esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
It can be seen from the above research that the cutter pressure is the main factor affecting the surrounding soil deformation when an EPB shield is tunneling in soft soil. Some scholars have also studied the stratum deformation of EPB shield tunneling in sandy pebble stratum. This stratum has low fluidity, high permeability, poor cementing ability, and high single-stone strength, which is different from the physical and mechanical properties of soft soil. Shield tunneling control is also different from soft soil, so some scholars have also conducted preliminary research on this stratum. The surface monitoring results of Chengdu Line 1 show that the surface settlement caused by the shield tunnel is mainly affected by the pressure of the cutter, the friction between the shield shell and the surrounding soil, and the gap of the shield tail. The surface deformation of the sandy pebble stratum is V-shaped, and its influence range is smaller than that of soft soil. The shield tail gap is the main reason for the ground loss [13]. [PERSON] et al. [14] found that the influence of grouting pressure on surface deformation is greater than that of chamber earth pressure. The weight of the shield machine and the friction between the shield machine and the surrounding soil have little influence on the ground settlement. [PERSON] et al. [15] pointed out that surface deformation is related to the grouting pressure at the shield tail but not to the grouting volume. The above research shows that when the shield tunneling passes through different strata, the stratum deformation law and control methods are different. Nowadays, most of the strata that EPB shields pass through are soft soil stratum, and some projects are sandy pebble stratum. There is little research on other strata. This project relies on the section from Fangxi Road station to Liansheng Road Station of Hangzhou Metro Line 3. The EPB crossing stratum in this section is mainly a round gravel stratum. The round gravel stratum is mainly composed of gravel, pebble, and sand. The stratum has a large difference in particle size. In addition, its anisotropy is greater than that of the soft soil layer and sandy pebble stratum. It is an unstable layer that is therefore prone to collapse when disturbed. In order to explore the law of stratum deformation and the control methods to reduce formation deformation during EPB shield tunneling in round gravel stratum, systematic, close-up, and high-density field monitoring tests were carried out in this section. Thisresearch provides beneficial information for engineering similar projects to control stratum deformation.
## 6 Discussion  
Analyzing the results, obtained by the unconstrained nonlinear optimization od exponent weights using the MATLAB(r) _fminsearch_ function, which utilizes the simplex search method, additionally verified using gradient-based methods, it may be noticed that the application of an individual elementary metric leads to PLCC and SROCC values not exceeding 0.7 for the best metric (FSIM) regardless of the chosen division into regions. Nevertheless, the application of the combination of even two metrics increases the Pearson's correlation significantly reaching the values over 0.8 for three \"best\" combinations. However, removing the FSIM metric from the combination decreases this value to only 0.7081, hence Feature Similarity should be considered as the basic metric used in all combinations.  
Further increase of the PLCC for the combination of three metrics allows one to specify the other metrics leading to even better performance (CW-SSIM and MCSD, used also in further combinations of four and five metrics) reaching 0.8472 with further increase to 0.8537 for five metrics, also leading to a substantial improvement of the rank order correlations. The increase of the linearity of the relationship between the combined metrics and MOS values may also be observed in Figure 4, particularly for combinations of 3 and 5 elementary metrics. As presented in Table 4, even better results may be achieved limiting the number of blocks for the combinations of four and five individual metrics, obviously with additional advantageous shortening of the overall computation time.  
In comparison to previous studies, the obtained results are superior to those presented in the recent conference paper [58], where PLCC = 0.8353 has been obtained for the combination of various approaches, previously applied only for sample classification purposes. However, the methods applied in the aforementioned paper, apart from the use of FSIMc metric, are based on various additional calculations including preprocessing with the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, entropy calculations [3,4], use of the Histogram of Oriented Gradients (HOG) and--more importantly--entropy of the depth maps obtained using the additional 3D scanning.  
Considering the practical applicability of the proposed approach, the use of even a monochrome camera is effortless, being enough to acquire the necessary data without the need of the time-consuming 3D scanning of the samples, which makes the real-time applications almost impossible, or at least expensive and troublesome, especially in low-cost solutions.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 2 Related Work
However, the proposed model does not consider optimal and reliable policies for data routing and incurs additional network overheads. Based on the literature review, it is seen that sensor-cloud is used in various applications such as healthcare, military, smart cities and environmental and monitoring, etc. Due to the unreliable and dynamic infrastructure of such applications, energy efficiency, data security, and node-levelauthentication are the most demanding challenges. It is observed that most of the proposed solutions do not consider the limitations of low powered sensors while developing a sensor-cloud solution. Although some of the existing sensor-cloud solutions decrease the overhead of network nodes, they lack the optimal data aggregation and routing decision. On the other hand, the knowledge-based security models improve energy efficiency and network lifetime, but these solutions offer data reliability with the additional cost of computation and energy consumption on sensor nodes due to events initiation and management. Furthermore, most of the proposed sensor-cloud solutions do not focus on the measurement of data security and node-level authentication in the presence of malicious threats, which result in compromised network trustworthiness. Therefore, it is concluded from the aforesaid solutions that the sensor-cloud infrastructure is suitable for large-scale network regions to increase network scalability with the addition of proper data management. However, the constraints of low powered sensor nodes should be taken into consideration while developing a solution. Therefore, the main contribution of the proposed solution is to develop a secure and authentic sensor-cloud architecture for the improvement of data gathering and energy efficiency. Moreover, the proposed architecture offers lightweight cryptosystems to estimate data security in terms of confidentiality, integrity, and authentication. Besides, with the integration of cloud to sensor networks, the proposed architecture optimizes computational overheads on sensor nodes with minimum energy consumption.
# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs  
The use of fuzzy logic in the design of controllers for RACs is mainly focused on the actual modeling or simulation of the system. As it was previously mentioned, the variables most used in a control system for RACs are the temperature and relative humidity; therefore, in the following subsections, the main studies in this engineering field are discussed. These studies show the scope of fuzzy logic applied to chillers, air conditioning systems, domestic refrigerators, and heat pumps.
## 3 Vibration Localization Characteristics and Their Influence Factors
### Eigenvalue Splitting
#### 3.2.2 Eigenvalues Under Different Coupling Strengths  
The masses of the resonant units are assigned based on the E2 example, and the eigenvalues of the CMS are calculated for coupling coefficients \(R=0\), \(R=0.1\), and \(R=0.2\). The results are shown in Figure 6.  
Figure 5: Eigenvalues for different mistuning strengths.  
Figure 6: Eigenvalues for different coupling strengths.  
It can be seen that the eigenvalues of the CMS increase overall as the coupling strength rises. When the coupling strength is relatively small (\(R<0.2\)), the relative intervals of the eigenvalues of each resonant unit are less affected by the coupling strength; when the coupling strength continues to increase (\(R=0.2\)), the eigenvalues of the minimum and maximum order modes of the CMS undergo an abrupt change. This is because as \(R\) increases, the influence of the coupling strength on the second-order perturbation of the eigenvalues is enhanced, particularly for the resonant units with the smallest and largest eigenvalues. This abrupt change in eigenvalues broadens the distribution range of the eigenvalues, but it causes the resonant frequencies of certain resonant units to deviate from those of other units. As a result, the number of the resonant units that can be effectively utilized is reduced, which in turn affects the overall vibration reduction performance. Therefore, it is advisable to minimize the coupling strength between resonant units as much as possible.
## 5 Proposed Scheme
### Simulation Results and Discussion  
We compare our proposed scheme with the random resource allocation scheme (labeled as RRA) presented in [3] and resource allocation scheme with RF of 1 (labeled as RA with RF = 1) presented in [11]. In RRA scheme, multiple D2D pairs residing in a cell can reuse the uplink cellular resources at the same time. However, due to coexistence of both cellular users and D2D pairs in same region of a cell imposed the aggregate interference. Whereas in the case of the RA with RF = 1 scheme, allowing only one D2D pair to access a cellular resource at a time cannot accommodate high capacity of a system.
## 4 Results and Discussion
### Statistical Performance of Satellite Precipitation Estimates
Figure 6 shows the temporal variations of averaged spatial precipitation and statistics for the selected grid boxes. Table 3 lists the statistical summary of seasonal comparisons including spring (March-May), summer (June-August), autumn (September-November), and winter (December-February) by computing at the daily scale. Overall, the patterns of monthly mean precipitation for all satellite precipitation products exhibited similar fluctuations with gauge observations. Precipitation in the summer is the main water source over the TP, while precipitation in the winter only contributes a minor part of annual precipitation. The performance of satellite precipitation productions showed distinct seasonal variations. The statistical indices performed better in the summer than the other three seasons with high correlation, low relative error, and better detection for rain events (see Table 3). For instance, the CC value of the four satellite precipitations ranged from 0.59 to 0.76 during the summer, while a lower CC occurred in the winter. We also note that the RMSE was larger during the summer months than that of the winter season (Table 3 and Figure 6c). This is because RMSE could be affected by large precipitation bias. The RB results indicate that GSMaP-MVK overestimated the reference precipitation, except during the winter, and IMERG-UC, IMERG-C, and GSMaP-Gauge underestimated the gauge observations in all seasons. Similar to the results of [PERSON] et al. (2019) and [PERSON] et al. (2020), satellite precipitation products showed higher error and poor capability of rainfall detection in the winter months. During the winter season, although the DPR improved the skill of snowfall observations, satellite precipitation products still showed unsatisfactory  
Figure 5: Spatial distribution of (**a,d,g,j**) correlation coefficient (CC); (**b,e,h,k**) root-mean-squared error (RMSE); and (**c,f,i,l**) relative bias (RB) between the four satellite precipitation estimates and CGDPA at the 132 grid central points. performance over the TP. This can be attributed to the limitation of passive microwave retrievals and IR information at cold or snow-covered background surfaces [22], suggesting that the current GPM-era estimates still have room for improvement in the winter.
## 4 Experiments Setup
### Text Preprocessing and Encoding  
The purpose of preprocessing is to remove noise and redundant information from the dataset and to convert natural language text into a format understandable to the classifier. In this study, a text preprocessing stage includes the following standard practices:  
1. Converting all letters to lowercase;
2. Removal of stop-words;
3. Removal of digits (numbers) and special characters;
4. Whitespaces formatting.  
Figure 1: The methodology of the study.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset Characteristic** & **Value of the Characteristic** \\ \hline Number of authors & 100 \\ Number of texts & 1100 \\ Dataset size, symbols & 375,618,852 \\ Dataset size, words & 62,603,142 \\ Dataset size, sentences & 5,216,929 \\ The average length of text, symbols & 973,342 \\ The average length of sentence, words & 14.3 \\ Maximum number of texts per author & 20 \\ Minimal number of texts per author & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the literary texts’ dataset.  
A feature vector is constructed based on the processed text. According to the formed feature space, the classifier is able to identify common dependencies and correlations in all objects (texts) belonging to a given class (author), including texts that have not yet been used for training.  
A set of \(n\) features measured for each text is fixed during the formation of the feature space. In this case, all \(n\) features are numeric, so the feature description of a text a the numeric vector of dimension \(n\). Elements of the vector are frequencies of punctuation marks, parts of speech, unigrams, bigrams, and trigrams of symbols, the most popular words of the Russian language (based on the frequency dictionary [51]).  
The values of the resulting vector will always be in the range [0, 1]. However, it is obvious that the letters, especially vowels, are found in the text much more often than bigrams, trigrams of symbols, etc. To bring the features to a common scale without losing information about differences in ranges, minimax normalization was used.  
Texts are encoded using One-Hot Encoding to work with NN. The principle of this method is to convert categorical variables into a binary vector. The vector consists of zeros at the positions of all features, except one. The position corresponding to the numerical value of the feature is taken by one. The method is used to transform input data of NN and classical ML methods.
# Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
## 4 Case Study
### Network Structure  
Our experiments were conducted using a graph structure consisting of 10 bases (agents). As presented in Figure 2, a base can assume the role of root, inner, or terminator. A root base can only send products to others, an inner base can receive and send products, and a terminator base can only receive products. In particular, the simulation involves transporting a quantity of product from a root agent (origin base) to a terminator agent (destination base). Additionally, each base stores a finite amount of product (current stock). Moving a quantity of product between bases incurs storage and transportation costs, as well as time consumption. The cost of transporting a product from a base \(b_{i}\) (delegator) to a base \(b_{j}\) (delegatee) depends on the storage capacity and the storage cost of \(b_{j}^{\prime}\), which is added to the transport cost (throughput cost). Both storage and throughput costs are defined per unit of product. In contrast, the transport time depends on the throughput of the edge \(e_{i-j}\), considering the total quantity of product to be transported between the bases.  
The delegation process starts at the root base and spreads through the network until it reaches the terminator base, signaling the end of an iteration. During an iteration, a delegator can perform several delegation actions, as its goal is to reduce the quantity of product in its stock to zero. An iteration is represented by a series of delegations required to transport a quantity of product from the root base to the terminator base.
## 4 Simulation Experiments
### Robustness of the Proposed Technique  
To verify the robustness of the proposed technique, an outlier contaminated case is generated based on the simulation in the previous subsection. Three consecutive outliers are added to the discriminator output after the jamming environment changes, while other simulation parameters remain the same. This means that ten percent of the observations in the window are contaminated by outliers for the RPCs, which is the case in Section 4.1.  
Figure 7 shows the carrier tracking errors with and without phase compensation techniques under the outlier contaminated case. A comparison of Figures 6 and 7 reveals the following. (1) From the original curves in the two figures, it is known that several outliers in the discriminator output do not significantly affect the tracking results because the duration of the outliers is not long enough. (2) Comparing the SPC curves in the two figures, it is verified that the performance of the SPC is poor because its phase bias estimation is easily destroyed by outliers. (3) The performance of the RPC with averaging estimation is severely degraded by the outliers in the observation window due to the lack of outlier rejection capability. (4) The RPC with robust estimation shows its robustness to observation outliers and maintains high-precision carrier measurement accuracy under this severe condition. The results (2)-(4) agree with those in Section 4.1, and also validate the robustness of the proposed technique.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 2 Materials and Methods  
### System Structure and Geometric Srtifacts  
The nanotomography system consists of a cone beam X-ray source, a mechanical platform, a detector, and a computer control system. We built the coordinate system shown in Figure 1 to describe the geometry of the nanotomography system more clearly. Ideally, the geometry satisfies both of the following relationships: (1) The center ray is perpendicular to the plane where the detector is located and intersects with the detector center; (2) The plane composed of the rotation axes \(Z\) and \(X\) is parallel to the plane where the detector is located, and the projection of the rotation axis \(Z\) on the detector is the center column of the detector, that is, the straight line where \(Z_{1}^{\prime}\) is located. The geometric parameters of a nanotomography system can be expressed using the seven parameters shown in Table 1.  
Any deviation between the geometric parameters of the actual nanotomography system and the ideal geometric relation model will produce geometric artifacts. The geometric artifacts in the reconstructed image mainly show blurring and ghosting. The details of the reconstructed image are lost, making it difficult to obtain useful information from the image. In practice, not all misaligned parameters will have the same effect on the quality of the reconstructed image. The \(\eta\) defined in Table 1 and the lateral offset of the rotation axis \(\Delta u\) shown in Figure 1 have a more serious impact on the system [1; 3; 21; 22]. This paper mainly calibrates these two parameters with significant influence.  
\begin{table}
\begin{tabular}{c c} \hline The distance from the X-ray source to rotation axis & \(SOD\) \\ \hline The distance from the X-ray source to the detector & \(SDD\) \\ \hline The position of the axis of rotation on the detector plane & \(u_{c}\) \\ \hline The position of the mid-plane in the detector plane & \(v_{c}\) \\ \hline The rotation angle of the flat plate detector along the \(u=u_{c}\) & \(\theta\) \\ \hline The rotation angle of the detector around the \(\gamma\) axis & \(\eta\) \\ \hline The rotation angle of the detector along the \(v=v_{c}\) & \(\varphi\) \\ \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of nanotomography system.  
Figure 1: Geometric model of a laboratory nanotomography system.
## 2 Materials and Methods
### Monolayer Infection and Imaging  
Prior to proceeding with the co-culture assay, we verified the permissiveness of BeWo and HUVEC cells to our ZIKV isolate. Confluent BeWo and HUVEC monolayers were infected with 1000 plaque-forming units per well in a 12-well plate. After 48 h, the samples were fixed with paraformaldehyde solution, 4%, in phosphate buffered saline (PBS) (ThermoScientific CAT#J19943-K2)and blocked in 5% lamb serum. Primary antibody staining with an anti-flavivirus-group antigen, Clone D1-4G2-4-15 (BEIresources NR-50327), was performed to visualize ZIKV. The staining was conducted overnight at 4 \({}^{\circ}\)C. The cells were rinsed in PBS and then incubated with secondary antibodies at room temperature for 1 h. The secondary antibodies included AlexaFluor 647 goat anti-mouse (Invitrogen #A21235). The cells were rinsed, and then, coverslips were mounted with ProLong Gold Antifade Reagent with DAPI (Cell Signaling Technology, Danvers, MA, USA, catalog #8961S) and incubated overnight at 4 \({}^{\circ}\)C. Images were taken with an Olympus Fluoiview 3000 confocal microscope and processed using the Olympus Fluoiview FV10-ASW 4.1 software package.