esa-sceva/satcom-chunk-collection huggingface dataset:
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### In Situ Polymerization  
In recent years, in situ polymerization has become a popular technology for creating conductive nanocomposite because of its varied and advantageous approach to polymerization that directly uses 90 mixtures of monomer and nanoparticles with various additives. [PERSON] et al. highlighted the differences between in situ polymerization and other surface modification approaches, such as nanoparticle fusing, and the benefits of in situ polymerization over other methods [218]. Due to the intense interaction between two side-by-side polymer chains, most conductive polymers are thermally unstable and not soluble in different solvents. However, in situ polymerization can polymerize in various ways depending on the conductive polymer for different applications, such as in situ chemical polymerization, in situ electrochemical polymerization, in situ vapor phase polymerization, and in situ polymerization in a supercritical fluid [219].  
[PERSON] et al. mentioned that in situ polymerization can reduce the impedance of the electrolyte interface and bring interfacial compatibility and stability to solid-state lithium batteries, thereby simplifying the process of polymerization [220]. Highly electrically conductive and thermally stable graphene/polyaniline (GN/PANI) nanocomposite was synthesized by in situ polymerization [221].  
In situ polymerization, according to [PERSON] et al., aids in the efficient dispersion of carbon SWNTs in polymer composites in the presence of sonication, which was previously challenging due to the non-reactive surface of SWNTs [17]. The resulting nanocomposite is electrically conductive and optically transparent, with improved mechanical characteristics and thermal stability at low concentrations (0.1% of vol). [PERSON] et al. used ferric p-toluenesulfonic acid (FePS) and FeCl\({}_{3}\) as oxidants to investigate in situ polymerization of PEDOT on nylon 6, PET, and poly (trimethylene terephthalate) (PTT) fabrics and compared different properties of PEDOT/nylon-6, which has the best conductivity of all the nanocomposites; however, PTT/PEDOT is the most stable [222].
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 2 Materials and Methods
### Mix Design and Test Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Size (mm)**} & \multirow{2}{*}{**RCA (\%)**} & \multirow{2}{*}{**Water (m\({}^{3}\))**} & \multirow{2}{*}{**Cement (m\({}^{3}\))**} & \multicolumn{2}{c}{**RCA (m\({}^{3}\))**} & \multirow{2}{*}{**NA (m\({}^{3}\))**} \\ \cline{5-6}  & & & & & **RMC** & & **OA** \\ \hline \multicolumn{6}{c}{**Apparent Volume of Mortar**} \\ \hline \multicolumn{6}{c}{**Volume of Mortar According to EMV Concept**} \\ \hline NAC & 4.75–19 & 0 & 0.619 & 0 & 0 & 0.381 \\ \hline \multirow{2}{*}{RAC1-AP-25} & \multirow{2}{*}{4.75–19} & \multirow{2}{*}{25} & \multirow{2}{*}{0.567} & \multirow{2}{*}{0.052} & \multirow{2}{*}{0.095} \\ \cline{5-6}  & & & & & 0.294 & \\ \cline{5-6}  & & & & 0.103 & 0.191 & \\ \cline{5-6}  & & & & 0.619 & & 0.381 \\ \hline \multirow{2}{*}{RAC1-AP-50} & \multirow{2}{*}{4.75–19} & \multirow{2}{*}{50} & \multirow{2}{*}{0.516} & \multirow{2}{*}{0.103} & \multirow{2}{*}{0.191} \\ \cline{5-6}  & & & & 0.619 & & 0.381 \\ \hline \multirow{2}{*}{RAC2-AP-25} & \multirow{2}{*}{4.75–19} & \multirow{2}{*}{25} & \multirow{2}{*}{0.598} & \multirow{2}{*}{0.021} & \multirow{2}{*}{0.095} \\ \cline{5-6}  & & & & 0.619 & & 0.381 \\ \hline \multirow{2}{*}{RAC2-AP-50} & \multirow{2}{*}{4.75–19} & \multirow{2}{*}{50} & \multirow{2}{*}{0.578} & \multirow{2}{*}{0.041} & \multirow{2}{*}{0.191} \\ \cline{5-6}  & & & & & 0.619 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mix proportions of concretes by volume. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**ID**} & \multirow{2}{*}{**Size (mm)**} & \multirow{2}{*}{**RCA (\%)**} & \multirow{2}{*}{**Water (kg)**} & \multirow{2}{*}{**Cement (kg)**} & \multirow{2}{*}{**Sand (kg)**} & \multirow{2}{*}{**NA (kg)**} & \multirow{2}{*}{**RCA (kg)**} \\ \hline NAC1 & 4.75–19 & 0 & 175 & 389 & 720 & 1003 & 0 \\ \hline NAC2 & 4.75–19 & 0 & 175 & 389 & 720 & 1003 & 0 \\ \hline RAC1-AP-25 & 4.75–19 & 25 & 160 & 357 & 660 & 752 & 336  
added and mixed for 90 s. Afterward, water and superplasticizer were added and mixed for a further 120 s to obtain homogeneous concrete mixtures. The slump and air content of fresh concretes were performed according to KS F2402 and F2421 [47; 48]. All concrete mixtures were cast into plastic molds, demolded 24 h after casting, and cured in water before testing. For hardened properties, compressive strength, elastic modulus, and splitting tensile strength were tested according to KS F2405, F2438, and F2423 [49; 50; 51]. The evolution of compressive strength was observed at 7, 28, and 56 days using standard cylindrical specimens with a size of 100 \(\times\) 200 mm, and the elastic modulus was measured using two strain gauges simultaneously with compressive strength. Splitting tensile strength was measured at 28 and 56 days. Three specimens were tested for each property of hardened concrete.
## 3 Results and Discussion
### Temperature-Dependent Raman Spectroscopy
The peak position and FWHM of these phonon modes at each temperature in our results are consistent with theoretical Raman line shape. The relationship between the peak frequency and temperature was written as [52]:  
\[\omega(T)=\omega_{0}+\chi T, \tag{4}\]  
where \(\omega_{0}\) is the frequency of vibration of these phonon modes at absolute zero temperature, \(\chi\) is the first-order temperature coefficient of these phonon modes. The evolution of the Raman peak position, \(\omega\), follows a linear dependence with the temperature. The different Raman modes \(\chi_{i}\) of the first-order temperature coefficients can be fitted linearly. The values of temperature coefficient for each Raman mode are listed in Table 1.  
Figure 4 and Table 1 indicate the temperature evolution for the three Raman modes A\({}_{1g^{\prime}}^{1}\), E\({}_{8}^{2}\), A\({}_{1g^{\prime}}^{2}\) measured on the Bi\({}_{2}\)Se\({}_{3}\) hexagonal single-crystal nanoplates sample. For the Bi\({}_{2}\)Se\({}_{3}\) nanoplates, the first order temperature coefficients are \(\chi_{1}=-1.258\times 10\)\({}^{-2}\) cm\({}^{-1}\)/K, \(\chi_{2}=-1.385\times 10\)\({}^{-2}\) cm\({}^{-1}\)/K, \(\chi_{3}=-2.363\times 10\)\({}^{-2}\) cm\({}^{-1}\)/K. Bi\({}_{2}\)Se\({}_{3}\), a typical thermoelectric material, has temperature evolution of Raman mode further relative to other two-dimensional materials, such as, the transition metal dichalcogenides (TMDCs) (MoS\({}_{2}\), MoSe\({}_{2}\), WS\({}_{2}\), and WSe\({}_{2}\)), black phosphorus, trichalcogenides ( TiS\({}_{3}\) ), which are summarized in Table 2. The calculated \(\chi\) coefficient was listed in Table 2 and the \(\chi\) of other materials were summarized in Table 2. The calculated \(\chi\) coefficient was listed in Table 2 and the \(\chi\) of other materials were summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Raman Mode** & **Peak Position (cm\({}^{-1}\))** & **FWHM (cm\({}^{-1}\))** & \(\chi\) **(cm\({}^{-1}\)/K)** \\ \hline A\({}_{1g}^{1}\) & 72 & 4.83 & \(-1.258\times 10\)\({}^{-2}\) \\ E\({}_{8}^{2}\) & 130 & 7.06 & \(-1.385\times 10\)\({}^{-2}\) \\ A\({}_{1g}^{2}\) & 174 & 7.30 & \(-2.363\times 10\)\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the Raman peak position, FWHM, and temperature coefficient for different Raman modes in the large Bi\({}_{2}\)Se\({}_{3}\) hexagonal single-crystal nanoplates. Figure 4: The peak position and temperature (K), FWHM and temperature (K), the peak position and FWHM of A\({}_{1g}^{1}\) **(a)**; E\({}_{g}^{2}\) **(b)**; A\({}_{1g}^{2}\) **(c)** Raman active modes in the temperature range of 83–603 K.  
for comparison, indicating the comparable \(\chi\) coefficient and application of Bi\({}_{2}\)Se\({}_{3}\) nanoplates, and thus, our temperature-dependent Raman study can be exploited as large nonlocal signals to measure the temperature in graphene--Bi\({}_{2}\)Se\({}_{3}\) heterostructures device [53].
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Solving Phase I (Search): Maze Exploration
#### 3.3.2 The Algorithm  
The pheromone used to implement the AA-based algorithms is virtual, stored as matrices in individual agents. The virtual pheromone, therefore, is a message-passing coordination mechanism between the agents in the system. The pheromone matrix helps the agents keep track of the pheromone intensities, while at the same time, it helps them roughly map out the explored area.  
```
initialization;  possible moves = [stay, right, left, forward, backward]; whiletarget not founddo forach antdo  Generate list of all possible next states;  Acquire pheromone information of all next states;  Roulette Wheel \(\leftarrow\) generates probabilities of moving to each next state;  Spin Roulette Wheel to pick next state;  Update current position and pheromone levels;  end while  end while
```  
**Algorithm 1**The basic AA Algorithm used for decision making, where each model differs in probability generation. The AA, iAA, iAA-B, and iAA-R models are based on the same algorithm; they differ mainly in the equation used for probability distribution generation.  
As each agent explores the search space individually, their pheromone matrices would differ. Hence, when two agents pass by each other, they exchange their copies of the matrix to combine their search results and come up with a more inclusive version of the search space and pheromone intensities. For a general _\"bird's eye\"_ view of the process, the depiction of the search is shown in Figure 7.  
Figure 6: All AA-based decision-making models prioritize their 5 possible directions of movement (1–4 are shown above, with the option to remain stationary being the 5 th). AA prioritizes moving to a cell with a higher pheromone level, while all iAA-based models prefer moving to a cell with a lower pheromone concentration. The iAA-R algorithm (rightmost image, above) checks for average pheromone in neighboring _regions_, while the other three approaches (AA, iAA, and iAA-B) only check pheromone levels in the immediate neighboring _cells_.
## 2 Materials and Research Methods
### Numerical Analysis Method
The combustion method is chosen for the experimental mixing-controlled combustion (MCC) AVL combustion, that predicts the rate of heat released (ROHR means the amount of heat generated in unit time per unit volume of the combustion chamber) and the NOx emissions, based on the quantity of fuel in the cylinder and the turbulent kinetic energy introduced by the injection of fuel. The first law of thermodynamics, applied to the combustion cylinder, dictates that the change of the internal energy in the cylinder equals the sum of the piston works, fuel heat input, wall heat losses, and the enthalpy flow due to blow-by. This is applied to AVL BOOST(r) to calculate the thermodynamic state of a cylinder:  
\[\frac{\mathrm{d}(\mathrm{m}_{c}\cdot u)}{d\alpha}=-P_{c}\cdot\frac{dV}{d\alpha }+\frac{dQ_{F}}{d\alpha}-\sum\frac{dQ_{W}}{d\alpha}-h_{BB}.\frac{dm_{BB}}{d\alpha} \tag{1}\]  
where \(\mathrm{m}_{c}\) is the mass in the cylinder; \(u\) is the specific internal energy; \(P_{c}\) is the cylinder pressure; \(V\) is the cylinder volume; \(Q_{F}\) is the fuel energy; \(Q_{W}\) is the wall heat loss; \(\alpha\) is the crank angle; \(h_{BB}\) is the enthalpy of blow-by; and, \(m_{BB}\) is the blow-by mass flow [22]. Figure 3: AVL BOOST® model for a four-stroke marine diesel engine. The heat transfer to the walls of the combustion chamber, including cylinder head, piston, and cylinder liner, can be calculated as follows [22]:  
\[\mathrm{Q_{wi}}=A_{i}\cdot a_{i}\cdot(T_{c}-T_{wi}) \tag{2}\]  
where \(\mathrm{Q_{wi}}\) is the wall heat flow; \(A_{i}\) is the surface area; and, \(T_{c}\) and \(T_{wi}\) are the gas temperatures in the cylinder and wall, respectively. The combustion in direct injection compression ignition engines can be considered by two processes--premixed combustion (PMC) and MCC. The combustion model in this study used MCC [22], where:  
\[\frac{\mathrm{d}\mathrm{Q_{total}}}{d\alpha}=\frac{dQ_{MCC}}{d\alpha}+\frac{dQ _{PMC}}{d\alpha} \tag{3}\]  
where \(\mathrm{Q_{total}}\) is the total heat release over the combustion process [K]; and, \(Q_{PMC}\) and \(Q_{MCC}\) are heat inputs for the premixed combustion and cumulative heat release by the MCC [K], respectively. The NOx formation model is based on the well-known Zeldovich mechanism, and black carbon formation is described by two steps--formation and oxidation. The net of rate of changes in soot mass \(\mathrm{m_{soot}}\) is the difference between the rates of soot formed \(\mathrm{m_{soot}}\) and oxidized \(m_{soot.ox}\)[22]. \[\frac{\mathrm{d}\mathrm{m_{soot}}}{d\alpha}=\frac{dm_{soot~{}form}}{d\alpha}- \frac{dm_{soot~{}ox}}{d\alpha} \tag{4}\]
## 3 Results and Preliminary Analysis
### Preliminary Analysis
#### 3.2.2 Relationship between Axial Stress and Strain  
The axial strain and stress can be calculated respectively by Equations (1) and (6) at the testing temperature; the transverse and longitudinal Poisson's ratio can be obtained by three-dimensional digital image processing technology. Based on the test data, Figures 5 and 6 present the stress-strain curves of the longitudinal and transverse specimen, respectively. It can be observed that the axial strain range of the straight section of the stress-strain curve is small at low temperature; for example, the linear range of the longitudinal specimen is [0, 2%] at \(-40\)\({}^{\circ}\)C, and that of the transverse specimen is [0, 1%]. At 20 \({}^{\circ}\)C, the axial strain range of the straight section of the relationship curve is enlarged, as the linear range of the longitudinal specimen reaches [0, 30%] and that of the transverse specimen is [0, 20%]. With the same axial strain, the lower the temperature, the greater the axial stress.  
Figure 5: Relationship between axial stress and strain of longitudinal specimen at (**a**) 4–60 \({}^{\circ}\)C; (**b**) \(-40\)–0 \({}^{\circ}\)C.  
Figure 6: Relationship between axial stress and strain of transverse specimen at (**a**) 0–60 \({}^{\circ}\)C; (**b**) \(-40\)–0 \({}^{\circ}\)C.  
The stress-strain relationship of PVC GEM varies greatly with the increasing testing temperature, exhibiting a hard and strong form at low temperature and a tough and strong form above room temperature. Therefore, the stress-strain curve of PVC GEM is influenced by the temperature, and the mechanical properties obtained at the temperature specified in the specification do not truly reflect the merits of the material. Combined with the actual temperature in construction and operation, several suitable representative temperatures should be selected for the mechanical properties of the GEM and evaluate the merits of the material to ensure the safe operation of the project.
## 2 Design of the Plasmonic Sensor
The reflected light in the interface prism-LiIO\({}_{3}\) reaches the other optical fiber and it transports the light to the photodetector. When the laser strikes the interface prism-Au with an angle greater than the critical angle, an evanescent wave is produced on the surface. If both the thickness of the layers and the geometry of the sensor are adequate, it is possible to excite two superficial plasmons propagating through the surfaces. The sensor we have designed is based on intensity interrogation. This allows us to differentiate the refractive index of the medium with which our sensor is in contact. The principal advantage this sensor offers, compared to the others which work with angle interrogation, is that ours has no mobile parts, no friction hysteresis and backlash are eliminated, the response time is minimized and we save energy. For the theoretical model of surface plasmon resonance reflectance, the transfer matrix method was used to solve the Fresnel equations for the multilayer [29; 30] with the WinSpall software package. This sensor has been designed to differentiate the quantity of CO\({}_{2}\) in ppm in air. For a given angle of incidence \(\theta\), we obtain different reflectances for different concentrations of carbon dioxide. These values are sufficiently different from one another to effectively differentiate the reflectance with the photodetector in the endface of the optical fiber. We know that the concentration of carbon dioxide is not distributed homogeneously all over the planet. Our sensor is able to measure the concentration of CO\({}_{2}\) each instant, it can measure the concentration at the moment of the installation to know the level of CO\({}_{2}\) and decide from what amount of carbon dioxide the alarm signal will be given, either indoors or outdoors. We do not need a sensor of reference.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 3 Analysis of Results
### Tensile Test
#### 3.1.4 Strength Analysis  
The tensile test results are presented in Table 4. The yield stress and ultimate strength of the tensile steel samples were calculated using Equations (2) and (3). There is another common method for calculating the yield stress or ultimate strength of corroded steel. This method is that divide the yield load or ultimate load of corroded steel by the cross-sectional area of the uncorroded tensile steel sample. However, this method underestimates the above-mentioned values. Therefore, this study did not use this method to calculate the yield stress or ultimate stress of the corroded steel. \[f_{\text{yc}}=\frac{F_{\text{yc}}}{(1-\eta)A_{0}} \tag{2}\]  
\[f_{\text{uc}}=\frac{F_{\text{uc}}}{(1-\eta)A_{0}} \tag{3}\]  
where \(F_{\text{yc}}\) is the yield load of the corroded tensile steel sample; \(F_{\text{uc}}\) is the ultimate load of the corroded tensile steel sample; \(f_{\text{yc}}\) is the yield strength of the corroded tensile steel sample; \(f_{\text{uc}}\) is the ultimate strength of the corroded tensile steel sample; \(A_{0}\) is the cross-sectional area of the non-corroded tensile steel sample. Figure 8: Fracture mode: (**a**) flat fracture; (**b**) oblique fracture; (**c**) step fracture; (**d**) multiple fracture. As presented in Table 4, the yield strength and ultimate strength of the corroed steel tended to decrease as the corrosion time increased. Therefore, the relationship between \(f_{\rm{yc}}\) (\(f_{\rm{uc}}\)) and \(f_{\rm{y}}\) (\(f_{\rm{u}}\))is affected by corrosion rate (\(f_{\rm{y}}\) and \(f_{\rm{u}}\) are the yield strength and ultimate strength of the non-corroded steel). Thus, the yield strength and ultimate strength of the corroded steel can be expressed by Equations (4) and (5), where \(\alpha\) and \(\beta\) are the corrosion influence coefficients, which was considered to be related to \(\eta\) in this study. \[f_{\rm{yc}}=\alpha f_{\rm{y}} \tag{4}\]  
\[f_{\rm{uc}}=\beta f_{\rm{u}} \tag{5}\]  
Figure 9a shows the relationship between \(f_{\rm{yc}}\)/\(f_{\rm{y}}\) and the corrosion rate, and Figure 9b shows the relationship between \(f_{\rm{uc}}\)/\(f_{\rm{u}}\) and the corrosion rate. As can be seen, the relationship between \(f_{\rm{yc}}\)/\(f_{\rm{y}}\) and the corrosion rate and the relationship between \(f_{\rm{uc}}\)/\(f_{\rm{u}}\) and the corrosion rate is approximately nonlinear. Through polynomial regression analysis, Equation (6) can be obtained to describe the relationship between \(f_{\rm{yc}}\)/\(f_{\rm{y}}\) and the corrosion rate and Equation (7) can be obtained to describe the relationship between \(f_{\rm{uc}}\)/\(f_{\rm{u}}\) and the corrosion rate. From the viewpoint of R\({}^{2}\), the regression accuracy is acceptable. These two formulas can be used as empirical formulas to evaluate the yield strength and ultimate strength of corroded steel in an underground environment. \[f_{\rm{yc}}=(10.45\eta^{2}-2.84\eta+0.99)f_{\rm{y}}\quad{\rm{R}^{2}}=0.91 \tag{6}\]  
\[f_{\rm{yu}}=(15.51\eta^{2}-3.22\eta+0.99)f_{\rm{u}}\quad{\rm{R}^{2}}=0.97 \tag{7}\]
# Human Activity Recognition (HAR) in Healthcare, 2 nd Edition
## 3 Gaps in HAR Adoption  
Despite significant advancements, Human Activity Recognition (HAR) continues to face several critical challenges that hinder its large-scale adoption:  
* Model Generalizability: There is a lack of standardized benchmarks and datasets that reflect the diverse conditions found in real-world healthcare environments. Most existing datasets are collected under controlled circumstances, which fail to capture variations in sensor placement, user behavior, and ambient noise.
* Power and Computational Efficiency: Deploying HAR on wearable devices necessitates lightweight models that are optimized for low power consumption and minimal latency.
* Limited Interoperability and Clinical Integration**: Many devices and systems are not fully compatible, complicating the integration of data from various sources. Promoting common standards and open platforms is essential to enhance communication between different technologies. Additionally, the integration into healthcare workflows remains limited, partly due to inadequate compatibility with electronic health records and other information systems.
* Data Privacy and Security: The ongoing collection of sensitive data raises significant ethical and legal concerns. It is crucial to ensure compliance with regulations, such as the General Data Protection Regulation (GDPR), and to strengthen user trust.
* Accessibility and Inclusivity: Solutions should be designed to be accessible for individuals with low digital literacy or disabilities.
* Training of Healthcare Professionals**: The effective adoption of HAR technologies requires ongoing training and skill updates for healthcare professionals.  
To address these challenges, the following steps are recommended:  
* Encourage multidisciplinary research involving engineers, doctors, psychologists, and designers.
* Promote public policies supporting innovative health technologies through funding and incentives.
* Develop ethical governance models for using AI in healthcare, balancing innovation with protecting individual rights.
* Foster active participation from patients in developing and evaluating technologies to ensure that solutions are beneficial and centered around their needs.