esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Response Mechanism Analysis
#### 3.4.2 Results of Structural Equation Modeling
Temp had the largest coefficient of direct positive effects on ET (0.34), the negative effect coefficient of VPD on the ET was the largest (\(-\)0.44), precipitation had the largest total effect on ET, which was 0.62, and temperature had the largest negative effect of 0.07 through VPD. The path coefficient was the largest (\(-\)0.44), and precipitation had the largest total effect on ET change at 0.62, while temperature had a total effect on ET of 0.07, with the negative effect through VPD having an influence coefficient of \(-\)0.39. The model fit indices were as follows: RMSEA values were 0.01, 0.01, and 0.05; CFI values were 0.99, 0.99, and 0.99; and GFI values were 0.99, 0.99, and 0.99, respectively. The results showed that the effect of each hydrothermal light factor on WUE production was related to the effect on NPP and ET production under different elevation zones. At elevations less than 3700 m, Temp did not have a direct effect on WUE, and had an indirect effect on WUE mainly through its effect on SM, VPD, and PAR, with an effect coefficient of 0.49; precipitation had a negative effect on WUE at this elevation partition, with a total effect of \(-\)0.37; and the direct effect of VPD on the change in WUE was 0.52; between 3000 and 3700 m elevation, both Temp and Prec had a direct negative effect on WUE, and Temp attenuated the direct negative effect it produced through its indirect positive effect on ST, VPD and PAR, with a total effect coefficient of 0.89, which was related to the fact that Temp produced a direct positive effect on ET at this elevation zone. The results showed that ST produced the largest total effect on WUE, with a direct effect coefficient of 0.84, and the higher the ST, the more suitable the soil environment was for vegetation growth, favoring vegetation respiration and photosynthesis. Prec produced a total effect of \(-\)0.36 on the change in WUE, the increase in precipitation increased ET, and the vegetation evapotranspiration was greater than the photosynthesis rate, suppressing WUE. At elevations greater than 3700 m, Temp had a direct positive effect on WUE, which was consistent with the  
Figure 13: SEM of ET: (**a**) <3000 m, (**b**) 3000–3700 m, and (**c**) >3700 m elevation zones; path coefficients that pass the significance test of \(p\) < 0.05 are shown. positive effect of Temp on changes in NPP at this elevation partition. Under this elevation partition, SM had no direct effect on WUE, and PAR had almost no effect on WUE with a coefficient of 0.05; WUE was mainly affected indirectly by the effect of Temp on ST and VPD and the effect of Prec on VPD. The model fit indices were as follows: RMSEA values were 0.01, 0.01, and 0.05; CFI values were 1.00, 1.00, and 0.97; and GFI values were 0.99, 0.99, and 0.98, respectively.
## 4 Results and Discussion
### Influence Analysis of Tunnel Construction Parameters  
Figure 19a-c show the cloud diagrams of vertical deformation, horizontal deformation, stress and axial force of the pipeline during full-section excavation, three-step reserved core soil excavation, and three-step excavation. Under the three working conditions, the maximum vertical deformation of the pipeline was 46.3, 31.7, and 17.3 mm, the maximum horizontal deformation of the pipeline was 14.6, 4.14, and 8.79 mm, the maximum stress of the pipeline was 1.53, 1.11, and 1.18 MPa, and the maximum axial force of the pipeline was 63.9, 54.8, and 52.0 kN. As the full-section excavation had the greatest disturbance to the surrounding rock of the tunnel, the vertical and horizontal deformation of the pipeline were large, followed by the three-step excavation, and the vertical and horizontal deformation of the pipeline caused by the excavation of the reserved core soil of the three-step was the smallest. The variation trend of pipeline stress and axial force was the same as that of deformation, but the variation amplitude was small. Therefore, from the perspective of controlling the deformation of the surrounding pipelines and ensuring construction safety, the three-step reserved core soil method should be selected as the construction method for the double-arch tunnel.  
Figure 18: Vertical deformation of pipeline with different pipeline materials.  
Figure 19: Vertical deformation, horizontal deformation, stress and axial force of pipeline with different excavation methods: (**a**) Full-section excavation; (**b**) Three-step reserved core soil excavation; (**c**) Three-step excavation.  
The settlement of the top of pipeline 1 above the central axis of the tunnel is shown in Figure 20. Under the same other factors, the settlement caused by full-section excavation at each position of the pipeline was significantly greater than the other two excavation methods, and the pipeline settlement caused by the three-step reserved core soil excavation was the smallest. The maximum settlement caused by full section excavation was 36.57 mm, the maximum settlement caused by three-step excavation was 27.15 mm, and the maximum settlement caused by three-step reserved core soil excavation was 14.89 mm. Compared with the other two construction methods, the three-step reserved core soil method can effectively control the stress and deformation of the underground pipeline.
# Veins Depth Estimation Using Diffused Reflectance Parameter
## 5 Simulations  
The Monte Carlo model for light propagation in multi-layered tissues (MCML) is used to simulate light propagation in the skin layers. This is a stochastic process which can be used to simulate physical phenomena of particle behavior in highly scattering mediums. MCML model was proposed by [PERSON] et al. in [20]. This model simulates the photons transportation in the layered turbid medium and records multiple parameters like diffused reflectance 'Rd', absorption 'A' and transmission 'T' for the analysis. Photons are considered as particles in this method while ignoring their wave-like nature. The limitations of this method include the simulation time and noise included during simulation. The program called MCML (Monte Carlo Multi-layer) has gained much attention within the biomedical optics community and today it is considered as the de facto gold standard for light propagation in layered biological tissues. These simulations are fed with the optical parameters for each of the layers depending on their optical properties against the applied illumination wavelength. Table 1 tabulates these optical parameters which are obtained from literature [19,21] for the illumination wavelength \(\lambda\) = 800 nm. Here, the absorption coefficient is denoted by \(\mu_{a}\), scattering coefficient by \(\mu_{s}\) and anisotropy factor is denoted by g. The refractive index of each layer is denoted by n and the thickness of layer by t in millimeters. The number of photons simulated in each of the iterations was 25 million. The width of the hypodermis is increased in every iteration which can be considered as the varying depth of veins. The thickness of first layer is 0.02 mm, second layer 0.18 mm, third layer 1.3 mm and the fourth layer is from 0.1 mm to 3.0 mm. Total of 30 iterations are performed which make the total thickness of layered medium as 1.6 mm at first iteration and 4.5 mm at the last iteration. For each of the iterations, the diffused reflectance parameter 'Rd' was recorded against the total thickness of layered medium, i.e., veins depth. This value of 'Rd' is then used for the calculation of veins depth analytically with the proposed mathematical model given in Equation (7). The total elapsed time for each simulation was 9 h 55 m on the Intel(r) Core(tm) i7-260 CPU with 3.40 GHz processor speed with 16 gigabytes random access memory (RAM).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **n** & \(\mu_{a}\) & \(\mu_{s}\) & **g** & **t (mm)** \\ \hline Stratum Corneum & 1.55 & 3.3 & 1050 & 0.85 & 0.02 \\ Epidermis & 1.34 & 4.0 & 90 & 0.80 & 0.18 \\ Dermis & 1.4 & 0.8 & 90 & 0.85 & 1.3 \\ Hypodermis & 1.44 & 1.36 & 12 & 0.75 & 0.1–3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optical parameters of skin layers at \(\lambda\) = 800 nm
## 4 Verification of Energy-Based Impact Formula
### Scabbing Limit Assessment  
Based on the energy-depth relation, a new scabbing limit thickness formula is proposed and the thickness required to absorb 95% spalling and tunneling energy is suggested. Figure 9 shows the panel thickness (\(h\)) divided by the predicted scabbing limit thickness (\(h_{sc}\)), based on the new proposed formula. The modified NDRC, Hughes, and UK Atomic Energy Authority (UKAEA) formulae predict the penetration depth well, as shown in the Paper I [22]. Only the experimental results obtained from the test program conducted by [PERSON] [29] are used for the analysis of scabbing limit thickness, because all other experimental results in other studies presented unclear scabbing depth and failures.  
Figure 8: Ratio of tested penetration depth-to-scabbing depth.  
Figure 9: (**a**) Proposed formula; (**b**) modified National Defense Research Committee (NDRC); (**c**) Hughes formula; (**d**) Army Corps of Engineers (ACE) formula. Panel thickness (\(h\)) divided by predicted scabbing limit thickness (\(h_{sc}\)).  
As shown in Figure 9, the specimen with the scabbed failure is assumed to lie below 1 [30]. Table 2 shows the accuracy of the scabbing limit thickness formula. The four formulae predict that all specimens incur scabbing failure. However, each formula shows different prediction results, where the specimens should not scalp on the rear face of the concrete panel. The proposed scabbing limit thickness formula is predicted such that 139 specimens do not incur scabbing failure. No scabbing failure is observed in 172 specimens, and the proposed formula is \(\sim\)81% accurate. In addition, in 33 specimens, scabbing failure is predicted to occur but does not occur. The NDRC, Hughes, and ACE formulae are 66%, 43%, and 66% accurate, respectively. Therefore, our proposed scabbing limit thickness formula turns out to be the most accurate.
# Ni/Zn Layered Double Hydroxide (LDH) Micro/Nanosystems and Their Azorubine Adsorption Performance
## 2 Experimental Procedure
### Reagents and Solutions
#### 2.1.2 Adsorbent Characterization Techniques  
The synthesized bi-transition metal hydroxide based nanohybrid was characterized using several techniques in order to identify the hydrothermal process product properties, such as structural, textural, morphology and composition, which influence its adsorption performance for environment applications.  
Powder X-ray diffraction patterns of our solid adsorbent were collected on an XRD D8 ADVANCE-BRUKER AXS diffractometer equipped with a copper anticathode tube (\(\lambda_{\text{CuK}\alpha}\) = 1.5406 A) and a graphite monochromator rear blade, operating at 40 kV-40 mA and employing a scanning rate of 0.2\({}^{\circ}\) s\({}^{-1}\) in the range 10\({}^{\circ}\)-90\({}^{\circ}\).  
Specific contact surface area (SSA) and porosity measurements were carried out using N\({}_{2}\) adsorption/desorption mechanism at nitrogen liquid temperature (77 K) on a Tristar an ASap2420 sportometer (Micromerities) surface area analyzer using Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods, respectively.  
Raman scattering measurement was carried out at room temperature using a Horiba JobinYvon Lab-RAM Aramis confocal Raman spectrometer equipped with a cooled CCD camera and an automated XYZ table at a laser excitation of 532 nm. Using a D2 filter, the estimated laser power that reached the samples was 0.33 mW.  
The morphological aspect of this nanohybrid was identified via a field emission scanning electron microscopy FESEM (JEOL 6700-FEG microscope) operated at 3 kV.  
Fourier-transform infrared (FTIR) spectra were recorded using a Bruker Vertex 77v spectrometer with a 4 cm\({}^{-1}\) resolution in [400-4000 cm\({}^{-1}\)] range controlled with an Opus software analysis.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 2 Single-Pulse Variable Duration (SPVD) Modulation
### Example for \(k=3\)  
For the sake of clarity, the look-up table for \(k=3\) is provided in Table 1, with the different waveforms \(\mathcal{S}=\{s_{1}(t),s_{2}(t),...,s_{7}(t)\}\) being given by Figure 1.  
Moreover, Figure 2 depicts an example which aims to illustrate the difference between the waveforms sent by the new modulation and TS-OOK for the transmission of the same bit stream with \(k=3\) for a Gaussian first derivative pulse [6], normalized pulse amplitude \(A=1\) and \(t_{s}=5\) ps [7]. Among others, this example portrays the trade-off between low energy and high data rate. More specifically, one can observe that the proposed modulation needs 80 ps and 4 energy bursts (pulses), while TS-OOK needs 60 ps and 6 energy bursts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vector** & & **Waveform** \\ \hline
000 & \(\longrightarrow\) & \(s_{0}(t)\) \\
001 & \(\longrightarrow\) & \(s_{1}(t)\) \\
010 & \(\longrightarrow\) & \(s_{2}(t)\) \\
011 & \(\longrightarrow\) & \(s_{3}(t)\) \\
100 & \(\longrightarrow\) & \(s_{4}(t)\) \\
101 & \(\longrightarrow\) & \(s_{5}(t)\) \\
110 & \(\longrightarrow\) & \(s_{6}(t)\) \\
111 & \(\longrightarrow\) & \(s_{7}(t)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mapping for Modulation Order 8.  
Figure 1: Set of eight different waveforms.  
Figure 2: Waveform comparison between Single-Pulse Variable Duration (SPVD) and TS-OOK.
## 2 Solution Scheme of Perturbation Method  
### Theoretical Background  
For a viscously damped linear system with \(N\) degrees of freedom (DOFs), the equation of motion for the forced vibration can be expressed as:  
\[[m]\big{[}\ddot{u}\big{]}+[c]\big{[}\dot{u}\big{]}+[k][u]=\{f(t)\} \tag{1}\]  
where \(\{u\}\), \(\big{\{}\dot{u}\big{\}}\), and \(\big{\{}\ddot{u}\big{\}}\) are the displacement, velocity, and acceleration vectors with the dimension \(N\times 1\), respectively; \(\big{\{}f(t)\big{\}}\) is the force vector; \([m]\), \([c]\), and \([k]\) are the symmetric mass matrix, damping matrix, and stiffness matrix which are all \(N\times N\) square matrices, respectively.  
It is well-known that the modal superposition method is a powerful method for evaluating the dynamic response of a linear system. For non-proportionally damped structures, the undamped mode shapes cannot uncouple Equation (1). To uncouple the equation of motion with non-proportional damping, the \(N\) second order differential Equation (1) has to be reformulated as \(2N\) first order differential equations in state space [11]. By adding the identify \([m]\dot{[u]}=[m]\dot{[u]}\), and defining:  
\[\{y\}=\left\{\begin{array}{c}\{u\}\\ \{\dot{u}\}\end{array}\right\},\ [A]=\left[\begin{array}{cc}[c]&[m]\\ [m]&[0]\end{array}\right],\ [B]=\left[\begin{array}{cc}[k]&[0]\\ [0]&-[m]\end{array}\right],\ [F(t)]=\left\{\begin{array}{c}\{f(t)\}\\ \{0\}\end{array}\right\} \tag{2}\]  
the following state-space equation is derived from Equation (1):  
\[[A]\dot{[y]}+[B][y]=\{F(t)\}, \tag{3}\]  
in which, \(\{y\}\) is the state vector with the dimension \(2N\times 1\), \([A]\) and \([B]\) are \(2N\times 2N\) symmetric, real matrices. The related eigenvalue problem is:  
\[(\gamma[A]+[B])\{\psi\}=\{0\}, \tag{4}\]  
in which, \(\gamma\) and \(\{\psi\}\) are complex eigenvalues and associated complex mode shapes. For a system with \(N\) DOFs, there are \(N\) pairs of eigenvalues \(\gamma_{j}\) and \(\gamma_{j+N}=\overline{\gamma}_{j}\), and to each such pair there is a corresponding complex conjugate pair of eigenvectors: \(\left\{\psi_{j}\right\}\) and \(\left\{\psi_{j+N}\right\}=\left\{\overline{\psi}_{j}\right\}\) (\(j=1,2,\ldots,N\)).
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 3 Open RAN Movement and the Related Technologies
### Open RAN Movement
Further details about TIP OpenRAN project group are written in the next section. A year after the formation of the community, the first trials of Open RAN started in India and Latin America [16].
## 6 Demonstration and Results
### Results of Mapping the Concepts from a New Source to Existing Concepts in the Ubiquitous User Model
Tables 5-7 are the similarity matrixes for pair of sets of neighbors (\(N_{s}N_{T}\)) for the concepts _name_, _calories_ and _weight_ in that order. As it was expected, Table 5 shows no high similarity of the neighbors of concept name because \(c_{s}\) and \(c_{bt}\) are homonymous. As we recall, from the process of element level matching for _calories_ concept (Table 3), two best suited concepts in different sub collections were found. Therefore the neighbor set \(N_{T}\) included concepts from both sub collections. This strategy helps disambiguate the sense of the concept of the source by analyzing its similarity with the concepts of the two sub collections in conflict. In the case of the concept calories the sense was clearly disambiguated. Table 6 shows how to equivalences were found in the neighbors' similarity matrix and both of them belong to the same sub collection: the sub collection corresponding to physical activity and training domain. These means that the sense of extracted data from the source, corresponds to calories burned in the domain of physical activity instead of intake of calories, in the domain of food and diet. The similarity matrix of the concept _weight_ described in Table 7 shows that one concept _equivalence_ was encountered in the same sub collection as the _equivalence_ found in the element level matching process. This means that weight from the source is classified in the correct domain and sub collection (_Health O&M_) and therefore both concepts are not homonymous. The third step of the structure level matching is applying a set of IF THEN rules to determine the one-to-one mappings between the concept \(c_{s}\) of the source concept scheme \(X_{s}\) and the best concept \(c_{tb}\) from the set of labels \(C_{t}\) of the target concept scheme \(X_{T}\). In Table 8 we present the results of both processes, element and structure level matching, and the final mapping determined for \(MAP(c_{s}c_{tb})\) after the process of rule application. As we stated in rule 3, adding a new concept in the most relevant collection for _name_ can be considered but the label of \(c_{s}\) must be concatenated to the immediate ancestor label for disambiguation. This new label for the skos:Concept would be _exercise_name_, which is more expressive of the real sense of the concept. \begin{table}
\begin{tabular}{|c|c|c|} \hline
**Weight** & Date & Height \\ \hline
**polar-exercise-data** & 0.75 & 0.33 \\ \hline
**calendar-items** & 0.5 & 0.44 \\ \hline
**exercise** & 0.43 & 0.4 \\ \hline
**result** & 0.46 & 0.43 \\ \hline
**user-settings\_height** & 0.623 & 0.503 \\ \hline \end{tabular}
\end{table} 
Table 7: Similarity matrix for pair of sets of neighbors (\(N_{s}N_{T}\)) of concept _weight_.
## 3 Experimental Results and Analysis
### Evolution Characteristics of Spatial Shifting Along Measurement Lines  
In order to more clearly depict the displacement and sliding behavior of the bedding planes during the failure process and to reveal the displacement-strain evolution of anisotropic bedding during failure, characteristic monitoring lines were arranged on coal rock samples with different bedding angles, as shown in Figure 9, to record the displacement changes. The monitoring results of the characteristic displacements for coal rock samples with different bedding angles are shown in Figures 10 and 11. These figures present the vertical spatial displacement along Monitoring Line 1 and the horizontal spatial displacement along Monitoring Line 2 for different bedding angles, along with the variations corresponding to different loading stages.  
Figure 9: Feature line selection diagram.  
As shown in Figures 10 and 11,the vertical and horizontal spatial displacement distances for coal samples with different bedding angles are not uniformly distributed along the monitoring lines. The vertical spatial displacement along horizontal Monitoring Line 1 is notably influenced by the bedding angle. For coal samples with bedding angles of 30\({}^{\circ}\) and 60\({}^{\circ}\), the vertical displacement shows a characteristic shift along the bedding planes, whereas for coal samples with bedding angles of 0\({}^{\circ}\) and 90\({}^{\circ}\), the vertical displacement is parallel to the horizontal Monitoring Line 1. However, for the coal sample with a bedding angle of 90\({}^{\circ}\), significant lateral displacement during failure causes a large variation in the vertical displacement along the horizontal direction.  
The distribution of the horizontal spatial displacement along Monitoring Line 2 is significantly influenced by the end effects of the sample during loading. The horizontal displacement at the bottom of the sample is larger, while the top is constrained by the platen, resulting in smaller horizontal displacement at the upper end. As the load increases, both the vertical spatial displacement at the monitoring points along Monitoring Line 1 and the horizontal spatial displacement at the points along Monitoring Line 2 gradually increase. However, for coal samples exhibiting X-shaped conjugate shear failure, there is a sharp reverse increase in the horizontal spatial displacement near failure, just moments before the sample undergoes catastrophic failure.  
Figure 10: Vertical spatial migration evolution characteristics of Monitoring Line 1: (**a–d**) The bedding angles are 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\), respectively.
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 3 Response Surface Analysis
### UCS of Geopolymer-Stabilized Soft Soils
#### 3.1.3 Effect of Alkaline Activator Content on UCS of Stabilized Soil  
Figure 3 shows the compressive strength variations in the fly-ash-based geopolymer-stabilized soils with alkali activator content (modulus of 1.0 and a slag content of 30%). As can be seen from Figure 3, the UCS of the stabilized soil did not always increase with increasing alkali activator content, and it reached the highest value when the alkali activator content was 6%, after which the strength of the geopolymer-stabilized soil decreased with increasing alkali activator content. The increased OH\({}^{-}\) in the system led to excessive early deposition of the silicate products, which hindered the later reaction of the Si and Al [30]. However, when the activator content was too high, the excessive amount of alkali metal ions M\({}^{+}\) affected the charge balance of the system, which was not conducive to the formation and development of the structure [31]. When the hydration reaction occurred for a certain amount of time, the reaction products could only cover the surface of the structure, which hindered the contact between the geopolymer and the soft soil, resulting in excessive alkaline activator residue, which was not conducive to the development of the overall structure of the solidified soil.