esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CPS Architecture, Layers and Components  
In this section, CPS architecture, along with its layers and components, is discussed. A CPS consists of several components and can have a complex structure between the physical system and cyber software. It is difficult to analyze its entire architecture. Therefore, the CPS architecture is classified into three main layers; the physical layer, the network layer, and the application layer. Intuitive and simple layered architecture is presented in Table 2.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Kinetic-Mechanical Neutralizers
#### 4.2.3 Nets  
Nets are used to trap and immobilize m-drones. They can be projected by a net cannon (an example is shown in [101]) or can be carried by other drones (an example is shown in [102]). Nets are useful for neutralizing small drones, which are difficult to intercept by guns or guided missiles (see bibliography in [9]). They can then be equipped with parachutes to assure a safe descent for the drone/net assembly and to prevent collateral damages to other facilities or for forensic analysis. In any case, the effective neutralization range is short.
## 4 Experimental Results
### Dynamic System Identification  
The multiple time delays of the nonlinear dynamic system are expressed as follows:  
\[\gamma_{p}\ (t+1)=f\left(\gamma_{p}\ (t),\ \gamma_{p}\ (t-1),\ \gamma_{p}\ (t-2),\ u(t),\ u(t-1)\right) \tag{20}\]  
where  
\[f\left(x_{1},\ x_{2},x_{3},\ x_{4},\ x_{5}\right)=\frac{x_{1}x_{2}x_{2}x_{5}( x_{3}-1)x_{4}}{1+x_{2}^{2}+x_{3}^{2}}. \tag{21}\]  
Dynamic system identification was performed to change the parameters of the IT2 FNN using the proposed DGCPSO. Table 1 lists the initial parameters of the DGCPSO settings, including the total number of particles (NP), inertia weight \(\omega\), acceleration constants \(C_{1}\) and \(C_{2}\), and the generation and number of fuzzy rules. The compared items consist of the fitness value of the best, worst, average, and the standard deviation (SD). Table 2 and Figures 9-11 reveal that the proposed method exhibited superior learning performance to other algorithms [24; 25; 26; 27].  
Figure 11: Cont.  
Figure 10: Dynamic system identification of **(a)** DGCPSO, **(b)** DE [24], **(c)** PSO [25], **(d)** QPSO [26], and **(e)** CPSO [27].
## 4 Double-Criteria Drift Streaming Peaks over Threshold (DCDSPOT)
### Multi-Tier Thresholds
The method of setting multi-tier thresholds based on biDSPOT and using two criteria to identify anomaly is called Double-Criteria DSPOT (DCDSPOT). DCDSPOT considers the target parameter and the source parameters at the same time, which can reduce the false detection caused by the instability and noise of the target parameter during anomaly detection. As a result, the performance of DCDSPOT in reducing false negatives is out \begin{table}
\begin{tabular}{c c} \hline \hline
**Criterion 1: for Target Parameter** & **Criterion 2: for WSP** \\ \hline \(x_{i}>TU_{h}\) or \(x_{i}<TL_{l}\) & / \\ \(TU_{m}<x_{i}\leq TU_{h}\) & \(v_{i}>STU_{m}\) or \(v_{i}<STL_{m}\) \\ \(TL_{l}\leq x_{i}<TL_{m}\) & \(v_{i}<STL_{m}\) or \(v_{i}>STU_{m}\) \\ \(TU_{l}<x\leq TU_{m}\) & \(v_{i}>STU_{h}\) or \(v_{i}<STL_{l}\) \\ \(TL_{m}\leq x<TL_{h}\) & \(v_{i}<STL_{l}\) or \(v_{i}>STU_{h}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Anomaly cases determined by double-criteria and multi-tier thresholds. standing. Figure 6 shows a case of anomaly detection by DCDSPOT, Figure 7 compares the biDSPOT and DCDSPOT methods and shows how DCDSPOT eliminates false negatives. Figure 6: An example of anomaly detection using DCDSPOT. In the first and second sub-figures, the red curve represents thresholds calculated by \(q_{1}\) and \(p_{1}\), the orange curve represents thresholds calculated by \(q_{2}\) and \(p_{2}\), and the light blue curve represents thresholds calculated by \(q_{3}\) and \(p_{3}\). Figure 7: A case that illustrates how DCDSPOT eliminates false negatives. A sequence is judged as normal by biDSPOT, but these are false negatives. By setting multi-tier thresholds for WSP and target parameter, false negatives that meet \(TUL_{m}<x_{i}\leq TUL_{h}\) and \(v_{i}\)\(<\)\(STL_{i}\) are corrected.
## 4 Effects of Different Mode Bond-Slip Fracture
### Numerical Model of Bond-Slip  
In reinforced concrete, the interaction between the reinforcement and the concrete is highly complex. The interaction is governed by secondary transverse and longitudinal cracks in the vicinity of the reinforcement. This behavior can be modeled with a bond-slip mechanism where the relative slip of the reinforcement and the concrete is described in a phenomenological sense. The mechanical behavior of the slip zone is then described by the interface element with a zero thickness, as shown in Figure 23. The constitutive laws for bond-slip which have been proposed are mostly based on the total deformation theory, which expresses the tractions as the function of the total relative displacements. In Diana, the relationship between the normal traction and the normal relative displacement is assumed to be linearly elastic, whereas the relationship between the shear traction (bond stress) and the slip is assumed to be a nonlinear function.  
In this study, the nonlinear function between bond stress and slip is represented by the CEB-FIP 2010 model described earlier, since the model adequately considers the variation of bond stress under cyclic loading. The Bond-slip unloading/reloading of FIB Model Code 2010 is built into Diana, as shown in Figure 24a.  
In the first part, the power function of the bond-slip stress \(\tau\) is developed from 0 to the maximum bond-slip stress \(\tau_{max}\) at the relative slip displacement \(s_{1}\). In the second part, the bond-slip stress \(\tau\) remained constant at \(\tau_{max}\) until the relative slip displacement \(s_{2}\). In the third part, the bond-slip stress \(\tau\) is decreased linearly to the ultimate bond-slip stress \(\tau_{f}\) at the relative slip displacement \(s_{3}\). After the relative slip displacement \(s_{3}\) the bond-slip stress \(\tau\) remained constant at \(\tau_{f}\).  
Figure 23: Bond-Slip Interface Element.  
Furthermore, the original CEB-FIP 2010 model considers the effect of plastic deformation of reinforcement on bond strength, forming a 3-dimensional bond stress-slip-strain model, as illustrated in Figure 24b. As the reinforcement deforms plastically, the bond strength decreases exponentially. When the reinforcement strain reaches 100,000, the bond strength is approximately only 10% of the original. However, the built-in model in Diana only considers cyclic loading and does not account for the effects of reinforcement plasticity. Therefore, based on the result of reinforcement damage measured in the Case-2 experiment, considering the mid-term elongation of approximately 4000 in the north side reinforcement, we established a new bond-slip cyclic model with a maximum bond-slip strength which is around 3.47 MPa.
## 4 Discussion
### The Effect of Different Classifiers on the Results of Each Classification  
Among the machine learning classifiers used in this study, the XGBoost algorithm showed the best classification results at all three altitudes, with classification accuracies of 87.63%, 88.24%, and 84.03% at the altitudes of 1600 m, 1800 m, and 2000 m, respectively. The main reason for the different performances of the four classifiers is their different capabilities. First, the classification accuracy of the KNN algorithm is greatly influenced by the training samples, especially due to the unbalanced number of samples among various tree species in different altitude study areas, which can easily lead to misclassification [43]. Secondly, RF is essentially composed of multiple decision trees, and its results are obtained by voting on the N classification results. However, for decision trees with multiple categories, errors generated at the upper layer can propagate to the lower layer, leading to less satisfactory classification results [51]. Thirdly, the SVM algorithm has a relatively simple classification idea. During classification, the system randomly establishes a hyperplane to classify the samples. Although it can reduce the occurrence of misclassification, it is also shown that when the training samples are not uniform and the feature type changes greatly among different altitude study areas, it leads to more difficult data training, and the classification effect is not as good as other classifiers [5]. In contrast, the XGBoost algorithm has better classification performance, a simpler model for training, and is less susceptible to training data than the other three algorithms. Therefore, it exhibited the best classification accuracy [52].
## 2 Thermal Barrier Coating
### Thermal Properties of Thermal Barrier Coatings
#### 2.4.4 Thermal Conductivity Dependence on Deposition Shape  
Several methods have been proposed to fabricate TBCs with low thermal conductivity while using EB-PVD to achieve high corrosion resistance and durability. The method of repeatedly depositing each column of the columnar structure with two materials has been presented (Figure 17a). Thermal conductivity can be reduced by 25% using this method. In the EB-PVD deposition method, the superalloy is tilted at a certain angle and rotated to form a zig-zag columnar structure to induce the scattering of phonons (Figure 17b). This approach reduces the thermal conductivity by up to 40% [82], which increases the blade's acceptable temperature. The thermal conductivity of the TBC can be lowered by inserting impurities into zirconia to induce scattering of phonons due to point defects or by repeatedly depositing different materials.
## 3 Artificial Neural Network
### Regularization: Early Stopping  
Early stopping is a crucial technique in training machine learning models, particularly neural networks. Its primary purpose is to prevent overfitting and ensure the model's better generalization capability on new, unseen data. During the training process, a model endeavors to minimize its error on the training data. However, excessive training can lead the model to \"memorize\" noise and specific characteristics of the training data rather than learning general patterns. This phenomenon is known as overfitting. An overfit model  
Figure 1: Feedforward neural network.  
performs exceptionally well on data it has previously encountered but struggles with predictions on new and unknown data. Early stopping helps mitigate this issue. To implement early stopping, the original dataset is typically divided into three distinct subsets:  
* **Training set:** This dataset is used to train the machine learning model, allowing it to adjust its parameters (e.g., weights in a neural network).
* **Validation set:** This portion of the data is used to monitor the model's performance during the training process. The model does not train on this data; instead, its performance is merely evaluated on it. This monitoring indicates how well the model can generalize to new data.
* **Test set:** This dataset is kept completely separate and is only used at the end of the training process for a final, unbiased evaluation of the model's performance.  
The early stopping process unfolds as follows. In each training step (or \"epoch\"), the model's error is calculated on both the training and validation data. Initially, both errors are expected to decrease. As training progresses, the training error may continue to decline (as the model memorizes the data), but at some point, the validation error will begin to increase. This inflection point signifies the onset of overfitting. Early stopping is triggered when the validation error increases for a specified number of consecutive epochs. At this juncture, training is halted, and typically, the model's best parameters (weights and biases) from the epoch where the validation error was at its minimum are selected and saved.
## 2 Background and Methods
### Carbon Emission Accounting Boundaries and Methodologies
#### 2.2.2 Carbon Emission Accounting Methodologies
\(\rm CH_{4}\) and N\({}_{2}\)O emissions were converted to CO\({}_{2}\)-equivalents using IPCC-recommended GWPs by Equations (10) and (11):  
\[\rm M_{CH_{4}}=m_{CH_{4}}\times GWP_{CH_{4}}withGWP_{CH_{4}}=28\cdot kg\cdot CO _{2}/kg\cdot CH_{4} \tag{10}\]  
\[\rm M_{N_{2}O}=m_{N_{2}O}\times GWP_{N_{2}O}withGWP_{N_{2}O}:265\cdot kg\cdot CO _{2}/kg\cdot N_{2}O \tag{11}\]  
Total direct greenhouse gas emissions (\(M_{d}\)) from WWTP operations were calculated using Equation (12):  
\[\rm M_{d}=M_{CO_{2}}+M_{N_{2}O}+M_{CH_{4}} \tag{12}\]  
Indirect Carbon Emission Accounting Methodologies for Wastewater Treatment Plants  
The indirect greenhouse gas (GHG) emissions from WWTPs primarily originate from electricity consumption and chemical reagent usage, with electricity consumption being the dominant factor. During the operation of WWTPs, equipment such as blowers, pumps, aeration devices, and agitators consume significant amounts of electricity. The indirect GHG emissions from electricity consumption can be calculated using the following Formula (13):  
\[\rm m_{e}=E_{e}\times EF_{e,p} \tag{13}\]  
where:  
\(m_{e}\) represents the annual carbon emissions (in kgCO\({}_{2}\)-eq/a) from electricity consumption at the WWTP. \(EF_{e,p}\) denotes the average CO\({}_{2}\) equivalent emission factor for the regional power sector (in kgCO\({}_{2}\)/kW\(\cdot\)h), adopting the 2021 power sector CO\({}_{2}\) emission factor published by the Ministry of Ecology and Environment and the National Bureau of Statistics of China. For instance, the average provincial power sector CO\({}_{2}\) emission factor for Shandong Province in 2021 was 0.6838 kgCO\({}_{2}\)/kW\(\cdot\)h.  
\(E_{e}\) signifies the annual electricity consumption of the WWTP (in kW\(\cdot\)h/a). Additionally, various chemicals such as flocculants, coagulants, carbon sources, and disinfectants are added during the wastewater treatment process. The production of these chemicals also results in GHG emissions. The CO\({}_{2}\) emissions from chemical consumption can be accounted for using the formula (14):\[\rm EF_{cH_{2}O_{6}}=\frac{2}{3}\times 6\times\frac{44}{180}=0.98(kgCO_{2}/kgC _{6}H_{12}O_{6}) \tag{5}\]0  
where:  
\(m_{c}\) denotes the annual carbon emissions (in kgCO\({}_{2}\)-eq/a) from chemical consumption at the WWTP. \(M_{c,j}\) represents the annual dosage of the j-th chemical (in kg/a). \(EF_{c,j}\) signifies the CO\({}_{2}\) equivalent emission factor of the j-th chemical (in kgCO\({}_{2}\)-eq/kg). The specific parameters are detailed in \[\rm EF_{cH_{2}O_{6}}=\frac{2}{3}\times 6\times\frac{44}{180}=0.98(kgCO_{2}/kgC _{6}H_{12}O_{6}) \tag{5}\]1  
\(r_{c,j}\) denotes the effective content (%) of the j-th chemical. The data presented in Table 5 are sourced from Supplementary Table A.0.4 of the Assessment Standard for Carbon Mitigation in Municipal Wastewater Treatment Plants (T/CUWA 50055-2023).
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 5 Numerical Simulation of Linear MIMO FSE Receiver for MDL-Impaired Optical Channel
### Numerical Simulation Results
In the low \(\overline{SNR}_{in}\) regime (5 dB, Figure 6(a)), the STD of the degradation is lower when compared to the high \(\overline{SNR}_{in}\) regime (60 dB, Figure 6(b));
* The performance degradation measured as \(AL\) is milder than measured as \(L(i)\) when more than 95% coverage of the channels is considered. Note that the difference is negligible when median values are taken into account. Figure 6: \(ML_{05}\) (up) and \(AML_{05}\) (down) as defined in Section 5.4 for \(\overline{SNR}_{in}\) = 5, 6.2, and 10 dB (**a**) and \(\overline{SNR}_{in}\) = 15, 30, and 60 dB (**b**) for different values of the transmitter roll-off factor \(\alpha\). Note that \(\sigma_{g}=0\) corresponds to a channel without MDL.
## 3 Application of the Process Method
### Enhanced Expert System Design
Improving system performance with experience is accumulated in algorithms that make a final decision based on artificial intelligence-learned algorithms and human-conditioned facts. In this case, the historical data from experiments are used to create a computer-based model that supports decision-making. However, an expert system  
Figure 6: Smart hydraulic press with a real-time PR-MPC mechanism within an expert system. summarizes the algorithm's approach to automatically recognize the conditions and react to decision-making. During the implementation phase, the experimental results on the 5G network were performed in order to determine whether a hydraulic press on a 5G network is sufficient for industrial use. To conduct experiments on the compatibility of industrial and research productions with the 5G network, we determined the communication protocols and communication ports for an individual subsystem forming a 5G network. The results show that communication can be facilitated via the User Datagram Protocol (UDP) or Transmission Control Protocol (TCP) in combination with Internet Protocol (IP). The devices with their communication protocols and connections are listed in Table 1.  
The connectivity of each subsystem in the 5G network is based on its connectivity characteristics, so two 5G modems are used (all SIM modems are set to provide the static IP of the 5G modules in the network):  
* Teltonika Networks TRB50 gateway (Teltonika Networks, Vilnius, Lithuania), connects directly to LAN port of industrial subsystems,
* QUECTEL RMU500-EK (Quectel, Shanghai, China), connects via USB port. In experimental devices that support a local area network (LAN) connection, a TRB500 modem is used. Furthermore, in cases where the manufacturer's subsystem integrates a USB port, the RMU500-EK can be used, as no additional tasks, such as port forwarding and communication debugging, are required after connection. Therefore, the TRB 500 was used on the control PC and the RMU500-EK is used on the Beckhoff PLC CP3718. Both 5G modems support the 5G NSA mode, which is currently installed in the laboratory (Figure 6). The IP addresses of both 5G modems are set to the public IP so that communication can be established through the 5G network [3]. After setting up the ADS communication protocols based on the 5G IP addresses, the secure connection was established [50]. Since the control loop for controlling the hydraulic press processes is updated every millisecond and the latency of the 5G network does not allow such a low communication latency, the time-critical control processes are calculated by the Beckhoff itself, and the results are sent to the control PC via the 5G network.
## 7 Conclusions and Future Work  
This paper introduces the Structural Influence and Preservation (SIPA) Algorithm as a solution to the challenge of preserving stable node structures in online dynamic network visualization. The SIPA employs a novel strategy to compute and propagate node influence within the network based on structural changes and incorporates a node aging mechanism. The algorithm's performance is evaluated through layout result analysis and quantitative metrics. Results show that SIPA can better maintain the relative node positions and shapes of stable structures, thereby enhancing the overall layout stability. In addition, given that comprehending network dynamics solely through network layout results is challenging, we complement the algorithm with an interactive visualization system, enriching the layout results with various interactions and views of temporal context, network features, animated graph-diaries and network snapshots. Case studies and user experiments demonstrate the system's effectiveness in analyzing and tracking network changes over time.  
In future work, we would like to study the possible techniques to extend our research to address the challenges of visualizing large-scale networks. We will adopt multilevel approaches and community detection algorithms to partition extensive networks into manageable layers, allowing users to analyze network dynamics at various levels. Additionally, we plan to develop adaptive algorithms to automatically adjust the multiple parameters within our current algorithm, enhancing its adaptability and efficiency. These efforts will make large-scale dynamic network analysis more accessible, expanding the applicability of our visualization framework in the process.  
Conceptualization, G.W. and Y.W.; methodology, G.W. and H.C.; software, R.Z., H.C., J.L. and W.G.; validation, H.C., W.G. and F.W.; formal analysis, G.W. and R.Z.; investigation, G.W.; resources, Y.W.; data curation, R.Z.; writing--original draft preparation, G.W. and H.C.; writing--review and editing, G.W.; visualization, G.W.; supervision, Y.W.; project administration, G.W.; funding acquisition, Y.W. 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 with grant number 61872304, and the Talent Project of Sichuan University of Science and Engineering with grant number 2020 RC20.  
Not applicable.  
Data Availability StatementData available in a publicly accessible repository. The data presented in this study are openly available in [[https://snap.stanford.edu/data/](https://snap.stanford.edu/data/)]([https://snap.stanford.edu/data/](https://snap.stanford.edu/data/)).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Theoretical Background of the Leaky Lamb Wave Propagation and Generation
For example, in Figure 2a, at 2 MHz\(\cdot\)mm, the modes A0, S0, and A1 may propagate together. One can note in Figure 2b that the modes A0 and S0 present a high attenuation, whereas the mode A1 presents a lower leakage by re-emission. The phase velocity of the modes is evaluated using the real part of the wave numbers \(c_{p}=\frac{2\pi f}{K}\). The group velocity \(c_{g}\) is derived from the phase velocity: \(c_{g}(\omega)=c_{p}(\omega)^{2}\Big{[}c_{p}(\omega)-\omega\frac{d_{cp}(\omega )}{d\omega}\Big{]}^{-1}\), where \(\omega=2\pi f\). Both phase and group velocities are plotted respectively in Figure 3a,b. A real signal is never purely monochromatic. Its spectrum is defined by a band. That is why group velocities are of interest. In the example given by Figure 3b around 2 MHz\(\cdot\)mm, the modes have different group velocities. On the bandwidth, the group velocity of the mode A0 is rather constant, whereas the group velocity of the mode S0 varies by up to 100%. This highlights the dispersive property of the Lamb wave, described in detail in [9]. By reciprocity of the re-emission shown in Figure 1, Lamb waves can be generated in an immersed plate by a steered incident beam. The acoustic wave propagates in the fluid  
Figure 3: (**a**) Phase velocity of leaky Lamb waves; (**b**) Group velocity of leaky Lamb waves, as a function of the product frequency \(\times\) thickness. Arrow indicates the bandwidth of a real 2 MHz\(\cdot\)mm signal and highlights the dispersion phenomenon in the propagation of the Lamb waves. Figure 2: (**a**) Wave number of leaky Lamb waves normalized by the plate thickness; (**b**) Attenuation of leaky Lamb waves, as a function of the product frequency \(\times\) thickness. to the plate, where a reflected wave and a transmitted wave appear [31]. One can find the angles \(\theta\) that maximize the transmitted wave in the plate [32]. Those angles tie in with the angles that can be found using Snell's law, and the phase velocity of each mode is  
\[\frac{c_{0}}{\sin(\theta_{\mathrm{i}})}=\frac{c_{\mathrm{p}}}{\sin(\theta_{ \mathrm{Lamb}})} \tag{3}\]  
where \(\theta_{\mathrm{i}}\) is the beam incidence angle in the fluid, \(\theta_{\mathrm{Lamb}}=\frac{\pi}{2}\) in the plate. \(c_{0}\) is the wave velocity in the fluid. \(c_{\mathrm{p}}\) is the phase velocity of the aimed mode at a given product frequency \(\times\) thickness. The beam angles in the case of our experiments are plotted in Figure 4. For example, at 2 MHz\(\cdot\)mm, the A1 mode can be generated by an incident wave with an angle of \(10^{\circ}\).