esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hypothesis Development
### Attitudes  
Emotional readiness explains how emotions affect teachers' achievements in performing their duties [36]. Instructional emotional readiness reflects the impact of teachers' emotions on their teaching performance. In the DTPB model, attitude is considered to be the degree to which an individual is positive or negative about a particular behavior [15; 38],  
Figure 1: The proposed model with the initial hypotheses.  
and it has a corresponding impact on whether to implement the target behavior [39], which is similar to the concept of emotional readiness. As a result, to reduce the conceptual overlap of influencing factors and simplify the model, attitudes and emotional preparedness were studied as the same dimension. The attitude of teachers was mainly evaluated from whether they could generate positive emotions in their implementation of STEM education and achieve good results in STEM education. Attitudes' influence on an individual's behavioral intentions has been strongly supported by previous studies [25; 40]. Therefore, the following hypothesis was proposed:  
**Hypothesis 1:**_The attitudes/emotional readiness of teachers towards STEM education have a significant impact on teachers' intentions to implement STEM courses._  
According to the deconstruction of the DTPB model for attitude, attitude was also decomposed into three aspects: perceived usefulness, perceived ease of use, and compatibility. Perceived usefulness mainly concerns the perceived STEM benefits of individual teachers when they choose to offer STEM courses. Perceived ease of use refers to the degree to which a person finds it easy to use a specific system [41], as well as the degree of difficulty for teachers who implement STEM courses. Compatibility refers to the degree of fit of technology and existing potential value and experience [42]. It also refers to the degree of adaptation between STEM education and the teacher's teaching concepts, existing teaching experience, etc.  
Therefore, the following hypotheses were proposed:  
**Hypothesis 1a:**_Perceived usefulness has a significant impact on teachers' attitudes toward engaging in STEM education._  
**Hypothesis 1b:**_Perceived ease of use has a significant impact on teachers' attitudes toward engaging in STEM education._  
**Hypothesis 1c:**_Compatibility has a significant impact on teachers' attitudes toward engaging in STEM education._
# Mail Man Ferry: A Novel Routing Protocol in Intermittently Connected Networks
## 5 Performance Evaluation
### Performance Results and Discussions
The efficiency is proven by being able to resist the decreases in the node cooperation percentages in which it maintains similar delivery probability, while Epidemic and Prophet could not. The target node average message delay is now investigated. Table 2 compares the average latency for the three protocols used in our experiment with varying cooperation percentages. This demonstrates that the Mail Man Ferry approach has the lowest average delay when all nodes cooperate. This is because messages are better-directed to their destinations when they are carried by nodes that are anticipated to go along predetermined paths, such as Mail Man paths. On the other hand, the latency rises as the percentage of participating nodes falls. A similar concept is applicable in Epidemic and Prophet, but with much higher latency. Mail Man Ferry starts with 68 min, while Epidemic and Prophet start with 89 and 84, respectively. This shows that Mail Man Ferry not only maintains suitable delivery probability, but also maintains lower average latency to deliver the messages to the nodes.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 9 Analysis and Discussion
### Computation Overhead  
The vehicle computation time for the pseudonyms changing process is analyzed in this section. Figure 20 shows the average computation time of the proposed scheme CMC using number of neighbors in the transmission range. A higher number of transmission range neighbors will take more time to compute the pseudonyms changing process as compared to lower transmission range neighbors. At the start of the simulation, there is a lower number of transmission range neighbors and a lower computation burden on the vehicles for the pseudonym changing process. The proposed scheme's average computation cost is compared with TAPCS [25] and IndMZ [24], as in Figure 21. The CMC creates a lower computation overhead than existing schemes. This is because of the utilization of road context information and pseudonym changing process for vehicle anonymisation. IndMZ scheme produces a much higher computation cost due to the generation of fake pseudonyms in larger quantities. The TAPCS generates a lower cost of computation than IndMZ by utilizing the vehicle traffic conditions in the road environment, although the proposed scheme CMC has a lower computation overhead as compared with the existing scheme. However, this overhead can be reduced further to optimize the computation and communication time of vehicles by applying a privacy scheme.
# Seismic Behavior of Triple Tunnel Complex in Soft Soil Subjected to Transverse Shaking
## 6 Conclusions  
This research has been carried out to study the dynamic behavior of a triple tunnel complex in soft soil and the effect of different parameters on the overall behavior in case of transversal shaking by an earthquake. From the detailed study conducted, the following conclusions are drawn.  
1. The increase in the lining thickness from 0.4 m to 0.8 m decreases the surface displacements from 0.088 m to 0.045 m, reduces the normalized tunnel deformations from 6.37 to 1.12, and the resisted seismic-induced bending moments increase from 8698.27 kN-m/m to 9568.54 kN-m/m, respectively.
2. The increase in the thickness of the inner structure from 0.3 m to 0.7 m decreases the surface heave from 0.088 m to 0.069 m, respectively.
3. When the amplitude of the IM increases from 0.1 g to 0.5 g, the surface heave increases from 0.088 m to 0.106 m while the tunnel uplift increases from 0.013 m to 0.116 m.
4. The change in embedment depth of the tunnel from 0.25 to 2 decreases the surface heave from 0.120 m to 0.051 m while the normalized tunnel deformations are also reduced. In other words, the deeper embedded tunnel would act more rigid in comparison to the shallower embedded tunnel with the same lining thickness.
5. The shape of the tunnel complex also plays an important role in its seismic behavior. The triple tunnel complex results in a surface heave of 0.083 m as compared to 0.113 m and 0.136 m, which are 36.44% and 63.85% more in the presence of a rectangular tunnel complex and the equivalent horizontal twin tunnel complex, respectively.
6. The maximum resisted seismic-induced bending moments by the triple tunnel complex are 33.37% less than that of the rectangular tunnel complex while 6.55% more than the equivalent horizontal twin tunnel complex at the tunnel invert. Looking to the resisted seismic-induced bending moments along the tunnel perimeter, it can be noted that, at the normalized perimeter of about 0.375, 0.5, and 0.625, the resisted moments in case of the triple tunnel complex are about 30.20% more while, at the crown level, about 79.86% more than at the rectangular tunnel complex.  
From the conclusions drawn, it is evident that the triple tunnel complex resists more seismic-induced bending moments than that of the equivalent horizontal twin tunnel complex and the rectangular tunnel complex. The produced surface heaves are the least among the three shapes as well. This highlights the performance of a triple tunnel complex against the transversal seismic vibrations.  
Figure 10: (**a–d**) Comparison among triple tunnel complex, equivalent twin tunnel complex, and rectangular tunnel complex in terms of: (**a**) surface displacements, (**b**) normalized tunnel perimeter, (**c**) maximum moments, and (**d**) maximum thrust generated along the perimeter.
## 4 Discussion
### Strengths and Uncertainties  
This work assesses the spatiotemporal variation characteristics and future trends of NPP in the research region and the impact of various drivers on NPP in 20 years based on geographical detectors. Unfortunately, the geographical detector can only test the coupling of the spatial distribution of two variables and is not able to detect the coupling between three or more driving factors [57,58]. Furthermore, only nine driving factors were considered in this paper: Indeed, numerous climatic factors and human activities also affect the vegetation growth process [77,78], and more determinants should be included in future studies, e.g., solar radiation, soil type, and ecological engineering projects [79,80].  
The time lag effect of NPP on various driving factors is also not considered in this paper, which should be further studied on different time and spatial scales in the future [14, 77, 78]. Besides, only one NPP data product was used in this paper, and further work should include more types of NPP to reduce the uncertainty in assessing the spatiotemporal variability of NPP and driving force analysis [81, 82]. Despite the above limitations, our study reveals the spatiotemporal variation characteristics of NPP, future change trends, and the response of NPP to various driving factors in the research region during 2000-2019. The results of these analyses provide a reference for coping with global climate change and regional socio-economic development.
# Slot Waveguide Enhanced Bloch Surface Waves
## Materials and Methods
### Towards Fabrication  
Although experimental results are not presented in this article, it is important to consider the feasibility of the device and take into account the fabrication processes involved in the final realization of the structure. As already mentioned, the waveguide base is intended to be fabricated by means of the nano-imprinting of a silicon stamp, pattern by electron beam lithography, into an Ormocore(r) layer with a 500 nm thickness. The multilayer will be created using atomic layer deposition, which is a technique allowing a precise control of the layer thickness together with a highly conformal coating of the waveguides. The materials involved are TiO\({}_{2}\) and SiO\({}_{2}\), which can both be deposited by ALD using plasma-enhanced deposition. At this step, the multilayer is on the sides of the waveguide but also on top.  
The multilayer has a higher average effective index than the waveguide, which yields a possibility for the hybrid BSW-slot waveguide mode to leak into this layer. To avoid this, a reactive ion etching of the top layers can be performed. The two materials constituting the multilayer can possibly be etched using the same gases, but at different rates. It leads a possibility to over etch the multilayer and damage the polymer waveguide. [PERSON] et al. have demonstrated recently that a thin layer (only few nanometers) of aluminum oxide can act as a perfect etch stop layer for this RIE processes, enabling the fabrication of coatings by ALD only on the vertical sides of waveguides [16; 17]. With the refractive index of Al\({}_{2}\)O\({}_{3}\) at this wavelength being nearly the same as that of the polymer, the response of the structure is not affected by this protective layer and allows for an etching of the top layers. As a result, only the sidewalls remains coated and the design structure can be fabricated.  
It is important to remark that a layer nearly 1-\(\upmu\)m-thick and composed of two materials has to be etched. Although it is feasible, it requires a careful adaptation of the processes in order to avoid rough surfaces and etching of the side layers.
## 2 Implementation of the Nevanlinna-Pick Constraint into Structural Dynamics
### Practical Considerations  
When the NP constraint was implemented into the sample structural systems, we were required to consider certain properties of the Pick matrix. We observed that the positive definiteness of the Pick matrix changed at certain frequencies in a system. As an illustrative example, consider a 1-DOF mass-spring-damper system with a mass of _0.1 kg_, a stiffness of _0.1 N/m_, and a damping constant of _0.01 N/m/s_. Figure 1 shows the corresponding Bode gain and phase plots of the system.  
In numerical simulations, the eigenvalue test was used to determine the positive definiteness of the Pick matrix in Equation (1). We found that the Pick matrix was negative definite at frequencies around the natural frequency, while it was positive definite at all other frequencies. In other words, the positive definiteness of the Pick matrix was violated when the frequency range included the natural frequency of the system, which, in this example, was _1 rad/s_. It was also observed that both the frequency range in which the Pick matrix was evaluated and the damping constant affected the positive definiteness of the Pick matrix. The positive definiteness of the Pick matrix was violated with damping constants above almost _0.03 N/m/s_, in this example.  
When the Bode plots corresponding to different damping constants were examined, it was found that the change in the slope of the Bode gain curves resulted in a negative definite Pick matrix. This occurred because of the flattening of the response curve around the peak of the frequency response curve. The Bode gain curve flattened as the damping constant increased, which is associated with a decrease in the derivative of the transfer function. Figure 2 clearly shows that the peak frequency of the response shifts to the left as the damping increases, but that the natural frequency of the system remains constant. As a result, the positive definiteness of the Pick matrix was affected by both the frequency range (related to the natural frequency) and the damping constant.  
Figure 1: Bode gain and phase plots of a mass-spring-damper system with the natural frequency of \(\omega_{n}=1\)_md/s_.  
In short, the positive definiteness of the Pick matrix is influenced by the peak frequency of the response and the damping constant of the system. The peak frequency of the response almost coincides with the natural frequency at lower damping constants, while it shifts significantly away from the natural frequency at higher damping constants. In addition, the Bode gain plot flattens as the damping constant increases. As the damping constant increases, the frequency range in which the Pick matrix is negative definite enlarges around the peak frequency of the response. For this reason, the frequency range under consideration during optimization is divided into two regions: first the left side of the peak frequency of the response, and, second, the right side of the peak frequency of the response. This excludes the non-smooth part of the curve that renders the Pick matrix negative definite.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 3 Proposed Solution
### Data Extraction Algorithm  
At the receiver side, we first demodulated the stego channel and then we extracted the embedded data from the phase components of the stego signal. To achieve this, we first found out the embedding locations in the magnitude spectrum \(|S_{s}(m,k)|\) using the embedding keys: _Threshold_, \(\Delta(m,k)\), \(SLB\) and \(FBL\). Then, we mapped the embedding locations to the phase spectrum. Secret data segments were extracted and then reassembled as shown in Algorithm 2:  
```
Input:\(s_{s}(m,n)_{Modulated}\) Output:\(\delta(m,k)\)
1\(m=1\), \(n=1\)while\(m\leq M\)do
2 while\(n\leq N\)do
3\(s_{s}(m,n)_{Demodulated}\gets OFDM/QPSK(s_{s}(m,n))\)
4\(FFT(s_{s}(m,n))\)\(|\phi_{s}(m,n)|\leftarrow|\phi_{c}(m,n)|\)
5 while\(F_{HDmin}\leq k\leq F_{HDmax}\)do
6 if\(10*log_{10}(|S_{c}(m,k)|)\geq threshold_{dB}\)then
7 if\(\Delta(m,k)_{dB}\geq SLB_{dB}\)then
8 Extract \(\delta(m,k)\) from \(|\phi_{s}(m,k)|\)
9
10 return\(\delta(m,k)\)
```  
**Algorithm 2**Demodulate \(s_{s}(m,n)\) and Extract \(\delta(m,k)\)  
Data extraction from the phase spectrum is shown by the block diagram presented in Figure 6.  
Figure 5: Block diagram for the embedding process.  
Figure 6: Block diagram for data extraction.
## 4 Empirical Analysis
### Results of Empirical Analysis
Comparative Analysis of the Effect of Financial Crisis Early Warning with the Emotional Tone of MD&A and Financial News  
According to the rank of AUC, this paper selects five models with the best performance to compare the early warning effect. Based on the values of four evaluation indexes, Figure 3 is plotted, which shows in detail the early warning effect of the five models incorporated with the emotional tone of MD&A and financial news. AdaBoost-MD&A represents the early warning effect of the AdaBoost model based on traditional financial indicators and the emotional tone indicators of MD&A. AdaBoost-News represents the early warning effect of the AdaBoost model based on traditional financial indicators and the emotional tone indicators of financial news. The other four models also use the same rule of labeling. It can be seen from Figure 3 that the five models incorporating the emotional tone of financial news are more effective than the models incorporating the tone of MD&A in all four evaluation indexes. The average value of the AUC of the thirteen models considering the tone of financial news is 0.0277 higher than that of MD&A in terms of warning effect. It is also 0.0310 higher in terms of prediction accuracy, 0.0289 higher in non-ST listed company sample, and 0.0332 higher in ST listed company sample. Figure 3: Models warning effects based on different textual emotional tones. Figure 2: AUC values of 13 machine learning models under three sets of input feature variables. 4.5.5. Comparative Analysis of the Performance of Each Machine Learning Model for Sample Recognition  
This paper uses the average value of the effect evaluation indexes of the thirteen machine learning models based on three different groups of input feature variables and ranks each effect evaluation index to compare and analyze the performance of each machine learning model for sample recognition. The specific results are shown in Table 7. There are seven models with AUC above the mean, six models with ACC above the mean, seven models with SEN above the mean and nine models with SPE above the mean. There are four models with all four groups of effect evaluation indexes exceeding the mean, namely DBGT, AdaBoost, random forest and Bagging. These four models also rank in the top five in terms of AUC and ACC, and both the rankings of SEN and SPE remain in the top positions with good and smooth forecasting results. In addition, the BPNN model, whose AUC ranks sixth, ranks first in terms of SPE. It means this model has the highest prediction accuracy for ST listed companies. The traditional logistic regression, ridge regression and Lasso regression models have poor performance in identifying samples. Decision tree models rank last in the value of AUC, ACC and SPE, but rank second for identifying non-ST listed companies. The purpose of early warning mainly lies in the identification of ST listed companies, so the decision tree model does not belong to the focus of this paper.
# Settlement Foundations by Exploring the Collapse of Unsaturated Soils
## 2 Adopted Numerical Framework Using EBBM Theoretical to Predict Collapse  
The BBM model, a derivative of the Cam clay model, has been extensively applied in the study of unsaturated soils, especially with significant use in finite element method simulations. It is formulated based on the hardening plasticity framework, which adapts the modified Cam-Clay model (MCCM) to account for unsaturated states. However, the BBM model has limitations in its computer implementation. In response, [PERSON] and [PERSON] (2011) [PERSON] (2018) proposed a modification aimed at addressing the limitations, enabling the modeling of elastoplastic behavior under hydraulic and mechanical stress cycles, namely, the Extended Basic Barcelona Model (EBBM). It incorporates three new features: (1) incorporating a distinctive smooth yield surface; (2) considering the Lode angle's influence through a cross-section of the yield surface and a smooth failure envelope; and (3) integrating the two-yield surfaces concept. These features were implemented to simplify its implementation and avoid issues associated with yield surface nonconvexity. Several simulations of the hypothetical scenarios outlined by [PERSON] et al. (2015) are replicated using EBBM, and the results obtained are remarkably comparable to those predicted with the BBM, indicating that the extended version can serve as an effective alternative for the original BBM. The compression deformation during wetting-induced collapse is a complicated aspect of unsaturated soil behavior, where there is a considerable interaction between hydraulic and mechanical issues. Numerical models provide valuable insights for exploring this phenomenon and obtaining a deeper understanding of the various parameters affecting it. The hydromechanical model adopted the finite elements program COMSOL to perform the numerical analyses related to wetting-induced collapse. The H-M model incorporates the EBBM as a constitutive model in plain strain and utilizes the Richardsequation [49] for simulating the wetting process. The signs of the pressures, strains, and stress are considered positive when under compression, following the convention of soil mechanics. Two independent stress factors are incorporated by the EBBM model [25; 48]: suction s = ua \(-\) uw and net mean stress p = p \(-\) ua, where p stands for mean stress, uw stands for pore water pressure, and ua stands for pore air pressure.
## 3 Results and Discussion
### Characterization of Constituent Materials
#### 3.2.2 Embodied CO\({}_{2}\) of Binary and Ternary Cementitious Matrices (B1-B10)  
Then, the sustainability potential of all binary and ternary cementitious matrices was calculated with respect to the embodied CO\({}_{2}\) (E.CO\({}_{2}\)) of PC (930 kg CO\({}_{2}\)/tonne), LS (32 kg CO\({}_{2}\)/tonne) and FA (4 kg CO\({}_{2}\)/tonne) [36; 37]. The results are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Formulation** & **PC** & LS **E.CO\({}_{2}\)** & **FA E.CO\({}_{2}\)** & **Total E.CO\({}_{2}\)** & **Compression Strength** & **Strength/E.CO\({}_{2}\)** \\  & **Composition** & **E.CO\({}_{2}\)** & **(kg/tonne)** & **(kg/tonne)** & **(kg/tonne)** & **28 Days (MPa)** & **(MPa/kg/tonne)** \\ \hline B1 & PC86 LS14 & 799.8 & 4.5 & 0.0 & 804.3 & 48.4 & 0.06 \\ B2 & PC80 LS20 & 744.0 & 6.4 & 0.0 & 750.4 & 25.8 & 0.03 \\ B3 & PC70 LS30 & 651.0 & 9.6 & 0.0 & 660.6 & 31.4 & 0.05 \\ B4 & PC66 LS14 FA20 & 613.8 & 4.5 & 0.8 & 619.1 & 45.6 & 0.07 \\ B5 & PC51 LS1 FA53 & 474.3 & 4.5 & 1.4 & 480.2 & 31.2 & 0.06 \\ B6 & PC36 LS14 FA50 & 334.8 & 4.5 & 2.0 & 341.3 & 24.8 & 0.07 \\ B7 & **PC80 LS20 FA20** & **558.0** & **6.4** & **0.8** & **565.2** & **51.3** & **0.09** \\ B8 & **PC4512 FA53** & **418.5** & **6.4** & **1.4** & **426.3** & **46.3** & **0.11** \\ B9 & PC80 LS20 FA50 & 279.0 & 6.4 & 2.0 & 287.4 & 24.9 & 0.09 \\ B10 & **PC60 LS40** & **559.8** & **12.8** & **0.0** & **572.6** & **41.0** & **0.09** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Embodied CO\({}_{2}\) associated with strength of binary & ternary cementitious matrices.  
Figure 10: Cylinder compressive strength of binary and ternary cementitious matrices (B1-B10).  
By observing the results provided in Table 6, it is evident that B6 to B10 had the lowest total E.CO\({}_{2}\). Furthermore, B7, B8 and B10 had the lower environmental impact and exhibited the highest mechanical properties in terms of compressive strength compared amongst the initial pastes. As an effect, B7, B8 and B10 were the three selected bases for further optimization with the addition of microsized elements.