esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Effect of Different Water Content on Methane Adsorption Performance
From the figure, it can be seen that the density distribution of CH\({}_{4}\) in the crystalline cells is different under different water contents. With more water molecules, there is less CH\({}_{4}\) adsorption, the red dotted distribution decreases, the gas density distribution in some positions decreases, and the gas distribution is close to the position of water molecules. This is because the number of adsorption sites on the surface of the coal body is certain, but there is competitive adsorption behavior between molecules. The gas with strong adsorption capacity can replace the gas with weak adsorption capacity and occupy the adsorption site, while the gas with weak adsorption capacity is desorbed [22; 23]. H\({}_{2}\)O can better combine with the oxygen-containing functional groups in the coal, which is more conducive to the occupancy of the adsorption sites, and it has a repellent effect on CH\({}_{4}\). Therefore, with the increase in water content, the CH\({}_{4}\) adsorbed by the coal body gradually decreases. In addition, during the coalification process, the short fat chains of coal molecules will be shed, and a large number of ultra-micro pores will be formed in the later stage. These pores provide a large specific surface area, which is the main space for the adsorption of gas molecules [24]. In order to better explore the effect of H\({}_{2}\)O on CH\({}_{4}\) adsorption, H\({}_{2}\)O and CH\({}_{4}\) were used as probe molecules to detect the pore structure of the unit cell. The molecular dynamics diameters of H\({}_{2}\)O and CH\({}_{4}\) were 0.26 nm and 0.38 nm, respectively. Figure 8 shows the pores detected by probe molecules with different diameters, and the blue part represents the detected pores. From the figure, it can be clearly seen that the pore distribution detected by the H\({}_{2}\)O probe molecules is denser and the free volume of the pore structure is larger. A larger free volume in the coal pore structure means that gas molecules have more freedom in the pore structure. This improves the chances of contact between gas molecules and the coal surface, which promotes the adsorption of gas molecules by the coal [25]. For H\({}_{2}\)O with smaller molecular dynamics diameters, it is easier to enter the pore structure of coal. The H\({}_{2}\)O that  
Figure 8: The porosity of probe molecules with different diameters (**a**) with the diameter of (H\({}_{2}\)O) as 0.26 nm (**b**) with the diameter of (CH\({}_{4}\)) as 0.38 nm. Figure 7: Methane adsorption sites with different waters: (**a**) 0%H\({}_{2}\)O; (**b**) 1.1%H\({}_{2}\)O; (**c**) 4.35%H\({}_{2}\)O; (**d**) 8.31%H\({}_{2}\)O.  
enters the pore structure first will take priority during adsorption and first adsorb on the surface structure of coal, thereby reducing the probability of CH4 adsorption.
# Evaluation of Glottal Inverse Filtering Techniques on
## 5 Discussion
As a general trend, our results revealed that the performance of GIF methods decreases with female vocal tract signals with regard to male vocal tracts for all error measures but H1H2. This worsening would probably increase if the F0 vocal range was also adjusted to each type of vocal tract (male vs. female), which is something that might be explored in future works. The development and study of GIF methods has relied on the use of synthetic speech because it provides a ground truth that facilitates the evaluation and comparison of GIF methods, as well as the tuning of the GIF parameters. Nevertheless, in order to move to the analysis of real speech (i.e., without ground-truth references of glottal flow signals), other evaluation metrics should be considered. Furthermore, a strategy for the selection of optimal GIF parameters should be devised. Regarding the addition of expressiveness to the numerical generation of voice, previous works have been developed using ST-QCP to analyse the characteristics of GS and VT in aggressive and happy female vowels [9; 14]. However, the results obtained in the present study suggest that maybe it would be better to use QCP without the spectral tilt correction when dealing with female speech. The analysis methodology followed in this study includes the tuning of GIF parameters that was driven by a certain error metric. This error metric was defined as the MAE-Wave, which accounts for differences in the glottal flow time-domain waveform, as in the works by [PERSON] et al. [31] or [PERSON] [25]. However, the error metric can also be defined as a weighted sum of different glottal error measures (e.g., including others based on the frequency domain), similar to what [PERSON] et al. performed to determine the AME function parameters of the QCP method in [28].
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 3 Proposed Technique
### Circuit Design  
The voting process described above needs multiple evaluations of the PUF output, but multiple power-ups of the circuit are inefficient. Instead, the SRAM cell can be modified with minimal changes to implement the scheme. Instead of using start-up values during power-up, the circuit can be converted to a pre-charge/discharge circuit. This modified circuit is shown in Figure 4a. The clock signal first enables the paths from \(V_{dd}\) to \(OUT\) and \(\overline{OUT}\) and pre-charges the nodes to the supply voltage. During evaluation, depending on process variation, the output will settle in _logic-1_ or _logic-0_ due to the mismatch in the strengths of the discharge paths. The timing diagrams are shown in Figure 4b. The cell output is registered by the counter, as shown in Figure 3, on the rising edge of the delayed clock signal. The clock delay is to allow the cell to settle to its stable value during evaluation. This design is similar to changes required for enabling TMV in related work [10], Sense-Amplifier PUFs [25] and meta-stability based true random number generators (TRNGs) [26]. The circuit changes above have minimal impact on the cell area. The UP/DOWN counter circuit can be implemented using flip-flops along with the required logic for initialization and saturation detection.
# Induced Effect of Gold Nanoparticles (AuNPs) and Halide Ions on Pyridoxine Molecule Stability
## 3 Results and Discussion
### The Influence of Chloride Ions on Electrochemical Behavior of Pyridoxine
The presence of gold nanoparticles reduces absorbance; in other words, the nanoparticles prevent, to a greater or lesser extent, the electrooxidation of chloride ions. The absorption peaks corresponding to the vitamin are modified so that pyridoxine can no longer be identified qualitatively. Spectral changes are identified for both the supporting electrolyte and the pyridoxine; however, the maximum absorption corresponding to gold nanoparticles does not undergo significant changes. This is a common feature of the electrochemical behavior of pyridoxine in the presence of chloride and fluoride ions. The electrolyte solutions NaCl_B6, NaCl_AuNPs and NaCl_B6_AuNPs were subjected to electrolysis at constant current density in association with UV-Vis spectrophotometric analysis. The UV-Vis spectra recorded every minute, over a period of 10 min, are shown in Figure 7.  
Figure 6: UV–Vis absorption spectra of electrolyte solution containing NaCl/B6/AuNP systems; b—before and a—after performing cyclic voltammetry. Figure 7: UV–Vis absorption spectra of sample as the electrochemical processes progress over time in NaCl/B6/AuNP systems (i = 50 mA \(\times\) cm\({}^{-2}\)). Figure 7 shows a strong solvatochromic effect in the NaCl_B\({}_{6}\) system, compared to the NaF_B\({}_{6}\) system, due to the interaction of chloride ions with the pyridoxine ring. It is observed that pyridoxine molecules are electrochemically degraded from the first minutes of electrolysis. In the presence of chloride ions, gold nanoparticles are also degraded from the first minutes of electrolysis. Thus, the processes take place at a much higher speed, and no effect of delay or catalytic activation of AuNPs can be observed. The three spectra in Figure 7 are characterized by the appearance of a maximum absorption, of increasing intensity, at the wavelength of 293 nm, attributed to the formation of chlorinated oxygen species [51].
## 3 Results
### Skin Contact Capacitive Images
#### 3.1.1 Skin Water Content  
Table 1 shows the mean and the standard deviation of measured skin Epsilon values of different volunteers at the different skin sites. Epsilon values are in arbitrary units, but proportional to the skin water content. The results show that the lips and nose have the lowest water content, whilst the cheek, eye corner and under-eye have the highest water content. The lips and nose also have the lowest standard deviation, whilst the neck, eye corner and under-eye have the highest standard deviation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Skin Sites** & **Count 1** & **Mean** & **Standard Deviation** \\ \hline Lips & 86 & 21.42 & 5.75 \\ Nose & 116 & 23.13 & 4.65 \\ Forehead & 232 & 24.05 & 6.40 \\ Chin & 157 & 25.28 & 6.99 \\ Neck & 108 & 26.15 & 8.19 \\ Cheek & 239 & 26.46 & 7.36 \\ Eye Corner & 92 & 27.32 & 7.89 \\ Under-eye & 144 & 28.00 & 7.93 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: The measured skin Epsilon values of different volunteers at the different skin sites. Epsilon values are in AU (arbitrary units, proportional to water content).  
Figure 2: Samples of capacitive images (256 \(\times\) 300 pixels) from different skin sites.
# Survey on Power-Aware Optimization Solutions for MANETs
## 2 Related Work
### Energy-Aware Routing Protocols in MANETs  
Even though improvements in battery design have not yet reached that great a level, where a device can be able to operate for a longer period, the majority of the proposed routing protocols have not emphasized energy consumption at all. In MANETs, energy-efficient routing is one of the important problems that must be considered. Keeping this issue in mind, many researchers have suggested very efficient policies. Specifically, in MANETs, energy-aware routing is undeniably the utmost design benchmark, since all the nodes are attached and operated with low-powered battery devices. The shutdown of an intermediate node, due to power failure, not only affects the node's system itself but also its capability of relaying packets on the behalf of others, and hence it ultimately reduces the lifetime of the network [15; 59; 60]. With the untimely shutdown of a node, the entire network system suffers a burn, since, as soon as a node stops due to power failure, forwarding packets on the route will get completely disconnected. However, the preceding node keeps on unnecessarily re-transmitting the same packets up to a certain threshold, and thus the power of that preceding node is also needlessly wasted. Then, that preceding node knows that the path is disconnected, and it ultimately notifies the source about it. Finally, the source node again performs route discovery, and this process also consumes a significant amount of energy in the network. Many authors [61; 62; 63; 64; 65; 66; 67] have proposed energy-aware routing schemes for the dynamic environment of MANETs. All these schemes have conveyed a new aspect for the unnecessary consumption of the battery energy of a node. What they have suggested is that the node's battery energy can not only be consumed during its active participation in forwarding and receiving packets (active state) but also when it stays in energy preserving and idle medium (wireless) listening mode (in-active state). As a result, some of the energy-aware routing schemes consider active and in-active energy consumption states. Bearing in mind the active energy consumption state, energy-aware routing schemes reduce energy consumption, while nodes are forwarding and receiving packets. In this direction, researchers have suggested a method that regulates each node's radio (i.e., transmission) power just enough to reach the neighboring node and not more than that. On the other hand, in energy-aware routing schemes that consider the inactive
# A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems
## 3 The Proposed Method Based on the LS Method
The whole procedure of the proposed method in every OFDM symbol is organized as follows:  
1. The CFRs at pilot subcarriers are estimated by (4). 2. The CFRs at virtual even and odd pilot subcarriers are estimated by (17) and (21) respectively. 3. According to step 1 and step 2, the CFRs vector \(\hat{\mathbf{H}}_{p,PLS}\) at pilot and virtual pilot subcarriers can be obtained. 4. The CIR \(\hat{\mathbf{h}}_{PLS}\) of the \(N_{p}\) points can be calculated by the \(N_{p}\)-points IFFT of \(\hat{\mathbf{H}}_{p,PLS}\). 5. Set the values of \(0.5N_{p}\) points of the second half of \(\hat{\mathbf{h}}_{PLS}\) be zero to obtain \(\widetilde{\mathbf{h}}_{PLS}\). 6. The complete CFR \(\hat{\mathbf{H}}_{PLS}\) can be obtained by \(N\)-points DFT of \(\hat{\mathbf{h}}_{PLS}\).
## 3 Results
### Speed Correlation  
The adjustment (r\({}^{2}\) = 0.985) suggests that the average speed in the channel can be established from the rotor speed using Equation (9):  
\[y=-0.001x^{2}+0.0215x-0.1906 \tag{9}\]  
where \(y\) is the average velocity of the channel (m/s), and \(x\) is the angular velocity in rpm (Figure 2). This correlation equation allows making interpolations between 16 to 44 rpm and extrapolations for rotor speeds of approximately 46 to 60 rpm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\omega\) (rpm) & \(V_{lab}\) (m/s) & \(V_{pll}\) (m/s) & \(V^{\prime}\) (pil/lab) & \(V^{\prime\prime}\) (lab/pil) \\ \hline
30 & 0.3485 & 0.3515 & 1.0086 & 0.9915 \\
35 & 0.3949 & 0.4058 & 1.0276 & 0.9731 \\
40 & 0.4494 & 0.4563 & 1.0154 & 0.9849 \\
45 & 0.4903 & 0.5068 & 1.0337 & 0.9674 \\ \hline \hline \end{tabular} Correspondence pilo/laboratory (\(V^{\prime}\)); laboratory/piloite (\(V^{\prime\prime}\)).  
\end{table}
Table 2: Correspondence of linear velocities to different angular velocities.  
Figure 2: Correlation of average velocity vs. angular velocity; (\(\blacksquare\)) Experimental Data, (\(\multimap\)) Fitted model.  
The data presented diverge from those of [PERSON] et al. [7], which relates the velocity in m/s of three different Raceway bioreactor configurations at 35, 40, and 45 rpm, these angular velocities produce flow velocities of 0.12, 0.14, and 0.19 m/s correspondingly. Speeds, especially at 30 rpm, are very low and close to the speed limit that is considered part of the dead zones. From another point of view, the data shown in Figure 2 present speeds from 0.40 to 0.50 m/s at stirring speeds from 35 to 45 rpm. This speed difference is associated with the configuration of the curved blade agitator and the geometric relationships that it maintains with respect to the dimensions of the Raceway since it ensures flow speeds that keep most of the bioreactor in turbulence, a condition that is favorable for microalgal growth [8].  
Similarly, the relationship between the speed presented by the tracer on the walls of the Raceway was studied by adjusting the speeds next to the curved section vs. the straight section of the wall, but there was no model to explain the observed. Changing the approach, the distribution of velocities near the wall was analyzed and found to follow a gamma probability distribution (Figure 3). The statistical model explains both the speeds in the straight part and in the curve of the Raceway. With the parameters of the gamma function, the geometry of the Raceway bioreactor can be associated with the angular velocities, in this way, just by varying the parameters of the gamma function, the geometric parameters for Raceway of different sizes can be obtained, which could function as a different scaling-up method.
## 4 PSI-CA Protocol Constructed Based on DH Key Exchange Mechanism  
Before proposing the first unbalanced PSI-CA protocol of this paper, we introduce [PERSON]'s PSI-CA protocol, constructed based on the DH key-exchange mechanism [35]. As shown in Figure 1, the specific process is as follows, where \(\alpha\) is the private key of a large medical institution, \(\beta\) is the private key of a small health app developer, and \(H\) is the hash function negotiated by both parties.
## 3 Results
### Agreement between GPPOLCI-FAPAR and GPPE  
We first compared the performance of GPPOLCI-FAPAR against that of the GPPE across all sites, including for 2017 and 2018 (Figure 5). Results showed that the US-WCr site obtained the best performance (R\({}^{2}\) = 0.76), followed by the US-Ton site with R\({}^{2}\) = 0.65, the US-Rls site with R\({}^{2}\) = 0.64, and the US-Bi2 site with R\({}^{2}\) = 0.55. The R\({}^{2}\) values between the GPPOLCI-FAPAR and GPPE at US-Bi1, US-Rws, and US-KFS were all below 0.5. In terms of the RMSE, the US-Bi2 site produced the maximum error (RMSE = 9.77 g C m\({}^{-2}\) d\({}^{-1}\)), which is nearly twice or more than any other sites (Table 3). This situation was mainly caused by thesubstantial underestimation of GPPOLCI-FAPAR during the peak growth period of corn. This cause of underestimation is also applicable to alfalfa and grass sites. US-Rws had the lowest error with an RMSE of 1.23 g C m\({}^{-2}\) d\({}^{-1}\) (Table 3). Part of the difference between the US-Rws site and other sites in terms of RMSE was due to the low mean GPP in CSH.  
Figure 6 illustrates the temporal variation of GPPOLCI-FAPAR and GPPE at all sites. Overall, the GPPOLCI-FAPAR effectively matched the GPPE and generally captured the seasonal variations consistent with the GPPE at all sites. However, a remarkable difference remains between the GPPE and GPPOLCI-FAPAR at several sites. Notably, the GPPE values in most sites were underestimated by the GPPOLCI-FAPAR, except for US-Ton site. As shown in Table 3, the GPPOLCI-FAPAR tracked the GPPE well at the US-Ton site, with the second lowest bias of 0.64 g C m\({}^{-2}\) d\({}^{-1}\). Similar to RMSE, the US-Bi2 site had the highest bias (\(-8.44\) g C m\({}^{-2}\) d\({}^{-1}\)). Interestingly, the US-WCr site with a large RMSE also showed the lowest bias. The reason is that part of the bias is offset by the compensation of GPP underestimation in several months in 2017 with overestimation in 2018.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site ID** & **R\({}^{2}\)** & **RMSE** & **Bias** \\ \hline US-Bi1 & 0.45 & 5.18 & \(-\)3.6 \\ US-Bi2 & 0.55 & 9.77 & \(-\)8.13 \\ US-Rls & 0.64 & 1.36 & \(-\)0.88 \\ US-Rws & 0.5 & **1.23** & \(-\)0.89 \\ US-WCr & **0.76** & 2.42 & \(\mathbf{-0.61}\) \\ US-KFS & 0.45 & 5.54 & \(-\)4.03 \\ US-Ton & 0.65 & 1.39 & 0.64 \\ \hline \hline \end{tabular} Note: Highest R\({}^{2}\), lowest RMSE, and lowest bias value are shown in bold. Significance levels: \(p<0.001\).  
\end{table}
Table 3: Coefficient of determination (R\({}^{2}\)), root mean square error (RMSE, g C m\({}^{-2}\) d\({}^{-1}\)) and bias (g C m\({}^{-2}\) d\({}^{-1}\)) between GPPOLCI-FAPAR and GPPE for seven sites.  
Figure 4: Temporal dynamics ofscalar factors (\(f(\mathsf{T}_{\min})\) and \(f(\mathsf{VPD})\)) in the left column; fraction of absorbed photosynthetically active radiation (FAPAR), photosynthetically active radiation (PAR), and GPPEC in the right column for all seven sites in 2017 and 2018. (**a–g**) represent the sites the same as Figure 2.
## 1 Introduction
By contrast, high-frequency noise above 1 Hz is typically caused by transportation and industrial facilities, other human factors, and natural factors, such as wind and waves [23; 24]. In particular, high-rise buildings are greatly influenced by natural factors such as wind, as well as artificial factors due to mechanical vibration and walking load. Figure 1: Results of the numerical integration of velocity and displacement without baseline correction from the acceleration measured in the shaking table test; (**a**) Acceleration; (**b**) Velocity; (**c**) Displacement. Previous research on strong motion has identified the causes of noise in the frequency domain of accelerometers [14]. A variety of processing and filtering techniques have been developed to compensate for seismic accelerometers [15; 25]. However, not much research has been done on correction and filtering methods for accelerometers installed in high-rise buildings. The data obtained from accelerometers installed in a free field depend on the characteristics of the ground, and the artificial influence is relatively small. However, artificial factors have a great influence on the noise and dynamic characteristics of high-rise buildings. Therefore, it is necessary to verify the method by which acceleration data of high-rise buildings is converted into displacement data. In particular, acceleration measured over long periods leads to large error and baseline drift in displacement in the diagnostic assessment of high-rise buildings. The displacement of high-rise buildings can be measured by GPS, but high accuracy is required for soundness assessment, and the corresponding GPS is very expensive. High-speed imaging can measure the displacement of a structure, but it is difficult to perform measurements at all times, and challenging to apply this technique to high-rise buildings [26]. Therefore, it is most efficient to install an accelerometer in high-rise buildings and convert this data into displacement. In this study, the shaking table test was performed to verify the conversion methods. This involves converting the data from an accelerogram installed in a tall building into velocity and displacement data. In the shaking table test, the displacement of a 10-story model building under strong motion was measured using high-speed imaging. High-speed images were taken at 1000 frames per second, capturing the dynamic behavior of the model building. Then, this displacement was compared with the displacement obtained by processing the acceleration data. This study applied three methods for correcting and converting acceleration to velocity and displacement. The characteristics and the applicability of the conversion methods for carrying out diagnosis of high-rise buildings are evaluated in this study.
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 3 Proposed Neural Network Architectures
### Mlp  
MLP is one of the most common neural network model structures for regression and classification, which propagates information layers by layers. MLP consists of layers containing more than one perceptron with activation functions and their connections; it receives input data by the input layer, and these data are transmitted throughout the hidden layers and the output layer through connections. Then, the output layers return the predicted values. One of the typical ways for calculating an output value for each node is as follows:  
\[y=\varrho\left(\sum_{i}w_{i}x_{i}\right) \tag{1}\]  
where \(y\) is the output value of each node, \(x_{i}\) is the input from connected node \(i\) in the previous layer, \(w_{i}\)'s are learnable weight parameters, and \(\sigma\) is an activation function. Typically, hyperbolic tangent function, logistic function, and rectified linear units are selected for activation functions.  
Tables 1 and 2 represents the architectures of the MLP models in this study using the daily and hourly input data, respectively; we determined these through trial and error. \(T\) and \(m\) in Tables 1 and 2 denote time length of input data and the number of input variables. For example, with three days of input data in MLP with daily input data, the number of input nodes is 51: 3 (days) \(\times\) 17 (variables). Likewise, with three days of input data in MLP with hourly input data, the number of input nodes is 1080: 3 (days) \(\times\) 24 (h) \(\times\) 15 (variables).