esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Antioxidant Activity
#### 1.1.2 Antioxidant Activity of Salvia Extracts--In Vitro Cellular and In Vivo Methods
Due to the traditional use of several _Salvia_ species as memory-enhancing agents, researchers have also focused their attention on the evaluation of these plants as candidates for new Alzheimer's disease (AD) drugs, able to ameliorate the oxidative effects associated with this disease. In particular, [PERSON] et al. [77] reported that the treatment of AD-induced rats with a methanolic extract of _Salvia triloba_ at 750 and 375 mg/kg body weight for three months, induced a significant decrease in serum MDA (-19.65% and -12.9%) and NO levels (-36.21% and -21.94%), and a simultaneous increase of serum total antioxidant capacity (TAC; 33.39 and 26.67%) and SOD levels (17.27% and 4.20%) respectively, in comparison to an AD-induced untreated positive control group [77]. In vivo studies also support the hypothesis that _Salvia_ phenolic-rich extracts may counteract distinct mechanisms of inflammation and oxidative processes, namely the wound healing capacity. Indeed, in a study performed by [PERSON] et al. [72], biomechanical parameters were evaluated after topical application of ointments obtained from ethanolic extracts of _Salvia Kronenburgii_ Rech. f. (SK) and _Salvia euphratica_ Montbret, Aucher & Rech. f. var. _euphratica_ (SE; 0.5% and 1% (_w/w_)) in incision wound model skin samples from diabetic rats. The authors showed that such treatment reduced NO levels in the SK (1%), SE (0.5%), and SE (1%)-treated groups--which were significantly different from the positive control--after 14 days of ointment application [72]. Moreover, MDA levels of SK and SE ointment-treated groups (0.5 and 1%) were reduced in comparison to the positive control-treated group after 14-day application, while an increase in non-enzymatic antioxidant GSH levels was observed in the SE group (0.5%) on day 7 [72]. The wound tissues had increased levels of ROS production, while the reduction of NO and MDA levels observed in these tissues, were postulated by the authors as related to the antioxidant capacities of the extracts, particularly due to their richness in phenolics [72].
# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
## 4 Experimental Results and Analysis
### MATLAB Simulation Comparison
Compared with the traditional genetic algorithm, the A* algorithm and the algorithms from references [26], the improved genetic algorithm in this paper demonstrates significant advantages in terms of path length, smoothness, iteration speed, and computational efficiency, with high practical application value.
## 2 Materials and Methods
### Statistical Analysis  
All data were recorded in Microsoft Excel (version 16; Microsoft Corp., Redmond, WA, USA) and analyzed using IBM SPSS Statistics (version 26; IBM Corp., Chicago, IL, USA). Correlation heatmaps were generated using GraphPad Prism software (version 10; GraphPad Software, San Diego, CA, USA). Descriptive statistics for continuous variables were reported as means \(\pm\) standard deviations and as medians with minimum and maximum values to provide a comprehensive summary of the data distribution. The normality of data was assessed using the Shapiro-Wilk test, as well as skewness and kurtosis values. Based on these assessments, it was determined that accuracy, completeness, response time, and CLI scores were normally distributed, whereas FRES, FKGL, GFI, and SMOG Index scores did not follow a normal distribution. Accordingly, statistical comparisons were conducted using tests appropriate for each variable's distribution. For normally distributed variables, a one-way analysis of variance (ANOVA) was performed, followed by [PERSON]'s post hoc test when significant differences were found. For non-normally distributed variables, the Kruskal-Wallis test was used, with [PERSON]'s post hoc test applied to identify significant pairwise differences. To evaluate the strength and direction of associations among evaluation metrics (accuracy, completeness, response time, and readability scores), [PERSON]'s rank correlation coefficient (\(\rho\)) was employed due to the presence of non-normally distributed variables. Additionally, effect sizes for between-group comparisons were calculated using eta-squared (\(\eta^{2}\)) for both parametric and non-parametric analyses in order to assess the practical significance of observed differences. A \(p\)-value of less than 0.05 was considered statistically significant for all analyses.
## 2 Experimental Program  
### Materials, Sample Preparation, and Curing  
Type I ordinary Portland cement conforming to ASTM C150 [21] (Standard Specification for Portland Cement), fly ash (FA) meeting the requirements of ASTM C618 [22] (Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete), and micro silica-sand with a maximum particle size of 200 \(\upmu\)m were used in this study. Polycarboxylate high-range water reducing admixture (HRWRA) in accordance with ASTM C494 [23] (Standard Specification for Chemical Admixtures for Concrete) was added as a percentage of cement weight to adjust the workability of the tested mixtures. Furthermore, two types of fibers were used: 8-mm long polyvinyl-alcohol (PVA) and 16-mm long nickel-titanium (NiTi) shape memory alloy (SMA) fibers conforming to ASTM F2063 [24] (Standard Specification for Wrought Nickel-Titanium Shape Memory Alloys for Medical Devices and Surgical Implants). Table 1 provides the mechanical characteristics of the SMA and PVA fibers, while the mixture proportions for all tested specimens are given in Table 2. The first number in the mixture label indicates the PVA fiber content, while the second number is the SMA fiber content. For instance, ECC2-1.5 refers to an engineered cementitious composite incorporating 2% PVA fiber and 1.5% SMA fiber by volume fraction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mechanical Properties** & **Ultimate Tensile Strength (MPa)** & **Diameter (mm)** & **Length (mm)** & **Young’s Modulus (GPa)** & **Elongation (\%)** & **Density (kg/m\({}^{3}\))** \\ \hline SMA & 869 & 0.635 & 16 & 41 & 38 & 6450 \\ \hline PVA & 1620 & 0.039 & 8 & 43 & 6 & 1300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical characteristics of SMA and PVA fibers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mixture** & **Cement** & **Fly Ash** & **Silica Sand** & **w/cm** & **HRWRA** & **PVA (\%V)** & **SMA (\%V)** \\ \hline ECC2-0 & 1 & 1.2 & 0.8 & 0.26 & 0.012 & 2 & 0 \\ ECC2-0.5 & 1 & 1.2 & 0.8 & 0.26 & 0.012 & 2 & 0.5 \\ \hline ECC2-1.0 & 1 & 1.2 & 0.8 & 0.26 & 0.012 & 2 & 1.0 \\ \hline ECC2-1.5 & 1 & 1.2 & 0.8 & 0.26 & 0.012 & 2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mixture proportions.  
All ECC mixtures were prepared using a 20-L concrete mixer. First, all solid ingredients, including the cement, FA, and silica sand, were dry mixed for one minute. This was followed by HRWRA and water addition over another three minutes of continuous mixing. Subsequently, PVA and SMA fibers were randomly dispersed and mixing resumed for another three minutes until fibers were uniformly distributed. Subsequently, the required test specimens from each mixture were cast. Specimens were demolded after 24-h and cured in sealed plastic bags for 7-days at T = 21 \(\pm\) 2 \({}^{\circ}\)C) without external moisture supply, which is a common curing method for ECC. Thereafter, specimens were stored in lab conditions (T = 20 \(\pm\) 2 \({}^{\circ}\)C and RH = 55 \(\pm\) 5%) until testing.
## 3 Materials and Methods
### Feature Selection Using ANOVA  
The developed machine learning model's fine-tuning depends on feature options. Using ANOVA (analyses of variance), a rigorous statistical procedure, we refined this study feature set. ANOVA is successful in identifying and selecting EEG variables that significantly differentiate learning styles [46]. ANOVA [47] is used to examine the distribution of feature values across classes, discovering characteristics with significant differences that may be used to categorize learning styles. In this study, ANOVA was used to find discriminative traits that reveal important brain processes underpinning classroom learning styles. Since they comprise the most relevant data for distinguishing EEG signal-based learning approaches, the selected features improve the classification model. This feature selection approach boosts model performance and interpretability by selectively picking features. Thus, cognitive processes in the ever-changing classroom may be better characterized. ANOVA improves the statistical rigor of the feature selection procedure and ensures that the selected features are statistically significant and can detect learning-style differences in classroom EEG recordings. Statistical significance was defined as \(p\)-value < 0.05.
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 5 Experiment
### Results
#### 5.3.2 Test Retrieval Results of Word-Based, Stem-Based and Root-Based Retrieval  
Three experiments were conducted for word-based, stem-based, and root-based retrievals. The aim of the experiments was to investigate the effectiveness of morphological analysis (i.e., stem vs. root) on Amharic language. Table 9 reports the effects of morphological analysis on Amharic retrieval effectiveness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Stem-Based**} & \multicolumn{2}{c}{**Root-Based**} \\ \cline{2-5}  & **With Stopword** & **Without Stopword** & **With Stopword** & **Without Stopword** \\ \hline AMP & 0.14 & 0.51 & 0.24 & 0.70 \\ NDCG & 0.37 & 0.71 & 0.50 & 0.86 \\ bpref & 0.15 & 0.48 & 0.27 & 0.66 \\ R-prec & 0.17 & 0.49 & 0.29 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Stem-based and root-based retrieval with and without stopwords on 2 AIRTC.  
Figure 2: Retrieval effectiveness with and without stopwords.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Text Representation**} & \multicolumn{4}{c}{**Precision**} & \multirow{2}{*}{**NDCG**} \\ \cline{2-2} \cline{4-6}  & **P@5** & **P@10** & **P@15** & **P@20** & **MAP** \\ \hline Word & 0.56 & 0.49 & 0.44 & 0.40 & 0.43 & 0.47 \\ Stem & 0.62 & 0.53 & 0.47 & 0.43 & 0.57 & 0.71 \\ Root & 0.79 & 0.70 & 0.61 & 0.55 & 0.70 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Word-based, stem-based, root-based retrieval.  
We can observe that morphological analysis has an impact on retrieval effectiveness, both stem and root-based retrievals are more effective than word-based retrieval. The root-based retrieval is the best of the three text representations. As expected, the retrieval effectiveness of the three text representations decreases from precision at 5 documents (P@5) to precision at 20 documents (P@20) also due to the scarcity of relevant documents in the test collection. Root-based retrieval achieves 0.70 for P@20 while stem-based retrieval is 0.53 and word-based retrieval is 0.56.  
Figure 3 depicts the overall recall-precision (R-P) curves for the stem-based and root-based text representations. The top line (in blue) is the root-based retrieval effectiveness, whereas the bottom line (in red) is the stem-based one. In this figure, we could see that the performance of the root-based representation is better than stem-based representation at any level of recall.
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane
### Transmission Media
#### 6.3.1 Wired Media  
Wired transmission media transmit electrical signals from a given source to a given destination. The most frequently used material for electrical wires is copper. Copper electrical wires can be categorized into two types: twisted-pair cables and coaxial cables. Twisted pair cables are the most common wired medium used in LANs, consisting of insulated copper wires twisted together to reduce electromagnetic interference from other cables. In coaxial cables, a copper core is surrounded by a layer of insulation, a woven shield, and an outer jacket [430]. These cables are used in LANs as they provide better resistance to electromagnetic interference compared to unshielded twisted-pair cables. Copper wires have excellent electrical conductivity to carry signals with minimum degradation, high reliability as they can withstand harsh environmental conditions, are compatible with both analog and digital signals, and are cost effective compared to optical fibers. However, they have a lower bandwidth, are susceptible to electromagnetic interference, are susceptible to eavesdropping and hacking, etc. [431].
## 5 Experiments  
### Multi-Feature Optimization Clustering Segmentation  
In the experiments, we test with several \(256\times 256\) color images which are obtained from WebGIS (for example, Google Map/Earth) to assess the performance of the proposed algorithm with Matlab7.0 running on a desktop PC with 2.0 GHz CPU and 1.0G RAM. The results of kernel-based FCM with prototypes in feature space (KPCM_F), FCM with spatial constraints (FCM_S) and the proposed method are compared.  
Figure 3 (a) is \"map1\" original image. In KFCM_F and FCM_S, the initial class is set to 7. Figure 3 (b), (c), (d) shows the results of \"map1\" image segmentation. KFCM_F and FCM_S only divide the image into 3 classes. They use a separate color feature and cannot segment accurately. The proposed method divides the image into 7 classes. Figure 3 (b1), (b2) are the \"road\" and \"lake\" of KFCM_F. Figure 3 (c1), (c2) are the \"road\" and \"lake\" of FCM_S. We can see that in KFCM_F and FCM_S the \"lake\" and \"terrain\" are considered to be the same and cannot be segmented exactly. Figure 3 (d1), (d2), (d3) are the \"road\", \"terrain\" and \"lake\" of the proposed method. It can be seen that the proposed method has a better performance to segment accurately the \"road\", \"terrain\" and \"lake\".  
Figure 4 (a) is \"map2\" original image. Figure 4 (b1), (b2) are the \"road\" and \"lake\" of KFCM_F. Figure 4 (c1), (c2), (c3) are the result of FCM_S. Figure 4 (d1), (d2), (d3) are the \"road\", \"terrain\" and \"lake\" of the proposed method. Each method divides the image into five classes. Since KFCM_F and FCM_S can easily get into local optimization, we can see that from Figure (b2), the \"lake\" and \"terrain\" of KFCM_F are segmented into the same class. The \"road\" segmented from KFCM_F and FCM_S have less irrelevant information, but other shapes are mixed and cannot be segmented accurately. The proposed method can avoid getting into local optimization to get an overall better segmentation result.  
Figure 4: _Cont._  
Figure 3: The segmentation result of “map1” image: **(a)** “map1” original image; **(b1)** “road” of KFCM_F; **(b2)** “lake” of KFCM_F; **(c1)** “road” of FCM_S; **(c2)** “lake” of FCM_S; **(d1)** “road” of the proposed method; **(d2)** “terrain” of the proposed method; **(d3)** “lake” of the proposed method.
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 5 Simulation
In contrast, ECA-LF-PC can achieve a similar reservation performance with the minimal processing cost among all ECA versions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Target** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline SNR/dB & 25 & 17 & 30 & 28 & 25 & 20 & 20 \\ Range bin & 0 & 35 & 44 & 50 & 70 & 86 & 89 \\ Doppler/Hz & \(-\)30 & 6 & \(-\)6 & 39 & \(-\)50 & 50 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Target parameters. Figure 5: Two-dimensional cross-correlation function of different ECA versions. (**a**) Without suppression. (**b**) ECA without extension. (**c**) ECA with extension. (**d**) ECA-B. (**e**) ECA-S. (**f**) ECA-CF-PC. (**g**) ECA-LF-PC with symmetric weights. (**h**) ECA-LF-PC with asymmetric weights. (**i**) ECA-LF-PC with varying weights. Figure 6: The two-dimensional cross-correlation function’s Doppler slice of different ECA versions: (**a**) global view and (**b**) zoomed-in view. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **SNR of Target Three/dB** & **Periodic Modulation** & **Time Cost/ms** \\ \hline ECA with extension & 29.02 & No & 1755.17 \\ ECA-B & 17.62 & Yes & 23.13 \\ ECA-S & 23.13 & No & 113.84 \\ ECA-LF-PC & 28.63 & No & 3.62 \\ ECA-CF-PC & 27.97 & No & 3.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different ECA versions.
## 5 Conclusions  
We analyzed the effects of the time sampling interval on the angular combination characteristics of Moon-based Earth observations. A theoretical model was deduced for the three observational angles based on the analytical orbits of the Moon and Sun. Subsequently, an ACNI was proposed to characterize the angular performance of the Moon-based platform. Furthermore, the effects of different positions of observed points on the angular distribution were determined. Finally, the effect of time sampling interval on the angular combination was analyzed based on variations in the ACNI. The results show that the ACNI can be used to characterize the variation in the angular combination of the Earth observation platform. We found that the time sampling interval has different effects on the angle combination for the different observed points and mainly affects the observed points in mid-low latitude zones. According to the sensitivity to the time sampling interval, the Earth's surface can be divided into two zones. When the time sampling interval increases from 10 min to 2 h, the maximum loss of the ACNI is more than 50% for the observed points in the mid-low latitude zone; when the time sampling interval increases to 4 h, the loss rate reaches 70%.  
Figure 11: Division of the Earth surface into two zones from the perspective of angular sampling and sensitivity to the time sampling interval.  
**Author Contributions:** Conceptualization, Y.D., H.G., G.L. and J.H.; methodology, Y.D., G.L. and H.Y.; validation, Y.D., H.W. and M.Z.; writing--original draft preparation, Y.D., G.L. and H.Y.; writing--review and editing, Y.D., H.G., G.L., H.Y., J.H., H.W. and M.Z.; visualization, Y.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Key Research and Development Program of China (No. 2020 YFE0202010), the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (No. QYZDY-SSW-DQC026), Qian Xuesen Youth Innovation Fund of China Aerospace Science and Technology Group (No. Y-KC-JT-QXS-014), and National Natural Science Foundation of China (No. 41590852 and No. 42101413).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Latest Applications in Distribution Systems
### Line Parameter Calibration  
Besides topology, the operation, control, and analysis of distribution systems are also built on accurate model parameters such as line impedance. However, such information is often unavailable, incomplete, or outdated due to the system upgrade, especially in secondary distribution systems. Some utilities only have single-line diagrams of their systems without detailed three-phase line parameters; other utilities possess system models, but they are often incomplete or outdated due to frequent system expansion and reconfiguration [75]. Conventional parameter calibration methods either require the widespread installment of costly sensors such as PMUs or assume a simplified single-phase distribution network mode, which is not suitable for industrial practice. To overcome these challenges, several AI techniques are applied to solve the parameter calibration problem for three-phase distribution lines.  
In [61], a dynamic Bayesian network (DBN) with the consideration of external environment factors such as temperature and humidity is constructed for line parameter calibration. Such consideration is based on the observation of seasonal changes of line impedance parameters, due to the line absorbing moisture from the surrounding environment resulting in a decrease in insulation resistance. Thus, the DBN models time-varying correlations between specified environmental factors, network measurement data, and line parameters. By representing these correlations as probabilistic distributions, robustness to data noise and errors is enhanced. Physics knowledge, in the form of power flow equations, is embedded in the graphical learning models of [62, 63]. Specifically, a three-phase power flow-based transition function is constructed in [62] to replace the transition functions of GNNs. Then, the gradient of the voltage magnitude loss function with respect to line parameters is derived and utilized to update line parameter estimates through the iterative application of the stochastic gradient descent method. Reference [63] reduces the computation complexity of the proposed PGNN model and addresses the partial observability issue by eliminating unobservable nodes of the power flow equation via Kron reduction [76].
# Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
## 6 From Ideal Security Towards Perfect Security by SKD Based on CR KSG and Winnow IR Protocol
Summary of Additional Assumptions Made in in the Design of Proposed Perfectly Secure SystemFor clarity, let us summarize the assumptions made in the design of the proposed autonomous perfectly secure system from \[H(W_{i}|S=s)\geq R(W_{i}|S=s)\, \tag{93}\]8 In addition to the assumptions outlined for system from Figure 1, (see Section \"Summary of Assumptions Made in the Design of Proposed System\"), the system in Figure 11 assumes:  
1. The secret key output \(K_{s}\), from the SKD system is used for encryption of an independent message source \(M\) under the Vernam cipher, requiring statistical independence between \(K_{s}\) and \(M\). 2. The distilled key \(K_{s}\) is uniformly distributed \(K_{S}\sim B(0.5)\), ensuring maximum entropy, and its length is dimensioned to match the secrecy margin, i.e., \(|K_{s}|=\Delta_{K_{G}}\), maintaining effective secrecy when used for encryption. 3. The Winnow IR protocol is implemented with puncturing, ensuring that the leakage of parity bits over the public channel \(S\) does not reduce the entropy of the reconciled sequence, satisfying \(H(W_{i}|S=s)=H(W_{i})\). 4. The system operates with sufficient block lengths and a chosen compression rate to achieve the perfect secrecy region within a negligible error, ensuring that \(H(K_{s}|Z,S)=H(K_{s})\) holds to within an error bounded by \(\frac{1}{\ln 2}\,2^{-t}\), where \(t\) is the compression rate of the universal hash function. 5. The CR SKD block is assumed to function as an ISAR cryptosystem, maintaining the secrecy margin on the key even under unlimited ciphertext exposure, with \(H(K_{G}|Z,S)\geq\Delta_{K_{G}}\). 6. The authenticated but public communication channel remains assumed secure against injection or modification by the adversary, consistent with the assumptions made for system from \[H(W_{i}|S=s)\geq R(W_{i}|S=s)\, \tag{93}\]9 \begin{table}
\begin{tabular}{c c c} \hline \hline \(|K_{G}|\) & \(\left|\widetilde{K}_{s}\right|_{max}\) & \(n_{IR0}\) \\ \hline
128 & 0.0797 & 502 \\
256 & 0.0956 & 946 \\
512 & 0.1043 & 1833 \\
1024 & 0.1088 & 3606 \\
4096 & 0.1124 & 14,244 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximum effective rate of secret key generation \(\left|\widetilde{K}_{s}\right|_{max}\) and the corresponding basic block length \(n_{IR0}\), for typical key lengths \(|K_{G}|\) and a security margin \(t=34\). These additional assumptions collectively enable the system to transition from ideal secrecy to practical perfect secrecy in the Shannon sense, ensuring the effective and autonomous generation of secure keys for Vernam encryption under practical conditions.