esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stiffness Analysis for ALS-EHA
### Frequency Characteristics of Stiffness
Another technique is to reduce the natural frequency of the hydraulic spring and the equivalent inertia of the rotating components, which can be devised to retain lower than the structural modes and in the normal response ranges of flight control actuators. the hydraulic stiffness of the liquid column \(K_{h}\) must be maintained as high enough for the high-frequency stiffness. Therefore, according to \(\omega_{n}=\sqrt{K_{h}/M}\), the natural frequency can only be lowered by increasing the equivalent inertia \(M\). From \(M=J_{m}/k_{h}^{2}\), is to be increased by enlarging the inertia of the rotating components of the motor-pump set or reducing the hydraulic reduction ratio \(k_{h}\). a larger motor is selected to enhance \(J_{m}\). For the same power, a large motor outputs large torque but low speed. Under the condition that the active area and speed of the cylinder are the same, judging by \(D_{p}\omega_{m}=A_{a}v_{a}\), the lower-speed motor needs the larger \(D_{p}\) and \(k_{h}\). So, a larger motor may not necessarily have a larger equivalent inertia. To enhance power density, the smaller high-speed motors are trending. the selected motor size will affect the ALS-EHA stiffness, but it is not the primary aspect for stiffness in selecting a motor. a larger cylinder area will increase \(K_{h}\), but a larger area requires a greater \(D_{p}\), so \(k_{h}\) will not vary, which means that the ALS-EHA will still possess a lower stiffness than the natural frequency. For ALS-EHA operation, it is an ideal approach to have a low hydraulic reduction ratio to improve the ALS-EHA stiffness performance below the natural frequency, since the stiffness is relatively constant among a wide range of high frequencies. It is beneficial to improve the dynamic response of ALS-EHA and reduce its tracking error. This is another advantage over EHA-FPVM  
**Remark 3**. : _The above analysis for ALS-EHA design needs to be considered when selecting the actuator area, pump displacement and motor speed. a combination of high-speed motor and small displacement pump is advantageous for system stiffness, but more energy is needed for acceleration._  
Figure 11: EHA stiffness without control input.
## 4 Influential Factors in Fluoride Adsorption
### The Influence of Temperature on the Adsorption of F\({}^{-}\)
It is concluded that, of the 17 papers analyzed showed in the tables, it was found in 12 of them that an elevation of temperature up to 60 \({}^{\circ}\)C during the process does not bring significant improvement for the adsorption capacity of F\({}^{-}\). It is also emphasized that this finding is very important for the use of adsorption for defluoridation, since a real system should operate at room temperature, and in this system the increase in temperature for an eventual gain in adsorption capacity is not economically feasible. [PERSON] et al. [77] studied the effect of temperature and pH on the zeta potential of \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) and adsorption of fluoride ions. The authors reported that when the temperature increases from 10 to 40 \({}^{\circ}\)C, the pH of the point of zero charge (pH\({}_{\text{PZC}}\)) shifts to smaller values, indicating proton desorption from the alumina surface. The pH\({}_{\text{PZC}}\) increases linearly, allowing the estimation of the standard enthalpy change for the surface-deprotonation process. In addition, the authors reported that the increasing of the temperature from 25 to 40 \({}^{\circ}\)C lowers the adsorption density of fluoride. [PERSON] et al. [76] studied the effects of temperature on the adsorption of fluoride onto bone char made from cattle bones. The authors argue that the adsorption capacity was not influenced by temperature in the range from 15 to 35 \({}^{\circ}\)C. A comparison of fluoride adsorption capacities among several adsorbents revealed that the adsorption capacity of the bone char was 2.8 and 36 times greater than those of a commercial activated alumina (F-1) and a commercial activated carbon (F-400). Adsorption is commonly associated with exothermic processes, where heat is released during adsorption [79]. This phenomenon is consistently observed across various adsorption systems [80]. On the other hand, the occurrence of endothermic adsorption is less common and may raise questions about the thermodynamic feasibility of the process. In endothermic processes, the entropy of the system must increase for the reaction to occur spontaneously, according to the second law of thermodynamics [81]. This may seem contradictory, as the transition of the adsorbate from the solvated state to the adsorbed state is usually accompanied by a decrease in entropy. Therefore, the occurrence of endothermic adsorption may appear implausible at first glance. However, it is essential to consider that other factors, such as changes in the system's structure and free energy, can influence the feasibility of adsorption [26]. In some cases, despite the decrease in entropy during adsorption, other thermodynamic contributions, such as variations in enthalpy, can offset this effect and allow the process to occur endothermically. Additionally, specific experimental conditions, such as the presence of solvents or other components in the system, can significantly influence the thermodynamic behavior of adsorption [82].
# First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
## 3 Data and Methodology
### Topographic Wettness Index  
The topographic wetness index (TWI) is a parameter used to match runoff- producing elements in the landscape [29]. Different target areas with similar TWI values should have similar hydrological dynamics [30]. As such, TWI is an indicator of the relative propensity of soils to become saturated to the surface because of the local topography. In this work, TWI values were calculated based on the GA2 algorithm [31]. GA2 calculates the outflow gradient of each target area and uses precalculated up-land values from HydroSHEDS for the catchment area [32]. It can be expressed as follows [31]:  
\[\text{TWI}=\ln\left(\frac{\text{A}}{\tan\beta}\right), \tag{14}\]  
where A is the specific catchment area [33; 34]. TWI values are low at ridges and high in valleys. Generally, humid areas generate higher TWI values, although there are some exceptions such as desert areas where high TWI values do not correlate with high flow accumulation.
# Nonlocal and Size-Dependent Dielectric Function for Plasmonic Nanoparticles
## 1 Introduction
Inspired by the studies of the size-dependent dielectric function as mentioned above, in this paper, we move a step forward in this direction by developing nonlocal and size-dependent (NLSD) dielectric approach that incorporates the spatially local, nonlocal, size and even analogous quantum-size responses of the material. As an example, we investigate how the electron energy loss spectroscopy (EELS) and optical responses of gold spherical dimers of various radii and inter-particle gaps are modified once the nonlocality and finite-size effects are considered. These examples allow for a straightforward interpretation of our results. Unlike those complex configurational and resource consuming methods, such as quantum mechanical simulations based on the time-dependent Kohn-Sham density functional theory (TD-KSDFT), our approach provides a powerful and simple tool to calculate and analyze the EELS and optical response ranging from classical to quantum regimes.
# An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes
## 5 Conclusions  
For the problems in the data forwarding process of the OppNet, this paper analyzes the relationship between the nodes and proposes a forwarding strategy based on the cosine similarity of data packets between nodes. By calculating the cosine similarity of data packets between nodes, the strength of social relationships between nodes is defined. By defining the upper threshold and the lower threshold, it is used to filter the neighbor nodes, and finally one or more valid transmission paths can be obtained in the sub-net.  
Simulation experiments show that the cosSim algorithm performs much better than the traditional routing algorithms, the S&W algorithm and the Epidemic algorithm. The cosSim algorithm can improve the delivered ratio of the OppNet while effectively reducing the delivery delay and reducing the routing overhead. The cosSim algorithm provides a novel solution for the problems existing in the process of information transmission in OppNets.  
In fact, the social properties of the nodes in the OppNet are very complex. The transmission process of messages between nodes is affected by various factors, such as the node's mobile model and the community where the nodes are located. The comprehensive analysis of the above factors is the focus of the next step.  
Conceptualization, Y.L.; Methodology, Y.L. and J.W.; Validation, J.W.; Formal Analysis, Y.L.; Resources, Z.C.; Writing-Original Draft Preparation, Y.L.; Writing-Review & Editing, Y.L. and L.W.; Visualization, L.W.; Supervision, J.W.; Funding Acquisition, C.Z.  
This research was funded by the Research Foundation of Central South University (Grant No. 2018 zzts606).  
This work is supported by the National Natural Science Foundation of China (Grant No. 71633006, Grant No. 616725407). This work is supported by the China Postdoctoral Science Foundation funded project (Grant No. 2017M612586). This work is supported by the Postdoctoral Science Foundation of Central South University (Grant No. 185684). Also, this work was supported partially by the \"Mobile Health\" Ministry of Education--China Mobile Joint Laboratory.  
The authors declare no conflict of interest.
# Modularity-Based Incremental Label Propagation Algorithm for Community Detection
## 3 Results
### Nmi  
In this experiment, LFR networks are applied to test the performance of MILPA, LPA, LPAMP, Fastgreedy and Louvain. GN has a worst-case complexity \(y(n^{3})\) on a sparse graph that is infeasible in large networks, so it is not taken into consideration. The NMI of each method in different networks of each group is shown in Figure 3. The \(mu\) value in Every group is sampled at intervals of 0.1 at [0.1, 0.5].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Networks** & **Node Number** & **Edge Number** \\ \hline
**Karate** & 34 & 78 \\
**Dolphins** & 61 & 159 \\
**Polbooks** & 105 & 441 \\
**Football** & 115 & 616 \\
**Email** & 1133 & 5451 \\
**Hamsterster** & 2426 & 16631 \\
**DM-CX** & 4040 & 76717 \\
**Facebook** & 4039 & 88234 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Real-world network datasets.  
From the results of Figure 3, the proposed MILPA is proved to be the best, followed by Louvain and LPAMP. Especially in those networks with 5000 nodes, the NMI values of other methods show a significant downward trend, whereas that of MILPA are always close to 1 as the \(mu\) varies. LPA and Fastgreedy have rather worse performance that the NMI values of the two methods significantly decrease as \(mu\) grows in both groups. Besides, LPA is unstable due to its large performance gap between the two groups. In those networks with 1000 nodes, when \(mu\geq 0.3\), LPA has a sharp decline of NMI and finally cannot detect communities at all.  
MILPA, LPAMP and Louvain, as modularity-based methods, all have relatively stable performance in the LFR networks. MILPA and LPAMP which are based on LPA show their improvements comparing to LPA. As a greedy algorithm, Fastgreedy is easy to fall into local optimal solutions, resulting in the worst performance.
## 3 Results
### Convergence--ROC Curves  
Receiver operating characteristic (ROC) curves represent a fundamental visualization method in ML evaluation. These curves plot the true-positive rate (sensitivity) on the y-axis against the false-positive rate (1-specificity) on the x-axis across different classification thresholds. This visualization encapsulates the fundamental trade-off between sensitivity, which measures the ability to correctly identify abnormal cases such as diseased retinas,  
Figure 3: Learning curves illustrating the training and validation loss over training instances. The curves demonstrate the model’s convergence during training and its generalization capability on unseen validation data.  
and specificity, which quantifies the ability to correctly identify normal cases such as healthy retinas.  
Multiple ROC curves with corresponding area under the curve (AUC) values are shown in Figure 4. The ROC of Class 1 demonstrates an AUC of 0.98, while the ROC of Class 0 similarly achieves an AUC of 0.98. The microaverage ROC exhibits the highest performance with an AUC of 0.99, and the macroaverage ROC maintains consistency with an AUC of 0.98. These AUC values, approaching the maximum value of 1.0, indicate high discriminative ability of the developed ML models in classifying OCT images. The visual confirmation of this high performance is evident in the curves' steep ascent near the y-axis and their proximity to the upper-left corner of the plot.  
In the OCT classification context of this study, ROC curves serve multiple critical functions. First, as shown in Equation (8), AUC is utilized as part of the weighted scoring function (\(\psi\)) to select the optimal model (\(m^{*}\)) through 5-fold cross-validation. Second, sensitivity over specificity (\(\beta\) = 2 in their weighting scheme) is prioritized to minimize false negatives, which is particularly crucial in disease detection scenarios where missing abnormal cases carries higher clinical risk. Third, the high AUC values (0.98-0.99) quantify the models' ability to distinguish between normal retinas and specific pathologies targeted in this study. Fourth, ROC curves allow the selection of optimal decision thresholds that balance sensitivity and specificity according to clinical priorities.
## 3 Results
### The Effect of High-Hydrostatic-Pressure Treatment on Volatile Aromatic Compounds
#### 3.2.3 PCA Analysis  
To further compare the effects of different HHP treatments on the flavor of Marselan dry red wine, PCA was conducted on the 17 volatile aromatic compounds detected in the experiment with an OAV > 1. The results are shown in Figure 7, with PC1 contributing 38.93% and PC2 contributing 19.28%, cumulatively accounting for 58.21% of the total.  
Figure 6: Effects of different HHP treatments on volatile aroma compounds with OAV > 1. Changes in volatile aroma compounds in wine as processing time varies with constant pressure (100 MPa-600 MPa).  
Figure 7: PCA plot of volatile aromatic substances with OAV > 1 under different HHP treatments.  
Samples L-200-10 and L-300-20, located on the positive half-axis of PC1 and the negative half-axis of PC2, mainly reflected ester compounds with herbaceous, floral, and fruity aromas. Among them, L-300-20 showed a high positive correlation with ethyl acetate (fruity aroma), hexyl acetate (pear and other fruit aromas), ethyl butyrate (strawberry and apple aromas), and ethyl caprylate (pineapple and pear-throat aromas) [29], indicating that treatment at 300 MPa for 20 min can significantly enhance the fruity and floral aromas of Marselan dry red wine. Samples L-100-20, L-200-30, and L-300-10 were positively correlated with both axes of PC1 and PC2, mainly displaying information on ester compounds, such as ethyl caprate (fruity aroma, fatty flavor) and ethyl hexanoate (green apple aroma) [30], contributing significantly to the floral and fruity aromas of Marselan dry red wine. Samples L-100-10, L-400-20, and L-500-30 scored higher on the negative half-axes of PC1 and PC2, mainly reflecting terpene compounds such as beta-damascenone (sweet apple) [31] and hexanol, which contribute a grassy aroma to the wine. HHP treatment groups such as L-CK, L-500-10, L-500-20, and L-600-10 were more concentrated, distributed on the negative half-axis of PC1 and the positive half-axis of PC2, mainly showing information on compounds such as phenethyl acetate (floral and fruit aromas) [32], with fewer aromatic compounds in the area, indicating that treatment at 500-600 MPa for 10-30 min has no significant impact on the aroma of wine.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Total byte performance
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contains** & **Size [Byte]** \\ \hline Size(\(\text{RREQ}_{\text{AODV}}\)) & 24 \\ \hline Size(\(\text{RREP}_{\text{AODV}}\)) & 20 \\ \hline Size(\(\text{RREQ}_{\text{DSR}}\)) & \(\sum_{k=1}^{n+1}(12+4(k-1))\) \\ \hline Size(\(\text{RREP}_{\text{DSR}}\)) & \(12+4(k-1)\) \\ \hline Size(ID) & 4 \\ \hline Size(HC) & 1 \\ \hline Size(LT) & 8 \\ \hline Size(LI) & 8 \\ \hline Size(Hash) & 32 \\ \hline Size(\(\text{IDB}_{\text{Sig}}\)) & 276 \\ \hline Size(\(\text{IBSAS}_{\text{Sig}}\)) & 768 \\ \hline Size(Nonce) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for performance evaluations. As shown in Figure 3, when \(n=5\) (i.e., the number of intermediate nodes between the source and destination is 5), it takes twelve hops for successful route construction in the comparison methods. In comparison with ARAN, if the second node or less from the source has a route to the destination and the RREP is generated as a responder, the route can be constructed more efficiently. In contrast, compared to SAODV, the routes can be constructed within four hops using the proposed method. Moreover, our proposed method is always more efficient than ISDR+ because the signature size of IBSAS is larger than that of the ID-based signature. These results imply that the proposed method has a condition for \(N\) wherein its communication efficiency is superior to that of existing methods in terms of the total number of bytes. As the total number of bytes depends on \(n\) (and \(N\) in the proposed method), \(n\) affects the condition under which the proposed method is superior to the compared methods. Based on this fact, we calculated (a) the condition of \(N\) for \(B_{\text{IR}}\leq B_{\text{SAODV}}\), (b) the condition of \(N\) for \(B_{\text{IR}}\leq B_{\text{IR}}\), and (c) the condition of \(N\) for \(B_{\text{IR}}\leq B_{\text{ISDR+}}\). Figure 4 summarizes the maximum value of \(N\) at which the total number of bytes of the proposed method is smaller than that of the compared methods; these values were obtained based on Equations (18)-(23) and Table 1.  
Figure 4 demonstrates the maximum value of \(N\) at which the proposed method is superior to the compared methods under various network sizes. As evidenced by Figure 4, the region of \(N\) in which the proposed method is superior widen  
Figure 4: Threshold of \(N\) for \(n\), showing that the proposed method is superior to the existing method in terms of the total bytes. Figure 3: Comparison of the total byte number characteristics with each method at \(n=5\). method, that is, the proposed method tends to improve communication efficiency further as the network size increases.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 5 Experimental Results and Performance Analysis
### Performance Comparisons  
In this subsection, we compare the proposed method with the two SVD domain watermarking schemes in references [8; 14]. The method in [8] is a pure SVD-based watermarking scheme, while the method in [14] is a hybrid domain watermarking scheme. Both of these two methods have resolved the false positive problem by embedding the principal component of a watermark into the host image in the SVD domain. To make a better comparison for these three methods, we perform these methods on the images shown in Figure 4 and other test images. Here, we take image Lena and image Barbara as two examples to show the comparison results. Tables 3 and 4 list the NCC values of the extracted watermarks under different attacks. The host images used in Tables 3 and 4 are image Lena and image Barbara, respectively. The PSNR values of the watermarked images are also given in the tables. From the tables, it can be seen that the watermarked images obtained by the proposed scheme have higher  
Figure 6: Distorted images and the extracted watermarks: (**a**) No attack; (**b**) Salt and pepper noise (0.001); (**c**) Salt and pepper noise (0.005); (**d**) Gaussian noise (0, 0.0001); (**e**) Median filter (3 \(\times\) 3); (**f**) Average filter (3 \(\times\) 3); (**g**) Image sharpening; (**h**) JPEG (quality factor \(Q\) = 90); (**i**) JPEG (\(Q\) = 80); (**j**) JPEG (\(Q\) = 70); (**k**) Rescaling (2, 0.5); (**l**) Rescaling (0.5, 2); (**m**) Cropping (top 25%); (**n**) Cropping (middle 25%); (**o**) Cropping (right 25%).
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 3 Proposed Methodology
### Geo-Clustering Algorithm  
The first level of the problem at hand is to extract a set of vernacular regions within an urban area, based on user-generated sets of tags. It may be formalized as follows: \"Given an area, extract a set of homogeneous AOIs, in terms of the sets of tags that have been assigned by users at the process of tagging photos taken therein and shared within a social network\". In other words, at this level we do not use any other prior knowledge, except from the harvested tags of geo-tagged photos.
## 3 Experimental Work
### Tests and Methods
#### 3.3.4 California Bearing Ratio (CBR) Test  
In the structural design and assessment of pavements, the California Bearing Ratio (CBR) of the materials used in the base and sub-base courses has been widely used. Because of its simplicity and usefulness, the exam is well-known around the world. As a result, the test can be easily adapted by engineers to assess materials for use in road construction and to experimentally determine the soil strength under regulated water content and density circumstances. In this test, the force necessary for a standard-area plunger to pierce a soil sample is determined. The pressure that must be applied to pierce a standard layer of crushed rock at the same depth is divided by the measured pressure. The ASTM Standard [37] outline the CBR test for laboratory-prepared specimens. Soaked CBR tests were carried out in this study in accordance with [37] to imitate field situations in which the soil is flooded with water, which can come from either groundwater or rainwater permeating the layers. All of the soil specimens were subjected to soaking CBR tests, similar to UCS testing, to determine their CBR correlation with the P-wave results. Triple specimens were used and tested under each condition, and the average of the results wasreported.