esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The stepless flow control system offers important energy-saving technology for reciprocating compressors in the petrochemical and oil refining industries. Optimizing the movement characteristics of the unloader and suction valve is vital to improving the energy-saving level of the system and reducing the overall operating cost. However, the system has many of the characteristics of multi-system coupling and multi-parameter crossover; thus, it is difficult to optimize the key control parameters. In this study, to optimize the system inlet pressure, return pressure, and return spring stiffness parameters, a working model of the flow control system based on multi-system coupling was established. Using the ejection and withdrawal speeds of the unloader, the flow displacement deviation, and the gas work deviation of the control system as the optimization parameters, we used the response surface method to establish an optimization proxy model between the objective function and key parameters. Additionally, verification of the model's accuracy and sensitivity analyses were completed. Finally, a double optimization scheme based on a non-dominated genetic algorithm (NSGA-II) was proposed. Simulation and experimental results show that with optimization of the return spring, oil inlet pressure, and oil return pressure, the unloader's kinematic characteristics were also optimized at full load. The impact energy of the ejector and withdrawal speed of the unloader were reduced, and the compressor flow control error was less than 5%, which effectively improved the comprehensive working performance of the stepless flow control system.  
stepless flow control; reciprocating compressor; coupling model; co-simulation; multi-objective optimization +
Footnote †: journal: Applied Sciences
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
###### Abstract  
The aim of this study is to understand the hemodynamic responses in the heart-aorta system under physiological states and use this understanding to enhance the hemodynamic response analysis of cardiovascular fluid-structure interaction (FSI) models. This article developed a heart-aorta FSI model by constructing a structured fluid domain using the S-ALE method. The model realized a cardiac blood pumping pattern by applying a time-varying displacement load to the left ventricle (LV). The simulation reliability of the model was effectively verified by comparing the hemodynamic responses to the literature data. The FSI analysis in different physiological states showed that the altered ejection volume due to changes in LV systole displacement was a key factor influencing the hemodynamic response. As LV systole displacement increased, blood velocity, flow rate, and wall shear stress (WSS) showed a significant linear increase. The effect of changes in blood viscosity on the WSS demonstrated a significant linear correlation. However, the effect on blood velocity and flow rate did not present any significant difference. The S-ALE method used in this paper can rapidly generate fluid domains, providing technical support for the development of personalized medicine in the cardiovascular field.  
fluid-structure interaction; physiological boundary; hemodynamics; S-ALE method +
Footnote †: journal: Applied sciences  
Article
## 3 Method
### Functional-Type Identification of Buildings Not Containing POIs
#### 3.5.2 Calculation of Characteristic Similarity Weights  
The entropy weight method was used to find the weight of each similarity characteristic. This method objectively assigns weights according to the degree of variability of each indicator. Information entropy is used to calculate the entropy weight of each indicator, and then the entropy weight of the indicator is used to correct the weight, so as to obtain a more objective weight of the similarity of characteristics [43]. The entropy weight method is calculated using the following steps:  
1. Selection of samples. In the study area, \(n\) samples with \(m\) indicators are randomly selected to construct the original raw data matrix \(X=x_{ij}\) (\(i=1,2\ldots,n;j=1,2\ldots,m\)), where \(X\) denotes the value of the \(j\)th indicator of the \(i\)th sample. The sample matrix used in this paper consists of 4 similarity indicators for 71 pairs of samples.
2. Normalisation of indicators To solve the problem of non-uniformity in the units of measurement of the indicators, the indicators are normalised. All values of the normalised matrix \(x_{ij}^{\prime}\) are adjusted to be within the interval \([0,1]\). The normalisation calculation method is shown in Formula (10).  
\[x_{ij}^{\prime}=\frac{x_{ij}-\min\big{(}x_{ij}\big{)}}{\max\big{(}x_{ij}\big{)} -\min\big{(}x_{ij}\big{)}} \tag{10}\]
3. Calculation of the indicator weight \(p\)-value The weight of the indicator \(p\)-value is calculated as shown in Formula (11), where \(p_{ij}\) denotes the weight of the \(j\)th indicator value of the \(i\)th sample.  
\[P_{ij}=\frac{x_{ij}^{\prime}}{\sum_{i=1}^{n}x_{ij}^{\prime}} \tag{11}\]
4. Calculating the information entropy of the indicator The information entropy \(e\) of the indicator is calculated as shown in Formula (12), where \(e_{j}\) denotes the entropy value of the \(j\)th indicator with a constant term \(k=1/\ln(m)\), satisfying \(0\leq e_{j}\leq 1\), the maximum value of \(e_{j}\) is 1.
5. Calculating the weights of each indicator The weight of each indicator is calculated as shown in Formula (13). The equation describes the weighting of indicator \(j\), which represents the redundancy of information entropy and is calculated by Formula (14).  
\[W_{j}=d_{j}/\sum_{j=1}^{n}d_{j} \tag{13}\]  
\[d_{j}=1-e_{j} \tag{14}\]  
In this study, the entropy weighting method was utilised to obtain the similarity weights of each feature, as shown in Table 3.
# Energy Management of a Multi-Source Power System
## 4 Demonstration Example and Results
### Object Pickup Scenario-Simulated For Drone Flight  
The following simulation represents the situation where a drone must travel to a specific location to pick up an object and return the object to the drone's base. During this simulation, ideal conditions are considered where the drone does not face any disturbances or turbulence during its flight. The assumed demand profile for this simulation is shown in Figure 1. Initially, the drone starts traveling to the location where the object is located. At time step 4, the drone reaches the object's location and descends to pick up the desired object. After the drone picks up the object, it continues its flight to return to its base.  
Figures 2-4 display the system voltages, current, and state-of-charge, respectively,  
In the object pickup simulation, both the switching sequences of the dynamic approach and heuristic approach were able to meet the demand of the drone, but that of the standard approach did not. After both batteries and the super capacitor were completely depleted, the standard approach was unable to meet the demand of the drone in the 10 th time instance. However, although both the switching sequences of the dynamic approach and heuristic approach met the demand, the dynamic approach provided a sequence of switching superior to that of the heuristic approach. The obtained objective function value of the dynamic approach was 9% lower with a value of 6536.2, while the heuristic approach was 7123.3. However, the switching sequences of the heuristic approach had a lower average power consumption of 3361.2 W compared to the dynamic approach's 3720 W. This was due to the switching sequence of the dynamic approach resulting in significant power consumption in time steps 6 and 7. From Figure 4, the following can also be seen. The dynamic approach resulted in battery 1 having a SOC level of around 35% and battery 2 with a SOC level of around 50% after 10 s. While the SOC level of the supercapacitor was unchanged and remained at 100% throughout the time period. It is worth noting is that none of the batteries enter charging. In contrast, the heuristic approach seems to favor using the supercapacitor as a result of which the supercapacitor is completely discharged, re-charged, and discharged to SOC level zero again within the ten second time period. Battery 1 ends at a SOC level of 40%, and battery 2 ends at a SOC level of around 70%. The differences in behavior can be attributed to the optimization of the objective function value as mentioned above. The power used to meet the demand of the drone during each time step is shown in Figure 5. Figure 6 shows as additional examples the switching sequence and the sources used to meet demand shown in Figure 5(a,b at each time step.  
Figure 5: Power consumption comparison.  
Figure 6: Switching sequence and sources used to meet demand.
## 4 Experiment
### Predicted vs. Actual Plot  
Figure 4 depicts four scatter plots comparing actual values with expected values obtained from various ML models and ensemble combinations in Set 1. Each plot includes a diagonal line representing parity between projected and real values, where proximity of points to this line indicates higher prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Feature Set 1**} & \multicolumn{2}{c}{**Feature Set 2**} \\ \cline{2-5}  & **R2 Score** & **RMSE** & **R2 Score** & **RMSE** \\ \hline PyTorch+RF & 0.9181 & 290.87 & 0.9040 & 314.95 \\ PyTorch+GBR & 0.9150 & 296.35 & 0.9055 & 312.49 \\ PyTorch+XGB & 0.9097 & 305.51 & 0.9022 & 317.86 \\ PyTorch+LR & 0.9043 & 314.52 & 0.9071 & 309.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance Comparison of ML Models With PyTorch.  
Figure 4: Comparison of model performance across feature set 1.  
The ML models combined with PyTorch performed exceptionally well, as shown by the clustering of data points (represented by green dots) along the diagonal line. This suggests that the stacked model effectively captures underlying data patterns, yielding both higher R\({}^{2}\) scores and lower RMSE values. These metrics reflect more precise and reliable predictions compared to models that do not use PyTorch.  
Set 2, depicted in Figure 5, shows scatter plots comparing actual values (x-axis) to anticipated values (y-axis) for each model. These diagrams indicate ensemble models combining PNN with RF, GBR, XGB, and LR. The plots illustrate that the stacked models exhibit tighter clustering around the diagonal line, implying enhanced prediction accuracy over standalone models (shown by orange dots). However, some data points deviate from the diagonal line, particularly at higher actual values, suggesting that prediction errors increase with the scale of the target variable.
## 1 Introduction  
Soil is the main environment for the development of crops, guaranteeing a large part of the high yield. In recent years, there has been a trend of gradual transition from conventional tillage to new no-till techniques, culminating in no-tillage agriculture. Compared to other tillage practices, no-tillage techniques provide several economic, environmental, and agronomic advantages. It is a technique that has had generally positive effects on soil [1; 2; 3] by improving nitrogen levels [4], in addition to increasing grain yields. In the 2020-2021 growing year, the area of autumn cereals in Morocco reached 4.3 million hectares (ONICL, 2020). Cereals are grown in multiple of the country's agroclimatic zones in rotation with other annual crops (MAPM, 2020). Morocco's wheat imports in the 2020/2021 crop year were estimated at 6.2 million metric tons, which is approximately 35% higher than imports in 2019/2020. This significant increase was mainly the result of low domestic production and the suspension of import duties (ONICL, 2020).  
The average Moroccan consumes 200 kg of wheat per year, which is three times more than the worldwide average. As in the other Maghreb countries, this cereal, particularly through bread, is a basic element in the diet. Durum wheat is the raw material for many byproducts (bread, semolina, pasta, pastries, and biscuits) and has received considerable attention, owing to its adaptation to semi-arid environments ([PERSON] et al., 2019). Wheat grain is an energetic food containing, on average, 70% starch, 12% protein, 9% pentosan and cellulose, and less than 5% free sugars, lipids, and minerals ([PERSON] et al., 2019).  
Nitrogen remains one of the most important elements for cereal production ([PERSON] et al., 2019). Its efficient use is decisive for the improvement of production in terms of quantity and quality ([PERSON], 1966; [PERSON], 1966; [PERSON], 1966). Nitrogen fertilization of cereals should take into consideration the nature and the dose to be applied. Cereal nitrogen requirements vary depending on the growth phase ([PERSON], 1966; [PERSON], 1966). The objective of our study is to investigate the effect of no-tillage techniques and nitrogen fertilization on soil fertility and the yield of five durum wheat varieties recently developed in Morocco--three new varieties (Louiza, Faraj, and Nachit) and two germplasm (M.G and LC)--in a long-term NT (18 years) trial at the INRA Merchouch experimental station. Interactions between two or three factors can inform recommendations for farmers with respect to the optimal combination of tillage type, nitrogen dose, and variety.
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 2 Materials and Methods  
### Dataset  
We used mining-induced microseismic data and seismic data to verify our proposed method. Our mining-induced microseismic data came from the Huangtopo Copper and Zinc Mine, located in Hami City, China. For the specific conditions of this mine refer to the existing literature [39; 40; 41]. A mining-induced microseismic monitoring system was installed to monitor the safety of goaves in the mine. The system and goaves are described in Figure 1. Because natural seismic events and mining-induced microseismic events have apparent differences in frequency band and magnitude, the existing microseismic monitoring system only collects mining-induced microseismic events. However, as shown in Figure 2, due to the similarity between non-seismic vibration signals (such as blasting and mechanical vibration signals) and microseismic signals, some automatic methods must be used to distinguish them. We collected a dataset including 5400 mining-induced microseismic signals, 5400 blasting signals, and 5400 mechanical vibration signals.  
Figure 1: A mining-induced microseismic monitoring system was installed to monitor the safety of goaves in the Huangtopo Copper and Zinc Mine.  
The seismic dataset contains 9963 seismic records, and these records came from seismic activity around the world. The seismic data came from the Center for Engineering Strong Motion Data (CESMD) and the Northern California Earthquake Data Center (NCEDC). Each seismic record has 1500 sampling points, and the sampling rate is 200 Hz. Figure 3 shows the amplitude distribution of all used seismic records.
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
## 3 Fairness Index Based on Rate  
For the above system, the rate set of \(M\) users is defined as \(\textbf{{R}}=\{R_{1},\ldots\ R_{m},\ldots,R_{M}\}\), and the total throughput of the system can be defined as:  
\[R_{sum}=\sum_{m=1}^{M}R_{m} \tag{6}\]  
Figure 1: Downlink NOMA system model.  
The average transmission rate of all users is:  
\[\overline{R}=R_{sun}/M \tag{7}\]  
The mean square value of users' transmission rates is:  
\[\overline{R^{2}}=\frac{1}{M}\sum_{m=1}^{M}{R_{m}}^{2} \tag{8}\]  
The sample variance of users' transmission rates \(T\) can be expressed as:  
\[S^{2}=\frac{1}{M-1}\sum_{m=1}^{M}{(R_{m}-\overline{R})}^{2} \tag{9}\]  
The fairness index related to sample variance of users' transmission rate is designed as:  
\[F=1-\frac{S^{2}}{\overline{R^{2}}} \tag{10}\]  
Index \(F\) has the following properties:  
1. When the transmission rates of all users in the system are equal, the maximum value of \(F\) can be obtained (\(F_{\text{max}}=1\)).
2. The minimum value of \(F\) can be obtained (\(F_{\text{min}}=0\)) when only one user's transmission rate is not 0 and all the other users' rates are 0.
3. The value range of \(F\) has nothing to do with the number of users, channel conditions, or transmission power, but only with the distribution of users' transmission rates. For the system shown in Figure 1, the average throughput of the \(n\)-th cluster is: \[\overline{R_{c}}=\frac{\sum\limits_{n=1}^{N}{R_{c-n}}}{N}\] (11)  
The mean square value of transmission rate of \(N\) clusters is:  
\[\overline{R_{c}^{2}}=\frac{\sum\limits_{n=1}^{N}{R_{c-n}}^{2}}{N} \tag{12}\]  
Referring to the sample variance of transmission rates given in (10), intra cluster fairness \(F_{c-n}\) and inter cluster fairness \(F_{c}\) are respectively defined as follows:  
\[\begin{split} F_{c-n}&=1-\frac{\left[\frac{R_{c-n} ^{\text{s}}-\frac{1}{2}(R_{c-n}^{\text{s}}+R_{c-n}^{\text{w}})}{2}\right]^{2}+ \left[R_{c-n}^{\text{w}}-\frac{1}{2}(R_{c-n}^{\text{s}}+R_{c-n}^{\text{w}}) \right]^{2}}{\left[\left(R_{c-n}^{\text{s}}\right)^{2}+\left(R_{c-n}^{\text{ w}}\right)^{2}\right]}\\ &=1-\frac{\left(R_{c-n}^{\text{s}}-R_{c-n}^{\text{w}}\right)^{2}} {\left(R_{c-n}^{\text{w}}\right)^{2}+\left(R_{c-n}^{\text{w}}\right)^{2}}= \frac{2}{\frac{R_{c-n}^{\text{w}}}{R_{c-n}^{\text{w}}+\frac{R_{c-n}^{\text{w} }}{R_{c-n}^{\text{w}}}}}\\ \end{split} \tag{13}\]  
\[F_{c}=1-\frac{\sum_{n=1}^{N}{(R_{c-n}-\overline{R}_{c})}^{2}}{(N-1)\overline {R_{c}^{2}}} \tag{14}\]
## 3 UWB Positioning Scheme  
### Single-Station ADS-TWR Technique  
The principle of single-station asymmetric double-sided two-way ranging (ADS-TWR) is shown in Figure 2. The method is to send the return signal to device A when ADS-TWR device B first received the message from device A and then use the returned message as the second ADS-TWR start message. Finally, the device passes the return message to device B to end the ranging [19].  
Figure 1: Block diagram of an UWB indoor wireless location system.  
Figure 2: Time sequence of the station SDS-TWR ranging principle.  
As shown in Figure 2, the transmission time between device A and device B is \(T_{pror}\) (in seconds, s). \(T_{round1}\) (s) is the time that device A starts sending messages to receive a return message. \(T_{round2}\) (s) and \(T_{prply2}\) (s) are parameters for unilateral asymmetric bidirectional ranging with device B as the starting device. Finally, \(T_{round1}\) (s), \(T_{round2}\) (s), \(T_{prply1}\) (s), and \(T_{npply2}\) (s) can be used to calculate \(T_{pror}\) (s), then \(T_{pror}\) (s) is multiplied by the speed of light c (in meters per second, m/s) to obtain the distance of \(D\) (m) between the two devices:  
\[T_{pror}=\frac{\left(T_{round1}\times T_{round2}-T_{reply1}\times T_{reply2} \right)}{\left(T_{round1}+\ T_{round2}\ +\ T_{reply1}+T_{reply2}\right)}, \tag{1}\]  
\[D=T_{pror}\times c \tag{2}\]
# Responses of Laterally Loaded Single Piles Subjected to Various Loading Rates in a Poroelastic Soil
## 4 Parametric Study on Pile Cap Geometry
### Effect of Pile Lengths  
The effect of pile lengths on the pile response of the laterally loaded single piles was also examined in this study; various pile lengths of 15.5 m, 17.0 m, 18.5 m, and 20.0 m were considered. The ultimate lateral displacement of the laterally loaded single piles for undrained loading, partially drained loading, drained loading, and dry soil is, respectively, 12.0 mm, 17.1 mm, 19.0 mm, and 21.7 mm (Figure 6). However, for each of the loading cases, the results in Figure 6 show that the pile length has no influence on the corresponding lateral load-lateral displacement result. The result is plausible because, as seen from Figure 3, all the lateral displacements diminish at depth equals or greater than 13 m, which is the fixity point of the pile. Thus, as long as the length of the pile is greater than this point, the lateral displacement profile should therefore be unaffected. It should be noted that these studies only monitored the displacement generated during the incremental loading stage; therefore, these displacements were not their ultimate displacement, as the excess pore-water pressure dissipated, in particular, for the cases of undrained and partially drained loadings. Further  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cap** & **2 kN/s** & **2 kN/h** & **2 kN/Day** & **2 kN/Step** \\
**Width** & **(Undrained)** & **(Partially-Drained)** & **(Drained)** & **(Dry)** \\
**(m)** & **(m)** & **(m)** & **(m)** & **(m)** \\ \hline w/o cap & 14.8 & 17.2 & 20.2 & 23.3 \\
0.6 & 14.3 & 17.1 & 19.9 & 23.1 \\
1.0 & 13.0 & 16.1 & 18.7 & 21.9 \\
1.8 & 10.2 & 13.4 & 17.1 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of cap width on displacement of pile cap under various loading conditions and in dry soil.  
Figure 5: Effect of pile cap width on lateral displacement of pile cap: (**a**) undrained (loading rate = 2 kN/s); (**b**) partially drained (loading rate = 2 kN/h); (**c**) drained (loading rate = 2 kN/day); and (**d**) dry soil (loading rate = 2 kN/step).  
displacement associated with the dissipation of excess pore-water pressure is expected to take place during the consolidation stage.
## 4 System Design and Analysis
### Optimal Network Utilization Mechanism
#### 4.2.1 Long Flow Scheduling and Network Utilization Analysis  
Load balance based on ECMP can only be achieved if the network is symmetric, because ECMP is stateless. Under the influence of the randomness of the hash algorithm and flow size, the distribution of network traffic at a certain moment in an entire network is not uniform. ECMP cannot cope with network topology asymmetry caused by switches and link failure. At the same time, Uniform traffic distribution tends to result in insufficient link bandwidth, which is a hot spot as shown in Figure 5. As an example, assume there are \(n\) long flows with a peak bandwidth requirement of \(d\) in the network, and distribute them in \(m\) links with capacity \(c\). If \(nd>mc\), there will be an overall bandwidth shortage. If \(nd<mc\), there will also be a link congestion. All in all, ECMP protocol is less flexible.  
The performance of the entire network can be measured by two main metrics, the FTC and throughput. The goal is to minimize FCT for shot flows, while maintaining an acceptable throughput for long flows. The FCT is described as the interval between the start time stamp \(t_{s}\) when the first packet of a flow leaves the source server and the end time stamp \(t_{\epsilon}\) when the last packet of the flow arrives at the destination server [30]. The ideal FCT is the best achievable where only the service times of the intermediate nodes and the transmission time of the packets are considered. Because it is not easy to get the FCT of the network, previous studies have indirectly measured the flow completion time through bandwidth, and the more balancing the bandwidth utilization, the shorter the FCT.  
As described in Section 2, minimizing network packet loss rate instead of balancing link utilization is more suitable for data center network traffic load balancing. Packet loss is the most direct cause of the increase of FCT, and the main reason for the packet loss are link congestion and switch congestion. We should consider both link resources and switch resources to reduce packet loss rate. Link resources can be represented by link remaining bandwidth. Switch resources can be represented by switch idle memory. Data center load balancing can be described as the matching problem between traffic and network resources, that is, the overall load balancing of the network is realized on the basis of minimizing the network packet loss rate.  
It is worth noting that the utilization of network resources for each available path should be described as the maximum link or switch utilization on the path rather than the sum of link or switch utilization [42].  
Figure 5: Host-spot Link and Deployment of Sonum.