esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Density  
Figure 5 presents the variation in density with the age of specimens using as reference the values obtained at the standard curing age of 28 days [6]. The presented data are the average value of 20 measurements for the age of 5 years and 30 measurements for the early age.  
It can be observed that even though all specimens were kept in the same conditions, the reference kept its value for the density as compared to 28 days. The change from 28 days to 5 years was less than 1%. On the other hand, all three mixes of RuC exhibited a much larger decrease in the density by as much as 12% for RuC80. A possible explanation could be that, even though the same water/cement ratio was used for all mixes, due to the hydrophobic nature of rubber particles, the excess water available for the hydration of the cement paste evaporated due to the storage conditions. Since the amount of cement was the same for all mixes (Table 1) and the curing and storing conditions were identical, it was  
Figure 4: Assessment of the dynamic modulus of elasticity.  
Figure 5: Variation in density with age of specimens.  
thought that the rate of hydration was similar for all mixes, and therefore the excess water was lost by evaporation.  
According to a recent study on rubberized concrete [18], the porosity and density of micro-cracks in RuC increased with the curing age, although the determined pore radius decreased. All other chemical reactions and rate of strength increase were similar to normal concrete. Earlier findings [19] showed that keeping concrete specimens in hot and, especially, dry conditions would result in overall lower values of the strength and elastic properties that are closely related to the density of the material.  
In a similar study [20], it was concluded that the addition of rubber particles increased the number of cracks inside the concrete, which favors the migration of chemically unbound water, weakened the ITZ between the rubber aggregate and cement paste, and entrained more air during the mixing process. As a direct, immediate consequence, the density of rubberized concrete decreased compared to the normal concrete.  
The presented trend is in line with data available in the scientific literature and with the general consensus that adding rubber to the concrete mix leads to a decrease in the density of rubberized concrete compared to the equivalent reference mix [11].
## 3 Experiments  
### Simulation Model Input Conditions  
The structure of the after-treatment system is shown in Figure 1, and the integrated after-treatment system adopts the technical route of DOC+DPF+SCR+ASC. Among them, the SCR system mainly consists of an injector, a mixing section, and an SCR carrier. The U-shaped after-treatment and tightly coupled layout can enhance the utilization of vehicle space and reduce the heat loss of exhaust. The static mixer is of the swirl plate type, consisting of a rotating stream ray and a porous tube, arranged coaxially to promote the mixing effect of the airflow, and improving the evaporation decomposition and uniform mixing of UWS. The injector model is a 3-hole Bosch high-flow nozzle with a nozzle diameter of 190 \(\upmu\)m. The diameter of the circle where the spray hole is located is 1.9 mm. The spray pressure is maintained at 7 bar (relative pressure), and the spray frequency is 1 Hz. The spray rate is adjusted by controlling the duty cycle. Fitting the particle size distribution using the Rosin Rammler method resulted in a particle reference diameter of 85 \(\upmu\)m. The uniformity index of the particle size distribution is 3.3154. The computational domain includes carriers of SCR and ASC, both of which have a straight-through structure and consistent structural parameters. The relevant parameters are shown in Table 2.  
\begin{table} \begin{tabular}{c c c c c} \hline \hline
**Code** & **Chemical Reaction** & **A** & \(\mathbf{E_{a}}\) **(\(\times\)104 )/mol** & **Source** \\ \hline R1 & \(Urea(g)\to HNCO(g)+NH_{3}(g)\) & \(4.900\times 10^{3}\) & 2.3 & [23] \\ R2 & \(HNCO(g)+H_{2}O(g)\longrightarrow NH_{3}(g)+CO_{2}(g)\) & \(2.500\times 10^{5}\) & 6.22 & [23] \\ R3 & \(Urea(m)+HNCO(l)\longrightarrow B\ddot{u}pert(m)\) & \(3.517\times 10^{11}\) & 7.545 & [24] \\ R4 & \(Urea(m)\longrightarrow HNCO(l)+NH_{3}(g)\) & \(2.000\times 10^{4}\) & 7.4 & [24] \\ R5 & \(B\ddot{u}pert(m)+HNCO(g)\longrightarrow Cya(g)+NH_{3}(g)\) & \(9.397\times 10^{20}\) & 15.868 & [24] \\ R6 & \(2\ddot{B}iuret(m)\longrightarrow Cya(g)+HNCO(g)+2 NH_{3}(g)\) & \(2.327\times 10^{24}\) & 38.155 & [25] \\ R7 & \(B\ddot{u}pert(s)+HNCO(g)\longrightarrow Annelle(s)+H_{2}O(g)\) & \(5.244\times 10^{18}\) & 24.85 & [25] \\ R8 & \(Cya(s)+NH_{3}(g)\longrightarrow Ammellide(s)+H_{2}O(g)\) & \(7.013\times 10^{20}\) & 29.665 & [26] \\ R9 & \(2\ddot{B}iuret(m)\longrightarrow Ammellide(s)+HNCO(g)+NH_{3}(g)+H_{2}O(g)\) & \(2.327\times 10^{26}\) & 25.776 & [24] \\ R10 & \(Ammelide(s)+NH_{3}(g)\longrightarrow Ammeline(s)+H_{2}O(g)\) & \(2.518\times 10^{21}\) & 31.063 & [26] \\ R11 & \(B\ddot{u}pert(m)\longrightarrow Urea(m)+HNCO(l)\) & \(1.107\times 10^{21}\) & 20.823 & [24] \\ R12 & \(Cya(s)\longrightarrow 3 HNCO(g)\) & \(1.001\times 10^{3}\) & 11.842 & [24] \\ \hline \hline \end{tabular}
###### Abstract  
Shield tunneling is becoming the preferred construction scheme for metro construction because of its advantages of fast construction speed and small disturbance. However, limited by process defects, the stratum movements induced by the construction of shield tunnels still affects the safety of nearby underground structures and aboveground buildings. Therefore, the reliable prediction of stratum movements is important. Described in this paper is an analysis method of the Greenfield stratum movements (Greenfield is an area of land that has not yet had buildings on it, stratum movements means the movement of various soil layers) caused by shield tunnel construction combining an elastic half-space model of mirror source-sink method with the use of modified analytical method. Based on the theoretical formula in this paper, not only can the curve of surface settlement trough be calculated, but also the three-dimensional displacement field of deep soil can be obtained. By comparing vertical and horizontal contour maps of Greenfield stratum movements, good consistency between theoretical formula results and centrifugal test results are shown. This solves the defects and limitations of existing two-dimensional formulas; furthermore, based on this, it is convenient to evaluate the effect on the other skewed underground structures through the elastic foundation beam and other similar methods; therefore, this paper can provide a wide guidance and service for the design and construction of underground engineering in the future.  
shield tunnel; stratum movements; analytical; mirror source-sink method; centrifuge modelling test +
Footnote †: journal: Applied Sciences
## 6 Simulation Results and Discussion
### Optimization Results
#### 6.1.4 PV/Battery System  
The PV/battery system is the second-lowest-cost alternative for electrifying the specified island. The simulation result indicated that the LCOE of 0.258 USD/kWh, NPC of USD 175,506 with zero pollutant emission gasses. In this system, in order to deliver the desired power to the community with 0% capacity shortage, a PV size of 60 kWp, and a  
Figure 10: BGED relative to **(a)** Stand-alone diesel system, **(b)** PV/Diesel/Battery system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Minigrid System**} & **LCOE** & **L** **(USD/kWh)** & **NPC (USD)** & **Emission** & **RF (\%)** & **Unmet** & **Load (\%)** & **BGED (km)** \\ \hline PV/diesel/battery & 0.175 & 119,139 & 5159 & 86.4 & 0 & 1.25 \\ PV/battery & 0.258 & 175,506 & 0 & 100 & 0.09 & 2.15 \\ PV/wind/diesel/battery & 0.304 & 207,388 & 18,324 & 55.3 & 0 & 2.66 \\ PV/wind/battery & 0.323 & 219,736 & 0 & 100 & 0.09 & 2.86 \\ Stand-alone diesel & 0.346 & 235,734 & 38,261 & 0 & 0.02 & 3.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Possible configurations for the selected site.  
battery size of 75 kWh were required. However, due to the intermittent nature of solar energy, the PV/Battery system must be of a relatively big size to cope up the day and night loads. Having a big size leads the system to have high excess electricity (66.3%). On the other hand, when we calculated a capacity shortage of 5%, the excess electricity was reduced to 35.6% and the unmet load was 4.5%. This assured that in the PV/battery system, when 0% capacity shortage is considered, the system size will be higher. When a capacity shortage of greater than zero is considered, the system is incapable of handling peak loads. Considering these limitations, the authors continued to search for another optimal solution to electrify the island.
## 3 Results and Discussion
### Catalytic Mechanism
As shown in Figure 10a, the ratio of Fe3\({}^{+}\)/Fe2\({}^{+}\) increased from 1.03 to 1.25 after the degradation of SMX, indicating that Fe(II) was partially converted to Fe(III) during the reaction, as Fe2\({}^{+}\) can react with HSO5\({}^{-}\) (Equations (18) and (19)). Similar to other bimetallic catalysts, Bi also plays an important role in Bi@Fe/CN/PMS system. As shown in the high-resolution Bi 4f spectrum of fresh and used Bi@Fe/CN-3 (Figure 10b), the peaks at 159.5 eV and 164.8 eV, corresponding to Bi (V) in Bi 4f\({}_{7/2}\) and Bi 4f\({}_{5/2}\), appeared in used Bi@Fe/CN-3 while not being present in fresh Bi@Fe/CN-3 spectrum. The results indicated that Bi\({}^{3+}\) was partially converted to Bi\({}^{5+}\) due to the direct reaction between Bi\({}^{3+}\) and HSO5\({}^{-}\) (Equation (20)), which encourages the production of active free radicals [36]. \[\text{Fe}^{2+}+\text{HSO}_{5}^{-}\rightarrow\text{Fe}^{3+}+\text{SO}_{4}^{ \bullet-}+\text{OH}^{\bullet-} \tag{18}\]  
\[\text{Fe}^{2+}+\text{HSO}_{5}^{-}\rightarrow\text{Fe}^{3+}+\text{SO}_{4}^{ \circ-}+\text{OH} \tag{19}\]  
\[\text{Fe}^{3+}+\text{HSO}_{5}^{-}\rightarrow\text{Fe}^{2+}+\text{SO}_{5}^{ \bullet-}+\text{H}^{+} \tag{20}\]  
\[\text{Bi}(\text{III})+2\text{HSO}_{5}^{-}\rightarrow\text{Bi}(\text{V})+2 \text{SO}_{4}^{\bullet-}+2\text{OH}^{-} \tag{21}\]  
\[\text{Bi}(\text{V})+2\text{HSO}_{5}^{-}\rightarrow\text{Bi}(\text{III})+2 \text{SO}_{5}^{\bullet-}+2\text{H}^{+} \tag{22}\]  
Figure 10: XPS spectra of (a) Fe 2p, (b) Bi 4f and (c) O 1s in fresh and used Bi@Fe/CN-3 samples. Additionally, previous studies have demonstrated that surface lattice oxygen can be converted to \({}^{1}\)O\({}_{2}\)[53]. Compared with fresh Bi@Fe/CN-3, the lattice oxygen of used Bi@Fe/CN-3 dropped significantly from 89.52% to 59.63% (Figure 10c), which suggests that lattice oxygen plays an important role in the degradation of SMX. Due to the conversion of lattice oxygen to reactive oxygen species (O\({}^{*}\)) (Equation (23)), \({}^{1}\)O\({}_{2}\) was further produced during the reaction with PMS (Equation (24)) [54]. The possible mechanism of PMS activation in Bi@Fe/CN-3/PMS system was proposed and illustrated in Figure 11.  
\[\text{O}_{\text{Lattice}}\rightarrow\text{O}^{*} \tag{23}\]  
\[\text{O}^{*}+\text{HSO}_{5}^{*}\rightarrow\text{ }^{1}\text{O}_{2}+\text{SO}_{4}^{2-}+\text{H}^{+} \tag{24}\]
# A Method to Accurately Estimate Fish Abundance in Offshore Cages
## 2 Proposed Fish Counting Algorithm
### Theoretical Background
#### 2.1.2 Backscattering Coefficient  
When the individual targets are very small and there are many in the sampled volume, their echoes combine to form a integrated received signal. However, the echo intensity is still a measure of the biomass in the water column. The basic acoustic measurement is the volume backscattering coefficient, \(s_{v}\), given by  
\[s_{v}=\frac{\sum\sigma_{bs}}{V} \tag{2}\]where \(V\) is the sampled volume occupied by a scattering medium or multiple discrete targets. Similarly, area backscattering coefficient, \(s_{a}\), is a measure of the energy returned from a layer between two depths \(z1\) and \(z2\) in the water column. It is defined as the integral of \(s_{v}\) with respect to depth through the layer and is given by  
\[s_{a}=\int_{z1}^{z2}s_{v}dz \tag{3}\]
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
## 2 Approach
### Data Processing  
Each data acquisition attempt produced 838 traces, with each trace sampled into 4183 points, corresponding to specific positions along the fiber. This setup provided 838 time-domain points for each position along the fiber. The time-series data for each position was then subjected to fast Fourier transform (FFT), generating a 3D plot showing the amplitude of the signal across available frequencies and distances along the fiber (Figure 5a).  
Due to the setup configuration, the amplitude and position of the detected acoustic event showed slight fluctuations between data acquisition attempts. To minimize the impact of these variations on the results, the signal values were averaged in the spatial domain within the region where the acoustic event occurred (Figure 5b) for each frequency. Subsequently, an average value of the acoustic perturbation signal at each frequency was obtained across 100 data acquisition attempts. This data processing approach will hereafter be referred to as a \"measurement\".  
The error \(\sigma\) for this technique was calculated as:  
\[\sigma=\frac{MSE}{\overline{A}}, \tag{7}\]  
where \(MSE\) is the mean squared error between the two consecutive measurements of the same FUT segment and \(\overline{A}\) is the mean signal value (Figure 6). The average \(\sigma\) value for these measurements was found to be 492 rel. units, which is approximately five times lower than that for the most similar FUTs in the study--\"Fiber 6\" and \"Fiber 1\"--making the technique's error level acceptable.  
Figure 5: **(a)** Data obtained from the FFT of the test signal acquired with the Corning SMF-28 fiber in the DAS setup, with a red square highlighting one of the cross-sections; **(b)** a cross-section view at the 2000 Hz signal frequency, where the red square indicates the points selected for averaging.
## 2 Analysis Model and Experimental Verification
### Friction Coefficient Curve for Special Operating Conditions  
In the analysis process of finite element software, the contact problem is the most important part of the entire process. In the past, the finite element analysis of the vibration behavior of WLRB is mostly steady-state, and the friction coefficient is a stable value that does not change. However, in the real process of initiation, the friction coefficient is time-varying, is strongly related to factors such as rotational speed, and has a strong impact on vibration behavior. During the startup process of WLRB, the rotational speed is too low to form a dynamic pressure water film. The friction state is mixed lubrication or even dry friction, with a large friction coefficient, and the frictional vibration behavior is also significantly different from that of the stable operation of the bearing. The friction coefficient in this article is measured through experiments and correlates with the rotational speed, which also ensures the authenticity and accuracy of the finite element analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (kg\(\cdot\)m\({}^{-3}\))** & **Young’s Modulus (MPa)** & **Poisson’s Ratio** \\ \hline Copper & 8900 & 106,000 & 0.35 \\ Rubber & 1271 & 13.71 & 0.47 \\
45 carbon steel & 7800 & 206,000 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Configuration** \\ \hline CPU & Intel (R) Core (TM) i7-9700 \\ GPU & NVIDIA GeForce GTX 1660 Ti \\ RAM & DDR4 16 G (8 G \(\times\) 2) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computer configuration.  
The pretest consists of two parts. The first part is to acquire the friction coefficient under low-speed operating conditions. The second part is to acquire the friction coefficient and rotational speed loading curve during the startup process. UMT-TriboLab friction and wear testing machine is used to conduct preliminary experiments, as shown in Figure 2.  
The step of the low-speed operating condition pretest is to apply a load of 50 N to the block and apply a rotational speed of 50 rpm (linear speed of about 0.09 m\(\cdot\)s\({}^{-2}\)). Friction coefficient curve is extracted within 4 s after the friction coefficient stabilizes, as shown in Figure 3. As can be seen from Figure 3, the friction coefficient is stable around 0.15, and, in the subsequent analysis, the friction coefficient for low-speed operating condition is set to 0.15.  
The pre-experimental step of the startup process is to apply a load of 50 N to the block and apply a rotational speed of 216 rpm (linear speed of about 0.4 m\(\cdot\)s\({}^{-2}\)). The friction coefficient curve and speed curve are extracted within 1 s of startup process, as shown in Figure 4. The obtained friction coefficient curve and speed curve are written into the software for subsequent calculations.  
Figure 3: COF curve and speed loading curve of low-speed operating condition.  
Figure 2: Photos of test device: (**a**) UMT-TriboLab; (**b**) test placement.
###### Abstract  
Inverse Synthetic Aperture Radar (ISAR) serves as a valuable instrument for surveillance of space targets. There has been a great deal of research on space target identification using ISAR. However, the polarization characteristics of space target components are rarely studied. Polarimetric Inverse Synthetic Aperture Radar (PolISAR) comprises two information dimensions, namely, polarization and image, enabling a more comprehensive understanding of target structures. This paper proposes a space target structure polarization interpretation method based on component decomposition and PolISAR feature extraction. The proposed method divides the target into components at the stage of modeling. Subsequently, electromagnetic calculations are performed for each component. The names of these components are used to label the dataset. Multiple polarization decomposition techniques are applied and many polarization features are obtained. The mapping correlations between the interpreted results and authentic target structures are improved through preferential selection of polarization features. Ultimately, the method is validated through analysis of simulation and anechoic chamber measurement data. The results show that the proposed method exhibits a more intuitive correlation with the authentic target structures compared to traditional polarized interpretation methods based on Cameron decomposition.  
automatic target recognition; component decomposition; polarization interpretation; feature extraction
## 2 Results and Discussion
### Validation of the Quantitative Method
#### 2.2.1 Linearity of Calibration Curve in Different Wine Matrix
Reproducibility of 14, 16, 18 and 20 were the best of all compounds, with an RSD of less than 10%. This study showed that all the terpen in the standards and wines were stable and did not degrade or react under the chosen storage conditions (\(-18\)\({}^{\circ}\)C).
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 4 Proposed Platoon-Based Coordination Scheme to Minimize Traffic Congestion
### Mechanisms for Platoon Control
#### 4.4.1 Adaptive Cruise Control (ACC)  
In order to maintain the platoons' safety movement, the platoon leaders are set to run the ACC model [39]. In the ACC model, a vehicle can obtain the distance information from the preceding vehicle through the onboard sensors, thereby controlling the distance between each vehicle by adjusting the velocity and acceleration. According to the ACC system, the velocity \(v_{j}\) and acceleration \(a_{j}\) of a vehicle at the position \(x_{j}\) are defined as follows:  
\[\begin{cases}v_{j}=\frac{dx_{j}}{dt}=\dot{x}_{j},\\ a_{j}=-\frac{1}{T}(\dot{\epsilon}_{j}+\lambda\delta_{j}),\\ \delta_{j}=x_{j}-x_{j-1}+L_{j-1}+v_{j}T\\ \dot{\epsilon}_{j}=v_{j}-v_{j-1},\end{cases} \tag{9}\]  
where \(\dot{\epsilon}_{j}\) is the relative velocity between vehicle \(j\) and \(j-1\), \(j-1\) is the tailing vehicle of the front platoon, \(T\) is the time gap, \(L_{j-1}\) is the length of the vehicle in front and \(\delta_{j}\) is the distance error between the actual distance \(x_{j}-x_{j-1}+L_{j-1}\) and the desired distance \(v_{j}T\). The distance \(v_{j}T\) grows proportionally with velocity and, for both safety and stability reasons, \(T>1\). \(\lambda\) is a design parameter (default set to 0.1).
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
###### Abstract
Thanks to the fundamental nature of STLC, the phase distortion of the transmitted signals caused by wireless channels is compensated at the receiver. In other words, on the AP side, the signals of each STA are still aligned on their axis with only the channel gain applied; hence the AP can detect the transmitted signals of each STA through an ML detector without joint ML detection. On the other hand, the AP requires each STA's transmit power and effective channel gain called partial CSI to detect transmitted signals. This is indispensable in the uplink STLC-NOMA system for blindly decoding the signals transmitted from STAs, but it has not been well-elaborated in literature [22; 23]. Therefore, we further design a blind energy estimation (BEE) scheme for the proposed low-complexity STLC-NOMA system based on expectation-maximization (EM) for Gaussian mixture model (GMM) [24; 25]. Although a machine learning-based blind decoding method for STLC systems has been proposed in Ref. [26], our algorithm can be implemented without a learning engine; hence it has low-complexity and high cost-efficiency. More specifically, the AP exploits the EM algorithm that infers the parameters of a GMM as the blind transmit power and effective channel gain estimation method for both STAs. As a result, our main contributions in this paper can be summarized as follows. * We proposed a novel low-complexity STLC-NOMA system for two-user uplink IoT networks. We employ amplitude-shift keying (ASK) modulators for both STAs, and align them respectively to each axis in the I-Q plane;
* We theoretically analyze the exact BER performance of the novel uplink STLC-NOMA system. The Monte-Carlo simulations validate that the mathematical BER expressions of the proposed system are precisely the same as the numerical results;
* We design an EM-based BEE method estimating the transmission power and effective channel gain of each STA at the AP. Then, the AP can detect the signals transmitted from the two STAs via an ML detector. The remainder of this paper is organized as follows. In Section 2, we describe the system model of the proposed novel low-complexity uplink STLC-NOMA system. In Section 3, we mathematically analyze the exact BER performance and spatial diversity order for each STA. In Section 4, we design a blind energy estimation (BEE) scheme based on the EM for GMM. In Section 5, numerical results are presented. The conclusions are drawn in Section 6.