esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The settlement of municipal solid waste (MSW) is an important issue that needs to be considered carefully during the design, construction, and operation of sanitary landfills, as excessive settlement of MSW affects the storage capacity and safe operation of the landfill [1; 2]. The settlement of MSW includes three parts: primary compression settlement, secondary compression settlement, and degradation settlement. Primary compression settlement accounts for a large proportion of the total settlement, especially before the closure of the landfill [3; 4]. Consequently, studying the law of MSW settlement and calculating the primary compression settlement are important for predicting the total settlement of the landfill accurately.  
Domestic and overseas researchers have conducted plenty of research works on the law of MSW primary compression settlement. The MPCI (modified primary compression index), which is related to the dry weight, sample composition, pore ratio, moisture content, and size, is an important parameter that describes the primary compression characteristics of MSW. The MPCI decreases with an increase in dry weight, increases with an increase in water content, and increases with an increase in organic matter content [5; 6; 7; 8]. While little research has been conducted on the influence of temperature on MSW primary compression, it can be concluded that temperature has a significant impact on the primary compression of soil based on the existing literature [9; 10; 11; 12]. MSW contains kitchen, plastic, fabric, paper, and rubber matter and other components which are more sensitive to temperature compared to soil. Therefore, the primary compression of MSW will be affected by temperature, and studying the effect of temperature on MSW primary compression is greatly significant.  
Therefore, it is important to predict landfill settlement accurately and estimate landfill storage capacity correctly by studying the influence of parameters such as temperature, moisture content, dry weight, and composition on the MPCI. Currently, there is little research on how these parameters, especially the temperature, affect the MPCI. Some researchers have established a multi-parameter model to predict the MPCI through indoor experiments [13], but they did not consider the impact of temperature, and the prediction effect needs to be improved.  
Accordingly, the main contributions of this article are as follows: (1) we conducted an MSW consolidation test considering temperature control and degradation inhibition to obtain the MPCI of the samples; (2) analyzed the effect of parameters such as temperature, dry weight, organic matter content, and moisture content on the MPCI according to the results of the test and literature to create an MPCI regression model; (3) and validated the reliability and accuracy of the model with the results of an indoor bioreactor test considering temperature control.
# Phytochemical, Antimicrobial and Cytotoxic Activities of Gaultheria Trichophylla [PERSON]
###### Abstract  
Gaultheria trichophylla fruit is used by the indigenous people to treat asthma, headache, and as an appetizer in the alpine and sub-alpine regions of Western Himalaya. No studies exist on the antimicrobial significance of this species. The current study describes the phytochemical composition, in vitro cytotoxic, and antimicrobial effects of different extracts of _Gaultheria trichophylla_. In antimicrobial assay, four different bacterial strains (_Escherichia coli_, _Bacillus subtilis_, _Pseudomonas aeruginosa_, and _Staphylococcus aureus_) as well as four fungal strains (_Helmuthosporium solani_, _Fusarium solani_, _Aspergillus flavus_, and _Aspergillus fumigatus_) were used. Qualitative phytochemical screening showed the existence of different active compounds. Quantitative phytochemical screening showed the existence of phenolic contents in the range from 3.27 \(\pm\) 0.44 mg GE/g to 14.17 \(\pm\) 088 mg GE/g, whereas flavonoids were from 8.08 \(\pm\) 0.48 mg QE/g to 26.9 \(\pm\) 0.23 mg QE/g. The elemental analysis quantified essential minerals of life importance such as Na (3.24 \(\pm\) 0.05 \(\mu\)gg\({}^{-1}\)), Mg (1.93 \(\pm\) 0.08 \(\mu\)gg\({}^{-1}\)), and Ca (1.83 \(\pm\) 0.056 \(\mu\)gg\({}^{-1}\)), while none of the heavy metal levels were high from the permissible limit of WHO. Cytotoxic assay showed moderate activity in terms of LC50 of (50 \(\mu\)g/mL) for methanolic extracts. Antifungal assay of methanolic and other extracts against different tested fungal strains showed a zone of inhibitions from 29 \(\pm\) 1.154% to 86.66 \(\pm\) 0.09%. As an antibacterial, the MIC values were from 7.5 mg/mL to 15 mg/mL for the tested extracts. The observed biological potentials were at the expense of its phytochemical composition, however, further confirmation in animal models and responsible phytochemical isolations in pure form is needed.  
Gaultheria trichophylla; extracts; pharmacogenostic; phytochemicals; elemental; cytotoxic activity; antimicrobial activity +
Footnote †: journal: Applied
# Moment-Based Parameter Estimation for the \(\Gamma\)-Parameterized TWDP Model
## 3 Moment-Based Estimators for the \(\Gamma\)-Parameterized TWDP Model  
In order to derive moment-based estimators for the \(\Gamma\)-parameterized TWDP model, the expression for \(n\)-th moments of a signal envelope \(r\) given by ([4], Equation (6)) is expressed in terms of \(\Gamma\), as:  
Figure 1: \(\hat{\Delta}_{mean}\) (with absolute error bars) vs. \(\Delta\) for (**a**) \(K=1\), (**b**) \(K=3\), (**c**) \(K=10\), and (**d**) \(K=30\). The solid line shows a linear regression fit to the data. The unit slope dashed line is illustrated as a benchmark.  
\[\mu_{n}=\mathbb{E}[r^{n}]=\frac{\left(\frac{n}{2}\right)!\Omega^{\frac{n}{2}}}{(1+ K)^{\frac{n}{2}}2\pi}\sum_{m=0}^{\frac{n}{2}}\left(\frac{\frac{n}{2}}{m} \right)\frac{K^{m}}{m!}\int_{0}^{2\pi}\left(1+\left(\frac{2\Gamma}{1+\Gamma^{2 }}\right)\cos(\theta)\right)^{m}\mathrm{d}\theta \tag{7}\]  
and for even values of \(n\), obtained as a simple closed form expression:  
\[\mu_{n}= \tag{8}\]  
where \(\mathbb{E}(\cdot)\) is the expectation operator.  
Obviously, the \(n\)-th moment of signal's envelope \(\mu_{n}=\mathbb{E}[r^{n}]\) depends on three unknown parameters: \(K\), \(\Gamma\), and \(\Omega\). Consequently, estimators of these parameters can be constructed from at least three different moments. Thereby, it is shown that estimation accuracy is the largest when the lower-order moments are used [4]. Accordingly, moment-based estimators of the \(\Gamma\)-parameterized TWDP model should be generated using second-, fourth-, and sixth-order moments, since only even moments can be obtained from (8). Thereat, in order to further reduce the estimation complexity, the impact of parameter \(\Omega\) on \(K\) and \(\Gamma\) could be canceled out by properly defining ratios between the fourth- and the second-order as well as the sixth- and the second-order moments of an envelope [4]. It finally leads to the system of two equations:  
\[\frac{\mu_{4}}{\mu_{2}^{2}}  
By analyzing the discriminant of the polynomial (13), it can be shown that, for any values of \(K\) and \(\Gamma\), the considered polynomial has one real root and one pair of complex conjugate ones. Thus, following the [17], one can find the real root of polynomial (13) as:  
\[1+\hat{K}=\frac{-b+2\operatorname{Re}(Z)}{3a} \tag{16}\]  
where \(\operatorname{Re}(\cdot)\) gives the real part of the complex number, and \(Z\) is defined as:  
\[Z=\left(p+\sqrt{q^{
# A Choreography-Based and Collaborative Road Mobility System for L'Aquila City
## 3 Chorevolution Approach
### CHOReVOLUTION IDRE Underlying Technologies
In particular, the IdM is able to query the services for supported application contexts and played roles; force a specific application context for a certain service (put in \"maintenance\" or disable/enable). The Service Inventory is a sub-component of the IdM. It acts as a central repository for the description models of the services and things that can be used during the synthesis process. **(3) The Cloud layer** executes concrete service choreography instances on a cloud infrastructure and adapts their execution based on the actual application context. At execution time, for each choreography, in the CHOReVOLUTION cloud, there are (i) a set of choreography instances at different execution states; and (ii) a set of virtual machines executing a custom-tailored mix of services and middleware components to serve different parts of the choreography. VMs are installed and configured with services according to selectable policies. Due to the fact that EE is based on Apache Brooklyn, the CHOReVOLUTION IDRE is not constrained to a specific Infrastructure as a Service (IaaS) platform (e.g., Open Stack [47], Amazon EC2 [48]). The IDRE mainly targets three types of users described as follows:  
**Service providers** interact with the CHOReVOLUTION Studio to define the description models (i.e., interface and security models) of existing services and then publish them into the Service Inventory. The benefit they obtain is to foster and ease the reuse of their services by developers, hence increasing the opportunities to be involved in new businesses. **Choreography developers** interact with the CHOReVOLUTION Studio to (i) model a choreography by using the Choreography Modeler; (ii) Realize the modeled choreography through the automatic synthesis of BCs (for solving heterogeneity issues), CDs (for solving coordination issues) and As (for solving interface mismatches). This is done by exploiting the Synthesis Processor. **Choreography operators** interact with the CHOReVOLUTION console to (i) deploy and enact the generated choreography-based application through a structured process that involves the back-endlayer; (ii) monitor the status of the execution cloud environment; and (iii) monitor the execution of the choreography instances and managing their lifecycle.
# Context-Based Model for Browsing the Web Through Voice
## 7 Results and Discussion
### Web Search Results  
Participants were asked, after each of the four searches carried out, whether the results obtained coincided with their user profile and requests. The results were as follows:  
* Searching for an image: as evidenced in Figure 15(a), 60% stated that the results were in agreement, 20% indicated that they did not coincide, while the remaining 20% expressed that they corresponded slightly;
* Searching for a video: as shown in Figure 15(b), 41% said the results were consistent, 36% commented that they did not correspond, and the remaining 23% expressed that they slightly coincided;
* Searching for a web page: as seen in Figure 15(c), 75% stated that the results were consistent, 5% commented that they did not correspond, while the remaining 20% expressed that they slightly coincided;
* Searching for a term: as depicted in Figure 15(d), 70% said the results were consistent, and 30% said they slightly corresponded.  
Considering the four searches carried out, 61.5% of users stated that the results coincided with their profiles and requests, 23.25% expressed that the results corresponded slightly, while 15.25% indicated that the results did not fit their profiles and requests. It is concluded that the matching of contexts and requests achieved that 84.75% of users obtained relevant content in their first interactions. However, the opportunity for improvement through the implementation of a dynamic method for the analysis and matching of  
Figure 15: Results of the prototype capability assessment.  
contexts is recognized. This will enable the expansion of the context, allowing a better understanding of the user's needs and the characteristics of the web resources. Likewise, the integration of a dedicated search engine that operates considering the user's context can be contemplated.
## 1 Introduction  
In recent years, stereoscopic 3D (S3D) technologies have attracted considerable research interest. S3D measurement can be classified into S3D content measurement (e.g., visual discomfort, quality of experience, or unnatural experience) and S3D image quality measurement (IQM) (e.g., the measurement of compression distortions or transmission impairments for S3D images). However, research on S3D content measurement is still limited. In recent decades, blind S3D-IQM has been attracting attention in the research field of S3D imaging [1; 2; 3]. State-of-the-art blind S3D-IQM methods learn to measure S3D quality via regression from the subjective scores of training samples. For instance, in [4], [PERSON] et al. introduced a blind S3D-IQM method that extracts both 2D and S3D quality-aware features from an S3D image and a depth/disparity map. Next, a regression model is adapted to obtain a final quality value from the above-mentioned features. Similar studies were also conducted in [5]; [PERSON] et al. proposed a blind S3D-IQM method based on a \"cyclopean\" view and disparity map. [PERSON] et al. introduced blind S3D-IQM based on the self-similarity of binocular features [6]. Other relevant existing studies can be found in [7; 8; 9; 10; 11; 12; 13; 14]. These methods require human-scored S3D images for training; however, obtaining subjective scores through subjective evaluation is often expensive, cumbersome, and time-consuming, which limits their usability in practice. Thus, a significant problem is triggered: can we learn to measure S3D image quality blindly without relying on human-scored S3D images?  
Quality measurement of 2D images has been extensively studied, and several excellent methods have been proposed [15; 16; 17; 18]. For example, in [16], [PERSON] et al. presented a quality-aware clustering metric, which can learn centroids to act as a quality-aware codebook for measuring distorted image quality. Inspired by [16], we propose a blind S3D-IQM method that learns a quality codebook of binocular rivalry responses (BRRs). The main contributions of this work are three-fold.  
First, in the offline training phase, we learn a quality codebook from the BRRs of the original and distorted S3D images; this codebook will be used to replace human subjective scores to learn blind S3D-IQM methods.  
Second, a BRR-weighted local binary pattern (LBP) histogram scheme is used to extract quality-predictive features; this scheme is effective for describing degradation patterns.  
Third, in the online testing phase, a distance-based weighting scheme is used for perceptual quality pooling. Experimental results obtained using the publicly accessible LIVE S3D dataset indicate that the proposed method performs competitively and provides quality prediction performance and generalization ability.
## 2 Distributed Inertial Network System and Aircraft Elastic Deformation  
### Inertial Network System Structure  
In order to ensure the reliability of the inertial navigation system, the single node of the inertial network adopts redundant inertial sensors, that is, the number of similar inertial sensors in a single node exceeds three. According to the normal theory, the reliability of the navigation system increases with the increase of the number of inertial sensors. However, with the increase of the number of inertial sensors, the cost, volume and weight of the navigation system also increase accordingly, and when the number of sensors increases to a certain number, the growth of the reliability of the system is not obvious [28].  
Figure 1: Fusion algorithm based on distributed inertial network and flush air data sensing.  
The number of inertial sensors in a single node of the inertial network has been studied in detail in reference [28]. According to the proposed theory, after considering the reliability, economy and weight of the inertial navigation system, the optimal number of sensors in a single node of the inertial network is 6. This paper adopts the theory proposed in reference [28] and configures 6 similar inertial sensors in a single node.  
After determining that the number of sensors in a single node is six, the configuration orientation of these six sensors in the three-dimensional space is determined by the principle of minimum mean square error of least squares estimation. This is discussed in detail in reference [29]. Finally, it is found that the measurement axes of the six inertial sensors are along the normal direction of the six planes of the dodecahedron, and the angle with the coordinate system is \(31.7^{\circ}\). This configuration structure minimizes the mean square error of the least squares estimation. This configuration structure is also used in this paper.  
Each inertial network node is composed of the above-mentioned six sensors and microprocessors. Each network node is connected to other nodes by a wired or wireless network. The inertial information and local navigation estimation information of each node are shared in the network, thus avoiding a single point of failure [30], as shown in Figure 2. In the microprocessor of each inertial network node, the distributed information fusion algorithm is used to realize the navigation information fusion.
## 3 Results and Discussion
### Multivariate Analysis
#### 3.1.1 Harvest Discrimination  
Weather conditions significantly affect the compositions of grapes and wine, as shown in Figure 1a. Data concerning temperature, rainfall, and solar radiation between seasons (Figure 2) suggest that these factors may have contributed to variations in must content, biosynthesis of volatile compounds, and nutrient accumulation, which could have impacted the wine composition. Temperature can affect gene expression and enzyme activity, affecting primary and secondary metabolisms [22, 23]. Radiation influences photosynthesis, secondary metabolism, and vine water status, impacting shoot development, berry ripening, and fruit composition [7].  
Figure 1: Partial least squares discriminant analysis plots. (**a**) Score plot of the two principal components of Chardonnay sparkling wines trained on lyre (L) and GDC (G) systems in two seasons (2017 and 2018) and a replicate of vinjifications (V). (**b**) Loading plot of the two principal components.  
Regarding the compounds separating the vintages (Figure 1b and Table 1), the right side is correlated with the 2018 vintage, with the following compounds: dodecanoic acid (ID28), nonanoic acid (ID42), isoamyl acetate (ID2), and hexyl acetate (ID18), while the compounds 2-ethylhexyl salic (ID11), acetaldehyde (ID15), ethyl-9-decenoate (ID30), and furfural (ID32) presented correlations with the 2017 vintage.  
The 2017 vintage was differentiated by aldehydes (compounds related to wine oxidation); higher maximum and minimum temperatures at flowering and harvest stages; being a dry vintage at flowering, ripening, and harvest stages; and higher radiation at berry formation, _vetransion_, ripening, and harvest stages.  
The compound isoamyl acetate, correlated with the 2018 vintage, is related to the presence of the amino acid leucine, a precursor for the formation of isoamyl alcohol, which is subsequently esterified to isoamyl acetate through acetyltransferase enzyme action. It is an ester that can impact fruity aromas [24]. Additionally, this vintage is associated with lower temperatures, higher rainfall, and lower radiation--climatic conditions that could preserve the amino acids.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Key Generation Results  
Figure 6 shows the key generation results using the chaotic equations' output values and applying the control condition to convert these outputs into binary values to obtain the final key bit. After generating the key, we performed the NIST test, as shown in Figure 6, and the generated encryption keys were evaluated using the NIST statistical test suite, where they successfully passed 13 out of 16 tests. Although the key sequence exhibits high unpredictability, the unsuccessful tests--related to linear complexity and random excursions--indicate that a more chaotic parameter adjustment may improve uniform distribution features. However, the suggested key generation system retains high unpredictability and resilience to brute-force attacks, as most tests were successful.  
Figure 6: Outputs of the chaotic equations.  
These values represent the output of the 9D chaotic system, generated programmatically and used for key generation.
# A Classification Model Based on Interval Rule Inference Network with Interpretability
## 5 Interpretability Analysis of IRIN Structures  
In this section, the interpretability of the IRIN classification model constructed in Section 4 will be introduced. To alleviate the problem of the \"black box\" mechanism, IRIN's classification model should be interpretable. The authors of [26, 27] proposed a general interpretability criterion. Through the analysis of the general interpretability criterion, the constructed IRIN interpretability criterion is as follows.  
* The reference value of the attribute can be effectively distinguished. When constructing an IRIN, each attribute is assigned at least one reference value with a different meaning. In the present work, the set of referential value of each attribute is \(A=\) (\(L\),0.0),(\(M\),0.5),(\(H\),1.0), where \(H\)represents \"high\", \(M\) represents \"medium\", and \(L\) represents \"low\".
* A complete membership function. IRIN can convert data with any type of representation into the corresponding interval data, where each datum can match at least one reference value, and at least one rule can be activated in rule inference.
* The structures and parameters have actual meaning. As shown in Table 1, all the parameters of the IRB automatically generated by IRIN have actual meaning, so that is able to deduce a reasonable causal relationship.
* Standardization of the matching degree. The standardization of the matching degree helps the understandability of language terms and provides guidance for the classification of IRIN.  
Criterion 5.  
* [1] Reasonable information transformation.  
Data in any type or hybrid type need to be converted into interval belief distribution at the data transformation stage. Note that the boundaries of the interval belief distribution obtained by Equations 3 and 4 are not independent interval belief structures and meet the requirement of normalization. Therefore, the data are equivalently expressed in the interval belief structures.
* [1] A transparent inference engine.
* [1] The feedforward process of IRIN is the inference process of IBRB, which uses IER as the inference engine to ensure the interpretability of the rule base in the whole inference process.
* [1] The simplicity of the rule base.
* [1] The numbers of rules, parameters, and reference values for IRIN are moderate to improve the readability of IBRB.  
These criteria guarantee the interpretability of IRIN classification and provide a reference for establishing an interpretable classification model.