esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structure Prototype and Finite Element Model
### Description of the Structural Prototype Layout
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Conductor Lines** & **Overhead Ground Wires** \\ \hline
**Type** & AAC (Aluminum Alloy & ACS (Aluminum Clad Steel) \\
**Name** & Aster 570 & ASTM \\
**Section (mm\({}^{2}\))** & 570.22 & 77.55 \\
**Overall diameter (mm)** & 31.05 & 11.5 \\
**Unit weight (kg/m)** & 1.574 & 0.472 \\
**Modulus of elasticity (GPa)** & 56 & 140 \\
**Coefficient of linear** & \multicolumn{1}{c}{} & \\
**expansion/\({}^{0}\)C** & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mechanical characteristics of the conductor lines and ground wires. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **Profile Size (mm)** & **Unit Weight (N/m)** & **Area (mm\({}^{2}\))** \\ \hline
**Peak Legs** & L60 \(\times\) 60 \(\times\) 5 & 44.83 & 581.9 \\ \hline
**Cage Legs** & L120 \(\times\) 120 \(\times\) 11 & 195.22 & 2537 \\  & L150 \(\times\) 150 \(\times\) 14 & 310 & 4004 \\ \hline
**Cage Primary** & L50 \(\times\) 50 \(\times\) 5 & 36.99 & 480.3 \\
**Cage Primary** & L70 \(\times\) 70 \(\times\) 7 & 72.50 & 939.7 \\
**Bracing** & L80 \(\times\) 80 \(\times\) 7 & 83.28 & 1082 \\  & L100 \(\times\) 100 \(\times\) 10 & 147.54 & 1900 \\ \hline
**Cage Horizontal** & L60 \(\times\) 60 \(\times\) 5 & 44.83 & 581.9 \\  & L70 \(\times\) 70 \(\times\) 7 & 72.50 & 939.7 \\
**Bracing** & L80 \(\times\) 80 \(\times\) 7 & 83.28 & 1082 \\  & L100 \(\times\) 100 \(\times\) 8 & 119.49 & 1551 \\ \hline
**Top Cross Arm** & L60 \(\times\) 60 \(\times\) 5 & 44.83 & 581.9 \\  & L80 \(\times\) 80 \(\times\) 10 & 116.35 & 1511 \\ \hline
**Middle Cross Arm** & L70 \(\times\) 70 \(\times\) 6 & 62.69 & 812.7 \\  & L100 \(\times\) 100 \(\times\) 10 & 147.54 & 1915 \\ \hline
**Bottom Cross Arm** & L60 \(\times\) 60 \(\times\) 6 & 53.17 & 684 \\  & L80 \(\times\) 80 \(\times\) 7 & 83.28 & 1082 \\ \hline
**Main Legs** & L150 \(\times\) 150 \(\times\) 14 & 310.00 & 4004 \\  & L180 \(\times\) 180 \(\times\) 16 & 426.74 & 5504 \\  & L180 \(\times\) 180 \(\times\) 18 & 476.77 & 6191 \\  & L200 \(\times\) 200 \(\times\) 18 & 531.70 & 6911 \\  & L200 \(\times\) 200 \(\times\) 20 & 58
## 7 Experiments
### Comparison with Related Research
As indicated in Section 4, the method employed in the present study did not require the entire sequence but rather only one or two pedestrian gait cycles for effective recognition. In summary, the model and preprocessing approach proposed in the present study are more easily adaptable to other input situations compared with those in [9]. The present study conducted far simpler preprocessing than did [8] and applied a superior pedestrian \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dataset** & **Accuracy** & **Parameter Number** \\ \hline \multirow{2}{*}{[5]} & Generated from Human & \multirow{2}{*}{83.8\%} & \multirow{2}{*}{Over 1.2 million} \\  & 3.6M dataset \& CASIA & \\ \hline \multirow{2}{*}{[6]} & iGAIT & \multirow{2}{*}{98.0\%} & Over 160 million \\ \cline{2-3}  & CASIA & & \\  & (Silhouettes) & & \\ \hline \multirow{2}{*}{[9]} & TUM-GAID(RGB) & \multirow{2}{*}{98.0\%} & VGG-based \\  & & & Over 20 million \\ \hline \multirow{2}{*}{[8]} & TUM-GAID(RGB) & \multirow{2}{*}{99.78\%} & WRN \\  & & & (k = 4, N = 3) \\ \cline{1-1}  & & & Approximately 4.285 million \\ \hline \multirow{2}{*}{Ours} & TUM-GAID(RGB) & \multirow{2}{*}{98.38\%} & WRN\_ex3D\_v3 \\  & & & Approximately 3.173 million \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparisons with related research. detection method to eliminate excessive input. Moreover, fewer parameters were used in the present study than in [8; 9]. Finally, the present study modified the original WRN and developed a 3D-2D concatenated network with enhanced representation that could be applied to solve similar temporal problems. The accuracy rate obtained by applying multiple data segmentation methods to the same dataset revealed several findings. Regarding the method employed in the present study, the objective was to modify the original WRN model and verify the possibility of improving NN performance for solving gait recognition problems concerning time series. Therefore, the method employed in the present study differed from that employed in [8; 9] in that the proposed method used half of all the pedestrians for NN training and the other half for transfer learning to verify the effectiveness of descriptors. Because of these differences, the comparison of accuracy rates detailed in Table 10 merely served as a reference rather than for comparing the efficiency of the different methods.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 3 RDV: The Base Scheme
### The Routing Metric  
Since RDV is designed to improve delivery ratio, it adopts routing metric by which the paths with large bandwidth, i.e., large vehicle traffic volume, is preferably selected as the packet forwarding paths. To compute the routing metric for each link \((n_{s},n_{r})\), RDV computes the expected number of vehicles \(V\) that go from \(n_{s}\) to \(n_{r}\) in a unit time using the mechanism of SVS. Then, as customary in the Internet routing to incorporate link bandwidth into the shortest-path computation, RDV defines its routing metric as the inverse of the traffic amount. Namely, the metric \(M_{l}^{(vol)}\) of the link \(l\) is defined as  
\[M_{l}^{(vol)}=\frac{1}{V}. \tag{4}\]  
Note that this value easily fluctuates with time, which leads instability of the forwarding paths. To prevent the fluctuation, it is strongly recommended to apply a smoothing technique such as taking the moving average over time.
# Study the Effect of Operating Temperature on Performance in Dry Scroll Vacuum Pump
## 3 Results and Discussion
### Influence of Operating Temperature on Energy Efficiency Ratio of Dry Scroll Vacuum Pump  
In recent years, the world's energy supply has become increasingly tight, and the energy crisis continues to deepen. Under the general trend of global emission reduction development, the industry has paid more attention to resource and environmental issues and has made efforts to build a resource-saving and environment-friendly world. Energy conservation in modern society should aim to improve energy efficiency.  
With the ISO 21360-1:2012 [40] and ISO 21360-2:2012 [41] issued, the measurement method of volumetric vacuum pump performance has been confirmed. But the energy efficiency concept for volumetric vacuum pumps has not been confirmed. The concept of energy efficiency ratio (EER) for different volume vacuum pumps should be proposed.  
Pumping speed is one of the main performances of vacuum pumps. In this study, the energy efficiency ratio (EER) of the scroll vacuum pump is defined as the pumping speed (volume flow rate of gas transported) per unit of consumed power at a certain operating pressure.  
The input energy of the scroll vacuum pump includes the energy consumed in the gas compression cycle, the energy consumed by mechanical friction, and the motor loss energy. The input power (\(P_{i}\)) of the scroll compressor can be calculated as Equation (15):  
\[P_{i}=P/\eta_{m}\eta_{mo} \tag{15}\]  
where \(\eta_{m}\) is mechanical efficiency, \(\eta_{m}\) = 90%, \(\eta_{mo}\) is motor efficiency, \(\eta_{mo}\) = 90%.  
Figure 10: Instantaneous torque changes with rotation angle.  
In this study, the EER of the scroll dry pump takes the ratio of pumping speed (volume flow rate) to input power at a given suction pressure, which is calculated by Equation (16):  
\[EER=S_{th}/P_{i} \tag{16}\]  
where \(S_{th}\) is the pumping speed and \(P_{i}\) is the input power.  
The EER calculation results are listed in Table 8. When the operating temperature decreases from 338.15 K to 298.15 K, the EER is increased by approximately 1.745%. Figure 11 shows the change in EER with operating temperature.  
It can be seen from Figure 11 that the energy efficiency ratio of the scroll dry pump is gradually increased as the operating temperature is lowered. The key to improving the energy efficiency ratio is to reduce the gas compression power and increase the pumping speed so that the power per unit consumed during pump operation can transport more gas. Therefore, increasing the heat dissipation capability of the scroll pump can increase the energy efficiency ratio.
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 3. Experimental Apparatus and Testing Methods
### Method for Processing Experimental Data  
The size and velocity of particles were investigated in a dust-mist field utilizing different dust suppressant materials. The original experimental data were collected using an accompanying data software system, which included real-time particle size and velocity. The data collected through the PDPA system were subjected to matrix transformation to align it with the established spatial coordinate system, ensuring accurate representation and facilitating further analysis [16]. The matrix transformation is shown in Formula (1):  
\[\begin{cases}x&E1x&E1y&E1z\\ y&=E2x&E2y&E2z\\ z&E3x&E3y&E3z\end{cases}\begin{cases}vx\\ vy\\ vz\end{cases} \tag{1}\]  
where \(x\), \(y\), and \(z\) represent the coordinate system components; \(v\) is the orthogonal velocity, m/s; and \(E\) is the final transformation matrix that is used to convert non-orthogonal velocity components to orthogonal velocity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measuring Points** & **Spray Pressures** & **Test Materials** \\ \hline
5.0 cm, 7.0 cm, & 2.0 MPa, 4.0 MPa, & Water, SDS, \\
9.0 cm, 10.0 cm, 15.0 cm & 6.0 MPa & OP-10 and DA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measuring parameters selected.  
Figure 5: Layout of specific measuring points.  
To undertake scientific examination and comparison of the huge volume of data acquired, the Sauter Mean Diameter (D32) method was used to examine the original data [16; 32]. The D32 calculation formula (2) is expressed as follows:  
\[\text{D32}=\sum NiD_{i}^{3}/\sum NiD_{l}^{2} \tag{2}\]  
where \(N\) represents the volumetric frequency of droplets, m\({}^{3}\cdot\)s, and \(D\) denotes the droplet diameter, \(\upmu\)m.  
This method offers a precise and dependable approach to examining the experimental data and facilitates the examination of the relationship between influencing variables [16]. Figure 6 displays the data acquisition system of the PDPA device, which provides measurements for Diameter, Time, and Vx, Vy, and Vz.  
Diameter represents the droplet size; Time refers to the instantaneous moment when the system collects droplet data; Vx, Vy, and Vz are the velocity components in the X-, Y-, and Z-axis directions, respectively [16; 33]. Over the course of the experiment, the PDPA sampling points were realigned to the origin based on the previously described configuration of the dust-mist flow field for measurement purposes. The parameters for each measurement point within the dust-mist flow field were tested in sequence, adhering to the specified step size.
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 3 Results  
### Cavitation Morphology  
Figures 2 and 3 provides the predicted cavitation morphology of water-flow case as well as the experimental images obtained under the same operating condition. For poppet valves with a sharp valve seat, cavitation is mainly triggered at two quite distanced positions at pressure drop of 40 bar, including the vortex cavitation within the shearing layer of potential core and the attached cavitation beyond the poppet trailing edge, as shown in Figure 2a. At a pressure drop of 45 bar, the cavitation intensity is substantially elevated, according to the dense distribution of cavitation structure. In experimental images, the cavitation within the shearing layer is initiated at a short distance of approximately 0.2 mm from the sharp valve seat, while the distance in the numerical prediction is approximately 0.2-0.3 mm, indicating a close agreement. The cavitation structure beyond the poppet trailing edge exhibits a sparse and scattered distribution and covers a region of 4.1 mm \(\times\) 3.5 mm in simulation results, with an approximation to the experimental image of 3.9 mm \(\times\) 4.0 mm, as shown in Figure 1b. In the chamfered valve seat configuration, cavitation as well occurs inside the chamfered groove at 30 bar pressure drop, besides the free shearing region of potential core and the poppet trailing edge, as shown in Figure 3. As the pressure drop is increased to 44 bar, the cavitation structure at the shearing layer of potential core extends upstream to the attached cavitation within the chamfered groove, and such phenomenon is present in both simulation and experiment images. The cavitation beyond the poppet trailing edge at 44-bar pressure drop covers an area of 4.3 mm \(\times\) 3.0 mm in the simulation result, rather consistent with the experimental image of 4.4 mm \(\times\) 3.4 mm.  
Figure 2: Cavitation morphology identified by iso-surface \(\alpha\)l = 70% in simulation for sharp valve seat case (experimental image at left side by [PERSON] et al. [4] is also included for comparison). (**a**) water flow with 45 bar pressure drop through sharp valve of 0.6 mm openness; (**b**) water flow with 40 bar pressure drop through sharp valve of 0.6 mm openness.
## 3 Adaptive Routing Optimization Algorithm
### Analysis of Node Relations
#### 3.2.1 Historical Encounters  
The historical encounters of nodes can be successfully collected with the wireless terminal devices after a period of network running. They usually include information such as the number, duration, and time interval of encounters, and may also include the node's position, speed, and direction of movement. Hence, we can obtain the movement laws of nodes over a period of time and measure the strength of the relationship between nodes. For example, the more frequent the contact between pairs of nodes are, the closer their relationship is.  
In the previous researches [16; 30; 31; 32; 33], there are some metrics which can be used to analyze the relationship between nodes, such as the number of encounters, the frequency of encounters, and the total or average duration of encounters. Unfortunately, they have some shortcomings and cannot exactly evaluate the relationship between nodes in different situations. Therefore, we define a new metric based on the historical encounters as follows:  
\[w_{ij}=\frac{T}{\int_{0}^{T}f(t)dt} \tag{1}\]  
where, \(w_{ij}\) is the measure metric and denotes the reciprocal of the average delay of messages forwarding in the time \(T.f\) (\(t\)) is a function and denotes the forwarding delay. The message is forwarded at once during the encounter, while it can only wait for the next encounter in other cases. Therefore, we have:  
\[f(t)=\left\{\begin{array}{cc}0&\text{duringtheencounter}\\ t_{next}-t&\text{otherwise}\end{array}\right. \tag{2}\]  
Furthermore, the interval time of encounters between nodes \(v_{i}\) and \(v_{j}\) is \(t_{k}\), and \(k=\)1, 2,\(\cdots\), \(n\). Then, we specify the metric as follows:  
\[w_{ij}=\frac{2T}{t_{1}^{2}+t_{2}^{2}+\cdots+t_{n}^{2}}=\frac{2T}{\sum\limits_ {k=1}^{n}t_{k}^{2}} \tag{3}\]  
Clearly, the larger \(w_{ij}\) is, the closer the relationship between node \(v_{i}\) and \(v_{j}\) is.
## 3 Results and Discussion
### Statistical Evaluation--Warp Bow  
Statistical evaluation based on the Box 2D plot at the significance level (\(p<0.05\)) confirmed a significant difference between lumber with and with no spiral grain in warp bow. The resulting \(p\)-value for the given warp reached \(p=0.00001\) (Figure 13).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification of Lumber with Spiral Grain According to the Standard (DIN 4074-5)** [23]. \\ \hline
**Sample** & **Bow (mm)-O** & **LS 7** & **LS 10** & **LS 13** & **Reclaimed Wood** \\ \hline
3-2/To/T & 21.49 & & & O \\
5-1/To/R & 18.93 & & & O \\
2-1/To/T & 23.23 & & & O \\
4-1/To/R & 21.94 & & & O \\
10-1/To/T & 17.35 & & & O \\
7-1/To/R & 36.92 & & & O \\
6-1/To/R & 9.68 & O & & O \\
6-2/To/T & 32.59 & & & O \\
8-2/To/T & 26.63 & & & O \\
3-1/To/T & 13.65 & & & O \\
9-1/To/T & 16.26 & & & O \\
7-2/To/T & 26.2 & & & O \\
8-1/To/R & 20.16 & & & O \\
5-1/To/R & 13.34 & & & O \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of lumber with spiral grain according to the standard (DIN 4074-5) [23].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification of Lumber with No Spiral Grain According to the Standard (DIN 4074-5)** [23]. \\ \hline
**Sample** & **Bow (mm)-O** & **LS 7** & **LS 10** & **LS 13** & **Reclaimed Wood** \\ \hline
1-1/N/R & 12.74 & & & O \\
8-2/N/T & 13.14 & & & O \\
8-1/N/R & 10.55 & O & & \\
10-2/N/T & 10.51 & O & & \\
7-1/N/R & 11,92 & O & & \\
3-3/N/R-T & 6.71 & & O & \\
2-2/N/T & 7.89 & & O & \\
11-1/N/T & 16.63 & & & & O \\
3-2/N/R & 8.68 & O & & & O \\
10-1/N/T & 12.98 & & & & O \\
5-2/N/T & 17.11 & & & & O \\
6-1/N/R & 6.86 & & O & \\
2-1/N/T & 4.97 & & & O \\
6-3/N/R & 6.89 & & O & \\
9-2/N/T & 1.81 & & & O \\
8-3/N/T & 3.32 & & & O \\
9-1/N/T & 1 & & & O \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of lumber without spiral grain according to the standard (DIN 4074-5) [23].  
When comparing tangential and radial lumber, no significant difference was confirmed, which is also indicated by the resulting value of \(p\) < 0.5801, i.e., the warp does not depend on the initial position of the log, Figure 13B.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 5 Discussion
### Exoskeleton Design
Future developments should aim to reduce this latency through both hardware optimizations, such as components with reduced flexibility while maintaining ergonomic design, and improved control algorithms capable of predictive force modulation. Additionally, the use of actuators with higher acceleration and speed characteristics can significantly reduce the perceived delay. Replacing the current motor with a drive system optimized for dynamic response would allow the exoskeleton to adapt more rapidly to sudden changes in user movement, thereby improving the overall support performance during faster movements and dynamic high-load scenarios. Therefore, the actuator represents another limiting factor, operating near the lower threshold of the required torque and angular velocity. Future improvements in actuation could not only increase the system's responsiveness but also enhance the user experience by integrating more compact and quieter actuators, making the exoskeleton less noticeable in quiet work environments such as hospitals. Another important aspect is material optimization, where the integration of advanced lightweight composites can help reduce the overall weight while maintaining the necessary structural integrity. The exoskeleton was originally designed for healthcare professionals, but its use can be expanded to other physically demanding jobs. Possible applications include logistics, warehouse work, and personal caregiving at home. Adapting this technology to these fields would allow a broader range of users to benefit from its support.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
###### Abstract  
In the context of global warming, the strength and frequency of drought events are projected to grow in the future, and the onset of drought can have dramatic effects on vegetation growth in terrestrial ecosystems. Central Asia is the largest non-territorial drought area in the world, and the response of vegetation to drought events is extremely sensitive in the area. However, few studies have quantified and compared the vegetation gross primary productivity (GPP) response to the lagged and cumulative effects of drought. In this research, the solar-induced chlorophyll fluorescence GPP and Standardized Precipitation Evaporation Index (SPEI) were used to analyze the time and space patterns of vegetation GPP and the SPEI in Central Asia and to quantify and compare the lagged and cumulative effects of drought on the GPP of various vegetation types. During the period from 2000 to 2018, the general trends of vegetation GPP showed a slight increase in Central Asia, with the ratio of variation being 1.35 g C m\({}^{-2}\) y\({}^{-1}\) and a spatially decreasing distribution from north to south. SPEI showed a trend of decreasing and then increasing over a period of 19 years, with a slight decreasing (drying) trend and a rate of change of \(-0.02\) y\({}^{-1}\), and the overall spatial pattern was drying out from north to south. In 13 months, 72.44% of regional droughts had lagged impacts on vegetation. The maximum correlation coefficients of vegetation and the lagged effectiveness of drought were concentrated in the range of 0.15-0.35, and the high correlation was distributed in southern and northwestern Kazakhstan, which are prairie regions. Of the regions in Central Asia, 75.86% showed cumulative drought effects concentrated at 9-12 months. The maximum correlation coefficients were concentrated in the range of 0.20-0.50, and the high correlation regions were primarily situated in south Kazakhstan and east Uzbekistan. Comparing the correlation coefficients of the lagged effect of vegetation GPP and SPEI with the cumulative effect shows that the cumulative rather than lagged impacts of drought on vegetation cover were found in 86.75% of the regions in Central Asia. This research enhances our comprehension of the influence of drought events on ecosystems in arid regions and has a certain reference value for helping arid region ecosystems to cope with global climate change.  
2023
## 1 Introduction
### On Secure Communications
#### 1.1.1 Secure Key Exchange  
Secure communication systems employ ciphers to encrypt messages (plaintext) and to decrypt encrypted messages (cypertext). While the creation of a secure and efficient cipher is a complex problem, this problem may be solved simply. Ciphers operate with secure keys that form a momentary shared secret between [PERSON] and [PERSON]. Sharing (exchanging) the key securely is the difficult task. A communicator system cannot be more secure than its key. The security of the key exchange can be conditional or information-theoretic (unconditional).
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 2 Orbit Dynamics
### Modified Equinoctial Elements
If \(x_{7}\) denotes the mass ratio and \(T\) the thrust magnitude, for \(x_{7}\) the following equation can be obtained:  
\[\dot{x}_{7}:=\frac{\widetilde{m}}{\widetilde{m}_{0}}=-\frac{u_{T}}{c} \tag{5}\]  
where  
\[0\leq u_{T}\leq u_{T}^{(max)}\bigg{(}u_{T}:=\frac{T}{\widetilde{m}_{0}}\text{ and }u_{T}^{(max)}:=\frac{T_{max}}{\widetilde{m}_{0}}\bigg{)} \tag{6}\]  
Symbol \(c\) represents the effective exhaust velocity of the propulsion system. The magnitude of the instantaneous thrust acceleration is \(a_{T}=u_{T}\widetilde{m}_{0}/\widetilde{m}=u_{T}/x_{7}\) and is constrained to the interval \(0\leq a_{T}\leq a_{T}^{(max)}\), where \(a_{T}^{(max)}=u_{T}^{(max)}/x_{7}\). Moreover, the thrust acceleration can be expressed as \(\mathbf{a}_{T}=\mathbf{u}_{T}/x_{7}\), where \(\mathbf{u}_{T}\) has magnitude constrained to the interval \(\left[0,u_{T}^{(max)}\right]\). In conclusion, the spacecraft dynamics is described in terms of the state vector \(\mathbf{x}:= \begin{bmatrix}\mathbf{z}^{T}&x_{6}&x_{7}\end{bmatrix} ^{T}= \begin{bmatrix} x_{1}&x_{2}&x_{3}&x_{4}&x_{5}&x_{6}&x_{7}\end{bmatrix} ^{T}\), whereas the control vector is \(\mathbf{u}_{T}\), directly related to the thrust acceleration. Equations (2), (3) and (5) represent the state equations.