esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Data Preparation  
#### 2.2.1 Optical Data  
We used pre- and post-landslide event Sentinel-2 data (Table 1). The data selection was made based on the cloud cover estimates (<30%) provided by the European Space Agency (ESA) for pre- and post-event datasets, using the Google Earth Engine (GEE) platform.  
Figure 2: **(a)** Photograph showing the Hitardalur landslide and the landslide-dammed lake soon after the event occurred, i.e., taken on the afternoon of 7 July 2018 (photograph: © [PERSON]). **(b)** View from the landslide deposition area towards the landslide source area (photograph: © [PERSON]; 7 July 2018). **(c)** Water from the dammed lake finds a new way (photograph: © [PERSON]; 7 July 2018). **(d)** View from the Fagraskögarfjall mountain towards the Hitardalur valley showing the landslide deposition area and the new watercourse flowing out from the lake into the Stekka river (photograph: © [PERSON]; 23 November 2018).
## 4 Conclusions  
In this work, the time consumption of the data preprocessing for ML was enhanced. Regularization using LASSO was integrated, instead of ElasticNet, to reduce the preprocessing procedure and the computational time. Moreover, the computational time and performance of different ML models based on regularization technique were evaluated. The ML performance was evaluated using the ROC curves and AUC scores for each specific class. The classes were RMa LoS, RMa NLoS, UMa LoS, and UMa NLoS. The ML algorithms used are KNN, SVM, k-Means, and GMM.  
The obtained results show that the adoption of LASSO is better than ElasticNet, as it performs the same unwanted features removal in a shorter time. These unwanted features are esD, asD, DS, as they achieved 0 regularization coefficient values. The LASSO runtime was 0.33 s, while the ElasticNet runtime was 0.67 s. Then, the ML algorithms were performed. The time results show that the KNN training time and testing time were 0.011 s and 0.17, respectively. The model training time of the linear SVM is 0.14 s and the testing time was 0.015. Therefore, the SVM was the fastest supervised learning algorithm used, as its runtime was faster than KNN and it achieved a 0.994 AUC score. The k-Means showed the slowest classification time with 0.26 s and the worst performance by an overall score  
Figure 7: ROC and AUC scores for each scenario versus the others.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **KNN** & **SVM** & **k-Means** & **GMM** \\ \hline
**Accuracy** & 99\% & 99\% & 97\% & 98\% \\ \hline
**Fit \& Predict Time (s)** & 0.181 & 0.155 & 0.26 & 0.087 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time evaluation of each algorithm.  
of 0.983. The runtime of the fit and predict function of GMM was 0.087 s and achieved an overall score of 0.989 AUC score in ROC curves. The runtime of the GMM clustering may be considered as the fastest one among k-Means and the supervised algorithms. The GMM was the most time-efficient algorithm among both supervised and unsupervised learning algorithms.  
For future work, the optimization of GMM for scenario classification task can be considered. Long Short-Term Memory (LSTM) can be investigated in wireless channel classification and prediction. The LSTMs were developed to deal with the vanishing gradient problem that can be encountered when training traditional networks. Moreover, the study of the Doppler effect is a good point to be considered in a future work.  
Conceptualization, A.Z., M.H.A. and W.K.B.; Data curation, A.M.; Formal analysis, A.Z., A.M., M.H.A. and W.K.B.; Investigation, A.Z., A.M., M.H.A. and W.K.B.; Methodology, A.Z., A.M., M.H.A. and W.K.B.; Resources, A.M.; Software, A.M.; Supervision, A.Z., M.H.A. and W.K.B.; Validation, M.H.A. and W.K.B.; Writing--original draft, A.M.; Writing--review & editing, A.Z., M.H.A. and W.K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No data is available.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Bioactive Compounds of OM Extracts  
#### 2.6.1 Spectrophotometric Determination of Total Polypphenols  
The assays to evaluate the total polyphenol content (TPC) and individual polyphenols were comprehensively described in our previous study [24]. Utilizing the Folin-Ciocalteu assay, we determined TPC. Briefly, 200 \(\mu\)L of the sample and 200 \(\mu\)L of Folin-Ciocalteu reagent were mixed, and, after 2 min, 1600 \(\mu\)L of aqueous sodium carbonate solution (5% \(w/v\)) was added in a 2 mL Eppendorf tube. Immediately afterward, incubation of the samples was conducted in a water bath at 40 \({}^{\circ}\)C for 20 min, and the absorbance at 740 nm was immediately recorded. As several protocols use a solvent in which gallic acid has the highest solubility (i.e., methanol) [25], the total polyphenol concentration (C\({}_{\text{TP}}\)) was calculated (Equation (1)) through a calibration curve ranging from 10-100 mg/L of gallic acid (R\({}^{2}\) = 0.9996) in methanol, with the results being expressed as mg of gallic acid equivalents (GAE) per g of dried weight (dw).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Design Point**} & \multicolumn{4}{c}{**Independent Variables**} \\ \cline{2-5}  & **X\({}_{\text{1}}\) (R, mL/g)** & **X\({}_{\text{2}}\) (C, \% v/v)** & **X\({}_{\text{3}}\) (P, P/min)** & **X\({}_{\text{4}}\) (\(t_{\text{r}}\) min)** \\ \hline
1 & 3 (60) & 2 (50) & 1 (12) & 3 (20) \\
2 & 1 (20) & 2 (50) & 2 (30) & 2 (12) \\
3 & 1 (20) & 3 (100) & 3 (48) & 3 (20) \\
4 & 1 (20) & 1 (0) & 1 (12) & 1 (4) \\
5 & 3 (60) & 1 (0) & 3 (48) & 2 (12) \\
6 & 2 (40) & 1 (0) & 2 (30) & 3 (20) \\
7 & 2 (40) & 2 (50) & 3 (48) & 1 (4) \\
8 & 2 (40) & 3 (100) & 1 (12) & 2 (12) \\
9 & 3 (60) & 3 (100) & 2 (30) & 1 (4) \\ \hline \end{tabular}
\end{table}
Table 2: The four variables that Taguchi design involves along with their corresponding measured response values.  
\[\text{TPC (mg GAE/g dw)}=\frac{\text{C}_{\text{TP}}\times V}{w} \tag{1}\]  
In Equation (1), \(V\) (expressed in L) and \(w\) (expressed in g) denote the volume of the extraction medium and the dry weight of the sample, respectively.
## 2 Background and Related Work
### Dataflow Graphs and Neural Network Models
The inference rate of the communication links between each pair of devices is calculated as the minimum value between the transfer performance of each link divided by the total communication requirement of the two partitions involved in this link:  
\[\text{inference rate}_{links}=\text{min}\left[\left(\frac{\text{link performance}}{\text{communication load}}\right)_{dq}\right],\forall d,q\in 1,...,p, \tag{3}\]  
in which \(dq\) represents the communication link between devices \(d\) and \(q\). Thus, taking into account the previous equations, in the partitioning of Figure 1b, device A can perform \(18/0=\infty\) inferences/s, which means device A does not limit the inference rate. The communication link between device A and device B can perform \(4/8=0.5\) inference/s. Device B can perform \(18/18=1\) inference/s. Therefore, the inference rate of this partitioning is 0.5 inference/s, which is the minimum value among the inference rate of the devices and the communication links. It is worth noting that this partitioning is valid because both partitions respect the memory limit of the devices.
## 1 Introduction
These advancements are anticipated to yield positive impacts across diverse applications, ranging from autonomous driving to intelligent surveillance systems.
## 2 Theory and Methodology
### Univariate Interpolation Schemes
#### 2.2.3 Interpolatory Univariate Quadrature  
Considering the UQ goal of estimating specific statistics given by Equation (2), we may use an available interpolation \(\mathcal{I}_{i}\left[g\right]\) to derive an appropriate quadrature scheme. For example, assuming an univariate interpolation given by:  
\[\mathcal{I}_{i}\left[g\right]\left(y\right)=\sum_{j=1}^{m(i)}s_{i,j}l_{i,j} \left(y\right),\]  
the expected value of the QoI can be estimated as:  
\[\mathbb{E}\left[g\right]\approx\mathbb{E}\left[\mathcal{I}_{i}\left[g\right] \right]=\sum_{j=1}^{m(i)}s_{i,j}w_{i,j}, \tag{8}\]  
where \(w_{i,j}\) are quadrature weights given by:  
\[w_{i,j}=\mathbb{E}\left[l_{i,j}\right]=\int_{\Xi}l_{i,j}\left(y\right)\varrho (y)\mathrm{d}y.\]  
Quadrature schemes for the estimation of statistics other than the expected value can be derived in a similar way, by applying the expectation operator along with the associated functional \(\phi\).
## 2 SR Basic Model of Compressed Sensing  
Compressed sensing theory gives a method to reconstruct the down-sampled signal through nonlinear optimization technology [25]. Suppose that the desired high-resolution image is represented as an n-dimensional column vector \(x\in R^{n}\), where \(n\) is a larger value. Theoretically, \(x\) can represent any one-dimensional signal. For this problem, \(x\) is an n-pixel gray image (or an n-pixel image of a channel of RGB image) that has been converted into an \(n\times 1\) dimensional vector. SR is to obtain the HR image \(x\) from the LR image \(y\in R^{m}\), where \(m\ll n\). The goal of this paper is to recover the high-resolution signal \(x\) using only \(y\) as the input.  
This problem is ill posed. Due to the reduction of resolution and the loss of information, an LR image can be mapped to multiple potential high-resolution images. In this case, the key assumption of compressed sensing theory needs to be applied, that is, the transformation shape \(\widetilde{x}\) of signal \(x\) is sparse under a certain base \(D\), and there are \(k\) non-zero factors in \(\widetilde{x}\). It is a reasonable assumption that high-resolution images can be compressed in different transform domains, such as the wavelet domain, Fourier domain or artificially constructed dictionary. At this time, according to the rationality of the above assumptions, combined with the image degradation process, we can write the compressed sensing super-resolution model thus:  
\[y=CHD\widetilde{x} \tag{1}\]  
where \(C\) is the subsample matrix corresponding to the degradation process; \(H\) is the matrix multiplication form of blur kernel \(h\); and \(D\) is the sparse basis.
## 4 Experiment and Model Validation
### Model Validation  
Based on the above SCB test, the load-displacement curve of the asphalt concrete specimen was obtained. At the same time, the load-displacement curve was also obtained from the CZM-based FE model developed in this paper. Figure 7 shows the comparison between the load-displacement curves from the experiment and the numerical simulation. It could be observed that the numerical results had a similar trend with the experimental data for both \(-10\)\({}^{\circ}\)C and \(0\)\({}^{\circ}\)C. The curves gradually climbed to the peak point and then decreased. For the experiment, the measured peak loads and the displacements were 4.07 kN and 0.74 mm under \(-10\)\({}^{\circ}\)C, and 3.88 kN and 1.01 mm under \(0\)\({}^{\circ}\)C. While for the numerical simulation, peak loads of 3.99 kN and 3.61 kN were observed for \(-10\)\({}^{\circ}\)C and \(0\)\({}^{\circ}\)C, respectively. The corresponding displacements under \(-10\)\({}^{\circ}\)C and \(0\)\({}^{\circ}\)C were 0.79 mm and 1.10 mm, respectively. The relative difference in the peak load between the numerical and experimental results was 1.97% at \(-10\)\({}^{\circ}\)C and 6.96% at \(0\)\({}^{\circ}\)C. The relative difference in the displacement between the numerical and experimental results was 6.76% at \(-10\)\({}^{\circ}\)C and 8.91% at \(0\)\({}^{\circ}\)C. The values of the relative difference show that the accuracy of the CZM-based FE model is acceptable for the cases discussed in this paper. Moreover, for both the experiment and the numerical simulation, a larger peak load and smaller displacement was observed under lower temperature conditions. This means the numerical model reflects a similar trend as the experiment. Therefore, the CZM-based FE model presented in this paper is reliable.  
Figure 6: Experimental setup.  
Figure 7: Load-Displacement comparison curve.
## 2 Materials and Methods
### Preprocessing of Images for Creating the Models  
The CT images were retrieved from the picture archiving and communication system. To convert the images for use by the training database, the CT images were converted from digital imaging and communications in medicine (DICOM) format to joint photographic experts group (JPEG) format using a dedicated DICOM software (XTREK view, J-MAC SYSTEM Inc., Sapporo, Japan). The window width and level of DICOM image were used to preset values in the DICOM tag. The DICOM images were converted to JPEG images with a size of 512 \(\times\) 512 pixels. JPEG files were sorted into folders according to the class that each image belonged to.
## 2 Materials and Methods
### Sensitivity Analysis of Parameters  
A total of 17 flow hydrological parameters, including drainage system parameters, soil hydraulic parameters, and soil temperature parameters, were selected for the sensitivity analysis using Latin Hypercube One-Factor-at-Time (LH-OAT) method, which is commonly utilized for screening and sensitivity analysis. In this method, only one parameter varies. In contrast, the other parameters remain constant, which allows a specific attribute to the change in model output concerning change in the input parameters. Sensitive analysis was performed for multiple objective functions: NSE, RSQ, and RSME representing daily drainage flow, PBIAS, and total cumulative flow representing long-term water balance and relative yield of corn. The relative yield of corn was taken as an average of 2019 and 2020 from the DRAINMOD output. Two types of sensitivity indicators were calculated: relative and absolute.  
Relative sensitivity Index (RSI) defined by [9] was used for quantitative evaluation of the sensitivity of the model output with an absolute change in the model inputs.  
\[Sij=\frac{|O(x1,\ldots xi+\Delta xi,\ \ldots xp)-O(x1,\ldots xi,\ \ldots xp)|}{(O(x1, \ldots xi+\Delta xi,\ \ldots xp)+O(x1,\ldots xi,\ \ldots xp))/2}*\ \frac{ xi}{|\Delta xi|}\]  
Absolute sensitivity Index was defined as  
\[Sij=\frac{|O(x1,\ldots xi+\Delta xi,\ \ldots xp)-O(x1,\ldots xi,\ \ldots xp)|}{| \Delta xi|}\]  
where \(S_{ij}\) is the relative partial effect of a parameter (\(x_{i}\)) out of total \(p\) parameters and O refers to the output of model for the considered objective functions. Partial sensitivity indices (both relative and absolute) were considered in terms of magnitudes only rather than considering signs for eliminating the cumulative effects.  
The parameter xi was then varied by factor \(\Delta x_{i}\) = 5% from the range \(-80\%\) to \(+80\%\) of the base value in the step interval of 5%. The best parameter sets after calibration were taken as the base value, and the simulation was repeated. The final sensitivity index \(S_{xi}\) was then computed by taking the average of these partial effects. The higher the value of \(S_{xi}\) represents the outputs are more sensitive to the given parameters.  
\[Sxi=\frac{\sum_{j=1}^{N}Sij}{N}\]  
The sensitivity analysis was performed for both fields by repeating the same procedure.
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 2 Materials and Methods
### Radio Network Architectures for Corridor Coverage
Let \(N_{base}\) be the number of base positions spaced at \(R\) intervals  
\[N_{base}=\left\lceil\frac{D}{R}\right\rceil \tag{3}\]  
Out of these base positions, approximately half are duplicated  
\[N_{3}=\left\lceil\frac{N_{base}}{2}\right\rceil+\left\lceil\frac{N_{base}}{2 }\right\rceil\times 2=3\times\left\lceil\frac{N_{base}}{2}\right\rceil \tag{4}\]  
For simplicity, we can approximate  
\[N_{3}\sim\left\lceil\frac{3D}{2R}\right\rceil \tag{5}\]  
This staggered redundancy pattern provides a high level of fault tolerance. Strategy 3 is particularly useful for corridors where operational risk is moderate to high, but where budget or logistical constraints are present. Strategy 4. Fully overlapping coverage with complete duplication. In this strategy, radio repeater stations are placed along the UAV flight corridor at intervals of \(R\), as in Strategy 2, ensuring full overlap between the coverage zones of adjacent stations. However, unlike Strategy 2, each of these positions is occupied by two fully independent and redundant radio repeaters (Figure 5). This means that every planned location along the corridor contains a pair of co-located repeaters working in parallel. Strategy 4 requires the same number of locations as Strategy 2, but twice the number of actual repeater units:  
\[N_{4}=2\times N_{2}=2\times\left\lceil\frac{D}{R}\right\rceil \tag{6}\]  
As a result, each point along the corridor is covered by at least four independent radio units--two from the local station and two from the adjacent one--significantly increasing the overall communication reliability. Figure 4: Configuration of deployment strategy 3. Figure 5: Configuration of deployment strategy 4.
## 2 Results and Discussion
### Principal Components Analysis of the Green Tea Samples  
Principal components analysis was conducted using only the mean concentration for the 14 compounds reported in Table 2. Because the volatile compounds found in less than four samples were not included in the PCA, many of the green teas could have unique flavors that would differentiate them from other teas in ways not shown on the PCA biplot (Figure 1). Principal components (PC) 1 and 2 explained 40% and 27% of the variation on the data, respectively. Toluene, benzeneethanol, jasmone, and linalool were the main vectors for PC 1. PC 2 accounted for linalool, benzaldehyde, geraniol and hexanal.  
It is clear from the PCA map (Figure 1) that geography of the green teas in this study was not the primary differentiator based on volatile chemical composition. In fact, Japanese samples \"map\" in both the right-hand upper quadrant and the left-hand lower quadrant suggesting quite different volatile composition. Similarly, Korean samples occur in every quadrant showing that the composition of teas depends on many factors in addition to the inherent geographical characteristics of the growing location. Aspects such as the picking time, processing, packaging, and sample age all can impact the volatile composition.  
Benzeneethanol, toluene, benzaldehyde, hexanal, nonanal, geraniol and jasmone were commonly present in China 5 and Japan 3 tea samples. Concentrations of toluene, benzeneethanol and jasmone in these two samples were higher than most of samples in the study.  
Kenya 2 and Korea 3 had a similar composition of aroma volatile compounds and were distinct from the rest of the samples. Concentrations of linalool were especially high in these 2 samples. Sri Lanka 1 had a comparable low volatile compound profile with Kenya 2 and Korea 3. In Korea 4 hexanal, jasmone and linalool were the main volatile compounds.  
Eighteen samples, China 1, China 2, China 3, China 4, China 6, China 7, India, Japan 1, Japan 2, Japan 4, Japan 5, Japan 6, Kenya 1, Korea 1, Korea 2, Sri Lanka 2, Tanzania and Vietnam, were grouped together in the PCA biplot. These samples had two things in common: (1) the absence of some of the 14 volatile compounds that were semi-quantified in these tea samples, and (2) concentrations of the compounds in these samples (7 out of 14) that were much lower than other samples.
In this paper, a Hybrid Linear Iterative Clustering and Bayes Classification-based GrabCut Segmentation Technique (HLC-BC-GCST) is proposed for significant extraction of cytoplasm and nuclei of Pap smear cells by deriving the benefits from Simple Linear Iterative Clustering Process (SLICP), GrabCut-based graph cut model, Bayes classification [11] and minimum flow algorithms in a systematic manner to facilitate an effective segmentation in the process of cervical cancer detection. In this proposed HLC-BC-GCST approach, first, SLICP is used to ensure rapid clustering and sustain the pixel block count during segmenting Pap smear cells. Then the GrabCut is used for formulating a graph cut model based on which the error is minimized to a significant degree using an integrated Bayes classifier. Further, iterative enforcement of the GrabCut-based graph cut model and the second level of SLICP are employed to improve the quality of clustered pixel points considered from the cervical cancer Pap smear cells. Finally, two potential Gaussian Mixture Models (GMM) [12] are determined to achieve the maximum degree of segmentation by applying the minimum graph cut algorithm that determines the optimal boundaries from the Pap smear cell test. The experimental investigation of the proposed HLC-BC-GCST approach achieves better results when compared with the standard metrics. The mathematical model developed for implementing the proposed method HLC-BC-GCST was derived from published or other sources to enable the proposed model for economic incorporation and be feasible for clinical trial implementation. The cost ratio is identified as incremental with the effectiveness of cost, and the year life cost gained per year estimates the overall cost-effectiveness of the model with the impact of the proposed method. The paper is structured as follows: The existing segmentation schemes using graph cut algorithms are summarized with their pros and cons in Section 2. The sequential steps involved in implementing the proposed HLC-BC-GCST approach with their significance are presented in Section 3. Section 4 highlights the results of the experiments conducted to quantify the potential of the proposed HLC-BC-GCST approach over the considered baseline detection schemes used to detect cervical cancer. Section 5 depicts concluding remarks on the potential contribution of the proposed HLC-BC-GCST approach with its feasible plan of future research focus.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 1 Introduction
Many approaches have been applied to the 5G environment in order to enhance the network performance [24]. The machine learning approach is effective and widely applicable. Thus, it can be implemented with the conventional congestion control mechanism to satisfy the demands of 5G Internet of Things (IoT) nodes/sensors networks. The aim of applying the machine learning method to congestion control is to predict the optimal parametric setting and path to send/receive data. Finding the optimal node reduces data traffic and packet loss and improves throughput. In this work, a machine learning approach is utilized for prediction based on support vector regression using two parameters, namely, file transfer history and the measurement of simple path properties. This study presents a new methodology for improving the congestion control mechanism based on the machine learning decision tree (DT) approach. The rest of this paper is organized as follows. Section 2 presents related work on machine learning utilization for network optimization and prediction, congestion control mechanisms, and studies regarding congestion control improvement. Section 3 explains the mathematical model of the proposed congestion control method. Section 4 discusses the experimental results and demonstrates the effect of specific performance factors and the use of DT prediction. Section 5 presents the conclusions and directions for future work.