esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 6 Evaluation Metrics for Localization Techniques
### Realization Cost  
Generally, every system requires a realization cost for its execution. For the localization schemes, overall incidentals are dispersed for communicating and the computational time. The communication cost continually impacts information exchange between the sink node, source node and the central regulating component throughout the localization process. The computational time represents the processing cost that arises in the network databases and at the terminal. Computational time is well associated with the expected dimension accuracy of the scheme. Consequently, choosing a suitable localization scheme must be essentially conciliated among the expected dimension accuracy and a reasonable computational cost to reduce the realization costs.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 4 Results and Discussion
### Porosity Derived from Electric Propagation Time Method  
Well E, drilled later than the others, exhibits a distinct resistivity contrast between oil and water zones in the LHS-LD reservoir, as evident in Figures 5-7. The electric propagation time (EPT) log, which is only available in this well, was used to investigate the viability of the method in capturing the oil-water contrast. The gamma ray log, deep and shallow induction logs, electric propagation time (EPT) log and Sw calculated using Archie and Simandoux equations in Well E are compared in Figure 8. Porosity calculated using EPT (PhiTPL) was compared with Phi calculated using the neutron and density methods (mean porosity of neutron and density porosities (PhiND), density total porosity (PHIT) and neutron porosity (NPHI)). The brown colour indicates water, which can be seen in the shale.  
In the LHS-UD, bigger separations between PhiTPL and the three other curves indicate oil (green) in the tight as well as the porous zones. Therefore, EPT certainly indicates oil in the upper tight part of the reservoir despite the Sw value being close to 1 when calculated using the Archie equation and above 1 using the Simandoux equation.  
However, the porosity curves are so close in the LHS-LD that the result is inconclusive regarding whether this is oil or water. The separation of PHIT_TPL with NPHI is indicative of water in the entire LHS-LD section, while the water zone is distinguishable in the Sw curves.  
The separation of PHIT_TPL with PHIT_ND and PHIT is too small to provide a confirmative conclusion regarding the type of saturation. Low contrast can be caused by water influx due to the rise of OWC. Accordingly, EPT and ILD methods can have different sensitivity to the presence of water according to the conditions of the reservoir.  
Other methods may be required to determine water saturation such as the pulsed neutron-neutron method, which has shown good results in a low-resistivity depleted oil reservoir [31].  
Additionally, the perforations in this well cover the upper shale (K-member), which has sandstone layers, for which the EPT and both water saturation curves indicate water that can be mobile in these layers. Initially, UHS and LHS were considered as single reservoirs with a single OWC. Later, they were separated into two different reservoirs with unclear and undetermined OWC contacts [16].
## 4 Fault Diagnosis Based on Traditional and Interval Analytical Redundancy Relations
### Diagnostic Thresholds and Data Acquisition  
Positive, negative, or 0 residuals are possible. They have no dimensions and no physical significance. According to the literature [43; 44], threshold deviation is defined as \(\varepsilon_{i}\), and the absolute value type threshold is:  
\[\left\{ \begin{array}{l}Upper\ threshold=r_{i}+\varepsilon_{i}\\ Lower\ threshold=r_{i}-\varepsilon_{i}\end{array} \right. \tag{35}\]  
After considering the negative factors such as uncertainty \(\varepsilon_{i}=\Delta\theta_{i}\big{|}u_{\theta_{i}}\big{|}\), the absolute value type diagnostic threshold is:  
\[\left\{ \begin{array}{l}Upper\ threshold=r_{i}+\Delta\theta_{i}\big{|}u_{ \theta_{i}}\big{|}\\ Lower\ threshold=r_{i}-\Delta\theta_{i}\big{|}u_{\theta_{i}}\big{|}\end{array} \right. \tag{36}\]  
Table 6 shows the defined uncertainty value, which is 1% of the original value. In addition, the measurement uncertainty error is defined as 0.1%. According to Figure 13, as illustrated in Figure 14, the diagnostic simulation model is created in the LMS Imagine. Lab AMESim simulation platform. It is the same sensor that is shown on the diagnostic bond graph. The premise of the model-based simulation experiment is to prove the validity of the model. According to the professional maintenance manuals of Boeing and Airbus [45; 46; 47], as well as the computer-based training videos of maintenance professionals, under normal circumstances, the retraction time of landing gear is 7.5 s and the extension time is 10 s, which may vary slightly with different aircraft types. Technical manuals stipulate that actuation times cannot be changed more than 1 s. Actuator action is opposite to landing gear body action. When the retraction/extension actuator is extended, the landing gear retracts; when the actuator is retracted, the landing gear is retracted. The results of the diagnostic simulation model are shown in Figure 15. The retraction time of the actuator is from 2 s to 12.86 s, and the extension time of the actuator is from 2 s to 9.17 s.  
As shown in Figure 15, the error bar sampling interval between the simulation effect curve and the absolute ideal curve is 0.001 s. Introducing [48] sum of squares of errors (SSE), mean-squared error (MSE), and model effect evaluation index \(R^{2}\):  
\[\left\{ \begin{array}{l}SSE=\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}} \\ MSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}\\ R^{2}=\frac{SSE-MSE}{SSE}\end{array} \right. \tag{37}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) are the ideal value sampling points represented by the red curve and the simulation effect value sampling points represented by the black curve in Figure 15,
# Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
## 2 System Model
### Metrics of Interest  
In the considered system, we are interested in the cell coverage and efficiency in the case of a direct connection versus use of terrestrial and aerial (UAV) re-transmitters in an urban environment for a sub-mmWave network. Specifically, we are interested in such metrics as the spectral and the spatial efficiency (16) and (17), to estimate the channel transmission rate and coverage, and the BS density (18), to compare the number of BSs necessary to serve a \(r_{0}=1\) km area for direct and indirect connection.
## 1 Introduction
### Introduction to Bibliometrics, Science Mapping, and Topic Modeling  
Bibliometrics involves a set of approaches or techniques for measuring research outputs using scientific publications indexed in big bibliographic databases [25]. With the sustainable increasing number of scientific papers being published in different research fields, bibliometrics has grown into a valuable method aiming to progress the assessment of scholarly quality and productivity. The contributions of bibliometrics to science progress mainly involve several aspects, for example, enabling the assessment of progress, recognizing groups of relevant domain scientists, recognizing reliable publication sources, serving as an academic foundation for evaluation of new developments, assisting the implementation of specific intelligent information systems, and proposing novel indexes to evaluate scientific output [26; 27; 28; 29; 30]. In the bibliometric analysis, performance analysis and science mapping are two major approaches for exploring a specific research area [31; 32]. Performance analysis concerns the assessment of the impact of academic outputs of researchers, institutions, or countries. Science mapping focuses on displaying the conceptual structures of particular research fields and their evolutions across time using network analysis, for example, co-words, co-citations, or co-authorship [30]. Performance bibliometric analysis concerns the measurement of scientific outputs using quality and quantity indicators like article count, citation count, and Hirsch index (H-index). Science mapping analysis serves as a spatial representation of the correlation of different areas. It aims to monitor an academic area and to delimit research areas for the determination of its structure and evolution [33]. In addition, topic modeling generates estimations of a list of topics that are hidden within a collection of textual data [34]. STM is a newly proposed unsupervised machine learning approach for analyzing text corpora on a large scale [1; 2; 35; 36]. Topic models use a set of machine learning algorithms and a type of dimension reduction approach to extract latent topics from data. Topic models identify the co-occurrence of terms to further sort them into three latent topics. Each topic comprises terms, each with different assigned probabilities. Additionally, topic models assume that each document comprises different latent topics, each with different assigned probabilities in each document [37]. Bibliometric analysis and topic modeling have been popularly and widely adopted to present a general overview of various research fields (e.g., [38; 39; 40; 41]). Some representative works include bibliometric analysis on, e.g., text mining-enhanced medical research [42], research that utilize AI techniques in processing EHRs data [43], and research of event detection using social media data [44].
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 3 Privacy-Preserving Distinct Counting Computation
### Main Idea
As mentioned in Section 3.3, the approximation of distinct counting needs several more FM sketches in which the hash functions are different. After taking Step 1 to Step 3 for \(d\) times and according to Equation (8), the aggregator can get the final result \(C(S)\), the number of distinct elements in the sensing dataset. Figure 2: An example of the process that user 1 deals with his original dataset. Here, for example, the elements in the dataset are all integers. User 1 hashes elements of his dataset into 8-bit strings and do the bitwise OR operation to get a string as the representation of his dataset. Then each bit of this string is coded to a 5-bit string. **Remark 1**. : _In Step 2, it is worth noting that there is a probability of \(1-1/2^{q}\) to occur such a situation, where \(r_{i_{j}}\) equals to 1 but \(T(r_{i_{j}})\) is coded to be \(0^{q}\). Thus in Equation (11), the coding scheme requires that if this situation happened, \(r_{i_{j}}\) should be recoded until \(T(r_{i_{j}})\) is not \(0^{q}\). In that step, each bit of the string \(\Lambda_{i_{j}}\) from MSB to LSB, would be expanded into a \(q\)-bit string \(T(r_{i_{j}})\) under the action of our coding scheme (11), which is suitable for encryption operation._  
**Remark 2**. : _Notice that during the whole process, in order to reduce the computing time, we employ a knack here which is that PPDC determines bits of FM sketch \(S\) from the last bit to the first bit. When the aggregator applies FM sketches, the purpose is to find out the position of the last bit in \(S\) that is 1 and regard it as an index. This is to find out the position of the first bit in \(S\) that is 1, when PPDC starts finding from the last bit of \(S\). This transformation means the aggregator does not have to determine all bits in \(S\), after all, what the aggregator needs is the index to calculate the number of distinct counting of the dataset rather than the whole \(S\). Through this knack, PPDC can leave out a lot of computing steps, thus improving the efficiency._
## 4 Numerical Results and Discussion
### Stability Analysis of Large Section Tunnel under Different Excavation Sequences
#### 4.2.1 Deformation Analysis of Surrounding Rock  
Figure 15 shows the vertical displacement of the surrounding rock at the target section in the two working conditions at the time of excavation to the target section. The influence of the two working conditions on the vertical displacement of the surrounding rock was the same. The vertical displacement of the two working conditions was relatively close because the rock mass parameters and excavation steps were consistent. The maximum vertical displacement of surrounding rock in Condition 1 and 2 was 2.631 and 2.620 cm, respectively. However, the comparison of the displacement at the tunnel vault shows that the blue area with the same value as Condition 1 had a smaller distribution range in Condition 2 and was separated from the vault by a layer of the light blue area. In other words, when the two working conditions were excavated to the same length, the vertical displacement of Condition 2 was smaller and was conducive to settlement control during construction.  
Figure 16 shows the horizontal displacement of the surrounding rock at the target section under two working conditions when tunneling reached the target section. Figure 16b illustrates that the surrounding rock around the tunnel and near the surface had consistent horizontal displacement in a large range with a trend of left displacement. As shown in Figure 16a, a horizontal displacement trend to the right was observed at the arch waist of the right pilot tunnel and the upper and lower parts of the middle pilot tunnel. An opposite horizontal displacement was also observed in local areas. The maximum horizontal displacement of the surrounding rock in Condition 1 and 2 was 0.8500 and 0.8866 cm, respectively.  
Figure 15: The vertical displacement of surrounding rock under various conditions from excavation to target section (unit: m): (**a**) Condition 1 and (**b**) Condition 2.  
In terms of the overall displacement of surrounding rock, in the tunneling process, the vertical and horizontal displacements of the surrounding rock were better controlled under Condition 2, and the horizontal displacement of the surrounding rock around the tunnel was relatively uniform. Compared with Condition 1, the support interval between the first and second primary linings in Condition 2 was shorter, and the two primary linings could work together more appropriately to effectively control the displacement of the surrounding rock.
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 2 Methodology of the Multi-Classification Multi-Leak Identification (MC-MLI) Scheme
### Feature Extraction for the MC-MLI  
The features are defined as the parameters that can be commonly found in all situations but demonstrate distinctive characteristics. Based on the data analysis, ten (10) features are extracted for pattern recognition and classification. They are listed in Table 1.  
Figure 1: The development flow of the proposed multi-classification multi-leak identification (MC-MLI) scheme.  
The feature vector \(X\) with features entry \(x_{i}\) for \(i=1,2,\ldots,M\), is formulated as follows:  
\[X=\left[x_{1}\ x_{2}\ldots\ x_{M}\right] \tag{3}\]  
where \(M\) denotes the number of features and is equal to 10 in this case.  
Note that the extracted features will be further analyzed and weighted at the stage of designing an adaptive kernel. Further, the proposed MC-MLI is said to be adaptive because it combines multiple unique classifiers that are customized to the assigned pipe sections. Eventually, the overall detection performance will be enhanced. The variation of data quality among different pipes will influence detection performance. For example, the pressure in a pipe at a faraway location might remain at a low level. False alarms might frequently occur if all classifiers utilize the same feature vector. In this paper, the customization of the feature vector for each pipe section will overcome the captioned challenge and thus improve overall detection performance.
# Entangled Frequency-Tunable Microwave Photons in a Superconducting Circuit
## 3 Parameters and Spectrum of the System  
To demonstrate the feasibility of our proposed circuit, the parameters in the numerical simulation are set to the values that can be readily achieved experimentally with standard micro-fabrication techniques [35; 36; 40; 41; 42].  
Using Equations (1) and (2) and the parameters mentioned above, we calculate the spectroscopy of the system with \(\hat{H}(\Phi_{\rm dc})|\psi\rangle=E(\Phi_{\rm dc})|\psi\rangle\), where the eigenenergy \(E(\Phi_{\rm dc})=\hbar\omega(\Phi_{\rm dc})\) is in the unit of frequency as shown in Figure 2. The numerical solutions to Equation (1) agree well with analytical solutions to Equation (2). Due to the existence of the coupling between the two modes, the spectroscopy has anti-crossings [35; 36] in the vicinity of \(\Phi_{\rm dc}=(k+0.5)\Phi_{0}\) (\(k\in\mathbb{Z}\)). The coupling strength varies with the dc flux bias periodically as shown in Figure 2.  
Figure 2: **(a)** Spectroscopy of the circuit as a function of the dc flux bias \(\Phi_{\rm dc}\). The dashed (solid) black line (left axis) represents the resonant frequency \(\omega_{a}/2\pi\) (\(\omega_{b}/2\pi\)) of the mode \(a\) (\(b\)) with analytical solutions using Equation (2). The data marked by diamonds and circles are the numerical solutions according to Equation (1). The red line (right axis) is the effective coupling strength \(g/2\pi\). **(b)** The enlarged view of the blue dashed box in (a) explicitly displays the anti-crossings between the two modes.  
Figure 1b shows the proposed schematic measurement setups to characterize the quantum physical properties of the output fields of the source. These measurements have been realized in the research of continuous quantum variables in the superconducting nonlinear microwave circuits [27; 29; 43; 44], with which we numerically demonstrate the entanglement property between the output signals in our proposed source.  
Because the swapping of quantum states destroys the anisotropy of the entangled photons and transforms them into isotropy [45], the existence of swap interaction between different modes will reduce the entanglement of the continuous-variable microwave on average when the swap and entanglement interactions appear simultaneously. To eliminate the swap interaction with moderate radiation in the circuit, we apply a pump filed [37] with \(\alpha_{p}=2.404\), which sets \(\chi_{0}\approx 0\) and \(\chi_{1}\approx 0.519g\) in Equation (8), as shown in Figure 3. Then, Equation (8) can be rewritten as  
\[\hat{H}_{I}=-i\chi_{1}(\hat{a}\hat{b}-\hat{a}^{\dagger}\hat{b}^{\dagger}). \tag{9}\]
## 3 Results and Discussion  
### Total Phenolic Content  
The hawthorn fruit contains a variety of phenolic compounds such as proanthocyanidins, tannins, flavonoids, and phenolic acids [2]. Many cell wall-bound phenolics and phenolic glycosides (e.g., apigenin glycosides, vitexin rhamns) are also found in hawthorn fruit samples, and the bioavailable beneficial properties of these phenolic compounds are limited. In this assay, the cellulase enzymatic support resulted in a positive effect on the free phenolic production, as the TPC increased from 885 \(\pm\) 13 mg GAE/100 g DM to 1700 \(\pm\) 128 mg GAE/100 g DM (\(p<0.05\)) when the cellulolytic enzyme cocktail of \(R\). _miehei_ was used for extraction (Figure 3). The addition of pectinase to the enzyme cocktail further increased the amount of extractable phenolics to 1899 \(\pm\) 27 mg GAE/100 g DM (\(p<0.05\)) (Figure 3).  
In a previous study, cellulase treatment increased the phenolic content of grape, apple, and pitahaya extracts, and treatment with a combination of cellulase and pectinase further increased the yield of free phenolics for most residues [26]. More recently, carbohydrate treatments were applied efficiently for the extraction of phenolics from other plant residue materials such as sorghum grains [30], the dry extract of _Beta vulgaris_[31], and mulberry fruits [32]. For hawthorn samples, solvent-based treatments have been the most frequent techniques to prepare phenolic-enriched extracts. [PERSON] et al. [5], for instance, used ultrasound extraction in 80% methanol and determined 40.04 mg GAE/g DW of total phenols in _C. orientalis_. [PERSON] et al. [3] reported 2.12-30.63 mg GAE/g of fresh fruit TPC in _Crataegus oxyacantha_, depending on the solvent extraction methodology used. Treatment with an organic solvent-water mixture was an effective technique for the extraction of phenolics in _C. orientalis_ subsp. _orientalis_ as well [33]. The 2.86 to 13.81 mg GAE/g DW TPC yield obtained was comparable to those found for _Crataegus microphylla_ (2.47-8.63 mg GAE/g DW) [34] and _C. orientalis_ (13.36 mg GAE/g DW) [35] achieved with ultrasound or microwave combined solvent extractions. Anyway, our study is the first demonstration that  
Figure 3: Total phenolic content (TPC) of hawthorn fruit extracts obtained by acetate buffer (C), cellulase (S1) and cellulase–pectinase (S2) extractions. The values shown are averages of the data obtained from three measurements; error bars represent the standard deviation. The different letters above the columns indicate significant differences according to one-way ANOVA followed by [PERSON]’s multiple comparison test (\(p<0.05\)).  
a carbohydrates enzyme-assisted treatment is also an effective methodology for high-yield phenolic extraction in hawthorn fruit samples.