esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Design of TOD Compensation Using the Birefringent Plate  
Here, we aim to compensate the TOD of \(1.3\times 10^{6}\) fs\({}^{3}\) for the multi-PW with a 150-fs level design. As detailed in Figure 6a, we first analyze the variation of the maximum amplitude of TOD and free-spectral range (FSR) with the length of the birefringent plate using Equations (1)-(7), where the parameters are \(\theta=90^{\circ}\), \(\phi=17^{\circ}\), and T = 21 \({}^{\circ}\)C. Similar to the GTI, the maximum amplitude of the TOD increased, but the FSR decreased with increasing plate length. Figure 6b shows the GVD and TOD change with \(\phi\), in which the length of the plate is 6.5 mm, and the other parameters are the same as in Figure 6a. In addition, the TOD can be adjusted from positive to negative with the scope of \(\pm 2\times 10^{6}\) fs\({}^{3}\), which can compensate for the residual TOD of \(1.3\times 10^{6}\) fs\({}^{3}\) for the multi-PW design when \(\phi\) is set to \(17^{\circ}\). Furthermore, the GVD is small and can be neglected. Additionally, the transmission, GVD, and TOD curves changing with wavelength are also shown in Figure 6c,d, respectively, when the birefringent plate is set to the compensation TOD of \(1.3\times 10^{6}\) fs\({}^{3}\).  
Figure 5: (**a**) Intensity contrast for bandwidths of 3 nm, 5 nm, and 7 nm, which includes the TOD of \(1.3\times 10^{6}\) fs\({}^{3}\) and FOD of \(-3.9\times 10^{8}\) fs\({}^{4}\); (**b**) the contrast only with the FOD, with other parameters the same as in (**a**); (**c**) the contrast for bandwidths of 7 nm, 11 nm, and 15 nm including the TOD of \(1.3\times 10^{6}\) fs\({}^{3}\) and FOD of \(-3.8\times 10^{7}\) fs\({}^{4}\); (**d**) the contrast only considering the FOD, with other parameters the same as in (**c**).  
As shown above, the dispersion scheme can support the bandwidth of approximately 7 nm and the TOD of \(1.3\times 10^{6}\) fs\({}^{3}\), which can be used to compensate for the demanded TOD for the multi-PW design. In addition, similar to the results of crystal cascade increasing the bandwidth [33], we observe from Figure 6 that the birefringent crystal can also be cascade designed to afford larger bandwidth with the same TOD simultaneously in the dispersion compensation. From the results, the residual TOD for multi-PW design can be compensated by choosing the thickness and controlling the inclination angle, the in-plane angle, and the temperature of the birefringent plate independently.
## 3 Node Satisfaction: A Metric for Optimization
### Interpreting Satisfaction  
It is easy to see from Equation (1) that satisfaction \(S_{i}\) of node \(i\) takes values in the range \([0,1]\), with a maximum value when all \(b_{i}\) connections are established with node \(i\)'s top \(b_{i}\) ranked neighbors. In the general case of \(c_{i}\leq b_{i}\) connections, \(S_{i}\) takes the value of \(\frac{c_{i}}{b_{i}}\) minus a penalty if the connected nodes are not the top choices in the preference list. More specifically, using the previously defined connection list \(C_{i}\) of node \(i\), observe that for each connected node \(j\) the penalty is proportional to the difference between its rank \(Q_{i}(j)\) in the connection list of node \(i\) and its rank \(R_{i}(j)\) in the preference list of node \(i\):  
\[S_{i} = \frac{c_{i}}{b_{i}}-\frac{\sum\limits_{j\in C_{i}}\left(R_{i} \left(j\right)-Q_{i}\left(j\right)\right)}{b_{i}L_{i}}=\] \[= \frac{c_{i}}{b_{i}}-\frac{\sum\limits_{j\in C_{i}}R_{i}\left(j \right)-\frac{c_{i}\left(c_{i}-1\right)}{2}}{b_{i}L_{i}}=\] \[= \frac{c_{i}}{b_{i}}+\frac{c_{i}\left(c_{i}-1\right)}{2b_{i}L_{i} }-\frac{\sum\limits_{j\in C_{i}}R_{i}\left(j\right)}{b_{i}L_{i}}\]  
As defined in Equation (1), satisfaction \(S_{i}\) also measures the deviation of the connection list \(C_{i}\) from the optimal case. The following example (Figure 1) illustrates this notion of deviation:  
The optimal case for connections in \(C_{i}\) would be to occupy the top \(c_{i}\) slots in node \(i\)'s preference list (_i.e._, for node 31 to have \(R_{i}(31)=0\)). For each node \(j\) that deviates from this optimal case a penalty must be paid, giving to node \(i\) a satisfaction of:  
\[S_{i} = \frac{c_{i}}{b_{i}}-\frac{R_{i}\left(31\right)-Q_{i}\left(31 \right)}{b_{i}L_{i}}-\frac{R_{i}\left(7\right)-Q_{i}\left(7\right)}{b_{i}L_{i}}\] \[-\frac{R_{i}\left(11\right)-Q_{i}\left(11\right)}{b_{i}L_{i}}- \frac{R_{i}\left(22\right)-Q_{i}\left(22\right)}{b_{i}L_{i}}=0.708\]  
Figure 1: Example of satisfaction computation for node \(i\) with \(b_{i}=4\).  
In short, the interpretation of satisfaction (Equation (1)) we consider here can be described simply as follows: a node \(i\) is totally satisfied when it connects to its \(b_{i}\) \"most wanted\" preferences with its \(b_{i}\) degrees of freedom; otherwise, each \"opening\" in the list and/or each degree of freedom that remains unused (not matched) is reflected in the node's lower satisfaction.
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
## 4 Simulated Results of the Robust Design  
Figure 9 shows the noise test results of the initial and optimized TEM horn antennas. As shown in Figure 9a, the reflection coefficient of the antenna varies according to the noise factors in the OA matrix experiments. In particular, the fluctuation of the reflection coefficient level around 17.5 GHz exceeds the \(-10\) dB criterion. The robustness of the initial antenna calculated according to Equation (8) is 1.45. The noise test result of the robustly optimized antenna with robustness of 1.27 is presented in Figure 9b. Unlike the noise test result of the initial antenna, all of the reflection coefficient levels in the noise test result fall under the \(-10\) dB criteria in the 6-18 GHz band. The robustness of each experiment in the noise test ranges from 0.57 to 2.29. The minimum and maximum values are observed in experiments 4 and 9, respectively. The parameters of the optimized antenna are as follows: \(a=12.54\) mm, \(b=7.22\) mm, \(d=0.83\) mm, \(s=5.03\) mm, \(l_{s}=3.48\) mm, \(l_{c}=6.08\) mm, \(l_{r}=5.02\) mm, \(l_{h}=25\) mm, \(\alpha=0.092\), and \(\beta=0.1\).
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### Vehicle Decision  
The routing protocols in VANETs presented in the current literature depends on a certain number of factors. Therefore, to follow a standard nomenclature for the characterization, we consider three key factors as follows:  
1. Distance: between the current vehicle and the candidate vehicle;
2. Neighbor quantity: the number of candidate vehicles' neighbors;
3. Relative velocity: between the current vehicle and the candidate vehicle.  
To quantify the three factors, we select a nine-point fuzzy scale system. The fuzzy number is put forward as a triangular fuzzy number (TFN), denoted as follows [22]:  
\[\widetilde{\text{A}}=(\text{l},\text{m},\text{u}), \tag{1}\]  
where l and u are the lower and upper bounds of the fuzzy number \(\widetilde{\text{A}}\) respectively, and m is the modal value. In this paper, we assigned four values to the linguistic variable as presented in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Linguistic Variable** & **Triangular Fuzzy Number** \\ \hline Lower & \(\widetilde{\text{2}}=(1,2,3)\) \\ Low & \(\widetilde{\text{4}}=(3,4,5)\) \\ High & \(\widetilde{\text{6}}=(5,6,7)\) \\ Higher & \(\widetilde{\text{8}}=(7,8,9)\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Triangular fuzzy number.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & **Position** & **Velocity** & **Neighbor** & **Road** & **Source** & **Target** & **Link** & **Sequence** & **Lifetime** \\
**Vector** & **Vector** & **Quantity** & **ID** & **Intersection** & **ID** & **ID** & **ISO** & **ISO** & **ISO** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Beacon message.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Node ID** & **Position** & **1-Hop** & **2-Hop** & **Joint** & **Road ID** & **Link Sequence** & **Vehicle Quantity** & **Lifetime** \\ \hline a & \((\text{X}_{u},\text{Y}_{a})\) & Yes & No & - & - & - & \(\text{n}_{a}\) & \(\text{t}_{a}\) \\ b & \((\text{X}_{b},\text{Y}_{b})\) & No & Yes & a & - & - & \(\text{n}_{b}\) & \(\text{t}_{b}\) \\ - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Static node neighbor table.  
Next, we will illustrate the details on how to evaluate the candidate factors.
## 4 Active Control Experiment
was no vibration isolation effect on the 37 Hz and 60 Hz line spectrum in the frequency domain, and the 110 Hz line spectrum was attenuated by 24.16 dB. It can be seen that the hybrid vibration isolation system achieved a certain vibration isolation effect at a high frequency. After the active control was turned on, the error signal converged quickly in the time domain, and the amplitude was reduced by more than 80%. In the frequency domain, the 37 Hz and 60 Hz line spectra were attenuated by 38.86 dB and 26.16 dB, respectively, the 110 Hz line spectrum was attenuated by 11 dB on the basis of the passive vibration isolation, and the control effect is good. At the same time, it can be seen that the input current was well constrained within 8 A, which shows that the adaptive suppression algorithm can well constrain the output signal under large interference and ensure that the electromagnetic actuator can effectively isolate the vibration within the normal excitation current. The robustness of the algorithm was further verified. When the experiment ran for 20 s, an impact was applied to the system base, and the error signal could quickly be stabilized. In addition, when the shock was applied again between 21 and 22 s, the error signal could still quickly be stabilized to the convergence value. The acceleration response time history is shown in Figure 20. Through the continuous impact on the system, it was shown that the suppression algorithm could still effectively control the output signal. When the error signal increased sharply, the weight increment \(\Delta f\) tended to zero, so that the weight coefficient of the controller remained unchanged; therefore, it is concluded that the ASFXLMS algorithm has a strong anti-interference ability. Figure 19: Actuator input current amplitude. Figure 20: Time history of the acceleration response before and after impact control.
###### Abstract  
Communications technologies are an integral part of efficient monitoring and reliable control in smart grids, but enhanced reliance on these technologies heightens the risk of cyber assaults. Recently, a new type of stealth, or covert, assault in smart grid networks has been discovered, which cannot be ascertained by legacy bad-data detectors using state estimation. Due to the delay-sensitive nature of smart grid networks, swift detection of abnormal changes is immensely desired. In this paper, we propose two Euclidean distance-based anomaly detection schemes for covert cyber-assault detection in smart grid communications networks. The first scheme utilizes unsupervised-learning over unlabeled data to detect outliers or deviations in the measurements. The second scheme employs supervised-learning over labeled data to detect the deviations in the measurements. Unlike the classic detection test, the proposed schemes tackle an unknown sample with low computational complexity, leading to a shorter decision time. To improve detection accuracy and further reduce the computational complexity and the associated time delay, we employ a genetic algorithm-based feature selection method to choose the distinguishing optimal feature data subset as input to both of the proposed schemes. The evaluation is carried out through the standard IEEE 14-bus, 39-bus, 57-bus and 118-bus test systems. Simulation results show that compared to the existing feature extraction-based detection schemes, the proposed schemes show significant improvement in covert cyber deception assault-detection accuracy.  
anomaly detection; cyber assaults; Euclidean distance; feature selection; genetic algorithm; smart grids; state estimation +
Footnote †: journal: Applied Sciences  
_Article_
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
## 3 The Proposed Method
### The Life Prediction According to Specific Task Requirement  
The general procedure of the final life prediction with considering the mission profile is illustrated as Figure 4. According to the reference degradation models, the final life of the samples to be predicted under specific mission profile can be obtained as follows.  
Figure 4: The process of the life prediction.  
Figure 3: The process of the reference degradation models constructing.  
Step 4.1: Partition the operating condition of the samples to be predicted. For the samples to be predicted, partition this sequence into \(C\) new sequences \(\left\{\mathbf{OF}\mathbf{V}_{j}^{ti}\middle|j=1,\dots,C\right\}\) according to the operating condition, where \(\mathbf{OF}\mathbf{V}_{j^{\prime}}^{ti}\cap_{j\
eq j^{\prime}}\mathbf{OF}\mathbf{V}_{j}^{ti}=\varnothing\), \(ti=1,2,\dots,mn\), \(mn\) is the number of the samples to be predicted.  
Step 4.2: Predict the life of each constant operating condition of the test samples. According to the STM, in which the specific process refers to [20,21], the life candidate sets \(\mathbf{RUL}_{ck,j}^{ti}\) and the weights \(\mathbf{W}_{ck,j}^{ti}\) can be obtained, where \(ck=1,2,\dots,cn\), \(cn\) is the number of the candidate sets. Then, the life of each constant operating condition \(SRUL_{j}^{ti}\) of the samples to be predicted can be calculated as follows.  
\[SRUL_{j}^{ti}=\sum_{ck=1}^{cn}\mathbf{RUL}_{ck,j}^{ti}\cdot\mathbf{W}_{ck,j}^{ti} \tag{4}\]  
Step 4.3: Obtain the proportion of each operating condition in the mission profile. According to the future mission profile \(\mathbf{SV}^{ti}\), determine the proportion of each operating condition \(R_{j}^{ti}\) which is needed to complete the specific task.  
Step 4.4: Predict the final life of the samples to be predicted under a specific mission profile. According to the life of each constant operating condition and the proportion of each constant operating condition in the mission profile, the life of the samples to be predicted \(FRUL^{ti}\) under a specific mission profile can be calculated as follows.  
\[FRUL^{ti}=\sum_{j=1}^{C}SRUL_{j}^{ti}\cdot R_{j}^{ti} \tag{5}\]
## 3 Results and Discussion
### Experimental Correlation between UCS and \(\mathrm{v_{P}}\) in This Study
#### 3.2.3 Discussion  
Table 5 shows the results of all the performed regression analyses and their goodness-of-fit statistics, when relating UCS (dependent variable) first with \(\mathrm{v_{P}}\) and then with \(\mathrm{v_{P}}\) and other rock parameters for the studied limestone.  
Simple regression analyses found that UCS is related to \(\mathrm{v_{P}}\), and the best fit was obtained with a quadratic model (\(R^{2}=78.40\%\)).  
Figure 4: Predicted against experimental UCS results of the multiple regression analysis.  
Figure 5: Comparison of theoretical and observed probabilities for standardized residuals of uniaxial compressive strength.  
Nevertheless, it is well known that \(\
u_{\rm p}\) depends, among other physical properties, on the n, \(\rho\), and S\({}_{\rm r}\) of a rock, as explained in the introduction. Thus, it seemed worthwhile to attempt to obtain a better fit by incorporating some of these independent variables into the analyses. For such a task, we performed multiple regression analyses considering a full saturation level and obtained two relevant results.  
First, although it has been stated that \(\
u_{\rm p}\) depends on \(\rho\), the results showed that \(\rho\) does not have statistical significance for the predicted UCS. This may be explained by the fact that there does not seem to be a clear relationship between UCS and \(\rho\) based on the results of this study (Table 3). The maximum UCS was obtained on a mean \(\rho\) specimen, whereas both maximum and minimum \(\rho\) attained UCS values between the lower ones (UCS \(<60\) MPa).  
Second, n does have statistical significance for the predicted UCS. It is known that n affects strength, and the experimental results obtained in this study (Table 3) confirmed this: the largest UCS was obtained for the specimens with lowest n and the minimum UCS values were obtained for the specimens with largest \(n\).  
Furthermore, two different multiple regression models were tested, and we found that both multiple regression analyses featured better goodness-of-fit statistics than all of the simple regression analyses (Table 5). This means that it is worth considering more independent variables when trying to predict UCS starting from ultrasonic pulse transmission tests.  
Finally, it has to be pointed out that, despite there being many authors who have attempted to predict UCS from \(\
u_{\rm p}\), there is still no universal equation that can explain such a relationship at the laboratory scale for intact rock. Thus, this relationship for intact rocks at the laboratory scale still needs to be investigated before trying to extrapolate results at a real scale, where discontinuities will obviously play a fundamental role in the relationship between rock mass strength and ultrasonic wave velocity.
# Intrinsic Interference Use for FBMC-IOTA Systems
## 4 Evaluation of IIU
### Effect of Carrier Frequency Offset (CFO)  
The received signal (after equalizing the channel effect) in the presence of CFO becomes  
\[r(t)=s(t)\exp\left(2\pi if_{0}t\right) \tag{20}\]  
Hence the desired symbol \(a_{k,l}\) is demonstrated as  
Figure 1: BER performance with perfect channel estimation and synchronization.  
\[\hat{a}_{k,l} \tag{21}\] \[=\int r(t)g_{k,l}^{*}(t)dt\] \[=\int s(t)\exp\left(2\pi if_{0}t\right)g_{k,l}^{*}(t)dt\] \[=\int\sum_{n,m}a_{n,m}g_{n,m}(t)g_{k,l}^{*}(t)\exp\left(j2\pi f_{0 }t\right)dt\] \[=\sum_{n,m}a_{n,m}\int g(t-n\tau_{0})g^{*}(t-l\tau_{0})\exp\left(2 \pi if_{0}t\right)\] \[\exp\left(j(m+n-k-l)\pi/2\right)\exp\left(2j\pi(m-k)\
u_{0}t \right)dt\]  
Denoting \(t=x+\frac{(n+l)\tau_{0}}{2}\), \(\hat{a}_{k,l}\) can be calculated by (22). Moreover, considering that \(f_{0}=\delta\
u_{0}\), (22) becomes (23) which shows that a frequency offset will change the weights and some of the intrinsic interference will be added to the desired signal. Under this condition, new weights should be calculated. Figure 2 shows how the FBMC-IOTA system with 15 kHz subcarrier spacing are affected by frequency offset of 60 Hz. As indicated by the figure, the gain obtained by IIU reduces due to the frequency offset. This follows from the fact that frequency offset will distort the desired signal and subsequently affects the calculation of parity symbols in the algorithm.  
\[\hat{a}_{k,l}=\sum_{n,m}a_{n,m}\int g(x+\frac{l-n}{2}\tau_{0})g^{* }(x-\frac{l-n}{2}\tau_{0}) \tag{22}\] \[\exp\left(j(m+n-k-l+(m-k)(n+l))\pi/2\right)\exp\left(2\pi j((m-k) \
u_{0}+f_{0})x\right)\exp\left(2\pi if_{0}\frac{n+l}{2}\tau_{0}\right)dx\] \[=\sum_{n,m}a_{n,m}(j)^{m+n-k-l+(m-k)(n+l)}\exp\left(2\pi if_{0} \frac{n+l}{2}\tau_{0}\right)A_{g}\left((l-n)\tau_{0},(m-k)\
u_{0}+f_{0}\right)\]  
\[\hat{a}_{k,l}=\sum_{n,m}a_{n,m}(j)^{m+n-k-l+(m-k)(n+l)}\exp\left(\pi j\delta \frac{n+l}{2}\right)A_{g}\left((l-n)\tau_{0},(m-k+\delta)\
u_{0}\right) \tag{23}\]
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 5 Recognition Strategies
This matrix has several rows, depending on the cardinality of the \(WS_{T}\) array. The columns depend on the number of services recognized for that particular node. 4. Creation of the primary candidates, filtered by the matching recognition function \(M()\); creating the initial \(WS_{CT}\) set. 5. For each \(WS_{i}\), \(WS_{T}\) is decomposed into sub-patterns \(\rho(WS_{i})\). They generate the corresponding recognizer modules \(\Gamma\), which are responsible for the core of the recognition process from the QoS parameters and \(\Delta\)KS, or by \(\Delta\)PS, according to the user's preferences. For each \(WS_{c}\), its recognition module \(\Gamma\) is called and its ranking (\(\varphi\)) is calculated. 6. The recognition function selects the candidates from the calculation formalized in Equations (4) and (5). When the recognition is valid, \(WS_{c}\) is stored in the MC matrix. Equation (6) formally defines the recognition element, where \(n\) is the number of \(WS\in WS_{T}\) and \(m\) is a value representing the number of WSs that satisfies an ACM node. The element \(x\), defined in Equation (6), is a WS used for solution pathways. \[MC_{nxm}=\exists_{x}\left\{x\mid x\in \begin{bmatrix}WS_{c_{1,1}}&...&WS_{c_{1,m}}\\ WS_{c_{2,1}}&...&WS_{c_{2,m}}\\...&...&...\\ WS_{c_{n,1}}&...&WS_{c_{n,m}}\end{bmatrix} \leftrightarrow(KS(x)\bigoplus PS (x))\right\}.\] (6)
7. The process is not valid if a \(WS_{c}\) does not exist for any \(WS_{i}\). 8. The \(I(M_{c})\) function is responsible for integrating the solution pathways defined in \(MC\), a process conducted by grouping the candidate services for each WS of the model and selecting them, according to the condition \(\Delta\)WSC that satisfies the composition model. Figure 7: Ar_WSDS recognition system. ```
Input: ACM,\(WS_{g}[]\) Result: MC[][] \(WS_{T}[]\) = load(ACM); \(WS_{CT}[]\) = M(\(WS_{g}\)); \(MC[0..|WS_{T}]\) - 11 [ ] be_a_new_matrix; \(i=0\); for\(WS_{i}\in W_{T}\)do \(\Gamma=\rho(WS_{i})\); \(j=0\); for\(WS_{c}\in W_{CT}\)do if\(\Gamma(WS_{c})\) and \(\Gamma\in\)\(KS\)then  isC = KS_Recognizer(\(WS_{c}\)); else  isC = PS_Recognizer(\(WS_{c}\));  end if if\(iS=true\)then \(MC[i][j]=WS_{c}\); \(j=j+1\); else  delete \(WS_{c}\) in \(WS_{CT}\);  end if if\(M[i]=\emptyset\)then  Error no candidate services;  exit;  end if \(i=i+1\);  end if  Create solution pathways based on MC; \(I(MC)\);  end
```  
**Algorithm 1**Pattern recognition algorithm of web services.