esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 3 Response Surface Analysis
### Carbon Emissions and Economic Analysis of the Geopolymer Stabilizer  
After collecting the average prices of local suppliers, an economic analysis was carried out on the geopolymer stabilizer with the optimal mix ratio in this paper. The cost details are shown in Table 7. As can be seen from the table (ignoring the cost of water), the cost of silica-alumina raw materials for geopolymers (193.64) is much lower than that of cement, and the total cost of geopolymers is higher than that of cement. However, fly ash and slag are industrial wastes. If they are not used, the local waste treatment standard is 300 yuan/ton. Therefore, the overall economic benefits of geopolymers are still significant. From the cost analysis of geopolymers, it can be seen that the cost of alkali activators is relatively large. Some scholars [36] have proposed to reduce the degree of incorporation of alkaline solution. [PERSON] et al. [37] proposed that the slag was activated by waste alkaline  
Figure 13: Pore diameter distribution of the soft soil stabilized using different mixture ratios.  
water from a chemical company to prepare a geopolymer. This also shows that the cost of geopolymer still has great development potential.  
Geopolymers enable the construction industry to meet the sustainability requirement and reduce the global impact (lower carbon cost). Compared with cement, geopolymer can reduce carbon emissions by 44-64% [38]. Different types of industrial wastes and maintenance methods will affect the carbon emissions of geopolymers, but alkali activators are the main source of carbon emissions from geopolymers, accounting for 20-65% of the total emissions [39; 40]. In the future, choosing a low-carbon alkali activator and reducing the dosage will be an important avenue for further carbon reduction of geopolymers.
## 4 Results  
### Factor Scores  
The scores of natural and human factors were shown in Table 4, the factor scores varied greatly for different periods. Natural factors scored higher than human factors for all periods, and temperature obtained the biggest negative score, land use obtained the biggest positive score. From the low to high emission scenarios, the score of population increased rapidly, and the score of vegetation decreased.  
For the base period, temperature obtained the biggest negative score, and land use and elevation obtained the biggest positive score. Precipitation and temperature were factors that contributed significantly to vegetation vulnerability for the near future period and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Base Period**} & \multicolumn{4}{c}{**Near Future Period**} & \multicolumn{4}{c}{**Distinct Future Period**} \\ \cline{3-8}  & & **SSP1-2.6** & **SSP2-4.5** & **SSP5-8.5** & **SSP1-2.6** & **SSP2-4.5** & **SSP5-8.5** \\ \hline Precipitation & \(-\)0.67 & \(-\)0.69 & \(-\)0.62 & \(-\)0.56 & \(-\)0.69 & \(-\)0.65 & \(-\)0.60 \\ Temperature & \(-\)0.79 & \(-\)0.85 & \(-\)0.80 & \(-\)0.75 & \(-\)0.86 & \(-\)0.84 & \(-\)0.79 \\ Elevation & 0.77 & 0.81 & 0.80 & 0.83 & 0.82 & 0.81 & 0.87 \\ Vegetation & \(-\)0.61 & \(-\)0.63 & \(-\)0.60 & \(-\)0.50 & \(-\)0.66 & \(-\)0.62 & \(-\)0.51 \\ Population & 0.60 & 0.59 & 0.70 & 0.83 & 0.68 & 0.78 & 0.89 \\ Land use & 0.78 & 0.64 & 0.70 & 0.72 & 0.74 & 0.80 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Natural and human factor scores of different periods in MRB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **2019** & **2020–2059** & **2060–2099** \\ \hline Population density/person-km\({}^{-2}\) & 121.17 & 197.79 & 261.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Population density of near future period and distinct future period in MRB.  
Figure 2: Vegetation ecological water (unit:10\({}^{10}\) m\({}^{3}\)) of near future period and distinct future period in MRB.  
distinct future period. From low to high emission scenarios, for the near future period, the scores of precipitation, temperature, and vegetation decreased, while the scores of land use and population increased. The situation was the same for the distinct future period.
## 3 Results
### Excited Jet  
The results obtained in the previous section have shown that for the cases in which the turbulence intensity is low, the solutions obtained in the Cartesian and cylindrical coordinate system differ significantly. In this section, we analyse how these findings translate to the excited jets and the formation of the side-jets. To trigger them, forcing is applied according to the Equation (9) defined in Section 2.1. As discussed by [PERSON] and [PERSON] [29], the forcing is effective only when its amplitude is higher than the level of inlet turbulence intensity. Hence, the amplitude \(A/U_{0}=5\%\) and the frequency based on \(\text{St}_{\text{excit}}=0.3\), which corresponds to the subharmonic frequency of the preferred mode in the natural jet, are assumed as forcing parameters. Note that this frequency is associated with the vortex pairing process, and, as shown by [PERSON] and [PERSON] [30], the forcing at this particular frequency causes the generation and largest growth of an additional maximum in the profile of the axial velocity fluctuations.  
Figure 7 shows the profiles of the axial velocity fluctuations along the axis obtained for different levels of the turbulence intensity. The forcing significantly amplifies the fluctuations and causes the emergence of the above-mentioned second maximum of the fluctuations at \(x/D\approx 3\). This localisation agrees very well with [30; 31]. The observed development of the perturbation close to the inlet is attributed to the nonlinear interactions of the primary structures that emerged due to the Kelvin-Helmholtz instability in the direct vicinity of the nozzle. They interact and form larger structures at \(x/D\approx 3\). Note that this distance is independent of _Ti_. On the contrary, the maximum level that is attained by fluctuations is visibly dependent on _Ti_, and it turns out that it is larger when _Ti_ is low, i.e., \(Ti=0.1\%\) and \(Ti=0.01\%\). This means that a high level of natural turbulence prevents the formation of strong vortical structures and their downstream evolution and amplification. Concerning the impact of the coordinate system involved in the simulations, it can be seen that the fluctuation profiles are similar for the cases \(Ti=1.0\%\) and \(Ti=0.1\%\). Again, a strong influence of the coordinate system is seen for \(Ti=0.01\%\), especially when the profiles downstream of the location \(x/D=5\) are compared. The local maxima occur in almost exactly the same localisations as for the non-excited jets (\(x/D\approx 8.0\) and \(x/D\approx 10.0\)), but the one predicted in the Cartesian coordinate system is visibly higher. The next section examines the extent to which the turbulence intensity and coordinate system in which the simulations are performed affect the prediction of the formation of side-jets.  
Figure 7: Profile of the axial velocity fluctuations along the jet axis in excited jets in the Cartesian and cylindrical coordinate systems at \(Ti=1.0\%\), \(Ti=0.1\%\) (**a**) and \(Ti=0.01\%\) (**b**).
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Electrical Model
#### 2.6.2 Battery  
The battery model used in this paper assumes that the voltage of the battery is considered equal to the battery's electromotive force EMF, which is firmly related to SoC in series for an internal resistance \(R_{\text{BS}}\) and several resistor-capacitor blocks [7; 34; 35]. \(R_{\text{BS}}\) includes all the resistances between electrodes, current collectors, and electrolytes [34]. Those resistor-capacitor blocks represent the charge transfer and diffusion process [7]. Figure 7 shows the model used in this paper. Where the \(RC_{1}\) block has a more considerable time constant, therefore, corresponds to the slowest response, on the contrary, the \(RC_{2}\) block resembles a quick answer.  
The battery state of charge SoC is calculated as:  
\[\text{SoC}(t)=\text{SoC}(t_{0})-\frac{1}{C_{n}}\int\limits_{t_{0}}^{t}I_{B}(t)dt \tag{49}\]  
where \(\text{SoC}(t_{0})\) is the initial value of the SoC, \(C_{n}\) is the nominal capacity of the battery (in ampere-hours). \(I_{B}(t)\) is the instantaneous current (positive for discharge and negative for charge). As discussed earlier, the EMF is heavily associated with SoC. Analyzing the circuit of Figure 7 the battery voltage is defined by:  
\[V_{B}(t)=\text{EMF}(t)-R_{BS}(t)I_{B}(t)-V_{RC_{1}}(t)-V_{RC_{2}}(t), \tag{50}\]  
where \(R_{BS}(t)I_{B}(t)\) is the voltage drop across resistor \(R_{\text{BS}}\), \(V_{RC_{1}}(t)\) and \(V_{RC_{2}}(t)\) are the voltage drop across resistor-capacitor blocks \(RC_{1}\) and \(RC_{2}\) respectively, given by:  
\[V_{RC_{i}}(t)=V_{RC_{i}}(t-\Delta t)+\frac{\Delta t}{C_{i}(t)}\bigg{(}I_{B}(t) -\frac{V_{RC_{i}}(t-\Delta t)}{R_{i}(t)}\bigg{)} \tag{51}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \(\eta\) & 3.25 [-] \\ \(V_{q}\) & 1.12 [eV] \\ \(I_{\text{sc}}\) & 0.41 [A] \\ \(V_{\text{oc}}\) & 2.564 [V] \\ \(R_{\text{s}}\) & 0.2242 [\(\Omega\)] \\ \(\Delta I_{\text{Sc}}\) & 0.07 \\ \(R_{p}\) & \(\infty\) [\(\Omega\)] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the solar panel model.  
The values of internal parameters of the battery were approximated through look-up tables (LUTs), which were made through intermittent discharging experimental results [34] and are presented in the Appendix A.
# Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
## 2 Materials and Methods
### Dataset  
We used a total of 80 Computed Tomography Pulmonary Angiograms (CTPA) from the CAD-PE challenge [14; 18; 19]. While the dataset was not obtained for this work, we describe it here for the sake of clarity. The original scans were supplied from six different hospitals and coordinated by the \"_Central Diagnostic Radiology Unit_\" in Madrid (Spain). SIEMENS Somaton Sensation 40 scanners were used for the data acquisition with a pixel size between 0.58 and 0.85 mm and a slice thickness oscillating between 0.75 and 1.5 mm.  
Each scan was analyzed independently for the presence of PE by three radiologists with more than 15 years of experience establishing the reference standard. In the case of discrepancy, a majority voting scheme was employed. Furthermore, each radiologist marked regions of interest (ROI) around all the visible dots using the sagittal and coronal views. The regions of interest were segmented by applying an intensity threshold, binary closing operations and connected component analysis. Each of the individual connected components represented a different embolus clot, the Figure 1 shows an example of these findings. There was a total of 167 emboli in 60 training cases, with a per-case total average volume of 5.04 \(\times\) 10\({}^{3}\) voxels. The number of emboli per scan ranged between 0 and 21. A variation in the reference standard was performed by dilating the border of each embolus with an epsilon value as tolerance margin (\(\epsilon\) = 2 mm and \(\epsilon\) = 5 mm). This modification affected the number of emboli and their size in the reference segmentations, but was only used for test purposes, leaving the original reference standard (\(\epsilon\) = 0 mm) to train and validate the system.
## 6 Simulation Analysis
\begin{array}{l}\frac{\mathrm{d}x_{i}(t)}{\mathrm{d}t}=-x_{i}(t)+u_{1 }(t)\\ y_{\text{BER}}(t)=-3.3x_{i}(t)-9\end{array} \right. \tag{34}\]  
\[\left\{ \begin{array}{l}\frac{\mathrm{d}x_{i}(t)}{\mathrm{d}t}=-x_{i}(t)+u_{1 }(t)\\ y_{\text{BER}}(t)=-2.4x_{i}(t)-14.8\end{array} \right. \tag{35}\]  
From Figure 3, it can be observed that when the BER of the wireless communication system reaches the target value of \(10^{-4}\), the signal-to-jamming-plus-noise ratio of the system under QPSK modulation is \(-1.5\). Furthermore, when \(\mathrm{SJNR}<-1.5\), the bit error rate of the system under QPSK modulation is greater than the target value of \(10^{-4}\), while the BER under BPSK modulation remains within the target range. Therefore, to ensure that the system's BER remains within the target value while switching to the optimal modulation and coding scheme, the switching intervals for the two modulation and coding schemes are set as \(\mathrm{SJNR}<-1.5\) and \(\mathrm{SJNR}>-1.5\), respectively. The system's state-space equations are then given by:  
\[\left\{ \begin{array}{l}\dot{\mathbf{x}}(t)=\mathbf{A}\mathbf{x}(t)+\mathbf{ Bu}_{1}(t)=\left[\begin{array}{cc}A_{1}x_{1}(t)+B_{1}u_{1}(t)&x_{1}<-1.5\\ A_{2}x_{2}(t)+B_{2}u_{1}(t)&x_{2}>-1.5\end{array}\right]\\ \mathbf{y}_{\text{BER}}(t)=\left[\begin{array}{cc}-3.3x_{1}(t)-9\\ -2.4x_{2}(t)-14.8\end{array}\right]\end{array} \right. \tag{36}\]  
Figure 9 presents a simulation comparison of the BER curves for the proposed algorithm, the power control algorithm based on LQR for unmodeled switching systems, and the multi-parameter control anti-jamming algorithm for wireless communication systems based on PID under random jamming conditions. From Figure 9a, it can be observed that the communication system's transmission channel is AWGN with a signal-to-noise ratio of 30 dB, and the channel is subjected to random pulse jamming with power levels of 1 dBm and 5 dBm. Figure 9b shows the BER curves of each system as a function of time. The analysis reveals that the power-controlled communication system without the switching mechanism experiences a sudden increase in BER under higher power jamming, peaking at \(10^{-3.833}\), which significantly affects the transmission reliability of the communication system. The BER of the multi-parameter control anti-jamming algorithm for wireless communication systems based on PID shows considerable fluctuation and slower response speed. In contrast, the proposed algorithm's bit error rate is almost never above the target bit error rate.
## 3 Results
### Transmission of Liver Normal Tissue and Malignant Tumor Tissue  
Next, we prepared formalin fixed samples with a thickness of 200 \(\upmu\)m in which normal tissue and tumor tissue were mixed and measured the transmission of the samples at a frequency of 0.1 THz. The boundary between the normal and tumor tissues could be visually confirmed. Fibrosis advanced around the tumor tissue, and vacancies such as blood vessels also increased around the tumor tissue. We selected 2 mm\({}^{2}\) tissues with fewer vacancies by visual observation and transmission data were measured, from which the mean and standard deviation of the transmission was calculated. Table 1 shows the measured transmission of normal and tumor tissues for some samples. The transmission of the normal tissue was distributed in a relatively narrow range of 33-37%, whereas the transmission of the tumor tissue was distributed in a rather wide range of 31-38%. There were some samples in which the transmission of the normal tissue was higher than that of the tumor tissue and some samples in which the transmission of the normal tissue was lower than that of the tumor tissue. However, the \(p\)-value for each sample was \(\leq\)0.05, so that the transmissions for the two tissues were significantly different. Although the difference in transmission between tissues was small, our experiments suggest that the malignant tumor tissue can be distinguished from the normal tissue by THz imaging. We have calculated the \(p\) values when we hypothesized that the permeability of tumor tissue is equal to that of normal tissue.
## 5 Mechanical Properties
### Precipitation Strengthening  
Precipitation strengthening is a key strategy to improve the mechanical properties of Mg alloys. It is known that basal precipitates can strengthen against twinning, resulting in an increasing critical resolved shear stress (CRSS), which is necessary for the continued plastic deformation of Mg-Al alloys. Also, the precipitates can act as obstacles to hinder the movement of dislocations during deformation. The Orowan mechanism can be applied to express the contribution of precipitates to the increasing strength, and the equation can be calculated as follows [90]:  
\[\Delta\sigma_{0}=\sqrt{3}\bigg{(}\frac{Gb}{L}\bigg{)} \tag{4}\]  
where \(L\) is the average distance among the precipitates. The sizes and morphologies of precipitates are important factors that influence the mechanical properties, and high-performance Mg alloys are usually typically accompanied by fine and dispersed precipitates [94].  
Figure 9 shows the effect of aging precipitates on the mechanical properties of Mg-1.4 Gd-1.2Y-0.4 Zn investigated by [PERSON] et al. [94]. The results show that the aging metaphase \(\beta^{\prime}\) phases were distributed in the grains uniformly, and the \(\beta^{\prime}\) phases can strengthen the aged alloy by hindering the dislocation motion. The aging treatment can further increase the UTS from 373 MPa of as-extruded alloy to 461 MPa and YS from 303 MPa to 395 MPa, while elongation decreased to 6.0%. The hindering dislocation motion could strengthen the strength but cause a premature fracture.  
[PERSON] et al. [96] investigated the aging hardening response of Mg-8 Sn-2 Al alloy with Bi, Ag addition, and the results showed that Mg2 Sn precipitates formed in peak-aged alloys. Bi and Ag elements can act as heterogeneous nucleation sites and restrict the growth of precipitates to enhance the increasing microhardness from 62.2 HV to 69.9 HV.  
To solve the aging softening of Mg-Li-Zn based alloys, [PERSON] et al. [97] designed a new Mg-Li alloy that exhibited a stable age-hardening mechanism. The results showed that a semi-coherent MgLi\({}_{2}\) (ZnAg) precipitate can lead to an important age hardening of the Mg-Li alloy after water quenching.
# Study on Vibration Characteristics of Multi-Beam Structures with Stick and Slip at Joints
## 3 Result Analysis and Discussion
### Multi-Beam Situation
#### 3.6.2 Influence of Excitation Position  
Due to the nonlinear connection factors, the position of the applied excitation force will affect the structural transfer matrix, leading to differences in the propagation of elastic waves. Figure 18 illustrates the frequency response curves of the structure under different excitation force application positions. The observation point positions remain consistent with those in Section 3.6.1. The length of each segmented beam is taken from the symmetric case Combination 1 in Section 3.6.1. Five excitation point positions are selected, with the coordinates \(x_{1}\) = 0.2 m, \(x_{1}\) = 0.4 m, \(x_{2}\) = 0.2 m, \(x_{3}\) = 0.2 m, \(x_{3}\) = 0.4 m, and the coordinate index indicates which beam the excitation force is acting on. When the excitation force is applied at positions 1 and 5, the modal frequency response curves at each observation point are closest to the linear \"sharp\" state. Compared to other action positions, positions 1 and 5 are closer to the outer side of the structure, and as the excitation position moves toward the interior of the structure, the resonance frequency range of the frequency response curve increases, and the 'peak clipping' becomes more pronounced. From the equivalent stiffness variation curve (Figure 19), the phenomena of the resonance frequency shift and the change in symmetry of the equivalent stiffness are more clearly observable.  
Figure 17: The changes in the equivalent stiffness of the connections under different length combinations, for which the coordinates were (1) \(L_{1}\) = 0.6 m, \(L_{2}\) = 0.4 m, \(L_{3}\) = 0.6 m; (2) \(L_{1}\) = 0.65 m, \(L_{2}\) = 0.4 m, \(L_{3}\) = 0.55 m; (3) \(L_{1}\) = 0.7 m, \(L_{2}\) = 0.4 m, \(L_{3}\) = 0.5 m; (4) \(L_{1}\) = 0.75 m, \(L_{2}\) = 0.4 m, \(L_{3}\) = 0.45 m; (5) \(L_{1}\) = 0.8 m, \(L_{2}\) = 0.4 m, \(L_{3}\) = 0.4 m. (a) Joint 1; (b) joint 2.
For deep ultraviolet applications integrated on \(c\)-plane (002) sapphire substrates, the ZnGa\({}_{2}\)O\({}_{4}\) thin films were prepared using the diethylzinc (DEZn), triethylgallium (TEGa), and oxygen (99.999%), and were transformed from \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) to ZnGa\({}_{2}\)O\({}_{4}\) with increasing DEZn flow rate [14]. This is because the sufficient tensile strain in Zn-doped \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) provides a driving force for ZnGa\({}_{2}\)O\({}_{4}\) formation, obtaining the carrier concentrations up to 6.72 \(\times\) 10\({}^{16}\) cm\({}^{-3}\) and the resistivity down to 67.9 \(\Omega\)-cm. For applications of green storage and long persistent phosphors, ZnGa\({}_{2}\)O\({}_{4}\) spinel ceramics doped with Mn\({}^{2+}\) ions were prepared by a solid-state reaction at 1200 \({}^{\circ}\)C in air and demonstrated a relatively long and strong green afterglow due to the holes released from shallow traps of zinc vacancies [15]. Although the related studies on CO gas sensors are diverse and have made major breakthroughs, much less attention has been focused on the theoretical calculation of CO gas sensors. In addition, the ZnGa\({}_{2}\)O\({}_{4}\) layers are suitable as gas sensors, which can successfully detect CO, CO\({}_{2}\), SO\({}_{2}\), NO, and NO\({}_{2}\), and have wide bandgap of 5.2 eV, excellent optical characteristics, such as transparency in the near ultraviolet region, good conductivity, high resistance, as well as high thermal and chemical stability [1,14]. Accordingly, we have performed sufficient models to accurately predict CO gas adsorption on clean ZnGa\({}_{2}\)O\({}_{4}\)(111) and Pd-decorated ZnGa\({}_{2}\)O\({}_{4}\)(111) surfaces. Our analysis will focus on the work functions variation, adsorption energy, and the catalytic properties of palladium.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 3 Modeling Analysis  
### FANETs Communication Structure  
Figure 2 illustrates a typical example of a UAV ad-hoc network structure, which encompasses several common application scenarios for FANETs. In this network, each UAV serves as a node, with the ground station acting as the destination node. If a drone transmits a signal from the starting node (labeled \"source\" in Figure 2) to the ground station, other drones in motion act as relay nodes to forward the signal until it reaches the ground station, labeled by a red arrow in Figure 2. Communication is limited to nodes within the communication range. The primary challenge is to determine the optimal path for transmitting the signal to the destination without data loss while also optimizing the end-to-end delay.
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
## 2 Physical and Mathematical Model  
### Geometrical Model and Mesh  
Figure 1 shows the modified covering airfoil based on NACA0012. The covering is made of Fe-Mn-Si shape memory alloy, which is set at a cavitation-prone position. Additionally, the constant pressure fluid (\(p_{in}=1\) bar) is passed into the covering. Under normal working conditions, the internal and external pressure difference does not deform the memory metal within a certain range. However, cavitation of the airfoil occurs under abnormal operating conditions, and under the condition of increasing pressure difference between the inner and outer sides, the center of the memory alloy covering will deform and eventually form an eggshell bulge and cavitation will suppressed by impeding backflow [29].  
According to the literature and the research results of other experts and scholars on multiphase flow models, the Euler-Euler model allows the simulation of multiple independent and interacting phases; that is, the interaction between the cavitation bubble and the liquid can be simulated more accurately. Therefore, the Euler model is adopted as a multiphase flow model for simulation in this manuscript.  
Figure 1: Schematic diagram of improved covering airfoil. (**a**) The cross-sectional view of the airfoil and the variable area of the airfoil. (**b**) 3D diagram of a variable airfoil.