esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 5 Analysis and Discussion
### Simulation Tools and Methods  
Six discrete simulators were identified in the survey--CloudSim [6], DISSECT-CF [35], SCORE [58], ONE [76], FogNetSim++ [61] and CloudLightning Simulator [22]. Despite the small range of underlying platforms, this is significantly more than reported in recent resource management surveys. In line with [20], CloudSim was by far the most popular cloud simulation framework used in resource management research surveyed in this study (see Table 3). Seventeen articles used CloudSim and a further ten articles used variations or extensions of CloudSim including ElasticSim [48], CEPSim [27], DynamicCloudSim [56], MultiRE-CloudSim [50] and iFogSim [28]. The popularity of CloudSim can be attributed to the maturity of the simulator, the support community, and the modelling design approach which supports brokering of workloads between multiple date centres; a design that can be re-purposed for a distributed edge infrastructure. After the original CloudSim, the most popular extension of CloudSim used was iFogSim which was used in six studies, the most popular simulator for fog computing research for obvious reasons. Only eight articles used simulators other than CloudSim or variants. While the CloudLightning Simulator was used in three articles, it was used only by the originators of the software. Only two simulators were designed for fog computing specifically, iFogSim and FogNet++; both ONE and the variation of the CloudLightning Simulator in [66] were designed or adapted for edge computing scenarios.  
As CloudSim is overwhelmingly the most popular base simulation platform in the survey, by default, DES is the most popular modelling technique. Only the CloudLightning Simulator supports DTS although the SCORE simulator implements a hybridisation of discrete-event modelling and multi-agent systems to achieve scalability. Given the complexity and scale of computing in the C2T continuum and IoT scenarios, the use of DES-based simulators call into question the generalisability of results from simulation research on resource management based on DES.  
It should also be noted that 19 articles presented simulation frameworks or extensions to simulation frameworks, four of which related to fog or edge computing [28; 59; 61; 66].
## 1 Introduction  
The increasing share of renewable energy sources draws attention to a critical challenge. The availability of wind turbines and photovoltaic solar cells is limited and difficult to predict. They usually provide a fluctuating feed-in to the grid, so energy reserves, e.g., conventional thermal power plants or energy storage systems, are necessary to establish a balance between electricity supply and demand. To maintain the security of supply and improve the flexibility of future power systems, various solutions can be adopted, such as improving the efficiency of technical processes in areas such as thermal power plants, as well as cement and metallurgy industries, the use of advanced thermochemical conversion technologies such as gasification, the expansion of high-voltage transmission infrastructure, the promotion of renewable energy sources, the use of large-scale energy storage systems, and the use of highly flexible power generation units with carbon capture and utilisation, such as combined-cycle power plants.  
Given this background, and to support the development of thermochemical conversion processes for solid fuels and renewable energy, this Special Issue addresses the following topics, among others:  
* Gasification and combustion of biomass, refuse-derived fuel, tire-derived fuel, solid recovered fuel, sewage sludge, and low-rank coal;
* Technological combinations of thermochemical conversion processes based on renewable sources (power-to-fuel);
* Carbon capture, storage, and utilisation (CCS/U) technologies (carbon-capture-to-fuel);
* Renewable energy for heating and cooling to reduce peak demand, with energy storage systems to mitigate grid imbalances;
* CFD and process simulations of thermochemical conversion processes for solid fuels and renewables.  
The editors are pleased to present to the scientific community the best and latest advances in thermochemical conversion processes for solid fuels and renewable energy in this second volume of a compact, peer-reviewed Special Issue. This new Special Issue is a continuation of the previous Special Issue \"Thermochemical Conversion Processes for Solid Fuels and Renewable Energies\", which was closed in October 2020 including 10 valuable, peer-reviewed papers [1]. The second volume of the Special Issue contains fundamental scientific studies on the latest research progress in the development and optimisation of gasification processes, renewable energy source \"solar energy\", the synthesis of new hybrid nanocomposites and nanofluids, carbon capture, and energy storage systems. The submitted manuscripts were reviewed by recognised and expert reviewers. Manuscripts were accepted only if they made a clear contribution to technical and scientific knowledge and highlighted the most important recent developments and new findings in this field of research. Accordingly, 22 manuscripts were submitted, and 15 of them were accepted and published in this Special Issue (acceptance rate of 68%).
###### Abstract  
Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (\(\Psi_{\text{stem}}\)), assisting in irrigation decision-making for commercial vineyards. However, VI-\(\Psi_{\text{stem}}\) correlations are mostly reported at the leaf or canopy scales, using proximal canopy-based sensors or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of high-spatial resolution (3-m) near-daily images acquired from Planet's nano-satellite constellation to derive VI-\(\Psi_{\text{stem}}\) correlations at the vineyard scale. Weekly \(\Psi_{\text{stem}}\) was measured along the growing season of 2017 in six vines each in 81 commercial vineyards and in 60 pairs of grapevines in a 2.4 ha experimental vineyard in Israel. The Clip application programming interface (API), provided by Planet, and the Google Earth Engine platform were used to derive spatially continuous time series of four VIs--GNDVI, NDVI, EVI and SAVI--in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in \(\Psi_{\text{stem}}\) across the experimental vineyard ([PERSON]'s-\(r\) = 0.45-0.84; N = 60). A simple linear regression model enabled monitoring seasonal changes in \(\Psi_{\text{stem}}\) along the growing season in the vineyard (\(r\) = 0.80-0.82). Planet VIs and seasonal \(\Psi_{\text{stem}}\) data from the 82 vineyards were used to derive a 'global' model for in-season monitoring of \(\Psi_{\text{stem}}\) at the vineyard-level (\(r\) = 0.78; RMSE = 18.5%; N = 970). The 'global' model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of \(\Psi_{\text{stem}}\) in Mediterranean vineyards, substantially improving the efficiency of conventional in-field monitoring efforts.  
Google Earth Engine; grapevine; irrigation; Planet; time series; stem water potential; VI; vineyard +
Footnote †: journal: remote sensing
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Application Challenges
#### 6.2.2 User Acceptance and Trust Establishment  
User concerns about ETC system privacy and security represent important factors affecting acceptance. With frequent data breach incidents and increasing public privacy awareness, user demands for personal information protection have become increasingly strong. Data collected by ETC systems, such as location information and travel habits, possess high sensitivity, with users demanding higher requirements for the transparency, security, and controllability of data usage.  
Insufficient technical transparency exacerbates user concerns. Most current ETC systems lack sufficient transparency in data collection, processing, and usage phases, making it difficult for users to understand how their data is being used and impossible to effectively supervise and control data usage. ETC systems in some regions still experience problems with system stability, recognition accuracy, and response speed, with equipment failures, misidentification, and billing errors occurring periodically. This information asymmetry not only affects user trust levels in systems but may also trigger broader social controversies and policy risks.
## 2 Experimental Materials and Methods  
### Raw Materials and Experimental Procedure  
Si\({}_{3}\)N\({}_{4}\) ceramics were prepared using \(\alpha\)-Si\({}_{3}\)N\({}_{4}\) (SN-E10, \(\alpha\) phase \(>\) 95%, D50 = 0.297 \(\upmu\)m; Ube Industries, Ltd., Tokyo, Japan), Al\({}_{2}\)O\({}_{3}\) (99.9%, D50 = 0.2 \(\upmu\)m; Taimei Chemicals Co., Ltd., Tokyo, Japan), and Y\({}_{2}\)O\({}_{3}\) (99.9%, D50 = 0.4 \(\upmu\)m; Shenzhen Lanyi New Materials Co., Ltd., Shenzhen, China) powders as raw materials.  
According to a certain ratio of m(Si\({}_{3}\)N\({}_{4}\)):m(Al\({}_{2}\)O\({}_{3}\)):m(Y\({}_{2}\)O\({}_{3}\)), the raw material powder was weighed and added to anhydrous ethanol as a medium and then placed in a Si\({}_{3}\)N\({}_{4}\) ceramic jar. Small Si\({}_{3}\)N\({}_{4}\) balls were added to the jar in a mass ratio of 2:1, and planetary ball milling was carried out at a speed of 300 r/min for 6 h. The milled slurry was dried on a rotary evaporator and then sieved through a 100-mesh sieve. The sieved powder was then molded under a pressure of 20 MPa to a size of \(\Phi\)50 \(\times\) 3 mm; then, cold isostatic pressing was performed under a pressure of 200 MPa for 5 min. The sintering process was carried out under a 0.1 MPa N\({}_{2}\) atmosphere, with a heating rate of 15 \({}^{\circ}\)C/min from room temperature to 1200 \({}^{\circ}\)C, then a rate of 10 \({}^{\circ}\)C/min to 1400 \({}^{\circ}\)C, a rate of 5 \({}^{\circ}\)C/min to 1600 \({}^{\circ}\)C, and a rate of 3 \({}^{\circ}\)C/min to 1825 \({}^{\circ}\)C; the samples were cooled at a rate of 10 \({}^{\circ}\)C/min to 1200 \({}^{\circ}\)C and finally cooled with the furnace. The sintering process was as follows: after holding at 1650 \({}^{\circ}\)C for 2 h, the temperature was raised to 1825 \({}^{\circ}\)C and held for 2 h. The composition and relative density of the samples after sintering are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & \multicolumn{3}{c}{**Molar Ratio**} & \multirow{2}{*}{**Firing Condition**} & \multirow{2}{*}{**Theoretical Density (g/cm\({}^{3}\))**} & \multirow{2}{*}{**Relative Density**} \\ \cline{2-2} \cline{6-6}  & & & & & 1650 \({}^{\circ}\)C, 2 h; & 3.27 & 99.08\% \\ \hline
1825-SY-90 SN & 92 & 5 & 3 & 1650 \({}^{\circ}\)C, 2 h; & 3.27 & 99.69\% \\
1825-5Y-90 SN & 90 & 5 & 5 & 1650 \({}^{\circ}\)C, 2 h; & 3.27 & 99.69\% \\
1825-6.25Y-90 SN & 90 & 6.25 & 3.75 & 1650 \({}^{\circ}\)C, 2 h; & 3.28 & 99.70\% \\
1825-6.8Y-90 SN & 90 & 6.8 & 3.2 & 1650 \({}^{\circ}\)C, 2 h; & 3.29 & 99.40\% \\
1825-7.5Y-88 SN & 88 & 7.5 & 4.5 & 1650 \({}^{\circ}\)C, 2 h; & 3.30 & 99.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample composition, sintering process, and relative density.
## 2 Materials and Methods  
### Biological and Chemical Materials  
Recombinant _E. coli_ strain SG13009[pREP4] (Qiagen, Hilden, Germany), transformed with pTES-His\({}_{6}\)-OPH plasmid encoding His\({}_{6}\)-OPH synthesis was used for enzyme production. Gene encoding OPH synthesis was obtained from _Pseudomonas diminuta_ VKM B-1279 cells [25].  
As a basis, the known crystallographic structure of OPH dimer (PDB number 1 QW7) was used with preliminary modification by His6-tag as described in work of [PERSON]; [PERSON], [PERSON]. (2017) [26].  
We used the following PFCs: perfluorohexane (HaloPolymer, Moscow, Russia), perfluorodecalin (Acros Organics, Geel, Belgium), as well as new compounds of the perfluoroether class with molecular formula [C\({}_{3}\)F\({}_{3}\)O(C\({}_{2}\)F\({}_{4}\)CF\({}_{2}\)O)\({}_{3}\)C\({}_{2}\)F\({}_{4}\)]\({}_{2}\) (polyether I) and 3,6-dioxaperfluoro-5-methylnonane C\({}_{3}\)F\({}_{7}\)OCF(CF\({}_{3}\))CF\({}_{2}\)OC\({}_{2}\)F\({}_{5}\) (polyether II). The latter two compounds have higher molecular mass and higher gas transfer capacity when compared to PFD (Table 1). The concentration of PFC additives in the growth medium was 0.1, 1, 3, and 5% (\(v/v\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PFCs** & **Molecular Mass (g/mol)** & **O\({}_{2}\) Solubility at 298 K (\% v/v)** & **Density (kg/m\({}^{3}\))** \\ \hline
1,1,1,2,2,3,3,4,4,5,5,6,6,6-tetradecafluorohexane & & & \\ Perfluorohexane & 337.90 * & 55.5 & 1.678 * \\ CF\({}_{3}\)(CF\({}_{2}\))\({}_{4}\)CF\({}_{3}\) & & & \\ \hline
3,6-dioxapperfluoro-5-methylnonane & & & \\ (Polyether I) C\({}_{3}\)F\({}_{7}\)OCF(CF\({}_{3}\))CF\({}_{2}\)OC\({}_{2}\)F\({}_{5}\) & & & \\ \hline
1,1,2,2,3,3,4,4a,5,5,6,6,7,8,8,8a-octadecafluorodecalinPerfluorodecalin & 461.98 * & 45.0 & 1.930 * \\
1,1,1,2,2,3,3,4,4a,5,5,6,6,7,8,8a-octadecafluorodecalinPerfluorodecalin & & & \\ \hline
4,7,10,13,16,19,22,25-octaoxaperfluoro- & & & \\
5,8,11,14,17,18,21,24-octamethyloctacosane & 1746.26 & 53.0 & 1.804 \\ (Polyether II) (C\({}_{3}\)F\({}_{3}\)CO(C\({}_{2}\)F\({}_{4}\)CF\({}_{2}\)O)\({}_{4}\)C\({}_{2}\)F\({}_{4}\)]\({}_{2}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physico-chemical properties of perfluorocarbon compounds (PFCs) used in this study.
## Materials and Methods
### Handover Prediction Models  
#### 2.2.1 Method of Time Measurement  
The MTM calculates the overall execution time based on the predetermined durations of basic motion sequence, body motion, and various physical task parameters [46]. The MTM breaks down human motions into extremely small steps to accurately describe the tasks performed by humans and calculate the time required in a standardized manner [47]. The unit of time of MTM is the TMU, with 1 TMU = 0.036 s.  
In this study, the MTM was employed to calculate the standard assembly time, which was then used to set the cycle time over which the robot would perform fixed tasks. Table 1 presents a decomposition of the actions for assembling one orange block and one green block, which take 56.02 TMU and 73.32 TMU, respectively. To complete an assembly task (assembly of three orange blocks and one green block), the orange block action must be performed three times before a green block is added, thus taking a total of 241.38 TMU (\(\sim\)8.7 s). This value was verified in a pretest involving 10 participants, for which the average assembly time and twice its standard deviation was 8.31 \(\pm\) 0.82 s for the completion of one assembly task; this is consistent with the standard assembly time of 8.7 s calculated using the MTM.  
In the MTM handover prediction model, each handover of the UR3 robot lasts 8.7 s according to the calculated cycle time. The calculation benchmark was based on the completion time point of the previous handover. Due to the fact that the UR3 robot requires 0.45 s to move from the start point to the handover point and the network information transfer latency is approximately 0.15 s, the trigger is released 0.6 s before the predicted handover time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Action** & **Symbol** & **TMU** & **Action Symbol Description** & **Experimental Action** \\ \hline \multirow{4}{*}{Assembly of each of the three orange blocks} & \multirow{4}{*}{R38C} & \multirow{4}{*}{16.28} & R: Extend the hand & Extend the hand for 38 cm \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of block assembly actions and their duration, as calculated using the method-time measurement MTM.
## 4 Real Data Applications  
In the real data test, we used the land seismic data from eastern China. The CRP gathers after prestack time migration were used to generate velocity spectra. The number of CRP gathers was 500. The maximum offset was 2200 m. The longitudinal time was 2.5 s. The sampling interval was 2 ms. The stack section is shown in Figure 11(a). Figure 11(b) is the CRP gather at CDP 203, and Figure 11(c) is the velocity spectrum at CDP 203.  
The velocity spectrum at CDP 203 was used as the tracking target of the training data set. Since there was no obvious boundary between the energy clusters in the velocity spectrum in Figure 12, we used the sliding window to realize the target tracking. The time window length is given as 250 ms. The tracking windows are shown in Figure 12(a). The real data samples were made in the same way as the theoretical data. The training process had 500 iterations, and the learning rate was set to 0.0001. The position of the window was changed and the same training process repeated until the tracking of the target in the whole time range was completed. The tracking results of all CDP points were connected to form the final tracking paths as shown in Figure 12(b). Comparing Figure 12(b) with Figure 11(a), the tracking paths are consistent with the interfaces. The faults between CDP 200-CDP 350 and 1.3-1.8 s can be correctly identified on the tracking results. Along the tracking path, the parameter to be inversed can be extrapolated from the well, and the initial inversion model consistent with the structure can be obtained.  
Figure 11: (**a**) Interval velocity curve at CDP 30; (**b**) extrapolation velocity result under the paths constraint.  
Figure 12: (**a**) Stack section; (**b**) CRP gather at CDP 203; (**c**) velocity spectrum at CDP 203.
## 4 Computer Simulation
### Evaluation of Rician Fading Impact on and Effectiveness of Proposed Method Using FSS
Therefore, the proposed nonlinear estimation method demonstrates robustness against variations in the K-factor. Furthermore, in the proposed method utilizing FSS, the coefficient \(b_{2}\) exhibits approximately a 10% degradation compared to the proposed method without FSS. This degradation is likely due to the fact that the uncompensated LED signal, which is used for nonlinear coefficient estimation in the proposed method, experiences a decline in BER performance when FSS is applied. However, the proposed method is not able to achieve a significant reduction in the K-factor. Figure 12: BER performance of proposed method in Rician fading environment (\(K=10\) dB). Figure 13: Estimation accuracy of proposed methods in Rician fading environment. when considering the overall system BER performance, the impact of this degradation in estimation accuracy is minimal. However, when considering the overall BER performance of the system, the impact of the degradation in estimation accuracy is minor. Since the benefits of employing FSS outweigh this slight degradation, the system performance improves as previously described.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Experimental Results
#### 6.2.3 Resource Sharing with CBR and TCP Traffic  
In the third experiment series, Client 0 sends CBR traffic at different rates \(R_{0}\) while Client 1 has a single saturated TCP connection. Figure 6 shows the obtained goodput for both clients.  
Without ABC, the goodput of Client 0 increases with increasing transmission rate while the goodput of Client 1 decreases because it can only use the bandwidth left over by Client 0. If the transmission rate of Client 0 exceeds the capacity of the bottleneck link, Client 1 achieves hardly any goodput. This is different with ABC. Client 0 can increase its goodput only up to 32 Mb/s by increasing its transmission rate to very large values, but the remaining capacity is utilized by Client 1. Thus, ABC approximates fair resource sharing even under challenging conditions.
## 2 Magnetorheological Isolation System
### Control Algorithm and Control Law  
In order to successfully conduct the test, the numerical simulation of the control system was carried out before the test. The accuracy of the numerical calculation and the satisfaction of the fast calculation in the test depends on the control algorithm chosen by the controller. The LQR linear optimal control algorithm was used in this control system and it has the advantages of simple algorithm and strong robustness.  
The LQR is an algorithm commonly used in control. It uses a quadratic performance index as an objective function, using the state equation as a constraint condition, through the variational method to meet the minimum control force of the control requirements, as shown in Equation (1). Equation (2) provides the control force at each moment and is the gain matrix.  
\[J=\int\limits_{0}^{t_{f}}\Big{[}Z^{T}(t)QZ(t)+u^{T}(t)Ru(t)\Big{]}dt \tag{1}\]  
\[u(t)=-\frac{1}{2}R^{-1}B^{T}PZ(t)=-KZ(t) \tag{2}\]  
In reference [4], the above equation has been deformed. It is pointed out that the essence of the algorithm is to change the stiffness matrix and damping matrix of the controlled structure, so that the response of the structure is minimal, as shown in Equation (3) [51].  
\[u(t)=-KZ(t)=-\Big{\{}\begin{array}{cc}-K_{x}&-\;K_{\dot{x}}\end{array}\Bigg{\{} \begin{array}{c}x\\ \dot{x}\end{array}\Bigg{\}}=-K_{x}x-K_{\dot{x}}\dot{x} \tag{3}\]  
Considering the characteristics of the damping force that the MR damper exerts on the structure, the continuous control rule is shown in Equation (4) and the switch control rule is shown in Equation (5).  
\[u=\left\{\begin{array}{cc}u_{\max}&f_{opt}\times\dot{x}_{b}<0\\ f_{opt}&f_{opt}\times\dot{x}_{b}<0\\ u_{\min}&f_{opt}\times\dot{x}_{b}<0\\ u_{\min}&f_{opt}\times\dot{x}_{b}<0\\ u_{\min}&f_{opt}\times\dot{x}_{b}\geq 0\end{array}\right.\Big{|}f_{opt}| >|u_{\min}| \tag{4}\]  
\[u=\begin{array}{cc}u_{\max}&f_{opt}\times\dot{x}_{b}<0\\ u_{\min}&f_{opt}\times\dot{x}_{b}>0\end{array}, \tag{5}\]  
where \(u_{\max}\) is the maximum damping force that the MR damper can provide while \(u_{\min}\) is the minimum damping force that MR damper can provide. \(\dot{x}_{b}\) is the relative velocity of the isolation layer.\(f_{opt}\) is the  
Figure 2: Six story reinforced concrete frame structure.  
calculated optimal control force and \(u\) is the final calculated damping force applied by the damper which can be achieved by controlling the input current to drive the damper.