esa-sceva/satcom-chunk-collection huggingface dataset:
# Psychoeducation on Stress and Anxiety Using Virtual Reality: A Mixed-Methods Study
## 2 Materials and Methods
### Virtual Reality-Based Psychoeducational Experience on Stress and Anxiety  
MIND-VR is an immersive VR psychoeducational experience on stress and anxiety designed by some of the authors of this study. The funds necessary for its technical development ($5000) were raised through a crowdfunding campaign [31]. MIND-VR works on the Oculus Quest 2, and it can be downloaded for free, both in Italian and English, on the project website ([[https://mind-vr.com/free-download/](https://mind-vr.com/free-download/)]([https://mind-vr.com/free-download/](https://mind-vr.com/free-download/)) (accessed on 15 July 2022)).  
This VR-based psychoeducational experience was intended to be easy-to-use, pleasant, and educational [31]. In its design and development, we applied principles and practices of User-Centered Design (UCD) for VR experience [84]. Because MIND-VR was created for people who are not necessarily VR experts, great attention was given to creating a simple and intuitive interaction model (e.g., teletransportation for navigating the VR environment). Furthermore, following the conceptual framework defined as \"emotional design\" [85], we developed MIND-VR with extreme consideration of the graphic and sound quality of the experience.  
Based on previous literature underlying the relaxing effect of naturalistic virtual environments [29; 86], we chose a small tropical island as the setting. We avoided inserting elements on the island that could be excessively distracting for individuals compared to the psychoeducational content (Figure 1). On the virtual island there are three paths that describe different aspects related to stress and anxiety: (1) definitions (first path); (2) causes and symptoms (second path); (3) main treatments (third path) (Figure 2). The completion of each route takes about 15 min.
# Location-Based Relay Selection in Full-Duplex Random Relay Networks
## 4 Location-Based Relay Selection in FDRRNs  
In this section, we investigate relay selection algorithms for FDRRNs using (5). To begin, we analyze the relationship between the relay location and the outage probability of an FDRRN.
## 2 Related Work
While both explore intelligent resource management, the former emphasizes autonomous transportation systems, and the latter focuses on edge network optimization. Resource allocation using advanced learning schemes, such as deep-Q learning, has also shown promise for secure spectrum allocation in 6G environments [26], aiming to optimize both spectrum use and resource management. This approach is comparable to studies that explore energy-efficient dynamic resource optimization in 6G IoT ecosystems, focusing on sustainability [24]. Another study introduces robust resource control based on access point selection for 6G IoT, highlighting the importance of adaptability in network infrastructure [22]. This contrasts with resource allocation through graph attention models that incorporates knowledge collaboration to enhance decision-making processes in 6G-enabled IoT [23]. Collectively, these studies illustrate the breadth of ongoing research, federated frameworks, or hybrid optimizations, tackling key challenges in resource management, security, and efficiency across 6G-enabled IoT networks. The proposed trust-optimized 6G-IoT framework improves upon existing resource allocation models by integrating trust management, economic optimization, and delay-tolerant routing. Unlike current methods that focus on isolated challenges such as interference control [25] or federated learning for security [17], the proposed approach creates a holistic solution by combining security, energy efficiency, and adaptive routing. The proposed model here will address the shortfall of static models that cannot adapt to changes in the network conditions by considering trust scores, real-time dynamic pricing, and energy-conscious routing decisions with scalability and robustness in 6G IoT networks.
## 3 Results and Discussion
### Thermal Analysis of Microcapsules  
The DSC thermograms for wall materials, as well as for the blank and AMO-loaded microcapsules, are shown in Figure 9.  
The thermogram of GE (Figure 9: GE) presents an endothermic peak at 64.36 \({}^{\circ}\)C, which is associated with water loss overlapping with denaturation [41,65]. According to [PERSON] et al., an endothermic phenomenon during denaturation is largely attributed to the disruption of hydrogen bonds, leading to protein unfolding, with the denaturation temperature reflecting the thermal stability of proteins [69]. Another peak at 222.85 \({}^{\circ}\)C is attributed to the melting of the crystalline fraction and thermal degradation [70]. For GA samples (Figure 9: GA), the broad endothermic peak occurs at 67.89 \({}^{\circ}\)C.  
Similar endothermic events have been reported by other researchers across a wide temperature range--for GE at 70.96 \({}^{\circ}\)C [65] and 62 \({}^{\circ}\)C [71], and for GA at 69.87 \({}^{\circ}\)C [65], 65 \({}^{\circ}\)C [71], 63.31 \({}^{\circ}\)C [62], \(\sim\)75 \({}^{\circ}\)C [72], 79.3 \({}^{\circ}\)C [73], and 100-150 \({}^{\circ}\)C [74]--all of which are attributed to water loss. Given the fact that both GE and GA are natural products, the variations in these values can be attributed to differences in sample composition, moisture content, degree of purity, and storage conditions [65,69,72].  
Regarding the microcapsules (Figure 9), the main endothermic peaks are observed at 67.89 \({}^{\circ}\)C and 68.13 \({}^{\circ}\)C for the PL and MC, respectively. These temperatures are higher than that of pure gelatin (64.36 \({}^{\circ}\)C), which is consistent with the results reported for other protein-polysaccharide systems. In these systems, the denaturation temperature of the protein in the coacervates was found to be higher than that of the pure protein, indicating that coacervates provide greater thermal stability [41,65,71,75,76,77]. The elevated temperature observed for the coacervates suggests enhanced thermal stability compared to the pure  
Figure 9: The DSC curves of gelatin (GE), arabic gum (GA), AMO-containing microcapsules (MC) and unloaded microcapsules (PL).  
protein and indicates their potential use as wall materials for the encapsulation of thermally sensitive core materials [77].  
The DSC thermograms of blank microcapsules displayed a minor endothermic peak at 222.85 \({}^{\circ}\)C, corresponding to the melting of the crystalline fraction and thermal degradation of the GE component, while the AMO-loaded microcapsules exhibited a sharp endothermic peak at a higher temperature (225.85 \({}^{\circ}\)C). The observation that encapsulating essential oil in gum arabic/gelatin microcapsules shifts the endothermic peaks to higher temperatures and increases the energy required for denaturation and melting, compared to the unloaded microcapsules, suggests changes vs. the PL and interactions between the wall and core materials [41; 65; 76].
# Carbon Dioxide Uptake by Brazilian Cement-Based Materials
## 2 The Brazilian Cement Sector
### The Brazilian Cement Industry's Potential for Reducing Carbon Dioxide Emissions by 2050  
As a developing country, Brazil has a significant infrastructure program to be implemented in the wake of the increase in population and urbanization patterns. Therefore, the demand for cement in the coming decades will rise. The energy-intensive and emission-intensive cement industry would thus be obliged to limit the emission of greenhouse gases. The cement industry globally accounts for approximately 7.4% (2.9 G  
Figure 3: Apparent consumption in Brazil from 1965 to 2022 (million tons per annum) *source: author original.  
Figure 2: Carbon dioxide emissions broken down by area by the cement production in Brazil (2022) *source: author original.  
all anthropogenic carbon dioxide emissions. By contrast, this share is about 2.6% in Brazil because of a set of actions that the Brazilian cement sector has been implementing for years. Consequently, Brazil is one of the countries worldwide that emits the lowest amount of carbon dioxide per ton of cement. Figure 4 shows a comparison of the carbon dioxide emissions per ton of cement (2015) between Brazil (572 kg CO\({}_{2}\)/ton of cementitious) and some other countries. The Latin American and world average in 2015 were 588 and 637 kg CO\({}_{2}\)/ton of cementitious, respectively.  
The Brazilian cement sector has developed a Cement Technology Roadmap presenting some alternatives for mitigating carbon dioxide emissions from the national industry in the short- and long-term, and for the commitment of reducing carbon dioxide [36]. Accordingly, the Brazilian cement sector has committed to a 34% reduction in carbon dioxide emissions due to the cement production, to 375 kg/ton of cement by 2050 from 564 kg/ton of cement recorded in 2019.
# Analytical Model for Angular Distortion in Multilayer Welding under Constraints
## 2 Methods
### Mechanics of Welding Distortion
Here, the degree of constraint \(K\) must be 1 or above, and constant \(C\) depends on the material properties of the base metal and wire, the wire diameter, and the welding process. This constant includes errors while deriving Equations (1) through (8), and its value can be experimentally obtained through bead-on-plate welding for \(K=1\) without the external constraints. In this study, we maintain the same material and welding process to keep the constant \(C\) unchanged.
# Optimal Agent-Based Pickup and Delivery with Time Windows and Electric Vehicles
## 4 Agent-Based Simulation System
### Agent-Driven Consensus-Based Allocation  
The consensus mechanism is built as a negotiation-based process as depicted in Figure 4. The negotiation occurs between all Vehicle Agents when a new pickup and delivery orders pair is generated. Next, the pickup and delivery orders are submitted to all the vehicle agents. Subsequently, each vehicle agent evaluates its cost based on the constraints defined by the model in Section 3 and generates a bid value as the total distance traveled by the vehicle (Equation (1)), considering the new orders. The best bid value is then used to allocate the pickup and delivery orders to a vehicle agent. The whole process is described by Algorithm 1.  
```
1:\(V\leftarrow\) List of vehicles
2:\(O\leftarrow\) List of newly generated orders
3:for\(o\)\(\backslash\)\(O\)do
4:candidates\(\leftarrow\)\(\emptyset\)
5:for\(v\) in \(V\)do
6:if\(v\) can feasibly handle \(o\)then
7:\(candidates\leftarrow\)\(candidates\cup\{v\}\)
8:endif
9:endfor
10:ifcandidates is not emptythen
11:\(selected\_vehicle\leftarrow\)Negotiate(candidates)
12: Assign \(o\) to \(selected\_vehicle\)
13:else
14: Reject \(o\)
15:endif
16:endfor
17:functionNegotiate(candidates)
18:\(best\_vehicle\leftarrow\)Randomly select a candidate from candidates
19:for\(v\) in candidates do
20:if\(v\)'s proposal is better than \(best\_vehicle\)'s then
21:\(best\_vehicle\gets v\)
22:endif
23:endfor
24:return\(best\_vehicle\)
25:endfunction
```  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Vehicle** & \\ \hline unique\_id & A unique identifier for the vehicle. \\ speed & The vehicle’s speed (cells per step). \\ capacity & The freight capacity of the vehicle. \\ battery\_capacity & The maximum charge level of the battery. \\ battery\_consumption\_rate & The battery consumption rate per simulation step. \\ orders & The list of orders for the vehicle. \\ charge & The vehicle’s current battery charge level. \\ recharge\_rate & The vehicle’s charge rate per simulation step. \\ pos & The position of the vehicle. \\ path & The path taken by the vehicle. \\ plan & The plan of the vehicle. \\ load & The current load of the vehicle. \\ time & The current time of the vehicle. \\ current\_order & The order currently served by the vehicle. \\ current\_order\_index & The index of the current order to be handled in the order list. \\ needs\_charging\_threshold & This is used to determine when a vehicle needs recharge \\ needs\_charging & Indicates if the vehicle needs to charge its battery based on the \\  & \(needs\_charging\_threshold\times battery\_capacity\). \\ total\_orders & The total number of orders for the vehicle. \\ distance\_travelled & The total distance traveled by the vehicle. \\ number\_of\_charges & The total number of times the vehicle has been charged. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the vehicle agent.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Results for Steel Strands
#### 4.2.1. Prestress Loss in the Prestressing Stage  
With Equations (1) and (8), the prestress loss of steel strands in the prestressing stage at different distribution angles (20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\)) can be obtained given the slab width of 3.0 m, 4.5 m, 9.0 m, and 12.75 m, as presented in Tables 8-11, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Distribution Angle (\({}^{\circ}\))**} & \multirow{2}{*}{**Length of Steel Strand (mm)**} & \multicolumn{4}{c}{**Prestress Loss (\%)**} & \multicolumn{2}{c}{\(\frac{\alpha_{1}}{\alpha_{\text{m}}+\alpha_{\text{I2}}}\) **(\%)**} \\ \cline{3-6}  & & \(\sigma_{\text{I1}}\) & \(\sigma_{\text{I2}}\) & \(\sigma_{\text{I1}}\)+\(\sigma_{\text{I2}}\) & \(\frac{\alpha_{1}}{\alpha_{\text{m}}+\alpha_{\text{I2}}}\) **(\%)** \\ \hline
20 & 26,314 & 3.2 & 10.0 & 13.2 & 24.2 \\
25 & 21,296 & 3.9 & 8.2 & 12.1 & 32.5 \\
30 & 18,000 & 4.7 & 6.9 & 11.6 & 40.1 \\
35 & 15,691 & 5.3 & 6.1 & 11.4 & 46.8 \\
40 & 14,002 & 6.0 & 5.4 & 11.4 & 52.4 \\
45 & 12,728 & 6.6 & 5.0 & 11.6 & 57.0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Theoretical prestress loss of steel strands given the slab width of 9.0 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Distribution Angle (\({}^{\circ}\))**} & \multirow{2}{*}{**Length of Steel Strand (mm)**} & \multicolumn{4}{c}{**Prestress Loss (\%)**} & \multicolumn{2}{c}{\(\frac{\alpha_{1}}{\alpha_{\text{m}}+\alpha_{\text{I2}}}\) **(\%)**} \\ \cline{3-6}  & & \(\sigma_{\text{I1}}\) & \(\sigma_{\text{I2}}\) & \(\sigma_{\text{I1}}\)+\(\sigma_{\text{I2}}\) & \(\frac{\alpha_{1}}{\alpha_{\text{m}}+\alpha_{\text{I2}}}\) **(\%)** \\ \hline
20 & 37,279 & 2.2 & 13.9 & 16.1 & 14.0 \\
25 & 30,169 & 2.8 & 11.4 & 14.1 & 19.6 \\
30 & 25,500 & 3.3 & 9.7 & 13.0 & 25.3 \\
35 & 22,229 & 3.8 & 8.5 & 12.3 & 30.7 \\
40 & 19,835 & 4.2 & 7.6 & 11.9 & 35.7 \\
45 & 18,031 & 4.7 & 7.0 & 11.6 & 40.1 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Theoretical prestress loss of steel strands given the slab width of 12.75 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Distribution Angle (\({}^{\circ}\))**} & \multirow{2}{*}{**Length of Steel Strand (mm)**} & \multicolumn{4}{c}{**Prestress Loss (\%)**} & \multicolumn{2}{c}{\(\frac{\alpha_{1}}{\alpha_{\text{m}}+\alpha_{\text{I2}}}\) **(\%)**} \\ \cline{3-6}  & & \(\sigma_{\text{I1}}\) & \(\sigma_{\text{I2}}\) & \(\sigma_{\text{I1}}\)+\(\sigma_{\text{I2}}\) & \(\frac{\alpha_{1}}{\alpha_{\text{m}}+\alpha_{\text{I2}}}\) **(\%)** \\ \hline
20 & 37,279 & 2.2 & 13.9 & 16.1 & 14.0 \\
25 & 30,169 & 2.8 & 11.4 & 14.1 & 19.6 \\
30 & 25,500 & 3.3 & 9.7 & 13.0 & 25.3 \\
35 & 22,229 & 3.8 & 8.5 & 12.3 & 30.7 \\
40 & 19,835 & 4.2 & 7.6 & 11.9 & 35.7 \\
45 & 18,031 & 4.7 &
## 3 Methodology
### Data Preprocessing, Feature Selection, and Feature Scaling  
Following the data acquisition, the dataset was rigorously preprocessed to optimize its structure for ML and DL tasks. Initially, categorical features such as 'protocol_type','service', and 'flag' were transformed into numerical values using label encoding. Then the redundant features with no variance, such as 'num_outbound_cmds', were removed, as they do not contribute to learning and may degrade model generalization.
## 2 Development of FBG Stress Sensor  
### Basic theory of FBG  
The FBG sensor is a wavelength-modulated fiber sensor. The sensing information of external parameters is obtained by modulating FBG center wavelength. The mathematical expression is:  
\[\lambda_{B}=2n_{eff}\Lambda \tag{1}\]  
where \(\lambda_{B}\) is FBG center wavelength, \(n_{eff}\) is the effective refractive index of the fiber core, and \(\Lambda\) is the grating period. Equation (1) shows that the parameter \(\lambda_{B}\) depends on parameters \(n_{eff}\) and \(\Lambda\). As a result, any physical processes that can change these two parameters will cause FBG wavelength drift (Figure 3).  
When the FBG is subjected to uniform axial stress, the relationship between its change in wavelength, \(\Delta\lambda_{B}\), and axial strain, \(\epsilon\), can be expressed as follows:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=(1-P_{e})\epsilon \tag{2}\]  
where \(P_{e}\) is effective elastic coefficient and can be defined as:  
\[P_{e}=\frac{n_{eff}^{2}}{2}[-(P_{11}+P_{12})\
u-P_{12}\} \tag{3}\]  
where \(P_{11}\) and \(P_{12}\) are elasto-optical coefficients, and \(\
u\) is Poisson's ratio. For specific fiber materials, the parameters \(P_{11}\), \(P_{12}\), and \(\
u\) are constant. Equation (2) indicates that there  
Figure 3: Schematic diagram of FBG sensor principle.  
Figure 2: Three-directional vibrating wire pressure sensor: (a) sensor face; (b) clipped corner; (c) sealed washer; (d) cable.  
is a linear relationship between FBG wavelength drift and strain change under uniform axial stress.  
The temperature change, \(\Delta\lambda_{B}\), can also cause the change of FBG wavelength, and the relationship can be expressed as:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=(\alpha+\zeta)\Delta T \tag{4}\]  
where \(\alpha\) and \(\zeta\) are coefficients of thermal expansion and thermal light, respectively, which as generally constant. Equation (4) indicates that there is also a linear relationship between FBG wavelength drift and temperature change.
## 1 Introduction
Most existing studies focus on rock slopes with uniform lithology, while research on layered rock slopes with variable lithology is relatively limited, especially for rock slopes with weak interlayers. In fact, the presence of weak interlayers makes such slopes more prone to instability under blasting disturbances, with varying dip angles affecting both the likelihood and mode of failure. Furthermore, as noted in prior studies, current research on this topic primarily relies on numerical simulations, with few studies utilizing full-scale model tests. Based on this, this study conducts on-site blasting experiments on a typical rock slope with weak interlayers, selected from the Phase I Youth Hub Project of the Pinglu Canal in China. It investigates the attenuation patterns of blasting vibrations within these slopes, focusing on how slope elevation and bedding dip angles influence vibration propagation. The findings provide essential insights for ensuring the stability of weak interlayer rock slopes and support the safe, efficient progress of the Phase I Youth Hub Project of the Pinglu Canal.
## 5 Guidance and Control Algorithm
### Rendezvous
#### 5.1.2 Sliding Mode Control  
The linear sliding variable is utilized to compensate for errors from nonlinear terms and perturbations and is designed as  
\[\mathbf{s}=\dot{\mathbf{r}}_{e}+\lambda\mathbf{r}_{e}, \tag{5}\]  
where the error vector is \(\mathbf{r}_{e}=\mathbf{r}-\mathbf{r}_{d}\) and its time derivative is \(\dot{\mathbf{r}}_{e}=\dot{\mathbf{r}}-\dot{\mathbf{r}}_{d}\).  
If \(\mathbf{s}=0\), then the Lyapunov function \(V=\frac{1}{2}\mathbf{r}_{e}^{T}\mathbf{r}_{e}\) derives the time derivative \(\dot{V}=-2\lambda V\). This guarantees that the error converges to zero when the sliding variable is equal to zero.  
The time derivative of the sliding variable is derived as  
\[\dot{\mathbf{s}} =\ddot{\mathbf{r}}_{e}+\lambda\dot{\mathbf{r}}_{e} \tag{6}\] \[=\ddot{\mathbf{r}}-\ddot{\mathbf{r}}_{d}+\lambda\dot{\mathbf{r}}_{e}\] \[=\mathbf{f}+\frac{1}{m}(\mathbf{u}+\mathbf{d})-\ddot{\mathbf{r}}_{d}+\lambda\dot{ \mathbf{r}}_{e}\] \[=\mathbf{\zeta}+\frac{1}{m}u,\]  
where \(\mathbf{\zeta}=\mathbf{f}+\frac{1}{m}\mathbf{d}-\ddot{\mathbf{r}}_{d}+\lambda\dot{\mathbf{r}}_{e}\) includes the nonlinear dynamics \(\mathbf{f}(t)\) and external disturbance term \(\mathbf{d}(t)\). \(\mathbf{\zeta}\) is assumed to have a known bound, \(D\).  
Let us define the sliding mode control law \(u\) as  
\[u_{SMC}(t)=-K\mathbf{s}(t),\]where \(K\) denotes constant control gain. Lyapunov theory was utilized to prove the stability of the dynamic system using the control input.  
\[V=\frac{1}{2}s^{T}s \tag{8}\]  
By differentiating the Lyapunov function (\(V\)), the time derivative of the Lyapunov function is derived as follows:  
\[\dot{V}=s^{T}\dot{s}. \tag{9}\]  
Substituting Equations (6) and (7) into Equation (9) yields  
\[\dot{V} =\mathrm{s}^{T}\dot{s} \tag{10}\] \[=\mathrm{s}^{T}\bigg{(}\xi-\frac{K}{m}s\bigg{)}\] \[<\|\ddot{s}\|\bigg{(}D-\frac{K}{m}\|s\|\bigg{)}\]  
where \(\|\cdot\|\) is a vector two-norm and \(D\) is the known bound of \(\xi\). Assuming that the sliding variable is in the region \(\|s\|>\varepsilon\), then  
\[\dot{V}<-\beta V^{1/2} \tag{11}\]  
where \(\beta=\sqrt{2}\cdot(\frac{K}{m}\cdot\varepsilon-D)\) is positive. Therefore, if the control gain is chosen as \(K>\frac{Dm}{\varepsilon}\), the sliding variable is bounded as \(\|s\|\leq\varepsilon\).
# Carbon Dioxide Uptake by Brazilian Cement-Based Materials
## 1 Introduction
### General Background on the Carbonation as a Stage in the Decarbonization Roadmaps
[PERSON] [27] compared the carbon dioxide uptake calculated with the standardized method described in the European Standard EN 16757 [28] to that calculated with the parameters measured experimentally. It was concluded that the predictions of the European Standard EN 16757 underestimate the carbonation rate, underestimate the carbonation degree (indoor exposure), and overestimate the carbonation degree (outdoor exposure). Accordingly, several adjustments to the parameters given in the European Standard EN 16757 were recommended. In addition, [PERSON] et al. [29] studied the carbon dioxide uptake of several types of Portland cement concretes after a 10-year natural carbonation and compared the experimental results with the ones calculated by applying the EN 16757 procedure. The standard procedure underestimates the carbon dioxide uptake of concretes made with additions, mainly as a consequence of their carbonation depth underestimation, especially for concretes with a high ground granulated blast-furnace slag content [30]. [PERSON] and [PERSON] proposed an optimal slag-blended concrete mix design to mitigate the impact of global warming considering compressive strength and carbonation resistance [31]. Finally, it should be taken into account that the net carbon dioxide emissions increase as the compressive strength increases and the carbon dioxide uptake ratio increases [32]. The aim and novelty of this paper is to present, for the first time in the technical literature, a proper factual calculation of carbon removals in Brazil by the carbonation of cement-based materials. The remainder of this paper is structured as follows: following the introduction, Section 2 presents an outlook of the Brazilian Cement Sector's current situation; Section 3 provides an overview of the method to estimate the carbon dioxide removals due to carbonation; and Section 4 presents an analysis of the cement production and consumption in America and the carbonation results of Brazilian cement-based materials.