esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Activity and Results
### Analytical Acoustic Analysis of Combustion and Post-Combustion Chambers
Specifically, referring to investigations on combustion instabilities due to resonant coupling for standard engines, the combustion chamber can be reasonably assumed to be a closed cavity due to the short nozzle and the injector plate [23,24]. The same happens in the post-combustion chamber of a hybrid rocket engine. Therefore, the assumption of a closed-closed post-combustion chamber makes sense. Instead, still in the case of a standard engine, the presence of an orifice between the manifold and the injector suggests the use of an acoustically closed inlet [22]. An acoustically open end can also be assumed if the injector tube is connected to a large volume, which could be typical of a combustion chamber [21]. In a hybrid rocket engine, the choke between the pre-chamber and the combustion chamber allows the assumption of a closed acoustic boundary, while the connection of the combustion chamber to a much larger post-combustion chamber approximates an acoustically open boundary. On the other hand, when considering a low Mach number flow through the throttle orifice (between 0.03 and 0.06), the behavior is closer to the open-open case [21]. Considering all configurations normally encountered in the study of instability phenomena for conventional engines (i.e., all those presented above), an atypical acoustic boundary condition seems to coexist in hybrid rocket engines. Specifically, it is the closed-open condition associated with the post-combustion chamber (in conventional engines, this cavity is representative of the combustion chamber), which is normally considered closed-closed. This could be attributed to the small radial size difference between the combustion and post-combustion chambers, which provides a different impedance at the interface compared to traditional rocket engines. Needless to say, this is speculation and needs to be further investigated. Regarding the coexistence of different coupling conditions, theoretical studies support the possibility of simultaneous configurations [19].
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### Modified and Extended Upper-Bound (MUB) Pushover Analysis  
The upper-bound (UB) pushover analysis method, initially developed for 2D tall building frames, tends to underestimate seismic demands at lower levels of the structures [19]. To address this issue, a study [62] suggested combining the seismic demands from the UB pushover method with those from a conventional pushover analysis, aiming to better regulate seismic demands at lower building levels. Additionally, the study proposed an extension of the UB pushover method to accommodate tall buildings with unsymmetrical plans. This extension, termed the extended upper-bound (EUB) method, incorporates upper-bound lateral forces with patterns tailored to various unsymmetrical-plan tall buildings and considers higher-mode effects in calculating these forces. The results indicate that the EUB method can accurately estimate inelastic seismic demands of unsymmetrical-plan tall buildings, considering both higher modes and torsional effects.  
Initial calculations: perform calculations as outlined in Steps 1 to 4 of Section 4.8 above.  
Pushover analyses: After applying gravity loads, conduct both the upper-bound pushover and conventional pushover analyses. Continue these analyses until the control node, located at the roof's center of mass, reaches the target displacement determined in the previous step.  
For medium-rise unsymmetric-plan buildings, perform a conventional pushover analysis using an inverted triangular load pattern, and use a uniform force distribution for high-rise buildings until the target displacement is reached. For torsionally flexible systems, perform the conventional pushover analysis using the lateral force _fs_1, which is derived from Equation (30).
* Perform the upper-bound pushover analysis using the upper-bound lateral forces _fs_i derived from Equation (30) until the target displacement at the roof is reached.
* Determine the peak seismic demands. Calculate the peak values of the seismic demands individually for the upper-bound pushover and conventional pushover analyses. Denote the peak values obtained from these analyses by \(r_{\textit{fs}i}\) and \(r_{1}\), respectively.
* Determine the envelope of results. Calculate the envelope (\(r\)) of the results computed in the previous step as follows: \[r=\left\{\begin{array}{l}max\Big{\{}r_{1},r_{\textit{fs2}}\Big{\}}\qquad \qquad\textit{for the TS and TF systems}\\ max\Big{\{}r_{1},r_{\textit{fs2}},r_{\textit{fs3}},r_{\textit{fs4}}\Big{\}} \qquad\qquad\textit{for the RSS Systems}\end{array}\right.,\] (32) where \(r_{1}\)= \(r_{\textit{TLP}}\) for the TS and TSS medium-rise systems \(r_{1}\)= \(r_{\textit{LILP}}\) for the TS and TSS high-rise systems \(r_{1}\)= \(r_{\textit{fs1}}\) for the TF systems
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
###### Abstract  
Since cloud images captured from different views possess extreme variations, multi-view ground-based cloud recognition is a very challenging task. In this paper, a study of view shift is presented in this field. We focus both on designing proper feature representation and learning distance metrics from sample pairs. Correspondingly, we propose transfer deep local binary patterns (TDLBP) and weighted metric learning (WML). On one hand, to deal with view shift, like variations of illuminations, locations, resolutions and occlusions, we first utilize cloud images to train a convolutional neural network (CNN), and then extract local features from the part summing maps (PSMs) based on feature maps. Finally, we maximize the occurrences of regions for the final feature representation. On the other hand, the number of cloud images in each category varies greatly, leading to the unbalanced similar pairs. Hence, we propose a weighted strategy for metric learning. We validate the proposed method on three cloud datasets (the MOC_e, IAP_e, and CAMS_e) that are collected by different meteorological organizations in China, and the experimental results show the effectiveness of the proposed method.  
ground-based cloud recognition; transfer deep local binary patterns; weighted metric learning; convolutional neural network +
Footnote †: journal: Applied Sciences
###### Abstract  
By integrating \(H_{\infty}\) control into iterative learning boundary control (ILBC) with the method of lines (MOL), this paper suggests a novel scheme to reduce the vibrations of the uncertain vibrating string system in the presence of iteration-varying distributed/boundary disturbances. The dynamics of the string system are defined by two kinds of differential equations, namely: (a) non-homogenous hyperbolic partial differential equation (PDE) and (b) ordinary differential equations (ODEs). Firstly, MOL is employed to attain the string dynamics in the form of a state-space system instead of a PDE system. Secondly, ILBC is developed in a super-vector framework and integrated with the \(H_{\infty}\) control for decreasing the perturbations of the uncertain string system in the presence of iteration-varying distributed/boundary disturbances. Along the time, position, and iteration coordinates: (a) the boundary deflections of the string system are controlled; (b) the vibrations along the string are attenuated to zero; and (c) the external disturbances are excluded. Based on the \(H_{\infty}\) algebraic approach, performance/stability conditions and global convergence of the closed-loop string system are assured. Conducted simulations illustrate that the suggested scheme is efficient for diminishing the vibrations of certain and uncertain vibrating string system.  
distributed parameter system (DPS); \(H_{\infty}\) control; iterative learning boundary control (ILBC); method of line (MOL); partial differential equation (PDE); string system; system parameter uncertainties +
Footnote †: journal: Applied
## 1 Introduction  
### Global Change and the Terrestrial Carbon Fixation  
Global industrialization has caused the enrichment of atmospheric greenhouse gases (GHGs) globally [1]. In the atmosphere, GHGs have changed the process of radiation transmission [2]; further, more radiation is being trapped in the atmosphere [3, 4]. The long-lived GHGs have dominated 47% of global warming, in particular, CO\({}_{2}\) takes up 80% of this increase alone [5]. In this context, terrestrial and oceanic carbon sinks have been found to increase continuously over the last 60 years [6, 7, 8, 9]. The terrestrial ecosystem responds to climate variability and climate change [10, 11, 12]. Global warming and land-coverchanges have jointly accelerated carbon turnover [3; 4]. The terrestrial carbon residence time decreased by one year in vegetation and by nine years in soil from the 1980s to the 2000s [13]. As the primary driver of terrestrial carbon accumulation or sequestration [14; 15], GPP is not only the total carbon amount taken up by plants within photosynthesis [16] but also the largest CO\({}_{2}\) flux in global carbon cycling [17]. Variations in temperature and precipitation, as well as extreme events, have affected GPP and the ecosystem respiration (ER) [18; 19; 20]. Moreover, in the terrestrial carbon-nitrogen cycle, such as CO\({}_{2}\) fertilization [21], nitrogen deposition and other processes [22], a coupling interaction occurs with the systematic change of terrestrial carbon fixation [23]. Then, thickened tropospheric CO\({}_{2}\) subtly influences GPP [24; 25]. In network observations and ecological modeling studies, to represent the carbon that is actually fixed by the undisturbed biosphere through plant photosynthesis, the net ecosystem productivity (NEP) is a central concept in terrestrial carbon cycling [26]. NEP was originally defined as the imbalance between GPP and the ER [27]. Observations from networks of eddy covariance (EC) and footprint analysis provide rapid and accurate estimations of the exchange of mass and energy [28]. Furthermore, fluxes of CO\({}_{2}\) and water (H\({}_{2}\)O) are indicators of ecosystem carbon and hydrological balances [29]. The EC micrometeorological technique, as well as the ecology-based biometric methods, are used to efficiently monitor the net ecosystem exchange (NEE) [30]. NEE is the net atmosphere-biosphere exchange of CO\({}_{2}\)[31]. In the detailed mechanism, NEE is the sum of the ecosystem CO\({}_{2}\) fluxes that do not involve the organic carbon either in its sinks or sources, as well as NEP [27]. Normally, NEE approximately equals to the negative NEP [26].
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
## 2 Database and Machine Learning Algorithms  
### Database  
Conventional ultra-high-cycle fatigue tests are very time-consuming and take nearly four days to complete \(10^{7}\) cycles at a frequency of 30 Hz; however, 20 kHz ultrasonic fatigue experiments consume at least 400 times less time than conventional ones [19]. Therefore, the establishment of a database based on ultrasonic fatigue experiments can reduce the test time during the model validation phase and also facilitate the replenishment of the database in the future. In this study, 84 ultrasonic fatigue experimental datapoints were collected from published works to establish a database. The database contained commonly used metals in engineering and materials formed by special methods (for example, GH4169 alloy formed by selective laser melting). It should be noted that the loading conditions for all ten materials were ultrasonic and the loading stress ratio was \(-1\). Ultrasonic fatigue tests were conducted in a stress range of 100-900 MPa, and the fatigue life of all ten materials was achieved at \(10^{7}\) cycles. The data sources and the test conditions are listed in Table 1, and the stress distributions of the materials are shown in Figure 1. It should be noted that GCr15-bearing steel was not involved in the training of the model, but only as a final test of the model.
## 2 Maximum Power Transfer Capability of Grid-Connected Systems at the Rated PCC Voltage
### Ideal Maximum Power without Current Limitation  
Firstly, an ideal case without current limitation is analyzed as follows. Substituting \"\(V_{pcc}=V_{g}\)\" and Equation (2) into Equation (4), the per-unit values of \(i_{d}\) and \(i_{q}\) can be derived as:  
\[{i_{q(pu)}}^{2}+\frac{2\cdot SCR}{\sqrt{{{({R_{g}}/{X_{g}})}^{2}}+1}}\cdot{i_{ q(pu)}}+[{i_{d(pu)}}^{2}-\frac{2\cdot SCR\cdot{{({R_{g}}/{X_{g}})}}}{\sqrt{{{({R_{g}}/{X_ {g}})}^{2}}+1}}\cdot{i_{d(pu)}}]=0 \tag{5}\]  
Considering Equation (5) as a quadratic equation of \(i_{q(pu)}\), thus, only when Equation (6) is satisfied, does Equation (5) have solutions.  
\[\Delta=\frac{4\cdot SCR^{2}}{{{({R_{g}}/{X_{g}})}^{2}}+1}+\frac{8\cdot SCR \cdot{{({R_{g}}/{X_{g}})}}}{\sqrt{{{({R_{g}}/{X_{g}})}^{2}}+1}}\cdot{i_{d(pu) }}-4\cdot{i_{d(pu)}}^{2}\geq 0 \tag{6}\]  
According to Equation (6), the maximum value of \(i_{d(pu)}\) can be calculated, as given in Equation (7). Because the PCC voltage is considered a constant in this section, the per-unit value of the current \(i_{d}\) is equal to the per-unit value of the inverter active power \(P_{inv}\). Hence, the ideal maximum power of the inverter can be represented by Equation (7).  
\[P_{inv(pu)max}=i_{d(pu)max}=SCR\cdot[\frac{{{({R_{g}}/{X_{g}})}}}{\sqrt{{{({R_{ g}}/{X_{g}})}^{2}}+1}}+1] \tag{7}\]  
According to Equation (7), the relationship between the ideal maximum inverter power \(P_{inv(pu)max}\) and the SCR with different \(R_{g}/X_{g}\) ratios is shown in Figure 3. It can be seen that the relationship between \(P_{inv(pu)max}\) and the SCR is linear. In the purely inductive (R/X = 0) case, SCR = 1 is a critical point because when the SCR is lower than 1, the maximum power \(P_{inv(pu)max}\) is lower than 1, namely, the maximum output power of the inverter is limited naturally under weak grid conditions. Hence, this natural characteristic between \(P_{inv(pu)max}\) and the SCR can be considered a standard to be compared with the maximum power operating point at the stability boundary of different control strategies.  
Figure 3: Relationship between ideal maximum inverter power \(P_{inv(pu)max}\) and SCRs with different \(R_{g}/X_{g}\).
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Mobility Models
#### 2.2.3 Semi Random Circular  
Semi Random Circular is a path-planned mobility model centered around UAV-enabled surveillance and target monitoring of an already known area [52]. This model makes the examined FANET circulate an area of interest, rendering it realistic for the emulation of e.g., search-and-rescue missions.
## 3 Results and Discussion
### Phylogenetic Analyses  
The 16S rRNA sequence of the 2 De strain has been submitted to GenBank and was assigned the accession number MK570454. The sequence was aligned and compared with known sequences in the GeneBank database. The analysis indicated that the closest related species was _Pseudomonas baetica_ a390 with 99.80% similarity. The following closest related species were: _Pseudomonas unsonensis_ Ps 3-10 (98.99%), _Pseudomonas migulae_ NBRC 103157 (98.91%), _Pseudomonas reinekei_ MT1 (98.91%), and _Pseudomonas helmanticensis_ OHA11 (98.83%). The highest ANI for the 2 De strain was observed for _P. baetica_ a390 (100%) and _P. helmanticensis_ OHA11 (99.40%). Figure 1 presents a phylogenetic tree of the 2 De strain and the 10 closest type strains of other _Pseudomonas_ species and shows the isolate cluster separately in a group as a sister taxon to _Pseudomonas baetica_ a390.  
\begin{table}
\begin{tabular}{c c} \hline
**Bacterial Strain** & **Decrease in Mn Concentration (\%)** \\ \hline Control & \(1.20\pm 0.53\)\({}^{\text{f}}\) \\
2 Ge & \(60.12\pm 1.22\)\({}^{\text{abc}}\) \\
2 De & \(71.07\pm 1.22\)\({}^{\text{e}}\) \\
2 Dm & \(12.32\pm 2.43\)\({}^{\text{d}}\) \\
2 Dl & \(65.47\pm 1.22\)\({}^{\text{ae}}\) \\
2 Dl0 & \(54.93\pm 2.43\)\({}^{\text{c}}\) \\
2 Gh & \(62.04\pm 1.22\)\({}^{\text{ab}}\) \\
2 Dj & \(61.68\pm 2.43\)\({}^{\text{ab}}\) \\
2 Gm & \(11.40\pm 3.65\)\({}^{\text{d}}\) \\
2 Gi & \(57.88\pm 1.22\)\({}^{\text{bc}}\) \\
2 Dl2 & \(64.84\pm 1.22\)\({}^{\text{a}}\) \\ \hline \end{tabular}
\end{table}
Table 2: Decrease in Mn concentration in eleven samples containing 2% (_w/v_) of the soil from metallurgical waste heap and a single, isolated strain. Error bars are \(\pm\) standard deviation (\(n=3\)). Values within a column followed by the same letter are not significantly different at \(p<0.05\) (LSD).
## 2 Methods
### Path Optimization
#### 2.4.3 Path Smoothing  
To make the final path smoother, \(B\)-spline curves are used to make the robotic arm trajectory smoother. The \(B\)-spline curve can be represented using the Equation (16):  
\[p(u)=\sum_{i=0}^{n}P_{i}B_{i,k}(u) \tag{16}\]  
Figure 7: Geometric envelope method illustration.  
where \(P_{i}(i\in[0,n])\) represents the i-th control point of the \(B\)-spline curve, \(B_{i,k}(u)\) represents the \(i\)-th \(k\)-th order \(B\)-spline basis function. The basis function can be defined using the [PERSON] recursion formula:  
\[B_{i,k}(u)=\left\{\begin{array}{ll}1,\;u_{i}\leq u\leq u_{i+1}&k=0\\ 0,\;\text{otherwise}&k=0\\ \frac{u-u_{i}}{u_{i+1}-u_{i}}B_{i,k-1}(u)+\frac{u_{i+k+1}-u}{u_{i+k+1}-u_{i+1} }B_{i+1,k-1}(u)&k\geq 1\end{array}\right. \tag{17}\]  
In Equation (17), if both the numerator and denominator are zero, then the term is assumed to be 0. If the denominator is zero and the numerator is not zero, then the term is assumed to be 1. \(u_{i}(i(e[0,n+k]))\) is called the knot vector, which is defined as Equation (18):  
\[[u_{0},u_{1},\cdots,u_{k},u_{k+1},\cdots,u_{n},u_{n+1},\cdots,u_{n+k}] \tag{18}\]  
Based on the knot vector, \(n+k\) segments of the \(B\)-spline curve can be generated. The knot vector can control the shape of the \(B\)-spline curve and its local characteristics near the control points. Commonly used knot vectors include uniform knot vectors and quasi-uniform knot vectors. A uniform knot vector is a vector with equal intervals between the nodes, which can create a uniformly distributed curve. A quasi-uniform knot vector has duplicate elements at both ends of the knot vector, which can make the tangent of the spline curve at the beginning and end parallel to the line connecting the control points.  
In this paper, the path points generated by the improved FMT* algorithm are used as control points, and quasi-uniform cubic \(B\)-spline curves are used as the curves for trajectory planning to make the planned trajectory smoother.
## 2 Materials and Equipment  
### Materials  
This work investigates the impact of salt type on oil recovery from sandstone and carbonate rocks. Potassium-, sodium-, and calcium-based high-pH GLDA solutions were used. Rock samples from Indiana limestone and Berea sandstone formations were used. The rock properties were evaluated by measuring the core porosity and permeability. The limestone cores had an average porosity of 15% and a permeability of 10 mD, and the sandstone cores had an average porosity of 18% and a permeability of 75 mD. Table 1 lists the rock properties, pore volume (PV), initial water saturation (S\({}_{\text{wi}}\)), and the used chemicals for each experiments.  
Arabian medium oil with an API (the American Petroleum Institute) gravity of 32 was used in this study. The composition of this crude oil is provided in Table 2. In addition, Gulf seawater (SW) with a total dissolved solid (TDS) of 57.3 g/L was used to recover the oil during the secondary recovery process, and formation brine (FB) with TDS of 213.7 g/L was utilized for saturating the rock samples and establishing the initial water saturation. Table 3 lists the ionic composition of the synthetic SW and FB used in this work. All used brines were clear, and no precipitation was observed, to avoid the plugging of the rock samples during the flooding experiments. Moreover, the calcium-based GLDA chelating agent (Ca\({}_{2}\)GLDA), the potassium-based chelating agent (K\({}_{4}\)GLDA), and the sodium-based chelating agent (Na\({}_{4}\)GLDA) were used. All GLDA solutions had the same concentration of 5 wt % and pH of 11, which minimized the experimental uncertainty, and the impact of salt type on the oil recovery can be reasonably assessed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rock Sample** & **Porosity (\%)** & **Permeability (mD)** & **PV (cm\({}^{3}\))** & **S\({}_{\text{wi}}\) (\%)** & **Fluid Used** \\ \hline ILS\_1 & 15 & 11 & 26 & 26 & Ca\({}_{2}\)GLDA \\ ILS\_2 & 14.5 & 10 & 25 & 23 & Na\({}_{4}\)GLDA \\ ILS\_3 & 15.3 & 9.4 & 27 & 23 & K\({}_{4}\)GLDA \\ BSS\_1 & 18.3 & 74 & 32 & 28 & Ca\({}_{2}\)GLDA \\ BSS\_2 & 17.5 & 73 & 30 & 27 & Na\({}_{4}\)GLDA \\ BSS\_3 & 18.1 & 78 & 31 & 32 & K\({}_{4}\)GLDA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock properties and the used fluids during core flooding experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Moles (\%)** \\ \hline Pentane & 1.22 \\ Hexane & 5.11 \\ Heptane & 10.15 \\ Octane & 16.68 \\ Nonane & 14.34 \\ Decane & 13.96 \\ Undecane & 11.31 \\ Dodecane plus & 27.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Oil composition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ions** & **SW** & **FB** \\ \hline Bicarbonate & 0.17 & 0.35 \\ Calcium & 0.65 & 19.04 \\ Chloride & 31.81 & 132.06 \\ Magnesium & 2.16 & 2.44 \\ Sodium & 18.04 & 59.49 \\ Sulfate & 4.45 & 0.35 \\ TDS (g/L) & 57.28 & 213.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of synthetic seawater (SW) and formation brine (FB).
## 4 Simulation and Results
The voltage in the fourth year, due to the annual increase in load, gets the worst value, with an average of less than 0.93. Nevertheless, at the end of the planning, the network would no longer have this problem and therefore the voltage would increase significantly. The problem solution has a great impact on the reliability of the network. Table 5 indicates the size of the EENS index before and after solving the problem (without DG and with DG). As it is shown in the Table, the expected reduction in unsupplied energy is significant. In the fourth year, this amount has reached from 21,058.9 to 11,579.8. The run times of each optimization method for 33-bus test systems are indicated in Table 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Optimization Methods** & **Run Time for 33-Bus Test System (s)** \\ \hline GA & 2452 \\ PSO & 1126 \\ TS & 1989 \\ Proposed method & 1490 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The run times of the proposed method and other optimization methods to solve the multiyear network expansion planning problem on 33-bus distribution networks. Figure 4: The average network voltage in different years. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} \(EENS_{t}\) \\ **(kWh/year)** \\ \end{tabular} } & \multicolumn{4}{c}{**Without \(DG\)**} & \multicolumn{4}{c}{**With \(DG\)**} \\ \cline{2-9}  & **GA** & **PSO** & **TS** & \
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} & **GA** & **PSO** & **TS** &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline \(EENS_{1}\) & 19,201 & 21,251 & 29,234 & 18,191 & 17,692 & 18,490 & 28,221 & 12,847.9 \\ \(EENS_{2}\) & 21,298 & 23,390 & 31,402 & 19,101 & 16,721 & 18,432 & 27,172 & 11,910.6 \\ \(EENS_{3}\) & 23,295 & 24,607 & 32,521 & 20,056.1 & 16,734 & 17,456 & 26,639 & 11,724.7 \\ \(EENS_{4}\) & 23,971 & 24,806 & 32,902 & 21,058.9 & 15,892 & 16,786 & 26,015 & 11,579.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The \(EENS_{t}\) values for each year of the planning period.