esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 5 Volumetric Efficiency Characterization  
The volumetric efficiency of a piston pump is the ratio of actual output flow rate \(Q\) of the piston pump to theoretical maximum flow rate \(Q_{T}\) (flow rate without compression and  
Figure 8: Comparison between simulated and experimental values of the plunger pump outlet flow.  
Figure 9: Comparison between the simulated and experimental values of the liquid film leakage of the flow distribution pair.  
leakage), reflecting the flow rate loss due to fluid compression stagnation and leakage of the friction sub-film. The calculation formula is as follows:  
\[\begin{array}{ll}\eta_{\rm v}&=\frac{Q}{Q_{T}}=\frac{Q_{T}-(Q_{L}+Q_{P}+Q_{Z}+Q_ {H})}{Q_{T}}\\ &=1-\frac{Q_{L}}{Q_{T}}-\frac{Q_{P}}{Q_{T}}-\frac{Q_{Z}}{Q_{T}}-\frac{Q_{H}}{Q_ {T}}\\ &=1-\eta_{1}-\eta_{2}-\eta_{3}-\eta_{4}\end{array} \tag{7}\]  
where \(Q_{L}\) is the CO\({}_{2}\) compression retention volume loss; \(Q_{P}\) is the liquid film gap leakage volume loss of the flow distribution vice; \(Q_{Z}\) is the liquid film gap leakage volume loss of the plunger vice; and \(Q_{H}\) is the liquid film gap leakage volume loss of the sliding shoe vice. The theoretical flow rate calculation formula was as follows:  
\[\begin{array}{ll}\mathrm{Q}_{T}=\frac{\pi}{4}d^{2}\omega D\tan\beta Z \end{array} \tag{8}\]  
where \(d\) is the plunger diameter; \(D\) is the plunger distribution circle diameter; \(\omega\) is the plunger cavity rotation angular velocity; and \(Z\) is the number of plungers. The selected number of plungers was 7. After calculation, the theoretical flow rate of the model in this study was 284.35 L/min.
## 3 Security Weaknesses of Cao et al.'s Scheme
### Informal Security Analysis
#### 3.2.4 Single Point of Failure  
In [PERSON] et al.'s scheme, the MTC group leader \(MD_{n}\) collects the access request of the group member \(MD_{i}\) and aggregates it. Afterward, \(MD_{n}\) sends the aggregation messages of an access request to serving network \(SN_{j}\). However, if \(MD_{n}\) node is compromised, off-line or break down, the access request of massive MTC nodes cannot be delivered to \(SN_{j}\). It limits the security and the performance of the proposed system. Therefore, [PERSON] et al.'s scheme does not offer resistance against a single point of failure attack because the massive MTC nodes cannot be able to access the service when \(MD_{n}\) does not work.
## 5 Conclusions  
In conclusion, our data support the notion that the CatWalk XT(r) gait analysis can be seen as an objective and consistent tool for assessing the impairment and recovery of locomotor function after severe cervical contusion/compression SCI in rats. The CatWalk XT(r) parameters we used did not only reflect the spontaneous recovery after the injury. They also had a generally high correlation with the widely used scaling system of the BBB open field score, and especially with the histological assessment of the percentage of preserved tissue. We found that among static CatWalk XT(r) parameters, the print intensity parameters showed an early onset of a significant improvement in both, the forelimbs and the hindlimbs during the process of spontaneous recovery. On the other hand, kinematic parameters, such as the regularity index, the stride length, the swing speed, and the average speed, which are more dependent on the recovery of general locomotion and coordination of the animals, discriminated a significant recovery in the later stages 5-6 weeks after the injury. Among the kinematic parameters, the regularity index and the stride length showed a good correlation with the BBB open field scores and the percentage of preserved tissue, underlining their value for behavioral testing at later stages after SCI. Finally, among the CatWalk XT(r) parameters measuring the speed of the animals, the swing speed demonstrated a superiority compared to the body speed and the average speed with less variation and better correlation with the BBB open field scores and the percentage of preserved tissue.  
Conceptualization, G.Z. and A.Y.; methodology, G.Z., A.Y., M.S., L.R., T.S. and K.Z.; software, G.Z., A.Y., M.S. and L.R.; validation, G.Z., A.Y., T.S. and K.Z.; formal analysis, G.Z. and A.Y.; investigation, G.Z., A.Y., M.S. and L.R.; resources, T.S., A.U. and K.Z.; data curation, G.Z., A.Y., L.R., T.S. and K.Z.; writing--original draft preparation, G.Z. and A.Y.; writing--review and editing, A.Y., M.S., L.R., J.W., T.S., A.U. and K.Z.; visualization, G.Z. and A.Y.; supervision, A.Y., T.S. and K.Z.; project administration, A.Y. and K.Z.; funding acquisition, A.U. and K.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All experiments met the requirements of \"the animal protection law and animal research regulation of the Federal Republic of Germany\" and were approved by the Animal Care Committee of the federal government (file reference G-211/15).  
Not applicable.  
The data generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors acknowledge the help of [PERSON] and [PERSON] from the Interdisciplinary Neurobehavioural Core (INBC) Facility at the University of Heidelberg with the neurobehavioural testing.  
The authors declare no conflict of interest.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Navigation
#### 3.1.1 Neural Network Based Gravity Vector Estimation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Method 1**} & \multicolumn{2}{c}{**Method 2**} \\ \hline
**Set** & **Alg.** & **MSE** & **R** & **MSE** & **R** \\ \hline Train & SCG & \(5.81\times 10^{-3}\) & \(7.22\times 10^{-1}\) & \(5.82\times 10^{-3}\) & \(7.24\times 10^{-1}\) \\ \hline Validation & SCG & \(5.89\times 10^{-3}\) & \(7.21\times 10^{-1}\) & \(5.98\times 10^{-3}\) & \(7.25\times 10^{-1}\) \\ \hline Test & SCG & \(5.94\times 10^{-3}\) & \(7.18\times 10^{-1}\) & \(5.85\times 10^{-3}\) & \(7.21\times 10^{-1}\) \\ \hline Train & BR & \(6.95\times 10^{-5}\) & \(9.98\times 10^{-1}\) & \(9.83\times 10^{-4}\) & \(9.55\times 10^{-1}\) \\ \hline Validation & BR & \(6.92\times 10^{-5}\) & \(9.98\times 10^{-1}\) & \(9.80\times 10^{-4}\) & \(9.54\times 10^{-1}\) \\ \hline Test & BR & \(6.95\times 10^{-5}\) & \(9.97\times 10^{-1}\) & \(9.81\times 10^{-4}\) & \(9.56\times 10^{-1}\) \\ \hline Train & LM & \(5.63\times 10^{-5}\) & \(9.98\times 10^{-1}\) & \(7.31\times 10^{-4}\) & \(9.60\times 10^{-1}\) \\ \hline Validation & LM & \(5.61\times 10^{-5}\) & \(9.98\times 10^{-1}\) & \(7.21\times 10^{-4}\) & \(9.72\times 10^{-1}\) \\ \hline Test & LM & \(5.70\times 10^{-5}\) & \(9.97\times 10^{-1}\) & \(7.32\times 10^{-4}\) & \(9.65\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MSE and R values for neural network based gravity vector estimator. two additional vectors during terminal flight, i.e., the speed vector and the line of sight vector, a hybridization approach is also presented to improve performance.
## 3 Interpolation Algorithm  
The proposed algorithm is designed to interpolate between two adjacent models, \(G_{1}\) and \(G_{2}\). This section provides an overview of the algorithm, while its properties are analyzed in subsequent sections. The method incorporates certain aspects of hyperbolicgeometry. In [24], Section 2 provides a concise overview of hyperbolic geometry fundamentals, also centered on the Poincare disk model. This is expanded in Section 3, which uses a practical example to highlight how hyperbolic distance provides a proxy metric for comparing system dynamics. These foundations set the stage for exploring connections between hyperbolic metrics, system norms, and stability analysis. For further details, refer to [25; 26].  
In On the Correspondence of Hyperbolic Geometry and System Analysis [24], Section 2 provides a concise overview of hyperbolic geometry fundamentals, also centered on the Poincare disk model. This is expanded in Section 3, which uses a practical example to highlight how hyperbolic distance provides a more accurate metric for comparing system dynamics than Euclidean distance. These foundations set the stage for exploring connections between hyperbolic metrics, system norms, and stability analysis.  
The fundamental idea is that as the parameter varies, the poles of the model follow specific trajectories in the complex plane. However, since the model is only known at certain discrete points, only sampled positions of these pole trajectories are available. An interpolation approach can therefore involve replacing the original trajectories with artificial ones that connect the known points. A crucial aspect of this strategy is ensuring that the selected artificial trajectories adhere to the system's known a priori properties (see Section 2).  
In the previous study [20], hyperbolic lines were proposed as artificial trajectories connecting known samples. This choice was made because it preserves stability and provides a guaranteed upper bound on the deviation from the known models. In this paper, all of the possible trajectories that satisfy this condition are presented, paving the way for incorporating prior knowledge into the root loci if a particular problem is given. For further discussion, the hyperbolic line remains as a particular example because in a later section, Section 4, it serves as a reference for generalization.  
The input data of the algorithm consist of static finite element method (FEM) data related to the structure at the two extreme ends of the parameter range. This means that the FEM model provides information about displacements regarding a certain load that is discussed later, at the minimum and maximum values of a given parameter.
## 3 Development of Thermal Condition Monitoring System
### Overview of the Communication Link
The reasons being that when buried it can inadvertently be excavated; trees may fall on them, especially in rural or remote applications. The cost of optical fiber repairs is overwhelmed by this outcome. In this regard, the use of spread-spectrum radio concept has been ensured, with the advantages that once the system gets installed there are no additional recurring costs, such as license fees or leasing and does not experience any physical breakdowns. There are modalities in place for signal improvement against Wi-Fi and other interferences; thus, the system reliability can be justified. Invariably, signal congestion can be controlled with appropriate communication techniques application [21; 55; 56; 57; 58; 59]. Table 1 illustrates the types of ZigBee devices and characteristics in practice. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics.** & **Xbee ZB** & **Xbee-PRO 2B/C** & **Xbee-PRO 868** \\ \hline Indoor Range & 40 m & 90 m & 550 m \\ Line-of-Sight Range & 120 m & 1.5 km/3.2 km & 40 km \\ Radio Frequency Data Rate & 250 kbps & 250 kbps & 24 kbps \\ Frequency & 2.4 GHz & 2.4 GHz & 868 MHz \\ Transmit Power & 1.25 mW/2 mW & 10 mW/63 mW & 1 mW/315 mW \\ Encryption & 128-bit AES & 128-bit AES & 128-bit AES \\ Transmit Current & 35 mA/45 mA & 205 mA/120 mA & 500 mA \\ Receive Current & 38 mA/40 mA & 45–47 mA & 65 mA \\ Number Channels & 16 & 15 & 1 \\ Regions & Europe, Japan, Australia, UK, Canada, USA, Global & Europe \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ZigBee device characteristics comparison [48; 58; 60].
## 5 Analyses and Discussion of the Whipping Factor  
### Comparison Results of the Vertical Bending, Torsional, and Horizontal Bending Components  
In Section 4, we showed the average whipping factor in each component under head or bow quartering seas as the analysis results. In this section, we will compare each stress component in detail. Figure 7a shows the cumulative frequency distribution of the whipping factor of the data with a significant wave height of 3.0 m or higher. It can be observed in Figure 7a that the maximum value of the whipping factor is approximately 2.35 in the vertical longitudinal bending component. However, the maximum values of the whipping factor in the torsional and horizontal bending components reached only approximately 1.4 and 1.6, respectively, and were mostly small. It transpires that the whipping factor in the torsional and horizontal bending components is considerably smaller than that in the vertical longitudinal bending component.  
\begin{table}  
\end{table}
Table 4: Statistics table of whipping factor in bow quartering sea, (**a**) vertical bending component, (**b**) torsional component, (**c**) horizontal bending component.  
Figure 7b shows the exceedance probability distribution of the longitudinal stress peak values of the raw data with a significant wave height of 3.0 m or higher. The vertical bending, horizontal bending, and torsional components are plotted in this figure. It is observed that the torsional component is approximately one-third of the vertical longitudinal bending component. Therefore, the torsional component in whipping is not large. Moreover, it is observed that the horizontal bending component is approximately one-fifth of the vertical longitudinal bending component. Therefore, its influence is considered to be considerably small. From these results, it was found that the torsional and horizontal bending components in whipping were not dominant.  
These results may be because the wave impact pressure works in the vertical direction and the action position is not so far from the hull centerline, so that torsion and horizontal loads are not induced significantly.
# Impact of Environmental Factors on Stilbene Biosynthesis
## 2 Biosynthesis of Stilbenes and Stilbenoids  
Stilbenes and stilbenoids are biosynthesized through the phenylpropanol pathway, which is also responsible for the biosynthesis of numerous primary and secondary metabolites including flavonoids, coumarins, hydrolyzable tannins, monolignols, lignans, and lignins [44]. Generated by the shikimate pathway, the aromatic amino acid _L_-phenylalanine is the primary starting molecule of the phenylpropanol pathway (Figure 2). The non-oxidative deamination of _L_-phenylalanine to form _trans_-cinnamic acid, catalyzed by phenylalanine ammonia-lyase (PAL; EC 4.3.1.24), is the entry step for the carbon channeling from primary metabolism into the phenylpropanol secondary metabolism. PAL is ubiquitous in plants [45], and it is undoubtedly the most studied enzyme involved in plant secondary metabolism [46]. Cinnamic acid can be bound to a coenzyme A (CoA) molecule by cinnanate:CoA ligase (CNL; EC 6.2.1.-) to form cinnamoyll-CoA. Alternatively, cinnamic acid can be hydroxylated by cinnamate 4-hydroxylase (C4H), a cytochrome P450 enzyme (EC 1.14.14.91), to form _p_-coumaric acid. Some plants (mainly monocots but also dicots) possess a bifunctional phenylalanine/tyrosine ammonia-lyase (PTAL, EC 4.3.1.25) that efficiently deaminates both _L_-phenylalanine (PAL activity) and _L_-tyrosine (TAL activity) [47; 48; 49; 50]. These plants can directly produce _p_-coumaric acid using _L_-tyrosine as a substrate, bypassing the requirement for _L_-phenylalanine and C4H. A molecule of CoA is then bound to _p_-coumaric acid by 4-coumarate: CoA ligase (4 CL; EC 6.2.1.12), generating _p_-coumaroyl-CoA, which provides an active intermediate in numerous branches of the general phenylpropanol pathway [51].
## 5 Conclusions  
In this paper, the silting behavior of a permeable pavement was simulated in detail, and the permeable performance of the permeable Marshall specimen during the silting process was studied. The relationship between the noise and the degree of congestion on the permeable pavement was then explored, and a method of non-destructive detection of the degree of congestion on the road was proposed. Finally, we summarized several current methods of cleaning permeable pavement, evaluated several new cleaning methods, and put forward some suggestions on the protection of permeable pavement blockage. The main conclusions of this article are as follows:  
1. Through the mesoscopic simulation process of blocking behavior, it was found that: the clogging process can be roughly divided into three stages, (1) early fast clogging stage, (2) middle slow clogging stage, (3) terminal clogging stable stage. According to the position of the blockage, it can be divided into shallow blockage on the surface and deep blockage on the inside. The role of water flow in the blockage process cannot be ignored. It is the main factor that promotes the lateral movement of the blockage, sometimes promoting blockage and sometimes slowing it down.
2. The critical particle size of the blockage is particles within 0.3-0.6 mm. Particles above 1.18 mm gather on the road surface and it is difficult for them to enter the road surface. The particles below 0.03 mm are basically washed away by the water flow and cannot form a large blockage. Therefore, when cleaning the blockage of the permeable asphalt pavement, the 0.3-0.6 mm particles should be considered.
3. The simulated blockage test of the PAC-13 Marshall specimen found that: during the clogging process, the weight, porosity, and water permeability of the test piece became larger as the degree of clogging deepened, and the range of change was first large and then small. This shows that the early blockage occupies a larger proportion.  
Figure 13: Noise growth rate and quality growth rate.  
The tire drop test in the laboratory verified that the permeable asphalt pavement had better noise reduction performance than the ordinary densely distributed pavement.
* According to the noise change in the process of indoor simulation of road blockage, a new method of non-destructive detection of permeable road blockage is proposed.  
Conceptualization, X.G.; software, S.C.; formal analysis, S.C.; investigation, X.L. and C.Z.; data curation, S.C., W.C. and X.L.; writing--original draft preparation, S.C.; writing--review and editing, X.G.; project administration, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Province Science and Technology Development Plan (CN) (Grant No. 20190303033 SF).  
Not applicable.  
This research was funded by the Jilin Provincial Science and Technology Development Program (CN) (Grant No. 20190303033 SF). This financial support is gratefully acknowledged.  
The authors declare that there is no conflict of interests regarding the publication of this paper.
## 3 Results
### Variable Selection and Final Random Forest Model  
The stepwise backward variable selection process resulted in high values of the Overall Accuracy and the Kappa Coefficients (starting in average at 81% and 72%, respectively, with 24 predictors) until 14 predictors remained in the model. From that point, these values started to decrease, until they reached the Overall Accuracy and the Kappa Coefficients of 62% and 43.0%, respectively, with two predictors (Figure 2). The last variables kept in the process were nNDMI and NDWI, but the removal of the DEM, Re1, and S1-VH caused the largest drops in the Overall Accuracy and the Kappa Coefficient (Table S3). To maximise the classification accuracy, we selected the RF model with 14 predictors.  
This predictor set was thus used to compute the final RF with 5000 trees, which achieved an Overall Accuracy of 81% and a Kappa Coefficient of 72% (Table 3). At the class level, the classification accuracies achieved at least 70% for most habitat types. The few exceptions were the User's Accuracy for rocks (69%), and the Producer's Accuracies for shell beds (59%) and bare sediment mixed (36%). The highest User's Accuracy was observed for shell beds (91%), while bare sediment sandy scored the highest Producer's Accuracy (93%). This model was used to generate the final classification for the entire intertidal area.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predictions**} & \multicolumn{8}{c}{**Reference**} \\ \cline{2-10}  & **Rocks** & **Shell Beds** & **Macrolgae** & \begin{tabular}{c} **Bare Sedi-** \\ **ment Sandy** \\ \end{tabular} & \begin{tabular}{c} **Bare Sedi-** \\ **ment Mixed** \\ \end{tabular} & \begin{tabular}{c} **FCB** \\ **Sandy** \\ \end{tabular} & \
\begin{tabular}{c} **FCB** \\ **Mixed** \\ \end{tabular} & **Total** &
\begin{tabular}{c} **User’s** \\ **Accuracy** \\ \end{tabular} \\ \hline Rocks & 95 & 0 & 41 & 1 & 0 & 0 & 0 & 137 & 69.3 \\ Shell beds & 3 & 107 & 0 & 8 & 0 & 0 & 0 & 118 & 90.7 \\ Macroalgae & 7 & 3 & 685 & 23 & 3 & 3 & 90 & 814 & 84.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix and independent validation statistics of the final random forest model, computed with the best set of predictors (14 predictors). All the values of the validation statistics range between 0 and 100%. FCB: fiddler crab burrow areas.  
Figure 2: Variation in mean Overall Accuracy and mean Kappa Coefficient along the stepwise backward variable selection procedure. In each step, 25 random forests were computed, and an independent validation was performed for each. The red circle and dashed line mark the threshold point selected as the best variable set (14 predictors) to perform the supervised classification.
## 6 Discussion and Conclusions
### Future Research Directions  
#### 6.2.1 Scalability  
One of the major challenges in ICN is ensuring scalability and performance. Maintaining efficient and effective content distribution becomes increasingly complex as the content volume and the number of users grow. This includes addressing issues related to large-scale content naming, routing, and caching mechanisms [124; 125]. The dynamic nature of content creation and consumption patterns further complicates the development of scalable solutions that can adapt in real time while maintaining high throughput and minimal packet loss, which are essential for a good QoE.
###### Abstract  
Ten-Koh is a 23.5 kg, low-cost satellite developed to conduct space environment effects research in low-Earth orbit (LEO). Ten-Koh was developed primarily by students of the Kyushu Institute of Technology (Kyutech) and launched on 29 October 2018 on-board HII-A rocket F40, as a piggyback payload of JAXA's Greenhouse gas Observing Satellite (GOSAT-2). The satellite carries a double Langmuir probe, CMOS-based particle detectors and a Liulin spectrometer as main payloads. This paper reviews the design of the mission, specifies the exact hardware used, and outlines the implementation and operation phases of the project. This work is intended as a reference that other aspiring satellite developers may use to increase their chances of success. Such a reference is expected to be particularly useful to other university teams, which will likely face the same challenges as the [PERSON] team at Kyutech. Various on-orbit failures of the satellite are also discussed here in order to help avoid them in future small spacecraft. Applicability of small satellites to conduct space-weather research is also illustrated on the Ten-Koh example, which carried out simultaneous measurements with JAXA's ARASE satellite.  
**Keywords:** space environment; plasma; radiation; single events; particle detectors; Langmuir probe; magnetometer; rapid spacecraft development; small-satellite; Ten-Koh