esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Shielding Efficiency
Thus, \(SE_{A}\) and \(SE_{R}\) contributions are important and must be improved at both low and high-frequency regions to develop effective electromagnetic shielding materials. Figure 4: Dependence of AC conductivity values of graphite (**a**) and carbon black (**b**) filled samples on the frequency and compositional parameters. Figure 5: Absorption shielding efficiency values of graphite (**a**) and carbon black (**b**) samples. The total shielding efficiency of the graphite-added samples is slightly higher than the carbon black-added samples, as shown in Figure 7. \(SE_{\rm T}\) values for graphite-added samples are 20-21 dB for the L band and S band; 19-21 dB for the C band, X band and partially Ku band. As the frequency increases, a small decrease can be mentioned from the L band to the Ku band. For carbon black added samples, it can be said that the \(SE_{\rm T}\) values change little in different band ranges. \(SE_{\rm T}\) for CB1 8 dB in L band, S band and C band; 9 dB in X band and 10 dB in partially Ku band. CB3 is 10 dB, CB5 is 14 dB, and CB7 is 17 dB, and these values remain almost constant from the L band to the Ku band. Although the AC conductivity values of the carbon black-filled samples are higher, the higher shielding efficiency of the graphite-filled samples is due to the segregation of carbon black in the matrix. These values are very close to each other in graphite-added samples, but differences can be observed in carbon black-added samples according to the filling ratio. Graphite's layered structure can lead to higher contact resistance between filler particles, especially at low wt%, hindering the formation of an effective conductive network [42]. This makes graphite composites less sensitive to lower wt% filler content for achieving good EMI shielding performance. When working with higher graphite filler ratios, it is predicted that these \(SE_{\rm T}\) values will not be close to each other. Shielding efficiency values change as the carbon black filler ratio increases. Generally, it can be said that the results in this study show consistency when compared to other studies shown in Table 3. It's important to consider that the provided Table 3 includes different filler amounts, concentration levels, production methods and frequency ranges for various materials and composites. The specific performance may vary based on factors such as the dispersion of the filler and the measurement setup. \[\sigma_{AC}=2\pi f\varepsilon_{0}\varepsilon^{\prime\prime} \tag{1}\]0 Reflection shielding efficiency values of graphite (**a**) and carbon black (**b**) samples. \[\sigma_{AC}=2\pi f\varepsilon_{0}\varepsilon^{\prime\prime} \tag{1}\]1 Total shielding efficiency values of graphite (**a**) and carbon black (**b**) samples. The shielding efficiencies of the samples with both types of filling materials vary little depending on the frequency. This is important in terms of keeping the efficiency of the shielding material constant in applications in the 1-14 GHz range.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 6 Conclusions  
Experiments on the average end-to-end delay, packet delivery rate, and node survival rate were conducted to verify the performance of AODV-EOCW against those of vanilla AODV and AODV-UU. The simulation results show that AODV-EOCW has obvious advantages over the two routing protocols, and it can effectively improve the survival rate and lifetime of the network by selecting a less congested and more stable link to forward packets. According to the experiment results, the AODV-EOCW protocol is suitable for both MANET and WSN applications. Finally, there are two major limitations in this study that could be addressed in future research. First, the residual energy level thresholds that were used to select judgment matrices are designed empirically. How to select the thresholds at runtime will be our focal research point. Second, the proposed protocol has not been verified in real applications. The authors are considering implementing the AODV-EOCW protocol in a homemade mobile sensor node network to investigate how the protocol performs in real applications.  
Methodology, Y.J.; Software, M.Y.; Writing--original draft, Y.J.; Supervision, H.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Model of the Single-Stage FLTD
### Surface Impedance Boundary Condition  
Under the influence of a pulsed current with rise-time less than 100 ns, the skin depth of good conductors in the FLT cavity is very small, leading to a rather short length of spatial step to accommodate the fast-changing field. As depicted in Figure 10, by using the SIBC, EM field distribution on the surface of conductors can be obtained without calculating the internal field distribution of the medium [40]. Therefore, introduction of the first-order SIBC in TD-FIT can effectively reduce the number of DoFs.  
Since the SIBC is defined in the frequency domain, vector fitting techniques and first order rational functions are used for approximation of the convolution operation. Similar to the lumped element, surface equivalent current \(\widehat{\mathbf{i}}_{\text{SIBC}}\) generated by the first-order SIBC is added to the right-hand side of Ampere's law:  
\[\widetilde{\mathbf{Ch}}^{n+1/2}=\mathbf{D}_{\varepsilon}(\widetilde{\mathbf{e} }^{n+1}+\widetilde{\mathbf{e}}^{n})/2+\widehat{\mathbf{i}}_{\text{S}}^{n+1/ 2}+\widehat{\mathbf{i}}_{\text{L}}^{n+1/2}+(\widehat{\mathbf{i}}_{\text{SIBC} }^{n+1}+\widehat{\mathbf{i}}_{\text{SIBC}}^{n})/2+\mathbf{D}_{\varepsilon}( \widetilde{\mathbf{e}}^{n+1}-\widetilde{\mathbf{e}}^{n})/\Delta t, \tag{30}\]  
\[\widehat{\mathbf{i}}_{\text{SIBC}}^{n+1}=c_{0}\widetilde{\mathbf{e}}^{n+1}+ \sum_{p=1}^{P}\chi_{p}^{n+1}, \tag{31}\]  
\[\chi_{p}^{n+1}=e^{-\alpha_{p}\Delta t}\chi_{p}^{n}+\frac{c_{p}}{\alpha_{p}} \bigg{(}1+\frac{e^{-\alpha_{p}\Delta t}-1}{\alpha_{p}\Delta t}\bigg{)} \widetilde{\mathbf{e}}^{n+1}+\frac{c_{p}}{\alpha_{p}}\bigg{[}\frac{1}{\alpha_{ p}\Delta t}-e^{-\alpha_{p}\Delta t}\bigg{(}1+\frac{1}{\alpha_{p}\Delta t} \bigg{)}\bigg{]}\widetilde{\mathbf{e}}^{n}, \tag{32}\]  
Figure 10: Comparison of grids without and with the surface impedance boundary condition (SIBC). (**a**) Without SIBC, (**b**) with SIBC.  
where \(P\) is the number of rational functions, \(c_{0}\), \(c_{p}\), and \(\alpha_{p}\) denote coefficients and poles of the rational function approximation, respectively [40].  
Obviously, Equation (30) is still an explicit update for the electric field and there is no need for reduction in the time step from the CFL stability limit.
## 5 Discussion
### Vulnerabilities Mitigation
#### 5.2.6 PHYS, PORT, STAT: Thefts and Inappropriate Accesses  
This section primarily discusses physical security aspects that may be implemented at a company facility to prevent a data breach. We understand that the mitigation approaches related to thefts of the company assets in possession of an employee outside of the company's premises are embraced in Section 5.2.4.  
The physical-security design in a company starts in the architectural-arrangement stage of the facility construction. Crime Prevention Through Environmental Design (CPTED) strategies may be employed during this phase. Through this approach, criminals are deterred and more easily detected by the physical layout of the space.  
The main CPTED principles are natural surveillance, access control, territorial reinforcement, and maintenance [84]. As an example, it is stated that fences should be at least 3 ft (about 1 m) high to deter casual trespassers and at least 8 ft (approximately 2.5 m) high to deter purposeful infiltrators [85].  
Other physical controls should be implemented to prevent incidents, especially in more sensitive areas, such as data centers. Examples include the use of a keypad (preventive), guards (deterrent), security cameras (detective), and alarms (corrective).  
Nonetheless, all these security measures will be rendered useless if the human factor is successfully explored. An adversary may, for example, covertly sneak through a door opened by authorized personnel, a practice known as tailgating, or they may convince someone to let them enter, for example, by saying that they forgot their badge and are in a hurry. The latter is a social engineering tactic known as piggybacking. An effective countermeasure to these intrusions is the use of a mantrap.  
However, intruders do not always perpetrate physical incidents. Authorized guests, for example, may perform unauthorized activities, and, in that case, additional physical countermeasures must be put in place.  
One possible gap is the direct observation of devices' screens and keyboards. In such cases, an adversary may obtain sensitive data such as passwords through shoulder surfing. To hinder this activity, it may be necessary to relocate the devices.  
Another security measure regarding the employee's workstation is the implementation of a Clean-Desk Policy, which enforces that all desks within the company must be clear of objects and documents. After successfully implementing this policy, an intruder cannot steal a sensitive document from a worker's desk.  
NIST SP 800-12 [86] Chapter 15 reviews other physical security practices. Physical safety, which aims to protect people's physical integrity, life, and health, is another relevant topic in this discussion. However, as these incidents do not usually result in data breaches, which are the focus of this work, we do not include them in the discussion.
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 3 Performance Analysis
### OP of the Full Passive RIS System  
The outage probability of passive RIS can be written as  
\[P_{outp}=\Pr(\gamma_{p}<\gamma_{th})=F_{\tilde{I}_{B}^{2}\tilde{I}_{D}^{2}} \left(\frac{\gamma_{th}}{\overline{\gamma_{p}}N^{2}}\right) \tag{27}\]  
where \(\overline{\gamma_{p}}=\frac{P_{S}}{\sigma_{D}^{2}}\). \(F_{\tilde{I}_{B}^{2}\tilde{I}_{D}^{2}}(z)\) can be obtained by using the convolution formula. Its integral expression is processed with ([28], (3.471.9) (6.592.2)). The result is displayed as follows  
\[F_{\tilde{I}_{B}^{2}\tilde{I}_{D}^{2}}(z)=\frac{\left(\mathrm{Y_{S}}Y_{D} \chi\right)^{m_{D}}}{\Gamma(m_{S})\Gamma(m_{D})}G_{1,3}^{2,1}\left(\mathrm{Y_{ S}}\mathrm{Y_{D}}\chi\left|\begin{matrix}1-m_{D}\\ m_{S}-m_{D},\end{matrix}\right.\begin{matrix}0,&-m_{D}\end{matrix}\right. \tag{28}\]  
where \(G_{1,3}^{2,1}(\cdot)\) denotes the Meijer-G function with the order 2, 1, 1, and 3 [28].  
The outage probability of passive RIS is  
\[\begin{split} P_{outp}&=F_{\tilde{I}_{B}^{2}\tilde{I}_{D}^{ 2}}\left(\frac{\gamma_{th}}{\overline{\gamma_{p}}N^{2}}\right)\\ &=\frac{\left(\mathrm{Y_{S}}\mathrm{Y_{D}}\gamma_{th}\right)^{m_{D }}}{\Gamma(m_{S})\Gamma(m_{D})\left(N^{2}\overline{\gamma_{p}}\right)^{m_{D}} }G_{1,3}^{2,1}\left(\mathrm{Y_{S}}\mathrm{Y_{D}}\gamma_{th}\left|\begin{matrix}1- m_{D}\\ m_{S}-m_{D},\end{matrix}\right.\begin{matrix}0,&-m_{D}\end{matrix}\right. \begin{matrix}0\\ 0,&-m_{D}\end{matrix}\end{matrix}\end{split} \tag{29}\]
## 4 Results and Comparison
### PNR-TDNN Experiments
#### 4.2.3 Discussion of PNR-TDNN Ablation Experiments
For experiment 7 on dataset C, a 23.77% EER improvement and a 1.88% enhancement in the MinDCF are achieved. These results suggest that the enhancement mechanisms proposed in this paper demonstrate superior performance in speaker verification tasks under conditions of strong noise and reverberation. In addition, the EER of experiment 8 is slightly better than experiment 9, indicating that larger pre-trained models still have a few advantages in backend applications, but the improvement effect is not significant. For example, for dataset A, experiment 8's EER improves by 4.5% compared to that of experiment 9. Considering the miniaturization requirements of smart microphone devices, choosing smaller pre-trained model is still the preferred choice. Finally, we performed significance tests on the model. Specifically, five cross-training sessions were conducted using dataset A, and then the t-test was cross-validated using the mean and variance of the calculated EER. As shown in the Figure 12, this reflects that the five models are statistically significantly different, and also shows that our proposed model has a very uplifting effect with respect to the other four baseline models, and improvements are not due to random variations.To better present the results, as shown in Figure 13, we present the best EER comparison plots of the four baseline models with the proposed T-Hu+EC+FAM on dataset A, dataset B, and dataset C.  
Figure 11: Metrics of different PNR-TDNNs in dataset A/B/C: (**a**) EER in dataset A; (**b**) EER in dataset B; (**c**) EER in dataset C; (**d**) MinDCF in dataset A; (**e**) MinDCF in dataset B; (**f**) MinDCF in dataset C. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Methodology**}} & \multicolumn{2}{c}{**Dataset A**} & \multicolumn{2}{c}{**Dataset B**} & \multicolumn{2}{c}{**Dataset C**} \\  & & **EER (\%)** & **MinDCF** & **EER (\%)** & **MinDCF** & **EER (\%)** & **MinDCF** \\ \hline
1 & Hu + EC (original Res2 net) & 6.38 & 0.188 & 8.37 & 0.193 & 6.17 & 0.157 \\
2 & Hu + EC (reverse Res2 net) & 6.51 & 0.188 & 8.11 & 0.190 & 5.97 & 0.163 \\
3 & Hu + EC + F & 5.82 & 0.181 & 8.09 & 0.192 & 5.93 & 0.161 \\
4 & Hu + EC + A & 6.14 & 0.180 & 7.76 & 0.191 & 5.38 & 0.165 \\
5 & Hu + EC + M & 5.82 & 0.183 & 7.67 & 0.191 & 5.71 & 0.158 \\ \hline
6 & Hu + EC + AM & 5.88 & 0.182 & 7.58 & 0.192 & 4.87 & 0.155 \\
7 & **Hu + EC + FAM** & 5.65 & **0.178** & 7.47 & 0.190 & **4.84** & 0.156 \\
8 & **S-Hu + EC + FAM** & **5.40** & 0.180 & **6.94** & 0.190 & 5.65 & 0.161 \\
9 & **T-Hu + EC + FAM** & 5.66 & 0.182 & 7.52 & **0.185** & 5.74 & **0.153** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: PNR-TDNN’s best EER and MinDCF in multiple enhancement methods (Note: Hu = HuBERT, EC = ECAPA-TDNN, S-Hu = small HuBERT, T-Hu = tiny HuBERT, F = fusion, Res2 net, A = SE-attentive mean, M = multi-heads attentive statistics pooling).
## 1 Introduction  
The exponential growth of data traffic demand, huge energy consumption and large amounts of global carbon dioxide emissions severely restrict the sustainable development of wireless cellular networks. According to the statistics, the data traffic volume demand in the fifth-generation (5G) wireless communication network will increase of 1000\(\times\) by 2020. Moreover, the limited spectrum resources also constrain the network capacity improvement [1]. Therefore, the network energy efficiency (EE) which considers both spectral efficiency (SE) and energy consumption has been valued not only as an important network performance indicator for modern wireless networks, but also for the operational expenditure reduction and sustainable development [2].  
Heterogeneous networks (HetNets) consisting of macro base stations (MBSs) and low-power pico base stations (PBSs) can improve SE by reusing the spectrum geographically [3] and enhance the wireless link quality by shortening the distance between the transmitter and the receiver [4]. Therefore, HetNets are deemed as a promising technique to support the deluge of data traffic with high network EE. Nonetheless, due to the complex two-tier heterogeneous architecture deployment of HetNets, the challenging inter-tier interference and PBS deployment density will deteriorate the network EE if they are not treated carefully, which are the concerns of this paper for aiming to improve the network EE.
# In Situ Rubber-Wheel Contact on Road Surface Using Ultraviolet-Induced Fluorescence Method
## 2 Methodology  
### Experimental Setup  
Figure 1 shows the dimensions of the rubber-wheel, road surface replica, and the assembled image of the experimental apparatus for the current study. The contact area was measured using a smooth surface of rubber-wheel and a patterned transparent urethane material copied from the actual road surface. The experiment used macro-rough and micro-rough road surface (based on Michelin report 2001), which has the best friction coefficient in damp and wet conditions [7]. The road surface replica was covered by fluorescence liquid of pyranine solution representing wet conditions in the rainy season. The rubber-wheel was cylindrical, with an outer diameter, inner diameter, and width  
of 79 mm, 41 mm, and 32 mm, as shown in Figure 1. The inner side of the rubber was fixed on a gear-shaped aluminum with 16 mm inner diameter. The rubber-wheel was connected to a shaft through an aluminum hole for free rotation. Also, the shaft was connected to a linear guide instrument with a vertical loading adjustment and flexible speed for dynamic condition tests. Consequently, the driving force was produced by a linear guide instrument instead of rubber-wheel rotation. Figure 1b shows the contacting part image of road surface replica made of urethane material. This is a rectangular surface of 98 mm \(\times\) 108 mm in lengths and  
In this apparatus, the contact area was observed using a high-speed digital camera (HAS-U2; DITECT, Ltd., Japan) with 1-inch CMOS sensor, adjustable gain number and image size. The camera produced a maximum of 7500 frames per second (fps) for recording images of high-speed movement. The excitation light source was provided by ultraviolet (U.V.) light with a 365 nm peak of wavelength. The apparatus mechanism differed from the laser-induced fluorescence (LIF), which uses a half or dichroic mirror to reflect the light source perpendicular to the observing surface. The current experiment utilized a ring-shaped U.V. device as a light source for excitation, located beneath the road replica. The dichroic filter and high-speed camera were placed in the middle and below the U.V. light source, respectively. The high-speed camera directly captured the image of contact between rubber-wheel and road surface replica without utilizing a half-mirror or dichroic mirror. Also, the working distance of the high-speed camera and rubber-wheel contact could be modified based on a wide range of the image. The green-dichroic-filter (passing wavelength between 505 nm and 575 nm) was placed on the top of the camera to capture the only green color of fluorescence light for analysis. Hence, the apparatus enabled us to conduct an experiment for in-situ observation and measure a wide range of contact area. The captured images were then recorded to P.C. for further analysis. The detailed outline of the observation system is shown in Figure 2.
## 4 Discussion
### Prediction Models
In the WQ region, s4_df_e_9 (multiyear average of mean diurnal LST difference in winter) and yr_n_i_9 (multiyear average of annual minimum night LST) were the most influential covariates. Compared with the BQ region, the winter LST seemed to have a more substantial impact on soil PSFs prediction at the WQ region since a total of four winter LST covariates were used in the ILR-RF models. The vegetation (i.e., EVI, NDVI, and GPP) and LST (i.e., yr_n_i_9 and s2_df_a_1/9) covariates are the common predictors used in ILR-RF models at both regions, indicating vegetation and LST were crucial in predicting soil PSFs of the surface layer at both study areas. Many studies found that using machine learning methods can generate soil maps with higher accuracy and smoother transitions compared with polygon linkage approach [22; 63]. Nevertheless, [PERSON] et al. [19] indicated that most studies emphasized the prediction and accuracy of the predicted maps for applications, while few studies accounted for existing soil knowledge in the modeling process or quantified the uncertainty of the predicted maps. Current research still falls short in revealing the underlying mechanism and the internal relationships between the soil properties and potential covariates [19; 26]. Some studies found that even irrelevant predictors, i.e., pseudo predictors, can generate reasonably accurate evaluation statistics of target properties [67; 68]. [PERSON] et al. [69; 70] attributed the reason to scale and information content and proposed the \"information horizon\" for the interpretability of spatial environmental predictors. Besides, pedogenetic knowledge is still crucial as the key step in DSM [25; 26]. However, it is hard for non-expert users to assess the feasibility of a large number of potential covariates [26]. With this respect, [PERSON] et al. [71] and [PERSON] et al. [26] suggested to acquire reference from the formalized covariate selection knowledge in existing applications and applied those covariates into studies of their own. For future developments, machine learning could incorporate three core elements: Plausibility, interpretability, and explainability, which will trigger soil scientists to couple model predictions with pedological explanation and understanding of the underlying soil processes [19]. Expert knowledge can be incorporated into DSM, but DSM can also lead to the discovery of knowledge [26; 72]. The combination of data-driven and knowledge-based methods can promote even more significant interactions between pedology and DSM [72].
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 3 Method and Testing Architecture
To make database querying more efficient, indexes were added to all columns used in queries, and, where necessary, compound indexes were added to further improve response times. To analyze the impact of the optimizations, two applications in Java were implemented, using Intellij IDEA [13], one for relational MySQL, and one for document-based MySQL. Applications were used to carry out several CRUD operations for different amounts of data before applying these optimizations and, after, in order to be able to observe the efficiency and impact on the performance of optimizing the database.