esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband
### Performance Figure Derivation
#### 3.2.4 LDPC Decoder  
The decoder leverages redundant parity bits within each codeword for iterative forward error correction (FEC), operating on the received LLR values. Increasing the number of iterations decreases the probability of bit error propagation further through the RX DBB [42; 43]. However, fewer iterations yield shorter time delays.  
The IEEE 802.11 ad LDPC codewords consist of 672 bits, whereas the resulting data-word length at the decoder's output depends on the code rate. To accurately model the corresponding delays, the performance figures are derived from [44], where the authors report a 5.3 Gbps throughput and a latency of 150 ns for 5-iteration 13/16-code-rate decoding. They also make note of the number of processing cycles consumed per iteration for all code rates, except 7/8. The latter is thus not part of the present study. The described performance figures lead to the delay functions contained in Equations (4) and (5):  
\[TD(R_{c},i)=\left(5.3\,\mathrm{Gbps}\cdot 10^{-9}\cdot\frac{13}{C(R_{c})} \right)^{-1}\cdot\frac{i}{5} \tag{4}\]  
\[PD(R_{c},i)=150\,\mathrm{ns}\cdot\left(\frac{1}{4}+\frac{3}{4}\cdot\frac{C(R_{ c})}{13}\cdot\frac{i}{5}\right) \tag{5}\]  
where \(TD\) and \(PD\) represent the throughput- and data propagation delay, \(R_{c}\) is the selected code rate, \(C(R_{c})\) stands for the number of incurred processing cycles, and \(i\) marks the number of incurred decoding iterations. \(TD\) and \(PD\) both depend on the ratio between \(C(R_{c})\) and the number of iterations for the reference code rate, 13 when \(R_{c}=\frac{13}{16}\). Moreover, they are governed by the quotient between \(i\) and 5, the reference number of iterations studied in [44]. A constant 25% of the reported propagation delay is assumed to be latency caused by buffering, memory access, and I/O operations. The remaining propagation delay factor scales with the code rate and number of iterations.
## 5 Results
### Throughput Analysis without Nonlinear Effects
#### 5.2.1 Rayleigh Channel  
First, we evaluated the UCCF M-MIMO network and compared it against a state-of-the-art network-centric approach under the idealistic i.i.d. Rayleigh channel, which is widely used in the literature, e.g., [4; 7]. The resulting CDFs of the average UE rates under the Rayleigh radio channel for both the network-centric approach and the UCCF M-MIMO network architecture, with varying numbers of RUs serving a single UE, are depicted in Figure 5. The CDF for the network-centric architecture exhibits an interesting staircase shape. The stairs correspond to the maximum user throughput that can be achieved under a given MCS (vertical lines). This is because, under an uncorrelated Rayleigh radio channel, there are no spatial correlations between UEs. More specifically, when a high number of antennas is used, the radio channels between users become orthogonal, and as a result, there is no interference, i.e., the so-called _favorable propagation_ occurs. While the number of UEs is relatively small and each UE can be served by only a single RU, the maximum number of spatial layers is not exceeded. As a result, all UEs experience almost static radio conditions, and in every time slot, the scheduling decision is exactly the same. This is not the case when considering the UCCF approach, where a single UE can create spatial layers within multiple RUs, causing situations where some UEs are not scheduled within a particular time slot (or are granted a limited set of RBs), which affects the decisions of the scheduler in the subsequent time slots. This is why the CDFs for the UCCF M-MIMO network architecture have smooth shapes. However, one can see that only the UCCF architecture with two RUs serving a single UE provides significant gains for users characterized by the worst radio conditions (10 th percentile), without deteriorating the median or 90 th percentile of the average UE rate distribution. Increasing the number of RUs to three, four, five, and six causes significant degradation. This is the result of equal power allocation, i.e., while there is no interference under an uncorrelated Rayleigh radio channel, the system becomes noise-limited. In such a case, the fact that the transmit power must be distributed among more UEs is not always compensated by the benefits of the joint UCCF network. For example, in the extreme case of six RUs, the median gain in the average RSS from the UCCF network is about 1 dB (see Figure 4), but power is always divided among all spatial layers (in the case of micro-RUs, it is about a 9 dB reduction in the average RSS). Moreover, in some situations, the RU allocates the same power to users who are both close and far away from it. As a result, power allocated to the far UE is \"wasted\".
## 2 Basic Theory
### VMD Decomposition Principle
#### 2.1.3 Selecting IMFs and Reconstructing the Signal-Kurtosis  
After signal decomposition by the VMD, choosing the appropriate Intrinsic Mode Functions (IMFs) for signal reconstruction is a crucial research focus. Several criteria, such as correlation [22] and energy content [23], can guide the selection of IMFs. Kurtosis is particularly apt for the selection of IMFs due to its sensitivity to the atypical, transient burst characteristics of PD signals. Researchers already proved the utility of kurtosis in the extraction and identification of significant PD signal denoising [24].  
The kurtosis is calculated by dividing the random variable's fourth central moment by the standard deviation's fourth power [25]. A dimensionless factor assesses how far the signal deviates from the normal distribution. The formula for kurtosis is as follows:  
\[K=\frac{\int_{-\infty}^{+\infty}[x(t)-\bar{x}]^{4}p(x)dx}{\sigma^{4}} \tag{7}\]  
where \(x(t)\) is the instantaneous amplitude, \(\bar{x}\) is the average of the amplitude, \(p(x)\)is the probability density, and \(\sigma\) is the standard deviation.  
The discretization formula is as follows:  
\[K=\frac{1}{N}\sum_{i=1}^{N}\left(\frac{x_{i}-\bar{x}_{i}}{\sigma_{t}}\right)^{4} \tag{8}\]  
where \(x_{i}\) is the value of the signal, \(\bar{x}_{i}\) is the average of the siganl value, \(N\) is the length of the signal, and \(\sigma_{t}\) is the standard deviation.
## 2 Materials and Methods
### Data
#### 2.2.11 ENSO Indices  
The El Nino/Southern Oscillation (ENSO) is used to represent the changes in the equatorial Pacific Ocean and is the main factor of global climate change. ENSO indices are used to characterize the intensity of an ENSO event. Two ENSO indices were selected to represent the El Nino-Southern Oscillation. The first one is the Multivariate ENSO Index (MELv2, [71; 72; 73]). It is calculated every two months by combining the Empirical Orthogonal Function (EOF) of five different variables (sea level pressure, sea surface temperature, zonal and meridional surface wind, and outgoing longwave radiation over the tropical Pacific basin in the area between 30\({}^{\circ}\)S and 30\({}^{\circ}\)N and 100\({}^{\circ}\)E and 70\({}^{\circ}\)W). The second one is the Nino 3.4 [74], representing the average equatorial sea surface temperature (SST) in the region 5\({}^{\circ}\)N-5\({}^{\circ}\)S and 170\({}^{\circ}\)W-120\({}^{\circ}\)W. These two indices were de-seasonalized and normalized by their mean values and standard deviations, respectively.  
Strong El Nino/La Nina events are defined when the ENSO index anomaly is larger than 1 or smaller than \(-\)1 for more than six continuous months. Additionally, for each ENSO index, we put the moderate-to-strong El Nino events (anomaly > 1) and moderate-to-strong La Nina events (anomaly < \(-\)1) together for calculation of the correlations between soil moisture, NEP, CGR, and ENSO, as well as for estimates of the differences in these variables between El Nino and La Nina events in different continents. The neutral ENSO events are then defined as the absolute value of the ENSO index being smaller than 1.
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 4 Cost and Sustainability Evaluation  
### Cost Evaluation  
Several researchers have conducted cost evaluations comparing the use of RHA and LS with traditional stabilizers, like lime and OPC, for clay soil stabilization worldwide. Conducting an extensive cost analysis for the stabilization process involves several factors, including material, transportation, application, performance, and environmental and regulatory costs. RHA and LS are often byproduct materials and can be sourced at low cost in some areas, but lime and OPC are commercially produced, with generally higher raw ma  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **M\({}_{\rm r}\) Values** & **Notes** & **Source** \\ \hline Unstabilized clay & 20–60 MPa & Baseline values before stabilization & [122] \\ Stabilized clay & 100–200 MPa & Significant improvement due to & \\ (RHA + Lime) & (7 days curing) & pozzolanic reactions; values depend & [123] \\  & 150–300 MPa & Long-term curing increases M\({}_{\rm r}\) due & \\  & (28 days curing) & to continued reaction & \\  & \textgreater{}300 MPa (optimized & Observed in some studies with & \\  & mix, extended curing) & well-optimized RHA-lime mixes & [125] \\ \hline \end{tabular}
\end{table}
Table 3: Resilient modulus (M\({}_{\rm r}\)) parameter for non-stabilized and stabilized clay soil.  
terials and production costs. RHA and LS may require less transport cost if sourced locally from agricultural or industrial facilities, but lime and OPC often require transport from production plants, potentially increasing costs, especially for remote sites. The application process for all stabilizers is similar, involving mixing, compaction, and curing. Labor costs are unlikely to vary significantly. RHA and LS may require higher quantities compared to lime and OPC to achieve similar performance, impacting total costs. Additionally, waste material use (RHA and LS) can reduce environmental costs and long-term maintenance. Utilizing RHA and LS supports waste recycling, reduces disposal costs, and potentially qualifies for subsidies or incentives, while OPC production has a high carbon footprint, potentially incurring environmental compliance costs. Summarized cost comparison results from various studies are provided in Table 4.  
These studies demonstrate that using RHA and LS for clay soil stabilization can be a cost-effective alternative to traditional methods, offering both economic and environmental benefits.
## 5 Discussion
### Factors Influencing Sinkhole Formation
This finding supports the conclusion by [99], who indicated that areas near drainage lines are highly sensitive to sinkhole formation. Similarly, many studies in the literature have emphasized that water flow pathways contribute to the expansion of karstic cavities and the formation of sinkholes [4; 14]. A positive correlation between slope (SLP) and elevation (ELV), suggesting that sinkhole formation is more likely in areas with gentle slopes and lower elevations. This finding aligns with previous studies that have highlighted the importance of topographic factors in controlling sinkhole susceptibility [4; 52]. Meteorological factors play a crucial role in understanding the climatic conditions that contribute to sinkhole formation. MAP has been identified as one of the most influential factors in the SHAP analysis. An increase in precipitation elevates groundwater levels, accelerating the dissolution of karstic rocks. This finding is also supported by [108], emphasizing the direct impact of rainfall on karstic processes. Also, variables such as MAT and MAW indicate that temperature and evaporation rates influence groundwater movement. In particular, high temperatures can increase evaporation rates, leading to a drop in groundwater levels and the collapse of karstic cavities [4; 44]. This aligns with the observed sinkhole formations in the study area. Environmental and anthropogenic factors have played a significant role in explaining the impact of human activities on karstic processes. WDS has emerged as a highly significant factor in the SHAP analysis. Intensive groundwater use, particularly wells drilledfor agricultural irrigation, leads to a decline in groundwater levels and deterioration of karstic cavities. This finding supports the view that groundwater extraction accelerates sinkhole formation [40; 41; 44]. Moreover, the variables PST and PRD demonstrate that areas with high human activity expose increased sinkhole susceptibility. This reflects the adverse effects of increased surface load on karstic rocks [1; 59]. The NDVI variable, representing vegetation cover, has shown that vegetation has a slowing effect on karstic processes. A positive correlation was observed between well density (WDS) and proximity to settlements (PST), suggesting that areas with high human activity and intensive groundwater use are more susceptible to sinkhole formation. This finding aligns with previous studies that have highlighted the role of anthropogenic factors in controlling sinkhole susceptibility [40; 41].
## 5 Analysis of Results
### Static Application Validation
It should be clarified that after the 3 h data set processing, the three-dimensional position errors at 30, 60, 120, and 180 min are computed for each day, and then the RMSE values over the three days are computed. As given in Figure 7, the 3D position errors decreased as the observation widow increased, with obvious impacts in the GLONASS, Galileo, and BeiDou models; furthermore, GPS real-time PPP is superior to the GLONASS, Galileo, and BeiDou solutions, with RMSE values less than 0.4 m for all products. On the other hand, the 3D errors are less than 2.0 m for both the GLONASS and Galileo solutions and 2.5 m for the BeiDou solution. For the GPS processing model, BKG, CNE, and WHU products are better than GFZ and IGS products over the four time windows; also, the obtained GLONASS solutions obtained through BKG products are better than those of the other products. Additionally, improvements in the 3D positioning accuracies are obtained with time window increments for CNE, GFZ, IGS, and WHU products. For Galileo 3D RMSEs, similar results are achieved for BKG, CNE, and IGS products, and the attained 3D RMSE values from WHU products obviously decrease with observation time window increment. The GFZ's 3D RMSE values are an exception. Furthermore, WHU products clearly outperform GFZ and IGS products for real-time BeiDou solutions. The three-dimensional positioning accuracy is examined further by calculating the cumulative distribution function (CDF) of the 3D position errors (Figure 8). It can be noticed that for GPS PPP models, about 90% of the 3D position errors are equal to or less than 0.5 m utilizing the five products, with superiority to WHU products; in contrast, 90% of the three-dimensional position are less than 2 m for GLONASS PPP processing model. An exception is the obtained 3D position from WHU products; additionally, about 90% of the Galileo PPP errors are smaller than 1 m for BKG, CNE, and IGS products, and less than 1.5 m for GFZ and WHU products. The significant superiority of the BeiDou 3D accuracy utilizing WHU products compared with GFZ and IGS products is clear. Figure 8: _Cont._  
Figure 7: 3D position RMSE for different PPP solutions using various real-time products.
## 3 Results and Discussion
### Ruggedness  
The results of ruggedness tests are shown in Table 7; the same sample was measured twice for each experiment. By entering the data of Table 7 into the MINITAB statistical package and applying \"General Linear Model\", Table 8 is obtained, which is the analysis of variance table. Table 8 shows the \(p\)-values, which for all four factors (B-E) are greater than 0.05. Therefore, it is found that factor B, C, D, and E do not affect the measurement for 95% significance level. However, factor A (concentration of methanol solution in acetonitrile)  
\begin{table}
\begin{tabular}{|l c c c c c|} \hline \multicolumn{6}{|l|}{**ANOVA: DEHP mg/L versus Day**} \\ Factor Type Levels & Values & & & \\ day & random & 8 & 1; 2; 3; 4; 5; 6; 7; 8 & \\ Analysis of Variance for DEHP mg/L & & & & \\ Source & DF & SS & MS & F & P \\
**day** & 7 & 0.0122750 & 0.0017536 & 2.86 & **0.082** \\ Error & 8 & 0.0049000 & 0.0006125 & \\ Total & 15 & 0.0171750 & & & \\ S = 0.0247487 & R-Sq = 71.47 & R-Sq(adj) = 46.51\% & \\  & & & & Expected Mean \\  & & & & Square for Each \\  & & & & Term (using \\  & Variance & Error & unrestricted \\ Source & component & term & model) \\
1 **day** & 0.00057 & 2 & (2) + 2 (1) \\
2 **Error** & 0.00061 & & (2) \\ \hline \end{tabular}
\end{table}
Table 6: Analysis of variance table for estimation of method precision.  
has \(p\)-value = 0.041 \(<\) 0.05. So, it is found that the deliberate change in factor A affects the measurement for a significance level of 95% [12].  
The solution, with a concentration of 1.00% methanol in acetonitrile, is prepared in a 500 mL volumetric flask by transferring 5.000 mL of methanol and bringing the flask to volume with pure acetonitrile. The transfer of 5.000 mL of methanol is conducted with a 0.500-5.000 mL pipette, which has an accuracy, according to the manufacturer, of \(\pm\)0.5%, which means 5.000 \(\pm\) 0.025 mL. For the preparation of the solution of the deliberate change (0.99%), the same pipette was used, transferring 4.950 mL of methanol. Note that the deliberate change is not exactly the lower limit of accuracy, because the pipette adjustment scale does not allow transfer of 4.975 mL. This particular pipette was internally tested by the laboratory and found to be \(\pm\)0.2% accurate. Therefore, the deliberate change is very large relative to the precision limits of the pipette, and we can consider that such a magnitude of variation is unlikely to occur by chance. However, since this factor has a significant effect on the measurement, the tolerance limits should be indicated in the method instruction and the control method. Finally, it should be noted that, in addition to the accuracy of the pipette, the accuracy of the 500 mL volumetric flask and the deviation of the solution temperature from 20 \({}^{\circ}\)C affect the overall accuracy of the preparation of the 1.00% methanol in acetonitrile solution; however, the two last parameters can be considered negligible.
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 4 Experimental Results  
The results show an improvement in the existing protocol. Our model has successfully calculated all the paths and routed the packets along the calculated path. The system selected the top three distances from all the calculated paths. The verification and validation of CRPR are discussed below.
## 3 Characterization of Position-Tracking Error in Robotic Catheter Systems  
Smooth and safe tool manipulation requires developing a precise position-tracking ability in the RCS presented above. Position tracking involves iterative geometric mapping between the axial commands (viz. linear or angular displacement) issued via the master device and the actual response from the slave device. This suitably helps to maintain steady translation and rotation movements between the master and slave platforms for robot-assisted catheterization. First, we characterized the RCS parts to establish the presence of motion lag or backlash gaps between the master and slave platforms. Existing studies hypothesized sources of motion lags in RCSs to be from actuated gears, belts, and pulley configurations in linear systems and that become evident during axial translations [21, 37]. Practically, this could cause nonlinear but observable marked differences between the motion applied at the master device and the actual cannulation achieved with the slave side. Although new technologies in linear drive systems seek to produce low-backlash devices, none can eliminate the occurrence of position error when using the master-slave setup for R-PCI.  
To characterize this situation in our RCS, we performed several experiments to observe the tracking errors between the master-slave platforms. This includes navigation trials performed with varying conditions, i.e., by issuing different position values at the master side and other trials under constant displacement. In all trials, higher-order motion data were derived from the position and velocity outputs from the in-built encoders' readings.
## 5 Conclusions  
The problem of driver fatigue detection has been discussed in this paper. The proposed approach comprises both feature extraction and classification tasks. The feature extraction task has been accomplished using FFT and DWT. The classification task has been performed using machine learning and deep learning methods including SVM, RF, LR, MLP, QDA,  
Figure 18: Brief Comparison of The Proposed System in different scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Year** & **Feature Extractor** & **Classifier** & **Accuracy** \\ \hline
**[PERSON] et al. [40]** & 2014 & Multimodal Analysis & ANN & 83 \\
**[PERSON] et al. [41]** & 2015 & FFT & ANN & 90 \\
**[PERSON] et al. [14]** & 2016 & AE and SE & SVM & 90 \\
**[PERSON] et al. [15]** & 2016 & Entropy Rate Round & & 88.2 \\
**[PERSON] et al. [16]** & 2017 & FE & SVM & 95 \\
**[PERSON] et al. [25]** & 2020 & DNN & DNN & 73 \\
**[PERSON] et al. [42]** & 2020 & Deep Transfer Learning & Deep Learning & 93 \\  & & FFT + DWT & SVM & 96 \\
**Proposed** & 2023 & FFT + DWT & RF & 96 \\  & & FFT + DWT & LR & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Brief Comparison among The Proposed Models and Works in The Literature.  
KNN, DT, 1D CNN, and C-CNN. In addition, the proposed models are carried out on EEG, EOG, EMG, and ECG signals, which are included in the DROZY dataset. Furthermore, two scenarios have been carried out for the classification, multiclass and binary-class scenarios. The proposed method achieves an accuracy of 96% with superior performance among the proposed efforts prior to fatigue detection.  
However, the proposed approach has a drawback which is represented in the low performance of deep learning methods. Therefore, the performance can be enhanced in future work with some ideas. Using different feature selection and feature engineering algorithms will improve the performance by selecting some features with optimal importance. Another type of enhancement is to employ different optimization techniques to improve the performance of the deep learning approach. Furthermore, recurrent models can be considered and tested by the DROZY dataset.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing--original draft preparation, A.S. and M.M.; writing--review and editing, visualization H.M. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R137): Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  
The authors declare no conflict of interest.