esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results Analysis and Discussion
### Transient Temperature Field  
Figures 11-14 show the instantaneous temperature distribution at different times in the central cross-section of circular, three-lobe (forward) shaped, three-lobe (backward) shaped, and four-lobe-shaped cooling holes (Cases 1-4).  
Figure 11: Instantaneous temperature distribution at different times on the central section of Case 1.  
Figure 10: Comparison of total pressure loss for various cooling holes.  
Figure 12: Instantaneous temperature distribution at different times on the central section of Case 2.  
Figure 13: Instantaneous temperature distribution at different times on the central section of Case 3.  
It can be seen that the large-eddy simulations (LES) effectively simulated the flow field at the outlet of the gas film. Based on the instantaneous temperature distribution at different times in the central cross-section, the folded structure created by the mixing of cooling air and main flow air can be clearly seen.  
Figure 9 shows that a significant amount of localized high-temperature gas occurs near the wall surface starting, at X/D = 0 at the orifice of the circular holes. In conjunction with Figure 6, this indicates that near the mouth of circular holes, high-temperature gas enters the cooling airflow laterally, resulting in lateral mixing not observed in other structures and reducing cooling efficiency. In other cooling hole structures, such high-temperature gasses either did not occur (Case 2) or occurred later (Cases 3 and 4) in this phase, due to certain blowdown effects at this blowing ratio. Shaped holes can bring the coolant closer to the wall surfaces while avoiding such lateral mixing at the hole outlets.  
It can be seen from the figures that shaped film cooling holes did not have lateral mixing as described above, but there were cases where the main flow air approached or entered their outlets, as at T+30t in Figure 13 and T+60t in Figure 14, respectively. In circular film cooling holes, on the other hand, the cooling air at the outlets penetrates deeper into the main airflow than in other types of holes.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 5 Results and Discussion
### Prediction Model for Maximum Vertical Displacement of Pavement Layer  
In the training set, a combination of the grid search method and the five-fold cross-validation method was used to optimize the parameters of the machine learning model. R\({}^{2}\) was chosen as the main evaluation index to select the optimal combination of parameters, and the final evaluation result was the mean value of the five-fold cross-validation results. The optimal combination of parameters is learning_rate = 0.05; max_depth = 8; min_child_weight = 2; and n_estimators = 230. The prediction performance of the optimized model was verified on the test set, and the MAE of the XGBoost model on the test set was 0.041 The relationship between the predicted and true values of the XGBoost model on the test set is shown in Figure 20, from which it can be seen that the predicted values of most samples in the model are very close to the true values.  
The XGBoost model was compared with other traditional machine learning models. The models were evaluated using three metrics, MAE, RMSE, and R\({}^{2}\), and a grid search combined with cross-validation was used for each model to optimize its parameters. Table 9 shows the prediction results of each traditional machine learning model on the test set, and it can be seen that the XGBoost model shows good performance. The XGBoost model was compared with the KNN model, and the RMSE was reduced by 55%, the MAE was reduced by 52%, and the R\({}^{2}\) was improved by 260%. The XGBoost model was compared with the SVM model, and the RMSE was reduced by 44%, the MAE was reduced by 39%, and the R\({}^{2}\) was improved by 62%.  
Figure 19: Importance of input feature variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline XGBoost & 0.011 & 0.013 & 0.865 \\ KNN & 0.019 & 0.025 & 0.631 \\ SVM & 0.020 & 0.026 & 0.590 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with other traditional machine learning models.  
The importance of the input feature variables for the interpretability analysis of the developed prediction model using the SHAP architecture is shown in Figure 21. It can be seen from the figure that the most important feature variable is the spacing \(D\) of the diaphragm, and the relatively more important feature variables are the elastic modulus \(EI\) of the upper layer of the pavement, the thickness \(T\) of the steel bridge panel, and the thickness \(H2\) of the lower layer of the pavement.  
Figure 21: Importance of input feature variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R2** \\ \hline XGBoost & 0.041 & 0.052 & 0.861 \\ KNN & 0.092 & 0.109 & 0.239 \\ SVM & 0.073 & 0.085 & 0.533 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with other traditional machine learning models.  
Figure 20: Relationship between predicted and true values of XGBoost model on the test set. Line: predicted values, points: true values.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 3 Digital Twinning throughout the Life Cycle of Physical Assets
### Uses and Life Cycle Phases of Physical Assets
#### 3.1.4 End of Life  
Finally, when the asset no longer meets the needs and expectations, it may undergo more profound interventions, such as renovation or replacement, or even be deactivated or dismantled, marking the end of its useful life. Although this phase is often under-represented in the literature on DTs [10], it is extremely relevant to the asset's life cycle. According to [PERSON] [60], the end of life is one of the phases with the most deficiencies in organisational policies and strategies. Determining the optimal time for replacement is a recurring challenge in physical asset management activities, as multiple variables and inputs from different organisational levels contribute to this decision. Integrating relevant information for such decisions (such as replacement costs, operation and maintenance costs, available budget, condition, risk, lifespan, etc.) from dispersed information sources is a significant interoperability challenge.  
Some assets can still hold value when they reach the end of their useful life, such as historical or material value (selling the asset raw materials). In the latter case, the asset itself can be reintegrated into the life cycle of other assets, as with the incorporation of recycled aggregates in road pavement construction. These opportunities can improve the economic performance of asset life cycles and contribute to reducing the environmental impact of the new asset through material circularity. However, to achieve this, it is essential to know the type and quantity of materials available for incorporation. This information spans the entire life cycle of the asset, from the moment it was designed to the renovations it underwent over time. This type of information is particularly relevant when environmental or safety risks are associated with handling or disposing of certain materials. Therefore, knowing the type of materials involved and their location is key to ensuring efficient and safe end-of-life activities. Although much of this information can be included in BIM models, it is often not. Even highly detailed BIM models are not updated throughout the life cycle of the infrastructure. It is therefore critical to develop suitable workflows and semantic enrichment processes that enable non-geometrical information to be updated regularly [61; 62].  
When dismantled, the asset ceases to have physical materiality once again. Only its records of existence (digital thread) remain in the digital space. These records can be used, from a continuous improvement perspective, to optimise the management of the life cycle of current or future assets.
## 4 Results and Analysis
### Compare of Heat Fluxes Calculated Using Different LST Datasets  
To determine which of the LST downscaling methods is more suitable for calculating the subpixel surface heat fluxes, the downscaled LSTs obtained using the DisTrad and GWR methods were used to calculate the surface heat fluxes (here, we call these heat fluxes the DisTrad-LST and GWR-LST heat fluxes, respectively). These three methods were evaluated using (1) validation of the heat fluxes calculated with different LST downscaling methods using ground-based observational data and (2) quantitative analysis based on the spatial distribution and scatterplots of the energy balance components (i.e., H and LE).
# Design and Control of a Pneumatic Muscle Servo Drive Applied to a 6-DoF Parallel Manipulator
## 3 Results
### Synthesis of Angular Position Control System  
The servo drive presented in this chapter is used to achieve the desired angular position for a single arm of a parallel manipulator. Its schematic, with the locations of all components marked, is shown in Figure 20.  
It consists of two pneumatic muscles (4). In each of them, one end is immobilized by connecting it to the stationary base (7) through a force-sensing load cell (6) with a range of \(\pm 2\) kN. The free ends of the muscles are connected together by a towhed belt (2) driving a gear wheel with a pitch diameter of \(d_{p}=63.66\) mm, thus forming a towhed belt transmission through which the antagonistic action of the muscles is converted into the rotational motion of the arm. The pitch diameter of the pulley and the length of the muscles allow the servo drive to move in the range of \(-\frac{pi}{3}\) rad to \(\frac{pi}{3}\) rad, and the maximum force generated by a single muscle allows for the generation of a torque equal to \(M=25.46\) Nm. The servo drive is also equipped with two pneumatic pressure-control servo valves (5) that control the operation of the muscles and an absolute angular position sensor (1) with a resolution of 16 bits per revolution, allowing the measurement of angular position with an accuracy of 0.000096 rad. The diagram also shows an electric servo drive (3), which was used in the research setup to load the muscle servo drive with a specified torque. Figure 20: Schematic diagram of a pneumatic muscle servo drive. Then, the synthesis of the control system for the angular position servo drive was carried out. Figure 21 shows the control system diagram for the entire servo drive. The servo drive control system consists of two independently operating control systems, one for each muscle, in which the controlled variables are the linear positions of the muscles. The desired angular position signal of the servo drive is converted into a desired linear position of the first muscle and input into the inverse model of the first muscle. Similarly, the current angular position signal of the servo drive is converted into the current linear position value of the first muscle and input into its inverse model. The current force signal generated by the muscle, which comes from the strain gauge force sensor CL21 msrs produced by ZEPWN (Marki, Poland), is also input into this model. Then, the control signal is determined based on the inverse model of the first muscle. Any discrepancies between the setpoint and the actual linear position value of the first muscle, resulting from inaccuracies in the model, are compensated for by a PID controller. The signal generated by the controller is added to the signal generated by the inverse model, and the sum of the signals is converted into a control voltage for the valve of the first muscle using a digital-to-analog converter. The PID controller parameters were adjusted during tests of the servo drive on the test bench. A similar operating principle is applied to the control system of the second muscle.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 3 Research Model and Hypotheses
### The Task-Driven Issue  
In the context of the IS, the TTF model is often used. Previous studies using TTF models such as testing the intention to use KMS proposed by [44] used the TTF model in which the tasks and characteristics of KMS were positively related to task-technology suitability.  
Task characteristics and technology characteristics in this study refer to [43; 46]. Other researchers propose a mixture of the TTF and TAM models for the use of cellular devices [75]. Other researchers find that task characteristics play an important role in determining task-technology suitability in statistical modeling and project management tools, the task modeling in the DMTs [52] integrates TTF with TAM to investigate the determinants of user acceptance of m-commerce [76].  
Thus, by involving the TTF model in this study it is expected that users will find e-health laboratories useful when the system helps users in their health screening activities [69]. In particular, if an adequate e-health laboratory can meet the information needs of users in an effective health screening activity, users will consider the e-health laboratory as a suitable system for their routine health checks. As discussed above, this research proposes the following hypotheses:  
**Hypothesis 1 (H1).**: _The task characteristics of e-health laboratory have positive impact on the task-technology fit._  
**Hypothesis 2 (H2).**: _The technology characteristics in e-health laboratory have a positive impact on the task-technology fit._  
Figure 3: Conceptual model.  
In information systems theory, the relationship between task-technology suitability and its use has been well established [49; 52; 77; 78]. Previous research [49] has expanded the post-acceptance model with the combination of TTF and ECM to inspect the intention to IS continuation. The results show that task-technology suitability is clearly related to perceived usefulness.  
Task-technology fit is listed in several articles [77; 79; 80; 81]. It is a proposed model to examine the connection between the perceived task-technology suitability and system factors in the goal of encouraging students to use e-learning systems [78]. In particular, the study [52] has found that the fit among task and technology had a positive influence on perceived usefulness in the use of DMTs. Overall, the results of the study indicate that the task-matching technology positively influences the perceived benefits. This study offers the next hypotheses:  
**Hypothesis 3 (H3).**_The task-technology fit has a positive impact on perceived ease of use._  
**Hypothesis 4 (H4).**_The task-technology fit has a positive impact on perceived usefulness._
## 4 Experimental Results
### Energy Study in a Conventional PV System with Solar Tracking  
In order to compare the energy generated by a fixed PV system versus one mounted on the proposed STS, two polycrystalline Perlight PLM-270-60 solar modules were considered; their electrical parameters at standard test conditions were as follows: module area of 1.627 m\({}^{2}\), nominal maximum power point of 270 Wp DC, open circuit voltage (Voc) of 38.23 V, short circuit current (Isc) of 9.13 A, maximum power voltage of 31.22 V and maximum power current of 8.65 A. The temperature coefficient of maximum power point is \(-\)0.41 %/\({}^{\circ}\)C, while the temperature coefficients of Voc and Isc are \(-\)0.11 V/\({}^{\circ}\)C and 0.004 A/\({}^{\circ}\)C, respectively.  
The detailed PV model of the System Advisor Model (SAM) was used to estimate the electrical output of a PV system. Module and inverter specifications were input into data along with the array information, such as the number of modules and inverters. SAM software performs power production estimations dynamically using typical meteorological year (TMY) information with a time step resolution of 1 h. In this way, it simulates the dynamic operating conditions of the PV system such as irradiance, ambient temperature, wind speed, etc., and determines their effect on energy production [40]. During the comparison, the fixed modules were oriented to the south with an inclination of 22\({}^{\circ}\), which is the optimal orientation for PV systems in Aguascalientes, Mexico (latitude N 21\({}^{\circ}\)50'39\({}^{\prime\prime}\) and longitude W 102\({}^{\circ}\)20'38\({}^{\prime\prime}\)). For the PV system with tracking, the losses caused by tracking error were set at 2\({}^{\circ}\). Figure 13 shows four representative scenarios for different months where the electric power generation is compared between a fixed PV system and a PV system with the developed dual-axis STS. Sunny days were selected for better comparison. As expected, on all days, the energy production with the tracking system was greater because the PV system receives radiation directly throughout the day. In the same way, Figure 14 summarizes the net DC electrical energy generated before the inverter throughout a year in Aguascalientes, Mexico. The developed STS allowed enhancing efficiency up to 37.5% compared to a fixed PV system.
## Materials and Methods
### Finite Element Simulation  
According to these optimal structural parameters of the composite sound-absorbing structures obtained in Section 2.2.2, finite element simulation of the sound absorption property of the composite structure was conducted in the virtual acoustic laboratory [28; 29; 30; 31], and the constructed simulation model is shown in Figure 3. The size of the standing wave tube was 60 mm \(\times\) 60 mm \(\times\) 300 mm, and its front surface was treated as the acoustic source inlet for transmission of the incident sound wave. The composite structure was installed at the back surface of the standing wave tube. Meanwhile, there were two microphones fixed on the standing wave tube. Through giving the investigated frequency range and detecting the sound pressures at the two microphones, the sound absorption coefficient of the composite sound-absorbing structure for a certain frequency range was obtained.  
Finite element simulation of the microperforated polymethyl methacrylate panel was realized through confirming the acoustic transfer relation admittance from its optimal structural parameters and selecting the appropriate tolerance data in the built finite element simulation model, which was loaded between the back surface of the standing wave tube and the front surface of the polyurethane foam, as shown in Figure 3. Moreover, Finite element simulation of the investigated polyurethane foam was conducted through determining its acoustic characteristic parameters in the finite element model, which consisted of the fluid parameters (such as the sound velocity, mass density, specific heat ratio, Prandtl number, dynamic viscosity, and so on), the geometric parameters (such as the static flow resistivity, porosity, tortuosity, characteristic viscous length and thermal length, and so on), and the elastic parameters (such as the Young's modulus, mass density, and so on). Furthermore, the plane wave with a certain frequency was loaded at the acoustic source inlet and treated as the incident sound wave, and the sound-transmitting medium in the standing wave tube in Figure 3 was air. When the incident sounds wave reached the composite sound-absorbing structure, some of them were absorbed and the others were reflected. Through detecting the sound pressures of the incident sound wave and those of the reflected sound wave at the two microphones in Figure 3, the sound absorption coefficient could be calculated based on the standing wave tube method [22]. By this method, sound absorption characteristics of the composite sound absorbing structures were simulated in this study.  
Figure 3: Finite element simulation model for sound absorption property of the composite structure.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 5 Results and Discussion
### Network Lifetime and Stable Period  
Figure 4 displays the relationship between the number of dead nodes and rounds and reflects the lifetime and stable period of nodes in the WBAN. It can be seen from the figure that in TSAER, the first dead node appeared in round 4100, and the last node died in round 7050. In IM-SIMPLE, the first dead node appeared in round 3412, and the last dead node appeared in round 6160. In M-ATTEMPT, the first dead node appeared in round 3070, and the last dead node appeared in round 5740.  
Compared with iM-SIMPLE and M-ATTEMPT, TSAER obviously has a longer network lifetime and stable period. This paper adopts the method of calculating the node receiving probability and forcing the hot node to sleep, which effectively saves energy consumption. Calculate the receiving node probability according to the temperature condition of each node, obtain the data receiving condition of the available nodes in the link, avoid the rapid generation of hot nodes, and balance the network energy. The transmission mode of M-ATTEMPT is single-hop and multi-hop hybrid transmission. In order to ensure the timeliness of data transmission, emergency data are transmitted in a single-hop mode, but for nodes far away from the sink, it will cause a large amount of energy consumption. Furthermore, M-ATTEMPT uses a backoff method to solve the generation of hot nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Initial energy & 0.5 J \\ Transmission energy & 16.7 nJ/bit \\ Receive energy & 36.1 nJ/bit \\ Data aggregation energy & 5 nJ/bit \\ Amplifier energy & 1.97 \(\times\) 10\({}^{-9}\) \\ Initial node temperature & 37 \({}^{\circ}\)C \\ Node temperature threshold & 43 \({}^{\circ}\)C \\ Node temperature low & 39.5 \({}^{\circ}\)C \\ threshold & 0.02 \({}^{\circ}\)C \\ Temperature drop at nodes after each round of sleep & 0.02 \({}^{\circ}\)C \\ Specific heat T\({}_{\text{s}}\) & 3600 \(|\)J/kg\({}^{\circ}\)C\(|\) \\ Thermal conductivity K & 0.498 \(|\)J/ms\({}^{\circ}\)C\(|\) \\ Blood perfusion constant b & 2700 \(|\)J/m\({}^{3}\)s\({}^{\circ}\)C\(|\) \\ Fixed blood temperature T\({}_{\text{b}}\) & 37 \({}^{\circ}\)C \\ Time step \(\delta_{\text{t}}\) & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
When the node receives a data packet and the temperature reaches the threshold, it will return the received data to the previous node, which alleviates the node temperature rise but increases the probability of data retransmission and consumes additional energy. In iM-SIMPLE, the temperature state of the node is not considered. When the temperature of the intermediate node increases due to repeated data transmission, there is no mechanism to reduce the high temperature of the node. At this time, continuous work will consume additional energy.
## 3 Hydrogen Production Technologies
### Background Concepts
#### 3.1.1 Steam Methane Reforming  
This is a process in which methane is heated, with steam (usually also with a catalyst), to produce a mixture of carbon monoxide and hydrogen [44]. Methane, coming from natural gas, reacts with steam under a pressure up to 25 bar, splitting into carbon monoxide (later removed) and hydrogen molecules--as shown in Equation (1). Because this is an endothermic reaction, heat must be supplied to the process for it to occur:  
\[\mathrm{CH_{4}+H_{2}O\ \xrightarrow{Heat}\ \ }\ \mathrm{CO+3\,H_{2}} \tag{1}\]