esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation Between Landscape Patterns and Surface Water Quality
#### 3.3.2 Landscape Configuration and Surface Water Quality  
The results from the Spearman correlation analysis indicated that when the radius of buffer zones is small, only forest land is correlated to the surface water quality indicators. In contrast, when the radius of buffer zones is large, only the urban area is correlated with the surface water quality indicators. In the 200 m radius buffer zones, the landscape configuration indices of forest land were mostly correlated with COD. COD concentration had significant negative correlations with _CONTIG_, _COHESION_, and _AI_ (_p_ < 0.05) and a significant positive correlation with _DIVISION_ (_p_ < 0.05). TP concentration had a stronger significant negative correlation with _NP_ (_p_ < 0.01) and a positive correlation with EC (_p_ < 0.05) (Table 4). In the 2000 m radius buffer zones, the urban area had significant correlations with NH\({}_{3}\)-N, TP, EC, and the general surface water quality. NH\({}_{3}\)-N had a significant positive correlation with the _COHESION_ of the urban area and significant negative correlations with _NP_ and _DIVISION_ (_p_ < 0.05). NH\({}_{3}\)-N had no correlating relationships with _CONTIG_ and _AI_. TP had significant correlations with all landscape configuration indices, significant negative correlations with _NP_ and _DIVISION_ (_p_ < 0.01), and significant positive correlations with _CONTIG_ and _COHESION_ (_p_ < 0.01). COD and DO had no correlations with the landscape indices of urban areas. EC had significant positive correlations with _CONTIG_ and _AI_ (_p_ < 0.05) and significant negative correlations with _NP_ and _DIVISION_ (_p_ < 0.05). In general, the urban area in the 2000 m radius buffer zones had the greatest impact on the general surface water quality as their correlation was relatively high (Table 5). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicator** & **NP** & **CONTIG** & **COHESION** & **DIVISION** & **AI** \\ \hline COD & \(-\)0.13 & \(-\)0.67 * & \(-\)0.57 * & 0.60 * & \(-\)0.59 * \\ NH\({}_{3}\)-N & \(-\)0.43 & \(-\)0.26 & \(-\)0.27 & 0.42 & \(-\)0.11 \\ TP & \(-\)0.87 ** & 0.34 & 0.30 & \(-\)0.12 & 0.45 \\ DO & \(-\)0.47 & \(-\)0.14 & \(-\)0.32 & 0.44 & 0.15 \\ EC & 0.52 * & 0.53 & 0.51 & \(-\)0.39 & 0.47 \\ Score & \(-\)0.52 & \(-\)0.28 & \(-\)0.25 & 0.40 & \(-\)0.13 \\ \hline \hline \end{tabular}  
* the correlation is significant at the 0.01 level;
* the correlation is significant at the 0.05 level.  
\end{table} 
Table 4: Correlations between landscape configuration indices of forest land and surface water quality indicators (200 m radius).
# Orientation and Exposure Efficiency of a Solar Tracking Surface in Clear Sky
## 2 Materials and Method
At a change of the altitude of location by 100 m, the value of air mass is approximately reduced by 1.2% and this should be considered while comparing the modeled fluxes of solar energy with natural. In actinometry, air transparency is quantitatively assessed by these parameters, which are dependent on the optical mass along the beam path under conditions of clear sky. According to the peculiarities of interaction with solar beams, atmosphere is divided into ideal and real. Ideal atmosphere consists only of the main gaseous molecules of air components without carbon dioxide, water vapor and aerosols. In the atmosphere, solar beams become weaker due to interaction of light with the molecules of air by two mechanisms, Rayleigh scattering and selective extinction of the waves of a definite length of the solar spectrum by air molecules and their temporary fluctuations. The total effect is estimated by the integral factor of attenuation \(\delta(m)\), which depends on the optical mass of air on the beam path. Intensity of the surface flux \(G_{b}\) is conventionally described by the equation, similar to the equation of Lambert for optically homogeneous environments [34] (8):  
\[G_{b}=G_{sn}e^{-\delta(m)m} \tag{8}\]  
Similarly to homogeneous transparent environments, the value \(\delta(m)\) is also called the optical thickness of the air. Hence, in actinometry, the exponent multiplier is called an integral coefficient of air transparency (9):  
\[p=e^{-\delta(m)}=\sqrt[n]{\frac{G_{b}}{G_{sn}}} \tag{9}\]  
At average latitudes, the perfect model is best manifested by transparency of highland air, and on the plain under penetration of cold and dry arctic air. Previously, it has been numerically defined by the experimentally adopted pyrheliometric equation of [PERSON] (10):  
\[\delta_{p}\;(m)=\frac{1}{9.4+0.9m} \tag{10}\]
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Discussion  
The conducted experiments involving length extension and length preservation methods have provided valuable insights into the performance of the WPE-based dereverberation approach. The results, as summarized in Tables 1 and 2, demonstrate the effectiveness of both methods in improving the performance compared to the conventional WPE while considering different aspects of the evaluation metrics. The length preservation method, which incorporates the early samples of crossband components, has shown competitive performance compared to the length extension method. Remarkably, the length preservation method achieves comparable or superior results across various evaluation metrics, including FWSegSNR, CD, and PESQ. This indicates that by utilizing the crossband components in an optimized manner, the length preservation approach offers an attractive alternative for dereverberation tasks. Notably, the length preservation method achieves these performance gains while maintaining the same computational complexity as the conventional WPE. This is a significant advantage, as it allows for efficient real-time implementation without sacrificing the quality of dereverberation results.  
Overall, the findings highlight the importance of considering different approaches and parameters in the WPE-based dereverberation framework. The length preservation method presents a promising avenue for further exploration, offering competitive performance with improved computational efficiency. Further research can investigate the method's  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & \(L_{\text{bb}}\) & **Optimal Gain** & **Optimal \(L_{\text{cb}}\)** \\ \hline \multirow{3}{*}{FWSegSNR} & 14 & 1.24 & 2 \\  & 15 & 1.24 & 2 \\  & 20 & 1.23 & 5 \\ \hline \multirow{3}{*}{CD} & 14 & 1.33 & 2 \\  & 15 & 1.34 & 3 \\  & 20 & 1.33 & 5 \\ \hline \multirow{3}{*}{PESQ} & 14 & 1.74 & 2 \\  & 15 & 1.75 & 3 \\ \cline{1-1}  & 20 & 1.77 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of experimental results (length preservation).  
Figure 7: Performance evaluation (Pres.) for \(L_{\text{bb}}=20\) (optimal PESQ) in terms of (**a**) FWSegSNR, (**b**) CD, and (**c**) PESQ.  
robustness across various real-world scenarios and explore potential optimizations to enhance its effectiveness in different reverberant environments.
# A Novel Reduction Circuit Based on Binary Tree Path Partition on FPGAs
## 3 Related Works  
The number of adders used in some reduction circuits is affected by other factors. For example, the number of adders in the partially compacted binary tree (PCBT) [17] is related to the number of input elements, and the number of adders in modular fully pipelined architecture (MFPA) [18] is affected by the number of stages of the adder pipeline. They tend to use more adders than designs with fixed adders. The symmetric method (SM) and the asymmetric method (AM) proposed in [12] use only one adder, but they can only handle one data set at the same time. Some reduction methods can handle multiple sets simultaneously. [PERSON] et al. also proposed the fully-compressed binary tree (FCBT), dual strided adder (DSA), and single strided adder (SSA) in [17], and the number of adders they used is 2, 2, and 1, respectively. [PERSON] et al. also proposed two reduction methods, area-efficient modular fully pipelined architecture (AeMFPA) and the alternative design of AeMFPA (A\({}^{2}\)eMFPA) in [18], which focus on reuse and portability, and the number of adders they use is 2. The delayed buffering (DB) proposed by [PERSON] et al. in [19] uses only one adder, and it has good performance. Reference [20] proposed a reduction circuit based on a binary tree. However, the use of seven floating-point adders greatly reduces its appeal. Finally, a tag-based random order vector reduction circuit reported in [21] also uses only one adder and has the ability to process multiple data sets at the same time. However, the buffer size becomes an irritating issue since it depends on the number of data set. We have previously conducted related research on reduction circuit in [22; 23], and proposed the state-based method (SBM). In this paper, we propose a simpler method than SBM.  
Figure 2: (**a**) A simple reduction circuit example; (**b**) use a combinational logic reduction operator to calculate \(y[0]=\sum_{j=0}^{2}x[0][j]\); (**c**) use a \(p\)-stage pipeline reduction operator to calculate \(y[0]=\sum_{j=0}^{2}x[0][j]\), where \(p=2\); (**d**) if an operation is required to be performed once in a clock cycle, the solution (**c**) can only reduce the input data to \(p+1\) partial results, where \(p=2\).
## 4 Experimental Results and Comparison
It can be performed by using LUT, built-in multipliers, internal memory block, and DSP blocks. The LUT method is used in the proposed work. Xilinx field programmable gate array (FPGA) Vivado 2019.2 tools and Verilog hardware description language were used for synthesis and implementation. The Xilinx Artix-7 XC7A35T-CSG324-1 chipset was adopted to evaluate the performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**[PERSON] and [PERSON] [20]**} & \multicolumn{3}{c}{**This Work**} & **ADP** \\ \hline \(n\) & **Delay (ns)** & **Area (LUT)** & **ADP** & **Delay (ns)** & **Area (LUT)** & **ADP** & **Saving** \\ \hline
8 & 19.488 & 197 & 3780.04 & 17.37 & 133 & 2310.21 & 38.88\% \\ \hline
16 & 25.047 & 597 & 14,953.06 & 22.254 & 461 & 10,259.09 & 31.39\% \\ \hline
24 & 31.024 & 1461 & 45,326.06 & 29.74 & 943 & 28,044.82 & 38.13\% \\ \hline
32 & 33.583 & 2190 & 73,546.77 & 32.166 & 1491 & 47,959.51 & 34.80\% \\ \hline
40 & 39.271 & 3481 & 136,702.35 & 37.614 & 2621 & 98,586.29 & 27.88\% \\ \hline
48 & 39.723 & 4970 & 197,423.31 & 38.086 & 3560 & 135,586.16 & 31.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of area-delay product of this work with [PERSON] and [PERSON] [20]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**[PERSON] and [PERSON] [20]**} & \multicolumn{3}{c}{**This Work**} & **PDP** \\ \hline \(n\) & **Delay (ns)** & **Power (W)** & **PDP** & **Delay (ns)** & **Power (W)** & **PDP** & **Saving** \\ \hline
8 & 19.488 & 0.054 & 1.0524 & 17.37 & 0.047 & 0.8164 & 22.42\% \\ \hline
16 & 25.047 & 0.135 & 3.3813 & 22.254 & 0.118 & 2.6260 & 22.34\% \\ \hline
24 & 31.024 & 0.279 & 8.6557 & 29.74 & 0.21 & 6.2454 & 27.85\% \\ \hline
32 & 33.583 & 0.406 & 13.6347 & 32.166 & 0.318 & 10.2288 & 24.98\% \\ \hline
40 & 39.271 & 0.565 & 22.1881 & 37.614 & 0.469 & 17.6410 & 20.49\% \\ \hline
48 & 39.723 & 0.735 & 29.1964 & 38.086 & 0.584 & 22.2422 & 23.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of power-delay product of this work with [PERSON] and [PERSON] [20]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**[PERSON]**} & **This Work** \\ \hline \(n\) & **Power (W)** & **Power (W)** & **Power Saving** \\ \hline
8 & 0.054 & 0.047 & 13\% \\ \hline
16 & 0.135 & 0.118 & 12.59\% \\ \hline
24 & 0.279 & 0.21 & 24.73\% \\ \hline
32 & 0.406 & 0.318 & 21.67\% \\ \hline
40 & 0.565 & 0.469 & 17\% \\ \hline
48 & 0.735 & 0.584 & 20.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of dynamic power of the proposed multiplier with [PERSON] and [PERSON] [20].
# Structural Features and Properties' Characterization of
## 3 Results and Discussion
### Morphology Study  
Microscopy is one of the most obvious and reliable methods to study the structure and morphology of polymer compounds. SEM images of the samples' chipped surfaces are presented in Figure 8.  
Figure 8: _Cont._  
Figure 7: The tgA slope ratio-epoxidized soybean oil (tgA–ESO) content relationship.  
The image shows two incompatible polymers. By analyzing the radiation intensity distribution over the elements, it is revealed that the low-viscosity PLA makes a continuous phase where NR is distributed as separate inclusions. With the introduction of ESO, the average size of NR inclusions drops from about 10 \(\upmu\)m (M0 sample) to a few \(\upmu\)m (M15 sample). In comparison with the data obtained earlier [14; 32], this result can be considered quite satisfactory, given the high percentage of NR. One can observe the samples' surfaces become more homogeneous once ESO is introduced. Similar results were observed by researchers [33], who concluded that the observed results were due to improved polymer compatibility. Thus, one can confirm the efficiency of ESO as a compatibility enhancer in PLA-NR compounds. However, in our research, we failed to achieve a finer and more even distribution of polymer components or the formation of a continuous NR mesh structure, as it had been demonstrated in earlier studies utilizing other additives to the PLA-NR system (e.g., epoxidized NR [34], phenolic resin [24]).  
Moreover, the images of the M15 sample register traces of greater deformations in the NR, indicating that the NR component is responsible for the deformation/elongation of the material. As was mentioned in earlier studies, this fact confirms the assumption of the improved interface interaction between NR domains and the PLA matrix. In samples with lower ESO content, rubber domains are much less deformed or not deformed at all, which negatively affects the specific elongation at break.  
It should also be mentioned that SEM uncovered some heterogeneous areas with excess ESO content in the M20 sample. This confirms the assumption that the optimal oil content does not exceed 10-15 mass parts.
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
## 3 Theoretical Background
### 5G/NR Network Deployment Today
During the standardization process, the comparative performance of different DM-RS patterns was analyzed in [7], where simulations resulting in expected frame error rate (FER) and spectral efficiency for various SCS configurations (15 kHz, 30 kHz, 60 kHz) and relative speeds (40 km/h, 120 km/h, 240 km/h and 500 km/h) pointed to the optimal DM-RS density for various use cases. In moderate mobility scenarios and typical speeds for urban environments of 40 km/h, configuration with one or two DM-RS symbols was advised. This encouraged further investigation, which started with Doppler frequency shift analysis, as detailed in the next section.
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 3 Methodology
### Data Preparation and Extraction  
The faulty data were extracted using the Simulink model from Figure 2, and the waveforms were generated from the model to show the frequency of fault occurrence. The graphs in Figures 4-7 show the waveform that validated the presence of a fault in the network. The fault current and voltage were generated and used for machine-language training to classify and locate faults in the transmission line. The waveform displayed in Figure 4 showed standard sinusoidal voltage and current waveforms.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Class** & **Fault Type** & \(\mathbf{L_{1}}\) **(a)** & \(\mathbf{L_{2}}\) **(b)** & \(\mathbf{L_{3}}\) **(c)** & **G (g)** \\ \hline
1 & a-g & 1 & 0 & 0 & 1 \\
2 & b-g & 0 & 1 & 0 & 1 \\
3 & c-g & 0 & 0 & 1 & 1 \\
4 & a-b & 1 & 1 & 0 & 0 \\
5 & a-c & 1 & 0 & 1 & 0 \\
6 & b-c & 0 & 1 & 1 & 0 \\
7 & a-b-g & 1 & 1 & 0 & 1 \\
8 & b-c-g & 0 & 1 & 1 & 1 \\
9 & a-c-g & 1 & 0 & 1 & 1 \\
10 & a-b-c & 1 & 1 & 1 & 0 \\
11 & a-b-c-g & 1 & 1 & 1 & 1 \\
12 & No fault & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fault types in binary representation  
Figure 3: The data processing model for machine learning.  
Under the no-fault state, the waveform is sinusoidal and has no distortion due to noise or fault, so the resultant waveform was standard, as seen in Figure 4. When the fault occurred, the fault current of the power transmission line became abnormally high, while the fault voltage decreased to a low value.  
Figure 4: Three -phase at no fault condition.  
Figure 5: Three-phase to ground fault (a-b-c-g).  
Figure 6: Double-phase to ground fault (a-b-g).  
Figure 5 shows a three-phase to ground fault where the current and voltage waveform of phase \(V_{a}\), \(V_{b}\), \(V_{c}\) and \(I_{a}\), \(I_{b}\), \(I_{c}\) were distorted by a sudden decrease in their magnitude. In Figures 6 and 7, the voltage and current of phases B, C and A, were also distorted due to faults in the line. All these waveforms showed a distortion due to faults. The switching time of the fault model was set at 0.2 s and the fault location was 250 km along the transmission line. Figures 4-7 illustrate fault detection in four different scenarios: no-fault, single-phase-to-ground-fault, double-line-to-ground fault, and three-phase-to-ground fault. The fault current and voltage data were generated, and machine language was used in training the data to detect, classify and locate the fault on the transmission line. The current in Figures 5-7 increased drastically, and the voltage fell to zero, as shown in Figure 6, confirming the transmission line fault.
## 2 Factors Affecting the IOP Response to Exercise
### Nutritional Ergogenic Aids
These findings indicate that caffeine may have a protective role in maintaining retinal health and may even reduce the damage caused by elevated IOP in some contexts, though more research is needed to confirm these effects in humans. Although caffeine is one of the most widely studied ergogenic aids in terms of its impact on physical and cognitive performance, there remains a significant gap in our understanding of its long-term effects on IOP, particularly during exercise. The current body of research, while informative, does not provide conclusive evidence on how regular caffeine consumption influences IOP over extended periods. Additionally, it remains unclear whether the short-term rise in IOP observed after caffeine intake is clinically significant for individuals with glaucoma or those at risk of developing the condition. As the relationship between caffeine and IOP is still not fully understood, further research is necessary to explore both the short- and long-term effects of caffeine and other nutritional supplements on IOP levels, ocular health, and retinal function. Interestingly, nitric oxide supplements have been proposed to improve muscle performance and training adaptations. Research indicates that dietary nitric oxide supplementation improves peak power during isokinetic resistance training [89]. Additionally, nitric oxide plays a vital role in maintaining IOP homeostasis, with disruptions in its signaling linked to glaucoma and related conditions [90]. This compound has also been identified as a promising strategy for reducing IOP in glaucoma patients by enhancing outflow through the conventional outflow pathway [91; 92]. Furthermore, nitric oxide supports ocular blood flow and regulates vascular tension, potentially offering protection for the optic nerve [93]. These findings suggest that nitric oxide supplementation could be a valuable adjunct in glaucoma treatment, particularly by targeting the conventional outflow pathway [94]. In addition to caffeine and nitric oxide, other nutritional ergogenic aids, such as creatine, beta-alanine, and branched-chain amino acids (BCAAs), are commonly used to improve athletic performance. However, their effects on IOP and ocular health remain largely unexplored. For the rest of the substances, future studies should aim to investigate the impact of these supplements on IOP during different types of exercise, particularly those that may increase IOP levels, such as strength training and high-intensity interval training. By expanding our understanding of how various dietary supplements influence IOP and retinal function, we can provide evidence-based recommendations for athletes, particularly those with eye conditions like glaucoma, ensuring that supplementation strategies support both performance and long-term eye health.
## 3 PRI-SWT Dual-Modal Fusion Model for CFRP Defect Detection  
In this study, eddy current testing (ECT) was performed on CFRP defects, including cracks, delamination, and low-velocity impact damage, using a high-precision nine-grid eddy current probe system. The resulting ECT images were augmented using data augmentation techniques, generating training and testing sets for the dual-modal fusion PRI-SWT model. After obtaining these images, machine learning and deep neural network methods were applied to classify the defects. The core aim of this study is the development of the Dual-Modal Parallel Real-Imaginary/Swin Transformer (PRI-SWT) model, which integrates both the real and imaginary components of the eddy current signals. Dual-modal fusion is the process of integrating and leveraging information from two distinct modalities--such as text and images, audio and video, and different types of sensor data--to enhance the understanding and performance of a system. This approach capitalizes on the complementary strengths of each modality, enabling more comprehensive data representations and improved outcomes in tasks such as classification, recognition, and prediction. This dual-modal fusion approach is pivotal as it allows the model to leverage the complementary information from both signal components, enhancing detection accuracy and robustness in classifying CFRP defects.  
The integration of real and imaginary data is crucial for capturing the complex characteristics of defects. Traditional methods, which often rely on single-modality inputs, may miss critical information, potentially leading to misclassification. By incorporating dual modalities, the PRI-SWT model offers a more comprehensive understanding of defect features, presenting improved generalization across various defect types and conditions. The following sections present the CFRP defect detection and classification algorithm used in this study, highlighting its innovative aspects and the critical role the PRI-SWT model will play in advancing detection capabilities.
# Application of Artificial Neural Network to Somatotype Determination
## 2 Materials and Methods
### Sensitivity Analysis of the ANN  
To identify critical parameters and their degree of impact on the ANN outputs (somatotypes), a sensitivity analysis was performed. This analysis indicates the importance of individual network input variables (see Table 1) and consists of checking how the network error behaves with a change of independent variables. For each input variable, its average value was entered (the variable ceases to provide any information) and the final prediction error was checked. No increase in error means that this variable for the ANN is not significant and can be omitted.