esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Graph-Attention ReadOUT  
\(H\) in Equation (8) can be used as a prior to calculate the spatial attention vector:  
\[\kappa_{space}=s(H) \tag{8}\]  
\[\widetilde{h}_{G}^{\sim}=H\kappa_{space} \tag{9}\]  
where \(s(\cdot)\) is attention function. \(\widetilde{h}_{G}\) is the spatial attention graph representation \(h_{G}\).  
Inspired by the attention mechanism, a novel attention-based readout function is designed. As presented in Figure 3, the attention-based readout module follows the Transformer's attention based on key query embedding, with the key embedding calculated from the node feature matrix \(H\), and the query embedding acquired from the unattended graph representation vector \(H\kappa_{space}\).  
\[\begin{array}{l}K=Q_{key}H\\ q=Q_{query}H\Omega_{mean}\\ \kappa_{space}=\text{{sigmoid}}\Big{(}\frac{q^{T}K}{\sqrt{D}}\Big{)}\end{array} \tag{10}\]  
where \(Q_{key}\in R^{m\times m}\), \(Q_{query}\in R^{m\times m}\) represents a key query parameter matrix that can be learned, and \(K\in R^{m\times m}\) and \(q\in R^{m}\) correspond to an embedded key matrix and an embedded query vector, respectively.  
To enhance the classification accuracy and generalization capabilities of the GIN model, additional regularization techniques are applied to the adjacency matrix, including orthogonalization. In this regard, orthogonal regularization \(L_{OR}\) is operationally defined as follows:\[L_{OR}\ =\left\|1/n\cdot H^{T}H-I\right\|_{2} \tag{11}\]  
where \(k=\max(H^{T}H)\). The scaling term \(1/n\) preserves the orthogonality and equal length of the columns in the matrix H.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.1.5 LU Model  
The LU Model [68] is based on the correction of the rainfall rate modification factor's distribution and an exponential rain cell structure. The comparative results show that it offers the lowest RMS and standard deviation (STD) over the full range of the percentage of the time.  
(1) The distribution of rainfall rate projection on the Earth is defined as:  
\[R(x,p)=R_{\max}(p)e^{-b|x-L_{G}|} \tag{20}\]  
where \(b=1/\rho\) here \(\rho\) (km) is the same radius for which the rainfall rate is fixed \(R_{\max}/e\).  
(2) The attenuation along with the slant link:  
\[A(p)=k[R(p)r(p)]^{\alpha}L_{S} \tag{21}\]  
where \(r(p)\) is the rain rate adjustment factor, and \(R(p)\) is the rain rate exceeded for \(p\%\) of an average year.  
(3) The coefficients \(a_{i}\left(i=1,2,\dots,6\right)\) are taken using the genetic algorithm (GA) and annealing algorithm; thus, exploiting the rain databank of the ITU-R for the slant link:  
\[r(p)=3.78(1-\frac{0.85p^{0.065}}{1+0.12L_{S}})R(p)^{-0.56+\frac{151}{L_{S}}} \tag{22}\]The procedure to determine attenuation by this model is shown in Algorithm 5.  
```
// Assumption: abrupt change more than 1 dB is neglected
1 Compute \(L_{S}\)
2 Calculate \(L_{G}\)
3 Calculate \(R(x,p)\) /* Equation (20) */
4 Determine \(r(p)\) /* Equation (22) */
5 Compute \(A(p)\gets k[R(p)r(p)]^{\alpha}L_{S}\) Return \(A_{p}\)
```  
**Algorithm 5**LU Model [68]  
Advantages  
This model introduces the concept of \"rainfall rate correction factor\", and it shows monotonic attenuation behaviors with an elevation angle compared to other rain attenuation models. The unusual behavior of rain attenuation (such as a nonmonotonic change in attenuation with respect to elevation angle and percentage of time or rainfall rate) was improved.  
Limitations  
The monotonically changing behaviors of rain attenuation are a valuable property of the LU Model, but applicability is limited by low latitudes and low elevation angles.
## 3 Results and Discussion
### Spatial Variations of Air Pollutants  
#### 3.4.1 Differences between Pollutant Concentrations at the Roadside and the On-Site AQMS  
The sub-sites were located approximately 100-400 m apart, so the differences in pollutant concentrations between sites could be attributed to the local traffic and built environments under common meteorological conditions. We considered the relative difference (RD) in air pollutant concentrations between the monitoring sub-sites and AQMS placed on the roof of a 4-story building (\(\sim\)15 m a.g.l.) at Site-4 (\(\sim\)30 m from a single-lane road) (Figure 1). In summer, the RD varied significantly at each sub-site, but CO concentrations were generally 12-37% higher at the roadside sites compared to the AQMS values, indicating an impact of traffic emissions (Table 3). Daytime ground-level ozone was, however, significantly lower at all monitoring sub-sites (including Site-4) than at the AQMS, implying the importance of the chemical loss of ground-level ozone by freshly emitted NO (NO-titration effect). The relative elevation of CO and depletion of O\({}_{3}\) at the roadside locations was more prominent at highly trafficked sites (Sites-1 and 2) than at Site-4, where the traffic flow was minimal. As with CO, the NO\({}_{2}\) concentrations at Site-1 and Site-2 were higher than at the AQMS, but the concentrations at Site-4 were lower than at the AQMS, likely due to the conversion of NO to NO\({}_{2}\) by O\({}_{3}\) or the transport of NO\({}_{2}\)-rich plumes from adjacent roadways to the AQMS. The RD in PM\({}_{2.5}\) was small at all sites, and the AQMS concentrations were slightly higher (3-7%) than at ground level on average, implying the dominance of the secondary formation of PM\({}_{2.5}\) in summer. However, considering the uncertainty of the sensors, we cannot exclude the possibility that these small differences in PM\({}_{2.5}\) might have been caused by errors in the instrumental methods. For the cold wave period in winter, the RD values of air pollutant concentrations between each monitoring sub-site and the AQMS were remarkably different from those in the polluted period (Table 3). For the cold wave period (clean air), the RD of CO at each sub-site was similar to the value in the summer, with only slightly lower values (\(-\)2-24%). The RD of NO\({}_{2}\) was much higher at Site-2 and Site-3 than in summer (112-115% vs. 34%).
###### Abstract
However, there have been only a few precipitation validation studies conducted in South Korea using GPM IMERG data [39; 40; 41; 42], and none of them have directly applied it to flood hydrological analysis. [PERSON] et al. [39] and [PERSON] et al. [40] demonstrated the proof of GPM ground validation studies using the radar network and gauge observations, respectively. [PERSON] et al. [41] assessed precipitation products from GPM using gauge-based precipitation data of Far East Asia during the pre-monsoon and monsoon seasons. [PERSON] et al. [42] implemented a validation of GPM IMERG by comparing it to multiple gauge-based analysis products (e.g., HRPPs and CMORPH) over East Asia. Therefore, it is necessary to conduct a study that explores the application of GPM IMERG precipitation data in hydrologic flood simulation, compares the modeling results with rain gauge observations and ground-based radar precipitation estimates in data-rich regions. With that, the suitability of space-borne satellite radar precipitation data in flood simulations also can be investigated. Hence, this study aims to perform a comparative application of rain gauge, ground- and space-borne radar precipitation products for flood simulations, specifically focusing on evaluating the effectiveness of the half-hourly GPM IMERG final run product using the HEC-HMS model in the Yongdam dam watershed--a representative dam area characterized by mountainous terrain and an abundance of monitoring stations (water level, discharge, etc.) and data in South Korea. By addressing the existing research gaps, this study endeavors to implement a rigorous scientific investigation and analyze the comparative modeling results in regions where observational data are relatively abundant. Furthermore, this study seeks to evaluate the effectiveness of incorporating satellite precipitation data (i.e., GPM IMERG; space-borne radar product) into flood hydrological modeling simulations in regions with limited observational data.
## 5 Discussion
### Model Selection Rationale
At the same time, users can customize holidays or special events according to actual conditions, so as to more accurately capture the impact of these factors on PM\({}_{2.5}\) concentration. This flexibility and the ability to effectively handle seasonal and special events are highly consistent with the characteristics of PM\({}_{2.5}\) concentration changes in Nanning urban area. Therefore, choosing the Prophet model can better analyze the impact of seasonality and special events on PM\({}_{2.5}\) concentration in Nanning urban area city and improve the accuracy of prediction, especially during special periods such as holidays. The LightGBM model is an efficient machine learning framework based on gradient boosting decision trees, which has unique advantages in processing large-scale data and mining complex nonlinear relationships. The change in PM\({}_{2.5}\) concentration in Nanning urban area is the result of the combined action of multiple complex factors. The relationship between these factors is intricate and nonlinear, and it is difficult to accurately describe it with traditional linear models. For example, the relationship between temperature, humidity, wind speed, wind direction and other meteorological factors and PM\({}_{2.5}\) concentration is not a simple linear relationship. Topographic features can also have complex indirect effects on PM\({}_{2.5}\) concentration by affecting the diffusion and transmission of pollutants. By constructing a gradient boosting decision tree, the LightGBM model can automatically learn complex patterns and relationships in the data and effectively mine these potential nonlinear relationships. When processing large-scale data, it exhibits high computational efficiency and low memory consumption, and is suitable for modeling long-term, high-resolution PM\({}_{2.5}\) concentration data in Nanning urban area. In addition, the LightGBM model has good scalability and flexibility, and the model performance can be optimized by adjusting parameters. Based on these advantages, the selection of the LightGBM model helps to reveal the complex driving mechanism behind the changes in PM\({}_{2.5}\) concentration in Nanning urban area, improve the accuracy of predictions, and provide strong support for more accurate predictions of PM\({}_{2.5}\) concentrations.
# Effect of Using Plastic Waste Bottles on Soil Response above Buried Pipes under Static Loads
## 3 Results and Discussion  
The effect of different parameters and experimental testing results are presented in this section. Two non-dimensional parameters named bearing capacity ratio, BCR, and percentage reduction in settlement, PRS, are adopted, similar to the previous studies [2; 30], to evaluate the increase in bearing capacity.  
BCR compares the ultimate bearing capacity of the plastic bottle-reinforced sand (q\({}_{\text{reinf.}}\)) to that of the unreinforced sand (q\({}_{\text{unreinf.}}\)) at the same surface footing settlement and given by Equation (1).  
\[\text{BCR}=\frac{\text{q}_{\text{reinf.}}}{\text{q}_{\text{unreinf.}}} \tag{1}\]  
PRS, defined by Equation (2), compares the settlement of the plastic bottle-reinforced sand (S\({}_{\text{reinf.}}\)) to that of the unreinforced sand (S\({}_{\text{unreinf.}}\)) at the same applied pressure.  
\[\text{PRS}=\left(1-\frac{\text{S}_{\text{reinf.}}}{\text{S}_{\text{unreinf.}} }\right)\times 100 \tag{2}\]  
Depending on the failure criterion stated by [31], the failure is defined for all model tests as the load causing a settlement equivalent to 10% of the width of the model footing. BCR value is calculated when S/B is equal to 10%. This is because (1) the footing settlement of a plastic bottle-reinforced bed is always lower than that of an unreinforced bed, and (2) most of the time, the foundation is designed for practical situations under certain circumstances; large settlements of basis are not accepted and are limited to small values. Nonetheless, the ultimate bearing capacity and BCR ratios reported at S/B = 10% for all tests with various reinforcement circumstances are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Series** & **Pipe** & \begin{tabular}{c} **Reinforcement** \\ **Condition** \\ \end{tabular} & **Variables** & **Constants** \\ \hline Series 1: three & Yes & Unreinforced & DR = 30, 60, and 80\% & — \\ \hline Series 2: nine & Yes & Bottle reinforced & \begin{tabular}{c} DR = 30, 60, and 80\%; \\ u/B = 0.5, 1 and 1.5 \\ \end{tabular} & \begin{tabular}{c} b/B = 1.04 and \\ H/B = 1.4 \\ \end{tabular} \\ \hline Series 3: six & Yes & Bottle reinforced & \
\begin{tabular}{c} DR = 30, 60, and 80\%; \\ b/B = 1.56 and 2.08 \\ \end{tabular} &
\begin{tabular}{c} u/B = 0.5 and \\ H/B = 1.4 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental study plan.
# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 6 Experimental Results and Performance Evaluations
### Calculated Results  
The experimental results were performed using TSODT and a traditional classifier that exploits the built-in sci-kit-learn library in Python. As stated in the explanation of TSODT, it is flexible for the selection of internal decision trees, so we performed decision tree space exploration. We recorded the execution of TSODT for decision trees (1, 2, 3..., 10) and this resulted in the selection of three decision trees (N = 3, input of Algorithm 1) which provided efficient execution. Hence, we used three decision trees for further computation. The achieved results are given in Table 2. The first column presents the type of classifier while the second column shows the execution time (in ms) required for training the data. Columns three to six give the numerical values for the constructed confusion matrix over each classifier, respectively. The remaining columns (i.e., columns seven to eleven) provide different parameters in terms of accuracy, error rate, precision, recall and F score. Therefore, the corresponding values for these parameters (accuracy, error rate, precision, recall, Fscore and AUROC (area under receiver operating characteristic)) in Table 2 are calculated using Equations (A1)-(A5), respectively, as given in Appendix A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Status of the COVID-19 Patients**} & \multicolumn{2}{c}{**Classifier Prediction**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline True (sufferer) & TP & FN \\ \hline False (non-sufferer) & FP & FN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constructed confusion matrix.  
As shown in Table 2, the proposed TSODT algorithm (Algorithm 1) requires 55.6 ms to train the model. The combined utilization of the decision tree and a tabu search algorithms, at the same time, result in 98.1% accuracy. Apart from the higher accuracy and the required execution time, the error rate is only 3.7% (see column eight of Table 2). The additional parameters, i.e., precision, recall, and Fscore, result in more than 97% (as shown in columns nine to eleven in Table 2).
## 3 Results and Discussion
### Image Dataset Establishment
#### 3.1.2 Actual Degraded Image Acquisition  
The acquisition of actual degraded images and their corresponding original images necessitated precise alignment of the optical axis, ensuring that no variation occurred during the process. To accurately replicate a real UDC system, we constructed a holding frame using 3D printing, designed to align with our simulation conditions. This frame allowed us to securely position a commercial smartphone, facilitating the capture of original images. Subsequently, we carefully inserted the UDC panel into the frame, ensuring that there was no alteration to the optical axis or field of view, to obtain the degraded images. The capturing process for the setups without and with the UDC panel are illustrated in Figures 8a and 8b, respectively. This method ensured consistency and accuracy in our image acquisition process.  
Figure 8: Image acquisition moments by fixed camera by holding frame (**a**) without UDC panel, and (**b**) with UDC panel.  
Figure 7: Process of generating virtual degraded images.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Traffic Filtering and Policing  
IEEE 802.1 Qci per-stream filtering and policing (PSFP), also known as ingress policing/gating standard, allows filtering and policing individual traffic streams based on rule matching. This TSN sub-standard protects time-sensitive flows from excessive bandwidth usage, burst sizes, incorrectly configured endpoints, and malicious attacks [39].  
In short, thanks to their identifier, streams are first filtered according to per-stream policies. Then, a gating mechanism regulates the stream. Finally, stream metering ensures bandwidth limitations before a frame is queued for forwarding. To the best of our knowledge, no adaptations of IEEE 802.1 Qci are included in the TGbe roadmap. However, the necessity of a potential extension of this TSN sub-standard to wireless networks has already been pointed out in previous related work [25].  
A traditional approach in IEEE 802.11 to protect time-sensitive traffic is to avoid channel overloading (i.e., to limit the traffic load, the number of traffic flows, and/or the number of STAs allowed to transmit data) in a given band and time period:  
* Whereas IEEE 802.11e admission control mechanisms limit the number of traffic flows per service class in a BSS, IEEE 802.11 aa extends this capability to an entire OBSS. Both could be used by WiFi 7 alone or in combination with IEEE 802.Qci-inherited policies to control how traffic arrives to transmission buffers.
* Multi-link operation and the incorporation of the 6 GHz band in IEEE 802.11 be foresee the emergence of traffic-aware multi-band admission control and policing systems. For instance, under the asynchronous mode of the multi-link operation, and depending on the network conditions and existing load, the 6 GHz band could be fully and exclusively dedicated to time-sensitive traffic. Several multi-link load balancing policies are introduced and evaluated in [28], showing that distributing the traffic over the different interfaces may not be the best approach as it makes the traffic more vulnerable to the activity of the neighboring BSSs.  
Future admission control mechanisms may also be required to support scheduled operation. Thus, in the aforementioned example, the 6 GHz band would become even more exclusive, by only accepting time-sensitive traffic coming from devices able to operate in contention-free mode.
## 3 Compositional Data on Date Seeds and Date Seed Coffee Products
### Minerals  
Several studies have analyzed the mineral composition of date seeds. Table 3 provides an overview of the mineral contents of different sources. Considering that date seeds are used for coffee surrogate preparation and consumed daily in the same recommended intake levels as coffee, generally, no health risk is expected. The mineral intake concentration is within the reference quantity. Compared to coffee and barley-coffee drinks, date seed coffee has the lowest mineral content. The mineral content of date seed coffee is only slightly higher in sodium, iron, and zinc compared to _Coffea arabica_ coffee, but still within the recommended dietary intake level [42]. A high intake of the trace element nickel occurs through the consumption of coffee. However, since date seeds contain less nickel than _C. arabica_ coffee, substituting the daily coffee beverage intake with products made from date seeds does not pose any health risks [43]. Additionally, date seeds are a good source of calcium and potassium [23].  
Mineral contents may vary more between varieties, but, overall, it is unlikely that the recommended daily intake will be exceeded.
## 4 Experimental Verification  
### Experimental Installation of Bearing Faults  
In order to verify the proposed method, this paper used data from the bearing run-to-failure tests on a specially designed test rig under normal load conditions. Four Rexnord ZA-2115 double-row bearings (REXNORD, Milwaukee, WI, USA) were installed on a shaft. The rotation speed was kept constant at 2000 rpm, while at the same time a radial load of 6000 lb was applied onto the shaft and bearing by a spring mechanism. All of the bearings were force lubricated, and their flow and temperature were regulated by an oil circulation system. Debris from the oil was collected by a magnetic plug installed in the oil feedback pipe in order to collect evidence of bearing degradation. The data sampling was 20 kHz and the data length was 20,480 [3].  
Table 2 gives the bearing characteristic frequencies for different faults and the corresponding parameters of the experimental bearing, including the outer-race fault frequency, \(f_{\mathrm{O}}\); the inner-race fault frequency, \(f_{\mathrm{1}}\); the rolling element fault frequency, \(f_{\mathrm{B}}\); the fundamental cage frequency, \(f_{\mathrm{C}}\); and the ball spin frequency, \(f_{\mathrm{BS}}\). These frequencies can be calculated according to the corresponding formula [3].
## 3 Results
### Effects of the 80% EtOH Extracts of BR, BRB, and MBR on Anti-Muscle Atrophy Activity  
The effects of 80% EtOH extracts of BR, BRB, and MBR at 50 and 100 \(\upmu\)g/mL concentrations on the viability of C2C12 myotube cells were evaluated using the XTT assay, and the results revealed that these concentrations had no toxic effects on the cells (Figure 6A). To further evaluate the effects of the BR, BRB, and MBR extracts on the DEX-induced viability of C2C12 myotube cells, extracts with 50 and 100 \(\upmu\)g/mL concentrations were treated with 200 \(\upmu\)M DEX. The results are shown in Figure 6B; cell viability significantly increased in the groups treated with BR, BRB, and MBR compared with that in the DEX group. As shown in Figure 6C, the diameter of myotubes in the DEX group decreased to 62.86% compared with that in the control group. However, when the C2C12 myotubes were co-treated with 100 \(\upmu\)g/mL of the BR, BRB, and MBR extracts and 200 \(\upmu\)M DEX, the myotube diameter increased to 75.39%, 79.70%, and 65.23%, respectively, with the 100 \(\upmu\)g/mL BRB extract showing the most significant effect. Therefore, high concentrations of the 80% EtOH extract of BRB can effectively inhibit DEX-induced myotube atrophy. Similarly, upon co-treating with higher concentrations of the BR, BRB, and MBR extracts and DEX, the fusion index of the C2C12 myotubes increased significantly to 79.89%, 82.96%, and 71.07%, respectively (Figure 6D). In summary, these results demonstrate that relatively high concentrations of the 80% EtOH extracts of BR, BRB, and MBR inhibit DEX-induced C2C12 myotube atrophy, with the 100 \(\upmu\)g/mL BRB extract showing the most significant effect.  
Figure 6: Effects of the 80% EtOH extracts of BR, BRB, and MBR on DEX-induced C2C12 myotube cells in terms of (**A**) cytotoxicity, (**B**) cell viability, (**C**) myotube diameter, and (**D**) fusion index. (**A**) Cytotoxicitywas evaluated at different concentrations of the BR, BRB, and MBR extracts. (**B**) Cell viability of C2C12 myotube cells co-treated with different concentrations of the BR, BRB, and MBR extracts and 200 uM DEX. (**E**) Jenner-Giemsa staining of C2C12 myotube cells, and representative photographs captured under a fluorescent microscope (200\(\times\) magnification). All values are expressed as mean \(\pm\) SD. Bars labeled with different lowercase letters indicate statistically significant differences at \(p<0.05\).