esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A more energy-concentrated time-frequency result denotes better time-frequency location and better characterization of the time-varying feature. In this paper, the new synchrosqueezing algorithm based on the generalized S-transform (GST), namely the SSGST method, squeezes and reconstructs the complex coefficient spectra produced by the GST along the frequency direction so that the energy distributions on the time-frequency spectra are concentrated around the real instantaneous frequency of the target signal, thus showing satisfying time-frequency resolution. SSGST is a promising technique for the extraction of time-varying features of seismic signals. Results of the three synthetic examples demonstrate that the SSGST method can correctly obtain time-frequency representations of the signals and depict instantaneous frequency information better compared with CWT, GST, and SSCWT. Moreover, SSGST can reconstruct the original signal with small errors. The feasibility and validity of the SSGST method in seismic time-frequency analysis for hydrocarbon detection is confirmed via an applied example. Therefore, the SSGST method can be used as a new high-precision time-frequency analysis method in seismic signal processing and analysis.  
**Acknowledgments:** The work presented in this paper has been supported by the National Natural Science Foundation of China (No. 41304111), key project of the Science Technology Department of Sichuan Province (No. 2016 JY0200), the Natural Science project of Education Department of Sichuan Province (No. 14 ZA0061, 16 ZB0101 and 17 ZA0030), the Sichuan Provincial Youth Science and Technology Innovative Research Group Fund (No. 2016 TD0023), and the Cultivating Programme of Excellent Innovation Team of Chengdu University of Technology (No. KYTED201410).  
**Author Contributions:** [PERSON] conceived the idea of this research. [PERSON] and [PERSON] built a detail analysis framework. [PERSON], [PERSON], and [PERSON] conceived, designed, and performed the experiments under the supervision of [PERSON]. [PERSON] and [PERSON] analyzed the results and provided a detail interpretation. The paper was written by all the authors. All the authors treat the suggestions of reviewers seriously.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 13: The details of Figure 12a near hydrocarbon reservoir.
## 3 Results
### Between-Session Reliability in Isometric Belt Squat Test  
Descriptive statistics between sessions for IBSqT, including their respective reliability tests, SEM, SWC0.2, and ES, are presented in Table 3. Like intra-session tests, PF and RPF demonstrated excellent reliability values (ICC = 0.98; 0.99) and acceptable variability (4.2-4%), with high sensitivity to performance changes in both variables. Force at all time points showed moderate to excellent reliability (ICC = 0.70; 0.90) with variability values ranging from 10.4% to 18.5% (Table 4). For RFD, ICC values range from moderate to good (0.72-0.89), with CV ranging from 21.3% to 48%. IMP across all time windows showed moderate to good reliability (ICC = 0.67-0.77), with none of the time windows meeting the acceptable variability criterion (CV = 13.7-17.7%). Paired \(t\)-tests revealed no significant differences between test sessions for all kinetic variables (\(p>0.05\)), except for IMP 0-50 ms (\(p\) = 0.020; ES = 0.5 [moderate]). Additionally, RPF demonstrated good sensitivity to performance changes, as SWC0.2 scored higher than SEM. According to the Bland-Altman analysis, the average bias for peak force (PF) was \(-\)53.38 N, with a greater SD of 465.8 N and LoA between \(-\)966.3 N and 859.6 N (Figure 3). In the body mass-relative measurement (PF/BM), IBSqT showed a bias of \(-\)0.3545 N/kg, with broader LoA between \(-\)12.26 N/kg and 11.55 N/kg. For the force at 50 ms (Figure 4), IBSqT showed a higher bias of \(-\)147.7 N, with greater variability (SD = 324.6 N) and LoA between \(-\)783.9 N and 488.5 N. For the RFD at 50 ms, IBSqT showed a bias of \(-\)261 N, with LoA ranging from \(-\)9070 N to 8548 N. At 150 ms, the force showed a bias of \(-\)97.97 N, with LoA between \(-\)1090 N and 894.2 N. The RFD at 150 ms had a smaller bias of \(-\)20.82 N, but with greater variability (LoA between \(-\)6309 N and 6268 N) (Figure 5). For impulse at 150 ms, IBSqT showed a bias of \(-\)23,772 N\(\cdot\)ms, with LoA ranging from \(-\)145,832 N\(\cdot\)ms to 98,288 N\(\cdot\)ms. At 250 ms, the force showed a bias of 27.6 N, with broader LoA ranging from \(-\)1153 N to 1208 N. The RFD at 250 ms showed a higher bias of 503.2 N, with LoA between \(-\)3475 N and 4481 N. For impulse at 250 ms, IBSqT demonstrated a pronounced bias of \(-\)15,658 N\(\cdot\)ms, with even wider LoA between \(-\)244,259 N\(\cdot\)ms and 212,943 N\(\cdot\)ms (Figure 6).
## 5 Conclusions  
In this paper, a flow channel with a trapezoidal baffle was suggested, and geometric parameters of the baffle were optimized based on CFD simulation, ANN, and single-objective optimization methods, to improve the net power output of the PEMFC. 1. A flow channel model with trapezoidal baffle was suggested, and a 3D and multiphase CFD model of the PEMFC was established. The ANN method was used to train the surrogate model, and the single-objective optimization method was used to optimize the geometric parameters of the trapezoidal baffle, and the CFD model and the ANN model were verified, respectively. 2. The velocity, pressure, and oxygen distribution in the straight flow channel model, rectangular baffle model, and the optimized trapezoidal baffle model were analyzed, and the results showed that the optimized trapezoidal baffle model can effectively improve the transport of oxygen towards the GDL. With voltage at 0.3 V, the power density of the optimized model was 4.0% higher than that of the straight flow channel model, and the pressure drop was only 37.83% that of the rectangular baffle model. The optimized trapezoidal baffle model showed better performance than the straight flow channel model and less pressure drop than the rectangular baffle model. 3. The PEMFC had its highest power output with a voltage of 0.4 V. The rectangular baffle model with higher power density can achieve higher net power output than the trapezoidal baffle model, even with relatively high pump power requirements. However, for on-road PEMFC with 0.6 V voltage, the influence of pump power was significant, and the optimized trapezoidal baffle model had a net power increase of 1.47% compared to the rectangular baffle model at 50% pump efficiency and 3.94% at 30% pump efficiency. The performance of the optimized trapezoidal baffle model surpassed that of the other two models in practical application. Conceptualization, G.Z. and C.W. ; methodology, G.Z. and C.W. ; software, C.W. ; validation, G.Z. and C.W. ; formal analysis, G.Z. ; investigation, G.Z. ; resources, S.B. and G.L. ; data curation, K.S. and G.Z. ; writing--original draft preparation, G.Z. and K.S. ; writing--review and editing, G.Z. and H.C.; visualization, G.Z. and C.W. ; supervision, G.L., S.B. and K.S. ; project administration, K.S. ; funding acquisition, G.L. and S.B. All authors have read and agreed to the published version of the manuscript. Figure 18: Net power density with different pump efficiencies: (**a**) at 0.4 V; (**b**) at 0.6 V.  
**Funding:** This work was funded by Key Research and Development Program of Shandong, China [grant numbers 2020 CXGC010404 and 2020 CXGC010406], Asset & Laboratory Management Department of Shandong University, China [grant numbers sy20232305], and the Undergraduate School of Shandong University, China [grant numbers 2022Y155]. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available in article. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## 5 Conclusions  
This study showed that multiple high-resolution satellite sensors can be used to derive models for predicting yields of smallholder farms. The calibrated models that were trained using ground data roughly captured one-third of the variation in surveyed yields, and more than one-half at aggregated administrative units. An alternative scalable uncalibrated approach that did not require yield measurements for calibration performed almost as well as the calibrated approach, especially at the aggregated units. The proposed preferred model successfully captured the spatial pattern of yield gradient at a regional scale, and highlighted the within- and between-field yield heterogeneity at a local scale. We also tested multiple approaches in this study to improve the uncalibrated model performance, among which using MTCI instead of GCVI and using a fixed ratio of yield to biomass had consistent benefits in this region. Further improvement can be expected in the near future when more frequent satellite observations throughout the growing season become available (e.g., from Sentinel-2 and Planet constellations). Overall, yield mapping in this region is very challenging due to the considerable landscape heterogeneity, small field sizes, and prevalence of intercropping. Yet our results suggest that the recently available satellite missions are a viable tool to estimate and understand yield variation at the field scale across smallholder farms in Africa, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.  
The following are available online at www.mdpi.com/2072-4292/9/9/931/s1.  
**Acknowledgments:** We acknowledge [PERSON] and [PERSON] for leading field survey data collection in Bungoma. We thank One Acre Fund for leading the crop cut measurements and in particular [PERSON] and [PERSON] for coordinating the data collection. We also thank Planet for providing the RapidEye imagery and Terra Bella for providing the Skysat imagery. Partial support for this project was provided by the Feed the Future Innovation Lab for Sustainable Intelligence (Cooperative Agreement No. AID-OAAA-L14-00006), funded by the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived of the work. All authors collected and processed the data. [PERSON] and [PERSON] wrote the first draft of the manuscript, and all authors contributed to the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Simulation and Engineering Application of Temporary Stress Field in Coal Mine Roadway
## 3 Construction of Stress Distribution Model for Temporary Support  
As shown in Figure 2, the surrounding rock in the roadway is considered to be an infinite beam, which conforms to the assumption of stress distribution.  
Therefore, the effect of the temporary supporting force on the surrounding rock can be considered as a normal uniform force acting on a semi-infinite plane. It is evident that exists a radial stress caused by force \(\sigma_{1}\) at any given point.  
\[\sigma_{1}=\int{Fz^{3}\Big{(}z^{2}+(x-\xi)^{2}\Big{)}^{-2}d\xi}=0.5 Farctan \big{(}\xi z^{-1}-xz^{-1}\big{)}+0.5 Fz(\xi-x)\big{(}z^{2}+(\xi-x)^{2}\big{)}^{ -1} \tag{1}\]  
The radial stress \(\sigma_{2}\) induced by the temporary support force \(F\) at any given point can be determined by integrating \(\sigma_{1}\) over the interval \(x_{1}\)-\(x_{2}\)  
Figure 1: Relationship between tunneling system and surrounding rock (a—unexcavated area; b—unsupported area; c—temporary support area; d—permanent support area; 1—excavating system; 2—temporary support system; 3—anchor drilling system;).  
\[\begin{split}&\sigma_{2}=-2\pi^{-1}\int_{x_{1}}^{x_{2}}Fz^{2}\left(z^{2 }+(x-\xi)^{2}\right)^{-2}d\xi\\ &=-F\pi^{-1}\left[arctan(xz^{-1}-x_{1}z^{-1})-arctan(xz^{-1}-x_{2 }z^{-1})+z(x-x_{1})\left(z^{2}+(x-x_{1})^{2}\right)^{-1}-z(x-x_{2})\left(z^{2} +(x-x_{2})^{2}\right)^{-1}\right]\end{split} \tag{2}\]  
The temporary support force \(F\) in the headspace can be determined by integrating \(\sigma_{2}\) over the interval \(x_{2}\)-\(x_{3}\):  
\[\sigma=-F\pi^{-1}\Big{[}(x_{1}-x_{3})arctan(x_{1}z^{-1}-x_{3}z^{-1})-(x_{2}-x _{3})arctan(x_{2}z^{-1}-x_{3}z^{-1})-(x_{1}-x_{2})arctan(x_{1}z^{-1}-x_{2}z^{ -1})\Big{]} \tag{3}\]  
The distribution characteristics of the temporary support force can be derived from mathematical model of Equation (3). The distribution of the temporary support force \(F\) in the unsupported area can be classified into three distinct stages, as shown in Figure 2(a). The temporary support force hardly attenuates in area I. The temporary support force decays more rapidly in area II. The temporary support force attenuates to a very small value in area III and remains nearly constant thereafter. As observed from Figure 2(b), the temporary support force initially increases and then decreases as the distance from the roof increases along the z-axis, peaking at a distance of 3 m. It is crucial to ensure that the unsupported area remains within the area I at all times, aligning with the actual working conditions on-site.  
Figure 3: Stress change regulation along excavation direction (**a**) and height direction (**b**) of the roadway.  
Figure 2: Temporary support stress distribution model.
## 4 PUPSO Approach with the Objective Function of LCOE
### Flow Chart of PUPSO Algorithm  
The above mentioned UPSO algorithm is carried out in a sequential form and the optimization process is usually time-consuming when the speed and position of a large number of particles are updated. In this study, further efforts are made to improve the computational efficiency by proposing a parallel UPSO (PUPSO) approach, where the  
Figure 4: Design parameters of the tower.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Range** \\ \hline Length of the \(\hat{p}^{st}\) steel section \(H_{s}^{j}\) (mm) & 500–70,000 \\ Thickness of the \(\hat{p}^{st}\) steel section \(\hat{t}_{s}^{j}\) (mm) & 10–25 \\ Outer diameter of the top end of the \(\hat{p}^{st}\) steel section \(D_{st}^{j}\) & 2686 (\(j=1\)) \\ Outer diameter of the bottom end of the \(\hat{p}^{st}\) steel section \(D_{sb}^{j}\) & \(D_{st}^{j-1}\) (\(j>1\)) \\ Steel segments & 1–3 \\ Length of the concrete section \(H_{c}\) & 7500–77,000 \\ Thickness of the top end of the concrete part \(t_{cl}\) (mm) & 180–500 \\ Thickness of the bottom end of the concrete part \(t_{cl}\) (mm) & \(t_{cl}\)-500 \\ Outer diameter of the top end of the concrete part \(D_{cl}\) (mm) & \\ Outer diameter of the bottom end of the concrete part \(D_{cl}\) (mm) & \(>D_{cl}\) \\ Area of prestressed reinforcement (mm\({}^{2}\)) & 31,150–62,300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables and their ranges.  
computing body is divided into several concurrent tasks on the basis of different particles when evaluating the objective function. The flow chart of the PUPSO algorithm is shown in Figure 5.  
The PUPSO algorithm starts by reading the initial conditions of the PCSH tower model, including the number of particles, the optimization parameters, and the termination condition. The termination condition for this implementation is the maximum number of iterations. Then, the particle swarm is generated randomly in the range mentioned in Section 4.3 and sent into the fitness value function. Based on the fitness value function, the fitness value of every particle can be determined and fed back to the PUPSO algorithm. Based on the returned values, the parameters are modified as described in Section 4.1. Then, new particles are generated according to the modified parameters and sent to the next loop. The optimal solution is then obtained after a number of cycles.
## 3 Aeroacoustic Model
### Engine Acoustic Source Model
#### 3.1.3 Noise towards the Engines Inlet and Outlet Plane  
The sound field, calculated in the form of a modal solution at the point of origin, has to be propagated to the inlet and outlet planes of the engine. At these planes, the radiation of the sound into the far-field takes place. In this process of propagation from the origin to the inlet or outlet planes, the acoustic modes change their complex amplitude and propagation capability as a function of the altering geometry and flow conditions.  
Figure 4: Illustration of used coordinate system of the acoustic engine noise model.  
The propagation is computed according to Jaron [12] between the locations 1 and 2. Here, the assumption is made that the acoustic power for a mode pair (\(m\),\(n\)) remains constant as long as it remains cut-on, i.e., its cut-on factor \(\alpha_{m,n}\) is a real number. The new amplitude at location 2 reads  
\[A^{\pm}_{mn_{2}}=A^{\pm}_{mn_{1}}\bigg{(}\frac{R_{1}}{R_{2}}\sqrt{\frac{\rho_{ 0}a_{0}R_{2}}{\rho_{0_{1}}a_{0_{1}}k_{1}}}\sqrt{\frac{\alpha_{mw_{1}}}{\alpha_ {mw_{2}}}(1-M_{2z_{2}}^{2})}\frac{(1+\pi\alpha_{mn_{2}}M_{yz_{2}})}{(1+\pi \alpha_{mz_{1}}M_{xz_{1}})}\bigg{)}\exp\left(i\int_{1}^{2}k_{x}^{\pm}\cdot\pm| \mathrm{d}\mathrm{x}|\right) \tag{5}\]  
In Equation (5), the speed of sound is given by \(a_{0}\), \(k\) is the free-field wave number, and \(M_{x}\) is the axial Mach number. Note that the exponential term accounts for the propagation of acoustic modes in a slowly varying duct. Equation (5) clarifies that the propagation only accounts for a scaling of the modal pressure due to a change of the cross section under the assumption of a constant modal power and a filtering of cut-on/cut-off modes. Note that a scattering of power into different radial and circumferential mode orders during propagation is not modeled. For more detail see [PERSON] [12].
## Materials and Methods
### Water Quality and Membrane Type  
The feedwater was prepared using deionized water and NaCl (Sigma-Aldrich, general purpose grade) to create synthetic brackish water with a salt concentration of 5 g/L. The concentration was calculated from EC values that were measured with conductivity electrodes (Burkert 8222, Germany) using a conversion factor of \(k\) = 0.59, determined by calibration with NaCl dissolved in deionized water (BWT Moro 350, Germany) at 20 \({}^{\circ}\)C.  
The UF membrane (DuPont Dizzer P4040-6.0, Wilmington, Delaware, USA, membrane area: 6 m\({}^{2}\)[56]) was chosen as a pretreatment to remove large particles and protect the RO membrane against fouling, while a loose 4\" spiral-wound brackish water RO membrane (DuPont BW30-4040, Wilmington, Delaware, USA, [57]) was used for desalination. The BW30 membrane exhibited 24% recovery and 97.5% retention when treating 5-g/L NaCl saline feedwater with the system operating at 300 W of power and a transmembrane pressure (TMP) of 10 bar under steady-state conditions [40]. The membrane-specific parameters (flux, TMP, retention, recovery and SEC) were calculated using well-known relationships, detailed in Equations (1) to (5) [58; 59] below.  
\[\mathrm{J}=\frac{Q_{P}}{A}\, \tag{1}\]  
where J represents the flux (L/m\({}^{2}\cdot\)h), A is the membrane-active area (m\({}^{2}\)), and \(Q_{p}\) is the permeate flowrate (L/h).  
\[\mathrm{TMP}=\frac{P_{inter-vessel}+P_{C}}{2}-P_{perm}\, \tag{2}\]  
where TMP represents the transmembrane pressure, \(P_{inter-vessel}\) is the pressure after the UF membrane (bar), \(P_{C}\) is the pressure in the concentrate stream (bar), and \(P_{perm}\) is the relative pressure of the permeate side (0 bar)  
\[\mathrm{R}=\left(1-\frac{EC_{P}}{EC_{F}}\right)\times 100\ \%\, \tag{3}\]  
where R represents the recovery (%), EC\({}_{\mathrm{P}}\) and EC\({}_{\mathrm{F}}\) represent the electrical conductivity of permeate and feed (\(\mu\)S/cm), respectively.  
\[\mathrm{Y}=\left(\frac{Q_{P}}{Q_{F}}\right)\times 100\ \%\, \tag{4}\]  
where Y represents the recovery (%), \(Q_{P}\) and \(Q_{F}\) represent the flowrate of permeate and feed stream (L/h), respectively.  
\[\mathrm{SEC}=\frac{P_{pump}}{Q_{P}}\, \tag{5}\]  
where SEC represents the specific energy consumption (kWh/m\({}^{3}\)), \(P_{pump}\) is the electrical power of pump motor (W).
## 4 Controller Design
### Linear-Quadratic-Integral Controller Design  
In this paper, the LQI controller [32] is also employed for the position-loop servo control and is compared with the performance of the EMPC controller. Taking crane control as an example, in the design of the LQI controller, the ultimate goal is to track the position reference of the lunar rover. Therefore, in practice, the optimal control scheme should be applied as follows:  
\[u_{c}^{LQI}=-K_{LQI}^{C}\begin{bmatrix}x\\ x_{i}\end{bmatrix}=-K_{LQI}^{C}\begin{bmatrix}e_{X}(k)\\ y_{ux}(k-1)\\ \int e_{X}(k)\end{bmatrix} \tag{31}\]  
where \(e_{X}(k)=X_{r}(k)-X_{u}(k)\) is the tracking error. Assuming that the reference signal is constant in the future, the state equation for the reference signal is:  
\[X_{r}(k+1)=X_{r}(k) \tag{32}\]  
Combining Equations (31), (32), and (17) results in the following equation:  
\[\begin{bmatrix}e_{X}(k+1)\\ y_{ux}(k)\\ \int e_{X}(k+1)\end{bmatrix}=\begin{bmatrix}1&-T_{s}&0\\ 0&1&0\\ 1&-T_{s}&1\end{bmatrix}\begin{bmatrix}e_{X}(k)\\ y_{ux}(k-1)\\ \int e_{X}(k)\end{bmatrix}+\begin{bmatrix}-{T_{s}}^{2}\\ -T_{s}^{2}\end{bmatrix}u_{C}(k) \tag{33}\]  
Similarly, we can obtain the spatial state equation for position tracking control of the trolley by following the same procedure as we did for the crane moving system. In both the crane and trolley moving systems for position servo control, the weighting matrices used in the LQI controllers are as follows:  
\[Q_{LQI}^{C}=\begin{bmatrix}40,000\\ 0.00001\\ 1\end{bmatrix},\,Q_{LQI}^{T}=\begin{bmatrix}10,000\\ 0.00001\\ 1\end{bmatrix}, \tag{34}\] \[R_{LQI}^{C}=R_{LQI}^{T}=0.1,\]  
Figure 8: Critical regions. The figure illustrates the critical regions generated by the EMPC controller for the moving trolley system.  
During the parameter selection process of the weight matrix, special consideration is given to prioritize accurate tracking. As a result, the error state is given a high penalty. Additionally, the integral error state is also assigned a high penalty to minimize the deviation from the steady state as much as possible. Furthermore, the plant input weight is chosen relatively high to prevent any significant changes in the system. Then, the state feedback gain \(K_{LQI}\) for the LQI controllers can be determined by solving the algebraic Riccati equation, as illustrated in Equation (20).