esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Technical Challenges for 6G Development
### Network Security Challenges  
Security emerges as a pivotal concern in the context of 6G wireless networks, particularly with the implementation of the Satellite-Terrestrial Integrated Network (STIN) technique. In the 6G landscape, it is imperative to extend beyond traditional physical layer security and encompass diverse security aspects, including integrated network security. Hence, novel security methodologies, characterized by low complexity and high efficacy, warrant thorough investigation.  
In pursuit of this objective, certain physical layer security techniques proposed for 5G can be extrapolated to suit 6G networks. Notable examples include LDPC-based secure massive MIMO and secure millimeter-wave (mmWave) techniques, which may find applicability in ultra-massive MIMO (UM-MIMO) and terahertz (THz)-band applications. Addressing integrated network security involves establishing a robust management mechanism for different function keys across various security domains. A distributed key management mechanism, as proposed in [273], which considers both unicast and multicast communication key management, emerges as a promising solution for STIN. Efficiently managed and implemented, these physical-layer and network-layer security techniques can collectively constitute a well-integrated security solution, adept at safeguarding confidential and private information within 6G networks [273].
## 2 Results and Discussion
### Sensory Analysis
The 44 odor-active compounds characterized by GC-O were used as X-matrix, and the 7 flavor notes obtained by sensory evaluation were Y-matrix, which generated the correlation load diagram of PLSR as shown in Figure 2. The two ellipses represent the variance contribution rate of 50% (small) and 100% (large), respectively. The volatiles between the two ellipses had a good correlation with the seven flavor notes. As could be seen from Figure 2, the contribution rate of X variable was 85%, and that of Y variable was 62%, indicating that the PLSR model can further verify the correlation between volatile compounds and flavor notes. The specific analysis was as the following: \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sensory Attributes**} & \multicolumn{3}{c}{**Mean Score**} \\ \cline{2-4}  & **T1** & **T2** & **T3** \\ \hline mushroom & 6.27b A & 9.39a & 6.03b \\ nutty, malty & 3.14b & 2.37c & 4.37a \\ fatty, green & 5.89a & 4.19b & 4.98 ab \\ floral, sweet & 0.98b & 0.76b & 3.39a \\ sulfury, musty & 4.12a & 4.23a & 4.18a \\ roasted potato & 2.07 ab & 1.34b & 2.89a \\ roten cabbage, corn & 8.13a & 7.96a & 7.89a \\ \hline \hline \end{tabular}  
* Values with different roman letters (a–c) in the same row are significantly different according to the Duncan test (\(p<0.05\)).  
\end{table} 
Table 4: The mean intensity values of the seven attributes for the three truffles in descriptive sensory evaluation. Figure 1: The aroma profiles of truffle samples obtained from T1, T2 and T3 samples. The seven notes with \({}^{n_{m}\
eq}\) are significantly different between samples (\(p<0.05\)). T1 sample was thought to have much more fatty, green and rotten cabbage, corn aroma than other two samples. From Table 1, Als of hexanal (**A5**), octanal (**A19**), nonanal (**A22**) in T1 were higher than those of T2, T3 and flavor note of these compounds were closely related to the \"green, fatty, and waxy\"; Dimethyl sulfide and 2-acetylthiazole had high Als and concentrations in T1, which contributed to the rotten cabbage, corn notes (Figure 2). In T2 sample, mushroom and sulfuric, musty attributes were clearly detected. 3-octanone (**A15**) and 1-octen-3-ol (**A31**) had both higher concentration and OAV than other two samples, which provided typical mushroom aroma (Figure 2). According to GC-O, bis(methylthio)methane (**A17**) was regarded to contribute rough onion and garlic flavor. T3 sample was much more described as \"nutty and malty\", \"floral and sweet\", \"roasted potato\" than other two samples. 2-methylbutanal (**A2**) and 3-methylbutanal (**A3**) were thought to relate to \"nutty and malty\". Benzeneacetaldehyde (**A39**) and phenylethyl alcohol (**A44**) had sweet rose-like fragrance. The note of \"roasted potato\" mainly came from 3-methylthiopromanal (**A32**).
## 6 Conclusions  
To effectively balance the latency and energy consumption performance while meeting the diverse transmission needs of data in UWSNs, we proposed a mixed packet forwarding strategy based on wakeup threshold and dynamic access probability with priority scheduling for CHs. We derived a series of system performance index expressions by constructing a discrete\(-\)time queueing model with preemptive priority. We provided numerical results and analyzed how various parameters affected network performance. By comparing with the traditional mechanism, access probability mechanism, and wakeup threshold mechanism, we objectively analyzed the superiority of the proposed mixed mechanism. The numerical results indicated that compared with the traditional mechanism, the mixed mechanism proposed in this paper was less affected by network load in terms of latency and energy consumption (for example, the range of \(\eta_{2}\) under traditional mechanism is approximately 92.630, while the range of \(\eta_{2}\) under the proposed mechanism is approximately 26.706; the range of \(\epsilon\) under traditional mechanism is approximately 0.058, while the range of \(\epsilon\) under the proposed mechanism is approximately 0.041), and performs better than traditional mechanisms within a specific parameter range (for example, when \(0.053<p_{2}<0.068\)). Compared with the single access probability mechanism and wakeup threshold mechanism, the mixed mechanism could effectively balance latency and energy consumption performance.  
Figure 14: Comparison energy consumption of the CH.  
In this paper, we assume that the arrival process of packets is a single\(-\)input process that obeys the geometric distribution, and in future work, we can further construct a parallel\(-\)input queueing model to analyze the network performance with multiple sensor nodes, so as to improve the accuracy of the model. In addition, in this paper, our numerical results are derived from the ideal environment. In future work, we will consider non\(-\)ideal conditions such as forwarding failures or mistakes, and further build a queueing model that is more in line with the actual network environment. Moreover, this paper only divides the CH into two states (sleep/working mode). In future research, we will consider further dividing it into multi\(-\)state mode (such as sleep/idle/working mode [30]) according to the actual environment to improve the accuracy of our model.  
Conceptualization, N.L.; Methodology, Z.X.; Validation, J.L.; Formal analysis, Z.G.; Resources, L.F.; Project administration, H.G.; Funding acquisition, L.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Independent Project deployed by Innovative Academy of Marine Information Technology, Chinese Academy of Sciences (No. CXBS202102).  
Not applicable.  
All relevant data are within the paper.  
The authors declare that there are no conflicts of interest regarding the publication of this manuscript.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 4 Design of Multi-Constraint Multi-Strategy Hopping Algorithm
### Path Similarity Constraint
#### 4.3.1 Selection Cycle Constraint  
The path similarity constraint enhances the unpredictability of path hopping but at the same time results in these paths with smaller similarities being repeatedly used over multiple hopping cycles after the constraint. If the attacker listens for a long time, it is easy to crack the hopping rule. Therefore, when selecting a path, it is necessary to constrain the use cycle of the path as shown in Equation (8) and preferentially select those paths that have not been reused or that have never been used within a certain hopping cycle.  
\[T_{now}-T_{recent}\geq\beta\,or\,T_{recent}=0 \tag{8}\]  
where \(T_{now}\) represents the current number of cycles for which path jumping is being executed, and \(T_{recent}\) represents the number of cycles since alternative path \(i\) was last used. The value \(\beta\) represents the threshold for the selection cycle of alternative paths and is used in conjunction with the path similarity constraint. If only the path similarity constraint is used independently, it may result in lower-speed paths being repeatedly used in a short period. On the other hand, if only the selection cycle constraint is used independently, the similarity of alternative paths is not effectively ensured, and there may be a situation where jumping paths are cyclically used within a certain range. The value of \(\beta\) is typically set based on the size of the jump space. A too-high \(\beta\) value will narrow down the selection range of alternative paths, leading to the cyclic use of paths in the overall jump space and reducing the unpredictability of jumps. Conversely, a too-low \(\beta\) value will result in paths being reused too quickly, reducing the effectiveness of defense against attacks.
# An Optical Interference Suppression Scheme for TCSPC Flash LiDAR Imagers
## 6 Experimental Results
It is important to note that increasing the length \(L\) of the LFSR has the undesirable consequence of reducing the effective PRF of the system. Table 2 shows the effective PRF of a system running at a nominal PRF of 1 MHz and system clock of 80 MHz, for various lengths of the LFSR. It is therefore impractical to use long LFSRs in real-time applications. One way to overcome such a limitation is to use a different random number generator (RNG) architecture that can generate long, non-repeating sequences of small values. In our experiment, this was achieved by applying a modulo operator to the state of a 32-bit LFSR. The modulo was implemented as a bit-mask on the last \(W_{m}\) LSBs of the LFSR, and the width of the bit-mask was selectable in \(W_{m}\in[1,2,3,4,5,6]\). The periodicity of the resulting sequence is determined by the length \(L\) of the LFSR, while the maximum applicable delay \(T_{\text{d,max}}\) is defined by the bit-mask width \(W_{m}\) and the system clock frequency \(F_{\text{clk}}\) according to the relation \(T_{\text{d,max}}=(2^{W_{m}}-1)F_{\text{clk}}\). Figure 12 reports the suppression ratio for a victim using a long LFSR (\(L=32\)) as a function of the bit-mask width \(W_{m}\).
# Intersatellite and Downlink Agile Attitude Maneuvers
## 5 Attitude Maneuvering for Downlink Connection
### Numerical Simulations in Nominal Conditions  
In this work, the operational orbit is nearly circular, repeating, and with critical inclination. The repetition period equals 1 nodal day, and includes 15 nodal orbital periods. All orbit elements are reported in Table 2. The minimum elevation angle \(\varepsilon_{min}\) is set to \(10^{\circ}\). Figure 4 depicts the elevation time history during the 5 visible passes (in a single repetition period) over the ground station, sited at geographical longitude of 9.4 degand latitude of 39.7 deg. The initial attitude conditions are  
\[\underline{\omega}(t_{0})=\begin{bmatrix}0&0&0\end{bmatrix}^{T} \tag{52}\] \[\underline{\theta}(t_{0})=\begin{bmatrix}0&\frac{\pi}{4}&\frac{ \pi}{2}&\frac{3\pi}{4}\end{bmatrix}^{T}\] (53) \[\underline{\theta}(t_{0})=\begin{bmatrix}0&0&0&0\end{bmatrix}^{T}\] (54) \[R(t_{0})=I_{3\times 3} \tag{55}\]  
Matrix \(Q\), which is related to the inertia estimator dynamics (15), includes large values if the knowledge of the mass distribution of the spacecraft is satisfactory. Conversely, small values correspond to poor knowledge of it. For this simulation, \(Q\) is set to  
\[Q=10^{-8}I_{6\times 6} \tag{56}\]  
The gains used in the numerical simulations are reported in Appendix A.  
Figure 5 illustrates the eigenangle time history, which provides a clear indication of misalignment. This time history exhibits some spikes immediately after the end of tracking intervals, when the spacecraft must reorient to point toward the direction where the ground station will appear in the subsequent visible pass. Instead, during visible passes, the eigenangle reaches modest values, e.g., below 0.1 deg, as shown in Figure 6, referred to the fourth tracking interval. Figure 7 portrays the time histories of the components of the error angular velocity, which reach again their maximum values at the beginning of reorientation arcs. In contrast, during visible passes, these components quickly drop to zero, as illustrated in Figure 8. Precision in pointing is testified by the two mean values \(\bar{\Phi}_{ei}\) and \(\bar{\omega}_{ei}\), defined in Equations (5) and (6),  
\[\bar{\Phi}_{ei} =6.20\cdot 10^{-2\circ} \tag{57}\] \[\bar{\omega}_{ei} =2.60\cdot 10^{-4\circ}/\text{s} \tag{58}\]  
The actual torque components, depicted in Figures 9 and 10, reach values of order of tens of Nm. The angular rates of the gimbals are portrayed in Figures 11 and 12 and do not exceed 30 deg/s. Finally, the time histories of net motor torques, shown in Figure 13, turn out to be of order of 1 Nm.  
Figure 4: Elevation \(\varepsilon(\textbf{t})[^{\circ}]\) during a repetition period.  
Figure 8: Components of the error angular velocity \(\omega_{e1}\)(t), \(\omega_{e2}\)(t), \(\omega_{e3}\)(t)\({}^{\circ}\)/s] during the fourth tracking interval.  
Figure 10: Actual torque components \(T_{a1}\)(t), \(T_{a2}\)(t), \(T_{a3}\)(t)[Nm] during the fourth tracking interval.  
Figure 9: Actual torque components \(T_{a1}\)(t), \(T_{a2}\), \(T_{a3}\)(Nm)
# GPP of a Chinese Savanna Ecosystem during Different
## 2 Materials and Methods
### Flux and Meteorological Data  
Starting in May 2013, climatic variables were simultaneously measured via an open-path EC system, which directly provides a high-temporal-resolution dataset of the energy/carbon/H\({}_{2}\)O fluxes between the ecosystem and atmosphere [42]. A triaxial sonic anemometer (CSAT3, Campbell Scientific Inc., USA) and a high-frequency open-path CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzer (Li-7500, LI-COR Inc., Lincoln, NE, USA) that comprised the EC system were mounted at a height of 13 m above the ground on the flux tower. The flux measurements were complemented by a series of meteorological measurements, such as relative humidity, air temperature (Ta), soil temperature, incoming and outgoing shortwave and longwave radiation amounts, and incoming and reflected PAR amounts.  
Both FLUXNET and ChinaFLUX have created a set of standard quality assurance and quality control methodologies to control and ensure the quality of flux data. Standard quality assessment and control (QA/QC) protocols were followed in accordance with their application, as suggested by [42], with the instrument setup and data processing details provided in [43]. In summary, processing included despiking high-frequency data, applying coordinate rotations to align horizontal wind to the mean wind direction, correcting the effects of air density fluctuations via Webb-Pearman-Leuning (WPL) calibration, filtering fluxes with a low friction velocity (0.2 m/s), and identifying and rejecting outliers.  
Gap filling and partitioning procedures were applied to the flux data through an online method recommended by FLUXNET [44]. This method is established as the standard protocol by EUROFLUX and is maintained by the Max Planck Institute, accessible at http:  
Figure 2: Workflow of this study. The light blue rectangular boxes denote the key processes, and the dark green rectangular boxes denote the important data and result outputs.  
//www.bgc-jena.mpg.de/5622399/READdyProc, accessed on 1 April 2024. Net ecosystem exchange (NEE) values were partitioned to determine ecosystem respiration (Re) following the methodology outlined by [45], and the GPP was derived as the sum of the NEE and Re. Further information on the data and their processing can be found in other studies [6].
# Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk
###### Abstract  
This research acquired data from the Central Weather Bureau Observation Data Inquiry System (CODIS) for historical weather information, such as observation time, temperature, humidity, wind speed, global radiation, etc., and constructed a historical weather database by using Excel software. Least square support vector machine (LSSVM) was used to forecast wind speed and solar radiation; then, the power output of wind and solar was derived. Considering factors of the demand response and the load and electricity pricing, a maximized risk income model of the virtual power plant (VPP) is established based on conditional value-at-risk (CVAR). An enhanced bacterial foraging algorithm (EBFA) was proposed to solve the risk dispatch problem of a VPP in this paper. In an EBFA, the stochastic weight trade-off is embedded to improve the behavior pattern of individual bacteria to enhance their sorting efficiency and accuracy in a high-dimension solution space. Various moving patterns of EBFA were considered for improvement, which were demonstrated by using a VPP system on Penghu island, Taiwan. Many scenarios were created, including various seasons, power rebate preings, and confidence levels, so the maximal risk and return of VPP could be simulated and analyzed. Simulation and tests show a positive result for a VPP to perform the power dispatch by maximizing risk income. This paper also provides a guideline for the VPP to handle the risk management.  
virtual power plant; demand response; conditional value-at-risk; bacterial foraging optimization 2021  
18 August 2021  
10.3390/app11167752
## 2 Numerical Simulation
### Vehicle Loading Model  
A single-axle four-wheel load model was constructed to simulate the rear axle wheels load of the vehicle simulator in the full-scale NE-ALT system, which applied vehicle loads on the asphalt pavement in the field site using a standard tandem-axle eight-wheel frame. The dimension of the vehicle load model is shown in Figure 3. The vehicle load was applied in the form of a half-sine function, as shown in Equation (4). Two levels of wheel-ground contact pressure were taken into account, which were 0.7 MPa and 1.4 MPa. Three levels of vehicle speed were selected for further discussion, which included 20 km/h, 60 km/h, and 100 km/h, respectively. The loading durations corresponding to different vehicle speeds are summarized in Table 2. The duration of 0.05 s without any vehicle load was added before and after the formal loading for the purpose of convenient data analysis. In this study, the moments when vehicle loads reached their maximum values at different vehicle speeds are calculated and presented in Table 2. For example, when the vehicle speed is 20 km/h, its formal loading duration is determined at 0.036 s, and it will take 0.05 s and the half of 0.036 s to reach the maximum loading value, that is, 0.068 s.  
\[\left\{\begin{array}{c}p=p_{\max}\sin\left|\frac{\pi t}{T}\right|,0\leq t\leq T \\ P=0,T\leq t\leq T_{a}\end{array}\right. \tag{4}\]
## 3 Results and Discussion
As the temperature increased, the viscosity decreased due to increased molecular motion within the oil, leading to a decrease in shear stress. However, these changes differed at each experimental temperature, especially when comparing the cases with and without the additive. The introduction of the fly-ash additive resulted in an increase in shear stress, particularly at higher temperatures. The presence of the additive impacted the structure and properties of the oil, altering its viscosity and load-carrying capacity. Empirical regression equations were derived from the experimental data to characterize the shear stress-shear rate relationship for the different oils at various temperatures. The viscosity values were calculated from these equations, and it was observed that as the temperature increased, the viscosity of the oils decreased. The additive also influenced the viscosity-temperature relationship, with a smaller magnitude of viscosity reduction for oil A compared to the other oils. The additive enhanced the lubricating properties of the base oil by reducing the impact of temperature on viscosity, as the additive particles acted as better thermal conductors. Therefore, this study provided valuable insights into the effects of temperature and the fly-ash additive on the viscosity and load-carrying capacity of industrial oils. The findings highlight the potential benefits of using such additives to improve the performance of lubricating oils under varying operating conditions, particularly at higher temperatures. Further research can focus on exploring the long-term effects and optimizing the concentration of additives for different types of oils.
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 2 Method Development
### Proposed Methodology
#### 2.1.1 Input Data  
The proposed work uses an RGB UAV-captured image defined as i, and a shadow image mask M as input data. Urban region shots were taken by a setup consisting of a UAV device (DJI Phantom 4) with a 12.4 MP camera mounted. At a variable flight height, capturing altitude was set for each image in order for the shot to contain enough information while preserving a good ground resolution. As it has been observed in the scheme displayed in Figure 2, the scenes captured different shadow regions, where such regions might contain self-shadows and cast shadows that depend on the light source occlusion.  
The shadow mask (\(M\)) used was computed using the method developed in our previous work [42].  
Figure 1: Proposed methodology.  
Figure 2: Image capturing scheme.
# Finite-Time Set Reachability of Probabilistic Boolean Multiplex Control Networks
## 5 Conclusions  
This study investigated the finite-time set reachability of PBMCNs. For the finite-time set reachability analysis, the PBMCNs were extended to the RLDS via STG reconstruction technology. On this basis, a necessary and sufficient condition for the finite-time set reachability of PBMCNs was obtained. Finally, the proposed method was illustrated by a numerical example. The main results are as follows:  
1. Considering the systematic nature of biological models, in this paper, the PBMCNs are proposed based on the PBCNs. The model can simulate complex gene regulatory networks, such as cancer networks.
2. In order to demonstrate the set reachability of PBMCNs, the set reachability issue of PBMCNs is transformed into the set reachability issue of RLDS by the STG reconstruction technique, and a necessary and sufficient condition for the finite-time set reachability of PBMCNs is obtained.  
Although the PBMCNs proposed in this paper can simulate more complex gene regulatory networks, this model still has the limitation that the computational complexity \(O(2^{KN+m}\times 2^{KN+m})\) will further complicate as the number of nodes increases. Therefore, we will focus on reducing the computational complexity of the PBMCNs in further research.  
Conceptualization, Y.C. and Y.S.; methodology, Y.C. and Y.S.; software, Y.C.; validation, Y.S., S.L. and F.L.; formal analysis, S.L. and F.L.; investigation, Y.C.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, S.L. and F.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (No. 51671075, 51971086), Heilongjiang Postdoctoral Fund for scientific research initiation (No. LBH-Q16118) and Fundamental Research Foundation for Universities of Heilongjiang Province (No. LGYC2018 JC004).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.