esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **N** & **Est Median** & **Sum of Ranks** \\ \hline ABC-DE & 30 & 0.89043 & 81.0 \\ iSSO-DE & 30 & 0.88961 & 69.0 \\ PSO-DE & 30 & 0.88851 & 51.0 \\ SSO-DE & 30 & 0.89117 & 99.0 \\ \hline S = 30.16 & DF = 3 & \multicolumn{2}{c}{\(p=0.000\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Experimental results of [PERSON]’s test. The Freidman test results of the four algorithms for the abovementioned experiments of three seasons are shown in following Table 24. The \(p\)-value was 0.000, which represents a significant difference in fitness values between different algorithms. Additionally, in terms of sum of ranks, it could be found that the sum of ranks value of SSO-DE was the largest, which shows the fitness performance of SSO-DE was the best among all algorithms in the experiments of the three seasons. Figure 11: Box and whisker diagram for summer. *: Outliers. Figure 10: Box and whisker diagram for spring (autumn). Furthermore, the [PERSON]'s test (post-hoc test) experimental results of the four algorithms including ABC-DE, iSSO-DE, PSO-DE, and the proposed SSO-DE in the three seasonal data experiments are shown in Table 25. The results showed that there was no significant difference between the other three algorithms with SSO-DE but the \(p\)-value compared with PSO-DE was very close to 0.05. The reason for this result should be that the fitness value performance of SSO-DE in the summer experiment is slightly inferior to that of ABC-DE and the standard deviation performance of SSO-DE is not as stable as that of PSO-DE. Although the experimental data in the three seasons obtained by SSO-DE performed better than those found by PSO-DE and iSSO-DE, the difference is not particularly significant as shown by [PERSON]'s test. However, in the three data sets, the running time of SSO-DE was much shorter than other compared algorithms, and [PERSON]'s test in the previous paragraph also confirmed that the comprehensive performance of SSO-DE was the best.
## 3 Results  
### Finite Element Analysis  
The tilting behavior as a function of the applied moment \(M\) is shown in Figure 5. The corresponding analytical relationship is: \(M=K_{rot}\alpha_{t}\), where the equivalent rotational stiffness \(K_{rot}\) is given by Equation (8).  
For the explored cases, the analytical formula coincides with the results of the finite element analysis. Therefore, the bodies deformability has a negligible effect. The most critical condition is that with the highest load \(F_{b}\), which is the one shown in Figure 5. The small difference between the loading and the unloading phase is attributed to the numerical error. In a different set of simulations, the friction coefficient was changed, but it had negligible influence on the results. Figure 5b follows the example loading history shown in Table 1.  
In Figure 6, the equivalent von Mises stress is shown at two different time steps, one before the tilting moment is applied and the other when its maximum is reached. A shift of the contact area is visible between Figure 6a,b but, apart from the shift, the stress distribution seems only slightly affected by the moment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Value** & **Unit** \\ \hline Bearing Diameter & 90 & mm \\ Pad Machined Diametral & 0.4 & mm \\ Clearance & 130 & mm \\ Bearing Outer Diameter & Steel & \\ Pad Material & Steel & \\ Journal Material & Steel & \\ Shell Material & Steel & \\ Lubricant & ISO VG 32 & \\ Oil Supply Temperature & 50 & \({}^{\circ}\)C \\ Number of pads & 5 & \\ Load type & Load Between Pads & \\ Pad Arc & 52\({}^{\circ}\) & \\ Pad Offset & 0.6 & \\ Preload & 0.55 & \\ Pad axial length & 45 & mm \\ Housing radius, \(R_{h}\) & 65 & \\ Pivot back radius, \(R_{b}\) & 58 or 64 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the simulated bearing.  
The software also gives as output the contact status: sticking, sliding or no contact. Sticking prevails where contact occurs while the sliding area is negligible in agreement with the reversibility of the loading and unloading phases observed in Figure 5.
# Fatigue Crack Growth in Managing Steel Obtained by Selective Laser Melting
## 4 Conclusions  
The fatigue crack growth of the AISI 18 Ni300 managing steel made by Selective Laser Melting was studied numerically and experimentally. The experimental study considered \(\Delta\)K as the crack driving force while the numerical work used the plastic CTOD. The main conclusions are:  
The FCG rate, da/dN, plotted versus \(\Delta\)K showed a classical Paris law regime with a typical value of the slope. This indicates that cyclic plastic deformation is probably the mechanism controlling fatigue crack growth. The influence of stress ratio and specimen thickness was found to be relatively small, indicating a reduced level of crack closure. The analysis of fracture surfaces by SEM showed propagation between deposition layers.  
A complementary analysis was developed, based on plastic CTOD range, to better understand crack growth mechanisms. The level of plastic deformation at the crack tip was found to be relatively low, which also explains the relatively low level of crack closure. Consequently, the curve da/dN versus plastic CTOD range is clearly above the curves for other materials.  
Conceptualization, F.A., J.F. and C.C.; formal analysis, F.A. and P.P.; investigation, J.J., J.C. and L.S.; writing--original draft preparation, F.A.; writing--review and editing, F.A.; funding acquisition, J.F.  
The authors acknowledge sponsorship under the project Hibsilaser, funded by the European Regional Development Fund (FEDER), through the Portugal-2020 program (PT2020), under the Regional Operational Program of the Center (CENTRO-01-0145-FEDER-028789) and the Foundation for Science and Technology IP/MCTES through national funds (PIDDAC).  
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.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 2 CVDP Network Design
### Loss Function  
This network is mainly used to accurately predict the hopping frequency in the one-time slot, and this problem can be regarded as a multi-classification problem. In this paper, the received frequency hopping signal in one hopping time slot is first transformed into a time-frequency spectrogram \(X\) and then fed into the network whose mapping function is denoted by \(\Lambda(:\Omega)\), where \(\Omega\) is the parameters of the network model, and then the network output is fed into a classifier with \(N\) classes. And finally, the hopping frequency in the one-time slot is estimated, and the cross entropy is chosen as the loss function as follows:  
\[\begin{split}&\mathbf{p}_{i}=\Lambda(X:\Omega)\\ &\xi=-\frac{1}{B}\sum_{i=1}^{B}\sum_{n=1}^{N}a_{in}\log(p_{in}) \end{split}. \tag{12}\]  
where \(\mathbf{p}_{i}=[p_{i1},p_{i2},\dots,p_{iN}]\) is the output probability of the \(i\)-th sample on the classifier; \(B\) is the number of samples per training batch; \(N\) is the number of categories; \(p_{in}\) is the output probability of the \(i\)-th sample in class \(n\) on the classifier; \(a_{in}\) is the \(n\)-th label corresponding to the \(i\)-th sample. And One-hot is used to encode the sample labels.
# Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation
## 1 Introduction
Under the specific assumptions for the antenna patterns, the first-order approximation of the estimation is derived. The results are given in an analytical form and in some comprehensive diagrams. The output errors in the monopulse receiver were analysed in terms of the mean square error (MSE). In [14], a minimum variance-adaptive monopulse estimator was developed, which adaptively chooses the optimum difference weight, which is applied at the subarray level. The worst distortion of the beam patterns and the ratio of the difference to sum beams occurs with the main beam jamming. A monopulse characteristic is defined as the ratio of difference beam pattern to sum beam pattern in the vicinity of the look direction. The Taylor expansion of the adaptive monopulse characteristics has been derived [15; 16]. This minimum variance adaptive monopulse scheme (MVAM) is compared with the approach proposed in [15; 16] for severe main beam jamming and clutter scenarios. In this paper, Gaussian noise is used to model measurement uncertainty. The effect of Gaussian noise on the accuracy of the azimuth estimate and the elevation estimate is rigorously derived. Subsequently, an explicit expression of the MSE is also derived. In comparison with the previous studies on the performance analysis of the amplitude comparison monopulse algorithm [13; 15; 16; 17; 18; 19; 20], we present a more explicit representation of the MSEs in this paper. Many previous studies focused on how the performance of the conventional amplitude comparison monopulse algorithm can be improved by proposing new algorithms or by modifying conventional amplitude comparison monopulse algorithm. Our contribution in this manuscript does not lie in how much improvement can be achieved by proposing a new algorithm. Our contribution in this paper lies in a reduction in computational cost in getting the MSE of an existing amplitude comparison monopulse algorithm by adopting analytic approach, rather than the Monte Carlo simulation-based MSE under measurement uncertainty due to an additive Gaussian noise. That is, the scheme described how analytic MSE can be obtained much less computational complexity than the Monte Carlo simulation-based MSE.
## 3 Network Anomaly Detection
### The Structure of Network Anomaly Detection
#### 3.2.1 Autoencoder-Based Feature Transformation  
To start with, let us visualize the configuration composed of encoder \(E\) and decoder \(D_{1}\) as an individual autoencoder \(AE_{1}\). When deploying an autoencoder for anomaly detection on unlabeled data, the input data serve as its own label. This prompts the neural network to decipher a mapping relationship, consequently creating a reconstructed output. An anomaly within the original input data can be suspected if the discrepancy between the reconstructed output and the original input surpasses a certain threshold. Two fundamental components encompass an autoencoder: the encoder and the decoder.  
The encoder processes time window data \(W\) as an input and primarily focuses on encoding the input data into a condensed latent representation \(Z\). This process encourages the neural network to discern the most informative features. The output generated by the encoder can be characterized by a standard neural network function that is succeeded by the rectified linear unit (ReLU) activation function, as expressed by:  
\[Z=ReLU(a_{e}\mathcal{W}+b_{e}). \tag{5}\]  
where \(ReLU(\cdot)\) stands for the ReLU activation function, while \(a_{e}\) and \(b_{e}\) represent the encoding weights and biases, respectively.  
In order to reconstruct the latent representation back to its initial dimension, the decoder generates the reconstructed features, which can be mathematically formulated as:  
\[\hat{\mathcal{W}}=ReLU(a_{d}Z+b_{d}), \tag{6}\]  
where \(\hat{\mathcal{W}}\) corresponds to the feature formed by the reconstruction, while \(a_{d}\) and \(b_{d}\) are the decoding weights and biases, respectively.  
However, the decoder attempts to reconstruct the data as accurately as possible, thereby minimizing the reconstruction error \(\left\|\mathcal{W}-\hat{\mathcal{W}}\right\|_{2}\). This endeavor could inadvertently overlook minor anomalies. Simply evaluating the possibility of anomalies by reconstructing the errors alone is not good, so the model also needs to be refined based on the idea of adversarial.
# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 2 Modular Hardware Platform for the Extreme Edge of IoT
### Software Integration and Usability  
To provide additional software support for the proposed Cookie node beyond the embedded libraries developed to use the platform (in case of needed), the Contiki-NG Operating System has been integrated in this hardware node. Contiki-NG started as a fork of Contiki OS [16], with the intent of focusing on the new 32-bits platforms, and the available partial porting of Contiki for EFR32 core [17] has been adapted to the Cookie Edge Node and completed using some of the libraries for the EFR32 from Silicon Labs. In addition to this, the porting has been conceived to provide fully support to the new hardware elements of the proposed solution, including the management of the power and functional islands, the self-diagnosis of the sensor node based on the power consumption monitoring cross-correlated with the advanced power profiles, and the enhanced security capabilities of the Cookie. Moreover, based on the modular architecture of the Cookie platform, and since the vertical connectors have been exploited to make full compatibility of the new hardware design with already existing or future Cookie layers, the different analog and digital signals and their relationship with the connected hardware elements are properly addressed in the implemented porting.  
Most of the work to complement and enrich the initial porting and the adaptation to the proposed hardware node can be classified into the following categories:  
* Adaptation of the pinout and other purely hardware-related issues, such as mapping ports to new locations.
* Completion of unfinished functions and missing parts of the Software Abstraction Layer (SAL). Since the porting is still a work in progress, some work needed to be done in this area.
* Particularization of generic functions calling to platform-specific ones for each target or board. Since each board provides the user with its own set of Hardware Abstraction Layer (HAL) functions and lower level functions belonging to each MCU, some parts of the higher level needed to be properly connected to the lower layers. This also includes adapting the drivers for the interaction with the peripherals.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **EM0** & **EM1** & **EM2** \\ \hline All islands disabled & EM0-000 & EM1-000 & EM2-000 \\ Temp sensor enabled & EM0-100 & EM1-100 & EM2-100 \\ Cryptchip enabled & EM0-010 & EM1-010 & EM2-010 \\ Inertial sensor enabled & EM0-001 & EM1-001 & EM2-001 \\ Temp + Crypto enabled & EM0-110 & EM1-110 & EM2-110 \\ Temp + Inertial enabled & EM0-101 & EM1-101 & EM2-101 \\ Crypto + Inertial enabled & EM0-011 & EM1-011 & EM2-011 \\ All islands enabled & EM0-111 & EM1-111 & EM2-111 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Combined low-power modes of the Cookie Edge Node, allowing the creation of a more advanced range of power profiles.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 7 Results
### Resilience against Failures  
To prove the adaptability and resilience of the routing protocol implemented in HARE, the network was subjected to the deliberate shutdown of two of its STAs. With respect to the GW, it was programmed to send 50 beacons with the following sequence: beacon #1 was a _network association primary beacon_, beacons multiple of 10 were _data primary beacons_ asking for _statistics packets_; the rest of beacons were _data primary beacons_ asking for _application packets_. In addition, the _disassociation mechanism_ was programmed in the GW to remove an STA from the network if not receiving any of its data packets during one _primary beacon_ (\(N_{\text{pd}}=1\)).  
Once the initial _network association mechanism_ is finished, the network was organized in four rings, as shown in Figure 21a. After beacon #4 (**A**), STA #1 was switched off, but it did not imply further problems to the network, as this STA did not have any children. However, after beacon #12 (**B**), STA #4  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Rings** & \(T_{p_{\text{min}}}\) **(s)** & \(T_{\text{max}}\) **(bits/s)** \\ \hline \(R=1\) & 41 & 25.76 \\ \(R=2\) & 66 & 16 \\ \(R=3\) & 91 & 11.60 \\ \(R=4\) & 116 & 9.10 \\ \(R=5\) & 141 & 7.49 \\ \hline \hline \end{tabular}
\end{table}
Table 10: \(T_{p_{\text{min}}}\) and \(T_{\text{max}}\) values in function of \(R\).  
Figure 20: Computed throughput for the different error configurations.  
was also switched off, and it forced the network to reconfigure itself. The path to the GW of STAs #2, #3 and #5 was broken, and they had to look for a new route by using the _STA association mechanism_ of successive _data primary beacons_. After beacon #15, all active STAs (i.e., all of them except #1 and #4, which remained off) had a path to the GW and the network was stable again (see Figure 21b).
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 4 Discussion  
### Analysis of Data Sampled at Different Sampling Rates  
As shown in Figure 10a, at a sampling rate of 6.25 MHz, the waveform of the MVB physical layer barely exhibits the characteristics of pulse waveforms. The excessively low sampling rate leads to severe distortion of the MVB waveforms. Pulses occupying 0.5 BT in the waveforms do not display a steady state. The severe distortion is not only reflected in the waveform images but also in the CRC results of the MVB frames. Most of the results are wrong, which means the frames are erroneous. However, under normal test conditions, all frames output by the MVB device are error-free, which makes the decoding results at this sampling rate unreliable. Therefore, the sampling rate of 6.25 MHz is not applicable to this study.  
As shown in Figure 3, there is a significant difference in the average edge distortion under different sampling rates. The average edge distortion corresponding to 62.5 MHz and 125 MHz is the same, which is more credible, while the average edge distortion under the sampling rates of 12.5 MHz and 31.25 MHz is significantly lower. The definition of edge distortion is the time difference between the idealized and the actual zero crossing The MVB conformance test requires that the edge distortion does not exceed 2%. As shown in Figure 7, the falling edge and the rising edge in the MVB waveform usually last for a short time, and it is extremely difficult to directly collect the actual zero crossing, so the feature-extraction algorithm fits several sampling points along a straight line to approximate the actual zero crossing. However, a lower sampling rate (31.25 MHz and below) is not enough to capture enough data to fit the waveform, and only data within the edge can be used for fitting, causing the estimated actual zero crossing to deviate from the true value, resulting in a difference in the extracted average edge distortion.  
Among other feature parameters, there is little difference in the average steady-state amplitude and the average overshoot, while there is a significant difference in the average slew rate. The average slew rate is obtained by calculating the average change speed near 100 ns of the zero crossing. However, just like the actual zero crossing, it is difficult to accurately obtain the value of 100 ns under low sampling, and only sampling points near 100 ns can be selected instead. For example, the sampling interval of a 12.5 MHz sampling rate reaches 80 ns, and the interval to calculate the slew rate may reach 160 ns--far more than 100 ns.  
According to the comparison of waveform feature quality at various sampling rates, 62.5 MHz can be regarded as a better sampling rate. Compared with sampling data at lower rates, it can obtain higher-quality waveform features; compared with 125 MHz, the amount of data generated in the same time is halved, reducing data storage costs and computational analysis pressure.
## 7 Results and Discussion  
To demonstrate the effectiveness of the proposed model, the intent detection results for the Urdu web queries dataset were presented and compared with the baselines in terms of the accuracy and F1 scores. Finally, the ablation results were reported highlighting the contribution of the various components in the proposed model.  
The proposed model was trained using the annotated Urdu web queries dataset by utilizing the train and evaluation sets given in Table 7. Figure 4 describes the training and validation accuracies and loss during the training of the proposed U-IntentCapsNet model. The optimal number of epochs, 40, can be seen from the training accuracy and loss curves, as at epoch 40 the training set converged to the highest value and the loss was at the minimum.  
Table 8 shows the label-wise intent detection results with the proposed U-IntentCapsNet model. It is evident that the INFO intent had the highest F1 score, 0.9439, while the NAV and TRAN intents had comparatively lower and similar F1 scores, 0.7553 and 0.7555, respectively.  
Figure 4: Accuracy and loss curves during training of the proposed U-IntentCapsNet model.  
In Table 9 the confusion matrix for intent detection with the proposed U-IntentCapsNet model is presented. It is evident that the majority of the confusions were between the TRAN-INFO and NAV-INFO classes. One obvious reason is the lexical similarity between the TRAN-INFO and NAV-INFO queries. For example, \"Geo News\" (the name of a local TV channel) was NAV, and \"Kashaf Drama\" (the name of a drama) was TRAN while the \"News drama industry\" was INFO. The majority of the INFO queries contained terms occurring individually in queries with a TRAN or NAV intent.
## 4 Results and Analysis
### Grid Virtual Division Scheme Performance  
Based on the prototype of the experimental system already deployed in Figure 6, this section first designs the comparison experiment between the traditional RoI extraction (as the benchmark) and the proposed schemes. In practical application scenarios, the effect of transmission frequency, LED shape or quantity on ROI extraction needs to be considered, so we set these three metrics to quantify the experimental results. We finally report the experimental results provided by the baseline and grid virtual division schemes under variable transmission frequency, variable LED shape and size and variable LED number, respectively. It is worth noting that more than 10,000 bits are recorded during each test to count the bit error rate (BER) of the proposed scheme at different data rates. Furthermore, we have calculated the processing time of machine-learning-based models (such as YOLO and Transformer). As the seventh-generation models in the YOLO series are more flexible and have faster processing times, we used the latest version of YOLO7 for testing and removed the loading times of the model to compare with the proposed method. However, YOLO7 still has a processing time of 12 ms after removing the loading time of the model, which is much longer than the selected baseline of Gaussian Blur, so we omit to report its results for following tests.