esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Comparison of EVI Anomalies with Weather Data and Crop Yields  
#### 5.2.1 The Years 2003 and 2018  
Comparing the negative anomalies in vegetation condition to other data sets, such as meteorological data or yield statistics, supports the identified drought patterns. A recent study has shown that the dry and hot weather conditions of 2018 are connected to an amplified hemisphere-wide wavenumber 7 circulation pattern [35]. The same atmospheric circulation pattern was present during the European meteorological drought and/or heat events of 2003, 2006, and 2015 [35]. In 2018, patterns of precipitation deficit (Figure S3) and the spatial picture of EVI deviances are relatively similar with a lag of approximately one month. This is especially the case for May/June onwards. At the beginning of the vegetation period of 2018, the weather conditions were already dry and warm [36]. However, the spatial patterns were heterogeneous, with a dry north and strong precipitation events in the rest of Germany [36]. We revealed that the vegetation was overall not yet stressed under these conditions. Apart from small areas in the north, EVI deviances were actually positive in April and May 2018. This leads to the assumption that the plant-available water was not a limiting factor in spring and that the high temperatures were triggering an early phenological onset [36]. The water availability assumingly became too low only later in the year, as the vegetation photosynthetic activity was discernably below average. In contrast, the entire year 2003 was very dry in large parts of Germany and from May onward was extremely hot, especially in June and in August [37; 38]. Apart from some areas in southeastern Germany, which experienced strong precipitation events in summer 2002, the year 2003 already started rather dry from a hydrological perspective as discharges were already lower than normal [39] and spring conditions were relatively dry [37]. Plant-available water was therefore limited, which is also visible in the negative vegetation conditions from the beginning of the vegetation period in 2003 revealed in this study. This is also supported by the analysis of the effects of high temperatures and water shortage on plant physiology and biochemistry, showing that heat only has a negative influence during dry conditions and not in moist conditions [28; 40].  
Regarding the crop yields of the considered field crops, both 2003 and 2018 were below one standard deviation from the mean for the different crop types. For two crops (potatoes and winter wheat), the harvests were lowest in 2003, for rye and winter cereal and silage maize in 2018. It can be assumed that, for the agricultural sector, the negative consequences of these two extreme years were similar, with differing effects on different crop types, depending on their water requirements at crucial phases throughout the year. The major difference was the positive start of 2018, especially the ripening phase of crops that was affected in this year, leading to large yield losses.
# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Supervision** & **Method** & **Feature** & **AUC (\%)** \\ \hline \multirow{4}{*}{Unsupervised} & [PERSON] et al. [25] & C3D RGB & 65.51 \\  & BODS [26] & I3D RGB & 68.26 \\  & GODS [26] & I3D RGB & 70.46 \\ \hline \multirow{6}{*}{Weakly Supervised} & [PERSON] et al. [27] & C3D RGB & 75.41 \\  & 3D ResNet [28] & R3D RGB & 76.67 \\  & [PERSON] et al. [29] & C3D RGB & 78.66 \\  & [PERSON] et al. [30] & C3D RGB & 79.49 \\  & GCN-Annmaly [31] & C3D RGB & 81.08 \\  & MIST [32] & C3D RGB & 81.40 \\  & MIST [32] & I3D RGB & 82.30 \\  & [PERSON] et al. [33] & I3D RGB & 82.44 \\  & [PERSON] et al. [34] & C3D RGB & 83.28 \\  & [PERSON] et al. [34] & I3D RGB & 84.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frame-level AUC performance on UCF-Crime. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **C3D** & **I3D** & **R3D** \\ \hline KTH [35] & 0.79 & 0.96 & 0.82 \\ UCF-11 [36] & 0.84 & 0.98 & 0.84 \\ KISA Datasets & 0.81 & 0.59 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Action Recognition Evaluation Performance. The test image is streamed from the image transmission server to the test computer in real-time using an Real Time Streaming Protocol (RTSP)-based Very Large Cone (VLC) player [37] as shown in Figure 11. We read the video list, and load the video information in an appropriate order. In the xml file in which video information is recorded, there are ROIs and abnormal behavior scenarios to determine intrusion and loitering. Table 5 shows the KISA evaluation criteria for each scenario. The event start time defines the event recording time in each video. When the situation suggested in the scenario occurs, we performed abnormal behavior analysis and recorded the detected result and time. Since abnormal behavior detection performance is evaluated on a real-time basis, there is a detection time, and if this item is violated, it is classified as false detection in the evaluation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Event** & **Video DB** & **Count** \\ \hline Intrusion & & 150 \\ \hline Loitering & & 150 \\ \hline Violence & & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Configure KISA datasets. Table 6 shows the evaluation results of four scenarios for KISA Datasets. Performance evaluation was performed using following equations. \[Precision=\frac{TruePositive}{TruePositive+FalsePositive} \tag{6}\]  
\[Recall=2\times\frac{TruePositive}{TruePositive+FalseNegative} \tag{7}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]  
The real-time abnormal behavior detection accuracy was 94.65% on average in four scenarios, which is suitable for real applications.
## 5 Discussion  
### Relationship between Drivers and NPP Changes at Each Stage  
The driving factors affecting vegetation NPP changes may differ at different stages, and the main contributors may differ for each time period. In this study, the relationships between each driver and vegetation NPP changes were analyzed using a structural equation PLS-SEM with a five-year cycle by integrating soil, climate, socioeconomic, and topographical factors. This research first validated the reliability and validity of the PLS-SEM model (Table 5). VIF was used to detect multicollinearity in the variables and provided values between 1 and 5, indicating that there was no significant covariance between the factors. A composite reliability (CR) greater than 0.7 and not lower than 0.6 indicated that the model was reliable, and an average variance extracted (AVE) greater than 0.5 demonstrated that the model was valid. The data in this study satisfied the above conditions well and prove that the model was reasonable and reliable [42]. The PLS-SEM model of the relationship between the latent and explicit variables for the changes in NPP in 2001-2020 is shown in Figure 20. The results show that changes in human activities always had a negative impact on NPP changes, with path coefficients of \(-0.017\), \(-0.075\), \(-0.041\), and \(-0.006\) for 2001-2005, 2005-2010, 2010-2015, and 2015-2020, respectively. For the periods 2001-2005 and 2015-2020, topographical change had a positive impact, with path coefficients of 0.096 and 0.234, respectively. Soil change had a positive impact in 2001-2005, 2010-2015, and 2015-2020, with path coefficients of 0.078, 0.463, and 0.220, respectively. In addition, the path coefficients and main impact factors of the climate  
Figure 19: Spatial distribution of the contribution of (**a**) climate change and (**b**) human activities to NPP changes. Figure 18: Spatial distribution of (**a**) the slope of the predicted NPP and (**b**) the slope of the NPP generated by human activities. changed. The impact of climate (evaporation and precipitation) on NPP changes in 2001-2005 was positive (\(\beta\) = 0.375). The impact of climate (evaporation and temperature) was positive (\(\beta\) = 0.092) in 2005-2010 and became negative in 2010-2015. The impact of climate on NPP changes (evaporation and solar radiation) was positive (\(\beta\) = 0.151) for the period of 2015-2020.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 3 Simulation Results and Analysis
### Electric Quality Metric Comparison  
Many of the design metrics for radiation-hardened SRAMs exhibit trade-off relationships. For example, increasing the supply voltage (VDD) can improve critical charge, RSNM, WWTV, RAT, and WAT, but it also increases HPWR. In addition, the proposed LDRH16T cell demonstrates strong performance in terms of read stability and leakage power, but it has a slight drawback in read access time. To account for these factors, we propose an electrical quality metric (EQM) for evaluating the overall performance of SRAM cells. The formula for this metric is defined as follows [17; 24]:  
\[EQM=\frac{Q_{\text{C}}\times\text{RSNM}\times\text{WWTV}}{\text{HPWR}\times \text{RAT}\times\text{WAT}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Structure** & **Qc (fC)** & **RAT** & **WAT** & **RSNM** & **WWTV** & **HPWR** \\  & **(fC)** & **(ps)** & **(ps)** & **(mV)** & **(mV)** & **(mV)** \\ \hline RHBD10T [13] & 12.63 & 34.54 & 89.75 & 135 & 179.46 & 66.51 \\ SIS10T [14] & 11 & 38.97 & 86.67 & 50 & 190.45 & 56.56 \\ We-Quatro [11] & 24.27 & 16.56 & 56.57 & 71 & 297.96 & 50.5 \\ QUCCE12T [12] & 28.17 & 15.79 & 58.7 & 89 & 290.36 & 53.47 \\ EDP12T [16] & \textgreater{}150 & 39.35 & 57.19 & 39 & 295.77 & 56.66 \\ SEA14T [15] & \textgreater{}150 & 40.39 & 56.29 & 20 & 298.96 & 54.46 \\ SAW16T [17] & 44.69 & 29.46 & 45.55 & 52 & 337.31 & 35.36 \\ LDRH16T & \textgreater{}150 & 139.81 & 55.75 & 160 & 301.87 & 31.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison.  
Figure 10: Equivalent circuits used to simulate (**a**) a positive and (**b**) a negative injected noise.  
A cell with a higher EQM is considered to exhibit better overall performance. Figure 12 presents the EQM values of the proposed LDRH16T cell and all other comparison cells. As shown in the graph, the LDRH16T achieves the highest EQM value among all designs. This result confirms that the proposed cell offers superior overall performance.
## 5 Analysis of HFT Results
### Fracturing Efficiency  
This time, the loss of the injected fracturing fluid was examined to inspect the fracturing efficiency by fracturing fluid injection. The fracturing fluid injected to generate HFs in the formation can be lost by being pushed out through the fracture surface into the contacting formation due to high internal pressure. Since the fracturing fluid as much as the lost amount cannot be used for fracturing, it is considered a factor affecting the fracturing efficiency. The continuity equation assuming that fluid flow propagates long fracture of the UFM model includes \(q_{L}\) term, which is the leak-off volume rate as described in Equation (8). The \(q_{L}\) is yielded by \(h_{L}\), leak-off height, times velocity, \(u_{L}\), at which fracturing fluid infiltrates into surrounding permeable medium as in the Equation (9). The permeability of formation may affect this velocity, \(u_{L}\). For this reason, the average porosity and permeability of the adjacent well formation and the leak-off ratio determined in each model were depicted in Figure 8. In the actual model, the average porosity and permeability were 7.7% and 3.9 \(\times\) 10\({}^{-15}\) m\({}^{2}\), which are 5.5% and 88.7% higher in comparison to that of the proposed model. The leak-off ratio, which indicate the ratio of lost fluid to the amount of injected fluid, was calculated to be 11.1% higher. Although the porosity of the two models is similar, the difference in permeability of 88.7% might have affected the calculation of leak-off ratio, which represents the loss of fracturing fluid to the surrounding formation.  
Under this influence, the HFs generation results of both models were examined. By investigating the results of the length, height, average width, and conductivity of the HFs created per unit injection amount of the fracturing fluid, a detailed analysis was performed to identify how the difference in the horizontal well orientation of the two models had influenced the fracturing efficiency. The HF volume of the actual model was about 3% larger, the HF width of the proposed model was slightly thicker, and the conductivity was almost the same, so the difference between the results was insignificant as listed in the Table 3. In other words, the difference between the two models is insignificant in terms of the HF length, height, width, and conductivity, so it was found that the azimuth of the horizontal well had little effect on the fracturing efficiency.  
Figure 8: Average porosity (%) and permeability (10\({}^{-15}\) m\({}^{2}\)) of actual and proposed models and their leak-off ratio (%) results.
## 2 Truck Platooning Speed Control Based on Multi-Objective Optimization
### Model Construction Based on Model Predictive Control
#### 2.1.2 Control Cell Analysis  
(1) Calculation of platooning position  
Within the proposed method, downstream traffic congestion is relieved by controlling the speed of truck platooning, represented by the speed of the cell where the truck platooning is located. Because the truck platooning process is dynamic, its location also changes continuously. Therefore, when carrying out MPC, it is necessary to predict the location at each successive moment. The following formula is used to achieve this:  
\[x_{p}(t+1)=x_{p}(t)+v_{p}(t+1)*t \tag{6}\]where, \(x_{p}(t)\) is the current position of truck platooning in the road network, \(x_{p}(t+1)\) represents the position of truck platooning in the subsequent moment, \(v_{p}(t+1)\) denotes the truck platooning speed during the predicted time interval, and the speed of the truck remains constant.  
\[xcell_{p}(t+1)=\frac{x_{p}(t+1)}{L} \tag{7}\]  
Here, \(xcell_{p}(t+1)\) represents the predicted location (that is, the cell where truck platooning is located at the next moment) and \(L\) represents the cell's length.  
Single-bottleneck control cell analysis  
Specifically, \(x_{p}(t)\) represents the instantaneous position of truck platooning in the road network at the end of the \(t\)-th time interval. The cells that were controlled in this study include all cells within the interval \(\big{[}xcell_{p}(t),xcell_{p}(t+1)\big{]}\). Within this interval, it is also necessary to consider the relationship between the locations of truck platooning and congestion (assuming m) to determine the cell that requires control.  
1) \(xcell_{p}(t)\geq m\)  
When the above formula is satisfied, there is no need to control or predict the subsequent location of truck platooning.  
2) \(xcell_{p}(t)<m\)  
When the above formula is satisfied, only the cells in \(\big{[}xcell_{p}(t),xcell_{p}(t+1)\big{]}<m\) are controlled.  
3) Multi-bottleneck control cell analysis  
Assume that there are two bottleneck cells m and n at the \(t\)-th time interval in the road network, and \(m<n\).  
1) \(xcell_{p}(t)\geq n\)  
When the above formula is satisfied, there is no need to control or predict the subsequent location of truck platooning.  
2) \(m\leq\text{xcell}_{p}(t)<n\)  
When the above formula is satisfied, only the cells in \(m\leq\big{[}xcell_{p}(t),xcell_{p}(t+1)\big{]}<n\) are controlled, and the optimization objective is calculated with the object of n.  
3) \(xcell_{p}(t)<m\)  
When the above formula is satisfied, the cells in \(\big{[}xcell_{p}(t),xcell_{p}(t+1)\big{]}<m\) are controlled, and the optimization objective is calculated with the object of m. The process then repeats for the interval \(m\leq\big{[}xcell_{p}(t),xcell_{p}(t+1)\big{]}<n\) and object of n.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 1 Introduction
A reduction in the lower limit of transmit power consumption by the proposed schemes will contribute to the implementation of green communication in next-generation systems. The remainder of this paper is organized as follows. In Section 2, the system model of hybrid three-hop relay networks is presented. In Section 3, optimal power allocation schemes are proposed for three-hop AF-DF and DF-AF relay networks, respectively. Section 4 shows the simulation results, and the conclusions are drawn in Section 5.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 5 Performance Evaluation  
In this section, we will conduct simulations and analyze the result compared with the current UWSNs routing protocols VBF, HHVBF, DBR and VAPR. Here we use NS-3 simulation tool, a discrete event simulator [32], to verify the effectiveness and validity of NCRP. The relative simulation parameters are set in Table 1. The underwater MAC protocol we use here is CW-MAC [26], which is a MAC protocol based on a slotted contention window scheme. If the channel is sensed busy, the nodes will choose a random slot and resend packets. In our settings, if two nodes are 400 m apart, the packet loss rate of beacon messages, data packets and feedback packets are 0.019, 0.100, 0.014, respectively. When the two nodes are 1 km apart from each other, the packet loss rate becomes 0.07, 0.40, 0.07. So the reliable transmission range of data packets is about 400 m, but 60% of the data packets can still reach the destination if the transmission range becomes 1 km. In the mobility model, we assume that the nodes mainly move horizontally due to the function of floating bodies and mooring lines. Based on the underwater applications and channel varying, we set 60 data packets in each transmission block. In our simulations, each run lasts 7000 s. Unless otherwise specified, we report the average value of 50 runs.  
Figure 5: (**a**) The process of deleting nodes; (**b**) The process of adding/updating nodes.
## 3 Data Sources  
The occultation observation data are selected from the RDEX-format occultation observation data received by the GNOS radio occultation detector on Fengyun 3E satellite on 4 January 2022. According to the instrument design, the GNOS instrument's occultation data sampling rate is 50 Hz for closed-loop and 100 Hz for open-loop, and the accuracy of the occultation data is comparable to that of similar international occultation products [34].  
As for precision orbit determination, the positioning data also come from the BDS positioning data of GNOS. The GNSS precise ephemeris selects the GBM fast product released by the GFZ analysis center of IGS, and uses the simplified dynamic precise orbit determination to obtain the FY3E precise orbit and clock deviation. Precision orbit determination of low-orbit satellites is a repeated iterative process. Firstly, the initial orbit of the low-orbit satellite is obtained by pseudorange and carrier phase measurement, which could calculate the initial orbit parameters. Then, the orbital parameters are further corrected by the least squares LSQ. A more precision orbit is then calculated with new orbital parameters. Through multiple iterations and residual judgment, the precise orbit and orbital acceleration parameters are finally obtained. In addition, the time arc of each precision orbit determination is 30 h, so for the adjacent two days, there is a 6 h orbit overlap, which is used to evaluate the accuracy.  
The position accuracy of the final FY3E precision orbit is at the centimeter level, and the speed accuracy is on the order of millimeters per second. This accuracy can usually be used for subsequent occultation data processing. It is worth noting that the single- and dual-frequency occultation processing use the same FY3E orbit data, so we believe that the influencing factors of the occultation products in the study are only reflected in the difference between the single- and the dual-frequency occultation processing.
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 2 Biosynthesis of Steroidal Saponins
### Biosynthesis of Cholesterol/\(\beta\)-Sitosterol  
2,3-oxidosqualene is usually cyclized from a linear to a polycyclic structure by various oxidosqualene cyclases (OSCs), and the cyclization of 2,3-oxidosqualene also marks the demarcation point between sterol and triterpene metabolism in plants. 2,3-oxidosqualene is cyclized by cycloartenol synthase (CAS) to generate the plant sterol precursor cycloartenol [18]. Cholesterol/\(\beta\)-sitosterol are synthesized from cycloartenol under the catalysis of a series of enzymes, such as sterol side-chain reductase 1/2 (SSR1/2), sterol C-24 methyltransferase (SMT), C-4 sterol methyl oxidase (SMO), cyclopropylsterol isomerase (CPI), sterol C-14 demethylase (CYP51), sterol C-14 reductase (C14-R), sterol 8,7 isomerase (8,7 SI), sterol 5(6) desaturase (C5-SD), and 7-dehydrocholesterol reductase (7-DR) [18; 20; 21; 22] (Figure 2).  
Figure 2: Possible biosynthetic pathways of steroidal spapins in plants. Biosynthetic pathway of 23-oxygenate (light green): square is catalyzed by SDE to form 2, 3-oxide squalene (light yellow), which is also the demarcation point between sterol and triterpene metabolism. The biosynthetic pathway of cholesterol and \(\beta\)-sitosterol are depicted in pink and purple, respectively. The biosynthetic pathway of steroidal spapins is marked in blue. Dashed arrows indicate that there may be multiple catalytic steps.
## 2 Materials and Methods
### Materials Properties  
There are two types of the developed self-insulating concrete masonry unit (SCMU) used in the construction of the SCMSW walls as shown in Figure 2. All SCMSW specimens were constructed with the same SCMUs, mortar and grout properties. Table 2 shows the average value of the compressive strength for the masonry materials used in SCMSW specimens while Table 3 presents the properties of the insulation material (expanded polystyrene (EPS) foam) which was inserted in the units. Additional details and description of the materials and methods of testing are reported in the published study [15].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Wall**} & **Height** & **Length** & **Thickness** & **Aspect** & \multicolumn{3}{c}{**Reinforcement**} & **Axial** \\  & **(mm)** & **(mm)** & **(mm)** & **Ratio** & **Vertical** & **Horizontal** & \(\rho_{\mathbf{v}}\%\) & \(\rho_{\mathbf{h}}\%\) & **Stress** \\  & & & & & & & & (N/mm2). \\ \hline SW1 & 1590 & 1590 & 240 & 1.0 & \(\mathcal{O}\)16\#200 mm & \(\mathcal{O}\)8\#200 mm & 0.46 & 0.115 & 0.00 \\ SW2 & 3190 & 1590 & 240 & 2.0 & \(\mathcal{O}\)16\#200 mm & \(\mathcal{O}\)8\#200 mm & 0.46 & 0.115 & 0.50 \\ SW3 & 3190 & 1590 & 240 & 2.0 & \(\mathcal{O}\)16\#200 mm & \(\mathcal{O}\)8\#200 mm & 0.46 & 0.115 & 1.0 \\ SW4 & 5780 & 1590 & 240 & 3.6 & \(\mathcal{O}\)16\#200 mm & \(\mathcal{O}\)8\#200 mm & 0.46 & 0.115 & 1.0 \\ SW5 & 5780 & 1590 & 240 & 3.6 & \(\mathcal{O}\)20\#200 mm & \(\mathcal{O}\)8\#200 mm & 0.718 & 0.115 & 1.0 \\ SW6 & 5780 & 2190 & 240 & 2.6 & \(\mathcal{O}\)16\#200 mm & \(\mathcal{O}\)8\#200 mm & 0.667 & 0.115 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Self-insulated concrete masonry shear wall (SCMSW) specimen details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SCMU** & **Mortar** & **Grout** & **Masonry Prism** \\ \hline
6.7 & 14.7 & 24.7 & 9.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average compressive strength of masonry materials (N/mm2).  
Figure 1: Self-insulating concrete masonry shear wall (SCMSW) specimen configurations.  
For the steel reinforcement properties, samples were taken from the same steel reinforcement used to construct the masonry shear walls. The samples were subjected to tensile testing according to the standard procedure specified by Chinese standard (GB/T 228-2002) [18]. The average yield strengths for the \(\mathcal{O}\) 8-, \(\mathcal{O}\) 16- and \(\mathcal{O}\) 20-mm reinforcing steel were 358, 442.5 and 445 N/mm\({}^{2}\), respectively.