esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The use of smart technologies, including sensors, actuators, and artificial intelligence (AI), in a home, building, or environment may affect the quality of human life, well-being, productivity, energy-saving, and safety [1; 2; 3; 4; 5]. The word'smart' is becoming a trend for enhancing the quality of the built environment, including home, building, transportation, construction, and city [5]. [PERSON] et al. [6] showed how smart home offers a mechanism for achieving energy efficiency goals and how it offers users a lower-cost approach for saving energy. The building automation mechanisms and smart home assistive systems can help users to have better quality of life, and, at the same time, their behavior can be analyzed by AI. The behavioral analysis can help the system to predict user needs and optimize the use of devices and resources, including energy. At a basic level, the smart technology helps the user to turn devices on or off, and scholars in various disciplines, such as healthcare or architects, use a variety of sensors and actuators for many purposes. However, there is a need to learn from different practices and merge them to use the same system, as well as analyze the data collected from their devices for energy management at the same time. [PERSON], [PERSON] and [PERSON] [3] discussed that the management of user side or demand estimation is critical and challenging in terms of renewable energy generation, hence, required to be addressed by smart homes and smart grids solutions. The possible solution will be an integration of algorithms and optimization of sources, which can assist in developing an automated home energy management system (EMS). Recently, smart technology has been rapidly evolving, and more applications are being examined and offered by scholars and practitioners. The use of Internet of things (IoT) is increasing, which creates a network of objects from sensors and actuators to smartphones or tablets, and reports show that there will be a rapid growth of 25 to 50 billion connected devices by 2020 in terms of wireless telecommunication [7]. Thus, there is an urgent need to revisit previous works and review the state-of-the-art and technological capabilities and evaluate their implementations. The objective of this review article was to better understand what type of sensors and technologies are being used in smart homes and identify future directions. This article aimed to identify the emerging themes in the literature and synthesize different practices under different contexts and map them schematically. Table 1 presents the key terms and concepts used in smart home literature using sensors and AI. It lists all keywords and their definitions to achieve a common understanding of relevant concepts to the subject and increase the clarity of communications without needless repetitions.
## 3 Results and Discussions
### Performance Characterization of Microcapsules  
#### 3.2.1 Particle Size and Surface Morphology  
The reaction conditions in the emulsion at different process parameters and preparation stages, as well as the prepared microcapsules, were observed and analyzed using optical microscopy and scanning electron microscopy. The optimal process parameters for microcapsule preparation were determined as follows: oil-to-water ratio of 7.5:200, emulsifier concentration of 1 wt%, emulsification speed of 2500 r/min, initiator amount of 2 g, and core-to-wall ratio of 4:1.  
As shown in Figure 6, the microcapsules under this parameter present a uniform particle size and have good sphericity, indicating that the reaction process of the microcapsules is relatively stable and the reaction effect is good. The particle size distribution is between 20 \(\upmu\)m and 70 \(\upmu\)m, with an average diameter of 35.17 \(\upmu\)m, showing a good normal distribution. The maximum frequency is around the ideal particle size of 35 \(\upmu\)m, accounting for about 30%, while the proportion of microcapsules in the particle size range of 25 \(\upmu\)m to 45 \(\upmu\)m reaches 80%, indicating that the particle size distribution of the microcapsules is relatively concentrated and well-controlled.  
Figure 5: Effect of core-to-shell ratio on the shell structure and morphology of microcapsules, **(a)** 8:1, **(b)** 8:2, **(c)** 8:3.
## 3 Experimental Results and Analysis
### The Change Law of Dynamic Compressive Strength  
Figure 7 shows the variation in dynamic compressive strength with axial pressure for sandstone specimens with temperature-water coupling.  
As shown in Figure 7, the dynamic compressive strength of sandstone samples increases as the axial pressure increases. The dynamic compressive strength of the temperature-water-coupled sandstone specimen is 96.28 MPa when there is no axial pressure, 91.89 MPa when the axial pressure is 0.5 MPa, and 61.15 MPa when the axial pressure is 4 MPa, which represents a decrease of 30.74 MPa. This phenomenon occurs because the internal structure of the sandstone sample is degraded by the action of the axial pressure, which weakens the load-bearing capacity of the specimen and thus reduces the dynamic compressive strength of the specimen. The dynamic compressive strength of the sample is thereby reduced. As the axial pressure increases, the internal structure of the sandstone sample is further degraded, internal defects are aggravated, and the bearing capacity of the sample is weakened, resulting in a decrease in the dynamic compressive strength of the sample. The dynamic compressive strength of the sandstone specimen decreases linearly with an increase in axial pressure. The fitted functional relationship is shown in Equation (1), with a correlation coefficient of 0.9709 and an obvious negative correlation:  
\[\sigma(P)=96.524-8.869P\ \ (R^{2}=0.9709) \tag{1}\]  
Figure 6: Dynamic stress–strain curve of sandstone specimens.  
Figure 7: Dynamic compressive strength changes with axial pressure.  
where \(\sigma(P)\) represents the dynamic compressive strength of the sandstone sample, and \(P\) represents the axial pressure.
## 3 Guadalajara Metropolitan Area and SITEUR
Within the public transport system, SITEUR serves nearly one tenth of the journeys, i.e., nearly four hundred thousand trips on work-days [45; 46; 47]. Due to the large investments that a mass public transport system (MPTS) implies, SITEUR should increase its contribution to the modal split. In addition to SITEUR, private enterprises operate public transport services in the GMA. Although the fare is the same for SITEUR as it is for buses, the privately licensed operators provide poor transport services by bus and microbus and are not fare-integrated to the SITEUR. As illustrated in Figure 3, SITEUR mainly serves the centre of the GMA. It represents an improvement in the intermodal transport in the GMA, through LRT, BRT, and high-quality bus services. Two of the higher capacity lines are north-south oriented through the centre of the city. The second LRT line and high-quality bus routes are east-west oriented. These buses serve some areas in the periphery of the city, where the lower socioeconomic dwellings are concentrated [41; 48; 49; 50]. As shown in Table 1, there is a large difference between mode capacities of SITEUR vehicles. Nonetheless, the peak-time frequency (a.m.) does not differ greatly among the different modes, except for one bus line [48]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Length [Km]**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{1}{c}{**Peak-Time**} & \multicolumn{1}{c}{**Capacity**} \\  & & & **Stops/Stations** & **Frequency** & **[Passenger/** \\  & & & **[Minutes]** & **Vehicle]** \\ \hline \multirow{3}{*}{LRT} & 1 & 16 & 20 & 5.33 & 684 \\  & 2 & 9 & 10 & 4 & 600 \\  & 3 & 21 & 18 & 7 & 500 \\ \hline \multirow{3}{*}{BRT} & Macrobus & 16 & 27 & 6 & 164 \\  & MiMacro Periferico * & No data & No data & No data & No data \\ \hline \multirow{3}{*}{Bus} & SITEUR 1 & 12 & 29 & 6 & 47 \\  & SITEUR 1 B & 12 & 30 & 8 & 80 \\ \cline{1-1}  & SITEUR 2 & 13 & 26 & 7 & 47 \\ \cline{1-1}  & SITEUR 3 & 18 & 54 & 7 & 80 \\ \cline{1-1}  & SITEUR 4 & 22 & 88 & 15 & 80 \\ \hline \hline \end{tabular}  
* This BRT line was not included. It was under construction when the analysis was performed. Source: Authors based on [45; 46; 47; 48].  
\end{table} 
Table 1: Characteristics of Modes in the SITEUR. Figure 2: Modal Split in the Guadalajara Metropolitan Area (Percentage of Work-Day Journeys). Source: Authors based on [45; 46; 47; 48; 3].
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 4 Discussion
### Viscosity Effect on Cavitation Inception  
The difference in flow dynamics also leads to variation in the inceptive cavitation morphology. Figure 10 illustrates the cavitation inception for the four cases. For the poppet valve with a sharp valve seat, cavitation inception occurred approximately at pressure drop of 30 bar and 32 bar for the water- and oil-flow, respectively. The first appearance of cavitation in the oil-flow is located within the free shearing layer as transitory vortex cavitation, while the inceptive cavitation in the water-flow case involves additionally the attached cavitation at the poppet trailing edge with a comparable size. It suggests that under the operating condition of cavitation inception, the attached cavitation at the poppet trailing edge, which in the water-flow case has a size comparable to the vortex cavitation within the free shearing layer, is absent in the oil-flow case. For the poppet valve with a chamfered valve seat, the earliest traces of cavitation are mainly initiated as attached cavitation within the chamfered groove at pressure drop of 25 and 30 bar for water- and oil-flow case, respectively. However, the inceptive cavitation morphology is stably organized as an attached cavity ring, while that in the water-flow case displays the feature as detached vortex cavitation, as shown in Figure 10. And the attached cavitation at the poppet trailing edge is evidently present neither in the water- nor the oil-flow case. The difference in either susceptibility to cavitation or inceptive cavitation morphology is undeniably correlated with flow dynamics. Due to higher viscosity, the oil-flow case tends to facilitate shearing strength at the free shearing layer, and suppress flow separation and the resulted detached vortex, interpreting the deviation in inceptive cavitation morphology.
## 3 Numerical Results
### Velocity of Airflow Throughout the Building Interior
This distribution indicates weak airflow, with zones of stagnation and turbulence--particularly near the fire source, where the lower fan interferes with the combustion gases. None of the measured points reach the 2 m/s threshold, meaning that scenario S2 does not provide effective protection against smoke infiltration into the stairwell. The placement of the fan at ground level contributes to the mixing with hot smoke, further reducing the system's overall efficiency. In scenario S1, the airflow is well directed and downward, and constant infiltration from the stairwell into adjacent rooms is observed near the doors. The velocity values in these areas are close to the functional threshold of 2 m/s, indicating the presence of an effective aerodynamic barrier capable of limiting smoke ingress into the protected zone. The vertical airflow regime remains stable throughout. In scenario S2, the infiltration velocity is significantly lower, and the airflow near the doors is fragmented and unstable. The uneven pressure distribution--caused by air being introduced at both the top and bottom of the stairwell--prevents the achievement of the critical sealing velocity, especially in the lower zone, where the proximity to the fire source increases the risk of smoke contamination. In conclusion, only scenario S1 ensures coherent and directed infiltration flows capable of acting as an active barrier against smoke penetration. In scenario S2, performance is hindered by the lack of sufficient differential pressure, while in scenario S0, the effect is completely reversed, with uncontrolled smoke ingress into adjacent rooms, significantly amplifying risks for occupants. Figure 12 illustrates the velocity distribution through the slice at X = 7.50 m, highlighting that scenario S1, with superior pressurization and a unidirectional downward jet, provides the most effective smoke control in the stairwell by maintaining a stable flow and an efficient aerodynamic barrier. In scenario S2, the interaction between opposing jets and the fire-induced flow generates instabilities in the lower region, reducing the overall protection efficiency. Scenario S0 allows for the formation of an uncontrolled stack effect with high velocities, promoting the rapid contamination of the evacuation space. Figure 12: Velocity distribution in the slice at X = 7.50 m at time T = 360 s.
## 5 Results
### Computing Speed  
The computing times required to design the band filters were analyzed using MATLAB. The results were an average of computing the filter gains and coefficients for \(100,\!000\) random command gain settings. Table 7 shows the results obtained by using a \(15\) inch MacBook Pro (2017) computer with a \(2.8\,\mathrm{GHz}\) Intel Core i7. The computing times are presented so that the gain optimization and coefficient update are separated, because the neural networks replaced the gain optimization, whereas the filter coefficients were calculated and updated independently of the gain optimization algorithm. Furthermore, the computing times of the third-octave and Bark GEQs should not be directly compared with each other, because they had a different number of band filters, and the latter design needed more iterations.  
Figure 14: Example design using the proposed SGEB and NSGEB methods showing the largest errors for the entire validation dataset for both Bark band designs. The largest errors occurred at \(157\,\mathrm{Hz}\), between the second and third command points.  
Figure 16: Example design using the SGEB and NSGEB methods showing their typical performance for a randomized command gain setting.  
Figure 15: Zigzag test, similar to Figure 12, using the proposed SGEB and NSGEB methods. The largest errors occur at the center frequency of the first Bark band at 50 Hz.  
Table 7 shows that the least-squares methods, SGE3 and SGEB, required 408 us and 526 us, respectively, to calculate the optimized gains, whereas the calculation of the filter coefficients took 126 us and 101 us, respectively. Although SGEB had only 24 EQ filters compared to the 31 EQ filters used in SGE3, the gain optimization was slightly slower, since it used two rounds of iterations in the design, compared to only one iteration in SGE3. However, the filter coefficient update was faster in SGEB than in SGE3, since it was done in the same way in both designs and the Bark band GEQ had fewer filters.  
As can be seen from Table 7, the gain calculation time with the neural networks was superior to that of the WLS method. The average gain optimization time was 5.1 us for NSGE3 and 4.4 us for NSGEB. This meant that the neural-network based third-octave design NSGE3 was approximately 80 times faster than the original SGE3 method, whereas the neural network based Bark design NSGEB was up to 120 times faster than SGEB.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 3 Results
### Analysis of Beam Ray Incident Angles on a Cross-Section of a Breast in Water
and when using 513 receiving transducers in 1 measurement projection, the maximum diameter of the measured breast cross-section \(D_{b}\) cannot exceed \(\sim\)180 mm. For such a diameter of the breast cross-section in the horizontal plane, the area of the ring in the breast tissue with a width of about 21 mm from its border constitutes the area of the largest refractions of ultrasound wave beam rays, with the refractions decreasing for beam rays received by the receiving transducers closer to the transducer No. 1 (Figure 14), up to the transducer No. 183 when the angle of incidence of the ray is about 50\({}^{\circ}\) (then the refractions are small--see Figures 9-12). This means that for such a large breast section, distortions of the reconstructed image due to refraction of wave beam rays will appear in the area of this ring and will be greater the closer to the breast/water border they are. For smaller breast section diameters, the width of the \"distortion ring\" decreases accordingly (Figure 14). The existence of the area of the largest refraction due to the increase of the angle of incidence at the water/breast interface also causes difficulties in correct measurement of breast tip cross-sections (near the nipple), as shown by results of model calculations presented in Figure 15 (in the vertical plane). Figure 14: Visualization of results of calculations of the area of the largest ultrasound wave refractions in the breast section for 3 different diameters of these sections (\(D_{b}=180\), 120, 40 mm) in the horizontal plane. Figure 15: Visualization of results of calculations of the area of the largest refractions of the ultrasound wave in the breast section for 3 different diameters of these sections (\(D_{b}=180\), 120, 40 mm) in the vertical plane.
## 4 Experiments and Analysis
### Experimental Parameter Settings  
In addition to the parameter settings of the four sub-models and E3W, the relevant parameters for the other models used in the experiments were set as follows: The dimension of the words was 100, the number of hidden layers was 769, the learning rate was set to 1 \(\times\) 10\({}^{-5}\), the maximum length of the sentences was 19, the dropout was set as 0.5, and the learning decay rate was 0.9. When CNN convolutional kernels were involved, the convolutional kernels were set to (2,3,4), and the number was 128.
## 3 Real-Time Hybrid Simulation (RTHS)
### Transfer System: Human-Structure Interaction Multi-Axial Test Framework (HSI-MTF)  
Transfer systems (TrSs), as components in real-time hybrid simulations, are intended to accurately and consistently exchange information between the experimental and numerical substructures within the simulation loop. This system ensures that the dynamic interaction between the physical specimen (anthropic load induced by crowd gait, \(F_{k,j}\)) and the computational model (dynamic response of the reference footbridge, \(\bar{a}_{s,total}\)) is synchronized in real-time. In order to ensure the fidelity of the real-time synchronization requirements in the simulation, this study adopted the human-structure interaction multi-axial test framework (HSI-MTF) as the TrS prototype, which was used in previous studies of human gait on surfaces with lateral movements [17] and completely described its development by [PERSON] et al. [28].  
The HSI-MTF is located in the Laboratory of Seismic Engineering and Structural Dynamics at the Universidad del Valle, Cali, Colombia, and was developed to assess the multi-axial dynamic loads induced by pedestrians walking on surfaces with lateral and vertical movements. [PERSON] et al. [28] discovered the HIS-MTF, shown and detailed in Figure 3, into five principal components: the unidirectional shake table to induce lateral movements with displacement of \(\pm\)80.0 mm, the lateral support system to support the dynamic loads generated, a sole brand treadmill model F65 with an effective action area of 72.0 cm \(\times\) 150.0 cm and operating speeds ranging from 0.50 to 10.0 m\(\cdot\)s\({}^{-1}\), a load acquisition system that was developed to measure the tridimensional anthropic loads generated by the human gait, and the vertical hydraulic system to induce vertical movements with displacement of \(\pm\)75.0 mm. In addition, the upper section of Figure 3 shows how the human gait is developed on HSI-MTF, and the TSs were instrumentalized to measure the vertical load wearing pressure insole and joint displacement using active motion markers of a low-cost motion capture system.  
The lateral movement system implements a robust model control type \(H_{\infty}\) coupled with a Kalman filter, and the vertical movement system incorporates a proportional-integral-derivative (PID) model and an anticipatory function control. Both were integrated to ensure the fidelity, stability, and accuracy of multi-axial displacement tracking. These requirements are necessary for the proper development and stabilization of the simulation in real-time.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 4 MADM Handover Algorithm
### Handover Decision-Making  
The main objective of our MADM handover algorithm is to choose the best network in \(n\) available networks (integrated with the CCN processing module) for each application, with the QoS requirements based on the \(m\) attributes (including QoS attributes and user preferences) and two WBAN traffic categories.  
The following are the steps that are followed in our algorithm (detailed in Sections 4.2.1-4.2.3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definition** & **Unit** \\ \hline \(L_{phy}\) & Length of Physical header & kbit \\ \(L_{machtr}\) & Number of MAC headers & kbit \\ \(L_{maxft}\) & Size of MAC footer & kbit \\ \(R_{data}\) & Data rate of the network & kbit \\ \(d\) & Distance from the sender to the receiver & meter \\ \(S\) & Speed of light & m/s \\ \(l\) & Payload length of the data packet & kbit \\ \(a\) & Length Factor & - \\ \(bo_{slots}\) & Number of back-off slots & - \\ \(T_{dfs}\) & Distributed inter-frame space time & ms \\ \(T_{sifs}\) & Short inter-frame space time & ms \\ \(T_{boslots}\) & Time for a back-off slot & ms \\ \(T_{up}\) & LTE uplinks transmission time & ms \\ \(T_{buff}\) & LTE buffering time & ms \\ \(T_{re}\) & LTE retransmission delay & ms \\ \(T_{UL\_sch\_r}\) & LTE uplink scheduling request & ms \\ \(T_{UL\_sch\_g}\) & LTE uplink scheduling grant & ms \\ \(T_{UE}\) & UE delay estimated time & ms \\ \(T_{e\_NodeB}\) & e\_NodeB delay estimated time & ms \\ \(T_{core}\) & Core network delay time & ms \\ \hline \hline \end{tabular} MAC: Media Access Control; LTE: Long Term Evolution; UE: User Equipment.  
\end{table}
Table 1: Definition of parameters.