esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Implications of Electrical Generation on Fuels Cost
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Natural Gas (KSCF)** & **Gas Oil** & **Crude Oil** & **Heavy Oil** & **Total Cost** \\  & **Standard Cubic Feet** & **(Barrels)** & **(Barrels)** & **(Barrels)** & **(KJD)** \\ \hline
**2012** & 264,080,165 & 11,913,629 & 16,566,894 & 38,557,558 & 2,423,012,351 \\
**2013** & 253,461,108 & 9,237,306 & 11,323,855 & 46,967,101 & 2,327,992,356 \\
**2014** & 313,936,191 & 11,153,661 & 14,409,093 & 37,954,682 & 2,435,107,934 \\
**2015** & 350,979,921 & 8,570,450 & 4,849,437 & 46,722,496 & 1,288,525,905 \\
**2016** & 378,535,102 & 5,731,758 & 4,057,944 & 48,460,342 & 1,010,903,300 \\
**2017** & 374,964,177 & 5,196,552 & 9,194,665 & 41,591,383 & 1,297,642,956 \\
**2018** & 403,438,524 & 3,623,846 & 6,236,988 & 42,956,365 & 1,694,031,251 \\
**2019** & 433,605,520 & 5,376,675 & 3,439,839 & 39,107,701 & 1,442,738,912 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The consumption of fuels and the total cost of fuel consumption between years 2012–2019 [21; 22; 23; 24]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fuel**} & \multicolumn{3}{c}{**Price**} & \multirow{2}{*}{**Unit**} \\ \cline{2-3}  & **Low** & **Average** & **High** & \\ \hline Natural Gas & 0.294 & 0.375 & 0.456 & KD/MSCF \\ Gas Oil & 11.846 & 18.904 & 25.963 & KD/bbl \\ Crude Oil & 8.084 & 14.085 & 20.085 & KD/bbl \\ H.F.O. & 6.496 & 13.398 & 20.300 & KD/bbl \\ LNG & 1.265 & 2.626 & 3.987 & KD/MSCF \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average prices of the different fossil fuels used in power generation in Kuwait. forecasted fuel budget for the Kuwait power and desalination plants in millions of Kuwaiti Dinars up to 2030 using all proposed models. All models elucidate consistent trend through the gradual increase in fuel cost due to increasing power generation. The gap between the upper model (HWSS) and lower model (HWTS) outcomes, as it can be noticed in Figure 8, increases at the end of the tested period compared to the beginning because of the rise in expected power generation difference, which exceeds 9 million kWh total annual production. The moderate growth of the fuel budget might be attributed to the rising dependency on natural gas on other liquid fossil fuels. Relying more on natural gas as a significant fuel source is evident in the electricity value between them. The primary contribution of fuel to the total fossil fuel budget comes from natural gas with LNG, followed by Heavy fuel oil, gas oil, and crude oil for the year 2020, while gas oil and crude oil have the same contribution by 5% in the year 2030 as illustrated in Figure 9.
## 2 Mathematical Models
Furthermore, this modified model no longer requires the end-of-cycle residual oil saturation (Som) needed by the Stone model. For \(S_{o}>S_{orw}\) (Stone I):  
\[k_{ro}=k_{rocv}\cdot SS_{o}F_{w}F_{g} \tag{22}\]  
For \(S_{o}<S_{orw}\) (The modified Stone I model):  
\[k_{ro}=\beta_{o}\cdot k_{rocv}\cdot SS_{o}F_{w}F_{g} \tag{23}\]  
\[SS_{o}=\frac{S_{o}-S_{om}}{1-S_{om}-S_{vaco}} \tag{24}\]  
\[SS_{w}=\frac{S_{w}-S_{vaco}}{1-S_{om}-S_{vaco}} \tag{25}\]  
\[SS_{g}=\frac{S_{g}}{1-S_{om}-S_{vaco}} \tag{26}\]  
\[F_{w}=\frac{k_{row}}{k_{rocv}(1-SS_{w})} \tag{27}\]  
\[S_{on}=\frac{(S_{o}-S_{om})}{\big{(}1-S_{wc}-S_{g}-S_{om}\big{)}} \tag{19}\]0  
Research on the current state and development history of relative permeability models reveals that relative permeability is a function of saturation. Three-phase permeability is derived from two-phase relative permeability through linear interpolation or saturation interpolation. Currently, the most widely applied three-phase relative permeability models are the Stone I model, Stone II model, and Baker model.
# Numerical Investigation of the Pile-Soil Interaction Problem under Dynamic Loads
## 3 Finite Element Analysis
### Soil Parameters  
The stress-strain behavior of natural soils under dynamic loads is largely nonlinear, and the shear modulus generally decreases with an increase in shear strain [38]. Deterioration in shear modulus significantly affects the performance of the foundation system [39]. For this reason, it is important to use a soil model suitable for nonlinear soil behavior in soil dynamics problems. Particularly during dynamic loading, inertial forces and strain rate effects inherent in the loading come to the fore rather than the shear strain magnitude. In this study, the Hardening Soil Small model, which is also included in the Plaxis program, was used to best reflect the dynamic soil behavior. The parameters of the sand used in the analysis were determined by [PERSON] et al. [13], and the properties of clay soil were obtained from the study by [PERSON] [40]. The formulas in [PERSON] et al.'s [41] study were used to determine the parameters of the sand in the numerical model. Additionally, the clay parameters used in the analysis were obtained from the study by [PERSON] et al. [42]. The soil parameters used in the analysis are presented in Table 3. In the analysis, the pile element was selected as a plate element as recommended in the literature. In determining the pile parameters, the transformations in the Plaxis program manual were used, taking  
Figure 2: Numerical model of the experiment of [PERSON] et al. [13].  
into account the elasticity modulus in the study of [PERSON] et al. (2013). The properties of piles and pile caps used in numerical analyses are summarized in Table 4.
## 2 Literature Review
### Connected Vehicles Trends  
A review of the latest trends in CV technology development sets the stage for understanding the topic classifications. The transportation industry expects CV technology to revolutionize driving safety, efficiency, and convenience [4]. The recent literature on CVs focused on advancing security, traffic management, and cooperative control systems. [PERSON] et al. (2023) examined the integration of 5G networks in vehicular cloud computing, highlighting the potential of vehicle clusters to share resources and data in a mobile cloud environment [5].  
The security of CVs is another area of major concern. [PERSON] et al. (2023) reviewed cybersecurity techniques focusing on CVs, covering threat analysis and intrusion detection [6]. [PERSON] et al. (2023) focused on the cybersecurity challenges of in-vehicle communication [7], while [PERSON] et al. (2022) examined attack detection and resilience from a vehicle control perspective [8]. [PERSON] et al. (2023) explored the use of blockchain technology to enhance security in vehicle networking [9], and [PERSON] et al. (2023) proposed a blockchain-based secure communication system for CVs [10].  
[PERSON] (2023) conducted a systematic literature review of how autonomous vehicles manage traffic at junctions, exploring various methodologies that include ML [11]. [PERSON] et al. (2023) developed a real-time control algorithm for CVs to optimize fuel use in signalized corridors [12]. [PERSON] and [PERSON] (2022) surveyed cooperative intersection management strategies for CVs [13]. [PERSON] and [PERSON] (2023) proposed a method to improve arterial signal coordination using CV data [14], and [PERSON] et al. (2022) reviewed merging control strategies at freeway on-ramps [14]. [PERSON] et al. (2022) discussed the development of cooperative driving systems for CVs [15].  
[PERSON] et al. (2022) reviewed cooperative perception technologies that combine local and edge sensing data for improved situational awareness [16]. [PERSON] et al. (2023) utilized CV data to develop crash prediction models. [PERSON] et al. (2023) provided insights into predictive cruise control under cloud control systems, emphasizing the role of predictive algorithms in traffic efficiency and safety [17]. [PERSON] and [PERSON] (2023) focused on using CV data for traffic conflict prediction [18]. [PERSON] et al. (2022) examined the role of digital twin simulations in various traffic management applications [19].  
The above literature review revealed that trending topics in CV development include vehicular security, traffic management, cooperative control and perception systems, and overall driving safety. These trends suggest that while the industry has made considerable progress in the development of technologies for CVs, challenges in security, real-time control, and effective traffic management remain, necessitating further research and innovation in these areas.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
###### Abstract  
Affect detection combined with a system that dynamically responds to a person's emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human-Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices.  
affective computing; emotion recognition; feature extraction; machine learning; wavelet transform +
Footnote †: journal: Medical Engineering, School of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago 7820436, Chile, [EMAIL_ADDRESS]
## 2 Method of Multi-Ray-Tracing in Tunnels
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Dielectric Constant \(\epsilon_{r}\)** \\ \hline Concrete & 5.31 \\ Glass & 6.27 \\ Brick & 3.75 \\ Wood & 5 \\ Wet Ground & 10 \\ Nylon & 3.2 \\ Silicon & 11.6 \\ Teflon & 3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative dielectric coefficients of construction materials at 28 GHz. Figure 2: Reflection coefficients for different construction materials at 28 GHz: (**a**) transverse electric (TE) reflection; (**b**) transverse magnetic (TM) reflection. The multi-reflection inside the tunnel not only causes variations in the field intensity due to constructive or destructive interference, but also causes a phase shift \(\Delta\phi\). This is a frequency-dependent quantity that may cause a dispersive group delay. The normalized field at the receiver can be expressed as follows:  
\[\frac{E_{r}}{E_{LOS}}=\sqrt{\frac{P_{r}(Tunnel)}{P_{r}(LOS)}}\cdot e^{j\Delta\phi} \tag{11}\]  
The incremental group delay with respect to the line-of-sight path can be calculated by the phase derivative:  
\[\Delta\tau=-\frac{1}{2\pi}\frac{d}{df}\Delta\phi \tag{12}\]
# A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup
###### Abstract  
Prefix caching is one of the notable techniques in enhancing the IP address lookup performance which is crucial in packet forwarding. A cached prefix can match a range of IP addresses, so prefix caching leads to a higher cache hit ratio than IP address caching. However, prefix caching has an issue to be resolved. When a prefix is matched in a cache, the prefix cannot be the result without assuring that there is no longer descendant prefix of the matching prefix which is not cached yet. This is due to the aspect of the IP address lookup seeking to find the longest matching prefix. Some prefix expansion techniques avoid the problem, but the expanded prefixes occupy more entries as well as cover a smaller range of IP addresses. This paper proposes a novel prefix caching scheme in which the original prefix can be cached without expansion. In this scheme, for each prefix, a Bloom filter is constructed to be used for testing if there is any matchable descendant. The false positive ratio of a Bloom filter generally grows as the number of elements contained in the filter increases. We devise an elaborate two-level Bloom filter scheme which adjusts the filter size at each level, to reduce the false positive ratio, according to the number of contained elements. The experimental result shows that the proposed scheme achieves a very low cache miss ratio without increasing the number of prefixes. In addition, most of the filter assertions are negative, which means the proposed prefix cache effectively hits the matching prefix using the filter.  
prefix caching; two-level Bloom filter; prefix expansion; IP address lookup; Internet router +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Tracing of Contamination Hotspots and Presumable Emission Sources via Mapping of _gl Values_
The contrast of each element urban anomalies is characterized by the ratio (RP) of the 75 th percentile to 25 th percentile of its geochemical index gl. The size (areas) of hotspots and contrast of elemental anomalies is reflected in the following sequence of respective RP ratios of \(aE\)'\(N\) index: Fe (11.1) \(>\) Mg (6.1) \(>\) Zn (4.1) \(>\) Cr (3.4) \(>\) Sr (2.5) \(>\) Cl, Ca (2.4) \(>\) As (2.3) \(>\) Cu, P (2.2) \(>\) K (2.0) \(>\) Ni, V (1.9) \(>\) Pb (1.8) \(>\) Br (1.7) \(>\) Mn (1.5) \(>\) S (1.4). Meanwhile RP ratios of \(nEF\)'\(Al\)'\(N\) reflect relative anthropogenic part of analytes in dust as well as the variety of their presumable emission sources and their uneven distribution in the city. According to these RP ratios, the elements are arranged as follows: Cu (22.1) \(>\) Cr (7.8) \(>\) Mn (3.3) \(>\) K (3.0) \(>\) P (2.7) \(>\) Sr (2.5) \(>\) Zn (1.9) \(>\) Pb (1.8) \(>\) Fe (1.7) \(>\) V, S (1.6) \(>\) Br, Ca (1.5) \(>\) Ni (1.7) \(>\) As, Cl (1.2) \(>\) Mg (1.1). These RP ratios could help to perceive the indices presented in Figures S1-S17, to increase the readability of maps, to highlight the main hotspots of analytes enriching dust, and to find out their dominant emission sources. It is obvious that the main source of Fe is iron ore stevedoring, of Mg is dolomite, and of Sr and P is apatite and phosphoric. The anomalies of Cr can be caused by close distance to iron ore stevedoring and dust re-suspended from former topsoil anomaly in the neighborhood. Hotspots of As and Pb are also at close distance. Meanwhile Zn, Cu, Ni, and V are widely spread in all urban territory. The influence of KCl fertilizers stevedoring is slightly visible from K and Cl spread. Therefore, more detailed research of stevedoring sites is necessary in future, i.e., not only near iron ore stevedoring (B site), but also in other stevedoring sites (C, D, E). The spread of S and halogens Br and Cl is scattered. Actually, their hotspots, as well as hotspots of many other analytes (Mn, V, Ni, etc.) can be masked by high content of iron ore stevedoring dust. It is possible that to reduce this masking, other analytes (e.g., Fe, Mn, or Ti) can be used as reference elements or even complex \(nEF\) indices. Examples of such attempts have been shown earlier [25; 112]. Figure 15: The frequency of elemental \(nEF\)’\(Al\)’\(C\) and \(nEF\)’\(Al\)’\(N\) values of twenty TSP sampling sites in three levels indicating possible origin of emission source of analytes. Explanation: mcu are intervals when the thresholds are selected taking into account common publications [45; 46; 47], crs are those when thresholds suggested in [40] are selected. The red color shows the intervals where the presumable origin of analytes is anthropogenic, green color when it is crustal, while yellow color when it is mixed.
## 6 Analysis of Retrofit Effect of SSDB Systems
### Natural Vibration Characteristic  
The natural frequency of structure is an important parameter that reflects the dynamic characteristics of structures, and it is also related to the structural mass and stiffness. In general, during strong motions, concrete cracking and rebars yield might induce the stiffness of structure decrease and make the structure lose its bearing capacity, further leading to the change of natural vibration characteristics of structure. Therefore, the effects of CSSDB systems on the dynamic characteristics of MD-RC frames under different layout schemes are investigated in this section. For this purpose, frequency ratio (FR) factor is defined as \(FR=f_{r}/f_{ur}\), where \(f_{ur}\) is the frequency of MD-RC frame without retrofitting (i.e., un-retrofitted) and \(f_{r}\) is the frequency of retrofitted MD-RC frame under three layout schemes. Figure 9 shows the natural frequencies ratio (FR) of retrofitted MD-RC frame and un-retrofitted MD-RC frame under DS1 and DS2 damage states. All FR values exceed 1.0, and the maximum value of FR is about 4.0 and the minimum value of FR is about 1.5, indicating that retrofitting systems have effectively improved the overall stiffness of MD-RC frames, as shown in Figure 9, intuitively. In addition, no matter which mainshock (i.e., MS1 and MS2) induced MD-RC frame, the FR value increase with the order of layout schemes (i.e., LS-1, LS-2, and LS-3). Especially for the first-order frequency, the amplitude of FR significantly increases. For the second- and third-order frequency, the changes in FR is not evident from LS-1 to LS-2, while both retrofitting schemes are different from LS-3. The results indicate that, with the increase of the number of retrofitted stories of MD-RC frame, the structural stiffness increases more obviously. Furthermore, for MD-RC frame with different damage states (i.e., DS1 and DS2), the natural frequency of moderate damaged structures (DS2) has significantly increased than that of minor damage (DS1), and with the decrease of modal order, the difference between DS1 and DS2 becomes increasingly obvious.  
Figure 9: Frequency ratios (FR) of DS1 and DS2 damage states MD-RC frames with and without retrofitted by three layout schemes: (**a**) MS1 mainshock; and, (**b**) MS2 mainshock.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives
### Conflict Management Perspective
#### 5.2.3 Peacemaking and Peace Enforcement  
The role of remote sensing in peacemaking and peace enforcement in many parts of the world has been pivotal for decades. Further case studies and examples of systematic exchange and sharing of conflict-related remote sensing data among both developing and the developed world can be helpful in the resolution of ongoing conflicts. Moreover, remote sensing can support and strengthen civil-military cooperation. With an increase in the number of satellites orbiting the earth, a huge amount of earth data has a significant potential for its use in peacemaking and peace enforcement. The synergistic approach of civilian science and remote sensing can help consolidate the environmental safety standards among the troubled regions [115].
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
## 3 Experiment Results
### Experimental Analysis of Pseudorange Noise  
Similarly, the experimental data given in Section 2.1 are used to perform the experimental analysis of pseudorange noise according to the Equation (8). CC is represented as the pseudorange noise.  
The time series of CC values of BDS-3 C35 at B2a are given in Figure 4. The gray line indicates the satellite elevation. There is a certain correlation between the pseudorange noise and satellite elevation. The higher the satellite elevation, the smaller the noise, and vice versa. In Figure 5, the time series of CC values of four BDS-3 satellites at different signals are depicted. The performances of the four satellites are almost the same, and all of them show elevation-dependent and periodic characteristics. Corresponding to the high signal power, the B2a signal possesses the lowest noise level among the three signals B1I, B3I, and B2a.  
Figure 2: Average C/N0s against the elevations of different signals. The left panel is only for the BDS signals, while the right panel is for all the signals of BDS-3, Galileo, and GPS.  
Figure 3: Comparison of _C/N0_ values at different signals for different receiver types. The horizontal axis in subfigure (**a**) represents 15 stations with the receiver type JAVAD, while that in subfigure (**b**) represents 24 stations with the receiver type LEICA, that in subfigure (**c**) represents 29 stations with the receiver type SEPT, and that in subfigure (**d**) represents 40 stations with the receiver type TRIMBLE.  
Similarly, the time series of CC values for BDS-2, Galileo, and GPS satellites are given in Figure 6. For BDS-2, the GEO and IGSO satellites have larger noise levels than MEO satellites, which can also be related to their higher orbital altitude. Possessing high signal power, BDS-2 B3I, GPS L5, and Galileo E5b show smaller noise than other signals. It is worth noting that although GPS L2 has a low signal power, its noise level is not significantly greater than the other signals, which indicates that the noise level is not only related to the signal power.  
Figure 4: Time series of CC values of the BDS-3 C35 satellite at the B2a signal during DOY 134–140 in 2019. The satellite elevation over time is indicated by the gray line.  
Figure 5: Time series of CC values of the four selected BDS-3 satellites at B1I/B3I/B2a during DOY 134–140 in 2019.  
Similar to the comparison of _C/N0_ on each station in Figure 3, STDs of CC values at different signals on each station are depicted in Figure 7, which are used to reflect the code noise level. Comparisons of different signals on different stations have almost identical characteristics, indicating that the pseudorange noise levels of different signals have little relationship with the receiver type. It is worth noting that the Galileo E5 signal exhibits optimal performance due to its high signal power.