esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Based on the idea of bi-layer optimization, this paper solves the location problem of logistics UAV parcel-receiving stations and the planning problem of public air routes. The genetic algorithm is used to determine the UAV parcel-receiving station location combination among alternative locations, and the MMAS algorithm is used to determine the public air-route configuration, where the air route between nodes in a public air route is the result of the A* algorithm.  
Simulation results show that the proposed logistics UAV parcel-receiving station location model and public air-route planning model are effective. The average cost of the upper-layer model and the lower-layer model is reduced by 5.12% and 4.48%, respectively, when the two models are combined into a bi-layer optimization model and solved using the nested GA-MMAS algorithm. It can be seen that the upper-layer model of parcel-receiving station location and the lower-layer model of public air-route planning influence each other, and the bi-layer optimization model proposed in this paper is valuable.  
In the future, we will further study the construction method of the logistics UAV air-route network, add optional routes and alternative routes to public air routes, and improve the capacity and toughness of the logistics UAV transport air-route network. Another promising direction is to study the door-to-door logistics scenario, which can be further studied in combination with multi-level takeoff and landing points, parcel-receiving stations, and hub-and-spoke logistics networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station Count** & **20** & **25** & **30** & **35** & **40** & **45** & **50** \\ \hline Calculation time (s) & 4.01 & 6.23 & 8.33 & 11.96 & 15.31 & 19.86 & 24.21 \\ Iteration times (s) & 32.89 & 34.00 & 32.48 & 35.23 & 35.01 & 36.42 & 36.45 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Weight combination results in upper and lower layers.  
Figure 15: Average selected station count in each generation of the genetic algorithm.  
**Author Contributions:** Conceptualization, H.Z.; methodology, F.W., D.F. and G.Z.; software, S.D. and C.D.; writing--original draft preparation, F.W.; writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (71971114) and the Fundamental Research Funds for the Central Universities (No. NQ2022012).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge with thanks the support for this work by the College of Civil Aviation at Nanjing University of Aeronautics and Astronautics.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results

Table 4: The classification results of the CWT-SCNN, CSP-SCNN, fast Fourier transform (FFT)-SCNN, and short time Fourier transform (STFT)-SCNN methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{4}{c}{**Mean Kappa Value**} \\ \cline{2-5}  & **CSP-SCNN** & **FFT-SCNN** & **STFT-SCNN** & **CWT-SCNN** \\ \hline S1 & 0.286 & 0.364 & 0.453 & **0.478** \\ S2 & 0.465 & 0.463 & 0.599 & **0.622** \\ S3 & 0.296 & 0.341 & 0.299 & **0.347** \\ S4 & 0.905 & 0.887 & **0.923** & **0.923** \\ S5 & 0.666 & 0.754 & 0.769 & **0.847** \\ S6 & 0.448 & 0.410 & 0.443 & **0.733** \\ S7 & 0.344 & 0.358 & 0.330 & **0.459** \\ S8 & **0.848** & 0.794 & 0.829 & 0.822 \\ S9 & **0.685** & 0.640 & 0.618 & 0.684 \\ \hline Average & 0.549 & 0.556 & 0.585 & **0.657** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The mean kappa value results of the CWT-SCNN, CSP-SCNN, FFT-SCNN, and STFT-SCNN methods. time-frequency image signal using CWT. Then the image signals perform 10\(\times\)10-fold cross validation on SCNN and CNN. From Figure 5a,b, compared with CWT-CNN, eight of the nine subjects obtained the highest classification accuracy and kappa value using the CWT-SCNN method. In addition, the CWT-SCNN algorithm has the highest mean classification accuracy and mean kappa value. Compared with the CWT-CNN method, the mean classification accuracy and mean kappa value improved by 2.9% and 5.3%, respectively.
## 5 Conclusions  
In this paper, we proposed a measurement-based TDL channel model for A2G communication applications in rural areas for different UAV flight phases. Among all of the considered scenarios, a maximum of 14 channel taps with a maximum delay of 650 ns were used in the model. The K-factor value 27.58 dB for the corresponding UAV vertically landing scenario is the largest among all of the considered scenarios. For the high altitude scenarios, the K-factor value increases from 15.96 dB at 300 m to 17.23 dB at 800 m, which also validates the dependence between the K-factor and the UAValtitude. In addition, we analyzed the amplitude distribution characteristics of each tap, where the first tap can be well modeled by Rice and Weibull distributions. Other channel taps can be fitted with a normal distribution. Further, we also evaluate the delay-Doppler spectrum, where we found the LoS component is always the dominant component in considered scenarios. The Doppler spectrum caused by MPC reveals significant differences in different flight phases. In taking-off and landing scenarios, the effect of the MPC on the Doppler spectrum is very small compared to the LoS path. In high altitude scenarios, the Doppler caused by MPC shows dependence on the UAV altitude, where the higher the UAV altitude, the less MPC is observed. In addition, the \"Bell\"- and \"Jakes\"-type spectra are used to model the Doppler spectrum for individual channel taps.  
Conceptualization, Y.L. and W.W.; funding acquisition, Y.L. and W.W.; investigation, Y.L., Y.H, Z.L., J.Y. and D.S.; methodology, Y.L. and W.W.; software, Y.L. and Y.H.; data acquisition, Y.L., Y.H. and W.W.; supervision, W.W.; validation, Y.L.; writing--original draft, Y.L. and W.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant 2020 YFB1807001, in part by the Innovation Capability Support Program of Shaanxi under Grant 2022 TD-41, the Key Research and Development Program of Xi'an under Grant 24 ZDCYJSGG0006, the Natural Science Basic Research Program of Shaanxi under Grant 2023-JC-QN-0686, the National Natural Science Foundation of China under Grant 61871059, and the Joint Fund for Equipment Pre research of the Ministry of Education under Grant 8091B032226. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.  
The datasets collected and generated in this study are available upon request to the corresponding author.  
[PERSON] is employed by Beijing Metaradio Technologies Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
## 1 Introduction
### Motivation and Related Literature
Moreover, the SatCom networks are a promising architecture for supporting the Internet of Remote Things (IoRT). Thus, in [22], the model-free reinforcement learning is exploited for the joint resource-scheduling and IoRT data-scheduling problem subject to the maximization of the amount of the IoRT data of the overall satellite IoRT network. Finally, in [23], the authors apply a multiobjective reinforcement learning for cognitive SatComs, where the uncertainty in the thousands of possible radio parameter combinations and the dynamic variation of the radio channel over time result in a continuous multidimensional state-action space. This requires a fixed-size memory continuous state-action mapping and not the traditional discrete mapping. The authors give trade-off analyses considering the execution time, the performance accuracy and the computational cost of the proposed scheme.
## 2 Materials and Methods
### RNA Extraction, cDNA Synthesis, and Real-Time Polymerase Chain Reaction (PCR)  
RNA was extracted using TRIzol Reagent (Thermo Fisher) following the manufacturer's instructions. RNA concentrations were measured using the Cytation 5 Cell Imaging Multimode Reader (Agilent, Santa Clara, CA, USA). cDNA synthesis was performed using a SmartGene Compact cDNA synthesis kit (SamJung Bioscience, Daejeon, Republic of Korea). Real-time PCR was performed using a SmartGene Real-Time PCR master mix (SamJung Bioscience). The real-time PCR conditions comprised polymerase activation and initial denaturation at 95 \({}^{\circ}\)C for 2 min, followed by 40 cycles of denaturation 95 \({}^{\circ}\)C for 5 s and annealing/extension at 60 \({}^{\circ}\)C for 30 s. The primers used in this study are listed in Table S2 (Supplementary Materials).  
Enzyme-Linked Immunosorbent Assay for Tumor-Necrosis Factor (TNF)-\(\alpha\) and Interleukin (IL)-1\(\beta\)  
After RAW 264.7 macrophages were treated with LPS and CHA for 24 h, the conditioned medium was collected to measure TNF-\(\alpha\) and IL-1\(\beta\) levels using QuantikineTM ELISA kits obtained from R&D Systems (Minneapolis, MN, USA) following the manufacturer's protocols.
# Data Traffic Modeling in RPAS/UAV Networks with Different Architectures
## 6 Discussion
Let us just say that, for example, doubling the number of users from N = 5 to N = 10 leads to a twofold increase in the parameter AU for the link \"BS-RPAS1\" (TS = 1000 bits and other parameters similar to those discussed above). In general, our research shows that using the obtained data on traffic characteristics (Figures 3-5), it is possible to choose the right operating conditions and successfully exchange data between the base station and users of remote cellular networks using RPAS/UAV integration. In the future, we will expand our research to RPAS satellite radio access networks, coordinated data transfers between ground base stations and predicting channel behavior in critical conditions. In our next work, we will present the results obtained for models containing instead of RPAS1 (Figure 2) the low-orbit satellite Iridium. NetCracker software tools allow you to create a network, carry out the necessary simulation experiments, determine the final characteristics, change the topology and modify the network equipment for further improvement and development. The capabilities of the NetCracker software for predicting traffic parameters were tested in our work [38]. The obtained dependencies of the message transit time (1.4-1.9 s) on the number of satellites and aircraft were experimentally confirmed by Aireon [39], which provided air traffic surveillance data to its partners. Tracking more than 10,000 aircraft with ADS-B 1090 Extended Squitter receivers for Iridium NEXT satellites, the system delivered data to air traffic control centers with a delay of less than 1.5 s [40].
## 3 Case Study: Dairy Industry
### Dairy Plants  
The case study relates to four geographically dispersed dairy plants (\(F=\{f_{1},\ldots,f_{4}\}\)) and one distribution center (named \(f_{5}\) for modeling's sake, then \(F^{*}=\{f_{1},\ldots,f_{5}\}\)).  
The processing units have production capacity according to each product type, see Table 1. There are six products produced (\(p_{1},\ldots,p_{6}\)) by the four dairy plants; the relationship between products and plants is as follows:  
* \(f_{1}\) produces \(p_{1}\), \(p_{2}\), and \(p_{3}\).
* \(f_{2}\) produces \(p_{5}\).
* \(f_{3}\) produces \(p_{1}\) and \(p_{2}\).
* \(f_{4}\) produces \(p_{3}\), \(p_{4}\), and \(p_{6}\).  
The dairy plants \(f_{1}\), \(f_{3}\), and \(f_{4}\) can receive products from other units for completion; considering the entry of the unfinished product as input for a new one, they maintain the following relationships (see also Table 2):  
* \(f_{3}\) can accept \(p_{1}\) from \(f_{3}\) and produces \(p_{2}\).
* \(f_{4}\) can accept \(p_{3}\) from \(f_{1}\) and produces \(p_{4}\).
* All factories can send finished products to \(f_{5}\) (distribution center). In this case study, we have only one distribution center (DC), also called \(f_{5}\), for notation's sake.
###### Abstract  
The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 \(\times\) 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.  
Keywords:remote sensing images; deep learning; deep convolutional neural network; real-time target detection; context feature scene perception +
Footnote †: journal: Applied Sciences
## 2 Case Study
### Model Performance Evaluation Indices  
In this study, output data from the numerical analysis were compared to data measured using the consolidation time. Numerical methods have been used to predict the behaviour of embankments located on soft grounds. In order to predict the accuracy of the developed models, the correlation coefficient (\(R^{2}\)) and the average percentage difference (\(PD_{average}\)) were employed. The value of \(R^{2}\) was derived from the graph of the equation, where the linear-square fit line fitted the least The \(PD_{average}\) was performed on two settlement data, which were predicted and measured based on the following equation:  
\[PD_{average}=\left(\frac{\frac{|V_{1}-V_{2}|}{(V_{1}+V_{2})/2}\times 100\%}{n} \right), \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{\(\gamma_{\text{unsat}}\)} & \multirow{2}{*}{\(\gamma_{\text{sat}}\)} & \multirow{2}{*}{\(\
u\)} & \(E_{\text{ref}}\) & \(c_{\text{ref}}\) & \(\phi\) & \(\psi\) & \multirow{2}{*}{\(\Lambda\) *} & \multirow{2}{*}{\(\kappa\) *} \\  & & & (kN/m\({}^{3}\)) & (kN/m\({}^{3}\)) & & (kN/m\({}^{2}\)) & & (\({}^{\circ}\)) & & \\ \hline Fill material & D & 16.5 & 18.0 & 0.3 & 15,000 & 10 & 20 & 0 & - & - \\ Sand cushion & D & 17.0 & 18.5 & 0.3 & 20,000 & 0 & 30 & 0 & - & - \\ Clay silt & UD & 13.1 & 13.6 & 0.15 & - & 75 & 0 & 0 & 0.10 & 0.02 \\ Silty clay & UD & 13.3 & 13.8 & 0.15 & - & 43 & 0 & 0 & 0.12 & 0.03 \\ Soft clay & UD & 13.6 & 14.0 & 0.15 & - & 43 & 0 & 0 & 0.11 & 0.03 \\ Silty clay & UD & 14.0 & 14.5 & 0.15 & - & 11 & 0 & 0 & 0.13 & 0.03 \\ Clay silt & UD & 14.5 & 15.8 & 0.15 & - & 10 & 0 & 0 & 0.14 & 0.03 \\ \hline \hline \end{tabular}  
* \(\gamma_{\text{unsat}}\) = unsaturated unit weight; \(\gamma_{\text{sat}}\) = saturated unit weight; \(E_{\text{ref}}\) = reference modulus; \(c_{\text{ref}}\) = reference cohesion.  
\end{table}
Table 5: Material properties.  
where \(V_{1}\) and \(V_{2}\) are the predicted and measured data, respectively, and n is the number of data in a series of datasets. The higher the \(R^{2}\) values and the lower the \(PD_{average}\), the higher the accuracy of the predictions of the developed models.
## 3 Materials and Methods
### Lateral Displacement Definition  
Through aerial video observation, vehicles moving along the road cross-section are ubiquitous in the overtaking scene, which is quantified by the lateral displacement. This study introduces two lateral displacements, including the absolute displacement in the road reference frame and the relative displacement in the vehicle reference frame.  
Figure 4: Cumulative frequency distribution of average speed difference in overtaking scenario.  
Figure 3: Vehicle motion reference frames. (**a**) Road reference frame; (**b**) Vehicle reference frame.  
As shown in Figure 5a, in the road reference frame, the position coordinates of the overtaking vehicle and the overtaken vehicle are \((x_{f1},y_{f1})\) and \((x_{p1},y_{p1})\) at one moment and are \((x_{f2},y_{f2})\) and \((x_{p2},y_{p2})\) in a later moment. Then the absolute displacements \(Y_{f}\) and \(Y_{p}\) of the two vehicles are calculated by  
\[Y_{f}=\frac{y_{f1}-y_{p1}}{\left|y_{f1}-y_{p1}\right|}(y_{f2}-y_{f1}) \tag{2}\]  
\[Y_{p}=\frac{y_{p1}-y_{f1}}{\left|y_{p1}-y_{f1}\right|}(y_{p2}-y_{p1}) \tag{3}\]  
When \(Y_{f}\) or \(Y_{p}\) takes negative values, the vehicle laterally approaches the other vehicle. Conversely, it laterally departs from the other vehicle in the case of positive values.  
As shown in Figure 5b, in the vehicle reference frame, assuming that the position coordinates of the overtaking vehicle at two moments are \((x_{r1},y_{r1})\) and \((x_{r2},y_{r2})\), the relative displacement of the overtaking vehicle is  
\[Y_{r}=\left|y_{r2}\right|-\left|y_{r1}\right| \tag{4}\]  
Similarly, when \(Y_{r}<0\), parallel vehicles laterally approach each other; when \(Y_{r}>0\), they laterally depart from each other. The relationship between relative displacement and absolute displacement is  
\[Y_{r}=Y_{f}+Y_{p} \tag{5}\]
# Influence of Impeller Speed Patterns on Hemodynamic Characteristics and Hemolysis of the Blood Pump
## 3 Results
### CFD Results of the Hemodynamic Characteristics and Hemolysis
The red dotted line region in Figure 15a shows the slow flow area at the outlet of the blood pump. At the peak pressure moment, the flow velocity at the outlet of the blood pump increased and the slow flow area disappeared, as shown in Figure 15b. Then the flow velocity in the red dotted line region in Figure 15c became smaller compared with peak time. Therefore, the pulsatile pattern washed away the surface of the blood pump outlet region and inhibited the adhesion of blood cells. Figure 14: (**a**) Flow trace of blood under shear stress in the whole flow field of the blood pump. (**b**) Track of blood flow in the spiral groove. Figure 15: Flow-field variation characteristics of the blood pump at different moments in the pulsating speed pattern. The panels (**a**–**c**) show the velocity vector of the flow field inside the blood pump when the aortic pressure increased (**a**) at 0.9 s, (**b**) was at peak at almost 1.2 s, and (**c**) decreased at 1.4 s. Black arrows indicate the direction of fluid flow. The shear stress distribution in the spiral groove in the sinusoidal speed pattern was compared with that in the constant speed pattern at different moments. The shear stress distribution in the spiral groove under the constant speed pattern was more uniform and hardly changed with time, as shown in Figure 16d-f. However, the internal shear stress of the spiral groove in the sinusoidal speed pattern had time-varying characteristics. The maximum shear stress was different at different times. The maximum shear stress appeared in the rising stage of the impeller speed, which basically reached 157 Pa, as shown in Figure 16a. The maximum shear stress in the sinusoidal speed pattern was slightly higher than that in the constant speed pattern. Therefore, the HI and shear stress characteristics of different impeller speed patterns were established. The results showed that the shear stress increased to a certain extent in the variable speed condition, and the HI in the pulsatile and sinusoidal speed patterns was higher than that in the constant speed pattern.
# Evaluation of Horizontal Shear Performance of Composite Girders Using Separable Shear Connectors
###### Abstract  
The increased volume of heavy vehicles and use of de-icing agents on concrete bridge decks accelerates the deterioration of these structures. Therefore, the rapid replacement of these structures has attracted considerable attention, with prefabricated bridges being the preferred option. Conventionally, horizontal shear connections between girders and precast decks have incorporated rebar stirrup shear connectors. Although effective for initial construction, this method renders dismantling of aged decks complex, because rebar connectors are fully embedded within girders. This study introduced an embedded separable shear connector that minimizes deck-breaking and facilitates easy reinstallation by the simple separation of the deck from the girder. Horizontal shear and flexural tests on composite girders and comparisons with various design codes were conducted to evaluate this connector. The results of horizontal shear tests confirmed that securing sufficient embedment depth is necessary to prevent the pull-out failure of shear connectors. Additionally, prestressed concrete composite girder flexural tests with improved design verified that the detachable shear connectors exhibited an approximately 60% improvement in flexural performance compared with conventional reinforcement shear connectors.  
prefabricated bridges; horizontal shear connection; separable shear connector; environmental impact; deck reinstallation
## 5 Error Analysis  
### Crack Parameters  
To judge the accuracy of the double-probe penetration detection method, the average value of \(\omega_{K}\) under two different \(K\) values was used as the corresponding standard value. The product of this value and the maximum wave height was used as the wave height of the crack tip and thus detected the Cracks S-3-1 to S-3-15. After obtaining the location of the crack tip, the depth and angle of each prefabricated crack were calculated using Equations (3)-(6). The error analysis is summarized in Table 6. Due to uncontrollable factors such as test operation, the crack angle error in some specimens was large, so these data were not considered in the error analysis.  
As shown in Table 6, the relative error of crack depth detection was less than 25%. When the deflection angle was between 10 and 30\({}^{\circ}\), the detection precision rose with increasing crack depth. During crack angle detection, the relative errors were all within 30%: it can be inferred from the several cracks with large angle test errors that the crack with a deviation angle similar to the angle of incidence of the wave from the probe (30\({}^{\circ}\) or 45\({}^{\circ}\)) was nearly parallel to the incidence wave, which also affected the accuracy of penetration detection.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Crack Number**} & \multicolumn{2}{c}{**Crack Parameters**} & \multicolumn{2}{c}{**Test Results**} & \multicolumn{2}{c}{**Measurement Error**} & \multicolumn{2}{c}{**Relative Error**} \\ \cline{2-9}  & **Depth \(d\)/mm** & **Angle \(a\)/\({}^{\circ}\)** & **Depth \(d\)/mm** & **Angle \(a\)/\({}^{\circ}\)** & \(d\)/mm** & \(\Delta a\)/\({}^{\circ}\) & \(\delta_{d}\)/\% & \(\delta_{a}\)/\% \\ \hline S-3-1 & 3 & 10 & 2.8 & 1