esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Predictive Model Based on ESN
### Other Models  
In the prediction of pitch motor temperature, we will compare the prediction results of ANFIS (adaptive network-based fuzzy inference system), NN (neural network) and NN+dropout model with those of ESN, so as to find a more effective model. To facilitate the understanding of these methods, we briefly describe their main ideas as follows.  
The ANFIS model [17] not only has a strong learning ability for neural networks but also can easily include prior knowledge of related problems. As a result, it has been widely used in industrial problems. The ANFIS model mainly consists of two modules, that is, the structural learning module and the parameter learning module. In the former, the input and output pairs are used to extract fuzzy rules through an expert's experience. Conversely, in the latter, the parameters of the model are updated continuously by a neural network, so that the parameters of fuzzy rules and membership function have adaptive abilities. In a word, ANFIS utilizes the fuzzy information system to set the corresponding fuzzy rules (such as the selection of membership function and the determination of parameters) and employs a neural network to learn the involved parameters. Due to the space constraints, more details can be found in the article [17].  
In the NN (neural network) model [18], we used the neural network with three hidden layers to learn the function between the input parameters related to the pitch motors and their temperature. In order to improve the generalization ability of the model, \(L_{2}\) regularization is added to its objective function. In the learning process, SGD (stochastic gradient descent) [19] is employed to minimize the objective function.  
In this paper, we also combined dropout [11] in deep learning with NN to train the model. Dropout can be deemed as an ensemble method that integrates many large neural networks. However, the separate training of large neural networks needs a lot of computing resources. By contrast, dropout achieves fast training of subnetworks by randomly deleting some hidden units in the network, and finally integrating the subnetworks in a special manner.
## 6 A Case Study
### Discussion  
The major advantages of the proposed set and methodology over the existing studies are summarized below.  
* From Table 7, the disadvantages of DHFS are apparent. As in DHFS, the proportion figures of the assessments are ignored, which means that all possible assessments in DHFS have the same occurrence probability, which is unrealistic because those values that occur multiple times have lost their importance. On the other hand, the inspection of Table 5 shows that PDHFS contains not only the membership and non-membership parts, but also take into account their associated proportion figures, and, thus PDHFS constitutes an extension of DHFS.
* Table 13 indicates that PHFS only assigns to each element a membership grades along with proportion, while PDHFS assigns to each element the membership grades as well as the non-membership grades along with their associated proportions. Thus, PDHFS provides a more flexible expression way for DMs to express their opinions from different prospectives in a more conformed to the reality approach.
* The score function and the deviation degree results of PDHFS shown in Table 6 are quite similar to those displayed in Table 8 for DHFS. However, from Table 14, we observe that these values are different from those derived for PHFS. The main reason of this difference is that the non-membership part of DM opinions is ignored in PHFS and thus partial information is lost. This comparison guarantees that the values of the score function and the deviation degree of PDHFS are reasonable and interpretable.
* From Table 15, we can see that the ranking order of traditional approach is different from the proposed method and the method in Ref. [22]. This difference is due to the effect of proportions. Although neglecting the proportional figures reduce the complexity of calculations, but its neglect leads to unsatisfactory ranking results.  
Various remarkable similarities and dissimilarities of the novel approach with other approaches are summarized in Table 16.  
\begin{table}
\begin{tabular}{l l l} \hline \hline Traditional TOPSIS & \(x_{3}>x_{4}>x_{1}>x_{2}\) \\ TOPSIS [22] & \(x_{3}>x_{1}>x_{2}>x_{4}\) \\ Proposed TOPSIS & \(x_{3}>x_{1}>x_{2}>x_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 15: Results comparison.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline _Observation_ & _Novel Approach_ & _Reference [22] Approach_ & _Traditional Approach_ \\ \hline Expression of the initial decision judgment. & IFNs & IFNs & IFNs \\ Expression of the collective decision judgment. & PDHFSs & PHFFs & IFNs \\ Judgment. & Additional information demanded for converting the MAGDM into & No & No \\ MADM. & Can naturally reveal the preference judgment of the DMs group. & Yes & partially & No \\ Adapted distance measure for the separation of each alternative from the FS/NIS. & Proportional dual hesitant & Proportional hesitant normalized Hamming distance & Dual hesitant normalized distance \\ \hline \hline \end{tabular}
\end{table}
Table 16: Similarities and dissimilarities of the novel approach with other approaches.
# An Efficient Blockchain Transaction Retrieval System
## 3 System Implementation
### Retrieval Structure Generation and Analysis
#### 3.3.2 Performance Optimization Analysis of BB+ Tree  
This section will analyze the time and space complexity of the B+ tree and BB+ tree. The data structure features of the B+ tree are as follows: internal nodes store corporate index relations, while leaf nodes store actual data. The retrieval of a key value often depends on the height of the tree, so the time complexity is  
\[\text{Read}_{B+\text{ Tree }}=O(\log_{d}n)+s \tag{3}\]  
where \(d\) is the order of the B+ tree, the total number of key-value pairs (number of leaf nodes) is \(n\), and \(s\) is the additional query time cost. Although the logarithmic time complexity is excellent, with the rapid expansion of the search space, even logarithmic complexity cannot guarantee the search speed. Faced with the problem of retrieval space expansion,  
Figure 4: The BB+ tree. we propose a new structure, BB+ tree, whose time complexity consists of two parts: the time complexity of the Bloom filter and the time complexity of the subB+ tree, as follows:  
\[\text{Read}_{BB+\text{ Tree }}=O(k)+O(\log_{d}n/2^{m})+s \tag{4}\]  
where \(k\) is the time required to calculate k hash functions, and \(m\) is the number of bit array. According to formula k in the Bloom filter, there is a relationship between \(m\) and \(n\):  
\[\text{k}=\frac{m}{2^{\lceil\log_{2}n\rceil}}*\ln 2 \tag{5}\]  
Substituting the expression of the above equation k into the formula, we can obtain  
\[\text{Read}_{BB+\text{ Tree }}=\frac{m}{2^{\lceil\log_{2}n\rceil}}*\ln 2+\log_{d} \frac{n}{2^{m}}+s\approx\frac{m}{n}*\ln 2+\log_{d}\frac{n}{2^{m}}+s \tag{6}\]  
Taking \(n\) as the variable, we take the derivative of the time function of BB+ and compare it with the time function of B+. It can be found that the magnitude of the change of the BB+ tree is smaller than the derivative result of the B+ tree with the increase in n, which indicates that the growth rate of the BB+ retrieval time is slower than B+ tree's time with the increase in retrieval space. \[\text{Read}^{\prime}_{B+\text{ Tree }}=\frac{1}{n\ln d} \tag{7}\]  
\[\text{Read}^{\prime}_{BB+\text{ Tree }}=-\frac{m}{n^{2}}\ln 2+\frac{1}{n\ln d} \tag{8}\]  
The space consumption of the B+ tree depends on the number of nodes. In actual operation, the node's size matches the size of the disk page (usually 4, 8, or 16 K), so the size of the B+ tree is multiplied by the B+ tree nodes number and the disk page size. In addition to the space consumption of the B+ tree, the space consumption of the BB+ tree structure also includes the space consumption related to the Bloom filter.
## 2 Discrete Element Parameter Model and Parameter Calibration of Soil-Soil Interaction
### Physical Test of Soil Accumulation Angle  
The experimental method employed in this study involved the use of a steel plate lift, as illustrated in Figure 2. During the experiment, the steel plate was initially wedged against one side of a cube. Subsequently, soil was poured into the cubic chamber until the latter was completely filled. A universal testing machine was utilized to lift the steel plate upwards at a speed of 0.03 m/s, resulting in the formation of a particle pile, as depicted in Figure 3. Finally, a tilt angle gauge was employed to measure the angle of soil accumulation. The physical experiments for the angle of repose were repeated 10 times, and the average value taken, ultimately yielding an actual soil pile angle of 35.55\({}^{\circ}\).
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### 2.2.2 H/\(\alpha\) Decomposition Parameters  
In 1997, [PERSON] and [PERSON] proposed a polarimetric information extraction method based on the decomposition of the \(3\times 3\) coherency matrix (3) of the target [31]:  
\[\mathbf{T}=\mathbf{U}_{3}\left[\begin{array}{ccc}\lambda_{1}&&\\ &\lambda_{2}&&\\ &&\lambda_{3}\end{array}\right]\mathbf{U}_{3}^{H} \tag{3}\]  
where \(H\) stands for transpose conjugate, and \(\mathbf{U_{3}}\) can be parameterized by Equation (4):  
\[\mathbf{U_{3}}=\left[\begin{array}{ccc}\cos(\alpha_{1})e^{i\phi_{1}}&\cos( \alpha_{2})e^{i\phi_{2}}&\cos(\alpha_{3})e^{i\phi_{3}}\\ \cos(\alpha_{1})\cos(\beta_{1})e^{i\phi_{1}}&\sin(\alpha_{2})\cos(\beta_{2}) e^{i\phi_{2}}&\sin(\alpha_{3})\cos(\beta_{3})e^{i\phi_{3}}\\ \sin(\alpha_{1})\sin(\beta_{1})e^{i\gamma_{1}}&\sin(\alpha_{2})\sin(\beta_{2 })e^{i\gamma_{2}}&\sin(\alpha_{3})\cos(\beta_{3})e^{i\gamma_{3}}\end{array}\right] \tag{4}\]  
The three eigenvalues of the coherency matrix \(\mathbf{T}\) are real numbers, arranged as \(\lambda_{1}>\lambda_{2}>\lambda_{3}\), \(\mathbf{U_{3}}\) is the unitary matrix, whose column vectors \(\overrightarrow{u}_{1}\), \(\overrightarrow{u}_{2}\) and \(\overrightarrow{u}_{3}\) are the eigenvectors of \(\mathbf{T}\):  
\[\mathbf{T}=\sum\limits_{i-1}^{3}\lambda_{1}\overrightarrow{u}_{1}\cdot \overrightarrow{u}_{1}^{H}+\lambda_{2}\overrightarrow{u}_{2}\cdot \overrightarrow{u}_{2}^{H}+\lambda_{3}\overrightarrow{u}_{3}\cdot \overrightarrow{u}_{3}^{H} \tag{5}\]  
The probability of three eigenvectors can be calculated by:  
\[P_{i}=\frac{\lambda_{i}}{\sum\limits_{j=1}^{3}\lambda_{j}} \tag{6}\]The polarimetric entropy, which describes the randomness of the scattering mechanisms, can be defined as:  
\[H=-\sum_{i=1}^{3}P_{i}\log_{3}(P_{i}) \tag{7}\]  
The mean scattering angle \(\alpha\) is defined by:  
\[\alpha=P_{1}\alpha_{1}+P_{2}\alpha_{2}+P_{3}\alpha_{3} \tag{8}\]  
The entropy \(H\) is a measure of the randomness of the scatter mechanism. It is base-invariant and closely related to eigenvalue \(\lambda\), which represents different components of the total scatter power. For a clean sea surface, Bragg scattering dominates, so \(H\) is close to 0. In contrast, for oil slick-covered areas, the scattering mechanism becomes more complex; stronger random scattering results in higher entropy values. Moreover, for biogenic slicks, although the scattering power is lower, the main scattering mechanism is still Bragg, resulting in lower entropy compared to oil-covered areas. This way, \(H\) can be used to distinguish oil slicks and weak damping look-alikes.  
Usually jointly used with \(H\), the mean scattering angle \(\alpha\) reflects the main scattering mechanism of the observed target. On slick-free sea surfaces, \(\alpha\) is expected to be less than \(45^{\circ}\) as the Bragg scattering is dominant. In slick-covered regions, larger \(\alpha\) can be measured, as a more complex scattering mechanism is present.
## 5 Simulations Results and Discussions  
This section presents the simulation results and discusses the developed model for optimal EM via power usage scheduling. Our proposed system model simulations are carried out for two case studies: (a) home is not equipped with DGs, and (b) home is equipped with DGs. The load is served via a power supply from the EUC and DGs. These case studies are created to evaluate the validity and applicability of the proposed system model for achieving our desired objectives. Furthermore, the proposed GWDO-based ECS is compared with existing algorithms in aspects of optimal EM. In the proposed system model, we have considered six appliances as a load of the consumer, which are smart appliances connected via WiFi to the ECS. The GWDO algorithm-based ECS schedules the operation of smart appliances using an energy pricing signal considering consumer and EUC constraints. Distributed generations such as PV, WT, ESS, and DGs are shown in Figure 3. The RTP signal is depicted in Figure 4.  
Figure 3: DGs: (**a**) PV energy generation, (**b**) wind energy generation, (**c**) DGs generation, and (**d**) EVs charging/discharging with DGs.
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 4 Specific Implementation  
### Initialization  
The private key generation center sets safety parameter \(\lambda\) and random number \(r\), the user provides \(ID_{u}\) as his unique identifier to the private key generation center, which will use \(KeyGen(ID_{u})\rightarrow(PK_{u},SK_{u})\) to generate the public/private key pair \((PK_{u},SK_{u})\) of the data subject. Service providers and regulatory agencies can obtain key pairs separately through the same process.
# Saturation Effect on the Coherency Loss of Spatially Varying Vertical Ground Motions
## 4 Conclusions  
In the present paper, the effect of the influence regularity of the site saturation degree, the height of the groundwater level, and the thickness of the surface water layer on the coherency loss of vertical SVEGMs was studied based on the 1D wave propagation theory, the dynamic-stiffness matrix of the surface water layer, and the effect of site saturation on the amplification of seismic wave. The results showed that (i) generally, the state of full saturation is the critical state determining whether the vertical motion is amplified or suppressed, and natural frequencies of spectral ratios increase as SD, GWL, and WLT increase; (ii) as the site saturation degree and water table increases, the value of the lagged coherency loss for the vertical seismic motions gradually increases (i.e., gradually approaches that for bedrock motion), and at the frequencies where vertical motion is suppressed significantly by the surface water layer, the value of coherency loss is extremely low; (iii) there is always a remarkable change of phase angle showing up around the frequency point corresponding to each peak of spectral ratio curve, and the curves of phase angle gradually shift towards high frequency end as spectral ratio varies with increasing SD, GWL, and WLT. However, the present results are based on a local homogeneous soil site, and random soil properties are not considered, which will be taken into consideration in future work.  
Conceptualization, E.Y.; methodology, E.Y.; software, Z.Y.; validation, Z.Y. and Y.R.; formal analysis, W.L. ([PERSON]); investigation, W.L. ([PERSON]); resources, Y.R.; data curation, X.W.; writing--original draft preparation, E.Y.; writing--review and editing, Z.L.; visualization, W.L. ([PERSON]); supervision, X.D.; project administration, W.L. ([PERSON]); funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Knowledge Innovation Program of Wuhan-Shuguang (No. 2022010801020242), the National Natural Science Foundation of China (Nos. 51908236, 51978304, 51778260, and 52079009), and the China Postdoctoral Science Foundation (2019M652649).  
The basic data used to support the findings of this study are included within the article and available from the corresponding author upon request.  
The authors thank the anonymous reviewers for their detailed comments that helped to improve this article substantially. The first author is grateful for his wife, [PERSON], for support and care.  
The authors declare no conflict of interest.
# Imogolite Nanotubes: A Flexible Nanoplatform with Multipurpose Applications
## 2 Imogolite Synthesis
### The Selection of Precursors  
The sources of Al and Si precursors have also been varied. INT synthesis are commonly performed with Al(ClO\({}_{4}\))\({}_{3}\), AlCl\({}_{3}\) and Al alkoxide (ASB: aluminum tri-sec-butoxide), Al(NO\({}_{3}\))\({}_{3}\) salt being less employed, while the silicon precursor is most often Si(OH)\({}_{4}\) or an alkoxide (TEOS: tetraethoxysilane) [40]. However, regardless of the nature of the Al and Si precursors used, the synthetic imogolite nanotubes are always identical with inner and outer diameters of 1 and 2.5 nm, respectively. Conversely, [PERSON] et al. suggested that the nature of the protic acid used (HCl, HClO\({}_{4}\) or CH\({}_{3}\)COOH) may control the relationship between the INT precursor's shape, formed in the early stage of the synthesis (also referred as \"proto-imogolite\"), and the resulting nanotube shapes [44]. More recently, [PERSON] et al. evidenced that replacing NaOH with KOH strongly impacts the morphology of the nanotubes in addition to producing a large amount of disordered structures [45]. INT diameter slightly increases by less than 20% while their average length becomes shorter, suggesting that the nature of the counter ions affects the hydrolysis of Al and Si precursors [46; 47].
## 4 Methodology
### Classification Models  
#### 4.4.1 Machine Learning  
Traditional ML algorithms, such as Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), have proven to be advantageous in several text classification projects [68; 69; 70], particularly Arabic text classification [71; 72]. Due to the fact that Naive Bayes is based on [PERSON] theorem and the assumption of conditional independence between features, it is an effective classification algorithm [68]. On the other hand, SVM works well with small-to-medium-sized datasets [69], LR provides insights into the relationship between features and class probabilities [70], and RF combines multiple decision trees to make predictions. A description of each algorithm in detail is as follows:  
* **Naive Bayes (NB)**: This classifier is composed of a number of algorithms that are based on the Bayes Theorem and assumes independence of the attributes. It is based on estimates, in which the model adjusts its probability table using the training data and predicts new observations by estimating the class probability based on its feature values. The small amount of training data needed by NB results in storage space savings. It also yields quicker results and is not sensitive to missing data [73].
* **Support Vector Machine (SVM)**: is a supervised machine learning model that classifies input data based on dimensional surfaces by finding the maximum separating hyperplane between different classes [74]. SVM offers data analysis for both classification and regression analysis. After that, it chooses a boundary that maximizes the distance between neighboring members of various classes [75]. SVM offers the advantage of resolving overfitting in high-dimensional spaces. As a result, by choosing a plot from a large selection, it can simulate non-linear acceptable boundaries. It is widely used in text classification projects [76; 77].
* **Logistic Regression (LR)**: is a classifier that establishes a link between features and likelihood of the outcome [78]. It is based on the logistic function, an S-shaped curve that maps real value numbers to values between 0 and 1 [79]. LR is reliable for classifying problems [80], and it prevents overfitting.
* **Random Forest (RF)**: this classification algorithm uses a decision tree model that is based on bootstrap aggregation techniques, called bagging [81], which is an ensemble method that combines the predictions from several ML algorithms. Bagging also reduces high variances that can be produced by the algorithm, which is due to its sensitivity to the training data. RF is scalable and robust to outliers.
## 4 Proposed Solution
### Trajectory Optimization Algorithm Based on DDPG
#### 4.3.1 Observation Space \(\mathcal{O}\) and State Space \(\mathcal{S}\)  
For each UAV at time slot \(t\), its observation space \(\mathcal{O}\) contains three elements: the UAV's position, the remaining energy \(E^{f}_{remain}\) of the battery carried by the UAV, and the coverage \(D^{k}_{cover}\) of the user (referring to the total time slots covered by the user from the UAV's launch to the current time slot). In the scenario set out in this paper, the environment is partially observable. The state space \(\mathcal{S}\) is the set of all possible states, which summarizes the current environment and is the basis for the agent's decision-making. The state space includes the space observed by the UAV and the energy consumption \(E^{f}_{cost}\) of the UAV in the current time slot, as described in Table 1.  
Figure 3: Explanation of the basic principles of DDPG.  
Where \(a_{range}\) represents the boundary of the maximum horizontal flight area of the UAV set in this article, \(E_{max}\) represents the maximum power of the UAV battery, and \(D_{range}\) represents the maximum operating time of the UAV set in this paper. Therefore, state space can be defined as:  
\[\mathcal{S}\triangleq\{s_{t}\}=\left\{x_{f},y_{f},\,z_{f},E^{f}_{remaint},\,D^{ k}_{cover},E^{f}_{cost}\right\} \tag{18}\]