esa-sceva/satcom-chunk-collection huggingface dataset:
# Elucidating Super-Compressible States in Inner Structure of Vehicular Flow
## 8 Statistical Compressibility in Two Lanes Separated
The slope of the obtained regression line is understood as an empirical value of the statistical compressibility of the traffic sample and is denoted by \(\chi_{emp}\). Figure 10: Schematic representation of a freeway segment where the discussed traffic experiments were implemented. In Figure 11, one can spot the evolution of the statistical compressibility depending on traffic density in both lanes. The results of the analysis clearly show that even a physical barrier preventing vehicles from changing lanes did not erase the differences between the fast lane and the main lane. The substantial difference between the two compressibilities, which has been detected in previous research on two-lane freeways, is also clearly visible in the new data. And since these deviations can no longer be explained through the argument of increasing/decreasing the number of vehicles, this deviation must be a direct consequence of the different decision criteria of individual drivers in the two lanes. The second (rather surprising) finding is the fact that super-compressible states occur not only in the fast lane but also in the main lane. And since the super-compressible states indicate (in accordance with theoretical derivations) the presence of an attractive force component, it can now be argued that the interaction forces (which represent the physical transcript of the decision-making processes running in the driver's brain) are mixed. This means that the repulsive force preventing collisions is also accompanied by an attractive force that corresponds to the driver's effort to shorten the distance to the preceding vehicle. These findings are all the more surprising because the speed is limited to the same value in both lanes. Thus, we find that although both traffic lanes are technically set to identical parameters, the driver's brain does not choose the appropriate lane completely at random. On the contrary, drivers make spontaneous decisions based on their own driving style. It seems that more sporty drivers choose the fast lane to a greater extent, which is then reflected in an increased compressibility value. The consequence of this division is the fact that the statistical fluctuations of the traffic micro-quantities recorded in the fast lane are significantly more striking. The difference between the two lanes is also clearly visible in Figure 12, where a shift towards higher speeds is visible in the fast lane. Figure 11: Empirical values of statistical compressibility \(\chi_{emp}\) detected in traffic data measured in an area where traffic flow was significantly restricted due to reconstruction works. Data samples from the main (right) and fast (left) lanes were analyzed separately. Both lanes were separated by a barrier preventing lane\(-\)changing. The speed was limited to 80 km/h.
## 3 Experiment
### Strength at Cyclic Loading  
We will rewrite Equation (23) in the following form:  
\[\frac{C}{n}\int_{0}^{t_{p}}\left(t_{p}-\tau\right)^{\frac{1}{n}-1}\sigma\left( \tau\right)d\tau\,=\,1, \tag{24}\]  
where  
\[C=[A(n+1)]^{\frac{1}{2}}. \tag{25}\]  
Having performed the differentiating of the integral Equation (24) in parts, we will have  
\[C\int_{0}^{t_{p}}\left(t_{p}-\tau\right)^{\frac{1}{n}}\!\!d\,\sigma\left(\tau \right)=1. \tag{26}\]  
The failure condition of the asphalt concrete (24) was represented in the form of the Riemann integral. Now, it has been written in the form of Stieltjes integral (26).  
In accordance with the scheme of cyclic loading (Figure 6), we will introduce the following notation:  
\[t_{k}=(k-1)\cdot P, \tag{27}\]where \(k\) is the cycle number; \(t_{k}\) is the beginning of cycle \(k\).  
The stress at the time moment \(t\) in cycle \(k\) we present by the expression  
\[\sigma_{k}(t)=\sigma[h(t-t_{k})-h\left(t-t_{k}-t_{0}\right)], \tag{28}\]  
where \(\sigma_{k}(t)\) is Heaviside unit function  
\[h(t-\tau)=\begin{cases}1,&t\geq\tau,\\ 0&t<\tau.\end{cases} \tag{29}\]  
Having substituted the expression (28) into Equation (26), we will have  
\[C\,\sigma\overset{1}{\underset{k=1}{\sum}}\left[\left(t_{*}-t_{k}\right)^{ \frac{1}{h}}-\left(t_{*}-t_{k}-t_{0}\right)^{\frac{1}{h}}\right]=1. \tag{30}\]  
When obtaining expression (30), it was considered that  
\[\delta(t)=\frac{dh(t)}{dt}, \tag{31}\]  
where \(\delta(t)\) is delta-function of Dirac, and it has the following property:  
\[\int\limits_{0}^{t}f(t-\tau)\,\delta(\tau)\,d\tau=\int\limits_{0}^{t}\delta(t -\tau)f(\tau)\,d(\tau)\,=\,f(t). \tag{32}\]  
Considering expression (6), we will have from expression (30):  
\[C\,\sigma\,P^{\frac{1}{h}}\overset{N}{\underset{k=1}{\sum}}\left[\left(N-k+1 \right)^{\frac{1}{h}}-\left(N-k+1-\frac{t_{0}}{P}\right)^{\frac{1}{h}}\right]=1. \tag{33}\]  
Considering expression (4), we will represent expression (30) in the following final form:  
\[\left(\frac{P}{t_{p}}\right)^{\frac{1}{h}}\overset{N}{\underset{k=1}{\sum}} \left[\left(N-k+1\right)^{\frac{1}{h}}-\left(N-k+1-\frac{1}{P}\cdot t_{0} \right)^{\frac{1}{h}}\right]=1. \tag{34}\]  
The obtained expression (34) provides an opportunity for determination of the loading cycle number to failure of the asphalt concrete at tension. It considers the duration of relax period between loading cycles and the history of loading. The analysis of expression (34) shows that the cyclic strength of the asphalt concrete depends on stress (\(\sigma\)), parameters of long-term strength (\(A\), \(n\)), frequency of loading \(\left(\frac{1}{P}\right)\), duration of loading period \(t_{0}\) and relax period \(P\)-\(t_{0}\), and ratio of loading period to the long-term strength \(\left(\frac{P}{t_{p}}\right)\). In this case, the parameter \(n\) is a key characteristics, which shows for what order the long-term strength of the asphalt concrete \(t_{p}\) decreases (increases) with the increase (decrease) of the stress \(\sigma\) for one order.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## Appendix A Absolute Error Between theoretical and practical measurements
### Suburban Area Results  
Figure 17 shows that in suburban intra-handover zones, the experimental power variation (difference between two consecutive measurement points) is predominantly concentrated between 0 and \(5\,\mathrm{dB}\)m, indicating that small power changes are the most frequent. The theoretical values also show peaks in this range and additionally in the 8.7 to \(11.2\,\mathrm{dBm}\) interval, although with fewer samples. Notably, some higher variation ranges (around 20 to \(25\,\mathrm{dBm}\)) appear only in the theoretical data, likely due to the absence of experimental samples in those intervals. This suggests that the theoretical results have a broader distribution, especially toward medium and large power variations, which are less common in practice.  
Figure 16: Comparison of experimental RSSI and theoretical RSSI in inter-handover and non-inter-handover cases.  
Figure 18 complements this by illustrating that the experimental median variation is 5 dBm, splitting the data evenly. The experimental distribution is positively skewed, with greater dispersion among higher values. Conversely, the theoretical median is higher at 7.8 dBm and shows a negative skew, with a greater spread in the lower half of the data and a concentration toward higher values. The theoretical box plot also presents more extreme values, as indicated by the longer upper whisker.  
In the suburban inter-handover zones, Figure 19 shows that both the theoretical and experimental data are mainly concentrated between 0 and 6 dBm, indicating that small changes predominate. However, while the theoretical data exhibit a peak around 6 dBm,  
Figure 17: Histogram of theoretical and experimental power variations in suburban intra-handover zones.  
Figure 18: Box plot of theoretical and experimental power variations in suburban intra-handover zones.  
the experimental data are more evenly distributed across 6 to 11 dBm. Additionally, the experimental data include outliers exceeding 30 dBm, reflecting occasional abrupt power changes not fully captured by the model.  
Figure 20 further shows the experimental median at 5 dBm with a positively skewed distribution and several outliers above 25 dBm. The theoretical median is slightly lower at 4.5 dBm and shows a negative skew, with a greater dispersion in the lower range and outliers up to 15 dBm, although less extreme than the experimental ones.  
Overall, while the theoretical model captures the general behavior of power variations in suburban environments, it exhibits somewhat larger deviations than in urban settings,  
Figure 19: Histogram of theoretical and experimental power variations in suburban inter-handover zones.  
Figure 20: Box plot of theoretical and experimental power variations in suburban inter-handover zones.  
particularly with respect to higher-magnitude fluctuations during handovers. This reflects the more complex and variable propagation conditions typical of suburban areas.
# A Site-Specific Response Analysis: A Case Study in Hanoi, Vietnam
###### Abstract  
A series of one-dimensional (1-D) site response analyses were performed using the nonlinear (NL) and equivalent linear (EQL) approaches to assess the applicability of the Vietnamese earthquake-resistance design code TCVN 9386: 2012. Six soil profiles were selected from three districts in Hanoi (Vietnam). A number of ground motions compatible with the rock design spectrum were used as input for carrying out analyses. The results highlight that the calculated response is higher than the design spectrum for site class C and lower for site class D. The normalized response spectra of the EQL approach results are higher than those of the NL approach. Moreover, the peak ground accelerations at the surface from EQL analyses are greater than those of the NL method because the latter generates a higher amount of nonlinearity. The results from the NL approach also illustrate that the deamplification phenomenon occurs in the soft soils of the Hanoi region (e.g., soil profile P3 and P5 of site class D). Additionally, the shear strains calculated from the NL method are closely matched with those from the EQL method, the difference between them increasing with a decrease in soil stiffness.  
earthquake; seismic; design response spectrum; one-dimensional site response analysis; nonlinear approach; equivalent linear approach Article  
A Site-Specific Response Analysis: A Case Study in Hanoi, Vietnam
## 5 Conclusions
For monitoring application of special sites for which early warning or rapid response actions would also required, as landslides, special nodes that incorporate additional communications hardware, e.g., Internet connection, satellite phones, VSAT, _etc._, may serve as entities able to communicate data and parameters to outside of the network, such as a disaster management center. In particular, in the future it is intended to use the GFZ-WISE at landslide sites to monitor and analyze a combination of parameters (e.g., the S-wave velocity, ground motion, groundwater level variation, and rain gauge), and together with dedicated decision making algorithms, to detect and locate changes within the landslide. Hence, the GFZ-WISE system might provide real time early warning information about the possible landslide activation after earthquakes or meteorological events. Results of the tests performed indicate an excellent performance of the innovative instruments when used in seismic site effect surveys. In fact, the GFZ-WSU displayed for frequencies higher than 1 Hz a performance comparable to other standard, high sensitivity and higher costs seismic stations. Moreover, during a field experiment, the GFZ-WISE was found to be effective in providing to external users real-time access to the data. Therefore, results shown in this study indicate that in the near future, dense arrays of low-cost wireless sensors might be successfully and profitably deployed for the purpose of site-effects studies and monitoring activities, providing a worthwhile contribution to the reduction of seismic risk in urban areas.
## 6 Frequency Domain Optimization
### Frequency Domain Calculations for Assessing Measurement Quality
Equation (11) uses the assumption that the feedforward controller is designed to perfectly cancel the impact of the wind speed disturbance on the generator speed, as long as perfect preview information is provided. Following the pitch controller tuning recommendations in [PERSON] et al. [57],the closed-loop transfer function \(T_{\alpha_{gam}u_{off}}\) used in this paper is chosen to have a second order response with natural frequency \(\omega_{n}\) = 0.6 rad/s and damping ratio \(\zeta\) = 0.65. More information about the combined feedback-feedforward controller is provided in Section 7.1. Detailed descriptions of how the PSDs \(S_{u_{off}u_{off}}\left(f\right)\) and \(S_{d_{off}u_{off}}\left(f\right)\) as well as the CPSD \(S_{u_{off}u_{off}}\left(f\right)\), used to compute all of the performance metrics discussed here, can be calculated from a frequency domain wind field model using Fourier properties are available in [PERSON] et al. (2016) and [PERSON] (2016).
# Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0
## 5 Conclusions  
A position location-searching method suitable for both 2D and 3D sensor networks and based on relative distance relationships was proposed. This method can be used for indoor and outdoor scenarios, and it was demonstrated that its accuracy can be improved without the need to increase the infrastructure requirements. Since in Industry 4.0, traditional indoor and outdoor localization methods are not suitable due to the lack of reliability of distance estimation, the proposed method provides a reliable alternative to solve the need for position location information. The proposed method is robust, as it does not rely on the goodness of estimated distances. Its performance was assessed for multi-hop environments using a low-cost distance estimation method (hop count) considering several ANs for 3D networks. The 3D PL searching method is based on the space discretization once the searching space has been defined and enclosed by a set of ANs and on a vertex weight assignment due to distance relations. It was shown through simulations that the PL searching method improves as we increase the number of ANs in the operational area/space. However, using only four ANs combined with a small discretization step (\(L=\frac{1}{8}\) m) is sufficient to achieve the best performance reported. Additionally, marginal  
Figure 11: Error (\(e/d\)) analysis for different transmission ranges, \(r_{0}\), in outdoor deployment.  
improvements can be achieved when the number of nodes in the network increases, as better routes and therefore better distance estimations become available.  
Conceptualization, R.V.-H., D.M.-R. and C.V.-R.; methodology, R.V.-H.; software, R.V.-H.; validation, R.V.-H., [PERSON]-R. and C.V.-R.; formal analysis, [PERSON]-H. and [PERSON]-R.; investigation, R.V.-H.; resources, D.M.-[PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the SEP-CONACyT, Research Project 256237, the School of Engineering and Sciences, and the Telecommunications Research Group at Tecnologico de Monterrey.  
All the authors of the present paper declare no conflict of interest.
## 6 Discussion
Our model, however, uses climate and terrain data, which are easily accessible and can be updated in real time. Machine learning algorithms: While machine learning is already employed for landslide prediction, our innovative combination of cellular automata with these models allows for the analysis of terrain dynamics more effectively, improving the accuracy and reliability of predictions. Support vector machines (SVMs): These are used for classification and regression but require careful parameter selection. Compared to random forest algorithms, they can be less effective with large datasets. Markov chain-based models: These are used to predict changes in state over time but do not capture complex spatial interactions as effectively as cellular automata do. Neural networks: This includes convolutional models (CNNs), which excel in analyzing spatial data. However, they require a large volume of training data and can be computationally intensive. Our study demonstrates the effectiveness of the integration of cellular automata and random forest models for the prediction of landslide susceptibility. This innovative approach combines the ability of cellular automata to model spatial and temporal dynamics of terrain with the robust analysis of climate and terrain data offered by random forest models. Our methodology offers high spatial accuracy, which is crucial for identifying vulnerable areas and implementing preventive measures, and is particularly useful in urban environments and regions with complex topographies. This study aims to develop a system for assessing sensitivity to landslides. However, the intricate mechanisms underlying landslide occurrences must be considered. Despite a careful selection of evaluation factors based on the existing literature and tailored to the unique conditions of the study area, the model has limitations that must be acknowledged. Expanding the archive of landslide assessment factors is essential to enhancing the assessment framework and ensuring a more comprehensive evaluation of landslide risks. Future studies should incorporate a broader range of environmental and anthropogenic variables into the model, ultimately improving the accuracy and applicability of hazard predictions.
## 4 Conclusions  
As DESs are generally formed by mixing HBD and HBA, there is a significant margin to be exploited in making the greener gas hydrate inhibitors. Many substances have been tested to be potential gas hydrate inhibitors but have shown either biodegradability or performance issues. However, mixing compounds to make DESs may be a beneficial act. In this work, the DESs were made at a 1:7 molar ratio for the combinations of TEAAC:MEG, TEAAC:Glycerol, TEAB:MEG, and TEAB:Glycerol. The DES, TEAB:Glycerol showed a significant average suppression temperature (T) of 2.4. The Hydrate Liquid-Vapor Equilibrium (HLVE) data for CO\({}_{2}\) were evaluated through the T-cycle method at different temperature (273.15-283.15 K) and pressure (2-4 MPa) conditions in the presence and absence of 5 wt % aqueous DES solutions. The concentration of the best performing DES was then increased from 5 wt % to 10 wt % to observe any changes. The hydrate inhibiting behavior was also compared to other conventional and ILs-based THI, where the DES (TEAB:Glycerol) proved to be superior. It is recommended that further studies are made on DES to maximize its usage and move towards greener industry.  
Conceptualization, V.S. and B.L.; Data curation, V.S.; Formal analysis, V.S. and N.H.; Funding acquisition, Z.K.; Investigation, V.S. and N.H.; Methodology, V.S. and N.H.; Project administration, Z.K.; Resources, B.L.; Supervision, B.L. and A.S.M.; Validation, V.S.; Writing-original draft, V.S.; Writing-review and editing, V.S. and A.S.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pressure (MPa)** & **Pure Water** & \begin{tabular}{c} **TEAAC:Glycerol** \\ **5 wt \%** \\ \end{tabular} & \begin{tabular}{c} **TEAAC:MEG** \\ **5 wt \%** \\ \end{tabular} & \
\begin{tabular}{c} **TEAB:MEG** \\ **5 wt \%** \\ \end{tabular} &
\begin{tabular}{c} **TEAB:Glycerol** \\ **5 wt \%** \\ \end{tabular} \\ \hline
2.0 & 69.60 & 66.95 & 70.02 & 77.37 & 74.47 \\
2.5 & 66.45 & 64.42 & 67.01 & 73.71 & 70.88 \\
3.0 & 62.96 & 62.34 & 63.64 & 69.57 & 67.04 \\
3.5 & 59.22 & 58.68 & 59.83 & 65.44 & 62.71 \\
4.0 & 55.58 & 56.38 & 55.86 & 60.56 & 58.18 \\ Average (\(\Delta\)H\({}_{\text{diss}}\)) & 62.76 & 61.75 & 63.27 & 69.33 & 66.66 \\ \hline \hline \multicolumn{6}{c}{Expanded uncertainties U (T) = \(\pm\)0.1 K, U (P) = \(\pm\)0.01 MPa, U (mass fraction) = \(\pm\) 0.0001 g, U (H) = \(\pm\) 1.2 kJ-mol\({}^{-1}\)} \\ \multicolumn{6}{c}{(0.95 level of confidence).} \\ \end{tabular}
\end{table}
Table 7: Dissociation enthalpies (\(\Delta\)H\({}_{\text{diss}}\)) of DES systems at different pressure conditions.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank Universiti Teknologi PETRONAS, Malaysia, for the financial and technical aids provided. Special thanks to those who have contributed to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Temperature
Figure 6 illustrates the energy generation comparison between a single and staged digester operated at different temperature conditions. Temperature optimization by employing various technological options such as reactor design [187], OLR or HRT variation [191], pretreatment [192], and post-treatment [190] have been the major topics investigated in the existing literature. Moreover, incorporating computer simulation with the use of advanced modelling tools, such as ADM1 (Anaerobic Digestion Model No. 1), is gaining popularity in recent research [193]. Figure 6: Higher energy generation from a two-stage thermophilic-mesophilic digestion compared to a single stage mesophilic or thermophilic digestion alone—modified from [PERSON] et al. [189].
## 4 Multiple Metrics Implementation
### Implementation Overview and Description
#### 4.1.4 Relevant Link Metric Values and Pruning  
RLMVs are link metric values obtained for each path to a destination. The values are derived from ETX, ML and MD link metric algorithms for different paths to a destination. The number of paths to a destination recorded by the routing table differs, depending on the size of the network. The pruning technique is used to trim the number of paths to a destination.
# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 5 Experimental Results and Discussion
### Machine Learning
#### 5.1.2 PPG  
The accuracy of the emotion classification utilising PPG signals is shown in Table 6. The RF had the highest classification accuracy for the arousal and dimensional emotions, with 67.30% and 40.00%, respectively. Whereas the SVM obtained the highest PPG-based ERS classification accuracy for valence at 64.94%. Table 7 demonstrates that compared to other algorithms, SVM and RF performed comparably well in identifying emotions based on the overall results, where they were either the best or the second best algorithm. Interestingly, although the features extracted were fewer, it was observed that the accuracy of the PPG-based ERS built for the valence and dimensional emotional model were better than the ECG-based ERS. Meanwhile, for arousal the difference was marginal.