esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
To assess the rheological characteristics of plasticized PVC formulations, the melt flow index (MFI) serves as a critical parameter. This index is commonly utilized to evaluate the recyclability of polymer materials and to inform processing method selection. In this study, we investigated the rheological properties of PVC compositions that included a blend of dioctyl phthalate (DOP) plasticizer and various adipate mixtures. Specifically, a DOP to adipate ratio of 5:1 (by weight) was employed in preparing the PVC composition. The temperature-dependent behavior of the MFI for these PVC compositions was analyzed under the following formulation (in weight hours): PVC--100; plasticizer--100; TBLS (tertiary butyl lithium sulfate)--1. The composition of the plasticizer was as follows: DOP--83.3; adipate--16.7. The applied load during testing was set at 49 N (Figure 4). This investigation highlights how different plasticizers, particularly those based on adipic acid, can significantly impact the flow characteristics and processability of PVC composites. Previous studies have shown that the introduction of environmentally friendly plasticizers can enhance the melt flow properties compared to traditional phthalate-based options, indicating a shift towards more sustainable materials in polymer applications. Experimental studies have demonstrated that increasing temperature significantly enhances the fluidity of PVC melt compositions, independently of their specific formulations, exhibiting a nearly exponential relationship. Notably, the incorporation of DOP with adjates in PVC formulations does not diminish the melt fluidity when compared to similar mixtures containing DOP solely. In fact, mixtures of DOP with esters tend to increase the melt flow index (MFI) of polyvinyl chloride compounds. Among the additives, adjates featuring aliphatic alcohol groups show superior melt fluidity within the examined temperature range. Conversely, compositions utilizing adipate with aromatic alcohol groups display intermediate fluidity values between those of industrial variants like DOA and DOP. The synthesized derivatives of adipic acid exhibit a pronounced plasticizing effect on PVC, primarily characterized by an enhancement in the fluidity of the PVC melt. This suggests that these adipate derivatives could be advantageous in optimizing processing conditions and improving the performance characteristics of PVC-based materials. In general, fundamental parameters such as the solubility, compatibility, polarity, molecular weight and functional groups of plasticizers play a crucial role in determining plasticizing properties [44; 45; 46]. The structure of the substituent reduces the glass transition  
Figure 7: Dependence of the MFI of PVC composition on temperature. temperature, and the volumetric end fragments separated by a linear chain effectively separate the macromolecular chains of the polymer [47; 48; 49].
## 2 Algorithmic Basis for Urban Residential Vacancy Rate Observation
### Application of the Algorithm in Urban Residential Vacancy Rate Observation
For example, by performing deep learning processing on remote sensing images, the residential vacancy behind the nighttime light changes can be more accurately identified; combined with drone aerial photography technology, the changes in residential areas can be monitored in real time, providing dynamic data support for urban planning; the combination of the Internet of Things and data analysis algorithms can collect and analyze residential electricity and water consumption data in real time, further refining the observation of vacancy rates. The comprehensive application of these technologies will undoubtedly provide more accurate and efficient technical means for observing urban residential vacancy rates and provide a scientific decision-making basis for the healthy development of cities.
## 3 Analysis and Discussion
### Study on GRU Parameter Settings
#### 3.1.2 Sensitivity Studies on the Hyperparameters  
Apart from the network structure, the hyper-parameters of the proposed network (e.g., dropout ratio and learning rate) have an important influence on the model's performance [79].  
Dropout RatioDropout is currently widely used in various types of deep neural networks, which prevents network overfitting by randomly dropping a part of neurons during training [80] and improves model performance. In RNN, no temporal dimensional connections are chosen randomly to be dropped in order not to impair the network's ability to remember in the temporal dimension. Since parameter tuning involves much computation, we chose the typical eight network structures (networks 4, 6, 7, 9, 10, and 13) for our study. The dropout is tested in the input, hidden, and output layers of the GRU, respectively, ranging from 0.05 to 0.6, with 12 samples at a uniform interval of 0.05, which gives a total of 216 networks tested. Only the test results for the two network structures are shown in Figure 6 to keep things simple since the other groups follow similar trends.  
According to Figure 6, trends can be summarized as the change in dropout ratio does not make the GRU model accuracy change significantly for the selected range, and the dropout is not a key factor for the GRU network to recognize the input features. Nevertheless, on the whole, the input layer is more sensitive to dropout changes, and the degradation of GRU model validation performance is more significant. Therefore, to prevent potential overfitting and ensure model performance, the dropout ratio is set at 0.5 for the output layer and 0 for the input and individual hidden layers in this section.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Number of Layers** & **Number of Cells in Each Layer** & **Accuracy (\%)** & **Parameters on Graph** \\ \hline
1 & 1 & 32 & 86.94 & 4581 \\
2 & 1 & 64 & 87.19 & 15,301 \\
3 & 1 & 128 & 87.38 & 55,173 \\
4 & 1 & 256 & 87.62 & 208,645 \\
5 & 2 & 32 & 87.54 & 10,917 \\
6 & 2 & 64 & 87.69 & 40,261 \\
7 & 2 & 128 & 88.06 & 154,245 \\
8 & 2 & 256 & 87.44 & 603,397 \\
9 & 3 & 32 & 87.62 & 17,253 \\
10 & 3 & 64 & 87.37 & 65,221 \\
11 & 3 & 128 & 87.53 & 253,317 \\
12 & 3 & 256 & 87.27 & 998,149 \\
13 & 4 & 32 & 88.30 & 23,589 \\
14 & 4 & 64 & 87.56 & 90,181 \\
15 & 4 & 128 & 87.19 & 352,389 \\ \hline \end{tabular} _Note:_ Values of other hyperparameters are as follows: Learning rate = 0.001; batch size = 128; maximum iterations = 100; dropout ratio = 0.3 for the output layer, 0 for the input and hidden layers.  
\end{table}
Table 1: Network configurations, accuracies (validation set), and the number of parameters on the graph.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 3 Proposed Method
### Vision Transformer
Compared with ViT, DeiT-S introduces a new distillation procedure based on a distillation token, which plays the same role as the class token to reproduce the label estimated by the teacher. Benefiting from the knowledge distillation strategy, DeiT has fewer parameters and computation consumption.
## 4 Results
### _eTL_ and _iTL_ Variables According to the Game Situation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Load** & **Variable** & **Game Situation** & \(M\) & \(SD\) & \(X^{2}\) & \(p\) & \(E^{2}R\) \\ \hline Acc & No opposition & 125.54 & 54.72 & 174.628 & 0.00 * & 0.12 \\  & Individual game & 117.22 & 54.96 & & & \\  & Inequality SSG & 156.79 & 47.04 & & & \\  & Equality SSG & 119.11 & 28.59 & & & \\  & Full game & 177.35 & 38.51 & & & \\ \hline Acc/min & No opposition & 16.71 & 7.27 & 311.586 & 0.00 * & 0.21 \\  & Individual game & 14.72 & 5.97 & & & \\  & Inequality SSG & 20.42 & 5.42 & & & \\  & Equality SSG & 22.71 & 5.28 & & & \\  & Full game & 20.79 & 3.66 & & & \\ \hline Acc\({}_{\text{max}}\) & No opposition & 2.43 & 0.54 & 93.898 & 0.00 * & 0.06 \\  & Individual game & 2.78 & 0.63 & & & \\  & Inequality SSG & 2.56 & 0.63 & & & \\  & Equality SSG & 2.67 & 0.60 & & & \\  & Full game & 2.86 & 0.46 & & & \\ \hline Acc\({}_{\text{avg}}\) & No opposition & 0.62 & 0.14 & 44.245 & 0.00 * & 0.03 \\  & Individual game & 0.60 & 0.13 & & & \\  & Inequality SSG & 0.57 & 0.12 & & & \\  & Equality SSG & 0.56 & 0.12 & & & \\  & Full game & 0.63 & 0.12 & & & \\ \hline Dec & No opposition & 125.10 & 54.93 & 174.820 & 0.00 * & 0.12 \\  & Individual game & 116.68 & 55.13 & & & \\  & Inequality SSG & 156.55 & 47.11 & & & \\  & Equality SSG & 118.92 & 28.61 & & & \\  & Full game & 176.87 & 38.22 & & & \\ \hline Dec/m & No opposition & 16.65 & 7.30 & 311.456 & 0.00 * & 0.21 \\  & Individual game & 14.65 & 6.00 & & & \\  & Inequality SSG & 20.39 & 5.44 & & & \\  & Equality SSG & 22.67 & 5.29 & & & \\  & Full game & 20.74 & 3.63 & & & \\ \hline Dec\({}_{\text{max}}\) & No opposition & \(-\)2.59 & 0.57 & 47.737 & 0.00 * & 0.03 \\  & Individual game & \(-\)2.68 & 0.70 & & & \\  & Inequality SSG & \(-\)2.49 & 0.70 & & & \\  & Equality SSG & \(-\)2.73 & 0.70 & & & \\  & Full game & \(-\)3.01 & 0.51 & & & \\ \hline Dec\({}_{\text{avg}}\) & No opposition & \(-\)0.50 & 0.12 & 26.582 & 0.00 * & 0.02 \\  & Individual game & \(-\)0.48 & 0.12 & & & \\  & Inequality SSG & \(-\)0.47 & 0.12 & & & \\  & Equality SSG & \(-\)0.48 & 0.12 & & & \\  & Full game & \(-\)0.56 & 0.12 & & & \\ \hline PL & No opposition & 5.85 & 2.21 & 171.643 & 0.00 * & 0.11 \\  & Individual game & 5.21 & 2.29 & & & \\  & Inequality SSG & 5.85 & 2.38 & & & \\  & Equality SSG & 4.34 & 1.40 & & & \\  & Full game & 8.22 & 2.87 & & & \\ \hline PL/min & No opposition & 0.78 & 0.29 & 89.673 & 0.00 * & 0.06 \\  & Individual game & 0.65 & 0.24 & & & \\  & Inequality SSG & 0.77 & 0.31 & & & \\  & Equality SSG & 0.83 & 0.27 & & & \\  & Full game & 0.96 & 0.30 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inferential results of the variables studied independently according to the game situation.
## 3 Results  
In total, 133 video-verified headers were recorded. For all headers, the median and interquartile range (IQR) for PLA was 13.5 g (IQR: 9.9-17.5 g) and PAA was 1094 rad/s\({}^{2}\) (IQR: 675-1595 rad/s\({}^{2}\)). Approximately a quarter (25.6%) of headers were below 10 g, 59.4% were below 15 g, and 87.2% were below 20 g.  
When categorized by header type, 37.6% (n = 50) were found to be pass attempts, 22.6% (n = 30) were flick-on attempts, 18.0% (n = 24) were clearance attempts, 14.3% (n = 19) were interceptions, 3.8% (n = 5) shot attempts, and 3.8% (n = 5) were headed controls. By the preceding ball delivery method, 45.1% (n = 60) were passes, 31.6% (n = 42) were long balls, 13.5% (n = 18) were goal kicks, and 9.8% (n = 13) were cross deliveries.  
For the header type, there were no significant differences in kinematics, with shot attempts having the highest median PLA and PAA (Figure 2). For the ball delivery method, goal kicks had a significantly greater PAA than long balls and pass attempts (Figure 3).  
Figure 2: Heading kinematics based on the header types clearance (n = 24), flick on (n = 30), headed control (n = 5), interception (n = 19), pass (n = 50) and shot (n = 5). Tabulated results are available in Appendix A.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 9 Conclusions  
While CVR and other techniques have been postulated for many years, the issues of deployment have always led to roadblocks. Several of these roadblocks have caused promising solutions to be shelved. In today's rapidly changing smart grid arena, there are enabling technologies that effectively address the roadblocks, and can make cost-effective, functional \(\overline{\text{DVO}}\) deployments a reality. By leveraging modern signal processing technology and grid-location-aware sensors, large-scale \(\overline{\text{DVO}}\) deployments can be implemented accurately and effectively, while providing numerous other system benefits. Employing cost-effective sensors at the customer premises eliminates the danger of reducing the supplied voltage below regulatory limits and enabling \"high definition\" \(\overline{\text{DVO}}\) with in-line control points (voltage regulators, capacitors and other voltage control devices on the power line). Furthermore, by incorporating ML algorithms and constant feedback for the real-time data gathered from the \"high definition\" \(\overline{\text{DVO}}\) system, voltage level control can be optimized over a wider range of operational conditions and dimensions of the dataset, which maximizes the potential for voltage reduction and power savings.  
In short, we believe that an ML-based approach to \(\overline{\text{DVO}}\) can be valuable, particularly when driven by incomplete data from service endpoints, accumulated over time, and providing a unique approach to \"unwinding\" both geospatial and schematic \"locations\". Specifically, the upstream/downstream correlation paradigm presented here has two primary benefits:  
Figure 11: Spectrogram of downstream current disturbances (DCP) captured at the substation (UCP). The “cross-talk” between phases is clearly indicated.  
In addition to the DCP correlation template, out-of-band messaging from the smart meter which traps the DCP also carries geospatial information, time information, and other deployment context which can be lost or corrupted during construction or emergency events. Thus, the smart meter \"identifies itself\" to the correlation algorithm(s) via logistical data as well as operational data which directly indicates presence on a specific feeder and phase.
2. The fact that the DCP travels intact to the substation and can be used to identify the feeder and path of electrical deployment [36] means that the DCP/UCP correlation \"completes the picture\" to provide robust, real-time situational awareness for the grid operator.  
The implementation of the proposed architecture alongside an existing CVR deployment would be straightforward, and the two system outputs would be complementary. The fact that the architecture proposed here leverages already-deployed \"smart meters\" to provide information to upstream systems for geospatial and schematic location refinement is significant. This approach can be leveraged alongside CVR systems to implement a richer form of \(\overline{\text{DVO}}\).
# 5G Mobile Phone Network Introduction in Colombia
## 4 MIMO Systems
### MIMO and 5G  
Since spectrum resources are valuable and limited, the fifth generation of mobile communications commonly known as 5G new radio (NR) improves spectrum efficiency by increasing communication channel capacity. It relies heavily on the use of MIMO antennas in mobile terminals, demonstrating multipath propagation with a higher data rate and link reliability, which are the main features of 5G. MIMO, with multiple transmitances receive antennas builds of huge information flows, thus, improving the channel capacity along with the advantages of high data rates and performance within the Gigabit/sec range without the need for power, bandwidth expenditure, or additional retransmission devices, while operating at frequencies of Sub-6 GHz [31]. On the other hand, MIMO antennas work better when they fully utilize a rich dispersion environment. Many studies show that MIMO antennas at relay nodes play an important role at 5G by providing extended coverage at cell edges as well as for indoor applications, even though they require high bandwidth and high gain to reduce atmospheric disturbances at millimeter frequencies [4; 10; 18].
## 6 Experiments and Results
### Feature Analysis before Graph Theory Representation  
We further analyzed the dataset before applying graph theory representation. We generated a figure illustrating the count of unique values for each training feature (Figure 9). This enabled us to identify potential patterns and gain a better understanding of the dataset's characteristics. By examining the distribution of unique values, we could determine which features were more informative for the classification task and which features might require additional processing or transformation. Upon examining the distribution of unique values as demonstrated in Figure 9, we observed distinct distributions of unique values among the features. Some features exhibited a wide range of unique values, indicating their potential informativeness in the classification task. These features may possess discriminative power, enabling them to contribute significantly to the classification model's performance. On the other hand, certain features displayed a limited range of unique values or a concentrated distribution within a specific range, suggesting a potential need for additional processing or transformation.  
Figure 8: Components that best describe the data.  
Figure 9: Count of unique values per dataset feature.  
In addition, we investigated the distribution of classes in the dataset. Figure 10 illustrates the distribution of class frequencies, offering valuable insights into the composition of our dataset. By closely examining the plot, we identified potential class imbalances, where certain classes were overrepresented or underrepresented compared to others. This observation raised concerns regarding the fairness and effectiveness of our models in predicting minority classes or achieving accurate predictions across all classes. Understanding the implications of class imbalance, we recognized the need to address this issue proactively. To mitigate the adverse effects of class imbalance on our models, we considered several techniques, including oversampling, undersampling, and synthetic data generation.
###### Abstract  
Osteoarthritis patients often experience pain and inflammation in joints. To understand whether antioxidant supplements could reduce inflammation and could potentially be applied for osteoarthritis treatment, vitamin E and resveratrol were investigated in this study. Hydrogen peroxide was used for inducing inflammation in porcine chondrocytes, and the lumen density was measured for the level of oxygen-derived free radicals. In total, 5, 50, 100, and 200 uM of vitamin E and resveratrol were analyzed for cell viability and the effect of lumen density. In addition, the expressions of IL-1\(\beta\), TNF-\(\alpha\), MMP-1, MMP-13, and COL2A1 were investigated when treating chondrocytes with 100 uM vitamin E, 5 uM resveratrol, or their combination before or after hydrogen peroxide induction. Inducing chondrocytes with hydrogen peroxide for 30 min increased the level of lumen density sufficiently and stimulated the expression of inflammatory genes. All the concentrations of vitamin E and resveratrol showed good cell viability. Single treatment with either vitamin E or resveratrol reduced inflammatory gene expression, and combined treatment increased the expression of collagen type II. The results suggested that post-treatment with both vitamin E and resveratrol has a better effect on the reduction in inflammation in osteoarthritis-like chondrocytes.  
Keywords:antioxidant supplement; osteoarthritis; inflammation; vitamin E; resveratrol +
Footnote †: journal: Medical  
the overall status of reactive oxygen species, including oxidants such as peroxides, superoxides, hydroxyl radicals, and peroxynitrites [11; 12]. In OA patients, the oxidative stress index is higher [13]. It has been shown that oxidative stress down-regulates prolidase and up-regulates lipid peroxidation, resulting in the reduction in collagen type II synthesis and cartilage damage [1; 13; 14]. Therefore, intake of food and vitamins that can function as antioxidant supplements has been considered beneficial for reducing oxidative stress.  
Two antioxidant supplements, vitamin E and resveratrol, were investigated in this study. Porcine chondrocytes were induced to mimic OA cells using hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and the inflammatory gene expression in the OA-like chondrocytes was examined. Different concentrations of vitamin E and resveratrol were tested for cell viability and for reducing the level of H\({}_{2}\)O\({}_{2}\)-derived free radicals. The effects of vitamin E and resveratrol on inflammatory gene expression before or after the chondrocytes were induced by H\({}_{2}\)O\({}_{2}\) were analyzed to understand how vitamin E and resveratrol affect inflammatory gene expression in OA.
## 6 Other Flow-Rate Estimation Methods for Comparison
### Flow-Rate Estimation Using a Visible Camera  
To estimate the flow rate by attaching different sensor to the load cell, we conducted the flow-rate estimation using a visible camera. In this approach, the side area of liquid in the ladle was measured using a visible camera. As presented in Figure 14, the shaded parts refer to the measurement areas, and it was assumed that these areas were on the same plane for simplifying measurement.  
Figure 14: Measurement of side area of liquid in ladle.  
Figure 15 presented the parameters of the ladle for obtaining the liquid volume in the ladle. In Figure 15, \(D_{1}\) m and \(D_{2}\) m denote the depth of the liquid in the ladle, and \(A_{1}\) m\({}^{2}\) and \(A_{2}\) m\({}^{2}\) are the side areas of the liquid in the ladle.  
To estimate the pouring flow rate, the volume \(V_{c}\) m\({}^{3}\) of the liquid in the ladle was calculated as follows:  
\[V_{c}(\theta) = V_{1}(\theta)+V_{2}(\theta), \tag{43}\]  
where \(V_{1}\) m\({}^{3}\) and \(V_{2}\) m\({}^{3}\) are the volumes of the liquid in the front and rear parts of the ladle, respectively, as follows:  
\[V_{1}(\theta) = A_{1}(\theta)D_{1},\;\;V_{2}(\theta)=A_{2}(\theta)D_{2}.\]  
The side areas \(A_{1}\) m\({}^{2}\) and \(A_{2}\) m\({}^{2}\) are varied with the tilting angle \(\theta\) deg of the ladle. The depths \(D_{1}\) m and \(D_{2}\) m are constant without regard to the tilting angle of the ladle.  
Then, the pouring flow rate \(q_{c}\) m\({}^{3}\)/s could be estimated by differentiating volume \(V_{c}\) with respect to time and can be denoted as follows:  
\[q_{c} = \frac{dV_{c}(\theta(t))}{dt}. \tag{44}\]  
In the experimental implementation, the differential form described in Equation (44) could be represented by the backward difference as follows:  
\[q_{cn} = \frac{V_{cn}(\theta_{n})-V_{cn-1}(\theta_{n-1})}{T_{c}}, \tag{45}\]  
where \(T_{c}\) s is the sampling interval of the visible camera and is given as 0.06 s in this study.  
This approach is difficult to apply in the practical pouring process in the casting industry because the ladle generally consists of gypsum and metal, which means that the liquid volume in the ladle cannot be measured by the visible camera. However, our purpose in this study is to verify the appropriateness of the flow-rate estimation method by DKFs, which is able to be applied in the practical automatic pouring machine. Therefore, in this study, since we used water as the target liquid and a clear acrylic container as a ladle, we could apply this flow-rate estimation approach using the visible camera to the automatic pouring machine.  
Figure 15: Parameters of ladle for obtaining liquid volume by a visible camera.