esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Analysis
In subsequent studies, the laser ablation processing was performed in a vacuum or in an inert atmosphere. Moreover, the SEY difference of the same metal samples which were processed under different atmosphere will be compared and analyzed in the future. Geometry and surface chemical states change effects were the two main reasons for the SEY reduction of laser processed Mo metal. Previous studies demonstrated that the geometry effect was the main reason for SEY reduction [34; 35]. The earlier studies [16; 36] indicated that the oxidation states of laser treated copper was in terms of the SEY increase. Reference [22] investigated the SEY curves of dozens of metals before and after cleaning, which demonstrated that the surface oxidation can induce a SEY increase of 0.2-0.5. However, surface morphology was also an important factor affecting the SEY values. After laser processing, the Mo metal on the surface of the untreated Mo sample was  
Figure 7: XPS core-level spectra of molybdenum (O-1s) for (**a**) untreated Mo and (**b**) laser treated Mo sample #4.  
almost fully oxidized. Although, Mo oxidation may have contributed to the SEY increase. While, laser induced nano- and micro-structures reduced the SEY. The balance effect was the SEY reduction of Mo samples. Thus, the degree of the contribution of surface chemical states changed on the reduction of SEY was not clear and requires further investigations. As reported in references [9; 10], the electron energy in MDC varied between 2 keV-24 keV, which may bombard on the inner surface of the MDC. When these electrons with the energy of 2 keV-24 keV impacted the inner surface of the MDC, the secondary electrons and backscattering electrons emitted formed the surface with the energy of 1 eV-24 keV, then part of the scattering electrons interacted with the inner surface and the beam in MDC. At 2000 eV \(\leq E_{\rm P}\leq\) 3000 eV, the SEY of untreated Mo ranged from 1.07 and 1.0. The SEY of sample #6 was 0.77\(\sim\)0.94 at 2000 eV \(\leq E_{\rm P}\leq\) 3000 eV with the incident angles of \(90^{\circ},80^{\circ}\) and \(60^{\circ}\), which was the lowest one in this study. Moreover, at 100 eV \(\leq E_{\rm P}\leq\) 2000 eV, the SEYs of sample #6 were less than one. Therefore, the laser processing parameters of sample 6 was preferable for SE mitigation in MDC.
## 3 Results  
### Flow Stress Prediction Using DEFORM-3D Simulation  
Figure 2 compares the peak stress obtained from experimental uniaxial compression testing and DEFORM-3D simulation for three titanium (\(\alpha+\beta\)) alloys. The peak stress values are very close except for the simulation performed at 10/s for the experimental Ti-4.5 Al-1V-3 Fe and Ti-6 Al-1V-3 Fe alloys and the commercial Ti-6 Al-4V alloy, where there  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Number** & **Description** & **Value** \\ \hline
1 & Workpiece material & Titanium alloys \\
2 & Workpiece height (mm) & 12 \\
3 & Workpiece diameter (mm) & 8 \\
4 & Strain rate (/s) & 0.1, 1, 10, 15, 20, 50 \\
5 & Workpiece temperature (\({}^{\circ}\)C) & 800, 850, 900, 950 \\
6 & Initial die temperature (\({}^{\circ}\)C) & 200 \\
7 & Coefficient of heat convection (N/(s.mm.\({}^{\circ}\)C)) & 0.02 \\
8 & Coefficient of heat transfer (N/(s.mm. \({}^{\circ}\)C)) & 5 \\
9 & Coefficient of friction (lubricated) & 0.3 \\
10 & Finite element method elements & 26,247 \\
11 & Element nodes & 5352 \\
12 & Simulation environment (\({}^{\circ}\)C) & 25 \\
13 & Friction type & shear \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forging simulation parameters using DEFORM-3D.  
Figure 1: Deform-3D interface for computing constitutive constants.  
were observable deviations. This was expected because the simulations may not always accurately represent all the experimental testing conditions [22; 23]. For example, a fixed coefficient of friction of 0.3 was used in the simulations for simplicity, but it varies rapidly under certain experimental conditions, affecting some simulation outputs like flow stress. Despite this deviation, Figure 3 shows a good correlation between the imposed deformation parameters and peak flow stress obtained from the FEM simulation. There was a high correlation coefficient (R2 > 0.98) between peak flow stress and the strain rates for different test temperatures, even when the stimulation was extended from a 10/s to a 1000/s strain rate. This means that the constitutive parameters used to determine the peak flow stress during numerical simulation effectively describe the flow behavior at strain rates that are extremely higher than those tested in the laboratory.  
Figure 2: Peak stress obtained from the experiment and DEFORM-3D simulation (**a**) Ti-4.5 Al-1V-3 Fe; (**b**) Ti-6 Al-1V-3 Fe; and (**c**) Ti-6 Al-4V. _S represents simulated peak stress, and E represents experimental peak stress_.
## 3 Results and Discussion
### Toughness and Tencity  
The effect of CRM, PP, and TPS contents on toughness and the maximum force of asphalt binders are illustrated in Figures 4 and 5, respectively. Figure 4 shows that toughness is increased linearly with an increase in modifier content. CRM has the highest toughness, followed by the TPS and PP modifiers. Thus, the capability of the CRM-modified binders to resist the permanent deformation is better than the TPS- and PP-modified binders. Moreover, our results indicate that CRM has a higher dissipated energy during the fracturing process. Thus, the higher the dissipated energy in the fracture, the better the asphalt mixture quality is. Concretely, CRM is the best modifier for enhancing the mechanical performance than both the TPS and PP modifiers.  
Figure 4: Effect of modifier content on toughness.  
Figure 5: Effect of modifier content on max force.  
Figure 3: Effect of modifier content on viscosity.  
Figure 5 shows how the maximum force was increased linearly with the rise of CRM, PP, and TPS concentrations. It is observed that the addition of CRM yielded a better maximum force than PP and TPS modifiers. Hence, it can be deduced that CRM has a more significant force during the fracturing process. Tenacity has been considered as an essential factor for performance evaluation of modified asphalt. Therefore, Table 5 illustrates the tenacity of the modifiers. Therein, it is observed that CRM has small values in the tenacity property and is thus the best one in the performance evaluation, followed by PP and TPS.
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 4 Results and Discussion
### PWV Comparison  
To assess the accuracy of the tomographic result, the PWV value for the location of the radiosonde station is calculated using the water vapor density of the voxels acquired from the three schemes and compared with PWV derived from the radiosonde data at UTC 0:00 and 12:00. In this experiment, eight tomographic solutions are selected daily to calculate PWV. As shown in Figure 9, the trends of the PWV time series are basically consistent and the results of Scheme #3 agree worst with that from the radiosonde. Compared with Scheme #1, it is clear that Scheme #2 is more consistent with the radiosonde data, indicating that the proposed method can improve the tomographic accuracy. The statistical results are listed in Table 1, which show an advantage of Scheme #2 compared with other schemes in terms of RMS, MAE, and bias.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **RMS** & **MAE** & **Bias** \\ \hline Scheme \#1 & 5.06 & 4.40 & 4.11 \\ Scheme \#2 & 4.00 & 3.39 & 2.76 \\ Scheme \#3 & 5.72 & 5.21 & 4.23 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical results of PWV between various schemes and radiosonde for the week of DOY 152–158, 2017 (unit: mm).  
Figure 8: Average number (histogram) and percentage (line) of voxels crossed by signal rays for Schemes #1 (the first bar in each set), #2 (the second bar in each set), and #3 (the third bar in each set) of DOY 152–158, 2017.  
Figure 9: Comparison of PWV time series derived from radiosonde and various tomographic results for the period of DOY 152–158, 2017.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 6 ML Performance Review in IDS
### Binary Classification  
#### 6.2.1 Observations  
Tables 7-9 indicate that most of the studies achieved an accuracy rate higher than 90%, with a few achieving near-perfect accuracy. More advanced preprocessing techniques like dimensionality reduction and feature engineering often resulted in improved performance. DT, RF, and ensemble algorithms like XGBoost repeatedly ranked high in the best-performing category.  
In the TON_IoT dataset, DT and RF achieved perfect accuracy, benefiting from employed preprocessing in [55; 61]. Similarly, XGBoost demonstrated exceptional accuracy (99.9987%) on the BoT-IoT dataset, underscoring its robustness [45]. This can also be attributed to carefully designed features and a controlled attacking setting. High performance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **F1 Score [\%]** & **Highest Accuracy ML Technique** & **Dataset** \\ \hline
[62] & 99.6 & - & - & - & MLP algorithm & Bot-IoT \\
[63] & 99 & 99 & 99 & 99 & KNN & Bot-IoT \\
[58] & 99 & 99 & 99 & 99 & LSTM & TON_IoT \\
[39] & 98.9 & 99.47 & 99.31 & 99.35 & LSTM & CIC-DDoS2019 \\
[35] & 98.72 & 97.81 & 97.35 & 98.26 & EWEA & Edge-ToT \\
[53] & 98.5 & 98.2 & 95.9 & 97.4 & KNN & TON\_IoT \\
[34] & 98.1 & - & - & - & DT & custom \\
[57] & 97.81 & - & - & 97.81 & RF & UNSW-NB15, IoT23, TON_IoT \\
[51] & 97.66 & 98.24 & 97.66 & 96.57 & RF & MedBiT \\
[75] & 96.32 & 93.12 & 84.55 & 88.63 & Voting (DT, MLP RProp, Logistic Regression) & TON_IoT \\
[50] & 96.17 & 96.92 & 96.17 & 96.02 & RF & MedBiT \\
[70] & 95.59 & 79 & 64 & 68 & Extratree & UNSW_NB15 \\
[41] & 94.21 & - & - & J48 & Edge-ItoT \\
[36] & 86.1 & - & - & 75.8 & CCN & IoTID20 \\
[66] & 78.35 & 81.79 & 78.35 & 77.65 & APSO-CNN-SE & UNSW-NB15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._often reflects an ideal scenario where distinguishing features are quite clear. However, the controlled nature of the dataset might not capture the full complexity found in real-world environments.  
The CIC-DDoS2019 dataset showed the worst accuracies and the widest performance spread compared to the other two datasets. This variation could correspond to the complexity of the DDoS attack patterns that are present in the dataset. Deep learning techniques that include CNNs may underline the potential complexity of the dataset, where performance could be improved when spatial or sequential representations of data are the basis for such methodologies.  
The general tendency in the performance of the studied models indicates that models based on neural networks require more time and impose higher computational overhead [61], while XGBoost, DT, RF, and ET are faster. However, in the CIC-DDoS2019 dataset, CNN-based models outperformed tree-based and SVM-based alternatives (Table 9).
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Results  
#### 3.7.1 Processing Time for Homomorphic Encryption and Model Accuracy  
To assess the computational impact of Homomorphic Encryption in Split Learning, we measured the time required for encryption and processing on the server, alongside model accuracy on unaltered (clean) datasets. The results, summarized in Table 2, indicate that encryption and processing times remain efficient across datasets, with minimal impact on model accuracy due to encryption.  
Figure 2: Client-Server Communication in Split Learning with HE and ZKP.  
These results demonstrate that Homomorphic Encryption imposes a manageable computational load, maintaining model accuracy levels between 85-98% on clean datasets. This stability is essential for secure decentralized learning without compromising performance.
# Waveguide Manufacturing Technologies for Next-Generation Millimeter-Wave Antennas
## 4 Stereolithography (SLA) and Post-Metallization Process
Moreover, the proposed 3D-printed manufacturing process can be considered as a lightweight and low-cost solution for 5G high data rate communications. Table 4 shows a comparison of different types of antennas fabricated with SLA to demonstrate the maturity that the technology has acquired during recent years. This fabrication method provides high-gain antenna array designs with efficiency levels above 90%, as shown in [58] and [59]. The technology is widely proven in Ku, K, and Ka bands. However, satisfactory work can also be found in higher bands, as shown in [60]. Some designs with polarization versatility, such as those shown in [53] or [58], can also be implemented with SLA technology obtaining high quality and good performance.
## 4 Discussion  
Summing up the observed results, it can be concluded that reactive magnetron sputtering of titanium in Ar-O\({}_{2}\)-H\({}_{2}\) gas mixtures with high hydrogen content allows the deposition of films whose visual appearance is similar to the titanium oxide with a significant amount of titanium in Ti\({}^{3+}\) state. However, neither of the applied methods provided unambiguous evidence for the existence of Ti(III) oxide or oxygen vacancies. This suggests that during film deposition, hydrogen not only reduces TiO\({}_{2}\) and stimulates the formation of oxygen vacancies, but also presumably creates bonds with titanium and occupies oxygen sites in crystal structures of Ti(IV) oxides. Earlier studies with various oxyhydrides [4] demonstrated that when hydrogen occupies oxygen sites, it can stabilize the crystal structure of the corresponding oxide and extend its stability to lower oxygen contents [4,21]. We assume that in currently investigated TiHO samples, hydrogen also stabilized the Ti(IV)  
Figure 4: Tauc plots (blue curves), trendlines for the linear parts of the plots (solid red lines) and respective band gap values (dotted red lines) of TiHO films.  
Figure 5: Estimation of photocatalytic bleaching of MB solution using different TiHO films.  
crystal structure and averted the nucleation growth of crystalline Ti\({}_{2}\)O\({}_{3}\) or other titanium suboxide phases.  
The absence of Ti\({}^{3+}\) component in XPS spectra also might be related to the fact that in TiHO samples, instead of oxygen vacancies, there are highly polarizable H\({}^{-}\) anions. These anions can hinder the Ti\({}^{3+}\) signal in two ways. First, although hydrogen has a lower charge than oxygen, it is still higher than the efficient charge of oxygen vacancy. Second, there is high thermodynamic potential for the hydrogen anions to combine and form neutral hydrogen molecules. Indeed, recently, researchers demonstrated that full oxidation of some oxyhydrides is even easier to achieve than oxidation of corresponding suboxides [4]. This means that in oxyhydride materials, the chemical bonds of hydrogen with the cations is not strong, and hydrogen might be relatively easily replaced by oxygen from the atmosphere. Accordingly, there is a probability that during TiHO sample transfer from the deposition chamber to XPS, hydrogen from the top layers of the TiHO samples was rapidly replaced by oxygen from the atmosphere, and after this oxidation, only Ti\({}^{4+}\) peaks were acquired. Similar reasoning also can be used to explain the unexpectedly low photocatalytic activity of TiHO films. First, due to the relatively high reactivity of hydrogen in oxyhydrides, by placing TiHO samples into aqueous solutions, we might initiate additional TiHO sample oxidation by hydrolysis. This potentially may eliminate the beneficial Ti\({}^{3+}\) cations from the sample and reduce the induction of electron-hole pairs. On the other hand, if hydrogen is not removed from the oxygen sites, its high polarizability might serve for the higher recombination rates of photoinduced charges.
# Implementation of Context Aware e-Health Environments Based on Social Sensor Networks
## 5 Social Sensor System Design and Discussion
For this reason, and because most of the medical devices require some user intervention (press button, cuff placing, _etc._), it is necessary to minimize the number of these actions. All these considerations have been taken into account in NASISTIC project development. * The users and social/medical staff have shown significant interest in participating in the NASISTIC pilot deployment phase. With regards to users, personal interviews between them and Red Cross assistants have derived some first conclusions. On the one hand, they accept the use of proposed system, which is perceived as an additional element within the household. On the other hand, they have found of particular interest knowing some biomedical parameters (blood pressure mainly) on a daily basis as security data related to health state. From the social point of view, the interest of users has not been as high as with medical parameters; in contrast, in this case, family members (second users) have shown interest in issues such as temperature variation in the monitored homes, flood, _etc._ With regards to social/medical staff, the main results obtainedare the following: response times are reduced due to real time interaction capabilities offered by the social sensors, and the general service provides them certain levels of self-control. Although specific survey forms have not been implemented, the daily personal contact between users and social/medical staff during the project deployment has made obtaining these first satisfaction data possible. * The large amount of stored data enables the development of additional projects related to social behavioral patterns. Meanwhile, biomedical data allow the knowledge of user's health state and mood, while other activity data, such as watching TV, use of HVAC system, water consumption, or length of phone calls, can provide information about user's behavior and social relationships. All this information can be combined with user activity on the Internet and specific web social networks like Facebook, Twitter, _etc._ This makes broader monitoring applications focused on following up with users with chronic social problems such as exclusion, loneliness, energy poverty, _etc._ possible. * Technical workers, engineers and architects involved in the deployment indicate that the proposed system is simple to integrate and maintain, reducing overall installation and operational costs. Thus, start-up system requires minimum intervention by technical worker because elements are pre-configured from factory or service provider; the worker only has to verify the correctness of system operation the first installation time. In addition, the system maintenance neither demands a great number of home interventions by engineers or technical workers unless some element must be repaired or replaced.
## 2 Materials and Methods
### Fabrication of Silver-Nanowire-Embedded Flexible Transparent Electrodes  
Figure 1 depicts the process used to fabricate silver-nanowire-embedded flexible transparent conductive electrodes and shows a representative scanning electron microscope (SEM) image of them. We first prepared a 0.3 wt % silver nanowire solution dispersed in deionized water (Nanopyxis, South Korea). The nanowires in the solution had a diameter of \(35\pm 5\) nm and length of \(25\pm 5\)\(\upmu\)m, generally capped by polyvinylpyrrolidone (PVP) to make a solution [29; 30; 31]. Glass substrates sized \(2.5\times 3\) cm were cleaned by sonication in acetone and isopropyl alcohol at 60 \({}^{\circ}\)C for 15 min, followed by drying in an oven at 100 \({}^{\circ}\)C. We then spin-coated polymethylmethacrylate (PMMA) at 3000 rpm for 60 s on the glass substrates to form a releasing layer with low adhesive force. The releasing layer should have adhesive force with silver nanowires weaker than the adhesive force between the UV-curable polymer and silver nanowires for complete detachment of the silver nanowires from the releasing layer. In detail, while the glass substrate has an adhesive force of 169.5 mJ m\({}^{-2}\) with the silver-nanowire film having a polar surface energy of 131.3 mJ m\({}^{-2}\) and a nonpolar surface energy of 10.3 mJ m\({}^{-2}\), the PMMA surface has an adhesive force of 112.5 mJ m\({}^{-2}\) with identical silver-nanowire film [28]. After drying the PMMA-coated glass substrate at 180 \({}^{\circ}\)C for 30 s, the silver nanowire solution was spin-coated on it at 600 rpm for 60 s. After drying at 100 \({}^{\circ}\)C for 1 min, a silver nanowire network for electron conduction formed on the substrates. To embed the silver nanowires into a polymer and fabricate silver-nanowire-embedded electrodes, ultraviolet (UV)-curable SU8 or NOA polymers with different viscosities were spin-coated at 250-1000 rpm for 60 s and cured under UV light irradiation for 30 min. Silver nanowires are embedded into UV-curable polymers by a principle of mechanical interlocking during the curing process [26; 28]. The delamination of the UV-curable polymer completed the fabrication of silver-nanowire-embedded flexible and transparent electrodes. Due to the low adhesive force of the PMMA-coated surface with silver nanowire, the silver nanowires could be fully detached from the substrate [26; 28]. In addition, light scattering was insignificant in the silver-nanowire-embedded transparent electrodes in SU8 or NOA polymers because the electrodes were very flat [26; 28]. Then, to investigate whether the electrodes were well constructed, their surfaces were observed by SEM (JSM-6500F, Jeol).
# Rapid Parametric Modeling and Robust Analysis for the Hypersonic Ascent Based on Gap Metrics
## 5 Simulation Analysis
### Robust Analysis  
The initial parameters of the tracking controller are obtained at the start point in constant dynamic pressure climb. The gap metric is computed for the open-loop system varying due to flight condition. The normalized coprime stability margin (NCSM) of the closed-loop system is calculated for the same controller obtained at the design point. Here, we provide two examples to illustrate the robust analysis based on gap margin, as shown in Figures 7 and 8.  
The value of NCSM and the gap metric are expressed in dB. The tendency of the gap metric shows a significant relation with dynamic pressure. Furthermore, it decreases as the velocity deviates from the design point, revealing the variation in open-loop dynamics with flight conditions. As for closed-loop dynamics, the nominal controller at the initial  
Figure 8: NCSM and gap metric of ascent tracking at Mach 7.8, \(h\) = 26,495 m.  
Figure 7: NCSM and gap metric of ascent tracking at Mach 6.5, \(h\) = 23,408 m.  
point is used for all cases in the flight envelope. Hence, the NSCM reveals the robustness of the closed-loop system if we keep the controller the same. The nominal value of NSCM for the start point is approximately 0.1. It dramatically decreases as the flight altitude increases by 2 km, whereas it remains as velocity increases. Moreover, simulation results show the same tendencies for the terminal points compared with the initial point. The evaluated gap margins for these two cases are provided in Figure 9. The circle in these figures represents the initial flight condition of ascent, whereas the square is the terminal point. Besides, the blue line denotes the constant dynamic pressure profile, and the orange line denotes the constant Mach number profile.  
A positive gap margin reveals the robustness of the closed-loop system, whereas a negative gap margin indicates that the closed-loop system lacks robust stability. Simulation results reveal that the trajectory tracking control is more sensitive to altitude variation. A deviation of 800 m will cause stability problems, whereas the range of velocity deviation is about 210 m/s. It indicates that climbs with constant velocity for hypersonic vehicles need more attention for precise altitude control.