esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thickness and Air Permeability of the Electrospun Nonwoven Fabrics  
Figure 4 shows the thickness and air permeability test results of PLA and HPO/PLA electrospun nonwoven fabrics. Thickness is an essential characteristic of textile materials since it has a direct influence on performance attributes such as permeability. The thicknesses of the PLA, 3 HPO/PLA, and 5 HPO/PLA electrospun nonwoven fabrics were measured as 215 \(\pm\) 12 \(\upmu\)m, 221 \(\pm\) 8 \(\upmu\)m, and 224 \(\pm\) 6 \(\upmu\)m, respectively. The volume of the electrospinning solutions was used to regulate the thickness. Although all electrospun nonwoven fabrics were made in the same way, the slightly higher thickness value of the HPO/PLA blended fabrics can be attributed to the existence of lower surface tension that reduces resistance to applied electric force, causing fibers to accumulate in a narrower area on the collector. Thus, the presence of HPO into PLA electrospinning solution increased nonwoven mat thickness.  
Figure 3: TGA thermograms of the PLA, and HPO-loaded PLA electrospun nonwoven fabrics.  
Air permeability is important for wound dressing, to allow oxygen in which plays a vital role in wound healing [82]. The role of oxygen in wound healing can be summarized as follows; the oxidative killing of bacteria, re-epithelialization, angiogenesis, and collagen synthesis [83]. Parameters that may affect the air permeability of materials include fiber diameter, thickness, and porosity [84]. The air permeability of the PLA, 3 HPO/PLA, and 5 HPO/PLA electrospun nonwoven fabrics were measured as 9.02 \(\pm\) 0.55 cm\({}^{3}\)/cm\({}^{2}\).s, 11.79 \(\pm\) 1.23 cm\({}^{3}\)/cm\({}^{2}\).s and, 11.93 \(\pm\) 0.77 cm\({}^{3}\)/cm\({}^{2}\).s, respectively. Only a minor difference in air permeability was observed after the addition of HPO in PLA electrospun nonwoven fabric. The air permeability values indicated that the addition of HPO to the PLA electrospun fabrics caused an increase. Air permeability is still a complicated phenomenon for electrospun mats because airflow may affect the geometry of nanofibers due to the mats' lightness and thinness [85]. This increase in air permeability can be explained by the fact that the fine fibers are more affected by the air flow since the intermolecular interaction changes with the addition of HPO to the structure. [PERSON] and [PERSON] reported that the air permeability values of herbal extract loaded-thermoplastic polyurethane electrospun mats were increased after the addition of extract into a polymer [37]. The measurement results showed that the presence of HPO in the PLA electrospun nonwoven fabric had a minor influence on the thickness and air permeability of samples. The fact that all samples are breathable is promising for the development of PLA-based electrospun biomedical materials such as wound dressings.
## 3 Overview of Pseudo-Dynamic Testing and Result Analysis
### Experimental Results and Analysis
#### 3.4.2 Maximum Seismic Response Load and Displacement
Figure 18 compares the load-displacement curves obtained from the first floor of both PD-FR at 200 cm/s\({}^{2}\) and PD-CFT-MF at 200, 300, and 400 cm/s\({}^{2}\). Figure 19 compares the displacement-time hysteresis loops of the first floor of both PD-FR at 200 cm/s\({}^{2}\) and PD-CFT-MF at 200, 300, and 400 cm/s\({}^{2}\). Table 5 summarizes the maximum seismic response load and the corresponding response displacement at the maximum load of PD-FR and PD-CFT-MF, along with the comparison of the resultant seismic response strength ratios and displacement ratios, which are important parameters for seismic performance evaluation. Figure 14: Seismic response load-displacement and displacement-time hysteresis curves (PD-FR, 200 cm/s\({}^{2}\)): (a) load-displacement curves; (b) displacement-time hysteresis curves. Figure 16: Seismic response load-displacement and displacement-time hysteresis curves (PD-CFT-MF, 300 cm/s\({}^{2}\)): (**a**) load-displacement curves; (**b**) displacement-time hysteresis curves. Figure 17: Seismic response load-displacement and displacement-time hysteresis curves (PD-CFT-MF, 400 cm/s\({}^{2}\)): (**a**) load-displacement curves; (**b**) displacement-time hysteresis curves. Figure 15: Seismic response load-displacement and displacement-time hysteresis curves (PD-CFT-MF, 200 cm/s\({}^{2}\)): (**a**) load-displacement curves; (**b**) displacement-time hysteresis curves. Figure 19: Comparison of seismic response displacement-time hysteresis curves (first floor). Figure 18: Comparison of seismic response load-displacement curves (first floor). According to the figures and table below, the test frame retrofitted with the CFT-MF system exhibited seismic response strength about 1.72 times higher than that of the reference test frame (PD-FR) when the same seismic acceleration was applied at 200 cm/s\({}^{2}\). The difference was even larger when the seismic acceleration was higher: about 2.45 times at 300 cm/s\({}^{2}\) and about 3.09 times at 400 cm/s\({}^{2}\). PD-CFT-MF also exhibited smaller seismic response displacements than the reference test frame. The difference was about 0.19 times at 200 cm/s\({}^{2}\), about 0.38 times at 300 cm/s\({}^{2}\), and 0.67 times at 400 cm/s\({}^{2}\). The results confirmed that when the same seismic acceleration was applied at 200 cm/s\({}^{2}\), the seismic response displacement was about 81% lower in PD-CFT-MF than in the reference frame PD-FR. This indicates that the CFT-MF system was able to effectively enhance the ability of the test frame to absorb the applied seismic energy.
# Low Profile Monopole Meander Line Antenna for WLAN Applications
## 4 Parametric Study
### Upper Meander Line Length  
Figure 4 depicts the effect of the upper meander line (ML) length and shows that changing the length will provide different reflection coefficients and slightly shift the frequency. It is clearly demonstrated that when the meander line is at its optimal length, L1 div = 22.84 mm, the reflection coefficient is \(-\)22.69 dB at the center frequency of 2.43 GHz, while halving the length of the meander line to L1 div = 11.42 mm results in a reflection coefficient of \(-\)9.87 dB at the center frequency of 2.65 GHz. At 2.35 GHz, when the length of the ML is increased to L1 div = 25 mm, the reflection coefficient decreases to \(-\)11.73 dB. Finally, when ML is removed, the reflection coefficient is \(-\)9.25 dB at the center frequency of 2.69 GHz with L1 no line = 0 mm.  
We observed that there were no bands when we halved the length of the ML and removed it entirely. Nonetheless, as the ML length was increased, we observed a band with a slightly shifted frequency, whereas at the optimal line length, we observed a frequency concentration at 2.43 GHz. We are attempting to reduce or increase the length of the meander line in order to determine the effects of the different meander line lengths until we obtain the concentrate operation frequency. The figure below shows the geometric of the upper ML length.  
Figure 3: Depicts the proposed ML antenna’s surface current distribution.
## 5 Experimental Results  
### Data Collection  
In order to verify the effectiveness of the proposed method, a manufacturing enterprise in Sichuan Province and its collaboration enterprises are selected as sample data, and the survey method is a questionnaire survey. First, design a questionnaire based on the constructed collaborative performance evaluation index system, and distribute it to the enterprise and its partners to collect data. Then, manufacturing enterprise managers, technical experts and authoritative scholars evaluate the collaboration performance between different enterprises, and use the evaluation results as the target value. Finally, 50 pieces of enterprise collaboration performance evaluation data originating from 25 collaborative enterprises were obtained, and each piece of data contained 21 evaluation indicators. Inspired by the experimental settings of [9; 10; 15], we randomly select 40 pieces of data as training data and the remaining 10 pieces of data as test data. Matlab_R2021b software is used for programming and operation, the 21 items of data of the enterprise are used as input values, and the corresponding evaluation values are used as target output values. The collaborative relationship between enterprises is shown in Figure 2.  
The raw data after standardization is shown in Table 2, where \(G\) represents the indicator data and \(E_{i}\) to \(E_{j}\) represents the collaboration performance of enterprise \(E_{i}\) and \(E_{j}\).
## 2 Materials and Methods
### MRAC Data  
The MR data for attenuation correction was collected with a 3D T1-weighted fast field echo sequence with echo time of 2.16 ms, repetition time of 4.18 ms and a flip angle of 10 degrees. The geometry correction on the MRI system is applied with the option value set as \"default\". In total, three MR-based attenuation maps were created: (a) soft tissue (0.096 cm\({}^{-1}\)), bone (0.151 cm\({}^{-1}\)), and air (3-class model), (b) soft tissue with higher mu-value of 0.100 cm\({}^{-1}\) and air cavities (2-class model), and (c) a higher mu-value of 0.100 cm\({}^{-1}\) for all soft tissue and ignoring all air cavities inside the head (2-class model without air).  
All the MR-based attenuation maps, including the 3-class model were created from the T1-weighted images with an SPMS-based method described in an earlier study [22]. The 2-class model was created by assigning the same attenuation coefficient for both the bone and soft tissue. Finally, in the 2-class model without air, the air cavities inside the head were ignored in the segmentation pipeline. The less accurate attenuation maps cannot be expected to perform as well as the 3-class model and thus the focus of the study is to evaluate the effects of TOF when attenuation maps are inaccurate rather than compare the results of different attenuation maps to each other.
# New Advances, Challenges, and Illustrations in Applied Geochemistry
## 1 Introduction
### Methods for the Determination of Geochemical Background Values  
Contribution 1, a study on geochemical background values, is an important foundational work in earth sciences. The types of methods used to determine geochemical background values can be classified as fixed-value and unfixed-value methods. Fixed-value methods define a fixed value as an elemental background value in a specific area, whereas unfixed-value methods indicate that different values are determined as the elemental background values within a specific area, with even each background value corresponding to each sample within a specific area.  
Utilizing fixed-value methods, [PERSON] et al. (contribution 1) introduced three ways in which to determine geochemical background values, as follows: the iterative method [11], the frequency histogram method, and the multifractal concentration-area method [12]. In this paper (contribution 1), the final geochemical background value was obtained by averaging the values calculated using the three methods for each analytical item.  
For the unfixed-value method, which is the ideal method to use, each geological sample has a different geochemical background value due to different degrees of weathering and lithology in each sample, as proposed by [PERSON] et al. [13]. In their study [13], the geochemical background values of 27 trace elements in a sample could be calculated from the sample's major oxide contents using empirical equations. Recently, the equations for Li [14] and Cr [15] were further improved, to help determine their geochemical backgrounds more accurately.
## 4 Methods
### NER Evaluation Metrics  
NER systems are usually evaluated by comparing their outputs against human annotations. The comparison can be quantified as either an exact match or relaxed match. The method used in this article was an exact match.  
NER involves identifying both entity boundaries and entity types. With 'exact-match evaluation', a named entity is considered correctly recognized only if both its boundaries and type match are ground truth. Precision, Recall, and F1-score are computed on the number of true positives (TP), false positives (FP), and false negatives (FN).  
1. TP: entities that are recognized by NER and match the ground truth;
2. FP: entities that are recognized by NER but do not match the ground truth;
3. FN: entities annotated in the ground truth that are not recognized by NER.  
Precision measures the ability of a NER system to present only correct entities, and Recall measures the ability of a NER system to recognize all entities in a corpus. F1-score is the harmonic mean of precision and recall, and the balanced F1-score is most commonly used:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{5}\]  
Figure 6: Visualization of self-attention.  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{6}\]  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{7}\]  
**Author Contributions:** Conceptualization, Y.X.; methodology, Y.X.; software, H.X., X.D., C.L. and X.L.; validation, H.X., X.L., D.W. and Z.S.; formal analysis, H.X. and X.L.; investigation, S.L., Y.Z., C.Z. and S.C.; resources, K.L., C.Z. and X.L.; data curation, K.L., C.Z. and X.L.; writing--original draft preparation, H.X. and K.L.; writing--review and editing, H.X.; visualization, H.X.; supervision, Y.X.; project administration, Y.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data and evaluation scripts can be provided by the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 3 Methods  
In this study, the 865 nm band of the DPC is calibrated using the cross-calibration method with the observations of the 865 nm band of the POSP as the reference data. After that, the 910 nm band of the DPC is calibrated according to the DPC calibrated at the 865 nm band.
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 4 DDQN-MTGPSR Protocol Design  
### Broadcast Beacon and Routing Table Optimization  
DDQN-MTGPSR periodically broadcasts beacons containing the local node's ID, location, residual energy, mobility mode (speed and direction), estimated total waiting delay, and neighboring topology information (NBT). Upon receiving a beacon, a node calculates the SNR and parses the packet, storing the information in its routing table, referred to as \(RT\) in this study.  
Table 3 is the improved GPSR routing table RT, which needs to be maintained, and if no broadcast packet is received from a neighbor node for a period longer than T, the neighbor information is removed from the routing table.
## 3 Method
### Feature Coding Based on Mapping Mechanism and PCA  
The output of the improved Swin-Unet was a single-channel feature image. To enhance the robustness of the algorithm, we proposed a feature-encoding method that combined the mapping mechanism and principal component analysis to further suppress the non-feature pixels of the feature image. The mapping mechanism we designed was based on the sine function, as follows:  
\[fm(x,y)=\frac{255.0\times(1+\sin(f(x,y)\times\theta\times\frac{\pi}{255}-\frac {\pi}{2}))}{2} \tag{1}\]  
Above, \(f(x,y)\) represents the numeric value of image pixel, \(fm(x,y)\) represents the mapped pixel value, \(\pi\) (radians) stands for 180 degrees, and \(\theta\) is the adjustment factor. Obviously, once mapped, the value of the pixels whose value is less than the median value will be reduced, which is beneficial for enhancing the robustness and tampering sensitivity of the algorithm. When \(\theta\) is set to 1, the mapping examples of the pixel value are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Raw Value of Pixel** & **Mapped Value of Pixel** \\ \hline
60 & 33.27 \\
64 & 37.62 \\
110 & 100.22 \\
128 & 128.28 \\
160 & 177.19 \\
255 & 255 \\ \hline \end{tabular}
\end{table}
Table 1: Examples of mapping of different pixel values.  
As shown in Table 1, this mapping mechanism, based on sine function, was able to suppress small values while enhancing large values.  
The feature matrix processed by the mapping mechanism was then decomposed by PCA. Since the first few columns of the principal components summarized the main features of the matrix, we chose the first column of principal components as subject-sensitive features of the HRRS image. The first column of principal components were binarized into a 0-1 sequence.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 2 Related Work
### End-to-End VC Based on Variational Autoencoder  
End-to-end VC approaches integrate all stages of the speech generation pipeline (content front-end, acoustic, and vocoder) into a unified architecture. Recent studies have increasingly adopted the variational autoencoder (VAE) framework as the foundation for such models. A major breakthrough in this direction was the introduction of VITS by SK Telecom [22; 23], which unified VAE-based representation learning, stochastic duration modeling, and adversarial training into a single architecture. This integration eliminated the need for explicit alignments and has been shown to improve synthesis quality, efficiency, and style transfer.  
Building upon the VAE framework, FreeVC [24] enhances alignment strategies to achieve higher-quality voice conversion, while Glow-WaveGAN2 [25] improves prosodic control and timbre consistency. DINO-VITS [26] incorporates semi-supervised learning to boost robustness under low-resource conditions. The effectiveness of VITS-based models has been validated against various competitive benchmarks. For instance, in the 2023 Singing Voice Conversion Challenge [27; 28; 29], models based on the VITS architecture achieved competitive rankings in naturalness and similarity evaluations. Despite their robustness and controllability, current VAE-based systems still face challenges in capturing fine-grained timbral variations and complex semantic representations typical of natural human speech.
## 5 Models Development
### Performance Evaluation  
To evaluate the performance of the models, 70% of the data has been randomly devoted to the training phase, and the other 30% is assigned to the testing phase. Then, the root mean squared error (\(RMSE\)), Pearson correlation coefficient (\(r\)), and determination coefficient (\(R^{2}\)) are employed as performance indices of the models. These statistical indicators can be characterized as below:  
\[RMSE=\sqrt{\frac{\sum\limits_{k=1}^{S}\left(P_{k}-T_{k}\right)^{2}}{S}} \tag{8}\]  
\[r=\frac{S\left(\sum\limits_{k=1}^{S}T_{k}\times P_{k}\right)-\left(\sum \limits_{k=1}^{S}T_{k}\right)\times\left(\sum\limits_{k=1}^{S}P_{k}\right)}{ \sqrt{\left(S\sum\limits_{k=1}^{S}T_{k}^{2}-\left(\sum\limits_{k=1}^{S}T_{k} \right)^{2}\right)\times\left(S\sum\limits_{k=1}^{S}P_{k}^{2}-\left(\sum \limits_{k=1}^{S}P_{k}\right)^{2}\right)}} \tag{9}\]  
\[R^{2}=\frac{\left[\sum\limits_{k=1}^{S}\left(T_{k}-\overline{T_{k}}\right) \cdot\left(P_{k}-\overline{P_{k}}\right)\right]^{2}}{\sum\limits_{k=1}^{S} \left(T_{k}-\overline{T_{k}}\right)\cdot\sum\limits_{k=1}^{S}\left(P_{k}- \overline{P_{k}}\right)} \tag{10}\]  
where \(P\) and \(T\) are the predicted and target values, and \(S\) is the total number of training or testing samples, respectively.  
This is also important to note that all the codes were developed in the MATLAB environment.
## 2 NB-PLC and RF Characterization
### RF Characterization
#### 2.2.2 RF Noise  
In addition to the AWGN, a major design challenge for wireless transmission on unlicensed bands is the presence of strong interference produced primarily by uncoordinated transmissions and neighboring impractical devices on the same frequency band.  
Such interference is characterized by three statistical models of impulsive noise: (1) the stable alpha-symmetric variable (S\(\alpha\)S), (2) the Middleton class-A (MCA), and (3) the Gaussian mixture (GM). The symmetric stable alpha-unsystematic variable is similar to the GM model, and the MCA probability density function (PDF) is a special case of the GM PDF. In the literature, to the best of the authors' knowledge, there are no works that present noise modeling in the 863-870 MHz wireless frequency band. Assuming the analogy between the sub-1 GHz and the 2.4 GHz band, authors in [44] model noise as a two-component Gaussian mixture random process and obtain the best distribution that fits the major source of RF interference (RFI) in a wireless channel from the RFI toolbox [45].  
Therefore, the probability density function of the GMM (Gaussian mixture model) is computed as a summation of complex Gaussian distributions with zero mean and unit variance \(\sigma_{k}^{2}\) and is given in Equation (8) as follows:  
\[f(x)=\sum_{k=1}^{K}\pi_{k}N_{c}\Big{(}x|0,\sigma_{k}^{2}\Big{)} \tag{8}\]  
where \(\pi_{k}\) is the mixing probability and \(\sigma_{k}\) is the variance of the \(k\)th Gaussian component.  
Both RF and NB-PLC technologies have their advantages and drawbacks, and the choice between them depends on factors such as the specific requirements of the utility company, the geographical characteristics of the environment, and regulatory considerations. In the next section, the authors present the environment characterization.
# Analytical Algorithm for Oxygen Concentration of Aircraft Fuel Tank in Various Inerting Stages
## 3 Analytical Algorithm  
The conventional way of solving the implicit ordinary differential equations, like (5), (6), and (8), needs the initial values of the oxygen concentrations and their derivatives [23]. The accuracy of computed results depend on the empirically given values that are difficult to be properly set. This section proposes an analytical algorithm to solve the dynamic model without the assumed initial derivatives of gas concentration.  
In most of flight process, the gas concentration in the unlage always falls because of fuel consumption, and the unlage is mainly under the pressurization or the exhaust process. During the descent phase, the gas concentration increases by injecting NEA to maintain the pressure difference between the unlage and the atmosphere. Therefore, the unlage washing can be concluded into 3 inerting stages, which are the descent phase, the pressurization process, and the exhaust process. The derivatives of variable, such as oxygen concentration and unlage pressure, can be calculated according to the state of each inerting stage based on Formulas (5), (6), and (8) to eliminate the uncertainty brought by the assuming the initial values.  
The analytical algorithm of solving the dynamic model of unlage washing is illustrated in Figure 2. The algorithm has three main steps, as follows.  
1. Identification of the inerting stage at the \(i\)th moment. During the descent phase, calculate the \(z^{\prime}_{Uov}(i)\), \(z^{\prime}_{Unv}(i)\), and \(\dot{v}_{I}(i)\) by the descent phase calculation method. In other flight phases, if the fuel tank is pressurized, calculate the \(z^{\prime}_{Uov}(i)\), \(z^{\prime}_{Unv}(i)\), and \(p^{\prime}_{U}(i)\) by the pressurization process calculation method. Otherwise, calculate the \(z^{\prime}_{Uov}(i)\), \(z^{\prime}_{Unv}(i)\), and \(\dot{v}_{O}(i)\) by the exhaust process calculation method.
2. Calculate \(z_{Uov}(i+1)\) and \(z_{Unv}(i+1)\) according to \(z_{Uov}(i)\), \(z_{Unv}(i)\), \(z^{\prime}_{Uov}(i)\) and \(z^{\prime}_{Unv}(i)\).
3. Repeating the previous two steps until the end of the flight to obtain the \(z_{Uov}\) regarding time.  
Figure 2: Analytical algorithm flow chart to calculate the unlage oxygen concentration.