esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Grashof-Type Four-Bar Mechanism  
### Kinematic Analysis of the Four-Bar Mechanism  
Generally speaking, the four-bar mechanism can be represented in the schematic diagram in Figure 1. In this figure, \(l_{i}\) is the length of the \(i\)-th bar, and \(q_{i}\) is its corresponding orientation with respect to the horizontal for all \(i=1,\ 2,\ 3,\ 4\). In this representation, the points \(A=[x_{a},y_{a}]^{T}\) and \(D=[x_{d},y_{d}]^{T}\) are fixed and are measured with respect to an \(XY\) coordinate system. On the other side, the point of interest \(E=[x_{e},y_{e}]^{T}\) (i.e., the point at which a desired trajectory is developed) in an \(XY\) system can be calculated from the point \(B\) using the length \(l_{b}\) and the angles \(q_{3}\) (the orientation of the third bar) and \(q_{b}\) (a fixed angle). Moreover, \(q_{2}\) is assumed as the input angle for the mechanism.  
To determine the trajectory or path generated by \(E\) from the possible values of the input \(q_{2}\), it is necessary to carry out a kinematic study of the mechanism. This study describes the unknown angles \(q_{3}\) and \(q_{4}\) in terms of the known input \(q_{2}\) based on the closed-loop equation presented in Appendix A. With this information, the position of the point \(E\) can be calculated. Therefore, by knowing all the angles of the four-bar mechanism in Figure 1, it is possible to determine the position of the point of interest \(E\) for any input \(q_{2}\) as follows:  
\[\begin{array}{l}x_{e}=x_{a}+l_{2}\cos(q_{2})+l_{b}\cos(q_{3}+q_{b})\\ y_{e}=y_{a}+l_{2}\sin(q_{2})+l_{b}\sin(q_{3}+q_{b})\end{array} \tag{1}\]
## 2 Evaluation Indices and Data Acquisition
### Data Acquisition  
In the kinds of army synthetic division/brigade tactical communication networks shown in Figure 1, the network management equipment can be accessed at any node in the network the individual desires. The architecture is similar to the MIML-IDPS System [48], which can form a 3 tier or 2 tier management structure. The 3 tier network management structure includes a first-level network management center, several second-level network management centers and third-level node devices. The first-level network management center is responsible for controlling all nodes in the network and can divide the network into different administrative areas, which are managed by the second-level network management center. The last level devices are the backbone network nodes and the detachment network access nodes. These nodes act as network management agents, which can control the instructions of network management devices to specific devices and also report the status of devices to network management devices.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index Name** & **Index Definition** & **Abbreviation** \\ \hline Information encryption intensity & \begin{tabular}{c} Average value of correlation coefficient between enciphered \\ information and original information. \\ \end{tabular} & X1 \\ \hline Network throughput & The data transmission volume of the whole network per & X2 \\  & unit time. & X3 \\ \hline End to end average delay & The average value of the delay from the source node to the destination node. & X3 \\ \hline Network state perception & \begin{tabular}{c} When the state of the network node changes, the accuracy \\ and delay weighted value of the network management \\ device perception changes. \\ \end{tabular} & X4 \\ \hline Timeliness of network adjustment & \
\begin{tabular}{c} The time delay from the network adjustment instruction to \\ the successful network adjustment \\ \end{tabular} & X5 \\ \hline Success rate of temporary network access & Success rate of temporary users. & X6 \\ \hline Voice interruption rate &
\begin{tabular}{c} The ratio of the number of voice communication \\ interruptions to the total number of calls. \\ \end{tabular} & X7 \\ Voice link building time & The average time of voice users from dialing to successful & X8 \\  & chain building. & X9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of Evaluation Indexes.  
We collected 31 groups of real data from participating units in several war-games performed in Northern China. The amount of data obtained is very large, and it is necessary to preprocess the data before evaluating network performance.
## 3 Network Models and Related Definitions  
### Network Models  
#### 3.1.1 Network System Model  
Referring to the 3D characteristic of UWSNs in this paper, we assume that \(n\) nodes are distributed randomly and uniformly on the water surface, that they are floating with the help of buoys, and that the area of 3D monitoring space is \(L\times W\times H\). A sink node is fixed at the center of monitoring water surface. The typical UWSNs model is described in Figure 1, including the following preliminaries:  
1. The node has no ability to perceive global coordinate information in 3D network space, and merely the node perceives own depth information.
2. Each node has a unique identity ID.
3. The boundary effect is negligible because the node communication radius \(Rc\) and sensing radius \(Rs\) are significantly smaller than the length \(L\), width \(W\), or height \(H\) of the 3D monitoring space.
4. All nodes use a uniform acoustic signal to transmit and receive information, and the sink node transmits the information to the base station by radio signal.
5. All nodes are identical and have the same initial energy \(E_{init}\), sensing radius \(Rs\), and the initial communication radius \(Rc_{init}\). Moreover, the energy consumption of the sink node is negligible.  
Figure 1: 3D UWSNs system model.
## 3 An Efficient Time Reversal Method
Subsequently, the transfer function of the desired Lamb wave mode propagating in the structure, \(G_{d}\)(\(\omega\),r), is calculated as follows [34]:  
\[G_{d}(\omega,r)=\frac{V_{d}(\omega,r)}{V_{A}(\omega)}, \tag{10}\]  
where \(V_{d}\)(\(\omega\),r) is the Fourier transform of \(v_{d}\)(t,r). For a narrowband excitation signal \(V_{n}\)(\(\omega\)), whose bandwidth falls in that of \(G_{d}\)(\(\omega\),r), the corresponding response signal \(R_{n}\)(\(\omega\),r) can be expressed as follows [34],  
\[R_{n}(\omega,r)=G_{d}(\omega,r)V_{n}(\omega), \tag{11}\]  
Substituting Equation (10) into Equation (11), the narrowband response \(R_{n}\)(\(\omega\),r) is obtained as,  
\[R_{n}(\omega,r)=\frac{V_{d}(\omega,r)}{V_{A}(\omega)}V_{n}(\omega), \tag{12}\]  
It means that the narrowband response could be directly calculated, without requiring hardware operation. The response signal is then reversed in the time domain. Similar to Equation (4), that could be achieved by taking complex conjugate of the signal in the frequency domain. Next, the time-reversed signal, \(R^{*}_{n}\)(\(\omega\),r), which acts as a secondary excitation signal, is virtually reemitted back to the structure through PZT B. By taking advantage of the properties of the Green's functions, it can be easily shown that the elastic response of medium when launching the same excitation function from position A and recording in position B is the same as the one captured at A when launching at B, with no dependence on the geometry of the sample [35]. Hence, the reconstructed signal \(R_{nr}\)(\(\omega\),r) received by PZT A could be estimated as,  
\[R_{nr}(\omega,r)=R^{*}_{n}(\omega,r)G_{d}(\omega,r)=G^{*}_{d}(\omega,r)G_{d}( \omega,r)V_{n}(\omega), \tag{13}\]  
Substituting Equation (10) into Equation (13), the reconstructed signal could be rewritten as,  
\[R_{nr}(\omega,r)=\frac{|V_{d}(\omega,r)|^{2}}{|V_{A}(\omega)|^{2}}V_{n}(\omega), \tag{14}\]  
Figure 3: Illustration of ridge and its neighbor area in time-frequency domain. The ETRM has following advantages. Firstly, multiple narrow-band reconstructed signals can be obtained through only one broadband actuating-sensing step. Secondly, the response signal and the reconstructed signal only contain a direct wave of a Lamb wave mode. Therefore, there is no need to determine the two time intervals for extracting the re-emitted components and the reconstructed components. Thirdly, the narrow-band extraction process is also a filtering process, which will improve the SNR of the reconstructed signal. In a word, the proposed technique improves the efficiency and accuracy of the TRM.
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 3 Results
### VOCs in Tomato  
The volatile organic compounds from tomato fruit under different pollination treatment was analyzed by OPLS-DA. The distribution of samples on the score plot (Figure 6) clearly shows a separation between bumblebee pollination treatment and non-bumblebee pollination treatments, but also, to a lesser extent, between PGR treatment and vibrator treatment. This suggests that tomatoes pollinated by bumblebees produced the most distinctive volatile profiles in fruits. A total of 21 compounds which had been reported to be positively correlated with consumer liking and 11 compounds negatively correlated with consumer liking were identified in this study (Table 1). To check the differences in those volatile compound profiles associated to each pollination treatment, HCA was applied. Based on the data set of consumer liking related compounds, an obvious clusters of different pollination treatments were found among different pollination methods (Figure S1). Tomato fruits from bumblebee pollination produced the greatest consumer liking related compound profiles among the three pollination treatments (Table 1). Figure 5: The content of fructose (**A**), glucose (**B**), sucrose (**C**), citric acid (**D**), and malic acid (**E**) in tomato fruit by different pollination treatments. A total of 66 tomato fruits were collected and each of three tomato fruits grouped into one biological replicate. Eight replicates were analyzed in treatments of ‘bumblebee’ and ‘PGR (plant growth regulator)’, and six replicates were analyzed in treatment of ‘vibrator’. Boxes indicate quartiles with the median marked as a horizontal line. General linear models followed by Duncan post-hoc method were used to compare the content of fructose, glucose, and sucrose, and non-parametric Kruskal–Wallis one-way ANOVA followed by the Dunn–[PERSON] post-hoc method was used to compare the content of citric acid and malic acid by different pollination treatments. Different letters indicate significant differences at \(\alpha\) = 0.05. ‘FW’ indicates fresh weight.
## 4 Flow Field Characteristics behind the WGB
### Mean Wind Characteristics
#### 4.1.3 Vertical Velocity (\(w\)) Distributions  
Figure 15 shows the effect of various WGB cases on the vertical wind velocity on the bridge deck. It can be observed from Figure 15 that a higher vertical wind velocity occurs near the windward side of the bridge deck, which gradually decreases with increasing distance from the barrier, showing a trend of decreasing with distance. The distribution of vertical wind velocity is similar to that of transverse wind velocity, with a max wind velocity ratio of 0.18, slightly smaller than the lateral wind velocity ratio. Compared with vertical wind barriers, the vertical flow guidance effect of WGB with a height of more than 4 m is comparable to that of vertical wind barriers.  
Figure 14: Effect of various WGB cases on transverse wind velocity for the bridge deck: (**a**) Case 2; (**b**) Case 3; (**c**) Case 4; (**d**) Case 5; (**e**) Case 6; (**f**) Case 7.  
Regarding the effect of \(\theta_{1}\), the test results of Cases 3 and 4 show that increasing \(\theta_{1}\) leads to an increase in the vertical wind velocity ratio in the root of the WGB. The vertical wind velocity ratio at 1.65 m increases from 0.1 to 0.15, which reduces the vertical flow guidance effect. At the same time, the vertical wind velocity in the backflow area near the top of the WGB gradually decreases. Regarding the effect of \(h_{1}\), the test results of Cases 3 and 5 show that changing \(h_{1}\) leads to greater differences in the distribution of vertical wind velocity, similar to that of lateral wind velocity. Compared with \(h_{1}\) = 350 mm, when \(h_{1}\) = 500 mm, the max vertical wind velocity ratio on the windward side decreases by more than 0.07, and the max vertical wind velocity also occurs 1.65 m above the windward side of the track. Regarding the effect of \(B_{1}\), the test results of Cases 4, 6, and 7 show that increasing \(B_{1}\) can effectively reduce the wind velocity near the bridge deck and enhance the flow guidance effect at a height of 3 m to 4 m. When \(B_{1}\) is increased from 250 mm to 300 mm, it has a more significant effect on the external flow field above the shear layer. When \(B_{1}\) is increased from 300 mm to 350 mm, it has a more significant effect near the top of the WGB.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Ministatellites
#### 3.2.2 TCSAR (260 kg)  
TECSAR, also transcribed as TechSAR, is the first Israeli SAR satellite designed and developed by Israel Aerospace Industries Ltd. (IAI) for technology demonstration of Israeli Ministry of Defense. TCSAR-1, also called Ofeq-8, was launched in 2008 onto a 403 km \(\times\) 581 km orbit with 41\({}^{\circ}\) inclination whereas TECSAR-2, or Ofeq-10, was launched in 2014 onto a 384 km \(\times\) 609 km orbit with 140.95\({}^{\circ}\) inclination [88]. Out of the satellite mass of 260 kg, the SAR payload accounts for 100 kg with 1.6 kW peak power, which is powered by a 45 Ah lithium-ion battery with solar panels capable of generating 850 W of power [89]. This level of miniaturization has been realized by adopting a deployable, umbrella-shaped parabolic antenna. The reflector-based parabolic antenna of TECSAR distinguishes itself from active patch arrays of TerraSAR-X and Cosmo-SkyMed constellations [90; 91; 92]. The paraboloid reflector consists of skeleton ribs and knitted mesh, the latter of which weighs less than 0.5 kg in mass. At the center of the paraboloid is a rigid dish made of carbon fiber reinforced plastic (CFRP) to combine its advantage in surface homogeneity with the lightweight of knitted mesh in Figure 15 (left) [93]. Among the observation modes of TECSAR, the wide-coverage ScanSAR mode achieves the largest swath up to 100 km with a resolution between 8 m and 20 m. The stripmap mode, mosaic mode, and spotlight mode allocate observation time to a band area, several points, and a single spot, respectively. The resulting resolution decreases from 3 m to 1.8 m and 1 m.  
Figure 14: Rendition of NovaSAR-1 and its SAR imagery: (**left**) Antenna array, internal components, solar panels. (**right**) Scene of Suez Canal and the Red Sea obtained in the ScanSAR mode. Source: SSTL, Airbus DS.
## 3 Combined Numerical Forecast Model and Machine Learning
### Machine Learning Models
#### 3.4.2 Hyperparameters Optimizations  
The hyperparameters of each model, including the learning rate, number of epochs, activation function type, and layer sizes, were optimized using grid search optimization [73].  
Figure 4: ML model output (high-resolution output shown here) is postprocessed through the ML model. (**a**) CNN model architecture. (**b**) MLP model architecture.  
This method systematically explores a predefined set of hyperparameter values, creating a grid of all possible combinations. Each combination is then evaluated for model performance, allowing the identification of the optimal set of hyperparameters that maximize prediction accuracy. The exhaustive grid search approach is effective for fine-tuning models, and the selected hyperparameter values from the 324 examined combinations are presented in Table 2 and illustrated in Figure 4. Notably, the best prediction accuracy was achieved with a learning rate of 0.0001 and 100 epochs.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 3 A 4D Airspace-Temporal Grid Framework (GeoSOT-AS) for ADS-B Data Organization
### GeoSOT-AS Height Dimensional Profiling Rules and Methods
#### 3.2.2 Height Dimensional Coding Methods  
Under the operation of level-by-level dissection across the height dimension, the right and left tree characters were assigned values of 1 and 0, respectively, for each binary tree, in order to dissect to the lowest level, and achieve the requirement of 150 m hierarchy. The specific dissection and coding schematic is shown in Figure 6.  
In general, a distinct height code can be utilized to indicate the range of height layers, as per typical civil aviation guidelines for ADS-B data analysis which specify a separation of 300 m between height layers. For instance, if an aircraft is flying at an altitude of 8400 m, the altitude layer multi-level profile principle mentioned above represents its operation using the code 0 00010001 for high-level airspace.
## 2 Related Works
### Structure of the Neck  
In the neck, feature maps output from the backbone will be processed and transmitted to the prediction part of the detector. The neck of the early DL-based detectors only directly transmits the last feature map of the backbone to the prediction part. The shallow feature map contains rich location information but low-level semantic information, whereas the deep feature map is the opposite; thus, this structure is not conducive to object detection,especially for small objects. In order to improve the detection performance of detectors for small objects, [PERSON] et al. [3] proposed a neck structure that directly transfers the feature maps of different levels from the backbone to the prediction part of the detector for multi-scale detection, and proves that the utilization of a shallow feature map is beneficial for small object detection. However, shallow feature maps still lack high-level semantic information, while deep feature maps are still short of location information. FPN [16] is designed to transfer the high-level semantic information to the shallow feature map through the bottom-up structure to further improve the detection performance of the detector for small objects. In order to make the deep feature map possess rich location information and high-level semantic information, BFPN [40] has been developed to fuse the penultimate feature map and the last feature map based on FPN, while PANet [41] adds a top-down structure based on FPN to transmit location information to the deep feature map. Both BFPN and PANet can improve the detection performance for middle and large objects while maintaining a high detection accuracy for small objects.  
YOLOv4 adopts the PANet as the framework in the neck. However, YOLOv4 suffers from the problem of feature loss in ORSIs due to many convolution operations in the neck. Therefore, a short-cut connection based on a residual is introduced to each CBL\(\times\)5 in the neck for strengthening the transmission of object features without an increase in the computational burden and inference time.
# Measurement of Amount for Steel Abrasive Material Transported by Special Scraper Conveyor
## 4 Discussion  
The results obtained from evaluation of the performed experimental measurements enabled us to determine the time interval, which is necessary for transport of the given amount of the steel abrasive materials, STEEL GRIT GH50 and GH80, using the above-mentioned and described special scraper conveyor. Due to the abrasive material being conveyed, the conveyor rakes are made of rubber to last as long as possible in this abrasive environment. The elasticity and flexibility of the rubber rake means that if the filling system is improperly filled and set, the transport efficiency of the device may decrease. The amount of transported material depends on the efficiency of the gathering process and on the moving force, which is required in order to compensate all the motional resistances. The graphs containing the measured and evaluated values are useful to specify the transported amount of material in the case of an adjusted shifting speed level. Higher level of the material shifting speed increases efficiency of the transport process and vice versa. This finding is visible in Figure 9. It can also be seen that the STEEL GRIT GH50 and GH80 materials have similar traffic curves, which means a similar traffic amount over time, as evidenced by the outputs according to Table 3. The analogous dependence is valid between the rising values of conveyor feeding and increasing efficiency of the conveying process. However, there is a limitation in this case because the amount of the transported material in the conveyor must not exceed the values in the interval 15-25 kg plus weight of the already mentioned durable filling charge, which is placed on the bottom surface of the conveyor trough. Exceeding this material weight limit causes an instable operational state. Changing of the shifting speed of the scraping membranes allows variable shifting of the instability limit, which is visible in Figure 11, where unstable areas occur at 30% and 60%, but increasing to 90% of the speed stabilizes the transport process. According to Table 3, the highest possible transport quantity is reached at a speed of 90% and a filling of 15 kg for the STEEL GRIT GH80 material--namely, 1.634 tons per hour. Thanks to all these important findings, it can be seen that for this type of transport equipment it is necessary to determine the boundaries where the equipment is still operating stably. Without the use of this knowledge, unnecessary losses can occur in practice due to the system being overwhelmed, which can lead to its malfunction. The results also show that if there is an unintentional congestion of the system, the operator can increase the feed rate to 90%, thus setting traffic curves from an unstable zone to a stable zone, or if the device is more congested, to a slightly unstable zone and after eliminating the problem. When the filling is reduced, the system can return to the original feed rate.