esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussions: The Influence and Benefits of PPGIS
### Defining the Management Visions and Scopes  
[PERSON] and [PERSON] (2017), [PERSON] and [PERSON] (2018), and others mention that PPGIS could help stakeholders identify the characteristics of a refuge, map the residents' values and preferences for the refuge, understand local land use and the locations of possible conflicts, and assist stakeholders in understanding the potential relationship between environmental resources and social culture, which were needed for decision-making.  
PPGIS provided and displayed the environmental resource distribution and hydrological conditions of the refuge and its surrounding areas, substratum elevations, the depths and localities of mud, and even the river profile. These were key items of information for the platform meeting, which helped the participants understand the links within the overall refuge water system. The results helped to inform the Suao Township Office, the management authority of the six-sluice gate, as an important stakeholder. As all of the stakeholders learned about the land use of the wetlands and surrounding areas and the possible impact of management, in 2015, they supported expanding the spatial scope of the conservation plan, i.e., this refuge, to include surrounding paddy fields, agricultural lands, estuaries, hills, etc., so that the refuge could be considered from the larger landscape perspective and so that the zero loss of wetlands can be more viably considered.  
Based on the geospatial information of the refuge, PPGIS helped the stakeholders build up a collective mechanism to allocate objectives, budgets, and human resources for implementing management. In addition, PPGIS also specifically linked the budget execution and division of labor under the management objectives so that stakeholders could work together on medium- and long-term planning of the refuge.  
\"Because of the discussion, it was later discovered that the six-sluice gate had great influence on the water source of the refuge. Therefore, it was decided to invite the people of the Luodong District Office to attend the meeting.\" (LC03)\"Because the map visualizes the water system data, it allows us to clearly see the relationship between these water systems and the refuge. The birds in the refuge will also go to the adjacent fields during floods... so during management discussions, we want [to] be included.\" (AC02) \"These tidal current investigations and substratum elevation monitoring, presented through PPGIS, can turn the land area issues in the refuge into issues for discussion. A substratum elevation monitoring was undertaken last year. We know what to do and how to do it this year. An elevation monitoring was done, and we know the changes in the water level, elevation, and silt distribution in the refuge. The elevation in the refuge is relatively high, and the water is easy to discharge.\" (AR01) \"When discussing management work items, we put the things that the community usually does on the map; therefore, the ways of implementing and dividing the labor are clearer. Everyone has a mechanism for coordination.\" (LC05)
# Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition
## 2 Method Overview  
### Mrsvd  
#### 2.1.1 The Two-Division Recursion SVD  
The two-division recursion SVD is based on the singular value decomposition, adding the idea of the two-division25, and reconstructing the signal into a Hankel matrix with two rows, realizing a decomposition of complex signals into sub-spaces of different levels, it is also called MRSVD.  
For an one-dimensional signal \(X=\{x_{1},x_{2}\ \dots\ \ \ x_{N}\}\), a Hankel matrix A is constructed by using the two-division recursion SVD method as shown in Equation (1):  
\[A=\left[\begin{array}{ccc}x_{1},x_{2},&\cdots&x_{N-1}\\ x_{2},x_{3},&\cdots&x_{N}\end{array}\right] \tag{1}\]  
Then, the singular value decomposition of the matrix A is performed to obtain Equation (2):  
\[A=\sigma_{11}u_{11}v_{11}^{T}+\sigma_{12}u_{12}v_{12}^{T} \tag{2}\]  
where \(\sigma_{11}\) and \(\sigma_{12}\) are the two singular values obtained by the first decomposition and \(\sigma_{11}>\sigma_{12}\),\(u_{11}\) and \(u_{12}\) are the vectors formed by the first two rows of the first decomposition of the matrix U. \(v_{11}\) and \(v_{12}\) are the vectors formed by the first two rows of the matrix V. The decomposition diagram is shown in Figure 1. Align symbols correctly.  
\(\sigma_{11}\) is the larger singular value, the corresponding reconstructed sub-signal \(A_{1}\) is the approximate signal obtained by the first decomposition; the smaller singular value \(\sigma_{12}\) corresponds to the reconstructed sub-signal \(D_{1}\), which is the detailed signal obtained by the first decomposition. The approximate signal \(A_{1}\) is selected to continue to construct the Hankel matrix with the number of rows 2 for the SVD  
Figure 1: MRSVD decomposition diagram.  
decomposition of the next layer, and the original signal is decomposed successively into a set of detail signals and approximation signals with different resolutions.  
Reconstructing matrix \(A_{0}\) using Equation (3):  
\[A_{0}=A_{M}+\sum_{j=1}^{M}D_{j} \tag{3}\]  
It can be seen from the above decomposition process that the signal reconstruction requires only a simple summation of \(D_{j}\) and \(A_{j}\) to obtain the SVD approximation signal \(A_{j-1}\) of the upper layer, the original signal \(A_{0}\) can be obtained by analogy, where M is the number of decomposition layers.
## 2 Problem Statement  
Currently, most studies set the water head at the tunnel face to zero (i.e., atmospheric pressure) when calculating seepage at the tunnel face [24; 25; 26]. This setting is reasonable when the permeability coefficient of the strata is low because the seepage flow is limited even if the water pressure at the tunnel face is zero. However, as the permeability coefficient of the strata increases and the water head rises, setting the water pressure at the tunnel face to zero will result in a large seepage flow, which in turn affects the stability of the tunnel face. Therefore, seepage flow control at the tunnel face must be considered in seepage analysis.  
As shown in Figure 1, for a tunnel with an excavation diameter of \(D\), a cover depth of \(C\), and a groundwater level depth of (\(C\)-\(l_{w}\)), with the tunnel bottom as the reference water head plane, seepage will occur at the tunnel face when the water head at the tunnel face is less than \(h_{0}\) (\(h_{0}\) being the vertical distance from the groundwater level to the tunnel bottom).  
For estimating the seepage flow at the tunnel face, [PERSON] [31] proposed a method using circular equipotential surfaces. Subsequently, [PERSON] [32] extended this method to three-dimensional analysis and derived a formula for calculating the seepage flow at the shield tunnel face. For the estimation of seepage flow at the tunnel face in water-rich strata, as shown in Figure 1, a simple transformation based on [PERSON]'s method [32] can be obtained:  
\[Q=\frac{\pi kD(h_{0}-h_{F,A})(h_{w}+D/2)}{h_{w}} \tag{1}\]  
where \(k\) is the permeability coefficient, and \(h_{F,A}\) is the water head at the center of the tunnel face.  
For convenience in subsequent analysis, let the pressure head at the tunnel face be expressed as a proportion of the total head:  
\[h_{\text{F},A}=\eta(l_{0}-D/2) \tag{2}\]  
where \(\eta\) is the tunnel face water head coefficient.  
Then, the total head at any point on the tunnel face can be expressed as  
\[h_{\text{F}}(y)=\eta(h_{0}-D/2)+y \tag{3}\]  
where \(y\) is the vertical distance from the point to the tunnel bottom.
# Dynamic Infection Spread Model Based Group Testing
## 4 Proposed Algorithms and Analysis
### Comparison of Dynamic Individual and Dorfman Algorithms
: When \(T/2\) individuals are uniformly randomly selected from a set of individuals that are guaranteed to have at least one infection, with size \(2(\bar{\alpha}(t)+\bar{\lambda}(t))/T\), we have  
\[E\left[\sum_{i=1}^{T/2}\mathbbm{1}_{\bar{\alpha}_{i}(t)=1}\right] \sum_{i=1}^{C}\mathbbm{1}_{\bar{\alpha}_{i}(t)=1}\geq 1\right]\] \[=\frac{E\left[\sum_{i=1}^{T/2}\mathbbm{1}_{\bar{\alpha}_{i}(t)=1 }\right]}{P\left(\sum_{i=1}^{C}\mathbbm{1}_{\bar{\alpha}_{i}(t)=1}\geq 1\right)} \tag{49}\] \[=\frac{T\bar{\lambda}(t)}{2(\bar{\alpha}(t)+\bar{\lambda}(t))} \left(1-\frac{\binom{\bar{\alpha}((t)+\bar{\lambda}(t))/T}{2(\bar{\alpha}(t)+ \bar{\lambda}(t))/T}}{\binom{\bar{\alpha}(t)}{2(\bar{\alpha}(t)+\bar{\lambda} (t))/T}}\right)^{-1}\]where \(\tilde{U}_{i}(t)\) represents the infection status of the \(i\)th selected individual for testing, at the time of testing phase and \(C=2(\tilde{\alpha}(t)+\tilde{\lambda}(t))/T\). In the case of \(\tilde{\alpha}(t)+\tilde{\lambda}(t)<T^{2}/4\), \(T/2\) individuals to be tested individually are chosen from multiple groups where each of them is guaranteed to have at least one infected individual. Therefore, the term in the denominator of the right hand side of (49), i.e., \(P\left(\sum\limits_{i=1}^{C}\mathbbm{1}_{\tilde{U}_{i}(t)=1}\geq 1\right)\), is replaced by the probability of the event that multiple subsets of size \(C\) having at least one non-isolated infected member, which is a subset of the event that only one subset of individuals of size \(C\) having at least one non-isolated infected member and thus, having lower probability. Therefore, (49) is also a lower bound for the average number of detected and isolated infections at time instance \(t\), for the case of \(\tilde{\alpha}(t)+\tilde{\lambda}(t)<T^{2}/4\). For a given state of the system at the time of the testing phase, i.e., \(\tilde{\alpha}(t)\) and \(\tilde{\lambda}(t)\), as we show in Lemmas 8 and 9, using dynamic Dorfman type group testing algorithm becomes advantageous with respect to the dynamic individual testing algorithm when \(\tilde{\alpha}(t)\geq 2(\tilde{\alpha}(t)+\tilde{\lambda}(t))/T\) and  
\[1/2 <\frac{\binom{k(t)}{2(k(t)+\tilde{\lambda}(t))/T}}{\binom{k(t)+ \tilde{\lambda}(t)}{2(k(t)+\tilde{\lambda}(t))/T}} \tag{51}\] \[=\frac{\prod\limits_{i=0}^{C}(\tilde{\alpha}(t)-i)}{\prod\limits _{i=0}^{C}(\tilde{\alpha}(t)+\tilde{\lambda}(t)-i)} \tag{52}\]  
where \(C=2(\tilde{\alpha}(t)+\tilde{\lambda}(t))/T\). In the next section, we present numerical results of our two proposed dynamic algorithms, as well as their weak versions, under various sets of system parameters.
## 3 Methods
### Experimental Setup  
Figure 6 shows the experimental setup to conduct the HFUVAS and CS experiment of the quadrilateral microlens array. On the Moore Nanotech 350 FG machine from Nano-technology Inc. USA, an ultrasonic tooling system with an operating frequency of 104 kHz from Son-X GmbH Germany [22] was set up. The input current of UTS2, which was proportional to the vibration amplitude, was set as 30 mA with a corresponding amplitude of about 0.75 um in the cutting direction. The workpiece material was Mirrax 40, which is remelted stainless steel used for precision injection molding and press molding. The workpiece was mounted on a fixture and then vacuumed on the spindle. A nominal cutting speed of 100 mm/min was used when the microlens array was manufactured, followed by a programmed position code. A sampling rate of 2.5 um along the cutting direction was used to create the position code with respect to the target line. Using a white interferometer from Nexview, Zygo Ltd., the machined structures' geometry form and surface roughness were assessed. The point data of the 3D structure was exported from the measured software  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Machining Parameters** & **CS** & **HFUVAS** \\ \hline  & 1 mm tool nose radius, 0\({}^{\circ}\) rake angle, 15\({}^{\circ}\) clearance \\ Diamond tool & angle, 50 nm tool edge radius, less than 0.25 μm arc \\  & waviness \\ Workpiece material & Mirrax 40 steel \\ Depth of cut (μm) & 5, 3, 2, 1 \\ Nominal cutting speed (mm/min) & 100 \\ Tool path generation & Segment cutting with smooth tool path \\ Lubricant & Coolant (Claisol 330/odorless kerosene, MQL) \\ Vibration frequency (kHz) & No & 104 \\ Vibration amplitude (μm) & No & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The machining parameters of the cutting experiments.  
and then reconstructed on the Matlab software for form accuracy analysis. The morphology and element analysis of the machined structure and the cutting tool was measured by a scanning electron microscope (SEM, TESCAN VEGA3) equipped with energy dispersive spectroscopy (EDS).
# Resonant Fatigue Tests on Drill Pipe Connections with Different Geometries and Sizes
## 2 Materials and Methods  
### Description of the Resonant Test Rig  
The test rig designed by the University of Pisa, in collaboration with ACTA SrI, is shown in Figure 1. Two supports were settled at the two nodes of the first natural frequency of the structure, so they were only functional to support the weight of the pipe. Two fixed masses were added to reduce the first natural frequency of the structure. In order to obtain this, an alternative solution was to make the drill pipe longer instead of adding mass, but this was difficult to accomplish. An asynchronous electric motor,driven by an inverter, spun an eccentric mass at a frequency near to that of the first natural frequency of (bending) vibration of the drill pipe. The link between the electric motor and the eccentric mass featured homokinetic joints to freely allow angular and displacement misalignment. Pictures of the two lasers employed to measure the horizontal and vertical displacements at a specific location and about the rotating mass during the motion are shown in Figure 1b,c, respectively.  
An FE model, obtained with the Ansys Workbench software 2021 R2 was performed to find the correct lengths of the two sides of the drill pipe and then the positions of the two nodes corresponding to the first natural frequency. The correct lengths of the two sides were set to have the same stress values at the two referenced points in Figure 2. These two points are in correspondence with the strain gauges locations, which will be described below.  
Figure 1: (**a**) Test rig during testing operation, (**b**) laser sensors to measure the horizontal and vertical displacements while vibrating, (**c**) eccentric rotating mass during testing operation.  
Figure 2: Bending displacement and stress, obtained through FE simulation, corresponding to the first natural frequency vibration modal shape.
# Systematic Literature Review of MBSE Tool-Chains
## 4 Discussion
### Detailed Analysis from Findings
#### 4.2.5 Contributions  
To identify the current research trends regarding MBSE tool-chains, we defined seven types of contributions to distinguish the selected papers.  
* _Framework/method/technique_ indicates that the contribution of the article is about the framework of the tool-chain, the methods used by the tool-chain, and the technology involved in the tool-chains.
* _Guideline_ indicates that the contribution of the papers is to provide guidance for building the MBSE tool-chains.
* _Lesson learned_ emphasizes the experience summary of the tool-chain construction process.
* _Model_ indicates that the contribution is about the modeling.
* _Tool_ indicates that the contribution of the paper is about the use of the tool itself.
* _Survey_ indicates that the contribution of the paper is about a survey.
* _Tool-chain architecture_ indicates that the contribution of the paper is about tool-chain architecture.  
From the results, we found that more than half of the papers focused on the _framework/method/ technique_ of MBSE tool-chains, which indicates that the research on MBSE tool-chain is at the stage of concept design. The relatively fewer contributions to tools and models illustrate that tool-chains are not widely used in the industry. This is consistent with the results in Figure 3, which show that most of the papers were proposed in R&D projects. In addition, a framework development for tool integration is very important to construct a tool-chain for different use cases, because a good framework provides more flexibility for tool integration from the perspective of **RQ 3**, such as a model-driven framework [41], a CPS open standardized platform [42], and a framework to support collaborative virtual prototyping of a system-of-systems [43]. As a lesson learned from these studies, a good integration framework is required for software designers before building MBSE tool-chains.
## 4 Practical Formula for Bearing Capacity and Reliability Analysis
### Formulation  
The following represents the relationship between the transverse stress (\(\sigma_{\theta,s}\)) in the steel tube and the radial stress (\(\sigma_{\pi,c}\)) in the strengthening region of infilled concrete:  
\[\sigma_{r,c}=\frac{\rho}{2(1-\rho)}\sigma_{\theta,s} \tag{4}\]  
Figure 10: Simplified model for calculating the bearing capacity of DS-CFST short columns under axial compression. (**a**) Stress distribution in the external steel tube; (**b**) Stress distribution in the infilled DS concrete; (**c**) Overall simplified model.  
Figure 9: Stress nephogram comparison for stub columns with varying DS replacement ratios at mid-height sections.  
\[\rho=\frac{D^{2}-D_{c}^{2}}{D^{2}} \tag{5}\]  
As illustrated in Figure 11, the ratio of axial stress (\(\sigma_{L,s}\)) and transverse stress (\(\sigma_{\theta,s}\)) to yield strength (\(f_{y}\)) at ultimate strength changes with the ultimate strength of the FE models (\(f_{sc}=N_{u}/A_{sc}\), \(A_{sc}=A_{c}+A_{s}\)).  
Figure 11 illustrates that, at ultimate strength, the axial to transverse stress ratio in the steel tube can be described by the following average values:  
\[\sigma_{L,s}=0.63f_{y} \tag{6}\]  
\[\sigma_{\theta,s}=0.51f_{y} \tag{7}\]  
In the infilled concrete, the relationship between axial and radial stresses is as follows:  
\[\sigma_{L,c}=f_{c}+k\sigma_{r,c} \tag{8}\]  
Here, \(k\) is the lateral pressure coefficient, with \(k=3.4\) for circular sections ([PERSON] et al. [44]).  
Using static equilibrium principles, the ultimate bearing capacity (\(N_{u}\)) of axially loaded DS-CFCST stub columns is:  
\[N_{u}=\sigma_{L,c}A_{c}+\sigma_{L,s}A_{s} \tag{9}\]  
Substituting Equations (4)-(8) into Equation (9), the ultimate bearing capacity (\(N_{u,r}\)) of DS-CFCST stub columns is expressed as:  
\[N_{u,r}=f_{c}A_{c}+k_{r}f_{y}A_{s} \tag{10}\]  
In Equation (10), the steel tube confinement factor \(k_{r}\) for DS-CFCST stub columns is approximately 1.58, compared to a factor \(k_{1}\) of 1.7 for CFCST stub columns ([PERSON] et al. [44]), indicating a slightly reduced confinement effect for the steel tube on DS concrete.
# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 4 Methodology
### Triple-Graph Masked Autoencoder
#### 4.1.2 Node Feature Reconstruction
```
Input: Knowledge graph \(\mathcal{G}_{k}=(\mathcal{E},\mathcal{R})\), encoder, edge decoder, degree decoder, feature decoder, connection masking rate \(\varphi\), feature masking rate \(\mu\), weights \(\lambda_{1}\),\(\lambda_{2}\),\(\lambda_{3}\);
1:while not converged do
2: Mask \(\mathcal{G}_{k}\) with rate \(\varphi\) to obtain \(\mathcal{R}_{mask}\) and \(\mathcal{R}_{remain}\);
3: Mask \(\mathcal{G}_{k}\) with rate \(\mu\) to get entity sets entities \(\mathcal{E}_{mask}\);
4: Obtain the perturbed feature matrix \(\mathbf{X}^{\prime}\) of \(\mathcal{E}_{mask}\) according to Equation (9);
5: Perform GNN encoding on \(\mathcal{R}_{remain}\) and \(\mathbf{X}^{\prime}\) according to Equation (4);
6: Calculate loss \(\mathcal{L}_{edge}\) according to Equation (5);
7: Calculate loss \(\mathcal{L}_{deg}\) according to Equation (6);
8: Calculate loss \(\mathcal{L}_{f}\) according to Equation (7);
9: Calculate \(\mathcal{L}_{m}=\lambda_{1}\mathcal{L}_{edge}+\lambda_{2}\mathcal{L}_{deg}+ \lambda_{3}\mathcal{L}_{f}\);
10: Update model by minimizing the reconstruction loss \(\mathcal{L}_{m}\);
11:endwhile
12: Return the trained T-GMAE. ```  
**Algorithm 1** Triple-Graph Masked Autoencoder (T-GMAE)
## 2 Materials and Methods
### Methods
#### 2.3.2 PCA Modified AHP Weighting Method  
To address the issues of unclear weight significance, subjective judgment, and the challenges of large-scale application in previous indicator weighting methods [80], this study employs an effective and adaptable PCA-AHP method to determine the weights for each indicator. The method offers advantages in decomposing complex problems and integrating both subjective and objective information.  
Initially, certain collinearity and correlation exist among factors evaluating the ecological quality of the study area, such as LAI and GPP [81]. To address this, PCA was employed for data dimension reduction and to ascertain the contribution of each indicator to the principal component. Principal components were selected based on the principle that cumulative variance should exceed 95%. Assuming \(p\) principal components were selected, with a total of \(n\) indicators, each principal component can be represented by a linear combination of indicators and their coefficients \(\sigma\). The coefficient \(\sigma\) of indicator \(i\) in principal component \(p\) can be calculated using the formula:  
\[\sigma_{i}^{p}=\frac{\beta_{i}^{p}}{\sqrt{\alpha_{i}}} \tag{10}\]  
where \(\alpha_{i}\) and \(\beta^{i}\) mean the eigenvalue and the corresponding value of indicator \(i\) in the eigenvectors of principal component \(p\), respectively.  
We used \(\sigma_{i}^{p}\) and cumulative explained variance ratio of the first \(p\) principal components \(\varphi_{p}\) to calculate the composite score coefficient \(\gamma_{i}\) using Formula (11):  
\[\gamma_{i}=\frac{\sum_{j=1}^{n}\varphi_{p}\sigma_{i}^{p}}{\sum_{k=1}^{n} \varphi_{k}} \tag{11}\]  
Finally, normalization has been operated on \(\gamma_{i}\) of indicator \(i\) to obtain the final weight \(\omega_{i}\).  
\[\omega_{i}=\frac{\gamma_{i}}{\sum_{i=1}^{n}\gamma_{i}} \tag{12}\]  
The \(\omega_{i}\) of each indicator composed the weight matrix \(w_{p}\) obtained by PCA. However, these principal components may lack clear physical meaning, posing challenges in attributing ecological quality assessments. To address this, we introduced the Analytic Hierarchy Process (AHP) method to incorporate expert knowledge and adjust objective weights by assessing the relative importance of indicators through pairwise comparisons. A judgment matrix was provided to five experts, who scored different indicators by comparing their importance for ecological quality on a scale from 1 to 9. We utilized the judgment matrix between several indicators under a specific criterion layer to test whether the single-level sorting results passed the consistency test. Subsequently, we obtained the final weights (\(w_{A}\)) until the overall hierarchical sorting also passed the test. The weight of the final indicator (\(w\)) was determined as the average of \(w_{p}\) and \(w_{A}\), integrating subjective and objective information. The results are presented in Table 2.
## 6 Challenges and Future Research Directions  
The implementation of power splitting (PS) techniques in practical SWIPT systems presents significant challenges, including power loss due to signal degradation in wireless channels and reduced performance as the number of devices increases. These factors complicate the design of power optimization algorithms, which are crucial for balancing spectrum efficiency (SE) and energy efficiency (EE) in the system. In this paper, we address these issues by proposing a complex regularized zero-forcing (CRZF) precoding scheme, integrated with a power splitting optimization method through a joint optimization approach. Nonetheless, further experimental studies are necessary to evaluate the real-world effectiveness of PS techniques. There are several challenges related to SWIPT implementation in mMIMO-NOMA systems. This section highlights the challenges and future research directions related to the implementation of SWIPT in mMIMO-NOMA systems.