esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Brake Proportioning While in a Steady-State Turn  
Although bicycle balance and stability in an idealized model comprises a 7-degree of freedom, non-linear and non-holonomic system [30] when performing a steady state turn, the steering angles can be simplified to Equation (10) [31].  
\[\delta=\frac{\text{Lcos}(\theta)}{\text{rcos}(\phi)} \tag{10}\]  
where \(\delta\) is the steer angle, \(L\) is the wheelbase length, \(\phi\) is the steering axis angle, and \(\theta\) is the lean angle. A bicycle with a 1.1 m wheelbase length and 20\({}^{\circ}\) steering axis (70\({}^{\circ}\) head tube) will have a steering angle of fewer than five degrees for all turning radii greater than 14 m. As the lean angle increases, the steering angle decreases even further. For the high-speed and large-radius turns found in emergency braking scenarios, we can assume that the primary method of turning is in leaning the bicycle. Further work outside the scope of this research would be required to analyze low-speed high steering angle scenarios.  
With both tires leaning over at the same angle, both tires lose an equal amount of their available traction proportional to the lateral force generated. Therefore the combined turning and braking scenario is analogous to a longitudinal-only low-grip scenario, such as braking in snowy or wet conditions. Therefore the same cautions apply as Sections 5.2 and 5.3. When turning at the maximal lateral grip, any additional braking force to the rear or front wheel will cause either wheel to lock. Thus an automotive-type proportioning valve that provides equal front and rear brake force at low pressures is preferable. Under lower lateral loads, a higher proportion of front brake force can then be used, commonly referred to as trail braking.
# Polymorphic Adversarial Cyberattacks Using WGAN
## 2 Related Works
### Taxonomy of Intrusion Detection Techniques Using ML and DL
#### 2.1.2 Anomaly-Based Intrusion Detection  
The anomaly detection technique uses several statistical methods, machine learning, and deep learning in IDS. The authors in [34] proposed a kernel-based IDS that can detect anomalies, such as DDoS attacks. It uses the ML technique named K-means clustering to classify between adversarial and standard examples. In [35], the authors employed a statistical analysis-based anomaly detection to identify normal and malicious data. An adaptive threshold profile is maintained for normal and abnormal behaviors together with a trust management scheme. An ML network anomaly detection technique based on the Isolation Forest classifier is proposed in [36]. Several anomalous behaviors are identified through statistical feature extraction methods. The authors in [37], provided a comparative analysis of several unsupervised network anomaly detection techniques, such as K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), and Local Outlier Factor (LOF). Their results indicated that DBSCAN gives the best performance based on the detection rate. Another unsupervised ML clustering -based technique is employed in [38]. The authors employed Sub-Space Clustering (SSC) and One Class Support Vector Machine (OCSVM) to identify both known and unknown attacks.  
Several DL algorithms are employed for unsupervised network anomaly detection. In [39], the authors introduced a Deep Autoencoder model for network flow-based anomaly detection. While the detection is only based on the distribution of traffic features, investigation of other network features is left for future work. The authors in [40] introduced a hybrid anomaly detection classifier consisting of a CNN model and an unsupervised Deep Autoencoder (DAE) model. In this case, CNN is used for traffic profiling and the unsupervised DAE is used for unsupervised detection of normal and anomalous traffic. In [41], the researchers compared several anomaly detection techniques, such as K-means, Deep Autoencoding Gaussian mixture model (DAGMM), Self Organizing Maps (SOM), and Adversarial Learned Anomaly Detection (ALAD) to identify normal and anomalous network flows. Based on their analysis, ALAD is the best performing model since it learns from adversarial samples and provides stable results on minority attacks.
# Phase Transition and Controlled Zirconia Implant Patterning Using Laser-Induced Shockwaves
## 2 Materials and Methods
### Laser Shockwave Imprinting  
Two similar approaches with different sacrificial layers were employed for the pattern imprinting [38]. In the first approach, a thin 10 \(\upmu\)m layer of graphite was sprayed using a dry graphite lubricant to function as a sacrificial layer, to prevent melting and ablation of the sample. A TEM copper grid was then placed on top as a template and ablative layer. The entire setup was sealed with BK7 glass, which confined the plasma generated by the laser-ablative layer interaction. In the second method, aluminum foil was used as a sacrificial layer to protect the material surface from melting and direct laser contact [39]. A TEM copper grid was placed on the zirconia surface and covered by a single sheet of 16 \(\upmu\)m-thick aluminum foil and a BK7 glass slide to seal the setup.  
The prepared samples were irradiated under a 3 mm-diameter focused laser beam, forming plasma. Plasma forms when zirconia is heated to extremely high temperatures, causing it to vaporize and ionize. This ionization process strips electrons from atoms, creating a hot, electrically charged gas [37; 40]. The plasma expansion on the ablative layer was contained using glass slides, creating a localized pressure of 9 GPa and a temperature spike of 6500 K at a laser fluence of 2 J/cm\({}^{2}\). Extreme pressure and temperature stamps the TEM grid, causing plastic deformation on the zirconia surface, resulting in an imprinted grid pattern on the surface [41]. For samples patterned using the first approach, the graphite layer was washed with acetone.  
Surface morphology was studied using scanning electron microscopy (SEM, Jeol 6510 LV, Tokyo, Japan), atomic force microscopy (AFM, Nanosurf Flex AFM, Liestal, Switzerland), and a drop shape analyzer. The laser parameters were optimized to control protrusion heights, ensuring the patterns achieved the desired depth and uniformity. These analyses provided a detailed view of the pattern quality and surface modifications achieved, helping to understand the effectiveness of laser-assisted template imprinting techniques.
## 4 Conclusions  
In this study, using the AM filter proposed by [15], designs that also take into account reliability criteria in topology optimization were carried out. For density-based topology optimization, reliable designs are obtained based on the build direction in a typical powder bed-based AM process. In this way, non-printable parts with parts that need support structure are removed from the design area. The proposed method has been tested using three different numerical examples commonly used in the literature. In this context, the topologies of reliability-based optimization problems were found to diverge from deterministic outcomes. Likewise, analyses employing the AM filter demonstrated that additional material was unnecessary for self-supporting structures. To verify the accuracy of the obtained results from the example solutions, the optimum topologies were 3D printed and examined. The use of the AM filter, as observed in the examples, significantly reduced printing time and resulted in less material usage. Additionally, there is a need for additional investigation to adapt this method to three dimensions, thereby addressing more complex engineering challenges.  
Conceptualization, F.M. and I.K.; methodology, F.M., I.K. and A.T.S.; software, F.M. and I.K.; validation, F.M. and I.K.; formal analysis, F.M. and I.K.; investigation, F.M., I.K. and A.T.S.; resources, F.M. and I.K.; data curation, F.M.; writing--original draft, F.M.; writing--review and editing, F.M., I.K. and A.T.S.; supervision, I.K. and A.T.S.; project administration, I.K. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by TU Delft.  
Not applicable.  
The data are available upon request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Optimization Algorithm**} & \multicolumn{3}{c}{**Calculated Displacement (mm)**} \\ \cline{2-4}  & **MBB** & **Cantilever** & **L-Shape** \\ \hline
**DTO** & 0.0418 & 0.0500 & 0.1057 \\ \hline
**AM-DTO** & 0.0419 & 0.0511 & 0.1150 \\ \hline
**RBTO** & 0.0415 & 0.0384 & 0.0973 \\ \hline
**AM-RBTO** & 0.0425 & 0.0392 & 0.0981 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the reliability values of topology optimization designs.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 7 Performance Evaluation
### Network Performance
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Numerical Values** \\ \hline Duration of Time slot, \(\omega\) & 52 \(\mu s\) \\ Bit error rate (BER) & \(2*10^{-6}\) bits/s \\ Minimum physical (PHY) layer header & 6\(\omega\)\(\mu s\) \\ Arbitration inter frame space (AIFSN) & 2 \\ Data rate & 650 Kbps \\ MAC service data unit (MSDU) length & 256 octets \\ Short Inter-frame space (SIFS) duration & 160 \(\mu s\) \\ MAC header length & 14 bytes \\ PS-Poll & 6\(\omega\)\(\mu s\) \\ PS-Poll-ACK & 6\(\omega\)\(\mu s\) \\ Block acknowledgment (BA) & 6\(\omega\)\(\mu s\) \\ Maximum termy limit, R & 7 \\ Max. number of antennas in AP, \(A_{ap}\) & 1 \\ Number of antennas in STA & 1 \\ Bandwidth & 4 MHz \\ OFDM symbol duration & 40 \(\mu s\) \\ Number of bits in an OFDM symbol & 54 \\ Modulation and Coding scheme, MCS & 0 \\ Beacon interval, BI & 1 s \\ RAW slot & 200 ms \\ Minimum contention window size \(W_{min}\) & 31 \\ Transaction opportunity (TXOP) limit & 2 packets \\ Idle mode energy consumption \(\omega_{i}\) & \(18.2*10^{(-9)}\) J \\ Receive mode energy consumption \(\omega_{r}\) & \(17.9*10^{(-6)}\) J \\ Transmit mode energy consumption \(\omega_{t}\) & \(15.8*10^{(-6)}\) J \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for model of the UAV network at MAC level. Figure 5: Per node throughput.
## Appendix B Overview of U-Net-Based Autoencoder Architecture  
The U-Net architecture used in the experiments was built using specific layer blocks described below:* **Convolution Block**: \[x =\text{Conv2D}(num\_filters,3,\text{padding}=\text{``same''})(input)\] \[x =\text{BatchNorm Normalization}(x)\] \[x =\text{Activation}(\text{``relu''})(x)\] \[x =\text{Conv2D}(num\_filters,3,\text{padding}=\text{``same''})(x)\] \[x =\text{BatchNorm Normalization}(x)\] \[x =\text{Activation}(\text{``relu''})(x)\] return x
* **Encoder Block**: \[x =\text{conv\_block}(input,num\_filters)\] \[p =\text{MaxPool2D}((2,2))(x)\] return x, p
* **Decoder Block**: \[x =\text{Conv2 DTranspose}(num\_filters,(2,2),\text{strides}=2,\text{padding }=\text{``same''})(input)\] \[x =\text{Concatenate}()([x,\text{skip\_features}])\] \[x =\text{conv\_block}(x,num\_filters)\] return x  
Encoder Path (Contraction Path): The _encoder_ extracts features at different scales using convolutional layers and max-pooling operations to progressively reduce spatial dimensions. The _encoder_ block is applied three times sequentially with 128, 256, and 512 filters, respectively. Each convolution block output (\"x\") is stored for later concatenation in the _decoder_.  
Bridge: The bridge connects the _encoder_ and _decoder_ paths by applying a convolution block with 1024 filters.  
Decoder Path (Expansion Path): The decoder increases image resolution while combining features from _encoder_ layers through concatenation. Similar to the _encoder_ path, three _decoder_ blocks are applied sequentially with 512, 256, and 128 filters, respectively.  
Output Layer: The output layer consists of a convolution operation with a single filter of size \(1\times 1\), followed by a linear activation function for regression prediction:  
\[outputs =\text{Conv2D}(n\_classes,1,\text{padding}=\text{``same''},\text{activation }=\text{``linear''})(d4)\]  
Summary of Network Architecture  
1. **Encoder:** \[\bullet\] Block 1: 128 filters \[\rightarrow\] MaxPooling. \[\bullet\] Block 2: 256 filters \[\rightarrow\] MaxPooling. \[\bullet\] Block 3: 512 filters \[\rightarrow\] MaxPooling. \[\bullet\] **Bridge:** 1024 filters. \[\bullet\] **Decoder:** \[\bullet\] Block 1: 512 filters. \[\bullet\] Block 2: 256 filters. \[\bullet\] Block 3: 128 filters. \[\bullet\] **Output:** Convolutional layer with linear activation.
# Recent Advances of Exciplex-Based White Organic Light-Emitting Diodes
## 3 Approaches for Exciplex-Based WOLEDs
### WOLEDs Based on Exciplex Emitters
#### 3.2.3 WOLEDs Using Exciplex and TADF Emitters  
Similar to phosphorescent emitter, the TADF emitter could harness both singlets and triplets. Therefore, by combining TADF emitter and exciplex, it can be another scheme to develop high-performance WOLEDs. [PERSON] et al. combined TADF emitter 4,4'-(9H,9'H-[3,3'-bicarbazole]-9,9'-diyl)bis(3-(trifluoromethyl))benzonitrile (pCNBCzoCF3) showing blue  
Figure 5: Working mechanisms for (**a**) WOLED-I; (**b**) WOLED-II [173].  
emission with the orange exciplex emission from pCNBCzzoCF\({}_{3}\)/4_A_\({}^{\prime}\)/4_A_\({}^{\prime}\)-tris[phenyl(m-tolyl)amino] triphenylamine (m-MTDATA) interface, fabricating highly efficient \"warm-white\" OLEDs with a maximum EQE of 18.8% and 19.3 lm W\({}^{-1}\), respectively [174]. Besides, [PERSON] et al. reported an extremely simple but high-performance WOLEDs [175]. For their device, the high-quality white light could be achieved via only single molecular emitter. More specifically, the doping-free p-i-n WOLED has been formed by sandwiching the intrinsic TADF material between p-type transporting layer and n-type transporting layer, achieving high-quality white light with the CRI as high as 91. Besides, a peak total PE and EQE as high as 68.5 lm W\({}^{-1}\) and 28.4% could be obtained. As shown in Figure 6, 40 nm TAPC and 55 nm TmPyPB have been selected to be p-type transporting layer and n-type transporting layer for the p-i-n WOLED, respectively. Besides, 2 nm 9,9_\({}^{\prime}\)/9_\({}^{\prime\prime}\)/_-((6-phenyl-1,3,5-triazine-2,4-diyl)bis(benzene-5,3,1-triyl))tetrakis(9H-carbazole) (DDCzTrz) has been used to be TADF EML. As a result, the three-layer structure of TAPC/DDCzTrz/TmPyPB has been formed. Besides, ITO/MoO\({}_{3}\) and LiF/Al have been used to be anode and cathode, respectively, ensuring that holes and electrons could be effectively injected. The emission mechanism for white light was to simultaneously produce the blue TADF light which was originated from the TADF emitter DDCzTrz and the exciplex emission which was generated from the p-type layer TAPC (donor) and DDCzTrz (acceptor). Besides, both S\({}_{1}\) and T\({}_{1}\) of the exciplex are lower than those of TAPC and DDCzTrz (e.g., the T\({}_{1}\) of TAPC and DDCzTrz are 2.87 and 2.53 eV, respectively), suggesting that excitons on the exciplex would not be quenched by the donor and acceptor, ensuring the efficient orange light otherwise the exciton leakage would deteriorate the performance.  
Figure 6: (**a**) The device architecture for p-i-n WOLEDs. (**b**) Molecular structures of TAPC, TmPyPB and DDCzTrz. (**c**) The working mechanism. The red arrow and black arrow are the hole and electron flow, respectively [175].
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 3 Experimental Measurements and Results  
### Electrical Conductance Measurement from Early-Age ECCs  
As mentioned before, the ECC specimens (PE1, PE2 and PE3) had small differences in their initial water content, and moreover, the PVA fibers had unknown water absorption in each batch of mix. All of these factors would lead to a challenge in distinguishing ECC specimens using conventional resistivity measurement methods. The nondestructive microwave near-field technique is a powerful way to detect such small differences at lower microwave frequency regimes, as illustrated in Figure 4. It should be mentioned that the measurement of the PE1 specimen commenced on Day-2 whereas the measurements of the other two started on Day-1. This was owing to the difficulty of modus dismounting as the PE1 mix had the minimum water content per unit volume but needed a high amount of HRWR in order to maintain the target workability. Figure 4a shows the detected normalized conductance of all specimens having their values crossover (transition) [21] (see also Figure 7) on Day-3, whereas Figure 4b shows the effective conductance in proportion with the water contents of PE1, PE2 and PE3 in order, across the R-band. It is observed that the electrical conductance decreases daily according to a way of exponential decay although the values are hardly to be observed in frequency domain of Figure 4. To verify this observations on the daily decay values of conductance in frequency domain, the measured effective conductance was further expressed in temporal domain as shown in Figure 5. The relative small values of electrical susceptance were not investigated in this study.  
Figure 3: Destructive compression applied on an ECC specimen on Day-3.
## 4 The Accumulated Data Analysis within the M-PVMS System
### Power Prediction Module of Power Generation with GRU Algorithm  
Figure 11 shows the structure of the power generation prediction module. First, we stored power generation, insolation, module temperature data, public data insolation, and cloud cover data in the M-PVMS, and then merged them in the same local time.  
Second, in data preprocessing, we refined the merged data with two steps as follows: (1) preprocess through formatting, cleansing, and sampling with the merged data, and (2) transform with scaling, decomposing, and aggregating with the preprocessed data. Then, we classified them into the category of seasonal, trend, and noise with Seasonal-Trend decomposition using base Loess (STL) implementation.  
Third, with the gated recurrent unit (GRU) algorithm, we executed training with the transformed data for predication. At this point, we assigned the value of dropout with 0.3 against the overfitting learning, that is, just training them except 30% of all neural nodes. To measure a training error, we used the mean squared error (MSE) method for precision and root mean squared error (RMSE) method for error of test data. The nearest zero value of MSE and RMSE may be the highest accuracy.  
After learning, the training result was about 0.000 MSE of the error rate and the test result was about 0.015 RMSE of the error rate, which has a good prediction performance.  
This means that the average error rate is about 1.2% of the real power generation value in the prediction through the verification data. Prediction models include those that predict power generation after one hour and those that do so after one day. Therefore, if the real power generation continuously exhibits significant differences from the predicted power generation, it is possible to anticipate the aging of a generator device and replace it in advance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multicolumn{2}{c}{**Local Public Environment Data of Korea Public Data Center**} & \multicolumn{2}{c}{**Environment Data of Solar Power Plant**} \\ \cline{2-6}  & **Total Cloud Cover** & **Lower-Middle Cloud Cover** & **Insolation** & **Vertical Insolation** & **Horizontal Insolation** \\ \hline Current output & \(-0.4314\) & \(-0.3613\) & \(0.7016\) & \(0.9803\) & \(0.9843\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation coefficient measurement table between power generation data and public data.  
Figure 11: Structure of power prediction module.  
Figure 12 shows the graphs that predict data for 12 h when the sun was passing by predicting the amount of power generation per hour. Figure 13 shows results for 105 days obtained by predicting data per day. The red line represents the prediction and the blue one indicates the real power generation value. Consequently, there is little difference between the predicted total power generation and the real total power generation from 0.01% to 0.02%.
## 3 The Processing Method for Integrated Radar-Communication Signals
### DFT-Based Minimum Mean Square Error Channel Estimation  
The integrated signal contains not only unknown transmission symbol data but also known chirp signals. Therefore, we can use them as training symbols for the min  
Figure 8: Mean square estimation of CFO estimation techniques.  
Figure 7: STO performance estimation based on correlation maximization and difference minimization.  
imum mean-square error (MMSE) channel estimation. By minimizing the cost function \(J(\hat{H})=\left\|Y-X\hat{H}\right\|^{2}\), we can obtain the least squares (LS) estimate of the channel as follows:  
\[\hat{H}_{LS}=\left(X^{H}X\right)^{-1}X^{H}Y=X^{-1}Y \tag{19}\]  
Considering the LS solution in the equation, let \(\hat{H}_{LS}\triangleq\hat{H}\) and we define MMSE as \(\hat{H}\triangleq W\hat{H}\) using a weighted matrix [20]. The mean squared error (MSE) of MMSE channel estimation \(\widehat{H}\) can be expressed as follows:  
\[J(\widehat{H})=E\{\left\|e\right\|^{2}\}=E\{\left\|H-\hat{H}\right\|^{2}\} \tag{20}\]  
where \(e=H-\hat{H}\) is the error vector, and when the error matrix is orthogonal to \(\hat{H}\), we obtain the minimum mean square error. In this case, the weighting matrix is \(W={R_{H\hat{H}^{H}}{R_{H\hat{H}}}^{-1}}\), where \(R\) represents cross-correlation operations.  
Channel estimation based on the discrete Fourier transform (DFT) can improve the estimation performance of MMSE by eliminating noise beyond the maximum channel delay [22]. Let \(\hat{H}[k]\) represent the channel gain of the \(k\)-_th_ subcarrier obtained by the MMSE channel estimation method. We take the inverse discrete Fourier transform (IDFT) of the estimated channel \(\left\{\hat{H}[k]\right\}_{k=0}^{N-1}\), i.e.,  
\[IDFT\{\hat{H}[k]\} =h[n]+z[n] \tag{21}\] \[=\hat{h}[n],n=0,1,\cdots,N-1\]  
Then, the remaining L channel coefficients are transformed to the frequency domain \(\hat{H}_{DFT}[k]=DFT\left\{\hat{h}_{DFT}[n]\right\}\). From Figure 9, by comparing the results of various types of channel estimation, it is obvious that the DFT-based channel estimation method improves the estimation performance. The MMSE estimation performance is better than the LS estimation performance.  
Figure 9: Comparison of different channel estimation methods. (**a**) LS linear interpolation estimation without DFT technique; (**b**) LS linear interpolation estimation based on the DFT technique; (**c**) LS spline interpolation estimation without DFT technique; (**d**) LS spline interpolation estimation based on the DFT technique; (**e**) MMSE estimation without DFT technique; (**f**) MMSE estimation based on DFT technique.