esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to Reflectarray for Compact Antenna Test Range
### Wideband Multi-Frequency Optimization of the Near-Field Figures of Merit
After the POS, the layout is simulated at three different frequencies within the 5G FR n257 band to assess the performance. Before POS, results are only shown at 28 GHz since ideal phase-shifters are assumed. Compliance (%) indicates percentage of surface in \(\Omega_{1}\) that complies with specifications. Table 2 gathers the performance data of the optimized antenna. A compliance of 100% in region \(\Omega_{1}\) (see Figure 3) is achieved. Compared with the initial layout obtained after the POS, the worst magnitude ripple was reduced from 5.02 dB to 0.98 dB, while the worst phase ripple was reduced from 24.12\({}^{\circ}\) to 6.87\({}^{\circ}\). Figure 8: For linear polarization \(X\), representation of the magnitude and phase of the NF that complies with a magnitude ripple of 1 dB and phase ripple of 10\({}^{\circ}\) before (**top** row) and after (**bottom** row) the multi-frequency optimization. (**a**) Magnitude at 27 GHz before optimization. (**b**) Phase at 27 GHz before optimization. (**c**) Magnitude at 28 GHz before optimization. (**d**) Phase at 28 GHz before optimization. (**e**) Magnitude at 29 GHz before optimization. (**f**) Phase at 29 GHz before optimization. (**g**) Magnitude at 27 GHz after optimization. (**h**) Phase at 27 GHz after optimization. (**i**) Magnitude at 28 GHz after optimization. (**j**) Phase at 28 GHz after optimization. (**k**) Magnitude at 29 GHz after optimization. (**l**) Phase at 29 GHz after optimization. Similar results were obtained for polarization \(Y\). Figure 7: Main cut \(y=0\) mm of the near field after the wideband multi-frequency optimization of the layout for the (**a**) magnitude and (**b**) phase.
## Appendix A Derivation of DTFT Magnitude Deviation Formula  
Given a sinewave with an amplitude \(A\), frequency \(f\), and initial phase \(\varphi\), at sample \(k\), the signal can be desribed using the following formula  
\[x[k]=Asin(2\pi fk\delta t+\varphi) \tag{1}\]  
Figure 8: Methods comparison for implementation on a STM32 microcontroller with hardware acceleration.  
where \(\delta t\) is the sampling period. In this case, the real \(\Re(Y)\) and imaginary part \(\Im(Y)\) of Discrete Time Fourier Transform (DTFT) at frequency \(f\) take the following formula, given \(N\) samples:  
\[\Re(Y)=\frac{1}{N}\sum_{k=0}^{N-1}Asin(2\pi f\delta tk+\varphi)cos(2\pi fk\delta t) \tag{20}\]  
\[\Im(Y)=\frac{1}{N}\sum_{k=0}^{N-1}Asin(2\pi f\delta tk+\varphi)sin(2\pi fk \delta t) \tag{21}\]  
By expanding the imaginary part:  
\[\Im(Y)=\frac{A}{2N}\sum_{k=0}^{N-1}cos\varphi-cos(4\pi k\delta t+\varphi) \tag{22}\]  
\[\Im(Y)=\frac{Acos\varphi}{2}-\frac{A}{2N}\sum_{k=0}^{N-1}cos(4\pi k\delta t+\varphi) \tag{23}\]  
The sum expression in the previous formula can be also written as following [35]:  
\[\Im(Y)=\frac{Acos\varphi}{2}-\frac{AR}{2N}cos(\varphi+(N-1)2\pi f\delta t) \tag{24}\]  
where \(R=\frac{sin(2\pi f\delta tN)}{sin(2\pi f\delta t)}\).  
The same analogy could be repeated for the real part, which gives  
\[\Re(Y)=\frac{Asin\varphi}{2}-\frac{AR}{2N}sin(\varphi+(N-1)2\pi f\delta t) \tag{25}\]  
The squared magnitude of single-sided AC analysis based on DTFT \(\left\|X_{DTFT}\right\|^{2}\) could be expressed as \(\frac{1}{4}(\Re(Y)^{2}+\Im(Y)^{2})\), which is:  
\[4\|X_{DTFT}\|^{2}=A^{2}\Big{(}\frac{1}{4}+(\frac{R}{2N})^{2}+\frac{R}{2N}(sin \varphi sin(\varphi+(N-1)2\pi f\delta t)-cos\varphi cos(\varphi+(N-1)2\pi f \delta t)\Big{)} \tag{26}\]  
\[\text{expanding }y=(sin\varphi sin(\varphi+(N-1)2\pi f\delta t)-cos\varphi cos( \varphi+(N-1)2\pi f\delta t)\]  
\[\text{gives:} \tag{27}\]  
\[y=-cos(2\varphi+2\pi f(N-1)\delta t) \tag{28}\]  
\(4\|X_{DTFT}\|^{2}\) becomes:  
\[4\|X_{DTFT}\|^{2}=A^{2}\Big{(}\frac{1}{4}+(\frac{R}{2N})^{2}-\frac{R}{2N} cos(2\varphi+2\pi f(N-1)\delta t)\Big{)} \tag{29}\]  
The deviation formula for \(\overline{\|X_{DTFT}\|}\) can be expressed as \(A-\|X_{DTFT}\|\):  
\[\overline{\|X_{DTFT}\|}=A\bigg{(}1-\sqrt{1+\Big{(}\frac{R}{N}\Big{)}^{2}- \frac{2R}{N}cos(2\varphi+2\pi f(N-1)\delta t)}\bigg{)} \tag{30}\]
## 5 Experiment
### Experiment Setup
#### 5.1.2 Evaluation Metric  
In our experiments, we utilize two primary metrics: Query Match (QM) and Answer \(F1\). The Query Match (QM) accuracy evaluates the congruence of the whole predicted sequence with the ground truth, achieved by decomposing the predicted SPARQL queries into distinct triples. On the other hand, the answer \(F1\) score provides a measure of accuracy between the ground truth answer and the answers derived from the predicted SPARQL queries, which is frequently employed in similar works in the field.  
For one example, assume the predicted SPARQL query is \(Q_{p}\) and the ground truth (true label) SPARQL query is \(Q_{t}\). We convert the triples in the WHERE clauses of \(Q_{p}\) and \(Q_{t}\) into sets, denoted as \(S_{p}\) and \(S_{t}\), respectively. The QM metric for one example can be defined as follows:  
\[QM=\begin{cases}1,\text{ if }S_{p}=S_{t}\\ 0,\text{ if }S_{p}\
eq S_{t}\end{cases} \tag{4}\]  
In other words, if the predicted SPARQL query and the ground truth query have the same set of triples, regardless of the order, the QM metric is 1. Otherwise, if the sets of triples do not match exactly, the QM metric is 0. This definition ensures that the QM metric is insensitive to the order of triples in the WHERE clause. As long as the two sets have thesame elements, they are considered a match. This flexibility makes the QM metric suitable for evaluating text-to-SPARQL tasks that consider triple ordering. The overall QM score for the test set is the average QM score for each example in the test set.  
The answer \(F1\) is a metric that measures the accuracy of the results obtained after executing a query. Assume \(A_{p}\) is the predicted answer and \(A_{q}\) is the ground truth. Let \(P\) represent the precision, which is the ratio of the number of correct results obtained from the predicted query to the total number of results. Similarly, let \(R\) represent the recall, which is the ratio of the number of correct results obtained from the predicted query to the total number of correct results in the ground truth query.  
\[P=\frac{|A_{p}\cap A_{q}|}{A_{p}} \tag{5}\]  
\[R=\frac{|A_{p}\cap A_{q}|}{A_{q}} \tag{6}\]  
\[F1=\frac{2\times P\times R}{P+R} \tag{7}\]  
The formal mathematical definition of the answer \(F1\) score is the harmonic mean of precision and recall, as expressed by the equation above. Also, the overall answer \(F1\) score for the test set is the average answer \(F1\) score for each example in the test set.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 4 Discussion
### PU Activity Pattern Needs to Be Considered  
In order to have successful spectrum access, nodes need to exchange the sensing information; thus, control signaling between two CR nodes needs to be uninterrupted by neighboring CR nodes, as well as by PU activity. On the other hand, the PU may access and use the licensed spectrum on an occasional basis, or in some cases, PUs might have specific transmission patterns, for example a television station that broadcasts and uses dedicated spectrum for a pre-determined time and duration. Therefore, in the design of a control channel transmission, scheduling and dynamic power control need to be wisely designed to adapt to the PUs' pattern.
## 4 Classification of Auto-Configuration Protocols
### Stateless Protocols
#### 4.2.4 Aipac  
_Automatic IP Address Configuration in Mobile_ Ad Hoc _Networks_ (AIPAC) [25] is a protocol for IP address auto-configuration using a reactive approach in the IP address assignment, so it should manage duplicate addresses; it is also focused on maintaining the address uniqueness after network merging due to the node mobility. The protocol has as its priority the supporting of the following features of _ad hoc_ networks: limited resources of the devices and the unreliability in the wireless channels.  
Each network is identified with its _NetID_. When two networks merge, and the merger is persistent, the _NetID_ should be unified. To accomplish that, it uses a gradual fusion mechanism. This allows a node to pass from a _NetID_ to another one, according to network changes observed by the node. This procedure allows a homogeneous system to be made in the case of multiple overlapping networks, according to the evolution of their topologies.  
This protocol does not guarantee the uniqueness of the assigned IP addresses, but it ensures that messages are routed correctly. Each node in AIPAC is aware of its neighbouring radio, so the amount of information stored by the node is limited to the nodes within the radios distance.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 3 ANN-Based Design Methodology for Low-Power Interconnect  
### Limitations in Frequency Domain Design  
The characteristics of the eye diagram determine the signal quality in the time-domain simulation. However, this simulation requires the information of channel properties from the frequency-domain. In the conventional design procedure, the frequency-domain design is also the most time-consuming stage due to the iteration simulations to find the optimal structure. Moreover, it also requires lots of effort in analysis to ensure design optimization. Furthermore, technology development grows the complexity of design with the increase in the number of design parameters. This complexity requires not only more simulations but also exceeds the analysis ability of the designer. While the time-consuming simulation expects an alternative method to minimize the design cycle, the optimization procedure demands an analytical approach to achieve the optimal design. Therefore, a surrogate model that quickly generates accurate data is essential to reduce the design time. Furthermore, an objective function utilizes this model to assure the design optimization for low-power applications.
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 3 Experimental Results and Analysis
### Algorithm Iterative Stability Analysis  
BP neural networks and the model-driven deep learning networks need to be trained to achieve good generalization capabilities, but the iterative process is not required to solve the problem after the algorithm is trained, and iterations only exist in the algorithm training process so that it can reduce the defect detection time [38; 39]. The iterative process of CSI, the BP neural network and the model-driven deep learning network are compared to analyze the stability in the iterative process. The iterative process of each algorithm is shown in Figures 7 and 8, where the output value of the objective function and cost function are processed in absolute value.  
Figure 7 shows the change in the output value of the objective function during the solution process for CSI. The iteration process is the detection process of the CSI for the homogeneous double defects of radius 2 cm, which can be seen as the output value of the CSI objective function can converge to a stable range after about 90 iterations. If more iterations are used in the solution process, the detection time cost of the CSI will be increased. As can be seen from Figure 8, the BP neural network converges to the stable range only after 150 training iterations, while the model-driven deep learning network converges to the stable range only after 80 training iterations. Thus, the model-driven deep learning network reduces the number of training iterations by improving the cost function, and it can reasonably control the number of iterations in the network training process to reduce the time cost and improve network training efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Contrast Source** & **BP Neural Network** & **Model-Driven Deep** \\  & **Inversion** & & **Learning Networks** \\ \hline Mean Square Error & None & 0.2679 & 0.1345 \\ Single Detection Time & None & 0.077 s & 0.065 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean square error and average single detection time for each algorithm.  
Figure 7: CSI iterative solving process.  
Figure 9 shows a group of abnormal training processes of BP neural networks. Although the method converges to a stable value after 130 iterations, the \"spike\" phenomenon can be observed obviously on the iterative curve. These \"spikes\" are caused by abnormal training data, which leads to a sudden change in the output value of the cost function. These mutations seriously affect the performance of the algorithm. The improvement of cost function is an effective way to avoid this phenomenon. The results of Figure 8b show the effectiveness of this change. It can be observed that the stability of the iterative process reflected in Figure 8b is significantly better than that in Figure 9 when using the same data.
# Analysis of the Impact of Small Group Behavior on Cruise Ship Emergency Evacuation
## 4 Cruise Ship Evacuation Simulation Model Construction
### Congestion Area and Duration of Congestion for Two Evacuation Methods without Groups  
During the process of evacuating a cruise ship, congested areas have a significant impact on the efficiency of evacuation. According to the International Maritime Organi  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter Evacuation** & **Evacuation Method I** & **Evacuation Method II** \\ \hline Average value/s & 985.26 & 648.54 \\ Minimum value/s & 715 & 506 \\ Maximum value/s & 1132 & 878 \\ Standard Deviation/s & 47.67 & 27.21 \\ Median/s & 982 & 631 \\ \(P_{5}\) & 934 & 558 \\ \(P_{50}\) & 981 & 530 \\ \(P_{95}\) & 1016 & 698 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Important parameters for evacuation without groups.  
Figure 5: Frequency distribution of evacuation time without groups.  
zation's MSC.1/Circ.1533, to determine if an area is congested, we check its population density. If the density exceeds four people per square meter, it is determined that the area is a congestion area. Additionally, if the congestion in this area accounts for more than 10% of the total evacuation time, it is regarded as having a substantial impact on the ship's evacuation.  
At t = 170 s, the personnel density thermal map for the evacuation of the cruise ship without grouping is shown in Figure 6. Based on the congestion determination method above, there are congested areas in evacuation mode I, including areas I-a, I-b, and I-c as shown in Figure 5a, and there are congested areas in evacuation mode II, including areas II-a and II-b as shown in Figure 5b. Figure 7 displays the variation in personnel density in specific areas without any group effect. The congestion duration of Area I-a ranges from 365 to 651 s, while for Area I-b it ranges from 482 to 671 s. For Area I-c, the congestion lasts from 382 to 805s. Area II-a experiences congestion for a duration of 372 to 641 s, while Area II-b experiences congestion for 410 to 724 s.  
Figure 6: The personnel thermal distribution map of congested areas during cruise ship evacuation.  
Table 7 presents the continuous congestion time data without group interaction. The data show that the maximum congestion density in the five selected areas exceeds four people/m\({}^{2}\), and the maximum congestion density is area II-b. Additionally, the continuous congestion time in all five areas accounts for over 10% of the total evacuation time, with some areas even exceeding 40%. Among them, region II-b has the largest ratio of congestion time to total evacuation time. These data indicate that congested areas during the evacuation process have a significant impact, so it is necessary to prioritize control and properly guide passengers in these congested areas [38].
## 3 Proposed Framework and Model Training
### Performance Evaluation  
As shown in Equations (2)-(5), accuracy, precision, recall, and F1 score are commonly used to measure the classification performance of models and are suitable for both binary and multi-class problems. Accuracy measures the proportion of correctly classified samples to the total number of samples. Precision is the ratio of correctly predicted positive samples to the total number of samples predicted as positive. Recall is the ratio of correctly predicted positive samples to the total number of true positive samples. The F1 score combines precision and recall and is calculated as the harmonic mean of the two.  
\[accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{2}\]  
\[precision=\frac{TP}{TP+FP} \tag{3}\]  
\[recall=\frac{TP}{TP+FN} \tag{4}\]  
\[F_{1}=2\times\frac{precision\times recall}{precision+recall}\times 100\% \tag{5}\]  
where _TP_ represents the number of samples that are truly positive and are correctly predicted as positive by the model. _TN_ represents the number of samples that are truly negative and are correctly predicted as negative by the model. _FP_ represents the number of samples that are actually negative but are incorrectly predicted as positive by the model. _FN_ represents the number of samples that are actually positive but are incorrectly predicted as negative by the model. For multi-class problems, you can treat the current class as the positive class and group all other classes as the negative class, making these metrics equally applicable.
# Privacy-Preserving Monotonicity of Differential Privacy Mechanisms
## 5 Privacy-Preserving Monotonicity
According to the privacy metrics and utility metrics of this paper, the increasing of privacy metric leads to the decreasing of utility metric as privacy budget decreases. Conversely, the decreasing of privacy metric causes the increasing of utility metric as privacy budget increases. Thus, we only give the monotonicity property of privacy metric of differential privacy as privacy budget changing based on privacy metrics and utility metrics of this paper. In view of this, we can know the monotonicity property of utility metric of differential privacy as privacy budget changing. That is to say, the monotonicity of privacy metric can show the trade-off between privacy and utility of differential privacy based on privacy metric and utility metric of this paper. In other words, privacy-preserving monotonicity can show the trade-off between privacy and utility of differential privacy. According to the model of the trade-off of differential privacy, the privacy metrics, the utility metrics, and the identification of the problem, we have the following definition of privacy-preserving monotonicity. **Definition 8** (Privacy-Preserving Monotonicity). : _A privacy metric \(PM\) of differential privacy, the privacy-preserving monotonicity is defined as \(PM_{\varepsilon_{1}}\geq PM_{\varepsilon_{2}}\), when \(\varepsilon_{1}\geq 0\), \(\varepsilon_{2}\geq 0\) and \(\varepsilon_{1}\leq\varepsilon_{2}\)._According to Definition 8 and the privacy and utility metrics of this paper, given \(\varepsilon_{1}\geq 0\), \(\varepsilon_{2}\geq 0\) and \(\varepsilon_{1}\leq\varepsilon_{2}\), we have \(PM_{\varepsilon_{1}}\geq PM_{\varepsilon_{2}}\), then \(UM_{\varepsilon_{1}}\leq UM_{\varepsilon_{2}}\). Conversely, given \(\varepsilon_{1}\geq 0\), \(\varepsilon_{2}\geq 0\) and \(\varepsilon_{1}\geq\varepsilon_{2}\), we have \(PM_{\varepsilon_{1}}\leq PM_{\varepsilon_{2}}\), then \(UM_{\varepsilon_{1}}\geq UM_{\varepsilon_{2}}\). Thus, the increasing of privacy metrics leads to the decreasing of utility metrics as privacy budget decreases. Conversely, the decreasing of privacy metrics leads to the increasing of utility metrics as privacy budget increases. Thus, the definition of privacy-preserving monotonicity can describe the trade-off between privacy and utility, and it also shows that privacy-preserving level decreases as privacy budget increases and that data utility increases as privacy budget increases. Conversely, privacy-preserving monotonicity shows that privacy-preserving level increases as privacy budget decreases and that data utility decreases as privacy budget decreases.
## 3 Case Study
### Calculation of Diesel Engine without Shock Absorbers in Impact Condition
#### 3.4.1 Main Bearing Lubrication of Diesel Engine without Shock Absorbers in Impact Condition  
Shown in Figure 18 is the peak oil film pressure curve of the first main bearing of the diesel engine without shock absorbers under an external impact environment. In Figure 18, at 0.863831 s, there is a peak oil film pressure of 2549.09 MPa at the first main bearing, which far exceeds the peak oil film pressure required by the AVL bearing evaluation point (must be less than 100-150 MPa).  
As shown in Figure 19, it is the peak asperity contact pressure curve of the first main bearing of the diesel engine without shock absorber in the external impact environment. At 0.361813 s, there is an asperity contact pressure of up to 215.686 MPa at the first main bearing, which far exceeds the asperity contact pressure recommended value required by the AVL bearing evaluation point (must be less than 50 MPa).  
Figure 17: Under impact condition, the maximum crankshaft stress is 363 MPa at 0.177778 s, which is located at the eighth crank pin.  
As shown in Figure 20, it is the minimum oil film thickness curve of the first main bearing of the diesel engine without shock absorbers in an external impact environment. At 0.361298 s, there is a minimum oil film thickness of 0.0111803 \(\upmu\)m at the first main bearing, which is much lower than the AVL bearing evaluation requirement (must be greater than 1.5 \(\upmu\)m).  
Figure 19: The peak asperity contact pressure curve of the first main bearing of a diesel engine without shock absorbers under the external shock environment. The x-axis represents time in seconds, and the y-axis represents the peak asperity contact pressure in units MPa.  
Figure 20: The minimum oil film thickness curve of the first main bearing of a diesel engine without shock absorbers under the external shock environment.  
Figure 18: The peak oil film pressure curve of the first main bearing of the diesel engine without shock absorbers under the external shock environment. The x-axis represents time in seconds, and the y-axis represents the peak oil film pressure in units MPa.  
Combining the results of evaluation indicators such as peak oil film pressure, peak asperity contact pressure, and minimum oil film thickness, it can be seen that the lubrication state of the main bearings of the diesel engine without the shock absorbers is unusable under external impact.