esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Determination of the Elastic Parameters
### Prediction Method for Unsaturated Elastic Modulus  
All of the elastic moduli calculated above are tangent moduli, regardless of whether they are \(E\) or \(H\), while the calculation of lateral swelling pressure or lateral pressure is based on an incremental method, according to Equations (3) and (6). Therefore, in order to predict the unsaturated elastic modulus under certain net normal stress and matric suction states, the normalization of \(E\) by saturated \(E\) magnitude (\(E_{sat}\)) was subsequently performed, collapsing the data onto consistent power functions of normalized suction (Figure 10). The correlation coefficients exceeded 0.98, allowing predictive Equation (15) to estimate \(E_{unst}\) from known saturated conditions. The values of the fitting parameters and their correlation coefficients are shown in Table 4. This simple expression captures modulus evolution, which is important for constitutive modeling.  
\[E_{unst}=E_{sat}\left[1+\alpha\left(\frac{s}{s_{0}}\right)^{\beta}\right], \tag{15}\]  
where \(\alpha\) and \(\beta\) are fitting parameters, and \(s_{0}\) is the initial matric suction of the soil.  
The unsaturated modulus with respect to matric suction can be predicted in the same way. To normalize the data, Figure 11 plots \(H_{unstst}/H_{\text{past}}\) and \(H_{hunsst}/H_{\text{Inst}}\) versus \(s/s_{0}\), collapsing onto single best-fit power curves, as per Equation (16). The values of the fitting parameters and their correlation coefficients are shown in Table 5.  
Figure 10: Normalization fitting curves of \(E_{\text{unsst}}/E_{\text{sat}}\) to \(s/s_{0}\).  
Figure 9: Variation curves of (**a**) \(H_{v}\) and (**b**) \(H_{h}\) with different matric suctions.  
(more than 0.99) affirmed the predictability of this expression in terms of estimating the unsaturated elastic modulus \(H_{\text{unstat}}\) from the saturated conditions.  
\[H_{\text{unstat}}=H_{\text{sat}}\left[1+\lambda\left(\frac{s}{s_{0}}\right)^{ \eta}\right], \tag{16}\]  
where \(\lambda\) and \(\eta\) are the fitting parameters.  
Moreover, it was observed that the datasets in Figure 10(a),b were quite close to each other after normalization; hence, united fitting based on all of the datasets was further performed, and the fitting results show that the correlation coefficient is 0.9777, where the fitting parameter of \(\lambda\) is 4012.631 and \(\eta\) is 1.639, respectively. For convenient use in practical engineering applications, the united fitting method is recommended in the prediction of the unsaturated elastic modulus \(H_{\text{unstat}}\).  
The implementation of the proposed methods provides a practical means for determining the anisotropic expansive soil compressibility represented by \(H_{\text{unstat}}\), which is a critical parameter within the constitutive framework for improved unsaturated expansive soil analyses.
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
###### Abstract  
A reduced reference quality assessment algorithm for image retargeting by earth mover's distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point be local information, and the histograms of saliency feature as global information. Those feature information is extracted at the sender side and transmitted to the receiver side. After that, the same feature information extraction process is performed for the retargeted image at the receiver side. Finally, all feature information of the reference and retargeted images is used collectively to compute the quality of the retargeted image. An overall quality score is calculated from the local and global similarity measure using earth mover's distance between reference and retargeted images. The key step in our algorithm is to provide an earth mover's distance metric in a manner that indicates how the local and global information in the reference image is preserved in corresponding retargeted image. Experimental results show that the proposed algorithm can improve the image quality scores on four common criteria in the retargeted image quality assessment community.  
reduced reference; image quality assessment; earth mover's distance; human visual perception 202119776[[https://doi.org/10.3390/app11209776](https://doi.org/10.3390/app11209776)]([https://doi.org/10.3390/app11209776](https://doi.org/10.3390/app11209776))
## 3 Literature Review
### RescueNet  
[PERSON], [PERSON], and [PERSON] presented the RescueNet [6] dataset in their paper, which focuses on post-disaster scene understanding using UAV imagery. The dataset contains high-resolution images with detailed pixel-level annotations for ten classes of objects, including buildings, roads, pools, and trees, which were collected by UAVs following Hurricane Michael. The authors employed state-of-the-art segmentation models like Attention UNet [21], PSPNet [22], and DeepLabv3 [23], achieving superior performance with attention-based and transformer-based methods. The findings demonstrated RescueNet's effectiveness in improving damage assessment and response strategies, with transfer learning outperforming other datasets like FloodNet [24]. The dataset was observed to have limited generalization to other domains and to require a time-consuming annotation process, despite its detailed annotations.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 3 Materials and Methods
### Information Gain  
The information gain technique, or mutual information, is one of the most used filter criteria. The IG criterion is modeled after the concept of entropy in information theory [47]. The entropy measures the impurity or uncertainty in a group of observations, while information gain computes the decrease in entropy before and after adding an attribute. An attribute with a high IG value is usually preferred to those with low IG values. Assuming \(X\) and \(Y\) are features in a dataset, the information gain of \(X\), given \(Y\) is represented mathematically, is:  
\[G(X|Y)=H(X)-H(X|Y) \tag{1}\]  
In (1), \(H(X)\) denotes the entropy of \(X\) and \(H(X|Y)\) is the conditional entropy for \(X\) given \(Y\)[48]. Meanwhile, \(H(X)\) and \(H(X|Y)\) can be represented as:  
\[H(X)=-\sum_{x\in X}P(x)log_{2}(x) \tag{2}\]  
\[H(X|Y)=-\sum_{x\in X}P(x)\sum_{y\in Y}P(x|y)log_{2}(P(x|y)) \tag{3}\]  
Therefore, given two features \(X\) and \(Z\), a response variable \(Y\) is more correlated to \(X\) than \(Z\) if \(IG(X|Y)>IG(Z|Y)\)[49]. Lastly, the information gain technique considers each feature separately, computes the IG value, and outputs its importance to the response variable.
# Structural Monitoring of a Drawbridge in Operation: Signal Analysis
## 1 Introduction
Additionally, the main signal processing methods that were employed are also presented, which, combining structural and process information, allow for the classification and framing of different events, providing valuable information.
## 3 Parameter and Performance Comparison  
### Key and Signature Size  
Two relevant parameters are compared in the analysis of algorithms: key size and signature size, for which smaller values are better. The data on key length and signature length are presented in Figure 5[19; 23; 26]. They are similar to the data reported for Dilithium and Falcon in [27].  
The two lattice-based algorithms (Crystals-Dilithium, Falcon) have public keys with comparable sizes, while Sphincs+ has a significantly shorter public key length, as seen in Figure 5. On the other hand, Sphincs+ has the largest signature length.
## 2 Design Concepts
### Design Considerations for Antenna Components
#### 2.1.3 Feeding Structure  
Commonly used feeding structures include coaxial probes and printed feedlines. Both types of feeding structures can excite an antenna through direct contact or proximity coupling. With proximity coupling, the primary radiator is excited through the gap between it and the feeding structure. This allows a higher degree of freedom for tuning the impedance  
Figure 2: Open prism without a bottom face: (**a**) Trimetric view from above; (**b**) Trimetric view from below (inside of the open prism is shaded with red lines).  
matching of the primary radiator and the resonant frequency of the feedline. Therefore, proximity coupling was adopted. Compared to a coaxial probe, a printed feedline has additional degrees of freedom to match an antenna, such as the shape and the width of the feedline. In addition, a printed feedline with a potentially larger width can provide stronger coupling than a coaxial probe with a limited diameter. Hence, in this work, we used printed lines as the feeding structure. To realize dual-polarized operation, the feedlines are placed orthogonally along the X- and Y-axes. To suppress cross-polarization, differential feeding was implemented. By adjusting the excitation/matching conditions of the orthogonal differential pairs, three dual-polarization schemes, namely dual-linear, slant dual-linear, and dual-circular polarizations, could be realized.  
As established previously, in addition to its role in exciting the primary radiator, the feeding structure in this design needs to act as a broadside radiator at a close enough frequency to that of the primary radiator to allow resonance merging. The feedlines were suspended above the horizontal base of the ground cavity to provide broadside radiation. One end of each feedline is connected to an excitation port located in the ground cavity sidewall, while the other end is open to provide proximity coupling to the primary radiator (i.e., the radiating open prism). This way, the feedlines act as physically wide horizontal monopole antennas connected to a vertical ground plane and backed by a horizontal ground plane. Their large width makes them broadband capable.
###### Abstract  
Vegetation water use efficiency (WUE) is a crucial indicator for elucidating the interconnections between the carbon and water cycles of ecosystems and for discerning the response of vegetation ecosystems to climate change. Gansu Province in northwestern China is facing significant ecological water-related challenges. However, the response of vegetation WUE to climate environmental factors in this region remains unclear. In this study, the MODIS vegetation gross primary productivity (GPP) and evapotranspiration (ET) datasets were used to calculate the vegetation WUE in Gansu Province and, combined with meteorological data, Theil-Sen median trend analysis and partial correlation analysis were used to determine the spatial and temporal characteristics of vegetation WUE in this region and its response to climate environmental factors. Finally, the random forest model was used to rank the importance of climate environmental factors. The results indicate the following: (1) The average values of vegetation WUE, GPP, and ET in Gansu Province from 2000 to 2020 were 1.46 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), 510.22 gC\(\cdot\)m\({}^{-2}\), and 343.68 mm, respectively, and their spatial distribution was high in the southeast and low in the northwest, which was closely related to the distribution of vegetation in the region. (2) Over the past 20 years, the vegetation WUE in this region showed a slowly decreasing trend in general, with a decrease rate of 16.57%. There were significant differences in the WUE of different vegetation types, among which forest WUE was the highest and grassland WUE the lowest. (3) The trend prediction of WUE in Gansu Province was performed by using the rescaled extreme difference method, and the Hurst index was 0.45, which means that the vegetation WUE in this region is expected to increase in the future. (4) In general, precipitation was the main factor influencing the change in vegetation WUE in Gansu Province, followed by vapor pressure deficit (VPD), temperature, and soil moisture. This study provides strategy support for the coupling process of vegetation ecosystems and the sustainable development of agriculture and animal husbandry in Gansu Province and has scientific reference value for promoting and planning the sustainable development of vegetation in arid and semi-arid areas.  
water use efficiency; Gansu Province; trend analysis; partial correlation analysis; Hurst index; importance analysis of random forest models +
Footnote †: journal: Sustainable Energy
## 6 Discussion
### Design Recommendations for Fabric-Based Interface for HAR Interactions
#### 6.1.5 Consider Resistive Sensing Technique to Capture Both the Wrist and Touch Inputs  
Prior studies on fabric-based interfaces demonstrated the advantages of deploying resistive sensing technology to detect unobtrusively both wrist gestures [46] and thumb-to-index inputs [24]. They used fabric-based strain sensors to capture wrist movements and pressure sensors to detect legacy-inspired touch inputs. Our proposed design supports both wrist and thumb-to-touch input methods. To successfully recognize the gestures from our user-defined set, we recommend using two opposing fabric-based piezoresistive strain sensors [55] on the wrist and pressure sensors [56] on the index finger. The strain sensors can be sewn while the pressure sensors can be glued to the hand glove to allow detecting both wrist and thumb-to-index touch inputs.
## Results and Discussion
### PCA of Spectroscopic Data
#### 3.3.2 3D Component Score  
The 3D component score graphs of PC1 versus PC2 versus PC3 for the 1 st derivations of FT MIR spectra (1780-875 cm\({}^{-1}\)), 1 st derivations of FT NIR spectra (6900-4000 cm\({}^{-1}\)) and normalized FT Raman spectra (1705-825 cm\({}^{-1}\)) are represented in Figure 3. These graphs illustrate discrimination of the basidiocarp samples originating from sixteen _P. ostreatus_ strains. The samples were assigned according to the amount of proteins and glucans, i.e., with low, medium and high contents of these components. It is evident from all these graphs that the samples po4, 3009 and 3029 with high protein contents (~16.6-17.5%) as well as kryos, hk35, p80 and fotios with low protein contents (~6.2-8.5%) were more or less separated from the rest. Samples spoppo and po3, which contained ~14.8 and ~13.9% proteins, respectively, were located in close proximity to the clusters of high- and medium-protein samples. Unfortunately, there was no discrimination of the basidiocarp samples based on the content of total glucans, although in some cases it was possible to localize samples with a high content (po5 and po2) or, on the contrary, a low content (kryos and po1) of these polysaccharides.  
Figure 3: The 3D score plots of PC1 versus PC2 versus PC3 for the PCA of spectroscopic data: (**a**) FT MIR, (**b**) FT NIR and (**c**) FT Raman.  
Difficulties in distinguishing basidiocarp samples based on total glucan content can be explained by the fact that the amount of total glucans determined by the enzymatic set includes polysaccharides, which, along with the difference between \(\alpha\)- and \(\beta\)-anomeric polymers, also differ in glycosidic bond configuration and branching [14; 26; 50]. Oyster mushrooms contain a number of water-soluble homo- and heterogalactans, first of all branched mannogalactans that contribute to polysaccharide vibration bands [51]. For example, in a 3D score plot for FT Raman spectra (Figure 3c), the points of sample 3009, which contained a significant proportion of mannose and galactose in the monosaccharide composition, were displaced from the clusters of the other two high-protein samples, 3029 and po4, that may be associated not only with the higher glucan content, but also with the contribution of mannogalactan. The effect of fungal chitin, which is structurally similar to fungal \(\beta\)-d-glucans, should also be taken into account [29]. Moreover, the presence of chitin in the basidiocarp also influences the sample discrimination by protein content using PCA of vibration spectroscopic data, since both biopolymers contain amide groups that contribute to the spectra in a similar way.
## 1 Introduction
### Organization  
The rest of this paper is organized as follows. Section 2 develops a simple problem and a general problem statement. Section 3 introduces the DIRECT algorithm and NSGA-II algorithm. Section 4 presents the proposed approach. Section 5 contains engineering cases. Concluding remarks are discussed in Section 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Type** & **Existing** & **Parameter Variability** & **Performance Function** & **Optimization** \\  & **Literature** & **Assumption** & **Assumption** & **Problem** \\ \hline Robust point & [2] & Prescribed & Analytically known & Single-objective \\  & [12] & Prescribed & Analytically known & Multi-objective \\ \hline \multirow{4}{*}{Solution space} & [20] & Unprescribed & Analytically known & Single-objective \\  & [16] & Unprescribed & Analytically known/black-box & Single-objective \\  & [18] & Unprescribed & Analytically known/black-box & Single-objective \\  & [22] & Unprescribed & Analytically known & Multi-objective \\  & The proposed method & Unprescribed & Analytically known/black-box & Multi-objective \\ \hline \hline \end{tabular}  
* The meanings of the notations used in this table are as follows. Robust point: the authors aim to find a robust point. Solution space: the authors aim to find a box-shaped solution space. Prescribed: the design parameters are either measured or assumed and then represented by mathematical concepts such as value ranges and probability distributions. Analytically known: the explicit expression of the performance function is known. Black-box: the explicit expression of the performance function is unknown.  
\end{table}
Table 1: A summary of the existing literature from different aspects according to the type of design, assumptions on the parameter variability, performance function, and type of optimization problem.
# Base Pounding Model and Response Analysis of Base-Isolated Structures under Earthquake Excitation
## 3 Engineering Case and Numerical Simulation
### Effects of Pounding
#### 3.2.1 Impact Force  
Figures 11 and 12 show the time-history curve of the impact force under El Centro earthquake excitation. Every peak in the curve represents a bounding. As shown, bounding did not happen just once, but repeatedly during the earthquake.  
On the basis of models A and B, models with 7, 9, and 12 stories were built to study the effects of story and on bounding. Figure 13 shows the curve of impact force variation with the number of stories under the El Centro earthquake excitation.  
In Figure 13, it can be seen that the impact force first decreases with an increasing number of stories, but then decreases. This pattern also can be observed in under the other two excitations. Therefore, the number of stories in a building is not a good standard to estimate the magnitude of the impact force. More factors such as the type of structure, and material characteristics should be considered.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 4 Services
### File Transfer Service  
A file transfer service consists of the transmission of digital files through a communication channel from one system to another. The file transfer protocol (FTP) may be used to perform file transfers over TCP networks. FTP has a client-server architecture, where a client sends or receives files from the server. A file transfer service has strict requirements in terms of packet loss rates.  
The most extended metrics used to evaluate the QoE perceived by users in a file transfer service are the download time, the end-to-end (E2E) throughput and the MOS. Although several models for the estimation of the subjective MOS for a file transfer service can be found in the literature, all of them have in common a dependence on the E2E throughput perceived by the users [23]. In this line, [24] shows that the perception of QoE of file transfer service clients presents a logarithmic relationship with the throughput achieved in transmission and with the file download time. In this work, with the aim of providing a broader view and not focusing on a specific MOS model for FTP services, the E2E throughput achieved by users has been used as an indicator of the QoE perceived by them.
## 2 Results
### Optimization of HS-SPME on the CW/DVB Fiber  
Based on the behavior of the PDMS fiber, as discussed in the previous section, the extraction of MB was not affected by t. We also observed that interaction of t with the other two factors was not significant in the surface models of A\({}_{\text{TOT}}\) and A\({}_{\text{ED}}\), whereas the coefficient of the pure t term was positive in both cases. This means that the shape of the surface of these two responses does not depend on t, although, for a fixed T, %NaCl pair the value of the responses increases when t rises. In light of these findings, an investigation of CW/DVB and DVB/CAR/PDMS fibers was carried out by fixing t at its maximum level (30 min) and by exploring only the influence of %NaCl and T. These factors were varied within two levels and an additional point was considered in the center of the 2\({}^{2}\) DOE, where three replicate experiments were performed. Table 3 displays the matrix of the experiments for the CW/DVB fiber together with the experimental responses. The comparison of the responses provided by this fiber with those collected with the PDMS sorbent reveals a generally less intense chromatogram in the first case. As can be seen, A\({}_{\text{TOT}}\) variations observed within the various DOE points were comparable to the fluctuations due to random factors, evaluated by the replicate experiments in the central point, except for the point (T = 50 \({}^{\circ}\)C, %NaCl = 30), in which A\({}_{\text{TOT}}\) was noticeably greater. Therefore, the trend of A\({}_{\text{TOT}}\) within the experimental domain was evident and the maximum condition for the response was unequivocally identified at T = 50 \({}^{\circ}\)C and %NaCl = 30, without the need of developing a response surface model. Concerning the extraction efficiency of ED (A\({}_{\text{ED}}\)), T seems to have a negligible or poor effect and the maximum of this response is observed at the lowest %NaCl level. On the contrary, extraction of MB (A\({}_{\text{MB}}\)) is inhibited at low NaCl contents and the maximum for its peak area in the GC chromatogram is achieved at the maximum level of both factors (T = 50 \({}^{\circ}\)C and %NaCl = 30). In summary, a low NaCl content promotes extraction of ED, whereas the addition of NaCl and the increase in temperature improve that of MB.