esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Mathematical Formulation  
Basic mathematical equations concerning the present simulation are given in this subsection in order to properly explain the methodology followed. These equations are used in order to simulate properly the examined biomass-based heating/cooling system and the scope of their presentation is a clear description of the followed methodology.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structural** & **Materials** & **Thickness** & **k** & **\(\rho\)** & **c\({}_{\text{p}}\)** \\
**Component** & **Materials** & **(cm)** & **(W/mK)** & **(kg/m\({}^{3}\))** & **(J/kgK)** \\ \hline \multirow{2}{*}{Roof} & Concrete & 25 & 2.1 & 2400 & 800 \\  & Insulation & 10 & 0.035 & 40 & 800 \\ \hline \multirow{3}{*}{Ground} & Floor & 1 & 0.07 & 800 & 1000 \\  & Concrete & 25 & 2.1 & 2400 & 800 \\  & Insulation & 10 & 0.035 & 40 & 800 \\ \hline \multirow{3}{*}{Wall} & Plaster & 1 & 1.389 & 2000 & 1000 \\  & Brick & 12 & 0.889 & 1800 & 1000 \\ \cline{1-1}  & Insulation & 8 & 0.035 & 40 & 800 \\ \cline{1-1}  & Brick & 12 & 0.889 & 1800 & 1000 \\ \cline{1-1}  & Plaster & 1 & 1.389 & 2000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of the structural elements and material thermal properties.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Heating set point & 20 \({}^{\circ}\)C \\ Cooling set point & 26 \({}^{\circ}\)C \\ Floor area & 400 m\({}^{2}\) \\ Length & 20 m \\ Width & 20 m \\ Height & 3 m \\ South window total area & 10 m\({}^{2}\) \\ Infiltration and natural ventilation & 1 air change per hour \\ Appliances and lighting specific gain & 7 W/m\({}^{2}\) \\ Occupants & 7 persons sited in rest \\ Specific load per occupant & 100 W/occupant (ISO 7730) [26] \\ U-values of the ground & 0.304 W/m\({}^{2}\)K \\ U-value of the roof & 0.318 W/m\({}^{2}\)K \\ U-value of the external walls & 0.365 W/m\({}^{2}\)K \\ U-value of the glazing (85% of the window) & 2.80 W/m\({}^{2}\)K \\ U-value of the frame (15% of the window) & 2.27 W/m\({}^{2}\)K \\ U-value of the window & 2.72 W/m\({}^{2}\)K \\ g-value of the window & 0.755 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information for the examined building.
# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 3 Materials and Methods
### Evaluation Methods  
A single measure cannot be used to determine the performance of the algorithm and compare different methods. Commonly used methods to compare the performance of different approaches include precision, recall, accuracy, and F-score. Accuracy is defined as the ratio of the number of samples that are classified correctly to the total number of samples and its expression is shown in Equation (9). Precision and recall are two measures widely used in the fields of information retrieval and statistical classification to evaluate the quality of the results. Precision measures the precision of the retrieval system and recall measures the recall rate of a retrieval system, whose expressions are shown in Equations (10) and (11). It is expected that the higher the precision, the better the results are. The higher the recall, the better the results are. The F-score integrates the results of the precision and recall and its expression is shown in Equation (12); when the F-score is high, both precision and recall are high. The following equations [48; 49] show the calculation of the evaluation factors, where TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Kernel Function** & **Pace** & **Accuracy** & **Precision** & **Recall** & **F-score** \\ \hline \multirow{4}{*}{Linear} & 0.78 m/s & 0.590 & 0.680 & 0.600 & 0.638 \\  & 1.00 m/s & 0.660 & 0.710 & 0.670 & 0.689 \\  & 1.25 m/s & 0.720 & 0.720 & 0.710 & 0.715 \\  & 0.78 m/s & 0.770 & 0.780 & 0.780 & 0.780 \\   rbf & 1.00 m/s & 0.710 & 0.770 & 0.690 & 0.728 \\  & 1.25 m/s & 0.750 & 0.780 & 0.740 & 0.759 \\  & 0.78 m/s & 0.490 & 0.250 & 0.500 & 0.333 \\   poly & 1.00 m/s & 0.500 & 0.250 & 0.500 & 0.333 \\  & 1.25 m/s & 0.610 & 0.610 & 0.610 & 0.610 \\  & 0.78 m/s & 0.500 & 0.250 & 0.500 & 0.333 \\   sigmoid & 1.00 m/s & 0.610 & 0.640 & 0.600 & 0.650 \\  & 1.25 m/s & 0.630 & 0.630 & 0.630 & 0.630 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of SVM algorithm under different parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**K** & **Pace** & **Accuracy** & **Precision** & **Recall** & **F-score** \\ \hline \multirow{4}{*}{2} & 0.78 m/s & 0.690 & 0.740 & 0.670 & 0.703 \\  & 1.00 m/s & 0.620 & 0.700 & 0.630 & 0.663 \\  & 1.25 m/s & 0.730 & 0.760 & 0.740 & 0.750 \\  & 0.78 m/s & 0.760 & 0.780 & 0.760 & 0.770 \\  & 1.00 m/s & 0.690 & 0.700 & 0.700 & 0.700 \\  & 1.25 m/s & 0.720 & 0.730 & 0.730 & 0.730 \\\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{12}\]
# The Hyperloop System and Stakeholders: A Review and Future Directions
## 3 Analysis and Results
### State of Progress for the Hyperloop System  
Based on the literature findings, the current state of progress for the hyperloop system is provided by focusing on the components of the system. These are:  
* The vehicle (also called pod or capsule), which includes an aerodynamic fuselage (similar to the construction of a commercial aircraft), the interior, and the electric subsystem.
* The infrastructure, which includes the tube, the sub-structure, and the stations. The tube encloses and maintains the low-pressure environment, ensuring minimum air leakage. The infrastructure also contains the pressure maintenance system and the power substations, which provide a considerable reduction of air drag, allowing for a smooth travel of the pod with speeds of up to 1200 km/h. Infrastructure characteristics depend on the type of levitation and propulsion system.
* The communication system, which creates an autonomous environment, exchanges data, and coordinates operations, ensuring safety and comfort.
## 2 Materials and Methods
### Spectra Acquisition and Pre-Processing  
The spectra of the olive oil samples were recorded in triplicate (three different sub-samples) using a 6200 JASCO spectrometer (Jasco Corp., Tokyo, Japan) equipped with a ZnSe 45\({}^{\circ}\) Horizontal Attenuated Total Reflectance (HATR) crystal plate (PIKE Technologies, Madison, WI, USA) with a refractive index of 2.4 and a penetration depth of 2.0 \(\upmu\)m at 1000 cm\({}^{-1}\). The Spectra ManagerTM Code of Federal Regulations (CFR) software ver. 2 (Jasco Corp., Tokyo, Japan) was used for spectral measurements in the wavenumber range of 4000-400 cm\({}^{-1}\) by accumulating 100 scans with a resolution of 4 cm\({}^{-1}\) and a total integration time of 2 min. Between each sampling, the crystal was cleaned with detergent, distilled water, and acetone and dried with lint-free tissue. For every three measurements, a background spectrum was recorded using only the crystal without the sample.  
For the measurement, 0.8 mL of olive oil was deposited on the surface of the crystal. Overall, 192 spectra (64 olive oil samples \(\times\) 3 replications) were acquired and used in model development, following a 70:30 dataset partition, representing the calibration (134 spectra) and testing (58 spectra) datasets. For external validation, 48 spectra (16 olive oil samples \(\times\) 3 replications) were acquired from additional olive oil samples to test the accuracy of the classifier to categorize unknown samples. The acquired spectra were subjected to baseline correction and smoothing using the Savitzky-Golay (S-G) second derivative (d = 2) filtering algorithm to reduce noise. To explore the impact of smoothing on classification performance, second- and third-order polynomials (p = 2, p = 3) and different window sizes (w = 11, w = 12, and w = 13) were employed to correct baseline shifts and enhance spectral resolution.
# Improving the Energy Efficiency of the Modular Buildings
## 4 Results and Discussion
### Energy Consumption and Savings  
Based on the power of equipment (Table 9) and the time taken to dry, the energy consumption of each of the four systems was calculated and is depicted in Figure 24 where the highest value of 21.175 kWh is due to the baseline case, as the 3 kW heater was running for 7 h, followed by heater and air mover scenario that consumes about 20.8 kWh, which is not very different from the baseline scenario, as the drying time in this case was still high. However, when adding a dehumidifier, the consumption dropped to 15.3 kWh, as the dehumidifier is low-power equipment, and the drying time was the shortest among all the four scenarios. Finally, the lowest energy use came from the dehumidifier + air mover, which are low-energy equipment even though the drying time was longer than in the previous scenario. Also, it is worth noting that in cases where the dehumidifier was OFF, the extractor fan was running to release the excess of moisture outdoors.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Power kW** \\ \hline Heater & 3 \\ Air mover & 0.437 \\ Extractor fan & 0.025 \\ Dehumidifier & 0.775 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Power of equipment used in the experiment.  
Figure 23: Relative humidity variation with DC temperature in (**a**) heater ON, (**b**) dehumidifier ON and air mover ON, (**c**) heater and air mover ON, (**d**) dehumidifier, air mover, and heater ON.  
Dehumidifier and fan power were worked out through the Amps used from the operations manual, as no energy meter was installed to take real energy data.  
Figure 25 shows the energy savings realised from each of the three scenarios compared to the baseline scenario. The DH and AM scenario reached 60%, the highest level of savings, followed by H, AM, and DH with 27%, and lastly, 2% of H and AM, which is not viable at all. The H, AM, and DH scenario could show further savings if a low energy heater was used instead of the 3 kW fan heater. The computations indicated that incorporating a 1 kW infrared heater as a component of the upcoming hard schemes scheduled for implementation in the summer could yield a 58% savings. This aspect warrants experimentation to assess and compare the drying outcomes of the 1 kW heater against the existing 3 kW convective fan heater.  
After spotting that the quickest way to dry garments is using H, DH, AM, further testing should be carried out to investigate the impact of varying temperatures on the drying time.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 4 Results and Discussion
### Discussion
It is clear that the resynchronization energy overhead must be lower than the energy benefit gained by the EEMIP method. A rough recommendation is to use the method for communications in which at least 90% of messages are transmitted during the scheduled time slots (i.e., synchronized). Compared to the existing related works (analyzed in Section 1), the proposed method uses a unique combination of QoS-based prioritization and congestion control and timing channels (duty cycling) to increase communication efficiency. It can be seen as a combination of ideas from different works. Duty cycling [14; 15] is a common approach to enable power-saving state of sensor devices; however, we use a unique synchronization mechanism. The medium access incorporating duty cycling to improve energy efficiency can be achieved by modification of link-layer protocol, such as in [13], or it can be influenced by the application protocol as in our case, which eliminates the need for modification of the existing protocol. Proactive planning was used in [16]; however, it was used for Internet connections and we use it to eliminate collisions. A number of works incorporated prioritization into the IoT sphere [18; 19; 20] to increase reliable delivery of critical communication. However, we have used the priorities in a unique way that also enables us to use them for determination of communication frequency (i.e., periodicity, time intervals). Thus, data with higher priorities are sent more frequently than data with lower priorities.
## 1 Introduction
For systems with complex structures and huge model sizes, such as space optical mirror components, finite element method analysis has the problems of a large calculation volume and low solution efficiency. The simplified model method cannot accurately reflect the true response of the system to external excitations and cannot really meet the needs of engineering practice. To improve the solution rate and simplify the solution method, the state space method can be used to solve the dynamic characteristics of the system. [PERSON] et al. proposed a new method of calculating the dynamic response of a linear system using multiple exponential damping models by introducing higher-order derivatives of the displacement vector as the state variable [10]. Based on the extended state space formalism, an implicit time integration method using linear approximation was developed to analyze the dynamic response of multiple non-viscous damping models of structural systems [11]. [PERSON] used the state space method to solve the eigenvalues and response of the fluid-structure-coupled vibration system [12]. [PERSON] et al. used an effective simplified modal state space method to predict the dynamic response of sandwich beams at a low computational cost [13]. Figure 1: Simplified method of finite element modeling of a space mirror: (**a**) single-point model, (**b**) 2D shell model, (**c**) 3D solid model. [PERSON] et al. derived a new extended state space method for damping systems and calculated the transient dynamic responses for structural systems using multiple damping models [14]. In recent years, state space theory has been increasingly applied to the fields of mechanics of materials and structural mechanics. The state space method can describe the relationship between input and output, revealing internal system features. In addition, the using vector-matrix and other powerful mathematical tools on the computer to solve more complex problems reduces the dependence on the designer's experience. Therefore, the analysis method based on state space theory has become an important tool for researching the dynamic system [12]. Based on modal analysis and state space theory, this paper provides a method of analyzing the frequency transfer characteristics of various complex structures. Dynamic analysis is performed on a spatial optical mirror with an diameter of \(\phi\)740 mm, modal analysis on the primary mirror assembly is performed using the finite element method, modal information such as the natural frequency of the mirror assembly is obtained, and the single input and single output of the mirror assembly state space model are established. The DC gain method is used to reduce the modes to simplify the model. The frequency transfer characteristics from the input to the output point are analyzed, and the system's frequency response characteristic curve is drawn. Finally, a mechanical vibration test is performed to verify the accuracy of the simulation analysis.
## 4 Numerical Results and Sensitivity Analysis  
In this section, a detailed presentation of a numerical example and sensitivity analyses of the obtained solutions is provided for the considered problem of pricing and remanufacturing decisions. Therefore, this section is dedicated to examining the solutions obtained from the proposed model studying the decentralized configuration of the dual-channel supply chain with the considered reverse flows, focusing on the sales of pharmaceutical products. The variations during the planning horizon of the different obtained optimal decisions are discussed in the numerical example's subsection, while the sensitivity analyses delve into assessing the robustness of the obtained solutions according to several parameters of interest. Thus, the proposed model was implemented and solved using Wolfram Mathematica 12.0, a software tool equipped with a range of global and local optimization techniques and capable of addressing integer, linear and nonlinear problems. The optimal solutions were obtained by solving the optimization problem (15) to obtain the total profit \(\pi_{M}\) of the manufacturer. Also, the online total profit \(\pi_{O}\) was calculated according to Equation (3) and the total profit \(\pi_{P}\) of the pharmacy was obtained from Equation (5). The data used in this optimization problem are presented in the following table, where \(P\) stands for product and \(mu\) denotes a money unit. Table 1 illustrates the different values of the parameter used to conduct the numerical example and the sensitivity analyses below. The proposed data could describe a dual-channel supply chain specialized in the manufacturing, remanufacturing, wholesale and selling of pharmaceutical products. As listed, all the experiments in this paper were performed in a multi-periodic environment composed of 12 periods. The variation from one period to another in the size of the market when the product is free, denoted as \(A(t)\), intended to examine the evolution of the obtained pricing and remanufacturing strategies under different market sizes and the adaptability of the obtained optimal results according to different scenarios. In addition, the online selling process was considered more self-sensitive to changes in its proposed prices than the pharmacy's selling process. This gap in the self-price sensitivities between both distribution channels was caused by the absence of AS in the online platform. These provided services rendered the pharmacy more attractive to the clients and less sensitive to fluctuations in its proposed selling prices \(P_{p}(t)\).
## 2 Data and Methods
### Well Logging Data  
In this research, the well logging data pertinent to a vertical wellbore, called Well A, in the Aboozar oilfield were used as the raw data. The recorded data belonged to the limestone formations at a depth from 4350 m to 4500 m. The corresponding well logs are gamma ray log (\(GR\)), caliper log (\(CAL\)), Poisson's ratio (\(PR\)), total porosity (\(PIGT\)), density log (\(RHOB\)), true formation resistivity log (\(RT\)), temperature (\(TEMP\)), compressional wave velocity (\(V_{p}\)), and shear wave velocity (\(V_{s}\)). Figure 3 illustrates the plot of the different well logging data used in the current research. It is noteworthy that the Poisson's ratio values in the \(PR\) log have been measured independently of the \(V_{s}\) and \(V_{p}\). Moreover, the rock density fluctuated between 2.39 g/cm\({}^{3}\) and 2.40 g/cm\({}^{3}\) for the entire profile. Therefore, it can be expressed that the rock density was relatively constant in our research.  
Figure 1: The location of Kharg island and adjacent oilfields.  
Figure 2: The simplified stratigraphy petroleum systems and tectonics offshore of the Persian Gulf.
## 1 Introduction
However, very few studies have been conducted on the application of ANNs to predict the compressive strength and the flexural strength of UHPFRC. [PERSON] presented an ANN model to predict the compressive strength of concrete containing industrial byproducts at the age of 28, 56, 90, and 120 days [40]. [PERSON] et al. estimated the compressive strength of steel fiber reinforced lightweight concrete by ANN [41]. Moreover, [PERSON] et al. developed an ANN model to predict the flexural performance of confined hybrid fiber reinforced high strength concrete beam with varying the volume fraction of steel and polypropylene fibers [42]. As for predicting the effects of steel fibers on the compressive strength and the flexural strength of UHPFRC, because of the high adaptability and high accuracy, the ANN models are suitable for evaluating the mechanical behavior of UHPFRC. The objective of this study is to develop two ANN models to predict the compressive strength and the flexural strength of UHPFRC. To achieve this purpose, 162 compressive strength data sets and 102 flexural strength data sets were collected from published literature to develop models and to evaluate the effects of steel fibers on the compressive strength and the flexural strength of UHPFRC, respectively. Finally, the proposed models were compared with several analytical models to evaluate their reliability and predictability. Furthermore, the ANN models can be utilized to predict the flexural strength and the compressive strength of UHPFRC, determine the volume fraction of steel fibers in UHPFRC, and optimize the UHPFRC mixtures.
## 4 Results and Discussion
### Pressure Measurements
#### 4.1.2 Case 1--First Stage of the Reduction of the Blood Vessel Lumen  
Figure 5a shows the fluid pressure values in the left ventricle and aorta. This time, the shapes of the measurements (red and blue colors) were also similar to each other during the inflation of the ventricular balloon, i.e., the pumping of fluid into the hydraulic system that corresponded to cardiac contraction. This means that there were no significant changes between the aortic and ventricular pressure waveforms during balloon inflation. The pressure values in the ventricle were slightly higher for this period compared to the pressure values in the aorta. The maximum fluid pressure \(p_{max}\) values, measured in the aorta and the left ventricle, are shown in Figure 5c. The measurements were obtained during the testing of the hydraulic system with the control valve partially closed (the first stage of the reduction of the blood vessel lumen). The maximum values of fluid pressure in the aorta, depending on the frequency \(f\) of operation of the device between 60 and 160 cyc/min, ranged from 124 mm Hg for 60 cyc/min to 168 mm Hg for 120 cyc/min. The differences in the maximum fluid pressures in the hydraulic system in the left ventricle and aorta, depending on the frequency \(f\) of operation of the device in the range of 60 to 160 cyc/min, were from approximately 27 to 37 mm Hg.  
The pressure in the pneumatic system, as measured by a sensor located near the balloon set for the external gas supply system, varied across operating frequencies of 60, 100, 120, 140, and 160 cycles per minute. The pressure range was approximately from about \(-\)164 mm Hg to about 733 mm Hg (Figure 5b). The maximum gas (helium) pressure in front of the balloon set, varied from 464 mm Hg for 60 cyc/min to about 733 mm Hg for 160 cyc/min (Figure 5c).