esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment of a Nonlinear Creep Model Considering Temperature-pH Damage  
The Nishihara creep model can accurately explain the two stages of primary creep and steady-state creep in rock, but it cannot accurately describe the accelerated creep stage of rock. To describe the influence of temperature and pH on the creep characteristics of rock, this study replaces the viscoelastic element in the Nishihara creep model with a viscoelastic element that includes the influence factors of temperature and pH based on the Nishihara creep model. To describe the accelerated creep stage of rock mass, this study replaces the viscoplastic elements in the model with nonlinear viscoplastic elements. A nonlinear creep model under the coupled effect of chemical corrosion and temperature is constructed after two revisions to the Nishihara creep model, as shown in Figure 5.  
Figure 4: Influence of pH on the strain of viscoelastic element at the same temperature.  
Figure 5: Nonlinear creep model considering temperature–pH damage.  
Figure 3: Influence of temperature on the strain of viscoelastic element in an acidic environment.  
Let the total stress of the creep model be 1 and the total strain be 2.  
When \(\sigma\leq\sigma_{\mathrm{s}}\) in the creep model, only the viscoelastic and elastic elements participate in the creep, and the nonlinear viscoplastic element 2 does not deform. The equation of states is given as follows:  
\[\left\{\begin{array}{l}\sigma_{1}=E_{1}\varepsilon_{1}\\ \sigma_{3}=E_{2}\varepsilon_{3}+\eta_{2}(T,pH)\dot{\varepsilon}_{3}\\ \sigma=\sigma_{1}=\sigma_{3}\\ \varepsilon=\varepsilon_{1}+\varepsilon_{3}\end{array}\right. \tag{7}\]  
When \(\sigma>\sigma_{\mathrm{s}}\) in the creep model, elastic element 1, nonlinear viscoplastic element 2, and viscoelastic element 3 participate in creep. The equation of states is given as follows:  
\[\left\{\begin{array}{l}\sigma_{1}=E_{1}\varepsilon_{1}\\ \sigma_{2}=\sigma_{\mathrm{s}}+\eta_{1}(n,t)\dot{\varepsilon}_{2}\\ \sigma_{3}=E_{2}\varepsilon_{3}+\eta_{2}(T,pH)\dot{\varepsilon}_{3}\\ \sigma=\sigma_{1}=\sigma_{2}=\sigma_{3}\\ \varepsilon=\varepsilon_{1}+\varepsilon_{2}+\varepsilon_{3}\end{array}\right. \tag{8}\]  
\(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are stresses of elastic element 1, nonlinear viscoplastic element 2, and viscoelastic element 3, respectively. \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\) are the strains of elastic element 1, nonlinear viscoplastic element 2, and viscoelastic element 3, respectively. \(\eta_{1}(n,t)\) is the viscosity damping coefficient of the nonlinear viscoplastic element 2.  
By arranging Equations (7) and (8), the creep equation of the nonlinear creep model considering temperature-pH damage is obtained as follows:  
\[\left\{\begin{array}{l}\varepsilon=\frac{\sigma}{E_{1}}+\frac{\sigma}{E_{2} }\left(1-e^{-\frac{E_{2}\sigma[pH-pH_{0}]}{\eta_{2}}t}\right)\\ \varepsilon=\frac{\sigma}{E_{1}}+\frac{\sigma}{E_{2}}\left(1-e^{-\frac{E_{2} \sigma[pH-pH_{0}]}{\eta_{2}}t}\right)+\frac{\sigma-\sigma_{1}}{\eta_{1}}t^{n }\quad(\sigma>\sigma_{\mathrm{s}})\end{array}\right. \tag{9}\]
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
## 3 Examples
### Field Data Examples  
We used 2D field data to demonstrate the effectiveness of the WRT, as shown in Figure 9a. There were 126 offsets in total, the offset spacing was 24 m and each offset had 650 sampling points. The sampling interval was 4 ms, and the recording length was 2.6 s. Figure 9b shows the waveform and its upper envelope for trace 1 and trace 55.  
To test the reconstruction performance, we designed empty regions (from trace 61 to trace 70) in the seismic data, as shown in Figure 10a. From Figure 10b,c the WRT was significantly better than the CGRT with respect to the resolution in the Radon panel. Then, we used the CGRT and WRT to reconstruct the field seismic data gather. The reconstruction result is shown in Figure 11, and the area between the two red lines is the reconstruction region. It is clear that the reconstruction region of the WRT was better than the CGRT.  
Figure 8: (**a**) Waveform at first trace; (**b**) waveform at reconstruction trace; (**c**) waveform residual at first trace; (**d**) waveform residual at reconstruction trace.  
Figure 12 shows the waveform of the original data and the inversion results at trace 10 and trace 64 at the reconstruction region. The black line represents the original data, while the red line and the blue line represent CGRT and WRT, respectively. Compared with the CGRT, for the field data, the WRT kept the waveform more accurate and preserved the AVO. The SNR of the reconstructed results for the CGRT and for the WRT are 4.32 dB and 9.71 dB, respectively.  
Figure 10: (**a**) CMP gather with empty regions; (**b**) CGRT Radon model result; (**c**) WRT Radon model result.  
Figure 9: (**a**) Field CMP gather; (**b**) waveform and upper envelope of the two traces.  
Figure 11: (a) CGRT inverse transform result; (b) WRT inverse transform result.  
Figure 12: (a) Waveform at trace 10; (b) waveform at reconstruction trace 64; (c) waveform residual at trace 10; (d) waveform residual at reconstruction trace 64.  
For the field data, the differences in the calculation time were more apparent. The running time of the CGRT was 2850.137 s, while that of the WRT was 114.257 s. This proves that WRT is at least one order of magnitude faster than CGRT.
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 3 Statistical Methods and Results
### Elemental Abundances in Moon Samples with Low-Ca and High-Ca  
Although elemental abundances in moon samples were derived based on statistical distributions such as normal, log-normal, and alr-normal distributions, some items clearly deviate from their ideal distributions, such as CaO, FeO, Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), P\({}_{2}\)O\({}_{5}\), Cr\({}_{2}\)O\({}_{3}\), etc., as shown in Figure 2. It is worth mentioning that the distribution of CaO contents is near a bimodal distribution (Figure 2). In order to derive more meaningful elemental abundances, moon samples were classified into two types, as low-Ca and high-Ca samples, based on the CaO content boundary of 13.5% used in this paper.  
In the total 2365 analyzed moon samples, there were only 1979 records with valid CaO contents. According to the content boundary of 13.5% CaO, there were 1468 records classified as low-Ca samples and 511 as high-Ca samples. Therefore, moon samples with low Ca were about three-quarters of the total moon samples collected in this paper, and samples with high Ca were about one-quarter.  
With respect to the low-Ca moon samples and high-Ca moon samples, we used the same statistical methods as adopted for the total moon samples to calculate the elemental abundances of each type separately. The abundances of each type are also listed in Table 3, along with their counts of samples (n\({}_{0}\) and n).
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 2 Method
### Generation of Yield Gain Surfaces from Spectral Models
#### 2.6.2 Generation of Spectral Models for Yield Prediction  
The values of band surface reflectance and multispectral index brightness were extracted through the generation of regions of interest using part of the points of the point-type vector files (Figure 2C,F). Each georeferenced yield value was related to the respective digital number in the image. It is important to note that the 100 sampling locations were randomly selected with a minimum distance of 5 m between adjacent locations. After selecting the points, we calculated the Pearson model between yield and spectral values from the images with the respective derived vegetation index.  
We used the original bands of the PlanetScope imagery and the Normalized Difference Vegetation Index to generate the prediction models. We only used the original bands and index as predictor variables to avoid overfitting the generated models, which can occur when the number of parameters used in the regression is high.  
Analogous to the Kriging step, yield surfaces were generated considering two scenarios: algorithms structured with yield and reflectance values derived only from areas that received treatment and from areas that did not receive treatment. Notably, the prediction models for Br1 and Br2 were not used to estimate yield outside the plot from which the training samples were extracted. Therefore, 80% and 20% of the samples were used for the training and validation of the algorithms, respectively.  
In this step, the machine-learning algorithm used to generate the yield prediction models was the MLP Neural Network type. It was configured with three neurons in an intermediate layer. The learning rate was 0.3 and momentum was 0.2. This step was performed using Weka version 3.9.5 software ([[https://waikato.github.io/weka-wiki/downloading_weka/](https://waikato.github.io/weka-wiki/downloading_weka/)]([https://waikato.github.io/weka-wiki/downloading_weka/](https://waikato.github.io/weka-wiki/downloading_weka/)), accessed on 1 August 2021).
# Exploring Pre-Trained Models for Skin Cancer Classification
## 1 Introduction
This study presents a comprehensive evaluation of four pre-trained deep learning models--two convolutional neural networks (CNNs) (ResNet50 and VGG19) and two vision transformers (ViTs) (ViT-b16 and ViT-b32)--for skin lesion classification using a publicly available dermoscopic dataset. While CNNs have been widely applied in dermatological diagnostics, the potential of ViTs remains underexplored. This study provides a comparative analysis of CNNs and ViTs, assessing their respective effectiveness in skin cancer detection. A key novelty of this research is the systematic integration of XAI techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and integrated gradients, to enhance model transparency and interpretability. Unlike previous studies that primarily focus on CNN-based architectures or apply XAI techniques to a single model, this research evaluates multiple deep learning models across different architectures, offering a broader and more in-depth analysis of model explainability. By incorporating XAI, this study ensures that model decisions are interpretable, thereby improving clinician trust in AI-driven diagnostics. The primary contributions of this study are as follows:  
* Comparative evaluation of CNNs (ResNet50 and VGG19) and vision transformers (ViT-b16 and ViT-b32) for skin lesion classification, addressing a gap in prior research that predominantly focused on CNNs. * Improving the performance of the models employed and benchmarking them against models from previous research that utilized the same dataset in this analysis. * This study emphasizes the crucial role of interpretation methods, highlighting their importance in enhancing model transparency and building confidence in the results. By applying both (LIME and integrated gradients) techniques to the best-performing model, the study strengthens the interpretation and validation of the model's outcomes. * Exploration of transformer-based models in dermatology, assessing their viability against traditional CNNs.
## 4 Simulations and Results
### Simulation Metrics  
The performance of the proposed protocol was evaluated in terms of the average residual energy, the average number of surviving nodes, the average round when the first death node occurs, and the number of packets received by the base station.  
The average residual energy is a measure of network energy consumption. When the residual energy of a node falls below a threshold, the node is unable to join a cluster and sends/receives packets normally so that becomes a dead node. The average residual energy is calculated by:  
\[\text{Average residual energy}=\frac{\sum E_{i}^{n}}{N\cdot N_{SR}} \tag{26}\]  
where \(E_{i}^{n}\) denotes the residual energy of node \(i\) at the \(n\)th round. \(N_{SR}\) denotes the number of simulation groups.  
The average number of surviving nodes is also a measure of network energy consumption and is calculated by:  
\[\text{Average number of surviving nodes}=\frac{\sum N_{s}^{n}}{N_{SR}} \tag{27}\]  
where \(N_{s}^{n}\) denotes the number of nodes in the network with residual energy greater than 0 at the \(n\)th round.  
The average round when the first death node occurs is calculated by:  
\[\text{Average round when the first death node occurs}=\frac{\sum R_{d}}{N_{SR}} \tag{28}\]  
where \(R_{d}\) denotes the round when the first death node occurs.  
The number of packets received by the base station can be obtained directly by experimental simulation.
# Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach
## 3 Problem Description and Mathematical Model
### Mathematical Model
The first term (a) is the expected value to cover the demand of demand cell \(i\in\mathcal{I}\) when increasing the number of ambulances by one at each center for each time. An additional explanation of the first term was presented in [8]. The second term (b) is the cost incurred when a potential center \(c\in C_{new}\) is newly used. The third term (c) is the cost incurred if an ambulance is assigned to location \(c\) at time \(t\) and relocated to location \(c^{\prime}\) at time \(t+1\). As mentioned before, we computed the relocation cost as the difference in the distance back to the base center. Finally, the fourth term (d) minimizes the number of cells with less than one ambulance within the golden time. Constraint (2) implies that demand cell \(i\) is covered by at least \(k\) ambulances if \(k\) of the ambulances deployed in the centers serving demand cell \(i\) cover demand cell \(i\). Constraint (3) means that the sum of the number of ambulances deployed to all centers is equal to the total number of available ambulances at each time point. Constraint (4) is a constraint that assigns \(z_{c}\) a value of \(1\) if potential center \(c\) is used at least once in the planning horizon. Constraints (5) and (6) are constraints that connect the values at time \(t\) and time \(t+1\) by the ambulance's relocation. Constraints (7) and (8) are constraints that are logically established based on the number of ambulances, \(k\), that minimally cover demand cell \(i\). To give an example of the constraint (7), we assumed that an arbitrary demand cell \(i\) is covered by at least three ambulances. Since demand cell \(i\) is covered by at least three ambulances, we can say that it is also covered by two ambulances. Similarly, it is covered by one ambulance. To give an example of the constraint (8), we assumed that an arbitrary demand cell \(i\) is not covered by at least four ambulances. Since demand cell \(i\) is not covered by at least 4 ambulances, it cannot be covered by more than 5 ambulances.
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 8 Conclusions  
In the last decade, sentiment analysis enabled by NLP, machine learning, and deep learning techniques has also been attracting the attention of researchers in the educational domain in order to examine students' attitudes, opinions, and behavior towards numerous teaching aspects. In this context, we provided an analysis of the related literature by applying a systematic mapping study method. Specifically, in this mapping study, we selected 92 relevant papers and analyzed them with respect to different dimensions such as the investigated entities/aspects on the education domain, the most frequently used bibliographical sources, the research trends and patterns, what tools were utilized, and the most common data representation techniques used for sentiment analysis.  
We have shown an overall increasing trend of publications investigating this topic throughout the studied years. In particular, there was a significant growth of articles published during the year 2020, where the DL techniques were mostly represented.  
The mapping of the included articles showed that there is a diversity of interest from researchers on issues such as the approaches/techniques and solutions applied to develop sentiment analysis systems, evaluation metrics to assess the performance of the systems, and the variety of datasets with respect to their size and format.  
In light of the findings highlighted by the body of knowledge, we have identified a variety of challenges regarding the application of sentiment analysis to examine students' feedback. Consequently, recommendations and future directions to address these challenges have been provided. We believe that this study's results will inspire future research and development in sentiment analysis applications to further understand students' feedback in an educational setting.  
In future work, our plan is to further deepen the analysis that we performed in this mapping study by conducting systematic literature reviews (SLRs), as also suggested by [108].  
Conceptualization Z.K. and A.S.I.; methodology F.D. and Z.K.; Investigation and data analysis; writing--original draft preparation; writing--review and editing; supervision, Z.K., F.D., A.S.I, K.P.N. and M.A.W.; project administration, Z.K. and F.D. All authors have read and agreed to the published version of the manuscript.  
The APC was founded by Open Access Publishing Grant provided by Linnaeus University, Sweden.  
Not applicable.  
The authors declare no conflict of interest.
# Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
## Data and Methods  
### Study Area  
Beijing, located at 39\({}^{\circ}\)56\({}^{\prime}\) N latitude and 116\({}^{\circ}\)20\({}^{\prime}\) E longitude, extends geographically from 115\({}^{\circ}\)25\({}^{\prime}\) E to 117\({}^{\circ}\)30\({}^{\prime}\) E and from 39\({}^{\circ}\)26\({}^{\prime}\) N to 41\({}^{\circ}\)03\({}^{\prime}\) N. The city experiences a temperate monsoon climate, with hot, wet summers and cold, dry winters. Surrounded by the Taihang and Yanshan mountain ranges, Beijing is positioned near the Bohai Sea to the east and the Yellow River to the south. This climate and location support a rich plant life and contribute to the city's agricultural potential. Beijing, extending over 16,410.54 square kilometers and comprising 16 districts, boasts a varied landscape that includes both mountainous regions and plains. The mountainous areas, which constitute around 10,200 square kilometers or 62% of the city's total area, and the plains, spanning 6200 square kilometers, together create Beijing's rich geographical diversity. The city's average elevation is 43.5 m; the plains typically range from 20 to 60 m in elevation, while the mountains peak over 1000 m. In the context of this study, areas above 500 m are classified as mountains, and those below are classified as plains. The plains of Beijing are mainly used for farming and urban development, whereas the mountainous areas are largely covered by forests and grasslands. Over the past twenty years, Beijing has seen a significant increase in urbanization, with the rate increasing from 77.52% in 2000 to 86.6% in 2020, and it is expected to reach 99.46% by 2030 [24].
## 3 Feature extraction and dimension reduction
### Traditional multivariate analysis methods
#### 3.2.2 Auto-regressive modeling  
Auto-regressive (AR) analysis is an efficient feature extraction tool for time series data, widely used for speech analysis [39, 40]. As an all pole model (filter), a p-order AR model can be expressed as:  
\[\textbf{x}_{n}=a_{0}+\sum_{i=1}^{p}a_{i}\textbf{x}_{n-i}+\varepsilon_{n} \tag{2}\]  
where **x** is time series data, \(a_{i}\) (i=1,...,p) are AR coefficients, \(a_{0}\) is a constant, and \(\varepsilon_{n}\) is an error estimate. Briefly, using AR coefficients, we can represent the \(n\)th value **x\({}_{n}\)** with its previous \(p\) values: **x\({}_{n\text{-}1}\)**, **x\({}_{n\text{-}2}\)**,..., **x\({}_{n\text{-}p}\)**. The goal of an AR model is to estimate the AR coefficients that can fit the original data series as closely as possible through an optimization process.  
AR coefficients systematically characterize a changing trend in the data series, so in addition to the common effects of signal treatment, such as noise removal and dimension reduction, the AR model shows its significant advantage in dealing with mild signal shifts in the time series (such as chromatograph data). AR modeling can permit us to directly work on time shifted chromatograph data without using time alignment as a preprocessing step. This advantage has recently been proven in a GC/MS based sensor design for bacterial identification [41].
# Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine
## 3 The Proposed Adaptive Weight Online Sequential Extreme Learning Machine
### Benchmark Data Sets  
In this subsection, we make a comparison between AWOS-ELM and OS-ELM on six time series prediction problems consisting of three artificial time series and three actual time series. Among them, a monthly milk production dataset, having 100 training and 44 testing data, and an electricity production dataset, having 350 training and 102 testing data, are obtained from the well-known Time Series Data Library. The sunspot time series, having 2500 training and 690 testing data, is the monthly mean total sunspot number from January 1749 to December 2015 and is obtained from Solar Influences Data Analysis Center. Pseudo periodic synthetic time series, having 8000 training and 1801 testing data, is found from the University of California, Irvine (UCI) repository. The other two chaotic series, viz. Mackey-Glass and Logistic time series, are generated according to mathematical equations. The Mackey-Glass series \(\left\{x_{k}^{mg}|k=1,2,3,\dots\right\}\) is generated according to the following differential delay equation [25]  
\[\frac{dx^{mg}(t)}{dt}=\frac{a(t-\tau)}{1+x^{mg}(t-\tau)}-bx^{mg}(t) \tag{30}\]  
where \(\tau=17\), \(a=0.2\), \(b=0.1\), and \(x(0)=1.2\). The Logistic series \(\left\{x_{k}^{lo}|k=1,2,3,\dots\right\}\) is produced by the following recursive equation [26],  
\[x_{k+1}^{lo}=\lambda x_{k}^{lo}\Big{(}1-x_{k}^{lo}\Big{)} \tag{31}\]  
where \(\lambda=3.5\). The prediction performance comparison between AWOS-ELM and OS-ELM on benchmark data sets is given in Table 1. The mean and standard deviation of weights for AWOS-ELM algorithm are listed in Table 2. The results in Tables 1 and 2 are the mean values of 50 trials.  
As observed from Table 1, the training time for AWOS-ELM is close to that for OS-ELM in various data sets, just as we expected. When the hidden nodes number and chunk size are set to be consistent, AWOS-ELM has lower RMSE and lower standard deviation than OS-ELM in most data sets. This implies higher prediction accuracy and the superior prediction stability of AWOS-ELM algorithm due to the accessed confidence coefficient. Furthermore, from Table 2, we can find that the mean weights are less than 0.92, which implies that the proportion of low confidence data is considerable. Additionally, the standard deviations are quite large, which implies that there is a great difference between the weights of normal data and that of low confidence data in the AWOS-ELM algorithm. Thus, the AWOS-ELM can efficiently increase the prediction performance.