two errors.
###### Abstract  
Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.  
manuevering target tracking; interacting multiple model (IMM); unscented Kalman filter (UKF); Gaussian distribution; sensor fault; cubature principle; adaptive matrix gene +
Footnote †: journal: Applied Sciences
## 3 Analysis of Effects of Co-channel Interference on EBPSK Waveform
### Effects of Different Signal to Interference Ratio (SIR) on Time-Domain Waveform  
When signal power remains unchanged, the amplitude of the interference decreases with the increasing of SIR, _i.e.,_ the energy of the corresponding sine is weakened, thus, the enhancement role for signal becomes poor according to section 3.1. Therefore, the impulse amplitude decreases, but the amplitude difference remains unchanged. Figure 4 gives the response to filters in the time-domain of no interference, \(SIR=0 dB\) and \(SIR=10 dB\), respectively, where the initial phase of the interference is 0.  
Figure 3: The effect of different initial phase on impulse amplitude. (a) No interference. (b) The initial phase of the interference is 0. (c) The initial phase of the interference is \(\pi/2\). (d) The initial phase of the interference is \(\pi\).
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 5 Conclusions  
To improve the prediction accuracy of single forecasting models, this paper proposes a combined forecasting model using LSSVM optimized by QGA as a combiner to predict the misalignment fault of wind turbines based on vibration and current signals. The specific method is to extract the time-domain, frequency-domain and time-frequency domain feature parameters from the signals at first. After being normalized, the extracted features are used to predict by IMGM (1, \(n\)) and QGA_LSSVM separately and the predicted values are then regarded as the inputs of the combiner, which dynamically assigned non-linear weight coefficients to them. The simulation results indicate that the prediction accuracy of the combined prediction model is higher than those of single models and the running time is shortened. This new model is suitable for fault prediction with deterministic and random trends. In future research, the kurtosis predicted by the proposed model will be combined with the kurtosis threshold to achieve early warning of wind turbine misalignment faults.  
[PERSON] and [PERSON] contributed to paper writing and the whole revision process. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51577008).  
The authors are grateful to the anonymous reviewers for their constructive comments on the previous version of the paper.  
The authors declare no conflicts of interest.
## 4 Discussions  
### St63 Gly37 Synthesis  
The \({}^{1}\)H NMR spectrum interpretation of the product St63 Gly37 is carried out by comparison with the peaks of the native starch found in the literature [20]. The spectrum of native starch is shown in Table 5. The glycerin has two chemical shifts at 4.3 ppm corresponding to the proton of the CH, and at 4.4 ppm corresponding to the protons of CH\({}_{2}\).  
Chemical shifts of native starch and St63 Gly37 bio-copolymer are comparable. The starch and glycerin signals are also found together, thus proving the success of the grafting reaction. The glycerin OH signals can be confused with those of the starch OHs (H\({}_{8}\), H\({}_{9}\)).  
After concluding that glycerin is well grafted on starch, the question that still remains is how this grafting takes place. Glycerin grafting may occur preferentially on the 7-position of the starch. It could also occur on positions 8 or 9 of the starch (see Figure 2). On the other hand, glycerin can be grafted via its OH-13 or OH-14 functions. Indeed, the possible reacting OH hydrogens are: H\({}_{7}\), H\({}_{8}\) and H\({}_{9}\). For the positions H\({}_{8}\) and H\({}_{9}\), they are still visible on the \({}^{1}\)H NMR spectrum displayed in Figure 3 and Table 3. We can; therefore, conclude that H\({}_{7}\) reacted to give modified patterns according to the structure shown in Figure 2. The presence of the peaks at chemical shifts of 4.4 ppm (triplet) and 4.5 ppm (doublet) correspond, respectively, to the CH and CH\({}_{2}\) of the structure.  
The \({}^{1}\)H NMR spectrum also enables the calculation of relative amounts of starch and glycerin in St63 Gly37. The copolymer contains 63% starch and 37% glycerin. The chemical shifts are represented in the following Table 6.  
The FT-IR technique is largely used for the characterization of starch as a natural polymer [20]. Examination of the FT-IR spectrum confirms the grafting of glycerin on the starch, by the CH\({}_{2}\) bands and the characteristic OH bands of glycerin, as reported in Table 7. Figure 4 shows the FT-IR spectra of the native starch in comparison with the St63 Gly37 copolymer. It is visible that the C-O-C bond bands change frequency because of the effect of the proportion of glycerin in the copolymer. The two spectra are almost of the same nature. The assignment of the different vibration bands of the native starch have been previously reported [20]. The comparison of the two FT-IR spectra shows a slight displacement of the elongation bands.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Shifts 8 (ppm)** & **Attributions** \\ \hline
3.2–3.7 & H 2,3,4,5,6 \\
4.4 & H 11 \\
4.5 & H 10 \\
4.6 & H 13, 12 \\
5.1 & H 1 \\
5.4–5.6 & H 8,9,14 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Chemical shifts \(\delta\) (ppm) of St63 Gly37.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Shifts \(\delta\) (ppm)** & **Attributions** \\ \hline
3.6 & H 2,3,4,5,6 \\
4.6 & H\({}_{7}\) \\
5.1 & H\({}_{1}\) \\
5.6 & H\({}_{8}\), H\({}_{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Chemical shifts of native starch [20].
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 3 Result and Discussion
### Field Testing Results
#### 3.2.1 Best-Performing Anti-Fouling Phosphonium Ion Gels
P888 ion gels with biocides showed denser coverage at FR 10-30 on days 14, 43, and 63. We hypothesized that this was due to the copper(II) oxide causing a lower hydrophobicity in the coating. Advancing contact angle measurements were used to characterize the hydrophobicity of the surfaces. Tributylphosphonium- and trioctylphosphonium-sample advance contact angle measurements are summarised in Table 3 (Supplementary Section S4). It is evident from the table that the contact angle increases as the trialkylphosphonium's attached alkyl chain length increases. It is worth noticing that [AOT]\({}^{-}\) falls within the category of anionic surfactants [9]. These compounds are best characterized by their negatively charged ions. Because of these ions, AOT can interact with water molecules and other polar substances, reducing surface tension. P444[77; 21,0] shows the highest contact angle for tributyl phosphonium samples whereas P444[49; 48,0] shows the least. This indicates that the contact angle degree decreases with an increased weight percentage of free ionic liquid in the formulation. This trend agrees with all the tricotyl samples, including the one having Cu\({}^{2+}\) in it. The contact angle values [P888 VB][AOT] were doubled as compared to [P444 VB][AOT], proving that trioctyl(vinylbenzyl)phosphonium docusate is more hydrophobic than tributyl(vinylbenzyl)phosphonium docusate. Adding Cu\({}^{2+}\) to [P888 VB][AOT] reduces the contact angle to a more hydrophilic end.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## 3 Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
### Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
```
0: System matrices as defined in (2)-(4); estimation threshold \(\kappa>0\)
0: Optimal control strategies \(\mathbf{L}_{p}^{*}\) (pursuer) and \(\mathbf{L}_{e}^{*}\) (evader)
1:\(t\Leftarrow 1\), \(m\Leftarrow 1\)
2: Initialize boundary conditions
3:for\(t\Leftarrow 1\) to \(N\)do
4:\(\mathbf{\psi}_{t}\Leftarrow\) Compute via (9)
5:if\(\mathbf{\psi}_{t}\) is invertible then
6:\(\mathbf{L}_{e}^{*}\Leftarrow\) Solve using (10)
7:\(\mathbf{P}_{p,k},\mathbf{P}_{e,k}\Leftarrow\) Update via (7) and (8)
8:endif
9:endfor
10:for\(m\Leftarrow 1\) to \(M\)do
11:\(\epsilon_{m}^{m}\Leftarrow\) Evaluate from (36)
12:\(\hat{\mathbf{L}}_{e,m+1}\Leftarrow\) Refine via (37)
13:if\(\left\|\hat{\mathbf{L}}_{e,m+1}-\hat{\mathbf{L}}_{e,m}\right\|\leq\kappa\|\hat{\mathbf{L}} _{e,m}-\hat{\mathbf{L}}_{e,m-1}\|\)then
14:\(\mathbf{L}_{e}^{*}\Leftarrow\hat{\mathbf{L}}_{e,m+1}\)
15:return\(\mathbf{L}_{e}^{*}\)
16:break
17:endif
18:endfor
19:\(\mathbf{L}_{p}^{*}\Leftarrow\) Derive from (13)
20:return\(\mathbf{L}_{p}^{*}\)
```  
**Algorithm 1** Stackelberg Equilibrium-Based Model Reference Adaptive Control* Lines 3-9 (Offline Reference Calculation): The algorithm first calculates the optimal control gains for a finite horizon N. It iterates backward in time, computing the coupling matrix \(\mathbf{\psi}_{t}\) and solving for the optimal gains at each step using the Riccati updates from (7) and (8). This produces the optimal evader gain \(\mathbf{L}_{e}^{*}\), which will serve as the reference. * Lines 10-18 (Online Pursuer Adaptation): This loop represents the online adaptive process. The pursuer evaluates the estimation error (line 11) and refines its estimate of the evader's gain, \(\hat{\mathbf{L}}_{e,m}\), using the robust update law from (37) (line 12). * Lines 13-16 (Convergence Check): The adaptation continues until the change in the estimated gain becomes small, determined by the threshold \(\kappa\). At this point, the algorithm has converged to a good estimate of the evader's current strategy. * Line 19 (Final Pursuer Strategy): With the best available estimate of the evader's strategy, the pursuer calculates its own optimal control gain \(\mathbf{L}_{p}^{*}\) using Equation (13). This gain is then used to control the pursuer spacecraft.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Kinetic-Mechanical Neutralizers
#### 4.2.2 Collision UAVs  
In this case, a dedicated UAV (drone), equipped with detection and tracking capabilities, follows the malicious drone in order to collide and destroy it. The neutralizer drone requires high speeds to chase the malicious drone and, typically, it is effective for small drones located in protected areas. Collision UAVs can employ detection methods based on computer vision techniques and can carry explosives to maximize damage during impact with the m-drone. They can cause collateral damage, as in the case of projectiles,and compared to the latter they are characterized by a higher neutralization delay. In conclusion, these neutralizers are disposable systems, acting as a hybrid system halfway between a drone and a missile. An example of a collision UAV is shown in [100].
# Event-Based Path-Planning and Path-Following
## 5 Discussion
In the future, this will allow for us to apply formal methods and software tools to analysis of DESs intended for path planning. Besides, we plan to develop a convenient tool for specifying DES with the possibility to integrate new rules and actions in the path planning process. Although the focus of the paper is on path planning in an unknown environment, it also concerns the path-following problem. The designed path-following controller demonstrates acceptable performance: the steady-state tracking errors in the along path direction \(s_{e}\) and in the across path direction \(y_{e}\) do not exceed 1 m (see Figure 10). Analysis of the simulation results showed that the tracking errors are primarily affected by jump changes in the path curvature. At the same time, such jumps occur quite rarely at junction points of two adjacent path segments, so the AUV has time to reduce errors before reaching the next junction point. It should be noted that increasing \(D_{m}\) in the RCOA algorithm can reduce the number of path segments that are generated by the waypoint guidance algorithm and, therefore, the number of jumps. In turn, a path generated by [PERSON] curve-based algorithm consists only of three segments.
###### Abstract
These BLIMFs refer to components with distinct, narrow-frequency bands, enabling a more precise representation of the signal's oscillatory modes by confining each mode to a specific frequency range. VMD has a solid mathematical foundation to avoid mode mixing and is robust to noise and sampling, unlike EMD [17]. One of the key benefits of VMD for denoising is its ability to separate the desired signal into a few initial BLIMFs, while most of the noise is contained in the final BLIMFs. This means that by discarding the modes with noise, a good approximation of the true signal can be obtained through partial reconstruction. VMD-DFA is a method for performing partial reconstruction by identifying and removing trends (i.e., non-random patterns) from VMD-BLIMFs data sequences using the DFA method [18]. This process helps to improve the accuracy of the reconstruction by eliminating the influence of trends on the final result. An extension of VMD-DFA incorporating discrete wavelet transform (DWT) (VMD-DFA-DWT) has been proposed to minimize the mode mixing in the previous methodology and to further improve the signal-to-noise ratio (SNR) of denoised signals [19]. This paper develops a novel hybrid approach using a multi-step process that combines VMD, DFA, and SWT to denoise signals. The main motivation is to address the inadequacies of the current denoising methods and hence more effectively separate and remove noise from signals. First, the BLIMFs are classified into noise-dominant and signal-dominant groups using a novel metric based on DFA. The key idea is to introduce an adaptive threshold that relies on the higher frequency content of each mode, rather than fixed DFA slope scores. This threshold value quantifies the change in frequency and long-range correlations when transitioning between modes. A higher slope indicates a larger increase in frequency or a greater decrease in long-range correlations, indicating more noise. As a result, the mode following the one with the highest slope marks the beginning of predominantly noisy modes. The next step is to employ SWT to remove the noise from the noise-dominant components and finally reconstruct the denoised signal. The simulation experiments were performed for benchmark test signals, as well as a few ECG signals, which demonstrate superior denoising performance of the proposed method in comparison with existing methods. The remainder of this paper is organized as follows. Section 2 presents a few preliminaries on the signal model and EMD, and Section 3 provides a concise overview of the existing EMD-based denoising methods. Section 4 outlines the development of the proposed method in comparison with existing VMD-based methods, and detailed remarks on the computational complexity analysis are presented in Section 5. Section 6 details comprehensive computer simulations to test the proposed method's performance in denoising a variety of benchmark signals and a few practical examples that had been purposely distorted with additive white Gaussian noise (wGn). Finally, Section 7 concludes this paper and offers a few remarks on possible directions of future research.