esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Polystyrene Particles with Precisely Controlled Degree of Concaveness
## 3 Results and Discussions  
### Formation of PS Particles with Controlled Degree of Concaveness  
A schematic representation of the synthesis is summarized in Figure 1. The method combines the powerful seeded emulsion polymerization with the well-known principle of \"like dissolves like,\" i.e., polymers tend to dissolve in solvents of similar polarity. The polystyrene (PS) particles with a single dimple are prepared by seeded emulsion polymerization [26; 27]. Then the PS dimple particles are dispersed in a solvent mixture of DMF/Water. Here the choice of solvents is based on three facts. Firstly, DMF is miscible with water and can form a one-phase solvent for the PS particle. Secondly, DMF has a boiling point of \(\sim\)152 \({}^{\circ}\)C and is less volatile than other common organic solvents, making it perfect for our long-term dissolution experiments. Thirdly and more importantly, the solubility parameter of PS is very close to that of DMF, but is very different from that of water (with the value of PS, 16.6-20.2 (Mpa)\({}^{0.5}\), DMF, 24.7 (Mpa)\({}^{0.5}\), and water, 48.1 (Mpa)\({}^{0.5}\)) [28; 29]. According to the principle of \"like dissolves like\" principle, PS can be dissolved by DMF, but to a much less degree by water. Hence, by systematically changing the mixing ratio of DMF and water, we control the amount of PS dissolved. After the dissolved PS chains were removed, the particles were found to buckle due to material loss. And as a result, the degrees of concaveness are precisely tuned by this dissolution method [24].  
The dimple particles are synthesized by seeded emulsion polymerization with programmed feeding of the cross-linker. From the SEM images, uniform-sized particles with one single dimple are observed (Figure 2). The diameter of the dimpled particle and the size of the concave (maximum length of the open end) are found by image analysis to be 3.0 \(\pm\) 0.1 \(\upmu\)m and 500 \(\pm\) 20 nm, respectively. The results are consistent with our previous work on the synthesis of anisotropic particles [23; 25].
## 2 Materials and Methods  
A commercial-grade simulator eigenmode solver and propagator was used to perform calculations for the waveguide and MMI study [46]. The refractive index of the SiGe alloy was obtained via linear interpolation of the index with respect to germanium concentration (x) in the alloy, as described by Equation (1), where \(\mathrm{n_{Si}}\) and \(\mathrm{n_{Ge}}\) are the respective refractive indexes of Si and Ge.  
\[\mathrm{nSi_{1-x}Ge_{x}=n_{Si}(1-x)+xn_{Ge}.} \tag{1}\]  
For the simulation, experimental values of the Ge refractive index were used [47]. As the simulations were performed over a wide range of wavelengths, the refractive-index dispersion was taken into account for the Ge-rich SiGe waveguides. As an example, the refractive-index evolution of the \(\mathrm{Si_{0.2}Ge_{0.8}}\) alloy is shown in Figure 1. For instance, at a wavelength of 3 \(\upmu\)m, \(\mathrm{n_{Si}}\) = 3.43 and \(\mathrm{n_{Ge}}\) = 4.04. It is worth noting that, for SiGe waveguides, the mode dispersion dominates over the material refractive-index dispersion in the MIDIR [34]. Consequently, the mode-dispersion engineering approach by means of geometric parameter optimization was chosen to obtain the polarization-insensitive and wideband integrated photonic components.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Experimental Results and Discussions
The model's closeness to the upper left corner of this particular ROC curve indicates that it has a high capacity for accurately classifying occurrences. Excellent discrimination between the positive and negative classes indicated by the reported AUC of 0.9965. This high AUC value indicates that there is little overlap between the classes and that the classifier is very good at differentiating between benign and malicious instances. Overall, the ROC curve and its AUC offer a thorough assessment of the model's performance, emphasizing how well it distinguishes between the two classes while reducing incorrect classifications. We summarized the performance of proposed model on the custom dataset using the classification report shown in Table 6. The precision of 0.99 for class 0 and 0.98 for class 1 indicates very few false positives. The recall of 0.98 for class 0 and 0.99 for class 1 indicates very few false negatives. The F1-scores of 0.99 (class 0) and 0.98 (class 1) confirm a strong balance between precision and recall. The overall accuracy is 99%, correctly predicting 17,444 out of 17,444 samples. Finally, macro and weighted averages both are  
Figure 12: The ROC-AUC curve of the Char2B model of TPR vs. FPR. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metrics** & **Loss** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Recall (\%)** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} \\ \hline Training set & 0.0216 & 99.32 & 99.54 & 99.08 & 99.31 \\ Testing set & 0.0214 & 98.50 & 98.27 & 98.69 & 98.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the proposed model on the custom dataset. 0.99, demonstrating consistent and unbiased performance across classes. Therefore, this report reflects excellent model performance with near-perfect metrics and the model is highly effective and well generalized, showing minimal error across both classes.
## 3 Guidance Algorithms
For this reason, the desired orientation with respect to the LVLH frame is obtained with the following definition of the orientation (Euler) angles  
\[\phi_{d_{x}}(t)=\arctan\frac{E_{y}(x)}{E_{x}(x)} \tag{14}\] \[\phi_{d_{y}}(t)=\arctan\frac{E_{x}(x)}{E_{x}(x)}\] (15) \[\phi_{d_{z}}(t)=\arctan\frac{E_{y}(x)}{E_{x}(x)}, \tag{16}\]  
where the function arctan produces angles in the four quadrants of the Cartesian plane with \(\phi_{d}\in[-\pi,\pi]\). For example, the rotation matrix for the \(x\) rotation is  
\[\Phi_{d_{x}}= \begin{array}{cc}\begin{bmatrix}1&0&0\\ 0&\cos\phi_{d_{x}}&-\sin\phi_{d_{x}}\\ 0&\sin\phi_{d_{x}}&\cos\phi_{d_{x}}\end{bmatrix}.\end{array} \]  
The total transformation matrix for the desired orientation is  
\[[\Phi_{d}]=[\Phi_{d_{z}}][\Phi_{d_{y}}][\Phi_{d_{x}}]. \tag{17}\]  
From this matrix, we can easily evaluate the desired quaternion and the desired \(\Sigma(q_{d})\in\mathbb{R}^{3,3}\) as in Equation (9). In the last part of the maneuver (forced motion), we assume that the Chaser has to be aligned with the Target, so the desired attitude in terms of quaternions is \(q_{d}=[0,0,0,1]^{T}\), which means the body frame aligned with the ECI one.
# Environmentally Friendly Green \(O\)-Alkylation Reaction for Ethenzamide Synthesis
## 4 Synthesis
### Synthesis of Ethenzamide (**6**)  
#### 4.2.1 Synthesis of Ethenzamide (**6**) in Conventional Conditions in Ethanol  
In a round-bottomed flask, 0.020 mol (0.8 g) of sodium hydroxide was dissolved in 15 cm\({}^{3}\) of water, then 0.02 mol (2.7 g) of salicimide (**5**), 10 cm\({}^{3}\) of methanol, and 0.026 mol of ethyl iodide were added. The reaction mixture was refluxed for 3 hours until the alkaline reaction disappeared, then the residue was evaporated to dryness. After purification by crystallization from methanol, 1.42 g (43%) of ethenzamide was obtained, melting point 125-128 \({}^{\circ}\)C [mp lit. 129-133 \({}^{\circ}\)C]. \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 8.24 (dd, J = 7.8, 1.8 Hz, 1H), 7.91 (brs, 1H), 7.48 (ddd, J = 8.4, 7.4, 1.9 Hz, 1H), 7.11-7.06 (m, 1H), 6.99 (d, J = 8.3 Hz, 1H), 6.03 (brs, 1H), 4.23 (q, J = 7.0 Hz, 2H), 1.54 (t, J = 7.0 Hz, 3H), \({}^{13}\)C NMR (101 MHz, CDCl\({}_{3}\)) \(\delta\) 167.21, 157.29, 133.37, 132.58, 121.13, 112.30, 64.72, 14.86; \(R_{f}\) = 0.58; IR 1643.19 (C=O), (NH\({}_{2}\)) 3367.74 cm\({}^{-1}\); MS: m/z 166 (M+), R\({}_{t}\) = 3.79 min. (Supplementary Materials)
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 4 Evaluation
### Congestion, Awareness and Topology Control
The minimum transmission power is established by the topology control TOWER for the rest of vehicles since A-DCC is designed to utilize a fixed transmission power. Figure 10 shows that when the communications range requirements increase, the minimum transmission power is limited by the awareness control process MINT. However, when the communications range requirements are relaxed to 10-50 m, the percentage of vehicles with a required minimum transmission power established by the awareness control process is reduced; this is the case because the transmission power level needed by the topology control process increases. Figure 10 also shows that higher traffic densities increase the number of neighboring vehicles, and therefore the percentage of vehicles with a required minimum transmission power established by the awareness control process, and not by the topology control process as it requires lower transmission power levels. These results demonstrate that COMPASS is capable to adapt its coordination process to the context conditions, even when considering multiple interacting protocols and diverse operating conditions. Figure 9: Average of the \(CBR\), \(D_{p}\) metric and number of neighbor vehicles when applying COMPASS to coordinate the operation of A-DCC, MINT and TOWER. The results are shown for a traffic density of 75 veh/km/lane and two possible ranges of the communication range (\(CR\)) requirements: between 50–200 m and between 10–50 m.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 5 Design Guidelines
### Step 2--Selection of the Manufacturing Technology  
In this step, the best technology for the implementation of the new radenv-ADC is identified. The options were limited in Step 1 to those in line with the application requirements. For example, a low-voltage technology could be incompatible with the monolithic implementation of a radenv-ADC requiring a wide voltage front-end. In fact, technologies without radiation characterization data are usually discarded, but those with latch-up immunity and minimum TID degradation are marked as preferential. Particularly, deep sub-micron CMOS nodes offering SOI-FinFET, UTBB-FDSOI [13], high-\(\kappa\) dielectrics, or devices with Ge or SiGe p-channel should be preferred [11; 12]. For all the considered technologies, the following features should be analyzed:Inter-device isolation options--LOCOS (LOCal Oxidation of Silicon), STI, DTI (Shallow or Deep Trench Isolation), buried layers, SOI, etc.--to evaluate the SEL sensibility.
* TID degradation and possible countermeasures: ELT availability, transistors minimum aspect ratio to mitigate performance degradation, etc.
* Effective volume of the technology devices to calculate the collected charge at an ion strike for different energies (for SET simulations).
* Availability of rad-hard libraries and IP-cores.  
The selected technology is the one offering the best trade-off between electrical performance, hardening capability, and manufacturing price (usually following this prioritized order for radenv-applications). After the technology's choice, the process options (top-metal thickness, use of buried layers, etc.) and the technology devices to be used in the design are selected. Additionally, missing rad-hard cells considered important for the design are identified for their later implementation in Step 4.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 4 Selective Channel Estimation Based on Correlation Coefficient
In low-SNR regions, \(\rho_{TT}(m)\) becomes larger than \(\rho_{CSI}(m)\) for the following reason:  
\[\text{In the limit }\gamma\to 0\] \[\Rightarrow H_{CSI}(m,k)\sim\hat{H}(m,k)\ \ \because\text{large demapping error}\] \[\Rightarrow H_{TT}(m,k)\sim\hat{H}(m,k)\] \[\because h_{TT}(m,n)=\hat{h}(m,n),\ \ n=1,2,3,\cdots,L\] \[\because\rho_{CSI}(m)<\rho_{TT}(m), \tag{14}\]  
where \(\gamma\) represents the SNR, and \(\sim\) denotes \"similar in distribution.\" In low-SNR regions, a large demapping error owing to a considerable noise component lowers the accuracy in the initial channel estimate \(\hat{H}(m,k)\). Moreover, frequency interpolation on incorrect channel estimates causes the propagation of uncertainty in the channel estimate \(H_{CSI}(m,k)\), resulting in a low correlation coefficient between \(H_{CSI}(m,k)\) and \(\hat{H}(m,k)\). By contrast, as shown in Equation (9), \(H_{TT}(m,k)\) obtained by DD-TT includes most channel components of the initial channel estimate \(\hat{H}(m,k)\) within the \(L\)-th tap of \(H_{TT}(m,k)\). This results in a higher correlation coefficient with the initial channel estimate than \(H_{CSI}(m,k)\). Conversely, in high-SNR regions, \(\rho_{CSI}(m)\) is larger than \(\rho_{TT}(m)\) for the following reason:  
\[\text{In the limit }\gamma\rightarrow\infty\] \[\Rightarrow H_{CSI}(m,k)\sim\hat{H}(m,k)\ \ \because\text{ decreasing demapping error}\] \[\Rightarrow\rho_{CSI}(m)\approx 1\] Figure 2:0  
In high-SNR regions, demapping errors due to noise effects become negligible, which improves the accuracy of channel estimate \(H_{CSI}(m,k)\). Thus, the correlation coefficient between \(H_{CSI}(m,k)\) and \(\hat{H}(m,k)\) is close to 1, which is the maximum correlation coefficient. Likewise, the channel estimate \(H_{TT}(m,k)\) generally has a high correlation coefficient with the initial channel estimate \(\hat{H}(m,k)\) but has a lower correlation coefficient than \(H_{CSI}(m,k)\) owing to the removal of the channel information of \(\hat{H}(m,k)\) after the \(L\)-th tap. The TRFI scheme is affected by the accuracy of the initial channel estimate. The initial channel estimate is reduced in accuracy by demapping errors at low SNRs; conversely, it is improved in accuracy by the reduction of demapping errors at high SNRs. Therefore, \(H_{CSI}(m,k)\) obtained through the CSI can be used as an indicator for selecting the TRFI. Figure 3 shows the mean correlation coefficients \(E[\sum_{l=1}^{m}\rho_{j}(l)]\) between \(\hat{H}(m,k)\) and \(H_{j}(m,k)\) according to the SNR, and it proves Equations (14) and (15). As shown in Figure 3, before 24 dB, we can observe that \(H_{TT}(m,k)\) has a higher mean correlation coefficient than \(H_{CSI}(m,k)\). After 24 dB, both channel estimates have high mean correlation coefficients. However, \(H_{CSI}(m,k)\) approaches 1, whereas \(H_{TT}(m,k)\) maintains a value less than 1. Therefore, \(H_{CSI}(m,k)\) has a higher correlation coefficient than \(H_{TT}(m,k)\).
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 5 Conclusions  
Machine learning-enabled intrusion detection plays a vital role in modern cybersecurity systems by providing intelligent, adaptive, and efficient means of identifying and responding to cyber threats. Unlike traditional rules-based systems, machine learning models can analyze vast volumes of network traffic and system logs to detect subtle patterns and anomalies that may indicate malicious activity. Machine learning-based cybersecurity systems can continuously learn and evolve, allowing them to recognize emerging threats and attacks that would otherwise go unnoticed. By automating intrusion detection and reducing false positives, machine learning can enhance the accuracy and responsiveness of cybersecurity operations, ultimately helping organizations to better protect their sensitive data and critical infrastructure.  
In this research work, we go a step further to address machine learning-enabled intrusion detection with very limited amounts of labeled data, as is typically the case in real-world scenarios. Specifically, we explore various techniques for semi-supervised learning, focusing on self-training, co-training, and entropy-based uncertainty sampling. Through extensive experimentation and analysis, several key insights and findings have emerged.  
Initially, the concept of semi-supervised learning was introduced and highlighted in scenarios where labeled data are scarce and unlabeled data are abundant. As one of the fundamental methods in this domain, self-training was investigated thoroughly in this research. Iteratively training a model on initially labeled data and subsequently training it on unlabeled data demonstrated good effectiveness in improving classification accuracy across different datasets.  
The next model adopted co-training, a strategy that leverages multiple views or feature representations of data to enhance learning. Our experiments showed that co-training can outperform traditional self-training methods when multiple diverse sources of unlabeled data are available. By exploiting complementary information from different views, co-training effectively reduces the risk of overfitting and improves the robustness of the learned model.  
Finally we explored an entropy-based uncertainty sampling approach which chooses those instances with the highest entropy, indicating maximum uncertainty. This approach demonstrated superior performance in scenarios where data can naturally be represented in multiple ways, such as textual and visual features in multimedia data.  
Our experimental evaluation employed the CIC-DDoS2019 and UNSW-NB15 benchmark datasets across different metrics to validate the efficacy of the proposed methods. The results consistently showed that our proposed approaches enhance classification accuracy and provide robustness against noisy and limited labeled data, with the only exception being the logistic regression-based co-training model.
## 3 Results  
### Evolution of Amino Acids and Fruity Esters during Alcoholic Fermentation  
We investigated the dynamic evolution of 13 amino acids in spine grape and Cabernet Sauvignon during alcoholic fermentation (Table S4). Total amino acid content (except proline) of spine grape must was more than that of Cabernet Sauvignon must. In spine grape must, aspartic acid, valine, glutamic acid, GABA, and arginine levels were significantly (\(p<0.01\)) more than those in Cabernet Sauvignon grape must, while proline, alanine, phenylalanine, and isoleucine levels were significantly less than those in Cabernet Sauvignon grape must (Figure 1). With progress in alcoholic fermentation, almost all amino acids gradually decreased, except proline and tyrosine. Proline was not assimilated and finally produced by yeast, and tyrosine content decreased slightly during Cabernet Sauvignon fermentation and increased during spine grape fermentation.  
Evolution of fruity esters during alcoholic fermentation was monitored by HPMEG-MG-MS (Figure 2, Tables S5 and S6). Acetates and ethyl esters rapidly increased once fermentation started, and the highest ethyl acetate and short-chain fatty acid ethyl ester (SCFAEE) levels were detected at the end of fermentation. In contrast, acetates of higher alcohols (AHAs) and medium-chain fatty acid ethyl esters (MCFAEEs) decreased during the later stage of fermentation (day 7 to day 12). The level of ethyl acetate in spine grape wine was 58% of that in Cabernet Sauvignon wine, AHA was 5%, SCFAEE was 89%, and MCFAEE was 67%.  
Figure 1: Concentrations of total amino acid (except proline), alanine, isoleucine, and phenylalanine in Cabernet Sauvignon and spine grape must. Difference significant at 99% (**) confidence level.
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis
### Average Error Rate Expressions  
Applying maximum ratio combining to the direct link signal \(y_{\rm sd}\) in (1) and the \(R\) indirect link signals \(y_{\rm id_{r}}\) in (7) at the destination, the combined SNR is given by  
\[\gamma_{\rm cb}=\gamma_{\rm sd}+\gamma_{\rm id}=\gamma_{0}+\sum_{r=1}^{R} \gamma_{\rm id_{r}} \tag{31}\]  
where \(\gamma_{0}=\gamma_{\rm sd}=P_{\rm s}\abs{h_{\rm sd}}^{2}/\sigma^{2}\) represents the direct link SNR with the PDF of \(f_{\gamma_{0}}(x)=\frac{1}{\tilde{\gamma}_{0}}\exp\Bigl{(}-\frac{x}{\tilde{ \gamma}_{0}}\Bigr{)}u(x)\), \(\tilde{\gamma}_{0}=\tilde{\gamma}_{\rm sd}\), and the MGF of \(M_{\gamma_{0}}(\rm s)=1/(\tilde{\gamma}_{0}\rm s+1)\). Assumingindependence between \(\gamma_{0}\) and \(\gamma_{\rm id,}\), the average symbol error rates (SERs) for the indirect links and the combined link are expressed as  
\[\begin{split} P_{S,\rm id}&=\frac{1}{\pi}\int_{0}^{(M-1 )\pi/M}\prod_{r=1}^{R}M_{\gamma_{\rm idr}}(\mathsf{s})d\theta\\ P_{S,\rm cb}&=\frac{1}{\pi}\int_{0}^{(M-1)\pi/M}M_ {\gamma_{\rm id}}(\mathsf{s})\prod_{r=1}^{R}M_{\gamma_{\rm idr}}(\mathsf{s})d \theta\end{split} \tag{32}\]  
where \(\mathsf{s}=g_{\rm FSK}/\sin^{2}(\theta)\) and \(g_{\rm FSK}=\sin^{2}(\pi/M)\)[43].  
By replacing \(M_{\gamma_{\rm idr}}(\mathsf{s})\) in (32) with the upper bound \(M_{\gamma_{\rm idr}}^{\rm Up}(\mathsf{s})\) from (30), the approximated SER bounds for the indirect and the combined links are given as  
\[\begin{split} P_{S,\rm id}&<P_{S,\rm id}^{\rm Up}= \frac{1}{\pi}\int_{0}^{(M-1)\pi/M}\prod_{r=1}^{R}M_{\gamma_{\rm idr}}^{\rm Up} (\mathsf{s})d\theta\\ P_{S,\rm cb}&<P_{S,\rm cb}^{\rm Up}=\frac{1}{\pi} \int_{0}^{(M-1)\pi/M}M_{\gamma_{\rm id}}(\mathsf{s})\prod_{r=1}^{R}M_{\gamma_{ \rm idr}}^{\rm Up}(\mathsf{s})d\theta.\end{split} \tag{33}\]  
From (19) and (26), the asymptotic BER for BPSK for the \(r\)th indirect link is approximated as  
\[\begin{split} P_{B,\rm id,r}&\approx P_{B,\rm id,r}^ {\rm Asym}\\ &=\frac{1}{4\tau_{\rm Srr}+1}+\frac{1}{4\tau_{\rm rdr}\Gamma_{ \rm Srr}/\ln\bigl{(}4\tilde{\tau}_{\rm rdr}\Gamma_{\rm Srr}+1\bigr{)}}.\end{split} \tag{34}\]  
From (34), the help of (14-4-18) in [44], and after some algebraic manipulations, the asymptotic approximations for \(P_{S,\rm id}^{\rm Up}\) and \(P_{S,\rm cb}^{\rm Up}\) in (33) can be expressed for BPSK as [24]  
\[\begin{split} P_{B,\rm id}&<P_{B,\rm id}^{\rm Asym}= \binom{2R-1}{R}\prod_{r=1}^{R}P_{B,\rm id,r}^{\rm Asym}\\ P_{B,\rm cb}&<P_{B,\rm cb}^{\rm Asym}=\frac{(2R+1) }{4\tau_{\rm sd}+1}\prod_{r=1}^{R}P_{B,\rm id,r}^{\rm Asym}.\end{split} \tag{35}\]
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 2 Material and Methods
### Filtering of 50 Hz Signal and Higher-Order Harmonic Components  
The very high impact of power line interferences with EMG signals was observed during the research. In some cases, the higher-order harmonics of the 50 Hz component had relatively large amplitudes. In order to reduce these harmonics in the EMG recordings, either comb or notch filters can be applied [30; 31].