esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 5 Results and Analysis
### Analysis of Variance (ANOVA)
#### 5.3.2 ANOVA of Latency  
The factors and levels considered for the latency variance analysis are shown in Table 9. Routing protocols had three (3) levels, traffic distribution had five (5) levels, and traffic density had four (4) levels, as listed in the table.  
For the latency results, the ANOVA is shown in Table 10. From the table, we can observe that the main effects contribute 92.7% of the variation in the latency results. The traffic density (D) has the highest impact, with 91.1% of the variation. While the routing protocol (RP) has the least contribution with 0.2%. Traffic distribution (T) contributes 1.4% of latency variation. Additionally, the highest interaction is between the traffic density and distribution that has the most significant effect with 3.94% variation on the latency results compared to any other interaction combination. This indicates that the above factors interact to influence the latency result. The interaction between all three factors accounts for only 1.9% of the variation. The Q-Q plot of latency residuals is shown in Figure 18. We can observe that the residuals fall evenly around the least-squares line forming a trend with a heavy tail due to outliers. Hence, our simulations are concrete, and the variations in the results are not due to randomness.  
Figure 17: Quantile—Quantile Plot of Trip Time Residual.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Factor**} & \multicolumn{5}{c}{**Levels**} \\ \cline{3-8}  & & **1** & **2** & **3** & **4** & **5** \\ \hline RP & Routing Protocols & AODV & DSDV & DYMO & & \\ T & Traffic Distribution & 0:100 & 25:75 & 50:50 & 75:25 & 100:0 \\ D & Traffic Density & 500 & 1000 & 2500 & 5000 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Factors and their respective levels for latency.
## 2 In Situ Acoustic Emission (AE) Monitoring--Fundamentals
### Box 3. Comparison of accelerometer and piezoelectric AE sensor.
As data needs to be processed and stored on computer hard drives some dead time in recording occurs in between different windows. In the trigger mode approximately 10 AE events per second can be recorded. The signals of AE events are recorded when a specified threshold is crossed on one or more channels. As mentioned above, continuous recording of the waveform is possible in the last few years, as large computer storage is now available. Typical sampling rates are 500 kHz to 1 MHz. The P-wave and S-wave onsets are automatically picked after band-pass filtering of the traces by applying an adapted, speed-optimized short-term-average to long-term-average (STA-to-LTA) trigger algorithm [89]. After picking of the onsets, a least-square algorithm based on a gradient method is used to determine the location of the AE events. A location is valid if a sufficient number of P-wave and S-wave onsets are used for source location and the time residuals are small enough. With this location procedure working, noise without discernible onsets can be eliminated. For post-analysis, the digitized waveforms and location results are stored on hard disk. With fast Internet access, the AE measuring system can be remotely controlled. Transient noise of anthropogenic or electronic origin on the other hand is often localized and can pollute the seismic catalog, especially in active mines [36; 38; 87].
## 3 Results  
### In-Silico Assessment of the Proposed Variable Ranking Algorithm  
#### 3.1.1 Results on a Representative Simulated Dataset  
The results of the variable ranking obtained for the representative dataset of Section 2.2.2 are reported in Table 4 for both the standard RFE-Borda count method and the proposed algorithm, performed on B = 100 training set variants generated by bootstrap resampling. We can observe that the standard RFE-Borda count approach, which ignores variable correlation, commits some ranking mistakes: x\({}_{2}\) is ranked below x\({}_{3}\); x\({}_{5}\) is ranked in the 6 th position, below x\({}_{6}\); x\({}_{8}\) is ranked in the 9 th position, after x\({}_{15}\); x\({}_{9}\) and x\({}_{10}\) are ranked in the 14 th and the 18 th position, respectively, and they are surpassed in the ranking even by noise variables, such as x\({}_{18}\), x\({}_{19}\), and x\({}_{20}\). Conversely, the ranking obtained by the proposed approach, which considers the variable correlation, is almost completely correct; the only ranking mistake is that, also in this case, x\({}_{8}\) is ranked in the 9 th position, after x\({}_{9}\) and its correlated feature x\({}_{15}\). We can note that the proposed algorithm always assign the same rank to the highly correlated variables. Overall, the RMSE obtained is 3.07 for the standard RFE-Borda count method and 0.50 for the proposed approach.
## 2 Design of the Proposed DDL-TDC
### Calibration Flow of DDL-TDC  
Figure 5 illustrates the timing diagram of the proposed DDL calibration. In the beginning, the TDL-A takes \(2^{9}=512\) cycles to reset _Cal_Mem A_ and then uses \(2^{17}=131\),072 cycles to count the hits of code density testing in the _Cal_Run_ state of the TDL-A calibration FSM. Finally, TDL-A uses \(2^{9}=512\) cycles to obtain the calibration map and stores the parameter in _Cal_Mem A_. For these (\(2^{9}+2^{17}+2^{9}\)) cycles, TDL-TDC A executes calibration, and TDL-TDC B runs the time conversion at the same time. The operation changes for the next (\(2^{9}+2^{17}+2^{9}\)) cycles; in those cycles, the TDL-TDC A runs time conversion, and the TDL-TDC B executes calibration. As TDL-TDC A executes the calibration, _cal_start_ and _cal_stop_ connect to _Start_A_ and _Stop_A_; the input signals _Trigger 1_ and _Trigger 2_ connect to the input signals of TDL-TDC B (_Start_B_ and _Stop_B_). In this work, we chose 6 ns as our clock period. Thus, the calibration time of TDL-TDC A is \((2^{9}+2^{17}+2^{9})\times 6\) ns = \(792,576\) ns \(\simeq 0.8\) ms. In this 0.8 ms, TDL-TDC A executes the calibration, and TDL-TDC B runs the time conversion. However, in the next 0.8 ms, TDL-TDC B executes the calibration, and TDL-TDC A runs the time conversion. Consequently, the proposed DDL-TDC performs calibration every 0.8 ms. This time is short enough to adapt to changes in the environment; the ongoing conversion is suitable for ToF LiDAR applications.  
Figure 4: Finite state machine used in DDL-TDC.  
Figure 5: Timing flow of the calibration in the proposed DDL-TDC.
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 5 Conclusions
This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A5A8026986 and No. 2022R1F1A1073745), Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0020536, HRD Program for Industrial Innovation), and the Institute of Information & Communications Technology Planning & Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded (20%) by the Korea government (MSIT) (IITP-2025-RS-2020-II201462). Not applicable. The DTU dataset used in this study is publicly available at [[https://surfsplating.github.io/](https://surfsplating.github.io/)]([https://surfsplating.github.io/](https://surfsplating.github.io/)) (accessed on 1 March 2025). The Mip-NeRF 360 dataset used in this study is publicly available at [[https://jonbarron.info/mipnerf360/](https://jonbarron.info/mipnerf360/)]([https://jonbarron.info/mipnerf360/](https://jonbarron.info/mipnerf360/)) (accessed on 1 March 2025). The Tanks and Temples dataset used in this study is publicly available at [[https://github.com/hbb1/2d-gaussian-splatting](https://github.com/hbb1/2d-gaussian-splatting)]([https://github.com/hbb1/2d-gaussian-splatting](https://github.com/hbb1/2d-gaussian-splatting)) (accessed on 1 March 2025). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
3 DGS 3D Gaussian Splatting  
2 DGS 2D Gaussian Splatting  
DDGS  Dual-Dimensional Gaussian Splatting  
TnT  Tanks and Temples dataset  
NVS  Novel view synthesis  
TSDF  Truncated Signed Distance Fusion  
MLP  Multi-layer perceptron  
SLAM  Simultaneous localization and mapping  
SuGaR  Surface-Aligned Gaussian Splatting  
PSNR  Peak signal-to-noise ratio  
SSIM  Structural similarity index measure  
LPIPS  Learned Perceptual Image Patch Similarity  
CD  Chamfer distance
# Polymer-Stabilized Silver (Gold)-Zinc Oxide Nanoheterodimer Structures as Antimicrobials
## 2 Materials and Methods
### Methods
#### 2.3.2 Antimicrobial Activity Tests  
The determination of the minimum inhibitory concentrations (MICs) of the preparations in relation to the strain of microorganisms used was carried out using a serial microdilution method in accordance with the standard procedure [84]. Initial concentrations of VM/Ag\({}^{0}\), SM/Ag\({}^{0}\), and EM/Ag\({}^{0}\) were 1.35, 1.35, and 1.5 mg/mL, respectively; VM/Ag\({}^{0}\)/ZnO, SM/Ag\({}^{0}\)/ZnO, and EM/Ag\({}^{0}\)/ZnO were 1.9, 1.9, and 1.4 mg/mL, respectively; and VM/Au\({}^{0}\) and VM/Au\({}^{0}\)/ZnO were 1.7 and 1.8 mg/mL, respectively. The description of the experiment is given in the protocol published elsewhere [79]. Briefly: The tests were carried out in 0.2 mL volume using sterile 96-well standard polystyrene plates. The final concentration of the investigated microorganism is 105 CFU/mL. Trypticase-soy broth (Bio-Rad Laboratories, Hercules, CA, USA) was poured into 0.1 mL in each well of the plate. The number of wells was determined by the necessary dilution range, the last well was left for setting negative control. The solutions of studied composites in fourfold ratio to the above-mentioned concentrations in the volume of 0.1 mL were pipetted into the first well containing 0.1 mL of broth.  
After mixing, 0.1 mL of this mixture was pipetted to the second well, also initially containing 0.1 mL of broth and so on. The procedure was repeated until a sufficient number of dilutions were made. For inoculation, a microbial suspension of test microorganisms prepared on 0.45% saline solution was used. A measure of 0.1 mL of the inoculum was introduced into testing wells with the appropriate dilution of the composite, as well as in the last well with a nutrient broth without the test preparation (positive control). The plates covered with a sterile film were incubated at 35 \({}^{\circ}\)C overnight (48 h for _C. albicans_). The culture growth in the presence of the composites was compared with the reference wells without the composites (positive control) under visual control (wells in plates were viewed in transmitted light). The MICs of the samples were determined by the lowest concentrations of preparations in the well, which suppress the apparent growth of the microorganisms used. Statistical processing of data based on the results of 3 measurements was carried out.
## 4 Analysis and Results
### Data Analysis of Teachers' STEM Education Intention
#### 4.2.1 Reliability and Validity Tests
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Constructs** & **Cronbach’s** & **Composite** & **Aplha** & **Composite** & **AVE** & **Items** & **Standardized** & **SMC** \\ \hline  & & & & & AT1 & 0.804 & 0.646 \\ \cline{4-6}  & & & & & AT2 & 0.899 & 0.808 \\ \cline{4-6}  & & & & AT3 & 0.916 & 0.839 \\ \cline{4-6}  & & & & AT4 & 0.857 & 0.734 \\ \cline{4-6}  & & & & AT5 & 0.899 & 0.808 \\ \hline  & & & & PU2 & 0.843 & 0.711 \\ \cline{4-6}  & & & & PU3 & 0.921 & 0.848 \\ \cline{4-6}  & & & & PU4 & 0.911 & 0.830 \\ \cline{4-6}  & & & & PU5 & 0.92 & 0.846 \\ \hline  & & & & PEU1 & 0.713 & 0.508 \\ \cline{4-6}  & & & & PEU3 & 0.849 & 0.721 \\ \cline{4-6}  & & & & PEU4 & 0.732 & 0.536 \\ \hline  & & & & CO1 & 0.655 & 0.429 \\ \cline{4-6}  & & & & CO3 & 0.665 & 0.442 \\ \cline{4-6}  & & & & CO4 & 0.868 & 0.753 \\ \cline{4-6}  & & & & CO5 & 0.843 & 0.711 \\ \hline  & & & & SN1 & 0.925 & 0.856 \\ \cline{4-6}  & & & & SN2 & 0.912 & 0.832 \\ \cline{4-6}  & & & & SN3 & 0.845 & 0.714 \\ \cline{4-6}  & & & & SU2 & 0.751 & 0.564 \\ \cline{4-6}  & & & & SU14 & 0.848 & 0.719 \\ \cline{4-6}  & & & & SU15 & 0.901 & 0.812 \\ \hline  & & & & & CI1 & 0.931 & 0.867 \\ \cline{4-6}  & & & & CI2 & 0.767 & 0.588 \\ \cline{4-6}  & & & & CI5 & 0.895 & 0.801 \\ \hline  & & & & STI1 & 0.889 & 0.790 \\ \cline{4-6}  & & & & STI2 & 0.938 & 0.880 \\ \cline{4-6}  & & & & STI3 & 0.962 & 0.925 \\ \cline{4-6}  & & & & PA1 & 0.822 & 0.676 \\ \cline{4-6}  & & & & PA2 & 0.837 & 0.701 \\ \cline{4-6}  & & & & PA3 & 0.939 & 0.882 \\ \hline  & & & & PBC2 & 0.923 & 0.852 \\ \cline{4-6}  & & & & PBC3 & 0.901 & 0.812 \\ \cline{4-6}  & & & & PBC4 & 0.891 & 0.794 \\ \cline{4-6}  & & & & PBC5 & 0.818 & 0.669 \\ \hline  & & & & SE1 & 0.921 & 0.848 \\ \cline{4-6}  & & & & SE2 & 0.936 & 0.876 \\ \cline{4-6}  & & & & SE3 & 0.875 & 0.766 \\ \cline{4-6}  & & & & SE4 & 0.76 & 0.578 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Convergent validity analysis in CFA (confirmatory factor analysis). Structural model analysis  
In order to evaluate the model fit, according to the research of [PERSON] [55], [PERSON] et al. [56], [PERSON] [57], and [PERSON] [58], we applied the comparative fit index (CFI), incremental fit index (IFI), Tucker-53 Lewis index (TLI), chi-square (x\({}^{2}\))-to-degree of freedom (df) ratio, root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR) values. The traditional rule of thumb in CFA (confirmatory
## 2 System Model
### Channel  
In this paper, the authors have considered the geometrically expanded Saleh-Valenzuela model [45]. The channel \(\mathbf{h}_{d,k}\) (\(\in\mathbb{C}^{N_{T}\times 1}\)) associated with the \(k^{th}\) UE within the \(d^{th}\) is considered to be the cumulative effect from \(\mathcal{L}_{b,k}\) and can be expressed as  
\[\mathbf{h}_{d,k}=\sqrt{\frac{N_{T}}{\mathcal{L}_{d,k}}}\sum_{l=1}^{\mathcal{ L}_{d,k}}\mathbf{a}_{d,k}^{(l)}\mathbf{a}\Big{(}\Theta_{d,k}^{(l)},\Phi_{d,k}^{(l )}\Big{)}. \tag{5}\]  
where \(\mathbf{a}_{d,k}^{(l)}\) reflects the complex gain within the \(l^{th}\) path and corresponds to the \(k^{th}\) UE within the \(d^{th}\) beam. Furthermore, it has been presumed to have complex Gaussian characteristics with a mean of 0 as well as a variance of 1. \(\sqrt{\frac{N_{T}}{\mathcal{L}_{d,k}}}\) symbolizes the normalization factor. Herein, \(\Theta_{d,k}^{(l)}\) as well as \(\Phi_{d,k}^{(l)}\) symbolize the azimuth as well as the elevation angle of departure (AOD) pertaining to the \(l^{th}\) path. The normalized array response vector is introduced by the factors \(\mathbf{a}\Big{(}\Theta_{d,k}^{(l)},\Phi_{d,k}^{(l)}\Big{)}\). Consider the \(N_{T}\) elements uniform linear array (ULA), wherein \(\mathcal{N}_{1}\) elements are arranged in a horizontal direction as well as \(\mathcal{N}_{2}\) elements in a vertical direction [\(N_{T}=\mathcal{N}_{1}\mathcal{N}_{2}\)].  
Under such element distribution, the array response vector \(\mathbf{a}\Big{(}\Theta_{d,k}^{(l)},\Phi_{d,k}^{(l)}\Big{)}\) can be conveyed as  
\[\mathbf{a}\Big{(}\Theta_{d,k}^{(l)},\Phi_{d,k}^{(l)}\Big{)}=\mathbf{a}_{az} \Big{(}\Theta_{d,k}^{(l)}\Big{)}\otimes\mathbf{a}_{el}\Big{(}\Phi_{d,k}^{(l)} \Big{)} \tag{6}\]  
where  
\[\mathbf{a}_{az}\Big{(}\Theta_{d,k}^{(l)}\Big{)}=\frac{1}{\sqrt{\mathcal{N}_{1}} }\Big{(}1,e^{\text{j}\text{e}\text{j}\text{e}\text{j}\text{e}\text{i}\text{e} \text{j}\text{e}\text{i}\text{e}\text{j}\text{e}\text{j}\text{e}\text{j}\text{e }\text{j}\text{e}\text{j}\text{e}\text{j}\text{e}\text{j}\text{e}\text{j}\text{e }\text{j}\text{e}\text{j}\text{e}\text{j}\text{e}\text{j}\text{e}\text{j}\text{e} \text{j}\text{e}\text{jthe BS as well as the UEs have perfect knowledge of CSI. The authors have also tested the proposed system under the imperfect CSI condition. As a part of the channel modeling and further exploration, the authors have considered that all the arrivals are Nakagami-m distributed and have tested the proposed system under the expanded Saleh-Valenzuela channel with the Nakagami-m distributed small-scale fading model.
## 3 Conventional and Proposed Methods  
### Conventional Methods  
#### 3.1.1 Equal Power Allocation Method  
The equal power allocation method is a simple approach in which each transmitting antenna transmits signals with equal power. This method does not consider channel state information (CSI), and the same power is allocated to all antennas.  
When the total transmission power \(P_{\text{total}}\) is equally distributed among all antenna elements, the transmission power of each antenna \(P_{i}\) is expressed as follows:  
\[P_{i}=\frac{P_{\text{total}}}{N}. \tag{19}\]  
When each element of the \(M\times N\) planar array antenna transmits signals to the receiving end, a phase shift is required depending on the position of each antenna element. When the signals from each antenna element \((m,n)\) interfere and combine at the receiving end, the received signal power \(R_{\text{rec,equal}}\) is expressed as follows:  
\[R_{\text{rec,equal}}=\left|\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N}\sqrt{ \frac{P_{\text{total}}}{N}}\cdot h_{m,n}\cdot e^{i\theta_{m,n}}\right|^{2}. \tag{20}\]  
In this context, \(h_{m,n}\) represents the channel gain of the antenna element \((m,n)\), and \(e^{i\theta_{m,n}}\) denotes the phase shift term for beamforming, which is determined based on the position of each antenna element and the transmission angle. Further details are provided in Section 3.2.2 regarding the adaptive selection method of \(\beta\).
## 5 Conclusions  
This paper proposed a novel model and method for spiral level gear transmission efficiency optimization. Two optimal steps are conducted, and several conclusions could be drawn according to the simulation and research:  
(1) A hybrid PSOGSA algorithm for SBG transmission efficiency was proposed, considering the loaded contact analysis and lubrication conditions. The proposed model combined geometric analysis, mechanical analysis, and lubrication analysis in an efficient way.  
(2) Transmission efficiency optimization was realized by two steps. One is control parameter optimization, which is of high speed and has a flexible wide range. Another is machining parameter optimization, which contains more machining parameters and higher accuracy. These two steps could achieve fast convergence and global optimal search.  
(3) According to the numerical example, the transmission efficiency of spiral level gear pairs could increase to 98.78%, an increase of more than 4%, based on two-step optimization. The factors with influence on transient effects are definitely improved, including entertainment speed, relative sliding velocity, friction coefficient, etc.  
The present method and model could also be applied into other gear design processes or even other fields. However, it should be pointed out that the determination of the optimization range should also take load bearing strength and other performance indexes into account to guarantee the rationality of the optimal design. The results also need verifications by experiments in further studies in the future.  
Conceptualization, X.H., S.-K.L. and Z.L.; Data curation, S.G.; Funding acquisition, S.-K.L.; Methodology, X.H.; Project administration, S.-K.L.; Resources, Z.L.; Supervision, S.-K.L. and R.Z.; Writing-original draft, X.H., S.G. and L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2020 YFB2010200), National Natural Science Foundation of China (No. 52105060), Natural Science Foundation of Jiangsu Province (No. BK20200428), and the Basic Science Research Program through the NRF of Korea (NRF) funded by the MEST (NRF-2020R1A2C1011958).  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Numerical Results
### Capture Probabilities  
For showing the capture probabilities under Rayleigh, Rician and Nakagami-\(m\) fading, we plot the analysis results in Figure 2a, Figure 2b and Figure 2c according to (11), (22) and (30), respectively. In addition, we also give the simulation results in Figure 2, which accord with the analysis results. Besides, we give Table 1 for some special values of capture probabilities.  
Figure 2a shows the capture probabilities with different values of capture ratio \(z\) when the values of \(n\) (the number of packets transmitted in the same slot) are 2, 3 and 4 in Rayleigh fading channel. As we can see from the figure and Table 1, the capture probability decreases when the capture ratio \(z\) increases. Furthermore, if \(n\) increases, the capture probability decreases as well and decreases faster as a function of \(n\) than \(z\).  
Figure 1: Each user randomly chooses a slot in the frame of length \(L\) when the number of active users (\(N\)) = 8.  
According to (22), the capture probability in Rician fading channel depends on the factor \(K\), the capture ratio \(z\) and \(n\). From Figure 2b and Table 1, the capture probability decreases when \(z\) or \(n\) increases like that of Rayleigh fading. In addition, the capture probability decreases as the Rician factor \(K\) increases. Because larger \(K\) means that the ratio of the power of LOS component to the average power of scattered components is higher, which results in a lower possibility that the condition for capture is satisfied.  
Also, we plot the capture probabilities in Nakagami-\(m\) fading channel based on (30), which are decided by the shape parameter \(m\), the capture ratio \(z\) and \(n\). As seen in Figure 2c and Table 1, the capture probability decreases when \(z\) or \(n\) increases like that in Rayleigh and Rician fading. Moreover, the capture probability also decreases as the shape parameter \(m\) increases. That is to say larger \(m\) makes the condition for capture is more difficult to achieve.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fading**} & \multicolumn{3}{c}{\(z=2\)} & \multicolumn{3}{c}{\(z=4\)} & \multicolumn{3}{c}{\(z=10\)} \\ \cline{2-10}  & \(n=2\) & \(n=3\) & \(n=4\) & \(n=2\) & \(n=3\) & \(n=4\) & \(n=2\) & \(n=3\) & \(n=4\) \\ \hline Rayleigh & 0.667 & 0.333 & 0.148 & 0.400 & 0.120 & 0.032 & 0.182 & 0.025 & 0.003 \\ Rician & 0.506 & 0.123 & 0.022 & 0.209 & 0.020 & 0.001 & 0.061 & 0.002 & 0.000 \\ Nakagami-\(m\) & 0.584 & 0.210 & 0.063 & 0.285 & 0.048 & 0.007 & 0.091 & 0.005 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Capture probabilities under different fading channels for \(z=2,4,10\). (\(K=3\), \(m=1.5\)).  
Figure 2: Capture (cap) probabilities when \(n=2,3,4\). (**a**) Rayleigh (Ray) fading; (**b**) Rician (Ric) fading for \(K=2,3\); (**c**) Nakagami-\(m\) (Nak) fading for \(m=1,1.5,3\). Ana., analysis; Sim., simulation.