esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 3 Model and Method of Range Dimension Deceptive Jamming
### Range Dimension Deceptive Jamming Method
After signal processing such as frequency mixing at the receiving end, the matched filter is performed by \(\phi_{m}(t^{\prime})\) within the signal processing device, and the final signal obtained after the matched filter is  
\[ \begin{array}{ll}y_{n,m}&=\zeta_{j1}e^{-i4\pi f_{m}\tau_{j}/c}e^{j\pi(m \sin\theta_{j}+n\sin\theta_{j})}\\ &=\zeta_{j1}e^{-i4\pi m\Delta f_{j}/c}e^{j\pi(m\sin\theta_{j}+n\sin\theta_{j})} \end{array}  \tag{29}\]  
Figure 4: Receiver processing flow chart of the spoofing interference model. where \(\xi_{j1}{}^{\prime}=\xi_{j1}e^{-i\pi\int\!\sigma_{j1}/c}\), \(\xi_{j1}\) are the complex coefficients after matched filtering. Consider a self-defending interferer, there are \((r_{j}=r_{0},\theta_{j}=\theta_{0})\), to detect the desired target, its steering vector is set to  
\[\mathbf{w}_{0}=\mathbf{S}(r,\theta)\big{|}_{r=r_{0},\theta=\theta_{0}}=\mathbf{ a}(r_{0},\theta_{0})\otimes\mathbf{b}(\theta_{0}) \tag{30}\]  
The normalized antenna pattern is  
\[ \begin{array}{ll}B(r,\theta)&=\left|\frac{\mathbf{w}_{0}^{h}\cdot\mathbf{S} (r,\theta)}{MN}\right|^{2}\\ &=\left|\frac{1}{M}\sum\limits_{m=0}^{M-1}e^{j2\pi[-m\Delta r\frac{2(r-r_{0}) }{c}+m\frac{(\sin\theta-\sin\theta_{0})}{2}]}\right|^{2}\times\left|\frac{1}{N }\sum\limits_{n=0}^{N-1}e^{j2\pi n\frac{(\sin\theta-\sin\theta_{0})}{2}} \right|^{2}\end{array}  \tag{31}\]  
The weight vector of the deceptive jamming signal generated by the jammer is  
\[\mathbf{w}_{j}=\mathbf{S}(r,\theta)\big{|}_{r=r_{0},\theta=\theta_{0}}= \mathbf{a}(r_{0},\theta_{0})\otimes\mathbf{b}(\theta_{0}) \tag{32}\]  
So, the beam gain is normalized to obtain  
\[G=B(r,\theta)\Bigg{|}_{r=r_{0},\theta=\theta_{0}}=\left|\frac{1}{M}\sum \limits_{m=1}^{M}e^{\theta}\right|^{2}\times\left|\frac{1}{N}\sum\limits_{m=1} ^{M}e^{\theta}\right|^{2}=1 \tag{33}\]  
From Equation (33), we can know that the false target deceptive jamming and the real target can achieve the same large beam gain, and the power of the jamming will not be attenuated after signal processing. By changing the size of the time delay \(\tau\), the false target can emerge at various range bins, thereby creating a significant range dimension deceptive interference effect as a way to interfere with the regular operation of radars.
## 3 Results and Discussion
### Impurity Concentration Profile  
After the trial, a total of five samples was taken from every 100 mm along the aluminum bar and analyzed via an OESpark spectrometer. The relation between the concentration of the impurities Fe and Si in the solid phase of the analyzed samples and its initial composition was defined as \(C_{S}/C_{0}\) and represented in Figure 15. In the case of no or very low impurity removal, this ratio would be equal or very close to unity. The smaller this ratio than unity, the more removal via zone refining can be interpreted.  
Figure 14: Variation of the growth rate at the freezing interface along the bar.  
Figure 15: Relative concentration profile of Fe and Si along an aluminum bar subjected to one pass zone refining at 0.8 mm/min.  
As seen above, the increased Fe and Si content at the length of 100 mm coincides with the region where higher growth rate values are observed in the Figure 14. The reason is that when the coil moved to this region, the heat transferred from the induction heater to the ends of crucible will be embedded. This leads to a sudden increase in the moving rate of the growth front and does not allow enough time for the solute to fully segregate into the molten zone ahead. In this period, the molten zone length also begins to decrease to its lower plateau of around 150 mm.
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 3 Results
### Changes in Spectral Response of Crabgrass to Herbicides  
The plant spectral images acquired by different sensors, RGB, CF, and IR thermal sensors, show clear differences depending on the herbicide mode of action and the time after herbicide treatment (Figure 2). Spectral changes by herbicide treatment became apparent as the time after herbicide treatment progressed. The fastest spectral change was observed in the paraquat (PSI) treatment in all spectral images, followed by tiafenacil (PPO) and glufosinate (GS), while the slowest one was observed in penoxsulam (ALS), followed by isoxaflutole (HPPD) and glyphosate (EPSPS). Spectral changes in the RGB images include dehydration, chlorosis, and necrosis, which depend on the herbicide mode of action and the time after herbicide treatment (Figure 2A). In the case of the CF images, the most notable and fast change was observed in the paraquat (PSI) treatment, even at 3 HAT, and a distinctive difference between the herbicide modes of action was observed from 48 HAT (Figure 2B). A relatively slow change but still clear difference between the herbicide modes of action was also observed in the IR thermal images (Figure 2C). The ANOVA of all spectral parameters estimated by a spectral image analysis showed a very high statistical significance for both herbicide and time and their interaction (Table 2). Considering F values, herbicide affected the RGB and CF images more significantly than time, while it affected the IR thermal images less significantly than time. In particular, the difference in \(\mathrm{F_{v}/F_{m}}\) and \(\mathrm{F_{d}/F_{m}}\) between herbicide and time were the greatest, suggesting that these CF parameters may be useful to diagnose herbicide modes of action.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Spectral Parameter**} & \multicolumn{1}{c}{**Source of Variation**} & \multicolumn{1}{c}{**F Value**} & \multicolumn{1}{c}{\(p\) Value**} & \multicolumn{1}{c}{\(p\) Value**} \\  & & & & & **Summary** \\ \hline \multirow{6}{*}{RGB} & \multirow{3}{*}{mNDI} & Herbicide & 1063.089 & \textless{}0.0001 & *** \\  & & Time & 606.495 & \textless{}0.0001 & *** \\  & & Herbicide \(\times\) Time & 95.708 & \textless{}0.0001 & *** \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{ExG} & Herbicide & 1816.526 & \textless{}0.0001 & *** \\  & & Time & 1185.414 & \textless{}0.0001 & *** \\ \cline{1-1}  & & Herbicide \(\times\) Time & 166.365 & \textless{}0.0001 & *** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of two-way ANOVA of each spectral parameter of crabgrass after treatment of herbicides with different modes of action.
## 2 Governing Equations  
The flow equations are mainly described by the mass, momentum, and energy conservation equations. The energy equation is presented in its specific total enthalpy form. The equations are, respectively, as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{v}) =0 \tag{1}\] \[\frac{\partial\rho\mathbf{v}}{\partial t}+\
abla\cdot(\rho \mathbf{vv}) =\
abla\cdot\tau-\
abla p\] (2) \[\frac{\partial\rho l_{0}}{\partial t}+\
abla\cdot(\rho\mathbf{v} l_{0}) =\
abla\cdot(\lambda_{eff}\
abla T)+Q^{l_{0}} \tag{3}\]  
where \(\tau\) in Equation (2) is the stress tensor which includes laminar and turbulent stresses. Assuming a Boussinesq assumption, \(\tau\) is expressed as  
\[\tau=\mu_{eff}\big{(}\
abla\mathbf{v}+\
abla\mathbf{v}^{T}\big{)}-\frac{2}{3} \mu_{eff}(\
abla\cdot\mathbf{v})\mathbf{I}-\frac{2}{3}\rho k\mathbf{I} \tag{4}\]  
\(\mu_{eff}\) and \(\lambda_{eff}\) are effective viscosity and effective thermal conductivity, respectively, defined as  
\[\mu_{eff}=\mu+\mu_{t},\quad\lambda_{eff}=\lambda+\lambda_{t}\]  
\(Q^{l_{0}}\) in the total enthalpy equation consists of energy sources such as viscous dissipation. An auxiliary equation, i.e., the ideal gas law, is required to relate density to pressure and temperature, stated here:  
\[\rho=\frac{p}{RT} \tag{5}\]The system of equations above is closed by the SST \(k-\omega\) turbulence model [34]:  
\[\frac{\partial\rho k}{\partial t}+\
abla.(\rho\mathbf{v}k) =\overline{\tau}_{ij}\
abla\mathbf{v}-\beta^{*}\rho\omega k+\
abla. \big{[}(\mu+\sigma_{k}\mu_{t})\
abla k\big{]} \tag{6}\] \[\frac{\partial\rho\omega}{\partial t}+\
abla.(\rho\mathbf{v}\omega) =\frac{\rho\gamma\overline{\tau}_{ij}\
abla\mathbf{v}}{\mu_{t}}- \beta\rho\omega^{2}+\
abla.\big{[}(\mu+\sigma_{\omega}\mu_{t})\
abla\omega \big{]}+2(1-F_{1})\frac{\rho\sigma_{\omega 2}}{\omega}\
abla k\
abla\omega \tag{7}\]  
The model's parameters, blending functions, and variables are retrieved from Ref. [34], and may not be stated here.
## 3 The Proposed BS-CS Method
Thus, using the updated block index \(T_{t}\), we can have the multipath magnitude of the UWA channel with LS algorithm:  
\[\gamma^{k}=\operatorname*{arg\,max}_{j=T_{t-1}}||\mathbf{X}[j]^{T}\mathbf{r}^ {k-1}||_{2}, \tag{9}\]3  
Next, the residual error \(\mathbf{r}^{k}\) is updated by:  
\[\gamma^{k}=\operatorname*{arg\,max}_{j=T_{t-1}}||\mathbf{X}[j]^{T}\mathbf{r}^ {k-1}||_{2}, \tag{9}\]4  
Iteration stops when the maximum iteration number \(\kappa\) is reached, and the channel estimation result \(\mathbf{\hat{h}}_{t}\) is obtained. Otherwise, the above iterations continue. Finally, if there are some false detections in computing the additions step due to the small addition threshold \(\alpha\), their elements need to be removed from the support estimate. The proposed BS-CS algorithm is summarized in Algorithm 1. The computational complexity of BS-CS is equal to that of simple CS, which is \(\mathcal{O}(N^{3})\), where \(N\) is the length of CIR [17]. ```
1:(1) Running the BOMP: initialization of \(\mathbf{r}^{0}=\mathbf{y}_{t}\);
2:for\(k=1:\kappa\)
3:\(\gamma^{k}=\underset{j=T_{t-1}}{\arg\max}||\mathbf{X}[j]^{\mathsf{T}}\mathbf{ r}^{k-1}||_{2}\);
4:\(\hat{T}_{t}^{k}=\hat{T}_{t}^{k-1}\cup\gamma^{k}\);
5:\(\mathbf{h}_{t,tmp}=(\mathbf{X}[T_{t}^{k}])^{\dagger}\mathbf{y}_{t}\);
6:\(\mathbf{r}^{k}=\mathbf{y}_{t}-\mathbf{X}[T_{t}^{k}]\mathbf{\hat{h}}_{t,tmp}\);
7:endfor
8:(2) Computing the additions:
9:\(\mathbf{\hat{y}}_{t,\text{res}}=\mathbf{X}\mathbf{b}_{t}+w_{t}\);
10:\(\mathbf{b}_{t}=\mathbf{h}_{t}-\mathbf{\hat{h}}_{t,tmp}\);
11:\(\Delta_{t}=\{i\in\{T_{t}^{k}:|(\mathbf{b}_{t})|>\alpha\}\}\);
12:\(\hat{T}_{t}=\hat{T}_{t}\cup\Delta_{t}\);
13:(3) BOMP update: initialization of \(\mathbf{r}^{0}=\mathbf{y}_{t}\);
14:for\(k=1:\kappa\)
15:\(\gamma^{k}=\underset{j=T_{t}}{\arg\max}||\mathbf{X}[j]^{\mathsf{T}}\mathbf{ r}^{k-1}||_{2}\);
16:\(T_{t}^{k}=T_{t}^{k-1}\cup\gamma^{k}\);
17:\(\mathbf{\hat{h}}_{t}=(\mathbf{X}[T_{t}^{k}])^{\dagger}\mathbf{y}_{t}\);
18:\(\mathbf{r}^{k}=\mathbf{y}_{t}-\mathbf{X}[T_{t}^{k}]\mathbf{\hat{h}}_{t}\);
19:endfor
20:(4) Deleting some small false detections. 21:Output\(\mathbf{\hat{h}}_{t}\). ```  
**Algorithm 1**\(\mathbf{x},\mathbf{y}_{t},d,\kappa,\alpha\)
# Audio Feedback with the Use of a Smartphone in Sailing Training among Windsurfers
###### Abstract
The key element here is to acquire real-time information that is relevant to the training race (e.g., the optimal course in relation to the position of the distant windward mark), which can extend the range of stimuli from the environment. Useful information can be sourced from GNSS devices (Global Navigation Satellite Systems) which are currently widely available. The implementation of visual feedback that reflects a sailor's position relative to the racecourse in real-world on-water training conditions is problematic due to the limited focus on GNSS devices. However, using the sense of hearing seems to be a promising alternative. The present concept was developed within the framework of a research project in which the SoniSailing application for mobile phones was created to support tactical decisions via an auditory display, using the example of windsurfing. In the preliminary stage of the project, the functionality of the application interface and the reliability of navigational measurements were studied [12]. Furthermore, different sound transmission devices (in-ear headphones, a wireless speaker, bone conduction wireless headphones) as well as various modes of sound information (pure tone, speech, repeated sound) were tested [13]. Ultimately, bone conduction wireless headphones and a voice generator were considered optimal for the main purpose of the project. Using the SoniSailing application, the windsurfer's actual course can be described promptly in relation to the race route in real on-water conditions during the training process. Recent research suggests that auditory cues based on satellite navigation are a sensory augmentation that helps to create a stronger connection with the environment [14]. Augmented auditory display supporting basic tactical decisions seems to be promising in sailing sports, primarily due to the demanding environment and the necessity of constant course control in regard to the route of the regatta. Hence, the aim of the study was to assess of usefulness of the augmented audio feedback provided in real-time with the use of a smartphone application, based on the opinions of windsurfers at different sports levels.
# Lateral Dynamic Response of Helical Pile in Viscoelastic Foundation Considering Shear Deformation
## 5 Parametric Study
### Space Response Analysis of Helical Pile
#### 5.1.3 Influence of Pile-Soil Stiffness Ratio  
Keeping \(E_{\mathrm{s}}\) unchanged, \(E_{\mathrm{p}}/E_{\mathrm{s}}\) is set as 1000, 5000, and 10,000, and the other parameters are shown in Table. Figure 10 depicts the influence of the pile-soil stiffness ratio on the LDD, BM, and SF of the helical pile with space response. Overall, all the LDD, BM, and SF of the helical pile visibly increase as the pile-soil stiffness ratio increases. The reason for this phenomenon is that, when the pile-soil stiffness becomes large, the ability of the soil to resist deformation decreases compared to the increasing pile stiffness. Therefore, in the lateral dynamic design of the helical pile, it is necessary to set a reasonable pile-soil stiffness ratio, rather than blindly increasing the pile's body stiffness.  
Figure 9: Influence of dimensionless frequency on LDR of helical pile. (**a**) Lateral displacement envelope. (**b**) BM envelope. (**c**) SF envelope.  
Figure 10: Influence of pile–soil stiffness ratio on LDR of helical pile. (**a**) Lateral displacement envelope. (**b**) BM envelope. (**c**) SF envelope.
## 2 Basic Principles
### Principle of Controlled Mixing Strategy  
In order to achieve the effect of roller mixing while avoiding as much collision damage as possible, this paper proposes a Dual-Channel Mixing Scheduling Strategy (DCMS) in combination with the existing DDSGL circulation system. The schematic diagram of the DCMS principle is shown in Figure 4a,b. The original external circulation system had single-line operation for rollers in the cleaning and drying mechanisms. This is changed to a dual-channel operation mode with two conveyor belts. The rollers are distributed proportionally on two equal-length conveyor belts. The two conveyor belts transport the rollers at different speeds and merge the rollers on the two belts into single-line operation at the outlet of the conveyor belts.  
The flowchart of the study on DCMS is shown in Figure 4c. Firstly, the DCMS model is established and relevant parameters are defined, followed by a single-factor simulation calculation of the parameters in the model. Subsequently, a comprehensive evaluation system for roller mixing is established, with statistical analysis of the simulation calculation results and determination of the optimal combination of parameters. Finally, the structure of the DDSGL system is improved based on DCMS, and a single-factor lapping test of parameters is conducted in the improved system. During the experiment, the batch diameter variation of the roller is measured, and discussions are made in combination with the optimal combination of simulation calculation parameters. The main purposes of the grinding test include: (1) verifying whether DCMS can improve the convergence efficiency and precision of the batch diameter variation of rollers in the DDSGL process; and (2) verifying whether the DDSGL experimental group corresponding to the optimal combination of parameters determined by the established DCMS model and the comprehensive evaluation system of roller mixing is the best performing group in terms of the convergence trend of the batch diameter variation of rollers.  
Figure 3: Operation mode of cylindrical roller in DDSGL system.
## 3 Results and Discussion
### Influence of the Spectral Index  
The spectral index serves as a complement to the DN or reflectance for the phenological normalization, which is a key parameter in this study. A performance evaluation of each spectral index for each band is performed in order to select the optimal spectral index for each band, as shown in Table 4. It can be found that the same tendency is shown at all sites. ExG show high performance in the blue and NIR bands, while the performance in the red band is relatively low. In the case of ExGR, the performance of the red band is satisfactory, while the blue, green, and NIR bands are somewhat inferior in performance. Furthermore, VEG has the lowest performance overall, and CIVE has a normal performance as a whole, but it does not show optimized performance. On the other hand, Com shows the highest performance in the green band, demonstrating reasonable performance as a whole. In other words, it is confirmed that there is a considerable difference in the influence of each spectral index on each band. Therefore, ExG, COM, ExGR, and ExG are selected as the optimal spectral indices for the blue, green, red, and NIR bands, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Method** & **Band 1** & **Band 2** & **Band 3** & **Band 4** \\ \hline \multirow{4}{*}{Site 1} & ExG & **0.1440** & 0.2027 & 0.3373 & **0.1545** \\  & ExGR & 0.1459 & 0.2044 & **0.3073** & 0.1579 \\ \cline{1-1}  & VEG & 0.1464 & 0.2063 & 0.3355 & 0.1576 \\ \cline{1-1}  & CIVE & 0.1460 & 0.2048 & 0.3316 & 0.1578 \\ \cline{1-1}  & COM & 0.1462 & **0.1987** & 0.3326 & 0.1570 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the NRMSE for each spectral index.  
Figure 11: Experiments on the radiometric resolution: (**a**) accuracy by setting different radiometric resolutions, (**b**) training time by setting different radiometric resolutions.
## 4 Analyses of the Applications of Digital Twin Models
### Manufacturing
#### 4.1.2 Benefits and Challenges of Digital Twins in Manufacturing
Regarding the interoperability between Digital Twin and CPS, identified challenges include issues related to cybersecurity and managing large volumes of transmitted andprocessed data. Cybersecurity is a primary factor in protecting sensitive data and preventing cyberattacks, which can compromise the integrity of the entire system. Additionally, handling and analyzing large amounts of data require robust data infrastructures and advanced algorithms to ensure that relevant information is efficiently extracted and used.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 3 Method and Implementation
### Flow Privacy Metric  
As the attacker's goal is to correlate communication flows from the same source, identity privacy can be measured based on the number of _clusters_ into which an attacker classifies the set of flows from a client machine. A successful attack will create a single cluster, as the attacker correlates all flows with the same origin. A totally ineffective attack will create one cluster _per flow_, failing to correlate any pair of flows from the same origin.  
\"Clusters per flow\" quantifies the effectiveness of a defence against this attack by indicating how frequently an attacker must create a new cluster for the flows it detects. With one cluster per flow, every new flow is a new cluster and so no flow correlation is possible. With less than one cluster per flow (i.e., more than one flow per cluster), the attacker is able to start correlating flows. The degree of _flow identity privacy_, \(P_{f}\), can therefore be expressed as the ratio of flows from a node (\(f\)) to the number of clusters the attacker creates (\(c\)):  
\[P_{f}=\frac{c}{f} \tag{1}\]  
A \(P_{f}\) value of 1 is the best value for privacy, and means \(c\) and \(f\) are equal: each flow is in its own, unique cluster, and no linking information is exposed, completely thwarting the correlation attack. In the worst case, when all flows are linked to the same source, \(c\) is 1 and all flows are in that same cluster, so \(P_{f}\) will approach a limit of zero; each new flow decreases \(P_{f}\) by increasing the linkage between the flows that are visible to the attacker.  
Figure 2 shows the mathematical behaviour of this metric over time as an attacker observes flows and clusters them based on an estimate of the source identity, e.g., in our case, by inspecting the source address bits. Depending on how effective the clustering method is, that attacker might correlate more or fewer flows, resulting in the different lines shown. Whenever the attacker is forced to create a new cluster, \(c\) increases, which increases \(P_{f}\).
# Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems
## 3 Results
### Comparative Analysis of Prediction Results  
To evaluate the accuracy and robust generalizability of MV-LSTM in discrete manufacturing quality prediction, we compared and analyzed the results of the pre-processed training samples and the test samples from the collected dataset with SVM and univariate LSTM.  
According to the QIXI manufacturing company, the input set of the production of drinking fountains on a single processing line consists of raw material quality, semi-finished product sampling, and finished product testing data. The main purpose of the input layer is quality prediction, and this layer selects quality evaluation and quality-related indicators with greater weight. This can be expressed as follows:  
\[X=\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{n}\end{bmatrix}=\begin{bmatrix}Q_{1}&q_{1}&l_{1}\\ Q_{2}&q_{2}&l_{2}\\ \vdots&\vdots&\vdots\\ Q_{n}&q_{n}&l_{n}\end{bmatrix} \tag{18}\]  
In equation above, \(Q\) denotes the quality evaluation value; \(q\) is the power; and \(l\) is the flow data of semi-finished products serving as the input layer to simulate the actual production process based on the quality data of each product to help companies conduct real-time quality monitoring. In the quality-oriented process, we assumed that the power and flow data in the measurement dataset contribute more to the overall quality variability and, therefore, are received greater attention in the quality prediction step.  
The comparison of quality predictions, as shown in Table 3, indicates that the prediction error of the univariate LSTM was the largest. This could be attributed to the fact that the univariate LSTM only reflects the dynamic characteristics of the univariate time domain and does not consider the quality level. Another possible reason for the large prediction error could be the improper adjustment of hyperparameters. On the other hand, the multivariate LSTM showed an improvement compared to both the traditional machine learning support vector machine and the univariate LSTM.  
A prediction analysis of the model was conducted using a dataset that was generated by preprocessing and normalizing actual production data. To establish production line prediction models, SVM, univariate LSTM, and multivariate LSTM algorithms were employed.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **RMSE** & **MAE** & **MAPE (\%)** \\ \hline SVM & 0.1719 & 0.07274 & 0.86158 \\ LSTM & 0.40957 & 0.3043 & 3.5848 \\ MV\(-\)LSTM & 0.02755 & 0.01639 & 0.19421 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The prediction performance of the different methods.  
Figure 6: Quality evaluation data.