esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### COP Position in Anteroposterior and Mediolateral Direction  
Our findings revealed an anterior shift of the COP during uphill walking compared to level walking, with this shift becoming more pronounced as the slope increased. Conversely, during downhill walking, the COP shifted backward compared to level walking, with this backward shift being more prominent at a \(-10\%\) slope. Furthermore, the mediolateral shift of COP increased during uphill and downhill walking compared to level walking, and this shift tended to increase, although not always significantly, as the slope became steeper (Figure 8). These findings are consistent with previously reported data that have shown a greater shift in the anteroposterior direction and mediolateral excursions of the COP at uphill inclinations of 15 and 25 degrees [65].  
The increased anteroposterior displacement of COP during uphill walking can be attributed to participants' postural adjustments, necessitating them to lean forward to counteract gravitational forces and sustain balance [15]. [PERSON] et al. [15] hypothesized that by inclining the trunk forward, the center of mass would shift slightly ahead of the foot's center, causing gravity to propel the body forward relative to the center of support. By shifting the center of mass forward, individuals can effectively distribute their body weight over their feet and propel themselves upward against the force of gravity. Given the method of determining the COP in this study (by connecting the trajectories of the COP from the forefoot of one side to the rearfoot of the contralateral side during selected gait cycles), our data support this hypothesis, indicating that vGRFs are exerted more anteriorly on the plantar foot areas. In contrast, when descending, individuals must lean backward to control their speed and prevent falling [15], leading to a more backward application of vGRFs and, ultimately, a posterior shift of the COP. Moreover, the mediolateral displacement of the COP tended to increase, although not always significantly, with increasing slope. This indicates that individuals may need to make greater lateral adjustments to maintain balance and stability on steeper slopes while navigating uneven terrain and adapting to changes in slope. In our study, these adjustments were manifested with increased foot rotation during uphill walking and increased step width during downhill walking, both indicating an expansion of the base of support.  
Figure 8: Mean COP position in the anteroposterior and mediolateral directions under various walking conditions. The mean displacement of COP in uphill and downhill walking conditions is presented as multiples of the mean COP position in the anteroposterior and mediolateral directions determined in the C walking condition.
## 3 Results
### Heavy Metal Detection Using the PLS and PCA Methods  
Heavy metal concentrations observed using the PLS technique had lower values as compared to the PCA technique (Table 3). The PLS and PCA methods showed a significant difference on both 0.001 and 0.05 probability levels in quantifying mostly Cd and Pb in the water samples. There were no significant differences in the methods on the detection of Ag, Cu and Zn. Concentrations above 45 mg L\({}^{-1}\) for Zn were obtained between October to December 2019. Even though Zn values declined in the successive sampling period, detections were still high in the water samples. The PLS and PCA methods detected the same concentrations in borehole water sources, below 0 mg L\({}^{-1}\) for Ag, Cd, Cu and Pb (QM 8) and Cu, Pb (QM 5 and QM 7). In the study, mostly the PCA method showed concentrations below 0 mg L\({}^{-1}\) for Ag values in both river and borehole sources. The only exception was in site QM 8, where the PLS method also detected values below 0 mg L\({}^{-1}\) Ag. On the other hand, the PLS method strongly detected concentrations below 0 mg L\({}^{-1}\) for Cd and Pb.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Model Variables**} \\ \hline Metal & Ag & Cd & Cu & Pb & Zn \\ PLS & 6 & 5 & 3 & 4 & 10 \\ PCA & 3 & 5 & 3 & 7 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Principal component values for the Partial Least Square (PLS) and Principal Component Analysis (PCA) models based on ATR FT-IR data output.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**October-December 2019**} & \multicolumn{4}{c}{**January-February 2020**} \\ \hline  & & & & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**Source**} & \multicolumn{1}{c}{**Site**} & \multicolumn{1}{c}{**Element**
# Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis
## 2 Results
### Cannabiroid and terpen in Hops and Cannabis
Data represented in Figures 5 and 6 indicate that the concentration of myrcene in fresh, undried hops was reduced from 1.9 to 0.3 g 100 g dry matter\({}^{-1}\) (MAHD) and to 0.7 g 100 g dry matter\({}^{-1}\) (freeze-dried) and for fresh, undried cannabis, the concentration reduced from 0.3 to 0.1 g 100 g dry matter\({}^{-1}\) (MAHD) and to 0.2 g 100 g dry matter\({}^{-1}\) (freeze-dried). [PERSON] et al. (2017) showed that compared to fresh, undried carrots, myrcene was reduced from 2.3 to 0.4 g 100 g dry matter\({}^{-1}\) (MAHD) and to 1.6 g 100 g dry matter\({}^{-1}\) (FD). This shows that freeze-drying resulted in a higher terpene retention compared to MAHD for these crops. Major terpene content was similarly determined and compared for cannabis and hops subjected to the same postharvest drying conditions (Figures 5 and 6). The average total terpene content from fresh, undried cannabis and hop samples was 4.3 g 100 g dry matter\({}^{-1}\) and 3.3 g 100 g dry matter\({}^{-1}\), respectively. ANOVA analyses showed that the increase in the total terpene content to 4.4 g 100 g dry matter\({}^{-1}\) and 3.6 g 100 g dry matter\({}^{-1}\) for cannabis and hops, respectively, by pre-freezing was not significant (\(p<0.05\)). For freeze-dried  
Figure 5: Concentration of terpen in cannabis in fresh undried, pre-frozen undried, microwave-assisted hot air dried, and freeze-dried cannabis (C. _sativa_) biomass. Bars with the same letter are not significantly (\(p<0.05\)) different. and microwave-assisted hot air dried hop samples (Figure 6), the average total terpene significantly (\(p<0.05\)) reduced to 1.5 g 100 g dry matter\({}^{-1}\) and 1.2 g 100 g dry matter\({}^{-1}\), respectively. However, freeze drying preserved the total terpen (3.9 g 100 g dry matter\({}^{-1}\)) in cannabis samples compared to microwave-assisted hot air drying (2.8 g 100 g dry matter\({}^{-1}\)) (Figure 5). The high temperature used during MHAD significantly (\(p<0.05\)) reduced total terpene content in the fresh, undried samples from 4.3 to 2.8 g 100 g dry matter\({}^{-1}\). Terpenes evaporate easily in MAHD since the cannabis and hop structures and dimensions permit its evaporation even at 35 \({}^{\circ}\)C, while freeze-drying uses a relatively very low temperature which limits the evaporation of terpen [60]. Hence, freeze-drying, rather than hot-air drying, is recommended to help preserve terpen in hops and cannabis during postharvest processing.
## 3 Results and Comparative Experiments
### Datasets and Experimental Environment  
The dataset in this study contains depth and coordinate information of seabed topography. This dataset includes eight datasets, each collected from sampling areas in different sea regions, with each sampling area being a rectangular region of the same size (5 nautical miles \(*\) 4 nautical miles). The dataset contains seabed coordinate data and seabed elevations within their respective areas. Table A1 in Appendix B.1 shows part of the data from the sixth sampling area. This dataset was obtained using single-beam echosounder models several years ago to illustrate seabed topography variations and features, serving as a critical reference for designing multibeam echosounder survey lines.  
The datasets have been preprocessed to remove noise and anomalies, ensuring experiment accuracy. Each data group contains 250 rows and 200 columns, totaling 50,000 seabed coordinate data points. Each cell represents the seabed elevation at a point within the sampled area, expressed in negative meters, indicative of water depth. The coordinates are measured in units of \(2\times 10^{-3}\) nautical miles. The data cover a range of seabed depths from shallow to deep, displaying complex underwater ridge features.  
Figure 9: This figure shows the projection of the plane containing the MBES system onto the YOZ plane in this maritime area when \(x=0\).  
This study conducted experiments and comparative experiments within the sampled maritime area to validate the effectiveness of our model. Based on understanding the model used in this study, a series of assumptions were made about the experimental environment. These assumptions are fundamental to the model's validity and impact its ability to solve practical problems. Assumptions include but are not limited to a constant transducer opening angle of 120\({}^{\circ}\), the homogeneity of the seabed geology (not affecting the measuring beams' reflection), the measuring equipment's precision, and the margin of error in the data processing. Through these assumptions, we ensure the model's applicability and the experimental results' reliability.  
The experimental environment for this study was based on Python 3.9, primarily utilizing NumPy 1.21.0 for numerical calculations, Pandas 1.3.0 for data analysis, Matplotlib 3.4.2 for data visualization, and SciPy 1.7.0 for scientific computation. All experiments were conducted on a Windows 10 Professional computer equipped with modern multi-core processors, ensuring processing efficiency and the reliability of the results.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 2 Calculation Process of the ED Model  
The ED model assumes that after rockbolts are loaded along the axial direction, an ED model can be used to depict the relative movement of the b/g face. After this ED model is merged into the bolting system, the axial performance of rockbolts comprises three phases: the elastic phase, the ED phase, and the debonding phase.  
In the elastic phase, the tensile capacity and deformation of rockbolts can be calculated as follows:  
\[F_{b}=\frac{\pi D_{b}\tau_{1}}{\lambda s}\tanh(\lambda L)u_{b} \tag{1}\]  
where \(F_{b}\): tensile force of rockbolts; \(D_{b}\): rockbolt diameter; \(\tau_{1}\): shear strength; \(\lambda\): a coefficient; \(s\): slippage at the ultimate load; \(L\): installed length; and \(u_{b}\): tensile deformation of bolts.  
In the ED phase, the tensile capacity and deformation of rockbolts can be calculated as follows:  
\[u_{b}=\left(\frac{1}{E_{b}}+\frac{A_{b}}{E_{r}A_{r}}\right)\left[\frac{2\tau_ {2}L}{D_{b}}+\frac{4\tau_{1}}{\lambda D_{b}}\tanh(\lambda L_{e})-\frac{2\tau_ {2}L_{e}}{D_{b}}\right](L-L_{e})+s \tag{2}\]  
\[F_{b}=\pi D_{b}\tau_{2}(L-L_{e})+\pi D_{b}\tau_{1}\tanh(\lambda L_{e})/\lambda \tag{3}\]  
where \(E_{b}\): rockbolt modulus of elasticity; \(E_{r}\): rock modulus of elasticity; \(A_{b}\) and \(A_{r}\): cross-section area of rockbolts and rock, respectively; \(\tau_{2}\): residual strength; and \(L_{e}\): elastic section length.  
Attention is paid to the fact that based on Equations (2) and (3), the tensile force and deformation of rockbolts mobilised with the elastic section length. Moreover, for the ED phase, there was a non-linear relationship between tensile force and deformation of rockbolts.  
In the debonding phase, the tensile force and deformation of rockbolts can be calculated as follows:  
\[F_{b}=\pi D_{b}\tau_{2}\bigg{(}L+\bigg{(}\frac{1}{E_{b}}+\frac{A_{b}}{E_{r}A_{r}} \bigg{)}\frac{2\tau_{2}}{D_{b}}L^{2}+s-u_{b}\bigg{)} \tag{4}\]  
Based on Equations (1)-(4), the entire force-deformation curve can be plotted.
## 4 Control System Design
### The Storage Battery Charger Controller
#### 4.3.2 The Fuzzification Process  
In this stage, the crisp values of the controller inputs are converted into fuzzy sets. Therefore, the converted data can be used by the inference algorithm to manipulate the control rules. It is assumed that \(e\) and \(\Delta\epsilon\) are within the limits [\(e_{\min}\), \(e_{\max}\)] and [\(\Delta\epsilon_{\min}\), \(\Delta e_{\max}\)], respectively. The values (\(e\), \(\Delta\epsilon\)) should be normalized within [zero, one] with the help of:  
\[\left[\begin{array}{c}e_{n}(i)\\ \Delta\epsilon_{n}(i)\end{array}\right]=\left[\begin{array}{c}\frac{e(i)-e _{\min}}{e_{\max}-e_{\min}}\\ \frac{\Delta\epsilon(i)-\Delta e_{\min}}{\Delta\epsilon_{\max}-\Delta\epsilon _{\min}}\end{array}\right] \tag{6}\]  
The periods of each linguistic value of (\(e_{n}\), \(\Delta\epsilon_{n}\)) are selected to be a fuzzy partition with seven parts, assigning the linguistic values of NB: negative big, NM: negative medium, NS: negative small, ZE: zero, PS: positive small, PM: positive medium, and PB: positive big. The membership function with the triangular waveform is assigned to the linguistic values. The membership functions of (\(e\), \(\Delta\epsilon\)) are shown in Figure 5a.  
In the same way, let the limits of (\(\Delta k_{p}\) and \(\Delta k_{d}\)) be [0,1]. Let (\(\Delta k_{p}\) and \(\Delta k_{d}\)) be the changes of the proportional and derivative gains. These were assigned to adjust the proportional and derivative parameters. The equation is given by:  
\[\left[\begin{array}{c}k_{p}\\ k_{d}\\ k_{i}\end{array}\right]=\left[\begin{array}{c}k_{pp}\Delta k_{p}\\ k_{dd}\Delta k_{d}\\ \frac{(k_{p})^{2}}{\Delta k_{d}}\end{array}\right] \tag{7}\]  
where (\(k_{pp}\), \(k_{dd}\)) are constant factors for the proportional and the derivative gains.  
The Gaussian membership functions, shown in Figure 5b, are assigned to the linguistic values of (\(\Delta k_{p}\) and \(\Delta k_{d}\)). These values are assumed to be either big or small.  
The gain of the integration part of the PID controller can be calculated using the relation in Equation (7).  
Figure 5c shows the membership function for the parameter (\(\alpha\)). Its linguistic values are set to be S (small), MS (medium-small), M (medium), or B (big).  
Figure 5: The membership functions of the parameters: (**a**) (\(e_{n}\), \(\Delta e_{n}\)), (**b**) (\(\Delta k_{p}\) and \(\Delta k_{d}\)), and (**c**) \(\alpha\).
## 2 Related Work
### Image Gradient \(L_{0}\)-Norm Minimization  
The successful application of the image gradient \(L_{0}\)-norm in image processing has inspired scholars to start developing it for limited-view and SVCT reconstruction. The main principle of the image gradient \(L_{0}\)-norm is to count the number of non-zero elements. The mathematical expression is denoted as follows:  
\[||\
abla x||_{0}=\sum_{p}\psi(|\left(\partial_{i}x\right)_{p}|+|\left(\partial _{j}x\right)_{p}|) \tag{6}\]  
where \(p\) (\(p\) = 1, 2, \(\ldots\), \(N_{1}\times N_{2}\)) represents the location of (\(n_{1}\), \(n_{2}\)) in the image. The (\(\partial_{i}x\))\({}_{p}\) and (\(\partial_{j}x\))\({}_{p}\) denote (\(x\)(\(n_{1}\),\(n_{2}\)) - \(x\)(\(n_{1}\) - \(1\),\(n_{2}\))) and (\(x\)(\(n_{1}\),\(n_{2}\)) - \(x\)(\(n_{1}\),\(n_{2}\) - \(1\))). \(\Psi\) is a counting operator, which can be defined as follows:  
\[\psi(|\left(\partial_{i}x\right)_{p}|+|\left(\partial_{j}x\right)_{p}|)=\left\{ \begin{array}{l}1\text{ if }|\left(\partial_{i}x\right)_{p}|+|\left(\partial_{j}x \right)_{p}|\
eq 0\\ 0\text{ otherwise}\end{array}\right. \tag{7}\]  
It could be observed from Equation (7) that the gradient \(L_{0}\)-norm does not penalize the values with large gradients, which indicates that edge information and more small structures are more likely to be retained.
## 2 Results and Discussion
### Changes of Volatile Components with the Flowering Period  
The quantity of volatile components increased with the flowering period, peaking during stages 3-6 of the inflorescence and decreasing after stage 7, with almost no odor at stage 9 (Figure 2). The level of emitted volatile components changes with the flowering period, with the strongest odor during stages 3-6 in _Onc._ Rosy Sunset and not in the initial flowering stage. This result was repeated in a variety of flowers. For comparison, the flowering period of _[PERSON]_ Nobby's Pacific Sunset was 51 days, and the odor was strongest at 8-20 days after flowering (DAF) [8]. _Phal. bellina_ emitted the highest levels of geraniol and linalool at 5-7 DAF, and the flowering period was 14 days [6]. The odor was strongest at 2 or 3 DAF for _Narcissus tazetta_ var. _chinensis_ with a 6-day flowering period [17].  
Four of the major volatile compounds were linalool, benzaldehyde, \(\beta\)-myrcene, and \(\beta\)-carophyllene, and their relative percentage change in composition with the flowering period is shown in Figure 3. The relative percentage of linalool was much higher than that of other volatile components during all phases of the _Onc._ Rosy Sunset flowering period, and the level significantly increased during the first flower bloom of the inflorescence. These changes were similar to those in the total relative content. The relative percentage of benzaldehyde was higher during stages 7-8, which may be affected by the decrease in the relative quantity of linalool. No significant changes of \(\beta\)-myrcene and \(\beta\)-carophyllene were observed during the flowering period (Figure 3). _Onc._ Sharry Baby had a higher quantity of volatile compounds during the flowering stage than in the initial flowering stage, and the relative content of alkenes, alcohol, and esters was found to increase with the flowering stage [10]. However, the total relative content significantly lowers at stage 5 in our experiment, but rises again at stage 6, presumably due to the colder weather. Although the experiment temperature was controlled, the environment still varied in temperature because of a cold snap. [PERSON] et al. [12] reported that the temperature will affect the volatile emissions in _Onc._ Sharry Baby, and the amount of aroma components and total relative content were both higher at 30 \({}^{\circ}\)C than at 10 \({}^{\circ}\)C. [PERSON] et al. [22] also indicated that for _Gongora bufonia_, the odor was strongest at 10:00, which was the period in which the temperature began to rise.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
### Summary of Network Development Status
#### 3.2.2 Mobile Satellite Networks
Only satellites deployed in recent years, such as Inmarsat fifth generation satellites, have begun to support broadband Internet services. Emerging LEO satellite broadband Internet constellations such as Starlink need to be equipped with special routers to convert satellite signals into Wi-Fi, microwave, and other wireless networks to realize Internet access by user terminals. The development trend of mobile satellite communication in the future will mainly include four aspects: increasing satellite antenna aperture and satellite power; multi-beam antennas will be widely used and the number of beams will increase; broadband business, IP services, and data transmission rates will be continuously improved; and the trend of integration with ground mobile communication network will increase. In the future, satellite and ground communication networks will no longer be isolated, but an integrated communication network of heaven and Earth will exist. The integration of the 5G mobile communication network and satellite communication network [101] will also become a new development direction, and the world will enter a new era of broadband Internet. In May 2020, Galaxy Aerospace launched the first 5G satellite, the \"ynihe-1\" broadband communication satellite. Furthermore, VestaSpace, an Indian small satellite manufacturer, unveiled plans for its own satellite 5G constellation. In addition, 3 GPP, ITU, and other organizations are also updating the standard version of 5G and satellite communication. These trends and changes will provide some technical accumulation and reference for the heterogeneous network ManyNet and 6G communication networks that integrate satellite communications in the future.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 2 Related Work
### Deployment of Static Nodes in Vehicular Networks  
Static nodes have been considered as useful devices to support communications of vehicles. A typical style of static node is called RSU (Road-Side Unit), which often provides single-hop communication with vehicles. The well-known example of such systems is DSRC (Dedicated Short Range Communications), which has already been standardized as IEEE1609 families [15], and also have practically deployed all over the world such as ERP (Electric Road Pricing) in Singapore [16] and VICS (Vehicle Information and Communication Systems) in Japan [17].  
RSU also works in combination with multi-hop vehicular networks. To enhance the coverage of RSU, [PERSON] et al. proposed TSF (Trajectory-based Statistical Forwarding) [18] that delivers packetsfrom RSUs to a target vehicle via multi-hop communications of vehicles. Reversely, [PERSON] et al. and [PERSON] et al. respectively proposed D-Greedy (Delay-bounded Greedy forwarding) [19] and PROMPT (cross-layer Positionbased coMmunication PROTcol for delay-aware vehicular access networks) [20], which forward packets from vehicles to RSUs via multi-hop communications. [PERSON] et al. regard the problem of packet delivery among vehicles supported by wired RSUs as an optimization problem and solved the problem to provide better performance on packet delivery [21]. [PERSON] et al. proposed a geographic routing protocol called IEGRP (Infrastructure Enhanced Geographic Routing Protocol) that is enhanced by wired RSUs located at intersections to exchange packets among vehicles [22]. However, they all assume that RSUs have wired connection to networks, which requires high building costs.  
On the other hand, [PERSON] et al. proposed an approximated algorithm to compute the optimal locations of wired/wireless static nodes in the roadmap that supports delay-bounded propagation of information in VANETs [23]. This study tries to reduce the building cost of RSUs while maximizing the effect on enhancing the performance of vehicular ad hoc networks.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 4 Challenges and Development Trends in Reflector Antenna-Based SAR Technology
### Risks for Reflector Antenna-Based SAR
#### 4.3.2 High-Power System Failure  
Traditionally, reflector antennas rely on single or few high-power feeds (usually hundreds of Watts to kilowatts). Compared to the low-power TR modules of phased array antennas, concentrated high-power feeding poses significant challenges:  
(1) Thermal Management Difficulty: Heat dissipation is inefficient in the space vacuum, making high-power feeds and associated beam switching switches (if used) prone to overheating.  
(2) Multiactor Discharge: High-power microwave signals in vacuum or low-pressure environments can easily trigger multipactor discharge (secondary electron multiplication) within waveguides, connectors, or switches, leading to permanent component damage or failure. The failure of the HJ-1C satellite was analyzed to be directly related to multipactor damage in its high-power switching components. However, by increasing the pulse repetition frequency and ambiguity suppression in signal processing, usable SAR images were still ultimately obtained.