esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Main Geomorphological Features of the Pit Rim Structure
### Slope and Aspect Analysis
#### 3.5.2 Aspect Analysis
Figure 15: (**a**) Results of the percentage accumulation of inside and outside aspects of the pit rim structure, and (**b**) results of the percentage area of independent ridges’ aspect. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ridge ID**} & \multicolumn{8}{c}{**Aspect Area (\(\text{m}^{2}\))**} \\ \cline{2-10}  & **Plane** & **N** & **NE** & **E** & **SE** & **S** & **SW** & **W** & **NW** \\ \hline \#1 & 0.01 & 6586.20 & 5028.36 & 3914.48 & 1088.00 & 154.36 & 271.72 & 979.80 & 4257.40 \\ \#2 & 0.00 & 20,161.44 & 17,915.04 & 20,475.36 & 17,484.52 & 9828.68 & 4932.96 & 9321.76 & 18,851.72 \\ \#3 & 0.00 & 2290.16 & 5329.04 & 4300.60 & 1421.40 & 207.28 & 32.76 & 24.04 & 291.00 \\ \#4 & 0.01 & 18,483.92 & 15,158.84 & 17,698.52 & 17,095.16 & 9922.72 & 4056.36 & 5190.56 & 13,938.32 \\ \#5 & 0.01 & 14,422.28 & 11,037.36 & 10,155.16 & 6055.44 & 1794.56 & 438.96 & 1636.84 & 11,816.48 \\ \#6 & 0.00 & 10,045.24 & 11,741.88 & 17,222.00 & 23,738.36 & 15,751.04 & 6537.28 & 4878.08 & 8349.20 \\ \#7 & 0.00 & 19,496.32 & 22,968.28 & 17,132.08 & 6399.96 & 840.28 & 362.96 & 1725.24 & 8527.40 \\ \#8 & 0.00 & 17,487.48 & 14,534.44 & 17,289.08 & 19,436.40 & 12,519.68 & 5063.96 & 4088.32 & 12,726.52 \\ \#9 & 0.00 & 5817.92 & 10,059.12 & 7825.68 & 4031.08 & 703.68 & 75.68 & 80.64 & 985.64 \\ \#10 & 0.00 & 6870.80 & 9685.80 & 11,449.72 & 10,466.28 & 4309.24 & 642.64 & 159.76 & 1637.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical results of the aspect areas of independent ridges. The aspect of the ridges was largely concentrated in the clockwise direction from north to the southeast (Figure 15b), and the maximum value of the ridges was evenly distributed (Table 9). The plane aspect was the lowest, mainly distributed on the #1, #4, and #5 ridges. Most of the ridges were northwest, whereas ridges #3, #9, and #10 were not dramatically increased. The distribution of the aspect of the #2, #4, and #8 ridges and the distribution of the #1 and #5 ridges were relatively similar. Although the maximum value of the aspect of the different ridges outside was different, a certain law was maintained in the distribution of the aspect. The aspect was primarily distributed in the north, northeast and east, and the east and northeast accounted for the highest proportion. The lowest proportion was in the west and southwest, whereas the proportion in the northwest tended to be increased again. The ridges in the northern part of the pit rim structure (#1-#5) primarily accounted for a greater proportion of northwest aspect than the ridges in the south.
## 3 Methods  
The process flow of this study consists of three main parts: data preprocessing, machine learning modeling, and model validation (Figure 2). The input variables were first preprocessed to have the same spatial extent and resolution. Multicollinearity of the variables was then tested using the variance inflation factor (VIF). Monthly forest fire density through the stratified sampling was finally generated in the preprocessing stage. An ensemble model combining two machine learning models with different input variables was developed in the modeling part. The ensemble model was cross-year validated (One-year-out cross-validation) and further compared to the existing forest fire risk indices. One-year-out cross-validation refers to excluding one-year data for validation and training the model with the remaining five-year data. By repeating this process for each year (i.e., six times), all six years were cross-validated. The spatial distribution of the hourly forest fire risk index was qualitatively evaluated as well.
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 2 System Model  
As shown in Figure 1, we consider a cache-enabled D2D-aided cellular network with single-antenna BSs in downlink transmission. The locations of the BSs and users are modeled as two homogeneous Poisson point processes (PPPs), denoted \(\Phi_{b}\) and \(\Phi_{u}\) with densities \(\lambda_{b}\) and \(\lambda_{u}\), respectively, such that \(\lambda_{u}\gg\lambda_{b}\). We assume that each random user has a local cache to store content and can also act as a D2D transmitter. Let \(\
u=[0,1]\) denote the proportion of cache-enabled users who serve as D2D transmitters upon receiving a request for content. The transmission powers for each BS and each device transmitter are \(P_{b}\) and \(P_{u}\), respectively. Assume that a fixed bandwidth \(W\) is shared by the D2D link and the cellular link. The cache storage size of each device is denoted with a size \(M\) (representing the number of contents), where each content is assumed to have the same size \(S\). The content library set is denoted by \(\mathcal{K}=\{1,2,\ldots,K\}\), where \(K>M\) is the library size and all users request content according to a popularity distribution. The popularity of content can be modeled as a Zipf distribution [31]:  
\[p_{k}=\frac{k^{-\eta}}{\sum_{l=1}^{N}l^{-\eta}} \tag{1}\]  
where content with a smaller \(k\in\mathcal{K}\) has higher popularity and the parameter \(\eta\geq 0\) indicates the skewness of the content popularity distribution.  
Consider a probabilistic caching strategy in which each user independently stores content \(k\) with probability \(q_{k}\). Thus, the distribution of users caching content \(k\) follows a PPP with density \(\lambda_{u,k}=\
u\ \lambda_{u}\ q_{k}\). Note that some D2D transmitters may not be able to participate in cooperative transmission to the requesting user because they may be serving other users or remaining silent for some other reason. Let \(\tau\) be the time required for sending the desired content, which is calculated as \(\tau=\frac{S}{W\log_{2}(1+\xi)}\), where \(\xi\) is the given signal-to-interference ratio (SIR) threshold of successful transmission [21]. Moreover, the probability that a D2D transmitter is available to handle a request is defined as the available transmission probability, which follows a Poisson distribution with the average sending rate of content per second \(\lambda_{t}\) and is denoted by \(\Omega=\left(1-e^{-\lambda_{t}\tau}\right)\) according to [32]. Given a predefined average sending rate \(\lambda_{t}\), we calculate the offloading probability of the network in the next section.  
Each requesting user obtains the content from its serving node, called the closest node, based on strongest received power \(\left\{P_{r,j}\right\}_{j=u,b}=P_{j}\ h_{j}\ r_{j}{}^{-\alpha}\) of the node either from D2D communication mode or from BS mode, where \(P_{j}=\left\{P_{b},P_{u}\right\}\) is the transmission power; \(h_{j}\) is the channel power gain; \(r_{j}\) is the distance between the requesting user and its closest node; and \(\alpha\geq 2\) denotes the path loss exponent.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.1 Consensus Algorithms  
Even though the PoW mining method overcomes the problem of transaction reliability, it wastes substantial resources. Mining incentives can also result in extremely concentrated mining pools. With just seven transactions per second, the decentralized architecture provides a long-term consensus period [38]. The BFT method is used to create the consensus mechanism in the consortium chain, which overcomes the problem of the PoW algorithm's low efficiency and excessive resource consumption. For complete P2P communication monitoring of abnormal activity, the BFT algorithm offers a high number of signatureconfirmations [166]. However, because of the high communication complexity, the system has substantial overhead, which affects consensus competence. This falls well short of BC's expectations for IoT security. As a result, to satisfy the development of BIoT requirements, consensus methods must be updated and enhanced [57; 76].  
In most cases, IoT devices communicate tasks to gateways that can complete the work. Off-chain solutions may increase functionality by transmitting data outside the chain to reduce latency [8]. While several projects are aimed at incorporating complete BC nodes into IoT devices, mining remains a major stumbling block in the IoT because of its restrictions. The IoT is largely made up of devices with limited resources, yet it has tremendous processing capacity on a global scale. Babelchain, for example, adapts PoW for IoT applications using a new consensus method named proof of understanding (PoU). PoU, rather than hiring miners to decode hash problems, translates from other protocols, requiring fewer energy resources [74]. While tackling important difficulties in IoT connectivity, the effort is mainly focused on practical processing. Rather than agreeing on transaction status, participants agree on the communication's meaning (i.e., format, action, and content).  
[PERSON] et al. [90] proposed a consensus method based on game theory. A variable number of validators is decided dynamically on the basis of game theory in their consensus method. The chance of danger is reduced by selecting just a legitimate number of honest validators. [PERSON] et al. [109] proposed an AI-based consensus method to combine the benefits of PoW, PoS, and DPoS. [PERSON] and [PERSON] [167] discussed designing consensus mechanisms for different applications including BC type, scalability, fault tolerance, throughput, and bandwidth. However, all these solutions are yet to be further investigated [57].
## 3 Results and Discussion
### Sensory Analysis  
The sensory attributes of the bread samples are included in Table 5. Higher levels of protein concentrate additions led to a stronger intensity in the colour of the crumb and crust. There was no statistically significant difference (\(p>0.05\)) in the bitter and asrtingent flavours between the bread samples. Crumb firmness increased with protein concentrate addition and was positively correlated with the values of hardness provided by the texture analyser (\(r=0.9493\)). As the addition of protein concentrates increased, mass adherence during chewing increased in comparison with the bread. However, this increase was not significantly different (\(p>0.05\)) between the control and soy protein breads and 5 PP. Only higher levels of PP (10% and 15%) addition brought about a significant increase in mass adherence. Hence, a good correlation between the protein content in the bread and mass adherence was found (\(r=0.8544\)).  
According to the hedonic test (Figure 4), the soy breads had a good overall acceptability, with a score varying from 6.3 to 6.8. Moreover, the SP breads did not significantly differ (\(p>0.05\)) from the wheat control bread. On the other hand, breads with a pea protein concentrate addition were less liked compared with the soy breads. The overall acceptability noted for the 10 PP and 15 PP breads decreased significantly (\(p<0.05\)) compared with the control bread and SP samples.  
The results from the bread note are presented in Table 6. These results were in line with those obtained through other methods. Thus, positive correlations were observed between the physical methods and bread note methods for volume (\(r=0.9969\)), porosity (\(r=0.9578\)), and elasticity (\(r=0.8356\)). In addition, a highly positive correlation was found between the overall acceptability performed by the panelists and the total score calculated through the bread note procedure (\(r=0.9770\)). The L* parameter was positive correlated with the crumb colour in the bread note (\(r=0.8392\)). There was also a negative correlation between L* and crumb colour in the sensory analysis (\(r=-0.8254\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Samples** & **Control** & **5 PP** & **10 PP** & **15 PP** & **5 SP** & **10 SP** & **15 SP** \\ \hline Colour of crust & 2.21 \(\pm\) 0.84  & 2.50 \(\pm\) 0.71  & 2.83 \(\pm\) 0.62  & 2.21 \(\pm\) 0.54  & 3.00 \(\pm\) 0.43  & 4.08 \(\pm\) 0.51  \\ Colour of crumb & 1.75 \(\pm\) 0.97  & 2.29 \(\pm\) 0.78  & 2.54 \(\pm\) 0.86  & 2.50 \(\pm\) 1.00  & 2.13 \(\pm\) 0.96  & 2.50 \(\pm\) 1.07  & 3.04 \(\pm\) 1.05  \\ Bitter flavour & 1.00 \(\pm\) 0  & 1.08 \(\pm\) 0.29 [FOOTNOTE:8]Footnote 8: The results are mean \(\pm\) standard deviation (n = 12). Values followed by different superscript letters in the same row are significantly different (\(p<0.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 3 Photodetectors Based on 2D Material-Si Heterojunctions
### Other 2D/Si Heterojunctions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Materials** & **Measurement** & **R/A\(\cdot\)W\({}^{-1}\)** & **D\({}^{\star}\)/Jones** & **EQE** & **Time (Rise/Down)** & **Ref.** \\ \hline Bi\({}_{2}\)Te\({}_{3}\)/Si & 635 nm/\(-\)5 V & 0.017 & 2.5 \(\times\) 10\({}^{11}\) & & & [133] \\  & 635 nm/\(-\)5 V & 1 & & & & \\ \hline Bi\({}_{2}\)Se\({}_{3}\)/Si & 808 nm/\(0\) V & 2.6 & 4.39 \(\times\) 10\({}^{12}\) & & 2.5 \(\upmu\)s/5.5 \(\upmu\)s & [134] \\  & 808 nm/\(-\)1 V & 24.28 & 1.21 \(\times\) 10\({}^{12}\) & & 2.5 \(\upmu\)s/5.5 \(\upmu\)s & [134] \\ \hline Bi\({}_{2}\)Se\({}_{3}\)/Pyramid & 1550 nm/\(0\) V & 3.06 \(\times\) 10\({}^{-8}\) & 1.37 \(\times\) 10\({}^{5}\) & & 0.52 ms/0.44 ms & [136] \\ Si & 2700 nm/\(0\) V & 1.8 \(\times\) 10\({}^{-8}\) & 1.53 \(\times\) 10\({}^{6}\) & & 0.585 ms/0.535 ms & [136] \\ \hline Ti\({}_{3}\)C\({}_{2}\)/Si & 910 nm/\(0\) V & 0.402 & 2.3 \(\times\) 10\({}^{13}\) & 60.3\% & 0.14 ms/1.6 ms & [140] \\ \hline Te/Si & 808 nm/\(0\) V & 6.49 & 7.79 \(\times\) 10\({}^{12}\) & 998\% & 26 ms/30 ms & [148] \\ \hline Te/Si & 405 nm/\(-\)2 V & 249 & 1.15 \(\times\) 10\({}^{11}\) & 76,350\% & 3.7 ms/4.4 ms & [66] \\ \hline Te/Si & 1064 nm/\(0\) V & 437.24 & 4.86 \(\times\) 10\({}^{11}\) & & 920 ns/200 \(\upmu\)s & [61] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of properties of Other 2D material/silicon heterojunction devices.
## 2 Spectral Density Estimation
### Spectrum-Adapted Polynomial Regression/Orthogonal Polynomial Expansion
In the field of graph signal processing [4], it is common to analyze or modify a _graph signal_\(\mathbf{b}\in\mathbb{R}^{N}\), where \(b(i)\) is the value of the graph signal at vertex \(i\) of a weighted, connected graph \(G\) with \(N\) vertices, by applying a graph spectral filter \(f_{l}\). The filtered signal is exactly the product (1) of a function of the graph Laplacian (or some other symmetric matrix) and a vector, the graph signal \(\mathbf{b}\). In Figure 6, we show examples of collections of such functions, commonly referred to as _graph spectral filter banks_. In the right three columns of Figure 5, we examine the relative errors incurred by approximating \(f_{l}(\mathbf{A})\mathbf{b}\) for the lowpass, bandpass, and highpass graph spectral filters \(f_{1}\), \(f_{3}\), and \(f_{5}\) shown in Figure 6a. We expand on the applications of such graph spectral filters in Section 5. We make two observations based on the numerical examples:  
1. The spectrum-adapted interpolation method often works well for low degree approximations (\(K\leq 10\)), but is not very stable at higher orders due to overfitting of the polynomial interpolant to the specific \(K+1\) interpolation points (i.e., the interpolant is highly oscillatory). 2. The proposed spectrum-adapted weighted least squares method tends to outperform the Lanczos method for matrices such as si2 and cage9 that have a large number of distinct interior eigenvalues. Figure 5: _Cont._  
Figure 5: Approximations of \(f(\mathbf{A})\mathbf{b}\) with \(\mathbf{b}=\mathbf{VI}\) and \(f(\lambda)\) equal to \(c^{-\lambda}\) (first two columns) and lowpass, bandpass, and highpass spectral graph filters (last three columns, respectively).
###### Abstract  
The distribution of heavy metals in the agricultural areas from a cattle-producing area near the Zhangbei Dam grassland, Hebei province, China, was investigated for providing the basis for the control of pollution in a rural farmland. Fifty-three surface soil samples including 28 soils of potato and 25 soils of oats were collected and analyzed for the distribution of Cd, Cu, Pb, Zn, Cr, As, Hg, and Ni. Furthermore, the ecological risk of soils contaminated with heavy metals was evaluated by employing the single factor index, the Nemerow comprehensive pollution index and geo-accumulation index, and potential ecological hazard index. The results showed that the contents of the eight heavy metals in soil planted with potato were below the risk screening values. The single factor index in soils planted with hulless oats showed that the concentrations of Cd, Hg, and Ni surpassed the Chinese screening limits by 8%, 4%, and 8%, respectively. According to the geo-accumulation index, 4% of samples reached the level of medium ecological risk for Cd. According to the results of ecological risk assessment, the studied soils generally showed a mild pollution degree. According to the health risks assessment results, the carcinogenic risks should be considered non-negligible. Arsenic is the dominant carcinogenic pollutant for human beings in the county. The main sources of pollution are mining and application of fertilizers.  
heavy metal; risk assessment; agricultural soil; dam grassland +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Prediction Model Construction and Performance Evaluation  
Twenty-two models--that is, \(R\), \(L\), \(T\), \(RL\), \(RT\), \(LT\), and \(RLT\) models in CT-only, DD-only, CD, and DL models--were constructed to compare treatment failure prediction performances. Significant features were selected using Coxnet, and the CPH model was optimized using the LASSO (L1 norm) penalty and 10-fold cross validation. LASSO is widely used in radiomics to select features. If the coefficients of the features are zero, they are considered irrelevant to treatment failure. In other words, only features with coefficients were related to treatment failure, and only significant features could be selected. The Coxnet algorithm using the \"glmnet\" package in R software (R Foundation for Statistical Computing, Vienna, Austria) version 4.0.2 was employed to select significant features in the training dataset. The coefficients of the features selected by Coxnet represent the degree of contribution of each feature to the prediction of treatment failure. Moreover, to consider multicollinearity, pairwise correlation coefficients between selected features were calculated, and no highly correlated features (>0.70) were identified. The Rad-score was calculated as follows:  
\[Rad-score\ =\ \sum_{i=\ 1}^{n}\beta_{i}\times SF_{i}, \tag{3}\]  
where \(n\) is the number of selected features, \(\beta\) is the coefficient of the selected feature obtained by the Coxnet, and the \(SF\) is the value of selected feature. Ultimately, a prediction model was constructed using a Rad-score-based CPH model.  
C-indices and balanced C-indices were employed to assess model performance. The balanced C-index was calculated as the average value of the C-indices in the training and testing datasets. Moreover, all HNSCC cases in each dataset were stratified into two groups by the median of the Rad-score of the training data, and the significant difference between the two groups was evaluated by Kaplan-Meier curves (treatment failure-free probability curves) with the log-rank test (\(p<0.05\)).  
The optimal number of selected features was determined by changing the number of selected features (\(n\)) from two to the sample size (\(N\)), in order of larger absolute values of the coefficients, based on the condition \"\(N>10\times n\)\" [37]. The optimal number of features was determined as the one representing the presence of statistically significant differences between Kaplan-Meier curves and the highest C-index of the test dataset. In the case of the absence of statistically significant differences, the optimal number of features were decided only with the C-index.
# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 3 Proposed Method for Defect Depth Recognition  
Figure 2 illustrates the framework of the proposed defect depth recognition strategy.  
Two key steps are as follows:  
1. Principal components extraction: The first step is to obtain several irrelevant principal components from the raw thermal sequence that contains redundant information. The procedure of preprocessing the raw thermal sequence by a principal component analysis (PCA) contributes to eliminating correlation and saving the expense of training for network model.
2. Defect depth recognition based on GRU: The second step is training and testing the GRU model by making the principal components as input datasets to realize defect depth recognition.
## 3 Results
### Raw Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy/** & **Recall/** & **Precision/** & **F1-Score/** & **AUC/** \\  & **Training Accuracy** & **Training Recall** & **Training Precision** & **Training F1-Score** & **Training AUC** \\ \hline Logistic Regression & 0.92/0.92 & 0.05/0.05 & 0.52/0.53 & 0.08/0.09 & 0.83/0.84 \\ Decision Tree & 0.92/0.92 & 0.05/0.07 & 0.44/0.61 & 0.09/0.13 & 0.80/0.86 \\ Random Forest & 0.92/1.00 & 0.04/1.00 & 0.43/1.00 & 0.08/1.00 & 0.81/1.00 \\ Gradient Boosting & 0.92/0.92 & 0.04/0.07 & 0.49/0.71 & 0.08/0.12 & 0.83/0.86 \\ XGBoost & 0.74/0.76 & 0.75/0.87 & 0.20/0.23 & 0.32/0.37 & 0.82/0.89 \\ CatBoost & **0.74/0.78** & **0.77/0.85** & **0.20/0.22** & **0.33/0.35** & **0.83/0.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results after optimization. Figure 3: Confusion matrix of logistic regression performance. Particularly, the macro average F1-score, even in the best case scenario, reached only 0.58. This indicates that the model's performance diminishes when both classes are considered equally, irrespective of their imbalance.
## 6 Thematic Taxonomy of UWSNs
### Sea Surface
#### 6.2.3 Sound Receiver  
A sound receiver is an essential device in underwater acoustic applications and consists of a transducer, amplifiers, and data acquisition boards. An underwater sound transducer is a device that converts acoustic energy to electrical energy (hydrophone) or vice versa (projector). Consequently, an amplifier can be connected to the hydrophone to increase the amplitude of the sound, which can then be measured using a voltmeter. The outputs in the analog wave can be converted into digital signals using a data acquisition system or, vice versa; digital signals can be turned into analog signals for processing purposes. A Data Acquisition (DAQ) board consists of an analog-to-digital converter (ADC), TTL level logic inputs, onboard timer, and digital-to-analog converters (DAC).  
In carrying out the tasks of underwater wireless sensor networks, real implementation is usually quite challenging due to the environmental factors especially in deep sea and also high operating costs. Therefore, most studies are currently using simulation as another alternative. In conducting the simulation for UWSN, the conditions and environmental properties give a significant impact on the performance system.  
Table 4 lists some recent studies that take into account all the factors including the environment, and system parameters in obtaining the results of the study. Methods, advantages, and description of the study are reported for further review.