esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Parameter Settings  
The HSI in three datasets is sampled into blocks. The size of blocks in the Salina Valley dataset is \(6\times 6\times 204\), that of blocks in the Pavia University dataset is \(8\times 8\times 103\), and that of blocks in the Houston University dataset is \(6\times 6\times 144\). Then, the blocks in each dataset are divided into the training set, the validation set, and the test set with equal spacing and non-overlapping. In Salinas, the training set, the validation set, and the test set, respectively, account for 3.76%, 3.76%, and 92.56%. At Pavia University, the ratios are 6.64%, 6.64%, and 86.72%. At Houston University, the ratios are 18.6%, 18.6%, and 62.8%.  
The above data of different proportions are input into the network for training and testing. To ensure that the number of pixels in the training set, the validation set, and the testing set is consistent with those of other methods, in Salinas, the experiment is repeated nine times with different training samples. The experiment number is ten times and six times that of the Salina for Pavia University and Houston University datasets, respectively. The computer configuration for this experiment is as follows: an i7-8700 K processor, 32 GB of RAM, and a GTX1080 GPU. The kernel size and output size of each dataset are shown in Tables 1 and 2. Taking the Salinas Valley data as an example, input1 passes through the SFCI branch to obtain 256 feature maps and 256 feature maps of the same size as the SAS branch. Then, these features are concatenated together and predicted by a softmax layer.
## 2 Results and Discussion
### Physical Characterization  
The particle size distribution of the microencapsulated compounds is a crucial factor determining the release rate and sensory impact of the encapsulated samples.  
Figure S1 in the Supplementary Materials presents the particle size metrics of different wall materials. The median particle sizes (Dx (90)) of the control, CMC, acacia, gelatin, and maltodextrin are 12.71 \(\pm\) 0.10 \(\upmu\)m, 131.67 \(\pm\) 0.67 \(\upmu\)m, 10.71 \(\pm\) 0.11 \(\upmu\)m, 126.58 \(\pm\) 5.65 \(\upmu\)m, and 14.27 \(\pm\) 0.17 \(\upmu\)m. The control group shows a concentrated particle size distribution between 1.36 \(\upmu\)m and 81.01 \(\upmu\)m. Maltodextrin-based microcapsules exhibit a similar but slightly narrower distribution, indicating smaller and more uniform particles. In contrast, CMC-based microcapsules display a much broader size distribution, with a peak around 13.56 \(\upmu\)m and a range extending up to 374.85 \(\upmu\)m, suggesting wide variance in particle size. Gelatin-based microcapsules are smaller than CMC, with a peak near 7.65 \(\upmu\)m and extending up to 374.85 \(\upmu\)m. Acacia-based microcapsules have a distribution similar to the control and maltodextrin groups. These findings highlight that the choice of encapsulation material significantly affects the particle size distribution of microcapsules, which, in turn, may influence their functional properties and potential applications.  
The particle size of microcapsules is typically correlated with their release characteristics. Smaller microcapsules ranging from 1 to 10 \(\upmu\)m, due to their relatively larger surface area, generally exhibit faster release rates, whereas larger microcapsules ranging from 100 to 500 \(\upmu\)m may facilitate a slower and more sustained release. However, smaller microcapsules may have higher solubility, which means they have better application prospects for instant tea [25]. In this study, the microcapsules prepared with gelatin and CMC have a larger particle size, which may indicate better retention of fragrance and nutrients. However, this could also result in poorer solubility. In contrast, microcapsules made with maltodextrin and acacia have smaller particle sizes, so they might not retain the fragrance as effectively [25].  
Maintaining low moisture content is essential for preserving the aroma, stability, and quality of instant tea powder during storage and handling. In this study, the moisture contents of the gelatin, CMC, acacia, and maltodextrin were recorded to be 3.23%, 3.98%, 4.67%, and 4.98%, respectively, demonstrating the superior moisture retention of gelatin. Meanwhile, the moisture content of the control sample was higher, at 6.02%. For enhanced stability, it is recommended that the moisture content for jasmine instant tea powder be maintained between 3% and 5% [36].  
The Scanning Electron Microscopy (SEM) image (Figure 3) confirms the particle size distribution of different wall materials. The particle sizes of the control group, gum arabic, and maltodextrin are smaller and more uniform, whereas the particle sizes of gelatin and CMC are larger and more variable.
# Hydropower Functional Zoning with Crowdsourced Geospatial Data: A Case Study in Sichuan Province
## 2 Materials and Methods
### Data Source and Preprocessing
#### 2.2.2 Other Data  
The normalized-difference vegetation index (NDVI) was applied with the Medium Resolution Spectral Imager (MERSI) II NVI product of the FengYun-3D satellite, launched by the China Meteorological Administration. The spatial resolution was 250 m and the time resolution was 10 days. In addition, the MERSI-II NVI product generated annual average standard NDVI datasets from 2019 to 2021 after performing waveband operations through projection transformation, mosaicking, and image tailoring. For the digital elevation of the data, ASTER's Global Digital Elevation Model (GDEM) with a spatial resolution of 30 m was used, which was downloaded from the National Scientific Data Service Platform. As far as the soil-erosion-intensity data are concerned, the basic geological data from 2019 were applied and calculated, with references to Technical Regulations on Water and Soil Conversation Monitoring. The land-use data applied the data with a spatial resolution of 1 km from 2020, and meanwhile, the applied geological-disaster-point data from 2019 were downloaded from the Resource and Environmental Science Data Center, Chinese Academy of Sciences. The runoff data from 2015 were download from a Big Earth Data Platform for Three Poles [44].
## 5 Result and Discussion
### Evaluation Metrics  
The metrics for comparing and analyzing inference performance through four deep learning frameworks are as follows.  
1. CPU utilization: this refers to the measurement rate of CPU usage that occurs during inference performance. Nvidia AGX Xavier has eight cores, and the usage of eight cores is calculated as an average. The value is expressed as a percentage and has a value between 0 and 100.
2. GPU utilization: this refers to the measurement rate of GPU usage that occurs during inference performance. The value is expressed as a percentage and has a value between 0 and 100.
3. Accuracy: this refers to the average accuracy of person and car objects that occur during the measurement of inference performance. Even if the number of persons and cars is measured differently for each frame, it is calculated as the overall average of the measured objects. In addition, when objects other than person and car are detected, the accuracy of other objects is discarded in order to reduce errors in false detection and non-detection. The value is expressed as a percentage and has a value between 0 and 100.
4. Latency: This is the execution time measured when performing inference on the image per frame used for inference of the sample video. The value is expressed as milliseconds.
5. Power: This refers to the power consumption of the Nvidia AGX Xavier used for inference. The value is indicated by W.
# Public-Key Cryptography Based on Tropical Circular Matrices
## Appendix A An Example of Protocol 1 with Small Parameters  
We choose the parameters \(k=5\) and \(s=t=9361\) and the entries of the matrices in \([0,2^{15})\). The public matrix \(Y\) is as follows:  
\[Y=\begin{pmatrix}8630&29,391&21,921&18,968&25,014\\ 15,306&5461&18,973&800&1786\\ 7986&27,430&22,510&11,233&30,900\\ 2398&6071&25,269&27,186&4328\\ 18,306&10,527&16,873&11,565&9569\end{pmatrix},\]  
1. [PERSON] selects at random two \(t\)-circular matrices \(P_{1},Q_{1}\) as follows: \[P_{1}=[297,21,730,\,15,290,\,10,135,\,19,522]^{9361}\] \[Q_{1}=[21,654,\,19,077,\,27,810,\,23,876,\,1267]^{9361}\] [PERSON] computes \(K_{a}=P_{1}YQ_{1}\). She sends the matrix \(K_{a}\) to [PERSON]. \[K_{a}=\begin{pmatrix}26,578&19,555&38,342&32,846&29,893\\ 3350&25,959&16,386&21,160&11,725\\ 24,783&18,911&30,607&33,184&22,158\\ 5892&13,323&16,996&23,702&26,279\\ 11,133&29,231&21,452&27,798&21,563\end{pmatrix}.\](2) [PERSON] selects at random two \(t\)-circular matrices \(P_{2}\), \(Q_{2}\) as follows:  
\[P_{2}=\left[1059\,4901\,20,575\,21,400\,4378\right]^{9361}\]  
\[Q_{2}=\left[8556\,14,895\,30,549\,31,378\,15,257\right]^{9361}\]  
[PERSON] computes \(K_{b}=P_{2}YQ_{2}\). He sends the matrix \(K_{b}\) to [PERSON].  
\[K_{b}=\begin{pmatrix}18,245&27,756&29,434&23,095&24,081\\ 18,102&15,076&16,754&10,415&11,401\\ 17,601&18,918&20,596&14,257&15,243\\ 12,013&15,686&31,029&20,282&13,943\\ 15,855&19,528&26,488&21,180&17,785\end{pmatrix}.\]  
(3) [PERSON] computes \(K=P_{1}K_{b}Q_{1}\). [PERSON] computes \(K=P_{2}K_{a}Q_{2}\).  
\[K=\begin{pmatrix}25,645&29,170&38,681&40,359&34,020\\ 12,965&29,027&26,001&27,679&21,340\\ 16,807&28,526&29,843&31,521&25,182\\ 15,507&22,938&26,611&33,317&31,207\\ 19,349&26,780&30,453&37,159&31,178\end{pmatrix}.\]
# Nonparametric Probability Density Function Estimation Using the Pade Approximation
###### Abstract  
Estimating the Probability Density Function (PDF) of observed data is crucial as a problem in its own right, and also for diverse engineering applications. This paper utilizes two powerful mathematical tools, the concept of moments and the relatively little-known Pade approximation to achieve this. On the one hand, moments encapsulate crucial information that is central to both the \"time-\" and \"frequency-\"domain representations of the data. On the other hand, the Pade approximation provides an effective means of obtaining a convergent series from the data. In this paper, we invoke these established tools to estimate the PDF. As far as we know, the theoretical results that we have proven, and the experimental results that confirm them, are novel and rather pioneering. The method we propose is _nonparametric_. It leverages the concept of using the moments of the sample data--drawn from the unknown PDF that we aim to estimate--to reconstruct the original PDF. This is achieved through the application of the Pade approximation. Apart from the theoretical analysis, we have also experimentally evaluated the validity and efficiency of our scheme. The Pade approximation is asymmetric. The most unique facet of our work is that we have utilized this asymmetry to our advantage by working with two mirrored versions of the data to obtain two different versions of the PDF. We have then effectively \"superimposed\" them to yield the final composite PDF. We are not aware of any other research that utilizes such a composite strategy, in any signal processing domain. To evaluate the performance of the proposed method, we have employed synthetic samples obtained from various well-known distributions, including mixture densities. The accuracy of the proposed method has also been compared with that gleaned by several State-Of-The-Art (SOTA) approaches. The results that we have obtained underscore the robustness and effectiveness of our method, particularly in scenarios where the sample sizes are considerably reduced. Thus, this research confirms how the SOTA of estimating nonparametric PDFs can be enhanced by the Pade approximation, offering notable advantages over existing methods in terms of accuracy when faced with limited data.  
Pade approximation; moments; nonparametric probability estimation 202518888
## 3 Results  
The search in the databases identified 1035 (ScienceDirect), 1220 (PubMed), 932 (IEEE Xplore), and 32 articles (SURF database). Screening the references of the identified articles revealed 27 other relevant articles. After the exclusion of duplications, 1807 journal articles remained. After titles and abstracts were screened, 1776 non-relevant articles remained. Thirty-one (full-text) articles were assessed for eligibility. Twenty-three more were excluded because eye-tracking was not performed in these studies. They dealt with training the visual search strategy, or the studies were concerned with the accuracy of the eye-tracking measurement in VR. Another study was excluded because it re-analyzed the data of an already included study. A total of seven (in situ vs. video presentation: n = 4; perspectives in video presentations: n = 2; reality vs. VR: n = 1) studies was included (ScienceDirect: n = 2; PubMed/SURF database: n = 1; Citations: n = 5; see Figure 1). Eye-tracking technology is a frequently researched topic in sports science. The conducted studies cover a wide range of different subject areas (see [7]). However, a meaningful representation of such studies focusing on a specialization on the influence of athletes' perspectives (in situ vs. video presentation or in situ vs. aerial view) has been lacking so far.  
Nevertheless, the research status allows for conclusions to be drawn about differences in gaze behavior. A reason for the weak state of research could be the complex execution of eye-tracking studies. Researchers have to create natural game situations or real opponents' actions for a study with an in situ design. To create a video presentation design, they have to record the videos of actions, which is laborious. Because of this complexity, VR technologies come into focus.
###### Abstract  
Domestic, agricultural, and industrial waste has been investigated as a substitute for activated carbon adsorbents. For instance, the transformation of tea waste to biochar can be utilized as a substitute for activated carbon adsorbents. In this study, tea waste-based biochar adsorbents (biochar,/reduced graphene oxide (biochar/rGO), biochar/reduced graphene oxide/deep eutectic solvent-cetyltrimethylammonium bromide (biochar/rGO/DES-CTAB), and biochar/reduced graphene oxide/deep eutectic solvent-glycerol (biochar/rGO/DES-glycerol)) were synthesized by simple thermal treatment of tea waste and sucrose followed by modification with reduced graphene oxide and deep eutectic solvents. The obtained materials were characterized using a range of spectroscopy techniques, Fourier Transformed Infrared Spectroscopy (FTIR), X-ray diffraction (XRD), Scanning Electron Microscopy-Energy-Dispersive X-ray Spectroscopy (SEM-EDS), Brunauer, Emmett, and Teller (BET) surface area analysis, and pH at point of zero charge (pH\({}_{\text{PZC}}\)). The obtained results showed that the principal material, i.e., biochar was modified, and FTIR results confirmed the presence of added functional groups. SEM images revealed surface structural changes, and BET showed a decrease in pore size from 10.16 nm to 6.87 nm. The synthesized materials were applied for the removal of ZDV and phenol from the aqueous medium. Batch adsorption studies were conducted to optimize operating parameters such as the adsorbent dose, solution pH, contact time, and initial concentration. Pseudo-first-order (PFO), Pseudo-second-order (PSO), and intraparticle diffusion (IPD) kinetic models were determined to investigate the mechanism of the adsorption process. The coefficient of correlation, R\({}^{2}\), was used to determine the best fit of the kinetic models. The adsorption results showed that the DES-glycerol-modified adsorbent was more efficient in removing the pollutants ZDV and phenol than biochar, biochar/rGO, and biochar/rGO/DES-CTAB adsorbents. In addition, the results showed that an acidic medium of pH 2.00 and a contact time of 1 h 30 min and 30 min is sufficient for removing ZDV and phenol, respectively, from an aqueous medium.  
biochar; deep eutectic solvents; adsorbent; organic pollutants; wastewater
## 2 Background and Proposed Method
### Speaker Verification Model  
Speech features, including MFCC or Mel-Spectrograms, are extracted through the processing of audio by a characterize. Subsequently, these extracted features are utilized as input for various neural networks to derive deep embedding features. In the final step, the likelihood of the speaker's identity is determined through the application of an activation function. Among the commonly employed baseline models in the field are TDNN, ECAPA-TDNN, Res2 net, Resnet-SE, Eres2 net, and CAM++. The ECAPA-TDNN employs several advanced approaches, including channel-dependent and context-dependent statistics pooling, 1-dimensional Squeeze Excitation Res2 Blocks, and a multi-layer feature aggregation and summation mechanism. CAM++ employs the D-TDNN [50] as its backbone architecture, incorporating strategies like context-aware masking.
## 6 Conclusions  
In this study, three-dimensional linear and nonlinear finite element modeling of SHS columns under pure compression was carried out. Steel material was used, including plasticity damage, which includes the isotropic hardening plasticity behavior. The results from the numerical analysis of the present study were validated by the results from the mathematical formulation. A comparative study was performed, using finite element analysis to estimate the critical load and design buckling resistance with analytical calculations following Eurocode 3.  
Focusing on the results and discussion presented in this study, the next conclusions are described below:  
* For the elastic buckling or Euler load determination, the finite element method resulted in a close calculation from the mathematical formulation, with a variation between 0 and 6.8%.
* For the design buckling resistance and the critical load determination, the deviation between the finite element method and the mathematical formulation is between 0 and 14.7%. The higher relative errors are obtained for lesser cross-section sizes in the study.
* According to the needs for the design of the columns, by increasing the length and reducing the cross-sectional size, the design buckling resistance also decreases. These demonstrate that the length and size of the column cross-section can meaningfully increase the structural behavior of SHS columns. Also, the material properties stiffness is incorporated as a great effect in its resistance.
* Three-dimensional linear and nonlinear finite element modeling was demonstrated to be the best instrument for researching the performance and behavior of steel columns under pure compression, incorporating the correct material properties and boundary conditions.
* Finally, the great meaning of the present investigation was to include the effect of initial column imperfections, including simultaneous nonlinearity with material plasticity damage and the study of all these combined effects on the impact of buckling resistance.
* The tested numerical method will be possible to implement in other future column analyses where other parameters could be different.  
Additional and further studies are suggested to improve the evaluation and behavior of the numerical development presented but extended to other column cross-section configurations, including the variable column wall thickness.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 5 Discussion
### Theoretical Consideration for Diagenetic Overprints  
Using the original C-O isotope compositions of carbonate to decipher depositional environments requires a thorough understanding of the effects of diagenesis. The \(\delta^{18}\)O values of sedimentary carbonate are very sensitive to diagenetic alteration. On the one hand, if the \(\delta^{18}\)O values are less than \(-10\)%, it is likely that the primary isotopic signals of carbonate rocks have been highly altered and may no longer provide the original information of the sedimentary conditions [21]. On the other hand, if the \(\delta^{18}\)O values range from \(-10\)% to \(-5\)%, alternation may occur to variable degrees while depositional \(\delta^{18}\)O composition can be preserved [21; 22; 23]. Moreover, the relationship between \(\delta^{18}\)O and \(\delta^{13}\)C values can also determine whether it is affected by diagenetic alteration or not: a high correlation between both isotope values represents later alternation between depositional and diagenetic signals, whereas data discreteness suggests primary fraction during precipitation of carbonate minerals [21; 24; 25]. The \(\delta^{18}\)O values of the studied samples, except for PM04-22-1, PM04-23-1, and PM04-24-1, are mostly greater than \(-10\)%. There is no apparent linear relationship between \(\delta^{18}\)O and \(\delta^{13}\)C values (R\({}^{2}\) = 0.296, Figure 2). It can be inferred that the samples are less affected by diagenetic alteration and can reflect the original information of carbonate precipitation. Therefore, it is reliable to analyze the sedimentary environment of the studied carbonate rocks by O and C isotopes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Sample No.** & **V** & **Cr** & **Cu** & **Sr** & **Th** & **U** & **Ba** & **Sr/Ba** & **Sr/Cu** & **Th/U** & **V/Cr** \\ \hline PM04-0-1 & 4.64 & 3.01 & 3.67 & 280 & 0.47 & 1.38 & 26.4 & 10.61 & 76.29 & 0.34 & 1.54 \\ PM04-1-1 & 3.39 & 2.35 & 1.84
# Research on the Prediction of Operator Users' Number Portability Based on Community Detection
## 2 Modeling and Experiment Comparison
### Comparison of Feature Selection Methods  
Feature selection refers to the selection of N features from the existing M features, to optimize the specific indicators of the system. It is the process of selecting some of the most effective features from the original features to reduce the dimension of the dataset. It is an important means to improve the performance of learning algorithms and a key data preprocessing step in pattern recognition.  
Feature selection includes three methods, namely Filter, Wrapper, and Embedding [20].  
Due to the variety of feature selection methods and according to the different combinations of feature selection and learners [21], in this paper, we tried different feature selection methods, compared their advantages and disadvantages, and selected the most suitable method for this project.  
In this article, we attempted to evaluate and compare 125 attributes. The encapsulated code takes about 9 h to run, which is much longer than the filtering and embedded algorithms. Both the filtering and embedded algorithms can generate visual graphics, which are easy for researchers to observe and can be deleted by setting relevant thresholds.  
From the perspective of project complexity, the filtering algorithm is independent of the specific learning algorithm and has the advantage of high operational efficiency. However, the filtering algorithm needs to be run twice. The first run is used to calculate the correlation between attributes and labels, and the second run is to calculate the correlation between two attributes. The embedded algorithm integrates feature selection into the classification model. Although it only takes one step, it runs the prediction model again after filtering the feature attributes due to the subsequent feature selection of the trained model cannot be performed.  
The advantages and disadvantages of the three feature selection methods are compared in Table 3:  
To sum up, in this paper, we chose to use the filtering method for feature selection because this method is not as time-consuming, easy to operate, does not get stuck in a local optimum solution, and can observe the correlation between two attributes.