esa-sceva/satcom-chunk-collection huggingface dataset:
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 4 Simulation Experiment and Performance Evaluation
### Experiment on Real WMNs Network for HMAA Algorithm  
Figure 4a shows the static WMNs network structure diagrams of the simulation experiments. The NTT network has 57 nodes and 81 edges. Each link in these networks has three weights, which corresponds to Cost, Delay and Jitter. All of the parameters in these networks can be found at the following URL: [[http://code.google.com/p/eftpas/downloads/list](http://code.google.com/p/eftpas/downloads/list)]([http://code.google.com/p/eftpas/downloads/list](http://code.google.com/p/eftpas/downloads/list)) (accessed on 13 October 2022). All of the red points are denoted as nodes of the network, the black wires as the wireless links of WMNs in Figure 4a. The green circle denotes the source node S and the green star denotes the destination node **T** in Figure 4b, respectively. The final anypath \(P_{s}^{*}\), the blue path shown in Figure 4b, is obtained by running proposed HMAA algorithm. At the same time, we also ran two other algorithms on this real WMNs network. The final result is the same that the anypath path obtained by running SAF and MAP algorithms is also the blue path in Figure 4b. Therefore, we can draw a conclusion that in the real wireless multimedia network, the proposed HMAA algorithm is as effective as the existing SAF and MAP algorithms in finding the anypath path.
## 4 Experiment and Validation  
In this study, it was possible to evaluate the proposed method by utilizing the bearing dataset from the Case Western Reserve University (CWRU) Bearing Data Center [43] and the production line dataset. Meanwhile, it was compared to three comparable methods: VGGNet [44], Conv5, and ProtoNet [45]. A brief description of the methods being compared will be provided. We performed a 10-way cross-domain diagnostic case on the CWRU dataset and a 4-way cross-domain diagnostic case on the production line real operating conditions dataset, respectively. Additionally, we displayed the outcomes using tables, graphs, confusion matrix, and visualization by t-Distributed Stochastic Neighbor Embedding (t-SNE) [46]. All of the methods used in our tests were performed in the same environment using Pytorch [47] and an NVIDIA RTX Titan GPU.
## 3 Results and Discussion
### Characteristic Wavelengths  
To reduce the complexity of the prediction model and improve prediction accuracy, it was necessary to further reduce the dimension based on the characteristic wavelength. The characteristic wavelength extraction results shown in Figure 4 were used for KPCA dimension reduction, and the results are shown in Figure 5.  
The KPCA dimensionality reduction method uses the Gaussian kernel function, and the 3D distribution map of the first three principal components with the highest contribution rate is shown in Figure 5A. The results show that KPCA improved the clustering effect of the samples. The contribution rates of the principal components and the cumulative contribution rates are shown in Figure 5B. To retain the main characteristic information of the original sample data, principal component selection requires that the cumulative contribution rate reaches more than 95%. The KPCA dimension reduction results for the characteristic wavelengths of the NIR spectrum are shown in the \"Dimension reduction of KPCA\" section of Table 2. The results show that the KPCA method effectively reduced the data dimension while retaining most of the features of the original sample.
# Reconstruction of Ancient Lake after Peat Excavation--A Case Study about Water Quality
## 4 Discussion
### Dominant Aquatic Plants and Communities  
Communities observed in waters were _Phragmitterum_, _Caricetum ripariae_ and _Caricetum acuiformis_. The common reed community (_Phragmitterum_) plays an important role in the overgrowing of reservoirs and have high phytomas production, which could be a problem for the reservoir if taxa will spread to the open water. It can be found in mineral and peat-bogged habitats with a wide range of water trophic statuses. The _Caricetum ripariae_ is present in ditches, canals, moors, lakes, ponds and old riverbeds. It plays an average role in the overgrowth of water bodies and prefers eutrophic waters. _Caricetum acuiformis_ is present in eutrophied lakes, ponds and in peat bogs, and plays a role in the final stage of water overgrowth (29). The presence of macrophytes can additionally improve water transparency and oxygenation in the reservoir (30). Additional surveys on aquatic plants were made in the year 2017 and differences were not observed in comparison to botanical data gathered in presented studies (for the year 2014). The increase in plant cover was low and this ratio was estimated for about 2 percent. The removal of all the peat deposit  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Unit** & **Part A** & **Part B** &
\begin{tabular}{c} **Reservoir after Connection** \\ **of Both Parts (2014)** \\ \end{tabular} & **For Cyrinids** \\ \hline Total phosphorus & mg P/L & 0.086 & 0.079 & 0.082 & Appropriate \\ pH reaction \({}^{*}\) & - & 8 & 8.05 & 8 & Appropriate \\ Ammonium & mg NH\({}_{4}\)/L & 0.22 & 0.25 & 0.24 & Appropriate \\ Nitrite & mg NO\({}_{2}\)/L & 0.022 & 0.023 & 0.023 & Appropriate \\ Copper, dissolved & mg Cu/L & \textless{}0.003 & \textless{}0.003 & \textless{}0.003 & Appropriate \\ Zinc, total & mg Zn/L & \textless{}0.005 & \textless{}0.005 & \textless{}0.005 & Appropriate \\ Dissolved oxygen & mg O\({}_{2}\)/L & 9.2 & 8.7 & 8.9 & Appropriate \\ BOD-5 & mg O\({}_{2}\)/L & 3.4 & 3.6 & 3.5 & Appropriate \\ \hline \hline \end{tabular} \({}^{*}\)Median.  
\end{table}
Table 4: Water quality parameters (mean values) for the estimation of the usefulness of the reservoir for living cyprinids fish species during the period 2013–2014.  
was necessary to maintain the good quality of lake water. In the typical procedure part of peat can remain and cutaway reservoirs can be covered by aquatic plants very quickly as in Przebedowo Lake (Wielkopolska region, Przebedowo, Poland), which was intensively covered by macrophytes in the year of putting into service.  
It can be presumed, that decades after establishment of this lake, it initiated natural processes in the riparian zone which will result in the development of aquatic plant classes typical for peat lakes such as _Plragimitetea, Lemnetea minoris, Potametea_ and mosses from the _Sphagnopsida_ class [31; 32].
## 3 Methodology
### Model Selection and Evaluation  
To establish a comprehensive and performance-diverse benchmark, three distinct machine learning models, representing a spectrum from interpretable linear approaches to highly powerful ensemble methods, are meticulously employed in this study:  
* **Logistic Regression (LR)**--Serving as our interpretable linear baseline, LR provides transparent coefficients that directly indicate the impact of each feature on the probability of success. Its mathematical formulation, \(P(y=1|x)=\frac{1}{1+e^{-x^{1}\hat{y}}}\), models the probability of the target class based on a linear combination of input features.
* **Random Forest (RF)**--This robust bagging ensemble method strategically constructs multiple decision trees and aggregates their predictions, thereby significantly reducing variance and mitigating the risk of overfitting inherent in single decision trees. The aggregated prediction, \[\hat{f}_{RF}(x)=\frac{1}{T}\sum_{t=1}^{T}f_{t}(x),\] leverages collective intelligence for enhanced stability.
* **XGBoost (XGB)**--As a cutting-edge gradient boosting framework, XGBoost is renowned for its exceptional predictive performance and efficiency on structured data. It systematically minimizes a regularized loss function, \[\mathcal{L}=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum_{k=1}^{K}\Omega(f_{k}), \quad\Omega(f)=\gamma T+\frac{1}{2}\lambda\|w\|^{2},\] where regularization helps prevent overfitting and improves generalization.  
A rigorous hyperparameter optimization process is conducted using 5-fold cross-validation, ensuring that each model achieves its optimal performance across diverse subsets of the training data [28]. Specifically, key parameters for XGBoost and Random Forest, including max_depth\(\in\{3,5,7\}\), learning_rate\(\in\{0.01,0.1,0.3\}\), and n_estimators\(\in\{100,300,500\}\), are systematically tuned to maximize model efficacy.  
Model performance is meticulously assessed using a triad of robust evaluation metrics: accuracy, F1 score, and ROC-AUC. Accuracy represents the overall proportion of correct predictions, calculated as  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN},\]  
while the F1 score, a harmonic mean of precision and recall, is crucially employed to measure performance under class imbalance:  
\[F1=2\cdot\frac{P\cdot R}{P+R}.\]  
ROC-AUC evaluates the classifier's discriminatory power across all thresholds, providing insight into its generalization ability.
## 3 Methodology  
This section presents an integrated machine learning (ML) and deep learning (DL) framework for the prediction and mitigation of ADI. The methodology incorporates DSP and ML techniques to characterize ADI behavior and optimize special subframe configurations in TD-LTE systems, with the aim of minimizing interference effects. The experimental setup evaluates the performance and accuracy of the proposed models using real-world TD-LTE network data under practical operating conditions.  
Further, ADI ducting heavily depends on the carrier frequency of the radio waves. The degree of interference varies across frequency bands due to their distinct propagation properties. Lower frequencies (e.g., sub-6 GHz) with longer wavelengths diffract and propagate more effectively through atmospheric layers, making them more prone to ducting. Conversely, higher frequencies (e.g., mmWave) with shorter wavelengths experience greater attenuation and are less affected by ducting. At 0.5 GHz, atmospheric ducting is particularly pronounced, as the long wavelength enables radio waves to be trapped in ducts, traveling hundreds of kilometers with minimal loss. This extended range heightens interference risks, as signals from distant transmitters (e.g., base stations) can interfere with receivers far outside their intended range. Therefore, we focused on the low-frequency ranges of the TD-LTE network to develop the ADI mitigation system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Year** & **Methodology** & **Results** & **Network** \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & \multirow{2}{*}{2019} & Remote Interference Management & Detection probability: 0.900 & 5G New Radio \\  & & Reference Signal (RIM-RS) & False alarm probability: 0.002 & (FR1) \\ \hline \multirow{2}{*}{[PERSON] et al. [17]} & \multirow{2}{*}{2020} & DSP, LSTM, and CNN &
\begin{tabular}{c} Symbol error rate is reduced \\ from 0.37618 to 0.0003 \\ \end{tabular} & \multirow{2}{*}{QAM-OFDM} \\ \hline \multirow{2}{*}{[PERSON] et al. [3]} & \multirow{2}{*}{2017} & Adjustment of the Guard period & - & TD-LTE \\ \hline \multirow{2}{*}{[PERSON] et al. [14]} & \multirow{2}{*}{2017} & Adjustment of the Guard period & - & TD-LTE \\ \hline \hline \end{tabular}
\end{table}
Table 3: An overview of the existing mitigation approaches.
## 2 Characteristics of the Sectors in Focus
### Water Management  
Water management is one of the components of natural resources management with special environmental and societal implications. It has a direct impact on the preservation of ecosystems, food production in agriculture, as well as the health and fundamental living conditions of humans. The UN's Sustainable Development policy goals include ensuring the availability and sustainable management of water and sanitation for all [33]. Within the EU, the Water Framework Directive has been adopted to establish coordinated action in the area of water policy [34], which commits the member states to achieve good qualitative and quantitative status of all ground and surface water bodies (rivers, lakes, transitional waters, and coastal marine waters up to one nautical mile from shore). The member states are also obliged to develop water management plans for each national river basin district as a basis for making decisions that affect the state of water resources and the principles of their management in the future. These plans affect not only the shaping of water management but also other sectors including industry, municipal management, agriculture, forestry, transport, fishing, and tourism.  
An important element of integrated water management is continuous water monitoring, which applies to surface- and groundwater and covers many quality control aspects: biological--fish, benthic invertebrates, and aquatic flora; hydromorphological--river bank structures, river continuity, or substrates of river beds; physical-chemical--temperature, oxygenation, and nutrient conditions; and chemical--environmental standards for river basin-specific pollutant concentrations. The purpose of monitoring is to determine the quality status of water bodies and thus to form the basis for taking the necessary improvement actions. This is the foundation of water protection, especially protection against pollution, including pollution that leads to eutrophication, mainly from the housing and municipal sector and agriculture (biogenic pollution), and industrial pollution. An important con stituent of operational water management is also the remote management and operation of hydraulic structures in the water industry, especially in the areas of flood protection, drought prevention, and water supply.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 3 Parameters to Be Considered for the Design and Analysis of a Flexible Antenna
### Bending Analysis  
Flexibility, as well as bending tests for on-body measurements, need to be performed in realistic environments. The mechanical deterioration that includes bending, stretching, etc., degrades the performance of the antenna by shifting the frequency of resonance, a change in the gain and radiation patterns, etc., of the antenna for any specific application.  
Moreover, the application of a flexible antenna in a wireless body area network involves continuous bending of the antenna across different curvatures of the body. The human body exhibits different curvatures at different positions of the body [41]. Therefore, the positioning of the antenna across such different positions shows different antenna characteristics.  
The bending analysis of the flexible antenna across different radii shifts the resonant frequency of the antenna. This is because when the antenna is bent, it varies the effective length of the antenna and thus varies the resonant frequency.  
The bending of the flexible antenna also changes the gain of the antenna which affects its performance. It is observed that as we increase the radius of the curved surface, the gain and antenna performance increase and on further increasing the radii, it decreases. However, for the surface-like arm, which is similar to a planar surface, the performance is optimum. Therefore, while increasing the bending radius, the antenna gain increases since the antenna's effective areas are more and thereby contribute a good gain. The bending of the conformal antenna should therefore be optimized in such a way that the antenna performance, such as the resonant frequency, gain, etc., should have a very negligible impact on the antenna performance. Figure 4 shows the effect of bending on the gain for a slotted dipole antenna under flat and bend conditions [42].
## 3 Results and Discussion
### Droplet Impact on Horizontally Moving Micro-Pillar Surfaces
#### 3.2.4 Contact Time  
When droplets impact moving surfaces or surfaces with directional anisotropy, the droplets experience asymmetric spreading and retraction in two directions, forming a short axis and a long axis [56; 62]. Typically, the short axis of the droplet retracts earlier than the long axis; so, when the short axis retracts to its minimum, the droplet detaches from the surface. For example, when a droplet impacts a moving surface, the long axis of the droplet extends along the direction of motion, while the short axis is perpendicular to the direction of motion. The model in this study not only considers the horizontal motion velocity of the droplet, but also accounts for the droplet penetrating into the micro-pillar space and undergoing a rolling phenomenon. This rolling phenomenon leads to additional viscous dissipation, making the contact time model in this study more complex. Figure 14 shows the variation in the droplet contact time with \(We_{t}\), which is mainly divided into two stages.  
First, in the range of \(0.6<We_{n}<1.8\), the contact time decreases as the \(We_{n}\) increases. At this stage, the penetration depth of the droplet is relatively small, and the viscous  
Figure 14: Variation in the droplet contact time with \(We_{n}\) on horizontal moving micro-pillar structured surfaces with different \(We_{t}\)s.  
Figure 13: Variation in the dimensionless maximum spreading diameter of droplets with \(We_{t}\) and \(We_{n}\). (**a**) Cases with \(We_{n}\) = 0.6-4.2; (**b**) prediction case with \(We_{n}\) = 5.4.  
dissipation effect caused by droplet rolling is not significant, leading to a reduction in the contact time. Second, in the range of \(1.8<We_{n}<5.4\), the contact time increases with the \(We_{n}\). Here, the penetration depth of the droplet increases, resulting in greater viscous dissipation. Although the droplet exhibits asymmetry during spreading and retraction, the contact time still increases with the Weber number. Therefore, the contact time model in this study is essentially the result of competition between viscous dissipation caused by the rolling phenomenon and asymmetric spreading. The rolling phenomenon increases energy dissipation during droplet contact, while asymmetric spreading promotes droplet retraction and detachment from the surface. The interaction between these two factors determines the contact time of the droplet.
# Application of Ultrafiltration for Recovery of Bioactive Phenolic Compounds from Rose Wastewater
## 2 Materials and Methods
### Methods
#### 2.2.6 HPLC Analysis of Phenolic Compounds  
High-performance liquid chromatography (HPLC) analysis was conducted using a Nexera-i LC2040C Plus UHPLC system (Shimadzu Corporation, Kyoto, Japan) containing a UV-VIS detector, a binary pump, and a Poroshell 120 EC-C18 column (3 mm \(\times\) 100 mm, 2.7 \(\mu\)m), thermostated at 26 \({}^{\circ}\)C. The flow rate was set to 0.3 mL/min and the injection volume was 5 \(\mu\)L. Detection of derivatives occurred at a wavelength of 280 nm. The mobile phase comprised 0.5% acetic acid (A) and 100% acetonitrile (B). The gradient began at 14% (B), increased linearly to 25% (B) between 6 and 30 min, and reached 50% (B) by 40 min.  
Compound identification was confirmed by comparing retention times with standard solutions and calibration curves of various phenolics, including neochlorogenic acid, gallic acid, 3,4-dihydroxybenzoic acid, catechin, chlorogenic acid, vanillic acid, epicatechin, caffe acid, \(p\)-coumaric acid, rutin, ferulic acid, ellagic acid, naringin, quercetin-3-\(\beta\)-glucose, rosmarinic acid, myricetin, cinnamic acid, luteolin, quercetin, naringenin, kaempfer, and apigenin. The concentrations of individual phenolic compounds were reported as mg per 100 g of dry weight (DW) \(\pm\) standard deviation (SD). The HPLC standards were the following: gallic acid (100%); protocatechic acid (\(\geq\)97%); catechin (\(\geq\)98%); vanillic acid (\(\geq\)97%); \(p\)-coumaric acid (\(\geq\)98%); rutin (\(\geq\)94%); hesperidin (\(\geq\)80%); rosmarinic acid (\(\geq\)98%); quercetin (\(\geq\)98%); and kaempfer (\(\geq\)97%), and were purchased from Sigma-Aldrich (Steinheim, Germany).
# Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength
## 4 Results and Discussion
### Accuracy Assessment of Predictive Models
As for the testing phase, it was shown that the DA-ANN surpassed the WOA- and IWO-based neural ensembles in terms of both _RMSE_ and R\({}^{2}\). This is while the _MAE_ of this model was larger than WOA-ANN and IWO-ANN. Taking into consideration all three criteria, the IWO-ANN and DA-ANN presented the same generalization capability (testing scores = 10). It means that the computational parameters suggested by these two algorithms construct more promising ANNs compared to the WOA (testing score = 7). All in all, the final position of each model is determined by a total raking score (TRS), as the summation of all partial scores. As Table 2 denotes, the IWO-ANN (TRS = 22) emerged as the most accurate model of this study, followed by WOA-ANN and DA-ANN (TRS = 16), and typical ANN (TRS = 6). Also, the observed discrepancy between the results of the training and testing phases of the DA-ANN cannot be ignored. As explained, in spite of the lowest learning capability among the proposed neural ensembles, it achieved the prediction as accurate as that of the IWO-ANN. Since the IWO presented the best-fitted parameters of the ANN, the neural equation of this combination is presented in this part (Equation (5)). Note that this is a predictive formula that can properly estimate the SSS of soil using the mentioned independent factors, due to the satisfying performance of the IWO-ANN model. In fact, the values presented in this equation indicate the weights and biases of the unique output neuron of the optimized ANN. As the formula expresses, the activation function of the output layer is \"purelin\", in which the input is directly released as the response (i.e., f(x) = x). \[\text{SSS}_{\text{IWO-ANN}}=1.1762\times Z1-1.0995\times Z2-0.5416\times Z3+0.1 767\times Z  
\[Tansig\left(x\right)=\ \frac{2}{1+\ e^{-2x}}-1 \tag{7}\]  
Lastly, it should be noted that the extent of the used data can be mentioned as a limitation for the current study. More clearly, since the data were collocated from a real-world construction project, there were obviously special characteristics for the soil in the studied area. This led to limited values of key factors, and consequently the SSS. Therefore, the extent of data should be considered for using the developed models (as well as the SSS predictive formula) of this research. In addition, from an artificial intelligence point of view, implementing ideas such as optimizing the input factors and executing k-fold cross-validation techniques can be potential suggestions for enhancing the quality of prediction. Furthermore, we believe that utilizing well-known optimization algorithms such as the GA and PSO along with more recently developed algorithms will result in helpful comparative studies. However, due to the pivotal aim of this work, such suggestions, which need to be meticulously investigated, might be noted as favorable subjects for future studies.